diff --git a/documentation/5/.buildinfo b/documentation/5/.buildinfo
index e9fe5f698..e541c6a96 100644
--- a/documentation/5/.buildinfo
+++ b/documentation/5/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
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+config: 11c3898f4b31274f6d5f7bd9b6b34e69
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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index 7103373b3..2928dc2d7 100644
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+++ b/documentation/5/_downloads/1eed1518b8928bc24e721527ef1f9970/mnist_mb_classifier.ipynb
@@ -35,7 +35,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
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diff --git a/documentation/5/_sources/index.rst.txt b/documentation/5/_sources/index.rst.txt
index ff9e0f103..e9f2ec6b1 100644
--- a/documentation/5/_sources/index.rst.txt
+++ b/documentation/5/_sources/index.rst.txt
@@ -20,6 +20,7 @@ Note, this documentation is under construction. If you cannot find what you are
     custom_models
     bibliography
 
+    tutorials/index
     userproject/index
 
     Reference documentation <source/pygenn>
diff --git a/documentation/5/_sources/tutorials/1_neurons.ipynb.txt b/documentation/5/_sources/tutorials/1_neurons.ipynb.txt
new file mode 100644
index 000000000..83164b1d9
--- /dev/null
+++ b/documentation/5/_sources/tutorials/1_neurons.ipynb.txt
@@ -0,0 +1,334 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Defining populations of neurons\n",
+        "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n",
+        "![image.png](data:image/png;base64,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)\n",
+        "\n",
+        "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t2ihZLXh5VD-",
+        "outputId": "510653d0-3172-4c5f-c101-1bfe66297121"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8GngV4fThkhM"
+      },
+      "source": [
+        "## Build model\n",
+        "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "q6WNelXsbjy1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from pygenn import GeNNModel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "261uLnJsgyeE"
+      },
+      "source": [
+        "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "EDpiDOK0gkEz"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial1\")\n",
+        "model.dt = 0.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LrfXpMqfjRBe"
+      },
+      "source": [
+        "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tU2M4MgFjRae"
+      },
+      "outputs": [],
+      "source": [
+        "izk_init = {\"V\": -65.0,\n",
+        "            \"U\": -20.0,\n",
+        "            \"a\": [0.02,     0.1,    0.02,   0.02],\n",
+        "            \"b\": [0.2,      0.2,    0.2,    0.2],\n",
+        "            \"c\": [-65.0,    -65.0,  -50.0,  -55.0],\n",
+        "            \"d\": [8.0,      2.0,    2.0,    4.0]}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YrOQPgYBjuym"
+      },
+      "source": [
+        "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "zc-e5Lu2j_Yq"
+      },
+      "outputs": [],
+      "source": [
+        "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u8wu06PZkBnS"
+      },
+      "source": [
+        "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "GNBjEGWPj_3Q"
+      },
+      "outputs": [],
+      "source": [
+        "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IGKUIiaGkA0Z"
+      },
+      "source": [
+        "Generate code and load it into PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "d0mK72rYkiYe"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cNs18ywkkq6T"
+      },
+      "source": [
+        "# Simulate tutorial model\n",
+        "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "nWFVfYfdkobN"
+      },
+      "outputs": [],
+      "source": [
+        "voltage = pop.vars[\"V\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wv-hDOIe3Hgy"
+      },
+      "source": [
+        "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "99MBe7JKk5Ut"
+      },
+      "outputs": [],
+      "source": [
+        "voltages = []\n",
+        "while model.t < 200.0:\n",
+        "    model.step_time()\n",
+        "    voltage.pull_from_device()\n",
+        "    voltages.append(voltage.values)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ug6S1h-z3k7v"
+      },
+      "source": [
+        "Plot the voltages over time in 4 seperate panels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "RsVbAbIPlEO8",
+        "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b",
+        "scrolled": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x800 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Stack voltages together into a 2000x4 matrix\n",
+        "voltages = np.vstack(voltages)\n",
+        "\n",
+        "# Create figure with 4 axes\n",
+        "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n",
+        "\n",
+        "# Plot voltages of each neuron in\n",
+        "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n",
+        "    axes[i].set_title(t)\n",
+        "    axes[i].set_ylabel(\"V [mV]\")\n",
+        "    axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n",
+        "\n",
+        "axes[-1].set_xlabel(\"Time [ms]\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h4yw3JiNpXOM"
+      },
+      "source": [
+        "Exercises\n",
+        "---\n",
+        "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n",
+        "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "1_neurons",
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/2_synapses.ipynb.txt b/documentation/5/_sources/tutorials/2_synapses.ipynb.txt
new file mode 100644
index 000000000..081dc4173
--- /dev/null
+++ b/documentation/5/_sources/tutorials/2_synapses.ipynb.txt
@@ -0,0 +1,466 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Tutorial 2 - synapses\n",
+        "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t2ihZLXh5VD-",
+        "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8GngV4fThkhM"
+      },
+      "source": [
+        "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "q6WNelXsbjy1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "261uLnJsgyeE"
+      },
+      "source": [
+        "## Build model\n",
+        "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "EDpiDOK0gkEz"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial2\")\n",
+        "model.dt = 1.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mki7b8R8xhAv"
+      },
+      "source": [
+        "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n",
+        "\n",
+        "\\begin{align}\n",
+        "    \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n",
+        "\\end{align}\n",
+        "\n",
+        "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "AkMk7Ml4tOxM"
+      },
+      "outputs": [],
+      "source": [
+        "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n",
+        "              \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XboW6qxrxnok"
+      },
+      "source": [
+        "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "dWf4f4Bpxl7u"
+      },
+      "outputs": [],
+      "source": [
+        "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n",
+        "            \"RefracTime\": 0.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B3hhcDILxeki"
+      },
+      "source": [
+        "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "id": "5AECcjzMs8Iz"
+      },
+      "outputs": [],
+      "source": [
+        "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n",
+        "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n",
+        "\n",
+        "exc_pop.spike_recording_enabled = True\n",
+        "inh_pop.spike_recording_enabled = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QypcRqLi0hgq"
+      },
+      "source": [
+        "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "WpmzQu0UuPky"
+      },
+      "outputs": [],
+      "source": [
+        "exc_synapse_init = {\"g\": 0.0008}\n",
+        "inh_synapse_init = {\"g\": -0.0102}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58kevKNm0rfi"
+      },
+      "source": [
+        "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n",
+        "\\begin{align}\n",
+        "    \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}}  \\delta(t - t_{j}).\n",
+        "\\end{align}\n",
+        "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "id": "VnbedWiB0oAF"
+      },
+      "outputs": [],
+      "source": [
+        "exc_post_syn_params = {\"tau\": 5.0}\n",
+        "inh_post_syn_params = {\"tau\": 10.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nJ1JwSAO1qNi"
+      },
+      "source": [
+        "We want to connect these with a fixed probability of 0.1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "id": "ciwtEyzB0nte"
+      },
+      "outputs": [],
+      "source": [
+        "fixed_prob = {\"prob\": 0.1}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HWvUT89Z106p"
+      },
+      "source": [
+        "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n",
+        "Each of these synapse populations all configured with:\n",
+        "*   `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n",
+        "*   The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n",
+        "*   The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n",
+        "*   The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 21,
+      "metadata": {
+        "id": "rD6K22qZtxId"
+      },
+      "outputs": [],
+      "source": [
+        "model.add_synapse_population(\"EE\", \"SPARSE\",\n",
+        "    exc_pop, exc_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"EI\", \"SPARSE\",\n",
+        "    exc_pop, inh_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"II\", \"SPARSE\",\n",
+        "    inh_pop, inh_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"IE\", \"SPARSE\",\n",
+        "    inh_pop, exc_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbability\", fixed_prob));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FiAsrqRx5OgZ"
+      },
+      "source": [
+        "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "0I-7lZP4vWE2"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load(num_recording_timesteps=1000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1JLVx3u1281A"
+      },
+      "source": [
+        "## Simulate model\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8HhNMK4C4d6f"
+      },
+      "source": [
+        "Simulate the model for 1000 timesteps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 23,
+      "metadata": {
+        "id": "v0lT7gaIviev"
+      },
+      "outputs": [],
+      "source": [
+        "while model.timestep < 1000:\n",
+        "    model.step_time()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SUzXrYxr4kO5"
+      },
+      "source": [
+        "Copy the recorded spike data back from the GPU and extract the spike times and IDs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 24,
+      "metadata": {
+        "id": "bDJLu6Kwvn7W"
+      },
+      "outputs": [],
+      "source": [
+        "model.pull_recording_buffers_from_device()\n",
+        "\n",
+        "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n",
+        "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jS5OtCX15CCJ"
+      },
+      "source": [
+        "Plot spikes and rates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 850
+        },
+        "id": "9rWE-Rvjvo5I",
+        "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n",
+        "\n",
+        "# Define some bins to calculate spike rates\n",
+        "bin_size = 20.0\n",
+        "rate_bins = np.arange(0, 1000.0, bin_size)\n",
+        "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n",
+        "\n",
+        "# Plot excitatory and inhibitory spikes on first axis\n",
+        "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n",
+        "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n",
+        "\n",
+        "# Plot excitatory rates on second axis\n",
+        "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n",
+        "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n",
+        "\n",
+        "# Plot inhibitory rates on third axis\n",
+        "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n",
+        "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n",
+        "\n",
+        "# Label axes\n",
+        "axes[0].set_ylabel(\"Neuron ID\")\n",
+        "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n",
+        "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n",
+        "axes[2].set_xlabel(\"Time [ms]\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lkZXMKuC42jG"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "2_synapses",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/comp_neuro_101/1_neurons.ipynb.txt b/documentation/5/_sources/tutorials/comp_neuro_101/1_neurons.ipynb.txt
new file mode 100644
index 000000000..b447e4fd2
--- /dev/null
+++ b/documentation/5/_sources/tutorials/comp_neuro_101/1_neurons.ipynb.txt
@@ -0,0 +1,334 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Defining populations of neurons\n",
+        "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n",
+        "![image.png](data:image/png;base64,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)\n",
+        "\n",
+        "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t2ihZLXh5VD-",
+        "outputId": "510653d0-3172-4c5f-c101-1bfe66297121"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8GngV4fThkhM"
+      },
+      "source": [
+        "## Build model\n",
+        "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "q6WNelXsbjy1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from pygenn import GeNNModel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "261uLnJsgyeE"
+      },
+      "source": [
+        "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "EDpiDOK0gkEz"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial1\")\n",
+        "model.dt = 0.1"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LrfXpMqfjRBe"
+      },
+      "source": [
+        "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "tU2M4MgFjRae"
+      },
+      "outputs": [],
+      "source": [
+        "izk_init = {\"V\": -65.0,\n",
+        "            \"U\": -20.0,\n",
+        "            \"a\": [0.02,     0.1,    0.02,   0.02],\n",
+        "            \"b\": [0.2,      0.2,    0.2,    0.2],\n",
+        "            \"c\": [-65.0,    -65.0,  -50.0,  -55.0],\n",
+        "            \"d\": [8.0,      2.0,    2.0,    4.0]}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YrOQPgYBjuym"
+      },
+      "source": [
+        "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "zc-e5Lu2j_Yq"
+      },
+      "outputs": [],
+      "source": [
+        "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u8wu06PZkBnS"
+      },
+      "source": [
+        "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "GNBjEGWPj_3Q"
+      },
+      "outputs": [],
+      "source": [
+        "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IGKUIiaGkA0Z"
+      },
+      "source": [
+        "Generate code and load it into PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "d0mK72rYkiYe"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cNs18ywkkq6T"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "nWFVfYfdkobN"
+      },
+      "outputs": [],
+      "source": [
+        "voltage = pop.vars[\"V\"]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wv-hDOIe3Hgy"
+      },
+      "source": [
+        "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "99MBe7JKk5Ut"
+      },
+      "outputs": [],
+      "source": [
+        "voltages = []\n",
+        "while model.t < 200.0:\n",
+        "    model.step_time()\n",
+        "    voltage.pull_from_device()\n",
+        "    voltages.append(voltage.values)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ug6S1h-z3k7v"
+      },
+      "source": [
+        "Plot the voltages over time in 4 seperate panels"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 718
+        },
+        "id": "RsVbAbIPlEO8",
+        "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b",
+        "scrolled": true
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1500x800 with 4 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Stack voltages together into a 2000x4 matrix\n",
+        "voltages = np.vstack(voltages)\n",
+        "\n",
+        "# Create figure with 4 axes\n",
+        "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n",
+        "\n",
+        "# Plot voltages of each neuron in\n",
+        "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n",
+        "    axes[i].set_title(t)\n",
+        "    axes[i].set_ylabel(\"V [mV]\")\n",
+        "    axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n",
+        "\n",
+        "axes[-1].set_xlabel(\"Time [ms]\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h4yw3JiNpXOM"
+      },
+      "source": [
+        "Exercises\n",
+        "---\n",
+        "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n",
+        "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "1_neurons",
+      "provenance": [],
+      "gpuType": "T4"
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/comp_neuro_101/2_synapses.ipynb.txt b/documentation/5/_sources/tutorials/comp_neuro_101/2_synapses.ipynb.txt
new file mode 100644
index 000000000..dec918779
--- /dev/null
+++ b/documentation/5/_sources/tutorials/comp_neuro_101/2_synapses.ipynb.txt
@@ -0,0 +1,466 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Adding synapses\n",
+        "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "t2ihZLXh5VD-",
+        "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ],
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8GngV4fThkhM"
+      },
+      "source": [
+        "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "q6WNelXsbjy1"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "261uLnJsgyeE"
+      },
+      "source": [
+        "## Build model\n",
+        "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EDpiDOK0gkEz"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial2\")\n",
+        "model.dt = 1.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mki7b8R8xhAv"
+      },
+      "source": [
+        "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n",
+        "\n",
+        "\\begin{align}\n",
+        "    \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n",
+        "\\end{align}\n",
+        "\n",
+        "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AkMk7Ml4tOxM"
+      },
+      "outputs": [],
+      "source": [
+        "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n",
+        "              \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XboW6qxrxnok"
+      },
+      "source": [
+        "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "dWf4f4Bpxl7u"
+      },
+      "outputs": [],
+      "source": [
+        "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n",
+        "            \"RefracTime\": 0.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "B3hhcDILxeki"
+      },
+      "source": [
+        "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5AECcjzMs8Iz"
+      },
+      "outputs": [],
+      "source": [
+        "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n",
+        "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n",
+        "\n",
+        "exc_pop.spike_recording_enabled = True\n",
+        "inh_pop.spike_recording_enabled = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QypcRqLi0hgq"
+      },
+      "source": [
+        "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WpmzQu0UuPky"
+      },
+      "outputs": [],
+      "source": [
+        "exc_synapse_init = {\"g\": 0.0008}\n",
+        "inh_synapse_init = {\"g\": -0.0102}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58kevKNm0rfi"
+      },
+      "source": [
+        "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n",
+        "\\begin{align}\n",
+        "    \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}}  \\delta(t - t_{j}).\n",
+        "\\end{align}\n",
+        "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "VnbedWiB0oAF"
+      },
+      "outputs": [],
+      "source": [
+        "exc_post_syn_params = {\"tau\": 5.0}\n",
+        "inh_post_syn_params = {\"tau\": 10.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nJ1JwSAO1qNi"
+      },
+      "source": [
+        "We want to connect these with a fixed probability of 0.1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ciwtEyzB0nte"
+      },
+      "outputs": [],
+      "source": [
+        "fixed_prob = {\"prob\": 0.1}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HWvUT89Z106p"
+      },
+      "source": [
+        "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n",
+        "Each of these synapse populations all configured with:\n",
+        "*   `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n",
+        "*   The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n",
+        "*   The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n",
+        "*   The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rD6K22qZtxId"
+      },
+      "outputs": [],
+      "source": [
+        "model.add_synapse_population(\"EE\", \"SPARSE\",\n",
+        "    exc_pop, exc_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"EI\", \"SPARSE\",\n",
+        "    exc_pop, inh_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"II\", \"SPARSE\",\n",
+        "    inh_pop, inh_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+        "\n",
+        "model.add_synapse_population(\"IE\", \"SPARSE\",\n",
+        "    inh_pop, exc_pop,\n",
+        "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+        "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+        "    init_sparse_connectivity(\"FixedProbability\", fixed_prob));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FiAsrqRx5OgZ"
+      },
+      "source": [
+        "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0I-7lZP4vWE2"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load(num_recording_timesteps=1000)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1JLVx3u1281A"
+      },
+      "source": [
+        "## Simulate model\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8HhNMK4C4d6f"
+      },
+      "source": [
+        "Simulate the model for 1000 timesteps"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "v0lT7gaIviev"
+      },
+      "outputs": [],
+      "source": [
+        "while model.timestep < 1000:\n",
+        "    model.step_time()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SUzXrYxr4kO5"
+      },
+      "source": [
+        "Copy the recorded spike data back from the GPU and extract the spike times and IDs"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bDJLu6Kwvn7W"
+      },
+      "outputs": [],
+      "source": [
+        "model.pull_recording_buffers_from_device()\n",
+        "\n",
+        "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n",
+        "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jS5OtCX15CCJ"
+      },
+      "source": [
+        "Plot spikes and rates"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 850
+        },
+        "id": "9rWE-Rvjvo5I",
+        "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 2000x1000 with 3 Axes>"
+            ],
+            "image/png": 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HrirHnK2OJ6k/oeFjZdqxxi+TWbF4DPnQNO40lAVWRPI6ZCjIt/CdiQ9FJJGfmj7ZDxGytby9YDtGy3FrDLyP+VHIL6H5GNtcsDtwFio4anjDAjErVqyQp556Sp544on037/6V/9Kfv/3fz/9f3t7uwwMDKTfefbZZ+UnP/mJXHjhhSIicuGFF8pTTz0lP/vZz9JnHnroIZkzZ46cffbZ6TM8Bp7BGBUcJYSMG/2cVioh/PY8ljjUPvFCcshZuOx5TIKOYc3waxI2lAHMhDyhKGIzODi5F74zY/g7702Y3f4Xku9YDgctbEIMzWPAUIwmxvLj8LiegMA+rTAcEdMFnidn9LLD6KNbkp8vPdlwRjYE1NjBoqHlrQUC6/BryRrmnypyYDAxRAFelgd+3zVQDBbhuTKDgj/f1eAnXXMTh2EsLOvL6LxjdvK39o7wd8rOigZWoNl4ipnb6p0NR0QtUXhnnZCUNt92clFBDDlHWHnUNL69PzHERRIFZXS/iNSLSoI+kzBGzvmgyKKL8u+stSZjzJidOec/8ULyuaXcXnFH/nzCqcfBTY1THUQGaKe39ZzlhI5RZDBmTfxAt8cn4MhedJEfkGvGCa2f/dYnkjG/9Ql73sDJirUJ36lJ5ji44o5sD09fkdEM+MCex5JnLOeIdrjrOcMYf27A5+HLV0vqgaxLlhnZ3pV3bMUEPiwesuiixPEhteQF7AhdfFWGDyi1lnPIqmrAGLNOyJz3ZY5czV89I5GV9cmx8pZvzfBL/K5pnp9pbEfBMbxyfbLmleuz5/nses40HbDAeYjh197atJF36DVJnSUM2jnojbe9P8loq08mVaPspeO9+uH9mazDWCJF+saaT16STwrx+L9FC0zri69KfnqZ7JbjpIwerazlM1YkfwsFQSA7EXTd85hI72ezMxUTzNS4s3jvroHytT28Lh/I1fyue2Hys8xRFRP0LnNEA5C8NLwn+Tf4ROODWpEGWRc8fUVcVZd+L3im1DK57TkEPPqH7uPpQKFzgzMeksfPDSR4++H9yd92P5roXZetLX6PnbjNBBq/dKnILd3JT54X9KjDr2X0gmDasj6R45JEQTn0anlQiR1qGmIqXxm0U11/Bh7U3pW9F3zAAjjndj+aBUy9wHBrR6arDaxLPrccjQwI2CMwoHX7kG6mQe8r5gd9wwvyW8DnPAbw7vmn+rxOJ4gw/bPeg0S2XQOZPKxPJmOcsSJ/Zs5o0OHkWLyNKSIydiCT/1qWNgOQ5+1dcYlzVvIBn03sPwI77EjWtiLb/3zGdEBey3XI5JeezAfxQnYXYFlfwmPGDyc0yecBOMce89y1HjnreBGpJ2vG+nfeU9RLdWsqraMhsU4kP48yHse2nOc3sHwoVuWTDuzp93v8j8/2roHk/+MjNv/mxB4kQjVT1VtXPzEmJ4FAn/N4DtY064RMj7aqjLTvQeOS372qPxxIYjtv64Zkjz7xQvL3NS+J3DKU/F/jGL8jkCSSzY070vC+4lzsvDcf0EfHk533ZGcYfOPy9UU+GZNEwWDZfPqzGNs4pDd487Jsdx3YZ9yxjQJYfFWRPtkPEUpk94Kwiy4S6f1ceYLXPX15/s38KLQPux9tnLcGH9P+zBjbuIJoeMMCMccdd5wsXrw492/WrFly/PHHy+LFi6W7u1v6+vrk+uuvl3/+53+Wxx9/XP7dv/t3cuGFF8oFF1wgIiKXX365nH322fLhD39Yvv/978vmzZtlzZo1cu2110pHR6Lc//Ef/7H8+Mc/lj/7sz+TH/7wh/KFL3xBvvKVr8jq1ZFMoIIwNKOgQIhBmWJFBLC9Pzn0CMKUVQCA8YHhX7Y2r+yUZYBwNhEcEmVtr7gsEIa5zhqJFTYeQ/MqgbzMXgsnnBUgErdPGmIzN5avzjvTrAwnGIB4cGoi+dkxp3x/IbAgGF56smiAWlke7PivS16og7bK2mLpdbPSFlJoQ8LKysC0xvLWFFJ6tbD3skMsQOBz5vEN507dD4JqYOXRC3ixsY+qIKkVlQSNOxgjT38tfz45cKsdop5yy05LGHpYc6yCZr3DMjjwXu2Exhoe+IR/l4u19wza4WtByPFiQWxm0uR48rfJ8WKmlMY7jHp21HFwFDTDzhDPSaMdkxqguIYyAJf1JcoxHCKtDUdga0fesRXDw61nIH9Eis74Vf2JoV4WWLOqGjAGeN/gjkxueXNiA4CVbzhPMQdW1oEPOHG39+fb1Ey3bYFuU8fj8B4wLOvLZDToi9fHzjTwgVknJDg5crA4h2acrCHZt3VD1vpBZ+DGOgc5+7JnaSOosKq4VxMjvqxjGgJ9HHo1ef7Qq+FgmT4rkIndCxMjD0ErXbkQcpyUBRZQDQljXiScyRzSN0LOitAYVgu4EK9lp8PWDXlnDesUOqEj5ECw8GglAGn55uEYz4COkJjUNdd2WkEX1AY24wp7tWug+F7goK2r6Gwswz8gVKE0Xb2KP4M+OjGS56XWflg8JRT4AgzuyH7yfu8akFR3mhgryqwY2XJPX9KSbOJwMekH80LQle2B0JxDTnUO0F2+Pu/IY+DxkYC28x4/MAw5vmJtRqfgOWXZvCKZc1s7r2PxCNB0Y/ESPg/ajrTOuUhc1Sz4B2S3FfDjBJHxseJnoCEksk0QPwb88P78u3UiI9sOXiKB1hktWw50rm0QvSac8cvX+/us7QNNF7xPzHfYkcyOUcu5aTlQvbkA9JmKvW9F0xXr/nzGVqzNPoPzXlfvpdWfDdh5b17n162pNF8Evlo7/Ioj63eWrSvWJrpMaJ34vu5MgrFY59Xy1NsPHh+0jiQVC3jOgPGS1o8ABFbPWFEMCuJz4MDjOdArBp9I9qS1o1hZxrZis3zMWgf+VuazshKjLHoYWJfJtJeezNsjgLauPI/oWZp9xme4mYqkEDDv07IbOuysE3xfCILrzeLI+pu1B5Y+iXf3fi4JmrBsZH2V+YEVJNW6sadravASNEJ8kMfm71kt5JsNplUQhDcsEBMDGzZskPe+971y1VVXySWXXCILFiyQr371q+nnra2t8k//9E/S2toqF154oVx99dXyB3/wB/IXf/EX6TOnnXaabNq0SR566CE599xz5bOf/az8zd/8jaxcufKNWNKvH1hZAFCmQ2WdMJp0BYDnHBHJMzcu7bXmg+fB6DgjP+T4hPEDuP8GUhJqWeQ/Vth4DO05MuRYcDST2Ssy/ci1J+D1ZWWcYeAZ1tv7RX6+N/n/yUsyJ+jiq8qdVpwhxQILWS9tHUUnDdMVHP+11kzx5cx7kURB3FziYGQ8cjZc6PI2zzEvUhSwWjkFeGvSTlqA55yKpQOmR+CYK0VixwFeNq/JGw5sbLa2N5SVzxYVA54HAg7IctLnE5mvO+/NG4ahntHemi0InWVtdGmDw8uAgoE0NZ5XmKxMMYtPYi/KHL4hxwsg1jnNtA7lu2dpUenTBprVDg7rZ6emdoZ4TprWDsnxdsYtzhVadLByy5dX8vnjjHwOdMfwcCvoDbpDlZb+fogutfOKg1Ha+dbaaG9TU2OUGV0iWSWSlekF/oZzycHT2DZQGryAOAcnvWpNTV+hgCoyWct6iXvz57MQqgQJVdFp3uWdLdB+57ykapT5Nt63fHUjI51kHQdgQy1Q+TNrHnxWWCZqWuHKhe39fpDL46O8X+l5J4dKbKBbjxULmtbQAm5yLMxrWQdixySq/ECv3nkuCxqXrVVEObvv9dt/QS8EHXFikgZLxljynXm01mXKqlnK1iUSdnKEzmmoXTGvB/ooKqa46lLLZ3aYMo6tYBp/j+Ug73ddJNNzOvLBcL12b63sDBkZsu/eO/Rq0R4I4c46P2wTxSRrMV9so/beMbqols8Wj7ISjHDGdMVHM6Bp3cIFPwOa53aIVtJFWdCOdSDIbiuouqwvq/KbGs/0Z3wGGmpvtOtE0sKyPklpbUK1kdOOPrYdvEQC1kFYPgPA57gNlHboI6mROxZ4YOGcbSZLN3nTmcWOCN6ay/Rbix9yIgLWxa0YQ6DpSusRXJWnnfcI0CGQjASmxasSumnvyuv8ujWV1nknGnd+6gosLxAQavsa4t/4PncmYdB35sUkh/H4VnJQSLdhH4N3954FXBHLtMz35Hg6CKpEW9oyXYLtQSvwG9LRNVj7oH1qOoEkNumIk3IAJy/J8wKcqZXr83blR7fkqzNi9Z7Q/LRsRlBUj8k6vyd/YE8yjmJxo5/DWd70sexvoHWm+Rj5jud0oNML2LHeXxawg41staXzoOD7aQD0EKblZhIRKiiFX6pAzJYtW+Qv//Iv0987Ozvl85//vOzbt08OHTokX/3qV9O7XwCLFi2S+++/Xw4fPiyvvPKKfOYzn5G2trbcM5deeqns2LFDxsbGZNeuXfKHf/iHv4DVVCAiNlPhTBDPkPJK1jVTAWPkTN1lffnS3tD3WaDAcLPKNfm7h15NBA+EDYwKOK40hJi+x9C49YPFdEPZEZZTptkxgNfxsbyTAfMaHSoGaTxlYuuGxLAQSRRODiaVOcGB8+cG8grsqn6R7jeXV2tYjmiuwIHwRNakN5aX5cSXY1qGFYTX5jX5qgHLAWIppyFj0TojnoIaSwf8vlX9SUbkTS9l74/NhGC86rYcnXOT/+ssLQatyIzuT75nBSbSs1LPG4ZcRaOdKGit9eCa6Tn3eJ7My3QQwquWgUGJ+5Og+DQTONVjhvjM7kftz7wAoAWscH50i8gtw8lPXaWnDbTOeSKdc/JGTqi1R5ljVjsQ2Ij0ejfz93S1CwdldKC7GWMmx6vmZA6cmCCsPn/gjbhjZFlfxm/3v5Dws/bZ2b0DIbAcnKg25UoTgEXTMMJ6zosLblq4gWd3/ql5gwLnm+/BYEDiAydA8FytDGyvitZyVlgynh3RfKdBTBWdZ4Tpd3HbPst5h+9pWccB2FALVJ3drZ9j52aoNQ/0otNXJAaoF+SKyR7kIEbIIertF0OZM8Yag+lQJJ9duHx14nRB1jPO28RY3jEpkndY63aI7EDUDngGTxfVjl6OiMRUEODd0wly8ByYR+skA066KZNZ3h6GKikthyyAq03wTg4m6IxPBAIWXZTpflwJj7PJd6dwUEYHb/h7Sz+crG3ph/P7vWJt5lQH/mJonPcpvWusJgXdJnQuYhNQAM3oHCJZ4tnEWOKUq7Umv8dU3eqAJ/Mo3XaK58PVWc3M3eILli7k0c8ZKxI60xW9ej2s73m6/hV3JLIb6wjtIboFcGAFNGS1++k5L/+Tv8O8iu8y9WSkJT8YoLdA57MypWtSTBbzeDSfDeYzVuUanrU6IjCwrVhGI/y5lViDdUEBhh4coxtu7/cTUETyznvdfo8dx9BLsffYM9ZXNY+xdGUP8F3o6NZdKSEZLJJ8/8wrip0eRPLnl22CMsD4aOmHNk+MRx0s396fyOLOOQnPZ7vFAyugwbTs3ZPDgGDRjNl2otDR2Jkitu7azF01VoAboJNyRBI/F8tQDhiwjYB2eVhjmd6j56dlOOso2FPmOTym9oWEEpOsZFaLJ4R0Ja7MAl6R1HbZ2qJOY+1bGR48mmG9vyxgN3E4sRm0jy3GF7msr9Fee56kya8Pr/P10+kk5lWQg1+qQEwFv4LgMRUvS8HKovLGE8kEo87U9ZifNvBCwrGMQbLRFRKw1kVwZe+CUPcuWdTt1CzhxfgUyQtjvszdGgMKSXtH3pmdZqbU/cohDfw5Mugsw8f7Liv2zPCtYIbnlGLllJ1+aBHWc164TZk1zvCe/OWYFs3B8NHtZfR4VsCI8cRKlHbWckDTUlCbVTY82N7vZ0Nr4GxUnR0T019bG0FwWOLs8eecLcXBKSszB2tFa62Yi2h5PlYrBq/Mnsumt27IMtz4uaUfFpnTk92Z4gVyLNDOMet5/M1qGYC5x7YuY+cbK5yhKj2eI/OLGKMEDhN9T4rnyEVLCq8KRUREaokTgg2HkHO+GUWTs8rQz9rKoIzhWyKZjET2Ffljo5VyPT476bzydYum4cxB26syw14DzqJIVrGCdSDDHQo/5oA92PdC8rd9L8QZEnxPVChIonGDebIjGk7QgXV20Mc7M5inlV0PeSvit6nT4zMvGTuYXPY9/NP8hcIaNzyGNV8OCIacb/xZSoS14tpDQS0ey2t1YkGsfhY6p3rf+H4ofQ456xlLRcIAcKPvVwJu0W5Jt2WxHHsiRYe5l3SBrPxQhrf+W2gvmBa0Hq6dopaOovl32Vnw9tCqpIQOtvvR/D0VvB52huH+mphkAuaZqKIFHaPqCb8DX3x/SOg5vDeEPwbWJ1gW6QDsLUNZQEffvecF2kKt/iwo2z8N3EaS7aAr7kj+HqJD790L35k5rFvasnOB78MBph2hsU6ugXUZjViV5Bb/wL0IACuwxfYVnKLQ9dG6moFluEUfmEdbV+Z49t7J3zv0av6nB6CNQ6+GO02IhG1o7LdutYq5QVcMVQBZTkuMX5ZRb3VE0Di0ZHoz6+RxUHnTPjO7V6us1ZFIg6/WJe2Wod/FznutJ3sJoSK2bqwhVLXrycu6+inSXFDf4z2a7rHW0IX2PE9u6SdS1G30PYicLDLv1OQ7+GmB5axnWg7drwt8nLHCt2t3/J3fgrpMdnu+Em0DePoffCcW37PWH9vqG7/rcUEXbKtbbf20L07rKKE7HwHaX2jdH6nxZr3bW6fW0djXALxygN6zqcYOZi2ZPdxre1afN68aOqT36/EsX6SV0IR77QCevGpWd6jAhCoQU8HrC54xwpUVoYyS0Hj4Xte84oWtISMIBp7OuNGCRl9WysKl7B0MlnKjQTO0kLN6gIxBDlDAyVCfLDr/UofsPZljVrdkY4EHhQSZLRCIui0HZ0V4ysSyvqx0deV6G9ceaMWeGX7IUeABLqLDetgwCd3dwOBlolj0gPFxHwpndcdkVHnKjv7cMjosp/6m64sXPnp40wL6/hv8bGi9FuCCs1CbEdY8Px1w0J9zKyUdwOV3876hEmXxVeWKcGgftm7IBzIshwjKptnxh+dw8arlCPMu6vOMSHZq6Eqo1gb9HTmYN6LAR6zstBB9NnNJsGXkhMbWuNb3pHhO9pByDQOu+80irzybjOdd5BlSxBn0GrDfdWm0DmvgvKa+5/Etnc2Vfq+e0Ae3UAvxupBizoayV2mS0oUU123hw1Lk9f/ZYYcs2tGhzJBBlRxkJO8B8FCTeLlRBpZxo2X8sr7kbga8m6FM9rMjV1eHYd1MXyJ5Q12Pr9uuTY0nyOILhZtxCn/p0kYiQbudOOC1zYPst6p/Q7IIgHsvjsUe8lxjzynvaXtH8Rxy1rPWwbRjA7JUg6YVgOe00pU0DAhczTw+nOGtg3yQKzC4mTeKZOvGXEf3x91/YIFO9gmNwXuhnaks5yFfIFe1vMeZ2fNYUQZ7wPSIdeMOHfAdJD55QSf9XCzdaWeUlnHYN8vZop1NZUHJZmQD8zyRuAqzkG3iOevwu3YEYW2ocpJaQgOQUfg+Ep70O0NOYpH8mQWN6Epy1gsZZ1q3CZ1Tdopy8EQHiS3c8Zwx35Xrk+QWEd+By3C0bXhjkgUBMfoLnnnl2QTvz95ffHfoHgO+783C+ar+5OyOHy7OOeSI1HyS5yri601Pf03SKjxUjbA+D+AA64bFmZ/DuqtLJE8Plp4skqcfBM7Zjvdkaahq19OVQ2ebnbQer/H0Q5F8IEPLUh1s53nqgJ5IxqOxbn0PIidrvfRk8gx+WgBezhXi7MgPtWxkXQ9z0gkQuNDeakGtaVIH+PhzliEa957+B99JqIMGt/jzgmn6ffhdj2u19LX8Btqu1zKX99TTtzXN6BZ4oeScmCQoXYnJvgbglfmeZVPx+hGM4X1kWg7Zw141tCdT+Tmd0MCtTpne8C7wNvhVvaTZWB9oBUGoAjEVvDHAlRUhg7zMYQfl16oOsICdTDrjRgsaFkjN9BnV8/f6tDJohhZicGxEehelameAVtBx2aG+JN1yAIlkRi4yFU9fkVdsYVBu+lgiYMralsUYDtqBEmvsh4CVMR2siI3uhzJR9P9xISbuc+CMNVYUPKXBUkQtw80yOnS2HkC3ZvIUAT0/nBsrw8oz5DSueNxQNpQOgmpDOXb+/G7+/aNbsiqO0F0Qeh+00qkzdSyHCPYO/c13P5o5I1GRYznvQhf1Wdk+7NTAeoCbyYnk2amJ5DO+mLtrbtx9GswDYSifvKT8PIYyP62yeY1r654Uz2mplWvL4Q6cjx8uZmLxnurABQMCk9b3UEU4OWG3DtM8ArSiqxEuWys5xzA7bkOVaSGHPK/Ny6Jdvjrv6OQ5wzBhfFh8jDOQdcLDoouSfcLdXZABbKjz+edKrGOVgWUZN9a5t+498zJ5AZ5jD/TJ64YzaniPpPeAWPTNZwJ7I5I/f83IVFzIOjluJw5Y2fShICjmaFUbi2T3M+28N/88z8k606HPeK6heWvdDVWwun3Ksr6s+gBBD9Y5RDLHBmDnvUVnsdcq0HNksDPKWndMgIur8Yb3FC/0RtAFzgAA85iyJJSyIGhMkJSfsdrLat4PuarlPTtqcdZC96ABBzjrmga0A9Jz8OusZ6Y7TauaL7IzSusG3uX1Ifpn8AJtZXvk6aBle4kMX66stxyz/Pv2/nybJgb83jk3y0Dne1lC1QGh9WlnJKo4QpnlANBYz1L/nAJ4nWjrAv6v+fKh1xI7yWrJp+kuNuElpKdYCVKaNurqp4VLjMdORE9/wXvBh8ZHiu/WLW2t9Vh3P3hztnRijQfQE/Yy1FGC58tzhJPSokltD+PCdsxB6/DAnZfIJ2IHkPGf9i5fH/KCLSK+I1rbUpYcwvo9f4GlH+rzpc+lDrbzfFbvtCuxPR7FesruR7NkM4vOGPguNpF823urogPA1cqeTQAamNNTnLvWAxi/PD4q7XX1mQ6IalrAc+i6wIB5cMeEWB3bSxDQ51IHxhi8hAcePyQLNQ2g0vW5gfJ1aNmEPRYpJl7gpw4AanvbsqlYH4fc5MCMtQ5vnhZgbmihbAHrqKjyx57z9QM6GMx3Vh6N362CIFSBmAreGLB6hVvgKYNW9pjOHPiLE7LLmQFgNItX2ZlnnBVhKUCWwzQEWzeE+7R6hlbIAGPnkM72gZNHOwPSd9eyC9diWihoRzMbBuxwSvtZ17PyaiiBVvl0TPsEa+9DAitEK3g3MhV6lhaDFezcDOFfC3BWvtjpw3tfl6KTLBQE0kYFFNHnBvxWWN68oQi3zcwMH/29UPAB8+PLZ3UAarzh8MFPgOUc1Iqn7qktkqePkCIfO3/83jZTZPin+XdZimNsVmoowIjPuC+4SN6oRsaJ5bxjBd9rc2TROme9wIACX+BWcXD6eMFcTee45LomWTuyQ69OL7sdezG6P2+4gqZh2C9fncd5mYPIO5vs4OKMVW146Axh73JEDkzq1kKpc7OelXQzaB6hqyN4PHYM87nxKtOAg1CbRQtXGvQdN/q+DP6OxceOHCwmPFjG5xV3ZC3l+I4Yq21WmaxqBqy1h849vw8ycOc9djBG8ysrcI51I0sbFXrtXb5zAfwHmYK9n8ufP+tOLADmgLuxwMt7luaNUK/9gR6nLBDKDq4H12RyMOVDq8K8m3HpZcwCl3w3lDVvrbuVBZu8oDE7d1saiRUtbUXj1XIAascev8viv/qslLXT1Zeg6wu9Rfy2M6gQkpp9ebrGtxVA84KPoTGs77A+hsu4t/cXA8+6QiymRSNDs+27rMCtXk8oYOY5o6AbYN8sOcNjhs4ft3LzwNKJdNDD0kf1nCB/dOVSyJnGesiKtfm1ePcRPdfQmdCKyWoHZfFXXV0aY+PotS66KHGcLv1wWE/y8AN5pnVZvqtOJBxA94IVZWeT52LxVh1QsSohrDF1QNHjU9gn2BxWi7VQS1sdqLRwo+9YC60Vl63jjpK6JGeR20nCxuEWqTzXxavyCVWW/NKB5DNW5M9liF94wLTLLZ+7FyZ3soS+F+ryEEMzrKuG5FxoTM3zvXMZ4jnWuebndbIF3oOgSq0lOc8bFifVwLA3eT6oYMdPyMnxkXAnCARwBp8oVmZifWMNXjD806Itp/UAjV+W7SGfWZluHLKR2xst42uBcULni/f0jBXJQJNj5Y77ZvT5GBsfOh5s1dDYWjZZewy+eMaKYkBy85rEvziyP3m2Ljadso4xNZHXv8bHEj58+LXp72taIVwvt4s5GRzJ2Egc1LaWSLa3MW0YK5g2VIGYCn5xoBk2LtQTCTu9tVMJTGHXgG0UIXMA7TvY8ek5VAEcaLAUoNiLkRF8WPjOsPAMVRKEyiqtFj9Q6OFoZ0j7xXZlawhleVjvEskbBuxwEpFMwtQS4cBtJsoEqAWxzrLQZzrLycsCDxkYIZxohyw7fdj4ZmFnjaPxrOmCs2K8Vli8XnaEAS9vuyJ/H4lI9v1ZJ4RbAbDDBu0EGUftDYfPxEh5wE0rnnx/jhVkbZZWvMy4ZX2NVgGKJ8Rmw3oZ23yOrPXiWIwdTAyAOT2ZMzLUbkRnBeo2Rx6tp4pePW8g4/JKfTE38CTiG0nsREGP7JDjQ+NIf5buRQN0pZF3/sqCDNbZ1C1rVvUnZ1IkMzxgGOsybb7LhN+3fHWxaoTnoFtaMQ40jwhV31g8AhnDHCjR7/cMcOYPoA1Ne8jYwh03D67JMplxXwZ/h52nGBOXDYvY55jXBScLV6ha9B06hxrKqlZ4zp6j3ANuO2BlKnvBQA4yIvCGoAwq9PRlvKG5az4AmrR662PfcTeWSBbI4Qy9sox8LzCi+XZNsueQFS2SBDC0883j7+yon3VCsaUZ8BC6G0qkqLvFyBMG63lUuLa2+3Sj994D6/v6rJT11AdfBE+xLvT22s4AP72fzWe1673WwRDmJZs+1qChofzYobYZ3Ifc08N33pOnT80frHYrMaD3tJkAP0PIkah5N1eTsazuXphcRo3A03Tm6ck6Ddp2GFiX6HP67DOf0rTGd0+gzSRam5YlMOHZ3Y/mKyZFsnff05cFi+Eohw0BfFl6D8s076J5DyfWWq0AbEylkebDmke3zZRchn4oKIhgkM5m9+YmEh+gKWszLGLzJbZn0eoZPxF0wD6hxZoVbAkB1hO6k07rOV5AC7qrSKbLQV60NewW6IFokcqteyDf4HBHQpVlq2j/AidbaL2nTJcN4SV0JwaP5eHE4ufWd1hXDfEEPT+RvF5l6dfed63zxbLeSkLkeYpk78F9U/BXDO/J2oTtvDcvn9sbfgz8hLxcfFWe5hnPTFvcZUSvAX4TJNp4baYAVnB5xdrEkT56oCgnrO/pv3tV2qt3ZvqC59/yvi9S5EVoiYdKV+Y1Ma0PvXVYvEzTTFrlXC8PGDEwX+azgvFR9fLgmvxdYNh7JBdoOkXnCYy9+Kp8Enp7R8PWIh05ZON4tjS3cbOA14EWoEjGDiUYaD3F0tGrSpmjhioQU8HrB9oZwgzb+78Gy6kER2PdeHb1znwWYExJKgOcLC0t02MuWAsMhx/en2/lpcFbS8z9EPy+rRvCPce1cLSM29B78NmexxJ87nksr4CyQ0r32baMZS+Y5GWQ4u+eAqLHBISMIo1vvtgwVklmob3nsXzLCza+Y7PpAJou9P0TCLbps8PBSBinlhILwPdxGSK3AtBjX7Y2n3nBTviUfuv59n0hpxfmhSqNk5dkijEyIcvAMhr1vJmmrJZaFt0w7+JsK8sxyefIMnygeE2NJzg+MBhnnMaeH/08gmJs/FmOeSiX+FuID2NsZPJrYyjk+PDG5eo07WjQdMP7HHOXEwyH9pn2/Tf6Ak9L4cQc2KkMWNaXVQEefq3II3TrG8aB5hHaMe/xCMbBjS8kvb89R4pnNIXWhPFRUYVDPd4wOGBseMBrxJlevKq8moWdLKGLy8ucpQy6pYtOkNBJHd7llRbsfyH7/zkfDGd5soMUwUrc3/HcgB/UKgsGefJQ38elnUV8NxbwOd5onWq1nNSZpnwuLR3OqrJrm5mNN2N2vPE2sC4LhOqWogxlgRWN01gce9/f3p8ZzlpvY9AXu2t9OIQH/U59bnW7P4vH6wtivXUzD0JCBtpUoIL0/o/7wZAaI0I5QDz9tuwz3QpIJzNph2TsXvKavcqWmOen+wwAegLu+0qdg/fYbSvL5snyOfR+pjmcL26V6AVdGZ5rOHNaOxL81yRzsoFfoPWWpm8EfeDUFikGjlNbop5VCkEHsrLxLZnWbLDVaj1dNgYn4WjZzXxYB+TWvCRyy1Cm/4XeE0pI4WA/5qHvhvJoEvYNkjkgB++/odxZzgFFvn8Dsk4ko+uBdc3dPaV5Ylm7zbJEFowB+XbGirxupIP0nF3OCYQ6mCOS0QzLcT3/0LkMJcx4oGWIdScGywbtE+B7IbxAHONR4ydWB+O5cPtCy6Hr6WYWeGdFzxN7NGN2Zmvhu9CBWtrySZReQt6q/jzNs87IgUnuMsLr2t6fVX8h4GFVnvJ9oRxc3rwmkcG7Hy1vVRgK1IeSVULJJJiblWhh7YnlS0GETMv6GL8WntOy0AIESaFzlY3JPoBZxycT1G1Gt/dn60Z1VHoXWGOd+nxjzQjcMs/XfiHGjSWH+HzEJPZYoG07+OjKgIOAzVRJVdAUVIGYCo494NBD2YbQ0JlB/H/P6W05lUIXyvHnvZ/LmF+ssEelxOR4nhmy8A0Z0FCMTl5SzPZn3OC73lq0UgkhrS+YZkUs5GTTmUypQFQCK3QXjg4ycVYHnJLtM21FJmSgMm4tZZKf4csJQ9kfLGDhfOd2UaYzsJ5dbGgpydb7tOAPZbcdei3J6r3t5CIdasEvkuwVSmI50xiKtYifhcmZXhjfcs5qemWnvzW2dYEeMpIBtey/Uc4JbnMFpyXTsEcTIrYi4GWRDqxL5nrFHfmWPta+ssKL9dSkaGDA2d81L3/ng65q6Jid/R4KDFuBSKu3rwV4nvuhawfOwndmbRrGx/JGQSgQoDP5vEq1he8sVtp5xhNXp1kZnpx9pzNOvXkCf1ZVB58v3JEBRwLGtO7xmneq7Qzg4LbO9NJ8IOQw0E5sy5Dj5wbWJXduDO/JjB2dpceVipZjzwoMg8eKNAJ6DQc6+pCXOfl4jaG2I9b3vDtpyjLiPLnOlZvb+zMDBvID9ADDhA3EMlnPsnPRReGLjq0AZuheKL0uT0/RsnjrhnzgAvukaYvvxsoFbusZLvjdzPtE8nzA0j20HFrWlzgooHusWFs860zXvGZ+N/aTW5rFJGfE4JKfAR2F9ETe51pgfNAJjF7tRNF4CFVxMW/3qp50kIwdbWUJNjgfaK+yayB5JypI61PZ81Yrxs55jXZttfw9dvpOFYYzVmTBCMwDsmPRRYn+ju/qu4lig3nNPjsdmM74ul1VCrWwQ9wKCnrVCxYwzTH/6ZyTT8zY3i/y873JZ6h2oCnmfnJALR2zXqyEf3hd9jBkitUiWtsSbEOI2BnBq3dmCQ6Wnm/tUYjPYVzWQRhwZqxKQsvp2UzQFXBPX+KAtQLkPDd2qkO2aIenXrNIPpmjLhlfqUt8pQbLWXa4PvMNqpLbb1fyWTjRPDHUblPjztML2baz2qDq8WDjaFsIWe3gVxbNWHqsdy6392d3iIbudWPQMoT5CDt/obtgfKuyDDJZB2AZjzoZUtuQ2m7lKg7QUxp0rdkOXeCMdTMPQskE/HfLr4Jnln44qTJDVSvwWHbesTbmdzowyWDpkSLFxLU0IaaR3DE5lvflcCvD0L1KIuWBem0rhID1AvAVqWd2uF6TSPI7qi7Yl4IWy1oP8Pxa2icY6/DXOlcIN14Sq66+TuVWLWsJuHx1oqN0vzkJroXkLuZk2SoaN+gSwd0iWNf3ql61DeLZjkhswD7wHlsJfWXBr5DvtoJoqNXrdU9kV9CAAwcOSHd3twwPD8ucOUZ2bQUZ4GDXJxPFZXIsERoIhngHe8PihBngUrOaJAYeGEj3wozZTwduP7XhLK6JSD0/Hs9LJFEgpZ4oHJ94If/9znmJU9WbE6+jY3bC1AefSAyPy9dn64EwhQJlKeEQvKv6k16UEAa3DBXfB+EwsC7BeVtHYpjo8bHWhe+03/3pUxPFmdcOKPtuCPTe8+9wIHU2HDWgH8Yv8N82M8lcgMJk7QPjZPXO4u8x89N/E8noonNeonzxvPA3/R38H3NNoUGHIokxumsgw/sMoi+R/NxvOzlR2Npnitz0Ujy+PRzE4KYM7ulLMjlFErouc9jGzFkk+//uR/3x9Trv6UsUepw3/O3pryVnYvxw3lnl0RGfv0UX5ecF+oShoelMJMm6+uiWPI74PHugz/PWDYmSM7Lf3iPrTDLuPD4rkp3x6dCAx8t5/LLxvP3G/3ndvF8h2tL89/BrDQOH+L5INke0h9Pfr7Umzin0/9Xr2d5PGcWS5wkWfkJrHViXyTsLlwhwga7bOrJ51VoTx7pFN5q2sW5vX8BbWtpFjlswPT4/XdDnY/3JiQHaNjPJJvOA90vLDOsZ8ADQA+iE11gm61nH6V6YOCdYfnlyBPhkvO74uyQoqPmFdyatsZ4baBjsY5lsmHm8zQssOuQAsqYT69xhjBBfCoF3NjAe8F5Gf3rvrb3U+8ln1BorlcuNn5aeluqSEtYHNU17v5+8pJEY8NPsnTfv83GGfQudy+39SSbtxEjiPIB+IVLkedD50Lp2/HCGK8yxY062Zo8msX9tMxvOI+NZBq1rNiM7mpFZMc/qZ2LltUgcfWnQ+4m96jkvq0726LnZtTF86dKE37S2i5z1/oSu9bmz9NXez+XPP9OWSF5XgjzT9gfbYL2fnZ5M0etlPD68zrddLDx5Nqcngy29WSTht+yMs/gb5IW1p55ddev8TM/U/EAkbxO1deRx7vFOrR9ZeiN0DW0DM3j6X2qnMtSSVq1aVui5ePLKsgebAX3WeDyR+POj+RXOEsvuGOclgOUIy+zQ9zRfCtmokEsi4f3G+bdsNx5f20marscO5PcJ34UOXkZLzG9uGQ7jrhk8e7Ywn3ucG0+nYR5v6XgWhPRInhPbtyJ5OaJ5bZkfLUZPsGwFi1+wfnXOBxM9c3RICnaUtn81jwvN1/Nr8fvbZibrb2nLrjPw8B4rt2G/WTwffFck0ZXAC7XcCvnKLFkXY6tYukSM303TJ59Lkcwn6Mk36+zH6hbHwof0KwjNxA2qipgKji1s3ZAxsbddkc+KDUW1OQOgrNXPdKDW+IlMLCsT+ME1yTv5Am0rs09H1nUmSPfCLANl3wtJ1BwtwThTxLo7BaCzptNWL1fZeOMsyPHDiTP/h/cn42+mbANUvPzwfhtPusSX1xZb4WKBrrThLNiJRmuUM1YUqy40/rn8Nyb7g3/XmT/W2nhN+JtIo491w8CoZY/kMm+0wqGz+BnaurL/77w3n2XD89drQdu9cXUXS9l6ynBl3fHBGaqhbAfQaGwQxsv85Tnz/3P3rUyGM0Sf/pqkVQrIbIKTsS6StpLiLGoPN9470HZCtwzj73MrHeDIqpTSmSv6PCN7zasEAO/SbRRCfBaZcJetDWdd6rnx7/od+KzsTiwem88K49i6GN46n1ZmF/CHzLszr0josue84pnq/VxRWWf+DHoB8P1J4LWd8+xMLz1f5neaR1gZmjr7EHQ9MZbsm+4zz3TjZYmW0Tl4y9REMtdnDRlRlk3nfe5lTgJ0C42JRgYefnqgK/pYZugzdcUdjctta4lRhwoNTVfM0y39Q8sovjTYymLH3iNrmivcUJk1uMPnAwx6rFX9WeUmst3aOrI5hOgQmXCoEoylkxBfssDab26bxZUV0AGtCjENmCf4ML6n+R6O8NiwyC3diQPNGgtGq0iiJ3p6Gvf41vogrxU0vfOehM/qKrFdA5Je8ju8J9/PXuNP3zMU0yYLlU67BvK+Ud3WBJ91zi3eLYM5pxWdtWL2o6YHPrMhvb2ufrJsss4xg0ejlm5RRs+YJ5/z9I6ae30ZCKipn4DQ/Fm33Loh26tDr2ZZz7gUXKR5PHgAvWRyvNhGkHmhec8GzZ3v2ntuIOPdnCGuK3lxiXM76b6x2dneelkPAR2NDBXHs84o2vDhTlGrbU9Ib0YVJzK/GT98PrlFocWjdEUA2rq1dkhOxmucIWu6vSPDuXV3CAPfNal5LOaNChIRKcRU9JyhY2MPYaf2LM3Ocu9nixW42/uL1Rr6riDgsuz+gzLQdoUn6yxa5Cx2tCcCTrgt23SA+cX4SIaDe/rydhfPS8sQXtvAOkkdxpBLLKd0ZRm3C4Os27zGbydvnT3uYAAah3zA3MruH8FzLY0KlZZ2+zmGkI1T9izbCDg3OC+erYXL32tSPN+8P/f0JcGF204O+4xM+7aW7RXDrOOTM7ToooQu0NGAz96XLk32DRUm2g60fFRsY+68p4hP1pvRng3VG5aeyO02LZ3T2ivPr8Xvx10sU+PlbfzKKugAsLssns/V18wLddvV8bH8Twac7a65RR/MnJ7k91knFHlOqJKL/W5fujSvy/L39LlknyACY5oWrrgjGQd8B90+yux4Xtd0+XMFVUVMDFQVMU1AKIPFi4zrKD5Hqq1s9dh5eJmgXjSZsyDBmELZOKHMUI6WYx16LqEId9n8rfXoKD+yYxCVx8WcnPHp4VWv7WgqHTQ+PvFCcXwRO7MglHUQiytvrFB2nZnlX0sEK2cVlGUM8X6I5H9/cE2WwXzmFfGZHB5NNpOZEMoSsrIiQ9mZzWSPilDGSS0JUpZlFmH8OT3J/Sqh9+iKGKxBVzhwVgpXGyCz0ZujVYGG/YSCZGW4ezgK7ZneIx7juYF8hVhsRYwel6t7dNZlqLIM9GHhJYZPW8/rDLqyDOPpZDrjPV42lMYPzqiI5HgmqggsWvR4syUrdFYhXqOrG2adkBj8/L5msgJjAPRVa0mMHgDvQxnOvYxbXreIv/ecVc/VtB5YNBnKwGy2Yis2GzeUDagzGnOZfofz44TkHOOJ99zLkrX0izK57skFa69i9BbGDfYo1UucPdDZorGypUy/9DL8ATo7UKRcT9Og9b5N1yd/t/grZ0SLZNVfuhqTeXQzOhhnSZ6xopF1W8vokPET4of8DGfEggeiooflFDJ8Q+emjI6mkwmvqwhi16bfpatodYUln9MyvTGmKogz4kGreGdMZajFZwBMTyJFXUfjQuvlFs3wmFzNzbzL0x9E7LVNt9qBK3BClRzm2iSs25a9u9nM+DNWFPkZxjm4NwmQiYSr/DxZw2Dpm3wurQoMlhuossSZCdlY07HRNS2Ar8RWhvB8p6P/WN+1bE2dxc482MJxjL0KYFsF9h9X2osU9SXv/Df7bl29/7NnKYBez/NPXWFhVfaKFOVDjO+nmfmH/ESh/Y/RV0Iy3tIL+N1ciTd6INu7mH0omx/zUJFszzAXrdeIFINluuJZ74unXzR7vsqquKdrp2j7n/UNkaKPJcZnZ9GuRUsh/Fg+LYwz79TMXuOOGuxbqLUma9IVKTH0GqoeC52TEH6YJ5RVmVdQClVFTAVvHIQyWLxMPo6Yc0aVzugtA4706ig8Z6DprBdEkxGdR9b9wLrMGWtVDIQyQ7myxFv3oov8zEYvkxXrsbIMdj+alAfjEmiuukAfYShZPeeFK41QwTI+lseJxnMI+Dl9eTVnwXoXufHf9GfWXqMPsXfZpB7Ly67Td5Hgud7PFnvBhjKGlvXl75nRtI1MocvXx1dMLOvL33nDoLO7kN3wpUuzLB2M42UJ1SS/HmRUIWvNujsodC+OBZh/e5ddVaHpCuMfGCx/z6r+pMT5ppfy/OOKOxq0V0uMPa5m0VU0oTnqbCLugS2SXeTNF3pv77fvjtHjadjxd8m7d/xd8rt1b017R/NVarz3Xs/h7f3FjBieq+ZPsXwae4zKGc6uxrx0H1sPYt5pPcP7pnlESAlGxjocFXxXE4PFm3XWP84o9gBnL+313nDQjx/OKiv1nSvWe5rNMGbAOZsxO5kP7oiZHCtmUFnyUKTIR5BpxzLS2hONn5Xri+vVa9M0CnyE+uQzTyujVc4sm3WCf3+HSDgbED2+Ue2DTGyuHEAWqDVnBkuX0PwXzzw3UOTZ+AzyGOfe05u0TNmwOPk75qD3xKJJzTN5kdYe8BrLLqctww0DnBmYpwbs4TkfzGeIN8NbWcdY1pe9E1mQDNA707uYGhVpvGbQoEh271sscJYkaKP7zYneqXUK69Jori7BM9AfITfrk4nDgfGzqj/JJgWvZBrh/4f2i89eM5mWWp6FeH3oXfpyZfCzlC/TfUraQW1lHnsAemntKFYx4p3MO63zJZLovHyXAGB7f8KDUd2j9SMLF6z3t8+UHO8CzXBmvq6iB+j1M++11jZdGDuQ7C/LYouPYm1jB+mPNbsqsgy8ZAtUT3BFgz6H4NWan7ZT1dnJS/KVKxY/Dc3b0smZLpjeMHdUSDw3UGwBpG0szFnb6LH6B9MC38uHyhDvrDLos9CM7mOdI8vW1BW37IhddFHG1/Fez161gO+uWXRRvtKedRTg6sjBzLblO3ew7tNXZMk8MfhHRddLT2a8o60rzz+xv1s3ZNUWqNyCIxs41PLBwrd1VxDA4yN6DNjSInnbC2DZzNY5YZpHZdjuR+2Kys55SSIf5sEyhfec7agYGgjND+9mvblzXsKTO+c0/D0N/TetIqplPAnjghegWknffwe600kenqzxwJN3en0x55Sfgaw5/Fq+gnjrhvz9WKALzz9i7feyvrCOwP5MDsDeOj8JtjC98jhoL4o74BiPJy/JfuIcsn8NvrfNazJ5ovVz7HfP0iLu+MyK5H1OWtfR+gr4TlmVubVPFUwbqkBMBcceLMGiDywbedNR5CwIOcWtZzAvPL+qPx9EgrOza65d6m0pxJazguGevuwiML70tWzdGkdW2aB2Wqxc31C26pK7nA+XlYec2uzo1YG1GAHNQm/rhqLjkvGEANj8U/0gmXbKcquzkOHK4I0FsISiiK1IwNAKXXCOMb3PeVzvOaxz0/VZIMULfPB4rBQM7pBCoMEKGmlHMQeO4AQaHfKdr157M/037eQooyuMr1uBxQArJXyJoEgS2Nn9aHY5LC7K/dKl/hz1nuGg4LywUo61ozWFZaCElHBuWySSKXAtLYnR5RktZeeT9967VF1f/H1PX3KecRGuBl4HOyT03oOeYezzZdLgaWyYh6DM8eo9o53xfJ51EBftuqSWzBfnDm1Bjhz026Nhr3l8kfwZZaMAl153zcucs4Ba/ldXBjRrPDFoeQba162u4IS12h6wo59h/qnZ/0P6AfBjGcRWMgK3F8D80VqU+TyP712uys+xI/WKOzLDygsIaJ6Mcfii6/GDyfwxVktb9hmCX2XBRws82VFTP735WrLU4neW7oQ2GdBlygI3y1dnbfUWr0o+C8mMsstpPbASNUSyC+AtGdJMQoGnr+lWpXjvgcHi9wfWJfzjzCvyvBzyqK2j4UxobCDL75j58N5x662yYBlA65M6UBUK4nPiAb8vlj/x2WvGQa7lGfN6rzUtdDjrXaDZuuT5MoJm+pJc7Swsk0+hViRWyzBP12KnH+8H2xjtXfnWOZatwbp0fTKp0NABgVhHp/4765MIBHfMzvSwZm0/2BjchgWOYIuPYm1TE3mcTCfT16Jj/O3prxWdg5aer8cALfR+NrPPkJRiJS3wvGFPf+nSPE0znYCvtM9MdCwdQGc86oQ0K1HLmk/s+WZa4PY/PeflE3RCNKF5VujdOhhs6U7WWYRMOPRqEee833gv9Bz8tOav+Q4ndc2YnbWBYh0FwUbYtrwPrEdaAXUP/7DrOfDztisSx/yugWzO4HEIQrd1FWl4YF2GU4uPhuiHgVuWatzp9pGeflNGgyG7zEr80IkK2n5gulnVn+kY+ox58wjxOz5by/qSMccPJ3MBn5kxW+Q9n5a0Yp95vkiGe9zTpNtYx7ZlZDvGmnOMvBPJ749nK1p7iAQr3IF05GCjrX0DQBdo+bprIP9ebhHJ5x772dIuMvxTP9kKOEBig05AYbpAIH3hO4t4PPRq/icCYfgc9DwxUuQvGOs9n04qYbjrRhmOtU2j+TXzZLQVhZ/B87kcjb1ZQQpVa7IIqFqTRYKVJQTQZYv6QizOruDnmim3Db3feybUnkrE/n9I0JS1u+J1WxfPhy5z9OYMsEqlPZx46/Te2SyedTmvvkjTLLltKBIxl57yBbO4DA3z0mXG3nwtPHqtpXjd6QXgkldIj7bNQnCdUnxfaL/g6KlJkrkx+ITfjiIG2Oj11mfh82japU0HrDGsVg7ctg+XBN7SnY3T+7mMlkJtGkSKeMfvoTMdM29Ni7FtlZrFY0yLhdClikxr3E7SupSQxz19RfZeBLqZHxzLc6TnaPEBkeLFsbqsG3hFoCp06bs1Pq+pjMc+8ImkRVhru8jvfLqcd/GYzbRTKgNP/uj2CNa5B1i0YJ0VrR9wSyeLx4VkG7fQQMuastYn05WtIvn/M39JvaS1pCJBt2trtuXU9n6/DZF+LkaGhy65Do2nz42W2dNpHRgjZzzgPWJ+AvqyeHksvsrmXYafnqX59l1lvJzHHztYfsl5mZ48ncuGNc3H6uJei7flq/Ot5kTyLdimI/uPZs/KdHXre7olpb5cN3Y+Zfp3GWidxuP3+tJgnC2R8D6yfq3lG8t3nDVv7lruYq26PRi3aYk99yE6C8kHbtEVOlMhsMZmuRPDZ5ppTR36bHt/1gIR4NG4hzs+l9wO0dMvPNtDpHlaZp6vL30Xyc8z9kyF/A8Ys4zOQjojv4/3Ubf6DdlEWtcsmzufN6sVMejOu5g7BrhdJuaMv7G/AnQHPaS9K7GJY3Dq0b5ujYRW0hYtQwe39qaMB2Mcq/2t10K6jBYsKNN/Qp97eErtMMn7zZb12Re9W/M/fUXGn05fUc6/tY4u0pxuFtLbPVvR4incnhn6neYXTK8aD2U6kGVz6H0SyXCweJWvt2j+6vmnLNsJNIgWbNOx5fhdXls6a2/0/HFGTl6SdGXQcvxY2pm/YlC1JqvgjYFQdFRnMyC7W8SO+IIBw4dRl3LgiPz2fpH1JyeCKxThDmXV6GyEUJWPleVi4YSzkto7iuuuiZ9phb/pOQOs7PaYbDWMzxlQ3vc4kzMEOvKuW5MxPeBZXOJZM/Cr4bK1WbWPLpfX1UseXVoXMYcuYMQ4aWZGLS9M0c4hdEGtBV5WCNbZhizMmZnA87KzsIec3fDRLeF2FN58mK5F8pVR1t5YdOnRqgWxGTWhOaN8HuXqfBk9Z5YiG52z0vmySOCTsxuBW49HaMDaY1pfWBksSz+cz3rRmVgelPEq/bed9xazwHSbALQ55HaHGOP+G/IZoFi3VcHE43KVHNNJMzTjgV4vMr84W5B5tr7UdFlfsSoIGa1bN2QXcaL6wuLbAGS14/wCQrSzrE+kPpX8f2qqPFtNj2lVcJZltHmg58m8XcTOLsUzi1clPzlDDMA0r9eDbPuTl/itjHg/eD18dvETCgRavXE7CytjLCRbOaPS4gNbN2Tva+uiCpCr8q2ucBaazfrfusFvQxSTqa4/C7UE4TEtOuBzowHyddYJzWU1T6clFWcq7rw3T5N8JsADNn2sOJ9Y+bO9P7to2qJn6CPMV/a/UNThQryc8cKZ8pY+tmFxlhlaV3/HGqFT1ZpYp9YnY3VxrfsxcNWRbsE2nczK0Pc0DjSthXR/HoNpSWfb6iz63Y9mFR6h+fC7LP27DHC2UGW3a8Dmhdx2eeuGvN1l7SPmiExYri7kqg44zth2s0DLXay1Lsm4PUszPh97OTAgRGd4tzU3ZK3DftD7rXVfa0+sM4S/WRUN1ry4PU7M+JbcEclnwHcvDNM4gPfw/hsyukY1oM7SZjvAa2lWpg97sKwvsy1qkgUKh3/aqCitJX/Tmf6xoM8w44Q7c2gA7VrVApi3bl2pqwRDNhHrjCG8cSUi9DqmHS3fJsayll2xFWbYX1R6c9UAeMjK9fk5sh5iVW5ZAP5h8Y20+rOx3+ARsDd1G1reG9ZtRcI0mPobOopVzHseyy6n14CqpFgZZe19jK7JZ0rjKbXDZhZbpnodP9hGQ8UVKnxCPEikqKNDl23Gx6FtW3b8AwfaPrD8U4dezfQRfO+MFUVepauSgW99ob2WuV6FL+8TzuLiVck8LLsde6u7q1h836IBXYWHfRaxz7Nl1zFPCOnT2meqO71MjDTawe3I5PjD6+JkWAXRUAViKjh2EHKgaQMGZXlgfPieVkjK2kkBLMcblISd92bPaeVcO3OacQJaxpRmTDC4UWrNPZp1GyjdGgoABeXwa7ZD5GjBEhqMT1ZWLYemBXqOev/ZIMezaIUDh5Bu+8Dz2v1oJoDRNuHBNXYwzNtTzOHpr5ULYx4HxikcI55ip/HogXYcMl6X9SUX+N4yHA6kaMOqGUernqdF13qvrb9bTogQrcbghqHMaOIgGRxPcMLCUYX3tTaCLvgpkpT7ouyXgwmd8yQ1DiynsYeXZs6p1TpGG1IcXGv27FsKI86X7guNdy1fnWS83TJX5E1nZsaYHhfl3trxWXa3AvZodH9CM6GgNyDWUWIFr3U7Mu4XvGJtYqRY8+M7UpjPL1+d/USg2TJQuBc4FN4Yug/xojLasmg0FIyygsCxvMtyVmgaeOXZhE6evT8b23KWYww4gQ+9Gna0shNIrx16BbfewHlPHQ7SvBNYOxM1H+DA28r1Df49VDTupyPHgbeW9uQdTBtlDt1YuvNkgf5cJONHIsWxIV9fetLHr8aBbn/FEOL/rCO0tmVZvTzG8tWSHep6/H5rYN2SZb12sHGrGE0rZbw8ljaYFnFHkUhRd0LbQ5HmdQOA1sVBB1+6NL8veu48F+ZLzN+mG3zXyTQh2rUCuVaAmeegg/EIXuCnth9Cdxp5gWdL/y4DnK3JiWw+GH/zmvzdPhyY3/dCOHiq7RhLhrPjHDyWW2BphxS3hgPPR2Bn6YeTSpj9L2SySQexQufew69ImKZ4DVpGlum+MRDSU2Jp3bJprYSvuvpeiMYZOuaI6fSGTB89kD3rORxjA+dlsgfzzLU0rSeyo3OuvU4N3n5ZCTXAiT6vMY5yvSbWnXTw2koWCdn6FrCtjDnhvPG7td4DOeQFGi3cIaDCQX+Phvhsh9oJW3Rs0Qz0ZNgjfN+e1jOtIDrf1xlyWEMOafyHzvt0EkQsvLEs9PCa2lSS8Eae48r1th3mvc+bewwP+tKlDZpoz56bHJOC3lMGobaomHOoVb72f/H3EHzkgBwHerw9tWQu7nxCWy4LdPtaxqO2/bV8sXCOdYhkNGslCIuEfTFs1/F5C+nTOA+3nZy15cY4kP+4NqBnqaS6c12tpVk/TgUFqAIxFRw7CCl92oBhBtHMZahligQr4QDO4taBEeu7se+2IuWaeS7ryzJUHvhEPnKto9FWxpFIpqCEeoTz/LRhXAZaaGzvz3q/D6zLK6vaoRkLev9DQglr198RyQctIIBxjwMueuSyz1DgCu+uTyZBHBE781kLf6ZXvjvDUqxDtIXxETiazh0oDGxYNWs8Wk4CNpy1omGdI9DJznvDgtnL7CsDpkNt6GKeCJJdtrYRcOmQ1GHJ8wfNpHeASF4pwhqR3dj7WT9YzIA7IfhuCL1uCy+WsVVmSMUGJaxsMnYkv+0KWwFlh6PuhwvAXHs/aztkPWfC1g35DPyyy7g1zdx/Q5h+ND/Wjk9tnFjnFOeZ70jxFG/s3857sj7tnuMlVt5oo17jMrTnFo1avBs4xAWsmBPfZRYKwoay+thRkF5yPZKNEco25Tl7Cj8brCx3NZ/mDGVciIxzv70/X92g98bCMebkVbpBXnP/6umA9W7g7bgFWXCH5UjIoRtypnkVpHp/t/dndLF5TXFsdoJox5Al12AMQicK3dOSypd7ijQPHaFzXrbH2nG3rC+RDyLSdFYng6cDWQ42phXtkIsFb9+Yr+skEFwmj5/L+uLvD/BA8xTMa3BH0ZmJPb2nT2RkKPk7HKHAw6KLEr1zz2PlFSTe31FV+cP7izpFs8Edi2fW1M8zVmROQgQIUIW7dUM+uBTrgF/W518w7IFOCgJ9dS9MZDb0MP18TcJ8NxZnmsdyBaZ2SPFdIPrdrIsAeN4ifnBL8xydgKWd4J4+L2I738FPRZqzp0TyDteygKAHXmAJZ33nvckZO2NFMVO9LHgFOdI5N8ssB1hOSk/vwd/LqjpDAX0dvE+hobfjzFl3iDJ4NjifYf1OneyiaTemWsC7Ly+0dpF4OmBbSyR/3vjdoPfnBpJnrfsdvLkAdxz8KAN9tof3JH4DXXGNdyJBxKMZ/H1qPOPp2kbms73wncmYuGcYMtmymzAP7+4mfr/F+3jOInF2lwWWT8N6F+DAYHxg1QPYoicvKQYreC0acDfp5LhtMwHK7BJOePXu1LUCjNAhcLfr+OFikhH2/fBr9h2fzQRKQoEOT6fg/Qh9Zv3uvcPTgT1bSOujMWPhnaP7szuH6pKXI9ym76NbMh+ITuCItWcrcKEKxFTwiwGdTacvb/cgdBmmSFhQ49I0zh6wDFKLWVtZpSGFOsTwJhrOp6nxfNYGA1+Yy+uyGK0HnmEsUq6Y81q2bpBUU6hJvk3M1g3JHjaTlb+9v+GUqiWK3pcuTZw5s06wx8Da0f6HS0ytoAW3X7CErgfL+iTV0tN2YwQxTjk2ErWg5bZY1r5h/FD2YSxow8q6mDbUqo8VISgczw3kM8n4nOhzdE9fVhnR3hXGvQ4ElBn9wDvKl09eks+EZ6cu4xCBhFpL4vRhg4Zpy5tfTKs+DTGt7dhgg6Oa+VcoY46dDxoHFs639+fL0nkf4UiGEaUzyTiYxC2iGDznHOaxeU3eaatpaPEqv+rDwl1dMidBiH7KLt7WxmAoMIx94LOB74Ce2Pgf3JGnn2YMPoDFayALUfnXbCBTB6NEMoOQL2AVycsOrfizw8NKJmDn/s57GlVXMxsl/VfF4wBz5lZxLCPhMBJJeFVM27UVa5M9g1OGg40cBA0ZZ2W8wHJkAZox3K13W3gDHewaCLeP5O8Cl5s+Fq4g1WtlvWXicLGVATtB2FmrgxAcWLXaGlp0oat/mIeKJHK2JmHenvKDerFyNbZtH58jkeJ+Ms60XjUdg9VzJnCmt5YVuEweP/U4DM0E9Pkz8IHuhXkePkB7iopUvkCd12A5MwExjhE4WXGxLcsE7YgvW6u1dt22j+0W6H7jhzPdHMFz3LPh2Qt6bWXyCgA9HhWkrO9g/LTaty1bk0hzWfghCAWndfIOg+UoZ12kHS14u/LvKGsZg4p4tDjl/bZoLURPlk03vCcLAMckGG3vzztcmwkIMk/TeGR9BG3VkL0863jJ8bRQZZbGuw6kWU5K67uWfPDWo2UM9FJdwYWxez+XJBosuihbi66s1HTIZ5M/4zOsaUAnu4QSP/S6sD/nfDDRb4Z/mg/KWviaDrCtpYOOuqUTBxustkHeXGL5j4UHTgIBTer1c3s5iy9jnat3ikxNJL9PjGR6ynMD+cDL9v6sFSlsb8hkj8d5LYL1+7Uui88wZ9a7m5HpGMurRmSAQ7zMLooB2KKDO4qJNyF/V8/S/E+vukLjwAogQx/WNKnlEwcYQcdjB7P27NCp8b3djyYt4ziY4AXjLR+hVY2tA7mx/C5EPxpCunkoqdvSH7Rdx2PpAJcVxGqfmdEk5MgP78+fL/1+r8q5gmlBFYip4BcDmoHBgMJPj3HV1U/PUWIxeHaaAViBwhjcUinkRNHvDinNvB59rwKvW/+Onx6jFfGNR8wPfZdZiFuKuYfz5aszReD0FXFtYixgZws7u5BpgZ96Htae6r9x9j5oC3/b/Wg+yzlkfKP0Ms2UVXhgIWY5PnHnT1tHEY/cFksLbosOpwPa0IFhtWugGMC07hUAAIdskIEWR4eytnRwRuvsQYxZa8lazHnr0kagPp9eRtlLTyYGIsqYkZnjnUsEEtCuTSQx2kf2Jy2SPIVKGw6ek86aa0xrO3aowQFlGU2W0sfOB64G8JQhK4vJU0Dh0AB9Q4Gvtfpn31IouVIKQWj81GDda2UB0wycBKEsc13OHjKsrGAJg3U2IF9qkhm9CCqB/3qVCfpdIZrnDEPIQNxzgouzj4Z/eP2/QceLVyW/s1PRM9p15hiCJCJJ1VXMPmtg3GgZyQ6jmvjl+Qw6gKyTHPTeeBVuenxOdAg5smINd08+WHTK1Q+xjv9U/6gXeU/oLLDe0j4z7/RZvjqhUT3n7f1ZhS0qTzmwGsI/A+52gEzmPurcG5/Xo3m33m8AO7C0UyJkVIdwHDK2+XME4632gHo/eAyWz7qiJHTvj4ZQ9n5oreADIvmzzfcMcHtAS2/A/+FQ5PuEPKco/x3rRBKObpETchLpxAirzWHIbmEZz61RoCuGWtlo3XLWCZnzJURvZQ52kawirH12Ob/xdG/9vG5P7OHJqnoBlDl7oTdevj7/Dk9HwDvbGsHGmhT31LsbhAH2zvBP89WseL5GbhLWqTXwOQq11Nbn3pL7XPGv8Qj5wnxM0xPOk9ZD2YGpcRByUnISiGVXe+DZ56yX4h4CTmYTKZ4jtBiFbqn3enQo+Z6mA16L5+Rk3EIv5PsmrXPCyV9o2TR+uDh+bBJX6NyntCh5fO5/QXItv1m+LXxnXvZx9wY9l5DDNgS8v5omef3wgYyP+bwWOGjrEsGdesxv9Z1iAE3jnkNctwjWwM9aPI51Fi2/YnRwJJ0AvMQP+Eu65pbryyFeAuB7wXSyS9recLLI1z66JX9HqZc8rXFgdSPwKj6toA30BcDURCNAQMC6H7fGZp8Cy2LPxmYc6nuyALHnl8Fqqx9at5UAsOexsI9O053F28HPvLb3K9bmu4JgXLbXW1rCftnp4KeCHFSBmApeX/AYiTYUPQNAK7Sx2cXeeLpfrEixqsFzouh3h5Rmfj/6ei5elSkqWDfwM+9UEaklzgRtbIbWZSmcH92SZeXprDJktkMps3C0rK+YYcW4tspHLQUA2SzjY/mSR51p4QljXrsW7vo7rNw9/bUs6PDcQL7Nmv5eqC+odsCyAgZY1S/S/Wa7bVyoTQUUee7DPB3Qhs7uRxODeaxRxg6BycG1UJaNdnIgywmti5Dhrg0cXmuZYA59bp1bDo7CkOBqBu9cclUKnOJpi6TDJYhtIG50OKEf614ka64xre3YodZsO7o065WUJzhlLJyW9YhlBVRfuAkFvqXFLym3cMCXWupAJ55v5iJRXrdeg8fndTtHbbRvWJwZ2pvXxM3FCqQxT8DeL/1w8QLJEIRoHhmGqCjqmpf/bllrrxB4yQoieb6og3kc5LJ6lGO8K+7IgjGhoE0I+DnLuQy6wB0YcECwbLOc8UzLHbOT71t45Ao3Ps+6Okc7SD169eR6yNHjnVs8y9UPnsOacXn/DUmChXXxasgZJJLtQe/nEoepbolgzXnrBkn5KSpPObDq3ZVizYVlMmiSKyW1415X3OjsQU5k0HeB8fw9nhVyxISMbf5cy1ZdLc3AY1y2Npkvy+fQ/XDemWOjOuSoLHO46PFEskpU3UJHV3EguQLVhPo5z7GK/1stsqxgphUEAu2GAiegE7T7uWxtPjAImgPfgVzg9jtMz1q3fOnJzPkS4o1aj7f2mqv+tNxmHsAOx03XJ0EI4Es7ZK32xNyW1qMR7zPtJNP72oxjmOWCvo+A+ZHHk5f1ZY50i/46uvPPjwzZ89HOKdCVTuCC7NDtQBlHVmBSz5nvtdP0NLgja/vNwDZriDd5a0P7Z55zyBnMCQxWUPqKOzId0brvDPfhYC06iYLPLyrvLlvr02FM5Uf67oasqoldjctnAOeyfWZzlSVlzn8A9kffRcg0h+d0K8CR/YmuyxnunpxHAh/b1aHzh/M264TkO163DA6ohHjt1g2Z7S6Srff0FfadYr2fCwcrmN95Nox+VtMW6wg6gdAL+ljAewXeZLUK1XsaAu88i2Tz2f9C8jvwrZ3+gJGhcEAHCRZ8v6JIntdt70+SHNE6EePg3D03EE5OYXmOxLbFV+X3bHhPPjjbvTBrjW0FYEaGbJmmn9N3b/EzsXYqwNpD5pOhJFzLzmS7lfUHrWdbtFB23qx2fZw8Pjke9stOF0cVpFAFYio4Oig7hLGMxFOcpuvUZYWc38sKAUBnscdGeEPOVKt9C/eqh8CA4fbSk5JzHLJw084uVgD5dy1EGfe6qgWGVVnGns5sWb6aHAf3+soE47i9Ix/Y+eiWRIE69Gq5UNLOYghyy8DUmVPoF8tt1jznVMghyE4jq4LDmz875SHsN12fleAfi/Y1ms6hbE+NFzPyZh2fKCwhxVU7ORCUbCfBjPexczO2ssECz3mIv4tkTl1d3mzhSp/LxauyMdP2FzP9+fB+1Kck06Zq+T0uy4bSraT4fDJPYCVW48PD1fxTk/mU3QlkGZ5aAYXTSNP3oVeTn5PjfjaRxgEyGIErr/VDTfxzd09f0kLv1nl2Kz12jHpnjzMDLacbG9oThnPBAstRZPGEB9dkGfoxQRKP115xh6RMdOJwlkCAADZfosmgAw8A3Z4yxPssoxRORQ5yeQ5WXkNZVnsZbnR/dM1/wSPYAaHv/4Gc2rzGd/Ba5451A6Y7zr7ltoyc6GBVYcYGFC0+aMl1Eb/1iqVjMQ+1ki0smvD4kQ5qeHvKSQAIyMboWCH6RDCwrSPLCj70anFfUS1k3QnD+teex4p3gfH8uxeKjB/M+DnjAO/U56uMxvE5y9bGVKNgWV92kTUuOIYssDIyvfmwUR1qGaz3zHNqpu3Q6lkQHOfRu9Q9tzc1n59bzn/9HPN5fQG8FcRZ1leesMB0wrrH1g2Z0z+VfWLfteAF3NnxHqpuFYmrTl/WlzmstdzWeie/Y3BHlrGtK6p1oo1331DoXFt8F7qHZ/chsGAFGJn3o4oCdo3VZlJ/l2Uk1teztPidtPXxqiw5wpIXOmkQZ3BiLD9mahd15e83uKcvkS1HDmY8TbfjYvDoCWBVTXk2q16LtzZu/6znYTmDB59Ifg7vye8j04JlO6S8sSMfPOAAHSeR6PPbbCKE9QzfN6n9BvoMYA2o6oqtLGm2xQ/sYFTAWJVXHDiwWm/r+zJFiufR6gIBvfwvTije6RaiUw+/+r4Vkbwc2HlvXsYwnTTjo/HaYnnz0sEK4KbM7kxt/I/5AXLslbZPRLIxQ61VGdj3YJ3nUFIArxkJKOiW4enDfBdvyNeU0lq9qM96dt/uR4v41PvNHQewHt3Onded6n71JGCtkzQ0fvQ9e3pdzYA+l/y+ULUjdArQIFfk6yQKDZYuIRLWbSw6FEmSx1vQ4tSwMz0bqoJpQa1er9fLH/v1hgMHDkh3d7cMDw/LnDlz3ujp/HLBp0/NWqR84oX8BU+LLkoUy5rknQTdCzPFuQw0Y4qFDYuzd4lk/4eBFjNe7Lu39+fXuawv/35vrXhGJBEu+17IxhBJGC4M2PpkNpYeOx2nIUw75yVOBWv+bDzCWNGOLf25SDYO9lAkca60deTX7eGPx2D88Now/pGDCU3x3nXOSxRffNY5L/msJkk10eAOSR09yOLGe/XeWPPy8GGthT8v22d87/BrWRVGrTVRMGLGLKNXi86xN2teyuYAWoo5ex7dWGdZpLnzbAHWgD3WNIK/W3sUi398J3Smwbvm9GTnqbUtCURILXHSxfKg7f2JQp5caiJptp7eg3v6kuAZYPGqTGG01nT7qQ1nCZ114EzEP+8WznCGJseSc8zVidbZ0Z9ZeMT8wIM8YDmhg3e3zs8cWiIJ3q64o/hu5hPN0J/mcaGz7/EwjRv+3i1zJQ3esWHXDOC8YW8mxrK7CGbMDtM8ZDLej2eAV+Y/WMPpK/I40Puj8eB9LwTeOde8hfGsZanmh9b5t878pusbk6glvecZz1q2lfES0J1Icl533ivpOe9+c1GuifjjlsljxhvOgcYPQ+hc6c8hJz1Zz8kiMWvR63luwNcNYmB7fxI4mxhJ5Dqvh3UnTU88D94rzFl/BryWGcVl51qfL2sMb52s68XMBX/zzs2DaxJ+0T5T5MwrwjTB46d6SoS889am900kez/LNhFbd9FnVOModI60TgfZyntiQUiPSGWuJHr6oVeT96C6nmXx2IG4OXpnPNZGKZNhIT6g54Fnay1JAg/0gtH9iR456/g4O6EZvdmbg55vaN/1nrFuVEa7zD+ma4/y/uv3sf6ndSHGXdpaRySlI5EM77G6CX8WwqWnCz+8Lm/Hx9iv1nr0XP/ihISmai2SJg01g2/Wjz0b0bKJY/BU9pl+Tut7oTMWo5eIZDTrnTMNMfq1xUs0n5h1QhI8sfw0lu21fDXZM1LcD4sP7X40ww/uG9Xz1nPld7TNTBzCZTZHGTR7TmK+b+0DywlNhzF01qwtG3qedUbI3ZAfyNo/rQ9rPYXPMs9t96OJTtzelegerAcyTazqb8535eFCpKj3iCS6D1oFar0Za2N9q1m/UDO8g/GG82DpxlrX13ohzgf4tAWef7BZmrT0WW990zmXv+LQTNygqoip4Oigrn7qPp7WJdFeFo4F0422cqWAznQAIy7LVimLQPNzujLEa9vAa16+WtKMlX0vJAIITm7dG5fxpvGI35FZ2RiykDXCDFNn4mNeyOrgzAjeg+Wrs+zWt11RbCvC68P7gSNkMXEVDK9NZ+UsX52tDVkkdckqXYBz3DWD7F6e77K+4hwtmtIl2R4t6M/1XmhASXtdsgvncJ8M48drnaPfq88MP8uXEa5cn597qPqJgTM3eK0aj14mBY9jZb5an+E+H31HCdZWk2JGUiz+Nf5CmVTgXTDQay0iZ70/GZ8N7Vi+heALMuusyjkOwmAOWNOsExLn320nZ+/C2W7vKlZkWDRq8Ts+Ux2zs4sO9VnXe47PBtb5bQ1DvdEZkHn9w/vtKgSGOT3592EP0Y4KWdIx+wKccDsbnVXFwPjg//OF1BoHaLXRPnP6mUIYf/xwIj+RaWm13dDrTm3lWp7WrPuL0EZOZ8JxZrHGA9OFVdHlgT7PwAu3j3p4XTGbP8QPLTmrz/iyPmqHeV5+TvxsGS/BXXLzTpVcyzUu40eLq7aO8rsJsPbhPYn88ap9sMZQ5RugrPUKZ9TqrHM+WywrQ/dxaeC2O9wWZTqtC5b1ZS2DdHY3yx+rKk3zCG5NoSsBrcAH1s60hnMtteTv3/pE/nnrfPEYWu5B5otklWN6Llov4/3X+jUDsldbO4pt8yw+iXm2dkjqFNH9zfX3LP0SFQpcbcVVNkw/+v/Qj3BZO79P06Gei8Wn+d6uskuPQ3Rdo/9z5jf+3tKWvOOMFXZbY8aLJ2ea0fkY988NZOeNv8N8QFdq6X1D5vF7Pp3Jbshx8GrdLk/zDs9G8rLE9RyYRrUN4bWK1nuG/eia6zvvdGeBo7ljLW3RUy+2LWT9T1crwI7iO5ZEEjoCtHeU6yYWbO/P7uvw2pKBRvh3bceHzoPHB6y5dsxOfnZ2F/fRomvmi1gLAPajthExX69iLuRDwGfoVODBsr5iFVhI1lp3y1nz0OfMu1MCuDpjRdZ20JOn2t9htW7UrRB1m2y2EbCnXLmJC++98bduyJ9pzy7guQ6sk1z3gZXr83yizBdUpv/rqguP52iwbEbwmsmx7HtoCWnxlBBPB3+edUJztmyI5oHXK+7I74keC3o/KmAXXeTrw8yL2ZbVdw8tuihJSLp8fb493tYNxTOjfVej+5Ozb+k7Hi74/1z5hVbbqADju1MYPyJF+RzSy7398GBgXVYxD77sVZTynT1bN2TtwU9ekswBFdB18YH3bXu/3zXCmj/TpdZnPX5dBWGOGqpATAVHB1rAWn08mZFzaafnzBPJOyE84cQGhmYSmuGPHcwu3g5dpMXjaKbogaXUw+Hx7P2+wbisLzEaRDLFyionLVPGgVd21lkQUsS1gsuCVu8jnCNaAdV76gV3uDST12Ypf1oRw/fYwYKMSpRQlhkRLJj4Hhut1GjDzRpT07RuCZQ6zjuSCpXuhcX7ZHQgL+Qc1LTLz/JlhLsfzZz4+k6VEOjWUhpw5nb8XfhuBZ6nnrOmO/QE1neUaGcaWjg049Dz9s1SKrg9BbL3tCIW4lu8/4xzOKvNzBry8ug7dnS7QpGsDdHl64vz8vq7h2hG867Q2WFHuucUCwW6GLBsqy3Yqv4sK1ckcX5bgcRlfck4cOBPN2gfAi+gn25bLcMB9n7RRfk2FTznkMziz2pqDoxXjWPt7EoNwrl541PfX6SztDBXzQMsZR5zD901poHPM+OFjYo6/Y5e/CF+GNPzXSRrs4efofl5tAvD/aUn806lleuzljV7HsuCmzH392gHmHY+w6HM7wu1wCjjdxNj+ffhO9zelGWY1fIlFHhL5V1X9gvuFQi1F/J4euhODJY/3jjL+rK7FJBsAR3HCuCEcInzA+RNjuefX9WfOT6sRAkt9zhQ5RmznuPRmh+Pz3o53/XFdMXnlXUvy9i35J7GuddmBzRRkyIfA152P5oEb7rmFe/14haU0GH4s9tPTXQDS4Z5LVO1ruad/e39CR7aZxYTh8DHOmZnPMgap8zhIRLmcSG5pvv1w/HLY+y8V9Ke/SLFwAzAkjFtjYDekYP2d5avzgeJ3aSAelg+sMOpmeQZBuiJWKMXbMS4If3VAyt4IbVicoHl6NNnD8/0LE1w2NpoBYN7ThhCwVPLwWrhWtMV6zZ8r9A9fXl+tL1fZP3JWZtYL7is54mzgztRrSC/ZSuyLQT6Esm3mwPvBm53P5q/mzC0bm3XAaxWbqFxQroy3y3nfd8C6DzMn1jHgn7htQMXCZ8XzEHb9zjDCCiy0xpw+fpsL3Dhvd5/pimW3Z5847mmvKyWJb5pfwwn3YSC8Qw66IDPQzwnBEzXbR3hpE9A6I5b6JVo4+eB5U+LaRFp0R3+hjZWOglSj6GTCfhOXZ3ogbUhONM5L2lzNfzTRD6xnwXJTSeemdHWyH5b32H8sz3CuF18laTJqOARCEQhWeHhdeUBPq2Xx+yHB9B/OEHA+y7sGNi62m45Y0WW9OEBrw1tskeG4ubPuND6rOU3fT1s7l9DqAIxFRwdeNlVZX08t24IZ+mzE6Isgx3VN5YiDEOPlbsQk+VxNFMM4UALYIytnRA6U0b3d+ce057DQ18cyPMIGSz6PhEWZlrBZUGrhZbV7xu40735reCOd0G5l2HJTg0I8d2PZoYUYPYC21HJwMEcOHJFkp+syGvHQ4wxqOlMJH/ZINZgKUVeJg3eK+I70oAr3s+nvyapEx9nCGOEAhkYt3Nu+MzhUlMEN3UAis+YPm+MA157SLkYO5Cv3ACEMo1E/H2zFAjwro9u8TNPQnyL91/zLU9hYcURQRus4eQl2Wc6OLX70eJ9H6BrnaUa4nfMu3Y/ms9s8uD0Fcmedc4prq3MsQoA37N6jovkAx3gF1xCjndgTSND+aB9aB6g19tOLg/ssaOfaSm9tPyz+SBxyEAWCcss/uz0FfnvbO/311Sn/4BXwUDzej5zsEUH+zUP0DxTJG/chjLVLdBnkjMJV6wlPlAPG5hethccjV+6tDyQboG3Bn1PAjst8Zl1x0PIWNGGFTuYUSmoA3F6v0O4BXBlps5M1fQSymAsWw8HinHfCqqQrISWMkPOuxMjFNzXoPUQ7cTzaNVyRGzvp+qqpf67LCcy619cwQUdUWfVi2TysSZFR5bea6Y7DnLgsl5cumqdBR7Lyl7mrE6PrlnOWLzSShDSF5djjxa+MzvbNfEztvEZBz6b1dWAd895P7o/cTx+4gVbL9YXaGuw8O3p8FaGaYh3ofIJyGfHLwDZxvipHWchwN5NTcRVrGhdQKTRa77m40ck73CyqgkssM6aJfcAXlA0VmfRY+AupK65xepcz9Gnz+jqncma65NZIpKVMBUKnoaCsww6iK51G407nIuH12XJUk9/Lc/H9Lz4d0tv8HCJ7+k7vTR96bPN/MOzSzXwHJf1xVfNaVtMpCgfwEPg4OYxQ0Ha1o4smYNxN7I/W5tIcibGDiY2Qdl9WaE1sL7HOIZDXlfY4bu6qknvv3Wny/4XwrYiALyMbU8eX99fFvL5MFjBJ5E8z2lGP2S61klFHgAv+q6j5asz3bGlrSgTypLcQkFGgOVXAS/SvIfXwXulK1QY97pyggN7zw0kfGVqQkTqSbCJ/Sw6uQn3ksw/1a8QR7AWfEXT3C1DIje9VJSfkH/aDrb2vtmARwgs/cf7rk580nOLTTwDWPf16DmI2HbSPX1JpaBOPGD8NXNuKnChCsRU8MZA6AIpkeyAh5Sq2Oob7egOtc/hcXiOIr4CoWF7f8NhXkucEJwxpjNl9Dw5+8er3IGTcnSoOUWMmbhWknkemgFb+LFKXvkzFiJwfJS1AirLcPEMLQ7QWPjgDEBNJzCip8bzjoZQwMkz3KyAihaclgAOZdIAWGFn2tXKCZwV53xQCk58y5jT2ZFlraVw5kCEYwfzRgOc/zwO/187Gnjtmp54bdgbXfHhOT3L2glaCgTva6iUHkEIXeoeCqh5CsuqfpHuN+erXnDu979QVCoB2pnCZwAODPCckFLJay5z0HBwnDOx+N1lJeUAbRCK5HHJgY5FF/kVjSvW5qsRYtoYgF6twJ4Gr8Wk5QRFy0Yr6w0Qkln8GSva2EdvTVZLBMv45LOBgK4FmvYtY0+3lgTPD1WbeqD5Hy6i1VV5VgY+enKLZPJ55z35YHFof2MzjUWK2fX8XS9gZ+GPwWvVUJPM+aWNrjNWSIG3ewD+Pn4w+R0tbzQ/YXopq5AL8U6RYlb9K89mz+lzFGvI6ee0URgaB5+B/4Fvx/IqkTxNfHSLyC3Dyc+yeVo08vTXiMYlazNZk+Qdm9dkMpl1nlieqp1YdcnzpjIHgvU5spXbu3y61nI+xrkNXbalLfllciwZDy1N4OzSrfkwR66M1nsfmgPLauDdClojuG9dXg3Avj43EO/QBz51RYuVWIIL3C0AzlFVcc4Hi0lEqMy8fH3yHauFngZ9ntGWz7roW9swOgGuPiUFPhZKskJmOAeVtKPbC657TteyeUIuWsE4Bh1g5MpdfU4x51knZDpEyJkaqtDj+Vq81vqbBk0XGn/4XWfJM/+AbgKZpYNm2n6WWlKtZuGTHYHQJ9opsBiymQDgH+Chll2q6VXrdF7VHHDWTCUSeMihV/0xGYAvVNVxVwfwJ24J9tKTyZmaGMsHkZsNyGgAjlEBh3aLs04IJ7Z5NMTnjmV1KGHJslPYDzQ6lPwNe6730aMTK/ik56qDlCEo01ksmuHgBAKAmAvoD238uN2ZJQ+44kwH7TmRx/NZ6MCwl+jBe1UXf5/1+eHEJPAQtF1sbcsnbunkpvpU8tzgE0UcQvZOjIVpTssKkXzHGG3TW3TDPK6ZQL0FsQEbfi8Cds3YEgyYc895UmorMM0wPbEfwPObNrO2Clyo1et1HR+sQEEzl+782oElDGJgw+Lk8FuXk/KYYBLdC5MDr9833ffHrIEvZOuYncxDJLsoy/se1iaSV/aXry5eVhZaD+4J4Avhtvc3LkI9nI0PZbaZdfI8+HL7ZX35S2l7P2cHTLZuyF/41+xldxYAb7zX+hJBjKUvUdPf5XeHLmpGe57QZXF6H6x5Wuuz5u/hRcSmg7ILxTGXsotivfcN/zRZd7MXDN/Snf2/93PxNImLxK3L5vTlibw2vozZ22uMoS/3DO1B7NwYmHdpfE+HH+nvrD85wWXbzKSdnQXWRZN6XuCt+iJWby0dcxI6L6Mf6yJu3isRyV3kXnahL8/Derfmp7hQun1mEqSycB7iqyIJTUyOJYZn6MJ5xs/JS8L8LpYveHi15u9dpM7/b4bOwO/aZhbP63T5NIO+fDZ0EbwH3qXL+oJUfq5rbnYpO4N2trXNTPa9cFmoZBdxx9CrxgPL+Gb0kFjZIZLRAwKmFv71eLhwU2pJwDdmfs3wMDyLy5OZV2POwK9I87TmQexFuwy3nZxdRA9Zz3KFx9Q6kbcfzdIIzl+IT2qZXKZHeBDSo61nPN4p0txF6Pw8685671mOTIxll+zq8yTS3Pox/s/3Jgk2kBPec9BjT16SZUdrfHm6CI+jLxAHWHqFd+m2xr2WfTG6vmVbNXveYu2zGJ3WkgEWrvDOVDbVkgBQ6FJnzQemYx+yzmjpdDF4smhW2xZleuXRyN+QTNb0KZJUgXh8E/TaPjOpBNPykOfJ7U31hc4hO9iyYcousrZ4sMUXymggRNshXGP+lk0m4vOSmPPG9tQtw7a+askKPkdlOgjjz9N58czokKQyiG2J6eg2Gk+eTe/xTxF7z0I0Px0I8WRP/mjezjq2tgs3r0laMYOnaVzhHSL+njIeOucU8QV+2NLSaJ3akNcsz7RNHNINYv0XmoZYz+mYbeuH1tnE/Ns6En2A5xS7P1jPsaDdkO4U+32R5vRpttHYVmkGQv4wT9/TvOSBTyT6U89SO+mogiA0EzeoKmIqODrwskfLQGcIeGPqCLB+33TfH7MGzlpbvlpSKVkv+d7y1SK1xtFCxQAYn84eeHBNMsaDa5LfOcKMCPQZK/IlonCgefd4eMBj6+oYriLA5cNtM23BgcyEwSfiyiRj94izX5DJoLPMOIvqwGDWy9bLFOB3cmYRPoOgRoskRPsZV1CO8LOMJnl8ZHWKhDOr9BjAMUrEUY7t4WzFWv9SUwCvKXVMdhWzIzevyTJzXSCkLOtrXM7bgFALCpwd9EIWybI3cK/S019LFDJkyelMQca/zlbR7QQ5q6eM/urqpwd4v5V1Od2KAMx1e3+WFZhmBxpg3UeADLGTl+SrFCyc81rAh1FdcGCw+D6+kJGzlfWerFhbrFqz2oRoCGX6LF+dz3rmffKMAZ2pY53Rto5Ecbcus7TwM7gjXDGk1+BlC2ma5TY1OrNVt5DwKn5israW9WU8ArTFLfZ4zNB4ep38LGRWe6N39s578tlWMXKAq5z4Wc2HuYVZXTIaQXb44lXFDOiJkWIlGQAVNKF2qAxetRRD2b6Esji5Sgi4QxBGanYFqG55hIzDxVfFZ641o1PhWeB/dCh7P7KvGULVYoAYWuY2JbEZi9ymAZm3jJNQ20DoAbiPKpbP81nh87dibbESFHTH1Qd4d2xVDAPTVkg/0vsAGtq8Jn+urTMJ0HvAZ9XTl/iuwFTm1jJcMa8LtTjS78f4U407fMYP27SB53Bx9eCOJMgvNOdZJyTBzLFGRQqyli08e21qLL3Cq7i1MmBZ9sXQutVtoFk7KWSfhbJg+T14jmUY8wuvmoXv3GI+3b0w0+FBX979BJBh1v1lGodcnQbdKRbX+jJ20C7bFl5LHD0frrYKvduSOyH9iXHOHRc8wDxbG9WTWj/i/ed7NjRo2g3ay62ZLXnjC5lDVdtLrMsjQa4uYbmm6TX27lc8q9uwgU8DL5vXZLoxWj299GTcXuKz7oXJ72h3qXGFNtxojYhL1ZnnlJ1xxp+nk0PWQEZCf4jBFa+H+TDrU1qv4TNq8QS9D8zTQjQ/HWCZpdcBvGl5r3k7n3ue19YN+fZ+mhZYxvGe4g4V9nNgP6BzsR4Iv87kRDaxgXVFPUR3NLDwaO1VqGMJgDtYwHbHmrQtr8eAf8y6X9OruNV8ge3z6dIu4306dMbrsvQpD1hHhK2y8x77frYQNKPvWfrL1g2Z/vTSk+W6dQVHBVUgpoKjA+vAN+sU0kqvFhrspNTv0877kLLjzcdzLAFaO5J5oOe517KFASWWo/vzQQ49HzgHUieBwhEUPi4RhcbAbSJiAe/m+xRYuA+sSxyUXfMSg8jCHZSOtq44ARXjsNLrhbDwvqsNME0ngOWrs7L6M6/Ir4eVO1yozt/Hs6evSNaJLCzLWLbwgHcP/zQLuLEA5O/pMn6N45r4irPlqNVwT19S6XTbyflztHJ9ovgsuijDy0SDFicMmgTAWbT4quQ7YwclpUs2bDWsWCtphqDu7w9DEfvqOV9CDmmsi9vjea1LrLlZzhENloMBwIp5rINQOwzmnZr8fcbs+F7XIpkSvu+F5H6JTR9L9t1zokGJRUAJxuDJS4pz5wt/LXq3nH5ld0JpnPL54/drg4ADVc0GeXVwTl8sbc0Lcorx460hxtnNTtzt/fmWC1hPWcsJHYy0HE4ixXOvlW7OmEbgc+E7/fH0OvW7eXw4fUSSZAa+zyV0NrRuANC9ltmZdMaKjEY+uqXYFgTzspzc6BHfs7Q5gwuB410DRce85Vwrg+39fvtTfaZQAaTHramfiy4SmdOTv9si9l0xeNABWDhwJscyYxz4FckCxyFa8O7PYOAkmVgegLtqIGN2DeTfHWobKJLn7ZZz3QLNE/TZ0bqYSD5Zx3tGw3QDftY6QTsTI8oZOMeXo9oBPt5ozTvv1GKgHO/EHWiHX8veKfXE+eHZA956+f14nu/wQaKTtXZOQhk/nG/RgSANkhSG98TxRYZmWnrodXkB2VAA3uoj36wzyePBIsV2tgyhID1/ru9p4gvidz+a8Q7dPu3MK/IywLqfAPYgV2swn9E8mc/woVfjkh3wf7RRY7pg/nbFHclF1CLJvQcWWMGpUKDX2t+Q7sE492xuBk4A1LxHf0dfJq0BAYRQMFikyFuspBBc/A0dRbeq0vjkfWNZx/aJZZPyGhnX2ha1kjuYt+HZ0F5iriL5dpdaX9UJe9APRcqD7QC2hXh+GtguXnRRtjcL31nent3iwyFa48BFqDWfpnnYLixXGGJtLwZ9Xy/8NrDNRYrJbGgLXJO8rNIVGMC91DI/C8+vGT8HJ3MgoILxcFZx96hIoiPF+ixCoOnYGwP8lO/64cBGmWzHOnm+er81nbGNj+8y7cask8dkvMfadd66PH2qDLrfnP0/9k63MiizdbFOpkMdyLLkYBWoOSqoWpNFQNWaLBLALNE6RpcQhr5nlXMCvJJEhpjWNmUlipi/V0YZC7ok+8Bgfm08V5Hyssft/fnSS6tlGc+f56v/VlayqMuDreettiD6Pfw7BJy3xma+i3fP6UnwarVmCrWyYnzz/5evztOg/n26rYa4PUH3m8tbjIFmsHYLJ97ehiBtUeOMzyXWM2Y3V4rrtY3wQJ9nqyWM1f7Kw8F02rY0Cx6u7+lLghPtXVkP9li6B2j+tOljkvMONHtuuAUFt7fR89fnBd+zyt7R1ifUDkzPK9S6UOMtdE5DZ0Gk+f3f3p/hOFZGMb5iz4UHVtse7D1aRlql+Tx/i6db8k2f+7K2JYx7tPyabus2PpsivlzR+xsCvfeaZzfLn0XK+az3fdZzROz2lc3oEDFtiPBur2WlRxvcCosdDaF3NQv8brSqRHtF/qys1ee6E5K2Gq3tImtfLY69rC9/hlasjeMBjF+RZG7tHXHtKPUcRHwaisUVeJB+d5nupGmgGVmjx/ecWM2Ob81ZJAt6hfRykUY1FcmsOT3594X2/8YXRL50aVLR0rNUZOmHs2dTWVpL2tJ6a0c7UG5zCIf+znsbD9WzFrDTaRfiwdHoypZMit3fMtr1xoFMsdpRNbseq80r7CXIM+a1bTMT2Widdeu9Visjqz2O1XZQ6y+Ma8jLlvYk6S7U1i6EL09/Ddm8zexviB81awta3wm1iGy21a9nYz43kG936u2vh9MH12RtD1G1Cf06xJe0TqFlGiDUJrh7YRKUm+5e6mdE/L0Jfc/T2yy9yWt/JRKvn3j2rUdrZbTsnUXPpxPjMwrNW/ukFr4za1+oxzzaNlCeT8hrhxxLD9pmA3gtwMt0h1icWutmHqhlewzo/ca6Q+emmRaEPO+F7xT54f1J4KTnvPgWg2V0ENPymPVC+FNEkv9bLe34ezHt5rw1h/xRGn+xvO3XHKrWZBW8MYDDj+qOWvjx3Pesck5ATf20IBTpXb46X/HhRXAxf7QhmH9qfDsP/T7O3NFReZ7r8E+Tv+EnIBRp9jL3raxcndlcloGg2yvo57f3Z9kCnJHFGSRg7lZmjAU6g8nKrsd38e4Dg8WsZzxbE3/N3v9BgyJZGbqmSZ2NpDMCkCnI1S+6NcyuATs7Smd1aLCyMjTeGDT9nPNBEb60DXN84BPJc7i8uy5ZayqrRZUFwKNu72bNQ6R4KSNfdgt6WdVfbMdirXdZn9+25Vhma/C7edynvyYi9UThfXBNXqmxzo6ez/b+pHoF2Z/L+rJWFi3t4XPjZVtftjarAjt5STFzDqDPS13yZe/8br7w1+IrDMhoD7XyYrxt+lgxi8hqCQHcM45D++/Bsr5idWMM4IxyK4Dp0Bjzb6wZxgX4W2uHv/f6HPAYWlbxubeqRiEzxg5m+MY6RcJtLBgnnXOyVpVwXHI2d0iuYD2brrezrBnHvHadte3xQ53Bzc/p74R4qsY/9mjFWj+LmC+etUBn0ZVV7+Hduj2KJTsZzzXJqsCQ5ck6ipfpGsp+17TP79btFfkzrc9pPom2GpPj9r6L5M8Q8wCuPNYAnoLqHLZz6/ZXXMB6RDJeCL2AL/z2+MPWDdlL9bt5rVamLD5H21JU9Wne5EFZWzWsjSs+y8bXOhtoK1QNyc+tWJtVTlkVSXr/NQ3hYveXnszraW1dkjoxrHEAb7si09NOX5HhfFV/IzO1sUm4h89q7eSdle394UxyrSsDz/rieMtpoi9L13thQYj/ec8xcBWKRd9c0VamA+FzphMeX9uUEyMZH9O2HNsg4L9WxafOvPeqjlAN9dxAsTIEZ7alPXEsLv2wb7OE8LV1g92+OFSdbe2vJyND+olnl2H/xseKn+vvWNU5AM1bvblY8+BxUf3IGeVMN1gnw9iBTCfkbhOTY5I6ObWcxfywz9A9cd68TgG63Tjr9DiXoLvpAO+3Z6dbEDrblv7F+qRlB6AypKU9Tg4A+Ox5focyWsZZ3HlvwkchF5Gtr2lLy4cysHDFfhumb9CjhUsGXfl++6mZrA7pw9w679CrRdpifQP0adFDa0f+J4CrPay98c6ptts14HsixT31qo3xHS3rNDBNxtp50/mOSOKjmTicyP1Dr+b9WtZ6tSzzaNzqoKGB9cKWtkxX7X5z3tflyRGv3VxID7VkBetmVnUXxm72nFVgQlUREwFVRUwkINLe7EV1xyLDp+wZfYmZlaWDqDJn5iEjS49b9j4r02v56mL2k5e1YGVIiNgZtlhbrSXp66gzTUXsyxljM+O8dTGDti5JjxnPywjzIIbGvCyt0Lti5m9ls4hk+4RMvlD1y1+ckOxRrUVkzik+7mOybKzvAfCelnaR93y6uO5b5krOA8R0hax8K8stNtsOYK1D/83LErKylOCc4moZDw/TyRb1wMvynHVCEnAQkdSz52XgWFloXL1irdtbW+y5sbISPbzG8oGyi5vxzlpLki2Ky/74Pbsfzc4brx3z47Yi53zQz8xuln80A2VZcstX+5Wc05FZzFuxZouHTJeGrfPAeyn1Yoaexr9IMbsLewLwLsf2YHt/EoQRsbOGvexNzMXiD4yzlNfVsqx479Ja7yxY/4+5hLpMBjdbZRWqwLPog+f63ECWDWzxY5HiPELZ3KGs2hCNlvEb68JWjO9dlIxsZazBkt+6IqalPXGAerxDz9OqvrKqCFnnE/Fp1+NbnqzRMgxVAtaFvDGZrR7/9uBYVQROB/SZ1fKfL0JGVS3LVsggT38MVQSH+AWDd1bKKt30WYFNEOLJ0C+8dzZz/kJ0WCZbLZoIVULE0JCFbz73yFZu70rwxFnSXhU7n1OWZyF7E+88uDexGVmu6TMYqvoIneeysx4LHi/GmF7liEUjnk1qQagixtNvPP3e+y5sclQf8busLH0tc0UyXbOlXeS4BbZe5VWIx1T/WHshUqwI1GvGWWmfKTLz+OZ1OgvPgNi9EYnTIfgMeZeJW3OJrYzS+NNyP70fT4qf7bxX0orF2ArZEA4tWoWOavEBa9+synfMPVRNxHjmKnnwLZxn6EdeNYK3xzzeroHi3sR0FglVN8b6f7Sd51XOWusBX9Nz1DTv7U+IvrUvBLxDV+PEVKV58w/NBRUx7TOTIJpFx6zDLb4q0Ycmx7J7T0O+B89OnY78ma4t+msAVUVMBW8MoGf6/heKfbBDWXplGVyhzzF+WS92fYmZdXG1lU3sXVweyjYRyaLG53wwy3B7eF3x4nDONLe+v3x1Pjpdk+J7oaBMTeSzqJARgPUzvnD5NsaKuXyWsye0Uq8vSS/bU943fReIRTP426KLEoMTmQrAA3/Hy9LSWQMMMfO3spH4bhf03UX1i3UJ+FSD/upTflZiKIMIz95/Q/I7emHfo3CHi9amxovr3t6fZFuINPrbtmYXFwMXV9xhGxtldK8h5mJRL8tL7wF+5zuTtm7wac3KUImZfxnP4nHRGxv7bmXgYLxZJxSrPOpSzILn9fB8OeM2RMsM9cZP5ncYExmluAhy96NxSlWt8bNrrv0cMorbOpPfgSNey6r+ZM0YcPnqPE+AwYWMVC/bFHgIXeQ8XdB3VWzdkK+SG8A8a+Fz6tGRHh9ru+KOjHdY1Sia3pleNe3qLCd9HpB5u/iqfMY7z8W6bBnZ+Jz13LM0wc2ZVzSH52V9WTY8KrgYZzxv72wwXjTfbevK/1zWl1VQwLl3/w1JeyOmfx6f/4/3PDeQz0zk88x4BOy8Nz6LzoM9j0laSYa5wIiyzjdkPC5BRiUYv4/1Cz0PfQcDg1W1ZvWuF8mv29pDPifWha16b3Eu7r8h4RcIwuBsWnweuG6fmfw+NWFnowP0/uuKWew3zgnkF2f38/5qGjpjhcjogYz/Whml+D6q2fgujcvXN/S7evY3vCtUyeTdGVYGZbQa4kNHC3xm9f1xWzdkGayr+vP6BADJEtAN+TJtkXz2Pq9TByN0Viuv0zsry1dnFarWnQz6DKcZ/PX83iO7niv7oXNytcU9fYkzB+dR7wWfP8arpoXQZ7w2TRP6Li/r+dCdnqzboPIPeF/Vn7Snkjrt2WSmD2MPgGc+p3z/YEg2Az94J2xGrqzAM8g2rxeHya1HV3JYul6zsoDBk5GwAa2scO9eM4DOpLcA58m6gFzTtbYFPF1cO/ego3TMLtp1NfUTuMAfnhto2DGN36cmfNsMOISNDd4OPgC7iO/TtIDlKcuC7f35cymS0c14E/dH6POsZS/+HqpWsvQZy9ZlGQ18YE18N441LoBtkLLKB+BP0/Kq/gT31l0g0IlEEr4Bn8LogXJcWuvVc8DnqBLnc8Hr3d6ftLi8ZW52Nyf8MVrHsuSjpVOIJIEA4Jnv72lrzINp31qTSN7XwxVcLPc0/rWvyeNPWAtXDGq8eNUYnGxXVsFh8UrMEbzculPH8g1Yuv34WN4Xsqwvf3bAQz1dvww8fqd9OtDR2zp82zatyqpn+tDM47OqTg3W3mk+Ox35U+bnqyAKqkBMBccOLKYecnzGGmuh57Tw8pgImAycpZYRur0/uwBQaonC5ZUjlzEtViBgzIBxs8ODLzHjtYrkjSXd4oUN89YOSSPj7Jhf1le8cBP4gsCC4wKtqfDTgpDTkx13VnYTt2ZghXTCKH+3aEYrm/ry2pCxw3P0HE6s4JQJFRY+7BjTZcTWPEB/fDG0Nk6t4BSvgdvsQUnbeU8+INnSnjzfs7S47q0bskDN6JCkFxczHj0jdTqOw9hLFpf15emXzz1fOs5tdULzsJQE7WSxyqJD9KcNF+2wti64xXdRXo9MvO6FidLHF5qG8I0zy5Ukre1hHFhON63EwjHBTv9QMPT0Fb6jRSTbc91eS9MOeBmysrWCCwuDDQ3G/T19IsM/zdrtNSNPyi4d5ffip+ZxUIbbu8Ln1DOu+cJxvTbwo5OXlDtX2aGiaZc/swI4OItWK0ALgIPWtmzuGPfQq77hXwYwDve/UEwIaCbQav19ZcOhv3K9HRTFuRrckX83j2O9C3Rx5GBGh3yBaorHxoPtXX7wKNaQsWSYJxtrUmyv6b2PeRCfo0UXJa13Fl2UPMc0yLzdM95FssxHy1nEbTgsR7CeK87FnJ7sexNjxTYK1n5hPAR40EqTHQcs9/T+c1ILPuM2R9qBy3+z5qRbhFjtHnUAituRaf2OdUmWFZaD1LuI3YOy7HWdDBWT2BPzTitoib2fdUJRBuh19ixN/o+fGEM7fnQrqLFG4hQnT4Uu29YOLh14RPDA4408DnTExavye4/2a7XWhO8O70naQUL3AM6RHS6S8KZN14flekinK3O+iYSduBq8YDkDkpusPeU5tZNDFPxD65t4dv6pSfJT+8wsWBPbsibV2c/LBxpvPzXZh655WUvKMp2ZcRMKkFkwXXsZ9qEOAmqbDzTILTetsVl38vQciyd4e6Odg5oPAi/cMjCkiy7rS5KERDIZnZ6pq/x3g0ePjyVfZIesl5RhAdvrOokF5xLz4kSYWH6sW14haQ88AXzKuwDeWn8ocI8AMFq1Y03AKQeeLbyyDRJqcwvgAII+HyvWZokTAL2+uuRt5GYSAoCHHX9X/MxqF8jrRUIA1qkD3jz3UKIIAjeQowBUxOAMhQLeDDqBTCQ7P2esyOxQnSjDiZFa/jOP4eSsIwdtvFh7rm1n5n0xZww8QSTjP6HEIevdy1dTy1op2i8c1EXCHe9TTEtcgHUecX6llq1B499KWkiDgbVw+1fwa7TE470DH8HcLflTllxzrBNufk2hak0WAVVrskiwjLXG3x475SNy/Y/Pl2suPV2uvmBR8llsqwN6buOFm+SuLbuycUIGojcv7zu6dQWYYEw5soLHvnKHvOWZv5bRBf9KThvali8v5PLJ1GiqJW1TDJxs3LY7v2aeKxSOWqs8dtYn5fxn/rO0yZTdSoRxoUuzYy5ii8G1Bbo1Ay74Eim/wFAkP1+vHDRibiYe9RwtWtRZkVpQBcqgg+/08GSVfFv44dYbgEZZrXnerDlPjOXLm2EIlZUJe3OCQwj0jTYh2BevtR3Au9QvbfsmwXlt3LZbXvpfn5dr2u6T2StuCNNpSpc1ScvagQOHj8XSfm7fW/+XfeZ4DjGXenP7PIB3zgnAi35y9r+Xd/7eDdkHmhfwhZhwlDCum2wr8nz3BdK5938X3+sB4xiKuEi+pRM/w2XtN+9L5/eSnCgDvzOQ4N06myWtYrB3n/uNx+WdL/6tLzfKeGYZzYS+b+Haa0MTuricLkbfeOm387wo1CoE+Lc+89YeK2dDe6/bLVnfixw3Sm6yjEdrT7xbJPyexjzqwz+VmtRlSlqk5Zb9+fmlrU4jx2wWmJeLhFs7eqD0qxXfWiEnyyuSa7eFMZkGddtWTxYyzoFjXHhcJg+cuU5Ji7TIVPI3vsTZ+H5QBjMPRPCisdbC9yy9pAy3ofYw3CYLQXoPD3j3z/fm8Udr/LffOi/T/c75YL7VapO6UgFCvN/SW3DB9tFc4urJRqZBEb91ngJXDnrvFclf7q3W8thX7pC3PfOX0tneKh2X32zbEsDXly5NAr2NFmlB2tI6B7XlGv3hgzI2MSUzWyakfWpURCSTd1v+dXapOS60H2nMXUSk93NFvj5dmE6bOuYPoRZvsWNv75eDA3fIo0fOkItmPJfoeyK2DBr+qQjaxt68b3qyiuclktLIS3KiHNfZJrNHXyrnvZaMjdH9yvBizY35t0iOfjdu2y3v+9ZF0i0Hw3qvNweR8FotniCS8sKNb/5zm/51WybvTGGNHo1Mh1er9R3sPFlm3/jDoxuPQbcVna78t1pesV3d2M+Dt79NZo++JBPSIo+f/Z/CPC/UgtT5rCnb1vM78Gcatzgf3E7Qsv+ZJttnymO/eb088qNXEhuwNlp8Hu8TSb6HtpW8VkAzl8Nv77dbZGrblwMU3nie7JuG7DblnuYlzbQd08+zLSZS8NP95k++Ui53DVnnyYcc3X2317YTG2MeHLhD7pq4Uk5+17V5Gm2GD4fstGb8JRaO2QfRNTe+rVhIfonYdrTeu8552Z13Hi8v47nT0QN+TaBqTVbBGwNWRLXxt+t/fL68ODQid23ZlX3mZFtt3LZbLr79Ydm4bXfhubu27ErH2bhtt1w8cJpsvHCTyazTMRrM6KVNn5Lr7t7hfuexUz4iL8mJ8vyC92Tz0hfxRcLCZ74oJ8srsnDv5kJ1w9TOr4rUJ5OfuJgbPw2cYM03f2NnipN0riddnjqV3vLMX8umyQtkQlr8jAAra1PEzvbQuJx8V+47G7ftlvNufVDOu/VBue7uHXLerQ/KWWsfkPNufTDbO6yJs3jhNJKanXXFdNTYu4PPfUcuHrtTHjvtWjv6H5FZzLSj4bFTPiKDcoKsH/6d/NxFZN/mT6dZkYOb/nP+c85+Mt4demcBONNPZWbk6JkzJtJs65m5DN1//4Nz5cWhEfnM5meL7+E5N7KDH/uNa7M9bmTLPnbKR9L91TgpAJ0x0PfEU1+V6557e25fHjvlIwl91ifl4MAdxXGMS/0eO+Uj8mDtIpkKZUk24K4tu+RD4/cmRrHKpmEcbty2W+44dIUc7Dw5PXtjk1MpDjZeuCnhE1i3Q18FXkXzSPddnbmNk+/KvoM9r0lpptjGN/+5rJf/jwzKCfLqnHPiMn9E5Pofny8Xjv5Xuf7H5+fmfN1zb5fzDnxOznv63yT8kCsaLN4cwa8PDtwhMrpfDtY7pWPv9oQHPvPF4losvOnscd1WUSSfKdWgledPulwuvv3hlCf+t/H3JXjfuiE9t/s2f1pOu3GTnLX2W/LYKR8JVlRh767/8fnhLC3FMwtrKuNJje+nZ49wgbU8dspH0rGTM9CSrjd93ru4fOsGkRVr5WDnyXLHxO/JZzY/m+dFej8b8904+S6549AVsq/tJLnj0BU5GarX/g/dfyinf3KTXHf3jtx6N27bLS9t+lR59mNjbemzrR3BtknNjGvyXpUR/9gpH5GLn/t92bhyR3KXFr9b6Q0FXthY7wNykUzUW+RbcmGqk1y34O/k4oHTZGyyESxAG78IOdUUgMZnzC5v7eiB0q/+2/j7Uh5dqDQm/rxx8l1y8didicxQc9k4+a4UX1+YuFJ+Wj9B/uFN12WtOVFhzFmRy/rkurt3ZPQUmOv/arlY9tdnybDMTuRlgB60zpjbR9A12s7QWkO65r7NnxYZ3pP8DAFVvRTe3dAtD+7bK1KflElpkTta/yjDJwP2sqORUcvVAI255nS/Vf2J493SlcqyTS1YvjrhI+AH6jOttzz2G9cm/Os3ro1/h/FOlo0HB+6Qi29/ONN5z/mgPH/S5TLR4IkaUjl39w65+PaHU538lKe/mFuDaW805MNjv3Gt3NH20URPWLE29+xbnvlr6ZaDsm98RvGMad6KapbGT82bCmdJJOM/O7+d0N2qftk/0SHdclAOT7XJaNsc2V+flck7yJO3Xi8Xy/8jG//1t/PVNQ256u6jAZ5+4+kC+jsFOwzy+If/v0T3uH9hU2PnYFmfrJQvyEcPXyMr5Qs2fwWtt3XJlLTIg7WLUh36ugV/J6d/dYFc+Vdb42QKz2v56jz+J67MzfngwB0iw3uK9gKfvRLdz9VXLbyouU01+Pfo+KRYlcV3bdkl/2X89+QlOTGzwRq4u+65t/s8WNty9D3IfehimiccfO47id7/3HeKshl4qScBkPX1/3eRNtQaNY3kZIdhQ0bxu+WrZX99luyvz5LPHKYWq8v6MptA2cHRgO4JM4+PsiPcs7direxrO0nW/PyD8g+dqzI8KH30L0ffKxP1FmmTKXnLM39dmE5ufKVL597pfNaUbUs2UMG2MqqNfvpP/zlZm24nSPpIgdZ6Pydy00ty/Y/Pl88f/Ndy7vBnbX9B4x31hoO6Prgjmwv0G7TZbVQp4DybNiuvcc1LSULtqv4i3THNqjPDekHurK9Ym+ILvivoljH8W6Ro/xXmYv2uAPbIP3SuyuMca1Ft45g2zPdrAK6wz2hJSb4m0F6O7jw7sTHm7NGX5EPj9xZplOjsCxNXZrqKca6DugzWbvhL9PnVNl2K94bN8dJIm1z33NuL58/aG0/OKbvFsnXTefzGtQWdF3S+b/Oni/vchE+gguagqoiJgKoi5uiBo9giEsykuPj2h+XFoRE5ZW6XfOfGy9xxwJCt53JjrHheXtr0Kflv4++Tv596l0zWJf2ONV5rTeTW9y/Oza2p7A8RueXPPyZ/VPu6PFE/U9477ye5KPuDf/FeuWzyO/Jw68Vy+Xs+GFXJcfM3dubmfd6tD8rQyLjM7WqXJ67YY1YdNTtnD7z9wN9FRFprIpPESaw9SaGJjGZUN/zl6Hvlb0Z/W0RErjy3R+780NJ0fecvmieP795fus4QPngteu5rblotH2v7BxER+czEv5Etx70v97k37sZtu9NAyCVvPTFqjh5+ML+5Xe0yq6OtkEmm9z5HHzfnnRQ8r4+vPFOuvmCRucchnFhzxhn71x3/Vy6b/I5smrpA/nT8P8jzt/emODo0NiG9Rx6Qa9ruk7vbr5Ib1vyXMC5oHta51PgXEbcihtcoIjn+IFs3yPrh35G/Gf3tFMcmvp25afyE+J25NxFnoqn9cPDDe12TrIsk46GZbCvNmz4w8S350Pi9cnf7VTI2MSV/WP+a/E39/fLQzPfmcFi2py5OAmfjlLldKR9HJhYyltcdvip1cpXhTvMV/CxUyCgIya0QWN/TNMK/gzY9+ajp3+UdJfMBT7dogw0iwAvGOf8P7d+Uk3s/Gcxq+8zmZ6X3yANybft90tP7n5IHHDq8+PaH5dKff9Md13uHJ8u1DpHLHjxtfkFvECnqDvsOHZGR8cnc3gB3187+ttww6/4C3qYrk6My6SPgurt3yKYnB6V3SSJL9fhltC5i0C3N5eKB03K0IZLoCLvO/2qwwvj0T26SybqkfIHXqnnqbZuekdHxKbl90Xb5N6P3uHPVtDY0krTlZLq2KkiBo3N6uuXpweGc/rXmptXyx233yRcnrpT1t9kOvo3bdsubHrxW3jX1HWlZ/Lty8XO/b/LvO9b/mXxo/F754sSVsnHyXQU+JkLyA9WVaq3Tyk6mMWK+X8rfSBf5f+04O4ev6QDmdF33I3LJyxvlr6feL18+cpnM7WqXj688M+UzQyPjQRsAZ/GPOv9Z/rD+Nblr4krZctz7UhyHxgDfrYnIug8szvGLz/3G43EVNiKFyihNy1q/F0kymU95+ovpfL9z42XyD3fdIhfv/Tv5zoIPy53Dl5gyVO+TJ/9j9qZZmbZx225Z+/WdUhcpyqoVz8vwpj+XqXpdPjvxb1x53Awth57VshA8Ce8DrxER+f3W/5XJn0g+qmmD348zzXsnIrn9+zfX3JI87PBwV18N2MSYA95/d/tVcsPKMwvjW/wf4wwOjUhdGvz6U70mzrX9YM1Zy4T1m34gf1j/mny59kE59Xf+xNXZWXZY9ou15o3bdsuarycZ2YV5NykjT71xU/r/F27PxpmufhczD8smCL3vtBs3oY+GPH97cY8wJmytHL0F1hN6p9bZRcJ+HA8Ka1XZ/D/9p/8sd01cKX8/9S7Z9bt7zUoabXdoeWnx0xQa73tpeFQW1F+RJ+u/If/fmZ8t8Equ5HjkR69k52nNf4njUZF0p+06kfxZ13Isp5dH2JzT3Sdrjt67m3m/iTtVEfPYKR+Rf/+Dc0Uk8Z1senIwXXvvkp68LyVQfRiqiAGd/Y+GHPLso9hzH7uPml6vbv1frn/Sw69b2d04QxgPeg7z65Df1NJFY2zlo/Ux/ipCM3GDKhATAVUgJh5iDmbISRw7RtlzlqJmOe1ZwPQu6UkZvhd04L+HlNmfHRiV8am6dLW3yA/WvSf3mXbwNWtwiEjO2LFw6M35aPEskhewHGh44KmXZHyqLu2tNbn5fefknp1OUIvnD2MZsJ6M4ljloGyNLKx47t97fp/c9/1BERHpam+Vm3rPyn2uBRs7I2Hgi/iGVAxYY4boM8ZYErEDknjWwok3psb1bZuekZHxqZT+8c6u9hYZHZ+STsKjh3f8//xF89xzaa29DIeecnrW2gfSOb/77AWy6clBmdHWmnOyhvATy+/4PeANFlz5V1vlyReHZckp3XLfnyyPfl8ZaAey5UiIHQP0COPse8/vS3niO06bL5/Z/KwMj4xLXfJ7ZwWXpwveGWe8X/LWE+Wb3x9M6S5mrSHjw5InIr7BoT8rm/9Za78lI+OTIpLwOjiQ2QlpvVvvx3QC8nj++Fkz5OnB4YKznnHD8MLtvblzfmRiyvyuxu2MthYZGZ+KosNjofh7BpGIFIx7lm+P/OgVESkGr5k3iCT87NDYhExM1aVnbqfsHR5N8eAaY5FrCQUwPR5uvYMDHpazzQJPp/JoGrh7y/yZ8tSLw6Vnb+O23Wlw5X3nJsZ2yJB1HW8KWE975EevmM7TkFOKk0yQBOIFshj0fDOZ2CrvPvukwtnFeT5+1gx58sVhEckb9MGgtYPP2ESVGBnarN5mOk2amBc7uuF01A7+UHA5xKPPXzRPvvn9QamLSFd7i8yf1WGOgTmIFB1+MXzcg5gEMOusM88RERmbmJSOttacPqD1BB4fZwCfAR/enGPonIHlAssqyw7T/FSPcbR2ix5HrwW/t9RqMj5VT/WzWDstNtmJk7AgX5hnxSQMiPiOTdYVYpPUQvzOkt0WvYr4zmBrfz18WbZLmX2rv8N0BxzEQEj36mpvlR+s+53CfpbZPiF/g/cd1u+w3tAYMTYE69ie/aL3KpQweCx09pi95QSI1w4dMfUNzTM9GzzEZ8oSEkXE1dWmE5SL8aNYPDnEj/hZxgHPvRkeqnECew57EaLnZgMWoYQTTpixknzdRJUmbAKcofaWmrxpTqfrWzkWPiSPfpuh1zK/qZ6L9sVYuNPr4bP3k32HzXkwHHVw+lcYqkDMMYYqEBMPMZnOzTAUDdNxxJQ5DLzMijKFCs6Mmoj00HfZQcVGHj6DUhTKxAvNX+OYnZ9WdquHq5hsfs8wtfZOZ7N6Slvs/lsZsBAoMERFwo6e6QoKPUeNcxakmmbwXd57kXhlIaT0X3f3DtOhbO1ZaH+9Sp3Q3nsVGTEOQC+QpMfE53O72mVsYkpGxielvaUmszraTHwAJ804Czywqg70WWWDJpRZ4o2rqxvY8Nc49zLzPJxbYO3DtKq0FHhVFiEDeckpvmEl0lygIgamw2csowRO9d+i+cNQgHOwLECIoBcb96G57Ds0Vqo8W0bj3K52+fnoeJSxXOZciOEfszpa5cWhUVlySrf83rKFuVaIIdmmnQQI1nlOrWNJG953uFKsuyFfeA36ex7fsYJUcLyFjLGY9Vx39440KaDMoeX9DePE8MzY+ZY51T1+wfPQgRfNH5hW2VDXjjeNw1CVjfcdvbZQIoAH1tqwbyJSqkeJJEFYkZIMXwP4fIUSVay5Ws7XZgK4TAvMJxH40gEWj79pPYodUZ6zInaOnNjjOeCZ7qyAhzdms5UmnmPE4s/6XRycYjpi2ffxlWe6tFCmH1vz5AA9ki0wR+BrOnuj8dms3VI2Tow848CVDgDqJLyyxA6LLsBLcM4853aMHo7nUR0hUpQzIeci07WVaGC9MxQAtJ4XwltZFWZMAoH3nZA8i8ElV3lY9NvMObGqGKzvccDnpt6z3Dl6gSFP7lp6dUzVH++Vh7OjtbWiKuEdnLLcQKKGpRtYOh3WFuK1XtDeslc8n4sHWh4f19keLStieUEoOOHxOC+hE/jaOzxSmrDTTMKHXo8XPGL5vOSUrCIZdAO9or0lCaJjvs34eJiW33Ha/FwSEO/pdGl+unaLx2um4zfduC2rFBRJ9MnQXLz98GiL/z/dxLhfVajuiKngFwobt2W9EK+59PRU+bhri9079OMrz2w4Wifj7p8gAKPg+1Jiv4P58HyvvmCR9C7pkdaayPmL5snVFyyS79x4WcpU8N3Hd+/P/V1E0u91trem42P9ItL4WZMXh0Zk7dd3ChoBHToyUVAOeE5l8z9/0bw0u+3qCxalToJNTw7mvnf1BYtSBmqNe82lp6fvD70vFoCP3iU9cteWXangxPhY49jEZNR4ei862lqlq70l58iY1dEmrx06InURGRmfLMyZ1xjCcegzjMN0jXHPXzSvoPAMU+WOt5YQ3LVllwyNjMvQyHhhPY/v3m+u9XvP75O9wyPyveezyxR5jry2qy9YJB9feabM6miTR370Su488d5rnFxz6ekyt6td5na152jGohe9Xvz+8ZVn5mjOG3NsYjLN8hufqqdKmYW/x3fvT+l/OnsLwNw+vvLMFHcfX3lmbt6HxpKze9umZ9zzExpX/w4F3eJp7S213E+G2DOKe0GghDNtWTzNA8bhxm275dDYROrk4TE0Ts5fNC8d4+nB4dycmT/dtumZdJ56v6bLjzTePfBo/uoLFsmsjjYZn6pLXUSeenE4h0sRkc72FpcGGBegZfwsm0tHW2vuXFj8A3Lg+Fkz0v245K0nynGdxfMEwPquu3uH3PyNnSafwTPHz5qRykU9R/BeNJ5/7dCR3N/1ObcA8/z4yjOlsz1RRS3ZENr/0Gfeufd4Mfaru6u9kAWv37Vx2+5U7j6+e39hnK72FqmJyClzO1OZqN+tzwrzAo/W+V1DI+O5tVnjgS70Ptz5oaWy61O9qTNQ338D3PG9Qha/A042PTno6mU8DxFJz9DFtz8s3/x+prvo8RlXTFu8b3O72gvGseZ55/R0pz+9vbf+jr/d+aGlcuv7F0fxegaWS+cvmpde0TA8Mh7UgXqX9EhNkupbzKPZ97P+dU5Pt3mOAR4th/RmC4D3R370SgGXdZEc3xRJeOfcrnY5NDZRvL9KMv59U+/Z6T5858bL5PHd+2VoZFx+PlrUs2Lw8uLQiIxNTKY45uzVF4dG5JvfHySdXeSJmy+XjrZWGRoZN+/cw/dEJHqPmNZZDuI+G5Y/zJ9Bk5e89URprYm0kW4Q4negn94lPQX9KzRn/hzrfPLF4ZSuta569QWL5ImbL5cnbr485xhm3aFMF7N0WW9OIeAzHeKFGs8YHzIJgS7+3qYnk2qqIxOT6fh6jaBpkYwumO+KZOdUJH8+gWvwSaYLvf73nduTfq93SU9OjvC7rTWPjE/J0Mi4bHpy0OSvGuciidyZP6sjt8cArP38RfMKej344UPP7M3dQaN5b0wAWH8HY2tZzLi0ZCpwDrvOkzOwUw6NTRRk5MZtu3O6GOtO5/R0u7Q62tAFR8cnC7TK8pif0/hBgJ/XZq33kreeWHpmapLZ6h7OQrZWzNkGHXktpvn7wKmWW3WRnIzx9gzvYzr01oW/IxgpUqQx/m6I3izgc84tbtkvgTN+5V9tTe+z1DY54+m6u3ekPE37Iay5g08juczihYyv8xfNkxltiR4L/dVbm+cj4/mW+aD4jEG+iSS2BeSXphsEYTBWrK0vkucZd23ZJSPjU1KX4p56/rUyAP+/bdMzuf297u4dQf7mybdYe5ZxwPoK9Elvnp/Z/GzOZ8Q0wjTI/2/Gr1WBD1UgpoKjBn0wy4xGrQQ242CLCfRY3/EcKiJhJS6k9EOpvqn3rJxRdev7F8vcrnY5rrM9NbjrIvLi0KiIiIxP1qW1lmQTDw6NyG2bflAwRHVwCwIK0XmeLwdANISU0DKHlLXmkDBgI0MrXGwcdLS1RgkUvY6hkXEZGZ+SWR1tqaA+f9G8grKlnakQJJax7+FJO8vv2rJLFnR3ikiibAN3WhH+zOZn0yw6EUkN+maCjezIO3/RvAIt8GcAS1nw5oh5wiFhBZi08AUOPr7yzFwPY8swDIFFc3DcIkB0ytwu6WjLlIb2hkY2NjFpKlkeT7CciTFzEylmeeCziYbiNzo+ZRqQhQu9S3CgFXSe481XniOnzO2Sm688pzCGZRjGgHVWzlr7gJx2Y+CCbJGCIqYNH+BaRHIGJegR/Mlz5I42qj/0uzDnZp2gIj5/Cxl81ruBr05SZLWTUDtj9Pu7Gk4d/OR5WE4WnDNkr1l7DLn19OBwuh9wUgKP+ns4C3CAJ3NqNfcEDjctFxlf2pBiOegp6JAFTEM473zuAV5AmT/TtOEFNazL4DXt6oAs8Kh5o040AFx9wSKZP6ujkXZRyzne9HOMo9s2/SDdk5BjlMOyoeB3PmAmroGqjXTtRASfAX6sYOA5Pd1Sk4SHaWc1xv/56Lhc8tYT0wqoF4dGpLO9JZdU4tGM3mfI/0veeqKJJ4bXDh1JfzZjqDNoRxmP4Y3Jcunx3ftTh2ldJKgDveO0+dJSyydblBm8Hk/rXdIjrx06IpP1xHllzVPTMjtT2VlUFiS0gNc3NjGZvntuV7vc1Ht26gAXKTqMQ/op01ozewq8iNSkLiLzG7ocxj1lbpe0NfSNusTZGBjzkreeGO2U8Jx8CAJhXdphhrU+8qNXZLIu0tZaS9/Nskvr6RxUtBJkYhzewM+SU7pT2vISaXiut236QbomHSS1nr+vIZu++f3BwmfQ6bXepfffSuaICTTwGt599oLUGaiDftrmshxVIpJL5vHei3fw+QRewSc50C2S58N3fmiprP9AYnO/47T5ufMc4o+8d5ZuxuAlUmlgp6wOyAHHo+NT6f56/Ij5jCWzrbXwvK67e0fqzGY+pgG6DXifp1dbQVHLMY9kI2Twv3boiCs33nduQkPvO7cn95kOburnmA+XJV94TnLGKWTp+87tKQQttc4V8r+EfA0xEPLLaBphWzBkC+nAsKcvIlFiqHHevKCdpffGAJ6/8tzknPG51MksT744nCZbsnNc+684eGrZ+Pr9OOsikuOFmi5hjz++e7+MjE9JSy3RSUJ6jqbBUCJdyJE/3KD7R370Ss6PqJ8DH0KSYld7S2pPx9KgFQTScmzjtt0yoy2x2Wa0tUbrjRu3ZQm5I+NT8uLQiNzXkO2Q8dYcdRA6JPc8sHBQEztRWURkbGIq/ck+I0524TOm9cLp6NMV5OENDcTcddddsmTJEpkzZ47MmTNHLrzwQnnggQfSz0dHR+Xaa6+V448/XmbPni1XXXWVvPzyy7kxfvKTn0hvb6/MnDlT3vSmN8kNN9wgExMTuWe2bNkib3/726Wjo0POOOMM+fKXv/yLWN6vDWhGHMMwQgq8B2BSUBpZSQhlXen58Hw3btst+w6NFRzb/D4rcu0xSHxHJBF2FLAXJK+1t9Zksp4EZiBwRfKGKBssrATypWV4VmdZaTyHhKQFof2LFQY6q42Ng5Bh4s1R0wtn5ohITtnyBL+IuAqbdvaJZMYOvo9A2lMvDpcGIbq72lOnomfQe3vBjjxka7Diwp9hDGS9cjDOcqho6GhrdRUeLyjDOI3Jigg5rvS4GAvGwZXn9sjN7zsnDc54lQu8Bu0AODQ2EcwI1hBS5tgostZiVRhgvFB2IxT0GEcUPhMRN0sXgOpDzFUbJpwJFMr4YVqwMtV00A7OXC4n104gHvd95/ak89T0ysHUY6HwhQw+bdQxvhBwR9CQ51TmqIRT591nLyjMg51vHAxkhzhoxwpCsBMFfxORoEECB7hI5oxkfsFVndoYZ3wxjcbKBtAFZ2DqwLclvy1j03unFyixAtYeD9KyxOKNXnuN42fNyP0EeDx/47bduWopbYRxsGjdBxZH6U4h/q2fYyP9xaERGaa2RtoBxQBaeO3QkbSdm14r6Ak0M6ujLb0M+qbes13dJQShzFwdpInFgwc6wK7H8JzLLJfQkkikqANZjgsrMGLRjCdHH9j5kkzWk59lPEHTsuW8CwUeAVaSztUXLErpqqOtteAQDwUxQvoR05q3p9b3EcCYmJwqyDCc71kz2tK/4fNQ4M9K5IoN1uEzOD47KVDPAUy9VhFJdSKWXbGB8LLKcNZRNH7u+5Pl6ZnV+gSPgSrXsipQa20iksNFKIDDn+l34/kyx6nF8x/fvV9uff9i0/GsbS7tqALNW7ajfi94xNjEVMHmA59E4AfBR703fAb4PIcctLx3SJ4r07EsumJ6YjtKJyXhu9ChuZMEg5ad08lIR8XSyPhksPJb8z4r4KKfhT7EeqrWxUJJRxiXaUjvHyfb6eeYD4eSL7QNx/sEnN73/cGUJ0KWMt9BxwTWjbxE29AZs4KyGnRVEeMXc5rb1d6QZbXcWREpt4Uwf4sW0GlCxNZ58QwSGjy/UNl7uaoTgVWsEzSz5JRud84sKyy9P8TfcNZ1MNUKImN9zPtCeo5XOSRSpAtvrndt2ZXuAY8rkrTl+s3/dH+aMIjPkKx4U+/ZwbFDe4Jzz3KM6RUtohHIiPGhYS2ttWIlSqiLQsjPEguMA+z1+wz/QgYJ1icmp8zORgj+gy9qvfBogq8VJPCGBmLe/OY3y+233y6PP/64/O///b/lsssuk/e///3y9NNPi4jI6tWr5Zvf/Kb84z/+o3z729+WwcFB+d3f/d30+5OTk9Lb2ytHjhyRRx99VP72b/9WvvzlL8uf//mfp888//zz0tvbK7/9278tTzzxhPzpn/6p/NEf/ZFs3rz5F77eX1WIyVCyvgPGJ+JnazJoA5GFGSveeA5tMrQhzfP9zOZnU0fkIz96JWhwW4avbsWhDaUlp3SnLRDmdCbK+cRkcjEkf1YWnNCKHleahHBXJiSPJXCWzTdVVhsrvCLh/bbmaGW2sGHC++YJfjgILIUt5OzD90+Zm1TEtLXW3CAEOyPgBPccZiEHAmc1seNbZyvB+fvaoSMFh5Y+L4x3nqennHpBGcZJmcJjOagto1GPo529+NwrMbcM6VFqbQYFOgZCwUvd0kdnp1nVStoRZjneOUgRm2WCartQm5uQYQlaam+plZafM1iOJ8vZKSJBZzUHut9x2nz5+ajfMi2Gb4WC8QyWQc1Z/x6+tEG09us7czIn5DwN4QztZXgfNQ5Fik5Ui3bwNy9zlSt5dPAS6+FsNMuZo4NV04XurvZ0j73gB4Pet5Bj2guUWNWjIV7mOZY4EGc5LZ8eHM79BHg6hZZHG7ftzhm+OiCknZ8WhPi3fo6N9NZaYpqhksZrY6Fxpx3xmPMjP3ol1y6vLIhlgWXce5m5+qzF4iH0bq4Yakb2gb9hTlZvfO3gwPk6rrNdvrJ9j6z5eiZjvOCLntN4o7QKP1l+Wq1KQ+0l+W+hPdP80dI19NjTbSkUGpPPlPf9iYZOgPfq+SIQjWQXrw2hh69mHCrsPEXA/pK3nmjSOJzTb5k/M6pCwYKQ3gl9BtnZ1nwt2WrRJRxYLbWsOsMLaLFei6SUm3rPTsdlXUe/mz/LdL+p3PjNJAvpgFZMW0DWsxBsLuv2wMH179x4mXS05d0xfOa4PSIHbPSzsc5xaw9hQ9+26Rl3zhbopEHOqAYOLB2aO0nwvJ58MS87LZltBa+ZXrm9I4+vq2usJB8LZ5qX44yARrQuFko6KpOjV1+QJdvpRAOWDRbod7Azm20Vrevrrgh8QXpsgpj3d+Yroyooy/uoq4rYDmAb3jornNXvjW/9rvE6t6vdDWBqO67Mh1Jmg3ByCtPMfX+yPK1w0xWzLCvecdr89O9lAVL+Xbd8ZJqZDu+z1hZKzPbohPfgkreeWEhwRItorpTUgOSOm7+xMz3jMfa0fsZKIoBtF2OLckXyTb1nydyu9tTOfvfZC6Irv5vh34wDbQ9a/CiDRNkZn6rnbAydhBDSC5uhjwqK8IYGYt73vvfJFVdcIb/5m78pb33rW+W2226T2bNny7Zt22R4eFj6+/vlc5/7nFx22WVy/vnny3//7/9dHn30Udm2bZuIiDz44IPyzDPPyMaNG+W8886T97znPbJu3Tr5/Oc/L0eOJO0IvvjFL8ppp50mn/3sZ+Wss86S//Af/oOsWrVKNmzY8EYu/VcamnX2lz0PpqN71rMwY8VbGzBsSHvvaNhd6XM3f2Nn4X26l6LlCABjgqHxe8sWyvO398oP1v1Owclx358sl3UfWCzzVdasiN8C7B2nzXcDKzEC5/VinBu37U5bCmx6cjDXX3njtt25CpmyDMaY0kdWEkQkt7/aIdBM9oqFn0yZTaikraXmVliEsixi3iVSzGri7F9cUody5FCWqh7fMphCjn/+e0iZCtGd5aBmx15IKdNZOzGZXvxdrrKwIERbMcFLKzuNq5U0oE8zZ3paEOKHPGdu51Sm9FuOc+D0TXM65fnbw1npZcEzNvj0/TEiduCVx9RVEt6ehviWl9mlQQddteNEBz69uSN7a2xiqmAgl2WF8Tzg8Oa2XBgP/0dQNyb44fE8/jvmiGd4PYfGJnKf6bOGNTXbVhSgq7Q0eHsdCpZjPRy4sHi+VT0aSiTxHEv8Pstp6bULtfgaAm5zu9pTPYTHGpuYzPHM2ECttQ6R7Cxa42jjW/NdPX5ItmKtYxOTMjQynrZqKHOKWmDRBAd3eC1eb3m8mx1vsTpTTY3hJV8wWDwzJvjETl04JTEPjMmZtJYcbaUJsw4c08bE2h/LYQmwcKgDh/iuSL51pVeNjvV6CRGWs0HLa5GiE9HLENXz1WfAuscjhK+QQ2Xjtt1u4pBOQNGOMA7w6vMXawuE9E7oM1bXAYBVgWLdJYW2MVP1zOHoBd48ByPWhFZ+qIrmBDjWg1j3s86kB3r/YxyHno6i++vHvFPErl7mM4d5aCe0luvscPf0dJaV+BtsaB3Emg6g1RMCITrYrGnXSkgIJQZZwWum6Ts/tDS1u3nfYipiLdC8vKw9Otaj73yK6TQBG6km2Ts4aBcK8nnv0Po1t7P7+MozC10R8HtZgmgIeE/BV/S5tO6nsKqJymQuzoQ+G2UBccarTvTU+jbmwG3RQ1Bmg+hEKMiE6+7ekd7j8pXte3KBQ56XlWhgnfGQvqrPYYj3ld1RogPLMTSjZTr2gHUUtoVEpFA1qnGgz3iz51tE0iQCEUmTK9E+N8YW1XLu56NZIOmhZ152vxcre5r1sYYAZ6ZLVepoGrVkiXePWwXNwS/NHTGTk5Py93//93Lo0CG58MIL5fHHH5fx8XF517velT7ztre9Td7ylrfId7/7XRER+e53vyu/9Vu/JSeddFL6zMqVK+XAgQNpVc13v/vd3Bh4BmNYMDY2JgcOHMj9qyAemnX2lz0PpoO+q6zEwxhnAa8ZCEfa9TsgXNZ9YHGqCEMBst4HuPqCRamz5fhZM1IhCsb00DN7zTJOfZGyx1BjncGMuxjmXOYEienH6xnegCQL4OzUGXDXll25CpmQIagzYWIcqh9feaZZicBjwnDTLXDYIGal5HvP7yvgAfPmdhDN4I7xFnJiWUa8laGknb/6XbEBKI0j/fcyYe8pt+xk6F3SkzrcQ726AZpONU74cz1/zgIB1EQKbY+89fE+6sARg+Vcs2hb0/VNvWcHFVo26HVGExvZTMuh4KZ2nOuAJ95jZfbrObFz3nJmWo7bGN4FQJWE/g47kvWZtRywMTKIn7GcYY/86JVc1Yv+LnydHW0tOeepFawK8V2vpQ+Pp38PgXce2XDTZ4bXM0GZUXru2slp0WhZVZK1ltjgAnBvBWGx/8gYZn6mxy8LHmuHpnX5cMhx4LUL1WeHWzM9cfPlsu4Dmd7C92XxmZouX9Z/05eJaodDWcDbM/Q1H+YWnWWtQzyw9CFu28RzKbtI16s00voAQ3dj75u5166MBwGfCNDz2PguqnCXnNKd7ofOpLV4CnQCON54LqFAVQg8R79FZ9xeSMstHs+7IFckz/PLKlwYsFbLici6AX+mcaLPAFfIaPACmpYT/eoLFqU60NDIuNy26Rn3vOl5iOQDvBrvni0Qy1tZn7nzQ0vl/EXz0r72Hs6toBfmjLt22ltq6Wdey8ZrLk2qiQeHRuTW+54uOGexl0hmYwe1iKRn09vfMvB0LhFfr/f05kd+9EqUjmvttXcPCQdMvMpDyHUvOUsHE/gOmru2FO8iKWtfh8+stqKP/OiVwh1MDDqQwnQEOYuAHNuOGB9VUMMj46VBDgacnwXdneme6n2wWjBp/Zf9CxZ9aKf3mq/vDNrUep9wb5bVAaBZ/0oZIDmSbcXpJEuI2IEAEcnxFSugjySN79x4WS4Q60FsMkQoIC6S7dNtm54p7CEq9HUrQH13TbMBb2sN2HO0n8f/cU8jggrMI4BX1s2sgHhIX/V4GMBLfrCe81ozx+raHu4QBIKPh9s7WzjGGT+np9u0c8v2ij9vb6nJkQlfR/HWp+8dhe9GRApVYdOBWN2yTOaz/Lyp9+zC2SwLBIUqZyuIhzc8EPPUU0/J7NmzpaOjQ/74j/9Yvva1r8nZZ58te/fulRkzZsjcuXNzz5900kmyd+9eERHZu3dvLgiDz/FZ6JkDBw7IyMiIWPCpT31Kuru7038LFy48Fkv9tQEvCweg/1Ym9FmAcJYCO/1Cl1BypF2/w3oOzhDtYNHCXl+WDCGalDQWs4osJ6Xui+rhKcR4kR0RugskFqyMoTIHjEi2R+s/sDjtHc3KKoyyttaau9/WGmOUzqsv8CsRYFjDcPOUH3ZO3rVlVwEPG7ftTrNU4DjRzqmyXsbaYPIUIH1+PrP5WZnR1pKrMgC9eg5Ny2nh3cPAzh1rT8voyVNueSwEJscmJgtjxjpvQ+W8ZRlqcPBbGVh6fbyP+syWKabW35oxnDiApjOXQ0a251zDXoxNTBYupNctHkNKVSgwXGYk8t/YaNXZVpwNGgrWemfWu7vEA8378X926NcD312n2gdo42G6mYTAgQ4AHu15FMkHcbkH8Fe270nX+lundOfkrCWHwIcsGo2pSkLG7L7GBerscCq7FFw7NfE97H9HW0uBH2jHJL9D8xzLoWldPjxdp18oaGDRpG4/NF068P422rhMlPENnHxl+x63ktUy9K17DPIXtse3DilzcvC7+Kw0w2+1wezd8RVyrHp0bgUwWU6DBq2WmVkQItGbXjt0JP17TLsQTjDS/K0sUOWB5+iHU5f5BSp5nnpxWNZ+fWfhTiiRYtY8Q8ihV7av0+G9ZTiZ0WYn+oiEK2bLnBRlLbT0GeAAbyhoEwrKePxVv5v1WNZ1OXCIvcczrCOBdmd1tKXjei0bRSQNyo03Ltbky4OZ51oVe3yPznQA79HybOO2fKU/g9Y7PTr1ArwxuiJ+F8naIenv8flDwoCVnKX1E76D5ppLTzfvLLHsE03XHDAFbWkchLLpWdex7iRD69a2llr67o621rS7RDNOQKxxcGg0De40qyvq3y27j30WgLI7bjjpgztn6PNcxiMs4G4cwBd0p9Dl4Rp00CnkG9C6orYdbv7GTnnL/Jkikp9XDN/UoAPf/PdQhQHmODo+VaiigK01NjGZC8zxXoTuroqFjduSylCRRBbCz4X29V4rXcarxXv0Pb2WvlpmO8fuhbZNyxI19Fq0TLfsTb5CwHtOJDvjrx06Ytq5ZfYtPm+tJedP29x6PVawnLuYwC+HvcT9srHgJXo0ExTzeASSQgBlthcAPOq4zqSbg06sqKA5eMMDMWeeeaY88cQT8thjj8k111wjH/nIR+SZZ5rrUXqs4ZOf/KQMDw+n//bs2fOGzueXGcqUAIsJxzB3y+HJ9whYTr+QUd8MeA4WT4iz4ERvWpHEDOFel3ieI/O6L6plqFvKN+MSzoNQhmQZAFcLupMMzHN6unPvCDlgGDcidhsiXIA6MVl39yPk2PbGBXgCHYa1pSDAgLEcONjHGW2tct3dO+Tmb+xM8cyKAOPGa0djzTEmeCCSCcqR8SkZGhl3+7viDgNk8IuEe/vyvLVxmxnScSXG3rlgJwAcDh1trQUjgul8uko46BUZuF72YGjeAN7HmCBTzPzYSLDG0AE03XqG8aovVWaHrtdTGobGIz96pdDmyLr80QIvMMyOgLJSZS9YqYOKnrGpKxO8Kpjp8n4OqHY0gp9eJnyZkWfhzQJPRsZk1VqgA7nY37fMn5kaZjjz4EHcAglOX2QDal4RqlTjxIJQexZcjooLMNnhVHYpuAdsoGpnteZJeAffs/WZzc/KWWsfkLVfT86edoAcK9BnJybRgPmH3gPPOSJS5N86+HnK3C75rYaByE5m7DlnZGKux8+aIad/cpN87/l9rpNUJN9HnavHQkZ6KJCndULdmhRnpcyxxjIBFxU/8qNXCvqA3i8ro9IKmJbtOdaZOXqKdz6E3o31fe/5fXLajZvkrLXfKui81t57c4kBThCwHIPMu5mPdba3pgFevhNKJOMBVtZ8jEMP8/IqJK01TCd7mZ1AZXfExCRPfXzlmWnbrt9SFzPreQIPVkZ9iM5DQZlY/go96Mpze3L6xuO796djiRRbJeG5iclE54Oz9bq7d6SZwdCRWe/RoNsMeWuL0aNjwTqnjA89D+6KwMEEre+Vtca21sZnLnQXEJ8/3WbVSxxivmW1S9R4jaFrpi19VjWt6mpu8O/vPb9PBodGcsFZtG6d1dGWqyAtC0hjztfdvaMQCEN7I6tNcFkLJg2W3cc+hCvPDduFjD+cL22PIdkg9L2Q0zUUwNettkPJEGVtn1hesbxgYNpCQJb5kUdfZR0nmvUrAS/fufGytBoMe8S2FrcM5u+U6fmxQRrcUyySyMI7P7Q0Tfq570+Wm5XVIkklk5cg5QUINYTogvEAne/Kv9oa1IEs2/RY6NDXXJpvD8vvjpXpZfNgGxN7jzZ0aEtmjaMr8bFmXbmMvXzHafObsk11IkXZdy0Zz2N45wgtBLEmCzd4L/wNQyMJT7ISKyqIhzc8EDNjxgw544wz5Pzzz5dPfepTcu6558p//a//VRYsWCBHjhyRoaGh3PMvv/yyLFiQlKYtWLBAXn755cLn+Cz0zJw5c6Srq8ucU0dHh8yZMyf3rwIbQk5FrbSBARw/a0YpY/acU/pOhlD7rpBg9hhaMw48DhDN6miTj688U+780FLpbmSIdXe156LweP6RH72SlsN6TlBeK4SgpYB4zoNm1wJc7R0eFZHMGYd3eJl3+n36AmusnTNydKlvDIT2EoESOM8YtPNaK0fYN+3AufNDS6VnbpeMjE+mCk1NJM3+w1zYEXznh5ZK75Ie2fTkYMEpdt3dO3KKaZkC5IGnVD30zMsyNDKeZrhpY1BDaE+nE2xgsJwAXs9ufaZjnelamYAi8NSLw4UAJuOhLDPv4tsflnecNj9VfjWtN9PWxXKo6ACLDsCISEGBKwsEhwJ7MNJBFyJ5pyUbwWhHwhcd6r3yMmlh/FtVNVawkltDAMqcYVZlAjtSm6Fhj7a0MwEZmvp7oTZusXgDWFWRFi6mcy55f9Fqk3kIWmUuOaXbzU7Xc/GcOyLFKlHMQeOJM6j4LCHbGvy6TIbhPF/y1hML/NU7OywTHnpmb65tAByu3AqimeSGGJnLY3oBR4suddYdoJme2NbevXboSKHVhs7igyH6nRsvM+lIr40dh5/Z/KzUJWsPGapKDAWp9Lqsd5XtzXm3PpjqXdbcrcppvMfKqOTzJeIni+iEEjb2PYczvnfNpaebfHXTk4NpMBP6TIzzx6Jpi+48+WWdex0Qh0767rNPkrld7dLVcHjqJAGvOja2uls7uUPrb4Z/8tpDlTsan6GgNT//pjlJwhPr2QB9PgDWefOcKh6P1vpwaN3vOG2+LOjuSlsF8X7FnOGJRmXLky8m99ngkuWaSOqMwqXIYxOTKc1c2cT9LtPRo0M82gtWoNJfjwPeC3mn20dC37NstLKgYSgRT3/X45sezYcSPXDerRZGIbrGuT+usz3nJLaAx2De8c3vZzwNwDp7M1XPXPGhA2HvPnuBtNbybY6sucXslW5Dqp/12pRq0MFtq3ODJf+1zRRT8Ybvz+1qT7P+EWQOJUPooJNnR3rVB5q28JP5kZaVG7ftluvu3pFWpunL2rXMCPlLrPutNm7bXdgjK2HF4kcx7aF5jmX2wuDQSGkLO7yHdQKLZnWLrpizqOeqdb4nXxwO6kBesKJsLZqutVy7+oJiB4LQ9611lfELtjFD1cOezdPR1pI7v55cCgVWdGKJxmWMDmPJeOaxkFk4R0geOEBVpTrhROtVGO+UuZ1RQeYKwvCGB2I0TE1NydjYmJx//vnS3t4uAwMD6WfPPvus/OQnP5ELL7xQREQuvPBCeeqpp+RnP/tZ+sxDDz0kc+bMkbPPPjt9hsfAMxijgqMDK9By26YfmAKCldYy54bFyMuMCM7G8Iw5MD7MURts03V28XdYOIcE0mijysFqrcZrfaqRlXrf9wcLwlQ7DyzjOWYtYKwLujuDjuaQQgGHC0ArlhBMoQxQD0J4DGV+aOUTzsGxiamccLEMWrzznJ6spPSJmy/POX50JYLnFLMcKTFOPvRGBbDAY6UbfUdrImZWSggvHq5RZWP1zGewlAcN7zhtvmu88ZnmeXlOlY3biu0ioHS2tdYKbVDKjEgdDAmdl7IWJkxHOiuIAyxQqjgA42Xzexlq2qHrKX6MY33puz5XobPkKYTX3b0jXYPFP/h7MHj2Do8WWv95GXR6DA6GoLd06HkLvLOh6dEL7jfTG7csuOhVRaLlJJ4NBQE9vszv1kY0G1C/t2yh9Mztkpt6zyoYn/rMaJxYiRcwsEXExNNTVIHz+O796Tl45EevuO+y1oi5IbkhRqbwelCphyABX8wZazyHZK41Riiz00tsgUPQwmeZc6RsfG4Th33QWXzsREL14YLuzij8oB1ZZ3ur6ZT2jOVQ4JlxKxKu3GSdj6tLkSACOi/bb+v88d9igiCWsY+sy0veeqL7HauKIdb4jaFjiy54PbrFK4973d07UrkBByln6D9x8+Uyf1ZHoSLAksUWfkJ6TFkiEusl11ya3UPCZ087L/XvunInhM9YB5S2VayxxiaSakHcE8RV6gBPXoeSJmKcUZYTVs8NP7WODTnOLVju2rIrbU+MZKxEb80qpWHHxLZ61OdfpBgItXQny/az9pTbtlhz4Qzic3q6C8Eo1vd0QofGtQbvzHvfZec161EePeLc8GXkemw+gzF0zece7a5i5DHr1KARzEMn2XnzCJ3JzvaWAo/gewBDvLHMXyBSlOexdreec1mAi+U/t4zTQRuPbrSeAt0v1PpU43s6QSUG1p9hD3hnnfHIwZe65AP6ml5D/hJvfA0sO0R8/QI0edeWXQV/U1mQBvuB5KNaY22htvCMW92qXz+LQPpP9h0u1UnL8AIdc8kp3YV9tfbaS07QgO9qn5Al13SrNf6+iG1nNAMhn2OI7+EZ1t9C9izzXs1X+P446CIeP/f20eIjnLCIvexsz989OVXPzzEE4J8itSh+UEEY3tBAzCc/+Ul55JFH5IUXXpCnnnpKPvnJT8qWLVvk93//96W7u1v6+vrk+uuvl3/+53+Wxx9/XP7dv/t3cuGFF8oFF1wgIiKXX365nH322fLhD39Yvv/978vmzZtlzZo1cu2110pHR4eIiPzxH/+x/PjHP5Y/+7M/kx/+8IfyhS98Qb7yla/I6tWr38il/8qAFWgZGZ80DaOy1k0i5W01IPQ082GGhWwMq1UXDIAR58KsEOP1GF/ook8LQi1BeK0Yg8unQ9Uk2oCMNQpFMsYKByk7mlmQehklDHBqeU6WUAZomXARKRpbsZkfLKRHxydTY1LEFvx4JzKGobSLZLSpHRSeU0wrTaG16nXPn5XwslPmduUEHpRukaTFxSlzs57w/O6Q0AZYDv7Hd+8PtlTgAIZWHmIdkzHBKA1aQRTJDAS0wOM2KGVOD9D00Mh4adai5wzE53yxqA5EgjbYaYc7c/TdP54zOia4JiIujtlYtsYI8Wgen9vx8JlB7+2yPr5WUCCkSFsBOmRPwnS3HPSMCytgoHmjdirpNm77Do1JTRJeM7erXfYdGisNVIaMEn4HAr7efRWhIKDODEPFDhst2oi2AmtexjtDKAMT34OB7VUrsEw7f9G8qIBaKPttusAXFF99wSL5wbr3yPO35/vklxl3Ojtc32NnVTJ4wU7L2R9ymojk91Ubbpo/ZZVI9dTRgIuVoRN5DjoAsvj3Do8GHYnZZ7Dwkp86w9QLNOvPygKCHuA5viQVZ7rsbPJZgjHL56/Z+1YsvlM2hpeA9I7T5qcOukveeqKcd+uDaWWBlTXK68LeY22hCpRhasfKDj7QPZIiOPlBr7OskhS4tuZRpsdyVbPV8ojbXY42Kt7Ysaedl/p3/f4YGaz5pqf3WHeSiCT02dHWKi8O2VXqAE/3jdX99by8Vp963WjXo9v24Jk1X98pIiLrP5Alh0A3E0n0h9Zaprd61S8hvVXvgxUItXQn67LkWF7iwWuHjhSCUVqe6/FDe4R94JY4DF4gxQvQWAFwTw/U8wp1HGBgXcZq+cXPnXfrg/Kb/+l+Oe3GfIcM0Ah4CWTlfd8fLNwryKD1F5FMztzUe3Ya5BPJVySKhB24Hs0wXWp5Hnv2QK9rv25flA4AX5vV0ZYG7b1WfKAL644gpg1tm8QmQ2gceHdRiNjBCw5QehfEA5gXaXq6bdMzafBL3zfIz2ocxSbJsuwo0zc5EOT5pzy9jv1U3B6NbWsvgfPODy3NnWHN/zC+SL5NeVnCiIUX6Jj3/cly0y8XsuH12lmvAi1qn5Blh/I503Tm2RkxPpbQOqy/ab0Qc2BZXqYjYN/AV8YmJuXi2x/O+aQsuREb4NJz5cQJtD68qfesnAxZ0miT2k7BcIDW2ZvpDFJBObyhgZif/exn8gd/8Ady5plnyooVK2T79u2yefNmefe73y0iIhs2bJD3vve9ctVVV8kll1wiCxYskK9+9avp91tbW+Wf/umfpLW1VS688EK5+uqr5Q/+4A/kL/7iL9JnTjvtNNm0aZM89NBDcu6558pnP/tZ+Zu/+RtZuXLlL3y9v+pg9dBliMmmKGMu3ueekuFBV3tLafm7ZuKes0sb6GWOaMyRW4KE4DffdJyIZAxSK5yMAzjmB4dGCj3cQ8ABA88Q1g4zLdD1BbEehEp9yxz31v6Drv7vz34uLw4lLd+8NaK6pC6JEYl5hBzQWdbGZEGB0XfQ3PmhpXLr+xenbV4AaF9n9dIuM/604/OstQ+kBgzg//7s50ElYjrnijOxQll5IpJ7LnQWLYPJAnZIaDrBmGgXwTRi0Zbn9ACw8qNbNWC+a7++08yO0njrbG9Nf+pgAGel4BzpO3Ms4PMXcx8BzymUUaX3auO23amSVsajOZMG9yl1tbemF6syTi3AmUWLkthWNMnYSRZtW0tNTpnbJd1d7QWDCcb+GqdVYsh44MpOvgj4ri3ZXQGvHToiszractn1GiwHs14n45LlgXX2QnJNZ4ahYidUucM4YAO5jB/FZmDqZxlu6j07Xd9Dz7ycuup1VYCeo3ae6KxCq7IhZIyF2op4xgY7sq2EAebz1p6FZA3zCADPQyejlGXj6t/RwvHFodHU0cBtgazLkjVgPjoLnIHXDWft6PhUaUClbP68BzG8kOcLp+/6DywutB/Tc+Z34wwNG4F6/k7M3QJWgDh0rkNOUOYdj/zolbTaR19cbq0Le4+1WUlL3E5WRILV6CL55Ae9x6Drh57Za55F7ZSyErBu2/SMnHZjvpIs5KiweCjLZ40frE3/rtfSTIKTnqfWezSPsYKulk4OPoU2qriYWONBJHy3osZfqPUTzxX3vuAnyzeADjR/fOWZadLExFRdJusiP9l3WESSbGcdHGT7qawlkwf8DP5vtcm1xrLOs9Y1Lf0Yc0Y7H30vH0Djl8cuC856gZRmaDMmGUUknDjA84a+NDwyLu8+e4GcMrdL3jJ/ZiqfdOLW+FRd6pLvkKFtyNjL7rX+ohMSAJrPlLW19GiGzw3fJ2rJtzJncF2yOZcFHR/50StpO1lP3/Lkt9YjYn0D3rws3S/Gpj2us11qUtQ1tYP7oWdeTs/PTb1np7Z7V3treuflpicHC/cNsm5ZRtsesOwQyQfrQsl5ni6jzzPrL0gQ5JbYXhBFQ0hH9e6s0nAskiNDoHVarVfdtWVXgddaevmMtqxaPTbgPJ0AeygxyJo/ZBPbdmWBCq2TIumio601lZG4tzBGx/ZAd9wQkXTOmlfc9yfL5ZS5XTI+WS/gS+MX9PzQM3uD9zZVEAdt5Y+8ftDf3x/8vLOzUz7/+c/L5z//efeZRYsWyf333x8c59JLL5UdOypCeT2AFZ47P7Q0NQj1Z7FMHQJDGx34m/4cn4Hh4V3e+5Ap582JGfehsYnUwScihfYgg0MjUpcsqANH+c3f2JlmfJzT0y17h0fS+UEJXfv1nWnpfpnSDOfJ1FRdPr7yzHR8OBQ17jCvTU8O5hyqof0I4QyfXXz7w7k7SHRGhTfGxm27c8Ga0Lv0/gJfEBwWfQDQ5gA/rXVgf9EKBIrRnR9aWnA+o+VG75Ie+c6Nl8lZa78lIiIHRsfl9E9uSi9zZ5xu3Jbck1NvzH1WR1tKT+cvmpejhdBamK7hmMUe4GK/pweH03JmrNnb47J3eYZiWYDAehdKiKHw63l49MsAh8Qpc7tKaZXPbEiB5PkynjraWmVkfCrNwtP8BvPlzFoPrzf1nlV4B/iISJLpNzYxlTtHGvQe8vnTOGE6gaILZ7Y2DPAdLvHm+etnQ7SEy0M3bkv6K7/jtPly15Zdsu/QERmfmixkynpr3HfoiIyMT8qhsYk0Y7EMOtpaZGR8UmZ1tMl3brwsh2dWuodG8g5JPn8hXgjHw+j4ZM64x7qBAxEp/M6gjQAoxHCQ4XtMP3rfGWJ4tOYboItDYxNy5V9tlacHh6V3SU+B17FT/JpLT8/xPqs3viWPvaCWNQ7zYs6/euiZvaa8sNbINHtobCJt/aLpmJ2gvFYL9J5ZzjDwJvyfZSEc66F9tGRNCNiIZ/3iri27ZO/wSOqQeMdp8wt8XNMX7jBb0N0pLw6Npu/QZ8+TFTyf1w4dSccXKeIVrfUueeuJ6ZmyeIt3Bqz58/w8+cDAGctl87VkFc4Sxvj56Lh87/l97v6WzUXT7c3f2Cm3vn9xjicwcD9v1gM0blgX07JVz/H8RfNkcGhEOttb5d1nn5SOq+HqCxbJ957fJ5ueHJRL3npigWaxr+f0dKe4tdYqImniwej4VO58eTSg4a4tu1K9h/Xa0Pess8fyOfQcaBefh8aNsXO03oM17aNqa72eEL/nAMVdW3bJ8bNm5HRLAPTn2zY9E6UXhvDJfGjvcCLLYJvgPYfGJuTKc3tSns/89JpLT0/vz4TuDZkNPiYiaaXrZzY/m/LWYVXZZO0D23beXnn2CX/v4tsfDtIhr2dWR1tO92dbVKQuLw6NyqEjEzI+WS/lVXrs0Hko09nhQAzRZJl+D+hd0iPf/P5gLtBgzRtyvC7Z/Yanf3JTzmH+4tCIzO1qT+0WjA/43vP7ZHBoRG7b9AMRkfScg5480PqLp8uy7YXvfe/5fXLzN3bK957fF20v63MKvwfsVAs/ei5vmT9Thl8cls7G3VPa1rXeB1siREse7TRbvenN38PBxm1ZxTjzIS33hkbG0/tW0f5aJLGLRsYnU/8F6AmyY/6sDnlxaETmz5qRyuTeJT3yjtPmy2c2PytjE5Pp3W8xtkQI33w2NH/Q+LDsUUuXAf1dfPvDsu/QmIyMT8lcukcY+inOQFd7a07/1XO5+oL8HSbM/7R+dPHtD+eCHqH1vN7AehXrNGW8cWR8Kr3n9GcHEt2Vk1FD7yrzsel3abuNcaPnDxnGNgi3rH/HafMLOi/r0GzDXnPp6XLbph/IyPikjE1MubqGxl0ZIOHzvFsfTPUoEWnKJ2XhFWf1vkaFcdWibHpQq9fr9fLHfr3hwIED0t3dLcPDwzJnzpw3ejq/VHDerQ+mLX2euPnynBNFRFJh4BmaFmhhAyGCcfhzCFO8/2iB5z82MZkKSzjUkZm+6clBaanVZHyqnns38AGAwom58+chvPA83jJ/Zs6B5imI+Pvxs2bI04PDOQPZwuPR4ObjK89MjXTLWceA92LNZc47hrPWPiAj41PS1d4iP1j3nsJaWZnAunk+GleWM8TDx2k3bkoVwudv7y3sbWstuY/FolWRrJpDO3PK8M/ONjiCmfZv2/SMjI5PpYE83oPf/E/3y/hUXdpbavJ///MVQdzqd4mIXHluT+pU9+irbN/OWvstGRmflK72VvnBut8x31k2TjNB3JDT2AM+C5ZiBb4FHjM2kQQXtGIc846u9pac8tPV3iLzZ3W4a9M8NYQTvIMNW6Yv6zswjltrIrs+1Zvi77jONhkamZAlp3TLfX+yPMgv9Gf4HbySz5vlVNfztvi3t69l/A884bZNz6R4v/Lcnpwj4Oej4+5ZxHvP6elOM3ab2XdvPjH07H0n9vt6DM2TAMCHxRevvmBRjkY8HhcjRzStWfM8ftYMeZLujGGI4ZXaQcGOaQQ6wePKxmOaC/FBTdMhnHgyiI0pEcn9zfqM9R2RJDP0N980O5V5oO+YfdFyKqQ7TUeG6vEB7IAt0w2tc651nFjdg+l4OjoQLgsWkRzP0ok9IbnFc0FAzKJJHgM61oy2VhkZn3Tnfd3dO+Sb3x+UtpZarlVXCCeWPq2/E5JF3pniz1j2Yd3cwoX3powvst7j7fl0kr88/IhIQZ6JFOXD8Mh4Tsf35mKdG9BSTSStAoiRG5rvWXpiEugZS5NMrDnG4q5M9/fohPmpxZ/wOQeG21sSuwo4aYZOml2Tlh0iks6R/2/pU5qXaBxA9tVEpIfaQDH9sH1WRrvN2C6WftuMvl1mg1vfReCpvbUmN7/vnNxatTwVsekaOBMpl9WhNZTRJz/LttauT/WadFE2Dx6/TCawHOAz643BuML/Pf0gREfTtak8PcjDgUh+7/S6WG6IZAlK4FGwj0BPIras1etqZr/0vJrxHTVzTllmIMDM0NXeKjf1npUmb7JtIpK0duR36HOobSTPUR/DB49GZoZgOnaQNYb2iYjYdkUMhOjbsiNDeseVf7VVnnxxWNpba2nAnfmKtpHLeDJ8Xu0tNXnTnM7cXrIs9OwZltXQty27IsTXPbvdW/vR7MWvKjQTN3hDW5NV8KsHV1+QlbuJlPcbt0BHpK+5NK5Hs1VGeDTz59ZVPAdEs9taa4VLVtE2B4CLAoEDLmcPXdTJ9278ZN9hWdDdJe84bX46RzDl6+7ekZb0owzxtUNHCu0KvAyq6eLmri3FclsN2I/zF83LlQrjgjJkPYWhpn4mwDSA/2Pd+h4VTSvIdixrh4Res/iJ8tkljbt9rBLZay7NSpr5nhzgisu4gR9uxYA549lzerqlJkkGCDKH+B4DXMgnkihp440b18an8iqfdzbwLsB93x9M+/7qdhDeudOAvqdWL26RuNJnpvGy88zZJ/ystWamyVPIOObWZSL5gOHQyLjMn9VhtrMJAXiGrg6JaUcmkvWNxfwtvKHEuYWOB/MV/Z2N27IS73N6uuW8Wx9Me/wPjSTZZMhyZZ6ncal5Mn7nu25E8m1wmG7Q17mtpZbecaDbSWFeuiWFRz/896svyO5WEsm3BxGR1EGiZdPGbVl7ttcOHcm1YGsW9Hxi9pzPGLd+1GfPO88wWCz5iR7AIpKW1a/5+s60VzO+c93dO1KeMKOttdCnWu89A7fKYloL3Tlk3X0gIqWyig0mbjECHHO2463vXxzk9YBQax7gXETSnvOcJeeNr/cOv/OFxvgbMol1lqRFOyPjkzmZF5qDdX5ZTnnP8XwhDzAfztzUMu38RfOkqz3Z+0NHsopAzWNDOpAlb/A3tP1BlZA3BnCi207F4IiBW5DiQumxianCHEN36F1zadY+J9RihsdEi46bes8K0i/as4xP1dOsTM1PoSda7RFj5DqvBxeVo4pUzx/8gxMQ0PLy/EXz5OZv7JTjZ81oqh0i6z0e6Ha5oT31PuOzISIFXQgBtCdfHJahRhDGw4PXPgV0iHsB6pLdmWDptho/GMtrX4bvwYZBayU9R8ZBiA4gxyEL9b1UInYbWdbNmO42bkta7ELG4x4ckaQ9LHAC/o4zLBJutWbNO7Qmre9h7w+NTaR2i2e/al5y6MhE7idaUL7v3KxyXtOPxVc9HYH5sLblQvoZ4yBkB1gyKkbmgxaxh+NkUDCdxOhD3Oa2GTtV6z1ltimvT7cK1fc3WjjWeOTWSvruF71W4FKfWe9yd4uGH/nRK+bnGg+xPhG9Nta9MX+RuLPHnQWS+ykyGaB1Mea14FE39Z6doyf+Lt/DpPmWSLiNlwbdDkvrrt5ay3R5a782PZkFYVprkrZZ62hrkasvWJRWCg6NjMuC7s7cWAz6HD70zN60RRS/mx33fBdLM0GYY+FPE/FbpDczPtNN75KeVLcsuxfYA2636Om34FNHGh0sYIfqdsCwlycm66m8EJGCzcF7p88U+4DgL5iYqhfsBIzB949pYJv7oWf2yt7hEXlg50vy4tBI2u4M+Pf4ume3a2DbLVSxWEEYqkBMBUcFVh9ddsqV9Ru3QDMHT5FhB8wlbz1R1tJ9AJ7SFMP8LacizwHOT5FawRkgKufhN990nPx8NF9WDycOO1vYYNbKC0of2biEsrXpycGUYYqIy/i1IRVy5IXww2NaTNxSkB7fvT/nuCpz1DPgEreOtpbc2GXzsOYrkg9wWf3IGd599gJprSU/RTI6vO9PlqfGhb4o8OoLFslb5s9s7PdzhflyRp/lkOM53/r+xfKTfYdTB4t1jjAGHKvtDY88O135Od2jHO9qJ08+HCwiEm2MMfAF2IAYh4gOSJVdKojvHhqbMHsOawVL06QO9FjnHn+zHIZ6Td4acX/FldRnOnT+RKTgBPIA/Xdh+3Kpu+d8GBmfkp65XWmgAXDK3M6cQeop0PyZiASdCGxoMd1oxyHPeeO2pL0fIKTglTlh2aGGc/+W+TPllLnZ3TIWzbADCPPWCnjsPPhzpnHrO94ZYz6DeyOsPYYTlINMzLdweTLj9L7vD+acTnyRNS5wD8ljBm3ggNZCzlOsef0HFsuVDd5x5bk9uct1GV/AIQeIQnMKGZveXPSeX/lXW12ca5zEBi35UlnrGctJjUSOrkaCxzWXnp7O8Svb97jrss4vy2S+F0mvEXMTycsDOBeOTEym6waOHt+9X+bP6kjOeIM5jU1MpmcS48KhZPE46yywEwv0al1oy0Ez3pcQHYTkDeZ9XGd7eg9WR1tLUwG47z2/T6bqiZNRRApBXg5ieWN+7/l9Jp8Bf4AD03KsenqPXgPTr+VgZPzwvTCMK7QQYX0Ez4FHoKJpOpe+xujxVlCaIRTkwNn4+Mozpau9RYZGxguO2yWndKd6kocHL/gBZ/o7TpufBhgm60n2LfeXL9O5wHfu/NBSc0+hy+jPLRyE3qX1XD2Gl7CAfUXwEXP4zOZnc0E6nGcEc+DEOn/RvBRXoT3ToM+Spb95dh5s1sd37y/Yr3wGtMyZaPA5/ESSFDKuLfo5Za599yQHTrUcEZGCLafpnPUzvovCc9J7MipG5vMeMk1MB+780FJZ94HFMr+k5ZAGBIZFEprTdzRoPwWvL+buWhE/SUbExk+9MS/gGDIWd7fqM2kFarV9awWJeK3Q/5DwF0rmsdZ226ZnZA3p3uzo1jakF4xCZV8WDG5JZb4OTF19waLUT/SO0+bn8MFBOR0sYT1F6xSxSVN6zyCX7vv+oKlTxALbjDjDSKjsam9tXJR+do4e2X+2d3g01dM1L9ZzBg/FT4tW4F8I7bu+X1Pr1yLNBWa0z81KAonl5Xrtd35oqdzUe7b0zO2Sn+w7XDpH628cfMX5GZuYMuejA7X6ziwOuENefGbzs6nc+sr2Pblzz/Rp+YBwjhCY5PvsOHnNCzSz3Yu7lFgHZ7xru4X3KXQ/ML8LtlvVlmz68IbeEVPBv3xgpzJ+xz8AhGUzY/L3tQGtP7/6guz+EpGE2Vj3dNz8jZ1yXGd7LihiGed6fA3Ituloa5H5s2bkhEpXez62icxN6zLj8xfNkxeHRnI9vTHO3K52OTIxKZP1JDLOwMpW75KetL+0LiHkvdE9pDUzBh5Q3ou/M+AZKHzXXHp6oSxSG3a89/j+b53SnRrhZaD7nmJsz6jk/zO94P3oN1uTrHLAU95ieuo+9Mze3EWBV1+wKC3VRFk1zxdKARTsu7Zk92QAeM5snFlCET0+dZsDrBd4uOZS+24WvAulumi3192oUkAmYzNg3YGg6c36DH3CrWc8gBOAW4hw6wcRkX2HxlLl6JK3npieA8tZpWkB+OEeuzqQps+u/h29Y99x2vwUL9Z4IlmgcG5Xe3p3jQdMz9zujNeiQZ9JLon/zo0r3HeBV704NCLX3b0jdS6AX3j3b+jzx7Sv71fhcw6ON7erPdiOsazPM8Y9f9G8tLXQ04PDcuv7F8tnNj9rXr5t8Q6RvAKuWx+W4cGicZFi/2FPdgInI+OT6TwsmZLxlDETL3p84OShZ/bKkYmptJ3QkYkpk6dYwHuDlkvn9HTL//3Zzwv9wi3gOV19waJ0vy++/eGcPAK+kP04t6u94EDGPDy5wTi11uTtObdOK1tP2fvwDkuv4Xnwdxm49RSyV0WyOVr8U597DVgn5BB6b/McMTbk5jk9eTkOvQQ4euHVQ/LiUNZGoaOt1QzqemDpYYw7zBl0wPLckjchGVQGWjfj1pI8Ft8XpHUuDlxZd5VgfBFx9SpPRj7yo1ekLlmGLb7DjtXQfVbWu6A74/lhCtpb93EwrtAaCJEKtHgUydqyoV2VpWPFBM0s/sbzClXtAA94FsERjMFj6p7qqJgEHXKCVIjXMn75jqnv3HhZKodHx6dyOIGcYfsqBspsGF6Pd58f1gT5pvVcQIi3/P/Z+/sgK8/0Tgz+nf5udaRuNS8zuInF8GqNVoBARDUER1Ms8RuN7PSCVOuUa7VFthJnk1oyltbe1caaIBazEjtKFfFuISfsH66Uk6WsZLayQbC9M4zKY6zMxBhHhUAIGc0yGM3SwwxWi5a21d2c7j7vH+f8nvN7rue67uc5LU3t2NNXFQWc85z7uT+u+/r+oG4CNPUyDwZ7u3Oyka7h0Ze+mdurVUN9uHF7trQ3gL1LLENEfEnhl+6JLaela7G6w27pjaNzYGkbOknJ4what9/KExxH9V7Vw8gjI3mAuLZ2ZDDDK64xpU9F+JPaN+0VWEXf13VomS2vl1InBnbSEt4hLUNURccnDdGeg3SOKG2JYOeG1Th1YRIDvd2YX1jM8I4lpzz5MdLbdL4A3PcqvUjNjd9pgA6BOK+07kUpC8j52LOhXcXbU92vKvqfZ5MhPhFUTqHzyZMpOsUZoMmXTkoQkmbvdTKe6oxZoNvUx7j20njhOf23ltOrSr+jdwNtXOnpqmGp0XDlVrVB3J6dzdnLrHwdnaHHO1WG0FJrDAIhKK2Iyqx5+87xB3u7cg7XqnLf9vWjeP3dWxl/as6v4QaFWHuG6jjkAZE+CAC3W3K57dls95+yt9WZj5y+gv6erkKwu+6dldMptxGnurqaZdP6e7oLv9U95Zxff/dWpXJ9y8XTFcjDSkbMCnxiSCnWe3ekI1WreNij8Y+fbaa3r39uAquG+jIPbn9Pdy4vhRF6ZAaDvV2YbBkUq0TdW/AiqfiZLUPE9GOvtrGWu7CRSDs3rM5KV+zeOpaLKGF0Wl9PU4GxZVK8rBQt5aHvsUwWKKb32ggJZl9456Ep8/bs+Q6vhBh/b6PAdAwqX54SpjgSnSmNmd21Zu3pVOaAt0cezIngyueYjbJ2ZKDweysUfPu5n8vKjtjyTEC+HBobBSswI+K9qY+zcjGM2ldh6NiZq4VUeAUK80P9PRluc+xTFyZzuFRmQOt0LxmRsmlsOEzVV/AibLQ84KlWWv1bN6axb9f9uTOySkvV6Bxv/vaz6P8ACmPbiD0PovVzDw6+einDZ6bzUzDimmzkmt6nvTvW4YVW5JX3Dr7n0Ze+mTOonLowmaMXVcvL8DdU3JRu6bw0oofzqoJ7Hj/h7964/gHWjjRT/tcMD2RCJ+8U8cneL82C0cgoSxPtPkRZEXoHLb7YjBs9Ry31xHvi8RTuo+VDHhx9alsWODDbipwiLl39yjhOPv2FjsupMbr0/Zk7mK0vZY17U2Cj5+y+6n4x0h5o9k7z+IvSVuXNVe65nRdLq2n0u94FD2yE6nJkI/0eQKH8qBocCIywrmIEs+vsqtVyn2kTbhsBTr6pfPz42WajXsIb1z/IxlhqIIxG3rlhdaG8a9lciR/EV5aOVbmkCr22oPTWy37z5D4Le3e0I+qtzMXAgE1jw7mzr5IJw3fbkhT8bWTkVoh6x1h5yeNbR05fKdS3Vx6jc6FBZbHRzg5QII3Yvn40Jy96cqvidxV9wbtTXraKNVwcfPWSW4pDZUjtM8Z189lOsv6Vv3HdAy3azjJWkQFsOeDRV+5hVNaVoHye/MfKLCk6ZvHSZloN9jZ7H6WMx7pXpCkXb0wX8M7LdonuuuK8veup8pScd39Pdy47IJr72pFBzLX4IDMHLXglqQCURiQzm5DP3j3Q7sXl0RQ9N5ao6ZRfqE7h3cUynuaNpXT72JmrrrMotc9AO1OU+6VrsxHsuj/emFyDlju1RuGUbM5s79GhvpwcxgyPvp7u7L3e3U6teeeG1aihKa9ZXrdqqA8HX72E+YWljB4pf+WZ0DFk58wgHGYkW9lp745iWTHKvEdOX8HDh76BBw98Lae/A80szlRZ9BS9s/tDGXywtzvHWzVjT2WkTrI4jj61LctEUR6/XPq7b9f9uYLq0Vz4+fb1o5UysxSYcaoVLQjE0aH+npDG84y1Okkk50T2F+pSxCu1A823gruAYrlBq6fae5rad7W3MdsykqE8OqdznplfyFW44Vgezhw/my9dbedH2sF7OtjbnStlb8HagCw94Txttqndu+jOUNY6uHtTNn5Eo3V/mIFaBp3crxWIYcURswKfGKoIdN6F7aT00MhgL1YN9eUEZqa3N9A0HGg5AdungwLEs48/UKj5mJqnN29LBPkZhTeyREZzR4Ip902j6yhYvXb5Jm7P1jG/sJgxZjI6CnqMTrNgo1g8Bd4SY3UAReMBKBgT7XNe+bNHX/o9fO65CQCNJJ4oc/ScPIz4fctp7FwFB+1+l/3G7pEaGogTu6WUDp/7pc//dGvcv9SRsUXr/to50Kjj4awChZlTFyZzPTGo8PC9keLqnX8D+Vrd3tzLGHJKQaMSwOh73qvoN4onLHehBhKWjqnV2hkfQNNwGzkK9+26P+egrTJ/+1n0f9IjrTUeRQJTgJtfWCo4BeweaDP1lHHtkxiD9e4TBnq7cvhCY3VZpoAqbt5ctHRDlR4c3Ett5GkVMPYhaEb8NTPUbk7P5UqmaHlHNQow20CjGA890XTgWkeBllPxDP7cZ+UHdu89g4E6kljq6f2ZO6XGDjUUpO4mDQUsdaXGHA88/jg1M184f8/YGM2Ba3z+xKWs5MS0NHfV/WIZH6DtLEjNA/BLflCW2PPyt5LzYmm1k09/IavjXQZ0YC/X2OXNo6z86ItPbk46zlI04MjpK1lPMRpiUtmqkWKrTdkfWXdv5tx5aO1wNq9nXjmPAycuYapVW7pK1qm3Du4Dad/hicuZwq+0HWg7sYB2bxuvxKDiib2L1nBfpXyrlblYT9v2RNI7nnLweCUp+Fv2AmEwh+ewi4xTNJbzb88QSaih7XSwOKWGVfKm3VKO0ztLOsPtHCMc4/dlQQqPvvRN7NywOhfA5O25Nf7W0DQ02fOlLO85dYHOehOcuzaFj+bqmax18NVLuRKOirspmdELXPL2guWoDr56ye335fFkK68++/gDGf+pelcBmICAWsFRztLD+k6ra6ks4JW/8vRIjs+7ft/oXSEu2LuuZ2znpHfD7psdh89SR4hoqsefnn38gaxkkwVbnop3QA3sHk2x5+Y5g3VvIgfXyGAvpqVUXwQpB6Dus5aZbJf9bvcVqhLoY/FE16YOcLuHhycuV+oF4slz1gZg12UDDY4+tQ3Dg705x2MUMBAF9VD3JzAjRHXEufpiliFKnCy7s5wHg3Boc0jpOGpXYebPbH2pwB8oX0e9FiNbxCPr7s2V1VNnxWMbP5s5Xva8/C2cvDCZ2US0bFungTfqbKuis3tjaNCbBrpFc7GOt06M26QPHp2wNik6zCwcO9MOULWGep0L5W2Vu4Giw0ntQErfWW5Q16Z7Yu9pSna2MgqAUhkqmvPCUiOrcBMFThK0/5PFC++ejg714eTTX8j0xsi5o3hn5xntgfI9fc77TaTnW3zVPfHmGQU6LjdIZAWasOKIWYFPDCljno1w9S6sbUqtoEQ9ZYxWIcsKZXaOGtlsFTpLWFLZFdF6hls13Ln+qvumYzKKf84pTWSNXN73FGRI4KnAR95+zsWr2W4jJGhMBPJMlfOiwZnrYYmuG7fnkkySv+/tqmHy9mzOSKb7PNBbJFu6l5GS3km0lgdeBJcaZ61wEfUmieqE0vjonakXjX/87PVCRDHvQU93Lbs3Vmn0Iq64N+rQsFFmkWMqimCtCp5xIzWOFfzsGBRKG418x6a3Ws11rULA+TNykZFDnUZ6pH6jfaJ0vrZ2uTpZo8wz/b3nhDl+9nqujIyt756ap1WeqQjRqDUy2Iv94xtz+JLKfLDv0gbV9jvPEcExPEHVOn49PNKodL1Dmo2o9enVyaz7bsuNAMjRRKv8L8cA70VMlgnYEXhGBC+KlbTysY1rMmP/4Yl3wnHtWBoM8bW3vp97ViPwU8qmOk6Ynt9o/d7i9t4d67I+MtYoVyUDxxot2DA5dce43ynjr4Iatj8pz+E8NLDEGkasQShl9PJ6JRBqaGdDbV8/ikdf+ibuG70rx1+iu6hzJD+kc0cdDyzPRWNUlcxAXZOXAQnky0fpvA5PvFMI7tBghWgvbF3wKOIwMh6r04T8JLq/kZPZm5t91spm5N9Aul9gHudr5u/8OlSm0qbv1mBNXqFZ4GWBSCqX6HjevfG+B4pOMZW1oh6VHl0dGezNgrq4P1a+sjTe9iZIGZ/5/1MX8n20NANAgU7p1y7/wF2rOmijYDYdn38rXqgxVfFP+wtwbcsJxKHsCbQNXfr7qEF5NLZGrVfhg9qPSHFB32vvup5xpP/ZfTt+tp09aR0uno4Q7VnK+G9htr6URS5bZ2pk4CeNttn33rui9380V88Mt6kziGgm1847f/SpbdnZsPk40C4t5uGnB9HdixzgQFO/jvZYx0g5hq1cpWdYFmgQBXhFQT08Q1JqHVd7VZAXKG3h/DTT/JNC26HadrjarCT2R9H+Ul7GKfebAWivv3srk+9t9QrVky6awMz+nu5lyeFRYEHKuG/vcKp3UCTrWD5YVZc+fvY65hcWUUOTP0WO+b071iWDiFK6pM7F0kkdXx1OagdSvdGTPfR89J4eP9vuoxat/dGX2j0AvaARbw3enDVYJBqL72PvZuoZKnt799TawjzeFvXt4jzLsk1t1mnKThO9m3qZ1983tY9V5fcVSMOKI2YFfqSQihqzad4e49GLbhnBfaN3Zc95QlYE2pzPEq0UYSkTzLje27N13GmlBzNlNxW1RlDi/VArnZEEURUHKsZeFISCZlhwvkDs7Y9A90gFKC9KiAL14YnLkjbfnKdtIE9QpvrmwS9iqdFAA3kjmSoR+8c3ls43ilZS8BwSKdAIriiVGmjjkDZ/SwmEFMTU+GiZp+KsCnsUtulgYy32ob6enNFCm+Jawc9bI3+nDs2U0LkcwzOhigE7FWlkx1CjkZqWBnrzhgDrJB6QaPCUMBwJNhotYw1DtoY35/va5ZtZZB7333MKpPbMgo1wYv8AZtBFhsRnXjmP509cys70wIlLWRT+G9c/yJQiADk6mHIKW2VcHSd2jyNBP5qvxRUPj3QPtXmuKh9Hn9qW4fn29aPZe3Vt/NwaPnUvqp5PhD/b14+iq5bPdLQRiKl3euOrgRQo0n51qBHm6oulhmCOpVBfaoSOWUtzlHa8dvlmNgYbmaeMPF5zXT0roIHPPdfMdonANkyucseq7j+NEl55uJRSlIrMrVJ+1BoQonMmvSEft012dQxbXitlmNQ57tt1v1vKyN5tz1Dl7YU1imjAiD5ne/TNmpIbnEN3DRjojeVOi2NRA/WUDGFxPqIJnrwS0bwIJ200cap8WpHHNrK9inCa74ky+pS2p5wvdkw1OJY5Lb3vPVzv1HnNcXkPlGdodpv3O+voSRmf+X+VM/btageV8B08b+Iuy8ZYXNM1WWMMx7BlZIb645KV565NFe5dA3B5kYVIXtIz9nCyiuxX1hg95SBXmSJy9FVp1j49W3d5PO8tAxKY1VRlf8oMrtF+aNUF6phtR3St4Cy3+tWbB79YMGx7em/kfEhlYkfzt05bypkM+uD3tieiZnyU0ZPo7nnrIM5Qz/b0fR3DM26m5KpoD1PlliMDrJ4N9ZvhlqyjDeEBZPIt300n6BvXPyjI39FalwNc14tPbi5kJb0/c6dQwYPO6FMXitnfDEADENJxBpdp6SfKcrq3Hu5UDXKwdyIll0R6otp+ojKQnn6e4lmqtzL46PV3b+Uc83Y+NkNe55W6XzoXSycjuUT54hvXPyjIhSk7iMpVqeoo1nmRctpE77QyDYCcc8eTM4BGhneek1T5i8oGKXuh2gbKbFK656pnVHX0eO8G2tlSWjbW4rrHIzrNaF8BH9KW3BVYgU8IbK543+hdOPn0F3LfkWir4GhBLzqJJkFTJJdjAPbAKl5e019tYmqZ175dzYZ48wuLGaG0TRcJVmAGkDU2G2rVXyfoXj36Ur6JcSRUabO1sr3m5zs3rM41TrSgUeuHnticG0uFU43In19YQncN+Nz/Z8gd0zKRvp4uzNWX8NDafLPoMkFc15Zq4k3Yt6vdfJAlnLRxpGcw4WfPvHIeB1+9hHPXpnLNPYE2DgGNzLGWKjOlxi8qKtoUsUzZOXam3VjSnjkhagDrCSfR+6xiUvV3Fjy8LxvHvtveBx1Lx/jTP5vBxRvT6O2uZQ48axhhfd2f+czdGY165pXzuTPVOSsd0HVotIx+x9+oYkyjwmwi881rYFsF7PvUaLpqqA+/9PmfLqzl2JmruDmdN6w3gjH1N2UKsv4OyOOc/Y5NEe2ZLufdHh49+tI3c7/lfAi8R6Sr337u53KCsQepO6E4w6aJbHDu3W0VjFMRRql32u+BJv/RTKBICWJT4d1bx8J38N9HTl/JIiu/+sffy+6YKiV6tkrjHll3b04BJ+43MzLaTcyr0hOOz+ebZTCRi5S0NMc24OwEyFsOT1x252obmXu0wzt7+zl/t2qoD2/dmMZAbzf2jz+Y3WnyN76PRpmZ+YWwVCDnMHl7Nsv+8xqC8znbSJk0jn9HtJxznK0v5YzYtqHzuWtTOTob7YXFJwXOm/2NdE6DvV0F457SmeicLO5Z/qnfR7xacZ6l8NiI1luDrjmieXzWzlEb3ZK2Eaw8YWlff093tkeHTr6Ng69ecueZ2p/U+aSgE7nBA++9OmbkWPJwzJtLWe9I+379P3Gbpc6I43b/eJc5X5ZT7W01WWZTYMqZeo78bX8ri9LizKRxljM7XUtYkeeRJhw5fSXXcDx1pnynrs373q7ZcxYsh+4D/rnpeIrHnY797OMPJGVxT9fywMNT+5nKhQy48+ar9L9mvuvv6SrgtqcL2XcrfdO9s7SEupWCbbKt8/ToIY2dQNvJyGcfPPD1nPOcAWZV8MKuSY2zdh1836MvfTM0JHKtXnP7Y2eahmI1HHtnX5W+6ZlRntqydhgnn/5CzoBP3kmZjlkjQNOpQcepvjeSv729S/GpTvU2pQ08B757oNUbaqC37Rj2ZA7vvgH5PjzWrmTBzjsl2+r/rS2oilxi7UVq+/HsDZyT9mPdvn60gK92D0hzamjqafMLi5kznk4Cu269f5FNysJX//h7uHF7Fl/94+8lnXdV5QG7x5E+d+7aFCZvz7r8h3qSOi9S61nOHeT/yccnb89isLcb/T1NeXJ0qK9wv+x7rB7uzcE+AzTLqRNHUntu9Qxvv1M6JeWJBpp9HGfmF/Dgga9hrr6ErlYA0eGJd7I5eE6X5cp9K5CHlYyYFfjUwEs39epKlqXflWWe8HvW4Nb+HApeBEMq8tQDnRvnAsQRMHt3ND3hs/WlrDTLiESvpNLBLYPxwNYHjoSqKhFoCpyLl4bLPXvmlfO59Hs1GFCJuD1bx/RsHWOtxti93TXMtRpBR/1N9IyPnWnX5f+lz/+0O4fo/Lg3h06+nTFmGow82LujHa0HtPuoVIkMUoeUZVAancioiLJx9VzKDLI2O0qjOaNIWBvNEOFBCuxd7LTRMsHifQR6V6PoJJ758ycuuRHwWcPopUZOCKeCNTLYm9VDVRplz9TL/gEsHVDXRUwjKDjenq1nPRmY+abf2d9WpV16tvbZtyenXZq2b9f9WVTWnq1jbp8t7zdlNFZ/Z3HOztNG+DMqKHq3Bx4PItjfKh5GdHXfrnTZgNRe6LOkFxdvTOdovOV1GsFogc+W1ay20fs1IFMoozu/d8c6vPPCL+DaS+km2lyX9nY4+fQX8KcvtRtCUqmIeA8ztGpo0rKHWpmSD60dLqVLVTJKmHmpGZj23DqVAxTIU2aDsiZ2DfruCH+9z/m7izemc+W8IqBRpr7UyEonROt8yDQQrUKTj5+9nhnOWPvbRqtX2VfdH0+58yLv7J4qv+PeMUJb17J/fGNmrPSyeKJzsrBvV77UhI5BGcL2MFR460axrG5Kzo1oXjTHVLk1K1dqlKTdk3qrXro6SnUcL0uH8+X8nnnlfOUscAtV76VG+afKcEQZXNF5WigrR5iiV8RtljqLStyoHHXszNVMihjq78HVr4zjvamPceP2LF5/91ahTFNU2os4o4ZOoCmPE/g8gMKzauhM6VbcX3W2png5wfboOHTq7VyGRCdn4EFVelaFVnmlsRhV/sMP51BDu/+gzdDTccp0MZX9yhyAmsVoZbUquG3frb9JlREHimV2vZKyKX2NDvIaJpY0bgABAABJREFUiiVw2LR6T6tcEKsKlO2Ht76oLKHOz5OllLZwDC0/pgGHkawT4VZEQ5XGM4CEf1sZVP8PtAtK2ox/7oPOLcJRPscgIfIRPb+qeptd6xvXP8gqeDDr7rGNn8XakUHsH3+wQMujjMpIjirTQVIye1XY8/K3mkF73bXk/lq+bUt7W3vDwVcv4ZlXzmeBq3SkpYDz37lhdZYZpcEUpNllvC+qYqBgcVEhVT2mqn0hOkfqCP09XdkY1O1WDfXl5L1O1qOg77N32upulL/vG70rlNOWuwf2N+TDM/MLYYlhpTtA3hlUVaekfMJ2MPWlRpZhRdoyZzLKl1v1ZAXSsOKIWYFPDTyBzOttUSboWU+7VbSoRL7fyhrxIjei90SKAX8HlNdQ3blhdakhrAZkDWmjdHBL2KxBwVuTLUHgQSeCISEl2HAfT16YxGx9CcOtxqWAr/A0gKwx9tJSo2D0STniIqMD/13FUVKXRmPaLM7bC3VkjAz2oq+ny2Xods5RyYPmO9spo/zOKzMVnQmNp0BcEsOWyOHesRSeFUqrNm2ssldehEQnhs0yIZqg52/TutWIQLDC4vGz+brdz7xyPotsAZCl6XvNVFNCh3V+2WcGe7ty39k7omn1tjlmSqhM0a4IqNwz62fT2HB4pjb1fKi/JzMOUVi1xi/v/kcKm77XOkysklWlQauF42ev4+SF6n1mvDPW+tJA0RCTMpiqk88q+bYUFpvOa9p4ZPSx+1rWkFLv5d4d+frQ9r55Z8K+Gnxn1QbPkWMtgoHeZvQuyym+PTntPp9Squ3eHDtzFb/0+Z/GWnHkWyXLPl8V2vjf5C+DjuHDzvf42euYmpnPMlQi5cyj6cQ1dSiR/3sGWKApuwz2drkKpa7byk6WJhMvld/SSMyShzof/q13QOuXR+DdwajcQYRTnlFRldVUffRoLp6MEhn2+H46W06KM4T7oeWoCLpXUWmN42fzZTcivuSVW7PGRnVUUdGeb5XQZUQ/+YTtw5cyVFh5m82Zy4ygKZ5vDZ9RSRjbn0GNT8fP5nul2XGsQdmbI1AsU5ICL9AjVWIUyOO78n/28VCZv8zwZ400NGy/+OTmnLNcHZgM3uGz+l00Z+vEAZCjE8rLGQigBkGuic7segshvfJ4NuCrCnjOCOsc8PRDD9c82ky5ik5v9h+0RvtOQOX+FFhZZu+OYqN6oF22rKoji/cBKJYR1ztugwW9krLaY8ju8d4d67B/fCPGRtpltCJjvOpvZTKF3n2V9TuRpZT3qcEfyON3qvm8HUPB02+Zbc152EASKxfq/599/IGsTPP+8Y1JWqWOPs+ZrmdJPqL8P0UT6JhU3dNbK8fjvgMI98pCZPj3gkG8M2QgLKtTeKUYFfTeULcknaoaEBf1KqZMRF2FgUlR8BPXpXeE5aI/mqsXerEo2B7Me3dUK20PwA1qInQqp0V7ZPWng69eyvE8jsdydm9PThd4oN3nCCJdgneDwWWKu0rX3nIcUlXWbXWCVKACad7CUsOlnWXlWa399NATm13HPNfIcr6DvV2Zg3ztyAC6a82Arcj+4717BZYHtUaj0Sh/7CcbPvzwQwwPD2N6ehr33HPPv+vp/NhClKJMYGmYkcHeXGkvK1imxuEYttSKEgc+M9jbhTsLS7lxyKQXG01hU1NA+Tv7edXvOT5LCwBN4kbD/H2jdxVKjFQFu3csJ5CaS6dzt8+yDvDhiXdy6eI1IKsnr2cHtJWtnRtWZ8b/sn32jKRAHp+Yku2lLfP3/M6WcgHyEQPRXqTmRYZjv6syH7uuKmfy8KFvZAaSNw9+Mfs8NSeOByAnABNXeJ52Pyyk3uE9t2qoLxNUy860bA8sPvHf565N4WQrSteWA7Cp/BzD3pH7vzyRq29dZX5cpy0ZoevnOU/N3MFsfbFwZpaepc7d2zd9T3SnyuY+PdtssDoy2JuVP0yNoTSAzwIozNvDdT03jy6vHRnMSqx014CrXxmvzANSe6f4v2XtcGbgV2N1tOZO1mH3lu9g+bG+nmbZhbIzsnQWgItndo5lOGufq0IzRgZ7MyNMDcDYyGB2fyy/tu/w9onj1gAMS1aV/lbHT+GknrneextooIY+vfceLY32sgo/8/Yjmi+AHE0uu7MR3U/xA51jGS+yf3triGQsoHjOdl72bKuuW4F3f9NYvjSpt69VaGhVfqy/Vd5BnEvx+n27mqX9tAwaA3qiO+vtVWof7dmn5q933eLrM6+cz3hpmSxOsPLnI+vuzQWJKJ3Q7zw6FtHw6A4DMd+xPFfxcai/J0dflfZxXynLAtVkxDJI4U8Veatsz8vw95NAGU1PzTn1W4928TPC2pEBsGyat2feeURyGcHyV55/DU1X+mBvF0aH+l2+VYUPaUk51fMA5OTt5fLq6Hcp3kqI+EX0bi11pr3C7HjennljTs3MY7a+5D7v6eDR+BFN8O6UrpnyBHmYlSWjfeYYdh86uRsW71K8JrJFfBJQveztyels3fa+8Mw7lSk8UNm7k/Gq7pW+w/IElSPfPPjFcEyr05AOlPHp5m9/Dzduz2HtyABm5hdL71YZlNmxPDrk6cop/KEc1dVVQ32x4dJfbw3L0TMAFGhJJG/rs1b25npULiHd9ux5y913S0MA5PbS0hfupafbpb4jeHIJnYNq1yizLXlgdWVPd07pUKnzLpODquLKTyJ04jfoOCPmO9/5Do4cOYJf+ZVfwdNPP43f/M3fxHe/+91lT3YF/nyDRkJsXz8aNj3UiDpGXDEqw3pVUw2g6MU99MTmQoaJfaa/p7sQsQ8gF12i3mkb8WE93FFEiPVwq2dzrr6URaG8PdkuMeKl4Nv3aQQXo2p3blidi8a35c68cVJz92DfrnzEMz309JY30DZQqZdcIxOOPrUt22c7RzuXyKvu4QHrp3peeUbZnHz6C7j20jjeeeHnsXdHO0WXZRCidPRU1LTOWSNuvAwd7gnxM8pgsJE6+gzPl1GR/C6aE5DPtFABBkAhIjYVxeCV4fLwhuv0ejVVjZSw+3oga+KZ779AHGAknuKdjYCPykxp5F4Ume4Bo2VsBBzXyKi/hcUl1NCMMtaztLWGU3TG7pvi2hvXP0hmTHhRN0Azs4WR7AAKEY0evWD02aaxdjNzDxcsrh+euIyDr17Kov91fC9DhNFG9jzs3ffGsOvmdy8+uRnvz9zJRf9VabzJs/Iie1N4wXdwzv09XeG9VuB+jm9p9mRhJkoUpV0VZ1NR03Yf+H9GggHNciFl5Q69SDby80dfajeJbgCF9WhUIEvybBprRtzZ5tK6T/be2zUzYwHIR/TarI3UXnItB1+9lCmULC3Je5NqhG7nu2+X37S+U4jKQCpt4h3UiGx7P0mDXrv8g1wUqTf/Q09sziKWuWcqtwHFskF837OPP1Bp3d49tCWdiDu6r5p1bO9XdG9fu3wzJwd4ZXMsz1Q+qtHTnLdG1e8f35iLMNT98HBF98pGwus8qkbJa7TsuWtT2e9Z3kfla8DH5Wi+3HsA2V4AzbtNPkvc3L5+NBmh6vER8jnismaUevySsgFlU0b9Mlv92ccfyGibltG17x0e7HVpmGaxqY7j0XLFYT1TL/I94gXeWJwvzyjSd8rGqQq6B2W8T/GE/RLJ8y0OebSriPM17Nnablht3++dnfJfb2+tHsMxbLaXRsan5F2v0sGLko1AnH/28Qey97L0kGadrn9uAg8e+Lor79m9i2QzwOetur9eFkxK1gTa9yECmzFsgWP293SHWRrMqqmhXSqRmWtzptxnxNe9ueuaKU9sGhvOePfJC5PY8/K3cP+XJ3Du2lRyHdwHS1cVvwE/M0L5p45hn9u7I9+kPYIU/fG+495cNOUwVT9PnbHKFFGZMH0/Ze+IT6nD0o5n90rHjPbV8gSL6/t2NbPh7h7odX/PkvY9Xe0Zl+urNfN3fv2d0Fk7R8+mQTl41VBfqPuU4Q/lqIVWFs/0bL3SPNVekuJ7ng5gs1kIEW+x+8cKFrRzKd+21SOWA2pzUfvC3h3rcvYqzag6fvZ6pnsza1T5A+nLXD1uF+DNYd+u+wt2DQUPLwi6b1ZX9nRn2rZ0bdQTAD/r17OJWaBOST63AsuDjhwxX/nKV7Bx40b8+q//Ov7P//P/xD//5/8cf//v/3385b/8l3HkyJEf1RxX4McYVBhOpWQrM7aKjxV+qxh/SaCjut2PrLsX07P1glH62Jl8M1Sbnq41VCPmbFNa7Vq0PMjurWNZyqimOM7WF7Hn5W+F4+j/Jy5OZrUbSVytgUXn6M3bM6JEYBk8BZ394xvDMh9eb4ZojlbZiFKOFQ9SglJVJxOF/JMXJnOGYu6ZNbZHBsBjZ67moq2Ig9aww/VoCR91rhFfvDOMnDnRnI6fvZ6lON9pCUJWkdf9joxYFqoYf7W3iLd3Clbw0vGPnL6SOTFZsoJ3cWZ+AYO9XQXhmmfn4YZVGm3prQiqOmDbin0zEnKhVaYi6uVAQdjuqQqxVthWXEsZogHg8MTlnBPL0tdDT2zO7rIqPt7+UZh7f+ZOqQKqApvXD0rryHMsOmrpJC/bc88YSvC+Uzo8M7+QOcP4PlsOgWPMLyxmtKjsnvAdFFQ7NZzZO856vGqgWI6SlwIrpHNftN64Rk965ZIs6FnxrJnCX0NcbuG1yz/AYqP5t9dLjlDVAUU+ct/oXeabvBKd2lc13qE1dy0vlCob6tG0vTvWYXSoP8e7UxA54NWZGwWPWGO54p39jrjmKecpJ1tZGRE1XKXW7Tky7FxTJZ3Yw2K2vliYS0Q75lrZKpQDvCAK/laNxpERWCOAtSk0HTLPPv6A6xz39pvlZdSYxP/b77wxuY+UE/KlGRuoAfjhh3N4+NA3sGqoL+cE94J/IpzQACAaT8lnqzoKPD6S4nMRvzx25mo2n/6e7gIPsLROx/GMdyprqrxtHe4Kz7xyHs+fKPbbsc5vwJcDVc+g85f/ZgkdQuTA8s6LY3ZSJisydFqw79SS1PY74iJpl343LHqSZvsyCtgaHS0PIP+toV36zNJhW9JJ7ydxpmpfSI/mePPie8lHTrYCdQ5PXM71G7C6r3eWnmymmWjW0e05DhQivZulmTmGharlzSKdw9MP1QnKQKHdrd4wGsTijVnWR+z9lrPs/Zk7Ob3bOie8deo+eH02UrxLncQevbJn7AUb8hk6jTRAx/Zi02AR67TcYkqCW1AHaSRTlIHK3pZPVQHv/kT7pTYMoOgY02ACr4won/vOD/9tJt9xvmX0Tg3SUV+uFHgyos7ROtQoB1+8MZ3xF957tT9VCVpm6e1Ga285h7IS/BpEXLY+Kzdbx3vEW6yDhnxXaVyZ/F+mJ6nNhTqNBtyRNtE5yz2lLKL8GSjSVdo/HnLKt/H9gL/fniOyCk51Yj9V26uuDSjqCbpXGhiT0r28lhQr0BlUdsT8/u//Pp5//nns378ff/Znf4bvf//7uHnzJm7duoXnnnsOzz33HF5//fUf5VxX4McQ1BgFxA2qCVT8I4NyFVBhM6rbrUZpLxpOFWllCsp4ot9QcI+M428e/CLeeeEXsswQRqFsXz+ai5a8eCOO+NT/j28ZK0SXRlESqdq4UfM2b29pJHv93VtJBVb3O2oWa9dlDQmRomadblGUeVmEEgVOZZSWaXiCWBSd9si6ezHY25U1OSMOWoXOi3awOKTOO6+5pgo4dCJGChaFoLJn2QhPFUo9k06bo25fP4o1w4OF6Glv74iDz59oRlB4QlQNyPq4AG1BgoJhmeFNFYxU83YL2iMj5Ti0wMaTu7eOuVFhjJCkIGxBz9g6GjxBLQI1NKqiao1a1iHr3a1UI0YVTKl4ev12eLZsbh6No1FSHNPuecpg4d3dc9emWlkeNdyerWeG0lVDfQWjnypamkWZMvYCxQjD1JxTwOdInxqt+VdV8hSIw9bJb99l58TeEA00yzA8f+JSZSXMux9sHPvCk5uz/bHrYbnL2fqiW2e+U4gyKRg139/TVepMUOPds48/kMvwtNkQUQCFhU4MG6kAC0KZYnjf6F2hkYpOquHBZn3wiCYRlA7w3tSAjFfZtVfNpvTkLws2IlDfZbM/1ZBh90QNErxn3TVgzfBAuE+6/5ERWHmkGskiR1aEJzajwI6RckToPmoU/qax4Wx/+nu60UCzdx4zs0njPFqu/7e4fu7aFG5Oz+K1yz/IgiZocOkEz3Udz5+4VGjAmwJPd/AyxC1f00AZjw/q+NbBHmV3VTE+pORAyn/WiEXDRlkmTaohc+SUreLgT8k8Fo+Vdlv8OTzxTrYunQtlhtGh/qx/ZtWAE53jmwe/mBmAvbVGjhLFGXX42h5DijerhvoKNCdysiofIczVm2V1dIyIVthz9O5UDciMlqR91iFrzzvSuyPjdRV8IKSCN9jf4dSFydAJeuiJzRmfpaE00lVTBmggz7eOPrUty1wqc06U7QOQ5l1lZ1j8vnk5ZubrBccsnUZaFtybA2kV6RyArDKEBp5ZYz/38PV3bxV6FVaxx1gHRaTzsX8JP9PvomAYLzNB113mYE7dGwahLCw1Qh5r91oN0p5dosxhXQUvKG8fnrico5kKajvQPmSebYXz3L5+FHcPtPmZXY/VkXk/NYi4jKcrvVMdSr+njUYdldZBQ/1Z+9pYnUb5eJXgILW5cI+B9t21jgSrc1sbjAUGeL4/c8edh91vxQXuy7lrU9kZVJGjPDrOMc5dm0rKNhqUae1Oule2ckYEn4bu9pMOlR0x//Sf/lP8rb/1t/Abv/EbuPfetjA6OjqKf/gP/yF++Zd/GceOHfuRTHIFfnxBjVFR82qgaIzsRNBPfR8Z6MpK33hRNlYBi37DsT2DtxcRRuFHa4jX0G7czP2yyqEy0jsL7UgBNWhbw611QHUKHJfCn9fozRO2mO6rzWIJVN4Z4afKjhpFgbwi5QkVKQOVZgSoEkWm8v7Mnaz8gWUaZc4cIB/9w/OYuDiZw4u1I4NYNdSH+7884RoX7LPEs1Qj4KjZmgLH3bM13RhWoYaiYYBnwnlVAa9Zot1DLRdGOHVhMidEkX688OTmXONYQgPIKRwUxHS+6th74/oHubIEdu+iyE6bYhwpDYoP336u2WDUiwpTIdU7F/3eOhpTtMUCnSC7t44lGylawZXvoPJ74MSlXAS+51j1Sn7s23V/zinH/WGpLt2T42ev44cfNmvCrxrqSwq79owtf/B4iT3LestrevHGtNu8OiqJU1UR8aCqQsvnGMXJ+acyciJc4Lrp5LeGDItPQPNOHdyzSWjwXPZ8lbXrXOgEfWzjmtB4zfG0SaTNVqtiLLSGMCql5EU0mtoIRutMsOMBKChHKaU65bS0e14V7F7pGqK7wnmpod0CIy1vzzaNZZ6ipWu1jcTXjgxiuNVPyDMEROuOnKdemTdP/rMBJnSSjg715e6wOkTsPGjke2/qYyw2gMnbc7lI/tT+231ReUEbwnOunlIdKdidRhR6Yz77+AO5KPz3Z+5k5VmfffyBzNEK5B1QNH4sNoADJy5luEzcsmehdJX0wss4qboOgm3A64G9n4pX/MyTD8nXNPjF8kG7TvICGt6i7C41rEeBK7ZEmn5G55s6f6lfkI7RueZF4EdgnQ0pYzH3kAEonvHbGvNVHlWeb2nTnBiRWTbOuwsc08t4tu+3nwEI15riI0oXqHepfMp9YkYLS0srzUk5WTn+HpHLjj61LVc2WaGqI5PrpEN5fqEduc6MXu6/xfPUHY3erzqsV85R9yuiwSwHN9DbVZhDGd3QvdVguJHBXvzwwzmsf64YaBVlw1nnhF0nHW7dtXZ5KBvJnuJd3h6mDPeUtW7PLhRsGmtHBgAAvd1t2s3sFS3zTvtLWUaino++Z35hMVcqLgVWDrotDc6976kfWf22zMYDtLOAVUaJHMyeIzTS5xnw9NDa4UpypqV3ClXsWF6lA/tb1TXmpM8cHYd7to5lsjXLRfPec6xoX+05WXlVnaQ6Lw0i7kR29QKBvD3x9pL6M7MVj525ms2POo3y8ZQ8T1CbC/dY726ZrbBMf9f3eHji8To7lsqAVYMQIp134uJkDg+s45tVf4BiILvdq76eriwogWD5Acu2fZKScT/pUGs0GoHvNQ/r16/HP/tn/wxf+MIX3O//7//7/8bf/Jt/E9euXftUJ/jjAJ003VmBGKxBLfoOaBrV5+pL2L21SRy1+VQ0zqMvdd5gMzWn1HNV58D/W4jmyOe9ZmXdNeDugd6wgTJQbNxVZa36ObMP2DTRNh21zTG1Cdmzjz+AQyffRn2pkTVOB4D1z01k0ZMvPrk518yNTQOBuJGf7lXqvPieGprNprln2qC97IxTze/03WzMbRsKHz97Hc+faEYq14CslJvXUFPHTTUdBarjdhV89p7hZ7bBvcIzr5zHqQuTGOjtxv7xB7Pf2sZ/3lz1mftG78LFG9O5poleg9D22n8vU1j0Dui/vfu2p0U7tDGxNia086zaIJ579Uma5y6HhnWCA4pPQNthorTE3gdtoMsItunZOhoo3gdLr21D1agJpV0L4UWnYXK07io4vuflb+HijWn0dtdwcPcmHDr1NuqtMDOvmWinzkcPqvK36LtH1t2L1y7fxFx9CQ+tHc4M6kBx/7kn9hyzBp21GupLjcJaFSL8XzM8gMnbc4V77q0FaDe51Ca5KXwhnazSaFkjBtVprLisirn+28PxVGNNux/3f3ki2//B3vbzlg/qPKrKHZ8UPHyKmtwrVHnG8vToPdxDvetKEwH/rFJ8NZKteBbdNeDqV8bd3xw4canQhBtAKLP0dtUyB22Vs/P2hc3ilYfZuZaBx3e4vlVDfXjrxnR2F3VPPVoV0RnFZfJdrvnBA1/LAoRS923vjnW5udLZ7j0XzSk1XqTIp2STFG8gznnN7pUPjm8Zy0qh6H0GmjKE6hzL4fkPHvg6ZuuLGOztxjsv/Lw7bwVL+0hftVkx99425K4Cdi3cV2ZYkGfoXjKIbLC3C++88Au5OQIIz+DctalCM+IIUg2kPX6gTbo9Xahsn713W9pBmgI0jaJW1k/h/yeVKyJ9xN6HkZZjHGjyqf3jD7rymO3DUaZz2D0EirTD7gH3y/IOuz+Uz1RPtGuvyvOBts5Xpp+nwMqkqbVWHTP1m2deOS+6SQ8+mltwG2xbuYrrLpOxvH30zpy4rzysTBci3Tx1YTJHM3TO8wuLmQ3H8ooqsp/eayCPP3at0T0v00XZOD3VCN7eQ+WPkR2Fc0zZEyI8iWimXYtHLyMc0Hko3Xjz4Bdd/uTNy56BPnPk9JVMV7T3JrWXOpeIXqjuZvk1bUZVeEHZepZrO9HvaPshnyz7DaGKHFQGVWQzlW1SuAzk6aGHh0D6rH/SoRO/QeWMmB/84Af43Oc+F36/fv163Lx5s/IkV+AvHpRFF1SJHmDUjmYc2IjBKNKyLDLVgyqRGUAx6j9ai/V261xYDzlKJdXfA+2ID36mPR6ihsHae4E1ZhktZOfMd2uknfaD+fZz7YbKj6y7161Ha/eSho23bkxn6xqQMlMHX72Ua6i6d0c7GyQqyxRFkSg039MsH7F761jBs89IuwhHVUCI0jH13VE6qu7LQG93pfI+XmSRzsuWoPPWwCagWq+8LBLQiyhNZbXZ+taEnRtW55rUe+emeMvIf5Z2K2sQqhH61pgE5MsD7Nt1fy5S9+hT2zKnggWdZ0oQsuvRSC+Li1UirHQMe07e3uk8yjKjODYzPADkIiKZZdHX042v/vH3CinpLMGyc8NqDPX3ZIJ1qvE830djgJZZo6DnRZzbMarQ1DJBmHMkjtUXGzh25iqG+nrC/UrxJY3+qXqmEW0s++6N6x9g//hGjI0MZlH7QLuMid0TLyuCdOngnk2FLCRbgsDiGn/77ef+fxgbGczuucVpXcuxM/mMRs6Z0aQEyz8tjqbKWdr3KS5rZL9GW0fZVYyUZZaW7qu9X5o1qVlyyrP0TlXpufVpgUfDWU70vamPQ3xm9Povff6nw2cYFc6/tW8ekN9D7vOpVtS49oGKzsqCvRvefdTmy3xGaUsUTam0ibSW8gdxT0sUVqHdOmdG/CsPi0o16Pj6by8bjDT74o3pjOc+f+JSoWymhYiWaeaG7b3T39OUzxgRrWPovy2P5Hfnrk1lcoedFx1kmm2rEbFc+/b1o6WZyECxFBDvvS3LQ/xQWVkzHzQr8vV3b+Uii3V8q3NU7YOjQBzR7BDeh1VDfUnaByArK6MlX7gndNZr5moKj5V/ci3cV5bu0+ANrlXLnto52vlaOZlloR5Zd2+Bl1a5c8fPXsfUzHxWGs7iA4AMv2zmBrMbquqE2ldw7451uZ6Y2i/PyiHevauqWyrseflb+NxzzTI8yluVPpEGzC8sZufVLnndyPEn7pENSFC6WDY/8kXtlWZlYcpvmhFuv9P9udjqIce/U6C/tXhHebVWa+LA8yf80raEFL6p7gC0Mw+8tVaF1G9IW7prwFB/b9hge+eG1bnSUpEeYNes/av4fmb6AW2ZyyudHGVpqmz1xvUPCj1j1X6hfbZ0bop7X/3j7xXOg7RRG7ZTfjwg56v8P9KbUvqUfs/eliytrplf9h5GZ+p9rueWAv3t0ae2ZVl0SrfsWmymg3UyaPZuKqOD1ScY6KFgq5dE677dcsIAzYzbSB6O5jK/sBhm7lF3qwFZ5oXajDhOlfYIkS5h5ZSITpTpcQTNavJ+Y8eP+td2IpPqGPa+qS1RSxkeOX0FUzPzbtYL+/Ja/E3xgxVYHlR2xMzNzaGvry/8vre3F3fu3Am/X4G/+JAiONYpoN95jFSV2VQNQn1nqhyPLRcWvdd7vgqoQVsFBK0P/djGNbmSB96ekYBq/UbAb7wYAccsa0yo5aKid3l7SoUdyDcXnplfACtgUDA+cvoK9o8/mDHJxQbw2uW8wzYSllIGUm8tjOrcvn40+60ybct87Z5VzZxJzZ3/f7FVXktri0f9SlKGQ8/o762BThIgbsTmQRUjGIFGsN7uWk4xtD1nUmOcuzaVKdVjIwOFpn4ebGmlkq8dGcgETy3loZEa565NFYRf9kWwTbx1nqnyMHY9KeG+qrLmOVy9d9l5pBo8Ery+DlQitO+JpQ97d7RLsLz+7q1MEaRjV4VWCs0sT0SBTc/GKi1KV/fuWIc9W/P10qtAan9tf4qRVjPgmfkF7NywOruL943eFfID+//IYOHxiDInWpXv1MjEe8xSKAplyhXxCECGIyeDEgQerkWOEPudDRIgvD05XWr0iMa3c4uMfuRNxF81oNlyjtahHQUyfDTXPuujT20Dy4Owp4o3f6+XUNXeVFUNkalnrFMgBVXoEw3z/T3dBfxXA5HedZaeYQ9AoLhHXqlDHS9FA7T5crQW4owGj6jSOL+wiBu3ZzP5gw6ZQ080s/GiWuPcfw0g4dqs8Rrwe6YpzUzJIVwXafaWtcO5vmO2bGYVOH72Ol5/9xaGB3uxf/zBXL8joC3DpbJSgdg4p//3HOwN+X8k66T2wzpUgLyRSR0kaoy3ze71PV4QBWHvjnWZzEGZxwuI0c9TDhAtGcrfnDTlViLapw5ryi8s5clAIytHpPQwlu/VHk7qqIrkHLuGyFnngZ6R5aU616g34bEzV3NGXTtv/g5ANj7L16TKNCpE72aQkZVRyoxrStdSQSwW1EGhJe2ANn0inVenkdJsIF0+i9+XlV1VnVb7nNpxIidcSuYhX+2uwd0X7q/eZ73vfGb/+EZce2kcWtclxdvK+N/dA70ZvVXHm7dWb6+8gKPIvrBmuLkHm8aGC8/ZBtsatMK5lznRLF3WtdOZd3jiHezd0S7PRMN/ZHOxcqe9N9Z+QfyyNheCltC1ASXasJ2gzqKUo0/naz9XmwWN06RvLK1ue7iqM7Oq00dpNxCXPLe/ZaZU1GeFa7HBOCn5Qp8t6wHbCS7z85HB9r25OT2XycNeuXKdC2U1oJYFcdk+yOwxpE4/Bnqx56dXWtq+18qaVgdROSWiE2V63KAEA6d+U9VOsBznbyrgjuc/1N+TBaDMOr13SX/6e7oLpen27ljn9s5cgeVDZUcMAPz2b/82jh496v757d/+7R/VHFfgzwl4kRopp0CKkWqkXKoGYWSkscY0m7Wg71XHif2eUMa8VHCMItBZq1rH5ZxtQ1GgWL8xmlu0J2WNCWfuNB0vC0uNsEeJnikNEaqwWyWLAvGScDUSfzLqufpSLipShetOHWAWVFCz+2FxxFtnVKM6gpRwpIZt7VdiDRkpoyrny74zUTM3dZIcemJzzhiVAu/OKug9+s4P/y0AoKerK+cQml9YLDWo89mJi5OZUn1zeq4gbHpC4C99/qdb6/1LrvKvQuipC5OFurnsi6BZWhZU8agaUVpmwE7tpSosnRjWrPDoOQlsXwfFa1XASR82jQ0XHNMAcg0G7XspNLNOMZvu2rNJGdy3rx/FWCsSS/fS7r+N6KmyXxQWR4f6s8gpNq22/VNS/EGbgqtBxUbo8Z0p414UoakNbtWAUlaXuIqjWnGE0ACSDgKL4ylji/23GjBTRjb9XSc9VixuRQ5s67TkXDzlXo2Utlb6zeliDXcCM0VYr16NWlX6fljlWeeihl01ntpngGIzek9GodN2ama+1Olh+9Ho2lTp1fN4bOMaAHn+q3ydCjCAwl3T8SKwclIqCtYGj1BBp5GS8ocNboiyYa3xnp/t23V/wTCpz2u245HTVzKaOTO/kMxwVcPPyae/gGsvjef6THjZ4HYMZsg+eODrpZnMlpdGGYCRcY6fb1k7nJOjuRatyx7JOim6bumcJwNbvqXGeO85fY8XWUyZ4b2pj3NZ45Z+cC6pHi4268f2z0jJXzrft0wWgTpPbG8zi1ucm0bq2n21+KQ8IIrY7QQ8478a1HQ+ts+S/i6SudX4SwdVlYhdu1bdB9uLiuDxLKsDkq6lsqjsvSX/3LJ2OEfHSD9o5KMhWbMaIv7qvQ9AZpCLwGax6hqVxlv9mZDCL7S0wagBtnefU3rwbieoh+/T7IYUnSEvGujtKpxtmdwf6eUp+wJli/dn7hRoMOW1+YUlV4YG2tmB1vDKdbPH1J6tYwU5zmbpKa4R7z2bi92HlANE+4tYmwv5mfYu4zPan5bgOXaqBHDoflg5Xe8lgyce2/jZAt1I6fg6PpAPYtX9TvVRtXsaBTaUBYyp/NIJP1WnnJVJy+xeetbMhqMtQs8wuh82u5qf6W+8HkPkz29PThccDZGsEMmuvBtaDSLSScp0rtGhfrzwZP7e2B7JQPV+YFWfU/B6/igNsnab3q5aRwEDVYLHVqAzqNwj5nOf+xxqtVrpcys9Yn6ywdat5MX3ahlHwo1+TqKxdqRanxCCV1NYBW39v51zJ3Wr7ZisVcnMCo1s6e2qYanRcMfV2qupWqRla4/m5T2vPVVeeHJz9hyQr51Zpc4y3zM1M5/VkmatZR2DijwNVV4vHO+sy2pz254QVTz0dm9S66yCc6l3PLLuXnztre/n+uekxrTfscZ7M5NksPAbO3cP/6M9TK1b6/USj2sArr00XsD5KvihNe8f2/jZQl8BDwfK8E9xq6+nC7P1JbfesfY0SGU9VdmPCEe11rwdv8o90vHsOVm6RAOffV8neG3nlcIV7SHCcnFebeUqdN2bV0RDgXRfhNS+6dmwnjWBOGLH132msqZRx2tH2rWNgU9WQ5x7ABR713waoDiyc8PqrPY0DTpVeiJ1AkovgKbReWGpUagTXpX+lPGz6MyPnL6C+YVF9Pd0Y+eG1TkDatRnooZ8jwQg7qmi5+bx6kiG8HBL6ZHuBQB3Xl5N9wjsnQI6xzPdYyDdx8m7s7YnXFX5xM5B65BX4TXKT6I+NhHN9NZse2p4Pcp0LMpCvAu2D0dK7qkCHq5w3Mnbsxmt09KcHl/y9ou/83oEeb+rKidWhRTOlclNpHf3jd6V0zmq4priMs+3hna/Pz3v42f93kQe3/N6elAnqFJn3fbyqbLHSo9tn5yUfAL4fSjsmjrtmWPPQPtHqsGJtfZtT6iyuwuUywpl+p+VhzwZKyUHWvnlq3/8vbAfSlXepzSUtDs1h2j9Vc9Y8bqsB0VK147ebfc12iMrv3j9xyKakOLr3j5TN7H4WEXHSe17JOdqT8DdW4u9YeDM2ZP5bU8y/j7V98TTJVI2l2gtVdZvv9ezA1A4X+WbVfsbpqDKnJfLg6uOb3vv/vDDuayMO/kBfx/1T6qi4+v+l/Vh4hy1p6321rU6cgqPvd6+XJvtNwa0z5+9dm1fE3un9bmjT22r3FMl6pvySWijt4cpGtFJz8Dl2pkUKCdonxoPV1Qn2LM1r+ta+gz48vynra/+RYIfSY+YP/3TP8W1a9dK/6zACnjwS5//6UJEVeRdVi+25xGOvOsK1kuuHuHovfMLi1mppbJ0djsHjslMhPEtY4Xx60uNrDRXFKnDKBamXHoE2dZsT83Li2oiaLkB3RM7hj0DzyPO3+8f34i1I4N4bOMaTM3M48CJS3jmlfO5NHv2sVGGR4Zu62ZzLl50owIjJJaWGpUZWNk6CRRWGDVSFZ555TwOvnopE1QoeLG8SgqP7bkx6pSp08Rl7pON4PCicVjH375Pn9Uxn3nlfCZQzS+0IylteYoqWR189v2ZO1mEIaM5NTLJOwN+FkVs7NvVzvrQyOxDJ9/Goy99E9vXj+LqV8axf/xB1NB0dOp9sPicikJh5Mzk7dksAk1xNBV5XLWHVXRnNcqed+PZxx9wU4eV3pVF1ej+MhrKlm45frZdToVOmO4acnXrCVXos75XFbIbt2ez7BBGS5ZFIem67XMa/dZozZkl0Rqtd9qIJS+jyEaxexF6qewGBY3U5Fp/VKCRiUef2obBVr+ugd7uDJ9OXphMRo0eP1uMko/Wxt8CTYNlfamBBprRWVHpJ91jO569C1GvNgA5nsGUe0YkMxLOyxTknG2k8TOvnM8iQ08+/YVCtKOev51XFEGud5jvVYVX957R4mNSklGB+GvPQc9Gy4/aMKqqkW16n6O7SLq41GhGAEY0oEwWi+bEM+UdrsJr+C6taa9ZBECxxBXnoefJcSiDAHGPMuX5zMC5PVvHfaN35XAMKPYATK3J2xf9Hf89v9DM9unpqmUR4veN3oWP5urYuWG1S0P1rPRezC8s5b6zdKCq/NTJmrw5pfoz2jGU3rHPFp2wHJOZ2BHovdT+CV7pk7072n1EtFSUtxYA2T1m35Sh/p5CJla0L49tXFMp6t9bS1R+zHt23677S/teWbn8+Zacr8CsrP/vlyew/rlmxD7P8vDEZTz60jfRIxHxGrlMYLYuo7Y9GVZxpYrskXrm+Nl8pP3eHevcagEerqs8rFnIGr2d2nMLiudeZLonF5etP+qrpjhHWYa09tnHH8i+B5CjnZRhNGPSe//xs/k+PxZszxi+79y1qezeqZ6Qiny3+6M9Sj3gb9+ebDthuEeUWYBifyqFlIxq95kZ0NvXj+Z64erzmkVo32Pxkboh7wrl1NlW3xMG32iWJHUiyieWZ6b0H8vv7v/yRKF3WVXazv26PVvP+ss9tnFNVrK6TM60uOtBtJ6qaybYO1KWZaXr1BJcN27PZrYAoGl3Upn0vamPc3/zXcy8ppxsbVxWpqnSh0l1zIFW1r/KpCkbQ2RX8PA3si+prmXvkP5fyxAfP5vvq+fpJ4SoXYEHmp3ZiSyj/J22gT0vfyubE216UWUabyxr3+kEaIPbP74x2xvNhFdbJYH6COdPXZi9Favq9CuwPOioNNkKrEAZWIGRFzjVPNyCx2BUUa+SkppSrKM5s8mrZYzRHMkUVQmiE+fUhSbDUIMdGyqyNIbnXKDy+PZksXY015Ey9qaMWhYiY5FlQsrkHz70jVxTs8gh88b1D7ISVBMXJwsM0TPi8xmWDjo88U42l/EtY4V1KaNK9RGKwO6VNRIRdJ+r1OEnqKFz1VBfoSxDJ8yeZ/XYxs9mCjJx+uCrlzK8Yw1+oNhXaKC32KDagjLcfFmdBtaONHvfUPiJjFgWyoTWqvvglfYD8sqJRr7XlxoFA45tLqlrtqXyIkOPVZ4sjka0qapQqM2yIxwvE4yqGE91/3hnveawVPAJW9YO5wxV9uwih5NnUPPu20JLSfEMrp5wGilICuqs275+NIcHtoyU5T+KW3SCA8266YwYZ+kQnXN0RtZxN9gqhWF715StezmgjTmVViov2rtjXWaEeOaV85kRnEJ5am2WrrP8XZcYrq1DNTJ2eUCHsDqG7Xx41uwPpA407w7SEfenfzYDoE1nUs5/Nfh2ErmmdzjlONy7Y11muLs5PZd7TvsWAMVeYN7Z9Pd05xopR8+lDP62fKrFRfL7Uxcmc7yVNb45fop22e+sEYI9q8r2XA15fT1drgEwNQ86SPn3sTPt0h9ejzLOR2mJ7ZkUlZKq4mj25qq/47/7e5oy5lB/D669NI53Xvj5rE8GGxGnjFrKP/t7unLfWToQyYmWb6aca5HRISUTWL5UVb9Qp5zFea8PBfdVS6nZNT586BtZj8WofIt1huh5eYFbEV5qmSygaXAjT41oVQq3PNlGDV8N5Ov8e2uyPESBfQuXGigYm2dbOhDQ7oVknYovPrkZ78/cwY3b7XJFA61edKoHWAdSFd1NcYs6GtDWr7RUohroon218jD52cFXL2W9QLq6agX87CTIRGUl4j55WlWnbmT4tE25bb9Mi5NWhvloru7uLeHYmXafn9ffvZWVbiPP15JsdvxobbZ8XbRulptSh5LH31R+Z19EpflVAgais1MjtNJQ8ia9R55sYXUovQPUDXlXtISmAu8j73Q0/wi3KQuy9+Txs9czfmd7l1l8sf0b1YkyLWVzgTyORvIAgNyepnQhj2Y/fOgbOTnGc2RYiBy+qeAUK3cCDXTXmj2SKMN5wWwKWhZT5WTvvcoPWfaNd6oMZuuLmTPM8qLDE5ezQDxbnrOnq5ajuWWyscqT1paX0t9SfXQjm5jelah0KCHlMCd4Th/l73MtGnfxxnQ2p/dn7uTsbCnaYfu7Lsf54dEbBqXpeSi+dbVwhbaNUxcms3s5PVsvBCF6gWErsHyo7IiJesPYPyvwkw2WOZBIAdWah3tjKJCwlNUUt0Dj5sz8Qug8UIeM11jWzpFMkcLiwVcvZYp7A83mikC7Fu9jG9fkoiK1DimJ7nQr5W/T2HAyIq0G30jJ/SVTWg7RjPafe89YDkZfeoacR9bdm9XcHN8y1lFUCmG2vph9f/SpbQVjnTIqZtmwxq0HnuJpx/QcdqrU7d46VmDGkdFBa6W+PTmdM9yWOTCooNuoIFWQAWQZHvMLi7l7dnjicqakEAf2j290DSXqiFg11JcJAqoc2GjPToSElNCqCmUkVNu7kYrUUmCwpa017EUWpoQ8Ox+NQHv40Dcy5eLoU9tCAdQ6kCNh7PjZ67k67ppFoI5T7z5VidJKAX/jKd8a5fRLn//pgkEJQA5HqViosqwOJs6X62MpqRqAh8TR49Gg5UTE7t2xDvMLi7g9W8ehk28DaJfesU7cFA+zAr9GQ9k5R0YhVfyV30TO92jdKYjwS8/rjesfYI/Di46fbWc/MVKqZsYuC4YgPX9/5g4OPbE51zOMjg41BkQRjva+0tDMv4lfq4b6csZS2x8oFflIYwIDAEhL7Z6kDC8pPquGjFS/OwtR3yo1yHqOUFV0gTaeW2OedbBYXmANtDT4MQKSUe3Hz17P8cuBVsDJR3P1bP8BhAb8FN3ifN64/kGBttra+LrfpCvak8waOlI0ks5g/m1pY8S7raONtfA7CRDxwDsri5P82zoFvGxaBcULzdDTZsZ8pmZ+5+2B7j+d1CpTWedamZFEZSEgHzhBAwFQ1C/s76xThZAy5KTWSaO9Z+SoCp5jOMJLz6gEoECrLFRxhFnwaL7nrDr61LYcjtvAkRqQjbNpbDijVwxKW2hVCWDZR86F+21552Mb1xRkdutAioya6pxV4Lndnq3jw5ZDQZ0u7M3zViKy3O6lypTsBVJfbCT5dxmP0XdEhu0yg3IVuVCrF1gZyAaRUccpy15W+ZHPA8gc5OwD+Uuf/+nc+BqAB+SDyzTQTb+zWRq2F5iVPRSX3zz4RezcsDrThUjz7X5Wlcc8B7Puw2x9CWMt+uXxMoLVoYA2P9U91jPivdzTqmDA+xgFcHjz1XfTzqHOMZ7/Q2uHXdnCc7ZYQ3ED+cwrzZyy42gQaLTu1Bkorfec1ynHvvLgH37YvNNKJ+z7AOTkzpHBXsy1spSAWhYoYWW4lCwZycn2+/mFRdSXGhgZ7A3Lkum7SI+j96lOan/LrM5TFybdrBTdE71vzFSnE9fKDfrbY2euhjxO75RnXyK/APysNs6NmSubxoZdPAN8WUHxmRme5Hte39xUULjyr6oVNFKQ2hvyKKDNm1RWpMpG2dnq/MuRd1bAh8qOmH/8j/9x6Z9/8k/+yY9wqivw5xFs5NcnISpAZxkfCiwTwSh5r8SUdcgAaeeRJ4wuiMWJwqkaEzQqUssEkCg20IxmfG/q48zgru/n3Lza8NYoaCP4PilYoaO/px2dxugSCr+vv3sL+8c3YmxkENvXjyaVdsugKSgOmpTyVPktIM3gou+rKCZU6taODOLoU9sKzDgyBLMEGYAMR6pGcEZRQdo8/NnHH8iMXgBy94xp6RMXJ5ORMzS0UGlk9Owb1z/A0ae2ZSU0rFDYiaE/9azOIzLw2rthcUjLwfB+vPjk5uzOLyw2MkOoNkYnRAaaaO4agUY8KCt3Yh3IkXHqyOkrmWJCA72XoWKNqnYvl3Pv7W/0vnlOS7s+L3JRI8R5Hvz72Jl25CXQjqhXo4waGbXsghpxFWciXDt+9npWX7++1MidRdVGxIzs57ueeeU8Jm/PZnfR7ndkFFLFX/lNypjWqWOtzFCgyjWdiKocE2iM0WwKi8tlhr6Dr17CQyYqz/JXq5zzd9ZoZpVU4hczDnT/PUXGuxekzXTc0uls96Rq1GVqr6tG7wP5jFVtqKplg85dm8oMs+TBAHKKrtJMj0bYiGp1SntOEpZ6YWav7kkNzdIIercVZ+cXFnPG/WhOkQFQwWay6X7T0LdpbDgLCOmk8aqWbU09a3HfBoREtCWFT17ULoDCWdkx7L0kaDZtJINzrAWpW893aWac1yjcG8s7e/uu19+9lTnrgLyRRNdm6Sjnr+XmSBfUUeXRX+8cVaYqo68RrUvpNWUOj6p0XQ2wdBaT72xfPxr+jrLGgRN5GSX17ojm0/hrcZ44rms9+tQ2XHtpPDM0a1Py/eMbMTLYm2W4kN/bfbLR18xKjQJaqsiaqUxHqm8aFU0ZeyAwVnrvVZlyfMtYSIO8+UU0get+ZN29hSwCW6460jMiOqZOyyr3hv+njmPvuhf0Rkc67+rIYG9m5LNrTwXgEdS5rd+xWfVsK0vjkXX3ZvQ05bgleDKFldcjI6ldt7cu7gMzW6dm7mRzZumlVBnKCKwTDgDWDA9mtIH30TP+6z7afeFaN40NF+RUnv/7M3dCPk6dpgaEeqQGf2nghOe0idatOK1BGlY3oQzvVdnQoCu7DyovsbSYlz0R3eOP5vKlVTnHc9emwpJcQP5uenKyN0fqWNOzdTfYz9pcWM7KK4X37ed+Dru3juXsDvqdlu+0zkFCZHtRWTOSG1Re8EDvlCfL8Uw9HU3HZ+YKy6cD7XYFniPVu4tD/T3ZvxksVaaDK64qXdEqJ8uF1N6QR3E9LKF/6InNWeUEBuqlaPsKfHKo7Iip0h/mu9/97o9yrivw5xg6qdUIpCN6PcGsasQqIVViyjpkIg860FSOWcuzBmSKqhKwqkIUieLM/EIWNV5DnimljAcew+mUaHrP8zNGhNXQrnVts4J0X1NOBzU8qyH+0Ze+maWSP7bxszmDBAVkW97s03Aynbs2hUdf+iZ2bljtZkzYaDBlxvq9OgrVcUWBNRL0Ugql/lujoffuWFcwbvOesdyRF4lro46s0mgVSm+PP62917VFhiSg2TTXE35s9L46KNASp+h81R4uZQqqveNAMcKQZ12Dn02jcyzLILBOVC37VVYegv9P0ZkqdMA+YwXB1Pj6ne6lRohbI7oqYbbnSrRWLQ9GB5ziTISX1lhcJSjA0igblX/qQrPUw1wrcy+1J52cgwee4y0FKYcUHVpRFhi/2yMNZHVf7djWmaqGe77jvamPc3WjU8EZ+3bdn92pkxcmcwqcPV+vJCXnoL3eUvtO2nzPQDtStZNI4hTPVAcGaStQPTuYQL46V1/MZUUqTdPMXC+7JFqHvbvqlPaMKzToP7R2OON1943eles3Zw0stvyrrr3MIBzxmagcKfEHAL7zw4+ycgsp2dNG/FuHipd9YuUXwuGJy1nGkPcemw3iOTB5pjb7SB3RnlySkjNTMjh/S/n12ccfyN1fzoOlS9QhFI3FSHIgn6VD+jK/sJQZXrReuV2LXRfnrwYb4olm21WNKLUylQfeedMApXPyfuMFD1hDWBQkY/GS0N/TneMHkbNFoYE8rS+T4SzN10h+j94DfsS2XT/HHurvyWUTeb/VEpR8t/L7yIAP+PJaFPBgS2kqTdGa+xF4/FmdCaND/QUaxDXueflbhaxOCyobv/7urZxB2itXbfUMOlMjHqg9GHheUU9GBUvndb4RfeC+KP/3zr7MuaaBYvodHWaDvV25gATNjk31tfT4CvXx2y3DttJSvaOWH6RkQJ7jbH0RA6Kv2b1jRoAGL6SyJjgHKzt5Z2DvPufrObTfn7njBpB2Yuegnh3pXSlDtw1CTIHXi4/jseeQOvmoDwIo4IeHj1rtw4K3D+pk4F3hHJlJ4lXYANp301ZoSe23OkesjcHiqIdfBP5m+/rRzIkXGfRr6AxU1tQyZZaWpJxjVd9jcd2Tz7mPimdWXk05No6fvV6o6hCdjRdM5pXwXw6kdB39jufOPrNeDy696ymZdwU+GdQajYbnZF4BgQ8//BDDw8OYnp7GPffc8+96On8uIUXsPXj0pW/ixu1ZrB0ZLNQ79cbi88rovOdo0N80NpxFXndqTOa7agCGB3sxc2cB9cUGBnu78c4LP9/RWHZNM/MLuN0qwTTU31NpfvwtvegAslIkqX1MrY3PHz/bTBelcf/OwlJu7wBk72Y6JZtc8zuuCWgb9/geXScFUO4rgOx3NAikzlf3wc7B/pvPc20cv8o+leGy4qKdczTGw4e+kZ37mwe/GL7PW4O3/7oHdo6cH9COQu3kbnYKuh/jW8bCedGgALTxV3/vnY2uxcOtj+bq2RlsGhvOGhf2dtdQX2y4+23fuW/X/RmeVKVH0Vj8bXRuqTOweHB44jLm6kvYLQbzsjNI4bd9hjhZA9wMvLI5RjQ4Wk/qd5Y+Aj5NiOCZV85nJSxqaCoo0V5UpcUPHvgaZutLGOztwjsv/ELp3gBw73mKXiieUGAvo1GpPdcz5pjjW8awff1oTmG/PVt38bXsHEnDuWfEa+6ljpnCSe4ToRMctLylbO90/jRqRXTB+93UzDxm60vZb5SveLjTqSwEtOUWe1Y7N6zOeP7ODatz67R0PaKvVdbo8UxVjom70V3Z8/K3cPHGNNaODACoVaJ3nwSUdlHB2ZOgkx5P1DMkHyH+8HnKRONb2mOvf24iiwC+9tK4+x7dO4uTek7zC0uYrS/maJbHr6vg1HLwro3jdzBbX8zheGoOQF720zXybAZ7uzA61F/53nlyLiP4PTlqqL+noBN4oHerDD8sfqd4WPSbMt1G76gN0NCz53drRwZzfDGFT53c++g3Hr339rhMpk3Nqcp+792xLjy7TvWeaA+i++K9t4rOqnRPzxZo8v2rX8nTC28uvCuEwd6uLBDL3gdPx+FZAW18PXdtKpOPLI1bzh5WoTXR+J5MzzGr4LHHs1K/q3L/VR6J7iEQ8z9vLO0Zu9us06MjVXUPKzvp/atCQ/UMPg390JO9lfZXpUeKx1Xk/Sp0wcr6nszrfafzqrI3KVtUX093Vmpc+Zado5UzU6B7vmdrU2b0ZFIAoW5bJsdWWWPVfapCazwaulzZMaWT63l7uG9l8YgOpM5I1/La5ZsZDdDxAHTMt6O12e+6a0BfT1dBb0nJdFa3+1HJ7n9RoBO/QeWMmBVYgQhSHlhCWfSVhcjbT6HKlhfat6vY58GLymEa/XtTH2fRIlXmr8AIuwaaBquFVh0M1q23863inbYKV5RGmfqtRsd6ETlRdJ3dRxvtTOFn//jGXFPAI6evZGexff0o9u26P8cgNfKJcOpCviG2rtPuK4AsOob1O1kqh0KCFw0Xle+wOKhrS5UFspCK9gKKqdye0Fj1Pth1auQd066574za1z3wSmbZqCOux0bNdXovItC7ecpEaek7GCVpM1ZSkT/87sUnm30Xjp9tZ59oU+VDT2zOlYrjnY1AG7QqnnhzKDtLb/4WVxm5lcIHfc+R01eyFH6WM4hA90TnkIo85GdUnLzoVw/s3UjdFf3OPmf3lP/XzBlLE1JApbOGdtZidNcZ8WPLv9h3pCJlO7k73HdGYmpZKO11oc3uU5Dac8VrRl+dvDCJwxPvZLwDKNZS9qI0vfVxLewfxgh6jX7lbzVS2o7HqDHWOdfIvrI9trxFm+4u5/5asJFhtn620gsABTrc6fuAfPmfvTvWYeeG1VltbUboMYvDy2bhvFK9MDxI8UyNYrTZPjbjkA7wG7ebtdWZudBJVJ3tCcNz8CK9iT8sMQakoww9nqhnyLt34/Ys9rz8rQzHvEa7tqyZgmZppDI2GLXLEhEPtSL1oyauysO5D/Z+LAfv+BvOgyXJuGekU0C+/IjnQLBg+86VAcd84/oHbn/G42fzPaU8ncCDKlGonrwazY/v0swfGyVflg2lWSJaOkR1Hx3nvtG7cuXVrFyViuL1aGiKVigeVd1j+z7yG45Xtt9Au9yS7n90dlbmWo4smyp17JVFTMmpnm7CvSNoT8no93t3tLMLta8D6YWV+/fuyGe719A8K72jx85cdbPMuJ5UZkykU3K+QNz8nPdj1VBfbgzdP91fxcmo/LTdK/5f8d/iQlTiUsFmbCvoHbWZfSno7+l2M6Xs/FN45fEClZ1s1oxnK/BK9ek7O+Eburf8t5ZW6mkh/GyrrKk9xwifyI+BNv6m7rPKSzonu5eqV9g95v2fmV8I+/WW2QII3h5yjvvHH8xlk0RzpLO1Cigus/Qf5RnlYSnd9sjpK1hstHt8la1T19ip7FGFXnOMr136Pm7cnsWhU2+7z6bkQn2f5cE8y/mFxVxJbivnsIzgqQuTufNXOU4zOT3Qe8g+UZTtuU9qD+lETi6zl5AHsFQ3y68BKJyRrq+KDLQCy4MVR8wKfGKoyoxSYIknUCQKfJenIKjAaQVJz9DzSeZPoamGpqNg99axsKRJqmSAKs80SlnFI1KqvWaNrHmqwqIKxCzjkyLslmGmGjey+bXXK0WNJipkMm3cY8x2X5ky+ebBL+L9mTu59FwKCZ4AoedexYh/6InNbo3tiMF7pRb0NxTsmcpdhWFFqeZWULLGWRUCbP8cqyBz/ueuTWWKGwUNMmc6SjzjeCQklwk+ejdtnW19hzoMgGppyJ6hMCpVpQrTbkdJUWDt37cnp3N40onwofvtfU7FoBNDhgdlv+We0HCrn5M2PXzoGwDaxjVGXQ4P9mbK+POtBoaHTr6dFHCr0uCq91RBlWrSBK/cR6SgDQ/25tKtU2DLvyiURXhFPCVVUmL7+tGCIV0dxVUMysfP5ssO8jPuicVrwlwr2p10167bc9CnnN0MSqCQr2XCVAnRqDylA9xb61yossfWCER6XeX+RufzzCvn8bnnJvC559pNgKdb0d6Wlii9YIkIr+fKcoC/15ry3tpJ76KSC9a4VnVeKg9oDxrb7wjIly/qbTXg6e2ulRroI1CDWcQH7T4cfWpbdndSDkzSFWYhKM7YsrYXb0wnDTiREUhlgzeuf5Ar/ai8VUs/8D0q/2ivCI6tfVM8J3h0vs+8ch7rn5vAgwe+ngwWApp4rXudkpuJJzs3rC7QWu+OpegiweMX1nltDRla7rdq8JFnTARifcQaKjgOnQT9PV0Fwxrg19P3jHDkd1piiWuloebtyemsvBrQpqeHJy5XbgKua2fpvFQ5VUJfj9//xJ6p3tlUaWjt+adyncU18jqgaCT15MJPqqMq0KnR19Od4QlxAIidD9bAfeiJzRlf10ChFLSdoxszZ7B17ii/0WA5VhqYX1h0m6JrY3qg2JfKQqSP22AF+93Dh76R8bC3J6dzDpYjp6+gt6tWKP1EPUVhOfzU0kXi76ax4XAs68yxMo/e0TI8054fKWdXRHt0zSln/vb1owWa5dkKNo0N5xwNkWxbZa89uRBoG/N7umqZfgcUe3BYZ5vVk4B8gEsVmcqTTzsJjFxYarj9enU/P4nDd++OfD+uyKFRhUcSSJ8o69myX9aWkZKNh1s6wchgL3744RzWPzeBPS9/q2AL0LlGfMXDbzqMdf9TTvB6yyPHv+2zfHfVoE8NkPCCa+xatGeYnr86HBcWl5J4oPdQ77+1oVTVixU855F+x/n3dtUK/COaZ6cO2RXoDFYcMSvwiaFqLeYUqHEmJcjYyEWCJzwo4bDE1EbOlTFTVZTJvF54cnMWiepF8it4Eb0qNDOTIVIcrPCozRrV6GAj37iWwxPvZBElKeXKrt1GnOk+M0Kj1lqL4oFGDeiZpeos8zkyfitAjgz2Ynq2nota8KLhrTIeMQ8PPxhJpkqjPQMtLeA511LR1xFE81SGTQO5l8Vjz8lzTFJgoYCiUXNU0Hq6am7dZq7NU7rK7q4qg49tXOMaLbmfdBiwVw8/r+IQsuN5e6zR4ymhQutEpyJ9LHjCqN0XqxhExqLIEUihXBUTdXR4wpzn7OHnDaDgTAXymXlq/GfPHS/byotGjGhw6ndVwDvrqg6QFA7ps5FC4dVZ1j2PeKK3TlUk9Hs1eqccEgrHzrSbndJgrWuweL1Hovd5NwCE2YZelKbnbOP9pZCvUWYWHy3NtHseRdfaqDZ7Bkqvq+JWhIfM5ASQlZFosdMCLbFjaH+rKgpqCogrpNNebxyOZeehvTCswShSfFPygHf/PAfFsTNXUV9qYO3IIA7u3pQZYiInZvRuRoxvGhsuOClT96KTpqcpnGmWVQO2rG1HrqfolpUhdM624bb2eWi+y+/jA7R7ZGj0u65NgzEiHkqYuDgZGpl0P7x7au890A6gSWWZ6H0mDbbBCh54cl0ZXTx3bapyFhj7FyhtL+u9UiXQx0aQWvk9WiMh5RyxBh114APNqNdUtH/kYLf90CJQfmPP2nOGEH9SwWuqO1iDoeX1jOqdrS/i8MQ74TyXY9BKGT6PPrUNYyODmK0v4uCrl3I4U9XpQ97cSTa+QuQMtvxG8V97dUVN0S0vKMNxTx+3cqR+R1rXQJt+a/+h27N11JcaWZS4rpfZhsw86dTBdvxsPjucODQ2MphVeuBYNgPTW7eV2cvwzDrjue+eA8db2zOvnM8Coo6duerS2ZQDnu/bvn40C8Zjc3LygGhPq+y1Jxc++/gDmX7Z39Oda2gO1HK6hOIT0Ha2qD6rwREp3lb1TOz5cL/oMGMvPM9u8mk5fCPjeaQb2LlaOPrUtqynCx34DMRTuSQlG6vDkI6LesspdfHGdIGv6tpTgVuETuik6rWeHAYA063+TXz3QG/TPjUzv4D7v9wOoLKOFw168OiplXP6e7oxMtiLxzauKdgcCQstHTlam8ovtjJGSu+qCqm9pd1uqL8nDCYC4mDD5TgbVyANK46YFfjEYBvXRZe0TLDRSI0ogs1GphCsIT0a3xJd/pbExirPanizDf+8SBMgX5pj54bVudICdm+iJuR2vlapphCrSoK399yXOalBmlKuLAFPMVQScNbuVzzoaUXA0lhVRanzohT0O45XA7KohZMXJrHn5W+FY1qI8FONg1Ss1GhilcGI0XkZRJ9kXlZQB5pnePdAby7iyYussgZMpsuyMaQa5nmWQ/09BSWB92JmfiGLVFMDcxTd4UU5eXeHzde1gbg+l3IIWcdnVWFVaZH3jEY1K5QZUvV7LVGnNM2jRR7ep5yxVjFRR4ct12KdchppRycQaZAqT6oY6Hn3tozAZZk4dm/suiMjV4pPKHhCahWlizQ+wiGFiP4BSCoa2vx2uQKr51xNGX1Jo1iyhOeja1C8jupoV3EmR85ua8hWw4+uRfke50c81vJpCp6DRu9NJwakFHj0QDP5mN0BtJW/6Pe2nv/M/AIGe7syWpBSmHQc/ptliXq6awX61IlBourepOSBqkEO9jeAnw1gwZ4nI8bZn444c/SpbTlalZJBy5TIlHMXqOFPXxrHL33+pyspolraY9+u+3OyAZ1iQL5Rfc38X2m1LfG3b1c7k5WOTsCnF9F5a+SshwvqULb31N57W1KritPYlseJ5PcIImMBvzt5IV86KiUjcw5Am7ZTZvICqYBiuVDF18iBfOT0lUyOtUb+1Pw854gaVq0cBSBrKs3Mjegd/NyWMK1yX6pmztg7G8n6GnGsZ2PPmBl+BC/Lxt6fTgxKKacY5TjKQQAq0Valp8QXlu9M8fXjZ6+HclHZPSQOqAyneqk96w9bBlvympRMHenjkRNSoQZkuMAxGHmvPFLBykSd8nhvfyibU7+j/kKH4MkLRRkuktnLDKeRbOWtwxqAH33pmwV6Zn/nOZo8p4w1mns6gN3TVMCtvWdcq46h2dakT/09bV2U5fJYCYO4QDyIZE5vvmq8T/GHiNYeOX0lV9miiv0iOscyOH62GRR24MSlpPxm52xls4g+KJ0i3SHNWjXUF9JAG8CitGSL45xS/EgFbnl7Zc/CC4Zr8+emHHby6S9kz2r5Yr6b5VQXlhpYbDT5Q2QDLCvZT1D6YZ2nWr5QS8mmwAYaeE55Tw/QeXqfefYgPgu076Lug+VzNhhPbaGfZnbpCgC1RqPRKH8sD0tLS/jX//pf44c//CGWlvK18Hbu3PmpTe7HBTppuvOTCKoMkWiuHSk2irr/yxOZYjpmFHOFR1/qvFEcCYc2HdN5AXDHsQ0l+W42hON6NJ22SqPAKs2JbbPnlFKp40cNvOz4Op9VQ31468Y0Bnq7sX/8wdL5R3MoU3xtk1kgbhxMY7VtLhmNr2f17OMP4PkTl7LvrKEnglQDcsUhbUjm4XQ0z1SD0hTw7ICmkPPe1McA2mdc9Y55OMDPbJM+727ov7111dAUeKKx9u4oNpd85pXzOHVhsoB7ut9AWziIojB0jmxUP9DbbHTo7UW0H0CbFnXXgDXDg7lnUjhojdd2/OicAL8xc7Q+3g0KwRSa9O7bfdc7lWraHe1Jau84fxoSdV7RnfPwTp8l/QPy85y8PZsZqjw+Yc+nCt301lIDskg9pdWp/YneZc9PDfBljUmj5o8cp2yfLe/j+tTo671b78ChJzbnGsB30gxT8S7CizL6pd8fOHHJbXbu7b1+1knD76q4ovPj/szcWcjKIgDFJqf6e8oB2oSVSrG9m0BMRwFk/AFAJjNE59BpM9NIVjp3bcql3Z8EIh5sz8by0tTZRXu1Z6vfULUKrbd7w321TXPLZAGuU9+9b9f9uTureKDPKf68efCLBRmId4X9CUhHAJQ2oK4CVXmF3g+lH0BRnvDoBefuNQ3W39g9rsJ/gXbT79Tdnl9YRH9Pd452peRFC5HsZ3WKFJ1KyfBVaBbQpA2UjfaPP1jAeT472NuV478W1zydKoIy2ux9BviypsWlCC90HXcWlrBpbDhz0lIuSM1fm6ZXbRyewrdOeGVVfqXvuzk9m+PXZXKIxWHlQR7vUVmc8KfCf6M96BRS943fk67Zs+lENo9k6lRj+tT9qEqTytZehv/es0rLgGYwyGfuGVgWPfTkwuXKRdF3kQxEPPd4AICCzaSKDOB9pjQ3uv8pWss5A23eoeeS0sM6BaXd9n0pmcjqylaej3Qk1U24P6ShKi94/KyqfabK894eeLpiSq6133nPRAFn0f4PtmwK1gZYJq97Y5bZMKvQUk+2LbNPRWeiY9FOYPmmvpN7oPpIpM+vQB468Rt07Ig5e/Ys/sbf+Bu4fv067E9rtRoWF+Par39eYcURUx2qCEp9PWnjaVWjc9m7IwFUBZSpmTuZw8Aq7Xt3rAuJeBkz9oxrlkmsf24iZ3CyTNYb33Pw2PWnhE2gXKn6JKDEmsC1eEaJlKBkwZ7vnpe/hYs3ptHbXUN9sdGRowRAkunTCBUxKp6v/a6qI8YzJqtjiWD3pSre6bzsM9GZee+ywhsVI8VDT4GvYvi3ApAKl9aoa5XWCM8856C39mdeOZ9Fl21Z29yrVUN9eHtyOkefWGYvMmqkhLxOlK7oPACECo79Lnq33rVDT2xO4rX+PsKzqsbMlFMaiA2VVAq6upr32t6lBw98HbP1xUwZ9cZPzZ//joT3T6pgW6VZwSp1QPXzju6mPV+g7cAB2sotBW/iquKuZtx1yh+qKL1AkWbqvitOkScP9nbjnRd+Ptzn1F0HfOeypWNl43hGDgXPmVAV7zoxovC33JuUYThyvKecemUOZACVaF4Edi2egbQTx0vZ+OSl6szzDICdKNncVw1GsMqtvaMa2EHwnOqRMdXiqzqBKNMCTbrCO1zVaFvlzOwcq8iYSgeAIo9K7VfkDI7uBuUEninQpnfcP2/+kVxoeU1VemPHBhDiu6XD0Zg6RhX851o1A+/FJzcXxuH9VaP7nq1jeO3yzczw/KKR0cv6a6UCgKqcfTReSr4rc9x6xiQF1QWAJq3YvbVooP6kRtcqwRtVnMCvXb6JufpSNkelWynaHhlvVabWwLnerhrqSw3X2c85VQkQSYHVRz1HtEIka6YCOFJylafnpRw4n6bRsW17yDv09fsjp69gulW+TfenHQzQlAOs0buMx3uBhlWDs5brBKvqePbw+Gf++3+F+lIDXTXgp4YHKwW4AshsA80yVrVSGp3aK4sz6qRpyqvNc4ycY2WQOm87lg3ejc6A9MGjfZbPvv7urZxj5upX2vJSUf70cRbweWhVx7Xd21SgZwq3luMk5n4oLyB0Ggyu+2V1sOgsPJvWlrXDWcaPdw56D1L03wYbAA3cuD2HLWuH8fbkdE4GKLOfLUcv/kmGH6kj5uGHH8aGDRtw6NAh/NRP/RRqtVru++Hh4c5n/GMOK46YalD1oi5Xwe6ECFqmYaMHVND3mBChihHZE/QoQJA5e55kK7x4jiOgKTR9NFd3BSgPOo366xQ6Ecj4vJe9YhX6lEDcKT5FEQmAb6T0mLZGmFi8oBNN1+RlfwC+8c07IzVU2owYQirySP/fiYMrEqItsy8ThiLjZpnwRQeICpA0dgHIegNRydrdMhrM1ZfwkAgUnRqQ7fl6GXsaJVglu+TToIFetBjxIMqeKHuXlzFhDYoKqWgxxWXAN6pGz0cG6zJ6qMqmdVh6NFGNLBFelAntnfApvXfEY/Ifjaz0FB2PBnrOiogOeIqCZ4xXXLYGOqCaYTgVuVnm0LL3S+mLRmdFtK/KmXnPqXIH5A2VqSw8u8Z2tudidp5R9Dv3vSw7JWUYiJShsih1j34DRYdKtF+q2N03ehcu3pgG0HYiVs1sjsaP6GTKyJCiJR54Y3m8UY3i6owcdDIty/A8FbFpDUhVaQ/H5O81chVAFkxg8VhlKTXaLid4R8+MuOfxjqpBICmct5m3kVNS74AaxiwvqBIYc/zs9VxmUdVMZm9/ItlyOcFlqfFT8vvnnpvI/p2668qXLG2sWgGAUNVwEwUwKe8HkMnQ/T1dSRldoVNHrheMR9pgo6GjvaziZKl63ilI0U81InZyx3W/VL5+4cnNlQ36KUNlmYyr77AOcS8jBijKmrqGmfmFTOf+zj/6T105mg5ejzf+KAMUFSKHvnXQAAiNt0qvuH+ec8ZCxI86cTin1lTF2Kxg9RmLVylZH/ArB6isaeXtqhl+qblZ2mjnWJVOpJyKEc1IyUg65zXDA7hxey6cj4LasrxAMaDotC4LkgKq2z28Mbz76WWolAWBRnvv7RtlKcp+0X6UBWpZ2kSZLdKRlQ4qLr2YyMC1cygL2FMZCWj35jp1YRI9XbWsX9RyAwVWnDNF6MRv0HGPmO985zv4R//oH+HBBx/EyMgIhoeHc39W4CcXSCCPnSmvHch6xsfPpmsy7t1RrVmVvpv/nhMnjB1n3652DVs2LNbGtwQ+R+ain2uNTPtcfamR/c3vAOT2x/ah0LqYOj7QVg5s74bjZ4v1I73GlkCzOVd/T3dp41JvTD2n1Dl7/YJsLfhVQ304+OolPLLu3lx9d9bkZWPRMnxiTdRz16YKOKLnYRveaT1ib58I2tjaAutY93bVcmOw9vtsfRFHTl/J6mzaJmweXhEfTj79hVxNWj0D+ztbt1f7kqTWFp2Z7juQL/mme6z/1z4Q3nnt3dFuRPjMK+ezeqxAs57w+zN3Cg2g2WiPbv6G/H3qwiTYaFSjOh5Zd28Bd1O1X+358v+7t45la7M19CM6osKn3YNO7xDfwSaLfP742eu5s6pKG4F2T45H1t2b1ZDmHntA+qN1ujln7d9z7Ey7MfDUzB13jV5foGNn8rW6gSLtAPK1bPk7Au+e1iHnXs/cWcitJVojjbEenlQ9S45l+xvtH9+ItSOD2D++0eUxdn8AZJHKb09Oh3unc/dwEmjTgZ0bVudwWd+t61O6+NU//h4+99xE1nsrauRuQXtWefvH+zXQ212gLzz7tyenC3gRrTs6j+Nni70OCDs3rM71FbDzjM5X8XX/+MaMN9seD5bXn7owmdw7ewa6f9G+R33luHYqRsrfrLxStq8fzTV7d7w9OZ09Q/7Ac+zprhXml7ofOr6Hs8xo4N/ePpGPHjl9JUnb9e4RPN6oNEfredt64jpnNli1+Pn6u7dw43a+zwl/k2oA64GlDbbX0sTFyVxJF7unKlum3mX3VWmdx9soh5J3aI+k1y7fxGID+M4PP8rm4Z2zfhbxwYHeruScvT4LNaDAC6ZNlCvxU+WQwxOXw54tKfD253ar5w3Qpm3WWFF29pEcr+N79IS/27J2ONn3h+9/bOOaHC3UGvcAQjrs3fGyc+bvKOO/dWM6d+7kexMXJzFxcRINAHcWFjN6bWULD1R/UiM86XoEj238bLZu0jbtr5aimxH+6ud63hF9TO0tkO7PMb/QrkLizVHlSZ2r7tfurW3Zd++OuFcn16b3r8oeeHNSHUdlsSOnr2Cgtwsjg82G2NF7LbAJfL4ZfJMu8U6SRs8vLOZ4Y5muVHZuVcDe691bx7L95/potwDaPXSscfTgq5dyPWLbvyuvRhPxo5QuEckhVi+N+kbq2HYflf8enngn0yU0GAQA1o4MZHSJdFR5pMVXT3cvwx+7NtKkN65/kPste9gQdwcDW4CCdxfsZ7pPEU6mZCSgTUfphClbq8rKLz65OQvELMf1RuE5nX/U/6qMFnr4SZrn9bxkz7z7Ru/Coy99E+euTVXae2snoBOmuwbsH3+w0G8vGi9lm2F/uYXFRiizK08/duYqertrue+qQoQvdj+3tHrXMNhxbGQQ9aWGy+er2HM7sfmuQAwdO2L+w//wP8S//tf/+kcxlxX4cw4eMYiMkEp8yqAKY9B3UyhoABmBSTGN7etHsWZ4ENvXjxbGjQQUGqAp9Nnntqwdzv6OhJ/Uu1RQJbNhA3jP6aT/VuOdOkOs8SuCaHxCpBQ888p5SSlvZE4IVRRqaBvPrcOFTeQpPHnGYAKZp45T5dyiZ7hPe17+VtbwLoUXNPQc3LMp5xhTxW2+FQVRQ9P4o3vWiRE9ZayzThT+//2ZO8nxtbGfvbfqpKwyxyr4rQYfNSY8+tI3sWqor9Dkb//4gxgZ7MVwq/b9yGCzgefIYC96pFk2BW6uXfeKkYIRnaGxavv6UTz60jexff1ooQH20ae24dpL43jnhZ9390HP45lXzmPy9mxmALF3Tw10VZQ/4tL0bN1V6oFqtFGNyHTuNA3KD+YUQbsuIN9U25szaS3QdD6mDHl2bd5YVMYPvnopmyeAnLDLhpG/8NBPuQ6eG7dnsdDS6AZbDX+9/Tl3bQo3p2fxtbe+7+JJyhnlKZuHntgcKrfW6e7tgTq11gwPVOYVHg5YugA0HSU2eIDre+aV89kYzIDg3ymgYcDbE7s+7oHinVU8vWaVqXV777ZOPvJPRsPSqauBDYp3ZWsh7weQBZPwDIB8U3WlVbpnkfJWBhr97NGjFH4y+MAL4rDGcRodNo21g6vIu3iOQ33Nuzk9W8/wR402kbwVgRoGLVj8AJA0Snvv83ij7r/elYfWxkFl9r5p4EIVYxT/r3KDguUjCveN3gWg+Y75hcXM4E7js8cLUnvv3TvFZU+2uXugF49t/GyuQe7xs9ezKG4aEquAxX/iwP7xja5TlwE8a0cGMyMlf2NL9R07czVnsFR6oXJI2Xyr7iubx/M8ImdT2V2IAhj27bo/5zix8+Ld+84PP8LYyKDb00mdQqSF3B/VOarIcgdfvZTjG2Vw5PSVjPb2dNXQXWs3i9401nQe9fV0Z7Iys7kY2BTpih6+ci88B6OVy964/kG27u3rR3NGs3PXpjA9W8+MahYi+m35RZnj0e6t/T4KltLnBnu7kvQ15cyx8kkV2Y33r8oeWLCNxW3wEWkJHSdHTl8pvBfI8wzl83rXvfs9W1/K9I0qvMnud9QYPQUcgw50G7jC9dFBs3vrWIE/KG9mGVoa0q1jJ6UbVNU/PWd2tCd3DzR1NKuv6zzsbxRHrCNJ7QZAreBsUXyOZE2VtzUQo8w5a502Hu4RHtu4Bt01Xx+x79a7l9LlovNJyUhAWx/esnY449UpoKzMfz/zynlXBuZ76XRaWGzg+ROXXFkvBR4t9PiIPkeax7Ow32ngWpWAEq5VbVy6fwDQ19PUMcmfOC8rm1nbjMpL/T2tvVpqhEEN1tF/cPem0PGfukcRWL3k5NNfKNB5TweM9s1Cp/rLCvjQsSPm6aefxt/7e38Pv/M7v4M33ngDFy9ezP1ZgZ9c8JiHRzDKoowUUoae6N3WKGbHiBRp77uUwOX9jpF27019jBef3Ixf+vxPFzIAqhjfdR5WCF811Jf9rYTUy4RRI0FV47p1atkxPSMfkHeI3Lg9J/Un2+rj7q1jhWgVCimPbVyTYxKeMVjXRfAyVhTKhBegrcRevNF2EnHvygQNy4yp1JERA8B7Ux+7e1YFImbpGf6jSHALGoFojZtU1D0cqSrc2+fU4KNRlzduz2aOOd0bjcpjxNNsfQlD/T2ZcX5ksBdHn9qGQ09sTt6BGopRQWVCTScRcHz21IV2JKdGBc0vLBYMdFUdhUP9PZmxxAqhACrhaEQjOBYA97c0PlgjxLlrUzmF4dATmzPniCoanTgb9XnSF67ZZrSNDvWjAf8ukZY8tHa4kKVlz5R3gNmL/L0Kr1b560TwLMMhuz865s3puWU5au0+qCJhjfh6xzwFhsBIaa7Jvptn9ci6eyvdGQ8v+JlmSJaNE0U42jOyWQ80llKpjvAOiLMubTCJPQOONdTfU+A9Kcc64fjZ65mhfeeG1dnnvIsz8wulmSeq6HqyTiS/kIccemIz3p+5k43NTDXKMzR8NYAMf4B8+cNOIuVS9EJp1c3pWdw3elcmVcwvLLpZUfYzz7Gq42pk6Pszdwp7R4iMSc8+/kDBGauQkjsVNCPA0nlmKHXXmlGxDQCjQ30FI+8zr5zH+ucm8OCBr2fv0zNW+k2jPHmChyNHTl/J5kmerGvVNeze2pbHOqWBEdDhrxmH6tRKZQ3yniu9UDmE2QB0Ktkzo8Hp+RNFXFB4/d1baADo78kbxDsNnoh4zN4d6/DCk5szXIsi1efqS+Hd07PtxDCpoBHKKUdHNF4NTbq42Gg6+2/cnsX7M3cwNjKI2foi3p+5k8vmKjNMcR8UXwGEjn37jP1OjWa8i8xwr5IJlJKhy+SHqk4NpWULmfziO/1Jr19/91apMye1rui76NnUGKp/6BxVfrydcIDZd5y7NpUF5tkMht1bxzL5RXnpqQvFAD7umcc7dP91/tFvLHAMwOeNVgZKGZWVphFfmakJIAwA6xQ8Z7YXRMJ5zNaXCgFNimd2H9V4rcEPx85czbI+qFdY3ZYy1uvv3krqrAQ+z2y8VIa37vO5a1NZAIAnp2nmDD+rEhgV6XJV7VweaFUNwLed2HVqsB/XEmXSPLZxDdaODArNaYLFsyo2F+XpxHUPV1NOMBtIogGv1gkR8WTlMdvXj+LI6Su5ihs3bs/iwIlLePjQN3I6PoACTuoZcg8eWjscOsBtgBSAXLaVgvJ7e48i3CnDqZQOyEBFL8tIf9+Jjr8CPnTsiPnFX/xFvPPOO/jlX/5lfP7zn8fDDz+Mbdu2ZX+vwAqURXyqsbrsAkfGFvse+7kyZjuGVWA84wUVkyjrQo3gGkmrkXZUDKpk/6gwao3Ddg/fakUqv3VjumCwjhhO1ewGoOjUsoYx70yPn72OPnE8sDxCExpYOzKYNZS1mQeRYycVjcM5MJ22ynoA3+BsoYamop4SnFPnQ3j28QcyBR8oz0RSsNExQFOoYqmbY2eu5gw2ALIsgo/m/D3TMa0zjN8dnngnuxve/VqOc0ph+/rRQtRlFAXPfbVCuCfksZSO3gEaClluIXK+lEXMlAGftRldGtGVMtClICWE6vfzC0th+YBjZ65ifqGpINlU8dRa6Ujk33zuZAsHD516GwAdpjU0AHztre+HeFE1oqfqmr29pLHhvamPs++JN/adGj1Wk98rjk/NzOccTCnB047fCQ5x7FSZTAvcT3WukY+wBAwVCZ0Dywmp41bvYLPBKbK/lcfYtVCJuXugN4teteuncpUyVqRwI/qdvtvuo9J73R9GWNN4bcezeGcNLvqsOh89Q1nEdyP+aY30VAaVL2qUXVnmiSq6niPRk3sov9AQyaCP3u5aLlPNGuOJPzR07dywumN6VwU/FK9pQAdqub2IojoVj63CTzmN/6aRWvfOu292zy19iPBa7551YHplVWwgw90DvYU91vNUA7K9j9boETmWFEeAfI8hlhCamV/I/cbKYykamJIvPCOlx0OrBoboHA890Zwj5RAbzKG0ygsAKjO6ark2D6zDzAZ6dWIEV6BcxMh47+7p2dpo4ki+taARyp7spvqV7pXKY5SNOY6lRwpe/Xp9z8z8QiEynnTXKyNoy3YCCPVVvYOUscoygTpxcFhcinQVz/lBWsbs37n6ous09srlAbEcVYbfVZwOZc8ojdM5Wh2wv6crO8/Uvp4Up4oaV+8eaBpXVUdmVD/LS1so03Go61JP1N9UMaLznkaBK8fPtnuZAPlgmoimRY7GKEOT76nqPGL2jfJVK2tQhraBbzpHj35xvu9NfZxzumqmo2by27UD1YI++LyWZPaCE4D8fYtkQM7d2qc8/FFd1ntflAX7adzFMvhorp4521S+sOtk9uC3n/u5TPbaI7R8sQE8f6JJG6sGGqr8WDWb2H5HXrZv1/25MudV6XCk3/B+04Zze7ae0/G97HTvPjII97XLN8Og7qrlnwFkNrEyOpD63HvG8vIU3ivefRo4+JMOPeWP5OHatWs/inmswF8gUEZU1lgeKI8gir5TQhsZFkmoUwSOxBhoe4FZW5hgDWP0jM/VF3ORtJvGhrOSLo+suzfzsHMt0XqtIcCuW9c30GomRoFCn626r52AnifHo7JAYBSBNgq0Dd5ef/dWFqGh87Lz57ynZubD2t12TzpZB/FC9+fZxx/IhN+xkcFcVo4+R/D2/Ny1KRw5fQXzC4vo7+nGzg2rM4GmSsPT1FwJA9K8UhvyUiAEmsIMsweiMamk2u9qwfPcBxqmm4b94vyi/aHQovMqO0N+/+hL38wa93Eedl22T8yxM80GdYuNtpKmNbJT98ubfwpSNKoKpO6pnZt9ln8ePvSNrMHg8bPFpsHFWEl//voeoN2jRQ2HvNP1dspbVlag3jIQe3hhnV/R/to1M/qX86p690lTiTf8jA6L19+9hbsHevFLn/9pvDf1cbauR9bdi5vTs1nzV6BaJptdU6d0+fjZ65lxu4xfHjl9JWuwCyBnvOE6tLwL50AaR97G/dm+fjRb42SrvjT/Tq2FgQ43bjebP2vk175dzaa7pEdD/T2VcYNGXmsotvS4ypi6P/y8Ct4Bzf3TJtJAU2km/weauHH0qW2536ZwVL9TWkVF0MosxNdjZ65m5dXYBHVqZh63Z+s4IE0+dX19PV2Ya5Vh8ebF/eTd5vj8PTMw6ouNzMkJ5Htm6XiPvvRNd885f68pvM677Cy1WT3lrv6eLowO9WFmfgE3bs/i5vRsIUtLZQ17B46duYpVQ324OT2b8TU9exrCtOGplX8iiGie0iblS3t3NMsY2uAS7rONwNV5WDmWDc+5B9pHjg6twxOX0d/TnYvq1HcpLinNUjqT4pOkpV40qHfeqee9O5XCmU5A6Rjx4sbt2VxjZqBNB713qgyp33n6CPeMz09cnMTRp7blGkR7AUZWZlV+FskQnsyg+6d30q7TO1eeUTRHBa794KuX0Nfqb/T6u7ey33n4BSCrIDC/sITZ+iIOT1wO57qw1Mgc1pYOe5Aq82UNfkef2pbpLUAjF8FNvmb3vRPZsaoO6wHf88i6e7P5KE2jQ4J8hLR9uXPqZG7eM4qHSuNUXn1k3b0Z/wDaeh/XyXWnwN47jm0j1yO5jO8hP3n+xCX0dtVQX2pkY1HX1RI//K3ymxQQr7yzF9E6qxjg7beulf+3+BfhoqeTpYDzKus7MjzYW1kWAtp7pw3aVUfU+VImVN3H8inFJ9W7+Txp7M4NqzMcocxw4MQlHDl9Jfc7Twa0c+e7lc8Sf5SuUD7i+3imXhYs97zKXTw8cRkHTlzCQG93VpJS+UMEeqbUOzhHfaddJ/GeRvpDT2zOGs2fujCZo9FKHyP9FWgGaSo/887QgjotPb2yCh1W+deb85/+2Qwu3phGb3cN+8c3ZnMhj1JQnOS8RgZ7sXZkEJO3Z7Ps8TK+yXnpPCL5Qt/r/bZMVvXu5/Gz19FVq2Gx0ciVJyZ4stInlcF+kqHjjJh169Yl/6zATx7YqAJGDAJxpELV6OgUVPUC792R93hrLVkLKqRzHC/roqcVztXTihSl0cuW8ti7o539AzSJ94ETxWjNfbvyKfh7d8RRAKyxv3/8wWx93rPevkYRICmvNvcBQGk2xO3ZOh4+9I3M6z8y2JuVBwL8siV2/pw3a/sOt5ThTktFeRHUUTrn3h3rClGRBG9/+RmAQjYU07NtdHHVeXpz9Wqnew15BxMN2FM1ohn5PDYykEWgefvFKG1GqkVCTgp/7fq1L48HvBueYZbfexkNChMXJ3PrSc2PQiCjfb05l+EHkG+smIp40fWURZdEtFKzhGjk0yhIrR9dJQqTYxB39f82YwJAFiG1Ze1wiBfe/us7oz3mvlJpTO2R7oO+z56p7pEq6s8+/kDGAxZMyTJvbgp2TYBfijI6w6p8kHNvwC/LRT7CyO+oZKfuD/HzwIlLGW/TyMHUfSFdAWoZ7yQO6RgRv7bf2fNO0e2qY+o6qKx50Vz2/7aPFA3hxIwqjV8V7PhKq6zMolnDfI7Gd54raUyjNZbujdLr19+9lYzgjjIp8xHh7Qw/ZhTZ9UTnwXnZ8gfHz+bL0pSd5WMb12DNcFOGoPLM6HebYXnoic2FBtV6BzSTmcYu5Ws2Y8CeURXgvL0MklQ5zQiqGsO0txn3QHGHMtVcS1bptFmrlVejZ7V8jMU/Tx5R+Tuitfq5xTfNMLGyrpaw8kDH4r+fffwBvPjk5lw51QjHLX31xuVzpKU2+4kOsqhskpVZq9Tbj86Gv/eyRiyd1H23Ja4s8B7u3LAaM/MLqKHpvLA9IHT+Kh8pb2ZwyWyr3JrSD6/0S6Tf6PztfbNyQkr+Yv8Y7VXlZd5VrT5QpsN6WROcI9CUL2xfG4sj5COWFkZ4kdITUnMueyaFh+y3+trlmwCawXcvPrnZ5X3q2OHe7JEIfTsHZoPzXXo+Si/sezT4r25KMVngmNzjVDkwvocli61sSfzc05LZU33zjp+9ntkTKBtbQ/cnKR3klUOiwd7acCiPeuWoqsjNNlPI3mFWPYjoGNdY1huU9Ov1d2/lyjNq5oP+zus3473bwx9LV3i29ky9LFigvKwlv59ryXksoejtiwf2TL13RmPZ8ubsi9zTXcvJoFVojn1H2RkSjpy+kjktKVtF2cmpMSx9UN7HYKSlFg3g/tuSzfY+UE6jnZF8anzLWOEcvSofdq8i+SJaEx10ET9MwbEzVzOapzZNgicrdSIXr0Aeao1GI81hHLh69Sr+yT/5J3jnnXcAABs3bsTf+Tt/B/ff/xfzID788EMMDw9jenoa99xzz7/r6fzYAT3ojBZaOzKIbz/3c8koLf5GS2OUPUcj13IyPTTSjMKSRgFw3MMTlzFXX8LureloL2Z71NB0FDCSg0YtZkXcN3oX3roxjYHebvT3dOWilatEPne6zrLfR2fF9WhGSzSeRq7wTJ555XwuPVzHt+ebirTkWBMXJ7FpbBjvz9zJKRf6zmit9tlUBLqNuuD+jAz2FrK5vHlzL8hgX3/3Vla3+aG17fnbMTSKzVvTcs8+FXns3SXC/V+eyASHq18ZD8euEqWSmpc3H0INwLWXxt3f6Jl69ELxVyOBNDKYjktvf3RtALJ7au9Dag+BPJ3RCB8Ke979SuGufv/Iunvxtbe+j/pSA1vWDmd1gL29Tp1TtIYUTfbop9K76F1leOzNRWkUaaSeL7MgojPw3qu4ZufNqFueje43Mx/4Xdn5p/a3bE/03HZuWI3X372V8RHd209yD8vwE621EjQitewMGTUKNDMmZuuLGX8si2pL3efUfamy9ggX1JBGXIv4jP5m1VBfFiF3cPemjni4xY2qa6nKwwAUcIhgebaOwUg7Kxfou5XXaamSxQZymSpHn9pWmBezN8nTSX81yyR1p+w51IAsW9Y2aVdg9pJGitqxbOaYx6s1Cyk6qyryrq6zCi2xY68a6sPbk9OVshFS80rJCYQ9L38LF29MY0tLjrFyEeBnNHiRmZ7M6K0/4jPRXbQ4yqjT7lrTaE4ZnTQ8ytCPZFL+e2pmvpDx3cmel91zfv/DD+dQX2pgsLcL77zwC+Fznvxy4MSl7E400MazsiybCOz9oLxaJhtb+Y7PP7Lu3oweWX5g5TfLmwdbmeBlci3QpueWjkX3rQznuDbiAGkOkKe1KdrgnUEV2p+SjyI6kuIVVXDy09K5LVTRTfSuUhaLZJ2q9NPKEjqmZhVvcfQ10sC1IwOYmW86BZWvck4pmdHugdUzOj1HBeUrVWVjex4p+p2Sw8psOFV13WhNvMNKy6vYTbQSxx7HjhPpElaX0T3R6hOUbzrhqSlcTY1V5W4CeftLGY/6NO4y36l7wrkN9jazsAd6u/HYxs+GevZ9o3eFsoza13ZvHcvdNwAF/mB5XhnoHlD3UVlS95n46/GHFC5Znd7LOrK6VopedwK6fyoTlOGG2t6+88OPKtlCPy18+osGnfgNOs6IOX36NDZu3Ihz585hy5Yt2LJlC/7oj/4ImzZtwmuvvdbRWF/5ylfw+c9/HnfffTc+85nP4Mknn8SVK6bx4NwcvvSlL2HVqlX49/69fw+/+Iu/iB/84Ae5Z9577z2Mj4/jrrvuwmc+8xn8/b//97GwkI9mPnPmDP6D/+A/QH9/P/7SX/pL+J3f+Z1Ol74CAdAjar38KY+0elHtczbizWYpdFJPkWAzXbx6lNqkqyyF2/b/4BwBtBq1d+P2bB0Xb0xnkQp8jtHKnjJYJZKtLGqL4EW16Vl52R9l49lIDwL3q9ZaozZNi8bRKBJdI8/q/Zk7uRq0qfnqONZDH+3juWtThagL/hZI94XhvwFkESVHn9qGZx9/AEuNZqkEnb8dg5GPNfiRrfququete+tFZqUiF6KIHDt21NupbI4eLeB8ep0IfKB9zw+dfDvbazZMtGNplpt+f/SpbVkT2GNnrmZjaq8doFhLuwYfyqIFbZQQ57Fzw+qspJvdI30uFUk4cXEyM3QzSifaa3tOGilq07AJKZrsZQqStqQil1IRUPad+hmjaFUx0ihbb/6p95J+6O9Ye75felupUKmR46z7X3b++i6PVrF/hdf4c++OdubmqQv57Drldal7WAaKn9yjg69ews4Nq7P92blhdRZ9ePDVS5UjyTWDaK6+mPHHof5mBdxURoYdP9XsU3GzCm1M9YSzskBEI/Xz96Y+BgAM9fmZjqm12fE7XYu3FxYXeHavv3srw3HSn6mZOy79sb1edP3K90cGe9HX04Ujp6/kohFn60u5etJKF46cvlLg6eRPAAr3MgXcP9b3byDdt4M9gbRXih3LZo7ZdfM7G1FO4LtT98SjP3x/1CtAgXjEzB2vn15ZxHFqbR4ek8e8PTmdzZ9NfZWXsrSNN44nM9roYP1Mo5Wr8Cm7Pz1dTc69aWw4a/Td01XLaAl5ViprxGZn3Ljdzs5O7bWNiK0aCZybf3etJXvUkmfJflwa/UxeNdCbz1aOejCWgdJGbeLs9b3y1sdz0n4tUa8xpfdK05j9/9jGNQCa5WsiOXLfrvtzEe5lmUk8r1PSB9Tj3dxzL/uQ2WQebVB88OrtK/+N7r7OmeOtGuorlACtonfbz6PnUvcrwt8qYHUTD1ceavXro5zs9WFIzdMDK0sob9I+MW9PThfsEG05u5brM2N7jXF/NPDBA12PpYmpxt8RKF8hfYtkT0u3eB4A3J5qAJLVQ/RdQL48ru5JWQN4b1zKIqQ/QFvPLpO5tP+UR/OirHkrS+n+s1cwnR3e2VTB75SuX9bjLjXO0ae24cUnN+fwXHW+KnalTsFmCrVtJ7VM5vL6FlM+vXhjOqOJdq7sf6f9gcg3NFuG/YdTfdE80D0gbpOu61qoB9rKI3wPZYz5haVQf/bsjWpnSunPM/MLWaab108OyGeV84x5bx9qVaiI+mFZ4F19e3Iao0P9lWyhnxY+/SRDx46Y5557Dr/2a7+GP/qjP8Jv/uZv4jd/8zfxR3/0R/jVX/1V/Pqv/3pHY/3BH/wBvvSlL+Hs2bN47bXXUK/X8cUvfhEzMzPZM7/2a7+GU6dO4Z//83+OP/iDP8Dk5CT+2l/7a9n3i4uLGB8fx507d/D//D//D/7X//V/xe/8zu/gH/yDf5A9c+3aNYyPj+M//o//Y7z55pv41V/9Vfytv/W3cPr06U6XvwIOVFHqot94z6qAeu7aVC4l9fjZuIF7CiyRTKX40TjulTk4fvZ6JmTQaMG0ao+Y9nY1FavB3mZUc8pAaplvxHTLHDRkZnZMa2TUOacMXxa8s6MCtHvrGN48+MXKTdMiQ6w1UKiR0iu/kDIiR/uoSpFVYjxnk87LlkXhu46dSZcO4T6x/I+tp+vty3IYHZU0Zf6pOxelX1vhPTJCLGeOFFKG+nowMtibZaxY0LIAkVCgBhwLem5UgmicUaMYm2/v3LAaL4hQq2suM2p4Di0q4pFBkM9E5aw4f63V6qU3p0AdgJFiW+Y4p9J17tpU4fPIkBrdPQqPLHlgjYQ0Ktm5eU07PfCME5GSZcu6KR6rw/3YmavJ89czZDmMwxPv5J7R0jPe+bUNzXmnpJaFWi4cP5tvMqsKghrtuUYgrwTrGerceTa7pSzI7q1jOR6bcix7zq0ULiqkeCGVExoKuIc6tpUFqhqxOF7Z/fPwkPuRoqFW3tDyIymgc4SOZI6nDglvjEiGs+c81N+TOQcBZAaPQWliynVzDvMLiwUjkxppO3Eqcp77xzeGpbG8QB6PPumZUtZ79KXfC8tkekZRNTgBKMgt+i5dp8pikYOHoEa6qEzNp6kMc20MphnfMpadfQOdl+MD0iVc9DMAOQOFR+dTQR093U2VtuksbRKvnu5a4T22RJ7yt5k7pBdt4woNPfeN3oXnndLCQBwMdPxsu7de5MDX8o4pOcGC0iCvfK018JbJc/a+kzZaXLDg8fnUOaXuovd56o7ob70grgg8553qGZ6ziLREdQA69C1tUMesJxd6BrrIWK64a52xVXmlgme8S0HkxKoaNKDvowNWS1Dxzrw39XFl/C8LYohkauUJ+8c3ZmXNeDZ6fqrL6R7Ys1PDKmlkmS5tz9bSuypBP8pXgDYeefhreUS0ToWUXuXdFa+UWdQA3oI9r6NPbcvoj+f8Te1Jyr6jDiieHVAMFEo5T5b7nQJljlVDfbngz04dV3Zdaoso43Vlej2hyn1vO6ybPJj8zjqBuLYtUqrLzlVlA9XN7V1YWGx0bHvkHvCMNIhYexBZx6K3x+1Q7EZBnlz/3AQePPB1ACjgow1u8uRTdRgfO3M1w4uTLd0xeg4oBjPvH99Yat87frbZFwZo8sKqeFz1uRWIoePSZAMDA3jrrbfwMz/zM7nP3333XWzZsgVzc3PBL8vh1q1b+MxnPoM/+IM/wM6dOzE9PY3Vq1fjd3/3d/Gf/Wf/GQDgT/7kT/Dggw/iD//wD7Fjxw587Wtfw1/9q38Vk5OT+OxnPwsA+Kf/9J/i13/913Hr1i309fXh13/91zExMYFLl9oNTf/6X//ruH37Nr7+9a+XzmulNNnyQRXOqkTy+Nl2qYwa8iUoSLCrpKjqeFUbf+lctVzTmuHBQvq7pkHa33rr9uZh03BrqFbKxVuPTUfU1F4tLdBJ6nNVsOWEgDhtP5q//dyWc1K88EoPRHsVrfGTlmyI0oxT5QqqpKR2soboGa+8UxWw4xCnWBpCS4zQAEYGHM0xdTd4n+14+tyqob6svJ+WF9N3RaXJdBwtg6MpuvZ7e65VyyhGoKnyUTmjVEk8bx62lEHZvLhGLbES7VN0nlFZw6qg4x+euIzZVpQx5x+VBdD1pO5CRHO9NXUyvjdGWfkBlsipoVluj2Ow9AzQNF5H5W5sNKjF1+WUJkuVHPHudapsUSdllQC//AvLWXXVmiXNqpb9icZVGq53jvxwOWWFAL+hdYpepO6R8rDU75QuKt0iHY7wn+WJ7P0GkPuubB88euGVirHr88aI9onPVMXlqndf987DTQ9nKOsRtERnSm7z+HiKFxGUlm4aG87xt+iuAX6plSp70wm9iO52mYwbzaGqbFxlrDLQvQdQKKvDcauUR6qh6VDW5xRPXjQl8aI52/JBWv6Sd1NL/LGsi5V1vH/zbkVyQ1kZK/v/qBRRit5H5S7LeHCnjoPl4la0hog+VRkztR9VZDnOnXLQYOu8U7SL+7xmeAA3p+c61lu8OQKdl9+t+gzX2OQjd3JVIYB2+V+C8jrq+0C18m/e2UZ8turelOloVehYtB8pPAbyd1vLfgIIddYU3nXKwwhlvCzSFyOc0jKFnZTvKtu/qs9EcgKA5N6xEX1UMrIMvLWpfenQE5tzeP7a5ZtuWaiyPVL+RueZd29Ser03bhlNs/gbld8rw/9I57C6Ls+D+pU3j5S9R/kUEMvzZfsd4fv65yYyFw2dzlGJ3Gje9s6euzaVKz+nJWLt3e5UT9a16vhlZXk/TRviXzT4kZYmW716Nd58883C52+++SY+85nPdDpcDqanm2mgo6OjAIA33ngD9Xod/8l/8p9kz/zlv/yXcd999+EP//APAQB/+Id/iIceeihzwgDA448/jg8//BBvv/129oyOwWc4hoX5+Xl8+OGHuT8rEEPKW26jMKr8TiMamFLHzAF6XzsxLnseYw84/rEzzahKjWJSr28VD7AXpXR44nJYCouRjuw3Y+fJvWL0ENCOeuF3Woqh6TwayNU/t1EWUSSVnkvVyCfNwDj4apNJaiq3jdTwziP6fHq2nq2bkXle6QEPiH9e+n+qCV8KUufPNbK8UKpcQxR5p1CV0XGdz59oZgvZexJFwCleaRSf3a+5VrNUIN/3h+tTXLL4YzO9Dk9cxo3bzZJjGlVoaQXHPPn0F7LGw4wes/uaymrg/4F2GRzNdOGadW3Reds7U/V+EJaWGu45Mvpt5s5CLvJdx7d4p/9P0Vndy8c2rslKDUT7dOzM1cK9OX62HdW7aWy4EFEVpU0r6Pha6uWRdfdWptEp0PHt2eqaOL6mfdt90jPSz/jvsvIDu1tRlswS4buH+nuyLALdA6CIS0P9PVnD1p0bVuci8JezV/t2xWVXvAzAsrHKntf12NJ2dEItNtoZb9Ozdex5+VuleKTgZSgdP3s9Z+Rhen6VbE/AvwcaVWj3TKP+lN55Zd2OnbmaGXJtdJ/imRcNWwPQ39Md3vNjZ/INcy3evmBKV3hg7w1xlKX59u7IZ3bwHUAxorQKbqVw2d6HFI3TtaZw8/jZ61mZES2JQbli7UhbZiJ4jWP5Dm0EzT1g5tX8wmKYybRvV7v84ltSvtby3eNnr2cZXUA+izdafyd7zHdE2cX6nX2H5fnMFDk8cbnw/rJsndTYnYA20GV5E8V3jssyI+QVum7S7gZQiCamTrBnazEzJJozz9rbkyOnr2QG4yY0M2JGh/oKso7KeOeuTWX4zfV6e/zIuntzJQktXbF3RXHXjhNF50flLr17WwUXPIj2ViPwabjS+6LAtZJeA6jE+7xS2B6N4V1hxkdZFP+xM1ezYBRmgKRoF3ndzem5HM+LZHvvO52jxcnU2pbzDNd44/Zs5oQhWCcMcYv3lxnRe3e0sy7sXUzNh3y2LKvAk1tVR+NcU1mkndCo6Df6OfdsfqFZ3lXLfmpGNSGFd8rPbcaNt0429yYeKT2NdEOFqLIG3wU06dTJhG7M8/LsEGVlcnWeB04U56n0x7PpcH5WhwCauLR/fGPhfZZ/ejqhd1/UvkQe/dFcHdvXj+LOwlLGf3S8siwplVd53qlsEU+v94C6w+1WaVLSW68Up95ZADmdI7ITKF+uUi6RGTIPrR2uLCNGfOqrf/w93JyeBbNbVg31he+1Zxvhu1Y02Lfr/kJFjMjuot8ByMnaWn5ufmExx5NYzaCK/BTtEc9Y5Qne04j2Lpefr0Aeejr9wX/9X//X+G/+m/8G3/3ud/Ef/Uf/EQDg29/+Nv6H/+F/wN/9u3932RNZWlrCr/7qr+LRRx/F5s3NSIibN2+ir68PIyMjuWc/+9nP4ubNm9kz6oTh9/wu9cyHH36I2dlZDA4O5r77yle+gkOHDi17LT9pYIkwQY13HuNQZcQam6nkW087Py8D/d2+Xe1o6zLDEaNGj5y+gjcPfjFnpLfvVYMxBR2+x4viUcNbtB67XrtXN6dns4aZVCqoyI0M9mJ0qC/z7KvATqWTRk9vPfZdGiXAOuDRHqshTNO1vbVE58HPWcpk54bVOea52EAmNDDlUufhee337bo/2x/i53K9+Poe/SyKNh3o7UJ/Tzdm5hfwzCvns7lpZAXHe/Slb7rzie6WBY0Smbg4iaNPbcs9T0GP/+bfPF9G8LPEDPeLjg2NsNF5pnCV71GF6PjZ65nyWV9qZBEodrzUGXl4pfTCluLQMnLqQNF7o44WC3pH7byqnM+zjz+AwxOXMVdfyhn4FKhkL7YsMjSE6PjWAaTzOHdtCjenZ5NlDPQ9ShPUsUNDxs4NqzNaw3Pkub0/cydrTMjvGKlz6sKkW97u2JlmZtPk7VlMzdzBQ2uHcfFGM/CCUUo0oHhOPoL9XPfhkXX3ZuNrA0aWEnn93VsZXmi2icd/yuCRdfcW9lvxhIIzwe61pVd2bUCb1mnkMseows907z185TvLeI2H21X4sL5L92vvjnWZsx5oGr9v3J5DA8jhRBUHue4rI976evJl3ZRXlO0RALdWuz0rL7JS6Z3+bZ2npDuvv3srR/ctPyXf00byOh/LM7jHUfmgTs6st1WuZ2xkIJMjUjTOynKWF0e/S+GyveuRPAEUs1xS8yRoSQygHZV690Avtq8fLfy2hqLc9uCBrwFo0muO1ZTDmlGms8bZ6gHlhOnZeo7eKn+2UdERaLNVNp4mXRzo7Q6dYWrg1/1TulMWCEKwDmY9N+W7ZWNWASt/Ac0a9ORxqXvv4SzQ7EPCiFl7748+ta3jwB1PVuKeTLVK+BL6e7owOtQX8mTi6sTFyaxMJ/WAiYuTBf7v9UgiXdm+frRwN/t7ujFbX8r6oeg4UUlOZtsTN2/czvMNpf02EjkCpYW2UbaC1uhXnm75G9e5d8e6Ak7r+fBdEX4qnbG4pbSkSgaGnu3CUgOrhvqScojqcJYfcN2HJy7j4KuXsr3y5H7OkRUm7Fmk6HVK34nWSDxnU202u/9wro6lRjNLvL7YwBvXP8D29aPZ/bV0X9ddpsNZXmF5JX9PvUflDV0/ncrWMRm9nzh4eOLysnRMnTv5R29XM2N409gw3vn+h6gvNbJyekAa75QneaUDOTeeKQ3zejc4xmArKFZ5lNJyvs9bs+pn6ni28jPH0L08cvpKpm9QP/X0HJ4Jz6qB4h22cqh+Z2Uwvn9Qem6VGbYjndCujfoBcc7yRaWpyqeUt9m103bCPo+pu1Km11vgutSBGsn3VueuqstF52ntDbwXa0cG8f7MndwYnl5GGN8yhlMXmvoBZaLFRlvnuD3b1D+1/6ri7aMvfTPLED9w4lJhfIXHNn42Oz/q59SRaYPz7Hkp3Vfn8/Chb2C2voTp1v1gkE2KbnMvIzna2jm0+sz4lrGsbx8dZ4Bvi1mBzqHjjJgDBw7gH/yDf4CXX34Zf+Wv/BX8lb/yV/Bbv/Vb+I3f+A08//zzy57Il770JVy6dAn/+//+vy97jE8LvvzlL2N6ejr7873vfe/f9ZR+rCHyGh85fQWz9aWw4ZMXhWAh5dlNgSVmVeqQHztzNUsnjKKqvPG97xiByKyI509cwlgr2tKLpiNEEQPcK+1Lw8hs20STXnqtJ85xGcnNxnBeZEsUdZGKUKU3fbC3KzNi8Z1Asc6mdx78fHSoP8uk0RrVdo42GsPz2u/dka8XqxHLqT3wgFELjBI4PPGOGy3L/X9s45qsv1E0t7L61/ZuRZkHe3esK9Q6tvtVQ7NXkTJ7ALnMs/6ebrdnAhUkzcKKhG6dM//NzBxd45a1+awmxf3U/fJoghf5de7aFJ4/ccmtNX/szNXCvalCY+y8dK1RdszeHevwzgu/kDUetBHPGs022IqkoSHEnn80j7LeNfY9keCkjb7vHmjXrt23q12nd9VQX87BrmekTQFtFNvbk+3I7/dn7uDFJzdnTr83rn+QownRuu3nug9qdCL9bgBZM10PD6jOdhrVk6qd7YGHs7bxsNJd0tMffjiH9c+160jzvqX4meJhqlG97l1U27uTrBf7f32XxU+NLOcp2LrRhFS2le4ro0Vn64sFXpQC3QfybosnelbKQ0gDLb3TTCYLvFcAchGmlp9q/x67XtusG1h+U24Fzn+hlaV0c3ouWW/d/o5rotxjo1I9Gmmj+QhWDknJgl4z7NQ8X3xyc6H5qsptuq/k5y882c4u5RpIp/t7urOxVA7TDEwFypqMsn328QcwLP0niF9Akz/vH9+YzN7lnJjtc/HGdIZbr12+iQaaRv7IaKn76O1Xir/xOfKH3Vvb/cu0/wP5+nLq3+u7lR5Y+auT8S3OUq5Tx3yntecjsLjL/7OePtCWRYiLNHgwuvj1d29le8wsfUYbkx/Zu89nlI/bviS6x0DTKHTf6F24/8sT2PPyt0rlBs0s5/t1/0mXXn/3VmXj9OGJdzIZ28sCaEPD/N12mEYyJM/d0yH4LnW6atSzbdqte2cd+GVAPv6ZewbQQNMAmJJDiDMWJ/XuafYEv7Pnz7Uvx1EQ0cjUGps9Ch7MSv69/u4tvHnwi/ip4ebdG+rrydHgqHKDZrExq578xZ51mS5BeafVEqGgXxKUvtt9SJ0VHRpHTl/pqBcP5861jwz2Zv1E35v6OPs3AFfOssDvdm5Y7WaAE4jjXtNzjsE94NnYs+KepDJmqI9zGZZeedln07P1jP7093SHMg7f39/THfYU0QAX22c20mn7e7qTPMDufxWd7fjZ63jwwNew/rkmH7N2Cq9BPICQt/FeztaXClkwHq7qnQLirC+7TpVnIvmePJm9nCN7ks3WtKAOqKhyhd3rlAx89KltGBsZxGx9EW9c/yDLumMf564aMt4azYVBJnTy8XNWIiHYeRx9alvmyHzt8s3QnpfSfRW0d6nuRwTWHkq7XIo23T3Q1KEYmORVcNm7Yx1WDfXh+ROXsOflb4XvX4E0dOSIWVhYwD/7Z/8Mf+Nv/A38m3/zbzJHxb/5N/8Gf+fv/B3UarXyQRz4lV/5FfzLf/kv8fu///v49//9fz/7fM2aNbhz5w5u376de/4HP/gB1qxZkz3zgx/8oPA9v0s9c8899xSyYQCgv78f99xzT+7PCsRglQw18mpzQ3vp+btOy6JUgSrEjEAlT1MSF6RBuPe8FbpVWFeGpSmKN6fncPUr49i+frSSA0AZESOnrPHOGrpVmPCUSKt8ekKTMhFdV8qoR4Vxtr6Ej+byaeeeIJBS6pVxE0fen7mTfb9n61jOecHntVyZl/ZqDQHcg7J58TMbFaXp9jZa9tvP/Ry+dun7WeSPZ9RUvCtrykhIGZxUeLProJGaJS+s0aismZs2gU4pubp+Rlh4+Prik5tx8ukvhAKud39tOu/xs8WUdaAtlKjiHhn0y0ocHj+bL+2gxg+Lu2XKWUohfP3dW5nCynPwFIPIoVI1Zd1rkKzRbHo31CC9d8c6jA71ZwYDdbArvdPUfevsG98yllOQPCcpzzcqMWId1bZhLiUQ0m9ttKrnpsbV5fAfjcTtFCI8Ubp77toUbs/WUV9qoIFm+aLlOguBdtmvg69eyvbeu2PTs3WcuzYFoOgoqvIuT+D33uUpmoeeaNIE5ZHPvHI+V0oqBeS1vV01DPX3YP/4xqxuc4rf6tzsPfIMAyljrzWWkU+ps4X3isqUbfjL+867ARTLZXh4Z3FceUCK33rfPbQ235y7rISpleXokKXCqnun+OLtL6Gqc/n42WYZ2RqAvp7upOMnMogTRz0NJpJvD756KVf+imOoET1ymBLPuL/Ei4/m6plR3TPMR6C0FmgaFYhbNkPF7osN4vDOlUENKf42OtSPF1oOLjVsezKjyqvK061xzK7v2JmroQxEB3YVvg7k9Q/yvO5ak6ZHOJnax+WAOuvYW9GWclWZfba+hLGRwSxricYRljHyyh3u3LAaH8016brluboGDWJhQ3g69F67fLPSerz952cfzTXL2hw69XZh7+w+UraerS9mxmc1Qrehlv1tHaaRDugFQPFZWwq7KkQOfLvO1J5VCXzwxlPZjBkDNCZaGlQl+Mt7j6cjs2R0Ffzfu2NdZvi061YnAeVaZgLou5955Xx2N2aNQTSl71uefvzs9cxIzA7Jql/q+gEUHOkpp5t1aACoVEbWnqlmd6kTiP9eWGq4cpYFPe9UCVDuIQDcnJ7NZEAF8jrebcobdw80z9U6eT19jXOqEtyhxmbqLSlZXXVa3gfPMc05MjjSC4TRs9eywBasjlYWoEj5TIOUT12YzJVKtL+zstWmsWEcfPVSoZyeOlxVb7W6YlWe5dEZj5ZEtLaBJh6TXzMIkfN6/d1bhWxN7zwB5PRIlYnsnqXsBtZpyf08uGcTxkYGsdRAxlujuezemtdh9T1aRm7VUF/ByUQ5bK6+VNATvvrH38PnnmsGPlRxlOgdevbxB/DIuntx4MQlPHjg62GgjN0XdarrfVU95U6r9G6KxjKjiH+vQOfQkSOmp6cHf/tv/23Mzc0BAO6++27cfffdy355o9HAr/zKr+D/+r/+L3zzm9/E+vXrc98/8sgj6O3txe/93u9ln125cgXvvfcefvZnfxYA8LM/+7N466238MMf/jB75rXXXsM999yDjRs3Zs/oGHyGY6zApwvKEEm4U4JASpBYDqSYoT5jo4bfEkKiUX2eUmiFbrsGGloe29guibdpbDisc8ooP0agKdMAYmEuMhLwuWgNOzesdiPjrJBZRcjjd9onRrNNPOO1FXqs08AaYNSAbyNZ+TxL0ERRIDrOoSfyBlhr0PeMR/093Vg7MpiVbRns7cr2kb0gdB11rTnhAMel0usZnBgZ+Hwr8kvrylYZ2/aMsc4iD3+5B7ZeMFDs1dSJo9M6xVJgnzt+th2FTgH6+ROXsv0B2kIaf0elerC3GQmskbTWaJbaR969g682S7doZpCXSRDtSZU9S0Xy6R7YvkuR0TLl7Ivqxdu7Yec/vmUsE/pXDfVlBklrcOS957+PPrUN/T3dWVRktN5TFyYrKa8eHWZUORVhKvQAsn4OhyfeSToLLXg0lJHMGtFcFRQPrJIw2NuFyVZ0toLNNEpFMlllg8rsxRvTOQOmrlufS9Vit/th1xIZKMr2eGpmHgckqoy4oMbWMppHJ2ZPdy1nSLXG/pRxvkpWiTU22nuqdEYVfzoXlbeSnqtzTA351vjQ5kVdmRFLaSsNs9qfxutVo6Dz57/fn7lTcOpbg0oq0vKxjWuyz5TmdMI3Uk5v+36Ndkw5fuzZW94U9dGxOK+ZfCn5ywLHsRkMOiYd0JvGhgv4Fd190nLKJvcM9GaK+u6tYxkdjs6OjtHt60cLjgAgnwFgz+742euFXjjWsE2wjnT+7lRJLXLlVSoDqROFtGI5ugSz1A49sbmUpluDRafZlBYHNSPMc/J6dIDQ1hFqYYamdVxFOgPQDmJhhgDdHLNSsshbA8Gj8/yM4jDlYo/ucPxBkd2G+poV1BcWGwUn61zLYcNsLyvTpmQ8OhVWtYKTooAknRsdlltMb4JUIExEg5555TwePvSNTHZMZV+V4Rzx4Gc+U80eY+mwd57e+WjZnjJZwY5p+yl4TgLSU834VLmQwWDEDwaGWbqi/MrS2mNn2tmINgPEM0YysJB8/fDEO6HTzRpY1dHq8byU3gk0nV3q7E9lrqQM7Pt23Z/xBnWm2XP2ss/4vaUtKm/QIaQOFpXhbNCnGpvV2QfkHV+ala+/S9kjAGQZ+6uG+kInEPmIFwijukXKaalBGXo36Six79beKTToD/R25zI/In2U9EzleOLkuWtTOSeJ6q1lMkrU5yQVJBPtPR3/zTvadBrarCnOi2etziPFBZ6Jzo/nqzKKykRAkf/reiO9LyWX6lxUh33t8k0cO3M1l8XPd2nfP+7zQ2uHc38reM4M6yhJ7fmpC5OhU8s6Wq1dzuvR6wU3R3dv7chA7u8V6Bw6tiJs374d58+fL3+wAnzpS1/C8ePH8bu/+7u4++67cfPmTdy8eROzs02hcHh4GP/Vf/Vf4e/+3b+L3//938cbb7yB//K//C/xsz/7s9ixYwcA4Itf/CI2btyI//w//89x4cIFnD59Gs8//zy+9KUvob+/HwDwt//238Z3v/td/Hf/3X+HP/mTP8H//D//z/jqV7+KX/u1X/tU1rECefAurC2NlILjZ/Npm6nnPKW0ijLsKZsDrVqgL5qoPuss8ITuSFCm8Ld2pF3Pkv+33nSNQGNPFk+YKxO4dGzLTDm3N65/kDMC6d5FQmbEsAlalgBoR/F+9Y+/V2Ao3MNVQ32ZMk7Dv1cGIeU48Aw2UWSEOuhUUaNBf35hMVR2KQgc3LMJa0ea5UI01dkK070SwXcq0ZwwFQk3J1k3NDhVKVGiwvbExcmkABvhk9YLLjv7FHRirIjmohk5apCdrS9mzeft+qhUM0vjlJSVYMkUT5jb8/K3sP65CTx44Ot4ZN29GBnszRor856mouHVeZSKxgbSd8risGek4Tui0iHW2ce+FMfPXi8YnfU+ACisRYVRCv0c18tW4G817X3mzkLub2+9GvnqGf2ikgxKu6gIq0LPuzRnGseWRYh5+KtNTMsiyyxEzrZz16ayKDmgXapLcVjXqfQ0cnaq4klBOSrDQaU3RY8s77RrUd6hd8orA8HvNDpQS6oobSRPrgZN/JlfWHSzR1JBANaxBLSjIiNlz+KhGj51/wEUeKu9gynnhiqMWprURs/TuAAg9+/IqWFL4q0d8cv20KBCg4GlOeqAUkfW6+/eyvbZ7h2V9vmFJdeo65VDIFRxAuraPIdBVd6kcpQ1OEVz8sAzenOtHJPZfDR66hr07mvDXBobh/p7chk6NArafYzmqTxGccCei90b0qzp2XqOBlnDtpWT+TvK3hHt0XuiMhD3jfz50Mm3wzKGEWj0OZ3SKdquOFPmLPTAM3DTkGedvAppZ3Yc9KOOK4/XEb92blid8f/3pj7OjdHbVcvpCstxQJH/dNdQ4N/672deOY+5+hIGe7uzsn0MElCaTbxj5pgHKWMi75ftCWAN+krniHvamwAo0nHlb5ZOKj0tCzjhHFiKSwN+eD81g/itG0V5zDvzyBnn6Sd6PryfLL25aWw4wwtbQtSOGeFvmW2g7dRtZ4Y8tnFN5pCwjjbLr4BinzbeMXV+WdlGeak6KWbrRR0xBbb0pmcrsHP0skEoz2k/qMjgbN+zd8c6LDXafMXiJ2kYe8+oA73t7EVhfMtzPVmF2aoMRlHQ+2nlx5QtIgXHzlzN5MlUub/t60cr8XG1Vdh7tG/X/bm1aTCqdVLqfj37+AN454VfwLWXxrMAIru/9gy5X4RNY8O5bHGrG1kHYEpXTJWt8+aRAq2+Qf3WZlFxD6zzyOOLQF5mPDzxTibTWudOSk6M5BkAWY/Vc9emCmuNeB3tIxq0RFxRHZY4waw7L/vOc2Z4ZxiB0sbIBmCDs85dm8rRJpUzyc+sHGDhmVfO48btudb/aslnVyCGjh0x/+1/+9/i7/29v4ff+q3fwh/+4R/i4sWLuT+dwLFjxzA9PY1du3bhp37qp7I//8f/8X9kz/zjf/yP8Vf/6l/FL/7iL2Lnzp1Ys2YN/sW/+BfZ993d3fiX//Jforu7Gz/7sz+LvXv34m/+zb+Jf/gP/2H2zPr16zExMYHXXnsNW7duxf/4P/6P+O3f/m08/vjjnS5/BRyoQqSVOAPpUiHKSFPlSCICXEUZVoMmmcX+8Y25CCqrAKtzxQrdLNtw6OTbmJlfwGBvF2bmF3JMj/Py6sZTWertKhKzvTvaKaFAsXSBjfRPRQR6xNbLPPH2rmxsFXA1ek+9/ByX72bfCKDN2Kr2XVDjjhoarMMlFXXEc7XGWR3/3LUpTM3M4/lW6qembavgBeSF6YO7N2WsaUAa/tn9TEXC7d7aFGJ7u2oFZT8VxabZXczEqmpo9qKuyhS3TpyDKSiLwhkebNYrZTTRYG9cM9jOWZWLUxcmQwPXRYlmYe8SRkpT8WRJGetISUUuVlGI7fyVHnlGGnWsWgOC/oYKNNAW9j2HciR0qjCnRnWbofXMK+czJ9bxs9ezcn78e6F1Ufi3d16MgB2R8iDeHbZ7Zw2z1gi/uxXNyuw1b20WfyMDb5mROAWewjQzv5CLQtyzdQxXvzLulu/zBHSuwUbV5aOhmtRIFYG2oeedzBhp6ZHOlw5RoFjbOXKMeT2y9DugHR2oJVU82mjvkG/UaLSCAWq57BHeUZ2npTeeY4nloqLa+BYPbZksDaiInPzWSGQDCFJOIDUEq0H/2ccfyP7N8kTMltFzUzlAo5S5L8R/S0NspKU6oJSmzC8sFhwg+u5UJF+q74JGwB589VJ2PsqfbaCEpToezlYxMEd0uyzgQQ0xUebAbtPrTftR0fBDIP4ygMQbt8xg5u3HoSc255zYlEs9+VTBGssBhHLDvl3t3haPbVyTlIXK5FKWPaovNVzDlAf8nhlI5FEp2q57+ci6e3Hk9BVMV8je1PdZnUCNvZ4jwKN1/D91AWZrew4JdVx5+K33nwFJM/MLWDsymJUo7OnuKmRwVZHp8nNu4o63VqBtDGR0752Fxew7jYy3xusqZeg8iLLLrazh6SNldFv5m/29ymRl2RI8X5a1YUktDTLRDGLKuIuNpixmnQsReLIT36PR47yf/Pv9mTvZfTtlSojaPVFc0H/bsslAPkCp7dR9sOCc8LIVrMxr9W01ugL5TBeVbQDkDPWaYV816867b5YGenQ75ey3GW6Rgd3KtYrvip+807dn6+jpruUCf1R+ZPUHdcbawBsv8OzoU9sKZeksTM/WC1n7ZTQmwieVj61jPxVIZIMtbJY0nToqY+/d0a68MDO/kJ0JswlS+0VIyYeRPDIy2JuT4zUbg+uyZVH5vaVHNriDa9M7mJKL7Ll7peUBZLaUKj0BrYNL38OylTW0deSoJxB1FvZRi9agd8rqBFZPTmWlEVeG+noKfZBS+Dwzv5j7G4h7KXtAZ96LT+Z54fGz1zE1Mw+gGJxldTIrZ1p7gSdPpcq/r0B16NgR89f/+l/HtWvX8Mwzz+DRRx/Fww8/jG3btmV/dwKNRsP981/8F/9F9szAwAD+p//pf8LU1BRmZmbwL/7Fv8h6vxDWrVuHf/Wv/hU+/vhj3Lp1C0eOHEFPT0/umV27duH8+fOYn5/H1atXc+9YgU8GVYi0Cgllyu6+Xe002ih6l895BLjMYcBsga7WO6IIZVVQvAgJZbCE+lIDt2frmKsvZUK4FWY94ZHKErMtorqk1okEINnMUpnp8bPXc/U6Pcbg7V3VaAg1HPN8jp25mnn5t6wdLhhNN40NZwZ1ltCYnq1XMoZEkYlqrFXBa35hMVe3U5VqvluzQDSyijWJZ+uLOQamWUCeME2jCg0NnSqMR5/ahhee3IylRqOg7HuGfqZkM7KpBuA7P/wo6dyweFDFQdSJAcsaFzSLrSpuWaGQtdIpgFQRArRsQ09XLXcXlH6waZ9G6HENVDw1iyRlaNJInFR5oGgfrHDtGeUj4Z1RL0C734cK+17D+ajEhhqMtO7/0ae24dATm7P6vxMX0ynSA9LQOQLPuaXNq6MIZI+OqxE+6qFkjWEKhycuZzX4OzEO2nVHEVZq9BsQ5+L29aO5Mayz3QroVnD26l57tdLbhp6mAsCIdoXobs/WF3M0xXOMRQqx8u6dG1Zn0YE268WLCCTuWaWae8CGrnr2UTZlChQvgHa8FzNtIpoVlcmy77UBA9HcPEWZzlAABaOxN8Zrl29m67B4bp05/Mxm8bAkKAA3O1cNPeoYX2j1OfL2LqVE20yFCCgTETR4R8+QBvfe7lqhPI7nzFXoJCM0MhBFhhgLqazXvTv88mmphsJV3qtBJxamWwY6oIlr9jc7N6zGyGCzsatX5kKfVT1g7451YS1/b92eQ5blneYXFjEy2OuWCYnkSH7PDCTtxRDhge4lSyo14GfIRu9TfpsyuNr5e/8n3nsZwQpt43rzB6uG+twzIR+ot/bkvamPc7SUd97SrbI1szeZ9gy1PJE0jq1m1wwPFPYcQNKZaflF6t5uXz+KNcODBV5rHT1AMVLd4qMXNMO12t+rfJ0ytOn50iFGR6fCQG9XzqGppfo8fdGDyCBdBhqINuD0p4lkY1vS1/Ifj4fZu5IKklH9xerblPtPXpjM6cTa85O8Ww3J/T3dGBnsxWMb14T83+KfR0f0s5TjlXDu2lTucxvoocFYFgdVrrV9+apE23v2EGbVW8cPs7YOT1wujEMnjnWKeaVz2Y9K75TXi8hm03hOCC/jqcp9UFBbhfYQIz5p3x6OfeiJzQVdsUxfiPQ73g1mmpH26RluXz9aOn5kXGfgsKVVOp9UVpDlS16gsi0r7vFk1Uc1uGJ+YREPH/pGFsxD2yBLfKXkCOUrQNzUXu+U7QWswTwaWOXZRzS7lLSKvA6Iy6bagMVOIMWPaSsBkM2rSuUD6xxkDxreb+KALf++AsuDjh0x165dK/z57ne/m/29Aj9ZUBa1aJtRk7mWMaTP3NMUwm0anworZYqcJ9gcP3s9y9CoLzYKDMEr02EFJiuQaBkolpKhIdwjrNZAq/OLnDVWadIoMKYlDvR2h1EpNE6qEFLV6UJGe+T0lVIjujLltkJTw5++1Izutut8f+ZOFg3FEhpVFFuLd2rQBNoMV0tasbYnhWwy6Y/m6vjTP5vJKdWPrLs3FwncrlndnUWyMeNJGxZaqNJzoMwZESlTNort4KuXskyyof4e1IDWejtvKF42VwA5ATdFA+xaNIvNCnGR4qy4Gt3LMtAsoaH+ntye6vncM9CLay+N450Xfr6S8V2NQl5/Jd7l6dl6YY9s9KWlR1WF93PXpty+FLacEJCuoe8J0fwdBUtNg1Yh+/kTl7BmeCDnxKKS0t/TnZWgGh7sTZaZ8hR4ntWzjz9QKRPFRhoCKBh+VOGIjGFtB2zxDkXGQXuPrWHDnin/v398Y2bAt5kt1ngQzSXVAF7P1iqlVGpo6NG1KE9MGaI93uMpxPxOebf9bdm9mG45JTx+ZmtKp2hDylCnePHs4w9kBvD+nu7STLcqTroyQzFBS7cAyCm0R05fyZzvLL+lTgAqT8TfwSAzk3fK0omyKFhdgzX00CDI7DfyYEuPIhmO2Wya1aa99GzpH6/0np4hDe5LS40QJyJ+0gmf8WQgGyxSFnig5xiVb6PThIaZiP50gotqKFEjmZdxoQbAof4ezNaXMryIAjVsFlU0tyoykWYD8N0nn/5CrkyIBt5Y4LsZ2erRAGsEtfoBDWFVsjL4Phryygzddm9smb2Z+YXMOezRUG+/WEbkrRvT7pkwcGiw1bcAKDZITgWUWFBDPbOL2TNUx1CHbOua4ub0XDYGz5HOWc+paB3zZRDRXys7eJHqZbqP8r5Ug3E7f2vEJw19f+ZOQUYhbX5s45ocDVWHOHGGGdxl7+wUNBBt//jGZNlk1XuZ2QMUM9AiRzaf9UqhAgh/Y++RZkAdO3M14xcsJV0DCnK6dcB62cd8znO6WYdSmUyq/7c9Vmygh66vqlwLtEumAciCFJlFrOVHVddNZcRqM3ILkR587toUlhpt2cb+3uNLHlhdOJIl1VFi7R4M1Lhv9K7c2DYIT53SQDFDQstwLcd5rXMG2oE2LGVI2mfvjeKaLROo6+D6Vg31ZXjNPWc/IosvdJK8daNY6s3jU8p3VX9T438Z7eG+9vd052wzvO9qG4xsasqjU/oAs7bojFV5wDo0U/O2ziTNXEzxe9WTLVSVhTx6pLoDAyZtYBXtetqLytp5mLUI5EvW2/LvK7A86NgRs27duuSfFfjJgqpRfgpVlNpURJoV4CNBwyNQntCthM4jpDZaxUYWsMbw2pFBnHz6C9i36/5M6QeKZdgiZ0tq/ZEiC7TTEvePPxhGIuq8U0qj93salwHkjOg2OtlGyaQaWHrP67qjOfJ3jFTnfupvNeqGjrCB3q5Cr519u9rpr1o+zWNY77zwC3jxyc0YHerD9vWjmQCkad2HJy4XehZ9Gga5aE88JxuViGcffyCLKKHxLTKiatRixOzb+/5OQajohAbYSCxrnIpog3XAVjGKWWFUozeoxFIp06iglFHLe7c1CvE5XTMNWnaPbPSlxZOqa7V9KaiMs9kegOzc2H/rvtG7CngR0R0V9ElvmJ2k0eg3p+dyTqzI0B05yVOGRE/BqQplfCOi9do0uNN3RGDfRQUNQI7W6ljWeJAaO1K+bSk+G70H5JtqepmhuheDvd1hU+PjZ4t93iIDySPr7i0Y0KJ7ocZhflfFCNrJfY6+t44e747wPV5mgYWqeKylW0i31gwPZNGZdL6/cf2DghOA+0QesH98Y7L0E5DHO5s1Z+l1ag2MvKOB4oUnN2c8WLNeozHYNH2uvlhwSF68MV1oRuuV3lN4SLIlPglEuOTJNDZowDq8qijXthGrGqcip5hCFR4SNWclH/EyLjRKtqz0WSr4yNvXqjKRBgVYOfP1d2/l9i0y6nqRrcrTb9yexYETl1z9oGrpEH0f8dq+Kzo3oKlDaNNxylzDg72uEdYCz4qV5QZ6u9x7xyyRxzZ+tuDMTskq0Z1QQ70NIrJyH50JgyazQssX2h6OFmdswJK3J+RNN27PYrC3u4CTFndTMp+O62WcVgl+icZUXs4yeDQWc19sdQOWnGb/Ext8EZW203V4MltV2SwFqvc20M4wtXwkcmTbeSgOqXOwjHYcfWobXnyyLSORX/S0+iDt3jpWuNM6v4hX2ufKoEwmVZpMPZW6thfwRdm4ij5l97jdm7CBGpr6LANoWXqRwbSRYzEq/eutjeeoZQj3OL/n77zofc5j54bVOSdH5NACYnnx2JmrWRag9ozy1mCzelJ8hLjZifNa56zykbcHEb7Z8nUKb0ljeJaDawA5WSq6y16JdQvFcrPNfV0zPJDbI3tOlu6ovM2M27sHel1nUWRTq8qjj5+9njmHNbuYfEX33pP3l+vIJkQZY0A1+1BUSYZnMdcK7vPKF6ocHQVbqN0mVbJ+BZYHHTti/rf/7X9L/lmBnywoEzyW2+Q7uuCRAQ0oOjw8gUkJCuspqtJLQ6rNZLHEcGZ+IYtI2zQ2nBN+1ADCCBPbW6Fs7yJFtuzZKobUThxguieMzPSaKnqOIq/0Uep5roXMwGNuZBCM8NVoWcuMD756KYvG5fPKlPfuaKe/shnplrXDOHbmao5hedHZ3nmwHF3Uq6Zsz1PNcaue2wtPbs7WpxFzqd/biA8vmobnxYgRvR+dKB80zjOqpapxKiWMRMKQzSLQ6A2W6jl5YTLrj9Hf0+02Dky9S5WigcBY7xnHrdGuSvR+Cmy9cyrg78/cySlPAKCNLFPReg8f+gamZubD0g+8q9KXsGDk1GdV2PQU7Oez1Od3wjHKQOt9e4JkmYIK5I2gLEdRJeInugcpHqj7QFo7PVvPyv1wvtZ4kAKloUqzbA1gzeRTgwqAnKJvjSSz9SWsHRksZIxZRw/xjCUzo34sxFU1oPGd3r2ggV+jtJW3eqVVvKCBTpUnNawpj/KMSFEEZ+RQTs1Jx+de3Zyey6IzrWNbDUb8LnLAKM6kjCycO9djozCBYnSvBjKoM8tmvUZjeE26+3qaUfNb1g4XyhzZGvOcM7OC3pv6OFdGUc/DK30SQcQbPJnGro30viwrQmVXypK11ueRYWY5DmqC5RfEk1RGr0bJprIUbGSlBn1ERtd9u5oZEpO3Z3NyCMfkM/vHH8wMuqnegmqsTQWcEKzjooFio+nlAg1LQNsIncqU8hwg/A3QvkOpwCee4z0DvZkMlHIweAa5FE2OjFNRRobKfbxHfHZUKgxopDohKnflOQX0TqgcT33gzsJiMiAuAu+uaaUELaVTdVxvTOWRqlvY5yNDZOTg4N55mfYeXvDusCyNlsaskhVs1zky2JsZf21PDi/rTHVr1We9KgiRDvz8iUsZLfF0ZpY0Les3yX/bbFF9DkApXymj10qT6XCeX1jK8VZLb717FZ2BJyewagSATK5vmN9GsnNZSU3VK3g3eqQEPX+/ff1oQUZi9D7pC+U8r/eHvfNVShk+su5e2D6F9hkA7h2zz/GsKc97ZbHK7ol3tz1HT3QWtnydguqpF29MY6nRtH+kKlo8+/gDGOztwlx9Kev9QiBtYLCC4tO+XfdnWZjtpu7FNep5WqAzZXSov9RZlLpTqT2nzY5VF/Rzyw+j3tHcJ6vrqcMwCuDRMukWUnyd+xNVkqF+7jnajp9t94+hTS8KtlCHli33Z4OPV6BzqDUaDUtnk3DvvXlkqNfr+Pjjj9HX14e77roLU1PlkYB/3uDDDz/E8PAwpqencc899/y7ns6PNUTG56pG6eW+b2Z+Abdn61g7MpgxfMIzr5zHxMVJjG8Zc4UEOwbQzCp48+AXs+8YaWSJFZCvI0yD+smW8WlksBcfzdUzgXexAXeOALDn5W/h4o1pbFk7nCvjVWX96jgBkDVD+6T7/vChbzTrNqPZKF332L7bvoe/HeztwuhQf/YdmS7naX/76EvfzPaS+83vOCaBApY+w0gvZoho9LTdd7s/+m4VqG/cnsXIYC+G+nsK6+W/gQZu3J5Db3cNB3dvyvAkOm8LqXWn5hx9zr0iLqdAf6sli65+ZTz3/dTMHczWFyuNWXWdhycuY66+hJ7uGuqLDXfsFB7reI+suze76wCye2jT0T/33ET2e+KIvjd6n8UP3WOLh6m5e3hWBareZ/uc0rHXLt/EXH0Ju7f69FDnp3sHFHE+uvvePPQ+WPzW9wHIKSHR3Ly9u//LE9m9HzNjdHq/OsH1MlqYepa0cH5hMTMQrR0ZzPjRcu6aR7MOT7yTOVJffHJzSJ/0LPYYHPHwz+PBygMHe7vwzgu/UKBHipM0onpNPL11cc78P3lrd62pfNpz03cP9fcs6+7puwFk71W64skLKfpR9rmFsvHLoFP5zLu/3ENPJlLZ4NDJt1FfamDtyEDWhNTj96k9iOiH7pHKKA3k+bzKC5rVS9zw8KcsuvuZV85nuL1n6xi2rx8tPZPl8A2uPyUrfVoQycgpfEvJd9GZqWxm6bBdG2m5yiF2L/ftuh8HTlzK+LfuJcdTOkpZfLC3C3cWlrL12vXzHSo/lp1VJxDRMSvDsJ9Z6r4QZ/V+EjrVA5arLyie0KhUxsMi2ZvQzPwbzJ2DHaeT+XL8wd6ujM/q/a3Cu1Pv9fTIKvSkyhrK6ADxd9PYMN6fuYNVQ314e3I6d5/te8r0YgVLYyNdtiof07O298rKiRG9qIrXDPIBmnLhtZfGc9/Zs/doXacyvOUrVWQNO56ez+vv3qrEL3SuigOK4ykZHUABz6xN4ZOA8uPuWjOoRmXGiYuTmfxmz5uODZXhABT4j64pZRfy9twD+0wk61v5iPpDDcALpol6asyqdrMUDYp4r9UB1emmPNYDy4+tngT4eP4z//2/Qn2pgd6uGr7zj/7T5B57Y3j4HNGrqnYCzw6keH7u2lSOni5Hj4zWaO1Iam/ZNDacW6OVlyIcLcMT7w57e14VDz1e16k+9RcdOvEbdJwR88EHH+T+/Nt/+29x5coVfOELX8Arr7yy7EmvwF8MsB7iss8/rfcBbUHNer6jvgeMrCCB0RRIPud5xOnxZykj7Seyb1e+38TODasLZYKiqIiLkjJa1bus+8pIGUYZAXHJnarw7OMPZMK3LQtgozEYdagRrWq04pw0ywZAIT1eoxqiiIM9Utfbi7RmBsLurWPJpoR2DV5EBT/TiBp9J8dgTet6KwuHvYO0hxDBO4vUuvX5T+OOpXCBNZNtrf1vP5cvS7VciCLnuW82G00FyGNniplSum+aks0sAipDmnGjNYmZahtFrNh3RdGrFpc0AtOeS6r5oV27jTQvO2NPaOIde+P6B7nG6BEecJ0siUD64kUvR1FZdr56Tt69Y0Qa69NHa/TuKIG0dvfWsewecW1V7xfnZkvIpM6HjUq1cagXea1ncezMVZy7NpWLmiOtWjXUl6ORVYHzYXaAlhTS9djzUND/29IG3ll7PNjWjgeK0WLKW6tEcXJuXhYAz11rePM9PAPNoon4cHQfjp+9XsgS8rIIz12bytbvKUWpPU9F8jHqjCXPtq8fXVYGXVXewX3g3Eh/lQ9q7Wtv/iz1ceP2XC6aO6KTtmwCkI+kjXrhEFJlM2rIRzpOz9Zx/Ox1F3/KcFDlOzbEjrIICHZ/9C54Z6L7v3PDanw0V8e5a1Ph+fH5TjJ77Jq8Ekbcfy8KOMq49Wg+s5bOXZvKspoYWWsjzTmOzfL09vLYmXwkq8eXNBOTcuFcfclt2E16x3eo/EgjRurepeQq/c7ig8pEmslLmuJljO3bdX+uZBHHBPIlhDuB6G6W4ZPiiWZZpJoQ23dxD0YGm9nSm8aGc+fgyZ1l56HAsViHv7vWpKM6RkouUPDuoeIb5RjrhEllm5edlfb0sDoTcYZZzm+1Gp8zG9Xbqyjbza5Z+SfPgdl4Xk+GKll5lC/VseDJicfO+Fk77N0ElJd927tjXSbvM7PEk82p29u+ipHOZWmbp8tpP9Ey2mz3TjOYlV9EOK8849vP/RzenpzOaBrnX9bXZu+OdYWqEfr/MvpW1kOWUEPTQakZuMThOeOE8e6GZgdoKSnOg/cDiBu0l51hdC4KKuuznw7LugENrB0ZLDhhvDGjdXrZ3Sm9MsV7+bzqgCwHt2lsuJTOq11A95hZ2b1dNTeD9eCeTVg7MoiDezaV6puenYbzJk1L9dtN0VLdc28eH821eaalp3p+uq9RFo8Hyp85R+1hfOiJzbk7y/V49M/OP8UH7R3W+dg913GUV9l95f/ZW8qrkrMC1aHjjJgI/t//9//F3r178Sd/8iefxnA/VrCSEVMdqnhUgTjy5NN4n43Us+/xIlu9KMqqEXBedAO92BqFR+J96sIkBnq70d/TlYvWYEYMUN27bPdVowNtBNpyPfmpzArPM273TSNZ3jz4RTc6OWqgVyXDp5PMj08DNCJW36mfd9eaTUdJXF8siYjx3sFoqO3rR5NRYYQoasE7Y70j/T3dmG41w4vuRBWoEp1m1078ANjvoJiBkIp49vaN9+uxjZ/FG9c/yO6/Rkfpe4lbOi8+60VbVb1HGgHTXN8i+nu6sXPD6kzQK4uYW07klI2EspGzneBhFP2l//60I2zV8ZbiD2XPRXTBi37ivY2i8aPISBs5TdoX0WGbbUI8APKRfbzvQJt2VNnLVCSm3o394w8mjYnMUktlTdlzSPH2sr3k7zrBrdQ8PByPopO9e19D05nHNVgexv0kjdb7zCjuKtHQVcBmp6XoRRmOVI14i2SkFL32aBCAjMcAflRtRIOi8WxG0viWsRydT+GYBoGURden9krvB4DKkeXeu4AivuueTN6ezXoqvNDKZItoXcQjO8ELyz+i36foa7Qe4i/PsRNa6803RWcY1aoR1koLlebbLJTlQpUoee9sVF+YX1jMRSsDxQxGpUlAMZuXd3TnhtUF2acTma5T2dryVs7RiwqP3lVFzku9v4x36H6n7onKBVHmY0ru8+bvZXl1ImdY2sc5WX2BGSCDvd1454Wf72ivUlmnlp89eODrmK0vJt9T5Vw65UseH/RoPZCuuqDR6XZ8L1vDyzBK3S3FtbI5Rzpa6u6l6LbFiWiuVWX01L0BihH2XjaczdzSd9L+sXZkAEAtKRdyrSmdJpK/vEywst9466TsoRmAAETuSd+9Kpmuni1K91Vx16P7ulf2eW9NVTODuE7dryiDNfWeKvfe8rlUxkqZXOXtq82MPvTE5mz8KEPYk0mr2tS8tXEe9s4eOX0lsxt42SxVeORydHAgz6uYcWv3eblyxU8C/EgzYiLo6enB5GSxMdQK/MWGqp5ZL+qIjTA/Se3llCd4rr7kRiqqZ1wj0bgWjd6qEnFloxv27mg3OZ1fWMqIGcu1NIBcKid/d/LpL2DL2mbUgZdFUbb+vTvW4YVWHwEvY0HnqZEcZdErVXocAO3mi/r5sTNXs/qit2frePDA17P+BwCyKFir5DCCw9bC9SIeUvPTdXnRaMsBjRLXd9oMDK3FaiM0UlE2QD5ajVERQDGKNAVVnmNfmwbaURdlc4ugSmTf8bPFRpOM1nhs42fDhnVAOuKZZ/unfzaDrlrzfjGyjfdfazpzTzR7TGkBo1xmWzTEi4Tj/L0I0mdeOZ+rTTzU34PZ1l6r0dZrwKhQ9SyiiEIdw3N02ibLFix9iaKXI6j6nPcbjbhPrVn7cVSJ4D12Jl9jW++zRsZ5c9d91v1j5DRhWP6tsG9Xu6fOG9c/MNHetWxsRp9tWTucpH3e+BqxqueqTd8jYwjp7mx9CWMjgwWjpBf1qAI652h5rt03G83qyQepdXoQ4dq+XfnIcYVnXjmPyduzhYiuBlDa5+W1yz/IIlbJ79cMD2CyxQ+rZFdUgX272pFrZfSibO+4RwCSchvxSLN9H1l3Lw6+eimLtreZR5YGEebqS8mGqVENbB3vkXX35iLveI/IIyN6YfFKs8xslL7dA76fUcTaF0GzClMZ1wT7ma7Nw1ul4ZQjaJhP0dOIR0Z4Ye8ikO8P5d3V5YDSJS/y1Zuf3X8vitvrMcj/n7rQ5LN0wihttz23bH8D5eOdZBil+LXeKYsLlAcAZLR30MiPVn5XXFaZSWUqZjIB6chwwnJlZMUTzW7UPk+pHoBA+z7WanDpgb4nultVeMfeHXHPPqCN5yoXWFq+d0fcy9LigM7Py/LyaLJdh47pyXOKv/xNM1vts+Gep/illR+jbMG5lh7LvwlWTqjKl6wcreehc7N8kE4CvoP3w+tBA7R5jmYMHz97Pcu6HN8yltMXvOwh1X0168WuOyXrp/Yl1cDb+709N8UJrt/rAVdVRte1eHTE8mjC/MJihoPcx7cnpwvrfn/mDoBm/zuPN+q/I50GiPUdgtfIXnHc2kTsmZIuER+UllDWbEIjvHsWP62Rm7/TfmLMDAGQyV1WXqFeoxmsHq4T+C4vG9lCGQ2KMljtGFpRIsJ/az8ib/xorsnzyNsv3sjjkadHpPiOzYzmeqJ+R/a+qSxj+1BGvVN0jtZ2tX39KNYMD2aO09uzddxZWCpkt5b1jYnmrFCmK+t5WhqRoicr0Dn0dPqDkydP5v7faDTw/e9/H7/1W7+FRx999FOb2Ar8+QCrUFaBR9bdi5vTs+jqqmXliBSqRiZGv9u5YTVef/dW5kmm4uN5yZka+Pq7tzLmNTLYm0UmdwqMSmCGQX9PF0aH+jKhnTDY2437Ru/C25PTOHdtKlvXW62MGP5dtlZvTdEe2e8Y3cJ5ReeYGlOjAHQe+u9z16aymsyz9cWsFI0q0goawaFMW5UfK0hG81P8vDk9mytbVRUs/jEKxjYgJbABto10seOUOfd0nTwrlg7x7oM6185dm0pmEzC6RuvFjm8Zy0WCdGp04b22zZKtoZYRFHZ8TcXX31XJSqNwzYwyChCvv3srwx9bfmXalH7S7xeW2nTJ4mAVfCOu6Tq9aKXIIB7hiRWq+JyNxtHfqKB+7tpUrqlxdBapOelaOq0bXTXy0TsfC1yzRkXTeXvk9JVCvwD9HVPKaRwhztsSPHZOaqRUXNa+AlQevHfrWXBcvtPjFVROdb0pvtQJrbagdDcSrjU67fkTlzDY24w2J9/QMyHPtXTbygz27FPzrBrxZvfElsMkTFxsBkbcWVhM3lN9F+evgQfb14/ijesf5LIXxoI97DRCrYxXKER7F0Wq00gVvVMjB0kfa2g6Gu3dV/q/d8e6rCfRgJSB9CAqkaP39LXLNzN5iu8kr2J5R6/8kTee4oHiqII66ADkaLqVkTwZyMpS9rOyu6hnrpH5Hl0CUMojo/fZeVn+zPP38CSicd76PRymIYD8S2my7tHM/EJhvCq0cEB6gVi6Y/cwcijzzK2xLAKuU9dm90D5BIBcwIbOC2jTIsvXz12bymgNEJdr2ber2YPv9mwd8wtL4bwJp1qBYqcuNGVk74w9+mUNVN739hwtLZ9rnRVFL8opXhRydLeq4IWeBcGjh5vGhnHxxnRG51OOYitvRTLbt5/7uVD3SK0jkum889Df6rr4Dj0X7yxT8qPVYx5a29yjh1oBhLoW0lm7nir8r8rechw+b50EzErWZzU73upTM/MLGb3gfeIzHl55Mo1mOB84cSnLmPTWqo4fz6Aa8UVdz6qhvhzPTeGF3b9OwbvXdr4N5AN97L0nTtI24+2nyvPemju1c1hQeV9/A/g2EfIArkdB950yVk9XraU/1go4rHioJa1u3G42u+c9peF93677s3vU21XLBQDavZmZX8Bgbxdm5hcy5x33NMIj4i2Agh1G55vSKwlHn9pWaldR3pqi1x6u6dkMtPSO3q4aPnPPgEvvU/LDs48/kBuvjF9UuW88u+nZeq4fp6Vhdj8iPqQ2GnsfUrRB57tv1/05ubyMl+r3Vc9zufRkBdrQcUbMk08+mfvz1/7aX8Nv/MZvYMuWLfhf/pf/5UcxxxX4MYZ9u9K1zT2PK4nIUF9PIZPBRrYAbWZxeOJyMlqLz9HQP1tfKkQHWGXMW4tGGlSNsLaROA2061bbKI49W8cwOtSHi6aO7/Gz1zPBsaerlqxbb9/ZaQTx3h3rCpEcy/Fs791RHiWvkSca0epFBwL5cwDyWQvnrk3h5vRsViu/DHRdVSI2PLD7q1ELejbHz17HgROXMsHJ7gnH8XpHWLD7qmcVnfe+Xe2oniiaGygy29GhfjTQvJdetFBV8OrM2zq2Hp7xWTUU6F4BKEQM2t+uGR4AAKwdGcgiW44+tS0XnalRJEdOX8kMpsQz/Z6K5Za1w5X7V+j6bPTb3h3rssyYsv4OXPuR01fw4IGvYf1zbZqn+2cFqdSYPIdTFyZzPUmq3nmPxumeVKGTdpzoc/IApaEecM1Hn9qW4Yc6b6I90b4sR05fyUU/7dvVjtiyuKtGXKXn3D/9vxpEUhFR+juPV5Rl6CwXvIhvbx76rHe2s/XFXGQW50ieS1qnc7f7ZnEitU591qufHc2XkVu2D5TyBNLFZx9/AG8e/GKGD5yXnb/2LeC8Bnq70F1DwTAVraHT87JnoJ8/88r5rFa53TvvznYKuu6h/mIMl1UO2U8sFZWt41pZgGu4PVvPDGTe+95qRUUC7UhRy5c1Wh9o0pX+nrgXlTroiDteRLPnQPaMa7ZfnIfjKXmZc07x/whShs8UDTt+9npG72bmFwpzi+6pHSM6e3t/tUa7jr/cKO794xuzSFquP5Kh7FwsHwdQ2PPU2lJnZHvk3Z6tZ5G+Vu6jzGDhtcs/QAN545wC52aDoMrmT6cp9Q+gmAnlrc3yvirRs5aWUx4bNI5ba7iz6029+5PAd374bwE0s3kaQC6Axb43Bct5rpN16P4888p5HDhxCVMSwEHw+KV3ll5WlKfHHDtzFe9NfQygSYOtLKHR4tHvLXQSoa86AnviMYBs7458dQh1lNgMMTVG65zLzsOTaca3jGWOOw9n9M4dO9Puj+kZVFM6MufMnhLe71P9RSJenPq+DCct3ed91Xu/d8e6nG3G20/K854MU1V/TkGU7aB69iPr7s34+Kax4QJekK+8/u6tbL+oH9SXGlnQSJQB9cb1D7KsRc0atHqZnt3CUiPUg8lHmD1hexp3mqVp5+tllagO8fChb+DBA19zM0Cid6bonMdD1AZC2fLgnk0hTpbJDymbir0H/J59ajRIQLOXeI7MEKzBzx5TKLuTnr2gLCNG16NyOWnCgROXMhrLs9/z8rfw/IlLlWRLT39cgeVDx46YpaWl3J/FxUXcvHkTv/u7v4uf+qmf+lHMcQV+jCHFnCNDCYU0/tsK+TayhYSbDTbVSKxETAm8/TeNa5b47NywOixrxfmUCY0sGrGqFc1Mpm1TY62RitBAU/jVferp7sqEDq8pW7TW5cKnqcAAeaFp09gwumtNo/boUF/WdHbfrvvdJrCpuXwSR4EngKWYYKQURE6wY2eu5up6P/PKeax/bgIPHvh6VqoK8EvklAH3BPBLh9DYSeEiMhqpc4QCrTL15TqrAN9JAOSN/d7Z8lktdaPp39Yhq7jCz25Oz7U+qYVlCF+7fBOLDeC1yzez3w8P9mbPqsDCTATWn61yv6yDyzpz9R6n8I7vA5ApanTWKh5Yg18VZ8hAb1dGn3ZuWJ05HFIGXruPVslW53lZ+ZHISGdxR2vTVqVJvH/qvPEMstrA1q6X0IwgXkyeexnN9PDVO6OUkk/6WLavVUD5sTrm7BzPXZsKy4pQqdmytknTB3u7w2yGGnxaFzmiqgRzeM9Oz9ZzCkGKZ2v5qkdf+ia2rx/NlXSxypgNCtH56x3nvPaPb8TVr4zj/Zk7hXPrxMhkoYoD0+ONfOeqob5c2ZBUOU8FLY0BIIeTXiCAlmhJ8XiFyHkHIHPO0+CsjjTuOUt3adPeiEfv23U/Bnu7sNQA7hu9KzwHGtT6Wr1t7HlHgQaRce3tyWam5sUb09narDLryXnHz7az8EgLvTuQwvkUHeLa1OBBGmyNQJERzN7RqkZXbeTuyf0ESy/s+yK+Z40tKWcw8Yx/2zuuxhx79h5tTtE0lTNo1IvKGEbGR0bb1lvGuchoqbzOlgzyjGwsD0v948CJSwXjmre2FC+McE3pBXFheLA3M7KRNulzdoyUk7ET8OihLbdl11T1vbo3nRq6veeto0TP45SUvia/1h6FQLscj/5WDbEpHUvpkZ5/A/ngr707/AbRdr52jcRJ68DwQO+O55DQ/aSOs2lsOAx6efbxB3KOz6rnps8efWobXnhycyYfAyjwB/4/KuVlx/T4ol1PSpbQ8ybNOjxxOWl4f/7EpUrlCaN9UboFtAMIrKxfdTyuGei85GsVvYi6+uGJdzKZhXz8/Zk7OVxWvjK/sOjauJjhmCo5SuB4xBnqvwdfvYSZO+2sGWZXpRzhnt6vOmOke3gymt51AAVeoc5NBsvYQJ+q+k7VcwKQsx95a7Lgvc8GLdlzsXtsZU2CPqd48VBLP+KZRU6LZ145n3N+KL0+PHHZxS2gPCMmsn+SB9BJrGd/UarwWP3cgsoX0RxXoDosu0fMnTt3cOXKFSwsLJQ/vAI/EXD8bFwXUYFREV50JgkIlU4VeBllumZ4wDW62Ggy/TffR+XH9o/Yu2NdzlucMvwQ+O4bt5tG4Lcnp5NRbHaNe7a2jd1FobdRaGwaKT5limoEqWiHTwoqNL0/cweHntic1YUl8VavvOcw2PPytzA9W8dgb3emINFRsGlsOFkrmmCdgRZHqxgwNDW9isOPwhSjatmvhI1IgXzdXAtlDFD7W9j5p6KK1Dmiyr8y9ShaqJM5An6UfzSOlubgPlMR8xyyiitWAF011JdTUtWw1I4MXcJ9o3dlAk/kbKganRgZ5ezn8wvN7IFVQ32ugZegkTwETwC0Bj/FBXs+NDTsH9+Yqx/PKHlr6InwivRGFRM1ogGoHLUW0W4qmVoerwpNs7zDvoPn0NNVyxRgr79FOzMr79QrM17bPfOilqpG9ZFOTc3Mtxo4LxV4k31+OTScPSWo+KlixYwh7y6cfPoLuPqV8cxwZ52BQNwnx0LK4JHCQ41AUyOQKh52HCDfVFXvnz0vli31otoi4zOA3Lnpe5SfVDHweI5KG7moa2XAgzrR+c63J6ezDA+7j2VnY2U1jw4fP9uOCL09W885+Ghwmpq5k3NIWUM6918NLf09Tbo31N9TMBhwDfvHN+bupTUWWvy9s9Ckm29PTod7cPSpbRgbGcxlEagBNAo0sE5qgp7JgZaRS++b8gdLL2yfPO/sPJzX75bjuAGAwd4u1IDMsOAZwVKOHhv4pHPrbzm5WKLG0m4LKedXxPciPLc0h0Z34pvKoPd/eQJf/ePvufsT0ZLU/bLncfdAEV84h5n5hcyhrevr7apl5+MB39HTYsq9XTWMmr6TGvDB+bM8LPUPRmmXra2K0cY7H2ZB9fd0Z3fYjp/SF1N43Al4xsrdW/PBSNqrIwoyLINO5+vtHfsjnLowWXBwqZy4b9f9uezfhZYy2WjNX3VrpaupYCxLj3ZuWJ3JUmX9ywj2fK1zgr1Vy/ZJDZ/kfVGEOHWc92fuVMocV30x5SSPcH6ovydz7EQZh2U9+wheUEuV9RBfGyjSbPa+tPKknl2VYEdPvqbMqPwikvWtUzF1P167/IPcPG0gQyT72jG9c7MOTAZsaKCbAtdAHga09YMXn9wc9sNL8SO1G9EZurDYyMY8+tS2Qk9D6+SOsomOn/WDinQ/vUAD3nWuDWjzCtW9RwZ7MdgK8lNen9r7Mllf5653UI3/y6X/1tls5QHrbI5kTSt38bn3pj7OMmdSQW+2T5HyOtor1BnnnZcHVl8H2tlW1gZFGY3BdXu2juXsoR6Pazum0mWHV6AadOyI+fjjj/HLv/zLuOuuu7Bp0ya89957AICnn34aL7300qc+wRX48wMU0igwp5rNRYQkJaR5zdyicSyRV0MGa0NGTecouJRFPXHMtSPNskibxoZzDC8ytqmxfM/WttD77OMP5OrOU0HrpFSM7lnV6PQoquOTgHrqWYKBe67NZ62T4/jZZnkveuhpPOLa6ShgxLEyNY8pM4rudisqyOJoiqFZnKni8FMDNRtu93bXMmGFGQlaFiXlHEpFv3n/T4E6R6hsPbLu3o7GSO31coQiW+6JY9AopcZ4T4jlZ9vXjwJAljp88sKka4QlvHVjOhN4eE+AYvNwoDziJjLK6ecjg71ZRh9LEgJthcJzYO/dsS6jD49tXJN7p2fw03O0CrxHxziGGnr0u07x6tATmzMaFmV96byid1DJtGn/ZTQqMlDyHYwG6umu4YUnN+cMU2qEpzGOfxNsWcSIx3A9XtSSVVAjIJ1iCQdmXXpKtd3XCNSRpI65bz/3c5kBrgbkymtwX5VG69r5O88Z+P9n731j67zO/MDfJXlFXrMOaapKNBRijeqOvJZkKaobjQq7gjAdx5MSkr37Iah3hH4pOoCAjbDoGltnZY/q2uqoU2OxlVFo0RbFdqHCO/kkS8smjjGGVnB2NMoajmRZht0VFCUQRxnXNGmHIalL8u6He3/n/t7nfc55z6WcJjvDBzBEk+973vPnOc//PynHVQzn7TpSeKiGGDUCeYY727CUsgn7i5y+eDMaZaZZc1X7refWVmL7k3Q2dW4ezr9x7Xbhd7rWj+fulJzoqiyn8C4lU9h5e/eM6yYM9NUKjY7VyGHXrA4q5UtHXn0nBGLs27qh4OCKBaXoXLxyT0C5HF3Ouk9fvImzl7vKeyzQQGUAoMs7Tj69Ozjm6DgkD2BU8+BAObuMBqGpmXkcefWd5Hxp1LZyZxVdjK37mSceDGVLGUzjveM5Ca3TUTPRCF62le6ZBY/PWqNJFR309u/U+XImJcchr9amwPbsSece2XxfdiCaBn+p/GPXOzPfDOUOKUffmpnH8OBAoN92Xcrvh9e1DXvNTkaT7j/pOoDSnRoc6C9EaVfpVzlGm8P7iw2NVSeoCtiJ4av9fW5AQswZrAb3k0/vxktP7QgBJrHoYztmyjBcJVdV6a23ZrrlL4fq5dKKj2/bWDCmqaFP+x4CfnbG9NydQpYo50S8to7VCx9+FJwJMSNw1TrVmEl5Yr65jBqK/NkD3qWP5+5kR4hbUH3Z9iWMyfb2vZhB2XMq5pSMsmu0QS0571odHECQe+jItfKk6hyxqgiK58zKXz+8rlQWOFb6XPHp3OWiQVzlcHsXtNyT6oqpMty6V8TZ45Pv49ZMMStIHZjcKwZsqB5iQWXcnOAWzxHhZcRSRjmwa7wwpu1pmHJyKyitjcnSNosVQEGmtpmhqnsPDw6EID916trgJp2v5dWxclt8brbTq1CN/6uxXQAoOZt176yzObZP/P/c0uUqE9t57Nw0Euw9vFMEOtD13gDlkqE5MDjQH0o20gZFGY3Bdb30T35825eSwYlrkAc9O2K+9a1v4cqVKzh//jyGhobC73/7t38bf/RHf/S5Tm4N/v8FFKjYMOyNaz8t1FNUyI3IVEKrRoWqaHVL5PkcjS1e2RsqhrlKXddY2yabH8/dCQwPKEexeQySjoU9W8bw8usfBEM9gKSClnL0eJFuqflrVMfnBYziAdrnr1HualRW5fLl1z8IvSEAoCMrBgdXVYkazyCgygeV01jtYgsezvTC8Ok4/OK9Q9izZQyfLXQNq8Q1loaKOYeqygTweQCVRgB1LnDct29+kn0XFbz9t7XPVxOpwjGAWlKxUlDnpwrUjPr780/bGWt0mAJtJTbnnuSsI2aU098PDw4EYw9BFQrNQFA8J+5NXpkqCO8ASqUfPCXPOhEVaDSkYOYZN4HVpX4DftaX4k3svVz6lQO6DjYDHhzoLxmO1AgfcyDwztDJF+MxsagqPhMzaCqkylakjKepMWMOZAt7towV6qsD5XsQM1DknC/vq9ezJMfo763Jlo3SxqWek4UGn3cdAyu/HcMD72x5L1kCjHBrZqFAZw/vbzfPZt8nj2d5wP0mDnvlZWjk8DICTj69O4l3KTqXwx9U9htt1DE8OFAovxSrvW/nr+ekRgdm7803V4KDRc9D11xVwkSzPlMGZGucIFgDVSzK0+6pypdUwIFuVHOsljkNQmcvT7mR2UDX0JJat84tR+6xxsLYOyonVTnZlSdopCaNYi+cfS8YySx4fNYaTarooHfeGkhA+kFjyKbRoWAoie2Drt/j4578z98xOMADzksddPydDW6LGbiYNejtP9/T/bOGxVh2f4peWAMj4BuNSBfuHaq771c5E3nHmGGsmWpVMkMuP4vxa3UiWgN8yjBcRUtz9FaWv2QJN6X75Gs0Hm8fHwlyxIFd46XyRzFnuZ0T8ZpGOy2vvRpDqN5DNWYC3YjtFhCqKFSdp826tJDad5U3rUM2Jtvb92LBIynd/9DezUE+T5UB47s8N7ue2DcY9AN05Z7Qn3dwIOpkjVVFoCGYgZKTV6ZCVj4zbnXvYvKm4hOdiuSpyv/tXTiwq+uc0H1OleFWXkecpUNnodnNutJ+Yvw27xsDVZk91ItBPOVssfQC6N4lPQNdC4M7+S/3YP3wumR/D8vjYvOkY9TjMbGAvlTwFM/zjWu38cC3JrF+eJ3rnLNnb+fO7My2w+ehAo/qxflFsDiua+jVueM9r0Ho5KmUiS98+FHo/Qq07ZDUQ85d7t6pRuduaEm6WJ/HqgAE7ZH58usfYG5xKbRl8Nbp0RwF7ldOCck1qIZaq9VqVT/Whc2bN+OP/uiPsHfvXtx77724fPky/tpf+2v4f//f/xd/42/8DXz66ae/qLn+0uDTTz/FyMgIZmdn8YUvfOGXPZ1feXj0xJu4NTOPGtrEc7RRDxGCvUKM+Pf6ngrt703NYmLneKXn98ir7+Bsp8bywV3jhQghKnJqqCJBYyTA2zc/wfrhdaH2okar67Pq9OFzc4tLmOlEAPzw2NdKRndm9WwabQRD42r3brV7XAVfeeF7YQ0Aws/DgwMlwePU+euYnrsTal/rOyrs8n3urzdn/e6+rRtw7vIUhur9ODrxUOnZI6++g8krU0l84P543zzy6jvR8XVfeb7qAOQ9aZceWsZCcwUHdhXnYeena+M+6v4A8d4anA9xq9ceHLF94X5wPRbPc3EP6OI11+e9b/eE3+V66Nhq1Puw0BFqdF/svOx9VnqRwrPc/Xlk831BYHnmiQdx6cZ0Yf76faCL899/9reC0Zp3HUDYY733MXrnnXUv913PNEZn9Pv6vXuH6oW1xN6xtFDPhb/jnm0fHwl9e6rm7t0/oG1Ue7eTbefR19i4yg8ooMbeyd23qvnyHvz5pwtorrTQqPfh/Re/nj1OLs6evtjORPT49emLN3F88lqBPlV9I/V37g3QNroc2DWOPVvGXPztdQ0WB3lvuKe8z+Q1jXofxoYHe/qOd7ZKg5ZbbSXqztJKAV+BLn0D2vTo+h9MRMf07scjm+8LDkEPt1J459E5u3e8aymemTqbGM3r9Qz1zk/sHA+BKrPzTbQAd+9raGcvLS6thIjqF5/a4dIT/k75aUpGTclfAEp8uIoHencqBpbueHzR41cAonNWHIvxvl7vXmo9MX5EXk1dgVADcOPERNZ3c3C6l/NW+qR8uGpPdC7EQSv7aqbUrZl51PtqaK60sHPTCM5+8zF3z3imeraWP8TOnedp5Qvuv33Wflv1EMW7mLxhZV0P32Iyo73PMRrG9zWbCEAw6FbJ8948cvQAeyaWDnOMXmQUOxc9D/25iiarLjE8OBDOLSUn8pw0Wwnw79FqaUTsOXsPvXOlbBDDa2/9gM97ezkH7771osP0ohM98K3JYPCnLGBBZYuJneOlOeXcF7VH5No/YvMAuneNPH7f1g3ZelKVzkw+8vCmvPPzaFGM16XON3aOSmesrp26XwAK1VesXstv8EyIr3Zeer78tv0u8QgAXnqqd53ewyG9o8888WDU5pQjD07NzAdHHfHcOzfSGYtPdl9jtKpqbTGZLMXPUvw+JnN59NfyVHT2Y+NIo0C7GBjt0eGYjJXLM62dMaabVumvdi296Ll/WaAXv0HPjph77rkHV69exV/7a3+t4Ii5fPky9u3bh9nZ2bua/K8irDlieoOYMg74QmaKCaqw2MtFjwn8nnIQe1cJpiqGnqIW+7YaXl56qqt4WkO6FYStsdYSxpihOMZAUgprr3u5mvd03c888aDLcNWAPjY8mGRwBM8wmWtcAcqCcMqQ5DF0ChhAGRdUubOGRrs/MTy356wG2aMT20rCiWeoIlhlLcfJkfqbHe9unBZA99ysAc1C7MysUt1Xaxs5CAcTBi97v2KCiHcXYsZDRmBpr6fYHFKOn9j4Oo7F+dRZ9+Jc8WhGysClhm0grjjbOSst9ObUC/3mHNWxsG/rhmDMpNGvCs9y1xlzROTunV2j0hfSOw1qSDmA7L7G6F9qbfW+GlZardI9B6r5ncUxz9nrGflItyz+9uLUTdEjAKVvxmhg1dpIXzaODOH27AK2j4/gx9M/B5BWIFUJopFJgztyeWyV8ZnK2vhoe36550hQpb6KjxA8Hgn4RoO7gZjyq3JW7K54PHY1DneL20CXzn37Bz/BlVuzrmFd+aeNKK+SRVfDW/WcPltoFngQ5RGVpziPHPoRkwUAXybT9WiwBYH3ZjWGwhg/q3Ju5jhUPLzOwWUvCEnlWsqWKstqxo0dI0YT1RCSQyetkTL2TsywnxO8k4uz1iALwL3bMT1u3UAf5psrBX7Fe9UL39D1pnBF945yiBdYVeW8i+mmlp4qvQLixt/YXFPn2uv+WMg9Y2vMjen/OWtRHLQOCb2zeqdi+5WCHFkzZ38tDqgOTLqa47xTRzx5W0zOju1rrvyc0m96Ca5IGc6rbDo5zimFmO5WRat6cSa+/PoHWFxaxuBAu0xqbB88+UfxNeVUJ/CMPRwCfLtZim9afmbHiO2Fp5+tNnjTsyuozEC9OIanXjABkHe3c+wsFnoJ2NC5AnBpecqhwiAjPgtUO5w9u4RnUwOKzkDrPI0FiAJYVUDDGnShF79Bz6XJ/ubf/JuYnJwM/1+rtfP5/u2//bf4W3/rb/U63Br8BYRDe7u1I1m3+tT5Ysq8/gzEU+sP719dDUg7Psdhrw5tanr6YrG0BEv5KNgmiKlGbvptlqBgzV4FXduhvcX+IjZN0+4D91jLQXjr1jnF0hoVuBda55hjstxCVXM8jqMEWlM1da2c/7HXroZUSVu78tDebmqzNo0DuuWGFHrpTWDrxXsNqy0OUai+NdNu/E2wuKBlxTh/xQH9XQzPufdMX2aptcGB/sL73FOWFNJSNzxT1nfWNHvFfTtnC6yRT6WAcwZQOq+7hUs3pqM4Zuu7cv9Onb8eBL7lVrcmdg0IzQ7tegl2/6vOQ++QllnQ/VOaQ4g1wOS4tmcS18d9jaVvW+C3edZAt8RYrBavvRukGbZWbozGWOC89dtV843Red1LfS52nqfOXy9EWGsKNRsYa+8PbxxvnaQt2uMptnavdnBq7yw/UHp3YNd4OM/c/Sek1qYldPit5korNAPmvGwZCx3Tm8/h/fFeOB4tt9FAuhe561WcP7z/gYKhhnd0bnEJ/TXg/rF7svbNK+vHO8iyY1duzYZ7wl5Vdp9VlnnpqR24caJYdgLIr/kcwy3+jaWsOD/SHJVbUrX32SPIlsWzfGRxaaXEI5W2eHc6dl97BfYcOXW+XYdf+1nE7grno7wxJkelgOPQIKH0+b2pdiAa/1WwvIHztWXpvLvl8dbUXp6+2C2fB3QDAbh3avyJlahJgZUT9F1PJiPwrNr4s4yZ+SZm55vYs2UM3392dXXKY7xjfacH2HrpBebJYXYfVT4FUOpNlIPDKoNS9gVqBdlyeHAADdErLP6xbBn/TfGHXDqp+596x8oPLHFW1dtM97iqZ4g2yL50Y9qVgWwpPuUrgwP92DTawNcf/jVsHGnzEa9fUKyEs7dexSFPR1WDFuURymp2jFjZw5huCiBaoriKB1u81v/X5zwcWo28bvUS7oG9F4tLK+FfXXfq23buLIXFvpGkO7qHemcb0kPC09Gr7m9M1vTWH7s3qqcTF7kXCnu2jGHjSAM/nv55aTzOk7Kr9lfVslq6X1Xz4ppipVC99/Ve2nLIsT3Vu2dLb8f0D4LV71Lf8fbc8ucYVNEHlc2eeeJB3OnIO3Yf1Gai9EfnQVpIOYP4e+y1q1KOuw0sT28bwHPOavTmGn48/XNsHGmU5E8+o/vvnbF3H5WH5e5p7Iy8cnd6r84Zm8L28ZECjigOAUja3yzE7CyerSsHrBysdEL5ZkpGf//FrwcdwNr+PN1M98yTqfmNmflmgTaSZwEIdgO1vSleqEyXa2e4Gx6yBl0oF3+tgH/2z/4Zvv71r+PatWtYWlrCv/yX/xLXrl3D//1//9/4v/6v/+sXMcc1+BWGlEdUicD64XX48fTPC4Kkevk1OodNkR/ZfF9QdHsFHQ9AYRx6lUm8njtzFQDwwtn3sNJqFRpI2nlyLBVOUt8+tHdzgfnYaOZYpFRq/ql12v8/+MpbuHJrFptGh4JSnlK4ua7bs/NB6eKYc4tLuDUzHxT445PXcOy1q67X3Ardqfqf9NjT2KD7YCOvyHhZXmio3h+EFT2fGM5QKOHPVBiBNl54kQPeGRyffB8AMNDfhy9+YZ2LCxtHhnBrZgEbR9q9SVJ3JYYL64fX4fbsvJu2HIuU0ajWl1//oFAKzkaB2HOa2DmOc5ensG6gvK8xsBEYqwVNf6ZQwHkp7NkyhrdvflIQOo9PXsN8RzkheKntdr0E/Tv/P+e+KSwuLaOvVsNyq4Xt4yOlMTTaJDYua0bH1h77/r6tGzB5ZSrU9bXftsI375uNFtK7EfuW/T0FRdIXu7+xPfcyO3T/CanI0theHd7/QMCJtgLctUQOrxvA4tIyZuebOPLqOzj59G53jo9svi/wIQvEeSq0OjelWZyL9ztvffZ+xvAwhoN2X3X/2SBZ+QtpOmnhob2b8dDz38F8cyUo/d4crIGba7N7kZonx/Ui9hRSdy62V6R/z5+5iks3pkMj2aWVVqhpbvHG8pq5xaXAD3Rtj2y+D1MdOtOOXBwCUCsZNg/t3VzAIW8fqWCyVn4upPZk+/gIrtyaxWhjAJ8tLBUc1i+//gHmmyuhNJ+9N6cvtvvpeJliarwnj2EJMM6DfP7Chx+5kdIxWpALfH+0US8o1KfOX49G6Vp5y8p/sXu5GtDSSBZUHojJo9aY5t0tzpkR4t5e2gzkU+evY3puEfMdJ4mOa8+Jz3v3ObbOQ3s3lyIqq6FtffLwUKEq6lL3Up+lM+zdW7PRdXAvaRTjeDYKHwDGhgdL+BObs9K/6U4j8aXlFfTXUCpXaM+BMDjQj/nmSihV4tEPj8+ncNjKjF4fs6r12GdXGxU7VO8L+Hju8hRGnP6ces+9eeh5UGfZNNoIz6VkqdieEFdSuhV11O+8285Qf/n1D1wDtQcx3U75oKcfdunFH+O5M1eDU+Ll1z+IlqyxMlKsz44nv6TO06MfHm0fHOjDfHMZgwN9UZ5VZUPoSm61UFrYuy+EoxMPhTW3UNQ5UzKj7rPdCwCF8rixe5OSlUYbdYwNryu8E8Nz/VtD+qgwkzG2/tge23nxzijN4/vsIUH9L3VuthQj5RjV6wEUbA+0C8WMvJ5+Z/dEZdm7sRHFcMHiCdDNgLP7YG0mSn9iOoGWNnz75id44ckd4Q6PDa/DrZmFMHZKj6J82Q6KKf5Nvzm3uBTWzLFiND+mj+Tuca6Md2jv5qCjWV2DthjiiPZS0wAbq8Mq766ys1i+ofO1+rTujeK1l1XTCz/2wNPNFJ/smriH9b4aNo02QnbUI5vvw54tY4X5rh9eF3ob7du6AdNzi1hornTw8j63gssa/OKh59JkAHD9+nWcOHECly9fxs9+9jP8jb/xN/CP//E/xsMPP/yLmOMvHdZKk8UhlcLHaJYWyumWKVhNff1ewEtVtJAqYwSUUy0/jxS9X8S6f/3Zbvbaj0zN7ZQQHiuRpOcJoFD3U8e7dGO60F/HK81FiJXDUqanpQBYg59KXFUpAPstr+9PbD9iEEu51/dsinUv55uTSp4q7aRlvg7sGo+mUntrjs0zVo7kbsscKKgDKpZq7c1vy7OTpah6r+QfUDZ8VK27Ci88o01O6SxrLFd61GsKeFU6fs73to93nZus3ZxzJ6pqWOfeq9wSM8TDdQP9mG8uR9dsy+Vo7V2lK155u9h87O88/ueVp0j1Pvovxe9oiNW55vIAIF5KNLWO1fLF2Fg55TWVTwFdngG0HfheHW79pu3zMrFzHG9c+2mnp0w/Bgf6SiWFYuv18EOf8cqVxM4vdw9TpcOqSuvl1pqO4YClhfZu9mr4y/lmVRmJ1Bikf59X+ZnU+cRKruSs0St5kiqvV4V3QLyWeWqMHKi6p1wLe7jZ0lS97nfsPNmfsa8GrLTivSqVXtgyYHaO9nuxOev+Wcjdz89Dp7BQVTrQ3t9fpL52+mK3RMtQvc3LLY+Pldrxzt8rnZTSLTyc8fbEAy3fCPhnWlWaOLYnVbRQ9dVNo41SzwGlLVUlQj25xKOJObi4WqdOSm54ZPN9QV/L6Y+nc7Hlf2J8KTVHnRsNtoTV3GOgWo6y76XKD66WRujZ23FTPNHbG8Av95tz97w5p/CBeiFlf48n5M5fx431ZanqpZUaQ/kggEq80/0kvPRU99s5vcI+D72iV72+F7nQjmNtRjF6yeAwlmRPlbrLXbcX5Mt/Y30Yc8vefR6QkqN039Qmd+PEhIu71o4GpHvOVs3r85ZL/iLCL7Q0GQA88MAD+Df/5t/g0qVLuHbtGk6fPv0X1gmzBvlw+mI5xX+kYwwYqvdllRg7ffFmdpRWL3NROLS3mKpY75SX2jQ6FFJEJ69Mue8SNMODQo2Xom1//soL38NDz38nlP9ROLw/vwxban0KOzeNFP5V8FLnuTdeWQE9z5FOCRBNIdZICI12iZXmIsSyZRg5w2+MNupYN9BO29XyF9yvWCkA3TMtDXXhw48KpZjsHFN7bEuteWuzKdZVKdn6PeKCl0rO5zTVnc/zOc6vhe6+MvJav+PNPYaHNr1Yn+PeP38mXQaiCm85n487UaSxjDM7P+JivXOBGaV96nwxLZvje6VoYutWvPbWcunGNABgeLBt6K/31yrvsI6pP3MOOXXYdR8P74+XggLiKcQs27LcamcJtACMDa8Lzx2ffB+3ZuZDBpgH+m2vhI73bQ8PYvtvf08heaGjiKliY/eEDuPBgf5C+rdX3s7O0d7XI6++g6mZ+VLJJm8/dL6kY0A7cjAX73JpfBVwbXSfK93QdfN7AEp3hDjKdHk+Y+mGLftJunB88lrWWjgHpv3rnuhdTpWbOXX+elBMRhv1cNYAQi8XlkLy6B5p7uBAf8DphWY7Io//EqycErtns/PNMF+976kymroexRmLFx7ee6VugG65ice3bcTc4lLgB7oH9gxja0vxdJZ+yyllpOuza7Fr976p5W8sWNnMjpGSuWL7/sjm+wp7ZO9GDC9jJVdy9tUr+caIxufPXMWRV98p7J2HVzquGnRj5eN0P1O0yDsz4p0ni3H+gwP9BX6T2m9LT21Gk+Wfh/c/gI/n7gBoO2EAHz+4LyPitFQdxM4xto8petTo6D5aMikHUvfLA+7XwVfewgPfmsSRV98p/U330rvr5Pdzi0vZZQV70Vvs+h7fthF9NeA3vvhXCmNYnUC/HaMJJ5/eXSoHk9ItPJzJXQvv8s5NI1H6feHDj0J2YEyGtJB6jn9jNeRNo0OhbBxL9VjawjO+d6he+P2RV9/Bc2faxjnKSN5+AD4NJVCnZZR1Dr7qO6rrWF7PiHcNKMiVhw7t3Yyx4cGg/1h6p/J1in7r3Hjm1DGUp3tr9HQsy+tSBk2+55Uf5Fxt6b4qUBkvVqIw9w48svm+UMKU+lers5+nL97EuU4pay0LrOuK4UkKH5jJPDjQLd915NV3Qvm6l1//oCd5mnNhqVPqr6qH8fvWsWDPd8+WsYJMpXwQQEk/s/vAb+7cNBL2VddO3Urlcr6j5cZT+xgDz/bA6gxV+OXRY+JUTI7zIFXKnUF0C82VELzEktl2nfr/OXbAk0/vxuH9D4SSXpwHULbV9KKfK42ze5wDh/bGSxDrfj7cse2Njw4FHq+4pvru9vGRUOJy/fC6gj6bO79cXrYG+bAqR8warAFBFT5PoOHfj05sKxFn7+KTedm+BLHnY5BS7hVI7ABgeu4O7h2qo4YuEctRPtRhYIUa+/PMfBPzzRVXSe1F8cpV/s9+8zH86MREqXFsClL7pedtDfNqLFAGxv4rsTGrDLCssQogZME8bBxMsehDXc/Lr38QGGyj3h9qlMcMBinHjqfg2TXa/amqu6m4Yh1inlF0cKAvnIUVKC98+FFB0M5dF5CPh95zFMRjUHUvU4bY2HdPX7yJCx9+hHuH6oWePUCxv8Ujm+8LwtEjm+9Do96HqZn5YLDoGqyLCl+Vg4YCHNPJV1ZalXsX66WQu/ee8Mvz9uo/e4aMl1//AFduzYZnvJrYngHac7hT0POcjN45p5zAVWvntw7sGi887+0Jeybt27oheTc90DJLj554M9S0v7O0HL6ZMngCKAjGAMKdrTLep4wfq4XBgb7Cv/wO6yTbYAIaI1naQEvPeXxHFQf+nSUFWDbQOoIt8Ayt8g507zL588uvfxDus9Z55n168akd+OGxr+Hk07vD3eD8U85K0lw1gNDQcGDXePj9S53xU/jK0gZKF3u975b+WOdozDD5zBMPlvqQAF0nm9czLqX8EXLOT+mK4rp3760spWtJ9bPiHDy81vmobObRLm//Ff+tA+Dtm58U9kgdX6n7qjQnxfu8fVXZQYMJ5pvtfkBnL08V7q/nYPUMjJ4Bx5MvUjKDPTM9by0pQuDc6KxjMIDnwFYDlDVYqeGFRjmgawzm7wmW7qmhxOox1EGUBniQCoBQ3ef7z/4Wjk485BrtV+twt+/xjK7cmi31+fP20t710xdvYr7D55srrZKjNmaA6dVhpPNn38F3hV6k7l6VoThllIs9p/OvujcE3uWz33wsa+0p47DyMLtu5c/82z99cgd+dGIC33/27wAo9xxQ4LysjqO4MdLJ6rf7AaCkN1k6rMbmHEc6UOz3YGkV16yyB4DgFO1FHlKDMsdLGayBMv3WufHMjx3YXnA6eBC7K0qrYk4Uz4hLIK2cu7MUdaTbsarOQ/eA529tNR5c+PAjtNDWDfZsGSs5dlrRN/MDZa0+SB1Fe3BMXpkK31rsyObencwxIPNMY86z1Fj2Lqju+cwTD0b1cbvus998DCONeij1lqJpKdob02e9/ffW88a129F7r+A59K19KqbP63fZm5Bl0QlHXn0nlBbmOetYqUAWu64Y3qmcqIFb1lbTi36utqXVOE2BMt/w7CMMOLk9uxB4vNXHKT9+PHcnBCQy8JL6LPfquTNXcfCVt7LsdauVXdagCNk9Yvr6+lCr1ZLP1Go1LC0t3fWk1uD/P0DhlXDqvF93HkjXwlYli0zMgn1eU18BFNJgdRzvOwqMlJtvroTSLTZN277rrVsjJnQN+jOF0PZ3y0pqLnBuXh3JXLC9BHQtsTHtuhU8R8Om0UahjNWx19o1+7368R5QYLVA5vPx3J3A9IB2dJcqujRoMv2eSvPY8DrMLS4Fx04V2DRrrf1P43aqZjvg19+1f4/hvn4vt8xBDEdjDpi7ARpruI4Y6Do8POPv5haXgoM0BXq+QPuM7yytuDWxHz3xZsCTdpR724h17vJUQYG1wtup89fDfAGUaBXT5TeODOH27EK0Rr5GwOldYcO+1PO94MqFDz8KWQOcrzVkEF8JjXo/9mwZC/tAfB8fLa/JO7eUk1GF8m5N6XzaZ7938undrgPF2xPeAct7gOoUa1t/ud5XQ3Ol3f/Hjm/B4iVrm+fQPK7ZGpB7ed8DvaMUnDlHlt3QYAKWUiA+qZI9PXcHM/PtTBedl+VL7brog5ieuxOMfPY8T1/spuDTcZGqj2zpLdBt/GzvPMG+mxMlqGdLvAPafdduzczj2z/4SeVZ6H2gUpTioVXzAMrO0Rgt0PeI/6yt7tUNJ6Roy+mL3TJO9uz1XRqJVrM+/XZORDvred8/dg8ePfGmOx+Ol9MzguMT52Nj6do93Erd2RTv8+am3+a4j2y+r7DPjG63BhFPLrF7rnPVb/EZ3k9Gget6te49jQQ1tI0mjGT1gMZ3/qv74O03wbs/ny00S+dKvkc6pnKhyoxefw97tjHw5P3YPGNjVeknMYi9V++rYaXVCjz79MWbmJ5bdJsb6x5buVD7xQGIjmEhh1eRPxKG6v3BCNSQMmXe3UvxXPtdfdb+PceYFpMZvNKWFlL9QPUb3Gcth2Npgu0/sVp5gO9pGVoGcsVoFIMfWihnNyje8GfteebRLerbNfi6gtJeBgzYMkneumwZH5X9LC1X6FU2UJrYqy6nsrjtc2bLWHEvSL9ffv0DfLbQ6XW33CoY47kHFjctDsfsIjFnvKUtOk+eoyd3XboxjamZ+YBf+n5KV7Z4bfVB1VE4z0c23xfkP4/fcEz2W/XoV0p/9c7S6x9p74LHY1Og50fHA5DPR2Kg+pd39kCxVxj/ptbeVNN2q1/qeACCjOKVvrJ0XQNOuUZ1tNGplavDWfqT6imq42gvJitT5oDlp/a+W4jxFXvWpK9zi0uFXn5zi0tYXFrG4EB/WIvKzR4O237Ah/c/EPplM5DKk/F1TtqLk39bg94hu0fMa6+9Fv3bn/zJn+DkyZNYWVnBwsLC5za5XxVY6xHTG8SIo/19Sqj1/gYUFSStuQ10e4hojVyOlapZnaqb/nkYwCz0WnvbGqq0DjKQX3PWGzfnnHLes3us9T4ZAWrrVMZqenqGNk8Q15+1xrcqQBQQvFqvAJJ4oeu0PSV0zpynxR8P37Uu72rrtqb6l1hlwrtfqWdyFMwUHlQ9q30qvN46tv+HxQn7Le5LDe3IPns/YkrEvq0borWn9VuKP7Fzy61Jy+c0SpdN3709zBnXm+toox4MU6n9437MzjdL/bti/QzsNz06CSAoPu9NzYbmxHqfeVdSvYBS3+sVqu6iZ3wknaZhy+Jk6lvqhKnqN5Yz39XWe47NSyNAvd4+1oFNIM+xvd/sGECRLx185S1cuTWLnZtG8I2vftldG7D6fl+2B5D9/ucJtu9aaq66j16/idVAqn9CDCxPVTzq5X6xVjeQ7vunZ6OG4dwgAjvvlIySQ6OB6n4Jq5WjYqB79dJTxbrw64fXraofl66D0d6TV6ZCEIDSKKC6l0wKL+xzVnaxNEn70pEmpEp4aN138t8UnauSAXLOVX9PGhbrl+RB1V3Plactv8yVQ4G4/Kp/8/YHKN8NHZtGnhrKfQV5x7wx7Fxtvx0PvDr2NAJRlovtR2xfPdzx6EQuD/XOTeetvVV6kZvtN3iG94/dU5KFYjpyVa+CGM7oHsT0KU8H7/LZfrz/4u9EcT2l26r8G6NHdk9S8rE3NlDGu1+E/Kjf7FUm885GZePRRh2LS8sd2tiu3LDSavPRL35hELdmFjpl6f6OuwfKBz25NmdfYv2NlA54PejsXOzeWFuL7etk72gv/Cm2ltidTeFDlc2oitZ441bhi+qylK0P7Cr33MwBnQttMSobeA45u+cqo/zGF/9KUk+L0dle8S+mR2qGtepTvdjSqmimB1W99u5Gho3NLSXD6nwAuD//8NjXViVLHt7/QOir1Vfr9pIBqucD5PfM+ssCvfgNsh0xHnzwwQd49tlnce7cOfzu7/4u/uk//afYvPkvnkdszRETh9UYBfiONdDpZc8hTEdefQfnLk9VMgpPiVQh0xpuP2/BDVi9s0T3AugaP/gzG+PZZ3MElphishqBwgrZXoNBKhPW+GqVDXVs0HD1+LYvJQWSmGFmYmdXoawS6nMFU52zKrCWEVkmp86cqgbGFmL3LEfxyXEi8JkqQcB7NyVweU41oF1n+T8d/7vRMa2zghFP1oEDFI0RuUKgrtszQlc5sxRy7zOf+/NPF9BcaQVlJKac9iLA2fVaR2gMVJBSAbeXZrmxOSmw6aQ1fk3NzAejnXX4VH2zKjo1NU81Gr741A6XFlrDYu4Z2fVX4WJqzTEH/Gr4kmf8ynW46/4AXQfO9vGRoCQpDbHjW0d2zBBpaXTOGQJt3GU0nc6xV95mn/H26NETf1wwhlTtK8cjrgNx+lqlnObIDznGYi9qNofmb3l2Mqzh4K443Ty0d3OpXGjVHfeg6o7nPgN05cWBvlooO5VjVOuVzvD/LQ1Q54Vtmlq17zGcVOO5pVH6XAxvOJ9GvQ93llaCTJ7zvuURdKzUO/sLVBvSrbErx6CWutM6Zzt/4oDNok852Hsx9seMhZ4ByI6Twj/9G4ConJ1yjngOG9uUuMpoljJM2rkCZcNMqnn96YvF5urMwrQO9qogHL2f6gy+G6O09xxpAQOLrFM6d7+8vculB6mgrqq7Ys8eiDdttmf6zBMPut+ucqD0EjSmAVsx456e785NXTnEBlb1CrlOlxzbR66sbJ0aln9YqKHdmLtXXSUXPJpjjbUHEgFGMV4BoBOU0nboWdyirmd1/lx9JjUXa19IOWTsvOxzHi1L0eIcesB5Mnuc383RZy2oXYBjxNab47RlRl4NwLjJxIrtc+qepxxAHq89vP+BgqOeOpulEyn5GWjh1swCdm4ayS7V79lXcuwesbOtktmr7m7M7gQgyyEVkwXseVjHsA2u1oyhWEDrX3boxW+wqh4xU1NT+If/8B/i4YcfxtLSEn74wx/i3//7f/8X0gmzBmnQBltA+6J7NQP193znyq1Z3JqZx+JS10tPOLy/XYdw+/hIodahjnPy6d0YH21gvrmMj+fuRGv+nzrfLfOyfngdHvjWZKizPnllKtSDBro1cTXNMgc4L9aq79Zxv1ZoJLcaOLy/W2t0SBp+tlCuUct9071UILEl8a2q2xmbT+y8gG4qqjKqWzPd+tS2EfuhvcUG6rMdhqKNUm1zdYtnHOPQ3nb6tTYP11qyt2b8xqqM5IudE/sMvH3zk8K7bNROZmX3SesBe04Y3W/OS9fGn9n4U1OEj7z6TrQvjp6J1/sj9kzq2di7esYWZ1ShIH4AQHO5VWgma8eM1QDWMU+db6cXa51z7SmQwmM+RyeMPqfvXboxjduz3dTqlLB16cZ0tF6qClBLne7BC82V0r3RuSpO6zix/jX6vO1vwudj9Bko4laqh0pVjyHOif2bgC6dGunQMdb7Hqr3uQ2sj7z6TqHhsHeW/B3v9anzxRrl+o5dN8sqDNX7A82weL9v6wb019r9qIiTqRIOdv0Hd42HqPXV9Hs5db5bZ9j2pagC75w5L9IfD78ULE3lWliiZmLnOH48/XMACP+v/RJ0b1jOjf/aO8y6xfYuKq6lcPeNaz8tnOFqeBvHV/7v4dDcIsvq1cK+xtat9/7ArvHQhDWHP8d+l8LrqjVaXpuSAzw4sKt4T7WW+3Md/sneNew7MNpoy1YbR4Yqx7fAszh3eSrggdebSHv7xPCZNe2bK63SPUrtgZ1DjMfZ82G7Mpb70+9sHx+pxIXYN7x7O1TvK9EofY6G2xitGhzox3ILeG9qNor71pBgy8bRYD48OJCkVcTZSzem8dlCsb9EylgROyPSp1Pnrwe5m2VYrMzIniSTV6bC/L2+XbGz9eagMo6tlc93bB8ZO443LvdJ68Gn5OwYfVT6emhvt4wM9bAaUOqv48nXHAOIN01XXYXyMMc6d7ncjJljvfz6B5hvrmB8tFGQORY6/TQ9XDq8/4HgACGP1HuhfVAe2XxfKCXlAffEoy+6F5bWEOd5/3h+2gclV++L9cOy8zjy6js4df56llzv3RXSfy1Fap/XNeuZ3j92D547c7Xwru4hdWnLx1N7rrSGstxCRsloPd/3pmaDQdKWp/LuRUqWSMl0ut6qnp+xsRRU39E7qnu+abTMN4fqfVnj54K1X5DmqLz3yOb7cGjvZowND6KF6nJV9i4ut7rBB0vLK3jgW5NAQbNrj9kCMN9cLujrb1z7KZZb7RK03pnlwJ4tY4Xef0BZ51Kay/3nc0ob9Oytjuv16NS7USVvay+xHB1boSujtunc4tJyAcdiZXu9nir6bcr2LCHpzUHtPJyv1cNS9if+7bkzV3Hk1XcKOsep89dR7whUA321YKvhnqtTISY/s4fre1OzsBCjB14PUO9MrO1Ge1Z684rZr6pkWOWbQNfu5M3TA9UprSyp63rmiQdDpSHirNp69axjdHcN8qEnR8zs7Cz+8T/+x/jrf/2v47333sMf//Ef49y5c9ixY8cvan5r8CsOJNA0pFnCRFBCxHfqprG2AgmENdrnKEYW1AD13lS7keV8czlE9FqiCqQb4HlglXXWw2czVQWPWVjD4+mL3YZ9AIKQdnTiIYw26mjU+wpNHe2+xbzpWgs09myqQe5XXvgejk9ew9ziUsHwzLGUIXBs1gyfnrsTBHtvf6mwtoBSo1R1aljmbuHQ3s2FxnjqbLAKsRrf7DlZiCkOVsDimISZ+SYWl9oRLvcO1Uvjpgx5/Hlxabl0JqqIeAw4h7nbZ+yzKYVF8SeGM1YIVKXi3OWpwrPWEKPnpsq9xR/9tgrIFmeA7j2jYM/nYkYR62j2wHMI2L3TM9XG31wnAPc+2/2hsfOFs++VzkCBe7K4tBI14gIoNPSu+nbKwa6CJ+d09puP4aWnig08Z+ab+GyhiX1bN2DTaLuR8fU/mMCeLWOF8Wm0IY6knL9qkFCDilf3mOtONfgmaINDb48tPSXYO2WNHqm9jRlAUs4FbzwaXVV5tIbZ2L32gO9e+PCjwNNUER9p1LFnyxjuHfLnqz29gHQTSs+YCZRxl/1kgDY/V9qhxlnPGZUyugJdhdrDIf6dYyi/ib1z6vx1nHx6N26cmMD7L/5OIbJS+X4Kzz16l2s0smAduN5eeXDy6d1BQZu8MlXgnwT2riHOfLbQNgrcnu29bDF7SQ301QrGE2swzQkA0r5UsTubcrRTPrA8zvI+7m3H314o8aByLYNMcqKWY4Z6roUN4QHfSH588lqYh7duylpKS5V/esFJdk4q++XcM/JW7UuVMjSlzojvEvdm55tYP7yucC467vbxkWQDb95LoFWQXT0aSqOlBo/ot0gfGvU+TM3M48ir75TW4o3LPVcHQ0zOvnRjOsgGVUZZyzeH6v3BYeA5o2N7HZO91aCsvGigo+9pj7UYTeX6Duwaj8onh/ZuDiXMaChW3Ke8z8y9mENH9wToGmftXnhOGs7z6MS2Ag1loEcOeMFWKQc75Uzbr0XHIy8EuvTgyKvvYMuzk3jo+e8GnI0FZVjHrzZ4JtiAMr3zHn+3jmDveQZeavCLrkub2GvwzsTO8XAW+7ZuSBpBY7/z1kGw+2Nlvxj/STlYLT1ROLR3c6An3cCPdubPptEGHt+2seSkjekhsf1TsPaLyStTOLy/3cvSynu5cqgC7yLnv7TSwnILwTjOHl6H9z+ARr0PNSDgAu01gB98WgX2rC0Ntc5E0tx9Wzfgs4Um7h+7B6ONOmbnm64Tweq4nhMy18nqOdF5tuuH1xV4UWqtGuwHVAcREqxcyPcYmEcnUZXzN+ZwiY3P9wlnL0/h+OQ1TM3M441rP8WtmXkMDw5g02gDw4MDQRYbHOgvjWH1B35z56YRN+gPaMtH7SCia6X9t3sXC/r1HMrEAT3/WFAMkO80to69GB2xd17vl8VFO8a9Q9MoELMAAQAASURBVG07I/dSbb3WaZPjBFqDOGSXJvvDP/xD/PN//s+xceNG/LN/9s/w5JNP/qLn9isDa6XJ8oFExktbVkOJ/s4rY8Xn7HurLZnDv7EslleOIDXHqpRBrX06ONAfMhXqfTUsrbQqa4HbngxVqbG6rljKq/1OTvkNZaL2OVtyh9Fo3ngK+l7qWW8tsXkxtT6n7rxVTLw9SdW7taX0dByvzJr2VmD/DZb+SJXZ89bD72i6MqMSU6UeYvtb9YwFW7oklhqeWxJPy/PYVNZeyupxXyxeWPqRSm+OpVgr9FJ+wOKCpjXbfjg59y22x4QfnfB7t2jZCXtuqRJ7qX3QfdS7R6WDUHW/Y31HbHm/h57/Luaby6GEQQzs/D2e4pUKAXw6kOIHvdDT1Bxje2t7+1hczgUthXPvUL20D7o/QG89bGxN4PvH7gl1pAEEGqXNT2O9zLSMiZYeZCmFqr4zXgm8VPmfKqg63xxZIPZO7AyV77/w5I6e72IujuTiYM68td/Px3N3An9gPxSLT3y+3l/DQxu/UNkTSsGrKa/lRTjf2Fnr78lDq2iKt3fkG0C55FJs71J0MSVr5t53b82xUqUsKVdDu6RNLuTIRzlz93h3bgkT/blKtlHeR/nU4s7h/fFysgTeSwWbzZziGV5vy1QZTk8eyO1lYOWql57Kq39P0F49qVKp3lyrvqM0ulEv61y9lra2OBIrS9traSBPxrfn4Omp2gePQX6bRrulXWqo7vOgd4x77q3F4pZXwvnoxEOFd4FuWSmWFwKqdbbYGd+t/FjVxyl1hrl6pL2XWsK8qh/X6YvdEnkPbyrqe4o/ln70Imt4592LXBjjdxY/Yn2FgDIP+42j/xHN5Rb6am26qTJoryX2LHh77d03j64SJ8i7ayiXY++F96To6MuvfxAanmsP0f4asHGk4cqkubwuVbZMS+x5MrveHd7f2Dw8Plilk1XZzHrVP7im1ZSSo96nYHX1Klreq9wP5JfcBeK9tGJ2oT1bxgo9Q8kHvVLGHv3L0U1jYHWknZtG8O6tWbeHm+4r0A4g7qVU/RoU4RdSmuzZZ5/FwsIC/vpf/+v49//+3+O/+W/+G/e/NfjLDRodWhUxQQ8sS8BUlRMC0unAqXf5N5Yw8zz7nle5KsqEoJFRhBoQvPca+eh9h97mjSNDeOBbk1g/vK6UGuuV1KJn3Iv04tyPvdZO9fSit72986IKgG60AUHT8VMR1nyvKureevm9efF7XmowU1pja2O5CmWwGhXJCCQLFGjem5otRSZ6GVs0rAHdprUs/VED3GhvXY/iB3/2Ive1fJSetRcV/JUXvhfSeWM47AH3fL7ZdiKdvexnhujZpKL5hjrRGI16f+Gu2DE8UFyI4YW9W+uH1wFAJ9rl/TDWgV3jyewA/m7PlrFoiS5CDBdSoDiZupc6N41gYdkvjahXWkUcJN5xTpZ+VpUZIyhN0MhNRgQRvCw6jf4jbwDg0q3Z+SZOX7wZ6PPRiYey9pFjxKLdiA+M/PWisar4gf1WFb5WgY2oBro9Vg7vfyAapWXfj0VhvvDkjtI+WAMx0L7Tpy/G0+oVyKsbnSjq96ZmQykJjUY/Pvl+NAWeDjn+XUsPLi6tFKLMvGhwG/022qjj4K5ytmPu+XDdAJI4we+nyq7wHUZns3RlDDTKrEr2sd/w6F1sbV6Eu+KPXavSJ4sLzGr68fTPA+1iWYJNo40SreTzzeUWrtyaTfIRC8RfljLUfm8639hZayQieehCczkaURqDRr3LfxeXVgp3xUZGM9qxIbzOzisla+by56ooWwXNwuwFuK9etofyHuLX8x0ZLFUuj2s/+fTuYHyw0eExXh8DHZM8puscXgm4kxMZfPriTawbaJ+dZu0rbdG98XiG0giVfZiFb/dH+TDH3bd1Q8hGiuEr3xvoq4Xsj16MZqcv3gxR04MD/YXztntFnP/2D36C27PzlbSNe0T5wCsBl1PeScHiheoLnt6itF0zdSx4Mr7dC+WlnAfpGf9l+evZ+SYa9X68+NSOyrLK3p6ncIuZtpwPMxY0K9SLQNdIcMrFMbD8mvO1JWv1PGz0tYLKJHb/VXfRDA8LKT3S8jLFAy13VUVbT52/HvaTpdMV12IZVSlaHDtvoFway9uzZ554sGRTieEHdWP+qzphik8sdazErRZK32J1CgCV8qEHHv0mHp395mMlOuPJN9QHrBNGx4/p/1UyEseYmW9ivrmCzxaaeOPaT8Pfto+PFPC3quyex8e8ihYErbZAY73iL789sXM8yCGs0hCbx54tY0G29Mr6E8g/njtztXRvc++2B73SdQLP+eCu8cB7H940UsoeTdHy1ehlKhfZEvaeLKJ2Fs9eo7YA2oQIC51gBNJCHd+jI1W6Kc/moee/UzofVqMhXOk4YQh6rlpOk+0ibGsIfa8XGrAGach2xPz9v//38Y1vfANjY2MYGRmJ/rcGf3khZtAA0kqmLSekRMcaClNls1IGzZhRBYjXO9b3qnpnKGFkCYyhep+b/urtGQ2+t2cXgkJAZsN6jVYR5H7ElH01aHkltGL7c2jv5miJhR8e+1ooN8RyGIf2bk4adKsYp67FCg5//mk7dXl9x5HFfgtAKzyr6/ZKSNkyTcpgdU+s4EG8sOX3Yvum/69GUP6sTdXsOVYJD176pxpCDu9/IEQy2TPgOukUSgkolsHqPUk9SyXg1PnryTImRye24cWndmDMUQRjAjO/o6VGco2RWkphoROtP9opo6T7Q2H0oee/WxC+PENkDOwZ6pnFjPiLSyshw8Cbv75HoXbTaCM0G9TSaZZWKQ7SMJxSdFOgNIFz4N6/8OSOYGyxPWlijhItK7V+eB2eeeLBEMHsOUpy9zzFgwiLS8ulM019zzpMLA+pmmOM9/H31rhMAZ0ZBvzX3s3YuHoX7T6ocm97+NigA8+pa/sqUDmsoa0wsbTkgkS1WV5NxaSGtsI5sXM8KAuDA304tHdzSYn04NDebvmOPVvGsgNAFNRw7u2jKjsPPf8dbHm2HSQR+441xFYFcKhhK1eBtHiXUorU8aZ0wCqYMcckUA4C0b+Rf3pyjt4bDSqx+59S8K1jlfOx/SBihkM6jc9dngpOxBZ6K29C45ziKPfu3OW2XDXboYvsBVcDcHRiGw7sGsedpeVKo3WV/OqBZ+zw5AQaI194cocbUKAli9Qha50jFj+9sp0t+AaiGG7HaFjMmBzbOy3tSGM0jTrqRNN9Ij4eefWdAg7yvMdHGzh2cHs00CBF/+386ax84ckdIYvMw8HFpa4x3SunFbvrw4MDbg9DD2zwBg0z+7ZuSK7JOh+0NKH3Te4l5RHPuRM721hQgIcXpGncBwZbPHriTbxw9r1kMAPBk/HtXnh3geVuWHLphSd3hIjjheaye29iMpHuS65sAQB1QVArD6sssGfLWMBlysWx89Pfe3fUk4lSzorUeninbOCFnYfqkQCishB18Jn5Jo5PXsPh/eVyPCm6w2d5pnqHSZ8tnSWf8crFWd04FiwU0728IJTYftoANbUbkE+wDJiCLZfs6fUc+2yiX5sHWprc0loPcu+D6oUEq//nGozVYL3cKsqvP57+eenux8rnWuDdtwFxCrZsG0ui2Xu0Z8sYxoYHAXQzYizttPpiqqy/hVTfvCpHZE6JXQ9iOH/y6d0hKPb9P/u0MLb3vlcau5eAhJNP7w7BXFoSzzpH7J2Kga6f728aHQp3LBbwZemIyjOx9agTUXmfOqEbnSCQnZtGCmXe1fkCQGTc/pJDqYonrMHqoRz+HYH/7X/7336B01iDvwhghSGgWLbn9uy8a1ghoSexSRHQmKedBKUWeS82rjfnnPdSMDjQj/nmSqE+Zww4bxoft4+P4Mqt2UIdZX6fe6TABrQ0YNm58z1NV370xJslwm7X+cjm+6LnldoTGjlV6UgxEQUvWr/ZqXVKxYHKP2vLvn3zE5x8ejcu3ZgOqbAKpy/eDGVvWO+bzgotF2UNIYoXFAwobGwcGcLUzEJIdU+li1pnJNM97Tmm9jTGjNUQoo4FC4f356eXx+7Dzk0joRxN7FnicqNejnzU9TFd1rtz3lr5HZZW4Jnn4NTEzvEQff3wpm75CAoQvBs0Vs43l8NejTbqHUfJfLifsTnaNVqw94nPfuWF75VSsXUvrHGOdXOPvPoO9mwZw7qBdlmKiZ3jye9rarQa6fUOVAHfIy3RdXMv5xaXCmertF1B379yaxZnv/lYwJ9ewK45Rc+5Vs6x7dD1HWAKasxeTXo27x8VN37vkc33YWpmviAIKy0hNJzGrHRKx2i0FZS9M2CmAufC8oZa1sDuI795/9g9+HjuDvZsGQtOh4/n7oTyWuuH1+HKrTbNphDPOVg82rNlrFSOMRVRx7HsPGPBH5oJqXtB53uqf4+ORYfYe1OzpdJCFuwac+7Xob2bcenGNI69dhWXbkxnl3RI4by9f6QD/F3s7vNe0VFlaWaMv6txUe8NjWcsU7ZpdAiPnnizUCIvJoPZ+VTRK3732GtXsa6TBdNCW1bS8m9AnqJLnPdKk7JkinXujDTqOLR3c9i7c5engnLt8WDKBptGG0meCHQDLe4fuyfcf33G0qgqGZcZaeR9LA3oyfG6TxM7xwulUbXUoMV5jzedvniz0AhawT5vDXD2DiutU7lNccbKvnyPNIR7pXSV3+U5MtCgSr6181c53+O5ljfx79xTa8QnTdO9jp2zLROjcuMLT+7A82euhvuRAn5j/fA6vHurXd6Z6/Jwi/OZW1wqZZqfvpguL2NlPo7v4QXL8enech8Jqebv6qj08CxVivIbX/0yDu19LDwLtBtazzeXQ0ZWbB97oUEeqLz9xXuHou8rXuidtX+LyVEen4jJRBZfc0AdBNTF+L3jk+9jvrmM45PvJ+U8O0ee90JzJegm882VgOPKn5UmKB/7xle/HPjxxM7xUglDb69V/yXtmY3ItJZnVtHpHPBoV4pfA0X8B+DaB4D2npEO0qGXM1/NSLK0NvVe1b1QGkGY2DleoC0AovRJx+cca2jzbi2X6z2fyyNz5BbK0G1a0i4hp9l1LIvKNbH8/Z4tYyUZUfH6wocfFXRH7x7TGM+SbCmenbrbylOom3tnluLdFid5b2gD4tgKHp/g7z28SeGU9gSlnKg6BnUV6txVQWKcx9ziUgfna0FvsPPg86zawR5jOTSBcgLPUG0W5IfkYcQxAs+0hnIJaY6tPCDFE9Zg9ZCdEbMGa1AFnhecl5cGUM+wooKUjTCwkSdVnvYWyhkBvc45B1KRnIz2tso530tFaNimxoRDe7tefht9kAK+p42jvawPC2oIy4maYrQpUHNLSuREpqTOgoqDjR7hszZlnnDqfDfib3CgPzT3tEBDiJY5sHNRJxAFy+OT18J5HJ98vxQpdPCVt/Drz07i4CtvhTG99PwU6D7q2dNZt318JKyTjkgb7Z+TkQTEz+DsNx/Dj05MhEyM1LO2gV7sG49svq9whzQ6XfeQz3sRQFV4dfLp3fjRiQn86MREiO6wzYHfvvkJDu4aD5llQDfSm+VsNIsihdexqBHeJ0aREnhv7h+7xy3nYhvy3VlaCVHHNA6PO+WAFKyx2Qp3udFDfNZLzeffvJJg3vjeHb9/7J7Cv7mge26bInrRVqkMQw+If+uH1xXorh2fd/PgK28V8Jprp7GIQOUPKDsCOK/+GkIJP3vfUlGY+qyetxWmqVBrSUQt7+M50JixqePY7DTyrxTOKR4pnqQMtNwbNaCqAaeqBIgaMjWiV2myR1N0HuRFStstWL6bS+9Voe0lmjOWPaT7SjrAd7y76d0Z4gKzUWO0wxrEvXkRL27PLgRDni03k1q3xROP9vK77cyt/kJwTmrOse+qgm4jlI9OPBSNlD59sVviig3Rq5qFe3SRczz22tVCBKPK1Hbv7djaKN6CBq8sLi27c/Eynilz0Yn6zBMP4ofHvpbEecsftRF0VXZFLBpT5+vNPca3VK7QM7ROYL0DQJyWVN1TOx+lN3TU79u6IURbf/sHP8FnC82QqaJz5jyYQQAgmlFlM5c0++PQ3s1RmdjuP2n9r//V4SD/AojirUd/CZRtqko9bR8fyY4+B8pyCDNWUiX5YjRA+Yw3/1j5mse3fQn9NeDxbV8qja9zVEd37O6mdK+YgT8VnW71pBjvOLy/G/XvZfPpmJwjkC7/FgPViwDguU4J5XavljaOLZhgJTtve6eqSjGmZFk9Ly/rzwNbQpM04eXXPwiVCPZt3ZAs2bhaW4SCR7tIH06dbztRWaqc58YSuM+fuRroPPFa+ZnSQWbbp/gKgbjE7OecMuVAl+c8f6acmc1xWT6Juht5kQY3VfFVGqqBts2GGdYjkjlgn1cdNidLIKVnWVqi90ErOgBtHeJOp4pC6juaTXnpxnTSATE8OICjE9uS9/bSjekSL1JQW0SKj8fWas+H9gxmkug3FDzbQOocUn9TmqL7aHVnlVlSwG8BZRyMz6NV+Nejs96+6hlqGXuPzmkWEdCWv198qtirRh2JXkZ0L3aDNaiGNUfMGnxu4CkYVA5srUFLVGKEyY4ZIwAsbVMFMeNcygjjgTJ7q4hphI81Ntt19upoOn3xJs5e7gqHXimKFKgAkVIAlCnFzsYzNLNkzSOb74tmmFig4nDpxnThLLi2l57qltRI1ZaNrVdTMWPlojzB3joHqcCONrrRfQvNlSCss5ngzHwzCOOMCr9ya3bVzEtxQhUDGrXem5oNBsWBvlpIye8FVJnq1TBvzyuFiyoQXvjwo4Iyrg4DoBhtFTNqpvDKggq4l25MY2pmHo16f4gWefGpHXj/xd8pOBOsgmUVJqvE652yBiLP+E9j+rtSj9rOV2mBGlFyFDelwTQ25/YWUFoYc3ZYSDnVqwxtVDqs8lFFkz06pMZJW2JLFUpv72I8goZPKuXWOMm7eeXWbNLIRCBtatT7cO9Q0RjGUgnrBrpRtatxmlkB2uLMvUNlpVgNg7E5rxvoC+9587JOZw9XYw5ZRq+2kHYyaQk+G12ne8AyRHOLS6FM1myHRttSKIBPUziP0UY98CKl7RZy5QgLsZ4xKYdvbj1u63jz5uuVlYwppXYe1glKOUXrrduzo/FejQApmq5KpNJapb2M7uX4Lz61I8qXUrQ81j/HAkuzqYOG+EhHuXXY2DWpAuw5FLVRL8dRmTo1R+vAt3Dy6d0hAAGoufeZ5Xb5r56FhzOxc9NnbVndHAOK10dC55uikXaPVK7Q/oCeMx9AKdCAtJC0xM7bM3h4OKi6xNs3PwnltbQEGIFz1r6aOgbXoPTUlt1SYzxxLDY3u/+nzl/HOenv9MwTD0b5vRecwT1ZP7wu9OBJ9Uz8eO6OWyZZ9zg1/2989ctugFbsfnu9erwAICvP6RiWHlc5WVMZmZ4DlLSOhkrKppRzrPPAc16zlI/2+7Nnx333HFGWzuXK4DFgLyRd50JzJThSWkDB2J8KQgHaWQYbRxqhUoDVTdRpb50JKpfQke6VpVawuA6gYITV3iJekFnKuNmLLJEybN+aKZYV5O+Y6UsTcA3dqhGe/YX3HUCSr+jekMeffHp3KLfu0QwPvIBWjsugHzqGeRdV50+VxCMNJR7V0DW4Kz21ZfisgbuqvxHBO0tLS3TPSbsP7hoPuEV65AUyKl4R9KxTNpwUkKawn6QFtUWk5IHUWhVYbq7tfKmFsWM8Rm0D9g7EaL09C0+O9vSYlCNR6av2H84tycleiwvNlRDEV0Vn7e/3bd2AGrq9DO33eD4sq8tAzxivsSXfAER1kTVYHWSXJluDNegVyMw2jTbcpuap1OJeQZX41Bj2uwoUcNVYGkufJ0EjxNbCPdC52TI1Oi7/n8TNRjDoN6vKEcX26dDebvor4DdutH17vH21v1djwqMn3iyV2oidDVOAz12eKpSosWtLRXXo320pgVgUCpVYrkPLfCnj09I2J5/e3Vlb20hJRWHyyhTWDfSHKEHrFGRJr6o1VK3FlmC7NdMt1/X9Z38LW56dBNDuKxFLMfe+xfIUxK+cOVrIwUVPmKDwq9+1P9v58m8ebubsMXFuvrkcHEJaqsreSR1TQe8z+wNQqfb2I7amgb4aVlqtUg16+z7T2ImjOftt72Gu4VbPiiWEWLom9X6MxlYZ2liW8d6hAWx5djKU/lMjHPeFoI4mD2+eP3O1oFQSUntn58nxbFkPC/y7li7kPLxyNEqL7X7ZBrMeHfRKAHng4TOAQKP7a+2ItxjPsvhPA83Y8GA2fdA56Fjff/a3QmkZwM9mtfc5dnYxPqW0XA2d1vhFJ9Di0nJJ0fIM0V65Ro6jckQvNPTk07uDgVRLS9gSLcqbAGRFmKb4MMcbbdTdrEPLL2NOE/JQNZDNN4vlWqr2g+dIQwPQlb+YETI1M1/oeWYNmjEabnmifU7vANelEcWp5+y6FB89WSZWPsWjPxM7x0vlkSjDsVSnztHOxdIue6cGB/ow31wuRFMqsNzuoDiGYzjj8V+vbOmp8+2o17FO/z/dVwuH93fL4qhM6q3Vc2wpvfHAw+O3b35SMLTxWxZflJbotzgmy6Z4Zee4Ni37cunGNG7Pzgc+sm6gP+gKBM/YTzmEtIL7DrQd7l75Wp1/ChSXj09ew3xzJZTMjJUutHt/aG+3jBjvb3OlFeQKfc7Scq+0p87f7m3qXnI8fledwHonU7TK4qt9NqXjxfY1l08orWMWiZ5BSk6xOMm7a0t0nb7YzUq1JRkVDr7yFm7NzKPeX4uWvc5ZD/m/9nMa6mQ42LJHCrYUmF0nz9+7t3SULHSCPs5dLpZVevTEm5hvrmBTRcY5UNZJXn79g2CEtd/V8oO99BhUGpSrRxKUPiyttEI5yVPnr2N6bjH0QNu3dUMo5Tx3pxyIo/PxcC1nXrnyEctmcf5Va1tcWg7BQ1pGLjYnz5nHkqJcp9JxoFya2PKlqtLFthQ90C1JO3llqhTcRnmQoPOn/M65AsWynN/+wU9CmftvfPXL0fLIWt44xjvvHRoINg9PH5lbXAoOPCCehWT3NnY2GpT3wpM7ovjivR+769Rd1UZVZYPU+XpjKyhek77G+GpsnGeeeDC61lw7nKc78r/TF7uZlLQ5AC088K1J9NW6k+W+Uu/XuSgOA1i1rrMGXVjLiFmDXxioF9Z6gO3/H9rre1urQD3QMQEgN7rRKvMUEGPNBxlFt2/rhkJUDddC4q4e9EN7NxcivGLe5JTREihG5q4GdP02Ap1/r4oYBxCNRMyNugeAoY7wPVTvC175584U06fJGFJlRNTTn4qo1chZNfrYjAX+bnFpxU2nZaYOo68e3/al4IAZ6QjhfI4lvarOlcDnzkljRKAYzWhrKwPdlPxGvb/wnVTUAg0i9tupOZ6+mG6yHAPFK+7Pi0/tKOGQCkapu2Hxzps/187Scc+fuYqBvq7Qsbi0XDh7b236HRXs+I2Z+WYYhw5SC95cuQfq3Kg6q16iD7VRphcdk4LYc1Xv07HLfzX6kO/xb321rhGZUVUz80ulCLdYKTHS6M8W2sKlxSHSFjWgV4GXDcg6zRpdy6hklqLk3ZxbXA49qYhP3tmn9tPyDcVJ0jkvm8OC4lIq0l6jXu23gWJkbg7+eHiaG0HPDBYaMlI4r/w/lsVj95g/2xKRxKX55kqIEiPQAM1/T1+8iY/n7hRoO39P5586mD1I0VDOhfOwJVq4BjYljpWpsd+LyUgcb9/WDcGpzDlx/z1807uiTZftOeTQLCtXeM3KCUMdg76NVLSZZQpVtNPKbYrj+q4axtYPr8uWc+xcuLZPF5qFMfTbqb3X51Su8dbHcoPar0mjs/dt3ZDMZrW0Th3gNuLT22elv3y2Fxnx0N5iFmPqLG1kZw7P1L1MNeeNlX3ysvL4Ny+jwq5Ns8LoZAFqGB9tBD7ozZflMlnGbGa+GWgEJRzN3PfWnVumk3Th8W0bsWm0gaMT21ydyToRVXYmHgz01QpGKsrY3hyszhQ7N4Uq3PIc60Axc6JKxrU8nfSLZXyqMmK9MSxwPlrWyuKcvRuxMs16ZzUj0SvRdep8twTPj6d/Hp0/s0Gby63CGEdefQdbnp3Eb/xP/9GN2FdQeYP8tb/Wlf9sRpfui2aqe9HosTun+EEZcaCv5pY2rZKTAYTyXscnr5V4N4Fnbcv3VoG9oylaH6N1vEPNlRb6asCP/vNcyOI4OrEt6GEaXLXUsap7cmMM11Jyn8oTXknZI6++g6+88D089Px3CgEY2l/KwyHSz8GBfpfOxfZEaYDlbylZwO5rKrte4fTFm8FxYulObnCcgpXT7Ty1zH2MhtqS2TH6TScMv8v1UJ6fmW9ipOPUTWUhcc9o64vhsb3zXuY+EM8YfPTEmzj4ylt44FuTWD+8LsjK1P+rbIGKkym5SkHxmvPXOxvTwyzE1hrjFfb3h/e3S9HWUOZ/tPX01xBsDrdmFrDcApZWWqVqMUDcqWbXnEvP1qAMa46YNfiFgTWK5ZQYy2Hk3vNe+rQ1wHqptgqqONE4n0pDVCVZU3RtOp8VJjxF2xqNY2muVmBYLRzauxkHd5UFXM5BDTbWAZLDmGIM3pYJOn3xJgYH+jHaqIdeCIRzl6eipZ5SyvWQ0yyeQMYBINTLteVMNLKA5dZsOq06CRQP6AhhLVVbEiB2rlbp41pjDT+BrjKhhn8KxqxTTUbsCSwEFej2bd0Q7c9gnWLWSFYlYFhIOV9TeFalIMWMaDRQtNAWQmgHGBzoLxl5Umvj+ECx/MDgQH/PwjRxCeg6JGI0oUp41L3TskV0avDsq5R/nRufU7pj8dqmx1M5f/fWbMHwrsZEOn9XWggKK/F50+hQoWRNStmpMiRZA7rdHw947y98+FHBoKzlF05f7JYAiZ33QjNuXIrtM+dm+wMoTgJp4VjBMyBzPof2dksfbB8v9tzScksKi0vLUYeHh6deDW0Ahbr/1vFioSp4gvw/VQ4uJYvod2L83vLdWP1y69ROQYrOWD5ha95zDcTrVmdOFrdzAhmU33vOD2vw1/f0DhSMOJ0Gqyyr4WXZpOrl2/MgHtFhcHRiW0mepKFBS794OGl7PXngKbdemclU78MqnktYaaEgK+m3PUe6N0fKNTUg2k/DcxIAbR7mlfixfMRmtHvGRsA3gisd5rhvXPspllttOc8zAlnINY4xkpv/evhLo99XXvheKYhL10reQ3p17nKxREsKT/g3LxMlBZ7TOGaUIf6x5BBpBA2rqZLN3PMqRxFQLp9mzyFWgtLyZ+LBQH8N9w7VRQbrK5yR7RXoyc2nL8ZLaFYZNj25gPtKGUz7MeX0HVVeRB1jeu5OKAPm0eRcuPDhR4WsCMu/cmQ6vbO2zK89Ny0bSCeZtwfMBt25aaQwxuSVqULGkyd/6NypI98/dk9JHtFeVHbfyA9UbqYswDV7xk/Fj8e3bUR/DRjo7yvJR7F+ZBYWOuW9FporlTJ6rvytz6v8y8x76mi5ziO9i6QX5y5PlegqjbiUNTy5MTZ/G4Rl36czUMvx2nObb3Z7oFgemjK8s8G5gjof7V4pDUjRi17OK4Un9v6s1umn81IHfooXxb6hwTPWxqJ7x15bB3d1bQ08t8Wl5VAKi3xP9yMVbKQ9CxW0tCDpFoPtFDyHOudFHL9yaxa3Z+eDTNqCXxZNccvS8hxHA+VVZp/GAkdSck5qrTHwcI72SIvPyq+pd9X7auG+W77uOdU0WMHy/zVYHaw5YtbgvxjkCKAes4gZEPR5T5mwBtjcOVHovfDhR1GDlJ2revDp+Dk++b5rUI4plPpsLNr5jWu3O3XXb5fWoMpljpCfanBvDSLKUHIYk1XAvKgejaYg42EDwBraqem23IjNNKLR4cir72B2volGvb9kpLHGGEbiUYn1xtd1P/PEgyVDsGfUWD+8LtTFt4YNddbYc+W5q9LHuRydeCgaqaqp1SlFowpUEbrw4UfRZnR2zamo+Rh4d9mLkPfwjM8D6T42sXM8sGs84Nb9Y/cUmjGqAs765Q0pLaXz1si20UY9pMSrUqjzrVLArVHLGrK8b6eEHkbnPXfmalCoFVZjCLD4ZO+vKtuEoU5WFlB2HEzsHC/tsUYCH9g1jjtLy5WNJoF4BBHg9y1KObqAcrZBvRO9qw7rFB3kNw84BmgLVlGxSgQNfnrfqiLOFKxRzzY+1lr8aoBSI5j2OAAQdXiosx3oZjVYOklaMzPfxHNnruLSjemCQmkdFCmDiPL/VA+O2D20QRM6j5QhmkClTvd7tNE2MNq/2W97NNTjE/z+C0/uKPFqa2i1uK3/72V6sQF8iq5bgz/fOz55rZD5o+8S9194cke0JJKl+dZ5B3SbPxOPtPyHPUMaa4FyQ3Xi5OH9DxSMULlgHXmUB4h3c4tLJYNr1XgMhKGRw3MmX/jwo0J2YAy4dyONetQZ6TkJbE8Dy4O7e9c2YtDAZuX0qruijkyOOy+BEdYIlLN/sSAORnI3l1sFeQHoRuCq49YGcdl5qJNvSEonWf4Ry8qzctHpizfx0PPfwZZny85Ay+uq+D31jp2bRsJ9UyP7ob2bCz2SPMdsTmalpQsxWczSanX0UJ5h5kMsilp5K4Dovfcc2TrfGC6prhDr2UO5hWAj2D0ZSnkRafJ8c7mEXzlyMr9BfAVQohExOS72+9SdtXjGAJ6x4XVuuUK++42vfhk/OtHu2aljcA/qfbVgBI8FeAHdPadO9t7UbEne84yZnlOQNFl7N/F5z7Fn+5t6AQOpLN7TF29iSJwXqTu7Gtnb0gTi4lC9r5TJZIME7Ri8i/VORYAWir13Du3djPdf/DpunOj2kKrKLtJ1aRAWwcodM6bhvD03Goa9QEAv87d7X1uBx1j9ZHhwoLRXnkG5F0eI/X6VkZ1QAwqZXED53GIy6mrg0o3pqN7sVdVg5uPxyWvBoXn2m4+VnKHcs8GB/qiMovqhDcJVW0yVrcHSPmam0rmiDnXOi3IV0H53abnl8jBvX61dkcHZKRlP5QSrt+QEV+ieLLfaenxVwBD32GYAx3RTxTMAIUtufLS6/KKOkdOTbw3yYc0Rswb/xSCHSXlgHQGeU8Nr5K0G2Jgx22OiLDGk6fIe0VYipE4NKphkTDGvvx0jx8nByJv5ZrcRl40o0Qiu1TBxTxGlEmQbf8fARvHY/6ezam5xqZRtcnRiG8ZHG/iNL/6VZKknTf9nBBb3XktIKKOyRpSUNz/FcFS41OjEqj21UTlAMTIkNYcUpJxngG+Q9t4HEDVoWkOObbKcA95dBsqGeo20ABBVhnLwW2nE2PAgWmgrenQAUlClA+PdW7Ol2vWe8MS/MYLrwocfJUu0xKK5rKITa/aquOOtm79j802g67BVo0OMDqcM1taIH8MXzvWlp3YUnIgWh08+vRs3Tkzg/Rd/x91jRh6fuxxvNMk9pbKQusMASgqENX5TUeA7FO6bnZraKqimIoFSPMmCGpO07Jf2HSFdjDkJLMQMLKoovPz6B/jKC9/D9NxiKRMLQME4q3yFig9Q5meewqS4q0ZKvUe20avnDNC9UFxgPwUAUXpknb8aGJHCLcULG5GtWTx6L3lONH6m5u/RUI/OpOaohlaWFVNFU/9fHfd6VkCR/saCTzge0H6PdIZ11XU9ivue49OLUiWuMhBG6WSM/nhGOdtQXXGy6Cguqj9VjnM1uNNAcPLp3dGm1h7P1TEpM5795mNZ5U1imS4KDAbw5AwPrENGv6/RsrdmFgB0y9haucQaTmIOX5aD3TTaKPSDUDmqSp5Q57UnE8TKs6qctXGkm3WpjgLPYHX6YrdfxuPbNuKlp/ygHCt/p7L4GexiaV+ujnTwlbfw689O4kf/eS7gUK9yYq/GR6ULKWeCNV7zXrIXH9Cln0A3CIqgvNXipaXJMT6RMmxSV7iztJwMsNM52swZ75yU74903quhnd2xmvKFmvlgg8FOX2yXwPTOIIZDqTtrQfWbnGAWC3u2jGF8tIFjB7cHR7oN8LLyJXUy0utjr10tGD4VX72sO93/4cGBQg8xvgt0ZRrinZ53rMwxgBJN02fmmysYH21H8MecrDzTHCdc6h3N5MiVFyyPPHZwe/jb2ctTBYOvddbGMh/tHqjzUks/e7RfA2f13H547Gv44heGAp5oaT72Ma0Bbqk+rUjg6ZaeXUON0p4DKwdyjexaitvjtzGHDs//+OS1qM7nOWM16CB2V60eo46ThWY5w8jKrxqQ2Kj3JWUU9urU+caC6FSGAFCSj5ipsrTcKtCmg6+8hefOtEvG0nl0ULLJbcBXTF7j2hhERJnU7qUNpIuVxMx1XtCJ3S7NuBIypXsBWynFOvViVWYUcuWQu3USrkEb1hwxa3BX0MtFzBH8PaVdo0FiY1iCqO+pUcDO1zLR0UY9EECgFZhBVV17hUannFSj3l8gyBc+/Ci5VynjHoHpwtwrfU8jsWL7GYNUdJSOZVO+ewEdl1lKSyutkoJBRvFuJ3LVK5thHVwarf7y6x+EzJRzl7tp6jaqbc+WsWjmTGp/AD/jRKP8bfQh126jcoByBEdu2blURI89wyojhyojnkHT3kNvT6qcPXYeKQVIn+PdW1xajgqvNtpG56fKHIUUKsfaH4OC51C9r9DzqRfwlMsqemfvVOwsFXe8dfN3jHar99dCI9J5yQrMEb6swGajffTstPSiFTh7UW70Xe0b5Rn2cvbfW5tX2sNzIAAo4HGvwnDuHPUMFpdWwh6c/eZjJYOftx4vSEAzMi2oQcL2Q9Ez5TdZT57QFI8x+02Q7wJdhckaNW3Jq0N7uxkB28dHCvNPOXkt7moj39g+qxPFBjuk7qjihY0a1zl6ND1Gb6tw2XtP5+jJOXRw0dDJ7NKUEUUDEmw0ekzRpJJOGYON12P03tt/Oy9v7zyw9Mf28YjxPl0n0MKtmfnQl8qWQrVrj/2/VcaVp6QM+t6YqXMHugbrGlCK/LcGMzUw0nCgpfNyjIDf/sFPglFOo2Upm3gNwIFykAY689Vmy9bwdnRiW8AlL6s3Buq89mQCOrgYCMC/8T4DwO3ZhRBsQf3Aw0XyQTUi27NS+ZuOAaCbYWTlosP7Hwg8evt41+Fu9ykFjDy/IpHnOmfdx5RDORdieLu4tFwyPPNZBrRZoxSj2mN9X+hcv3Rj2t1rpcmWT3h3Qo2ZL7/+gdtzREG/GZNpq85Jja4fz90p6Ao5crKCOrqVTmvslgae5eKQ8iIb6a2lpYEyHav6hpVPveAKz6jLXlY1IGQEaxAd58H5nbs85VaAsLq0Go2trOcF69hx9m3dUOr7E5MvSCt6ybjsxY7iGYyrzoa/U96kjnDute2J0QsutZ3rbdtHrBQ75Uxb9tcbS7/JzFCgfR9Iq1WetwEYKsvyzGL8+OXXP8juBWJB8cfqyN5zXoCLxSNvD7xSxzp/S/O1X0lM5uR5MDCLNpIa4AYwe/OiLOz16OGZsDwtUMxWVvnU6hIx/gB0M0EP7Bov6JgeX4xVfrFytZ6dDSYA2g4961RXWeTU+eur6vVjgWM06n1JPsU5MviGzkkGXHEOtuRaqgoMwXMoeTQq1764BmlYc8SswV2BNqmrgpyIN0vo7UWvchJU1aOtGk9rMw4O9AdmAOTX5acCeHTioYIyCxTLP1jCVsWoT1+8iT1bxqKlQ04+vbtQhsDbzxjkEFQ14nnMP+V5t6A1763RnbY+CapxHWjc0wsffoS3b35SYPaEAekIykiiXNxI/c3bVxvl7xk+YoLM95/9raCwvXHtdjQl1Tp3UkJm6l0LHu55Sq2eec7d7PWbqfcGB/pdpSMWQcf5nRNlTksx0UC3fXwENbSjuGig055PQNzwnWosbtfnKeD2Tul7l25MlxoDU6BmiRgv+4BK0BfvHXIdtykjccxgDfhZYJ7xVs8wFrVZBWwgenRiW8kIq2D3NDaXw/sfCMq9nYsaa/VeXroxHX62wrBXbiZXkbY40lWIu6YV4i/LOcQMDTwrXbNt7G7XmhPFpkrSyad3hwydeoeeanNmr6SiNWp6hhgqSB/P3Sk8axtie/Mn7qaMatwXdeTnBDvot2KZCrkOV4/He71McsGTc7R3kC0H6vXi4bx0L6ucJjR699eAhztlkGwJUG+uGoVrDdcxg0OO8dP28cgpVcmsjpUWok4+3S87N2tw53PKU2IGfW8NMbA83kZ3WyMBz9pzJrdQpneLS8tRhVrLtuldYXSpGjN4Tw++8lbJiGFLyOTI/jFHkSf7UarzZILYN/U+q0ElhvvcF2YlxMrzqPxNx4AaBb21r7TatJ4Nlb05p3iK9uaw+2SDxhQ3lBb2Ep1vjVZAt8SYze6hnADUSg6I/lq8FB3nr0FU3h6kSpEqL4zdtZhhzoMY3qaM9/a9lKM4BSmHjfLTZ554sBCQkBsAo7xIZWSOn+q9V7UGff+5M1fx7R/8JCv7k2Mzo4gwM98sOUiIS55cmDo3ygx69z19RschjVd52ONvari0GZdAuySy/ktIyZMWD3L5nf7dk8soYysNsT0xcnU04tzgQF+01x/ByjB2TOUl3GM6ber9teBcsfK8DcCo0k+Aov6V2wsktg+9VGzQOXl45DmDD+wqO1Ss/vjcmasFZyxLVVr9SeelWTCDA/3Bgexl9Kd0fBvoyufV8abluz1bn+KFp+PQsas0XPcyBCGq8SgC1u6k949jAigEK1mnutLhRzbf55bx60U31DM9OrEt2htL50i84d3mnD08VrwAfN4FdMvev3Htdghm9ex5sQygNegN1hwxa3BXoE3qPg+whN4qxzGwhp6YkOc1kyOoE4DChEZWpLInrFCiQjgbeLERodf7IQWWaceiXK0Q4+1nav+qDARqxNs02u6hQOZlHS+eIVdBm7HFYKXVZYKe0b8b6VqsI02FDwCG1w2EviDMNLHRQVaoSTEwGz0BlGvVczw2D1QlIif6aiGRkmr3oZeIhF6jFzylVs88B2esAAX4mSqpb1sDoo4BtKPwbXaLznmoXq4fq0rGx3N3QmNR3itr4E0ZvrVkR2pPUgqSGm64Ni3PxbUCbXxiDWa9X9bIPre4hD1bxvDSUzuiNDSHbhG0tJFCbM2nzscbl1cJpx4f8O6shRzl3jNEemejEY3M/ohB1d3yDPfd6MSu0uBFuFdF0Ns1H9g1Hnpspfb3h8e+hrHhweAUSEVC0mA4vG6g4MhQo6iu0xo1+T2vtJrlyaksFwspo1rKycm9rsp0jRlGUw5XBVXw7N7ovFJg6bA60LQ8jy1fY50+/E6qvw/Lh9CIp3IVS3/8ePrnALq1xz0ZSPffu4upMg1Vxs8qOhujLTlKukY9p85Vn7OyQaqmfpUBFyjTEltqwhoJeNZ69l5WbbcfTC0aIEJH61C9P2nQPX3xZjCYX7k1W5JJ6DiqMsjlPmdlPxtwlAvK820ZGy/zmvvy4lM7ei7DmlpPqlyxPhcz7P36Xx3GaKOOH0//PJwNn7eZKJ6sfOy1q25zawsqZ6lhkzqI9nSkrEQ5QSsKUL6w9EnxS/EaaBuyPXnX6j8xp7hnzPTwpRdDmZVde6mU0Ou3Uu9afmrl1VxZvysjF01B9qxS+q43RwDB2Af4mVspoy51ZQW7lnuH2nwwpy+cgjrxPd6qcrLl2RrAoXILnyMNqaHtJLbfZ2nHWBnr2Y6umCtf9Are/dDs61S5brunOXcTKDuZrK1GwY7J+bIk7spKK8gN1lnnzTOXH5OOrbSKAVg5oHTL4k/sHuo5VMkyxE3PoaLzJ9AZGwt+0Xm9/PoHhaCJfVs3ZAUCeOtfaK64Tjh1sMScTZyj4oWV96jjawCTPd9jB7dj02ijUHovB8gL+W/M7uc51TUz3sN/awOx+GnpqrXhVeEQ9UjSKDvnWAYdx33+zNVChn3bqdZtgXBrZj7Ietae93lkAK3BmiNmDe4SNLOhF6gS5CjoAukURYISRKBNrKfnFoMBhPCu00zOwmijHgTcmBAU86B7BjPOn40Ibe8HT9BVyGXadg69CP29CHt8VusmW8eLZ8glWKeNjf5pSOPDHMOLbZKuism+rRtCXxCWhgOKjaQZRZK7B140i/0ZQKHJZczw5UVfPdyJUOrrq5WYtd0H+/8pJ0eO8crWZGaJrks3pktnnrNfKkBZ51FO7VrO1ZYFsIqK7fujgpz2KvHmbI2bR159Bxc+/Aj3DtWDozBmBPSMRKtRmjwBaaiTmjxkat0DbSNFTGHi/8/MN3F88lrUAOvdQ6aGX7oxXdpzgufI8EAFa08478WAwf2x0ea97D8FUtsok3th8V+NE/Z5r9xMyinpKdZcC5vEcl5aQkbxuWpvuOaTT+/G+GijssG3zhtANBLyyKvvYGpmPpRctFHmahRVg35OtsSp89eDUeLKrVkcfOUtrBsoGvZWa7xKOTltyYGcCDbl+VXn7UUJ8n32kvLqhHtgvzU8OBDKQNjyPDEFHEDBSLt+eF3UaWJx1Ub5cyxLv/U9q1DmGvJyIeYg03nYcpVWSffeSwWPKN+K3ZWYMSo2Vg7vUwe4Gh607Ip1pGsmNsuk8jy85tt89+s7fg39NeDxbV8q8Qc7fwIbxVuDAHmTzXCLGTdTmVDWwJWi8zm4ZWVq29zb7otn9E9B1fnGmhTH5mjH1X401mipvRI4XxoX923dIL2hus2te1mHNeZqc28FrShg+TOzfV84916JprIM3uPbNgYnJOlVLGupSpY4tLedrXDstas4+MpbSYOyBc8BT9oHVFdKSOkIsW95Ecipd21gWxV/0n1py8jbSnfPk0lJa2J0werF9HlvGh1KzsPuFXVl6oKNen/pzGfmmxgbHgw6f86ZWsOtxWWLryrPkNZSRqQN4b2p2YIxftNoAyONupv1ylKEtiShzRZLRZqnaNDdykqpvoYxJ4mni3rzoJPJOhEV7LqtTqz7FuMX9r6lAlfVsbnQQ0k5fZdzJh2NleT09jtlY8rZc6XvzKxf5zgA1el95NV3wh0AUAiW02CEXpy5xF27z7xvMYepnWMsWMsLJqg6316AMhH/jZ1JSu705BT+voauE4k08vkzV0MZ6ZQdsQqHqEfGdK7YWvTcNFiLOqkNV/LsDbl8Zg3S8Et1xFy4cAEHDhzA+Pg4arUazpw5U/h7q9XC7//+7+PXfu3X0Gg08Nu//dv4T//pPxWemZ6exu/+7u/iC1/4AkZHR/EP/sE/wM9+9rPCM1euXMHf/tt/G0NDQ/jyl7+MP/zDP/xFL+0vDVSleXsM+fTF6kaDrA1L5bGXy07izCbaKgwxOp7/qvGRBv1eouwoXFjvNNerRixrVIsJuhZUiaBB3GM6saiHXGMnQfck5iij04T7ZaM2VahN1S/1jNljw4N48akdhZRMj5l4QjK/QU/9hQ8/Co1WF5eKNVY1K6oq+lrnqAyXEcnt1OlWYVwtW2VxN4XTjD5vLrdKypvdB/v/KSdHjsBn37NlJ7R5or3XXkkhdYpRuObaU0ZmezesgcyLTLGKhEbn2SjkI6++gy3PTuKh578LAIXsANt0NzY3AMloq5gByouEUeDaHt+2ERtHGnh825fCWrn/VWWBbEQLUDYYePeQ62Y2DtA9N2uItcKjNXxSsKaBvsopmGtAS5UPSo3lGeZ1LxT/j712Fb/xxXtDfwT7fNU99OZtx7BRV1q/nEJ1qr9ZCnKd/ARGoE7sHC+VxDt3ud3ceKG5HOZB/nDwlbeC46jKYW7nx3NU5frKrVnMN1cw0qgHmSLFx3LWpmdj+02lGvRWGfZyHE0s9fDMEw+GO/npQhMPfGsyyctj37IGn9Q+q/FdnZDLrbbxKOY00bPxHPqUk7RE18FX3sLUTLv/ytTMfFAwPcNdSv7LBc84qsYKr6xOjFdaI0esbJ3lW95zveC/3k+Vt2K4NdvhR2p4iJWXsmvRtdIY7xngbbQ4eQAN4cSH9cPr0F8DDu4axze++mUA3Ya2ahA4fbGbOTN5Zapw9nRIVtFQz0BK4N79xv/0H7Hl2clAj3q9v58tNAvl32LQi3EqVZc/VvKQa7JZKPw95Q3tR2NlIOVvlq8xwEQz3HgusXVU8T4Lil8xPkRnJnuOLS4th3E1W4n4yKyrN67dLu0TnTPUkWLBaPwmx/KMpR4/sbK/0oCcfo5WX7CGVL37/Bblrxp8mVdlS88xUnVGFnqRX1IOawYeLC61g0BWgpXXmvXSe7WuU+Lq8W0bMT7aLvOtMpDqXTEe4J0pZVyWIFcDNY3qNQDTc3dK8qW9S0PSw8GT5azRGOjqdl5JQqUJ2qPH2k541l5fTLsPVgfh31dTFtXuTUoGVr5knUwW1PnIO2od1MzC5b/2e55jhP3jYnTY7hXLmGs58xjou6msgF7vod0T8tmYQ464MHllCvPNlU6Tdz8Ai2fCvrnMaNw02ij0C7J0NcYPlU/FsueswzS2D4oDniPak2WtI1XXmCNXxmxZue94soAXvHhobzd7a7ZT4hdAoXcrf+c52lQ29yDmnKu627TNEBaXVgp0TAMRD0pQtIL2c1uD1cMv1REzNzeHXbt24V/9q3/l/v0P//APcfLkSfyv/+v/ij/90z/F8PAwnnjiCSwsLIRnfvd3fxfvvfce3njjDfyf/+f/iQsXLuD3fu/3wt8//fRTfO1rX8PmzZvx9ttv41/8i3+Bf/JP/gn+9b/+17/w9a2Br7i8/PoH0ZI1Gi0GlPsRWPCIDplvva9WMsA/vu1LIeKP86PxkXUsNYsmZjy1woX1TnO9k1faRqw7S8vJaBPryLEGEAooLFPkEVm7T704sDzm6Sm+yuhsBKYVRvh9AAXBkY6K7eMjJeNdL9Fj+p6X/sxv05kwONCHel+7LMfBV94qZEXFGqJ531LDwIUPPwrMlzXoGeGdivxJ4TTxt6rMHhAvB5MTSe+tT9/TfZ/YOV4QCrw6+FpKS8GWtMqJvvL2Q7Or1BlinYbcE8UJ/f9jr10NxuX55nL4u2YhqCgeE2yqonG0xJJVlm2EoY6finTJFewZ2aMNtb1MIDXWKd6pEPb2zU+CYPzI5vsKWVEcRyN+LHhrBYpGpiOvvoPnzuQZ0HIyJFMCueLOoyfeDEZkOstpxGVUJns/9KJMxc5Ux/AU2VQ5kBwjoLe/qff4N83WtKXBiAv8V/mD9pSwa0o5IvUcfzz981AuyqsalTKUrTbYYKG54io3auy0tLOX8jbWMHNo7+ZwJ1daKOxvL+CNGwN1kALFhraWzlsjHs9GHfMa+ED6zeeu3JpFq7M2VTD//LMFHJ98v2BUPHU+XrKwCohTNpI+VQKpai+9wBigLPuR96X4Vsw4RPDup9Zo9/CYxt4W4Dant2NbI5nF6ZThSPdEZXE67ogP73bu/YUPPyo5N62DmzCxc7x09imjgf4tZlAjLWqutNACAj0Cyj0gYsCSxDWgsndUrkxdxadSfDzGu1Te0H40KdnSynUACka4VB8QxXmgmHGQCiqJ4ZfSGfJZlsJTmSKGg0A3sESjdylXUs5UB4HuJR3+lsd4jm41clvZX2VXymipfpmKCzZq2epa/BZ57Uij7hq6lUerw/TzqNVvz9Nz1KvcqM/zHAcH+gvyZKqkrP3eZwvtIMrhwYHg3LVBP2/f/KRwv1L30jrSLM4rjrApPI3ZqbvEHg4nn94ddUrYoJ+U0dR+y7ubetb2b57sYmU5q5NXyU3qKLR70wuk+jqpgyC3T0tKDrT94xRPLE/RsxheNxD+tQ5SC5ZGaaAcM01SpberwJPL7TrVIUqctM3kPWA1BaVnj2/bGJznesfUSRmbI3GBtjPbuy6HX1onp2Ydq4NdZVnVhwGUMtCBNJ/S+efq1Z5TnmuzDmr9nspwgwP9gS426l16CVQHC3lg3+nFGaU8enCgr7CfbDcxKkFxFnopI70GcfilOmK+/vWv46WXXsJ//V//16W/tVot/C//y/+C5557Dk8++SR27tyJ//1//98xNTUVMmfef/99fPe738W//bf/Fr/5m7+Jxx57DK+88gr+j//j/8DUVBsx/sN/+A+4c+cO/t2/+3fYvn07/t7f+3s4cuQI/uf/+X/+L7nUv5RgBQMSJjZeA9pOEiXyVKJzUveBeImnHx77Gr74haFggLeRJhyXUR4kiDYSPuUY8IQLS5xjqcgWSAApeJ67XMxMoOLilSmKKZs22iellFoGE8vkiIHHcLkmMiEKjnSAXLk16wqgnoBDweih57/jRo4Sp/ivCsJaHqnZCdG6cms2KDuNel+0IVrVeheXloMSz5IKVU6dKrAOHHuOCp4RLebkyDFK7NkyVjqLlzqN+1JCCIBStlkMUs24YxArk6WKLJVnaxgCEKLIqdwMSckDOmrZoPjk07tDyUUVTO23qxrVad1uFdIe2XxfyNJiM3Zv/F4cqRYYlfr4to1RPACKmUIAAt4dndhWoAHqZPOah8bqNdt18GxeOPteAQdUkKsyMsYctynwcJ/v0ohMZ7lX5q5XsM6nh57/DrY8241gjSkHnlGIa8vBh5QhSY0hpKfTc4shwppj23rzxAXWPFf+QKPWQKeMohc8wLONrWdxaSXQ5S8M1Qu4ZY1Aq9kTharoTBrFeRaEqsg0q/x4ih3v5KbRoRKfyKWJvQSleMYcjz94xiHKbarYe8+RjnFN/JfQXG4FpwzQjqDTUhW9ZB+fvtjNrmAkPdcUK4HkOdpoYAGQVL6Ja9o/yYtC9ub56Ik3cXzyWkkh9pzSNB5o9LtCUVkuNqdXWhyjhTZgQPlWiv6oQ4sZGIxU9xpR27kBRfl6z5axAq9M8Va7VzHHBoMACKONgYLukMMfuP9D9T7XCaeQa7Dh3FbDPzSrIOWMypHn7H23huGYgTQVOAIUz0ZLraT4s/6epbS+eO8ggGLzcouDSifJbxaXlktGZaBYPuX0xZuF/gfMQrA8RiFmAI9BzLAdCwyz+2x1La7dKxWmoDxa7wCdUikncBX9smddxdv0eduPQPsnxDI1LC6p017vAoBStoM1KNs5MXhISyzbYBfFEQJlc4825gaQeXuVa2hV2qvz4JoP7irLppbOn754s1Tm1erFVb13Vb8CUMDVFD6pQ8Jmccbeo4OA+pL+3eocKTnQ3jXFGctTVK+2vDQVHGFplP7sBbHk3D29E5zz9vFiNr63zhee3BFw0jaTJ/C7o426W01B7WKevuYZ9S2d535pj7Iqfsl9saWYNeuYcwPidJnnqhnoFk+UJmh5thR/5vwOvvIWHvjWJNYPryvdO/ZK1FKrAAr805aK7erZDyXpfK4MkUvbvfG98uGnzncDZrysT36P+sHd2rz+ssOvbI+YGzdu4Pbt2/jt3/7t8LuRkRH85m/+Jv7kT/4EAPAnf/InGB0dxd/8m38zPPPbv/3b6Ovrw5/+6Z+GZ/bt24d167pliJ544gl88MEH+OQT38i/uLiITz/9tPDfGuSBEgQrGJAQLnXzlUNEugKF75waqSlCxb8pIbHPv33zk2CE27d1QymBOsbgYwYb66FnKq1NRY4BHQm1WrH0CEvAkXBbASxHmU09Z4XaZ554EMODA6XnYlHBKWeBCgWPbL6vYKh/+fUPomU5dEzuN8vN2TUQp/ivGpvVqbGzU2u23l/D49s2hsimKoXdNl2mYsmU+6F6P85+87FSmT5rdPWibHKYqBqF9NkqZ4A3RhWeEKwQlVJ8AATcPDrxUGFsizO9RlHYSBOFw/sfCGnVVkijYUi/T+Xm6MQ23Dgxgfdf/J0Qqa7GWc3isQ5B7r916tpz5J3ds2UsGCwndo7j7ZufhCwtK/gq9GL0UTh98WZwnHiKucUlW6eW39Z7o062lJH5/rF7orh86cZ0UHqbK60CDpDeH9w17hrDYtFLsb2rUtyALj6rA1Xvdi8Kt52vVVK0xJ9n4K2iyVXf03dixnc1hpA3M/JU1+qVG7VNzIkbjORqrrRKjYutQ8c6hLrp7G2aXeucW8xQ4UV85u4LQQ0cvRhIcx1+9vsevQBqpf39PCLLPKe8Xau3N5Z3q9ymir1nRCJec03rhwex3GobxAk0nhL/c0pVeHup9GnT6FDhfntOolQWY1VGnY4JoGQISDnOeA6MKPTGVCM1jQcvPhUviRbrtRUb2/s97x15W46jQo2Pw4MDgV6ooTiVKaZR1Mdeu1ooLZXifd56PD4MAC8+1W0K/tnCUsGA6QWCWZzQKH6CNUDlyGgpp1YvoPPQEn7WGeU53avAOvpjGb1W5koZNwmec1ufUzpDmYNZ5O/emo3ur+oPrVZ3j9So7GUJ0phE4xfXkMomtDTTOqRi+2kdXNaZ5jnx+Xsa5jw9MoY7yqM1EGZxaRlfeeF7AW90zt5dj8kQvfDHlF5gDa0cU/mdlrxi+U5mAvEuLDRXSjJ37FuUv7TM652lrvxl74DiCOXYA7vG3az/zxNS8oxmoOs8OHf2AoqdA9Dlz+OjjVJEe8yxbfma5T1qF0jxDp4JDfN2XirTAQhl6IBuictcXdT+3T4bczLYLHyrV/QajGrPIZV17IEGu3HONhs/tU6gWxZdy66ThqWqN1Tp9lV9QXW/gFpB94yB0laeDf/1AoU856Odv6Xt1n5XA8Iecz8ARNfGc2Om7ZVbs6VAOQZNa6lV61C2pfsJVXQ+R4awPKqXzH3V5XQfSD8BPxiea/947k6yNcUa5MGvrCPm9u22wP6lL32p8PsvfelL4W+3b9/GF7/4xcLfBwYGMDY2VnjGG0O/YeEP/uAPMDIyEv778pe/fPcL+gsMMYMZiTNrjVPoOLBrPERLAD6RT5VPiBnlLHgGKOsoIYHktzQS3grQuYqVEkaNDgCqI1/pSGi1umU8rNHECp8p41QqUsTbK2sAs4b/1B5YAUtrthLevvkJjk48JEwbScMIxwQQGqp7az2wq11OaqjeXxCwXjj7XmH+Z7/5GDaNNtBcbhXKBPSifOg6acRfaC6XnC22zmnMCJQjpKlRSJ9N3ZPYGFV4omen+JpytgF5De6AYpZYjoFDI3+8samsUehRQSR2h4FuSbP7x+5xG4J687eRdjEFQ0ENlhQ02YQ05WhOpbanIh690o+WLisuLUjEegxiTjYC8VB7T3BOvAMsB1dDOXvMM/57c16ts0p5kd3Pb3z1y+Hb9gxT9NqeQcpZxPPWeuIASg5Ge99S0ZMx5+kLT+5wI6e1rjp/btT7Sr1AvHXFHKG28bcNHmBEmzrZyY8ZMegZoj0Djmc49HiOdQjZs4rhjBoFvIbhFv90zFhmQgqH9X3rtFpNhJs3R13r6YvVvfnsPKt4hjVWXOlku87ML+FgR5aSuJtShGcK0kbQWqEEnvYpsuupmrN+z9tzGucIj2y+L1kqlnt2oGM48LK7gG5/MRtx6M2DpaQ8g7lHczx8570DkHW+FmLGmlwavNzqlrsgr04ZU+3fPD7Mde+UZs587sKHHxWCpSxO8Fv7tm4IsmjsvsXwSeU9j+6sBrSfE6+OzQhYP7wOc4tLwbBkZe4cSNE0je7lGavspz0YARTkct0rlU/1PlPmqPfVCpn+nlNJZaSHO/3aYk3lvfXpGmJ0P5WVmlMuyY6tzr2UXLgafPFkEsWZmflmkLN0zt5558yhqn+Ct6+WF9uSbMrv1FkLtArfshms1qmsuGLXotlelr/aPSGOWKM9M2k0k9nK5SlZPHUXU9m32lcrdW6qn/Mc2OPPi9731q5/twEhaqit0n9iwDJ1pB02Q4UyneVP64fXRWXvXJ4TWydQLKFt/8agMToGgbxzVh6fyjr2YKATsTLQV+vZGUpgWfT3pmYLd7CKhsX2U+mK1YueE1lSHcpWJ4iBpa0xWsS5xap8kPdSntB7anV/OqyHOvyK5dRSwXCxlkE8I56blkVjQDX5d5WdZjXyfux8c2109rsp+mnnt1r8XAMffmUdMb9M+Na3voXZ2dnw309+8pNf9pR+pSGmxGtUhHqg92wZw/DgQPBwA7WScSAlcOUQARsdExNk3r75idskrypaMCWckjC2oVYgwFbQsQSOjqCHN41kNR2lIMPmgxZWozADvdeS1XcO7y+mtFJZU+Wbwl2qob2OSSb//otfD0Kh7t3Jp3djfLSB+eZyIaq4udKKRsL3wkT0vNWoSyMNFQV1tpw1Dc9jRqBe5sNGyXxWFfPcMhUppyVQTKmtKi9E6EWQ0IaVKcGAPwPpEjIERtHFDDYKek7vTbVLU40NrwvfiN1vCmbaUJ3vqIKhyrJ1vBzauxljw4PBMWPnqDWTe8lgsk5LGpSts5b7wzVag6EHKdzR8W05LwrbQFfRGGnUS9ljVZGZ1oFehWeqMFJJtRGA3h7au5jKVLDvxwztAPD+i1/HjRPFeuKkfdr7KMfAy/FTzikvclrrqr998xP88NjXMDY8WCijcOy1q6WSSqRbOk+C0u99WzeUeqyl9pi44kUo65xTyqz+TvE/1+BkFZEYL/XwP+V485ozpxxr1hG5GiNdFX8/db5bbuDThWZUscqVGayRBkAwiO/cNBIyL+rSlIEZwiznkLrHqqjTGMu+f2qc9e5o6m7EemvYLAgrI2qEYKpUbMxwoGeqRjgbeV3lcLVBLl5GrPcOaXROfyELNqAhF8hj6HinIf3Fp3YEg6Ea2nLx3p5vrAl26h2PLvK7lv/H8EnlCAAu/e/FuAIUsyx4c1gWkrj+3tQsZuabGGnUg56Uoy/Y7/A9ngHvwBvXbidpueoHNsPNOnLV4MX7TJnj2MHtIdOfcpUN/lLjVS/92nL1HSsrEPTOVPUOUrAO/RjurNaYxcj5s5enSkEbijPaXwbw9yPHORNz8HrrtkY9oMyLgWLgjRq7bXlDBpZY3uRlq1hZQIM2vEAfDywf1EwarklLTgHlagmxYJBc0HfUseydG1DWz5VGaLCCpbXeuOyPua5DbzwDLO9pVcZDLDgllqGiuv73n/2t0B/Fk717oa0xfZBZ/o1Olr861bWHaEpfqOLVqb22wOwM4myO09j+P+WS7eMjIeiGGUkeDcvdRwYrMLjLnomumTYRBpjEvmGdoIoLVfPSe2aDW2P3FOji5NGJbYHnA0UaZYPhWGoMQKi+AXTPlOemmaxa9efU+euVlUtWI+9bGpviUV5gm/2uRz+pC6V4whrcPfzKOmI2bmynKf70pz8t/P6nP/1p+NvGjRvx53/+54W/Ly0tYXp6uvCMN4Z+w8Lg4CC+8IUvFP5bgzjElPjTF7v1Vwc6kU9ap5NGFs+DnhK4PMHQRvSqUuIRDTVgq4IZq0Ebm59HiNQzrv1pgHK5FkvgWDv5P/35zwqpjSlmpqVochlr1XNcH5lqTumrKuJsFQO+EzOM6JhA2QAbM4RO7BwPhu+dm0ZKzB1AIXJI6xRbJTAGLNWjjjyWwvCAqfTeWnOYGgV7TX/luEyZvTUzj+c6dbpVqEytxeKBjVKx+Jpr+EmBJ3x7gkHumCmnaEoJpgHZ6wUSOxOrGCrQ8Hju8lRQllkKgdGtVF44r/XD60KjeDueNlUkxAQqNRrcP3ZPiOa6dGM6pKVbA3Mq0qgXUKXXjqURRcODA9Fz8s7aO4NUzWLrfLcluizuerhhvxmLpPTeTxnaPfCcQ9bA+9yZrpHMjp+iG7E7Zuestai5X7ak0rd/8JOSsqO0VOvA63PWAWj3OIV3Hp3x1qv8wRqagWKqvafkWUVkNf0BUo4w66zNeb/qb56zOhalae8EYaWFqGJVNX4Mjrz6Dt69NYtGvR+//leHg5I70F/DS0/tKBiOPCXZ2wM6AJndsNJqBQc2wbujn4eiaM9AIwRTpWJzx7O/Z9CRpe0pHhkrj6nZb+pktKU4PBrlnXnOc957PAeWuFNDujrQKK/8+acL0UxR/a51Cnn0wutlFitbYt/PpbUqR9DoTtqX4/xNwaG9m/Fi597QmGmbM5PWEQ9yGl0r8AzOXp7CkVffCXeWJYCV5nt6HveYuGujkIGurAx0DUaW9qtcBaAk1/eaxdYLHN7fLVmjckUsY6EKrBHL4k4qwChnPdqfSe+PluqrCqzhfdNIckKK98ScVvZvpGF5Tt+u0fL+sXvwwLcmcfCVt0qZHoDPzwk2GMJbg6fvpAyMthePBpCk9geAG0iq368K9gLijmV7/3QuOmd+T513KXqtAY2eDpa6p7E5es7ZnKCPVH9dljY8Pvm++20FKwPx2zbLX436esdS+oL9XS7eeXMEUAie856vcvxQLvl47k64VV7vNjtezO7hBSsce+0qto+PhCoSNjjUyiS5ziniCQNVUrilfWAtb7D3NMabrOMPQOGuaFYY5dejE9tKc7EVOB498SYe3jRSqLKhpc553rrfKUdNlSMrh8Zam2tKbuLYqgutNmhgDfKg3ATiVwS2bNmCjRs34o//+I/xla98BQDw6aef4k//9E9x+PBhAMDf+lt/CzMzM3j77bfxyCOPAADefPNNrKys4Dd/8zfDM0ePHkWz2US93o7ofOONN/Dggw/ivvuqjctrUA1kogQqIXOLS6H+qhomSGx5qalkxAwS+jerhJHA3J6dLxhQNBPBAy2lw/cO7e2mJvdSq93OaXCgH/MdY9b9Y/cUhKmTT+/Gni1jQVmz6+N6NKoJaGcp6Dx17/kex6IwrH+3YJkjFRBb/qKX0lcK+7ZuwOSVKezbuiGsN3aGKVBcmplvhjRYr/63foN4xrXqmjkGBeZzl6fw4lM7wnfUgUegkMZ9UicZ8f/RE29iZr6J0UYdw4MDeGTzfQXD++cFR159B5NXpoJyoGsBgOfOXEWj3h+aJNu1KCgeqCCkZ/P2zU+wZ8tYISLT4oPFY0LqrC/dmA7npMYRex+q9k7vgP4/f/Zok9KFPVvGAo7a92Pf88akA7aFduTMnaUV14Cve3LstauhRxXHVHpo58LzmltcKvRwovK/abSBdzvN5+ebyyGyC+gaGry581u6P/ZsYlB19uyL441lz+Nu7oniMgVrSxOWW8Ab127jgW9NugqezokG11gUpcUDC49svg+3Z+eTRsXD+9vZgzSY8nunL97Ec2faNHzyylRpDqcv3izQbF0r5+XxCfuz1qKe2DmOyStT2D4+go/n7oR9Y7kpzpffUlpKBZtzYqT1cgtBaWeDdK/EkgWruFQB8V/LCFh84DcfPfFm+L2e0aG9m3HpxnSoW8999uiR7jEVpRgdi9HAXLoVk3n4nuVXejafLTTDvR8eHMDOTSN4b2q2dMYKyuuGBwdcfujB5JVuFKnKT4MD/bh0Yxq3Z+exfXwEw3N3CvQlBof2bg5zaXSc0t57emcePfEm1g+vw3tTs5jYOd5T7Worh+oZkLfbMhVVNIDg4YAafb//7G8FvAS6+HPpxnThDnjOTU9+BFA4O8VH63w8PnktlN+x5RljfFm/dXzyGp4/czXwGQ9XvPe3j4/gyq02LvL+Erxmw978VUaxcknqfvH/9RktoWVpa0w+tuf/lRe+F+5OTK/x5h8DO35MLvDkthyY2DmOs50SPWcvl3UeyrhV8yPuWj2MPABo6wS2f6Ld10N7Nwf5Vp1iXLuFXvZSv2vpOOkMy2Trmmfmm+Fu5Oguh/c/gOfPXHXlrao5x/6m32VPS6CFWzML4f6Q5g8PDlTigd43T6dMrU11Ub5PZ+atmflCtngOLHXLRwTZlTKHx9eOvXYVLzy5ozS+d6729yl9B2jrVucuT2Go3o+jEw9hz5axoP/E9sXec52DyhrKu7016Dwndo7j3OUpTM/dCc6o2BrtnFRm5fcb9b5gk7BzseevMimzGqwc8/1nf6skG+XISoScu6pZjnbd1G3nnbLKlq6oPMNeGCqzWrpSAwp9Y8lTvL32fsfsP12n3Wu7V1ZWjtl7rL4Qs48xwG+o3h/tFaJ6V8zuYcfnvD6eu4Px0UbBuB97J6YfevOhnWe0k+kZsxlpsDPP1HsOgMubPJzkGdX7amiutELpdMV5D6ycSP18XJyNNnBQcVLtjpQ/PHlfdS1v/indPXYGqmdp0LvVn6tkqTW4O/ilZsT87Gc/ww9/+EP88Ic/BADcuHEDP/zhD/HjH/8YtVoN//1//9/jpZdewtmzZ/Huu+/i7//9v4/x8XE89dRTAICHHnoIv/M7v4N/+A//IS5duoTvf//7+O/+u/8Of+/v/T2Mj7cNYP/tf/vfYt26dfgH/+Af4L333sMf/dEf4V/+y3+Jf/SP/tEvadV/scCLvFdlkBFQGrlxaG9+ZK/9mzVCWO83mSsVZkYLWeB7jARWQyCjo1KlgRR0TkdefSdkAQFtwZJ/o6FAvf12ffRQay1kKoletJsl2jp/L2rcrp8EOtanRZ/LAa6RBJ4GGe8Mc5qMcq/mFpfQXwMWl1ZCRK2mnusZxH7mWgAUHBdUA2IRRrnA8RmhcPLp3aVGaLG+H/Ye2SgIjbrQSFLiD0vCEBaa5UiR1Jz5zKUb05jqRBrZqM4UHsbusL2v+jtGRXt1jntl9KlMCe85lkokzue+D5R7uPDdwYH+sOdHJ7YVSiGwhJPO4dT56z1Hc1sctnTw8P4HQso90M2qaXR+Z/txxcrxpCLWq/pcEVSRqYrIssbGVBRhrJ6/7oHnYKIBc6G5Usgis2v0cDYGqUi3HCf2ob1+09RDezeHHhueM8/S7Jw5e/uqRl3S7Pf/7FPcnp3HpRvTANqN0fmvCvYWDw/t3VzIjrH1nzlf3vcULVQemhMlTJ6vmaGx3mmKJxp4cfrizZJCZHt8Kb3i/BmdqY29PUWqypCYGwWp8/f4s54Nz4DnxOaaZ7/5WBad7YX/s6xJo96PjSNtnKn31wo8692OgW3PlrGs76vCrVlUAEq4zLNiY1XKHrn0ytJdG0mbmy3lnaOHA7NyHwC/zB7lHL3nXtNdlnrTbBodT9dmf6/ld4BilGQqep/jLHT6nwF5MiqBhrb3pmZDBo9Xes7uXwrnU06PFMTk4Zdf/yBklMb6GOYC8bCqd0MM9AytvBiTGTxcVPnF8ibKLywbrX27Unvp6WHFEs1lPujxBEuD9Vnv27n0SffBG8v2Qjj22tVC76aq0rz6DaAbQOdBbM4pnmUNuW0a2CbsdKjnZCnoHFQ/0P3xdHrCob3FHnQqy/K8SDu+8sL38NDz36nMytcyPkP1PvTXipUMdM4pGmz1cFvtgEbKGlBwcOidOmtKUuXQDx3D3kN71qk16LfevvlJqTSWfQaolo34/aMT20JUv86FWfnKHzn3mflmoQKD/XbKNtOLDG35swZU2CoqHK8hZcUspOwZQFHn0XGpW/Du9mr7SH3bjmXnwMDOucUlHJ98vxDoSSDtAhDGj52BV27bzlN1M5vZSHyyuK3VIGL74/EiW+ZS16Q6NAA3y4Plko9PXgNQ7MHlrcvb+1gmG+nd9NxiCKIFiqXTPVuVd/didNiWa7PgyX5W3rdjxvbQ090tnSP/Vz3Lnqd9xyt9mqObrUE1/FIdMf/P//P/YPfu3di9u22s+kf/6B9h9+7d+P3f/30AwP/4P/6P+OY3v4nf+73fw1e/+lX87Gc/w3e/+10MDQ2FMf7Df/gP+K/+q/8Kf+fv/B383b/7d/HYY4/hX//rfx3+PjIygu9973u4ceMGHnnkEfwP/8P/gN///d/H7/3e7/2XXexfULCMR4VJJaarNbBaiBELTXUnw0gJbXyPJRNoiDm0t9jQyytnYkEVV0aFErymYABK49KoQ2OYlnDgmr0akJ5wlFIC7Po5vhXKKYgDiBonPNCUUTqEjk9ei9aZrRLUaIhprrSw3OqWerNMlWuYW1wqCHCeMYJMkQZGzjtVLgDopg3bBpLengJwlYGYgbvKsKpje8Z7GjUaHUXmwK7xQhk0nYueYVep66blqgLgGf16qZdt76vSB54hnWupBtK9gL1TiqusRz3QVws4/8jm+4JAMjvfrBS4OC86almDl32MvDJ7anAHuvc/t3a1juM5DHmPT52/jse3fSms7fFt7fKbj2/7UsHQENtTFVpHG3X82ew8fv3ZdqkIQqxvkBVWcxQZK2Qee+1qoCEcT8cH4DoudG88pVENmAd2tQ3G9Q4OWGUnZgzxIMc4VTVO7LkUbtCgwNR3juH1VLNzZU8AGhitUZe0tpvVUCv8q4bVmON6/fA6AEBfpz+I8hje91igw6nz10MmLSMaqYTFHHSqJDIbx1sbUOZ9sRR8Ksg0LltcJS1fMFGZKceJhZQC5j1j5+8pWHSgsWQEA2G0VFUMLK9TupJqUkwD6otP7cD7L/4OpmYWAHSjndcNtEuFej1V9NsWd3MVbhovAIR+JOSPWpalF6XRfkNLk/XyHuCXF2uZ9zxniZYA8YwfVq5Qp7fl7Tw/oCjjWHlCDeFe/wNrpFF6eu9QHd/+wU8Kssbz0tQ35txiz6ovfmGoVHrO7p+nS8T0i1warO8rbQW62c85Z68GF4sHXu+GXsA6E6ze5RnPPVy08ku9Uzb64K5xPPPEg6GPZ8zxZvml4pRXbpLj2z6GyhOArpFJv8PxY43BPfqX4n00xsfKXlK2J07aYIgUPqmxsMrw5kHMyRr7rncnLD+O7YkGTXiBbClDdkyWVf2AZUpZ4s6WPtJ5kX406v14fNtGbBxp4Btf/bJ7x2Nlge1+qIyqv7/w4UdBv2EZZ91/AultLz04PVD+aeUUuwara+X0E60KIEs5ifj/qV4sgO8Y8UDn5jnzvbM/8uo7oZcqbTA0+HJetpcM0NZn+mvtf62+R6c+9y63dLXitc0+yAUrl3vnAHTtGotLy+GsgbaDhfKkLeXOOzrbyRhJ3YFU+Ty7bp2fbQVg9xZAFJ8I9ry9Pi52TQDCHnBOKlsym4v/2jP1+EQOb+IcSKuGBwcKNFRlFKvferw1RodtcAH/vm/rhtK+xs4mpmvpHladvd4v2ipr8veYrY9BFdY5mHJUrUE+1FqtltUH1sDAp59+ipGREczOzq71izHASwp0U3Rvzcxj02gjmZoci5Jb7XPeO7aMhTcOCRPTQr//7G/h4Ctv4cqtWezcNIKz33wMQDfl0FsXyxGwYbEt78I9obedc9Bvk9hrWatUaSBvjUpAVyNEEH7j6H9Es2PVsOvlPvTX4BrkuReNel+IlKwBwbDGd3LnqOONDQ8mn9e56XnaPWP6K+dFA9F8J4vkh8e+Vpifh9NaHsyWXuPPerYvPLkjel6cm1dqKLdElM7HM9w+8K3JYKgabdRDGSs11NyamQ/nxtT8Xu9c1TztPaKhpgWUzrgXGsG9u3/snlDWwLtTdDTxnHVOhBjO65xPnb+O6blFzDdXXJzR+bNE3eLSMgYH+rNKM60GPBoVm7u3x4BfVkH35fD+B0L5JuKa0jFL43g+p8630+XfvTXr4paOoWUU+E2l0byTKbxIrVPXZveFtKGKf8W+c7ew2ntkfx87B8tvAAQ+p+ek5assDbI8ACjzVqU3do6k6UDR4B/bAz5Pes3xFD8P7hovlePw5kfctaW5YrJB6vfcF7s/q8WJu8WlKv6nOMOgEcsvUjSE49bQjfjmXQFQeO+h57+D+eZKoOl8P1WmUOUo0mZLn0jn923dUBhny7OTgZffODFR2FOW+COs5m7TmKBzy3kvRZ9i5a4UvPsMlMuRpXCQ8ksML3LWzVKbsfdpUFPorwEbRxrhjhJvWOLNox3e3vVyL3Rf923dEDIte+G5Ofck513Lp1IyWs4a9X7QQc21WWMMv53SDzz5xepCVXNM3Vl+mzw1hX8xfq3vVgUA5cwlpY/1chbeM6RBAJJrjfHuu6H/sXdTemvsfSBeio/3q0qWtXR3VO49S6BW8ShPXs2FmH6mfX+A4vdPX7yJ45PXsNBcwYGOPBGTPXqBmEzm2Rl6hYee/y7mm8to1Pvx/ou/s6oxPLqU4h8pG4UnRwNF2UDPmOWigLYcytJ0NbSDCWOygo5h9fmUjlDFd+38OS/KilV6aC79tPKEyrL6PaDI62Pj363sGLMnVcmUHtg7rHdIMyztvNXOAqD0M9DWDd9/8euV+pw3j9SaU3JC7FxtGUPAp5uWZqrsam1LqXNM6SMpeu3tE+nwhQ8/wux806Vt3h2z8m8Vzv9lhl78Br/UjJg1+IsB9CJ73twYqDc5Fcm0mpJBmqJHAsTv2bIiAEoRKu9NzRb+BfIj6xjFrKU/+K5Nt1Qvs9d8MxW5zvV4ZX9ikQo5e3fk1XeCE4brVtAIAa/Bm6b40/lyoBNVphkwnOOlG9NuWjIjIQAUIvtZgoN/txF2NiLARhGqssoSTiONOgYHiqRQ8Y7jatM37ZVio8n4s0aacr2xqAwboRaLUImBLbFjwZY3ikXXHZ3Yhhef2oGxTkS7hdhdTUXOKth7xMjg/hpK2RqpaEfF1+fPXA1Cs95Z4gGAEAFKIV8b6zGKhuU4PJz3IlSOTmwrROXYCBn+P8vL0LmwWoN9KrLMzjMWxZaiDXb+dh/4N5upoVF++hznyIjoK9K75vjktVI5BI7B6HdmItgoQruGFL4R7Du2RIVnyLJjez8D5Qy6uwHlU7HyIECcHzESkVknNlqL+6j04F3pAQMA3/jql0PDT4/HEJdIz73IMJapqvfVSlkYjDoGEKJ/dW/tvQfaPODArvFCVoeu/ZxkO3BvaLjTqC1mR2hZutidSP2eBtpHNt8Xpem9wOfh0Kvif4oztlG694wdlz2VWkApGtpGmZM2Hp3YhsP7u+VStdRJDugZMHKRvQb5+yOvvhMMoAd2jYf9pCyooPhTRTd6laMsTQAQHV/5PZ/zMjhJp2bmm66MwTnu2TJWGF+f4dlsHy+X+6lat8pzqfK46oTZNDpUaHDd6DQ+tkbq3Khjj76kniWO2LJuuaB3IKe8ZOz+WDr29s1PQqN6+25Vc2Kgm827uLQSzoZlVA7vL0bQczzvvqXkl1RvTS8zkXOaW1wKOMyIWxq3mJnn0aVYlpby49zseQ88PYlnBPilXnLuvYeT1HMa9fhdAeK8O/Xd0xe7GU8enYjpybFvWdqn37a4ZdedI8se2rsZWtJUM43Ie7g/ujbK4tS1bNnP1Br092/f/CQ4/fVuPfPEg4EeeVkLtkwjQatj9AoxmYwBY1eM/NUbtMy/vcPJp3eHLKaUrGn1KWZhajS8J0dbHNT/p0xB5wNXMVTvx54tY6GSiMV5D6+p260fXhcto5TKlAaK2QeEWAljC6SfNaAk8/LbiodKe2lXAYrVUCyvj2VT9WIni83dk/Xt2acy7glWFuRa6YSJyfp6puRnc4tLIcOpUe/D4EB/QV9TXTUmt8burK45VskCaNucVlpteqFl/LR8IGUZT95Qmjkz3yxUAvDsITFci9lYDu31y1t746ludPLp3Zi7sxTuXEyXfGTzfdGsshROrkE+rDli1uCuwF70XOVVCZAaSiyB1xTO3LkAZeGd32NTsrOXu/0p7Jyr+jZYAdAjUqpoeBF+j554M6xppFHHyad3h72IGURi+/d5gBqNCTs3jbhGqJjRlX/XNEoS/BixtmWObO3UmfkmPlto16pV4Y+1u/l9GuSn5+4UyiroeGQs+7Zu6CiiD4V0WCqLXvqydYpYRULPQplXr2WnPMUi95zVQWaZMRUTlmzh+mN3NCUMWIcmDVpaazT1vr1rKhjY8jmeE81zcqgKQsG+3l/Dni1jYd9GG3UMdRQwpnxb4TPlJNPfs8zbpRvTpbVYQzGF2BruHojLz5+56u6JNVremimm93vgGezUacP70Ygootb4aNOn6WgDEOr/N+r9oU8L770aopV2cG89vpIyYtm9sXWorRDtOcut8uTVllaeleMQqnqO81hcWqksD+LhKg2HLOGo56V7fPLp3ah3yoYNdP61wRGxEm1aC95TtIG2cn/jxAS++IUht/+N5Qf2vPRsqTiffHp3UDhYpo6lY7TklUdjSBuH6n2lsnSx80j1FiEPOHu57PiuCjLxfufRzBx88pRapWNKr1UOURlH8dpzcvI9lr+soZvJxGetwZqK9anz10OptCpIlRABysZmPQvOS5udahRlDW155rOFdF+l2J5XzQ2IO+KrxldeZp9nKR3C+uF17r1MGSZ4NmrgSeGWJ7t6NFm/rTC3uIyNI43Q4JqO9RrasgfL1M3dWSoZj7y7EytL5YGWhmF5zdwykwS9A14ZKwueY8zbI08migUAeMBgIf5rv6uOPZXHUqC1+9UYeOHDj6L4sdDsGpQ5l+ZKqyQbA4jKV9qrz9sX4u++rRuiJQHVgH/64k33jnq8yeoolkfGAr28+Vmed/0PJnB04qHoXdHv9+Jwtw5GyyvpGLd6chU+xvq1pRw7ubLs7dmF8K8NAtH90dJFFz78CHOLSwEvbNlPgpXNvLVRxweKd2tseBAvdUpo2n2xuj9LzP3GF++NrrMqQIrlxoFWYR7s61nvr0X75VWBlu7MlT+9+du99Gga7y/vDffKs7M888SDgf8DRSer4iRlCmZed9fVV8ALi/M2WID0pgUEnQfolsjlmvh7BmB5e0F9hrKl16/IPq/6HrM+rT6iNgNrTzp1/rpbUqpXO49XWrsKYrK+1RdyA0M9WdAGIHu0xT5HWY2OkrHhwbCvdl/4rgb2VtFZjlHlXKKN4c7ScqGMn6WFKp/qmFZ2UKeup6vklBC28oSVU4iXVYGYGnRtdWsNNk7t5Wr42RoUoRzysAZr0AOQ2fbqEGDkDYHpbWT8hMGBfsw3V7DQXAnGhqq5aKqhEupDe9slLKrg5NO7Sym6Xmo752rXAnQJJVNvaTg6tHczjk++j/nmMup9tagxajWE7W5KH3h7942vftl9j+vVv1mwe+LtEdBNU/caUh/e3y1JBCAwTZY7oGDzyOb7QkQme5t43/KiGz9baBbq0JKxsdyDFYosLgDF0hoUuM5ensKP/vNcKcU4Vs6Gf1e8iu2b3Xf+zPJn9jwsLqqxSuetURqxc+Xf6NDkfrXQ7emgc6rCO7s+K2DE5uThq5braC63whhMkWfZwH1bN+D0xZuYnlsMEVSPnngzOkcLFFDOXZ4qpTTH7rAtX2fBozOxubRkf2P7HMNVCzoGcXfyylQwouk98Obj4awdn+Uevv7wr4V9YPkr3nu7b/wdEI+65Ds0DHt3lb9Thy8j7RRiNFyVJ1UclDZx7XYvSI+3j4/gx9M/B1AsI6NKr73LL7/+Aeaby6WavFWg67broWJLgzudGox8fWTzfbg9Ox+ao/Le2HHs3ubwFbsGu9/emLdm5kPPKyoSdt9pIDh9sZ2eTwXG8h67tzRcHdrbTdfnufEuTc3Mo4U2Led9IGwfHwmRrBb3H9l8H6Zm5jE9dyfweu65rk3f8/ap6m7ZZ/j/ymOoYAMojKUyDnvBeaA4k6IF3FdVIHlOt2fnS/zBg5iMUPX3iZ3jIdtRFfzRRh33j92D96ZmQ5kTG6lu6SdLMOieV/Ew3QOPR6UcJjwvAJW0Gugamey9VJwj/vPvl25MF/4GFOVt3QelY5QXqmiy5TVWNqBTYKRRx9s3PwnR9EtiBPD2kPMAuhlG9jx0D/ntFtpNinMDYFKgmcaWBnAuOT3FYnQwRa8tWJnUO3Og3GOL87TfIY3nz3rG03N3MDM/X6Bb/L7FU5XRN44M4fbsgluy14OhTulCNnfnd4i/j554M5S0sqDzP/baVVeHSNEU/Rt59bqB/kKD9Bj9TY176cY0bs/O49KN6c/NOKXyh5a2AdKZQjHaZeV4vetA2ZGn8inLUjKrPIa/5JEMjortmXUe8UxtKUsFK5vZvdKyaFquh2WSFK8VlC8C3ftPuuvxWcszYnx7cKC/QMfOfvOxMB/yr1Pnr5dK/6ZAaUKOvOCBt5dK0717GdNr9G8qV/E7Fk9U5vzO1T8rrIv4XkMcD/hNoFi2lPYAW9WEtErveEwPpmzpAfGe5R1ffv2DkFHh2UVSdD7G5+x+6rP2jJ954sGSPmLvZuyuWlnfk4W4v4tLy1GnuO4/11wFsTl5PUkUJ6lTfvsHPyncRcrsk1emKu+O8pjUvbE2Kv5s9Qr9/zeu/TTI/dSvamjLQKkyqSn6oXdC9SGgbNuyMpzavHTPd24aCeURFVL0dQ0+f1jrEZMBaz1i7h6qlNmYcRmI18WMQao2pBrHcup+stY5QbN41JgYc3ysH14XDDZMraSgWUO5nrm3R97vvXrIrMvfXwOu/0F33Ko9Se0fgOz3LOQaMFLvxMZQp9OFDz8Ke9ow/ScsXnl1tVl/upc68AR7Dl6tdDJgrZNdA0KvEs4lhU8KvZ5PyplYVZs05/dA3BjbC94Beb13Urhk69FeujEd6rgODvSV6hYDKNQWju2F/u7bP/gJrtyaLfTZqNrHKsipxavCrnV42fP09rNKqWC5pV5orV1vqlbvaKMe6tF6tWZ1HFsPP0YDeNfsfO14ug+M6kr1s/Hohe53jqKjNeMJln+QN2wabWB67g7mOxGgBxP1sXsBnZOlbxafFIes4hjD61RPhLsBqwzq+d7tXGxddXvmXq8i26PD9v/Sb2gPHAJrW+v8c/vApfZTzxBAgccoLUntR4qWe3JG7F3FHwCBnqX6wvS63qpv99cQjKmk6wACP6jiU56yXNUTqZd5x/Yrdj4WV6yMYO8JUD5vNfTYs/Hknhh+rqbfBMfm/gPxPj/23dg+e/0HbP+C1dCgGN+3/cl0/r3yy88TlM5U0UevlwRr3A/01QrlpXXsWL8mi6O291hVP8iY3MC1qNEttsekfZQpUt9O9bPQQK4agHEJjsvp42QhpYd5sFqZUd9nn0VLp3vpk2B1A6Dbey3WKyXFH3LphY59YNd4IbgJ8OV+1a89nSnWfyW3jxBxx6NVymtIcwi9yomkj/W+GlZarUL/nFz8sXvSKx5587MyBZBH4zw9aeemdiCShyeKI8Q5APjRiYnCPBhQYfleTDeM6T2nLxb7AFEX6FV/U3lB791qzsD2GqmSkVJ9SPT3Vh6O2dHsXnk9jawMofIFgFLArKe7eucV01tz6IoC72K9r4bmSqtw76v4D5Cm872cqYcXlBNyHKux87Xje2fg0RiPpuXYU3N49hqkoRe/wZojJgPWHDH5ECMkvRhkLRHulQBQCKv313DswPa7IqxqTNN0wtMXu80IrZIPlBkNn9PLpg2GU+Dtnceo7rYZqPdsL9E5OfPuFaqMbgDQV0OnhmfX2JUzJ9uINdUMtcoJ5DXdo1DAn7/4haHAGGl0pbBTZYSP7QlQZMAxR8Zq78DdnmGvdzf2vdUYgdSQCCA0jlYlAUBBCVJBVIVzbUbPLCreZ95/Lxqq1zuXkxHD52MGIsVXr4l1ynmSY4BPnWmuUlfl7NS7yPsRwwnPGO6dYc65WLqa+kYV0Ilf7xi6gKJCr8oLm9kSehWSY1DlnFCli1ClAMQcXtax/XkI6zlKlP3ZGu/sumNOPqDsYCGdqPfX0Fxuob8G3DtUbDhuFR91xDfq/YHOr9ZQVQVWcU5lXa52LLu/nkM/RzFMzaXX/Yjhtm3IDLTLv/yn4383OlbKmGkNzTx/zjM171xaGTNI8DlLQ+23eX/ZEFafO7z/gYLxycoFqfsQky2scauKniqPTEFqLz0DEeeYwvXcIDB18MRw2ZPnc9eWA73Qdg0SeynRBFmDAlQOsMEf3r56vCKF549svi9E9/MeEQdU/vLOKDcoIPaeOsZ4H6yTxgsQ0YAaq5OtRhdNNT/3GkN7+3o3TumcQKqqsSwfJE21Z5gymObeu5SzrYonqH7OM7f4o3rej/7zXMlBEwuCiq3NcwJZg34K9D6QVi8urQQ5oRfD7Wohx6kKlJ1M9tmU8Z/R+MozUwFY6rixZ0Mg/dIzs/y4aq2fR/BQTrBCSh7SMVJBFN6cUo6SGA1JyeYxXcnKfjH6DCAaxBL7Ruxsqn7Pv1GeebjTW4h3kUE4Hp1P/a4XXTKGC3rOvdIGjsEMKw1ciTmMeAZW9ksFDeh9yrU/5u7DGhShF7/BWmmyNfhc4dT5Yro7CYIylyoBzfaFObQ3XbLCAtNRWZ7IGgJIwHR+MXhYUvc0subU+W46+FC9vzAmUCyppIqCKikeEfSIuk1DBIopyco4WZvbQq97SMhpVmrnzf1myQbWy1yNJ93uKxnweKcRJNB2wgDderkph5Ti4anz1wtrs2neHh4DxTJHthyYjn/pxnQwyi2ttEpRKOcuT2FxaQX7tuaXJgH8sm/e3Ogk8tLwc/GB61ntGfaKd1Z4rfp9LzA40B/GmdKImhqwvNIuNca+Mnq3VCkFulGXXqS34isF7OOT1yqFfW+fUkZoLc3Hv3t0bahjCB6qd+8GcZLP6Rnb73lj2hJXKeA98pxFKYgZBBU8hUVp4dziUiiVpHOONWUEig2R+Y1Y2cwYcA6Pb9tYMojrfjL6cV0nS1IzMFLl8qr4l+KN3SP7PP+uRkhvTHv/vG+sH16HWzPzPe1VFXhzTvFaoFuTnXeDz7M8KPnw4tJKwAuOZSOq+c5SxwmjUX/ePbk926UrnmG7ak9VVtBm197PVLhWa0wg2LNklDn5n1VetYxNCr/05xyZq4rGW3qYolt0qhCWlluhwaq3T4f2bnYdOLoOG/Xp8XwFBgRx/VU8OLZ2nddoo174Nku77Nu6oRBEoCX9lDe8ffMTnHx6d+G7sfI2QLE0k8qCVfK0Zp5/JmV9qpxypCFa25+g563153N4kK7JGpnUoKGGML5jywTzjs7dWUJzuYWhev+qjHiroe0eNOp9leMc2DUezoOlhlhCRoODWI7M45E5MtieLWOFDHUF7S2TugtWd0jRNpUvTj69OzgZ1bhpZTXFTV1/Dv3kvrLnlveuLW+l75F28neH9m4ulGci5MhYVs/h3fJKSXFfc/iEpXenzl+PZiWn+FvV3eSeAN3yP7afAvmRV3IU6Mq3AAr6muIq16H9T23JKnuWKeC6Hj3xZqmstX3X00UpW843VzDfXOkYs5fCOjz8iUGVwToWTOjRRNWJU0Z1z4Hg43LXAQYglIX29pI/c2yeNef0558tBFoLlMuWa18NO197l6pkYrtO747H3rP2BasL6n1W5yaN6cR3Pqtnw/e4x+0+kvFy4jH5Ipeuk457PJDzn1tcKpQiq5LtrB0hto8qV/P/FdePTmzDqfPX8Y2vfrlwFwcH+jA2vK6wFv225X0qwz1/po1PxyevFe5NFd/zZIUc2uCNwQqcLVm73lPNBNayw7yH882VqJxD3Oe9sTa9Kv7wedhg1sCHvl/2BNbgLxYc3u83VD20t9xIWht+E05fvImFjjGKhvVegc2xG/X+giFPDTZeA7TTF8vN7rSZXGydRyceKozJv7EPAZtonnx6N158akdoZud902sIzWaXZEbaHPvSjWk8d6bdaM9r9roa8Brcx5qVcj7aSPwrL3yv0GT5wocfhfFyQPdE9/XU+euYb650asEvhOfZeHlxaRlfeeF7OHd5qiB463iKhxYP7N9ijV6pwG0fHym8Y0EZ3cObRgrnrM3Cq5rF5+4VUG42ebfA9TGqQ5uT584p9Xcqjmy4GtvP1D4r6D0/8uo7mO2k/atR9NbMPIbqXdbHWvUtoISjVEyAdsmbfVs3YNNou9God14ebVEDRGw/dA/sWpROqvLKefD73rcf3/Yl9Nfa/wJFZdTivXfGMVoZA91/NahQuCMtPLR3c7IZup63/lyFW0oz2QPl1PnrJee+dwZtuto+bDYhPrS33Fg+Bhzj+OT7uDUzjwsfflRqcKn7aZv7Pr5tYxDE3701G8WZqjOxSnZsrl954Xs4PnktKFPkWV7jWHv/9Bv8mcYNr66wPbfYOXq/t7+zvNYaT4Hi3Ti8v91Uc7nVvt9Ke/msjqVZLmwsfmDXeMADa0zQOWmDcBoxcptc0qg402lO6/187nL3Z1VSq2gj95Bnq2es/P7Q3nbvoJZzjpQFto+PFBxROd9mP65U0/OqsdShprzCyheH9m4OchYb7raASvmIjZnt+ZLuspnuM088WGq06ymvdMIAfp3tFC3z/lYDCt8GyoEyxEOgje/aKzGHhulzVvbz7lpMnmZAyHtTsyX6mZL/SUPevTUbpUE5DYNja+K3FRf4d91bXbuVH8nDhte14xjJK6xcE6NjvG85tD2FI6lG3XYc8pqP5+4E+mHlTq7DAxsgZuek+MBm3tRxeK/GR4eSuoSONTPfxGcLzXC3da90rRZHiSPPPPFgoMMvPLmjEJymuFkld9u16P3qRefy+AP3knf4wocf9dRsXYOu7N4pf9C/xeZqcffU+evYPj5SeV65crkC5b71w+sCjjJ4gvRKx1c5zgJlp4O7ivuq81KdiLaBdZ07o3hNPADa+E1ZPxY05NG9SzemC2fonRHvWb2vhhrajeOrvhWD1JmmcCFGy3mfDuwaLzljrXzLfiEeLr998xPs2TKGjSPtu0Kd/MKHHyVlQbsenuOxA9sx2qhjcKAv0GHylWeeeDCJIyr75zojdW95x3N0X3X0PLL5vhJ+LC4tl+744EB/QS/y5qDr4h4PDsSbvnvz6kUOVTk6JjMwWOeNa7eTe6LfSPFuixd27Z5tjH9THs795zj6bcv71LFGm8B8c6Vwb2J7x/nqOVs9cv3wuiwayfkfMHQsputocM4zTzwY9MYaEJVzCPcO+XYt3V9P7uhFD1+D3mDNEbMGnyuoogKUBXagLMwqkTh1vthoMmaktKCE4eTTu3HjxATef/F3XCOlJYwpBSmmwB7auzkIAJduTJeY+2cLXUJuFTllFEr8FBaXlt0moJYBqYBHQ0GOEpcLKiB7xpxjr10NTJsGD5tm2yuooq77enj/A2jU24axnZtGAsM6dnA7hgcHAgMd6KsVnBExYdXDA+IagKhwxMbb/Nebuyr4Lz21Az+e/nnhnFOOHs4lx1BJ/Dk+eQ2PnngTe7aM4fofTATFkwbB1Pg5eMI5tiNG3o8+Z/eg6u8UKDwhejX4q07Qs53ss6WVVjhHnsnRiW14qWOse9hpVKdnyEyY+eZySVFMAZX4A7vGk3chpjBxLbPzzZKitG/rhpJwR3wGunTXKvnbx0eCkxooOz0Z9U9H1rHXroaoarsuD68Yybx+eF1Y1/DggCuEekpqFVjcsbQzJriq0crC6Ys3Ax0bHOhHo96H2flmcBDlGhs4Hxr4CWos8sZSRaHj88NAx3mozc/t8958rPPLOsZU4Z2ZbwaaeeHDj4Iym6N46pqsgd4rS2bnwf22d14d+l5wgq6f4yp/UIMqgBC0wAbDQJH2qvHVU0jnmysY70Rbxwz/ek9OPr07yAW9gs5LDXb6MyNCAQTcyKGTVjFLGRCtQ4qgjYtjBlmVEchLX379gxBAkcqsrXLW0SCoTZNj8O0f/AS3Zubxo/88Fww23MfpuUVXnoxl/xL/PINDitexAerOTSPuXfWcmRrNaA3bXvkrnhUjTQF0jOpFOpeiGfotfc7uRyqIBSgaIdX4ab/NdxeXVkr0m+8N1fsL+3Hwlbfw689O4uArbxW+bfWDqrugBvGUEUv5JmkV6SG/Qec10HVUxM7z9MV26TsaMGPyQMrhbUH5cIxGevgSkztTfN1bW1XABvGBOsTUzELUGGvnSMc594r4bfc5dmdt0Ie+kxusRP2MAXW6r9yrlLPC6n10+lj+4OnDqbMgeOugjsSxNMgl5eSLBVZwb2NyBIBoQE1Md6fcd+XWbJZBPOVE1n2N6Wt0QjLTZHy0ATZr9+4XfxeT9bneSzemS8GlVnbyzoiOSQClYLyYTJe6K7G9eWTzfaWAVLtvlpanKmBY+dY6EGKy5+SVqUJ2qsUlBpIen7yWtLdYZ4uuQfUOCyr7x+ipt8fKK1QvqtKd+Q09U95noBbodCzwQ+++6rP8HvfYBmXE1pH6vQdeYIIdg3Y6oBtoGAusTgUIKMScK96z9m+KCx7vpcNEeZ+VVVmNoFHvC3amlBOa94EBd/ptBgpr5p0HXccNXDqWo3sy8Gi0UcdIo45LN6YDjlDfn567U9D7Yjo50HawHZ+8lgyerrLxrEFvsOaIWYNfGMQIIoCCMKtCtjUCpKI6lMjHHBqEFEHju56ClKPAWuHr1PluFD1T62NKtgXuyWCnXA0Fdrs/3DMqAqz3GFPiUozY/o3RZEA3jVrHO3u5a8xhKZdnnngQLzy5IzAwNRR7SkWOcAl0FRMau37ji/difLSBb3z1ywWGpWc20F8LgreOV+UUSuGazjkWVW+/VRWFElMcOJeUoZJRC7Mdp5fNuqhiuJ5BW42f3nwZdbHQXE6eX1UWVew87PMx/E19WwV2gma/2Ge//+xvhWw34rE1nL/w5I4gwAFl57GCjS7PyXSyxhGlkzY6PRY5GNs3e5c+nrsTVUQv3ZgOdGv98Dqcvew7SlKRMRQ8aay156zPWyU11yFIxURpkzc3/VlpkP0O6TWVIhqNJ69M9UQ3CY16X4HeWeU2hss0QAHtc09F+cXA3nsv8pFOg3pfLbw3t7iEqZl5NOr9JcXTA10Tf/547k4Uz+08tHSYt48tVBvbLY20jkgNrNDzr6K9GrFrlWCP/luHYoq/V9EuzmvPlrHQQFuVM2a4Ner9ATeqZB/dfxqg1RBt+XPKiEu8OPbaVTdwRWUi8tLFpeWeFFstyaHj8/7YTB1r0Dh98WbIRrlya7ZAhzVgw55PzFCppWRSjl995/TFmzj7zcfwoxMThZK2Cspn7DhVzluCjTR97sxVHHn1nYIMl6Kn1niW2g/vu54R8tT56wXjp90XvutlYNgsQX7bO09PP7Ayw1de+F5wgPRCR5VvkieSHqqhDUAoIxMLAqDjmYarwYH+qIHL/r8dR5/LDRDQMYF4gJENEtP3aMSaW1wKuKn8IYUPQDsoS53IMd2Q+EHHOfcqllWaun/eeR7e3858eOHJHaHEnoI6FRS3LG1V2TFmuI7xgRjfIh2u0h0IzDjQbJ9DezdjbHgw/P+5y1NhTd/+wU+i8/D2lDyCZV6tgf3Ya1dDqVvyP2us9fQpdUrw9ynHU0reVMg1Nqfw58ir7wQ5CPBlfS+AiOOwWgLvhqWDQFd2WurU1CZfjM09JU+k9oaBPWMd50RK7uC5elkcFqx8S9AofZU9bZawlQUJC82V5HqsU0JB9Q6giAuKW+qwiTki7d7u2TJWyCJIZWZwnjE7Enne4tJyIUsodqa8/6RBvej1BPKfquAq7/uxsVVnZRaHBgzG5pOia/Z87bM8x31bN4Q5VOkaOoe3b35SCBLnnlBWBdqy5dGJbRgbHqwMHKJcOLe4VMrQ3DQ6VMi8i0EvDg191pPZNZCOGaU/nv55cPY+f+ZqgY/beT3zxIMhmJpViWbnm6uy8axBb7DmiFmDu4KUgTRGEC1RViHbi56LRXB93l5ZCv1AXGhRIFPfODIUIrhY6oUmLtZn1H1QZ8ri0nJwXFAgUOOIMv7nzlwNUTjcs4/n7pQEPYJnhPP2yjsXEnWmv1KAYiQPDajqNKMSYBkYhbKXX/8gREflCJe6BxpF5RnnD+3dHAwPqZJ2VUqv4pqN9GJK9tJyK0tpULCMs0ppyHEeMSqFqeQxQ44XseaNT+NnbG4HOmVeDuwaDxETxyevleZV5SywBnLilOKMNRApvlTde65956Z2hP7j2za6Dls1Migee5HAFOAUJ3uFGK1MRW9ax3QOeM5AGiut0U+F33PSZPxdKauTihy1Z7FxpN27abmFQsQgQfffKqkp44x1Tiltun/snmiJM0IqYkr3WHFw48hQck72b8SNoxPb3Ahwjw/qz4xsUnrbK66lIsXUkH7hw4/QZHMtAM2VFloA7iwtB0NVysCVMmYQaAhlhqGNwNRgCyqJ+7ZuCBG9jOKKRQ7GvsvxFpeW0V/rZmfFFDcLWrpGnWGkeZb+W4fi4f3tqOQpE5Gsc6sKjojROK8sRarsng1+oVNYncO5Bl06pYlD3F8qY0qzWQIGaPPjHMWWQOVPz/fIq+8Eoy732Wb9cWzds03SS+70xXaJNKA6SpjjtL/ZQgxi79BImYoQ1XnbcVLOWw9UIZ68MlWQ4VLyccqwk8ILz1mghttU6Sq+q+V4lFZ4Rhieo54nUNYPFGdoGNRABs9pxPnoefH794/dE77r0Tu9e7EgADqeAQRZ356BF6hlM8JTuOU5I1NlXlL44M1FS32+ffOTQPM8Q4zFB0ZW04m8b+uGJE/knDWb25NpPNoVW6d1MsWeI+0/e3kK64fXBRpGqNJxvX1IOTg9B2WVw8GelY1E1+8N1fuCwZe6k2blx/aUDe7ZY8AGd5AH2IoMuq+KG1rib8+WsVAy0paU89ZeZTC3+5Hirapje/jD7I07S8tRWd+WN9P5awlzLztD950624tP7UiWbMqVA2NybZX+r+8yi+PSjemSTM39eHzbRgDFDMjTF28GY7b29vWywKxMyoCg8dEht9qDZvkBcB2jVgaLrVcdNp5jwTNQp3ik914KlzXQNiZVWL2HwZb8XgoXvL8r//GyenLB0gstua8BgzawOravHsyK4zsWeKB3xVuHvdOe00D3ZPt4t7qKljOuunN0qi2ttMJ8aPuYW1wuBDwCvgxn55Y6l9yzV2cwgELfGeXjlm9QvidtolPGm39u7+g1yIM1R8wa3BWkDKSeU8VT1FJRTdZI6SmAh/c/UKmIpP7fGsljUQU2zZrGyqmZhRDBxWjLFuDWq9V9efvmJ4WSHdYo5ynB5y5P4SsvfA/Tc4vRslYEK/D0ojDwd7Z0Cwl1VY8Me8YUZsgAYlGYMaDwy34fnnAZi6ric8cnr4U0aM1e0j1XXKMCQwP1Qicle2mllaU0xM5C5xQTjPk8gKjCTSbORpAxQ44XwaXjAwgNAx/ZfF+hbJAaKd6++Uk4d0ZMzDdXSmv3MhVioDhlhUcVfplmPz13B+uH15UEK09IYIS+Cm7ajN0qHzHDuY5/6cZ0cj29lveoUrxiUUGpkhWeIkABUY1+AAqNuTVzaKiDD6ONeiHiMjVXALg92+3dpI4dD+xdIT6vH15XimRWo4Om5h/e/wCu3Jp1IzJz52zpJOH27EJJUPbm4Y0TM7DY71tFJdconktn7N941+wdZXNvKrKXbkzj9ux8wPcUb499U2m+4rm3H0BbcfjOu3+GOyEDYRkvv/5BoF9e+ZlY5gaVXTpUcjJU+DOzTCd2jhfOijRPHVgASg7FQ3s3Y6HD1+0dsIbiWCaL0js9Y48va+BBjgG8F+XbwqUb01hptR0Z3F+ei0bEaj+8fVs3ZPF7jcg79trVAq1V3vX2zU8Ke2fvswaMQEypp85fD+Unxjq1uy148qXub6Pen6S91kiZihBNRRXaUitV8sIb134afu7rq5Xk49R8c529MYMo7yH5hNfXQ7/DgJY3rv004LCWKPWjdmvm3zZYmS1GE+4d6jZDthlhXAszvXXeKuNbegd0I0cXRA6K7XGj3ja+2QAVD3+ZjWMNqxa3uFfWMGIDZVQuj5V6VkeqnYt1QOn3Ygazk0/vLjg/vAAdGnM1Et8zfMbkYc47J4PIC3DxjOyE96ZmSz09rfMDQHReObw8dq+r7judyjUglPjTZ7v9S7eVMsLp6E7NT3UGT17Sco+bRtvll08+vbtA05RfW/02pwQWjbGqj6QMl/o3/ZYGolVlj6ZKKqojiUZWS6uUlsfKEun9iOnnKfkxxser5NocI+764XV44FuTIctJ5ReO45U3VdzzAiFjcsihvZvxxS+0netTMwvRs6nSLa0MFnNCaX9Xlb2BboCXdXRbm4YGEHgBDynHoXXwA2nH/Knz3RJg+7ZuiN5ZfhNA6e+c/8FdZZ5X5ZhXSNEL7uW9Q/XA/4+9drWwr1UOUs0a1X3wHM05MjTB41WkFUDbTmBtjOqsjQGzjgc6vZ7WD68TZ+RKNNiAAclfeeF7Bb1cn/HOxQZ32PsEwK3wwbJlNlgFKGf8KW2K9fZL8c81WB2sOWLW4K4gJqh74Hm09feekTolTOt7JLY0xsUiu7z/twY0z2jg9bIY6ERyDPTVgoG0IUIvFQk1ZluGwpId64fXRY0VzzzxYHhuqFOSZL65kqzz2AvYaDHvTOwZVn3XKuNk1KONOhaXVgopzDHmrGfE2r7NlRY+W2iGMVXpIoMAikoRmS6NMEA3MiSFs1RgBvpqePTEm3h400jICPHm6P1/rtAcAyuMKONOKTM6vgqf3vjt7IJ+DA8OhPGp0Gj6rq7L7oHOVbOocsDDKXu2jFSbby4XalfbffKUNR2rWw6lFW0gbedz5NV3ggOvqjlrlbCaQyurjOk594/KJ41eQLlnE8+ekUyPb9tYUOBzIqot7VBDRq1WvIenL94MGYD3j91T6hOiPShsJLMKj2qk0bnZiEx+MxbtHNtD0tqJnePJZti9lgjQPVOjXmyfU2OsRpHSb/MesMzE0YltQZE9ffFmqdxITrN1C0rzqeB7WUuH9nbLHjZXWlhutc2tzHDUMmYx8AwXOXX8uY9aasvLMr10YxpDHbxodPpXpMo8kHdYI1ju/WVZEY2o4/tUQGlM1ShLixMx45Tds1yIRQyfvliM3NS12ohqBXtumnGjtFbpikevlQbFnPvEx3pfzc1WAny5UJuhzjeX8e0f/CRZ5sUaKb0Al8P702UmbamVKnlB+1I1l1ulbAoLMcNNCidSirgaTIEyr1G8Z0DLfLPbuFizubySiKtR/Imfo416qfGuNYKqm0e/EbvH3A8ajjRy1IKluUCxBFaMJtw/dk8pa4CGYtK0mBxrHUR8RhsWe/xG76k1SMUMu7HStqcvdvvivPz6B+GMGYx1a2Ye5zrOL62znzprj+/FdDlrpNNxY3t+8undIVsj1d8IQFLuT0GOQasK3+lUbqGYHcl7qOd4dGIbNo02Qs+qFK4SUg4JoFi2SUF7K80tLoUALyvzphwPFk+0zLelmTHnjo6vAXWp7FGgaNBP2R7oILe0SnWSnB5EMZl0NbLf3egN/Dt1K4Kle6fOd8vKKn9TWc/T+2KOEf0b7Sk8m31bNwRHI+9yrm4Zo1U0UpOvquwdc3Sz54bSqtjalA+mAk7VSUcHAY3z6pTmnPgc4PPoHH3Dc/ylSuIB6f5P1vmuJcFjvCnleCbeMdglRmtzZWiCrpVnc+HDj0pN67kmW3I3JRN9ttAM1QT0TAcH+pK8Q8v2Ko9XeT1mN9SxvD3VAF3ybS9Ypaq6h5Wrc/jnGqwO1hwxa3BXYI1KOZczJWRao4/HtL1UR2WiJP76nP2mNwdPiLWgihpTbfns95/9LRyd2FYqrcI1kJE/d+YqvvLC9/DtH/wkCNM0PsZKRLz/4tdx40S7dnZOvXULVZEDvSgSVVENVMZtpDeZga0NHvu2d2Z6xjGly45HAV2dZJ5hz66LCszw4EDBQLdny5grNHlzjkV1eHfF21crjABdI0csatqO/+PpnwNA+NfusdZ35fgs06RCtzY65vkyi0ajGr0mgr2Cjf5Q46wqABrh4kVr2rHUaGkbSMfw2pZfiCmQq12fCuHenVrN+IxonG8uhwwqeyZKP+h0iPUXsWBrqavDlPW1V1ooZZ9xz5nFAqB0d7Sm9AtP7gDQpelWeOQ7jMiMKUVVDjQVwuebK+irtWuwW6derhDbi+EwZYzxeF0sWKAXYGbd49s2liJ6PedWTrN1D9jn5N3OeZ/t1Ky3eD3eKTk02hjAptF2uQ4tBciSAVVOe3WO8KxSdfyVpwC+s5L4M99cwfhoA49v+xKAdA8d8o6jE9sKv9f5p/p4qGHDnjENprZEByM19fmYcUrX1oszj6UHN44MlWg0HafWSJLK/LBzsMZmzvfk07vDXl348KNSzyc1+HIci9eUQVZardADyoI310N7uz0rgHKJHwue4cP+7dDezYXsTMWL0xdvYt1A1xls31Pge/VuCpDby+zIq+9gy7OTeOj577qBSoRUv7iUIq4GulQT4SOvvlNyaJ46fz1EUdMxbA1Ed6P4e/dCQc+X//LblOcXmislYxT34+CussPNA61xT0f3WSdrlGfjBZxotq86G9Voont8YFe5XBT3XuUnT0fKDSYgWAO/Oqra0MVR9uOM9e+LgccrLc9POUlzcMjr62HHYHAcHbq5c7Dz1TnFjJ4WyP/p5NCIer2H3CPlg0B1UEPVHqQi/q2xcWx4MNqPSOVfDcqxGRVqiFaaCRTpdayvFnsTDXWyOIFu1oZ3VjGDrJ6bGl09XNy5aQSTV6aSBuzU/YrhUy+yX0qviAF5L8s6H53Y5tIIWxHDGntje+utjbI97Sk8Gw1GAZCtW3pOAnVApBzYNM5readzl6cKgWFA3DGg9w/oBmsBxaA0VvggHVaZ7sKHH4Xf6ZnYrHzFm170DeuYTzkdbDUNezdU3ub9u3/snoKTQ3mTtZeo/s49Y7Zyjq6VqhBhg03oxCUXskEHMdtVzHbm2SVSzkjLO9rzqBV4vMrrqnvEMlc8PT23rK3HD8mDDr7yVokGrjlffnGw5ohZg7sCKyTkGA1TF1qNPqxRa9NtvQjiQ3u7kYiPbL4PxyevBcZ2fPJaSfDzogU9IZZAgv/iU23DoE1RVeiWbVouMHttVjkz3wwNSIGygTe1d7F666s12gK9Nd+qYkw0/H624EfeWSOKFSxj0Zp6xql9ijnhjk5sw0tPdUurUXAio/Fq0pJxqhNCs6M0OoZ4RQfikVffCWsD0k3e7b7aPdA90ywwGzXt4YBVXhR4n9SIZhk8hW5tdMyGcMODA0GYsyW+YmDnWEVDOA8aZ1UB4Pnpt1PRPvZM1ZHkNZ8GuoLwwV3lEnCrjYRUsNl2arSigfH5M37PgRhwzo16fyGzQcEaeOy9SdFpFdAt7dBsCyo9z525Gpqfj4pRswYU7jcNmLGeOXZOqf9XepTKitCzAbrRXKmyWB6kIhxTYHkTx7h0Y9qNXvYil3PKIypQWKdjJGas27NlLOkUSIGOqQa3yStTpbswNdMuaTc7v5Q835TT3jaQ1r+lss/IUxiFp9kmxG11Cigu9Nqs0uJyLBvKM2zoemrOmKSBAEqGE8943ovyDqDTp6V9TlqCUMciX1WjolcagrTMKzNpS0t6d8hzAgHlSGcPX2w9eQWN6LYlQQl0ethGpr3KX5qdqYZIysDjow3XEOqtb3jdQHBIe73Mzl2eKsgKVYZjL2o+RdNsFGZsnucutx2aI406jk48FObA6GegVmkgSu2zZ+j07oUFLfmqctihvZtxZ2nFddylHG7evNT4lZq3BiSosd/KyjHaqE5jBieQr1/48KPQwLuKt+bSB5WlrFFaDVM2AAtoBwLQOZVjoPZ4JdAtD6b3qKqMa9V9rXLqKF6osVHxp5fyoSpTpfaA/P/O0gqeeeLBwl2xdFjXwUbN9w7VC/09et0XnZvFEStfaxBJbE1W71Z8euaJBws6H3FocKAPpy/eLMhnsQoBRycewmijjsGBvpLOrnOyRmYA7toO7d0cDPU7N42UHO2H9xfL5SqkSmoqWP6mvMEGbMbkP6+KRwz4zp4tY7j+BxP4xle/DKCdCax3yuJuClfsvL0x+PvnzlzF8GB/4WxUDu0luK+KfsRsCLq32utnqN5XyJK3vDXmqNLn7JxOPr0bN05M4P0XfyfgjCfTnTp/vZQFxm9olnkv+oa9v3pP7Tw1KMbeDZW39S56QcWenm5/n2PbsftdZUv08GCkQ1dswJ4GkgB+LyIFnsOBXeMYHhzAni1jQWYH0n2myTsGB/oK+6+6tMoBXtB4bA9i5bRzaMBZ009sZr4Z+HrKab0am+MadGHNEbMGdwUxZks4+Mpb+PVn2x7WFFhhsQYEoSpVtkiBBIm9VwhMz68S9D0h1o7tGX+BYjQxjQK1zph89/FtXyr0OWGTuka9r1KZ85i9J0jECK6m+XpEMybEpgjt4tJy4W+6f5pNkYpupLDIsiYamVCliOkcgW5zzyoDlypyOdFFHI9OCH7LOlhsBIM6KzzFJAWxs1QFyjOQ6ns0hnWNUy14YPE+5gjT76lw0IsTzztfzvnl1z9Inr0HsQhm0oHUWDwXltFjlKYVOlLRgb0YM6v2ifRCjVY8MSr8uXuzZ8sYNo40QoPcmIFa8Sd1byyoMVON+qTzLG/GjBagLaBTUGWZjIc7/yrYyNBeDca6PuJ1KitCn923dUOI5gLiZbG8+6l4/JUXvoeHnv9OT9GIQFFRVyV+/fC67MjlHMHbNvf2jHU02qacAin+oGM+vm1jUGa3j4+USp1pCaCqMWO8XwMxeommt7xdjReeU4BOHyCOT1UyBmlML9GtuqYRMVRZx3NOFNuRV9/BsdeuhujhHLCZgbE95LNejwFbksiL+vfO2PJ4BcVlG03tjZWi56TR2neBRgagbZj7+o5fA+BnAPSSVcz1sNeOLcPoGXrs//NZ4gHna0EdodaIr/OOGdWrFO5UzxB+c9NoAwOdi0M5KhZUkqJxVUbqmJHY9k2Jzd+C8jpvbVWZDFbmUXw9uKvcXFodPGrs94ywCjHDjfJ1oGhcTjnkLt2YxtTMPI5Pvp/kYTH+cOnGND5baIbeBopbLKNI46L2FbDBdzGwgVPWoK/OEc+BV3Vfq/5um7bzee4xgAINqTJYKZ6lZB4bdOA52wEEhzdxTxs1e7KkdUTEHEkp5z6B/6/GzBiuWf3D4pN+Q51r9rxj/WOAbgkqBhxRRtNAAHt+tneHAg31arAnWKd/DHKN57pvHl5YPOX5LS4tl2h5DA9jMiT5uKfHe+/lzNuePeGW9OECihkxLAeaE4xWxT+9ddsyYqobPb5tY6DD2mPTCw6KnWmVDnNo7+ZCP6qUrshvUHbyMns9UBubzkUDZu08KSt9PHencDesg1Tvoq1Y8dDz38GtmfmQ/RPbl9je5eg3KZmD31TaYW1QzIhnLzkF23/angN5lzptqmQUdfqozq1yiA30AIp2xtj4tk1DCvesXZRA2yTQrfjjBWDn9OhZg2pYc8SswV2Bx2yVYTPrQ7M/POCFfuPabQDdZtFV0SIEy9y1DJVNz69y5PQiGOncGeVCBv7iU0WHDgWLpU6z368//GuF9N9UeSKtO8o15DB77os1rtnxYwYhj+CnatLrXBh9ZfvF6FqZpUGjhhWuc0CbBHtRATGmZRXbVASdnuszTzwYFAV1sFDAYfS9RlHnGLn1+zFDRMoYb8+RQjR7S6uRinjBaHjFJxVOrUGTmU4/+s9zYayUEcOCd75c6+LSSkj11bNPMfvcPjnePHpRwmPCfC+RSLG5arYdjQdqtALiJdmqoha9SCMaji7dmC4ZUHhv1PDvgRozdX+v3GpHQ3220MSlG9M4df46dm4aKURVnb54M6nIEm+ZreFFweY4N/Q9qwhaWqvOO9IENtTsE6GU4OEWfwcg9PHKcWrFQHs6vXtrNqrs2bnkOK5sLXNrrItlwOQas05fbKf0z3acGm/f/ARHJ7ZhfLSBH0//vFTqTEt5cUyvB0vOffOahefijXVO6l7y3nz7Bz8p1Zf2xqmSMaoMqwrWEK3vWcczUC6xZvfhnOkBlLM3NjPQG5dj2B4DVNYZFNBCO9K1KlPNRveyP5z+jcZcryxaLn0mTeC+aKQiFXXyLM2C1Xnn3DuCniFliHUDfYHHpbLAYoaflLOC9+ulp8ol/qwxRKM6SSdj2aLe2j2awHkOrxso7IG+r/fu0o1p3J6dx6Ub06Xxq4zUMcOO7ZvCtR159Z2CY9gaXpTXeWuz5Vu8s/bwZ7RRd5tLq2ymoTNVDgpruPGC12xkeQx/uK6YzhADxSu7L4pblHmAbsldOhe84LsUjWJmmhr0AZSc0laurdrPlD7FAB02bVdDLvc4FhGv56trUjyrolu29A/7H9AZaA3pQLF818aRoZKcZx0Ri0vLwXEZC2zQ/ajiIzFc60WG9mQ6OnCsfB3T+TXYQgMB+Mz9Y/fg9uw83rj20ywnQ+xvLJeruAN0S2RX8dtYQFzse7S7UKceHOgvyfe5ASKkh8STiZ3jYT+OT74ffS+GJ6mzZ4apltfk2ARG6DOAw5M3YxkCKf6puqfaEJRWaVCpJw/boCLr0MzBcd4/3mfFZWuX4PiUnejs60UntFUDgHJJPw8/lH+o05e0z5asO3X+egiKvrO0XKIZOXff4rfSbfLx50xpWp5jKrveAzqOdc+tYyMFMWdXyvFM2UdtSFU2i9S9O7y/66h/+fUPAm/ywBvzpad24NjB7auqgrAGq4M1R8wafG5gDQIvv/5B8Kzu3DSSpfAvdIxX882VgtJdBZa5q+JpewfkMMVcgdL7W6qkSH+tbYTQ6JxT568n04hVkQOKJa6qmJoK16ONOhr1vlJWRsogpEzCClg2y0MNaEA3Csk2JFNBmAYPG4nC9HlVlqsiYrRUSKo2vq7NRg72eq5qwKSA8+Ppn2O51e7J0kuph5jgqu+qMV730RroAIQ686z3q4JcrHeGOnOAYo8PoGt0uCKG4V4MUFYI0XWz7MBIo17Y41RptVgUHOdLQ1oqqs9zLMYcYSnjbhXdyHUAW6PVaKMeLckWm49+yxq1tHmpBabBq+G/F6j31UoNL9lbiVFV3v4ycmrLs5OhDwVQNm5VOdAsqDKbQ2t1XqwD3lxulZ5L0Yp9WzcEWttrtoMqALY8QiyjbjUKh2cs4F3at3VDMJbRcESw+x/DadKiFroKrPIi2+fMGlhiWVwp4PiW33jzJnh3lsZxluXgOJq2b50CFno5gxz8sNGaKX6mynLMcTbUKRvIHkA5TudUJontLWKf5Tc0GODO0rKbqeY5FACELGmNgFfDoVeOR9dQFRmr+KrlKRaXltGo92Gl1Y7y1/Jn6pTpxaBo5VHNuFVjrRfRGDMQ5+CIt8d23tb55WVSeDA9t4jnOyUoY8/GMm4oX5HmqKPQ7m2VnBb724FOE3YGZnFtLOVFx3AquMOLVI6Vuuvynrala/v4SBZuaNYYg2hiDoq7AXsf7NpsH4OcMRSvGEigAQXWuKxOL81otIESHv2mo4xBXHr2mhVgHXgASgbJXIjxPwAlnuA5DZQP9pLhbOdg9TXr9OL3dO+/8+6fheduzy6EMkKaqcUeD21oK5wLzZXoPFPO36oeaDlR2imI6Ug0TsbK+KnTV/UpPkPnzHxzOasHZ9W8CKSnVQE6niHf7rfXC492l1RD+xwdhH0SW2iXjGVZrIVOr5b55nJwvKnzIFc2V5vCkVffwfC6AYw26jh2YHvJnnGwQ7NpR2oBlQGjnmE6xj+t7ukBS6StM9m2/K4NKrJyiToMYhnyKbna4hPx4sfTPw/VR9T+EpPjvLOnrA2kAyl5b1QfsHKCZz+yfFLXGsu4876t+K10W4NwVwvUuTR4uxd9xGbp3ZqZL5Wsp73luTNXcenGdElfsxl73Gc9w1gPLHvuh/Z2qwMAiOq6py/exJ9/2i4xvL5TqlRlLGvv8gKVq8p/rkEerDli1uCuwCP+ashtrrSwabSBs998LMmoeaEP7BovlCbzGKtCzAPtESeb3le1phSjsMazmMBp4d6hdkkyGqGso8QTMtR4beuTVgk/apx55okHcWepLASmhGJlEt43NK2VTJfn1m3AVmxIdnj/A8EQR6OfpqFr+rwqyxoRo5EPzzzxYHBwcY4xpmUhZqjPVQaq9q8XyPkmezZwf7XPiSoeFBDHRxuFer8UnLw0YqAcqWvrwNK5s2l0KGt/PYjteZypt8y/XYhFwR177Wq094p1WqpjMSfSluCVxkgZe4FyZFvqzKsytOx8POMa0DUgU0nX5qUxSBn+LXC9/TXg2MHtwTDFaDrShZRyoQrN7dkFbBrtNgVX8AxhsT1UAdgadZXWxvDCM0SlnG/EhwsffoQfHvsa3n/x64Gu8bmDr7xVKmMTwxs1GByd2JZVNm61RgwgfpdSvEIVNHWS69wZoUz6tG/rBix0zvrChx+V5qyKhJ6P54z3DNYs9eSVu7LlNewa9f9tzX4CM7x6dRSlziBVwjUVKBEbj2BpFO/P49s2FjLacmWKGH5ppgu/peem0eIsS7h9fCRqHLDGU69MjOd40j2LGbG8dalMwiyeruO/ZvqE1Erv54ClM6pw6305vP+BwrnFHK0AgvP6+OT7bpaU0lc1VKmsEDOmA106yXIpGnWvoPT7vanZpJGS5Tg88ByFqX3MoXd8hv0PtG8KjbGxLDgLnpOGTsc9W8bcfWRfJTr2rb5w+uLN0AuOvS0BoCZolnKG5EDOfbDl5d6++QlefGpH6GOQM67yZwYS/Hj65yUeSRrh7eeFDz8Kclsqe4XvpLLnl5ZbuD07j2//4CfBaJyS87y1qdHb43+UHa2jWMFzyuT2BbWghmzOyzoD+T0N5miutApyP7PwiPc8CzpngBb6a8D46FBJ5tK5eJlqPGs9R4I14PN3OWV3LCiP0TFtyWt7FqRBqk/xLlLHadT7e84Iy4UUznqGfELKKFwViOHtewpqKJa0PLCrS4tPne/2QmLwhYeXFjgHAKVeowBKpalJW7/+8K+FMbzeiJ6jk2dq1+zJSSldS6uJkBao3cnabbxzUMc/dV91zMToStU56VpTcpzuA4CCHELaUVVa3I7Hs/ICnwhKo/dsGSvsD5BfutHKLB6wl1AMYrob0NbLGbytY3DfUrRaz2r98DoAwNydpYLeyawjoK2Pp8qoxZzbsQAR794pX4rZFU+dv45mJ9Lj3YqKRXadqd+tQe/gS8NrsAaZ8Mjm+3B7dr6U7aBERRkUDciPnnizxBytgZLvWgOjAv82t7hUUO7027lj2XfnFpcqv3vq/HV8/9nfSo7L788tLhW89+r9TglHdm/0Z7v/qXe/8sL33NJPdnydMwVsPUfdA03ZVYMs/7/NgFolRq01Vy0onnzn3T9Dc6XVMcjWMD13B/PNYkNeNTZwjqmeN6oU8DzsuXkGOu+MPEGPkRGpqB0KkvpuDp5zPhTUtTEc753dD757e3Yey61ifxsLik/eOPPNlSBE5ZYjU9AIFm8faGAqnlM3Mo/CI8Hi5iOb78Otmfkg+HCdVXff4oV91rsjFqpozvNnroa0Z89Apkop8T8G3ny8e6rKGmvcDw70YXCgzxVaibuPbL4vGKJTtNJ+l3isUZeTV6awZ8tYcg8P738AxyevYaG5gomd48FpZJuCe/eaOEWjpa7dlhTQeVq6bNe5Z8tYIbLJ+1au0aBrlGvf27OXu3ui+2f5iuJETj+RHP4GtMvTTV6ZwsTO8UJ5nBx+HaNNpC927kCb97DxI0t0puas33n0xJuF8fU7AEr7ZZVOHY9j8VuxNR/e384k4x7xOwDw6391GN/46pcLd520Q++2hZQhxCvhaumAp2DHZKbYHKyxlePTOX7pxnQSz2LfHhzoL/Tl02d5XuRNPLeP5+64+OTJO4+eeLOUrVJFTywttvtov8n5krYMDvRhvrmMwYE+PL7tSwEXLnz4UYiWtjTU3iuPDzBj2N51/i4Xjk++H/Z8vhk3hljaoTI6gNColtGaMZmUz01emSplRVn6nYIUjcqRhz1ZKEXv+Lwt66u0lbK40h8PUvK25Q1cy/rhdXhvarZgJLdrID1ckDvU6vxy02gj4IcFxW1dg6WB3p229H398LqwNourMTpj6ac2lqYsoXqUBY/3WfmUdNfuQQpX9m3dgMkrU8HQRLrq8aYYqCxZJQeq8b8KYmP0Yij/bKGJ5RbC+Zx8ene0hyHv5cObRvDx3J0wPnWGseHBgjxKfGUA3dTMQgia8Oap94hAGqNA2rhuoB/zzWUcn7yGY69dDX0G1aDs6U8eeLKH7h0DA3L0Kf5MHefw/gcK/W3uFu4fuwczt2ZR76/h2IHtWbzL442cv9UzUrKZt87YN6z+qs/t2TIW/p9700L3XlXJn3rHGRijdhAtk6Vgna50BMTkTU+eTt2pHP0OQEnuBNrneuXWLL79g59EacTh/Q/g+TNX0UK7cofVg3sxZuv5sNoB9fWYvuDhwXNnruLbP/hJqU+f5R0erVfdirJfLIM0RkN7scupzKJ/f+aJBwMe3FlaLr1nx9Bv2P9P3R+gzH88YLDF0nKr4LixvMG7hzq+t//2Z0LKdlQlIz93ps0/6HAljd4+XuQVa/CLhbWMmDW4K7CGMfXsHtrrR/K9ce12iP4C/ChLfdd6+hU877p9X5UKRlZ5jefst23klBfhqQacTaMNrB9eV4h41sgBoFteDGinA8aEodzIZlV8csGWfvIgFZFJ4JptrUuWFWPtVJsRw4wM6xAi8Hsnn96NlY42ent2oRO18FCIkE3VLY4Zk2y5jaosJE+Iie2TnT/nEsMZ+24Onh/eX25Kyb+PNuqYnlvEV174XvgdFSvtWaMN4phpxMhD4hPPKnYP9edcXAVQKOuizlu7v3pOTBtvAW6jXQUV2LWXj+KnF7FFQS/WtNADG0Xl0RyOpSUJT53vRgB5kZ58l2fz3Jl2FHLVPnv3lOtgCSqu04uuV8E4VRc59g7H4zcZaZXKHNCoJPYQ2bNlLFruJedcOC7pDyPcuT9AsX484JfdUZxg1J8FnU8qqo53lpk+AMKdA+DeMZttE4vwXM3+eH0N7P6wlJ3Xc8X7ptfDiNkreu95DzXSvga/BIiO7/Xe8mhhjF95+xOTUQ7t3YyTT+8OpR+Uv567PFWou5wqdaegcohdJ7NE+K8+b0voxM6Y+Kw1/RUnlS8yu4F0RjNPU9k39tvsm3P/2D2FM9VnbTShjmEbncdonJU1Us/ac9RMtZxa+5yn7t2eLWPYONKmTfu2buhEC5czCu29snxAo+Yt7uqzOSUfNCClr0Nn2WNRwdJ8vW/aqJbzj4GlyTbD5/0Xv44bJ/zSdQopGuXJTjFcsHKIBctjAbh0zMofKbyyjkxmJNksIeWLZ7/5WKmkn549z6NR7w8VAUYbdRzYVZ0xYWUFNe5oVK13p7nXNIKrQc5mt2sE9/HJayGwhOfBZ0jnFzv9AFSP4phKI/S8tTQmZdR9Wze4ZfiqemfwnBqdzCqW503hiT1v0nzqKVWZWKk7m8Ip/i3Vh8lmjPDOVmVl6r08+83HknyEZ8FykQygG+iUhWLpZ8WtWCYLeTzb690/dk+gjQvNdrm4heZKoJWcC52AVTqxvduWx3gZlAp6/2L05NDeeJnxqjP1gEbalZWWO572bkxlzsf0DA/s2hr1PkzNtPu85NA8dSjbrCMv2t6jx17minc+KsN5Uf8851Pnrxf4lnXaeDQlJ4M+BrzXB4Ue6zpz+iAf2rsZLz61I+iBQNz2UAW6vljAqe2ba/GAcOXWbEnPU7uExS8G7PDeeVVaFDwHGvkI9QsAIZOE/+oZHXn1HUzNzLtZoYf2trPnczLULW5a3LCyqL5Du97BV95K4g5lpAO7xgsVNxgkzh6LKduSnm/s56q15cChvZvx0lPtygNHJ7YB8EvPV0Gv92kNylBrtVqt6sf+csOnn36KkZERzM7O4gtf+MIvezq/UtBLFA9hy7OTgRltGm2ELAf2QrjbeQDlyGd6zRldxshYm7mTWgPfIdHXSDV+f2pmPqyNyjqjFVmihXPxxvG+xfe8OWqkcdXepfbIQi/j6vh2bUBbGbqztBK87NNzi5hvrpTG9nBJI0xtNI7OT5+jYuntK9dVA0Jz9Kr9stGGvewT/07Q5/i3Rr0PY8OD2XdIcVfXp99Sw4PFM43g3rd1A96++UnIChht1DE8ONDTfeZ8Yu/quaqAaKPONJr8oee/GwxNB3eN49zlqXCvXpJzs3tRRY9i5+XtSS97YMHO4/TFmyEy8cCuIo4+svk+nLs8haF6P45OPASgW9rvbKePS7vUVyOK173MI7ZOpTm8R7k0MTan2HnYbBTF19yzTH2D8wLK9PWh57+D+eYK6n01fPELQ4X3dKxLN6bD/gNtQ9nRiYd65nd2n8hvdO3e3imuAijcm9zzj+2PF/kEtPGO/LiGbk1uQirjw1svaY/SBn6HdFUzRVNr83BtNXwqZ3/4e0Yxsn70QnMl8HTOl/OvAeFue/hx8JW3grKec4a9ylYpXgMU9w9A4a7T6GhpQlW2zwPfmgx8nmNVGeJ1bZSX+mvA9T+YiNITjxdTia3aS/KSqvueS+e4d0D5HFMZMYf2bi7wfJbn4zlV0Ur7e66rUe/HQnMZLbRlzhsnJgrndv/YPSEj4+TTuwu0kfdSszZS8lNsT6roddU+e3zKyuxeFl9s33uZn8VxK1vG5qnRspTT7d88HcH+XWWvFA3z5h+TUXuR9VN3S9fFtZIvACjxbV2LRm7H5Avvm6ONesj4SOlGKd2Na1K8jtGlGJ5YvVSfS51ZSh5JrcfKzzF84f/H9JLV6OQevuga9Tx0z7156XfJH3jmekfJD3duGsHZbz5W2Isa2gGDynM8GhyjUSlZ9sir7wS5znvfuw/cE+9MU+em7zHD1pO5Du9/oKA7272q4ou5Z83zqAEYF4dTTDbQu5+yV6TAk9E8Wwq/C7R1Pu++6p6naIT9tspmVXI3IZcfP3rij3Frpl1O+fvP/p3kXni44dGoFK/LmWOVXEzZoa+GEKzG7+i7SsOVB3i6foo/WTwDivYvZrR5cqD9m7cHyr+5r73IdIf3d7ND9DueXY/PaFBQ1bnkyh5Vd3g1tL2XsVaTEVNlA/jLCr34DdZKk63BXQE9tUA+UTmwqy1k9NVqgSh/nvOw5UcO7d0chCAtT0FhS1NclWhaQdcTgAlkUo16XygX0UI7alEjbAC4Ahn3bnFpGYMD/di3dUO0BIvua6wMVq97ZKGXcQma6aLGHaCd7s2IO0apMSrGGmV1Tppyr3PWyDugGImqCrEFpoc/vKm6caoKHjEGwxIIdi05DMwr3ZDDiKtKFGmmUUyhoCDKvV0Ng1emDaBUgsIKcM+dudqO+J7+OYDuHfRSjrUfzNs3P8HDm0aCEVNLjKjyB/glDq2Qpn/TNdu74ZXlyAU7D41esyUJj712tVCLmmthTV3PEZkL/Kauhb+jc+v45LWAJ6Q5jMKvAqWJttyk7n1sXixRYPH10RNvRkuGxZyz+ow6+Ox+kT43V1qlNXJexGGFheZy6VxzwRopPIU9BTQQeutJgRod7L1UBV95DJM2h8RBrPuSUiwIxCVPoLelxkYb7b5pzZXW/8feeYfHVZ35/ztNxbKKJVfJtmzLvRtXDBjTQxxKSEJCcEIg7G4MgU2ybJYsEJYlLCRLNoSQ8MtuEkIwYUMJLV6aDca44Yp775ZcZMnqbcr9/THzXr1z5tw7dySNRuX9PI8f29LMveeec+4573mr6REXrw+B8PziEUJ836a9x8nhRn02/nNahdK9HvNAy42qM4v7Rc1Bu34ij1h6FhWnylOrvqf9hBwdVAqy0lBa1YiCrLSo1Gp2/UPrM9Ba+4B/jxRGQQOWaauAWMUC9RON+6TCXNu9jT7PZSJ1PlhBKcYCISNmbFTZzwr1XlbvopoiSF0v+J6vjpEqS9P9rFIvUrq0qyYOwtFz9dgekWuov2jcqkurYSAcLUNe5BTNpXs/+L1VwzQfQ7VPrN6jeL/T/f7ZlbFpJWmevb2tLOr9BmCucXz+xZsf/Nmy0r2OPEDV8eTp2NTfqX3I9zOe4sxpNLtdH84ZmW877+j7G45UxqyNVnuauj5wB44Pdp/Wrn/8e2p7ubK5tWh87PtcGYnIcAFaucLu7KY+PymedesS3y/ouryf8zJ9yM9KM38eb8zszjFWchJf8/i7qK5NOvmB35df02qe2O3Z/DtWKT6p/fwaVqmRCNWgwMeA6tfwOjZ8jlj1Ia1Fdim5+Wf5+gkgyrmGrw3q+PG5ST+jKDCuD1DPPdT+h9/ciewMn/kclOVBJ3M9u/JQVF/x8+yckfkxNb34mYbawcfSSm5I87pNZzA+FupaSc9A61umz4OrJg6K0Xu0FStdCt3XKuW1bs/RrREqBlrXHPXMQX2j6q6s3iOaTzQXWmvGucxrWY0Bl/3pWUnHwNcoq73O6noq8fQ3JDtQmsBV+8vNPlQN6Lr9rjkQQlVjdN/E2+OB6HMZd/pR0wDPLO6HsqpGVNa3YFJhLnaUViPN6zEjwjiqDkF9v6zGQl0jCJ6FgZ4JCI/ulKJwWyiq2eo5nfQFoE9TaieXO72uE+haXJ9ileLSzrDkVA4XrJHUZEKHQQuiLkUHD1+jAmwUrpfpc8dNw5AISxbGFua1CuGkVExAbIoItTD84nmtKbOsUgBdNXFwlAex2wVU1jfjoTd2mmGNdM+K+paYYm6N/hCqGv3YfOx8VMFiur662FGbdIsyDwOlsEEaB11KpHjXdRqCmJvpw9O3zDDDuil0lcLH1WLK1N+6om+6sOYlC0uQ7g2nBaG/qbB2WuT/Vn1CyrDtpdVRz6F7Nqs+56ipKnixQbpWXqbPLPCsM9jEKx7K20bzRJc6hq6jRvrMGZkfN7UNH29dXyxdH5uGg4RFKs7Ln4MLDPw+u8qqY9KF6Z6dxtWFsAKRjDCU2s9p2Ky6JvH0EaoARKhpOdQxsIM+d+9LW2P6iz8r72+1KDxvF62VTrzMdSxZGFvck9qRwbz8S6vCqXuyM/RF7AF96DY9B8/xbdX3artoHNTQa7oWgJh1HIg2ulqN4eJ5xVgwdgCqG/14bNmeqHHLZCmFlq7XF3xfsrAk5pA/pSg3bnoRdZ2l/+vWU7u1G4hOdbJ4nn2aDCt4qiF1btHP1D2GUuJcNXFw1HdVg5ndO0jr4vGI0ZVD16L94L5rxpkpKLmxQkXtr2dXHoqqG0Jt4qm21JRy8dZ5vmfOLO5nFnDnqTQWzytGfla6ebjnKRaWLIxOf8j7idIWXD+tMO4hS+1fdQ8g4yp/DuofKjqqpu+kvt1VVh137nE5wcV+rraL1qfrp+lTCRJqyi7qc5IBK+pbYvY2vt7Q5yk9HSn+eCo9q7V5wdgB8LjC7686Nk7SL/K+pf7KSvdaGvms9k+eTonSm+kKzC5dfwwPmsXb95g/p9RABJc9SJm5vbQaI+9fhoKsNDP1CaVQo3V+87HztmsJf1Z+WH9rW3gM39pWZipEnKTMi/c7ADHpCenz3OuU3p8MnydqX+FrEJ9/uvVCVXhwGYXa5yQtHBB2qGoJxCo1rfoQaI0o4GNndT91Hqlry70vbTXnSTxZiNLNvL2tLKrvrOQate/4/vP0LTPM9W/zsfNRSmlqJylI1dRXdKah93hwbrgQPE/TTKloczN9ZlpMXepfOkdxxyMVXYpTembqE6vUj1w2AWA7Zlz2BxCVJpi/K6qcZCXPq2uT3XqtpjjWpWOia6pzha/zfG5ZOQWo7VfP1Xw+xfPqV/ddWh9VeVgd98Xzis2xfzuyFqlY9Rd/dnUfVs+hNDd5PxpoTdWnO7/xlFn0t7ru6WQuUoKSrM/nrTpHVLmar9FqsXvOk+/tQ6M/ZJ7PrfqLziQeV9hpyUC4/gY5hm04Umm5v1EqWi5XU3pBvr7pdCl8fdC9zzrHOauzB8HTp5G8qUuLpjurqO8RPQ85f9JcUPVNdikG+f1a50dGTMrPNK/bzKhC7XryvX2Oz6FkqNxwpFL7HXpv071u5GX6UN3oj9vmxfOKke71RJyTomugWDnRqGvO4nnFZtrJOSPzTWM6gKhzLtWRbPQHUVHfgsK8TNNRkaO7L3+n7M4pujUiT3k36PwMhCPI3rrnYuQq9XWd6I7s2qFrL8n2OhnASQoyu3mirvlVkbHnKY1V7GSneGcJIT5iiBE6DDoo2Hk0csUCEF74Hlg0sc1Kf4562FULAOoWRqBVuavWuVFzgFu1iQsIm4+dj0oPYhitwgzlXQRahTNSNgAGXAB8bpfZf6pwpFvs7PpJJ1zQc6uHD7tn0+UA1mF1oFSFLi648o1UNdDw9qoLPglY1Manb5lhuVlzVG8Hej7KmWtV68WqT9RNlB+euABNeZfJ84QLa/E2MiuFgdom1WPBTii3QzfOz648ZM5jUsSoh1z+HKrRgWofUPRMvD7mRiWunF00tdDS4KNjycIS+CKJqguy0rRtVBX9al5b3iePvLULI+5fhjEP/J+twLJse5nZX29vK7PNaf70LTNw5IlF2PPo52L6zu79c7JG8kMVHShUhS0p3gHEHPj4/XR1RXg/6w6cVvsBPeeT7+2LMhTza9GBSa1RQs8zqTDXNjqMIn7UNeGBRRPMNV9VevP5wdchjyusLI6nHKff6WpH6bAydl3/q9VR77Pus05Qlf/qOOn2GDpwcyWY2pZ4BwL6PQDzGmqOc34I52uJ0+dUlbXcwEPGAwOIGgPdeqEqTWnP3HzsfJTCUV33ycBJ9RnoQBn2FgwbAFVFC9WciXfIsjvI6QzFHCsFhVXdJR1cTrguYmRZMHaA5birz6ZitVfwtVy9Nl9v6PPHKxtiauI5NQqSsYKUGjplP8dqHsa7H/+9qvSlel+kaKACs+paQjT5g7jvmnFmbnl+Tar9RnUOaE0zEO3wQHX1phTlahW0ds/M32P1GflnVflDJZ6MwxUvVjIRKSofWDQhal+hNlIdNCvU+WXlGOFUsaDuh9yIq9vPuGJfvTfJMvx7OiUW0Dp/VSM7Ybd+ZvjcZt/xOlGJyohqX+rqCqhnDM6Bs3VmIXhy5qF26M4RqhGSY1fzUufIoirc7WpMqM9MxgqgNUqe3m+S/and5FTH+1Y3B3V1C+OtTZzmQCjqbyunQ93azceK6kaqMhGN7UNv7Ixac4BYZTX/np28qPYz/x6tBfkReZ3QzWtaE+3g84nLpCTnqP1D51Cam6v2l0cZzklupfmumy/cudRuLbGaa3zeqn2sytXU7uZAyNYwqWaRsEOtsUQGITrb6M6HqvOqk3VA1x98LXSif1iysLX2jVqrSzUgWxludGcVKyfLdK8n6rPcMEzriu5cqba5tV6wK2qNorO22xWOXLFzhCHUd8POWY3rXMgoQg5NTs7UOlRjIX9OqzXHbo1Qx8POuEz6NpozVnoIXT8ROsP6RU+E62Ry50T1s0vXh9MX6856VE9HVx+Yo7aXO07qZAAn75LdGPL3kkOOOlbGIr5POTUICc4QQ4zQbrgBxMrTTqdYqGr0o7bJH3UNWiCdbjj857RRktLHLuqDH45UL4xWReWEqMXZqk1cKOMbLBXtIsjIQvdfPK/Y3IhKq5pgABiYk5GQ13M8S7uTYnrqM+iu//CbOx1F0egOlASfJ/Q5CvMlD1Mr76GCrDSMvH8ZJjz0rnnt2qZozz5eiNjKO/3pW2aYBcq4QM0L38bDqs9JWKA0FTrFWnMgFCWsOdnI2nJoV41CiRjUdHNEJwCph1x+D1Vg0KUhsIM/JzfekAemWoyxICstxiOLrqPztleNp2qUQF6mD9kZPtP7i+a+PxS+lj9oRCmX6Zo0BxdNLTS96TN8nhhBWKew4c+uO6DaKWftUA8UfIzo+ZcsLDE9x/mBj9/PTlmuExD5IUi3nukMxfxaamQMtYPmXUV9i21hcYr4oWfh1+drvpWQz/+/aGqhbf87FUxV5aUqxNsVS+RrtNr/Vvui+o46EeTpuapZfRo744UO+j3NKVr3rOYqtXPOyPw2Kwe5gYfvuxwrxZf6e1XJoR5Q+Ryi9YnSFdBe0ugPxvQTv4bdIUtd53k7uKE43mGfG3Qpyla3Xtt9l68f6vM43Ves9goAlgpwp4Yju3ePr8nq7506QKjrvNUc4p6GpIjhXvIET5FLc0ZnaANgplDlxnT6LC+uTvORnA74nknPSc4g6gHcbgxVgxkvNEtF2ym1ZaJOAmqfOS1erO4rdnNS/Z5d9HhbjdyTCnNNZbwu2gSIdcDgbbFymom3p1hF2OneQxq/qyYONmVtNaJE3Zvs+sOuL/l+anWua4qskV63y+xDnYxL+4dO4W4XuWS3P9L7S8XirdIgqdcCEGWs4M8DQGs4UZXiar8tnlccc814/RtLWCZt9AcTVpLpziiUulB9Fw3ATMuzbHuZuafw//OxV9dvp/uN1frK57WuUDzH6ixi1d/U53StcCRhq/xoZRxS70XtSvd6LI3SiSq66T1dtb/cXKPvu2acmSqV5E8eSaZbC9UsEnbQeDywaKK5b9PPKLJEd05XZe62eO9bFYu3ut7iecVoCYTMCGWOLopfJ+/rzir8c/w8TX1vda5J93piDD668xI/g6jrkwswx9yJI4wqq1DEodW5hZ+byJAVMsKRNHbjRmtuujf6TJvImsP7leQUnYMm72Mr4zJdS3e+WLo+nG6ODOe8n6zefRorblgkQw9fs/n53Ihcgxs4+dmWIvRJ12mHOi/aavCw+x5f5ym1swswHTKtjEVAqxOIKr8kKjsJ0YghRmg3doKF1WFE9epUrxFvw1HvxTdKADGeGE4sy2rIrfoZVbGhLj7Vjf4oCzopPUjx//D1k6KEwKXrW0NQpxblRj2vncHJaaiiTrhIdAHl4+TEs0U3PnQfHgWiHmDsjDtr7r8cOyJ5zsljktejoQ2UhAtVAFIPufE8mOLB+5wLjSQsvLWtDPe+tFWrWOMGn0S8hOxSbKht4v+ng6GVQKs7sNJnFs9rTQ/zwe4zAPT5dYl4RkH1e/HmH12vor4lynjGoT4nj0o1nSCgNyDwuage/Phhjby/aO5TZA8QK/zxOfj0LTOw59FrceSJRaZBl4euO0nhSP2memHy9chOqazre53Bjnsi2ikmElWWxzM46QzFHBKyF4wdoFW06QzD/OBFET8/udHeq9QqvQaf80/fMsN2P1B/Z6Ukosi7x5btUYT4kO1+oGI3jk6xUrwtWVhiKr+sDORW17Hq23Sv23Z/o3F75K1dcdPNqH2gpuii+wIw5w9XylgpYoDo1An0f6sIOgCmgpvjiwghPo/Ldr+O9/6qhh/V+MwLq1s9i5X8o16fo+4BdjKGnaLETrHrRGacMzI/xpudFLPD8/uYawuNg5VxiNbaDUcqHaed4mMFREd1Aa0KWW6koWeiaB1SoJHy5vpprelT1RoTOkMb0Oq4wNcoK4XY4nnFGJiTEfU9q3s4HUNCNdovnleMpkiNmyZ/KEamsPISVaF9+IPdp23TvXF0641Tg6DVc9utJYBeoccdAkiW5fsZv368d4HmyczifpbzGojeV3gUGndA0Y2h6tzA+4YUt3ZG4nhQu6hmIikRVTmB2kZKn6x0r5nCkjyI+RiqKZo5dsYKu/2R9gDuBW4Hl/F10Qm6yAerNME62iIbc9lKbauqVFRlXp72ht556iN6x7ni8L5rxkXVjeMe2yQr0P/5OjVnZD4G52aakSd2c8qJsUSVt+I5+VjtbXbe6VyGf2DRRNMRRZU1+XuoRsfYyRd0Le48ZjfG4VRYrbXE1HVKNU7RO0hOafz9s0uByOV0df/n1DcHsGp/eczvaPxI5qZ1vL3e+/x9s7ueldMGj7ig/qR9OZ4BVrfn2kWoqf1rd+7ifcbX3off3BmV6rY5EDSfwc6BQpVVKupbTNmJ7gNAe25SDVnq5/n8pN+pTnt8zeF7kWog4sZbnlL1eGWD7btgJVvSWKrnC0Dv6OdUlqV553K1joNVv9PayA2c/GxLc9NpqnOd3ojWcqc40fOt2l9u7oGPRqKJ7RxVeD1Oq34T2oYYYoR2oyoorLxYOGr6Hyslh7ooWC3ItLk8csNkc0Pk7XGyAOtCbjlWig2eOoLnYKZDhapEcSGcC5tytlLuSZ2yXT0YOjEoObVQ64Q4VUjjm79dSgtCp+QnQRVoNbrolKy6TZuw8m6nQyS/r5Uni52HxwOLJsbd8Oh5uKBEVDf6owpN68Lx+bzim3aiHgW6903naedEaaxGQqnzi4TARn8wZrwSNQqq8zSe8sNO0U2KkYKstCillJpOENAbEABEXVs9PFkpu26ePcw09qrCn6pYVY3QPHSdpwexU1BaHexIyAVge+hT+97KYKdTDKtt4e+z1TtlpQRV23fvS1vx0Bs70RwIWSrfrAxDNGdX7S+PmVO6dEaqglY9oKprFv2uKU6UnN3B0Op3dM0mf1DpOwPPrjxkuR8AsSk2rMbRiqXro+s8WSneVK8snVCvO2zxMVbfT1VRZbXOUMSZXboZvuerBxyCH5zVOgN2hmi1bfz3qjKT1i1+6LrvmnHISgsbcbLSvDHrq53Cyel6qlMQxFMm6j7nRDEfD36NDUcqURYxMqrvmHpNvoepxlq7+9PBe0dpteXaokYoE7QmUKSKmsZEhcaK3jsAMWuG3c+AaC95nj6VH+wXjB0Qs0ZbRTLzYtlqah11PDh2yjW+7gPWDiLquEyJOCUU5mVEXfvJ9/aZBmanUFoMnewXrx2qs02895s/N9/7dGsJrWNUH8cuLafqKW2liLN6F2itsqvhYuWUw88uL288gdKqRry88YS2rTTWau0GtT8S8WYHWtNBc4c4qoNQWtUY5ZykpikGEPXcuvkebz/gYxxvf7RTrDpxLKDoYqu2We3/umvbycaqzKeTrdK9nqj0Rer36d8ALNPe8H+r78LiecV4NOII9cCiiTGyAf1fjaSxmud27yX9XE2BpvaTahhVjQlW85ecoOh66r15+/n+rDoh2sl+8d4dmkM7IlHPVsZq6j+q4wHAPGMGDcRkqVDlE5Kp4tUZ4nNJjUKx+pwTHYnTcy31V0FWGkp+tAzD8/uY6xKPBrLCrhYRN9CQXOA0G4V6/oynH1ANGEDrXmoVdc/vxVPdkoIccMU4nFrJ7GqqVxX6vM6hVmfI4ikJdY5OqvMa7cF8L7KTk/h6wX9v1be8//maRsZHXZ86jbIFoo1+9HyRZBpR+hq132ltVGsg8ZR4qjOB1RmD+jKe05LOeGr1rnF5mNeVo/fAyR747Mroepw6I6CTPhb0uAyDpppgRU1NDXJzc1FdXY2cnJxUN6fLctETH6K0qhFFeZnmhqQTVpeuP2YKH6oSTncNK2jxDxrQ3rO+OYAqll6F30/nDUQLsJXQb+VBBLTmoW8OhKKiHtTnoOcDwqnK/CEDU4ty8dY9F8fc66E3dsJA2MuOwpDthP9E+9DJtXR97OR7vB15mT5kpXvNz9uN25Pv7UN1ox8G9GPKr6GbV3zc+SE4Xlvt+u3el7aa9SaAsFBE43LfNePMZyFBCgh7tN88e1hMm3XzPpE5b9XeRJ6TM/2R98135LOHr47pVxJy07weNPqDcCGsJOWej4m2m+DzwOMCHrlhMgA4eo6SHy0zv3fo8UVR17SaJyTQcG9y9f3VGQWsrqFrLx8TAFHzv7K+GY3+UNT7TJ+3e37dXNe9W3Z915Z3lqA+sXqfdd+nOWnVrzR+QOy6Quuw1Xo85l//D/6QAZ/bhQP/8fmo+9sdynhf8zmn23/C7crA6eom7bXs9jA7rv/VamwvrYbP48LD103CY8t2o9EfQqbPjQcWTWzTOxwP6leaf0Drs9P9NhyptC2sS6h9aLX2WL2fapvUdWZSYS4q6lts57/Vugfo57+6xumeh1+Lzzt639W9SLc3656NFD+6zwHR85UUKLp5add3TrDrX/Waaj/arQkc/k6r75h6TbX/gfjyBe/3TJ/bPBwvGDsgap1Qx1tdE+LNTav+1q1HdnKIbnyoLW6XyzQ6Zvrc2PPotXHbwfuL7y9O9t94MobVe/DB7tNo8ofMVH/Uj+TUwOUeujb1vwswIwLsxjSsYDBQWtUEn8cFf9CwfSar9cBuPbfaY+KNmzqnrd6feNi9Xxy173Trj+66/HkefGOn+fujT8TOb7v10En74/Uv7YvD8/tge2l11HXU9vD2k7JVJxs7lTnUPnLa71bXU2UYfg2+d7fl/U30bOa0XVbnW6f/tntfqW1OzjF217D6LJ3d1DVFhb+XP2HvCWC/j9idd9R3zamMZ/c5u/3hbE0T/CHDnD/qZ0lWnFqUa0bd8TMmf1bd3Imnz+Cf1+2rumdJROZ18u7yZ+b7Ce3LdmsVfbesqtFUEtP3nIy9KhfEk6ucvLtWug2n31XHTZUfeRudyux21wfi70VArJzP763KAoumFsas5U7Ws0T0Wurnnexp6tjYXeO+a8ZZ7kt2Z0wnqO+Ruu44WUdVuRmwlgUnPPSOee4j3RVg/67btQGAdo4L0SRiN5CIGKFdxLOMWnn7qLn96Trx6pBwnl0ZHdKveugCrYuUVdF6bmWeMzLf1utBtYTTZ3mdkwcWTTC9k8iLgcOt9IHIQVytXUFRF7Rg8tBkO08cfg+rPuTjpXpw0DNzbyPu8aSz5ttB7dB5Q+s86skDiYQq3TyitgKxnpt83Kl434YjlY5TgFnltuZGGI8L8Ebc/WhcKBUdeZUA4dQgOm9o7jHZljmv61/VUyYR1EgodX7xArkU9VXV6MfbEQ9RmuN2Xh1W/6a5RYeLJ9/bZ5vyin/XKv809YvqOcM9cSidB/cssqs1YnWNxfNiU/7wMaF/17cEUFrViEBEwcQ9Z9QUjTqPMiB2rttFlOn6LtF3FpFx5n3C32erdZI/P813Ejb5ZyknsM/jijKGlVa1ppezSoVI6yb9zecFr0WhzrvK+ma40JqPnivt+LtI7zAv1qrrz3jegTooFN8fNCLfa90tyHPOibd+IrTWwWrdRyYV5kbNtXhp6QgaXx4pptuT7Op70HtZkJVmPiulMbl59rCYuWX3jvF9wWrtoLztdG8gNmKHr6F83lnJNLnMKKF7Nmobv7davwqIjW7hnsi6dEh0fzuvU93P7GoPqX2sjqe6JlilVEyL5KjP9HmwaGqhWWdLbTOHy0PUx1afpUgLF4AHFk0091I13RKlzSAvSTVFjtXctPIqpL7RrQNcDqTv2clo5OFLRhgAZoovO3Sygp2XrTp37GRC9ff0vMu2l6HRHzIjvflc5TKcGjWqS8tE1+QRL/xdOV3dBADmHmknC+nmJ59rVs/F2xEv+p1YNDWcTs7ndiE7w4fjlQ3g0XBtwW5+0HpB64vdGPP+42lFKH0qT6OaKEvXhz1uJzz0DqY/8n5UKleO2r8kw1c1+qPONfy6Om/fzcfOa1NN8bVHd/+l6+OnmqJ551S2VN8FLgfxseOp+eI9p1V/WX1HN0fU7/JUfurnreYY/zlfu9Qo/8XzilGQlYYH39iJ63+1OqZPeQSFrn3xlNpW7yWAKPnCqt7n4NyMqPao+wgpXdXxVs87Otmet8nqXKCOlVU6Mp3cTWPg9ZD6zaX9LL0/u8qqY+Su6y3qOVitCXYsntfq1Q+4oiKG1Oe0SwenQmsXnX9orvBr0jM//OZOc0x5vRC1MDqfC7QmUZ2pwbkZ5p6n63ee1vSiJz40oxzf3lZmOVaceHsoXUOn24gXEQO0zgse6Wp3Pncqs6vXV8++VvuZWoeJ9w+/N08TT+n41dStQOwZlt7ZeJG4uj50ogdTUcfGarxrmvx48I2dKMhKMw0tVY1+MzIo0X4H7NPlquumup9Z7QVWtSxVeDRPRqRubabPA3rXdeuWbl/nega7LDNC4oghRmgXdgd4K3SLhk4JEg+eK52nH1ENALrCflZKbLvNWF0wrdIckVCj5gemRZhSGFARNl3tCp4CId3rNn+vKnPowKSrw6DWvFHHy+pnPL0PYJ/myg4rhRFXZgPRRiBV0W11cNOND1dOWxXvoz7TGQZ44Ul+8ORp0bIzfPBGCuKme91Roe5zRuabRhmnhkSnc54rVrixjtCNidVhUIXn5raCjCb0HlGf6PKL82cjQyW9m6rgxw19AGw3eH5Nnm6MvzfqO6xT2pLCm4S61sO+gRH3L4s5eFpdY+n6YzhbE1YgUcoA9aC75v7LEQi2Gg7W3H85NhypNMdSTdFo9bwqupB9O2FfXbus5gW1WU3vSIcb7hGja5tOMcmfg+594GxtuE+4e1+EDF9svl8OrZvkpW3VT+q6TkrFivoWSyMuz0nP33tdf2ZGhFonKRu5IpXvRVRglf6mtfftbWUx67rT99kKKuQNhHMyq8/jpN4QzQ9+yNe1Sy3OzqEi39tZ4V+rAq3xDiZ831TXDm7IVBUl1M+7yqrjGl10a7NVKk91LnIljU75q64tvCj724qigKPKC3b7OdBqfCjKy4gYGg3Hinq+JlileqD3y+MCHlg0QXsYt1pvVOVOvHlOew59riArzUz3qjsoqw4RVnPTah2hdbWmyY/SqnBxeiff41BbyXkj0+c21w9ax+zQpWNUC2Vz1Nz4atFatV0AYt4DbqjK8Lmj1gieCpjWAsDaSUanUOf9RvPzummFjlM6EaoilMvKqjJYZ0gmGZrq2vBxpHRy/pCBqka/mVKOHEecYjdH1GerbWpVjqipsKxkYf67t+65GEefWBQTZU84KXJPcm2jPxTjFa32vZWTyODcjKg15/pphXHXdhVVHtGtO3ZONHzekaih269147PhSCXqmwPI9Llj9sWl64+ZSi3d+8vXHCsDSyJnG7Wv1c/qzgdOrmtnmKRoJjWqacnCkphUaNRfp6vDzm9WhlcrQxONk5piiM716rmirCosd5MjmLqPOFUWWjl1qEbveH1K11ENR/ZnZSPqb1V+5w4DqpJ+zsh87TxS9zqn0PVJDiV0a4zOoQSI1UXQ2qXK+Or5jJxPyBi/q6xa69SgvlO0Lnk9Lhx6fBFOVzeZe56u39V6okSGzx13LYpnWCRU/YV6b668t9rTeEowIHYv1Tll2KGOC+8bp84/tBbaKf55OmH6fXMgaDkf6bOqQVd9Tppvuto+9Jnh+X2inG90/as6bKjPQHsM+cio656BVqMv/56Tc5k65wk1hXa8/Yz3Ha13dH1K46mutQvGDjCNLw8smogjTyzCnkc/B7AEsuoaRzoScpSz0t8lYggTrBFDjNAunCqAE/X2cYJV/lBVoAJgFuAl4wQQe/CMZ1m2sgqrwqOVsK0Kb1whoPMI4nnnrQRmbl0noVf1LLfyWiLUSBAqGK3zZlafj6OOu5XnC+97nWJejZ7hz2ulbF66/lhUUTs7r1deSFbNJ89Dv6mfqABhps+NqkY/AkFDO+7qJhrvwGsl+OmEIa5Y0c0Fdc7r5psOXjieNl1eR4LDN39elBGwz8lNyqfmQChG8KPr8sO2aoQjQUwneKrvjfoO6wwDAKKUWEB4PpZGDneqAGY13598b5/p2azz/iRUwwFXsNoJNU7WxOZA0Bzvd3aeQtAA3tl5KuZzukP8k+/tszTiqt5TfK7FO+RaHVK50ZuiM0i4pfvnZfrwwKKJtoZJ1QhndSDh96XaXJk+j7bNugP5VRMHWwr3QNjTyIB9/mq1T1btL4/yZFU9/mjt9bpjvZWcvM866B5UgF6H00OR7rCRaLtUD+LBuRlRhSDV+eIkWg1AzHvE22Wn5OB9oM47Wouv/9VqrSe/2ibd/qRT/lrdkx/Yqd4Vr3tF4/DYsj0ordLnO9cVA6Z3hnKOl1a1Ki2ongu12Up2A2DpEatb16kdzYFQQvPDSplk5c28q6waBsJ1zHRjTcpzXpdFh9V6S2NCB3Q+f9V93Oo9obZSXbEHFk3EnkevxZEnWg1Cun1fNeDw9V7X5/T5SYW55vzmMqKVsZorTLmx9ScRp6KrJg42v1vV6Meq/eUxDhxW6wB/X7hCnRt25ozMtzTc2l1bN3Z2ymWdIZlkIAP6PY3vHdCqoONjt5erDgPcYMC/p74XumfW9b06H9U1h8tZ3LksL9MHn9sFF6Ijb+NB7wspRsuqmkzlF0WlktKIisLrnMb4M+jW20QNORTpqvNk1vUl1fskY5Rq7CbHjg92n4lZL/l+5sRRRG1DPNTP6s4HTq5rtzfxyCrVuHldJCKDCsPz89ey7WXatd/OSKDKm29Fzq7q/kbtpT3RqpYcd9LUOdyoZw/dZz7YfTrGsGTVp2r77SIBCPJUT/d6Ys6vgN5hQDeX+M/4WSsefG2gfy8YOyAqelitt2q1rlI7+D5D351SlGt7ViTnRYqE0dX8U1F/r+55djomNarogUUTtbWD7HQ3ur7T6S+sjCZL17c68lqdw+hspI4Zfc+p07I6LmobufOouo7Q95ZtL7OMDNTpOVoNex6rZkXB5Rr1OWm+AbHOADQuOyJOXfwspnvXucOG2g/0f1r3fB6XKYtl+txmTVe+F/GaK48t262dE0C0o9uGI5WmE46qn+I1pO0MO+r46s7o9O/Nx87DAJCflRY1V2hs8iycaXV7CunvdDoVoe2IIUbocOIJCzrsPIWsqG8JmH/zdBTcq0tdmOwOZ9ROblnWwQ8AumLAVhu4E4GYonievmWGaTziv1cFZlrgecEzADEe3VyZom7easqJRn8IuZk+0wvPinheQuqzuxAWsh55a5epcNApEziqUsUqLFRVDvN0JKrHEPfP4UqaxfNaozO48lUVKgIhw2yDXQHteN5vVun53tYUhuWKQz4XVC92J/ONt4V7ydI96YCpiyZS+4SeTT0o899zr3/V69vqmvxZyKBIhgtVuKa5T7+LZ8wDolOCkTKJIgaK8jISjjywS3OjHqjovczwuS2NPLw/AMR4otG8S/d6zGfzRzQ49LeuLdRP5CVql2qIHyD4uqMaj6yuT96BQKzRm0dnzCzuZ1tE1QqdlzhvP5+TXBil/rRTTgH2BSnVQ2+8PYv3uXo4IKH2yff2odEfQqFiNKG52hZnBf5s910zzjwA8H2SDg5ceaRC79Bjy3bHKIudtovuN6UoN0qdWVbVFFUIko9FvGvzfZMfDlQlnTqWdhE7HFqLeUovnfKa0O1POuWvFfx5H1g0IaIomBAj01AdukDQiLkmL2jPnRx4n0wtalVa2Cnx1DlvNc/53kljRe2gPZaiBq1QjQ4q6jvK90EenRY7Bi7lbz1WazHtMaSU5h7w6j5upbChdZSnaeXKzemPvK8tCK8acABEyZpqBC59vqK+xZzfdkZoVWGqzmlVwQhYp/u1eletZH+uGLGLmrK7tt1nVVl56Xrr6HGgNbWKOv587+Ae47oCwRwrBZ3Vfkl7K83SeAb+DUcqUVbViMeW7bF0RnDiIc/PRiRDU9T+wJwMGEgsDQvvf57O9q1trbIl1YADWuXOeFEb6u+4E5GaWktFtz6pv1f3HG4AVw10/N9kAObtJQlswdgBUePL52BbUxID0dEngP58sGRhSZSR3U7G1O1NPLJKPT+TEpinOaI0fmlej7n2B4Lhtdxu7efvg3omSPd6tMpS2ht1Tm5AtKMVf9edrCP0mSZ/KCrCwknEMH2uutFvOZ+50YOeQT2/Wn1HN2f4M6kR1rprUPv52sBlaVqTH35zpzmOb20rM9O46ox2/NlVmUuNQF88r9Xb/rFle8z9k0dp66LW1LM27fmAK2bP06HO9XjymJ0jj2o8j7d2qUYTPt7NgWCUMy2PYOB9TLJYommh1HFR28flFzKGTSrMjepjSudstXaqinkrw56K7hzL+4fawmUnAFFraF6mL0qnw5/b6l1RI9c4b91zMYryMuEPGqYxPt3r0abB5bJakz9kOyco0pV0LIV5mTFzddX+8iinIiv4u0zjm+ZtNRap0Uu6tUs1oKn9pe4pXE8XT58rJIYYYoR2oXshdQugkxyZQOzB3w4z5U/QiDEmWHnJ6w5nqtFEF0miPrMTL10SgFVPazuBmP+OC0qqEAG0RvuQlypfWMPC6sQoQY8rZTjqQVCnMLTqB9VLiBb8e1/aam4GVAuAUnr5I4YMCsu0OyCpShWrjZTuTZ53VoIS94q975pxUV5JfBzUDZc+X5SXiSkRJZaaq9TKG4bPLVI48HlBnrDk5Zzhc8d4a3PFIZ8LqiKDrqt6bliNW4Dlqqc6N1beGk7ngYpao8HKmKYeFGjNoHljADHzV/Vy0V1Pd3jRGfL8ISMy912W7wpvI82Hn9w42TbNjaoAovfygUUT4/Rs9LV0ygo6zC1ZWGKZG15Xq8hJqiF+gOCHlniGLrt3iGjNj22t5OGKSjslnepNpWsTX+OcCpG0plTWN5t58vkhiB96rVKRELzPrby6mgNBc13hSj7qH7v9wwo+9wAgPyvdVISp6cC4MYS+q4ajN0aUEzxdlq5duj7gSuIjTywy1wRKRadTgto9s2psU9d8vkfbKWDsjGhkNPW5XTGe/DplS1uNZbrn5f9WZRoiEDIs20+pc8jYTH1S2+THiP5ZSPO68fa2MjOFEFfiFWSlmXsS99C3e3fUAznttbTF7FAiDVVUo4OVkk3tq6dvmRE1r2PHwFD+Tgyae/6QgdxMX9Rar95L/b+dsZiv61XMCMr3XdVBh3uY6uRQ3fyz8oik3wUNoMkfjHLmodoQ6vtvl+7XytigW3tpb3UaNaVT9nD4vCQl9dFz9TGf0Tke6GracPjZJV5khVWbrJyz1H5bPC+6/hT/rKo4Wba9zFTacO/9eKj7qupswddhLm/bGbF0zBmZH5XOlkOKWqC1zpCVols1ZOjWOlK428kJTvdPruyntqt70+J5xZEIqdY28n9bzRFuPFLXhHhyCX8+NX20lWOB+jmnTo4q9A6oClk+NpTGj5wEivIyo+qgOknnyY2PlA1CR7yxVBWHHKtUzCQrUXYEnjpcNRqTjuLBN2Lr+dU2WUfX8eelvYoU/VYy0NL1rVkcaM4Arc5E3GHLrt+s5ldzIBgTkcwzQxCUxtXKgLF4XnR6ODtZiBxcGiP7Tn1zIMoBQ7e2qusk7fkkL6hGC6voBB26ucnXffXMSobFgqw02+e0+l20viLaQYT26xYlgpcbbxJJC6WOi64dZ2uaMPL+ZThwtg5A2HjG+1iXzlltm84Jl/rLKtLe6lzE11D13uoaWtvUOnfjOUfQ/VTnaQ53WFLTU6tGDTI+Z/o8uG5aoVmbmNeepP6wqqmnwwV7Qxvvb+oXyjKxan95lFOjqiOh94Geh36mrqmqzonr6dp7zhGisc5VIQgOoBeZv5D08nOcFriK5xnCuW5a2Jtz0dRCzBmZH9UOEm64EsvOaEKfP13dCLfLFVVMFYhW+qjPTIYO+v/M4n4orWo0n5cLSVbQ9QEDpVVNKMqLTtdip3Tinib0TLpnJm8K/h3d5xbPC4dcUoi5VRvUflg8rzXNAxW4L8rLxK6y6khqD1J0h59xUmGuNpqCo7vH4nnFuOiJD6OeX+0T9Xv0b/I6p3BM/ju7+3IOnK2LCoOlZ6D5phsjul59c8BUqNHPyqoaYSCsDKFD7+Zj581oHif9w6+75v7LMf2R903hRyd80TU3HKnEWxGlala6N66HuA67vgIQd2zoGeubA1Fhw7RmZGd40RwImkpgdf7y6y1dfyxKuCKBRH0HlywswWPLdqPJHzKFt9KqRpRWNWJqUS5OVzdq78UVSY/cMNny3aa1hKfgoO/z6DvqH7tx5oc1nhJGfbZEFPT0eVV5brXGqdD6QMpbp+8Q9V9eps80ZKjvqDofrMaC1l2a+/z3VusGb4d6cFP7n+YtpSfJy/RFGdX5dfic43OErmPX5yT0A2Fh+q1tZaaSx8k+qGs7jU+a12MqRqiNpDzlbab5ylN50Bx78r195iFuwkPvoNEfsq2fw/vAbkzUfZO/D05Q78HfOQBR/7ba/63aSjywaKJ2H6GDjTo+uj2Yjw+/RiLvK18DNh87j6lFudhVVo1FUwujxmnxvOKofuXrxJKFJXjojZ2mIY0knNPVTZG0Za3PUPKjZaaCnpQyXHFkNSejD+TRCgbuYU7wvqHxWzB2gLkPxdv/1P6hz3FnCEol5jRNhg5SElU1+s1DK6CXnXTtJGMxfw51XQdaI8Ksrqe+M1T7heqNLVlYYu4HXC6h+6ksWVhiyoVZ6V4sWViCByO56SlFp9WeZTcefB6oChwuhwLhuZXudSPd6zaN3jz9HLXbSn5V+5LkGd5+kld5Wi+rPuZwpTdd95EbJmvlF3WO8jlpJf/oZEfduqjOayCs0Hl7WxkyfB40+YMwEG7jnJH55ufUawGt6zrJnUCsfKT2zUVPfBglw7Rl7K//1WpsL62GL6JhU99ZVabnbaDrchnxvmvGmTIcpeDj+67ad+q46n6nrtX0Tuie94FFE6I+q77furG3I5G1lSIZdc5S/HOTCnOxvbTa9HS32+tUeF9QZMTxygazLgEQ++6oY8rlRGLDkUrzPeSRrPz9zMv0Rd0HiJY5+XlfJz+rY6DKlPQ7/l3+O5J5+D7E5TRupHh7W5n5uWdXRivLgdh5oVsXgNh3kLdLsYlox5Fk1aK8TO18V9P4cfmZ6rvx/YefEa3mmh26dZWeyesO61gyfW5kpXstzwTq9/h4U5pDLi/o+gdojSJV11r+nGvuv9xUUi9Z2BrRQ3sz7ztKRR3+e1hCfUA/152FaN9T90hqN89g4VQ20sG/S7Is0KqDoOe0WpPi6YJ077Rdm3X9xGu56CDpkr+LXL/odK1T92BaB4ryMpGV7kFpVROy0lsdddX9jc+9kh8tg4HwvOAytbou8nvztZDv1/Hk3ceW7UZZVWOMIdjq81b7EXdgsVpfVeLJf0JiuAzDUNd4QaGmpga5ubmorq5GTk5OqpvTLXG6aagLU3tedhLs8zJ9lgI1bxffGNXP07WK8jLNDdvqeeizZP1WD9k6xQwXAjkk0FHb6P5cKOUbASng4wmyT98yQ/sMqlBK99P1gQ66f6bPjfys9BhB7ulbZkRdB0BUX3FDhg4ro4c6llbft+o3J1C7XQj71lIf8/EOGrCdI7r5lulzoyUQwqTCXFTUt0T1PfWPVZ9b9YfdPFDb8fLGE9heWo2pRbmWBV7pEG33Gd21nYyN1Xuq66t43kB0LUTGx+p6QKuwy/sZiH7ndIIT/57Ve8DnOBdGaQ1Qn5nGnHuO6tY/3fuVyIHazujj5P0mRt4fFjhdAI48sSih9d3q/VXb4UI4B/iCsQNs14VEDyS6z1s9+9L1x0xlz3XTYtcM3fMAcDRH1PtyJSjQ6j0Vb53StZ0U6dSHBK0rJMg3+kPI9HmQ7nXHrPe6NSRZ+7jd3HOiNFPXPDrcx9uv731pq6nMfGDRBNt28uvbHZjUZ9Htd07eMafzVB0nO8MPXz+AcDqFKUXhfYffJxGll1WbSeFrt1bF6xu7fczJXKTvuwBtxIOT9QiAaYAEYt9pJ/2hk6cSeRarPfWhN3aa67ChtM3peq5em8ae5BFVUZ7o3AVgOX/477hSUp0P1H80jvx7XL7kKdR8bhcG5mS0SVFFcHkCCK/JXMHiVB6It+eqsqMVVvP13pe2moYifg3dfa1kiHjz2Ol6Hu9ZeRt1c6EgK800MtPe5/Qsp5PpdH1qNZd16xG/J7XRau7q7qWupdSPC8YOsDTAJtKv6u94u2gNVveGeOud2hdcPtWdJ5ygznUg1rvf7hlJpvG4gMG5mZZ7rO794fOH0Mni1ZFaUWp/VdY3m844C8YOMKPRQka4fgQ5bKj9Gq9ddrINzWVdm3XjrVsTSLbRyXd2bWiPkl+Hug9avVPx9nH1zA3ojXZ2Oha+1gKwnEf0XuvarNtznOiGrNpoN6bkNMPlWo8LSPO60egPWco2dqjPaXfGiff9ePNJd46P9z2nnwMQla6NryV2uh4r3QK9b6pTCgAcjZxz7frKiX5Jd28g/t6vwtdD1TnEbl7x9nPZnvejur4CiekHhMTsBpKaTOgUuNXcLkR08TzrVEOJsmSh8zRbQGuhLCoqrnr68JBE1aqsu+8jN0zG5mPnLYsu8xBnILwQ52WGg9TyMr1Rocp0TUrJRR5Kq/aX24bdkofH0vWtKaF4gV71GdSNgXsjUBvoZ/zaBIWY8oLbc0bmIzvDhw92n8b0R95HQVaaGfK7ZGFrGHS8lHC8fbpCdYvnxU89wCOzdO23gnvgWBUfVENOeXvoXjx1BH3vgUUTcejxRaiob4np+3h9rvbHhiOVKPnRMgzP72Ob3oXPX+7do16f/k/epdtLq+P2me7d4H2hFiWmZ1SLonNhwqr4pgrPh8/z8avtenZldOTdkoXR+XCtBOnF8+xT6RF83BbPiw4NV73rSqsaUc2MMOp6oZunau5sO9T3gq8/VrnqdfBx43Vu1GtSChNdSjFqCx1eHnpjZ0y6E1oTDIQ9UueMzI/7jImgm59Wz754Xmt4vm6ttVqPsjN8yPS5TW91q3dGnSfXR9Jh+NyumOKTVujaTvl9r5tWGJO6kvZhUiw3+oNRRXFpvvEc5rw/2rqPq4Xveb+o+5uujxORB9Q+saqVQAqVlkiNMP6uWe0P9c0B27z7/N6qJ2q8d4zDn5vaokuzaFXE/tmVsQVJ+f5MxeJp3+H34cXTrdYPdTzUAx8QPtRyWYCur6ZF4n2jmwN2xVx19UXoPhTJQsZIq7lFaR916SiB6GgaNUrYSSpdnkJSh26s470Dz65srUVB6f3s5DW1b+j66vhSSoq37rk46l13Iv/Rz1VFCW+7lZxGMna8d2XDkcqoa1KaG6ohR9e4dsoQy/FwippKWfUKJzlC91mOKgvSHkmyME/5ZCeXqvOV+nTOyPyoOknq5/m80cnoVp/l7ec1K+1Qx5evXWobdemXKYKe59+nuaDKdOp9naZPUc9z6n2WLCwxZc7apnAKyseW7YnqI2r7Q2/sjFmXdeeuzcfOR8mCurOhU6zOAeT1H89YY5c2VrfXWKUVSwR+TiJHAEqbSdid4XjNgtaaM4blvsifh/Zgqn3E0zTxufXojZO1eylFVQKtZ0jDaN3fVDkQQEy7dO+17nnJwE6ONGqb1bORlQzKUxcCsed5uzbw91I9r7UFVa/A32O6P/WZk/N4vBT3/JrUV5ROk2pp8He8ICstZrzoGvS9qkZaB3ZHpW1S5T2nqfXt5Cp+TuKe8ksWttbaJfndQGz6vXio5w6SBeeMzLfc063kNhV1H+FnBqvvqf1mV8qA9xtf81UHzXhZeCrrm/HQGztjah9xPQSl+aa6sZRRxUCsDonW+kdumBzTj/xzvH9UmScRaD2cVJhrRkar/aOOB537qP38s1Z1xgin5SWExBFDjNCh2B3OKM+pnaIdaHuedasDJldAWQmwz648ZG56jf5gVIFbyqfIFUt8UVq6/hgmPPQORt4fFlbUjUddaLmBiBfHXHP/5chK90Xu5YtaTFUFJhDt0UGbHheY1c2NGz14YU7d4V134FGVcPEU7tR35JXV6A+hqtFvHrRoI6ANwEtJQS3Gk7dPVdY4MapwYwoVCHcyH2me0AZGXqLUD1Y5yTl0LzI26ZRkur6PpwRThUAq+Ev5fK0U9fx7fANWr88FaCKekKm2SR0nXpTY6nCojo3T1IZqPnx+iOCHbzUnMxdGuWJSZen6Y3jkrV0orWrEyxtP2LbFimdXtub2p5zzZHTQFWrkY7Jg7AB4XPFz4dphlZff7hCsjpta54avaaRcoLn+0BvRaykp9XghX3UNoTkZry6F2j9qm3Vrgm5/0a1bqoGqORCMOZzyXMDqetcSCJnFGe0UXHwtoQOeqvDiyjv1eXTjpssZr9uXiHSvx/wdvXtUpBaA5Z5p1c+qAQJo3XOo8L1uLdQpiKzkAbUdfO9zYpQHopU7/JoPv7nT7AeSBej6VOPsrW1l2v2G35unBovnHWm119G4OHFA0PW7Vdt099HtiWrbdAeye1/aigeZfEdrnFpIm57jrW1lMc9DbeP5u50Uc9XVF6H78HozunWC1z6wqikAwLI2iGrQV7FS0NmtSXaGYi73cKeDqyYOtqx9YNUmJ8obVcFL67GV/KdeX9c/XKnD5VPVYYHD90kyvJAcx41RqqLb7jmdyIx8rDN97qj1lN41F/usk2vyd4NkYZ4H3258uDJbdQzjChb183Z7nZPi8U4VjHZGxM3HzkfVcgJaU/7x+hC86LuVsRBAXAWxndOAlcMfvw85u9E60+QPavuTnsdKTrfavxJVwumMQDzVXlhGCUX9nO+JfG+zMxw9tmw3Sqsa8cHu05bnEB06WX/Mv/4fRtwfdr6ga8wZmY8WNt7NSi0Mq2tymYacx0qrmiz3Rd5vdjVdVcWu7kx23bRCsx9VRyGaZ7w/dfu103WXG9itfs+N9lb7Pa9foZ7nrRxOaD0gmaW+ORBl6E4EVd5XldxWz6VzhlANgby+lJ1xUDVuZKV70egPmeNG7/iOiExqJV9xebzJH4pae/iciScP2EF9RPX57n1pa4xj4YYjlebc8LldMWdFp1i9z1a1LunnOrmN0BlwCSv5lZ+ZaE+vbw6Y4xLvvE/Polvn7fSIfL9Uax9xueGtey7G0ScWgerGAohas63OC+rZgferagSzknniQevh8coGW4M+13sUZKVZzhldHWKgVV51WttXSBwxxAgdit3hjAsXdi+vUyWK3b359dWN0kqRvWRhSZSnDh0kaQElJX5BVpopnKzaXx61qJMXF994rBZa8pLkuWR5sXW7AzsvmsoVgaqnDBcKuPcAKR7UCIN4fa+z5nOvMp3lHwhvXpk+t1kQnr5HoZxBA8hK81p69qrtUw/ZToRc1ZvBKSTskuCjGnFUBSNvA/UHHTR1nqu6vrcSlNU+V+c2Makw13Zz5N+z8u4BWufb9ZGDyNSi3LheEXbvG2+javghobOyviXmcObUOKv2D8/vvuFIJWqbwgKL3cEtngBH9aO2l8ZGEPHPxVOmqO8kGaO4IUn9vFODlB10DcAw+9vKA4or3wlaM3SGYl7E2esOq6jIY0v1fuWFfNW+5s+pjgc/BKnGVY6VwQBAjJDJlYOqwwA9G/eK5++86pkIIMrDikfK2SmqeB/rohza6j2r65MPdp9GbZMfU1l0H28/V0rb7ZlW7dcpP8jo4YvMCysv/rxMn1krQl3jOLxgKh8nIFbZrToo6JQ7XLEaNGB6mAWNcDg/XT+T1cexiqCI1/92ymuCP7eT9Y+UpVZKp7YemNS26dYg7r0ORCsh3t5WFmOABZwpK9R1hdZ1fh3AiPJq5YofroDSGZB47QO7SEe+TnPZjO5jlS5THTcrQxeX23SODABi5B6+V9hFXj+2bHeUwlp9b+zgBnY1EsNOwaxGJfD+oXZZOQMRqhKN+p+iMHMzffhg9xnz8+QUwNvB54xuT6OxsDJyc2U5XV81uAOIUczYrdH8mrQ36OaUznBF/f70LTOiDAlWSn27NRGILXxshVMFo50Rkc8HugbVRqC/AUQVfbfrR7UQve7+fIydOnWp+NwuM7qUr8n3XTMuKsLHSk4HoJXrSQlHe69ujnLs9oBWGSXcj1WNfkx/5P2oe9OayWuX6gxHFP3Bo0Cs2qU77/G+57IyQUpXIhA0LA1qVu8T1bwBWuekapizUww7hfqVF6hXZXb+OepP1VHTSdQO9SNfl41If1lFrtsZmZ6+ZQaOPLEIex79XMzvVGM5/zdPkR4IGWZttwyfx9YhSH0e1chu5WhD8PlJ7wPdC0BU/6r7ilW7rM6z6hhkRHQTaoQcvw6961OKck2nNNU4Re891TdJBOojHhGongf5WpeV7o05K3Yk3MigO49z+HjrztZW/a7qiIDWuq52ZyZVP6JzErDTZfHxXDS1MK5DApcnuT6Pz1kA5tmhORC0dOxpq34zHi5Y782k99hVVm05Z+KdTeONYXvOpb0dMcQIHYrVIYJHhlh5J3bkva2uH0+YVVNQtHoaBaMs6CRHUhozntZo+iPvo7K+2TwUWR0AefFbOqDTJmYlsKgHKwCWSm87JcHxyoY2KXS5gKl6lakborp57Xn0Wnz28NU4eq7ePPzxw7jqtWM3VnYCls4z66InPkR9SyDq+3bh9lyoJ+E0ZBhaIw4pGKcW5cYIczQm5JmrpoOwgubDI2/tMj2NuVcPFWfWCeg/uXGybVSHHVYKdkoPdeBsneOUSbxN6jj95MbJMYYfHpGW7vVEKU7JUPPsykO2qZ7UOclTiZDxFNAbkmiebDhSaeldvGRhCSJ6ZBTlZdiuM+pcoDm14UhlTB/TOsDT9unGxKlC1k6xQNdI93rM/o6nGObjZhW+Tm0lQZW8yHQCIo0prQ3q+6Aaq9SUC3QIevjNnZbGVXUM1GfiSlGuHFQdBkhA56m6+LxS+4jWMXpn5ozMj4qQUBWxujHVeWbaec+qY27lLUz3avKHTE9A9Xf3XTPONsWLldeo7uDCf0ZGj2unDIHHBVw1cbC5l/EDN3ktWhmd6Fmp7WpKRV0f2xmH+ThWNfqRm+lDUV4mAqHWmcCVUg8smhiTWsXqoKimd7MK77d7r1VFPaAf33jK0nhGoIff3KlNR+dEmUHet2RkA2Cm2fN6XDEGWDvjhW5fps/Rvrd4XmvUHOAyo0BVxQ+/Pk/N2hYHIJ0yia/d6r6kGzcdNF8fW7bbkUzHozr5v7lS+N6XtqKsqhGZPo/5rpMSh94b/u7r4O2nMeFGZav+4j/XfYbmj5UzkPo5dd2haMz7rhmHpkjaHRdiI87UOaOuCzwy1MrIrfNYVQ3WBlo9452m76BoWJ0Xrirj8whFK4M4GQay0r3YcKSyTekdeWSZup84TQ2rO//RHKJ28PeB5tWCsQMslc10HfWZ1GhG3fdUQ6JOUWd11qK2PXz9JBx6PDZtz+J5xVERPqrhUD0bXfTEh3hs2Z6YvclKKa4+t2oEIsWl6nBABmpdtI+VsZn323WRdXtKUaszl1W7aG5yYyA3lnNZWYV+RxGmqjEfiN0v6b7HKxsARNeFUA1z/JyteuFbGROsfqfOPyunOYI7K1i1g8P7l6J9XNBnveD3TjRdkM4opBpK8zJ9ptPhddMK8cCiCSjKy8RVEweZjh668ye//mPL9pgRDvHkQmLDkcpIzZ3W6Gy7dZn3JTco2ynj1f/TO37VxMGmstrKuSY/Kx2PRs7WBBmp+P34+dyJ0UqFjIyTCnNj+ovWvOunFUY54XYUXPbiRgauFwMQs8eosqdqKOHrPv8dzd8FYweYhiVVF6Tbu9Q1M9EopMXzotOx8XnN9XIk0wGwlHVoTb3vmnFsPrii1lorRzAVp4ZO3ZhZ1Qni/aOmz9edmyiDBWB/Diec6CQEe1yGYRjxP9a7SaTojhBdUJKEo6K82AKniRTzdXpPvtjbHQDo92oRVR6Wy9vMi75eN60wqoAaLxR30ROxBSntiunShqIWoYxX9Ivuw9vo5OBP37MrZM6/q/6citZm+tzY8+i1UUXcSflPwrGu3wFgxP3LzH9PLcrFgbO1jgrFOZ0zat/Q/11AzJhZPbeuEBovhsiL9VGbyRBF833Jwtgi7Vb9qsLnIfGTGydHjZVdAeB48z9R1Pa0p2inFbzg7PXTYgu1qlg9t9rnvDhtSyAUU5CP+kSdJ7p78DGmg3BzIGw4UsdYnYd8TvGCv/y5nRbt5e1Rx1a3Nlj1t1Whcrv5qV7f6dqhW/foGk4LK/K1Mc3rMfNf07qsfpe3le5L1+HrMhVgnVQYvR6pqSDVueDkHc70uc2DDBn04xV8d3oPqzGhueYCUMgEaULtQ7siyG3Fqu3qHkTrGKBfN4HoYrRUONftChfL5XtPUV5sYWHdPNSty7p5Ru/l1CJ94XKPK1wPiNZhmmNWa7M6H9uyny1df8wsIsrHl/rJagz58/H9iw6iiaw7umsC0F5HlX/aCt+DrmfvJs0jvj+/vPGEKZe8dc/FAKLX2Xhrl92zFmSlYUdptblu0lqmrt1OZTQuWxqIX2ydXxeA9t9lVY0wANODl/eFKsPFe08TnRMcu3U93vXtxoaPhVrYnX+PFysHYP77vmvGmXM10+c28+1navZCq3bNLO4XU2zdyZjr9iRd8XR1nYxXYJ2vSeo7aNWX/J3g+xT1ld1cdjJOVnNVffe4PKDu0+p1rOaik/mib+MKlFY1oSgvA2vuv0J7bQBR742uODtgXSSZni/eGUS9ht180u0xS9eHU4uF25mYXKdiNU9X7S+PKmrP5Qars5eu/bRGAa1KQopMtZIT+TvRHAhGnRvVtcbqPVUj2XTtB6LfH7v5p9vfnJ6/eButzpZ2Y6bqJfj6oFt726o34N91obXmmjr/rM7aTu4x8v5lUecu3fpqBZcN+J7vFPWMaTevZxb3szw3AbHvJz2P3R5qN+ftSLQ4vJ2uJ953NxypNPcKwkqG0K379I5znSD9zmrdt3rGtug3rPqBjxMQLVsmeh7nawE/3/E22o2vuv7EOwslOobqXsDl9UTmqxCfROwGEhEjdDi00NCCC0SnjQCsc/S29546rxH6Hfd0IOsvL1IMICq1Drfwcqvz07fMML2G1JynSxa2RseQlwqFddLfvG1kTVajQayKyqseLTysnmqV8JzsnKXrW9P4LBg7wDI03a5f1bB1XuidnoWMMGq4KLWde0dV1LegJRCd1s3KY8DK60VF55lVlBed55fuY5X3mnvb0XiR9yLQ6pXPC3jzkHuK3rAKG7fqb4LmG6UCu35aqycitUf1iLHqt3j3svoe987gockkZHQ0PKJBl+ecY+WBQXOkmglWvDgteakAsR6SdB9KfaKL5ODeJQCiah+p/at6inBPJw73BIyXxkpFN7Y8xZvdd1ftL4cBmF7FHCvvF91zqW2gdgPRKcDo/3zdo/XrbaVuEKViUb0uuVBOnnqZvnBkjy5KS43g03nD3XfNOHO9rahvifJypbVEF4lCz/HsSvvC9U3+Vo9rHukV773U9a0VVp5zPBqBjw0VY79q4qCYekG6eaf+TLcnOV13qE+rI8VPgda8y7Ru8kgpLk+UVjWaSlMKWCEDic77DIhNB6V6LaoexwDMPjr6xCIc1dSN4nsdX4eprYB+jeLtpP0h3n6mer1yb2qvOzraRPX8VWUuui+9b3Sw1qXdsoJfl48xrY3UZsLKeztR+J5D9Yu47MTTx1AqHJ4Sh/YUF6BNQWK3D/J1iCKiKZKQ15nhY67zVtatqzR/SD6J51nI105eH4DLPV6W/o88eI9XNuCiJz7EVRMHm/shYP2etrU4K+9HHr1olRrK6nmt9hU+7yrqW2JqYfHv8bnHo714TnpufFCjQ3VzgkdScPmOy9f8vEPtobWGe6PT77i3PJfxM31uVDf6MbO4X9T81rWN+lP1euVtVvdzLmdRP1Q1+mPORapMzfvHrnYMH2N1PvFxUtdFddx1dTC4fKquRSp28kxpVVPU31brAO07jf6QdtzsvO+tziDq59Rr6N4RdY/hazalXwaAlojMbrVeq1jNJ9qTlywsMec8GXnjZTHQtZ+ecUqkEPbUotyo9KC6rArqmrpg7ICYcyNF3FLkEoCo97Sq0Y/Hlu02zwYuc/xjIw7VuaZbC6m/eH0jJ2OqGw8+j56+ZQYeuWGyefa3m7uEAZg192id4TI1YTefuOx3/a9Wx0Tc0ncfvXGymVJPvRb10xSW8la9hxW8RmxpVSNW7S+3TOuuRgTz94l0Elbo3u8lC6NTplIbqL18/ClzQ4tFbSN+PudzyU6G5jqqRFLp6dIzqqh97/RMofL0LTPwkxtbz89Aqx7Iat0iGcUFfW1iq/MZ7xs6m729rUybrtjqe3b9wNcZoHUtAMLnSL6X2qWsA6LHUVeTi2RjqzWNo+oP1TOCehbie2QiNUSpP7h+ls9XJ+cAoePoVYaYX//61xgxYgQyMjIwd+5cbNiwIdVN6nFwobk1dURrMTSuELIrKpvI/dTDjfo7yrnK04iQ8JTu9cQIz7rNX13saUOhgwxXPqoFKemAo4Z3qtflWB1MVMGNh9XHK6r37Ep9QU9+v5nF/TD9kfcx4aF3tPkyyQB13bRwXk23KzzAkwpzo56FH3RVhS3gMjf0JQtLYlIMWAkKXODVjbOVkY/aRamCKKyW2qgLaVWLXatCE1fGkwDF27f52HlbhXg8JQS1+a17LtamReCf0RnTrIrD6frMzgjHFcj0vFZFL52iKtr5wc9K6c1r9Fw/rdBWIQG01iVR+8nKiMU/R6lPdKEZ3g/TAAEAAElEQVS+qgJWrXfA26IqdimdAg9tB6KLas4ZmY+Zxf3w8Js7zTQW8YyO6nPwFG+UXs0u3Jn3FR8jp4JdPMOMbn5xZVZpVSMyfG5zDVCFTaBVqc0PHq39G5Ym65sDMQcdvk7r1glVUbJkYUlUDQWr/YCwenfoHnmZPlw3rTAqLaeVMUpn+HCaV1yF1i9SyDYHglqlGR1yKHWiThGme06eTslKEcf7VH0v1DQEdgW2VQUjpQXLZDVmdO84gKi9vrrRH6NkAmINsrrnX7KwBL6I0YMKD1NfAbEGRlVBRahzTj2o6945q7pQ5LVphd04elkKMb5HOilwrlOUzSzuh8r6ZvNz3EHBzunGibGZ9x2lO9PVqeJMZUo+gtqam+mLOdjyf6upSejnlPd7UmGuOW4FWWlmvnha3wm7VGi6fUJVtPN7W62d3BDA+zkr3WsaW9S5rqbIsZJFEq1HRsqxH0fmx2PLdptOB2QQ1j2Hbvx05wM7ZQbvT6v0uav2l5spR8mxJDeyJvO6T1zZaqe00hlIefpita26/tc9B8+1ryqbOVZt46kw48HXIK7MpHPRhiOVZjpV3TvMZWHdHsXHWJ1P/Nmt9mK6R1WjH7VN0RHiqpIqnkxtBTmGeVytEdX8rErwfYfGXnUwBOyNd7p3XIfqzKIa6IryWguff7D7jLa9uhpvdn2kzifdnrBkYYmptMzN9Nnub/z/QGyNIpKDuTyszif1enwO8XOj3XmGv8/0fnpcwKM3RjtR0Hd16XJ1jpE0T0h20jmn6fb0R97ahdKqRvz4zZ2WOhCnyvL7rhlnjgfV3AMQI1NbjQ+g1zVsL22tU2KXUlm9Fq+5xn/nxKiflWYty6ioBghVNuCo76NV32ZnhNdB1bkGiK1ZqdaysZJhKAUldyhSDQHkzAHo6zfZQe8A1YSlMzU/W6t9n8g6qbaZ1oDrmR5I18fEqv3lUel+SYandIJWDsf8vtRuA7HpFu3aq7ZLnec8JT5FeBF8vY7nMM7vqdvHVIcZfnbQna25/pB0SJSuz8rZgu+FaspA3gdqfxC7yqqxeF5retNV+8ttDfZtSbknWNNrDDF/+ctf8IMf/AAPP/wwtmzZgmnTpuGaa67B2bNnU920HgUtCFnpXtOzg3uZkzKEf45vOIkczOl+usMl/x15SHDFuZ3wxNEtYjoFPt3ryff2xQhXC8YO0EbP2D2v1cHEzuOI+jjDp3+t+aKuKv/Uwz15+av9yg0Uz65sLcZI3pZ242Z18FKNHlaCAhWidCnX1SlQdEpOEpzJcwhATGE93XhYKZ+4Mp6UmVyxxhXi8QTqePPeiWBuVexeJzBbHc7UjZof0u2KGSeCqmgnwQGAVulNa4U/ZKAwLzMmBJs/Cy+gWpCVhgkPvYMR9y/DhIfedbym2CmIVIUvr3cAxEaB8fYBrR6MXJB5+pYZyM30mYYTOmQ0+YO2/c0FY/VgRTQHQjGRZDTGC8YOsDR8qYJdIv3lxCuSoEPCVRMHm2uAKmxypbYuLzwZuAMhI+ag01oU1rD13OXPwKP84mH17qj7oJ1xX6f8ttvX1M+oyhYO9U2TPxS1b9G6SDWJhuf3iXkmuwLSBNWfAGIP2fzAwaOa6B66oqa6+6hKLDKUxqu3RX2S7nWbqTLUOhyqIsbK+LV4XnFM4WG7WjQAtIcftX/4vLA6TKl9wr3u7Oqc6fqSfkYGHNWAHM8b00pRtvnYedMTG2iNsHrwjZ2496WtlvPHqcKJUGUFDu/nm2cPQ1FeJm6ePSyqv9UxVw/L5LmpMwRSLZrjlQ3muPE1or45YBogAEQpV3RODlxmsZojdsZaXZ9yj3HVMElyqO491b1HuoLTdnso7VsUqdbkD5lK1qw0b0Kyg+58YCU/8v58duUhU7lDxkCa0wC0kYmL50XXhtRF4+r638pQ5UJ0ZKsT5w8A2rMFOWmkeT1xnXns3iWna5Cay/4t5lVvt55Y1XtS28u9i+0UUur3dIpPXndUnRNWNdL0hE8UuveUw/cdUjiTgyH/npUhB3B+vrUzoqtOeJSeNfy+hOdxXqbXfHet3hu1LVZrtPr9R2+cbLnnJPI8TueTlTJVPYtyxS1f5/j7Sc4b5PADtBq61HHjCkddFBuAGPlUp/RXo15JjggZ0OpA7MZChY8HyUO0znOZOt41qP10hqQz9qTCXEdRFyrNgWBUPzgx6nPHNiAsk1q9KzojHEV5U2SVk4hEQt1v1PeEIh3JuM/PwqqxwsogaGUIWLKwJCqa38pYpVs76B2giG06U/OICbXv7c63Knz/4+cnVQ6zMjIB0e+IaojWGQFVnQSfMy7EOjzq2kufieckRusM1xvostxYOX3ofk9YOcyo/aWOMekPqbSDASA/4hRI1ySnYt5G2guBaOcyXS3Uh98MpzZWjZc6XYXaz+qalqjOVoil19SImTt3LmbPno1nnnkGABAKhTBs2DDcc889uP/++22/KzVinGOlHOSKMXrBdXlQ7epeJHI//ju7PL5Wn6PfXfREbK5LXT59Nec+b79VjnTeJx2Ro9+uL9T86PQsaj9T3Qiv24WsdC8WjB1gmYuS15hI97rNXLW5rOZCW/LO2z2fLge91c+B6PGjuZXpcyM/Kz1q86fv8c/T7+l7vDCkXb/z+fTB7tPa+jfqnOD3XXN/a82MSYXh2gTxcoKqimeqh0I1AKzmvHo9tR26+1jNMbtxs+qfcM2HFrNOha6GABnRgNZcwfwaPI8w0FqngA7vBH8m3Tir7Yv3jOrn6JrqPKH2Uy0ZADG5WHX1SuLVS1HnLodfj+7nQtgL0O57/D4PvbEzqs6AmovfKU7X17auEbrr8zzgHBobXY0nwkneZR30LLr85Wpb472Liexrdrm0rfYYmgP0jqi5kOPtTdSGgqw00zChjqHVNQDY7vVtWWN08H5Sc1vr5hsfP7WtSxaW4JG3d8EfNODzuHDgsc9jwkPvotEfRKbPgz2Pfk773HZ7ra69XHlnJwe1dx3mz7V4XrGljOL0GgDM2gRAdN0NXmdNJxcmMvft0MlqqpxgdV3d2KufUWurAIiqkbejtNpcL488schyP6C2OJkjNCd4TQO78bFbTxNda3WyE7VBl7Of1s3BuRk4Xd1kyh5877OrycSxm6s6+OdpbXe7XPCHjKg6BVY1mexqLVj1nVVNinjPo3sGuj6X2+zqOsTrJ7XP2rLP0neA6Jp9HfGc9J6R7KeTyfl7YyX3qLIbYVWPTwefFwvGDnBUl4LXf6N202ft1lLdODhdA/l8mzMyH48t242mSK00Ludy7PZWJ3Jgotid/52u6ep7Su20W5t535CBissy6nuvGwfazwFEyc2As3pLgL6em3rvUT9ahpCBqForHVknV3eeclqDkSti6blJxgtHjrkcrcOqbJfImUo9v8Wbn7r3ze4cz2tu0bs+PL+P5fnGqm6wXf/ZrU3qd7j8Rffg3wWi5WUreUI9E/CaQ3Y1bdT20P34Nfi8uF5Ti1PXB1b6knj31a0d5OAcCBm4blqhGWljJw+p8le89c6pDOxEVrR6xniyD29DVrrXsh/t9hG1bp5at8auHqSVTkO9D98vO1rP11OQGjEKLS0t2Lx5M6688krzZ263G1deeSXWrVsX8/nm5mbU1NRE/RGcYWVtV70IVCu9aomN5wkS7378d9wKT15YT763Lya3sJp3EYi2dtO/dWlHdCle4sG9IoDWFFdttSzb9YWaJ5Ys6GruS4riGJiToU0Vw72EqMZEflaa6UVgAGYkjdMICqcWdXUOxfs5PSv9nNcKsUqDoPPQAGDr9Wflnblqf7mZWiJeeg+1HbS5bY943MULV6a55EL4EJHu9Zjvl877wmquxPP+sPLksEP9jvpeUqQTh99X9drk11y2vSzK24S/U4umFrJ0Ep6ouWHliZPIM6oedKqXDe/rrHSvGWUGtNbEoDZxT1Q7r28rryEVuh69l3mZPjPVmt33eJsfvXEyPK7wO81TFSSK0/XVKeqc1K3zKm5X9Dts5+Vn1/920LMArSkw1Pde9RxT048Q6vup87TjnrG61C0EpVzQpUUjz21dqLu6N/E9gOYfebzrUtPwa/B7x9vr27LG6OAeaQRPZ6bCx08XafTwdZNQlJeJaycPwfRH3jeVNur6xZ+byw3x5jhf53Te8+o92rMOJ+IVqbZRHUdqA+ULdyE6DStPN6i2V9eORJ+N3g3u6arrc7vr2sl2KoGgYb6LPL3OdUq6Dqv9gJ6byyxWc+TZlYe0BmUr7OZaomut1eet0nTQurnm/ivM9VPd+/j3nI6zk8/xeUSyeIBCcxg8VQxB+wjtkTqZV5dCKBFP43jPQH3N5Tb1d1beqbp3Wr1fW/ZZ+s5PInUxnTyL0+cEwmss1adr8ofM8w955VMqacA6Q4DV/XSRs1bwdVfdS63OJrqzhPo7p1GKTuVzPt+eXdmaZpqmuQuAL5Jy0ud2xd1b48n6Vlh9zs4DPZH9htY8NeUcEJtGVNc36nMtnteacofQjQPfx1U5nUexA7HymtV1dWeXnEgUcG5E2Rov3ZJTaGxJLuFZEezkXXVO5GX6kOlzm+sd7XGnq5scr8Oq/OJ0/BfPK45Kt6WOkdM5ysdBbROAqLNjVaMfu8qq40bsUDSG1TOraxP/udWZhX4HIKrP+ZhweZlHBuraQGcgfhbimTms9Eu6+/Hz2pKFrTV0dH2k6wMnUZJOZFJ6h0KGYZmmUwc/45BhKV69l3hwWQBAVKSV1TXpuQBE6TTivQ92/Wi3j6hzme9HOl2Zqmuxy25Bz8P3S7vUpIIzekVETFlZGYqKirB27VpceOGF5s9/+MMf4uOPP8ann34a9fl/+7d/wyOPPBJzHYmI6TisvOSdeMy0F+7pxQX6eFEHdrTFS0z1BrCzVLcXnUeME+8sKws5oPewJi+fRDx8OsIrPhm0dU6qXkF2HixW11YjYhL1ukjUo9QKnYdXoteK9x3d751G5qjvbEdG7MS7hlMPGrqHlTduIiT6fO19t6y8bNpyjfasrxwnz2TnHQi0PerFCXZj3VaP1HgRXE4iAhMZf7v1w64tVtdIZO3raFkg0Xcv3trJ98C2rO0dQUevw+29HlcWkNGvrfJdsta49o4Lj5owEOu5moyxprmriyjpTNrTDqv3z6lneaJ9bLXfWM2TeDKO1feSMacT8ba3+3wy16COWEd0Htzcs5Z7rzuN0OuovaIt0YztvWeicwNAlFeyXcYHp/dyuo7Ge4+ceMLb0Zb1Il4fOpUZrdY4VWfQnrnQUWczq+u2JyLGbs50lPwej0SjOhNdA/jzUESMXeaPjlhj2vNMgHWkoxNoXlc3+k25xUlkZUecrZ20zek11YhAp9+LFymdSFvsIq3aum4m2gYr2vqetkdH0Jl62+5GIhExYojRGGKam5vR3NxaeLSmpgbDhg0TQ0wPoaOUoh1NZy9qbVW+JCokJ6Md3YGe8Fypeobu0HfSxtSQTKVuZ9KRz9GZCrmu3KedQVeVH1JJKvuks+ZjZyukejOduW51tmG4rXSVdiSLZCmou2s7Opv2Ggu78vxsb9u6057flcfBCR3tMNIV6ArP1F37rr105Hqu68OOcLJIJV21Xd0dMcQotLS0oE+fPnj11Vdx4403mj+/7bbbUFVVhTfffNP2+1IjRhAEQRAEQRAEQRAEQRAEQRAEQmrEKKSlpWHmzJlYsWKF+bNQKIQVK1ZERcgIgiAIgiAIgiAIgiAIgiAIgiB0JN74H+kZ/OAHP8Btt92GWbNmYc6cOXjqqadQX1+P22+/PdVNEwRBEARBEARBEARBEARBEAShh9JrDDFf/epXUV5ejh//+Mc4ffo0pk+fjnfffReDBg1KddMEQRAEQRAEQRAEQRAEQRAEQeih9IoaMe1FasQIgiAIgiAIgiAIgiAIgiAIgkBIjRhBEARBEARBEARBEARBEARBEIQugBhiBEEQBEEQBEEQBEEQBEEQBEEQkkSvqRHTHih7W01NTYpbIgiCIAiCIAiCIAiCIAiCIAhCqiF7gZPqL2KIcUBtbS0AYNiwYSluiSAIgiAIgiAIgiAIgiAIgiAIXYXa2lrk5ubafsZlODHX9HJCoRDKysqQnZ0Nl8uV6uZ0KWpqajBs2DCcOHEibkEiQRCEtiDrjCAIyUbWGUEQko2sM4IgJBtZZwRBSDayzsRiGAZqa2tRWFgIt9u+CoxExDjA7XZj6NChqW5GlyYnJ0deQEEQkoqsM4IgJBtZZwRBSDayzgiCkGxknREEIdnIOhNNvEgYwt5MIwiCIAiCIAiCIAiCIAiCIAiCILQZMcQIgiAIgiAIgiAIgiAIgiAIgiAkCTHECO0iPT0dDz/8MNLT01PdFEEQeiiyzgiCkGxknREEIdnIOiMIQrKRdUYQhGQj60z7cBmGYaS6EYIgCIIgCIIgCIIgCIIgCIIgCD0RiYgRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEmKIEQRBEARBEARBEARBEARBEARBSBJiiBEEQRAEQRAEQRAEQRAEQRAEQUgSYogRBEEQBEEQBEEQBEEQBEEQBEFIEt5UN6A7EAqFUFZWhuzsbLhcrlQ3RxAEQRAEQRAEQRAEQRAEQRCEFGIYBmpra1FYWAi32z7mRQwxDigrK8OwYcNS3QxBEARBEARBEARBEARBEARBELoQJ06cwNChQ20/I4YYB2RnZwMId2hOTk6KWyMIgiAIgiAIgiAIgiAIgiAIQiqpqanBsGHDTPuBHWKIcQClI8vJyRFDjCAIgiAIgiAIgiAIgiAIgiAIAOConIl94jJBEARBEARBEARBEARBEARBEAShzYghRhAEQRAEQRAEQRAEQRAEQRAEIUmIIUYQBEEQBEEQBEEQBEEQBEEQBCFJiCFGEARBEARBEARBEARBEARBEAQhSYghRhAEQRAEQRAEQRAEQRAEQRAEIUmIIUYQBEEQBEEQBEEQBEEQBEEQBCFJiCFGEARB6JGcr2/B2dqmVDdDEARBEARBEARBEARB6OWIIUYQBEHocYRCBr707Fpc84tVqGpoSXVzBEEQBEEQBA2hkIFXNp3AicqGVDdFEARBEAQhqYghRhAEQehxHK9swOFz9Tjf4Mfqg+dS3RxBEARBEARBw2tbTuKfX92Of319R6qbIgiCIAiCkFTEECMIgiD0OHaWVZv//mS/GGIEQRAEQRC6Ih/uPQsA2Hi0Ev5gKMWtEQRBEARBSB5iiBEEQRB6HDtLa8x/f3KgHIZhpLA1giAIgiAIgkogGDIjl5v8Iewqq4nzDUEQBEEQhO6LGGIEQRCEHscuFhFTVt2EQ+X1KWyNIAiCIAiCoPLZiSrUNgXM/286WpnC1giCIAiCICQXMcQIgiAIPQrDMLCzNGyIGZCdDiAcFSMIgiAIgiB0HVbtD8tnHrcLALDp6PlUNkcQBEEQBCGpiCFGEJJARV0zXt96EsGQpEMShM6mrLoJ5xv88Lpd+Ma8YgDA6gNSJ0YQBEEQBKEr8XHEEPOlC4oAAJuOnZd0soIgCIIg9FjEECMISeDf3t6N7/9lG/534/FUN0UQeh0UDTNmUDaumDAQALDucAVaAlIAVhAEQRAEoStQWd+C7RGZ7buXjUGax41zdc04XtmQ4pYJgiAIgiAkBzHECEIHEwoZZhqkDUckz7EgdDZU6HVyYQ4mDM5B/75paGgJYstxSXchCIIgCILQFfjkQDkMAxg/OBvDC/pgclEOAGCjpCcTBEEQBKGHIoYYQehg9p6uRVWDH0C4AKUgCJ3Lroh35eSiXLjdLlw8uj8AqRMjCIIgCILQVVi1P5w29tKxAwAAs0fkAwA2HxNHNkEQBEEQeiZiiBGEDmbd4Qrz38cqGnC+viWFrRGE3sfOMjLEhD0rLxkTPuB/InViBEEQBEEQUo5hGFgVcZBZEDHEzCzuB0AiYgRBEARB6LmIIUYQOph1hyqi/r/tZFVqGiIIvZCztU04U9MMlwuYMIQMMeGImB2l1agUw6ggCIIgCEJK2XOqFuW1zcj0eTBrRNgAQ4aYg2frxJFNEARBEIQeiRhiBKEDCYYMbDgSNsSUDMgCIOnJBKEzofowJQP6ok+aFwAwMCcD4wdnwzCANQclKkYQBEEQBCGVUDTMhSUFSPd6AAAFfdMxKnJ+2nxMomIEQRAEQeh5iCFGEDqQPadqUNMUQN90L26ZMxwAsE0MMYLQaZj1YQpzon5OUTFSJ0YQBEEQBCG1fLwvLI9RfRhidnG4TswmMcQIgiAIgtADEUOMIHQglJZszsh8M7z+sxNVMAwjlc0ShF7DztJwRMykwtyon/M6MfI+CoIgCIIgpIb65gA2HasE0FofhpgZSVO26Whlp7dLEARBEAQh2fQaQ0xtbS2+973vobi4GJmZmZg/fz42btyY6mYJPYx1h8OGmAtHFWBiYQ58HhfON/hxorIxxS0ThN7BzrJwRMykouiImDkj85HmdeNUdRMOldelommCIAiCIAi9nnWHKuAPGhie3wcjCvpE/W72iHBEzPaT1WjyB1PRPEEQBEEQhKTRawwxd955Jz744AO88MIL2LFjB66++mpceeWVKC0tTXXThB5CIBjCxiNh7615o8L5jidGioV/drIqhS0ThN5BdYMfJ8+HjZ5qREyGz4O5I8OH+08OSJ0YQRAEQRCEVED1YRaM7Q+XyxX1uxEFfVCQlYaWYAg7I+lmBUEQBEEQegq9whDT2NiI1157DT/72c+wYMECjB49Gv/2b/+G0aNH49lnn01184Qewq6yGtQ2B5CT4cXESH2KacPyAACfHa9KXcMEoZewKxINMzy/D3IzfTG/v3g01YkRQ4wgCIIgCEIq+Hh/xBAzZkDM71wuF2ZRejKpEyMIgiAIQg+jVxhiAoEAgsEgMjIyon6emZmJ1atXp6hVQk+D0pLNGVkAjzvs3TU9YojZJhExgpB0KC3ZZCUtGUF1YtYdqkBzQNJdCIIgCIIgdCZHz9XjWEUDvG4X5kccZFRmFYcjmKVOjCAIgiAIPY1eYYjJzs7GhRdeiEcffRRlZWUIBoNYunQp1q1bh1OnTsV8vrm5GTU1NVF/BCEe66k+TEmB+TOKiNlZWg1/MJSKZglCr2FnaXitVtOSEeMHZ6N/33Q0+oPYcqyqE1smCIIgCIIgUFqymcX90Dfdq/0MRcRsPnYeoZDRaW0TBEEQBEFINr3CEAMAL7zwAgzDQFFREdLT0/H000/jlltugdsd2wWPP/44cnNzzT/Dhg1LQYuF7oQ/qj5MvvnzkQVZyMnwojkQwr7TtalqniD0ClojYvSGGLfbhUvGUHqy8k5rlyAIgiAIggCsiqQlu3RcbFoyYlJhLtK9bpxv8OPwubrOapogCIIgCELS6TWGmJKSEnz88ceoq6vDiRMnsGHDBvj9fowaNSrmsz/60Y9QXV1t/jlx4kQKWix0J3aUVqO+JYi8Pj5MGNyaFsntdrXWiTlRlZrGCUIvoK45gCPn6gEAkwr1qckAMEOM1IkRBEEQBEHoLFoCIaw9FM4goKsPQ6R53WZ6501HpU6MIAiCIAg9h15jiCGysrIwZMgQnD9/Hu+99x5uuOGGmM+kp6cjJycn6o8g2EFpyeaOzIc7Uh+GmC6GGEFIOntO1cAwgME5GejfN93ycxdH8pHvLKtGRV1zZzVPEAShW1Ld6Mfj7+zBsYr6VDdFEIRuzqZjlWhoCaJ/33RMHGJ/vqb0ZBvFECMIgiAIQg+i1xhi3nvvPbz77rs4cuQIPvjgA1x22WUYP348br/99lQ3TegBrIt4d80bVRDzu2lD8wAA28QQIwhJY1cppSWzP9gPzMnA+MHZMAxgTeS9FQRBEPT8z6rD+O3Hh/Gvr+9IdVMEQejmfBxJS7ZgTP8YxzWVWSPCqZ43H6tMersEQRAEQRA6i15jiKmursbdd9+N8ePH45vf/CYuvvhivPfee/D5fKlumtDNaQmEzLD5C0s0hphIRMzB8jrUNvk7s2mC0GvYWVYDIJxXPB4LxobTYXyyX+rECIIg2PHJwXAax7WHKnCisiHFrREEoTuzan94PbGrD0NcMLwfXC7gaEUDymslglkQBEEQhJ5BrzHE3HzzzTh06BCam5tx6tQpPPPMM8jNja+wE4R4bD9ZhUZ/EPlZaRg7MDvm9wOy01GUlwnDAHacrE5BCwWh57PTjIiJv67zOjGGYSS1XYIgCN2V6kY/dpysAgAYBvDalpOpbZAgCN2WszVN2HOqBi5Xa5pYO3Izfea5SqJiBEEQBEHoKfQaQ4wgJAu7+jCEWScmotAQBKHjaPIHceBsHYD4qckAYPaIfKR73Thd04SDke8JgiAI0Xx6uAIhA3BFRJtXN59EKCTGa0EQEmfVgXA0zJSiXBTY1PLjUJ2YTVInRhAEQRCEHoIYYgShnayLGGJ0ackI0xBzvKoTWiQIvYt9p2sRDBkoyErD4JyMuJ/P8HkwZ2Q49/gnEcWAIAiCEM3aSB2tm2YMRXaGFyfPN5rOJ4IgCImwyqwPEz8tGUGGmI3HxBAjCIIgdD47S6txurop1c0QehhiiBGEdtAcCLbWhxllbYihOjHbJCJGEDqcnWXhtGSTinLhctkXfyVa05NJnRhBEAQdayL1Ya6YMBDXTysEALy86UQqmyQIQjckGDJMectJfRhiVnHYaWZXaTUaW4JJaZsgCIIg6Nhy/Dyuf2Y1vvmHTyWdudChiCFGENrBthPVaA6E0L9vGkYP7Gv5uclFOfC4XThT04xT1Y2d2EKhPQQlBUu3YGdpDQBgcmH8tGTEJRGPzPWHK9EckMO9IAgC52xNEw6crYPLFXY0uXnWMADAOztPo6bJn+LWCYLQndhZWo3zDX5kp3vNLAFOGNovE4Ny0hEIGfjsRFXS2icIgiAIKv+z6jBCBrD/TB32nalNdXOEHoQYYgShHayLpO2YO6rA1hO/T5oXYweFC05uk4NEl+dweR2++Js1mP3Ychw5V5/q5ghx2EURMYW5jr8zfnA2+vdNR6M/iM2S8kIQBCEKSks2cUgO+mWlYerQXIwblI3mQAhvbytLcesEQehOfBxJSzZ/dAF8HufqB5fLhVkjwlExm49VJqVtgiAIgqByvKIB7+06bf7/g11nUtgaoachhhhBaAfrDofTdtilJSOmDwsribeKIabLYhgGXt50Al/41WpsPV6FyvoW/Peqw6lulmCDPxjC3tNhD5XJRc4jYlwuFxaY6cmkTowgCAKH0pJdNDq8TrpcLnxl1lAAwMubTqasXYIgdD+oPsylYwcm/N1ZxZE6MUfFaUYQehLVjX784oP9OHm+IdVNEYQYnlt7BCED6JPmAQB8sEcMMULHIYYYQWgjTf4gthyvAgBcWOLEEJMHQCJiuirVjX7c89JW/PDV7WhoCWL84HAE0+tbT6KqoSXFrROsOHi2Di2BELIzvBie3yeh714yVurECIIgqBiGYUbEzGfyzY0ziuB1u7DtRBX2S4oGQRAcUN3oN53QFkTkrkSYHYmI2XL8vKQMFqIIyXzo1vzs3b345YoD+MHL26T+htClqG704+WN4ZqIP7lxMlwuYPvJaikxIHQYYogRhDay9XgVWgIhDMhOx6j+WXE/Py1iiNlxsloOEl2Mzccq8flffoK/bT8Fj9uFf75mHJbdewkmFeagyR/CSxukOHFXZWcppSXLsU0PqIM8vXeW1qCirrnD2yYIgtAdOVbRgNKqRvg8LswZmW/+vH/fdFwxIezR/som2RcFQYjP2oPnEAwZKBmQhaH9EnOYAcKpZPukeVDbFBADsGCyq6wa0x55Hw+/uTPVTRHaQGV9C17bEo6u3XCkEusOV6S4RYLQyl82Hkd9SxBjB/XFF2cUYUZEj7d8t0TFCB2DGGIEoY2QwHBhnPowxJiB4YNEfUsQB8/WJbt5ggOCIQO/XH4AN/92PUqrGjE8vw9e/c6FuPuy0fC4XbjjopEAgD+tOwp/MJTi1go6dpXVAAAmJ1AfhhiYnYEJQ8LpzFYflPRkgiAIALDmUHg9nDGsH/qkeaN+d/OsYQCAv24plX1REIS4UH2YBWMHtOn7Xo8bFwwPpyfbJDX9hAhPvLMXtc0B/HnDcVTWS+aC7safPz2GJn+rDPHU8gMSFSN0CfzBEP645igA4M6LR8HlcuGqiYMBAO+LIUboIMQQIwhtZD0ZYhykJQMAj9uFKUVhZbGkJ0s9pVWNuOW/1+MXy/cjGDLwxRlFWHbvxZgROewBwBemDUH/vuk4Vd0UVaxN6DpQRMzkosQNMQCkTowgCILC2oORtGSjY+WbS8cOwIDsdFTUt+DDvWc7u2mCIHQjDMNg9WHaZogBgJmROjGbjlZ2SLuE7s2nhytMud0fNPDG1tIUt0hIhOZAEM+vOwYA+NG145HmcUtUjNBleGfnaZRVN6F/3zRcP70QAHD1pEEAwvq/miZ/Kpsn9BDEECMIbaDJH8Rnkfow80Y5M8QArXVitoohJqW8s+MUrn1qFTYcrURWmge/+Oo0/OKr05Gd4Yv6XLrXg1vnDgcAPBfxjBC6DsGQgd2nIhExRTltusYlY8KKgU8OlIsnliAIvZ5QyDCVIZS+keP1uHHTBUUAgFc2nezUtgmC0L04eLYOZdVNSPO6MXek8/OSCtWJ2XRUImJ6O4Zh4Mn39wEABudkAABe3nRCZPhuxNvbTqG8thmDczJwx8Uj8bU54UjbXy4/kOKWCb0dwzDwu08OAwC+MW8EMnweAEDJgL4YNSAL/qCBj/dJbVmh/XQpQ0xNTU3CfwQhFWw+dh4twRAG52RgRIHzfMdkiJGImNTQ0BLAj/66HUte3IKapgCmDcvD//3jJfjijKGW37l13nCkedzYfOy8jFsX48i5ejS0BJHp82Bk/75tusasEf2Q7nXjTE0zDkjKQEHoUJr8Qfx+9RHsKqtOdVMEh+w9XYvK+hb0SfNg2tA87We+MjOsNPlo31mcrW3qxNYJgtCdoLRkc0fmIzPN0+brTB+eB7crHM0uxZJ7Nx/vL8fGo+eR7nXj+TvmIM3rxt7TtWaqYqFrwxXdt80fAZ/HjSULS5DmcePTI5VYd0iiYoTUsenYeWw/WY00rxuL5w2P+t1VE8NRMR9IejKhA+hShpi8vDz069fP8Z/8/HwcPnw41c0WeiEkJFxY4qw+DDEtYojZd6YWjS3BZDRNsGBXWTWu+9VqvLThBFwuYMnCErz6nQtRXJBl+72B2Rn4wrQhAIDn1hzpjKYKDiHl7oQh2fC4nb+HnAyfxyxGLenJBKHjaAmEcPeLW/Do33bj+3/5LNXNERyyNlIfZs7IfKR59ceE0QP74oLheQiGDLy+RVLCCIKg5+MOSEsGAH3TvZhYGI58lqiY3othGPj5+/sBAN+YV4xxg7NxdUQ5+vKmE6lsmuCQtYcqsPd0LTJ9Hnx9TljRPSQ304yKeWr5/lQ2T+jlkJHwSxcUoaBvetTvaK35aN9ZqZEotJsuZYgBgFdffRUffvhh3D8rVqxAWlpaqpsr9FKoPsy8UfkJfW9IbgYGZqcjGDKwUzyEOwXDMPD71UfwxV+vxaHyegzKSceL356Lf/ncePg8zpbAOy4aCQBYtuMUztSI929Xgbzf2lofhljA0pMJgtB+AsEQvv+Xz7AiUkNk/5k6HDlXn+JWCU5YczBsiLmoJDYtGefmWWGliaSEEQRBR5M/iA1HwjVd2muIAYBZxZSeTOrE9Fbe23UaO0qrkZXmwZKFJQBa96I3tpaiyS9Ojl0dUnTfPGsocvu0pgSXqBgh1RyrqMf7kWgX0v1wpg/rh/5901DbFMCnh2UfEtpHlzLEFBcXY8GCBbj00kvj/lm4cCFGjRoFn88X/8KC0IE0tASw7WQVAODCUfaKChWXy2VGxUiaq+RTXtuM2/+4EY/+bTdagiFcOWEQ3vnHBZivyXtvx+SiXMwZkQ9/0MDS9ceS1FohUXaWho2ZkwvbZ4i5ZGx4Pqw/XIHmgBziBKE9hEIGfvjadizbcQo+jwvD88PpO5dLKH+Xxx8MmYrT+aPt6zksmjoEmT4PDpXXS907QRBi+PRIJZoDIQzJzcDogW1LH8uZNaIfgHDqGKH3EQy1RsPccfFI01v9otH9UZibgZqmgKQM6uIcPFuLj/aVw+UCblcU3UNyM/HV2ZFaMSskKkbofJ5bcxSGASwcNwBjBmXH/N7jduGK8eGomPd3n+7s5gk9jC5liDly5AgKCpwX8tu5cyeGDRuWxBYJQiybjp6HP2igKC8Tw/IzE/4+1YkRxUVy+Xh/Oa795SdYua8c6V43Hr1hEv7nmzORn9W2SLrbLxoBAHjx0+PicdUFMAzDNMRMKspp17XGDcrGgOx0NPlD2CwpLwShzRiGgYfe3Im/bimFx+3CM1+/AHdE1s4P9oiCpKuz7UQV6luCyM9Kw4TB9utqdoYPn58STtv5iqSEEQRBgQoaXzp2QEJpnK2giJg9p2pQ1xxo9/WE7sVb20px4GwdcjK8uPOSUebPPW4XvjwzXOtT0pN1bX6/+igA4KoJgzCif2xqcIqKWX+40sw+IgidQXWD31w/7rx4lOXnqE7M8t1nJBpcaBddyhDDOX78OJqbm2N+HgqFcPz48RS0SBDCtKYlS6w+DDFdImKSSnMgiJ/8bTdu+8MGnKtrxrhB2XjruxfjGxeOaNdB8KqJg1CUl4nK+ha89VlZB7ZYaAsnzzeipimANI8bYwbGeq0kgsvlwiVjwlExq6ROjCC0CcMw8JNle/Dip8fhcgH/dfM0XDNpMK6MHFo2Ha1EZX1Lilsp2LHmYKT+3agCuB3U3frKrLDy6+1tp9DQIopRQRBaWRVJ97qgA9KSAcDg3AwM7ZeJkAFsPS5OM70JfzCEX3xwAADwD5eWIDczOiPKl2eGHXNXHzyH0qrGTm+fEJ+Kumb8dctJAIgypHEK8zJx8+ywXPHL5Qc6rW2C8NLG42hoCWL84GxcZBMRfvGY/sj0eVBW3WSmSBeEttBlDTEjRozABRdcgEOHDkX9vLy8HCNHxubsE4TOYl0b68MQU4bmwuUKK5LP1cUaG4W2c6i8Djf9Zi1+t/oIAOCbFxbjze9ehHGD26eoBwCvx43b5hcDAP6w5oh4QaQYioYZNzjbsqB0IkidGEFoH//1wX78PrL2/vSmqbhhehEAYGi/PpgwJAchA/goUjNG6JqsORQ2RMdLS0bMHZmP4oI+qGsO4J0dkqZBEIQwpVWNOHi2Dm5X/HpTiTCrOJKeTKKXexWvbDqJ45UN6N83zcxQwBle0AfzRuXDMIDXNp/s/AYKcXnx0+NoDoQwdWguZkfSDOq4a+Fo+DwurDtcIVExQqfgD4bwxzVHAQDfvnikreNuhs+DBZGU5u9LKkShHXRZQwwATJgwAXPmzMGKFSuifi4KUCFV1DUHsP1kWAF8YYnzNHqcnAwfSgaEcyVLVEzHYBgG/rLxOL7w9GrsKqtBvz4+/M83Z+Hfb5iMDJ+nw+7z1VnDkenzYO/pWqyXIm0pZWdZpD5MO9OSERdF6gbtKqsRA6kgJMivPzqIX314EADw7zdMws2zo9PGXjVhIABI/vYuTENLwPQyd6o4dblc+EokJcwrmyUljCAIYVbtDzu1zBjeL6ogd3uZNSLsBLfpmMjgvYUmfxC/+jAcHXHXwtHok+bVfu7mWWG549XNJxEKia6oK9HkD+JP644CiK/oLsxjtWIkKkboBP5vxymcrmlC/77puH56YdzPXzVxMAA50wjto8saYlwuF37zm9/gwQcfxKJFi/D0009H/U4QUsGmo5UIhgwMy8/E0H592nydaUPzAIghpiOobvTjuy9txb+8tgON/iDmlxTgnX9cYObw7Ehy+/jwpZlhL+8/rDnS4dcXnLOzNBwOPLEwt0OuNyA7HROHhI06aw5KejJBcMofVh/Bf763DwDwo2vH45sXjoj5DB1aVh0olxpbXZSNrP5dcYFz+eamC4bC5QLWH67EsYr6JLZQEITuAtWHoWjjjmJWxJN+6/EqBIKhDr220DV58dPjOFXdhCG5Gfj63OGWn7t28hD0TffieGUDPj0ihrquxFvbynCurgVDcjPM2nJ28KiYTyUqRkgihmHgfz45DAC47cJipHvjO/BePn4g3K5wvbITlQ3JbqLQQ+myhhiKevn+97+P119/HT/+8Y/xd3/3d2hpkfziQuow05KNbFs0DDF9WFh5vFUMMe1i49FKfP6Xn2DZ9lPwul34l8+Nx9Jvz8Xg3Iyk3fNb88OpEZfvOYPjFbL5pgLDMLCLImIKOyYiBgAuiYQar9ovhhhBcMJLG47j3/+2GwDwj1eMwT9cWqL93OSiHAzOyUBDSxDrDsmhuiuyNmKAnl+SWP27wrxMXBJRtr4qKWEEodfjD4ZMh5ZLx3WsIWbswGxkZ3jR0BLEnlO1HXptoetR3xzAbz4KR9vee8UY2ywHmWkeXDctrOR/ZZNEaHYVDMPA7z8JOy9+a/4I+Dzx1Y+FeZlmhNMvV0hUjJA8NhypxM7SGqR73bh1XrGj7+RnpZnRmcv3SFSM0Da6rCGGc+2112Lt2rX46KOP8IUvfCHVzRF6MesjCqS2piUjpg8Le3RtO1ElqfbaQCAYwlPL9+Orv12H0qpGFBf0watL5mPJwhJHBYbbw+iBfXHp2AEwDOD5SJi10LmcrW3GuboWeNwuTBjScYYYXidG3ktBsOf1rSfxr6/vAAD8w4JR+N6VYyw/63K5cOXESHoyObR0Sag+DKVpTISbZ4XTk722+SSCkhJGEHo1n52oQm1zAHl9fJhS1DFRy4Tb7cJMqhMj6cl6PH9cexQV9S0oLuiDL0fSYNrxlYjy/v92nkJtkz/ZzRMcsPrgOew7U4s+aR58bY51RJPKXZeFo2LWHpKoGCF5UF3hL80civysNMffuzqSeeX9XXKmEdpGlzXEXHrppUhLa30ZJk6ciE8//RR5eXmiIBNSQk2THzsiBcLnjWqfIYYKjNc0BXBUoioS4uT5BtzyP+vx1PIDCBnATRcUYdm9l2D6sLxOawMVinx54wnUNQc67b5CmJ2R93D0gL4dWgNoZnE/pHvdOFvbjANn6zrsuoLQ03hnxync98p2GAbwzQuLcf+14+NGUVw5IXxoWb77jORv72JUNbRgV1k43WNbHE2unDAIuZk+lFU3SWpHQejlUH2YS8YMgCcJzlGzqU7M0fMdfm2h61Dd6MdvPz4EAPj+lWMdRVLMGJaH0QP7oskfwt+2n0p2EwUH/C4SDXPzrGHIzXReL6pIomKEJHPkXL0Z0XLHRSMT+i6lwN9wtBJVDZKxSUicLmuI+eijj5CXlxf1s4KCAnz88ccIhSQnrND5bDpaiZABFBf0QWFeZruuleZ1mymVPjshBwmnLNt+Ctf+8hNsPHoefdO9+OXXpuO/bp6Ovun6wo3JYsGYASgZkIXa5gBelfD3Tofqw0wq6rhoGADI8HkwN2JkJUWCIAjRfLT3LO79360Ihgx8ZeZQ/Nt1kxylsrqwpABZaR6crW02nRqErsG6QxUwjHDE56CcxFN7Zvg8uDFS4PRl2RMFoVfz8X6qD5N4dJ0TeESMOGf2XP5n1WHUNAUwdlBfXDctfgFtIBx9+5VI5IzsRaln/5lafLy/HC5X4opuIDoqZoPU/RE6mOfWHIFhhGu+jB7YN6HvFhdkYdygbARDBj7adzZJLRR6Ml3OEFNTU+PojyB0NpTX/sJ2RsMQ0yIRHNtOiEIqHoZh4ME3duDuP29BbVMA04fl4f/uvQQ3TC9KSXvcbhe+FREo/7j2qHh3dzI7zfowHZvyAmhVHHxyQLy6BUFlzcFz+Ielm+EPGrhuWiGe+NJUx+kg070es16A5FTuWphpydqRdpVSwry/64x4BwpCL6WirtXQfunYjq0PQ0wbmgefx4UzNc04eb4xKfcQUsu5umb8YU04kuIHV41LKLLqixcUweN2YevxKhw8K3WEUskfImmfrpk4GMML+iT8/aK8TFO2+OWK/R3aNqF3U9XQglc2hesa3nlx4kZCoDUq5oPdcqYREqfLGWLy8vLQr18/yz/0e0HobNYd7pj6MASl0tp6oqpDrteT+c3KQ1i6/jhcLuC7l43GK9+5sE0CXUfypQuKkJPhxdGKBvGE6GR2RQ75kzs49zgAs+j0p0cq0OQPdvj1BaG7suloJe58fhNaAiFcNXEQ/uvmaQmnnZFDS9dk7cGwfDO/DfVhiMlFuZg4JActwRDe2lbWUU0TBKEbsfrgORgGMH5wNga2IbrOCZlpHkyKOOJInZieybMrD6GhJYipQ3NxzaRBCX13YHYGLhsXrklHilah8zlX14y/bi0FANx5SdsU3QBw18IS+DwurDlYgY1H5X0XOoY/bziORn8QE4bktFm3R2eaj/eVozkgOgMhMbqcIeajjz7Chx9+iA8//BArVqxAeno6XnjhBfNn9HtB6EyqG/1m/vT21ochyBCzp6xGFm8bVu47iyff3wcAeOzGKbjvmnGO8gQnmz5pXtwSKTr43JqjqW1ML6Kirhll1U0AgImFHZuaDADGDuqLgdnpaPKHsPmYpA0UBADYfrIKtz+3EY3+IBaMHYBnvj6jTevwZeMGwuN2Ye/pWpyolPpoXYFT1Y04fK4eblf75ZuvzJKUMILQm6G0ZBT9mCxmjwg7ZW6UOjE9jlPVjXhh/TEAwD9dPc5R6lMV2ote21IKf1BS2qeCpeuPoSUQwrRheWY6wbYwtF8ffHlmJCpmudSKEdpPSyCE59ceBRCOhmnLGgMAU4pyMSgnHfUtQayNZM4RBKekXpupcOmll5p/Fi5cCI/Hg3nz5kX9/NJLL011M4VexoYjlTAMYFT/rDblT9cxPL8P+vXxoSUYwt5TEjqt41hFPe59aSsMA7hlznB8fe7wVDcpim9cWAy3K+wBuP+MjGFnQAbRkf2zklIbyOVymVExqw5InRhB2HOqBt/4/QbUNgcwd2Q+frt4JtK9njZdK69PmqlAk6iYrsGaSDTMlKLchArp6rhxehHSPG7sLK3BrjJJuyoIvYlQyMCq/eE0h5eOSa4hZmZxPgBgsxhiehy/+vAgWgIhzBmR3+Y6Q5ePH4j+fdNwrq4ZK/eJLN/ZNPmDeGFd2JjWHkU3cfdlJfC6XVh98JxExQjtZtmOMpypacbA7HTH9ad0uN0uXDlBIv2FttHlDDGC0BWh+jDzOigtGRBW+FKdmM8kPVkMDS0B/MMLm1HTFMCM4Xn4t+snprpJMQzt1wfXTBoMIFzwTUg+ZIiZlIRoGGLB2EidmP1SJ0bo3Rw8W4dv/P5TVDf6MWN4Hn7/rdnITGubEYagQ4vUiekarD0YXufak5aM6JeVZqZqkJQwgtC72HO6BufqmtEnzYOZI5KbRnxW5Pr7ztSiusGf1HsJncfxiga8vDEcUXnfNW2LhgEAn8eNL84I1xF9RSI0O503PytFRX0LivIyce3kwe2+3tB+fVprxUhUjNAODMPA7z4J62xumz8Cad72qcN5ymWpGSwkghhiBMEB66k+TAelJSOmDc0DAGwTQ0wUhmHgh69ux97TtejfNx3P3tp2D+xkc0ekwNtft5TifL0UKE42O8uSVx+GuCiikNx9qgbltc1Ju48gdGWOVzRg8e8+xbm6FkwqzMEfb5/TIVFodGj59EilKNBSjGEYWHMobIi5qKT9hhgA+HIkJcybn5VK2lVB6EVQNMyFowqSLrP375uOkf2zAABbjktUTE/hqeX7EQgZWDB2AOaMzG/XtUhx/+HesyLLdyJc0f2t+SPg7aB04nctbI2K2SRRMUIbWX+4ErvKapDhc+Prc9qfaeXCkgL0TfeivLYZ205Wtb+BQq+hWxhi2hvOKAjtoaqhBXtOh73w545qn1CoMn14HgCJiFH53SdH8Lftp+B1u/Ds4gswODc5BT87glnF/TC5KAfNgRBe2ng81c3p8ewqjRhiCpNniOnfN92MuFlzUKJihN5HWVUjvv679Thd04QxA/vihW/PbXfaKqK4IAtjB/VFMGRg5f6zHXJNoW0cKq/HmZpmpHndpod5e1kwZgAG52TgfIMfK/bI+ApCb+HjyHq+YGxy05IRVHdCUhX1DA6cqcXrn4WLu9939dh2X2/soGxMG5aHQMjAG5Gi8ULyWXXgHA6crUNWmgdfnTOsw647LL+PWfvnlyskKkZoG79ffRgA8OWZQ9EvK63d10v3esyaaJKeTEiELmeIuemmm6L+NDU14Tvf+U7MzwWhs1h/OFwfZvTAvhiY3bEGAYqIOXyuXjyDI6w+cA6Pv7MHAPDwdRMxe0THGr86GpfLhdvnh6Ni/rT2mBSFTCI1TX4crQgX+E5majIAUidG6LWcrW3C4t99ipPnGzGioA9evHMu8jvgsMKhqJj35dCSUtZGomFmDu+HDF/HeLB73C58aWY4JczLkhJGEHoFdc0BbD4Wjky5tJMMMVRvbNMxiYjpCfzXB/thGMA1kwZhauR83F5ujijuX950AoYhaYM6g999ElZ0f3X2cORkdIwDD3HXwtHwul345IBExQiJc7i8DssjDkJ3XDSyw6579USpEyMkTpczxOTm5kb9Wbx4MQoLC2N+LgidRbLSkgFAflYahuf3AQBsL63q8Ot3N05UNuCel7YgZIQ9FRbPK051kxzxhWlD0L9vOk7XNOHdnadT3Zwey+5IfZiivMwO8WKxgwqErj5wTg5vQq/hfH0LvvG7DTh8rh5FeZl48e/mYWBOx0ckUp2Yj/eVoyUgxutUQRF/F43uWPnmKzPDXrCr9pfjdHVTh15bEISux7pDFfAHDQzP74MRkZRhyWZmcdhRa9uJKtlHujk7S6vxzs7TcLmAf7p6XIdd97pphUj3unHgbB22nazusOsKevadrsUnB87B7QJuv2hEh19/WH4ffHmmRMUIbeMPkXq+V04YiFED+nbYdReOGwiv24UDZ+tw9Fx9h11X6Nm0P9l3B/Pcc8+lugmCEAUZYuYlwRADANOH5eF4ZQM+O15leuH3Rpr8QXxn6Wacb/Bj6tBc/OTGyd0mLWG614PF84bjqeUH8Ic1R3DdtMJUN6lHspPSkhUlNxoGAGaO6IcMnxtna5ux/0wdxg3OTvo9BSGVVDf68Y0/fIp9Z2oxKCcdf/67uSjKy0zKvaYNzcOA7HSU1zZj/eGKTktlI7QSDBlYdygs38wf3TH1YYgR/bMwZ0Q+NhytxGtbTuLuy0Z36PUFQeharNofjh7urGgYACgZkIV+fXw43+DHzrJqXDC8Y9IrCp3Pk+/vAwDcMK0QYwd1nLydk+HDtZMH443PyvDyphOYPiyvw64txEJpnz43eTCGRRxNO5q7LxuNVzefxCcHzmHzsUrTICsIdpyvb8Grm08CAL598agOvXZupg9zR+VjzcEKfLD7DP5uQcdeX+iZdLmIGEHoSlTUNWPv6VoAwLwOrg9DTIsIhb25wJdhGPjRX3dgV1kNCrLS8P8Wz+ywNCmdxa1zi5HmcWPr8Sqp+ZMkdkUiYpJZH4ZI93owd2TY+PqJpCcTejj1zQHc/twG7CwNr8Ev3jkXxQXJ82p2u124csJAAMDyPRLKnwp2l9WgpimA7HQvphZ1/JpKudxfkZQwgtDj+ThiiOlMo7rL5TKVsJuPSnoylRfWH8M1v1jV5WvobDpaiZX7yuFxu/C9K9tfG0bl5lnhCM23PytDY0uww68vhCmvbcYbW8sAdLyim8OjYp5aLlExgjP+vOE4mvwhTCrMSYpO76oJkp5MSIwuZYi56aabUFNT4/jzt956K86elUKgQvL49EhYeB03KBsFfdOTcg/yzvnsRFWvVVb8ce1RvL61FB63C898/QIUJskLO5kMyE43I2Gei4S+Ch0LRcRM6oSIGAC4JJKebNWBc51yP0FIBU3+IO58fhO2HK9CbqYPL3x7LkYPTH4EGNWJWb77TK/d+1LJmkh9mLmj8uH1dPxx4PNThiArzYOjFQ3YKErSXkV5bTPe2laG8/UtqW6K0AkcPVeP45UN8HlcuLAkOdkDrKA6MV3d2NDZGIaBZz86iH1navHN328w01B2NQzDwH++F46GuXnW0KSktZs3qgBD+2WitjmA93ZJ+uhk8cL6Y2gJhjBjeB5mFic3Ou3uy1prxWyWGlFCHJoDQfxx7VEAwJ2XjExKxpUrI2eaTccqUVHX3OHXF3oeXcoQ8+abb6K8vBw1NTVx/1RXV+Ptt99GXV1dqpst9GBa05IlL+x1UmEOvG4XztW1oLSqMWn36aqsP1yBnyzbAwD4189P6PRDXEdC+XCXbT8lefE7mMaWIA6Vh9f7zoiIAVo9Oz89XIEmv3jRCT2P5kA4JeS6wxXom+7F83fMwcTCzjF0zi/pj0yfB2XVTWa0m9B5kGJufknHpiUjstK9WDR1CIBwVIzQs2kOBPHOjlO48/mNmPf4Ctz70lbc9eIWMbL2AigaZmZxP/RN79ys57MihpjNx87LXGMcOFuHssg5pNEfxO1/3IiP9nY959XVB8/h0yOVSPO4cc/lY5JyD7fbZdYte1n2oqTQ5A9i6fpjAIA7kxgNQwzL74MvXSC1YgRn/G3bKZTXNmNQTjoWTUlO+vih/fpg4pAchAxgRRdca4WuR5cyxBiGgbFjx6Jfv35x/+Tn56O+XoohCcmF8qcn0ziQ4fNgwpCw4mvbid5VSLCsqhF3v7gFwZCBG6cX4o4kFPbrTCYX5WLOiHwEQoYpkAodw57TNQgZ4cijZBQP1zFmYF8MyklHcyCETeLRLfQwAsEQ7n1pK1buK0eGz40/fGt2p+ZPz/B5sGBs2AggofydS3MgaHqQX9TB9WE4lBJm2Y5TqGsOJO0+QmowDAM7Tlbj4Td3Yu5/rMCSF7dg+Z6zCIYMuFzAusMVWHOwItXNFJJMa32YgZ1+78lFuUjzulFR34IjUiTZZOW+sCJwfkkBrpo4CC2BEP7+hU14d+epFLesFcMw8GQkGubWecOTmg3hSzOL4HIBaw9V4ERlQ9Lu01t5fWspKutbUJSXiWsmDeqUe1JUzKr95RIVI1hiGAZ+tzqcqeS2+SOQ5k2e+vvqSZKeTHBOlzLEfPTRR/jwww8T+lNUVBT3usFgEA899BBGjhyJzMxMlJSU4NFHHxXPGcGW8tpmHDhbB5cLZq2IZDFtWNjD/7MTvUeQaPIHsWTpZlTUt2DikBw8ftPUpISKdjZ3XDwCAOUilSiKjmJXJC3Z5E7y1gfC+ccvGROOipE6Mb2DE5UN2NYLajwFQwb+6ZVteG/XGaR53fjdN2djzsjOL3h6ZSSnstSJ6Vy2Hq9Ckz+E/n3TMXZQ36TdZ2ZxP4zqn4WGliD+b3vXUQCqbD9ZhbWHzsm5wCFna5vwP6sO43NPfYLrnlmN59cdQ1WDH4Ny0vGdS0uw/AcLcNuFIwAA//n+PunXDuB8fQvWHarocn3ZHAhibcRpjQzrnUm614NpQ8NnqE2ijDVZuS8ss141cRB+c+sF+MLUIfAHDdz9561487PSFLcuzAe7z2DbyWpk+jy4a+HopN5raL8+uCgS/flKpGC30DGEQgZ+H1F0337RiKSkOtUxvECiYoT4rDtcgT2napDp8+Drc4Yn9V6UcvmTA+VSj0qIS+fGD8fh0ksvTcp1f/rTn+LZZ5/F888/j0mTJmHTpk24/fbbkZubi3vvvTcp9xS6P58eCR8sxg/OQb+stKTea9rQPCzF8V4TEWMYBh56Yye2naxGXh8ffvuNmchM86S6WR3CVRMHoygvE6VVjXjzs1J8dXZyN/3ews7ScOqiyUkoKm3HJWP649XNJ7HqwDn8qFPvLHQ2205U4Wv/vR6N/iCWfnsuLh7T+UqlziAUMvDA6zvw5mdl8LpdePbWC1L2rJePHwi3C9hVVoPSqkYUdcP6YN2RtWZasoKkOkC4XC58ZdYw/PTdvXh50wncPHtY0u7VVj7aexZ3/mkTgiED4wZl485LRuL66YVI9/YMmaSjaA4EsWLPWby2+SRW7i9HMBQ2CKR53bhm0mB8eeZQXDy6Pzzu8Hy667IS/O/G49h2ogrL95w1FRRC4tQ0+XH9r1fjRGUjfvi5cUlXWifC5qPn0egPYkB2OiYO6TxHGc6sEfnYePQ8Nh2tNKPwejN1zQEz4nHhuIHwedz45ddmIMPnwaubT+J7f/kMzf5QStfjUMjAf32wH0BYeT8gOzl1WDlfmTUUqw+ew2ubT+J7V4yB2939nf+6Ah8fKMfBs3Xom+7FVzt5Tt192Wi8uuUkVu0vx5bj53HB8OTWphG6H7//JGwk/Mqsocjrk1x93sQhOaYOaPXBcyL3CLZ0qYiYZLF27VrccMMNWLRoEUaMGIEvf/nLuPrqq7Fhw4ZUN03owlBasmTWhyFmDM8DAOworUYgGEr6/VLN0k+P45XNJ+F2Ac/ccgGG5fdJdZM6DI/bhdvmFwMAnltztMt5L3ZXdpaFjZSTOqk+DHFxJG3PnlM1OFsrdX96KkfP1eOOP25EYySK7Uevb0dDS89LpWQYBv79b7vxvxtPwO0Cfvm1GbhiQuoOCgV9082iriskKqbTWBORby4anfyabDddUAS3K+ytfri8a9V13HaiCndF0qO6XcC+M7X451e345KffoRnVx5CdaM/1U1MKYZhYPvJKvz4zZ2Y89gK3PXiFqzYG049NmN4Hh774mRsfOBK/OqWGbh07ADTCAMAA7Mz8K35IwEAP39/H0IhkYXayo/f2IkTleEakk++t888n3QFqD7MJWP6pyyqfVZkD5GImDBrDp6DP2iguKAPRvbPAhA+m/zsS1OxeN5wGAbww9e240/rjqasjW9vL8Pe07XIzvDiHxaUdMo9r5k0GNkZXpRWNZpRXEL7IUX312YPQ3aGr1PvHY6KCWfH+eVyiYoRojlUXocVe8/C5QJuv2hk0u/ncrlM48sHu08n/X5C96ZXGGLmz5+PFStWYP/+sOfFtm3bsHr1alx77bXazzc3N6Ompibqj9D7WHc4Uh9mVPIVFaP690V2uheN/iD2n+laioqOZuPRSjzy1i4AwL98bnyP9Dr/6qzh6JPmwd7TteY8EtpOcyCI/WdqAQCTizrX47Kgb7p5TypuLfQsztU147bnNqCivgWTi8LeTCcqG/Ff7+9PddM6nN99cgR/XHsUAPCfX55mFlNPJa2HFjHEdAZ1zQEz/d78kuTvv4NyMrBwXLh2RFdKCcONrwvGDsDGB67E/deOx6CcdJytbcZP392L+Y+vwL+/vRsnz/eumgJna5rw248P4ZqnVuH6Z9bgT+uOobrRj8E5GbhrYQlW/NOleP2ui3Dr3GLkZlor3r5z6Shkp3ux93Qt/raj66am68q8vvUk3visDB63C/NLChAygHte2tplHEM+NuvDDEhZG8iYf7i8HhV1zSlrR1eB0pItVMbE7Xbh0Rsm49sXhxWSP35zF/571aFOb18gGMJTEaX5318yCrl9Okd5n+Hz4Ibp4ULdL2860Sn37OnsOVWD1QfPwe0CvpWiOq/fvWwMPG4XPo5ExQgC8YdIyrwrJwwyjdLJhs40KyL18gTBil5hiLn//vvxta99DePHj4fP58OMGTPwve99D7feeqv2848//jhyc3PNP8OGSZhzb+NsTRMOl9d3Sn0YICwcT43Uidl2sirp90sVZ2qacNeLWxAIGVg0dQj+fsGoVDcpKeT28Zl5a/+w+mhqG9MDOHCmDv6ggbw+vpSkLjLrxOwXQ0xPo745gDv+uBHHKhowLD8Tf/jWbDz2xckAgD+sOYLPelC9mP1navGfkcK4D183EV+aOTTFLQpDdWLWH65ATVPvjkDoDDYcqUAgZGB4fp9Oi0a9eVZ4rr22+WSXiPrlxtcpRbl49tYLUNA3XN/kkx9ejie/Mg3jBmWjviWIP6w5gkv/cyXufWkrdpb23PSxTf4glm0/hduf24B5j6/A4+/sxf4zdUj3unH9tEL86Y45WHP/5fjh58ajZICzukJ5fdJw5yVhOe+pD/Z3ibHvThyrqMdDb4Qdl/7xijH43W2zMG5QNs7VNePel7amvD/P1DRh7+lauFytclIqyOuThjEDw3OytxftNgwDH+87CwCmAZzjcrnw4KIJ+O5l4fR2//F/e/HL5Qc6NXr/tS0nceRcPfKz0nD7xcn3UudQ6rp3d51GdYPIG+2FasNcO2UIhvZLTXaL4QV9cNMMiYoRoqmsb8FrW8LOP3d24jozZ2Q+cjK8qKhvEcOgYEuvMMS8/PLLePHFF/HnP/8ZW7ZswfPPP48nn3wSzz//vPbzP/rRj1BdXW3+OXFCvCZ6GxTFMHFITqd56kwbmgcA+Ox4Vafcr7NpDgSxZOlmlNc2Y/zgbPznl6emLI1BZ0CeQSv2nsGxivrUNqabQ8qvSYU5KZkzl0Sitj45KMWcexL+YAh3/3kLtp+sRr8+Pjx/+xwMzA57739xRhFCBvAvr25HS6D7Kw/9wRB+8PJnaAmGcPn4gfjW/BGpbpLJqAF9UTIgC/6ggY8jnrxC8lhzsPPSkhGXjx+E/Kw0nK1txicHUmvQ1hlfs9JbS2amed348syhePd7l+CPt8/GRaMLEAwZeGtbGb7wq9X4+v+sx0f7zvaIvcAwDHx2ogoPvrEDc/9jBe7+8xZ8tK8cISMcZfD4TVOw8cEr8fQtM7BAST3mlDsuHoF+fXw4fK4ef93SNYqEdwf8wRDu/d/PUNccwJwR+bj7stHok+bFbxZfgKw0D9YfrsQvlqc2anNVJBpmSlEu8pNcSzMes0aE00j39vRkB87Woay6CWleN+ZZZHRwuVy475pxuO/qsQCAXyzfj5+9t69T1rTmQBBPrzgIALhrYQn6pnduueIpRbkYPzgbLYEQ3tpe1qn37mmcrWnCm5+F1/TOVHTr+O7lo82omK2i/BYA/PnTY2jyhzClKBdzRia/zADh87hx2fiwEVwi/QU7urQhJhAIYPny5fjtb3+L2tpwWpqysjLU1SWWuumf//mfzaiYKVOm4Bvf+Aa+//3v4/HHH9d+Pj09HTk5OVF/hN7F+k5MS0ZMG5YHoOdGxDzy9m5sOV6FnAwvfvuNmeiT1rnCd2dTMqAvFo4bAMMAnl97LNXN6dbsKgunh5zcyfVhiJnF/ZDp86C8thn7IinShO6NYYQL1q/cV44Mnxu//9ZsjGJe3g99YSLys9Kw70wt/t/HnZ+6o6P59UcHsbO0Bnl9fHjipildzgh+ZSSUf7nUiUk6lGKxM9KSEWleN26cHvZYTWVKGG58zc9Kw5/umGtZJNrlcmHhuIF48c55+Ns9F+PG6YXwuF1Ye6gCtz+3EZ976hO8sulEtzTUnqlpwv/7+BCu+sUq3PjrNVi6/jiqG/0YkpuBuy8rwYf/dCleWzIft8wZjpx25vzPzvBhycJwDYhfrjiA5kCwIx6hx/PU8v3YdiIsM//ia9NNI1jJgL544ktTAQC//ugQPtybujVzVcSomsq0ZIRZJyZSpL63sjISDTNvVAEy0zy2n/3u5WPw4KIJAIBnVx7CI2/vTrox5qVPj6O0qhGDctKxeF5xUu+lw+Vy4cuRaOBXJD1Zu3hh/TH4gwZmFvfDjOH9UtqW4oKs1qiYFRIV09tpDgTx/Lqw7uXOS0Z2+pnn6omDAYQNMT3BaUdIDl3WEHPs2DFMmTIFN9xwA+6++26Ul4e9bn7605/ivvvuS+haDQ0NcLujH9Xj8SAU6n6HJ6FzoEKYF5Z0niFmRsQQs/9MLeqbe1aR6Jc2HMefPz0Olwv45S0zUFzQOXk6Uw0Vhnt50wnUSsqdNrOzLBIRU5QaQ0y614O5o8LeNJKerGfwi+UH8PKmk3C7gGduuQAXKIfI/Kw0PHzdRADAMx8exMGz3dcAt+NkNZ75MOyB+ugNkzEwJyPFLYrlqkh6so/2noVf0hcljXN1zdh7OjyX53eifAMAN88OK7+W7zmTkjoOMcbX22Y5zhk+uSgXT31tBlb98DLcefFIZKV5sO9MLf751e245Gcf4tmVh1Dd2LX3+CZ/EG9vK8O3ntuACx9fgSfe2YuDZ8Opx26cXoil356L1f9yOf75mvFRRumO4JsXjsDA7HSUVjXifzeI8jMe6w5V4Dcrww4AT3xpakxK1uumFeK2C8NK7O//ZVtKahgFQwY+ORA+my/oAoaY2ZGImB2l1Wjy915jH9WHuWycszG585JR+MmN4XSsf1x7FP/6+g6EklTXoKElgGc+Cs/rey4fgwyfvaEoWXxxRhG8bhe2n6zG3tNSB7gtNLYEsXR9RNGd4mgYgqJiVu4r71FphYXEeXvbKZTXNmNwTgY+P6Xza2FeOm4A0jxuHDlXj0PlPbv2s9B2uqwh5h//8R8xa9YsnD9/HpmZrQLoF7/4RaxYsSKha1133XV47LHHsGzZMhw9ehSvv/46/uu//gtf/OIXO7rZQg/gVHUjjlY0wO0CZndiKOPAnAwMyc1AyAgfJHoKW4+fx8NvhnNc33f1OFymyVncU1kwpj9KBmShrjmAV7tQkeLuRCAYwp5TFBGTuuhEyn++6oCkTuru/PnT43g64jH3kxunmNEYKtdPK8Tl4weiJRjCv7yWPOVEMmnyB/GDlz8z63JdN60w1U3SMmN4PxRkpaGmKYCNR7qXR3MwZHSbqAhyMhk/OBsFffWRIMli/OAcTB2aC3/QwBufdX5KGG58/fXXL2iTB29RXiYe/MJErP3RFbj/2vEYlJOOMzXN+Om7ezH/8RX497d3p0QprhIKGThcXoe/bS/Dz97di289twGzH1uOe17aipWR1GOzivvhiUjqsae+NgMXj+nfptRjTsjweXDP5eGaFM98dBCNLb1XUR6PqoYWfP8vn8EwgK/OGmapRPrXRRMwbWguqhv9uPvFLZ0eabSjtBpVDX5kZ3hNR7JUMiw/EwOy0+EPGth+suecoRKhrjmAjZGIIF19GCsWzyvGk1+ZBrcLeGnDCdz3yrak1B96fu0xnKtrxrD8TLNWSyoo6Jtu1qZ7ZZOczdrCX7eexPkGP4blZ+LqSYNT3RwA4aiYL5q1YlKbtlFIHYZh4HefHAYQThPv83S+urtvutd05n5f0pMJFnRZQ8wnn3yCBx98EGlp0TlnR4wYgdLSxHIM/+pXv8KXv/xl3HXXXZgwYQLuu+8+/MM//AMeffTRjmyy0EOgtGSTi3LbnZIhUaZHDjM9xZPjbG0TlizdgpZgCJ+bNBh3RdJT9BZcLpcZFfPHtUe7pSI31Rw+V48mfwhZaR6MSGEk1YJInZgNRyp7tbdld2f57jN48I0dAIB7rxiDr88dbvlZl8uFn9w4GX3Tvdh87DxeWN/9Ugz+4oP9OHC2Dv37puPRGyanujmWeNwuXB7JqdydDi2GYeD2P27E3P9YjhOVqVfAx2PtoXBE30WjOy8tGecrLCVMZ6Zr4MbXx744BVdM0BtfnZKb6cN3Li3BJz+8HE9+ZRrGDcpGfUsQf1hzBJf+50rc+9JWs7ZZsmlsCeKzE1X486fH8eAbO3DTb9Zg8r+9h8t//jG+++et+M3KQ1i5rxy1TQEU5mbgnstH46P7FuLVJfPxtQ5IPeaUr84ejqH9MlFe24zn1x3tlHt2NwzDwP2v7cDpmiaM6p+FH0eiMnWkez349a0XIDfTh20nq/Efy/Z0Ykth1vO6qKQ/vClQdKm4XC7MHhE2rm7spenJ1h48B3/QQHFBH8fRfsSXZw7FL782Ax63C3/dWop7/3drhzoY1DT5zTSv37tiLNK8qZ0zX5kV3ote31rabRwpugqhkIHfrz4CALh9/sikGfDbwncvC0fFfCRRMb2WtYcqsPd0LfqkeXDLbOszXrK5KuLkJ3ViBCtSLzlZEAqFEAzGKrtOnjyJ7OzshK6VnZ2Np556CseOHUNjYyMOHTqEn/zkJzFGHkEAWFqyTqwPQ5h1YnqA8NASCOHuF7fgdE0TRg/siydvntbl6hJ0BjddUIScDC+OVTTgw71nU92cbgcpsyYV5sKdQmF/9MC+GJyTgeZAqNce8uubA3jmwwPY0M0iFogtx8/juy9tQcgAbp41FN+/ckzc7xTmZeJfPjcOAPCzd/eitKox2c3sMDYdrcR/R7zCHr9pSsqLKcfjKlYnprvkVF65vxyr9pfjfIMfv1l5MNXNicuag2H55qLRnS/fAMD104qQ5nVj7+la7CztnJQw3Pj6j1eMwS1zOu5gnuZ148szh+Ld712C5++Yg4tGFyAYMvDWtjJ84Ver8fX/WY+P9p3tsPlcXtuMj/eX49mVh3DPS1txxc9XYtLD7+LGX6/Bv76+A0vXH8eW41VoaAkiw+fGtGF5uGXOcDx642T89a75WP0vl+Ofrh6XsJK2I0jzuvGPV4TX3P/38SHUSLrWGF7acALv7joNn8eFp2+Zgaw4hcyH9uuDX3x1GgDg+XXH8Pa2zos0o+jgSx2mwOoMZhaHsxhsPtY7i3Wv3B8ek4VtTBV33bRCPHvrBUjzuPF/O07jrhc3d5jj0e8+OYLqRj9GD+yLGyNRC6nk0rEDMCA7HZX1LSmts9QdWbn/LA6X1yM73YubZ6cusknHiP4SFdPboWiYm2cNQ26fznWo5tCZ5rMTVThb05Sydghdly5riLn66qvx1FNPmf93uVyoq6vDww8/jM9//vOpa5jQ41l/OKxknNfJ+dMBYNrQPAA9wxDz2LLd2Hj0PLLTvfjtN2aib5wDZU+lT5rXVPw8t/ZIilvT/SBl3aSi1KUlA8J70CWRqJhPDvS+OjGGYeCfXt6GJ9/fj6/+9zo88+GBbhXhdbi8Dt/+40Y0+UO4bNwAPPZF5wXrb51bjNkj+qG+JYgHXt/RLYwEDS0B/NMr22AYYU/XqyzSr3UlLh7TH+leN06ebzTrmHRlDMPAU8tbi8K+uvlkl0hLZcWJygYcr2yA1+3CnJGpMcTk9vHhc5E0Ji93QqFkbnz96qxh+J4D42tbcLlcuHTsALx45zz87Z6LceP0QnjcLqw9VIHbn9uIzz31CV7ZdMKx53UwZOBQeR3e3laGn767F7f9IZxabPZjy3HbHzbgp+/uxdvbynCovB4hA+jfNw0Lxg7Ady4twdO3zMDyH1yKXY98Dm/efREev2kKvjGvGBcM75dSZwYgXJth1IAsVDX48ftPRB7iHDxbi3//WziN7w+vGY/JDmviXT5+EO6+LBxtfv9r2zslH311gx9bj4eNHV2hPgxBETGbjlZ2K/mkIzAMw4xSSiQtmcrVkwbjv785E+leN5bvOYu/+9OmdqcSrKxvwe8jytEfXDW2S0RQeD1ufOkCitCU9GSJ8LvI2n3L3OFd8mzPo2J6gj5FcM7Bs7X4aF85XC7g9otGpLQtg3IyMG1oLgwDWL5HHHGFWLqsIebnP/851qxZg4kTJ6KpqQlf//rXzbRkP/3pT1PdPKGHUlrViOOVDfC4XWbhx85k6tBcuF1AWXVTt7aev7r5JJ5fF07j84uvTkdJBxd+7W58c/4IeNwurDlYgX3dQMHYldhZ1hoRk2ouiSgcVu3vfXVi/nvVYby76zRcLsAwgCff34+/f2ETqhu6vlfz2dom3PbcBpxv8GPa0Fz8+tYLEsoZ7Ha78MSXpiLN68bKfeV447PE0qOmgife2YtjFQ0ozM2wTW/TleiT5jWNncu7QSj/yv3hQ36Gz41pkdonlHqlK0JpyaYNy0up8oRSwrz5WWlS0zzGGl8nd0pU7uSiXDz1tRlY9cPLcOfFI5GV5sG+M7X451e345KffYhnVx5CdWPrutnQEsCW4+exdP0x/OvrO3Djr9dg8sPv4Yqff4x7XtqKZ1cewsf7y1Fe2wyXCxg1IAtfmDoEP/zcOPzx9tnY8MAV2PTgVfjTHXNw/7Xjcf20Qowe2LdLKDtVvB43fnDVWADA71cfwfn6lhS3qGvQHAjinpc+Q5M/hEvG9Me3Eyx+/f0rx2LeqHzUtwSxZOlmNLQEktTSMGsOnUPICEcKF+Vlxv9CJzFhSA4yfR7UNAVwsJcVSD5wtg6lVY1I87oxr50ZHRaOG4jnbp+NPmkefHLgHG57bgPqmts+p/7fx4dQ3xLEpMIc0xDfFaC96KN9Z3GmG5+5O5PdZTVYe6gCHrcLt80fkermaBnRPws3To9Exaw4EOfTQk/i96uPAgCunjgIxSlMZ060pic7neKWCF2RLmuIGTp0KLZt24YHHngA3//+9zFjxgw88cQT2Lp1KwYO7D3FvoXOhdKSTSnKTYmiIivdizEDw6n3umtu0x0nq/Gvr4fTgHzvyjGWhbB7E0V5mbhmUrgfnlsjXqBOCYUM7CkLR8RMTnFEDABcPLo/XC5g7+nabm0oTZS1h87hp+/uBQD8+w2T8dMvTUFaxFvyumdWY1dZ1y2MW9ccwB1/3IgTlY0oLuiD339rNvqkJb62lwzoa6bV+fe3d6Oirrmjm9phrD5wDn+KGMJ/9uVpnV7rrD1QAd0P9nRtQ4xhGPhlJBrmG/OK8aPPTwAAvLzxJE5Vd830dWZashRE+3Lml/RHUV4mapoCSasHpDO+dnYdi6K8TDz4hYlY+6MrcP+14zEoJx1naprx03f3Yv7jK3Dn8xtx+c9XYtLD7+Gm36zFg2/sxJ8/PY7PTlSh0R9Eps+DGcPzcOvc4Xjsi5Px+l3zseuRa/DhPy3EM1+/AHctHI2F4wZiYHZGpz5Xe/n85CGYMCQHdc2BLm247Ex+9u4+7DlVg/ysNPz8K9MSjlzyetx4+pYZGJCdjv1n6vDgGzuTGrlJkRcLxnSdaBgA8HncmDE8D0DvqxOzcl/Y43reqAJkpnnafb35Jf3xwrfnIDvdiw1HKrH4d59GGZCdcqamCc+vPQoAuO/qcSmPyuOUDOiLmcX9EDKAv27p+g42XQGqDfP5KUO6lBFW5Z7Lw1ExH+49K1ExvYSKumb8dUs4uu3OS0aluDVhro4YntccqkB9O4zZQs+kyxpiVq1aBQC49dZb8bOf/Qy/+c1vcOedd8Ln85m/E4SOZv3hSH2YFCoqpkfqxHRHQ0xFXTP+4YVNaAmEcOWEgbj38uSkAemO3H5R2MPx9a2lqBQvUEccr2xAbXMA6V43RneBqKr8rDRMjkTmrD7YO9KTna5uwr0vbUXICNc7Wjx3OL46ezhe+858DO2XieOVDbjpN2s7Jc1QoviDISxZuhk7S2tQkJWG52+fg/5909t8vb9fMAoThuTgfIMfj7y9uwNb2nHUNPnxw1e3AQgbCC4ek5qi7G3ligmD4HIB209W43R11zV2frw/XAg2w+fG3y8owbxRBZgzMh8twRB++/HhVDcvBsMwsDbiaDJ/dGrnhMftwpdmUkqYjl83uPF1RDuMrx1FbqYP37m0BJ/88HL8/CvTMH5wNupbgli+J5xn3zCAgdnpWDhuAO5aWIJf3TIDK/7pUux85Bq8ftdFeOyLU3Dr3GLMGN4vpc/RUbjdLtx3dTgq5vl1R3uVU4OOlfvOmsrN//zyVAzMaZthbWB2Bn51ywy4XWGl8l82JmdPNgyjS9aHIWYVh9OTbT7au+rErNzXvvowOmYW5+PPfzcPeX18+OxEFb7+P+sTPr888+FBNAdCmFncDwu74Hy5eVbrXtQd0s6mkrM1TXhrW9hglWjUXmczon8WbpheCECiYnoLL356HM2BEKYNzTX3gVQzZmBfFBf0QUsg1CuzaQj2dFlDzGWXXYbKylhvlurqalx22WUpaJHQG6CImPaGdbeHaRFDzLaTVSlrQ1sIBEO4+89bUFbdhFH9s/BfX53epTyfUs2s4n6YUpSL5kAIL204nurmdAsoLdn4ITmd7s1sRW+qE9MSCOGuFzfjXF0Lxg/OxmM3ttZVmTI0F3+752JcNm4AmgMh/PDV7fjRX7cnNdVQIhiGgX95bTs+OXAOmT4P/vCt2RjRzgLVPo8bP/3SFLhdwFvbyrpkgddH396NsuomFBf0wf3Xjk91cxJmQHa66YywvItGxfDaMIvnFmNAdti4RxFTf95wvMspl/efqcO5umZk+Fo9xlPJVyKGmNUHz6G0quMiiGKMr3e0z/jakaR53fjSzKF45x8vwZ/umIOHvjARf7pjDjY+cCU2PHAl/nj7HPzwc+Nx3bRClAzomqnFOorLxw/EjOF5aPKH8MxHB1PdnJRRXtuM+14JG85vu7AYV0xoXwT5vFEFuO+acQCAH7+1CztLOz5a9cDZOpyqbkK61425Izs/hXM8ZkXSSm861nsMMXXNATMCqKONHVOG5uKlv5uH/n3TsKusBl/773U4W+tsfztR2YD/3Rg+79x39bhOSQ2ZKIumFiLT58Hhc/XYcrz3zJm28Kd1x+APGpg9op8pp3Vl7rl8DNwu4MO9Z7G9m+lUhMRo8gfxp3VHAQDfvmRUl1lrXC4XrqJI/26QclnoXLqGZkuDYRjal6iiogJZWanP+Sf0PE5UNqC0qhFetyullnQSbrafqO5WxSYff2cv1h+uRFaaB7/9xsxulQ6nM3C5XGbhuBfWHYM/6Kxob29mZ2kkLVlh6tOSERczQ0xP9557bNlubDlehewML377jZkx6S7y+qTh97fNxg+uGguXC3hpwwl85f+tw4nK1Bcsf/L9ffjrllJ43C785tYLTAN3e5k6NM8MeX/g9Z2obeo6NXKW7z6DVzafhMsFPPmVacjqgkVUnUA5lbuqIWbVgXOt0TCXtqY/mF9SgJnF/dASCOG3q7pWVMyaSATf7BH5SPe2P21NexmW3wcXjiqAYQCvbe6YQsnc+NonzYPnbp/dJXKEq7hcLiwYOwDfvngkFowdYBryehMulwv/fHXYYPDShuNdYs/obEIhA/e9sg3n6lowblC2md6wvXxnQQmuGD8QLYGwc1RNB+9R5NU7d1QBMnypX0tUZgzPg9sVjqjuagbxZLH24Dn4gwaG5/fByHY6nOiYMCQH//v3F2JQTjj13Vd/ux5lDgzov1xxAP6ggYtH909ppgk7+qZ78fkpQwCEU4t2NVbuO4v/XnUIVQ2pzaTQ2BLE0k/DKW+/fXHXSPsUj5H9s3DjjEitmOUSFdOTeWtbGc7VtaAwNwPXTu46daiA1jPNh/vOIiC6H4HR5QwxN910E2666Sa4XC5861vfMv9/00034YYbbsA111yD+fPnp7qZQg+EomGmDctLqQJr7KC+yPC5UdscwOFz3aPY5Jv/n737Do+i7NoAfm9N770nJISEJITee0dFVIqgCPYuihXfT+VFfS2IimBviF1UFBXpvXcCCaQnpPfek935/tjdCNISsrszm9y/68olSXZmD5LdzDznOeeczG1trfD2rFh093IQOSJpur6XD9ztrVBQ1YAN8RzcdjWG2SPRfk4iR/KPfkEusFEpUFLTiMSCarHDMZnfT+RitX7OyPJbe192QVMul2HBuO5YfddAuNiqcDq3Ejes3Isd+n7lYvjmQCY+2KGbPfD6zTEYE2HcuXILx4cjyM0W+ZUNWLoxyajnvlbltU1YtFY3m+v+Ed0wIFh6O5XbyrB7bH9qaYcGBJuCrhomGYCuGub8+Rwyme61AADfHTqH4mrpzBHan6ZLxAwTuS3Z+WYN0LeEOZZtlE0n5ydfP7i9L3r5O3f4nGQ6Q8PcMTTUDc0aASu6YOuYr/ZnYldyMayUuvkuxkpqyOUyvD0rFn7ONjhXWodnfo4z6qaRXcmG+TDSeS85n4O1ChHeus07XaUqZqf+32RMDw+T7QQP87THmgeGwM/ZBhkltZj1yZU33aQW1bTOazBUaUmVoT3ZX6fyUNcknWuObw5k4q6vjuC1vxMx4s0deGdL8jXN6TGGX4/noKKuGYGutq0Ly5bAUBWzjVUxnZYgCPhij24N6s5hwVBJpIOGQb8gF7jYqlBR14wjXaxlJl2ZtH5SATg5OcHJyQmCIMDBwaH1cycnJ3h7e+P+++/Ht99+K3aY1AkZ5sMM7ibuApZSIUeMfuH5ZLZ0h2AbJORV4rlfTwEAHhkTisnRPiJHJF1WSgXuGBwEAPhSn7iiSxMEobWthmEuixRYKRWt7xF7Ujpnv9fEgiosWqt7TT82NqxN7VJGhnvgz8eGI9bfCZX1zbj7qyN4d0syNGau6tsYX4CX/kgAADw5IRyzBgQY/Tls1Aq8fnMMAOCbg+ckMRT4hXXxKKlpRHdPeyycEC52OB0S5mmPYDdbNGm02COxnsq7U0pwIqsCVsoLq2EMRnZ3R2yAruXS53ukURXTotHiULruZ3RYqHQWTydH+cDBSonssnoczCjt0LkuSL7eEoMxPYybfCXTMCzQ/no8B2nFlrHxyBjO5FXhjQ2JAIAXro9ED2/jbl5ytlXjo7l9oVbIsSmhsHWjVEfVN2lwKEP3XjLKiLNIjK1/sK6rgRR+N5uaIAjYZZgPY+L3vSA3O6x5cAiC3WyRU16PmR8fQPplXrfvbk2GVgDGR3pJvo3VwBBXBLvZorZJg79Pi79JThAEfLAjFS+uS4AgAF6OVqhubMGKbSkY8eZ2rNyWYtZqbK1WaL1nvXtYsEW1zQxxt8NNvXVVMV0x4d8V7E0tQVJhNezUCtw6IFDscC6iVMgxNkJ3H735jPjvLyQdkkvErFq1CqtWrcLixYvxxRdftH6+atUqfPLJJ3j++efh7i6dG0nqHARBwAF9ImZIN/F/vgwXrSezpZ05L69twgPfHENDsxajwj3w5ARp73qSgtsGBUKtkONkdgVOsB/xZeVVNqC8rhlKuQzh3vZih3OBEd11CxCdcU5MVUMzHtS/pkd0d8cT49u+qO/vYos1Dw7B7YMCIQi6thR3fXUE5e0c7nqtjmaW4fEfT0AQgDkDA/HY2DCTPdfQMHfc2l+X5HnuV3Fn4/wZl4f1p/KhkMvwzqzekmwX0x4ymQzjJdhT+YJqmMEXVsMYyGQyPD5O93P39YFzKK0RvyrmVG4lqhtb4GSjQk8JtXm0UStwQ6xumO7PR6+9Jcz5ydenJoRjVn/jJ1/JNPoGumBchCe0AvDulmSxwzGL+iYNFvx4Ak0aLcZHemKufnOOsfXyd8aLN+janb2xIRFHjZCUOJRRiqYWLXydrBHmKa3rsvMZ5sQc6wIVMalFNcitqIdaKTfLfFM/ZxuseWAIunvao6CqAbM+OYikf1WHJ+RVYv2pfMhkwFMTpb8xRCaTYab+98aao9mixiIIAt7YmIi3NumqrR8bG4YDi8bhw9v7orunPaoaWvD2lmSMWLoDH+5MRa0ZqoZ3JBUhvaQWDtbK1v9PluTRsWGQy4CtZ4twOsc8G1ybWrRIKazG36fz8f72FKw7mWtR7eYtyef6aphZAwLgZCPNtviGKrItZwo7fVtzajvJJWIMFi9ezFkwZDbnSuuQX9kAlUKGfiLOhzEwzDOIk3BFjEYrYMGPJ5BTXo9AV1usmN3HonbJiMXDwQpT9YtPq/ZlihuMhBmqYbp7OUhipsH5RobrkrWHMsokM5zeGLRaAU+tiUNmaR38nG3w3jW8pq2UCvzv5hi8PTMW1io5dicX44aVexGXXWGaoPVSi6pxz+qjaGzRLW69Mi3K5MMa/3NdJDwcrJBeXIuV28XZaVdU1YAX18UDAB4dE4YYf+lUj3WEFHsq7zmvGuaBS1TDGIzp4YloP0fUN2uMthO9IwxtV4d0c5Pc72hDS5gN8fnXNMvi/OTrbYMC8agJk69kGk/qF2r/OpWPs/lVIkdjeq+uP4PUohp4Olhh6YxYk/6emjs4CFNjfdGiFfDo9yc6nBhubUsWbroWWMZgmPOZkFdlloVqMe3UV8MM7uZ20Rw/U/F0tMaP9w9GpI8jSmoaMfvTA63X7ADwzmZdUvWGXr6I9JFO8v9KbunrB7kMOJxRhoySWlFi0GgF/Oe3eHyyS1dN+3/XReKpiT0gl8twXYwPNj4xEivm9EE3DztU1DVj6cYkjFi6A5/uTkN9k+nuRQwL3bcNCrTI2YPdPOwxTV8V89424yb8axtbcCqnAmuP52DpxkTc//VRjH17JyJf2ogJ7+7Gw98dx7LNyXj8x5OY89lBZJV2vXloppRcWI1dycWQy4C7hoaIHc5ljQx3h5VSjpzy+k7d1pzaR7KJGAD45ZdfMGvWLAwePBh9+/a94IPImAxtyXoHOJvtQvZKDBUxZ/OrJLvQ+9amJOxJKYGNSoFP5/WDk600dyFI0V3DggEAf5/OR0Fl1xgm2l4JeboFmWgJ7eA2CPWwh4+TNZpatDic0XlaX3y8Ow1bzhRCrZDjw9v7wtVOfc3nmt7PH789PAzBbrbIrdC1sPj+UJZJdgIVVjVg/pdHUFnfjD6Bzlg5py+UZugR7GSrwivTogEAn+xKx5k88y4iCoKA59eeRkVdM6J8HTvVInS/IBc463sqS6HP//nVMLcPunQ1jIFMJsOCsbpZMav3Z4o+ZHdfqq5yb2iY9IYl9w5wRndPezQ0a/FXXH67jr0w+eqFl280ffKVjC/K1wnX99K1tH17c+euitkYX4DvDmUBAN6Z1btDv2PbQiaT4fVbYhDqYYeCqgY88dPJDrUL3a1PxEi5LRkA+DrbwNfJGhqtYPJNIGIzzOMbbeZ/Ezd7K/x432DEBjijvK4Zcz47iONZ5TieVY5tiUVQyGVYOL67WWPqCB8nm9Zq91+Omb8qpqlFi8d/PIEfDmdBLgPenB6D+0ZeuOFDIZfhxlhfbFk4Cstv7Y0QdzuU1TbpZsgs3YEv9mYYfc0gPrcSB9JLoZTLcOfQYKOe25zOr4o5P2nYVqU1jTiUXorvDp3Dkj8TcMcXhzD09W2IWrwJN76/D0+uicOHO9Ow+Uwh0otrodEKsLdSIjbAGVNjfWGjUuBQRhkmLd+N1fszWR1jJIaWeZOivBHoZityNJdnq1ZihH6umpQq/Ulckk3ErFixAnfddRe8vLxw4sQJDBw4EG5ubkhPT8eUKVPEDo86mX/akkljocLP2Qbu9mq0aIXWBWkpOZxRho936fqxvzWzV+tgTGqbaD8nDAxxRYtWwDcHM8UOR5ISDPNh/KS3w18mk7VeUHWWOTH7UkuwTN8KYcm0qNaqvI6I9HHEukeHY0JPLzRptPjPb6fx9M+njLpzr7qhGXeuOoLcinp0c7fDF/MHmDWZPjnaG1OivdGiFfDcr6fMWr3x87EcbEssglohxzuzektuQGVH6Hoq6/rdb5XATcuelBIc11fDPHiFahiDCT29EOnjiNomjajzwBqaNa2JrKESmg9jIJPJWluJtaclzMXJ1z5mSb6SaSwcH65fJCvstC1b8yvrW2evPTCyG4abadi9vZUSH83tBxuVAntSSq55TkJOeR3SimuhkMswNEx67yX/ZmhP1pmHI9c0trTOwRndw/zJMSdbFb69ZyAGBLuguqEFd3x+CP9ZexoAML2vH7p5SLd93aUYfhf9eizXrPMN65s0uP+bo/jrVD5UChnev63vFWddKOQy3NTHD1sWjsRbM3ohwNUGJTWNeOWvMxi5dAdW7880WkLGcP1yfS8f+DjZGOWcYgg9rypm+dZLvwcKgoDcinrsTCrC53vS8fzaU5j58X70eXkz+r26Fbd+ehD/91s8Vu3LxJ6UEuTpN1K626sxKMQVtw8KxH+n9sS39wzCwefH4fR/J2LdI8Owck4fbHpiJAaFuKK+WYPFfyTgts9ZHdNRJTWNWHsiFwBw7wjpVsMYnN+ejAiQcCLmww8/xKeffoqVK1dCrVbj2WefxZYtW7BgwQJUVkq3XRNZHkEQWlt3DA6VRiJGJpMh1t8ZACS3m0sQBLy5UTdkdM7AANzQy1fkiCzT3fqqmO8PZUm26klM8XmGRIw0k3ydaU5MXkU9HvvhBLQCMLOfP2YbccC9k40Kn8zth+cmR0Au0w1lvvnDfcg0QuuHphYtHvz2GM7mV8Hd3gqr7x5o8h3Gl7JkWhQcrZU4nVuJL/eZZ9E9p7wOL/95BoCutY+xhz1LwQTDnJiz4vZUFgQB7+kXL28fFARPx8tXwxjoqmJ0FUqr9mWist58g3XPd+xcOZpatPBytEKohzTb/d7Uxw8KuQwnsyuQUnj1lg1iJ1/J+MI87XFLX12bus5YFaPRCnjypzhU1DUjxs8JT0007zzFcC8H/O9mXfXmiu0prZUt7bE7WXet0yfAWbJ9+M/XP1jXnuzouc5Ttfxv+1NL0KwREOhqixB3cd7fHaxVWH33QAwLc0NtkwaJBdVQKWRYMM5yqmEMxvf0hLOtCgVVDWbbZFXV0Iz5Xx7GzqRiWKvk+Hz+AFwX49OmY5UKOWb2D8D2p0bjjVti4Odsg6LqRiz+IwFjlu3EtwfPoanl2jcHFVQ24I+4PADAPcOlv9B9Nf9UxRRic0IBNsYX4IMdqVj400lMXbkXUYs3Ydgb23HnqiN4df1Z/HA4G0cyy1Fe1wyZDPB3scHoHh64d3gI3rglBr88OAQnX5qAoy9MwE8PDMH/bo7BncNCMLy7O7ydrC+o0A10s8UP9w3GkhujYKNS4GB6GSa/txtfH2B1zLUy/Hz3DnBG30DxxwpczdgIL8hkwOncSuRX1osdDkmAZBMxWVlZGDp0KADAxsYG1dW6m7M77rgDP/zwg5ihUSeTUVKLoupGqJVySb2RG9qTnZRYImZ7YhGOnSuHtUqOhe0Y5E0XmtDTG/4uNiiva8bv+h0dpFNU3YDCqkbIZJBsf+lhYe6QyYDEgmoUVVlue7nGFg0e+u44ymqbEOXriFduijZ6ex+5XIaHRofi23sGwc1OjcSCakx9f2+HdgVptQKe/SUO+1JLYadW4Ku7BiDAVZyydE8Ha7xwfU8AwDtbknGu1LT9xXV/91OoaWxB30Bn3Dfi6hUalmhkuAfUCjnOldYhtahGtDj2ppbg2LnyNlfDGEyK8ka4lz2qG1vwlUjzwAxtyYaFuku2bZeHg1Vr9dPPx3Ku+FipJF/J+B4f1x0qhQx7U0taN0d1Fp/sTsOB9FLYqhV4b3ZvqJXmv/2+pa8/5gwMhCAAT/x0st0LQbuSdS2wRkq8LZlB/yBdRcyJrAqzVjeY0059Qm10D3Fn9tiqlfhi/oDW9/E7hwbD30W6bYIux0qpwE36qomfj175d5ExlNY0Ys6nB3E4swwO1kp8c8+ga2r7p1LIMXtgIHY8PRqv3hQNHydr5Fc24IXf4zFm2U78eDgLzddQrf31gUy0aAUMDHFFL/3mUEsW6mGPG/UzWu//5hge/PYY3tqUhN9O5OJ0biXqmjRQKWTo7mmPKdHeeGxsGN6b3Rt/PTYcZ5ZMxt7nxuKruwbihRt6YvbAQPQPdoWzbduvP+RyGeYPDcbGJ0ZgYIgr6po0eGldAm7//BCyy1gd0x4NzRp8c+AcAF01jFSvb8/n4WDVus4ohUp/Ep9kEzHe3t4oK9PtYgkMDMTBgwcBABkZGaLujKTOx9CWrE+AM6xV0tlVaWgNFJdTIWoc59NqBbylb19059CQNu0MpktTyGWYPyQYgG7HNN/X/mFoxxfqYQ9btTQHQ7raqRGjb5tmyVUxr/x1BnHZFXCyUeHjuf1M+h44NMwd6xeMQN9AZ1Q3tOC+r49i6cbEa2rn9eamRPx+Mg9KuQwfze0negu7mf39MSzMDQ3NWjy/9rRJX8/fHjqH/WmlsFEp8Pas3pIbwG4sdlbK1rkmm0W6adHNhtFVw9w2KLBdv/Pkchke08+K+WJvOqqvYRh9R+3TL2hLvZWQoSXM2uM5l10wklLylYwvwNUWs/XteJZtTuo010Qnsytah5f/98YoUds1LZ7aE1G+jiirbcKj359o8+Jss0aL/am69xKpz4cx6OHtAAcrJWoaW5BYIL0Wzx0lCAJ2Jf2TiBGbtUqBz+b1x4bHR+A/10WKHc41m9lfV5m35UwhymtNN98tr6Iesz45gIS8KrjZqfHj/YMxQN9O71qplXLMHRyEHU+PxpIbo+DpYIXcinosWnsaY9/eiZ+PZrf5eruuqaV1ntW9naAaxuCJ8eFwtlXBVq1AL38n3NLHD89M6oFP7uiHbU+NwpmXJ2PLk6Pw0dx+eGpiD0zr7YdoPyejVt0Gudnhx/sGY/HUnrBWyXEgvRSTlu/GNwfPsTqmjdadzEVpbRP8nG0wOcpb7HDazNCeTKx7GpIWySZixo4diz/++AMAcNddd2HhwoWYMGECbr31Vtx8880iR0ediWHn3RCJtCUzMLQmO1daZ9KLwfb481QeEguq4WCtbNfOYLq0WQMCYKtWIKmwutPtAO2I1vkwvtKshjGw9Dkxvx7LwbcHsyCTActn9zbLoqa3kzV+vH9I69DPD3emYd6Xh1FS09jmc6zal4FPdqUDAJbO6CWJHboymQyv39wL1io59qeVtmveRXtklNTi9b91rSGfvy5CtHYk5mK4adl6Vpybln2ppa3VMA+NCm338dfF+CDUww5VDS34Wr97z1wq65txWr+RY1iYtK5v/m10Dw+426tRUtOEHYlFl3yM1JKvZHyPjg2DlVKOY+fKsTPJMn+vnq+msQWP/3gCLVoB1/fywcx+/qLGY61S4KPb+8HBWolj58rx5obENh13IqsC1Y0tcLFVWczrTiGXoU+Qvj1ZJ5wTk1pUg9yKeqiVcgzpJo1Eu0IuQ6SPo0XsTr+cKF8nRPk6okmjxbqTpulWkFFSi5kfH0BacS18nayx5sEhiPI13uvKWqXA/KHB2P3sGLx4Q0+426uRXVaPZ345hfHv7MJvJ3KuWiX267EcVNY3I9jNFuP0bWI7g2B3Oxx/YQISlkzCH48Oxzu39sYjY8IwKcoboR72Zpu1KJfLcNewEGx8fCQGBuuqY178PR5zv2B1zNUIgoDP9+jaQN81LNii5gMa7mkOppeiSoTNWSQtkv3J/fTTT/F///d/AIBHHnkEX375JSIjI/Hyyy/jo48+Ejk66iwEQcDBdF3l1ZBu0lqocLJVoZt+ke2kBKpimjVavLNFt6vvgZHd2lWOS5fmZKPCDP2NublmS1iC+Fzd7kWp3/APD9MlAPamllrcLqaEvEr85zfdUNUFY7tjTA9Psz23WinHf2+Mwoo5fWCrVmB/WiluWLEXx85dfbHk79P5ePkv3WyUZyf3aJ0rIAWBbrZ4aoKu9/+r688avWWdRivg6Z/jUN+swbAwN8wdFGTU80vReP0CwMnsChRVm7cFoK4aRvc7b87A9lXDGCjOq4r5bE86ahpbjBrjlRxKL4VWALq520l+yK5KIW99LV+qPZkUk69kfF6O1pivT9Iv25xkcb9X/+2ldfE4V1oHP2cbvHZTjCQWqAPdbLFsZiwA4PO9GdgYn3/VYwwzZUZ097CoCsz+hkRMG64tLI0hUTm4mxtnZBmZIWG6xgTtyc7kVWHmx/tbZ5z9/NBQhJqoSs5apcA9w0Ow+9kx+M91EXC1UyOztA4Lf4rDxHd34Y+4vEu+x2q1Ar7Yq7snvXt4iEW95ttCLpdJ4r0Y0CWGfrz/n+qY/WmsjqlrakF6cQ32p5Vg7fEcfLgzFYvXxeP+r49i2vt7Mei1bUgpqoG9lRKzjDjT1BxCPewR6mGHZo3QKTabUMdIMhHT0tKCV199FQUFBa1fmz17NlasWIHHHnsMajUXgMk40oprUFLTCCulHL0DncUO5yKGOTFxEpgTs+ZoNs6V1sHdXo27hnWeMmWxGRYdtiUWmWy2RLNGi6LqBiQVVGN/Wgn+OpWHbw5k4qOdaZLceROfp6uI6Snxipi+Qc6wVStQUtOIxIKrD5mWisq6Zjz07XE0tmgxuocHHhdpqOqNsb74/ZFh6OZhh4KqBtz6yQF8te/y7UcPpZfiiZ9OQhCAeUOCrqlCwdTuGhaMXv5OqG5owUvrEox67s/2pOPYuXLYWymxdEYs5J3s5vhSvBytEevvBEEAtp+9dKWEqexLLcXRc+VQK+V4aPS1/6zd0MsHIe52qKhrxrcHzVcVs7+1LZm0NplcjmHxa3ti0QVJNyknX8n4HhwVCnsrJRLyqrAxoeDqB0jUupO5WHs8F3J9xamTrXQG3E+K8sb9I3VV7c/8fAqZJVe+9tylT8RYWgK0f7ChIqZM5EiMb6d+Zs9oC/s3sQTTevtBrZDjTH4V4vUV+sZw7FwZZn96ACU1Tejp44g1Dw6Bn7PpN0nYqpW4f2Qo9jw7Bs9O7gFnWxXSimux4IcTmPzebvx9Ov+CRf9tiUXILK27YLMgmc751TEDgl1aq2Pu+PIQcsqld49+rQRBQFltE87kVWF7YiG+P5SFdzYn4dlf4jDvy8OY+O4u9PrvJvR8aRPGvr0Lt312CE+uicPSjUlYfeAcNp8pRFxOJYqqdR0U7hvRDY7W0vm92lYTeupaqXVkTip1DpJsvq9UKrF06VLMmzdP7FCokzO0g+oX5AIrpfR2FMUGOGPtiVycFDkR09CswYptuj75j44Jg52VJN86LFKohz1G9/DAzqRifLU/E4unRl3x8YIgoLZJg7KaJpTVNaGsthFltc2X/G95XTNKaxpR1XD5XdjrT+fhj0eGS2ZRt7KuGTnluiGyxizVNwUrpQKDu7lhe2IR9qQUSz5xBOh2uj255iSyyurg72KD5bf2FvXfPtzLAeseGYbnfj2Fv08X4L9/nsHxrAq8MT3mgvlAyYXVuO/ro2hq0WJSlBcWT42SzI628ykVcrw5vRemrtyLjQkF2HA6H1NifDp83qSC6tY5Ay9N7WmWm3epGB/phbicSmw5U4jZAwPN8pyCIOC9bbr/37cNDIRXB+ahKRVyPDImDE//HIfPdqdj3pAgs8y+2peqm101LFQabWuupruXA/oEOuNEVgV+P5GL+0eGWkTylYzL1U6Nu4eHYMW2FLyzJRmTorwtbkd2dlkdXvgtHgDw2NjuHZ79YArPTOqBE1nlOJJZjoe+O47fHh56yRlxJTWNOK1fjB7Z3TLeSwx6BzhDIZchv7IBuRX1neb3Zm1jC45k6Kp8pDAfprNxsVNjQpQX1p/Kxy/HcoxSnb8npRj3f30M9c0a9A9ywRd3DoCTjXkXke2slHh4dBjuGByEr/Zl4rM96UgurMHD3x1HhLcDFk4Ix8SeXvh8j6769LZBgZKd09kZBbvb4af7h2DV/ky8tSkR+1JLMend3fjP9ZG4bWCgJO95DHQbPhtRUNmAwqoG5Ov/W1Cp/6jSfTS1tG1GkZ1aAS8na/g4WcPL0Rrejuf92ckaPk428HCwMvHfyjQm9PTCx7vSsDOxCE0tWqiVkqyLIDOQ7LvruHHjsGvXLgQHB4sdCnViB9L182Ek1pbMIPa8ihhBEET7Jbx6fyYKqxrh52yDOYPMsxDWldw9LAQ7k4rx89Ec9A10QUV9M8prm1B2qY+6pjZfyJxPJgNcbNVwtVPDVf/fPSnFiM+twp+n8jCtt58J/mbtl6Cvhgl0tTX7Tcq1GNHdXZ+IKcEDFrBI+MGOVGxLLIJaKcfHc/tJosWgg7UKH9zWF1/szcDrGxLxR1weEguq8NHcfgj1sEd+ZT3mf3kYVQ0t6B/kgvdm95H0wlykjyMeGh2KldtT8dIfCRga6t6h3dDNGi2eXHMSTRotxkV4ij5nwNwmRHnh7S3J2JtagrqmFrMsDOxPK8WRzI5XwxhM6+2LFdtSkFVWh+8PZeHeEaadsVZU1YCUohrIZNKbf3clM/sF4ERWBdYczcHoHp4WkXwl47t3RAhW789EalENfj+Ri+kW9J7XotHi8R9PoLqxBf2CXPDY2DCxQ7oklUKOlXP64voVe3A2vwr//SMBb0zvddHj9qboErqRPo7X1J5RTLZqJaJ9HRGXU4mjmWXwk8h1bkftTytFk0aLQFfbTj8nTiyz+gdg/al8/HYiF4umRFwySdlWG07nY8GPJ9CsETAy3AOfzO0najs5B2sVHhvXHfOGBuPLvRn4cm8GEguq8cA3xxDuZY/kwhoo5TLMHxIsWoxdlVwuwz3DQzA2whPP/ByHo+fK8X+/xWPD6QK8MT0G/i6mn+XZFoIg4Gx+NXYkFWF7YhFOZJWjrZ3U3O3V8HK8MMni7aT/0P/ZwQIrXdqqT4Az3O2tUFLTiEMZpRjRncn0rkqyiZgpU6Zg0aJFOH36NPr16wc7uwsvNG688UaRIqPO4vz5MIMlulAR6eMAtUKO8rpmZJfVI9DN/L+Aqxqa8dGuNADAwgnhkqwcsnQjursjzNMeqUU1eOyHE206xlolh5udFVzsVHC1s4Krrf6/dv/+ry7p4mSjumjxeuW2FLy9JRnLNidhcrS3JP5tDW3Jov2kX10CoPUC6nBmGeqbNJLu1b07uRjv6GdevDotWlIzeGQyGe4d0Q29/J3xyPfHkVxYg2nv78PiqT3x+Z4M5Fc2INTDDp/P79+hG2JzeXRsGP4+nY+04lr87+8zWDoj9prP9f72VCTkVcHZVoXXb5HGnAFz6uHlAH8XG+SU12NPSgkmRXmb9PnOnw3T0WoYA5VCjkfGhOK5X0/j413pmDs4yKQ/x4a2ZFG+jpJItrbVDbE+ePmvBKQW1WDmxwcsJvlKxuVorcKDo0Lx5sZELN+WjKmxvhaza3TFthQcz6qAg5USy2/tLelBwt5O1nhvdh/c8eUh/HgkG/2DXS9qRbS7tS2ZZVXDGPQLctUnYsols+Goo3Yk6duS9fDoctcD5jI8zB0+TtbIr2zA1rOFuKGX7zWdZ83RbCz69RS0AnB9jA/evbW3ZN7LnGxUWDghHHcNC8bnezKwal8GkgtrAABTY33h7WRZidfOJMTdDj89MASr9mXgrU1J2JtagsnL9+A/10VizsAAUV739U0a7EstwfakIuxILEJ+5YVzG1UKWWtixcvJGj76pMr5SRdPRytJrDWISS6XYXykJ348ko0tZwqZiOnCJJuIefjhhwEA77zzzkXfk8lk0Gg05g6JOpnkwhqU1TbBRqVArL+z2OFckpVSgUhfR8RlV+BEdrkoiZjPdqejoq4ZYZ72uLlP57iJkRqZTIYXb+iJpRsTYaNSwNVODTd79T8VLJf4MMau8HtGhOCbg+eQXVaP7w5m4e7h4s/+ic+tAiD9tmQGoR528HWyRl5lAw5nlmGURPt155TX4fEfT0AQgDkDAyQ74HBgiCvWLxiOR78/gcMZZXjml1MAAE8HK6y+e6DFLCpbKRV4c3ovzPzkANYczcG03n4YFtb+hazTOZV4f0cqAOCVadEWtyPZGGQyGSb09MKqfZnYeqbQ5ImYA+dVwzxoxCq3m/v4Y8W2VORW1OOHw1kmnbVmaW3JDBytVbgu2gdrT+Sisr7ZopKvZFzzhwbhi70ZyC6rx5qj2Zg7OEjskK7qUHpp6/v1/26JQYCrNHYvX8nw7u54Ylw43t2ajBd+P41oP0dEeOs2wmi1AnbrK2Kkem1zNQOCXfDlvgwcPVcudihGIQgCdumHPLMtmeko5DJM7+uP93ek4uejOdeUiPlibwZe0c83u7V/AF67JUaSGwqcbdV4elIP3D08BJ/uTkd8biWenBAudlhdnkKu26A2NsITz/xyCsfOleM/v53Ghvh8vDG9l1laLWaX1WFHUhG2nS3CgfTSCzpyWKvkGB7mjjERnhjZ3QN+zjaSaXMudRN6erUmYpbcyGrvrkoaKflL0Gq1l/1gEoaM4UCa7uaif7CLZHanXEqf1vZkxhsY2FbF1Y34Ym8GAODpieGSvIDsLEaFe2D9ghH45aGh+HRef7x+Sy88OzkC947ohlv6+mN0D0/08neGv4ut0Vrz2KqVeGK87mJ75fYUVDU0G+W8HfFPRYxlJGJkMlnrbpY9+p2jUtPQrMHD3x1HeV0zYvycrjqHSGyeDtb47t5BrcOE7a2U+OqugZIpyW+r/sGuuEO/ePj82tOob2rftUtDswZPrjkJjVbA9b18MDX22nZkdgYTIr0A6Aa5a9ra/+Aa6KphdPPQ5gwIMOqOULVSjofH6BI7H+9KQ0Ozaa5lBUForYgZeg3JP7EZ2p9aWvKVjMtWrcSj+tfLyu0pJnu9GEtlXTMW/nQSWgGY3tcfN1rQ+/VjY8MwMtwDDc1aPPztcdQ06uYKnsmvQklNI2zVCvQPkt6cm7boF+wCAEgsqJLENW5HpRbVILeiHmqlHEO6Wd77uyUxVIftTilGXkV9m48TBAHvbEluTcLcNyIEb0yXZhLmfK52aiyaEoFv7x1kEUnkrqKbhz3WPDAEL1wfCSulXFcZ/u5u/HA4C4Jg3OvhFo0Wh9JL8fqGs5jwzi6MWLoDL61LwK7kYjS1aOHvYoN5Q4Kw6q4BOPnSRHw+fwBuHxSEAFdbJmHaYViYO2xUCuRXNrRuQKWuR7qrz0Qm1tqWTKLzYQxiA3QL0iezzb+b64Mdqahr0iDW38nku5BJHLP6+yPUww7ldc34dFe6qLHUNLYgo6QWgK6ljqUYoW/ZsUe/c1Rqlvx5BqdyKuFsq8JHc/taxO5ylUKO/1wXib8eG46NT4xATwv6eTjfs5Mj4OtkjayyOryzJaldx767JRkpRTVwt7fCK9OiTRShZRgQ4gpHayVKa5twIst0vwsPpJXicGYZ1Ao5Hhpt/NkOM/r5w8fJGoVVjfj5aLbRzw8A50rrkFtRD5VChgH6RUhLMiDYFb8+NAR/LRhucclXMq45gwLh52yDwqpGfHvwnNjhXJYgCHj+t1PIq2xAsJstlkyT9maHf5PLZVh+a2/4OFkjvaQWz/16CoIgYHeKbnPJ0FA3SW9YuxJPB2sEudlCEIATWRVih9NhO/XVMINCXCXdCrczCHa3w8AQVwgCsPZ4TpuO0WoFLPnzDFZs023oeHpiOP5zXSR3vFOHGKpj/n58BPoGOqOmsQXPrz2N+auOtCtJeClltU347UQOHv3+OPq+sgW3fnoQn+xKR0pRDRRyGQaGuGLRlAhsWTgSe54dg5enRWNMD0+LuJeUKmuVorXKdMuZApGjIbFY5lUVUQdptQIOZuh2jEo+EaNvmxafV4VmTfuHtF+rnHLdUGEAeGZSBC8iOymlQo5nJ0cAAD7fm47CqoarHGE6Z/OrIAiAj5M13O2tRIujvYaFukMmA5IKq0X9/3cpa45m44fDWZDJgBWz+1jcwma0n5PFxXw+eysl/ndzDABdm4q47Io2HXc0swyf7tElRt+4JQaudl27KkClkGNMhCcAYMuZQpM8hyAIWK5fPJkz0LjVMAZWSgUeGq3b5f/hzjQ0thh/l/8+fbVvn0AXo1VPmlu/IFd4OnS9Nnx0ISulAgvG6RKiH+5Ma63UkJqfj+bg79MFUMpleG92H9hbWd7rztVOjfdv6wulXIb1p/Lx9YFzrS2wRlpoWzKDfkG6hPTRzDKRI+m4ncmG+TCeIkfSNczqr2vj+/OxnKtWH7RotHjml1P4an8mAODlaVF4dGx33j+T0YR62OPnB4fiP9dFQK2UY3dyMSa9uxs/HWl7dYwgCDiTV4X3t6fglg/3od+rW7Dwpzj8dSofVQ0tcLFV4eY+flgxpw+OvzABax4YggdHhaK7lwN/lo1oQk9dpf9mE93TkPQxEUNdUmJBNSrqmmGrVqCXv7RbIIW428HRWommFi2SCqrN9rzLt6agSaPF0FA3DO/O8vfObGJPL/QLckFDs7Z1SLUYEnJ1bcksqRoGAFzs1Oilb6UmpaqY+NxKvPB7PADgyfHhFr+YYqnGRHhiWm9faAXguV9PXdBj+VLqmlrw1M9xEARgZj9/jNdfrHd1hpuWLWdNc9NyIL0UhzN01TAPjjbebJh/m9U/AJ4OVsivbMCvx3KNfv79qbpNJpY2H4boUqb39UeIux3KapuwSt8qV0rSimuw+I8EAMBTE3sgVt9O2BL1C3LB89dFAgBeXX8Gx/RzVSx1PozBgGBdW7WjmZY9J6a2sQVHMnR/hzGcD2MW18V4w06twLnSOhzOuHwiz9AC+NfjOVDIZXj31ljMGxJsvkCpy1DIZbh/ZCj+XjACfQKdUd3Ygud+PY07Vx1BfuWlq2Pqmlqw9Uwhnl97GkPf2I7rVuzBss3JOJ5VAUEAIn0c8ciYUPz60FAcfWEC3r21N26M9YWTrcrMf7uuY2yEJxRyGRILqpFdVid2OCQCJmKoSzqYrluoGBDsCpVC2i8DmUzWemN3oo27qTsqtai6tQz7mUk9zPKcJB6ZTIbnp+iqYn46ko3UIvMl/M4Xn6frkxrlK+3k6KUYkpV7U6QxJ6airgkPfnsMTS1ajIvwxCNjjN9midrupRt6wsVWhcSCanyyK+2Kj31jQyLOldbB18kaL07taaYIpW9UuAdUChnSi2uRVlxj1HOfPxtm9sAA+DiZbgiqtUqBB0fpEj0f7Eg1aqWrVitgv74iZliYtKt9idpCqZDjifHdAQCf7klHZZ105nw0tmjw+I8nUN+swdBQNzygn2tmye4eFowp0d5o1gho0QoIcrNFkJud2GF1SH99RcyJ7HKzdhYwtv1ppWjSaBHoaosQd8v+N7EUtmpl63y+NUcv3Z6strEF96w+gs1nCqFWyvHR7X1xcx9/c4ZJXVCYpz1+eXAonp+iq47ZlVyMie/sxpoj2RAEAdlldVi9PxPzvzyM3i9vwb1fH8UPh7OQX9kAa5Uc4yM98b+bo7F/0VhseHwEnpkUgX5BLpKfZdRZuNipW383marSn6RN2ivQRCZyIN0y2pIZ9NEnYtra1qaj3t6cDK2gq5ToE2h5Peap/foHu2JCTy9oBWDpxvbNsjCWeH1FTLSf5SViRnTX7U7cm1oCrQmHibeFVivgiZ9OIqe8HoGutnhnVm8OURSZm70VFk/VzQ1YuT31ssnOvSkl+PqAbhbC0hmxcLTmbjQDB2tV6+/srUa+aTm/GuYhE1bDGMwZGAh3eyvkVtTjt+PGq4o5W1CF8rpm2KkVFr0zn+h8U3v5IsLbAdUNLfhk95UT2eb09uZkxOdWwdlW1Wl+z8pkMrw5oxeC3XQtQS29GgbQtfNxtlWhoVmLM3mWOxh5Z5KhLZkHWwSZ0cz+uqTK36fzL2qPWFHXhLlfHMK+1FLYqhX46s4BmMiZqmQmCrkMD4zSVcf0DtBVxzz76ykMem0bRizdgcV/JGBXcjGaWrTwd7HBvCFB+OquATj50kR8Pn8Abh8UBF9n0208oitrrfRnIqZLkmwiZtSoUfj6669RX9+xAVRE/6bRCjikT8QMCbWMRIxhQeWkGRIxp3IqsCG+ADIZ8DSrYbqU5yb3gFym61dq7l7aDc0apBTpdrlH+1lWazIA6BvoAlu1AiU1TXj4u+P46UgWcsrFKTVesT0FO5OKYaWU46O5fVlaLhHTevtiTA8PNGm0WPTr6YsSdlUNzXj2lzgAwLwhQWwJeQmmuml5T18Nc+sA01bDGNioFa2759/fkYoWI+3SNrQlGxgi/WpforaSy2V4ckI4AGDVvkwUVzeKHBGwJ6UYn+7WzfF6c3ovk8yUEoujtQpf3TUQ94/shkc7QTWtXC5DP/2msiMWOidGEATs1M/sGc22ZGbVN9AF3TzsUN+swfpTea1fL6puwOxPD+JEVgWcbVX4/r7BGBrG6zYyvzBPe/z60FAs0lfHFFU3QiGXYWCIKxZNicCWhSOx59kxeHlaNEb38IS1SiF2yARgYk9d0vZwZhkq6ppEjobMTbJ3aX369MHTTz8Nb29v3HfffTh48KDYIVEncTa/ClUNLbC3UiLaQmZRGBIxacU1qGowbVuGtzbpqiFu7u2HcC8Hkz4XSUuYp0PrYMrXNyS2efCfMSQVVEOjFeBmp4a3o+UtaKiVclwX4wMA2JhQgOd+PY3hb+7AmGU78cLvp7ExvgCV9aZvqbIjqQjv6QeOv3ZzjEW2eeusZDIZXr05BnZqBY6eK8e3h85d8P2X/zyDvMoGBLnZYpG+VSBdaHykLhFzLKscpTXGWYw9kFaKQ2ashjG4fXAgXO3UyCqrw7qTeVc/oA32tbYl42IQdS4TenohNsAZ9c0afLgzVbQ44nMr8fTPcbhn9VEAwO2DAjGpE+6AD3a3w3+ui4SnBV6PXUp//ZwYw9wbS5NWXIPcinqolXIM6cb3d3OSyWSt90aG9mTZZXWY+fEBJBZUw9PBCj/dPwS9WYVKIlLIZXhwVCi2PTkKq+4cgOMvTMCaB4bgwVGh6O7lwCo6CQp0s0UPLwdotAK2JxaJHQ6ZmWQTMcuXL0deXh5WrVqFoqIijBw5Ej179sSyZctQWMjyLbp2/8yHcYHSQnaMuttbwd/FBoIAxOdUmux59qeVYE9KCVQKGRbqdx9S17JwQjisVXIcO1eOzWYslY3P0/1cR/k5WezF4pvTe2HNA0OwYFx39A10hkIuQ0ZJLb49mIUHvz2GPi9vxrQP9mHZpiQcSCtFY4vGqM+fXVaHJ348CUHQLQ5N78ce1VLj52yD5/RJljc3JCK3Qlf1u+VMIX45lgOZDHh7Zixs1Uoxw5QsX2cbRPk6QhCAbUa6aVm+NRmArhrGnC0abNVK3Dfin6oYTQdbGja1aFuHCQ8N5UIddS4ymQxPT9Rdl353MAt5FebrmNCs0eLPuDzM+Gg/bli5F78cy0FTixZDQ93wwvWc42UJ+gcbKmLKzbrJyFh2JOqqYQaFuMJGzd3s5nZLHz8o5DIcO1eOjfEFmPHxfpwrrUOAqw1+eXAoenhz4yJJQ4CrLcZEeLIbgoWYGMX2ZF2VpFehlUolbrnlFqxbtw45OTm47bbb8OKLLyIgIAA33XQTtm/fLnaIZIEOpFlWWzIDQ1XMCRO1JxMEoXU2yJyBgQhwtTXJ85C0eTla457hIQCApRsTjdYy52ric3V9uy2lSu1SDGXgT04Ix9qHh+HESxPw6R39MH9IELp52EEr6OY8vb8jFXM+O4jeS7Zg/peH8fmedJzNr+rQ4kBDswYPfXcMlfXNiA1wxksc8i5ZcwcFoX+QC2qbNHjht9Moq23C82tPAwDuH9GtdecuXZqhPZkx5sQYqmFUCplZq2EM7hgSBGdbFTJKavHXqY5VxcTlVKCuSQNXOzUiuChEndDwMHcMCnFFk0aLldtTTP58JTWNWLktBcPf3I7HfjiBo+fKoZTLcGOsL359aCi+u3cQF8UtRIyfE9QKOUpqGpFVJk7b2I7YmWyYD+MpciRdk6ejNUbr5yU9+O0xFFY1ort+WHqgG++XiejaGO5pdiUXo6HZuBs0SdoknYgxOHz4MBYvXoy3334bnp6eeP755+Hu7o4bbrgBTz/9tNjhkQVp0fyzY9TSSrv76BMxcSZKxGw5U4iT2RWwUSnw6FjL7wlN1+6BUaFwsVUhrbgWPx/LMctzJugrYqL9Ok8rLUdrFSZGeWPJtGhsf2o09i8ai7dm9MK03r5wt1ejvlmDXcnFeHX9WUx5bw8G/G8bHv/xBH4+mo38yvbt9n1pXTzic6vgaqfGR7f3hZWSi0NSJZfL8Mb0XlAr5NiRVIyZH+9HSY3upp6ViFdnaE+2J6Wkwzct720TpxrGwN5KiXv1ie+V2ztWFbMvVdeWbEioW6cYGk70bzKZDM/oZxeuOZqDzJJakzzP6ZxKPLUmDkNf3463tySjsKoR7vZqLBjXHfsWjcWKOX3QL8jFYqt3uyJrlQIx/rrryyOZltWerLaxBUcydDFzPox4ZurbkwFArL8T1jwwBF6dpHUfEYkjxs8J3o7WqGvStG4Wp65Bsr0vioqK8M0332DVqlVISUnB1KlT8cMPP2DSpEmtF7533nknJk+ejGXLlokcLVmKM/lVqG5sgYO1Ej0tbOe9oSLmZHYFBEEw6g2gRitg2WZdNcxdw4Lh6cALy67M0VqFx8Z2x8t/ncG7W5IxrbevSVslNWu0SCyoBgBEWdjrsj18nW0ws38AZvYPgCAISCyoxr5UXTvAwxllKKlpxLqTea3zIkI97DA8zB3Du3tgUDdXOFpfusz8x8NZWHM0B3IZsHJOH1EWlKl9wjzt8djYMLy9JRlpxbVQymV4Z1ZvDtBsgyhfR/g6WSOvsgH7UkswTp+Yaa+D6aU4mK6rhnl4tHibD+YNDcanu9ORWlSDDfH5uKGX7zWdZ7/+Bm4Y25JRJ9Y/2BWje3hgZ1Ixlm9NxvLZfYxy3maNFhviC7B6f+YFc0Ri/Z1w57BgXBfjww0OFq5/sAuOnSvHsXNlmGFBrVv3p5WiSaNFgKsNurnbiR1OlzUu0hNjenjAWqXAWzNjYW8l2WU0IrIQMpkM43t64tuDWdh8phBjIlj12FVI9jeIv78/QkNDcffdd+POO++Eh8fFO0B69eqFAQMGiBAdWSpDpnlQiCsUFrZjNNrXCQq5DEXVjSioaoCPk/EWW9edzEVyYQ0crZV4YKT527OQ9Nw+OBBf7stATnk9vtybgUfHdjfZc6UW1aCpRQsHayUCu0hLPJlMhkgfR0T6OOLeEd3Q2KLB8XMV2Jdagr2pJTiVU4G04lqkFddi9YFzUMhliPV3wvDuHhge5o4+gc5QKeQ4lVOBl9YlAACemtiDQ7otyAOjQrH+dD4SC6rxyJiw1t26dGW6mxYvfH3gHLacKbzmRMx7W3WtjWb1F6caxsDRWoW7h4dg+dYUrNyWiuuifdpd0VLX1IITWbrF42FhltV2lai9np7YAzuTirEuLg8PjQ7r0HyG4upG/HA4C98dOofCqkYAgFIuw/W9fHDn0GD0CXQxVtgksv5BrvgE6RZXEbMzSd+WLNyTVVgiUinkWHXXQLHDIKJOZkJPb3x7MAtbzxbif9poVrV3EZJMxAiCgG3btqF///6wsbn8zbGjoyN27NhhxsjI0h1I1yViBnezvIUKG7UCPbwccCa/CiezKuATY5yFo6YWLd7VDyt+cHQoh7sRAMBKqcAzk3rg8R9P4uNd6ZgzMBBu9lYmea74XF1bsihfxy57k2mlVGBIqBuGhLrh6Uk9UFnXjAPppdibWox9qaXIKKnF8awKHM+qwIptKbBTKzComxsS86vQpNFiQk8vPDSKSVRLolbK8fU9A3H8XDkm9vQWOxyLMkGfiNl6tghardDum5aD6aU4kF6qq4YZI34rzruGhuCLPRlIKqzG5jMFmBzt067jj2SWo1kjwM/Zpssks6nrivZzwpRob2yIL8A7W5LwyR39232OuOwKrN6fib9O5aNJPwvP3d4Ktw8KxO2DAuHJlkOdTr8gXVIttagG5bVNcLFTixzR1QmCgJ1JxQCAMRFsS0ZE1NkM7uYKeysliqsbcTKnAn25AaRLkOSMGEEQMG7cOOTkmGc2AXUNLRotjhjmw4RaXiIGOK89WU6F0c7505EsZJfVw8PBCncODTbaecnyTe3li2g/R9Q0tuD9Hakme56EvCoAuqov0nGyVWFytDdevSkGO54ejb3PjcGb02NwQy8fuNqpUdukwfbEIuRVNiDYzRZvz4rlDhoL5OlgjcnXUAHR1Q0KcYODlRIlNY3X9Pvw/GoYPwm08nOyVeHOYcEAgPe2pUIQ2jcrZr9+PsywMLcum8ymruXJCeGQyYBNCYU41cb3gKYWLdadzMXNH+7DtA/2Ye2JXDRptOgd4Izlt/bG/kVjsXBCOJMwnZSrnRqhHrrWXue3n5OytOIa5FbUQ62UW9xsUyIiujorpQKj9PO/tpwpFDkaMhdJJmLkcjm6d++O0lLjDCwKDg6GTCa76OORRx4xyvnJMpzOrURtkwZONipEelvmHIo++kRMXHaFUc5X19SCFdt1C+yPjQ0z6RwQsjxyuQyLJkcCAL49eA5ZpXUmeR5DRUy0HxMxl+PvYotbBwTi/dv64uj/jcf6BcPx/JQIzOjnjy/uHHDZ+TFEnZFaKW+9adnazpuWQxKrhjG4e1gI7NQKnM2vwtazRe06dl+aIRHDhTrqGrp7OeDm3n4AgGWbk6/42OLqRry3NQXD3tyOx388iRNZFVApZLi5jx9+f2QYfn9kGG7q4we1UpK3xWREA4JdAQBHLSQRY6iGGRTiChs1ZxQREXVGE3vq2iwzEdN1SPaK84033sAzzzyD+Pj4Dp/ryJEjyM/Pb/3YsmULAGDmzJkdPjdZDkNbskEhrha7+9hQEXM6pxIabft2zF7KV/szUVzdiABXG8weENjh81HnM7y7O0Z0d0ezRsCyzUlGP79GK+BMvr4ixs8yE6TmJpfLEOXrhAdGhWLZzFiEetiLHRKR2U24xpuW97bpqmFmSqQaxsDFTo15+qrUFdtS2lwVU1HX1FpVaKnVvkTX4onx4VDKZdidXIzD+or3853MrsDCn05i6Bvb8O7WZBRXN8LDwQoLx4dj36KxePfW3uitv66mrsHQnuxo5sU/L1JkSMSM7sEBzkREndXoHp5QymVILapBRkmt2OGQGUg2ETNv3jwcPnwYsbGxsLGxgaur6wUf7eHh4QFvb+/Wj7/++guhoaEYNWqUiaInKTqQpkvEWPJCRZinPezUCtQ2aZBaVNOhc1XWNePjnWkAgIXjw7kTkC7ruckRAIA/4vJwOqfSqOfOKKlFXZMGNioFQtyZUCCithkdrrtpSSmqQWYbb1oOZ5Rhf5q+Gma09GYq3Ts8BDYqBU7nVrYuwF3NgbRSCAIQ7mUPTwe2VKKuI9DNFrMGBAAAlm1KgiAIaGrR4vcTubjpg3246YN9+O1ELpo1AvoEOuO92b2x77mxeHx8d75Wuqj++oqYU7mVaGzRiBzNldU2trQmGEf34HwYIqLOyslG1TrDesuZApGjIXOQbB+i5cuXm+S8TU1N+Pbbb/Hkk09eto92Y2MjGhsbWz+vqqoySSxkPs0aLY5m6srQDW9ylkghlyHG3wkH08twMrscPbwdrvlcn+xOQ1VDC8K97DFN396B6FKi/ZxwU29f/H4yD29uTMS39w4y2rkT8nSJnUgfBygstFKNiMzPyVaFgSGu2J9Wiq1nC3HviG5XPea9bboWRjP6BcDfRXpD7d3srXDHkCB8ujsd721LwegeHled+WJoSzY0lG3JqOt5bGwYfjmWg8OZZXj2l1PYmVyM4mrdPZxKIcPUXr6YPzS4taKcurZgN1u426tRUtOE+NxK9Atq3+ZOc9qfVoomjRYBrjbo5m4ndjhERGRCE3p6YW9qCbacKcT9I6W3WYyMS7KJmPnz55vkvL///jsqKipw5513XvYxr7/+OpYsWWKS5ydxnMqpQH2zBi62KvTwuvbkhRTEBjjrEzGVuHXAtZ2jqLoBq/ZlAgCentiDC+B0VU9N7IG/Txdgb2oJdicXY2S4cXbnGVrqcD4MEbXXhJ5e2J9Wii1nrp6IOZxRhn2ppVDKZXhkjHRvcO4b0Q2r92fiZHYF9qSUXPW9dn+qrtqX82GoK/JxssG8wUH4fG8Gfj6WAwDwdLDC3MFBmDMwEB4OViJHSFIik8nQL8gFmxIKcSSzXNKJmJ1Jullho8M9r5qQJyIiyza+pxcW/5GAY+fKUVLTCHd7Xr90ZpLuRaTRaPDrr7/i1VdfxauvvorffvsNGk3Hyoi/+OILTJkyBb6+vpd9zPPPP4/KysrWj+zs7A49J4nP0JZscDc3i50PY9BHv6svLrvims/x/vZU1Ddr0CfQubXPPtGVBLja4o4hQQCANzYkQmuEGUUAEJ+rq4iJ9mUihojaZ3yk7vfX0XPlKK9tuuJjDdUwM/tLsxrGwMPBCrcP0r3XvneVWTH5lfVIL6mFXAYM6ibdBUUiU3p4TBh6+TthQLALVszpg73PjcWCcd2ZhKFLGqBvT2bolCBFgiCcNx+GbcmIiDo7P2cbRPk6QisA288WiR0OmZhkEzGpqamIjIzEvHnzsHbtWqxduxZz585FVFQU0tLSrumc586dw9atW3Hvvfde8XFWVlZwdHS84IMs28F0XY9dS25LZmBor5BUWI36pvYnJrPL6vDD4SwAwDOTenCXFbXZI2PC4GClxJn8KqyLy+3w+QRBaE3ERPnxfZaI2ifA1RYR3g7QaAXsSLr8TcuRzH+qYaQ4G+bfHhjVDWqlHMfOlbduJLmUffpqmF7+znC0VpkrPCJJcbVT449Hh+PnB4fixlhfzjykK+oX5AIAOHau7IqJbjGlFdcgt6IeaoXcomebEhFR2xk2SG8+UyhyJGRqkr1SXbBgAUJDQ5GdnY3jx4/j+PHjyMrKQkhICBYsWHBN51y1ahU8PT1x/fXXGzlakrLqhubWYYfDwiz/YtbHyQZejlbQaAXE57V/cPq7W5PRrBEwPMydPeWpXVzt1HhQv4i5bFMyGpo7VqGYU16PqoYWqBVydPe07JaBRCQOw03L1rOXv2l5b2sKAGBmf38EuEq3GsbAy9Eac/RDyN/blnLZx+1P1c2H6QzXNkRE5hDl6wRrlRzldc1IK64VO5xLMlTDDOrmClu1ZDvJExGRERnuafamFl/ThmuyHJJNxOzatQtLly6Fq+s/rRbc3NzwxhtvYNeuXe0+n1arxapVqzB//nwolbyg6Uq2JxahSaNFNw87hHrYix2OUcT6OwMATmZVtOu45MJq/HZCV8nwzKQeRo6KuoK7h4XA29EauRX1+PbguQ6dy1AN08PbgTtYieiaGG5adiUVo7Hl4puWI5ll2Jtaoq+GCTN3eNfswdGhUCvkOJRRhoPpF1fFCIKAfWn6RAw3VRARtYlaKW+9jzqaWSZuMJfxT1syT5EjISIic+np4wg/Zxu42qpxrkyaGwXIOCS78mVlZYXq6uqLvl5TUwO1Wt3u823duhVZWVm4++67jREeWZCN8QUAgCnR3p2mDVfvQGcAwMmcinYdt2xTEgQBmBzl3drijKg9bNQKLJzQHQDw/o5UVNY3X/O5DBVd0WxLRkTXKNrXCV6OVqht0lyyjZehGmZGP8uohjHwcbLBzP7+AICV2y+uikkrrkVhVSPUSjn66lvtEBHR1bXOiTknvTkxtY0trZ0cOB+GiKjrkMlkWPvwUOxbNBYR3lwf6cwkm4i54YYbcP/99+PQoUMQBAGCIODgwYN48MEHceONN7b7fBMnToQgCAgPDzdBtCRV9U2a1l1Fk6N8RI7GeHrrd3LFZVe0+ZgTWeXYfKYQchnw9CS+DujaTe/rjzBPe1TUNePjXdc2swsA4nOrAOjaRBARXQu5XIbxkbqqmC3/6ql89LxqmEfGWE41jMFDo0OhlMuwL7X0op3b+/XVMP2DXGCtUogRHhGRReoXrEte70stkVz7lwNppWjSaBHgaoNu7nZih0NERGbk5WjdaTaP0+VJNhGzYsUKhIaGYsiQIbC2toa1tTWGDRuGsLAwLF++XOzwyELsSi5GfbMG/i42nWrXfYy/E2Qy3YyNkprGNh3z1qYkAMAtff0Rxnkc1AFKhRzPTY4AAHy5NwP5lfXtPocgCEjQV8RE+Xae1yYRmd/48+bEnD982TBfxdKqYQz8XWwxo5+uKmbF9tQLvrevdT4M25IREbXHoBBXuNtbIb+yAS+tixc7nAvsTC4CAIwO9+RiHBERUSck2USMs7Mz1q1bh+TkZPzyyy/45ZdfkJSUhN9++w3Ozs5ih0cWYmN8PgBdK67OdDHrYK1CmH7eTVuqYvamlGB/WinUCjmeGN/dxNFRVzA+0hMDgl3Q2KLFu1uS2318UXUjSmqaoJDLEOnDRAwRXbuhoW6wUytQWNWI0/rZU8fOlWFPiuVWwxg8PDoMCrkMu5OLcSJL10ZHoxVa27ANDXUTMzwiIotjq1ZixZzekMuAn4/lYM2RbLFDAqDbpPTPfBi2JSMiIuqMJJuIefnll1FXV4ewsDBMnToVU6dORVhYGOrr6/Hyyy+LHR5ZgKYWLbad1e0qmhztLXI0xmeY8XLyKokYQRDw1qZEAMBtgwLh72J5u4JJemQyGRZNiQQA/HIsB8mFF8/0upJ4/WJpmIc92+oQUYdYKRUYGa5btNqqb0+2XD8bZnpfy6yGMQh0s8XNffwAACv1VTEJeZWoamiBg5USMX5s7UhE1F5DQ93x1MQeAIAX18XjTF6VyBEBacU1yCmvh1ohxxAm2YmIiDolySZilixZgpqamou+XldXhyVLlogQEVmafWklqG5sgYeDFfoGdr5Btr3bmIjZlFCAuJxK2KoVFr0rmKSnX5ALJkd5QysASzcmtuvY1vkwnahlIBGJZ4K+PdnmM4WdphrG4JExYZDLgO2JRTidU4l9qbpqmEHd3KBUSPZSnohI0h4aFYoxPTzQ2KLFw98dQ1VDs6jxGKphBnVzha1aKWosREREZBqSvXsTBOGSraTi4uLg6uoqQkRkaTbFFwAAJkV5QS7vPG3JDAyJmLjsigt64p9PoxWwbLOubdTdw0Lg4WBlrvCoi3hmcg8o5DJsPVuEwxllVz9AL14/Hybal7u5iajjxvTwhEIuQ2JBNV78PQEAcEtfPwS6WW41jEGIux2m9dZVxazYnoL9aYb5MNwxTUR0reRyGd6Z1Rt+zjbILK3Dc7+cuuw9lTkYEjGjwtmWjIiIqLOSXCLGxcUFrq6ukMlkCA8Ph6ura+uHk5MTJkyYgFmzZokdJklci0aLzfr2JFOifUSOxjR6eDvASilHVUMLMkpqL/mYtcdzkFpUAycbFe4b2c3MEVJXEOphj1sHBAAAXt9wts03sAn61mTRbKtDREbgYqdG/yBd9euZ/Coo5DI8OqbzzER7ZEwYZDJgy5lCHEzXVcQMC3MXOSoiIsvmYqfGB7f3hUohw4b4AqzalylKHLWNLa0bmkb38BQlBiIiIjI9ydW8Ll++HIIg4O6778aSJUvg5PTPIp1arUZwcDCGDBkiYoRkCY5klqOstgnOtioMDOmcFVQqhRzRfk44dq4ccTkV6OZhf8H3G1s0rT3yHxodCicblRhhUhfwxLju+O14Lk5kVWBTQgEmXyX5WVrTiLzKBgBAT1+2JiMi45jQ0wuH9AtZ0ztJNYxBmKc9bujliz/j8tCsEeDhYIXunvZXP5CIiK6od4AzXri+Jxb/kYDX/j6L2ABn9Asyb1vrA2mlaNJo4e9ig1APO7M+NxEREZmP5BIx8+fPBwCEhIRg6NChUKm4eEzttzE+HwAwIdILqk7cPz3W3xnHzpXjZFYFbu7jf8H3fjiUhdyKeng6WGH+kGBxAqQuwdPRGveNCMGK7alYujEJ467yukvQD0Tt5m4HeyvJ/RoiIgs1oacX/vf3WchlnasaxuCxsWH4My4PADA01O2SLXyJiKj95g0JwuHMMqw/lY9Hvz+O9QtGwNVObbbn35lcBEDXZpPv7URERJ2XZFeoR40a1ZqEaWhoQFVV1QUfRJej1QrYlKBvSxbjLXI0ptU70BkAcDKn8oKv1za24P0dqQCABeO6w0atMHdo1MXcN7Ib3OzUSC+pxU9Hsq/4WEMihtUwRGRMQW52+GRuP3x114BOVQ1jEO7lgJt6+wIAJkd17usbIiJzkslkeHN6L3TzsEN+ZQOe+OkktFrzzIsRBKF1PszoHpwPQ0RE1JlJNhFTV1eHRx99FJ6enrCzs4OLi8sFH0SXczKnAgVVDbC3Unb6/um9/Z0BAGfzqtDYomn9+qp9GSipaUKQm23r/A4iU3KwVuGxsWEAgOVbU1Db2HLZx8bncT4MEZnGxChvjOjeeReyls6IxfoFwzElpnPOvyMiEou9lRIf3d4P1io5dicXt25qM7W04lrklNdDrZBjSKibWZ6TiIiIxCHZRMwzzzyD7du346OPPoKVlRU+//xzLFmyBL6+vvj666/FDo8kbFN8AQBgbIQnrJSduxIkwNUGrnZqNGm0OJtfDQCoqGvCJ7vTAQBPTgjv1K3ZSFpuGxSEIDdblNQ04vM9GZd9XEKuPhHjy0QMEVF7qJVyRPG9k4jIJHp4O+B/N8UAAN7dmoy9KSUmf86dSbq2ZIO6ucJWzZa9REREnZlkV2j//PNPfPjhh5g+fTqUSiVGjBiBF154Aa+99hq+++47scMjiRIEARv0iZjJ0Z2/bYdMJkOsv25BJi67AgDw0a40VDe0IMLbAVN7+YoYHXU1aqUcT0/sAQD4dHcaSmoaL3pMVUMzMkvrAABRbE1GRERERBIyvZ8/Zg8IgCAAj/94AgWVDSZ9vl3JurZko8I7bzUnERER6Ug2EVNWVoZu3boBABwdHVFWVgYAGD58OHbv3i1maCRhZ/KrkFVWByulvMv02I0NcAYAnMyuQGFVA77alwkAeHpiD8jlHPZI5nV9jA96+TuhtkmDldtSLvr+Gf18GD9nG7iYcQgqEREREVFb/PfGKPT0cURpbRMe/f44mjVakzxPbWMLDqXr1jlG9/A0yXMQERGRdEg2EdOtWzdkZOha20RERGDNmjUAdJUyzs7OIkZGUmZoSzYq3KPLlHb31idi4rIrsGJbChpbtOgb6IxxkbyYJ/OTy2VYNDkCAPDdoSxkltRe8P14Q1syP1bDEBEREZH0WKsU+PD2vnCwUuLouXK8tSnJJM9zIK0UTRot/F1sEOphZ5LnICIiIumQbCLmrrvuQlxcHABg0aJF+OCDD2BtbY2FCxfimWeeETk6kipDW7IpMZ2/LZlBrL8zACC9pBY/HckGADw7OQIyGathSBxDw9wxKtwDLVoBb22+8MY1QV8Rw/kwRERERCRVwe52eGtmLwDAp7vTsSmhwOjPsTNZNx9mdA8P3rsRERF1AZItGVi4cGHrn8ePH4/ExEQcO3YMYWFh6NWrl4iRkVSlFtUgpagGKoUMYyO8xA7HbFzs1Ah2s0VmaR1atAJGhntgcDc3scOiLm7RlAjsTinG+lP5uH9ERWsLvX8qYpiIISIiIiLpmhztg3uHh+DzvRl4+uc4RHo7ItDN1ijnFgQBO5N082FGh7OTARERUVcg2YqYr7/+Go2N/wx6DgoKwi233IKIiAh8/fXXIkZGUmXYpTQ01B1ONiqRozEvwyI3ADw7qYd4gRDpRfo44uY+fgCANzYkQhAE1DdpkFZcAwCI8mVrMiIiIiKStuemRKBfkAuqG1rw0HfH0NCsMcp504prkVNeD7VCjqFh3ERHRETUFUg2EXPXXXehsrLyoq9XV1fjrrvuEiEikrqNhrZk0V2nLZnB8DB3AMCNsb6sNCDJeGpiD6iVchxIL8XO5GKcLaiCVgA8HKzg6WgtdnhERERERFekUsjx/m194GqnRkJeFZb8ecYo592ZpGtLNqiba5eZbUpERNTVSTYRIwjCJfuk5uTkwMmJC810oeyyOpzOrYRcBkzo2XXakhlM7+uPn+4f3NrHmEgK/JxtMH9IEADgzQ2JOJVdAQCIZjUMEREREVkIHycbLL+1N2Qy4IfDWVh7PKfD59yVrGtLNirco8PnIiIiIssgua0Xffr0gUwmg0wmw7hx46BU/hOiRqNBRkYGJk+eLGKEJEWGtmQDQ1zhZm8lcjTmJ5fLMIhzYUiCHhkThp+OZCOxoBof7kwDwPkwRERERGRZRoZ7YMHY7nhvWwr+77d4RPs5IdzL4ZrOVdfUgkPpZQCA0T04H4aIiKirkFwi5qabbgIAnDx5EpMmTYK9vX3r99RqNYKDgzF9+nSRoiOpMrQlmxzV9dqSEUmZs60aD48JwxsbElFUrZv7FeXLRAwRERERWZYF47rjeFY59qSU4MFvj+GPR4fD3qr9SyoH0krRpNHC38UGoR52JoiUiIiIpEhyiZjFixcDAIKDg3HrrbfC2ppzBOjKiqoacCyrHAAwqQvOhyGSujuHBmP1/kzkVzYAAKL92JqMiIiIiCyLQi7D8lt74/oVe5FeXIvn157Gitm9L9lS/Up2Junako3u4dHuY4mIiMhySXZGzPz585mEoTbZdKYQggD0DnCGj5ON2OEQ0b9YqxRYOCEcAOBmp4afM1+nRERERGR53Oyt8P5tfaCQy/BnXB6+PXiuXccLgoAdSUUAgNHhbEtGRETUlUiqIsbV1RXJyclwd3eHi4vLFXeHlJWVmTEykrKN8fkAgCmshiGSrBl9/VHd0ILunvbc+UdEREREFqt/sCuenxKBV9efxSt/nUUvf2fEBji36di04lrklNdDrZBjaBhnfBIREXUlkkrEvPvuu3BwcGj9Mxfr6GrKa5twUD/ocDITMUSSJZfLcM/wELHDICIiIiLqsHuGh+BIZhk2JRTi4e+OY/2C4XC2VV/1uJ36apiBIa6wVUtqOYaIiIhMTFK/+efPn9/65zvvvFO8QMhibDlbCI1WQKSPI4LcOOiQiIiIiIiITEsmk2HpjFgkFuzFudI6PLUmDp/N6w+5/MqbSXcl/zMfhoiIiLoWyc6I+fvvv7Fp06aLvr5582Zs2LBBhIhIijbFFwBgWzIiIiIiIiIyHycbFT68vS/USjm2JRbh491pV3x8XVMLDum7OTARQ0RE1PVINhGzaNEiaDSai76u1WqxaNEiESIiqaluaMaelBIAbEtGRERERERE5hXl64QlN0YBAJZtSsKBtNLLPvZAWimaNFr4u9gg1MPeXCESERGRREg2EZOSkoKePXte9PWIiAikpqaKEBFJzY6kYjRptOjmYYfunryQJSIiIiIiIvOaPSAAt/T1g1YAHvvhBIqqGy75uJ1J/7Ql4zxcIiKirkeyiRgnJyekp6df9PXU1FTY2XEWCAEb4/MBAJOjvHkhS0RERERERGYnk8nw6k3RCPeyR0lNIxb8cAItGu0FjxEEATuTiwAAo8M9xQiTiIiIRCbZRMy0adPwxBNPIC3tnz6rqampeOqpp3DjjTeKGBlJQX2TBjsSdTuKpkT7iBwNERERERERdVW2aiU+mtsPdmoFDqaX4Z0tyRd8P72kFtll9VAr5Bga5iZSlERERCQmySZili5dCjs7O0RERCAkJAQhISGIjIyEm5sbli1bJnZ4JLLdKcWob9bAz9kG0X6OYodDREREREREXViohz3emN4LAPDhzjRsTyxs/d6ORF01zMAQV9iqlaLER0REROKS7BWAk5MT9u/fjy1btiAuLg42Njbo1asXRo4cKXZoJAEb4wsAAJOj2ZaMiIiIiIiIxDc11hdHM8uw+sA5LPwpDusXDIe/iy12Jf8zH4aIiIi6JskmYoqLi+Hh4YGJEydi4sSJF3zv9OnTiImJESkyEltTixZbz+p2F02J9hY5GiIiIiIiIiKd/1wfiZPZFYjLqcQj3x3H13cPwqH0MgBMxBAREXVlkm1NFhMTg/Xr11/09WXLlmHgwIEiRERSsT+tBNUNLfBwsELfQBexwyEiIiIiIiICAFgpFfjg9r5wslEhLqcSc784hCaNFn7ONgj1sBc7PCIiIhKJZBMxTz75JKZPn46HHnoI9fX1yM3Nxbhx47B06VJ8//33YodHItqUoGtLNinKC3I525IRERERERGRdPi72OLdW2MBAKdzKwHoqmHYVpuIiKjrkmwi5tlnn8WBAwewZ88e9OrVC7169YKVlRVOnTqFm2++WezwSCQarYDNCbq2ZJOjfESOhoiIiIiIiOhiYyO88PDo0NbPx/TwFDEaIiIiEptkEzEAEBYWhujoaGRmZqKqqgq33norvL2vbSZIbm4u5s6dCzc3N9jY2CAmJgZHjx41csRkakcyy1Ba2wRnWxUGdXMVOxwiIiIiIiKiS3pyQjhujPXFwBBXDO/uLnY4REREJCKl2AFczr59+zB37ly4urri1KlT2LdvHx577DH8/fff+Pjjj+Hi0vbZIOXl5Rg2bBjGjBmDDRs2wMPDAykpKe06B0nDxnhdW7LxkV5QKSSdRyQiIiIiIqIuTKmQY8WcPmKHQURERBIg2UTM2LFjsXDhQrzyyitQqVSIjIzEmDFjMHfuXMTExCAnJ6fN53rzzTcREBCAVatWtX4tJCTEFGGTCWm1QmsiZkr0tVVGERERERERERERERGZk2RLCjZv3ow33ngDKpWq9WuhoaHYt28fHnjggXad648//kD//v0xc+ZMeHp6ok+fPvjss88u+/jGxkZUVVVd8EHii8upQEFVA+zUCgwLY1k3EREREREREREREUmfZBMxo0aNuuTX5XI5XnzxxXadKz09HR999BG6d++OTZs24aGHHsKCBQuwevXqSz7+9ddfh5OTU+tHQEBAu+Mn4zNUw4yN9IK1SiFyNEREREREREREREREVye5RMx1112HysrK1s/feOMNVFRUtH5eWlqKnj17tuucWq0Wffv2xWuvvYY+ffrg/vvvx3333YePP/74ko9//vnnUVlZ2fqRnZ19TX8XMh5BELAxgW3JiIiIiIiIiIiIiMiySC4Rs2nTJjQ2NrZ+/tprr6GsrKz185aWFiQlJbXrnD4+PhclbyIjI5GVlXXJx1tZWcHR0fGCDxLX2fxqnCutg5VSjlHhHmKHQ0RERERERERERETUJpJLxAiCcMXPr8WwYcMuSt4kJycjKCiow+cm8zBUw4wK94CdlVLkaIiIiIiIiIiIiIiI2kZyiRhTWLhwIQ4ePIjXXnsNqamp+P777/Hpp5/ikUceETs0aqON8fkAgMlsS0ZEREREREREREREFkRyiRiZTAaZTHbR1zpiwIAB+O233/DDDz8gOjoar7zyCpYvX47bb7+9Q+cl80grrkFyYQ2UchnGRXqJHQ4RERERERERERERUZtJrseTIAi48847YWVlBQBoaGjAgw8+CDs7OwC4YH5Me9xwww244YYbjBYnmc/GeF1bsqFh7nCyUYkcDRERERERERERERFR20kuETN//vwLPp87d+5Fj5k3b565wiEJ2KSfDzOFbcmIiIiIiIiIiIiIyMJILhGzatUqsUMgCckpr8OpnErIZcCEnmxLRkRERERERERERESWRXIzYojOZ2hLNiDYFe72ViJHQ0RERERERERERETUPkzEkKSxLRkRERERERERERERWTImYkiyiqobcPRcOQBgEhMxRERERERERERERGSBmIghydqcUAhBAHoHOMPHyUbscIiIiIiIiIiIiIiI2o2JGJIsw3yYyayGISIiIiIiIiIiIiILxUQMSVJFXRMOpJcCACZHMRFDRERERERERERERJaJiRiSpC1nCqHRCojwdkCwu53Y4RARERERERERERERXRMmYkiSNiXo2pJNifYRORIiIiIiIiIiIiIiomvHRAxJTk1jC3anlAAApsSwLRkRERERERERERERWS4mYkhyticWoalFi27udujuaS92OERERERERERERERE14yJGJKcTfG6tmSTo70hk8lEjoaIiIiIiIiIiIiI6NoxEUOS0tCswY6kIgC6RAwRERERERERERERkSVjIoYkZXdyMeqaNPBztkGMn5PY4RARERERERERERERdQgTMSQpG/VtySZFsS0ZEREREREREREREVk+JmJIMppatNh6thAAMCWGbcmIiIiIiIiIiIiIyPIxEUOScSC9FFUNLfBwsEK/QBexwyEiIiIiIiIiIiIi6jAmYkgyDG3JJvb0glzOtmREREREREREREREZPmYiCFJ0GgFbDmjS8RMifYRORoiIiIiIiIiIiIiIuNgIoYk4WhmGUpqmuBko8Kgbq5ih0NEREREREREREREZBRMxJAkbNC3JZvQ0wsqBX8siYiIiIiIiIiIiKhz4Io3iU6rFbApQZeImRzlLXI0RERERERERERERETGw0QMie5UbiXyKxtgp1ZgeHd3scMhIiIiIiIiIiIiIjIaJmJIdBvi8wEAYyI8Ya1SiBwNEREREREREREREZHxMBFDohIEAZv082GmRPuIHA0RERERERERERERkXExEUOiSiyoRmZpHayUcozu4SF2OERERERERERERERERsVEDIlqo74aZmS4B+yslCJHQ0RERERERERERERkXEzEkKg2trYl8xY5EiIiIiIiIiIiIiIi42MihkSTXlyDpMJqKOUyjIvwEjscIiIiIiIiIiIiIiKjYyKGRLMxQVcNMzTMHU62KpGjISIiIiIiIiIiIiIyPiZiSDSGtmSTo9iWjIiIiIiIiIiIiIg6JyZiSBS5FfU4lVMJuQyYGMW2ZERERERERERERETUOTERQ6IwVMMMCHaFu72VyNEQEREREREREREREZkGEzEkik2GtmTRbEtGRERERERERERERJ0XEzFkdkXVDThyrgwAMInzYYiIiIiIiIiIiIioE1OKHQB1PU0tWtzaPwBF1Y3wdbYROxwiIiIiIiIiIiIiIpPpEhUx//3vfyGTyS74iIiIEDusLsvfxRZvTO+FL+8cIHYoREREREREREREREQm1WUqYqKiorB169bWz5XKLvNXJyIiIiIiIiIiIiIikXSZbIRSqYS3N+eREBERERERERERERGR+XSJ1mQAkJKSAl9fX3Tr1g233347srKyxA6JiIiIiIiIiIiIiIg6uS5RETNo0CB89dVX6NGjB/Lz87FkyRKMGDEC8fHxcHBwuOjxjY2NaGxsbP28qqrKnOESEREREREREREREVEnIRMEQRA7CHOrqKhAUFAQ3nnnHdxzzz0Xff+///0vlixZctHXKysr4ejoaI4QiYiIiIiIiIiIiIhIoqqqquDk5NSmvEGXaU12PmdnZ4SHhyM1NfWS33/++edRWVnZ+pGdnW3mCImIiIiIiIiIiIiIqDPokomYmpoapKWlwcfH55Lft7KygqOj4wUfRERERERERERERERE7dUlZsQ8/fTTmDp1KoKCgpCXl4fFixdDoVBgzpw5bTre0L2Ns2KIiIiIiIiIiIiIiMiQL2jL9JcukYjJycnBnDlzUFpaCg8PDwwfPhwHDx6Eh4dHm46vrq4GAAQEBJgyTCIiIiIiIiIiIiIisiDV1dVwcnK64mNkQlvSNV2cVqtFXl4eHBwcIJPJxA5HUqqqqhAQEIDs7Gy2cCMik+D7DBGZGt9niMjU+D5DRKbG9xkiMjW+z1xMEARUV1fD19cXcvmVp8B0iYqYjpLL5fD39xc7DEnjLB0iMjW+zxCRqfF9hohMje8zRGRqfJ8hIlPj+8yFrlYJY3DlNA0RERERERERERERERFdMyZiiIiIiIiIiIiIiIiITISJGOoQKysrLF68GFZWVmKHQkSdFN9niMjU+D5DRKbG9xkiMjW+zxCRqfF9pmNkgiAIYgdBRERERERERERERETUGbEihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESYiCEiIiIiIiIiIiIiIjIRJmKIiIiIiIiIiIiIiIhMhIkYIiIiIiIiIiIiIiIiE2EihoiIiIiIiIiIiIiIyESUYgdgCbRaLfLy8uDg4ACZTCZ2OEREREREREREREREJCJBEFBdXQ1fX1/I5VeueWEipg3y8vIQEBAgdhhERERERERERERERCQh2dnZ8Pf3v+JjmIhpAwcHBwC6/6GOjo4iR0NERERERERERERERGKqqqpCQEBAa/7gSpiIaQNDOzJHR0cmYoiIiIiIiIiIiIiICADaNM7kyo3LiIiIiIiIiIiIiIiI6JoxEUNERERERERERERERGQiTMQQERERERERERERERGZCBMxREREREREREREREREJsJEDBERERERERERERERkYkwEUNERERERERERERERGQiTMQQERERiSC7rA4/HM6CRiuIHQoRERERERERmZBS7ACIiIiIupq6phbc/vkhZJXVoVmjxbwhwWKHREREREREREQmwooYIiIiIjN7c0MissrqAADfH8qCILAqhoiIiIiIiKizYiKGiIiIyIz2p5Vg9YFzAAClXIbEgmqczK4QNygiIiIiIiIiMhkmYoiIiIjMpKaxBc/+cgoAcNugQNwY6wsA+PFwtphhEREREREREZEJMRFDREREZCav/30WOeX18HO2wX+ui8TsgYEAgD9P5aGmsUXk6IiIiIiIiIjIFJiIISIiIjKDPSnF+O5QFgDgrRm9YG+lxIBgF4R62KGuSYM/TuaJHCERERERERERmQITMUREREQmVt3QjOf0LcnmDQnC0DB3AIBMJsPsAbqqmB8OZ4kWHxERERERERGZDhMxRERERCb2v/VnkVfZgEBXWzw3OeKC793S1w8qhQyncysRn1spUoRE0tCs0YodAhERERERkdExEUNERERkQjuTivDjkWwAupZkdlbKC77vZm+FiVHeAIAfj7AqhrquM3lVGPL6dty7+giaWpiQISIiIiKizoOJGCIiIiITqaxvxqJfTwMA7hoWjEHd3C75uDn69mTrTuShrqnFbPERSUVTixZP/RyHkppGbD1bhBd+Pw1BEMQOi4iIiIiIyCiYiCEiIiIykVf/OoOCqgYEu9ni2UkRl33c0FA3BLjaoLqxBetP5ZsxQiJpeH9HKs7mV8HBWgm5DFhzNAef7UkXOywiIiIiIiKjYCKGiIiIyAS2Jxbi52M5kMmAt2bGwkatuOxj5XIZZuurYgxtzIi6ivjcSnywIxUA8PotMXjxhp66P29IxJYzhWKGRkREREREZBRMxBAREREZWWXdPy3J7hkWggHBrlc9ZmY/fyjkMhw7V47kwmpTh0gkCY0tGjy1Jg4arYDrY3xwQy9f3Dk0GLcPCoQgAI//eAJn86vEDpPaKK24Bjd9sI+VfUbS0KzB/9afwZxPD6KgskHscIiIiIioA5iIISIiIjKyJX8moKi6Ed3c7fD0pB5tOsbT0RrjIjwBAD8eZlUMdQ0rtqUgqbAabnZqvDwtCgAgk8nw3xujMCzMDXVNGty7+iiKqxtFjpTa4r2tKTiZXYG3Nydxxk8HZZTU4pYP9+OzPRk4kF6Kd7YkiR0SEREREXUAEzFERERERrQ5oQBrT+RCLgOWzYqFteryLcn+bc5AXXuytSdy0NCsMVWIRJIQl12Bj3amAQBevSkabvZWrd9TKeT48LZ+6OZuh9yKetz/zVG+JiSuqLoBG+J1lTDpJbU4m8/Kvmu17mQublixB2fyq+BkowIA/Ho8F+nFNSJHRkRERETXiokYIiIiIiMpr23Cf36LBwDcN7Ib+ga6tOv4keEe8HWyRkVdMzYlFJgiRCJJaGjW4Kmf46AVgBtjfTElxueixzjZqvDFnQPgZKPCiawKPPvLKVZZSNiPh7PRrPnn32f96TwRo7FM9U0aLPr1FB7/8SRqmzQYFOKKzQtHYnykJzRaAcu3pogdIhERERFdIyZiiIiIiIzkv38moKSmEWGe9lg4PrzdxyvkMszsHwAA+OFwlrHDI5KM5VtTkFpUA3d7Kyy5Meqyjwtxt8PHc/tBKZfhj7g8rNyeasYoqa1aNFp8f0j3njU+Utdicf2pfCbO2iG1qBo3fbAPPx7JhkwGLBjXHd/dOwhejtZYOEH3++TPU3lILODMJOra6ppa8N8/ErAzqUjsUIiIiNrF4hIxGo0GL774IkJCQmBjY4PQ0FC88sorV73I37lzJ/r27QsrKyuEhYXhq6++Mk/ARERE1CVsjM/HupN5UMhleHtm+1qSnW/WgADIZMDB9DJklNQaOUoi8R3PKsenu3UtyV67ORouduorPn5IqBtevSkaAPDOlmT8dYqVFlKz5UwhCqoa4GanxlszYmGllCOztA4JeUwatMUvx3IwdeU+JBVWw93eCt/eMwhPTgiHUqG7XY/ydcL1MT4QBODdLckiR0skri/3ZuCr/Zl49PsTKKxqEDscIiKiNrO4RMybb76Jjz76CO+//z7Onj2LN998E0uXLsXKlSsve0xGRgauv/56jBkzBidPnsQTTzyBe++9F5s2bTJj5ERERNRZldY04v/0LckeHNUNsQHO13wuP2cbjAr3AAD8eIRVMdS5NDRr8LS+JdktffwwMcq7TcfNHhiIe4aHAACeWhOHuOwKE0ZJ7bX6QCYA3ZwrFzs1xkboq2JO54sYlfTVNbXgqTVxePrnONQ3azAszA1/Pz4cw8LcL3rswgndIZcBmxIKcTqnUoRoicTX2KLB6gPnAAA1jS14df1ZkSMiIiJqO4tLxOzfvx/Tpk3D9ddfj+DgYMyYMQMTJ07E4cOHL3vMxx9/jJCQELz99tuIjIzEo48+ihkzZuDdd981Y+RERETUWb30RwJKa5vQw8sBC8Z17/D5Zg8IBAD8eiwHTS3aDp+PSCre3pyE9OJaeDpYYfHUy7cku5T/XBeJsRGeaGzR4r6vjyK/st5EUVJ7JBdW42B6GeQy4LZBuveu63vpZv6wPdnlJRZUYerKvfj1eA7kMuCpCeH4+u5B8HSwvuTjwzwdcFNvPwDA21uSzBkqkWT8cTIPxdWNcLZVQS4D/ozLw96UErHDIiIiahOLS8QMHToU27ZtQ3KyriQ7Li4Oe/fuxZQpUy57zIEDBzB+/PgLvjZp0iQcOHDApLESERFR5/fXqTysP5UPhVyGZTNjYaW8tpZk5xsX6Ql3eyuU1DRh29lCI0RJJL6jmWX4fG8GAOD1W2LgZKtq1/EKuQzvze6NHl4OKKpuxL2rj6KuqcUUoVI7fK2vhpnY0xu+zjYAgLERnrBWyZFVxvZk/yYIAn48nIVp7+9DWnEtvByt8P19g/HYuO5QyGVXPPbx8d2hlMuwM6kYRzPLzBQxkTQIgoAv9L9DHhgZinlDggEAL62LR2OLRsTIqL3qmzT49VgOSmsaxQ6FiMisLC4Rs2jRIsyePRsRERFQqVTo06cPnnjiCdx+++2XPaagoABeXl4XfM3LywtVVVWor794J11jYyOqqqou+CAiIiL6t+LqRrz4u64l2SOjQxHj72SU86oUcszs7w8A+OFItlHOSSSm+iZdSzJBAGb088e4SK+rH3QJDtYqfD6/P9zs1EjIq8KTP8VBq2XFhViqGpqx9nguAGDekKDWr9uqla3tyf46xfZkBjWNLXj8x5NYtPY0Glu0GBXugb8XjMDgbm5tOj7IzQ4z+wcAAN7alMRqI+pS9qaWILGgGrZqBW4bGIgnJ4bDw8EK6SW1+HRXutjhURsJgoDHfjiBp36Ow62fHkRVQ7PYIRERmY3FJWLWrFmD7777Dt9//z2OHz+O1atXY9myZVi9erXRnuP111+Hk5NT60dAQIDRzk1ERESdgyAIeOH30yiva0aEtwMeHdvxlmTnmz1Ad/2xJ6UY2WV1Rj03kbkt3ZSIzNI6eDta48UbenboXAGutvh0Xj+oFXJsTCjAss1s0ySWtcdyUNekQZinPYaEXphMuD7GFwCw/nQeEwYAEvIqMXXlXvwRlweFXIbnJkdg1Z0D4GZv1a7zPDY2DGqFHIcyyrA/rdRE0RJJz2d7dNUws/oHwMlWBUdrFV64PhIA8P6OVGSV8lrJEny5LxNb9dXeqUU1ePT7E2jRsA0vEXUNFpeIeeaZZ1qrYmJiYnDHHXdg4cKFeP311y97jLe3NwoLL2zrUVhYCEdHR9jY2Fz0+Oeffx6VlZWtH9nZ3IlKREREF/ojLg+bEgqhlMvw9qxYqJXGvawKcrPDsDA3CALw81Fei5DlOpheilX7MgEAb0yPgZNN+1qSXUq/IFcsndELAPDhzjT8eiynw+ek9hEEAd8c1A3NnjckCDLZhW21xkR4wEalQHZZPU7ndt3h8oIg4JsDmbj5w/3IKKmFr5M11jwwGA+NDoX8Kq3ILsXX2aZ1Fs+yzayKoa4hqaAau5OLIZMBdw8Laf36jbG+GBrqhsYWLRb/Ec/Xg8TFZVfgjQ1nAQDzhwTBWiXH7uRivLr+rMiRERGZh8UlYurq6iCXXxi2QqGAVnv5DPqQIUOwbdu2C762ZcsWDBky5JKPt7KygqOj4wUfRERkWUpqGpFXwUHOZBpF1Q1Y/EcCAOCxsd0R5WuclmT/NnuAbrFtzdEc7hYki1TX1IJnfzkFQFflNbqHp9HOfVMfPzw6JgwA8Pza0zjCmRlmtT+tFGnFtbBTK3BzH7+Lvm+rVmJspO7fe30XbU9W1dCMR74/jhfXJaCpRYvxkZ5Yv2AE+gW5dui8D48JhY1KgRNZFdiRVGSkaImk64u9utZjk3p6I9DNtvXrMpkML0+Lhkohw46kYmxK4Fw9qaqs170fNmsETI7yxn9vjMLyW3sDAL7an4lv9Yl9IqLOzOISMVOnTsX//vc/rF+/HpmZmfjtt9/wzjvv4Oabb259zPPPP4958+a1fv7ggw8iPT0dzz77LBITE/Hhhx9izZo1WLhwoRh/BSIiMrHKumZMeW8PRizdgQ92pHJ+ABmVIAj4v9/iUVHXjChfRzw8JtRkzzUxygsutioUVDVgV3KxyZ6HyFTe3JCIrLI6+DpZ4//0LWSM6ckJ4ZgS7Y0mjRYPfHOMbfzMaPX+TADA9H7+cLC+dJXTDTE+AHRzYrraTvW47Apcv2IP/j5dAKVchheuj8Rn8/rDxU7d4XN7Olhj/tBgAMCyTcm8zqFOrbi6Eb+fyAMA3Dcy5KLvh3na44GRumuxl/9MQG1ji1njo6sTBAHP/XIKOeX1CHC1wZszekEmk2FytA+emdQDALD4jwTsTSkROVIiItOyuETMypUrMWPGDDz88MOIjIzE008/jQceeACvvPJK62Py8/ORlZXV+nlISAjWr1+PLVu2IDY2Fm+//TY+//xzTJo0SYy/AhERmdjK7Skorm6ERivgrU1JmL/qMEpqGsUOizqJ30/mYsuZQqgUupZkKoXpLqeslApM7+sPAPjhcNZVHk0kLfvTSrD6gG6H69IZsZddrO8Iub41YLSfI8pqm3DP6iOo5uBfk8utqG/t8X/H4KDLPm50D0/YqhXIrahHXE7XaE8mCAK+3JuBGR/vR3ZZPfxdbPDLQ0Nx74huF7Vv64gHRnaDvZUSZ/KrsDGhwGjnJZKabw5kokmjRe8AZ/QNdLnkYx4ZEwZ/FxvkVTZgxfYUM0dIV/P1gXPYmFAAlUKG9+f0vaBF6cOjQ3FzHz9otAIe/u4Y0oprRIyUiMi0LC4R4+DggOXLl+PcuXOor69HWloaXn31VajV/+ws+uqrr7Bz584Ljhs9ejROnDiBxsZGpKWl4c477zRv4EREZBaZJbVYfSATwD+9h/eklOC69/bgAIfaUgcVVjVg8TpdS7LHx3VHhLfp25fOHhgAANieWISCygaTPx+RMdQ0/tOS7LZBgRje3d1kz2WrVuLzeQPg5WiF5MIaPPbDCWhYIWBS3x08B60ADA11Q3cvh8s+zkatwLhILwDA+lN55gpPNBV1Tbj/m2N4+a8zre131i8Ygd4BzkZ/Lhc7Ne4ZrqsOeGdLMn/mqVNqaNa0zqK67wrJTBu1AktujAIAfLEnA8mF1WaLka4sPrcS/9PPgFk0JRKx/3o/lMlkeP2WGPQLckFVQwvu+eoIymubRIiUiMj0LC4RQ0SXxtkBRDpvbkxEs0bAyHAPLJkWjT8eHY7unvYoqm7E7Z8fxHtbU7hYQddEEAQ8v/Y0qhpaEOPnhAdHma4l2fnCPB0wINgFWgH4+Wi2WZ6TqKNe//sscsrr4edsg/9cZ/yWZP/m7WSNz+cNgLVKjp1Jxa2LPmR8jS0a/HRE9140b8jlq2EMrte3J1vfyduTHc8qx/Ur9mLLmUKoFXIsuTEKH829cOe3sd0zIgRONiqkFtXgj7hckz0PWZ7Ocq376/EclNc1w9/FBpOivK742HGRXpjQ0wstWgEv/B7fqd9vLEW1fk5Wk0aLCT29cPew4Es+zlqlwCd39IOfsw0yS+vw0HfH0NTC9Q0i6nyYiCGycKU1jXjul1OIeHEjPtiRKnY4RKI6klmGDfEFkMuA/9Mv/IV7OWDdo8Mws58/tALw7tZk3PHFIRRVs7KA2ueXYznYnlgEtUKOt2fFQmnClmT/NntAIADgp6PZnAVAkrcnpRjfHdK10ntrRi/YWynN8rwx/k54Z1ZvAMCX+zLw/SG28zOFv0/no7S2CT5O1hgfeeWFUQAY3cMDdmoF8iobcCK7wvQBmplWK+CTXWmY9fEB5FbUI8jNFmsfHor5Q4ON2orsUhytVa2bApZvTUEzN2Z1aY0tGvx8NBuTl+9G9OJNOJpZJnZIHaLVCvhiTwYA4K5hIW267lo8tSdsVAoczijD2uNMTopJEAQsWnsa50rr4Odsg7f0c2Eux93eCl/c2R92agUOppdh8R9MphFR58NEDJGF0mgFfH0gE2OW7cRPR7PRohXw7pZkpLAMm7oorVbAq/od0LcOCEAP739apdiqlXhrZizemRULW7UC+9NKcd17ezgQktosv7IeL/95BgCwcEI4wq/QiscUrovxgYO1Ejnl9dibyp9bkq7qhmY8p29JNm9IEIaGma4l2aVcF+ODpyeGAwBeWheP/Xy9GN3q/bo2QbcPCmzTwqi1SoHxPQ3tyfJNGpu5GeYSvb4hES1aATf08sFfjw1HtJ+T2WKYPzQI7vZqnCutwy/Hcsz2vCQd5bVNWLktBcPe2IFnfjmFxIJq1DdrsPiPBIvevLEjqQjpJbVwsFLi1gEBbTrG38UWC8Z1BwC89vdZVNZxZphYvjuUhfWn8qGUy7Dytj5wtlVf9ZgIb0esvK0P5DLgh8PZ+GJvhhkiJSIyHyZiiCzQ0cwyTF25Fy+tS0BVQwt6+jhiYIgry7CpS/vzVB7isitgp1Zg4YTwSz7mlr7++OPR4YjwdkBJTRPu+PIQlm1KYms/uiJBEPDcr6dR3diC3gHOuG9EiNljsFErcHMfPwDAj0e4y5+k63/rzyKvsgGBrrZ4bnKEKDE8MiYMN/X2RYtWwIPfHkM6B/8azamcCpzMroBKIcOt+kq9tjC0J/v7dL5FLwyf73BGGa57bw92JBVDrZTjtZtjsHJOHzhYm64V2aXYqpV4eHQYAGDFthQ0NGvM+vwknvTiGvzfb6cx5I1teHtLMkpqGuHtaI2nJ4bDwUqJhLwqrD1huVUhn+1JBwDMGRTYrsrKe4aHIMzTHqW1TXhrc6KpwqMrSMirxMt/6TYwPTu5B/oGurT52LERXq0tTf/391lsTyw0SYxERGJgIobIghRVN+DJNScx4+MDOJNfBUdrJV6ZFoU/HxuOd2bFwlolx6GMMvxmwRfcRNeioVmDpRuTAAAPjQ6Fp4P1ZR8b5mmP3x8ZhtsGBUIQgPd3pOK2zw4hv7LeXOGShVlzNBu7k3ULbctmmrcl2fkM7cm2nClESU2jKDEQXcnOpCL8qJ8d8taMXrAzU0uyf5PJZHhjei/0DXTWDf5dfRQVdRz8awxfH9BVw1wf4wMPB6s2Hzcy3AP2VkrkVzbgRHa5qcIzC61WwAc7UjHns4MoqGpANw87rNNfV5i6Fdnl3DYoEN6O1sivbMCPh5ms78wEQcDB9FLcu/oIxr69C98dykJDsxbRfo54b3Zv7HluDB4d2x0Pj9El55ZtSkJ9k+Ul5+JzK3EwvQxKuQx3Dg1u17FqpRyvTIsGoKvKONkJWyJKWU1jCx77/gSaWrQYG+GJe4d3a/c57hkegjkDAyAIwGPfn0BiQZUJIiUiMj8mYogsQLNGiy/2ZmDcsl1YezwXMhkwZ2AAdjw9GncMCYZCLmMZNrVZRV0TPtiRiutX7Ok0LSy+3JeB3Ip6+DhZ4542XOxbqxR47eYYrJjTB/ZWShzONOxqLTJDtGRJcivq8cpfupZ3z0zsgTBPe9Fi6enriNgAZzRrBPzaSV671HlU1jdj0a+nAQB3DQvGoG5uosajG/zbH37ONsgoqcXD3x3n/IwOKq9twh9xeQCAO4YEt+tYa5UCE/Ttyf6y4PZkgiDgke+P461NSdBoBdzcxw9/PjockT6OosZlrVLgsXG6hff3d6RZ1MJ7UkE1Zn68Hx/sSGVV/xU0a7RYdzIXU9/fi9mfHsTWs7pr1vGRnvjx/sH489HhmNbbDyr9ZpG7hgXDz9kGBVUNrZUlluRzfczX9/KBr7NNu48fEuqGW/r4QRCAF34/DU0nqcSTOkEQ8MJvp5FeUgsfJ2u8PTMWcnn7E9QymQxLbozG4G6uqG3S4J6vjnITkoRotQI+3Z2G697bg28PnuPri6gdmIghkrgDaaW4YcVevPLXGVQ3tiDW3wm/PTwMr9/SC272F+5EvHd4N4R62KGkpgnLNieJFDFJVUZJLV78PR5DXt+OtzYlISGvCv/57TRSiyx7rlBJTSM+3JEGAHhmUg/YqBVtPvbGWF99L3dHlNc1465VR/D6hrNcrCMA+pZkv5xCTWML+gW54O7h5m9J9m9z9D3SfzqSzQUrkpRX/zqDgqoGBLvZ4tlJ4rQk+zcPh38G/+5PK8VL6xL4uumAn45mo6lFt/O+b6Bzu4/vDO3JDmWUYUN8AdQKOZbO6IV3ZsWKVvn1b7P6ByDQ1RYlNY34+kCm2OG0SXF1I+7+6giOZJbjrU1JeO7XU7wG+5fK+mZ8vCsNI97cgcd/PIn43CpYq+SYOzgQ258ahc/nD8Dgbm4XVWNZqxR4boruvfjjXWkoqmoQI/xrkl9Z35qwvZZqCoPnr4uEg7US8blV+O7QOWOFR1ew5mg2fj+ZB4VchpVz+sDF7upzYS5HrZTj47n9EOxmi9yKejzwzTG2XpSA0ppG3L36CF77OxFn8qvwwu/xmPbBXhw7Z9nVrkTmwkQMkUQVVDbgsR9OYM5nB5FUWA0XWxXeuCUGvz08DL0DnC95jFopxys36cqwvz10DnEsw+7yBEHAofRS3Pf1UYx9eye+OXgO9c0a9PRxRO8AZzS1aPHUz6csekbK8q3JqGlsQYyfE27q7dfu44Pd7fDrQ0Mxf0gQAOCTXem49ZMDyK1gq7Ku7vvDWdibWgIrpRxvzegFxTXs6DO2qbG+sFMrkF5Si0MZZWKHQwQA2J5YiJ+P5UAmA5bNjG1XQtzUIrwdsWJOH8hkwA+Hs7BqX6bYIVkkjVbAtwd1C5nzBgdfUwuuEeHucLBSorCqEceyLHPB5vM9usHRM/r7Y1b/ANFakV2KSiHH4/rq+I92paG6QdrV8Q3NGjzwzVHkVtTD08EKchmw5mgO7v/6KOqaWsQOT3RZpXX47x8JGPL6NryxIREFVQ3wcLDC0xPDsX/ROLx6Uwy6eVy5SndqLx/0DnBGXZMGb29ONlPkHffV/ky0aAUMCnFFjL/TNZ/Hw8EKz07qAQB4a1MSiqotJxlliZIKqrH4jwQAwFMTw9E/2LXD53S2VeOLOwfA0VqJY+fK8fza09xQIaJD6aW4bsUe7EwqhpVSjnlDglqTndM/2o9nfo5j5RLRVTARQyQxTS1afLIrDePe3ok/4/IgkwFzBwdix9OjMXtg4FVLe4eGuuPm1jLseJaJdlGG9gXTPtiHWz89iC1nCiEIwNgIT3x/3yCsXzAcH83tCwdrJeKyK/CpBbYsAICUwmp8f0jXC/3/ro+8ptJ3ALBSKrBkWjQ+ul33/+R4VgWue28PtpzhcMiuKrusDv9br2tJ9uzkiKsudpiLnZUSN/b2BaBbVCYSW2XdPy3J7hkWYpSFF2MbF+mF/9MP/n11/Rm2obwGO5OKkFNeDycbFabG+l7TOayUCkyI0rUnW2+B7cnSi2uwTT80+u5h4ldIXspNffwQ6mGHirpmfLk3U+xwLksQBDz36ykcz6qAk40KP94/GJ/c0R/WKjl2JBVjzqcHUdpFF/OOnSvDQ98ew+hlO/DV/kzUNWkQ4e2At2b0wl79/BfXNlYZyGQyvHiD7r1vzbFsnM2X/pyNmsaW1mv7e0dcezWMwW2DgtDL3wnVDS14TX9dR8ZX19SCR74/joZmLUaFe+DBkaFGO3eohz0+vL0fFHIZfjuRiw93phnt3NQ2Gq2AldtSMOezgyisakSohx3WPToML0+Lxo6nR2NWf38AwM/HcjBm2U58tS/Dojd6EpkSEzFEErInpRiT39uN1zckorZJg76Bzvjz0eF49aYYONu2vaz3+esi4GCtxOncSnzPMux2EwQBv5/IxYu/x2PdyVyLmrdT1dCMT3enYdRSXfuCUzmVsFLKcdugQGx9chS+vHMAhoa6QyaTwcfJBounRgEAlm9JQVKB5bUoe+3vs9AKwMSeXhhshHkEU2J88PeCEYj1d0JlfTPu+/ooXv7zDJpaeCHZlWi1Ap795RTqmjQYGOyKu9o5JNbUZg8IBABsiC/gAHIS3ZI/E1BU3Yhu7nZ4Wr/zWIruGR6C2QMCoNUP/rXE33liWn1Adz1564CADlU83dDLctuTfbkvA4IAjIvwFHVe2JUo5DIsnBAOQDdjQ6q/I97fnop1J/OglMvw0e190c3DHhN6euG7ewfD2VaFuJxKTP9oP7JK68QO1SxaNFqsP5WPmz/ch+kfHcCG+AJoBWBUuAe+uWcgNjw+AjP7B8BK2f7XXr8gV1wf4wNB0F03S72a4Oej2ahuaEE3dzuMi/Ds8PkUchlevSkaMhnw+8k87E8rMUKU9G//z959h0VxdWEAf3fpHZSuVFFBRMUOqFhj1FgSo4ndWBKjsZtiviSmqimmGjV2jT3R2KIxFuwoYMFCEQWlSBXpZdt8fyxsQmy0LcD7e559lN3ZuWcVhpk5957z4d6buJ1RAAdLI3w7snp9YZ6mW3NbfDJEed369ZFYHL5e95L5dVVmfikmrA/DsqO3oBCA4e2b4sDMbvB2VPZGszU3wlcvt8We6YFo3cQS+SUyfHwgCi/8dBZhXL1P9AgmYoh0QEpOMd7ccgnj1oUhPrMQtuaG+GZEW/w+LRCtm1R9Oba9hTHeLrsZ8tWRWGTmN8wZZdWRXyLFzO1XMGfnVfx64R5m77iK9p8fxSu/hGL16Tu4nVGgkxcwSdlF+PRAFAIWH8fiQzG4n1sCW3NDzOvXAuff643FL/o99qbB8PZN0NvbHhK5Agt+i6xTdbnPxmUhJDYT+mIR3htQe/0IXBqZ4rdpgZhS1g9k/bkEjFh1HknZDeNmAClLO4bGP4CJgR6+erlNrV9M1lSbplbwcbKERKbAnssp2g6HGrC/b6Zhz5UUiEXANyPbwthAd0qS/ZdIJMKnQ5WNfwtKZZi8KZzlMyopIasQp29lKldpd3Gr0b66ednBwlgfGfmliKhD9eQfFkrw+6VkALUzS1+dBrZ2go+TJfJLZVh9WvdWPP95LRXLjirLZH06tDUCvWxVr3Vws8HuNwPRxNoEdx8U4aWV53A9OVdboapdQakM684moOc3JzFj22VcScyBoZ4Yr3R0wd9ze2DTpM7o3tyuxiXw3n3eG4Z6YpyJy8LJW5m1FH3tkysErD+nLP83qZtHrZ1/tWlqrTp2fbj3BidY1bLfLyVj9+VkiEXAj6/6P9LHtraM7eqGiWWTo+buulqvjw264tztLAz44QzO3s6CiYEevhnRFstGtoWp4aO90dq72mDfjG744sXWsDY1QExaPkb+Eoo5O67UqR5VROrGRAyRFpXK5Fh+Ig59lp3E4Rtp0BOL8FqQO47P74mXOzSt0cnnmC5u8GtStgz7EJdhV8aNlFy88NNZHLyWCn2xCMPbN0ULB3PIFQIuJmRj8aEY9P32FHp9cxKfHojCudtZWj+Rv5z4EDO2Xkbw1yFYfy4BhRI5WjiY46vhbXD23d6Y1af5U0+GRSIRlrzkB8uyFVS/nKobS73lCgGf/xkFQHlSXttlowz1xfjghVZYO74jrEyUMzMH/niGs68agMQHRVhyKAYA8N4Ab7jbmmk5okeJRCKM6uwCANgRnqiTyWGq/x4WSvD+HzcAAFN7eKK9q42WI3o2Q30xVo5RNv5NfliMab9eQqmMjX+f5dey1TC9WtrDtbFpjfZlqC/Gc60cAQB/Xrtf49g0ZevFeyiRKuDrbImunrpXfu/fxGIR5petitlw7q5OJRyvJedg/m9XASjLu43u4vrINs3szPHH9ED4OFkiq0CCV1aH4pQOJw+qIyWnGIsPRSNg8XF8djAKyQ+L0cjMELP6NMe593rjy5fboIWDRa2N59rYFBMClYmIxX9G62zJoL9vpiEpuxg2pgYY3r5pre57wXMtYWtuiDuZhVh7VvcSlHVVXHo+PtyrPBeY27cFutRChYKn+WCQD3q0sEOJVIEpm8ORlssb/OogVwj49ugtjF13EVkFpWjpYIH9bwXh5Q5P/7nUE4swposbQub3xOgurqqVaL2+OYk1p+Pr1KRPInURCWq6em/fvn3VAhGJsH//fjRpUvVGy+qWl5cHKysr5ObmwtLSUtvhUD0REpOBTw7cxN2yJfedPRrhkyG+8HGqve+xyKQcDFtxDoIAbJ/aFQHN1HtiVFcJgoDNoffwxZ/RkMgVaGJtgp9G+6tuKiVlF+FETAaOx2Tgwp0HkPzrBMLcSB89Wtiij7cDera0U9sMoH+TKwT8fTMNa87E43Jijur57s1tMaW7J3o0t63yzLk/riRj7s5IGOiJsP+tbrX6fagOu8KT8M7ua7A01sept3vBppK1sqsjJacYs7ZfwaWymbvjA9zw/kAfnZ75TdWjUAh4dc0FhCVko6tnI2yb0lXnVsOUyy2WosviYyiRKrD7zUB0cNP9m+BUv8zafgX7I+/Dy94cB2d2q1PHxNsZBXhxxTnkl8jwkn8TLBvZVqearuuSIokMXRYfR36JDBte64ReLWteKigkJgOvbQyHnYURLizsAz0dPc6WK5XJ0e3LEGTml+L7V9phmL/uXa/+lyAIGLbiPCKTcjApyAMfDW6l7ZCQlluCIcvPIiO/FL1a2mHthE5P/b/PL5Fi2pZLOHf7AfTFInz1chu8VMs35zXtWnIO1p5JwJ/XU1V9PJvZmWFyN0+81L6JWo+juUVSBH8TgpwiKT4f1hpju9ZsdZs6vLTiHC4n5mBmby/Mf672S13uuZyMebsiYWwgxtG5wXBpVLPEckNXLJFj6M9ncSu9AN28bLFpUmeNHM/zSqQYvuI84jIK4NfECrveCKhRyUyqKD2vBLN3XMGFeGVZsVc7uWDRYN9q/RtfS87BR/tu4mpSDgDAy94cnwzxRdC/VkIS1QdVyRuoLREjFosxf/58mJs/e5ayIAhYunQpoqKi4Ompe0u9mYih2pT4oAifHryJY9HKRrH2Fkb43yAfDGnrrJabAB/svY4tFxLhZW+OQ7O6w1CfC+H+LbdYind/v4a/bqYBUPYa+frltrAyNXjs9gWlMpyNy8KJmHSciMmsMMtQJAL8XazRx8cBfXzs0dLBolb/TwtKZdgVnoQN5xOQlF0MADDUE2NoO2dM7u6hqtNaHYIg4PVfL+FoVDpaOVli31tBMNDTze+VwlIZen5zEpn5pfhgkI9GSoRI5Qos+/sWVpWtGPJ1tsTy0e3hoYOrJeo6mVyBlJxixGcVIiGzEEUSmcbGvvugCL9fSoapoR6OzOmh8xfo83ZdxZ7LKRjRoSm+HtFW2+FQA/LXjVRM23IZemIR9rwZiLYu1toOqcrOxmVhwoYwyBUCZvb2wqw+zXX29542bQ9LxMI91+HW2BQh83vWSnJaIlOg4+dHkVciw47Xu9ZKjzd12hWRhHd+vwZHS2OcebdXnfk+OROXiXHrwmCoL8apt3vCycpEa7EUSWQY+UsobqTkoYWDOXa/GQgL48efa/+bRKbA279HYt9V5eqpd5/3xrRgzzqXOD0Zm4EVIXcQdveffgmBzRpjandPBLew09ikj43nEvDxgSg0NjPEybd7Vur/QFMu3XuI4SvPw1BPjLPv9YK9hXGtjyEIAl5dfQEXE7LR18cBayd0rPUxGpJ3f7+GnRFJsLMwwqFZ3WFnof4JieUSHxRh2IpzyC6UYKCfI5aPaq+zk6fqklO3MjFv51U8KJTAzFAPi1/yw9B2NZt8oFAI+P1SMpb+FYPsQmXfskF+TvjfIB84W2vv95KukckV+OtmGq4n52LhQB9th0NVpDOJmLS0NNjbV27WlIWFBSIjI5mIoXqrRCrHypN3sPLUHUhkCuiLRZjUzQMze3up9SQ4t0iK3stO4kGhBO8+7403ezZT21h1zdWkHLy17TKSHxbDQE+E9wf6YGKge6Uv7hQKAddScnEiOh3HYzJw835ehdebWJugt7c9+vjYo6tn42rPckvNLcbGc3exLSwR+SXKm9I2pgYY29UN4wLcau1CJSO/BM99dxo5RVLM6dscc/q2qJX91rZvj97Cj8fj4NrIFEfn9ahW49LqOhmbgXm7IpFddnK6ZHgbDGnrrLHx6wtBEJBZUIqEzEIkZCkf8VmFiM8sQGJ2EaRy7Zba0tWZov8VcTcbL68KhYmBHsL+10enbqhQ/fWgoBTPfXcaDwolmNGrGd7uX3s9ujTt1wv3VCVVHC2NMSHQHaM7uz5xMkZDIwgCBvxwBjFp+bU+8eHt3yLx26VkjOvqhs+Gta61/dY2QRDw/PdnEJuej/cGeGNacN05jxYEAa+sVq7yHNPFFV+86KeVOBQKAdO3XsZfN9PQyMwQ+2YEVWmig0IhYOlfMap+NxMC3PDRYF+dX0kFAPceFOLTA1E4HqOcgGegJ8Lgts6Y3M0Dvs5V7wNaU1K5Av2/O434rEJM79kM7zyvO8fv6Vsv4dD1NLVPLolLz8eAH85AphCwZnxH9GvloLax6rO9V1IwZ+dViETA1sldKvR60pSwhGyMWXsBUrmgtlVUDYVMrsCyo7ew8qRy0qGPkyV+Hu1fq+W/c4uk+PZoLH69cA8KATAx0MPMPl6Y3M1Do9fzuia/RIqd4UnYcO4uUnKUk22PzesBL/vaK09J6qcTiZh79+7B1dW10jc0k5KS4OzsDD093fsBZCKGakIQBByNSsenZfV/ASDIqzE+GeKrsYPr7kvJmP9bJEwM9HB0Xg80tdHtWd7qJggC1p1NwNLDMZApBLg2MsXy0f5o09S6RvtNzS3GiZgMnIjOwNnbWSj9V/8YEwM9dGtuiz7e9ujtbQ97y2cnT64n52Lt2Xj8eS0VsrLyBZ62ZpjUzQPD2zdVyxLs/ZH3MWv7FeiLRdg7Iwitm2j+IvFp0nJL0PObEJRIFVgxpj0G+jlpJYZZO64gLEE5q3FUZ1csGtyqTpXl0ZT8EinuZhUhPqvgn4RLWfKloPTJK10M9cXwaGwGd1tT2Jiqr+zc43jYmuH1HnVjtq0gCOj33WncziioM8kjqvtmbLuMP6+lKuuFzwyq8xfPm0Pv4qcTt5GZr1zhamqoh5EdXfBakDvcGjfsVY/hd7MxYlUojA3EuLiwb60mqE7GZmDihnDYmhvi4vt9dfam+ulbmRi/PgymhnoIXdgHViZ1K0kXlpCNkb+EQl8sQsiCnlpZ6fn1kRj8HHIHhnpibJvaBR3dq9djZ93ZBHx2UNkfcKCfI74d2U5nz72KJXKsPHkbq07HqybgTQh0x9TunnC0qv2VHlXx9800vP7rJRjqi3FifrBOXBcmZRch+OsQKATgrznda7TKvzKWHo7BqlN30MTaBEfn9Xhs43F6sjuZBRj801kUSeSY3ac55vbT3uS93y8lY8FvkQBQZ0pH6pr7ZWW4I8rKcI/r6ob/DVJfGe6o+3lYtP8Gwu8qx/OwNcOiwa3QsxZKn9YlyQ+LsPHcXewIT1JdFzc2M8TYrm6YGOiu1tLrVPt0IhFTnzARQ9WVkFWIj/ffVDWYdLIyxgeDWmGgn6NGb/L9e0Zcv1YOWDO+4S7DflgowYLfIlUz0wb5OWHJcD9Y1vJM8mKJHOfvZOF4WWImLa9iI8E2Ta2Uq2W8HdC6iaXq+0GhEHA8JgNrz8TjYsI/5Qu6ejbClG6e6O1tr9Zl14KgnLV4+EYavB0tsP+tbjpVzm7Bb5H4/VIyOrrZ4LdpAVq7WS6TK/Dj8Tj8FHIbggB4O1pg+ej28LKvvVlDdYVEpkBidlFZokWZcLlTlmwpv7H5OCIR0NTGBB625vC0NYOnnRk8bJUPZysTlheopLVn4vH5n9Fo3cQSB2d213Y4VM8dvHYfb227Aj2xCHunB8GvqW4l66urVCbHgchUrD0Tj5i0fADKY1T/Vo6Y0t0DHdxs6kRytra9te0yDl5LxaudXLB0eJta3bdUrkDHz48ht1iKbVO7ILCZbtaLH78+DKdvZWJioDs+HuKr7XCqpfwzDG/fFMtGaraMZXlPDgD4dmTbGvd4ORB5H/N3RUIiV6CzRyOsGddRp1awCYKAIzfT8dnBKNXM5m5etvh4iK/OnCP+uzzX0HbO+OFVf22HhE8O3MSGc3fRvbktfp3cRe3jFUlk6PftaaTkFOvcyiBdVyKVY9jP5xCTu6sKAwAAcxBJREFUlo8Az8bYMqWL1hPp5Yk1Q30xtk/tyr6JVXAiJh3zdkUip0gKCyN9LB3eBoPaqH+ioyAI2Hs1BYsPxaiuF59r5YAPX2il86Wha+pqUg7WnonH4Rtpql5hXvbmmNLNA8P81dsrjNRH5xIxnp6eCA4OxqpVq2Bk9E/dyKysLHTu3Bnx8fHqDqFGmIihqiqRyvHTiTisOZ0AiVwBAz0Rpnb3xFu9vbQ24+ZWej4Gli3DXju+I/o2wGXYEXezMWv7FdzPLYGhvhgfvdAKY7pUfuVedQmCgKjUPByPzsDxmAxEljWrK+dgaYTe3vZwb2yGHeFJSMgqBADoi0V4oY0TpnT31OjKlKyysjPZhRKdWuZ9IyUXg5efhSAAe2cEoZ0O9CQ4G5eFOTuvIqugFCYGevh8WGsM71C3G8k+iSAIuJqUgxv385CQWaha5ZKUXQTFU84kbM0NVQkWTztz5Z+2ZnBpZMoTzVqQXShB18XHIZErcOCtbjp3Yzw2LR+OVsZ1bhZ5ZWUXSpBXLIV7A+gXlZlfiue+O4WHRVLM6u2FeTryu6E2CYKAc7cfYO3ZeJyMzVQ9387FGlO6e+B5X0fo15H+IDWVkVeCwKUnIFMI+HNWN7WUUSrvL6DNsllPE5uWj/7fn4ZYBJxc0AuujevmzaHIpBwM/fkcxCLg77nBGksIRNzNxug1FyGRK2r1Zvf5O1l4Y/Ml5JfK0MLBHBtf66wTfQbiMwvw8YEonC6bgOdsZYwPX2iF51trdgJeZejSOXVusRSBS46jUCLH5kmd0aOFnUbGLV8ZZKAnwuHZ3XWuDFBWQSky80vh46Rb95/e/+M6tl1MhK25IQ7N6l6pKg/qplAIeGOLsteprbkh9s4I0omVXrpMIlPg6yMxWHMmAYBykujyUe01/nsuv0SKH47FYcP5u5ArBBjpizG9pxfeCPasV9eJcoWySs66s/GqlUCAMlE/ubsHgptrrlcYqYfOJWLEYjG8vLxgbW2N/fv3w9HREQCQnp4OZ2dnyOVydYdQI0zEUFWVX1gCQHALOywa3KpW62tW15LD0fjlVDya2pjg6NxgtZS20kUKhYBVp+9g2d+3IFcI8LA1w/LR/lqpzQwoe7GcjM3EiegMnInLRKGk4jHQ0lgfo7u4YUKgm9Yaq/55LRUztikbMf8xPbDGZdtqShAEjF5zEaHxDzCkrTN+HKX92XvlMvJLMHfnVZy7/QAA8EawJ97t712vTqbkCgGfHYzCxvN3H/u6qaHeI4kWD1szuNua1dsb8Lpk5vYrOBB5H6O7uGKxjtzMFAQBXx2JxcqTd2BtaoC3+7fEq51ctT5rsrZIZAqsP5eAH4/HKXvAje2A/r6O2g5LbQRBwLQtl3DkZrpOrpZUh7j0fKw7m4A9V1IgKSs12sTaBK8FueOVTi71vifT98du4ftjcejoZoPf3wxUyxjlZb8amxni4vt9dC7JVd7HZkBrR6wc20Hb4dTI1M0ROBqVjhfaOGH56PZqHy8puwjDfj6HB4USPO/riBVjareRdnRqHiZuCEN6XikcLY2xaVJntHTUzo30wlIZlofcxtoz8ZDKBRjqifF6D09M79VMp0tezdt1FXsup6CTuw12vaG9VearTt3B0sMxaOlggb/mdNdYHIIgYMqmCByPyUCAZ2Nsm9pFJxJmEpkC684m4KcTcSiSyOHvao0p3TzR39dB68fIA5H3MXP7FYhEwOZJndG9uWaSZpVRWCrDy6tCEZ2ah5YOFtg9PRDmRrr786dNSdlFmLn9Cq6WTQ59Lcgd7w3w1mqp2Vvp+Vi07yZC45XX066NTPHRC63q/OThwlIZfr+UjPXnEnDvQREAZa+wIW2bYHI3D7Ry5v3l+kLnEjF6enqIi4vDggULcPHiRezduxedOnViIobqpcz8UgQtPQGJXIEfXm2HIW2ddeKkDlAuw+677BTu55bU+Qa7lfWgoBTzdkWqysMNbeeML17005kTs1KZHBfjs3EiJgNxGfno5+OAER1dYKYD8ZX3AWjhYI4DM7tp9eTsWFQ6pmyO0Kl61v8mVwj48XgcfjgeBwB40b8Jvhzepl7cqCyRyjFv11Ucup4GAOjV0g5e9ubwsC1LutiZwd7CSGeOcw3R+dtZGL32IsyN9HHx/T5aP35I5Qq8u/sa9lxOqfC8XxMrfDLUF+1d63bJiDNxmVi0/ybiMwtVz5kY6OH3NwO0luBXt31XUzB7x1Xoi0XY91ZQvf2cj5NVUIotF+7h19B7eFAoAQCYG+nj1U4ueK2bB5rowEz82iaVKxC09AQy8kvxw6vtMLSdemruS+UKdP7iGB4WSbF1ShcEaaHZ85Nk5Jeg29IQSOQK7H4zsM6XuolOzcOAH84AAA7P7q7WWfb5JVK8vDIUsen58HW2xG/TAtSSkEjJKcaE9WG4nVEAS2N9rBnfEV08G9f6OE8iCAL+vJ6KL/6MRmqusgRxz5Z2WDTYFx51YJVkam4xen1zEiVSBVaNbY/nW2u+76JUrkD3L0OQlleCr15ug5EdXTQ6flJ2Efp9dwolUoVO9Bc5fSsTH++/ifiswkdea2pjgteCPPBKJxetXMfezSrECz+dRUGpDG/18sKC/rq3KvZ+TjGG/nwOmfml6ONtj9XjO9abCUC15cjNNLz9WyTySmSwNNbH1yPa6sxEIkEQcPCa8phaXta9t7c9PnqhVZ1beZ6WW4JNoXex7WIicoulAAArEwOM7eqKCQHuOrGSjGqXziVixGIx0tLSYG9vj4ULF+KHH37A6tWr0a9fPyZiqN5ZfiIO3/x9C21drLFvRpC2w3nEkZtpeEO1DLuHztQrVocL8Q8we8cVpOeVwthAjE+G+GJkRxfeMK6k7EIJnvvuFLIKJFqtnyyVK9D/+9OIzyzEtOBmeG+A7iYQd19Kxru7r0GmENC9uS1Wju2gM0m/6sgtlmLq5giEJWTDUE+Mb0a2xZC2ztoOi/5DoRDQa9lJ3HtQhK+Gt8HITpq9kfFvBaUyvLnlEs7EZUFPLMLnw1qjRCrHt3/fQn5ZI8qRHZvinee9YWtu9Iy96ZaUnGJ8fjAKh28ok5K25oZ493lv7Lt6H2dvZ8HJyhj7ZgTVu4urjLwS9PvuNHKLpZjbtwVm922u7ZC0okQqx94rKVh7NgG3MwoAAHpiEQa0dsTU7p5oqwPlMmtL+apYW3MjnH+vt1onFSzccw3bw5IwqrMrlrykGyv6AGDZ37H46cRt+Lta44/punc+Xx3lPX/U2S9SrhAwdXMETsRkwN7CCPveClLr6u6cIgmmbIpAxL2HMNQT4/tX22Ggn/oTCnHp+Vi0/ybO31HO3nZpZIKPXvBFXx/7OnWdUf597tbYFEfnBmt8AtHeKymYs/MqbM2NcO69XlqZ9PVzyG18fSQWtuZGOD4/WCsruZMfFuHzg9H462b5+YURFg7wRvfmtsqJABfu4WGR8mauhZE+RnVxxYRAd41NBCiRyjF85XncvJ+Hzu6NsG1qF62vznmSq0k5eOWXUJTKFJja3QP/G9RK2yHphFKZHEsOxaiqG/i7WuOnUf46N7kRePIqwxm9vHS+osvN+7lYdyYB+yPvQ1ZWu9u9sSkmd/PA8A5NdXqVJNWMziVi9PT0kJqaCnt7ewDAli1bMHXqVIwaNQqbNm1iIobqDZlcge5fhSA1t6RWGlKqgyAImLxJeYEU2Kwxtk7RjWXYtUmuEPBzyG18f+wWFIKy+dnPo9trrWRBXfbXjTRM23IJYhGwZ7p2akhvDr2Lj/bdRGMzQ4S83ROWOl4O5mRsBqZvvYwiiRy+zpbY8Fon2FvUvRuzqbnKmaa30gtgYaSPX8Z30NlmygSsOHkbX/0Vq9Wbhpn5pZi0MRzXU3JhYqCHFWPao5e3veq1L/+Kwe+XkgEoSzDOf64lxnRx1dmL+XIlUjnWnonH8pDbKJEqoCcWYXyAG+b2awFLYwPkFkvx4opziM8sRFsXa+x8vWu9qWstCAKmbr6EY9Hp8HW2xN4ZQTDQ8f8vdVMoBJyKy8S6Mwk4eztL9XwndxtM7uaJfq0c6vwM3JG/hCIsIVsjvYDOxmVh7LqLsDE1QPj/+urE8aBYIkfg0uN4WCTFijHtNXJjXxPuZBag37enoFBjX5DPDkZh3dkEGOmLseuNAI0kKEukcszafgV/R6VDJAI+HuyLCYHuahmrvJ/BxvN3ISvrZ/Bmz2aYFtysTh73C0pl6Pn1SWQVlOKDQT6Y0t1TY2MLgoDBy8/iRkoe5vdrgZl9tJPkL5XJMeCHM4jPLMSEADd8MrS1xsYukcqx+nQ8Vpz85/xiQoA75vRrXuF6p1gix54ryVh3NkG1GldPLMIgPydM6e6h9hLSH+27gc2h92BjaoBDs7trrXR2ZZWXUAOAL4f74ZVOrlqOSLvuPSjEW9uu4HpKLgDgjR6eWNC/pc6fz93JLMDH+2/iTJzyXKuJtQk+GOSjc323FAoBJ29lYM3pBFVpNQDo7NEIU7t7oo+3fb0qWU6Pp3OJmH+viCkXGhqKF198EZmZmUzEUL3x141UTNtyGY3MDHH+vd46e0Ke+EC5DLtUplBryQlt+G+/jpc7NMWnQ305+6AGZu+4gn1X76OZnRn+nNVdo9/XucVS9Pw6BA+LpPhsWGuM6+qmsbFr4lpyDiZtDEdWgQQujUyw6bXOOtEnqrJupedjwvowpOaWwN7CCBtf68watjouI78EgUuUjbWPzOmh8cRzQlYhJqwPQ2J2ERqZGWL9xE6Pvcl36d5DLNp/AzdS8gAA3o4W+GxYa3Ryb6TReCsrJCYDHx+4qarr3NmjET4d6gtvx4o/D3ezCjFsxTnkFEnxQhsn/DTKX6cuEqtrw7kEfHIgCgZ6IhyY2e2Rz93QRd3Pw7qzCdgfmQKpXHlJ5dbYFJOCPPByh6ZaLxNYHTFpeXj++zPQE4tw7t3ecLRS70QCmVyBzouPI7tQgl8n60bPgS0X7uGDvTfg0sgEJxf0qvOJtX9b8Fskfr+UjO7NbfHr5C61uu/tYYlYuOc6AODn0e0xqI3mElhyhYBF+29gy4VEAMCbPZvhnf4ta+04LAgC9l5NweJDMcjMLwUA9GvlgI9eaAWXRro3o7wqdoQl4r0912FlYoBTb/eEtamhRsYNvfMAo9ZcgLGBGOff64NGZpoZ93HO3c7CmLUXIRYB+2Z0g19T9ZffPB6djk8ORCExW3l+0cWjET4d2vqp528KhYCQ2AysPfOfG77ujTCluwf6+NT+RIBD11MxfetlAMCG1zqhV0v7Z7xDN5T3OdMXi/Dr5C4IaKa5soW65OC1+3hv93UUlMpgY2qAZSPbord33em7IggCjtxMx2cHo5CSUwxAWQ7Yvaw8dnlfUmWPUnNYmWpuwmaJVI7dlx9NkL7QxgmTu6k/QUq6RecSMU+Snp6OmJgYBAcHV/o97u7uuHfv3iPPT58+HT///PMjz2/cuBGvvfZaheeMjIxQUlJS6TGZiKHKGr3mAs7feaDVMk6VVV5CzdbcCCcWBOv8KoPKOBuXhTk7ryKroBQmBnr4fFhrDO+ge6uS6pqHhRI89/1pZOaX4o0enlg40EdjYy85FI1fTsfDy94cf83urhMzZSvr3oNCjF8fhnsPlDem103oCP860BsjLCEbUzaFI69EhmZ2Ztg0qbNOLlunR73xawSO3EzHxEB3fDzEV2PjXk1SJh6zCyVwbWSKTZM6P7U+vlwhYHtYIr4+Equqm/yifxMsHOCtM2W9Eh8U4dODN3EsOgMA4GBphPcH+jy171vonQcYt+4iZAoBc/o2x5y+LTQZcq3bFZ6Ed3ZfAwC8N8Ab04KbaTki3ZWRp6wFvuVCxVrgo7soa4GrO5lRm97/4zq2XUzEQD9HrBijmQb15WOO6uyCJS+10ciYT6JQCOj77SnEZxXioxdaYVI3D63GU9uSsovQ65uTkCkE7Hy9a631VDl/Jwvj14VBphAwr18LzNLC6gZBUK6I/+bvWwCAl/yb4MuX29R41nfU/Tws2n8D4XcfAlCWmVk0xLfO3JB+FrlCwKAfzyAmLR+Tgjzw0WDNlHKavDEcx2MyMKaLK754UftlCWdtv4L9kffRtqkV9kwPUlsC9t6DQnx6IArHY/45v/jfoFYY3MapSonDGym5WHc2AQfUWAIp8UERBv14BvmlMrwR7ImFAzR3DVhTgiBg1o6rOBB5H9amBtg7PajO9RmpiRKpHJ8djMLWi8rkdCd3G/w4yl/nVzM9SbFEjpUnb2P1mXiUSBVP3K6RmWFZUsYMHqpEjTncGpvW2mTSzPxS/Bp6F1suJiK7rHeghbE+RndWlgx0roe9A+nZ6kwipjr+u4Lmxo0b6NevH0JCQtCzZ89Htt+4cSNmz56N2NhY1XMikQgODpXPAjMRQ5VxOyMffb89DbEIOPNub51v3loqk2PA92cQn1Wo8Zt2tU0mV+CH43FYHnIbgqCcYb18dPt63f9G045GpWPq5giIRMDv0wLQwU39s9eTsovQZ9kpSOQKrJ/YsU7N3imXVaAs1XQtORfGBmL8PLo9+vjo7uc4fD0Vs3dehUSmQAc3G6yb0FFjMyOp5k7GZmDihnBYmRjg4vt9NLJ6LSRGWYqvWCpH6yaW2DCxM+wsKtf7JbtQgq+PxGJHeCIEQdkAfU7f5pgQ6K61cgklUjlWnLyDVafuQCJTQF8swuRuHpjZp3ml+j3tDE/Eu7uVM8J/GuWPwXW0p9K+q8q6/YIATO7mgQ8G+dSLFT7qViSRYfcl5ezIu2WrqPTFIgxp64zJ3T3g66z+WdY1kVciRdfFx1EkkWP71K4am0F8/nYWRq9VlicL+19frZZLORaVjimbI2BhrI/QhX3qdJ+3J/nfH9ex9WIiOrs3ws43utb4Zzs+swAvrjiP3GIphrZzxvevtNPq8eK3iCS8t+c65DXs15dbLMV3R29hc+hdKATlLOy3enthSncPrfQyUafTtzIxfn0YDPRE+Htu8FMnU9SGO5kF6LPsFADgxPxgnVg1npFXgj7LTiG/VIbPh7XG2FpehV9+I3nV6fh/zi+6e2Bm78qdXzxJWm4JNp6/i20X7yGvRNmLz9rUAGO6uGJ8gDscqjnBpVQmx4hVobiWnIsObjbY8XpXnS9l9V8lUjleWX0BkUk58LQzwx9vBml0xYS23MkswIytlxGTlg+RCJjesxnm9m1RpyY0PolUrkBSdhESsgqRkFWI+KxCxGcWICGrEOl5pU98n0gEOFuZVFxFY2cOT1szOFubVCrxGpuWj3Vn47H3yn1I5MpkUFMbE0wK8sDITi718nyBKk9nEjH+/pUry3D58uVqjzFnzhwcPHgQcXFxjx1r48aNmDNnDnJycqo9BhMxVBnltVOfa+WA1WpqgFnbyutyi0XA/re6oXUT3b5B8DhpuSWYteMKwhKyAQCjOrti0eBWOlsWri6bt+sq9lxOgYetGQ7N6q72ZnnlTWW7edni18md6+xNwMJSGWZsu4yTsZnQE4vwxbDWeLWz7tUq3nT+Lj4+cBOCADzXygE/jvLnz1EdI1cI6PFVCFJyivH9K+0wzF+9ZSd3RSRhYdnNrh4t7LBiTPtqXYREJuXgo/03EZmUAwBobm+OT4b6arQnkSAI+DtKWfog+aGy9EE3L1t8PMS3ykn9zw9GYW1Zj4SdbwRopbdWTfx1Iw0ztl2GXCFgdBdXfDGsdZ09/mqLQiHgeEwG1pyJV52fAECAZ2NM7eGBni10s154eSm6Fg7mODKnh8b+32VyBbouOY6sAgk2T+qMHi20V57slV9CcTEhW+MrgDUpNbcYwV+fhESmqHE5uNyish5ZWYXwd7XG9qm60SMrJDYD07dUb5KAQiHg98vJ+PJwDB6UzXYe6OeI/w1qpfMT7WpiwvownLqVif6+DvhlnHqvZctXwfX1scfaCZ3UOlZVbDyXgI8PRMHSWB8nFvSErXnlvmee5nGllap7fvE0haUy/F42EaC83JmBngiD2zpjSjfPKpcY/uTATWw4dxdWJsq+MHX1ez8jrwRDfz6H1NwSdPOyxYbXOtW5hFJV7L2Sgvf/uI4iiRyNzQzx3SvttPo7VZMKS2WqBI0qUZNZgPisQuSXJSkfx1BPDLfGpvC0U66eKV9N42FrhsZmhjgTl4W1ZxNw+lam6j3tXa0xpbsnnmvlUC8SXFRzOpOI+eSTT1R/FwQBS5YswbRp09CoUcWZ1IsWLarW/iUSCZydnTFv3jy8//77j91m48aNmDJlCpo0aQKFQoH27dtj8eLF8PV98uz/0tJSlJb+k03Ny8uDi4sLEzH0RPllMwgLJXJsmdwF3ZrXnYbWM7dfwYHI+2jrYo09bwbWqTrYJ2MzMG9XJLILJTAz1MOS4W0wpI7OPq4LcoukeO77U0jPK8Xkbh748AX1lS64dO8hhq88D5EI+HNm9zrfn0QqV+D9PdfxW1mj8rl9W2BWHy+duLkpCAK+OhKLlSfvAADGdHHFp0Nb16ljAf3jh2Nx+O7YLXTxaISdbwSoZQxBELD8xG0sO1pW/qV9E3w5vGblXxQKAb9dSsKXf8Wqlvm/0MYJ/xvko/YyCglZhfh4/02cKrvAcrYyxgcvtMKAajYDlSsEvL45AsdjMmBrboT9bwXVmTIFJ2MzMHVzBKRyAS/5N8E3I9rqZMKgLrmWnIO1ZxLw5/VUyMvKx7RwMMeKMR10auXuv0tyaaMn2wd7r2PLhUS80tEFX76snfJk15NzMXj5WeiLRTjzbq86W8KlMj47GIV1ZxPQtqkV9s4IqtaxTipXYOKGMJy7/QBNrE2wd0ZQpZMdmhBZVjbzQaGyX9/mSV2eudLjenIuPtp/A1cScwAAzezM8MmQ1nXq2q66bqXn4/nvT0MhoFbL1v1XdqEEAUuOo1SmwI7Xu6KrmsapDplcgaE/n8PN+3kY3r4plo1sW6P9Pa7Z+Icv+KC/r/qajcsVAo5GpWPd2XhVOT0ACPJqjCndPBHcwu6Zv9eP3EzDG79eAgCsHd8RfVvp7mr+yrh5PxcjVoWiSCKHt6MFWjlZlq2IUPYVcbc1rXP9ZIsk/0o6ZCr/jMsowPWUXABAV89G+PFVf50p+atNgiDgQaFE9W8Vn1WIhKwCxGcW4t6DItXqlscxNhCrSqGJRcDzrR0xuZsnOrjpfrlx0iydScT8l4WFBSIjI+Hp6Vkr+9u1axdGjx6NxMREODs//uZraGgo4uLi0KZNG+Tm5uKbb77B6dOncfPmTTRt+vjeER9//HGFJFI5JmLoSTaH3sVH+27C084Mx+cF68TN1crKyCtB72WnUFAqwxcvtsaYLrrfDF0qV2DZ37ew6pTyxrGvsyWWj26v9mX0pCxD9NrGcIhEwM7XA9DZo/ZLlAmCgJdWnseVxByM7NgUX71cs4sgXSEIApb9fQvLQ24DUK7e+myor1Zn0UjlCry7+xr2XE4BACx4rgVm9NKNBBFVT2puMYKWnoBCUE+5D7lCwEf7bqhqTk/v2Qxv12JD5NwiKZYdjcWWC/egEABTQz3M7N0ck7t5wFC/dn9WiiQyLD9xG2vPJEAiV8BQT4zXe3hieq9mNb4gLyiV4eWV5xGTlg8fJ0v8Pi1A5xu3h955gIkbwlAqU2CQnxN+eLUdZ/nVovs5xdh4/i62X0xEfqkM1qYGWDeho0ZKfVbGmbhMjFsXBnMjfVx4X/Mlucobd1uZGCDiA+2UJ5u94wr2Xb2PYe2c8f2r/hofX5OyCkrR/csQFEvlWDO+I/pV8UarIAj4YK/yd4GpoR52vxkIHyfdu06+m6Xs15eYrezXt35ip8euUnxYKMHXf8die5iyVKaZoR5m922OiYG1/7tHl5WvVGnT1Ap7pwepJRH/4/E4fHv0FvyaWGH/W9VLAqrTlcSHeGnleQg1SEgVlsrw04nbWHc2HlK5AEM9Md4I9sT0nl5qryjwb1eTcrD2TDwO30hTTQTwsjfH5G4eeNG/yWNXryVlK/vC5JXIMKWbBz5Q48Q7Tfr7Zhqmb72s6qfzX05Wxqqm7x6qRvDmaGpjorVzIalcgeSHxaqkgXKFh/LPtLzH97wWiYBZvZtjVp/mnFRXCXKFgPs5xcrkTFmJs/iyf+f7ucWq3wcjO7lgUpAHXBqxbyo9XoNJxPTv3x+GhoY4cOBApd8jlUrh4+ODUaNG4bPPPnvsNlwRQ1UhCAL6fXcatzMK8PHgVpgYVPeaepaXorAyMcDx+cG1sgxbXVJyijFr+xVcuqec4TM+wA3vD/TRiTIIDcXbv0Xit0vJcG9sikOzu9f6DKKD1+7jrW1XYGKgh5Nv96x2bWNd9euFe1i07wYUAtDXxwE/jfLX6EVZuYJSGaZvvYzTt5Ql05a85IeRHV00HgfVvkkbw3EiJgOv9/DE+7VYWqdEKses7Vfwd1Q6RCLg48G+mBDoXmv7/7eb93OxaN9NRJQd6z1tzfDxEN9aKa8gCAIOXU/D539GITVXeSHbs6UdFg32rdWEfvLDIgz7+RyyCiTo18oBv4ztoLOrSy7de4hx6y6iSCJHH297rBzboUHdfNSkBwWlmLwpAleTcmCkL8by0e2rfBNcHaZsisCx6HSt9Q2UKwR0WXwcWQWl2PhaJ/TUcCP0+znF6P5VCOQKAQdn1s1yvVX11V8xWHHyDrwdLXBoVvcqHZ/Krx1EImDNON2eMZ+ZX4rJm5T9+kwM9PDzGH9V30G5QsCO8ER8fSQWOUVSAMDQds54f6BPvTv/rIzM/FL0/DoEhRK5Wkqclkjl6PblCWQVSPDDq+0wtJ16S6hWV3lCqrm9OQ7N7l7pxLAgCPjzeiq++DNadX7Rq+z8QpuN4pMfFmHjubvYEZ6EglJliabGZoYY29UN4wLcVNf+EpkCI38JxdWkHLR1scZvbwTUq3OB5IdFuJac+6+EhvLG+8Oyn/3H0ReL4NrY9J++Irbm8LA1QzM7M9hZGNU4kSgIAjLyS/+VaClQrXRJzC56YuII+KcxvSp5ZGuG1k2smCyoJSVSOZIfFsHRyoT9X+iZGkQi5t69e/D09MSePXswdOjQKr13xIgR0NfXx/bt2yu1PXvE0NOUNxg1NdTDhff7wNK47jWAk8kVGLL8HKJS8/Byh6b4ZoRurkA4GpWOBb9FIrdYCgtjfXw1vA0G+DlpO6wGJ7dYiue/P43U3JJav2FTKpOj77enkJRdjDl9m2NO3xa1tm9dcuRmGmZtv4JSmQLtXa2xbkIn2JgZamz8zPxSTNoYjuspypsSK8a0Ry9vzd70IvX5+2YaXv/1EhqbGSJ0YZ9auYjOKZJg8qYIXLr3EIb6YvzwSju1H38FQcAfV1Kw+FAMsgqUE2T6+zrgwxdaoalN9S4yb2fkY9H+mzh3+wEAZZPNRYN90dfHXi2zci/de4hRay5AIlNgWnAzvDfAu9bHqKkbKbkYteYC8ktk6OZli7UTOnJyg5oVSWSYue0KjsdkQCwCPh/mh9FdtNc7LCm7CMFfh0AhAMfmBWutZNqHe2/g1wv3MKJDU3yt4XPRJYei8cvpeHT1bIQdr6unrKOuySmSoPuXIcgvlWH5aH+80KZy5X1DYjMweWM4FALw/kBvvN6jmZojrbnCUhne/Pfkkxf94OVgjkX7bqpK+bR0sMAnQ311qlSWNvwcchtfH4mFs5UxTizoWau/D3aFJ+Gd3dfgZGWM0+/00tleHTlFEvRedgrZhRIsHOCNN4Kf/T1+Kz0fi/bdRGi88vzCpZEJFr3giz5qOr+ojvwSKXaGJ2HDubuqfjWG+mK85N8Ek7t5YFdEEtacSYClsT7+nNW9wdzQf1goQcKDismZ+MxC3H1QqCpL9ThmhnplvUTMVYmQ8pJn/70nlFssxd2sQsRnFfyrPJbyUSSRP3EMYwPxP31L/rV/T1szWJtq7tqRiJ6uQSRiPv74Y/zyyy9ISkqCvn7ls5NyuRy+vr4YOHAgvv3220q9h4kYepppv17CXzfTMLarKz4f5qftcKrtcqKyJ4cgALveUE/JqeoqKJXhmyOx2Hj+LgCgbVMr/DSqPVwbN4yTQ1106lYmJqwPA4Bare+8+vQdLD4UAwdLI4Qs6Fnn6vVWRcTdbEzeFIHcYik87cyw6bXOGrngScgqxIRKlOmguksmVyBw6Qlk5Jfi59HtMahNzRImyQ+LMGF9GO5kFsLSWB9rJ3TS6O+IvBIpvj8ah02hdyFXCDA2EGN6Ty+83sOz0jeICkpl+PF4HNafTYBMIcBIX4w3ezbDtOBmak867L2Sgjk7rwIAvn65DUbo0Mqz2LR8vLo6FA+LpOjkboNNkzrX6+OuLpHJFfjfHzewMyIJADCrT3PM7dtcKzfslh6OwapTd9DNyxZbpnTR+PjlLsQ/wKurL8DSWB8RH/TT2EzsglIZApYcR36JrF70Q6iK8r5innZm+HtOj2eW4LmVno+XVpxHQakMIzs2xZfD2+jMTeZnkcoVeG/3dey+nFzheQsjfcx7rgXGdXVjOUYoZ4H3/uYk7ueW4O3+LTGjl1et7FcQBPT//jRupRdUOrmhTb9FJOHt36/BxEAPx+YHP7FZfX6JFD8ci8PG83dV5xfTe3rhjeDKn6NomkyuwF8307DmTAIik3Ieef2XcR3Q39dR84HpGIVCQFpeiapclTKBokzUJGUX4SmLVWBrbgRPWzMIEJCQVYisAskTt9UTi+BiY/LPipuyRIunnRkcLIx1djU1Ef2jKnkDtV5p/fjjjxW+lslk2LhxI2xtKza7mzVrVpX2q1AosGHDBkyYMOGRJMz48ePRpEkTLFmyBADw6aefomvXrvDy8kJOTg6+/vpr3Lt3D1OmTKnGJyKq6H5OMY5GpwMAxge4azeYGmrvaoNXO7lie1giPth7HX/OqvwybHURBAH7I+/jiz+jkZGvnA09uZsH3n3eu14tk66LglvYYVRnF2wPS8Lbv0fir9k9atz/ILtQgp9OKPunLHiuZb2/GdjRvRF2vxmACevDEZ9ZiJdWnsfG1zrB11l95VCuljWuzS6UwLWRKTZP6qzVUgmkHvp6Yozo2BQ/h9zBjvDEGiViou7nYeKGMGTkl8LJyhibJnVGCweLWoz22SyNDfDR4FZ4pZMLPtp3AxcTsvHt0Vv4/VIyFg1uhT4+T75pKggC9l29j8WH/vk90q+VAz56oZXGZnoO82+CO5kF+OnEbbz/x3W4NTbTickO8ZkFGLP2Ih4WSdG2qRXWT+xU74+7ukRfT4ylw/3gaGWMH47H4cfjcUjPLcEXL7bW6M3gEqkcO8OVPZ/GBWi3T2An90awszBCZn4pzt3O0thKzV3hScgvkcHTzgy9G9jq0End3LHxfALiMwux9+p9vNzh8T1UgfKyeuEoKJWhi0cjfD7Mr84kYQDAQE+Mb0a0gaOVEX4OUfaZHN6+Kd4b4A07C90ty6xpxgZ6eOd5b8zZeRUrQm5jZEeXWvn3OR2XhVvpBTAz1MOrnbW3ArCyhrdvil0RSQi/+xCfHriJX8Z1rPC6IAjYe1W5ajez7PziuVbKVbu6vpJEX0+MF9o4Y5CfEy7de4i1ZxJwJCoNggBMDHRnEqaMWCyCs7UJnK1NEORV8R6mRKZAYnZR2aqWggr9WzLyS5FVUKpazV3O3sKoQv+Z8tUtLjamvLdB1ICodUWMh8eze2WIRCLEx8dXab9///03+vfvj9jYWLRoUbFsTc+ePeHu7o6NGzcCAObOnYs9e/YgLS0NNjY26NChAz7//HP4+1e+ASNXxNCTfHMkFstDbtebMgYPCyXo861yGba2Sw3EpOXho303EZaQDQBwa2yKj4f4opeGa4bTk+WXSPH892eQklOMcV3d8Nmw1jXa36J9N7Ap9B5aOVni4MxuDWb2T1puCSZuCENMWj7MjfTxy7gOj5zs14aQmAxM33oZxVI5/Joob7ryxkP9lfigCD2+DgEAnHmnV7VuCpy/k4U3Nl9CfqkMLR0ssHFSJzhZPX5GqKYIgoAD11LxxZ9RSM9TXuD28bbHR4Nbwa1xxaTif3+PuDc2xSIt/R5RKAS8tf0yDl1Pg42pAfbN6KbVVZ1J2UUY+UsoUnNL4O1ogR2vd2WJCy3adlE5CUYhAL297bF8tL/GkmK7LyVj/m+RaGJtglNv99T6ioDyc4Hh7Zti2Uj1lyeTyRXo+c1JJD8sxhcvtsaYLtpNRmnDL6fuYMnhGLg0MsHxeT0fe0OwVCbH2LUXEX73Idwam2Lv9CCNllStbedvZ8HSxKBB9AKqDoVCwLAV53AtOReju7hi8Ys1r/owbt1FnInLwqQgD3w0uG40gI9Ny8fAH89ArhCwfmJHVW+hqPt5WLT/BsLvKvvYediaYdHgVhrvbVWb7j0oRHRqPvq1cmCT9xrKL5HiblYR4rMKIBKJ4GlrBndbM/YZIarHdLY0WV3FRAw9TqlMjqClymaDK8a0x8B60qtkV0QS3vn9GkwN9XBsXjCcn7AMW11yi6X47ugt/HrhnqoEzYyeXphahRI0pDln47Iwdt1FAMDWKV2qnUC4k1mA/t+dhkwhYNuULghUQyJCl+WVSPH65ghciM+GgZ4I34xoW6sNTHdFJGHhnuuQKwT0aGGHlWPa13gFE+m+8pseb/XywoL+Lav03gOR9zF/VyQkcgU6ezTCmvEdYWWiOz3QCktl+PFEHNadUZYaM9QXY1oPT7zZ0wsSueKR3yMzezfHlO4eMNLX3u+RYokcI38JxfWUXHjZm2PP9ECt9JVLyy3ByF9CkZhdhGZ2Ztj5RoCqUS9pz9GodMzcfhklUgXaulhj/YSOaKyB/5ehy88iMjm3VksQ1URYQjZG/hIKC2N9RHzQV+0/s4eup2L61suwMTXA+ff6wMSw4Z1rFkvk6P5VCLIKSh+bjBIEAQt+u4bdl5NhYayPP6YHaa2PEGlO+c+iWAT8NadHjVbDxqTl4fnvz0AsAk69Xb3JIdqy+FA0Vp+Oh0sjE+yeFoifQ27j1wv3oBAAEwM9vNXbS+vnF0REpF1VyRtw/RtRNR2+noasAgkcLI3Qrx7Vkn65fVN0dLNBkUSOTw9EaWxchULA75eS0WfZSWw8r+wDMKC1I47NC8bMPs2ZhNFR3ZrbYmxXZXmBd36/hvwSabX2s+RQDGQKAX197BtcEgZQll7aNKkzXmjjBKlcwOwdV7HmdNVWiz6OIAj46Xgc3vn9GuQKAS+1b4J1EzoyCdNAvNpJ+bP526UkyORPbjb6X2vPxGPm9iuQyBUY6OeIzZM661QSBgDMjPSxcIAP/prTA928bCGRKfDjidvo++2pCr9HBvo54vj8npjRy0vrN0lMDPWwZnxHOFga4XZGAd7adqVK/y+1IaugFGPWXkBidhFcG5li65SuTMLoiH6tHLB1SldYmxogMikHL68KReKDIrWOeTUpB5HJuTDUE+PVTrrRu6ijmw3sLYyQXyLD2bgstY+35ozyd+24rm4NMgkDKI9Nb/VSroL/6fhtlEgrNo5edSoeuy8nQ08swoox7ZmEaSA6ezRCf18HKATgiz+ja7SvtWcSAAADWjvVqSQMAMzu0xxOVsZIyi5G0JcnsClUmYQZ1MYJx+cH68T5BRER1R1qS8T8+OOPKCkpqfT2q1atQn5+vrrCIap1m0PvAgDGdHHTei+V2iQWi/D5i62hJxbhr5tpCInJUPuYN1Jy8fKq81jwWySyCiTwtDPD5kmdsXJsBzS1qVsn6w3RwgE+aGpjgpScYiw+FFPl95+/k4Vj0enQE4vw3gAfNURYNxjp6+HHV/0xuZuyrOcXh6Lx2cEoKJ7WCfIp5AoBH+y9gWVHbwEApvdshmUj2tar4xU9Xb9WDmhsZoj0vFKExGY+c3uFQsAXf0bh87IbLhMD3fHTqPY6nQj3sjfHr5M7Y9XY9mhirTwOZRVI0MzODFsmd8GKMR2e2GBXGxytjLF2fCcYG4hx+lam6t9aE3KKJBi79iLuZBbC2coYW6d0gaOVscbGp2fr4GaD36cFoom1CRKylL3DbqTkqm288nPZF9o4aWT1TWWIxSLVKvM/r6WqdaxL97JxJTEHhnpijNVyfxxtG9XFFc5WxkjLK8G2i4mq54/cTMNXR5TndosGt0L35nbaCpG04L0BPjDQE+HUrUycvvXs84jHycgrwb6rKQCAyd2fXbpe15gZ6WNRWSk1qVyAl705tk7pgp9Ht9d45QgiIqr71HY3Zu7cuVVKrLzzzjvIzKzeL3ciTbuRkovLiTkw0BPh1c66MYOwNnk7WmJSkDsAYNH+m4/MjKstOUUSfLD3OoYsP4vLiTkwNdTDewO88dfsHujRghd6dYWZkT6+fllZx317WGKVLtSUN36VNyLHdHFt8LMsxWIRPnyhFf43UJmQWnc2AbN2XEGprGo/gyVSOd7ccglbLyZCJAI+GeKLd573rlNNdanmDPXFGF7WeHlHWOJTt5XIFJiz8yrWlM1afW+ANxYNblUn6oSLRCI839oJx+YF470B3vh0qC8Oz+6Bbs11c3WdX1MrfP9KOwDAxvN3seXCPbWPmV8ixYT1yl5UdhZG2Dq1a52bldxQlJet83GyRFZBKV75JbTaN0Cf5kFBKQ6WJTrG6VgS4oU2ykTM0ah0tZ2DAv/M0h/m7wx7i4adlDTS18OsPs0BACtO3kaRRIYbKbmYs+MqBAEYH+CG8QHu2g2SNM7D1gzjuroDUJbokldjctDm0HuQygV0cLNBe1ebWo5QM/r7OmLxi3744sXWODy7u1p6ORIRUcOgttokgiCgT58+0Nev3BDFxcXqCoWo1pXPIBzQ2qneXrjN7tsCByJTkZhdhBUhtzHvuar1F3gahULAzogkfPVXDB4WKUtZDW7rjPcHemu9ETRVT0CzxpgQ4IZNoffw3u5r+Gtuj0r1PthzJQU37+fBwkgfs8tuABAwtYcn7C2NsOC3SBy8loqsglKsHt+xUv+mOUUSTN4UgUv3HsJQX4wfXmmHAfWkhxVV3SudXLD6dDxCYjOQmlv82GNsfokU07ZcwrnbD6AvFuGrl9vgpfZNtRBtzZgY6mFacDNth1Epz7d2wtv9W+LrI7FYtP8m3BubqS1xVCSRYdLGcEQm58LG1ABbp3SBh62ZWsai2uFgaYxdb3RV/VxO2hiOr0e0wYv+tfdzuTMiCRKZAm2aWqGdi3Wt7bc2tHe1gaOlcnXGmbgstZQATnxQhCM30wAAU7p71vr+66LhHZpi5ak7uPegCF8ficVfN9JQLJWje3NbfPRC3WiuTrVvVh8v7L6cjJi0fPwWkYRXO7tW+r1FEhm2XFRONpjSre6thiknEokwukvlPzcREdGTqC0Rs2jRoiptP3ToUDRq1EhN0RDVnpwiCfZdvQ9AOTusvjIvW4b95tbLWHUqHsP8m8DTruarFa4m5WDRvhuITFaW2mjhYI5PhrRGQLPGNd43ade7A7wREpuJxOwifHEwGl++3Oap2xdL5PjmSCwA4K3eXjpTFkVXDG3XBLbmRnjj10u4EJ+NkatCsfG1zk8tJZT8sAgT1ofhTmYhLI31sXZCJ3T24O/WhqyZnTk6ezRCWEI2doUnY3bfignPjLwSTNgQjujUPJgZ6mHl2A5ckagh03s2w+2MAvxxJQXTt17CHzOC0KwWfs/+W4lUjtc3X0L43YewMNbHr5O71KjhMmmOhbEBNkzsjAW/RWJ/5H3M3RmJ9LxSvNHDs8arG+UKAVsvKFfJjevqpnOrJcvLk60/l4BD11PVkohZfy4BCgHo0cKOPxNlDPTEmNO3OebujMSGc3cBAM3szLB8dHvos6xpg2VtaoiZvb3w+Z/RWHb0Fl5o6wzzSvYa3H05BTlFUrg2MsVzvo5qjpSIiEj36Uwihqiu2BWRhFKZAq2cLNHBrW4ur66s51s7IriFHU7dysRH+27i18mdq32xnl0owVd/xWBnRBIEQZnomdO3OSYEurNnRT1haqiPb0a0xSurQ7EzIgnP+zmiV0v7J26/5kw80vJK0NTGBBMC3TUXaB0S5GWLnW90xcQN4YhJy8fwleexaVIneNk/etMo6n4eJm4IQ0Z+KZysjLFpUmfeXCIAwKjOLspETEQS3urtpSo3diezAOPXhSElpxi25kbY+FontG5ipeVoGw6RSIQlL/khMbsIl+49xOSN4fhjehBszAxrZf8SmQLTt17G2dtZMDPUw6ZJnfn/W8cY6ovx/Svt4GBphDVnErD0cAzSckvw4Qs1Kxt4IiYDKTnFsDE1wOC2zrUYce0Z1EaZiCkvT1abvapyi6TYFZEEAJhaB3tWqNOQtk3wc8gd3M4ogI2pAdZP7AQrk2evxqX6bXyAO7ZcuIe7D4rwy6k7mF+JSgkKhYD1Z5Xl/yYFudeJUqdERETqxrufRFUgVwjYUjaDcHyA7s0grG0ikQifDPGFob4YZ29n4c/rVW+aKlcI+DX0Lnp9cxI7wpVJmJf8m+DEgmBM6e7JJEw909mjEV4LVN7UeG/3NeSWlZ77r4y8Eqw6dQcA8O7z3jrdDFzbfJ2tsOfNQHjamSElpxjDV4Yi4m52hW3O38nCK7+EIiO/FC0dLLBneiCTMKQyoLUTLI31kZJTjDNxyl4Tl+49xPCV55GSUwwPWzPseTOQN+m1wNhAD7+M64Am1ia4+6AIb269BIlMUeP9yuQKzNl5BSdiMmCkL8a6iZ3qbG3+hk4sFuF/g1rhg0HK3mEbz9/FzO2Xa9Q7pbzE7shOLjr7+9ffxRrOVsYoKJXVeo+cbWGJKJLI4e1ogW7s9VCBnliEpS/5IcirMdZO6AS3xixjSMqk8HsDvAEoJ1Kl5j67rPyx6HQkZClXaI/oWP96qhIREVUH74ASVcGpWxlIzC6CpbE+hrZrou1wNMLd1gzTeypr7n96IAr5JY+/sf44l+5lY/BPZ/HhvpvILZbCx8kSv00LwLevtKu3vXUIeLt/S3jYmiE9rxSfHox67DbL/r6FIokc/q7Wqqa89GQujUzx+7RA+LtaI7dYijFrL6pq2x+IvI+J68ORXypDF49G2DUtgL2WqAJjAz1Vz5cdYUk4GpWOMWsvIKdIirYu1vh9WgBcG7Nxu7bYmhth3cSOMDPUw4X4bHy07wYEoeoNkcspFALe+f0aDl1Pg6GeGKvHd0RXT5b/rOumdPfET6P8YagnxqHraZiwPgy5xZU/Jyt3J7MAZ+KyIBIBY7vobond8vJkAKo1EehJJDIFNp5XztKf3M2j3k+qqo6O7o2wdUrXer/yn6qmv68jOrs3QolUga/LSgs/zdqy1TCju7jBrJKlzIiIiOo7JmKIqmBzqLLZ4MiOLjAx1M0ZhOowLbgZ3BubIiO/FN8djXvm9pn5pZi/KxLDV4YiKjUPlsb6+HSoLw68FYRO7uxXUd+ZGOrhmxFtIBIBuy8n41hUeoXXo1PzsOuSsiTIB4N8eBOkkhqZGWLblK7o62OPUpkCb265hFnbr2Dm9iuQyBUY5OeETZM6s4QIPdarnZWzUY9Gp+ONXyNQIlWgt7c9tk/twv5MOsDb0RI/jfaHWATsCE/CurIbWFUlCAL+t/cG9lxJgZ5YhOWj/RHMnj/1xuC2ztg4qRMsjPRxMUHZO6wyM9P/7deyc9k+3vZwaaTbCdhBZRM1jpWVJ6sNf16/j/S8UthZGGFIO90sy0aki0QiEf5XtjJvz+UUXC/r9/k415JzEJaQDX2xCBMCdTfhS0REpGlMxBBV0t2sQpwqK40wtmvDOqE0NtDDp0NbAwA2nk/AzfuPP/GWyRVYfzYBvb85id2XkwEAr3R0QciCnhgf4M5Gnw1IB7dGmNrdEwCw8I/ryCmSAFDeJFx8KBqCoLzB0sGNibmqMDHUw6qxHTCqswsUArA/8j4AYGKgO34a5a+zJWZI+7wdLeHvag25QoBCUB6bV4/rAFNDzlLVFb29HfD+QOVNri8OReNETPoz3lGRIAj47GA0toclQiQCvnulHZsj10OBzWyx840A2FsYITY9Hy+tOI9b6fmVem9hqQy7LynPz8YFuKsxytrRzsUaTaxNUCiR42RszcuTCYKAtWeUSc4JAW4w0ufvTKKqaOtijWFlCczP/4x64urN8p+zwW2duUqbiIjoXzR6V1QikSA2NhYymUyTwxLVii0X7kEQgJ4t7eBu2/DqJfdoYYdBfk5QCMCHe29Aoah44n0h/gEG/XgWnx6MQn6pDH5NrPDH9EB8+XIbzrZuoOb1a4FmdmbIzC/Fx/tvAgBO3srEmbgsGOqJ8d7z3lqOsG7S1xNj8Yt+WPBcC9iYGuD9gd5YNLgVxGyCSs/wVi8vWBrrY27fFlg63I/JcR00uZsHRnV2gSAAM7ddQUxaXqXfu+zvW1h/Tnnz68vhbTBER5uwU821crbEnumBaGZnhtTcEry88jzCErKf+b69V1OQXyqDh60ZuteB3igikUi1KqY2ypOFxj/Azft5MDYQY4wOl2Uj0mVvP+8NI30xLiZk42jUoxMGUnKKVT+vk7t5aDo8IiIinaaRK/CioiJMnjwZpqam8PX1RWKistn5zJkzsXTpUk2EQFQjxRI5dkUoSymND2i4F24fvtAKZoZ6uJyYo/r3SM8rwaztV/Dq6guITc+HtakBFr/oh70zguDPxsANmrGBHr4Z0RZiEbD36n0cvp6KxX9GAwAmBrnrfEkUXSYSifBW7+a4/GE/vN6jGcu7UaX08XFA5KLnMLtvc37P6CiRSIRPh7ZGV89GKJTIMXljBLIKSp/5vp9DbmN5yG0AwGdDfTGSjZHrvaY2ptj9ZiA6uNkgr0SGsesu4vBTkhWCIGDzeWVZsrFd3epM8r68T8zx6HQUS2pWnqx8lv7LHZrCxsywxrERNURNrE1UCZalh2MglSsqvL7p/F3IFQICPBujdRMrbYRIRESkszSSiFm4cCEiIyNx8uRJGBv/06C7b9++2LlzpyZCIKqRfVdTkFcig2sjUwS3sNd2OFrjaGWMuf1aAACW/hWD5Sfi0Pubk9gfeV/Z9LWrK0Lm98ToLq7QqyMX+KRe/q42eL1HMwDAzO1XEJdRABtTA8zo5aXlyOoH3kynquL3jO4z0BNj1dgOcG9sipScYry+OeKp/THWnU1QNU5+f6B3nSg5RbXD2tQQW6d0wXOtHCCRKTB922VsOn/3sduGJWQjNj0fJgZ6eLlDU80GWgNtm1qhibUJiiRynIzNqPZ+bmcU4ERMBkQiYHI3z1qMkKjhebNnM9iaGyI+qxBbL9xTPZ9fIsX2i8pJt1N7cDUMERHRf2kkEbN3714sX74c3bp1q3ADwNfXF3fu3NFECETVJggCNoeWzyBkgmFioDu8HS2QUyTFN3/fQqFEDn9Xaxx4qxs+H+bHGYb0iDl9m6O5vTlkZeXs5vRtwYbyRERPYW1qiHUTO8HSWB+XE3Pw3u5rj63Fv+1iIj47GAUAmNu3hSrxTQ2HsYEeVo7tgDFdXCEIwKL9N/HVXzGPfL+Un8sO829Sp34Hi0QivFBWnuxgDcqTrTurXA3T18cBHg2wxDBRbbIwNlBNzvvheBxyi6QAgJ3hScgvlcHTzgw9G/DkRSIioifRSCImMzMT9vaP/iIuLCzkzEzSeZfuPURUah6M9MUs9QFlf4ovXmwNQ30xGpsZ4uuX22D3tEAuPacnMjbQw7KRbWGkL4a3owVGd3HVdkhERDqvmZ05VozpAD2xCHuv3seKkxUnL+25nIz/7b0OAHgj2BOz+nClYUOlJxbh82GtseA55Y3RFSfvYP5vkaqSQWm5JThyMw1A3SyxW94n5kR0BookVe81+qCgFHsuJwMAprBnBVGteKWjC5rbm+NhkRTLQ+Igkyuw4dxdAMCUbp51pvwhERGRJmkkEdOxY0f8+eefqq/Lky9r165FQECAJkIgqrbyGYRD2znD2pSrPQCgg1sjnH23F86+2xsjOrrwRJueqU1Ta5x5txd+fzMQBmwQTkRUKd2a2+KTIb4AgK+PxKp6gBy6nooFv0VCEIAJAW5473lvTm5q4Mp7h301vA30xCLsuZyCyZsiUFgqw7awRMgUAjq7N4KPk6W2Q60yvyZWcGlkgmKpHCExmVV+/5YLiSiVKdCmqRU6ezRSQ4REDY++nhjvD/IBAGw6fw+rz8QjJacYjcwM8VL7JlqOjoiISDfpa2KQxYsXY8CAAYiKioJMJsMPP/yAqKgonD9/HqdOndJECETVkpFfgsM3lDc9xrPmegX2FsbP3ojoX/g9Q0RUdWO7uuF2RgE2nr+Lubuu4l52Eb45EguFAIzs2BSLBvsyCUMqIzu5wM7CCNO3XsbpW5l4dfUFpOWVAADG1cHVMIAyyTTIzxmrTt3Bn9fvq1bIVEaJVI5fL9wFAEzu5sGfFaJa1LOFHbo3t8WZuCx89ZeyV9nYrm4wNtDTcmRERES6SSPTkrt164arV69CJpPBz88Pf//9N+zt7REaGooOHTpoIgSiatkRlgSpXEB7V2uW3iIiIiKt+GCQD4Jb2KFEqsDSwzGQKQQMaeuMJS+14apUekQvb3tsf70rGpkZ4npKLjLzS2FvYYT+vo7aDq3ayvvEnIjJQGFp5cuT7buagqwCCZytjDHQr/IJHCJ6NpFIhPcH+qA8v2moL8a4rnUz4UtERKQJGqsP06xZM6xZswZhYWGIiorCli1b4Ofnp6nhiapMKldg28VEAFwNQ0RERNqjryfGT6P90dzeHADQ39cBy0a2hR6TMPQE7VyssfvNQLg2MgUAjOniBkP9ulsa1NfZEm6NTVEiVeBETEal3iMIAtaeSQAAvBbkwdKoRGrg42SJVzsp+6gOb98EdhZGWo6IiIhId2mkNJmenh5SU1Nhb29f4fkHDx7A3t4ecrlcE2EQVcnRqHSk5ZXA1twQA/zq7gxCIiIiqvssjQ3w+7RAhN/NRo8WdrypTM/kYWuGfTOCcPZ2Vp1eDQOUlydzwoqTd3DoeioGt3V+5ntO3cpEXEYBzI308UpnFw1ESdQwfTzEF0Fetujj7aDtUIiIiHSaRq7gBEF47POlpaUwNGTzc9JNm0PvAgBe7eQKI33WuSUiIiLtsjI1QN9WDnV6ZQNplo2ZIQa3da4X3zODqlierHw1zCudXGBpbKDW2IgaMiN9PbzQxhkmhrxmJiIiehq1roj58ccfAShnMK1duxbm5uaq1+RyOU6fPg1vb291hkBULbfS83EhPht6YhFGd3HVdjhERERERA1aKydLeNiaISGrEMdjMjDkKatiolPzcPZ2FsQiYGKgu+aCJCIiIiJ6ArUmYr777jsAyhUxq1atgp7ePzMkDA0N4e7ujlWrVqkzBKJqKV8N08/HAc7WJtoNhoiIiIiogSsvT7Y85Db+vHb/qYmY8tUwA/yc4FLWJ4eIiIiISJvUukY9ISEBCQkJCA4ORmRkpOrrhIQExMbG4siRI+jSpUuV9unu7g6RSPTIY8aMGU98z2+//QZvb28YGxvDz88Phw4dqulHo3osr0SKPZdTAADjA9y0HA0REREREQH/lCcLic1EwRPKk6XnlWB/pPJcfmp3T43FRkRERET0NBopFhwSEgIbG5ta2Vd4eDhSU1NVj6NHjwIARowY8djtz58/j1GjRmHy5Mm4cuUKhg0bhmHDhuHGjRu1Eg/VP3suJaNIIoeXvTkCmjXWdjhERERERATA29ECnnZmkMgUOB6d/thtNofehVQuoKObDdq5WGs2QCIiIiKiJ1BrabJ/S05Oxv79+5GYmAiJRFLhtW+//bbS+7Gzs6vw9dKlS9GsWTMEBwc/dvsffvgBzz//PN5++20AwGeffYajR49i+fLlLItGjxAEAZsv3AOgXA0jEom0HBEREREREQHK8mQv+DnhxxO3cfBaKoa2a1Lh9SKJDFsuJAIApnA1DBERERHpEI0kYo4fP44hQ4bA09MTMTExaN26Ne7evQtBENC+fftq71cikWDLli2YN2/eE2+Yh4aGYt68eRWe69+/P/bu3fvE/ZaWlqK0tFT1dV5eXrVjpLrl3O0HiM8shLmRPl5q31Tb4RARERER0b8MauOMH0/cxqnYTOSXSGFhbKB6bfelZOQWS+HW2BT9WjloMUoiIiIiooo0Upps4cKFWLBgAa5fvw5jY2Ps3r0bSUlJCA4OfmJJscrYu3cvcnJyMHHixCduk5aWBgeHiifhDg4OSEtLe+J7lixZAisrK9XDxcWl2jFS3bI59C4A4KX2TWBupLEFY0REREREVAktHMzhZW8OiVyBY/8qTyZXCFh3NgEAMCnIA3pirmwnIiIiIt2hkURMdHQ0xo8fDwDQ19dHcXExzM3N8emnn+LLL7+s9n7XrVuHAQMGwNnZubZCBaBMHOXm5qoeSUlJtbp/0k0pOcWqi7nxAW5ajoaIiIiIiP5LJBJhkJ8TAODPa6mq549Fp+PugyJYGuvj5Q5c2U5EREREukUjiRgzMzNVXxgnJyfcuXNH9VpWVla19nnv3j0cO3YMU6ZMeep2jo6OSE+v2MgxPT0djo6OT3yPkZERLC0tKzyo/tt64R4UAhDYrDG87C20HQ4RERERET3GoDbKRMzpW1nILZYCANadUa6GGdPVDWZc2U5EREREOkYjiZiuXbvi7NmzAICBAwdi/vz5+OKLLzBp0iR07dq1WvvcsGED7O3tMWjQoKduFxAQgOPHj1d47ujRowgICKjWuFQ/lUjl2BGuXPnE1TBERERERLqrhYMFmpeXJ4tKR2RSDsLuZsNAT4SJge7aDo+IiIiI6BEamSr07bffoqCgAADwySefoKCgADt37kTz5s3x7bffVnl/CoUCGzZswIQJE6CvX/EjjB8/Hk2aNMGSJUsAALNnz0ZwcDCWLVuGQYMGYceOHYiIiMDq1atr/sGo3jh0PRXZhRI4WRmjrw8bexIRERER6bJBbZzw/bE4/Hk9VbUCZnAbZzhYGms5MiIiIiKiR6k9ESOXy5GcnIw2bdoAUJYpW7VqVY32eezYMSQmJmLSpEmPvJaYmAix+J+FPoGBgdi2bRs++OADvP/++2jevDn27t2L1q1b1ygGql82h94DAIzp4gp9PY0sFCMiIiIiomoa5KdMxJyJy4RCUD43ubuHdoMiIiIiInoCkSAIgroHMTY2RnR0NDw86uaJcV5eHqysrJCbm8t+MfXQteQcDFl+DoZ6Ypxf2Bu25kbaDomIiIiIiJ6h/3enEZueD0DZ53Hb1OqVvSYiIiIiqo6q5A00MvW/devWiI+P18RQRFVWvhpmoJ8jkzBERERERHXEoDZOqr9P7e6pxUiIiIiIiJ5OI4mYzz//HAsWLMDBgweRmpqKvLy8Cg8ibXlYKMH+yPsAgPFs7ElEREREVGcMa9cEJgZ6aNvUCsEt7LQdDhERERHRE6m9RwwADBw4EAAwZMgQiEQi1fOCIEAkEkEul2siDKJH7IxIgkSmQOsmlvB3sdZ2OEREREREVEmujU1x6p2eMDPUh1gsevYbiIiIiIi0RCOJmJCQEE0MQ1QlcoWALReUZcnGd3WvkCQkIiIiIiLdZ29hrO0QiIiIiIieSSOJmODgYE0MQ1QlITEZSH5YDGtTAwxp56ztcIiIiIiIiIiIiIioHtJIjxgiXbS5bDXMyI4uMDbQ03I0RERERERERERERFQfMRFDDVJ8ZgFO38qESASM7eKm7XCIiIiIiIiIiIiIqJ5iIoYapC0XEgEAvVraw7WxqZajISIiIiIiIiIiIqL6iokYanCKJDL8dikJADA+gKthiIiIiIiIiIiIiEh9NJKIWbRoEe7du6eJoYieae+V+8gvkcG9sSl6NLfTdjhEREREREREREREVI9pJBGzb98+NGvWDH369MG2bdtQWlqqiWGJHiEIAjaH3gUAjO3qBrFYpN2AiIiIiIiIiIiIiKhe00gi5urVqwgPD4evry9mz54NR0dHvPnmmwgPD9fE8EQq4XcfIiYtH8YGYozo4KLtcIiIiIiIiIiIiIiontNYjxh/f3/8+OOPuH//PtatW4fk5GQEBQWhTZs2+OGHH5Cbm6upUKgBK18NM6xdE1iZGmg3GCIiIiIiIiIiIiKq9zSWiCknCAKkUikkEgkEQYCNjQ2WL18OFxcX7Ny5U9PhUAOSkVeCv26kAQDGBbhpORoiIiIiIiIiIiIiagg0loi5dOkS3nrrLTg5OWHu3Lnw9/dHdHQ0Tp06hbi4OHzxxReYNWuWpsKhBmhbWCJkCgEd3Wzg62yl7XCIiIiIiIiIiIiIqAHQSCLGz88PXbt2RUJCAtatW4ekpCQsXboUXl5eqm1GjRqFzMxMTYRDDZBUrsC2i4kAgPGB7toNhoiIiIiIiIiIiIgaDH1NDDJy5EhMmjQJTZo0eeI2tra2UCgUmgiHGqAjN9OQkV8KW3MjPO/rqO1wiIiIiIiIiIiIiKiBUPuKGKlUio0bNyIvL0/dQxE90ZYL9wAAo7u4wlBf462RiIiIiIiIiIiIiKiBUvsdaQMDA5SUlKh7GKInyiooxcWEbADAK51ctBwNERERERERERERETUkGlkaMGPGDHz55ZeQyWSaGI6oghMxGRAEwK+JFZpYm2g7HCIiIiIiIiIiIiJqQDTSIyY8PBzHjx/H33//DT8/P5iZmVV4fc+ePZoIgxqoY1HpAIC+Pg5ajoSIiIiIiIiIiIiIGhqNJGKsra0xfPhwTQxFVEGJVI4zcVkAgL6t7LUcDRERERERERERERE1NBpJxGzYsKFW95eSkoJ3330Xhw8fRlFREby8vLBhwwZ07NjxsdufPHkSvXr1euT51NRUODo61mpspFvO3c5CsVQOZytjtHKy1HY4RERERERERERERNTAaCQRUy4zMxOxsbEAgJYtW8LOzq7K+3j48CGCgoLQq1cvHD58GHZ2doiLi4ONjc0z3xsbGwtLy39uxtvbc4VEfXcsuqwsWSsHiEQiLUdDRERERERERERERA2NRhIxhYWFmDlzJjZv3gyFQgEA0NPTw/jx4/HTTz/B1NS00vv68ssv4eLiUmGVjYeHR6Xea29vD2tr6yrFTnWXQiHgWHQGAPaHISIiIiIiIiIiIiLtEGtikHnz5uHUqVM4cOAAcnJykJOTg3379uHUqVOYP39+lfa1f/9+dOzYESNGjIC9vT38/f2xZs2aSr23Xbt2cHJyQr9+/XDu3LknbldaWoq8vLwKD6p7rqXkIjO/FOZG+uji2Ujb4RARERERERERERFRA6SRRMzu3buxbt06DBgwAJaWlrC0tMTAgQOxZs0a/P7771XaV3x8PFauXInmzZvjyJEjePPNNzFr1ixs2rTpie9xcnLCqlWrsHv3buzevRsuLi7o2bMnLl++/NjtlyxZAisrK9XDxcWlSjGSbjgWpSxLFtzSDkb6elqOhoiIiIiIiIiIiIgaIpEgCIK6BzE1NcWlS5fg4+NT4fmbN2+ic+fOKCwsrPS+DA0N0bFjR5w/f1713KxZsxAeHo7Q0NBK7yc4OBiurq749ddfH3mttLQUpaWlqq/z8vLg4uKC3NzcCj1mSLc9//1pxKTl4/tX2mGYfxNth0NERERERERERERE9UReXh6srKwqlTfQyIqYgIAALFq0CCUlJarniouL8cknnyAgIKBK+3JyckKrVq0qPOfj44PExMQq7adz5864ffv2Y18zMjJSrdwpf1DdkpRdhJi0fOiJRejZ0k7b4RARERERERERERFRA6WviUG+//57PP/882jatCnatm0LAIiMjISxsTGOHDlSpX0FBQUhNja2wnO3bt2Cm5tblfZz9epVODk5Vek9VHcci1aWJevkbgNrU0MtR0NEREREREREREREDZVGEjF+fn6Ii4vD1q1bERMTAwAYNWoUxowZAxMTkyrta+7cuQgMDMTixYsxcuRIhIWFYfXq1Vi9erVqm4ULFyIlJQWbN28GoEwEeXh4wNfXFyUlJVi7di1OnDiBv//+u/Y+JOmUo2X9Yfr6OGg5EiIiIiIiIiIiIiJqyDSSiDl9+jQCAwMxderUCs/LZDKcPn0aPXr0qPS+OnXqhD/++AMLFy7Ep59+Cg8PD3z//fcYM2aMapvU1NQKpcokEgnmz5+PlJQUmJqaok2bNjh27Bh69epV8w9HOie3SIqLCdkAgH6tmIghIiIiIiIiIiIiIu0RCYIgqHsQPT09pKamwt7evsLzDx48gL29PeRyubpDqJGqNN0h7dt3NQWzd1xFc3tzHJ0XrO1wiIiIiIiIiIiIiKieqUreQKyJgARBgEgkeuT5Bw8ewMzMTBMhUANyLDoDANCXq2GIiIiIiIiIiIiISMvUWprspZdeAgCIRCJMnDgRRkZGqtfkcjmuXbuGwMBAdYZADYxEpsDJWGUihmXJiIiIiIiIiIiIiEjb1JqIsbKyAqBcEWNhYQETExPVa4aGhujatesjfWOIaiL8bjbyS2SwNTdEu6bW2g6HiIiIiIiIiIiIiBo4tSZiNmzYAABwd3fHggULWIaM1O5oVDoAoI+3A8TiR8vhERERERERERERERFpkloTMeUWLVqkiWGogRMEQZWIYX8YIiIiIiIiIiIiItIFakvEtG/fHsePH4eNjQ38/f0hEj15dcLly5fVFQY1IDFp+UjJKYaRvhjdvGy1HQ4RERERERERERERkfoSMUOHDoWRkREAYNiwYeoahkjlWNlqmO7NbWFiqKflaIiIiIiIiIiIiIiI1JiI+Xc5MpYmI004Fl1WlsyHZcmIiIiIiIiIiIiISDdopEdMuYiICERHRwMAWrVqhQ4dOmhyeKrH0vNKEJmcC5EI6O1jr+1wiIiIiIiIiIiIiIgAaCgRk5ycjFGjRuHcuXOwtrYGAOTk5CAwMBA7duxA06ZNNREG1WPHozMAAO1crGFvYazlaIiIiIiIiIiIiIiIlMSaGGTKlCmQSqWIjo5GdnY2srOzER0dDYVCgSlTpmgiBKrnWJaMiIiIiIiIiIiIiHSRRlbEnDp1CufPn0fLli1Vz7Vs2RI//fQTunfvrokQqB4rkshw9nYWAKBfKyZiiIiIiIiIiIiIiEh3aGRFjIuLC6RS6SPPy+VyODs7ayIEqsdO38qCRKaAayNTNLc313Y4REREREREREREREQqGknEfP3115g5cyYiIiJUz0VERGD27Nn45ptvNBEC1WP/LksmEom0HA0RERERERERERER0T/UVprMxsamwk3xwsJCdOnSBfr6yiFlMhn09fUxadIkDBs2TF1hUD0nVwg4EZMBAOjbyl7L0RARERERERERERERVaS2RMz333+vrl0TqVxJfIjsQgksjfXRyb2RtsMhIiIiIiIiIiIiIqpAbYmYCRMmqGvXRCpHy8qS9fK2h4GeRirtERERERERERERERFVmtoSMXl5ebC0tFT9/WnKtyOqqmNR//SHISIiIiIiIiIiIiLSNWrtEZOamgp7e3tYW1s/tom6IAgQiUSQy+XqCoPqsfjMAtzJLISBngjBLe20HQ4RERERERERERER0SPUlog5ceIEGjVS9uwICQlR1zDUgB2PzgAAdPVsDEtjAy1HQ0RERERERERERET0KLUlYoKDgx/7d6LacpRlyYiIiIiIiIiIiIhIx6ktEfNfOTk5CAsLQ0ZGBhQKRYXXxo8fr6kwqJ7ILpQg4l42AKCPj72WoyEiIiIiIiIiIiIiejyNJGIOHDiAMWPGoKCgAJaWlhX6xYhEIiZiqMpCYjKgEAAfJ0s0tTHVdjhERERERERERERERI8l1sQg8+fPx6RJk1BQUICcnBw8fPhQ9cjOzq7y/lJSUjB27Fg0btwYJiYm8PPzQ0RExFPfc/LkSbRv3x5GRkbw8vLCxo0bq/lpSBcci1aWJevH1TBEREREREREREREpMM0kohJSUnBrFmzYGpa85ULDx8+RFBQEAwMDHD48GFERUVh2bJlsLGxeeJ7EhISMGjQIPTq1QtXr17FnDlzMGXKFBw5cqTG8ZDmlUjlOHUrEwDQtxX7wxARERERERERERGR7tJIabL+/fsjIiICnp6eNd7Xl19+CRcXF2zYsEH1nIeHx1Pfs2rVKnh4eGDZsmUAAB8fH5w9exbfffcd+vfvX+OYSLMuxD9AkUQOB0sjtHa20nY4RERERERERERERERPpLZEzP79+1V/HzRoEN5++21ERUXBz88PBgYGFbYdMmRIlfbbv39/jBgxAqdOnUKTJk0wffp0TJ069YnvCQ0NRd++fSs8179/f8yZM+ex25eWlqK0tFT1dV5eXqXjI/UrL0vW18cBYrHoGVsTEREREREREREREWmP2hIxw4YNe+S5Tz/99JHnRCIR5HJ5pfcbHx+PlStXYt68eXj//fcRHh6OWbNmwdDQEBMmTHjse9LS0uDgULGElYODA/Ly8lBcXAwTE5MKry1ZsgSffPJJpWMizREEAceiMgCwLBkRERERERERERER6T619YhRKBSVelQlCVO+3/bt22Px4sXw9/fH66+/jqlTp2LVqlW1FvvChQuRm5ureiQlJdXavqlmbqTkIS2vBKaGegjwbKztcIiIiIiIiIiIiIiInkptiRh1cXJyQqtWrSo85+Pjg8TExCe+x9HREenp6RWeS09Ph6Wl5SOrYQDAyMgIlpaWFR6kG46WlSXr0dwOxgZ6Wo6GiIiIiIiIiIiIiOjp1Faa7L+OHz+O48ePIyMjAwqFosJr69evr/R+goKCEBsbW+G5W7duwc3N7YnvCQgIwKFDhyo8d/ToUQQEBFR6XNINx6LK+sOwLBkRERERERERERER1QEaWRHzySef4LnnnsPx48eRlZWFhw8fVnhUxdy5c3HhwgUsXrwYt2/fxrZt27B69WrMmDFDtc3ChQsxfvx41dfTpk1DfHw83nnnHcTExGDFihXYtWsX5s6dW2ufkdQvJacYUal5EIuAXi3ttB0OEREREREREREREdEzaWRFzKpVq7Bx40aMGzeuxvvq1KkT/vjjDyxcuBCffvopPDw88P3332PMmDGqbVJTUyuUKvPw8MCff/6JuXPn4ocffkDTpk2xdu1a9O/fv8bxkOYcLytL1sHNBo3NjbQcDRERERERERERERHRs4kEQRDUPUjjxo0RFhaGZs2aqXsotcjLy4OVlRVyc3PZL0aLxq27iDNxWVg4wBtvBNfN7yUiIiIiIiIiIiIiqvuqkjfQSGmyKVOmYNu2bZoYiuqp/BIpLsQ/AMD+MERERERERERERERUd2ikNFlJSQlWr16NY8eOoU2bNjAwMKjw+rfffquJMKgOO3UrE1K5AE87MzSzM9d2OERERERERERERERElaKRRMy1a9fQrl07AMCNGzcqvCYSiTQRAtVxx6KU/WH6+XA1DBERERERERERERHVHRpJxISEhGhiGKqnpHIFTsRkAGBZMiIiIiIiIiIiIiKqWzTSI4aoJiLuPkReiQw2pgZo72qj7XCIiIiIiIiIiIiIiCpNrStiXnrppUptt2fPHnWGQXXcsWhlWbLe3g7QE7OUHRERERERERERERHVHWpNxFhZWalz99QACIKgSsT0a2Wv5WiIiIiIiIiIiIiIiKpGrYmYDRs2qHP31ADczijAvQdFMNQTo3tzO22HQ0RERERERERERERUJewRQzrtaNlqmECvxjAzUmvekIiIiIiIiIiIiIio1jERQzrtaFR5WTIHLUdCRERERERERERERFR1TMSQzsrIL8HVpBwAQB9vJmKIiIiIiIiIiIiIqO5hIoZ0VkhMBgQBaNPUCo5WxtoOh4iIiIiIiIiIiIioypiIIZ11NCoDANDXh6thiIiIiIiIiIiIiKhuYiKGdFKxRI6ztzMBMBFDRERERERERERERHUXEzGkk87dzkKJVIEm1ibwcbLQdjhERERERERERERERNXCRAzppGPR6QCAvj72EIlEWo6GiIiIiIiIiIiIiKh6mIghnaNQCDgWXdYfphXLkhERERERERERERFR3cVEDOmcq8k5yCoohYWRPrp4NNZ2OERERERERERERERE1cZEDOmcY1HKsmTBLe1gqM9vUSIiIiIiIiIiIiKqu3iXm3ROeX+YfixLRkRERERERERERER1HBMxpFPuPSjErfQC6IlF6NnCXtvhEBERERERERERERHVCBMxpFOORWcAADq7N4KVqYGWoyEiIiIiIiIiIiIiqpk6l4j5+OOPIRKJKjy8vb2fuP3GjRsf2d7Y2FiDEVNVlPeH6cuyZERERERERERERERUD+hrO4Dq8PX1xbFjx1Rf6+s//WNYWloiNjZW9bVIJFJbbFR9uUVShN3NBgD09WFZMiIiIiIiIiIiIiKq++pkIkZfXx+Ojo6V3l4kElVpe9KOkNgMyBUCWjiYw62xmbbDISIiIiIiIiIiIiKqsTpXmgwA4uLi4OzsDE9PT4wZMwaJiYlP3b6goABubm5wcXHB0KFDcfPmTQ1FSlVxNLqsLJkPy5IRERERERERERERUf1Q5xIxXbp0wcaNG/HXX39h5cqVSEhIQPfu3ZGfn//Y7Vu2bIn169dj37592LJlCxQKBQIDA5GcnPzEMUpLS5GXl1fhQeolkSlwKjYTANCP/WGIiIiIiIiIiIiIqJ4QCYIgaDuImsjJyYGbmxu+/fZbTJ48+ZnbS6VS+Pj4YNSoUfjss88eu83HH3+MTz755JHnc3NzYWlpWeOY6VFn4jIxbl0YbM2NEPZ+H4jF7ONDRERERERERERERLopLy8PVlZWlcob1LkVMf9lbW2NFi1a4Pbt25Xa3sDAAP7+/k/dfuHChcjNzVU9kpKSaitceoJjUeVlyeyZhCEiIiIiIiIiIiKieqPOJ2IKCgpw584dODk5VWp7uVyO69evP3V7IyMjWFpaVniQ+giCgGPRGQDYH4aIiIiIiIiIiIiI6pc6l4hZsGABTp06hbt37+L8+fN48cUXoaenh1GjRgEAxo8fj4ULF6q2//TTT/H3338jPj4ely9fxtixY3Hv3j1MmTJFWx+B/iM6NR8pOcUwNhAjyMtW2+EQEREREREREREREdUafW0HUFXJyckYNWoUHjx4ADs7O3Tr1g0XLlyAnZ0dACAxMRFi8T/5pYcPH2Lq1KlIS0uDjY0NOnTogPPnz6NVq1ba+gj0H8eilWXJunnZwcRQT8vREBERERERERERERHVHpEgCIK2g9B1VWm6Q1U3+KezuJ6Siy+H++GVTq7aDoeIiIiIiIiIiIiI6Kmqkjeoc6XJqH5JzS3G9ZRciERAb2/2hyEiIiIiIiIiIiKi+oWJGNKq49EZAAB/F2vYWRhpORoiIiIiIiIiIiIiotrFRAxpVXl/mL6tuBqGiIiIiIiIiIiIiOofJmJIawpLZTh/+wEAoJ8PEzFEREREREREREREVP8wEUNacyYuExK5Am6NTeFlb67tcIiIiIiIiIiIiIiIah0TMaQ1R6OU/WH6+jhAJBJpORoiIiIiIiIiIiIiotrHRAxphUyuwImYsv4wLEtGRERERERERERERPUUEzGkFZcTc/CwSAorEwN0dLfRdjhERERERERERERERGrBRAxpxbFo5WqYXi3tYKDHb0MiIiIiIiIiIiIiqp94B5y04liUMhHTr5WjliMhIiIiIiIiIiIiIlIfJmJI4+5kFiA+qxAGeiL0aGGr7XCIiIiIiIiIiIiIiNRGX9sBUMNjZ2GEr19ug/s5JbAwNtB2OEREREREREREREREasNEDGmcpbEBRnR00XYYRERERERERERERERqx9JkREREREREREREREREasJEDBERERERERERERERkZowEUNERERERERERERERKQmTMQQERERERERERERERGpCRMxREREREREREREREREasJEDBERERERERERERERkZowEUNERERERERERERERKQm+toOoC4QBAEAkJeXp+VIiIiIiIiIiIiIiIhI28rzBeX5g6dhIqYS8vPzAQAuLi5ajoSIiIiIiIiIiIiIiHRFfn4+rKysnrqNSKhMuqaBUygUuH//PiwsLCASibQdjk7Jy8uDi4sLkpKSYGlpqe1wiKge4nGGiNSNxxkiUjceZ4hI3XicISJ143HmUYIgID8/H87OzhCLn94FhitiKkEsFqNp06baDkOnWVpa8geQiNSKxxkiUjceZ4hI3XicISJ143GGiNSNx5mKnrUSptzT0zRERERERERERERERERUbUzEEBERERERERERERERqQkTMVQjRkZGWLRoEYyMjLQdChHVUzzOEJG68ThDROrG4wwRqRuPM0SkbjzO1IxIEARB20EQERERERERERERERHVR1wRQ0REREREREREREREpCZMxBAREREREREREREREakJEzFERERERERERERERERqwkQMERERERERERERERGRmjARQ9X2888/w93dHcbGxujSpQvCwsK0HRIR1RFLlixBp06dYGFhAXt7ewwbNgyxsbEVtikpKcGMGTPQuHFjmJubY/jw4UhPT6+wTWJiIgYNGgRTU1PY29vj7bffhkwm0+RHIaI6YunSpRCJRJgzZ47qOR5niKimUlJSMHbsWDRu3BgmJibw8/NDRESE6nVBEPDRRx/ByckJJiYm6Nu3L+Li4irsIzs7G2PGjIGlpSWsra0xefJkFBQUaPqjEJEOksvl+PDDD+Hh4QETExM0a9YMn332GQRBUG3D4wwRVcXp06cxePBgODs7QyQSYe/evRVer61jyrVr19C9e3cYGxvDxcUFX331lbo/ms5jIoaqZefOnZg3bx4WLVqEy5cvo23btujfvz8yMjK0HRoR1QGnTp3CjBkzcOHCBRw9ehRSqRTPPfccCgsLVdvMnTsXBw4cwG+//YZTp07h/v37eOmll1Svy+VyDBo0CBKJBOfPn8emTZuwceNGfPTRR9r4SESkw8LDw/HLL7+gTZs2FZ7ncYaIauLhw4cICgqCgYEBDh8+jKioKCxbtgw2Njaqbb766iv8+OOPWLVqFS5evAgzMzP0798fJSUlqm3GjBmDmzdv4ujRozh48CBOnz6N119/XRsfiYh0zJdffomVK1di+fLliI6OxpdffomvvvoKP/30k2obHmeIqCoKCwvRtm1b/Pzzz499vTaOKXl5eXjuuefg5uaGS5cu4euvv8bHH3+M1atXq/3z6TSBqBo6d+4szJgxQ/W1XC4XnJ2dhSVLlmgxKiKqqzIyMgQAwqlTpwRBEIScnBzBwMBA+O2331TbREdHCwCE0NBQQRAE4dChQ4JYLBbS0tJU26xcuVKwtLQUSktLNfsBiEhn5efnC82bNxeOHj0qBAcHC7NnzxYEgccZIqq5d999V+jWrdsTX1coFIKjo6Pw9ddfq57LyckRjIyMhO3btwuCIAhRUVECACE8PFy1zeHDhwWRSCSkpKSoL3giqhMGDRokTJo0qcJzL730kjBmzBhBEHicIaKaASD88ccfqq9r65iyYsUKwcbGpsI107vvviu0bNlSzZ9It3FFDFWZRCLBpUuX0LdvX9VzYrEYffv2RWhoqBYjI6K6Kjc3FwDQqFEjAMClS5cglUorHGe8vb3h6uqqOs6EhobCz88PDg4Oqm369++PvLw83Lx5U4PRE5EumzFjBgYNGlTheALwOENENbd//3507NgRI0aMgL29Pfz9/bFmzRrV6wkJCUhLS6twnLGyskKXLl0qHGesra3RsWNH1TZ9+/aFWCzGxYsXNfdhiEgnBQYG4vjx47h16xYAIDIyEmfPnsWAAQMA8DhDRLWrto4poaGh6NGjBwwNDVXb9O/fH7GxsXj48KGGPo3u0dd2AFT3ZGVlQS6XV7gpAQAODg6IiYnRUlREVFcpFArMmTMHQUFBaN26NQAgLS0NhoaGsLa2rrCtg4MD0tLSVNs87jhU/hoR0Y4dO3D58mWEh4c/8hqPM0RUU/Hx8Vi5ciXmzZuH999/H+Hh4Zg1axYMDQ0xYcIE1XHicceRfx9n7O3tK7yur6+PRo0a8ThDRHjvvfeQl5cHb29v6OnpQS6X44svvsCYMWMAgMcZIqpVtXVMSUtLg4eHxyP7KH/t32VcGxImYoiISKtmzJiBGzdu4OzZs9oOhYjqkaSkJMyePRtHjx6FsbGxtsMhonpIoVCgY8eOWLx4MQDA398fN27cwKpVqzBhwgQtR0dE9cGuXbuwdetWbNu2Db6+vrh69SrmzJkDZ2dnHmeIiOoYliajKrO1tYWenh7S09MrPJ+eng5HR0ctRUVEddFbb72FgwcPIiQkBE2bNlU97+joCIlEgpycnArb//s44+jo+NjjUPlrRNSwXbp0CRkZGWjfvj309fWhr6+PU6dO4ccff4S+vj4cHBx4nCGiGnFyckKrVq0qPOfj44PExEQA/xwnnnbd5OjoiIyMjAqvy2QyZGdn8zhDRHj77bfx3nvv4dVXX4Wfnx/GjRuHuXPnYsmSJQB4nCGi2lVbxxReRz0eEzFUZYaGhujQoQOOHz+uek6hUOD48eMICAjQYmREVFcIgoC33noLf/zxB06cOPHIktUOHTrAwMCgwnEmNjYWiYmJquNMQEAArl+/XuEE4OjRo7C0tHzkpggRNTx9+vTB9evXcfXqVdWjY8eOGDNmjOrvPM4QUU0EBQUhNja2wnO3bt2Cm5sbAMDDwwOOjo4VjjN5eXm4ePFiheNMTk4OLl26pNrmxIkTUCgU6NKliwY+BRHpsqKiIojFFW/d6enpQaFQAOBxhohqV20dUwICAnD69GlIpVLVNkePHkXLli0bbFkyAIBAVA07duwQjIyMhI0bNwpRUVHC66+/LlhbWwtpaWnaDo2I6oA333xTsLKyEk6ePCmkpqaqHkVFRaptpk2bJri6ugonTpwQIiIihICAACEgIED1ukwmE1q3bi0899xzwtWrV4W//vpLsLOzExYuXKiNj0REdUBwcLAwe/Zs1dc8zhBRTYSFhQn6+vrCF198IcTFxQlbt24VTE1NhS1btqi2Wbp0qWBtbS3s27dPuHbtmjB06FDBw8NDKC4uVm3z/PPPC/7+/sLFixeFs2fPCs2bNxdGjRqljY9ERDpmwoQJQpMmTYSDBw8KCQkJwp49ewRbW1vhnXfeUW3D4wwRVUV+fr5w5coV4cqVKwIA4dtvvxWuXLki3Lt3TxCE2jmm5OTkCA4ODsK4ceOEGzduCDt27BBMTU2FX375ReOfV5cwEUPV9tNPPwmurq6CoaGh0LlzZ+HChQvaDomI6ggAj31s2LBBtU1xcbEwffp0wcbGRjA1NRVefPFFITU1tcJ+7t69KwwYMEAwMTERbG1thfnz5wtSqVTDn4aI6or/JmJ4nCGimjpw4IDQunVrwcjISPD29hZWr15d4XWFQiF8+OGHgoODg2BkZCT06dNHiI2NrbDNgwcPhFGjRgnm5uaCpaWl8Nprrwn5+fma/BhEpKPy8vKE2bNnC66uroKxsbHg6ekp/O9//xNKS0tV2/A4Q0RVERIS8tj7MRMmTBAEofaOKZGRkUK3bt0EIyMjoUmTJsLSpUs19RF1lkgQBEE7a3GIiIiIiIiIiIiIiIjqN/aIISIiIiIiIiIiIiIiUhMmYoiIiIiIiIiIiIiIiNSEiRgiIiIiIiIiIiIiIiI1YSKGiIiIiIiIiIiIiIhITZiIISIiIiIiIiIiIiIiUhMmYoiIiIiIiIiIiIiIiNSEiRgiIiIiIiIiIiIiIiI1YSKGiIiIiIjqhYkTJ2LYsGEaH3fjxo0QiUQQiUSYM2eO2sa5e/euapx27dqpbRwiIiIiIqpd+toOgIiIiIiI6FlEItFTX1+0aBF++OEHCIKgoYgqsrS0RGxsLMzMzNQ2houLC1JTU/HNN9/g2LFjahuHiIiIiIhqFxMxRERERESk81JTU1V/37lzJz766CPExsaqnjM3N4e5ubk2QgOgTBQ5OjqqdQw9PT04Ojpq9XMSEREREVHVsTQZERERERHpPEdHR9XDyspKlfgof5ibmz9Smqxnz56YOXMm5syZAxsbGzg4OGDNmjUoLCzEa6+9BgsLC3h5eeHw4cMVxrpx4wYGDBgAc3NzODg4YNy4ccjKyqpyzO7u7vj8888xfvx4mJubw83NDfv370dmZiaGDh0Kc3NztGnTBhEREar33Lt3D4MHD4aNjQ3MzMzg6+uLQ4cOVfvfjYiIiIiItI+JGCIiIiIiqrc2bdoEW1tbhIWFYebMmXjzzTcxYsQIBAYG4vLly3juuecwbtw4FBUVAQBycnLQu3dv+Pv7IyIiAn/99RfS09MxcuTIao3/3XffISgoCFeuXMGgQYMwbtw4jB8/HmPHjsXly5fRrFkzjB8/XlVSbcaMGSgtLcXp06dx/fp1fPnll1wBQ0RERERUxzERQ0RERERE9Vbbtm3xwQcfoHnz5li4cCGMjY1ha2uLqVOnonnz5vjoo4/w4MEDXLt2DQCwfPly+Pv7Y/HixfD29oa/vz/Wr1+PkJAQ3Lp1q8rjDxw4EG+88YZqrLy8PHTq1AkjRoxAixYt8O677yI6Ohrp6ekAgMTERAQFBcHPzw+enp544YUX0KNHj1r9NyEiIiIiIs1iIoaIiIiIiOqtNm3aqP6up6eHxo0bw8/PT/Wcg4MDACAjIwMAEBkZiZCQEFXPGXNzc3h7ewMA7ty5U6Pxy8d62vizZs3C559/jqCgICxatEiVICIiIiIiorqLiRgiIiIiIqq3DAwMKnwtEokqPCcSiQAACoUCAFBQUIDBgwfj6tWrFR5xcXHVWpnyuLGeNv6UKVMQHx+PcePG4fr16+jYsSN++umnKo9LRERERES6g4kYIiIiIiKiMu3bt8fNmzfh7u4OLy+vCg8zMzONxODi4oJp06Zhz549mD9/PtasWaORcYmIiIiISD2YiCEiIiIiIiozY8YMZGdnY9SoUQgPD8edO3dw5MgRvPbaa5DL5Woff86cOThy5AgSEhJw+fJlhISEwMfHR+3jEhERERGR+jARQ0REREREVMbZ2Rnnzp2DXC7Hc889Bz8/P8yZMwfW1tYQi9V/+SSXyzFjxgz4+Pjg+eefR4sWLbBixQq1j0tEREREROojEgRB0HYQREREREREddXGjRsxZ84c5OTkaGS8jz/+GHv37sXVq1c1Mh4REREREdUMV8QQERERERHVUG5uLszNzfHuu++qbYzExESYm5tj8eLFahuDiIiIiIhqH1fEEBHR/9u5QyOIYQAGgmKmBqbuvy1T49CAFPEvttuACrgZAQA/eJ4n994kyZwza63Kzvu+OeckScYY2XtXdgAAgP8SYgAAAAAAAEpckwEAAAAAAJQIMQAAAAAAACVCDAAAAAAAQIkQAwAAAAAAUCLEAAAAAAAAlAgxAAAAAAAAJUIMAAAAAABAiRADAAAAAABQIsQAAAAAAACUfML5AOnlXWdsAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n",
+        "\n",
+        "# Define some bins to calculate spike rates\n",
+        "bin_size = 20.0\n",
+        "rate_bins = np.arange(0, 1000.0, bin_size)\n",
+        "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n",
+        "\n",
+        "# Plot excitatory and inhibitory spikes on first axis\n",
+        "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n",
+        "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n",
+        "\n",
+        "# Plot excitatory rates on second axis\n",
+        "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n",
+        "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n",
+        "\n",
+        "# Plot inhibitory rates on third axis\n",
+        "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n",
+        "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n",
+        "\n",
+        "# Label axes\n",
+        "axes[0].set_ylabel(\"Neuron ID\")\n",
+        "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n",
+        "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n",
+        "axes[2].set_xlabel(\"Time [ms]\");"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lkZXMKuC42jG"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "2_synapses",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt b/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt
new file mode 100644
index 000000000..0555dc93e
--- /dev/null
+++ b/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt
@@ -0,0 +1,9 @@
+CompNeuro 101
+=============
+Building spiking neural network models in GeNN
+
+.. nbgallery::
+    :name: rst-gallery
+    :glob:
+    
+    *.ipynb
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/index.rst.txt b/documentation/5/_sources/tutorials/index.rst.txt
new file mode 100644
index 000000000..09c3e9316
--- /dev/null
+++ b/documentation/5/_sources/tutorials/index.rst.txt
@@ -0,0 +1,33 @@
+=========
+Tutorials
+=========
+
+CompNeuro 101
+=============
+Building spiking neural network models in GeNN
+
+.. nbgallery::
+    :name: comp-neuro-101-gallery
+    :glob:
+    
+    comp_neuro_101/*
+
+MNIST inference
+===============
+Perform MNIST inference by converting a pre-trained ANN to an SNN
+
+.. nbgallery::
+    :name: mnist-inference-gallery
+    :glob:
+    
+    mnist_inference/*
+
+Insect-inspired MNIST classification
+====================================
+Train a model of the insect mushroom body using an STDP learning rule to classify MNIST.
+
+.. nbgallery::
+    :name: mushroom-body-gallery
+    :glob:
+    
+    mushroom_body/*
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt b/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt
new file mode 100644
index 000000000..c4799e2aa
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt
@@ -0,0 +1,9 @@
+MNIST inference
+===============
+Perform MNIST inference by converting a pre-trained ANN to an SNN
+
+.. nbgallery::
+    :name: rst-gallery
+    :glob:
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diff --git a/documentation/5/_sources/tutorials/mnist_inference/tutorial_1.ipynb.txt b/documentation/5/_sources/tutorials/mnist_inference/tutorial_1.ipynb.txt
new file mode 100644
index 000000000..8b606b5f3
--- /dev/null
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+    {
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+        "# Classification of a single digit\n",
+        "In this series of tutorial, we are going to build an SNN capable of classifying MNIST by copying the weights obtained by training the following simple ANN using TensorFlow:\n",
+        "\n",
+        "![Using GeNN for spike-based machine learning.svg](data:image/svg+xml;base64,<?xml version="1.0" encoding="UTF-8" standalone="no"?>
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       fill="#000000" />
  </g>
</svg>
)\n",
+        "\n",
+        "Clearly, this is far from a state of the art architecture, but it still achieves 97.6% accuracy on MNIST. In this first tutorial we are going to build the basic SNN model, present a single test set image to it and visualize the spiking activitiy of the model.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "VXltW7HVbtcj",
+        "outputId": "10383607-e3f3-41b4-9751-b80d3a858cbc",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 147MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8tqbF5GldF0o"
+      },
+      "source": [
+        "## Download pre-trained weights and MNIST test data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N-2PV7LcdFg_",
+        "outputId": "177625f2-5aa4-4b98-8fec-aafd20520cdb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+            "To: /content/weights_0_1.npy\n",
+            "100% 402k/402k [00:00<00:00, 142MB/s]\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+            "To: /content/weights_1_2.npy\n",
+            "100% 5.25k/5.25k [00:00<00:00, 18.8MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+        "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AikBc4sfg1b-",
+        "outputId": "bb469225-f242-4f8f-c1ef-e997d97d2066"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BwadUz9Azxss"
+      },
+      "source": [
+        "## Build model\n",
+        "Import standard modules and required PyGeNN functions and classes"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "agqWFZjickfU"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import mnist\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pygenn import (create_neuron_model, create_current_source_model,\n",
+        "                    init_postsynaptic, init_weight_update, GeNNModel)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iuwdL6IE2MuS"
+      },
+      "source": [
+        "Define some simulation parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "C68EDXn6cj-O"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation timestep of model in ms\n",
+        "TIMESTEP = 1.0\n",
+        "\n",
+        "# How many timesteps to present images for\n",
+        "PRESENT_TIMESTEPS = 100\n",
+        "\n",
+        "# How much to scale input images\n",
+        "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9PCMgNPCz86O"
+      },
+      "source": [
+        "Because the ReLU neurons our ANN was trained with are best matched by a very simple Integrate-and-Fire neuron without a leak, define a custom model with:\n",
+        "*   A single parameter (parameters are common across all neurons in population) `Vthr` which specifies it's spiking threshold\n",
+        "*   A single `V` state variable to hold the membrane potential of the neurons\n",
+        "*   Simulation code which simply adds the incoming current `Isyn` (this is a built in variable provided by GeNN) to the membrane potential `V` (note, we're assuming that the membrane resistance is 1 here)\n",
+        "*   Threshold condition code which causes the neuron to emit a spike if it's membrane potential `V` crosses the threshold `Vthr`\n",
+        "*   Reset code which zeros the membrane potential `V` after a spike is emitted."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-7lzXzmQcgbt"
+      },
+      "outputs": [],
+      "source": [
+        "if_model = create_neuron_model(\n",
+        "    \"if_model\",\n",
+        "    params=[\"Vthr\"],\n",
+        "    vars=[(\"V\", \"scalar\")],\n",
+        "    sim_code=\"V += Isyn;\",\n",
+        "    threshold_condition_code=\"V >= Vthr\",\n",
+        "    reset_code=\"\"\"\n",
+        "    V = 0.0;\n",
+        "    \"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ATwobw4Mw2LG"
+      },
+      "source": [
+        "We are going to convert MNIST digits to spikes by simply treating the intensity of each pixel (multiplied by a scaling factor) as a current and injecting it into the neurons in the input population throughout the stimulus presentation time.\n",
+        "\n",
+        "To do this we use a very simple custom current source model with:\n",
+        "\n",
+        "*   A single `magnitude` state variable to store the per-neuron current to inject\n",
+        "*   Injection code which injects a current of `magnitude` every timestep (`$(injectCurrent, X)` is a function provided by GeNN for use in current sources).\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EEQyoL-zcu-A"
+      },
+      "outputs": [],
+      "source": [
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TnawdZkzyJQZ"
+      },
+      "source": [
+        "Create a new model implementing `scalar` variables as single-precision and generating code into tutorial_1 directory"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "J1VY795eeFa8"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial_1\")\n",
+        "model.dt = TIMESTEP"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p4wFWaMS1M8_"
+      },
+      "source": [
+        "\n",
+        "Load the weight matrices extracted from our original ANN\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6RVz8FPmc4n5"
+      },
+      "outputs": [],
+      "source": [
+        "# Load weights\n",
+        "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+        "weights_1_2 = np.load(\"weights_1_2.npy\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ik9NVB3-1Mdy"
+      },
+      "source": [
+        "Create three populations of Integrate-and-Fire neurons sized to match the shapes of the weight matrices and initialised so their membrane potential's are all initialised to 0mv and their spiking thresholds to 5mv."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ODYWtS28cxGi"
+      },
+      "outputs": [],
+      "source": [
+        "if_init = {\"V\": 0.0}\n",
+        "if_params = {\"Vthr\": 5.0}\n",
+        "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+        "                                       if_model, if_params, if_init)]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DPD-YOP81dGN"
+      },
+      "source": [
+        "Because, in this first tutorial we want to examine the spike emitted by each neuron, turn on spike recording for each population."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "elcdezDSeTe4"
+      },
+      "outputs": [],
+      "source": [
+        "for n in neurons:\n",
+        "    n.spike_recording_enabled = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fj3wbKso1j2d"
+      },
+      "source": [
+        "Add synapse populations to sequentially connect the three populations of neurons. These are all configured identically with:\n",
+        "*   `DENSE` connectivity meaning that they are connected with a basic dense weight matrix(see [documentation](https://genn-team.github.io/genn/documentation/4/html/d5/d39/subsect34.html)).\n",
+        "*   The built in `StaticPulse` **weight update model** which is used for spiking synapses without any sort of learning. This has no parameters and a single state variable `g` representing its synaptic weights which we initialise using our arrays of pre-trained weights.\n",
+        "*   The build in `DeltaCurr` **postsynaptic model** which specified that weighted incoming spikes are added directly to the postsynaptic neuron's membrane potential without any additional shaping occuring. This model has no parameters or state variables.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Sx1VOU5udixG"
+      },
+      "outputs": [],
+      "source": [
+        "model.add_synapse_population(\n",
+        "        \"synapse_0_1\", \"DENSE\",\n",
+        "        neurons[0], neurons[1],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"))\n",
+        "model.add_synapse_population(\n",
+        "        \"synapse_1_2\", \"DENSE\",\n",
+        "        neurons[1], neurons[2],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eQaIR8-ByoSI"
+      },
+      "source": [
+        "Add current source to provide input into the input population of neurons"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "yENqkr6KeMLp"
+      },
+      "outputs": [],
+      "source": [
+        "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+        "                                         neurons[0], {}, {\"magnitude\": 0.0})\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "38pYD7rxytBT"
+      },
+      "source": [
+        "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout a single stimuli presentation."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0Tf07KUOeP-X"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load(num_recording_timesteps=PRESENT_TIMESTEPS)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vBHoR-Eu2r6R"
+      },
+      "source": [
+        "## Simulate model\n",
+        "First we load the two numpy arrays containing the images and labels from the MNIST test set and verify that the size of the input images matches that of the first (input) population and that the size of the last (output) population is enough to one-hot encode all of the labels."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "qVWFKwiRehF8"
+      },
+      "outputs": [],
+      "source": [
+        "testing_images = mnist.test_images()\n",
+        "testing_labels = mnist.test_labels()\n",
+        "\n",
+        "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+        "assert testing_images.shape[1] == neurons[0].num_neurons\n",
+        "assert np.max(testing_labels) == (neurons[-1].num_neurons - 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "t-F5qx030CdT"
+      },
+      "source": [
+        "PyGeNN uses *memory views* to directly expose the memory used by the simulation to numpy. Copy the first testing image into the memory view of the current source's magnitude variable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3z1ccKHeejeB"
+      },
+      "outputs": [],
+      "source": [
+        "current_input.vars[\"magnitude\"].values = testing_images[0] * INPUT_CURRENT_SCALE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "U06Zwnuk0ihm"
+      },
+      "source": [
+        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)\n",
+        "\n",
+        "On most systems, memory accessible by the GPU and the CPU is seperate.\n",
+        "Therefore, we need to manually copy the values we just placed in the current source's magnitude variable to the GPU (if we're running GeNN on the CPU, this call will not do anything)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OfdQXEtd0jRi"
+      },
+      "outputs": [],
+      "source": [
+        "current_input.vars[\"magnitude\"].push_to_device()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OfUYJwC13VOk"
+      },
+      "source": [
+        "Simulate the model for `PRESENT_TIMESTEPS` (`model.timestep` tracks integer timesteps whereas `model.time` tracks time in ms)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4qSoinT4etKq"
+      },
+      "outputs": [],
+      "source": [
+        "while model.timestep < PRESENT_TIMESTEPS:\n",
+        "    model.step_time()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-Xg5AItK3ahj"
+      },
+      "source": [
+        "Download the recorded spikes from the GPU"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "wOhN-Qcuexjy"
+      },
+      "outputs": [],
+      "source": [
+        "model.pull_recording_buffers_from_device()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dwGQ4ygO3f2b"
+      },
+      "source": [
+        "Plot raster plots of the spikes from all neuron populations, illustrating the correct label for this image with a horizontal line"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "dBFluY10e7Ba",
+        "outputId": "fd82e034-e5ad-4c3b-cbd2-e998429cfab1"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Create figure with one axis per neuron population\n",
+        "fig, axes = plt.subplots(len(neurons), sharex=True)\n",
+        "\n",
+        "# Loop through neuron populations and the axis we're going to plot their raster plot on\n",
+        "for n, a in zip(neurons, axes):\n",
+        "    # Extract spike times and IDs and plot\n",
+        "    spike_times, spike_ids = n.spike_recording_data[0]\n",
+        "    a.scatter(spike_times, spike_ids, s=1)\n",
+        "\n",
+        "    a.set_title(n.name)\n",
+        "    a.set_ylabel(\"Neuron ID\")\n",
+        "    a.set_xlim((0, PRESENT_TIMESTEPS * TIMESTEP))\n",
+        "    a.set_ylim((0, n.num_neurons))\n",
+        "\n",
+        "# Add an x-axis label and translucent line showing the correct label\n",
+        "axes[-1].set_xlabel(\"Time [ms]\")\n",
+        "axes[-1].hlines(testing_labels[0], xmin=0, xmax=PRESENT_TIMESTEPS,\n",
+        "                linestyle=\"--\", color=\"gray\", alpha=0.2);"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "tutorial_1",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mnist_inference/tutorial_2.ipynb.txt b/documentation/5/_sources/tutorials/mnist_inference/tutorial_2.ipynb.txt
new file mode 100644
index 000000000..86611c7e3
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mnist_inference/tutorial_2.ipynb.txt
@@ -0,0 +1,827 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Classification of the entire test set\n",
+        "In this tutorial we're going to take the model we developed in the previous tutorial, run it on the entire MNIST testing set and calculate the overall classification accuracy.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "Qqz__TiIdE9x",
+        "outputId": "912641fe-072b-48d1-aa90-f911ab463cd3",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 149MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8tqbF5GldF0o"
+      },
+      "source": [
+        "## Download pre-trained weights and MNIST test data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N-2PV7LcdFg_",
+        "outputId": "1404acd1-ba2c-4c08-c620-c1ad71ece658"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+            "To: /content/weights_0_1.npy\n",
+            "100% 402k/402k [00:00<00:00, 127MB/s]\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+            "To: /content/weights_1_2.npy\n",
+            "100% 5.25k/5.25k [00:00<00:00, 23.6MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+        "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "ddb641da-6ec7-459f-db01-5157d2a17f49",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "l7UOIOeX1xeE"
+      },
+      "source": [
+        "## Build model\n",
+        "As well as the standard modules and required PyGeNN functions and classes we used in the first tutorial, also import `time.perf_counter` for measuring the performance of our classifier and `tqdm.tqdm` for drawing progress bars"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "agqWFZjickfU"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pygenn import (create_neuron_model, create_current_source_model,\n",
+        "                    init_postsynaptic, init_weight_update, GeNNModel)\n",
+        "from time import perf_counter\n",
+        "from tqdm.auto import tqdm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FMBcXoyd4yS1"
+      },
+      "source": [
+        "As before, define some simulation parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "KqBx7iO_kApE"
+      },
+      "outputs": [],
+      "source": [
+        "TIMESTEP = 1.0\n",
+        "PRESENT_TIMESTEPS = 100\n",
+        "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2QlVBYQG431K"
+      },
+      "source": [
+        "Create very similar neuron and current source models. However, to avoid having to download every spike and count them on the CPU, here, we add an additional state variable `SpikeCount` to each neuron which gets incremented in the reset code to count spikes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-7lzXzmQcgbt"
+      },
+      "outputs": [],
+      "source": [
+        "# Very simple integrate-and-fire neuron model\n",
+        "if_model = create_neuron_model(\n",
+        "    \"if_model\",\n",
+        "    params=[\"Vthr\"],\n",
+        "    vars=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+        "    sim_code=\"V += Isyn * dt;\",\n",
+        "    reset_code=\"\"\"\n",
+        "    V = 0.0;\n",
+        "    SpikeCount++;\n",
+        "    \"\"\",\n",
+        "    threshold_condition_code=\"V >= Vthr\")\n",
+        "\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lWMtozHB3OrM"
+      },
+      "source": [
+        "Build model, load weights and create neuron, synapse and current source populations as before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Sx1VOU5udixG"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial_2\")\n",
+        "model.dt = TIMESTEP\n",
+        "\n",
+        "# Load weights\n",
+        "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+        "weights_1_2 = np.load(\"weights_1_2.npy\")\n",
+        "\n",
+        "if_params = {\"Vthr\": 5.0}\n",
+        "if_init = {\"V\": 0.0, \"SpikeCount\":0}\n",
+        "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+        "                                       if_model, if_params, if_init)]\n",
+        "model.add_synapse_population(\n",
+        "        \"synapse_0_1\", \"DENSE\",\n",
+        "        neurons[0], neurons[1],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"))\n",
+        "model.add_synapse_population(\n",
+        "        \"synapse_1_2\", \"DENSE\",\n",
+        "        neurons[1], neurons[2],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"));\n",
+        "\n",
+        "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+        "                                         neurons[0], {}, {\"magnitude\": 0.0})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jdggjUe13tT_"
+      },
+      "source": [
+        "Run code generator to generate simulation code for model and load it into PyGeNN as before but, here, we don't want to record any spikes so no need to specify a recording buffer size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "K8kHbKMJ3kIY"
+      },
+      "outputs": [],
+      "source": [
+        "model.build()\n",
+        "model.load()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oMxrFcIP66CX"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rUxwsE323l37"
+      },
+      "source": [
+        "Just like in the previous tutorial, load testing images and labels and verify their dimensions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0Tf07KUOeP-X"
+      },
+      "outputs": [],
+      "source": [
+        "testing_images = mnist.test_images()\n",
+        "testing_labels = mnist.test_labels()\n",
+        "\n",
+        "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+        "assert testing_images.shape[1] == weights_0_1.shape[0]\n",
+        "assert np.max(testing_labels) == (weights_1_2.shape[1] - 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r-TFULk_3i8z"
+      },
+      "source": [
+        "## Simulate model\n",
+        "In this tutorial we're going to not only inject current but also access the new spike count variable in the output population and reset the voltages throughout the model. Therefore we need to create some additional memory views"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3z1ccKHeejeB"
+      },
+      "outputs": [],
+      "source": [
+        "current_input_magnitude = current_input.vars[\"magnitude\"]\n",
+        "output_spike_count = neurons[-1].vars[\"SpikeCount\"]\n",
+        "neuron_voltages = [n.vars[\"V\"] for n in neurons]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JCDP_sTa4HTL"
+      },
+      "source": [
+        "Now, we define our inference loop. We loop through all of the testing images and for each one:\n",
+        "\n",
+        "1.   Copy the (scaled) image data into the current input memory view and copy it to the GPU\n",
+        "2.   Loop through all the neuron populations, zero their membrance voltages and copy these to the GPU\n",
+        "3. Zero the output spike count and copy that to the GPU\n",
+        "4. Simulate the model for `PRESENT_TIMESTEPS`\n",
+        "5. Download the spike counts from the output layer\n",
+        "6. If highest spike count corresponds to correct label, increment `num_correct`\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101,
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+            "098d0a4e89024f8fa8c61ff3f5c477d5",
+            "bb812d858cf746be9e33b71e67fc04f6"
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+        },
+        "id": "4qSoinT4etKq",
+        "outputId": "01a98bc1-3bb6-4fab-b172-598e3f90fb2b"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/10000 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
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+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Accuracy 97.44%%\n",
+            "Time 11.930175114999997 seconds\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Simulate\n",
+        "num_correct = 0\n",
+        "start_time = perf_counter()\n",
+        "for i in tqdm(range(testing_images.shape[0])):\n",
+        "    current_input_magnitude.values = testing_images[i] * INPUT_CURRENT_SCALE\n",
+        "    current_input_magnitude.push_to_device()\n",
+        "\n",
+        "    # Loop through all voltage variables\n",
+        "    for v in neuron_voltages:\n",
+        "        # Manually 'reset' voltage\n",
+        "        v.view[:] = 0.0\n",
+        "\n",
+        "        # Upload\n",
+        "        v.push_to_device()\n",
+        "\n",
+        "    # Zero spike count\n",
+        "    output_spike_count.view[:] = 0\n",
+        "    output_spike_count.push_to_device()\n",
+        "\n",
+        "    for t in range(PRESENT_TIMESTEPS):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Download spike count from last layer\n",
+        "    output_spike_count.pull_from_device()\n",
+        "\n",
+        "    # Find which neuron spiked the most to get prediction\n",
+        "    predicted_label = np.argmax(output_spike_count.values)\n",
+        "    true_label = testing_labels[i]\n",
+        "\n",
+        "    if predicted_label == true_label:\n",
+        "        num_correct += 1\n",
+        "\n",
+        "end_time = perf_counter()\n",
+        "print(f\"\\nAccuracy {((num_correct / float(testing_images.shape[0])) * 100.0)}%%\")\n",
+        "print(f\"Time {end_time - start_time} seconds\")\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "tutorial_2",
+      "provenance": []
+    },
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+      "display_name": "Python 3",
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diff --git a/documentation/5/_sources/tutorials/mnist_inference/tutorial_3.ipynb.txt b/documentation/5/_sources/tutorials/mnist_inference/tutorial_3.ipynb.txt
new file mode 100644
index 000000000..9edbe9ebd
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mnist_inference/tutorial_3.ipynb.txt
@@ -0,0 +1,888 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Faster classification of the whole test set\n",
+        "The model we developed in the previous tutorial classified MNIST successfully but was rather slow. Like ANNs, to maximise performance when simulating small SNNs like this on a GPU, we need to simulate multiple copies of the model at once and run them on **batches** of input images.\n",
+        "In this tutorial we will modify our model to do just that as well as off-loading further computation to the GPU to improve performance.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "qWqAJtsiejvU",
+        "outputId": "8d659e2f-0fe0-4c97-e61f-46023ef5df18",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 182MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "8tqbF5GldF0o"
+      },
+      "source": [
+        "## Download pre-trained weights and MNIST test data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "N-2PV7LcdFg_",
+        "outputId": "b7d8e21f-45e9-408a-c840-e6a2992f4ea7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+            "To: /content/weights_0_1.npy\n",
+            "100% 402k/402k [00:00<00:00, 50.3MB/s]\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+            "To: /content/weights_1_2.npy\n",
+            "100% 5.25k/5.25k [00:00<00:00, 23.2MB/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+        "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "1cc89063-bcd7-4d47-afd8-5968008ac3ca",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jBVpqi2k5mNb"
+      },
+      "source": [
+        "## Build model\n",
+        "Import standard module and PyGeNN functionality as before and configure simulation parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "agqWFZjickfU"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "from pygenn import (create_neuron_model, create_current_source_model, create_custom_update_model,\n",
+        "                    create_var_ref, init_postsynaptic, init_weight_update, GeNNModel)\n",
+        "from time import perf_counter\n",
+        "from tqdm.auto import tqdm\n",
+        "\n",
+        "TIMESTEP = 1.0\n",
+        "PRESENT_TIMESTEPS = 100\n",
+        "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "OTkuiEAx5qMG"
+      },
+      "source": [
+        "As we're going to use it in a few places, we add an additional simulation parameter to define the batch size."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ejMfqnhAkrye"
+      },
+      "outputs": [],
+      "source": [
+        "BATCH_SIZE = 128"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fojA3yl_6KU9"
+      },
+      "source": [
+        "Define the custom neuron and synapse models in exactly the same way as before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-7lzXzmQcgbt"
+      },
+      "outputs": [],
+      "source": [
+        "# Very simple integrate-and-fire neuron model\n",
+        "if_model = create_neuron_model(\n",
+        "    \"if_model\",\n",
+        "    params=[\"Vthr\"],\n",
+        "    vars=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+        "    sim_code=\"V += Isyn * dt;\",\n",
+        "    reset_code=\"\"\"\n",
+        "    V = 0.0;\n",
+        "    SpikeCount++;\n",
+        "    \"\"\",\n",
+        "    threshold_condition_code=\"V >= Vthr\")\n",
+        "\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "93YuiQG7qzG3"
+      },
+      "source": [
+        "As we increase the batch size of our model, the cost of resetting the spike counts and membrane voltages will increase. To counteract this, we can offload tasks like this to the GPU using a *custom update* model. These are defined using very similar syntax to neuron and synapse models but have one additional feature - variable references. These allow custom updates to be *attached* to existing neuron or synapse populations to modify their variables outside of the standard neuron and synapse updates."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "I8KZoiK1nQlK"
+      },
+      "outputs": [],
+      "source": [
+        "reset_model = create_custom_update_model(\n",
+        "    \"reset\",\n",
+        "    var_refs=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+        "    update_code=\"\"\"\n",
+        "    V = 0.0;\n",
+        "    SpikeCount = 0;\n",
+        "    \"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "kDWkDTCWqwt3"
+      },
+      "source": [
+        "Create a new model in exactly the same way as before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BSSdg6ckl6im"
+      },
+      "outputs": [],
+      "source": [
+        "model = GeNNModel(\"float\", \"tutorial_3\")\n",
+        "model.dt = TIMESTEP"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "njWcYaZk5w7G"
+      },
+      "source": [
+        "Set the model batch size"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iOyB3Z6qkVBM"
+      },
+      "outputs": [],
+      "source": [
+        "model.batch_size = BATCH_SIZE"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "enyL8xum-OpC"
+      },
+      "source": [
+        "Build model, load weights and create neuron, synapse and current source populations as before"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Sx1VOU5udixG"
+      },
+      "outputs": [],
+      "source": [
+        "# Load weights\n",
+        "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+        "weights_1_2 = np.load(\"weights_1_2.npy\")\n",
+        "\n",
+        "if_params = {\"Vthr\": 5.0}\n",
+        "if_init = {\"V\": 0.0, \"SpikeCount\":0}\n",
+        "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+        "                                       if_model, if_params, if_init),\n",
+        "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+        "                                       if_model, if_params, if_init)]\n",
+        "model.add_synapse_population(\n",
+        "        \"synapse_0_1\", \"DENSE\",\n",
+        "        neurons[0], neurons[1],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"))\n",
+        "model.add_synapse_population(\n",
+        "        \"synapse_1_2\", \"DENSE\",\n",
+        "        neurons[1], neurons[2],\n",
+        "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+        "        init_postsynaptic(\"DeltaCurr\"));\n",
+        "\n",
+        "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+        "                                         neurons[0], {}, {\"magnitude\": 0.0})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3S_ZASOdrnj3"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7PW3c8ibpx9x"
+      },
+      "outputs": [],
+      "source": [
+        "for n in neurons:\n",
+        "    reset_var_refs = {\"V\": create_var_ref(n, \"V\"),\n",
+        "                      \"SpikeCount\": create_var_ref(n, \"SpikeCount\")}\n",
+        "    model.add_custom_update(f\"{n.name}_reset\", \"Reset\", reset_model,\n",
+        "                            {}, {}, reset_var_refs)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vv-XOushroKw"
+      },
+      "source": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "muUbvSHOooev"
+      },
+      "outputs": [],
+      "source": [
+        "# Build and load our model\n",
+        "model.build()\n",
+        "model.load()\n",
+        "\n",
+        "testing_images = mnist.test_images()\n",
+        "testing_labels = mnist.test_labels()\n",
+        "\n",
+        "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+        "assert testing_images.shape[1] == weights_0_1.shape[0]\n",
+        "assert np.max(testing_labels) == (weights_1_2.shape[1] - 1)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "awF6vfLX-TVM"
+      },
+      "source": [
+        "First of all, we determine where to split our test data to achieve our batch size and then use `np.split` to perform the splitting operation (the last batch will contain < `BATCH_SIZE` stimuli as 128 does not divide 10000 evenly)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BB0kXBmQkwCX"
+      },
+      "outputs": [],
+      "source": [
+        "batch_splits = range(BATCH_SIZE, testing_images.shape[0] + 1, BATCH_SIZE)\n",
+        "\n",
+        "testing_image_batches = np.split(testing_images, batch_splits, axis=0)\n",
+        "testing_label_batches = np.split(testing_labels, batch_splits, axis=0)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pd4BBHjtur5E"
+      },
+      "source": [
+        "## Simulate model\n",
+        "Our batched simulation loop looks very similar to the loop we defined in the previous tutorial however:\n",
+        "*   We now loop over *batches* of images and labels rather than individual ones\n",
+        "*   When we copy images into the input current view, we only copy as many images as are present in this batch to handle the remainder in the final batch\n",
+        "*   We specify an axis for `np.argmax` so that we get the neuron with the largest spike count in each batch\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 101,
+          "referenced_widgets": [
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+            "fdf6330a16a94010ad46847e57aa8dbf",
+            "0eefe97369fa4e91b591ee1ac2568a16",
+            "514826e392cc400796831ac477a1d9c3"
+          ]
+        },
+        "id": "4qSoinT4etKq",
+        "outputId": "1d566ed4-7151-4fdc-a67d-770d2b7d5958"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/79 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "15e7a28075234a688d35420b0d90cd3a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "Accuracy 97.54%%\n",
+            "Time 0.34431284400000095 seconds\n"
+          ]
+        }
+      ],
+      "source": [
+        "current_input_magnitude = current_input.vars[\"magnitude\"]\n",
+        "output_spike_count = neurons[-1].vars[\"SpikeCount\"]\n",
+        "neuron_voltages = [n.vars[\"V\"] for n in neurons]\n",
+        "\n",
+        "# Simulate\n",
+        "num_correct = 0\n",
+        "start_time = perf_counter()\n",
+        "for img, lab in tqdm(zip(testing_image_batches, testing_label_batches),\n",
+        "                     total=len(testing_image_batches)):\n",
+        "    current_input_magnitude.view[:img.shape[0],:] = img * INPUT_CURRENT_SCALE\n",
+        "    current_input_magnitude.push_to_device()\n",
+        "\n",
+        "    # Run reset custom update\n",
+        "    model.custom_update(\"Reset\")\n",
+        "\n",
+        "    for t in range(PRESENT_TIMESTEPS):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Download spike count from last layer\n",
+        "    output_spike_count.pull_from_device()\n",
+        "\n",
+        "    # Find which neuron spiked most in each batch to get prediction\n",
+        "    predicted_lab = np.argmax(output_spike_count.view, axis=1)\n",
+        "\n",
+        "    # Add number of\n",
+        "    num_correct += np.sum(predicted_lab[:lab.shape[0]] == lab)\n",
+        "\n",
+        "end_time = perf_counter()\n",
+        "print(f\"\\nAccuracy {((num_correct / float(testing_images.shape[0])) * 100.0)}%%\")\n",
+        "print(f\"Time {end_time - start_time} seconds\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y34o04ucAUjJ"
+      },
+      "source": [
+        "And...we get a speed up of over 30x compared to the previous tutorial"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "tutorial_3",
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diff --git a/documentation/5/_sources/tutorials/mushroom_body/1_first_layer.ipynb.txt b/documentation/5/_sources/tutorials/mushroom_body/1_first_layer.ipynb.txt
new file mode 100644
index 000000000..ee522bfba
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/1_first_layer.ipynb.txt
@@ -0,0 +1,529 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Presenting latency-coded inputs\n",
+        "In this first tutorial we build an input layer of spiking \"projection neurons\" for our mushroom body model which converts MNIST digits into latency-coded spikes.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "maq6gt0pgTiQ",
+        "outputId": "166cc2f9-55c9-423f-b724-34456abe7aa1",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 279MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "675537d0-38e4-4724-f244-67bbe4c39dac",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yV0JrchrfQKR"
+      },
+      "source": [
+        "## Build tutorial model\n",
+        "Import modules"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hl53yKXi9LiV"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "from copy import copy\n",
+        "from matplotlib import pyplot as plt\n",
+        "from pygenn import create_current_source_model, init_postsynaptic, init_weight_update, GeNNModel"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "u67gXzipEue5"
+      },
+      "source": [
+        "Load training images from downloaded file and normalise so each image's pixels add up to one"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "X9JrpOMu9LiZ"
+      },
+      "outputs": [],
+      "source": [
+        "training_images = mnist.train_images()\n",
+        "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+        "\n",
+        "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "mRl2x1HA9Lia"
+      },
+      "source": [
+        "## Visualize training data\n",
+        "Reshape first training image from 784 element vector to 28x28 matrix and visualize."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 430
+        },
+        "id": "N2jR9guR9Lic",
+        "outputId": "662c041c-99fb-437d-d34d-d619b4d2ac4e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": "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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axis = plt.subplots()\n",
+        "axis.imshow(np.reshape(training_images[0], (28, 28)));"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g0IfyML59Lif"
+      },
+      "source": [
+        "## Parameters\n",
+        "Define some model parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oncGyriW9Lif"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation time step\n",
+        "DT = 0.1\n",
+        "\n",
+        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+        "INPUT_SCALE = 80.0\n",
+        "\n",
+        "# Number of Projection Neurons in model (should match image size)\n",
+        "NUM_PN = 784\n",
+        "\n",
+        "# How long to present each image to model\n",
+        "PRESENT_TIME_MS = 20.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ddx0SZ80Fe9z"
+      },
+      "source": [
+        "Define a standard set of parameters to use for all leaky-integrate and fire neurons"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "---jKi0cFdec"
+      },
+      "outputs": [],
+      "source": [
+        "# Standard LIF neurons parameters\n",
+        "LIF_PARAMS = {\n",
+        "    \"C\": 0.2,\n",
+        "    \"TauM\": 20.0,\n",
+        "    \"Vrest\": -60.0,\n",
+        "    \"Vreset\": -60.0,\n",
+        "    \"Vthresh\": -50.0,\n",
+        "    \"Ioffset\": 0.0,\n",
+        "    \"TauRefrac\": 2.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lAgVgaYcFq68"
+      },
+      "source": [
+        "Make a copy of this to customise for our Projection neurons and increase the refractory time way above `PRESENT_TIME_MS` so these neurons will only spike once per input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9LLcZa-nFjN7"
+      },
+      "outputs": [],
+      "source": [
+        "# We only want PNs to spike once\n",
+        "PN_PARAMS = copy(LIF_PARAMS)\n",
+        "PN_PARAMS[\"TauRefrac\"] = 100.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCYjAoJf9Lig"
+      },
+      "source": [
+        "## Custom models\n",
+        "We are going to apply inputs to our model by treating scaled image pixels as neuronal input currents so here we define a simple model to inject the current specified by a state variable. Like all types of custom model in GeNN, the `var_name_types` kwarg is used to specify state variable names and types"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IR8PXBg69Lih"
+      },
+      "outputs": [],
+      "source": [
+        "# Current source model, allowing current to be injected into neuron from variable\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gn4DpkPQ9Lii"
+      },
+      "source": [
+        "## Model definition\n",
+        "Create a new model called \"mnist_mb_first_layer\" with floating point precision and set the simulation timestep to our chosen value"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gx-GsJhD9Lik"
+      },
+      "outputs": [],
+      "source": [
+        "# Create model\n",
+        "model = GeNNModel(\"float\", \"mnist_mb_first_layer\")\n",
+        "model.dt = DT"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AlMTvSBNHYSD"
+      },
+      "source": [
+        "Add a population of `NUM_PN` Projection Neurons, using the built-in LIF model, the parameters we previously chose and initialising the membrane voltage to the reset voltage."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "OnHOIAyVHAFH"
+      },
+      "outputs": [],
+      "source": [
+        "# Create neuron populations\n",
+        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+        "\n",
+        "# Turn on spike recording\n",
+        "pn.spike_recording_enabled = True"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sdYo9umiH06S"
+      },
+      "source": [
+        "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7e1if0YCG_7m"
+      },
+      "outputs": [],
+      "source": [
+        "# Create current sources to deliver input to network\n",
+        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-GU4oXOS9Lil"
+      },
+      "source": [
+        "## Build model\n",
+        "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-FE02Zoz9Lim"
+      },
+      "outputs": [],
+      "source": [
+        "# Concert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "# Build model and load it\n",
+        "model.build()\n",
+        "model.load(num_recording_timesteps=present_timesteps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcpTaaB39Lim"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "In order to ensure that the same stimulus causes exactly the same input each time it is presented, we want to reset the model's state after presenting each stimulus. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7ENTbLZpGvye"
+      },
+      "outputs": [],
+      "source": [
+        "def reset_neuron(pop, var_init):\n",
+        "    # Reset variables\n",
+        "    for var_name, var_val in var_init.items():\n",
+        "        pop.vars[var_name].view[:] = var_val\n",
+        "\n",
+        "        # Push the new values to GPU\n",
+        "        pop.vars[var_name].push_to_device()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hHUa3hbMGwWG"
+      },
+      "source": [
+        "As an initial test of our model, we loop through 4 stimuli and show the Projection Neurons spikes emitted by the model in response."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "I5Qsfgq99Lin",
+        "outputId": "0ac4adda-b2ad-496f-cb61-f906b8bbc035"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "for s in range(4):\n",
+        "    # Set training image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state for next stimuli\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "\n",
+        "    # Download spikes from GPU\n",
+        "    model.pull_recording_buffers_from_device();\n",
+        "\n",
+        "    # Plot PN spikes\n",
+        "    fig, axis = plt.subplots()\n",
+        "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+        "    axis.scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+        "    axis.set_xlabel(\"Time [ms]\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "1_first_layer",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mushroom_body/2_second_layer.ipynb.txt b/documentation/5/_sources/tutorials/mushroom_body/2_second_layer.ipynb.txt
new file mode 100644
index 000000000..67e290e4d
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/2_second_layer.ipynb.txt
@@ -0,0 +1,493 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Adding Kenyon Cells\n",
+        "In this second tutorial we add a large population of Kenyon Cells to the mushroom body and visualize their spiking activity in response to latency coded MNIST digits.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "R-jOIOlfheKy",
+        "outputId": "c11fde32-f153-4519-c8a3-ae516831fc77",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 69.0MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "cd4e1641-ae8b-48b6-d1a6-b53faad33f86",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yV0JrchrfQKR"
+      },
+      "source": [
+        "## Build tutorial model\n",
+        "Import modules"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hl53yKXi9LiV"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "from copy import copy\n",
+        "from matplotlib import pyplot as plt\n",
+        "from pygenn import (create_current_source_model, init_postsynaptic,\n",
+        "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+        "\n",
+        "training_images = mnist.train_images()\n",
+        "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+        "\n",
+        "# Reshape and normalise training data\n",
+        "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g0IfyML59Lif"
+      },
+      "source": [
+        "## Parameters\n",
+        "Define some model parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oncGyriW9Lif"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation time step\n",
+        "DT = 0.1\n",
+        "\n",
+        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+        "INPUT_SCALE = 80.0\n",
+        "\n",
+        "# Number of Projection Neurons in model (should match image size)\n",
+        "NUM_PN = 784\n",
+        "\n",
+        "# Number of Kenyon Cells in model (defines memory capacity)\n",
+        "NUM_KC = 20000\n",
+        "\n",
+        "# How long to present each image to model\n",
+        "PRESENT_TIME_MS = 20.0\n",
+        "\n",
+        "# Standard LIF neurons parameters\n",
+        "LIF_PARAMS = {\n",
+        "    \"C\": 0.2,\n",
+        "    \"TauM\": 20.0,\n",
+        "    \"Vrest\": -60.0,\n",
+        "    \"Vreset\": -60.0,\n",
+        "    \"Vthresh\": -50.0,\n",
+        "    \"Ioffset\": 0.0,\n",
+        "    \"TauRefrac\": 2.0}\n",
+        "\n",
+        "# We only want PNs to spike once\n",
+        "PN_PARAMS = copy(LIF_PARAMS)\n",
+        "PN_PARAMS[\"TauRefrac\"] = 100.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KldVFE9dJdv8"
+      },
+      "source": [
+        "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZvNwgTphJeM9"
+      },
+      "outputs": [],
+      "source": [
+        "# Weight of each synaptic connection\n",
+        "PN_KC_WEIGHT = 0.2\n",
+        "\n",
+        "# Time constant of synaptic integration\n",
+        "PN_KC_TAU_SYN = 3.0\n",
+        "\n",
+        "# How many projection neurons should be connected to each Kenyon Cell\n",
+        "PN_KC_FAN_IN = 20"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCYjAoJf9Lig"
+      },
+      "source": [
+        "## Custom models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IR8PXBg69Lih"
+      },
+      "outputs": [],
+      "source": [
+        "# Current source model, allowing current to be injected into neuron from variable\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gn4DpkPQ9Lii"
+      },
+      "source": [
+        "## Model definition\n",
+        "Create a new model called \"mnist_mb_second_layer\" as before but add a second population of `NUM_KC` neurons to represent the Kenyon Cells."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gx-GsJhD9Lik"
+      },
+      "outputs": [],
+      "source": [
+        "# Create model\n",
+        "model = GeNNModel(\"float\", \"mnist_mb_second_layer\")\n",
+        "model.dt = DT\n",
+        "\n",
+        "# Create neuron populations\n",
+        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+        "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "\n",
+        "# Turn on spike recording\n",
+        "pn.spike_recording_enabled = True\n",
+        "kc.spike_recording_enabled = True\n",
+        "\n",
+        "# Create current sources to deliver input to network\n",
+        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sdYo9umiH06S"
+      },
+      "source": [
+        "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7e1if0YCG_7m"
+      },
+      "outputs": [],
+      "source": [
+        "# Create synapse populations\n",
+        "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+        "                                     pn, kc,\n",
+        "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+        "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+        "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-GU4oXOS9Lil"
+      },
+      "source": [
+        "## Build model\n",
+        "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-FE02Zoz9Lim"
+      },
+      "outputs": [],
+      "source": [
+        "# Concert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "# Build model and load it\n",
+        "model.build()\n",
+        "model.load(num_recording_timesteps=present_timesteps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcpTaaB39Lim"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+        " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7ENTbLZpGvye"
+      },
+      "outputs": [],
+      "source": [
+        "def reset_out_post(pop):\n",
+        "    pop.out_post.view[:] = 0.0\n",
+        "    pop.out_post.push_to_device()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DfcqDTVXdoRq"
+      },
+      "source": [
+        "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "K9pAP8OrJUub",
+        "outputId": "a271c2ed-8aa0-428a-c437-de4d2dbd27c9"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "4105 KC active\n",
+            "4822 KC active\n",
+            "2048 KC active\n",
+            "924 KC active\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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LAQDFxcXYt28f6urqxDkbN25EVlYWCgoKur3/nfFY6Tjk2TPwWOm4eHeFYRgmKWBJESaamDRN0+Ldia6wcOFCXHfddRg2bBhqamqwdOlS7N69GwcOHMCgQYMwd+5crF27FitXrkRWVhbmz58PANixYwcAn+TAxIkTkZubi+effx4OhwO33347fvrTn4YlOdDQ0ACbzQaXy9UtXieGYRiGYbpOOOt30ksO/OMf/8Att9yCU6dOYdCgQbj88svxySefYNCgQQCA3/zmN0hJScHs2bPR0tKCkpISvPLKK+L61NRUfPDBB5g7dy6Ki4uRmZmJOXPm4Be/+EW8hsQwDMMwTAKS9J6mRIE9TQzDMAyTfISzfve4nCaGYRiGYZhYwEZTEhEvvRHWOWEYhmEYNpqSihc2HEK1swkvbDjUrfdlnRMmFNi4Zhimp8NGE2MI65wwocDGNcMwPR02mpKIhSVjkGfPwMKSMd16X9Y5YUKBjWuGYXo6vHsuSvDuOYZhGIZJPnj3HMMwDMMwTJRho4lhGIZhGCYE2GhiGIZhGIYJATaaGIZhGIZhQoCNpiTi/lVfYOTicty/6otuvW+89XfifX+GYRiGAeJUsPd//ud/QjrvjjvuiHFPkovyvTXwar7fL95yQbfdV9bfiYfsQLzvzzAMwzBAnIymn/3sZ0GPmUwmNDY2wuPxsNHkR2lRLsr31qC0KLdb7zv3ipFYseVI3PR34n1/hmEYhgESTKfpxIkTeOqpp/Df//3fuPLKK7F+/fp4dylkWKeJYRiGYZKPpNNpOn36NB5//HGMHj0au3fvxoYNG5LKYGIYhmEYpucTl/Ac4Xa78dJLL+G5557DwIED8frrr+MHP/hBPLvEMAzDMAyjJC5Gk6Zp+J//+R8sWbIEHo8Hzz33HO6++26kpqbGozsMwzAMwzCGxMVoKioqwt///nfMnz8fCxYsQJ8+fdDY2BhwHucGMQzDMAyTKMQlp2n//v1oamrC888/j7y8PPTv31/3Y7fb0b9//4jaXr58OUwmExYsWCBea25uRllZGQYOHIi+ffti9uzZqK2t1V1XVVWF0tJS9OnTB4MHD8bDDz8Mj8fTlWFGHdYrSmz4/WEYhunZxMXTtHnz5pi0u2vXLvznf/4nioqKdK8/8MADKC8vx7vvvgubzYZ58+bhxhtvxPbt2wEAXq8XpaWlyMnJwY4dO3DixAnccccdsFgseO6552LS10h4YcMhOJvceGHDIQAQ2/BZuygxYD2p5ObNT47x3xTDMJ0SF8kBr9eLF154AatXr0ZrayuuuuoqLF26FBkZGRG3eebMGVx44YV45ZVX8Mwzz2DixIn47W9/C5fLhUGDBuGtt94SSeZfffUVxo0bh4qKCkyZMgXr1q3Dtddei5qaGmRnZwMAXn31VTz66KM4efIk0tLSDO/fHZIDE5/6G5xNbtgzLMi0mlHtbEKePQPbF10Zk/sx4cGLbnJz2fKP+G+KYXohCS858Nxzz+HnP/85+vbti7y8PPzud79DWVlZl9osKytDaWkppk2bpnu9srISbrdb9/rYsWMxdOhQVFRUAAAqKipQWFgoDCYAKCkpQUNDA/bv36+8X0tLCxoaGnQ/sWZhyRjk2TOwsGQM5l4xEnn2DBZ8TCBumzIM2xddyQZTksJ/UwzDGBG3MiqvvPIK/u3f/g0A8OGHH6K0tBT/9V//hZSU8O24t99+G59//jl27doVcMzhcCAtLQ12u133enZ2NhwOhzhHNpjoOB1TsWzZMjz11FNh97Ur3DZlmG5B5sWZYaKH/98XwzCMP3HxNFVVVeGaa64R/582bRpMJhNqamrCbuv48eP42c9+hj/96U9IT0+PZjc7ZfHixXC5XOLn+PHj3XZvhmEYhmG6n7gYTR6PJ8DAsVgscLvdYbdVWVmJuro6XHjhhTCbzTCbzdi6dStefPFFmM1mZGdno7W1FU6nU3ddbW0tcnJyAAA5OTkBu+no/3SOP1arFVlZWbofhmEYhmF6LnETt7zzzjthtVrFa83NzbjvvvuQmZkpXvvLX/5i2NZVV12Fffv26V676667MHbsWDz66KPIz8+HxWLBpk2bMHv2bADAoUOHUFVVheLiYgBAcXExnn32WdTV1WHw4MEAgI0bNyIrKwsFBQVdHi/DMAzDMMlPXIymOXPmBLx22223RdRWv379cP755+tey8zMxMCBA8Xrd999Nx588EEMGDAAWVlZmD9/PoqLizFlyhQAwPTp01FQUIDbb78dzz//PBwOBx5//HGUlZXpDDuGiQe8K49hGCYxiIvR9Prrr3fr/X7zm98gJSUFs2fPRktLC0pKSvDKK6+I46mpqfjggw8wd+5cFBcXIzMzE3PmzMEvfvGLbu1nOBgtpIm+0CZ6/xIJ1n9iGIZJDOKi09QT6Q6dJhkjTZlE15xJ9P4lEmxgMgzDxI6E12liuo6Rpkyia84kev8SCdZ/YhiGSQzY0xQlutvTxDAMwzBM12FPE8MwDMMwTJRho4lhGIZhGCYE2GhiGIZhGIYJATaaGIZhGIZhQoCNpiTizU+O4bLlH+HNT47FuysMw/Rg+FnDMGrYaEoiZJHDWMMPTYbpvXTns4Zhkgk2mpKI7tQ24ocmw/ReWEeNYdSwTlOU6Gk6TaxCzTAMw/QGwlm/2WiKEj3NaGIYhmGY3gCLWzIMwzAMw0QZNpoYhmEYhmFCgI0mhmEYhmGYEGCjiWEYhmEYJgTYaEoiulM7KRl1mpKxzwzDMEzywEZTEtGd2knJqNOUjH1mGIZhkgc2mpKI7hScS0Zxu2TsM8MwDJM8JL3RtGLFChQVFSErKwtZWVkoLi7GunXrxPHm5maUlZVh4MCB6Nu3L2bPno3a2lpdG1VVVSgtLUWfPn0wePBgPPzww/B4PN09FENumzIM2xdd2S1ik915r2iRjH1mGIZhkoekN5rOPfdcLF++HJWVlfjss89w5ZVX4vrrr8f+/fsBAA888ADWrFmDd999F1u3bkVNTQ1uvPFGcb3X60VpaSlaW1uxY8cOvPHGG1i5ciWWLFkSryExDMMwDJOA9EhF8AEDBuBXv/oVfvCDH2DQoEF466238IMf/AAA8NVXX2HcuHGoqKjAlClTsG7dOlx77bWoqalBdnY2AODVV1/Fo48+ipMnTyItLS2ke7IiOMMwDMMkH71WEdzr9eLtt99GY2MjiouLUVlZCbfbjWnTpolzxo4di6FDh6KiogIAUFFRgcLCQmEwAUBJSQkaGhqEt0pFS0sLGhoadD8MwzAMw/RceoTRtG/fPvTt2xdWqxX33Xcf3nvvPRQUFMDhcCAtLQ12u113fnZ2NhwOBwDA4XDoDCY6TseCsWzZMthsNvGTn58f3UExDMMwDJNQ9AijacyYMdi9ezc+/fRTzJ07F3PmzMGBAwdies/FixfD5XKJn+PHj8f0fkDP1yHq6eNjOuD3mmGYZKRHGE1paWk477zzMGnSJCxbtgwTJkzA7373O+Tk5KC1tRVOp1N3fm1tLXJycgAAOTk5Abvp6P90jgqr1Sp27NFPrOnpOkQ9fXxMB/xeMwyTjPQIo8mftrY2tLS0YNKkSbBYLNi0aZM4dujQIVRVVaG4uBgAUFxcjH379qGurk6cs3HjRmRlZaGgoKDb+94ZPV2HqKePj+mA32uGYZKRpN89t3jxYsycORNDhw7F6dOn8dZbb+GXv/wlNmzYgKuvvhpz587F2rVrsXLlSmRlZWH+/PkAgB07dgDwJY9PnDgRubm5eP755+FwOHD77bfjpz/9KZ577rmQ+8G75xiGYRgm+Qhn/TZ3U59iRl1dHe644w6cOHECNpsNRUVFwmACgN/85jdISUnB7Nmz0dLSgpKSErzyyivi+tTUVHzwwQeYO3cuiouLkZmZiTlz5uAXv/hFvIbEMAzDMEwCkvSepkSBPU0MwzAMk3z0Wp0mhmEYhmGYWMFGE5Mw8DZ0hmEYJpFhoymJuH/VFxi5uBz3r/qiy20looHC29AZhmGYRIaNpiSifG8NvJrvd1dJJAOFDLhJw/rzNvROSERDtzfA884wDMFGUxJRWpSLVJPvd1dJJJ0cMuAqj32H7YuuxG1ThsW7SwlJIhm6vQmed4ZhCN49FyV491zkvPnJMazYcgRzrxjJBlMn8DzFB553hunZhLN+s9EUJdhoYhiGYZjkgyUHGIZhGIZhogwbTQzDMAzDMCHARlOSEk35gVDhXUQMwzBMb4aNpiRCNpSiKT9AGBlFvIuIYRiG6c2w0ZRErN7jM5RW76kJS34gVA+RkVGUSDIFDMMwDNPdmOPdASYyXrzlArx4ywUhnSsbQ51tmZ57xUixtVrFbVOG8ZZrhmEYptfCRlMSUZRnw95qF4rybGFdZ2QMEWwUMQzDMExwWKcpSrBOE8MwDMMkH6zTxDAMwzAME2XYaGIYptcRD8kOhumN9DSpGjaakgh+0DOJTDI9HGMh2cEwTCA9Taom6Y2mZcuWYfLkyejXrx8GDx6MG264AYcOHdKd09zcjLKyMgwcOBB9+/bF7NmzUVtbqzunqqoKpaWl6NOnDwYPHoyHH34YHo+nO4diiCw5wDCJRjI9HMOR7GAYJnJ6mlRN0htNW7duRVlZGT755BNs3LgRbrcb06dPR2NjozjngQcewJo1a/Duu+9i69atqKmpwY033iiOe71elJaWorW1FTt27MAbb7yBlStXYsmSJfEYEsMkJcn0cHzxlgtwZFlpyLIdDMNExm1ThmH7oit7zM7sHrd77uTJkxg8eDC2bt2KqVOnwuVyYdCgQXjrrbfwgx/8AADw1VdfYdy4caioqMCUKVOwbt06XHvttaipqUF2djYA4NVXX8Wjjz6KkydPIi0tzfC+3bF7bviicvHvb5eXin+/+ckxISnQUz6YDMMwDNMd9Ordcy6XCwAwYMAAAEBlZSXcbjemTZsmzhk7diyGDh2KiooKAEBFRQUKCwuFwQQAJSUlaGhowP79+5X3aWlpQUNDg+4n1pA+k79OUzKFRRiGYRgmWelR4pZtbW1YsGABLrvsMpx//vkAAIfDgbS0NNjtdt252dnZcDgc4hzZYKLjdEzFsmXL8NRTT0V5BJ2zev7lytdDFa9kGIZhGCZyepSnqaysDF9++SXefvvtmN9r8eLFcLlc4uf48eMxv2cwelrMmGEYJlok065OJvHpMUbTvHnz8MEHH2Dz5s0499xzxes5OTlobW2F0+nUnV9bW4ucnBxxjv9uOvo/neOP1WpFVlaW7ifWyH/8sXgQdLVNfjgxDJNocPoCE02S3mjSNA3z5s3De++9h48++ggjRozQHZ80aRIsFgs2bdokXjt06BCqqqpQXFwMACguLsa+fftQV1cnztm4cSOysrJQUFDQPQMJgWfLD6Da2YRnyw8YPggiMWC6+nDhhxPDMIlGMu3qZBKfpDeaysrK8Oabb+Ktt95Cv3794HA44HA40NTUBACw2Wy4++678eCDD2Lz5s2orKzEXXfdheLiYkyZMgUAMH36dBQUFOD222/Hnj17sGHDBjz++OMoKyuD1WqN5/B0NLnbxG+jB0EkBkxXHy78cGIYJtHg9AUmmiS95IDJZFK+/vrrr+POO+8E4BO3fOihh7Bq1Sq0tLSgpKQEr7zyii70duzYMcydOxdbtmxBZmYm5syZg+XLl8NsDi1XPp6SAyriLUMQyf3j3WcmMvh9YxgmmQln/U56oylR6A6jadZLH2NvtQtFebagO+kShcuWf4RqZxPy7BnYvujKmF3DxB9+3xiGSWbCWb97lORATyfRDSWZSGQQWDohOeH3jWGY3gJ7mqJEd3iaGIZhGIaJLr1aEZxhogHLJzAMwzD+sNGUBKgW8GRa1JOprwTLJzAMwzD+sNGUBKgW8ERa1I2Monj3NRKjbdKw/kg1+X4zDMMwDMBGU1Kg0j9KJE0kI6Mo3n2NxGirPPYdvJrvN8MwDMMAnAgeNXpzInii6/SwZhTDMAwTDNZpigO92WhiGIZhmGSFd88xDMMwDMNEGTaaGIZhGIZhQoCNJoZhGIZhmBBgo4lhGIZhGCYEuPZclKB8+oaGhjj3hGEYhmGYUKF1O5R9cWw0RYnTp08DAPLz8+PcE4ZhGIZhwuX06dOw2WydnsOSA1Gira0NNTU16NevH0wmU1jXNjQ0ID8/H8ePH+/VcgU8Dx3wXHTAc9EBz4UPnocOeC46iHQuNE3D6dOnkZubi5SUzrOW2NMUJVJSUnDuued2qY2srKxe/6EHeB5keC464LnogOfCB89DBzwXHUQyF0YeJoITwRmGYRiGYUKAjSaGYRiGYZgQYKMpAbBarVi6dCmsVmu8uxJXeB464LnogOeiA54LHzwPHfBcdNAdc8GJ4AzDMAzDMCHAniaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBNhoYhiGYRiGCQE2mhiGYRiGYUKAjSaGYRiGYZgQYKOJYRiGYRgmBMzx7kBPoa2tDTU1NejXrx9MJlO8u8MwDMMwTAhomobTp08jNzcXKSmd+5LYaIoSNTU1yM/Pj3c3GIZhGIaJgOPHj+Pcc8/t9Bw2mqJEv379APgmPSsrK869YRiGYRgmFBoaGpCfny/W8c5goylKUEguKyuLjSaGYRiGSTJCSa3hRHCGYRiGYZgQiKvRtGzZMkyePBn9+vXD4MGDccMNN+DQoUO6c5qbm1FWVoaBAweib9++mD17Nmpra3XnVFVVobS0FH369MHgwYPx8MMPw+Px6M7ZsmULLrzwQlitVpx33nlYuXJlQH9efvllDB8+HOnp6bjkkkuwc+fOqI+ZYRiGYZjkJK5G09atW1FWVoZPPvkEGzduhNvtxvTp09HY2CjOeeCBB7BmzRq8++672Lp1K2pqanDjjTeK416vF6WlpWhtbcWOHTvwxhtvYOXKlViyZIk45+jRoygtLcW//uu/Yvfu3ViwYAF++tOfYsOGDeKcP//5z3jwwQexdOlSfP7555gwYQJKSkpQV1fXPZPBMAzDMExioyUQdXV1GgBt69atmqZpmtPp1CwWi/buu++Kcw4ePKgB0CoqKjRN07S1a9dqKSkpmsPhEOesWLFCy8rK0lpaWjRN07RHHnlEGz9+vO5eP/rRj7SSkhLx/4svvlgrKysT//d6vVpubq62bNkyZV+bm5s1l8slfo4fP64B0FwuVxdngUk2/m/Ft9qlyzZp/7fi27j2Y/5bn2vfW/SBNv+tz+PaD4ZJdhLlb5rpHlwuV8jrd0LlNLlcLgDAgAEDAACVlZVwu92YNm2aOGfs2LEYOnQoKioqAAAVFRUoLCxEdna2OKekpAQNDQ3Yv3+/OEdug86hNlpbW1FZWak7JyUlBdOmTRPn+LNs2TLYbDbxw3IDXefNT47hsuUf4c1PjkW1LaN2VcdDfQ0AVmw5gmpnE1ZsOdJp+/ev+kJ5veq40f1Vx1fvqYFX8/1Wcf+qLzBycTnuX/VFWHNt1P+uIvfL/56hzH9XieTz0VvpLXNh9DfN9F4SZvdcW1sbFixYgMsuuwznn38+AMDhcCAtLQ12u113bnZ2NhwOhzhHNpjoOB3r7JyGhgY0NTXhu+++g9frVZ7z1VdfKfu7ePFiPPjgg+L/tGWRiRz5QXXblGGdnvvmJ8ewYssRzL1ipPJc/4eef7vy9XTuCxsOBbxG16/YcgSNLR44m9wB/aPz514xUtnXFzYcgrPJjWpnk/j/zqP1KN9bg9KiXGz7+iScTW7UOJugtd9L7rPq/vRvX5+/QbWz2XB+yvf6jKryvTXini9sOCTaH5iZhv01LpQW5eLFWy7otP90TbD5N3rP5PGv2VMDDT5jr/LYdwHzT+2H8/kIB6N2jY4bfRYjIRZtRqMv8ZiLeGD0N830XhLG01RWVoYvv/wSb7/9dry7EhJWq1XIC7DMQHSYe8VI5NkzQnpQqb4Jyt+C5bZU7b6w4ZAwlOh4i6ct4DV5oQCg7N/Oo/VwuJqw82h9SONsbPHoDBiCdrsOzExT3r/F40WqCZg0rD9aPF4AQIvHG2AwmYLMT2lRLlJNvt8yNBd7q12iT0YeBXn+QkW+Zk27V2zNnhqkWzoeQ9Rn1XsWzucjHI+IUbt0fNKw/p16Gl/YcChqXphYezrCmR+5L0ZzFarXNdE9VbdNGYbti65MasOPiQ0JYTTNmzcPH3zwATZv3qxT48zJyUFrayucTqfu/NraWuTk5Ihz/HfT0f+NzsnKykJGRgbOOeccpKamKs+hNpjYE86DSn5404OYFuVQFprGFo/utw9N2ZdJw/oj1QRMHT1I2b81UlhMtSAsLBmDPHuG+L+nTdMZMFNHD0KqCWhrv/3+GpdyrK2eNng1YN2XJ+Dx+k6m3zKabiyamJ+LRwzAkWWlePGWC0SfFpaMEQYYKZSMz7XpFj86d9aEXHGNTKjhRRkylNItKXistEDXPnkp/Oc6nM9HOEaHql1V/7d9fVLZJr0/AKJm6IRjIEZCOPMj98XoPYjEqEoWQ4phAMCkaVrgU7eb0DQN8+fPx3vvvYctW7Zg1KhRuuMulwuDBg3CqlWrMHv2bADAoUOHMHbsWFRUVGDKlClYt24drr32Wpw4cQKDBw8GALz22mt4+OGHUVdXB6vVikcffRRr167Fvn37RNs//vGPUV9fj/Xr1wMALrnkElx88cV46aWXAPjChUOHDsW8efOwaNEiw7E0NDTAZrPB5XKx16mbuWz5R6h2NsGeYUGm1Yy5V4zEs+UH0eT2IsOSigGZaah2NiHPnoHti64EAAxfVC6uz7NnBFwvLwqq9uXj455YhyZ3m649uo/M/au+ECEpOfxF7VtSTHC3aSjKs+FUY2unfZYxQTb3gKI8G/ZWdxheNL5g/fLvvz3DgqmjBwX0VQ69AAgI2VA/aJ787ymPH4ByLohg94okFBiJt4DeE9kYyrCkoNXTFlKfE91DEa++qu4rz7Xq85lM88okJ+Gs33H1NJWVleHNN9/EW2+9hX79+sHhcMDhcKCpyRcKsdlsuPvuu/Hggw9i8+bNqKysxF133YXi4mJMmTIFADB9+nQUFBTg9ttvx549e7BhwwY8/vjjKCsrg9VqBQDcd999+Pvf/45HHnkEX331FV555RW88847eOCBB0RfHnzwQfzhD3/AG2+8gYMHD2Lu3LlobGzEXXfd1f0Tw+gw+iZK326njh4kXmtye8Vv8hRNGtZfHM9o93RkWFKU18vQ9Y2tHmVI6uqCHKSafMaKv/dL7vPFIwYgx5aBi0cM0B2n9s2pvj6damxV9tmSYhK/5X9fN8HntZo1IRffLi/F6vmXK+cH0DB8UTlmvfSx7rjVnBow5spj38Gr+X4TcnhNDklS+3KYTeVxkNtUtS8jeyQiCVV1NbyiCu9azamiz6r3V75nJInswY5HkrRvdG68wk+q+3Y15Mcw3UlcPU3BJMtff/113HnnnQB84pYPPfQQVq1ahZaWFpSUlOCVV17Rhc2OHTuGuXPnYsuWLcjMzMScOXOwfPlymM0dee5btmzBAw88gAMHDuDcc8/FE088Ie5B/P73v8evfvUrOBwOTJw4ES+++CIuueSSkMbCnqbYMfGpv8HZ5IY9w4LdS6cHPU/+xko5SIDP8+FsciPDkoIBmdaARGTyGgT7xkuvE5YUEw4/d02n/ZPbIm9MfWMrmtxe2DMsaPG0CU+Y1ZwCZ5MblhQT2jRNlxwutyl/46bk7GBzMvGpDXA2eWDPMGPq6MEih4r4dnmp+Dd5gMbn+jxcwbw78jhdTW5o8Hm5jra3FcyTRoTjPeqqpykWyOOT35+FJWNC8p4YeVSMPn/BrguljXjSVU8Re5qYWBPO+h3X3XOh2Gvp6el4+eWX8fLLLwc9Z9iwYVi7dm2n7VxxxRX44osvOj1n3rx5mDdvnmGfmPijepBOGtYfDlcTJg3rrzNyiGZ3m+4bq7+nI9iOGWqXjA5Pm/HnVm5LTiQHfMnbFA7zecR87bnb2914oFa81uLxirHKXid5oVbNxelmj/jtbzAV5dl0fSWvz6nGVt1C679AUchu6uhBWLfvBNxtGswpJmFMpEmeGBm5f5217z8O/3ymWBHqohzMO6baUab6LBntyJI/vzLydUZ9TcRdX13d9ej/WUgG2NDruSREIjjTuzEKKchJy4TKZS8vann2dABAnj1dXH+dlGisCn8Fg9rNsKQg1QRcN0G/+yzT6gtvtXi8Qm9IDkNQ+IFCglZzqi486B8ea3Z7xWtWcyqeLT+IamcTVu+pUYYpVDvZ5ETz8bk2MRd59gzcNDlfGR4MNhd07ravT4r5zbSa28duFkZZs9urDLMY7bSLJJE/msnDoSYny2Ek+TOpCi9FksgezCiTrzPqa6hht+5Mvo51UnsiwiHFngsbTUxckB/aRg8YozwIamvSsP7iNYfLtw3f4WoW1188YoC4ngyAbV+fNFy06V6PlRaI3WcytOW/yd0WICMAdEgSjBrcTyy0owb3AwCMGtwPQwf0AdCRZ1WYZxM76qaOHiTys4iBmWm6OZPlB4gXb7lA9LWq/qzoZ7WzCc+WH9Rdb5RfRPPS4vEqjQYyygrzbBHlyagkFWRUC3w0F6WBmWm638Halz+Hwf7dFUIxLlTnyH0N1RjqzkW9N27f742GYm+BjSYmLsjeh3C8PoT8IKa2tn19UrxGCzn9BoIvFEY6TEZQUrYJEN4defEiT8w+aUcb/XtftUvsdKOQXVX9WZ1RR+0T+2tcuoey7JVSIRtTvvt4lYnOwXSIOoyyNpH8Lc//qcZWABC//VF5CmVUidYyqvctmosSSTzIUg+q9mOtHB6KcWH0BSJUYyia89edXqtkkSfojYZib4GNJibuGHk6QqXF4xUPVHkhV3miyLszdEAfsYAsLBmj3P1ktBAtnTUeefYMPH3D+cK7I19DoTJzqkkYirQHQrUXosXj1elImVP1J/VLN+seykZGib8xlWFJUXpKSIfIP4xG17dpEIKUMkYLMLUPQLng0XEah387oYa/ZMJZXOn9GZ9r6zTMZeTR6ar3JlKDQBUKNjKGojl/3em14rAXE2/YaGLiglFOSCRtWc2pSuVietBuPOAQnhLZu6Ba1MNRQSZ2Hq1X5glRqCwzrWPfBeWSt2mAPUO/H8NqThVJ4W5F0rmzyaPMY5H7r5qfojwbUk0+iQRVvTcZuX26njxesrQAEPoCTLlZwXKbgrUTybf2cJSp6f0hbaxg1xh5dLr6OY6GQRAtD0c489edoSgOezHxJq6SAz0JlhyIP8F2rMhGEOALo103IbdTyQF5F5JKnkCGrks1+TwxsiAi6SNVO5thzzAj02rB3CtG4qnV++Fu02BJMcHTpunEKfPs6ahraBHHgUDjSRaspL5SPbpQtrTTbsBUE/DU9ee3L46+fvqLa1L7k4b1F7XhjOZEVa9MFr9UySQYSRaEg1Fbqq35Rp8f+XWj3VGR7J4K5/5dvVekfSESUdqAYSIlacQtGSYYkQj6GdV+o9Qgc4pJlyhNBPsWa1Qmha4bn2sT3iW5LUoUdzZ1lGyhkJs51QSzX86Sfy05f4kDS4pJ174qUVs1P3J4Ut5dR0YN3dc/Z4qOVx77Tngx5Np5Kq+VylN3nVSGRXWNXI+uq6hCvkbekWBeGqOkcBVGu9yMxDGDtRXrpPhQxxfsbyVZco5kkrHPTPxgo4lJSMJZCOjc8r3qLfl0nHyqmVazcgGTjS75/qqCsqpF/3DdGZG8LS86JH+QalLXJqPt+2Q7FeXZdOG53PbrSfJgZuEQ3fWUqK2qQyePZdvXJ8Vrsjo57Rqj3Xvjc21KxW85UVw2ulTFh+VFlebixVsuUBpdhFyPTiYSdW2jXWbhhLEiCQmpdnfK4clwPt+xDgsaEamB19n1iWSocJ4UEw4cnosSHJ6LLuGEHGTxRwofAR0q0hRKkhWvZUVtqpMmh4+GDuiDvdUuFOXZMPyczIDrH3//S3F/CpUFCz+p+ieH52YWDglof8lfv0Sb5jOkTIBOnDLPnoG6081wezVYUk3ITDPD2eQWxy2pJhx+tkOxXBafbGrXUqJQnqzuLbdf42wKUPwOFpJRKYobhZcuW74J1c5m5NnTsX3RVbp2gtXmk2v/0ULnH0rt7LMSzTBWOCE7UlIngqmIh9LXeIgmhhOKU7/X4aujdyfRDAszyUk46zcbTVGCjSZjIn3gR/JQk0t+AAgoOSIfJwMpxUSJ2RacbnaLnJ8cW2DBW7l4LhXI9XmUTAFGm7zAd1YwWM6JkhXE8+zpATlRstE2a0JugOr3MzecL+5PBqJcRkY22ryaJhLTyXv0wd4aYbT9fZnPaDJ6/4KVu1EtkCMXl4v5PbIs0CiTDSGaSzlnSz6umt9YM+6J9aIMzsGnZ3Q6fnpdnn+jz3+wuYjHFvZYlEFJJMXsRDLgmPjAOU1MQhLOjhwZVSjH6DqV4KMsSSCLR5JmUlu7wbKwZIwu/KQKf8ySiuTSTjyHq1mp3Gykg0THS4s6FMvlgrwk1OmScqJM0m/K36FrivJsShVuqzlV9I/ypDxtHQYTABFepK9S8lcqOSSjCm+STIK/LpRKh0ueX/958A9DqSQJItlmH02a2wVHm/2ER1VQvx8rLYgoJBjv8FFXd+RFoo7encQjvMkkL3GtPcf0Lvy9B/74L5QE5c3IC6wMeVKeLT8g2reaU9HkboPVnCpCIY0tHtF+Y4tHGAjplhQ0uduQYUkR3zRvmzJMeLXoYbfzaL1o/+IRA1B57DuhMu7fP7mOGGkhkcfBf8v9O7uOo9rZhMYWN043e7DzaD3MqSZfbbdUE64uGCLCa6rFk+41s3CI6POon/tqMTa2eLB01viAeS9s944V5tlwuO60ENZUHe9szl/YcEgX3gQCdaFUSdkv3nJBgNfQv8aYf5+D1SCLR20y1fzIIbdZL30swrur518edv+M5iLWJJInyIiu9jXWn59gzzUVyTTvvRX2NDHdRqQ7clQ73WTIs9EkFeSVdaCMxBMfKy0QngAVqkRz+UGo6l84gp2kCO5s8ih3j337z0Z4NcDjJcNGE4t1YZ5NqIdvPOAQ32jlRHLVvMvinzT+WdLuNiq9Qr/9aWh2i9+q3XEyKk+T0bfvRPJEUF/vX/WFUjyVkPtM7+leSQU+GuKV3UU43q14e1Li7YkzIhxPVqKPhWGjiUkgIl0cyLORYUkRDyd595dqd5yRira8O44eemlm359LY4tb9yBULRrycWor05oqQoJy+M0ff5VwWnjJEKp2NuuMGjJgZKORmlU0H9A/FXJ4UzU+WZyTuHjEAOX7J5eEIRJ9cVApwstGs9H8FbUbtUWSJypRx2z0+TUi3uNKdMHLWO/UZLoXNpqYhMfom6wqZ2S1pK1EW71X76kReT7yg0z10FflUVEIy9nk0RllRjo+1Fa1s1l4n2SdJsqPIvy3ZvjbPf6Glr9o+MDMNGVOkmwIyv2j/Kc1ezqXbJBfl40COv7ChkMhexwi8T51J6qcNDnnzIibJucjz56Bmybni9ciqbHYHXTVqxTvhT6RvJJdpSeNpafCRhOT8NCi/mz5gU5rlwV70KiSdY2EDlXij0SGJUVnVBktGtRWUZ5NWWSXNJOonArpMhGyTUQ6TXLtPH9v0t5qF65rN8Sum9CRZyUbkqrFz5xiEou63D9VIrtsFNBxoEOHSlWGRfbqyeFLOpfe5+7wWBgZaLKBo9KZIkP82fKDyutVhki0aixGGyNNK5lIhD4ZpifBRhOTNDRL4ScjwTx5dxsZEGS0UMJuZ0KHcp4SLSpkm7R62jA+1+dpGZ9rM1w0yCi6aXK+OE/evUd9IcVwh6tZZ7TIniVadOXaeVnploB7GuWByeOnvphTU0T7qpywymPfKXO6iKmjBwWolPt79QjZKKFzG1s83eaJMfKuGBk4RrvnVIZIOB6ZSL1ukVynen+C9dVoDInkLWSYWMBGE5Pw0AJemGcLWGjlnWjBkrPp36vnX97pNvVgD3xaVMj4Ki3KVSYCByuTYeR1IEVue4ZZtC8bVRTKSzFBjF/2hNG5MqqxkOJ3hiVFZ7RQXwCtU6NFnjOV0bPt65MhexxURomnTes2T4zR4q/KWZv10scivKny5Ml01fsSzKgz+rIQrfyiYP2PpHyMUf8ZJplgo4lJGIyMFipTsvFArfL6YAthqGUgVIaYfP3FIwYIQ0yVnyKHbFQ5MXK/5OvJa3S62SPal5OnyevUpkGMXy6DYqTTRP0fNbgfUk3A1QU5OqOF+kJGy7avTyoXPzmPS75epYlllGgvzwmFGnPt6d1Wzywco4bmYm+1S4Rk5fkPtX+Rlk4xMork1+KdN2VkjMY7aZxhugobTUzCID9QVV6bJjdJC3iVi3Iw8UVVTpTRQheOp8i/f81ur3Lx2Hm0XrQpG0XkNRqfa1P2KdOq1z1qdnuVC6U51ffnLHu/5HndX+MSfZb7R2Nxt0uKt3jU/ZcL6srH5VBiZ8hzKr9XKnFQVf+NFtpg72kkRpfK6C1q93TKeW7BQpWqe4Zj0ATbqGAU9ot33lSksiIMkyywuCWTMMjil8+WH0ST24tnyw9iQGaaLhEbUAvSycJwsviiv46TLMgoC87J4oT+C5W/uKCqjIclxSdIWZhn0/WPyjRQPTd/TxZ5i8ioeWrNfsw83ydoOXX0IKyWdJto0b54xADdWCm8Ji9Icp/JuCKDSe4feZBSTL7+BzN+SAQ03ZKiFF+kRHGVojedoxL5G5/rE4qkPLHOPhedEaz9cMQFVfc0+qzRPYIlUtO1kRo0Rn2JphBmrMUV4yFEyjDRhD1NTMIgf0ttVnht8tp3lRXl2ZSeqGC7r/yNgBaPV/mtP9LSHHQueXr8BSHpXrQLrqHZrdv9Rgss1Y5zezXdAkvb+/Ps6SIkJPe1o3yJXtXbf1ykXu4P3SvTahbeOzm8R1IFowb3E9IOKvkCOVFcNb/BQk6q/DBV/40W22Dth/peBvOEGfVJDluq+tLZa6EQTigxVrlURDge2q7AuU9MosJGE5OQyIm2tBA0tvgMqar6s8rwCACxKMmJ1PRvyvmxmlOV4bVgD2iVDpFq9x2ZRf6CkBSKI9o06BSjVUahvMCunn85vl1eisYWb4Ahc/+qL4ToZZvWseX/2fIDIiRJBMvZontNHT1IvCbnKZG8wv4aV4D2lKxjFU6YKBZ5OMFCWqEaEuEkX8sY1UZU9S9SjBLBu0pXc5KilbOUqLlPbMwxbDQxCYnRlnn54U7/XlgyRixK8qKt8qTI19MD+tnyA8IQUeWRAB1GCe14o98AdLk98vXkCSLy/HSYOmrP+bbcDz8nU7fAkoEktyMv1LJOExkfze1CnM3uNnG9q8mnHO7fH2Lb1yeVC5Uq56pfui+yT78BvVEgG0IqT6A8/yrFcNXiJBuKRotXJF6dYNeoPh+q+ZFrD8Zq0TdKBO8qXc1JilbOUqLmPiWqMcd0H2w0MQkPLZCkA+S/I0v1oJeNGpUnRbXAN7nblIKVqtp1sk4S9W/ogD7CuyUbDR4/yW7ymBGq2nOyUUAGkrtNE+3LC7XcPBmKsqeOrqfT/Puj8tSpxDer6s+KBYM0pZxNHqUBIxutZCy1eLwBc3rblGHK3XeqxUk2FFWbBuTacJEQzGBQfT5kVAZ+rBb9aIb9IiGcsGUs7xMvEtWYY7oPk6b5F21gIqGhoQE2mw0ulwtZWVnx7k6PghKp8+wZ7WEw9WtyEuvSv34Jr+bzvBxZVhr0GgCY+NTf4GxyI8XkC3FRZXpClRx7/6ovUL63BqVFuSKXxwSfYWLP8IlNOpvcsGdYMHX0oHaPkC/R2p5hgbPd6wNAJJATGZYUDMi0ir4OzEzD3moXLKkmuL2azkM294qReGr1frjbNJjg03Iij0dH/+pR7WwW48uzp2PuFecpE5lpfLNe+hh7q10oyrPhVGMrqp1NyLCkoNXTpmvT5zUzdfpeUFK+PcOC3UunB7y/NP807mB9kudcToQnAyrV5DMaZa+g/3sdCfS5ofcplM+HilgnWXcnwd6LeIyrJ80rEx/CWb959xyTkMgPZdXuqUnD+sPhatLlwcjeB3lHFj1U6dxg3xLp60OwLfsvbDgk+vHtPxvh1YBv/9ko+kJGkT+0O25gZhr217gwdfQgndExadgAlO+twfhcn4Ey94qOgsOThvXvCFtpHeE3eaze9o5r6NBxavV4A7wi1LUaZ7PuetU3+n3t3q991S48fcP5IszY5G5D5bHvhLesscWr23VIhLOji65vbPF02icSKpXvIbcv7w40umc40OevscUDZ5M74PMh79RULdr0+atvbEWT2xv0vGRC9vrJGwCAQGM31kSyO5JhIiWu4blt27bhuuuuQ25uLkwmE95//33d8TvvvBMmk0n3M2PGDN059fX1uPXWW5GVlQW73Y67774bZ86c0Z2zd+9efP/730d6ejry8/Px/PPPB/Tl3XffxdixY5Geno7CwkKsXbs26uNlOuhqcq0qkXvSsP7CCyPvyFIpVsv3p0TxQqk2nGr3VYunQ7JATuSmnBxzqkmED+VEdLr/vuoOnaTti67Ct8tLsX3RVeL6w3WnleOjsJW7XXxy44FaXZjA305rcnt14Tv/rfwmk3qnmRzeSm9XD6ffAET4cdKw/rqQmiqUEslONDn8GQ50vVwbLpLwjpG4qlH/WjzeThXhg5VcSUbkz5cqP7A7c344ZMZ0J3E1mhobGzFhwgS8/PLLQc+ZMWMGTpw4IX5WrVqlO37rrbdi//792LhxIz744ANs27YN9957rzje0NCA6dOnY9iwYaisrMSvfvUrPPnkk3jttdfEOTt27MAtt9yCu+++G1988QVuuOEG3HDDDfjyyy+jP2gGgHFCpapgrnyu6kHtM0aCSwbIi5qs3k0GiiwVYLQ7Ti5JQgaER9oipzJ6zCkmpVFGidlNUm09VaI54b/4+ieWZ1hSUHmsvv3+9QGeEf+A/FNr9qPa2YTVe2rE/R8rLRDyAjQXsjimnPOkMjYiWTy7msfS1Z1NRn0O1j8ypqzmVKXgJX0Wr5uQ26lKejIh53FFKtURLRI1/4npmSRMTpPJZMJ7772HG264Qbx25513wul0BnigiIMHD6KgoAC7du3CRRddBABYv349rrnmGvzjH/9Abm4uVqxYgcceewwOhwNpab4FaNGiRXj//ffx1VdfAQB+9KMfobGxER988IFoe8qUKZg4cSJeffVV5b1bWlrQ0tIi/t/Q0ID8/HzOaQqRcPIQjM4N9TiFV/LsGUJo0oSOZOmUFF/OkD3DEhByWrHlCE64mtCmBeYg+eco+V+vyumR86v8hTuL8mzYV+0S/bO1t29JMaFN01BalIt1X56A26vBkmrC4H7pqHY2wZ5hQabVjLlXjMTj73cY/M+0h9coPCjnYfnfnxa8nUfrA3JW5PCXfFxuS5XT1F2LWbCctVAJ1mfV60avAd0fpmIYJjLCyWlK+N1zW7ZsweDBgzFmzBjMnTsXp06dEscqKipgt9uFwQQA06ZNQ0pKCj799FNxztSpU4XBBAAlJSU4dOgQvvvuO3HOtGnTdPctKSlBRUVF0H4tW7YMNptN/OTn50dlvL2FaAr2hRoeksMr8u4y8grJniIZ8hqQneRpTwYGfAYOtZthUf85Uahu6IA+ok/y7jqLrBkA3448c/tr5hSTuH5wllWcQyVP3F5NtDV19CAxD9Sk3PTwczKFOKbsEZDFM4nV7SVTVu+pUYpjqmrXBRMK7S4i9XLQZwVAp2V4ghWHJuQxx9v7ESs9IdYpYno7CZ0IPmPGDNx4440YMWIEjhw5gp///OeYOXMmKioqkJqaCofDgcGDB+uuMZvNGDBgABwOBwDA4XBgxIgRunOys7PFsf79+8PhcIjX5HOoDRWLFy/Ggw8+KP5PniYmMfBf1OhbP3kgbpsyTCQVy4ni5EnxD6/4e2rkhGRaQEYN7icSvfVlXHxb1SkPirxepE1kTvV5rlJMPs9SaVEutn19Es4mNzKtZpHzVO1sBuCTJCjKs4ndbRsP1MKrAev2ncBl7f3/xfXnB3i6yLvmn2hNv8lToxJ3lD118pzQb38hSzmRP5jWln/7XfXKRFqiQ5VIrDKKKLw794qRyo0IkaL6/HXV2IpVcnRPSbrmHXdMpCS0p+nmm2/GrFmzUFhYiBtuuAEffPABdu3ahS1btsS7a7BarcjKytL9MImD7HUwKtir8qSodJpWz79c5HGoEn0p0VsWaQT0OUgq5WvKD8pKt4j25URyWb8I8Hmfbpqcjzx7Bm6anC/ad7dpSsVv+Tp/wUl5LgZmponjs9o9cbMmdOSUAQiYk2B5LHJxXyPvRDCl8u7CSPuIPIme9vl9tvygUpBTRjW/Rorz5Xtrws4DC2dM0YDapTqD3e1xipani0UqmUhJaKPJn+9973s455xz8M033wAAcnJyUFdXpzvH4/Ggvr4eOTk54pza2lrdOfR/o3PoOBMa0XTdd7UtVXikWUq0NlJWNgr5qRJ95Z1mstFDocAMS6rwyMjH5Tp0RGeLcqbVrDM0CtvDa3IoTu4fLfrm1BTRpnxctbuPBC3lUF6m1Zfo/M6u44ZzLe++U821PJdkFDa2eEJ+z6P5WVP1XxVqo/BtKLvg5PfHyCik+S0tyo2aoROr8CC16y8z0F1Ey9jhHXdMpCSV0fSPf/wDp06dwpAhQwAAxcXFcDqdqKysFOd89NFHaGtrwyWXXCLO2bZtG9zujkTdjRs3YsyYMejfv784Z9OmTbp7bdy4EcXFxbEeUo8imt/eutqWvKiSMZJrTxcPSjkPh8pzDMxM6/RBKi9+8vW0kNCOs4UlY3Q5P7TT6OqCbHGNfFxWFydoR11ji0cs1ikmKHdfkaZSm9ZxXOUpk3f/yf0no8Vk6vCEyWOl6yk8SGHGzpB336kWKPn9JU8beXJCec9j7SlQGWVyHpzK0I0UlWRComNkdMQq9ylaxk40jUrO8+pdxNVoOnPmDHbv3o3du3cDAI4ePYrdu3ejqqoKZ86cwcMPP4xPPvkE3377LTZt2oTrr78e5513HkpKSgAA48aNw4wZM3DPPfdg586d2L59O+bNm4ebb74Zubk+VeQf//jHSEtLw9133439+/fjz3/+M373u9/p8pF+9rOfYf369fj1r3+Nr776Ck8++SQ+++wzzJs3r9vnJJmJ5re3cNpSPbTkRZ+MEYerWTwoKQ9o44FaZUFaI4wK0qr6L18jH1fVLqPdee42TVeQl5AXbTl9XdV/OdEZ8IUDZU8WGS2aBuWY6Hq5oLCKYNpMqgVKFf4qzLOFXLg3ks9aOIubyiiTt9mryujI7dKYFpaM0f07EhLJg0sYGR2ypEc0iXeCvQoO9fUu4io5sGXLFvzrv/5rwOtz5szBihUrcMMNN+CLL76A0+lEbm4upk+fjqefflqXtF1fX4958+ZhzZo1SElJwezZs/Hiiy+ib9++4py9e/eirKwMu3btwjnnnIP58+fj0Ucf1d3z3XffxeOPP45vv/0Wo0aNwvPPP49rrrkm5LFwGZXwiGYipqoMh7zN3xdW8qlvb190FQBg+KJycT0lVVNJkGB9MiodYbTlPdiWdPq3nGi+bt8JuNs0WFJMMKea0NRegBdAgGRCuiUFTe42WFJMGJyVrhi/OaAMysYDDjS525BhScHVBTkBiuRy/4zKoBBd3fIfK8mAUNsPRzJAVUYnlHYj+ax3dV5i1VZnjFhULj6fR5eXxuw+iQAnlSc/4azfCaPTlOyw0RQe0Xx4k9FE+OskyZpF37Y/wMc9sU4YDVTnTa5d5l+6xV/nSe4zLaDBjA6Vjo9siADQ9Z/GQMdpLICGamdzgI5TuiUVTVKejSXVhMw0c8D1daeb4fZqyLCkAtDE+K3m1E6NInmuDj49M2AsnWkXhUNXr6fPQbBxdNWoirTfRv3qavvxaqszwtk9yTDxhmvPMQmPv1HSGUYPemEUNDTD3aYF7DZTQUrXKk+Pf203MpYyLCnKkNCaPTXQ4Mstom/V8vZ96rMsQyCj6q9s9FEdOpKRIoMJ8GUpNfklJru9GjKtqe2SBam61wFfInO6QlNK3lIvz7PVnIomd5sI49F8dSbpIBPqQh2pZECoGLUvfybDMS6i1e9g9zRqPx59NcK/TiDD9BSSKhGc6TmEk5sQanmLTKvvO4DVnKrLaZLFGymfgwyRnUfrxb8POhpEno+cM9NRZ61NXCMjC1ES8vWURyIbR/LuOSKlPRF71oRc3fyQ0CSRbkkRgpiWFFOAqGaePV0kbVdLxXkzLCkikVkug6IqA0LJ8fev+kKZkyMnkhvtDjPaPaciktybruYOyXMu91lVm09Gnquu9CvS3Jiuzq/qXE5uZhg1bDQxCYmqYK7s4VE91IPtaKLaaw5Xs1hcZB0hMkrIE+NfhJYMjLb2ROnyvXrtIXNqu9GU2mE0ydeTUQF07G6Tk8Jpdxy1/+0/GzHrpY8xfFE5Zr30sW4stCON1MEHZ1nFTjUK9QEmkbSdZ08X8/dYaYFIZFYt5FNHDxLzbFQwWSWJ0NjiURoPRrvnEqV2XbA+q3SU5D4bzZWRfIXqnpH2lTCaP/m4kfwGwzAdsNHEJCRGmkmqRZe2wu+rdukMKFpUxud27M6SdYT8kcNQQIdXKM+eLna5ybuDZK9NqMgFeeU6doBvSz+NZW+1S1ccmOZB9iQRFIobmJmGxhbSPvIq50/lVZELHo/P9XnnxufaDBdQMsDcbZpS0NJo95zKU9XV3ZPR1Pkir1qOLV3pXVPtfjRCNaeRGn1G8ytD8zJpWH9x3EjcMx6wp4tJVNhoYhISo4e2yhNAmFNMui3htKhU1Z8V3hFZR0iGvC/yQ5u8QoBJeGpI4LDZ7TUMv6jCX/skA4+MIvJT+W/pv7ogB6kmX5kW6pNc+47GTwaUrPcEGBsVqtpxVfVnxW/VcXnMNL8ZkiGqMoSCGVIqccuuhm+jqTJO77/D1ayUZCApgotHDAg5JBaq9zRSgs2fykA2MqrjAXu6mESFjSYmITF6aKvKeNCinWk1R/TtX0Z+aKuMBlnokNh5tL7TMi1ywWA5D4oMuOsm5IrSKHLO0rovT4jadWQIDD8nE6kmXxFe6p/sCZNzpozCLxRqW72nBsMXlWPiUxukPC6vUpNKnn8a69UFOUpDVHVPmUjELWVi7RVReSplo5HGT4ZasJCjSjA0mPc0UowML9VnORGJt6eLYYLBRhOTkITzrVulyC2XAaG2hg7oozQk5ERq/9Ioc68YqTQavv1no8g/osVw9Z4aEbKT26dE4Z1H68VCKedBUf+3fX1SLKqU1J5pNYtcK6LF49Xl0ZBQZ32jW3jC5D5TKBDQOvV0EM4mjy7kaFRvjMa/8UBHgWuV902VHP/mJ8eEgZdrT1cu6EafBZUB0tWkcFX7pxpblZ4m2dPp7/2UDSDZEI1mTpOqL8EMLyNR1kQh3p4uhgkGG01MQtLVb92qRFe5oK78jZuMraIgitSqxUzOOaKSJ0ST26u7Rk46Jyj52+PVlLvr5EW/yK+2nNWcqvOkkeSALD0g35/CddXOjkR4eVEio4Xaz7Ck6DxVKqNOhVzbL9Q8qhVbjhiGv2KdFB7qTjM5D0juE831wpIxnRYxlg3RaOY0yYQT1o4FsdgVyTCJBBtNTEISycM9WG04aktO+pa/cas8CfKiRpIE7+w6rswp8k/kzrCk6BZAVdK5XCaF7tXi8al+y9pKAHDT5Hzk2TNwbXtB14UlY3QlPYygpO48qfaevHhReI5kbgdkWsPySJCBR+HFUN4z+f2l92p8rk15fSwWenn8oUomyDsF5T4ZGYh0L9qd6F8bMJqEE9aOBTRXL2w4pDSOIpFHYJhEghXBowQrgnedaCpCU+kQWd1Zbn/n0foAxeJgZTSW/vVLnU5ShiUVB5+eIf4/6udr4W7TkGIChtgyAvqvUkeWy7g8c8P5AcnsefYM0X9ZXJPKxMjtT3xqA5xNHt3xZ8sPosntRYYlFQMy0wLKqGz7+qSYqxaPN6AMizx+ulekKs+xUuLuCvI9aX6NlMTrG1vR5PaGrewdj/F1J6q/m2Dq+arPQm+aHw43JibhrN/saWIShnDCMKpvp3JISfY00blAR0FbWWeIco7e2XVctCV/I6dQGOGvwN0hqtnx56Tafbft65OdfqOWC+KqPBFN7eEvf4+Is8kTcFwO2VFOU5O7TYQJ5fwawpxqEmN+Z9dxVDubdHMSaT6M0fsa651kRvc0yn+iz4L8/soY9dXIq5TsnhaVPIi86UHGSB4hHnMRa3FP3gnYs2CjiUkYwglZqHSayBCqPPadboGn8Muz5QeUD0JKqqbdaf4PNwqFBYM0oVo9HUaLKuelxaM2eqh/jS1efLu8FKvnX65TLPfPOfIvuyIJkQtkbSd/CQKTSZ9fo9KZknO2iGiKL8rIYyVivdDIi3eoIatgxlWoivXB2o/HohrN3CPV+xtOGDBYrlt3EWtxT94J2LNgo4lJGMJ50AbTafLPk5GTumVPjLwAUs6PRVgfmnLRkA0RuXQGGSUUwmts8ejEK2lcQIfiuAz9v6HZLdqUd8eRAUiBdH8RTfKAUBmWqaMH6XSoaHxEm6ZXT1eVdJFztgij9yfYQtsxfnRqtMqK2om40AQbf1f7Go+xhqMYbkSslNi7i1iLe/JOwJ4F5zRFCc5pin3sPlj7qtfl/KapowehfK+vqG6b5jN6Dj49U1xP58rIOUVkmE0a1h+Vx77D3CtG4vH3vxTnFuXZdB4ZE3wGjFfzGTFPXX9+e05MC5rcbbBnWHT3s6SYdMnkpD5d7WxGnj0dk4YNQPneGqSYfOfZM8xYWDJWjPnZ8gNocrfp+i7nhviPL8XkM8C09r7mto+VcsFCef/CyWMhguWuRJorpepLqJ+7cK7prpyU7sx9MboX5+EwvYlw1m82mqIEG02xS+ikB7jRoiyjSgo3wWco+Cfy0rl03Fev7TyxaNC3btmokI0mMrDkRGsAwhCoPPZdwPVL/vol2jSfASNvviMZgdWSPEGGJTUgj4oML6o3558oDiCoUWWCT1TT3abBkmLC0lnjO010NkreBRAwPjnR/uIRAwKMTrl/XV2UI/nchXON0bnRMjB6ekJ0MNhAY+INJ4IzcSFWrnVV+E2FHB6Sw2/Ur8J2HSY5DAV0JJCT4KR/vTa6HoAyZEHHRw3uJ16TJQFUOj5kKJHhBPh+q2QE/A0mShj37//VBTmd5oZQ+O66Cbk68cwOOqw31ZZ8OSdMfq9V45O1qVSlO+KdMxLONUaJ6kY5c6HmByViSLI74ERpJplgT1OUYE9T7Aj1m6jRN3XZ+yR7Uug62RO1sGRMQPhpYGYa9te4AjxJZOSMXFwuQnKqxHF5HLKnSubb5b7rZEkC1XG5rRc2HOrU0yR7tf7e3i85JEaeMNlTRq/l2TNEWJEIxRMy7ol1aHK3IcOSgsdKC6ImX6AiHp4KlWRBh6eyI6Qreyp7mwcpVNjTxMQbDs/FATaauhfVgzbYw5cWaDmnadTgfthb7UJRng3Dz8lE+d4a5NjS4XA1BxgNqkVRpT0jG1UUkpL7Mu6J9UI7adTgvuL+X9a4Aowa2WiinKmiPBtWz788YC78c5b8daTktsjokvvyWOm4gPDnpGH9hVFDmk7BdKJo/OGE34IZsJEQj7CWKqdLHr9sKKly4tg4YJjEgcNzTI8nnDIUFCqikJjVnKrbUk9SBTXOjjIecqiEdreZU0zK8An1par+rKh3J6tM0047CrM1u72oqj8LAKiqP6sL1VEYRy7YK59LyLv3KBRJ+IfzZEmC4YvKMeulj3U6TjRvFOabNKy/TseK2r+6IEf5XtBY17TX3vMvo0LhqftXfRETDZ54hLVUn7WLRwxQllGhc8kQ5zBUcJJds4qJDon8OWCjiUlKwhEMpPIllnajR67nVpRnE0aRqT3nx7/2HG3xN0sKl3L7JG9AOkz+iyJtqScK82yiXp1/3Tq6Xi7oq2J1uyEoJ4wHIyvdovv/3mqXzigjqPDvxgO1uvNVtedUD7V0S4pSpJCMyvK9HUaVSvMo0gdlvLd0h2rA93SRy2jA+U0MkNifAzaamG4jmotCOIKBlKA9bkiWuIbqud00OV8YRW0ahNEgX9/hyTEJo0H2JJGOktXcYTTIRgEpimdYfPc51diqqz0ne4LIaKM6dC2eNtHW1NGDOq3nFQzyINFtLCkmpVHWLHnC5P6TV6uh2d3eJ69yfh4rLVAmepOxUFqUG+B9kd8/oxpwMolkYITq6Qr2maWx0PgTcaEgulOpnem9JPLngI0mptvozm8PsrgliU/uq3YFeD9WbDkijAqi2e3VXU+K1R5vRzK0XIaE/sDlXXnyAnnxiAHIsWXg6oJs5YNAlhyg8KAcslOFd2ShTeorYYK6jEu6JQWpJmBm4RDd/elc2l143YRcXf/JUyaHN1XhJ9kYkOePjr94ywWdGrrynBotzkafpVgv7nL7kXi6VJ44oPPdof7XxYPuVGpnei+J/Dlgo4npNrozPCGXUSFF7BQThCdD7gvl71hSTMJokK8no8HTpinLaKjCVzLPlh9EtbMJGw/UigeBPcO31d+eYRYhMhM6PE0kKyDLC8h9HtCuOD4gM02E1WTkxY0MmFZPm/CkyaVTyMNRVX9WKXlA82fPMAvJBtVDTWWoyXXqjN5fVZ+ChQJViu/Bxh8q4Xz+ulonUfX+TB09yHChiHfYIpE9AAzTHbDRxHQb4YTUumpAyQ/3w3VnAEBnWMj1ziivyCO5fORFmcJr103IFf1X1WuTkRO15bAXkWm1iN+yVhIZGo0tvnPptz/VzmbxW26X+imPf92XJ+DVOsbf5PbqSqeoyrjInGpsBQCcbvaI/snvjyq8pFpcjRZ8o9puL2w41KlRJhPJ4h6OQSKXyYmkXbl/4RRCjrfRYmQsx5p4e9oSBZ6H+MFGE5MwqOrJRSP8IhsVtCjL9c7IWNKAgHpvlce+E+G1i0cMEO2o6rXJi77cfmF70rktwyyMEtkoo+uum5CrXBBprE+t2d8uonhQ54m6boLPqJs1IVfpKXL7uaFSTHpPnH9OV/neGt380vsyPtcm+iy/P/TvFo83ICQnL65GhoZ8jUqcFECnRlmkITMycAdmpoXsCaWQr38xZBWqvqrEU0MxhBIxbNGd3q94e9oSBZ6H+MFGE5MwyN4fo4UknIKjslFBCw55j0qLcsXxPHu6eE1lwMmeDnlLvgq5ffLUOJs8SqNMlfMjGw10fzJ+mt1enSdKVh8nKCT4bPlBkQBOaJp+Iad7FbXnNJUW5Srn96CjQYT3ZKOP2rKaUzv1mAQzNFQGsGwc0L+pn8HyqCL1VJKBu7/GFbInVH5/jTAydMIxhEIdV3d6Ioz+VmWva7TuNWlY/4TxtMTD6xNvj2NvJiyjyev1Yu/evWhqCtypc/bsWezduxdtbW2KKxnGGNk7YxQGMHpoyMfJUwR06CDJhgb9e9KwDk9SOGVUCNlQkb1TZGAEM8poUbls+SaxuMj397/+ugm5uqRpFXJIUPYz0fUqhp+TKfose4VURpvKaJw6elDAmO5f9YV438hT5W9oGO2ao+sBKI0LlaconG/ioRpA8numMlQjXTxjkUvVnZ4II6NP/ruO1r0SSfMqHl6fRPQ49hbCMpr+7//9v/jJT36CtLRA93paWhp+8pOf4K233gq5vW3btuG6665Dbm4uTCYT3n//fd1xTdOwZMkSDBkyBBkZGZg2bRoOHz6sO6e+vh633norsrKyYLfbcffdd+PMmTO6c/bu3Yvvf//7SE9PR35+Pp5//vmAvrz77rsYO3Ys0tPTUVhYiLVr14Y8DiY6yIuXUfJsON/e6ToSX/RPLqZFN5j2kUr8UZV/IwtGyn0lrxJgEgut7FWjRaW6XVxzzR59eIyur2/3WPmjMrrIe+ZvINH9VeE1WUdJ9gqRsVAk7a6ToetlA0peKOn4qcZWZfjQCKNFSeUpCuebuMoAUqES7Az2+YxkfLInMxihjiuRPBHheOVCJZHGl0h9YWJPWGVUvv/976OsrAw333yz8vg777yD3//+99i2bVtI7a1btw7bt2/HpEmTcOONN+K9997DDTfcII7/8pe/xLJly/DGG29gxIgReOKJJ7Bv3z4cOHAA6em+nI6ZM2fixIkT+M///E+43W7cddddmDx5sjDeGhoaMHr0aEybNg2LFy/Gvn378JOf/AS//e1vce+99wIAduzYgalTp2LZsmW49tpr8dZbb+GXv/wlPv/8c5x//vkhjYXLqEQXVZmNcGpUqcpcUA01e4YvCZvaP93sDth9RuVGZIzKdcjlSp654fxOy2w88f6X0ODbMXfdhFydAKYJQK49Q9xrYGaaUDCn4+mWFFHbTa4LB/i8SU9d33H/d3Ydx95qF+wZZpxu9gTUzqN/y2Vkvv1nY6elW1TzG6wMi6qkjIxRHTqj952uH59rw6nG1m4pU6L6LKjmJFiZH/k4/VueP65RxzDdR8xqzw0ePBg7d+7E8OHDlcePHj2Kiy++GCdPqvM8Ou2IyaQzmjRNQ25uLh566CEsXLgQAOByuZCdnY2VK1fi5ptvxsGDB1FQUIBdu3bhoosuAgCsX78e11xzDf7xj38gNzcXK1aswGOPPQaHwyE8ZIsWLcL777+Pr776CgDwox/9CI2Njfjggw9Ef6ZMmYKJEyfi1VdfDan/bDRFF6PaZEYLqdGiRkVu7RkWDB3QJ8CoCGfxVtWek40OoF2FO9UEt1fTFcGVC9rKApWy0bX0r1/qjLoMSwqs5lTRf7nuHH2jl2vnAdC17W+UqWrr0Wvh1IYLZvwYvVfRqkPXnTXo5DGRUSobmHJfKFwoG0UAOv18ctglNsRjjvl9TXxiVnuusbERDQ0NQY+fPn0aZ8+eDXo8HI4ePQqHw4Fp06aJ12w2Gy655BJUVFQAACoqKmC324XBBADTpk1DSkoKPv30U3HO1KlTdSHFkpISHDp0CN999504R74PnUP3UdHS0oKGhgbdDxM9jMpsGIVCjHR85N1vlKidabVEFD6inJzDdadFTpBc247+7fZqok+kUj5qcD88W35AZ9TICuEAkGPzeVXtGWahvC33n3SeUkwIyKOSE7XpvHRJENOnPq7PixqYmWaYM6Ui2NZ51XsVzeRgwug9jyZyqE5+rwnVRgKgQ7zSaEddMFRhQd5+Hjqx1u+K1j2ZxCUso2nUqFHYsWNH0OMff/wxRo0a1eVOAYDD4QAAZGdn617Pzs4WxxwOBwYPHqw7bjabMWDAAN05qjbkewQ7h46rWLZsGWw2m/jJz88Pd4hMJ8gLCC2wlGgdbMu5jGoBlxOO5TprqsVW9aA0SlhudnfUnpNr29G/Lakm0ad91R0q5f7hNas5RXcvh8unyeRq6qhTJydik86TpnUkqsvHaS6XzhovjC6V5AA5s/bXuJQ6VEaLRzBJAZrfgZlp4no55yladejC0TuKBJU21ZufHFPW8VNtJFhYMiZgR2C4ekeqBTgehkCyEmv9rmjdk0lcwjKafvzjH+Pxxx/H3r17A47t2bMHS5YswY9//OOodS6RWbx4MVwul/g5fvx4vLvUo5Af6mvaE7Qp0XpgZlpYu+voddlrIu8uUy22qgdlsDIfKp2l1fMvx7fLS7F6/uWizt3M84eI46b29dXk51UCAgUzSZHbnGoSfaI6cA3NbmRafefb2j1Rc68YaegpUulEyZIDsiFDYyWjNZjRGExSgOZ3f41LuWX/nV3HUe1swju7Ov6GIlmoYrU4qcYv94+M0aWzxiuvD3WnUyhjVo0xHoZAshLJrrOufq54p1vPwmx8SgcPPPAA1q1bh0mTJmHatGkYO3YsAOCrr77Chx9+iEsvvRRz586NSsdycnIAALW1tRgypKNWVm1tLSZOnCjOqaur013n8XhQX18vrs/JyUFtrb5qO/3f6Bw6rsJqtcJqtUYwMiYYcuxffqin+yU7BxMUlK+RH1KUM5Nh6SioS7vWKBHa4WrSeZomDesf8Foo91JB+VONLR6Rs/P4+18C8AlK5tnTUe1sRoYlBa2eNkwdPUiXPC0WtnZX0MDMNBHuadM61MGdTR6hNO5pT4LySMlQ1A8yesgT9eItF4gdYSu2tOLiEQOENwToyM9R2HcAOt63HJtvHGTk+c+lnKi982i9OB4svCUnSoeC3Odo4p9v1uLxCv0sylOJxn3DGfPOo/Vdun8k89tbidXniklOwvI0/f73v8ff/vY3PPvsszhx4gRee+01/Od//idOnDiBZ599FmvWrMGMGTOi0rERI0YgJycHmzZtEq81NDTg008/RXFxMQCguLgYTqcTlZWV4pyPPvoIbW1tuOSSS8Q527Ztg9vdkSy7ceNGjBkzBv379xfnyPehc+g+TPcgh6Tkb3dXF+Qg1QRRr81/USYi3WYuh+qISMp0hJOnow/p+P5NteFkwUtZp8ndrly+v8alC//JkCFHKudyaZhgqMqgqMZKnrRgZU5q2o23qnp9XiPN5anG1oAiwOV7a5R19qL57TxaJXko+ubxaob9M7qnkaBnMFTyEJHA3g+GiYywjKaf//znWLVqFR555BHs3r0bjY2NOHv2LHbv3o1///d/xzXXXINTp06F3N6ZM2ewe/du7N69G4Av+Xv37t2oqqqCyWTCggUL8Mwzz2D16tXYt28f7rjjDuTm5oodduPGjcOMGTNwzz33YOfOndi+fTvmzZuHm2++Gbm5Pg/Cj3/8Y6SlpeHuu+/G/v378ec//xm/+93v8OCDD4p+/OxnP8P69evx61//Gl999RWefPJJfPbZZ5g3b14408NEiCp8Jj/UadE93ezL6TkVRK8omI4OJU0DJuVCI+srqcqcyOKKgC98plp0KIy4Zk+NzoBS5exQHlKm1SzulWNLVyYyU34SLdrjc2268B8Zk7InTdZpov6T+OTCkjFKxXG5DIpqXuUyMjJkVKRb1I8TlYFJhu/4XBvqG31faOh3tDEKRRkZODR+sj/dIRiiRurkkYbHaC5Li9Qld3ojkRjFvTWni+k6YYtb/tu//RtWr16te72xsREzZ85EXV0dNm/eHHJ7n332GS644AJccIFvt9KDDz6ICy64AEuWLAEAPPLII5g/fz7uvfdeTJ48GWfOnMH69euFRhMA/OlPf8LYsWNx1VVX4ZprrsHll1+O1157TRy32Wz429/+hqNHj2LSpEl46KGHsGTJEqHRBACXXnop3nrrLbz22muYMGEC/vd//xfvv/9+yBpNTNegBcRqTlV6MiJZKORFiYwOj7dNaRRkSIs9eT/kRGrKZWnxtAXUm5MhoyHdkqIzoGjRBToUyYcO6AMAGDqgjzAKHa5mZUFcKihMa/VBh36nJhmTzVIIU/ak0VxUHvtO6V0ItQyKrHguQ+N7rLRA+f6pDEwyfE81tuqMVtVCFs3izSpikUitUlSX2480T0ZVcieaJKMxEcn711tzupiuE5ZOEwD813/9F372s5+hvLwcV1xxBRobGzFjxgw4HA5s3bpVeHh6G6zTFDmx0DGR23y2/IAuJ8pfx0els7Tt65NCO8jV5NaVIvG/nvSJZHHIjQccATpMsk6PSpMJ0FDtbEZRni/3h3R8apxN8P8jlcU3L1u+CdXOZqSafEafvNVd1hHyhb9Mupwx+VxZfFOlPTRiUbkQ5DyqEP+M5vsj91/uayz0l0L9/I17Yp14T2WdLJW21MjF5fBqHbpZnYl3Rqt/kaLKJUwmgc1I5oe1k5KTWL1vMdNpAoCf/vSnWLp0Ka6//nps2bIFM2fORE1NDTZv3txrDSama3Q1P8SoTdqNZkkxBYTc5DZvmpwvvDNy+IpCXUV5NqV3wL8MyravT2JAu6dhQGaayBVq8XiV11NfKaF7b7VL54ko9MtbsqSYdP0nSQLK+R4o3fOFDYeEvEG1s1l4iuTwI5277euTnWoPBSvNEipyn+X3hzxUs6Tdh7InIFq74rpaG+7qghwh2WDUvrw7sKtSCLH2isRirruTSPKzOKcrOUkED2FYu+eIRx55BPX19bjqqqswfPhwbNmyBeeee260+8YwAAJrzoUL5e2Qp6fy2HcBBT/925e3wQ8/JzOgTfkbz/hcG/ZWu5Bi6gihkQFEu+II+vZO3pWMIHlAMpRYbUkxoU3TMLNwCJ5asx9ur4an1uwX9yd86uYW8X+TyafhRDT5FdxVyRMU5dl0iuYARHHjYBiVEQn2PgbbnRTt3WnB7m/0+ZJ3z8neF+ofjZs+X7SjkubKf17CJdY73eT2E3GnGKt4M0Qi7PoMKzx344036v6/du1aTJgwAXl5ebrX//KXv0Snd0kEh+dih+oBZvQaELhoz3rpY2EI3DQ5Xxwn+QF5SzxJAgAQ5UWIDEsqBrRv+/eVRPHl5aSYfOf6l1Gpqj8bEMpRlRwJFv5pbPHoko/9S6dkWFJFXhAhl2GhOneEJcWETKtZtA8g6mVMgO4rExJqu0ZlcIJdH2rJHnuGBZlWc0i153ghDp3uLI8Tz3sy8SNmtefuuuuukM57/fXXQ22yx8BGU/eieqjJr6lqp8l5JkeWlQa0JecE1TU0w92mwZJigjnVpMu5kYvslhblYvWeGl3fguU8yQaSqt6abNQdrjsdtCCvJcUEr6ahrX1HnWozl5zzRO2S8SfnWckLvJzT1dXcG1kHK5K2wrlXvPNwVMaQ6vMZrRp7vQ32NDGxJpz1O6zwXG80hpjuI5wHlUp8UnbdqlSr5YRcVVuypwkIrEIvGxWd5an4lxGhUNjGAw5ctvwjzL1ipNgR19jiEeOuaQ8B7a9xoV+6BU3uNpGPJRtOnjYN5vZQXarJ91vGX7uJdqrRWaQzJHtAAF9JFw2+HC1ZXFOei87el51H6+FwNWHn0Xrl/ETq6VHhn4cTS5e9Uf9UIa1w+pToC3SwQszdRTxChokYpmQSg7ATwRkmVhhp28jIOTmEnNyp0kaSt+HL0ALvL8ro3+7q+ZeL6+WE2VntCdJEMMVyuTYdhdvcbZrYym8ydey2kiUJqLgv5T8V5tl01/sX+PXXsfI34vx1higR3NyeKF9alKt7L0JNvpQFK1VlbIKVYTGq6adCbj8WSb1GxaFD1XaS+6T6TAJd15GKNfL72hOJ9/wyyQUbTUzCIC+EXd0loapnFuzhSPcFOhLCVQt5sN1fZIyR8VRalKs7V1WbjgydFFOHuGabBmGUyQV9yQgjb5O/UeQfnhuYmaa7v8qIU81FptWsNAo7jC6tUx2l8bkdtevk+aG5pHH618MzqpOnIta7n4x2lBkZ+OEsxLHQkQoHIyV7WYi0J5IIO7KY5CFsnSZGDec0RRejpG7VcVlnSE7kpqRoWSdJlfsi5xSpkrfHPbEeTW4vMiypeKx0XKf5O/K5B58OLC00fFG5+DflLWVYUnDw6ZkA9JpIhe072ei8ojwbDp5oEDlXgN57lGoC+qVbRP8zraliNx/db0CmtVOdJnlM5GkgQtFRkt8fqnlH+Of0JGKuTzjJ4TR+ORH82fKDAe9/pMnFsQ7fBcv1I4z63Z06UsnYPpP4xFSniWG6A9mToPomqDou1+OSPTnylnHVN3ryCpB3Z3+NSxlKaW73lDS7vbo+qcIX8rkqrwOF2igpO8+egasLcsR5ZKNo6PAskadpX7ULMwuHINUEzCwcIkqyyGVWZEjHydQ+/sdKC3SK1ZSLtPFArXJM1F6ePV3pCZQ1nwj5OM3lLKl2narMTTCldRWxDqkYebJUtQFbPB3hV/n9J7pDAymSeVF5kuR2wvGExeJ9ibUniDWbmHBgo4lJeIwe2qoyK1az76NtNaeI48EWZVVOj6qOXWGeTRJ37DBrZCFDQhaCVIX6KE+JfgMQRot/bg8typS6ZE4x6RKtySghZ9O+ar3Rl2PzlR2yZXTs+6CQ3f4aF1a3l3xpcnfUnpMX0o5woEnMifyeUF+2fX1SudCqSn/IcxKq+KNRnlF3IveF+g9oYv5UQqCRLs7hjDWSeZFL2qjaMep3NMPqRu0nC5wn1XNho4lJelSLMu06k4vr+gta+mNONSHHlhFQmJYW+Kr6syLnRxavpJymi0cMEA/KYEnnhGy0+Of8NLZ4RH7UrAm50qLsg0JA/kYJ4R9vp746mzxCETxYngoZP5QUX1V/Vtxr0rD+IeeEhYPRokgLEM1TsDyjWEH3n/XSxyL3R5XzJNfuk99/owXU6Hg4Y41kXlTXhNOObFSFel2wMateT0ZPULyNeiZ2cE5TlOCcptgRSS5IqIKYQMeW6pQUE9xeLaScm4lPbYCzyQN7hhkLS8YG5EzJOT8q8Uw5Z2jdvhO6nCT/2m6Ua2XPMON0sydg6zeNq76xBU3uNtgzzMi0WsScyXXuqP3c9tfz7BkYmJmmUzSXBTUtKSbMLByC8r01SDP7RDSD6WDJW9PJQCVjyz/nK5xt7CrxyO5cQOn+hAnA05J4KPUl2JiMPr/hfL67K//GKKewqwQbc08RleQ8qeSCc5qYuBNN93Qk355V4bWdR+uV/SNPjrvdneO/i4tyboYO6COuOd3s01k63exR5kzJ3zQ3HqiFV/OVN6HXZE+Ex2/7m3+tOfJKOZs87XpPtbodT3SvAZlWfLu8FLuXliglEfLs6SJkJB+nsIzVnCJyi6hPnjZN5DeRJ2xgZppyx5scZpPbV+V8hVOPjdpaWDImah6HcLw/dH9Kuk+3pCi9H8HGFGp4OZTPdyTyDMHG1RmRSE6EQ7Axh+p1TPSwVzJ6x5jQYE9TlGBPkx7VN8aulqsIBl0n7/6Sr1cpfsueEqpN1+GpsWDq6EHCa7Dt65NwNrmFora/90QWglSVbCHRSKDDqJI9TQA69XRdtnyTbvcb5Tb5764ryrNh9fzLA+aHPCA5tnQ4XM0BfQYQ4CmTx0f9oxCh/+48mr9g86/ywHT1m3hXrzfyaKi8i7H6/IZDJP2SCdWT053q7uHQUzxRTGLBniamW1F9+zPStlER6Tda1e452RNDidQ5tnSRqCt7SuhbIe1iW1gyRukdoUTxScP64+IRA0T+k+pbpZyzREnBslK33D61ZfZXqWyHdr8BEJ4iOtecYhK7/vZWu5TfwinRu9rZLO6pmuupoweJ90z2hKl0qOREc2qr8th3ym/XRvldoRLNRPCu5kTFK/dGtasznLkIddzyWMLxCkZCNDWtGCbWsNHEdBkjSQAimmEK1XXy7jnZKCH1cDIatn19Ep52t4nHqynDd/KOOFqoMq1msXgYqUTLidZkFFXVnxXXyMepLas5VakYTX0pyrMJQ83cLkFuTjXpjK1QFs8cW7purkmpe+OB2k4Xfdn4UW25lyUHjIjE6JFDU7FePFXGiXz/eCX6RvJ3ZXS9EbGe63DmksNeTLzh8FyU6M3hue4SnwsW/lEhh4QovEbYMyxwNblFeMuWYelUfNG/H8FCFnLoAID4d93pZri9vnInQ2z6nCc5FBYsUZxCafWNrWhye2HPsKDF4xWCmIBPwymlXZbAP4wy6udrO000JyFNQK9jFWoicCSJ2pGEusY9sS5ABDRSIgnzyKEx8rBF4zPf25OGe/v4mfgTs4K9DKMi1sUtycBwuJrg1SC0YzqDwkqAL7/IKSlqDx3QB4frTqPJ3YZ0S0rI5Tvkcb6w4ZDwWsk5Q3L4jhYCUidv0yAWaBKUnDSsv/CE7W0Ps8kK2qv31IidaOrgXQdkF/mHUTKtZp1RmG7RO5gpJ8qSatJ941f9WzXvZAQ2tng6PS/YXKrw1wmKNvLuxlChPLdwRDgBY6Of3u8XNhyKW65UPOHiuEwyweE5JuFRhd9kjHIiyBghRe39NS7x7yZ3m9B0oigXFcsNlTV+OUNyEWGgI5cpz54esGMvlDwRUu/ObVfkXlgyRhdeJFLaC/76h8lo919Rnk0ogstzJgoVax3XyyEZo/AMhUwopBXu+0PH71/1Rcgq1OG0rzovkjCPXCQ6nF1sqpy7cEgkzZ9k2b3GMLGCjSYmYQj2QKYFzl90kjAqruvP+Fwb8uw+lew8e7owKshTs7/Gpbte1ZZc+sPfc9PY4tH1afX8y/Ht8lIAHZ4c2ShQlRmRxS3J6Kt2NouFnsJt7jZNGH1tGpRGGy32h+tOdzpnnjZNGHLhGBU0PwDENXIiPuVMPVt+UPn+kFGwZk+N6JPq/rJgqQxd/8KGQ50u6PEyPijny/e5CzQEVflTMvFIfg7295NIBhzDxAM2mpiEweiBHMm3+xVbjggDJMPiW2xPNbZi+6Kr8O3yUmxfdJXw+pAOj5ycHUynRvYUXV2Qg1Qpduavu0QL0MDMtE4Tpi8eMUAYCvLuPBnVQkaLLvXfP9xI/29ytynnj66/boLak6cySox2ssmJ+Ko6bPJ7SUZFsN2D/v30Ny5CVSTvqvEh319VLy+YoUGfFd/nLvwk5ngkPwf7W+yq4jfDJDtsNDEJQyS7sAD1Yio/3MkAubogu1NBPSp8S8nYnYWn5IK3tChmWFKEJIBqS/7+GpcwtGSjQWUMyq+RV8wEiNco5FeUZxOLKvXfak7tdNFq8Xh1/TPy5NH4qSCtf59V8yPvPlTVYZOh+cu0mjv1uAQzHozCg0bXqzCSFFCFVyM1NBLRexPsbzHUOUzEMTFMNGCjiUkYjPJ8jMIYMvLD3UhHKNRFd+fRerGQ7pW0kWiBGTW4n9I7RIvm+FybUieKaGzxiJCWfJx0mmT/lVxklRb4oQP6CO+HHBLzD2cFI9hCR/NDRZD9oeM7j9aL/qt0nuQdfbKnJhLFbzn859+PaHhkjBZ9lVERzDiSawOqDNlIvyzEkmB/i6F6kFhPiempsNHEJAyhJhz7L4qRLHCqnCUAAYbWii1HhFeFcm7870MLzP4aV6chvcN1Z0TOkZyfQ4nn7vacotV7anTHSdPJnmEWhoY8JrrX3mqXKLNCJU+a3F5hoFD4zmpOVXq3jOZfNlpVBqxKEDQY8qIcibETzr0iwWguVEaF0TiCfU5jLR4ZCcHGH2r+GOspMT0VNpqYhCHSBy0ZEAMz05QPcnnXE6HKWZIXAtWikW5JCajnNkuq4ybv7pOvp/blnB4ylIYO6CMUvWVkTwztbqPac9u+Pqkbk5zTA/gMJdJvyrCkGIa/VEaj0XHVeyWH5IwwMmSNCOdekWD0WYzEkxJpvTWZ7soVCjb+UPPHGKanwuKWUaI3i1tGi0grq6tqy8mChUb1ut7ZdVzoFLm9WsD1RrXTVH1VHQc0VDubdfXoTABM0s49wOdRyrRahPgi1cQjLCkmob0k18nT4Gsnw5KCx0oLAoQ4+6Wb4WzyoCjPhpsm54vjstAmjVseM+lERbPel0pcsjfVFYtUeylR5kj1NxEKPV1ziklOuPYck5QY7VgLhipnSP5GTl6boQP6KL+lk6eHDCb/b/xGSb+qUJe/OOP2RVeK3CSqS0f4f21xNnk6zXNxt2k6TxSFqsjwGjW4nxDP3Hm0XnilnE0eAB26Vf7zJ4+btKfW7KkRxwdmpok8IjmnSKWzZITqnsE8LqHqPMXK+xKL+xuV4QlGouQKyX8T4YyfE8SZZCehjaYnn3wSJpNJ9zN27FhxvLm5GWVlZRg4cCD69u2L2bNno7a2VtdGVVUVSktL0adPHwwePBgPP/wwPB6P7pwtW7bgwgsvhNVqxXnnnYeVK1d2x/AYP8IRVFRRVX9WWRvOKOcoXQpldRaSkPuiMmpaPF5leI8WFTLqSotyRVK3Bp8it0yKSR9S9E/klkNulce+EzlPxP4aV6c5Pykm6BLFVaEYmpN0S4o4Trv/yvfW6NpXiTeqErVlVPeMNGct1gtxqPcPlucTrYLWQOLkCqnCz+F8wQn2d81SBUyik9BGEwCMHz8eJ06cED8ff/yxOPbAAw9gzZo1ePfdd7F161bU1NTgxhtvFMe9Xi9KS0vR2tqKHTt24I033sDKlSuxZMkScc7Ro0dRWlqKf/3Xf8Xu3buxYMEC/PSnP8WGDRu6dZyMfkEIZ3EgT0+LxyseyLJRoxIXlB/epLN0dUFOp/2Sd4fJRgslRVvNqcrFgxaVU42tYheZLBkgFLnbsZpTdLvnVIrekMwu2klHkgelRbm6nB//RPCsdItSO0nmsdIC6V4+5CLD8r9VOV2yp6qrGC20sfa+qNqXF3ejPB+VUdHVwrvxRu5/OP2ONFk+2WDjr+eS8EaT2WxGTk6O+DnnnHMAAC6XC3/84x/xH//xH7jyyisxadIkvP7669ixYwc++eQTAMDf/vY3HDhwAG+++SYmTpyImTNn4umnn8bLL7+M1lbfQvPqq69ixIgR+PWvf41x48Zh3rx5+MEPfoDf/OY3cRszo37oGKl0y8hGjUpc8J1dx1HtbMI7u44Lr87GA45O25S9K6qFgrb8TxrW31DH6KbJ+cizZ+CmyfkB97GaU3W756j/h+tOi5BbtdMX6qt2Nov2ry7IEZIHsjgmjc+cahKJ4OTdKsyzKcNrqsWNjLuq+rM6yQOVzhMJVcqClcHeM6P3UtUXVUkUQL2lPxbI76+RZEWoRkWieJHCJZr9ThbD0egz21OMPyaQhDeaDh8+jNzcXHzve9/DrbfeiqqqKgBAZWUl3G43pk2bJs4dO3Yshg4dioqKCgBARUUFCgsLkZ2dLc4pKSlBQ0MD9u/fL86R26BzqI1gtLS0oKGhQffDqIlWzkewnCeVp8co1CfrLBGkmC2X+5AJtmNLJV5phLzoUv9JxHLogD5KHaMmd5vw3qjELSlh2z/PqrHFF472eDWxuB2uOwPAJ4NAobrVQSQVCGqnscWjDD/SPVdsOSKENjOtZuVx1fyFs8AYfT66ikrx/NnyA8qQo9Huw1gYQz3Vk5EshqPRZy1ZjD8mfBLaaLrkkkuwcuVKrF+/HitWrMDRo0fx/e9/H6dPn4bD4UBaWhrsdrvumuzsbDgcDgCAw+HQGUx0nI51dk5DQwOampqC9m3ZsmWw2WziJz8/0GPA+IhkMTNKFJb/TQ9aMjSCqRgDHZ4I2ejwL9Db7PYqFyXZeyOPSRX+k40eo5wXStom75F/ThL1n5w2JhMw/JxMpLb/vmz5JgxfVI7GFre4v2zgUFkXubyLHJ5rkkJ0nSViu6V2VOKhLR6vmH/V+IO139Xt+9S/ScP6R22hkt9fuldzu9FavrdGqfjenV6FWBmITGhEqinHJD8JbTTNnDkTP/zhD1FUVISSkhKsXbsWTqcT77zzTry7hsWLF8Plcomf48ePx7tLCUssvnWpHkpy8rS8qNCiQB6VFzYc0oXH/HezFebZDD0Z8pjovlX1Z5VlNlQ5L7JOE+X/ABCeLPq/t8POEbvj2jT97jYytpxNHnF/2VBSlTExKm2i8rTIZWJkyGgETAEhUXn84Sh+G2Gk+N5VQ0BllNOclRblKnN6Jg3rH3PjI9YGYm/EaNOCCjaKei8JbTT5Y7fbMXr0aHzzzTfIyclBa2srnE6n7pza2lrk5PgSenNycgJ209H/jc7JyspCRkYGgmG1WpGVlaX7YdRE8oBRPciNCvbKydNG4pJy+5TUbGmvuutfe44eqgMz0wIW0tumDBP3bWzxKHfPqTxhcniQdqrJ9WrtGWbxm+5Phy0pJl3OkFzmlu4vG0WqMibya+R1SzV11LZTFdQNViaGDCRAE+NTjT9YTlJXF+1Id6J1hqrPwYooq8KjsfLexNpA7I3EWl2e6VkkldF05swZHDlyBEOGDMGkSZNgsViwadMmcfzQoUOoqqpCcXExAKC4uBj79u1DXV2dOGfjxo3IyspCQUGBOEdug86hNpj4oHqQq+q1yXja3TIe2T0jtZUr5Qypdp9lppnFPeVEcXqo7q9xdWr8udu0gORgeVHbeKA2QJkcgNi916ZBPLxbPFr7WDXhVaIeZ1rNMLcbeOZUE2wZFgAdBX1XbDmiXOCDJdJTgrc8bfJcqyQbZFSepnB2SXW19lp37UQLJ48lVt6b7jIQexOxVpfvTjjUGnsS2mhauHAhtm7dim+//RY7duzA//k//wepqam45ZZbYLPZcPfdd+PBBx/E5s2bUVlZibvuugvFxcWYMmUKAGD69OkoKCjA7bffjj179mDDhg14/PHHUVZWBqvVCgC477778Pe//x2PPPIIvvrqK7zyyit455138MADD8Rz6L0e1YNc3lGmQg5JGYlLqnafDR3QR+xOkz1B9FAdn2sLefeeypMie7rknCoK71lSTOLhLdeOk6Ewl6p2na29Np1cjy6URHqC7j919CDdPVWSAjLUf0AtDmqkUxSL2muxMARUeVRvfnIMs176GMMXleOdXccj2oYfDskuVZCIqDyxyUpvD7V2BwldRuXmm2/Gtm3bcOrUKQwaNAiXX345nn32WYwc6Xs4NDc346GHHsKqVavQ0tKCkpISvPLKKyL0BgDHjh3D3LlzsWXLFmRmZmLOnDlYvnw5zGazOGfLli144IEHcODAAZx77rl44okncOedd4bVVy6jEnuClWCg1wdmpmF/jUt8Y6QyD5S4LZcxAXwGUVGeDavnXw4AGLm4HF4NwkDyPy6XsAAg/t3Y4oGzyY0MSwoGZFoDSpNMGtYf5XtrMD7XJkJ/L2w4JMqgNDS70ab5PEW57Yvf4+9/qZyDb5eXBszF0r9+qfMSyfeU5yLHlg6Hqzno/NQ3tqLJ7YW93XPlX3omGKoyNTJGpT9iUVoj1uU65DE7m9zidXp/Yk28ypFwGRQfiToPidKvROlHqISzfie00ZRMsNEUP4zqmJFRQwRb3OV6WmRIyH/0werN0b8nDesvEp/l42QgyUaVbDTJfQOADEsqri7IFn1ZLQlEyh4Fap9qy8lGmWy01TibIP+R++rZtbYX9k3FwadnANAbAlSeRZ4LeXzyg9CoDhnNW7DrI8HooRzrGm3yXAEanE0e2DPM2L20JOr3UtHV8SV77bt4w/PQOck2P1x7julRGIXCjOQJaPs9ESwnSnbTy25uSsTeebS+Ux0eORFYFUaRdaDkLev+NLu9QZOOVTpMdO5Nk/PFPQdmpgEABmam6RLNKXynCv/JfZJDZqoyKfJ7YhReU82PimjWMAsWSosWsqTC6Wbf54t++xOL2nVdDclFGsZJ9lBgtD4LyT4PsaYnzw8bTUzCY/SAN6pj5m4LdKYabTOW/+hVu2tUfQr2oCBjhPAvXTKrfacb7Zjzlzyg8icppg5PU0OzzzvV0OxW6kCRjML+GpdINLeaU4VxQ21aUkxiIdl5tF70SU7OpnHJBZGNFM+N5lSF0fusKl0SykM5FnkesqGoSiQOZ3dgJP0LJ2cr1Np30b5vIhKtz0Kyz0Os6cnzw0YTkzAE+xZo9IAP99ujx6vpDCGVAUWCkzuP1isXRVWfVDvm5D6RUZRrT8dTa/aj2tmEp9bsFx6uTKsvl8hf8mBm4RCkmoDzpcK8smYTGTiNrR5hyOTYfDsFc2zpSkkAWbGbFpI17YrgL2w4JHb6bTxQK8Z1qrFV6VEK9QFpdF44RlU4u/Ni8a1XNipVicThGJWx/lauMhS6c1FLpB1d0ZrrRBoT071wTlOU4JymrhNpHFx1nZyz8dTq/Tpvkwk+/SLKwyEDKtUEHFnmS+SVk8LptWCo8kNUSeNEqkm/vV+V3C0vZtSWCT7ZAXuGBY0tHrjbNFhSTBicla5rP8OSglZPW0BSuyXVBLfXt8NtYGaaSHQHfEnxKe2yB/55Vs/ccH5AThLlUQXLYzIikpyacK6JdSKq0WfVKDk+HLo6lmC5eN3lBUim/JZQ5zqa729X+sFEB85pYpKSSL8Fqq6Tv+mTV4XEI6lYLaGSFAhHu8UoVEdeCfI0jc+16cJjKuRvsnS9XPyWvE8zC4eI/CXC49V0/adQncfbIQkgh+9IXqFN65A0kPunElTsqkxAOGESo9puKiLxpITjPTD6rHaWsxbufaOpwxSPLenJlN+SKFv2E6UfTCBsNDEJQ6xCBpS0S9pGpxpbdeE5SqSuqj8rHlRyyEW1qMkhPZU4ozwWMjAoUfhUYyuWzhqPPHsGls4aL66RH5Ry+I50kLztTuGhA/roSsbIRYcBn06V3H8yoK6bkCv6RCro43P1BiQdl8N3qvGpXoum0RFNxfBQCSenyigUG837RtPoiIcBk0z5LaHOTzhGcSz7wXQ/bDQxSUOwxUm16Kh2gsk5PbInhrxSjS0epTI1tS9XuV/drtK9ek+NaH/jAYc4LhtVKnFIOWdKVU/M3R6/c0txPIow7q9x6Qry+uPvSVPtxCMV8Kr6syIRvSivw9Mmz5+c30SoPE1GZW7Coas5SZHUEzO6jzw+I6MuHEOP7husdl00jY5kMmBigZGBG638vK7S29+nRIaNJiZpCLYQGS065BXxtGnCO6NK3qXj/iEnal+ucq9qv6n9+Oo9NbqCuvQAlI0W2dNFi/G6fSeEISVDBkxeexmY8bk2kaPlbtOE0ZNh6fCkyUaDat5UJWmoTMqz5Qd192+W5Ak6270WrMyNyoCR+2S0u8uodp3q+kjqiRktVPL4jBK9wzH0QpVkiCa9NZFZ9bfQW+eCiQw2mpi4E+pDy2gh2nigVixkz5YfFAYAhbLIa+O/qJNRUpjXsaVeFYohD45/TlKHJ6sD0kai3wB0fZLDY+QtcrcbbbKYpUx9oy85m7xEBBmAj5WOU8okqOZNLsNC59IYmt1e3eJynWSUdbZ7LViZG2p/zZ4apdGl8lCFsztOtRDGop5YsPEZSV6ESneGZHprzoxqjmM9F2yU9SzYaGLiTqgPrWALEV0v6x8Fq90GBK9dt7/GBa8GrPvyhLJPVNj3VGOrLueHHsRF7UbXrAm5GDW4HwCI30CHx6bZ7dW1pdKRMkm/yaigsZBGE6FKlJaNBgoFrtjyjfD4yOKMJE9AhmBhnk25pV42ylQEy/OgvqQbGF3hIC9+qoUwknpiRoubPL5Y5LR0Z0imOw20RDIaYl27TzXWcHLlunIfpntgyYEowZIDkRPq9tpwas/J3pqiPN+We194yxSw9ZoeajKzJEkCWniDlVmR26J+qSQLZr30sdjmf9PkfHHNkr9+Cdluoq3/hKrUity/bV+fDCjTIs8P9YVINQE5tgyxDdzhagqoXUelZ6K5pdro/Qv2/seiDIuKcLbG85bw0EkmyYGuYiR/Eq2SP71pTrsDlhxgkgqjb9j0rYrCW/6JuCrxRcr/ybOni91l1c5mkTOkSjSma4rybMpEZ/k1uc+q8JJqd5rsXZLx/9rSpvm0lgDfb/IKESaok7ub28u0+OcJkadHljyQv11TH/Ps6V36xm2UfB1sxxm9DqDTRH+5jEssiFRlPBZlUnoSvWknmJHobajXRHIfpntgo4mJO6Hq1MjhN9WiNTAzTdr9Jge4OqA8Hzn8RA+1gZlWcV44yb2UI9XY4hHjkHenEfI9ZUPLf7cbADxWWoA8ewYeKy1Q5kzJ4yejqjDPJvonH1cpjsscrjsDwJczRQ93VfjJ6H2S86hU50a640y1+1CFUXK4EcEWN6NEdaNdg4mUPxQPA6437QSLZKzddQ0THdhoYuJOqDo1103IFQu5vGjR9fuqXWJ3nGygyPqRlOcj6xwR5JHaW+0K66FEOVKeNk2MQ95pRQsVbd3f9vVJnWQAGS2EJcWku7+/J6wwT+8pIqNKNtBUBp48J3JSump3nGr88jUqZO+aans+veYvI6CSXJBR7T6UUbUfTUOlq2VIaKwDM9PClkGINvE24BLd65bo/WPiDxtNTNwJVafmxVsuEAuVyqiQd6rJoTQ5/OUf0pKh8BX9JijsREaD/4Kj8vTIqDxlsmSAfwFfc6pJ9/CmsdLuucN1Z/DOruOodjbhnV3HhTHU4ukIz6kWdVlvSU6Up91xllST0mNCfWmSEtlVXh0y2vw9WXLOmEpGgIydbV+fDHnHnFH74YhnGmHUlpxUr4LGShsNgskgdMeCHUlYJ5r9irfRZkSi94+JP2w0MXHHSKfGKNRD11NIa2HJGFFaZGBmmnLLvCr8RIrd9JugsFOz26tccMhAO9XYKhZ9eXs6GTVkVPnvuKLwnBxIVD28ZUNH9orR/a3mFNG/y5ZvwvBF5bhs+aYAo6fJ7dXlTInwXZreWPTvC3nscu3puv6R0UO7+gZmpunmlxbqhSVjorZjTvW63H44kgVGGLVF87/t65NK44Lmf3yuTXg6I9llpWozXEMmkrBOOPlbRiR6Lk6i94+JP2w0MQlDsAeWUf4LeYJ2Hq0XC8K+dqNin1RiZNTgvp0KJgbT9qEt+bn2dOWCo+q3bDRQKLCq/qzyevLQaAZtytpQ8r/pXNnTUe1sFr/JqJGRc6ZUfVaNj3b01Tiblf2TFcvl+TVaqOm+U0cPEguyKqk8WDvx0ESSjQdqC0CnnrBTja1CBkH1mY40ET3WqELh4dw3WPmZRCTR+8fEH5YciBIsORA7VFt25deW/vXLgO39o36+Fu42DZYUE9o0LeB4qNvcg7UfDqqK6MMXlYvj/pICwbb5f29xOdo0nyTB+bm2APmC+sZWNLm9sGdYkGlNRbWzGXn29PbX23Rtfbs8/HGMe2IdmtxtyLCk4OqCnAD5BVnyIVx9pBVbjgiZgzx7BmqcTdDg874djaCvscZoazkA5b9Vn1+jBdro89+dC3wk9+Xt8Uyiw5IDTI9k59F65TdWSkDOsaWL41Tc1qtpSg+SKlH5/lVfiOvl43T9+FxbQB5PKGEKVc6LLIlAnpZZUqK7qn3y5LRp0HnS/MUvG1s8mHvFee3egfPg8dt6V5Rnw6yXPsbwReWY9dLHIY9F9k7JO+Xovbhpcr7IGQtnJ5sqJ8nc7kkzy1n8MSKSkJORV0j2yHRVMbw7wnahkuhK5wQndDOxgj1NUYI9TbGDvqmmmnySAf7fWOm4Cb4Ql7/nRuVVkb0/LZ42nXJ4MHFH+RszgJC/Pas8TSMWlQtPynXtQpXjc2041diqC4PI7cveqQxLivD6eLyaTlXcBMDWPgf2DAtcTW4R+iPjURb/zLNnhO0JUAl1yp4ieX6M5krlvVDNWawI1RNi5EmSx0/vYTQ8Qar5CdbneHh1ElHok71bTDiwp4npURjp9FCiteyVKGpPri6SNJDkb59y/o68e4225MvHVVvi5e37qvblnBySH2hodovX5Np05LXZW+0S3i1V+7J3Si7T4vErw5JrT9dJHsg6UFQDTpUTFWz3ogpZqNNo95pqLCpkT6JRmZJoehJC9YQEq3dntDswkj4b5QEZJcVHY9dgqCTijjNO6GZiBXuaogR7mqJLON9e6VulL5fHHPSaYN4L8pqQJ8ueYcHCkjEBZVbk9mVPEP1fziki747s9SFSTcBT158v2t95tB7le2uQYjLB3abBnuEToaS+Ul/kZG7yqpng2323V0p4N8FnQFJOF0wdxYoBn5fqsdKCgPunmVPRJO0Q9J//YGVk6HrZUybPv5HXyMiT2Nl7Hi+PChB+zlKwPodzbleJRbuJ6GlimHBgTxOT9ITz7VW15VyVU0OCkuSFIchrIjtsVGVWAAS8JitCyyE+k6njN+U05dnTlbvzvv1nI7waMDjLKsYhe4r8DSYAupwfWdQS8HmvyPvkadN0BlOqCbi6IEfnvVizx+fp8jeY/Oefzluzp0Z3PW2531/jUr5n8lhUkCdqfK5a50pFPDwJwXYEqjxB4eyOU50bqncuXGIxb7zjjOlNsNHExJxYJNrKGC1a9G8yJKzmVOWW8WCK49Q+hYz8wy9kDKSYIBK+5aRtMioaW7wiUVrun1wbj1DpPMlkWs3it//9ry7IEdpUvjH5wnrkyak89p1u/BQqtEjhTdX8yyFF1fwFC5/KYyHk60mS4XDdacP3mkikhVolj6Cav2B9Vp2rqn0YDWIxb0Z/35yUzfQkODwXJTg8F5yuhgTI/R9OlXs5ZEDhoxxbOhyuZlFGJdxE42DhmRc2HIKzyS1CZpRYTTxzw/kB4bupoweJUNeaPTU6jSZ/yYC60y06b5EJwNM3dIT3ni0/oJMU8B+TKnwphxcnDesfEJ5ThY/k+VeFJ0ORb1AlegMImL9ETN4NFoYaubg8QJJCDmWGI79gdK9ExOjvm5OymUSHw3NMQtHV0g20QK/ZU9NpYdRg0Lf2GmdzQL25YPdUvR6sttnQAX0A+BKwaZxy0nYHHYaPrsyLoi+yOKXb2/n3GtmDA+jr3b35yTHhqZo6elBA6Zm5V3TUrvN4fYYXqakTNFZ53owED8PxLpAHjzx9c68YqfTexNtjEawwr0rSQg5lRkIiedKM5l31963yRHJSNtMTYKOJiTmRLADyAkUPXbm2nBGqnCT5etXurGB5VNSXFk9Hzo9cpmV/jS+85nA1i3E6XM3iNapZ5/Fq4p6qgsImQByXd//Rv+0ZZhFyk+eHcqZkSSN5LBT+2njAodyRRfND4cv9NS6l0RWstp1q912wnWaq+VcV5JV1oIzen3hz8YgBATUNg4UykxGjeTcKjyeSAcgwXYWNJiZhUHkXgI6HslxbzgjVt9urC3ICFmo5aVyWFDBCFpdUeRpIcHN8rk1IGnjaNHFP2SigAHkwf9JNk/Pb+z1WlOGQoUK8ciK7avxN7rZOPXW29kLFObZ0PLVmP6qdTXhqzX5lbTsZmsttX58MMHT95QdUqDx5qjmNt8cimAyCygMlC4EmqrEXKpHMe7zfK4aJFeoKnb2Yl19+Gb/61a/gcDgwYcIEvPTSS7j44ovj3a1egWxIyFvyCdqpBBjnfMjnUk4FAGXJi2fLD6LJ7cUJl08gc+fRel2blH8EmMTil94uLpneXvDW35ChHW1V9WeFeKVsAFDEzav5vEkkeUCLL0kU7K124XDdGTS5vXi2/KDoF/Vp6uhBASEg/3AdSRbUNTTD3aaJ8J2/pALhcDWL/rm9WkRCjfL80/9l/A0JyrmSE+3959S/zURG7uvOo/VwuJoMd8J1Rx5TJPeIZN6T6b1imHBgT5PEn//8Zzz44INYunQpPv/8c0yYMAElJSWoq6uLd9d6NKoq8F2tUi97rVTfemXvAHmC2jQEhIQAiPCWx9smQmqyuGQwDxnx4i0X4MiyUlw8YoAI02S0h28yLCk4XHcGQIchBeiFLKl/TW6vuI+cE0XildTm0AF9DOdHHj+F32RJBDk8GGp4xUiQUkb/ntDAu29PSiQhs2BzajRuo51wKk9brEh2rxfDxBs2miT+4z/+A/fccw/uuusuFBQU4NVXX0WfPn3w3//93wHntrS0oKGhQffDRAY9yOUq8EYY6diocmJkZO0g2p4fTEeJcLdpIpGc8pj217iU9zLKmWpu3+3W7A4s4bKwZAwaW6iOXEf/gA5Fb9noIJ0p2kG3v8almx9ajN2S5IIMhffqGlpEbg6FBG+anC/Oi1VuDiW9O5s83bagGxkPKp2vcMK3MkahKpWieKzo7WGzZM8vY+IPG03ttLa2orKyEtOmTROvpaSkYNq0aaioqAg4f9myZbDZbOInPz8/4BwmNCJ5kBt9e5dzYlQLpKwdRJ6g7YuuUhptZABlSInkcvuq/Buj0hdyorCMyqND/cuQrlElYhfldXjqVPOTYUkRRpmqjIy7Tes0edvI0AjHiyGfS16tPGn3YawJ1ZCR56Ly2HfK9yeSRGlVX2Rx1ljR25Oy2dPGdBXWaWqnpqYGeXl52LFjB4qLi8XrjzzyCLZu3YpPP/1Ud35LSwtaWlrE/xsaGpCfn886Td1EOLkZqnMjye2IZs6J3NY7u46L4rer518elT4H05RSXUeaQnIZFNU1Rvfv6nuSSIQzf4k+FqYDfq8YFeHoNLHR1E64RpM/LG7JMAzDMMkHi1tGwDnnnIPU1FTU1tbqXq+trUVOTk6cesUwDMMwTKLARlM7aWlpmDRpEjZt2iRea2trw6ZNm3SeJ4ZhGIZheies0yTx4IMPYs6cObjoootw8cUX47e//S0aGxtx1113xbtrDMMwDMPEGTaaJH70ox/h5MmTWLJkCRwOByZOnIj169cjOzs73l1jGIZhGCbOcCJ4lHC5XLDb7Th+/DgngjMMwzBMkkC7351OJ2w2W6fnsqcpSpw+fRoAWK+JYRiGYZKQ06dPGxpN7GmKEm1tbaipqUG/fv1gMpmML5AgK7e3e6l4HjrgueiA56IDngsfPA8d8Fx0EOlcaJqG06dPIzc3Fykpne+PY09TlEhJScG5557bpTaysrJ6/Yce4HmQ4bnogOeiA54LHzwPHfBcdBDJXBh5mAiWHGAYhmEYhgkBNpoYhmEYhmFCgI2mBMBqtWLp0qWwWq3x7kpc4XnogOeiA56LDngufPA8dMBz0UF3zAUngjMMwzAMw4QAe5oYhmEYhmFCgI0mhmEYhmGYEGCjiWEYhmEYJgTYaGIYhmEYhgkBNpqiyLJlyzB58mT069cPgwcPxg033IBDhw6J4/X19Zg/fz7GjBmDjIwMDB06FPfffz9cLlen7d55550wmUy6nxkzZsR6OF3CaC4A4IorrggY13333ddpu5qmYcmSJRgyZAgyMjIwbdo0HD58OJZD6TJGc/Htt98GzAP9vPvuu0HbTcbPxYoVK1BUVCTE54qLi7Fu3TpxvLm5GWVlZRg4cCD69u2L2bNno7a2ttM2k/Ez0dk89KbnBGD8megtzwmg87noTc8Jf5YvXw6TyYQFCxaI1+L2rNCYqFFSUqK9/vrr2pdffqnt3r1bu+aaa7ShQ4dqZ86c0TRN0/bt26fdeOON2urVq7VvvvlG27RpkzZq1Cht9uzZnbY7Z84cbcaMGdqJEyfET319fXcMKWKM5kLTNO1f/uVftHvuuUc3LpfL1Wm7y5cv12w2m/b+++9re/bs0WbNmqWNGDFCa2pqivWQIsZoLjwej24OTpw4oT311FNa3759tdOnTwdtNxk/F6tXr9bKy8u1r7/+Wjt06JD285//XLNYLNqXX36paZqm3XfffVp+fr62adMm7bPPPtOmTJmiXXrppZ22mYyfic7moTc9JzTN+DPRW54Tmtb5XPSm54TMzp07teHDh2tFRUXaz372M/F6vJ4VbDTFkLq6Og2AtnXr1qDnvPPOO1paWprmdruDnjNnzhzt+uuvj0EPuw/VXPzLv/yL7o/AiLa2Ni0nJ0f71a9+JV5zOp2a1WrVVq1aFc3uxpRQPhcTJ07UfvKTn3TaTk/4XGiapvXv31/7r//6L83pdGoWi0V79913xbGDBw9qALSKigrltT3lM6FpHfOgorc8Jwh5Lnrrc4Lo7HPR058Tp0+f1kaNGqVt3LhR9zmI57OCw3MxhNzpAwYM6PScrKwsmM2dlwHcsmULBg8ejDFjxmDu3Lk4depUVPsaa4LNxZ/+9Cecc845OP/887F48WKcPXs2aBtHjx6Fw+HAtGnTxGs2mw2XXHIJKioqYtPxGGD0uaisrMTu3btx9913G7aVzJ8Lr9eLt99+G42NjSguLkZlZSXcbrfu/R07diyGDh0a9P3tCZ8J/3lQ0VueE8Hmojc+J4w+F73hOVFWVobS0lLdewkgrs8KLtgbI9ra2rBgwQJcdtllOP/885Xn/POf/8TTTz+Ne++9t9O2ZsyYgRtvvBEjRozAkSNH8POf/xwzZ85ERUUFUlNTY9H9qBJsLn784x9j2LBhyM3Nxd69e/Hoo4/i0KFD+Mtf/qJsx+FwAACys7N1r2dnZ4tjiU4on4s//vGPGDduHC699NJO20rWz8W+fftQXFyM5uZm9O3bF++99x4KCgqwe/dupKWlwW63687v7P1N5s9EsHnwpzc8Jzqbi972nAj1c9HTnxNvv/02Pv/8c+zatSvgmMPhiNuzgo2mGFFWVoYvv/wSH3/8sfJ4Q0MDSktLUVBQgCeffLLTtm6++Wbx78LCQhQVFWHkyJHYsmULrrrqqmh2OyYEmwt5ESgsLMSQIUNw1VVX4ciRIxg5cmR3d7NbMPpcNDU14a233sITTzxh2Fayfi7GjBmD3bt3w+Vy4X//938xZ84cbN26Nd7d6naCzYO8QPaW50Rnc9HbnhOhfC56+nPi+PHj+NnPfoaNGzciPT093t3RweG5GDBv3jx88MEH2Lx5M84999yA46dPn8aMGTPQr18/vPfee7BYLGG1/73vfQ/nnHMOvvnmm2h1OWYYzYXMJZdcAgBBx5WTkwMAATskamtrxbFEJpS5+N///V+cPXsWd9xxR9jtJ8vnIi0tDeeddx4mTZqEZcuWYcKECfjd736HnJwctLa2wul06s7v7P1N5s9EsHkgetNzwmguZHr6cyKUuejpz4nKykrU1dXhwgsvhNlshtlsxtatW/Hiiy/CbDYjOzs7bs8KNpqiiKZpmDdvHt577z189NFHGDFiRMA5DQ0NmD59OtLS0rB69eqIrOh//OMfOHXqFIYMGRKNbseEUObCn927dwNA0HGNGDECOTk52LRpk3itoaEBn376adBckEQgnLn44x//iFmzZmHQoEFh3ycZPhcq2tra0NLSgkmTJsFiseje30OHDqGqqiro+5usnwkVNA9A73lOBEOeC3966nMiGKq56OnPiauuugr79u3D7t27xc9FF12EW2+9Vfw7bs+KCBLamSDMnTtXs9ls2pYtW3TbO8+ePatpmqa5XC7tkksu0QoLC7VvvvlGd47H4xHtjBkzRvvLX/6iaZpv98DChQu1iooK7ejRo9qHH36oXXjhhdqoUaO05ubmuIwzFIzm4ptvvtF+8YtfaJ999pl29OhR7a9//av2ve99T5s6daquHXkuNM23ZdRut2t//etftb1792rXX399wm8lNpoL4vDhw5rJZNLWrVunbKcnfC4WLVqkbd26VTt69Ki2d+9ebdGiRZrJZNL+9re/aZrm20Y8dOhQ7aOPPtI+++wzrbi4WCsuLta10RM+E53NQ296Tmha53PRm54Tmmb896FpveM5ocJ/F2W8nhVsNEURAMqf119/XdM0Tdu8eXPQc44ePaprh645e/asNn36dG3QoEGaxWLRhg0bpt1zzz2aw+Ho/gGGgdFcVFVVaVOnTtUGDBigWa1W7bzzztMefvjhAP0V+RpN820bfeKJJ7Ts7GzNarVqV111lXbo0KFuHFn4GM0FsXjxYi0/P1/zer1B20n2z8VPfvITbdiwYVpaWpo2aNAg7aqrrtItCE1NTdq///u/a/3799f69Omj/Z//83+0EydO6NroCZ+JzuahNz0nNK3zuehNzwlNM/770LTe8ZxQ4W80xetZYWpvmGEYhmEYhukEzmliGIZhGIYJATaaGIZhGIZhQoCNJoZhGIZhmBBgo4lhGIZhGCYE2GhiGIZhGIYJATaaGIZhGIZhQoCNJoZhGIZhmBBgo4lhGIZhGCYE2GhiGCapufPOO3HDDTd0+31XrlwJk8kEk8mEBQsWxOw+3377rbjPxIkTY3YfhmGMMce7AwzDMMEwmUydHl+6dCl+97vfIV6FDbKysnDo0CFkZmbG7B75+fk4ceIEXnjhBXz44Ycxuw/DMMaw0cQwTMJy4sQJ8e8///nPWLJkCQ4dOiRe69u3L/r27RuPrgHwGXU5OTkxvUdqaipycnLiOk6GYXxweI5hmIQlJydH/NhsNmGk0E/fvn0DwnNXXHEF5s+fjwULFqB///7Izs7GH/7wBzQ2NuKuu+5Cv379cN5552HdunW6e3355ZeYOXMm+vbti+zsbNx+++345z//GXafhw8fjmeeeQZ33HEH+vbti2HDhmH16tU4efIkrr/+evTt2xdFRUX47LPPxDXHjh3Dddddh/79+yMzMxPjx4/H2rVrI543hmFiAxtNDMP0ON544w2cc8452LlzJ+bPn4+5c+fihz/8IS699FJ8/vnnmD59Om6//XacPXsWAOB0OnHllVfiggsuwGeffYb169ejtrYWN910U0T3/81vfoPLLrsMX3zxBUpLS3H77bfjjjvuwG233YbPP/8cI0eOxB133CHCimVlZWhpacG2bduwb98+/PKXv2TPEsMkIGw0MQzT45gwYQIef/xxjBo1CosXL0Z6ejrOOecc3HPPPRg1ahSWLFmCU6dOYe/evQCA3//+97jgggvw3HPPYezYsbjgggvw3//939i8eTO+/vrrsO9/zTXX4N/+7d/EvRoaGjB58mT88Ic/xOjRo/Hoo4/i4MGDqK2tBQBUVVXhsssuQ2FhIb73ve/h2muvxdSpU6M6JwzDdB02mhiG6XEUFRWJf6empmLgwIEoLCwUr2VnZwMA6urqAAB79uzB5s2bRY5U3759MXbsWADAkSNHunR/uldn97///vvxzDPP4LLLLsPSpUuFMccwTGLBRhPDMD0Oi8Wi+7/JZNK9Rrvy2traAABnzpzBddddh927d+t+Dh8+HJHHR3Wvzu7/05/+FH//+99x++23Y9++fbjooovw0ksvhX1fhmFiCxtNDMP0ei688ELs378fw4cPx3nnnaf7iaWcgEx+fj7uu+8+/OUvf8FDDz2EP/zhD91yX4ZhQoeNJoZhej1lZWWor6/HLbfcgl27duHIkSPYsGED7rrrLni93pjff8GCBdiwYQOOHj2Kzz//HJs3b8a4ceNifl+GYcKDjSaGYXo9ubm52L59O7xeL6ZPn47CwkIsWLAAdrsdKSmxf0x6vV6UlZVh3LhxmDFjBkaPHo1XXnkl5vdlGCY8TFq8pHQZhmGSmJUrV2LBggVwOp3dcr8nn3wS77//Pnbv3t0t92MYJhD2NDEMw0SIy+VC37598eijj8bsHlVVVejbty+ee+65mN2DYZjQYE8TwzBMBJw+fVroLNntdpxzzjkxuY/H48G3334LALBarcjPz4/JfRiGMYaNJoZhGIZhmBDg8BzDMAzDMEwIsNHEMAzDMAwTAmw0MQzDMAzDhAAbTQzDMAzDMCHARhPDMAzDMEwIsNHEMAzDMAwTAmw0MQzDMAzDhAAbTQzDMAzDMCHw/wObiXkrqBpGdAAAAABJRU5ErkJggg==\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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JTj75ZPziF79AIBDAyJEj8eSTT+Ib3/gGAOBf//oXpk2bhm3btmHOnDl46aWX8NWvfhX19fUYPXo0AOCxxx7DzTffjCNHjiAnJ8f2+OJpEgRBEITMI+09TZ988onyZ/fu3bjgggugaRrOPvvsuPa5dOlSVFZWYv78+Ybnt2/fjmAwaHh+6tSpGDduHLZt2wYA2LZtG2bMmKELJgCoqKhAS0sLdu3apTxeR0cHWlpaDD/JYiC9RapjibdJEARBENLE03Ts2DHcdtttmDx5Mnbs2IENGzbg5Zdfjvn9Tz/9NP75z39i5cqVPV5raGhATk4OCgoKDM+PHj0aDQ0N+jZcMNHr9JqKlStXwu/36z9jx46Nub3xMpDeItWxxNskCIIgCCkWTcFgED//+c8xYcIErF27Fn/4wx/w5ptv4otf/GLM+zh48CB++MMf4s9//jNyc3OT2Fojy5cvRyAQ0H8OHjyYtGMNZP4T1bEk/4ogCIIgpMgIrmka/vjHP+KOO+5AKBTCfffdhyuvvBIulyvufW3fvh2HDx/Gqaeeqj8XDoexZcsW/OpXv8KGDRvQ2dmJ5uZmQ7SpsbERRUVFAICioiK8/fbbhv3S6jraxozX64XX6427vX3hkjnjByxJoepYA3l8QRAEQUhXUhJpKisrw3//93/joosuwvbt23HhhReitbW1Tx6hL3/5y9i5cyd27Nih/5x22mm4+OKL9d89Hg82bdqkv6empga1tbUoLy8HAJSXl2Pnzp04fPiwvs3GjRuRn5+P0tLSxJ58H5A8TYIgCIKQelKyes7p7NZqDoejx+uapsHhcMS1eo4zb948ffUcACxZsgQvvvgi1qxZg/z8fCxbtgwAsHXrVgDRyNTJJ5+M4uJiPPDAA2hoaMCll16K733vezGnHEjm6rmJy6sQ1gCXA9i3sjKh++6NM+9/BXXNbSgp8OGNW740YMcVBEEQhIEinvE7JdNzr7766oAe76GHHoLT6cTixYvR0dGBiooKPProo/rrLpcLL7zwApYsWYLy8nLk5eXh8ssvx9133z2g7bRC8jQJgiAIQupJSaQpHA5j1apVWLduHTo7O/HlL38ZK1asgM/nG+imJAzJ0yQIgiAIUWsHfeHOBD9s2udpuu+++/DjH/8YQ4cORUlJCX75y19i6dKlqWhKRpAqT5MgCIIgxEs2p6lJiWj64x//iEcffRQbNmzA3/72N6xfvx5//vOfEYlEUtGctCdVtecEQRAEIV6yOU1NSjxNtbW1+MpXvqL/PX/+fDgcDtTX1+Nzn/tcKpqU1qTK0yQIgiAI8ZLNaWpSIppCoVCPRJQejwfBYDAVzUl7Hr7oFDx80SmpboYgCIIgDGpSltzyO9/5jiE5ZHt7O37wgx8gLy9Pf+65555LRfPSjlSZ6jLNzCcIgiAIySQlounyyy/v8dwll1ySgpZkBqs21KC5LYhVG2oGVLxwM5+IJkEQBGGwkxLR9Ic//CEVhxXiRPI0CYIgCEI3KS3YK8TGDRVTUFLgww0VU/TnBqLEySVzxuONW74kUSZBELICKQ0l9BcRTRlKuubBkJuSIAjpSrreN4XMQURTBqD6R0/XPBhyUxIEIV1J1/umkDmkpIxKNpLMMiqZtIotk9oqCIIgCPGM3yKaEoTUnhMEQRCEzCPta88J8ZGtPqFsPS9BEAQhOxHRlAGs2lCDuuY2rNpQk+qmJBTxPwmCIAiZhIgmIWWIKVMQBEHIJEQ0ZQCqPE2cvkxzpcPUmOSBEgY76fB/KAhC7IhoygL6Ms2VDlNjMmAIg510+D8UBCF2RDRlAHY31r5Mc6XD1JgMGMJgJx3+DwVBiJ2MF02rV69GWVkZ8vPzkZ+fj/Lycrz00kv66+3t7Vi6dClGjBiBoUOHYvHixWhsbDTso7a2FpWVlRgyZAhGjRqFG2+8EaFQaKBPxRK7G2tfprnSYWpMBgxhsJMO/4eCIMROxudpWr9+PVwuFyZNmgRN0/DEE0/gwQcfxHvvvYfp06djyZIlqKqqwpo1a+D3+3H11VfD6XTijTfeAACEw2GcfPLJKCoqwoMPPohDhw7hsssuw1VXXYX77rsv5nZIniZBEARByDwGfXLLwsJCPPjgg/jGN76BkSNH4sknn8Q3vvENAMC//vUvTJs2Ddu2bcOcOXPw0ksv4atf/Srq6+sxevRoAMBjjz2Gm2++GUeOHEFOTk5MxxwI0STZtgVBEAQhsQza5JbhcBhPP/00WltbUV5eju3btyMYDGL+/Pn6NlOnTsW4ceOwbds2AMC2bdswY8YMXTABQEVFBVpaWrBr1y7LY3V0dKClpcXwkyzIME35msQDJAiCIAgDT1aIpp07d2Lo0KHwer34wQ9+gL/+9a8oLS1FQ0MDcnJyUFBQYNh+9OjRaGhoAAA0NDQYBBO9Tq9ZsXLlSvj9fv1n7NixiT0pBhmmAYgHKMuQFYTCYEI+70KmkxWiacqUKdixYwfeeustLFmyBJdffjl2796d1GMuX74cgUBA/zl48GDSjjUiLzpFOK5wSFqaRuVG2HdkBaEwmJDPu5DpZIVoysnJwYknnohZs2Zh5cqVmDlzJn75y1+iqKgInZ2daG5uNmzf2NiIoqIiAEBRUVGP1XT0N22jwuv16iv26CdZ7KoPGB6B9BIqciPsO7KCUBhMyOddyHSyQjSZiUQi6OjowKxZs+DxeLBp0yb9tZqaGtTW1qK8vBwAUF5ejp07d+Lw4cP6Nhs3bkR+fj5KS0sHvO0qKsuK4XJEH4l0EipyI+w7suRcGEzI513IdDJ+9dzy5cuxcOFCjBs3DseOHcOTTz6Jn/70p9iwYQPOPvtsLFmyBC+++CLWrFmD/Px8LFu2DACwdetWAN0pB4qLi/HAAw+goaEBl156Kb73ve+ldcoBWUknCIIgCP0nnvHbPUBtShqHDx/GZZddhkOHDsHv96OsrEwXTADw0EMPwel0YvHixejo6EBFRQUeffRR/f0ulwsvvPAClixZgvLycuTl5eHyyy/H3XffnapTiolL5owXsSQIgiAIA0jGR5rSBUluKQiCIAiZx6DN05StpJPpu79k07kIQqYi/4eC0DdENGUAlNRy1YYa223T/WaYTgb2bCHdr7mQfsj/oSD0DRFNGUBrR8jwCFgPlMm4GSZyUJaVdolHBkAhXuT/UBD6hoimDCAU0QyPgPVAmYybYSIHZVlynHhkABTiRf4PBaFvZPzqucHAuTOLUVVdb8jTtGTeRD3lACcZq+qsjiWkB7KSUhAEYWCQ1XMJQlbPCYIgCELmIavnBEEQBEEQEoyIJkEQBEEQhBgQ0ZQBpNOS8v62JZ3ORRAEQRDiQURTBnDXul2oa27DXet2pbop/V5JJ8vjhVgRgS0IQrohoikDCHalGghGUu/Z7+/ydlkeL8SKCGxBENINSTkgxEV/l7dbvf9Pb36spzWQ5fMCIKkuBEFIPyTSlAGUlfgNj9mIRBUEM5KAURCEdEMiTRnAumVnpboJSUeiCoIgCEK6I8ktE4QktxQEQRCEzEOSWwqCIAiCICQYEU0ZwDVPvYeJy6twzVPvpbopQgKQpfSCIAiZiYimDGDd+/UIa9HHZCCD+MAipneBkP89QcgsMl40rVy5EqeffjqGDRuGUaNG4bzzzkNNTY1hm/b2dixduhQjRozA0KFDsXjxYjQ2Nhq2qa2tRWVlJYYMGYJRo0bhxhtvRCgUGshTSSjx3IxlEB9YJFeVQMj/niBkFhkvml577TUsXboUb775JjZu3IhgMIhzzjkHra2t+jbXXXcd1q9fj7Vr1+K1115DfX09zj//fP31cDiMyspKdHZ2YuvWrXjiiSewZs0a3HHHHak4pR74PC7DYyzEczOWQXxgkaX0AiH/e4KQWWTd6rkjR45g1KhReO211zB37lwEAgGMHDkSTz75JL7xjW8AAP71r39h2rRp2LZtG+bMmYOXXnoJX/3qV1FfX4/Ro0cDAB577DHcfPPNOHLkCHJycmyPm8zVc31J/CjJIgVBEATBnnjG76zL0xQIBAAAhYWFAIDt27cjGAxi/vz5+jZTp07FuHHjdNG0bds2zJgxQxdMAFBRUYElS5Zg165dOOWUU3ocp6OjAx0dHfrfLS0tyTqlPmXh7m/mbkEQBEEQjGT89BwnEong2muvxZlnnomTTjoJANDQ0ICcnBwUFBQYth09ejQaGhr0bbhgotfpNRUrV66E3+/Xf8aOHZvgsxEEQRAEIZ3IKtG0dOlSfPDBB3j66aeTfqzly5cjEAjoPwcPHkz6MQVBEARBSB1ZI5quvvpqvPDCC3j11Vfxuc99Tn++qKgInZ2daG5uNmzf2NiIoqIifRvzajr6m7Yx4/V6kZ+fb/hJFrIsWRAEQRBST8aLJk3TcPXVV+Ovf/0rXnnlFUyYMMHw+qxZs+DxeLBp0yb9uZqaGtTW1qK8vBwAUF5ejp07d+Lw4cP6Nhs3bkR+fj5KS0sH5kR6YdWGGtQ1t2HVhu5UClxIiagSBEEQhOST8UbwpUuX4sknn8Tzzz+PYcOG6R4kv98Pn88Hv9+PK6+8Etdffz0KCwuRn5+PZcuWoby8HHPmzAEAnHPOOSgtLcWll16KBx54AA0NDbjtttuwdOlSeL3eVJ4eAKAjFDY8Aj1TCvDfZdWcIAiCICSejI80rV69GoFAAPPmzcOYMWP0n2eeeUbf5qGHHsJXv/pVLF68GHPnzkVRURGee+45/XWXy4UXXngBLpcL5eXluOSSS3DZZZfh7rvvTsUp9cDrdhkeAWN+F/67JMsTBEEQhOSQdXmaUkW65GmS/EyCIAiCEDvxjN8imhJEMkWTIAiCIAjJIZ7xO+On5wRBEARBEAYCEU2CIAiCIAgxIKJJEARBEAQhBkQ0ZQCLHnkdx99ShUWPvK4/J7mZBEEQBGFgEdGUAVTXBQyPgDrhpSAIgiAIyUNEUwZQVuI3PAqCIAiCMPBkfEbwwcC6ZWf1eO6Giil6PiZBEARBEJKP5GlKEJKnSRAEQRAyD8nTJAiCIAiCkGBENAmCIAiCIMSAiCZBEARBEIQYENGUAQzWnEzXPPUeJi6vwjVPvZfqpgiCIAiCiKZMYPXmfahrbsPqzftS3ZQBpaq6HmEt+igIgiAIqUZEUwawZN5ElBT4Bl16gcqyYrgc0UdBEARBSDWSciBBSMoBQRAEQcg8JOWAIAiCIAhCghHRJAiCIAiCEAMimgRBEARBEGJARJMgCIIgCEIMSMHeBEF++paWlhS3RBAEQRCEWKFxO5Z1cSKaEsSxY8cAAGPHjk1xSwRBEARBiJdjx47B7/f3uo2kHEgQkUgENTU1KC0txcGDByXtAKLqfezYsdIfXUh/GJH+MCL9YUT6oxvpCyOJ7g9N03Ds2DEUFxfD6ezdtSSRpgThdDpRUlICAMjPz5cPNkP6w4j0hxHpDyPSH0akP7qRvjCSyP6wizARYgQXBEEQBEGIARFNgiAIgiAIMSCiKYF4vV6sWLECXq831U1JC6Q/jEh/GJH+MCL9YUT6oxvpCyOp7A8xgguCIAiCIMSARJoEQRAEQRBiQESTIAiCIAhCDIhoEgRBEARBiAERTYIgCIIgCDEgokkQBEEQBCEGRDQJgiAIgiDEgIgmQRAEQRCEGBDRJAiCIAiCEAMimgRBEARBEGJARJMgCIIgCEIMiGgSBEEQBEGIARFNgiAIgiAIMSCiSRAEQRAEIQZENAmCIAiCIMSAiCZBEARBEIQYENEkCIIgCIIQAyKaBEEQBEEQYkBEkyAIgiAIQgyIaBIEQRAEQYgBEU2CIAiCIAgxIKJJEARBEAQhBkQ0CYIgCIIgxICIJkEQBEEQhBgQ0SQIgiAIghADIpoEQRAEQRBiQESTIAiCIAhCDLhT3YBsIRKJoL6+HsOGDYPD4Uh1cwRBEARBiAFN03Ds2DEUFxfD6ew9liSiKUHU19dj7NixqW6GIAiCIAh94ODBg/jc5z7X6zYimhLEsGHDAEQ7PT8/P8WtEQRBEAQhFlpaWjB27Fh9HO8NEU0Jgqbk8vPzRTQJgiAIQoYRi7VGjOCCIAiCIAgxkFLRtHLlSpx++ukYNmwYRo0ahfPOOw81NTWGbdrb27F06VKMGDECQ4cOxeLFi9HY2GjYpra2FpWVlRgyZAhGjRqFG2+8EaFQyLDN5s2bceqpp8Lr9eLEE0/EmjVrerTn17/+NY4//njk5uZi9uzZePvttxN+zoIgCIIgZCYpFU2vvfYali5dijfffBMbN25EMBjEOeecg9bWVn2b6667DuvXr8fatWvx2muvob6+Hueff77+ejgcRmVlJTo7O7F161Y88cQTWLNmDe644w59m/3796OyshJf/OIXsWPHDlx77bX43ve+hw0bNujbPPPMM7j++uuxYsUK/POf/8TMmTNRUVGBw4cPD0xnCIIgCIKQ3mhpxOHDhzUA2muvvaZpmqY1NzdrHo9HW7t2rb7Nnj17NADatm3bNE3TtBdffFFzOp1aQ0ODvs3q1au1/Px8raOjQ9M0Tbvpppu06dOnG471rW99S6uoqND/PuOMM7SlS5fqf4fDYa24uFhbuXKlsq3t7e1aIBDQfw4ePKgB0AKBQD97YXDyv9sOaJ9fuUn7320HUt0UWwayrZnUL4IgCJlIIBCIefxOK09TIBAAABQWFgIAtm/fjmAwiPnz5+vbTJ06FePGjcO2bdsAANu2bcOMGTMwevRofZuKigq0tLRg165d+jZ8H7QN7aOzsxPbt283bON0OjF//nx9GzMrV66E3+/XfyTdQP9YvXkf6prbsHrzPuXrf3rzY5x5/yv405sfx7zPa556DxOXV+Gap96zfP+iR17H8bdUYdEjr/e6L/5+3lb+fKxt5O3iv6uOt2pDjbJf+LFoH4seeV3ZFlW7rI4bK7H2WyzEc23t+uuap96L+3MiDDxW17wv/+eCMJCkzeq5SCSCa6+9FmeeeSZOOukkAEBDQwNycnJQUFBg2Hb06NFoaGjQt+GCiV6n13rbpqWlBW1tbfjkk08QDoeV2/zrX/9Stnf58uW4/vrr9b9pyaLQN5bMm4jVm/dhybyJytfNomr15n2YNX44tn/8CZbMm4hL5ozXtyVhU9/cBg1AVXU9Nu5uRFswjHur9hi2ra4LGB7N0L5aO0JobgvqbeSPvF30Ox2D3s/buO79ev3RAUADsP79epwxobDHfn0eJ1wOYNb44YZ2rdpQg+a2oP7Iz4G3d9WGGnSEImgLhnHH8x9gxfMfoLKsGFXV9Qhr0b7hx6X38/Ze89R7qKquR2VZMR6+6JQe/cbPkb+ffh+Rl4Nd9QHD+62uLb82Kni7+b5oHw2BNoQ1xLQvIXVYXfN4PguCkArSJtK0dOlSfPDBB3j66adT3ZSY8Hq9enoBSTPQN/i3ykvmjMcbt3wJAJQRklnjh+vigW6sVdX1yigMvU6rR4v8uWgLhgEAbcGwYb8eZ3QjpwPKb7gU6WntCOnHp7ZeMme8oV1L5k1ESYHPIPzsImiqY63aUKPvC3AgrAFbPjxiaHdHKHo+9EiYjw8A7V3nHtGAsBYVaNOL/QCA6cV+QxtV7eVChdpAC3OdDuDeqj2oa27DvVV7DOdA+6quCyCsdYtFM6p+s6KyrBguR/RRtY/KsuKY9zWYSXVEx+qax/NZSDWp7kMhNaRFpOnqq6/GCy+8gC1bthiycRYVFaGzsxPNzc2GaFNjYyOKior0bcyr3Gh1Hd/GvOKusbER+fn58Pl8cLlccLlcym1oH0L/UEVcVN8qrSI3QHTAp8iSOdLE9z9r/HA94gAADYF2+DxOtAUj8HmchmOsWDQdqzfvQ1Nrpz7Yq77hhiIaNESPz9n+8Sd6ux6+6BTFezX9kdrodEQFjMfpQDCiGbYiLpkzHpfMGY9pt78EICqOeLu9bhfaghF43S6EwhqCEQ0OAA2BNry9vwlzJ49EVXU95k4eCaBb+ACAwwEcbe0EQI/dbVwy78Su/tZw/C1VKCvxY3qxH9V1AYPAIrxupy7K2oNheN3d38N4xKw36Fxj4eGLTtEjTObPVKZHJlT/I8naf6ojOlbXK5OuY6r7UEgNKY00aZqGq6++Gn/961/xyiuvYMKECYbXZ82aBY/Hg02bNunP1dTUoLa2FuXl5QCA8vJy7Ny507DKbePGjcjPz0dpaam+Dd8HbUP7yMnJwaxZswzbRCIRbNq0Sd9G6B+qCIbqWyV/ziqKQ5Gehy86RY/48P2TkKEo0vRiP84uLYLLAZxdWmTYb3eEKyoczJGbGyqmoKTAh3NndkcwuK/G7ptxXXO7/khRGK/biZICH1Ysmq481g0VU/RvsR2hCAAgFNYMx+LbThsTjXJq6I4kmcXcvpWV+nGiOq1bKPE2mttdXRdAbdNnAIDaps/0Nvg80VuH1+3CuTOj0Z9zZxYb2kUU+KLfzUoKcpV9ZIXKl8U9S/ya23m0MiEqEE9Usr/7T2REJxP6NhlkUlRMSBwpjTQtXboUTz75JJ5//nkMGzZM9yD5/X74fD74/X5ceeWVuP7661FYWIj8/HwsW7YM5eXlmDNnDgDgnHPOQWlpKS699FI88MADaGhowG233YalS5fC6/UCAH7wgx/gV7/6FW666SZ897vfxSuvvIJnn30WVVVVeluuv/56XH755TjttNNwxhln4Be/+AVaW1txxRVXDHzHZCEqz5LqWyV/bvXmfcoojuobOUWXZo0frnt0DrdEB/69h4/haGunvi8Aht8BGCI3qgjGn978WN9+/fv1Bh+SGe4BomiSx+lAa0c0d1gorOlTkbf97QNlf91btUefUgSika639zfpkSSgO6q0q97ox8r1OG09YlwolZVEI0llJT0jSQAMU4G8P3gf8egPYd5XQ6Ad8cAHefJo0f7urdqDs0tH69fcyutEcA9YukYF7K5Zf+H/I32J6FhFwgZrxCWTomJC4khppGn16tUIBAKYN28exowZo/8888wz+jYPPfQQvvrVr2Lx4sWYO3cuioqK8Nxzz+mvu1wuvPDCC3C5XCgvL8cll1yCyy67DHfffbe+zYQJE1BVVYWNGzdi5syZ+NnPfobf/va3qKio0Lf51re+hVWrVuGOO+7AySefjB07duDll1/uYQ4XBg6rb3Kqb+Q8skLRI5r6agtGbL1HPEKiimDctX6XPn2X2xVlyTVN9RHr36/XIz4LZ4yBywEsnDEGoa72BCOa8ps59wa1M8FEUZx1Xftd93694RjkTyrwufVoGociAZyyEr/+uG7ZWThwfyXWLTtL75uyEj9cDmDRTKN3iOC+LqtrY95XZVlxXFEJfp3MEcC2YNhwzblHK1Ox6tNEwfurL1hFwgYy4pKpUa1MbbfQE4emaWY7hdAHWlpa4Pf7EQgExBSu4Mz7X0FdcxtKCnx6lEWF1bdZ1Qot7mmi5/jvh1vaEYxo8HmcKMzzxnR8wBgp4l4gACjweXS/UGVZsXLlmdVxR+TloLouAJcjGukq8Hn0lW8A9JV0DkRFUlV1VBAdbe3EknkTDVEp7tEKRTQEWSNdDqDI79OPC0BfidcZivRot90gPe32l/Rj3VpZ2uv77Hw5sX4OzJx8198NfQUAPznvpB4enQKfB3let+VnwmqFYLZitbKxt6htLPtKVb/19fOTajK13YOFeMbvtFk9J2QHVt+oYv02avVt1jwF8MYtX8KWD4/o0R8OeYcA6JEXfny7b31bPjyir1ijyEVJQa4eiVJFtS6ZM972uOQNIn1D03UE9wadMaEQRX4f9h7+VD9H8gSVFOQaPFpcMNH+Vd6ws0uLsG9lJR6+6BTDKje7nE5et0t/pOtwb9VupYfILlpi9zmwujZzJ4+EyxE9d1UErHu1YffiAdVnxux/6y/pHkGwO9d4+iLZkbBYiOf/OJ0Q/1P2IKJJSChWN+FYb7jxLEXmXhvVcYMRTRc/fR00aZVZa0f39JBVG6k9dFy7aRDyOgHdpnWCRA35mjpCYTS1RiMtTa1Bg7BTwc+Xtt24u0E5yPD+4GKK4FOXdO7twYjuIeKoBrJ4EhlaXRsSqoBDF36qbedOHqlfm4FY1p5s83Z/4eca62KMdCZZ4jfZpIPgFBJDWqQcELKH/ppZ41mKHOqKsNDKMqtpMrMfZkReDuqa2zAiL0fZBr5cn6axmlo70dwWFRM7VpyjbCOZyWlabsm8ibqh+96qPbi1cloPc/SofC/qmtsxKt9rMJj7fZ4e++e5pvjSfjMO0990/m3BiO6ZorYsmTdRN5jPGj9cKcJ435PA8fvcaG4LochvXBFnHsi4iVuVRsJsILb6/Kie58+R0bu1I4QdK84xtD2ZJNu83V/M/zexLMbIFFLd9+kwXSkMPBJpEhJKPN+o4olKqAgyYzU/Lk+UCXSLK4JWm+2qDyiPpzLMhsLRpf8dobBlGykic2tlqd4WnsPI3C7AuIqNknE6HN3TURSB8rpdhqgUj/6QqZteLzYt7ae0BUQba8slc8YbzleVMoBDkajmtujUonlFnCoJaUcorDThd4vWbmM8b5dd8lMOTXWapzxVULtWbajp9/ROOkUQ7P530qmtVsTz/5/q88mkSJeQOEQ0CSlDddOJ50ZEuYLokaAbL4kqWrVG8KzStCru9r99oHt0+MBPIiHI9mGVH0g1sHOfkgqalXM6AFqSoWnASx8cQlgDwpqmi5g8bzQwnOd1G9IPHH9cHlyObhFZ32wUMqbT70FffEbUbvNqNT5tSPv1ul1KDxiJ17rmdmUtP9VUIe97/rvdykTV+UaPnT2Dnt3/Tn/rDQ4EmSBE6DM6a/zwjJraFBKDiCYhZdglt7Tj1spSPbLDocHW43TogoUPxmSyPmNCoW6ipsSQVdX1vfqFvG6XQVTx/EDm46/aUGM4FkHJHgt8boNQmtEVMZpR4tfbFdGgi4xxhUMAAOMKhxhSDphX+OV6ev+39nmchgGUCzBq+x3Pf6AX5OUDGUWi8nOj04dHWzstowMkkOg9ZvMuideyEr/Sd6MqFcM/H/x3Eqc+j8t20FW1Kxuw+99RfVbTjUzwWNFndPvHn6R95E5IPJJyIEFIyoH+kUh/AC1PL/B5dH8LX/JLHpvo8nQX6prbDcvxt3x4RH+d8jbx9AZ00ywp8OnCiS/jb2rtQFswgoIuX5K5LcffUqVst8/jQlswrD8SFBWjQc/lAJyO7qSZ5Isq8LmR5/X06MNFj7yO6rqAnqaAklkSPNWB35QGATAu7VctVef9wX0mqoK/vG+594jg+yWfEt+WzoXySxF0DJ6iQQYzY3++vb+pR+HlZPlyMm2/8aAqYC1kNpJyQMg4VGF5u6XwVvCIDKFKlBh9jM4zFeZ59VVZ3NfDp9woGsMjTVSihK/mAqLChuq+mbFaMceN3vw1ig6Q6brIn2uYqqPpuEBbSOkHolQHbcGot2knE0wA4O46lpt5pSi9QVmJX+kd4dEpu+gAj4rZwY+l8leR2Ks2nQMdY2ddoE/f/jNp+Xo88P8r/llVvZ6s42bCfuOhv0lChcxGRJOQFqgGXiv/igo+3URemeq6gD7FxAdjnneIBFBTaweOv6UKZ96/SSkS+MBvddOkfQEO/XUydM+dPFIfmBfOGKPXnuOeJg73YVGkiUzXDYF2g6DgBnJC5QciHA6jcHO7ukSTy6Gf+xu3fFnPEq7CaqpHlf+J2uU2GdjtUF0Hh+mR4FnaiXiEUDoMxsnATtDyLwADedxE7negBW8mTCEKyUNEk5AWqFfCaPqjXVI7PoiTV4YwRyX4wE0CiKIwdc3tyv3zaIzVTZP2RQV5l8ybaBBYKi+Eq0tRuBxGGcDnzCk6wCNNvL9IX0U09coyEm76vjUYIlVcRMYKb4tdQlIyAOR53X1e8UTXRFM896c3P1b62+IRQukwEPZl8O/virlkRU2StbJNtd+BFryJPLdsjXBmMyKahLSFL8e3S2rHa4+R+Zp0gtNhn6mcw/dP7wsz65/Vsnja1w0VU/TXVcblWeOH6+/haRM4FHlyoFsI8UjTmfdv0iNjxj6L5mHiU5Q0MFJ/+H1uQwSMi0g6n0WPvN7rSiuaEqxvbjecI98XRTGKu7J4W01XxgJdE5/HqZv7rTJ+E/EIoVQvXwf6Nvj3VzCkg1jsL+l2DhLhzG7ECJ4gxAgeG/EYOU9YXoWIFhUQ/7eyssc+rMzZZPQmqB6ZuRYbrwM16ccv6sbqhTPG6BGr7R9/YkhGCRiN0ZS80udxYc89C2I6d2pfSYHPsG9eT27SqGE96tSRab2kIFcXlEC0pIh5BR29z2wi532iMmSTYZ7vZ5+i73l9Pat6dLQvaouVUTwWVJ+bdDAFJ5K+nE+29UE2EE+dObl+6YEYwYW0JZ5vVrSsnR4JigqQoKGbjvnbJqUcmDt5pMG7wX+nb4WE2+UwTFnQfskD5PM4DefAk1fGeu482aOVKZzM2lzo8OgOT1tAkTXeRj59RjmfCHqvCjpfMoJPL/YbvjmbM5pPGjVMGZn705sf6/08vVidUiAeVDmw0iE61BfsUjQk63z6urCiL/sfzGRahFOIDxFNQkKxu3Fa3VBUiffsTMN8X/zmo3t4uiIcWz48YhBCKp8RGa+9bpdyv9PGRL99TBo1zCC6KPu2OQu3qj9ov52haN22lz44hIUzxsDlABbOGGPwFrlNIirP6zKZnbst0apCwXxq01zQN9AWsuzbbnES3f/R1k6DuZvOnaYTd9UHLOubUT8fbe1UTlfGOhXI6c3gnilYCce+iA4rwWq3bTKmhWSqKYoIoexGRJOQUOxunFY3FNVqLCvvkN2+KDkliYWOUNgyKaLKd/PsOwdR19yGZ985qO+Tl17hootHf+z6g9pLGiYY1rBxd2NXMd1Gg5Bxu4yiqa653TKZJ0GFgjfublS+TuR6nLZ9y034PEUDnTtFtaYXG1MS2Hm4VN606rqAfu3tivu2tEenXVs7QrYZrtM18mH1xaEvosNKsNptq7o2/SXR3qJ0vX5EurdPSA4imoSE0peSHICxtIkKu2/Uvd3AzKvCeI4hEgH1ze16VIrnAqL90nTX9GK/XjNtRF6Ocqm7XX/Q1FdJQa4hNxPHXC/PnC+JG7nNq+PaWZ4n87SfSnTdW7VHN5DT+fJIFY+A0fm4XdHzrW36TFkjjgvZLR8eUQ7mqqlAq+tMESbyy4cimkFoq65/PCIk2VNXHCux3xfRYSVY7bal3+naJCJyl+gIS7pHrtK9fUJyECN4ghAjeGzEY5LkWGWg5j4Xc5Zunh2aZ5cOtAX1DNjndpmoKcN2gc+DcYVD9KzTtU2fobktqGfNLinwoSHQppus7/paz2zZ8fTBoUCbbnZ3OR0IhjV4XA6Ewprexv33V/baB2QsdzqiaQfKSvzYc6hFN7bzlXl82pGYcEuVfqxikznd43Rg2ph8vT8uOH1sDyM44FCa4ek8yYhv1Ue8P7hRXHWOHqcDEU3TxTUZ9jfubuzRhnhMtrwNQM+FAtmAqj9U2fPThXQ3SQ9k+9K9LzKdeMZva0eoICQBPijGA307Jsz7MA+2YS0aBaF8RBx3l5BwO7tN38Ny3frADkR9OBecPhb3Vu3W3zMqP7dHOQrertjLK3RPffEcS5Gu6FIwrOkr6dxOB868/5Ue0y8j8nJQ19yGEXk5aO0IRffWta+jrZ26RysU0fSyKR6XwzBVSJBw5KVg+MrE2//2AYCoOf1oa2eP1YTcDM9v7nYr5fgqSLqGvV1n1b6onyd0laZpC4b1/jLvqzfMn8u+fEbTHfNUMQC9TFC6nKtZHKSzQBjI9qmunZAaJNKUICTS1BO7b0fJ+vZE4iXHHa3hZo5g8KgTHzSoDVZ16uxqpd3+tw96jQ7R/nntOYoEmSNCZnweF26tnKY8FomeIn8uGgLtXakSmvT0BE2tQb00Cwk/QC1CVEy7/SU9FcLZpUU9InNzJ4/skaKBR2msxGRfoo58XyTwotf2I9Q1txvSGwxElCiTIgCZkLahr5HobCfdrlO2ISkHhLTAbs6/r56T3p4Duo3ggKb0eHA/kF1SRL6t6rh8NZfK36Q6R54ugJcwWTQz6utaNLO4R5kQs+eJH0vly+KJMCkSFIxoupcrnlVX3IBOxnWKZI0rHGKoaaZaHbeOlaBRrSaMxf9Gv1M5m6rqeqx4/gPUNbd1PUbPl6ZNE10WxIpU+1r6679KdfvN9MXXNRCG7GQfo7+Z3YWBQ0STkDTsboDx3CBVN/d4bvjc7EyiasuHR/Sb1TVPvae8aZEg2fLhkR65iihRJRBdzXV2aRFcjmhagt6EQaAtpD+S4dts/Pb7jLmpfB6nQaBxIUPGdKIjFDZkSJ9R4tdfI/ESz6orDok3ai2tKiRUq+OIXNM52A0EqiXyuV0ZwSvLivVViKauG9BiqsnORh1PGgG796u2Hehs2skQBwMh/JJ9jHQTr4I1IpqEpJHIb0eqm7tVsdHu1WTdHh6rJJR0s1r3fr0uqvgNjI7bEYrogz1vC/cOkcDaWRdQrkiiAYMXsOVlVCgis+79ej39QFmJX8+9xFm14V9dx/iXLly4dqht+kx/pN8Jc8oBfj6qZfxc6Pi6IlxOB5SrHfn76fqUlfh7TZVghWqJ/NmlRSjy+3DGhEJD8V5agUdlVgYq0pTsCEA8aQTs3q+6zm/vbxrQCEYyxMFACL9kH2OgxavQd8TTlCDE0xQ/Kv9CLHP3qnIkqlVXPo8TnaFIj5VW/PcD/2ntUdCXl0mhNnBfz557Furbqjw2Ta0daAtGepRvMZd3IQFC+6WiwQBwoMsTpVoVWFlWjHXvd+ezMpdVMe/X63ahuS0In8eJwjxvDzM79xlNXF6lT3FR+RS+wor8S3xlItDtj1rx/Af6+4v8vl79TSrTOb/m/LNAx+DXnPvNqJ8H2tOUbPrrZbF6v+o6DwSJ9OaIz0dIFPGM3yKaEoSIpvhR3fRiMYJOu/3lHsZmvjyd/m5q7URbMNzDxM1FAKUfIDxOB/be9xX9bxrkNURXuPVWa01VE+/Zdw72SF9AmE3UWz48jOa2EAp8btxQMVVfJberPtD1+hG93Xw/lA6B4ELJfAwSSNyMTgIt2jcb0NwWshRY1AaeggHoXqI/Ii+nR3oCqyXuAHrsyy7lgFW7APQQczKQWhP7Ss/0RUzjQqIQI7iQdsSa0TuWMLXK2Kx6XygcjdxQRmsV5qSU5hQFZDymhW1et8tyioGe3/LhEf05nkmcpg1LWPZx7q9q7vI6NbeFdA8WZcve8uERQ2ZuEh0FPo8+3Ud43S6DgZ2mDV/aeQjH31KFRY+8btkf1Ia2YPd0JG8j77fu/u5OoXC0tRNANO2Bql4cPwfVvqymk/i50WdGVRqHl2zJNPprNo7n/dy8n6mk2k+W6PcJmYGIJmFA6IuXwermc27XKjMA+koqlQHZagk/FxRkqF40s1hZi41qwDkAZfFf1Wow7n/imc5pYG8ItOuDPZUFaWmPJqcEon4h82o5s/Dj52A+zXGFQwxCh9pF/VFdFzCs1OOYEohb9t3ZpUV6f/Ps4aqBzKpeHPm2bq0sVfqr+DWlbedOHqk02fd3AE2Hga6/fp/BZiZOtZ8s0e8TMgMRTULSiGd5OaFaMWW++dC3ZBr4zWZkEjVWAoDXe7ODIk8a1MV/zUnn3rjlS/C6u/+tVMvxqSTLiLwcQ3JLeh9/P+F1uwzlTHgbzOw0ebQIMkuXlfhxxoRC3VBtPE702NR14wqHGAQaibGNuxv0a8vFnt1Axs9Bta3V++l5ygPF+5uXBQEQs/jhpvV0GOj6K/zieX86iMR0p6/XQ0zd2U1KRdOWLVtw7rnnori4GA6HA3/7298Mr3/nO9+Bw+Ew/CxYsMCwTVNTEy6++GLk5+ejoKAAV155JT799FPDNtXV1fjCF76A3NxcjB07Fg888ECPtqxduxZTp05Fbm4uZsyYgRdffDHh5zvYUAkKQD2o0U18RF6OHsWxWh1HWE0xkKDgERg+SPCVdNTG9V2r56yiIT42jWcV4aBjzJ08Uhm1oj6gHErm5fo8/YBZ8I0rHII8b1Rw5Hldhvp3PtMUY67HiXGFQ/T3UaSnqbUTJQU+3WukEgkkaqjrdtYFlNNz7V3Td6s370N+bnSqkB7N8ELEqrxX8RDPajG74r8851M6DHT9jZzE8/50EInpTl+vh+RUym5SKppaW1sxc+ZM/PrXv7bcZsGCBTh06JD+89RTTxlev/jii7Fr1y5s3LgRL7zwArZs2YLvf//7+ustLS0455xzMH78eGzfvh0PPvgg7rzzTjz++OP6Nlu3bsVFF12EK6+8Eu+99x7OO+88nHfeefjggw8Sf9KDCNVAZOcH2lUf6OFTMUdT7Krb03F5sVqep4mm986dWaxva1Vwl26AlINp7uSRhoK/qmnB7R9/0utN06o4MU8/YBYg1XUBwzQYLyo8adQwANFkmTR1xr1UNLXHfUpWIoEEDvVdrsdpyEVFr5/bNZ25ZN5EgyjikDgBoPQh9QXe36rkl1x0233WeM6nwTbQpYNITAYSQROSTUpF08KFC/GTn/wEX//61y238Xq9KCoq0n+GD++OOuzZswcvv/wyfvvb32L27Nk466yz8Mgjj+Dpp59GfX10Sfaf//xndHZ24ve//z2mT5+OCy+8ENdccw1+/vOf6/v55S9/iQULFuDGG2/EtGnTcM899+DUU0/Fr371q+Sd/CAgHqM3PV9Z1j0YW0WaeHX73o5L2baBbo9QWzCsnJo6u7RIOfATPNrC8ympzoGfm+omzo/Ps4NzKFLE4dNgHBJIzW0hvY1WwgyI+qPspsFWLJqu+414Lip63TytpyLWZIoq8RPLoMf3T9G0nXXdotvus3ZrZWnGm6E5yRIMmSREJII2sGTSZyNRpL2nafPmzRg1ahSmTJmCJUuW4OjRo/pr27ZtQ0FBAU477TT9ufnz58PpdOKtt97St5k7dy5ycrqzJldUVKCmpgaffPKJvs38+fMNx62oqMC2bdss29XR0YGWlhbDj2CP3WD98EWn2EYlSBBML/b3Otjy6S6KnHicDqVvik89qQbx3lbg9YbKBM2f6whF29gRMrbRPHXncToM02BcQFF/8Mgan7qkKTdzv6jOl+DXiUfmCCvvmZ2PTXX9VeLHPE2qaqtKVPOVeHaftWyLKsUjGJK1barJ1AhapoqPTPpsJIq0Fk0LFizAH//4R2zatAk//elP8dprr2HhwoUIh6MDWENDA0aNGmV4j9vtRmFhIRoaGvRtRo8ebdiG/rbbhl5XsXLlSvj9fv1n7Nix/TtZoQdWN0ASBEdbO3sduPl0F6/xpvIk8RVvKkEAdK+eIw+R2UsU6w2EL7vnEbBR+V4AwKh8ry6EKD3BwhljDH6gk7rKpNAjvY9e51OY3RnSo4RMy+24UFHdvFXeMe6p4uIlnjIpRF/L6XBRrVrVF89AlKmDFieefrTzC/Z1vwNJrGlMMoFMFR/p+tlIJm77TVLHhRdeqP8+Y8YMlJWVYeLEidi8eTO+/OUvp7BlwPLly3H99dfrf7e0tIhwSgDmhJe93fxG5OWgrrkNI/JyDIkUzTXPgKjBuS0Y0VdtmVdsTbv9JQBRITN38kg0BNr0AaUh0IZQRNOnvs4uLUJVdX2P0iZ07Fnjh+PM+1/R/T7ULjo3c6SH4J6lWV3NO3ysw2DC7s7pFE1VUF0X0H1g9P6NuxvRGQrrU5jDcj0Ia9FIVETTUFlW3CPbNmE271tdn/qu/t1VH8DR1s5efUpWmZt5MlCC95cKnvyS2kvPmdtgHoh6yx69akMNmtuCuthLF+LJem33/8KJx1sWz34HErvPaiZh/lxnCun62UgmaR1pMnPCCSfguOOOw0cffQQAKCoqwuHDhw3bhEIhNDU1oaioSN+msdG4tJz+ttuGXlfh9XqRn59v+BH6TyzfuOgbJi2t31UfMHzDpG/RRroTMNpFFfiAQl6mIBM6VgOO3bJ4OjeK9HjdLsM0W1lXYd2yEr/um+LHVSWGBLqn54i2oLFgL+F2OXQvFY8IcSM3jyCZ+/tPb36sv48Sak4v9hu+bapWx9kZsquq62P+lm0VSaA2kGA1Tw/aTfulK32NQNgVos6GCEG6n0M80ctMjZANRjJKNP373//G0aNHMWbMGABAeXk5mpubsX37dn2bV155BZFIBLNnz9a32bJlC4LB7rITGzduxJQpU3RTeXl5OTZt2mQ41saNG1FeXp7sUxJMxDJtQAMJJZ7kwgDozsNERWUXzSw2ZNvmAxFNY1H0x+t2KW/Gvi6vzA0VU2xv1lbnQEKkuCBX35erS324HA6sW3YWDtxfiXXLzjK8j7blHi2enJKM5aSbnA5g7+Fo2o29hz/VhUwooimFA79hkxDluZ5UAzfN8NU2fWZ4vyo9QTzm/3hEgkoIbfnwiFKw2mG1AjDV9FUY2AnSbBikVeeQTtOs8XwBTIf2CrGR0um5Tz/9VI8aAcD+/fuxY8cOFBYWorCwEHfddRcWL16MoqIi7Nu3DzfddBNOPPFEVFRUAACmTZuGBQsW4KqrrsJjjz2GYDCIq6++GhdeeCGKi6Om1W9/+9u46667cOWVV+Lmm2/GBx98gF/+8pd46KGH9OP+8Ic/xH/913/hZz/7GSorK/H000/j3XffNaQlEAaGWKYNaHClemRUuoOgPEyaBvxfV121F6rrEekSUrPGD9en32glXkTTDAZis1GZ6skB1iFpXkiYzoFPr1CagLrmdr3e221/i6a1CEY0LHrkdb1uW1mJH9V1AT0hJW1Dj2dMKMT2jz/Ro0a8Fl1EM64WpD4Nh8l0HtanGOdOHmloY25X4eBcU14q85QYFSWOBav+snq+t2kKu2lFX1caAbNgtZv2S9dphr62i08V00rCbMBuujKdpuximXJLp/YKsZHSgr2bN2/GF7/4xR7PX3755Vi9ejXOO+88vPfee2hubkZxcTHOOecc3HPPPQbTdlNTE66++mqsX78eTqcTixcvxsMPP4yhQ4fq21RXV2Pp0qV45513cNxxx2HZsmW4+eabDcdcu3YtbrvtNhw4cACTJk3CAw88gK985SuIFSnYmxji8XBYbasqRsqf27i7EW3BMHweF84uHd1r4VJ6X47bhbZguNfioFRAtMDnQZ7XrU8LqYrskmjihXM5JQU+1DW3weWI+phKCnw43NKOYCS6yi7P69b329Ie7FFKxeN06NuuWDTd4PNyACju2j8vhmtV3FfV53wwtuv7eK6THbzgLxdCVLw3luskJJ94/o/jwa5Ib7KOmywGsr2Z1jcDSTzjd0pFUzYhoin94DcJEg0lBT7UN7dBQ1Q87O8SL1ZMuKVKd0PxSJTqGM++c1CPFNEU27TbX0JbMAKfx6lHd4r8uWgItKOyrBjr36/X20JRHr5tjjv6XElBLppaO/XXvW6XUowBUcHkdjn0bffcsxCAUaAtmlnMRGSDcr87Vpxj2Y9cGAJQ9jPPAK8a6CYur0JYi06h7lvZ+3WgNhwKtCHSZWrfe1/3lxqVYJWBIXXYiZu+Yo40igiInWRdk2wgnvE7ozxNggDE7gMwel66jeAzugzXM0r8tvviBXspC7jVMVR+IF5rjZbu1zdHC/auNyXHpOmutmBE92XRc7QyjrAqR1JS4MOKRdMNxyXfFiee1VP8HMmvRSkazOkeVGVleJZu3t+9JeA0Q8egiJq5GDMd94aKKRnv1ckGkmXSVi2sSNUy/b76kVLlY0p343ymIKJJyAj4jUZ1s1TdiPhqM76cv7bpMwBREzPt696q3crSLOaCvVXV9ZY3PRJYbpZkUmUw5tv5uzKB+31uw+o58mVxuBFcZbgGuoUdPy7VWCMcsL6BqsQY70cSW153dxJJvi9eYob6lpfG4dfOqnagCvWKyG6ywdgcK5lgHh6I65FqEdBX0ZYqsTeY/keSiYgmYUDp6w3fKppBqBI0El63yyBIWtqj01kt7UF9X23BiF4ahbeRRASVOZle7DfUsePihAQWPQIwiAjab5jNiPNVfccflweXAzj+uDw9GsbhRnCr9AMk7PgN0sHEhjmzN/UPPZIo2vLhEb0P+OuqiA4/Fgm09e93F8GdXuzXI03xDHT8OlC7SI+WFOT2ufxKOtGXdqc6wpIIEnG9Ui0C+iraUi32hP4hnqYEIZ6m3uEry5rbgnEbOe1Mw9wgnOd16yupOkMRffqH3s9rxqkM2SXMJA2ghyGbJ848wDxR1MbpxX4cbe3EknkTseL5D3TfTpHf+N7e8LgchhxNQDTtAXmPQmFNN3qTf8mBaESMe6rM50bt5f3Fzd9nTCjsWh3XibZguIfh2m6A4h6uWytLe+yLfFKxwD0YqtVg91bt0Q39XrezVy+WlQ8t1d+6++IzSaf295W++muSde7Z0KdC3xFPk5B28NVbvX3LsvoWbTUdRfCID32TAxz6e3iRXx51UqEqs8JzCVmVUaFoyM66gB714r4d2hdFSzzmyrsMs2DyOB2YNGoYAGDSqGGGqNPZpUVwOYBcTzQiZE7BQMdxOqB/u2/tiEa4WjtChr6lyBiPZKm+0fMyLRxqy9mlRfq1VE019rYPgl8HVeJQ2q/V/u2SaqZDpKYvUYdUR1gSQX/zTyX62qXTZ0JIb9K6jIqQPfCcJb3d7BNRToCmjCb9+EUAUWFQWVbca6SKryZTlVkxYy6NsmTeRD3/k4ZojqiOUBgPX3SK4XhUsqUtGIHb5UAooumr5xwO6LmkzCkE3C6Hnuepui6gb+N0dAvKUDiizFFE6Qk0DfrAQFnJzXXoSFwS5mK/BK36W/9+veH8uMGc+oNH3lTHqqquV14TVY4i/vmgNAM8QsaPYfVZSqeSFfwcB1O0o7/5pxJ97dLpMyGkNyKahAFBdZOMZ5BQJSdULYXndcaCTBiYxYsZnizSblkzPxea5qKs1LEIDl4Hj1bHkdACegomFRHFtnS+5mgc9R2f2qL0CDNK/Ia6ffR8NKmmo8cgQn3j7soDlWuKtvHBZ9WGGoQ1YO/hYyjM8/Y4By5kVVhdBz6dQ9fMbhCOp6ZhqpBEh/YkS2T29/4kDB7E05QgxNMUP9Nuf1n3pNxaOS0mzxNH5XnhCSV5gVoeiVB5pZzOqIeowOdBRyiitysUiSAY1uBxObD33p7JTrmHh3Ic+TxOFOZ5LUXXmfdvQl1zO0oKcnEo0K6MLpUU5KKuuV33KRX4PBhXOETPA1XN0hrQ37Qtz83EscpbxfuZX5M99yww9FFlWbE+Pcb9YlZilAQlEa8/il9fAL3mf1J5nvg5qt5v1TepGiD7kuwzFftMF5Kdd0jyGg0exNMkZATckxKr54mj8rxcMme87tcBoC9p59/i+codmiIiD1FHKGxoFz3PPUb8/XxlGfmqbq0s1duiqo/G0x/k53oAAPm5Hn2FXoHPjTdu+TIO3F+Jc7tqzM2dPNJQm47Kq5QU5Op5oey+/fBVf1aeEl5+heDTaLT0PxTRdB8U7w/uU6L+IE9VRyjcIyLYGypvmXn6zVyQd/373bXW+Ot2NQ3TwdMSa+6seFae8Wunel+mrjoEkr8KTVa5CSpENAkpgwTBuTO7TdLmZI29YWWIDTGTtKriPR8gyahNA7vX7TK0i4sTgosPqySTscIN7Dz9wKJHXsfxt1Tpg96WD48YBAkXXipUy/FVgsgMmds9TocyCSX5p7iI5MKQD9J0fSgFg1UxZKuBm19fMqg/+87BXgf5XI9TKaTtBEk6DJCxtsFK4Kn6kV871fvSQSz2lWQb4rPBcC8kHpmeSxAyPdc/Yk1JwLflUyn8OTII8yk3vtRdNWXBp8zeuOXL+rZ8aT7twy49AW87PxYQFRPDct1obguhrMSP44/L019/aechPY2AOeN1gc+DQFtQN4339k9rri1H7SJo+lA19WBXa4+mIz1OB0bl5xqmQ83pC+h8zUZw87VT9bEZXs6G+p33NxVftquZZ/WZyaSB0ardfanLlql9IAiJJJ7xW4zgQlrAa5rZfdtWGWZp4F61oQY3VEzB9o8/QX2XUGhpDxoGB1XUgUdu+LaqJJI8XxKtEJs1frhyBRc/FgkXiigdbe3EzrqAvgrtnvNO0t9/x/MfIKJ1C6BoXqLdaAtGdPM1taEwL8cQcdIAQ7uAaKZwqnl3dmmR/px5uopn/C7My+mxQpDM63let2Fg5gMviRaqLberPqDXlqOBnV87q0SdHKrLx8UaRZ/Ix2S+pnbm70w1XlsZ2e1WgKnel66meEFIV2R6TkgLVJmm7ba1GhxouogiExFNXT/Nyt/Ct+WeJeLWylLdu2Q37WN1LFX7+XSA1x391+QlWXieJv57a0dPscFzL1Edu7qumnfbP/7EMu+V6jpwcaEqC2M1jUFTQ9OL/cppUkLVx2Yo/9PCGWOUU26031njh/dabkd1rpnsWeFTcqrrkMmeJUFIR0Q0CWlBPP4B1baqwZxwwBjNIBGxcXejPqiQCbukINewrZ1I4KJINUhzcbKoyyvFk2pq7JF7lqjOXDCi6fvkBYH579z4roInf1QJONXAC3QnwhyRlwMAGJGXE9fAfMaEQhT5faht+qxX8WJ17VRlVLg45X2vSn5pJ4qywbNiJwytFkBkg5hK9jmkqo+y4dpkMyKahLQlnpsHHwBpECY0GKMZqlV7NGXWEGiPKfJBqKIdVoP0gf+0IqxFk1PWNbfhrvW7DK9zEzX3NJEw4IV++e8h07aLZhYbDOrFXSZ2B6ALOP46N7YTPNq2qz4q0HbVB5TXRLVCkD/f2hFSCkteI1AlXuzqDaqElNWKymzF7jNntQAikw3gRLLPwW7/yRI32XBtshkRTUJawW9E/b150Io4n8epFBHFBbl6tIIX5OXxn3urdncJit36fnlEyG6qjx+X51YCoivQeEkXvtKJQ0LH7eoSSi6H4Xda7cdX+Bm9VO1dZwPl61xEUv9zf5FqBdaqDTXKAUNVGoXSE1CW8N4icxy7vlUJhsEglDjxnK9dCodMI9UpB5IlbhJ5XhK1Sjyyei5ByOq5/qFaPWc2tvaWpZtDq7EoYWRZSXT1Fq0squ/yOzkA+H0eNLcF9RVp5oK8HCp2SwZnXoSXr+biiR9puqikwIfDLe2GCJJ5pR43Lt/2tw8Mxy7wRfM50Sqz1o6QvtJuxaLpWL15n35eJHBUBYoB9Fi1t/3jJn3lIODQDfl5XnePPqY28iK8fMUcP9ZPuoztVgknacUbX11ntRqMPhc8cWi6FuFNJ6wSwGZTH6UqgWcmfOYkQWdsSHJLIeOgwbQjFO7hUzGbke2+4ZHHh/TJzrqA7bc3t9OhH1eVmwmAHkEp8kefL/LnGqIhdIz2YESfZuPHXbFoOkoKfPB1FdZt7QhbRtYo8uXpatfcySMNUStesJem14jpxX5DJMlcGHhXfcDgtarvikTVN7fb5suia0JGdcA6KSNt+/BFp+jXkfcHtaGaFTjm8G0p8tUWjCg9OnYRsMGK1fRcNsGntQeSTIhqZkNEkZMOkTMRTUJaQP/cXrdLOQDHM7VgLkJrro/Gzdfkf3K7nLqIWDLvxK79n6gnewSg35gbAlGR0RBoN4gPuonSdJl5mk0lOPhAxgUYeayCXVNbG3c3GsQJ6SCno9voTee19/Cnhj6i5JL0nunFfoPZnfon1+PU27hxd2MPnxOHm7d5u8nsvmhm97nbrfCywriaMCo0fSx5pcrzBCArhUFfsVq0kE1YTWunE6ka7NNB2CXy3NNB+Mv0XIKQ6bnEkIiQN4XrKS8RZbJWTW3tvS9aT47XkOOJH2lwBjTUNbf3mNri+6VpKj7dpKrxxqcTDvynVa8nt/fwp3rdt0mjhho8UA5EM5SrpsEWdT1PSbodAPZ3TSXyPuVTakD3VJ+qHhxP4HmA7UuFXXJK/rq58LJ5+s7qmsea0NRqqi+b6Ov/iEzVpI7B3PeJPPdkTYnGM36LaEoQIpr6h92gCPTuY1Lt6/CxdgTDWtd0mKaLolsrS3vst6m1A23BSA+PDiWs5IKDCwPexhXPf6ALFwA9fCR0LN4uXtKEfFVcIPHs4dyXRe31eZyYNGoYqusCetLNshI/Ljh9bM/jdnmq+HvKSvxYt+ysHn0Yj2jigtOubwHYZv/uC9xnRok0Of292aaTf6Wvg1CyziGd+iZdGcx9lAnnLqIpBYho6h+qgcCuyr3dvjgFXYZv82BN23LzNo8e5XndqGtu0wWNVWQG6I4gWZUNoaiTFYssIknU/nGFQ3ShA0D/3bwqz+NyYNSw3B59x8/hWHuwV5HBhdCeexb2eJ3fCHkZFeovbt72OB2IaJplZC4WA6/djdfODNzfb7uxlHoZKBI5CCViX4M5iiJkB2IEFzIOlU+JezDs/Bh83pz2xVMOcA8OXxZP23aGoubtlz44ZPD70OvR4r0+XTCp5tUfvugU7FtZiXXLztJ9BDwHEp1DXyFxVF0XMORO8pk8W8GwpjSo83OIplaA/miGZz1XYZUlXHUdecoBvm08Bl47LwMl0jxjQqHy9WwyxCbCp0L/L5RLy2ysj4ds6ltBsENEk5AWqAYCbny2KvtBqAbVhTPG6AM/3/+696OD9br36/XnaVotGFYHXs+YUBizoZYPPjT91hYM6+dAhmyeGRyAoV1cW6myZXPR0xaMGF5zwJiJnGq0HfhPq77N0dZOw6MZ3l+qTNLRVYa9J5Gk1X4zSvzKbe2EGyfW0jnmFXiq87FDJR56yzifKAYywzWPemb76jpgYI3Y6bDCS0geIpqEpNHfm4fdQMn3r0qauHF3AxoCbXh7f1NcbeEZwVXZsu0EHB/AKQrEo0G0uG9PQ4tlG/xd/p8Cn0cf7Cly5nE6UNv0GQCgtukzOBXRKx4tW98lxmhpv3mlHkeVnFKV7iGWc6d+qm36TClY7IQbR1XeRUVHKNzvAUslHgZiFVIyRItVSgtVjcG+RowyQWwNZBszoT+EviOiSUgafbl5WC1P5zmKVPtXlTNp68qXtP79esNgrsrDRHmRCnxujCscAgAYVzjEkC1bxaJHXsfxt1Rh0SOv689xwcKnuczlXaJm8G5RxUURtaEjFNb3z7OAc1yO6N8ORD1K5840Lr2mkitOR3dUgfcX73MSWOuZp0o11Qf0f2l/XwZpfh15u6lvvW5XStqVCJJxXLtSNJy+CsOB7K9MmEKU6crsJi7RFA6HUV1djba2nmbWzz77DNXV1YhEIop3CoMR1c3D7qZnJbRUg/yIvJwegzmfAvKw+myc1o6w4REAdqyowIH7K7FjRYXBLzSjawptBptK41M13Gekgg9E9DsvnVLYVQy3MC8HC2eMgcsRnVakNtDUm3n/qkSXGqD7eni0jESW1+20LTTMczYRvMAxnQP1gXlg4H3Tlyktq8+HqrwLb7ddu+I5VjrktkkUqv8Lc7LYvtDX3Fv9pa/tHsg22h1Lpu8ym7hE0//+7//iu9/9LnJycnq8lpOTg+9+97t48sknY97fli1bcO6556K4uBgOhwN/+9vfDK9rmoY77rgDY8aMgc/nw/z587F3717DNk1NTbj44ouRn5+PgoICXHnllfj0008N21RXV+MLX/gCcnNzMXbsWDzwwAM92rJ27VpMnToVubm5mDFjBl588cWYz0NQo7p5WN30VF4Zjiqz8c66QI/EkvxYlNQxz+u2HMBVNzCeLM9uCokLIF6AFuhZ8JdeP/64PJQU+HDB6WP1unB1zcZEmXY+Hy5kOBSF4efL20Hn+tIHh3TjO+/bs0uL4HIAZ5cW6e9R1aZ7e3+Tsg/jGZwoanRv1W7beoP0vNft6tV0Hs/x020aJRntseoP6rtZ44f3aQDnbR1IEZANUZx0+9wJ8RGXaPrd736HG264AS5Xz+rvbrcbN910Ex5//PGY99fa2oqZM2fi17/+tfL1Bx54AA8//DAee+wxvPXWW8jLy0NFRQXa29v1bS6++GLs2rULGzduxAsvvIAtW7bg+9//vv56S0sLzjnnHIwfPx7bt2/Hgw8+iDvvvNPQzq1bt+Kiiy7ClVdeiffeew/nnXcezjvvPHzwgbH+l9B/rG56dCPZ/vEntoMe7aNrVkovm0LQTXzu5JH6AMsHDy4oVAM3X4mlGlz4TW/dsrNw4P7oijlurjVPJfJzrKquV940+bTe3sNG4W+GG8zNZVLMdE8LOnRRRYb3YFjDs+8cRF1zG55956Ae0dvy4RH9fCnD+bkzi5XnYDWAxjo4tLPSKFafD4qM0bQlYG1Wj5V4BuCBEAYDKQio77Z8eKRXA70VvK12JvxEkg1RwGwQfoOZuERTTU0N5syZY/n66aefjj171GUXVCxcuBA/+clP8PWvf73Ha5qm4Re/+AVuu+02fO1rX0NZWRn++Mc/or6+Xo9I7dmzBy+//DJ++9vfYvbs2TjrrLPwyCOP4Omnn0Z9fdST8ec//xmdnZ34/e9/j+nTp+PCCy/ENddcg5///Of6sX75y19iwYIFuPHGGzFt2jTcc889OPXUU/GrX/0q5nMReodHYVQ3PRoUR+TlKAcn1VQMGaqDprIpKiM4R3Xj5QP3Xet3oa65DXet36VvS4V3exvY7UrB0DkW+XNty1m0mTxUDhgN6hyafvM4HT3EIB/IQuHoVF9HKGzwdfEpRpVnSSUiK8uKlWVtrMrCECofEqVC6G0lHom5nRZ16vry7T3dolJ9EQSZMNWjamMmtDtZZIPwG8zEJZpaW1vR0mK94ufYsWP47LPP+t0oANi/fz8aGhowf/58/Tm/34/Zs2dj27ZtAIBt27ahoKAAp512mr7N/Pnz4XQ68dZbb+nbzJ071zClWFFRgZqaGnzyySf6Nvw4tA0dR0VHRwdaWloMP4I1doMOTU3tZCu8OCqholqZxrclI7g5USSHvEHFBd1ChkdhCC4ArCIcdr4aOse65nZ9aozDRZE5dsTr5N1QMcVQ4y3E2knt4lEr6nuqyed1uwy+Lm5AV52DSrDyIry8P/h1UhXy5asRVfuygvrf7E8jkv3tPV2jA/0Vc31NpWCVpyvWNiZShA5mAZYspE+tiUs0TZo0CVu3brV8/fXXX8ekSZP63SgAaGhoAACMHj3a8Pzo0aP11xoaGjBq1CjD6263G4WFhYZtVPvgx7Dahl5XsXLlSvj9fv1n7Nix8Z7ioCLWQSeXFWTlqL6dWSVg5MvTCb7KjS+rp4G9IdCuD/A0LDvQffMgwWOOHqmmpp5956AywmXGnBOKDz7mbFE+j1PPt/T2/iZD9IcibcGIpp8XCakQS3RJ+ZJuqJhiEFUrFk1HSYEPKxZNV/Y3F4yqlAT8OSsBRfBpxXig65TndSsHaNXnQ5Vfyvx7rKRDdEDV7njScqiwOi+791kZzO22jbXd8SAeocQjfWqNO56Nv/3tb+O2227D5z//eZSVlRlee//993HHHXfgpptuSmgD05Xly5fj+uuv1/9uaWkR4dQLFJGwgkpqnF1aZFtSI9Z98lpu5mzalIn6rq+dpE8lUbqC2/4W9bJpQI96coBmKD3Ba8tRORHyNVGmayrvQdnE65vboCGaAiC/q7Zcgc9tOJ9n3zloqCc3adQwrH+/Hhqi6QD4dCHHnBwzFNEM+ZKoBMhd63ZFXzcJN1U5Eh4xagi06X1Hr/PM3lSrjwZT8/Wh8/F5nHo/xlKwd9b44WgItGHu5JH6ce1Kp5hv/KrfM2mKxBzxA+z/B8x9YC6ZYlVGRXUsjt1x7baN5/128P/BgaC/JX0ygYHu00wirkjTddddhxkzZmDWrFlYuHAhrrvuOlx33XVYuHAhTjvtNEyfPh1LlixJSMOKiqKrdxobjauDGhsb9deKiopw+PBhw+uhUAhNTU2GbVT74Mew2oZeV+H1epGfn2/4EfqOaiqnv9EBjT1yD48qEzXP+M2n/cwJwuua2w1+IdW3bBIsRf5cQ94j2pYSV+bnetDcFjWx0yNBBnOecoCM744u7xfQ/WiGm7cJnviRpupCEc1wPutZVnJzWZol8ybqKwunF/v11/lqQ7tVVTw6SMdd9369rZGYPh8bdzfoUS27Miy83apcU5k2IPTFuM5Xo8YzTZZJfTTQUUC7KIzqc5lp013pEFlNV+ISTb/61a/w97//Hffeey8OHTqExx9/HP/zP/+DQ4cO4d5778X69euxYMGChDRswoQJKCoqwqZNm/TnWlpa8NZbb6G8vBwAUF5ejubmZmzfvl3f5pVXXkEkEsHs2bP1bbZs2YJgMKhvs3HjRkyZMgXDhw/Xt+HHoW3oOELyUd2k+c0pnhU6dIPitecOH+sAABw+1qFn4t7T0KJcPccHdkp6yUuf8KktDk2vkc6qb27XPTjci8OTZ8YDldbWNBhySamgOngPX3SK7tui1XOrN+8z5J/i5+Pobmav9fWOtnbqr/NjWZnCid5uxub+5NB+27t8avRNnsSaCn4sLsozdUDoi3Gdr0ZV/Y9ZZYbP1D4aCOwEpepzKdNd2UNcounHP/4xnnrqKdx0003YsWMHWltb8dlnn2HHjh347//+b3zlK1/B0aNHY97fp59+ih07dmDHjh0AoubvHTt2oLa2Fg6HA9deey1+8pOfYN26ddi5cycuu+wyFBcX47zzzgMATJs2DQsWLMBVV12Ft99+G2+88QauvvpqXHjhhSgujn5gv/3tbyMnJwdXXnkldu3ahWeeeQa//OUvDVNrP/zhD/Hyyy/jZz/7Gf71r3/hzjvvxLvvvourr746nu4R+oHqJt3fsg7kf7m1stRg7ua/00DdxlbPcfK80ajQGL9PTy1gtYqNzsHHEkTyXFEEFzykpcz+ZhJ+9LzH6UBxV7Ss2BQtM7/X43QYvtmSYAA0fYC0yj+lschab0vK+bWxytNkV6OPRCb1l7k/Vcwo8esDkl2RXk4mRU4SQaw5rFQRXqF37AQl/xJBWP2/CJmHQ9M0zX6zKH/5y19w6aWX4plnnsGiRYv051tbW7FgwQI0Njbitddew5gxY2La3+bNm/HFL36xx/OXX3451qxZA03TsGLFCjz++ONobm7GWWedhUcffRSTJ0/Wt21qasLVV1+N9evXw+l0YvHixXj44YcxdOhQfZvq6mosXboU77zzDo477jgsW7YMN998s+GYa9euxW233YYDBw5g0qRJeOCBB/CVr3wl1q5BS0sL/H4/AoGATNUlAe4jAHp6MzgqT8GkH7+IYESDx+nAtDH5qK4LoKzEj9qmz9DcFoTP40RhntcQIeERE+67eXt/k75/s4cHiJrOaf8XnD6219d5pu9FM4v1/ZJniXxZBT4PAm1B3aOV63GhLRiGz+MCoKEtGIHTEX2Nv5+fQ2tHCM1twa6UAhrqmttRUpCL1o4wmtuCKPB5dG8Z77uT7/q7/jp5ojhn3v+KfixuwLd6PpbrZb7m5BfjbbBrl2BE5cWx8+cIPelvn8X6fyEMHPGM33GJJgD47W9/ix/+8IeoqqrCvHnzdMHU0NCA1157TY/wDDZENPVOIm/OdNMp8HmQ53X32KfqdTJWl5X4sW7ZWT3axUURgB4CjQsO+p32b74BTlxehbAWTXC5b2WlZftLCnw43NKui7mIpunvK/Lnoq65HQU+N/K8HiyZNxH3Vu3WTdTkdQKg+NuFWyun9TiHEXk52FUfQGVZsSENA4k3c9+Y+8jq2lm9HqvROxbRxfucBNK021/S+2PPPQt77FcwXhv+ZYD6WURT/PRX9Eifpx/xjN9xF+z93ve+hxUrVuBrX/saNm/ejIULF6K+vh6vvvrqoBVMgj2xzunHErpWJWDkkDmaVrLdtX4XdnZFdHbWBQzHoGX8G3c39shFdMmc8frUVDQhZDRqQxnIWztCymkQO68Nn7biRXj5lBuVV2luC+lt4SVOeG4lM23BsCE9garsDDe72/mj+upvUSUG5cRTOkeVC8hqmtSOdJoe6e9iBzv4/52dbzAd+iUd2mBHf6d6xS+W2cQdaSJuueUWPPjggzj++OOxefPmQb/cXiJNvRPrt6t4vsVZTdn1TBNgXOrudbv0qAVNeQEwCB2aLtry4RE0twXhdAARLRqV2VkX0KfJ9t/fM5JkB59Wau0I6ZEmt8upT7nxPEbcW0TvA9Djd8Ictaosi077OR0OBCMaCnwew/Z8WlA13Wi3hJpfM75U2W4KyC5iGE+EC+h9ytaqvameHuFtAZDwdsXTh6pIVCKPFQsy5SqkgqRNz51//vmGv1988UXMnDkTJSUlhuefe+65OJqbHYhoSgx2Uz18gOTTPvyGT34dEjolBbmYNb5QH/g37m7QBRSAHlNb7cGwLoruOe8kfd8EFxkqEWE1mNPvNCVnnlbjeJwOQ3mYAp8HHaGw3u6zS4t6CB2+30mjhvXwbZGY6m16jlbF8YFzwi1VPfrDShSpvEd2fcOnPvlgrRpArQRcX8V2f7/t93dffRV+ySAdvDqpEk3x5AwTso+kiaYrrrgipu3+8Ic/xLrLrEFEU3JRfSN3OaLJHM0RDnOkyedxwet26jdjlfgwbu/s4ZXh5m2V74cz7faX9YhRYV5OD18OYY74kFDyOB0Yle/Vp+iA6Oo6r9vJxJ5DP8aee6JpPviAw71WquOOKxyiNKP/RCGKuHeoMM/ba3SoL6Zxq8FatS8rv1iyEwraRcsSER1KpACze38yfDWJ2Geq/D50Hfk9hV/Pvvr6hMwgnvE7rozgg1EMCekBF0Xk15leHI2MmLNP0+o2EkJtwTC87m77ntftQlswAq/bxZbjd3NrZWmPbLhcKNkN0O1dU2vtwbCeyXrW+OHY8uERAN3TZ3MnjzREfPK8bjS3BbsET7thnxENhtIowUhEPzdiXOEQNNcFMK5wiGHVHgBDJvKW9qDhfPjKQoK8UEvmTezRH6s378PhY+1obgvqNeQIynpu5fdQZRrm144PPqp90VSj2S9GWc+pjxONVYbsRGZOtsvC3Zf3W0Wy+nssFYnI8p3ITOHxoFoly7Hrr772p4itzKPPnibBiESaEk883+5VgwOfUuMRFHo93m2pDTzaQWVYrFapkSeKR3c8LgeCYQ0lBT5dyDgQzb9EaQCaWjstp+4AY1RqxaLpPc7hwP09ozBcHPLXVb4Wq2/chN2UXSzXUUVfIzfJntYZiMGtvykBVNtaeaZU3jPBmmRFmtLJXzeYSVqkSRAGEvLHULkSQvXtnm97rD1oEAhOR89oRm/wb41R8RI2RFR4tMO8KpCEC00dTbv9JQDRbNe0Oi3UJZjMOZ8o6mSOMgFR39GeQy26UOJRqXur9vRaAJdq1hFUUkYF9S0JP6vElDO6pvVmlPj145ujTjSQUB+ar6MqYmcVuaFth3XV6jNPk9pFuFTEM9ANRAREdQzz56u39qreb+7PREXFBht217+vn49ERiqFgSHulAOCkGpoyS4A5fJk83RbRDMuZeYD0b1VewwRGsCYEoBPtRFWZUNUS5H5snhKReD3Rf1Gz75z0JCRmUq20CNn7+FPDdnF+RJ8s2AyZyHI9Zj/zY0bqGrp1TZ91ut0F88oTsc3t4P61krQqWp0WS3Hpm2pRh/3YfWVWNNgpBL+mepLe3l/8t8z4dwHA5J+IPMQ0SSkLVwYqPK38MGeb2uOpPg8Tst8Ne2mAb2sxG/wx6gK39rlknl7f5P+Om8XiS0+8B9uiUaVDre0I9D1fMBUvBeIijaqITd38kjDzdYskswT7lRLb9HMYmUpB6taeqrzpd9H5OXowpLnfDK3mTDnWAKgLJxs1bckOElQlpX4Da/HIwLs8kOlE/w69zc/EIf2NWv88LTPi2RHJuR2ErIHmZ4T0grzlInZm2FltDQbwRsC3VM5k0YNw/HH5emGbJVpnEzlF5w+1lBj7eGLTsHDF52Ca556DxOXVxlKlPDl8qs21Ogr1hoCbQhr0YFc9S2SL/GniAlPQZDrcaIjFAHLOIBcjxMbdzcirAEbdzcaVvNxkaRKqknG+S0fBtHcFsKz7xzU0xCs2lBjMMZT/8+dPLJHOgc+BUnRuY27G5XGeaB7Cs9qxaGq/p3VlCxdByu44d4O7v/KpIzOyZgiJM9dIg3hA00yTO2xICbuwYlEmoS0wipioCr+yiMvnPXvG6dydtYFDNNg/JspRX/2Hv4Udc1tuLdqjzL79Lqufa57v96yEC19e68sU0d0iKOtHYZHorArk3lhXo5htR8QzSXFpwpJbJGvCIhGesIacOA/rbrIu+ap93r0R3VdwJDVnPcjRe+2fHhEGeHg2dipLVZTDLVNnxkezdgV9I2HeArP9jdikw1TW1w4pnu0jaP6f0pkBC4esuFzIMSPiCYhrbC6AaqWlFsNlG7TfJXb6TDsl0/r0U24jQkSO58BPy4XWPS+MyYU6tvyGysJGTJ6mw3fVs8TfKqQpiBLCnKx9/CnALqTdFbXBQwiz9wfZSV+hLrCWKGIZohgcTFFqHwxND02wzRNFg+q62c3JcvFICeegbO/PpJUDdIq4pma4n1H53BDxZS099Twc+T/u0SqfEHp9DkQBg4RTUJaEc8NkN+0+I2VDNOE2+Ww3C+JGtIVxQW5yoGIm7StIiT0PjJAr9pQY2ijedm/uWocF0Ihk5vd53EaDOhNrdFklU2twR5Ga7Oni/qjwOfBgfsrsW7ZWQYBxiNYJKaCEQ3H31KFRY+8DhWqqTUzqogdRzXo2JmVKWq2nuW3ioVE+l7SybwbT7SDG+/T6RzsSNeITjx9KL6rxJAO/SiiSRhQ+vqhz/O6DI+A9QBL000kSkJhzfAtmw/mJIDIP1Tf3K68SdPUVnNbyBD14t98zavFWjtChjZywzMAOEyqiQshXkKFivTyvlOt6mN7NhT0pfMdVzhE7wMuwGawqJE5cmRepUb9OCIvp9cpyFiwWwWpElW0EtC8IpCu2aoNNcp9JWvgTfVNPJ5oh10h6XSFn6OdEE9X0lX4ZRrp0I8imoQBxe5DbzUI2U1d8RsrTfuQ7AhGNMsIhTkjeK7HaYgkUXsoemNetcVXnpkFTChijBaZozIRrTs9gNMBw9J9np1bJdD8XZEvvyk9AfWB2xV9P4+yVdcF9Ck7VbuOtnbqv9PhS0yRN4pW7GRiyuqaxnqDU025AOpv8rQS8NbKUkO7uNdKdcxkTaWk+iYeT7SDC+VMJZMiZByZyksM6dCPIpqEAcXuQ281CPHIiUpY8ZspiR4a+H0epyFCoUo/QMvxb60sNXh8ug2zDn1qixuneR4mmvIqKcjtkaaAnzvJISeLcJn0VQ86QmGDQOORL77kvy/TBVwk0u90XoDD0F8UrXA6oBvnra5pMm5wtBKQyrzwVVNv3PIlPRJhPmayBtt0uIlnO6kWpokgU8VeupEO/SiiSRhQ7D70VoPQikXTUVLgM5QMsbqJkuhxORy6EDq7tEif5hrRtUptRF6OPggf+E+r/n5z7Tizf4kbmLmAom/yb9zyZeU3ejp3v88DAMjP9RgiTYu6RNeimcU9olRm+Pso+jJp1DB9+o3XqSO4L4v3IT8fmnoMhSM9hNSs8cP1c6TmxWKc53mrVNiZv4FoweTjb6nSDe5V1fW2nqiBIBU38VimBFM9bRgPdm0VYSqkEyKahD4zkDdmuyR/Kr9PMKLpmbd59IimlnbWBfRpu+q6gB454SZpet9LOw/1mAriU4GxLHWnNpIva1zhEEOk6YwJhSjy+3DGhEK9DdyXpRJCQHf0hU+/BZmhmzjWHtIfuXDk50PRrGBE089LdY4zYlg9R8Js/fv1yuk3IpZM1WZvVZE/1/C+ZH8W00mExBJ5sfN4JZL+9o3d+aQqupBO11xIH0Q0CX0mGWFzK39LPG1RmZm5X4hP1ZkNxe3BsMH7YRZg5nZZreAjVOVbyJdlFgL83ElAcV8WF0JcbFH0xQ6egZsLRw5Ny3mcDj26pDpHSnNglYMJ6M7DZE55oOobwiqq4DHtoyFg9Lb19XMTK+k0RRRL5EXl8UqWCOhv36RrJCmdrrmQPohoEvpMOiWV420xG66dDhh8P3yqjn6nQdnvcxsGFxJgfMxWeWl4lIR/u+eDuTkxpBnuWeLJB1Xw9ARmykr8umG9rMSvnw8JnKOtnfrKPYfDeD40VZbndevRJdU5ctO71WBMEao8r1u54ikeobNwxhiDX6yyrNhwXMor1dIeTIowGIjPup3oJmKJvKg8XskSAf3tm3TwqaiwOy+JRA1OHJpmrlQl9IWWlhb4/X4EAgHk5+enujkZSyylCey2odebWjvQFoygwOfB3MkjUVVdj8qy6JRbWzAMn8eFwrycHuKkwOdBc1sQBV3eo+a2IDxOByKapi/Xpn1x39I1T72Hqup6OJ0OBMOa4f0FPg9uqJhiapdbN3Sbj0vt5REkKrMSFYAO/Rx4nqYD91f26CMaLAt8HuR53VgybyJu+9sH+nt+ct5JPfqTv//Zdw7q5VAuOH0sVm/eh1njh+tlVqj0SYHPgx0rzon5Ok27/SX9fPbcsxBAd7mckgKfocSJ6nn+nPkamt+fCdidY3/PR8p+JJZEXhshtcQzfkukSUgr4vkWbbUNvU4G6RsqphhyK/GpOtWKNg5FMLjHh+/rzPs34fhbqnDm/Zt0/1OQKR1ucu6O0kS6XjUejG9L++JMGjVMf+Rmdb6ykFCtEOTlZnjkTeUNent/k74tL9lC2z580Sn9vk585SERz0o8/hydj8fpSMupnliwO8f+kqyITqoiLok8bl/2la7TikJyEdEkpBVWNy+7mxp/nRIwPvvOQeW2fOUZQd6m/FyPQbzwVWxms3RHKGzIH8WnrGhFHR+oyHxNdITCumwyu37MOZ88Tgd21UfFy676gCFPE8/JRKhu6Bt3N+rTYVyAcWjKjMzbqzfvM0wFqq6DVcJBu2umep/VwE7Pv72/SV8hyLeladaFM8ak5VRPX7ETOvH8XySLRE77xdPevuZ868u+VKTrtKKQXEQ0CWnFXet3oa65DXet32V43u6mxl+naS1aEbdqQ41hgOYmanofrUgbVzjEsN9zWRoAukHyCAlfxk/belyOHnXyAOiih2RYKKzpv2swenzMXqo8rxtF/qh4KfLnGvI0UXs6QhFl6ROVD4kLMBW5HicTXd3Sjtp417pdyhpwquMmclWUXZLSaPQv8cvx01V8xPN/kSwSGXGJp719zfnWl30JAiGiSUgraGoraJqbskszwHMJ0SoxHkniAzSfmqL9UkRpV33AcLOllXRnTCjUj8VzM+V5o76lPK9H35ZWi3WEwoY2UmJI/Vx7ycVEq9OA7qiVVVZ0EoS0u+q6gHJ6bkaJX98XX0nH20jndnZpkTLFgzklQVV1vaWhm943a/zwhAkOqzIqdP07QhHb1WJ9ERLJXp0H9G3gtnvPQIiBREZcEll4eSCLOKsQo3h2IkbwBCFG8MSw6JHXddPxumVn9brtyXf9vYdhm8zOdc1t8Hmc6AxFehi2VYZYMnFXlhXjjAmF+utAdJA9fKwdwbBmMI/zVUl8X1btIpP0pB+/iGBEg8fpMAinRTOL9TaYS52QSVxFWYkf1XUBfX8lBblYMu/EHu3ixtXWjlCP/uJL1K36js6NspmXlfhR2/RZD7M777um1k60BcMxGcWtzMr0PDeg89fp3Hi7t3/8idKoa7cvFfya8nMQBCusjOJiyE8/xAguZBz0rez44/JQUuDDBaeP7fO+6Bum1+3Szdt23/p4Ykn+rZMiDBT5amfm8SXzJhrKetAx5k4e2avHh7xHeV63wcTNE3Ca8xKZ/U/8fWTU7hZg6rxIqohRRyisLKPSFozokSTedxTVcnUdf+/hTw1TnzwiQ7+3KYsKqxMw2tWxM095ms8N6J4atYo00PUlURVLxClTC8VKtCN1WH3+JP9TZpPWounOO++Ew+Ew/EydOlV/vb29HUuXLsWIESMwdOhQLF68GI2NjYZ91NbWorKyEkOGDMGoUaNw4403IhQKGbbZvHkzTj31VHi9Xpx44olYs2bNQJyewKAbSVV1ve0NhQaCcYVD9Okm1aBG4sWco4b7psg0Ts+t2lDT60BjnhbiHhsSCVs+PKKLLj6Vx3Mv0XOj8r0AgFH5XsOKuDyvsRCv1+0y+J94okvSVw50m9W5eFGtiOOoyqjQPqcX+5VTU1xE2k1t0L7MfjFVAka71XN8W36dqA1ed/f1Sffpm4FABujEE6v5HoDyMyP+qcwmrUUTAEyfPh2HDh3Sf15/vdvket1112H9+vVYu3YtXnvtNdTX1+P888/XXw+Hw6isrERnZye2bt2KJ554AmvWrMEdd9yhb7N//35UVlbii1/8Inbs2IFrr70W3/ve97Bhw4YBPc/BDt1IKsuKbW8oNBDs7CobsuXDI8oEjDwqwaMp3DdFooeea+0IYcXzH6CuuQ0rnv9AF2NEWzBiGIhUHpuOUFi/qXJBooqAcZ8SL99CpVZ8HqcusKzIz41OA/p9HuVNWrUijpvZVTdxClqZM37TuVOky5x9nYtX+p0MALvqA0qhwxMw2q2es0vWGE9EKFOFUDyoaicCEoHqD/013w+Gz102k/aiye12o6ioSP857rjjAACBQAC/+93v8POf/xxf+tKXMGvWLPzhD3/A1q1b8eabbwIA/v73v2P37t3405/+hJNPPhkLFy7EPffcg1//+tfo7IxmjX7ssccwYcIE/OxnP8O0adNw9dVX4xvf+AYeeuihlJ1zJtPfm/GB/7Tq011W0NJ9h3oWShcBZApetaHGIF54tmzzPsjgDABhrbuuG4mEAp/bIDJ4dnEasL1ulzJyQjdLij6ZBzJevoXEFE2Tmael+DnQ/sYVDukxjXZDxRR9Ks7hgH5c/jqfYqTnPczMzrel86FI2NHWTqUQumTOeP13EoOVZcXKASWWQYQigm/vb9K3VQmCRA5I2SAsrGojDmRtuoFkIK5ZOpjvhdSR9qJp7969KC4uxgknnICLL74YtbW1AIDt27cjGAxi/vz5+rZTp07FuHHjsG3bNgDAtm3bMGPGDIwePVrfpqKiAi0tLdi1a5e+Dd8HbUP7sKKjowMtLS2GH6Hv0wEUDaGis1XVRiM0vxnqS/c19BpVCIWjSSQ7QkYf0rplZ+HA/ZVYt+wsmJdB+Cym37hfiA/MPNHls+8cRF1zG/K8rl5vmnwg496kRY+8rqcMUHmaOBQBqm36TG8DT7HA20hRJU2DsjQKpWioqq7Xn+c5n1RCiEd87q3aoxc7Vl2zMyYU6mLQKvJhB28jwfs+GYNlJkxt2Z13PNOd2cBA5IyKNbmuRJKyk7QWTbNnz8aaNWvw8ssvY/Xq1di/fz++8IUv4NixY2hoaEBOTg4KCgoM7xk9ejQaGhoAAA0NDQbBRK/Ta71t09LSgrY269pfK1euhN/v13/Gju27cTmb6O+3LO6l4fCbIS3dP5flTuLo5u0uoRMKa0rD9p/e/FiPglBNs7NLiwz7UhWbpajHNU+9ZzBUkyGbpwRQ3cS5cKCaagtnjDFk3iavEx3d63YZUiXw41LWcqIjFDa0kSJRM0r8ymujSj9A8GzdgNqvwb1YquvAvVBWkQ876JpPL/b3KY1AtmZ87utUkEr8ZgOpyhklDB7SWjQtXLgQ3/zmN1FWVoaKigq8+OKLaG5uxrPPPpvqpmH58uUIBAL6z8GD6uzTg42+fsuimzf5c8xFd/nNkE9jxUIwomFdV8Ro3fv1yjxMh491IKwBL31wyJAxnKahfF3JHm+omKKMegDGNWu0f1VkhQsHvmIOhvdHhRfFt8YVDkFbV/mVtmDE4EkKKfI9cYM6RWT2Hj6m7B/KCbX38Kd633jdLmUUTzWtQ76mGSX+uCIf8QgZWt1Y2/RZj+LCN1RMsY1gDWTG54Gc1uurSLAzK2cqqcoZJQwe0lo0mSkoKMDkyZPx0UcfoaioCJ2dnWhubjZs09jYiKKiaLSgqKiox2o6+ttum/z8fPh81hXpvV4v8vPzDT9C37H75hvPzdDsyzFPuakGWG4O5xnDaV+3Vpbqx+eRGVX9NKB7FRufQiL4zZjXweNizTw9R1EoglaijSscokfLVGVhuEG9LRjpEfmhY9Mjte2Giim9rvwBuoUhnyrk0SU7U3Y8CSP5ykOVaVwVweLiZSAHwIGMUPS1zIpEUeyx6tts8LoJfSejRNOnn36Kffv2YcyYMZg1axY8Hg82bdqkv15TU4Pa2lqUl5cDAMrLy7Fz504cPnxY32bjxo3Iz89HaWmpvg3fB21D+xCSR19vPrG+b9qYfOWUm8oLQ8KqwGfMnaS6cVIU7Ghrp0EYzGDmbHoPn0ZTwXMvcbHWW7ZwwFgGhaJlfLk9ry2nMnfz6Tur4r0q+jKt8/b+JmUeJru+4diJOZUQ5scaSJ9JOkUorMRRX71lggjOwU5aZwS/4YYbcO6552L8+PGor6/HihUrsGPHDuzevRsjR47EkiVL8OKLL2LNmjXIz8/HsmXLAABbt24FEE05cPLJJ6O4uBgPPPAAGhoacOmll+J73/se7rvvPgDRlAMnnXQSli5diu9+97t45ZVXcM0116CqqgoVFRUxt1UygscPz5hLS8gpU7U5i67qfVZZq+l1B6LTW9Gs1y7UNbejpCC3K0N1BD6PE163y5Bp2+UActxO/fVbK0t7ZI8GoMzoq8oabfccPzZl9PY4HQhr3REvFSUFufr5UPbvwy3tCEY0+DxOtAcj0BAVY/vvr+xx3EBbUH/9nvNO6jVDMc9grDp3q2zqqzbUoLktqF8HKqlC227c3YC2YAQepwOj8nPjzpDM20XH8nmcKMzz9nqdBhNW2aetslUn+7jJYKAzbEtG7+wjnvE7rUXThRdeiC1btuDo0aMYOXIkzjrrLNx7772YODF6Q2xvb8ePfvQjPPXUU+jo6EBFRQUeffRRfeoNAD7++GMsWbIEmzdvRl5eHi6//HLcf//9cLu7kwdu3rwZ1113HXbv3o3Pfe5zuP322/Gd73wnrraKaIoffvOhb29WQkj1vvrmNmiICp27vnZSj4HdSkT4u8QKL3PicToQ0TTDYK4SVb2VArm3ancPEUBt4TfYE5ZXIdKVQHKMv1v8HG7p0EXTikXTsXrzPhwKtCGidU/X0esRLZoaweUAhuUaxRdFjnoTftRWn8eJPfcsjOna8Gk5Kr9Cq+fagmH4PC7suWeBvg9VyZWjrZ09SrnE0rcqgcbLpNC+uEBLhhjIZOzEbyJJtihL1bGE7CRrRFMmIaKpf9AN3apOmQqrAZRunDyy0hEK6yLh7NIi/X0A9N9JpKlqxxEFPg/mTh6pv2fLh0d6HINvqzqH42+p0n8vKfDp7SYRyKM/Ta0daAtGUODzoLUjpIumUfleFjkLGkqVcKG0ZN5E3LVul2G6r6zEj+OPy+tx3hxVFJAPtjwiyNtNUS1+Te3ELT9H3l+qNtjtK556cplAIqMaAxldAgYuypdukZ90a49gj9SeEzIO8pzQerFYfC68XpzKR8L9RjwJJV/9RgbiLR8eUSaGpOX6ZV3L9XtbPWemtSMU12oyvy8a/fT73MqSK7RKLhTR0BCIrq5rCLQbvEzm/rxkzvge/qjquoDSoM7hnhe7PE08k7kq0SV5vcxpJOh1ujbmrOe8b8g07nY69ESZqnY9fNEpWWXeTaR/Jtleq1R5yNItL5J4nrIbEU1CWqDKD2Q30FndpFXLqblI4Bm/SRxQ9nDzviglwN7Dn+rPUc6gyrJig8Ayr6ALRTRDG83nCACrNvyrawXZv9DcFs231NwW0tsV6spQvuXDIwZxwlfwUckVntOJ911JQa7h9QKf22DC5qZwwi6fEu8jngJCdb6U0qC26TPlgGJ1LNVg6HY5dKEcD5k6kPVX6Fhla08GA2EuzwTxm04LAYTEI6JJSAtU+YHsBrp4qohzkUAioqQgVxdRoXBEv+HzGzNfjk9wkcAHIopKUaLMc2cWG9rIo0cEF0p81R4JCUqF0BEKG47LV/CpcjrxPmjtCBteD7SFDKkSeE4nu74lVEILMA6cFB3ifUclcOgxlmMB3VFDwNFrmgKrQTVTB7L+Cp2BFIt9TVxqB7+mmSB+0y3yJSQWEU1CWqBaUm430KmiS39682N94B6Rl6M/x0XCG7d8GQfur8Qbt3xZ31eQRXT4jZkLGTto0AAcyuSbPMeRCp4dnLblx1Wdo+pb/a76gKHvzFOduR6nIULGczqparxxqA3rFEKL9wEfOD1dU2pzJ480pEogYhlk7KZvqV0k1MyD6mAdyAZSLCbrWDyfV6aKXyF7ENEkpAWqQc1KFKngQoeiRzu7arGt3rzPkAySQ+KBJ8DkN2bKCO52OWynBVQ3dNUUIo90cVSCI6x1l4Lh+7L6Vq/y+5BgpKzmt1aWGt5za2Wp/jz5tda/X99rUkQi15Q4lPcB9W2e1623lU9tEvFMuVglE1Ulv+RkwrROMsg2b9FgFb9C+iCiSUgL7AY1VfZou4zPZBqeNX64IcLBp5aoJt2kUcP0yAu/Mcc6LQTwaAh6bVdTa1B/XNTlU1o0s9gwdUVFcMnDHYpohn3x32kfBb6oODnwn1bDOdI58CSfXIDxunwkatwu9flShIuM8bdWlhquA9+XyjSuKoETz5QLTYFamcbNz/flGJlKtgpDu8zyQnqTbZ9LEU1CWhDPoEb/hCQsVm2oUXqL3C6nHuEo8kejOkX+XIOHh6aZqusCvRY2Va1QA9TeHrtz4T6pA/9p1YUOL9jLi9+SP4oLEv77lg8PI6x1+6Oq6wKGc6RtN+5u1NvFRRdfDUiiJi/H3bPh6I6GUekUwCho+XFjrW8Wz5SLnWmcUk+Y+z7Z0zqJGBj6uw/V5y4bBqx4Is5C+pFtX1hENAlpgWpQ4zdI/m2T/gm5sODb0sDa7XsxLtFX1WUD0OvNmEc4+LFUImHW+OE9zN/8hsHLlnChxKGVcmUlfn21GC86zI9LYolwIBplQ9cjvY/E2oi8HMNApJoys4vo0GpDcySK9y2d+71Vu3VhqRr0YplyofeNyMtRGvbN7etP7cK+kIiBob/7sJsezgbBEU+9QiE9yDYfmiS3TBCS3DIxWGWi5sn4aJsReTnYVR/okdySSnWE2Sd70cxiZakPyoxNWCWk5Akv87xu/ViUmJHKd5jba5fVet376lxPB7qSRPKEhNxL5PM4WdZyp0E4mbOe8+ScQLfvidpAUa6yEj/WLTurx3F56ROaVpv04xd7ZC9fMm8i3t7f1KOfqd0OAMUsmadqv1ZQe1yOaKoInp3cKlljIpMM2u0rEcdKRlLEWP6fBpL+nqOqLJGQOrIlkacktxQyFv7NmH9DUeWbOdraqU/V8G0ptxJRUpCr9NIA3SZon6f36bfWjm5Rwo/FTdSqb1SqCIddYkl+3KbWjq5njN9teLLOHSsqcOD+SpR1JZGcUeI3RIro+QKfWxdMPGqlinZZTd8RlDAzGNEM58inz+h5D4t62e3XKhpCXqrpxX6lr0uFVeSmLxEXuyhQIiJZyYiG8X1a/T8NJH2ZhudtFH9TepFtU2+xoDYuCEKC4dGV3qIK5pIdhPmfk0pm0HsoKzQA3Pa3DwzvpTxFBNVKu7dqD/bcswCXzBmvt888HUUDe1jTlNNV/Lj0d1/hRXjXv18PDdCjYHXN7YboEgnDjbsbDSVogKjfqLbpsx7CrCMUMUz1qY7LvzlSNIJHj1Tw9/DrR8+7XdFCxHlet6G/VPs1rzYkSGjWNn1miDD01t+zxg9HQ6CtR1oGq2P0htXnMpPgfU+Ru3j6IBHE04+q62T+f8tEsiU6A2TH/0W8SKRJGBBiLT3C4TdNnpeInt/+8SfKb+bmnEot7cbpKVXCSi5COGQgp2mh7R9/0m9fBWXwpkeCr6oz+60AY3SJ/FztwbDS48WTeRLtwe6s58b8U9RfDuU3R1WUjqJXZSV+Q3/wyAbtK9QV9htXOMRgnOdlcAir6JHqfFSRCJW3bfvHn9iutLQjG5a697cPEkE8/ZhtXhgim6Iz2fB/ES8imoQBQWU2VmGVyI4PgHamcTOm0msG6H1tTIRwyEAe0brz/5AIa2kPYtEjr+P4W6qw6JHXY2oLAD2Dd11zO5MrRjFHAolwOozTesVdOZ6KC3J1QTmD1cfj+YxoSmNGiV+ZENMqlUFvXHD6WJQU+HDB6WMtt6F90VTezrqAQTyrxKfdTTgU1nrNDm31+UlVXbR0Ip0G61imB7P1OmWrGBwsiGgS+k0sN0ArT5EZHlGw8mOo8iHxASFkUknmyBNPLknv83mcyqKzJPbOnVmst4V2H9Gg9APZeWkKugrzlhTk6k4ls67jNfIAwLxcg68GpG33Hj6mv67yftCU3faPPzF4kjg8lYG53fz6cnFitdKOrpOPraiLVTybIREYjGhdK/L22NY6s/r89JdMXYVmJSJTQSKPn67Xw6pd2SoGBwsimoR+019zJ8cq47PdcfmAwJfrlxT4sGLRdMP7uOCg991aWaqLOsr/dG/VHuUUEhddfJqKsDLcUnsDXSvdWjvChvfz30k8ktybUeI3TOtx8UHbtgXVRYdJ4LR2hHSRwafnuABSTaOqlq3zaTKrqU2Cm+V5f8Zj6u1OMhqlPRhW5myy2mciB6pMHfATKSL72+5EithUC0Ar0rVdQv8Q0ST0m3hugHY3Ej7o2RXqVEWfLpkzXh+YLzh9rDIpHk90qYpaqfxC/Ljce2Q3TcUFGEVGHF1KqCMUxvHH5cHlAI4/Ls+wLxKNFAeqbfrMMK3HxQd5hhxAr5GXUFd9ve0ffwJ319yf2+XQVwa2doQwvTgq3OjR3M88hQBFl3h/2eVh6us0GW1L2c/NxZBVx+IkUpxkwoBP53vNU+/1et5v72/qU7/YtduuvxMpYtN1uitd2yX0D8nTlCAkT1NsxLqKDjDmCuKrNOhGa7UKRZXLhe+rIdCGsBYd9Pet7JkPiecPAqJRl+nFfhxt7cSSeRMNq/MofxJvY1NrJ9qCYRT4PAi0BaHBmKOIcDqiz1NbhuV251aaO3kkqqrr4XREV54V+DxoaQ8iokXfN8bffdz65jbD9J75HChfEs9rteXDI/qxeBt5fidVHhzq89aOEJrbgj2OxfNlJTt3kgq+fwBplaNIRbL6Q5XXip83/Y84EBXm8faLXbv5/1M69bcgqJA8TULaYlUGQwX3rKi+mVplO6apo9aOkHK1kMpXw1/n/itq76767uK/PKO3KgpDhu6OUBgzWO4k2paIaDBEdrifi47rdjn0iI6rK0TlcjgMdepmmPIwzRo/3JAxnPqOpyHgPiRuKudRJ4KveKN90ftnjR9u6C+Vz0j1/ngFQqyRIv6ZsJq+TSdU/ZGIqBid7/RitfmfyO0q4hxvv9hdx3Ttb0HoLyKahAElnpupXRJIO2MrmYZXbzYWpVWZ0p995yDqmtvw7DsHlUuz+eDDl/7zwYMEA/mFvG4XjnblTjra2tkj2aPH6cCehhYA0B8JEkV8So2bt7kBnY4RaAv12l9cEHHxyj1e/BjUByqfk5X45deM+pEn0uwrd63fhbrmNty1flev2/HPhJ3oTlcSMWWnSgCrKkt0a2VpUvrFrr/T1bwtCHaIaBIGlETcTFWFYPlgSR4fp6M7TQCPvKjgIkTlu+FRGqv8P/R8ntet+7JUpvBR+V4AwLQx+Qh2tTUY1vR2h8LdooheJxGlgsSBm60S5Kvy6LhcEHFBwSNvPDM69QF/XVVfj8OjZdz/1F94PxF2/ql4oprpRCKjNOmQdkF1nfoqDEVsCalGRJOQVsSylJ1uuKs21PQorXLJnPF6ygFN6xZVvICtCr4iTjXFxMWAKtEmT8A5rnCI/j7VKjYydO+sCxiOa5UGAIiKOb4tRyXW+Eo8s3jxOB2GiBCPvNFKtzKW04m/rkosyqff+OpHGrApsubzOPs86KlWKdoNvJk6RZRIQZOstAvxYLeIo7/7EoSBRESTkLZYRQpInHSEIsobKPcREXy1GB+46ffuUitGQUGCgMqZdIQilok26fmddYGYMoa7nQ7DSjyOqhZefZfYokeC2sDFJV+VR6+TXAz3svaDBtm9hz9VphFQCUoexeOrH2lfKxZN16eC+jroqVYp8rbYRZ04gzVakaopynhWOfZlX4IwkIhoEgYUlWDhgxePLlndIEmceN1qE2tt02eGR8CY/4lHs2gQ7wiF9QGYR5VIEBARzTrVAT2vKoHCz43HungWcEp6WeBz674pCoz5PE5DtIyb0YmNuxuV+ZaojXQaEc06tYOqXaq+3/LhEf09uSx5pd1gaJeQ0gpV9nAuXlXRRyuSEa1IVyGWDu1KVuRMEFKBiCZhQLFa3UTwgZDfIPnNv9vfo/XIXs3pCIV7GF/NSQ+7V7Q5lCZqEgQkdMpK/IZ2qVaG0RSX+VgUwSLxEoxoBvHT3JX0srktpG9LwvDWylLkeaOiKs/rNpjRVbXn+Ko86jua1jOfg+o68HbxvldF+XjySg69jwTP6s37lOZ+OyENwFC6xnztuOnbKvrISUa0It2mjVR9LwhC/xHRJAwodnXOrCIRfFAik3Rz12qxde/XK1cGed0upfGVPEfcexQK0/Rb2GDIJkFwz3kn4cD9lVi37CxDu1Qry2L9NuzzOC0FB6106whF9Od4u7m4JBHp70o5MHfySMOqPeq71o6wMhGnyqw+adSwHqJs9eZ9tlE+1TUDug35PIqnGtit0kjw0jWqfqZ2AZry88P3Fesy/3iiNOk2baTqe0EQ+o+IJmFA4QOWavBSLVmPpSq7amUQiSdzOZNd9VHRtas+oA/Y3HxNRvJQRFNmDOfEU0uN2rNoZrEulHgqBJqKczq6zeARDfq0E2837w96nkTkxt2NhjxO9HugLahnJ7e6JtSPu+oDSt8W/T6ucIjebqsoC217Q8UUpYhUDexWq71URnAOiW2KGJp9cHaRINXr8USP0m3aSNX3/RWGVtjtNx2mCGMlnrZm0nkJiUNEk5BWqJasmyNFVEqD+31UoooLEp7nR5VQknMuK9VBWEVA7AoRqyIcvI4dN1GTP1vTehYZpvbSI++PYbluw3btwTB2dkXjdtYF9MgcyUKzT4lD/VhZVqz0bdHvO+sCerutBK1KSKhW1/GB3Wq117plZ+HA/ZW44PSxyoFKFQGLR3THE/XMBOySwfb2XLzY7Tfdpi57I562StqEwYmIJhO//vWvcfzxxyM3NxezZ8/G22+/neomDSpUg6p5oCOhcvfXTlJObRFckPA8P3zqylwY2Ot2Geq6qfISqUzJgLrel+rGavV+npmbVvs5HVBOuXHIC0XMKPEbzNnxQELzwH9ae90uFvO3anDg3jIualXviScLvEqAxZOXSPV6puZ5skI1DTsiL6ffwlD1f2o3DR8PAyky4mmrpE0YnEjtOcYzzzyDyy67DI899hhmz56NX/ziF1i7di1qamowatSoXt8rtecSg1VNK16zjmqp8W1UteM0RKe3fB4nJo0ahuq6AMpK/Ljg9LE96pPNGj9cn4pataFGr78GAM1tQfg8ThTmebFk3kTcW7UbbcGI7kky1zfj9b5UNfOm3f6S/n5KZWDG/FqBz4MbKqbo+3r2nYP6+Rxt7UBdc7vhuMZ6cE16bijiwP2VyuNOuKWqRx07XjuMrg+goa65HWUl/h4+L8Ku/tjE5VW91gBUHZdfJ7t6cv2t65bsOnmJJp722tWmSyeyrY5dpn2uBgNSe66P/PznP8dVV12FK664AqWlpXjssccwZMgQ/P73v09107KSeL5BcsO1XbI8ijCRTWnSqGHKPD8AlFNmHJq+awt2r8qySl9A3qEif65yaotnMqf3xwOPzPAM5k1dkScN0KMGxihJz6m+RY+8juNvqcKiR143PE9pDXg2dX6dqO9JhJGfSoXdN3G7GoD8uNTPWz48EnOyxv76jNLNp2RHPBEM1TRsX0lkJEi1r3Qz2ff3fDPtcyUYEdHURWdnJ7Zv34758+frzzmdTsyfPx/btm3rsX1HRwdaWloMP0J8xOOx4AOsndfEPCW101QaRTU9xl/nq9RI2PACJVbpC0hANATaezU+e90u5fv5Mn86rsfZXbCXC0duGm/vikhFNCjN290m6W646OKDAE0Let1O5TQX7ZdyShX5jdnJ44FPgxJ2qRA4/R18ss1bYiU4VVDfPXzRKf0ewBM53aTaV7qJDJleG9yIaOriP//5D8LhMEaPHm14fvTo0WhoaOix/cqVK+H3+/WfsWPH9thG6B07LwSHG65VXhN+I6Nl/GSmzrUwilu1ha9SI4FEU1Z1ze2GmzgXUHYr6VS+G14ahacfoP3med36ufL95+dGpw7zcz26cb2sxK+McFF/kdAqKcg1FA3mfcejaKq+of0ea4/6qKjYr4q+rFizuiZWQrU/ZNvgF4/gTCSJjASlW1RJRSa0UUge4mnqor6+HiUlJdi6dSvKy8v152+66Sa89tpreOuttwzbd3R0oKOjQ/+7paUFY8eOFU/TAKDyBMT6XG/PE9w/RaviFj3yuu4hsvLwJIO+noPdPvhzAJS/97ZfVR/F2va+nEMySPXxk0k2n5sgJJp4PE0imrro7OzEkCFD8Je//AXnnXee/vzll1+O5uZmPP/8872+X4zggiAIgpB5iBG8D+Tk5GDWrFnYtGmT/lwkEsGmTZsMkSdBEARBEAYnbvtNBg/XX389Lr/8cpx22mk444wz8Itf/AKtra244oorUt00QRAEQRBSjIgmxre+9S0cOXIEd9xxBxoaGnDyySfj5Zdf7mEOFwRBEARh8CGepgQhniZBEARByDziGb8l0pQgSHtKviZBEARByBxo3I4lhiSiKUEcO3YMACRfkyAIgiBkIMeOHYPf7+91G5meSxCRSAQ1NTUoLS3FwYMHZYoO3bmrpD+iSH8Ykf4wIv1hRPqjG+kLI4nuD03TcOzYMRQXF8Pp7D2pgESaEoTT6URJSQkAID8/Xz7YDOkPI9IfRqQ/jEh/GJH+6Eb6wkgi+8MuwkRIniZBEARBEIQYENEkCIIgCIIQAyKaEojX68WKFSvg9XpT3ZS0QPrDiPSHEekPI9IfRqQ/upG+MJLK/hAjuCAIgiAIQgxIpEkQBEEQBCEGRDQJgiAIgiDEgIgmQRAEQRCEGBDRJAiCIAiCEAMimmLg/vvvh8PhwLXXXgsAaGpqwrJlyzBlyhT4fD6MGzcO11xzDQKBQK/7+c53vgOHw2H4WbBgwQCcQWIx9wcAzJs3r8e5/eAHP+h1P5qm4Y477sCYMWPg8/kwf/587N27N8mtTzzm/jhw4ECPvqCftWvXWu4nUz8fd955Z492T506VX+9vb0dS5cuxYgRIzB06FAsXrwYjY2Nve4zkz8bvfXHYLx32H0+Btu9o7f+GGz3DqKurg6XXHIJRowYAZ/PhxkzZuDdd9/VX+/r9f71r3+N448/Hrm5uZg9ezbefvvtfrdVMoLb8M477+B//ud/UFZWpj9XX1+P+vp6rFq1CqWlpfj444/xgx/8APX19fjLX/7S6/4WLFiAP/zhD/rfmbaEVNUfxFVXXYW7775b/3vIkCG97uuBBx7Aww8/jCeeeAITJkzA7bffjoqKCuzevRu5ubkJb3syUPXH2LFjcejQIcN2jz/+OB588EEsXLiw1/1l6udj+vTp+Mc//qH/7XZ331quu+46VFVVYe3atfD7/bj66qtx/vnn44033rDcX6Z/Nqz6Y7DeO3r7fACD795h1R+D8d7xySef4Mwzz8QXv/hFvPTSSxg5ciT27t2L4cOH69v05Xo/88wzuP766/HYY49h9uzZ+MUvfoGKigrU1NRg1KhRfW+wJlhy7NgxbdKkSdrGjRu1//qv/9J++MMfWm777LPPajk5OVowGLTc5vLLL9e+9rWvJb6hA0Rv/WHXP2YikYhWVFSkPfjgg/pzzc3Nmtfr1Z566qkEtjp5xPP5OPnkk7Xvfve7ve4vUz8fK1as0GbOnKl8rbm5WfN4PNratWv15/bs2aMB0LZt26Z8T6Z/NnrrDxXZfu+w64/Bdu+I9/ORzfcOTdO0m2++WTvrrLMsX+/r9T7jjDO0pUuX6n+Hw2GtuLhYW7lyZb/aK9NzvbB06VJUVlZi/vz5ttsGAgHk5+f3+AZlZvPmzRg1ahSmTJmCJUuW4OjRo4lqbtKx648///nPOO6443DSSSdh+fLl+Oyzzyz3tX//fjQ0NBj25ff7MXv2bGzbti3hbU8GsX4+tm/fjh07duDKK6+03Wemfj727t2L4uJinHDCCbj44otRW1sLIHruwWDQ0EdTp07FuHHjLK9zNnw2rPpDxWC4d9j1x2C7d8T6+RgM945169bhtNNOwze/+U2MGjUKp5xyCn7zm9/or/flend2dmL79u2G9zidTsyfP7/fnxGZnrPg6aefxj//+U+88847ttv+5z//wT333IPvf//7vW63YMECnH/++ZgwYQL27duHH//4x1i4cCG2bdsGl8uVqKYnBbv++Pa3v43x48ejuLgY1dXVuPnmm1FTU4PnnntOuX1DQwMAYPTo0YbnR48erb+WzsTz+fjd736HadOm4fOf/3yv22Xq52P27NlYs2YNpkyZgkOHDuGuu+7CF77wBXzwwQdoaGhATk4OCgoKDO/p7Tpn+mejt/4YNmyYYdvBcO+w64/Bdu+I5/OR7fcOAPi///s/rF69Gtdffz1+/OMf45133sE111yDnJwcXH755X263v/5z38QDoeV7/nXv/7Vvwb3K06VpdTW1mqjRo3S3n//ff05qxByIBDQzjjjDG3BggVaZ2dnXMfZt2+fBkD7xz/+0d8mJ5V4+oPYtGmTBkD76KOPlK+/8cYbGgCtvr7e8Pw3v/lN7YILLkhIu5NFPP3x2WefaX6/X1u1alXcx8mUz4eZTz75RMvPz9d++9vfan/+85+1nJycHtucfvrp2k033aR8fyZ/NlTw/uAMhnuHCqv+ILL53qHCqj8Gy73D4/Fo5eXlhueWLVumzZkzR9O0vl3vuro6DYC2detWw/M33nijdsYZZ/SrvTI9p2D79u04fPgwTj31VLjdbrjdbrz22mt4+OGH4Xa7EQ6HAQDHjh3DggULMGzYMPz1r3+Fx+OJ6zgnnHACjjvuOHz00UfJOI2EEWt/cGbPng0AludWVFQEAD1WUTU2NuqvpSvx9Mdf/vIXfPbZZ7jsssviPk6mfD7MFBQUYPLkyfjoo49QVFSEzs5ONDc3G7bp7Tpn8mdDBe8PYrDcO1So+oOTzfcOFVb9MVjuHWPGjEFpaanhuWnTpulTln253scddxxcLldSPiMimhR8+ctfxs6dO7Fjxw7957TTTsPFF1+MHTt2wOVyoaWlBeeccw5ycnKwbt26Pq3Y+Pe//42jR49izJgxSTiLxBFLf5jZsWMHAFie24QJE1BUVIRNmzbpz7W0tOCtt95CeXl5Us4jUcTTH7/73e+waNEijBw5Mu7jZMrnw8ynn36Kffv2YcyYMZg1axY8Ho/hOtfU1KC2ttbyOmfyZ0MF7w8Ag+reocLcH2ay+d6hwqo/Bsu948wzz0RNTY3huQ8//BDjx48H0LfrnZOTg1mzZhneE4lEsGnTpv5/RvoVpxpE8OmXQCCgzZ49W5sxY4b20UcfaYcOHdJ/QqGQ/p4pU6Zozz33nKZp0ZVWN9xwg7Zt2zZt//792j/+8Q/t1FNP1SZNmqS1t7en4pT6Be+Pjz76SLv77ru1d999V9u/f7/2/PPPayeccII2d+5cw3t4f2iapt1///1aQUGB9vzzz2vV1dXa1772NW3ChAlaW1vbQJ5KQlBNz+3du1dzOBzaSy+9pHxPtnw+fvSjH2mbN2/W9u/fr73xxhva/PnzteOOO047fPiwpmma9oMf/EAbN26c9sorr2jvvvuuVl5e3iMcn02fjd76YzDeO3rrj8F477D7f9G0wXPv0DRNe/vttzW3263de++92t69e7U///nP2pAhQ7Q//elP+jaxXO8vfelL2iOPPKL//fTTT2ter1dbs2aNtnv3bu373/++VlBQoDU0NPSrvSKaYoQPiq+++qoGQPmzf/9+/T0AtD/84Q+apkXnp8855xxt5MiRmsfj0caPH69dddVV/b6AqYL3R21trTZ37lytsLBQ83q92oknnqjdeOONWiAQMLyH94emRZeS3n777dro0aM1r9erffnLX9ZqamoG8CwSh0o0LV++XBs7dqwWDoeV78mWz8e3vvUtbcyYMVpOTo5WUlKifetb3zL4Udra2rT//u//1oYPH64NGTJE+/rXv64dOnTIsI9s+mz01h+D8d7RW38MxnuH3f+Lpg2eewexfv167aSTTtK8Xq82depU7fHHHze8Hsv1Hj9+vLZixQrDc4888og2btw4LScnRzvjjDO0N998s99tdWiapvUvViUIgiAIgpD9iKdJEARBEAQhBkQ0CYIgCIIgxICIJkEQBEEQhBgQ0SQIgiAIghADIpoEQRAEQRBiQESTIAiCIAhCDIhoEgRBEARBiAERTYIgCIIgCDEgokkQhIzmO9/5Ds4777wBP+6aNWvgcDjgcDhw7bXXJu04Bw4c0I9z8sknJ+04giDY4051AwRBEKxwOBy9vr5ixQr88pe/RKoKG+Tn56OmpgZ5eXlJO8bYsWNx6NAhrFq1Cv/4xz+SdhxBEOwR0SQIQtpy6NAh/fdnnnkGd9xxh6Ei+tChQzF06NBUNA1AVNQVFRUl9RgulwtFRUUpPU9BEKLI9JwgCGlLUVGR/uP3+3WRQj9Dhw7tMT03b948LFu2DNdeey2GDx+O0aNH4ze/+Q1aW1txxRVXYNiwYTjxxBPx0ksvGY71wQcfYOHChRg6dChGjx6NSy+9FP/5z3/ibvPxxx+Pn/zkJ7jsssswdOhQjB8/HuvWrcORI0fwta99DUOHDkVZWRneffdd/T0ff/wxzj33XAwfPhx5eXmYPn06XnzxxT73myAIyUFEkyAIWccTTzyB4447Dm+//TaWLVuGJUuW4Jvf/CY+//nP45///CfOOeccXHrppfjss88AAM3NzfjSl76EU045Be+++y5efvllNDY24oILLujT8R966CGceeaZeO+991BZWYlLL70Ul112GS655BL885//xMSJE3HZZZfp04pLly5FR0cHtmzZgp07d+KnP/2pRJYEIQ0R0SQIQtYxc+ZM3HbbbZg0aRKWL1+O3NxcHHfccbjqqqswadIk3HHHHTh69Ciqq6sBAL/61a9wyimn4L777sPUqVNxyimn4Pe//z1effVVfPjhh3Ef/ytf+Qr+v//v/9OP1dLSgtNPPx3f/OY3MXnyZNx8883Ys2cPGhsbAQC1tbU488wzMWPGDJxwwgn46le/irlz5ya0TwRB6D8imgRByDrKysr0310uF0aMGIEZM2boz40ePRoAcPjwYQDA+++/j1dffVX3SA0dOhRTp04FAOzbt69fx6dj9Xb8a665Bj/5yU9w5plnYsWKFbqYEwQhvRDRJAhC1uHxeAx/OxwOw3O0Ki8SiQAAPv30U5x77rnYsWOH4Wfv3r19iviojtXb8b/3ve/h//7v/3DppZdi586dOO200/DII4/EfVxBEJKLiCZBEAY9p556Knbt2oXjjz8eJ554ouEnmekEOGPHjsUPfvADPPfcc/jRj36E3/zmNwNyXEEQYkdEkyAIg56lS5eiqakJF110Ed555x3s27cPGzZswBVXXIFwOJz041977bXYsGED9u/fj3/+85949dVXMW3atKQfVxCE+BDRJAjCoKe4uBhvvPEGwuEwzjnnHMyYMQPXXnstCgoK4HQm/zYZDoexdOlSTJs2DQsWLMDkyZPx6KOPJv24giDEh0NLVSpdQRCEDGbNmjW49tpr0dzcPCDHu/POO/G3v/0NO3bsGJDjCYLQE4k0CYIg9JFAIIChQ4fi5ptvTtoxamtrMXToUNx3331JO4YgCLEhkSZBEIQ+cOzYMT3PUkFBAY477rikHCcUCuHAgQMAAK/Xi7FjxyblOIIg2COiSRAEQRAEIQZkek4QBEEQBCEGRDQJgiAIgiDEgIgmQRAEQRCEGBDRJAiCIAiCEAMimgRBEARBEGJARJMgCIIgCEIMiGgSBEEQBEGIARFNgiAIgiAIMfD/A52WhU7ZEhMZAAAAAElFTkSuQmCC\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def reset_neuron(pop, var_init):\n",
+        "    # Reset variables\n",
+        "    for var_name, var_val in var_init.items():\n",
+        "        pop.vars[var_name].view[:] = var_val\n",
+        "\n",
+        "        # Push the new values to GPU\n",
+        "        pop.vars[var_name].push_to_device()\n",
+        "\n",
+        "for s in range(4):\n",
+        "    # Set training image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate present timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state for next stimuli\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "    reset_neuron(kc, lif_init)\n",
+        "\n",
+        "    # Reset synapse state\n",
+        "    reset_out_post(pn_kc)\n",
+        "\n",
+        "    # Download spikes from GPU\n",
+        "    model.pull_recording_buffers_from_device()\n",
+        "\n",
+        "    # Plot PN and KC spikes\n",
+        "    fig, axes = plt.subplots(2, sharex=True)\n",
+        "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+        "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+        "    print(f\"{len(np.unique(kc_spike_ids))} KC active\")\n",
+        "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+        "    axes[0].set_ylabel(\"PN\")\n",
+        "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+        "    axes[1].set_xlabel(\"Time [ms]\")\n",
+        "    axes[1].set_ylabel(\"KC\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FC8WZqKZMNNM"
+      },
+      "source": [
+        "Oh dear! Even with normalised inputs and controlling for the initial state of the model before presenting each stimuli, we get very variable numbers of active Kenyon Cells."
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "2_second_layer",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mushroom_body/3_second_layer_gain_control.ipynb.txt b/documentation/5/_sources/tutorials/mushroom_body/3_second_layer_gain_control.ipynb.txt
new file mode 100644
index 000000000..b04f4af1c
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/3_second_layer_gain_control.ipynb.txt
@@ -0,0 +1,537 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Feedback-inhibition based gain control\n",
+        "Based on the highly variable levels of Kenyon Cell activity found in the last tutorial, here we add feedback inhibition inspired by the Giant GABAergic Neuron (GGN) found in Drosophila and, with this in place, visualize the spiking activity of Kenyon Cells in response to latency coded MNIST digits.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "Ki3IZh5Jij4W",
+        "outputId": "efd99f19-ea69-4061-8012-f03535ca2715",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 82.3MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "d201ba31-0261-408c-b461-b3d7207c03ba",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yV0JrchrfQKR"
+      },
+      "source": [
+        "## Build tutorial model\n",
+        "Import modules"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hl53yKXi9LiV"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "from copy import copy\n",
+        "from matplotlib import pyplot as plt\n",
+        "from pygenn import (create_current_source_model, create_neuron_model, init_postsynaptic,\n",
+        "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+        "\n",
+        "# Reshape and normalise training data\n",
+        "training_images = mnist.train_images()\n",
+        "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+        "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g0IfyML59Lif"
+      },
+      "source": [
+        "## Parameters\n",
+        "Define some model parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oncGyriW9Lif"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation time step\n",
+        "DT = 0.1\n",
+        "\n",
+        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+        "INPUT_SCALE = 80.0\n",
+        "\n",
+        "# Number of Projection Neurons in model (should match image size)\n",
+        "NUM_PN = 784\n",
+        "\n",
+        "# Number of Kenyon Cells in model (defines memory capacity)\n",
+        "NUM_KC = 20000\n",
+        "\n",
+        "# How long to present each image to model\n",
+        "PRESENT_TIME_MS = 20.0\n",
+        "\n",
+        "# Standard LIF neurons parameters\n",
+        "LIF_PARAMS = {\n",
+        "    \"C\": 0.2,\n",
+        "    \"TauM\": 20.0,\n",
+        "    \"Vrest\": -60.0,\n",
+        "    \"Vreset\": -60.0,\n",
+        "    \"Vthresh\": -50.0,\n",
+        "    \"Ioffset\": 0.0,\n",
+        "    \"TauRefrac\": 2.0}\n",
+        "\n",
+        "# We only want PNs to spike once\n",
+        "PN_PARAMS = copy(LIF_PARAMS)\n",
+        "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+        "\n",
+        "# Weight of each synaptic connection\n",
+        "PN_KC_WEIGHT = 0.2\n",
+        "\n",
+        "# Time constant of synaptic integration\n",
+        "PN_KC_TAU_SYN = 3.0\n",
+        "\n",
+        "# How many projection neurons should be connected to each Kenyon Cell\n",
+        "PN_KC_FAN_IN = 20"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KldVFE9dJdv8"
+      },
+      "source": [
+        "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZvNwgTphJeM9"
+      },
+      "outputs": [],
+      "source": [
+        "# We will use weights of 1.0 for KC->GGN connections and\n",
+        "# want the GGN to inhibit the KCs after 200 spikes\n",
+        "GGN_PARAMS = {\n",
+        "    \"Vthresh\": 200.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCYjAoJf9Lig"
+      },
+      "source": [
+        "## Custom models"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IR8PXBg69Lih"
+      },
+      "outputs": [],
+      "source": [
+        "# Current source model, allowing current to be injected into neuron from variable\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pe-5DQ9hezIs"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "# Minimal integrate and fire neuron model\n",
+        "if_model = create_neuron_model(\n",
+        "    \"IF\",\n",
+        "    params=[\"Vthresh\"],\n",
+        "    vars=[(\"V\", \"scalar\")],\n",
+        "    sim_code=\n",
+        "    \"\"\"\n",
+        "    V += Isyn;\n",
+        "    \"\"\",\n",
+        "    threshold_condition_code=\n",
+        "    \"\"\"\n",
+        "    V >= Vthresh\n",
+        "    \"\"\",\n",
+        "    reset_code=\n",
+        "    \"\"\"\n",
+        "    V = 0.0;\n",
+        "    \"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gn4DpkPQ9Lii"
+      },
+      "source": [
+        "## Model definition\n",
+        "Create a new model called \"mnist_mb_second_layer_gain_control\" as before but add a second population of `NUM_KC` neurons to represent the Kenyon Cells."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gx-GsJhD9Lik"
+      },
+      "outputs": [],
+      "source": [
+        "# Create model\n",
+        "model = GeNNModel(\"float\", \"mnist_mb_second_layer_gain_control\")\n",
+        "model.dt = DT\n",
+        "\n",
+        "# Create neuron populations\n",
+        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+        "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "\n",
+        "# Turn on spike recording\n",
+        "pn.spike_recording_enabled = True\n",
+        "kc.spike_recording_enabled = True\n",
+        "\n",
+        "# Create current sources to deliver input to network\n",
+        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+        "\n",
+        "# Create synapse populations\n",
+        "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+        "                                     pn, kc,\n",
+        "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+        "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+        "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sdYo9umiH06S"
+      },
+      "source": [
+        "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7e1if0YCG_7m"
+      },
+      "outputs": [],
+      "source": [
+        "if_init = {\"V\": 0.0}\n",
+        "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "9wCO8tBLfLm8"
+      },
+      "outputs": [],
+      "source": [
+        "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+        "                                      kc, ggn,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+        "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+        "\n",
+        "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+        "                                      ggn, kc,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+        "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-GU4oXOS9Lil"
+      },
+      "source": [
+        "## Build model\n",
+        "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-FE02Zoz9Lim"
+      },
+      "outputs": [],
+      "source": [
+        "# Concert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "# Build model and load it\n",
+        "model.build()\n",
+        "model.load(num_recording_timesteps=present_timesteps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcpTaaB39Lim"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+        " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DfcqDTVXdoRq"
+      },
+      "source": [
+        "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "K9pAP8OrJUub",
+        "outputId": "bab547b5-0d49-4076-b996-ee47c2cb25c0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "283 KC active\n",
+            "272 KC active\n",
+            "253 KC active\n",
+            "316 KC active\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def reset_out_post(pop):\n",
+        "    pop.out_post.view[:] = 0.0\n",
+        "    pop.out_post.push_to_device()\n",
+        "\n",
+        "def reset_neuron(pop, var_init):\n",
+        "    # Reset variables\n",
+        "    for var_name, var_val in var_init.items():\n",
+        "        pop.vars[var_name].view[:] = var_val\n",
+        "\n",
+        "        # Push the new values to GPU\n",
+        "        pop.vars[var_name].push_to_device()\n",
+        "\n",
+        "for s in range(4):\n",
+        "    # Set training image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate present timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state for next stimuli\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "    reset_neuron(kc, lif_init)\n",
+        "    reset_neuron(ggn, if_init)\n",
+        "\n",
+        "    # Reset synapse state\n",
+        "    reset_out_post(pn_kc)\n",
+        "    reset_out_post(ggn_kc)\n",
+        "\n",
+        "    # Download spikes from GPU\n",
+        "    model.pull_recording_buffers_from_device();\n",
+        "\n",
+        "    # Plot PN and KC spikes\n",
+        "    fig, axes = plt.subplots(2, sharex=True)\n",
+        "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+        "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+        "    print(f\"{len(np.unique(kc_spike_ids))} KC active\")\n",
+        "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+        "    axes[0].set_ylabel(\"PN\")\n",
+        "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+        "    axes[1].set_xlabel(\"Time [ms]\")\n",
+        "    axes[1].set_ylabel(\"KC\")\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FC8WZqKZMNNM"
+      },
+      "source": [
+        "Much better!"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "3_second_layer_gain_control",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mushroom_body/4_third_layer.ipynb.txt b/documentation/5/_sources/tutorials/mushroom_body/4_third_layer.ipynb.txt
new file mode 100644
index 000000000..585fb194d
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/4_third_layer.ipynb.txt
@@ -0,0 +1,1081 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Output neurons and learning\n",
+        "In this tutorial we add Mushroom Body Output Neurons (MBONS) to the model and train the weights connecting them to the Kenyon Cells using a simple event-driven STDP rule.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "H6PHF3xTkMOD",
+        "outputId": "fb700b27-ffc5-4c6d-daae-84818f3137fe",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 101MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "3f2967bb-fa35-4599-a5d1-8e52a630a715",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yV0JrchrfQKR"
+      },
+      "source": [
+        "## Build tutorial model\n",
+        "Import modules"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Hl53yKXi9LiV"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "from copy import copy\n",
+        "from matplotlib import pyplot as plt\n",
+        "from pygenn import (create_current_source_model, create_neuron_model, create_weight_update_model,\n",
+        "                    init_postsynaptic, init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+        "\n",
+        "# Reshape and normalise training data\n",
+        "training_images = mnist.train_images()\n",
+        "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+        "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]\n",
+        "training_labels = mnist.train_labels()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZfxsGYr5kVv-"
+      },
+      "outputs": [],
+      "source": [
+        "from tqdm.auto import tqdm"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g0IfyML59Lif"
+      },
+      "source": [
+        "## Parameters\n",
+        "Define some model parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oncGyriW9Lif"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation time step\n",
+        "DT = 0.1\n",
+        "\n",
+        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+        "INPUT_SCALE = 80.0\n",
+        "\n",
+        "# Number of Projection Neurons in model (should match image size)\n",
+        "NUM_PN = 784\n",
+        "\n",
+        "# Number of Kenyon Cells in model (defines memory capacity)\n",
+        "NUM_KC = 20000\n",
+        "\n",
+        "# How long to present each image to model\n",
+        "PRESENT_TIME_MS = 20.0\n",
+        "\n",
+        "# Standard LIF neurons parameters\n",
+        "LIF_PARAMS = {\n",
+        "    \"C\": 0.2,\n",
+        "    \"TauM\": 20.0,\n",
+        "    \"Vrest\": -60.0,\n",
+        "    \"Vreset\": -60.0,\n",
+        "    \"Vthresh\": -50.0,\n",
+        "    \"Ioffset\": 0.0,\n",
+        "    \"TauRefrac\": 2.0}\n",
+        "\n",
+        "# We only want PNs to spike once\n",
+        "PN_PARAMS = copy(LIF_PARAMS)\n",
+        "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+        "\n",
+        "# Weight of each synaptic connection\n",
+        "PN_KC_WEIGHT = 0.2\n",
+        "\n",
+        "# Time constant of synaptic integration\n",
+        "PN_KC_TAU_SYN = 3.0\n",
+        "\n",
+        "# How many projection neurons should be connected to each Kenyon Cell\n",
+        "PN_KC_FAN_IN = 20\n",
+        "\n",
+        "# We will use weights of 1.0 for KC->GGN connections and\n",
+        "# want the GGN to inhibit the KCs after 200 spikes\n",
+        "GGN_PARAMS = {\n",
+        "    \"Vthresh\": 200.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KldVFE9dJdv8"
+      },
+      "source": [
+        "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ZvNwgTphJeM9"
+      },
+      "outputs": [],
+      "source": [
+        "NUM_MBON = 10\n",
+        "MBON_STIMULUS_CURRENT = 5.0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-CKgGZHBjKl2"
+      },
+      "outputs": [],
+      "source": [
+        "KC_MBON_TAU_SYN = 3.0\n",
+        "KC_MBON_PARAMS = {\"tau\": 15.0,\n",
+        "                  \"rho\": 0.01,\n",
+        "                  \"eta\": 0.00002,\n",
+        "                  \"wMin\": 0.0,\n",
+        "                  \"wMax\": 0.0233}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCYjAoJf9Lig"
+      },
+      "source": [
+        "## Custom models\n",
+        "As well as the models we defined before:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "IR8PXBg69Lih"
+      },
+      "outputs": [],
+      "source": [
+        "# Current source model, allowing current to be injected into neuron from variable\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")\n",
+        "\n",
+        "# Minimal integrate and fire neuron model\n",
+        "if_model = create_neuron_model(\n",
+        "    \"IF\",\n",
+        "    params=[\"Vthresh\"],\n",
+        "    vars=[(\"V\", \"scalar\")],\n",
+        "    sim_code=\n",
+        "    \"\"\"\n",
+        "    V += Isyn;\n",
+        "    \"\"\",\n",
+        "    threshold_condition_code=\n",
+        "    \"\"\"\n",
+        "    V >= Vthresh\n",
+        "    \"\"\",\n",
+        "    reset_code=\n",
+        "    \"\"\"\n",
+        "    V = 0.0;\n",
+        "    \"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cfKeAMLBjZ6u"
+      },
+      "source": [
+        "We now also need an STDP learning rule!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pe-5DQ9hezIs"
+      },
+      "outputs": [],
+      "source": [
+        "symmetric_stdp = create_weight_update_model(\n",
+        "    \"symmetric_stdp\",\n",
+        "    params=[\"tau\", \"rho\", \"eta\", \"wMin\", \"wMax\"],\n",
+        "    vars=[(\"g\", \"scalar\")],\n",
+        "    pre_spike_syn_code=\n",
+        "    \"\"\"\n",
+        "    const scalar dt = t - st_post;\n",
+        "    const scalar timing = exp(-dt / tau) - rho;\n",
+        "    const scalar newWeight = g + (eta * timing);\n",
+        "    g = fmin(wMax, fmax(wMin, newWeight));\n",
+        "    \"\"\",\n",
+        "    post_spike_syn_code=\n",
+        "    \"\"\"\n",
+        "    const scalar dt = t - st_pre;\n",
+        "    const scalar timing = fmax(exp(-dt / tau) - rho, -0.1*rho);\n",
+        "    const scalar newWeight = g + (eta * timing);\n",
+        "    g = fmin(wMax, fmax(wMin, newWeight));\n",
+        "    \"\"\")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gn4DpkPQ9Lii"
+      },
+      "source": [
+        "## Model definition\n",
+        "Create a new model called \"mnist_mb_second_layer_gain_control\" as before although we no longer need to record spikes from individual neurons:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Gx-GsJhD9Lik"
+      },
+      "outputs": [],
+      "source": [
+        "# Create model\n",
+        "model = GeNNModel(\"float\", \"mnist_mb_third_layer\")\n",
+        "model.dt = DT\n",
+        "\n",
+        "# Create neuron populations\n",
+        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+        "if_init = {\"V\": 0.0}\n",
+        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+        "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)\n",
+        "\n",
+        "# Turn on spike recording\n",
+        "pn.spike_recording_enabled = True\n",
+        "kc.spike_recording_enabled = True\n",
+        "\n",
+        "# Create current sources to deliver input to network\n",
+        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+        "\n",
+        "# Create synapse populations\n",
+        "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+        "                                     pn, kc,\n",
+        "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+        "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+        "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))\n",
+        "\n",
+        "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+        "                                      kc, ggn,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+        "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+        "\n",
+        "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+        "                                      ggn, kc,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+        "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sdYo9umiH06S"
+      },
+      "source": [
+        "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kiitnN5HjlNc"
+      },
+      "outputs": [],
+      "source": [
+        "mbon = model.add_neuron_population(\"mbon\", NUM_MBON, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "\n",
+        "mbon.spike_recording_enabled = True\n",
+        "\n",
+        "# Create current sources to deliver input and supervision to network\n",
+        "mbon_input = model.add_current_source(\"mbon_input\", cs_model, mbon , {}, {\"magnitude\": 0.0})"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0BQ2LzsPjvlv"
+      },
+      "source": [
+        "Add a new synapse group connecting ``kc`` into ``mbon`` with initially zeroed weights and our newly-defined STDP rule."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5hq70tYRjqZq"
+      },
+      "outputs": [],
+      "source": [
+        "kc_mbon = model.add_synapse_population(\"kc_mbon\", \"DENSE\",\n",
+        "                                       kc, mbon,\n",
+        "                                       init_weight_update(symmetric_stdp, KC_MBON_PARAMS, {\"g\": 0.0}),\n",
+        "                                       init_postsynaptic(\"ExpCurr\", {\"tau\": KC_MBON_TAU_SYN}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-GU4oXOS9Lil"
+      },
+      "source": [
+        "## Build model\n",
+        "Generate code and load it into PyGeNN (as we're no longer recording spikes, we don't need to allocate a recording buffer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "-FE02Zoz9Lim"
+      },
+      "outputs": [],
+      "source": [
+        "# Convert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "# Build model and load it\n",
+        "model.build()\n",
+        "model.load(num_recording_timesteps=present_timesteps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcpTaaB39Lim"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+        " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DfcqDTVXdoRq"
+      },
+      "source": [
+        "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "hQV_X3IGkIQm"
+      },
+      "outputs": [],
+      "source": [
+        "def reset_spike_times(pop):\n",
+        "    pop.spike_times.view[:] = -np.finfo(np.float32).max\n",
+        "    pop.spike_times.push_to_device()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 49,
+          "referenced_widgets": [
+            "e4a549fa740647a9a4c257b70347b6dc",
+            "9331ce4de0d845d7bdba6eaf19733e3b",
+            "e98cca67c6dd4268b7fea6f8136b31cd",
+            "5456c8d419f1474cbe8cf0c3eca6fddc",
+            "007cd41dc27e40aeb84f65b26c870a7c",
+            "7ed5e1c9a13142939fcd1f7cf78243a1",
+            "4f077d3f032f4a169a5f7eedf15f421c",
+            "7b2a461efd354bba8e96b6ed7b6c5f2d",
+            "a0225294218b49cfa976eb59639f2f22",
+            "7c55bec89f144381b8f495e3aba48699",
+            "e0ed35f603ed483abb7d79c569d45fd2"
+          ]
+        },
+        "id": "K9pAP8OrJUub",
+        "outputId": "fa63fb73-6484-4c47-b5b5-5011bdf4f58c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/60000 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e4a549fa740647a9a4c257b70347b6dc"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "def reset_out_post(pop):\n",
+        "    pop.out_post.view[:] = 0.0\n",
+        "    pop.out_post.push_to_device()\n",
+        "\n",
+        "def reset_neuron(pop, var_init):\n",
+        "    # Reset variables\n",
+        "    for var_name, var_val in var_init.items():\n",
+        "        pop.vars[var_name].view[:] = var_val\n",
+        "\n",
+        "        # Push the new values to GPU\n",
+        "        pop.vars[var_name].push_to_device()\n",
+        "\n",
+        "# Convert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "for s in tqdm(range(training_images.shape[0])):\n",
+        "    # Set training image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Turn on correct output neuron\n",
+        "    mbon_input.vars[\"magnitude\"].view[:] = 0\n",
+        "    mbon_input.vars[\"magnitude\"].view[training_labels[s]] = MBON_STIMULUS_CURRENT\n",
+        "    mbon_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate present timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "    reset_neuron(kc, lif_init)\n",
+        "    reset_neuron(ggn, if_init)\n",
+        "    reset_neuron(mbon, lif_init)\n",
+        "\n",
+        "    # Reset spike times\n",
+        "    reset_spike_times(kc)\n",
+        "    reset_spike_times(mbon)\n",
+        "\n",
+        "    # Reset synapse state\n",
+        "    reset_out_post(pn_kc)\n",
+        "    reset_out_post(ggn_kc)\n",
+        "    reset_out_post(kc_mbon)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KqM0qyChlzhP"
+      },
+      "source": [
+        "## Visualise and save learned weights\n",
+        "First of all we download the learned weights from device and get the memory view to access them through (in the same way as we used to access current source state variables)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "s4lAfhkdkuHY"
+      },
+      "outputs": [],
+      "source": [
+        "kc_mbon.vars[\"g\"].pull_from_device()\n",
+        "kc_mbon_g_view = kc_mbon.vars[\"g\"].view"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FINPVXN5mr5C"
+      },
+      "source": [
+        "now we plot a histogram of the weight distribution - as is typical when using STDP rules whose learning rate is independent of the current magnitude of the weight it is bimodal"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 465
+        },
+        "id": "OHD_SzOsmF1Z",
+        "outputId": "404ba978-42b9-4273-d2a0-07791d2c664a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1000x500 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "fig, axis = plt.subplots(figsize=(10, 5))\n",
+        "axis.hist(kc_mbon_g_view, bins=100)\n",
+        "axis.axvline(np.average(kc_mbon_g_view), linestyle=\"--\")\n",
+        "axis.set_xlabel(\"Weight [nA]\")\n",
+        "axis.set_ylabel(\"Count\");"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bi2A8qZWAASt"
+      },
+      "source": [
+        "So we can reproduce exactly the same PN->KC connectivity again and reuse the weights we've learnt we want to save them back to YOUR google drive. First mount the drive"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "DMyS30_Rm-oe",
+        "outputId": "be763ffc-cfc1-43f0-b49f-bf68070c25d4",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/drive\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JTZrJsB4AQH5"
+      },
+      "source": [
+        "Save the learnt weights"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kFQtm-CVmT20"
+      },
+      "outputs": [],
+      "source": [
+        "np.save(\"/content/drive/MyDrive/kc_mbon_g.npy\", kc_mbon_g_view)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iqF9kWhEATFL"
+      },
+      "source": [
+        "Download the PN->KC connectivity from the GPU and save the sparse connectivity."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "6xNyJgiGmR9Y"
+      },
+      "outputs": [],
+      "source": [
+        "pn_kc.pull_connectivity_from_device()\n",
+        "np.save(\"/content/drive/MyDrive/pn_kc_ind.npy\", np.vstack((pn_kc.get_sparse_pre_inds(), pn_kc.get_sparse_post_inds())))"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "4_third_layer",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
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diff --git a/documentation/5/_sources/tutorials/mushroom_body/5_testing.ipynb.txt b/documentation/5/_sources/tutorials/mushroom_body/5_testing.ipynb.txt
new file mode 100644
index 000000000..407973ad5
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/5_testing.ipynb.txt
@@ -0,0 +1,1007 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lGa0_oLb61zz"
+      },
+      "source": [
+        "# Testing\n",
+        "In this final tutorial we load the weights we trained in the previous tutorial into a static version of the mushroom body model and evaluate its performance on the MNIST test set.\n",
+        "\n",
+        "## Install PyGeNN wheel from Google Drive\n",
+        "Download wheel file"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "if \"google.colab\" in str(get_ipython()):\n",
+        "    #import IPython\n",
+        "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+        "    #%run \"../install_collab.ipynb\"\n",
+        "    !pip install gdown --upgrade\n",
+        "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+        "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+        "    %env CUDA_PATH=/usr/local/cuda"
+      ],
+      "metadata": {
+        "id": "NMiDVe_vmKPh",
+        "outputId": "689453af-68ee-472c-8b20-b71acddb5d6c",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+            "Downloading...\n",
+            "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+            "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "100% 8.29M/8.29M [00:00<00:00, 215MB/s]\n",
+            "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+            "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+            "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+            "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+            "env: CUDA_PATH=/usr/local/cuda\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Install MNIST package"
+      ],
+      "metadata": {
+        "id": "KVRtXVzIg07T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install mnist"
+      ],
+      "metadata": {
+        "id": "AikBc4sfg1b-",
+        "outputId": "b2af7639-4b7f-4e36-f516-bc9c1231aba2",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting mnist\n",
+            "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+            "Installing collected packages: mnist\n",
+            "Successfully installed mnist-0.2.2\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yV0JrchrfQKR"
+      },
+      "source": [
+        "## Build tutorial model\n",
+        "Import modules"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "Hl53yKXi9LiV"
+      },
+      "outputs": [],
+      "source": [
+        "import mnist\n",
+        "import numpy as np\n",
+        "from copy import copy\n",
+        "from google.colab import drive\n",
+        "from matplotlib import pyplot as plt\n",
+        "from pygenn import (create_current_source_model, create_neuron_model, init_postsynaptic,\n",
+        "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+        "from tqdm.auto import tqdm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "ayctC3Y0i8ks"
+      },
+      "outputs": [],
+      "source": [
+        "# Reshape and normalise training data\n",
+        "testing_images = mnist.test_images()\n",
+        "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1)).astype(np.float32)\n",
+        "testing_images /= np.sum(testing_images, axis=1)[:, np.newaxis]\n",
+        "testing_labels = mnist.test_labels()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "l5HX_B1Gohbq",
+        "outputId": "93732801-95a9-4ebc-dce1-a91f521a846a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Mounted at /content/drive\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount(\"/content/drive\")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "i-4iEfEdom33"
+      },
+      "outputs": [],
+      "source": [
+        "pn_kc_ind = np.load(\"/content/drive/MyDrive/pn_kc_ind.npy\")\n",
+        "kc_mbon_g = np.load(\"/content/drive/MyDrive/kc_mbon_g.npy\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "g0IfyML59Lif"
+      },
+      "source": [
+        "## Parameters\n",
+        "Define some model parameters"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "oncGyriW9Lif"
+      },
+      "outputs": [],
+      "source": [
+        "# Simulation time step\n",
+        "DT = 0.1\n",
+        "\n",
+        "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+        "INPUT_SCALE = 80.0\n",
+        "\n",
+        "# Number of Projection Neurons in model (should match image size)\n",
+        "NUM_PN = 784\n",
+        "\n",
+        "# Number of Kenyon Cells in model (defines memory capacity)\n",
+        "NUM_KC = 20000\n",
+        "\n",
+        "# Number of Mushroom Body Output Neurons (should match number of labels)\n",
+        "NUM_MBON = 10\n",
+        "\n",
+        "# How long to present each image to model\n",
+        "PRESENT_TIME_MS = 20.0\n",
+        "\n",
+        "# Standard LIF neurons parameters\n",
+        "LIF_PARAMS = {\n",
+        "    \"C\": 0.2,\n",
+        "    \"TauM\": 20.0,\n",
+        "    \"Vrest\": -60.0,\n",
+        "    \"Vreset\": -60.0,\n",
+        "    \"Vthresh\": -50.0,\n",
+        "    \"Ioffset\": 0.0,\n",
+        "    \"TauRefrac\": 2.0}\n",
+        "\n",
+        "# We only want PNs to spike once\n",
+        "PN_PARAMS = copy(LIF_PARAMS)\n",
+        "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+        "\n",
+        "# Weight of each synaptic connection\n",
+        "PN_KC_WEIGHT = 0.2\n",
+        "\n",
+        "# Time constant of synaptic integration\n",
+        "PN_KC_TAU_SYN = 3.0\n",
+        "\n",
+        "# How many projection neurons should be connected to each Kenyon Cell\n",
+        "PN_KC_FAN_IN = 20\n",
+        "\n",
+        "# Time constant of synaptic integration\n",
+        "KC_MBON_TAU_SYN = 3.0\n",
+        "\n",
+        "# We will use weights of 1.0 for KC->GGN connections and\n",
+        "# want the GGN to inhibit the KCs after 200 spikes\n",
+        "GGN_PARAMS = {\n",
+        "    \"Vthresh\": 200.0}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pCYjAoJf9Lig"
+      },
+      "source": [
+        "## Custom models\n",
+        "As well as the models we defined before:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "IR8PXBg69Lih"
+      },
+      "outputs": [],
+      "source": [
+        "# Current source model, allowing current to be injected into neuron from variable\n",
+        "cs_model = create_current_source_model(\n",
+        "    \"cs_model\",\n",
+        "    vars=[(\"magnitude\", \"scalar\")],\n",
+        "    injection_code=\"injectCurrent(magnitude);\")\n",
+        "\n",
+        "# Minimal integrate and fire neuron model\n",
+        "if_model = create_neuron_model(\n",
+        "    \"IF\",\n",
+        "    params=[\"Vthresh\"],\n",
+        "    vars=[(\"V\", \"scalar\")],\n",
+        "    sim_code=\n",
+        "    \"\"\"\n",
+        "    V += Isyn;\n",
+        "    \"\"\",\n",
+        "    threshold_condition_code=\n",
+        "    \"\"\"\n",
+        "    V >= Vthresh\n",
+        "    \"\"\",\n",
+        "    reset_code=\n",
+        "    \"\"\"\n",
+        "    V = 0.0;\n",
+        "    \"\"\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Gn4DpkPQ9Lii"
+      },
+      "source": [
+        "## Model definition\n",
+        "Create a new model called \"mnist_mb_second_layer_gain_control\" as before although we no longer need to record spikes from individual neurons:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "Gx-GsJhD9Lik",
+        "outputId": "cc9ee27f-3a6a-44f3-9221-f466f5352681",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "<ipython-input-10-dcbfda279a3c>:3: FutureWarning: Call to deprecated function (or staticmethod) dT. (The name of this property was inconsistent, use dt instead)\n",
+            "  model.dT = DT\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Create model\n",
+        "model = GeNNModel(\"float\", \"mnist_mb_testing\")\n",
+        "model.dT = DT\n",
+        "\n",
+        "# Create neuron populations\n",
+        "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+        "if_init = {\"V\": 0.0}\n",
+        "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+        "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)\n",
+        "mbon = model.add_neuron_population(\"mbon\", NUM_MBON, \"LIF\", LIF_PARAMS, lif_init)\n",
+        "\n",
+        "# Turn on spike recording\n",
+        "pn.spike_recording_enabled = True\n",
+        "kc.spike_recording_enabled = True\n",
+        "mbon.spike_recording_enabled = True\n",
+        "\n",
+        "# Create current sources to deliver input to network\n",
+        "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+        "\n",
+        "# Create synapse populations\n",
+        "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+        "                                      kc, ggn,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+        "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+        "\n",
+        "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+        "                                      ggn, kc,\n",
+        "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+        "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "KfQ1y4vto6T7"
+      },
+      "outputs": [],
+      "source": [
+        "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+        "                                     pn, kc,\n",
+        "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+        "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}))\n",
+        "pn_kc.set_sparse_connections(pn_kc_ind[0], pn_kc_ind[1])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "NCL3jEsbo3T0"
+      },
+      "outputs": [],
+      "source": [
+        "kc_mbon = model.add_synapse_population(\"kc_mbon\", \"DENSE\",\n",
+        "                                       kc, mbon,\n",
+        "                                       init_weight_update(\"StaticPulse\", {}, {\"g\": kc_mbon_g}),\n",
+        "                                       init_postsynaptic(\"ExpCurr\", {\"tau\": KC_MBON_TAU_SYN}))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-GU4oXOS9Lil"
+      },
+      "source": [
+        "## Build model\n",
+        "Generate code and load it into PyGeNN (as we're no longer recording spikes, we don't need to allocate a recording buffer)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "-FE02Zoz9Lim"
+      },
+      "outputs": [],
+      "source": [
+        "# Convert present time into timesteps\n",
+        "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+        "\n",
+        "# Build model and load it\n",
+        "model.build()\n",
+        "model.load(num_recording_timesteps=present_timesteps)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CcpTaaB39Lim"
+      },
+      "source": [
+        "## Simulate tutorial model\n",
+        "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+        " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DfcqDTVXdoRq"
+      },
+      "source": [
+        "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "K9pAP8OrJUub"
+      },
+      "outputs": [],
+      "source": [
+        "def reset_out_post(pop):\n",
+        "    pop.out_post.view[:] = 0.0\n",
+        "    pop.out_post.push_to_device()\n",
+        "\n",
+        "def reset_neuron(pop, var_init):\n",
+        "    # Reset variables\n",
+        "    for var_name, var_val in var_init.items():\n",
+        "        pop.vars[var_name].view[:] = var_val\n",
+        "\n",
+        "        # Push the new values to GPU\n",
+        "        pop.vars[var_name].push_to_device()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "MOZDBWPyqpR6",
+        "outputId": "27f6cd4f-20dd-4c43-bd27-38c9a080217a"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 3 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "for s in range(4):\n",
+        "    # Set testing image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate present timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state for next stimuli\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "    reset_neuron(kc, lif_init)\n",
+        "    reset_neuron(ggn, if_init)\n",
+        "    reset_neuron(mbon, lif_init)\n",
+        "\n",
+        "    # Reset synapse state\n",
+        "    reset_out_post(pn_kc)\n",
+        "    reset_out_post(ggn_kc)\n",
+        "\n",
+        "    # Download spikes from GPU\n",
+        "    model.pull_recording_buffers_from_device();\n",
+        "\n",
+        "    # Plot PN, KC and MBON spikes\n",
+        "    fig, axes = plt.subplots(3, sharex=True)\n",
+        "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+        "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+        "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data[0]\n",
+        "\n",
+        "\n",
+        "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+        "    axes[0].set_ylabel(\"PN\")\n",
+        "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+        "    axes[1].set_ylabel(\"KC\")\n",
+        "    axes[2].scatter(mbon_spike_times, mbon_spike_ids, s=2)\n",
+        "    axes[2].axhline(testing_labels[s], linestyle=\"--\", color=\"green\", alpha=0.3)\n",
+        "    axes[2].set_ylim((-0.5, 10.5))\n",
+        "\n",
+        "    if len(mbon_spike_times) > 0:\n",
+        "        classification = mbon_spike_ids[np.argmin(mbon_spike_times)]\n",
+        "        axes[2].axhline(classification, linestyle=\"--\", color=\"red\", alpha=0.3)\n",
+        "    axes[2].set_ylabel(\"MBON\")\n",
+        "\n",
+        "    axes[2].set_xlabel(\"Time [ms]\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 84,
+          "referenced_widgets": [
+            "59c468dd929b4032af2794e104a6bead",
+            "01987d7984054c00a64645fe31405598",
+            "c59f76cc65da4d56a292b29394cb99aa",
+            "91ed1bcac34b4326b4cae858559b34b6",
+            "e6139bdca7e84d7e896a6fbd996975b0",
+            "89eec271580d412d8b16008b16e56f54",
+            "a10c6c1d757b4ee68186be7e18ebad0b",
+            "7041aa97acfb47879eded8e76f3e9877",
+            "37851d9dcb1945a885483cef23e67e10",
+            "861cd646f49d4b80802eadf0d5566e00",
+            "a8f8f9a21be84cb8b1adc1859aa4ccf8"
+          ]
+        },
+        "id": "T1u8nh2Iqsmi",
+        "outputId": "9b893e03-1542-4279-fa22-c003944f5723"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/10000 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "59c468dd929b4032af2794e104a6bead"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "7263/10000 correct (72.63 %%)\n"
+          ]
+        }
+      ],
+      "source": [
+        "num_correct = 0\n",
+        "for s in tqdm(range(testing_images.shape[0])):\n",
+        "    # Set testing image\n",
+        "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
+        "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+        "\n",
+        "    # Simulate present timesteps\n",
+        "    for i in range(present_timesteps):\n",
+        "        model.step_time()\n",
+        "\n",
+        "    # Reset neuron state\n",
+        "    reset_neuron(pn, lif_init)\n",
+        "    reset_neuron(kc, lif_init)\n",
+        "    reset_neuron(ggn, if_init)\n",
+        "    reset_neuron(mbon, lif_init)\n",
+        "\n",
+        "    # Reset synapse state\n",
+        "    reset_out_post(pn_kc)\n",
+        "    reset_out_post(ggn_kc)\n",
+        "    reset_out_post(kc_mbon)\n",
+        "\n",
+        "    # Download spikes from GPU\n",
+        "    model.pull_recording_buffers_from_device();\n",
+        "\n",
+        "    # Determine the classification and count correct\n",
+        "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data[0]\n",
+        "    if len(mbon_spike_times) > 0:\n",
+        "        if mbon_spike_ids[np.argmin(mbon_spike_times)] == testing_labels[s]:\n",
+        "            num_correct += 1\n",
+        "\n",
+        "print(f\"\\n{num_correct}/{testing_images.shape[0]} correct ({(num_correct * 100.0) / testing_images.shape[0]} %%)\")"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "name": "5_testing",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.7.7"
+    },
+    "widgets": {
+      "application/vnd.jupyter.widget-state+json": {
+        "59c468dd929b4032af2794e104a6bead": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HBoxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HBoxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HBoxView",
+            "box_style": "",
+            "children": [
+              "IPY_MODEL_01987d7984054c00a64645fe31405598",
+              "IPY_MODEL_c59f76cc65da4d56a292b29394cb99aa",
+              "IPY_MODEL_91ed1bcac34b4326b4cae858559b34b6"
+            ],
+            "layout": "IPY_MODEL_e6139bdca7e84d7e896a6fbd996975b0"
+          }
+        },
+        "01987d7984054c00a64645fe31405598": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "HTMLModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "HTMLView",
+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_89eec271580d412d8b16008b16e56f54",
+            "placeholder": "​",
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+            "placeholder": "​",
+            "style": "IPY_MODEL_a8f8f9a21be84cb8b1adc1859aa4ccf8",
+            "value": " 10000/10000 [00:26&lt;00:00, 284.92it/s]"
+          }
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+          }
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+          "model_name": "DescriptionStyleModel",
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+            "_view_module": "@jupyter-widgets/base",
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+          }
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+        "7041aa97acfb47879eded8e76f3e9877": {
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+          "model_name": "LayoutModel",
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+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
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+            "grid_auto_flow": null,
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+            "min_height": null,
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+        "37851d9dcb1945a885483cef23e67e10": {
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+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "861cd646f49d4b80802eadf0d5566e00": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "a8f8f9a21be84cb8b1adc1859aa4ccf8": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file
diff --git a/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt b/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt
new file mode 100644
index 000000000..bead1cbd3
--- /dev/null
+++ b/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt
@@ -0,0 +1,9 @@
+Insect-inspired MNIST classification
+====================================
+Train a model of the insect mushroom body using an STDP learning rule to classify MNIST.
+
+.. nbgallery::
+    :name: rst-gallery
+    :glob:
+    
+    *.ipynb
\ No newline at end of file
diff --git a/documentation/5/_static/_sphinx_javascript_frameworks_compat.js b/documentation/5/_static/_sphinx_javascript_frameworks_compat.js
index 8549469dc..81415803e 100644
--- a/documentation/5/_static/_sphinx_javascript_frameworks_compat.js
+++ b/documentation/5/_static/_sphinx_javascript_frameworks_compat.js
@@ -1,20 +1,9 @@
-/*
- * _sphinx_javascript_frameworks_compat.js
- * ~~~~~~~~~~
- *
- * Compatability shim for jQuery and underscores.js.
- *
- * WILL BE REMOVED IN Sphinx 6.0
- * xref RemovedInSphinx60Warning
+/* Compatability shim for jQuery and underscores.js.
  *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
  */
 
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-
 /**
  * small helper function to urldecode strings
  *
diff --git a/documentation/5/_static/basic.css b/documentation/5/_static/basic.css
index d64b53cc2..1506b6540 100644
--- a/documentation/5/_static/basic.css
+++ b/documentation/5/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -324,17 +324,17 @@ aside.sidebar {
 p.sidebar-title {
     font-weight: bold;
 }
+
 nav.contents,
 aside.topic,
-
 div.admonition, div.topic, blockquote {
     clear: left;
 }
 
 /* -- topics ---------------------------------------------------------------- */
+
 nav.contents,
 aside.topic,
-
 div.topic {
     border: 1px solid #ccc;
     padding: 7px;
@@ -375,7 +375,6 @@ div.sidebar > :last-child,
 aside.sidebar > :last-child,
 nav.contents > :last-child,
 aside.topic > :last-child,
-
 div.topic > :last-child,
 div.admonition > :last-child {
     margin-bottom: 0;
@@ -385,7 +384,6 @@ div.sidebar::after,
 aside.sidebar::after,
 nav.contents::after,
 aside.topic::after,
-
 div.topic::after,
 div.admonition::after,
 blockquote::after {
@@ -611,25 +609,6 @@ ul.simple p {
     margin-bottom: 0;
 }
 
-/* Docutils 0.17 and older (footnotes & citations) */
-dl.footnote > dt,
-dl.citation > dt {
-    float: left;
-    margin-right: 0.5em;
-}
-
-dl.footnote > dd,
-dl.citation > dd {
-    margin-bottom: 0em;
-}
-
-dl.footnote > dd:after,
-dl.citation > dd:after {
-    content: "";
-    clear: both;
-}
-
-/* Docutils 0.18+ (footnotes & citations) */
 aside.footnote > span,
 div.citation > span {
     float: left;
@@ -654,8 +633,6 @@ div.citation > p:last-of-type:after {
     clear: both;
 }
 
-/* Footnotes & citations ends */
-
 dl.field-list {
     display: grid;
     grid-template-columns: fit-content(30%) auto;
@@ -668,10 +645,6 @@ dl.field-list > dt {
     padding-right: 5px;
 }
 
-dl.field-list > dt:after {
-    content: ":";
-}
-
 dl.field-list > dd {
     padding-left: 0.5em;
     margin-top: 0em;
@@ -697,6 +670,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -765,6 +748,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/documentation/5/_static/classic.css b/documentation/5/_static/classic.css
new file mode 100644
index 000000000..564c5bccd
--- /dev/null
+++ b/documentation/5/_static/classic.css
@@ -0,0 +1,269 @@
+/*
+ * classic.css_t
+ * ~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- classic theme.
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+@import url("basic.css");
+
+/* -- page layout ----------------------------------------------------------- */
+
+html {
+    /* CSS hack for macOS's scrollbar (see #1125) */
+    background-color: #FFFFFF;
+}
+
+body {
+    font-family: sans-serif;
+    font-size: 100%;
+    background-color: #11303d;
+    color: #000;
+    margin: 0;
+    padding: 0;
+}
+
+div.document {
+    display: flex;
+    background-color: #1c4e63;
+}
+
+div.documentwrapper {
+    float: left;
+    width: 100%;
+}
+
+div.bodywrapper {
+    margin: 0 0 0 450px;
+}
+
+div.body {
+    background-color: #ffffff;
+    color: #000000;
+    padding: 0 20px 30px 20px;
+}
+
+div.footer {
+    color: #ffffff;
+    width: 100%;
+    padding: 9px 0 9px 0;
+    text-align: center;
+    font-size: 75%;
+}
+
+div.footer a {
+    color: #ffffff;
+    text-decoration: underline;
+}
+
+div.related {
+    background-color: #133f52;
+    line-height: 30px;
+    color: #ffffff;
+}
+
+div.related a {
+    color: #ffffff;
+}
+
+div.sphinxsidebar {
+}
+
+div.sphinxsidebar h3 {
+    font-family: 'Trebuchet MS', sans-serif;
+    color: #ffffff;
+    font-size: 1.4em;
+    font-weight: normal;
+    margin: 0;
+    padding: 0;
+}
+
+div.sphinxsidebar h3 a {
+    color: #ffffff;
+}
+
+div.sphinxsidebar h4 {
+    font-family: 'Trebuchet MS', sans-serif;
+    color: #ffffff;
+    font-size: 1.3em;
+    font-weight: normal;
+    margin: 5px 0 0 0;
+    padding: 0;
+}
+
+div.sphinxsidebar p {
+    color: #ffffff;
+}
+
+div.sphinxsidebar p.topless {
+    margin: 5px 10px 10px 10px;
+}
+
+div.sphinxsidebar ul {
+    margin: 10px;
+    padding: 0;
+    color: #ffffff;
+}
+
+div.sphinxsidebar a {
+    color: #98dbcc;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+
+
+/* -- hyperlink styles ------------------------------------------------------ */
+
+a {
+    color: #355f7c;
+    text-decoration: none;
+}
+
+a:visited {
+    color: #355f7c;
+    text-decoration: none;
+}
+
+a:hover {
+    text-decoration: underline;
+}
+
+
+
+/* -- body styles ----------------------------------------------------------- */
+
+div.body h1,
+div.body h2,
+div.body h3,
+div.body h4,
+div.body h5,
+div.body h6 {
+    font-family: 'Trebuchet MS', sans-serif;
+    background-color: #f2f2f2;
+    font-weight: normal;
+    color: #20435c;
+    border-bottom: 1px solid #ccc;
+    margin: 20px -20px 10px -20px;
+    padding: 3px 0 3px 10px;
+}
+
+div.body h1 { margin-top: 0; font-size: 200%; }
+div.body h2 { font-size: 160%; }
+div.body h3 { font-size: 140%; }
+div.body h4 { font-size: 120%; }
+div.body h5 { font-size: 110%; }
+div.body h6 { font-size: 100%; }
+
+a.headerlink {
+    color: #c60f0f;
+    font-size: 0.8em;
+    padding: 0 4px 0 4px;
+    text-decoration: none;
+}
+
+a.headerlink:hover {
+    background-color: #c60f0f;
+    color: white;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    text-align: justify;
+    line-height: 130%;
+}
+
+div.admonition p.admonition-title + p {
+    display: inline;
+}
+
+div.admonition p {
+    margin-bottom: 5px;
+}
+
+div.admonition pre {
+    margin-bottom: 5px;
+}
+
+div.admonition ul, div.admonition ol {
+    margin-bottom: 5px;
+}
+
+div.note {
+    background-color: #eee;
+    border: 1px solid #ccc;
+}
+
+div.seealso {
+    background-color: #ffc;
+    border: 1px solid #ff6;
+}
+
+nav.contents,
+aside.topic,
+div.topic {
+    background-color: #eee;
+}
+
+div.warning {
+    background-color: #ffe4e4;
+    border: 1px solid #f66;
+}
+
+p.admonition-title {
+    display: inline;
+}
+
+p.admonition-title:after {
+    content: ":";
+}
+
+pre {
+    padding: 5px;
+    background-color: unset;
+    color: unset;
+    line-height: 120%;
+    border: 1px solid #ac9;
+    border-left: none;
+    border-right: none;
+}
+
+code {
+    background-color: #ecf0f3;
+    padding: 0 1px 0 1px;
+    font-size: 0.95em;
+}
+
+th, dl.field-list > dt {
+    background-color: #ede;
+}
+
+.warning code {
+    background: #efc2c2;
+}
+
+.note code {
+    background: #d6d6d6;
+}
+
+.viewcode-back {
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    background-color: #f4debf;
+    border-top: 1px solid #ac9;
+    border-bottom: 1px solid #ac9;
+}
+
+div.code-block-caption {
+    color: #efefef;
+    background-color: #1c4e63;
+}
\ No newline at end of file
diff --git a/documentation/5/_static/doctools.js b/documentation/5/_static/doctools.js
index c3db08d1c..d06a71d75 100644
--- a/documentation/5/_static/doctools.js
+++ b/documentation/5/_static/doctools.js
@@ -4,12 +4,19 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
 "use strict";
 
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
 const _ready = (callback) => {
   if (document.readyState !== "loading") {
     callback();
@@ -18,73 +25,11 @@ const _ready = (callback) => {
   }
 };
 
-/**
- * highlight a given string on a node by wrapping it in
- * span elements with the given class name.
- */
-const _highlight = (node, addItems, text, className) => {
-  if (node.nodeType === Node.TEXT_NODE) {
-    const val = node.nodeValue;
-    const parent = node.parentNode;
-    const pos = val.toLowerCase().indexOf(text);
-    if (
-      pos >= 0 &&
-      !parent.classList.contains(className) &&
-      !parent.classList.contains("nohighlight")
-    ) {
-      let span;
-
-      const closestNode = parent.closest("body, svg, foreignObject");
-      const isInSVG = closestNode && closestNode.matches("svg");
-      if (isInSVG) {
-        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
-      } else {
-        span = document.createElement("span");
-        span.classList.add(className);
-      }
-
-      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
-      parent.insertBefore(
-        span,
-        parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
-          node.nextSibling
-        )
-      );
-      node.nodeValue = val.substr(0, pos);
-
-      if (isInSVG) {
-        const rect = document.createElementNS(
-          "http://www.w3.org/2000/svg",
-          "rect"
-        );
-        const bbox = parent.getBBox();
-        rect.x.baseVal.value = bbox.x;
-        rect.y.baseVal.value = bbox.y;
-        rect.width.baseVal.value = bbox.width;
-        rect.height.baseVal.value = bbox.height;
-        rect.setAttribute("class", className);
-        addItems.push({ parent: parent, target: rect });
-      }
-    }
-  } else if (node.matches && !node.matches("button, select, textarea")) {
-    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
-  }
-};
-const _highlightText = (thisNode, text, className) => {
-  let addItems = [];
-  _highlight(thisNode, addItems, text, className);
-  addItems.forEach((obj) =>
-    obj.parent.insertAdjacentElement("beforebegin", obj.target)
-  );
-};
-
 /**
  * Small JavaScript module for the documentation.
  */
 const Documentation = {
   init: () => {
-    Documentation.highlightSearchWords();
     Documentation.initDomainIndexTable();
     Documentation.initOnKeyListeners();
   },
@@ -126,51 +71,6 @@ const Documentation = {
     Documentation.LOCALE = catalog.locale;
   },
 
-  /**
-   * highlight the search words provided in the url in the text
-   */
-  highlightSearchWords: () => {
-    const highlight =
-      new URLSearchParams(window.location.search).get("highlight") || "";
-    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
-    if (terms.length === 0) return; // nothing to do
-
-    // There should never be more than one element matching "div.body"
-    const divBody = document.querySelectorAll("div.body");
-    const body = divBody.length ? divBody[0] : document.querySelector("body");
-    window.setTimeout(() => {
-      terms.forEach((term) => _highlightText(body, term, "highlighted"));
-    }, 10);
-
-    const searchBox = document.getElementById("searchbox");
-    if (searchBox === null) return;
-    searchBox.appendChild(
-      document
-        .createRange()
-        .createContextualFragment(
-          '<p class="highlight-link">' +
-            '<a href="javascript:Documentation.hideSearchWords()">' +
-            Documentation.gettext("Hide Search Matches") +
-            "</a></p>"
-        )
-    );
-  },
-
-  /**
-   * helper function to hide the search marks again
-   */
-  hideSearchWords: () => {
-    document
-      .querySelectorAll("#searchbox .highlight-link")
-      .forEach((el) => el.remove());
-    document
-      .querySelectorAll("span.highlighted")
-      .forEach((el) => el.classList.remove("highlighted"));
-    const url = new URL(window.location);
-    url.searchParams.delete("highlight");
-    window.history.replaceState({}, "", url);
-  },
-
   /**
    * helper function to focus on search bar
    */
@@ -210,15 +110,11 @@ const Documentation = {
     )
       return;
 
-    const blacklistedElements = new Set([
-      "TEXTAREA",
-      "INPUT",
-      "SELECT",
-      "BUTTON",
-    ]);
     document.addEventListener("keydown", (event) => {
-      if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements
-      if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
 
       if (!event.shiftKey) {
         switch (event.key) {
@@ -240,10 +136,6 @@ const Documentation = {
               event.preventDefault();
             }
             break;
-          case "Escape":
-            if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
-            Documentation.hideSearchWords();
-            event.preventDefault();
         }
       }
 
diff --git a/documentation/5/_static/documentation_options.js b/documentation/5/_static/documentation_options.js
index a750e4d5e..b57ae3b83 100644
--- a/documentation/5/_static/documentation_options.js
+++ b/documentation/5/_static/documentation_options.js
@@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = {
     SOURCELINK_SUFFIX: '.txt',
     NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
-    ENABLE_SEARCH_SHORTCUTS: false,
+    ENABLE_SEARCH_SHORTCUTS: true,
 };
\ No newline at end of file
diff --git a/documentation/5/_static/jquery-3.6.0.js b/documentation/5/_static/jquery-3.6.0.js
deleted file mode 100644
index fc6c299b7..000000000
--- a/documentation/5/_static/jquery-3.6.0.js
+++ /dev/null
@@ -1,10881 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.6.0
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright OpenJS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2021-03-02T17:08Z
- */
-( function( global, factory ) {
-
-	"use strict";
-
-	if ( typeof module === "object" && typeof module.exports === "object" ) {
-
-		// For CommonJS and CommonJS-like environments where a proper `window`
-		// is present, execute the factory and get jQuery.
-		// For environments that do not have a `window` with a `document`
-		// (such as Node.js), expose a factory as module.exports.
-		// This accentuates the need for the creation of a real `window`.
-		// e.g. var jQuery = require("jquery")(window);
-		// See ticket #14549 for more info.
-		module.exports = global.document ?
-			factory( global, true ) :
-			function( w ) {
-				if ( !w.document ) {
-					throw new Error( "jQuery requires a window with a document" );
-				}
-				return factory( w );
-			};
-	} else {
-		factory( global );
-	}
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
-	return arr.flat.call( array );
-} : function( array ) {
-	return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
-		// Support: Chrome <=57, Firefox <=52
-		// In some browsers, typeof returns "function" for HTML <object> elements
-		// (i.e., `typeof document.createElement( "object" ) === "function"`).
-		// We don't want to classify *any* DOM node as a function.
-		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
-		// Plus for old WebKit, typeof returns "function" for HTML collections
-		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
-		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
-			typeof obj.item !== "function";
-	};
-
-
-var isWindow = function isWindow( obj ) {
-		return obj != null && obj === obj.window;
-	};
-
-
-var document = window.document;
-
-
-
-	var preservedScriptAttributes = {
-		type: true,
-		src: true,
-		nonce: true,
-		noModule: true
-	};
-
-	function DOMEval( code, node, doc ) {
-		doc = doc || document;
-
-		var i, val,
-			script = doc.createElement( "script" );
-
-		script.text = code;
-		if ( node ) {
-			for ( i in preservedScriptAttributes ) {
-
-				// Support: Firefox 64+, Edge 18+
-				// Some browsers don't support the "nonce" property on scripts.
-				// On the other hand, just using `getAttribute` is not enough as
-				// the `nonce` attribute is reset to an empty string whenever it
-				// becomes browsing-context connected.
-				// See https://github.com/whatwg/html/issues/2369
-				// See https://html.spec.whatwg.org/#nonce-attributes
-				// The `node.getAttribute` check was added for the sake of
-				// `jQuery.globalEval` so that it can fake a nonce-containing node
-				// via an object.
-				val = node[ i ] || node.getAttribute && node.getAttribute( i );
-				if ( val ) {
-					script.setAttribute( i, val );
-				}
-			}
-		}
-		doc.head.appendChild( script ).parentNode.removeChild( script );
-	}
-
-
-function toType( obj ) {
-	if ( obj == null ) {
-		return obj + "";
-	}
-
-	// Support: Android <=2.3 only (functionish RegExp)
-	return typeof obj === "object" || typeof obj === "function" ?
-		class2type[ toString.call( obj ) ] || "object" :
-		typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
-	version = "3.6.0",
-
-	// Define a local copy of jQuery
-	jQuery = function( selector, context ) {
-
-		// The jQuery object is actually just the init constructor 'enhanced'
-		// Need init if jQuery is called (just allow error to be thrown if not included)
-		return new jQuery.fn.init( selector, context );
-	};
-
-jQuery.fn = jQuery.prototype = {
-
-	// The current version of jQuery being used
-	jquery: version,
-
-	constructor: jQuery,
-
-	// The default length of a jQuery object is 0
-	length: 0,
-
-	toArray: function() {
-		return slice.call( this );
-	},
-
-	// Get the Nth element in the matched element set OR
-	// Get the whole matched element set as a clean array
-	get: function( num ) {
-
-		// Return all the elements in a clean array
-		if ( num == null ) {
-			return slice.call( this );
-		}
-
-		// Return just the one element from the set
-		return num < 0 ? this[ num + this.length ] : this[ num ];
-	},
-
-	// Take an array of elements and push it onto the stack
-	// (returning the new matched element set)
-	pushStack: function( elems ) {
-
-		// Build a new jQuery matched element set
-		var ret = jQuery.merge( this.constructor(), elems );
-
-		// Add the old object onto the stack (as a reference)
-		ret.prevObject = this;
-
-		// Return the newly-formed element set
-		return ret;
-	},
-
-	// Execute a callback for every element in the matched set.
-	each: function( callback ) {
-		return jQuery.each( this, callback );
-	},
-
-	map: function( callback ) {
-		return this.pushStack( jQuery.map( this, function( elem, i ) {
-			return callback.call( elem, i, elem );
-		} ) );
-	},
-
-	slice: function() {
-		return this.pushStack( slice.apply( this, arguments ) );
-	},
-
-	first: function() {
-		return this.eq( 0 );
-	},
-
-	last: function() {
-		return this.eq( -1 );
-	},
-
-	even: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return ( i + 1 ) % 2;
-		} ) );
-	},
-
-	odd: function() {
-		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
-			return i % 2;
-		} ) );
-	},
-
-	eq: function( i ) {
-		var len = this.length,
-			j = +i + ( i < 0 ? len : 0 );
-		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
-	},
-
-	end: function() {
-		return this.prevObject || this.constructor();
-	},
-
-	// For internal use only.
-	// Behaves like an Array's method, not like a jQuery method.
-	push: push,
-	sort: arr.sort,
-	splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
-	var options, name, src, copy, copyIsArray, clone,
-		target = arguments[ 0 ] || {},
-		i = 1,
-		length = arguments.length,
-		deep = false;
-
-	// Handle a deep copy situation
-	if ( typeof target === "boolean" ) {
-		deep = target;
-
-		// Skip the boolean and the target
-		target = arguments[ i ] || {};
-		i++;
-	}
-
-	// Handle case when target is a string or something (possible in deep copy)
-	if ( typeof target !== "object" && !isFunction( target ) ) {
-		target = {};
-	}
-
-	// Extend jQuery itself if only one argument is passed
-	if ( i === length ) {
-		target = this;
-		i--;
-	}
-
-	for ( ; i < length; i++ ) {
-
-		// Only deal with non-null/undefined values
-		if ( ( options = arguments[ i ] ) != null ) {
-
-			// Extend the base object
-			for ( name in options ) {
-				copy = options[ name ];
-
-				// Prevent Object.prototype pollution
-				// Prevent never-ending loop
-				if ( name === "__proto__" || target === copy ) {
-					continue;
-				}
-
-				// Recurse if we're merging plain objects or arrays
-				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
-					( copyIsArray = Array.isArray( copy ) ) ) ) {
-					src = target[ name ];
-
-					// Ensure proper type for the source value
-					if ( copyIsArray && !Array.isArray( src ) ) {
-						clone = [];
-					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
-						clone = {};
-					} else {
-						clone = src;
-					}
-					copyIsArray = false;
-
-					// Never move original objects, clone them
-					target[ name ] = jQuery.extend( deep, clone, copy );
-
-				// Don't bring in undefined values
-				} else if ( copy !== undefined ) {
-					target[ name ] = copy;
-				}
-			}
-		}
-	}
-
-	// Return the modified object
-	return target;
-};
-
-jQuery.extend( {
-
-	// Unique for each copy of jQuery on the page
-	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
-	// Assume jQuery is ready without the ready module
-	isReady: true,
-
-	error: function( msg ) {
-		throw new Error( msg );
-	},
-
-	noop: function() {},
-
-	isPlainObject: function( obj ) {
-		var proto, Ctor;
-
-		// Detect obvious negatives
-		// Use toString instead of jQuery.type to catch host objects
-		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
-			return false;
-		}
-
-		proto = getProto( obj );
-
-		// Objects with no prototype (e.g., `Object.create( null )`) are plain
-		if ( !proto ) {
-			return true;
-		}
-
-		// Objects with prototype are plain iff they were constructed by a global Object function
-		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
-		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
-	},
-
-	isEmptyObject: function( obj ) {
-		var name;
-
-		for ( name in obj ) {
-			return false;
-		}
-		return true;
-	},
-
-	// Evaluates a script in a provided context; falls back to the global one
-	// if not specified.
-	globalEval: function( code, options, doc ) {
-		DOMEval( code, { nonce: options && options.nonce }, doc );
-	},
-
-	each: function( obj, callback ) {
-		var length, i = 0;
-
-		if ( isArrayLike( obj ) ) {
-			length = obj.length;
-			for ( ; i < length; i++ ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		} else {
-			for ( i in obj ) {
-				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
-					break;
-				}
-			}
-		}
-
-		return obj;
-	},
-
-	// results is for internal usage only
-	makeArray: function( arr, results ) {
-		var ret = results || [];
-
-		if ( arr != null ) {
-			if ( isArrayLike( Object( arr ) ) ) {
-				jQuery.merge( ret,
-					typeof arr === "string" ?
-						[ arr ] : arr
-				);
-			} else {
-				push.call( ret, arr );
-			}
-		}
-
-		return ret;
-	},
-
-	inArray: function( elem, arr, i ) {
-		return arr == null ? -1 : indexOf.call( arr, elem, i );
-	},
-
-	// Support: Android <=4.0 only, PhantomJS 1 only
-	// push.apply(_, arraylike) throws on ancient WebKit
-	merge: function( first, second ) {
-		var len = +second.length,
-			j = 0,
-			i = first.length;
-
-		for ( ; j < len; j++ ) {
-			first[ i++ ] = second[ j ];
-		}
-
-		first.length = i;
-
-		return first;
-	},
-
-	grep: function( elems, callback, invert ) {
-		var callbackInverse,
-			matches = [],
-			i = 0,
-			length = elems.length,
-			callbackExpect = !invert;
-
-		// Go through the array, only saving the items
-		// that pass the validator function
-		for ( ; i < length; i++ ) {
-			callbackInverse = !callback( elems[ i ], i );
-			if ( callbackInverse !== callbackExpect ) {
-				matches.push( elems[ i ] );
-			}
-		}
-
-		return matches;
-	},
-
-	// arg is for internal usage only
-	map: function( elems, callback, arg ) {
-		var length, value,
-			i = 0,
-			ret = [];
-
-		// Go through the array, translating each of the items to their new values
-		if ( isArrayLike( elems ) ) {
-			length = elems.length;
-			for ( ; i < length; i++ ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-
-		// Go through every key on the object,
-		} else {
-			for ( i in elems ) {
-				value = callback( elems[ i ], i, arg );
-
-				if ( value != null ) {
-					ret.push( value );
-				}
-			}
-		}
-
-		// Flatten any nested arrays
-		return flat( ret );
-	},
-
-	// A global GUID counter for objects
-	guid: 1,
-
-	// jQuery.support is not used in Core but other projects attach their
-	// properties to it so it needs to exist.
-	support: support
-} );
-
-if ( typeof Symbol === "function" ) {
-	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-	function( _i, name ) {
-		class2type[ "[object " + name + "]" ] = name.toLowerCase();
-	} );
-
-function isArrayLike( obj ) {
-
-	// Support: real iOS 8.2 only (not reproducible in simulator)
-	// `in` check used to prevent JIT error (gh-2145)
-	// hasOwn isn't used here due to false negatives
-	// regarding Nodelist length in IE
-	var length = !!obj && "length" in obj && obj.length,
-		type = toType( obj );
-
-	if ( isFunction( obj ) || isWindow( obj ) ) {
-		return false;
-	}
-
-	return type === "array" || length === 0 ||
-		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.6
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2021-02-16
- */
-( function( window ) {
-var i,
-	support,
-	Expr,
-	getText,
-	isXML,
-	tokenize,
-	compile,
-	select,
-	outermostContext,
-	sortInput,
-	hasDuplicate,
-
-	// Local document vars
-	setDocument,
-	document,
-	docElem,
-	documentIsHTML,
-	rbuggyQSA,
-	rbuggyMatches,
-	matches,
-	contains,
-
-	// Instance-specific data
-	expando = "sizzle" + 1 * new Date(),
-	preferredDoc = window.document,
-	dirruns = 0,
-	done = 0,
-	classCache = createCache(),
-	tokenCache = createCache(),
-	compilerCache = createCache(),
-	nonnativeSelectorCache = createCache(),
-	sortOrder = function( a, b ) {
-		if ( a === b ) {
-			hasDuplicate = true;
-		}
-		return 0;
-	},
-
-	// Instance methods
-	hasOwn = ( {} ).hasOwnProperty,
-	arr = [],
-	pop = arr.pop,
-	pushNative = arr.push,
-	push = arr.push,
-	slice = arr.slice,
-
-	// Use a stripped-down indexOf as it's faster than native
-	// https://jsperf.com/thor-indexof-vs-for/5
-	indexOf = function( list, elem ) {
-		var i = 0,
-			len = list.length;
-		for ( ; i < len; i++ ) {
-			if ( list[ i ] === elem ) {
-				return i;
-			}
-		}
-		return -1;
-	},
-
-	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
-		"ismap|loop|multiple|open|readonly|required|scoped",
-
-	// Regular expressions
-
-	// http://www.w3.org/TR/css3-selectors/#whitespace
-	whitespace = "[\\x20\\t\\r\\n\\f]",
-
-	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
-	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
-		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
-	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
-	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
-		// Operator (capture 2)
-		"*([*^$|!~]?=)" + whitespace +
-
-		// "Attribute values must be CSS identifiers [capture 5]
-		// or strings [capture 3 or capture 4]"
-		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
-		whitespace + "*\\]",
-
-	pseudos = ":(" + identifier + ")(?:\\((" +
-
-		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
-		// 1. quoted (capture 3; capture 4 or capture 5)
-		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
-		// 2. simple (capture 6)
-		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
-		// 3. anything else (capture 2)
-		".*" +
-		")\\)|)",
-
-	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
-	rwhitespace = new RegExp( whitespace + "+", "g" ),
-	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
-		whitespace + "+$", "g" ),
-
-	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
-	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
-		"*" ),
-	rdescend = new RegExp( whitespace + "|>" ),
-
-	rpseudo = new RegExp( pseudos ),
-	ridentifier = new RegExp( "^" + identifier + "$" ),
-
-	matchExpr = {
-		"ID": new RegExp( "^#(" + identifier + ")" ),
-		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
-		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
-		"ATTR": new RegExp( "^" + attributes ),
-		"PSEUDO": new RegExp( "^" + pseudos ),
-		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
-			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
-			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
-		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
-		// For use in libraries implementing .is()
-		// We use this for POS matching in `select`
-		"needsContext": new RegExp( "^" + whitespace +
-			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
-			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
-	},
-
-	rhtml = /HTML$/i,
-	rinputs = /^(?:input|select|textarea|button)$/i,
-	rheader = /^h\d$/i,
-
-	rnative = /^[^{]+\{\s*\[native \w/,
-
-	// Easily-parseable/retrievable ID or TAG or CLASS selectors
-	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
-	rsibling = /[+~]/,
-
-	// CSS escapes
-	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
-	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
-	funescape = function( escape, nonHex ) {
-		var high = "0x" + escape.slice( 1 ) - 0x10000;
-
-		return nonHex ?
-
-			// Strip the backslash prefix from a non-hex escape sequence
-			nonHex :
-
-			// Replace a hexadecimal escape sequence with the encoded Unicode code point
-			// Support: IE <=11+
-			// For values outside the Basic Multilingual Plane (BMP), manually construct a
-			// surrogate pair
-			high < 0 ?
-				String.fromCharCode( high + 0x10000 ) :
-				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
-	},
-
-	// CSS string/identifier serialization
-	// https://drafts.csswg.org/cssom/#common-serializing-idioms
-	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
-	fcssescape = function( ch, asCodePoint ) {
-		if ( asCodePoint ) {
-
-			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
-			if ( ch === "\0" ) {
-				return "\uFFFD";
-			}
-
-			// Control characters and (dependent upon position) numbers get escaped as code points
-			return ch.slice( 0, -1 ) + "\\" +
-				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
-		}
-
-		// Other potentially-special ASCII characters get backslash-escaped
-		return "\\" + ch;
-	},
-
-	// Used for iframes
-	// See setDocument()
-	// Removing the function wrapper causes a "Permission Denied"
-	// error in IE
-	unloadHandler = function() {
-		setDocument();
-	},
-
-	inDisabledFieldset = addCombinator(
-		function( elem ) {
-			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
-		},
-		{ dir: "parentNode", next: "legend" }
-	);
-
-// Optimize for push.apply( _, NodeList )
-try {
-	push.apply(
-		( arr = slice.call( preferredDoc.childNodes ) ),
-		preferredDoc.childNodes
-	);
-
-	// Support: Android<4.0
-	// Detect silently failing push.apply
-	// eslint-disable-next-line no-unused-expressions
-	arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
-	push = { apply: arr.length ?
-
-		// Leverage slice if possible
-		function( target, els ) {
-			pushNative.apply( target, slice.call( els ) );
-		} :
-
-		// Support: IE<9
-		// Otherwise append directly
-		function( target, els ) {
-			var j = target.length,
-				i = 0;
-
-			// Can't trust NodeList.length
-			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
-			target.length = j - 1;
-		}
-	};
-}
-
-function Sizzle( selector, context, results, seed ) {
-	var m, i, elem, nid, match, groups, newSelector,
-		newContext = context && context.ownerDocument,
-
-		// nodeType defaults to 9, since context defaults to document
-		nodeType = context ? context.nodeType : 9;
-
-	results = results || [];
-
-	// Return early from calls with invalid selector or context
-	if ( typeof selector !== "string" || !selector ||
-		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
-		return results;
-	}
-
-	// Try to shortcut find operations (as opposed to filters) in HTML documents
-	if ( !seed ) {
-		setDocument( context );
-		context = context || document;
-
-		if ( documentIsHTML ) {
-
-			// If the selector is sufficiently simple, try using a "get*By*" DOM method
-			// (excepting DocumentFragment context, where the methods don't exist)
-			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
-				// ID selector
-				if ( ( m = match[ 1 ] ) ) {
-
-					// Document context
-					if ( nodeType === 9 ) {
-						if ( ( elem = context.getElementById( m ) ) ) {
-
-							// Support: IE, Opera, Webkit
-							// TODO: identify versions
-							// getElementById can match elements by name instead of ID
-							if ( elem.id === m ) {
-								results.push( elem );
-								return results;
-							}
-						} else {
-							return results;
-						}
-
-					// Element context
-					} else {
-
-						// Support: IE, Opera, Webkit
-						// TODO: identify versions
-						// getElementById can match elements by name instead of ID
-						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
-							contains( context, elem ) &&
-							elem.id === m ) {
-
-							results.push( elem );
-							return results;
-						}
-					}
-
-				// Type selector
-				} else if ( match[ 2 ] ) {
-					push.apply( results, context.getElementsByTagName( selector ) );
-					return results;
-
-				// Class selector
-				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
-					context.getElementsByClassName ) {
-
-					push.apply( results, context.getElementsByClassName( m ) );
-					return results;
-				}
-			}
-
-			// Take advantage of querySelectorAll
-			if ( support.qsa &&
-				!nonnativeSelectorCache[ selector + " " ] &&
-				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
-				// Support: IE 8 only
-				// Exclude object elements
-				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
-				newSelector = selector;
-				newContext = context;
-
-				// qSA considers elements outside a scoping root when evaluating child or
-				// descendant combinators, which is not what we want.
-				// In such cases, we work around the behavior by prefixing every selector in the
-				// list with an ID selector referencing the scope context.
-				// The technique has to be used as well when a leading combinator is used
-				// as such selectors are not recognized by querySelectorAll.
-				// Thanks to Andrew Dupont for this technique.
-				if ( nodeType === 1 &&
-					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
-					// Expand context for sibling selectors
-					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
-						context;
-
-					// We can use :scope instead of the ID hack if the browser
-					// supports it & if we're not changing the context.
-					if ( newContext !== context || !support.scope ) {
-
-						// Capture the context ID, setting it first if necessary
-						if ( ( nid = context.getAttribute( "id" ) ) ) {
-							nid = nid.replace( rcssescape, fcssescape );
-						} else {
-							context.setAttribute( "id", ( nid = expando ) );
-						}
-					}
-
-					// Prefix every selector in the list
-					groups = tokenize( selector );
-					i = groups.length;
-					while ( i-- ) {
-						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
-							toSelector( groups[ i ] );
-					}
-					newSelector = groups.join( "," );
-				}
-
-				try {
-					push.apply( results,
-						newContext.querySelectorAll( newSelector )
-					);
-					return results;
-				} catch ( qsaError ) {
-					nonnativeSelectorCache( selector, true );
-				} finally {
-					if ( nid === expando ) {
-						context.removeAttribute( "id" );
-					}
-				}
-			}
-		}
-	}
-
-	// All others
-	return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- *	deleting the oldest entry
- */
-function createCache() {
-	var keys = [];
-
-	function cache( key, value ) {
-
-		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
-		if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
-			// Only keep the most recent entries
-			delete cache[ keys.shift() ];
-		}
-		return ( cache[ key + " " ] = value );
-	}
-	return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
-	fn[ expando ] = true;
-	return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
-	var el = document.createElement( "fieldset" );
-
-	try {
-		return !!fn( el );
-	} catch ( e ) {
-		return false;
-	} finally {
-
-		// Remove from its parent by default
-		if ( el.parentNode ) {
-			el.parentNode.removeChild( el );
-		}
-
-		// release memory in IE
-		el = null;
-	}
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
-	var arr = attrs.split( "|" ),
-		i = arr.length;
-
-	while ( i-- ) {
-		Expr.attrHandle[ arr[ i ] ] = handler;
-	}
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
-	var cur = b && a,
-		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
-			a.sourceIndex - b.sourceIndex;
-
-	// Use IE sourceIndex if available on both nodes
-	if ( diff ) {
-		return diff;
-	}
-
-	// Check if b follows a
-	if ( cur ) {
-		while ( ( cur = cur.nextSibling ) ) {
-			if ( cur === b ) {
-				return -1;
-			}
-		}
-	}
-
-	return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return name === "input" && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
-	return function( elem ) {
-		var name = elem.nodeName.toLowerCase();
-		return ( name === "input" || name === "button" ) && elem.type === type;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
-	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
-	return function( elem ) {
-
-		// Only certain elements can match :enabled or :disabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
-		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
-		if ( "form" in elem ) {
-
-			// Check for inherited disabledness on relevant non-disabled elements:
-			// * listed form-associated elements in a disabled fieldset
-			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
-			// * option elements in a disabled optgroup
-			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
-			// All such elements have a "form" property.
-			if ( elem.parentNode && elem.disabled === false ) {
-
-				// Option elements defer to a parent optgroup if present
-				if ( "label" in elem ) {
-					if ( "label" in elem.parentNode ) {
-						return elem.parentNode.disabled === disabled;
-					} else {
-						return elem.disabled === disabled;
-					}
-				}
-
-				// Support: IE 6 - 11
-				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
-				return elem.isDisabled === disabled ||
-
-					// Where there is no isDisabled, check manually
-					/* jshint -W018 */
-					elem.isDisabled !== !disabled &&
-					inDisabledFieldset( elem ) === disabled;
-			}
-
-			return elem.disabled === disabled;
-
-		// Try to winnow out elements that can't be disabled before trusting the disabled property.
-		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
-		// even exist on them, let alone have a boolean value.
-		} else if ( "label" in elem ) {
-			return elem.disabled === disabled;
-		}
-
-		// Remaining elements are neither :enabled nor :disabled
-		return false;
-	};
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
-	return markFunction( function( argument ) {
-		argument = +argument;
-		return markFunction( function( seed, matches ) {
-			var j,
-				matchIndexes = fn( [], seed.length, argument ),
-				i = matchIndexes.length;
-
-			// Match elements found at the specified indexes
-			while ( i-- ) {
-				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
-					seed[ j ] = !( matches[ j ] = seed[ j ] );
-				}
-			}
-		} );
-	} );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
-	return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
-	var namespace = elem && elem.namespaceURI,
-		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
-
-	// Support: IE <=8
-	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
-	// https://bugs.jquery.com/ticket/4833
-	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
-	var hasCompare, subWindow,
-		doc = node ? node.ownerDocument || node : preferredDoc;
-
-	// Return early if doc is invalid or already selected
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
-		return document;
-	}
-
-	// Update global variables
-	document = doc;
-	docElem = document.documentElement;
-	documentIsHTML = !isXML( document );
-
-	// Support: IE 9 - 11+, Edge 12 - 18+
-	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( preferredDoc != document &&
-		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
-		// Support: IE 11, Edge
-		if ( subWindow.addEventListener ) {
-			subWindow.addEventListener( "unload", unloadHandler, false );
-
-		// Support: IE 9 - 10 only
-		} else if ( subWindow.attachEvent ) {
-			subWindow.attachEvent( "onunload", unloadHandler );
-		}
-	}
-
-	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
-	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
-	// IE/Edge & older browsers don't support the :scope pseudo-class.
-	// Support: Safari 6.0 only
-	// Safari 6.0 supports :scope but it's an alias of :root there.
-	support.scope = assert( function( el ) {
-		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
-		return typeof el.querySelectorAll !== "undefined" &&
-			!el.querySelectorAll( ":scope fieldset div" ).length;
-	} );
-
-	/* Attributes
-	---------------------------------------------------------------------- */
-
-	// Support: IE<8
-	// Verify that getAttribute really returns attributes and not properties
-	// (excepting IE8 booleans)
-	support.attributes = assert( function( el ) {
-		el.className = "i";
-		return !el.getAttribute( "className" );
-	} );
-
-	/* getElement(s)By*
-	---------------------------------------------------------------------- */
-
-	// Check if getElementsByTagName("*") returns only elements
-	support.getElementsByTagName = assert( function( el ) {
-		el.appendChild( document.createComment( "" ) );
-		return !el.getElementsByTagName( "*" ).length;
-	} );
-
-	// Support: IE<9
-	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
-	// Support: IE<10
-	// Check if getElementById returns elements by name
-	// The broken getElementById methods don't pick up programmatically-set names,
-	// so use a roundabout getElementsByName test
-	support.getById = assert( function( el ) {
-		docElem.appendChild( el ).id = expando;
-		return !document.getElementsByName || !document.getElementsByName( expando ).length;
-	} );
-
-	// ID filter and find
-	if ( support.getById ) {
-		Expr.filter[ "ID" ] = function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				return elem.getAttribute( "id" ) === attrId;
-			};
-		};
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var elem = context.getElementById( id );
-				return elem ? [ elem ] : [];
-			}
-		};
-	} else {
-		Expr.filter[ "ID" ] =  function( id ) {
-			var attrId = id.replace( runescape, funescape );
-			return function( elem ) {
-				var node = typeof elem.getAttributeNode !== "undefined" &&
-					elem.getAttributeNode( "id" );
-				return node && node.value === attrId;
-			};
-		};
-
-		// Support: IE 6 - 7 only
-		// getElementById is not reliable as a find shortcut
-		Expr.find[ "ID" ] = function( id, context ) {
-			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
-				var node, i, elems,
-					elem = context.getElementById( id );
-
-				if ( elem ) {
-
-					// Verify the id attribute
-					node = elem.getAttributeNode( "id" );
-					if ( node && node.value === id ) {
-						return [ elem ];
-					}
-
-					// Fall back on getElementsByName
-					elems = context.getElementsByName( id );
-					i = 0;
-					while ( ( elem = elems[ i++ ] ) ) {
-						node = elem.getAttributeNode( "id" );
-						if ( node && node.value === id ) {
-							return [ elem ];
-						}
-					}
-				}
-
-				return [];
-			}
-		};
-	}
-
-	// Tag
-	Expr.find[ "TAG" ] = support.getElementsByTagName ?
-		function( tag, context ) {
-			if ( typeof context.getElementsByTagName !== "undefined" ) {
-				return context.getElementsByTagName( tag );
-
-			// DocumentFragment nodes don't have gEBTN
-			} else if ( support.qsa ) {
-				return context.querySelectorAll( tag );
-			}
-		} :
-
-		function( tag, context ) {
-			var elem,
-				tmp = [],
-				i = 0,
-
-				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
-				results = context.getElementsByTagName( tag );
-
-			// Filter out possible comments
-			if ( tag === "*" ) {
-				while ( ( elem = results[ i++ ] ) ) {
-					if ( elem.nodeType === 1 ) {
-						tmp.push( elem );
-					}
-				}
-
-				return tmp;
-			}
-			return results;
-		};
-
-	// Class
-	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
-		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
-			return context.getElementsByClassName( className );
-		}
-	};
-
-	/* QSA/matchesSelector
-	---------------------------------------------------------------------- */
-
-	// QSA and matchesSelector support
-
-	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
-	rbuggyMatches = [];
-
-	// qSa(:focus) reports false when true (Chrome 21)
-	// We allow this because of a bug in IE8/9 that throws an error
-	// whenever `document.activeElement` is accessed on an iframe
-	// So, we allow :focus to pass through QSA all the time to avoid the IE error
-	// See https://bugs.jquery.com/ticket/13378
-	rbuggyQSA = [];
-
-	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
-		// Build QSA regex
-		// Regex strategy adopted from Diego Perini
-		assert( function( el ) {
-
-			var input;
-
-			// Select is set to empty string on purpose
-			// This is to test IE's treatment of not explicitly
-			// setting a boolean content attribute,
-			// since its presence should be enough
-			// https://bugs.jquery.com/ticket/12359
-			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
-				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
-				"<option selected=''></option></select>";
-
-			// Support: IE8, Opera 11-12.16
-			// Nothing should be selected when empty strings follow ^= or $= or *=
-			// The test attribute must be unknown in Opera but "safe" for WinRT
-			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
-			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
-				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
-			}
-
-			// Support: IE8
-			// Boolean attributes and "value" are not treated correctly
-			if ( !el.querySelectorAll( "[selected]" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
-			}
-
-			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
-			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
-				rbuggyQSA.push( "~=" );
-			}
-
-			// Support: IE 11+, Edge 15 - 18+
-			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
-			// Adding a temporary attribute to the document before the selection works
-			// around the issue.
-			// Interestingly, IE 10 & older don't seem to have the issue.
-			input = document.createElement( "input" );
-			input.setAttribute( "name", "" );
-			el.appendChild( input );
-			if ( !el.querySelectorAll( "[name='']" ).length ) {
-				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
-					whitespace + "*(?:''|\"\")" );
-			}
-
-			// Webkit/Opera - :checked should return selected option elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			// IE8 throws error here and will not see later tests
-			if ( !el.querySelectorAll( ":checked" ).length ) {
-				rbuggyQSA.push( ":checked" );
-			}
-
-			// Support: Safari 8+, iOS 8+
-			// https://bugs.webkit.org/show_bug.cgi?id=136851
-			// In-page `selector#id sibling-combinator selector` fails
-			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
-				rbuggyQSA.push( ".#.+[+~]" );
-			}
-
-			// Support: Firefox <=3.6 - 5 only
-			// Old Firefox doesn't throw on a badly-escaped identifier.
-			el.querySelectorAll( "\\\f" );
-			rbuggyQSA.push( "[\\r\\n\\f]" );
-		} );
-
-		assert( function( el ) {
-			el.innerHTML = "<a href='' disabled='disabled'></a>" +
-				"<select disabled='disabled'><option/></select>";
-
-			// Support: Windows 8 Native Apps
-			// The type and name attributes are restricted during .innerHTML assignment
-			var input = document.createElement( "input" );
-			input.setAttribute( "type", "hidden" );
-			el.appendChild( input ).setAttribute( "name", "D" );
-
-			// Support: IE8
-			// Enforce case-sensitivity of name attribute
-			if ( el.querySelectorAll( "[name=d]" ).length ) {
-				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
-			}
-
-			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
-			// IE8 throws error here and will not see later tests
-			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: IE9-11+
-			// IE's :disabled selector does not pick up the children of disabled fieldsets
-			docElem.appendChild( el ).disabled = true;
-			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
-				rbuggyQSA.push( ":enabled", ":disabled" );
-			}
-
-			// Support: Opera 10 - 11 only
-			// Opera 10-11 does not throw on post-comma invalid pseudos
-			el.querySelectorAll( "*,:x" );
-			rbuggyQSA.push( ",.*:" );
-		} );
-	}
-
-	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
-		docElem.webkitMatchesSelector ||
-		docElem.mozMatchesSelector ||
-		docElem.oMatchesSelector ||
-		docElem.msMatchesSelector ) ) ) ) {
-
-		assert( function( el ) {
-
-			// Check to see if it's possible to do matchesSelector
-			// on a disconnected node (IE 9)
-			support.disconnectedMatch = matches.call( el, "*" );
-
-			// This should fail with an exception
-			// Gecko does not error, returns false instead
-			matches.call( el, "[s!='']:x" );
-			rbuggyMatches.push( "!=", pseudos );
-		} );
-	}
-
-	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
-	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
-	/* Contains
-	---------------------------------------------------------------------- */
-	hasCompare = rnative.test( docElem.compareDocumentPosition );
-
-	// Element contains another
-	// Purposefully self-exclusive
-	// As in, an element does not contain itself
-	contains = hasCompare || rnative.test( docElem.contains ) ?
-		function( a, b ) {
-			var adown = a.nodeType === 9 ? a.documentElement : a,
-				bup = b && b.parentNode;
-			return a === bup || !!( bup && bup.nodeType === 1 && (
-				adown.contains ?
-					adown.contains( bup ) :
-					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
-			) );
-		} :
-		function( a, b ) {
-			if ( b ) {
-				while ( ( b = b.parentNode ) ) {
-					if ( b === a ) {
-						return true;
-					}
-				}
-			}
-			return false;
-		};
-
-	/* Sorting
-	---------------------------------------------------------------------- */
-
-	// Document order sorting
-	sortOrder = hasCompare ?
-	function( a, b ) {
-
-		// Flag for duplicate removal
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		// Sort on method existence if only one input has compareDocumentPosition
-		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
-		if ( compare ) {
-			return compare;
-		}
-
-		// Calculate position if both inputs belong to the same document
-		// Support: IE 11+, Edge 17 - 18+
-		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-		// two documents; shallow comparisons work.
-		// eslint-disable-next-line eqeqeq
-		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
-			a.compareDocumentPosition( b ) :
-
-			// Otherwise we know they are disconnected
-			1;
-
-		// Disconnected nodes
-		if ( compare & 1 ||
-			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
-			// Choose the first element that is related to our preferred document
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( a == document || a.ownerDocument == preferredDoc &&
-				contains( preferredDoc, a ) ) {
-				return -1;
-			}
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			// eslint-disable-next-line eqeqeq
-			if ( b == document || b.ownerDocument == preferredDoc &&
-				contains( preferredDoc, b ) ) {
-				return 1;
-			}
-
-			// Maintain original order
-			return sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-		}
-
-		return compare & 4 ? -1 : 1;
-	} :
-	function( a, b ) {
-
-		// Exit early if the nodes are identical
-		if ( a === b ) {
-			hasDuplicate = true;
-			return 0;
-		}
-
-		var cur,
-			i = 0,
-			aup = a.parentNode,
-			bup = b.parentNode,
-			ap = [ a ],
-			bp = [ b ];
-
-		// Parentless nodes are either documents or disconnected
-		if ( !aup || !bup ) {
-
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			return a == document ? -1 :
-				b == document ? 1 :
-				/* eslint-enable eqeqeq */
-				aup ? -1 :
-				bup ? 1 :
-				sortInput ?
-				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
-				0;
-
-		// If the nodes are siblings, we can do a quick check
-		} else if ( aup === bup ) {
-			return siblingCheck( a, b );
-		}
-
-		// Otherwise we need full lists of their ancestors for comparison
-		cur = a;
-		while ( ( cur = cur.parentNode ) ) {
-			ap.unshift( cur );
-		}
-		cur = b;
-		while ( ( cur = cur.parentNode ) ) {
-			bp.unshift( cur );
-		}
-
-		// Walk down the tree looking for a discrepancy
-		while ( ap[ i ] === bp[ i ] ) {
-			i++;
-		}
-
-		return i ?
-
-			// Do a sibling check if the nodes have a common ancestor
-			siblingCheck( ap[ i ], bp[ i ] ) :
-
-			// Otherwise nodes in our document sort first
-			// Support: IE 11+, Edge 17 - 18+
-			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-			// two documents; shallow comparisons work.
-			/* eslint-disable eqeqeq */
-			ap[ i ] == preferredDoc ? -1 :
-			bp[ i ] == preferredDoc ? 1 :
-			/* eslint-enable eqeqeq */
-			0;
-	};
-
-	return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
-	return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
-	setDocument( elem );
-
-	if ( support.matchesSelector && documentIsHTML &&
-		!nonnativeSelectorCache[ expr + " " ] &&
-		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
-		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
-
-		try {
-			var ret = matches.call( elem, expr );
-
-			// IE 9's matchesSelector returns false on disconnected nodes
-			if ( ret || support.disconnectedMatch ||
-
-				// As well, disconnected nodes are said to be in a document
-				// fragment in IE 9
-				elem.document && elem.document.nodeType !== 11 ) {
-				return ret;
-			}
-		} catch ( e ) {
-			nonnativeSelectorCache( expr, true );
-		}
-	}
-
-	return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( context.ownerDocument || context ) != document ) {
-		setDocument( context );
-	}
-	return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
-	// Set document vars if needed
-	// Support: IE 11+, Edge 17 - 18+
-	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-	// two documents; shallow comparisons work.
-	// eslint-disable-next-line eqeqeq
-	if ( ( elem.ownerDocument || elem ) != document ) {
-		setDocument( elem );
-	}
-
-	var fn = Expr.attrHandle[ name.toLowerCase() ],
-
-		// Don't get fooled by Object.prototype properties (jQuery #13807)
-		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
-			fn( elem, name, !documentIsHTML ) :
-			undefined;
-
-	return val !== undefined ?
-		val :
-		support.attributes || !documentIsHTML ?
-			elem.getAttribute( name ) :
-			( val = elem.getAttributeNode( name ) ) && val.specified ?
-				val.value :
-				null;
-};
-
-Sizzle.escape = function( sel ) {
-	return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
-	throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
-	var elem,
-		duplicates = [],
-		j = 0,
-		i = 0;
-
-	// Unless we *know* we can detect duplicates, assume their presence
-	hasDuplicate = !support.detectDuplicates;
-	sortInput = !support.sortStable && results.slice( 0 );
-	results.sort( sortOrder );
-
-	if ( hasDuplicate ) {
-		while ( ( elem = results[ i++ ] ) ) {
-			if ( elem === results[ i ] ) {
-				j = duplicates.push( i );
-			}
-		}
-		while ( j-- ) {
-			results.splice( duplicates[ j ], 1 );
-		}
-	}
-
-	// Clear input after sorting to release objects
-	// See https://github.com/jquery/sizzle/pull/225
-	sortInput = null;
-
-	return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
-	var node,
-		ret = "",
-		i = 0,
-		nodeType = elem.nodeType;
-
-	if ( !nodeType ) {
-
-		// If no nodeType, this is expected to be an array
-		while ( ( node = elem[ i++ ] ) ) {
-
-			// Do not traverse comment nodes
-			ret += getText( node );
-		}
-	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
-		// Use textContent for elements
-		// innerText usage removed for consistency of new lines (jQuery #11153)
-		if ( typeof elem.textContent === "string" ) {
-			return elem.textContent;
-		} else {
-
-			// Traverse its children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				ret += getText( elem );
-			}
-		}
-	} else if ( nodeType === 3 || nodeType === 4 ) {
-		return elem.nodeValue;
-	}
-
-	// Do not include comment or processing instruction nodes
-
-	return ret;
-};
-
-Expr = Sizzle.selectors = {
-
-	// Can be adjusted by the user
-	cacheLength: 50,
-
-	createPseudo: markFunction,
-
-	match: matchExpr,
-
-	attrHandle: {},
-
-	find: {},
-
-	relative: {
-		">": { dir: "parentNode", first: true },
-		" ": { dir: "parentNode" },
-		"+": { dir: "previousSibling", first: true },
-		"~": { dir: "previousSibling" }
-	},
-
-	preFilter: {
-		"ATTR": function( match ) {
-			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
-			// Move the given value to match[3] whether quoted or unquoted
-			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
-				match[ 5 ] || "" ).replace( runescape, funescape );
-
-			if ( match[ 2 ] === "~=" ) {
-				match[ 3 ] = " " + match[ 3 ] + " ";
-			}
-
-			return match.slice( 0, 4 );
-		},
-
-		"CHILD": function( match ) {
-
-			/* matches from matchExpr["CHILD"]
-				1 type (only|nth|...)
-				2 what (child|of-type)
-				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
-				4 xn-component of xn+y argument ([+-]?\d*n|)
-				5 sign of xn-component
-				6 x of xn-component
-				7 sign of y-component
-				8 y of y-component
-			*/
-			match[ 1 ] = match[ 1 ].toLowerCase();
-
-			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
-				// nth-* requires argument
-				if ( !match[ 3 ] ) {
-					Sizzle.error( match[ 0 ] );
-				}
-
-				// numeric x and y parameters for Expr.filter.CHILD
-				// remember that false/true cast respectively to 0/1
-				match[ 4 ] = +( match[ 4 ] ?
-					match[ 5 ] + ( match[ 6 ] || 1 ) :
-					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
-				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
-				// other types prohibit arguments
-			} else if ( match[ 3 ] ) {
-				Sizzle.error( match[ 0 ] );
-			}
-
-			return match;
-		},
-
-		"PSEUDO": function( match ) {
-			var excess,
-				unquoted = !match[ 6 ] && match[ 2 ];
-
-			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
-				return null;
-			}
-
-			// Accept quoted arguments as-is
-			if ( match[ 3 ] ) {
-				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
-			// Strip excess characters from unquoted arguments
-			} else if ( unquoted && rpseudo.test( unquoted ) &&
-
-				// Get excess from tokenize (recursively)
-				( excess = tokenize( unquoted, true ) ) &&
-
-				// advance to the next closing parenthesis
-				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
-				// excess is a negative index
-				match[ 0 ] = match[ 0 ].slice( 0, excess );
-				match[ 2 ] = unquoted.slice( 0, excess );
-			}
-
-			// Return only captures needed by the pseudo filter method (type and argument)
-			return match.slice( 0, 3 );
-		}
-	},
-
-	filter: {
-
-		"TAG": function( nodeNameSelector ) {
-			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
-			return nodeNameSelector === "*" ?
-				function() {
-					return true;
-				} :
-				function( elem ) {
-					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
-				};
-		},
-
-		"CLASS": function( className ) {
-			var pattern = classCache[ className + " " ];
-
-			return pattern ||
-				( pattern = new RegExp( "(^|" + whitespace +
-					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
-						className, function( elem ) {
-							return pattern.test(
-								typeof elem.className === "string" && elem.className ||
-								typeof elem.getAttribute !== "undefined" &&
-									elem.getAttribute( "class" ) ||
-								""
-							);
-				} );
-		},
-
-		"ATTR": function( name, operator, check ) {
-			return function( elem ) {
-				var result = Sizzle.attr( elem, name );
-
-				if ( result == null ) {
-					return operator === "!=";
-				}
-				if ( !operator ) {
-					return true;
-				}
-
-				result += "";
-
-				/* eslint-disable max-len */
-
-				return operator === "=" ? result === check :
-					operator === "!=" ? result !== check :
-					operator === "^=" ? check && result.indexOf( check ) === 0 :
-					operator === "*=" ? check && result.indexOf( check ) > -1 :
-					operator === "$=" ? check && result.slice( -check.length ) === check :
-					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
-					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
-					false;
-				/* eslint-enable max-len */
-
-			};
-		},
-
-		"CHILD": function( type, what, _argument, first, last ) {
-			var simple = type.slice( 0, 3 ) !== "nth",
-				forward = type.slice( -4 ) !== "last",
-				ofType = what === "of-type";
-
-			return first === 1 && last === 0 ?
-
-				// Shortcut for :nth-*(n)
-				function( elem ) {
-					return !!elem.parentNode;
-				} :
-
-				function( elem, _context, xml ) {
-					var cache, uniqueCache, outerCache, node, nodeIndex, start,
-						dir = simple !== forward ? "nextSibling" : "previousSibling",
-						parent = elem.parentNode,
-						name = ofType && elem.nodeName.toLowerCase(),
-						useCache = !xml && !ofType,
-						diff = false;
-
-					if ( parent ) {
-
-						// :(first|last|only)-(child|of-type)
-						if ( simple ) {
-							while ( dir ) {
-								node = elem;
-								while ( ( node = node[ dir ] ) ) {
-									if ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) {
-
-										return false;
-									}
-								}
-
-								// Reverse direction for :only-* (if we haven't yet done so)
-								start = dir = type === "only" && !start && "nextSibling";
-							}
-							return true;
-						}
-
-						start = [ forward ? parent.firstChild : parent.lastChild ];
-
-						// non-xml :nth-child(...) stores cache data on `parent`
-						if ( forward && useCache ) {
-
-							// Seek `elem` from a previously-cached index
-
-							// ...in a gzip-friendly way
-							node = parent;
-							outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-							// Support: IE <9 only
-							// Defend against cloned attroperties (jQuery gh-1709)
-							uniqueCache = outerCache[ node.uniqueID ] ||
-								( outerCache[ node.uniqueID ] = {} );
-
-							cache = uniqueCache[ type ] || [];
-							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-							diff = nodeIndex && cache[ 2 ];
-							node = nodeIndex && parent.childNodes[ nodeIndex ];
-
-							while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
-								// Fallback to seeking `elem` from the start
-								( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-								// When found, cache indexes on `parent` and break
-								if ( node.nodeType === 1 && ++diff && node === elem ) {
-									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
-									break;
-								}
-							}
-
-						} else {
-
-							// Use previously-cached element index if available
-							if ( useCache ) {
-
-								// ...in a gzip-friendly way
-								node = elem;
-								outerCache = node[ expando ] || ( node[ expando ] = {} );
-
-								// Support: IE <9 only
-								// Defend against cloned attroperties (jQuery gh-1709)
-								uniqueCache = outerCache[ node.uniqueID ] ||
-									( outerCache[ node.uniqueID ] = {} );
-
-								cache = uniqueCache[ type ] || [];
-								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
-								diff = nodeIndex;
-							}
-
-							// xml :nth-child(...)
-							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
-							if ( diff === false ) {
-
-								// Use the same loop as above to seek `elem` from the start
-								while ( ( node = ++nodeIndex && node && node[ dir ] ||
-									( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
-									if ( ( ofType ?
-										node.nodeName.toLowerCase() === name :
-										node.nodeType === 1 ) &&
-										++diff ) {
-
-										// Cache the index of each encountered element
-										if ( useCache ) {
-											outerCache = node[ expando ] ||
-												( node[ expando ] = {} );
-
-											// Support: IE <9 only
-											// Defend against cloned attroperties (jQuery gh-1709)
-											uniqueCache = outerCache[ node.uniqueID ] ||
-												( outerCache[ node.uniqueID ] = {} );
-
-											uniqueCache[ type ] = [ dirruns, diff ];
-										}
-
-										if ( node === elem ) {
-											break;
-										}
-									}
-								}
-							}
-						}
-
-						// Incorporate the offset, then check against cycle size
-						diff -= last;
-						return diff === first || ( diff % first === 0 && diff / first >= 0 );
-					}
-				};
-		},
-
-		"PSEUDO": function( pseudo, argument ) {
-
-			// pseudo-class names are case-insensitive
-			// http://www.w3.org/TR/selectors/#pseudo-classes
-			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
-			// Remember that setFilters inherits from pseudos
-			var args,
-				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
-					Sizzle.error( "unsupported pseudo: " + pseudo );
-
-			// The user may use createPseudo to indicate that
-			// arguments are needed to create the filter function
-			// just as Sizzle does
-			if ( fn[ expando ] ) {
-				return fn( argument );
-			}
-
-			// But maintain support for old signatures
-			if ( fn.length > 1 ) {
-				args = [ pseudo, pseudo, "", argument ];
-				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
-					markFunction( function( seed, matches ) {
-						var idx,
-							matched = fn( seed, argument ),
-							i = matched.length;
-						while ( i-- ) {
-							idx = indexOf( seed, matched[ i ] );
-							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
-						}
-					} ) :
-					function( elem ) {
-						return fn( elem, 0, args );
-					};
-			}
-
-			return fn;
-		}
-	},
-
-	pseudos: {
-
-		// Potentially complex pseudos
-		"not": markFunction( function( selector ) {
-
-			// Trim the selector passed to compile
-			// to avoid treating leading and trailing
-			// spaces as combinators
-			var input = [],
-				results = [],
-				matcher = compile( selector.replace( rtrim, "$1" ) );
-
-			return matcher[ expando ] ?
-				markFunction( function( seed, matches, _context, xml ) {
-					var elem,
-						unmatched = matcher( seed, null, xml, [] ),
-						i = seed.length;
-
-					// Match elements unmatched by `matcher`
-					while ( i-- ) {
-						if ( ( elem = unmatched[ i ] ) ) {
-							seed[ i ] = !( matches[ i ] = elem );
-						}
-					}
-				} ) :
-				function( elem, _context, xml ) {
-					input[ 0 ] = elem;
-					matcher( input, null, xml, results );
-
-					// Don't keep the element (issue #299)
-					input[ 0 ] = null;
-					return !results.pop();
-				};
-		} ),
-
-		"has": markFunction( function( selector ) {
-			return function( elem ) {
-				return Sizzle( selector, elem ).length > 0;
-			};
-		} ),
-
-		"contains": markFunction( function( text ) {
-			text = text.replace( runescape, funescape );
-			return function( elem ) {
-				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
-			};
-		} ),
-
-		// "Whether an element is represented by a :lang() selector
-		// is based solely on the element's language value
-		// being equal to the identifier C,
-		// or beginning with the identifier C immediately followed by "-".
-		// The matching of C against the element's language value is performed case-insensitively.
-		// The identifier C does not have to be a valid language name."
-		// http://www.w3.org/TR/selectors/#lang-pseudo
-		"lang": markFunction( function( lang ) {
-
-			// lang value must be a valid identifier
-			if ( !ridentifier.test( lang || "" ) ) {
-				Sizzle.error( "unsupported lang: " + lang );
-			}
-			lang = lang.replace( runescape, funescape ).toLowerCase();
-			return function( elem ) {
-				var elemLang;
-				do {
-					if ( ( elemLang = documentIsHTML ?
-						elem.lang :
-						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
-						elemLang = elemLang.toLowerCase();
-						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
-					}
-				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
-				return false;
-			};
-		} ),
-
-		// Miscellaneous
-		"target": function( elem ) {
-			var hash = window.location && window.location.hash;
-			return hash && hash.slice( 1 ) === elem.id;
-		},
-
-		"root": function( elem ) {
-			return elem === docElem;
-		},
-
-		"focus": function( elem ) {
-			return elem === document.activeElement &&
-				( !document.hasFocus || document.hasFocus() ) &&
-				!!( elem.type || elem.href || ~elem.tabIndex );
-		},
-
-		// Boolean properties
-		"enabled": createDisabledPseudo( false ),
-		"disabled": createDisabledPseudo( true ),
-
-		"checked": function( elem ) {
-
-			// In CSS3, :checked should return both checked and selected elements
-			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
-			var nodeName = elem.nodeName.toLowerCase();
-			return ( nodeName === "input" && !!elem.checked ) ||
-				( nodeName === "option" && !!elem.selected );
-		},
-
-		"selected": function( elem ) {
-
-			// Accessing this property makes selected-by-default
-			// options in Safari work properly
-			if ( elem.parentNode ) {
-				// eslint-disable-next-line no-unused-expressions
-				elem.parentNode.selectedIndex;
-			}
-
-			return elem.selected === true;
-		},
-
-		// Contents
-		"empty": function( elem ) {
-
-			// http://www.w3.org/TR/selectors/#empty-pseudo
-			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
-			//   but not by others (comment: 8; processing instruction: 7; etc.)
-			// nodeType < 6 works because attributes (2) do not appear as children
-			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
-				if ( elem.nodeType < 6 ) {
-					return false;
-				}
-			}
-			return true;
-		},
-
-		"parent": function( elem ) {
-			return !Expr.pseudos[ "empty" ]( elem );
-		},
-
-		// Element/input types
-		"header": function( elem ) {
-			return rheader.test( elem.nodeName );
-		},
-
-		"input": function( elem ) {
-			return rinputs.test( elem.nodeName );
-		},
-
-		"button": function( elem ) {
-			var name = elem.nodeName.toLowerCase();
-			return name === "input" && elem.type === "button" || name === "button";
-		},
-
-		"text": function( elem ) {
-			var attr;
-			return elem.nodeName.toLowerCase() === "input" &&
-				elem.type === "text" &&
-
-				// Support: IE<8
-				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
-				( ( attr = elem.getAttribute( "type" ) ) == null ||
-					attr.toLowerCase() === "text" );
-		},
-
-		// Position-in-collection
-		"first": createPositionalPseudo( function() {
-			return [ 0 ];
-		} ),
-
-		"last": createPositionalPseudo( function( _matchIndexes, length ) {
-			return [ length - 1 ];
-		} ),
-
-		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
-			return [ argument < 0 ? argument + length : argument ];
-		} ),
-
-		"even": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 0;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"odd": createPositionalPseudo( function( matchIndexes, length ) {
-			var i = 1;
-			for ( ; i < length; i += 2 ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ?
-				argument + length :
-				argument > length ?
-					length :
-					argument;
-			for ( ; --i >= 0; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} ),
-
-		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
-			var i = argument < 0 ? argument + length : argument;
-			for ( ; ++i < length; ) {
-				matchIndexes.push( i );
-			}
-			return matchIndexes;
-		} )
-	}
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
-	Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
-	Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
-	var matched, match, tokens, type,
-		soFar, groups, preFilters,
-		cached = tokenCache[ selector + " " ];
-
-	if ( cached ) {
-		return parseOnly ? 0 : cached.slice( 0 );
-	}
-
-	soFar = selector;
-	groups = [];
-	preFilters = Expr.preFilter;
-
-	while ( soFar ) {
-
-		// Comma and first run
-		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
-			if ( match ) {
-
-				// Don't consume trailing commas as valid
-				soFar = soFar.slice( match[ 0 ].length ) || soFar;
-			}
-			groups.push( ( tokens = [] ) );
-		}
-
-		matched = false;
-
-		// Combinators
-		if ( ( match = rcombinators.exec( soFar ) ) ) {
-			matched = match.shift();
-			tokens.push( {
-				value: matched,
-
-				// Cast descendant combinators to space
-				type: match[ 0 ].replace( rtrim, " " )
-			} );
-			soFar = soFar.slice( matched.length );
-		}
-
-		// Filters
-		for ( type in Expr.filter ) {
-			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
-				( match = preFilters[ type ]( match ) ) ) ) {
-				matched = match.shift();
-				tokens.push( {
-					value: matched,
-					type: type,
-					matches: match
-				} );
-				soFar = soFar.slice( matched.length );
-			}
-		}
-
-		if ( !matched ) {
-			break;
-		}
-	}
-
-	// Return the length of the invalid excess
-	// if we're just parsing
-	// Otherwise, throw an error or return tokens
-	return parseOnly ?
-		soFar.length :
-		soFar ?
-			Sizzle.error( selector ) :
-
-			// Cache the tokens
-			tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
-	var i = 0,
-		len = tokens.length,
-		selector = "";
-	for ( ; i < len; i++ ) {
-		selector += tokens[ i ].value;
-	}
-	return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
-	var dir = combinator.dir,
-		skip = combinator.next,
-		key = skip || dir,
-		checkNonElements = base && key === "parentNode",
-		doneName = done++;
-
-	return combinator.first ?
-
-		// Check against closest ancestor/preceding element
-		function( elem, context, xml ) {
-			while ( ( elem = elem[ dir ] ) ) {
-				if ( elem.nodeType === 1 || checkNonElements ) {
-					return matcher( elem, context, xml );
-				}
-			}
-			return false;
-		} :
-
-		// Check against all ancestor/preceding elements
-		function( elem, context, xml ) {
-			var oldCache, uniqueCache, outerCache,
-				newCache = [ dirruns, doneName ];
-
-			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
-			if ( xml ) {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						if ( matcher( elem, context, xml ) ) {
-							return true;
-						}
-					}
-				}
-			} else {
-				while ( ( elem = elem[ dir ] ) ) {
-					if ( elem.nodeType === 1 || checkNonElements ) {
-						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
-						// Support: IE <9 only
-						// Defend against cloned attroperties (jQuery gh-1709)
-						uniqueCache = outerCache[ elem.uniqueID ] ||
-							( outerCache[ elem.uniqueID ] = {} );
-
-						if ( skip && skip === elem.nodeName.toLowerCase() ) {
-							elem = elem[ dir ] || elem;
-						} else if ( ( oldCache = uniqueCache[ key ] ) &&
-							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
-							// Assign to newCache so results back-propagate to previous elements
-							return ( newCache[ 2 ] = oldCache[ 2 ] );
-						} else {
-
-							// Reuse newcache so results back-propagate to previous elements
-							uniqueCache[ key ] = newCache;
-
-							// A match means we're done; a fail means we have to keep checking
-							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
-								return true;
-							}
-						}
-					}
-				}
-			}
-			return false;
-		};
-}
-
-function elementMatcher( matchers ) {
-	return matchers.length > 1 ?
-		function( elem, context, xml ) {
-			var i = matchers.length;
-			while ( i-- ) {
-				if ( !matchers[ i ]( elem, context, xml ) ) {
-					return false;
-				}
-			}
-			return true;
-		} :
-		matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
-	var i = 0,
-		len = contexts.length;
-	for ( ; i < len; i++ ) {
-		Sizzle( selector, contexts[ i ], results );
-	}
-	return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
-	var elem,
-		newUnmatched = [],
-		i = 0,
-		len = unmatched.length,
-		mapped = map != null;
-
-	for ( ; i < len; i++ ) {
-		if ( ( elem = unmatched[ i ] ) ) {
-			if ( !filter || filter( elem, context, xml ) ) {
-				newUnmatched.push( elem );
-				if ( mapped ) {
-					map.push( i );
-				}
-			}
-		}
-	}
-
-	return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
-	if ( postFilter && !postFilter[ expando ] ) {
-		postFilter = setMatcher( postFilter );
-	}
-	if ( postFinder && !postFinder[ expando ] ) {
-		postFinder = setMatcher( postFinder, postSelector );
-	}
-	return markFunction( function( seed, results, context, xml ) {
-		var temp, i, elem,
-			preMap = [],
-			postMap = [],
-			preexisting = results.length,
-
-			// Get initial elements from seed or context
-			elems = seed || multipleContexts(
-				selector || "*",
-				context.nodeType ? [ context ] : context,
-				[]
-			),
-
-			// Prefilter to get matcher input, preserving a map for seed-results synchronization
-			matcherIn = preFilter && ( seed || !selector ) ?
-				condense( elems, preMap, preFilter, context, xml ) :
-				elems,
-
-			matcherOut = matcher ?
-
-				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
-				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
-					// ...intermediate processing is necessary
-					[] :
-
-					// ...otherwise use results directly
-					results :
-				matcherIn;
-
-		// Find primary matches
-		if ( matcher ) {
-			matcher( matcherIn, matcherOut, context, xml );
-		}
-
-		// Apply postFilter
-		if ( postFilter ) {
-			temp = condense( matcherOut, postMap );
-			postFilter( temp, [], context, xml );
-
-			// Un-match failing elements by moving them back to matcherIn
-			i = temp.length;
-			while ( i-- ) {
-				if ( ( elem = temp[ i ] ) ) {
-					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
-				}
-			}
-		}
-
-		if ( seed ) {
-			if ( postFinder || preFilter ) {
-				if ( postFinder ) {
-
-					// Get the final matcherOut by condensing this intermediate into postFinder contexts
-					temp = [];
-					i = matcherOut.length;
-					while ( i-- ) {
-						if ( ( elem = matcherOut[ i ] ) ) {
-
-							// Restore matcherIn since elem is not yet a final match
-							temp.push( ( matcherIn[ i ] = elem ) );
-						}
-					}
-					postFinder( null, ( matcherOut = [] ), temp, xml );
-				}
-
-				// Move matched elements from seed to results to keep them synchronized
-				i = matcherOut.length;
-				while ( i-- ) {
-					if ( ( elem = matcherOut[ i ] ) &&
-						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
-						seed[ temp ] = !( results[ temp ] = elem );
-					}
-				}
-			}
-
-		// Add elements to results, through postFinder if defined
-		} else {
-			matcherOut = condense(
-				matcherOut === results ?
-					matcherOut.splice( preexisting, matcherOut.length ) :
-					matcherOut
-			);
-			if ( postFinder ) {
-				postFinder( null, results, matcherOut, xml );
-			} else {
-				push.apply( results, matcherOut );
-			}
-		}
-	} );
-}
-
-function matcherFromTokens( tokens ) {
-	var checkContext, matcher, j,
-		len = tokens.length,
-		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
-		implicitRelative = leadingRelative || Expr.relative[ " " ],
-		i = leadingRelative ? 1 : 0,
-
-		// The foundational matcher ensures that elements are reachable from top-level context(s)
-		matchContext = addCombinator( function( elem ) {
-			return elem === checkContext;
-		}, implicitRelative, true ),
-		matchAnyContext = addCombinator( function( elem ) {
-			return indexOf( checkContext, elem ) > -1;
-		}, implicitRelative, true ),
-		matchers = [ function( elem, context, xml ) {
-			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
-				( checkContext = context ).nodeType ?
-					matchContext( elem, context, xml ) :
-					matchAnyContext( elem, context, xml ) );
-
-			// Avoid hanging onto element (issue #299)
-			checkContext = null;
-			return ret;
-		} ];
-
-	for ( ; i < len; i++ ) {
-		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
-			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
-		} else {
-			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
-			// Return special upon seeing a positional matcher
-			if ( matcher[ expando ] ) {
-
-				// Find the next relative operator (if any) for proper handling
-				j = ++i;
-				for ( ; j < len; j++ ) {
-					if ( Expr.relative[ tokens[ j ].type ] ) {
-						break;
-					}
-				}
-				return setMatcher(
-					i > 1 && elementMatcher( matchers ),
-					i > 1 && toSelector(
-
-					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
-					tokens
-						.slice( 0, i - 1 )
-						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
-					).replace( rtrim, "$1" ),
-					matcher,
-					i < j && matcherFromTokens( tokens.slice( i, j ) ),
-					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
-					j < len && toSelector( tokens )
-				);
-			}
-			matchers.push( matcher );
-		}
-	}
-
-	return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
-	var bySet = setMatchers.length > 0,
-		byElement = elementMatchers.length > 0,
-		superMatcher = function( seed, context, xml, results, outermost ) {
-			var elem, j, matcher,
-				matchedCount = 0,
-				i = "0",
-				unmatched = seed && [],
-				setMatched = [],
-				contextBackup = outermostContext,
-
-				// We must always have either seed elements or outermost context
-				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
-				// Use integer dirruns iff this is the outermost matcher
-				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
-				len = elems.length;
-
-			if ( outermost ) {
-
-				// Support: IE 11+, Edge 17 - 18+
-				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-				// two documents; shallow comparisons work.
-				// eslint-disable-next-line eqeqeq
-				outermostContext = context == document || context || outermost;
-			}
-
-			// Add elements passing elementMatchers directly to results
-			// Support: IE<9, Safari
-			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
-			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
-				if ( byElement && elem ) {
-					j = 0;
-
-					// Support: IE 11+, Edge 17 - 18+
-					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
-					// two documents; shallow comparisons work.
-					// eslint-disable-next-line eqeqeq
-					if ( !context && elem.ownerDocument != document ) {
-						setDocument( elem );
-						xml = !documentIsHTML;
-					}
-					while ( ( matcher = elementMatchers[ j++ ] ) ) {
-						if ( matcher( elem, context || document, xml ) ) {
-							results.push( elem );
-							break;
-						}
-					}
-					if ( outermost ) {
-						dirruns = dirrunsUnique;
-					}
-				}
-
-				// Track unmatched elements for set filters
-				if ( bySet ) {
-
-					// They will have gone through all possible matchers
-					if ( ( elem = !matcher && elem ) ) {
-						matchedCount--;
-					}
-
-					// Lengthen the array for every element, matched or not
-					if ( seed ) {
-						unmatched.push( elem );
-					}
-				}
-			}
-
-			// `i` is now the count of elements visited above, and adding it to `matchedCount`
-			// makes the latter nonnegative.
-			matchedCount += i;
-
-			// Apply set filters to unmatched elements
-			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
-			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
-			// no element matchers and no seed.
-			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
-			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
-			// numerically zero.
-			if ( bySet && i !== matchedCount ) {
-				j = 0;
-				while ( ( matcher = setMatchers[ j++ ] ) ) {
-					matcher( unmatched, setMatched, context, xml );
-				}
-
-				if ( seed ) {
-
-					// Reintegrate element matches to eliminate the need for sorting
-					if ( matchedCount > 0 ) {
-						while ( i-- ) {
-							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
-								setMatched[ i ] = pop.call( results );
-							}
-						}
-					}
-
-					// Discard index placeholder values to get only actual matches
-					setMatched = condense( setMatched );
-				}
-
-				// Add matches to results
-				push.apply( results, setMatched );
-
-				// Seedless set matches succeeding multiple successful matchers stipulate sorting
-				if ( outermost && !seed && setMatched.length > 0 &&
-					( matchedCount + setMatchers.length ) > 1 ) {
-
-					Sizzle.uniqueSort( results );
-				}
-			}
-
-			// Override manipulation of globals by nested matchers
-			if ( outermost ) {
-				dirruns = dirrunsUnique;
-				outermostContext = contextBackup;
-			}
-
-			return unmatched;
-		};
-
-	return bySet ?
-		markFunction( superMatcher ) :
-		superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
-	var i,
-		setMatchers = [],
-		elementMatchers = [],
-		cached = compilerCache[ selector + " " ];
-
-	if ( !cached ) {
-
-		// Generate a function of recursive functions that can be used to check each element
-		if ( !match ) {
-			match = tokenize( selector );
-		}
-		i = match.length;
-		while ( i-- ) {
-			cached = matcherFromTokens( match[ i ] );
-			if ( cached[ expando ] ) {
-				setMatchers.push( cached );
-			} else {
-				elementMatchers.push( cached );
-			}
-		}
-
-		// Cache the compiled function
-		cached = compilerCache(
-			selector,
-			matcherFromGroupMatchers( elementMatchers, setMatchers )
-		);
-
-		// Save selector and tokenization
-		cached.selector = selector;
-	}
-	return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- *  selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- *  selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
-	var i, tokens, token, type, find,
-		compiled = typeof selector === "function" && selector,
-		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
-	results = results || [];
-
-	// Try to minimize operations if there is only one selector in the list and no seed
-	// (the latter of which guarantees us context)
-	if ( match.length === 1 ) {
-
-		// Reduce context if the leading compound selector is an ID
-		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
-		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
-			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
-			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
-				.replace( runescape, funescape ), context ) || [] )[ 0 ];
-			if ( !context ) {
-				return results;
-
-			// Precompiled matchers will still verify ancestry, so step up a level
-			} else if ( compiled ) {
-				context = context.parentNode;
-			}
-
-			selector = selector.slice( tokens.shift().value.length );
-		}
-
-		// Fetch a seed set for right-to-left matching
-		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
-		while ( i-- ) {
-			token = tokens[ i ];
-
-			// Abort if we hit a combinator
-			if ( Expr.relative[ ( type = token.type ) ] ) {
-				break;
-			}
-			if ( ( find = Expr.find[ type ] ) ) {
-
-				// Search, expanding context for leading sibling combinators
-				if ( ( seed = find(
-					token.matches[ 0 ].replace( runescape, funescape ),
-					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
-						context
-				) ) ) {
-
-					// If seed is empty or no tokens remain, we can return early
-					tokens.splice( i, 1 );
-					selector = seed.length && toSelector( tokens );
-					if ( !selector ) {
-						push.apply( results, seed );
-						return results;
-					}
-
-					break;
-				}
-			}
-		}
-	}
-
-	// Compile and execute a filtering function if one is not provided
-	// Provide `match` to avoid retokenization if we modified the selector above
-	( compiled || compile( selector, match ) )(
-		seed,
-		context,
-		!documentIsHTML,
-		results,
-		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
-	);
-	return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
-	// Should return 1, but returns 4 (following)
-	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
-	el.innerHTML = "<a href='#'></a>";
-	return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
-	addHandle( "type|href|height|width", function( elem, name, isXML ) {
-		if ( !isXML ) {
-			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
-		}
-	} );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
-	el.innerHTML = "<input/>";
-	el.firstChild.setAttribute( "value", "" );
-	return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
-	addHandle( "value", function( elem, _name, isXML ) {
-		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
-			return elem.defaultValue;
-		}
-	} );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
-	return el.getAttribute( "disabled" ) == null;
-} ) ) {
-	addHandle( booleans, function( elem, name, isXML ) {
-		var val;
-		if ( !isXML ) {
-			return elem[ name ] === true ? name.toLowerCase() :
-				( val = elem.getAttributeNode( name ) ) && val.specified ?
-					val.value :
-					null;
-		}
-	} );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
-	var matched = [],
-		truncate = until !== undefined;
-
-	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
-		if ( elem.nodeType === 1 ) {
-			if ( truncate && jQuery( elem ).is( until ) ) {
-				break;
-			}
-			matched.push( elem );
-		}
-	}
-	return matched;
-};
-
-
-var siblings = function( n, elem ) {
-	var matched = [];
-
-	for ( ; n; n = n.nextSibling ) {
-		if ( n.nodeType === 1 && n !== elem ) {
-			matched.push( n );
-		}
-	}
-
-	return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
-	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-}
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
-	if ( isFunction( qualifier ) ) {
-		return jQuery.grep( elements, function( elem, i ) {
-			return !!qualifier.call( elem, i, elem ) !== not;
-		} );
-	}
-
-	// Single element
-	if ( qualifier.nodeType ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( elem === qualifier ) !== not;
-		} );
-	}
-
-	// Arraylike of elements (jQuery, arguments, Array)
-	if ( typeof qualifier !== "string" ) {
-		return jQuery.grep( elements, function( elem ) {
-			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
-		} );
-	}
-
-	// Filtered directly for both simple and complex selectors
-	return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
-	var elem = elems[ 0 ];
-
-	if ( not ) {
-		expr = ":not(" + expr + ")";
-	}
-
-	if ( elems.length === 1 && elem.nodeType === 1 ) {
-		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
-	}
-
-	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
-		return elem.nodeType === 1;
-	} ) );
-};
-
-jQuery.fn.extend( {
-	find: function( selector ) {
-		var i, ret,
-			len = this.length,
-			self = this;
-
-		if ( typeof selector !== "string" ) {
-			return this.pushStack( jQuery( selector ).filter( function() {
-				for ( i = 0; i < len; i++ ) {
-					if ( jQuery.contains( self[ i ], this ) ) {
-						return true;
-					}
-				}
-			} ) );
-		}
-
-		ret = this.pushStack( [] );
-
-		for ( i = 0; i < len; i++ ) {
-			jQuery.find( selector, self[ i ], ret );
-		}
-
-		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
-	},
-	filter: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], false ) );
-	},
-	not: function( selector ) {
-		return this.pushStack( winnow( this, selector || [], true ) );
-	},
-	is: function( selector ) {
-		return !!winnow(
-			this,
-
-			// If this is a positional/relative selector, check membership in the returned set
-			// so $("p:first").is("p:last") won't return true for a doc with two "p".
-			typeof selector === "string" && rneedsContext.test( selector ) ?
-				jQuery( selector ) :
-				selector || [],
-			false
-		).length;
-	}
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
-	// A simple way to check for HTML strings
-	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
-	// Strict HTML recognition (#11290: must start with <)
-	// Shortcut simple #id case for speed
-	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
-	init = jQuery.fn.init = function( selector, context, root ) {
-		var match, elem;
-
-		// HANDLE: $(""), $(null), $(undefined), $(false)
-		if ( !selector ) {
-			return this;
-		}
-
-		// Method init() accepts an alternate rootjQuery
-		// so migrate can support jQuery.sub (gh-2101)
-		root = root || rootjQuery;
-
-		// Handle HTML strings
-		if ( typeof selector === "string" ) {
-			if ( selector[ 0 ] === "<" &&
-				selector[ selector.length - 1 ] === ">" &&
-				selector.length >= 3 ) {
-
-				// Assume that strings that start and end with <> are HTML and skip the regex check
-				match = [ null, selector, null ];
-
-			} else {
-				match = rquickExpr.exec( selector );
-			}
-
-			// Match html or make sure no context is specified for #id
-			if ( match && ( match[ 1 ] || !context ) ) {
-
-				// HANDLE: $(html) -> $(array)
-				if ( match[ 1 ] ) {
-					context = context instanceof jQuery ? context[ 0 ] : context;
-
-					// Option to run scripts is true for back-compat
-					// Intentionally let the error be thrown if parseHTML is not present
-					jQuery.merge( this, jQuery.parseHTML(
-						match[ 1 ],
-						context && context.nodeType ? context.ownerDocument || context : document,
-						true
-					) );
-
-					// HANDLE: $(html, props)
-					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
-						for ( match in context ) {
-
-							// Properties of context are called as methods if possible
-							if ( isFunction( this[ match ] ) ) {
-								this[ match ]( context[ match ] );
-
-							// ...and otherwise set as attributes
-							} else {
-								this.attr( match, context[ match ] );
-							}
-						}
-					}
-
-					return this;
-
-				// HANDLE: $(#id)
-				} else {
-					elem = document.getElementById( match[ 2 ] );
-
-					if ( elem ) {
-
-						// Inject the element directly into the jQuery object
-						this[ 0 ] = elem;
-						this.length = 1;
-					}
-					return this;
-				}
-
-			// HANDLE: $(expr, $(...))
-			} else if ( !context || context.jquery ) {
-				return ( context || root ).find( selector );
-
-			// HANDLE: $(expr, context)
-			// (which is just equivalent to: $(context).find(expr)
-			} else {
-				return this.constructor( context ).find( selector );
-			}
-
-		// HANDLE: $(DOMElement)
-		} else if ( selector.nodeType ) {
-			this[ 0 ] = selector;
-			this.length = 1;
-			return this;
-
-		// HANDLE: $(function)
-		// Shortcut for document ready
-		} else if ( isFunction( selector ) ) {
-			return root.ready !== undefined ?
-				root.ready( selector ) :
-
-				// Execute immediately if ready is not present
-				selector( jQuery );
-		}
-
-		return jQuery.makeArray( selector, this );
-	};
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
-	// Methods guaranteed to produce a unique set when starting from a unique set
-	guaranteedUnique = {
-		children: true,
-		contents: true,
-		next: true,
-		prev: true
-	};
-
-jQuery.fn.extend( {
-	has: function( target ) {
-		var targets = jQuery( target, this ),
-			l = targets.length;
-
-		return this.filter( function() {
-			var i = 0;
-			for ( ; i < l; i++ ) {
-				if ( jQuery.contains( this, targets[ i ] ) ) {
-					return true;
-				}
-			}
-		} );
-	},
-
-	closest: function( selectors, context ) {
-		var cur,
-			i = 0,
-			l = this.length,
-			matched = [],
-			targets = typeof selectors !== "string" && jQuery( selectors );
-
-		// Positional selectors never match, since there's no _selection_ context
-		if ( !rneedsContext.test( selectors ) ) {
-			for ( ; i < l; i++ ) {
-				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
-					// Always skip document fragments
-					if ( cur.nodeType < 11 && ( targets ?
-						targets.index( cur ) > -1 :
-
-						// Don't pass non-elements to Sizzle
-						cur.nodeType === 1 &&
-							jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
-						matched.push( cur );
-						break;
-					}
-				}
-			}
-		}
-
-		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
-	},
-
-	// Determine the position of an element within the set
-	index: function( elem ) {
-
-		// No argument, return index in parent
-		if ( !elem ) {
-			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
-		}
-
-		// Index in selector
-		if ( typeof elem === "string" ) {
-			return indexOf.call( jQuery( elem ), this[ 0 ] );
-		}
-
-		// Locate the position of the desired element
-		return indexOf.call( this,
-
-			// If it receives a jQuery object, the first element is used
-			elem.jquery ? elem[ 0 ] : elem
-		);
-	},
-
-	add: function( selector, context ) {
-		return this.pushStack(
-			jQuery.uniqueSort(
-				jQuery.merge( this.get(), jQuery( selector, context ) )
-			)
-		);
-	},
-
-	addBack: function( selector ) {
-		return this.add( selector == null ?
-			this.prevObject : this.prevObject.filter( selector )
-		);
-	}
-} );
-
-function sibling( cur, dir ) {
-	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
-	return cur;
-}
-
-jQuery.each( {
-	parent: function( elem ) {
-		var parent = elem.parentNode;
-		return parent && parent.nodeType !== 11 ? parent : null;
-	},
-	parents: function( elem ) {
-		return dir( elem, "parentNode" );
-	},
-	parentsUntil: function( elem, _i, until ) {
-		return dir( elem, "parentNode", until );
-	},
-	next: function( elem ) {
-		return sibling( elem, "nextSibling" );
-	},
-	prev: function( elem ) {
-		return sibling( elem, "previousSibling" );
-	},
-	nextAll: function( elem ) {
-		return dir( elem, "nextSibling" );
-	},
-	prevAll: function( elem ) {
-		return dir( elem, "previousSibling" );
-	},
-	nextUntil: function( elem, _i, until ) {
-		return dir( elem, "nextSibling", until );
-	},
-	prevUntil: function( elem, _i, until ) {
-		return dir( elem, "previousSibling", until );
-	},
-	siblings: function( elem ) {
-		return siblings( ( elem.parentNode || {} ).firstChild, elem );
-	},
-	children: function( elem ) {
-		return siblings( elem.firstChild );
-	},
-	contents: function( elem ) {
-		if ( elem.contentDocument != null &&
-
-			// Support: IE 11+
-			// <object> elements with no `data` attribute has an object
-			// `contentDocument` with a `null` prototype.
-			getProto( elem.contentDocument ) ) {
-
-			return elem.contentDocument;
-		}
-
-		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
-		// Treat the template element as a regular one in browsers that
-		// don't support it.
-		if ( nodeName( elem, "template" ) ) {
-			elem = elem.content || elem;
-		}
-
-		return jQuery.merge( [], elem.childNodes );
-	}
-}, function( name, fn ) {
-	jQuery.fn[ name ] = function( until, selector ) {
-		var matched = jQuery.map( this, fn, until );
-
-		if ( name.slice( -5 ) !== "Until" ) {
-			selector = until;
-		}
-
-		if ( selector && typeof selector === "string" ) {
-			matched = jQuery.filter( selector, matched );
-		}
-
-		if ( this.length > 1 ) {
-
-			// Remove duplicates
-			if ( !guaranteedUnique[ name ] ) {
-				jQuery.uniqueSort( matched );
-			}
-
-			// Reverse order for parents* and prev-derivatives
-			if ( rparentsprev.test( name ) ) {
-				matched.reverse();
-			}
-		}
-
-		return this.pushStack( matched );
-	};
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
-	var object = {};
-	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
-		object[ flag ] = true;
-	} );
-	return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- *	options: an optional list of space-separated options that will change how
- *			the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- *	once:			will ensure the callback list can only be fired once (like a Deferred)
- *
- *	memory:			will keep track of previous values and will call any callback added
- *					after the list has been fired right away with the latest "memorized"
- *					values (like a Deferred)
- *
- *	unique:			will ensure a callback can only be added once (no duplicate in the list)
- *
- *	stopOnFalse:	interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
-	// Convert options from String-formatted to Object-formatted if needed
-	// (we check in cache first)
-	options = typeof options === "string" ?
-		createOptions( options ) :
-		jQuery.extend( {}, options );
-
-	var // Flag to know if list is currently firing
-		firing,
-
-		// Last fire value for non-forgettable lists
-		memory,
-
-		// Flag to know if list was already fired
-		fired,
-
-		// Flag to prevent firing
-		locked,
-
-		// Actual callback list
-		list = [],
-
-		// Queue of execution data for repeatable lists
-		queue = [],
-
-		// Index of currently firing callback (modified by add/remove as needed)
-		firingIndex = -1,
-
-		// Fire callbacks
-		fire = function() {
-
-			// Enforce single-firing
-			locked = locked || options.once;
-
-			// Execute callbacks for all pending executions,
-			// respecting firingIndex overrides and runtime changes
-			fired = firing = true;
-			for ( ; queue.length; firingIndex = -1 ) {
-				memory = queue.shift();
-				while ( ++firingIndex < list.length ) {
-
-					// Run callback and check for early termination
-					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
-						options.stopOnFalse ) {
-
-						// Jump to end and forget the data so .add doesn't re-fire
-						firingIndex = list.length;
-						memory = false;
-					}
-				}
-			}
-
-			// Forget the data if we're done with it
-			if ( !options.memory ) {
-				memory = false;
-			}
-
-			firing = false;
-
-			// Clean up if we're done firing for good
-			if ( locked ) {
-
-				// Keep an empty list if we have data for future add calls
-				if ( memory ) {
-					list = [];
-
-				// Otherwise, this object is spent
-				} else {
-					list = "";
-				}
-			}
-		},
-
-		// Actual Callbacks object
-		self = {
-
-			// Add a callback or a collection of callbacks to the list
-			add: function() {
-				if ( list ) {
-
-					// If we have memory from a past run, we should fire after adding
-					if ( memory && !firing ) {
-						firingIndex = list.length - 1;
-						queue.push( memory );
-					}
-
-					( function add( args ) {
-						jQuery.each( args, function( _, arg ) {
-							if ( isFunction( arg ) ) {
-								if ( !options.unique || !self.has( arg ) ) {
-									list.push( arg );
-								}
-							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
-								// Inspect recursively
-								add( arg );
-							}
-						} );
-					} )( arguments );
-
-					if ( memory && !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Remove a callback from the list
-			remove: function() {
-				jQuery.each( arguments, function( _, arg ) {
-					var index;
-					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
-						list.splice( index, 1 );
-
-						// Handle firing indexes
-						if ( index <= firingIndex ) {
-							firingIndex--;
-						}
-					}
-				} );
-				return this;
-			},
-
-			// Check if a given callback is in the list.
-			// If no argument is given, return whether or not list has callbacks attached.
-			has: function( fn ) {
-				return fn ?
-					jQuery.inArray( fn, list ) > -1 :
-					list.length > 0;
-			},
-
-			// Remove all callbacks from the list
-			empty: function() {
-				if ( list ) {
-					list = [];
-				}
-				return this;
-			},
-
-			// Disable .fire and .add
-			// Abort any current/pending executions
-			// Clear all callbacks and values
-			disable: function() {
-				locked = queue = [];
-				list = memory = "";
-				return this;
-			},
-			disabled: function() {
-				return !list;
-			},
-
-			// Disable .fire
-			// Also disable .add unless we have memory (since it would have no effect)
-			// Abort any pending executions
-			lock: function() {
-				locked = queue = [];
-				if ( !memory && !firing ) {
-					list = memory = "";
-				}
-				return this;
-			},
-			locked: function() {
-				return !!locked;
-			},
-
-			// Call all callbacks with the given context and arguments
-			fireWith: function( context, args ) {
-				if ( !locked ) {
-					args = args || [];
-					args = [ context, args.slice ? args.slice() : args ];
-					queue.push( args );
-					if ( !firing ) {
-						fire();
-					}
-				}
-				return this;
-			},
-
-			// Call all the callbacks with the given arguments
-			fire: function() {
-				self.fireWith( this, arguments );
-				return this;
-			},
-
-			// To know if the callbacks have already been called at least once
-			fired: function() {
-				return !!fired;
-			}
-		};
-
-	return self;
-};
-
-
-function Identity( v ) {
-	return v;
-}
-function Thrower( ex ) {
-	throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
-	var method;
-
-	try {
-
-		// Check for promise aspect first to privilege synchronous behavior
-		if ( value && isFunction( ( method = value.promise ) ) ) {
-			method.call( value ).done( resolve ).fail( reject );
-
-		// Other thenables
-		} else if ( value && isFunction( ( method = value.then ) ) ) {
-			method.call( value, resolve, reject );
-
-		// Other non-thenables
-		} else {
-
-			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
-			// * false: [ value ].slice( 0 ) => resolve( value )
-			// * true: [ value ].slice( 1 ) => resolve()
-			resolve.apply( undefined, [ value ].slice( noValue ) );
-		}
-
-	// For Promises/A+, convert exceptions into rejections
-	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
-	// Deferred#then to conditionally suppress rejection.
-	} catch ( value ) {
-
-		// Support: Android 4.0 only
-		// Strict mode functions invoked without .call/.apply get global-object context
-		reject.apply( undefined, [ value ] );
-	}
-}
-
-jQuery.extend( {
-
-	Deferred: function( func ) {
-		var tuples = [
-
-				// action, add listener, callbacks,
-				// ... .then handlers, argument index, [final state]
-				[ "notify", "progress", jQuery.Callbacks( "memory" ),
-					jQuery.Callbacks( "memory" ), 2 ],
-				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
-				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
-					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
-			],
-			state = "pending",
-			promise = {
-				state: function() {
-					return state;
-				},
-				always: function() {
-					deferred.done( arguments ).fail( arguments );
-					return this;
-				},
-				"catch": function( fn ) {
-					return promise.then( null, fn );
-				},
-
-				// Keep pipe for back-compat
-				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
-					var fns = arguments;
-
-					return jQuery.Deferred( function( newDefer ) {
-						jQuery.each( tuples, function( _i, tuple ) {
-
-							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
-							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
-							// deferred.progress(function() { bind to newDefer or newDefer.notify })
-							// deferred.done(function() { bind to newDefer or newDefer.resolve })
-							// deferred.fail(function() { bind to newDefer or newDefer.reject })
-							deferred[ tuple[ 1 ] ]( function() {
-								var returned = fn && fn.apply( this, arguments );
-								if ( returned && isFunction( returned.promise ) ) {
-									returned.promise()
-										.progress( newDefer.notify )
-										.done( newDefer.resolve )
-										.fail( newDefer.reject );
-								} else {
-									newDefer[ tuple[ 0 ] + "With" ](
-										this,
-										fn ? [ returned ] : arguments
-									);
-								}
-							} );
-						} );
-						fns = null;
-					} ).promise();
-				},
-				then: function( onFulfilled, onRejected, onProgress ) {
-					var maxDepth = 0;
-					function resolve( depth, deferred, handler, special ) {
-						return function() {
-							var that = this,
-								args = arguments,
-								mightThrow = function() {
-									var returned, then;
-
-									// Support: Promises/A+ section 2.3.3.3.3
-									// https://promisesaplus.com/#point-59
-									// Ignore double-resolution attempts
-									if ( depth < maxDepth ) {
-										return;
-									}
-
-									returned = handler.apply( that, args );
-
-									// Support: Promises/A+ section 2.3.1
-									// https://promisesaplus.com/#point-48
-									if ( returned === deferred.promise() ) {
-										throw new TypeError( "Thenable self-resolution" );
-									}
-
-									// Support: Promises/A+ sections 2.3.3.1, 3.5
-									// https://promisesaplus.com/#point-54
-									// https://promisesaplus.com/#point-75
-									// Retrieve `then` only once
-									then = returned &&
-
-										// Support: Promises/A+ section 2.3.4
-										// https://promisesaplus.com/#point-64
-										// Only check objects and functions for thenability
-										( typeof returned === "object" ||
-											typeof returned === "function" ) &&
-										returned.then;
-
-									// Handle a returned thenable
-									if ( isFunction( then ) ) {
-
-										// Special processors (notify) just wait for resolution
-										if ( special ) {
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special )
-											);
-
-										// Normal processors (resolve) also hook into progress
-										} else {
-
-											// ...and disregard older resolution values
-											maxDepth++;
-
-											then.call(
-												returned,
-												resolve( maxDepth, deferred, Identity, special ),
-												resolve( maxDepth, deferred, Thrower, special ),
-												resolve( maxDepth, deferred, Identity,
-													deferred.notifyWith )
-											);
-										}
-
-									// Handle all other returned values
-									} else {
-
-										// Only substitute handlers pass on context
-										// and multiple values (non-spec behavior)
-										if ( handler !== Identity ) {
-											that = undefined;
-											args = [ returned ];
-										}
-
-										// Process the value(s)
-										// Default process is resolve
-										( special || deferred.resolveWith )( that, args );
-									}
-								},
-
-								// Only normal processors (resolve) catch and reject exceptions
-								process = special ?
-									mightThrow :
-									function() {
-										try {
-											mightThrow();
-										} catch ( e ) {
-
-											if ( jQuery.Deferred.exceptionHook ) {
-												jQuery.Deferred.exceptionHook( e,
-													process.stackTrace );
-											}
-
-											// Support: Promises/A+ section 2.3.3.3.4.1
-											// https://promisesaplus.com/#point-61
-											// Ignore post-resolution exceptions
-											if ( depth + 1 >= maxDepth ) {
-
-												// Only substitute handlers pass on context
-												// and multiple values (non-spec behavior)
-												if ( handler !== Thrower ) {
-													that = undefined;
-													args = [ e ];
-												}
-
-												deferred.rejectWith( that, args );
-											}
-										}
-									};
-
-							// Support: Promises/A+ section 2.3.3.3.1
-							// https://promisesaplus.com/#point-57
-							// Re-resolve promises immediately to dodge false rejection from
-							// subsequent errors
-							if ( depth ) {
-								process();
-							} else {
-
-								// Call an optional hook to record the stack, in case of exception
-								// since it's otherwise lost when execution goes async
-								if ( jQuery.Deferred.getStackHook ) {
-									process.stackTrace = jQuery.Deferred.getStackHook();
-								}
-								window.setTimeout( process );
-							}
-						};
-					}
-
-					return jQuery.Deferred( function( newDefer ) {
-
-						// progress_handlers.add( ... )
-						tuples[ 0 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onProgress ) ?
-									onProgress :
-									Identity,
-								newDefer.notifyWith
-							)
-						);
-
-						// fulfilled_handlers.add( ... )
-						tuples[ 1 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onFulfilled ) ?
-									onFulfilled :
-									Identity
-							)
-						);
-
-						// rejected_handlers.add( ... )
-						tuples[ 2 ][ 3 ].add(
-							resolve(
-								0,
-								newDefer,
-								isFunction( onRejected ) ?
-									onRejected :
-									Thrower
-							)
-						);
-					} ).promise();
-				},
-
-				// Get a promise for this deferred
-				// If obj is provided, the promise aspect is added to the object
-				promise: function( obj ) {
-					return obj != null ? jQuery.extend( obj, promise ) : promise;
-				}
-			},
-			deferred = {};
-
-		// Add list-specific methods
-		jQuery.each( tuples, function( i, tuple ) {
-			var list = tuple[ 2 ],
-				stateString = tuple[ 5 ];
-
-			// promise.progress = list.add
-			// promise.done = list.add
-			// promise.fail = list.add
-			promise[ tuple[ 1 ] ] = list.add;
-
-			// Handle state
-			if ( stateString ) {
-				list.add(
-					function() {
-
-						// state = "resolved" (i.e., fulfilled)
-						// state = "rejected"
-						state = stateString;
-					},
-
-					// rejected_callbacks.disable
-					// fulfilled_callbacks.disable
-					tuples[ 3 - i ][ 2 ].disable,
-
-					// rejected_handlers.disable
-					// fulfilled_handlers.disable
-					tuples[ 3 - i ][ 3 ].disable,
-
-					// progress_callbacks.lock
-					tuples[ 0 ][ 2 ].lock,
-
-					// progress_handlers.lock
-					tuples[ 0 ][ 3 ].lock
-				);
-			}
-
-			// progress_handlers.fire
-			// fulfilled_handlers.fire
-			// rejected_handlers.fire
-			list.add( tuple[ 3 ].fire );
-
-			// deferred.notify = function() { deferred.notifyWith(...) }
-			// deferred.resolve = function() { deferred.resolveWith(...) }
-			// deferred.reject = function() { deferred.rejectWith(...) }
-			deferred[ tuple[ 0 ] ] = function() {
-				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
-				return this;
-			};
-
-			// deferred.notifyWith = list.fireWith
-			// deferred.resolveWith = list.fireWith
-			// deferred.rejectWith = list.fireWith
-			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
-		} );
-
-		// Make the deferred a promise
-		promise.promise( deferred );
-
-		// Call given func if any
-		if ( func ) {
-			func.call( deferred, deferred );
-		}
-
-		// All done!
-		return deferred;
-	},
-
-	// Deferred helper
-	when: function( singleValue ) {
-		var
-
-			// count of uncompleted subordinates
-			remaining = arguments.length,
-
-			// count of unprocessed arguments
-			i = remaining,
-
-			// subordinate fulfillment data
-			resolveContexts = Array( i ),
-			resolveValues = slice.call( arguments ),
-
-			// the primary Deferred
-			primary = jQuery.Deferred(),
-
-			// subordinate callback factory
-			updateFunc = function( i ) {
-				return function( value ) {
-					resolveContexts[ i ] = this;
-					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
-					if ( !( --remaining ) ) {
-						primary.resolveWith( resolveContexts, resolveValues );
-					}
-				};
-			};
-
-		// Single- and empty arguments are adopted like Promise.resolve
-		if ( remaining <= 1 ) {
-			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
-				!remaining );
-
-			// Use .then() to unwrap secondary thenables (cf. gh-3000)
-			if ( primary.state() === "pending" ||
-				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
-				return primary.then();
-			}
-		}
-
-		// Multiple arguments are aggregated like Promise.all array elements
-		while ( i-- ) {
-			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
-		}
-
-		return primary.promise();
-	}
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
-	// Support: IE 8 - 9 only
-	// Console exists when dev tools are open, which can happen at any time
-	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
-		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
-	}
-};
-
-
-
-
-jQuery.readyException = function( error ) {
-	window.setTimeout( function() {
-		throw error;
-	} );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
-	readyList
-		.then( fn )
-
-		// Wrap jQuery.readyException in a function so that the lookup
-		// happens at the time of error handling instead of callback
-		// registration.
-		.catch( function( error ) {
-			jQuery.readyException( error );
-		} );
-
-	return this;
-};
-
-jQuery.extend( {
-
-	// Is the DOM ready to be used? Set to true once it occurs.
-	isReady: false,
-
-	// A counter to track how many items to wait for before
-	// the ready event fires. See #6781
-	readyWait: 1,
-
-	// Handle when the DOM is ready
-	ready: function( wait ) {
-
-		// Abort if there are pending holds or we're already ready
-		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
-			return;
-		}
-
-		// Remember that the DOM is ready
-		jQuery.isReady = true;
-
-		// If a normal DOM Ready event fired, decrement, and wait if need be
-		if ( wait !== true && --jQuery.readyWait > 0 ) {
-			return;
-		}
-
-		// If there are functions bound, to execute
-		readyList.resolveWith( document, [ jQuery ] );
-	}
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
-	document.removeEventListener( "DOMContentLoaded", completed );
-	window.removeEventListener( "load", completed );
-	jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
-	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
-	// Handle it asynchronously to allow scripts the opportunity to delay ready
-	window.setTimeout( jQuery.ready );
-
-} else {
-
-	// Use the handy event callback
-	document.addEventListener( "DOMContentLoaded", completed );
-
-	// A fallback to window.onload, that will always work
-	window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
-	var i = 0,
-		len = elems.length,
-		bulk = key == null;
-
-	// Sets many values
-	if ( toType( key ) === "object" ) {
-		chainable = true;
-		for ( i in key ) {
-			access( elems, fn, i, key[ i ], true, emptyGet, raw );
-		}
-
-	// Sets one value
-	} else if ( value !== undefined ) {
-		chainable = true;
-
-		if ( !isFunction( value ) ) {
-			raw = true;
-		}
-
-		if ( bulk ) {
-
-			// Bulk operations run against the entire set
-			if ( raw ) {
-				fn.call( elems, value );
-				fn = null;
-
-			// ...except when executing function values
-			} else {
-				bulk = fn;
-				fn = function( elem, _key, value ) {
-					return bulk.call( jQuery( elem ), value );
-				};
-			}
-		}
-
-		if ( fn ) {
-			for ( ; i < len; i++ ) {
-				fn(
-					elems[ i ], key, raw ?
-						value :
-						value.call( elems[ i ], i, fn( elems[ i ], key ) )
-				);
-			}
-		}
-	}
-
-	if ( chainable ) {
-		return elems;
-	}
-
-	// Gets
-	if ( bulk ) {
-		return fn.call( elems );
-	}
-
-	return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
-	rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
-	return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
-	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
-	// Accepts only:
-	//  - Node
-	//    - Node.ELEMENT_NODE
-	//    - Node.DOCUMENT_NODE
-	//  - Object
-	//    - Any
-	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
-	this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
-	cache: function( owner ) {
-
-		// Check if the owner object already has a cache
-		var value = owner[ this.expando ];
-
-		// If not, create one
-		if ( !value ) {
-			value = {};
-
-			// We can accept data for non-element nodes in modern browsers,
-			// but we should not, see #8335.
-			// Always return an empty object.
-			if ( acceptData( owner ) ) {
-
-				// If it is a node unlikely to be stringify-ed or looped over
-				// use plain assignment
-				if ( owner.nodeType ) {
-					owner[ this.expando ] = value;
-
-				// Otherwise secure it in a non-enumerable property
-				// configurable must be true to allow the property to be
-				// deleted when data is removed
-				} else {
-					Object.defineProperty( owner, this.expando, {
-						value: value,
-						configurable: true
-					} );
-				}
-			}
-		}
-
-		return value;
-	},
-	set: function( owner, data, value ) {
-		var prop,
-			cache = this.cache( owner );
-
-		// Handle: [ owner, key, value ] args
-		// Always use camelCase key (gh-2257)
-		if ( typeof data === "string" ) {
-			cache[ camelCase( data ) ] = value;
-
-		// Handle: [ owner, { properties } ] args
-		} else {
-
-			// Copy the properties one-by-one to the cache object
-			for ( prop in data ) {
-				cache[ camelCase( prop ) ] = data[ prop ];
-			}
-		}
-		return cache;
-	},
-	get: function( owner, key ) {
-		return key === undefined ?
-			this.cache( owner ) :
-
-			// Always use camelCase key (gh-2257)
-			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
-	},
-	access: function( owner, key, value ) {
-
-		// In cases where either:
-		//
-		//   1. No key was specified
-		//   2. A string key was specified, but no value provided
-		//
-		// Take the "read" path and allow the get method to determine
-		// which value to return, respectively either:
-		//
-		//   1. The entire cache object
-		//   2. The data stored at the key
-		//
-		if ( key === undefined ||
-				( ( key && typeof key === "string" ) && value === undefined ) ) {
-
-			return this.get( owner, key );
-		}
-
-		// When the key is not a string, or both a key and value
-		// are specified, set or extend (existing objects) with either:
-		//
-		//   1. An object of properties
-		//   2. A key and value
-		//
-		this.set( owner, key, value );
-
-		// Since the "set" path can have two possible entry points
-		// return the expected data based on which path was taken[*]
-		return value !== undefined ? value : key;
-	},
-	remove: function( owner, key ) {
-		var i,
-			cache = owner[ this.expando ];
-
-		if ( cache === undefined ) {
-			return;
-		}
-
-		if ( key !== undefined ) {
-
-			// Support array or space separated string of keys
-			if ( Array.isArray( key ) ) {
-
-				// If key is an array of keys...
-				// We always set camelCase keys, so remove that.
-				key = key.map( camelCase );
-			} else {
-				key = camelCase( key );
-
-				// If a key with the spaces exists, use it.
-				// Otherwise, create an array by matching non-whitespace
-				key = key in cache ?
-					[ key ] :
-					( key.match( rnothtmlwhite ) || [] );
-			}
-
-			i = key.length;
-
-			while ( i-- ) {
-				delete cache[ key[ i ] ];
-			}
-		}
-
-		// Remove the expando if there's no more data
-		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
-			// Support: Chrome <=35 - 45
-			// Webkit & Blink performance suffers when deleting properties
-			// from DOM nodes, so set to undefined instead
-			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
-			if ( owner.nodeType ) {
-				owner[ this.expando ] = undefined;
-			} else {
-				delete owner[ this.expando ];
-			}
-		}
-	},
-	hasData: function( owner ) {
-		var cache = owner[ this.expando ];
-		return cache !== undefined && !jQuery.isEmptyObject( cache );
-	}
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-//	Implementation Summary
-//
-//	1. Enforce API surface and semantic compatibility with 1.9.x branch
-//	2. Improve the module's maintainability by reducing the storage
-//		paths to a single mechanism.
-//	3. Use the same single mechanism to support "private" and "user" data.
-//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-//	5. Avoid exposing implementation details on user objects (eg. expando properties)
-//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
-	rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
-	if ( data === "true" ) {
-		return true;
-	}
-
-	if ( data === "false" ) {
-		return false;
-	}
-
-	if ( data === "null" ) {
-		return null;
-	}
-
-	// Only convert to a number if it doesn't change the string
-	if ( data === +data + "" ) {
-		return +data;
-	}
-
-	if ( rbrace.test( data ) ) {
-		return JSON.parse( data );
-	}
-
-	return data;
-}
-
-function dataAttr( elem, key, data ) {
-	var name;
-
-	// If nothing was found internally, try to fetch any
-	// data from the HTML5 data-* attribute
-	if ( data === undefined && elem.nodeType === 1 ) {
-		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
-		data = elem.getAttribute( name );
-
-		if ( typeof data === "string" ) {
-			try {
-				data = getData( data );
-			} catch ( e ) {}
-
-			// Make sure we set the data so it isn't changed later
-			dataUser.set( elem, key, data );
-		} else {
-			data = undefined;
-		}
-	}
-	return data;
-}
-
-jQuery.extend( {
-	hasData: function( elem ) {
-		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
-	},
-
-	data: function( elem, name, data ) {
-		return dataUser.access( elem, name, data );
-	},
-
-	removeData: function( elem, name ) {
-		dataUser.remove( elem, name );
-	},
-
-	// TODO: Now that all calls to _data and _removeData have been replaced
-	// with direct calls to dataPriv methods, these can be deprecated.
-	_data: function( elem, name, data ) {
-		return dataPriv.access( elem, name, data );
-	},
-
-	_removeData: function( elem, name ) {
-		dataPriv.remove( elem, name );
-	}
-} );
-
-jQuery.fn.extend( {
-	data: function( key, value ) {
-		var i, name, data,
-			elem = this[ 0 ],
-			attrs = elem && elem.attributes;
-
-		// Gets all values
-		if ( key === undefined ) {
-			if ( this.length ) {
-				data = dataUser.get( elem );
-
-				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
-					i = attrs.length;
-					while ( i-- ) {
-
-						// Support: IE 11 only
-						// The attrs elements can be null (#14894)
-						if ( attrs[ i ] ) {
-							name = attrs[ i ].name;
-							if ( name.indexOf( "data-" ) === 0 ) {
-								name = camelCase( name.slice( 5 ) );
-								dataAttr( elem, name, data[ name ] );
-							}
-						}
-					}
-					dataPriv.set( elem, "hasDataAttrs", true );
-				}
-			}
-
-			return data;
-		}
-
-		// Sets multiple values
-		if ( typeof key === "object" ) {
-			return this.each( function() {
-				dataUser.set( this, key );
-			} );
-		}
-
-		return access( this, function( value ) {
-			var data;
-
-			// The calling jQuery object (element matches) is not empty
-			// (and therefore has an element appears at this[ 0 ]) and the
-			// `value` parameter was not undefined. An empty jQuery object
-			// will result in `undefined` for elem = this[ 0 ] which will
-			// throw an exception if an attempt to read a data cache is made.
-			if ( elem && value === undefined ) {
-
-				// Attempt to get data from the cache
-				// The key will always be camelCased in Data
-				data = dataUser.get( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// Attempt to "discover" the data in
-				// HTML5 custom data-* attrs
-				data = dataAttr( elem, key );
-				if ( data !== undefined ) {
-					return data;
-				}
-
-				// We tried really hard, but the data doesn't exist.
-				return;
-			}
-
-			// Set the data...
-			this.each( function() {
-
-				// We always store the camelCased key
-				dataUser.set( this, key, value );
-			} );
-		}, null, value, arguments.length > 1, null, true );
-	},
-
-	removeData: function( key ) {
-		return this.each( function() {
-			dataUser.remove( this, key );
-		} );
-	}
-} );
-
-
-jQuery.extend( {
-	queue: function( elem, type, data ) {
-		var queue;
-
-		if ( elem ) {
-			type = ( type || "fx" ) + "queue";
-			queue = dataPriv.get( elem, type );
-
-			// Speed up dequeue by getting out quickly if this is just a lookup
-			if ( data ) {
-				if ( !queue || Array.isArray( data ) ) {
-					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
-				} else {
-					queue.push( data );
-				}
-			}
-			return queue || [];
-		}
-	},
-
-	dequeue: function( elem, type ) {
-		type = type || "fx";
-
-		var queue = jQuery.queue( elem, type ),
-			startLength = queue.length,
-			fn = queue.shift(),
-			hooks = jQuery._queueHooks( elem, type ),
-			next = function() {
-				jQuery.dequeue( elem, type );
-			};
-
-		// If the fx queue is dequeued, always remove the progress sentinel
-		if ( fn === "inprogress" ) {
-			fn = queue.shift();
-			startLength--;
-		}
-
-		if ( fn ) {
-
-			// Add a progress sentinel to prevent the fx queue from being
-			// automatically dequeued
-			if ( type === "fx" ) {
-				queue.unshift( "inprogress" );
-			}
-
-			// Clear up the last queue stop function
-			delete hooks.stop;
-			fn.call( elem, next, hooks );
-		}
-
-		if ( !startLength && hooks ) {
-			hooks.empty.fire();
-		}
-	},
-
-	// Not public - generate a queueHooks object, or return the current one
-	_queueHooks: function( elem, type ) {
-		var key = type + "queueHooks";
-		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
-			empty: jQuery.Callbacks( "once memory" ).add( function() {
-				dataPriv.remove( elem, [ type + "queue", key ] );
-			} )
-		} );
-	}
-} );
-
-jQuery.fn.extend( {
-	queue: function( type, data ) {
-		var setter = 2;
-
-		if ( typeof type !== "string" ) {
-			data = type;
-			type = "fx";
-			setter--;
-		}
-
-		if ( arguments.length < setter ) {
-			return jQuery.queue( this[ 0 ], type );
-		}
-
-		return data === undefined ?
-			this :
-			this.each( function() {
-				var queue = jQuery.queue( this, type, data );
-
-				// Ensure a hooks for this queue
-				jQuery._queueHooks( this, type );
-
-				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
-					jQuery.dequeue( this, type );
-				}
-			} );
-	},
-	dequeue: function( type ) {
-		return this.each( function() {
-			jQuery.dequeue( this, type );
-		} );
-	},
-	clearQueue: function( type ) {
-		return this.queue( type || "fx", [] );
-	},
-
-	// Get a promise resolved when queues of a certain type
-	// are emptied (fx is the type by default)
-	promise: function( type, obj ) {
-		var tmp,
-			count = 1,
-			defer = jQuery.Deferred(),
-			elements = this,
-			i = this.length,
-			resolve = function() {
-				if ( !( --count ) ) {
-					defer.resolveWith( elements, [ elements ] );
-				}
-			};
-
-		if ( typeof type !== "string" ) {
-			obj = type;
-			type = undefined;
-		}
-		type = type || "fx";
-
-		while ( i-- ) {
-			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
-			if ( tmp && tmp.empty ) {
-				count++;
-				tmp.empty.add( resolve );
-			}
-		}
-		resolve();
-		return defer.promise( obj );
-	}
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
-	var isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem );
-		},
-		composed = { composed: true };
-
-	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
-	// Check attachment across shadow DOM boundaries when possible (gh-3504)
-	// Support: iOS 10.0-10.2 only
-	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
-	// leading to errors. We need to check for `getRootNode`.
-	if ( documentElement.getRootNode ) {
-		isAttached = function( elem ) {
-			return jQuery.contains( elem.ownerDocument, elem ) ||
-				elem.getRootNode( composed ) === elem.ownerDocument;
-		};
-	}
-var isHiddenWithinTree = function( elem, el ) {
-
-		// isHiddenWithinTree might be called from jQuery#filter function;
-		// in that case, element will be second argument
-		elem = el || elem;
-
-		// Inline style trumps all
-		return elem.style.display === "none" ||
-			elem.style.display === "" &&
-
-			// Otherwise, check computed style
-			// Support: Firefox <=43 - 45
-			// Disconnected elements can have computed display: none, so first confirm that elem is
-			// in the document.
-			isAttached( elem ) &&
-
-			jQuery.css( elem, "display" ) === "none";
-	};
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
-	var adjusted, scale,
-		maxIterations = 20,
-		currentValue = tween ?
-			function() {
-				return tween.cur();
-			} :
-			function() {
-				return jQuery.css( elem, prop, "" );
-			},
-		initial = currentValue(),
-		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
-		// Starting value computation is required for potential unit mismatches
-		initialInUnit = elem.nodeType &&
-			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
-			rcssNum.exec( jQuery.css( elem, prop ) );
-
-	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
-		// Support: Firefox <=54
-		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
-		initial = initial / 2;
-
-		// Trust units reported by jQuery.css
-		unit = unit || initialInUnit[ 3 ];
-
-		// Iteratively approximate from a nonzero starting point
-		initialInUnit = +initial || 1;
-
-		while ( maxIterations-- ) {
-
-			// Evaluate and update our best guess (doubling guesses that zero out).
-			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
-			jQuery.style( elem, prop, initialInUnit + unit );
-			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
-				maxIterations = 0;
-			}
-			initialInUnit = initialInUnit / scale;
-
-		}
-
-		initialInUnit = initialInUnit * 2;
-		jQuery.style( elem, prop, initialInUnit + unit );
-
-		// Make sure we update the tween properties later on
-		valueParts = valueParts || [];
-	}
-
-	if ( valueParts ) {
-		initialInUnit = +initialInUnit || +initial || 0;
-
-		// Apply relative offset (+=/-=) if specified
-		adjusted = valueParts[ 1 ] ?
-			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
-			+valueParts[ 2 ];
-		if ( tween ) {
-			tween.unit = unit;
-			tween.start = initialInUnit;
-			tween.end = adjusted;
-		}
-	}
-	return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
-	var temp,
-		doc = elem.ownerDocument,
-		nodeName = elem.nodeName,
-		display = defaultDisplayMap[ nodeName ];
-
-	if ( display ) {
-		return display;
-	}
-
-	temp = doc.body.appendChild( doc.createElement( nodeName ) );
-	display = jQuery.css( temp, "display" );
-
-	temp.parentNode.removeChild( temp );
-
-	if ( display === "none" ) {
-		display = "block";
-	}
-	defaultDisplayMap[ nodeName ] = display;
-
-	return display;
-}
-
-function showHide( elements, show ) {
-	var display, elem,
-		values = [],
-		index = 0,
-		length = elements.length;
-
-	// Determine new display value for elements that need to change
-	for ( ; index < length; index++ ) {
-		elem = elements[ index ];
-		if ( !elem.style ) {
-			continue;
-		}
-
-		display = elem.style.display;
-		if ( show ) {
-
-			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
-			// check is required in this first loop unless we have a nonempty display value (either
-			// inline or about-to-be-restored)
-			if ( display === "none" ) {
-				values[ index ] = dataPriv.get( elem, "display" ) || null;
-				if ( !values[ index ] ) {
-					elem.style.display = "";
-				}
-			}
-			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
-				values[ index ] = getDefaultDisplay( elem );
-			}
-		} else {
-			if ( display !== "none" ) {
-				values[ index ] = "none";
-
-				// Remember what we're overwriting
-				dataPriv.set( elem, "display", display );
-			}
-		}
-	}
-
-	// Set the display of the elements in a second loop to avoid constant reflow
-	for ( index = 0; index < length; index++ ) {
-		if ( values[ index ] != null ) {
-			elements[ index ].style.display = values[ index ];
-		}
-	}
-
-	return elements;
-}
-
-jQuery.fn.extend( {
-	show: function() {
-		return showHide( this, true );
-	},
-	hide: function() {
-		return showHide( this );
-	},
-	toggle: function( state ) {
-		if ( typeof state === "boolean" ) {
-			return state ? this.show() : this.hide();
-		}
-
-		return this.each( function() {
-			if ( isHiddenWithinTree( this ) ) {
-				jQuery( this ).show();
-			} else {
-				jQuery( this ).hide();
-			}
-		} );
-	}
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
-	var fragment = document.createDocumentFragment(),
-		div = fragment.appendChild( document.createElement( "div" ) ),
-		input = document.createElement( "input" );
-
-	// Support: Android 4.0 - 4.3 only
-	// Check state lost if the name is set (#11217)
-	// Support: Windows Web Apps (WWA)
-	// `name` and `type` must use .setAttribute for WWA (#14901)
-	input.setAttribute( "type", "radio" );
-	input.setAttribute( "checked", "checked" );
-	input.setAttribute( "name", "t" );
-
-	div.appendChild( input );
-
-	// Support: Android <=4.1 only
-	// Older WebKit doesn't clone checked state correctly in fragments
-	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
-	// Support: IE <=11 only
-	// Make sure textarea (and checkbox) defaultValue is properly cloned
-	div.innerHTML = "<textarea>x</textarea>";
-	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
-	// Support: IE <=9 only
-	// IE <=9 replaces <option> tags with their contents when inserted outside of
-	// the select element.
-	div.innerHTML = "<option></option>";
-	support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
-	// XHTML parsers do not magically insert elements in the
-	// same way that tag soup parsers do. So we cannot shorten
-	// this by omitting <tbody> or other required elements.
-	thead: [ 1, "<table>", "</table>" ],
-	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
-	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
-	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
-	_default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
-	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
-	// Support: IE <=9 - 11 only
-	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
-	var ret;
-
-	if ( typeof context.getElementsByTagName !== "undefined" ) {
-		ret = context.getElementsByTagName( tag || "*" );
-
-	} else if ( typeof context.querySelectorAll !== "undefined" ) {
-		ret = context.querySelectorAll( tag || "*" );
-
-	} else {
-		ret = [];
-	}
-
-	if ( tag === undefined || tag && nodeName( context, tag ) ) {
-		return jQuery.merge( [ context ], ret );
-	}
-
-	return ret;
-}
-
-
-// Mark scripts as having already been evaluated
-function setGlobalEval( elems, refElements ) {
-	var i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		dataPriv.set(
-			elems[ i ],
-			"globalEval",
-			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
-		);
-	}
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
-	var elem, tmp, tag, wrap, attached, j,
-		fragment = context.createDocumentFragment(),
-		nodes = [],
-		i = 0,
-		l = elems.length;
-
-	for ( ; i < l; i++ ) {
-		elem = elems[ i ];
-
-		if ( elem || elem === 0 ) {
-
-			// Add nodes directly
-			if ( toType( elem ) === "object" ) {
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
-			// Convert non-html into a text node
-			} else if ( !rhtml.test( elem ) ) {
-				nodes.push( context.createTextNode( elem ) );
-
-			// Convert html into DOM nodes
-			} else {
-				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
-				// Deserialize a standard representation
-				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
-				wrap = wrapMap[ tag ] || wrapMap._default;
-				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
-				// Descend through wrappers to the right content
-				j = wrap[ 0 ];
-				while ( j-- ) {
-					tmp = tmp.lastChild;
-				}
-
-				// Support: Android <=4.0 only, PhantomJS 1 only
-				// push.apply(_, arraylike) throws on ancient WebKit
-				jQuery.merge( nodes, tmp.childNodes );
-
-				// Remember the top-level container
-				tmp = fragment.firstChild;
-
-				// Ensure the created nodes are orphaned (#12392)
-				tmp.textContent = "";
-			}
-		}
-	}
-
-	// Remove wrapper from fragment
-	fragment.textContent = "";
-
-	i = 0;
-	while ( ( elem = nodes[ i++ ] ) ) {
-
-		// Skip elements already in the context collection (trac-4087)
-		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
-			if ( ignored ) {
-				ignored.push( elem );
-			}
-			continue;
-		}
-
-		attached = isAttached( elem );
-
-		// Append to fragment
-		tmp = getAll( fragment.appendChild( elem ), "script" );
-
-		// Preserve script evaluation history
-		if ( attached ) {
-			setGlobalEval( tmp );
-		}
-
-		// Capture executables
-		if ( scripts ) {
-			j = 0;
-			while ( ( elem = tmp[ j++ ] ) ) {
-				if ( rscriptType.test( elem.type || "" ) ) {
-					scripts.push( elem );
-				}
-			}
-		}
-	}
-
-	return fragment;
-}
-
-
-var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
-	return true;
-}
-
-function returnFalse() {
-	return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
-	return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
-	try {
-		return document.activeElement;
-	} catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
-	var origFn, type;
-
-	// Types can be a map of types/handlers
-	if ( typeof types === "object" ) {
-
-		// ( types-Object, selector, data )
-		if ( typeof selector !== "string" ) {
-
-			// ( types-Object, data )
-			data = data || selector;
-			selector = undefined;
-		}
-		for ( type in types ) {
-			on( elem, type, selector, data, types[ type ], one );
-		}
-		return elem;
-	}
-
-	if ( data == null && fn == null ) {
-
-		// ( types, fn )
-		fn = selector;
-		data = selector = undefined;
-	} else if ( fn == null ) {
-		if ( typeof selector === "string" ) {
-
-			// ( types, selector, fn )
-			fn = data;
-			data = undefined;
-		} else {
-
-			// ( types, data, fn )
-			fn = data;
-			data = selector;
-			selector = undefined;
-		}
-	}
-	if ( fn === false ) {
-		fn = returnFalse;
-	} else if ( !fn ) {
-		return elem;
-	}
-
-	if ( one === 1 ) {
-		origFn = fn;
-		fn = function( event ) {
-
-			// Can use an empty set, since event contains the info
-			jQuery().off( event );
-			return origFn.apply( this, arguments );
-		};
-
-		// Use same guid so caller can remove using origFn
-		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
-	}
-	return elem.each( function() {
-		jQuery.event.add( this, types, fn, data, selector );
-	} );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
-	global: {},
-
-	add: function( elem, types, handler, data, selector ) {
-
-		var handleObjIn, eventHandle, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.get( elem );
-
-		// Only attach events to objects that accept data
-		if ( !acceptData( elem ) ) {
-			return;
-		}
-
-		// Caller can pass in an object of custom data in lieu of the handler
-		if ( handler.handler ) {
-			handleObjIn = handler;
-			handler = handleObjIn.handler;
-			selector = handleObjIn.selector;
-		}
-
-		// Ensure that invalid selectors throw exceptions at attach time
-		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
-		if ( selector ) {
-			jQuery.find.matchesSelector( documentElement, selector );
-		}
-
-		// Make sure that the handler has a unique ID, used to find/remove it later
-		if ( !handler.guid ) {
-			handler.guid = jQuery.guid++;
-		}
-
-		// Init the element's event structure and main handler, if this is the first
-		if ( !( events = elemData.events ) ) {
-			events = elemData.events = Object.create( null );
-		}
-		if ( !( eventHandle = elemData.handle ) ) {
-			eventHandle = elemData.handle = function( e ) {
-
-				// Discard the second event of a jQuery.event.trigger() and
-				// when an event is called after a page has unloaded
-				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
-					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
-			};
-		}
-
-		// Handle multiple events separated by a space
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// There *must* be a type, no attaching namespace-only handlers
-			if ( !type ) {
-				continue;
-			}
-
-			// If event changes its type, use the special event handlers for the changed type
-			special = jQuery.event.special[ type ] || {};
-
-			// If selector defined, determine special event api type, otherwise given type
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-
-			// Update special based on newly reset type
-			special = jQuery.event.special[ type ] || {};
-
-			// handleObj is passed to all event handlers
-			handleObj = jQuery.extend( {
-				type: type,
-				origType: origType,
-				data: data,
-				handler: handler,
-				guid: handler.guid,
-				selector: selector,
-				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
-				namespace: namespaces.join( "." )
-			}, handleObjIn );
-
-			// Init the event handler queue if we're the first
-			if ( !( handlers = events[ type ] ) ) {
-				handlers = events[ type ] = [];
-				handlers.delegateCount = 0;
-
-				// Only use addEventListener if the special events handler returns false
-				if ( !special.setup ||
-					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
-					if ( elem.addEventListener ) {
-						elem.addEventListener( type, eventHandle );
-					}
-				}
-			}
-
-			if ( special.add ) {
-				special.add.call( elem, handleObj );
-
-				if ( !handleObj.handler.guid ) {
-					handleObj.handler.guid = handler.guid;
-				}
-			}
-
-			// Add to the element's handler list, delegates in front
-			if ( selector ) {
-				handlers.splice( handlers.delegateCount++, 0, handleObj );
-			} else {
-				handlers.push( handleObj );
-			}
-
-			// Keep track of which events have ever been used, for event optimization
-			jQuery.event.global[ type ] = true;
-		}
-
-	},
-
-	// Detach an event or set of events from an element
-	remove: function( elem, types, handler, selector, mappedTypes ) {
-
-		var j, origCount, tmp,
-			events, t, handleObj,
-			special, handlers, type, namespaces, origType,
-			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
-		if ( !elemData || !( events = elemData.events ) ) {
-			return;
-		}
-
-		// Once for each type.namespace in types; type may be omitted
-		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
-		t = types.length;
-		while ( t-- ) {
-			tmp = rtypenamespace.exec( types[ t ] ) || [];
-			type = origType = tmp[ 1 ];
-			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
-			// Unbind all events (on this namespace, if provided) for the element
-			if ( !type ) {
-				for ( type in events ) {
-					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
-				}
-				continue;
-			}
-
-			special = jQuery.event.special[ type ] || {};
-			type = ( selector ? special.delegateType : special.bindType ) || type;
-			handlers = events[ type ] || [];
-			tmp = tmp[ 2 ] &&
-				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
-			// Remove matching events
-			origCount = j = handlers.length;
-			while ( j-- ) {
-				handleObj = handlers[ j ];
-
-				if ( ( mappedTypes || origType === handleObj.origType ) &&
-					( !handler || handler.guid === handleObj.guid ) &&
-					( !tmp || tmp.test( handleObj.namespace ) ) &&
-					( !selector || selector === handleObj.selector ||
-						selector === "**" && handleObj.selector ) ) {
-					handlers.splice( j, 1 );
-
-					if ( handleObj.selector ) {
-						handlers.delegateCount--;
-					}
-					if ( special.remove ) {
-						special.remove.call( elem, handleObj );
-					}
-				}
-			}
-
-			// Remove generic event handler if we removed something and no more handlers exist
-			// (avoids potential for endless recursion during removal of special event handlers)
-			if ( origCount && !handlers.length ) {
-				if ( !special.teardown ||
-					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
-					jQuery.removeEvent( elem, type, elemData.handle );
-				}
-
-				delete events[ type ];
-			}
-		}
-
-		// Remove data and the expando if it's no longer used
-		if ( jQuery.isEmptyObject( events ) ) {
-			dataPriv.remove( elem, "handle events" );
-		}
-	},
-
-	dispatch: function( nativeEvent ) {
-
-		var i, j, ret, matched, handleObj, handlerQueue,
-			args = new Array( arguments.length ),
-
-			// Make a writable jQuery.Event from the native event object
-			event = jQuery.event.fix( nativeEvent ),
-
-			handlers = (
-				dataPriv.get( this, "events" ) || Object.create( null )
-			)[ event.type ] || [],
-			special = jQuery.event.special[ event.type ] || {};
-
-		// Use the fix-ed jQuery.Event rather than the (read-only) native event
-		args[ 0 ] = event;
-
-		for ( i = 1; i < arguments.length; i++ ) {
-			args[ i ] = arguments[ i ];
-		}
-
-		event.delegateTarget = this;
-
-		// Call the preDispatch hook for the mapped type, and let it bail if desired
-		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
-			return;
-		}
-
-		// Determine handlers
-		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
-		// Run delegates first; they may want to stop propagation beneath us
-		i = 0;
-		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
-			event.currentTarget = matched.elem;
-
-			j = 0;
-			while ( ( handleObj = matched.handlers[ j++ ] ) &&
-				!event.isImmediatePropagationStopped() ) {
-
-				// If the event is namespaced, then each handler is only invoked if it is
-				// specially universal or its namespaces are a superset of the event's.
-				if ( !event.rnamespace || handleObj.namespace === false ||
-					event.rnamespace.test( handleObj.namespace ) ) {
-
-					event.handleObj = handleObj;
-					event.data = handleObj.data;
-
-					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
-						handleObj.handler ).apply( matched.elem, args );
-
-					if ( ret !== undefined ) {
-						if ( ( event.result = ret ) === false ) {
-							event.preventDefault();
-							event.stopPropagation();
-						}
-					}
-				}
-			}
-		}
-
-		// Call the postDispatch hook for the mapped type
-		if ( special.postDispatch ) {
-			special.postDispatch.call( this, event );
-		}
-
-		return event.result;
-	},
-
-	handlers: function( event, handlers ) {
-		var i, handleObj, sel, matchedHandlers, matchedSelectors,
-			handlerQueue = [],
-			delegateCount = handlers.delegateCount,
-			cur = event.target;
-
-		// Find delegate handlers
-		if ( delegateCount &&
-
-			// Support: IE <=9
-			// Black-hole SVG <use> instance trees (trac-13180)
-			cur.nodeType &&
-
-			// Support: Firefox <=42
-			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
-			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
-			// Support: IE 11 only
-			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
-			!( event.type === "click" && event.button >= 1 ) ) {
-
-			for ( ; cur !== this; cur = cur.parentNode || this ) {
-
-				// Don't check non-elements (#13208)
-				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
-				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
-					matchedHandlers = [];
-					matchedSelectors = {};
-					for ( i = 0; i < delegateCount; i++ ) {
-						handleObj = handlers[ i ];
-
-						// Don't conflict with Object.prototype properties (#13203)
-						sel = handleObj.selector + " ";
-
-						if ( matchedSelectors[ sel ] === undefined ) {
-							matchedSelectors[ sel ] = handleObj.needsContext ?
-								jQuery( sel, this ).index( cur ) > -1 :
-								jQuery.find( sel, this, null, [ cur ] ).length;
-						}
-						if ( matchedSelectors[ sel ] ) {
-							matchedHandlers.push( handleObj );
-						}
-					}
-					if ( matchedHandlers.length ) {
-						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
-					}
-				}
-			}
-		}
-
-		// Add the remaining (directly-bound) handlers
-		cur = this;
-		if ( delegateCount < handlers.length ) {
-			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
-		}
-
-		return handlerQueue;
-	},
-
-	addProp: function( name, hook ) {
-		Object.defineProperty( jQuery.Event.prototype, name, {
-			enumerable: true,
-			configurable: true,
-
-			get: isFunction( hook ) ?
-				function() {
-					if ( this.originalEvent ) {
-						return hook( this.originalEvent );
-					}
-				} :
-				function() {
-					if ( this.originalEvent ) {
-						return this.originalEvent[ name ];
-					}
-				},
-
-			set: function( value ) {
-				Object.defineProperty( this, name, {
-					enumerable: true,
-					configurable: true,
-					writable: true,
-					value: value
-				} );
-			}
-		} );
-	},
-
-	fix: function( originalEvent ) {
-		return originalEvent[ jQuery.expando ] ?
-			originalEvent :
-			new jQuery.Event( originalEvent );
-	},
-
-	special: {
-		load: {
-
-			// Prevent triggered image.load events from bubbling to window.load
-			noBubble: true
-		},
-		click: {
-
-			// Utilize native event to ensure correct state for checkable inputs
-			setup: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Claim the first handler
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					// dataPriv.set( el, "click", ... )
-					leverageNative( el, "click", returnTrue );
-				}
-
-				// Return false to allow normal processing in the caller
-				return false;
-			},
-			trigger: function( data ) {
-
-				// For mutual compressibility with _default, replace `this` access with a local var.
-				// `|| data` is dead code meant only to preserve the variable through minification.
-				var el = this || data;
-
-				// Force setup before triggering a click
-				if ( rcheckableType.test( el.type ) &&
-					el.click && nodeName( el, "input" ) ) {
-
-					leverageNative( el, "click" );
-				}
-
-				// Return non-false to allow normal event-path propagation
-				return true;
-			},
-
-			// For cross-browser consistency, suppress native .click() on links
-			// Also prevent it if we're currently inside a leveraged native-event stack
-			_default: function( event ) {
-				var target = event.target;
-				return rcheckableType.test( target.type ) &&
-					target.click && nodeName( target, "input" ) &&
-					dataPriv.get( target, "click" ) ||
-					nodeName( target, "a" );
-			}
-		},
-
-		beforeunload: {
-			postDispatch: function( event ) {
-
-				// Support: Firefox 20+
-				// Firefox doesn't alert if the returnValue field is not set.
-				if ( event.result !== undefined && event.originalEvent ) {
-					event.originalEvent.returnValue = event.result;
-				}
-			}
-		}
-	}
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
-	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
-	if ( !expectSync ) {
-		if ( dataPriv.get( el, type ) === undefined ) {
-			jQuery.event.add( el, type, returnTrue );
-		}
-		return;
-	}
-
-	// Register the controller as a special universal handler for all event namespaces
-	dataPriv.set( el, type, false );
-	jQuery.event.add( el, type, {
-		namespace: false,
-		handler: function( event ) {
-			var notAsync, result,
-				saved = dataPriv.get( this, type );
-
-			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
-				// Interrupt processing of the outer synthetic .trigger()ed event
-				// Saved data should be false in such cases, but might be a leftover capture object
-				// from an async native handler (gh-4350)
-				if ( !saved.length ) {
-
-					// Store arguments for use when handling the inner native event
-					// There will always be at least one argument (an event object), so this array
-					// will not be confused with a leftover capture object.
-					saved = slice.call( arguments );
-					dataPriv.set( this, type, saved );
-
-					// Trigger the native event and capture its result
-					// Support: IE <=9 - 11+
-					// focus() and blur() are asynchronous
-					notAsync = expectSync( this, type );
-					this[ type ]();
-					result = dataPriv.get( this, type );
-					if ( saved !== result || notAsync ) {
-						dataPriv.set( this, type, false );
-					} else {
-						result = {};
-					}
-					if ( saved !== result ) {
-
-						// Cancel the outer synthetic event
-						event.stopImmediatePropagation();
-						event.preventDefault();
-
-						// Support: Chrome 86+
-						// In Chrome, if an element having a focusout handler is blurred by
-						// clicking outside of it, it invokes the handler synchronously. If
-						// that handler calls `.remove()` on the element, the data is cleared,
-						// leaving `result` undefined. We need to guard against this.
-						return result && result.value;
-					}
-
-				// If this is an inner synthetic event for an event with a bubbling surrogate
-				// (focus or blur), assume that the surrogate already propagated from triggering the
-				// native event and prevent that from happening again here.
-				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
-				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
-				// less bad than duplication.
-				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
-					event.stopPropagation();
-				}
-
-			// If this is a native event triggered above, everything is now in order
-			// Fire an inner synthetic event with the original arguments
-			} else if ( saved.length ) {
-
-				// ...and capture the result
-				dataPriv.set( this, type, {
-					value: jQuery.event.trigger(
-
-						// Support: IE <=9 - 11+
-						// Extend with the prototype to reset the above stopImmediatePropagation()
-						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
-						saved.slice( 1 ),
-						this
-					)
-				} );
-
-				// Abort handling of the native event
-				event.stopImmediatePropagation();
-			}
-		}
-	} );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
-	// This "if" is needed for plain objects
-	if ( elem.removeEventListener ) {
-		elem.removeEventListener( type, handle );
-	}
-};
-
-jQuery.Event = function( src, props ) {
-
-	// Allow instantiation without the 'new' keyword
-	if ( !( this instanceof jQuery.Event ) ) {
-		return new jQuery.Event( src, props );
-	}
-
-	// Event object
-	if ( src && src.type ) {
-		this.originalEvent = src;
-		this.type = src.type;
-
-		// Events bubbling up the document may have been marked as prevented
-		// by a handler lower down the tree; reflect the correct value.
-		this.isDefaultPrevented = src.defaultPrevented ||
-				src.defaultPrevented === undefined &&
-
-				// Support: Android <=2.3 only
-				src.returnValue === false ?
-			returnTrue :
-			returnFalse;
-
-		// Create target properties
-		// Support: Safari <=6 - 7 only
-		// Target should not be a text node (#504, #13143)
-		this.target = ( src.target && src.target.nodeType === 3 ) ?
-			src.target.parentNode :
-			src.target;
-
-		this.currentTarget = src.currentTarget;
-		this.relatedTarget = src.relatedTarget;
-
-	// Event type
-	} else {
-		this.type = src;
-	}
-
-	// Put explicitly provided properties onto the event object
-	if ( props ) {
-		jQuery.extend( this, props );
-	}
-
-	// Create a timestamp if incoming event doesn't have one
-	this.timeStamp = src && src.timeStamp || Date.now();
-
-	// Mark it as fixed
-	this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
-	constructor: jQuery.Event,
-	isDefaultPrevented: returnFalse,
-	isPropagationStopped: returnFalse,
-	isImmediatePropagationStopped: returnFalse,
-	isSimulated: false,
-
-	preventDefault: function() {
-		var e = this.originalEvent;
-
-		this.isDefaultPrevented = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.preventDefault();
-		}
-	},
-	stopPropagation: function() {
-		var e = this.originalEvent;
-
-		this.isPropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopPropagation();
-		}
-	},
-	stopImmediatePropagation: function() {
-		var e = this.originalEvent;
-
-		this.isImmediatePropagationStopped = returnTrue;
-
-		if ( e && !this.isSimulated ) {
-			e.stopImmediatePropagation();
-		}
-
-		this.stopPropagation();
-	}
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
-	altKey: true,
-	bubbles: true,
-	cancelable: true,
-	changedTouches: true,
-	ctrlKey: true,
-	detail: true,
-	eventPhase: true,
-	metaKey: true,
-	pageX: true,
-	pageY: true,
-	shiftKey: true,
-	view: true,
-	"char": true,
-	code: true,
-	charCode: true,
-	key: true,
-	keyCode: true,
-	button: true,
-	buttons: true,
-	clientX: true,
-	clientY: true,
-	offsetX: true,
-	offsetY: true,
-	pointerId: true,
-	pointerType: true,
-	screenX: true,
-	screenY: true,
-	targetTouches: true,
-	toElement: true,
-	touches: true,
-	which: true
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
-	jQuery.event.special[ type ] = {
-
-		// Utilize native event if possible so blur/focus sequence is correct
-		setup: function() {
-
-			// Claim the first handler
-			// dataPriv.set( this, "focus", ... )
-			// dataPriv.set( this, "blur", ... )
-			leverageNative( this, type, expectSync );
-
-			// Return false to allow normal processing in the caller
-			return false;
-		},
-		trigger: function() {
-
-			// Force setup before trigger
-			leverageNative( this, type );
-
-			// Return non-false to allow normal event-path propagation
-			return true;
-		},
-
-		// Suppress native focus or blur as it's already being fired
-		// in leverageNative.
-		_default: function() {
-			return true;
-		},
-
-		delegateType: delegateType
-	};
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
-	mouseenter: "mouseover",
-	mouseleave: "mouseout",
-	pointerenter: "pointerover",
-	pointerleave: "pointerout"
-}, function( orig, fix ) {
-	jQuery.event.special[ orig ] = {
-		delegateType: fix,
-		bindType: fix,
-
-		handle: function( event ) {
-			var ret,
-				target = this,
-				related = event.relatedTarget,
-				handleObj = event.handleObj;
-
-			// For mouseenter/leave call the handler if related is outside the target.
-			// NB: No relatedTarget if the mouse left/entered the browser window
-			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
-				event.type = handleObj.origType;
-				ret = handleObj.handler.apply( this, arguments );
-				event.type = fix;
-			}
-			return ret;
-		}
-	};
-} );
-
-jQuery.fn.extend( {
-
-	on: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn );
-	},
-	one: function( types, selector, data, fn ) {
-		return on( this, types, selector, data, fn, 1 );
-	},
-	off: function( types, selector, fn ) {
-		var handleObj, type;
-		if ( types && types.preventDefault && types.handleObj ) {
-
-			// ( event )  dispatched jQuery.Event
-			handleObj = types.handleObj;
-			jQuery( types.delegateTarget ).off(
-				handleObj.namespace ?
-					handleObj.origType + "." + handleObj.namespace :
-					handleObj.origType,
-				handleObj.selector,
-				handleObj.handler
-			);
-			return this;
-		}
-		if ( typeof types === "object" ) {
-
-			// ( types-object [, selector] )
-			for ( type in types ) {
-				this.off( type, selector, types[ type ] );
-			}
-			return this;
-		}
-		if ( selector === false || typeof selector === "function" ) {
-
-			// ( types [, fn] )
-			fn = selector;
-			selector = undefined;
-		}
-		if ( fn === false ) {
-			fn = returnFalse;
-		}
-		return this.each( function() {
-			jQuery.event.remove( this, types, fn, selector );
-		} );
-	}
-} );
-
-
-var
-
-	// Support: IE <=10 - 11, Edge 12 - 13 only
-	// In IE/Edge using regex groups here causes severe slowdowns.
-	// See https://connect.microsoft.com/IE/feedback/details/1736512/
-	rnoInnerhtml = /<script|<style|<link/i,
-
-	// checked="checked" or checked
-	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
-	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
-	if ( nodeName( elem, "table" ) &&
-		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
-		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
-	}
-
-	return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
-	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
-	return elem;
-}
-function restoreScript( elem ) {
-	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
-		elem.type = elem.type.slice( 5 );
-	} else {
-		elem.removeAttribute( "type" );
-	}
-
-	return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
-	var i, l, type, pdataOld, udataOld, udataCur, events;
-
-	if ( dest.nodeType !== 1 ) {
-		return;
-	}
-
-	// 1. Copy private data: events, handlers, etc.
-	if ( dataPriv.hasData( src ) ) {
-		pdataOld = dataPriv.get( src );
-		events = pdataOld.events;
-
-		if ( events ) {
-			dataPriv.remove( dest, "handle events" );
-
-			for ( type in events ) {
-				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
-					jQuery.event.add( dest, type, events[ type ][ i ] );
-				}
-			}
-		}
-	}
-
-	// 2. Copy user data
-	if ( dataUser.hasData( src ) ) {
-		udataOld = dataUser.access( src );
-		udataCur = jQuery.extend( {}, udataOld );
-
-		dataUser.set( dest, udataCur );
-	}
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
-	var nodeName = dest.nodeName.toLowerCase();
-
-	// Fails to persist the checked state of a cloned checkbox or radio button.
-	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
-		dest.checked = src.checked;
-
-	// Fails to return the selected option to the default selected state when cloning options
-	} else if ( nodeName === "input" || nodeName === "textarea" ) {
-		dest.defaultValue = src.defaultValue;
-	}
-}
-
-function domManip( collection, args, callback, ignored ) {
-
-	// Flatten any nested arrays
-	args = flat( args );
-
-	var fragment, first, scripts, hasScripts, node, doc,
-		i = 0,
-		l = collection.length,
-		iNoClone = l - 1,
-		value = args[ 0 ],
-		valueIsFunction = isFunction( value );
-
-	// We can't cloneNode fragments that contain checked, in WebKit
-	if ( valueIsFunction ||
-			( l > 1 && typeof value === "string" &&
-				!support.checkClone && rchecked.test( value ) ) ) {
-		return collection.each( function( index ) {
-			var self = collection.eq( index );
-			if ( valueIsFunction ) {
-				args[ 0 ] = value.call( this, index, self.html() );
-			}
-			domManip( self, args, callback, ignored );
-		} );
-	}
-
-	if ( l ) {
-		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
-		first = fragment.firstChild;
-
-		if ( fragment.childNodes.length === 1 ) {
-			fragment = first;
-		}
-
-		// Require either new content or an interest in ignored elements to invoke the callback
-		if ( first || ignored ) {
-			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
-			hasScripts = scripts.length;
-
-			// Use the original fragment for the last item
-			// instead of the first because it can end up
-			// being emptied incorrectly in certain situations (#8070).
-			for ( ; i < l; i++ ) {
-				node = fragment;
-
-				if ( i !== iNoClone ) {
-					node = jQuery.clone( node, true, true );
-
-					// Keep references to cloned scripts for later restoration
-					if ( hasScripts ) {
-
-						// Support: Android <=4.0 only, PhantomJS 1 only
-						// push.apply(_, arraylike) throws on ancient WebKit
-						jQuery.merge( scripts, getAll( node, "script" ) );
-					}
-				}
-
-				callback.call( collection[ i ], node, i );
-			}
-
-			if ( hasScripts ) {
-				doc = scripts[ scripts.length - 1 ].ownerDocument;
-
-				// Reenable scripts
-				jQuery.map( scripts, restoreScript );
-
-				// Evaluate executable scripts on first document insertion
-				for ( i = 0; i < hasScripts; i++ ) {
-					node = scripts[ i ];
-					if ( rscriptType.test( node.type || "" ) &&
-						!dataPriv.access( node, "globalEval" ) &&
-						jQuery.contains( doc, node ) ) {
-
-						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
-
-							// Optional AJAX dependency, but won't run scripts if not present
-							if ( jQuery._evalUrl && !node.noModule ) {
-								jQuery._evalUrl( node.src, {
-									nonce: node.nonce || node.getAttribute( "nonce" )
-								}, doc );
-							}
-						} else {
-							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return collection;
-}
-
-function remove( elem, selector, keepData ) {
-	var node,
-		nodes = selector ? jQuery.filter( selector, elem ) : elem,
-		i = 0;
-
-	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
-		if ( !keepData && node.nodeType === 1 ) {
-			jQuery.cleanData( getAll( node ) );
-		}
-
-		if ( node.parentNode ) {
-			if ( keepData && isAttached( node ) ) {
-				setGlobalEval( getAll( node, "script" ) );
-			}
-			node.parentNode.removeChild( node );
-		}
-	}
-
-	return elem;
-}
-
-jQuery.extend( {
-	htmlPrefilter: function( html ) {
-		return html;
-	},
-
-	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
-		var i, l, srcElements, destElements,
-			clone = elem.cloneNode( true ),
-			inPage = isAttached( elem );
-
-		// Fix IE cloning issues
-		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
-				!jQuery.isXMLDoc( elem ) ) {
-
-			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
-			destElements = getAll( clone );
-			srcElements = getAll( elem );
-
-			for ( i = 0, l = srcElements.length; i < l; i++ ) {
-				fixInput( srcElements[ i ], destElements[ i ] );
-			}
-		}
-
-		// Copy the events from the original to the clone
-		if ( dataAndEvents ) {
-			if ( deepDataAndEvents ) {
-				srcElements = srcElements || getAll( elem );
-				destElements = destElements || getAll( clone );
-
-				for ( i = 0, l = srcElements.length; i < l; i++ ) {
-					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
-				}
-			} else {
-				cloneCopyEvent( elem, clone );
-			}
-		}
-
-		// Preserve script evaluation history
-		destElements = getAll( clone, "script" );
-		if ( destElements.length > 0 ) {
-			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
-		}
-
-		// Return the cloned set
-		return clone;
-	},
-
-	cleanData: function( elems ) {
-		var data, elem, type,
-			special = jQuery.event.special,
-			i = 0;
-
-		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
-			if ( acceptData( elem ) ) {
-				if ( ( data = elem[ dataPriv.expando ] ) ) {
-					if ( data.events ) {
-						for ( type in data.events ) {
-							if ( special[ type ] ) {
-								jQuery.event.remove( elem, type );
-
-							// This is a shortcut to avoid jQuery.event.remove's overhead
-							} else {
-								jQuery.removeEvent( elem, type, data.handle );
-							}
-						}
-					}
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataPriv.expando ] = undefined;
-				}
-				if ( elem[ dataUser.expando ] ) {
-
-					// Support: Chrome <=35 - 45+
-					// Assign undefined instead of using delete, see Data#remove
-					elem[ dataUser.expando ] = undefined;
-				}
-			}
-		}
-	}
-} );
-
-jQuery.fn.extend( {
-	detach: function( selector ) {
-		return remove( this, selector, true );
-	},
-
-	remove: function( selector ) {
-		return remove( this, selector );
-	},
-
-	text: function( value ) {
-		return access( this, function( value ) {
-			return value === undefined ?
-				jQuery.text( this ) :
-				this.empty().each( function() {
-					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-						this.textContent = value;
-					}
-				} );
-		}, null, value, arguments.length );
-	},
-
-	append: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.appendChild( elem );
-			}
-		} );
-	},
-
-	prepend: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
-				var target = manipulationTarget( this, elem );
-				target.insertBefore( elem, target.firstChild );
-			}
-		} );
-	},
-
-	before: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this );
-			}
-		} );
-	},
-
-	after: function() {
-		return domManip( this, arguments, function( elem ) {
-			if ( this.parentNode ) {
-				this.parentNode.insertBefore( elem, this.nextSibling );
-			}
-		} );
-	},
-
-	empty: function() {
-		var elem,
-			i = 0;
-
-		for ( ; ( elem = this[ i ] ) != null; i++ ) {
-			if ( elem.nodeType === 1 ) {
-
-				// Prevent memory leaks
-				jQuery.cleanData( getAll( elem, false ) );
-
-				// Remove any remaining nodes
-				elem.textContent = "";
-			}
-		}
-
-		return this;
-	},
-
-	clone: function( dataAndEvents, deepDataAndEvents ) {
-		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
-		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
-		return this.map( function() {
-			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
-		} );
-	},
-
-	html: function( value ) {
-		return access( this, function( value ) {
-			var elem = this[ 0 ] || {},
-				i = 0,
-				l = this.length;
-
-			if ( value === undefined && elem.nodeType === 1 ) {
-				return elem.innerHTML;
-			}
-
-			// See if we can take a shortcut and just use innerHTML
-			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
-				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
-				value = jQuery.htmlPrefilter( value );
-
-				try {
-					for ( ; i < l; i++ ) {
-						elem = this[ i ] || {};
-
-						// Remove element nodes and prevent memory leaks
-						if ( elem.nodeType === 1 ) {
-							jQuery.cleanData( getAll( elem, false ) );
-							elem.innerHTML = value;
-						}
-					}
-
-					elem = 0;
-
-				// If using innerHTML throws an exception, use the fallback method
-				} catch ( e ) {}
-			}
-
-			if ( elem ) {
-				this.empty().append( value );
-			}
-		}, null, value, arguments.length );
-	},
-
-	replaceWith: function() {
-		var ignored = [];
-
-		// Make the changes, replacing each non-ignored context element with the new content
-		return domManip( this, arguments, function( elem ) {
-			var parent = this.parentNode;
-
-			if ( jQuery.inArray( this, ignored ) < 0 ) {
-				jQuery.cleanData( getAll( this ) );
-				if ( parent ) {
-					parent.replaceChild( elem, this );
-				}
-			}
-
-		// Force callback invocation
-		}, ignored );
-	}
-} );
-
-jQuery.each( {
-	appendTo: "append",
-	prependTo: "prepend",
-	insertBefore: "before",
-	insertAfter: "after",
-	replaceAll: "replaceWith"
-}, function( name, original ) {
-	jQuery.fn[ name ] = function( selector ) {
-		var elems,
-			ret = [],
-			insert = jQuery( selector ),
-			last = insert.length - 1,
-			i = 0;
-
-		for ( ; i <= last; i++ ) {
-			elems = i === last ? this : this.clone( true );
-			jQuery( insert[ i ] )[ original ]( elems );
-
-			// Support: Android <=4.0 only, PhantomJS 1 only
-			// .get() because push.apply(_, arraylike) throws on ancient WebKit
-			push.apply( ret, elems.get() );
-		}
-
-		return this.pushStack( ret );
-	};
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
-		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
-		// IE throws on elements created in popups
-		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
-		var view = elem.ownerDocument.defaultView;
-
-		if ( !view || !view.opener ) {
-			view = window;
-		}
-
-		return view.getComputedStyle( elem );
-	};
-
-var swap = function( elem, options, callback ) {
-	var ret, name,
-		old = {};
-
-	// Remember the old values, and insert the new ones
-	for ( name in options ) {
-		old[ name ] = elem.style[ name ];
-		elem.style[ name ] = options[ name ];
-	}
-
-	ret = callback.call( elem );
-
-	// Revert the old values
-	for ( name in options ) {
-		elem.style[ name ] = old[ name ];
-	}
-
-	return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
-	// Executing both pixelPosition & boxSizingReliable tests require only one layout
-	// so they're executed at the same time to save the second computation.
-	function computeStyleTests() {
-
-		// This is a singleton, we need to execute it only once
-		if ( !div ) {
-			return;
-		}
-
-		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
-			"margin-top:1px;padding:0;border:0";
-		div.style.cssText =
-			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
-			"margin:auto;border:1px;padding:1px;" +
-			"width:60%;top:1%";
-		documentElement.appendChild( container ).appendChild( div );
-
-		var divStyle = window.getComputedStyle( div );
-		pixelPositionVal = divStyle.top !== "1%";
-
-		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
-		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
-		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
-		// Some styles come back with percentage values, even though they shouldn't
-		div.style.right = "60%";
-		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
-		// Support: IE 9 - 11 only
-		// Detect misreporting of content dimensions for box-sizing:border-box elements
-		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
-		// Support: IE 9 only
-		// Detect overflow:scroll screwiness (gh-3699)
-		// Support: Chrome <=64
-		// Don't get tricked when zoom affects offsetWidth (gh-4029)
-		div.style.position = "absolute";
-		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
-		documentElement.removeChild( container );
-
-		// Nullify the div so it wouldn't be stored in the memory and
-		// it will also be a sign that checks already performed
-		div = null;
-	}
-
-	function roundPixelMeasures( measure ) {
-		return Math.round( parseFloat( measure ) );
-	}
-
-	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
-		reliableTrDimensionsVal, reliableMarginLeftVal,
-		container = document.createElement( "div" ),
-		div = document.createElement( "div" );
-
-	// Finish early in limited (non-browser) environments
-	if ( !div.style ) {
-		return;
-	}
-
-	// Support: IE <=9 - 11 only
-	// Style of cloned element affects source element cloned (#8908)
-	div.style.backgroundClip = "content-box";
-	div.cloneNode( true ).style.backgroundClip = "";
-	support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
-	jQuery.extend( support, {
-		boxSizingReliable: function() {
-			computeStyleTests();
-			return boxSizingReliableVal;
-		},
-		pixelBoxStyles: function() {
-			computeStyleTests();
-			return pixelBoxStylesVal;
-		},
-		pixelPosition: function() {
-			computeStyleTests();
-			return pixelPositionVal;
-		},
-		reliableMarginLeft: function() {
-			computeStyleTests();
-			return reliableMarginLeftVal;
-		},
-		scrollboxSize: function() {
-			computeStyleTests();
-			return scrollboxSizeVal;
-		},
-
-		// Support: IE 9 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Behavior in IE 9 is more subtle than in newer versions & it passes
-		// some versions of this test; make sure not to make it pass there!
-		//
-		// Support: Firefox 70+
-		// Only Firefox includes border widths
-		// in computed dimensions. (gh-4529)
-		reliableTrDimensions: function() {
-			var table, tr, trChild, trStyle;
-			if ( reliableTrDimensionsVal == null ) {
-				table = document.createElement( "table" );
-				tr = document.createElement( "tr" );
-				trChild = document.createElement( "div" );
-
-				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
-				tr.style.cssText = "border:1px solid";
-
-				// Support: Chrome 86+
-				// Height set through cssText does not get applied.
-				// Computed height then comes back as 0.
-				tr.style.height = "1px";
-				trChild.style.height = "9px";
-
-				// Support: Android 8 Chrome 86+
-				// In our bodyBackground.html iframe,
-				// display for all div elements is set to "inline",
-				// which causes a problem only in Android 8 Chrome 86.
-				// Ensuring the div is display: block
-				// gets around this issue.
-				trChild.style.display = "block";
-
-				documentElement
-					.appendChild( table )
-					.appendChild( tr )
-					.appendChild( trChild );
-
-				trStyle = window.getComputedStyle( tr );
-				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
-					parseInt( trStyle.borderTopWidth, 10 ) +
-					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
-
-				documentElement.removeChild( table );
-			}
-			return reliableTrDimensionsVal;
-		}
-	} );
-} )();
-
-
-function curCSS( elem, name, computed ) {
-	var width, minWidth, maxWidth, ret,
-
-		// Support: Firefox 51+
-		// Retrieving style before computed somehow
-		// fixes an issue with getting wrong values
-		// on detached elements
-		style = elem.style;
-
-	computed = computed || getStyles( elem );
-
-	// getPropertyValue is needed for:
-	//   .css('filter') (IE 9 only, #12537)
-	//   .css('--customProperty) (#3144)
-	if ( computed ) {
-		ret = computed.getPropertyValue( name ) || computed[ name ];
-
-		if ( ret === "" && !isAttached( elem ) ) {
-			ret = jQuery.style( elem, name );
-		}
-
-		// A tribute to the "awesome hack by Dean Edwards"
-		// Android Browser returns percentage for some values,
-		// but width seems to be reliably pixels.
-		// This is against the CSSOM draft spec:
-		// https://drafts.csswg.org/cssom/#resolved-values
-		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
-			// Remember the original values
-			width = style.width;
-			minWidth = style.minWidth;
-			maxWidth = style.maxWidth;
-
-			// Put in the new values to get a computed value out
-			style.minWidth = style.maxWidth = style.width = ret;
-			ret = computed.width;
-
-			// Revert the changed values
-			style.width = width;
-			style.minWidth = minWidth;
-			style.maxWidth = maxWidth;
-		}
-	}
-
-	return ret !== undefined ?
-
-		// Support: IE <=9 - 11 only
-		// IE returns zIndex value as an integer.
-		ret + "" :
-		ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
-	// Define the hook, we'll check on the first run if it's really needed.
-	return {
-		get: function() {
-			if ( conditionFn() ) {
-
-				// Hook not needed (or it's not possible to use it due
-				// to missing dependency), remove it.
-				delete this.get;
-				return;
-			}
-
-			// Hook needed; redefine it so that the support test is not executed again.
-			return ( this.get = hookFn ).apply( this, arguments );
-		}
-	};
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
-	emptyStyle = document.createElement( "div" ).style,
-	vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
-	// Check for vendor prefixed names
-	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
-		i = cssPrefixes.length;
-
-	while ( i-- ) {
-		name = cssPrefixes[ i ] + capName;
-		if ( name in emptyStyle ) {
-			return name;
-		}
-	}
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
-	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
-	if ( final ) {
-		return final;
-	}
-	if ( name in emptyStyle ) {
-		return name;
-	}
-	return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
-	// Swappable if display is none or starts with table
-	// except "table", "table-cell", or "table-caption"
-	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
-	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
-	rcustomProp = /^--/,
-	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
-	cssNormalTransform = {
-		letterSpacing: "0",
-		fontWeight: "400"
-	};
-
-function setPositiveNumber( _elem, value, subtract ) {
-
-	// Any relative (+/-) values have already been
-	// normalized at this point
-	var matches = rcssNum.exec( value );
-	return matches ?
-
-		// Guard against undefined "subtract", e.g., when used as in cssHooks
-		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
-		value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
-	var i = dimension === "width" ? 1 : 0,
-		extra = 0,
-		delta = 0;
-
-	// Adjustment may not be necessary
-	if ( box === ( isBorderBox ? "border" : "content" ) ) {
-		return 0;
-	}
-
-	for ( ; i < 4; i += 2 ) {
-
-		// Both box models exclude margin
-		if ( box === "margin" ) {
-			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
-		}
-
-		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
-		if ( !isBorderBox ) {
-
-			// Add padding
-			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
-			// For "border" or "margin", add border
-			if ( box !== "padding" ) {
-				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
-			// But still keep track of it otherwise
-			} else {
-				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-
-		// If we get here with a border-box (content + padding + border), we're seeking "content" or
-		// "padding" or "margin"
-		} else {
-
-			// For "content", subtract padding
-			if ( box === "content" ) {
-				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-			}
-
-			// For "content" or "padding", subtract border
-			if ( box !== "margin" ) {
-				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-			}
-		}
-	}
-
-	// Account for positive content-box scroll gutter when requested by providing computedVal
-	if ( !isBorderBox && computedVal >= 0 ) {
-
-		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
-		// Assuming integer scroll gutter, subtract the rest and round down
-		delta += Math.max( 0, Math.ceil(
-			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-			computedVal -
-			delta -
-			extra -
-			0.5
-
-		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
-		// Use an explicit zero to avoid NaN (gh-3964)
-		) ) || 0;
-	}
-
-	return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
-	// Start with computed style
-	var styles = getStyles( elem ),
-
-		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
-		// Fake content-box until we know it's needed to know the true value.
-		boxSizingNeeded = !support.boxSizingReliable() || extra,
-		isBorderBox = boxSizingNeeded &&
-			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-		valueIsBorderBox = isBorderBox,
-
-		val = curCSS( elem, dimension, styles ),
-		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
-	// Support: Firefox <=54
-	// Return a confounding non-pixel value or feign ignorance, as appropriate.
-	if ( rnumnonpx.test( val ) ) {
-		if ( !extra ) {
-			return val;
-		}
-		val = "auto";
-	}
-
-
-	// Support: IE 9 - 11 only
-	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
-	// In those cases, the computed value can be trusted to be border-box.
-	if ( ( !support.boxSizingReliable() && isBorderBox ||
-
-		// Support: IE 10 - 11+, Edge 15 - 18+
-		// IE/Edge misreport `getComputedStyle` of table rows with width/height
-		// set in CSS while `offset*` properties report correct values.
-		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
-		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
-		// Fall back to offsetWidth/offsetHeight when value is "auto"
-		// This happens for inline elements with no explicit setting (gh-3571)
-		val === "auto" ||
-
-		// Support: Android <=4.1 - 4.3 only
-		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
-		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
-		// Make sure the element is visible & connected
-		elem.getClientRects().length ) {
-
-		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
-		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
-		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
-		// retrieved value as a content box dimension.
-		valueIsBorderBox = offsetProp in elem;
-		if ( valueIsBorderBox ) {
-			val = elem[ offsetProp ];
-		}
-	}
-
-	// Normalize "" and auto
-	val = parseFloat( val ) || 0;
-
-	// Adjust for the element's box model
-	return ( val +
-		boxModelAdjustment(
-			elem,
-			dimension,
-			extra || ( isBorderBox ? "border" : "content" ),
-			valueIsBorderBox,
-			styles,
-
-			// Provide the current computed size to request scroll gutter calculation (gh-3589)
-			val
-		)
-	) + "px";
-}
-
-jQuery.extend( {
-
-	// Add in style property hooks for overriding the default
-	// behavior of getting and setting a style property
-	cssHooks: {
-		opacity: {
-			get: function( elem, computed ) {
-				if ( computed ) {
-
-					// We should always get a number back from opacity
-					var ret = curCSS( elem, "opacity" );
-					return ret === "" ? "1" : ret;
-				}
-			}
-		}
-	},
-
-	// Don't automatically add "px" to these possibly-unitless properties
-	cssNumber: {
-		"animationIterationCount": true,
-		"columnCount": true,
-		"fillOpacity": true,
-		"flexGrow": true,
-		"flexShrink": true,
-		"fontWeight": true,
-		"gridArea": true,
-		"gridColumn": true,
-		"gridColumnEnd": true,
-		"gridColumnStart": true,
-		"gridRow": true,
-		"gridRowEnd": true,
-		"gridRowStart": true,
-		"lineHeight": true,
-		"opacity": true,
-		"order": true,
-		"orphans": true,
-		"widows": true,
-		"zIndex": true,
-		"zoom": true
-	},
-
-	// Add in properties whose names you wish to fix before
-	// setting or getting the value
-	cssProps: {},
-
-	// Get and set the style property on a DOM Node
-	style: function( elem, name, value, extra ) {
-
-		// Don't set styles on text and comment nodes
-		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
-			return;
-		}
-
-		// Make sure that we're working with the right name
-		var ret, type, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name ),
-			style = elem.style;
-
-		// Make sure that we're working with the right name. We don't
-		// want to query the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Gets hook for the prefixed version, then unprefixed version
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// Check if we're setting a value
-		if ( value !== undefined ) {
-			type = typeof value;
-
-			// Convert "+=" or "-=" to relative numbers (#7345)
-			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
-				value = adjustCSS( elem, name, ret );
-
-				// Fixes bug #9237
-				type = "number";
-			}
-
-			// Make sure that null and NaN values aren't set (#7116)
-			if ( value == null || value !== value ) {
-				return;
-			}
-
-			// If a number was passed in, add the unit (except for certain CSS properties)
-			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
-			// "px" to a few hardcoded values.
-			if ( type === "number" && !isCustomProp ) {
-				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
-			}
-
-			// background-* props affect original clone's values
-			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
-				style[ name ] = "inherit";
-			}
-
-			// If a hook was provided, use that value, otherwise just set the specified value
-			if ( !hooks || !( "set" in hooks ) ||
-				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
-				if ( isCustomProp ) {
-					style.setProperty( name, value );
-				} else {
-					style[ name ] = value;
-				}
-			}
-
-		} else {
-
-			// If a hook was provided get the non-computed value from there
-			if ( hooks && "get" in hooks &&
-				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
-				return ret;
-			}
-
-			// Otherwise just get the value from the style object
-			return style[ name ];
-		}
-	},
-
-	css: function( elem, name, extra, styles ) {
-		var val, num, hooks,
-			origName = camelCase( name ),
-			isCustomProp = rcustomProp.test( name );
-
-		// Make sure that we're working with the right name. We don't
-		// want to modify the value if it is a CSS custom property
-		// since they are user-defined.
-		if ( !isCustomProp ) {
-			name = finalPropName( origName );
-		}
-
-		// Try prefixed name followed by the unprefixed name
-		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
-		// If a hook was provided get the computed value from there
-		if ( hooks && "get" in hooks ) {
-			val = hooks.get( elem, true, extra );
-		}
-
-		// Otherwise, if a way to get the computed value exists, use that
-		if ( val === undefined ) {
-			val = curCSS( elem, name, styles );
-		}
-
-		// Convert "normal" to computed value
-		if ( val === "normal" && name in cssNormalTransform ) {
-			val = cssNormalTransform[ name ];
-		}
-
-		// Make numeric if forced or a qualifier was provided and val looks numeric
-		if ( extra === "" || extra ) {
-			num = parseFloat( val );
-			return extra === true || isFinite( num ) ? num || 0 : val;
-		}
-
-		return val;
-	}
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
-	jQuery.cssHooks[ dimension ] = {
-		get: function( elem, computed, extra ) {
-			if ( computed ) {
-
-				// Certain elements can have dimension info if we invisibly show them
-				// but it must have a current display style that would benefit
-				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
-					// Support: Safari 8+
-					// Table columns in Safari have non-zero offsetWidth & zero
-					// getBoundingClientRect().width unless display is changed.
-					// Support: IE <=11 only
-					// Running getBoundingClientRect on a disconnected node
-					// in IE throws an error.
-					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
-					swap( elem, cssShow, function() {
-						return getWidthOrHeight( elem, dimension, extra );
-					} ) :
-					getWidthOrHeight( elem, dimension, extra );
-			}
-		},
-
-		set: function( elem, value, extra ) {
-			var matches,
-				styles = getStyles( elem ),
-
-				// Only read styles.position if the test has a chance to fail
-				// to avoid forcing a reflow.
-				scrollboxSizeBuggy = !support.scrollboxSize() &&
-					styles.position === "absolute",
-
-				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
-				boxSizingNeeded = scrollboxSizeBuggy || extra,
-				isBorderBox = boxSizingNeeded &&
-					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
-				subtract = extra ?
-					boxModelAdjustment(
-						elem,
-						dimension,
-						extra,
-						isBorderBox,
-						styles
-					) :
-					0;
-
-			// Account for unreliable border-box dimensions by comparing offset* to computed and
-			// faking a content-box to get border and padding (gh-3699)
-			if ( isBorderBox && scrollboxSizeBuggy ) {
-				subtract -= Math.ceil(
-					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
-					parseFloat( styles[ dimension ] ) -
-					boxModelAdjustment( elem, dimension, "border", false, styles ) -
-					0.5
-				);
-			}
-
-			// Convert to pixels if value adjustment is needed
-			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
-				( matches[ 3 ] || "px" ) !== "px" ) {
-
-				elem.style[ dimension ] = value;
-				value = jQuery.css( elem, dimension );
-			}
-
-			return setPositiveNumber( elem, value, subtract );
-		}
-	};
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
-	function( elem, computed ) {
-		if ( computed ) {
-			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
-				elem.getBoundingClientRect().left -
-					swap( elem, { marginLeft: 0 }, function() {
-						return elem.getBoundingClientRect().left;
-					} )
-			) + "px";
-		}
-	}
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
-	margin: "",
-	padding: "",
-	border: "Width"
-}, function( prefix, suffix ) {
-	jQuery.cssHooks[ prefix + suffix ] = {
-		expand: function( value ) {
-			var i = 0,
-				expanded = {},
-
-				// Assumes a single number if not a string
-				parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
-			for ( ; i < 4; i++ ) {
-				expanded[ prefix + cssExpand[ i ] + suffix ] =
-					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
-			}
-
-			return expanded;
-		}
-	};
-
-	if ( prefix !== "margin" ) {
-		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
-	}
-} );
-
-jQuery.fn.extend( {
-	css: function( name, value ) {
-		return access( this, function( elem, name, value ) {
-			var styles, len,
-				map = {},
-				i = 0;
-
-			if ( Array.isArray( name ) ) {
-				styles = getStyles( elem );
-				len = name.length;
-
-				for ( ; i < len; i++ ) {
-					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
-				}
-
-				return map;
-			}
-
-			return value !== undefined ?
-				jQuery.style( elem, name, value ) :
-				jQuery.css( elem, name );
-		}, name, value, arguments.length > 1 );
-	}
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
-	return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
-	constructor: Tween,
-	init: function( elem, options, prop, end, easing, unit ) {
-		this.elem = elem;
-		this.prop = prop;
-		this.easing = easing || jQuery.easing._default;
-		this.options = options;
-		this.start = this.now = this.cur();
-		this.end = end;
-		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
-	},
-	cur: function() {
-		var hooks = Tween.propHooks[ this.prop ];
-
-		return hooks && hooks.get ?
-			hooks.get( this ) :
-			Tween.propHooks._default.get( this );
-	},
-	run: function( percent ) {
-		var eased,
-			hooks = Tween.propHooks[ this.prop ];
-
-		if ( this.options.duration ) {
-			this.pos = eased = jQuery.easing[ this.easing ](
-				percent, this.options.duration * percent, 0, 1, this.options.duration
-			);
-		} else {
-			this.pos = eased = percent;
-		}
-		this.now = ( this.end - this.start ) * eased + this.start;
-
-		if ( this.options.step ) {
-			this.options.step.call( this.elem, this.now, this );
-		}
-
-		if ( hooks && hooks.set ) {
-			hooks.set( this );
-		} else {
-			Tween.propHooks._default.set( this );
-		}
-		return this;
-	}
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
-	_default: {
-		get: function( tween ) {
-			var result;
-
-			// Use a property on the element directly when it is not a DOM element,
-			// or when there is no matching style property that exists.
-			if ( tween.elem.nodeType !== 1 ||
-				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
-				return tween.elem[ tween.prop ];
-			}
-
-			// Passing an empty string as a 3rd parameter to .css will automatically
-			// attempt a parseFloat and fallback to a string if the parse fails.
-			// Simple values such as "10px" are parsed to Float;
-			// complex values such as "rotate(1rad)" are returned as-is.
-			result = jQuery.css( tween.elem, tween.prop, "" );
-
-			// Empty strings, null, undefined and "auto" are converted to 0.
-			return !result || result === "auto" ? 0 : result;
-		},
-		set: function( tween ) {
-
-			// Use step hook for back compat.
-			// Use cssHook if its there.
-			// Use .style if available and use plain properties where available.
-			if ( jQuery.fx.step[ tween.prop ] ) {
-				jQuery.fx.step[ tween.prop ]( tween );
-			} else if ( tween.elem.nodeType === 1 && (
-				jQuery.cssHooks[ tween.prop ] ||
-					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
-				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
-			} else {
-				tween.elem[ tween.prop ] = tween.now;
-			}
-		}
-	}
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
-	set: function( tween ) {
-		if ( tween.elem.nodeType && tween.elem.parentNode ) {
-			tween.elem[ tween.prop ] = tween.now;
-		}
-	}
-};
-
-jQuery.easing = {
-	linear: function( p ) {
-		return p;
-	},
-	swing: function( p ) {
-		return 0.5 - Math.cos( p * Math.PI ) / 2;
-	},
-	_default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
-	fxNow, inProgress,
-	rfxtypes = /^(?:toggle|show|hide)$/,
-	rrun = /queueHooks$/;
-
-function schedule() {
-	if ( inProgress ) {
-		if ( document.hidden === false && window.requestAnimationFrame ) {
-			window.requestAnimationFrame( schedule );
-		} else {
-			window.setTimeout( schedule, jQuery.fx.interval );
-		}
-
-		jQuery.fx.tick();
-	}
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
-	window.setTimeout( function() {
-		fxNow = undefined;
-	} );
-	return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
-	var which,
-		i = 0,
-		attrs = { height: type };
-
-	// If we include width, step value is 1 to do all cssExpand values,
-	// otherwise step value is 2 to skip over Left and Right
-	includeWidth = includeWidth ? 1 : 0;
-	for ( ; i < 4; i += 2 - includeWidth ) {
-		which = cssExpand[ i ];
-		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
-	}
-
-	if ( includeWidth ) {
-		attrs.opacity = attrs.width = type;
-	}
-
-	return attrs;
-}
-
-function createTween( value, prop, animation ) {
-	var tween,
-		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
-		index = 0,
-		length = collection.length;
-	for ( ; index < length; index++ ) {
-		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
-			// We're done with this property
-			return tween;
-		}
-	}
-}
-
-function defaultPrefilter( elem, props, opts ) {
-	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
-		isBox = "width" in props || "height" in props,
-		anim = this,
-		orig = {},
-		style = elem.style,
-		hidden = elem.nodeType && isHiddenWithinTree( elem ),
-		dataShow = dataPriv.get( elem, "fxshow" );
-
-	// Queue-skipping animations hijack the fx hooks
-	if ( !opts.queue ) {
-		hooks = jQuery._queueHooks( elem, "fx" );
-		if ( hooks.unqueued == null ) {
-			hooks.unqueued = 0;
-			oldfire = hooks.empty.fire;
-			hooks.empty.fire = function() {
-				if ( !hooks.unqueued ) {
-					oldfire();
-				}
-			};
-		}
-		hooks.unqueued++;
-
-		anim.always( function() {
-
-			// Ensure the complete handler is called before this completes
-			anim.always( function() {
-				hooks.unqueued--;
-				if ( !jQuery.queue( elem, "fx" ).length ) {
-					hooks.empty.fire();
-				}
-			} );
-		} );
-	}
-
-	// Detect show/hide animations
-	for ( prop in props ) {
-		value = props[ prop ];
-		if ( rfxtypes.test( value ) ) {
-			delete props[ prop ];
-			toggle = toggle || value === "toggle";
-			if ( value === ( hidden ? "hide" : "show" ) ) {
-
-				// Pretend to be hidden if this is a "show" and
-				// there is still data from a stopped show/hide
-				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
-					hidden = true;
-
-				// Ignore all other no-op show/hide data
-				} else {
-					continue;
-				}
-			}
-			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
-		}
-	}
-
-	// Bail out if this is a no-op like .hide().hide()
-	propTween = !jQuery.isEmptyObject( props );
-	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
-		return;
-	}
-
-	// Restrict "overflow" and "display" styles during box animations
-	if ( isBox && elem.nodeType === 1 ) {
-
-		// Support: IE <=9 - 11, Edge 12 - 15
-		// Record all 3 overflow attributes because IE does not infer the shorthand
-		// from identically-valued overflowX and overflowY and Edge just mirrors
-		// the overflowX value there.
-		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
-		// Identify a display type, preferring old show/hide data over the CSS cascade
-		restoreDisplay = dataShow && dataShow.display;
-		if ( restoreDisplay == null ) {
-			restoreDisplay = dataPriv.get( elem, "display" );
-		}
-		display = jQuery.css( elem, "display" );
-		if ( display === "none" ) {
-			if ( restoreDisplay ) {
-				display = restoreDisplay;
-			} else {
-
-				// Get nonempty value(s) by temporarily forcing visibility
-				showHide( [ elem ], true );
-				restoreDisplay = elem.style.display || restoreDisplay;
-				display = jQuery.css( elem, "display" );
-				showHide( [ elem ] );
-			}
-		}
-
-		// Animate inline elements as inline-block
-		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
-			if ( jQuery.css( elem, "float" ) === "none" ) {
-
-				// Restore the original display value at the end of pure show/hide animations
-				if ( !propTween ) {
-					anim.done( function() {
-						style.display = restoreDisplay;
-					} );
-					if ( restoreDisplay == null ) {
-						display = style.display;
-						restoreDisplay = display === "none" ? "" : display;
-					}
-				}
-				style.display = "inline-block";
-			}
-		}
-	}
-
-	if ( opts.overflow ) {
-		style.overflow = "hidden";
-		anim.always( function() {
-			style.overflow = opts.overflow[ 0 ];
-			style.overflowX = opts.overflow[ 1 ];
-			style.overflowY = opts.overflow[ 2 ];
-		} );
-	}
-
-	// Implement show/hide animations
-	propTween = false;
-	for ( prop in orig ) {
-
-		// General show/hide setup for this element animation
-		if ( !propTween ) {
-			if ( dataShow ) {
-				if ( "hidden" in dataShow ) {
-					hidden = dataShow.hidden;
-				}
-			} else {
-				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
-			}
-
-			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
-			if ( toggle ) {
-				dataShow.hidden = !hidden;
-			}
-
-			// Show elements before animating them
-			if ( hidden ) {
-				showHide( [ elem ], true );
-			}
-
-			/* eslint-disable no-loop-func */
-
-			anim.done( function() {
-
-				/* eslint-enable no-loop-func */
-
-				// The final step of a "hide" animation is actually hiding the element
-				if ( !hidden ) {
-					showHide( [ elem ] );
-				}
-				dataPriv.remove( elem, "fxshow" );
-				for ( prop in orig ) {
-					jQuery.style( elem, prop, orig[ prop ] );
-				}
-			} );
-		}
-
-		// Per-property setup
-		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
-		if ( !( prop in dataShow ) ) {
-			dataShow[ prop ] = propTween.start;
-			if ( hidden ) {
-				propTween.end = propTween.start;
-				propTween.start = 0;
-			}
-		}
-	}
-}
-
-function propFilter( props, specialEasing ) {
-	var index, name, easing, value, hooks;
-
-	// camelCase, specialEasing and expand cssHook pass
-	for ( index in props ) {
-		name = camelCase( index );
-		easing = specialEasing[ name ];
-		value = props[ index ];
-		if ( Array.isArray( value ) ) {
-			easing = value[ 1 ];
-			value = props[ index ] = value[ 0 ];
-		}
-
-		if ( index !== name ) {
-			props[ name ] = value;
-			delete props[ index ];
-		}
-
-		hooks = jQuery.cssHooks[ name ];
-		if ( hooks && "expand" in hooks ) {
-			value = hooks.expand( value );
-			delete props[ name ];
-
-			// Not quite $.extend, this won't overwrite existing keys.
-			// Reusing 'index' because we have the correct "name"
-			for ( index in value ) {
-				if ( !( index in props ) ) {
-					props[ index ] = value[ index ];
-					specialEasing[ index ] = easing;
-				}
-			}
-		} else {
-			specialEasing[ name ] = easing;
-		}
-	}
-}
-
-function Animation( elem, properties, options ) {
-	var result,
-		stopped,
-		index = 0,
-		length = Animation.prefilters.length,
-		deferred = jQuery.Deferred().always( function() {
-
-			// Don't match elem in the :animated selector
-			delete tick.elem;
-		} ),
-		tick = function() {
-			if ( stopped ) {
-				return false;
-			}
-			var currentTime = fxNow || createFxNow(),
-				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
-				// Support: Android 2.3 only
-				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
-				temp = remaining / animation.duration || 0,
-				percent = 1 - temp,
-				index = 0,
-				length = animation.tweens.length;
-
-			for ( ; index < length; index++ ) {
-				animation.tweens[ index ].run( percent );
-			}
-
-			deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
-			// If there's more to do, yield
-			if ( percent < 1 && length ) {
-				return remaining;
-			}
-
-			// If this was an empty animation, synthesize a final progress notification
-			if ( !length ) {
-				deferred.notifyWith( elem, [ animation, 1, 0 ] );
-			}
-
-			// Resolve the animation and report its conclusion
-			deferred.resolveWith( elem, [ animation ] );
-			return false;
-		},
-		animation = deferred.promise( {
-			elem: elem,
-			props: jQuery.extend( {}, properties ),
-			opts: jQuery.extend( true, {
-				specialEasing: {},
-				easing: jQuery.easing._default
-			}, options ),
-			originalProperties: properties,
-			originalOptions: options,
-			startTime: fxNow || createFxNow(),
-			duration: options.duration,
-			tweens: [],
-			createTween: function( prop, end ) {
-				var tween = jQuery.Tween( elem, animation.opts, prop, end,
-					animation.opts.specialEasing[ prop ] || animation.opts.easing );
-				animation.tweens.push( tween );
-				return tween;
-			},
-			stop: function( gotoEnd ) {
-				var index = 0,
-
-					// If we are going to the end, we want to run all the tweens
-					// otherwise we skip this part
-					length = gotoEnd ? animation.tweens.length : 0;
-				if ( stopped ) {
-					return this;
-				}
-				stopped = true;
-				for ( ; index < length; index++ ) {
-					animation.tweens[ index ].run( 1 );
-				}
-
-				// Resolve when we played the last frame; otherwise, reject
-				if ( gotoEnd ) {
-					deferred.notifyWith( elem, [ animation, 1, 0 ] );
-					deferred.resolveWith( elem, [ animation, gotoEnd ] );
-				} else {
-					deferred.rejectWith( elem, [ animation, gotoEnd ] );
-				}
-				return this;
-			}
-		} ),
-		props = animation.props;
-
-	propFilter( props, animation.opts.specialEasing );
-
-	for ( ; index < length; index++ ) {
-		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
-		if ( result ) {
-			if ( isFunction( result.stop ) ) {
-				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
-					result.stop.bind( result );
-			}
-			return result;
-		}
-	}
-
-	jQuery.map( props, createTween, animation );
-
-	if ( isFunction( animation.opts.start ) ) {
-		animation.opts.start.call( elem, animation );
-	}
-
-	// Attach callbacks from options
-	animation
-		.progress( animation.opts.progress )
-		.done( animation.opts.done, animation.opts.complete )
-		.fail( animation.opts.fail )
-		.always( animation.opts.always );
-
-	jQuery.fx.timer(
-		jQuery.extend( tick, {
-			elem: elem,
-			anim: animation,
-			queue: animation.opts.queue
-		} )
-	);
-
-	return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
-	tweeners: {
-		"*": [ function( prop, value ) {
-			var tween = this.createTween( prop, value );
-			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
-			return tween;
-		} ]
-	},
-
-	tweener: function( props, callback ) {
-		if ( isFunction( props ) ) {
-			callback = props;
-			props = [ "*" ];
-		} else {
-			props = props.match( rnothtmlwhite );
-		}
-
-		var prop,
-			index = 0,
-			length = props.length;
-
-		for ( ; index < length; index++ ) {
-			prop = props[ index ];
-			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
-			Animation.tweeners[ prop ].unshift( callback );
-		}
-	},
-
-	prefilters: [ defaultPrefilter ],
-
-	prefilter: function( callback, prepend ) {
-		if ( prepend ) {
-			Animation.prefilters.unshift( callback );
-		} else {
-			Animation.prefilters.push( callback );
-		}
-	}
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
-	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
-		complete: fn || !fn && easing ||
-			isFunction( speed ) && speed,
-		duration: speed,
-		easing: fn && easing || easing && !isFunction( easing ) && easing
-	};
-
-	// Go to the end state if fx are off
-	if ( jQuery.fx.off ) {
-		opt.duration = 0;
-
-	} else {
-		if ( typeof opt.duration !== "number" ) {
-			if ( opt.duration in jQuery.fx.speeds ) {
-				opt.duration = jQuery.fx.speeds[ opt.duration ];
-
-			} else {
-				opt.duration = jQuery.fx.speeds._default;
-			}
-		}
-	}
-
-	// Normalize opt.queue - true/undefined/null -> "fx"
-	if ( opt.queue == null || opt.queue === true ) {
-		opt.queue = "fx";
-	}
-
-	// Queueing
-	opt.old = opt.complete;
-
-	opt.complete = function() {
-		if ( isFunction( opt.old ) ) {
-			opt.old.call( this );
-		}
-
-		if ( opt.queue ) {
-			jQuery.dequeue( this, opt.queue );
-		}
-	};
-
-	return opt;
-};
-
-jQuery.fn.extend( {
-	fadeTo: function( speed, to, easing, callback ) {
-
-		// Show any hidden elements after setting opacity to 0
-		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
-			// Animate to the value specified
-			.end().animate( { opacity: to }, speed, easing, callback );
-	},
-	animate: function( prop, speed, easing, callback ) {
-		var empty = jQuery.isEmptyObject( prop ),
-			optall = jQuery.speed( speed, easing, callback ),
-			doAnimation = function() {
-
-				// Operate on a copy of prop so per-property easing won't be lost
-				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
-				// Empty animations, or finishing resolves immediately
-				if ( empty || dataPriv.get( this, "finish" ) ) {
-					anim.stop( true );
-				}
-			};
-
-		doAnimation.finish = doAnimation;
-
-		return empty || optall.queue === false ?
-			this.each( doAnimation ) :
-			this.queue( optall.queue, doAnimation );
-	},
-	stop: function( type, clearQueue, gotoEnd ) {
-		var stopQueue = function( hooks ) {
-			var stop = hooks.stop;
-			delete hooks.stop;
-			stop( gotoEnd );
-		};
-
-		if ( typeof type !== "string" ) {
-			gotoEnd = clearQueue;
-			clearQueue = type;
-			type = undefined;
-		}
-		if ( clearQueue ) {
-			this.queue( type || "fx", [] );
-		}
-
-		return this.each( function() {
-			var dequeue = true,
-				index = type != null && type + "queueHooks",
-				timers = jQuery.timers,
-				data = dataPriv.get( this );
-
-			if ( index ) {
-				if ( data[ index ] && data[ index ].stop ) {
-					stopQueue( data[ index ] );
-				}
-			} else {
-				for ( index in data ) {
-					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
-						stopQueue( data[ index ] );
-					}
-				}
-			}
-
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this &&
-					( type == null || timers[ index ].queue === type ) ) {
-
-					timers[ index ].anim.stop( gotoEnd );
-					dequeue = false;
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Start the next in the queue if the last step wasn't forced.
-			// Timers currently will call their complete callbacks, which
-			// will dequeue but only if they were gotoEnd.
-			if ( dequeue || !gotoEnd ) {
-				jQuery.dequeue( this, type );
-			}
-		} );
-	},
-	finish: function( type ) {
-		if ( type !== false ) {
-			type = type || "fx";
-		}
-		return this.each( function() {
-			var index,
-				data = dataPriv.get( this ),
-				queue = data[ type + "queue" ],
-				hooks = data[ type + "queueHooks" ],
-				timers = jQuery.timers,
-				length = queue ? queue.length : 0;
-
-			// Enable finishing flag on private data
-			data.finish = true;
-
-			// Empty the queue first
-			jQuery.queue( this, type, [] );
-
-			if ( hooks && hooks.stop ) {
-				hooks.stop.call( this, true );
-			}
-
-			// Look for any active animations, and finish them
-			for ( index = timers.length; index--; ) {
-				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
-					timers[ index ].anim.stop( true );
-					timers.splice( index, 1 );
-				}
-			}
-
-			// Look for any animations in the old queue and finish them
-			for ( index = 0; index < length; index++ ) {
-				if ( queue[ index ] && queue[ index ].finish ) {
-					queue[ index ].finish.call( this );
-				}
-			}
-
-			// Turn off finishing flag
-			delete data.finish;
-		} );
-	}
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
-	var cssFn = jQuery.fn[ name ];
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return speed == null || typeof speed === "boolean" ?
-			cssFn.apply( this, arguments ) :
-			this.animate( genFx( name, true ), speed, easing, callback );
-	};
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
-	slideDown: genFx( "show" ),
-	slideUp: genFx( "hide" ),
-	slideToggle: genFx( "toggle" ),
-	fadeIn: { opacity: "show" },
-	fadeOut: { opacity: "hide" },
-	fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
-	jQuery.fn[ name ] = function( speed, easing, callback ) {
-		return this.animate( props, speed, easing, callback );
-	};
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
-	var timer,
-		i = 0,
-		timers = jQuery.timers;
-
-	fxNow = Date.now();
-
-	for ( ; i < timers.length; i++ ) {
-		timer = timers[ i ];
-
-		// Run the timer and safely remove it when done (allowing for external removal)
-		if ( !timer() && timers[ i ] === timer ) {
-			timers.splice( i--, 1 );
-		}
-	}
-
-	if ( !timers.length ) {
-		jQuery.fx.stop();
-	}
-	fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
-	jQuery.timers.push( timer );
-	jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
-	if ( inProgress ) {
-		return;
-	}
-
-	inProgress = true;
-	schedule();
-};
-
-jQuery.fx.stop = function() {
-	inProgress = null;
-};
-
-jQuery.fx.speeds = {
-	slow: 600,
-	fast: 200,
-
-	// Default speed
-	_default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
-	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
-	type = type || "fx";
-
-	return this.queue( type, function( next, hooks ) {
-		var timeout = window.setTimeout( next, time );
-		hooks.stop = function() {
-			window.clearTimeout( timeout );
-		};
-	} );
-};
-
-
-( function() {
-	var input = document.createElement( "input" ),
-		select = document.createElement( "select" ),
-		opt = select.appendChild( document.createElement( "option" ) );
-
-	input.type = "checkbox";
-
-	// Support: Android <=4.3 only
-	// Default value for a checkbox should be "on"
-	support.checkOn = input.value !== "";
-
-	// Support: IE <=11 only
-	// Must access selectedIndex to make default options select
-	support.optSelected = opt.selected;
-
-	// Support: IE <=11 only
-	// An input loses its value after becoming a radio
-	input = document.createElement( "input" );
-	input.value = "t";
-	input.type = "radio";
-	support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
-	attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
-	attr: function( name, value ) {
-		return access( this, jQuery.attr, name, value, arguments.length > 1 );
-	},
-
-	removeAttr: function( name ) {
-		return this.each( function() {
-			jQuery.removeAttr( this, name );
-		} );
-	}
-} );
-
-jQuery.extend( {
-	attr: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set attributes on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		// Fallback to prop when attributes are not supported
-		if ( typeof elem.getAttribute === "undefined" ) {
-			return jQuery.prop( elem, name, value );
-		}
-
-		// Attribute hooks are determined by the lowercase version
-		// Grab necessary hook if one is defined
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
-				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
-		}
-
-		if ( value !== undefined ) {
-			if ( value === null ) {
-				jQuery.removeAttr( elem, name );
-				return;
-			}
-
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			elem.setAttribute( name, value + "" );
-			return value;
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		ret = jQuery.find.attr( elem, name );
-
-		// Non-existent attributes return null, we normalize to undefined
-		return ret == null ? undefined : ret;
-	},
-
-	attrHooks: {
-		type: {
-			set: function( elem, value ) {
-				if ( !support.radioValue && value === "radio" &&
-					nodeName( elem, "input" ) ) {
-					var val = elem.value;
-					elem.setAttribute( "type", value );
-					if ( val ) {
-						elem.value = val;
-					}
-					return value;
-				}
-			}
-		}
-	},
-
-	removeAttr: function( elem, value ) {
-		var name,
-			i = 0,
-
-			// Attribute names can contain non-HTML whitespace characters
-			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
-			attrNames = value && value.match( rnothtmlwhite );
-
-		if ( attrNames && elem.nodeType === 1 ) {
-			while ( ( name = attrNames[ i++ ] ) ) {
-				elem.removeAttribute( name );
-			}
-		}
-	}
-} );
-
-// Hooks for boolean attributes
-boolHook = {
-	set: function( elem, value, name ) {
-		if ( value === false ) {
-
-			// Remove boolean attributes when set to false
-			jQuery.removeAttr( elem, name );
-		} else {
-			elem.setAttribute( name, name );
-		}
-		return name;
-	}
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
-	var getter = attrHandle[ name ] || jQuery.find.attr;
-
-	attrHandle[ name ] = function( elem, name, isXML ) {
-		var ret, handle,
-			lowercaseName = name.toLowerCase();
-
-		if ( !isXML ) {
-
-			// Avoid an infinite loop by temporarily removing this function from the getter
-			handle = attrHandle[ lowercaseName ];
-			attrHandle[ lowercaseName ] = ret;
-			ret = getter( elem, name, isXML ) != null ?
-				lowercaseName :
-				null;
-			attrHandle[ lowercaseName ] = handle;
-		}
-		return ret;
-	};
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
-	rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
-	prop: function( name, value ) {
-		return access( this, jQuery.prop, name, value, arguments.length > 1 );
-	},
-
-	removeProp: function( name ) {
-		return this.each( function() {
-			delete this[ jQuery.propFix[ name ] || name ];
-		} );
-	}
-} );
-
-jQuery.extend( {
-	prop: function( elem, name, value ) {
-		var ret, hooks,
-			nType = elem.nodeType;
-
-		// Don't get/set properties on text, comment and attribute nodes
-		if ( nType === 3 || nType === 8 || nType === 2 ) {
-			return;
-		}
-
-		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
-			// Fix name and attach hooks
-			name = jQuery.propFix[ name ] || name;
-			hooks = jQuery.propHooks[ name ];
-		}
-
-		if ( value !== undefined ) {
-			if ( hooks && "set" in hooks &&
-				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
-				return ret;
-			}
-
-			return ( elem[ name ] = value );
-		}
-
-		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
-			return ret;
-		}
-
-		return elem[ name ];
-	},
-
-	propHooks: {
-		tabIndex: {
-			get: function( elem ) {
-
-				// Support: IE <=9 - 11 only
-				// elem.tabIndex doesn't always return the
-				// correct value when it hasn't been explicitly set
-				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
-				// Use proper attribute retrieval(#12072)
-				var tabindex = jQuery.find.attr( elem, "tabindex" );
-
-				if ( tabindex ) {
-					return parseInt( tabindex, 10 );
-				}
-
-				if (
-					rfocusable.test( elem.nodeName ) ||
-					rclickable.test( elem.nodeName ) &&
-					elem.href
-				) {
-					return 0;
-				}
-
-				return -1;
-			}
-		}
-	},
-
-	propFix: {
-		"for": "htmlFor",
-		"class": "className"
-	}
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
-	jQuery.propHooks.selected = {
-		get: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent && parent.parentNode ) {
-				parent.parentNode.selectedIndex;
-			}
-			return null;
-		},
-		set: function( elem ) {
-
-			/* eslint no-unused-expressions: "off" */
-
-			var parent = elem.parentNode;
-			if ( parent ) {
-				parent.selectedIndex;
-
-				if ( parent.parentNode ) {
-					parent.parentNode.selectedIndex;
-				}
-			}
-		}
-	};
-}
-
-jQuery.each( [
-	"tabIndex",
-	"readOnly",
-	"maxLength",
-	"cellSpacing",
-	"cellPadding",
-	"rowSpan",
-	"colSpan",
-	"useMap",
-	"frameBorder",
-	"contentEditable"
-], function() {
-	jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
-	// Strip and collapse whitespace according to HTML spec
-	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
-	function stripAndCollapse( value ) {
-		var tokens = value.match( rnothtmlwhite ) || [];
-		return tokens.join( " " );
-	}
-
-
-function getClass( elem ) {
-	return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
-	if ( Array.isArray( value ) ) {
-		return value;
-	}
-	if ( typeof value === "string" ) {
-		return value.match( rnothtmlwhite ) || [];
-	}
-	return [];
-}
-
-jQuery.fn.extend( {
-	addClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
-							cur += clazz + " ";
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	removeClass: function( value ) {
-		var classes, elem, cur, curValue, clazz, j, finalValue,
-			i = 0;
-
-		if ( isFunction( value ) ) {
-			return this.each( function( j ) {
-				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
-			} );
-		}
-
-		if ( !arguments.length ) {
-			return this.attr( "class", "" );
-		}
-
-		classes = classesToArray( value );
-
-		if ( classes.length ) {
-			while ( ( elem = this[ i++ ] ) ) {
-				curValue = getClass( elem );
-
-				// This expression is here for better compressibility (see addClass)
-				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
-				if ( cur ) {
-					j = 0;
-					while ( ( clazz = classes[ j++ ] ) ) {
-
-						// Remove *all* instances
-						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
-							cur = cur.replace( " " + clazz + " ", " " );
-						}
-					}
-
-					// Only assign if different to avoid unneeded rendering.
-					finalValue = stripAndCollapse( cur );
-					if ( curValue !== finalValue ) {
-						elem.setAttribute( "class", finalValue );
-					}
-				}
-			}
-		}
-
-		return this;
-	},
-
-	toggleClass: function( value, stateVal ) {
-		var type = typeof value,
-			isValidValue = type === "string" || Array.isArray( value );
-
-		if ( typeof stateVal === "boolean" && isValidValue ) {
-			return stateVal ? this.addClass( value ) : this.removeClass( value );
-		}
-
-		if ( isFunction( value ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).toggleClass(
-					value.call( this, i, getClass( this ), stateVal ),
-					stateVal
-				);
-			} );
-		}
-
-		return this.each( function() {
-			var className, i, self, classNames;
-
-			if ( isValidValue ) {
-
-				// Toggle individual class names
-				i = 0;
-				self = jQuery( this );
-				classNames = classesToArray( value );
-
-				while ( ( className = classNames[ i++ ] ) ) {
-
-					// Check each className given, space separated list
-					if ( self.hasClass( className ) ) {
-						self.removeClass( className );
-					} else {
-						self.addClass( className );
-					}
-				}
-
-			// Toggle whole class name
-			} else if ( value === undefined || type === "boolean" ) {
-				className = getClass( this );
-				if ( className ) {
-
-					// Store className if set
-					dataPriv.set( this, "__className__", className );
-				}
-
-				// If the element has a class name or if we're passed `false`,
-				// then remove the whole classname (if there was one, the above saved it).
-				// Otherwise bring back whatever was previously saved (if anything),
-				// falling back to the empty string if nothing was stored.
-				if ( this.setAttribute ) {
-					this.setAttribute( "class",
-						className || value === false ?
-							"" :
-							dataPriv.get( this, "__className__" ) || ""
-					);
-				}
-			}
-		} );
-	},
-
-	hasClass: function( selector ) {
-		var className, elem,
-			i = 0;
-
-		className = " " + selector + " ";
-		while ( ( elem = this[ i++ ] ) ) {
-			if ( elem.nodeType === 1 &&
-				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
-				return true;
-			}
-		}
-
-		return false;
-	}
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
-	val: function( value ) {
-		var hooks, ret, valueIsFunction,
-			elem = this[ 0 ];
-
-		if ( !arguments.length ) {
-			if ( elem ) {
-				hooks = jQuery.valHooks[ elem.type ] ||
-					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
-				if ( hooks &&
-					"get" in hooks &&
-					( ret = hooks.get( elem, "value" ) ) !== undefined
-				) {
-					return ret;
-				}
-
-				ret = elem.value;
-
-				// Handle most common string cases
-				if ( typeof ret === "string" ) {
-					return ret.replace( rreturn, "" );
-				}
-
-				// Handle cases where value is null/undef or number
-				return ret == null ? "" : ret;
-			}
-
-			return;
-		}
-
-		valueIsFunction = isFunction( value );
-
-		return this.each( function( i ) {
-			var val;
-
-			if ( this.nodeType !== 1 ) {
-				return;
-			}
-
-			if ( valueIsFunction ) {
-				val = value.call( this, i, jQuery( this ).val() );
-			} else {
-				val = value;
-			}
-
-			// Treat null/undefined as ""; convert numbers to string
-			if ( val == null ) {
-				val = "";
-
-			} else if ( typeof val === "number" ) {
-				val += "";
-
-			} else if ( Array.isArray( val ) ) {
-				val = jQuery.map( val, function( value ) {
-					return value == null ? "" : value + "";
-				} );
-			}
-
-			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
-			// If set returns undefined, fall back to normal setting
-			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
-				this.value = val;
-			}
-		} );
-	}
-} );
-
-jQuery.extend( {
-	valHooks: {
-		option: {
-			get: function( elem ) {
-
-				var val = jQuery.find.attr( elem, "value" );
-				return val != null ?
-					val :
-
-					// Support: IE <=10 - 11 only
-					// option.text throws exceptions (#14686, #14858)
-					// Strip and collapse whitespace
-					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
-					stripAndCollapse( jQuery.text( elem ) );
-			}
-		},
-		select: {
-			get: function( elem ) {
-				var value, option, i,
-					options = elem.options,
-					index = elem.selectedIndex,
-					one = elem.type === "select-one",
-					values = one ? null : [],
-					max = one ? index + 1 : options.length;
-
-				if ( index < 0 ) {
-					i = max;
-
-				} else {
-					i = one ? index : 0;
-				}
-
-				// Loop through all the selected options
-				for ( ; i < max; i++ ) {
-					option = options[ i ];
-
-					// Support: IE <=9 only
-					// IE8-9 doesn't update selected after form reset (#2551)
-					if ( ( option.selected || i === index ) &&
-
-							// Don't return options that are disabled or in a disabled optgroup
-							!option.disabled &&
-							( !option.parentNode.disabled ||
-								!nodeName( option.parentNode, "optgroup" ) ) ) {
-
-						// Get the specific value for the option
-						value = jQuery( option ).val();
-
-						// We don't need an array for one selects
-						if ( one ) {
-							return value;
-						}
-
-						// Multi-Selects return an array
-						values.push( value );
-					}
-				}
-
-				return values;
-			},
-
-			set: function( elem, value ) {
-				var optionSet, option,
-					options = elem.options,
-					values = jQuery.makeArray( value ),
-					i = options.length;
-
-				while ( i-- ) {
-					option = options[ i ];
-
-					/* eslint-disable no-cond-assign */
-
-					if ( option.selected =
-						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
-					) {
-						optionSet = true;
-					}
-
-					/* eslint-enable no-cond-assign */
-				}
-
-				// Force browsers to behave consistently when non-matching value is set
-				if ( !optionSet ) {
-					elem.selectedIndex = -1;
-				}
-				return values;
-			}
-		}
-	}
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
-	jQuery.valHooks[ this ] = {
-		set: function( elem, value ) {
-			if ( Array.isArray( value ) ) {
-				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
-			}
-		}
-	};
-	if ( !support.checkOn ) {
-		jQuery.valHooks[ this ].get = function( elem ) {
-			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
-		};
-	}
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
-	stopPropagationCallback = function( e ) {
-		e.stopPropagation();
-	};
-
-jQuery.extend( jQuery.event, {
-
-	trigger: function( event, data, elem, onlyHandlers ) {
-
-		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
-			eventPath = [ elem || document ],
-			type = hasOwn.call( event, "type" ) ? event.type : event,
-			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
-		cur = lastElement = tmp = elem = elem || document;
-
-		// Don't do events on text and comment nodes
-		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
-			return;
-		}
-
-		// focus/blur morphs to focusin/out; ensure we're not firing them right now
-		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
-			return;
-		}
-
-		if ( type.indexOf( "." ) > -1 ) {
-
-			// Namespaced trigger; create a regexp to match event type in handle()
-			namespaces = type.split( "." );
-			type = namespaces.shift();
-			namespaces.sort();
-		}
-		ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
-		// Caller can pass in a jQuery.Event object, Object, or just an event type string
-		event = event[ jQuery.expando ] ?
-			event :
-			new jQuery.Event( type, typeof event === "object" && event );
-
-		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
-		event.isTrigger = onlyHandlers ? 2 : 3;
-		event.namespace = namespaces.join( "." );
-		event.rnamespace = event.namespace ?
-			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
-			null;
-
-		// Clean up the event in case it is being reused
-		event.result = undefined;
-		if ( !event.target ) {
-			event.target = elem;
-		}
-
-		// Clone any incoming data and prepend the event, creating the handler arg list
-		data = data == null ?
-			[ event ] :
-			jQuery.makeArray( data, [ event ] );
-
-		// Allow special events to draw outside the lines
-		special = jQuery.event.special[ type ] || {};
-		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
-			return;
-		}
-
-		// Determine event propagation path in advance, per W3C events spec (#9951)
-		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
-		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
-			bubbleType = special.delegateType || type;
-			if ( !rfocusMorph.test( bubbleType + type ) ) {
-				cur = cur.parentNode;
-			}
-			for ( ; cur; cur = cur.parentNode ) {
-				eventPath.push( cur );
-				tmp = cur;
-			}
-
-			// Only add window if we got to document (e.g., not plain obj or detached DOM)
-			if ( tmp === ( elem.ownerDocument || document ) ) {
-				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
-			}
-		}
-
-		// Fire handlers on the event path
-		i = 0;
-		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
-			lastElement = cur;
-			event.type = i > 1 ?
-				bubbleType :
-				special.bindType || type;
-
-			// jQuery handler
-			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
-				dataPriv.get( cur, "handle" );
-			if ( handle ) {
-				handle.apply( cur, data );
-			}
-
-			// Native handler
-			handle = ontype && cur[ ontype ];
-			if ( handle && handle.apply && acceptData( cur ) ) {
-				event.result = handle.apply( cur, data );
-				if ( event.result === false ) {
-					event.preventDefault();
-				}
-			}
-		}
-		event.type = type;
-
-		// If nobody prevented the default action, do it now
-		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
-			if ( ( !special._default ||
-				special._default.apply( eventPath.pop(), data ) === false ) &&
-				acceptData( elem ) ) {
-
-				// Call a native DOM method on the target with the same name as the event.
-				// Don't do default actions on window, that's where global variables be (#6170)
-				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
-					// Don't re-trigger an onFOO event when we call its FOO() method
-					tmp = elem[ ontype ];
-
-					if ( tmp ) {
-						elem[ ontype ] = null;
-					}
-
-					// Prevent re-triggering of the same event, since we already bubbled it above
-					jQuery.event.triggered = type;
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.addEventListener( type, stopPropagationCallback );
-					}
-
-					elem[ type ]();
-
-					if ( event.isPropagationStopped() ) {
-						lastElement.removeEventListener( type, stopPropagationCallback );
-					}
-
-					jQuery.event.triggered = undefined;
-
-					if ( tmp ) {
-						elem[ ontype ] = tmp;
-					}
-				}
-			}
-		}
-
-		return event.result;
-	},
-
-	// Piggyback on a donor event to simulate a different one
-	// Used only for `focus(in | out)` events
-	simulate: function( type, elem, event ) {
-		var e = jQuery.extend(
-			new jQuery.Event(),
-			event,
-			{
-				type: type,
-				isSimulated: true
-			}
-		);
-
-		jQuery.event.trigger( e, null, elem );
-	}
-
-} );
-
-jQuery.fn.extend( {
-
-	trigger: function( type, data ) {
-		return this.each( function() {
-			jQuery.event.trigger( type, data, this );
-		} );
-	},
-	triggerHandler: function( type, data ) {
-		var elem = this[ 0 ];
-		if ( elem ) {
-			return jQuery.event.trigger( type, data, elem, true );
-		}
-	}
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
-	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
-		// Attach a single capturing handler on the document while someone wants focusin/focusout
-		var handler = function( event ) {
-			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
-		};
-
-		jQuery.event.special[ fix ] = {
-			setup: function() {
-
-				// Handle: regular nodes (via `this.ownerDocument`), window
-				// (via `this.document`) & document (via `this`).
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix );
-
-				if ( !attaches ) {
-					doc.addEventListener( orig, handler, true );
-				}
-				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
-			},
-			teardown: function() {
-				var doc = this.ownerDocument || this.document || this,
-					attaches = dataPriv.access( doc, fix ) - 1;
-
-				if ( !attaches ) {
-					doc.removeEventListener( orig, handler, true );
-					dataPriv.remove( doc, fix );
-
-				} else {
-					dataPriv.access( doc, fix, attaches );
-				}
-			}
-		};
-	} );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
-	var xml, parserErrorElem;
-	if ( !data || typeof data !== "string" ) {
-		return null;
-	}
-
-	// Support: IE 9 - 11 only
-	// IE throws on parseFromString with invalid input.
-	try {
-		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
-	} catch ( e ) {}
-
-	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
-	if ( !xml || parserErrorElem ) {
-		jQuery.error( "Invalid XML: " + (
-			parserErrorElem ?
-				jQuery.map( parserErrorElem.childNodes, function( el ) {
-					return el.textContent;
-				} ).join( "\n" ) :
-				data
-		) );
-	}
-	return xml;
-};
-
-
-var
-	rbracket = /\[\]$/,
-	rCRLF = /\r?\n/g,
-	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
-	rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
-	var name;
-
-	if ( Array.isArray( obj ) ) {
-
-		// Serialize array item.
-		jQuery.each( obj, function( i, v ) {
-			if ( traditional || rbracket.test( prefix ) ) {
-
-				// Treat each array item as a scalar.
-				add( prefix, v );
-
-			} else {
-
-				// Item is non-scalar (array or object), encode its numeric index.
-				buildParams(
-					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
-					v,
-					traditional,
-					add
-				);
-			}
-		} );
-
-	} else if ( !traditional && toType( obj ) === "object" ) {
-
-		// Serialize object item.
-		for ( name in obj ) {
-			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
-		}
-
-	} else {
-
-		// Serialize scalar item.
-		add( prefix, obj );
-	}
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
-	var prefix,
-		s = [],
-		add = function( key, valueOrFunction ) {
-
-			// If value is a function, invoke it and use its return value
-			var value = isFunction( valueOrFunction ) ?
-				valueOrFunction() :
-				valueOrFunction;
-
-			s[ s.length ] = encodeURIComponent( key ) + "=" +
-				encodeURIComponent( value == null ? "" : value );
-		};
-
-	if ( a == null ) {
-		return "";
-	}
-
-	// If an array was passed in, assume that it is an array of form elements.
-	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
-		// Serialize the form elements
-		jQuery.each( a, function() {
-			add( this.name, this.value );
-		} );
-
-	} else {
-
-		// If traditional, encode the "old" way (the way 1.3.2 or older
-		// did it), otherwise encode params recursively.
-		for ( prefix in a ) {
-			buildParams( prefix, a[ prefix ], traditional, add );
-		}
-	}
-
-	// Return the resulting serialization
-	return s.join( "&" );
-};
-
-jQuery.fn.extend( {
-	serialize: function() {
-		return jQuery.param( this.serializeArray() );
-	},
-	serializeArray: function() {
-		return this.map( function() {
-
-			// Can add propHook for "elements" to filter or add form elements
-			var elements = jQuery.prop( this, "elements" );
-			return elements ? jQuery.makeArray( elements ) : this;
-		} ).filter( function() {
-			var type = this.type;
-
-			// Use .is( ":disabled" ) so that fieldset[disabled] works
-			return this.name && !jQuery( this ).is( ":disabled" ) &&
-				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
-				( this.checked || !rcheckableType.test( type ) );
-		} ).map( function( _i, elem ) {
-			var val = jQuery( this ).val();
-
-			if ( val == null ) {
-				return null;
-			}
-
-			if ( Array.isArray( val ) ) {
-				return jQuery.map( val, function( val ) {
-					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-				} );
-			}
-
-			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
-		} ).get();
-	}
-} );
-
-
-var
-	r20 = /%20/g,
-	rhash = /#.*$/,
-	rantiCache = /([?&])_=[^&]*/,
-	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
-	// #7653, #8125, #8152: local protocol detection
-	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
-	rnoContent = /^(?:GET|HEAD)$/,
-	rprotocol = /^\/\//,
-
-	/* Prefilters
-	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
-	 * 2) These are called:
-	 *    - BEFORE asking for a transport
-	 *    - AFTER param serialization (s.data is a string if s.processData is true)
-	 * 3) key is the dataType
-	 * 4) the catchall symbol "*" can be used
-	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
-	 */
-	prefilters = {},
-
-	/* Transports bindings
-	 * 1) key is the dataType
-	 * 2) the catchall symbol "*" can be used
-	 * 3) selection will start with transport dataType and THEN go to "*" if needed
-	 */
-	transports = {},
-
-	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
-	allTypes = "*/".concat( "*" ),
-
-	// Anchor tag for parsing the document origin
-	originAnchor = document.createElement( "a" );
-
-originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
-	// dataTypeExpression is optional and defaults to "*"
-	return function( dataTypeExpression, func ) {
-
-		if ( typeof dataTypeExpression !== "string" ) {
-			func = dataTypeExpression;
-			dataTypeExpression = "*";
-		}
-
-		var dataType,
-			i = 0,
-			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
-		if ( isFunction( func ) ) {
-
-			// For each dataType in the dataTypeExpression
-			while ( ( dataType = dataTypes[ i++ ] ) ) {
-
-				// Prepend if requested
-				if ( dataType[ 0 ] === "+" ) {
-					dataType = dataType.slice( 1 ) || "*";
-					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
-				// Otherwise append
-				} else {
-					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
-				}
-			}
-		}
-	};
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
-	var inspected = {},
-		seekingTransport = ( structure === transports );
-
-	function inspect( dataType ) {
-		var selected;
-		inspected[ dataType ] = true;
-		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
-			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
-			if ( typeof dataTypeOrTransport === "string" &&
-				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
-				options.dataTypes.unshift( dataTypeOrTransport );
-				inspect( dataTypeOrTransport );
-				return false;
-			} else if ( seekingTransport ) {
-				return !( selected = dataTypeOrTransport );
-			}
-		} );
-		return selected;
-	}
-
-	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
-	var key, deep,
-		flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
-	for ( key in src ) {
-		if ( src[ key ] !== undefined ) {
-			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
-		}
-	}
-	if ( deep ) {
-		jQuery.extend( true, target, deep );
-	}
-
-	return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
-	var ct, type, finalDataType, firstDataType,
-		contents = s.contents,
-		dataTypes = s.dataTypes;
-
-	// Remove auto dataType and get content-type in the process
-	while ( dataTypes[ 0 ] === "*" ) {
-		dataTypes.shift();
-		if ( ct === undefined ) {
-			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
-		}
-	}
-
-	// Check if we're dealing with a known content-type
-	if ( ct ) {
-		for ( type in contents ) {
-			if ( contents[ type ] && contents[ type ].test( ct ) ) {
-				dataTypes.unshift( type );
-				break;
-			}
-		}
-	}
-
-	// Check to see if we have a response for the expected dataType
-	if ( dataTypes[ 0 ] in responses ) {
-		finalDataType = dataTypes[ 0 ];
-	} else {
-
-		// Try convertible dataTypes
-		for ( type in responses ) {
-			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
-				finalDataType = type;
-				break;
-			}
-			if ( !firstDataType ) {
-				firstDataType = type;
-			}
-		}
-
-		// Or just use first one
-		finalDataType = finalDataType || firstDataType;
-	}
-
-	// If we found a dataType
-	// We add the dataType to the list if needed
-	// and return the corresponding response
-	if ( finalDataType ) {
-		if ( finalDataType !== dataTypes[ 0 ] ) {
-			dataTypes.unshift( finalDataType );
-		}
-		return responses[ finalDataType ];
-	}
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
-	var conv2, current, conv, tmp, prev,
-		converters = {},
-
-		// Work with a copy of dataTypes in case we need to modify it for conversion
-		dataTypes = s.dataTypes.slice();
-
-	// Create converters map with lowercased keys
-	if ( dataTypes[ 1 ] ) {
-		for ( conv in s.converters ) {
-			converters[ conv.toLowerCase() ] = s.converters[ conv ];
-		}
-	}
-
-	current = dataTypes.shift();
-
-	// Convert to each sequential dataType
-	while ( current ) {
-
-		if ( s.responseFields[ current ] ) {
-			jqXHR[ s.responseFields[ current ] ] = response;
-		}
-
-		// Apply the dataFilter if provided
-		if ( !prev && isSuccess && s.dataFilter ) {
-			response = s.dataFilter( response, s.dataType );
-		}
-
-		prev = current;
-		current = dataTypes.shift();
-
-		if ( current ) {
-
-			// There's only work to do if current dataType is non-auto
-			if ( current === "*" ) {
-
-				current = prev;
-
-			// Convert response if prev dataType is non-auto and differs from current
-			} else if ( prev !== "*" && prev !== current ) {
-
-				// Seek a direct converter
-				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
-				// If none found, seek a pair
-				if ( !conv ) {
-					for ( conv2 in converters ) {
-
-						// If conv2 outputs current
-						tmp = conv2.split( " " );
-						if ( tmp[ 1 ] === current ) {
-
-							// If prev can be converted to accepted input
-							conv = converters[ prev + " " + tmp[ 0 ] ] ||
-								converters[ "* " + tmp[ 0 ] ];
-							if ( conv ) {
-
-								// Condense equivalence converters
-								if ( conv === true ) {
-									conv = converters[ conv2 ];
-
-								// Otherwise, insert the intermediate dataType
-								} else if ( converters[ conv2 ] !== true ) {
-									current = tmp[ 0 ];
-									dataTypes.unshift( tmp[ 1 ] );
-								}
-								break;
-							}
-						}
-					}
-				}
-
-				// Apply converter (if not an equivalence)
-				if ( conv !== true ) {
-
-					// Unless errors are allowed to bubble, catch and return them
-					if ( conv && s.throws ) {
-						response = conv( response );
-					} else {
-						try {
-							response = conv( response );
-						} catch ( e ) {
-							return {
-								state: "parsererror",
-								error: conv ? e : "No conversion from " + prev + " to " + current
-							};
-						}
-					}
-				}
-			}
-		}
-	}
-
-	return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
-	// Counter for holding the number of active queries
-	active: 0,
-
-	// Last-Modified header cache for next request
-	lastModified: {},
-	etag: {},
-
-	ajaxSettings: {
-		url: location.href,
-		type: "GET",
-		isLocal: rlocalProtocol.test( location.protocol ),
-		global: true,
-		processData: true,
-		async: true,
-		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
-		/*
-		timeout: 0,
-		data: null,
-		dataType: null,
-		username: null,
-		password: null,
-		cache: null,
-		throws: false,
-		traditional: false,
-		headers: {},
-		*/
-
-		accepts: {
-			"*": allTypes,
-			text: "text/plain",
-			html: "text/html",
-			xml: "application/xml, text/xml",
-			json: "application/json, text/javascript"
-		},
-
-		contents: {
-			xml: /\bxml\b/,
-			html: /\bhtml/,
-			json: /\bjson\b/
-		},
-
-		responseFields: {
-			xml: "responseXML",
-			text: "responseText",
-			json: "responseJSON"
-		},
-
-		// Data converters
-		// Keys separate source (or catchall "*") and destination types with a single space
-		converters: {
-
-			// Convert anything to text
-			"* text": String,
-
-			// Text to html (true = no transformation)
-			"text html": true,
-
-			// Evaluate text as a json expression
-			"text json": JSON.parse,
-
-			// Parse text as xml
-			"text xml": jQuery.parseXML
-		},
-
-		// For options that shouldn't be deep extended:
-		// you can add your own custom options here if
-		// and when you create one that shouldn't be
-		// deep extended (see ajaxExtend)
-		flatOptions: {
-			url: true,
-			context: true
-		}
-	},
-
-	// Creates a full fledged settings object into target
-	// with both ajaxSettings and settings fields.
-	// If target is omitted, writes into ajaxSettings.
-	ajaxSetup: function( target, settings ) {
-		return settings ?
-
-			// Building a settings object
-			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
-			// Extending ajaxSettings
-			ajaxExtend( jQuery.ajaxSettings, target );
-	},
-
-	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
-	ajaxTransport: addToPrefiltersOrTransports( transports ),
-
-	// Main method
-	ajax: function( url, options ) {
-
-		// If url is an object, simulate pre-1.5 signature
-		if ( typeof url === "object" ) {
-			options = url;
-			url = undefined;
-		}
-
-		// Force options to be an object
-		options = options || {};
-
-		var transport,
-
-			// URL without anti-cache param
-			cacheURL,
-
-			// Response headers
-			responseHeadersString,
-			responseHeaders,
-
-			// timeout handle
-			timeoutTimer,
-
-			// Url cleanup var
-			urlAnchor,
-
-			// Request state (becomes false upon send and true upon completion)
-			completed,
-
-			// To know if global events are to be dispatched
-			fireGlobals,
-
-			// Loop variable
-			i,
-
-			// uncached part of the url
-			uncached,
-
-			// Create the final options object
-			s = jQuery.ajaxSetup( {}, options ),
-
-			// Callbacks context
-			callbackContext = s.context || s,
-
-			// Context for global events is callbackContext if it is a DOM node or jQuery collection
-			globalEventContext = s.context &&
-				( callbackContext.nodeType || callbackContext.jquery ) ?
-				jQuery( callbackContext ) :
-				jQuery.event,
-
-			// Deferreds
-			deferred = jQuery.Deferred(),
-			completeDeferred = jQuery.Callbacks( "once memory" ),
-
-			// Status-dependent callbacks
-			statusCode = s.statusCode || {},
-
-			// Headers (they are sent all at once)
-			requestHeaders = {},
-			requestHeadersNames = {},
-
-			// Default abort message
-			strAbort = "canceled",
-
-			// Fake xhr
-			jqXHR = {
-				readyState: 0,
-
-				// Builds headers hashtable if needed
-				getResponseHeader: function( key ) {
-					var match;
-					if ( completed ) {
-						if ( !responseHeaders ) {
-							responseHeaders = {};
-							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
-								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
-									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
-										.concat( match[ 2 ] );
-							}
-						}
-						match = responseHeaders[ key.toLowerCase() + " " ];
-					}
-					return match == null ? null : match.join( ", " );
-				},
-
-				// Raw string
-				getAllResponseHeaders: function() {
-					return completed ? responseHeadersString : null;
-				},
-
-				// Caches the header
-				setRequestHeader: function( name, value ) {
-					if ( completed == null ) {
-						name = requestHeadersNames[ name.toLowerCase() ] =
-							requestHeadersNames[ name.toLowerCase() ] || name;
-						requestHeaders[ name ] = value;
-					}
-					return this;
-				},
-
-				// Overrides response content-type header
-				overrideMimeType: function( type ) {
-					if ( completed == null ) {
-						s.mimeType = type;
-					}
-					return this;
-				},
-
-				// Status-dependent callbacks
-				statusCode: function( map ) {
-					var code;
-					if ( map ) {
-						if ( completed ) {
-
-							// Execute the appropriate callbacks
-							jqXHR.always( map[ jqXHR.status ] );
-						} else {
-
-							// Lazy-add the new callbacks in a way that preserves old ones
-							for ( code in map ) {
-								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
-							}
-						}
-					}
-					return this;
-				},
-
-				// Cancel the request
-				abort: function( statusText ) {
-					var finalText = statusText || strAbort;
-					if ( transport ) {
-						transport.abort( finalText );
-					}
-					done( 0, finalText );
-					return this;
-				}
-			};
-
-		// Attach deferreds
-		deferred.promise( jqXHR );
-
-		// Add protocol if not provided (prefilters might expect it)
-		// Handle falsy url in the settings object (#10093: consistency with old signature)
-		// We also use the url parameter if available
-		s.url = ( ( url || s.url || location.href ) + "" )
-			.replace( rprotocol, location.protocol + "//" );
-
-		// Alias method option to type as per ticket #12004
-		s.type = options.method || options.type || s.method || s.type;
-
-		// Extract dataTypes list
-		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
-		// A cross-domain request is in order when the origin doesn't match the current origin.
-		if ( s.crossDomain == null ) {
-			urlAnchor = document.createElement( "a" );
-
-			// Support: IE <=8 - 11, Edge 12 - 15
-			// IE throws exception on accessing the href property if url is malformed,
-			// e.g. http://example.com:80x/
-			try {
-				urlAnchor.href = s.url;
-
-				// Support: IE <=8 - 11 only
-				// Anchor's host property isn't correctly set when s.url is relative
-				urlAnchor.href = urlAnchor.href;
-				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
-					urlAnchor.protocol + "//" + urlAnchor.host;
-			} catch ( e ) {
-
-				// If there is an error parsing the URL, assume it is crossDomain,
-				// it can be rejected by the transport if it is invalid
-				s.crossDomain = true;
-			}
-		}
-
-		// Convert data if not already a string
-		if ( s.data && s.processData && typeof s.data !== "string" ) {
-			s.data = jQuery.param( s.data, s.traditional );
-		}
-
-		// Apply prefilters
-		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
-		// If request was aborted inside a prefilter, stop there
-		if ( completed ) {
-			return jqXHR;
-		}
-
-		// We can fire global events as of now if asked to
-		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
-		fireGlobals = jQuery.event && s.global;
-
-		// Watch for a new set of requests
-		if ( fireGlobals && jQuery.active++ === 0 ) {
-			jQuery.event.trigger( "ajaxStart" );
-		}
-
-		// Uppercase the type
-		s.type = s.type.toUpperCase();
-
-		// Determine if request has content
-		s.hasContent = !rnoContent.test( s.type );
-
-		// Save the URL in case we're toying with the If-Modified-Since
-		// and/or If-None-Match header later on
-		// Remove hash to simplify url manipulation
-		cacheURL = s.url.replace( rhash, "" );
-
-		// More options handling for requests with no content
-		if ( !s.hasContent ) {
-
-			// Remember the hash so we can put it back
-			uncached = s.url.slice( cacheURL.length );
-
-			// If data is available and should be processed, append data to url
-			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
-				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
-				// #9682: remove data so that it's not used in an eventual retry
-				delete s.data;
-			}
-
-			// Add or update anti-cache param if needed
-			if ( s.cache === false ) {
-				cacheURL = cacheURL.replace( rantiCache, "$1" );
-				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
-					uncached;
-			}
-
-			// Put hash and anti-cache on the URL that will be requested (gh-1732)
-			s.url = cacheURL + uncached;
-
-		// Change '%20' to '+' if this is encoded form body content (gh-2658)
-		} else if ( s.data && s.processData &&
-			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
-			s.data = s.data.replace( r20, "+" );
-		}
-
-		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-		if ( s.ifModified ) {
-			if ( jQuery.lastModified[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
-			}
-			if ( jQuery.etag[ cacheURL ] ) {
-				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
-			}
-		}
-
-		// Set the correct header, if data is being sent
-		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
-			jqXHR.setRequestHeader( "Content-Type", s.contentType );
-		}
-
-		// Set the Accepts header for the server, depending on the dataType
-		jqXHR.setRequestHeader(
-			"Accept",
-			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
-				s.accepts[ s.dataTypes[ 0 ] ] +
-					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
-				s.accepts[ "*" ]
-		);
-
-		// Check for headers option
-		for ( i in s.headers ) {
-			jqXHR.setRequestHeader( i, s.headers[ i ] );
-		}
-
-		// Allow custom headers/mimetypes and early abort
-		if ( s.beforeSend &&
-			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
-			// Abort if not done already and return
-			return jqXHR.abort();
-		}
-
-		// Aborting is no longer a cancellation
-		strAbort = "abort";
-
-		// Install callbacks on deferreds
-		completeDeferred.add( s.complete );
-		jqXHR.done( s.success );
-		jqXHR.fail( s.error );
-
-		// Get transport
-		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
-		// If no transport, we auto-abort
-		if ( !transport ) {
-			done( -1, "No Transport" );
-		} else {
-			jqXHR.readyState = 1;
-
-			// Send global event
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
-			}
-
-			// If request was aborted inside ajaxSend, stop there
-			if ( completed ) {
-				return jqXHR;
-			}
-
-			// Timeout
-			if ( s.async && s.timeout > 0 ) {
-				timeoutTimer = window.setTimeout( function() {
-					jqXHR.abort( "timeout" );
-				}, s.timeout );
-			}
-
-			try {
-				completed = false;
-				transport.send( requestHeaders, done );
-			} catch ( e ) {
-
-				// Rethrow post-completion exceptions
-				if ( completed ) {
-					throw e;
-				}
-
-				// Propagate others as results
-				done( -1, e );
-			}
-		}
-
-		// Callback for when everything is done
-		function done( status, nativeStatusText, responses, headers ) {
-			var isSuccess, success, error, response, modified,
-				statusText = nativeStatusText;
-
-			// Ignore repeat invocations
-			if ( completed ) {
-				return;
-			}
-
-			completed = true;
-
-			// Clear timeout if it exists
-			if ( timeoutTimer ) {
-				window.clearTimeout( timeoutTimer );
-			}
-
-			// Dereference transport for early garbage collection
-			// (no matter how long the jqXHR object will be used)
-			transport = undefined;
-
-			// Cache response headers
-			responseHeadersString = headers || "";
-
-			// Set readyState
-			jqXHR.readyState = status > 0 ? 4 : 0;
-
-			// Determine if successful
-			isSuccess = status >= 200 && status < 300 || status === 304;
-
-			// Get response data
-			if ( responses ) {
-				response = ajaxHandleResponses( s, jqXHR, responses );
-			}
-
-			// Use a noop converter for missing script but not if jsonp
-			if ( !isSuccess &&
-				jQuery.inArray( "script", s.dataTypes ) > -1 &&
-				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
-				s.converters[ "text script" ] = function() {};
-			}
-
-			// Convert no matter what (that way responseXXX fields are always set)
-			response = ajaxConvert( s, response, jqXHR, isSuccess );
-
-			// If successful, handle type chaining
-			if ( isSuccess ) {
-
-				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
-				if ( s.ifModified ) {
-					modified = jqXHR.getResponseHeader( "Last-Modified" );
-					if ( modified ) {
-						jQuery.lastModified[ cacheURL ] = modified;
-					}
-					modified = jqXHR.getResponseHeader( "etag" );
-					if ( modified ) {
-						jQuery.etag[ cacheURL ] = modified;
-					}
-				}
-
-				// if no content
-				if ( status === 204 || s.type === "HEAD" ) {
-					statusText = "nocontent";
-
-				// if not modified
-				} else if ( status === 304 ) {
-					statusText = "notmodified";
-
-				// If we have data, let's convert it
-				} else {
-					statusText = response.state;
-					success = response.data;
-					error = response.error;
-					isSuccess = !error;
-				}
-			} else {
-
-				// Extract error from statusText and normalize for non-aborts
-				error = statusText;
-				if ( status || !statusText ) {
-					statusText = "error";
-					if ( status < 0 ) {
-						status = 0;
-					}
-				}
-			}
-
-			// Set data for the fake xhr object
-			jqXHR.status = status;
-			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
-			// Success/Error
-			if ( isSuccess ) {
-				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
-			} else {
-				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
-			}
-
-			// Status-dependent callbacks
-			jqXHR.statusCode( statusCode );
-			statusCode = undefined;
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
-					[ jqXHR, s, isSuccess ? success : error ] );
-			}
-
-			// Complete
-			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
-			if ( fireGlobals ) {
-				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
-				// Handle the global AJAX counter
-				if ( !( --jQuery.active ) ) {
-					jQuery.event.trigger( "ajaxStop" );
-				}
-			}
-		}
-
-		return jqXHR;
-	},
-
-	getJSON: function( url, data, callback ) {
-		return jQuery.get( url, data, callback, "json" );
-	},
-
-	getScript: function( url, callback ) {
-		return jQuery.get( url, undefined, callback, "script" );
-	}
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
-	jQuery[ method ] = function( url, data, callback, type ) {
-
-		// Shift arguments if data argument was omitted
-		if ( isFunction( data ) ) {
-			type = type || callback;
-			callback = data;
-			data = undefined;
-		}
-
-		// The url can be an options object (which then must have .url)
-		return jQuery.ajax( jQuery.extend( {
-			url: url,
-			type: method,
-			dataType: type,
-			data: data,
-			success: callback
-		}, jQuery.isPlainObject( url ) && url ) );
-	};
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
-	var i;
-	for ( i in s.headers ) {
-		if ( i.toLowerCase() === "content-type" ) {
-			s.contentType = s.headers[ i ] || "";
-		}
-	}
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
-	return jQuery.ajax( {
-		url: url,
-
-		// Make this explicit, since user can override this through ajaxSetup (#11264)
-		type: "GET",
-		dataType: "script",
-		cache: true,
-		async: false,
-		global: false,
-
-		// Only evaluate the response if it is successful (gh-4126)
-		// dataFilter is not invoked for failure responses, so using it instead
-		// of the default converter is kludgy but it works.
-		converters: {
-			"text script": function() {}
-		},
-		dataFilter: function( response ) {
-			jQuery.globalEval( response, options, doc );
-		}
-	} );
-};
-
-
-jQuery.fn.extend( {
-	wrapAll: function( html ) {
-		var wrap;
-
-		if ( this[ 0 ] ) {
-			if ( isFunction( html ) ) {
-				html = html.call( this[ 0 ] );
-			}
-
-			// The elements to wrap the target around
-			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
-			if ( this[ 0 ].parentNode ) {
-				wrap.insertBefore( this[ 0 ] );
-			}
-
-			wrap.map( function() {
-				var elem = this;
-
-				while ( elem.firstElementChild ) {
-					elem = elem.firstElementChild;
-				}
-
-				return elem;
-			} ).append( this );
-		}
-
-		return this;
-	},
-
-	wrapInner: function( html ) {
-		if ( isFunction( html ) ) {
-			return this.each( function( i ) {
-				jQuery( this ).wrapInner( html.call( this, i ) );
-			} );
-		}
-
-		return this.each( function() {
-			var self = jQuery( this ),
-				contents = self.contents();
-
-			if ( contents.length ) {
-				contents.wrapAll( html );
-
-			} else {
-				self.append( html );
-			}
-		} );
-	},
-
-	wrap: function( html ) {
-		var htmlIsFunction = isFunction( html );
-
-		return this.each( function( i ) {
-			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
-		} );
-	},
-
-	unwrap: function( selector ) {
-		this.parent( selector ).not( "body" ).each( function() {
-			jQuery( this ).replaceWith( this.childNodes );
-		} );
-		return this;
-	}
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
-	return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
-	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
-	try {
-		return new window.XMLHttpRequest();
-	} catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
-		// File protocol always yields status code 0, assume 200
-		0: 200,
-
-		// Support: IE <=9 only
-		// #1450: sometimes IE returns 1223 when it should be 204
-		1223: 204
-	},
-	xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
-	var callback, errorCallback;
-
-	// Cross domain only allowed if supported through XMLHttpRequest
-	if ( support.cors || xhrSupported && !options.crossDomain ) {
-		return {
-			send: function( headers, complete ) {
-				var i,
-					xhr = options.xhr();
-
-				xhr.open(
-					options.type,
-					options.url,
-					options.async,
-					options.username,
-					options.password
-				);
-
-				// Apply custom fields if provided
-				if ( options.xhrFields ) {
-					for ( i in options.xhrFields ) {
-						xhr[ i ] = options.xhrFields[ i ];
-					}
-				}
-
-				// Override mime type if needed
-				if ( options.mimeType && xhr.overrideMimeType ) {
-					xhr.overrideMimeType( options.mimeType );
-				}
-
-				// X-Requested-With header
-				// For cross-domain requests, seeing as conditions for a preflight are
-				// akin to a jigsaw puzzle, we simply never set it to be sure.
-				// (it can always be set on a per-request basis or even using ajaxSetup)
-				// For same-domain requests, won't change header if already provided.
-				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
-					headers[ "X-Requested-With" ] = "XMLHttpRequest";
-				}
-
-				// Set headers
-				for ( i in headers ) {
-					xhr.setRequestHeader( i, headers[ i ] );
-				}
-
-				// Callback
-				callback = function( type ) {
-					return function() {
-						if ( callback ) {
-							callback = errorCallback = xhr.onload =
-								xhr.onerror = xhr.onabort = xhr.ontimeout =
-									xhr.onreadystatechange = null;
-
-							if ( type === "abort" ) {
-								xhr.abort();
-							} else if ( type === "error" ) {
-
-								// Support: IE <=9 only
-								// On a manual native abort, IE9 throws
-								// errors on any property access that is not readyState
-								if ( typeof xhr.status !== "number" ) {
-									complete( 0, "error" );
-								} else {
-									complete(
-
-										// File: protocol always yields status 0; see #8605, #14207
-										xhr.status,
-										xhr.statusText
-									);
-								}
-							} else {
-								complete(
-									xhrSuccessStatus[ xhr.status ] || xhr.status,
-									xhr.statusText,
-
-									// Support: IE <=9 only
-									// IE9 has no XHR2 but throws on binary (trac-11426)
-									// For XHR2 non-text, let the caller handle it (gh-2498)
-									( xhr.responseType || "text" ) !== "text"  ||
-									typeof xhr.responseText !== "string" ?
-										{ binary: xhr.response } :
-										{ text: xhr.responseText },
-									xhr.getAllResponseHeaders()
-								);
-							}
-						}
-					};
-				};
-
-				// Listen to events
-				xhr.onload = callback();
-				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
-				// Support: IE 9 only
-				// Use onreadystatechange to replace onabort
-				// to handle uncaught aborts
-				if ( xhr.onabort !== undefined ) {
-					xhr.onabort = errorCallback;
-				} else {
-					xhr.onreadystatechange = function() {
-
-						// Check readyState before timeout as it changes
-						if ( xhr.readyState === 4 ) {
-
-							// Allow onerror to be called first,
-							// but that will not handle a native abort
-							// Also, save errorCallback to a variable
-							// as xhr.onerror cannot be accessed
-							window.setTimeout( function() {
-								if ( callback ) {
-									errorCallback();
-								}
-							} );
-						}
-					};
-				}
-
-				// Create the abort callback
-				callback = callback( "abort" );
-
-				try {
-
-					// Do send the request (this may raise an exception)
-					xhr.send( options.hasContent && options.data || null );
-				} catch ( e ) {
-
-					// #14683: Only rethrow if this hasn't been notified as an error yet
-					if ( callback ) {
-						throw e;
-					}
-				}
-			},
-
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
-	if ( s.crossDomain ) {
-		s.contents.script = false;
-	}
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
-	accepts: {
-		script: "text/javascript, application/javascript, " +
-			"application/ecmascript, application/x-ecmascript"
-	},
-	contents: {
-		script: /\b(?:java|ecma)script\b/
-	},
-	converters: {
-		"text script": function( text ) {
-			jQuery.globalEval( text );
-			return text;
-		}
-	}
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
-	if ( s.cache === undefined ) {
-		s.cache = false;
-	}
-	if ( s.crossDomain ) {
-		s.type = "GET";
-	}
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
-	// This transport only deals with cross domain or forced-by-attrs requests
-	if ( s.crossDomain || s.scriptAttrs ) {
-		var script, callback;
-		return {
-			send: function( _, complete ) {
-				script = jQuery( "<script>" )
-					.attr( s.scriptAttrs || {} )
-					.prop( { charset: s.scriptCharset, src: s.url } )
-					.on( "load error", callback = function( evt ) {
-						script.remove();
-						callback = null;
-						if ( evt ) {
-							complete( evt.type === "error" ? 404 : 200, evt.type );
-						}
-					} );
-
-				// Use native DOM manipulation to avoid our domManip AJAX trickery
-				document.head.appendChild( script[ 0 ] );
-			},
-			abort: function() {
-				if ( callback ) {
-					callback();
-				}
-			}
-		};
-	}
-} );
-
-
-
-
-var oldCallbacks = [],
-	rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
-	jsonp: "callback",
-	jsonpCallback: function() {
-		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
-		this[ callback ] = true;
-		return callback;
-	}
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
-	var callbackName, overwritten, responseContainer,
-		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
-			"url" :
-			typeof s.data === "string" &&
-				( s.contentType || "" )
-					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
-				rjsonp.test( s.data ) && "data"
-		);
-
-	// Handle iff the expected data type is "jsonp" or we have a parameter to set
-	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
-		// Get callback name, remembering preexisting value associated with it
-		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
-			s.jsonpCallback() :
-			s.jsonpCallback;
-
-		// Insert callback into url or form data
-		if ( jsonProp ) {
-			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
-		} else if ( s.jsonp !== false ) {
-			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
-		}
-
-		// Use data converter to retrieve json after script execution
-		s.converters[ "script json" ] = function() {
-			if ( !responseContainer ) {
-				jQuery.error( callbackName + " was not called" );
-			}
-			return responseContainer[ 0 ];
-		};
-
-		// Force json dataType
-		s.dataTypes[ 0 ] = "json";
-
-		// Install callback
-		overwritten = window[ callbackName ];
-		window[ callbackName ] = function() {
-			responseContainer = arguments;
-		};
-
-		// Clean-up function (fires after converters)
-		jqXHR.always( function() {
-
-			// If previous value didn't exist - remove it
-			if ( overwritten === undefined ) {
-				jQuery( window ).removeProp( callbackName );
-
-			// Otherwise restore preexisting value
-			} else {
-				window[ callbackName ] = overwritten;
-			}
-
-			// Save back as free
-			if ( s[ callbackName ] ) {
-
-				// Make sure that re-using the options doesn't screw things around
-				s.jsonpCallback = originalSettings.jsonpCallback;
-
-				// Save the callback name for future use
-				oldCallbacks.push( callbackName );
-			}
-
-			// Call if it was a function and we have a response
-			if ( responseContainer && isFunction( overwritten ) ) {
-				overwritten( responseContainer[ 0 ] );
-			}
-
-			responseContainer = overwritten = undefined;
-		} );
-
-		// Delegate to script
-		return "script";
-	}
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
-	var body = document.implementation.createHTMLDocument( "" ).body;
-	body.innerHTML = "<form></form><form></form>";
-	return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include scripts passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
-	if ( typeof data !== "string" ) {
-		return [];
-	}
-	if ( typeof context === "boolean" ) {
-		keepScripts = context;
-		context = false;
-	}
-
-	var base, parsed, scripts;
-
-	if ( !context ) {
-
-		// Stop scripts or inline event handlers from being executed immediately
-		// by using document.implementation
-		if ( support.createHTMLDocument ) {
-			context = document.implementation.createHTMLDocument( "" );
-
-			// Set the base href for the created document
-			// so any parsed elements with URLs
-			// are based on the document's URL (gh-2965)
-			base = context.createElement( "base" );
-			base.href = document.location.href;
-			context.head.appendChild( base );
-		} else {
-			context = document;
-		}
-	}
-
-	parsed = rsingleTag.exec( data );
-	scripts = !keepScripts && [];
-
-	// Single tag
-	if ( parsed ) {
-		return [ context.createElement( parsed[ 1 ] ) ];
-	}
-
-	parsed = buildFragment( [ data ], context, scripts );
-
-	if ( scripts && scripts.length ) {
-		jQuery( scripts ).remove();
-	}
-
-	return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
-	var selector, type, response,
-		self = this,
-		off = url.indexOf( " " );
-
-	if ( off > -1 ) {
-		selector = stripAndCollapse( url.slice( off ) );
-		url = url.slice( 0, off );
-	}
-
-	// If it's a function
-	if ( isFunction( params ) ) {
-
-		// We assume that it's the callback
-		callback = params;
-		params = undefined;
-
-	// Otherwise, build a param string
-	} else if ( params && typeof params === "object" ) {
-		type = "POST";
-	}
-
-	// If we have elements to modify, make the request
-	if ( self.length > 0 ) {
-		jQuery.ajax( {
-			url: url,
-
-			// If "type" variable is undefined, then "GET" method will be used.
-			// Make value of this field explicit since
-			// user can override it through ajaxSetup method
-			type: type || "GET",
-			dataType: "html",
-			data: params
-		} ).done( function( responseText ) {
-
-			// Save response for use in complete callback
-			response = arguments;
-
-			self.html( selector ?
-
-				// If a selector was specified, locate the right elements in a dummy div
-				// Exclude scripts to avoid IE 'Permission Denied' errors
-				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
-				// Otherwise use the full result
-				responseText );
-
-		// If the request succeeds, this function gets "data", "status", "jqXHR"
-		// but they are ignored because response was set above.
-		// If it fails, this function gets "jqXHR", "status", "error"
-		} ).always( callback && function( jqXHR, status ) {
-			self.each( function() {
-				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
-			} );
-		} );
-	}
-
-	return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
-	return jQuery.grep( jQuery.timers, function( fn ) {
-		return elem === fn.elem;
-	} ).length;
-};
-
-
-
-
-jQuery.offset = {
-	setOffset: function( elem, options, i ) {
-		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
-			position = jQuery.css( elem, "position" ),
-			curElem = jQuery( elem ),
-			props = {};
-
-		// Set position first, in-case top/left are set even on static elem
-		if ( position === "static" ) {
-			elem.style.position = "relative";
-		}
-
-		curOffset = curElem.offset();
-		curCSSTop = jQuery.css( elem, "top" );
-		curCSSLeft = jQuery.css( elem, "left" );
-		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
-			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
-		// Need to be able to calculate position if either
-		// top or left is auto and position is either absolute or fixed
-		if ( calculatePosition ) {
-			curPosition = curElem.position();
-			curTop = curPosition.top;
-			curLeft = curPosition.left;
-
-		} else {
-			curTop = parseFloat( curCSSTop ) || 0;
-			curLeft = parseFloat( curCSSLeft ) || 0;
-		}
-
-		if ( isFunction( options ) ) {
-
-			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
-			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
-		}
-
-		if ( options.top != null ) {
-			props.top = ( options.top - curOffset.top ) + curTop;
-		}
-		if ( options.left != null ) {
-			props.left = ( options.left - curOffset.left ) + curLeft;
-		}
-
-		if ( "using" in options ) {
-			options.using.call( elem, props );
-
-		} else {
-			curElem.css( props );
-		}
-	}
-};
-
-jQuery.fn.extend( {
-
-	// offset() relates an element's border box to the document origin
-	offset: function( options ) {
-
-		// Preserve chaining for setter
-		if ( arguments.length ) {
-			return options === undefined ?
-				this :
-				this.each( function( i ) {
-					jQuery.offset.setOffset( this, options, i );
-				} );
-		}
-
-		var rect, win,
-			elem = this[ 0 ];
-
-		if ( !elem ) {
-			return;
-		}
-
-		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
-		// Support: IE <=11 only
-		// Running getBoundingClientRect on a
-		// disconnected node in IE throws an error
-		if ( !elem.getClientRects().length ) {
-			return { top: 0, left: 0 };
-		}
-
-		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
-		rect = elem.getBoundingClientRect();
-		win = elem.ownerDocument.defaultView;
-		return {
-			top: rect.top + win.pageYOffset,
-			left: rect.left + win.pageXOffset
-		};
-	},
-
-	// position() relates an element's margin box to its offset parent's padding box
-	// This corresponds to the behavior of CSS absolute positioning
-	position: function() {
-		if ( !this[ 0 ] ) {
-			return;
-		}
-
-		var offsetParent, offset, doc,
-			elem = this[ 0 ],
-			parentOffset = { top: 0, left: 0 };
-
-		// position:fixed elements are offset from the viewport, which itself always has zero offset
-		if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
-			// Assume position:fixed implies availability of getBoundingClientRect
-			offset = elem.getBoundingClientRect();
-
-		} else {
-			offset = this.offset();
-
-			// Account for the *real* offset parent, which can be the document or its root element
-			// when a statically positioned element is identified
-			doc = elem.ownerDocument;
-			offsetParent = elem.offsetParent || doc.documentElement;
-			while ( offsetParent &&
-				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
-				jQuery.css( offsetParent, "position" ) === "static" ) {
-
-				offsetParent = offsetParent.parentNode;
-			}
-			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
-				// Incorporate borders into its offset, since they are outside its content origin
-				parentOffset = jQuery( offsetParent ).offset();
-				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
-				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
-			}
-		}
-
-		// Subtract parent offsets and element margins
-		return {
-			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
-			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
-		};
-	},
-
-	// This method will return documentElement in the following cases:
-	// 1) For the element inside the iframe without offsetParent, this method will return
-	//    documentElement of the parent window
-	// 2) For the hidden or detached element
-	// 3) For body or html element, i.e. in case of the html node - it will return itself
-	//
-	// but those exceptions were never presented as a real life use-cases
-	// and might be considered as more preferable results.
-	//
-	// This logic, however, is not guaranteed and can change at any point in the future
-	offsetParent: function() {
-		return this.map( function() {
-			var offsetParent = this.offsetParent;
-
-			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
-				offsetParent = offsetParent.offsetParent;
-			}
-
-			return offsetParent || documentElement;
-		} );
-	}
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
-	var top = "pageYOffset" === prop;
-
-	jQuery.fn[ method ] = function( val ) {
-		return access( this, function( elem, method, val ) {
-
-			// Coalesce documents and windows
-			var win;
-			if ( isWindow( elem ) ) {
-				win = elem;
-			} else if ( elem.nodeType === 9 ) {
-				win = elem.defaultView;
-			}
-
-			if ( val === undefined ) {
-				return win ? win[ prop ] : elem[ method ];
-			}
-
-			if ( win ) {
-				win.scrollTo(
-					!top ? val : win.pageXOffset,
-					top ? val : win.pageYOffset
-				);
-
-			} else {
-				elem[ method ] = val;
-			}
-		}, method, val, arguments.length );
-	};
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
-	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
-		function( elem, computed ) {
-			if ( computed ) {
-				computed = curCSS( elem, prop );
-
-				// If curCSS returns percentage, fallback to offset
-				return rnumnonpx.test( computed ) ?
-					jQuery( elem ).position()[ prop ] + "px" :
-					computed;
-			}
-		}
-	);
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
-	jQuery.each( {
-		padding: "inner" + name,
-		content: type,
-		"": "outer" + name
-	}, function( defaultExtra, funcName ) {
-
-		// Margin is only for outerHeight, outerWidth
-		jQuery.fn[ funcName ] = function( margin, value ) {
-			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
-				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
-			return access( this, function( elem, type, value ) {
-				var doc;
-
-				if ( isWindow( elem ) ) {
-
-					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
-					return funcName.indexOf( "outer" ) === 0 ?
-						elem[ "inner" + name ] :
-						elem.document.documentElement[ "client" + name ];
-				}
-
-				// Get document width or height
-				if ( elem.nodeType === 9 ) {
-					doc = elem.documentElement;
-
-					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
-					// whichever is greatest
-					return Math.max(
-						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
-						elem.body[ "offset" + name ], doc[ "offset" + name ],
-						doc[ "client" + name ]
-					);
-				}
-
-				return value === undefined ?
-
-					// Get width or height on the element, requesting but not forcing parseFloat
-					jQuery.css( elem, type, extra ) :
-
-					// Set width or height on the element
-					jQuery.style( elem, type, value, extra );
-			}, type, chainable ? margin : undefined, chainable );
-		};
-	} );
-} );
-
-
-jQuery.each( [
-	"ajaxStart",
-	"ajaxStop",
-	"ajaxComplete",
-	"ajaxError",
-	"ajaxSuccess",
-	"ajaxSend"
-], function( _i, type ) {
-	jQuery.fn[ type ] = function( fn ) {
-		return this.on( type, fn );
-	};
-} );
-
-
-
-
-jQuery.fn.extend( {
-
-	bind: function( types, data, fn ) {
-		return this.on( types, null, data, fn );
-	},
-	unbind: function( types, fn ) {
-		return this.off( types, null, fn );
-	},
-
-	delegate: function( selector, types, data, fn ) {
-		return this.on( types, selector, data, fn );
-	},
-	undelegate: function( selector, types, fn ) {
-
-		// ( namespace ) or ( selector, types [, fn] )
-		return arguments.length === 1 ?
-			this.off( selector, "**" ) :
-			this.off( types, selector || "**", fn );
-	},
-
-	hover: function( fnOver, fnOut ) {
-		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
-	}
-} );
-
-jQuery.each(
-	( "blur focus focusin focusout resize scroll click dblclick " +
-	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
-	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
-	function( _i, name ) {
-
-		// Handle event binding
-		jQuery.fn[ name ] = function( data, fn ) {
-			return arguments.length > 0 ?
-				this.on( name, null, data, fn ) :
-				this.trigger( name );
-		};
-	}
-);
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
-	var tmp, args, proxy;
-
-	if ( typeof context === "string" ) {
-		tmp = fn[ context ];
-		context = fn;
-		fn = tmp;
-	}
-
-	// Quick check to determine if target is callable, in the spec
-	// this throws a TypeError, but we will just return undefined.
-	if ( !isFunction( fn ) ) {
-		return undefined;
-	}
-
-	// Simulated bind
-	args = slice.call( arguments, 2 );
-	proxy = function() {
-		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
-	};
-
-	// Set the guid of unique handler to the same of original handler, so it can be removed
-	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
-	return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
-	if ( hold ) {
-		jQuery.readyWait++;
-	} else {
-		jQuery.ready( true );
-	}
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
-	// As of jQuery 3.0, isNumeric is limited to
-	// strings and numbers (primitives or objects)
-	// that can be coerced to finite numbers (gh-2662)
-	var type = jQuery.type( obj );
-	return ( type === "number" || type === "string" ) &&
-
-		// parseFloat NaNs numeric-cast false positives ("")
-		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
-		// subtraction forces infinities to NaN
-		!isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
-	return text == null ?
-		"" :
-		( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
-	define( "jquery", [], function() {
-		return jQuery;
-	} );
-}
-
-
-
-
-var
-
-	// Map over jQuery in case of overwrite
-	_jQuery = window.jQuery,
-
-	// Map over the $ in case of overwrite
-	_$ = window.$;
-
-jQuery.noConflict = function( deep ) {
-	if ( window.$ === jQuery ) {
-		window.$ = _$;
-	}
-
-	if ( deep && window.jQuery === jQuery ) {
-		window.jQuery = _jQuery;
-	}
-
-	return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
-	window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/documentation/5/_static/language_data.js b/documentation/5/_static/language_data.js
index 2e22b06ab..250f5665f 100644
--- a/documentation/5/_static/language_data.js
+++ b/documentation/5/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/documentation/5/_static/nbsphinx-broken-thumbnail.svg b/documentation/5/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 000000000..4919ca882
--- /dev/null
+++ b/documentation/5/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
+  <path d="m 80.856887,38.34474 h 2.903414 l 7.066397,13.332221 V 38.34474 h 2.092166 V 54.281494 H 90.01545 L 82.949053,40.949273 v 13.332221 h -2.092166 z m -13.812565,0 h 10.076555 v 1.814633 h -7.920343 v 4.718048 h 7.589439 v 1.814634 h -7.589439 v 5.774805 h 8.11248 v 1.814634 H 67.044322 Z m -14.335606,0 h 2.156212 v 6.735493 L 62.01672,38.34474 h 2.775323 l -7.909669,7.429324 8.475407,8.50743 h -2.839368 l -7.653485,-7.674833 v 7.674833 h -2.156212 z m -10.738365,1.462381 q -2.348349,0 -3.73601,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010158 1.376987,4.760746 1.387661,1.750587 3.73601,1.750587 2.34835,0 3.714663,-1.750587 1.376986,-1.750588 1.376986,-4.760746 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714663,-1.750588 z m 0,-1.750588 q 3.351736,0 5.358507,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358507,2.241606 -3.362409,0 -5.379855,-2.241606 -2.006772,-2.241607 -2.006772,-6.020315 0,-3.778708 2.006772,-6.020314 2.017446,-2.252281 5.379855,-2.252281 z m -14.100771,8.75294 q 0.693831,0.234835 1.344964,1.003385 0.661808,0.768551 1.323615,2.113515 l 2.188235,4.355121 H 30.410068 L 28.371273,50.193231 Q 27.581374,48.592083 26.834172,48.069042 26.097644,47.546 24.816726,47.546 h -2.34835 v 6.735494 H 20.312164 V 38.34474 h 4.867489 q 2.732625,0 4.077588,1.142152 1.344964,1.142151 1.344964,3.447804 0,1.505078 -0.704505,2.49779 -0.69383,0.992711 -2.02812,1.376987 z m -5.401204,-6.692797 v 5.657388 h 2.711277 q 1.55845,0 2.348349,-0.715179 0.800574,-0.725854 0.800574,-2.124189 0,-1.398336 -0.800574,-2.102841 -0.789899,-0.715179 -2.348349,-0.715179 z M 7.4709616,46.670707 v 5.838851 H 10.92944 q 1.739914,0 2.57251,-0.71518 0.843271,-0.725853 0.843271,-2.209583 0,-1.494404 -0.843271,-2.198909 -0.832596,-0.715179 -2.57251,-0.715179 z m 0,-6.554031 v 4.803443 h 3.1916204 q 1.579799,0 2.34835,-0.587088 0.779225,-0.597761 0.779225,-1.814634 0,-1.206197 -0.779225,-1.803959 -0.768551,-0.597762 -2.34835,-0.597762 z M 5.3147497,38.34474 h 5.5079473 q 2.465767,0 3.800057,1.024734 1.334289,1.024734 1.334289,2.914088 0,1.462382 -0.683156,2.327001 -0.683156,0.86462 -2.006772,1.078106 1.590474,0.341579 2.465768,1.430359 0.885968,1.078106 0.885968,2.700602 0,2.134863 -1.451707,3.298364 -1.451707,1.1635 -4.130961,1.1635 H 5.3147497 Z M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 h -5.657391 z" />
+</svg>
diff --git a/documentation/5/_static/nbsphinx-code-cells.css b/documentation/5/_static/nbsphinx-code-cells.css
new file mode 100644
index 000000000..a3fb27c30
--- /dev/null
+++ b/documentation/5/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/documentation/5/_static/nbsphinx-gallery.css b/documentation/5/_static/nbsphinx-gallery.css
new file mode 100644
index 000000000..365c27a96
--- /dev/null
+++ b/documentation/5/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/documentation/5/_static/nbsphinx-no-thumbnail.svg b/documentation/5/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 000000000..9dca7588f
--- /dev/null
+++ b/documentation/5/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
+  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
+</svg>
diff --git a/documentation/5/_static/pygments.css b/documentation/5/_static/pygments.css
index 08bec689d..84ab3030a 100644
--- a/documentation/5/_static/pygments.css
+++ b/documentation/5/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #E40000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #008400 } /* Generic.Inserted */
diff --git a/documentation/5/_static/searchtools.js b/documentation/5/_static/searchtools.js
index ac4d5861f..97d56a74d 100644
--- a/documentation/5/_static/searchtools.js
+++ b/documentation/5/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -57,14 +57,14 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, highlightTerms, searchTerms) => {
+const _displayItem = (item, searchTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
   const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
 
-  const [docName, title, anchor, descr] = item;
+  const [docName, title, anchor, descr, score, _filename] = item;
 
   let listItem = document.createElement("li");
   let requestUrl;
@@ -82,13 +82,12 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
     requestUrl = docUrlRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
-  const params = new URLSearchParams();
-  params.set("highlight", [...highlightTerms].join(" "));
   let linkEl = listItem.appendChild(document.createElement("a"));
-  linkEl.href = linkUrl + "?" + params.toString() + anchor;
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
   linkEl.innerHTML = title;
   if (descr)
-    listItem.appendChild(document.createElement("span")).innerText =
+    listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
   else if (showSearchSummary)
     fetch(requestUrl)
@@ -96,7 +95,7 @@ const _displayItem = (item, highlightTerms, searchTerms) => {
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms, highlightTerms)
+            Search.makeSearchSummary(data, searchTerms)
           );
       });
   Search.output.appendChild(listItem);
@@ -116,15 +115,14 @@ const _finishSearch = (resultCount) => {
 const _displayNextItem = (
   results,
   resultCount,
-  highlightTerms,
   searchTerms
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), highlightTerms, searchTerms);
+    _displayItem(results.pop(), searchTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, highlightTerms, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms),
       5
     );
   }
@@ -155,10 +153,8 @@ const Search = {
   _pulse_status: -1,
 
   htmlToText: (htmlString) => {
-    const htmlElement = document
-      .createRange()
-      .createContextualFragment(htmlString);
-    _removeChildren(htmlElement.querySelectorAll(".headerlink"));
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
     const docContent = htmlElement.querySelector('[role="main"]');
     if (docContent !== undefined) return docContent.textContent;
     console.warn(
@@ -239,6 +235,12 @@ const Search = {
    * execute search (requires search index to be loaded)
    */
   query: (query) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -266,6 +268,10 @@ const Search = {
       }
     });
 
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
     // console.debug("SEARCH: searching for:");
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
@@ -274,6 +280,40 @@ const Search = {
     let results = [];
     _removeChildren(document.getElementById("search-progress"));
 
+    const queryLower = query.toLowerCase();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          let score = Math.round(100 * queryLower.length / title.length)
+          results.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id] of foundEntries) {
+          let score = Math.round(100 * queryLower.length / entry.length)
+          results.push([
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
     // lookup as object
     objectTerms.forEach((term) =>
       results.push(...Search.performObjectSearch(term, objectTerms))
@@ -320,7 +360,7 @@ const Search = {
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, highlightTerms, searchTerms);
+    _displayNextItem(results, results.length, searchTerms);
   },
 
   /**
@@ -401,8 +441,8 @@ const Search = {
     // prepare search
     const terms = Search._index.terms;
     const titleTerms = Search._index.titleterms;
-    const docNames = Search._index.docnames;
     const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
     const titles = Search._index.titles;
 
     const scoreMap = new Map();
@@ -499,16 +539,15 @@ const Search = {
   /**
    * helper function to return a node containing the
    * search summary for a given text. keywords is a list
-   * of stemmed words, highlightWords is the list of normal, unstemmed
-   * words. the first one is used to find the occurrence, the
-   * latter for highlighting it.
+   * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords, highlightWords) => {
-    const text = Search.htmlToText(htmlText).toLowerCase();
+  makeSearchSummary: (htmlText, keywords) => {
+    const text = Search.htmlToText(htmlText);
     if (text === "") return null;
 
+    const textLower = text.toLowerCase();
     const actualStartPosition = [...keywords]
-      .map((k) => text.indexOf(k.toLowerCase()))
+      .map((k) => textLower.indexOf(k.toLowerCase()))
       .filter((i) => i > -1)
       .slice(-1)[0];
     const startWithContext = Math.max(actualStartPosition - 120, 0);
@@ -516,13 +555,9 @@ const Search = {
     const top = startWithContext === 0 ? "" : "...";
     const tail = startWithContext + 240 < text.length ? "..." : "";
 
-    let summary = document.createElement("div");
+    let summary = document.createElement("p");
     summary.classList.add("context");
-    summary.innerText = top + text.substr(startWithContext, 240).trim() + tail;
-
-    highlightWords.forEach((highlightWord) =>
-      _highlightText(summary, highlightWord, "highlighted")
-    );
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
 
     return summary;
   },
diff --git a/documentation/5/_static/sidebar.js b/documentation/5/_static/sidebar.js
new file mode 100644
index 000000000..c5e2692c4
--- /dev/null
+++ b/documentation/5/_static/sidebar.js
@@ -0,0 +1,70 @@
+/*
+ * sidebar.js
+ * ~~~~~~~~~~
+ *
+ * This script makes the Sphinx sidebar collapsible.
+ *
+ * .sphinxsidebar contains .sphinxsidebarwrapper.  This script adds
+ * in .sphixsidebar, after .sphinxsidebarwrapper, the #sidebarbutton
+ * used to collapse and expand the sidebar.
+ *
+ * When the sidebar is collapsed the .sphinxsidebarwrapper is hidden
+ * and the width of the sidebar and the margin-left of the document
+ * are decreased. When the sidebar is expanded the opposite happens.
+ * This script saves a per-browser/per-session cookie used to
+ * remember the position of the sidebar among the pages.
+ * Once the browser is closed the cookie is deleted and the position
+ * reset to the default (expanded).
+ *
+ * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+const initialiseSidebar = () => {
+  
+    
+  
+
+  // global elements used by the functions.
+  const bodyWrapper = document.getElementsByClassName("bodywrapper")[0]
+  const sidebar = document.getElementsByClassName("sphinxsidebar")[0]
+  const sidebarWrapper = document.getElementsByClassName('sphinxsidebarwrapper')[0]
+  const sidebarButton = document.getElementById("sidebarbutton")
+  const sidebarArrow = sidebarButton.querySelector('span')
+
+  // for some reason, the document has no sidebar; do not run into errors
+  if (typeof sidebar === "undefined") return;
+
+  const flipArrow = element => element.innerText = (element.innerText === "»") ? "«" : "»"
+
+  const collapse_sidebar = () => {
+    bodyWrapper.style.marginLeft = ".8em";
+    sidebar.style.width = ".8em"
+    sidebarWrapper.style.display = "none"
+    flipArrow(sidebarArrow)
+    sidebarButton.title = _('Expand sidebar')
+    window.localStorage.setItem("sidebar", "collapsed")
+  }
+
+  const expand_sidebar = () => {
+    bodyWrapper.style.marginLeft = ""
+    sidebar.style.removeProperty("width")
+    sidebarWrapper.style.display = ""
+    flipArrow(sidebarArrow)
+    sidebarButton.title = _('Collapse sidebar')
+    window.localStorage.setItem("sidebar", "expanded")
+  }
+
+  sidebarButton.addEventListener("click", () => {
+    (sidebarWrapper.style.display === "none") ? expand_sidebar() : collapse_sidebar()
+  })
+
+  if (!window.localStorage.getItem("sidebar")) return
+  const value = window.localStorage.getItem("sidebar")
+  if (value === "collapsed") collapse_sidebar();
+  else if (value === "expanded") expand_sidebar();
+}
+
+if (document.readyState !== "loading") initialiseSidebar()
+else document.addEventListener("DOMContentLoaded", initialiseSidebar)
\ No newline at end of file
diff --git a/documentation/5/_static/sphinx_highlight.js b/documentation/5/_static/sphinx_highlight.js
new file mode 100644
index 000000000..aae669d7e
--- /dev/null
+++ b/documentation/5/_static/sphinx_highlight.js
@@ -0,0 +1,144 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          document.createTextNode(val.substr(pos + text.length)),
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(SphinxHighlight.highlightSearchWords);
+_ready(SphinxHighlight.initEscapeListener);
diff --git a/documentation/5/_static/underscore-1.13.1.js b/documentation/5/_static/underscore-1.13.1.js
deleted file mode 100644
index ffd77af96..000000000
--- a/documentation/5/_static/underscore-1.13.1.js
+++ /dev/null
@@ -1,2042 +0,0 @@
-(function (global, factory) {
-  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
-  typeof define === 'function' && define.amd ? define('underscore', factory) :
-  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
-    var current = global._;
-    var exports = global._ = factory();
-    exports.noConflict = function () { global._ = current; return exports; };
-  }()));
-}(this, (function () {
-  //     Underscore.js 1.13.1
-  //     https://underscorejs.org
-  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
-  //     Underscore may be freely distributed under the MIT license.
-
-  // Current version.
-  var VERSION = '1.13.1';
-
-  // Establish the root object, `window` (`self`) in the browser, `global`
-  // on the server, or `this` in some virtual machines. We use `self`
-  // instead of `window` for `WebWorker` support.
-  var root = typeof self == 'object' && self.self === self && self ||
-            typeof global == 'object' && global.global === global && global ||
-            Function('return this')() ||
-            {};
-
-  // Save bytes in the minified (but not gzipped) version:
-  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
-  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
-
-  // Create quick reference variables for speed access to core prototypes.
-  var push = ArrayProto.push,
-      slice = ArrayProto.slice,
-      toString = ObjProto.toString,
-      hasOwnProperty = ObjProto.hasOwnProperty;
-
-  // Modern feature detection.
-  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
-      supportsDataView = typeof DataView !== 'undefined';
-
-  // All **ECMAScript 5+** native function implementations that we hope to use
-  // are declared here.
-  var nativeIsArray = Array.isArray,
-      nativeKeys = Object.keys,
-      nativeCreate = Object.create,
-      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
-
-  // Create references to these builtin functions because we override them.
-  var _isNaN = isNaN,
-      _isFinite = isFinite;
-
-  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
-  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
-  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
-    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
-
-  // The largest integer that can be represented exactly.
-  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
-
-  // Some functions take a variable number of arguments, or a few expected
-  // arguments at the beginning and then a variable number of values to operate
-  // on. This helper accumulates all remaining arguments past the function’s
-  // argument length (or an explicit `startIndex`), into an array that becomes
-  // the last argument. Similar to ES6’s "rest parameter".
-  function restArguments(func, startIndex) {
-    startIndex = startIndex == null ? func.length - 1 : +startIndex;
-    return function() {
-      var length = Math.max(arguments.length - startIndex, 0),
-          rest = Array(length),
-          index = 0;
-      for (; index < length; index++) {
-        rest[index] = arguments[index + startIndex];
-      }
-      switch (startIndex) {
-        case 0: return func.call(this, rest);
-        case 1: return func.call(this, arguments[0], rest);
-        case 2: return func.call(this, arguments[0], arguments[1], rest);
-      }
-      var args = Array(startIndex + 1);
-      for (index = 0; index < startIndex; index++) {
-        args[index] = arguments[index];
-      }
-      args[startIndex] = rest;
-      return func.apply(this, args);
-    };
-  }
-
-  // Is a given variable an object?
-  function isObject(obj) {
-    var type = typeof obj;
-    return type === 'function' || type === 'object' && !!obj;
-  }
-
-  // Is a given value equal to null?
-  function isNull(obj) {
-    return obj === null;
-  }
-
-  // Is a given variable undefined?
-  function isUndefined(obj) {
-    return obj === void 0;
-  }
-
-  // Is a given value a boolean?
-  function isBoolean(obj) {
-    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
-  }
-
-  // Is a given value a DOM element?
-  function isElement(obj) {
-    return !!(obj && obj.nodeType === 1);
-  }
-
-  // Internal function for creating a `toString`-based type tester.
-  function tagTester(name) {
-    var tag = '[object ' + name + ']';
-    return function(obj) {
-      return toString.call(obj) === tag;
-    };
-  }
-
-  var isString = tagTester('String');
-
-  var isNumber = tagTester('Number');
-
-  var isDate = tagTester('Date');
-
-  var isRegExp = tagTester('RegExp');
-
-  var isError = tagTester('Error');
-
-  var isSymbol = tagTester('Symbol');
-
-  var isArrayBuffer = tagTester('ArrayBuffer');
-
-  var isFunction = tagTester('Function');
-
-  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
-  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
-  var nodelist = root.document && root.document.childNodes;
-  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
-    isFunction = function(obj) {
-      return typeof obj == 'function' || false;
-    };
-  }
-
-  var isFunction$1 = isFunction;
-
-  var hasObjectTag = tagTester('Object');
-
-  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
-  // In IE 11, the most common among them, this problem also applies to
-  // `Map`, `WeakMap` and `Set`.
-  var hasStringTagBug = (
-        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
-      ),
-      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
-
-  var isDataView = tagTester('DataView');
-
-  // In IE 10 - Edge 13, we need a different heuristic
-  // to determine whether an object is a `DataView`.
-  function ie10IsDataView(obj) {
-    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
-  }
-
-  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
-
-  // Is a given value an array?
-  // Delegates to ECMA5's native `Array.isArray`.
-  var isArray = nativeIsArray || tagTester('Array');
-
-  // Internal function to check whether `key` is an own property name of `obj`.
-  function has$1(obj, key) {
-    return obj != null && hasOwnProperty.call(obj, key);
-  }
-
-  var isArguments = tagTester('Arguments');
-
-  // Define a fallback version of the method in browsers (ahem, IE < 9), where
-  // there isn't any inspectable "Arguments" type.
-  (function() {
-    if (!isArguments(arguments)) {
-      isArguments = function(obj) {
-        return has$1(obj, 'callee');
-      };
-    }
-  }());
-
-  var isArguments$1 = isArguments;
-
-  // Is a given object a finite number?
-  function isFinite$1(obj) {
-    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
-  }
-
-  // Is the given value `NaN`?
-  function isNaN$1(obj) {
-    return isNumber(obj) && _isNaN(obj);
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function constant(value) {
-    return function() {
-      return value;
-    };
-  }
-
-  // Common internal logic for `isArrayLike` and `isBufferLike`.
-  function createSizePropertyCheck(getSizeProperty) {
-    return function(collection) {
-      var sizeProperty = getSizeProperty(collection);
-      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
-    }
-  }
-
-  // Internal helper to generate a function to obtain property `key` from `obj`.
-  function shallowProperty(key) {
-    return function(obj) {
-      return obj == null ? void 0 : obj[key];
-    };
-  }
-
-  // Internal helper to obtain the `byteLength` property of an object.
-  var getByteLength = shallowProperty('byteLength');
-
-  // Internal helper to determine whether we should spend extensive checks against
-  // `ArrayBuffer` et al.
-  var isBufferLike = createSizePropertyCheck(getByteLength);
-
-  // Is a given value a typed array?
-  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
-  function isTypedArray(obj) {
-    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
-    // Otherwise, fall back on the above regular expression.
-    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
-                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
-  }
-
-  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
-
-  // Internal helper to obtain the `length` property of an object.
-  var getLength = shallowProperty('length');
-
-  // Internal helper to create a simple lookup structure.
-  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
-  // circular imports. `emulatedSet` is a one-off solution that only works for
-  // arrays of strings.
-  function emulatedSet(keys) {
-    var hash = {};
-    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
-    return {
-      contains: function(key) { return hash[key]; },
-      push: function(key) {
-        hash[key] = true;
-        return keys.push(key);
-      }
-    };
-  }
-
-  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
-  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
-  // needed.
-  function collectNonEnumProps(obj, keys) {
-    keys = emulatedSet(keys);
-    var nonEnumIdx = nonEnumerableProps.length;
-    var constructor = obj.constructor;
-    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
-
-    // Constructor is a special case.
-    var prop = 'constructor';
-    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
-
-    while (nonEnumIdx--) {
-      prop = nonEnumerableProps[nonEnumIdx];
-      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
-        keys.push(prop);
-      }
-    }
-  }
-
-  // Retrieve the names of an object's own properties.
-  // Delegates to **ECMAScript 5**'s native `Object.keys`.
-  function keys(obj) {
-    if (!isObject(obj)) return [];
-    if (nativeKeys) return nativeKeys(obj);
-    var keys = [];
-    for (var key in obj) if (has$1(obj, key)) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Is a given array, string, or object empty?
-  // An "empty" object has no enumerable own-properties.
-  function isEmpty(obj) {
-    if (obj == null) return true;
-    // Skip the more expensive `toString`-based type checks if `obj` has no
-    // `.length`.
-    var length = getLength(obj);
-    if (typeof length == 'number' && (
-      isArray(obj) || isString(obj) || isArguments$1(obj)
-    )) return length === 0;
-    return getLength(keys(obj)) === 0;
-  }
-
-  // Returns whether an object has a given set of `key:value` pairs.
-  function isMatch(object, attrs) {
-    var _keys = keys(attrs), length = _keys.length;
-    if (object == null) return !length;
-    var obj = Object(object);
-    for (var i = 0; i < length; i++) {
-      var key = _keys[i];
-      if (attrs[key] !== obj[key] || !(key in obj)) return false;
-    }
-    return true;
-  }
-
-  // If Underscore is called as a function, it returns a wrapped object that can
-  // be used OO-style. This wrapper holds altered versions of all functions added
-  // through `_.mixin`. Wrapped objects may be chained.
-  function _$1(obj) {
-    if (obj instanceof _$1) return obj;
-    if (!(this instanceof _$1)) return new _$1(obj);
-    this._wrapped = obj;
-  }
-
-  _$1.VERSION = VERSION;
-
-  // Extracts the result from a wrapped and chained object.
-  _$1.prototype.value = function() {
-    return this._wrapped;
-  };
-
-  // Provide unwrapping proxies for some methods used in engine operations
-  // such as arithmetic and JSON stringification.
-  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
-
-  _$1.prototype.toString = function() {
-    return String(this._wrapped);
-  };
-
-  // Internal function to wrap or shallow-copy an ArrayBuffer,
-  // typed array or DataView to a new view, reusing the buffer.
-  function toBufferView(bufferSource) {
-    return new Uint8Array(
-      bufferSource.buffer || bufferSource,
-      bufferSource.byteOffset || 0,
-      getByteLength(bufferSource)
-    );
-  }
-
-  // We use this string twice, so give it a name for minification.
-  var tagDataView = '[object DataView]';
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function eq(a, b, aStack, bStack) {
-    // Identical objects are equal. `0 === -0`, but they aren't identical.
-    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
-    if (a === b) return a !== 0 || 1 / a === 1 / b;
-    // `null` or `undefined` only equal to itself (strict comparison).
-    if (a == null || b == null) return false;
-    // `NaN`s are equivalent, but non-reflexive.
-    if (a !== a) return b !== b;
-    // Exhaust primitive checks
-    var type = typeof a;
-    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
-    return deepEq(a, b, aStack, bStack);
-  }
-
-  // Internal recursive comparison function for `_.isEqual`.
-  function deepEq(a, b, aStack, bStack) {
-    // Unwrap any wrapped objects.
-    if (a instanceof _$1) a = a._wrapped;
-    if (b instanceof _$1) b = b._wrapped;
-    // Compare `[[Class]]` names.
-    var className = toString.call(a);
-    if (className !== toString.call(b)) return false;
-    // Work around a bug in IE 10 - Edge 13.
-    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
-      if (!isDataView$1(b)) return false;
-      className = tagDataView;
-    }
-    switch (className) {
-      // These types are compared by value.
-      case '[object RegExp]':
-        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
-      case '[object String]':
-        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
-        // equivalent to `new String("5")`.
-        return '' + a === '' + b;
-      case '[object Number]':
-        // `NaN`s are equivalent, but non-reflexive.
-        // Object(NaN) is equivalent to NaN.
-        if (+a !== +a) return +b !== +b;
-        // An `egal` comparison is performed for other numeric values.
-        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
-      case '[object Date]':
-      case '[object Boolean]':
-        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
-        // millisecond representations. Note that invalid dates with millisecond representations
-        // of `NaN` are not equivalent.
-        return +a === +b;
-      case '[object Symbol]':
-        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
-      case '[object ArrayBuffer]':
-      case tagDataView:
-        // Coerce to typed array so we can fall through.
-        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
-    }
-
-    var areArrays = className === '[object Array]';
-    if (!areArrays && isTypedArray$1(a)) {
-        var byteLength = getByteLength(a);
-        if (byteLength !== getByteLength(b)) return false;
-        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
-        areArrays = true;
-    }
-    if (!areArrays) {
-      if (typeof a != 'object' || typeof b != 'object') return false;
-
-      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
-      // from different frames are.
-      var aCtor = a.constructor, bCtor = b.constructor;
-      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
-                               isFunction$1(bCtor) && bCtor instanceof bCtor)
-                          && ('constructor' in a && 'constructor' in b)) {
-        return false;
-      }
-    }
-    // Assume equality for cyclic structures. The algorithm for detecting cyclic
-    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
-
-    // Initializing stack of traversed objects.
-    // It's done here since we only need them for objects and arrays comparison.
-    aStack = aStack || [];
-    bStack = bStack || [];
-    var length = aStack.length;
-    while (length--) {
-      // Linear search. Performance is inversely proportional to the number of
-      // unique nested structures.
-      if (aStack[length] === a) return bStack[length] === b;
-    }
-
-    // Add the first object to the stack of traversed objects.
-    aStack.push(a);
-    bStack.push(b);
-
-    // Recursively compare objects and arrays.
-    if (areArrays) {
-      // Compare array lengths to determine if a deep comparison is necessary.
-      length = a.length;
-      if (length !== b.length) return false;
-      // Deep compare the contents, ignoring non-numeric properties.
-      while (length--) {
-        if (!eq(a[length], b[length], aStack, bStack)) return false;
-      }
-    } else {
-      // Deep compare objects.
-      var _keys = keys(a), key;
-      length = _keys.length;
-      // Ensure that both objects contain the same number of properties before comparing deep equality.
-      if (keys(b).length !== length) return false;
-      while (length--) {
-        // Deep compare each member
-        key = _keys[length];
-        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
-      }
-    }
-    // Remove the first object from the stack of traversed objects.
-    aStack.pop();
-    bStack.pop();
-    return true;
-  }
-
-  // Perform a deep comparison to check if two objects are equal.
-  function isEqual(a, b) {
-    return eq(a, b);
-  }
-
-  // Retrieve all the enumerable property names of an object.
-  function allKeys(obj) {
-    if (!isObject(obj)) return [];
-    var keys = [];
-    for (var key in obj) keys.push(key);
-    // Ahem, IE < 9.
-    if (hasEnumBug) collectNonEnumProps(obj, keys);
-    return keys;
-  }
-
-  // Since the regular `Object.prototype.toString` type tests don't work for
-  // some types in IE 11, we use a fingerprinting heuristic instead, based
-  // on the methods. It's not great, but it's the best we got.
-  // The fingerprint method lists are defined below.
-  function ie11fingerprint(methods) {
-    var length = getLength(methods);
-    return function(obj) {
-      if (obj == null) return false;
-      // `Map`, `WeakMap` and `Set` have no enumerable keys.
-      var keys = allKeys(obj);
-      if (getLength(keys)) return false;
-      for (var i = 0; i < length; i++) {
-        if (!isFunction$1(obj[methods[i]])) return false;
-      }
-      // If we are testing against `WeakMap`, we need to ensure that
-      // `obj` doesn't have a `forEach` method in order to distinguish
-      // it from a regular `Map`.
-      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
-    };
-  }
-
-  // In the interest of compact minification, we write
-  // each string in the fingerprints only once.
-  var forEachName = 'forEach',
-      hasName = 'has',
-      commonInit = ['clear', 'delete'],
-      mapTail = ['get', hasName, 'set'];
-
-  // `Map`, `WeakMap` and `Set` each have slightly different
-  // combinations of the above sublists.
-  var mapMethods = commonInit.concat(forEachName, mapTail),
-      weakMapMethods = commonInit.concat(mapTail),
-      setMethods = ['add'].concat(commonInit, forEachName, hasName);
-
-  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
-
-  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
-
-  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
-
-  var isWeakSet = tagTester('WeakSet');
-
-  // Retrieve the values of an object's properties.
-  function values(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var values = Array(length);
-    for (var i = 0; i < length; i++) {
-      values[i] = obj[_keys[i]];
-    }
-    return values;
-  }
-
-  // Convert an object into a list of `[key, value]` pairs.
-  // The opposite of `_.object` with one argument.
-  function pairs(obj) {
-    var _keys = keys(obj);
-    var length = _keys.length;
-    var pairs = Array(length);
-    for (var i = 0; i < length; i++) {
-      pairs[i] = [_keys[i], obj[_keys[i]]];
-    }
-    return pairs;
-  }
-
-  // Invert the keys and values of an object. The values must be serializable.
-  function invert(obj) {
-    var result = {};
-    var _keys = keys(obj);
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      result[obj[_keys[i]]] = _keys[i];
-    }
-    return result;
-  }
-
-  // Return a sorted list of the function names available on the object.
-  function functions(obj) {
-    var names = [];
-    for (var key in obj) {
-      if (isFunction$1(obj[key])) names.push(key);
-    }
-    return names.sort();
-  }
-
-  // An internal function for creating assigner functions.
-  function createAssigner(keysFunc, defaults) {
-    return function(obj) {
-      var length = arguments.length;
-      if (defaults) obj = Object(obj);
-      if (length < 2 || obj == null) return obj;
-      for (var index = 1; index < length; index++) {
-        var source = arguments[index],
-            keys = keysFunc(source),
-            l = keys.length;
-        for (var i = 0; i < l; i++) {
-          var key = keys[i];
-          if (!defaults || obj[key] === void 0) obj[key] = source[key];
-        }
-      }
-      return obj;
-    };
-  }
-
-  // Extend a given object with all the properties in passed-in object(s).
-  var extend = createAssigner(allKeys);
-
-  // Assigns a given object with all the own properties in the passed-in
-  // object(s).
-  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
-  var extendOwn = createAssigner(keys);
-
-  // Fill in a given object with default properties.
-  var defaults = createAssigner(allKeys, true);
-
-  // Create a naked function reference for surrogate-prototype-swapping.
-  function ctor() {
-    return function(){};
-  }
-
-  // An internal function for creating a new object that inherits from another.
-  function baseCreate(prototype) {
-    if (!isObject(prototype)) return {};
-    if (nativeCreate) return nativeCreate(prototype);
-    var Ctor = ctor();
-    Ctor.prototype = prototype;
-    var result = new Ctor;
-    Ctor.prototype = null;
-    return result;
-  }
-
-  // Creates an object that inherits from the given prototype object.
-  // If additional properties are provided then they will be added to the
-  // created object.
-  function create(prototype, props) {
-    var result = baseCreate(prototype);
-    if (props) extendOwn(result, props);
-    return result;
-  }
-
-  // Create a (shallow-cloned) duplicate of an object.
-  function clone(obj) {
-    if (!isObject(obj)) return obj;
-    return isArray(obj) ? obj.slice() : extend({}, obj);
-  }
-
-  // Invokes `interceptor` with the `obj` and then returns `obj`.
-  // The primary purpose of this method is to "tap into" a method chain, in
-  // order to perform operations on intermediate results within the chain.
-  function tap(obj, interceptor) {
-    interceptor(obj);
-    return obj;
-  }
-
-  // Normalize a (deep) property `path` to array.
-  // Like `_.iteratee`, this function can be customized.
-  function toPath$1(path) {
-    return isArray(path) ? path : [path];
-  }
-  _$1.toPath = toPath$1;
-
-  // Internal wrapper for `_.toPath` to enable minification.
-  // Similar to `cb` for `_.iteratee`.
-  function toPath(path) {
-    return _$1.toPath(path);
-  }
-
-  // Internal function to obtain a nested property in `obj` along `path`.
-  function deepGet(obj, path) {
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      if (obj == null) return void 0;
-      obj = obj[path[i]];
-    }
-    return length ? obj : void 0;
-  }
-
-  // Get the value of the (deep) property on `path` from `object`.
-  // If any property in `path` does not exist or if the value is
-  // `undefined`, return `defaultValue` instead.
-  // The `path` is normalized through `_.toPath`.
-  function get(object, path, defaultValue) {
-    var value = deepGet(object, toPath(path));
-    return isUndefined(value) ? defaultValue : value;
-  }
-
-  // Shortcut function for checking if an object has a given property directly on
-  // itself (in other words, not on a prototype). Unlike the internal `has`
-  // function, this public version can also traverse nested properties.
-  function has(obj, path) {
-    path = toPath(path);
-    var length = path.length;
-    for (var i = 0; i < length; i++) {
-      var key = path[i];
-      if (!has$1(obj, key)) return false;
-      obj = obj[key];
-    }
-    return !!length;
-  }
-
-  // Keep the identity function around for default iteratees.
-  function identity(value) {
-    return value;
-  }
-
-  // Returns a predicate for checking whether an object has a given set of
-  // `key:value` pairs.
-  function matcher(attrs) {
-    attrs = extendOwn({}, attrs);
-    return function(obj) {
-      return isMatch(obj, attrs);
-    };
-  }
-
-  // Creates a function that, when passed an object, will traverse that object’s
-  // properties down the given `path`, specified as an array of keys or indices.
-  function property(path) {
-    path = toPath(path);
-    return function(obj) {
-      return deepGet(obj, path);
-    };
-  }
-
-  // Internal function that returns an efficient (for current engines) version
-  // of the passed-in callback, to be repeatedly applied in other Underscore
-  // functions.
-  function optimizeCb(func, context, argCount) {
-    if (context === void 0) return func;
-    switch (argCount == null ? 3 : argCount) {
-      case 1: return function(value) {
-        return func.call(context, value);
-      };
-      // The 2-argument case is omitted because we’re not using it.
-      case 3: return function(value, index, collection) {
-        return func.call(context, value, index, collection);
-      };
-      case 4: return function(accumulator, value, index, collection) {
-        return func.call(context, accumulator, value, index, collection);
-      };
-    }
-    return function() {
-      return func.apply(context, arguments);
-    };
-  }
-
-  // An internal function to generate callbacks that can be applied to each
-  // element in a collection, returning the desired result — either `_.identity`,
-  // an arbitrary callback, a property matcher, or a property accessor.
-  function baseIteratee(value, context, argCount) {
-    if (value == null) return identity;
-    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
-    if (isObject(value) && !isArray(value)) return matcher(value);
-    return property(value);
-  }
-
-  // External wrapper for our callback generator. Users may customize
-  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
-  // This abstraction hides the internal-only `argCount` argument.
-  function iteratee(value, context) {
-    return baseIteratee(value, context, Infinity);
-  }
-  _$1.iteratee = iteratee;
-
-  // The function we call internally to generate a callback. It invokes
-  // `_.iteratee` if overridden, otherwise `baseIteratee`.
-  function cb(value, context, argCount) {
-    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
-    return baseIteratee(value, context, argCount);
-  }
-
-  // Returns the results of applying the `iteratee` to each element of `obj`.
-  // In contrast to `_.map` it returns an object.
-  function mapObject(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = keys(obj),
-        length = _keys.length,
-        results = {};
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys[index];
-      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Predicate-generating function. Often useful outside of Underscore.
-  function noop(){}
-
-  // Generates a function for a given object that returns a given property.
-  function propertyOf(obj) {
-    if (obj == null) return noop;
-    return function(path) {
-      return get(obj, path);
-    };
-  }
-
-  // Run a function **n** times.
-  function times(n, iteratee, context) {
-    var accum = Array(Math.max(0, n));
-    iteratee = optimizeCb(iteratee, context, 1);
-    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
-    return accum;
-  }
-
-  // Return a random integer between `min` and `max` (inclusive).
-  function random(min, max) {
-    if (max == null) {
-      max = min;
-      min = 0;
-    }
-    return min + Math.floor(Math.random() * (max - min + 1));
-  }
-
-  // A (possibly faster) way to get the current timestamp as an integer.
-  var now = Date.now || function() {
-    return new Date().getTime();
-  };
-
-  // Internal helper to generate functions for escaping and unescaping strings
-  // to/from HTML interpolation.
-  function createEscaper(map) {
-    var escaper = function(match) {
-      return map[match];
-    };
-    // Regexes for identifying a key that needs to be escaped.
-    var source = '(?:' + keys(map).join('|') + ')';
-    var testRegexp = RegExp(source);
-    var replaceRegexp = RegExp(source, 'g');
-    return function(string) {
-      string = string == null ? '' : '' + string;
-      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
-    };
-  }
-
-  // Internal list of HTML entities for escaping.
-  var escapeMap = {
-    '&': '&amp;',
-    '<': '&lt;',
-    '>': '&gt;',
-    '"': '&quot;',
-    "'": '&#x27;',
-    '`': '&#x60;'
-  };
-
-  // Function for escaping strings to HTML interpolation.
-  var _escape = createEscaper(escapeMap);
-
-  // Internal list of HTML entities for unescaping.
-  var unescapeMap = invert(escapeMap);
-
-  // Function for unescaping strings from HTML interpolation.
-  var _unescape = createEscaper(unescapeMap);
-
-  // By default, Underscore uses ERB-style template delimiters. Change the
-  // following template settings to use alternative delimiters.
-  var templateSettings = _$1.templateSettings = {
-    evaluate: /<%([\s\S]+?)%>/g,
-    interpolate: /<%=([\s\S]+?)%>/g,
-    escape: /<%-([\s\S]+?)%>/g
-  };
-
-  // When customizing `_.templateSettings`, if you don't want to define an
-  // interpolation, evaluation or escaping regex, we need one that is
-  // guaranteed not to match.
-  var noMatch = /(.)^/;
-
-  // Certain characters need to be escaped so that they can be put into a
-  // string literal.
-  var escapes = {
-    "'": "'",
-    '\\': '\\',
-    '\r': 'r',
-    '\n': 'n',
-    '\u2028': 'u2028',
-    '\u2029': 'u2029'
-  };
-
-  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
-
-  function escapeChar(match) {
-    return '\\' + escapes[match];
-  }
-
-  // In order to prevent third-party code injection through
-  // `_.templateSettings.variable`, we test it against the following regular
-  // expression. It is intentionally a bit more liberal than just matching valid
-  // identifiers, but still prevents possible loopholes through defaults or
-  // destructuring assignment.
-  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
-
-  // JavaScript micro-templating, similar to John Resig's implementation.
-  // Underscore templating handles arbitrary delimiters, preserves whitespace,
-  // and correctly escapes quotes within interpolated code.
-  // NB: `oldSettings` only exists for backwards compatibility.
-  function template(text, settings, oldSettings) {
-    if (!settings && oldSettings) settings = oldSettings;
-    settings = defaults({}, settings, _$1.templateSettings);
-
-    // Combine delimiters into one regular expression via alternation.
-    var matcher = RegExp([
-      (settings.escape || noMatch).source,
-      (settings.interpolate || noMatch).source,
-      (settings.evaluate || noMatch).source
-    ].join('|') + '|$', 'g');
-
-    // Compile the template source, escaping string literals appropriately.
-    var index = 0;
-    var source = "__p+='";
-    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
-      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
-      index = offset + match.length;
-
-      if (escape) {
-        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
-      } else if (interpolate) {
-        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
-      } else if (evaluate) {
-        source += "';\n" + evaluate + "\n__p+='";
-      }
-
-      // Adobe VMs need the match returned to produce the correct offset.
-      return match;
-    });
-    source += "';\n";
-
-    var argument = settings.variable;
-    if (argument) {
-      // Insure against third-party code injection. (CVE-2021-23358)
-      if (!bareIdentifier.test(argument)) throw new Error(
-        'variable is not a bare identifier: ' + argument
-      );
-    } else {
-      // If a variable is not specified, place data values in local scope.
-      source = 'with(obj||{}){\n' + source + '}\n';
-      argument = 'obj';
-    }
-
-    source = "var __t,__p='',__j=Array.prototype.join," +
-      "print=function(){__p+=__j.call(arguments,'');};\n" +
-      source + 'return __p;\n';
-
-    var render;
-    try {
-      render = new Function(argument, '_', source);
-    } catch (e) {
-      e.source = source;
-      throw e;
-    }
-
-    var template = function(data) {
-      return render.call(this, data, _$1);
-    };
-
-    // Provide the compiled source as a convenience for precompilation.
-    template.source = 'function(' + argument + '){\n' + source + '}';
-
-    return template;
-  }
-
-  // Traverses the children of `obj` along `path`. If a child is a function, it
-  // is invoked with its parent as context. Returns the value of the final
-  // child, or `fallback` if any child is undefined.
-  function result(obj, path, fallback) {
-    path = toPath(path);
-    var length = path.length;
-    if (!length) {
-      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
-    }
-    for (var i = 0; i < length; i++) {
-      var prop = obj == null ? void 0 : obj[path[i]];
-      if (prop === void 0) {
-        prop = fallback;
-        i = length; // Ensure we don't continue iterating.
-      }
-      obj = isFunction$1(prop) ? prop.call(obj) : prop;
-    }
-    return obj;
-  }
-
-  // Generate a unique integer id (unique within the entire client session).
-  // Useful for temporary DOM ids.
-  var idCounter = 0;
-  function uniqueId(prefix) {
-    var id = ++idCounter + '';
-    return prefix ? prefix + id : id;
-  }
-
-  // Start chaining a wrapped Underscore object.
-  function chain(obj) {
-    var instance = _$1(obj);
-    instance._chain = true;
-    return instance;
-  }
-
-  // Internal function to execute `sourceFunc` bound to `context` with optional
-  // `args`. Determines whether to execute a function as a constructor or as a
-  // normal function.
-  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
-    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
-    var self = baseCreate(sourceFunc.prototype);
-    var result = sourceFunc.apply(self, args);
-    if (isObject(result)) return result;
-    return self;
-  }
-
-  // Partially apply a function by creating a version that has had some of its
-  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
-  // as a placeholder by default, allowing any combination of arguments to be
-  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
-  var partial = restArguments(function(func, boundArgs) {
-    var placeholder = partial.placeholder;
-    var bound = function() {
-      var position = 0, length = boundArgs.length;
-      var args = Array(length);
-      for (var i = 0; i < length; i++) {
-        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
-      }
-      while (position < arguments.length) args.push(arguments[position++]);
-      return executeBound(func, bound, this, this, args);
-    };
-    return bound;
-  });
-
-  partial.placeholder = _$1;
-
-  // Create a function bound to a given object (assigning `this`, and arguments,
-  // optionally).
-  var bind = restArguments(function(func, context, args) {
-    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
-    var bound = restArguments(function(callArgs) {
-      return executeBound(func, bound, context, this, args.concat(callArgs));
-    });
-    return bound;
-  });
-
-  // Internal helper for collection methods to determine whether a collection
-  // should be iterated as an array or as an object.
-  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
-  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
-  var isArrayLike = createSizePropertyCheck(getLength);
-
-  // Internal implementation of a recursive `flatten` function.
-  function flatten$1(input, depth, strict, output) {
-    output = output || [];
-    if (!depth && depth !== 0) {
-      depth = Infinity;
-    } else if (depth <= 0) {
-      return output.concat(input);
-    }
-    var idx = output.length;
-    for (var i = 0, length = getLength(input); i < length; i++) {
-      var value = input[i];
-      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
-        // Flatten current level of array or arguments object.
-        if (depth > 1) {
-          flatten$1(value, depth - 1, strict, output);
-          idx = output.length;
-        } else {
-          var j = 0, len = value.length;
-          while (j < len) output[idx++] = value[j++];
-        }
-      } else if (!strict) {
-        output[idx++] = value;
-      }
-    }
-    return output;
-  }
-
-  // Bind a number of an object's methods to that object. Remaining arguments
-  // are the method names to be bound. Useful for ensuring that all callbacks
-  // defined on an object belong to it.
-  var bindAll = restArguments(function(obj, keys) {
-    keys = flatten$1(keys, false, false);
-    var index = keys.length;
-    if (index < 1) throw new Error('bindAll must be passed function names');
-    while (index--) {
-      var key = keys[index];
-      obj[key] = bind(obj[key], obj);
-    }
-    return obj;
-  });
-
-  // Memoize an expensive function by storing its results.
-  function memoize(func, hasher) {
-    var memoize = function(key) {
-      var cache = memoize.cache;
-      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
-      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
-      return cache[address];
-    };
-    memoize.cache = {};
-    return memoize;
-  }
-
-  // Delays a function for the given number of milliseconds, and then calls
-  // it with the arguments supplied.
-  var delay = restArguments(function(func, wait, args) {
-    return setTimeout(function() {
-      return func.apply(null, args);
-    }, wait);
-  });
-
-  // Defers a function, scheduling it to run after the current call stack has
-  // cleared.
-  var defer = partial(delay, _$1, 1);
-
-  // Returns a function, that, when invoked, will only be triggered at most once
-  // during a given window of time. Normally, the throttled function will run
-  // as much as it can, without ever going more than once per `wait` duration;
-  // but if you'd like to disable the execution on the leading edge, pass
-  // `{leading: false}`. To disable execution on the trailing edge, ditto.
-  function throttle(func, wait, options) {
-    var timeout, context, args, result;
-    var previous = 0;
-    if (!options) options = {};
-
-    var later = function() {
-      previous = options.leading === false ? 0 : now();
-      timeout = null;
-      result = func.apply(context, args);
-      if (!timeout) context = args = null;
-    };
-
-    var throttled = function() {
-      var _now = now();
-      if (!previous && options.leading === false) previous = _now;
-      var remaining = wait - (_now - previous);
-      context = this;
-      args = arguments;
-      if (remaining <= 0 || remaining > wait) {
-        if (timeout) {
-          clearTimeout(timeout);
-          timeout = null;
-        }
-        previous = _now;
-        result = func.apply(context, args);
-        if (!timeout) context = args = null;
-      } else if (!timeout && options.trailing !== false) {
-        timeout = setTimeout(later, remaining);
-      }
-      return result;
-    };
-
-    throttled.cancel = function() {
-      clearTimeout(timeout);
-      previous = 0;
-      timeout = context = args = null;
-    };
-
-    return throttled;
-  }
-
-  // When a sequence of calls of the returned function ends, the argument
-  // function is triggered. The end of a sequence is defined by the `wait`
-  // parameter. If `immediate` is passed, the argument function will be
-  // triggered at the beginning of the sequence instead of at the end.
-  function debounce(func, wait, immediate) {
-    var timeout, previous, args, result, context;
-
-    var later = function() {
-      var passed = now() - previous;
-      if (wait > passed) {
-        timeout = setTimeout(later, wait - passed);
-      } else {
-        timeout = null;
-        if (!immediate) result = func.apply(context, args);
-        // This check is needed because `func` can recursively invoke `debounced`.
-        if (!timeout) args = context = null;
-      }
-    };
-
-    var debounced = restArguments(function(_args) {
-      context = this;
-      args = _args;
-      previous = now();
-      if (!timeout) {
-        timeout = setTimeout(later, wait);
-        if (immediate) result = func.apply(context, args);
-      }
-      return result;
-    });
-
-    debounced.cancel = function() {
-      clearTimeout(timeout);
-      timeout = args = context = null;
-    };
-
-    return debounced;
-  }
-
-  // Returns the first function passed as an argument to the second,
-  // allowing you to adjust arguments, run code before and after, and
-  // conditionally execute the original function.
-  function wrap(func, wrapper) {
-    return partial(wrapper, func);
-  }
-
-  // Returns a negated version of the passed-in predicate.
-  function negate(predicate) {
-    return function() {
-      return !predicate.apply(this, arguments);
-    };
-  }
-
-  // Returns a function that is the composition of a list of functions, each
-  // consuming the return value of the function that follows.
-  function compose() {
-    var args = arguments;
-    var start = args.length - 1;
-    return function() {
-      var i = start;
-      var result = args[start].apply(this, arguments);
-      while (i--) result = args[i].call(this, result);
-      return result;
-    };
-  }
-
-  // Returns a function that will only be executed on and after the Nth call.
-  function after(times, func) {
-    return function() {
-      if (--times < 1) {
-        return func.apply(this, arguments);
-      }
-    };
-  }
-
-  // Returns a function that will only be executed up to (but not including) the
-  // Nth call.
-  function before(times, func) {
-    var memo;
-    return function() {
-      if (--times > 0) {
-        memo = func.apply(this, arguments);
-      }
-      if (times <= 1) func = null;
-      return memo;
-    };
-  }
-
-  // Returns a function that will be executed at most one time, no matter how
-  // often you call it. Useful for lazy initialization.
-  var once = partial(before, 2);
-
-  // Returns the first key on an object that passes a truth test.
-  function findKey(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = keys(obj), key;
-    for (var i = 0, length = _keys.length; i < length; i++) {
-      key = _keys[i];
-      if (predicate(obj[key], key, obj)) return key;
-    }
-  }
-
-  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
-  function createPredicateIndexFinder(dir) {
-    return function(array, predicate, context) {
-      predicate = cb(predicate, context);
-      var length = getLength(array);
-      var index = dir > 0 ? 0 : length - 1;
-      for (; index >= 0 && index < length; index += dir) {
-        if (predicate(array[index], index, array)) return index;
-      }
-      return -1;
-    };
-  }
-
-  // Returns the first index on an array-like that passes a truth test.
-  var findIndex = createPredicateIndexFinder(1);
-
-  // Returns the last index on an array-like that passes a truth test.
-  var findLastIndex = createPredicateIndexFinder(-1);
-
-  // Use a comparator function to figure out the smallest index at which
-  // an object should be inserted so as to maintain order. Uses binary search.
-  function sortedIndex(array, obj, iteratee, context) {
-    iteratee = cb(iteratee, context, 1);
-    var value = iteratee(obj);
-    var low = 0, high = getLength(array);
-    while (low < high) {
-      var mid = Math.floor((low + high) / 2);
-      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
-    }
-    return low;
-  }
-
-  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
-  function createIndexFinder(dir, predicateFind, sortedIndex) {
-    return function(array, item, idx) {
-      var i = 0, length = getLength(array);
-      if (typeof idx == 'number') {
-        if (dir > 0) {
-          i = idx >= 0 ? idx : Math.max(idx + length, i);
-        } else {
-          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
-        }
-      } else if (sortedIndex && idx && length) {
-        idx = sortedIndex(array, item);
-        return array[idx] === item ? idx : -1;
-      }
-      if (item !== item) {
-        idx = predicateFind(slice.call(array, i, length), isNaN$1);
-        return idx >= 0 ? idx + i : -1;
-      }
-      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
-        if (array[idx] === item) return idx;
-      }
-      return -1;
-    };
-  }
-
-  // Return the position of the first occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  // If the array is large and already in sort order, pass `true`
-  // for **isSorted** to use binary search.
-  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
-
-  // Return the position of the last occurrence of an item in an array,
-  // or -1 if the item is not included in the array.
-  var lastIndexOf = createIndexFinder(-1, findLastIndex);
-
-  // Return the first value which passes a truth test.
-  function find(obj, predicate, context) {
-    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
-    var key = keyFinder(obj, predicate, context);
-    if (key !== void 0 && key !== -1) return obj[key];
-  }
-
-  // Convenience version of a common use case of `_.find`: getting the first
-  // object containing specific `key:value` pairs.
-  function findWhere(obj, attrs) {
-    return find(obj, matcher(attrs));
-  }
-
-  // The cornerstone for collection functions, an `each`
-  // implementation, aka `forEach`.
-  // Handles raw objects in addition to array-likes. Treats all
-  // sparse array-likes as if they were dense.
-  function each(obj, iteratee, context) {
-    iteratee = optimizeCb(iteratee, context);
-    var i, length;
-    if (isArrayLike(obj)) {
-      for (i = 0, length = obj.length; i < length; i++) {
-        iteratee(obj[i], i, obj);
-      }
-    } else {
-      var _keys = keys(obj);
-      for (i = 0, length = _keys.length; i < length; i++) {
-        iteratee(obj[_keys[i]], _keys[i], obj);
-      }
-    }
-    return obj;
-  }
-
-  // Return the results of applying the iteratee to each element.
-  function map(obj, iteratee, context) {
-    iteratee = cb(iteratee, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length,
-        results = Array(length);
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      results[index] = iteratee(obj[currentKey], currentKey, obj);
-    }
-    return results;
-  }
-
-  // Internal helper to create a reducing function, iterating left or right.
-  function createReduce(dir) {
-    // Wrap code that reassigns argument variables in a separate function than
-    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
-    var reducer = function(obj, iteratee, memo, initial) {
-      var _keys = !isArrayLike(obj) && keys(obj),
-          length = (_keys || obj).length,
-          index = dir > 0 ? 0 : length - 1;
-      if (!initial) {
-        memo = obj[_keys ? _keys[index] : index];
-        index += dir;
-      }
-      for (; index >= 0 && index < length; index += dir) {
-        var currentKey = _keys ? _keys[index] : index;
-        memo = iteratee(memo, obj[currentKey], currentKey, obj);
-      }
-      return memo;
-    };
-
-    return function(obj, iteratee, memo, context) {
-      var initial = arguments.length >= 3;
-      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
-    };
-  }
-
-  // **Reduce** builds up a single result from a list of values, aka `inject`,
-  // or `foldl`.
-  var reduce = createReduce(1);
-
-  // The right-associative version of reduce, also known as `foldr`.
-  var reduceRight = createReduce(-1);
-
-  // Return all the elements that pass a truth test.
-  function filter(obj, predicate, context) {
-    var results = [];
-    predicate = cb(predicate, context);
-    each(obj, function(value, index, list) {
-      if (predicate(value, index, list)) results.push(value);
-    });
-    return results;
-  }
-
-  // Return all the elements for which a truth test fails.
-  function reject(obj, predicate, context) {
-    return filter(obj, negate(cb(predicate)), context);
-  }
-
-  // Determine whether all of the elements pass a truth test.
-  function every(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (!predicate(obj[currentKey], currentKey, obj)) return false;
-    }
-    return true;
-  }
-
-  // Determine if at least one element in the object passes a truth test.
-  function some(obj, predicate, context) {
-    predicate = cb(predicate, context);
-    var _keys = !isArrayLike(obj) && keys(obj),
-        length = (_keys || obj).length;
-    for (var index = 0; index < length; index++) {
-      var currentKey = _keys ? _keys[index] : index;
-      if (predicate(obj[currentKey], currentKey, obj)) return true;
-    }
-    return false;
-  }
-
-  // Determine if the array or object contains a given item (using `===`).
-  function contains(obj, item, fromIndex, guard) {
-    if (!isArrayLike(obj)) obj = values(obj);
-    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
-    return indexOf(obj, item, fromIndex) >= 0;
-  }
-
-  // Invoke a method (with arguments) on every item in a collection.
-  var invoke = restArguments(function(obj, path, args) {
-    var contextPath, func;
-    if (isFunction$1(path)) {
-      func = path;
-    } else {
-      path = toPath(path);
-      contextPath = path.slice(0, -1);
-      path = path[path.length - 1];
-    }
-    return map(obj, function(context) {
-      var method = func;
-      if (!method) {
-        if (contextPath && contextPath.length) {
-          context = deepGet(context, contextPath);
-        }
-        if (context == null) return void 0;
-        method = context[path];
-      }
-      return method == null ? method : method.apply(context, args);
-    });
-  });
-
-  // Convenience version of a common use case of `_.map`: fetching a property.
-  function pluck(obj, key) {
-    return map(obj, property(key));
-  }
-
-  // Convenience version of a common use case of `_.filter`: selecting only
-  // objects containing specific `key:value` pairs.
-  function where(obj, attrs) {
-    return filter(obj, matcher(attrs));
-  }
-
-  // Return the maximum element (or element-based computation).
-  function max(obj, iteratee, context) {
-    var result = -Infinity, lastComputed = -Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value > result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Return the minimum element (or element-based computation).
-  function min(obj, iteratee, context) {
-    var result = Infinity, lastComputed = Infinity,
-        value, computed;
-    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
-      obj = isArrayLike(obj) ? obj : values(obj);
-      for (var i = 0, length = obj.length; i < length; i++) {
-        value = obj[i];
-        if (value != null && value < result) {
-          result = value;
-        }
-      }
-    } else {
-      iteratee = cb(iteratee, context);
-      each(obj, function(v, index, list) {
-        computed = iteratee(v, index, list);
-        if (computed < lastComputed || computed === Infinity && result === Infinity) {
-          result = v;
-          lastComputed = computed;
-        }
-      });
-    }
-    return result;
-  }
-
-  // Sample **n** random values from a collection using the modern version of the
-  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
-  // If **n** is not specified, returns a single random element.
-  // The internal `guard` argument allows it to work with `_.map`.
-  function sample(obj, n, guard) {
-    if (n == null || guard) {
-      if (!isArrayLike(obj)) obj = values(obj);
-      return obj[random(obj.length - 1)];
-    }
-    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
-    var length = getLength(sample);
-    n = Math.max(Math.min(n, length), 0);
-    var last = length - 1;
-    for (var index = 0; index < n; index++) {
-      var rand = random(index, last);
-      var temp = sample[index];
-      sample[index] = sample[rand];
-      sample[rand] = temp;
-    }
-    return sample.slice(0, n);
-  }
-
-  // Shuffle a collection.
-  function shuffle(obj) {
-    return sample(obj, Infinity);
-  }
-
-  // Sort the object's values by a criterion produced by an iteratee.
-  function sortBy(obj, iteratee, context) {
-    var index = 0;
-    iteratee = cb(iteratee, context);
-    return pluck(map(obj, function(value, key, list) {
-      return {
-        value: value,
-        index: index++,
-        criteria: iteratee(value, key, list)
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-  // Groups the object's values by a criterion. Pass either a string attribute
-  // to group by, or a function that returns the criterion.
-  var groupBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
-  });
-
-  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
-  // when you know that your index values will be unique.
-  var indexBy = group(function(result, value, key) {
-    result[key] = value;
-  });
-
-  // Counts instances of an object that group by a certain criterion. Pass
-  // either a string attribute to count by, or a function that returns the
-  // criterion.
-  var countBy = group(function(result, value, key) {
-    if (has$1(result, key)) result[key]++; else result[key] = 1;
-  });
-
-  // Split a collection into two arrays: one whose elements all pass the given
-  // truth test, and one whose elements all do not pass the truth test.
-  var partition = group(function(result, value, pass) {
-    result[pass ? 0 : 1].push(value);
-  }, true);
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-  // Safely create a real, live array from anything iterable.
-  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
-  function toArray(obj) {
-    if (!obj) return [];
-    if (isArray(obj)) return slice.call(obj);
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-      // Keep surrogate pair characters together.
-      return obj.match(reStrSymbol);
-    }
-    if (isArrayLike(obj)) return map(obj, identity);
-    return values(obj);
-  }
-
-  // Return the number of elements in a collection.
-  function size(obj) {
-    if (obj == null) return 0;
-    return isArrayLike(obj) ? obj.length : keys(obj).length;
-  }
-
-  // Internal `_.pick` helper function to determine whether `key` is an enumerable
-  // property name of `obj`.
-  function keyInObj(value, key, obj) {
-    return key in obj;
-  }
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-  // Return a copy of the object only containing the allowed properties.
-  var pick = restArguments(function(obj, keys) {
-    var result = {}, iteratee = keys[0];
-    if (obj == null) return result;
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-      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
-      keys = allKeys(obj);
-    } else {
-      iteratee = keyInObj;
-      keys = flatten$1(keys, false, false);
-      obj = Object(obj);
-    }
-    for (var i = 0, length = keys.length; i < length; i++) {
-      var key = keys[i];
-      var value = obj[key];
-      if (iteratee(value, key, obj)) result[key] = value;
-    }
-    return result;
-  });
-
-  // Return a copy of the object without the disallowed properties.
-  var omit = restArguments(function(obj, keys) {
-    var iteratee = keys[0], context;
-    if (isFunction$1(iteratee)) {
-      iteratee = negate(iteratee);
-      if (keys.length > 1) context = keys[1];
-    } else {
-      keys = map(flatten$1(keys, false, false), String);
-      iteratee = function(value, key) {
-        return !contains(keys, key);
-      };
-    }
-    return pick(obj, iteratee, context);
-  });
-
-  // Returns everything but the last entry of the array. Especially useful on
-  // the arguments object. Passing **n** will return all the values in
-  // the array, excluding the last N.
-  function initial(array, n, guard) {
-    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
-  }
-
-  // Get the first element of an array. Passing **n** will return the first N
-  // values in the array. The **guard** check allows it to work with `_.map`.
-  function first(array, n, guard) {
-    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
-    if (n == null || guard) return array[0];
-    return initial(array, array.length - n);
-  }
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-  // Returns everything but the first entry of the `array`. Especially useful on
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-  // `array`.
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-  // Get the last element of an array. Passing **n** will return the last N
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-    if (n == null || guard) return array[array.length - 1];
-    return rest(array, Math.max(0, array.length - n));
-  }
-
-  // Trim out all falsy values from an array.
-  function compact(array) {
-    return filter(array, Boolean);
-  }
-
-  // Flatten out an array, either recursively (by default), or up to `depth`.
-  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
-  function flatten(array, depth) {
-    return flatten$1(array, depth, false);
-  }
-
-  // Take the difference between one array and a number of other arrays.
-  // Only the elements present in just the first array will remain.
-  var difference = restArguments(function(array, rest) {
-    rest = flatten$1(rest, true, true);
-    return filter(array, function(value){
-      return !contains(rest, value);
-    });
-  });
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-  // Return a version of the array that does not contain the specified value(s).
-  var without = restArguments(function(array, otherArrays) {
-    return difference(array, otherArrays);
-  });
-
-  // Produce a duplicate-free version of the array. If the array has already
-  // been sorted, you have the option of using a faster algorithm.
-  // The faster algorithm will not work with an iteratee if the iteratee
-  // is not a one-to-one function, so providing an iteratee will disable
-  // the faster algorithm.
-  function uniq(array, isSorted, iteratee, context) {
-    if (!isBoolean(isSorted)) {
-      context = iteratee;
-      iteratee = isSorted;
-      isSorted = false;
-    }
-    if (iteratee != null) iteratee = cb(iteratee, context);
-    var result = [];
-    var seen = [];
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var value = array[i],
-          computed = iteratee ? iteratee(value, i, array) : value;
-      if (isSorted && !iteratee) {
-        if (!i || seen !== computed) result.push(value);
-        seen = computed;
-      } else if (iteratee) {
-        if (!contains(seen, computed)) {
-          seen.push(computed);
-          result.push(value);
-        }
-      } else if (!contains(result, value)) {
-        result.push(value);
-      }
-    }
-    return result;
-  }
-
-  // Produce an array that contains the union: each distinct element from all of
-  // the passed-in arrays.
-  var union = restArguments(function(arrays) {
-    return uniq(flatten$1(arrays, true, true));
-  });
-
-  // Produce an array that contains every item shared between all the
-  // passed-in arrays.
-  function intersection(array) {
-    var result = [];
-    var argsLength = arguments.length;
-    for (var i = 0, length = getLength(array); i < length; i++) {
-      var item = array[i];
-      if (contains(result, item)) continue;
-      var j;
-      for (j = 1; j < argsLength; j++) {
-        if (!contains(arguments[j], item)) break;
-      }
-      if (j === argsLength) result.push(item);
-    }
-    return result;
-  }
-
-  // Complement of zip. Unzip accepts an array of arrays and groups
-  // each array's elements on shared indices.
-  function unzip(array) {
-    var length = array && max(array, getLength).length || 0;
-    var result = Array(length);
-
-    for (var index = 0; index < length; index++) {
-      result[index] = pluck(array, index);
-    }
-    return result;
-  }
-
-  // Zip together multiple lists into a single array -- elements that share
-  // an index go together.
-  var zip = restArguments(unzip);
-
-  // Converts lists into objects. Pass either a single array of `[key, value]`
-  // pairs, or two parallel arrays of the same length -- one of keys, and one of
-  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
-  function object(list, values) {
-    var result = {};
-    for (var i = 0, length = getLength(list); i < length; i++) {
-      if (values) {
-        result[list[i]] = values[i];
-      } else {
-        result[list[i][0]] = list[i][1];
-      }
-    }
-    return result;
-  }
-
-  // Generate an integer Array containing an arithmetic progression. A port of
-  // the native Python `range()` function. See
-  // [the Python documentation](https://docs.python.org/library/functions.html#range).
-  function range(start, stop, step) {
-    if (stop == null) {
-      stop = start || 0;
-      start = 0;
-    }
-    if (!step) {
-      step = stop < start ? -1 : 1;
-    }
-
-    var length = Math.max(Math.ceil((stop - start) / step), 0);
-    var range = Array(length);
-
-    for (var idx = 0; idx < length; idx++, start += step) {
-      range[idx] = start;
-    }
-
-    return range;
-  }
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-  // Chunk a single array into multiple arrays, each containing `count` or fewer
-  // items.
-  function chunk(array, count) {
-    if (count == null || count < 1) return [];
-    var result = [];
-    var i = 0, length = array.length;
-    while (i < length) {
-      result.push(slice.call(array, i, i += count));
-    }
-    return result;
-  }
-
-  // Helper function to continue chaining intermediate results.
-  function chainResult(instance, obj) {
-    return instance._chain ? _$1(obj).chain() : obj;
-  }
-
-  // Add your own custom functions to the Underscore object.
-  function mixin(obj) {
-    each(functions(obj), function(name) {
-      var func = _$1[name] = obj[name];
-      _$1.prototype[name] = function() {
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-    return _$1;
-  }
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-  // Add all mutator `Array` functions to the wrapper.
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-    var method = ArrayProto[name];
-    _$1.prototype[name] = function() {
-      var obj = this._wrapped;
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-        }
-      }
-      return chainResult(this, obj);
-    };
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-  // Add all accessor `Array` functions to the wrapper.
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-      var obj = this._wrapped;
-      if (obj != null) obj = method.apply(obj, arguments);
-      return chainResult(this, obj);
-    };
-  });
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-  // Named Exports
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-  var allExports = {
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-    VERSION: VERSION,
-    restArguments: restArguments,
-    isObject: isObject,
-    isNull: isNull,
-    isUndefined: isUndefined,
-    isBoolean: isBoolean,
-    isElement: isElement,
-    isString: isString,
-    isNumber: isNumber,
-    isDate: isDate,
-    isRegExp: isRegExp,
-    isError: isError,
-    isSymbol: isSymbol,
-    isArrayBuffer: isArrayBuffer,
-    isDataView: isDataView$1,
-    isArray: isArray,
-    isFunction: isFunction$1,
-    isArguments: isArguments$1,
-    isFinite: isFinite$1,
-    isNaN: isNaN$1,
-    isTypedArray: isTypedArray$1,
-    isEmpty: isEmpty,
-    isMatch: isMatch,
-    isEqual: isEqual,
-    isMap: isMap,
-    isWeakMap: isWeakMap,
-    isSet: isSet,
-    isWeakSet: isWeakSet,
-    keys: keys,
-    allKeys: allKeys,
-    values: values,
-    pairs: pairs,
-    invert: invert,
-    functions: functions,
-    methods: functions,
-    extend: extend,
-    extendOwn: extendOwn,
-    assign: extendOwn,
-    defaults: defaults,
-    create: create,
-    clone: clone,
-    tap: tap,
-    get: get,
-    has: has,
-    mapObject: mapObject,
-    identity: identity,
-    constant: constant,
-    noop: noop,
-    toPath: toPath$1,
-    property: property,
-    propertyOf: propertyOf,
-    matcher: matcher,
-    matches: matcher,
-    times: times,
-    random: random,
-    now: now,
-    escape: _escape,
-    unescape: _unescape,
-    templateSettings: templateSettings,
-    template: template,
-    result: result,
-    uniqueId: uniqueId,
-    chain: chain,
-    iteratee: iteratee,
-    partial: partial,
-    bind: bind,
-    bindAll: bindAll,
-    memoize: memoize,
-    delay: delay,
-    defer: defer,
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-    wrap: wrap,
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-    compose: compose,
-    after: after,
-    before: before,
-    once: once,
-    findKey: findKey,
-    findIndex: findIndex,
-    findLastIndex: findLastIndex,
-    sortedIndex: sortedIndex,
-    indexOf: indexOf,
-    lastIndexOf: lastIndexOf,
-    find: find,
-    detect: find,
-    findWhere: findWhere,
-    each: each,
-    forEach: each,
-    map: map,
-    collect: map,
-    reduce: reduce,
-    foldl: reduce,
-    inject: reduce,
-    reduceRight: reduceRight,
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-    filter: filter,
-    select: filter,
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-    every: every,
-    all: every,
-    some: some,
-    any: some,
-    contains: contains,
-    includes: contains,
-    include: contains,
-    invoke: invoke,
-    pluck: pluck,
-    where: where,
-    max: max,
-    min: min,
-    shuffle: shuffle,
-    sample: sample,
-    sortBy: sortBy,
-    groupBy: groupBy,
-    indexBy: indexBy,
-    countBy: countBy,
-    partition: partition,
-    toArray: toArray,
-    size: size,
-    pick: pick,
-    omit: omit,
-    first: first,
-    head: first,
-    take: first,
-    initial: initial,
-    last: last,
-    rest: rest,
-    tail: rest,
-    drop: rest,
-    compact: compact,
-    flatten: flatten,
-    without: without,
-    uniq: uniq,
-    unique: uniq,
-    union: union,
-    intersection: intersection,
-    difference: difference,
-    unzip: unzip,
-    transpose: unzip,
-    zip: zip,
-    object: object,
-    range: range,
-    chunk: chunk,
-    mixin: mixin,
-    'default': _$1
-  };
-
-  // Default Export
-
-  // Add all of the Underscore functions to the wrapper object.
-  var _ = mixin(allExports);
-  // Legacy Node.js API.
-  _._ = _;
-
-  return _;
-
-})));
-//# sourceMappingURL=underscore-umd.js.map
diff --git a/documentation/5/_static/underscore.js b/documentation/5/_static/underscore.js
deleted file mode 100644
index cf177d428..000000000
--- a/documentation/5/_static/underscore.js
+++ /dev/null
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ur(n,r,!1)},without:Kr,uniq:Jr,unique:Jr,union:Gr,intersection:function(n){for(var r=[],t=arguments.length,e=0,u=Y(n);e<u;e++){var o=n[e];if(!Er(r,o)){var i;for(i=1;i<t&&Er(arguments[i],o);i++);i===t&&r.push(o)}}return r},difference:Cr,unzip:Hr,transpose:Hr,zip:Qr,object:function(n,r){for(var t={},e=0,u=Y(n);e<u;e++)r?t[n[e]]=r[e]:t[n[e][0]]=n[e][1];return t},range:function(n,r,t){null==r&&(r=n||0,n=0),t||(t=r<n?-1:1);for(var e=Math.max(Math.ceil((r-n)/t),0),u=Array(e),o=0;o<e;o++,n+=t)u[o]=n;return u},chunk:function(n,r){if(null==r||r<1)return[];for(var t=[],e=0,u=n.length;e<u;)t.push(i.call(n,e,e+=r));return t},mixin:Yr,default:tn});return Zr._=Zr,Zr}));
\ No newline at end of file
diff --git a/documentation/5/bibliography.html b/documentation/5/bibliography.html
index 6fc7db12e..a7b2d3bb6 100644
--- a/documentation/5/bibliography.html
+++ b/documentation/5/bibliography.html
@@ -1,31 +1,33 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Bibliography &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
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-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="User projects" href="userproject/index.html" />
+    <link rel="next" title="Tutorials" href="tutorials/index.html" />
     <link rel="prev" title="Custom models" href="custom_models.html" /> 
 </head>
 
@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
@@ -114,7 +117,7 @@ <h1>Bibliography<a class="headerlink" href="#bibliography" title="Permalink to t
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
         <a href="custom_models.html" class="btn btn-neutral float-left" title="Custom models" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-        <a href="userproject/index.html" class="btn btn-neutral float-right" title="User projects" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+        <a href="tutorials/index.html" class="btn btn-neutral float-right" title="Tutorials" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
diff --git a/documentation/5/building_networks.html b/documentation/5/building_networks.html
index ed008d06c..9e65f8eaf 100644
--- a/documentation/5/building_networks.html
+++ b/documentation/5/building_networks.html
@@ -1,28 +1,31 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Building networks &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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     <script src="_static/js/html5shiv.min.js"></script>
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-        <script src="_static/doctools.js"></script>
-        <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -56,6 +59,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
@@ -175,11 +179,11 @@ <h3>Variables<a class="headerlink" href="#variables" title="Permalink to this he
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>InitVarSnippetBase</em><em>, </em><em>str</em><em>]</em>) – variable init snippet, either as a string referencing
+<li><p><strong>snippet</strong> (<em>InitVarSnippetBase</em><em> | </em><em>str</em>) – variable init snippet, either as a string referencing
 a built-in snippet (see <a class="reference internal" href="source/pygenn.html#module-pygenn.init_var_snippets" title="pygenn.init_var_snippets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">init_var_snippets</span></code></a>)
 or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">InitVarSnippetBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_var_init_snippet" title="pygenn.create_var_init_snippet"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_init_snippet()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the variable init snippet (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the variable init snippet (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
 </ul>
 </dd>
 </dl>
@@ -347,11 +351,11 @@ <h3>Neuron populations<a class="headerlink" href="#neuron-populations" title="Pe
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>pop_name</strong> (<em>str</em>) – unique name</p></li>
 <li><p><strong>num_neurons</strong> (<em>int</em>) – number of neurons</p></li>
-<li><p><strong>neuron</strong> (<em>Union</em><em>[</em><em>NeuronModelBase</em><em>, </em><em>str</em><em>]</em>) – neuron model either as a string referencing a built-in model
+<li><p><strong>neuron</strong> (<em>NeuronModelBase</em><em> | </em><em>str</em>) – neuron model either as a string referencing a built-in model
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.neuron_models" title="pygenn.neuron_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">neuron_models</span></code></a>) or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuronModelBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_neuron_model" title="pygenn.create_neuron_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_neuron_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the neuron model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the neuron model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the neuron model (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
 </ul>
 </dd>
@@ -388,12 +392,12 @@ <h3>Synapse populations<a class="headerlink" href="#synapse-populations" title="
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.weight_update_models" title="pygenn.weight_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">weight_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">WeightUpdateModelBase</span></code> (for example returned
 by <a class="reference internal" href="source/pygenn.html#pygenn.create_weight_update_model" title="pygenn.create_weight_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_weight_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or
 initialisers (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial presynaptic variable values or
+<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial presynaptic variable values or
 initialisers (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial postsynaptic variable values or initialisers
+<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial postsynaptic variable values or initialisers
 (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
 <li><p><strong>pre_var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em>) – references to presynaptic neuron variables,
 typically created using <a class="reference internal" href="source/pygenn.html#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
@@ -421,14 +425,14 @@ <h3>Synapse populations<a class="headerlink" href="#synapse-populations" title="
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>PostsynapticModelBase</em><em>, </em><em>str</em><em>]</em>) – postsynaptic model either as a string referencing a built-in model
+<li><p><strong>snippet</strong> (<em>PostsynapticModelBase</em><em> | </em><em>str</em>) – postsynaptic model either as a string referencing a built-in model
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.postsynaptic_models" title="pygenn.postsynaptic_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">postsynaptic_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">PostsynapticModelBase</span></code> (for example returned
 by <a class="reference internal" href="source/pygenn.html#pygenn.create_postsynaptic_model" title="pygenn.create_postsynaptic_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_postsynaptic_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the postsynaptic model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the postsynaptic model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or initialisers
 for the postsynaptic model (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – references to postsynaptic neuron variables,
+<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – references to postsynaptic neuron variables,
 typically created using <a class="reference internal" href="source/pygenn.html#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
 (see <a class="reference internal" href="#variables-references">Variables references</a>)</p></li>
 </ul>
@@ -485,11 +489,11 @@ <h3>Synapse populations<a class="headerlink" href="#synapse-populations" title="
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>InitSparseConnectivitySnippetBase</em><em>, </em><em>str</em><em>]</em>) – sparse connectivity init snippet, either as a string referencing
+<li><p><strong>snippet</strong> (<em>InitSparseConnectivitySnippetBase</em><em> | </em><em>str</em>) – sparse connectivity init snippet, either as a string referencing
 a built-in snippet (see <a class="reference internal" href="source/pygenn.html#module-pygenn.init_sparse_connectivity_snippets" title="pygenn.init_sparse_connectivity_snippets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">init_sparse_connectivity_snippets</span></code></a>)
 or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">InitSparseConnectivitySnippetBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_sparse_connect_init_snippet" title="pygenn.create_sparse_connect_init_snippet"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_sparse_connect_init_snippet()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the sparse connectivity init snippet (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the sparse connectivity init snippet (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
 </ul>
 </dd>
 </dl>
@@ -544,14 +548,14 @@ <h3>Synapse populations<a class="headerlink" href="#synapse-populations" title="
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>pop_name</strong> (<em>str</em>) – unique name</p></li>
-<li><p><strong>matrix_type</strong> (<em>Union</em><em>[</em><a class="reference internal" href="source/pygenn.html#pygenn.SynapseMatrixType" title="pygenn._genn.SynapseMatrixType"><em>SynapseMatrixType</em></a><em>, </em><em>str</em><em>]</em>) – type of connectivity to use</p></li>
+<li><p><strong>matrix_type</strong> (<a class="reference internal" href="source/pygenn.html#pygenn.SynapseMatrixType" title="pygenn._genn.SynapseMatrixType"><em>SynapseMatrixType</em></a><em> | </em><em>str</em>) – type of connectivity to use</p></li>
 <li><p><strong>source</strong> (<a class="reference internal" href="source/pygenn.html#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – source neuron group</p></li>
 <li><p><strong>target</strong> (<a class="reference internal" href="source/pygenn.html#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – target neuron group</p></li>
 <li><p><strong>weight_update_init</strong> – initialiser for weight update model, typically
 created using <a class="reference internal" href="source/pygenn.html#pygenn.init_weight_update" title="pygenn.init_weight_update"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_weight_update()</span></code></a></p></li>
 <li><p><strong>postsynaptic_init</strong> – initialiser for postsynaptic model, typically
 created using <a class="reference internal" href="source/pygenn.html#pygenn.init_postsynaptic" title="pygenn.init_postsynaptic"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_postsynaptic()</span></code></a></p></li>
-<li><p><strong>connectivity_init</strong> (<em>Union</em><em>[</em><em>None</em><em>, </em><em>SparseConnectivityInit</em><em>, </em><em>ToeplitzConnectivityInit</em><em>]</em>) – initialiser for connectivity, typically created
+<li><p><strong>connectivity_init</strong> (<em>None</em><em> | </em><em>SparseConnectivityInit</em><em> | </em><em>ToeplitzConnectivityInit</em>) – initialiser for connectivity, typically created
 using <a class="reference internal" href="source/pygenn.html#pygenn.init_sparse_connectivity" title="pygenn.init_sparse_connectivity"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_sparse_connectivity()</span></code></a> when
 <code class="docutils literal notranslate"><span class="pre">matrix_type</span></code> is <a class="reference internal" href="source/pygenn.html#pygenn.SynapseMatrixType.BITMASK" title="pygenn.SynapseMatrixType.BITMASK"><code class="xref py py-attr docutils literal notranslate"><span class="pre">SynapseMatrixType.BITMASK</span></code></a>,
 <a class="reference internal" href="source/pygenn.html#pygenn.SynapseMatrixType.SPARSE" title="pygenn.SynapseMatrixType.SPARSE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">SynapseMatrixType.SPARSE</span></code></a>,
@@ -589,12 +593,12 @@ <h3>Current sources<a class="headerlink" href="#current-sources" title="Permalin
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>cs_name</strong> (<em>str</em>) – unique name</p></li>
-<li><p><strong>current_source_model</strong> (<em>Union</em><em>[</em><em>CurrentSourceModelBase</em><em>, </em><em>str</em><em>]</em>) – current source model either as a string referencing a built-in model
+<li><p><strong>current_source_model</strong> (<em>CurrentSourceModelBase</em><em> | </em><em>str</em>) – current source model either as a string referencing a built-in model
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.current_source_models" title="pygenn.current_source_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">current_source_models</span></code></a>) or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">CurrentSourceModelBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_current_source_model" title="pygenn.create_current_source_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_current_source_model()</span></code></a>)</p></li>
 <li><p><strong>pop</strong> (<a class="reference internal" href="source/pygenn.html#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – neuron population to inject current into</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the current source model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the current source model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the current source model (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
 <li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em>) – variables references to neuron variables in <code class="docutils literal notranslate"><span class="pre">pop</span></code>,
 typically created using <a class="reference internal" href="source/pygenn.html#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
@@ -634,14 +638,14 @@ <h3>Custom updates<a class="headerlink" href="#custom-updates" title="Permalink
 <li><p><strong>cu_name</strong> (<em>str</em>) – unique name</p></li>
 <li><p><strong>group_name</strong> (<em>str</em>) – name of the ‘custom update group’ to include this update in.
 All custom updates in the same group are executed simultaneously.</p></li>
-<li><p><strong>custom_update_model</strong> (<em>Union</em><em>[</em><em>CustomUpdateModelBase</em><em>, </em><em>str</em><em>]</em>) – custom update model either as a string referencing a built-in model
+<li><p><strong>custom_update_model</strong> (<em>CustomUpdateModelBase</em><em> | </em><em>str</em>) – custom update model either as a string referencing a built-in model
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.custom_update_models" title="pygenn.custom_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">custom_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">CustomUpdateModelBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_custom_update_model" title="pygenn.create_custom_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_custom_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the custom update model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the custom update model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the custom update model (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>var_refs</strong> (<em>Union</em><em>[</em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em><em>, </em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em><em>]</em>) – references to variables in other populations to
+<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>] </em><em>| </em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em>) – references to variables in other populations to
 access from this update, typically created using either
 <a class="reference internal" href="source/pygenn.html#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a> or <a class="reference internal" href="source/pygenn.html#pygenn.create_wu_var_ref" title="pygenn.create_wu_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_wu_var_ref()</span></code></a>
 (see <a class="reference internal" href="#variables-references">Variables references</a>).</p></li>
@@ -682,16 +686,16 @@ <h3>Custom connectivity updates<a class="headerlink" href="#custom-connectivity-
 <li><p><strong>group_name</strong> (<em>str</em>) – name of the ‘custom update group’ to include this update in.
 All custom updates in the same group are executed simultaneously.</p></li>
 <li><p><strong>syn_group</strong> (<a class="reference internal" href="source/pygenn.html#pygenn.SynapseGroup" title="pygenn._genn.SynapseGroup"><em>SynapseGroup</em></a>) – Synapse group to attach custom connectivity update to</p></li>
-<li><p><strong>custom_conn_update_model</strong> (<em>Union</em><em>[</em><em>CustomConnectivityUpdateModelBase</em><em>, </em><em>str</em><em>]</em>) – custom connectivity update model either as a string referencing a built-in model
+<li><p><strong>custom_conn_update_model</strong> (<em>CustomConnectivityUpdateModelBase</em><em> | </em><em>str</em>) – custom connectivity update model either as a string referencing a built-in model
 (see <a class="reference internal" href="source/pygenn.html#module-pygenn.custom_connectivity_update_models" title="pygenn.custom_connectivity_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">custom_connectivity_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">CustomConnectivityUpdateModelBaseUpdateModelBase</span></code>
 (for example returned by <a class="reference internal" href="source/pygenn.html#pygenn.create_custom_connectivity_update_model" title="pygenn.create_custom_connectivity_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_custom_connectivity_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the custom connectivity model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the custom connectivity model (see <a class="reference internal" href="#parameters">Parameters</a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or
 initialisers (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial presynaptic variable values or
+<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial presynaptic variable values or
 initialisers (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
-<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial postsynaptic variable values or initialisers
+<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial postsynaptic variable values or initialisers
 (see <a class="reference internal" href="#variables">Variables</a>)</p></li>
 <li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em>) – references to synaptic variables,
 typically created using <a class="reference internal" href="source/pygenn.html#pygenn.create_wu_var_ref" title="pygenn.create_wu_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_wu_var_ref()</span></code></a>
diff --git a/documentation/5/custom_models.html b/documentation/5/custom_models.html
index dfd007ad8..7f1ebf4a4 100644
--- a/documentation/5/custom_models.html
+++ b/documentation/5/custom_models.html
@@ -1,28 +1,31 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Custom models &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
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+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
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+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+        <script src="_static/doctools.js?v=888ff710"></script>
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+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
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@@ -56,6 +59,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
@@ -164,10 +168,10 @@ <h3>Variable initialisation<a class="headerlink" href="#variable-initialisation"
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new model (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>var_init_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>var_init_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 required to initialise the variable</p></li>
 <li><p><strong>extra_global_params</strong> – names and types of model
 extra global parameters</p></li>
@@ -231,17 +235,17 @@ <h3>Sparse connectivity initialisation<a class="headerlink" href="#sparse-connec
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the snippet (only for debugging)</p></li>
 <li><p><strong>params</strong> – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>row_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity row by row</p></li>
-<li><p><strong>col_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity column by column</p></li>
-<li><p><strong>calc_max_row_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>row_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity row by row</p></li>
+<li><p><strong>col_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity column by column</p></li>
+<li><p><strong>calc_max_row_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 row length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_max_col_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>calc_max_col_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 column length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_kernel_size_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the size of the kernel if snippet requires one</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of snippet extra global parameters</p></li>
-<li><p><strong>param_names</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>calc_kernel_size_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the size of the kernel if snippet requires one</p></li>
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of snippet extra global parameters</p></li>
+<li><p><strong>param_names</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
@@ -302,14 +306,14 @@ <h3>Toeplitz connectivity initialisation<a class="headerlink" href="#toeplitz-co
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the snippet (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>diagonal_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity row by row</p></li>
-<li><p><strong>calc_max_row_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>diagonal_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity row by row</p></li>
+<li><p><strong>calc_max_row_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 row length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_kernel_size_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the size of the kernel</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of snippet extra global parameters</p></li>
+<li><p><strong>calc_kernel_size_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the size of the kernel</p></li>
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of snippet extra global parameters</p></li>
 </ul>
 </dd>
 </dl>
@@ -424,19 +428,19 @@ <h3>Neuron models<a class="headerlink" href="#neuron-models" title="Permalink to
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>sim_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>sim_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing a threshold condition
+<li><p><strong>threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing a threshold condition
 expression to test whether a spike
 should be emitted</p></li>
-<li><p><strong>reset_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the reset code
+<li><p><strong>reset_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the reset code
 statements to run after emitting a spike</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 <li><p><strong>additional_input_vars</strong> – list of tuples with names and types as
 strings and initial values of additional
@@ -464,6 +468,8 @@ <h3>Neuron models<a class="headerlink" href="#neuron-models" title="Permalink to
     <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">,</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">VarAccess</span><span class="o">.</span><span class="n">READ_WRITE</span><span class="p">)])</span>
 </pre></div>
 </div>
+<section id="additional-input-variables">
+<h4>Additional input variables<a class="headerlink" href="#additional-input-variables" title="Permalink to this heading"></a></h4>
 <p>Normally, neuron models receive the linear sum of the inputs coming from all of their synaptic inputs through the <code class="docutils literal notranslate"><span class="pre">Isyn</span></code> variable.
 However neuron models can define additional input variables - allowing input from different synaptic inputs to be combined non-linearly.
 For example, if we wanted our leaky integrator to operate on the the product of two input currents, we could modify our model as follows:</p>
@@ -475,6 +481,7 @@ <h3>Neuron models<a class="headerlink" href="#neuron-models" title="Permalink to
 <span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
 </pre></div>
 </div>
+</section>
 </dd></dl>
 
 </section>
@@ -518,50 +525,50 @@ <h3>Weight update models<a class="headerlink" href="#weight-update-models" title
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-synapse model variables</p></li>
-<li><p><strong>pre_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-presynaptic neuron model variables</p></li>
 <li><p><strong>names</strong> (<em>post_vars</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
 <li><p><strong>access</strong> (<em>types and optional variable</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
-<li><p><strong>pre_neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to presynaptic
 neuron variables</p></li>
-<li><p><strong>post_neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>post_neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>pre_spike_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic spike code</p></li>
-<li><p><strong>pre_event_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic event code</p></li>
-<li><p><strong>post_event_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic event code</p></li>
-<li><p><strong>post_spike_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic spike code</p></li>
-<li><p><strong>synapse_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the synapse dynamics code</p></li>
-<li><p><strong>pre_event_threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic event threshold
+<li><p><strong>pre_spike_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic spike code</p></li>
+<li><p><strong>pre_event_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic event code</p></li>
+<li><p><strong>post_event_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic event code</p></li>
+<li><p><strong>post_spike_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic spike code</p></li>
+<li><p><strong>synapse_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the synapse dynamics code</p></li>
+<li><p><strong>pre_event_threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic event threshold
 condition code</p></li>
-<li><p><strong>post_event_threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic event threshold
+<li><p><strong>post_event_threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic event threshold
 condition code</p></li>
-<li><p><strong>pre_spike_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run once per
+<li><p><strong>pre_spike_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run once per
 spiking presynaptic neuron. Only
 presynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>post_spike_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run once per
+<li><p><strong>post_spike_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run once per
 spiking postsynaptic neuron</p></li>
-<li><p><strong>pre_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run every
+<li><p><strong>pre_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run every
 timestep on presynaptic neuron.
 Only presynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>post_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run every
+<li><p><strong>post_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run every
 timestep on postsynaptic neuron.
 Only postsynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
-<li><p><strong>post_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>post_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
@@ -598,6 +605,8 @@ <h3>Weight update models<a class="headerlink" href="#weight-update-models" title
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+<section id="pre-and-postsynaptic-dynamics">
+<h4>Pre and postsynaptic dynamics<a class="headerlink" href="#pre-and-postsynaptic-dynamics" title="Permalink to this heading"></a></h4>
 <p>The memory required for synapse variables and the computational cost of updating them tends to grow with <span class="math notranslate nohighlight">\(O(N^2)\)</span> with the number of neurons.
 Therefore, if it is possible, implementing synapse variables on a per-neuron rather than per-synapse basis is a good idea.
 The <code class="docutils literal notranslate"><span class="pre">pre_var_name_types</span></code> and <code class="docutils literal notranslate"><span class="pre">post_var_name_types</span></code> keyword arguments} are used to define any pre or postsynaptic state variables.
@@ -633,6 +642,9 @@ <h3>Weight update models<a class="headerlink" href="#weight-update-models" title
     <span class="n">post_dynamics_code</span><span class="o">=</span><span class="s2">&quot;postTrace *= tauMinusDecay;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="synapse-dynamics">
+<h4>Synapse dynamics<a class="headerlink" href="#synapse-dynamics" title="Permalink to this heading"></a></h4>
 <p>Unlike the event-driven updates previously described, synapse dynamics code is run for each synapse, each timestep i.e. unlike the others it is time-driven.
 This can be used where synapses have internal variables and dynamics that are described in continuous time, e.g. by ODEs.
 However using this mechanism is typically computationally very costly because of the large number of synapses in a typical network.
@@ -642,6 +654,9 @@ <h3>Weight update models<a class="headerlink" href="#weight-update-models" title
 <span class="n">synapse_dynamics_code</span><span class="o">=</span><span class="s2">&quot;addToPost(g * V_pre);&quot;</span><span class="p">,</span>
 </pre></div>
 </div>
+</section>
+<section id="spike-like-events">
+<h4>Spike-like events<a class="headerlink" href="#spike-like-events" title="Permalink to this heading"></a></h4>
 <p>As well as time-driven synapse dynamics and spike event-driven updates, GeNN weight update models also support “spike-like events”.
 These can be triggered by a threshold condition evaluated on the pre or postsynaptic neuron.
 This typically involves pre or postsynaptic weight update model variables or variable references respectively.</p>
@@ -651,6 +666,7 @@ <h3>Weight update models<a class="headerlink" href="#weight-update-models" title
 </pre></div>
 </div>
 <p>Whenever this expression evaluates to true, the event code in <code class="docutils literal notranslate"><span class="pre">pre_event_code</span></code> will be executed.</p>
+</section>
 </dd></dl>
 
 </section>
@@ -682,14 +698,14 @@ <h3>Postsynaptic models<a class="headerlink" href="#postsynaptic-models" title="
 <li><p><strong>params</strong> – name and optional types of model parameters</p></li>
 <li><p><strong>vars</strong> – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>sim_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>sim_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 </ul>
 </dd>
@@ -721,17 +737,17 @@ <h3>Current source models<a class="headerlink" href="#current-source-models" tit
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to variables
 in neuron population current source is attached to</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>injection_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>injection_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 </ul>
 </dd>
@@ -780,17 +796,17 @@ <h3>Custom update models<a class="headerlink" href="#custom-update-models" title
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess" title="pygenn._genn.CustomUpdateVarAccess"><em>CustomUpdateVarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess" title="pygenn._genn.CustomUpdateVarAccess"><em>CustomUpdateVarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to variables
 in population(s) custom update is attached to</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 to be run when custom update is launched</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 <li><p><strong>extra_global_param_refs</strong> – names and types of extra global
 parameter references</p></li>
@@ -808,6 +824,8 @@ <h3>Custom update models<a class="headerlink" href="#custom-update-models" title
 <p>When used in a model with batch size &gt; 1, whether custom updates of this sort are batched or not depends on the variables their references point to.
 If any referenced variables have <a class="reference internal" href="source/pygenn.html#pygenn.VarAccess.READ_ONLY_DUPLICATE" title="pygenn.VarAccess.READ_ONLY_DUPLICATE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_ONLY_DUPLICATE</span></code></a> or <a class="reference internal" href="source/pygenn.html#pygenn.VarAccess.READ_WRITE" title="pygenn.VarAccess.READ_WRITE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_WRITE</span></code></a> access modes, then the update will be batched
 and any variables associated with the custom update with <a class="reference internal" href="source/pygenn.html#pygenn.VarAccess.READ_ONLY_DUPLICATE" title="pygenn.VarAccess.READ_ONLY_DUPLICATE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_ONLY_DUPLICATE</span></code></a> or <a class="reference internal" href="source/pygenn.html#pygenn.VarAccess.READ_WRITE" title="pygenn.VarAccess.READ_WRITE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_WRITE</span></code></a> access modes will be duplicated across the batches.</p>
+<section id="batch-reduction">
+<h4>Batch reduction<a class="headerlink" href="#batch-reduction" title="Permalink to this heading"></a></h4>
 <p>As well as the standard variable access modes described previously, custom updates support variables with ‘batch reduction’ access modes
 such as <a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess.REDUCE_BATCH_SUM" title="pygenn.CustomUpdateVarAccess.REDUCE_BATCH_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_BATCH_SUM</span></code></a> and <a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess.REDUCE_BATCH_MAX" title="pygenn.CustomUpdateVarAccess.REDUCE_BATCH_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_BATCH_MAX</span></code></a>.
 These access modes allow values read from variables duplicated across batches to be reduced into variables that are shared across batches.
@@ -825,6 +843,9 @@ <h3>Custom update models<a class="headerlink" href="#custom-update-models" title
 </div>
 <p>Batch reductions can also be performed into variable references with
 the <a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode.REDUCE_SUM" title="pygenn.VarAccessMode.REDUCE_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_SUM</span></code></a> or <a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode.REDUCE_MAX" title="pygenn.VarAccessMode.REDUCE_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_MAX</span></code></a> access modes.</p>
+</section>
+<section id="neuron-reduction">
+<h4>Neuron reduction<a class="headerlink" href="#neuron-reduction" title="Permalink to this heading"></a></h4>
 <p>Similarly to the batch reduction modes discussed previously, custom updates also support variables with several ‘neuron reduction’ access modes
 such as <a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess.REDUCE_NEURON_SUM" title="pygenn.CustomUpdateVarAccess.REDUCE_NEURON_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_NEURON_SUM</span></code></a> and <a class="reference internal" href="source/pygenn.html#pygenn.CustomUpdateVarAccess.REDUCE_NEURON_MAX" title="pygenn.CustomUpdateVarAccess.REDUCE_NEURON_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_NEURON_MAX</span></code></a>.</p>
 <p>These access modes allow values read from per-neuron variables to be reduced into variables that are shared across neurons.
@@ -841,6 +862,7 @@ <h3>Custom update models<a class="headerlink" href="#custom-update-models" title
 </div>
 <p>Again, like batch reductions, neuron reductions can also be performed into variable references with
 the <a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode.REDUCE_SUM" title="pygenn.VarAccessMode.REDUCE_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_SUM</span></code></a> or <a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode.REDUCE_MAX" title="pygenn.VarAccessMode.REDUCE_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_MAX</span></code></a> access modes.</p>
+</section>
 </dd></dl>
 
 </section>
@@ -878,16 +900,16 @@ <h3>Custom connectivity update models<a class="headerlink" href="#custom-connect
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-synapse model variables</p></li>
-<li><p><strong>pre_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-presynaptic neuron model variables</p></li>
 <li><p><strong>names</strong> (<em>post_vars</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
 <li><p><strong>access</strong> (<em>types and optional variable</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to synaptic variables</p></li>
 <li><p><strong>pre_neuron_var_refs</strong> – names, types and optional variable access
 of references to be assigned to presynaptic
@@ -895,20 +917,22 @@ <h3>Custom connectivity update models<a class="headerlink" href="#custom-connect
 <li><p><strong>post_neuron_var_refs</strong> – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>row_update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>row_update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 to be run when custom update is launched</p></li>
-<li><p><strong>host_update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements to be run
+<li><p><strong>host_update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements to be run
 on CPU when custom connectivity update is launched</p></li>
 <li><p><strong>extra_global_params</strong> – names and types of model
 extra global parameters</p></li>
 <li><p><strong>extra_global_param_refs</strong> – names and types of extra global
 parameter references</p></li>
-<li><p><strong>post_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
-<li><p><strong>pre_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
-<li><p><strong>post_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>post_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>pre_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>post_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="source/pygenn.html#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
+<section id="parallel-synapse-iteration-and-removal">
+<h4>Parallel synapse iteration and removal<a class="headerlink" href="#parallel-synapse-iteration-and-removal" title="Permalink to this heading"></a></h4>
 <p>The main GPU operation that custom connectivity updates expose is the ability to generate per-presynaptic neuron update code. This can be used to implement a very simple model which removes ‘diagonals’ from the connection matrix:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">remove_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;remove_diagonal&quot;</span><span class="p">,</span>
@@ -923,6 +947,9 @@ <h3>Custom connectivity update models<a class="headerlink" href="#custom-connect
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="parallel-synapse-creation">
+<h4>Parallel synapse creation<a class="headerlink" href="#parallel-synapse-creation" title="Permalink to this heading"></a></h4>
 <p>Similarly you could implement a custom connectivity model which adds diagonals back into the connection matrix like this:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">add_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;add_diagonal&quot;</span><span class="p">,</span>
@@ -942,6 +969,9 @@ <h3>Custom connectivity update models<a class="headerlink" href="#custom-connect
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="host-updates">
+<h4>Host updates<a class="headerlink" href="#host-updates" title="Permalink to this heading"></a></h4>
 <p>Some common connectivity update scenarios involve some computation which can’t be easily parallelized. If, for example you wanted to determine which elements on each row you wanted to remove on the host, you can include <code class="docutils literal notranslate"><span class="pre">host_update_code</span></code> which gets run before the row update code:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">remove_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;remove_diagonal&quot;</span><span class="p">,</span>
@@ -964,6 +994,7 @@ <h3>Custom connectivity update models<a class="headerlink" href="#custom-connect
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
 </dd></dl>
 
 </section>
diff --git a/documentation/5/genindex.html b/documentation/5/genindex.html
index 0c6e92bc6..535441842 100644
--- a/documentation/5/genindex.html
+++ b/documentation/5/genindex.html
@@ -4,23 +4,25 @@
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Index &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
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-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" /> 
@@ -52,6 +54,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
diff --git a/documentation/5/index.html b/documentation/5/index.html
index 5d5fb79ae..7bbad3427 100644
--- a/documentation/5/index.html
+++ b/documentation/5/index.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>PyGeNN documentation &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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     <script src="_static/js/html5shiv.min.js"></script>
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-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -54,6 +56,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
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 </ul>
@@ -98,6 +101,19 @@ <h1>PyGeNN documentation<a class="headerlink" href="#pygenn-documentation" title
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/comp_neuro_101/1_neurons.html">Defining populations of neurons</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/comp_neuro_101/2_synapses.html">Adding synapses</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mnist_inference/tutorial_1.html">Classification of a single digit</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mnist_inference/tutorial_2.html">Classification of the entire test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mnist_inference/tutorial_3.html">Faster classification of the whole test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mushroom_body/1_first_layer.html">Presenting latency-coded inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mushroom_body/2_second_layer.html">Adding Kenyon Cells</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mushroom_body/3_second_layer_gain_control.html">Feedback-inhibition based gain control</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mushroom_body/4_third_layer.html">Output neurons and learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorials/mushroom_body/5_testing.html">Testing</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
diff --git a/documentation/5/installation.html b/documentation/5/installation.html
index 5bdf94a99..88a1ac81e 100644
--- a/documentation/5/installation.html
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@@ -1,27 +1,29 @@
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 <html class="writer-html5" lang="en">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Installation &mdash; PyGeNN  documentation</title>
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-      <link rel="stylesheet" type="text/css" href="_static/css/theme.css" />
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+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
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@@ -55,6 +57,7 @@
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 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
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-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
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@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
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 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
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index d3fc0a87a..ff93b0c4e 100644
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 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
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 <html class="writer-html5" lang="en">
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+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Computation times &mdash; PyGeNN  documentation</title>
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+      <link rel="stylesheet" type="text/css" href="_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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     <script src="_static/js/html5shiv.min.js"></script>
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+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
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     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" /> 
@@ -53,6 +55,7 @@
 <li class="toctree-l1"><a class="reference internal" href="simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
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 </ul>
diff --git a/documentation/5/simulating_networks.html b/documentation/5/simulating_networks.html
index 8da99f32d..dbe81d347 100644
--- a/documentation/5/simulating_networks.html
+++ b/documentation/5/simulating_networks.html
@@ -1,27 +1,29 @@
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 <html class="writer-html5" lang="en">
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
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   <title>Simulating networks &mdash; PyGeNN  documentation</title>
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     <script src="_static/js/html5shiv.min.js"></script>
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-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="_static/doctools.js?v=888ff710"></script>
+        <script src="_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -55,6 +57,7 @@
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="source/pygenn.html">Reference documentation</a></li>
 </ul>
diff --git a/documentation/5/source/modules.html b/documentation/5/source/modules.html
index 1f3110c58..0dc3752dd 100644
--- a/documentation/5/source/modules.html
+++ b/documentation/5/source/modules.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>pygenn &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
@@ -53,6 +55,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../userproject/index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="pygenn.html">Reference documentation</a></li>
 </ul>
@@ -86,6 +89,52 @@ <h1>pygenn<a class="headerlink" href="#pygenn" title="Permalink to this heading"
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="pygenn.html">pygenn package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CurrentSource"><code class="docutils literal notranslate"><span class="pre">CurrentSource</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CustomConnectivityUpdate"><code class="docutils literal notranslate"><span class="pre">CustomConnectivityUpdate</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CustomUpdate"><code class="docutils literal notranslate"><span class="pre">CustomUpdate</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CustomUpdateBase"><code class="docutils literal notranslate"><span class="pre">CustomUpdateBase</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CustomUpdateVarAccess"><code class="docutils literal notranslate"><span class="pre">CustomUpdateVarAccess</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.CustomUpdateWU"><code class="docutils literal notranslate"><span class="pre">CustomUpdateWU</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.GeNNModel"><code class="docutils literal notranslate"><span class="pre">GeNNModel</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.ModelSpec"><code class="docutils literal notranslate"><span class="pre">ModelSpec</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.NeuronGroup"><code class="docutils literal notranslate"><span class="pre">NeuronGroup</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.ParallelismHint"><code class="docutils literal notranslate"><span class="pre">ParallelismHint</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.PlogSeverity"><code class="docutils literal notranslate"><span class="pre">PlogSeverity</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.SynapseGroup"><code class="docutils literal notranslate"><span class="pre">SynapseGroup</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.SynapseMatrixConnectivity"><code class="docutils literal notranslate"><span class="pre">SynapseMatrixConnectivity</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.SynapseMatrixType"><code class="docutils literal notranslate"><span class="pre">SynapseMatrixType</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.SynapseMatrixWeight"><code class="docutils literal notranslate"><span class="pre">SynapseMatrixWeight</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarAccess"><code class="docutils literal notranslate"><span class="pre">VarAccess</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarAccessDim"><code class="docutils literal notranslate"><span class="pre">VarAccessDim</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarAccessMode"><code class="docutils literal notranslate"><span class="pre">VarAccessMode</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarAccessModeAttribute"><code class="docutils literal notranslate"><span class="pre">VarAccessModeAttribute</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarLocation"><code class="docutils literal notranslate"><span class="pre">VarLocation</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.VarLocationAttribute"><code class="docutils literal notranslate"><span class="pre">VarLocationAttribute</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_current_source_model"><code class="docutils literal notranslate"><span class="pre">create_current_source_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_custom_connectivity_update_model"><code class="docutils literal notranslate"><span class="pre">create_custom_connectivity_update_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_custom_update_model"><code class="docutils literal notranslate"><span class="pre">create_custom_update_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_egp_ref"><code class="docutils literal notranslate"><span class="pre">create_egp_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_neuron_model"><code class="docutils literal notranslate"><span class="pre">create_neuron_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_post_var_ref"><code class="docutils literal notranslate"><span class="pre">create_post_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_postsynaptic_model"><code class="docutils literal notranslate"><span class="pre">create_postsynaptic_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_pre_var_ref"><code class="docutils literal notranslate"><span class="pre">create_pre_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_psm_egp_ref"><code class="docutils literal notranslate"><span class="pre">create_psm_egp_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_psm_var_ref"><code class="docutils literal notranslate"><span class="pre">create_psm_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_sparse_connect_init_snippet"><code class="docutils literal notranslate"><span class="pre">create_sparse_connect_init_snippet()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_toeplitz_connect_init_snippet"><code class="docutils literal notranslate"><span class="pre">create_toeplitz_connect_init_snippet()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_var_init_snippet"><code class="docutils literal notranslate"><span class="pre">create_var_init_snippet()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_var_ref"><code class="docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_weight_update_model"><code class="docutils literal notranslate"><span class="pre">create_weight_update_model()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_wu_egp_ref"><code class="docutils literal notranslate"><span class="pre">create_wu_egp_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_wu_post_var_ref"><code class="docutils literal notranslate"><span class="pre">create_wu_post_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_wu_pre_var_ref"><code class="docutils literal notranslate"><span class="pre">create_wu_pre_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.create_wu_var_ref"><code class="docutils literal notranslate"><span class="pre">create_wu_var_ref()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.get_var_access_dim"><code class="docutils literal notranslate"><span class="pre">get_var_access_dim()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.init_postsynaptic"><code class="docutils literal notranslate"><span class="pre">init_postsynaptic()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.init_sparse_connectivity"><code class="docutils literal notranslate"><span class="pre">init_sparse_connectivity()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.init_toeplitz_connectivity"><code class="docutils literal notranslate"><span class="pre">init_toeplitz_connectivity()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.init_var"><code class="docutils literal notranslate"><span class="pre">init_var()</span></code></a></li>
+<li class="toctree-l2"><a class="reference internal" href="pygenn.html#pygenn.init_weight_update"><code class="docutils literal notranslate"><span class="pre">init_weight_update()</span></code></a></li>
 <li class="toctree-l2"><a class="reference internal" href="pygenn.html#submodules">Submodules</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pygenn.html#module-pygenn.cuda_backend">pygenn.cuda_backend module</a></li>
 <li class="toctree-l2"><a class="reference internal" href="pygenn.html#module-pygenn.current_source_models">pygenn.current_source_models module</a></li>
diff --git a/documentation/5/source/pygenn.html b/documentation/5/source/pygenn.html
index ae7702bd5..89986ff24 100644
--- a/documentation/5/source/pygenn.html
+++ b/documentation/5/source/pygenn.html
@@ -1,28 +1,31 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>pygenn package &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -55,6 +58,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../userproject/index.html">User projects</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">Reference documentation</a></li>
 </ul>
@@ -365,10 +369,10 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>precision</strong> (<em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em>) – Data type to use for <code class="docutils literal notranslate"><span class="pre">scalar</span></code> variables</p></li>
+<li><p><strong>precision</strong> (<em>str</em><em> | </em><em>ResolvedType</em>) – Data type to use for <code class="docutils literal notranslate"><span class="pre">scalar</span></code> variables</p></li>
 <li><p><strong>model_name</strong> (<em>str</em>) – Name of the model</p></li>
-<li><p><strong>backend</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – Name of backend module to use. Defaults to one to pick ‘best’ backend for your system</p></li>
-<li><p><strong>time_precision</strong> (<em>Optional</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em>) – data type to use for representing time</p></li>
+<li><p><strong>backend</strong> (<em>str</em><em> | </em><em>None</em>) – Name of backend module to use. Defaults to one to pick ‘best’ backend for your system</p></li>
+<li><p><strong>time_precision</strong> (<em>str</em><em> | </em><em>ResolvedType</em><em> | </em><em>None</em>) – data type to use for representing time</p></li>
 <li><p><strong>genn_log_level</strong> (<a class="reference internal" href="#pygenn.PlogSeverity" title="pygenn._genn.PlogSeverity"><em>PlogSeverity</em></a>) – Log level for GeNN</p></li>
 <li><p><strong>code_gen_log_level</strong> (<a class="reference internal" href="#pygenn.PlogSeverity" title="pygenn._genn.PlogSeverity"><em>PlogSeverity</em></a>) – Log level for GeNN code-generator</p></li>
 <li><p><strong>transpiler_log_level</strong> (<a class="reference internal" href="#pygenn.PlogSeverity" title="pygenn._genn.PlogSeverity"><em>PlogSeverity</em></a>) – Log level for GeNN transpiler</p></li>
@@ -386,12 +390,12 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>cs_name</strong> (<em>str</em>) – unique name</p></li>
-<li><p><strong>current_source_model</strong> (<em>Union</em><em>[</em><em>CurrentSourceModelBase</em><em>, </em><em>str</em><em>]</em>) – current source model either as a string referencing a built-in model
+<li><p><strong>current_source_model</strong> (<em>CurrentSourceModelBase</em><em> | </em><em>str</em>) – current source model either as a string referencing a built-in model
 (see <a class="reference internal" href="#module-pygenn.current_source_models" title="pygenn.current_source_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">current_source_models</span></code></a>) or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">CurrentSourceModelBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_current_source_model" title="pygenn.create_current_source_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_current_source_model()</span></code></a>)</p></li>
 <li><p><strong>pop</strong> (<a class="reference internal" href="#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – neuron population to inject current into</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the current source model (see <a href="#id12"><span class="problematic" id="id13">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the current source model (see <a href="#id12"><span class="problematic" id="id13">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the current source model (see <a href="#id14"><span class="problematic" id="id15">`Variables`_</span></a>)</p></li>
 <li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em>) – variables references to neuron variables in <code class="docutils literal notranslate"><span class="pre">pop</span></code>,
 typically created using <a class="reference internal" href="#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
@@ -422,16 +426,16 @@
 <li><p><strong>group_name</strong> (<em>str</em>) – name of the ‘custom update group’ to include this update in.
 All custom updates in the same group are executed simultaneously.</p></li>
 <li><p><strong>syn_group</strong> (<a class="reference internal" href="#pygenn.SynapseGroup" title="pygenn._genn.SynapseGroup"><em>SynapseGroup</em></a>) – Synapse group to attach custom connectivity update to</p></li>
-<li><p><strong>custom_conn_update_model</strong> (<em>Union</em><em>[</em><em>CustomConnectivityUpdateModelBase</em><em>, </em><em>str</em><em>]</em>) – custom connectivity update model either as a string referencing a built-in model
+<li><p><strong>custom_conn_update_model</strong> (<em>CustomConnectivityUpdateModelBase</em><em> | </em><em>str</em>) – custom connectivity update model either as a string referencing a built-in model
 (see <a class="reference internal" href="#module-pygenn.custom_connectivity_update_models" title="pygenn.custom_connectivity_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">custom_connectivity_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">CustomConnectivityUpdateModelBaseUpdateModelBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_custom_connectivity_update_model" title="pygenn.create_custom_connectivity_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_custom_connectivity_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the custom connectivity model (see <a href="#id18"><span class="problematic" id="id19">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the custom connectivity model (see <a href="#id18"><span class="problematic" id="id19">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or
 initialisers (see <a href="#id20"><span class="problematic" id="id21">`Variables`_</span></a>)</p></li>
-<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial presynaptic variable values or
+<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial presynaptic variable values or
 initialisers (see <a href="#id22"><span class="problematic" id="id23">`Variables`_</span></a>)</p></li>
-<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial postsynaptic variable values or initialisers
+<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial postsynaptic variable values or initialisers
 (see <a href="#id24"><span class="problematic" id="id25">`Variables`_</span></a>)</p></li>
 <li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em>) – references to synaptic variables,
 typically created using <a class="reference internal" href="#pygenn.create_wu_var_ref" title="pygenn.create_wu_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_wu_var_ref()</span></code></a>
@@ -460,14 +464,14 @@
 <li><p><strong>cu_name</strong> (<em>str</em>) – unique name</p></li>
 <li><p><strong>group_name</strong> (<em>str</em>) – name of the ‘custom update group’ to include this update in.
 All custom updates in the same group are executed simultaneously.</p></li>
-<li><p><strong>custom_update_model</strong> (<em>Union</em><em>[</em><em>CustomUpdateModelBase</em><em>, </em><em>str</em><em>]</em>) – custom update model either as a string referencing a built-in model
+<li><p><strong>custom_update_model</strong> (<em>CustomUpdateModelBase</em><em> | </em><em>str</em>) – custom update model either as a string referencing a built-in model
 (see <a class="reference internal" href="#module-pygenn.custom_update_models" title="pygenn.custom_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">custom_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">CustomUpdateModelBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_custom_update_model" title="pygenn.create_custom_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_custom_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the custom update model (see <a href="#id34"><span class="problematic" id="id35">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the custom update model (see <a href="#id34"><span class="problematic" id="id35">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the custom update model (see <a href="#id36"><span class="problematic" id="id37">`Variables`_</span></a>)</p></li>
-<li><p><strong>var_refs</strong> (<em>Union</em><em>[</em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em><em>, </em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em><em>]</em>) – references to variables in other populations to
+<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>] </em><em>| </em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>WUVarReference</em><em>]</em>) – references to variables in other populations to
 access from this update, typically created using either
 <a class="reference internal" href="#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a> or <a class="reference internal" href="#pygenn.create_wu_var_ref" title="pygenn.create_wu_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_wu_var_ref()</span></code></a>
 (see <a href="#id38"><span class="problematic" id="id39">`Variables references`_</span></a>).</p></li>
@@ -500,11 +504,11 @@
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>pop_name</strong> (<em>str</em>) – unique name</p></li>
 <li><p><strong>num_neurons</strong> (<em>int</em>) – number of neurons</p></li>
-<li><p><strong>neuron</strong> (<em>Union</em><em>[</em><em>NeuronModelBase</em><em>, </em><em>str</em><em>]</em>) – neuron model either as a string referencing a built-in model
+<li><p><strong>neuron</strong> (<em>NeuronModelBase</em><em> | </em><em>str</em>) – neuron model either as a string referencing a built-in model
 (see <a class="reference internal" href="#module-pygenn.neuron_models" title="pygenn.neuron_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">neuron_models</span></code></a>) or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">NeuronModelBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_neuron_model" title="pygenn.create_neuron_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_neuron_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the neuron model (see <a href="#id42"><span class="problematic" id="id43">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the neuron model (see <a href="#id42"><span class="problematic" id="id43">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial variable values or initialisers
 for the neuron model (see <a href="#id44"><span class="problematic" id="id45">`Variables`_</span></a>)</p></li>
 </ul>
 </dd>
@@ -529,14 +533,14 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>pop_name</strong> (<em>str</em>) – unique name</p></li>
-<li><p><strong>matrix_type</strong> (<em>Union</em><em>[</em><a class="reference internal" href="#pygenn.SynapseMatrixType" title="pygenn._genn.SynapseMatrixType"><em>SynapseMatrixType</em></a><em>, </em><em>str</em><em>]</em>) – type of connectivity to use</p></li>
+<li><p><strong>matrix_type</strong> (<a class="reference internal" href="#pygenn.SynapseMatrixType" title="pygenn._genn.SynapseMatrixType"><em>SynapseMatrixType</em></a><em> | </em><em>str</em>) – type of connectivity to use</p></li>
 <li><p><strong>source</strong> (<a class="reference internal" href="#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – source neuron group</p></li>
 <li><p><strong>target</strong> (<a class="reference internal" href="#pygenn.NeuronGroup" title="pygenn._genn.NeuronGroup"><em>NeuronGroup</em></a>) – target neuron group</p></li>
 <li><p><strong>weight_update_init</strong> – initialiser for weight update model, typically
 created using <a class="reference internal" href="#pygenn.init_weight_update" title="pygenn.init_weight_update"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_weight_update()</span></code></a></p></li>
 <li><p><strong>postsynaptic_init</strong> – initialiser for postsynaptic model, typically
 created using <a class="reference internal" href="#pygenn.init_postsynaptic" title="pygenn.init_postsynaptic"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_postsynaptic()</span></code></a></p></li>
-<li><p><strong>connectivity_init</strong> (<em>Union</em><em>[</em><em>None</em><em>, </em><em>SparseConnectivityInit</em><em>, </em><em>ToeplitzConnectivityInit</em><em>]</em>) – initialiser for connectivity, typically created
+<li><p><strong>connectivity_init</strong> (<em>None</em><em> | </em><em>SparseConnectivityInit</em><em> | </em><em>ToeplitzConnectivityInit</em>) – initialiser for connectivity, typically created
 using <a class="reference internal" href="#pygenn.init_sparse_connectivity" title="pygenn.init_sparse_connectivity"><code class="xref py py-func docutils literal notranslate"><span class="pre">init_sparse_connectivity()</span></code></a> when
 <code class="docutils literal notranslate"><span class="pre">matrix_type</span></code> is <a class="reference internal" href="#pygenn.SynapseMatrixType.BITMASK" title="pygenn.SynapseMatrixType.BITMASK"><code class="xref py py-attr docutils literal notranslate"><span class="pre">SynapseMatrixType.BITMASK</span></code></a>,
 <a class="reference internal" href="#pygenn.SynapseMatrixType.SPARSE" title="pygenn.SynapseMatrixType.SPARSE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">SynapseMatrixType.SPARSE</span></code></a>,
@@ -655,7 +659,7 @@
 <dd><p>Load the previously built model into memory;</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>num_recording_timesteps</strong> (<em>Optional</em><em>[</em><em>int</em><em>]</em>) – Number of timesteps to record spikes
+<dd class="field-odd"><p><strong>num_recording_timesteps</strong> (<em>int</em><em> | </em><em>None</em>) – Number of timesteps to record spikes
 for. <a class="reference internal" href="#pygenn.GeNNModel.pull_recording_buffers_from_device" title="pygenn.GeNNModel.pull_recording_buffers_from_device"><code class="xref py py-meth docutils literal notranslate"><span class="pre">pull_recording_buffers_from_device()</span></code></a>
 must be called after this number of timesteps</p>
 </dd>
@@ -1631,17 +1635,17 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to variables
 in neuron population current source is attached to</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>injection_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>injection_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 </ul>
 </dd>
@@ -1686,16 +1690,16 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-synapse model variables</p></li>
-<li><p><strong>pre_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-presynaptic neuron model variables</p></li>
 <li><p><strong>names</strong> (<em>post_vars</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
 <li><p><strong>access</strong> (<em>types and optional variable</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to synaptic variables</p></li>
 <li><p><strong>pre_neuron_var_refs</strong> – names, types and optional variable access
 of references to be assigned to presynaptic
@@ -1703,20 +1707,22 @@
 <li><p><strong>post_neuron_var_refs</strong> – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>row_update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>row_update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 to be run when custom update is launched</p></li>
-<li><p><strong>host_update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements to be run
+<li><p><strong>host_update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements to be run
 on CPU when custom connectivity update is launched</p></li>
 <li><p><strong>extra_global_params</strong> – names and types of model
 extra global parameters</p></li>
 <li><p><strong>extra_global_param_refs</strong> – names and types of extra global
 parameter references</p></li>
-<li><p><strong>post_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
-<li><p><strong>pre_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
-<li><p><strong>post_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>post_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>pre_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
+<li><p><strong>post_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
+<section id="parallel-synapse-iteration-and-removal">
+<h2>Parallel synapse iteration and removal<a class="headerlink" href="#parallel-synapse-iteration-and-removal" title="Permalink to this heading"></a></h2>
 <p>The main GPU operation that custom connectivity updates expose is the ability to generate per-presynaptic neuron update code. This can be used to implement a very simple model which removes ‘diagonals’ from the connection matrix:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">remove_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;remove_diagonal&quot;</span><span class="p">,</span>
@@ -1731,6 +1737,9 @@
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="parallel-synapse-creation">
+<h2>Parallel synapse creation<a class="headerlink" href="#parallel-synapse-creation" title="Permalink to this heading"></a></h2>
 <p>Similarly you could implement a custom connectivity model which adds diagonals back into the connection matrix like this:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">add_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;add_diagonal&quot;</span><span class="p">,</span>
@@ -1750,6 +1759,9 @@
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="host-updates">
+<h2>Host updates<a class="headerlink" href="#host-updates" title="Permalink to this heading"></a></h2>
 <p>Some common connectivity update scenarios involve some computation which can’t be easily parallelized. If, for example you wanted to determine which elements on each row you wanted to remove on the host, you can include <code class="docutils literal notranslate"><span class="pre">host_update_code</span></code> which gets run before the row update code:</p>
 <div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">remove_diagonal_model</span> <span class="o">=</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">create_custom_connectivity_update_model</span><span class="p">(</span>
     <span class="s2">&quot;remove_diagonal&quot;</span><span class="p">,</span>
@@ -1772,6 +1784,7 @@
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
 </dd></dl>
 
 <dl class="py function">
@@ -1803,17 +1816,17 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.CustomUpdateVarAccess" title="pygenn._genn.CustomUpdateVarAccess"><em>CustomUpdateVarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.CustomUpdateVarAccess" title="pygenn._genn.CustomUpdateVarAccess"><em>CustomUpdateVarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to variables
 in population(s) custom update is attached to</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>update_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>update_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 to be run when custom update is launched</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 <li><p><strong>extra_global_param_refs</strong> – names and types of extra global
 parameter references</p></li>
@@ -1831,6 +1844,8 @@
 <p>When used in a model with batch size &gt; 1, whether custom updates of this sort are batched or not depends on the variables their references point to.
 If any referenced variables have <a class="reference internal" href="#pygenn.VarAccess.READ_ONLY_DUPLICATE" title="pygenn.VarAccess.READ_ONLY_DUPLICATE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_ONLY_DUPLICATE</span></code></a> or <a class="reference internal" href="#pygenn.VarAccess.READ_WRITE" title="pygenn.VarAccess.READ_WRITE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_WRITE</span></code></a> access modes, then the update will be batched
 and any variables associated with the custom update with <a class="reference internal" href="#pygenn.VarAccess.READ_ONLY_DUPLICATE" title="pygenn.VarAccess.READ_ONLY_DUPLICATE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_ONLY_DUPLICATE</span></code></a> or <a class="reference internal" href="#pygenn.VarAccess.READ_WRITE" title="pygenn.VarAccess.READ_WRITE"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccess.READ_WRITE</span></code></a> access modes will be duplicated across the batches.</p>
+<section id="batch-reduction">
+<h2>Batch reduction<a class="headerlink" href="#batch-reduction" title="Permalink to this heading"></a></h2>
 <p>As well as the standard variable access modes described previously, custom updates support variables with ‘batch reduction’ access modes
 such as <a class="reference internal" href="#pygenn.CustomUpdateVarAccess.REDUCE_BATCH_SUM" title="pygenn.CustomUpdateVarAccess.REDUCE_BATCH_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_BATCH_SUM</span></code></a> and <a class="reference internal" href="#pygenn.CustomUpdateVarAccess.REDUCE_BATCH_MAX" title="pygenn.CustomUpdateVarAccess.REDUCE_BATCH_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_BATCH_MAX</span></code></a>.
 These access modes allow values read from variables duplicated across batches to be reduced into variables that are shared across batches.
@@ -1848,6 +1863,9 @@
 </div>
 <p>Batch reductions can also be performed into variable references with
 the <a class="reference internal" href="#pygenn.VarAccessMode.REDUCE_SUM" title="pygenn.VarAccessMode.REDUCE_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_SUM</span></code></a> or <a class="reference internal" href="#pygenn.VarAccessMode.REDUCE_MAX" title="pygenn.VarAccessMode.REDUCE_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_MAX</span></code></a> access modes.</p>
+</section>
+<section id="neuron-reduction">
+<h2>Neuron reduction<a class="headerlink" href="#neuron-reduction" title="Permalink to this heading"></a></h2>
 <p>Similarly to the batch reduction modes discussed previously, custom updates also support variables with several ‘neuron reduction’ access modes
 such as <a class="reference internal" href="#pygenn.CustomUpdateVarAccess.REDUCE_NEURON_SUM" title="pygenn.CustomUpdateVarAccess.REDUCE_NEURON_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_NEURON_SUM</span></code></a> and <a class="reference internal" href="#pygenn.CustomUpdateVarAccess.REDUCE_NEURON_MAX" title="pygenn.CustomUpdateVarAccess.REDUCE_NEURON_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">CustomUpdateVarAccess.REDUCE_NEURON_MAX</span></code></a>.</p>
 <p>These access modes allow values read from per-neuron variables to be reduced into variables that are shared across neurons.
@@ -1864,6 +1882,7 @@
 </div>
 <p>Again, like batch reductions, neuron reductions can also be performed into variable references with
 the <a class="reference internal" href="#pygenn.VarAccessMode.REDUCE_SUM" title="pygenn.VarAccessMode.REDUCE_SUM"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_SUM</span></code></a> or <a class="reference internal" href="#pygenn.VarAccessMode.REDUCE_MAX" title="pygenn.VarAccessMode.REDUCE_MAX"><code class="xref py py-attr docutils literal notranslate"><span class="pre">VarAccessMode.REDUCE_MAX</span></code></a> access modes.</p>
+</section>
 </dd></dl>
 
 <dl class="py function">
@@ -1910,19 +1929,19 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>sim_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>sim_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing a threshold condition
+<li><p><strong>threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing a threshold condition
 expression to test whether a spike
 should be emitted</p></li>
-<li><p><strong>reset_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the reset code
+<li><p><strong>reset_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the reset code
 statements to run after emitting a spike</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 <li><p><strong>additional_input_vars</strong> – list of tuples with names and types as
 strings and initial values of additional
@@ -1950,6 +1969,8 @@
     <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">,</span> <span class="n">pygenn</span><span class="o">.</span><span class="n">VarAccess</span><span class="o">.</span><span class="n">READ_WRITE</span><span class="p">)])</span>
 </pre></div>
 </div>
+<section id="additional-input-variables">
+<h2>Additional input variables<a class="headerlink" href="#additional-input-variables" title="Permalink to this heading"></a></h2>
 <p>Normally, neuron models receive the linear sum of the inputs coming from all of their synaptic inputs through the <code class="docutils literal notranslate"><span class="pre">Isyn</span></code> variable.
 However neuron models can define additional input variables - allowing input from different synaptic inputs to be combined non-linearly.
 For example, if we wanted our leaky integrator to operate on the the product of two input currents, we could modify our model as follows:</p>
@@ -1961,6 +1982,7 @@
 <span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
 </pre></div>
 </div>
+</section>
 </dd></dl>
 
 <dl class="py function">
@@ -1992,14 +2014,14 @@
 <li><p><strong>params</strong> – name and optional types of model parameters</p></li>
 <li><p><strong>vars</strong> – names, types and optional variable access
 modifiers of model variables</p></li>
-<li><p><strong>neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>sim_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the simulation code
+<li><p><strong>sim_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the simulation code
 statements to be run every timestep</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
 </ul>
 </dd>
@@ -2056,17 +2078,17 @@
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the snippet (only for debugging)</p></li>
 <li><p><strong>params</strong> – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>row_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity row by row</p></li>
-<li><p><strong>col_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity column by column</p></li>
-<li><p><strong>calc_max_row_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>row_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity row by row</p></li>
+<li><p><strong>col_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity column by column</p></li>
+<li><p><strong>calc_max_row_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 row length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_max_col_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>calc_max_col_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 column length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_kernel_size_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the size of the kernel if snippet requires one</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of snippet extra global parameters</p></li>
-<li><p><strong>param_names</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>calc_kernel_size_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the size of the kernel if snippet requires one</p></li>
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of snippet extra global parameters</p></li>
+<li><p><strong>param_names</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
@@ -2121,14 +2143,14 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the snippet (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>diagonal_build_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – code for building connectivity row by row</p></li>
-<li><p><strong>calc_max_row_len_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the maximum
+<li><p><strong>diagonal_build_code</strong> (<em>str</em><em> | </em><em>None</em>) – code for building connectivity row by row</p></li>
+<li><p><strong>calc_max_row_len_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the maximum
 row length of synaptic matrix created using this snippet</p></li>
-<li><p><strong>calc_kernel_size_func</strong> (<em>Optional</em><em>[</em><em>Callable</em><em>]</em>) – used to calculate the size of the kernel</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of snippet extra global parameters</p></li>
+<li><p><strong>calc_kernel_size_func</strong> (<em>Callable</em><em> | </em><em>None</em>) – used to calculate the size of the kernel</p></li>
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of snippet extra global parameters</p></li>
 </ul>
 </dd>
 </dl>
@@ -2191,10 +2213,10 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new model (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from paramss</p></li>
-<li><p><strong>var_init_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string containing the code statements
+<li><p><strong>var_init_code</strong> (<em>str</em><em> | </em><em>None</em>) – string containing the code statements
 required to initialise the variable</p></li>
 <li><p><strong>extra_global_params</strong> – names and types of model
 extra global parameters</p></li>
@@ -2273,50 +2295,50 @@
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>class_name</strong> (<em>str</em>) – name of the new class (only for debugging)</p></li>
-<li><p><strong>params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>str</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em><em>]</em>) – name and optional types of model parameters</p></li>
-<li><p><strong>vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>params</strong> (<em>Sequence</em><em>[</em><em>str</em><em> | </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – name and optional types of model parameters</p></li>
+<li><p><strong>vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-synapse model variables</p></li>
-<li><p><strong>pre_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 modifiers of per-presynaptic neuron model variables</p></li>
 <li><p><strong>names</strong> (<em>post_vars</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
 <li><p><strong>access</strong> (<em>types and optional variable</em>) – modifiers of per-postsynaptic neuron model variables</p></li>
-<li><p><strong>pre_neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>pre_neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to presynaptic
 neuron variables</p></li>
-<li><p><strong>post_neuron_var_refs</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and optional variable access
+<li><p><strong>post_neuron_var_refs</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccessMode" title="pygenn._genn.VarAccessMode"><em>VarAccessMode</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and optional variable access
 of references to be assigned to postsynaptic
 neuron variables</p></li>
-<li><p><strong>derived_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names, types and callables to calculate
+<li><p><strong>derived_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Callable</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names, types and callables to calculate
 derived parameter values from params</p></li>
-<li><p><strong>pre_spike_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic spike code</p></li>
-<li><p><strong>pre_event_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic event code</p></li>
-<li><p><strong>post_event_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic event code</p></li>
-<li><p><strong>post_spike_syn_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic spike code</p></li>
-<li><p><strong>synapse_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the synapse dynamics code</p></li>
-<li><p><strong>pre_event_threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the presynaptic event threshold
+<li><p><strong>pre_spike_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic spike code</p></li>
+<li><p><strong>pre_event_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic event code</p></li>
+<li><p><strong>post_event_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic event code</p></li>
+<li><p><strong>post_spike_syn_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic spike code</p></li>
+<li><p><strong>synapse_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the synapse dynamics code</p></li>
+<li><p><strong>pre_event_threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the presynaptic event threshold
 condition code</p></li>
-<li><p><strong>post_event_threshold_condition_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the postsynaptic event threshold
+<li><p><strong>post_event_threshold_condition_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the postsynaptic event threshold
 condition code</p></li>
-<li><p><strong>pre_spike_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run once per
+<li><p><strong>pre_spike_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run once per
 spiking presynaptic neuron. Only
 presynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>post_spike_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run once per
+<li><p><strong>post_spike_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run once per
 spiking postsynaptic neuron</p></li>
-<li><p><strong>pre_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run every
+<li><p><strong>pre_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run every
 timestep on presynaptic neuron.
 Only presynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>post_dynamics_code</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – string with the code run every
+<li><p><strong>post_dynamics_code</strong> (<em>str</em><em> | </em><em>None</em>) – string with the code run every
 timestep on postsynaptic neuron.
 Only postsynaptic variables and
 variable references can be
 referenced from this code.</p></li>
-<li><p><strong>extra_global_params</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>]</em><em>]</em>) – names and types of model
+<li><p><strong>extra_global_params</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>]</em><em>] </em><em>| </em><em>None</em>) – names and types of model
 extra global parameters</p></li>
-<li><p><strong>post_vars</strong> (<em>Optional</em><em>[</em><em>Sequence</em><em>[</em><em>Union</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>]</em><em>, </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>str</em><em>, </em><em>ResolvedType</em><em>]</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>]</em><em>]</em><em>]</em>) – </p></li>
+<li><p><strong>post_vars</strong> (<em>Sequence</em><em>[</em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>] </em><em>| </em><em>Tuple</em><em>[</em><em>str</em><em>, </em><em>str</em><em> | </em><em>ResolvedType</em><em>, </em><a class="reference internal" href="#pygenn.VarAccess" title="pygenn._genn.VarAccess"><em>VarAccess</em></a><em>]</em><em>] </em><em>| </em><em>None</em>) – </p></li>
 </ul>
 </dd>
 </dl>
@@ -2353,6 +2375,8 @@
 <span class="sd">        &quot;&quot;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+<section id="pre-and-postsynaptic-dynamics">
+<h2>Pre and postsynaptic dynamics<a class="headerlink" href="#pre-and-postsynaptic-dynamics" title="Permalink to this heading"></a></h2>
 <p>The memory required for synapse variables and the computational cost of updating them tends to grow with <span class="math notranslate nohighlight">\(O(N^2)\)</span> with the number of neurons.
 Therefore, if it is possible, implementing synapse variables on a per-neuron rather than per-synapse basis is a good idea.
 The <code class="docutils literal notranslate"><span class="pre">pre_var_name_types</span></code> and <code class="docutils literal notranslate"><span class="pre">post_var_name_types</span></code> keyword arguments} are used to define any pre or postsynaptic state variables.
@@ -2388,6 +2412,9 @@
     <span class="n">post_dynamics_code</span><span class="o">=</span><span class="s2">&quot;postTrace *= tauMinusDecay;&quot;</span><span class="p">)</span>
 </pre></div>
 </div>
+</section>
+<section id="synapse-dynamics">
+<h2>Synapse dynamics<a class="headerlink" href="#synapse-dynamics" title="Permalink to this heading"></a></h2>
 <p>Unlike the event-driven updates previously described, synapse dynamics code is run for each synapse, each timestep i.e. unlike the others it is time-driven.
 This can be used where synapses have internal variables and dynamics that are described in continuous time, e.g. by ODEs.
 However using this mechanism is typically computationally very costly because of the large number of synapses in a typical network.
@@ -2397,6 +2424,9 @@
 <span class="n">synapse_dynamics_code</span><span class="o">=</span><span class="s2">&quot;addToPost(g * V_pre);&quot;</span><span class="p">,</span>
 </pre></div>
 </div>
+</section>
+<section id="spike-like-events">
+<h2>Spike-like events<a class="headerlink" href="#spike-like-events" title="Permalink to this heading"></a></h2>
 <p>As well as time-driven synapse dynamics and spike event-driven updates, GeNN weight update models also support “spike-like events”.
 These can be triggered by a threshold condition evaluated on the pre or postsynaptic neuron.
 This typically involves pre or postsynaptic weight update model variables or variable references respectively.</p>
@@ -2406,6 +2436,7 @@
 </pre></div>
 </div>
 <p>Whenever this expression evaluates to true, the event code in <code class="docutils literal notranslate"><span class="pre">pre_event_code</span></code> will be executed.</p>
+</section>
 </dd></dl>
 
 <dl class="py function">
@@ -2466,14 +2497,14 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>PostsynapticModelBase</em><em>, </em><em>str</em><em>]</em>) – postsynaptic model either as a string referencing a built-in model
+<li><p><strong>snippet</strong> (<em>PostsynapticModelBase</em><em> | </em><em>str</em>) – postsynaptic model either as a string referencing a built-in model
 (see <a class="reference internal" href="#module-pygenn.postsynaptic_models" title="pygenn.postsynaptic_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">postsynaptic_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">PostsynapticModelBase</span></code> (for example returned
 by <a class="reference internal" href="#pygenn.create_postsynaptic_model" title="pygenn.create_postsynaptic_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_postsynaptic_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the postsynaptic model (see <a href="#id46"><span class="problematic" id="id47">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or initialisers
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the postsynaptic model (see <a href="#id46"><span class="problematic" id="id47">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or initialisers
 for the postsynaptic model (see <a href="#id48"><span class="problematic" id="id49">`Variables`_</span></a>)</p></li>
-<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – references to postsynaptic neuron variables,
+<li><p><strong>var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – references to postsynaptic neuron variables,
 typically created using <a class="reference internal" href="#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
 (see <a href="#id50"><span class="problematic" id="id51">`Variables references`_</span></a>)</p></li>
 </ul>
@@ -2497,11 +2528,11 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>InitSparseConnectivitySnippetBase</em><em>, </em><em>str</em><em>]</em>) – sparse connectivity init snippet, either as a string referencing
+<li><p><strong>snippet</strong> (<em>InitSparseConnectivitySnippetBase</em><em> | </em><em>str</em>) – sparse connectivity init snippet, either as a string referencing
 a built-in snippet (see <a class="reference internal" href="#module-pygenn.init_sparse_connectivity_snippets" title="pygenn.init_sparse_connectivity_snippets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">init_sparse_connectivity_snippets</span></code></a>)
 or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">InitSparseConnectivitySnippetBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_sparse_connect_init_snippet" title="pygenn.create_sparse_connect_init_snippet"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_sparse_connect_init_snippet()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the sparse connectivity init snippet (see <a href="#id52"><span class="problematic" id="id53">`Parameters`_</span></a>)</p></li>
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the sparse connectivity init snippet (see <a href="#id52"><span class="problematic" id="id53">`Parameters`_</span></a>)</p></li>
 </ul>
 </dd>
 </dl>
@@ -2553,11 +2584,11 @@
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>snippet</strong> (<em>Union</em><em>[</em><em>InitVarSnippetBase</em><em>, </em><em>str</em><em>]</em>) – variable init snippet, either as a string referencing
+<li><p><strong>snippet</strong> (<em>InitVarSnippetBase</em><em> | </em><em>str</em>) – variable init snippet, either as a string referencing
 a built-in snippet (see <a class="reference internal" href="#module-pygenn.init_var_snippets" title="pygenn.init_var_snippets"><code class="xref py py-mod docutils literal notranslate"><span class="pre">init_var_snippets</span></code></a>)
 or an instance of <code class="xref py py-class docutils literal notranslate"><span class="pre">InitVarSnippetBase</span></code>
 (for example returned by <a class="reference internal" href="#pygenn.create_var_init_snippet" title="pygenn.create_var_init_snippet"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_init_snippet()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values for the variable init snippet (see <a href="#id56"><span class="problematic" id="id57">`Parameters`_</span></a>)</p></li>
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values for the variable init snippet (see <a href="#id56"><span class="problematic" id="id57">`Parameters`_</span></a>)</p></li>
 </ul>
 </dd>
 </dl>
@@ -2580,12 +2611,12 @@
 (see <a class="reference internal" href="#module-pygenn.weight_update_models" title="pygenn.weight_update_models"><code class="xref py py-mod docutils literal notranslate"><span class="pre">weight_update_models</span></code></a>) or an instance of
 <code class="xref py py-class docutils literal notranslate"><span class="pre">WeightUpdateModelBase</span></code> (for example returned
 by <a class="reference internal" href="#pygenn.create_weight_update_model" title="pygenn.create_weight_update_model"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_weight_update_model()</span></code></a>)</p></li>
-<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>int</em><em>, </em><em>float</em><em>]</em><em>]</em>) – parameter values (see <a href="#id58"><span class="problematic" id="id59">`Parameters`_</span></a>)</p></li>
-<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial synaptic variable values or
+<li><p><strong>params</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>int</em><em> | </em><em>float</em><em>]</em>) – parameter values (see <a href="#id58"><span class="problematic" id="id59">`Parameters`_</span></a>)</p></li>
+<li><p><strong>vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial synaptic variable values or
 initialisers (see <a href="#id60"><span class="problematic" id="id61">`Variables`_</span></a>)</p></li>
-<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial presynaptic variable values or
+<li><p><strong>pre_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial presynaptic variable values or
 initialisers (see <a href="#id62"><span class="problematic" id="id63">`Variables`_</span></a>)</p></li>
-<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>Union</em><em>[</em><em>VarInit</em><em>, </em><em>int</em><em>, </em><em>float</em><em>, </em><em>ndarray</em><em>, </em><em>Sequence</em><em>]</em><em>]</em>) – initial postsynaptic variable values or initialisers
+<li><p><strong>post_vars</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarInit</em><em> | </em><em>int</em><em> | </em><em>float</em><em> | </em><em>ndarray</em><em> | </em><em>Sequence</em><em>]</em>) – initial postsynaptic variable values or initialisers
 (see <a href="#id64"><span class="problematic" id="id65">`Variables`_</span></a>)</p></li>
 <li><p><strong>pre_var_refs</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>VarReference</em><em>]</em>) – references to presynaptic neuron variables,
 typically created using <a class="reference internal" href="#pygenn.create_var_ref" title="pygenn.create_var_ref"><code class="xref py py-func docutils literal notranslate"><span class="pre">create_var_ref()</span></code></a>
@@ -2906,7 +2937,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>name</strong> (<em>str</em>) – name of the parameter</p></li>
-<li><p><strong>value</strong> (<em>Union</em><em>[</em><em>float</em><em>, </em><em>int</em><em>]</em>) – numeric value to assign to parameters</p></li>
+<li><p><strong>value</strong> (<em>float</em><em> | </em><em>int</em>) – numeric value to assign to parameters</p></li>
 </ul>
 </dd>
 </dl>
@@ -3068,8 +3099,8 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>pre_indices</strong> (<em>Union</em><em>[</em><em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>ndarray</em><em>]</em>) – presynaptic indices</p></li>
-<li><p><strong>post_indices</strong> (<em>Union</em><em>[</em><em>Sequence</em><em>[</em><em>int</em><em>]</em><em>, </em><em>ndarray</em><em>]</em>) – postsynaptic indices</p></li>
+<li><p><strong>pre_indices</strong> (<em>Sequence</em><em>[</em><em>int</em><em>] </em><em>| </em><em>ndarray</em>) – presynaptic indices</p></li>
+<li><p><strong>post_indices</strong> (<em>Sequence</em><em>[</em><em>int</em><em>] </em><em>| </em><em>ndarray</em>) – postsynaptic indices</p></li>
 </ul>
 </dd>
 </dl>
@@ -3390,7 +3421,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <p>Array class used for exposing internal GeNN state</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
-<dd class="field-odd"><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – </p>
+<dd class="field-odd"><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – </p>
 </dd>
 </dl>
 <dl class="py property">
@@ -3410,7 +3441,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dl class="field-list simple">
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
-<li><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – data type of array elements</p></li>
+<li><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – data type of array elements</p></li>
 <li><p><strong>group</strong> – group array belongs to</p></li>
 </ul>
 </dd>
@@ -3452,7 +3483,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>variable_name</strong> (<em>str</em>) – name of the extra global parameter</p></li>
-<li><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – data type of the extra global parameter</p></li>
+<li><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – data type of the extra global parameter</p></li>
 <li><p><strong>group</strong> – group extra global parameter belongs to</p></li>
 <li><p><strong>init_values</strong> – values to initialise extra global parameter with</p></li>
 </ul>
@@ -3497,7 +3528,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>variable_name</strong> (<em>str</em>) – name of the variable</p></li>
-<li><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – data type of the variable</p></li>
+<li><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – data type of the variable</p></li>
 <li><p><strong>init_values</strong> – values to initialise variable with</p></li>
 <li><p><strong>group</strong> – group variable belongs to</p></li>
 </ul>
@@ -3542,7 +3573,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>variable_name</strong> (<em>str</em>) – name of the variable</p></li>
-<li><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – data type of the variable</p></li>
+<li><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – data type of the variable</p></li>
 <li><p><strong>init_values</strong> – values to initialise variable with</p></li>
 <li><p><strong>group</strong> – group variable belongs to</p></li>
 </ul>
@@ -3585,7 +3616,7 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
 <dt class="field-odd">Parameters<span class="colon">:</span></dt>
 <dd class="field-odd"><ul class="simple">
 <li><p><strong>variable_name</strong> (<em>str</em>) – name of the variable</p></li>
-<li><p><strong>variable_type</strong> (<em>Union</em><em>[</em><em>ResolvedType</em><em>, </em><em>UnresolvedType</em><em>]</em>) – data type of the variable</p></li>
+<li><p><strong>variable_type</strong> (<em>ResolvedType</em><em> | </em><em>UnresolvedType</em>) – data type of the variable</p></li>
 <li><p><strong>init_values</strong> – values to initialise variable with</p></li>
 <li><p><strong>group</strong> – group variable belongs to</p></li>
 </ul>
diff --git a/documentation/5/tutorials/1_neurons.html b/documentation/5/tutorials/1_neurons.html
new file mode 100644
index 000000000..2038cfa23
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+++ b/documentation/5/tutorials/1_neurons.html
@@ -0,0 +1,305 @@
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+  <section id="Defining-populations-of-neurons">
+<h1>Defining populations of neurons<a class="headerlink" href="#Defining-populations-of-neurons" title="Permalink to this heading"></a></h1>
+<p>In this tutorial we’re going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes: <img alt="image.png" src="data:image/png;base64,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" /></p>
+<p>(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com) ## Install PyGeNN wheel from Google Drive Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
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+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 118MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Import numpy, matplotlib and the main <code class="docutils literal notranslate"><span class="pre">GeNNModel</span></code> class from PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="n">GeNNModel</span>
+</pre></div>
+</div>
+</div>
+<p>Create a new model called “tutorial1” with floating point precision and set the simulation timestep to 0.1ms</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial1&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="mf">0.1</span>
+</pre></div>
+</div>
+</div>
+<p>Configure initial state for a population of Izhikevich neurons with a constant value for the <code class="docutils literal notranslate"><span class="pre">V</span></code> and <code class="docutils literal notranslate"><span class="pre">U</span></code> state variables and different values for the <code class="docutils literal notranslate"><span class="pre">a</span></code>, <code class="docutils literal notranslate"><span class="pre">b</span></code>, <code class="docutils literal notranslate"><span class="pre">c</span></code> and <code class="docutils literal notranslate"><span class="pre">d</span></code> parameters (because we are going to be using the <code class="docutils literal notranslate"><span class="pre">IzhikevichVariable</span></code> model, the parameters are also implemented as state variables so they can vary across the population of neurons)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">izk_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>
+            <span class="s2">&quot;U&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">20.0</span><span class="p">,</span>
+            <span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.02</span><span class="p">,</span>     <span class="mf">0.1</span><span class="p">,</span>    <span class="mf">0.02</span><span class="p">,</span>   <span class="mf">0.02</span><span class="p">],</span>
+            <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span>      <span class="mf">0.2</span><span class="p">,</span>    <span class="mf">0.2</span><span class="p">,</span>    <span class="mf">0.2</span><span class="p">],</span>
+            <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>    <span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>  <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>  <span class="o">-</span><span class="mf">55.0</span><span class="p">],</span>
+            <span class="s2">&quot;d&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span>      <span class="mf">2.0</span><span class="p">,</span>    <span class="mf">2.0</span><span class="p">,</span>    <span class="mf">4.0</span><span class="p">]}</span>
+</pre></div>
+</div>
+</div>
+<p>Add a population of 4 of these neurons (GeNN’s built in models are selected by specifying model as a string)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;Neurons&quot;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;IzhikevichVariable&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="n">izk_init</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;CurrentSource&quot;</span><span class="p">,</span> <span class="s2">&quot;DC&quot;</span><span class="p">,</span> <span class="n">pop</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;amp&quot;</span><span class="p">:</span> <span class="mf">10.0</span><span class="p">},</span> <span class="p">{});</span>
+</pre></div>
+</div>
+</div>
+<p>Generate code and load it into PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+<section id="Simulate-tutorial-model">
+<h1>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h1>
+<p>State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">voltage</span> <span class="o">=</span> <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;V&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">voltages</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">while</span> <span class="n">model</span><span class="o">.</span><span class="n">t</span> <span class="o">&lt;</span> <span class="mf">200.0</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+    <span class="n">voltage</span><span class="o">.</span><span class="n">pull_from_device</span><span class="p">()</span>
+    <span class="n">voltages</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">voltage</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Plot the voltages over time in 4 seperate panels</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Stack voltages together into a 2000x4 matrix</span>
+<span class="n">voltages</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">voltages</span><span class="p">)</span>
+
+<span class="c1"># Create figure with 4 axes</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+
+<span class="c1"># Plot voltages of each neuron in</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s2">&quot;RS&quot;</span><span class="p">,</span> <span class="s2">&quot;FS&quot;</span><span class="p">,</span> <span class="s2">&quot;CH&quot;</span><span class="p">,</span> <span class="s2">&quot;IB&quot;</span><span class="p">]):</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;V [mV]&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">200.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">voltages</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_1_neurons_19_0.png" src="../_images/tutorials_1_neurons_19_0.png" />
+</div>
+</div>
+<section id="Exercises">
+<h2>Exercises<a class="headerlink" href="#Exercises" title="Permalink to this heading"></a></h2>
+<ol class="arabic simple">
+<li><p>Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.</p></li>
+<li><p>Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful.</p></li>
+</ol>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="index.html" class="btn btn-neutral float-left" title="Tutorials" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+  <hr/>
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+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
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@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Defining populations of neurons\n",
+    "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n",
+    "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1MAAAJDCAIAAABhe9nkAAAgAElEQVR4nOzdT2zbVro//MPB3SVAxcJAgN6N6Kpdi4a9a4CBIRpt+lv5Dll42QzEQEA7wF2YGrV9V2/GGrGLF+hcQDCF8SyNiLnZ5TYQDaNAu4sh+m4bxfTmF8CAYKpAsta7eCZnGFKSaesfJX4/CAKZoqhD8ujo0fkr9Pt9BgAAAAAp8Lt5JwAAAAAAZgSRHwAAAEBaIPIDgKkQBGHYU67ryrIsy7KiKI7jMMZ839c0TZIkTdN834//LpIkWZbF/3QcRxTFax0BACBVEPkBLIkRkVbSbG9vm6bpuq5pmpqmMcYajcb6+rrneevr641GI/6hSqXS0dER/9O2bVVVRVGcfKIBAJYCIj+AZTC9sE8QBMMwNE3TNM3zPMaY7/u6rlONHd9o2zZV4wVr4Gjn6EbLshRFoceZTIYxVq/XVVVljKmqWq/Xo2kwTVNRFEmSDMMwDIMeW5alqqpt27ySjyK/qVwIAIDl0O/3MbwXJqsPk3Pl9Zz2lWeM7e/v9/v9/f19VVX7/b6qqrSl3++3Wi3amM1mz87OLi8vWaBUuby8zOfzfOeQQqGQyWRarVb/3dOMnghjrFar9ft9Oj4d8OzsLJPJUHro2Wazmc1mJ3ju8zLrTyzEM+98kSDzvhUwWMzbJ9AtFASh3/+feacZloEg3EO5MEGCIIy4nhOv6ou+VzAB9FgQhEKhENzHcRzHcVzX7Xa733//PS9V8vk8Y8x13WFv5ziOruue50XfJZSGy8tLasON7mlZVr1ed11X1/VcLmcYxjhXIAlQICcQSrYgZNEEip9FEfnBhKF8nCAK7EZfz2DwN40rH420RFE8Ozvjfekcx2k0GtlsVlGUXC63urrKS5VWq6XrummaoRZYPrCDH1OSpOPjY0mSPM/b3NykFuQRaQg9FkWx3W6vra21221JkiZ+EWYMBXICoWQLQhZNoPhZFP38ABbbDBpfbNtmjJmmWSwWGWOqqlarVXrKNE3TNG3brlQqwXiOKIpiWZau66HBtqIo0jEty6Lqw1KpRFts2y6VStdNoaqquq5T578bniQAQDog8gNYBlON/54/f64oysnJSa1WY4zR/7IsS5J0cnJimub+/r6maYqi9Hq9fD5vmiZ/raIoqqo+ePAgeMBKpbK3tyfL8tHREQ3+KBaLJycndECKL6+FRviG2qABACAKrb0wYWgTmaA4rb0zSANu6IyhQJ4eQbhXKMj0uNv9zTTvK8oaY8ww/u66Z7S903l1fn4RugUo2YJmlkUH3i/XfWkYB93ubysr74nirUrlC1n+cAaJSbj4WfTfpp0UAACA5HCcv9AD1325ufln36fODH/kO8jyV7Xa/fkkDiKi92t7++GTJ99RtGdZP25vP/S8f8w1jQsGkR8AjIJ6DlhWsvxhr/cmtNEw/r6y8p5h/GEuSYIR+P06P7/IZu/QRl3/TJLuzDVdiweRH0BC8SlU5p0QgOVkWT/ylkTiOO1G49nZGSqQkojfr1rt/vvva4WCrCiyJN1R1bvzTtqCQeQHAAApoijf0oOTk18rlS/4dt9/rWlVy/qTKN6eU9JggOj9Mow/qOrdk5Nfnz//tV5/+vz5r8HGergSRnjAhKEf9KTwOj9cz7RBgTw9gnCPX1vff/3++xr/U9P2stk7w2KIdJZsruvev39/ZWWFMWaapiz/s4p0liM8ovfLNB/z5vjQTUwzzOc3lO+/1vUfRFEVhHuatuf7r+edIgAAmINg3Z5pPn7x4hWqjkK2t7dN03QcxzCM7e3tabyFINzj/0bvye9Xvf7Usn6kx8E+fxBT6ur8dP2HXu/1/v6fGGPl8oHnXfBxQzAR6fxlPA2o80ut9BTIsxesQ2KMSdKXNEpUEO5ls3dyuQ/4U6GvhnSWbCOXz5lAFo1Ge4Mm0wnfL8ZYtfroxYtXjLGPPvoAs7oQrN42lCDcazYr1CHUcdpbW9+l5MRnJp3l4zQg8kut9BTICySdJRvNxE5rMNLy3LR9ZpEfxDe71l5BuOc47TEPMkut1kM+mMvzLjKZW/NNz6IThHum+XjeqQAAgMnTdb1cLiuKUq1WDcMIPnVl4ywk1qz7+fn+a9N8LElfCsI9SfrSNB8P62kXc09d/+Fa+U9R1qivgGk+LpcPYk7XKctfBfsiTCPcse2fR59I6EzjX0kAAIAb0HW93W47jtNutzVNCz6FyrnFNetZXTStSgOzZXnVdc+q1UeO4w7saRdnT9v+udF4dt00uO7L7e2HmcytZrNC6/Zc6fT0rFj8NNgFRJZXr/u+I9j2z3t7j0bvEDrT+FcSAADgZnq9HmMsk8lkMpmJH7zf/59gjQaiydmYaeRnWT8eHbmt1kOKtxRlTZZXt7a+s6wfdf2z6+7peRe6/kM+v3p6ehY/DbT8S6XyRfwp2j3vgjFWKn0+jT6k1NfwygSEzjT+lYRFh65+MEGh/vKW9aNt/8IYOzpyqRuMqn6ysfHxsEVRB+5PZY5pPq7Xn+ZyH4jiLVW9m6jJdRFb3Njx8THN6iKK4pMnT6bxFrgjs3d1a68sfyVJXwa3GMbfBeEetS22Wg/X1z+O+WZHR24+vxqsZlOUtXx+9ejIvcGe29v/r6re3dn5ffzUMsaq1Ucjwj7XfcknjeQ6nf/LGBsY9l35dlcefH3941brYav1sFj8dGCS2KAzjX8lB4q2Vpvm42FtzQOTDQBLQNc/c5y/UFsBPdD1z7a3H5rmfdf9L8f5S6Egb28/HL0/Y8yyfjw8/Knd/pvj/GV//0+GcZCc/t+hkg29065FlmXXdR3HaTabfDI/WHRXR347O78/P79w3Zd8i23/oqqfUG853m0uDsdxNzbCYeLGxseOE45XrtzTNB/3em+ivfRGp5b+LJcPhk0g1O3+Fg2eXPcsk7kVnAWQF2pXvt2VBxfF24qypihrwabkoIFnGv9Kjm9gsmHaUM+XcnMMUEKLolrW11e+pF5/apr3qdwTxdtxXgIA83J15EeV9oeHP9Gfrvvy/PwiVJMfGv3A/4Xqinq9N9H4Jpf7ILpm9ug9HaddLh88efJdNMC6MrX9/v+E/l15BSg9vd5ry/rT/v7XL1682tr6joK/OBdnHMPONP6VBIBFNMcmMFoUVVG+Nc3Htv1znM7Qp6dnoSaImF2oAWD2ru7nJ0l3VPUT2/6FJjc/PPwpk7kVCm5arYcDX7uy8t5EUhlESyvWavcHNr/GSe11SdKdWu0+byBW1btra18bxoHrrk3j7bjRZxpHsNqAdwoEABgBi6ICLLdYIzwKBdm2f3Hdl7L8YaPxLNojLWZIkcnc6nRehTZ2Oq+ik+qN2LNcPmCMyfIq1brRbo7TXll5jyKkK1MbNawjSKEgO85fQpGcKN4ulT6nZMR5u9EHH5GqEWca80oG24hzuX8f8V5RN042ACw0WhRVku6o6t1K5Yv339eujPzy+VUqA/kRut3fEhIvpnn0qCAIo3dAl5J0ihX5qerdcvnAcdxO51Wv9yY0qIIxNqwzL4/GiKLIz5//Gtrn+fNfFSXcb3TEnp530eu9CY2H3dr6jkckV6Y2itdZuu5ZuXzA/1xZec/3X5+c/JrL/bsk/WtlwG73Nx5jXfl2Iw4+OlUjzjTmlYw/frnb/W1SyQaAhVavP81kbtHQDd7nz3HaI37hl0qfG8ZBs1kRxdued1GtPmq3/2t2Kb5KqqK9oFarRQ9c163X66VSiUZp8D/nmjqYm1iRnyjeVtW7h4c/bWx8nM+vRhsfh81LEqofouqxYAniOO3T07NS6fPoC4ftGZq1hCZkDn6wr0xtVKhEC/2paVVFkZvNb+hP339t27/wGOvKtxt98BFCVWvBM6W5FeJcyWGCVYZ0RpNKNgAkGe9+LcurA6vlnjz5rlp9VK8/ZYx99NEHtEzq6IUudf2zXu+NplUZY93ub81mJfhTGeZFURR6YBjG8fGxJEl8u6qqm5uboWU5ICXizuenKHKj8ez8/KJS+SL6bMxfVLr+Wb3+VNOqwfmHCwWZgjlF+ZaXRKP3HDO111WpfFEuH2jaHjX77u096vVeB4882beLY8zrUyjIjcazXO4DWV71vItqtdnrYf0PgAnwfV/TtE6nk8vlms2mKIq03XVd+pbtdrsHBweyLDuOo+t6LpdjjNHSqDNI3rCyOrhdlj/kP3S50PQC0eMYxh/iNzLAjJ2fn0c30hTNkEJxIz9VvZvJ/NDrvYnTbW6E4+O/NhrP6vWn1IhQqXzBDxiaOmTEnjNLLaHi7PDwJ02rZjK3FEU+OPjPYN3eZN8upnGuT7NZKZcPqtVHvd6bfH61UtF6vTe85yIA3Fi1WlUUxXEc0zSr1appmrTdMAzDMBRFcV13e3vb8zzXdSuVypQCPt6zLbUNnRBULBY3NzdDrb2VSmXe6YL5+OecYYIgoICAiRCEe+g1PL7gfH6Y22+BSJLUbrdFUfR9f21tzfM82k7hID2mG2qapizLvD0uaMwCOTQ8C2X7RCx6yWaaZr1ep8q/QqEwZjUzYoYEip9FEfnBhC16+ZgEoVAPkd8CuTJkN03TcRzHcRRF6Xa7KysrtDG4QMI4BXJ0CmiU7ROBki0IMUMCxc+iM123FwAgtTzPK5fLL168OD4+ZoypqrqxsUEd/u7fv++673R3CS2wCwAwKVev4QEAADFls1nf9xljvu9ns1m+3fO8tbW19fV113Vp2Ieu61TPpyjK6elp6Dg3DvsQLwLAaIj8AAAmRlXVRqPBGGs0GqqqMsaoe1+5XG42m8FJNERRpF6AjuMUCoU5pRcAUgf9/GDC0BtmfOjnt7iis7rQ7RMEIRjeUVc/wzAm3s8PpmShSzZFUUJ5bEzIogmEfn4AAHMgiiIfw0uoLI6WyDTDy+xSBunmOM4EIz9YaBOI/AThXqv1cIJrPJjm407nlWX9SRDu1Wr3Zzw7qK7/kMt9gClJYV5QwwcAk6UoSr1eZ4yFgr+BkwrB0ktcnZ/nXZTLB2dn/5hXAiqVL9bWvioWPxXF2/NKAwAAwKSUy2X+fxB+ZKZT4kZ4VKuPisVP57jmoyTdURQZC1oAwECmaUqSJAiCIAiKoliWNe8UAVyhP8S80wXzkazIz/dfNxrPFGXOfREKBdm2f/Z9LGULAO8wDKNer5dKpVar1Wq1FEUJLtEGAJB8sSI/Qbhnmo+DW0zzMZ8pvtV6uL7+cfy39P3XpvlYkr4UhHuiqOr6DzzGsu2fGWOqeje4v2X9KMtf0c6atue6LxljsvyVJH0Z3M0w/i4I92KGa6PPSNc/6/XeUGIAALhGo3F8fEwr8CqKYhjG8fFxtVqdd7oAruA4jqZpVF1Nj2niSUihCdT5KcratbrElcsH1eqjUunzVuthrXbftn/WtH+Wm0dHbqHwToVftfqoXn/Kd37+/MXm5p9d9+XOzu/Pzy8oCiS2/YuqfjKpznmFgnx0hGF3MGsY3pFwmUwm5kaA5LAsS9O0bDZL9dPr6+svXrzAL5bUmtgIj+hikaRQkB3nL8Etjcaz3d3/4INne703vFPdixevNjbC1YfHx3/l8Zyq3l1d/bJafVSr/bFcPjg8/EmWP2SMue7L8/ML07w/qdOR5VXHQeQHAIy9nY2ZMVYqlTY3N0ulEo2R9DyvXq9jgCQkXLVatSyLphZnjImieHBwsL29jY4K6TSxyK/Vejhw+8rKe6EtNP2j5110Ov/X8y4OD3/iT52enu3s/D64c2iMrSjeLhY//f77/242v1HVT2z7F9P8I2Ps8PCnTOZWqJmYvRuPXmvqmZWV905Pz2LuDADLbWtrK/hnaIzk6ekpxnlAkp2fn/Owj8iyfH5+Pq/0wHxNLPKLGVT5/uty+aDReMYYy+dXV1bei4aGQdFn+ZZCQbbtX1z3pSx/2Gg8KxY/jb68VvtXLWAu9+9xUggAEIImeFho+XzeNM3g4oG2bQfXlb4BQbiHZTwW1A0jv273t9AWx2kP3HNl5T1qkCWaVu12f2u3/8Y3mubja/Wo63ReZbN3GGOqerdcPnAct9N51eu9CVUWkvgTMkfPCGDG0MkPAKbBNM2tra1Op0M9E2zb3tvbK5VK4xwTYd/iihv5dTqv+GPff23bv4R22Nr6buALQ/38jo7cWu1+MBYMdqfL51dDEVi9/jTY4Ov7r237Z6reE8Xbqnr38PCnjY2P8/nV4DHHP6NO51U+v3qtAwIAACSQoiitVqvRaOi6zhjTdb1SqQSrACFVYkV+hYLcaDzL5T6Q5VXPu6hWm71eePKUmOF/oSBXq48ymVuSdMd1z+r1p7TdcdqKsvbRRx+47ju963q912trX5dKn8vyKu2fydyuVL6gZxVFbjSenZ9f8C0xXXlGnnfx0UcfXOuYAAAACURNvaGhSKH2X0iPf7YuCYIwInSjznm2/XOv9yafXy2VPqcBuTeo7PX91w8e/EAVbKr6iareXV//eG3tK8aY79uW9eODB3/jh6V1exlj9frT8/OLbPaOosi12v13x3yovd6by8vmteZzufKMBOHe/v7Xuv7ZdU8QBOEemixvYERTL1qB02Z0gQxzsaAlGw1L39raarVawe29Xk/TtBufEbJoAsXPorEiv5nx/derq1/WavfnG3LRFIPXjSaBLGj5OHeI/BaCoiimaYaWvZ+4hBTIELSgJZsgCMOeKhaLNx6TjiyaQPGzaLJWb6Oue3OfQtlx3NBsMgBTNaJ0hqThc/sBJB9fnze6aC+mIkqtic3qMim12v3V1S8970KS7swlAZ530Wg8Ozv7x1zeHVJrEasTUkhRlHq9zhgLVfthMmdIMhQvEJSs1l5imo87nVeW9ae5vLuu/5DLfRB/OhgIWdA2kTmiCr8RFw2tvckxrHZ2sjcoUQUykEUv2RzHaTQaz58/Pz8/p3G++/v7oije7GjIogm0qP38YAksevk4e1cGdoj80gYFcgItdMlmWVa5XC4WixsbG5qmXV5ebm5uUqfVmx0QWTSBEPnB3Cx0+Th7V1b4MUR+6YMCOYEWumSTJMk0TVrAjcoT13W3t7c9z7vZAZFFE2hRR3gApEqcsA+SxrZtTdMkSRIEwXEcTdN83593ogBGwbq9EITID2CeEPYtFpr8dn19ncZFrq+v+75frVbnnS6AUWjd3uCW8dfthcWVuLG9ACmRnplclmmpgGq12m63JUmiP0VRtCxrbW3txv2lAGZgGuv2wuJCnR/AHKSqnbdcLs87CROTyWQ6nc68UwFwPbRub6/X4+v27uzsLM3vsbkQhHv0v6J8S/8k6UvL+nHe6YoFdX4As7ZYYZ8gCPv7+w8ePLi8vCyXy8+fP19ZWRFFsVarSZJk2za1e3a73W+++UZVVc/zyuWy7/uiKFYqlcPDQ8aYoigDJ0D2fb9cLjuOk8lkTNNM/qx4lUpF07RisRisOykWi/NOF8AVFEVJ/udrETnOX+iB779+/31tIRZ9ReQHMFNTCvumGk32er1+v69pWqFQoDjPcZxyudxsNnVdPzs7E0XR9/0HDx6oqqrrOq1v5rputVptNpvff//9sHUvyuVyLpezLGvMkYYzo+u6JEmNRkPTNPqzWCyiqRcSjnqjum54fSwsSDNBJye/FgrTXddxUjCrC0zYQs99MFW8Y991r0+cCf9uduRrvbsgCIVCIfgUjWx98eKFoigbGxt8zojgbo7jjEg/hYwTT/OiQ4GcQAtdsmma9vz582jHvhs3+CKLCsK9fv9/BOEej/aOjtx2+2+y/OFck4T5/GAeFrp8nJ5x6uRGRE4THyYSfSP+7qIoUvUebXcchxqPXNd1HMdxnG6367puMJijfUannz/FD5hkA0erTHwICwrkBFrokk0QhLOzMz4yaSIHTHkW5ZEfvw6u+3J7+6HnzW3pV0R+MDcLXT5Ow/gVcqPr/ILB31Tr/HRdp954jDHTNCnaE0Xx+PhYlmXf91dXV33f13U9l8sZhsH3GZF+TdPW19cNw3AcR9f1JLf2UrvY1tZWq9UKbu/1epqmYfW2pbfQJZskScfHx4j8Jiga+fGNc01SrCyKfn4A0zLVRliO/3ib3luQWq1WrVZlWe71ehsbGxQCWpZ1//79lZWVbrdLXQBrtdqDBw9kWV5ZWaF9CoUCdfuLHpPGjiiKIorikydPpn0K49ja2go94DDCAxKuVCptb28/efJkgsEfhDhOO59fnXcqYkGdH0zYQv8ynpTJxnwxW4qxyNsMzOYio0BOoIUu2Wjhmej28RoiUp1Fo/38ut3fDg7+cyH6+aHOD2CSplHP1+/3BUEYVqvH32hxv5YWSL/ft23btu3nz5+fn5+3Wq1Go7G/v8+7PwIkkGEYxWIxtIAbjIMC3wUNf1HnBxO20L+Mb2zane3ivG9UCm/EtJmmWa/XS6WSLMtbW1uXl5eapsmyPNmJXVAgT0+wkoYxZpr3ZflD131pGAfd7m8rK++J4q1K5Ytozc1Cl2yCIFxeXk7w9wmyaAJhhAfMzUKXj9cSirqSdtZX1hHCDYiiyFdvo5Zfz/PW1tYmOzfNNArkULOUad5XlLUrI55ok1an86pSCU9Xa5qP6/WnudwHonhLVe+q6l3+2tChJntSNzAwGZL05ZMn39G5W9aP1WozOkJzoUs2TdNUVb1xnV80kyNmSCC09gJMRcKjvaCBaRvWapzkE0kUWr1tQbvJ88UGXPfl5uaffd/e3n4YjHhGzEkxYqECy/rx8PCndvtvonjb91+vrX2dydxSlLUpn80knZ9fZLN36LGufyZJd+abnolbX1+nsfOy/M5UwzHnUdJ1vdfrTSdpC4AWaiPLEe8i8gMYJRonLXSQFD8cXOjTnJ7lWL1Nlj/s9d6wG0U80YUK6vWnpnlfFG8zxkTxtmV9PYUkT1etdv/997VCQVYUWZLuUJ3lMqG1s6MraMf5mNOyikdHR1NJWeIFwz6WmKrrMSHyA/iXdNaHDZy9GY3FAy3H6m2W9SNFb/EjHkX5lh7QQgXBp05Pz4I1fO8+/nbCSZ8EnipZXjXNPzLGDOMPqnr35OTX589/rdefPn/+K21fGjf+2PJ52qNRIywu9PODCZtSbxhaClZRFFovwXEc13XHXDghnXHeODCghDHm+/7JyUlo42RXH5l2P7+Tk18rlS8M4w+MMc+7oIjHtn9R1U9CEU+chQqG1YIsUD8/03xMV4O9bc6O7rPQ/fyifN/XNO3KdXsVRbEsS5Kk0HxGb6eamv8NnbZQnR9L8FljhAfMzTTKR8/zdF2nQooXQIqiNJvNOKPVRsQry1SUz9eYU0kvyo2wLOvBgwfR7clfwyMY8fDg5sqIJ85CBbL8VXAaM9N83O3+Zpp/XKDIT5K+5MNWhq3BtdCRn+u629vb5+fnwY3ZbHb0qjm0MPf6+jpj7OjoqFAo8EgxPTHDUkZ+v5t2UgCuS5Zl27aDj+v1Oo1Ko0o+ql9RVbXRaIw4jvAWY6w/xNRPJjWGXeE4WOBm3cAsT7NardZqtUXPSNQnjzFWrz+1rB/pMe/z5zjtEa8NLlRAe5ZKnxvGge+/Zox53kW1+qhU+j/TS/w0PHny3dGRK8tfyfJX1eqjJ0++m3eKJqxarSqK0mq1MplMs9lsNpvZbJYW3RlBURTf96m1l71dwDBtQnFeYsO+a0GdH0zY+L+MTdPsdDqWZfGpBGRZ5usO8To/z/O2t7cHrgkGMCWLu4ZHqK6L5jFhjFWrj168eMUY++ijD2hWl+Ceoxcq4Hua5mPHcVlgvpjoOyakzu/GFrrOj8/npyiKYRiKotCE5M1mM/4RQq29C303lxJae2Fuxi8fecBnmma32zVNM1joDHsMMAM0afO1evVRt1TGGPVS5dt5R9Vut3twcBCcbmO+BXKwCXhSey6BhY78RFE8OzsTRVHX9VwuRxlvnPITMUMCYT4/WGCSJNFP0sPDw4ODg3knB+BfTNPUNK1Wq4Wm9BsWC5qm2ev1qGZa13Uan0RPGYZBtS/UB2t0j6trWb7px8bBr0aaL4WiKOVyuVKp5HK5er0uy7LjONlsdt7pgvmIFfnRJ6dWu89/3rnuy/v3/7/T0zO2FB8nWf7q9PQseiK2/bOmVXd3/2PJRvgnX6FQoK5+VBGSz+c9zwt913qel8/n55M+SCvXdXu9XnSQx7Cf2vV6/fj4mB5XKpXNzc3ggHSKF2VZDnW9H0e0Q/p1xa/GS36FX/BqLHpz8zh83/d937btYrHY6XS2tray2eyTJ0/mnS6YjxuO8OBh3/TY9s+K8q0g3BOEe7L8lWk+5k/5/mvTfCxJXwrCPUn60jQfU+fiG9vZ+T29Y2g79VyhZ2GWaCRvqVSiP3d2dnjn4kKhQOGg4ziTnUoD4ErXHeFxfn7Of7FIkhSM8HiWNk2zUCiEXjh+ADfZ4yyiNJ97FPXwE0XRsqx+vx9dzwPSI1Y/PxrAlcv9O5/hnT5R06sMo8o2Vf1EVe9mMrds+5dG4xl/O0X5luajkuVV1z2rVh+tr3/MFxe6Ac+7WF39slj81LL+FNwuimomc3vYckYw0DR6wwyceopPNDXZ9wIY4bpdowb1i//Xn57nlcvlFy9eHB8fB+cnGqcT1QJNQjFtAyO/cS7s4vbzM02zXq+XSqWbrd4WhX5+CTThfn7DVmBcWXnvGom6jr29R/n8arP5TTABjcYz0/yjZf14dOS2Wg9po6KsyfLq1tZ3lvVjaBHx+CTpTj6/ats/ByM/133Z670pFj8d+2xgXDQkLVjJR48R9sGMqapqWZau6zH3pynTKKN6nhfsWUUjmSqVSvzxlXAtNCo5tGVeiZmvcVZvg+UTq7WXmlypvdVx2vyzVC4fRH9UyfJXkvRlcIth/F0Q7l2rQXZl5b1S6fPglkzmFi00eXTk5vOrocWC8vnVo6MBs3tQsnnTsKJ867ovHacty19RSzGfy2pn5/e93pvgRFaHhz8xxhZuYqplRX3h+Z/UcjHH9EA6ra+vl8tl0zSdd4gIktkAACAASURBVA3bv1QqVatVelytVqkDA+1fLpebzebEs/FSTj8GYxoxlSak0LXH9q6svFcoyBRmZbN3crkPQjvs7Py+XD5w3Zd8SndaFIjPHRpHqOnWtn+m1l7GmOO40cUlNzY+jvbSI9Xqo2z2jmne9/3X5fLB9vbDXu91rXZfku6Y5n8/ePC3jY2PZflDVb1bLh84jstjStv+JZ9fjbOEOQCkxHXrTmgALzWx8Z8rW1tb/X7ftm2auoj2nOA0uYj2OFwKgKhrR36y/KHj/IWq+kqlz6NjuyiEOjz8iSI/1315fn5hmvfp2WFdbgsFeWBHPb4/7+TX672Jhpu53AdUIxiVydw+Pv4rxZ2dzqvvv//v/f2vqV04l/v31dUvHceV5Q/fNvj+Qu/ieRfn5xe12v041wQgCXzfp75inuednJzQqicwWTeoJgnO4Rc8CGpcYI5irtsLS2ny8/lJ0h1V/YSHUIeHP2Uyt3gtXav1cOCrhnUZbLUeet7F0ZH7/ff/vbLy3g0mEQhWN9K78O6Aofq8UunzBw/+RrWVVIMYrVwESCDLssrlciaToVEvuq5j1PPM4BsUkm/Yur3zSg/M11TW7S0U5PPzC9d9yRhrNJ4FB0koytrAf7xpOERR1nT9s2bzm0JBrlYfMcYymVudzqvQbp3Oq0zm1sAjxB+GQnEezeRyePgTmnphITiOUy6Xj4+PPc/LZDKGYXQ6HXSCnBLXdSVJCq4a/P7773c6nXmnC2CUm63bC8tqKmt48D5znc6rXu9NcD68YWuBr6y8x4M/QbgXnDWaKMo/Oxcqivz8+a+hlz9//quijDs1kSjeVtVPDg9/KhY/PT09Q1MvTIQgCNd9SXB5OnZVs2Cj0ajVarwn2dbWVqvVchzHdV1FUTBl12TRN6iqqpqm0RenYRj4BoWEs22b1u1dX1+nxgHGWKPRQONAcpjm43r9aS73gSjeUtW7VBU1pcWvpxL5ieJtVb17ePjTxsbH+fxqsD5va+u7gS8J9vMrFOR6/Wko8qvXn+bzq/Ssbf/iOG0+FMNx2qenZ6GxwDdDB280njE09cKEjNmdq9/vj47/gsuuO46jqqpt28+fP9/Z2dne3q5UKvGnIIEr4RsUFlEmk6EHkiTRb0L69TLfVF1JEO4VCv/67UoDBgzjoNv9bWXlPVG8Val8EQwwQoGRZf1o278wxo6OXDqOqn6i65+57kvDOGCMdbu/HRz8Z6jJMfim3e5vpnlfUdYM4++u+8/VKzqdV+fnF5MdPGRZPx4e/tRu/00Ub/v+67W1rzOZW8Mm1BvftNbtVRS50Xh2fn5RqXwR3B7nYun6p5pWVZRvVfUTSbrjeRf1+tPz8wvqI6jrn9XrTzWtGpzJuVCQbzyZX5Cq3n3w4G90wGBTr6J8K8urWMMN5oJPt84GxX+00p2qqq7rNhqNhw8ffvXVV+12W5blYrG4urqKyG+CFvQbFFJucdftDQ39lKQvnzz5jmI1y/pxe/vhiKUWdP0zCgwE4V7wONvbD+kgrvty4BH4zq77cnPzz75vB7/9ZfmriTcJ1utPTfM+jUkQxduW9fVkjx8yrchPVe9mMj/cbCZkVb3bbDLb/rlcPuj13mQytxRFDgbmx8d/bTSeUTiYzd6pVL6Y1HzL1OBL09AEtw+cLBBgHHFacoMCy+288xLTNKnB8eTkpFKp/O///m+xWKR+fjzmc123Wq36vq/rOsb8jmNxv0EhzSqVimEYS7BuL33p02Nd/+xmffH5QWT5w9Exlix/GJo2xDD+frPBpqOdnp6FZikOPP52su/FYq7eBp53oes/jLNAXHos9BpHsxQn8hu4VtjAF/q+/+DBg2azSZPGKYrieR7NIVypVGitCFmWDcMolUqoBbwx13Xp8haLxXK53Gg06Bt0sv0pUSAnEEq2oNlk0Wi3NtN8XC4fFAqyosiSdCfUKWtYN7jQdsP4e6PxTFHk9fWPRx+E2ov5V7/jtDWtenb2j2vNTxxHzJSP7ucXP4tOZWzv8jk5+VWWV+edClgeNxj2wdHk+zSwlG8URZF6++m6bhiG67q9Xi+Xy1mWVa1W6Re/oiilUqnT6fi+L8uyJEmaprku6rOvgSr5DKx8D4vGNE1ZlqncUBTFtu15pygWRfmW/hnG3xljhvGHs7N/6Pqn3e5vhnFAG6/LNP/Ybv/X+vrHnc4rXf8hehD+puXyAR886vuvNa1qWX+aeNjHGMvnV2k6lLcpfHyzU4sJdX6xKMq3zWZlGvd7+eCXcRzBoG3E5RpY5xc9Tmgf27ZptCm17UqSFKyU8jyv1+t1Oh3qGri5uUkrSWAgcByKotA36FTfBQVyAi10yWaaJv0CpKzrOE6j0bAs68Z9P+ZY58dbWn3/9fvva3GqxELb4x8k+Kym7WWzd6bU3Z8qFynM8LyLtbWv2u3/kqQ7U6rzm1Y/vyWDdl6YoFCF35Xh3QjBwR/8T1VVgwU6H5Tg+36v15MkiVaVsCxLkqRer8cY63a7juPwb4VcLidJ0s2StPT4hQJYFPV6nbqC0J+KomxsbOzt7S1cr996/Wkmc4vGbfDuesG5PiZ7EF7dY5qPX7x41Wx+M5GziNL1z3q9N5pWZYx1u781m5WpTieMOj+YsIX+ZXwtNLQizp6h2C4YqI3u7XfdoHBYFeDe3t7Ozs7h4eHOzg5jjNorXdd1HIeaiSkWpI2bm5vtdhuR30Cmadbr9VKpFAr+JjurCwrkBFrokm1Yj+Ebn9G86vxc92W1+ujFi1eMsY8++oBmdeG7BSdkCU7HETrOlQcJ7kyjidfWvs5m7wRXjk1gfVD8LIrIDyZsocvHa4lfbt642L1Z0RyN/zzPs22b2nM1TSsUCjTIgyYl0XWdIr9isbi5ufnNN9/wmgCqFxwY1nieVy6Xm80mrR1CSwYvvWEdNCeb51EgJ9BCl2yaphWLxeAHmX4QXtnNl4Y0Mca63e7BwQH/wZOoLBpsvZ3vQa5FEO7Rg0ldSYzwALiCIAiWZQmCQHOdyLKsKIqmaZ7nMcYoTqJQybbt6D5UGg6r6aG1XDVNo25hMz2xd4eAUKQiSZJhGFRqVyoVWohCUZSjo6Ngcw9foIJv6fV69N1gWRYv9H3fZ4xlMhnas1wuHx0dMcZoQPHsznMe+kPMO10Ao6yvr2uaZpqm4ziO4/DVvZ23hr3QMAzDMBzHOTg42N7enl4KBeEe/ZveWyRK8Exnf9Y3rPOjhEbXWIOEoKHvc/lNtii/jAVBqNVqhmEE68Co43Oz2RRF8ezsTBRFmi2FMRbdZ0SFnKZp6+vrFB0ahvH999/Pss4vehA2pFKKTwFjWZZt291uN1QHQKvAUdR4enpKQ1mpOfjo6CiTydDVoLHDE0ntwqEof8R35w0kqkIFyKKUbANdOZnAsFOj6JAfJLiw5ASzaCj0SUPmj0Z745/15Ed4mOZjxpgsr05vORGAGaPIjKr0QnMcKIqyublJ/aApyIvuM0JwRbVKpfL9999PNuXXMnAUCOFluiRJJycn7fbgZbUty9rZ2Tk9PWWMVavVUqlEc5rQ7ICNRoMOVSgUHMfxPE9V1WVt/HVdd3t7+/z8PLgRMzlDwt04ZuVFhGmahUJhcin6l/TU8yVH3MivXD5gjNVq9xH5wZLJZDJUyUd/UknXbDZpAIRlWXt7ewP3GX1M3/cTFf0Ef6wHt9AkL7QKyLBRHfV6/fj4uFwue55H14QxdnR01Gw2j46OqHbTNM2joyNqEV5dXaUa0+sm0rIsRVGSPLiEN4hrmkbXgS7dvNMFMC3Uo/fFixfHx8fB7aNnGIEkQz8/SDtVVWmtC8aYaZrULY+iFsMwms3m+fn5wH1iHpOqxJKDd02jXoCrq6tUdTdsfodqtbqzsyOKYj6fr1ardO6u6+bzeVEUPc+jzo6dTmd3d5cuzvr6+snJycCj6bruOA4dxLZt6g3JK1Pr9Xqn05nKaU+Ibdu1Wk1RlPX1derpaJpm0m4xwKR4nre2tra+vu66bui3HMK+CZrxxbw68nOcNq+MLZcPghWz3e5vuv6DINwTRVXXf/D91/wp2/5Z0/aow6am7QUnp6aNtv3zsNdysvyVJH0Z3GIYfxeEewN3HsHzLjRtTxTVaGJuRhDuUfM3Z5qPb1Blfa3juO7LYev3ue5LfrVl+Svb/vm6KUmzWq3GGKM1LU5OTvhcd/fv36c2X8uyBu5TKBSGzetWq9Vo5rzJTvYxWTwEPDo6evDgwcCeQLlcrlgsFotFxtjKygq15DLGnj9/vrGxwRhzHIeq6HgIyBjrdDorKysD37TRaFCcRKNkarUa9RF0HEfX9dPT09kPiLkWPjmiJEnUJ1JV1UVZDgHgumj8fszpq24mnRFkv/8/wX8zfverW3tXVt4rFOSjI5cxFprPptF4RosZ93pvGo1njDHL+hP7Z9hX5bvZ9i+2/Uur9TDYUry39+j09Cz62qCdnd+Xyweu+1KWP+SHUtVPrrWWBk2Hnc3eoePv7T3a3PzzNNbdm7Zu9ze6CyGu+3Jz88/Z7J39/a8l6Y7nXRjGQa93veA4hXjrpyiK0WgjNBkyYyy6z4g2X+oGx/8MlZs8xhrY9272hjUEM8b4tM+MMcMweDx3cnKSy+XoMY3nPTo64hfk/Px8YExM87/s7+9T5QEfCJzL5Xq9nmVZjUaD3s73fWpcliSpVqsNazimRegzmczKykqwOX56FEUpl8uVSiWXy9XrdVrMDf38YFlRN2heCEx2JBOXzuBvjq6u85PlD/mMhaXS58HZCzOZ2+323y4vm6r6CWOMAjjG2N7eI8aYqn5yednkzxrGQfCwvd6bs7N/RF8bROsoHx7+RH+67svz84vQ4spXqlYfZTK3j4//qqp3VfXukyf/D481lwM/QV3/TFHWdP2z4+O/zjtRsJBCDcGhpYGDy7uVSiWqCOSNvzz6oaEeA4/f6XTW19cpPuMzBWqa1ul0MpkMtSDTW2ialsvlms1mLpejsdVRvu+7rut5nuu6sizPpsm1UqnQ5Ig0O9rW1pZt20+ePJnBWwPMXr/fdwLmnRyYjLFWbyuVPqfauPX1j237F76dKvOoqi+0MfhaWpwk9NogSbqjqp/Y9i80E/fh4U+ZzK1o5Dd6OkTHcYPVhJJ0Z2Y/Lyhh03472/5ld/c/glWYknRHUeRhVxXmji/dwf+cY2IGGrjiSHA7DwFlWZZl2fd9Hv14njesEdx1XT56o1qt0ngOxpgoitQ1kOoUPc87OTkxDIP+HzYhBVWsUujpOA4tTzJtVMlHjy3LwtgOAFg4U1y3V1U/WV//eMyDFAqybf9CDb6NxrNi8dPQDsOmQ+Tx1vn5xcrKe2Mm47pGTNJ43UAw9HL+Z6Eg8/rX6AmOiKchCa5cty05rowCGWOiKPKWVpr1cBjeRryxsdFoNHq9nuM4vV6PBovQQXgVIHWpHFaDSF0DaSG1brd70/MDAEiXqUR+2eyd8/OL9fWPx5/nWVXvlssHjuN2Oq96vTc7O78ftuewiCqTudXt/hbc4jjtXO7fJ7sccugt4iQs5nFarYf0wHXPyuUD/ieP9rLZO53Oq/jpAbixYVEgixfCKorCR0js7+/btu15XqVSYYxR5dmLFy8cx6GaP+oc6fv+sGZc13VLpRLtZprmzAbT0NK9NKVfoVCg5e9m89YAAOObSuSnKHKj8axef6qqdzOZW5ubfz49PQvWUcUnirdV9e7h4U8bGx/n86t8qAfHl1geNrcQtXvylZsdp7219d3+/te6/tn1z+xfgsGW778OVbDxVI15HMZYaALF6HyKqvpJo/GsVrvPG3x9//UydWRcVsmv7RstlP44gWCwFVgURR4w8TZTSZJyuRzv/7e+vn54eDisGVdRlO3t7ZOTkxcvXnz00UeO40x1BCKhMSVU0cgYc123Wq3ShIjTfmsAgIm4XuRXrT5yHPfKAK5S+cK2fz4/v1hd/eecLJnMLdO8f7MkUhx5fn5RqXwxbJ8R9WqVyhebm39WlG91/VPff10uH+TzqxT2Kcq3srzKg8L4CgW50XiWy30gy6ued1GtNgeOpb2yti/mcUarVL5oNJ6trX1dKn0uy6uue1avP73uQQDGNDoQjO4wEK+3o9XkPM8bUZkny3K73T45OaEdZtP9vNFotNtt3luRZnVeW1tD5AcAiyJu5Fer3a/Xn56fXwycWCREku4cH/+1Wn1ENViFgmya96PVdTGp6t1M5ode7020k18csvwhJUbTqjRApFb7Zwwa51wGajYr5fJBtfqo13uTz69WKlqv94aWOZn9cUTx9tnZP8rlA7pB2eydUulz9nbZFZi7K1fMjFr06kA26BQGXocRZzpsZukgURR5XDib1t6BE7jwJmwAgOQT+JqeKZxQx/MudP2HG7RBwwgLva55ogTXR19WI8LiZJ47dfLjrb2e59Xr9Y2NDR6nTiQATWeBnHAo2YKQRRMofhZNdeRn2z8/f/7rDVp7YQSUj5OShshvhGTWlV6ZqomkIZ0FcsKhZAuadha1rB+pzfDoyC0UZMaYqn6i65+Z5uN6/Wku94Eo3qI5eqeXhoUTP4tOcVaX5LOsZ81mZd6pABgg5WEfS2qdXzJTBbBkdP0z6o4vCPd4u5xl/Xh4+FO7/TdRvO37r9fWvs5kbkVHPcKVUh35oZ0XEmhR5vkDAJilev2paf5zFgtRvG1ZX887RYsq1ZEfQNKgqg8AYKDT07NgDR9q+24MkR9AIqCqDwAAZuB3804AQNoJgkBVfQj7AGAGDMOgFbej81CGnnIch2ZWVxRl7qtU5/OrrvuS/2majw3j73NMz+JC5AcwH8JbiPkAYGZM0+z1eq7ruq7b6/VM0xzxlOu6lUrFcRxaJnt6qaKFuOjfsH1Kpc8N48D3XzPGPO+iWn1UKv2f6SVpiaV6VheYhunNfWCapizLiqKYpmkYhuM4rusu3NoJfFqQOFcpOIcIosP0QIGcQEszq4skScfHx7QOjed5m5ubnucNe4qmrozOUjnZLBqN9vjBQ+uymuZjx3EZY93ub6Z5H139gjCfH8zNlMpHz/N0XacVuvgwCEVRms2mKIoTf7vJukEAF2cZXFhWKJATaGkiv9AwsuCf0acKhUK3211ZWWFvf3sH9pxF5Afxxc+iaO2FJArGPfS4Xq/TMglUyUe/QVVVbTQac0rjKMK7GGP9t+adNACAuFRVPTg4cBzHMIz79+/POzkwGYj8YDE4jkPRHvVKoco/RVEODw/nmzBhkH7EdQ8begmPIAEAxpHNZnnzrud5waWoo0/puk71fIqinJ6ezj61MA2I/GAxnJ6eUu+TIEmSploYDYzqrgzyJlWxx4/DDxusRAQAuIFSqVStVulxtVotlUrs7W/p6FOiKFIs6DhOoVCYUpJCbbto6p029PODCZtIb5ho15NhnVFGTH08foSUhMbZgSeIyf+WGwrkBFqafn6MMRohxxijAXMsUM6EnqJ23mn384OJwLq9sNgymYzneZIk8RkH8vk8bQnu5nlePp8fdpDlKKYHngX/wTZiHwCAgYIzuRBehoSeUhTFdd0ZJQtmBa29kES1Wm17e5v/vmSM7ezs0C9RxlihUKCneOe/dEIrMMzXiKnXEmVR0gkTlPybPscUCqHKA4DxTaMKyvd9TdN48EdoWvlo/7/UQivwckCBnEz4ZHHIosl0vdZeZGhIOFEUFUUJVvLRY4R9QWgFXg64cZBwyKILbWjveABYdKgCBACAEER+AAAAAGmBER4A8A7eWMxH+Q3r0xPd81rHBwCA2UOdHwC8g3em5FN8DRtDHd0zjmvtDAAAk4U6P4AlYdu2oiiKosiybNs2Y8zzPE3TFEXRNI0m5RIEwTAMGhljWRa90LIsWZZlWaYtW1tb7N31kWkLPyx/HN0ztE9oLDZnGIamaZqm8aWigBiGQfeCruo4+4eecl2XZ4/lnqHtymsYnC4K5o4miJZlWVEU3/cdx5EkifIqL6PmJZqYSSUvehzf96lkpuswsXMYaODaUwCwcDKZzOXlZb/fv7y8VFW13+8XCoV2u93v99vtNm1hjNVqNdqSyWSCL+Sv4sVC6EGtVisWi/1+/+zsjF4b3ZPv0263s9nswHQyxvb39/v9/v7+Pr0jEH71+v1+sVikO3Wz/aNPFQqFVqvVH3lrlsCV17DZbOKLLzmazWY+n6eCq1arqapaq9WofEiCaGImlbzocXZ3dym71mq13d3d8d9iBHwAAJaEqqr5fH53d7fZbNIWxlghoB8I0YKP6dngqwY+4AEfL5iie15eXtI+vBSLGpgG6Pf72Wz27OyMHp+dnV0Zn43YP/oUZQCyxJd99DXc3d2lxWfnkTQYIJ/P069T0m63a7Ua/URJgmhiJpW86HGy2Sz/6T7t32bocAOwPFzXdRzHcZxut+u6riiKvNWA+uQNW/LYcZxGo/HixQvXdfn26ANN01RV3dvbOzg4kGU5ugPfxzCM4+PjgbMtxlx2OYVCV+PKizNi/xFP0WKswxriF12ca4hclxyCINRqtcPDQ8bYRx99VKlUDMPodrvRlYLnQlGUUGKiWyZ15JkWjFONKwFgZjKZDP165hVvvLWLGvv6g+rboq9iQ+r8+m/bZ/P5/LAd+v1+s9nMZrMjmnEZY1S/GGyYg36kKu7K8nnE/gOfOjs7o9tHVQtLKc41xBdfcjDGeMsmFR37+/tUIrVaLV7UzEs0MZNKXvQ4Iz6/E4cPAMCSoB4zhUIhn89TaEVd92gjlTLRwiX6KhZoBY4WSZlMhndPie4Z3SeKyvp8Pq+q6hKHIDcw1dZeaqy/su/gootzDRH5zR31MNnf389kMsFGz+v++JkSnrzRiblu8gYelh9nlq29+AAAwCTNoNhaVhMZ4UHfo9GnVFVNTvep6RlxTThEfslRLBZ5nd/+/n6hUMhkMhS7t1qtYOfUuYgmZlLJix5nliM80N0BACbGNM3Dw8NvvvlGVdV5p2UhGYZBPfAURYkzP3Z0f95DKPSUIAg0uIEsaz8/NvKaEPTzSw7f98vl8vPnz9nbfn7dbtcwjIT083McJ5SY6JZJHdn3fU3TOp1OLpdrNpuiKE7wRELwAQAAAABIC8zkDAAAAJAWiPwAAAAA0gKRHwAAAEBaIPIDAID5EARhiceaACQTIj8AAACAuBb9FwsiPwCAVBAEwTRNTdMEQZAkic8a47qupmmiKAqCoGma67qhlyiKoijKsN2ChxVFkVaHo9WoJEmyLIt2syxLkiTagb+QMba1tcW3RPcJpSF4OiOSDQCjTHW2QAAASAjGGF8yodVq8SU9CoVCsVi8vLy8vLwsFovBibgZY9lstlartdvtYbvRYWllglqtFnwX+vPy8pLertVq0boatFoMvZZPszxiH56G4OmMSDYsGcZYrVbL5/OZTCY4y3G73VZVNZPJMMZUVeU5hPanNTNG70Mzj1J+q9Vq2WyWduPLC9HqarQPZXIePvG51vf397PZbGiNnGAaQqdD+zPG8vl8KFfPBiI/AIBUCH5X9ft9+iqlB3zFs1arxd5d4o+/ZNhuocMyxoLrU1Fsp6oq/8Kmr9vgs/R4xD4DlzMZkWxYMhR4nZ2d0a8Cnh9G/BqJ+Yul2WzSdorD+FtQVqR8RW/XbDb5T5pxfrHw49D70mrpM4aPCgBAKgS/rvqBaOny8pIWPaO1s0KRH3/JsN1Chx34Z3B51larFfz25dvj7BM0ItmwZEK/QHjeiPNrJM4+tJ3vRnV1/X5fVVW+GGC/39/d3aUfJOP8YikUCsHtfFH1Wfq3ybQZAwDAYiqXy47jlEolVVV7vZ6maePsNlCv19va2hp/n0mlBxYOXydNluXz83N6XCwWG41Gp9PxPO/k5GTg/nH2IZIkhd7UcRzeUZUxtrGxYdt2dJ9ms8kPWK/Xhx2fHB0dGYYRfPnA850qRH4AAGnheR5/7DgOreTbaDRarRaNn4h+sXExdxsok8lYlkV9qnzfD30Bx99nUumB5RAn+h/zF8uV+1/3F0sSYGwvAEBalMtlCpJs2240GrquM8ay2SxVPNi2TbURvu9HXxtzt4GKxeLe3p7neZ7naZrWaDT4U71e78p9BhonPbBw+I8W13XpFwtjrNFoWJZlGEZo3HdQnH2GoY6AvJGUxiqN2GfgDiH5fJ6PQ/d9XxCE2Q9LR+QHAJAWFF0JgrC3t8cr2J48eWLbtiAItm1blpXNZgfWc8TcbSCalmVtbW1tbU2W5f39fZ4eTdNoDpdh+wwzTnpg4dTrdfpVUK/XKd+yeNH/OL8QKpXK3t4e/VhyXXdzc5P/ILnxL5ZSqVStVh3H8X2/XC7n8/mBjcLTNeN+hQAAMBdsyFAJgIRjjNGw3NDMKe12m8/DQqOCaGRGMKvH2Sc0NpyP8Oi/HVDCGKMBvzTbC40F5inZ3d3NZDI04wyfDmbExy04fcxcZnUR+oHJaQAAYFkJgsA7xgEsEGTdyUJrLwAAAEBaYGwvAEAqoIUHABhjaO0FAAAASAu09gIAAACkBSI/AAAAgLRA5AcAAACQFoj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIC0R+AAAAAGmByA8AAAAgLRD5AQAAAKQFIj8AAACAtEDkBwAAAJAWiPwAAAAA0gKRHwAAAEBaIPIDAAAASAtEfgAAAABpgcgPAAAAIC0Q+QEAAACkBSI/AAAAgLRA5AcAAACQFoj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIi/lEfoIgOI4z4/ca9qaTTYwsy77v02FDRFHUdZ2eJYZhiKIoCIIkSaZp0kbPWd7gWAAAIABJREFU8yRJmlR6lkycm+U4TvAiX/fl03th/INM7wMSvDjTy/nBUw5RFIUnQFEU13UnlYCEuMFVDd6UEbl3sm86Ar+VjDHLsmRZpnsnyzIvpga+qeM4giDwP5fy/i4Q0zQFQQjesvhulg+jkvP9G3wj0zQlSeK52rIsfoSUZNp/m3cCpq5Wq+VyuXF2iM80zZ2dHVEU+ZFlWebPep5XLpd7vV6z2aSdG41GrVaTJMl13Wq1yhgzDEOSJEVRTNM0DGMiqVomcW7W1tZWq9VSFOVmL7/x+y6E4MWZXs4PCn4K6CPw4MED+ggYhmEYxsx+BCZW8KaMyL0jTOlW6rpOZRRFD1RMdTqd4DflCLi/81WtVmu1mqqqN3jtzfJhVHK+fxlj6+vrtFu1Wq1UKvSs4zgPHjzo9Xr0hZuWTNufB8ZYq9Vasje9vLzMZDJnZ2cj3m53d5df82w2u7+/z5+q1WrZbJYen52dZTKZ6SV1uc0ldw0TTEz8hE3vFKZx5BE5P/p2+/v7wWInm80m52ZNxA2u8M0yyTQEbyXdqWAZxTe22+3+oKS2Wq3Qd8ry3d8FMk5emng+TML3Lwl98/bf/fLtpyPTjmrtFQTBtm1FUSjwtyyLKkhFUQxVIBuGwZsseW1/sHI11ApAfN/XNI3XuAarWENvHeR5nqIo9CpN03jVrmmatD2UvGhlsu/7sixHG6foINS0QfVw/CWmaVKzLNXGRc+FMWbb9vr6+pUNtZlMhh6cn58HtxeLRf5LWpKkbDYb84d1qgRvlm3buq7zNkS6X3Rrtra2KA+EMtKV93pg7mLv5iL+QZAkid+jEZk5RJIkXdf5n5S1og0rrutemZ+DH6vQmQ77mIQuzixzPhFFMZvN8j9LpVKj0YjzwgQamBMYY67rRi/msBwSvCmhG8SGl7oDM/awDwUZWEqHBG/l0dFRPp8P5lXGmK7rtVot/iVa6Pu70EJ5aVj2i1NQBC369y+LfPOyd798WUoy7YiokDGWyWSKxeL+/n6z2cxkMrVardVq0WMeNddqtUwm02w2aTs/JgsE3aHfgvRUoVAoFAqtVqvVaqmqGgy6g28dSlWhUFBVlV5FR6D9KZBvtVr7+/vB5PFk0IPLy8t8Pp/P5y8vL6M78IPk83k6cr/f5wekg9P3VvRyqapaq9VC5xjcgV9D+rNYLPJzpN/QQbu7u6qqDr85KRW8WcEMye8X5bRarUY//kIZ6cp7PTB3BV9IzZT0vvQVSPduWGZmkeoc+rzwM8rn88ViMXqaPHm0fzQ/99/9WIXOdOCJRC/OtHN+6FNAxw9+qNvt9uhSKLGG5YTgxQxmoWE5JHhTQjeISoxmszmwWItm7GEfiv7wUjokeCsZY7u7uyOuQLSIi9b5Le79XXShvDQs+8UpKIIW9/uXo5Y32n/gPmnItFdEfvyTXygUgpd1f3+f35h8Ps+fohKQv3x05BfMWNEdhhU6lCPp8dnZGT0O7b+7u5vP50PJYIxRaRjMdqEd+JGpWOcnGDr4wGwRqkMeGGc3m83gS2q1Wj6fp6eoHOcJazabwVAYSPBmBW9K8H6FIo/gblfe64G5K/hCVVWDgRr/6h2WmaNh0NnZGc8Jwceh07wyP0ffKHSmo08kztW4cc4PvkVI9PfMiDI6yYblhOCNCN6g0cXdwFi5UCiEOoQEf4pEM/aID8WwUjokeCuDuWKggUVc9MgLen+XQPDKjyigriwoQsdc0O/fYGamjxJ/KvjNO/r0l8YVkd+IEnzgd9uw77xhJV2tVtvd3S0UCqFAfsR1p2Irm83u7u6OqFqIJoO9bWkNffcEd7jyRKLnMizN7G19AJfP5wdW411eXjabzd3dXQr+Rr9Lyg28Wf2rgq3RLw99PVPuKhaLwYBs2PsGDczMAxNTKBToRof6l0TfbvTZjcilAz8mw9Iz8ZwffIvgp4B+vod+1C1oITviq3FYuRenuLtBqcv/vEG2GZF4FqPOL1TEDYwpF/T+LoFomRDNfleWeCED91+I799o/WW/32+321QdHmp4WfpMe41ZXaIXgrbzjmvXpet6vV5fWVkxDCP+yHPDMM7OziqVSq/X29raGjgAttfrDUwV/SywbftaI3cymUyv14u/PyfLshLwzTff2LZNTwW74IiiqKqqaZq1Wm35uxckG89djDFd1+MPr75WZlZVlXLC4eFhnJF3w/LzCHE+Jle6cc4PCn4KdF23LCudmfxmxd2wUve6blBKFwqFaDnp+35wopBQERcaSgnJMSz7XbfEi7N/Mr9/eRfA4JevLMuGYaTwmzdu5KeqavBumabJe/6ur6/zpzzPC76K37CBHd4bjQbNXRJ/6LiiKI7jUB95y7Kazeb3338ffQvHcQYek5KtqmpoXr04b8r/HNZ5P5vNjs6gwQ9Dt9ut1+sDD0IPPM/jDcEwM4qidDodyl2WZfHcxeXz+WBm4BMxXiszq6ra6/VM0zw9PS2VSgP3GZGfR3+s2MiPybVMKucHTWSSsCQYlhOGuUFxFyp1+e/GGxhRSgcFb6Wqqqenp6Ev6UajQRUkN04JzMWw7HdliRcybP/kf/9y3W738PAwtPHGFVgLKu58fqqqapq2srJCw4JoOhx6Std1Xdc3NjYYY+Vymb8kn8/v7e1lMhnP84ZFOY7j0A40m53v+wOnBONEUTQM45tvvqHfAXt7e7zKpFqtZjIZGhPUaDSoTnigWq22trZWrVZj/vIuFotbW1u5XE6WZZ7UqI2NjefPn8ecPOmbb77RNK3X66mqSnnOcZxGo8Gvaq/X++ijj+IcCqJc183lcjeYEJsyGGUMy7Kid7NUKpXLZTq4bdt0B9k1M7MoisVisVqt5vP5YYkclp+v/FixkR8Tdp2LM5GcHyypaT6/YNxAZfr404bN3rCcMMzoHBK8KfxxqNQdJ7XDSumQ4K3Udf3k5ETTND7zmeu65XJ5f39/dCkdtLj3d8kMy34jSryBBcWw/Rfo+5e+fNnbbBmKZ1hKMu2IlmD2blsDH1YT7alDnS5pOz9mu92m/alzMYv0a+E7FAoFehwdShlyeXlJQ2Ip8bxjJmOMukyxt1XK0bMIHjM0Fm90P4PguWez2WH9o/f393m31oGnQCOG+Ebq7spvRGjMY6hzN5CBN6v/7v2iTMi7Hl+rn9/l5SUvOwqFQnQAWv/tMEnKDLyny7DMzN7tOBW8+ywyWVowncPy87CPVfDgwz4mwy7O9HJ+tMDZ3d0NdqammWYHXoTkG5gThl3MEcVd8KYEH/cD139050h2VT+//pBSOiR0K/tvu6LSvVNVdXRXsGgfrIW+v4sueIOGZb9hJV4oH3ID91+I798gGkBJZ0HHDD6bhkw7gTEE7Xab950c1mN92kbf5jEFT7D/dkDuwD2DM0mOg2ZyDo02gmVCk2sk/BbPIOfn8/no0GaYuPil9KQKMYL7mwZL9v2bhkw7gXV7Dw8Pt7e3Hcexbbterw/rt7S4HMfZ3NykfqmWZVWr1Z2dnYF7ViqVYXXR11Kv1yuVSvwmFVggvu87jkMtsAm/xdPO+dSqcrPVpeBa4pfSkyrEGO4vTMKMv3/TkmnHDx4vLy/51DihppyZYdP8zUFtZ3SC0ZrhkGw2O+bPDprrcpwjQJJRQ0ZoUqtkmnbOp6llx0sjxHKtUnr8Qozg/qbEMn3/piTTCv2bThMAAAAAAItlAq29AAAAALAQEPkBAAAApMWMIj9BEKa6/zjwXmlAV4Nfk2lfnAkeP5mHmuzRUptXBUHgOXNYFp3xxQkmI7VpGPjWqc2lsHxQ5wcAAABhwWA3GvgODIVj7hb/5dfdeUpHmMbG626fyJEJIj8AAACAtEDkBwAAALAkruyZgMgPAAAAIC0Q+QEAAAAkwgzGEiHyAwAAAEiuyYaDiPwAAAAA0gKRHwAAAEBaIPIDAAAASAtEfgAAAABpMbvID0vfAAAApA2+/ZMGdX4AE4ZiDmBp4OMMyweRHyw/lN2QQDxbIn8CwCwh8gMAAABIC0R+AAAAAGmByA8AAAAgLRD5AQAkC3r+AcD0IPIDgGtDaAJThQwGMD2I/AAAAADSApEfAAAAQFog8gMAAABIC0R+AAAAAGmByA8AABINAz4AJgiRHwAAAEBaIPIDAACAhEKN78Qh8gMAAIBxIURbFIj8lhk+hwAAABCEyA8AAAAgLRD5AQAAvAMNJrDEEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAGBpoddmCCI/AICEwjcWwNwt38cQkR8AAABAWiDyAwAAAEgLRH4AAAAAaZHEyG/52tQBAAAAkiCJkR8AQArhRy+kBLL6fCHyg7RAWQMAACTN3wiI/AAAAADSApEfAAAAXEOaK8yWQNojP2RfAAAASI+0R34AAAADoWoAlhIiPwAAAIC0QOQHAAAAkBaI/AAAICnQwAowbYj8AAAAANICkR8AAAAMhYrYJYPIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIy/w1iPwAACCJUvI1nEy4+NM2xyuMyA8AAADmDLHmdd34iiHyg5RCKQMAACmEyA8guRCeAgDAZCHyAwBIEIT7AFfCx2QciPwAAAAArmGhQ09EfgAAAABTlKhIEZEfAAAMlahvLAAYHyI/AAAAgLRA5AcA84QqpaTBHQFYboj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAgKthvpuEww2KCZEfAAAAQFog8psd/BwBgCRDGQWQBoj8AAAAANICkR+kjiAI/X5/3qkAAACYA0R+AAAQhpbf4O9D/FyEZYLIDwAgKRBeACyZBP5sQOQHkAqowkm4pH03AMB1LUoxi8gPAAAAIC0Q+QEAAAAss2B9JCI/AAAAgLRA5LecEtilFAAg+VB4TtCi9HtLG0R+AACQXPONHhAFjgkXcMbi/HSZReSHqB8AID4quFH5xOE6QJpNvCiYUZ0fPreQEmn4tk7DOc4SLmYUrgmk02xKV7T2AgAAAKQFIj8AAABIIvQWm4bERX5oSILJQo6CBFqUbInRFRDT9LI0ssHEJS7ym6VFKXxhInCvAW5glh8clMkwcchUUamO/AAAAOBaliCWin8KS3CyUYj8ABJqKUscCErm/U1mquYrzddk4gURSrbZC11zRH4AadHv95PWXRrfAXT6CbwICUwSwFTNsngcv+gb5wiI/CBdkhb6AHChKHAusVcSIlFchxS6Vhwz99Eki/49gshvRhY9oywTlOaQfMFcOpvSI/ou/X6fJ0MQhLkUYqHrkIQ0zD4BkEAL/T2CyG92FjqjwELD19XimmW5Mey9eAg4x4wUDEPnmAaWjk/TInbDSMN9mZRRkZ/v+5Ik+b4f3Kgoiuu6U04VjAUfAIhauHIckiYhWWiq5duVB0/IRYCBcHdiGhX5VavVUqkkiiJjTBAEx3EYY4ZhGIYxo9TBTeEDMBdpiLnTcI4JhMtOZlCyxXkL3A6IGlhRmsysMjTy832/0WgUi8XQdkVROp0ORYETN8sa5mTeD1h0aYi503COiZKoCz69knNRyuRE3Y4kW5QbOm3XGrky5nvFPMLvJEkK1uHpuq4oCmOs0WgoisIr/BhjW1tbpmkyxkqlUqPRGDN9SYAPcHrMd5zgDSxiP5vrwhfDwpl2nkzJyMr0GH/ikkmlZFGMX2sY55r/TlVV27b537Ztq6rKGDs5OVlfXw8eqNVqUYwoy3LwJXClFGbfZJrxOlQJPNRkjZ+w4NDRsZMDN7dA13/pfxEl3MCsMr38g9iRTSHP/25nZ+f8/JwGbdi23ev1KPJzHEeSpIGvoUpBjPO4FrRip8oEb/f4wwlDr51UDknUOcL4EFFdKwemObsOzCqJra+NWYs2bOMSfC6ip/Y7WZaz2Sz123McR1VVauHt9XqZTGbEsbrd7s3ecvw9x7fcH9olyKmLKJqpkhNjhRq7b5ywJJ8jwPhi5kNk13EkoRpvvsHr7I8QOrXfMcZKpdLh4SF7G/mNmaAr33Iie44PlXAwccFMNU4Gm2p7ypg5fyHOERYObn0yzWwW8WsVJnMfQjtOPeKwI4yfhvh+xxhTVfX09NRxHN7UyxgbUeFHM/ytrKzc+F3TBlHm0ht25W98R0J55sZZaAbdDed+jimRnEG1U0rJfDMACs9hbnZfktCqm4QEjDmwd0o9EH7HGJMkKZ/P67oerPD7/9u7u9u2kXaB46ODLUBauAApSAGi4W3AEFNAABrw7QJS4AJMwykggpgCAkvA3gawgBQgvnADMazcb2BtAQtTHfhczAkPX35pxM8h+f9dBLZMDYfkM8OHMyRzdnYWupNvv9/LHx4fH/v9vmEY6hU6iKneotQe/VpJv5QsNhKiK9Kkx4ytRra1VLCN7W6eUoabhzR5qLakmmhy0Ok8DzoqO6l9VjdnCZokr2WMhv7f+/zkcx7BzM80zeBL+6bT6cXFhXyry3a7lQ95FEXWjKneBq2rBQqMgWpytQwrKrBHrmYbOfVWT5N+gwG/pij8DrmjpkTzv+IkZ/Jaez6aoYSYrUg6YJ7nvXnz5unpKfqEr2EYjuMoJn/+1W3KZW70T+U9UBPNMtu6rlJX1xQHx/wK2T/psS2OH/BXbynVFJW+fI3b2FDZtrGMPZPtqrvYmtReh9oroCG5dcE9438SfBNT9ORe1IfHlhCqZ4YScn4YrUAorord3mwl+BL/D4/BYHB7ext9b58cCCxwzK/6UbFqmisDmR2RvjeOza6O/UqeotSPY8XbSIBVo/o+Ss86ZK4AgSqOGUIr6ZY1qfDBufwVSPpQfXXl7bG0/7d3Op1+/fpVPs/hcxxHzvkWoq2pWMXdGXPKdak4VxNq+1+lqKKG6I5SbMWQhw4plz51yKCDgXrU3SOKH+af6i2vhIqneivbYyJltrcoSUOOfoViK1D4QHpKF9PodVW5ulAMNaLjK3W296hcTWWUS6UyB+tcYFHqpVW8jU1X+2xvzpSrkJpQB20lzSqKyM1L8ofMH4amj5NWnb5wGSUcVdvYWd3Y3aVYQv46JC3s+y36UdnSc77y1ti+0b4q96QOV+f6OGrP+80yvbQqBw6DyyctXO82HqwYoZhZ7Tuw+lOAnnVohzIG/PLPIOcv4ajxwtDCSU1MkwE/UXHmV1fOV80aK966tqazFctw1Z5tb8SuKFvMFFVUSrpW7zYmVSz/VEvHFdVHpYwlKNZBhxneVnZoxQrtojL2WFKPkT+hzFNCkvxlHpWf5S8h8fq57Oiva15DvY+rctagwHVVs7qmXxmrzI0eNRAlf8gf0jmLCn6xwKLyl1bNNjZ9li1zryjyTU3m+Xq0tGyXTEXVIfPFSbF1aHQcJmnrdsFX7phfXdfo2k655rlWPnZdOTU95ytWIXsj1BwyFyWjKFhagUXlKU1ouY2tcXBKPYkObbnwPvnYvaHDTgA0Ufpsb8XNrK25UVvXpb8CJ8iKqM7/l1bUqbTYk7Eo7hyfUgKRqUiHtlx7HWqvAKCbGp7wKElbc6O2rkt/mu8NPWslNK5Yp+gQvbXXoYIKMDGKJkrM/Gq/mVq9ReVv3lWuS11b16WiyvCLHn3d9gY0UXuvqKKy6E3pNmtvQdVUIPP8O1CvxMyv3nOeSosq6h4gxdbb1jys9j46VmWVCR19PfcGNKF5VFQZvbHdZvX3ZXLZFkQaCkWNnO2tuG03Og/T+bpcHzxJgKYo6tU5xaqlAppctukz4atJNaC/hmV+7cj5Kuu7k4Yzaz9PaIWdgKZIynVETWFcewVqr0PoIXQ6EzRCYzK/WgbeylhdjX23Dt00gJw0acg69CFa1YHJVjSF1plfga8ZU1lXMCdrUxKmyXkCQH40YQA56XKDQixtX8hc1Oraty4A6CZ9bvgD0hGpAAAAXfE/dVcAAAAAFSHzAwAA6AoyPwAAgK4g8wMAAOgKMj8AAICuIPMDAADoCjI/AACAriDzAwAA6Ir/EUL0ej3Xdf2PPM8bjUae5wU/MQwj+AkAAAAaJ2bMbz6fX11dDQYD8SspHAwGl5eXq9Wq8uoBAACgML+Ffvc8b7VaPT8/hz63LOv09HQ6ncqMEAAAAI0TzvxWq5Vpmv6AnxDi3bt3i8XCtu2zs7PVamXb9lErkIWgZRrx3z0Te63UiNg7iOBspXYEJ1ovnPk9Pj6enZ3Jn19fX3u93mazMU1TCGGa5uPjY4Z10BhQF2IP2iI4AdQinPm5rmtZVuyio9FoPp+XXyUAAFAPBqRbKXipGc789vt9v9+P/Vq/39/v9yXWCwAA1I0B6XbjfX4AAABdEc78kgb8hBD7/X44HJZcHwAAAJQlnPmdnZ1tt9vgJ/4M7263++OPPyqqFwAAAIoWzvxM0wz+fx7T6fTi4sJxHPHfj/0CAACgcXqhGzk9z3vz5s3T09NoNIp+/vz8fOybnHu98CqAahB70BbBCW0RnK0XHvMbDAa3t7fr9Tr0+Wq14j/wAAAAaLSY1N7zvPPz84eHBz/P8zzv9PT06ekpQ+bH1QPqQuxBWwQntEVwtl7pB5gYQl2IPWiL4IS2CM7WC7/J2cdbvFEjwg96IjIBNB2pPQAAjVfgZQmJQbuR+QEAAHQF/3sbAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFekZX6u63qeJ4To9Xqu6x5bdLZv1cXfWJGj5ilfNAzD87xeAsdx/CWXy6VhGPJzwzCCfzJNc7vdZqhYa+Q8TB2MyfTvEpbtEAyVzGQwCCGikTAYDGazmfyr4zgp0UIw6KOM7i5nVnBQgcmG67q9Xq/AKiXVrdhdEWyG0WJV+mrbtgeDQa/XG41GSR31byk1ePfu3WazMU2zqE3SWXBjF4vF27dvCyzccZzLy8vBYLDZbOQn2+325ubG/9Vf3Ww2W61Wi8VCHrDtdjufz3/+/LlcLoUQtm3btt2g3KVwxGSBCMvWyN8u/GCQvy4WC8Mw/L/udrubm5v9fn9/f29ZVvBPQgjbtn/8+CHXTjC0W6d6YOlg31tg5xxqhlEH+2rHcWRfPRqNZEcthLBtW4Ta5msyuZrgD0fJ9q26FFLb2EJeXl76/f7z83PwQ3nAQkve3d0JIe7u7qIfPj09yV+Hw2GD9mrhgns4wyHrYEwmlUNYtknOUAkFQ2xp19fXseeLxWIhT37+JwSDJsro7nJmBerl5/9WbG+WR9mnD5Vm6EvauuFwGOyrF4vFcDgM/lWWmTjbK4dJ3717J6/y9/v9bDaT44rBMUPP8y4uLvw5oOg4f8oCvV5vuVyapimHJbfb7XK5lKOUpmn6kxfL5XI0Gsll5BhDrKTFkj7v9Xrr9do0TVmB4Mb6o6yx3z24ySHr9frs7Gw0GqUvJoT4z3/+Mx6PZ7NZ8MPZbCb7Vunq6mq1Wh0sqpVCh0kkhKXKAUpaRjEmRTlhqRKTSd8lLKsXPF4icFwGg0FwkmW328kD2uv1Li4u0ns2WeZRnW0oVFR6vNCGKAZDv98PfSIHHqbTqRxUkLoZDJqLjYrgbKAMueDtJev1OlSIYlYQjbTYppHULpKKTdqKIMdx/O56v9/H7orY9coJU/l5qP1GZ109zzMMIzotKwuRd8XI00dsxeT9EtGKqffJKf7555/gr9PpNLij/r9tpmeUi8Xi+flZCNHv9xeLxWaz2Ww24/F4MpnIxSaTyWQykZ9blhXMLoUQm80mfQG/WMuy+v3+eDzebDb39/fy82A1NpuNPNPEZsH39/ehxeRoRMrX5dqn0+nd3V1wY/2aJ5V5cJNDdbMsK3hBnJKwCyGur6+Tjoj09PSUctTaLXqYYsNS5QAlLaMSk6+lheXBmEwpk7CsXvB4+RHiR4t/5T2ZTCzLkodGHqbXQzHgl6PS2QZDRbHHC21IKBiiAROKf+nl5WU4HI7H45eXl+Dn3QwGDfnHMSkqptOpH13D4VAIcX9//5o8nqSYFYQiTQbP/f39ZrO5u7vzm0Zsu0gpNj22g4XLBeSFSnQrktYrR8tClXyNDHO+vLyMx+Ng2AcX8AsJ1jxYsbu7O7mroxU72AyjxyL6+XQ69Xe+Pyfj89um6mxvsOuX5x75s39milZFfj19Ab9Y+Sd/ScuyLMuSP0yn0+BWyc9DoovJw5by9dBGRcMoqcyDmxyqW2j0NfaL/tejJ+Oo9IBot9Bhig1LlQOUtIxKTL6WFpYHYzKlTMKyesHjNZlMgnvp7u4ueCL0//T8/Cx/VoyBozrblGKjJQeFgkHEkTlBkLw0ip5dXjsZDBo6GBV+dMlMzrIsGSHX19exHdqrWlYQ+tNkMgnNP/rJVrRdpBSbHtsy2Qp+dzqdxuZGKuu9vr4ej8fRTZYXY6GrneACfsnBmocqlnTjRLQZZsj8Xl9fF4vFeDyWzVZmgdHaZrnPL7TKxWJxfX09mUxCmWzwLJu+QGyZfnAEV+3vTb8/il0sdhWvkegM/il6lk3Z7ypblP5h5sGVpAI7IjYZkoK7VOUAxS6jEpPRVRcVlgdjMqVMxa1O/5CwPEroAEXJP8nxieFweH19nRS9STFwbGer3uMlbcjrrxPYJmA8HodSAblR0YuHg+tCZQ5GxcvLi/g1WiYvXWS6Mx6P/Xwo2KGllBm6fg6FU2zTUGkXKcVGYztlgaDM6xW/bngItYXYfZJS86SkLaX/j0rJ/KSXl5f7+/vr62uZ/IWKzfs+v9ls9uXLl5OTE9u2g1Pj6gtk43dJBZapqKQtEkJMJpPoM3HypRvVb2ZzqRwgwlIdYakoNr0WQti2/fz8fHvxd6M7AAAVt0lEQVR7u9/v3717F7wl7ijlHeIowzDMgI8fPwbv+vJv7wvd/YlmGQwGk8lku90+Pj7KA/3jx4/dbvfjxw/LskRxHVps0yiqXfj6/X7w3r7obamSynr3+33s1+Ww3Hq9Purp9VDFyhO8LXIwGFiW5TjOYrGI3nebN/NbrVaO49i2nfSY98EF0lmWFexxXNeVEel3SfL9AuPxOHgk/BtXk76uIqnMY7doOBwqHnXLsn78+BEKqdVqJXN2xWpD5QARloRlsSzLCu4ix3FkVmSapuu6o9FoNpstl8v7+/vPnz+LTDGgcoizhdbBYAieBT3Pe//+vRwWOlgydJASFTI+XdeVjywMh8MvX74Mh0P5nEGoQ8u89mDTkDVJahfZtiK4LcEFooWkrDf4QIbrurGtTLZry7L811uqCFUs6fE79T45yb///vvly5fYkkOfpL3PTwix3W7TX1QzHA5d1+33+7vdTr45xvO84NtoDi6Qbjqdvnv37u3bt/JZttVqtfn1Apugq6urm5ubt2/fjkaj9Xq93+9lTCh+Pbix/pM1SWUeu0V//PHH9+/fVfrf2Wz2+Ph4cXFxe3srW5q8tr67u/PLlwHUqdcphYQOU5TKAWpKWEY3lrDUk2VZFxcXJycn8pjO5/Pb21shxGAwsG3748eP8rr/06dPGbomKf0Qy1DJUKw4JhiEEB8+fPjnn38Wi8Xj42Pwcz9QCQbdpESFaZo3Nzfi1/EyTfPz58/yRrQkB7OCkFDTkB8mtYtsWxG7QGwqmbLe+Xze7/flM7npDWexWJyens7nc8Wh0GDF/MYbFW2GoRxR7sOUFX38+PHi4kKeFOTVmuu6q9VK9kUi2DZT5onl4Y8+uhicYH56epLp5GQykT+Hbgg4uEDspHXwnir/WZjYe9KDX5GbOhwOgzcjJ309tFH+xgb/FFum4hYFK+DfKxq7D6Mb4mfolmWFbqxeLBZJ9952QexhkvxdqnKAkpZRjMnXcsJSJSaTyiQsqxfasf4x7ff7/p2RLy8v8mk7/xzg322tEgOKnW0wVBR7vKBQMESXlI8E+jczxfLvbe9mMGgoeBxT+qtgIMn3dEaf5vGpZAXR+PHXfrBdpBSbshX+t/wF/DHpUP1T1ut3cUmFhzph8eu9CrELpNQ8tmKvcc0wJHgCSuqr5TMo/lfG43Ho8Zr/e5Is+k0ULvrK3MzG43FKswTUEZbwEQzospTrovyenp6Cjct/kiaqwGYYy2+bee/zg4rb29ukAd6jyKFa9XvCgBSEJXwEA1AS13XPz8/lcyHL5XI+n19eXsYuWVQzTKqG8NtmedklgobDYf5cXr7HtZD6AK+EJQIIBnSWKHPMT04xy/RLzvamLFxIM4wVbJu91+T7NgAAANAmzPYCAAB0BZkfAABAV5D5AQAAdAWZHwCgHr1ej1VoSG5RcLuSfk7/UPGvisu0rJBCVpRtLU3K/IpqXTq00nrroMMeaJ/QXk3/9eDnxy4DAIWgw2m9JmV+AAAAyIPMDwAAoCvI/AAAALqCzA8AAKAryPwAAAC6gswPAACgK8j8AAAAuoLMDwAAoCvI/AAAALqiMZkfbxUHAADIqTGZHwAAqB4jLy1D5gcAANAVZH4AAABdQeYHAADQFWR+AOJxcw8AlKTGDpbMDwAAoDCaXzaT+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR9QPM1f5gQA6CwyPwAAgK4g8wOgarvdmqZpmqZhGNvtVn5o27ZhGIZh2LatWI5hGOv12v91NpupfxcAkAeZHwBVtm3btu267l9//fX+/XshhOM4+/1+u91ut9v9fu84jko5l5eXruv6v67X68vLy7IqDQAI6L2+vtZdByXyxqlCatvr1b/V9dZBhz3QPsG9Gg3X2H2uGNV1Ha/oek3T9DM2+dfRaPTw8DAajYQQu93u/Px8t9sFS5hOp67rWpa13+9d1726urJte7fbnZ6eep4nhFiv158+ffJHEFupU/d9HhWrFcR2O1ZRJb9f8rcr1FMl9WYpO0FlFx1cpkGFqPTtisuUsTmNiVcyv9asva26kPn5HMdxXdd13dAy0V83m83Z2dnvv/++2WxM0/QXMAzj48ePlmXZtn1yctLu2d7utLhjt7QdaVnLji+ZH5mfLsj8WrP2tupI5rfb7W5ubv7++++Hh4fBYHAw8/PPHKEfHMf5+fPncrkMjhq2VXdaHJlfC5D5tT7z4z4/QGtaTRTKWdqzs7PtdjsYDIQQw+HQn97d7XbD4VCxKMuy1uv1drvt9/vtTvsAQCtkfgBU3dzc3N/fB2dmr66u5vO5/Hk+n19dXSkWNRqNTNO0bZtnOwCgSo0Zo2a2tzVrb6uSZnsLjPxjxdwd0utNJhP/V/m0h3zaVwhhmmbo2d6U2V4hxHK5/PDhw/Pzc+vH/LrT4pjtbQFme1s/29uYeCXza83a26oLmR+y6c6eJPNrATK/1md+v6V/AQBQGf+2zjZlEgC0wn1+AFC/Xq8XHAPQ6skeAG1C5gcANfNzPpn2kfwBKA+ZHwDUKfZ2H2Z7AZSEzA8AapaU5zHsh44g1KtE5gcgHsNOFUg54bH/AZSBzA8A6qH+6gcA1ehCiyPzA4DapKd9DPsBKByZHwDUoAtDCwA0xJucAaAeikN6Rf0XEaFckwFFoJsY8wOAqqkP+BWV8wVfGcj7AoEuI/MDgBpUNuQWek10cO0kf2iTlsVzeZtD5gcAlcrQoWc+B6Q8PkzyB3QTmR8AVO2oAb+co4MpXy82+SOJBBqBzA8AqlNleqSyrmLvIwSgPzI/AKhUNXf4qbwmOrRwtrXE3kcIQFtkfgBQkTwDYxm+q5iKZc7YyPmAJiLzA4DqZEuSjv1WBQ+RHDWmCKAWse2azA8AWqjUh0hI+4DmIvMDgCpU9gxEzlfAKC5G2gc0FJkfUAAebISKnNmSSphlTssU3/BC2gc0HZlf55CjANXL3+7Uk63MadnBL5L2AS1A5lcDci+ggypImArpW5IKIe0D2oHMDwDao5D/8COa/JH2Aa1B5gfgOLvdbjAYyJ89zzNNczQamabpeV69FeuClFG9oiYTQslf8F3NhZQPoF6dy/yYaQVyms1m+/1e/jyfz03T3O12pmnO5/N6K6azQtKmg4UUlZz5yR/vakYI59AW6FzmByAPx3FM0/R/Xa/X0+lUCDGdTtfrdXDJXq83m81Go5Ft2/IHx3Hkn5bLpWEYhmEsl8sqK99ihZ+PXwOKLRlAvXpNadVFTTfoMG1Rbx102APtE9qr0Z3c68W0NZVjodXx2m63tm27rutvTnC7QtvY6/U2m83Z2dnvv/++2WxM0/QXGAwGnud5nvfhw4f7+/tatqVKsUc/c1EiEg/6BMmxW1rgnmn3Kqrkh1OwmYv/vg0gtjdL2QkHd5FiZ9iyQtKXKa8mjYlXMr92rL2tOpL5maa5XC5Ho5Fi5hdaxv9BjhpalmVZln/LYIsVmxlES9Mn8yDzawEyv8oKSV+mvJow2wtAieu6j4+Ps9lM5m3y3+FwKB/s8DxvOByqF2Xb9uPj4/n5eXkVbrHg3C73XaF2BGGzNOZKhTG/dqy9rToy5ufzN8e27ZOTE9u2Hcf5999//Tv5gstEfxgMBs/Pz0KIN2/edOGJ4MLHhEInWn3CgzG/Fsgw5qcyfKXPSJs+haQvU15Nfkv5AgCku729vbi4+PLly9u3b9Xv2FssFnK0b7FYlFm71pJnZaFTzgegKRpzpcKYXzvW3lZdG/PDUVo2JpSCMb8WYMyvskLSl+E+PwAA0FTcC1i4zLuU2V4AKB2nPQCaIPMDgHK1aSoQQNOR+QGla814TwUbQpIEoCSt6YpzIvMDoIq0DACajic8AAAAuoLMDwAAoCvI/AAAAJS04GZBMj8AaJUWnJkAlKcZmV/L3pAOAKUi+UNOhFCLNSPzA9pHpWPlmgfH8uOKMzeAWGR+QMHU0zWyOgBAxcj8AKA9Xl9f5RUF1xVoE8awC0TmBwAAdPf6+kr+VwgyPwAAUBbuV9YNmR8AAEBXkPlVjcFqAGVjXkxiJwBR3cr8NOkFahz31mQPtAxzGQCApuhW5id43o09AABAh3Uu8wMAoCgtm0iJzmAwp1GGevcqmR8AAEAx9M+VyfyAcrVsSAAoCk0DSFJq62hA5qd/+gykiwaw4n/aW0510Ak83oumKCRQ25cqlLc5Dcj8gFZSadUt68gAIJYmfV3L0sekzSHzA4rEKAuATmlZttQFZH6VqjctICmpBp0ggFbiJNIOHcr8NAnZetMCkhIAQGbNPYm0bGwyz+YcyPxs2zYMwzCM7XYrhNhut6ZpmqZpGIbruorrMAxjvV77v85mM9u2s1U3pzYddbSbJhcqaDQe8gCK1Y4GlZb5OY6z3++32+3Hjx/n87kQwrZty7Jc13UcZzabKa7j8vIymCau1+vLy8s8lQb0kd4RxP5Vse/gQgUAhGb5Vgt65rTRQsMw/vrrL8MwhBDb7dYwjODoYmiksdfrTadT13Uty9rv967rXl1dybG93W53enrqeZ4QYr1ef/r0SY4gKtWv1xO/dnTOoVr/6zUO+dZYhwL3JIJCxzT919BXRPKxUFkGOEjn4Al2SqxCE37HJX/1O7Hov9GvpHdlKaGYXkKxhaS3iGoKKWRFedaSNub348ePr1+/yrld+Yn/5eVyOZlMQstblvX09PT582fLsna73c3Njfx8NBoNh0M54fv9+/djB/wKaVE6XDHUXofW9E2tV3uoAABKUnsP/1v6n09OTlzXdV33/fv3u91Ofmia5uPj43K5DC1smmboB5+c8LUsa71ePzw8FFHzo+mQ9+hQB5SnwPczEypAThWcX2s/hbeAPvuwqBdKqyyW3sOXvU/CY4AyabMsazabDQaDp6en0Wgkfo0Zuq4rF/A8782bN3IC169oaDA2OMAoJ3wfHh7+/PPPbFO9InWYt5py8qi3DjrsgbYKBrz4750sf0hpF9Ffo+XELgMo0jl4yp4nVZkO038VFat+tld9Jje9kGBt0zetgjnl/JtTSE1Ewj4Jz/bKET759IYcopMfyrnd2Wwm87Z//vmn3+/HrizWaDQyTdO27Vqmegssp7l10GEPdNCxu71l9wyhdq+6Pt6rZ63gU+yF9DmOFYyi6bOxOUcW02Z7F4vFxcXF169fT05OHMcRQnz79u3PP/88OTkZDAbfvn07qqKTyeTDhw/ROeIkRe1iHQ5VvXXQYQ9ASj8WpH0og7YRpW1WiqOkP2GQv/yigqSQhqBPa8pTE33HqGNnwQoZQal3prXiOuiwB1osuHtjZ3tDM+wpUZ2S9nG80D5lz5OqTP8Vspa2zvb6eXmwgxKRXZo+Lxm7ZNIyBxcopJBSJ2FDeyzPMqVuzoEnPOqSP8cP3WVVl3pHcRhDqoD67k2Jao4UULiyG1QHBywL36X6DApWObJY+8S0jplfzrOgVjlfXdXQZCd0TWhvR3f+wUvALsjWrzV3/3QwP0gSGhFvgeaGZSFUbl85qJA0SJ9CClH25miX+WU+Cwb3RWdzPn12Qgdl3uEqX9RtOilnJ6jVtlSga9sbKxQzpe6TCk7SrUlec0o/jvokZJoUcnCZamqiV+Z3bNpXZVdyUL1ZF4N8ujk4/pf+eWiZXq9XdoAdeyYj0pBN2TlTBYPobR2nP+oKkwG/YwtRXKaCmoQzv9qvY47d5mxtr6gRlDypZ/461Lt21DX4H8oCS1oLUJ5Sn+oor/xqVtEgDPgdVYgmA34imvnpHM1F1S04gnJsmUWlnqFRHMX5vthycq5d5yOuv1r2HocMzVXe9RJDfWULXX+mLKmY4uTMYJpYSMoyhdREOhiies32VsZ/1Dl9Bq3AZCulHPUhnAJ7nNg9UNK6ACRRz4Ra0CTLyJwquM2G+6djpbxMJOWvKgsoLqNJIfpsjnpn0tHMT1KZQaugndfYldCLAdXLcJ9GIYP0tWSZ5d2NzdxuULGDqf72ZpsWKztPUp82raYQfTZH8Xh1OvML0r9hA2iunAlQaJBe8evZ7k6JXgbnnIHyK19UN0vOF1VGPYNlqjyylj/tKyTFqXItla0o51rCX2lKWAO64SkZHDXQUnjeEy2zqBuR08uMUkwoY++oOfbRNPU1arsKoErR+GTMDwAyypzxFLLS2HtUCk9BNMlpGOcDikLmB2RR3iOKgKLuJCg8twsUiMwPyIiTBBRxnZAZQ31ATtHAJvMDgBKRT6RLmQ1nqA8ow//UXQEAQEcV9ZaKdEljrhWsAtAQmR8AQCOFvLzQl/T+kbJXAWiL2V4AgC7aMcPLJDJ0xpgfAKBO/lRpO3Iy0j5ojswPAFCb4OsJRWkJkyy86asACkHmBwCoWakJk//fxzV6FUBR+D9ngCz4/5oAAE3EmB8AAEBXkPkBAAB0BZkfAABAV5D5AUfjJj8AQEOR+QEAAHQFmR+aarvdGoZhGIbrukIIM6DX6223W5VCDMNYr9f+r7PZzLbtsmoMAEDdmLRCU41Go4eHByHE+fn5brfzP1+v19+/f3ccR6UQx3F+/vy5XC7lr4PB4OHhwTCM9G8x2wsAaChOYGgqP/0K5mGe552enj49PQ0Gg9DC0+nUdV3Lsvb7veu6V1dXtm3vdrvT01PP84QQ6/X606dPKoOFZH4AgIZithdNNZlMXNd1XXc4HPofrlarq6urUNonWZb19PT0+fNny7J2u93NzY0QYjQaDYdDOeH7/fv3y8vLyuoPAED1GLpAI+12u/l8/v37dyHEfr//9u2bnKIdDAbPz8/RzC86QOj/4E/4yunj0Wh0cO2M+QEZyP/czEcjAmrxW90VAI5gmqYQwrKsm5ubh4cHeX/edrs9Pz/3PG+9XpumGTvgl8KyrNPT06urq36/r5L2AcjMz/ZCWSCAypD5oUnkY7xCCDlX6+v3+/Kvk8nk2DJHo5FpmrZtM9ULAGg9Mj800sPDw3w+//vvv09OToQQ3759E0J8//7dsqwMpU0mkw8fPvhP+AIA0FbcrgQcjfv8gAyCDYdGBNSFMT8AACAE91+2V/BCi8wPAMrF2TRWu3dLc0c0m1tzKCLzA4DScTYNYYcAdeFNzgAAAF1B5gcAANAVes32tvu2j2bRdi6GIAEAIDO9Mj+hccIBTRAhAABkxmwvAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXaPdsLwAAOBZvvIKi30QgXPz3ZfAJn/AJn+j8Se04y0I3+rQOaK4XipXauzNiF/qrvZl0HL0EAGQWnu2lSwVU0FIAAE3EEx4AAABdQeYHAADQFWR+AAAAXUHmBwAA0BVkfgAAAF1B5gcAANAVZH4AAABdQeYHAADQFWR+AAAAXUHmBwAA0BVkfgAAAF0R/n97AW31er26qwAAQLP9Lx7OcZRDWHzyAAAAAElFTkSuQmCC)\n",
+    "\n",
+    "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t2ihZLXh5VD-",
+    "outputId": "510653d0-3172-4c5f-c101-1bfe66297121"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8GngV4fThkhM"
+   },
+   "source": [
+    "## Build model\n",
+    "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "q6WNelXsbjy1"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygenn import GeNNModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "261uLnJsgyeE"
+   },
+   "source": [
+    "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "EDpiDOK0gkEz"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial1\")\n",
+    "model.dt = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LrfXpMqfjRBe"
+   },
+   "source": [
+    "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "tU2M4MgFjRae"
+   },
+   "outputs": [],
+   "source": [
+    "izk_init = {\"V\": -65.0,\n",
+    "            \"U\": -20.0,\n",
+    "            \"a\": [0.02,     0.1,    0.02,   0.02],\n",
+    "            \"b\": [0.2,      0.2,    0.2,    0.2],\n",
+    "            \"c\": [-65.0,    -65.0,  -50.0,  -55.0],\n",
+    "            \"d\": [8.0,      2.0,    2.0,    4.0]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YrOQPgYBjuym"
+   },
+   "source": [
+    "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "zc-e5Lu2j_Yq"
+   },
+   "outputs": [],
+   "source": [
+    "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u8wu06PZkBnS"
+   },
+   "source": [
+    "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "GNBjEGWPj_3Q"
+   },
+   "outputs": [],
+   "source": [
+    "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IGKUIiaGkA0Z"
+   },
+   "source": [
+    "Generate code and load it into PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "d0mK72rYkiYe"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cNs18ywkkq6T"
+   },
+   "source": [
+    "# Simulate tutorial model\n",
+    "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "nWFVfYfdkobN"
+   },
+   "outputs": [],
+   "source": [
+    "voltage = pop.vars[\"V\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wv-hDOIe3Hgy"
+   },
+   "source": [
+    "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "99MBe7JKk5Ut"
+   },
+   "outputs": [],
+   "source": [
+    "voltages = []\n",
+    "while model.t < 200.0:\n",
+    "    model.step_time()\n",
+    "    voltage.pull_from_device()\n",
+    "    voltages.append(voltage.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ug6S1h-z3k7v"
+   },
+   "source": [
+    "Plot the voltages over time in 4 seperate panels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "RsVbAbIPlEO8",
+    "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Stack voltages together into a 2000x4 matrix\n",
+    "voltages = np.vstack(voltages)\n",
+    "\n",
+    "# Create figure with 4 axes\n",
+    "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n",
+    "\n",
+    "# Plot voltages of each neuron in\n",
+    "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n",
+    "    axes[i].set_title(t)\n",
+    "    axes[i].set_ylabel(\"V [mV]\")\n",
+    "    axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Time [ms]\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h4yw3JiNpXOM"
+   },
+   "source": [
+    "Exercises\n",
+    "---\n",
+    "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n",
+    "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "name": "1_neurons",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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+             
+  <section id="Tutorial-2---synapses">
+<h1>Tutorial 2 - synapses<a class="headerlink" href="#Tutorial-2---synapses" title="Permalink to this heading"></a></h1>
+<p>This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 98.5MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+<p>Import numpy, matplotlib and the main <code class="docutils literal notranslate"><span class="pre">GeNNModel</span></code> class from PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="n">GeNNModel</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_var</span><span class="p">,</span> <span class="n">init_weight_update</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “tutorial2” with floating point precision and set the simulation timestep to 1ms</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial2&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="mf">1.0</span>
+</pre></div>
+</div>
+</div>
+<p>For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:</p>
+<dl class="simple">
+<dt>:nbsphinx-math:<a href="#id1"><span class="problematic" id="id2">`</span></a>begin{align}</dt><dd><p>tau_{text{m}} frac{dV_{i}}{dt} = &amp; (V_{text{rest}} - V_{i}) + R_{text{m}}I_{i}.</p>
+</dd>
+</dl>
+<p>end{align}`</p>
+<p>We configure these using the parameters from (Vogels &amp; Abbott, 2005 <a class="reference external" href="https://doi.org/10.1523/JNEUROSCI.3508-05.2005">link text</a>). Note that the resting voltage is <strong>higher</strong> than the reset to provide a constant current input <strong>TODO</strong> get rid of this</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[15]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lif_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span> <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">49.0</span><span class="p">,</span> <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+              <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span> <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>So that the network starts in a non-pathological state, we want to randomly initialise the neuron’s membrane potentials so that they are between their threshold and resting potentials. GeNN provides <a class="reference external" href="https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html">various</a> initialisation “snippets” which can be used to parallelise variable initialisation but, here we are going to use <code class="docutils literal notranslate"><span class="pre">Uniform</span></code> to sample values from a uniform distribution.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">init_var</span><span class="p">(</span><span class="s2">&quot;Uniform&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;min&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span> <span class="s2">&quot;max&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">}),</span>
+            <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="mi">3200</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">lif_params</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">inh_pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;I&quot;</span><span class="p">,</span> <span class="mi">800</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">lif_params</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="n">exc_pop</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">inh_pop</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+<p>So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[18]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_synapse_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">0.0008</span><span class="p">}</span>
+<span class="n">inh_synapse_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.0102</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<dl class="simple">
+<dt>We are going to use an exponential synapse model where a single time constant <span class="math notranslate nohighlight">\(\tau_{\text{syn}}\)</span> to define it’s dynamics: :nbsphinx-math:<a href="#id3"><span class="problematic" id="id4">`</span></a>begin{align}</dt><dd><p>tau_{text{syn}} frac{dI_{text{syn}_{i}}}{dt} = &amp; -I_{text{syn}_{i}} + sum_{j=0}^{n} w_{ij} sum_{t_{j}}  delta(t - t_{j}).</p>
+</dd>
+</dl>
+<p>end{align}` To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[19]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_post_syn_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+<span class="n">inh_post_syn_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">10.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>We want to connect these with a fixed probability of 0.1</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[20]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fixed_prob</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>Now we have defined the synaptic weights (in GeNN, this is the responsibility of the <strong>weight update model</strong>), the synapse dynamics (in GeNN this is the responsibility of the <strong>postsynaptic model</strong>) and the connectivity parameters we can add the synapse populations to the model. Each of these synapse populations all configured with: * <code class="docutils literal notranslate"><span class="pre">SPARSE</span></code> connectivity meaning that they are connected with a sparse weight matrix. * The built in <code class="docutils literal notranslate"><span class="pre">StaticPulseConstantWeight</span></code> <strong>weight update model</strong> which
+is used for spiking synapses without any sort of learning. This has a single parameter <code class="docutils literal notranslate"><span class="pre">g</span></code> representing the synaptic weight used for all synapses. * The build in <code class="docutils literal notranslate"><span class="pre">ExpCurr</span></code> <strong>postsynaptic model</strong> which implements the exponential synapses described previously * The sparse connectivity is configured using the built in <code class="docutils literal notranslate"><span class="pre">FixedProbability</span></code> model described previosuly</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[21]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;EE&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">exc_pop</span><span class="p">,</span> <span class="n">exc_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">exc_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">exc_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbabilityNoAutapse&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;EI&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">exc_pop</span><span class="p">,</span> <span class="n">inh_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">exc_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">exc_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbability&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;II&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">inh_pop</span><span class="p">,</span> <span class="n">inh_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">inh_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">inh_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbabilityNoAutapse&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;IE&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">inh_pop</span><span class="p">,</span> <span class="n">exc_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">inh_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">inh_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbability&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">));</span>
+</pre></div>
+</div>
+</div>
+<p>Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[22]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-model">
+<h2>Simulate model<a class="headerlink" href="#Simulate-model" title="Permalink to this heading"></a></h2>
+<p>Simulate the model for 1000 timesteps</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[23]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">while</span> <span class="n">model</span><span class="o">.</span><span class="n">timestep</span> <span class="o">&lt;</span> <span class="mi">1000</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Copy the recorded spike data back from the GPU and extract the spike times and IDs</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[24]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">()</span>
+
+<span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">exc_spike_ids</span> <span class="o">=</span> <span class="n">exc_pop</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">inh_spike_ids</span> <span class="o">=</span> <span class="n">inh_pop</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Plot spikes and rates</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[25]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Define some bins to calculate spike rates</span>
+<span class="n">bin_size</span> <span class="o">=</span> <span class="mf">20.0</span>
+<span class="n">rate_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1000.0</span><span class="p">,</span> <span class="n">bin_size</span><span class="p">)</span>
+<span class="n">rate_bin_centres</span> <span class="o">=</span> <span class="n">rate_bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">bin_size</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span>
+
+<span class="c1"># Plot excitatory and inhibitory spikes on first axis</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">exc_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">inh_spike_ids</span> <span class="o">+</span> <span class="mi">3200</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Plot excitatory rates on second axis</span>
+<span class="n">exc_rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">rate_bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rate_bin_centres</span><span class="p">,</span> <span class="n">exc_rate</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1000.0</span> <span class="o">/</span> <span class="n">bin_size</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">3200.0</span><span class="p">))</span>
+
+<span class="c1"># Plot inhibitory rates on third axis</span>
+<span class="n">inh_rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">rate_bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rate_bin_centres</span><span class="p">,</span> <span class="n">inh_rate</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1000.0</span> <span class="o">/</span> <span class="n">bin_size</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">800.0</span><span class="p">))</span>
+
+<span class="c1"># Label axes</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Neuron ID&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Excitatory rate [Hz]&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Inhibitory rate [Hz]&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_2_synapses_28_0.png" src="../_images/tutorials_2_synapses_28_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="1_neurons.html" class="btn btn-neutral float-left" title="Defining populations of neurons" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+  <hr/>
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+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
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diff --git a/documentation/5/tutorials/2_synapses.ipynb b/documentation/5/tutorials/2_synapses.ipynb
new file mode 100644
index 000000000..209799ec9
--- /dev/null
+++ b/documentation/5/tutorials/2_synapses.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Tutorial 2 - synapses\n",
+    "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t2ihZLXh5VD-",
+    "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8GngV4fThkhM"
+   },
+   "source": [
+    "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "q6WNelXsbjy1"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "261uLnJsgyeE"
+   },
+   "source": [
+    "## Build model\n",
+    "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "EDpiDOK0gkEz"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial2\")\n",
+    "model.dt = 1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mki7b8R8xhAv"
+   },
+   "source": [
+    "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n",
+    "\n",
+    "\\begin{align}\n",
+    "    \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n",
+    "\\end{align}\n",
+    "\n",
+    "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "id": "AkMk7Ml4tOxM"
+   },
+   "outputs": [],
+   "source": [
+    "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n",
+    "              \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XboW6qxrxnok"
+   },
+   "source": [
+    "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "id": "dWf4f4Bpxl7u"
+   },
+   "outputs": [],
+   "source": [
+    "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n",
+    "            \"RefracTime\": 0.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B3hhcDILxeki"
+   },
+   "source": [
+    "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "id": "5AECcjzMs8Iz"
+   },
+   "outputs": [],
+   "source": [
+    "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n",
+    "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n",
+    "\n",
+    "exc_pop.spike_recording_enabled = True\n",
+    "inh_pop.spike_recording_enabled = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QypcRqLi0hgq"
+   },
+   "source": [
+    "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "id": "WpmzQu0UuPky"
+   },
+   "outputs": [],
+   "source": [
+    "exc_synapse_init = {\"g\": 0.0008}\n",
+    "inh_synapse_init = {\"g\": -0.0102}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "58kevKNm0rfi"
+   },
+   "source": [
+    "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n",
+    "\\begin{align}\n",
+    "    \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}}  \\delta(t - t_{j}).\n",
+    "\\end{align}\n",
+    "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "id": "VnbedWiB0oAF"
+   },
+   "outputs": [],
+   "source": [
+    "exc_post_syn_params = {\"tau\": 5.0}\n",
+    "inh_post_syn_params = {\"tau\": 10.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nJ1JwSAO1qNi"
+   },
+   "source": [
+    "We want to connect these with a fixed probability of 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "id": "ciwtEyzB0nte"
+   },
+   "outputs": [],
+   "source": [
+    "fixed_prob = {\"prob\": 0.1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HWvUT89Z106p"
+   },
+   "source": [
+    "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n",
+    "Each of these synapse populations all configured with:\n",
+    "*   `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n",
+    "*   The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n",
+    "*   The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n",
+    "*   The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "rD6K22qZtxId"
+   },
+   "outputs": [],
+   "source": [
+    "model.add_synapse_population(\"EE\", \"SPARSE\",\n",
+    "    exc_pop, exc_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"EI\", \"SPARSE\",\n",
+    "    exc_pop, inh_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"II\", \"SPARSE\",\n",
+    "    inh_pop, inh_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"IE\", \"SPARSE\",\n",
+    "    inh_pop, exc_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbability\", fixed_prob));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FiAsrqRx5OgZ"
+   },
+   "source": [
+    "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "id": "0I-7lZP4vWE2"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load(num_recording_timesteps=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1JLVx3u1281A"
+   },
+   "source": [
+    "## Simulate model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8HhNMK4C4d6f"
+   },
+   "source": [
+    "Simulate the model for 1000 timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "id": "v0lT7gaIviev"
+   },
+   "outputs": [],
+   "source": [
+    "while model.timestep < 1000:\n",
+    "    model.step_time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SUzXrYxr4kO5"
+   },
+   "source": [
+    "Copy the recorded spike data back from the GPU and extract the spike times and IDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "bDJLu6Kwvn7W"
+   },
+   "outputs": [],
+   "source": [
+    "model.pull_recording_buffers_from_device()\n",
+    "\n",
+    "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n",
+    "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jS5OtCX15CCJ"
+   },
+   "source": [
+    "Plot spikes and rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 850
+    },
+    "id": "9rWE-Rvjvo5I",
+    "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n",
+    "\n",
+    "# Define some bins to calculate spike rates\n",
+    "bin_size = 20.0\n",
+    "rate_bins = np.arange(0, 1000.0, bin_size)\n",
+    "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n",
+    "\n",
+    "# Plot excitatory and inhibitory spikes on first axis\n",
+    "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n",
+    "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n",
+    "\n",
+    "# Plot excitatory rates on second axis\n",
+    "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n",
+    "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n",
+    "\n",
+    "# Plot inhibitory rates on third axis\n",
+    "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n",
+    "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n",
+    "\n",
+    "# Label axes\n",
+    "axes[0].set_ylabel(\"Neuron ID\")\n",
+    "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n",
+    "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n",
+    "axes[2].set_xlabel(\"Time [ms]\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lkZXMKuC42jG"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "2_synapses",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/documentation/5/tutorials/comp_neuro_101/1_neurons.html b/documentation/5/tutorials/comp_neuro_101/1_neurons.html
new file mode 100644
index 000000000..22a927aaf
--- /dev/null
+++ b/documentation/5/tutorials/comp_neuro_101/1_neurons.html
@@ -0,0 +1,313 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
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+  <title>Defining populations of neurons &mdash; PyGeNN  documentation</title>
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+  <section id="Defining-populations-of-neurons">
+<h1>Defining populations of neurons<a class="headerlink" href="#Defining-populations-of-neurons" title="Permalink to this heading"></a></h1>
+<p>In this tutorial we’re going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes: <img alt="image.png" src="data:image/png;base64,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" /></p>
+<p>(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com) ## Install PyGeNN wheel from Google Drive Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
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+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 118MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Import numpy, matplotlib and the main <code class="docutils literal notranslate"><span class="pre">GeNNModel</span></code> class from PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="n">GeNNModel</span>
+</pre></div>
+</div>
+</div>
+<p>Create a new model called “tutorial1” with floating point precision and set the simulation timestep to 0.1ms</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial1&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="mf">0.1</span>
+</pre></div>
+</div>
+</div>
+<p>Configure initial state for a population of Izhikevich neurons with a constant value for the <code class="docutils literal notranslate"><span class="pre">V</span></code> and <code class="docutils literal notranslate"><span class="pre">U</span></code> state variables and different values for the <code class="docutils literal notranslate"><span class="pre">a</span></code>, <code class="docutils literal notranslate"><span class="pre">b</span></code>, <code class="docutils literal notranslate"><span class="pre">c</span></code> and <code class="docutils literal notranslate"><span class="pre">d</span></code> parameters (because we are going to be using the <code class="docutils literal notranslate"><span class="pre">IzhikevichVariable</span></code> model, the parameters are also implemented as state variables so they can vary across the population of neurons)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">izk_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>
+            <span class="s2">&quot;U&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">20.0</span><span class="p">,</span>
+            <span class="s2">&quot;a&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.02</span><span class="p">,</span>     <span class="mf">0.1</span><span class="p">,</span>    <span class="mf">0.02</span><span class="p">,</span>   <span class="mf">0.02</span><span class="p">],</span>
+            <span class="s2">&quot;b&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">0.2</span><span class="p">,</span>      <span class="mf">0.2</span><span class="p">,</span>    <span class="mf">0.2</span><span class="p">,</span>    <span class="mf">0.2</span><span class="p">],</span>
+            <span class="s2">&quot;c&quot;</span><span class="p">:</span> <span class="p">[</span><span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>    <span class="o">-</span><span class="mf">65.0</span><span class="p">,</span>  <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>  <span class="o">-</span><span class="mf">55.0</span><span class="p">],</span>
+            <span class="s2">&quot;d&quot;</span><span class="p">:</span> <span class="p">[</span><span class="mf">8.0</span><span class="p">,</span>      <span class="mf">2.0</span><span class="p">,</span>    <span class="mf">2.0</span><span class="p">,</span>    <span class="mf">4.0</span><span class="p">]}</span>
+</pre></div>
+</div>
+</div>
+<p>Add a population of 4 of these neurons (GeNN’s built in models are selected by specifying model as a string)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;Neurons&quot;</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="s2">&quot;IzhikevichVariable&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="n">izk_init</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;CurrentSource&quot;</span><span class="p">,</span> <span class="s2">&quot;DC&quot;</span><span class="p">,</span> <span class="n">pop</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;amp&quot;</span><span class="p">:</span> <span class="mf">10.0</span><span class="p">},</span> <span class="p">{});</span>
+</pre></div>
+</div>
+</div>
+<p>Generate code and load it into PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">voltage</span> <span class="o">=</span> <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;V&quot;</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">voltages</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">while</span> <span class="n">model</span><span class="o">.</span><span class="n">t</span> <span class="o">&lt;</span> <span class="mf">200.0</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+    <span class="n">voltage</span><span class="o">.</span><span class="n">pull_from_device</span><span class="p">()</span>
+    <span class="n">voltages</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">voltage</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Plot the voltages over time in 4 seperate panels</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Stack voltages together into a 2000x4 matrix</span>
+<span class="n">voltages</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">voltages</span><span class="p">)</span>
+
+<span class="c1"># Create figure with 4 axes</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span> <span class="mi">8</span><span class="p">))</span>
+
+<span class="c1"># Plot voltages of each neuron in</span>
+<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">([</span><span class="s2">&quot;RS&quot;</span><span class="p">,</span> <span class="s2">&quot;FS&quot;</span><span class="p">,</span> <span class="s2">&quot;CH&quot;</span><span class="p">,</span> <span class="s2">&quot;IB&quot;</span><span class="p">]):</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;V [mV]&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">0.0</span><span class="p">,</span> <span class="mf">200.0</span><span class="p">,</span> <span class="mf">0.1</span><span class="p">),</span> <span class="n">voltages</span><span class="p">[:,</span><span class="n">i</span><span class="p">])</span>
+
+<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_comp_neuro_101_1_neurons_19_0.png" src="../../_images/tutorials_comp_neuro_101_1_neurons_19_0.png" />
+</div>
+</div>
+</section>
+<section id="Exercises">
+<h2>Exercises<a class="headerlink" href="#Exercises" title="Permalink to this heading"></a></h2>
+<ol class="arabic simple">
+<li><p>Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.</p></li>
+<li><p>Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful.</p></li>
+</ol>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../index.html" class="btn btn-neutral float-left" title="Tutorials" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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+  <hr/>
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+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
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diff --git a/documentation/5/tutorials/comp_neuro_101/1_neurons.ipynb b/documentation/5/tutorials/comp_neuro_101/1_neurons.ipynb
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@@ -0,0 +1,334 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Defining populations of neurons\n",
+    "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n",
+    "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1MAAAJDCAIAAABhe9nkAAAgAElEQVR4nOzdT2zbVro//MPB3SVAxcJAgN6N6Kpdi4a9a4CBIRpt+lv5Dll42QzEQEA7wF2YGrV9V2/GGrGLF+hcQDCF8SyNiLnZ5TYQDaNAu4sh+m4bxfTmF8CAYKpAsta7eCZnGFKSaesfJX4/CAKZoqhD8ujo0fkr9Pt9BgAAAAAp8Lt5JwAAAAAAZgSRHwAAAEBaIPIDgKkQBGHYU67ryrIsy7KiKI7jMMZ839c0TZIkTdN834//LpIkWZbF/3QcRxTFax0BACBVEPkBLIkRkVbSbG9vm6bpuq5pmpqmMcYajcb6+rrneevr641GI/6hSqXS0dER/9O2bVVVRVGcfKIBAJYCIj+AZTC9sE8QBMMwNE3TNM3zPMaY7/u6rlONHd9o2zZV4wVr4Gjn6EbLshRFoceZTIYxVq/XVVVljKmqWq/Xo2kwTVNRFEmSDMMwDIMeW5alqqpt27ySjyK/qVwIAIDl0O/3MbwXJqsPk3Pl9Zz2lWeM7e/v9/v9/f19VVX7/b6qqrSl3++3Wi3amM1mz87OLi8vWaBUuby8zOfzfOeQQqGQyWRarVb/3dOMnghjrFar9ft9Oj4d8OzsLJPJUHro2Wazmc1mJ3ju8zLrTyzEM+98kSDzvhUwWMzbJ9AtFASh3/+feacZloEg3EO5MEGCIIy4nhOv6ou+VzAB9FgQhEKhENzHcRzHcVzX7Xa733//PS9V8vk8Y8x13WFv5ziOruue50XfJZSGy8tLasON7mlZVr1ed11X1/VcLmcYxjhXIAlQICcQSrYgZNEEip9FEfnBhKF8nCAK7EZfz2DwN40rH420RFE8Ozvjfekcx2k0GtlsVlGUXC63urrKS5VWq6XrummaoRZYPrCDH1OSpOPjY0mSPM/b3NykFuQRaQg9FkWx3W6vra21221JkiZ+EWYMBXICoWQLQhZNoPhZFP38ABbbDBpfbNtmjJmmWSwWGWOqqlarVXrKNE3TNG3brlQqwXiOKIpiWZau66HBtqIo0jEty6Lqw1KpRFts2y6VStdNoaqquq5T578bniQAQDog8gNYBlON/54/f64oysnJSa1WY4zR/7IsS5J0cnJimub+/r6maYqi9Hq9fD5vmiZ/raIoqqo+ePAgeMBKpbK3tyfL8tHREQ3+KBaLJycndECKL6+FRviG2qABACAKrb0wYWgTmaA4rb0zSANu6IyhQJ4eQbhXKMj0uNv9zTTvK8oaY8ww/u66Z7S903l1fn4RugUo2YJmlkUH3i/XfWkYB93ubysr74nirUrlC1n+cAaJSbj4WfTfpp0UAACA5HCcv9AD1325ufln36fODH/kO8jyV7Xa/fkkDiKi92t7++GTJ99RtGdZP25vP/S8f8w1jQsGkR8AjIJ6DlhWsvxhr/cmtNEw/r6y8p5h/GEuSYIR+P06P7/IZu/QRl3/TJLuzDVdiweRH0BC8SlU5p0QgOVkWT/ylkTiOO1G49nZGSqQkojfr1rt/vvva4WCrCiyJN1R1bvzTtqCQeQHAAApoijf0oOTk18rlS/4dt9/rWlVy/qTKN6eU9JggOj9Mow/qOrdk5Nfnz//tV5/+vz5r8HGergSRnjAhKEf9KTwOj9cz7RBgTw9gnCPX1vff/3++xr/U9P2stk7w2KIdJZsruvev39/ZWWFMWaapiz/s4p0liM8ovfLNB/z5vjQTUwzzOc3lO+/1vUfRFEVhHuatuf7r+edIgAAmINg3Z5pPn7x4hWqjkK2t7dN03QcxzCM7e3tabyFINzj/0bvye9Xvf7Usn6kx8E+fxBT6ur8dP2HXu/1/v6fGGPl8oHnXfBxQzAR6fxlPA2o80ut9BTIsxesQ2KMSdKXNEpUEO5ls3dyuQ/4U6GvhnSWbCOXz5lAFo1Ge4Mm0wnfL8ZYtfroxYtXjLGPPvoAs7oQrN42lCDcazYr1CHUcdpbW9+l5MRnJp3l4zQg8kut9BTICySdJRvNxE5rMNLy3LR9ZpEfxDe71l5BuOc47TEPMkut1kM+mMvzLjKZW/NNz6IThHum+XjeqQAAgMnTdb1cLiuKUq1WDcMIPnVl4ywk1qz7+fn+a9N8LElfCsI9SfrSNB8P62kXc09d/+Fa+U9R1qivgGk+LpcPYk7XKctfBfsiTCPcse2fR59I6EzjX0kAAIAb0HW93W47jtNutzVNCz6FyrnFNetZXTStSgOzZXnVdc+q1UeO4w7saRdnT9v+udF4dt00uO7L7e2HmcytZrNC6/Zc6fT0rFj8NNgFRJZXr/u+I9j2z3t7j0bvEDrT+FcSAADgZnq9HmMsk8lkMpmJH7zf/59gjQaiydmYaeRnWT8eHbmt1kOKtxRlTZZXt7a+s6wfdf2z6+7peRe6/kM+v3p6ehY/DbT8S6XyRfwp2j3vgjFWKn0+jT6k1NfwygSEzjT+lYRFh65+MEGh/vKW9aNt/8IYOzpyqRuMqn6ysfHxsEVRB+5PZY5pPq7Xn+ZyH4jiLVW9m6jJdRFb3Njx8THN6iKK4pMnT6bxFrgjs3d1a68sfyVJXwa3GMbfBeEetS22Wg/X1z+O+WZHR24+vxqsZlOUtXx+9ejIvcGe29v/r6re3dn5ffzUMsaq1Ucjwj7XfcknjeQ6nf/LGBsY9l35dlcefH3941brYav1sFj8dGCS2KAzjX8lB4q2Vpvm42FtzQOTDQBLQNc/c5y/UFsBPdD1z7a3H5rmfdf9L8f5S6Egb28/HL0/Y8yyfjw8/Knd/pvj/GV//0+GcZCc/t+hkg29065FlmXXdR3HaTabfDI/WHRXR347O78/P79w3Zd8i23/oqqfUG853m0uDsdxNzbCYeLGxseOE45XrtzTNB/3em+ivfRGp5b+LJcPhk0g1O3+Fg2eXPcsk7kVnAWQF2pXvt2VBxfF24qypihrwabkoIFnGv9Kjm9gsmHaUM+XcnMMUEKLolrW11e+pF5/apr3qdwTxdtxXgIA83J15EeV9oeHP9Gfrvvy/PwiVJMfGv3A/4Xqinq9N9H4Jpf7ILpm9ug9HaddLh88efJdNMC6MrX9/v+E/l15BSg9vd5ry/rT/v7XL1682tr6joK/OBdnHMPONP6VBIBFNMcmMFoUVVG+Nc3Htv1znM7Qp6dnoSaImF2oAWD2ru7nJ0l3VPUT2/6FJjc/PPwpk7kVCm5arYcDX7uy8t5EUhlESyvWavcHNr/GSe11SdKdWu0+byBW1btra18bxoHrrk3j7bjRZxpHsNqAdwoEABgBi6ICLLdYIzwKBdm2f3Hdl7L8YaPxLNojLWZIkcnc6nRehTZ2Oq+ik+qN2LNcPmCMyfIq1brRbo7TXll5jyKkK1MbNawjSKEgO85fQpGcKN4ulT6nZMR5u9EHH5GqEWca80oG24hzuX8f8V5RN042ACw0WhRVku6o6t1K5Yv339eujPzy+VUqA/kRut3fEhIvpnn0qCAIo3dAl5J0ihX5qerdcvnAcdxO51Wv9yY0qIIxNqwzL4/GiKLIz5//Gtrn+fNfFSXcb3TEnp530eu9CY2H3dr6jkckV6Y2itdZuu5ZuXzA/1xZec/3X5+c/JrL/bsk/WtlwG73Nx5jXfl2Iw4+OlUjzjTmlYw/frnb/W1SyQaAhVavP81kbtHQDd7nz3HaI37hl0qfG8ZBs1kRxdued1GtPmq3/2t2Kb5KqqK9oFarRQ9c163X66VSiUZp8D/nmjqYm1iRnyjeVtW7h4c/bWx8nM+vRhsfh81LEqofouqxYAniOO3T07NS6fPoC4ftGZq1hCZkDn6wr0xtVKhEC/2paVVFkZvNb+hP339t27/wGOvKtxt98BFCVWvBM6W5FeJcyWGCVYZ0RpNKNgAkGe9+LcurA6vlnjz5rlp9VK8/ZYx99NEHtEzq6IUudf2zXu+NplUZY93ub81mJfhTGeZFURR6YBjG8fGxJEl8u6qqm5uboWU5ICXizuenKHKj8ez8/KJS+SL6bMxfVLr+Wb3+VNOqwfmHCwWZgjlF+ZaXRKP3HDO111WpfFEuH2jaHjX77u096vVeB4882beLY8zrUyjIjcazXO4DWV71vItqtdnrYf0PgAnwfV/TtE6nk8vlms2mKIq03XVd+pbtdrsHBweyLDuOo+t6LpdjjNHSqDNI3rCyOrhdlj/kP3S50PQC0eMYxh/iNzLAjJ2fn0c30hTNkEJxIz9VvZvJ/NDrvYnTbW6E4+O/NhrP6vWn1IhQqXzBDxiaOmTEnjNLLaHi7PDwJ02rZjK3FEU+OPjPYN3eZN8upnGuT7NZKZcPqtVHvd6bfH61UtF6vTe85yIA3Fi1WlUUxXEc0zSr1appmrTdMAzDMBRFcV13e3vb8zzXdSuVypQCPt6zLbUNnRBULBY3NzdDrb2VSmXe6YL5+OecYYIgoICAiRCEe+g1PL7gfH6Y22+BSJLUbrdFUfR9f21tzfM82k7hID2mG2qapizLvD0uaMwCOTQ8C2X7RCx6yWaaZr1ep8q/QqEwZjUzYoYEip9FEfnBhC16+ZgEoVAPkd8CuTJkN03TcRzHcRRF6Xa7KysrtDG4QMI4BXJ0CmiU7ROBki0IMUMCxc+iM123FwAgtTzPK5fLL168OD4+ZoypqrqxsUEd/u7fv++673R3CS2wCwAwKVev4QEAADFls1nf9xljvu9ns1m+3fO8tbW19fV113Vp2Ieu61TPpyjK6elp6Dg3DvsQLwLAaIj8AAAmRlXVRqPBGGs0GqqqMsaoe1+5XG42m8FJNERRpF6AjuMUCoU5pRcAUgf9/GDC0BtmfOjnt7iis7rQ7RMEIRjeUVc/wzAm3s8PpmShSzZFUUJ5bEzIogmEfn4AAHMgiiIfw0uoLI6WyDTDy+xSBunmOM4EIz9YaBOI/AThXqv1cIJrPJjm407nlWX9SRDu1Wr3Zzw7qK7/kMt9gClJYV5QwwcAk6UoSr1eZ4yFgr+BkwrB0ktcnZ/nXZTLB2dn/5hXAiqVL9bWvioWPxXF2/NKAwAAwKSUy2X+fxB+ZKZT4kZ4VKuPisVP57jmoyTdURQZC1oAwECmaUqSJAiCIAiKoliWNe8UAVyhP8S80wXzkazIz/dfNxrPFGXOfREKBdm2f/Z9LGULAO8wDKNer5dKpVar1Wq1FEUJLtEGAJB8sSI/Qbhnmo+DW0zzMZ8pvtV6uL7+cfy39P3XpvlYkr4UhHuiqOr6DzzGsu2fGWOqeje4v2X9KMtf0c6atue6LxljsvyVJH0Z3M0w/i4I92KGa6PPSNc/6/XeUGIAALhGo3F8fEwr8CqKYhjG8fFxtVqdd7oAruA4jqZpVF1Nj2niSUihCdT5KcratbrElcsH1eqjUunzVuthrXbftn/WtH+Wm0dHbqHwToVftfqoXn/Kd37+/MXm5p9d9+XOzu/Pzy8oCiS2/YuqfjKpznmFgnx0hGF3MGsY3pFwmUwm5kaA5LAsS9O0bDZL9dPr6+svXrzAL5bUmtgIj+hikaRQkB3nL8Etjcaz3d3/4INne703vFPdixevNjbC1YfHx3/l8Zyq3l1d/bJafVSr/bFcPjg8/EmWP2SMue7L8/ML07w/qdOR5VXHQeQHAIy9nY2ZMVYqlTY3N0ulEo2R9DyvXq9jgCQkXLVatSyLphZnjImieHBwsL29jY4K6TSxyK/Vejhw+8rKe6EtNP2j5110Ov/X8y4OD3/iT52enu3s/D64c2iMrSjeLhY//f77/242v1HVT2z7F9P8I2Ps8PCnTOZWqJmYvRuPXmvqmZWV905Pz2LuDADLbWtrK/hnaIzk6ekpxnlAkp2fn/Owj8iyfH5+Pq/0wHxNLPKLGVT5/uty+aDReMYYy+dXV1bei4aGQdFn+ZZCQbbtX1z3pSx/2Gg8KxY/jb68VvtXLWAu9+9xUggAEIImeFho+XzeNM3g4oG2bQfXlb4BQbiHZTwW1A0jv273t9AWx2kP3HNl5T1qkCWaVu12f2u3/8Y3mubja/Wo63ReZbN3GGOqerdcPnAct9N51eu9CVUWkvgTMkfPCGDG0MkPAKbBNM2tra1Op0M9E2zb3tvbK5VK4xwTYd/iihv5dTqv+GPff23bv4R22Nr6buALQ/38jo7cWu1+MBYMdqfL51dDEVi9/jTY4Ov7r237Z6reE8Xbqnr38PCnjY2P8/nV4DHHP6NO51U+v3qtAwIAACSQoiitVqvRaOi6zhjTdb1SqQSrACFVYkV+hYLcaDzL5T6Q5VXPu6hWm71eePKUmOF/oSBXq48ymVuSdMd1z+r1p7TdcdqKsvbRRx+47ju963q912trX5dKn8vyKu2fydyuVL6gZxVFbjSenZ9f8C0xXXlGnnfx0UcfXOuYAAAACURNvaGhSKH2X0iPf7YuCYIwInSjznm2/XOv9yafXy2VPqcBuTeo7PX91w8e/EAVbKr6iareXV//eG3tK8aY79uW9eODB3/jh6V1exlj9frT8/OLbPaOosi12v13x3yovd6by8vmteZzufKMBOHe/v7Xuv7ZdU8QBOEemixvYERTL1qB02Z0gQxzsaAlGw1L39raarVawe29Xk/TtBufEbJoAsXPorEiv5nx/derq1/WavfnG3LRFIPXjSaBLGj5OHeI/BaCoiimaYaWvZ+4hBTIELSgJZsgCMOeKhaLNx6TjiyaQPGzaLJWb6Oue3OfQtlx3NBsMgBTNaJ0hqThc/sBJB9fnze6aC+mIkqtic3qMim12v3V1S8970KS7swlAZ530Wg8Ozv7x1zeHVJrEasTUkhRlHq9zhgLVfthMmdIMhQvEJSs1l5imo87nVeW9ae5vLuu/5DLfRB/OhgIWdA2kTmiCr8RFw2tvckxrHZ2sjcoUQUykEUv2RzHaTQaz58/Pz8/p3G++/v7oije7GjIogm0qP38YAksevk4e1cGdoj80gYFcgItdMlmWVa5XC4WixsbG5qmXV5ebm5uUqfVmx0QWTSBEPnB3Cx0+Th7V1b4MUR+6YMCOYEWumSTJMk0TVrAjcoT13W3t7c9z7vZAZFFE2hRR3gApEqcsA+SxrZtTdMkSRIEwXEcTdN83593ogBGwbq9EITID2CeEPYtFpr8dn19ncZFrq+v+75frVbnnS6AUWjd3uCW8dfthcWVuLG9ACmRnplclmmpgGq12m63JUmiP0VRtCxrbW3txv2lAGZgGuv2wuJCnR/AHKSqnbdcLs87CROTyWQ6nc68UwFwPbRub6/X4+v27uzsLM3vsbkQhHv0v6J8S/8k6UvL+nHe6YoFdX4As7ZYYZ8gCPv7+w8ePLi8vCyXy8+fP19ZWRFFsVarSZJk2za1e3a73W+++UZVVc/zyuWy7/uiKFYqlcPDQ8aYoigDJ0D2fb9cLjuOk8lkTNNM/qx4lUpF07RisRisOykWi/NOF8AVFEVJ/udrETnOX+iB779+/31tIRZ9ReQHMFNTCvumGk32er1+v69pWqFQoDjPcZxyudxsNnVdPzs7E0XR9/0HDx6oqqrrOq1v5rputVptNpvff//9sHUvyuVyLpezLGvMkYYzo+u6JEmNRkPTNPqzWCyiqRcSjnqjum54fSwsSDNBJye/FgrTXddxUjCrC0zYQs99MFW8Y991r0+cCf9uduRrvbsgCIVCIfgUjWx98eKFoigbGxt8zojgbo7jjEg/hYwTT/OiQ4GcQAtdsmma9vz582jHvhs3+CKLCsK9fv9/BOEej/aOjtx2+2+y/OFck4T5/GAeFrp8nJ5x6uRGRE4THyYSfSP+7qIoUvUebXcchxqPXNd1HMdxnG6367puMJijfUannz/FD5hkA0erTHwICwrkBFrokk0QhLOzMz4yaSIHTHkW5ZEfvw6u+3J7+6HnzW3pV0R+MDcLXT5Ow/gVcqPr/ILB31Tr/HRdp954jDHTNCnaE0Xx+PhYlmXf91dXV33f13U9l8sZhsH3GZF+TdPW19cNw3AcR9f1JLf2UrvY1tZWq9UKbu/1epqmYfW2pbfQJZskScfHx4j8Jiga+fGNc01SrCyKfn4A0zLVRliO/3ib3luQWq1WrVZlWe71ehsbGxQCWpZ1//79lZWVbrdLXQBrtdqDBw9kWV5ZWaF9CoUCdfuLHpPGjiiKIorikydPpn0K49ja2go94DDCAxKuVCptb28/efJkgsEfhDhOO59fnXcqYkGdH0zYQv8ynpTJxnwxW4qxyNsMzOYio0BOoIUu2Wjhmej28RoiUp1Fo/38ut3fDg7+cyH6+aHOD2CSplHP1+/3BUEYVqvH32hxv5YWSL/ft23btu3nz5+fn5+3Wq1Go7G/v8+7PwIkkGEYxWIxtIAbjIMC3wUNf1HnBxO20L+Mb2zane3ivG9UCm/EtJmmWa/XS6WSLMtbW1uXl5eapsmyPNmJXVAgT0+wkoYxZpr3ZflD131pGAfd7m8rK++J4q1K5Ytozc1Cl2yCIFxeXk7w9wmyaAJhhAfMzUKXj9cSirqSdtZX1hHCDYiiyFdvo5Zfz/PW1tYmOzfNNArkULOUad5XlLUrI55ok1an86pSCU9Xa5qP6/WnudwHonhLVe+q6l3+2tChJntSNzAwGZL05ZMn39G5W9aP1WozOkJzoUs2TdNUVb1xnV80kyNmSCC09gJMRcKjvaCBaRvWapzkE0kUWr1tQbvJ88UGXPfl5uaffd/e3n4YjHhGzEkxYqECy/rx8PCndvtvonjb91+vrX2dydxSlLUpn80knZ9fZLN36LGufyZJd+abnolbX1+nsfOy/M5UwzHnUdJ1vdfrTSdpC4AWaiPLEe8i8gMYJRonLXSQFD8cXOjTnJ7lWL1Nlj/s9d6wG0U80YUK6vWnpnlfFG8zxkTxtmV9PYUkT1etdv/997VCQVYUWZLuUJ3lMqG1s6MraMf5mNOyikdHR1NJWeIFwz6WmKrrMSHyA/iXdNaHDZy9GY3FAy3H6m2W9SNFb/EjHkX5lh7QQgXBp05Pz4I1fO8+/nbCSZ8EnipZXjXNPzLGDOMPqnr35OTX589/rdefPn/+K21fGjf+2PJ52qNRIywu9PODCZtSbxhaClZRFFovwXEc13XHXDghnXHeODCghDHm+/7JyUlo42RXH5l2P7+Tk18rlS8M4w+MMc+7oIjHtn9R1U9CEU+chQqG1YIsUD8/03xMV4O9bc6O7rPQ/fyifN/XNO3KdXsVRbEsS5Kk0HxGb6eamv8NnbZQnR9L8FljhAfMzTTKR8/zdF2nQooXQIqiNJvNOKPVRsQry1SUz9eYU0kvyo2wLOvBgwfR7clfwyMY8fDg5sqIJ85CBbL8VXAaM9N83O3+Zpp/XKDIT5K+5MNWhq3BtdCRn+u629vb5+fnwY3ZbHb0qjm0MPf6+jpj7OjoqFAo8EgxPTHDUkZ+v5t2UgCuS5Zl27aDj+v1Oo1Ko0o+ql9RVbXRaIw4jvAWY6w/xNRPJjWGXeE4WOBm3cAsT7NardZqtUXPSNQnjzFWrz+1rB/pMe/z5zjtEa8NLlRAe5ZKnxvGge+/Zox53kW1+qhU+j/TS/w0PHny3dGRK8tfyfJX1eqjJ0++m3eKJqxarSqK0mq1MplMs9lsNpvZbJYW3RlBURTf96m1l71dwDBtQnFeYsO+a0GdH0zY+L+MTdPsdDqWZfGpBGRZ5usO8To/z/O2t7cHrgkGMCWLu4ZHqK6L5jFhjFWrj168eMUY++ijD2hWl+Ceoxcq4Hua5mPHcVlgvpjoOyakzu/GFrrOj8/npyiKYRiKotCE5M1mM/4RQq29C303lxJae2Fuxi8fecBnmma32zVNM1joDHsMMAM0afO1evVRt1TGGPVS5dt5R9Vut3twcBCcbmO+BXKwCXhSey6BhY78RFE8OzsTRVHX9VwuRxlvnPITMUMCYT4/WGCSJNFP0sPDw4ODg3knB+BfTNPUNK1Wq4Wm9BsWC5qm2ev1qGZa13Uan0RPGYZBtS/UB2t0j6trWb7px8bBr0aaL4WiKOVyuVKp5HK5er0uy7LjONlsdt7pgvmIFfnRJ6dWu89/3rnuy/v3/7/T0zO2FB8nWf7q9PQseiK2/bOmVXd3/2PJRvgnX6FQoK5+VBGSz+c9zwt913qel8/n55M+SCvXdXu9XnSQx7Cf2vV6/fj4mB5XKpXNzc3ggHSKF2VZDnW9H0e0Q/p1xa/GS36FX/BqLHpz8zh83/d937btYrHY6XS2tray2eyTJ0/mnS6YjxuO8OBh3/TY9s+K8q0g3BOEe7L8lWk+5k/5/mvTfCxJXwrCPUn60jQfU+fiG9vZ+T29Y2g79VyhZ2GWaCRvqVSiP3d2dnjn4kKhQOGg4ziTnUoD4ErXHeFxfn7Of7FIkhSM8HiWNk2zUCiEXjh+ADfZ4yyiNJ97FPXwE0XRsqx+vx9dzwPSI1Y/PxrAlcv9O5/hnT5R06sMo8o2Vf1EVe9mMrds+5dG4xl/O0X5luajkuVV1z2rVh+tr3/MFxe6Ac+7WF39slj81LL+FNwuimomc3vYckYw0DR6wwyceopPNDXZ9wIY4bpdowb1i//Xn57nlcvlFy9eHB8fB+cnGqcT1QJNQjFtAyO/cS7s4vbzM02zXq+XSqWbrd4WhX5+CTThfn7DVmBcWXnvGom6jr29R/n8arP5TTABjcYz0/yjZf14dOS2Wg9po6KsyfLq1tZ3lvVjaBHx+CTpTj6/ats/ByM/133Z670pFj8d+2xgXDQkLVjJR48R9sGMqapqWZau6zH3pynTKKN6nhfsWUUjmSqVSvzxlXAtNCo5tGVeiZmvcVZvg+UTq7WXmlypvdVx2vyzVC4fRH9UyfJXkvRlcIth/F0Q7l2rQXZl5b1S6fPglkzmFi00eXTk5vOrocWC8vnVo6MBs3tQsnnTsKJ867ovHacty19RSzGfy2pn5/e93pvgRFaHhz8xxhZuYqplRX3h+Z/UcjHH9EA6ra+vl8tl0zSdd4gIktkAACAASURBVA3bv1QqVatVelytVqkDA+1fLpebzebEs/FSTj8GYxoxlSak0LXH9q6svFcoyBRmZbN3crkPQjvs7Py+XD5w3Zd8SndaFIjPHRpHqOnWtn+m1l7GmOO40cUlNzY+jvbSI9Xqo2z2jmne9/3X5fLB9vbDXu91rXZfku6Y5n8/ePC3jY2PZflDVb1bLh84jstjStv+JZ9fjbOEOQCkxHXrTmgALzWx8Z8rW1tb/X7ftm2auoj2nOA0uYj2OFwKgKhrR36y/KHj/IWq+kqlz6NjuyiEOjz8iSI/1315fn5hmvfp2WFdbgsFeWBHPb4/7+TX672Jhpu53AdUIxiVydw+Pv4rxZ2dzqvvv//v/f2vqV04l/v31dUvHceV5Q/fNvj+Qu/ieRfn5xe12v041wQgCXzfp75inuednJzQqicwWTeoJgnO4Rc8CGpcYI5irtsLS2ny8/lJ0h1V/YSHUIeHP2Uyt3gtXav1cOCrhnUZbLUeet7F0ZH7/ff/vbLy3g0mEQhWN9K78O6Aofq8UunzBw/+RrWVVIMYrVwESCDLssrlciaToVEvuq5j1PPM4BsUkm/Yur3zSg/M11TW7S0U5PPzC9d9yRhrNJ4FB0koytrAf7xpOERR1nT9s2bzm0JBrlYfMcYymVudzqvQbp3Oq0zm1sAjxB+GQnEezeRyePgTmnphITiOUy6Xj4+PPc/LZDKGYXQ6HXSCnBLXdSVJCq4a/P7773c6nXmnC2CUm63bC8tqKmt48D5znc6rXu9NcD68YWuBr6y8x4M/QbgXnDWaKMo/Oxcqivz8+a+hlz9//quijDs1kSjeVtVPDg9/KhY/PT09Q1MvTIQgCNd9SXB5OnZVs2Cj0ajVarwn2dbWVqvVchzHdV1FUTBl12TRN6iqqpqm0RenYRj4BoWEs22b1u1dX1+nxgHGWKPRQONAcpjm43r9aS73gSjeUtW7VBU1pcWvpxL5ieJtVb17ePjTxsbH+fxqsD5va+u7gS8J9vMrFOR6/Wko8qvXn+bzq/Ssbf/iOG0+FMNx2qenZ6GxwDdDB280njE09cKEjNmdq9/vj47/gsuuO46jqqpt28+fP9/Z2dne3q5UKvGnIIEr4RsUFlEmk6EHkiTRb0L69TLfVF1JEO4VCv/67UoDBgzjoNv9bWXlPVG8Val8EQwwQoGRZf1o278wxo6OXDqOqn6i65+57kvDOGCMdbu/HRz8Z6jJMfim3e5vpnlfUdYM4++u+8/VKzqdV+fnF5MdPGRZPx4e/tRu/00Ub/v+67W1rzOZW8Mm1BvftNbtVRS50Xh2fn5RqXwR3B7nYun6p5pWVZRvVfUTSbrjeRf1+tPz8wvqI6jrn9XrTzWtGpzJuVCQbzyZX5Cq3n3w4G90wGBTr6J8K8urWMMN5oJPt84GxX+00p2qqq7rNhqNhw8ffvXVV+12W5blYrG4urqKyG+CFvQbFFJucdftDQ39lKQvnzz5jmI1y/pxe/vhiKUWdP0zCgwE4V7wONvbD+kgrvty4BH4zq77cnPzz75vB7/9ZfmriTcJ1utPTfM+jUkQxduW9fVkjx8yrchPVe9mMj/cbCZkVb3bbDLb/rlcPuj13mQytxRFDgbmx8d/bTSeUTiYzd6pVL6Y1HzL1OBL09AEtw+cLBBgHHFacoMCy+288xLTNKnB8eTkpFKp/O///m+xWKR+fjzmc123Wq36vq/rOsb8jmNxv0EhzSqVimEYS7BuL33p02Nd/+xmffH5QWT5w9Exlix/GJo2xDD+frPBpqOdnp6FZikOPP52su/FYq7eBp53oes/jLNAXHos9BpHsxQn8hu4VtjAF/q+/+DBg2azSZPGKYrieR7NIVypVGitCFmWDcMolUqoBbwx13Xp8haLxXK53Gg06Bt0sv0pUSAnEEq2oNlk0Wi3NtN8XC4fFAqyosiSdCfUKWtYN7jQdsP4e6PxTFHk9fWPRx+E2ov5V7/jtDWtenb2j2vNTxxHzJSP7ucXP4tOZWzv8jk5+VWWV+edClgeNxj2wdHk+zSwlG8URZF6++m6bhiG67q9Xi+Xy1mWVa1W6Re/oiilUqnT6fi+L8uyJEmaprku6rOvgSr5DKx8D4vGNE1ZlqncUBTFtu15pygWRfmW/hnG3xljhvGHs7N/6Pqn3e5vhnFAG6/LNP/Ybv/X+vrHnc4rXf8hehD+puXyAR886vuvNa1qWX+aeNjHGMvnV2k6lLcpfHyzU4sJdX6xKMq3zWZlGvd7+eCXcRzBoG3E5RpY5xc9Tmgf27ZptCm17UqSFKyU8jyv1+t1Oh3qGri5uUkrSWAgcByKotA36FTfBQVyAi10yWaaJv0CpKzrOE6j0bAs68Z9P+ZY58dbWn3/9fvva3GqxELb4x8k+Kym7WWzd6bU3Z8qFynM8LyLtbWv2u3/kqQ7U6rzm1Y/vyWDdl6YoFCF35Xh3QjBwR/8T1VVgwU6H5Tg+36v15MkiVaVsCxLkqRer8cY63a7juPwb4VcLidJ0s2StPT4hQJYFPV6nbqC0J+KomxsbOzt7S1cr996/Wkmc4vGbfDuesG5PiZ7EF7dY5qPX7x41Wx+M5GziNL1z3q9N5pWZYx1u781m5WpTieMOj+YsIX+ZXwtNLQizp6h2C4YqI3u7XfdoHBYFeDe3t7Ozs7h4eHOzg5jjNorXdd1HIeaiSkWpI2bm5vtdhuR30Cmadbr9VKpFAr+JjurCwrkBFrokm1Yj+Ebn9G86vxc92W1+ujFi1eMsY8++oBmdeG7BSdkCU7HETrOlQcJ7kyjidfWvs5m7wRXjk1gfVD8LIrIDyZsocvHa4lfbt642L1Z0RyN/zzPs22b2nM1TSsUCjTIgyYl0XWdIr9isbi5ufnNN9/wmgCqFxwY1nieVy6Xm80mrR1CSwYvvWEdNCeb51EgJ9BCl2yaphWLxeAHmX4QXtnNl4Y0Mca63e7BwQH/wZOoLBpsvZ3vQa5FEO7Rg0ldSYzwALiCIAiWZQmCQHOdyLKsKIqmaZ7nMcYoTqJQybbt6D5UGg6r6aG1XDVNo25hMz2xd4eAUKQiSZJhGFRqVyoVWohCUZSjo6Ngcw9foIJv6fV69N1gWRYv9H3fZ4xlMhnas1wuHx0dMcZoQPHsznMe+kPMO10Ao6yvr2uaZpqm4ziO4/DVvZ23hr3QMAzDMBzHOTg42N7enl4KBeEe/ZveWyRK8Exnf9Y3rPOjhEbXWIOEoKHvc/lNtii/jAVBqNVqhmEE68Co43Oz2RRF8ezsTBRFmi2FMRbdZ0SFnKZp6+vrFB0ahvH999/Pss4vehA2pFKKTwFjWZZt291uN1QHQKvAUdR4enpKQ1mpOfjo6CiTydDVoLHDE0ntwqEof8R35w0kqkIFyKKUbANdOZnAsFOj6JAfJLiw5ASzaCj0SUPmj0Z745/15Ed4mOZjxpgsr05vORGAGaPIjKr0QnMcKIqyublJ/aApyIvuM0JwRbVKpfL9999PNuXXMnAUCOFluiRJJycn7fbgZbUty9rZ2Tk9PWWMVavVUqlEc5rQ7ICNRoMOVSgUHMfxPE9V1WVt/HVdd3t7+/z8PLgRMzlDwt04ZuVFhGmahUJhcin6l/TU8yVH3MivXD5gjNVq9xH5wZLJZDJUyUd/UknXbDZpAIRlWXt7ewP3GX1M3/cTFf0Ef6wHt9AkL7QKyLBRHfV6/fj4uFwue55H14QxdnR01Gw2j46OqHbTNM2joyNqEV5dXaUa0+sm0rIsRVGSPLiEN4hrmkbXgS7dvNMFMC3Uo/fFixfHx8fB7aNnGIEkQz8/SDtVVWmtC8aYaZrULY+iFsMwms3m+fn5wH1iHpOqxJKDd02jXoCrq6tUdTdsfodqtbqzsyOKYj6fr1ardO6u6+bzeVEUPc+jzo6dTmd3d5cuzvr6+snJycCj6bruOA4dxLZt6g3JK1Pr9Xqn05nKaU+Ibdu1Wk1RlPX1derpaJpm0m4xwKR4nre2tra+vu66bui3HMK+CZrxxbw68nOcNq+MLZcPghWz3e5vuv6DINwTRVXXf/D91/wp2/5Z0/aow6am7QUnp6aNtv3zsNdysvyVJH0Z3GIYfxeEewN3HsHzLjRtTxTVaGJuRhDuUfM3Z5qPb1Blfa3juO7LYev3ue5LfrVl+Svb/vm6KUmzWq3GGKM1LU5OTvhcd/fv36c2X8uyBu5TKBSGzetWq9Vo5rzJTvYxWTwEPDo6evDgwcCeQLlcrlgsFotFxtjKygq15DLGnj9/vrGxwRhzHIeq6HgIyBjrdDorKysD37TRaFCcRKNkarUa9RF0HEfX9dPT09kPiLkWPjmiJEnUJ1JV1UVZDgHgumj8fszpq24mnRFkv/8/wX8zfverW3tXVt4rFOSjI5cxFprPptF4RosZ93pvGo1njDHL+hP7Z9hX5bvZ9i+2/Uur9TDYUry39+j09Cz62qCdnd+Xyweu+1KWP+SHUtVPrrWWBk2Hnc3eoePv7T3a3PzzNNbdm7Zu9ze6CyGu+3Jz88/Z7J39/a8l6Y7nXRjGQa93veA4hXjrpyiK0WgjNBkyYyy6z4g2X+oGx/8MlZs8xhrY9272hjUEM8b4tM+MMcMweDx3cnKSy+XoMY3nPTo64hfk/Px8YExM87/s7+9T5QEfCJzL5Xq9nmVZjUaD3s73fWpcliSpVqsNazimRegzmczKykqwOX56FEUpl8uVSiWXy9XrdVrMDf38YFlRN2heCEx2JBOXzuBvjq6u85PlD/mMhaXS58HZCzOZ2+323y4vm6r6CWOMAjjG2N7eI8aYqn5yednkzxrGQfCwvd6bs7N/RF8bROsoHx7+RH+67svz84vQ4spXqlYfZTK3j4//qqp3VfXukyf/D481lwM/QV3/TFHWdP2z4+O/zjtRsJBCDcGhpYGDy7uVSiWqCOSNvzz6oaEeA4/f6XTW19cpPuMzBWqa1ul0MpkMtSDTW2ialsvlms1mLpejsdVRvu+7rut5nuu6sizPpsm1UqnQ5Ig0O9rW1pZt20+ePJnBWwPMXr/fdwLmnRyYjLFWbyuVPqfauPX1j237F76dKvOoqi+0MfhaWpwk9NogSbqjqp/Y9i80E/fh4U+ZzK1o5Dd6OkTHcYPVhJJ0Z2Y/Lyhh03472/5ld/c/glWYknRHUeRhVxXmji/dwf+cY2IGGrjiSHA7DwFlWZZl2fd9Hv14njesEdx1XT56o1qt0ngOxpgoitQ1kOoUPc87OTkxDIP+HzYhBVWsUujpOA4tTzJtVMlHjy3LwtgOAFg4U1y3V1U/WV//eMyDFAqybf9CDb6NxrNi8dPQDsOmQ+Tx1vn5xcrKe2Mm47pGTNJ43UAw9HL+Z6Eg8/rX6AmOiKchCa5cty05rowCGWOiKPKWVpr1cBjeRryxsdFoNHq9nuM4vV6PBovQQXgVIHWpHFaDSF0DaSG1brd70/MDAEiXqUR+2eyd8/OL9fWPx5/nWVXvlssHjuN2Oq96vTc7O78ftuewiCqTudXt/hbc4jjtXO7fJ7sccugt4iQs5nFarYf0wHXPyuUD/ieP9rLZO53Oq/jpAbixYVEgixfCKorCR0js7+/btu15XqVSYYxR5dmLFy8cx6GaP+oc6fv+sGZc13VLpRLtZprmzAbT0NK9NKVfoVCg5e9m89YAAOObSuSnKHKj8axef6qqdzOZW5ubfz49PQvWUcUnirdV9e7h4U8bGx/n86t8qAfHl1geNrcQtXvylZsdp7219d3+/te6/tn1z+xfgsGW778OVbDxVI15HMZYaALF6HyKqvpJo/GsVrvPG3x9//UydWRcVsmv7RstlP44gWCwFVgURR4w8TZTSZJyuRzv/7e+vn54eDisGVdRlO3t7ZOTkxcvXnz00UeO40x1BCKhMSVU0cgYc123Wq3ShIjTfmsAgIm4XuRXrT5yHPfKAK5S+cK2fz4/v1hd/eecLJnMLdO8f7MkUhx5fn5RqXwxbJ8R9WqVyhebm39WlG91/VPff10uH+TzqxT2Kcq3srzKg8L4CgW50XiWy30gy6ued1GtNgeOpb2yti/mcUarVL5oNJ6trX1dKn0uy6uue1avP73uQQDGNDoQjO4wEK+3o9XkPM8bUZkny3K73T45OaEdZtP9vNFotNtt3luRZnVeW1tD5AcAiyJu5Fer3a/Xn56fXwycWCREku4cH/+1Wn1ENViFgmya96PVdTGp6t1M5ode7020k18csvwhJUbTqjRApFb7Zwwa51wGajYr5fJBtfqo13uTz69WKlqv94aWOZn9cUTx9tnZP8rlA7pB2eydUulz9nbZFZi7K1fMjFr06kA26BQGXocRZzpsZukgURR5XDib1t6BE7jwJmwAgOQT+JqeKZxQx/MudP2HG7RBwwgLva55ogTXR19WI8LiZJ47dfLjrb2e59Xr9Y2NDR6nTiQATWeBnHAo2YKQRRMofhZNdeRn2z8/f/7rDVp7YQSUj5OShshvhGTWlV6ZqomkIZ0FcsKhZAuadha1rB+pzfDoyC0UZMaYqn6i65+Z5uN6/Wku94Eo3qI5eqeXhoUTP4tOcVaX5LOsZ81mZd6pABgg5WEfS2qdXzJTBbBkdP0z6o4vCPd4u5xl/Xh4+FO7/TdRvO37r9fWvs5kbkVHPcKVUh35oZ0XEmhR5vkDAJilev2paf5zFgtRvG1ZX887RYsq1ZEfQNKgqg8AYKDT07NgDR9q+24MkR9AIqCqDwAAZuB3804AQNoJgkBVfQj7AGAGDMOgFbej81CGnnIch2ZWVxRl7qtU5/OrrvuS/2majw3j73NMz+JC5AcwH8JbiPkAYGZM0+z1eq7ruq7b6/VM0xzxlOu6lUrFcRxaJnt6qaKFuOjfsH1Kpc8N48D3XzPGPO+iWn1UKv2f6SVpiaV6VheYhunNfWCapizLiqKYpmkYhuM4rusu3NoJfFqQOFcpOIcIosP0QIGcQEszq4skScfHx7QOjed5m5ubnucNe4qmrozOUjnZLBqN9vjBQ+uymuZjx3EZY93ub6Z5H139gjCfH8zNlMpHz/N0XacVuvgwCEVRms2mKIoTf7vJukEAF2cZXFhWKJATaGkiv9AwsuCf0acKhUK3211ZWWFvf3sH9pxF5Afxxc+iaO2FJArGPfS4Xq/TMglUyUe/QVVVbTQac0rjKMK7GGP9t+adNACAuFRVPTg4cBzHMIz79+/POzkwGYj8YDE4jkPRHvVKoco/RVEODw/nmzBhkH7EdQ8begmPIAEAxpHNZnnzrud5waWoo0/puk71fIqinJ6ezj61MA2I/GAxnJ6eUu+TIEmSploYDYzqrgzyJlWxx4/DDxusRAQAuIFSqVStVulxtVotlUrs7W/p6FOiKFIs6DhOoVCYUpJCbbto6p029PODCZtIb5ho15NhnVFGTH08foSUhMbZgSeIyf+WGwrkBFqafn6MMRohxxijAXMsUM6EnqJ23mn384OJwLq9sNgymYzneZIk8RkH8vk8bQnu5nlePp8fdpDlKKYHngX/wTZiHwCAgYIzuRBehoSeUhTFdd0ZJQtmBa29kES1Wm17e5v/vmSM7ezs0C9RxlihUKCneOe/dEIrMMzXiKnXEmVR0gkTlPybPscUCqHKA4DxTaMKyvd9TdN48EdoWvlo/7/UQivwckCBnEz4ZHHIosl0vdZeZGhIOFEUFUUJVvLRY4R9QWgFXg64cZBwyKILbWjveABYdKgCBACAEER+AAAAAGmBER4A8A7eWMxH+Q3r0xPd81rHBwCA2UOdHwC8g3em5FN8DRtDHd0zjmvtDAAAk4U6P4AlYdu2oiiKosiybNs2Y8zzPE3TFEXRNI0m5RIEwTAMGhljWRa90LIsWZZlWaYtW1tb7N31kWkLPyx/HN0ztE9oLDZnGIamaZqm8aWigBiGQfeCruo4+4eecl2XZ4/lnqHtymsYnC4K5o4miJZlWVEU3/cdx5EkifIqL6PmJZqYSSUvehzf96lkpuswsXMYaODaUwCwcDKZzOXlZb/fv7y8VFW13+8XCoV2u93v99vtNm1hjNVqNdqSyWSCL+Sv4sVC6EGtVisWi/1+/+zsjF4b3ZPv0263s9nswHQyxvb39/v9/v7+Pr0jEH71+v1+sVikO3Wz/aNPFQqFVqvVH3lrlsCV17DZbOKLLzmazWY+n6eCq1arqapaq9WofEiCaGImlbzocXZ3dym71mq13d3d8d9iBHwAAJaEqqr5fH53d7fZbNIWxlghoB8I0YKP6dngqwY+4AEfL5iie15eXtI+vBSLGpgG6Pf72Wz27OyMHp+dnV0Zn43YP/oUZQCyxJd99DXc3d2lxWfnkTQYIJ/P069T0m63a7Ua/URJgmhiJpW86HGy2Sz/6T7t32bocAOwPFzXdRzHcZxut+u6riiKvNWA+uQNW/LYcZxGo/HixQvXdfn26ANN01RV3dvbOzg4kGU5ugPfxzCM4+PjgbMtxlx2OYVCV+PKizNi/xFP0WKswxriF12ca4hclxyCINRqtcPDQ8bYRx99VKlUDMPodrvRlYLnQlGUUGKiWyZ15JkWjFONKwFgZjKZDP165hVvvLWLGvv6g+rboq9iQ+r8+m/bZ/P5/LAd+v1+s9nMZrMjmnEZY1S/GGyYg36kKu7K8nnE/gOfOjs7o9tHVQtLKc41xBdfcjDGeMsmFR37+/tUIrVaLV7UzEs0MZNKXvQ4Iz6/E4cPAMCSoB4zhUIhn89TaEVd92gjlTLRwiX6KhZoBY4WSZlMhndPie4Z3SeKyvp8Pq+q6hKHIDcw1dZeaqy/su/gootzDRH5zR31MNnf389kMsFGz+v++JkSnrzRiblu8gYelh9nlq29+AAAwCTNoNhaVhMZ4UHfo9GnVFVNTvep6RlxTThEfslRLBZ5nd/+/n6hUMhkMhS7t1qtYOfUuYgmZlLJix5nliM80N0BACbGNM3Dw8NvvvlGVdV5p2UhGYZBPfAURYkzP3Z0f95DKPSUIAg0uIEsaz8/NvKaEPTzSw7f98vl8vPnz9nbfn7dbtcwjIT083McJ5SY6JZJHdn3fU3TOp1OLpdrNpuiKE7wRELwAQAAAABIC8zkDAAAAJAWiPwAAAAA0gKRHwAAAEBaIPIDAID5EARhiceaACQTIj8AAACAuBb9FwsiPwCAVBAEwTRNTdMEQZAkic8a47qupmmiKAqCoGma67qhlyiKoijKsN2ChxVFkVaHo9WoJEmyLIt2syxLkiTagb+QMba1tcW3RPcJpSF4OiOSDQCjTHW2QAAASAjGGF8yodVq8SU9CoVCsVi8vLy8vLwsFovBibgZY9lstlartdvtYbvRYWllglqtFnwX+vPy8pLertVq0boatFoMvZZPszxiH56G4OmMSDYsGcZYrVbL5/OZTCY4y3G73VZVNZPJMMZUVeU5hPanNTNG70Mzj1J+q9Vq2WyWduPLC9HqarQPZXIePvG51vf397PZbGiNnGAaQqdD+zPG8vl8KFfPBiI/AIBUCH5X9ft9+iqlB3zFs1arxd5d4o+/ZNhuocMyxoLrU1Fsp6oq/8Kmr9vgs/R4xD4DlzMZkWxYMhR4nZ2d0a8Cnh9G/BqJ+Yul2WzSdorD+FtQVqR8RW/XbDb5T5pxfrHw49D70mrpM4aPCgBAKgS/rvqBaOny8pIWPaO1s0KRH3/JsN1Chx34Z3B51larFfz25dvj7BM0ItmwZEK/QHjeiPNrJM4+tJ3vRnV1/X5fVVW+GGC/39/d3aUfJOP8YikUCsHtfFH1Wfq3ybQZAwDAYiqXy47jlEolVVV7vZ6maePsNlCv19va2hp/n0mlBxYOXydNluXz83N6XCwWG41Gp9PxPO/k5GTg/nH2IZIkhd7UcRzeUZUxtrGxYdt2dJ9ms8kPWK/Xhx2fHB0dGYYRfPnA850qRH4AAGnheR5/7DgOreTbaDRarRaNn4h+sXExdxsok8lYlkV9qnzfD30Bx99nUumB5RAn+h/zF8uV+1/3F0sSYGwvAEBalMtlCpJs2240GrquM8ay2SxVPNi2TbURvu9HXxtzt4GKxeLe3p7neZ7naZrWaDT4U71e78p9BhonPbBw+I8W13XpFwtjrNFoWJZlGEZo3HdQnH2GoY6AvJGUxiqN2GfgDiH5fJ6PQ/d9XxCE2Q9LR+QHAJAWFF0JgrC3t8cr2J48eWLbtiAItm1blpXNZgfWc8TcbSCalmVtbW1tbU2W5f39fZ4eTdNoDpdh+wwzTnpg4dTrdfpVUK/XKd+yeNH/OL8QKpXK3t4e/VhyXXdzc5P/ILnxL5ZSqVStVh3H8X2/XC7n8/mBjcLTNeN+hQAAMBdsyFAJgIRjjNGw3NDMKe12m8/DQqOCaGRGMKvH2Sc0NpyP8Oi/HVDCGKMBvzTbC40F5inZ3d3NZDI04wyfDmbExy04fcxcZnUR+oHJaQAAYFkJgsA7xgEsEGTdyUJrLwAAAEBaYGwvAEAqoIUHABhjaO0FAAAASAu09gIAAACkBSI/AAAAgLRA5AcAAACQFoj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIC0R+AAAAAGmByA8AAAAgLRD5AQAAAKQFIj8AAACAtEDkBwAAAJAWiPwAAAAA0gKRHwAAAEBaIPIDAAAASAtEfgAAAABpgcgPAAAAIC0Q+QEAAACkBSI/AAAAgLRA5AcAAACQFoj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIi/lEfoIgOI4z4/ca9qaTTYwsy77v02FDRFHUdZ2eJYZhiKIoCIIkSaZp0kbPWd7gWAAAIABJREFU8yRJmlR6lkycm+U4TvAiX/fl03th/INM7wMSvDjTy/nBUw5RFIUnQFEU13UnlYCEuMFVDd6UEbl3sm86Ar+VjDHLsmRZpnsnyzIvpga+qeM4giDwP5fy/i4Q0zQFQQjesvhulg+jkvP9G3wj0zQlSeK52rIsfoSUZNp/m3cCpq5Wq+VyuXF2iM80zZ2dHVEU+ZFlWebPep5XLpd7vV6z2aSdG41GrVaTJMl13Wq1yhgzDEOSJEVRTNM0DGMiqVomcW7W1tZWq9VSFOVmL7/x+y6E4MWZXs4PCn4K6CPw4MED+ggYhmEYxsx+BCZW8KaMyL0jTOlW6rpOZRRFD1RMdTqd4DflCLi/81WtVmu1mqqqN3jtzfJhVHK+fxlj6+vrtFu1Wq1UKvSs4zgPHjzo9Xr0hZuWTNufB8ZYq9Vasje9vLzMZDJnZ2cj3m53d5df82w2u7+/z5+q1WrZbJYen52dZTKZ6SV1uc0ldw0TTEz8hE3vFKZx5BE5P/p2+/v7wWInm80m52ZNxA2u8M0yyTQEbyXdqWAZxTe22+3+oKS2Wq3Qd8ry3d8FMk5emng+TML3Lwl98/bf/fLtpyPTjmrtFQTBtm1FUSjwtyyLKkhFUQxVIBuGwZsseW1/sHI11ApAfN/XNI3XuAarWENvHeR5nqIo9CpN03jVrmmatD2UvGhlsu/7sixHG6foINS0QfVw/CWmaVKzLNXGRc+FMWbb9vr6+pUNtZlMhh6cn58HtxeLRf5LWpKkbDYb84d1qgRvlm3buq7zNkS6X3Rrtra2KA+EMtKV93pg7mLv5iL+QZAkid+jEZk5RJIkXdf5n5S1og0rrutemZ+DH6vQmQ77mIQuzixzPhFFMZvN8j9LpVKj0YjzwgQamBMYY67rRi/msBwSvCmhG8SGl7oDM/awDwUZWEqHBG/l0dFRPp8P5lXGmK7rtVot/iVa6Pu70EJ5aVj2i1NQBC369y+LfPOyd798WUoy7YiokDGWyWSKxeL+/n6z2cxkMrVardVq0WMeNddqtUwm02w2aTs/JgsE3aHfgvRUoVAoFAqtVqvVaqmqGgy6g28dSlWhUFBVlV5FR6D9KZBvtVr7+/vB5PFk0IPLy8t8Pp/P5y8vL6M78IPk83k6cr/f5wekg9P3VvRyqapaq9VC5xjcgV9D+rNYLPJzpN/QQbu7u6qqDr85KRW8WcEMye8X5bRarUY//kIZ6cp7PTB3BV9IzZT0vvQVSPduWGZmkeoc+rzwM8rn88ViMXqaPHm0fzQ/99/9WIXOdOCJRC/OtHN+6FNAxw9+qNvt9uhSKLGG5YTgxQxmoWE5JHhTQjeISoxmszmwWItm7GEfiv7wUjokeCsZY7u7uyOuQLSIi9b5Le79XXShvDQs+8UpKIIW9/uXo5Y32n/gPmnItFdEfvyTXygUgpd1f3+f35h8Ps+fohKQv3x05BfMWNEdhhU6lCPp8dnZGT0O7b+7u5vP50PJYIxRaRjMdqEd+JGpWOcnGDr4wGwRqkMeGGc3m83gS2q1Wj6fp6eoHOcJazabwVAYSPBmBW9K8H6FIo/gblfe64G5K/hCVVWDgRr/6h2WmaNh0NnZGc8Jwceh07wyP0ffKHSmo08kztW4cc4PvkVI9PfMiDI6yYblhOCNCN6g0cXdwFi5UCiEOoQEf4pEM/aID8WwUjokeCuDuWKggUVc9MgLen+XQPDKjyigriwoQsdc0O/fYGamjxJ/KvjNO/r0l8YVkd+IEnzgd9uw77xhJV2tVtvd3S0UCqFAfsR1p2Irm83u7u6OqFqIJoO9bWkNffcEd7jyRKLnMizN7G19AJfP5wdW411eXjabzd3dXQr+Rr9Lyg28Wf2rgq3RLw99PVPuKhaLwYBs2PsGDczMAxNTKBToRof6l0TfbvTZjcilAz8mw9Iz8ZwffIvgp4B+vod+1C1oITviq3FYuRenuLtBqcv/vEG2GZF4FqPOL1TEDYwpF/T+LoFomRDNfleWeCED91+I799o/WW/32+321QdHmp4WfpMe41ZXaIXgrbzjmvXpet6vV5fWVkxDCP+yHPDMM7OziqVSq/X29raGjgAttfrDUwV/SywbftaI3cymUyv14u/PyfLshLwzTff2LZNTwW74IiiqKqqaZq1Wm35uxckG89djDFd1+MPr75WZlZVlXLC4eFhnJF3w/LzCHE+Jle6cc4PCn4KdF23LCudmfxmxd2wUve6blBKFwqFaDnp+35wopBQERcaSgnJMSz7XbfEi7N/Mr9/eRfA4JevLMuGYaTwmzdu5KeqavBumabJe/6ur6/zpzzPC76K37CBHd4bjQbNXRJ/6LiiKI7jUB95y7Kazeb3338ffQvHcQYek5KtqmpoXr04b8r/HNZ5P5vNjs6gwQ9Dt9ut1+sDD0IPPM/jDcEwM4qidDodyl2WZfHcxeXz+WBm4BMxXiszq6ra6/VM0zw9PS2VSgP3GZGfR3+s2MiPybVMKucHTWSSsCQYlhOGuUFxFyp1+e/GGxhRSgcFb6Wqqqenp6Ev6UajQRUkN04JzMWw7HdliRcybP/kf/9y3W738PAwtPHGFVgLKu58fqqqapq2srJCw4JoOhx6Std1Xdc3NjYYY+Vymb8kn8/v7e1lMhnP84ZFOY7j0A40m53v+wOnBONEUTQM45tvvqHfAXt7e7zKpFqtZjIZGhPUaDSoTnigWq22trZWrVZj/vIuFotbW1u5XE6WZZ7UqI2NjefPn8ecPOmbb77RNK3X66mqSnnOcZxGo8Gvaq/X++ijj+IcCqJc183lcjeYEJsyGGUMy7Kid7NUKpXLZTq4bdt0B9k1M7MoisVisVqt5vP5YYkclp+v/FixkR8Tdp2LM5GcHyypaT6/YNxAZfr404bN3rCcMMzoHBK8KfxxqNQdJ7XDSumQ4K3Udf3k5ETTND7zmeu65XJ5f39/dCkdtLj3d8kMy34jSryBBcWw/Rfo+5e+fNnbbBmKZ1hKMu2IlmD2blsDH1YT7alDnS5pOz9mu92m/alzMYv0a+E7FAoFehwdShlyeXlJQ2Ip8bxjJmOMukyxt1XK0bMIHjM0Fm90P4PguWez2WH9o/f393m31oGnQCOG+Ebq7spvRGjMY6hzN5CBN6v/7v2iTMi7Hl+rn9/l5SUvOwqFQnQAWv/tMEnKDLyny7DMzN7tOBW8+ywyWVowncPy87CPVfDgwz4mwy7O9HJ+tMDZ3d0NdqammWYHXoTkG5gThl3MEcVd8KYEH/cD139050h2VT+//pBSOiR0K/tvu6LSvVNVdXRXsGgfrIW+v4sueIOGZb9hJV4oH3ID91+I798gGkBJZ0HHDD6bhkw7gTEE7Xab950c1mN92kbf5jEFT7D/dkDuwD2DM0mOg2ZyDo02gmVCk2sk/BbPIOfn8/no0GaYuPil9KQKMYL7mwZL9v2bhkw7gXV7Dw8Pt7e3Hcexbbterw/rt7S4HMfZ3NykfqmWZVWr1Z2dnYF7ViqVYXXR11Kv1yuVSvwmFVggvu87jkMtsAm/xdPO+dSqcrPVpeBa4pfSkyrEGO4vTMKMv3/TkmnHDx4vLy/51DihppyZYdP8zUFtZ3SC0ZrhkGw2O+bPDprrcpwjQJJRQ0ZoUqtkmnbOp6llx0sjxHKtUnr8Qozg/qbEMn3/piTTCv2bThMAAAAAAItlAq29AAAAALAQEPkBAAAApMWMIj9BEKa6/zjwXmlAV4Nfk2lfnAkeP5mHmuzRUptXBUHgOXNYFp3xxQkmI7VpGPjWqc2lsHxQ5wcAAABhwWA3GvgODIVj7hb/5dfdeUpHmMbG626fyJEJIj8AAACAtEDkBwAAALAkruyZgMgPAAAAIC0Q+QEAAAAkwgzGEiHyAwAAAEiuyYaDiPwAAAAA0gKRHwAAAEBaIPIDAAAASAtEfgAAAABpMbvID0vfAAAApA2+/ZMGdX4AE4ZiDmBp4OMMyweRHyw/lN2QQDxbIn8CwCwh8gMAAABIC0R+AAAAAGmByA8AAAAgLRD5AQAkC3r+AcD0IPIDgGtDaAJThQwGMD2I/AAAAADSApEfAAAAQFog8gMAAABIC0R+AAAAAGmByA8AABINAz4AJgiRHwAAAEBaIPIDAACAhEKN78Qh8gMAAIBxIURbFIj8lhk+hwAAABCEyA8AAAAgLRD5AQAAvAMNJrDEEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAGBpoddmCCI/AICEwjcWwNwt38cQkR8AAABAWiDyAwAAAEgLRH4AAAAAaZHEyG/52tQBAAAAkiCJkR8AQArhRy+kBLL6fCHyg7RAWQMAACTN3wiI/AAAAADSApEfAAAAXEOaK8yWQNojP2RfAAAASI+0R34AAAADoWoAlhIiPwAAAIC0QOQHAAAAkBaI/AAAICnQwAowbYj8AAAAANICkR8AAAAMhYrYJYPIDwAAACAtEPkBAAAApAUiPwAAAIC0QOQHAAAAkBaI/AAAAADSApEfAAAAQFog8gMAAABIy/w1iPwAACCJUvI1nEy4+NM2xyuMyA8AAADmDLHmdd34iiHyg5RCKQMAACmEyA8guRCeAgDAZCHyAwBIEIT7AFfCx2QciPwAAAAArmGhQ09EfgAAAABTlKhIEZEfAAAMlahvLAAYHyI/AAAAgLRA5AcA84QqpaTBHQFYboj8AAAAANICkR8AAABAWiDyAwAAAEgLRH4AAAAAaYHIDwAAACAtEPkBAAAApAUiPwAAgKthvpuEww2KCZEfAAAAQFog8psd/BwBgCRDGQWQBoj8AAAAANICkR+kjiAI/X5/3qkAAACYA0R+AAAQhpbf4O9D/FyEZYLIDwAgKRBeACyZBP5sQOQHkAqowkm4pH03AMB1LUoxi8gPAAAAIC0Q+QEAAAAss2B9JCI/AAAAgLRA5LecEtilFAAg+VB4TtCi9HtLG0R+AACQXPONHhAFjgkXcMbi/HSZReSHqB8AID4quFH5xOE6QJpNvCiYUZ0fPreQEmn4tk7DOc4SLmYUrgmk02xKV7T2AgAAAKQFIj8AAABIIvQWm4bERX5oSILJQo6CBFqUbInRFRDT9LI0ssHEJS7ym6VFKXxhInCvAW5glh8clMkwcchUUamO/AAAAOBaliCWin8KS3CyUYj8ABJqKUscCErm/U1mquYrzddk4gURSrbZC11zRH4AadHv95PWXRrfAXT6CbwICUwSwFTNsngcv+gb5wiI/CBdkhb6AHChKHAusVcSIlFchxS6Vhwz99Eki/49gshvRhY9oywTlOaQfMFcOpvSI/ou/X6fJ0MQhLkUYqHrkIQ0zD4BkEAL/T2CyG92FjqjwELD19XimmW5Mey9eAg4x4wUDEPnmAaWjk/TInbDSMN9mZRRkZ/v+5Ik+b4f3Kgoiuu6U04VjAUfAIhauHIckiYhWWiq5duVB0/IRYCBcHdiGhX5VavVUqkkiiJjTBAEx3EYY4ZhGIYxo9TBTeEDMBdpiLnTcI4JhMtOZlCyxXkL3A6IGlhRmsysMjTy832/0WgUi8XQdkVROp0ORYETN8sa5mTeD1h0aYi503COiZKoCz69knNRyuRE3Y4kW5QbOm3XGrky5nvFPMLvJEkK1uHpuq4oCmOs0WgoisIr/BhjW1tbpmkyxkqlUqPRGDN9SYAPcHrMd5zgDSxiP5vrwhfDwpl2nkzJyMr0GH/ikkmlZFGMX2sY55r/TlVV27b537Ztq6rKGDs5OVlfXw8eqNVqUYwoy3LwJXClFGbfZJrxOlQJPNRkjZ+w4NDRsZMDN7dA13/pfxEl3MCsMr38g9iRTSHP/25nZ+f8/JwGbdi23ev1KPJzHEeSpIGvoUpBjPO4FrRip8oEb/f4wwlDr51UDknUOcL4EFFdKwemObsOzCqJra+NWYs2bOMSfC6ip/Y7WZaz2Sz123McR1VVauHt9XqZTGbEsbrd7s3ecvw9x7fcH9olyKmLKJqpkhNjhRq7b5ywJJ8jwPhi5kNk13EkoRpvvsHr7I8QOrXfMcZKpdLh4SF7G/mNmaAr33Iie44PlXAwccFMNU4Gm2p7ypg5fyHOERYObn0yzWwW8WsVJnMfQjtOPeKwI4yfhvh+xxhTVfX09NRxHN7UyxgbUeFHM/ytrKzc+F3TBlHm0ht25W98R0J55sZZaAbdDed+jimRnEG1U0rJfDMACs9hbnZfktCqm4QEjDmwd0o9EH7HGJMkKZ/P67oerPD7/9u7u9u2kXaB46ODLUBauAApSAGi4W3AEFNAABrw7QJS4AJMwykggpgCAkvA3gawgBQgvnADMazcb2BtAQtTHfhczAkPX35pxM8h+f9dBLZMDYfkM8OHMyRzdnYWupNvv9/LHx4fH/v9vmEY6hU6iKneotQe/VpJv5QsNhKiK9Kkx4ytRra1VLCN7W6eUoabhzR5qLakmmhy0Ok8DzoqO6l9VjdnCZokr2WMhv7f+/zkcx7BzM80zeBL+6bT6cXFhXyry3a7lQ95FEXWjKneBq2rBQqMgWpytQwrKrBHrmYbOfVWT5N+gwG/pij8DrmjpkTzv+IkZ/Jaez6aoYSYrUg6YJ7nvXnz5unpKfqEr2EYjuMoJn/+1W3KZW70T+U9UBPNMtu6rlJX1xQHx/wK2T/psS2OH/BXbynVFJW+fI3b2FDZtrGMPZPtqrvYmtReh9oroCG5dcE9438SfBNT9ORe1IfHlhCqZ4YScn4YrUAorord3mwl+BL/D4/BYHB7ext9b58cCCxwzK/6UbFqmisDmR2RvjeOza6O/UqeotSPY8XbSIBVo/o+Ss86ZK4AgSqOGUIr6ZY1qfDBufwVSPpQfXXl7bG0/7d3Op1+/fpVPs/hcxxHzvkWoq2pWMXdGXPKdak4VxNq+1+lqKKG6I5SbMWQhw4plz51yKCDgXrU3SOKH+af6i2vhIqneivbYyJltrcoSUOOfoViK1D4QHpKF9PodVW5ulAMNaLjK3W296hcTWWUS6UyB+tcYFHqpVW8jU1X+2xvzpSrkJpQB20lzSqKyM1L8ofMH4amj5NWnb5wGSUcVdvYWd3Y3aVYQv46JC3s+y36UdnSc77y1ti+0b4q96QOV+f6OGrP+80yvbQqBw6DyyctXO82HqwYoZhZ7Tuw+lOAnnVohzIG/PLPIOcv4ajxwtDCSU1MkwE/UXHmV1fOV80aK966tqazFctw1Z5tb8SuKFvMFFVUSrpW7zYmVSz/VEvHFdVHpYwlKNZBhxneVnZoxQrtojL2WFKPkT+hzFNCkvxlHpWf5S8h8fq57Oiva15DvY+rctagwHVVs7qmXxmrzI0eNRAlf8gf0jmLCn6xwKLyl1bNNjZ9li1zryjyTU3m+Xq0tGyXTEXVIfPFSbF1aHQcJmnrdsFX7phfXdfo2k655rlWPnZdOTU95ytWIXsj1BwyFyWjKFhagUXlKU1ouY2tcXBKPYkObbnwPvnYvaHDTgA0Ufpsb8XNrK25UVvXpb8CJ8iKqM7/l1bUqbTYk7Eo7hyfUgKRqUiHtlx7HWqvAKCbGp7wKElbc6O2rkt/mu8NPWslNK5Yp+gQvbXXoYIKMDGKJkrM/Gq/mVq9ReVv3lWuS11b16WiyvCLHn3d9gY0UXuvqKKy6E3pNmtvQdVUIPP8O1CvxMyv3nOeSosq6h4gxdbb1jys9j46VmWVCR19PfcGNKF5VFQZvbHdZvX3ZXLZFkQaCkWNnO2tuG03Og/T+bpcHzxJgKYo6tU5xaqlAppctukz4atJNaC/hmV+7cj5Kuu7k4Yzaz9PaIWdgKZIynVETWFcewVqr0PoIXQ6EzRCYzK/WgbeylhdjX23Dt00gJw0acg69CFa1YHJVjSF1plfga8ZU1lXMCdrUxKmyXkCQH40YQA56XKDQixtX8hc1Oraty4A6CZ9bvgD0hGpAAAAXfE/dVcAAAAAFSHzAwAA6AoyPwAAgK4g8wMAAOgKMj8AAICuIPMDAADoCjI/AACAriDzAwAA6Ir/EUL0ej3Xdf2PPM8bjUae5wU/MQwj+AkAAAAaJ2bMbz6fX11dDQYD8SspHAwGl5eXq9Wq8uoBAACgML+Ffvc8b7VaPT8/hz63LOv09HQ6ncqMEAAAAI0TzvxWq5Vpmv6AnxDi3bt3i8XCtu2zs7PVamXb9lErkIWgZRrx3z0Te63UiNg7iOBspXYEJ1ovnPk9Pj6enZ3Jn19fX3u93mazMU1TCGGa5uPjY4Z10BhQF2IP2iI4AdQinPm5rmtZVuyio9FoPp+XXyUAAFAPBqRbKXipGc789vt9v9+P/Vq/39/v9yXWCwAA1I0B6XbjfX4AAABdEc78kgb8hBD7/X44HJZcHwAAAJQlnPmdnZ1tt9vgJ/4M7263++OPPyqqFwAAAIoWzvxM0wz+fx7T6fTi4sJxHPHfj/0CAACgcXqhGzk9z3vz5s3T09NoNIp+/vz8fOybnHu98CqAahB70BbBCW0RnK0XHvMbDAa3t7fr9Tr0+Wq14j/wAAAAaLSY1N7zvPPz84eHBz/P8zzv9PT06ekpQ+bH1QPqQuxBWwQntEVwtl7pB5gYQl2IPWiL4IS2CM7WC7/J2cdbvFEjwg96IjIBNB2pPQAAjVfgZQmJQbuR+QEAAHQF/3sbAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFekZX6u63qeJ4To9Xqu6x5bdLZv1cXfWJGj5ilfNAzD87xeAsdx/CWXy6VhGPJzwzCCfzJNc7vdZqhYa+Q8TB2MyfTvEpbtEAyVzGQwCCGikTAYDGazmfyr4zgp0UIw6KOM7i5nVnBQgcmG67q9Xq/AKiXVrdhdEWyG0WJV+mrbtgeDQa/XG41GSR31byk1ePfu3WazMU2zqE3SWXBjF4vF27dvCyzccZzLy8vBYLDZbOQn2+325ubG/9Vf3Ww2W61Wi8VCHrDtdjufz3/+/LlcLoUQtm3btt2g3KVwxGSBCMvWyN8u/GCQvy4WC8Mw/L/udrubm5v9fn9/f29ZVvBPQgjbtn/8+CHXTjC0W6d6YOlg31tg5xxqhlEH+2rHcWRfPRqNZEcthLBtW4Ta5msyuZrgD0fJ9q26FFLb2EJeXl76/f7z83PwQ3nAQkve3d0JIe7u7qIfPj09yV+Hw2GD9mrhgns4wyHrYEwmlUNYtknOUAkFQ2xp19fXseeLxWIhT37+JwSDJsro7nJmBerl5/9WbG+WR9mnD5Vm6EvauuFwGOyrF4vFcDgM/lWWmTjbK4dJ3717J6/y9/v9bDaT44rBMUPP8y4uLvw5oOg4f8oCvV5vuVyapimHJbfb7XK5lKOUpmn6kxfL5XI0Gsll5BhDrKTFkj7v9Xrr9do0TVmB4Mb6o6yx3z24ySHr9frs7Gw0GqUvJoT4z3/+Mx6PZ7NZ8MPZbCb7Vunq6mq1Wh0sqpVCh0kkhKXKAUpaRjEmRTlhqRKTSd8lLKsXPF4icFwGg0FwkmW328kD2uv1Li4u0ns2WeZRnW0oVFR6vNCGKAZDv98PfSIHHqbTqRxUkLoZDJqLjYrgbKAMueDtJev1OlSIYlYQjbTYppHULpKKTdqKIMdx/O56v9/H7orY9coJU/l5qP1GZ109zzMMIzotKwuRd8XI00dsxeT9EtGKqffJKf7555/gr9PpNLij/r9tpmeUi8Xi+flZCNHv9xeLxWaz2Ww24/F4MpnIxSaTyWQykZ9blhXMLoUQm80mfQG/WMuy+v3+eDzebDb39/fy82A1NpuNPNPEZsH39/ehxeRoRMrX5dqn0+nd3V1wY/2aJ5V5cJNDdbMsK3hBnJKwCyGur6+Tjoj09PSUctTaLXqYYsNS5QAlLaMSk6+lheXBmEwpk7CsXvB4+RHiR4t/5T2ZTCzLkodGHqbXQzHgl6PS2QZDRbHHC21IKBiiAROKf+nl5WU4HI7H45eXl+Dn3QwGDfnHMSkqptOpH13D4VAIcX9//5o8nqSYFYQiTQbP/f39ZrO5u7vzm0Zsu0gpNj22g4XLBeSFSnQrktYrR8tClXyNDHO+vLyMx+Ng2AcX8AsJ1jxYsbu7O7mroxU72AyjxyL6+XQ69Xe+Pyfj89um6mxvsOuX5x75s39milZFfj19Ab9Y+Sd/ScuyLMuSP0yn0+BWyc9DoovJw5by9dBGRcMoqcyDmxyqW2j0NfaL/tejJ+Oo9IBot9Bhig1LlQOUtIxKTL6WFpYHYzKlTMKyesHjNZlMgnvp7u4ueCL0//T8/Cx/VoyBozrblGKjJQeFgkHEkTlBkLw0ip5dXjsZDBo6GBV+dMlMzrIsGSHX19exHdqrWlYQ+tNkMgnNP/rJVrRdpBSbHtsy2Qp+dzqdxuZGKuu9vr4ej8fRTZYXY6GrneACfsnBmocqlnTjRLQZZsj8Xl9fF4vFeDyWzVZmgdHaZrnPL7TKxWJxfX09mUxCmWzwLJu+QGyZfnAEV+3vTb8/il0sdhWvkegM/il6lk3Z7ypblP5h5sGVpAI7IjYZkoK7VOUAxS6jEpPRVRcVlgdjMqVMxa1O/5CwPEroAEXJP8nxieFweH19nRS9STFwbGer3uMlbcjrrxPYJmA8HodSAblR0YuHg+tCZQ5GxcvLi/g1WiYvXWS6Mx6P/Xwo2KGllBm6fg6FU2zTUGkXKcVGYztlgaDM6xW/bngItYXYfZJS86SkLaX/j0rJ/KSXl5f7+/vr62uZ/IWKzfs+v9ls9uXLl5OTE9u2g1Pj6gtk43dJBZapqKQtEkJMJpPoM3HypRvVb2ZzqRwgwlIdYakoNr0WQti2/fz8fHvxd6M7AAAVt0lEQVR7u9/v3717F7wl7ijlHeIowzDMgI8fPwbv+vJv7wvd/YlmGQwGk8lku90+Pj7KA/3jx4/dbvfjxw/LskRxHVps0yiqXfj6/X7w3r7obamSynr3+33s1+Ww3Hq9Purp9VDFyhO8LXIwGFiW5TjOYrGI3nebN/NbrVaO49i2nfSY98EF0lmWFexxXNeVEel3SfL9AuPxOHgk/BtXk76uIqnMY7doOBwqHnXLsn78+BEKqdVqJXN2xWpD5QARloRlsSzLCu4ix3FkVmSapuu6o9FoNpstl8v7+/vPnz+LTDGgcoizhdbBYAieBT3Pe//+vRwWOlgydJASFTI+XdeVjywMh8MvX74Mh0P5nEGoQ8u89mDTkDVJahfZtiK4LcEFooWkrDf4QIbrurGtTLZry7L811uqCFUs6fE79T45yb///vvly5fYkkOfpL3PTwix3W7TX1QzHA5d1+33+7vdTr45xvO84NtoDi6Qbjqdvnv37u3bt/JZttVqtfn1Apugq6urm5ubt2/fjkaj9Xq93+9lTCh+Pbix/pM1SWUeu0V//PHH9+/fVfrf2Wz2+Ph4cXFxe3srW5q8tr67u/PLlwHUqdcphYQOU5TKAWpKWEY3lrDUk2VZFxcXJycn8pjO5/Pb21shxGAwsG3748eP8rr/06dPGbomKf0Qy1DJUKw4JhiEEB8+fPjnn38Wi8Xj42Pwcz9QCQbdpESFaZo3Nzfi1/EyTfPz58/yRrQkB7OCkFDTkB8mtYtsWxG7QGwqmbLe+Xze7/flM7npDWexWJyens7nc8Wh0GDF/MYbFW2GoRxR7sOUFX38+PHi4kKeFOTVmuu6q9VK9kUi2DZT5onl4Y8+uhicYH56epLp5GQykT+Hbgg4uEDspHXwnir/WZjYe9KDX5GbOhwOgzcjJ309tFH+xgb/FFum4hYFK+DfKxq7D6Mb4mfolmWFbqxeLBZJ9952QexhkvxdqnKAkpZRjMnXcsJSJSaTyiQsqxfasf4x7ff7/p2RLy8v8mk7/xzg322tEgOKnW0wVBR7vKBQMESXlI8E+jczxfLvbe9mMGgoeBxT+qtgIMn3dEaf5vGpZAXR+PHXfrBdpBSbshX+t/wF/DHpUP1T1ut3cUmFhzph8eu9CrELpNQ8tmKvcc0wJHgCSuqr5TMo/lfG43Ho8Zr/e5Is+k0ULvrK3MzG43FKswTUEZbwEQzospTrovyenp6Cjct/kiaqwGYYy2+bee/zg4rb29ukAd6jyKFa9XvCgBSEJXwEA1AS13XPz8/lcyHL5XI+n19eXsYuWVQzTKqG8NtmedklgobDYf5cXr7HtZD6AK+EJQIIBnSWKHPMT04xy/RLzvamLFxIM4wVbJu91+T7NgAAANAmzPYCAAB0BZkfAABAV5D5AQAAdAWZHwCgHr1ej1VoSG5RcLuSfk7/UPGvisu0rJBCVpRtLU3K/IpqXTq00nrroMMeaJ/QXk3/9eDnxy4DAIWgw2m9JmV+AAAAyIPMDwAAoCvI/AAAALqCzA8AAKAryPwAAAC6gswPAACgK8j8AAAAuoLMDwAAoCvI/AAAALqiMZkfbxUHAADIqTGZHwAAqB4jLy1D5gcAANAVZH4AAABdQeYHAADQFWR+AOJxcw8AlKTGDpbMDwAAoDCaXzaT+QEAAHQFmR8AAEBXkPkBAAB0BZkfAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR9QPM1f5gQA6CwyPwAAgK4g8wOgarvdmqZpmqZhGNvtVn5o27ZhGIZh2LatWI5hGOv12v91NpupfxcAkAeZHwBVtm3btu267l9//fX+/XshhOM4+/1+u91ut9v9fu84jko5l5eXruv6v67X68vLy7IqDQAI6L2+vtZdByXyxqlCatvr1b/V9dZBhz3QPsG9Gg3X2H2uGNV1Ha/oek3T9DM2+dfRaPTw8DAajYQQu93u/Px8t9sFS5hOp67rWpa13+9d1726urJte7fbnZ6eep4nhFiv158+ffJHEFupU/d9HhWrFcR2O1ZRJb9f8rcr1FMl9WYpO0FlFx1cpkGFqPTtisuUsTmNiVcyv9asva26kPn5HMdxXdd13dAy0V83m83Z2dnvv/++2WxM0/QXMAzj48ePlmXZtn1yctLu2d7utLhjt7QdaVnLji+ZH5mfLsj8WrP2tupI5rfb7W5ubv7++++Hh4fBYHAw8/PPHKEfHMf5+fPncrkMjhq2VXdaHJlfC5D5tT7z4z4/QGtaTRTKWdqzs7PtdjsYDIQQw+HQn97d7XbD4VCxKMuy1uv1drvt9/vtTvsAQCtkfgBU3dzc3N/fB2dmr66u5vO5/Hk+n19dXSkWNRqNTNO0bZtnOwCgSo0Zo2a2tzVrb6uSZnsLjPxjxdwd0utNJhP/V/m0h3zaVwhhmmbo2d6U2V4hxHK5/PDhw/Pzc+vH/LrT4pjtbQFme1s/29uYeCXza83a26oLmR+y6c6eJPNrATK/1md+v6V/AQBQGf+2zjZlEgC0wn1+AFC/Xq8XHAPQ6skeAG1C5gcANfNzPpn2kfwBKA+ZHwDUKfZ2H2Z7AZSEzA8AapaU5zHsh44g1KtE5gcgHsNOFUg54bH/AZSBzA8A6qH+6gcA1ehCiyPzA4DapKd9DPsBKByZHwDUoAtDCwA0xJucAaAeikN6Rf0XEaFckwFFoJsY8wOAqqkP+BWV8wVfGcj7AoEuI/MDgBpUNuQWek10cO0kf2iTlsVzeZtD5gcAlcrQoWc+B6Q8PkzyB3QTmR8AVO2oAb+co4MpXy82+SOJBBqBzA8AqlNleqSyrmLvIwSgPzI/AKhUNXf4qbwmOrRwtrXE3kcIQFtkfgBQkTwDYxm+q5iKZc7YyPmAJiLzA4DqZEuSjv1WBQ+RHDWmCKAWse2azA8AWqjUh0hI+4DmIvMDgCpU9gxEzlfAKC5G2gc0FJkfUAAebISKnNmSSphlTssU3/BC2gc0HZlf55CjANXL3+7Uk63MadnBL5L2AS1A5lcDci+ggypImArpW5IKIe0D2oHMDwDao5D/8COa/JH2Aa1B5gfgOLvdbjAYyJ89zzNNczQamabpeV69FeuClFG9oiYTQslf8F3NhZQPoF6dy/yYaQVyms1m+/1e/jyfz03T3O12pmnO5/N6K6azQtKmg4UUlZz5yR/vakYI59AW6FzmByAPx3FM0/R/Xa/X0+lUCDGdTtfrdXDJXq83m81Go5Ft2/IHx3Hkn5bLpWEYhmEsl8sqK99ihZ+PXwOKLRlAvXpNadVFTTfoMG1Rbx102APtE9qr0Z3c68W0NZVjodXx2m63tm27rutvTnC7QtvY6/U2m83Z2dnvv/++2WxM0/QXGAwGnud5nvfhw4f7+/tatqVKsUc/c1EiEg/6BMmxW1rgnmn3Kqrkh1OwmYv/vg0gtjdL2QkHd5FiZ9iyQtKXKa8mjYlXMr92rL2tOpL5maa5XC5Ho5Fi5hdaxv9BjhpalmVZln/LYIsVmxlES9Mn8yDzawEyv8oKSV+mvJow2wtAieu6j4+Ps9lM5m3y3+FwKB/s8DxvOByqF2Xb9uPj4/n5eXkVbrHg3C73XaF2BGGzNOZKhTG/dqy9rToy5ufzN8e27ZOTE9u2Hcf5999//Tv5gstEfxgMBs/Pz0KIN2/edOGJ4MLHhEInWn3CgzG/Fsgw5qcyfKXPSJs+haQvU15Nfkv5AgCku729vbi4+PLly9u3b9Xv2FssFnK0b7FYlFm71pJnZaFTzgegKRpzpcKYXzvW3lZdG/PDUVo2JpSCMb8WYMyvskLSl+E+PwAA0FTcC1i4zLuU2V4AKB2nPQCaIPMDgHK1aSoQQNOR+QGla814TwUbQpIEoCSt6YpzIvMDoIq0DACajic8AAAAuoLMDwAAoCvI/AAAAJS04GZBMj8AaJUWnJkAlKcZmV/L3pAOAKUi+UNOhFCLNSPzA9pHpWPlmgfH8uOKMzeAWGR+QMHU0zWyOgBAxcj8AKA9Xl9f5RUF1xVoE8awC0TmBwAAdPf6+kr+VwgyPwAAUBbuV9YNmR8AAEBXkPlVjcFqAGVjXkxiJwBR3cr8NOkFahz31mQPtAxzGQCApuhW5id43o09AABAh3Uu8wMAoCgtm0iJzmAwp1GGevcqmR8AAEAx9M+VyfyAcrVsSAAoCk0DSFJq62hA5qd/+gykiwaw4n/aW0510Ak83oumKCRQ25cqlLc5Dcj8gFZSadUt68gAIJYmfV3L0sekzSHzA4rEKAuATmlZttQFZH6VqjctICmpBp0ggFbiJNIOHcr8NAnZetMCkhIAQGbNPYm0bGwyz+YcyPxs2zYMwzCM7XYrhNhut6ZpmqZpGIbruorrMAxjvV77v85mM9u2s1U3pzYddbSbJhcqaDQe8gCK1Y4GlZb5OY6z3++32+3Hjx/n87kQwrZty7Jc13UcZzabKa7j8vIymCau1+vLy8s8lQb0kd4RxP5Vse/gQgUAhGb5Vgt65rTRQsMw/vrrL8MwhBDb7dYwjODoYmiksdfrTadT13Uty9rv967rXl1dybG93W53enrqeZ4QYr1ef/r0SY4gKtWv1xO/dnTOoVr/6zUO+dZYhwL3JIJCxzT919BXRPKxUFkGOEjn4Al2SqxCE37HJX/1O7Hov9GvpHdlKaGYXkKxhaS3iGoKKWRFedaSNub348ePr1+/yrld+Yn/5eVyOZlMQstblvX09PT582fLsna73c3Njfx8NBoNh0M54fv9+/djB/wKaVE6XDHUXofW9E2tV3uoAABKUnsP/1v6n09OTlzXdV33/fv3u91Ofmia5uPj43K5DC1smmboB5+c8LUsa71ePzw8FFHzo+mQ9+hQB5SnwPczEypAThWcX2s/hbeAPvuwqBdKqyyW3sOXvU/CY4AyabMsazabDQaDp6en0Wgkfo0Zuq4rF/A8782bN3IC169oaDA2OMAoJ3wfHh7+/PPPbFO9InWYt5py8qi3DjrsgbYKBrz4750sf0hpF9Ffo+XELgMo0jl4yp4nVZkO038VFat+tld9Jje9kGBt0zetgjnl/JtTSE1Ewj4Jz/bKET759IYcopMfyrnd2Wwm87Z//vmn3+/HrizWaDQyTdO27Vqmegssp7l10GEPdNCxu71l9wyhdq+6Pt6rZ63gU+yF9DmOFYyi6bOxOUcW02Z7F4vFxcXF169fT05OHMcRQnz79u3PP/88OTkZDAbfvn07qqKTyeTDhw/ROeIkRe1iHQ5VvXXQYQ9ASj8WpH0og7YRpW1WiqOkP2GQv/yigqSQhqBPa8pTE33HqGNnwQoZQal3prXiOuiwB1osuHtjZ3tDM+wpUZ2S9nG80D5lz5OqTP8Vspa2zvb6eXmwgxKRXZo+Lxm7ZNIyBxcopJBSJ2FDeyzPMqVuzoEnPOqSP8cP3WVVl3pHcRhDqoD67k2Jao4UULiyG1QHBywL36X6DApWObJY+8S0jplfzrOgVjlfXdXQZCd0TWhvR3f+wUvALsjWrzV3/3QwP0gSGhFvgeaGZSFUbl85qJA0SJ9CClH25miX+WU+Cwb3RWdzPn12Qgdl3uEqX9RtOilnJ6jVtlSga9sbKxQzpe6TCk7SrUlec0o/jvokZJoUcnCZamqiV+Z3bNpXZVdyUL1ZF4N8ujk4/pf+eWiZXq9XdoAdeyYj0pBN2TlTBYPobR2nP+oKkwG/YwtRXKaCmoQzv9qvY47d5mxtr6gRlDypZ/461Lt21DX4H8oCS1oLUJ5Sn+oor/xqVtEgDPgdVYgmA34imvnpHM1F1S04gnJsmUWlnqFRHMX5vthycq5d5yOuv1r2HocMzVXe9RJDfWULXX+mLKmY4uTMYJpYSMoyhdREOhiies32VsZ/1Dl9Bq3AZCulHPUhnAJ7nNg9UNK6ACRRz4Ra0CTLyJwquM2G+6djpbxMJOWvKgsoLqNJIfpsjnpn0tHMT1KZQaugndfYldCLAdXLcJ9GIYP0tWSZ5d2NzdxuULGDqf72ZpsWKztPUp82raYQfTZH8Xh1OvML0r9hA2iunAlQaJBe8evZ7k6JXgbnnIHyK19UN0vOF1VGPYNlqjyylj/tKyTFqXItla0o51rCX2lKWAO64SkZHDXQUnjeEy2zqBuR08uMUkwoY++oOfbRNPU1arsKoErR+GTMDwAyypzxFLLS2HtUCk9BNMlpGOcDikLmB2RR3iOKgKLuJCg8twsUiMwPyIiTBBRxnZAZQ31ATtHAJvMDgBKRT6RLmQ1nqA8ow//UXQEAQEcV9ZaKdEljrhWsAtAQmR8AQCOFvLzQl/T+kbJXAWiL2V4AgC7aMcPLJDJ0xpgfAKBO/lRpO3Iy0j5ojswPAFCb4OsJRWkJkyy86asACkHmBwCoWakJk//fxzV6FUBR+D9ngCz4/5oAAE3EmB8AAEBXkPkBAAB0BZkfAABAV5D5AUfjJj8AQEOR+QEAAHQFmR+aarvdGoZhGIbrukIIM6DX6223W5VCDMNYr9f+r7PZzLbtsmoMAEDdmLRCU41Go4eHByHE+fn5brfzP1+v19+/f3ccR6UQx3F+/vy5XC7lr4PB4OHhwTCM9G8x2wsAaChOYGgqP/0K5mGe552enj49PQ0Gg9DC0+nUdV3Lsvb7veu6V1dXtm3vdrvT01PP84QQ6/X606dPKoOFZH4AgIZithdNNZlMXNd1XXc4HPofrlarq6urUNonWZb19PT0+fNny7J2u93NzY0QYjQaDYdDOeH7/fv3y8vLyuoPAED1GLpAI+12u/l8/v37dyHEfr//9u2bnKIdDAbPz8/RzC86QOj/4E/4yunj0Wh0cO2M+QEZyP/czEcjAmrxW90VAI5gmqYQwrKsm5ubh4cHeX/edrs9Pz/3PG+9XpumGTvgl8KyrNPT06urq36/r5L2AcjMz/ZCWSCAypD5oUnkY7xCCDlX6+v3+/Kvk8nk2DJHo5FpmrZtM9ULAGg9Mj800sPDw3w+//vvv09OToQQ3759E0J8//7dsqwMpU0mkw8fPvhP+AIA0FbcrgQcjfv8gAyCDYdGBNSFMT8AACAE91+2V/BCi8wPAMrF2TRWu3dLc0c0m1tzKCLzA4DScTYNYYcAdeFNzgAAAF1B5gcAANAVes32tvu2j2bRdi6GIAEAIDO9Mj+hccIBTRAhAABkxmwvAABAV5D5AQAAdAWZHwAAQFeQ+QEAAHQFmR8AAEBXaPdsLwAAOBZvvIKi30QgXPz3ZfAJn/AJn+j8Se04y0I3+rQOaK4XipXauzNiF/qrvZl0HL0EAGQWnu2lSwVU0FIAAE3EEx4AAABdQeYHAADQFWR+AAAAXUHmBwAA0BVkfgAAAF1B5gcAANAVZH4AAABdQeYHAADQFWR+AAAAXUHmBwAA0BVkfgAAAF0R/n97AW31er26qwAAQLP9Lx7OcZRDWHzyAAAAAElFTkSuQmCC)\n",
+    "\n",
+    "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t2ihZLXh5VD-",
+    "outputId": "510653d0-3172-4c5f-c101-1bfe66297121"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8GngV4fThkhM"
+   },
+   "source": [
+    "## Build model\n",
+    "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "id": "q6WNelXsbjy1"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygenn import GeNNModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "261uLnJsgyeE"
+   },
+   "source": [
+    "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "EDpiDOK0gkEz"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial1\")\n",
+    "model.dt = 0.1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "LrfXpMqfjRBe"
+   },
+   "source": [
+    "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "tU2M4MgFjRae"
+   },
+   "outputs": [],
+   "source": [
+    "izk_init = {\"V\": -65.0,\n",
+    "            \"U\": -20.0,\n",
+    "            \"a\": [0.02,     0.1,    0.02,   0.02],\n",
+    "            \"b\": [0.2,      0.2,    0.2,    0.2],\n",
+    "            \"c\": [-65.0,    -65.0,  -50.0,  -55.0],\n",
+    "            \"d\": [8.0,      2.0,    2.0,    4.0]}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "YrOQPgYBjuym"
+   },
+   "source": [
+    "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "id": "zc-e5Lu2j_Yq"
+   },
+   "outputs": [],
+   "source": [
+    "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u8wu06PZkBnS"
+   },
+   "source": [
+    "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "GNBjEGWPj_3Q"
+   },
+   "outputs": [],
+   "source": [
+    "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "IGKUIiaGkA0Z"
+   },
+   "source": [
+    "Generate code and load it into PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "d0mK72rYkiYe"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cNs18ywkkq6T"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "nWFVfYfdkobN"
+   },
+   "outputs": [],
+   "source": [
+    "voltage = pop.vars[\"V\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "wv-hDOIe3Hgy"
+   },
+   "source": [
+    "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "id": "99MBe7JKk5Ut"
+   },
+   "outputs": [],
+   "source": [
+    "voltages = []\n",
+    "while model.t < 200.0:\n",
+    "    model.step_time()\n",
+    "    voltage.pull_from_device()\n",
+    "    voltages.append(voltage.values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ug6S1h-z3k7v"
+   },
+   "source": [
+    "Plot the voltages over time in 4 seperate panels"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 718
+    },
+    "id": "RsVbAbIPlEO8",
+    "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b",
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1500x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Stack voltages together into a 2000x4 matrix\n",
+    "voltages = np.vstack(voltages)\n",
+    "\n",
+    "# Create figure with 4 axes\n",
+    "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n",
+    "\n",
+    "# Plot voltages of each neuron in\n",
+    "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n",
+    "    axes[i].set_title(t)\n",
+    "    axes[i].set_ylabel(\"V [mV]\")\n",
+    "    axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n",
+    "\n",
+    "axes[-1].set_xlabel(\"Time [ms]\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "h4yw3JiNpXOM"
+   },
+   "source": [
+    "Exercises\n",
+    "---\n",
+    "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n",
+    "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "name": "1_neurons",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_2.html">Classification of the entire test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_3.html">Faster classification of the whole test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/1_first_layer.html">Presenting latency-coded inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/2_second_layer.html">Adding Kenyon Cells</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/3_second_layer_gain_control.html">Feedback-inhibition based gain control</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/4_third_layer.html">Output neurons and learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/5_testing.html">Testing</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../../userproject/index.html">User projects</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../source/pygenn.html">Reference documentation</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
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+      <li class="breadcrumb-item active">Adding synapses</li>
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+           <div itemprop="articleBody">
+             
+  <section id="Adding-synapses">
+<h1>Adding synapses<a class="headerlink" href="#Adding-synapses" title="Permalink to this heading"></a></h1>
+<p>This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 98.5MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+<p>Import numpy, matplotlib and the main <code class="docutils literal notranslate"><span class="pre">GeNNModel</span></code> class from PyGeNN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="n">GeNNModel</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_var</span><span class="p">,</span> <span class="n">init_weight_update</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “tutorial2” with floating point precision and set the simulation timestep to 1ms</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial2&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="mf">1.0</span>
+</pre></div>
+</div>
+</div>
+<p>For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:</p>
+<dl class="simple">
+<dt>:nbsphinx-math:<a href="#id1"><span class="problematic" id="id2">`</span></a>begin{align}</dt><dd><p>tau_{text{m}} frac{dV_{i}}{dt} = &amp; (V_{text{rest}} - V_{i}) + R_{text{m}}I_{i}.</p>
+</dd>
+</dl>
+<p>end{align}`</p>
+<p>We configure these using the parameters from (Vogels &amp; Abbott, 2005 <a class="reference external" href="https://doi.org/10.1523/JNEUROSCI.3508-05.2005">link text</a>). Note that the resting voltage is <strong>higher</strong> than the reset to provide a constant current input <strong>TODO</strong> get rid of this</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lif_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">,</span> <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span> <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">49.0</span><span class="p">,</span> <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+              <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span> <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>So that the network starts in a non-pathological state, we want to randomly initialise the neuron’s membrane potentials so that they are between their threshold and resting potentials. GeNN provides <a class="reference external" href="https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html">various</a> initialisation “snippets” which can be used to parallelise variable initialisation but, here we are going to use <code class="docutils literal notranslate"><span class="pre">Uniform</span></code> to sample values from a uniform distribution.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">init_var</span><span class="p">(</span><span class="s2">&quot;Uniform&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;min&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span> <span class="s2">&quot;max&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">}),</span>
+            <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;E&quot;</span><span class="p">,</span> <span class="mi">3200</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">lif_params</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">inh_pop</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;I&quot;</span><span class="p">,</span> <span class="mi">800</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">lif_params</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="n">exc_pop</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">inh_pop</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+<p>So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_synapse_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">0.0008</span><span class="p">}</span>
+<span class="n">inh_synapse_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">0.0102</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<dl class="simple">
+<dt>We are going to use an exponential synapse model where a single time constant <span class="math notranslate nohighlight">\(\tau_{\text{syn}}\)</span> to define it’s dynamics: :nbsphinx-math:<a href="#id3"><span class="problematic" id="id4">`</span></a>begin{align}</dt><dd><p>tau_{text{syn}} frac{dI_{text{syn}_{i}}}{dt} = &amp; -I_{text{syn}_{i}} + sum_{j=0}^{n} w_{ij} sum_{t_{j}}  delta(t - t_{j}).</p>
+</dd>
+</dl>
+<p>end{align}` To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">exc_post_syn_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+<span class="n">inh_post_syn_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">10.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>We want to connect these with a fixed probability of 0.1</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fixed_prob</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;prob&quot;</span><span class="p">:</span> <span class="mf">0.1</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>Now we have defined the synaptic weights (in GeNN, this is the responsibility of the <strong>weight update model</strong>), the synapse dynamics (in GeNN this is the responsibility of the <strong>postsynaptic model</strong>) and the connectivity parameters we can add the synapse populations to the model. Each of these synapse populations all configured with: * <code class="docutils literal notranslate"><span class="pre">SPARSE</span></code> connectivity meaning that they are connected with a sparse weight matrix. * The built in <code class="docutils literal notranslate"><span class="pre">StaticPulseConstantWeight</span></code> <strong>weight update model</strong> which
+is used for spiking synapses without any sort of learning. This has a single parameter <code class="docutils literal notranslate"><span class="pre">g</span></code> representing the synaptic weight used for all synapses. * The build in <code class="docutils literal notranslate"><span class="pre">ExpCurr</span></code> <strong>postsynaptic model</strong> which implements the exponential synapses described previously * The sparse connectivity is configured using the built in <code class="docutils literal notranslate"><span class="pre">FixedProbability</span></code> model described previosuly</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;EE&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">exc_pop</span><span class="p">,</span> <span class="n">exc_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">exc_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">exc_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbabilityNoAutapse&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;EI&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">exc_pop</span><span class="p">,</span> <span class="n">inh_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">exc_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">exc_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbability&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;II&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">inh_pop</span><span class="p">,</span> <span class="n">inh_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">inh_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">inh_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbabilityNoAutapse&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">))</span>
+
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;IE&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+    <span class="n">inh_pop</span><span class="p">,</span> <span class="n">exc_pop</span><span class="p">,</span>
+    <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="n">inh_synapse_init</span><span class="p">),</span>
+    <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="n">inh_post_syn_params</span><span class="p">),</span>
+    <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedProbability&quot;</span><span class="p">,</span> <span class="n">fixed_prob</span><span class="p">));</span>
+</pre></div>
+</div>
+</div>
+<p>Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="mi">1000</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-model">
+<h2>Simulate model<a class="headerlink" href="#Simulate-model" title="Permalink to this heading"></a></h2>
+<p>Simulate the model for 1000 timesteps</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">while</span> <span class="n">model</span><span class="o">.</span><span class="n">timestep</span> <span class="o">&lt;</span> <span class="mi">1000</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Copy the recorded spike data back from the GPU and extract the spike times and IDs</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">()</span>
+
+<span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">exc_spike_ids</span> <span class="o">=</span> <span class="n">exc_pop</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">inh_spike_ids</span> <span class="o">=</span> <span class="n">inh_pop</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Plot spikes and rates</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">20</span><span class="p">,</span> <span class="mi">10</span><span class="p">))</span>
+
+<span class="c1"># Define some bins to calculate spike rates</span>
+<span class="n">bin_size</span> <span class="o">=</span> <span class="mf">20.0</span>
+<span class="n">rate_bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1000.0</span><span class="p">,</span> <span class="n">bin_size</span><span class="p">)</span>
+<span class="n">rate_bin_centres</span> <span class="o">=</span> <span class="n">rate_bins</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">bin_size</span> <span class="o">/</span> <span class="mf">2.0</span><span class="p">)</span>
+
+<span class="c1"># Plot excitatory and inhibitory spikes on first axis</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">exc_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">inh_spike_ids</span> <span class="o">+</span> <span class="mi">3200</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+<span class="c1"># Plot excitatory rates on second axis</span>
+<span class="n">exc_rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">exc_spike_times</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">rate_bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rate_bin_centres</span><span class="p">,</span> <span class="n">exc_rate</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1000.0</span> <span class="o">/</span> <span class="n">bin_size</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">3200.0</span><span class="p">))</span>
+
+<span class="c1"># Plot inhibitory rates on third axis</span>
+<span class="n">inh_rate</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">histogram</span><span class="p">(</span><span class="n">inh_spike_times</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="n">rate_bins</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rate_bin_centres</span><span class="p">,</span> <span class="n">inh_rate</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1000.0</span> <span class="o">/</span> <span class="n">bin_size</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">/</span> <span class="mf">800.0</span><span class="p">))</span>
+
+<span class="c1"># Label axes</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Neuron ID&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Excitatory rate [Hz]&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Inhibitory rate [Hz]&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_comp_neuro_101_2_synapses_28_0.png" src="../../_images/tutorials_comp_neuro_101_2_synapses_28_0.png" />
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="1_neurons.html" class="btn btn-neutral float-left" title="Defining populations of neurons" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
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diff --git a/documentation/5/tutorials/comp_neuro_101/2_synapses.ipynb b/documentation/5/tutorials/comp_neuro_101/2_synapses.ipynb
new file mode 100644
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--- /dev/null
+++ b/documentation/5/tutorials/comp_neuro_101/2_synapses.ipynb
@@ -0,0 +1,466 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Adding synapses\n",
+    "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "t2ihZLXh5VD-",
+    "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8GngV4fThkhM"
+   },
+   "source": [
+    "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "q6WNelXsbjy1"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "261uLnJsgyeE"
+   },
+   "source": [
+    "## Build model\n",
+    "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EDpiDOK0gkEz"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial2\")\n",
+    "model.dt = 1.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mki7b8R8xhAv"
+   },
+   "source": [
+    "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n",
+    "\n",
+    "\\begin{align}\n",
+    "    \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n",
+    "\\end{align}\n",
+    "\n",
+    "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "AkMk7Ml4tOxM"
+   },
+   "outputs": [],
+   "source": [
+    "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n",
+    "              \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XboW6qxrxnok"
+   },
+   "source": [
+    "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "dWf4f4Bpxl7u"
+   },
+   "outputs": [],
+   "source": [
+    "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n",
+    "            \"RefracTime\": 0.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "B3hhcDILxeki"
+   },
+   "source": [
+    "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5AECcjzMs8Iz"
+   },
+   "outputs": [],
+   "source": [
+    "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n",
+    "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n",
+    "\n",
+    "exc_pop.spike_recording_enabled = True\n",
+    "inh_pop.spike_recording_enabled = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "QypcRqLi0hgq"
+   },
+   "source": [
+    "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "WpmzQu0UuPky"
+   },
+   "outputs": [],
+   "source": [
+    "exc_synapse_init = {\"g\": 0.0008}\n",
+    "inh_synapse_init = {\"g\": -0.0102}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "58kevKNm0rfi"
+   },
+   "source": [
+    "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n",
+    "\\begin{align}\n",
+    "    \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}}  \\delta(t - t_{j}).\n",
+    "\\end{align}\n",
+    "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VnbedWiB0oAF"
+   },
+   "outputs": [],
+   "source": [
+    "exc_post_syn_params = {\"tau\": 5.0}\n",
+    "inh_post_syn_params = {\"tau\": 10.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "nJ1JwSAO1qNi"
+   },
+   "source": [
+    "We want to connect these with a fixed probability of 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ciwtEyzB0nte"
+   },
+   "outputs": [],
+   "source": [
+    "fixed_prob = {\"prob\": 0.1}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HWvUT89Z106p"
+   },
+   "source": [
+    "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n",
+    "Each of these synapse populations all configured with:\n",
+    "*   `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n",
+    "*   The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n",
+    "*   The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n",
+    "*   The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "rD6K22qZtxId"
+   },
+   "outputs": [],
+   "source": [
+    "model.add_synapse_population(\"EE\", \"SPARSE\",\n",
+    "    exc_pop, exc_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"EI\", \"SPARSE\",\n",
+    "    exc_pop, inh_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"II\", \"SPARSE\",\n",
+    "    inh_pop, inh_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n",
+    "\n",
+    "model.add_synapse_population(\"IE\", \"SPARSE\",\n",
+    "    inh_pop, exc_pop,\n",
+    "    init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n",
+    "    init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n",
+    "    init_sparse_connectivity(\"FixedProbability\", fixed_prob));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FiAsrqRx5OgZ"
+   },
+   "source": [
+    "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0I-7lZP4vWE2"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load(num_recording_timesteps=1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "1JLVx3u1281A"
+   },
+   "source": [
+    "## Simulate model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8HhNMK4C4d6f"
+   },
+   "source": [
+    "Simulate the model for 1000 timesteps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "v0lT7gaIviev"
+   },
+   "outputs": [],
+   "source": [
+    "while model.timestep < 1000:\n",
+    "    model.step_time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "SUzXrYxr4kO5"
+   },
+   "source": [
+    "Copy the recorded spike data back from the GPU and extract the spike times and IDs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bDJLu6Kwvn7W"
+   },
+   "outputs": [],
+   "source": [
+    "model.pull_recording_buffers_from_device()\n",
+    "\n",
+    "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n",
+    "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jS5OtCX15CCJ"
+   },
+   "source": [
+    "Plot spikes and rates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 850
+    },
+    "id": "9rWE-Rvjvo5I",
+    "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 2000x1000 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n",
+    "\n",
+    "# Define some bins to calculate spike rates\n",
+    "bin_size = 20.0\n",
+    "rate_bins = np.arange(0, 1000.0, bin_size)\n",
+    "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n",
+    "\n",
+    "# Plot excitatory and inhibitory spikes on first axis\n",
+    "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n",
+    "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n",
+    "\n",
+    "# Plot excitatory rates on second axis\n",
+    "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n",
+    "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n",
+    "\n",
+    "# Plot inhibitory rates on third axis\n",
+    "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n",
+    "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n",
+    "\n",
+    "# Label axes\n",
+    "axes[0].set_ylabel(\"Neuron ID\")\n",
+    "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n",
+    "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n",
+    "axes[2].set_xlabel(\"Time [ms]\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lkZXMKuC42jG"
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "2_synapses",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/documentation/5/tutorials/comp_neuro_101/index.html b/documentation/5/tutorials/comp_neuro_101/index.html
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--- /dev/null
+++ b/documentation/5/tutorials/comp_neuro_101/index.html
@@ -0,0 +1,137 @@
+<!DOCTYPE html>
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+<li class="toctree-l2"><a class="reference internal" href="mushroom_body/1_first_layer.html">Presenting latency-coded inputs</a></li>
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+<h1>Tutorials<a class="headerlink" href="#tutorials" title="Permalink to this heading"></a></h1>
+<section id="compneuro-101">
+<h2>CompNeuro 101<a class="headerlink" href="#compneuro-101" title="Permalink to this heading"></a></h2>
+<p>Building spiking neural network models in GeNN</p>
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+  <div>Defining populations of neurons</div>
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+  <div><img alt="" src="../_images\tutorials_comp_neuro_101_2_synapses_28_0.png"></div>
+  <div>Adding synapses</div>
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+</section>
+<section id="mnist-inference">
+<h2>MNIST inference<a class="headerlink" href="#mnist-inference" title="Permalink to this heading"></a></h2>
+<p>Perform MNIST inference by converting a pre-trained ANN to an SNN</p>
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+<a class="reference internal" href="mnist_inference/tutorial_1.html">
+  <div><img alt="" src="../_images\tutorials_mnist_inference_tutorial_1_39_0.png"></div>
+  <div>Classification of a single digit</div>
+</a>
+<a class="reference internal" href="mnist_inference/tutorial_2.html">
+  <div><img alt="" src="../_static\nbsphinx-no-thumbnail.svg"></div>
+  <div>Classification of the entire test set</div>
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+<a class="reference internal" href="mnist_inference/tutorial_3.html">
+  <div><img alt="" src="../_static\nbsphinx-no-thumbnail.svg"></div>
+  <div>Faster classification of the whole test set</div>
+</a>
+</div>
+</div>
+</section>
+<section id="insect-inspired-mnist-classification">
+<h2>Insect-inspired MNIST classification<a class="headerlink" href="#insect-inspired-mnist-classification" title="Permalink to this heading"></a></h2>
+<p>Train a model of the insect mushroom body using an STDP learning rule to classify MNIST.</p>
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+  <div><img alt="" src="../_images\tutorials_mushroom_body_2_second_layer_21_4.png"></div>
+  <div>Adding Kenyon Cells</div>
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+<a class="reference internal" href="mushroom_body/3_second_layer_gain_control.html">
+  <div><img alt="" src="../_images\tutorials_mushroom_body_3_second_layer_gain_control_22_4.png"></div>
+  <div>Feedback-inhibition based gain control</div>
+</a>
+<a class="reference internal" href="mushroom_body/4_third_layer.html">
+  <div><img alt="" src="../_images\tutorials_mushroom_body_4_third_layer_31_0.png"></div>
+  <div>Output neurons and learning</div>
+</a>
+<a class="reference internal" href="mushroom_body/5_testing.html">
+  <div><img alt="" src="../_images\tutorials_mushroom_body_5_testing_22_3.png"></div>
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+<h1>MNIST inference<a class="headerlink" href="#mnist-inference" title="Permalink to this heading"></a></h1>
+<p>Perform MNIST inference by converting a pre-trained ANN to an SNN</p>
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+  <section id="Classification-of-a-single-digit">
+<h1>Classification of a single digit<a class="headerlink" href="#Classification-of-a-single-digit" title="Permalink to this heading"></a></h1>
+<p>In this series of tutorial, we are going to build an SNN capable of classifying MNIST by copying the weights obtained by training the following simple ANN using TensorFlow:</p>
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  </g>
</svg>
" />
+<p>Clearly, this is far from a state of the art architecture, but it still achieves 97.6% accuracy on MNIST. In this first tutorial we are going to build the basic SNN model, present a single test set image to it and visualize the spiking activitiy of the model.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 147MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Download-pre-trained-weights-and-MNIST-test-data">
+<h2>Download pre-trained weights and MNIST test data<a class="headerlink" href="#Download-pre-trained-weights-and-MNIST-test-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>gdown<span class="w"> </span>1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+<span class="o">!</span>gdown<span class="w"> </span>131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading...
+From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+To: /content/weights_0_1.npy
+100% 402k/402k [00:00&lt;00:00, 142MB/s]
+Downloading...
+From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+To: /content/weights_1_2.npy
+100% 5.25k/5.25k [00:00&lt;00:00, 18.8MB/s]
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Import standard modules and required PyGeNN functions and classes</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">create_current_source_model</span><span class="p">,</span>
+                    <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Define some simulation parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation timestep of model in ms</span>
+<span class="n">TIMESTEP</span> <span class="o">=</span> <span class="mf">1.0</span>
+
+<span class="c1"># How many timesteps to present images for</span>
+<span class="n">PRESENT_TIMESTEPS</span> <span class="o">=</span> <span class="mi">100</span>
+
+<span class="c1"># How much to scale input images</span>
+<span class="n">INPUT_CURRENT_SCALE</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="mf">100.0</span>
+</pre></div>
+</div>
+</div>
+<p>Because the ReLU neurons our ANN was trained with are best matched by a very simple Integrate-and-Fire neuron without a leak, define a custom model with: * A single parameter (parameters are common across all neurons in population) <code class="docutils literal notranslate"><span class="pre">Vthr</span></code> which specifies it’s spiking threshold * A single <code class="docutils literal notranslate"><span class="pre">V</span></code> state variable to hold the membrane potential of the neurons * Simulation code which simply adds the incoming current <code class="docutils literal notranslate"><span class="pre">Isyn</span></code> (this is a built in variable provided by GeNN) to the membrane potential
+<code class="docutils literal notranslate"><span class="pre">V</span></code> (note, we’re assuming that the membrane resistance is 1 here) * Threshold condition code which causes the neuron to emit a spike if it’s membrane potential <code class="docutils literal notranslate"><span class="pre">V</span></code> crosses the threshold <code class="docutils literal notranslate"><span class="pre">Vthr</span></code> * Reset code which zeros the membrane potential <code class="docutils literal notranslate"><span class="pre">V</span></code> after a spike is emitted.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;if_model&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthr&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span><span class="s2">&quot;V += Isyn;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span><span class="s2">&quot;V &gt;= Vthr&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    V = 0.0;</span>
+<span class="s2">    &quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>We are going to convert MNIST digits to spikes by simply treating the intensity of each pixel (multiplied by a scaling factor) as a current and injecting it into the neurons in the input population throughout the stimulus presentation time.</p>
+<p>To do this we use a very simple custom current source model with:</p>
+<ul class="simple">
+<li><p>A single <code class="docutils literal notranslate"><span class="pre">magnitude</span></code> state variable to store the per-neuron current to inject</p></li>
+<li><p>Injection code which injects a current of <code class="docutils literal notranslate"><span class="pre">magnitude</span></code> every timestep (<code class="docutils literal notranslate"><span class="pre">$(injectCurrent,</span> <span class="pre">X)</span></code> is a function provided by GeNN for use in current sources).</p></li>
+</ul>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Create a new model implementing <code class="docutils literal notranslate"><span class="pre">scalar</span></code> variables as single-precision and generating code into tutorial_1 directory</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial_1&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">TIMESTEP</span>
+</pre></div>
+</div>
+</div>
+<p>Load the weight matrices extracted from our original ANN</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load weights</span>
+<span class="n">weights_0_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_0_1.npy&quot;</span><span class="p">)</span>
+<span class="n">weights_1_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_1_2.npy&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Create three populations of Integrate-and-Fire neurons sized to match the shapes of the weight matrices and initialised so their membrane potential’s are all initialised to 0mv and their spiking thresholds to 5mv.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">if_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Vthr&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+<span class="n">neurons</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron0&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron1&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron2&quot;</span><span class="p">,</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)]</span>
+</pre></div>
+</div>
+</div>
+<p>Because, in this first tutorial we want to examine the spike emitted by each neuron, turn on spike recording for each population.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">neurons</span><span class="p">:</span>
+    <span class="n">n</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+<p>Add synapse populations to sequentially connect the three populations of neurons. These are all configured identically with: * <code class="docutils literal notranslate"><span class="pre">DENSE</span></code> connectivity meaning that they are connected with a basic dense weight matrix(see <a class="reference external" href="https://genn-team.github.io/genn/documentation/4/html/d5/d39/subsect34.html">documentation</a>). * The built in <code class="docutils literal notranslate"><span class="pre">StaticPulse</span></code> <strong>weight update model</strong> which is used for spiking synapses without any sort of learning. This has no parameters and a single state variable <code class="docutils literal notranslate"><span class="pre">g</span></code>
+representing its synaptic weights which we initialise using our arrays of pre-trained weights. * The build in <code class="docutils literal notranslate"><span class="pre">DeltaCurr</span></code> <strong>postsynaptic model</strong> which specified that weighted incoming spikes are added directly to the postsynaptic neuron’s membrane potential without any additional shaping occuring. This model has no parameters or state variables.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_0_1&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_1_2&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">));</span>
+</pre></div>
+</div>
+</div>
+<p>Add current source to provide input into the input population of neurons</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">current_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;current_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span>
+                                         <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+<br/></pre></div>
+</div>
+</div>
+<p>Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout a single stimuli presentation.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">PRESENT_TIMESTEPS</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-model">
+<h2>Simulate model<a class="headerlink" href="#Simulate-model" title="Permalink to this heading"></a></h2>
+<p>First we load the two numpy arrays containing the images and labels from the MNIST test set and verify that the size of the input images matches that of the first (input) population and that the size of the last (output) population is enough to one-hot encode all of the labels.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">testing_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()</span>
+<span class="n">testing_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()</span>
+
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+<span class="k">assert</span> <span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">num_neurons</span>
+<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">neurons</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">num_neurons</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>PyGeNN uses <em>memory views</em> to directly expose the memory used by the simulation to numpy. Copy the first testing image into the memory view of the current source’s magnitude variable.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">current_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">testing_images</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_CURRENT_SCALE</span>
+</pre></div>
+</div>
+</div>
+<img alt="pci-e_single_dual-400x142.png" src="data:image/png;base64,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" />
+<p>On most systems, memory accessible by the GPU and the CPU is seperate. Therefore, we need to manually copy the values we just placed in the current source’s magnitude variable to the GPU (if we’re running GeNN on the CPU, this call will not do anything)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">current_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Simulate the model for <code class="docutils literal notranslate"><span class="pre">PRESENT_TIMESTEPS</span></code> (<code class="docutils literal notranslate"><span class="pre">model.timestep</span></code> tracks integer timesteps whereas <code class="docutils literal notranslate"><span class="pre">model.time</span></code> tracks time in ms)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">while</span> <span class="n">model</span><span class="o">.</span><span class="n">timestep</span> <span class="o">&lt;</span> <span class="n">PRESENT_TIMESTEPS</span><span class="p">:</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Download the recorded spikes from the GPU</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Plot raster plots of the spikes from all neuron populations, illustrating the correct label for this image with a horizontal line</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create figure with one axis per neuron population</span>
+<span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">neurons</span><span class="p">),</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+<span class="c1"># Loop through neuron populations and the axis we&#39;re going to plot their raster plot on</span>
+<span class="k">for</span> <span class="n">n</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">neurons</span><span class="p">,</span> <span class="n">axes</span><span class="p">):</span>
+    <span class="c1"># Extract spike times and IDs and plot</span>
+    <span class="n">spike_times</span><span class="p">,</span> <span class="n">spike_ids</span> <span class="o">=</span> <span class="n">n</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">a</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">spike_times</span><span class="p">,</span> <span class="n">spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="n">a</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">n</span><span class="o">.</span><span class="n">name</span><span class="p">)</span>
+    <span class="n">a</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Neuron ID&quot;</span><span class="p">)</span>
+    <span class="n">a</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">PRESENT_TIMESTEPS</span> <span class="o">*</span> <span class="n">TIMESTEP</span><span class="p">))</span>
+    <span class="n">a</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="n">n</span><span class="o">.</span><span class="n">num_neurons</span><span class="p">))</span>
+
+<span class="c1"># Add an x-axis label and translucent line showing the correct label</span>
+<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">)</span>
+<span class="n">axes</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">hlines</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">xmin</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">xmax</span><span class="o">=</span><span class="n">PRESENT_TIMESTEPS</span><span class="p">,</span>
+                <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;gray&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
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+<img alt="../../_images/tutorials_mnist_inference_tutorial_1_39_0.png" src="../../_images/tutorials_mnist_inference_tutorial_1_39_0.png" />
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diff --git a/documentation/5/tutorials/mnist_inference/tutorial_1.ipynb b/documentation/5/tutorials/mnist_inference/tutorial_1.ipynb
new file mode 100644
index 000000000..cb722b7db
--- /dev/null
+++ b/documentation/5/tutorials/mnist_inference/tutorial_1.ipynb
@@ -0,0 +1,618 @@
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+  {
+   "cell_type": "markdown",
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+    "# Classification of a single digit\n",
+    "In this series of tutorial, we are going to build an SNN capable of classifying MNIST by copying the weights obtained by training the following simple ANN using TensorFlow:\n",
+    "\n",
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       fill="#000000" />
  </g>
</svg>
)\n",
+    "\n",
+    "Clearly, this is far from a state of the art architecture, but it still achieves 97.6% accuracy on MNIST. In this first tutorial we are going to build the basic SNN model, present a single test set image to it and visualize the spiking activitiy of the model.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "VXltW7HVbtcj",
+    "outputId": "10383607-e3f3-41b4-9751-b80d3a858cbc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 147MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8tqbF5GldF0o"
+   },
+   "source": [
+    "## Download pre-trained weights and MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "N-2PV7LcdFg_",
+    "outputId": "177625f2-5aa4-4b98-8fec-aafd20520cdb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+      "To: /content/weights_0_1.npy\n",
+      "100% 402k/402k [00:00<00:00, 142MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+      "To: /content/weights_1_2.npy\n",
+      "100% 5.25k/5.25k [00:00<00:00, 18.8MB/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+    "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "bb469225-f242-4f8f-c1ef-e997d97d2066"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BwadUz9Azxss"
+   },
+   "source": [
+    "## Build model\n",
+    "Import standard modules and required PyGeNN functions and classes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "agqWFZjickfU"
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import mnist\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pygenn import (create_neuron_model, create_current_source_model,\n",
+    "                    init_postsynaptic, init_weight_update, GeNNModel)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iuwdL6IE2MuS"
+   },
+   "source": [
+    "Define some simulation parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "C68EDXn6cj-O"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation timestep of model in ms\n",
+    "TIMESTEP = 1.0\n",
+    "\n",
+    "# How many timesteps to present images for\n",
+    "PRESENT_TIMESTEPS = 100\n",
+    "\n",
+    "# How much to scale input images\n",
+    "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "9PCMgNPCz86O"
+   },
+   "source": [
+    "Because the ReLU neurons our ANN was trained with are best matched by a very simple Integrate-and-Fire neuron without a leak, define a custom model with:\n",
+    "*   A single parameter (parameters are common across all neurons in population) `Vthr` which specifies it's spiking threshold\n",
+    "*   A single `V` state variable to hold the membrane potential of the neurons\n",
+    "*   Simulation code which simply adds the incoming current `Isyn` (this is a built in variable provided by GeNN) to the membrane potential `V` (note, we're assuming that the membrane resistance is 1 here)\n",
+    "*   Threshold condition code which causes the neuron to emit a spike if it's membrane potential `V` crosses the threshold `Vthr`\n",
+    "*   Reset code which zeros the membrane potential `V` after a spike is emitted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-7lzXzmQcgbt"
+   },
+   "outputs": [],
+   "source": [
+    "if_model = create_neuron_model(\n",
+    "    \"if_model\",\n",
+    "    params=[\"Vthr\"],\n",
+    "    vars=[(\"V\", \"scalar\")],\n",
+    "    sim_code=\"V += Isyn;\",\n",
+    "    threshold_condition_code=\"V >= Vthr\",\n",
+    "    reset_code=\"\"\"\n",
+    "    V = 0.0;\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ATwobw4Mw2LG"
+   },
+   "source": [
+    "We are going to convert MNIST digits to spikes by simply treating the intensity of each pixel (multiplied by a scaling factor) as a current and injecting it into the neurons in the input population throughout the stimulus presentation time.\n",
+    "\n",
+    "To do this we use a very simple custom current source model with:\n",
+    "\n",
+    "*   A single `magnitude` state variable to store the per-neuron current to inject\n",
+    "*   Injection code which injects a current of `magnitude` every timestep (`$(injectCurrent, X)` is a function provided by GeNN for use in current sources).\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "EEQyoL-zcu-A"
+   },
+   "outputs": [],
+   "source": [
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "TnawdZkzyJQZ"
+   },
+   "source": [
+    "Create a new model implementing `scalar` variables as single-precision and generating code into tutorial_1 directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "J1VY795eeFa8"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial_1\")\n",
+    "model.dt = TIMESTEP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "p4wFWaMS1M8_"
+   },
+   "source": [
+    "\n",
+    "Load the weight matrices extracted from our original ANN\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6RVz8FPmc4n5"
+   },
+   "outputs": [],
+   "source": [
+    "# Load weights\n",
+    "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+    "weights_1_2 = np.load(\"weights_1_2.npy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ik9NVB3-1Mdy"
+   },
+   "source": [
+    "Create three populations of Integrate-and-Fire neurons sized to match the shapes of the weight matrices and initialised so their membrane potential's are all initialised to 0mv and their spiking thresholds to 5mv."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ODYWtS28cxGi"
+   },
+   "outputs": [],
+   "source": [
+    "if_init = {\"V\": 0.0}\n",
+    "if_params = {\"Vthr\": 5.0}\n",
+    "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+    "                                       if_model, if_params, if_init)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DPD-YOP81dGN"
+   },
+   "source": [
+    "Because, in this first tutorial we want to examine the spike emitted by each neuron, turn on spike recording for each population."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "elcdezDSeTe4"
+   },
+   "outputs": [],
+   "source": [
+    "for n in neurons:\n",
+    "    n.spike_recording_enabled = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Fj3wbKso1j2d"
+   },
+   "source": [
+    "Add synapse populations to sequentially connect the three populations of neurons. These are all configured identically with:\n",
+    "*   `DENSE` connectivity meaning that they are connected with a basic dense weight matrix(see [documentation](https://genn-team.github.io/genn/documentation/4/html/d5/d39/subsect34.html)).\n",
+    "*   The built in `StaticPulse` **weight update model** which is used for spiking synapses without any sort of learning. This has no parameters and a single state variable `g` representing its synaptic weights which we initialise using our arrays of pre-trained weights.\n",
+    "*   The build in `DeltaCurr` **postsynaptic model** which specified that weighted incoming spikes are added directly to the postsynaptic neuron's membrane potential without any additional shaping occuring. This model has no parameters or state variables.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Sx1VOU5udixG"
+   },
+   "outputs": [],
+   "source": [
+    "model.add_synapse_population(\n",
+    "        \"synapse_0_1\", \"DENSE\",\n",
+    "        neurons[0], neurons[1],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"))\n",
+    "model.add_synapse_population(\n",
+    "        \"synapse_1_2\", \"DENSE\",\n",
+    "        neurons[1], neurons[2],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "eQaIR8-ByoSI"
+   },
+   "source": [
+    "Add current source to provide input into the input population of neurons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "yENqkr6KeMLp"
+   },
+   "outputs": [],
+   "source": [
+    "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+    "                                         neurons[0], {}, {\"magnitude\": 0.0})\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "38pYD7rxytBT"
+   },
+   "source": [
+    "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout a single stimuli presentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0Tf07KUOeP-X"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load(num_recording_timesteps=PRESENT_TIMESTEPS)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vBHoR-Eu2r6R"
+   },
+   "source": [
+    "## Simulate model\n",
+    "First we load the two numpy arrays containing the images and labels from the MNIST test set and verify that the size of the input images matches that of the first (input) population and that the size of the last (output) population is enough to one-hot encode all of the labels."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "qVWFKwiRehF8"
+   },
+   "outputs": [],
+   "source": [
+    "testing_images = mnist.test_images()\n",
+    "testing_labels = mnist.test_labels()\n",
+    "\n",
+    "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+    "assert testing_images.shape[1] == neurons[0].num_neurons\n",
+    "assert np.max(testing_labels) == (neurons[-1].num_neurons - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "t-F5qx030CdT"
+   },
+   "source": [
+    "PyGeNN uses *memory views* to directly expose the memory used by the simulation to numpy. Copy the first testing image into the memory view of the current source's magnitude variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "3z1ccKHeejeB"
+   },
+   "outputs": [],
+   "source": [
+    "current_input.vars[\"magnitude\"].values = testing_images[0] * INPUT_CURRENT_SCALE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "U06Zwnuk0ihm"
+   },
+   "source": [
+    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)\n",
+    "\n",
+    "On most systems, memory accessible by the GPU and the CPU is seperate.\n",
+    "Therefore, we need to manually copy the values we just placed in the current source's magnitude variable to the GPU (if we're running GeNN on the CPU, this call will not do anything)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OfdQXEtd0jRi"
+   },
+   "outputs": [],
+   "source": [
+    "current_input.vars[\"magnitude\"].push_to_device()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OfUYJwC13VOk"
+   },
+   "source": [
+    "Simulate the model for `PRESENT_TIMESTEPS` (`model.timestep` tracks integer timesteps whereas `model.time` tracks time in ms)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4qSoinT4etKq"
+   },
+   "outputs": [],
+   "source": [
+    "while model.timestep < PRESENT_TIMESTEPS:\n",
+    "    model.step_time()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-Xg5AItK3ahj"
+   },
+   "source": [
+    "Download the recorded spikes from the GPU"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wOhN-Qcuexjy"
+   },
+   "outputs": [],
+   "source": [
+    "model.pull_recording_buffers_from_device()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dwGQ4ygO3f2b"
+   },
+   "source": [
+    "Plot raster plots of the spikes from all neuron populations, illustrating the correct label for this image with a horizontal line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 472
+    },
+    "id": "dBFluY10e7Ba",
+    "outputId": "fd82e034-e5ad-4c3b-cbd2-e998429cfab1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Create figure with one axis per neuron population\n",
+    "fig, axes = plt.subplots(len(neurons), sharex=True)\n",
+    "\n",
+    "# Loop through neuron populations and the axis we're going to plot their raster plot on\n",
+    "for n, a in zip(neurons, axes):\n",
+    "    # Extract spike times and IDs and plot\n",
+    "    spike_times, spike_ids = n.spike_recording_data[0]\n",
+    "    a.scatter(spike_times, spike_ids, s=1)\n",
+    "\n",
+    "    a.set_title(n.name)\n",
+    "    a.set_ylabel(\"Neuron ID\")\n",
+    "    a.set_xlim((0, PRESENT_TIMESTEPS * TIMESTEP))\n",
+    "    a.set_ylim((0, n.num_neurons))\n",
+    "\n",
+    "# Add an x-axis label and translucent line showing the correct label\n",
+    "axes[-1].set_xlabel(\"Time [ms]\")\n",
+    "axes[-1].hlines(testing_labels[0], xmin=0, xmax=PRESENT_TIMESTEPS,\n",
+    "                linestyle=\"--\", color=\"gray\", alpha=0.2);"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "tutorial_1",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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+<li class="toctree-l1"><a class="reference internal" href="../../building_networks.html">Building networks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../simulating_networks.html">Simulating networks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../custom_models.html">Custom models</a></li>
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+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/1_neurons.html">Defining populations of neurons</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/2_synapses.html">Adding synapses</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_1.html">Classification of a single digit</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Classification of the entire test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="tutorial_3.html">Faster classification of the whole test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/1_first_layer.html">Presenting latency-coded inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/2_second_layer.html">Adding Kenyon Cells</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/3_second_layer_gain_control.html">Feedback-inhibition based gain control</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/4_third_layer.html">Output neurons and learning</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mushroom_body/5_testing.html">Testing</a></li>
+</ul>
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+             
+  <section id="Classification-of-the-entire-test-set">
+<h1>Classification of the entire test set<a class="headerlink" href="#Classification-of-the-entire-test-set" title="Permalink to this heading"></a></h1>
+<p>In this tutorial we’re going to take the model we developed in the previous tutorial, run it on the entire MNIST testing set and calculate the overall classification accuracy.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 149MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Download-pre-trained-weights-and-MNIST-test-data">
+<h2>Download pre-trained weights and MNIST test data<a class="headerlink" href="#Download-pre-trained-weights-and-MNIST-test-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>gdown<span class="w"> </span>1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+<span class="o">!</span>gdown<span class="w"> </span>131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading...
+From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+To: /content/weights_0_1.npy
+100% 402k/402k [00:00&lt;00:00, 127MB/s]
+Downloading...
+From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+To: /content/weights_1_2.npy
+100% 5.25k/5.25k [00:00&lt;00:00, 23.6MB/s]
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>As well as the standard modules and required PyGeNN functions and classes we used in the first tutorial, also import <code class="docutils literal notranslate"><span class="pre">time.perf_counter</span></code> for measuring the performance of our classifier and <code class="docutils literal notranslate"><span class="pre">tqdm.tqdm</span></code> for drawing progress bars</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">create_current_source_model</span><span class="p">,</span>
+                    <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">perf_counter</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<p>As before, define some simulation parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">TIMESTEP</span> <span class="o">=</span> <span class="mf">1.0</span>
+<span class="n">PRESENT_TIMESTEPS</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">INPUT_CURRENT_SCALE</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="mf">100.0</span>
+</pre></div>
+</div>
+</div>
+<p>Create very similar neuron and current source models. However, to avoid having to download every spike and count them on the CPU, here, we add an additional state variable <code class="docutils literal notranslate"><span class="pre">SpikeCount</span></code> to each neuron which gets incremented in the reset code to count spikes.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Very simple integrate-and-fire neuron model</span>
+<span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;if_model&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthr&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;SpikeCount&quot;</span><span class="p">,</span> <span class="s2">&quot;unsigned int&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span><span class="s2">&quot;V += Isyn * dt;&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    V = 0.0;</span>
+<span class="s2">    SpikeCount++;</span>
+<span class="s2">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span><span class="s2">&quot;V &gt;= Vthr&quot;</span><span class="p">)</span>
+
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Build model, load weights and create neuron, synapse and current source populations as before</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial_2&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">TIMESTEP</span>
+
+<span class="c1"># Load weights</span>
+<span class="n">weights_0_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_0_1.npy&quot;</span><span class="p">)</span>
+<span class="n">weights_1_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_1_2.npy&quot;</span><span class="p">)</span>
+
+<span class="n">if_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Vthr&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+<span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;SpikeCount&quot;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+<span class="n">neurons</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron0&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron1&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron2&quot;</span><span class="p">,</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)]</span>
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_0_1&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_1_2&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">));</span>
+
+<span class="n">current_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;current_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span>
+                                         <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<p>Run code generator to generate simulation code for model and load it into PyGeNN as before but, here, we don’t want to record any spikes so no need to specify a recording buffer size.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Just like in the previous tutorial, load testing images and labels and verify their dimensions</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">testing_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()</span>
+<span class="n">testing_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()</span>
+
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+<span class="k">assert</span> <span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">weights_1_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-model">
+<h2>Simulate model<a class="headerlink" href="#Simulate-model" title="Permalink to this heading"></a></h2>
+<p>In this tutorial we’re going to not only inject current but also access the new spike count variable in the output population and reset the voltages throughout the model. Therefore we need to create some additional memory views</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">current_input_magnitude</span> <span class="o">=</span> <span class="n">current_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span>
+<span class="n">output_spike_count</span> <span class="o">=</span> <span class="n">neurons</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;SpikeCount&quot;</span><span class="p">]</span>
+<span class="n">neuron_voltages</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;V&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">neurons</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+<p>Now, we define our inference loop. We loop through all of the testing images and for each one:</p>
+<ol class="arabic simple">
+<li><p>Copy the (scaled) image data into the current input memory view and copy it to the GPU</p></li>
+<li><p>Loop through all the neuron populations, zero their membrance voltages and copy these to the GPU</p></li>
+<li><p>Zero the output spike count and copy that to the GPU</p></li>
+<li><p>Simulate the model for <code class="docutils literal notranslate"><span class="pre">PRESENT_TIMESTEPS</span></code></p></li>
+<li><p>Download the spike counts from the output layer</p></li>
+<li><p>If highest spike count corresponds to correct label, increment <code class="docutils literal notranslate"><span class="pre">num_correct</span></code></p></li>
+</ol>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulate</span>
+<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">start_time</span> <span class="o">=</span> <span class="n">perf_counter</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+    <span class="n">current_input_magnitude</span><span class="o">.</span><span class="n">values</span> <span class="o">=</span> <span class="n">testing_images</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_CURRENT_SCALE</span>
+    <span class="n">current_input_magnitude</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Loop through all voltage variables</span>
+    <span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">neuron_voltages</span><span class="p">:</span>
+        <span class="c1"># Manually &#39;reset&#39; voltage</span>
+        <span class="n">v</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+
+        <span class="c1"># Upload</span>
+        <span class="n">v</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Zero spike count</span>
+    <span class="n">output_spike_count</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">output_spike_count</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">PRESENT_TIMESTEPS</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Download spike count from last layer</span>
+    <span class="n">output_spike_count</span><span class="o">.</span><span class="n">pull_from_device</span><span class="p">()</span>
+
+    <span class="c1"># Find which neuron spiked the most to get prediction</span>
+    <span class="n">predicted_label</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output_spike_count</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
+    <span class="n">true_label</span> <span class="o">=</span> <span class="n">testing_labels</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
+
+    <span class="k">if</span> <span class="n">predicted_label</span> <span class="o">==</span> <span class="n">true_label</span><span class="p">:</span>
+        <span class="n">num_correct</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="n">end_time</span> <span class="o">=</span> <span class="n">perf_counter</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Accuracy </span><span class="si">{</span><span class="p">((</span><span class="n">num_correct</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">float</span><span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mf">100.0</span><span class="p">)</span><span class="si">}</span><span class="s2">%%&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Time </span><span class="si">{</span><span class="n">end_time</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "e2a5b2d7a928414c921ef1945e969ff2"}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+Accuracy 97.44%%
+Time 11.930175114999997 seconds
+</pre></div></div>
+</div>
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+</script></section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="tutorial_1.html" class="btn btn-neutral float-left" title="Classification of a single digit" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+  <hr/>
+
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+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
+  </div>
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diff --git a/documentation/5/tutorials/mnist_inference/tutorial_2.ipynb b/documentation/5/tutorials/mnist_inference/tutorial_2.ipynb
new file mode 100644
index 000000000..e3d7c84e5
--- /dev/null
+++ b/documentation/5/tutorials/mnist_inference/tutorial_2.ipynb
@@ -0,0 +1,827 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Classification of the entire test set\n",
+    "In this tutorial we're going to take the model we developed in the previous tutorial, run it on the entire MNIST testing set and calculate the overall classification accuracy.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Qqz__TiIdE9x",
+    "outputId": "912641fe-072b-48d1-aa90-f911ab463cd3"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 149MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8tqbF5GldF0o"
+   },
+   "source": [
+    "## Download pre-trained weights and MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "N-2PV7LcdFg_",
+    "outputId": "1404acd1-ba2c-4c08-c620-c1ad71ece658"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+      "To: /content/weights_0_1.npy\n",
+      "100% 402k/402k [00:00<00:00, 127MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+      "To: /content/weights_1_2.npy\n",
+      "100% 5.25k/5.25k [00:00<00:00, 23.6MB/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+    "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "ddb641da-6ec7-459f-db01-5157d2a17f49"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "l7UOIOeX1xeE"
+   },
+   "source": [
+    "## Build model\n",
+    "As well as the standard modules and required PyGeNN functions and classes we used in the first tutorial, also import `time.perf_counter` for measuring the performance of our classifier and `tqdm.tqdm` for drawing progress bars"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "agqWFZjickfU"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pygenn import (create_neuron_model, create_current_source_model,\n",
+    "                    init_postsynaptic, init_weight_update, GeNNModel)\n",
+    "from time import perf_counter\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FMBcXoyd4yS1"
+   },
+   "source": [
+    "As before, define some simulation parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "KqBx7iO_kApE"
+   },
+   "outputs": [],
+   "source": [
+    "TIMESTEP = 1.0\n",
+    "PRESENT_TIMESTEPS = 100\n",
+    "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "2QlVBYQG431K"
+   },
+   "source": [
+    "Create very similar neuron and current source models. However, to avoid having to download every spike and count them on the CPU, here, we add an additional state variable `SpikeCount` to each neuron which gets incremented in the reset code to count spikes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-7lzXzmQcgbt"
+   },
+   "outputs": [],
+   "source": [
+    "# Very simple integrate-and-fire neuron model\n",
+    "if_model = create_neuron_model(\n",
+    "    \"if_model\",\n",
+    "    params=[\"Vthr\"],\n",
+    "    vars=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+    "    sim_code=\"V += Isyn * dt;\",\n",
+    "    reset_code=\"\"\"\n",
+    "    V = 0.0;\n",
+    "    SpikeCount++;\n",
+    "    \"\"\",\n",
+    "    threshold_condition_code=\"V >= Vthr\")\n",
+    "\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lWMtozHB3OrM"
+   },
+   "source": [
+    "Build model, load weights and create neuron, synapse and current source populations as before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Sx1VOU5udixG"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial_2\")\n",
+    "model.dt = TIMESTEP\n",
+    "\n",
+    "# Load weights\n",
+    "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+    "weights_1_2 = np.load(\"weights_1_2.npy\")\n",
+    "\n",
+    "if_params = {\"Vthr\": 5.0}\n",
+    "if_init = {\"V\": 0.0, \"SpikeCount\":0}\n",
+    "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+    "                                       if_model, if_params, if_init)]\n",
+    "model.add_synapse_population(\n",
+    "        \"synapse_0_1\", \"DENSE\",\n",
+    "        neurons[0], neurons[1],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"))\n",
+    "model.add_synapse_population(\n",
+    "        \"synapse_1_2\", \"DENSE\",\n",
+    "        neurons[1], neurons[2],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"));\n",
+    "\n",
+    "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+    "                                         neurons[0], {}, {\"magnitude\": 0.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jdggjUe13tT_"
+   },
+   "source": [
+    "Run code generator to generate simulation code for model and load it into PyGeNN as before but, here, we don't want to record any spikes so no need to specify a recording buffer size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K8kHbKMJ3kIY"
+   },
+   "outputs": [],
+   "source": [
+    "model.build()\n",
+    "model.load()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oMxrFcIP66CX"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rUxwsE323l37"
+   },
+   "source": [
+    "Just like in the previous tutorial, load testing images and labels and verify their dimensions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0Tf07KUOeP-X"
+   },
+   "outputs": [],
+   "source": [
+    "testing_images = mnist.test_images()\n",
+    "testing_labels = mnist.test_labels()\n",
+    "\n",
+    "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+    "assert testing_images.shape[1] == weights_0_1.shape[0]\n",
+    "assert np.max(testing_labels) == (weights_1_2.shape[1] - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "r-TFULk_3i8z"
+   },
+   "source": [
+    "## Simulate model\n",
+    "In this tutorial we're going to not only inject current but also access the new spike count variable in the output population and reset the voltages throughout the model. Therefore we need to create some additional memory views"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "3z1ccKHeejeB"
+   },
+   "outputs": [],
+   "source": [
+    "current_input_magnitude = current_input.vars[\"magnitude\"]\n",
+    "output_spike_count = neurons[-1].vars[\"SpikeCount\"]\n",
+    "neuron_voltages = [n.vars[\"V\"] for n in neurons]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JCDP_sTa4HTL"
+   },
+   "source": [
+    "Now, we define our inference loop. We loop through all of the testing images and for each one:\n",
+    "\n",
+    "1.   Copy the (scaled) image data into the current input memory view and copy it to the GPU\n",
+    "2.   Loop through all the neuron populations, zero their membrance voltages and copy these to the GPU\n",
+    "3. Zero the output spike count and copy that to the GPU\n",
+    "4. Simulate the model for `PRESENT_TIMESTEPS`\n",
+    "5. Download the spike counts from the output layer\n",
+    "6. If highest spike count corresponds to correct label, increment `num_correct`\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 101,
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+    "id": "4qSoinT4etKq",
+    "outputId": "01a98bc1-3bb6-4fab-b172-598e3f90fb2b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2a5b2d7a928414c921ef1945e969ff2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Accuracy 97.44%%\n",
+      "Time 11.930175114999997 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Simulate\n",
+    "num_correct = 0\n",
+    "start_time = perf_counter()\n",
+    "for i in tqdm(range(testing_images.shape[0])):\n",
+    "    current_input_magnitude.values = testing_images[i] * INPUT_CURRENT_SCALE\n",
+    "    current_input_magnitude.push_to_device()\n",
+    "\n",
+    "    # Loop through all voltage variables\n",
+    "    for v in neuron_voltages:\n",
+    "        # Manually 'reset' voltage\n",
+    "        v.view[:] = 0.0\n",
+    "\n",
+    "        # Upload\n",
+    "        v.push_to_device()\n",
+    "\n",
+    "    # Zero spike count\n",
+    "    output_spike_count.view[:] = 0\n",
+    "    output_spike_count.push_to_device()\n",
+    "\n",
+    "    for t in range(PRESENT_TIMESTEPS):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Download spike count from last layer\n",
+    "    output_spike_count.pull_from_device()\n",
+    "\n",
+    "    # Find which neuron spiked the most to get prediction\n",
+    "    predicted_label = np.argmax(output_spike_count.values)\n",
+    "    true_label = testing_labels[i]\n",
+    "\n",
+    "    if predicted_label == true_label:\n",
+    "        num_correct += 1\n",
+    "\n",
+    "end_time = perf_counter()\n",
+    "print(f\"\\nAccuracy {((num_correct / float(testing_images.shape[0])) * 100.0)}%%\")\n",
+    "print(f\"Time {end_time - start_time} seconds\")\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "tutorial_2",
+   "provenance": []
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diff --git a/documentation/5/tutorials/mnist_inference/tutorial_3.html b/documentation/5/tutorials/mnist_inference/tutorial_3.html
new file mode 100644
index 000000000..a7c9fa3b5
--- /dev/null
+++ b/documentation/5/tutorials/mnist_inference/tutorial_3.html
@@ -0,0 +1,460 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+<li class="toctree-l2"><a class="reference internal" href="tutorial_1.html">Classification of a single digit</a></li>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Faster classification of the whole test set</a></li>
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+            <a href="../../_sources/tutorials/mnist_inference/tutorial_3.ipynb.txt" rel="nofollow"> View page source</a>
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+           <div itemprop="articleBody">
+             
+  <section id="Faster-classification-of-the-whole-test-set">
+<h1>Faster classification of the whole test set<a class="headerlink" href="#Faster-classification-of-the-whole-test-set" title="Permalink to this heading"></a></h1>
+<p>The model we developed in the previous tutorial classified MNIST successfully but was rather slow. Like ANNs, to maximise performance when simulating small SNNs like this on a GPU, we need to simulate multiple copies of the model at once and run them on <strong>batches</strong> of input images. In this tutorial we will modify our model to do just that as well as off-loading further computation to the GPU to improve performance.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 182MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Download-pre-trained-weights-and-MNIST-test-data">
+<h2>Download pre-trained weights and MNIST test data<a class="headerlink" href="#Download-pre-trained-weights-and-MNIST-test-data" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>gdown<span class="w"> </span>1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+<span class="o">!</span>gdown<span class="w"> </span>131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Downloading...
+From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc
+To: /content/weights_0_1.npy
+100% 402k/402k [00:00&lt;00:00, 50.3MB/s]
+Downloading...
+From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF
+To: /content/weights_1_2.npy
+100% 5.25k/5.25k [00:00&lt;00:00, 23.2MB/s]
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+</pre></div></div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Import standard module and PyGeNN functionality as before and configure simulation parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">create_custom_update_model</span><span class="p">,</span>
+                    <span class="n">create_var_ref</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">time</span> <span class="kn">import</span> <span class="n">perf_counter</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="n">TIMESTEP</span> <span class="o">=</span> <span class="mf">1.0</span>
+<span class="n">PRESENT_TIMESTEPS</span> <span class="o">=</span> <span class="mi">100</span>
+<span class="n">INPUT_CURRENT_SCALE</span> <span class="o">=</span> <span class="mf">1.0</span> <span class="o">/</span> <span class="mf">100.0</span>
+</pre></div>
+</div>
+</div>
+<p>As we’re going to use it in a few places, we add an additional simulation parameter to define the batch size.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">BATCH_SIZE</span> <span class="o">=</span> <span class="mi">128</span>
+</pre></div>
+</div>
+</div>
+<p>Define the custom neuron and synapse models in exactly the same way as before</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Very simple integrate-and-fire neuron model</span>
+<span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;if_model&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthr&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;SpikeCount&quot;</span><span class="p">,</span> <span class="s2">&quot;unsigned int&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span><span class="s2">&quot;V += Isyn * dt;&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    V = 0.0;</span>
+<span class="s2">    SpikeCount++;</span>
+<span class="s2">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span><span class="s2">&quot;V &gt;= Vthr&quot;</span><span class="p">)</span>
+
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<p>As we increase the batch size of our model, the cost of resetting the spike counts and membrane voltages will increase. To counteract this, we can offload tasks like this to the GPU using a <em>custom update</em> model. These are defined using very similar syntax to neuron and synapse models but have one additional feature - variable references. These allow custom updates to be <em>attached</em> to existing neuron or synapse populations to modify their variables outside of the standard neuron and synapse
+updates.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">reset_model</span> <span class="o">=</span> <span class="n">create_custom_update_model</span><span class="p">(</span>
+    <span class="s2">&quot;reset&quot;</span><span class="p">,</span>
+    <span class="n">var_refs</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">),</span> <span class="p">(</span><span class="s2">&quot;SpikeCount&quot;</span><span class="p">,</span> <span class="s2">&quot;unsigned int&quot;</span><span class="p">)],</span>
+    <span class="n">update_code</span><span class="o">=</span><span class="s2">&quot;&quot;&quot;</span>
+<span class="s2">    V = 0.0;</span>
+<span class="s2">    SpikeCount = 0;</span>
+<span class="s2">    &quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Create a new model in exactly the same way as before</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;tutorial_3&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">TIMESTEP</span>
+</pre></div>
+</div>
+</div>
+<p>Set the model batch size</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">batch_size</span> <span class="o">=</span> <span class="n">BATCH_SIZE</span>
+</pre></div>
+</div>
+</div>
+<p>Build model, load weights and create neuron, synapse and current source populations as before</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Load weights</span>
+<span class="n">weights_0_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_0_1.npy&quot;</span><span class="p">)</span>
+<span class="n">weights_1_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;weights_1_2.npy&quot;</span><span class="p">)</span>
+
+<span class="n">if_params</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;Vthr&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}</span>
+<span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span> <span class="s2">&quot;SpikeCount&quot;</span><span class="p">:</span><span class="mi">0</span><span class="p">}</span>
+<span class="n">neurons</span> <span class="o">=</span> <span class="p">[</span><span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron0&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron1&quot;</span><span class="p">,</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">),</span>
+           <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;neuron2&quot;</span><span class="p">,</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                       <span class="n">if_model</span><span class="p">,</span> <span class="n">if_params</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)]</span>
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_0_1&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+<span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span>
+        <span class="s2">&quot;synapse_1_2&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+        <span class="n">neurons</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">neurons</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+        <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">weights_1_2</span><span class="o">.</span><span class="n">flatten</span><span class="p">()}),</span>
+        <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">));</span>
+
+<span class="n">current_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;current_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span>
+                                         <span class="n">neurons</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">neurons</span><span class="p">:</span>
+    <span class="n">reset_var_refs</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">create_var_ref</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="s2">&quot;V&quot;</span><span class="p">),</span>
+                      <span class="s2">&quot;SpikeCount&quot;</span><span class="p">:</span> <span class="n">create_var_ref</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="s2">&quot;SpikeCount&quot;</span><span class="p">)}</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">add_custom_update</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">n</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2">_reset&quot;</span><span class="p">,</span> <span class="s2">&quot;Reset&quot;</span><span class="p">,</span> <span class="n">reset_model</span><span class="p">,</span>
+                            <span class="p">{},</span> <span class="p">{},</span> <span class="n">reset_var_refs</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Build and load our model</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">()</span>
+
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()</span>
+<span class="n">testing_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()</span>
+
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span>
+<span class="k">assert</span> <span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="n">weights_0_1</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="k">assert</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">)</span> <span class="o">==</span> <span class="p">(</span><span class="n">weights_1_2</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>First of all, we determine where to split our test data to achieve our batch size and then use <code class="docutils literal notranslate"><span class="pre">np.split</span></code> to perform the splitting operation (the last batch will contain &lt; <code class="docutils literal notranslate"><span class="pre">BATCH_SIZE</span></code> stimuli as 128 does not divide 10000 evenly)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">batch_splits</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">BATCH_SIZE</span><span class="p">,</span> <span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span><span class="p">,</span> <span class="n">BATCH_SIZE</span><span class="p">)</span>
+
+<span class="n">testing_image_batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="n">batch_splits</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+<span class="n">testing_label_batches</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">,</span> <span class="n">batch_splits</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-model">
+<h2>Simulate model<a class="headerlink" href="#Simulate-model" title="Permalink to this heading"></a></h2>
+<p>Our batched simulation loop looks very similar to the loop we defined in the previous tutorial however: * We now loop over <em>batches</em> of images and labels rather than individual ones * When we copy images into the input current view, we only copy as many images as are present in this batch to handle the remainder in the final batch * We specify an axis for <code class="docutils literal notranslate"><span class="pre">np.argmax</span></code> so that we get the neuron with the largest spike count in each batch</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">current_input_magnitude</span> <span class="o">=</span> <span class="n">current_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span>
+<span class="n">output_spike_count</span> <span class="o">=</span> <span class="n">neurons</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;SpikeCount&quot;</span><span class="p">]</span>
+<span class="n">neuron_voltages</span> <span class="o">=</span> <span class="p">[</span><span class="n">n</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;V&quot;</span><span class="p">]</span> <span class="k">for</span> <span class="n">n</span> <span class="ow">in</span> <span class="n">neurons</span><span class="p">]</span>
+
+<span class="c1"># Simulate</span>
+<span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="n">start_time</span> <span class="o">=</span> <span class="n">perf_counter</span><span class="p">()</span>
+<span class="k">for</span> <span class="n">img</span><span class="p">,</span> <span class="n">lab</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">testing_image_batches</span><span class="p">,</span> <span class="n">testing_label_batches</span><span class="p">),</span>
+                     <span class="n">total</span><span class="o">=</span><span class="nb">len</span><span class="p">(</span><span class="n">testing_image_batches</span><span class="p">)):</span>
+    <span class="n">current_input_magnitude</span><span class="o">.</span><span class="n">view</span><span class="p">[:</span><span class="n">img</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],:]</span> <span class="o">=</span> <span class="n">img</span> <span class="o">*</span> <span class="n">INPUT_CURRENT_SCALE</span>
+    <span class="n">current_input_magnitude</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Run reset custom update</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">custom_update</span><span class="p">(</span><span class="s2">&quot;Reset&quot;</span><span class="p">)</span>
+
+    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">PRESENT_TIMESTEPS</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Download spike count from last layer</span>
+    <span class="n">output_spike_count</span><span class="o">.</span><span class="n">pull_from_device</span><span class="p">()</span>
+
+    <span class="c1"># Find which neuron spiked most in each batch to get prediction</span>
+    <span class="n">predicted_lab</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">output_spike_count</span><span class="o">.</span><span class="n">view</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="c1"># Add number of</span>
+    <span class="n">num_correct</span> <span class="o">+=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">predicted_lab</span><span class="p">[:</span><span class="n">lab</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span> <span class="o">==</span> <span class="n">lab</span><span class="p">)</span>
+
+<span class="n">end_time</span> <span class="o">=</span> <span class="n">perf_counter</span><span class="p">()</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="s2">Accuracy </span><span class="si">{</span><span class="p">((</span><span class="n">num_correct</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="nb">float</span><span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mf">100.0</span><span class="p">)</span><span class="si">}</span><span class="s2">%%&quot;</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;Time </span><span class="si">{</span><span class="n">end_time</span><span class="w"> </span><span class="o">-</span><span class="w"> </span><span class="n">start_time</span><span class="si">}</span><span class="s2"> seconds&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "15e7a28075234a688d35420b0d90cd3a"}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+Accuracy 97.54%%
+Time 0.34431284400000095 seconds
+</pre></div></div>
+</div>
+<p>And…we get a speed up of over 30x compared to the previous tutorial</p>
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+</script></section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="tutorial_2.html" class="btn btn-neutral float-left" title="Classification of the entire test set" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../mushroom_body/1_first_layer.html" class="btn btn-neutral float-right" title="Presenting latency-coded inputs" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/documentation/5/tutorials/mnist_inference/tutorial_3.ipynb b/documentation/5/tutorials/mnist_inference/tutorial_3.ipynb
new file mode 100644
index 000000000..fd8991a0c
--- /dev/null
+++ b/documentation/5/tutorials/mnist_inference/tutorial_3.ipynb
@@ -0,0 +1,888 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Faster classification of the whole test set\n",
+    "The model we developed in the previous tutorial classified MNIST successfully but was rather slow. Like ANNs, to maximise performance when simulating small SNNs like this on a GPU, we need to simulate multiple copies of the model at once and run them on **batches** of input images.\n",
+    "In this tutorial we will modify our model to do just that as well as off-loading further computation to the GPU to improve performance.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "qWqAJtsiejvU",
+    "outputId": "8d659e2f-0fe0-4c97-e61f-46023ef5df18"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 182MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "8tqbF5GldF0o"
+   },
+   "source": [
+    "## Download pre-trained weights and MNIST test data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "N-2PV7LcdFg_",
+    "outputId": "b7d8e21f-45e9-408a-c840-e6a2992f4ea7"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+      "To: /content/weights_0_1.npy\n",
+      "100% 402k/402k [00:00<00:00, 50.3MB/s]\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF\n",
+      "To: /content/weights_1_2.npy\n",
+      "100% 5.25k/5.25k [00:00<00:00, 23.2MB/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gdown 1cmNL8W0QZZtn3dPHiOQnVjGAYTk6Rhpc\n",
+    "!gdown 131lCXLEH6aTXnBZ9Nh4eJLSy5DQ6LKSF"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "1cc89063-bcd7-4d47-afd8-5968008ac3ca"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "jBVpqi2k5mNb"
+   },
+   "source": [
+    "## Build model\n",
+    "Import standard module and PyGeNN functionality as before and configure simulation parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "agqWFZjickfU"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from pygenn import (create_neuron_model, create_current_source_model, create_custom_update_model,\n",
+    "                    create_var_ref, init_postsynaptic, init_weight_update, GeNNModel)\n",
+    "from time import perf_counter\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "TIMESTEP = 1.0\n",
+    "PRESENT_TIMESTEPS = 100\n",
+    "INPUT_CURRENT_SCALE = 1.0 / 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "OTkuiEAx5qMG"
+   },
+   "source": [
+    "As we're going to use it in a few places, we add an additional simulation parameter to define the batch size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ejMfqnhAkrye"
+   },
+   "outputs": [],
+   "source": [
+    "BATCH_SIZE = 128"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fojA3yl_6KU9"
+   },
+   "source": [
+    "Define the custom neuron and synapse models in exactly the same way as before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-7lzXzmQcgbt"
+   },
+   "outputs": [],
+   "source": [
+    "# Very simple integrate-and-fire neuron model\n",
+    "if_model = create_neuron_model(\n",
+    "    \"if_model\",\n",
+    "    params=[\"Vthr\"],\n",
+    "    vars=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+    "    sim_code=\"V += Isyn * dt;\",\n",
+    "    reset_code=\"\"\"\n",
+    "    V = 0.0;\n",
+    "    SpikeCount++;\n",
+    "    \"\"\",\n",
+    "    threshold_condition_code=\"V >= Vthr\")\n",
+    "\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "93YuiQG7qzG3"
+   },
+   "source": [
+    "As we increase the batch size of our model, the cost of resetting the spike counts and membrane voltages will increase. To counteract this, we can offload tasks like this to the GPU using a *custom update* model. These are defined using very similar syntax to neuron and synapse models but have one additional feature - variable references. These allow custom updates to be *attached* to existing neuron or synapse populations to modify their variables outside of the standard neuron and synapse updates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "I8KZoiK1nQlK"
+   },
+   "outputs": [],
+   "source": [
+    "reset_model = create_custom_update_model(\n",
+    "    \"reset\",\n",
+    "    var_refs=[(\"V\", \"scalar\"), (\"SpikeCount\", \"unsigned int\")],\n",
+    "    update_code=\"\"\"\n",
+    "    V = 0.0;\n",
+    "    SpikeCount = 0;\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "kDWkDTCWqwt3"
+   },
+   "source": [
+    "Create a new model in exactly the same way as before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BSSdg6ckl6im"
+   },
+   "outputs": [],
+   "source": [
+    "model = GeNNModel(\"float\", \"tutorial_3\")\n",
+    "model.dt = TIMESTEP"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "njWcYaZk5w7G"
+   },
+   "source": [
+    "Set the model batch size"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "iOyB3Z6qkVBM"
+   },
+   "outputs": [],
+   "source": [
+    "model.batch_size = BATCH_SIZE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "enyL8xum-OpC"
+   },
+   "source": [
+    "Build model, load weights and create neuron, synapse and current source populations as before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Sx1VOU5udixG"
+   },
+   "outputs": [],
+   "source": [
+    "# Load weights\n",
+    "weights_0_1 = np.load(\"weights_0_1.npy\")\n",
+    "weights_1_2 = np.load(\"weights_1_2.npy\")\n",
+    "\n",
+    "if_params = {\"Vthr\": 5.0}\n",
+    "if_init = {\"V\": 0.0, \"SpikeCount\":0}\n",
+    "neurons = [model.add_neuron_population(\"neuron0\", weights_0_1.shape[0],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron1\", weights_0_1.shape[1],\n",
+    "                                       if_model, if_params, if_init),\n",
+    "           model.add_neuron_population(\"neuron2\", weights_1_2.shape[1],\n",
+    "                                       if_model, if_params, if_init)]\n",
+    "model.add_synapse_population(\n",
+    "        \"synapse_0_1\", \"DENSE\",\n",
+    "        neurons[0], neurons[1],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_0_1.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"))\n",
+    "model.add_synapse_population(\n",
+    "        \"synapse_1_2\", \"DENSE\",\n",
+    "        neurons[1], neurons[2],\n",
+    "        init_weight_update(\"StaticPulse\", {}, {\"g\": weights_1_2.flatten()}),\n",
+    "        init_postsynaptic(\"DeltaCurr\"));\n",
+    "\n",
+    "current_input = model.add_current_source(\"current_input\", cs_model,\n",
+    "                                         neurons[0], {}, {\"magnitude\": 0.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "3S_ZASOdrnj3"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7PW3c8ibpx9x"
+   },
+   "outputs": [],
+   "source": [
+    "for n in neurons:\n",
+    "    reset_var_refs = {\"V\": create_var_ref(n, \"V\"),\n",
+    "                      \"SpikeCount\": create_var_ref(n, \"SpikeCount\")}\n",
+    "    model.add_custom_update(f\"{n.name}_reset\", \"Reset\", reset_model,\n",
+    "                            {}, {}, reset_var_refs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "vv-XOushroKw"
+   },
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "muUbvSHOooev"
+   },
+   "outputs": [],
+   "source": [
+    "# Build and load our model\n",
+    "model.build()\n",
+    "model.load()\n",
+    "\n",
+    "testing_images = mnist.test_images()\n",
+    "testing_labels = mnist.test_labels()\n",
+    "\n",
+    "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1))\n",
+    "assert testing_images.shape[1] == weights_0_1.shape[0]\n",
+    "assert np.max(testing_labels) == (weights_1_2.shape[1] - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "awF6vfLX-TVM"
+   },
+   "source": [
+    "First of all, we determine where to split our test data to achieve our batch size and then use `np.split` to perform the splitting operation (the last batch will contain < `BATCH_SIZE` stimuli as 128 does not divide 10000 evenly)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "BB0kXBmQkwCX"
+   },
+   "outputs": [],
+   "source": [
+    "batch_splits = range(BATCH_SIZE, testing_images.shape[0] + 1, BATCH_SIZE)\n",
+    "\n",
+    "testing_image_batches = np.split(testing_images, batch_splits, axis=0)\n",
+    "testing_label_batches = np.split(testing_labels, batch_splits, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pd4BBHjtur5E"
+   },
+   "source": [
+    "## Simulate model\n",
+    "Our batched simulation loop looks very similar to the loop we defined in the previous tutorial however:\n",
+    "*   We now loop over *batches* of images and labels rather than individual ones\n",
+    "*   When we copy images into the input current view, we only copy as many images as are present in this batch to handle the remainder in the final batch\n",
+    "*   We specify an axis for `np.argmax` so that we get the neuron with the largest spike count in each batch\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
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+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15e7a28075234a688d35420b0d90cd3a",
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+       "  0%|          | 0/79 [00:00<?, ?it/s]"
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+     },
+     "metadata": {},
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+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Accuracy 97.54%%\n",
+      "Time 0.34431284400000095 seconds\n"
+     ]
+    }
+   ],
+   "source": [
+    "current_input_magnitude = current_input.vars[\"magnitude\"]\n",
+    "output_spike_count = neurons[-1].vars[\"SpikeCount\"]\n",
+    "neuron_voltages = [n.vars[\"V\"] for n in neurons]\n",
+    "\n",
+    "# Simulate\n",
+    "num_correct = 0\n",
+    "start_time = perf_counter()\n",
+    "for img, lab in tqdm(zip(testing_image_batches, testing_label_batches),\n",
+    "                     total=len(testing_image_batches)):\n",
+    "    current_input_magnitude.view[:img.shape[0],:] = img * INPUT_CURRENT_SCALE\n",
+    "    current_input_magnitude.push_to_device()\n",
+    "\n",
+    "    # Run reset custom update\n",
+    "    model.custom_update(\"Reset\")\n",
+    "\n",
+    "    for t in range(PRESENT_TIMESTEPS):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Download spike count from last layer\n",
+    "    output_spike_count.pull_from_device()\n",
+    "\n",
+    "    # Find which neuron spiked most in each batch to get prediction\n",
+    "    predicted_lab = np.argmax(output_spike_count.view, axis=1)\n",
+    "\n",
+    "    # Add number of\n",
+    "    num_correct += np.sum(predicted_lab[:lab.shape[0]] == lab)\n",
+    "\n",
+    "end_time = perf_counter()\n",
+    "print(f\"\\nAccuracy {((num_correct / float(testing_images.shape[0])) * 100.0)}%%\")\n",
+    "print(f\"Time {end_time - start_time} seconds\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "y34o04ucAUjJ"
+   },
+   "source": [
+    "And...we get a speed up of over 30x compared to the previous tutorial"
+   ]
+  }
+ ],
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diff --git a/documentation/5/tutorials/mushroom_body/1_first_layer.html b/documentation/5/tutorials/mushroom_body/1_first_layer.html
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+             
+  <section id="Presenting-latency-coded-inputs">
+<h1>Presenting latency-coded inputs<a class="headerlink" href="#Presenting-latency-coded-inputs" title="Permalink to this heading"></a></h1>
+<p>In this first tutorial we build an input layer of spiking “projection neurons” for our mushroom body model which converts MNIST digits into latency-coded spikes.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 279MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-tutorial-model">
+<h2>Build tutorial model<a class="headerlink" href="#Build-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>Import modules</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span>
+</pre></div>
+</div>
+</div>
+<p>Load training images from downloaded file and normalise so each image’s pixels add up to one</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">training_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="p">(</span><span class="n">training_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+<span class="n">training_images</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Visualize-training-data">
+<h2>Visualize training data<a class="headerlink" href="#Visualize-training-data" title="Permalink to this heading"></a></h2>
+<p>Reshape first training image from 784 element vector to 28x28 matrix and visualize.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+<span class="n">axis</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">training_images</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)));</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_1_first_layer_9_0.png" src="../../_images/tutorials_mushroom_body_1_first_layer_9_0.png" />
+</div>
+</div>
+</section>
+<section id="Parameters">
+<h2>Parameters<a class="headerlink" href="#Parameters" title="Permalink to this heading"></a></h2>
+<p>Define some model parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation time step</span>
+<span class="n">DT</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Scaling factor for converting normalised image pixels to input currents (nA)</span>
+<span class="n">INPUT_SCALE</span> <span class="o">=</span> <span class="mf">80.0</span>
+
+<span class="c1"># Number of Projection Neurons in model (should match image size)</span>
+<span class="n">NUM_PN</span> <span class="o">=</span> <span class="mi">784</span>
+
+<span class="c1"># How long to present each image to model</span>
+<span class="n">PRESENT_TIME_MS</span> <span class="o">=</span> <span class="mf">20.0</span>
+</pre></div>
+</div>
+</div>
+<p>Define a standard set of parameters to use for all leaky-integrate and fire neurons</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Standard LIF neurons parameters</span>
+<span class="n">LIF_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
+    <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>
+    <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+    <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>Make a copy of this to customise for our Projection neurons and increase the refractory time way above <code class="docutils literal notranslate"><span class="pre">PRESENT_TIME_MS</span></code> so these neurons will only spike once per input.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We only want PNs to spike once</span>
+<span class="n">PN_PARAMS</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">LIF_PARAMS</span><span class="p">)</span>
+<span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;TauRefrac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">100.0</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Custom-models">
+<h2>Custom models<a class="headerlink" href="#Custom-models" title="Permalink to this heading"></a></h2>
+<p>We are going to apply inputs to our model by treating scaled image pixels as neuronal input currents so here we define a simple model to inject the current specified by a state variable. Like all types of custom model in GeNN, the <code class="docutils literal notranslate"><span class="pre">var_name_types</span></code> kwarg is used to specify state variable names and types</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Current source model, allowing current to be injected into neuron from variable</span>
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Model-definition">
+<h2>Model definition<a class="headerlink" href="#Model-definition" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “mnist_mb_first_layer” with floating point precision and set the simulation timestep to our chosen value</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create model</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;mnist_mb_first_layer&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">DT</span>
+</pre></div>
+</div>
+</div>
+<p>Add a population of <code class="docutils literal notranslate"><span class="pre">NUM_PN</span></code> Projection Neurons, using the built-in LIF model, the parameters we previously chose and initialising the membrane voltage to the reset voltage.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create neuron populations</span>
+<span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;Vreset&quot;</span><span class="p">],</span> <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">pn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;pn&quot;</span><span class="p">,</span> <span class="n">NUM_PN</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">PN_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="c1"># Turn on spike recording</span>
+<span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+</pre></div>
+</div>
+</div>
+<p>Add a current source to inject current into <code class="docutils literal notranslate"><span class="pre">pn</span></code> using our newly-defined custom model with the initial magnitude set to zero.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create current sources to deliver input to network</span>
+<span class="n">pn_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;pn_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">pn</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover <code class="docutils literal notranslate"><span class="pre">PRESENT_TIME_MS</span></code> (after converting from ms to timesteps)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Concert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="c1"># Build model and load it</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">present_timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>In order to ensure that the same stimulus causes exactly the same input each time it is presented, we want to reset the model’s state after presenting each stimulus. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_neuron</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">var_init</span><span class="p">):</span>
+    <span class="c1"># Reset variables</span>
+    <span class="k">for</span> <span class="n">var_name</span><span class="p">,</span> <span class="n">var_val</span> <span class="ow">in</span> <span class="n">var_init</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">var_val</span>
+
+        <span class="c1"># Push the new values to GPU</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>As an initial test of our model, we loop through 4 stimuli and show the Projection Neurons spikes emitted by the model in response.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="c1"># Set training image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">training_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state for next stimuli</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+    <span class="c1"># Download spikes from GPU</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">();</span>
+
+    <span class="c1"># Plot PN spikes</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    <span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span> <span class="o">=</span> <span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_1_first_layer_29_0.png" src="../../_images/tutorials_mushroom_body_1_first_layer_29_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_1_first_layer_29_1.png" src="../../_images/tutorials_mushroom_body_1_first_layer_29_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_1_first_layer_29_2.png" src="../../_images/tutorials_mushroom_body_1_first_layer_29_2.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_1_first_layer_29_3.png" src="../../_images/tutorials_mushroom_body_1_first_layer_29_3.png" />
+</div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="../mnist_inference/tutorial_3.html" class="btn btn-neutral float-left" title="Faster classification of the whole test set" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="2_second_layer.html" class="btn btn-neutral float-right" title="Adding Kenyon Cells" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
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diff --git a/documentation/5/tutorials/mushroom_body/1_first_layer.ipynb b/documentation/5/tutorials/mushroom_body/1_first_layer.ipynb
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+++ b/documentation/5/tutorials/mushroom_body/1_first_layer.ipynb
@@ -0,0 +1,529 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Presenting latency-coded inputs\n",
+    "In this first tutorial we build an input layer of spiking \"projection neurons\" for our mushroom body model which converts MNIST digits into latency-coded spikes.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "maq6gt0pgTiQ",
+    "outputId": "166cc2f9-55c9-423f-b724-34456abe7aa1"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 279MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "675537d0-38e4-4724-f244-67bbe4c39dac"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yV0JrchrfQKR"
+   },
+   "source": [
+    "## Build tutorial model\n",
+    "Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Hl53yKXi9LiV"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "from copy import copy\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pygenn import create_current_source_model, init_postsynaptic, init_weight_update, GeNNModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "u67gXzipEue5"
+   },
+   "source": [
+    "Load training images from downloaded file and normalise so each image's pixels add up to one"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "X9JrpOMu9LiZ"
+   },
+   "outputs": [],
+   "source": [
+    "training_images = mnist.train_images()\n",
+    "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+    "\n",
+    "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "mRl2x1HA9Lia"
+   },
+   "source": [
+    "## Visualize training data\n",
+    "Reshape first training image from 784 element vector to 28x28 matrix and visualize."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 430
+    },
+    "id": "N2jR9guR9Lic",
+    "outputId": "662c041c-99fb-437d-d34d-d619b4d2ac4e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axis = plt.subplots()\n",
+    "axis.imshow(np.reshape(training_images[0], (28, 28)));"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g0IfyML59Lif"
+   },
+   "source": [
+    "## Parameters\n",
+    "Define some model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oncGyriW9Lif"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation time step\n",
+    "DT = 0.1\n",
+    "\n",
+    "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+    "INPUT_SCALE = 80.0\n",
+    "\n",
+    "# Number of Projection Neurons in model (should match image size)\n",
+    "NUM_PN = 784\n",
+    "\n",
+    "# How long to present each image to model\n",
+    "PRESENT_TIME_MS = 20.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ddx0SZ80Fe9z"
+   },
+   "source": [
+    "Define a standard set of parameters to use for all leaky-integrate and fire neurons"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "---jKi0cFdec"
+   },
+   "outputs": [],
+   "source": [
+    "# Standard LIF neurons parameters\n",
+    "LIF_PARAMS = {\n",
+    "    \"C\": 0.2,\n",
+    "    \"TauM\": 20.0,\n",
+    "    \"Vrest\": -60.0,\n",
+    "    \"Vreset\": -60.0,\n",
+    "    \"Vthresh\": -50.0,\n",
+    "    \"Ioffset\": 0.0,\n",
+    "    \"TauRefrac\": 2.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lAgVgaYcFq68"
+   },
+   "source": [
+    "Make a copy of this to customise for our Projection neurons and increase the refractory time way above `PRESENT_TIME_MS` so these neurons will only spike once per input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9LLcZa-nFjN7"
+   },
+   "outputs": [],
+   "source": [
+    "# We only want PNs to spike once\n",
+    "PN_PARAMS = copy(LIF_PARAMS)\n",
+    "PN_PARAMS[\"TauRefrac\"] = 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pCYjAoJf9Lig"
+   },
+   "source": [
+    "## Custom models\n",
+    "We are going to apply inputs to our model by treating scaled image pixels as neuronal input currents so here we define a simple model to inject the current specified by a state variable. Like all types of custom model in GeNN, the `var_name_types` kwarg is used to specify state variable names and types"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IR8PXBg69Lih"
+   },
+   "outputs": [],
+   "source": [
+    "# Current source model, allowing current to be injected into neuron from variable\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gn4DpkPQ9Lii"
+   },
+   "source": [
+    "## Model definition\n",
+    "Create a new model called \"mnist_mb_first_layer\" with floating point precision and set the simulation timestep to our chosen value"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Gx-GsJhD9Lik"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model\n",
+    "model = GeNNModel(\"float\", \"mnist_mb_first_layer\")\n",
+    "model.dt = DT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "AlMTvSBNHYSD"
+   },
+   "source": [
+    "Add a population of `NUM_PN` Projection Neurons, using the built-in LIF model, the parameters we previously chose and initialising the membrane voltage to the reset voltage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "OnHOIAyVHAFH"
+   },
+   "outputs": [],
+   "source": [
+    "# Create neuron populations\n",
+    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+    "\n",
+    "# Turn on spike recording\n",
+    "pn.spike_recording_enabled = True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sdYo9umiH06S"
+   },
+   "source": [
+    "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7e1if0YCG_7m"
+   },
+   "outputs": [],
+   "source": [
+    "# Create current sources to deliver input to network\n",
+    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-GU4oXOS9Lil"
+   },
+   "source": [
+    "## Build model\n",
+    "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-FE02Zoz9Lim"
+   },
+   "outputs": [],
+   "source": [
+    "# Concert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "# Build model and load it\n",
+    "model.build()\n",
+    "model.load(num_recording_timesteps=present_timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcpTaaB39Lim"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "In order to ensure that the same stimulus causes exactly the same input each time it is presented, we want to reset the model's state after presenting each stimulus. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7ENTbLZpGvye"
+   },
+   "outputs": [],
+   "source": [
+    "def reset_neuron(pop, var_init):\n",
+    "    # Reset variables\n",
+    "    for var_name, var_val in var_init.items():\n",
+    "        pop.vars[var_name].view[:] = var_val\n",
+    "\n",
+    "        # Push the new values to GPU\n",
+    "        pop.vars[var_name].push_to_device()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "hHUa3hbMGwWG"
+   },
+   "source": [
+    "As an initial test of our model, we loop through 4 stimuli and show the Projection Neurons spikes emitted by the model in response."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "I5Qsfgq99Lin",
+    "outputId": "0ac4adda-b2ad-496f-cb61-f906b8bbc035"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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N25u2XcqSJUvkcDi8S//+/QMpGwAAdDABneIZN26c9+thw4Zp1KhRSkxM1J/+9CdFRka2eXFNcnNzNWfOHO/3Ho+HkAIAQCfWqrcZx8TEaNCgQfr0008VHx+vuro6VVdX+7SpqqryXrMSHx9/wbt6mr6/2HUtTSIiImS3230WAADQebUqoHz99dcqKSnRNddcoxEjRigsLEy7du3ybi8uLlZZWZmcTqckyel06ujRozp16pS3zY4dO2S325WcnNyaUgAAQCcS0CmeefPmafz48UpMTFRFRYWefvpphYSEaPLkyXI4HJo2bZrmzJmjXr16yW63a8aMGXI6nUpNTZUkjR07VsnJycrOztayZctUWVmphQsXKicnRxEREUEZIAAA6HgCCihffPGFJk+erL///e/q06ePbr/9dhUVFalPnz6SpBUrVqhbt26aOHGiamtrlZGRoVWrVnmfHxISoi1btsjlcsnpdCoqKkpTp07Vs88+27aj6kDyi0q9H0LIpx4DAHAeHxbop70DQ9rS3SqvrlHfmEjtWzA66P0BAGAWPiywFZZvL1Z5dY2Wby++ZJv8olKlLd2t/KLSVvfnSk9S35hIudKTWr0vAAA6CwJKC+QVlKi8ukZ5BSWt3ldWaqL2LRjN6R0ApmvLF19AaxFQ/MzLGKy+MZGalzH4km2Y9QDQGbXliy+gtbgGBQAgiYv2EXyBHL8JKAAAoF1wkSwAAOjQCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCgAAMByCCh++LhxAEBXZ4VjIQHFz/LtxSqvrtHy7cVmlwIAgCnyCkpUXl2jvIIS02ogoPipPdfg82hFVki2AIDOy5WepL4xkXKlJ5lWQ6hpPVtURGiIauobFREaYnYpl/TdZJuVmmh2OQCATiYrNdH04wszKH7mZQxW35hIzcsYbHYpl2SFZAsAQDDZDMMwzC4iUB6PRw6HQ263W3a73exyAABAMwRy/GYGBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BBQAAWA4BxU9+UanSlu5WflGp2aVYqhYAANoTAcXP8u3FKq+u0fLtxWaXoryCEpVX1yivoMTsUgAAaFcEFD+15xp8Hs3kSk9S35hIudKTzC4FAIB2FWp2AVYTERqimvpGRYSGmF2KslITlZWaaHYZAAC0O2ZQ/MzLGKy+MZGalzHY7FIAAOiybIZhGGYXESiPxyOHwyG32y273W52OQAAoBkCOX4zgwIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgOInv6hUaUt3K7+o1FL7AgCgK2lVQFm6dKlsNptmzZrlXXf27Fnl5OSod+/eio6O1sSJE1VVVeXzvLKyMmVmZqpHjx6KjY3VU089pXPnzrWmlDaTV1Ci8uoa5RWUtDpgfHdf6PwIpADQdlocUA4cOKDf//73GjZsmM/62bNna/Pmzdq4caP27t2riooKTZgwwbu9oaFBmZmZqqur09tvv61169Zp7dq1WrRoUctH0YZc6UnqGxMpV3pSqwPGd/eFzo9ACgBtyGiBM2fOGNdff72xY8cO48477zSefPJJwzAMo7q62ggLCzM2btzobfvRRx8ZkozCwkLDMAzjL3/5i9GtWzejsrLS2yYvL8+w2+1GbW1ts/p3u92GJMPtdrek/Gb7f4V/M25bssv4f4V/C2o/6Bz4fQGAywvk+N2iGZScnBxlZmZqzJgxPusPHTqk+vp6n/VDhgzRgAEDVFhYKEkqLCxUSkqK4uLivG0yMjLk8Xh07Nixi/ZXW1srj8fjs7SHrNRE7VswWlmpie3SHzo2fl8AoO2EBvqEV155RYcPH9aBAwcu2FZZWanw8HDFxMT4rI+Li1NlZaW3zXfDSdP2pm0Xs2TJEv385z8PtFQAANBBBTSDcuLECT355JN6+eWX1b1792DVdIHc3Fy53W7vcuLEiXbrGwAAtL+AAsqhQ4d06tQp3XLLLQoNDVVoaKj27t2rlStXKjQ0VHFxcaqrq1N1dbXP86qqqhQfHy9Jio+Pv+BdPU3fN7XxFxERIbvd7rMAAIDOK6CA8s///M86evSo3n33Xe8ycuRIPfTQQ96vw8LCtGvXLu9ziouLVVZWJqfTKUlyOp06evSoTp065W2zY8cO2e12JScnt9GwAABARxbQNSg9e/bUjTfe6LMuKipKvXv39q6fNm2a5syZo169eslut2vGjBlyOp1KTU2VJI0dO1bJycnKzs7WsmXLVFlZqYULFyonJ0cRERFtNCwAANCRBXyR7JWsWLFC3bp108SJE1VbW6uMjAytWrXKuz0kJERbtmyRy+WS0+lUVFSUpk6dqmeffbatSwEAAB2UzTAMw+wiAuXxeORwOOR2u7keBQCADiKQ4zefxQMAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgOInv6hUaUt3K7+otEPsFwCAzoiA4mf59mKVV9do+fbiNt1vXkGJyqtrlFdQ0qb7BQCgMyKgtBNXepL6xkTKlZ5kdimWwswSAOBiCCh+5mUMVt+YSM3LGNym+81KTdS+BaOVlZrYpvvt6JhZAgBcTJvf6r6jy0pNJES0I1d6kvIKSphZAgD44Fb3AACgXXCrewAA0KERUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUOCVX1SqtKW7lV9UanYpAIAujoDiZ+aGI0rK3aqZG46YXUq7yysoUXl1jfIKSswuBQDQxRFQ/Gx9v0INxvnHlujIsxCu9CT1jYmUKz3J7FIAAF0cAcVP5rAEhdjOP7ZER56FyEpN1L4Fo5WVmmh2KQCALi7U7AKsZuXk4Vo5eXiLn+9KT1JeQQmzEAAAtILNMAzD7CIC5fF45HA45Ha7ZbfbzS4HAAA0QyDHb07xAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAAAAyyGgAPCRX1SqtKW7lV9UanYpALowAkoz8UcbXUVeQYnKq2uUV1BidikAujACip+ZG44oKXerZm444rOeP9roKlzpSeobEylXepLZpQDowggofra+X6EG4/zjd/FHG11FVmqi9i0YrazURLNLAdCFhZpdgNVkDkvQ1vcrlDkswWd9Vmoif7ABAGgnNsMwDLOLCJTH45HD4ZDb7Zbdbje7HAAA0AyBHL85xQMAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACyHgAIAACwnoICSl5enYcOGyW63y263y+l0atu2bd7tZ8+eVU5Ojnr37q3o6GhNnDhRVVVVPvsoKytTZmamevToodjYWD311FM6d+5c24wGAAB0CgEFlH79+mnp0qU6dOiQDh48qNGjR+sHP/iBjh07JkmaPXu2Nm/erI0bN2rv3r2qqKjQhAkTvM9vaGhQZmam6urq9Pbbb2vdunVau3atFi1a1LajAgAAHVqrb3Xfq1cv/fKXv9T999+vPn36aP369br//vslSR9//LGGDh2qwsJCpaamatu2bbrnnntUUVGhuLg4SdLq1as1f/58nT59WuHh4c3qk1vdAwDQ8bTLre4bGhr0yiuv6JtvvpHT6dShQ4dUX1+vMWPGeNsMGTJEAwYMUGFhoSSpsLBQKSkp3nAiSRkZGfJ4PN5ZmIupra2Vx+PxWQAAQOcVcEA5evSooqOjFRERoccff1yvvfaakpOTVVlZqfDwcMXExPi0j4uLU2VlpSSpsrLSJ5w0bW/adilLliyRw+HwLv379w+0bNPlF5Uqbelu5ReVml0KAACWF3BAGTx4sN5991298847crlcmjp1qj788MNg1OaVm5srt9vtXU6cOBG0voIVJPIKSlReXaO8gpI23S8AAJ1RwAElPDxc1113nUaMGKElS5bopptu0m9/+1vFx8errq5O1dXVPu2rqqoUHx8vSYqPj7/gXT1N3ze1uZiIiAjvO4ealmBZvr1Y5dU1Wr69uE3360pPUt+YSLnSk1r0fGZgAABdSavvg9LY2Kja2lqNGDFCYWFh2rVrl3dbcXGxysrK5HQ6JUlOp1NHjx7VqVOnvG127Nghu92u5OTk1pZiaVmpidq3YLSyUhNb9HxmYAAAXUloII1zc3M1btw4DRgwQGfOnNH69etVUFCg7du3y+FwaNq0aZozZ4569eolu92uGTNmyOl0KjU1VZI0duxYJScnKzs7W8uWLVNlZaUWLlyonJwcRUREBGWAgZqXMVh5BSUtnukIFld6kiXrAgAgGAJ6m/G0adO0a9cunTx5Ug6HQ8OGDdP8+fN11113STp/o7a5c+dqw4YNqq2tVUZGhlatWuVz+qa0tFQul0sFBQWKiorS1KlTtXTpUoWGNj8r8TZjAAA6nkCO362+D4oZCCgAAHQ87XIfFAAAgGAhoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoAAAAMshoPjJLypV2tLdyi8qNbsUNAM/LwDonAgofpZvL1Z5dY2Wby82uxQ0Q15Bicqra5RXUGJ2KQCANkRAaSZeqVuTKz1JfWMi5UpPMrsUtBH+rwGQCCgXmJcxWH1jIjUvY7DPel6pW1NWaqL2LRitrNREs0tBG+H/GgCJgHKBSx3weKUOtA/+rwGQJJthGIbZRQTK4/HI4XDI7XbLbrebXQ4AAGiGQI7fzKAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaAAAADLIaD4yS8qVdrS3covKg1KewAAcGUEFD95BSUqr65RXkGJz/pLBZFLtQesiEANoKMgoPhxpSepb0ykXOlJPusvFUQu1R6wIgI1gI7CZhiGYXYRgfJ4PHI4HHK73bLb7e3SZ35RqfIKSuRKT1JWamK79Am0NX6PAZgpkOM3AQUAALSLQI7fnOIBAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABAACWQ0ABEDT5RaVKW7pb+UWlZpcCoIMhoPiZueGIknK3auaGI2aXAnR4eQUlKq+uUV5BidmlAOhgCCh+Nr9XoQbj/COA1nGlJ6lvTKRc6UlmlwKggwk1uwCr6R4Wopr6BnUPCzG7lHaRX1SqvIISudKTlJWaaHY56GSyUhP5vQLQIsyg+Pn3zKHqGxOpf88canYp7YIpeACAFTGD4qerveJzpSd5Z1AAALAKm2EYhtlFBMrj8cjhcMjtdstut5tdDgAAaIZAjt+c4gEAAJYTUEBZsmSJbr31VvXs2VOxsbG67777VFxc7NPm7NmzysnJUe/evRUdHa2JEyeqqqrKp01ZWZkyMzPVo0cPxcbG6qmnntK5c+daPxoAANApBBRQ9u7dq5ycHBUVFWnHjh2qr6/X2LFj9c0333jbzJ49W5s3b9bGjRu1d+9eVVRUaMKECd7tDQ0NyszMVF1dnd5++22tW7dOa9eu1aJFi9puVAAAoENr1TUop0+fVmxsrPbu3as77rhDbrdbffr00fr163X//fdLkj7++GMNHTpUhYWFSk1N1bZt23TPPfeooqJCcXFxkqTVq1dr/vz5On36tMLDw6/YL9egAADQ8bTbNShut1uS1KtXL0nSoUOHVF9frzFjxnjbDBkyRAMGDFBhYaEkqbCwUCkpKd5wIkkZGRnyeDw6duzYRfupra2Vx+PxWQAAQOfV4oDS2NioWbNmKS0tTTfeeKMkqbKyUuHh4YqJifFpGxcXp8rKSm+b74aTpu1N2y5myZIlcjgc3qV///4tLRsAAHQALQ4oOTk5+uCDD/TKK6+0ZT0XlZubK7fb7V1OnDgR9D4BAIB5WnSjtunTp2vLli1688031a9fP+/6+Ph41dXVqbq62mcWpaqqSvHx8d42+/fv99lf07t8mtr4i4iIUEREREtKBQAAHVBAMyiGYWj69Ol67bXXtHv3bl177bU+20eMGKGwsDDt2rXLu664uFhlZWVyOp2SJKfTqaNHj+rUqVPeNjt27JDdbldycnJrxgIAADqJgGZQcnJytH79ev35z39Wz549vdeMOBwORUZGyuFwaNq0aZozZ4569eolu92uGTNmyOl0KjU1VZI0duxYJScnKzs7W8uWLVNlZaUWLlyonJwcZkkAAICkAN9mbLPZLrp+zZo1evjhhyWdv1Hb3LlztWHDBtXW1iojI0OrVq3yOX1TWloql8ulgoICRUVFaerUqVq6dKlCQ5uXl3ibMQAAHU8gx28+iwdA0OQXlXo/jLIrfQgngIvjs3haYeaGI0rK3aqZG46YXQrQ4eUVlKi8ukZ5BSVmlwKggyGg+Nn6foUajPOP+UWlSlu6W/lFpWaXBXRIrvQk9Y2JlCs9yexSAHQwBBQ/mcMSFGI7/8irP+shNHYsWamJ2rdgNKd3AASMgOJn5eThKlmSqZWTh/Pqz4IIjQDQNbToRm1dRVZqIq/8LMaVnuS96BIA0HnxLh4AANAueBcPAADo0AgoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoANpUflGp0pbuVn5RqdmlAOjACCh+Zm44oqTcrZq54YjZpQAdUl5Bicqra5RXUGJ2KQA6MAKKn63vV6jBOP/Y1fDKF23BlZ6kvjGRcqUnmV0KgA6MgOInc1iCQmznH7saXvmiLWSlJmrfgtHKSk00uxQAHVio2QVYzcrJw7Vy8nCzyzCFKz1JeQUlvPIFAJjOZhiGYXYRgfJ4PHI4HHK73bLb7WaXAwAAmiGQ4zeneAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOUQUAAAgOWEml1ASzR9ALPH4zG5EgAA0FxNx+2m4/jldMiAcubMGUlS//79Ta4EAAAE6syZM3I4HJdtYzOaE2MsprGxURUVFerZs6dsNlu79+/xeNS/f3+dOHFCdru93fs3W1cef1ceu9S1x9+Vxy517fF35bFLbTt+wzB05swZJSQkqFu3y19l0iFnULp166Z+/fqZXYbsdnuX/GVt0pXH35XHLnXt8XflsUtde/xdeexS243/SjMnTbhIFgAAWA4BBQAAWA4BpQUiIiL09NNPKyIiwuxSTNGVx9+Vxy517fF35bFLXXv8XXnsknnj75AXyQIAgM6NGRQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BJTLKC8vV1ZWlnr37q3IyEilpKTo4MGDkqT6+nrNnz9fKSkpioqKUkJCgqZMmaKKigqTq247lxu/v8cff1w2m02/+c1v2rfIIGnO2D/66CPde++9cjgcioqK0q233qqysjKTKm5bVxr/119/renTp6tfv36KjIxUcnKyVq9ebWLFbWfgwIGy2WwXLDk5OZKks2fPKicnR71791Z0dLQmTpyoqqoqk6tuG5cb+5dffqkZM2Zo8ODBioyM1IABAzRz5ky53W6zy24zV/rZNzEMQ+PGjZPNZtPrr79uTrFtrDljLyws1OjRoxUVFSW73a477rhDNTU1QaupQ95Jtj189dVXSktL0z/90z9p27Zt6tOnj44fP66rrrpKkvTtt9/q8OHD+tnPfqabbrpJX331lZ588knde++9lzyIdyRXGv93vfbaayoqKlJCQoIJlba95oy9pKREt99+u6ZNm6af//znstvtOnbsmLp3725i5W2jOeOfM2eOdu/erfz8fA0cOFB//etf9cQTTyghIUH33nuvidW33oEDB9TQ0OD9/oMPPtBdd92lSZMmSZJmz56trVu3auPGjXI4HJo+fbomTJigffv2mVVym7nc2CsqKlRRUaHly5crOTlZpaWlevzxx1VRUaH//u//NrHqtnOln32T3/zmN6Z8zEowXWnshYWFuvvuu5Wbm6vnn39eoaGheu+99654u/pWMXBR8+fPN26//faAnrN//35DklFaWhqkqtpPc8f/xRdfGH379jU++OADIzEx0VixYkXwiwuy5oz9hz/8oZGVldVOFbWv5oz/hhtuMJ599lmfdbfccovx7//+78EszRRPPvmkkZSUZDQ2NhrV1dVGWFiYsXHjRu/2jz76yJBkFBYWmlhlcHx37Bfzpz/9yQgPDzfq6+vbubL2cbHxHzlyxOjbt69x8uRJQ5Lx2muvmVdgEPmPfdSoUcbChQvbtQZO8VzCpk2bNHLkSE2aNEmxsbEaPny4Xnzxxcs+x+12y2azKSYmpn2KDKLmjL+xsVHZ2dl66qmndMMNN5hUadu70tgbGxu1detWDRo0SBkZGYqNjdWoUaM6zVRvc372t912mzZt2qTy8nIZhqE9e/bok08+0dixY02qOjjq6uqUn5+vRx99VDabTYcOHVJ9fb3GjBnjbTNkyBANGDBAhYWFJlba9vzHfjFut1t2u12hoZ1vMv5i4//222/1ox/9SL/73e8UHx9vcoXB4z/2U6dO6Z133lFsbKxuu+02xcXF6c4779T//M//BLeQdo1DHUhERIQRERFh5ObmGocPHzZ+//vfG927dzfWrl170fY1NTXGLbfcYvzoRz9q50qDoznjX7x4sXHXXXd5E3ZnmUG50tibXjn16NHD+PWvf20cOXLEWLJkiWGz2YyCggKTq2+95vzsz549a0yZMsWQZISGhhrh4eHGunXrTKw6OP74xz8aISEhRnl5uWEYhvHyyy8b4eHhF7S79dZbjX/7t39r7/KCyn/s/k6fPm0MGDDA+OlPf9rOlbWPi43/Jz/5iTFt2jTv9+qkMyj+Yy8sLDQkGb169TL+8Ic/GIcPHzZmzZplhIeHG5988knQ6iCgXEJYWJjhdDp91s2YMcNITU29oG1dXZ0xfvx4Y/jw4Ybb7W6vEoPqSuM/ePCgERcX5/Oft7MElCuNvby83JBkTJ482afN+PHjjQcffLDd6gyW5vzu//KXvzQGDRpkbNq0yXjvvfeM559/3oiOjjZ27NjR3uUG1dixY4177rnH+31XCij+Y/8ut9tt/MM//INx9913G3V1de1cWfvwH/+f//xn47rrrjPOnDnjXddZA4r/2Pft22dIMnJzc33apaSkGAsWLAhaHZziuYRrrrlGycnJPuuGDh16wbs06uvr9cADD6i0tFQ7duzoNB/FfaXxv/XWWzp16pQGDBig0NBQhYaGqrS0VHPnztXAgQNNqLjtXGnsV199tUJDQ5v1+9ERXWn8NTU1+ulPf6pf//rXGj9+vIYNG6bp06frhz/8oZYvX25GyUFRWlqqnTt36sc//rF3XXx8vOrq6lRdXe3TtqqqqlNN+V9s7E3OnDmju+++Wz179tRrr72msLAwEyoMrouNf/fu3SopKVFMTIz3b54kTZw4Uenp6SZV2vYuNvZrrrlGktr9b17nO3HYRtLS0lRcXOyz7pNPPlFiYqL3+6Zwcvz4ce3Zs0e9e/du7zKD5krjz87O9jkPL0kZGRnKzs7WI4880m51BsOVxh4eHq5bb731ir8fHdWVxl9fX6/6+voLrt4PCQlRY2Nju9UZbGvWrFFsbKwyMzO960aMGKGwsDDt2rVLEydOlCQVFxerrKxMTqfTrFLb3MXGLkkej0cZGRmKiIjQpk2bOsW71i7mYuNfsGDBBYEtJSVFK1as0Pjx49u7xKC52NgHDhyohISEi/5dGDduXPCKCdrcTAe3f/9+IzQ01PjFL35hHD9+3Hj55ZeNHj16GPn5+YZhnD+tc++99xr9+vUz3n33XePkyZPepba21uTqW+9K47+YznKKpzljf/XVV42wsDDjhRdeMI4fP248//zzRkhIiPHWW2+ZWHnbaM7477zzTuOGG24w9uzZY3z22WfGmjVrjO7duxurVq0ysfK209DQYAwYMMCYP3/+Bdsef/xxY8CAAcbu3buNgwcPGk6n84JTYh3ZpcbudruNUaNGGSkpKcann37q8zfv3LlzJlXb9i73s/enTnaK53JjX7FihWG3242NGzcax48fNxYuXGh0797d+PTTT4NWDwHlMjZv3mzceOONRkREhDFkyBDjhRde8G77/PPPDUkXXfbs2WNe0W3ocuO/mM4SUAyjeWN/6aWXjOuuu87o3r27cdNNNxmvv/66CZUGx5XGf/LkSePhhx82EhISjO7duxuDBw82fvWrX13y7agdzfbt2w1JRnFx8QXbampqjCeeeMK46qqrjB49ehj/8i//Ypw8edKEKoPjUmPfs2fPJf/mff755+YUGwSX+9n762wB5UpjX7JkidGvXz+jR48ehtPpDPoLMpthGEbw5mcAAAACx0WyAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAADAcggoAJrt4Ycf1n333dfu/a5du1Y2m002m02zZs0KWj9/+9vfvP3cfPPNQesHwJXxYYEAJEk2m+2y259++mn99re/lVk3n7bb7SouLlZUVFTQ+ujfv79Onjyp5cuXa+fOnUHrB8CVEVAASJJOnjzp/fqPf/yjFi1a5PPppdHR0YqOjjajNEnnA1R8fHxQ+wgJCVF8fLyp4wRwHqd4AEiS4uPjvYvD4fAGgqYlOjr6glM86enpmjFjhmbNmqWrrrpKcXFxevHFF/XNN9/okUceUc+ePXXddddp27ZtPn198MEHGjdunKKjoxUXF6fs7Gz97//+b8A1Dxw4UM8995ymTJmi6OhoJSYmatOmTTp9+rR+8IMfKDo6WsOGDdPBgwe9zyktLdX48eN11VVXKSoqSjfccIP+8pe/tPjfDUBwEFAAtMq6det09dVXa//+/ZoxY4ZcLpcmTZqk2267TYcPH9bYsWOVnZ2tb7/9VpJUXV2t0aNHa/jw4Tp48KDeeOMNVVVV6YEHHmhR/ytWrFBaWpqOHDmizMxMZWdna8qUKcrKytLhw4eVlJSkKVOmeE9N5eTkqLa2Vm+++aaOHj2q//zP/2TGBLAgAgqAVrnpppu0cOFCXX/99crNzVX37t119dVX67HHHtP111+vRYsW6e9//7vef/99SdJ//dd/afjw4Vq8eLGGDBmi4cOH6w9/+IP27NmjTz75JOD+v//97+tf//VfvX15PB7deuutmjRpkgYNGqT58+fro48+UlVVlSSprKxMaWlpSklJ0fe+9z3dc889uuOOO9r03wRA6xFQALTKsGHDvF+HhISod+/eSklJ8a6Li4uTJJ06dUqS9N5772nPnj3ea1qio6M1ZMgQSVJJSUmr+m/q63L9z5w5U88995zS0tL09NNPe4MTAGshoABolbCwMJ/vbTabz7qmdwc1NjZKkr7++muNHz9e7777rs9y/PjxFs1kXKyvy/X/4x//WJ999pmys7N19OhRjRw5Us8//3zA/QIILgIKgHZ1yy236NixYxo4cKCuu+46nyWYbyH+rv79++vxxx/Xq6++qrlz5+rFF19sl34BNB8BBUC7ysnJ0ZdffqnJkyfrwIEDKikp0fbt2/XII4+ooaEh6P3PmjVL27dv1+eff67Dhw9rz549Gjp0aND7BRAYAgqAdpWQkKB9+/apoaFBY8eOVUpKimbNmqWYmBh16xb8P0kNDQ3KycnR0KFDdffdd2vQoEFatWpV0PsFEBibYdZtIQGgmdauXatZs2apurq6Xfp75pln9Prrr+vdd99tl/4AXIgZFAAdgtvtVnR0tObPnx+0PsrKyhQdHa3FixcHrQ8AzcMMCgDLO3PmjPc+JjExMbr66quD0s+5c+f0t7/9TZIUERGh/v37B6UfAFdGQAEAAJbDKR4AAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5BBQAAGA5/x+uSjlTPf8u+QAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for s in range(4):\n",
+    "    # Set training image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state for next stimuli\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "\n",
+    "    # Download spikes from GPU\n",
+    "    model.pull_recording_buffers_from_device();\n",
+    "\n",
+    "    # Plot PN spikes\n",
+    "    fig, axis = plt.subplots()\n",
+    "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+    "    axis.scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+    "    axis.set_xlabel(\"Time [ms]\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "1_first_layer",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
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+      <li class="breadcrumb-item active">Adding Kenyon Cells</li>
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+            <a href="../../_sources/tutorials/mushroom_body/2_second_layer.ipynb.txt" rel="nofollow"> View page source</a>
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+             
+  <section id="Adding-Kenyon-Cells">
+<h1>Adding Kenyon Cells<a class="headerlink" href="#Adding-Kenyon-Cells" title="Permalink to this heading"></a></h1>
+<p>In this second tutorial we add a large population of Kenyon Cells to the mushroom body and visualize their spiking activity in response to latency coded MNIST digits.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 69.0MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-tutorial-model">
+<h2>Build tutorial model<a class="headerlink" href="#Build-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>Import modules</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span>
+                    <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="p">(</span><span class="n">training_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+
+<span class="c1"># Reshape and normalise training data</span>
+<span class="n">training_images</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Parameters">
+<h2>Parameters<a class="headerlink" href="#Parameters" title="Permalink to this heading"></a></h2>
+<p>Define some model parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation time step</span>
+<span class="n">DT</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Scaling factor for converting normalised image pixels to input currents (nA)</span>
+<span class="n">INPUT_SCALE</span> <span class="o">=</span> <span class="mf">80.0</span>
+
+<span class="c1"># Number of Projection Neurons in model (should match image size)</span>
+<span class="n">NUM_PN</span> <span class="o">=</span> <span class="mi">784</span>
+
+<span class="c1"># Number of Kenyon Cells in model (defines memory capacity)</span>
+<span class="n">NUM_KC</span> <span class="o">=</span> <span class="mi">20000</span>
+
+<span class="c1"># How long to present each image to model</span>
+<span class="n">PRESENT_TIME_MS</span> <span class="o">=</span> <span class="mf">20.0</span>
+
+<span class="c1"># Standard LIF neurons parameters</span>
+<span class="n">LIF_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
+    <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>
+    <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+    <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+
+<span class="c1"># We only want PNs to spike once</span>
+<span class="n">PN_PARAMS</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">LIF_PARAMS</span><span class="p">)</span>
+<span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;TauRefrac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">100.0</span>
+</pre></div>
+</div>
+</div>
+<p>As we’re now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Weight of each synaptic connection</span>
+<span class="n">PN_KC_WEIGHT</span> <span class="o">=</span> <span class="mf">0.2</span>
+
+<span class="c1"># Time constant of synaptic integration</span>
+<span class="n">PN_KC_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+
+<span class="c1"># How many projection neurons should be connected to each Kenyon Cell</span>
+<span class="n">PN_KC_FAN_IN</span> <span class="o">=</span> <span class="mi">20</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Custom-models">
+<h2>Custom models<a class="headerlink" href="#Custom-models" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Current source model, allowing current to be injected into neuron from variable</span>
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Model-definition">
+<h2>Model definition<a class="headerlink" href="#Model-definition" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “mnist_mb_second_layer” as before but add a second population of <code class="docutils literal notranslate"><span class="pre">NUM_KC</span></code> neurons to represent the Kenyon Cells.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create model</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;mnist_mb_second_layer&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">DT</span>
+
+<span class="c1"># Create neuron populations</span>
+<span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;Vreset&quot;</span><span class="p">],</span> <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">pn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;pn&quot;</span><span class="p">,</span> <span class="n">NUM_PN</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">PN_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;kc&quot;</span><span class="p">,</span> <span class="n">NUM_KC</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="c1"># Turn on spike recording</span>
+<span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Create current sources to deliver input to network</span>
+<span class="n">pn_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;pn_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">pn</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<p>Add a current source to inject current into <code class="docutils literal notranslate"><span class="pre">pn</span></code> using our newly-defined custom model with the initial magnitude set to zero.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create synapse populations</span>
+<span class="n">pn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;pn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+                                     <span class="n">pn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                     <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">PN_KC_WEIGHT</span><span class="p">}),</span>
+                                     <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">PN_KC_TAU_SYN</span><span class="p">}),</span>
+                                     <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedNumberPreWithReplacement&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;num&quot;</span><span class="p">:</span> <span class="n">PN_KC_FAN_IN</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover <code class="docutils literal notranslate"><span class="pre">PRESENT_TIME_MS</span></code> (after converting from ms to timesteps)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Concert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="c1"># Build model and load it</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">present_timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_out_post</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<p>Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; <strong>and</strong> the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_neuron</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">var_init</span><span class="p">):</span>
+    <span class="c1"># Reset variables</span>
+    <span class="k">for</span> <span class="n">var_name</span><span class="p">,</span> <span class="n">var_val</span> <span class="ow">in</span> <span class="n">var_init</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">var_val</span>
+
+        <span class="c1"># Push the new values to GPU</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="c1"># Set training image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">training_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate present timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state for next stimuli</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">kc</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+    <span class="c1"># Reset synapse state</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">pn_kc</span><span class="p">)</span>
+
+    <span class="c1"># Download spikes from GPU</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">()</span>
+
+    <span class="c1"># Plot PN and KC spikes</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span> <span class="o">=</span> <span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span> <span class="o">=</span> <span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">kc_spike_ids</span><span class="p">))</span><span class="si">}</span><span class="s2"> KC active&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;PN&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;KC&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+4105 KC active
+4822 KC active
+2048 KC active
+924 KC active
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_2_second_layer_21_1.png" src="../../_images/tutorials_mushroom_body_2_second_layer_21_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_2_second_layer_21_2.png" src="../../_images/tutorials_mushroom_body_2_second_layer_21_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_2_second_layer_21_3.png" src="../../_images/tutorials_mushroom_body_2_second_layer_21_3.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_2_second_layer_21_4.png" src="../../_images/tutorials_mushroom_body_2_second_layer_21_4.png" />
+</div>
+</div>
+<p>Oh dear! Even with normalised inputs and controlling for the initial state of the model before presenting each stimuli, we get very variable numbers of active Kenyon Cells.</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="1_first_layer.html" class="btn btn-neutral float-left" title="Presenting latency-coded inputs" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
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diff --git a/documentation/5/tutorials/mushroom_body/2_second_layer.ipynb b/documentation/5/tutorials/mushroom_body/2_second_layer.ipynb
new file mode 100644
index 000000000..3801d0a11
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/2_second_layer.ipynb
@@ -0,0 +1,493 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Adding Kenyon Cells\n",
+    "In this second tutorial we add a large population of Kenyon Cells to the mushroom body and visualize their spiking activity in response to latency coded MNIST digits.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "R-jOIOlfheKy",
+    "outputId": "c11fde32-f153-4519-c8a3-ae516831fc77"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 69.0MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "cd4e1641-ae8b-48b6-d1a6-b53faad33f86"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yV0JrchrfQKR"
+   },
+   "source": [
+    "## Build tutorial model\n",
+    "Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Hl53yKXi9LiV"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "from copy import copy\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pygenn import (create_current_source_model, init_postsynaptic,\n",
+    "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+    "\n",
+    "training_images = mnist.train_images()\n",
+    "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+    "\n",
+    "# Reshape and normalise training data\n",
+    "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g0IfyML59Lif"
+   },
+   "source": [
+    "## Parameters\n",
+    "Define some model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oncGyriW9Lif"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation time step\n",
+    "DT = 0.1\n",
+    "\n",
+    "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+    "INPUT_SCALE = 80.0\n",
+    "\n",
+    "# Number of Projection Neurons in model (should match image size)\n",
+    "NUM_PN = 784\n",
+    "\n",
+    "# Number of Kenyon Cells in model (defines memory capacity)\n",
+    "NUM_KC = 20000\n",
+    "\n",
+    "# How long to present each image to model\n",
+    "PRESENT_TIME_MS = 20.0\n",
+    "\n",
+    "# Standard LIF neurons parameters\n",
+    "LIF_PARAMS = {\n",
+    "    \"C\": 0.2,\n",
+    "    \"TauM\": 20.0,\n",
+    "    \"Vrest\": -60.0,\n",
+    "    \"Vreset\": -60.0,\n",
+    "    \"Vthresh\": -50.0,\n",
+    "    \"Ioffset\": 0.0,\n",
+    "    \"TauRefrac\": 2.0}\n",
+    "\n",
+    "# We only want PNs to spike once\n",
+    "PN_PARAMS = copy(LIF_PARAMS)\n",
+    "PN_PARAMS[\"TauRefrac\"] = 100.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KldVFE9dJdv8"
+   },
+   "source": [
+    "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZvNwgTphJeM9"
+   },
+   "outputs": [],
+   "source": [
+    "# Weight of each synaptic connection\n",
+    "PN_KC_WEIGHT = 0.2\n",
+    "\n",
+    "# Time constant of synaptic integration\n",
+    "PN_KC_TAU_SYN = 3.0\n",
+    "\n",
+    "# How many projection neurons should be connected to each Kenyon Cell\n",
+    "PN_KC_FAN_IN = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pCYjAoJf9Lig"
+   },
+   "source": [
+    "## Custom models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IR8PXBg69Lih"
+   },
+   "outputs": [],
+   "source": [
+    "# Current source model, allowing current to be injected into neuron from variable\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gn4DpkPQ9Lii"
+   },
+   "source": [
+    "## Model definition\n",
+    "Create a new model called \"mnist_mb_second_layer\" as before but add a second population of `NUM_KC` neurons to represent the Kenyon Cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Gx-GsJhD9Lik"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model\n",
+    "model = GeNNModel(\"float\", \"mnist_mb_second_layer\")\n",
+    "model.dt = DT\n",
+    "\n",
+    "# Create neuron populations\n",
+    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+    "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "\n",
+    "# Turn on spike recording\n",
+    "pn.spike_recording_enabled = True\n",
+    "kc.spike_recording_enabled = True\n",
+    "\n",
+    "# Create current sources to deliver input to network\n",
+    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sdYo9umiH06S"
+   },
+   "source": [
+    "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7e1if0YCG_7m"
+   },
+   "outputs": [],
+   "source": [
+    "# Create synapse populations\n",
+    "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+    "                                     pn, kc,\n",
+    "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+    "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+    "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-GU4oXOS9Lil"
+   },
+   "source": [
+    "## Build model\n",
+    "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-FE02Zoz9Lim"
+   },
+   "outputs": [],
+   "source": [
+    "# Concert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "# Build model and load it\n",
+    "model.build()\n",
+    "model.load(num_recording_timesteps=present_timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcpTaaB39Lim"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+    " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7ENTbLZpGvye"
+   },
+   "outputs": [],
+   "source": [
+    "def reset_out_post(pop):\n",
+    "    pop.out_post.view[:] = 0.0\n",
+    "    pop.out_post.push_to_device()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DfcqDTVXdoRq"
+   },
+   "source": [
+    "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "K9pAP8OrJUub",
+    "outputId": "a271c2ed-8aa0-428a-c437-de4d2dbd27c9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4105 KC active\n",
+      "4822 KC active\n",
+      "2048 KC active\n",
+      "924 KC active\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def reset_neuron(pop, var_init):\n",
+    "    # Reset variables\n",
+    "    for var_name, var_val in var_init.items():\n",
+    "        pop.vars[var_name].view[:] = var_val\n",
+    "\n",
+    "        # Push the new values to GPU\n",
+    "        pop.vars[var_name].push_to_device()\n",
+    "\n",
+    "for s in range(4):\n",
+    "    # Set training image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate present timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state for next stimuli\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "    reset_neuron(kc, lif_init)\n",
+    "\n",
+    "    # Reset synapse state\n",
+    "    reset_out_post(pn_kc)\n",
+    "\n",
+    "    # Download spikes from GPU\n",
+    "    model.pull_recording_buffers_from_device()\n",
+    "\n",
+    "    # Plot PN and KC spikes\n",
+    "    fig, axes = plt.subplots(2, sharex=True)\n",
+    "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+    "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+    "    print(f\"{len(np.unique(kc_spike_ids))} KC active\")\n",
+    "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+    "    axes[0].set_ylabel(\"PN\")\n",
+    "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+    "    axes[1].set_xlabel(\"Time [ms]\")\n",
+    "    axes[1].set_ylabel(\"KC\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FC8WZqKZMNNM"
+   },
+   "source": [
+    "Oh dear! Even with normalised inputs and controlling for the initial state of the model before presenting each stimuli, we get very variable numbers of active Kenyon Cells."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "2_second_layer",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html
new file mode 100644
index 000000000..9512b496f
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html
@@ -0,0 +1,502 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
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+  <section id="Feedback-inhibition-based-gain-control">
+<h1>Feedback-inhibition based gain control<a class="headerlink" href="#Feedback-inhibition-based-gain-control" title="Permalink to this heading"></a></h1>
+<p>Based on the highly variable levels of Kenyon Cell activity found in the last tutorial, here we add feedback inhibition inspired by the Giant GABAergic Neuron (GGN) found in Drosophila and, with this in place, visualize the spiking activity of Kenyon Cells in response to latency coded MNIST digits.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 82.3MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+</pre></div></div>
+</div>
+</section>
+<section id="Build-tutorial-model">
+<h2>Build tutorial model<a class="headerlink" href="#Build-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>Import modules</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span>
+                    <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+
+<span class="c1"># Reshape and normalise training data</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="p">(</span><span class="n">training_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">training_images</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Parameters">
+<h2>Parameters<a class="headerlink" href="#Parameters" title="Permalink to this heading"></a></h2>
+<p>Define some model parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation time step</span>
+<span class="n">DT</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Scaling factor for converting normalised image pixels to input currents (nA)</span>
+<span class="n">INPUT_SCALE</span> <span class="o">=</span> <span class="mf">80.0</span>
+
+<span class="c1"># Number of Projection Neurons in model (should match image size)</span>
+<span class="n">NUM_PN</span> <span class="o">=</span> <span class="mi">784</span>
+
+<span class="c1"># Number of Kenyon Cells in model (defines memory capacity)</span>
+<span class="n">NUM_KC</span> <span class="o">=</span> <span class="mi">20000</span>
+
+<span class="c1"># How long to present each image to model</span>
+<span class="n">PRESENT_TIME_MS</span> <span class="o">=</span> <span class="mf">20.0</span>
+
+<span class="c1"># Standard LIF neurons parameters</span>
+<span class="n">LIF_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
+    <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>
+    <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+    <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+
+<span class="c1"># We only want PNs to spike once</span>
+<span class="n">PN_PARAMS</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">LIF_PARAMS</span><span class="p">)</span>
+<span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;TauRefrac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">100.0</span>
+
+<span class="c1"># Weight of each synaptic connection</span>
+<span class="n">PN_KC_WEIGHT</span> <span class="o">=</span> <span class="mf">0.2</span>
+
+<span class="c1"># Time constant of synaptic integration</span>
+<span class="n">PN_KC_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+
+<span class="c1"># How many projection neurons should be connected to each Kenyon Cell</span>
+<span class="n">PN_KC_FAN_IN</span> <span class="o">=</span> <span class="mi">20</span>
+</pre></div>
+</div>
+</div>
+<p>As we’re now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># We will use weights of 1.0 for KC-&gt;GGN connections and</span>
+<span class="c1"># want the GGN to inhibit the KCs after 200 spikes</span>
+<span class="n">GGN_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="mf">200.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Custom-models">
+<h2>Custom models<a class="headerlink" href="#Custom-models" title="Permalink to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Current source model, allowing current to be injected into neuron from variable</span>
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><br/><span></span><span class="c1"># Minimal integrate and fire neuron model</span>
+<span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;IF&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthresh&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V += Isyn;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V &gt;= Vthresh</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V = 0.0;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Model-definition">
+<h2>Model definition<a class="headerlink" href="#Model-definition" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “mnist_mb_second_layer_gain_control” as before but add a second population of <code class="docutils literal notranslate"><span class="pre">NUM_KC</span></code> neurons to represent the Kenyon Cells.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create model</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;mnist_mb_second_layer_gain_control&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">DT</span>
+
+<span class="c1"># Create neuron populations</span>
+<span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;Vreset&quot;</span><span class="p">],</span> <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">pn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;pn&quot;</span><span class="p">,</span> <span class="n">NUM_PN</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">PN_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;kc&quot;</span><span class="p">,</span> <span class="n">NUM_KC</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="c1"># Turn on spike recording</span>
+<span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Create current sources to deliver input to network</span>
+<span class="n">pn_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;pn_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">pn</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+
+<span class="c1"># Create synapse populations</span>
+<span class="n">pn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;pn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+                                     <span class="n">pn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                     <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">PN_KC_WEIGHT</span><span class="p">}),</span>
+                                     <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">PN_KC_TAU_SYN</span><span class="p">}),</span>
+                                     <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedNumberPreWithReplacement&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;num&quot;</span><span class="p">:</span> <span class="n">PN_KC_FAN_IN</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+<p>Add a current source to inject current into <code class="docutils literal notranslate"><span class="pre">pn</span></code> using our newly-defined custom model with the initial magnitude set to zero.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;ggn&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">if_model</span><span class="p">,</span> <span class="n">GGN_PARAMS</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">kc_ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;kc_ggn&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">kc</span><span class="p">,</span> <span class="n">ggn</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+
+<span class="n">ggn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;ggn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">ggn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">5.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover <code class="docutils literal notranslate"><span class="pre">PRESENT_TIME_MS</span></code> (after converting from ms to timesteps)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Concert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="c1"># Build model and load it</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">present_timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU.</p>
+<p>Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; <strong>and</strong> the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_out_post</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">reset_neuron</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">var_init</span><span class="p">):</span>
+    <span class="c1"># Reset variables</span>
+    <span class="k">for</span> <span class="n">var_name</span><span class="p">,</span> <span class="n">var_val</span> <span class="ow">in</span> <span class="n">var_init</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">var_val</span>
+
+        <span class="c1"># Push the new values to GPU</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="c1"># Set training image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">training_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate present timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state for next stimuli</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">kc</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">ggn</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+
+    <span class="c1"># Reset synapse state</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">pn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">ggn_kc</span><span class="p">)</span>
+
+    <span class="c1"># Download spikes from GPU</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">();</span>
+
+    <span class="c1"># Plot PN and KC spikes</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span> <span class="o">=</span> <span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span> <span class="o">=</span> <span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">unique</span><span class="p">(</span><span class="n">kc_spike_ids</span><span class="p">))</span><span class="si">}</span><span class="s2"> KC active&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;PN&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;KC&quot;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+283 KC active
+272 KC active
+253 KC active
+316 KC active
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_1.png" src="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_2.png" src="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_2.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_3.png" src="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_3.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_4.png" src="../../_images/tutorials_mushroom_body_3_second_layer_gain_control_22_4.png" />
+</div>
+</div>
+<p>Much better!</p>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="2_second_layer.html" class="btn btn-neutral float-left" title="Adding Kenyon Cells" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="4_third_layer.html" class="btn btn-neutral float-right" title="Output neurons and learning" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, James Knight, Thomas Nowotny.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
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+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.ipynb b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.ipynb
new file mode 100644
index 000000000..77b595a8e
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.ipynb
@@ -0,0 +1,537 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Feedback-inhibition based gain control\n",
+    "Based on the highly variable levels of Kenyon Cell activity found in the last tutorial, here we add feedback inhibition inspired by the Giant GABAergic Neuron (GGN) found in Drosophila and, with this in place, visualize the spiking activity of Kenyon Cells in response to latency coded MNIST digits.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Ki3IZh5Jij4W",
+    "outputId": "efd99f19-ea69-4061-8012-f03535ca2715"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
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+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 82.3MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "d201ba31-0261-408c-b461-b3d7207c03ba"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: mnist in /usr/local/lib/python3.10/dist-packages (0.2.2)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yV0JrchrfQKR"
+   },
+   "source": [
+    "## Build tutorial model\n",
+    "Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Hl53yKXi9LiV"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "from copy import copy\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pygenn import (create_current_source_model, create_neuron_model, init_postsynaptic,\n",
+    "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+    "\n",
+    "# Reshape and normalise training data\n",
+    "training_images = mnist.train_images()\n",
+    "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+    "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g0IfyML59Lif"
+   },
+   "source": [
+    "## Parameters\n",
+    "Define some model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oncGyriW9Lif"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation time step\n",
+    "DT = 0.1\n",
+    "\n",
+    "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+    "INPUT_SCALE = 80.0\n",
+    "\n",
+    "# Number of Projection Neurons in model (should match image size)\n",
+    "NUM_PN = 784\n",
+    "\n",
+    "# Number of Kenyon Cells in model (defines memory capacity)\n",
+    "NUM_KC = 20000\n",
+    "\n",
+    "# How long to present each image to model\n",
+    "PRESENT_TIME_MS = 20.0\n",
+    "\n",
+    "# Standard LIF neurons parameters\n",
+    "LIF_PARAMS = {\n",
+    "    \"C\": 0.2,\n",
+    "    \"TauM\": 20.0,\n",
+    "    \"Vrest\": -60.0,\n",
+    "    \"Vreset\": -60.0,\n",
+    "    \"Vthresh\": -50.0,\n",
+    "    \"Ioffset\": 0.0,\n",
+    "    \"TauRefrac\": 2.0}\n",
+    "\n",
+    "# We only want PNs to spike once\n",
+    "PN_PARAMS = copy(LIF_PARAMS)\n",
+    "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+    "\n",
+    "# Weight of each synaptic connection\n",
+    "PN_KC_WEIGHT = 0.2\n",
+    "\n",
+    "# Time constant of synaptic integration\n",
+    "PN_KC_TAU_SYN = 3.0\n",
+    "\n",
+    "# How many projection neurons should be connected to each Kenyon Cell\n",
+    "PN_KC_FAN_IN = 20"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KldVFE9dJdv8"
+   },
+   "source": [
+    "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZvNwgTphJeM9"
+   },
+   "outputs": [],
+   "source": [
+    "# We will use weights of 1.0 for KC->GGN connections and\n",
+    "# want the GGN to inhibit the KCs after 200 spikes\n",
+    "GGN_PARAMS = {\n",
+    "    \"Vthresh\": 200.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pCYjAoJf9Lig"
+   },
+   "source": [
+    "## Custom models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IR8PXBg69Lih"
+   },
+   "outputs": [],
+   "source": [
+    "# Current source model, allowing current to be injected into neuron from variable\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pe-5DQ9hezIs"
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# Minimal integrate and fire neuron model\n",
+    "if_model = create_neuron_model(\n",
+    "    \"IF\",\n",
+    "    params=[\"Vthresh\"],\n",
+    "    vars=[(\"V\", \"scalar\")],\n",
+    "    sim_code=\n",
+    "    \"\"\"\n",
+    "    V += Isyn;\n",
+    "    \"\"\",\n",
+    "    threshold_condition_code=\n",
+    "    \"\"\"\n",
+    "    V >= Vthresh\n",
+    "    \"\"\",\n",
+    "    reset_code=\n",
+    "    \"\"\"\n",
+    "    V = 0.0;\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gn4DpkPQ9Lii"
+   },
+   "source": [
+    "## Model definition\n",
+    "Create a new model called \"mnist_mb_second_layer_gain_control\" as before but add a second population of `NUM_KC` neurons to represent the Kenyon Cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Gx-GsJhD9Lik"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model\n",
+    "model = GeNNModel(\"float\", \"mnist_mb_second_layer_gain_control\")\n",
+    "model.dt = DT\n",
+    "\n",
+    "# Create neuron populations\n",
+    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+    "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "\n",
+    "# Turn on spike recording\n",
+    "pn.spike_recording_enabled = True\n",
+    "kc.spike_recording_enabled = True\n",
+    "\n",
+    "# Create current sources to deliver input to network\n",
+    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+    "\n",
+    "# Create synapse populations\n",
+    "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+    "                                     pn, kc,\n",
+    "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+    "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+    "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sdYo9umiH06S"
+   },
+   "source": [
+    "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7e1if0YCG_7m"
+   },
+   "outputs": [],
+   "source": [
+    "if_init = {\"V\": 0.0}\n",
+    "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "9wCO8tBLfLm8"
+   },
+   "outputs": [],
+   "source": [
+    "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+    "                                      kc, ggn,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+    "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+    "\n",
+    "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+    "                                      ggn, kc,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+    "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-GU4oXOS9Lil"
+   },
+   "source": [
+    "## Build model\n",
+    "Generate code and load it into PyGeNN allocating a large enough spike recording buffer to cover `PRESENT_TIME_MS` (after converting from ms to timesteps)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-FE02Zoz9Lim"
+   },
+   "outputs": [],
+   "source": [
+    "# Concert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "# Build model and load it\n",
+    "model.build()\n",
+    "model.load(num_recording_timesteps=present_timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcpTaaB39Lim"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+    " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DfcqDTVXdoRq"
+   },
+   "source": [
+    "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "K9pAP8OrJUub",
+    "outputId": "bab547b5-0d49-4076-b996-ee47c2cb25c0"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "283 KC active\n",
+      "272 KC active\n",
+      "253 KC active\n",
+      "316 KC active\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def reset_out_post(pop):\n",
+    "    pop.out_post.view[:] = 0.0\n",
+    "    pop.out_post.push_to_device()\n",
+    "\n",
+    "def reset_neuron(pop, var_init):\n",
+    "    # Reset variables\n",
+    "    for var_name, var_val in var_init.items():\n",
+    "        pop.vars[var_name].view[:] = var_val\n",
+    "\n",
+    "        # Push the new values to GPU\n",
+    "        pop.vars[var_name].push_to_device()\n",
+    "\n",
+    "for s in range(4):\n",
+    "    # Set training image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate present timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state for next stimuli\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "    reset_neuron(kc, lif_init)\n",
+    "    reset_neuron(ggn, if_init)\n",
+    "\n",
+    "    # Reset synapse state\n",
+    "    reset_out_post(pn_kc)\n",
+    "    reset_out_post(ggn_kc)\n",
+    "\n",
+    "    # Download spikes from GPU\n",
+    "    model.pull_recording_buffers_from_device();\n",
+    "\n",
+    "    # Plot PN and KC spikes\n",
+    "    fig, axes = plt.subplots(2, sharex=True)\n",
+    "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+    "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+    "    print(f\"{len(np.unique(kc_spike_ids))} KC active\")\n",
+    "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+    "    axes[0].set_ylabel(\"PN\")\n",
+    "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+    "    axes[1].set_xlabel(\"Time [ms]\")\n",
+    "    axes[1].set_ylabel(\"KC\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FC8WZqKZMNNM"
+   },
+   "source": [
+    "Much better!"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "3_second_layer_gain_control",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/documentation/5/tutorials/mushroom_body/4_third_layer.html b/documentation/5/tutorials/mushroom_body/4_third_layer.html
new file mode 100644
index 000000000..7175889ab
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+++ b/documentation/5/tutorials/mushroom_body/4_third_layer.html
@@ -0,0 +1,610 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
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+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/1_neurons.html">Defining populations of neurons</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_1.html">Classification of a single digit</a></li>
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+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_3.html">Faster classification of the whole test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="1_first_layer.html">Presenting latency-coded inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="2_second_layer.html">Adding Kenyon Cells</a></li>
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+<li class="toctree-l2 current"><a class="current reference internal" href="#">Output neurons and learning</a></li>
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+             
+  <section id="Output-neurons-and-learning">
+<h1>Output neurons and learning<a class="headerlink" href="#Output-neurons-and-learning" title="Permalink to this heading"></a></h1>
+<p>In this tutorial we add Mushroom Body Output Neurons (MBONS) to the model and train the weights connecting them to the Kenyon Cells using a simple event-driven STDP rule.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 101MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-tutorial-model">
+<h2>Build tutorial model<a class="headerlink" href="#Build-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>Import modules</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">create_weight_update_model</span><span class="p">,</span>
+                    <span class="n">init_postsynaptic</span><span class="p">,</span> <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+
+<span class="c1"># Reshape and normalise training data</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_images</span><span class="p">()</span>
+<span class="n">training_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="p">(</span><span class="n">training_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">training_images</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">training_images</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="n">training_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">train_labels</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Parameters">
+<h2>Parameters<a class="headerlink" href="#Parameters" title="Permalink to this heading"></a></h2>
+<p>Define some model parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation time step</span>
+<span class="n">DT</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Scaling factor for converting normalised image pixels to input currents (nA)</span>
+<span class="n">INPUT_SCALE</span> <span class="o">=</span> <span class="mf">80.0</span>
+
+<span class="c1"># Number of Projection Neurons in model (should match image size)</span>
+<span class="n">NUM_PN</span> <span class="o">=</span> <span class="mi">784</span>
+
+<span class="c1"># Number of Kenyon Cells in model (defines memory capacity)</span>
+<span class="n">NUM_KC</span> <span class="o">=</span> <span class="mi">20000</span>
+
+<span class="c1"># How long to present each image to model</span>
+<span class="n">PRESENT_TIME_MS</span> <span class="o">=</span> <span class="mf">20.0</span>
+
+<span class="c1"># Standard LIF neurons parameters</span>
+<span class="n">LIF_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
+    <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>
+    <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+    <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+
+<span class="c1"># We only want PNs to spike once</span>
+<span class="n">PN_PARAMS</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">LIF_PARAMS</span><span class="p">)</span>
+<span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;TauRefrac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">100.0</span>
+
+<span class="c1"># Weight of each synaptic connection</span>
+<span class="n">PN_KC_WEIGHT</span> <span class="o">=</span> <span class="mf">0.2</span>
+
+<span class="c1"># Time constant of synaptic integration</span>
+<span class="n">PN_KC_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+
+<span class="c1"># How many projection neurons should be connected to each Kenyon Cell</span>
+<span class="n">PN_KC_FAN_IN</span> <span class="o">=</span> <span class="mi">20</span>
+
+<span class="c1"># We will use weights of 1.0 for KC-&gt;GGN connections and</span>
+<span class="c1"># want the GGN to inhibit the KCs after 200 spikes</span>
+<span class="n">GGN_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="mf">200.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+<p>As we’re now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">NUM_MBON</span> <span class="o">=</span> <span class="mi">10</span>
+<span class="n">MBON_STIMULUS_CURRENT</span> <span class="o">=</span> <span class="mf">5.0</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">KC_MBON_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+<span class="n">KC_MBON_PARAMS</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">15.0</span><span class="p">,</span>
+                  <span class="s2">&quot;rho&quot;</span><span class="p">:</span> <span class="mf">0.01</span><span class="p">,</span>
+                  <span class="s2">&quot;eta&quot;</span><span class="p">:</span> <span class="mf">0.00002</span><span class="p">,</span>
+                  <span class="s2">&quot;wMin&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+                  <span class="s2">&quot;wMax&quot;</span><span class="p">:</span> <span class="mf">0.0233</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Custom-models">
+<h2>Custom models<a class="headerlink" href="#Custom-models" title="Permalink to this heading"></a></h2>
+<p>As well as the models we defined before:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Current source model, allowing current to be injected into neuron from variable</span>
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+
+<span class="c1"># Minimal integrate and fire neuron model</span>
+<span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;IF&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthresh&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V += Isyn;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V &gt;= Vthresh</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V = 0.0;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>We now also need an STDP learning rule!</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">symmetric_stdp</span> <span class="o">=</span> <span class="n">create_weight_update_model</span><span class="p">(</span>
+    <span class="s2">&quot;symmetric_stdp&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;tau&quot;</span><span class="p">,</span> <span class="s2">&quot;rho&quot;</span><span class="p">,</span> <span class="s2">&quot;eta&quot;</span><span class="p">,</span> <span class="s2">&quot;wMin&quot;</span><span class="p">,</span> <span class="s2">&quot;wMax&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;g&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">pre_spike_syn_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    const scalar dt = t - st_post;</span>
+<span class="sd">    const scalar timing = exp(-dt / tau) - rho;</span>
+<span class="sd">    const scalar newWeight = g + (eta * timing);</span>
+<span class="sd">    g = fmin(wMax, fmax(wMin, newWeight));</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">post_spike_syn_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    const scalar dt = t - st_pre;</span>
+<span class="sd">    const scalar timing = fmax(exp(-dt / tau) - rho, -0.1*rho);</span>
+<span class="sd">    const scalar newWeight = g + (eta * timing);</span>
+<span class="sd">    g = fmin(wMax, fmax(wMin, newWeight));</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+</section>
+<section id="Model-definition">
+<h2>Model definition<a class="headerlink" href="#Model-definition" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “mnist_mb_second_layer_gain_control” as before although we no longer need to record spikes from individual neurons:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create model</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;mnist_mb_third_layer&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dt</span> <span class="o">=</span> <span class="n">DT</span>
+
+<span class="c1"># Create neuron populations</span>
+<span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;Vreset&quot;</span><span class="p">],</span> <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">pn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;pn&quot;</span><span class="p">,</span> <span class="n">NUM_PN</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">PN_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;kc&quot;</span><span class="p">,</span> <span class="n">NUM_KC</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;ggn&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">if_model</span><span class="p">,</span> <span class="n">GGN_PARAMS</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+
+<span class="c1"># Turn on spike recording</span>
+<span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Create current sources to deliver input to network</span>
+<span class="n">pn_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;pn_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">pn</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+
+<span class="c1"># Create synapse populations</span>
+<span class="n">pn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;pn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+                                     <span class="n">pn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                     <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">PN_KC_WEIGHT</span><span class="p">}),</span>
+                                     <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">PN_KC_TAU_SYN</span><span class="p">}),</span>
+                                     <span class="n">init_sparse_connectivity</span><span class="p">(</span><span class="s2">&quot;FixedNumberPreWithReplacement&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;num&quot;</span><span class="p">:</span> <span class="n">PN_KC_FAN_IN</span><span class="p">}))</span>
+
+<span class="n">kc_ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;kc_ggn&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">kc</span><span class="p">,</span> <span class="n">ggn</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+
+<span class="n">ggn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;ggn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">ggn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">5.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+<p>Add a current source to inject current into <code class="docutils literal notranslate"><span class="pre">pn</span></code> using our newly-defined custom model with the initial magnitude set to zero.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">mbon</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;mbon&quot;</span><span class="p">,</span> <span class="n">NUM_MBON</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="n">mbon</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Create current sources to deliver input and supervision to network</span>
+<span class="n">mbon_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;mbon_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">mbon</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+</pre></div>
+</div>
+</div>
+<p>Add a new synapse group connecting <code class="docutils literal notranslate"><span class="pre">kc</span></code> into <code class="docutils literal notranslate"><span class="pre">mbon</span></code> with initially zeroed weights and our newly-defined STDP rule.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">kc_mbon</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;kc_mbon&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                       <span class="n">kc</span><span class="p">,</span> <span class="n">mbon</span><span class="p">,</span>
+                                       <span class="n">init_weight_update</span><span class="p">(</span><span class="n">symmetric_stdp</span><span class="p">,</span> <span class="n">KC_MBON_PARAMS</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}),</span>
+                                       <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">KC_MBON_TAU_SYN</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Generate code and load it into PyGeNN (as we’re no longer recording spikes, we don’t need to allocate a recording buffer)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="c1"># Build model and load it</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">present_timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU.</p>
+<p>Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; <strong>and</strong> the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_spike_times</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">spike_times</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">finfo</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span><span class="o">.</span><span class="n">max</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">spike_times</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_out_post</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">reset_neuron</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">var_init</span><span class="p">):</span>
+    <span class="c1"># Reset variables</span>
+    <span class="k">for</span> <span class="n">var_name</span><span class="p">,</span> <span class="n">var_val</span> <span class="ow">in</span> <span class="n">var_init</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">var_val</span>
+
+        <span class="c1"># Push the new values to GPU</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="c1"># Convert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">training_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+    <span class="c1"># Set training image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">training_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Turn on correct output neuron</span>
+    <span class="n">mbon_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mi">0</span>
+    <span class="n">mbon_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[</span><span class="n">training_labels</span><span class="p">[</span><span class="n">s</span><span class="p">]]</span> <span class="o">=</span> <span class="n">MBON_STIMULUS_CURRENT</span>
+    <span class="n">mbon_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate present timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">kc</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">ggn</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">mbon</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+    <span class="c1"># Reset spike times</span>
+    <span class="n">reset_spike_times</span><span class="p">(</span><span class="n">kc</span><span class="p">)</span>
+    <span class="n">reset_spike_times</span><span class="p">(</span><span class="n">mbon</span><span class="p">)</span>
+
+    <span class="c1"># Reset synapse state</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">pn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">ggn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">kc_mbon</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "e4a549fa740647a9a4c257b70347b6dc"}</script></div>
+</div>
+</section>
+<section id="Visualise-and-save-learned-weights">
+<h2>Visualise and save learned weights<a class="headerlink" href="#Visualise-and-save-learned-weights" title="Permalink to this heading"></a></h2>
+<p>First of all we download the learned weights from device and get the memory view to access them through (in the same way as we used to access current source state variables)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">kc_mbon</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;g&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">pull_from_device</span><span class="p">()</span>
+<span class="n">kc_mbon_g_view</span> <span class="o">=</span> <span class="n">kc_mbon</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;g&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span>
+</pre></div>
+</div>
+</div>
+<p>now we plot a histogram of the weight distribution - as is typical when using STDP rules whose learning rate is independent of the current magnitude of the weight it is bimodal</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axis</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">axis</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">kc_mbon_g_view</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">)</span>
+<span class="n">axis</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">kc_mbon_g_view</span><span class="p">),</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">)</span>
+<span class="n">axis</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Weight [nA]&quot;</span><span class="p">)</span>
+<span class="n">axis</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;Count&quot;</span><span class="p">);</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_4_third_layer_31_0.png" src="../../_images/tutorials_mushroom_body_4_third_layer_31_0.png" />
+</div>
+</div>
+<p>So we can reproduce exactly the same PN-&gt;KC connectivity again and reuse the weights we’ve learnt we want to save them back to YOUR google drive. First mount the drive</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
+<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s2">&quot;/content/drive&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Mounted at /content/drive
+</pre></div></div>
+</div>
+<p>Save the learnt weights</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/kc_mbon_g.npy&quot;</span><span class="p">,</span> <span class="n">kc_mbon_g_view</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<p>Download the PN-&gt;KC connectivity from the GPU and save the sparse connectivity.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pn_kc</span><span class="o">.</span><span class="n">pull_connectivity_from_device</span><span class="p">()</span>
+<span class="n">np</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/pn_kc_ind.npy&quot;</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">((</span><span class="n">pn_kc</span><span class="o">.</span><span class="n">get_sparse_pre_inds</span><span class="p">(),</span> <span class="n">pn_kc</span><span class="o">.</span><span class="n">get_sparse_post_inds</span><span class="p">())))</span>
+</pre></div>
+</div>
+</div>
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+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="3_second_layer_gain_control.html" class="btn btn-neutral float-left" title="Feedback-inhibition based gain control" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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+    </div>
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diff --git a/documentation/5/tutorials/mushroom_body/4_third_layer.ipynb b/documentation/5/tutorials/mushroom_body/4_third_layer.ipynb
new file mode 100644
index 000000000..eebbfb99d
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/4_third_layer.ipynb
@@ -0,0 +1,1081 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Output neurons and learning\n",
+    "In this tutorial we add Mushroom Body Output Neurons (MBONS) to the model and train the weights connecting them to the Kenyon Cells using a simple event-driven STDP rule.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "H6PHF3xTkMOD",
+    "outputId": "fb700b27-ffc5-4c6d-daae-84818f3137fe"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 101MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "3f2967bb-fa35-4599-a5d1-8e52a630a715"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yV0JrchrfQKR"
+   },
+   "source": [
+    "## Build tutorial model\n",
+    "Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Hl53yKXi9LiV"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "from copy import copy\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pygenn import (create_current_source_model, create_neuron_model, create_weight_update_model,\n",
+    "                    init_postsynaptic, init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+    "\n",
+    "# Reshape and normalise training data\n",
+    "training_images = mnist.train_images()\n",
+    "training_images = np.reshape(training_images, (training_images.shape[0], -1)).astype(np.float32)\n",
+    "training_images /= np.sum(training_images, axis=1)[:, np.newaxis]\n",
+    "training_labels = mnist.train_labels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZfxsGYr5kVv-"
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g0IfyML59Lif"
+   },
+   "source": [
+    "## Parameters\n",
+    "Define some model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "oncGyriW9Lif"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation time step\n",
+    "DT = 0.1\n",
+    "\n",
+    "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+    "INPUT_SCALE = 80.0\n",
+    "\n",
+    "# Number of Projection Neurons in model (should match image size)\n",
+    "NUM_PN = 784\n",
+    "\n",
+    "# Number of Kenyon Cells in model (defines memory capacity)\n",
+    "NUM_KC = 20000\n",
+    "\n",
+    "# How long to present each image to model\n",
+    "PRESENT_TIME_MS = 20.0\n",
+    "\n",
+    "# Standard LIF neurons parameters\n",
+    "LIF_PARAMS = {\n",
+    "    \"C\": 0.2,\n",
+    "    \"TauM\": 20.0,\n",
+    "    \"Vrest\": -60.0,\n",
+    "    \"Vreset\": -60.0,\n",
+    "    \"Vthresh\": -50.0,\n",
+    "    \"Ioffset\": 0.0,\n",
+    "    \"TauRefrac\": 2.0}\n",
+    "\n",
+    "# We only want PNs to spike once\n",
+    "PN_PARAMS = copy(LIF_PARAMS)\n",
+    "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+    "\n",
+    "# Weight of each synaptic connection\n",
+    "PN_KC_WEIGHT = 0.2\n",
+    "\n",
+    "# Time constant of synaptic integration\n",
+    "PN_KC_TAU_SYN = 3.0\n",
+    "\n",
+    "# How many projection neurons should be connected to each Kenyon Cell\n",
+    "PN_KC_FAN_IN = 20\n",
+    "\n",
+    "# We will use weights of 1.0 for KC->GGN connections and\n",
+    "# want the GGN to inhibit the KCs after 200 spikes\n",
+    "GGN_PARAMS = {\n",
+    "    \"Vthresh\": 200.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KldVFE9dJdv8"
+   },
+   "source": [
+    "As we're now going to be adding our synaptic connections between the Projection Neurons and a new population of Kenyon Cells, also define some parameter for these"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "ZvNwgTphJeM9"
+   },
+   "outputs": [],
+   "source": [
+    "NUM_MBON = 10\n",
+    "MBON_STIMULUS_CURRENT = 5.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-CKgGZHBjKl2"
+   },
+   "outputs": [],
+   "source": [
+    "KC_MBON_TAU_SYN = 3.0\n",
+    "KC_MBON_PARAMS = {\"tau\": 15.0,\n",
+    "                  \"rho\": 0.01,\n",
+    "                  \"eta\": 0.00002,\n",
+    "                  \"wMin\": 0.0,\n",
+    "                  \"wMax\": 0.0233}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pCYjAoJf9Lig"
+   },
+   "source": [
+    "## Custom models\n",
+    "As well as the models we defined before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "IR8PXBg69Lih"
+   },
+   "outputs": [],
+   "source": [
+    "# Current source model, allowing current to be injected into neuron from variable\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")\n",
+    "\n",
+    "# Minimal integrate and fire neuron model\n",
+    "if_model = create_neuron_model(\n",
+    "    \"IF\",\n",
+    "    params=[\"Vthresh\"],\n",
+    "    vars=[(\"V\", \"scalar\")],\n",
+    "    sim_code=\n",
+    "    \"\"\"\n",
+    "    V += Isyn;\n",
+    "    \"\"\",\n",
+    "    threshold_condition_code=\n",
+    "    \"\"\"\n",
+    "    V >= Vthresh\n",
+    "    \"\"\",\n",
+    "    reset_code=\n",
+    "    \"\"\"\n",
+    "    V = 0.0;\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cfKeAMLBjZ6u"
+   },
+   "source": [
+    "We now also need an STDP learning rule!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "pe-5DQ9hezIs"
+   },
+   "outputs": [],
+   "source": [
+    "symmetric_stdp = create_weight_update_model(\n",
+    "    \"symmetric_stdp\",\n",
+    "    params=[\"tau\", \"rho\", \"eta\", \"wMin\", \"wMax\"],\n",
+    "    vars=[(\"g\", \"scalar\")],\n",
+    "    pre_spike_syn_code=\n",
+    "    \"\"\"\n",
+    "    const scalar dt = t - st_post;\n",
+    "    const scalar timing = exp(-dt / tau) - rho;\n",
+    "    const scalar newWeight = g + (eta * timing);\n",
+    "    g = fmin(wMax, fmax(wMin, newWeight));\n",
+    "    \"\"\",\n",
+    "    post_spike_syn_code=\n",
+    "    \"\"\"\n",
+    "    const scalar dt = t - st_pre;\n",
+    "    const scalar timing = fmax(exp(-dt / tau) - rho, -0.1*rho);\n",
+    "    const scalar newWeight = g + (eta * timing);\n",
+    "    g = fmin(wMax, fmax(wMin, newWeight));\n",
+    "    \"\"\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gn4DpkPQ9Lii"
+   },
+   "source": [
+    "## Model definition\n",
+    "Create a new model called \"mnist_mb_second_layer_gain_control\" as before although we no longer need to record spikes from individual neurons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Gx-GsJhD9Lik"
+   },
+   "outputs": [],
+   "source": [
+    "# Create model\n",
+    "model = GeNNModel(\"float\", \"mnist_mb_third_layer\")\n",
+    "model.dt = DT\n",
+    "\n",
+    "# Create neuron populations\n",
+    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+    "if_init = {\"V\": 0.0}\n",
+    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+    "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)\n",
+    "\n",
+    "# Turn on spike recording\n",
+    "pn.spike_recording_enabled = True\n",
+    "kc.spike_recording_enabled = True\n",
+    "\n",
+    "# Create current sources to deliver input to network\n",
+    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+    "\n",
+    "# Create synapse populations\n",
+    "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+    "                                     pn, kc,\n",
+    "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+    "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}),\n",
+    "                                     init_sparse_connectivity(\"FixedNumberPreWithReplacement\", {\"num\": PN_KC_FAN_IN}))\n",
+    "\n",
+    "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+    "                                      kc, ggn,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+    "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+    "\n",
+    "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+    "                                      ggn, kc,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+    "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "sdYo9umiH06S"
+   },
+   "source": [
+    "Add a current source to inject current into `pn` using our newly-defined custom model with the initial magnitude set to zero."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kiitnN5HjlNc"
+   },
+   "outputs": [],
+   "source": [
+    "mbon = model.add_neuron_population(\"mbon\", NUM_MBON, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "\n",
+    "mbon.spike_recording_enabled = True\n",
+    "\n",
+    "# Create current sources to deliver input and supervision to network\n",
+    "mbon_input = model.add_current_source(\"mbon_input\", cs_model, mbon , {}, {\"magnitude\": 0.0})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0BQ2LzsPjvlv"
+   },
+   "source": [
+    "Add a new synapse group connecting ``kc`` into ``mbon`` with initially zeroed weights and our newly-defined STDP rule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "5hq70tYRjqZq"
+   },
+   "outputs": [],
+   "source": [
+    "kc_mbon = model.add_synapse_population(\"kc_mbon\", \"DENSE\",\n",
+    "                                       kc, mbon,\n",
+    "                                       init_weight_update(symmetric_stdp, KC_MBON_PARAMS, {\"g\": 0.0}),\n",
+    "                                       init_postsynaptic(\"ExpCurr\", {\"tau\": KC_MBON_TAU_SYN}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-GU4oXOS9Lil"
+   },
+   "source": [
+    "## Build model\n",
+    "Generate code and load it into PyGeNN (as we're no longer recording spikes, we don't need to allocate a recording buffer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-FE02Zoz9Lim"
+   },
+   "outputs": [],
+   "source": [
+    "# Convert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "# Build model and load it\n",
+    "model.build()\n",
+    "model.load(num_recording_timesteps=present_timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcpTaaB39Lim"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+    " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DfcqDTVXdoRq"
+   },
+   "source": [
+    "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "hQV_X3IGkIQm"
+   },
+   "outputs": [],
+   "source": [
+    "def reset_spike_times(pop):\n",
+    "    pop.spike_times.view[:] = -np.finfo(np.float32).max\n",
+    "    pop.spike_times.push_to_device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 49,
+     "referenced_widgets": [
+      "e4a549fa740647a9a4c257b70347b6dc",
+      "9331ce4de0d845d7bdba6eaf19733e3b",
+      "e98cca67c6dd4268b7fea6f8136b31cd",
+      "5456c8d419f1474cbe8cf0c3eca6fddc",
+      "007cd41dc27e40aeb84f65b26c870a7c",
+      "7ed5e1c9a13142939fcd1f7cf78243a1",
+      "4f077d3f032f4a169a5f7eedf15f421c",
+      "7b2a461efd354bba8e96b6ed7b6c5f2d",
+      "a0225294218b49cfa976eb59639f2f22",
+      "7c55bec89f144381b8f495e3aba48699",
+      "e0ed35f603ed483abb7d79c569d45fd2"
+     ]
+    },
+    "id": "K9pAP8OrJUub",
+    "outputId": "fa63fb73-6484-4c47-b5b5-5011bdf4f58c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4a549fa740647a9a4c257b70347b6dc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/60000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def reset_out_post(pop):\n",
+    "    pop.out_post.view[:] = 0.0\n",
+    "    pop.out_post.push_to_device()\n",
+    "\n",
+    "def reset_neuron(pop, var_init):\n",
+    "    # Reset variables\n",
+    "    for var_name, var_val in var_init.items():\n",
+    "        pop.vars[var_name].view[:] = var_val\n",
+    "\n",
+    "        # Push the new values to GPU\n",
+    "        pop.vars[var_name].push_to_device()\n",
+    "\n",
+    "# Convert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "for s in tqdm(range(training_images.shape[0])):\n",
+    "    # Set training image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = training_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Turn on correct output neuron\n",
+    "    mbon_input.vars[\"magnitude\"].view[:] = 0\n",
+    "    mbon_input.vars[\"magnitude\"].view[training_labels[s]] = MBON_STIMULUS_CURRENT\n",
+    "    mbon_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate present timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "    reset_neuron(kc, lif_init)\n",
+    "    reset_neuron(ggn, if_init)\n",
+    "    reset_neuron(mbon, lif_init)\n",
+    "\n",
+    "    # Reset spike times\n",
+    "    reset_spike_times(kc)\n",
+    "    reset_spike_times(mbon)\n",
+    "\n",
+    "    # Reset synapse state\n",
+    "    reset_out_post(pn_kc)\n",
+    "    reset_out_post(ggn_kc)\n",
+    "    reset_out_post(kc_mbon)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KqM0qyChlzhP"
+   },
+   "source": [
+    "## Visualise and save learned weights\n",
+    "First of all we download the learned weights from device and get the memory view to access them through (in the same way as we used to access current source state variables)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "s4lAfhkdkuHY"
+   },
+   "outputs": [],
+   "source": [
+    "kc_mbon.vars[\"g\"].pull_from_device()\n",
+    "kc_mbon_g_view = kc_mbon.vars[\"g\"].view"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "FINPVXN5mr5C"
+   },
+   "source": [
+    "now we plot a histogram of the weight distribution - as is typical when using STDP rules whose learning rate is independent of the current magnitude of the weight it is bimodal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 465
+    },
+    "id": "OHD_SzOsmF1Z",
+    "outputId": "404ba978-42b9-4273-d2a0-07791d2c664a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axis = plt.subplots(figsize=(10, 5))\n",
+    "axis.hist(kc_mbon_g_view, bins=100)\n",
+    "axis.axvline(np.average(kc_mbon_g_view), linestyle=\"--\")\n",
+    "axis.set_xlabel(\"Weight [nA]\")\n",
+    "axis.set_ylabel(\"Count\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bi2A8qZWAASt"
+   },
+   "source": [
+    "So we can reproduce exactly the same PN->KC connectivity again and reuse the weights we've learnt we want to save them back to YOUR google drive. First mount the drive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "DMyS30_Rm-oe",
+    "outputId": "be763ffc-cfc1-43f0-b49f-bf68070c25d4"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount(\"/content/drive\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "JTZrJsB4AQH5"
+   },
+   "source": [
+    "Save the learnt weights"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "kFQtm-CVmT20"
+   },
+   "outputs": [],
+   "source": [
+    "np.save(\"/content/drive/MyDrive/kc_mbon_g.npy\", kc_mbon_g_view)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "iqF9kWhEATFL"
+   },
+   "source": [
+    "Download the PN->KC connectivity from the GPU and save the sparse connectivity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "6xNyJgiGmR9Y"
+   },
+   "outputs": [],
+   "source": [
+    "pn_kc.pull_connectivity_from_device()\n",
+    "np.save(\"/content/drive/MyDrive/pn_kc_ind.npy\", np.vstack((pn_kc.get_sparse_pre_inds(), pn_kc.get_sparse_post_inds())))"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "4_third_layer",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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diff --git a/documentation/5/tutorials/mushroom_body/5_testing.html b/documentation/5/tutorials/mushroom_body/5_testing.html
new file mode 100644
index 000000000..d49e19fea
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/5_testing.html
@@ -0,0 +1,608 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <title>Testing &mdash; PyGeNN  documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../_static/pygments.css?v=80d5e7a1" />
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+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+    <link rel="next" title="User projects" href="../../userproject/index.html" />
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+</head>
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+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
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+<li class="toctree-l1"><a class="reference internal" href="../../installation.html">Installation</a></li>
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+<li class="toctree-l1"><a class="reference internal" href="../../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/1_neurons.html">Defining populations of neurons</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/2_synapses.html">Adding synapses</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_1.html">Classification of a single digit</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_2.html">Classification of the entire test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/tutorial_3.html">Faster classification of the whole test set</a></li>
+<li class="toctree-l2"><a class="reference internal" href="1_first_layer.html">Presenting latency-coded inputs</a></li>
+<li class="toctree-l2"><a class="reference internal" href="2_second_layer.html">Adding Kenyon Cells</a></li>
+<li class="toctree-l2"><a class="reference internal" href="3_second_layer_gain_control.html">Feedback-inhibition based gain control</a></li>
+<li class="toctree-l2"><a class="reference internal" href="4_third_layer.html">Output neurons and learning</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Testing</a></li>
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+</li>
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+      <li class="breadcrumb-item active">Testing</li>
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+            <a href="../../_sources/tutorials/mushroom_body/5_testing.ipynb.txt" rel="nofollow"> View page source</a>
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+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Testing">
+<h1>Testing<a class="headerlink" href="#Testing" title="Permalink to this heading"></a></h1>
+<p>In this final tutorial we load the weights we trained in the previous tutorial into a static version of the mushroom body model and evaluate its performance on the MNIST test set.</p>
+<section id="Install-PyGeNN-wheel-from-Google-Drive">
+<h2>Install PyGeNN wheel from Google Drive<a class="headerlink" href="#Install-PyGeNN-wheel-from-Google-Drive" title="Permalink to this heading"></a></h2>
+<p>Download wheel file</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">if</span> <span class="s2">&quot;google.colab&quot;</span> <span class="ow">in</span> <span class="nb">str</span><span class="p">(</span><span class="n">get_ipython</span><span class="p">()):</span>
+    <span class="c1">#import IPython</span>
+    <span class="c1">#IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a</span>
+    <span class="c1">#%run &quot;../install_collab.ipynb&quot;</span>
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>gdown<span class="w"> </span>--upgrade
+    <span class="o">!</span>gdown<span class="w"> </span>1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+    <span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+    <span class="o">%</span><span class="k">env</span> CUDA_PATH=/usr/local/cuda
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
+Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
+Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)
+Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)
+Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)
+Requirement already satisfied: soupsieve&gt;1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4-&gt;gdown) (2.5)
+Requirement already satisfied: charset-normalizer&lt;4,&gt;=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.3.2)
+Requirement already satisfied: idna&lt;4,&gt;=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (3.6)
+Requirement already satisfied: urllib3&lt;3,&gt;=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2.0.7)
+Requirement already satisfied: certifi&gt;=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (2024.2.2)
+Requirement already satisfied: PySocks!=1.5.7,&gt;=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]-&gt;gdown) (1.7.1)
+Downloading...
+From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
+To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+100% 8.29M/8.29M [00:00&lt;00:00, 215MB/s]
+Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
+Requirement already satisfied: numpy&gt;=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
+Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
+Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
+Requirement already satisfied: wrapt&lt;2,&gt;=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated-&gt;pygenn==5.0.0) (1.14.1)
+pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
+env: CUDA_PATH=/usr/local/cuda
+</pre></div></div>
+</div>
+</section>
+<section id="Install-MNIST-package">
+<h2>Install MNIST package<a class="headerlink" href="#Install-MNIST-package" title="Permalink to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[2]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="o">!</span>pip<span class="w"> </span>install<span class="w"> </span>mnist
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Collecting mnist
+  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)
+Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)
+Installing collected packages: mnist
+Successfully installed mnist-0.2.2
+</pre></div></div>
+</div>
+</section>
+<section id="Build-tutorial-model">
+<h2>Build tutorial model<a class="headerlink" href="#Build-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>Import modules</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">mnist</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">copy</span>
+<span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
+<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span>
+<span class="kn">from</span> <span class="nn">pygenn</span> <span class="kn">import</span> <span class="p">(</span><span class="n">create_current_source_model</span><span class="p">,</span> <span class="n">create_neuron_model</span><span class="p">,</span> <span class="n">init_postsynaptic</span><span class="p">,</span>
+                    <span class="n">init_sparse_connectivity</span><span class="p">,</span> <span class="n">init_weight_update</span><span class="p">,</span> <span class="n">GeNNModel</span><span class="p">)</span>
+<span class="kn">from</span> <span class="nn">tqdm.auto</span> <span class="kn">import</span> <span class="n">tqdm</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[4]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Reshape and normalise training data</span>
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_images</span><span class="p">()</span>
+<span class="n">testing_images</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">))</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
+<span class="n">testing_images</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">testing_images</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)[:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+<span class="n">testing_labels</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">test_labels</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">google.colab</span> <span class="kn">import</span> <span class="n">drive</span>
+<span class="n">drive</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s2">&quot;/content/drive&quot;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+Mounted at /content/drive
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[6]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pn_kc_ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/pn_kc_ind.npy&quot;</span><span class="p">)</span>
+<span class="n">kc_mbon_g</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s2">&quot;/content/drive/MyDrive/kc_mbon_g.npy&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Parameters">
+<h2>Parameters<a class="headerlink" href="#Parameters" title="Permalink to this heading"></a></h2>
+<p>Define some model parameters</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Simulation time step</span>
+<span class="n">DT</span> <span class="o">=</span> <span class="mf">0.1</span>
+
+<span class="c1"># Scaling factor for converting normalised image pixels to input currents (nA)</span>
+<span class="n">INPUT_SCALE</span> <span class="o">=</span> <span class="mf">80.0</span>
+
+<span class="c1"># Number of Projection Neurons in model (should match image size)</span>
+<span class="n">NUM_PN</span> <span class="o">=</span> <span class="mi">784</span>
+
+<span class="c1"># Number of Kenyon Cells in model (defines memory capacity)</span>
+<span class="n">NUM_KC</span> <span class="o">=</span> <span class="mi">20000</span>
+
+<span class="c1"># Number of Mushroom Body Output Neurons (should match number of labels)</span>
+<span class="n">NUM_MBON</span> <span class="o">=</span> <span class="mi">10</span>
+
+<span class="c1"># How long to present each image to model</span>
+<span class="n">PRESENT_TIME_MS</span> <span class="o">=</span> <span class="mf">20.0</span>
+
+<span class="c1"># Standard LIF neurons parameters</span>
+<span class="n">LIF_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;C&quot;</span><span class="p">:</span> <span class="mf">0.2</span><span class="p">,</span>
+    <span class="s2">&quot;TauM&quot;</span><span class="p">:</span> <span class="mf">20.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vrest&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vreset&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">60.0</span><span class="p">,</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">50.0</span><span class="p">,</span>
+    <span class="s2">&quot;Ioffset&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">,</span>
+    <span class="s2">&quot;TauRefrac&quot;</span><span class="p">:</span> <span class="mf">2.0</span><span class="p">}</span>
+
+<span class="c1"># We only want PNs to spike once</span>
+<span class="n">PN_PARAMS</span> <span class="o">=</span> <span class="n">copy</span><span class="p">(</span><span class="n">LIF_PARAMS</span><span class="p">)</span>
+<span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;TauRefrac&quot;</span><span class="p">]</span> <span class="o">=</span> <span class="mf">100.0</span>
+
+<span class="c1"># Weight of each synaptic connection</span>
+<span class="n">PN_KC_WEIGHT</span> <span class="o">=</span> <span class="mf">0.2</span>
+
+<span class="c1"># Time constant of synaptic integration</span>
+<span class="n">PN_KC_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+
+<span class="c1"># How many projection neurons should be connected to each Kenyon Cell</span>
+<span class="n">PN_KC_FAN_IN</span> <span class="o">=</span> <span class="mi">20</span>
+
+<span class="c1"># Time constant of synaptic integration</span>
+<span class="n">KC_MBON_TAU_SYN</span> <span class="o">=</span> <span class="mf">3.0</span>
+
+<span class="c1"># We will use weights of 1.0 for KC-&gt;GGN connections and</span>
+<span class="c1"># want the GGN to inhibit the KCs after 200 spikes</span>
+<span class="n">GGN_PARAMS</span> <span class="o">=</span> <span class="p">{</span>
+    <span class="s2">&quot;Vthresh&quot;</span><span class="p">:</span> <span class="mf">200.0</span><span class="p">}</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Custom-models">
+<h2>Custom models<a class="headerlink" href="#Custom-models" title="Permalink to this heading"></a></h2>
+<p>As well as the models we defined before:</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Current source model, allowing current to be injected into neuron from variable</span>
+<span class="n">cs_model</span> <span class="o">=</span> <span class="n">create_current_source_model</span><span class="p">(</span>
+    <span class="s2">&quot;cs_model&quot;</span><span class="p">,</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;magnitude&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">injection_code</span><span class="o">=</span><span class="s2">&quot;injectCurrent(magnitude);&quot;</span><span class="p">)</span>
+
+<span class="c1"># Minimal integrate and fire neuron model</span>
+<span class="n">if_model</span> <span class="o">=</span> <span class="n">create_neuron_model</span><span class="p">(</span>
+    <span class="s2">&quot;IF&quot;</span><span class="p">,</span>
+    <span class="n">params</span><span class="o">=</span><span class="p">[</span><span class="s2">&quot;Vthresh&quot;</span><span class="p">],</span>
+    <span class="nb">vars</span><span class="o">=</span><span class="p">[(</span><span class="s2">&quot;V&quot;</span><span class="p">,</span> <span class="s2">&quot;scalar&quot;</span><span class="p">)],</span>
+    <span class="n">sim_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V += Isyn;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">threshold_condition_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V &gt;= Vthresh</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">,</span>
+    <span class="n">reset_code</span><span class="o">=</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    V = 0.0;</span>
+<span class="sd">    &quot;&quot;&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Model-definition">
+<h2>Model definition<a class="headerlink" href="#Model-definition" title="Permalink to this heading"></a></h2>
+<p>Create a new model called “mnist_mb_second_layer_gain_control” as before although we no longer need to record spikes from individual neurons:</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[10]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Create model</span>
+<span class="n">model</span> <span class="o">=</span> <span class="n">GeNNModel</span><span class="p">(</span><span class="s2">&quot;float&quot;</span><span class="p">,</span> <span class="s2">&quot;mnist_mb_testing&quot;</span><span class="p">)</span>
+<span class="n">model</span><span class="o">.</span><span class="n">dT</span> <span class="o">=</span> <span class="n">DT</span>
+
+<span class="c1"># Create neuron populations</span>
+<span class="n">lif_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="n">PN_PARAMS</span><span class="p">[</span><span class="s2">&quot;Vreset&quot;</span><span class="p">],</span> <span class="s2">&quot;RefracTime&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">if_init</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;V&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">}</span>
+<span class="n">pn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;pn&quot;</span><span class="p">,</span> <span class="n">NUM_PN</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">PN_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;kc&quot;</span><span class="p">,</span> <span class="n">NUM_KC</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+<span class="n">ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;ggn&quot;</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">if_model</span><span class="p">,</span> <span class="n">GGN_PARAMS</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+<span class="n">mbon</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_neuron_population</span><span class="p">(</span><span class="s2">&quot;mbon&quot;</span><span class="p">,</span> <span class="n">NUM_MBON</span><span class="p">,</span> <span class="s2">&quot;LIF&quot;</span><span class="p">,</span> <span class="n">LIF_PARAMS</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+<span class="c1"># Turn on spike recording</span>
+<span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+<span class="n">mbon</span><span class="o">.</span><span class="n">spike_recording_enabled</span> <span class="o">=</span> <span class="kc">True</span>
+
+<span class="c1"># Create current sources to deliver input to network</span>
+<span class="n">pn_input</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_current_source</span><span class="p">(</span><span class="s2">&quot;pn_input&quot;</span><span class="p">,</span> <span class="n">cs_model</span><span class="p">,</span> <span class="n">pn</span> <span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;magnitude&quot;</span><span class="p">:</span> <span class="mf">0.0</span><span class="p">})</span>
+
+<span class="c1"># Create synapse populations</span>
+<span class="n">kc_ggn</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;kc_ggn&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">kc</span><span class="p">,</span> <span class="n">ggn</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="mf">1.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;DeltaCurr&quot;</span><span class="p">))</span>
+
+<span class="n">ggn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;ggn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                      <span class="n">ggn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                      <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="o">-</span><span class="mf">5.0</span><span class="p">}),</span>
+                                      <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="mf">5.0</span><span class="p">}))</span>
+<br/><br/></pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+&lt;ipython-input-10-dcbfda279a3c&gt;:3: FutureWarning: Call to deprecated function (or staticmethod) dT. (The name of this property was inconsistent, use dt instead)
+  model.dT = DT
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[11]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">pn_kc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;pn_kc&quot;</span><span class="p">,</span> <span class="s2">&quot;SPARSE&quot;</span><span class="p">,</span>
+                                     <span class="n">pn</span><span class="p">,</span> <span class="n">kc</span><span class="p">,</span>
+                                     <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulseConstantWeight&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">PN_KC_WEIGHT</span><span class="p">}),</span>
+                                     <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">PN_KC_TAU_SYN</span><span class="p">}))</span>
+<span class="n">pn_kc</span><span class="o">.</span><span class="n">set_sparse_connections</span><span class="p">(</span><span class="n">pn_kc_ind</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">pn_kc_ind</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[12]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">kc_mbon</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">add_synapse_population</span><span class="p">(</span><span class="s2">&quot;kc_mbon&quot;</span><span class="p">,</span> <span class="s2">&quot;DENSE&quot;</span><span class="p">,</span>
+                                       <span class="n">kc</span><span class="p">,</span> <span class="n">mbon</span><span class="p">,</span>
+                                       <span class="n">init_weight_update</span><span class="p">(</span><span class="s2">&quot;StaticPulse&quot;</span><span class="p">,</span> <span class="p">{},</span> <span class="p">{</span><span class="s2">&quot;g&quot;</span><span class="p">:</span> <span class="n">kc_mbon_g</span><span class="p">}),</span>
+                                       <span class="n">init_postsynaptic</span><span class="p">(</span><span class="s2">&quot;ExpCurr&quot;</span><span class="p">,</span> <span class="p">{</span><span class="s2">&quot;tau&quot;</span><span class="p">:</span> <span class="n">KC_MBON_TAU_SYN</span><span class="p">}))</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Build-model">
+<h2>Build model<a class="headerlink" href="#Build-model" title="Permalink to this heading"></a></h2>
+<p>Generate code and load it into PyGeNN (as we’re no longer recording spikes, we don’t need to allocate a recording buffer)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[13]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Convert present time into timesteps</span>
+<span class="n">present_timesteps</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">PRESENT_TIME_MS</span> <span class="o">/</span> <span class="n">DT</span><span class="p">))</span>
+
+<span class="c1"># Build model and load it</span>
+<span class="n">model</span><span class="o">.</span><span class="n">build</span><span class="p">()</span>
+<span class="n">model</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">num_recording_timesteps</span><span class="o">=</span><span class="n">present_timesteps</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Simulate-tutorial-model">
+<h2>Simulate tutorial model<a class="headerlink" href="#Simulate-tutorial-model" title="Permalink to this heading"></a></h2>
+<p>As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset. This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU.</p>
+<p>Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; <strong>and</strong> the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">def</span> <span class="nf">reset_out_post</span><span class="p">(</span><span class="n">pop</span><span class="p">):</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">pop</span><span class="o">.</span><span class="n">out_post</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+<span class="k">def</span> <span class="nf">reset_neuron</span><span class="p">(</span><span class="n">pop</span><span class="p">,</span> <span class="n">var_init</span><span class="p">):</span>
+    <span class="c1"># Reset variables</span>
+    <span class="k">for</span> <span class="n">var_name</span><span class="p">,</span> <span class="n">var_val</span> <span class="ow">in</span> <span class="n">var_init</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">var_val</span>
+
+        <span class="c1"># Push the new values to GPU</span>
+        <span class="n">pop</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="n">var_name</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[16]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">4</span><span class="p">):</span>
+    <span class="c1"># Set testing image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">testing_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate present timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state for next stimuli</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">kc</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">ggn</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">mbon</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+    <span class="c1"># Reset synapse state</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">pn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">ggn_kc</span><span class="p">)</span>
+
+    <span class="c1"># Download spikes from GPU</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">();</span>
+
+    <span class="c1"># Plot PN, KC and MBON spikes</span>
+    <span class="n">fig</span><span class="p">,</span> <span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span> <span class="o">=</span> <span class="n">pn</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span> <span class="o">=</span> <span class="n">kc</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="n">mbon_spike_times</span><span class="p">,</span> <span class="n">mbon_spike_ids</span> <span class="o">=</span> <span class="n">mbon</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">pn_spike_times</span><span class="p">,</span> <span class="n">pn_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;PN&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">kc_spike_times</span><span class="p">,</span> <span class="n">kc_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;KC&quot;</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">mbon_spike_times</span><span class="p">,</span> <span class="n">mbon_spike_ids</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">testing_labels</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;green&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">((</span><span class="o">-</span><span class="mf">0.5</span><span class="p">,</span> <span class="mf">10.5</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">mbon_spike_times</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">classification</span> <span class="o">=</span> <span class="n">mbon_spike_ids</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">mbon_spike_times</span><span class="p">)]</span>
+        <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">classification</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s2">&quot;--&quot;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s2">&quot;red&quot;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s2">&quot;MBON&quot;</span><span class="p">)</span>
+
+    <span class="n">axes</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s2">&quot;Time [ms]&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_5_testing_22_0.png" src="../../_images/tutorials_mushroom_body_5_testing_22_0.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_5_testing_22_1.png" src="../../_images/tutorials_mushroom_body_5_testing_22_1.png" />
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_5_testing_22_2.png" src="../../_images/tutorials_mushroom_body_5_testing_22_2.png" />
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../../_images/tutorials_mushroom_body_5_testing_22_3.png" src="../../_images/tutorials_mushroom_body_5_testing_22_3.png" />
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[17]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">num_correct</span> <span class="o">=</span> <span class="mi">0</span>
+<span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+    <span class="c1"># Set testing image</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">view</span><span class="p">[:]</span> <span class="o">=</span> <span class="n">testing_images</span><span class="p">[</span><span class="n">s</span><span class="p">]</span> <span class="o">*</span> <span class="n">INPUT_SCALE</span>
+    <span class="n">pn_input</span><span class="o">.</span><span class="n">vars</span><span class="p">[</span><span class="s2">&quot;magnitude&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">push_to_device</span><span class="p">()</span>
+
+    <span class="c1"># Simulate present timesteps</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">present_timesteps</span><span class="p">):</span>
+        <span class="n">model</span><span class="o">.</span><span class="n">step_time</span><span class="p">()</span>
+
+    <span class="c1"># Reset neuron state</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">pn</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">kc</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">ggn</span><span class="p">,</span> <span class="n">if_init</span><span class="p">)</span>
+    <span class="n">reset_neuron</span><span class="p">(</span><span class="n">mbon</span><span class="p">,</span> <span class="n">lif_init</span><span class="p">)</span>
+
+    <span class="c1"># Reset synapse state</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">pn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">ggn_kc</span><span class="p">)</span>
+    <span class="n">reset_out_post</span><span class="p">(</span><span class="n">kc_mbon</span><span class="p">)</span>
+
+    <span class="c1"># Download spikes from GPU</span>
+    <span class="n">model</span><span class="o">.</span><span class="n">pull_recording_buffers_from_device</span><span class="p">();</span>
+
+    <span class="c1"># Determine the classification and count correct</span>
+    <span class="n">mbon_spike_times</span><span class="p">,</span> <span class="n">mbon_spike_ids</span> <span class="o">=</span> <span class="n">mbon</span><span class="o">.</span><span class="n">spike_recording_data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">mbon_spike_times</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">mbon_spike_ids</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmin</span><span class="p">(</span><span class="n">mbon_spike_times</span><span class="p">)]</span> <span class="o">==</span> <span class="n">testing_labels</span><span class="p">[</span><span class="n">s</span><span class="p">]:</span>
+            <span class="n">num_correct</span> <span class="o">+=</span> <span class="mi">1</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;</span><span class="se">\n</span><span class="si">{</span><span class="n">num_correct</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> correct (</span><span class="si">{</span><span class="p">(</span><span class="n">num_correct</span><span class="w"> </span><span class="o">*</span><span class="w"> </span><span class="mf">100.0</span><span class="p">)</span><span class="w"> </span><span class="o">/</span><span class="w"> </span><span class="n">testing_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2"> %%)&quot;</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<script type="application/vnd.jupyter.widget-view+json">{"version_major": 2, "version_minor": 0, "model_id": "59c468dd929b4032af2794e104a6bead"}</script></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+
+7263/10000 correct (72.63 %%)
+</pre></div></div>
+</div>
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+        <a href="4_third_layer.html" class="btn btn-neutral float-left" title="Output neurons and learning" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/documentation/5/tutorials/mushroom_body/5_testing.ipynb b/documentation/5/tutorials/mushroom_body/5_testing.ipynb
new file mode 100644
index 000000000..00585ca43
--- /dev/null
+++ b/documentation/5/tutorials/mushroom_body/5_testing.ipynb
@@ -0,0 +1,1007 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "lGa0_oLb61zz"
+   },
+   "source": [
+    "# Testing\n",
+    "In this final tutorial we load the weights we trained in the previous tutorial into a static version of the mushroom body model and evaluate its performance on the MNIST test set.\n",
+    "\n",
+    "## Install PyGeNN wheel from Google Drive\n",
+    "Download wheel file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "NMiDVe_vmKPh",
+    "outputId": "689453af-68ee-472c-8b20-b71acddb5d6c"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
+      "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n",
+      "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
+      "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n",
+      "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
+      "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
+      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
+      "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n",
+      "Downloading...\n",
+      "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+      "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "100% 8.29M/8.29M [00:00<00:00, 215MB/s]\n",
+      "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+      "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n",
+      "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n",
+      "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n",
+      "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n",
+      "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n",
+      "env: CUDA_PATH=/usr/local/cuda\n"
+     ]
+    }
+   ],
+   "source": [
+    "if \"google.colab\" in str(get_ipython()):\n",
+    "    #import IPython\n",
+    "    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n",
+    "    #%run \"../install_collab.ipynb\"\n",
+    "    !pip install gdown --upgrade\n",
+    "    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n",
+    "    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n",
+    "    %env CUDA_PATH=/usr/local/cuda"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "KVRtXVzIg07T"
+   },
+   "source": [
+    "## Install MNIST package"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "AikBc4sfg1b-",
+    "outputId": "b2af7639-4b7f-4e36-f516-bc9c1231aba2"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting mnist\n",
+      "  Downloading mnist-0.2.2-py2.py3-none-any.whl (3.5 kB)\n",
+      "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from mnist) (1.25.2)\n",
+      "Installing collected packages: mnist\n",
+      "Successfully installed mnist-0.2.2\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install mnist"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "yV0JrchrfQKR"
+   },
+   "source": [
+    "## Build tutorial model\n",
+    "Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "id": "Hl53yKXi9LiV"
+   },
+   "outputs": [],
+   "source": [
+    "import mnist\n",
+    "import numpy as np\n",
+    "from copy import copy\n",
+    "from google.colab import drive\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pygenn import (create_current_source_model, create_neuron_model, init_postsynaptic,\n",
+    "                    init_sparse_connectivity, init_weight_update, GeNNModel)\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "id": "ayctC3Y0i8ks"
+   },
+   "outputs": [],
+   "source": [
+    "# Reshape and normalise training data\n",
+    "testing_images = mnist.test_images()\n",
+    "testing_images = np.reshape(testing_images, (testing_images.shape[0], -1)).astype(np.float32)\n",
+    "testing_images /= np.sum(testing_images, axis=1)[:, np.newaxis]\n",
+    "testing_labels = mnist.test_labels()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "l5HX_B1Gohbq",
+    "outputId": "93732801-95a9-4ebc-dce1-a91f521a846a"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Mounted at /content/drive\n"
+     ]
+    }
+   ],
+   "source": [
+    "from google.colab import drive\n",
+    "drive.mount(\"/content/drive\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "id": "i-4iEfEdom33"
+   },
+   "outputs": [],
+   "source": [
+    "pn_kc_ind = np.load(\"/content/drive/MyDrive/pn_kc_ind.npy\")\n",
+    "kc_mbon_g = np.load(\"/content/drive/MyDrive/kc_mbon_g.npy\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "g0IfyML59Lif"
+   },
+   "source": [
+    "## Parameters\n",
+    "Define some model parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "id": "oncGyriW9Lif"
+   },
+   "outputs": [],
+   "source": [
+    "# Simulation time step\n",
+    "DT = 0.1\n",
+    "\n",
+    "# Scaling factor for converting normalised image pixels to input currents (nA)\n",
+    "INPUT_SCALE = 80.0\n",
+    "\n",
+    "# Number of Projection Neurons in model (should match image size)\n",
+    "NUM_PN = 784\n",
+    "\n",
+    "# Number of Kenyon Cells in model (defines memory capacity)\n",
+    "NUM_KC = 20000\n",
+    "\n",
+    "# Number of Mushroom Body Output Neurons (should match number of labels)\n",
+    "NUM_MBON = 10\n",
+    "\n",
+    "# How long to present each image to model\n",
+    "PRESENT_TIME_MS = 20.0\n",
+    "\n",
+    "# Standard LIF neurons parameters\n",
+    "LIF_PARAMS = {\n",
+    "    \"C\": 0.2,\n",
+    "    \"TauM\": 20.0,\n",
+    "    \"Vrest\": -60.0,\n",
+    "    \"Vreset\": -60.0,\n",
+    "    \"Vthresh\": -50.0,\n",
+    "    \"Ioffset\": 0.0,\n",
+    "    \"TauRefrac\": 2.0}\n",
+    "\n",
+    "# We only want PNs to spike once\n",
+    "PN_PARAMS = copy(LIF_PARAMS)\n",
+    "PN_PARAMS[\"TauRefrac\"] = 100.0\n",
+    "\n",
+    "# Weight of each synaptic connection\n",
+    "PN_KC_WEIGHT = 0.2\n",
+    "\n",
+    "# Time constant of synaptic integration\n",
+    "PN_KC_TAU_SYN = 3.0\n",
+    "\n",
+    "# How many projection neurons should be connected to each Kenyon Cell\n",
+    "PN_KC_FAN_IN = 20\n",
+    "\n",
+    "# Time constant of synaptic integration\n",
+    "KC_MBON_TAU_SYN = 3.0\n",
+    "\n",
+    "# We will use weights of 1.0 for KC->GGN connections and\n",
+    "# want the GGN to inhibit the KCs after 200 spikes\n",
+    "GGN_PARAMS = {\n",
+    "    \"Vthresh\": 200.0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "pCYjAoJf9Lig"
+   },
+   "source": [
+    "## Custom models\n",
+    "As well as the models we defined before:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "id": "IR8PXBg69Lih"
+   },
+   "outputs": [],
+   "source": [
+    "# Current source model, allowing current to be injected into neuron from variable\n",
+    "cs_model = create_current_source_model(\n",
+    "    \"cs_model\",\n",
+    "    vars=[(\"magnitude\", \"scalar\")],\n",
+    "    injection_code=\"injectCurrent(magnitude);\")\n",
+    "\n",
+    "# Minimal integrate and fire neuron model\n",
+    "if_model = create_neuron_model(\n",
+    "    \"IF\",\n",
+    "    params=[\"Vthresh\"],\n",
+    "    vars=[(\"V\", \"scalar\")],\n",
+    "    sim_code=\n",
+    "    \"\"\"\n",
+    "    V += Isyn;\n",
+    "    \"\"\",\n",
+    "    threshold_condition_code=\n",
+    "    \"\"\"\n",
+    "    V >= Vthresh\n",
+    "    \"\"\",\n",
+    "    reset_code=\n",
+    "    \"\"\"\n",
+    "    V = 0.0;\n",
+    "    \"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Gn4DpkPQ9Lii"
+   },
+   "source": [
+    "## Model definition\n",
+    "Create a new model called \"mnist_mb_second_layer_gain_control\" as before although we no longer need to record spikes from individual neurons:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "Gx-GsJhD9Lik",
+    "outputId": "cc9ee27f-3a6a-44f3-9221-f466f5352681"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-10-dcbfda279a3c>:3: FutureWarning: Call to deprecated function (or staticmethod) dT. (The name of this property was inconsistent, use dt instead)\n",
+      "  model.dT = DT\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create model\n",
+    "model = GeNNModel(\"float\", \"mnist_mb_testing\")\n",
+    "model.dT = DT\n",
+    "\n",
+    "# Create neuron populations\n",
+    "lif_init = {\"V\": PN_PARAMS[\"Vreset\"], \"RefracTime\": 0.0}\n",
+    "if_init = {\"V\": 0.0}\n",
+    "pn = model.add_neuron_population(\"pn\", NUM_PN, \"LIF\", PN_PARAMS, lif_init)\n",
+    "kc = model.add_neuron_population(\"kc\", NUM_KC, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "ggn = model.add_neuron_population(\"ggn\", 1, if_model, GGN_PARAMS, if_init)\n",
+    "mbon = model.add_neuron_population(\"mbon\", NUM_MBON, \"LIF\", LIF_PARAMS, lif_init)\n",
+    "\n",
+    "# Turn on spike recording\n",
+    "pn.spike_recording_enabled = True\n",
+    "kc.spike_recording_enabled = True\n",
+    "mbon.spike_recording_enabled = True\n",
+    "\n",
+    "# Create current sources to deliver input to network\n",
+    "pn_input = model.add_current_source(\"pn_input\", cs_model, pn , {}, {\"magnitude\": 0.0})\n",
+    "\n",
+    "# Create synapse populations\n",
+    "kc_ggn = model.add_synapse_population(\"kc_ggn\", \"DENSE\",\n",
+    "                                      kc, ggn,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": 1.0}),\n",
+    "                                      init_postsynaptic(\"DeltaCurr\"))\n",
+    "\n",
+    "ggn_kc = model.add_synapse_population(\"ggn_kc\", \"DENSE\",\n",
+    "                                      ggn, kc,\n",
+    "                                      init_weight_update(\"StaticPulseConstantWeight\", {\"g\": -5.0}),\n",
+    "                                      init_postsynaptic(\"ExpCurr\", {\"tau\": 5.0}))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "id": "KfQ1y4vto6T7"
+   },
+   "outputs": [],
+   "source": [
+    "pn_kc = model.add_synapse_population(\"pn_kc\", \"SPARSE\",\n",
+    "                                     pn, kc,\n",
+    "                                     init_weight_update(\"StaticPulseConstantWeight\", {\"g\": PN_KC_WEIGHT}),\n",
+    "                                     init_postsynaptic(\"ExpCurr\", {\"tau\": PN_KC_TAU_SYN}))\n",
+    "pn_kc.set_sparse_connections(pn_kc_ind[0], pn_kc_ind[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "id": "NCL3jEsbo3T0"
+   },
+   "outputs": [],
+   "source": [
+    "kc_mbon = model.add_synapse_population(\"kc_mbon\", \"DENSE\",\n",
+    "                                       kc, mbon,\n",
+    "                                       init_weight_update(\"StaticPulse\", {}, {\"g\": kc_mbon_g}),\n",
+    "                                       init_postsynaptic(\"ExpCurr\", {\"tau\": KC_MBON_TAU_SYN}))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-GU4oXOS9Lil"
+   },
+   "source": [
+    "## Build model\n",
+    "Generate code and load it into PyGeNN (as we're no longer recording spikes, we don't need to allocate a recording buffer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "id": "-FE02Zoz9Lim"
+   },
+   "outputs": [],
+   "source": [
+    "# Convert present time into timesteps\n",
+    "present_timesteps = int(round(PRESENT_TIME_MS / DT))\n",
+    "\n",
+    "# Build model and load it\n",
+    "model.build()\n",
+    "model.load(num_recording_timesteps=present_timesteps)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "CcpTaaB39Lim"
+   },
+   "source": [
+    "## Simulate tutorial model\n",
+    "As well as resetting the state of every neuron after presenting each stimuli, because we have now added synapses with their own dynamics, these also need to be reset.\n",
+    " This function resets neuron state variables selected by the keys of a dictionary to the values specifed in the dictionary values and pushes the new values to the GPU."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "DfcqDTVXdoRq"
+   },
+   "source": [
+    "Now, like before, we loop through 4 stimuli and simulate the model. However, now we need to reset the Projection Neuron and Kenyon Cell populations; **and** the synapses between them. Additionally, we want to show spikes from the Kenyon Cells as well as the Projection Neurons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "id": "K9pAP8OrJUub"
+   },
+   "outputs": [],
+   "source": [
+    "def reset_out_post(pop):\n",
+    "    pop.out_post.view[:] = 0.0\n",
+    "    pop.out_post.push_to_device()\n",
+    "\n",
+    "def reset_neuron(pop, var_init):\n",
+    "    # Reset variables\n",
+    "    for var_name, var_val in var_init.items():\n",
+    "        pop.vars[var_name].view[:] = var_val\n",
+    "\n",
+    "        # Push the new values to GPU\n",
+    "        pop.vars[var_name].push_to_device()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 1000
+    },
+    "id": "MOZDBWPyqpR6",
+    "outputId": "27f6cd4f-20dd-4c43-bd27-38c9a080217a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for s in range(4):\n",
+    "    # Set testing image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate present timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state for next stimuli\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "    reset_neuron(kc, lif_init)\n",
+    "    reset_neuron(ggn, if_init)\n",
+    "    reset_neuron(mbon, lif_init)\n",
+    "\n",
+    "    # Reset synapse state\n",
+    "    reset_out_post(pn_kc)\n",
+    "    reset_out_post(ggn_kc)\n",
+    "\n",
+    "    # Download spikes from GPU\n",
+    "    model.pull_recording_buffers_from_device();\n",
+    "\n",
+    "    # Plot PN, KC and MBON spikes\n",
+    "    fig, axes = plt.subplots(3, sharex=True)\n",
+    "    pn_spike_times, pn_spike_ids = pn.spike_recording_data[0]\n",
+    "    kc_spike_times, kc_spike_ids = kc.spike_recording_data[0]\n",
+    "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data[0]\n",
+    "\n",
+    "\n",
+    "    axes[0].scatter(pn_spike_times, pn_spike_ids, s=1)\n",
+    "    axes[0].set_ylabel(\"PN\")\n",
+    "    axes[1].scatter(kc_spike_times, kc_spike_ids, s=1)\n",
+    "    axes[1].set_ylabel(\"KC\")\n",
+    "    axes[2].scatter(mbon_spike_times, mbon_spike_ids, s=2)\n",
+    "    axes[2].axhline(testing_labels[s], linestyle=\"--\", color=\"green\", alpha=0.3)\n",
+    "    axes[2].set_ylim((-0.5, 10.5))\n",
+    "\n",
+    "    if len(mbon_spike_times) > 0:\n",
+    "        classification = mbon_spike_ids[np.argmin(mbon_spike_times)]\n",
+    "        axes[2].axhline(classification, linestyle=\"--\", color=\"red\", alpha=0.3)\n",
+    "    axes[2].set_ylabel(\"MBON\")\n",
+    "\n",
+    "    axes[2].set_xlabel(\"Time [ms]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 84,
+     "referenced_widgets": [
+      "59c468dd929b4032af2794e104a6bead",
+      "01987d7984054c00a64645fe31405598",
+      "c59f76cc65da4d56a292b29394cb99aa",
+      "91ed1bcac34b4326b4cae858559b34b6",
+      "e6139bdca7e84d7e896a6fbd996975b0",
+      "89eec271580d412d8b16008b16e56f54",
+      "a10c6c1d757b4ee68186be7e18ebad0b",
+      "7041aa97acfb47879eded8e76f3e9877",
+      "37851d9dcb1945a885483cef23e67e10",
+      "861cd646f49d4b80802eadf0d5566e00",
+      "a8f8f9a21be84cb8b1adc1859aa4ccf8"
+     ]
+    },
+    "id": "T1u8nh2Iqsmi",
+    "outputId": "9b893e03-1542-4279-fa22-c003944f5723"
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "59c468dd929b4032af2794e104a6bead",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "7263/10000 correct (72.63 %%)\n"
+     ]
+    }
+   ],
+   "source": [
+    "num_correct = 0\n",
+    "for s in tqdm(range(testing_images.shape[0])):\n",
+    "    # Set testing image\n",
+    "    pn_input.vars[\"magnitude\"].view[:] = testing_images[s] * INPUT_SCALE\n",
+    "    pn_input.vars[\"magnitude\"].push_to_device()\n",
+    "\n",
+    "    # Simulate present timesteps\n",
+    "    for i in range(present_timesteps):\n",
+    "        model.step_time()\n",
+    "\n",
+    "    # Reset neuron state\n",
+    "    reset_neuron(pn, lif_init)\n",
+    "    reset_neuron(kc, lif_init)\n",
+    "    reset_neuron(ggn, if_init)\n",
+    "    reset_neuron(mbon, lif_init)\n",
+    "\n",
+    "    # Reset synapse state\n",
+    "    reset_out_post(pn_kc)\n",
+    "    reset_out_post(ggn_kc)\n",
+    "    reset_out_post(kc_mbon)\n",
+    "\n",
+    "    # Download spikes from GPU\n",
+    "    model.pull_recording_buffers_from_device();\n",
+    "\n",
+    "    # Determine the classification and count correct\n",
+    "    mbon_spike_times, mbon_spike_ids = mbon.spike_recording_data[0]\n",
+    "    if len(mbon_spike_times) > 0:\n",
+    "        if mbon_spike_ids[np.argmin(mbon_spike_times)] == testing_labels[s]:\n",
+    "            num_correct += 1\n",
+    "\n",
+    "print(f\"\\n{num_correct}/{testing_images.shape[0]} correct ({(num_correct * 100.0) / testing_images.shape[0]} %%)\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "name": "5_testing",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "01987d7984054c00a64645fe31405598": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "HTMLModel",
+     "state": {
+      "_dom_classes": [],
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "HTMLModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/controls",
+      "_view_module_version": "1.5.0",
+      "_view_name": "HTMLView",
+      "description": "",
+      "description_tooltip": null,
+      "layout": "IPY_MODEL_89eec271580d412d8b16008b16e56f54",
+      "placeholder": "​",
+      "style": "IPY_MODEL_a10c6c1d757b4ee68186be7e18ebad0b",
+      "value": "100%"
+     }
+    },
+    "37851d9dcb1945a885483cef23e67e10": {
+     "model_module": "@jupyter-widgets/controls",
+     "model_module_version": "1.5.0",
+     "model_name": "ProgressStyleModel",
+     "state": {
+      "_model_module": "@jupyter-widgets/controls",
+      "_model_module_version": "1.5.0",
+      "_model_name": "ProgressStyleModel",
+      "_view_count": null,
+      "_view_module": "@jupyter-widgets/base",
+      "_view_module_version": "1.2.0",
+      "_view_name": "StyleView",
+      "bar_color": null,
+      "description_width": ""
+     }
+    },
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+    <link rel="prev" title="MNIST inference" href="../mnist_inference/index.html" /> 
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+<li class="toctree-l1"><a class="reference internal" href="../../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../upgrading.html">Upgrading from GeNN 4</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../building_networks.html">Building networks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../simulating_networks.html">Simulating networks</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../custom_models.html">Custom models</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../index.html">PyGeNN documentation</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../comp_neuro_101/index.html">CompNeuro 101</a></li>
+<li class="toctree-l2"><a class="reference internal" href="../mnist_inference/index.html">MNIST inference</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Insect-inspired MNIST classification</a><ul class="simple">
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+      <li class="breadcrumb-item active">Insect-inspired MNIST classification</li>
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+            <a href="../../_sources/tutorials/mushroom_body/index.rst.txt" rel="nofollow"> View page source</a>
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+</div>
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+           <div itemprop="articleBody">
+             
+  <section id="insect-inspired-mnist-classification">
+<h1>Insect-inspired MNIST classification<a class="headerlink" href="#insect-inspired-mnist-classification" title="Permalink to this heading"></a></h1>
+<p>Train a model of the insect mushroom body using an STDP learning rule to classify MNIST.</p>
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-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>User projects &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
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-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
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     <link rel="next" title="MNIST classification using an insect-inspired mushroom body model" href="mnist_mb_classifier.html" />
-    <link rel="prev" title="Bibliography" href="../bibliography.html" /> 
+    <link rel="prev" title="Testing" href="../tutorials/mushroom_body/5_testing.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">User projects</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="mnist_mb_classifier.html">MNIST classification using an insect-inspired mushroom body model</a></li>
 <li class="toctree-l2"><a class="reference internal" href="potjans_microcircuit.html">PyGeNN implementation of local cortical microcircuit model</a></li>
@@ -117,7 +120,7 @@ <h1>User projects<a class="headerlink" href="#user-projects" title="Permalink to
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="../bibliography.html" class="btn btn-neutral float-left" title="Bibliography" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="../tutorials/mushroom_body/5_testing.html" class="btn btn-neutral float-left" title="Testing" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
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diff --git a/documentation/5/userproject/mnist_mb_classifier.html b/documentation/5/userproject/mnist_mb_classifier.html
index 76ae2ddcc..29002dc81 100644
--- a/documentation/5/userproject/mnist_mb_classifier.html
+++ b/documentation/5/userproject/mnist_mb_classifier.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>MNIST classification using an insect-inspired mushroom body model &mdash; PyGeNN  documentation</title>
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-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
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-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">User projects</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">MNIST classification using an insect-inspired mushroom body model</a></li>
 <li class="toctree-l2"><a class="reference internal" href="potjans_microcircuit.html">PyGeNN implementation of local cortical microcircuit model</a></li>
diff --git a/documentation/5/userproject/potjans_microcircuit.html b/documentation/5/userproject/potjans_microcircuit.html
index 3ba66c897..2af004686 100644
--- a/documentation/5/userproject/potjans_microcircuit.html
+++ b/documentation/5/userproject/potjans_microcircuit.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>PyGeNN implementation of local cortical microcircuit model &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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     <script src="../_static/js/html5shiv.min.js"></script>
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-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">User projects</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="mnist_mb_classifier.html">MNIST classification using an insect-inspired mushroom body model</a></li>
 <li class="toctree-l2 current"><a class="current reference internal" href="#">PyGeNN implementation of local cortical microcircuit model</a></li>
diff --git a/documentation/5/userproject/sg_execution_times.html b/documentation/5/userproject/sg_execution_times.html
index da83c3ff6..32f642ead 100644
--- a/documentation/5/userproject/sg_execution_times.html
+++ b/documentation/5/userproject/sg_execution_times.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>Computation times &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
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     <script src="../_static/js/html5shiv.min.js"></script>
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
@@ -53,6 +55,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="index.html">User projects</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../source/pygenn.html">Reference documentation</a></li>
 </ul>
diff --git a/documentation/5/userproject/superspike_demo.html b/documentation/5/userproject/superspike_demo.html
index 30cd3abea..384291b19 100644
--- a/documentation/5/userproject/superspike_demo.html
+++ b/documentation/5/userproject/superspike_demo.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
 <html class="writer-html5" lang="en">
 <head>
-  <meta charset="utf-8" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
   <title>PyGeNN implementation of SuperSpike &mdash; PyGeNN  documentation</title>
-      <link rel="stylesheet" type="text/css" href="../_static/pygments.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css" />
-      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css" />
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery.css?v=61a4c737" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-binder.css?v=f4aeca0c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-dataframe.css?v=2082cf3c" />
+      <link rel="stylesheet" type="text/css" href="../_static/sg_gallery-rendered-html.css?v=1277b6f3" />
 
   
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
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-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js?v=b3ba4146"></script>
+        <script src="../_static/doctools.js?v=888ff710"></script>
+        <script src="../_static/sphinx_highlight.js?v=4825356b"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="https://unpkg.com/@jupyter-widgets/html-manager@^0.20.0/dist/embed-amd.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -55,6 +57,7 @@
 <li class="toctree-l1"><a class="reference internal" href="../simulating_networks.html">Simulating networks</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../custom_models.html">Custom models</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../bibliography.html">Bibliography</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../tutorials/index.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="index.html">User projects</a><ul class="current">
 <li class="toctree-l2"><a class="reference internal" href="mnist_mb_classifier.html">MNIST classification using an insect-inspired mushroom body model</a></li>
 <li class="toctree-l2"><a class="reference internal" href="potjans_microcircuit.html">PyGeNN implementation of local cortical microcircuit model</a></li>
@@ -101,8 +104,9 @@
 learning rule to learn the transformation between fixed spike trains of
 Poisson noise and a target spiking output (by default the Radcliffe Camera at Oxford).</p>
 <p>This example can be used as follows:</p>
-<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">superspike_demo</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="o">--</span><span class="n">record</span><span class="o">-</span><span class="n">trial</span> <span class="p">[</span><span class="n">RECORD_TRIAL</span> <span class="o">...</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">target</span><span class="o">-</span><span class="n">file</span> <span class="n">TARGET_FILE</span><span class="p">]</span>
-                       <span class="p">[</span><span class="o">--</span><span class="n">num</span><span class="o">-</span><span class="n">trials</span> <span class="n">NUM_TRIALS</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">kernel</span><span class="o">-</span><span class="n">profiling</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">save</span><span class="o">-</span><span class="n">data</span><span class="p">]</span>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">usage</span><span class="p">:</span> <span class="n">superspike_demo</span> <span class="p">[</span><span class="o">-</span><span class="n">h</span><span class="p">]</span> <span class="o">--</span><span class="n">record</span><span class="o">-</span><span class="n">trial</span> <span class="p">[</span><span class="n">RECORD_TRIAL</span> <span class="p">[</span><span class="n">RECORD_TRIAL</span> <span class="o">...</span><span class="p">]]</span>
+                       <span class="p">[</span><span class="o">--</span><span class="n">target</span><span class="o">-</span><span class="n">file</span> <span class="n">TARGET_FILE</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">num</span><span class="o">-</span><span class="n">trials</span> <span class="n">NUM_TRIALS</span><span class="p">]</span> <span class="p">[</span><span class="o">--</span><span class="n">kernel</span><span class="o">-</span><span class="n">profiling</span><span class="p">]</span>
+                       <span class="p">[</span><span class="o">--</span><span class="n">save</span><span class="o">-</span><span class="n">data</span><span class="p">]</span>
 </pre></div>
 </div>
 <section id="named-arguments">