OSRS_GE_LSTM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "#Forecasting Old School Runescape Item Prices"
      ],
      "metadata": {
        "id": "2i02v4qATBE9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this notebook we will use a technique called time series forecasting that predicts a future result. In this instance we are predicting the price of Runescape items. But how can we approach this? "
      ],
      "metadata": {
        "id": "iRU_2ZVfVhXk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Sequential Neural Networks"
      ],
      "metadata": {
        "id": "JsOmxFgEXnjI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sequential networks are a type of neural network that are designed to work with sequential data, such as language and stock prices. In these examples data is heavily influenced by what comes before and or after it. Todays stock prices are heavily influenced by the prices of the days before and in language adjectives and adverbs can greatly modify the meaning of the words preceding."
      ],
      "metadata": {
        "id": "Xbe7VVF5Y_dE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Recurent Neural Networks (RNN)"
      ],
      "metadata": {
        "id": "AdbhGYFQWqhq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recurrent neural networks use a feedback network to take in previous data. This means that in a recurrent neural network each neuron can be represented as having 2 outputs, a feed forward, and a feedback. By feeding back previous information into our model we have a network that can learn from both present and previous data."
      ],
      "metadata": {
        "id": "RKVamzz9WwF9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://builtin.com/sites/www.builtin.com/files/styles/ckeditor_optimize/public/inline-images/national/rnn-vs-fnn.png\">"
      ],
      "metadata": {
        "id": "a5mX9n1nbrhj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Recurrent networks also do this thing called unfolding. In the gif shown below we are given an example with a sentence. The word \"what\" represents our first word and the feedback network modifies everything by a shared weight as it continutes through the sentence until we get to our final word which in this case is \"?\". We then have a value that we can use for some kind of prediction or that can be fed into another layer of the network."
      ],
      "metadata": {
        "id": "q3DDpHx4byz9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This presents a big issue, vanishing and exploding gradients. Imagine that we had a sentence or sequence that had 50 words. As we unravel our recurrent layer each value will get multiplied by its weight 50 times. If we had a weight less than 1 our value will be extremely small. Using this value to back-propogate makes it almost impossible to adjust our network since any adjustments will be extremely small. The same applies to values greater than 1 and this results in an exploding gradient, instead of small adjustments we will be left with adjustments that are too large and result in overshoot."
      ],
      "metadata": {
        "id": "rl9vPoeKgUrd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://research.aimultiple.com/wp-content/uploads/2021/08/rnn-text.gif\">"
      ],
      "metadata": {
        "id": "LZpBPmnTfFVR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Despite these problems RNN's can still be functional in tasks such as language however for our price predictions we will be taking in long sequences of data and therefore we should look for other options."
      ],
      "metadata": {
        "id": "-qpXNFnQhumE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Long Short Term Memory (LSTM)"
      ],
      "metadata": {
        "id": "NERxbAzYh_K_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "A LSTM is a type of recurrent network that aims to resolve the Exploding/Vanishing Gradient problem by \"storing memory\". Instead of a constant unrolling to pass down past data an LSTM keeps both long and short term memory seperate and uses a series of sigmoid activation functions to modify these 2 paths. "
      ],
      "metadata": {
        "id": "7MoIIvSKiH58"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/9/93/LSTM_Cell.svg/1200px-LSTM_Cell.svg.png\">"
      ],
      "metadata": {
        "id": "0WsNJ9h0jAFz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this image our C chain represents our long term memory that gets passed down and modified between layers. The h layer represents our input, output, and short-term memory. From left to right the neuron takes an input x and uses it along with the previous short term memory to determine how much of our long term memory we are going to get rid of. After this we determine how much of our current short-term memory to add to our long-term memory. We finally take our short-term and long term-memory and use both to determine our output/new short-term memory."
      ],
      "metadata": {
        "id": "ydqPIqAHj_7e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This seems like the better option for our network. Let's build one!!!"
      ],
      "metadata": {
        "id": "ySbChpy5lJ6K"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Lets Code 🎆"
      ],
      "metadata": {
        "id": "2bJDNv0Ak4Ro"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's start off by importing what we know we need. Everything regarding our LSTM is included with the keras library. It is very similar to previous networks except we have LSTM layers instead."
      ],
      "metadata": {
        "id": "ZgVwCAzKlBf4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "NlRRFldv8GPx"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Dropout, LSTM\n",
        "from tensorflow.keras.optimizers import Adam"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Think back to the LSTM layer. Because of the sigmoid activation functions taking data and scaling it between 1 and 0, we can help it converge faster by scaling our entire dataset as such. This can be done with the MinMaxScaler from sklearn. We haven't used this yet however it works similarly to the standard scaler. We simply create an object and set our range as (0,1)."
      ],
      "metadata": {
        "id": "SXmeY0qNliVS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "pcXonlKJ9Sx3"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Data"
      ],
      "metadata": {
        "id": "NhqSCBYAmc8y"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data for this was collected by a youtuber called Chronic Coder. It was originaly from a OSRS API which collected and stored historical OSRS Grand Exchange prices. However it has since shutdown and this is what remains 🙁."
      ],
      "metadata": {
        "id": "8joPANHMmgS8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cA32Gmov9aTB",
        "outputId": "3e9fb25a-3f20-467c-ac89-7c320aad5c89"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2838, 3658)"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "dataset = pd.read_csv('/content/drive/MyDrive/overall_avg_ge.csv')\n",
        "dataset.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the dataset we have a timestamp and a corresponding price for just about every item. Items have been added since this dataset has been released though so the most recent items won't be in here."
      ],
      "metadata": {
        "id": "i4IArYAUm3BG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "drXJKJ8qAeQY",
        "outputId": "08e5406e-c1d6-451b-f18b-4d83f0c3db41"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       timestamp  Cannonball  Cannon_base  Cannon_stand  Cannon_barrels  \\\n",
              "0     1580705221         191       192371        191999          193439   \n",
              "1     1580705282         191       192371        191999          193439   \n",
              "2     1580705341         191       192371        191999          193439   \n",
              "3     1580705401         191       192371        191999          193439   \n",
              "4     1580705461         191       191057        189241          191074   \n",
              "...          ...         ...          ...           ...             ...   \n",
              "2833  1581044521         180       180000        186391          187126   \n",
              "2834  1581044641         180       180000        186391          187126   \n",
              "2835  1581044761         180       180000        186391          187126   \n",
              "2836  1581044881         180       180000        186391          187126   \n",
              "2837  1581045001         180       180000        186391          187126   \n",
              "\n",
              "      Cannon_furnace  Insect_repellent  Bucket_of_wax  Candle  \\\n",
              "0             193607                 0            774     295   \n",
              "1             193607                 0            774     295   \n",
              "2             193607                 0            774     295   \n",
              "3             193607                 0            774     295   \n",
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              "2836          180250                 0            625       0   \n",
              "2837          180250                 0            625       0   \n",
              "\n",
              "      Bronze_arrowtips  ...  Watchtower_teleport  Teleport_to_house  \\\n",
              "0                    0  ...                    0                524   \n",
              "1                    0  ...                    0                524   \n",
              "2                    0  ...                    0                524   \n",
              "3                    0  ...                    0                524   \n",
              "4                    0  ...                    0                521   \n",
              "...                ...  ...                  ...                ...   \n",
              "2833                 0  ...                    0                453   \n",
              "2834                 0  ...                    0                453   \n",
              "2835                 0  ...                    0                453   \n",
              "2836                 0  ...                    0                453   \n",
              "2837                 0  ...                    0                453   \n",
              "\n",
              "      Bones_to_bananas  Bones_to_peaches  Enchant_sapphire_or_opal  \\\n",
              "0                    0              1152                         0   \n",
              "1                    0              1152                         0   \n",
              "2                    0              1152                         0   \n",
              "3                    0              1152                         0   \n",
              "4                    0              1152                         0   \n",
              "...                ...               ...                       ...   \n",
              "2833                 0              1069                         0   \n",
              "2834                 0              1069                         0   \n",
              "2835                 0              1068                         0   \n",
              "2836                 0              1068                         0   \n",
              "2837                 0              1068                         0   \n",
              "\n",
              "      Enchant_emerald_or_jade  Enchant_ruby_or_topaz  Enchant_diamond  \\\n",
              "0                           0                      0                0   \n",
              "1                           0                      0                0   \n",
              "2                           0                      0                0   \n",
              "3                           0                      0                0   \n",
              "4                           0                      0                0   \n",
              "...                       ...                    ...              ...   \n",
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              "2835                        0                      0                0   \n",
              "2836                        0                      0                0   \n",
              "2837                        0                      0                0   \n",
              "\n",
              "      Enchant_dragonstone  Enchant_onyx  \n",
              "0                       0             0  \n",
              "1                       0             0  \n",
              "2                       0             0  \n",
              "3                       0             0  \n",
              "4                       0             0  \n",
              "...                   ...           ...  \n",
              "2833                    2             0  \n",
              "2834                    2             0  \n",
              "2835                    2             0  \n",
              "2836                    2             0  \n",
              "2837                    2             0  \n",
              "\n",
              "[2838 rows x 3658 columns]"
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2836</th>\n",
              "      <td>1581044881</td>\n",
              "      <td>180</td>\n",
              "      <td>180000</td>\n",
              "      <td>186391</td>\n",
              "      <td>187126</td>\n",
              "      <td>180250</td>\n",
              "      <td>0</td>\n",
              "      <td>625</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>453</td>\n",
              "      <td>0</td>\n",
              "      <td>1068</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2837</th>\n",
              "      <td>1581045001</td>\n",
              "      <td>180</td>\n",
              "      <td>180000</td>\n",
              "      <td>186391</td>\n",
              "      <td>187126</td>\n",
              "      <td>180250</td>\n",
              "      <td>0</td>\n",
              "      <td>625</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>453</td>\n",
              "      <td>0</td>\n",
              "      <td>1068</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2838 rows × 3658 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f8b2975-6745-45b9-a977-f9196458068d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f8b2975-6745-45b9-a977-f9196458068d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1f8b2975-6745-45b9-a977-f9196458068d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Choosing data"
      ],
      "metadata": {
        "id": "FhI8pU9eoZJo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Looking at the data we can see that we have 2800 rows only. This could be enough for our purposes but I don't want to split my data any further. So to solve this issue I am going to pick very closely related items in game. For, example, pizzas require mostly the same ingridients and are used as a way to quickly make profit whilst leveling up cooking."
      ],
      "metadata": {
        "id": "3JZzAfcfo4C6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "Gr6S8VRmAoGP",
        "outputId": "687120d4-0c19-460a-fe16-0d2c8f072042"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9701f428e0>]"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(dataset['Anchovy_pizza'][:10])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "9DkL-IDVCZbH",
        "outputId": "670f7830-0900-495c-f75a-e49f22a27d6a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9701c2e130>]"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "plt.plot(dataset['Pineapple_pizza'])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I'm going to pick Anchovy and Meat pizza since they have pretty similar prices, although the meat pizza varries wildly in price. I believe this is due to double xp events."
      ],
      "metadata": {
        "id": "tshmijr-rLDO"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "G_rqJDQ4C6J7"
      },
      "outputs": [],
      "source": [
        "train = dataset['Anchovy_pizza'][:2800]\n",
        "test = dataset['Pineapple_pizza'][:2800]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "5a5Ov2a5DANa"
      },
      "outputs": [],
      "source": [
        "train = np.array(train)\n",
        "train = np.reshape(train, (-1, 1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "6O4ArjVbDUEP"
      },
      "outputs": [],
      "source": [
        "scaled_data = scaler.fit_transform(train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "iZxxF6tQD090"
      },
      "outputs": [],
      "source": [
        "x_train, y_train = [], []\n",
        "for i in range(60,2800):\n",
        "    x_train.append(scaled_data[i-60:i,0])\n",
        "    y_train.append(scaled_data[i,0])\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before we feed our data into a network note that our LSTM requires 3d array in the format ` [data_size, amount of timesteps, features]`, otherwise we will run into a very common bug called dimension shuffling. "
      ],
      "metadata": {
        "id": "TiT31QjmAzRb"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "HsMbtrX-EJjR"
      },
      "outputs": [],
      "source": [
        "x_train = np.reshape(x_train, (x_train.shape[0],x_train.shape[1],1))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VPrI9sFbTuIe",
        "outputId": "bfead5a0-7301-42d3-d917-0a8071986d0c"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2740, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Building our model"
      ],
      "metadata": {
        "id": "LgIO-eDev9QR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a very simple model but let's walk through it. Firstly we declare a sequential network and name it model. We can now specify our layers. Firstly we start out with an LSTM layer of size 64, we have return_sequences set to true because we will be feeding our data into another LSTM. The next LSTM has 32 units. And lastly we have a Dense head for the network which gives us our price prediction."
      ],
      "metadata": {
        "id": "_4343udH_VO6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Each LSTM unit is a single neuron in the whole LSTM layer, each unit has hidden cells that are the length of our sequences.*"
      ],
      "metadata": {
        "id": "05H71DxOCHCH"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "aVZCRzwhEMSM"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=64, return_sequences = True, input_shape=(x_train.shape[1], 1)))\n",
        "model.add(LSTM(units=32))\n",
        "model.add(Dense(16))\n",
        "model.add(Dense(1))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "4pW76EHwEOfo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad02af0f-dfac-45f7-f537-a70142dac953"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/3\n",
            "172/172 [==============================] - 24s 98ms/step - loss: 0.0801 - accuracy: 0.0084\n",
            "Epoch 2/3\n",
            "172/172 [==============================] - 12s 67ms/step - loss: 0.0032 - accuracy: 0.0099\n",
            "Epoch 3/3\n",
            "172/172 [==============================] - 7s 44ms/step - loss: 0.0032 - accuracy: 0.0099\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f9701e94ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "model.compile(optimizer=Adam(learning_rate=0.0001),loss='mean_squared_error', metrics=['accuracy'])\n",
        "model.fit(x_train,y_train,epochs=10,batch_size=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Plotting Predictions"
      ],
      "metadata": {
        "id": "cyqlRb9Kv5Zd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "To predict how well we perform we will create a test dataset and an actual price dataset. We will feed the test dataset into our model and predict the price for the next day. Then we will plot them both to compare."
      ],
      "metadata": {
        "id": "XVVlLEm03c8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def ComparePredictions(item_name):\n",
        "  data = dataset[item_name][:2800]\n",
        "  \n",
        "  #scale test_x data\n",
        "  test = np.array(data)\n",
        "  test = np.reshape(test, (-1,1))\n",
        "  test = scaler.transform(test)\n",
        "\n",
        "  #trim test_y data\n",
        "  data = data[160:2800]\n",
        "  data = data.reset_index()\n",
        "  data = data.drop('index', axis=1)\n",
        "\n",
        "  #create predictions\n",
        "  X_test = []\n",
        "  for i in range(60,test.shape[0]):\n",
        "    X_test.append(test[i-60:i,0])\n",
        "  X_test = np.array(X_test)\n",
        "  X_test = X_test[100:]\n",
        "  X_test = np.reshape(X_test, (X_test.shape[0],X_test.shape[1],1))\n",
        "\n",
        "  closing_price = model.predict(X_test)\n",
        "  closing_price = scaler.inverse_transform(closing_price)\n",
        "\n",
        "  #plot \n",
        "  plt.figure(figsize=(20,10))\n",
        "  plt.plot(closing_price, label='prediction')\n",
        "  plt.plot(data, label='actual')\n",
        "  plt.legend(loc='upper left')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "FfOGeDZT63Xf"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Pineapple Pizza Predictions"
      ],
      "metadata": {
        "id": "TQAkol_O4XS0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ComparePredictions(\"Pineapple_pizza\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "mgaTHYhl-E8l",
        "outputId": "3157328a-af1a-41b1-a9e8-1aa0bfb6eb45"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 2s 15ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Meat Pizza Predictions"
      ],
      "metadata": {
        "id": "-x5VtNxm4aWL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ComparePredictions(\"Meat_pizza\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "sVuuciCM72FZ",
        "outputId": "92349c1a-222a-495c-fb04-1d07b3435fd6"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 1s 15ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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hvjnCOiAkH2yBJd9PIYQIrS9P1gKwLDuly3N3L81iVHwEv1h/NCgNhZusdu5Y/RVzf72Rr0saAr7+npQ3tLK70MSNC8ag0QRnctU1c9Mpa2hlb4AvtAeLqqp8fryacyalYNBp0Gs1XDk7nY3HqjC3Bq9865UvC0mKNnD7onEAxEa4+wa1tElT6UBrczi59aVd/HHTCW78207eC1Cwb/X2Ai78w1au/MuXvPRFfkDWOdw1tznYcryaK2eno/Ucs6IMOl66cz4GnYbvvrqHBgmaCiG+IcI2IBQREUFdXZ0EMQJEVVXq6uqIiIgY1O1WN1p5+Yt8NuVU4ezHndva5jb+34c57DhVG4S9E0KIwbW/qIFxyVGMiOt6LI7Qa/nVVTPIrWzihS0nA7rd/JpmrnlhB1/kuY+lBbUtAV1/b7Z4yp4umjYiaNu4aNpIIvXaoGfUBEuJqZWqxjaWTDgdKLx23mhsDhcfH64IyjYrzK1sOlbFjQvGtJcvev+22BxB2eZwtimnmqI6C8/dMpezMhN57J1DHCodWGC2xGThqY+OceHUEUwdFcfftxf261xLdLbjZC12p8qKqZ0zGjMSo/jbHfMpb7By++qvMIUg21IIIQItbEdIZGRkUFpaSk1N8Ovnh4uIiAgyMjIGbXtVjVaufWEHZQ3uUcoLM5P4w02zyUiM8unrHU4XD6zdz1cFJl7fU8KXj19AfKQ+mLsshBBBdai0gQWZST0+v3LaCK6ek85zn+VxVmYSSyd2zSTy1ydHK/nJW1+j0yi8cNs87l+7f1AvZD4/Xs3ohEgmpsUEbRvRRh2XzhjJ+oNlPHbxZBI9vXCGin3F7sli88cltj82c3Q841OjWXegjJsXjg34Nt/aW4oK3Hb26XVHGdwBIatdMoQCbcPRSpKjDVw2cxTLJqZwxZ+3c//a/fz7h+f0+9zmrb0luFSVX189nV35dfz4ja/JKW9kZkZ8gPd+eNl6ooZog5YF47oeqxdkJvG3O+Zz3//t48a/7eTF2+aRPSI2BHsphBCBEbYBIb1eT1aWjHccqmwOF/d4Umrf+cFiTtW08KsPcrjiz9v5y63zfLrIefazPL4qMHHX0kz+8WUh678u5w5PWrsQQgw11Y1Wys1WZvVxsfaba2aSU9HIg6/tZ939S8lMie7X9kpMFn75wVE2Hatm2qg4/nbHfNITIlEUBq3cQVVV9haaWDltBIoSnHIxr/vOm8C6g2W89EU+j10yJajbCrT9RQ3EGHVM6nBhqSgKV88ZzR82nqCsoZXRCZEB3eZHhys4a1wSY5JO36TRazXoNAqtEhAKKLvTxee51Vw2cxRajUJStIE/3zqXG/66kyfWHeYvt8zt1/tja14tc8YkMCo+sj27bFd+nQSEBkBVVbYcr2HJRHf5ZnfOn5LGq3cv5IG1+7n8z9v59pJMvrd8PCkxxkHeWyGEGLiwLRkTQ9uLW05xpKyRP9w0h/njkrhxwRjWP7iUlBgjd6z+ir9tPdVrOeAXeTX85fOT3Lggg59dPo2po+J4e1/pIL6C4LM5XBTWtrA9r5b3D5bx+u5iVm8v4C+b8/jjxhNSJifEN8zhMjMAszISel0u2qjjb3csAOD6v+70u99Pm8PJnz/L48I/bGXHqTqe+NYU3n9wKWOSotBqFBIi9ZgGKSBUUNtCvcXOvLGJfS88QJNGxHLZzFG8sqOQck9m6lBxqMzMzNHx7f1KvK6eMxqA9QcDO0GtsLaF3MomLp4xsstzkXotrbbgj7sfTnLKG2luc7B80ulm8vPGJvLIykn8+1AFb+wp8XudDRYbh0ob2tc5Ii6C8SnRQW9K/01XWGehrKG108+qO4vGJ/Pxj87hslmjWLUtn0W//YwtMoFMCDEEhW2GkBi6Sust/OXzPK6Ync7F00+fbI5PjWHdA0t59K2veerjXLafrOXnV0xjYlrnVNsKcys/ev0g2Wkx/PLKGSiKwrdmjOR/N56gtrltSN+BqW608vb+UjbmVHGkzIzd2XNQbFpOHB89fM4g7p0Qw9vzn59kenoc500O/CQsgDzPZJrJPpQXZKVE89Z9i7lz9W6ufXEHdy4ex3fPGd9nlsiW49X88oMcCmpbuGzmKJ68fCqj4jt/TWKUgXpL8BoVd3Sg2B3MmjsIASGAn14yhc+OVfPke0dY/e0FQc9KCgSXS+VkVRM3LBjT5bmxyVHMH5fIugOl3Hfu+IC9ng1HKwG4eHrXvk4RBq1kCAXYnkJ3SeCCzM7vgx+cO4Edp2r5+fqjZI+I7VQy2Pc661FVOmVcnz0+iX8fqsDlUoPWwP2bbk+B+2e1eHzPpb1eabER/OHGOXx/+QQu/tM2DpWag/b5IYQQwSIBIRFwL2w5hYLCE9/qmrIfY9Txwm3zWLOziN9/epyVf9zGiilpXDVnNEsnpqDXKty/dj9Wu5MXbptHpKefwbmTU/nfjSfYnlfL1XNHD/ZLGjCbw8Vzn+Wxals+NqeLOWMSuHtZFtlpsYxOiCQ11kC0UUeUXkekQcsT6w6zPU8yhIQYLMcrm/jdJ8eJMeo48suLg7KNvKpm0mKNxEf51i9kYlosHz+8nN9+dIw1O4t4dUchc8YksDArmYlpMaQnRJAUbSA+Uk9ZfSt/3nySrSdqyEqJZs3dC3u8wx1l1NJqG5wL/v3F9cQadWQHsX9QR2OSovjJxZP59Yc5/P3LQu5ZFv6l52UNrbTYnEwe2X2g8Jq5o3nyvSMcKGkIWKbVluPuMsLuevpF6rXSQyjA9hSaGJvUtZm8RqPw7M1zuf7FHdzz6h7e/P7iTmWDvTlU2oBWozAj/XR52Nyxifxrdwn5tS1B7dn1Tban0ERilJ4Jqb5//yaPjCXGqKNeJo8JIYYgCQiJgKo0W3lrbwk3LhjT5a60l6IofHtJJpfNGsU/vizg9d0lbDrmTrON1GuxOV08f+vcTplDM9LjSYo2sPVEzZALCJlb7XxvzV6+KjBxzdzRPHTBRMb3caKRFG2QEwshBtHre4oByEgMbJ+Wjk5WN5E9wr+LtPgoPc9cP4uHVkzkzb2lbD1ezert+d1mF8ZF6HjysqncuTizx94X4C0JGqyAUANzxiYMarbCXUsy2Xqihj9tPDEkAkInqpoAmNTD78bVc0fzzMe5vLqjMCABIZvDxYHiBm47u/uefJF6rUwZC7AjZY09Zv+kxBh59e6F3PDXndz28le8+4Mlnfo69eRQqZnstJj2G2cA88a6y1EPFNdLQKif9hXVM39ckt/ZeAlResyDlHkphBCBJAEhEVBv7i3B7lT5/vIJfS6bEmPk0Yun8OMLJ3G4zMyOU3WU1rdy9Zx0zh6f3GlZjUZheXYK207UDKlUaLvTxf1r97G/uJ4/3TTH52BWQpSeNoeLVpuz08meECLw7E4X73nGlbt66W02EKqqklfdzI3dlAX5IiMxikdWTuKRlZOwO10UmyxUma3UW+yYW+3ER+pZPimF2Ii+s48iDTrMrcG/cGlzOMmrauL8yeODvq2ONBqFs7OS2HaiBqvd2T5KPVydqHKXEvY0qSjGqOOGBWNYs7OQ/7x0KiPjI7pdzleHyxpoc7hYmNV9gMJdMiY9hALF3GqnrKGV23sZijEuOZrX7j2bq5/fwRPrDrPm7oW9BiRUVeVwmZkLzxiLPj4lhtgIHfuLG7otQRS9q21uI7+2hZvO8v975y7FlRt5QoihRwJCImBcLpU39pSwbGIKY5N9Gy0PoNNqmDs2sc8eE8snpfLewXKODqGRqn/ceIIvT9bxu+tn+ZXZlBjlHplcb7ERaQhexoIQAnYXmKi32EmNNVLXHJwT+uqmNiw2J+NT+zcxrCO9VsOE1Bi/Sho6itRrqDIHP0OooLYFh0vtsRQqmLxjvBtb7WEfECqqayElxkBcL8G8u5ZmsmZnIX/enMdvrpk5oO3tLqgH3OOzuxOp12AdpAyy4SC3ohGAKaN6fx9MTIvlkZWT+NWHOWw5UcP5vfSiKa1vxdRi69KgXqNRmDMmgQPF9QPf8WFob6H3veF/Jl5ClJ6GQQi0CyFEoMmUMREwO/PrKGto7dedFV+ck+3uh7EtryYo6w+0k9VNrNqWz/XzM/y+U5fguZgZjLv4Qgx3m45VYdBpuGp2OiaLDZcr8FlCJSYLgE+lIMEWqR+cpsHezBdfe6IEUoKnT9NQuEArqbd028unozFJUdx69lhe31PCqZrmAW1vT6GJCanRPQ5oMOi0tDklQyhQcivdJYHTRsX1uezti8aRkRjJHz490esk1tMTC7veHJs3NpETVU00t0nZn7/2Fpow6DTMGO3/Tce4CD2NQ+B4I4QQZ5KAkAiYDUcqidRrWTmt69SSQEiNNTI9PY6tJ4ZGQOi3H+USZdDyn5d2ba7dF2+ZmEx6ESL4tp6oYcmEZFJijagqWB2Bf9+V1HsCQkHsUeSrSINucAJClU1oNUpAsqL8lRDpzrJsGAI9PYpNFsb6ECh86IJsovRa/mvd4V6DBb1xulT2FJpYmNXzBCWDVoPNIQGhQMmrbiI+Uk9abN8TUg06DQ+vyOZwmZlPc6p6XC63sgmN0n2wde7YBFyqu+m08M+eonrmZCRg1PmfVRih12KVUkshxBAkASEREKqq8tmxKpZlpwQ1PX/5pFT2F9XTZA3vk/zjlU1szq3m3nPGk9zDXdjeRHq+h4PV+FWI4aqmqY38mhYWj08myhOItQThfVdiagXoMxNkMETqtYNSEnSiqonM5Kh+XVwNlDdDKNx7ejicLsobrIxJ6jtQmBpr5InLprIr38Tre0r6tb3jlU00WR2c1UO5GIBRp8EWhKDocFVUZyEzJdrnJsXXzB3N+JRo/rix5yyhvKomxiVHd3u+NWeMt7G0BIT80WpzcrTMzPx+lIsBRBo0IZ3O198gsRBCSEBIBERuZRPlZmuXBoeBtjw7FYdLZeepuqBuZ6Be/iKfCL2m1yaSvYkyuNt7SUBIiODaW2gC4KyspKAGYktMFlJjjWHRzybKoMVidwb9AuJEVVNI+gcBRBvdx9Bwn5ZVYbbidKk+ZQgB3HzWGBaPT+a3/z5GhbnV7+3t8fy+95ohpNNgk5KxgCmsayHTz76KNywYQ25lE009lH3lVTf3OEUsIcrA+NRo6SPkpwPF9ThcKgt7CZb2xj2db/DP2ZwulUfePMjkJzfw3Vf3UtVoHfR9EEIMbRIQEgHx2TF3avP5U4IbEJo/LpFogzas+wg1tzlY/3U5187LIDHa0K91RBrcb00pGRMiuPYW1WPUaZiRHt8eiA1KhlC9JSzKxcBdkup0qd2Org8Uq91JkclCdlpoAkKng3vhHdg4XUroW8BAURSevm4mdpeLJ9cd8Tuot7vARHp8RK+ZanqtIiVjAWJzuCirb2Wcn73Dkj3nDt2NMbc5XBTWtpDdy1j5uWMSOVDcEPCg74Hien72/hHueWUP31uzlzf3lOAMQs+1UNhdaEJRYN64/mYIuUtxBztT51+7i3l3fxkXTEljx6larnn+S4rqWgZ1H4QQQ5sEhERAbDpWzewxCaTFDmwcbl8MOg2LJySz9URN2KbHbsyppM3h4lo/poqdyZtFIAEhIYLrcJmZaelxGHSa9kBsMLJKKsxWRodBuRi4S4JgYL2SNudWUVjb80VHscmCqhKS/kHQISAU5sdQ7918f0bJj0uO5icXTeaz3Go+PFTh89epqsruQhNn9ZIdBO7P2WAGC4eT0noLLtX9M/NHfFTPgyWK6tzT+3pr1j5vXAJ1Lbb2UtWBUlWV339ynGte2MHb+0opN1vJrWzisXcOceffv6KmqS0g2wmlPYUmpoyMa59Q6C/vMadtEIOpLpfKy1/kM3dsAi/ePo83v78Yi93Jzat2SVBICOEzCQiJAatpauPr0gZWBDk7yOvCqSMoMbVyrKJpULbnr/cPljM6IZJ5Y/t3lwmkh5AQg0FVVY6VNzI93T39J1LvKdUMcBBBVVUqzVZGxvnfTywYvAGh/maBFNW1cPcre3noXwd6XKa4zp354mspVKBFeIJ7oezp4YuqRveFdFqcfzdT7lqaxfT0OH770TGfPyeK6izUNLX12j8IwKDVSoZQgBR53gfj/CgZA9qDEt0FhPKq3VPmeioZA3eGECX8pagAACAASURBVMCBksCUjf3qwxz+8vlJblyQwZ7/upCPHz6HrY+exzPXzWRfUT1X/mU7h0vNAdlWKNidLvYXNbCwn/2DACL1nszuQTxv25lfR2GdhTsXj0NRFGaMjue17y6iVYJCQgg/SEBIDNjnx6tRVVgR5P5BXiunjUCjwIYjvt8ZHSxNVjtf5NVy+axRaDS+NZDsjkwZEyL4SkytNLU5mDbKPWLY21Q60Cf0ja0O2hwuRvh50R8shgEGhFZtywegtx65xabQBoQMWg0aJfyD6lWNVmKMOmI8PY98pdUo/PyK6VSYrfxt2ymfvma3p3/Q2T5kCElAKDDKzf1rJt9rQKiqGUWBCak9B4QmjYghyqANSGPpN/YU848vC7lraSbPXDervT+XoijcdNZY3r5vCRpF4fq/7uDd/aVhm73dm0OlDbTanX1mz/UmFOdtH3xdToxRx6UzRrU/Ni09rj0odPcrewZtX4QQQ5cEhMSAfXasilHxEUwbFTco20uOMbIwK4kNRysHZXv+2FNowulSWT4pdUDridBJhpAQwZZT4b6jPc2TIRSsKWOVnrKgb0JAqNXm5P2D5QBoeokIFZssRBu0JPWzj9pAKYpCpF4b9kH16sY20vqZObYwK4nLZo7ir1tPUdbQd2nQ7gITiVH6XjNL4HRT6aF4YR9uKs1WNAqkxPj3PvBOyWvopodQQW0z6fGR7QGI7ui0GmZlxLN/gI2lyxpa+fn6oyybmMKTl03rdlLajNHxrH9wKXPGJPDIm19zz6t7OVkdnhncPfnocCUGrWZA527eUn9/Pz8sNgf7ivz/OblcKpuOVXPu5NQuwwqmpcdx15IsTtW0YJcG8SKAVFVlV34dR8rM8hnxDSIBITEgVruTL/JquWBKms8jVQPhkukjOVHVzKma5kHbpi925ZswaDUDKhcD0GgUIvSasL+YEWIoO1XjTqf3NmeNDFKGUFWYBYT0Wk9AqB8XCp/mVNLc5mBUfAR1LT33DSk2WRiTFDWonwtnijSEf0CoqtHKiAH03nv80ikoKDz+ziFcfTT33VNoYkFmUp8/E4PW/bxMGhu4SrOVtNgIdFr/Trd7m5JXWGchM6XvjKO5YxPJKW8cUNnkb/99DICnr5uJtpes5+QYI2u/ezZPXjaVr/LruPAP27hj9Ve89lUxp2qaw7bxtKqqlJgsrP+6nOWTUomL6F//IDhd6u/v9/uBtfu57sUd3WaD9eZQmZna5jZWTh3R7fNJ0T0HFYXoD1VV+clbh7h51S4u//N2vv/Pff0+X6pqtPKbf+dw28u7wr60ezjwL0dZiDPsyq/DYnNyYQ8fSMFy8YyR/OKDHDYcqeSB8ycO6rZ7s/NUHXPGJPR6585XRp2WNjlIChE0p6qbGRkX0X7x5c2caQvwhbA3Q2hkmASEDNr+Zwh9mlNFaqyRS2aM5I09JT0uV2yyMCFEDaW9IvRarGGeZVnVZGX+AG4gjEmK4snLp/Jf646w6ot87jt3QrfLVZqtFNVZuP3scX2u0/s+sDtV/KxkE2eobLT61TDcK8oTXGhp6/r7W1TXwiUdSoR6Mm9sIg6XypEyMwv6MUr9SJmZfx+u4EcXZvtU8qbTavjuOeO5Zu5o1n5VzJt7S3hi3WEAIvQaRidEkhJjJCXGSIRei1GvIUKnxaDT4I2XKSgoCrSHnhSl/d/uxxVUVFTVfXGqAi7V/X+XSqfnXCqex93BqNPLuZ8ra2glp9xMbbMNRYG7lmb6/T3qyNjeVNq/Y87nx91TcyvNVr8aWn+VXwfA0okp3T4fH+WZVNdqIzU2PPrXiaHtH18W8s7+Ur5/7njiI/X87pPj/OTtr/nLLXN9vvnjcqms2VnIMxuOt9+wKTZZem2SL4JPPurFgGw6VkWkXsviCcmDut1R8ZHMGZMQVgEhc6udo+VmHrwgOyDr86btCyGC41RtS6cpWEat54Q+wIHYak9AqL+lQYHWHvjyMyBkd7rYdqKGS2eMJC5Cj8XmxOlSu2QOuFzuu+7nTx5Y6exAhXvJmKqqVDW2DThz7NaFY9lxso5nNuQyOiGSK2and1lmW577orOni8eOOgUMw+NXdsiqMFuZ2Euvn57otBoMOg0We+cMIXOrnXqLnUwfmlTPGZMAwIHihn4FhF7+Ip9og5a7lmb59XXJMUZ+uCKbhy6YyMnqZg6WNJBb2USFuZWapjaOVTbSZnfR5nB6/na5gzWcDvKAO5jTF43iLg/VKB2CSYq7nFXBU9bq/X+HxxUFUmMjOG9yGtPT41gyIYXJIwd2Qep93/R3yliFudWvfdhTaCIrJbrHYE+ip+ywXjKERADkVTXx9IZcVkxJ4/FLpqAoCqoKv/vkOJdMH9nt586ZLDYHD79+kI05VZw7KZWLp4/kiXWHMbXYBuEViN5IQEj0m8ulsjGnivO6qV8eDJfOGMlTH+dSWm/xu2FjMOwpMOFSYfH4wATHDFrNoI4vFWI4UVWV/Jpmrppz+iSmvbdOgAOxVY1txEXoQnKc7E5/ewgdLGmgyerg/Mlp7U2jW+3OLg2Ra5rbaHO4QtZQ2ivcS8YarQ5sDteA794risL/3jib6iYrD79+gOqmNu5emtnpju22EzWkxhqZOqrvC06Dp4edNJYeuCqzlWU+BOG6E2XQYjkjQ6jYj6llqbFGxiRFsqfQxL3Lx/u17dJ6Cx8cquA7SzL7PYZdURSyR8SSHYA7/6onu0eF9oBOKMtRu2PQeUot/Xjf1DWfLrutMFt9/jpVVdlbVM9F03rOzk+IdGcI1cvFthggVVX57/ePEG3Q8vR1s9rfe/edO4FPjlbyyw+Osjw7lfiono8VjVY7d6zezeHSBn5+xTS+sySzfVq0BIRCT3oIiX47VGamqrGNlb18IAXTJTNGArDhSHg0l96VX4dBp2Hu2ISArM8ok16ECJq6FhtNVgfjU07fvR/o9K2et9VGShil7BvbS4L8e517C92NTxdmJXVowN21x4k3WJQR4oCQQRvex1DvSXAgGm9H6LWsuftsLpgygl9/mMMtL+1i56k6VFXFaney9UQN505K9ekiOljvg+Gmuc1BU5ujXyVjANEGXZcGxUUmd9+zccm+lWOeNymNbXk1tLR1fZ/25k+b8tBqFO5Z5l92ULAoioJGo6DVuP8Ot2AQgEHrfyD1eOXp5tv+9BAqrW+lwWJnzpiey03jIt2B+mY/f/ZCnGlzbjW78k38eOWkTjcwtBqFp66dSb3FztMbcnv8eovNwd3/2ENOuZkXb5/PXUuzUBSFZE+zfQkIhZ4EhES/fXq0Eq1G4YIpgzNu/kzjkqOZOiqOT8Jk2tjO/DrmjU0IWBaAjP4VInhKuhmLrtUo6DRK4ANCzTaSQzRtqzv9uXAB2FdkYnxKNMkxRiIN7ouN7hpKeu90p8dHDnBPBybcj6Hek+DEAP1uRBq0vHTnfH5zzQxOVjdzy0u7OO/3W7j1pV00WR1cPz/Dp/Xo25tKh2921VBQ0+TO/kjrZzDYneHW+WK+qK7rcas3l80ahdXuYtOxKp+3u2ZnIW/vK+WuJZmkJ4T2PTyUdOy95atjHQJCFj8CN0fLGwGYnt7zdF9vk+twzpIU4c/hdPHbj44xPiWaWxaO7fL89PR47lqSyet7ijnQzVRDq93J99bsY39xPc/ePJeLp49sfy4xSrLYwoUEhES/qKrKJ0crOTsriYSo0F3oXDJ9JHuL6qlu8j3VNhjMFjs5FY0sClC5GAS+h1Cbw0mDRQ66QoD7DitARlLnC55gBBFMLbaQjV/vTn9K41RVZV9RPfPHue9In84Q6nqxUeUJCPU3MyJQwr0PmzcgFMhgoaIo3Hb2OLb/9AKevnYm41OiOVndzD3Lsnz+fDK2ZwiF52SoocLkmcKXHNO/gFC0QdulqXRRXQupscb2Rvh9OSsziYzESNbuKu51OVVVya1s5L/fO8LP3j/KhVPT+I+LJvdrv4er08dV3wMweVVNJEUbiDJoafGjAX5ORSMahV57DkUEaWqmGF7eO1jOqZoWHrtkcvuE0jP9aOUk0mKNPPnekU4TBe1OFw++doDtJ2v5n+tn862ZnZvhG3QaDFoNzd1kGovBJT2ERL/sKaznVE0L9yzzry490C6dOZI/bjrBx4cr+faSzJDtx1cFdagB7B8EgS93eGDtATYdq6LgqW+FZbq1EIOprMEdEBqd0E1AKMBBBFOLrV9NXYOlPyVB5WYr9RY7szyNaiN7CQhVmK1EGbTERfTzFMNuBUtd18eNsRDR8x3xM4V7yZj3rmhiEG6qROi13LxwLDd3c0e3L8HqpTXc1DYPLOAXadB2uZgvrLP41FDaS6tR+M6STP7fv4+xK7+uU1DQ6VLZlV/Hx0cq+PRoFdVNbSgK3L00iye+NQVdDxd/30gtteBo6/yYooHYke6GRT7oz3G1oLaF8SnRFJks3Zbf9iSnvJEJqTG9ZqS3ZwhJQEj0k93p4rnP8pieHtcps+dMMUYdP7t8Og+8tp+/by/g3uXjcThdPPLm12w6VsWvrpreY4Zqd8c5MfgkIBRCqqpyrKKJab2kfIYbU4uN/UX1/HlzHglReq6ZOzqk+zNpRCxTRsay7kBZSANCu/JNGHUa5gSofxAEPlPBmzJe2ywjSIUoq28lPlJPbETnJoiBDiK4XCr1FhvjdPWQv/X0E5GJMGpWwLbjj/aSID9e5zFPicI0T1PiaE/JmLWbcoTKxlZGxkf4F3i2WaBsH6gu+PgxqOmmH4ExDq7/O2gNEJ0CI6b3usqwKRkzFUBD1wyNiLJyJinm9j4K4aK/JYWiszpPQCilnxlCUQYdVY2ds5+L6yw+TYrr6JaFY/nnriJ+/MZB/uf6WSREGticW80be4opN1uJ1Gs5f0oq501KY1l2yvArEzv5Gfzftd0/d8kzsOg+n1bTaTqfj4o8P8/a5rYu2WC9OVndxPTR8b0uo9dq0GsVLFIyJvpp3f4yik0WXr5zQZ+f59+aOZKLpo3gd58cJ3tEDG/sKeHjI5U8fukU7lyc2ePXRUlAKCxIQCiE3j9Yzo/eOMjqby9gxdTQNGb2hcXm4J39Zbx3oIz9xfWoqvuu02+vmdF+lziUrp03mt9+lEtBbQtZKb41Wgy0nfl1zB+XiFEXuO+HQacJSjPA/JpmCQiJYa+03tIlOwgGGESwWeDIO+A8fae5tc3JrZpj3J6zAfZXdF7+4UOQOK5/2xqA9rHzfmSAHKtwB4Qmj3TfwOitZKzCbGWUv+ViX/4Jtj5z+v9LH4akCaf/b2uBT/8L1l7veUCBx/IhqufMK4MuTCY1rr4IWqq7PHwlcJlBQWO9Egx9j+wdLNJUOjC8E6QSo/s3pevMCyWr3Ullo9WvDCGAaKOO52+dx3df3csdq3e3P35OdgpPXj6N8yenhcW5XMg0efpQrvgZRHUItn30KJhLfF5N+3HVx/dNq+30z/NYRaPPGUI2h4uS+lau9GHMd4ReLrZF/9gcLp7bnMesjHhWTO27V6yiKDx93Sy+9ewXfOcfe9Ao8F/fmtrnhMNIg1aClmFAAkIhtLvQBLhTgMPVhiMVPLHuCKYWG1NGxvLDC7JZlp3C1FFxXUYNh8qVs0fz1Me5vLW3hMcumTLo22+w2MitbOTHF04K6HoDmamgqqdreisbQ9tvyVcul8qLW0/xxp4SFo1P4olvTQ1pvyrxzVLW0EpmN5N6DDqNX4GSTo6th/UPdnooGvh/esAKXPa/kDoVqo7Cx49CfUFIAkLGfmSA5FY2MS45qv24H9nLlLGGBjP3x30EG94//eCkS2D8uT1voLkKIhLg5tdAHwHp87qWaky4wF1KlvepO4Bka+41IGQMlx5CbY0w43pYcHenh9/4dBs3lT/tvvDMPMfnTIRg608vFNFVXYuN2Ahdv28UTbcfZqnlU9jwCQCtFhv/rSvjvPJU2OCZjmiIhnP+A/S9Z/XMGB3Ppv84lx0na3G6VOaMTWBUiJu+hw3Vc4yYeQMkdCix3PSLrmVkvTD6WWrZPjEuJZpoY9d+UT0pNllwulSyUvu+ARpl0HabxSlEX97ZX0ppfSu/vmqGz9m+SdEG1j+0lI05Vcwbm8jUUX1XwEiGUHgIjyv6YarZ6uj0dzhRVZU/bcrj2c/c0eFVd8xn/rjEsOw9MzI+ggunjuBfu4v54YrsgE358tWufJO7f9CEwPUPgsCWOzR2+B3zJy05VJwulZ++c4i395UyZ0wC6w6UcajUzGv3Lgqr5rxi6KowW7vt+WXQamiz9/N9V1/k/vvHR91lTcD+4nq+t2Yff759IYtnTHQ/HzcKPgbMZf3bzgD1JwMkr7qJ7LTTDUy9x9lOFxubf4NafoBnraeYZcuHxlh3UMfWAhWHeg8I2VrcZXSZS3teJs0T8G/wfJ9dvX926sOlh5DL4b7QPOO1bdJrOEv7BuNPbYbcD+HEx6DRw5xbYUYPJSyD4HRJoTSVHoi6Flu/y8UAVtS9zgTXTjjgft/FOF3coHUSXayDUsX9e2W3QNZy958+xBh1XNRLH5BhyxsQUs7omaSLAEerz6vxt2SssNZ9MzgrOZoog87noR8Fte5AUlZKTJ/LRuq13WZxCtEbm8PFXzafZM6YBM6bnOrX16bFRnDb2b7f6IqULLawMIw6xoWfwjr3Qb28wfcPnMHgdKn89/tHePazPG6Yn8Hb9y1hQWZSWAaDvO5ZlkW9xc47+0sHfdu78uuI0GuYldF7Pbe/DNrAlTt07EPQEoQytED71QdHeXtfKQ+vyGbd/UtY/e2zKKht4faXv8Lcag/17okhzmp30mR1kBbXtaxpQFkl5mKIToX4DIhJg5g0ql1x1BJPXFKHk6o4T++19+8HU37/tjUA/gaEXC6VwjoL4zvckXY3LFVps9nB5YTWetj2O1zVubiA3LE3wxOl8J8l7uwgawO4XO5lvX86ZC7S1gzGvi9wANDovDvW++sMm4CQ8/Q+d1BjcfHz9L/Boych+yKwNkLRl/D16yHYydP8zXQY1lS18+90hz+mJgvJUf0rFwPQalSOuMajPl4M/1nCq+d+way21TT9ON/9vrrjPfeCTpkeOiCq52JUOeNmos7oV4aQRqOg0yg+H3O8gw0yEiP9CtwU1DYD+NQiIUKvlbHzwm+bc6spa2jlwfMnBv3aL9Kgk5KxMCAZQiHicqnkVbkP6k1t4XOB63SpPPb2Id7ZX8r3zx3P45dMCetAkNfZWUnMyojnhc9Pcd28jEHNEtp5qo4F45IC2j8IAjvtqMJ8OiDUFOYBoS3Hq3l1ZxH3LMvixyvdZXjLJ6Xytzvmc++avdzzyh7W3LOQKIMcvkT/VDe6T/K766Vl1GmxOfpxcvL163Dg/9ylTh3UW9zH906TpHRGmH4NHF0H1ccgaXCnNWo1ClqNgt3H40u5uRWbw9XpAiRCr+Fp3UvcvHELbDy9bMGSp7j6PQ0vL1pAewFvZAIc/zc8Ndqd0eA17Wo4/wl3sKStCQw9j1DuxHsnX+3952TQaXx+jUHjcgEqaLp+PjS22slIjHSX/dz2lvvBVef3+br6paGk8/e+RwoGxZ1FMpjBNJvDxXsHy/gir5aslGjuWZpF/ACCKYPm/Qfg4Npun1oL1OpGgPMoaP1/LTqNBhUVm9OFUaeloLaF+Ej96dJpb5DRGd6f6WGvpwwhfSQ4/Cux9yezu9LcSoReQ0KUHqPe9/O9ojoLiVF64iP7/p2SCU6iP947UEZKjNHv7KD+iNJrqTSHV2LEcCRXVCFS2Whtj9o3h0kJj83h4vF3DvHugTJ+fOEkHr4wO9S75DNFUXj80inc+tJXrN5ewAPnTxyU7ZbWWzhe1cR18wM/bS2QJWMdD7bhnCHkcLr41Yc5ZKVE89Mz+kGdNzmNZ2+ey4Ov7ef7/9zHy99eEPAgnBgeqpvcJ/lp3QSEDDqNX+N/29WecP99ydOdHm70ZLR1OXk/5yfugFAfZU/BYtD6fgHiLVHo2HMpQqdlgqYcs3E08Uu+437QGMvJ6HnAQUZ2bCod4cmetFtg4ffcWVTlByHnPfcfr+yLfNt5b3DF1XdAyOFScblUNJoQ3djwBne6CwhZ7cSd+Xuh0fb5uvxWtg9eusDnxZMWPAgsGbSAkLnVzt2v7GFfUT0j4oz8+1A5b+4p4Y83zQl4KXbAVee4m5/PvrnLU59u2cJFji/d5YAavfv3fuzZPq/a+yvbanNi1GkpqrOQ2TErROvNlAvfz/QhwZup2KVkzAh2/wJCej+Oq+7m+5EoiuLOTPXx/Vbe0MroRN/6P/mzXiEA2hxOPj9ezU1njUGnDX4hUZRByhrDgQSEQqSoQyNpSwgv0HMrG3lpWwFVjVZK6y0U1ln4j5WTeGjF0AkGeS2ZkMLF00fw7Gd5XDAlzadmZgO1Odc9OSYYU+ICWe5QafZMO4nSh2XPKq+NOVXk17Tw4m3z2staOvrWzFE8fd0sHnv7ED/81wH+dNPc4T0dZQioNFu559U9/PGmOUwa4WMGSJBVN7nfD2mxXUvGDDoNDa39eN+5HO6eE2dc8Jlb7eg0SvtUrnbejIEQXczptb6XNngDQh1LxjQaBQ0K9cZ04s99rP3xyi8LADpPGYtIOP3vcx+H6GT3hVbep+Cyw/sPgb0FDP6WjPX+vTN0KH2K6CYgMyhc3ZejqKqKudXeNVCoaAOfIdTsmXC24meQmNn7sh8+gq6tHqB/mXJ+UlWV/3z3EF+XNPDszXO4cnY6R8oaefiNA9y++iseu3gy31s+PnwzlZ0OSJ0MHd4DXqs2RnCR7kt46zunH/xxDsT7dgPJ+xFosTlJiHK/DxdkJp5eQOM9hoRPlvmQ5OohaKuLCHKGkJWRnrJlfyYi9jQQofv90UqZvfDLoVIzbQ4XSyak9L1wABj12vCYBjrMSQ+hECn2TBeYMjI2KKPFfXGkzMx1L+xgY04lTW0O0hMiWXXH/CEZDPL6zTUzSYjUc//a/dQ0+V773R9Ol8rb+0qZmBbDhFQfL2T8EMhyh8rGVlJijCRGG2juT/bDIPnHjkLGJkX12vjyxgVj+MUV0/jkaBVX/GU7eVVNg7iHwl8vbDnJ0fJGPvi6PNS70q7a01MrLa6bDKH+BmJ76BNjbnVngXS5oA1xuYdB5/tJWFGdhQi9pktGlaIBl9q58XBFoxWDVtO5+XtEh/5q3qlg+giYdiXMuA5SPBmdBt8uctqDK32VjGn9GwMdFN6g1Rm/G1a7C7tTJS6iuwyhAO+vt8dM9sXu73dvf/RR7Zkpdmfwm0rvLjDx0eFKfrxyElfNGY2iKMzMiGf9g8u4ePoInvo4lx/8334arWF6Ueuyd1sOZrU7OeZIp1UX7/7Zr/i5+4maXJ9XrdG4f38tNgdtDifl5jMCAe2BUbm7PiDtJWNnHKN1EX71EAL/Mi/dGULugJBRp6XNhz4qqqpSVt9KeoJvGUJh00dNDBm7C9wTsBdm9TzBM5Akiy08SEAoRIpNFnQahYlpMbSE4AJdVVV+9v4RIg06Nj5yLu8/sJTX7l005CdQpMQYeeG2eVSardy8aienapp9/lq708WRMjPbTtTw2bEqtp6o4cuTtewuMHGkzExhbQvVTVZabc72xtuHSs3cf96EoLyWjuUOA1VptjIizki0QReQkjFVVdmUU8VVz3/JBb/fEpCxpmUNrewuMHHTWWPQ9lHe8Z2lWfzznoWcrG7mr1sHvymv8I2qqnyRVwu4U+nDRXVTGzqNQlJU14l1/S7VdDm6LQvqNgsEfM5yCRZ/TsIqzO4LkDODWhpF6RIQqjRbGRFv7LxsxlnuzJSZN3a96AJ3L6G40e7R675oLxnrff+N/ZimFnA9lIx5AxxxkWcEERVN4DOEnJ5gita3CY3e428getj1dVPjxa2nSIkxcM+yrE6Pxxh1PH/rPJ68bCobj1Vxwe+38H+7ivpXzhlMTtvpTJ0OzK12Wohk3YrP4YkKmHOb+4mtz/i8ar1GARTMrXZKTBZUFTJTok4v4C0Zc4ZpsGyoaA8IdZch5F9vE1+Pqy6XSlWjtb201teekY2tDlpsTnfvMZ/3RwKGwncHiuuZkBo9aBN9A9keQ/SflIyFSFGdhdGJkcRH6rGEoIfQ/uIG9hc38P+unsGIbibtDGULMpN45a6z+P7/7ePy57Zz7zlZ3LE4s1MD2UarnbL6VkrrW8mvaWZ/cT1fnqzzOVvL6Envve/cCVw7LyMoryOQ5Q61zTZSY420tDn6P1Lbw+lyp/i/uff0RLfyhlbGDzBL6kNPBskVs9J9Wv6c7FRmjo6nriW4mWCi/46WN7aXG3n79oSDmqY2UmKM3faVGVhAqOtHaqPV0bVPDHQICIXmYs6fpvXlDVbS47tegGiVrjGZSrOVUXFnLJsxHx7+uucNnPOI+4+vvBdufpSMhUwPJWM99pbSaP3OSuiTd306307wNZ6g3UAzq3aequOWl3bx3gNLmTMmocvzdc1tbDtRww/Om9DtIAhFUfjuOeM5OyuZX314lCffO8Iv1h9l6qg45o5NYM6YBKanxzMhNXpQel10y+noNkOowdNMPi460v19j0nzPFHi86q9QXRTix1Ti3t9nTOEpGQsINqnjHXTQ8jfDCEfPz9qW9pwuNQOGUIa7M6++515J5P5nCEUwOEkYngoqG0hO23wyvvdE5UlaBlqEhAKkRKThbFJUcQYdSEpGdtwpAK9VuGK2b5dfA81Z49PZsPDy/nVh0d5bvNJnt9yinHJUSi4swOazuijMzohkitmp7NkQjIj4yMwaN3ZOQ6nO62/xebAYnPQ3OakyWqnurGNs7OSuHTmqKC9ho7lDgOdmmZqsTFpRCwOpzrgO6x/3HiCN/eW8uD5E5k7NoF7Xt2LqcXG+AEOI/jsWDXT0+MYmxzV98IeidEG6lu+WSN3yxtaee9gGdfPy+h2LPpQsjm3GkVxZ+55J3uFA1OLjZTY7i+OpjpJ8wAAIABJREFU+30C7bT3WDLWbYZQiHsIuUsJfDsJqzC3sjy76xtco4DzzAyhRiuzM7pe/AeUxseSsXDIEOqhP4m3r0eXkjFFczpjIVC8JWM+ZggpqAEpNdmZXwfAx4crug0IbcypwqW6e8P1ZmZGPG9+fzG7C0xsPVHDgeIG3tlXypqdRYD7YnrKqDimjYolIzGKUfERpMQYiY3QMXlkbHAnUrp6ft9Dh4CfosC8b8OJDT6vWq91BwbqW2ztGWXdl4yFWdbUUNPTlDFdhHtYgK3F53JWXz8/Kj2TX0d6Au2+3gAs9zcgJCVjwg9Ol0qJqZWV0wavWsSg0+BS3UNlghHYV1UVl0qflQfDnQSEQqTcbGXqqDiiDDraHK6gvRF6sulYNUsmpPg0tnLQqCoc/8g9frij1CmQPsfv1Y2Mj+CF2+ZzsrqJ9V9XcLK6CVWFZRNTSE+IZHRiJBmJUWSlRIfX98EjUOUOqqpS29xGSoyBBouNekv/15dX1cSLW09x/fwMfnLxZI6UmQGoG2BQpslqZ39xPfcu92/8dlKUnoJa38sCw93ar4r4+ftHcbhUtIrC988NTjniYNl+spbp6XFE6XVdgrChZLLYOo+B78Cg1fQvi87l7LZ0pLHVzpju0vtD3kPItwsFu9NFdVMbo7q5ANEoCh3bzKiqSoXZyiXTgxzI9HXKmNa9XHiXjA1CU+n2gFDXnlldeLKDdFoFxwAzCwyegEZtc/efD1uO1zA6IZJpPgyAUBSFs8cnc/Z499Qxp0vlVE0zR8vN5JQ3cqSskU+OVmE647No5bQRvHTnggG9jl457d0G2hos7v1IiOzwnFbvV3mXNyBkstgoMVmIj9STGH3G+kDGzg9UT02lvf3OPnoUrn7Bp1X5GoCp8ASEOvYQAmiz934DsKbZOxDBh/cyw7ccR1VVXv6igKvnju5UHSB6V97Qis3pItOPG7MDZewQDA3EdXBxnYUvT9VyrKKR45VNnKhqwqXC9p+eT+yZN2BEOwkIhYDN4aK2uY0RcREY9YF9I/jCbLFTUNvCjQvGDMr2fFbxNbx+a9fHE8bCjw73e7UT02J5ZGV4TDfyR6DKHVpsTtocLpJjDJTU+z7JojvPfpZHlF7Lf17qHgnvrTE+8yTcX7vyTThcardZCL1xZwh9M9Ll/7I5j99/eoJzslP4Iq92wEG2UGtpc3CguJ67l2WRW9HUfoEUDupbbIxJ7P6Ex6jT0Naf91wPPYQaQ91D6OQmKN7V+bH0uRh0iT4dW6oaragqpMd3DfIoitKpx1mDxY7N4Qp+GbKPJWPeC+rQZgh131S6sdX9eFzEGadhwRg73x4Q8v1kWO/Jkh0I7zS/VnvXn5OqquwpNHHu5NR+TRDTahQmjYhl0ohYrpl7+vFWm5PKRiu1zW0883EuJSZLzysJBGf3TaW7ZAiB+3fAj5+tRlFQFPfna25lE5NGnFGW3R4Y/WZ8BoZMT2Pnz3scdr0ALbU+r8rXAEyFJ9NnZIeSMYA2pxPo+X3qHZaSHONbtp8/08u+SXIqGvnNR8fYfrKWV+9eGOrdGTIK69wl/pkpPg54CICOmbw93Kfrk6qqfHS4khe3nuRIWSMA0QYtk0bGMnlkLLvyTZQ3WJk8UgJCPZGAUAhUN7lPsEfFR9DqacbbZu//G8FfR8vdWR0zRgd/LLtfvJlB174EGZ47ept/AwXbQrdPIRSococ6zx2lpGije5JFP2t161tsfHq0itsWjSU5xuhZp/uXtn6AF/t7i0zotQpzx/pXahIfqae5zYHTpQ7pdNAPD5Xz+09PcM3c0fz+htks/5/PqevhrvpQsbvQhN2psmxiCiUmC2UN4VMjbmqx9dgw0dsUVFVV/y5Uu+kh1ONocRi8HkIfPQamU6cvdlQXxKZjiP2HT8eW8gbPnexuMoS0SucpY2fe9Q4a7/fO15KxcOwhZO2hh5CiDXzJmLcPio8lY+AOpg30++YtE/UGvzoqqG2hrsXGWZmBnWQTadCSlRJNVko0E1Jj+Px4dUDX34WvJWPgCQj5/n5XAL1WS3GdhZzyRm5eeMZNPE1oy06/MXoqGYuIhzGL/Bo9b9BpfGoDUdnYhl57erCB91jVV3ZqbXMb8ZH69owiX/ZnOGYIeYPR3gCH8E1hnTuA3qk0NcgGeq1jsTl4+PWDbMypYmJaDP99+TTOm5zK+JRoFEVhx6laduV/5ek3OvSSAwaLBIRCoKrRWzsc0X6yPZgnrIc9ZT7T0+P7WNIHpXvho5/0nLKsM8BVz0Pa1L7X5fSctCaMgyRP6VBEfODT54eIQJU7eNP1k2MM7uyHfjaV/vfhCmxOV6fMMqNOg0ZhwI3RDxQ1MD093u9eSVEG9/IWm2PIpoKaLXaefO8Is8ck8D/Xz0KrUUiOMQz5Ztm7C0zoNAoLxiXx/sFyLCHoldYdu9NFo9VBQlT3vy/ekxO7U8WgG1hAyGJz4nCpoe0h1FwFi+6HS55y/3/9D+HEJ+h1Giytfb9vK8yenhXdBHk0ZzSVrmzsfNc7aHycMhYePYS8GUJn9BDyNB3uctzSaIKQIeTflDFU0Gk0Ay4Zq/I0kvcGRzraW1gPwFmZiQPaRm+SYgzU/3/23jtckrO+Ej4V+6a5M3dy1swojuIIRBACJEAgk4MNNpaxjVkBH4th99n1YmOvWYyNwcYJ412CbYJtgjEIC0wWSCAskEACaVAcaUaTZ27u3NXV9X5/vPVW6gpvxa6+t8/z3OfO3A7VoeoN53d+5zS1+ORuHIQohAQBWONUgElK7OtdlUXc9shZtLsGLvWu2ayWsZFCKBWCTKUBQBkDuvxJY7wtY/P1DjZM2sEGlkIo4rGs/Z8XFUdabZhZ9UrD8UX6nem99Cm9cTBX7+CqP/42/vqXD+AVV+4o9NhZ4ORSC4okcLckZgGnX2pctLs9/ObH78aPjyzgnS+6CG945r6+4vCGSfpe0nYyrHSMCKEBwK6ijlsqgLTJT3HwwKkqtq8dyyZS8PD3gJP3Ahe8sH8y1VvAY9+hpBEPIaSbF6szCUXMoVo6JMhaIbRxsmKloyXB7Y/MYtf6cex3+D0IgoAJVUZTS76B6fYM3HdiCb/61HNiP3bcNAttab2hJYT+3+2PYbnVxXtfeamVKrN+Uh16hdDBE8u4YMsajKsSJlUJjRTnSJZg6T9X1r4L/Ml1bsJZncLMgU8CoCQ9uwa54EMIBfrEAA6FUI6fi9YEtDow2d+KybtxCVMIiYKAnuGnEOIzPE0MNtdEbK4rZVAIsfnL59wYV6T+cywvDyFRpmQTJ6iHUMqWMVMh5EcI3X1kATMTCs5NmU4Zhg2TKro9gmpbz8cnkBCq+PEh2pZbXUyPKe5NuCjHJm8qsoi2uT68dIeHEBqZSmcDYgAQLP8sF+QxoLXI/VQKpyLHq1Ll9YycrXVieeJkmVY7TGDm28yWoyg8coZ2Onzke48PJSF0ZrmNzWvGCiUPVU4y1A/v+9pDuOvwAj742ivxsoCQpKysLVY6RoTQAGCnC4xZg0eRkXtH5pupI8ItdJsABOC1n+mfTOuzwAfO45fbsvs5jS/zSFwZEtgTebpzgw2CG6ZUVBQpEcHU7Rm487F5vOxA/4A7rkq+HhG8eGy2jnbXwBW74ivWJhSmECoH2RAXTU3Hv/zoCbzosm0uxd5URcbR+Zy9L3IEIQQ/P1nF9ftp1PJERUarJN8R8zLaVb+fbryf9ibzhqPAz2/GjHYWQIVeJ3GKZD4eQsxIe43XJwag46Ug5Vfdr58FHr+d/ptFXjtAY455zE9bWDMmY6rS/x4EAXDazJxZbkMUEKuCnQi8LWNlMJU2/NUH9Y6OKb/zIi8PIR5DaQC0UckkDFMQaYQQnDUVQowYdeLuIwu4as/6/JQ7ANaZ7TjLzYC2zbSw1F/+ZvLT415/KAUAMQ3oeTbnxCp0qJKI8zf7eQgJI0IoLYgR/H3EjJ6vSHxFt/mG5vIBskylI/YCc3UNl2znt3vIMq12mHDG3Gc1ClYmsw6QtGm+g8Lpahtbpos14Wbnftx5+oePz+MT/3kEr79mTyAZBAAzphp8pfiN5oURITQAnFpuY1yRMD0mc8tEs8SxhSZ+4dKMIgVZHKdvZcUcVHgJIWZ8KXsIoYi2gJWKNDJKJ5g58fpJs2VM78WW0B88sYx6R8czz9vYd9uEKqUiZB4+TUnRi7bG97RiLWPMi2vY8OWfnUStreP1z9jj+vukKqMxpAsKgI5xCw3NqmhPqhK0ngFNj6m6yQGMIF2jLwDTO4Dn/xG94bHvAj+/2VJpxSYRfBRCzEti0odMAWB6iuT0PX/hDbb/2jqv+o5AlgQuQuj0chvbAxQ/oii4PIROLrexZXos/4AE3pSxUrWMuc+BWlv3JdlyUwjFMJQG0iuElltddHsEk6qEaqvralmZrXVwZL6J1z51d+Ln54HVUpyiYBEKqxWv/3usd3RMVTyfueRQ9HCqNabHFbz52nNx/f7N/lX7mMllI/jA6Pm3iwFUIRSnZYyTaJ9vdFxJTnkphLJKqx02nDaJmXrB6abHF0xl0oDXOUlxutrGRVuL9dlJouQlhOD9X38I29eO4R2/cFHofWVJREUWh5akKwrDecYOOebrdEAXBAEVha8qkBVq7S4WGhp2r88oUpARQn5QzE0Et0LIrMJ4CaHVrhBKOZEvNTWMKxLGFAkVWYRBEDs9hvlOHdjVb/o8ocpopPAQeuRMDbIoYG+CVINxdbgVQl+57xT2bJjAk89x+2iMpyTZBo2DHp8yZ2vfoMEM0Ce0eV/ljJw0mcrQ+zbdbDG6JogQSuApwo0zDwAXvQR44+3Anmf23axIIteGf64evAGRAE/LWCt//yDANmjmNpUe4HkXEDvf6OiYrPiQAqKUfRFE77jnVQ7Iogg9xetghYi9myZhEKDuWIzfe5S24FyVo38Q4PSYy+n7ZwbRPgohSgh5vl9GCsYgcERBwO++8CJcFWS+nSepvFpAjHBCKIZCSJX5lHULdQ3rJ+1rkqdtpt3tod7RsXEqWcvYagIjhBpazzVH5Y2TpjKpW7B3UVY4YxZ1ioRtqM4/Tv/o8ALuPbqEtzznPC7lW9rC9WrASCE0AMw7eoeLVggdM9nrVIRQrwvc8ttAY5ZGGs/s9b+fKNNJlncytaJxR4QQYCuE0hNCtlzeliUblhKCB/cdX8bGKdU3PWgiZcvYI2fq2LtxMpFyZKJERENcLDU13PnYPG569r4+tdaEKqGlxVdylQUHT1YhCsD+bbTS5KzUrw2J1M0csw8D33qXK9nn8qUWPqHUMT7/GHDuc/seYimE4pIIRq9PBdKIVAhJ2Wzm7v1n4Oc32/8nBGjOAbueBmw/4L6veT4pnC1B8w0NuwLmC0EUYBBY5+mppbbLYyw3cPovlUohJHgJoR4mVT+FUA5zXs/f5yYYBIosptrULJqE0J4Nkzh4okpbqMz2p3uPLUGRhGyCLULA5oe0oQeBYGEaPuqrRqfXHw1upYJlqOgRcySVVwuI0Xd9WpDH4qWMcXiztbs9NLSeb8tY2GNZ5HwiD6FVphBiLWMA0NB0a+zJ/bgmEVXzaZMtO2rtLhpaD1sHRAjFIS0/c9dRrBmT8YtP2sl1/7Rep6sBI4XQADBX1yyPhaIJoaML1JckFSG0fAz42WfsdoQghZAgxJPbsknXaSotiKs3ZSyjyo4z9poZ7MVh4gHg/uPLuGzHWl9yIi3z/siZGi5IKFF1powNG+44NAfdIHj+xVv6bpusyNANMrRVvYdPV7Fn46S1IRuYbP3QrcAjXwMac9QYtLUIqbOMdUIdZOMFwCWv7HsI86CMvRn28xAyCSHf1iCAbuayaPf48ceBY3db7xHtJWD3M4ALbgh8iMLZMjZf16yUDi9YB0tHN0AIwanldv6R84BtjhxFCGVEqqcCU9n4tBP6t4xlPOcZBvCzT3O3KDEoIt/5EYQFByEEuI2l7z26iIu3TefuaZL7/MCIHR9CyPf7tZIFOb9fwjEGSfGNqkfwIFQhFM9DiCfmnannNjhMpXkUQrNmQMimOAqhMvioFQxGbOzbRMeeItvGbEJo+NakzgTsIhF3nq53dHzt4Gm88sodVpdAFNIWrlcDRgqhAWCh0cHlpreGpdgoKGXs+CIlhHbOpEiBYQvcrZcBJ34SXj2JI7e1WsYcg1HZU8Ya80DjbPbPK6lQRNrOEtrWUTsDtBZCn2pN9VFcogCYfRhjZpU4DgGp6QYOzdZx/cX97TUAMK5IVuUqLlpaD0cXmnjVlXwsf9+xh9hD6M7H5jFVka2xwIlxc6PU0nrWGDFMODzXwL6NtgFqmhSJVOg26O83fMsimj/x9Yfwse89jkdveqGv9xmLLI29GTZ0u03WRCOSEMqo3aO9BJz3PODVH+d+CE/LGGtR6FM6mBAFAQBBS+uh3e2h1e35ppFlDt6WsYx82FLBahlzbzgbWkGm0uwamOL0DTSvibQeQowQYq3AjBDSewbuO76M11y1K/Fz8yL3+YGpmgNbxrym0uZ5myWBM2oZS4/IlrEWJec41LqKRGPew9S9C3Xb15HBLg4Hn6tz5jorUcvYKiKEGLFx7qYpPD7bsLz8ijx2Ry+HZ2IcnE2gQMsCrFDNe47e9vBZaLqBl1webCTtxYQqpbK2WA0YEUIFgxBC4ybNBbbKMQlkidPLbYwpYrrEDbb4WLebEkKtpeD7xpHbWi1jXoVQSScyQoC/vRJoL+fy9NO/8GEA08Eb004N+OtL7c8tAH/B/vF3wKUXvQ3A02NtkI7MN9AzCM7f7K/iSeN3c+hsHYQAF25Nlno35iBOhg0/fHweT9kz42vAy7xFGloP6zKy+yoKhkFwZL6J6y60CcSiiW8L3RYlDxwV/Fq7i+lxpX+xbm2ERQAkvkKo1+1XgbQjWsay8hBqLQHj/f5egSCEq2WMVbKDUsPYmdvWe1aCRzEKIb64bTa/DtTPIcBUmnoIFWAqzcili18e62GKJKKuJz83F0yvrj0b6QBWbdHneuRMHU2thyt3xzhfE4IphDLfCGhN4PDtQPUE/b9vy5gfIcQUQnE+1wgSQlSAhceBB78MrN8HbLkkxnOH4PRBoHYa2Hedr2n2igIx+ghbC8x76+c3Rxuzr9kOVaYFHv3EfVBqR33vJpxYxg3iIZw7XwUepGufsbVX0MPVTgAP3uv7uLFDc7hcqGHTmudFvCEbRe8xyoAzVUpsnGN2QhS17tB0A3N1DTMTChabXdQ7OtbLOSduZoj5OpvviyWE4hZuvvHzM9g4pfZ5b4Zh3LRhGCEYK3yULx+qbR3dHrGkokW3jJ2pdbB1eiydLwlbrG6+mE6S51wdfN84clu9Q8kg52tjhBBndaZQGDolgy55FXDxy7J73vYy8OW3Q9GWQQmhgM2M1qBk0JN+3dcLheH3bz6Ii7auwetO/ynGdUpexVkcHDpbBwCc5428NVHhkEgH4fA8rV7v3ZiMEEqSTlAGLDU1PDbbwC8+2V8ZZZswD1/l9+RyC5puWK0igPN7KnhC1pp9KYjVlu4fA2+CKoRIAoVQv4dQXdOhymJwlTALDyFC6JgxxuvHwjyEoluC5s0WhcCWMfNt1ds6Tldpa3AxhNAwpYyx2Hm30i+wZSxrhRB7rjgtYyZhmKZlbLFBwwy2mgl1zMz93mPUUPrKXfkaSgMOD6Gsx9F7PgV8/R2OA7nTN3sGQVPr9RN+Ug4eQpMbgCPfpz+Tm4HfeTSb5/3wNfT3jf8GnP/8bJ6zrAhLGZsyW7r/7fXRzyNIqDzr+wAA+VMvpGs0H1wK4CMqgO/Yf1t3yS8DeDmuuu8Pgfkf+j7u2QCuViUQ5TcB8I2zqzFlbM6ct3aYnRBFkWFna7T4fd7mKdx9ZBG1dtelAis7Fhr9yrUiEGeeJoTgzsfm8KzzN1lqbh5MqjLO1Pi9wFYjRoRQwbAW2MxDSCmYEFpuY3NawzC2wNx0EfD2n4VL0ZVxKrflQU9zG0oD9iRdSkLI/By2XurrRZIY9bPAl98OJhyJTHrZfmXo8b/42XHcuHU3cPYDkMwEpTgVE0YIsX5sLyqylJiQSdvCWIoNXwIcPFEFAFy+w79KPqEMb3ra4TlG8tnni50iUbRCqNnXxlVtd0MNJmVzmIndLuMXOx8ULc6QhSFst0k3mGPxFBeKJIIQunkNWliximFQy5gsCiCgn+nJJbrY2l5oy1j4+SSJAkSBYwzNEz4KIb1noN01AkylM26TDkg5i4Ispm0Zo5sh5ndy2jR5vffoEjZMqti1Pv/zhCmEMq8Md+m8hf/yHWBsGthwnuvmhhbQKmqljGVIUP36LdTX8T8/BDz0leyel6G1mP1zlg1hptIHbgR2XhU9Tv/ss8CdH0JFNK/dbgt40m8AT72p765f+Mlx/P0dh/H5N19Nz5HP3gipR68PUW8DO54MvPRv+h73nS//E5574iNAaxaY4DPvt+beISuapQEjNthcVNj+ymwX27txEncfWUS76PVOSsw3NAgCMDMxGEKI5xx9bLaBubqGp+0NSF0MwLAn9xaBESFUMGwzObpI4kkWyBJnam1csTOlVNu5wJzZE35fXoWQYQA//L/9mxrXwr9kvbjEv/KbFWwvk+SLck030Or2sG6CboAlIT4B+dhsHTvWjVvVVi9UWYxtUs1wbKGF9ZNqcEtNBIpW2GWFgydZLLv/oi5uT3WZwAghJ4E4sO+p2wQUd89dtdXF9HiIQkhK4SHk0xYUTgjJwGPfAf7hBfGO5QTzI+FWCFGwNLVuz4AUQBawSmuQhJypqZZbXZxabkEWhWLk5pZCKHpjLXOmqeUGRu44PuOGuTANjp3PUiHkn3IWDDuFLp2pdAczkwpUWcTGKdXaLN17dBFX7l5XSHqiIomQRSGRh5BhEJyqtrEjjODccgmg9BfYLO8wrxKRs9XRBsfcP76O/kxtyu68cT4PI79WMsI8hEQR2Lw/+jkevx0AoDjVoFNbqNemB4+KCg6JBJO7D1jhK6J5PRiEAOqU/+Ok8/BcgBYNN5wb/ZpQEmP9grHQ0CAKsOLT2wV5TDIvzV0zE4UeNyssNDpYN67EUt5kgUoM4/MfHZ4HADxt34ZYxxhXJLRHhFAouHZhgiAcAVAD0AOgE0KuEgRhPYDPAdgD4AiA1xBCFgU6y/8NgBcBaAL4TULIPdm/9OGEt+LKYySXFQghOL3cxgsuTrlgZ9VWngWmPE43PH+2L+LFmc/pTcVhi8Yy+ghZld+cCCHzrQcuyjkSSJiRJ/OMYmuVOOfbkblGoDoIoOdw0o3+8cUmdqUwOC+FaWwCHDyxjB3rxjETIM0d1vcFUEJoQpWw2WFMaHkIFe1j0G31pSDW2rq1UHTD9BASRQC9TAihepBPDMOTfh149BvxjuOFMg6cfwOw99kxHkSgmAOM1jMC057mIhRCbOFYbek4tdzGlumxYhaTnC1jAE3LSqN0SQ2LkLE3iqFm45mnjCVTCCmSAN1IoRBqdrHeLHxtmR7DqeU2Fhu0VfaVV+5I/LxxwZP65IcPffcQ/vJbj+COdzwHO2fiGbkFeofl0TLGkKWyzFnE402JHWaQkJaxmFA4xr/5egcbJisuUlQU6HI3bFl3qmv6OC4eATZf5DjohO115MHqbBnTMDOhWuEcRa2jWMF/W8HKpKyw0NAG0uJmd8pEz3s/enwBm9dUsGdDvDFZlQdcGBoCxCnLP4cQMuf4/+8CuJUQ8j5BEH7X/P87ALwQwPnmz9MA/D/z9wgA5htuT4ZKga0U1ZaOjm4EbIZiII4E/dn/A3j4a3zPO74euPZ/uf9mtYyVkNm1Ftr5CO1YxUhPMYgtt+gEtdaUgLLNWpzz7fhiCy/YHqw+UGWaqhHWehKEE4st7N/GJ332gyAIVKE0ZIaJD5+uYf82f5NuAKgoAyJQMsDhuQb2bJh0LXaLbo21oDXit4zxKvMIAU7fb2+euk1fQmhNGCF09VvozwDAFEJhZMl8vYMJVQpUB0rmd7zc6uLofNPybMgdnCljAK3Yp1G6pIbPPMEIIV+yMOtkTZJsnpIlEd0U1+tyU7NMXfdsmMTBk8v4/iG6hLzmvI1hD80USQsWN99LDaOPzjd9CKHwsaFuEX6eNVKSljFeJVWWRKIzCGRVEEIks8Iemz/CmJ3FptZXDBJAoEoiSMi1/0TXXCt96c3uGyY2AP/tIKD2b5KHta0+DRYaHayfVDEWg2jI5LhmAWW76aM3bOu3ubqGDQUbSgPxVGw/eWIRT9m7PrbCtCJLxVsWDBnS7GRfDuA689+fBHAbKCH0cgCfIoQQAD8UBGGdIAjbCCGn0rzQlYJ5T9ykIAhQpeQKizg4bUq2M/MQ4qmonHc9/UkKkc8rYiAgMZRSsUAHOkauaJHV7eCB0aUQEgSbEOI83xodHfMNLdTjx9n2yGJ+eWAYBMeXWnj+JVu4H+N7fCm5qfUg0DMInphv4rkXbQ68zzBX9Y4uNHHhFjfZVZji6cgdwNIx+//Lx4Bptxoh0lTaVM5E+s48+k3g069x/63ift/1jm55qJQGgt0SBIS3xs03tEB1EGCPUcutLh6fa+CGlNcyN+K0jIli8Sljsw8DJ0xh9EkzMcix4axHKYTyMJWONU9RBVk3hUJoudW1lKnnb5nCVw+ewjcOnsa6CQWXp21bj4GkCqGaqfI5thi/ZYqlmk1VPMRz7JaxGMiSSHQml64KQsjIzKNSCQoQcIBeG/3XfkUWEXbJPdGZxMe2/R/cdIVjDb/wOHDXR4H7Pgtc9Vt9j1GHNHgjDZjSha1Ni/LymW9oWDMmY41ZcBo2D6GFhobzA8Jj8oQoCpBFIXKcXmpqOLHUwuuuPif2MSpKMfvsYQYvIUQAfFMQBALgI4SQjwLY4iB5TgNgK8EdABwrchw3/zYihEAvuOkx2ZU6UylI4cAMrVNvUBJWHBPBUgiV8EK2Kr/5eBsJYMaeyd99LS1MAAAgAElEQVR71VzUsg2wTQjxnW8nluhicNf6YHmmswIVhxCarXeg6UZsOb4XwzbQH19sQusZOHdT8MSrZuS5Qwihxc+CesIJITi11MZzL3STXYUohHpd4FMv799s7bIFqt0e9dSaHg9WCClWq2bEZrhNjcHxsr8F1mynF+zOp7ju0uj0sHdjRFTxgCBL1P9HvfvDAKq+97nu+Ek8AwZw6x39N46thUh0SJKIR87UsNDQsC9hWmBssLnn0K2BST6AAFzxWqgcaWqZ45a3AcccSUGCRKv4JuphCqG8Yudjm0qLwXPPTz4BLPlHagNUHHFT9zFcObsOePSluHznZSAE+I/7T+HlB7YX6lFRkaXY6ytCiKXiOrEUkkwTQCLUO7QQ0+cRxVrGfvwPAa2iAnDFrwAbz2cvhP9Fs0TWLOBqGVsFHkJhKWO8MM8FVRL6/ubFcqvrCl2wHitLlBAKeNxSs4snzr0eeLrDX6i5QAmhu//RnxBahR5C8w0N+7dOOzowilHqzDc0bJhUY7VAlQmDahkD+Ij7B07RdUqSrgLV9BIkhBTiXzeM4N3RP5MQckIQhM0AviUIwkPOGwkhxCSLuCEIwhsBvBEAdu/eHeehQ425eqdPklfUhtYytA6p+HIh4QIzEdgknWXFNCvkbCoNpDf29HpVsBYP3vPt2AJdDIb5/Lh9sPg3v2kTxhjUIVMIPTYbntoGZGfC/NufuRdfO3gaj733RamehxdLzS5a3Z7VQ89QiHm+0aNk0DPe5l4Yr91l/ZNV/af9FELmtRHbVPqcawINPusdHZMxSNIioUoidglnMfP9d9Fx1mdD9GJWrv6BZwFFDPojKqjIl+EHZivQuZuDz+lMIcrA5ouBYz+iP34wdKDbhCy9IBWpngh6G9jzLEoWAkBlmsaDm7BbxgJMpQHq1ZdFsSGu1535VdO5x2dZ16kDX347vWPIc94kEignesC37sI1b7wD6ydVLDY1vP6avfFef0pUEnhHnKl2LCNq5gcUB3VLIeQZZ9btpsEZB7/g/0BDB7Q68ML3xz6mtQ7J4rxZlQqhbMZpiYNYcqrnnFAlASSABDQMgqWmhnXjnvX7xHpg33VA9aTv41Zny5hmtowV6yFktaoVrEzKAoZBsNgcMCEUMU4/eKoGALg4ASHkLEoGeSaudnARQoSQE+bvs4Ig3AzgqQDOsFYwQRC2AThr3v0EgF2Oh+80/+Z9zo8C+CgAXHXVVQN0fCwWi00NMxPuiaAiS4UM1iyKMXWkYAFEiAXOeOGBIGdTaYBW8YOVCtGXDVvMWoQQM5XmrJgcX6SLwTAVT1I1C3vuXakVQtJQKYQen2UpXPkrhL5yHxVmtru9QiZBpijbsc7dllqkeT4m1gPr/TedVbOFMkwhJInMWyf9OdXWerFUc4WBECiSCAnme3zlR4DLX9N3t2f/6a245ryN+PNXX+G+4eGvAZ/5FYDQxdXiIv1cC1MICQLwljvD7/O+3QAxUrc+JYYyEXgesvjbwNh5wJxnMyCEEip6lSBlFZuLX/DHwDPe6vvY08stXP2n38H39nwcu3vHUJElfO3tz8JcvYNLQvzo8gBNwYx3LR86W7f+zdQ+cRBoGj6zB/jdJ4If+IELfAiYGB5CQDaJrE4PIT1EIbVSEJYyFhP8LWP9c5Aii4FL3VpHh0FgJca6MLkZWDjs+7jV1jKm9wwsNbtYP6la772wlrG6hl3rJ4ZSIVRr6yAEvudlEahwjNMPnKxi41QFm9bE73KxlHIhIRqrHZErBEEQJgGIhJCa+e8XAPgjALcA+A0A7zN//7v5kFsAvFUQhM+Cmkkvryr/oFt+G/jZ5wJv/njPoNP7e8xJ48IXoiK/Pv2GtrlAK0tOTGx0mczZhFDKC555axSpEIojnS4KOZtKA3QQi/QyCYG3NSGuh9DJ5RZUicYGByGpmuWkKcXfvi6dpxVVCA3PxPvEfBPTY3JoJcZK5cpI6nzobB2X7sh/I3bSJIS2exRClofQgCtmtXaId4sJWeD17gq/nRCCZreHidIRQsxDKHqjuRSwcbFBrCQXVRZTq/3ygJLSHDkPMPWJL1koOlSxUgaL80QeQrQYkTRljHnXKZJIs2lBk8ZSB1okQBJT6UNnaSV67bhizaEuRKxHQlsCwyBV3OqcOLASWXtIZw8KQHe8hsacuz1wYkNfcuPQI8uUMfMyEwLmh47eQ7tr9I+rhJpKI4C4WW7Sa2qdX0FXVmnLtA/suXd41khpwGwSZiZofLoiCYURM/MNDQd2rSs0LCgrVNvuROKiwacQquLi7clCaKyglq4BFD8NDQV4Zo0tAG42e+5kAJ8mhHxdEIS7AfyrIAhvAPAEAFZe/Cpo5Pwh0Nj512f+qsuMU/cB09uAi1/he/Pnf3QUW6YruH7/FlplPX2fWcFKMWAtHQP+5vJ+Fc3GC4G33mX9d6GhYd2EAllKOfEVqhByLnJKhrxMpR39rbIkoKtHLMpD+mGZuSVtWxFit4zNVjvYtKYS2nObtB3obK2NqYocmGDEi2HzEDqx1MKOCFVUJYOqXsOxkTlbawMYHCEkioLVw50fOBRzrHIfYiptns6pFULdHk3eGy9pNUqJmAc03UBT6/lXpB2YMRMzrzl3Q/q5JQekITaSI/x4LVMh5FupjJGgxoUkLd6mgqxnEBgGie1BVm3R6yzqHCsCSUylD881sKYiY8/GSYtE9keQh5AORRKscZwbsppckZNlAEfP4SH06DeAv3b4Hc3sBd7+0/THKBOIkUGB0/QBcrXr9Z8frqAPz/0UiZlK9z9usRlS0JUq7u/M+cyCMDiV5ACw7FEBj8lSIQohQggWPa1q7SEqVPqfl8UhyvpB7xk4dLaOZ12QLKGyIq0upVwSRO7ECCGPA7jC5+/zAJ7n83cC4L9m8uqGFZsuAp7/bt+b/vzOb+Jl52zH9c+/FKieAE78JHEsqoXmPJ3QnvomYNvl9G8HvwAc/7HrbpkZhuVspuzCUJhK59gyJoroplIIdTGmiNZGTRDoD+9m90ytjc3T4fLMpO1As7VOIumnF8PmIXRisRVq0g1ko6hh7VsAIjY12eHkchuqLGKDzzjDIwnOG5Z3SwgJyTwg0hoRs03/eErCMy8okhjajGItEMNajAnB+kkVn/ytp+Kqc2ayfYFZwCQ2Bho774M2Uwj5EUKWh1BGmwkSd57ypNAZBiqux0ZvLKuWQkgYuLq3IktYasZT3RxfbGHn+gmsqcguYp0XjY6OyYoc37xUHnOrczg+awtZ+i0yUuolfwVIjuv/0W8BD3wJ0Jq+EedDCyO7ljE5QnkZ1rasyGLg5bLUYgohnw27XPGcN57nlUIM4lcYvMQGLRjmT8xU2zp0g86HZVFEx8HACSE53Prh+GIrMowlDFYb3ypRyiVBOVeqKxSGQVBtdTE91u8hlMmAte864CLTPHbuEeDID1w3zzc6vhu12ChSIVTq2Hn2OeREjBECVQ6JTOZYaNc7PVf0rQC6OIhuh6E4W+1EDsBJI9LPZkQIVRRxqMz7Ti61cPW5G0Lvk4Wi5rgjLrlaECE0W+tgc4CiTC0oTTHMc6OhhbVy0McxhVDaqPJW2KZ/4CCRLWPLLbrBiFogCgCuvWBTVi8scyjigAihEDKg1e1Z7Qz9j8taIaS7n5cTsqkK0nsEcTuf2OaiDIqxJAW3E0st7JyZgCwKproyHuptPZR0DoSkBio9IpGl3yIjF7ZdAex4svsYD3yJGhhvPC/9ccqCDD2EogihsI23KgkgASQgIzXXek2lgcjzRhbDvChXFvoIoQiiISs4iT5RFKDK4lAqhML8FfNEVMvY4XnqvelK54vz/FIya4vVhBEhVCAaGjWF804EFUX071NPA9aLToi1MF1sdHHOhgyqOqOUMYoiTKVTxs43OjqmPEk2aoyK+dlaJ5K8SGqAPFfrYH/CfmDX8SXRalEoO5ZbXdQ6OpdvUlpFDTPtBpIl5STBbK2DjVP+JF9qJWQGaASl/zggQDAX0Olea9Mkn8rnIUTh3qz3b2KWmGeF7wKR3b/MmwxT6SILpSOMW5qBcUXyV5Cw+eT7f0GNm504dhdwx18B178b2HQB38ESet2x80MP2kiGEF7Mj2IYW8YIITi+2MLT921Ara0HjJ3RHkJrQtpSAyFX3JHvQOjn7L5fhmrqn99Mf0uesXztDvr75jcCY2uBDefTRLRhj3HOkBBSExNCVM0YlDLGxmPfljG5f73vek1y+VSSecFPIdQuQBXST0QNXhFt4dCtwJ1/B4AA6hTw8g/R69cB3/Ny4XHg6+/sJxsFCbj2HcCup2T2ElVJCPUCPTybjhBiCqFh6iYoGoOfrVccghcKrEo/Pe5YKJiS9sBFV9JjSir9u2EvZuYbWvrIeaBgD6FhaBnLmle1J/TA6N+A+3vBpOvOuwWmx3jQ7vaw3Opic4SKxzJAjjnQMjVJWmSmsCsAJxZZClc0MUsrJsnf1/HFFlRZhCgAtXb8pJwkmKsHq74qSs5pihyKOdb+MeEX9+2AIonRvjMRx2MKodIlWpgbBjVis74c1qIwRJDF8rVLtMJS/3ZfTX/f9TFAa1C1Bvu566PAw18Ffvov/AeL3TIGAMTa2CZRKTKCXi6irTwCcRUCy60u6h0dO2fGMa6KaCcYs+rOeTcOJLWfEOJFlmrqJTMJbf0+99+3HQDOu56uy+YeBe76yMpIIcvSVDrinA9TCFFCyP9xi80QxSZr6wswJJcHpZIcAAalEPIed6xM6bcP/Dtw+HZqEP/gLbT10zmv6BpqjSYU6FirEjs46MgdwCNfo4/r1OyfQ9+i81CGCO2GAHBknvq6Je1ySbpPWU0YKYRygf8GnaUE2C1jdtpL5oO17JggJAWEECw1tfSR80DBKWPD0DKW3+eQ9tyodfQ+NQSvp8ZsjS5MN68JV7OoCVrGWloPtY6ejYdQAtPQQeHUMjNdzl8hdGKxhZ0z41hoaIV5CM3WOrhyt7+XjCoV1TIWDKtlLLSdg1Azd97rLqA6zjyEyqoQUmQhMAkHsCvSkZ4CpVYHEE5SPevDhh+vHZY+t+1y4Jc+Dvzb64H3bve/zw/+Gli3G3jKG6Jfi6VkTagQ8nrYcRCvy60upioyaNfZYFVkakxlIlNW7pwZx6nldrjnRMC539B6mE6qEGovxX8ckG3xrLkA7H9Zv0/Q2DTwa1+g/77774H/+B9Auwoo5UsXjIUsTKXNc0FyKoR8zg9GlrrGVUbSy6ZCyOdxS80u1ozJ/m2YjBDSO/Qc8kCRhZRF5+GB16NprKDQEX+FUIkKlRMbgZu+A7xvN/CF/nnjjQDeOAbgzwBM7wDe/jObYPy1LwBTm+07/8n25GmIXiwdAyY3Rir9D881sGfjZHxfNhNJ9imrDSNCqEAExfopWaXvOC8UV8VgEk2tB90g2RiG5e2d40SpFUL5E2NKytj5RkfHVk/UryKJ0KKSy0DbxQBgUw6m0oxs2hTQXhQHlSEihNhnyhO/XElZYTq+1MKOdePo9oxCFEJ6z8BCUwtRCBXUMhaauqdjXJEg+aUmCV5lXkpT6bBo8UGDkEj1hmVi6udZMUTIpeDChRAPIa0X7i110YuBG/4U6Db7b1u7E7j5TcDsw3wvg80fvIUL8zpwegjFRb3TDW3LLBKVmN5lNiE0gfuOLycas9paD1sj5k1feM2B4xhys/Eri/b65jyNlw9DxWz37tSANVvSH3OQyLRljE8h5OfVokpiIH1abXWD1++MBAqInlfEvBM+C0J9Fjj6n6F32XbiOF6izGLsUR1YtxsVeYAtY2Vbl0oK8OpPAKfv77vpGw+cwaNnanjrefPAo9+kcw8biyTPeefX2sqDw98DWov2/5vzwFf+O3Dxy6HKbwtdxx+Zb+DAruTBFUnDb1YTyjFjrxIEpQvE8XThBruAzQuakVGZGIYNwkOolIQQM+vMixgjEbHzfC0yLuk6IdwbpFnTTDOqrSsJ886MOjdzECNRiFsBHiTOVukkGuSz40Ta9LSz1TbO37wRZ6pti5zIEwsNDYQAmwLaUstA3NU7Pa5WDkVKX1FtamU2lfZsXHxItOVWF4IAfy+UUquCTAh2WlbxsfPhaHZ7GAsjCuUKcPVbgm//1rsAvRV8uxOWQijePMXG9eC5Iox47WGyIpXiPIk77jAz/h3rxjGmSNANAr1nxDLIbnUjCL8g+MaH83oIZaSmJoRu2ibWh9/PIoSq6Y43SHRbwI//EVg8AqzZlslTUt+s4PFmudXFpCr5+mspkhDIAVbb/YE0FqwCsP8mvYxJi4nwnT8C7vlU6F1eBeBVEoB/BSBVMLb9Fiy2iyeExhSpECIqNi64gf548OXj9+CBehVvPe9hSggZPVsF5PUSk/3GqQic+TnwyZf637Z8HOqUFEhadvQeTiy28MoDO+Id04GkXqerCSNCKGuEVHSsyoBrUDcl7RyKjVhgF7B5QfsfOyEKTRkrMSFEcvIQ8igV6npEu0/IotvtZWBvkHgWB2c5W8YqCQbabBVCOXvTZIjZehszE4o1OYUhTVyqYRDLo6moXvbZuvmdBimEZKkQYioMTU2nG9UIyFwV1ei2IKCMCiHbbDkMy00N02M0MWWYIUtC6caHttbDuJKikCBXgC6nd0vCeYopyJK029V9WpUHBVapJ4RwtRucWGphUpWwbkJxzW0uQijiI2l1e8mu+4j48FBkUTw7cgdwzz/Rc2Y8ihBaQ38PIyF0x1/TJN6lo8CR79O/7XlWJk/NkzLmq/QhzFTa//urtkOMyplCKEC1sWJaxrotYHoncOO/Bt7lPV95AEcXmvjYxfcBd/89bRmrZdTeFILlVheyKFitwOVSCPG1+U6PK+4gH4sQ8hT5JJV/nDJ6wHfeQwMRAOB1X3K3n332RqCyJrQAemyhBYMAexIaSgO2h1DZ1gJlQjlm7FUCZird1zImp5S0+5FQHpM5377lpBiljFEU8Dmkrez4pZ3wPueZahuSKESauFWU+AOt1Y6WkYdQeSbecJytdiIJNoY0sfOLTQ26QSghJBdTqWIkX5D6SZVFLLXyXJhxKuai4qAJgSqnNfq3FUJl9RDiaRnjM5QuN2GkiOnabpMh2nB8Y5qAB2Wc38w3Sew8IdbGNsn809R0TCSJXc8BbH7q9gjUCBIUoN5rO2bGIQiCZfzd0Q1M+g5r/s9HCb+kptIJTZotU+kUY/1dHwMe+go1k9799PD7jjlaxoYJ9Vng2+8Cxmdo4tKBXwNe8pf9m97YcLdaOv/mhLXx9nmsIjNTaT/voS52rQ8Io+AwlV4RLWMA9UfdckngzQ8aNXSmDGDqDIBiTaXXjisW6UwLcSXctwSgxghHVjgwdHo+CSIgecYyuRI+Tn3j920CSG/RFrWprcD5LwD2XecuYpvkUNg6/uSS3cabFOqoZSwS5ZixVxoCqlBMpTPFNugOSXs2g7XjuLKXEGItYxl85aOUMYoCPgcaO59sY6r3DLS7Rt8GWJFFaBzPSSPE1UiFAGs9ieshJIkC1idMDHCiItPrh7cCPEjMhqRweVFRkptKO72KKoqIeid/U+mFBh1rNoTFzg84hpVXuRAvdj7CVDrJxrAAqJIYaiq93OoGRM4PEQgxCy7lqo4nVpAwyGMxCKG4CiE77AKAT7td9GdZ7/SwY505tsfxwckBzvmJR5l5tmaT9kwhFJdQp99vAgWYMg4054D37zWfaAHY/Qy+x2axVtLqwNbLgTd+N/q+rGXs5jcDt7wt+TGLBrsefuUzwDlXZ/70fq1gTgQqhBDuIVQLVQiZRaa/f767QCkIwPPfA1XatzIUQhyotrsu5Xkm5s53/h3wvQ+E3uWdHR3vMAjwfgXYdBEq8rsLC/PgQsTauN7RadiJk1jWO/5EqVQJN5W+958AdQ2w8XxAnQSu/DXgpR8MLp4TEho7f8IkhHbMJDevr4xMpSNRzpXqCkW11cWaitxnaJqPh5CbEMq0ZazQlLESE0J5fw6EQJFDzo2IhXajQwdXd4sMHXh5YpgXGho2+JdFXVAkAYIQb6Cdq3cwM6H6m/vGhLM3uHQR3x7M1jrYu4dP9lqRpdDUhTCcqTKPpgoqsoS5ev6S6UUzlWomQFVSnJIr+Jxqar0QxZvXVDrdAtqKnU+yMcwdJLJlbKnpV8lmKDfxSsEq9gPyzwhZgLe0kNh5HijjtH2CByTZPMU2toGfXcj7a2o6pioSynCeVBR7fljDcf+5egd7NtBKtFMhxItuz4BukGQeQle+jhIWxADu+1y8x7LCVBo1dacOVKb47rtuN/CcPwDqZ5Ifb1CYWA/semouTy0JCF3XhCl9rJQxv8eFeQjteSbwjLf1jwn3fAo4eS9k6dyVsRHmIJfrbR17N9rn8Jgiop32vR//Mb2uLn9N4F3ufPAMNN3Ai2dOAEf/E5Vzh0shVG+bxTJrHNGpSbnXPwiINpXu6cAlrwBu+BOOI9sJe0FrrpNLLYgCsCVFR0ESa4vVhhEhlDmCB6xq22eBTVjEcdYeQiyG0m0qPXwpYxnIoPNCEil+TChi8nOjbkZsJ42dX2x2MTMZfb4IgmBGivMPtItNDes5npsHFvPfKzchRAjB2Rq/QiiNqbTT/2lMKSb+dLmpmSbEwdVPHiIyT9Q7erDs3gEu4/WIxWlL60EShcjUmUEhqmVsOaxFYYiQRftf1giNneeBPEbVHDywTKXjHc/2EIp/zTY6OiYqMlCCAjm7/njGUkII5hwqzmCFUPD5ZBHBSeaiLRcDL/oz+u9D36b+PLyqV6t4luJc1+rAxDmcxxOAa38n+bFWMMLswWptPZDYoabS/d+fYRDUOzqmgxRC4+uAF7yn/+8Hv2A+r4iGVsI1dA7wqoBVWUQ3CxJgajPw4mCV0IcO34F1EypevPfbwMl7KRE1YEW0BY4xwQqgsVrGetQ42pswBpim0iFFxp4WuwVTlYM7ZU4stbB1eiyWsb/f8wMjQigMI0KoQFR9e4fpYN0zCHoGSaiYiPYQYgqhQMlpHCRcYCZCmRVCJH8PIZlrE+1/zjTMNiG/FkWexcFiU8P+rdNcrzOugd5io4t1E9nEWVvMf9cA0oeW5YZqW4emG/FaxhJWmGYdHk1FmUovNqkUPmgM421VTAzORc8Uh7cJTaZK95k1zWjx0rUxCu6WoCAsNbXhbxlD3Pa/jMBjOpxWIdSY47uvkaS1mdgtY2lMpXUAS08At77Hfw4XRNpOsH5v7GPwwqkQikJD66HdNSwftCQKoTZLFyzaO4yNM2mKZ50av0JohECEFQH8fB0ZmCqPwL2qq2s6CEmeEqxIQjakSCkQ3frkVCdmZ8kRjlrbXWyi3kXDQcIRQlDXdKypyI6WMYPuH2Wf9aqkAloj6MkAo+tPJIVAlaTAffDJpRa2r0veLkaff0QIRWFECOUC/wGr2vIy/PaABdBKnJQVueBjKj2pSqkYVguJFpgJkUXVKy8UZCqddBPN+pe9Mds01S56UFxq8prKAmpM477FpoZzN2Wz8LTSA0pumrhoeuzw+ialiWmfrXWwpiJjTJFQkcVCTKUXmxpmQki+XFpjY6LZ6WEiMmUspmozgPBJ7ROTMwRBgBJA3hFCUG3rfIrSshFeLhArdr4sHmOEkPSEUCwPoWQFHLYu6SNGI+binkHQ7hpUAdUAXYN8/wOA6HMuGV268bj+XbFeWxzESZfxGuPbxYaA8dPnfGIKoVTfbxI4N3JJoTWo58cI8eFJh4WBvvODEOLvYye49wIGAZxnTzVlQXfFxM5HgPlmTlXssUaVhEI8Jmseoo8W9Mr0mYe30hNi7hVEb8tYgEKoOe//ZD16rsYmhBweP95104mlFq7cNRPr+bwQBMG0LRgOkm4QGBFCBaLa7mK3jwRftRZeKUkP52DHCKFb3gZUpnDTcgs3Sj3g/76bXvAv/PPkhnoFKGMssLaGMqaMFWAqrUhCiFIhykPIv2VM5Ui1MwyCpYgNvhOVmAMtbzsaD9SoRXtJsNikhBDvZ5rGc2exqWHGJJ7GlGJSxqIIRK42rCwQsOgjhKDZ7QWnjHkW9GmNuFuaXvymkBfmpj4oIrmp9dAzSDaK0gFDsdKy+FKmsoP/sWgEOjCWhiyMkzIWN3bevA7YuaHp8dYlDb9WZVEG/tBH0fTenclTtTgRJ/Rgrm4SQqxlzFQXxfEgYemChV/7WZlKqyOFUFookkAJIQ9aXTquTgWMq4yA9F5xrLiXzAPUJsWHH3y+mVNjMmBe7rQNjxLVQfNd2uPSY7uJPt7CaxlQd3YTOL3Igkylw1rGgqLqgyAIAEggIdQzCE4vt7Hj8nQKISBdkXU1YPhXe2VDyLhRbXV9/DVsaXZXN4D0KdwUWy6mUuz2MgDgZH0JLVnHuTPrgYf/Azh6Z3JCiBEUqz1lLHeFEEk1qfgSQoRwVYtqHR0GQQyFEL9PByGUbMqqZUzJilDNGRYhxK0QSt7qRQk3RggVU6laamlWOo8fBl2l7PaoHJlHtaNI6X1nWml9YgqAJc32kGhsAxLkB1VuVZAJi9iwlS4qBu/n1MqCMFDGgeVjwB9tjL5vXEKIHSJIIWQhgEw0N2UTqhx9nkSZk2aAOC1jc6zV1lIImS1jXkI9RCVlm8mnvPbjXmNRptL1WeDYj4AvvjF4M2d07Tj5EZKBkEDlZb3tX6SzHieJAAgMz/llK4RiEkIOcnc1bIRrHTPJuSIBTfo3uwODQE5zSYZcjz2DoKn1TDV+tEly8Qh/HXXnXqFPIeTXMlahasLqSWB6u/s2gymEYnoImfvgTq8HwD7P5+oddHskdcsYEN/aYrVhRAgVCLu31Q1FTm7eCMD/WlfGgZf/nfXfP//onegZBM/85acBf7TelvUlOl6BCqFhIISyJsYcE48sCehGER0BE1W9jxCyZclRE9WSSV7wkjZxfDpqHR26QQLTqOJCsSrZJTxHHFhshKdweaGmqLQtZS0AACAASURBVGYsNTWrNa0iS9ANAr1nZNMyGoDFRhcXbA7O8WHnXX7S7fBzuq3zm73yqZnCj9dMmySVGzytDT77x1o7Q8+5AcPaEOgEyIaDToVMWoqe+iZgbC1/K/XaHTRZKQZkMZmHEJt3JiNbM2G2vuVLCFkKIQ6DV1shZJPpQDyFUDtzhVBcU2mf13r6IPDha+i/p7YAB270fw5RBq54bfyXOIILqiwCPqd1rcOI9ggPIc8lZymExpONx2oGnnilQcjawVIIVRTADFyzTOV7BsaRz3zsUkWawmLmXVSWVuUwuIhKVjggIabSlTW0IPGX+4FXfcydvpZBy5gTVuT8uvQGoRVZ4poHViuGf7VXRvhc/IQQNCwG2X0/xTFg5YV6R6dVL1GiBIa3QjT/GE214MHxu+nv1Z4yVgAxliaZiUnXvSoFy2Tv7EPA4dv7Hzg+g8V1zwfAT17EUX8sWcSISlPwfvYZd9vA3muBzRdxPReQAaFaEBZjkmyKJKJrJFtQOD2anJuaqRwJIUv1dfKntBLtxKYLoco7AQyidYeCe6NGCOQM1Eypk6QKgCIKvoRQte0xpA9FiRe7xKHALXRDFK0gSeUvtekC4Hl/mPzxUbDUCvHXJUGtyr6Q1fxbxmLMD7O1DgQBWD9hk+lAWDtyiTyELELI57UuHaW/r3snsP+lVEE+QsZwEu1C39+AMIWQ2+jfWwOsthMqhEzkkmRcQtRNhZCTjC5ifVhvO4g+M/yRKV50gyAqwKEQhBJpDr/RnvnZ/fjjwOIRYHJT/wOu/V/AjicD//4Wund0gu0t/TzjQmCP0+7z9MQiJYSyUAiFJZmNMCKECkNHN9AzSJ/BL2Az2OkH7HDmfM8G89iS2k8IffdPrIhKLqzdXUzbQFkVQvf+M3Df5+i/80wZE0UYZv9z3AS6oI2HytQP334X8MjXfR/bedFXAcQgL3gTpAiB8oMP4N3yfXj6Q5uBh+eAR7/hvs/aXcAFv0D/LYjAU94AbLow+NhiVtdPvlhsapBEITg61gM1Re87TXGjE7KVlNPt8W3SEkDTDTS0HiUQ/+O3gRM/dt9hfAbK1bcCoAszNvnng2CTZ8AmyMKgiHEW0ME+PFuny53SJUsC4CMWZSoP3nO1zLBan4oeH4LMxrUUseQFIunnxqrlExxpfrHMsRMiDrE1W9ewfkK1lJRx2s0Y2DhTOBlsmUr7fF+dGv192S8BG84t7jWtUigBhZd+1bYbbF4MVAglGY85bQJWAuqmQsipwGKXYar3H6HErDsJFZMQYm2Dmm4Eng9lQc15XrbND+zef6K/d1zV/4A1W4ErbwS+8XtAa8F9W1wPIQAgBKrkb/5/0lIIpSeEFElIXGBfDRj+1d6QIGwicKaM5Xl869h+hJDWALZcCvzGl/mesKgkirISQl9/J5VTbtoPTHD4OCQBsYkA3wS6iEnK2ni4GqcdvkQ9Ddh2AHjdzfbNT/wA+NyvQVueAyDztzfxDrTLx7Dtnr/AL0kVVI5NAKIA7H8Z8NK/obcfvh346u/Y5GRrgVaRX/DHgU+pOD6jMmOh0cXMhMKt9kna+67pBuod3VHljt/2EBdLLaZ+UoD2EnDRS4CX/S298TvvAe77fCHjXBiiK/fuVqq0C4dyp4yZptKifyW7FlmRLkHFMxKmf4ZYrvGhPSjCgBtuU+nAVpOAccxu27D9NALPlzBz0ozANtk8xNZcvYNNa2zPDEbaxTHlz47wi+shZN7fz0OoU6W/KyN/oCIQ1Jod5c1mp4xl5CHksgkox/iXClHEjE+yrkUIp17/BF+Prv2dp/NjGD53l3JNc4xbr/8asOvpwQ8cXw80vYSQ2TPH3TJmey4B/oTQmjE5sTrOiRVzHeSEESGUOfwHLJckz3P3IjxQGh3dPrak9C/C9DagTMT2GcgdjAT5wk3UF8mJZ/w28LQ3Ff+aCKFpHM/8b/nK9uFUjxkhC0z/iard7aEiixA9yiLZ6SEkKe7vfC1t69Hq8wC2xGtv4hloTb+I3+vehLe/9ff6o+cveSX9YXjfOZEeE8PSMhYntQ1wV7bj9L5b5IwjZQyIt6mJi7rlcaAAnTowPmOfVzK9botojQ2DtVHj2IjLEofiLWJx2tZSRosXgKDKZa0d7nUxTIjTMlQEBtZSFBNKQuVykymEeDyEpEphCiGe73+u3rEi5wFH7Hzf2iz4M2ln0RLoBK8S22qv93mfFiEU7PE2QlYgVruQF/VQDyECVRIhwEch1NExpoiJlbXKimoZi259cpLRhRTcfeZLi+Aow7wT8dVbHkhjMlB3jFuTm+ykZz9MrAcas+54+iQKITg/L/c69cRSG9vXplcHAcwuY6VcB9mj3Dq2FQSbQc7BVDriajdcDvjwVwjpGq3WlQ1bLwOe9mbgghuAvc+2fzo14PD3BvOael3ap69M5HQAt6k0kKzdoU+hYC4sVUmgZnd+DxpbCwDoNpcgCMDacX4PIa6B1iR3OpAtBUsoOFoKsmu5zBcLjZiEUMJxwWtebftg5LcwqbkqTHXfjUfu31OUYi7GRlyVhNQmnM2yegg5NphBbahMIZRXi2GRkAfRUhqWQjUULWMksfIyqi3GhQJSxqyCG8f7mK9r2DBlj9GqJEIQQlrGfMiawXsI+bzWdpWu+5T0xqwjBMBxLlhEu+f0qAeNq+b92Jzv/Qb9E4r5oUgiegaBUfIk1rSwTLsr9mdlK4Tye+91n4J/+dalwURazc9UGogmdSY3UVX/n2wFjvyA/s0ihGJ6CEn+5PvpagvbMjCUZsdImtq8GjD8q70hAZNRuxVCJoOd1YI1SMKtecgoSelPGdPbwMSGdMfPA8o48ML39//9o8/JfSEZiG6D/s6NELJhVTcSbE5bAQoFO8mC9E8RY+vobc1FTI8p3L5FCu9A26PfWVdQqJokCrJpPB1xbKA8CoAgLDW7OGcD/zmjJtyQWfH2E96knBwVQmwTqIqUEFI9yi8QKKaR9KAm5Db3Rs00leZ9nSFeMamjp3NGkNllra1DEIBJHh+YUieouNtuy4BMTKULACPS+lonI4jXQDW070HGaItpjlBjzA9LTQ3rHPOSIAg0qjhWyxg9TuGEn+Uh5Hmtj98GnL5/pA4qEEEtY37EgRO2qbT7Gqu19RR+bsS1jqwUkQ48INgtY/Z7ZAEW6cb/GLHtJhTzn8NAQDQ6OiSRjnUuT9QoQui5/xvY/iTgtvcCJ+8F9lyTImXMv1Pm9HIbl+1YG+u5giBLQunTiAeJESFUEMIWSXl7oPSRUb4KoU45FUJBKMCMMhBdlmeZNyHkrNL6TUhRMdtG/+aXEKsKRfwW9qZCaN/iHXirPA987z77ti2XABe+0PdYqswZO2+SO6pa4SObOL7nYfEQqra7fCSYCUvZELOyteQhhJhCKM+WMVZhWiuZi4GKlxAqkLgLJGjocQM34p4KbzdFNbVnEHR0AxNKSadY861ZHkKez6zWpp5z3nZTC2XmgBgsRWS5CONW5rHkGcPywAibe4Cgk4CtNyYUyT6vgkhDDsI/LaxxJ2Ij0DMIqm0daz0qzoosxTaVVmUxdghEavgphOYeBT71cvrvrZcX+3pWMYJaxmodHRU5uPVLtYp17r9X22kVQrbSfLhFnxFktEZb65yEnCrmP/5bLWN+yqSSzDthaGpUzSwIgpsQitoTbr2U7gt+8DdA9QT9m8EIIU41vDVP0+M655uO3sNcXcPW6exaxthefIR+DPXQUEoEVM+CZdT2Bj2vgaPv2JLqrxAaKkKoQo2wBwGtSX8r+Rtr20kvyRRCflVK27gQkLyLelECtlyGC8/cgwtxD/Adx22VaeD3jvkeSxY5PYRMhdDYGCeZxtFSkJ1pYL6otfVYnixJx4XFptkyNslSxuIn5cSFZUIsmuSdUyHkMVkcmIdQjFYOvjQKHh+RcndlB6XX0Yp0uRPSeCE74n/LgGHxEBIEAbLISfQ70OjomFClYDLRiSJSxgLijL1gxr3rPKT9mCL2k+khKql2NyPvsLjKO0YIOU2lG7P090s/CFzyivSvaYRokOCY8XrYGsBMAxNA+k2l2zp3+74LAjPWLxcpnhfo2OP+fOO0jIYi5Hq0lV8SvN5F5ViXRhSPnWOW4FQIcZxzggCs3QE89B9Aa9G28kjqIeT4vM5W6dp/29psWsZGHkLhGBFCBSFMIaRam/6EJyqvhJsNlH6m0j2NLs6GBXIFaM4P5thdRghlw1r3wTHxyDzV7YCJqt3teSK2bQ8hIEAhBABv+h5e+aHbMTOp4h9/4yn0b3f8JXDbnwYqyRSnUXUYTCJSHeM81+Qxi0QKQlLz0yLRMwjqHT1WlS9py9hCw9syZsfOZ4affNKOJQVwba2DL6pNbPmK+b36tCdEKw7Sgs9DiKeVQ5FEGIR+b0kq/Yx8K6dPjGN8CTCMrLW7K8I/CBhUS2mIh9CQtIwBlEyLS6Q1tP5NWfABKsDyceAfbgCe/25gd0iiTULwbgiXGSHkSdasyFLslLGBkH2WqbTj+2qbZtJbLrXUvyPkj7DY+bBxVZVFdEGLdU7UWl3snEm+3uQlRYcCIcSM69pjhaiA9Kos0ej4KJNKpkwNA1MIAfC0jHGKBC5+OXDf52hKMVMKiXFbxvpNpU8t02LB1owIIe5OhlWKlbHiKxt8BizbY0Puu192C9YgCbeHjPJtGRtChVDOcbWBYIRQ7i1jgCIm30QHxV47PYR8IYpY1gRs3zhOJf0ANY8DaMTk9La+h/C3jDGFEOfiRuJRCJW/ZYxd/3F8AJKOC0tNDWOKaJERVux8lqbSB78AzD0C7HgyAKBVl1AnBOLUJmDmRcDuq933J2RAG3MbbcvMN0K1Q9y+M1Kk50L/uNsx/ZoqCVNhikKYhxC/mq3E/WMkqu22eLDrsNTnhjk3KL7Kz6gCVM83PMMXl7wKqJ4CTt4DfPJltMgyuRF4422Zed4onOqIpUBCKKTYEWAqPRCyz2oZc5BXnRr9PTaKmy8StvLSfX7U2zpNcuqDIx4e/YRQNY1ikxDHOrK8a6Qs0PIJcsjE3Dkq7r6j9/ntqSWbd0KJtK6jm8BlKs15zj33D+gPAPz0M8Cj3wA2X8T/2gjxVQidWqb2HFkqhJJ0W6wWjAihgmD7+Pht0PMdrPuiLuWKT8tYZ8gUQhlJzY0ecOqn9PPYcgnfIrR2mv4u0lTa79yISlXSelbSlN9zhhV+6x3dTV6yCPGWPyHEHTvPWsYqnISQXIk0HR2G2HmrpaoAQmix2XUluFkKoSxNpbU6sOMq4HVfBAB8+qsP4pN3HsFDv+7vMQXwe3mkR7BiDuBT7Tire0lUPh1r019uFYgclDLW6WLTVFiBoMQkkAdp2m5TIWAB3tGpx4xQajNuCkUWYyuX/do2As+X855Hfw5/H3jwy8DiEbqZWHyC+lNkAFEUIHG0vjHvtbXj7lYHRRJjewgNRCHE1H5OD6HOMv09MpQuFGqAQqgWpRCSRDTh7yGU3FR6eII3IhGx5m1q/WRsEQXDlu9xh+czbzkVQs6WsSRz1IHX0h9uuL3+nITQ6YwVQtydDKsUI0Ioc/ifbA2Nmsn1pQ84Kud5SRpZypitEFLsyhGD3o7d8zlQZBVXe/ALwBdvov++4rXAKz8c/Zi7Pkp/T2xMf/wwuJQK8QexdtfPQ8hjKh2UTNfR3e2N4yYhdP/naZqAE+dcwz/QmgaiY+O8hNBYsOno0lHg5L1Qz3sRgBJVYnxgk7L8Vb6krXBLTc1ljMrOgUwVQp06sHan9d9qW8dUJfy9lcFDSJGEQDm/u5XKNuEMRMjilG0gy6sCMRUgIZXsfRv7jcGHC+X0z9B0o8TnBeC9DgI/t5BUU7vwxbmh2Pss+vP4bZQQai/zv1wOUE+w8HE0qGVMkTmLHSbamSmEEnoIOQkh1jJWGSmEioQaRLS3dexYF7z2YUmcTg+hdrcHTTdiBVLYcLdNlXmNlAX8yNjsUibDFTY2oeI25S+FqXRU8dg5Zg0ohY4pt1tdd8vYVEVOZajuBPUQKsH3UVKMCKGC0AipDKipB+sIOaM3ilFS3WQKIcPnISRVslEInb6ffh7bDgCnD/I9ptcFpncAG89Lf3xf2BOPylXdDkhV8k6Olps/W3T4P9QwCBqaR/a/fi9dcN7xV/0P2P8yKNO/zzXQEr0DATFaxmQ1+Hv+zK8CZ+6HctNtAMqz4fMDS+GKoxBKGpfqjajNpUKm1QHVrjpTf6Tw95aJdDsMHIseXrVPWtWZ1TIW1Z42CDg9ygI9hIJaG4YPZWsZ6+hG6ZVjDEkqqq2ukcwAF7B9boIIobs+BqzbDVxwQ6yn5dkILDX9TaUrvurX4M+kmbWHEG+VnlX2f/pp4Nhd9N9H76R/z8vvcAQfkJDY+S7WjAWptWxTaeI4v5KsHbxYWS1jwddDu9vD+knVdT8/5UnW8Lvmy2UqHY6m1rM8JwdFCDFVaVOzCaHTy+3M1EEA3fusjGsgH6yMFV/p0D9g9SkuHPfLW1pYN9vVppwKoYXDwGdvpP9nG6lh8xCKE1f7/b8ATtxj/396B/C8PwT+84PApv3AzqcAd3/M/kyCoEwA84eAfdcledWxIafYREd7CPmfb80ua290nK/rdgO/8xglApz4l9cAPc0aaAkhoa0QXa0FFcDEBGdCmzwWrAQ7cz8AQDK6EIVyL3bslrEkCqH4hND2dfYkquaRYtipu6Ll6+1uxIKVWNXPgXkIxWjlsHxHEiZT2Qqhcm/8A1vGYibilRcD8q4KU491y64QskFNpT2fWwTx2un2MLYm4VpibB397dcmvHwc+Or/pP9+11KsdgaVo6WZEUJeJYYiC1YLKA9azs1VkVizDZjaCjz6LfrDsOtpyVo/RogHx2dsKS89n3u9HVAYZsU6mbXz249L0m7uBnG0zZaDFM8LTa2HnTM5eAhxBFZ419pJC3r5IZxIsxVCg5j3aXhHRRbRchBCp6rtzPyDALPAMQQE3aCwElZ8Q4F6p9/sjCGzCn7A9d7UdAiCI+b2/BuA+ccpKcSw9XLgnGekO36RiOMh1NOB776Xtj1NbqI+Ng99BXjwFnr7/pcA+54DHPm++zPpex4NmH+U/pt56uQMS+7qXZRzIGgDbBNC/o9rmu1NE96Fy8T6/vdtmk4rkkiFZgYJjLIGgHa7TQmh8Rix89UTwN89LfxuJZeCrn3ki/im+rfY88VJgGczOLMHynUfAQBoeryFjDfJhJEbmVWqCDEVQjapVwta6DpQ2MY8YPPj1+fvD0erJs9n5nO8zjAYB8M/dr7d7UHrGfwmpiXfbFqx84VvhoI9hMp+XtgthfHapYCAQgTvORKmEJp7xP73P70CeN2XuJ+XbgTCv/+lloapitzXUqpIoqWy5kF2LWMxMbkB+J8PF3/cEfrg15ZMCIlU0jLywtkyVmUKoYiW7DDIZWpfSoVo30yvCthq3crSQ9HnuJs8JPhwpYzpmPCLnS8CgmBtRiZUyaMQauGCzZsyO5QSFhAwwogQyhwBu+ywlrG8vTWYnNFSbjzpdfRnmCGP0TSNuUPREsfl44ChA9f/H+DKGylB9I13AvXTwMYLbHf8N38//Hm0JvBe01B5vAhCiNiTiu/GNHhgMwyCdtfHENfhWUUXHSGJeLxJMXD3qMtGG6id8r2fvkz/PjnBKWG//FeA1hJ832vttFVJVjkW/LmitWi+Tn/sfuzTgFCHsfFJQFQb0dwh4JGvQ72WKuD6FhSNeaBTDXz42vYJ7BI6lNwUJYhrd0FJK5WtnQa6NPEBPY1ee6pDIdTRsXsygOSzWhUH62MQafbqqvCaFdUERCzgTBkrqULISpHqr2RztSiUnAQC0HfelWUzpOmGpQQoJVwthULs67Xd7WGMnfdxz5OxtbQ1+eu/B3zzD9y3ORWtj98GHPsRMLUl/PnkMWB6GxSWgtmpA41Z37tKS09gf2XJXRRauxOqJEKL8RnQcSaD73cYrrERfOFsy2dnQkc30O2R0FZcv2IdUwgl8hDyjIErPWHJL2UsM/+kkOuxqekYt0JmPL5Ng1yXWogm0gbtIQRCsFdZRKWuAwuT0A2CsdpRXFQRwwv1vFi702odjupkWK0YEUIFoaHpATJih6Q96cDB4Z0RpE4aWrDUjA89mf8xm8wYREkGXvRn8Y+pTlBZdu0UsCZiIZoRrOp2WOuKn0LBJJD8KpVMyhp02liJeH1JMQEgxGo96RoGxj//q8Bjt/redT2ADpExFUQeeHHO1fTHD4e+DfzzL1rR0gOrxHTbwF9d2t9O58AmAB/tvRive/Ungahr8QcfBL71v/2Vg/WzwF/upwRnAL4MAA+aPwDw4r+AIu1MrhB64k7g47/Q/3eHWoy2GPGZSg/qe2r5EaQB4POdCb7NihYvo4eQA35eFzYhvDKWB3LJNkMd3UBlEClUCZAkppdfiecDUQJe8WFgLkDpsnYXbe/+8DXAP3L6CL3uZttD6O+fB8w+5Hs3i376oOOPl70aivyWfnVByJorjlfZCCsQhEA211g9g1iEkBUsETSuEto2I8CtEMrCQyiN9UDpEBaf7vTyKdDcud01AtPNylKICINL1cmsQ4r2lP3pp/HFzluAQwA+SMmJ2ysA7jF/0uLyX4GylrYc6wZxBGqMwLAyVnxlg8+AVe/o2DXj2QSbd5NEIVcPlFQLtLLiyhuBiQ2hG2MXxqaBHU9Kf9wbPw/MPgxc4LNBzgo+SoUksn0AGHNVot2eVUEeQkk2hJYxum4AjbPA1suAp//Xvvs9cKqKP/xeHb8/noXHgvtzSqrmSA29Rcmgy14NnPs837t8+f7T+MiDO3BTDILAlxBqLdFz/qrfAnY+te8xXcPAO/7tPrzosm24fv8W4EtvBhpzUKTdyReDrKL+3D8Aps1kMUkBLrQj5iNNpQnxfz+ZIvz9tbWelWQRhbTkla0QKiMh1J+m5kQSv6syQ+Eh1TNHWAJdD5XApLtyQZGE2J+brzI1Dq745ej7/Oq/As2F8PvUzwDffhfQmIMqbaXXcv0ssPdamijqwQdvfRSKLOD/u9YMi/jenwGNWaiVeG0GnbTvvw+jjcuwgbVpO4kd1nYYZdYvCF5CKKWHUCFz7+BhGMQkNtyfk5JFymRk3L3epz5mSXOlMZUOGEa6Papcs15/ZQ3w67dQe40i0TgLAPjH9f8dv/XsC3FkvoEP3voo/suz9uHibSlTEm9/P9CYhbLBPheC02ZXL0aEUEGgptLBi4Qkvfr9CPHOWGkVq8oa4PJXF3/crZfRn4JgT2Zxk17ohtSPCGTJQsEKIZZKxzM8eI3RzSdduws40L/ofhQn8GPy04QRqsFQJDG2107m2PFk3/cMAD86cj+MsVOxZKpss+7brnDONcBlv9T35+V6B1801uHAnkuAA3soIQRK2HXSLkwufBGw5ZK+PzNvhEgPIWZuPaAFUqvbw8YpDiKSxK2oBiv0StsyZoIpEHuEgL3SLCrSpYGjRbbwSm3Ata7pBufYOkCYk4MsiT7Xa/A10TMItJ7hQ7xmTGrwpIzNP0YJIXjS0jZe4DtOf+nW27B/4zRwwCwc/eQTAMIMqfvfEyFkSDyiRsge/abSzunDLrT5rX0Ex78Ej6k0G4+Tr5kGrc7NDGFm/UwV79nriAJrfc3vvfu2qg3JZ9429wqu17/v2mJfhON7vVW5Dr914Do8cP8pfNG4BzcdeBaQlhD68T8AgLsbZwC+/2XHaNYqCI1OL3QRqCaId+VF04c1H2E4wJKZfGX7YbJ105jNr1LJWsYMwHfT0tDiEELm6+Sc/Kotsx8+Y/WBKmdBqOYHnpYqL8L9o/xhVSE93x1Pyk5StLo99AwSQiAU7SEUQIzHMHtNW1HtdMusELLBSEfn2+SrSA+DasFNVpclYYfGzpf7vGAIVQj5zB1sc2FvyspxnvC0FFdbXayd6B+jFVngJrF1g8AgWV335fjsRogPy6fRcCp9+JTXokCJRYYq5+P8EVCwW4FomutWP3sMNRMzYf/rsc+vU7CPCZSfEArbKwwC7Hs8tUxDg7JMGVOHqI1vEBiOVclQoX/QIYSgoQVU0Jm5Z44b2raWkcnhCMWCEEvNE/fc6F+YW0/qaBkLMkBnsfO8EwRx90uHzLvVXNQHA/YQipATAzFjvFnvu29safixLJ8CJ/lECFTZr9LPi4hjckrhB10x80sgccPHVDpsEclRrSyvh5DbVLrns3FJk2pTJkiiACHHluy46JTdVNrVUhjPQ4jNO2XZXACg86ildPK/ZgkhWGp2sdarXDUVZv0bCP/nGRZl4Ah5gviOq/bcHDRP0vvSljH7r7V2F1MVGZJPey8v7LbZcoyB6RBc8AH8yWjFV+mYDdq6j8IGEQrvohHheQb4E2mFwnyNVbMgdXq5hTFF7B+Tkx8gdVjISkeZVyUrBq1uD4QAEyEqnXQb2oj+1q4eeuwRyoZ+6XF4daN/gvRtGRPc1aJgQii+Qog3YrPa6qIii9lsGAT3xqUsGz4/1Nrd2CRYEgIlqAqZJ2HGV8EkuXulRRFzHZ2/dTa9hxB9nFrGPnXndWMtkLI1MS0bFLHguNlQsrA3NISBkiBhC/ArRAwWqiSGeiG1uj3oBvFVrsZRn1rKwNISwSMUAVbM6Tk9hDp0oxu1rvIzlU47Fltts2Xxs8kBltLFh9jwJ3WzAYtJ96qPBUHIVZkdH/5EmvX6SzJmV1t0/XFiqYXta8czTQNLHeC0wjGatXKB+wRmF1w/A+thsNMOHAEXTnMlegitEiQ2lQ4Z5KMIIVbJ4koZ85BM9kDrfy5W23rm/kEAVdgNvhITPHElaRkz15T+G9mAaz2o3SedQsg6qO9f2fnC0waY58IsCvxeao7UPJ7X6pvy14MsCr4pXmWCHKIQilJ8DQdMJdQgFYQeaMPWMub93ELIrsB0vQFH/Lq+f7/QD79z3hHZ7Tt2hqR7Zvr9juKR0rbs0gAAIABJREFUhwM+YSBODrIeRrQ7HuunEEpHCDmVEYNeI+UHS+nineMJgSoJsVrvYx03aK1tmnmXnYQL8xstDo4WyY4OwyA4utDErvWcacSRYKr7AfkJDgmGY1Uy5GgFMMhO5Okh1F6JKWOrBFyx8z4Ik+6rrsWKj4dQhyYmxJEo8w601dSLG3+ofhuXEiFJlS9JhSkoIS5PIoYpygIlx47FriqJ+VdnfDZQhJBYHkJ2/3+y19rpDsemXzI/DqeEut7pYkwRw1M4nJ/xEGxY5QTx6enh/7kMh4eQbSodZ+4JblUeLKJaRmoWqd0/RiuSCIO4SdMgZNoyNgTX1Qj+YGOni2jnTG8VBMFDCMUvJjmeDIC9jiwLKZ4cwddgkFIHyMiSI+B67CdUHMRgyb0tgfDicSHwfq6EnvNPzDdxzoasCCEKdcVcB/mg7KuS4YNP9Sy8R5NVMMXcGOxmd6QQGk6QxJGZgaw/IZacObBlTAs3QO9/mXbPPN10BU/a1VY3c0Np5vNQ5kG+2o7zvt0tg673FdEW5e9TQNL10EdGrrLxLfqcGdQCSesZMEiEt4mrBZHHcyHcQ6hS5jGXedeJbOPiTrVZEZHznop9tyTV8U637B5CNsKVVcGm0rbBahlIDWKPOwFjWbDhL7G+K57xs6MPh5n8CPmCCeScl06jo0MWhchzQ4R7bTZqGfOAl5jxjv95hfYEdoBklR6dBaIDaAZvK2K/xicWGqi1dezOTCEEV+JoOb6T8mE0axWAsAGDQZFTSNojNmwtrT8ScYThgCgKkKIiM7nSXmzweAhNcRtKm8/JqajItmXM23JZjg2fF4QQ8zONP+EqMdMxgtp9KjkSMVySY0ZADKh1p635R9IGIe0Celiip2WLELLfZ62TfgNSNig5tgzEhdYzyu0h5NxIxfRmK0f7QT+iFNj1kGhvayzgIOY7QS1zI6weODaeLg+hto7JihzsiUKCTaXTFtHUVdAy1o6wSchLIR2a6CuJ0EruV9O0xuzyjFk/PbYEANkSQhh8sEnZUZ4zYCXBM+CzGL2+AcNjipt+wOqfaHoGQUc3SrdAGyEEnvOH+jjEm1T8JylTPiwyhZD/YxudOCbk3ljTYJ8GAKi1ur6y/LTIU2HHjYD33NGpOmUiJsnGUub8x4UgDyEdqiQ6Npz295O6QhZUmTPHN54xZlAeQq2Y6UdpPReGoy0IkK2WMU9FOlHEcQlhEZHxWp8yOLDvX/WegZ5BhuLcAGirSf/cE+0hNNZHeA1AKSQEKS19PIRMw183ac88hILaDPqfh41t2RJ+ZVBZjRCNfrN+t6l0L6QoJLj+5RyqqmkVQoTYLWODXiPliLDCe2oPxdCULtYy768uLA35ELB+s4i0QSqEHJ8vgYAfPj4PANidVcuYx+u07CTdoDAcq5IhB48kLy83+rL29I/ADyUBWdjqBisimDeNAeJvsJlAzeKKnQ9B6sVNyPFLM/F6YC1UElyDcc0Q652urxlwoDFqBoh+fx4PodyVXP4mzwBnK4dpBgmA03fG53jdkqtATPjGI7e7K6NlzAFZEoonIsNMh8uuIHEQackUQuV6f1Hvoxpi+BurZYwphDIh/EZE0LDCMpV2nDL1TpdrXUUVQvT6I4SYptLpxmMrJGHYFUIc8en9a17TVDqHgjsAtELUx2VelzIwwcLg9oj9n+ttD89CEgXs2TCZ6ZFUeeQhFIZyzdorAnE9hChoy1j2gzVPu9oIJQVhxp4BCiGOybF/YWq37gQqhDQdk7HULMQdOx/yupqazpdeFgsD9hCK9NgJqiAFwFnZ7qswRXgI+RFuhKT07oloSY3RJjIoJVfcjbhstYkkVQj1yr/ph90y5vxKam0eQlgI+HeZ4G59KoPpPCMV2HhZdtANTcA1ENKq7FUoDhTE6aEWkKwZRAjF9J2wiefRems1gxUUnER7o9PjSm4UYJtKd3QD3R5JUUSjr0MQhKEgJ9IgLLwnz/Whvb7z9y4qhW9T6F6Bvr6B7xHN1zimSGhqPZy3aYpb0c15AGu9s5KvgzQYjlXJkKMZ5uLulLTnsGELS5saYTiQZDJrd3sYU0SIAUlhiiyGeAjFNJUGX2+uYRA04xpWh6Eg08C04EkZDELc9xWk7lIl0SJFskZL60EQ+KriqbzSohCy6OHbiLtbTIAIhVDI8crfMmaTzYDbQ6i+Ej2Eciq4xIVNTJZ5Pna3voQbq7vRLquHUIQXG/Ne85ub/Oe2AA+hYVGAjZAjiK/ystbRI9Y+9L6iQKy1WbVNWxmzaLNPt8coEyJMpZV+MjpPj8mwMW9QLfJx0NJ07vVbEbhk+xoAwNP2rc/8uUceQuEoxxmw4uAesII3hPkPWHESgEYoC7weQlGkgH+ltq9K6SFQwk2l46lZLFPpkN7chsYXu5oEap5EAzf8FyrJVXokeEER5NHkVXeY98vm8wl+fxOKFGyWCcCZpjiIBZJVuY/pIZT0M6OEULk2xRYc35NkpanZN9fbOlcle5ggxzRHTo0g0+EhS6FSREqkBc0VXgQWoAaSNuYmeC1iy7dNuotxRbKue+f9WMsYD6HOvt9hUYCNkA+sljGXqXSXy5vNGTtfCzE754dJ/ovlIMXzQlPrQZVES93rRHplMkfcvXfMIyXzEAoh0sYj1295w/58X/OU3di3cRKvuWpXhs9vr4OB5MrvlY6VteorKXg2hJl4CPlc0GXt6R+BH67FLCe0CIWCIrJFR/8504isZPm/RgDohrxOdh1kphByHb9ME68bzRQKobi977W2ju3rxnyeJ8eUjW4v3JAwRWpRVrAq91wbcWJ7LvAsHHy9YnpYl1maXn6wK9n08zEMgrq2gkylzYWmKomxjfnTI8RDqKxkoQdWWpJhG9OGth9ozFS6XOsNSggSEPhvi+qdYBLUbof2vG+/6z7LlDH2/APdqI3ADcf3pFrKS/tmqrwOuO4djxUA9PoIofTjsSqXX62SBkwV3wdCsimIBVyGYR0gqiRae7CyghFCA4NnfHvlgZ145VX7cjmUFRaygq+DNCjXrL0S4LNYshJuQhaBefX3/v/svUvMNVt63/Wsqtp7f7c+fbrbJ75023TnYkVg4QCtCBQUJFtWkIiIpSQigkEGljwEyUiEjBCCSUYGiVGEBxaTBGWSKBOE5GRIkFEACTJpAsjdxHbHPt19vvey67YYVK2qVavW/VJrrdrrJx2d73u/d++6rVqXZ/2f/7Mahp1lgv94NMJghyJlRTIpFaWMjSOGp3aA9ybBC4zhQnJzZT4NdyLL9zz4YBw5ZUzlsWPoIcQoBzfPXrFTP/k/7atdOO2QKY750g7aAedDUnckZr5XzYWqq+fCvZO/f6nQzDtm5JE8tT1grLEjTd/jVBesdBXPRPwzWsN2GA+SUigIhgAAV5naD3Cp0bpLn0TbwOv9FvRlsmIHZh5CeQX8CmFYUnE3VcZ6+HBTbxIgBMvc7Is5ZcxaIcSows+8EH5uxdVxQyqTJzU+bc8gqm4YE7nCKY0U38DzQkM/uEck9VnJKXhpe3h7qQV+LlQ1jwAeH2THihs5LySOvdS3FaWsUJ5VvG987uxUPEvKmOQ8n0hAyFv6IptymWYn72LsPvUL+s/+uR24x7kE3B18bnt4pxlwjqXkanUUQkz33FSqUuUqD6EUJlkC5n6gRmTHjCxA5rTOk6WMXeoqiQo72aWMaSg/aV7aIUm/QnIdIj6+ilVx3CpjJ0kJLASAKvNOPISmjbYePsg2w+Y2NZWd3/bHPhRCjcwgPicEQeaXbtwGNjaKrXDFLERzLoCETKUlvMZWCLEE3ERYys6f4T0IQBm1DkAnAjst2CwbqdTctJhK544yD1mQsiKblDYV4iqElqCNdkBo9hBiy85zzunpHjJlbNqJ0fW6CIJgIJOayssg1cG4izFRCdSBUQPOudOzgsrp/kgmYm9UwS4qEBlugiQP0ACYLdQuNbI+V9X7lwqLefa8cCEqvtOZStfo4Op2goCB17LkgaCVVcRjSnNucu9FAaEISiFGHSE7F37KmGBsk5DF8y0E51qtqZYA00YbxnqB9kkhNP15VQg59Mfzd6WcVu8DsvHOw2l9BaCs6Ms/Lg66EWeMYP722o3nXx+idR4MAFEq3eZAGbVCwLx40yKN88IxEey2d8015ZkLlwlKdjDtp6kEZecl3PuRk5ZAtTeSMsYciwSETI2fLxrlHJ9CpYwBMcneVvVIhRcHhZCJhxDGeJZNc3LZyS53gMnJS9vDO+mEgtmpi+EhtCuHreaiXWGJ3++m2+fSC/7p/8RDiCxAQhi/R2HuDia118HtjheoH3JJGZtYlZ/qClsAkrlOZEhASHTmX7x28CVBOs9FuIjgp6bWFeIa29qTQtpdQc02RRWASsVVbrRRHkII7TyEPvHgRxfLv88rksDMFNjgvXc46LzjRbLhf8sgCBd/8wopbQl8cWmIN2TazyQWecxKMuelE0sKCU2FFOkJdphW1ymkh3Bnx6Hs9aQQ2v/cTsWDoarQnNo2Cs+LVBnzrxCic4MjBIQUg5lxpb/NDj2bMiZ/5iPmmFdjLDZG1ULlkaTu3wiTQfrxz6g1WYhTaiZbI+J7P2TR5zZzIJesdfWr2uS1SL0oyo4fxaogSb9tAOgF+mleuzG9gBDV/4n6amFlPYyXcVRLIeR1cZXXO1ZYWaqMjUwqrk6VMQAY8bTB86PXqST4B+s0e0ol15hvLOaEWJ3oy8tHUqVLkKpmmvIfg2T8DoPfpuIhpCKBVnA29q1almO6LD6aMFVQbFIlColALUxNOzCxh4ncQ2gxfrb1u5G04Y+W6iMhm4HXzOviSF7aaVJn4+N1MShbKlMiLfcngFT2uR3UKWPLeYTL5Zdhk8rR1Ei+CBQsLjHGyoBsfFZ/MgDXlLFUF650dbs0zD3XDZqU2wZQ8xJJypigqum2n0ujbZDrEPHF3Z+pdNrvfeEISFyCmEqbKK/Ja9WPGL547eDDtRH4j5rRVAmlLznBvxf7d09SnMMjMlXkxUd1Mx8oN49TCuKHGzPIfKd4CPEpI9cB6BgtElNcO48Pycu+mEqn9MIXTLBRVdy7QaqGmFKs9gOVsYcQE5RZB799p/4c0EOIXGuKucHP84QBGZvl4cnHhjeh4HwXMQR/x9mtus4DvtuEUOxbJE8ZA1gCka65/NJDiL+XXLd84rO9PttS5ZNXUx5BeFJljN3JPp+HUIyy83vy2KDZKhQB9HdUp4BQKnONvYcQBtj1neOI4eOdYyo9/96SNqeh1Lx3qS2uCseDAcHWVFpvM2w1lQaY3rkvJNXvTM4HIF669lHI3j2yWTlaq5PlPq3cPm+uapWOqbQokBY7ZewIpmtHCJ3+PXDh7K0gEtsX77nj+3qwO5gA4JZOITAXBkh9AlrYwngIWQwq7cDZqWQVNbyUMYe0LpX5NZkUqYMH5kRNGVPwbJBSxWIyeL3Mz+4tR16+GKMGmJwoU8bYah9RPITMvVtsS5WvfW6iC0M6JRExCiGD1IY8IOXTD96pFQQn8yk7P7EoC+l+VbbbLAoIRS4/v5hKc05dZfhL0s3uuiljqau/CocxMIF2nXnVEhDqJ4WQdcl5hkdIGeP2qxivG4YBFOQyVeA14eq3hDa2qhEhOCBfbKE5vMBEPpSR6wBe2kHpH8I3b3TntRuhQqtUrpAfdgoh+U5lU1fA+0riIWS0IKRT23oMos796T4FRn3In9njx80NVnjsaFQZ3MJKnfUWY4tX0W5Bht1MpTU8kvRTxg5YmHMWn+0wQFMhqLXanmGqJnO8RQWSwcJwrSK1mkojBPBe5VkReYFvSpwKO3zTYYCEg4UMpF/VNeQWV9yJCeUhxOmrP0p9s7C4Mg13A87j4op8f2bv2sPCeU6sqbRQ7UN9Fs17de0wwufPHXz6ziEgxPoRJh6cUGPnm+nmoTgjeA/3KVfb4i2p3/N0UsYCB4UcLDgehfRnrLnBWTzpLAhDKRwmOaBNukohFcQdmHxwlO1CX+sKMOfzLpXAVLvwT+3gOV1s7yGU4m7Mc9vDu4vddZt47jxLPISugQJmw4ih7Uft64s1GN87+fvAo1F4YgmPNT+vN0lMsuSQYWGpanPvzT0rUh1bdums8XfHc1PsNtJ+g19db+MhlEjT2Jad3/LxPlXWE41NJpVp0llcFVKATRnTUgihdS7z+VMLX3l39XIuKo/HbBD0KfeeMUfmeEyGUEjLUq4u88Zr/Oq3ikBaSptXgecTkz1L7OeRJgm1gvMiTxkh+b0O5fBUhmEpvewFA4jxq7kRbysapEgZ5ppf6nFJ69KuarFVsywBGU6n/nTvrcyqdUiieoBgIHs2VgjNYAyXRmRszDF0nQNC22PNPhjz/XGaEAlMZKdjKvqYze4MtvRKU2FfdQ8Adtd3rZFCGSHyEcnAOJi5/z2V2iBKncmZpo5Qdp5Dm4WH0ApJZdddSL6m5CFEqyPm+dXWpWViVcXyz/vK6zsF/ddj+HEUpOBVJT1iAw+h+XdJuY+XtofPnzv4yntfAaFEDI4Dce8GsYfQ/E72Aa5fVqUriXmpgum+nbzP2tgWoCDt4AycvBVEgllYPItc6DcTFh8dB0fC3I1Z7FQXKNiFaYOgk+0wiKTru0FqG7zhp4z18PZSa6bWbFH5wzzd+yCG0gBUakOCkf8XWZVBBSaeO8Ly9nQOveeB8FniW8TDKXXNgVahmOMxBWLtFUI5TbLIe/PRi4lpeoQNRPIQBQxGuNZV2opdemfdsN8QB15T8RDiqGJbySYIxlBXCBDSVAilUsK5kASkyXy899BUSGtMIOrMj/cBfvDcwldcUsYANpsxTh6liSPue1ZLAet5h1WVLrwok+4peNZop7xFAGOlNYEvTCr3Phpl5AoMSanQThmzWIDIKCaH+dNUlVFEeyl7LZHJX2rErbjy1NoEbbbqD1HHbvfdesePmjKm4bFjFBBC+8DdKjmWeQiRhQ2v7PxsjOp5YvIi9C2i2Uu3jw7ckdRZEy5NpWlCyfcQkqWpxIU637ntDvN1frz3JzKUhnUxZKh08QIvUN/lVZZ89ZhSV9gCIPONxDagMO0htGepfikIaiOE4FJXHFNp/kaM7FhmJBw0LHDYP6+eKjv/4U0jCQSvP68QAgwIfveHr9CP2DFlbKuSS6filSWCudYwYuhHLBzjvXgI6VbpotUoAZVJPuiHcb5vMcekY/u506ROBiCfmUk2bBvaC68UNIelmodnWftrZhPQwh5hByYYHMnvyibmU9l5XsrYIJTOq89TXnb+6T74XWwy1asAYpedF5Rl7wZtBQ2LUHIsTd/ilZ0PMyHiHlPCMRJqnqm0gUKICiLYnCf5TC6VpAAAyGujX9Umh8XqXukSO22sHfLaoJG+r0wfhPG0+bUNiMRsJ/TzJ3/G3E0QAIB37LhH/d6t1lML2gSe1eTwrhV4jFT1RqVR/wwRZ3/382cAAG8pY2cut72k4gr61sbFkkPCsvkq8RCajhs5ACGqepnkXCVEfyewtihsSKkVnBJlSgUmHkJhFkphJiiFQ1ikvmYLU7lx6epLxBsknk3Tuhg1iyplzDZ1SsXFS8AjDJOptH2QDUCvXyApY7z0VK4Phgeeub5FPEj57zgpYzbKjEtdyZVMqtLiySqEANiNi6U88t3GQyj9BWsTQyHEQVX9MRmoNBMAvfvWjxhGnGaqpEytR/ow2YJdN82gLZ6NBQBKeTn99eNdPxW3mudU3/38BQDAPWVs5jwpYzxlHm/Ou52bAvif/0xpyOLN19Q9hO5dfuntrlxP7qXlwuO0goNgu9sXySKNZvEQskoZczRTLSSNujITP2VFFvW/NIjbaj46+PxMJTbFbTFkOsq64Euvo7c2lQazBZk4IOToISRJidNLGVtZzfMDTEwl59kOOv3g9j1qapGhtxzymUtG/S5tKv3JCT2EQnloCRGaDo9LkDdd9mbMOsoq6S595EsmwVneYyGVNXcKoekTADAF5reLSfHzLfOtR2dtGwNeU3HV86rZVHpRCM0BIWeFENmMQZEV1OFYffs47zCVMmrf/4tN5Kfj8gq4RLYy2MFPcQWQZxMcw2rEHu4Qpey8ijJyeeTn/rP/AT5/aoF+8eQpY57LZmubCxfSZr8wNSldKaxkw6RYTSlje/m8cdBm19Hyz/PZe9n59fjOpoFuJyD9V3NT6f3O1jKASYMzPby5VNyS4aHKrvIrmzEgzvUcPDG1KTt/VSmECEy/m7xCaHO+ZCd7TW04lYcQ5W8GcLR31f497ExSFxOA670hCXbRn0kHTCmEOKpYjaD2lacQEs23fCnAUo8bFrZw2sMcL5g8hGT9KvVZhKZWuqSMeSo7f611PfFSRlTZU650Wfp/F4WU4H2XHTdU5ocv5NkEB3FwgYWLZvrvI5LayJ01Fdp3V7opFddlwuo/ZaxUGcsbU9mpdLdkpqkqbkz+6W5fEaupqF1Ujr/EU+u77PzevC+1svNtP5n22d5TcSCH7+ezr5TDlJ33HHB+1vRII6wTs4DPiTdxG0wWatSO6sN4CGHohxFeukHPQyjlKlkcUlEQdsOYsNn4HhPvjTaZ3eYZtN9w4/HU9nBrqkWlTX0B9Xk934kgJZwze9cKKyT+8IWBOrpCCBAA/M4fkoCQQ8oY1XSm8eycC+ElsCEs/z73Y543ovhzbd4GWOz7Lg/iJ5XGHKK/YypnpqHYSo98ZiYZcG32vizqlIp5BzOQB0opg5ozq1wdgLe7ofAwEchYAaaUMQSwKz1vnta17WhFC4eXbgCMAd49WNn5VUFjcd2G5eKf20GYmnoLFDB7mT3S3qgWgYwnSXvwBOneDcYBGttqFEtAKKOFfzdi+OJ1epbGCqFUF6zUaSURMAaAdsCZBAoprzkQbVSxqcpTX7dp94m0DVn65vNdrVy91pWWurIosgsTs/KSpIy99vBes1gHgqm/emoHeHOp4Mtv/XkIDSNejK6zRarU2RfUAFjff9+BgHunCEQFOq4vHtFDyLZYyCPwOK3gAC51Za0QctrBlKSRvBZT6exRptkg/sRc1slfKuKpsG07xqbSFFfJAno17gxkKp1UrvbKcycuBa+DiYeQMDWNTqmz2iGT9C/zhELlkUa4NiGVGhIPIR1vD+Y9ss01J/c4aQ8h5r3vhxE+f24BAOAr7/0sQFKCBDZi75B3fV4KIZNyzXeZh1Dk/CdZcPaplRQ7IEU/2M0OzpxrLeFc5lsPDdU2hvm9+dFrpw7skM/htQ391KdvJaXqzc4nblp9WFRzXuey84o0WVEBF5OiIMHhBtLkAa1DkaxjPR0AAIqHkIwEWsF5mEp5w+bFI1XGuBMOjreGW2fNeeFL2fn8YDruxXBcM81GLAPdS+gx9bNxxPBk5fPDVEPjdOxEKaNUkljhwzTQ5fDigUxW+UsIz3NnuS75sUQLm1AKiXVCIbs+2fUcw70392651MgqhbcdtpUjk2ZuTu0wwg9eOgAA+NSTZ0USUJUaAY5sd/z3tBvYsuwJwqSZALD3LbPqelRAnF9Zc1CWBNdRC5K5m7/5VhrqqoI9I57GyNduNFL6kDb09U/fejsXsdI8I0SBGanSBXuad2gqkyjS9xBKIWUMHRAMWrkoit88MomN3Hkzdbjbhvba6aWMkMWK75SXUvUif0wrM0lTxmZIkIlWCBE/mA+a0maW7aR5O3iu5uoeU8YSMCvewg/IAgC8sdyBESqfOLs9L7xqZojxEPKdQ28oOT5ml9KP2WujnTKWmam0oJ3+YFYIfaq1cMlrsRolEMnzFBtyqDK2YmLGqhccPpL1PtcVWh8H81yeWkE6D+NBpPJxI31hHimBBf9wTKUxhh/OgXZ5QIhRp85j1Te/9t7bOa1p9WkGJ1xQVcsKtSGwpIzR77x0Qy8tkjCVZgmcZnwpZeeFJNQK8udS7z2ElBUsGDlniB38MKqMwlEIfRyEMlZ1J3+tK0CAgf5GUn7XSCHEDH6i9rumTobpcpqQ5cwdWJ+FzTtIKZ80AjnPXS8MuK2BJf8B5wqtKa9iWKXGsc+p7Q3MXumqeeO4S6tkf48lR1Pp126Ez5+mhYuvqjbx4SyGIu+Ot5mljEmrE+5SlXmB0HSCX7yUfoBp3FN6CDW1Moi9KoR8z7fSuYcFM4YBw4/mgNAnBgqhn/jkDQAA/Jt/4se8nUtzyGbMEUhSnxp+3xNsQ0yaJhvPM3GHSlmVQsrYQVxLypiQM9WXjY4PDyG7zpr/smOMi0IoZ+bHejFM95F38msFJYCtQugjCQhZqni4pXlnFnPlS1hT6dQmO6+OA66J2fwzTyEEAAAYEELaxqj7j4uPTQLOuj4HQZVc0vM07wcvFQKMp5LsjYGqY/EQSloJwihZe8pDyDgglPJ1TjSBqsyY0g1j2t5ShPldQghBXSGtqoDSxVEC5tIixd5zO8BnX7oJPkXSP1mF0L6vyeO9L4QHL+9PP64KIXUqLl7+/5NffgO/9R//W/CtH3NVCK3fa6o0zwlV+XcTL0Y+FlW6MA7smWiKecrbsQRul/RG3wnfAR9kMDPJh0uN5ja3vngv7QAIiToqz2WzmUlXO4yAcUoS7oINl0o1kO+fO4A8ZYUMkBhTflf32fjZ1EOI8umY2i/HQ2g2V1YFRq2gPCLiVBmTB0wA7Afcndm8JOjx0g6SaobygJ0tWoEWjoQ6aNl5BowxtINOQIiV7dupSrphBISmNJVcaPsRfvDcQYUAvvTmfPtEhweMheoxDLeMFEIAxEtL/Q6kmypJqRM5z+Wp7bU8hFR9VutbGZhAEK3gRjtM/SqAKmVszx/97IO7oTTFqVPGlo03UVEND4EZmSmz4J1fixmkec+TSBlDe6uVkFzqKvrGUKqkNnJnDU8h9NJNizRVxx5iQauKmhdSZV/tCMBGIcT6ydDfOf1l5CmELD2EmmqKvE8xUcZDqJ3OybbaFp+tR0Qdu5wk5x0WbJ/kAAAgAElEQVR/dfYQEi1k98fim0qrfDAM4F6fWRXD9XqOmwB0AwaMzRdq2qpNNhDfT8bBPifzXtmc1/QcunGE739xh0/fXaHKKJClZpuqeGzAeH8fuyGHlDFm/Kmq7TugqLhj29d5h1M1cP6Hzc+f7wO8445520D2ru/kvPcAKQbECofA6e+7AcMfPk3KS2lAiP2sr7GD+p7GcB6ZJnY2CaYqe11WD0W6/9iXu0/1nj/MGpHelGxQcpkEqXDyVnAs14bvIaQylAYIY3pmavhaSBOxP469h9Bl9hCiv4F4CH0wUgjtVW680yLV9oyqbRmSolmck0IIYyOz+RdFXxOi3Oa9H43S4Uw8kazZeZuYPoO11DSA+bm2OVSS2oHgez94gU/fae5ipxrsoknQ3HNKGcvg3lE0QoUQPyCyec8SaieiQJyuQkiVZkDaVvoBv8KR/O4PXwHAXCHkm+viB5Z5ugxXqcNZ61C/51x2XoDaQyiMd6M5Kg+hx8kiKR5CYsrI5ZFpIrCvMiY30t2aSltFLlXmwg/0sp+LbflqbYWQrMoYJh5C+ypjT62FqTTFWsp+3x7XanshA0JVcpF/111z3YXsME5pUdzjULnTPj3KAKYJhUnA+RI0p15RDttYIaRKGRMfLydDacL3fvBiZyid0KJfxLLhEtlU+p6NqfR6n3TSpQDW+Qa/7cdvI7zzGkYMr90orn6J1+Cw0lTasp9RksH7VWBZ35//bw4IfRIrFZcpXHNkuvZRqLxwnDcErAq4mBUFiUESKWOE4KXn1/dgnL0hC1u0WwFCqEYI/WOE0N+f//4thNA/Qgh9ByH0txFC1/nnt/nv35n//ZthTj09LjXapcu8tINYFcHz1nCKJPOrfiTxshesaVRtQ1DtRa4QIilj62ddTaUvnCATYTFXDxKcXANnUSL/MjPjzkIhxJQ7BqADxfKJiayi4K3xb6anV8UwrlJDux/cpZhIKixJyCItiNNm/+9//gRf0VUIZUZQM3Mu4gp0uanHpnQpfQ+h5OYblMcd+1yeW7006SurPuW8P613hVAJBGUJ0zb+2Q9f4Eu3ZpnHKT8XaGGcikoyBPdu8moVGbrXFYIKuV67WJkkCgIndc8FyiqkVSU2JHxvt1Ak9UwSw2Tk+o8A4J9Qf/8bAPDrGOM/DgCfA8CvzD//FQD4fP75r8+/9xBc6mrXrp87vZQxPx3WFn5+ayF5BAtTY4XQbgKyX5jTap4nGw8hTlATM8cCCBQQYu5TUyPoosqhJVJmK4UQFqvDBP4Vu8UY83zcFsT86zNSCFXHewi1ls/AtlR5N+B8FELMgPXZl95EOpFAxNwdF3iKJB8s5Iw/2/sm9xBKp+0LPISo6yPjElchZNh3BlMIFbLmn/3g1ajkvF9oD6ETpIyJlDpz0QiZb18IBTlZY23m2twiGmneczJ/S8fvMNR5yDZZCwStkQsh9A0A+HcA4L+d/44A4BcA4O/Mv/KbAPDL85//wvx3mP/9F1E6rS0o3JSxdoC3mguRxnOH9bqkjJUJSs6YRrRJyopqcEQAQI9TT6TKmG3ZeTIocsa+126AN5cqqGFtCI8cV0iq3BvLoKyuskFnMXZp/HssTRMKzWvDOHDKGIGvmLvWZs/Adiep7cfES0+Lz+3rn+oGhFK+vj1NAl4Ow4hhGDMICDE0dZV5lbEJ3n3X3QS5cNWV23eA/Huq118Izb5PxIDgdz5/hq+8VwWE2M/671+vMYLiQeBsTHW8ecj296515X3D8N4P0FRIqP6yVRl7RxRIMywKcgaOVwvng+7I9V8BwH8CAOQOfg0AfoAx7ue/fxcAvj7/+esA8DsAAPO//3D+/dMzlTXd/uy568X56QCbF9V3h1VMpc/BujBl2oYkr1n1zIlEFG8CQj28u9bWQRuy2BeljIU0lAaY358oOzGysvOjVMqsYq1SIZeTy9WAjh5CEjnvVGXMQCEUYTA2TmVZfLYUwSvBfWmHEa6ZTbLwPHn+qU/fRj4Tn+xNRWOWXCbtKDcFSVMJqrJwzNvr3eIIcX/3WKhADafoB4BAIUR9lvSdvLGNEE4hlFfwtQCbdvbcDvBHElBenjlVRkepPAV1Q2yIiY+LEEqy2AmhHcwU3mEJPHdn1MK+7RPOgLIlIIT+PAD8Psb4f/F5YITQryKEfhsh9Nvf//73fX51NLhl5w0Wwntpti4qw7O8FieFGWZhKm4be0WE+JlvKyhtFEJtb2cozXS0PFPpl26QB0ZdoE2T5zafCvd+hDdNbSjJNZe36laW871TpVVljC59WwU091UYP6oXavsdRQDzicNUdj71RRz/mv7YZx8sviv1a02j5PJahSr9+7XZqGoqrUCaqcH80TSc+74ohBTFDq7CSp8rWT3fQkD2beTHP7k5fd6N7Twy65QxAfd+EM9DyDy6cgnMSDZfRes7al6aRkBIoKxKIoPkSA+hI1TqeaLTEv4MAPy7CKH/BwD+FkypYv81AHyKECIrvG8AwPfmP38PAH4aAGD+9y8DwB+wX4ox/psY429jjL/92WefOV1EKqwpY+uL99qNEtPVfY67UyMVmAvbVjgqxIK/MNVdzLeiXQuh38/Ex/tgWHIeYJOjXhFTadi1xZd2CNAOmfenEZVHPghO0Oe1k0xUVGC8eu7snr2ukfj6ezfXHTKuKaGZ5BghdLj5t623yRKINTzXbsipytj0vnzrs/cAAPBzX/9yzJMJAG0ofOCu4FRdYvOjXD1mpoUUdd+kyriErm3nhUTOjeMhxB33zMzwvT/fx3BaeAiiKYQ4pdfzThkT+5ep5iHOHkK8+Q83CM5b16UZhDNK+Q8FfV9D9Xn0e9DE3xxKFeXIhTH+6xjjb2CMvwkAfwUAfgtj/B8AwD8AgL80/9pfBYC/O//5781/h/nffwvLdLYn4tpwTKXbKQ1HB91qHrqoSjEW8mBVCOm1DR35bFMhQIBhpMvO3/XbKo9rI+7MgyqEZpIsO++4a15VCJpKrRxczavFzy/ExOTeCUrd86Cq/QRNGWMNty1TdcgiUNmmOAbfSfvEcCZdf+3P/Un4n/76L0Ktmy6a2WKVBFbjKoS2Ssp0Yc369QK4qSuEuB5CrZ5CiBsQEvUzyT/fQhA4fSJJxf3pr74z+2yA/vXUKWMafc81SJVVjVS1JOal9vYSZ0N7XveAuLSEvwYAv4YQ+g5MHkG/Mf/8NwDga/PPfw0A/lO3U8wHXlnTl26At9LJxvr7vnNNXzt1GkkhfcQDuWCnth+Ui1/y76yHkFXKGHOefA+hPriHUDRprsxjR6ssu5xtIEeRFsVbjNCBGKv7I/dIMg04h8jll9EZm91uVSVi1Zm4tHj6i/4tb681/MSX4/tceIVaVFUVgrqKqyBcU4ryahvXuhJsRuwDIrtxZ3kGEQOImKRJc8rO32UKIfqz6kVErgqwQnh+/hvxlZeLJ17uKWNCpbK4wirAbMnh3UNIrZC+ht4Ac8C0SmxQgutGmIqjiaq2YmK0+sMY/0MA+Ifzn/8pAPxpzu+8AsBf9nBu2UF7qCAAGEcMr91o4CFU2ck5hd4ZLiWvC/GZO7Bld9uDQojKpwbYewjZSZvVO98v7QCfvrtafLf+8a91BS9dYh5CNrvmnImMKl1Q510P4yGks8O0l1CHqfYkCtDYKTNsd1TvfWKpMwUAcPWQcKddAkJ5qauaGkH3qqEQMkwfPRoXhZCOn1iwgF9marzCyq/+2W/B+PIvWHqzeQKv8yMAgC7rlDE+Or6ZThuGkjWWeM41H/fgDTAhwpS3BPrsA5OIStl5MWXW6hEyESCLbFL2XagQ4uS4u6WM8T0LbobllgtpoSzVzbQjHQ8TUr2KNoB+ug/mCiGeLxHe+mgBzEo53wohTuAk7sDL8RByUghJJjKcNCUAjhqQ8RBwGwS3x8R4CnibBrwuBy/Mtas7Mfe0MQzE0sdLPm3E5wQskwWre/s3YX9/u6xSimjlsl6qxWSmntK18d9nur1Kq4zRYxsZg0lAnfP++K8il8d7VWCg2sbXP30H/8Uv/5xe5VbyOa+LY44PVqJqFS2sAjMT7htRmh5CnHVduh5CDh6X3kCCP4c5xvUM70EgYreEU0EijyQg9LJMNvSrjPlcKOVa5vbhESxMdeWuXY8Fu5TU5IA2gJ75eO/hw83BQ4hjVE14bgcnfyIdQihgXPHhq6Gzs6XjF3b1vFNFFtcy3yIeR++Y2SozrqpA7IJG6kxSUOdrvfjIZLFKXV9TH5wyJgjapt02QCPVwn5RFhOex93TvYdLjdQbKFy1IP/5Nro+XIWTwXvuum2B/T3/bag52lj/QO6dWql8rSvvQQAdVWQaHkJ87skF8cNDNsPP+B648lgtITCrQmhqaGT3SVch4NsDhUxQcpOoF7asEkemA5NUe1HJ1onf1c5DyMH4meyi8s7rtRvgTeiAUDRprsxjx4OHUENV+REc6q7hF2a9U6VKSdVe4K6Kp7AL871iDsBcmbEEYg0l9lNANrc+N7fz1cFzFU9HcvUQairBfeNUNRVW3ImqIlv7HfYspo0K2Zi3TTe9SxaU7YDh2lSAMlHMFR6L85jp7t+vth/hqgrMNP6VyToePNfoynUQrxX60XhDLxyBAzR425dHfyYJktfMJHF2KWPzIk2aKkO9A/YLJbG5aYWmKiGFDJk7MITmSlO6CiFpQGj6zprxEBpHPE2ObUylF18ihUIo1MAzH/CaoDTXJqWK77mjqxBSBIQ87pDdO02FEC81NsRgLJj0dL3hQpxMHMhOkiiNV3S85BVCj0ls6T5JB88tIKR73+4ai7KYXOpq985OmyDqc75qLCLCpcyVAFN2JFVY+fxmujpKF7cNAQtVJBWASCP4wDPjHjPcvHKjeAiJyWtmkjhXJiBEDG7FqTLMQqlxXChxyqDmNvks8JEPKnvpuqwEPMAUZKoAwTgPdM9zW/WSMoZh0xYxxnPZed+LhcQ8hITVLyyvmzKD3Ady9rvzALyUsa0Pxt1j/0Iqm5kGvI7eMVtMpU3Nr+egqakJZ/Jl5wHA725cHhPKQ/sHqcdMBveLOv9LjbRUcm1KFWsA9qnXNZpb/dZDSLwJwvFfkVR79B4ILkqjB0NeSdSVukJQodyVEWJVvPDdozYsnTyELOd300ZcmkG4JPwOEVX9MVSfx3hpAuT+HoQhodE7f0jKDJsypl1lzLPZanomjwUtOJ1iUyPt3W2hQoj9XgSA5+b2dJ+rrbiUnV8WOtvzvPcjYAzBU8aaZHZiVl47d18NnR2mxVRacqzr/D3Y0+7la6dWJfE4esfM1kNI24STE4jPRyGU5kTVH1tzZKsqntbwUxfTDxZu2Y09whTSIel2z5sLPbWaCqFGTyH0aLvthcThGhynNUfyQTdopG4FSBnjr7H2gehU1SiPqGYuKWNiHqslBGbKUcc7hZCwyhjn875NpdW74oUcuBq0jW4QmUpvqRAsCqGPc0Dog0tAaFNlbGWp5BI4V/kay1RaEmCRl0PV47JZkIkXYwACnxxKaYQxwDCaBgHkx9S+PlpCHXLHjFN1D2A1UteFmHD2BvcLYzwFhFJe9PvYhctQvdA4V/F0I5+AkN0i0oeBfhCofgfBquAGAHi+KzyEls9y0gwsqnsWTgyvT9TtJ3ebdWH61xTT6o3h3BtuMPaAYNikxtcws040+KC7VjiE4GmWmlYAD0wiLeEcLB5C84yDVBmTB4SoHczGr9lq1+O0FyYFbfgVcuxNpQFgnhxPg+aiELIylZZ7Y5gGRu2Pr6+iOorJVNrwHbTw3CG54LLytr6rK9w1VEkTcXfMumGEppLfmy3b9iw+1/19HMbJqD27fjfDAI8S5pqumqlPoSDvS25BA/FCaq+ME5dgjt++SIB3pBYeT20P7zXSpHXUgveSol9YSGgewlRaTDU44YLcN3PCqZiF0C8Qa1UoTPWep5feHn6cKB5CYlJqCdlzYSYcL6YpY7YLpaWv2u9YXXLwKygI2KY76CuERriKpOvUwIbQqub5aJsyxsnNnb5y/flLO333W4cKZqpjA6Qw8HJyzDsXhdCs7OFVT2P9fETHoX7PvcrI/pgANh5CoZ6TeNJmM+mxMeFc0tMyW/Q/ArFTSkkgNrdg4YW7GbFnKv2ckqk0GxCc7jt9KdIqY/TYxgbTeR5R3lP0U6jQVjDGVumAxW3LF/HnSP7BGCsCM+sGj1sQgKNM0glENYmosph+ZFUzx+5f0AHx0+IhpENeM5PEWRRCc+NTKiPYBa2ovKslZcfqPFzqCjpR6gorXdeQsU4fQ8vc4/k+tVWd3VLxOU7nwZ7lSzu1ad3AqP3xK+hHvCj0YjOOmL9rboiWh9AwaJU/BQBvaXUkZeyN4XM93ENI19uDeY9sTDiJDDmbRb+PxUfKC1bGHPm4iTnerR/ySRkDoHvxppr6VZX3WOreWWsVWEohdDdTCLXDIPyd1K+/8IjwNs3SmB9ZwemDWs1+1XcxC4zx7CGkSlVLU5VFUuEfrc8qHkJiHqslBGbxEGJTxnQVQo3fCav/HatCLC410i4ZLlZEbAeqCvDiIfTUuptKk1L27KD9PH+3/ypjW5Zd3MPTQsQlSQHMAyYs07OX7x7K/Tu2KVDGA6HQRNZUIbTmcIctfevP5LnRmkCvx7vPC8a0FUI+AjgJB4EExN4dXwNCid87zoIGQJ5qijGe/dLSbfdkfkZ7qEkVQgBApyMDsL4THA+hMt96YHy+12H6iFSDE2aw751oE4YTDPPoMUkCKupAVAKqLEnVy3Q2KAIHKjE7D844MBqIVFrCKWB3oBaFkGxBuNnB9G8q/WjR37My7dIybYPTyRMZqJaHEKUQcjKVnr8EITQZFzMdO3kPXAMjquOTSXvYYIM+tmXZbUxd7zrmhhqVckx47QxNpWesU2Mt6Rzy5KWTOe4ki0xOE1/078jtfHVIK6WUKPPSDhbuWVIn2fGHChz1Qu+sdNKeWA8hjLF+lTGNdNv0/DgKBQBb64FcIEEeVaDdLXVLHFBRlbtPVZW1jEcp9VkHjBMXz0r5M5FQS8if6xIQmv7+3A5wrStoNF84+0iym7lwIVHoYGFTQasxqKhloJSHEKxt1b7sPGc3GcOmYydKOf8Kof2CDyCiFJT19XFVCFHVwfYLEfZYIv+OvYeQ9f0RXJ8y4MWM8cF2zITGj4b9IGPC2Ruca5upcfAj0FR6XjihyNVDqNHYUU3SMHtnKk7mZ9PPX7sRMAZ4JxzzeB5CkoCQhsGsEQkE0QqWBK+YZEeqwQkX1sCMYJ5FBWbaYVSmvgph3kfdgIpvZZI9e+U0QAIbFAhBcHUQ9ewQQidRyvknodE7f4iBM1lkv3aDorLSvvqOU2e985LB6cvTC1pcpQvT9RmbpCUgtLbVj3c/peEvTbXr2p+DBYSYYzubJvtlVdCE9xC696Oy2tdyfzyV29SvMrY/jyMnpTqVQCb278w0idQ/1/Rk2BK8LVryGGOmoPpBfQPP6yLFHVkRjPcSgDwY0hmnjx4Pue8kZWwppKAxLu2D6fznm8WzLQTGVYXicWzMxM9Gn/29uWsqhIhqt/fkMSkOqDD3vDlWEa0LmYPdHrDPOqNSzgeP1xICsksZawcjI91LXcHg0RR38s5IqepHQR8mWKhpOE48DrgTU2ZyUMFaZez53sO7a21QmpvPpa52iyGt1EkPXDV2soOg8tgxLTvPcGloZYNADSjz7yBKo8a23KbIt8gwFZCWUB+4Y+ailLxUGgoh6r1aFELZTLJsd0v9nkU41uu7upQdtmLvMVNXaPVay4RdtT2ZH0WKASEqpZj2EHrW8c0z8J2YUvTzeraFx+KMC2Fl6tZMswvquh5XL6CShIeQIIANAOlUog6uqmNTJ8+llPNBgqN3vlyqapOG86xUCDGfJx2WJ1NcafnxQlbw8585uyXE1FbLQ2jtg5/a3sFQervo2lcZU1Tbc4aYFRPjz9iD7wRRCL0xDcrudvXUygYdDyHfKXXmptLkPALvmLFKSYd+cHrvDFLGUl4YE3ipKGdMT0FsUD3u7viUupjDfd7fNwBev0EFQkXKuOUZxL9udsPuiahipabS5LMc3wnmktpSxOOxcelX2d/z2R8zPqWpeCxasxvfBam4nHkUAGsMr4lM8SkMqKzB5BHDxsw+BdJUMx8zTujMqR+RlFpC9iwpY1SVMbUqgifNNuw4BJHVImE+DxfNxYyJTwVCCEhT+3gf7AyleRVpMAa6Yw+mEGLGjugeQjtfH1eF0Posd9fEXLvQQ4j6vUVBZR0w41+fuq0dZe7ryUNoU3IbQSecyHGUEuSZl343OdxMRd3J1dNPxz8nTX8k/oJwpDZBACSFFDi+E7JFhHk/oyJ+EK1gS1qLf8KhabMHoU7F3RZ6sL/+/UaT/Liw+ffoKiFOABsgkT47uDqI9ZPTr9r8SCTQEs4DO+F46Xq5KkIYwfbXYeU4AS0QNHd2qHbUqXYtqI53UrNNf//itYMvvbEvOU9oOAohU3N1W1LzELp3vsrOqxeyWmXnG8v7Iyx1P8CtqQAZ7mYevWM2ealZpowZprdloRAiJGp8GgrdoLof+GlVSUy+DWkqUmVM3F5y8EciG26k3yGFFN7dZP0zk2ZA+gKBYiCIqfYZ1XtnxrZfJZ/z2i8za4zIKklneO+d5pgrrJZoiTCgwtskhXTmpYT00nwDz0eYIj1ZvweBSKUlnAKeh5CJka7/lDHPVS8Kx8GmO2iaAcp3LRgPIbS21Y+vvZ1CiGE67rZjf+0GeOPoo6NDNA8hwUBGUsZcF4HEW2wYsVgNOMgCQrA5D1/3RydNbQuT2nfQgNwOo96kh7PwutSVhgnl3tQ9m4W/j8VHygvWXbrEgZNAbpGHTNoFrZLbpUtJPISSTIlbA+IIAAYmZey9RsoYgFrZmKsCrPA4nCJljEG373FKGeOgG4haFJaJKVKSUggdTPEQ4vN4LSEg17oCBJhSCI1G6gDfC7YyQTkP17rSChQKvRw4VAiAfOUXr729QohadF1rtFtjPre9lk+DNTvjzzQGXvIszFPG9lUqAOTXNSmE5H3NWmXM0w6ZRhCKxzX4c/LoIWRYlSUHpYSfVJQUF/5ymsiTwCkwmd99uy476xKFkGpxlEDQ8DIrnciY97SYSuvN0a4NW3FwX4Y65SprhRjotnv298K8L+dQRvDLv+/fPXYe5VdB3mkGVHQM6YPDLQQgKUATi4PGieIhxCehlpA/JEK9KoR6tW8K3u/EmUeSxR5CxVT6HDQ12u9sSDp5fQ+htQTvl95cLM6Mn/ZId+wv3RjIUJovzY22E8MxMwZwGHDx9lluJ3Ksn88gUOusv6dTPlrKTvGgmQIjSo31PUESqF1cUmcbmSqAK1/fVnTLh9zOVweOb8A4LpUVj6bNJWWMo04FgL26ipOqvPfOitiu2H6HeDySoh9L2XnRZgXbfuSLae8p+gkE0QqWJJWOS6kkqzRLoLugnGct8yjP859lrJcfN52NSv789CGySBBnLhD9eaTHA7SE4yDlZBdT6c4yZcy6w9r+dSqDWh5xtrDpDhoKIXUQYv3OCsFiKv2j185byhg7F3ppB2cfHa1je94BckXfdFmOTgDlrlV23vb+iNPUbPLPj54gdYNhqs5G8WamKumyUAg9Js3cN8Wq9tJlWuSBeAjJDZW3PmUpQu79kjI2V7+UeghtxmDxIqIfRhjxgyyuCvnA2YzJO2VMlq56rLlzq1nRN1UPoTWbIHLgGSEAENsh+IMtO5/W80iBMnp5BCE056hPfzddCPveOS+m0ufhUleSVB+OqbRGJ49gCl5ijOHjvYdPPJhKTwuCvYfQ2zN7CEmq/AGYl2UXK2pGkKkBValpvlO1jMssz/dp70kSFv1qi/t3pqmRke8MCdrm0e9i8GPkmLKSYTsJBJCnPvk77LbSIkBmGzR0ULQhCiGx6W2aJYxnyE59VUGF8MZU+lIjZaotYbuI2N6DsOkXKb9fhT22/Qtm/u+fS3M+ZcSy8aZpKu1v/kPeeU3vosTu+8N7CHnykjoTj9cSAjNVbpr+bKoQuno0Wx1HbL4zXkiIfTqUzkKmlclAmUBDhaapxw9fOsAY4IN1QIjxEGL+dXoPAnoIwXYynsrA62uRdFEEUPphhH7E2h5CvlLqbAPOwT2EmHbeDqN1CteUa65476jjkd9tYu+6hSbDdJbYO7W5jsdrlTGJh5lyIyJ+e1lTxtaAkMm4NG3KUH0B/d5rLkoLJ4bXJ+r2k+zvBepfm+oE3imi1C1Fta91I8rfhjuAesPPt5m1HbkE8Q/yEGpO8B4EIKWWcA7myk3dMEI3YLWHEF3No/K3UCIGxGWCcg74XiZuRnFTuXAE//zjHQDAzkOIp2ZhdsefQ6WMCcp7xgsI8c0O7d/BbarX5ro2QQi9IKB7Sh3rW6SreDjIQ0iAi1JSbiq9P3+iJiIGtknisnDJCZHSLpLH2FTkIYf7zL9vsoWUsLpeQu1qTRmb/v7UDvI06Z0HkXgRsfTBWTzfwkOxUfvlnjK2R9cmwWkjiucXqEwP3yqIUgtAPJSHEM9PMLHnkQKP0BIOZVIIYXiZS06bmOkuEy/Thrp0VnT5Y31z4UKqsDmvWGmIquzkqc/Pllfw/S9aAAAvHkJNXe2Wya/dEMhUeovvKlqu+No1VgVQ7p1qp2o7MTEOxEhS4myuLVzgTmAq7eDdYlqqfJmcPsQkKy8Wc+RIHkL6qYtpcVnum9xQefrddK9vCQjNz3+qfqm/YXetkTCYGGZxVYJL+ZJm0MW0amYOGJed97Qhptvnha+qqgmrnE7G7xDNc8zA7wxm11Pneg98ELslnA7iIfQ6GxZKlRGs8zmbq++AiZdMIX0ulWQxQ1d7MZiYk4+tCiEfptKzQRzFS3uQh1AjD5wcDXkWTWX6Dko8hDjBmfviVSRf3Fw9B8w646pJW+PZZE2lKZrK0FSapIwZP/MIJNDJ3qAAACAASURBVFUJJzzHBoz5Ev34k29zGo0FTdq7zduAOEkZ+3gf4J3BJsiVLtnNvDtBF1cJqawKGtj2q+RzXrtlJm2qqqAf8VL4JjskSh19DyG/KWO743KUhfTvp0L7wKIBssFe2PJ4LSEwCE0eQh/nkqYmi2yfHiht2anOG8GgogoWyr0cGA+h+f9/4BoQYqoysROa4B5CeLsIT2Xgvc8msshxQq+SHOuaVyOEvO4QtpYmuUeaf2OM9c+T85yuWiac6+f6oOayAbAOCiHuH5OD7ZvgQIUQ0576AWe0QbOtrgVAv6+cRZmq3ScQ1CDnRoROz/cePsgqjHE+L+oL0g6IFQoTy6aZRsXadNl7BALw+h7+hruv+c9dMwi8FDOIGYCQFAJIqs86aJyQF+l5XBJqCedhHPESEHqvWghTL6pPD6F05IAFH5BghyqdUOjlwAGhyQD6975w8BDiqFkw8+NwZecT24nhSHJvLu8f3u7gbNMV1mPd+0mNyB/Y98/H+v5wrk9rd0laNS0EVIBmJPfQbqIxKYQE5ymYZFUIoE5aIaQOFp8DvqlorP6hG8dFbZMTwgUNrUwVVqyJ2K5YU9n5nSSX8VFpKr3vt7YG83QfXOZbBZd+lf09n+/NPrh7Jh8hYtasmou4zTvEfp3C45Lqhql6CD1SFgknGyeVjeOUKKOXZ4iHEAkImVRuIhFsZVWbHeLor3HJ60KSXBeFENWJcZpJZzAxJYvW3/3hKwD48RC6MAqhfhihHUYNc3V3lpSoRDr6bhi9KPQuilS4u0ohxOROm++MCLx5Bmx1fcEmSAHKYU8eQgYpY5ku+h+B2FUI+wEvqb9JIwik6KSMXSyr+R0BUWquHkIKU2mATdd3PVohlICqqmBJoum4PjedU6EdBmgqBJWob8Vb9aKvDTHddz4ZDyGOsgqlsHmFZpuJ4O9M8RBSEbIW9GOCAPoR4P46B4SkEw7+zrmJien26/aVh8qOVcZw1WO8TnNvJi5eqO9Npb/7+TMAAHz6zkYhtGVaEKzHeJ2DD2+v4dthtPKeMtNlm/dPqqjheQhNCqGbRtBNq4y6JsbXx07MDpDskrZg1A9uAmjIKHCVzaL/AbE2VbeB0yf0Q57BQh3lZdKK5MWbZfr/aGQqvbJNt+V7CD2iH0eBxbZ/EadkWiOwHkhl08wHuh6Ba9Eef/Mfvhr4aEW0He3saedqaZAjxUOITxm9PINgmnA8tToBoS0+Ow6rhVAhWXQrM62BQHUnT8ax//cPnuHWVA4qnq1PB93Nvszm6mEVQtMR6wpBhdIZeG2rcLGoFDVKhRDFrfG3M2LrIXRk2XkzL7X9O3OpK7XnDGPqno9vm0Nlj80kMuUJ5XZXEMBhw8UYZkd5zMlDaOVSMd5LEiWe2Ew9neseFlPpHt4bzs9E6solfSTIu5/OvSvkzfUMKWOc1HVuv8qmCXneiNItErAE1I/eqFTQ9djN0iAIB3oIJbJOSInUWkP2IADoMYaPs0LIZMLRLAs/946jDSFhLhyI3S7DIt2vOM+dGSCrCgEGBL//xR2++v5qt1OwSy+YjTvn818CQiFMpTnne6mriIaJrKTYdQHIeAjRz566dmlAaKc2csmd5nkI6Vzf/hwAAgbuOFX3rD2EagSDQVWWbsDLO1BIC/JcYk0E+2HMp20wKjkA+UKqHbAXA32/8M9lwFPa2Gs3yj0eOeoKUR/cDtM4VzbgHhhe29d9H9jf8/keBfIpTQXdjSknDzledTPlcVPyEBJUvXyY9SEbHJzmwTjR1M5YPEprOAyEpsnGx/s0QVBXbtpXQTGOYEu9M1KaoBVsWXa3NwtT/nOX5lNT1NSk46vvr87nCLB6SJAF9Et3hEJo5VpXyezE3PsRropS8DqogoH3ThX8dcydlqXEOSmEDkgZ8+AhBCCqysJPC0q+z+V6nyZ+zjZwjCQB4u2O66Y2xGd730hKRLcLim4Dr9x0qQTbVT+sCu73yipj2/mZSCHUahrbmpHevSvkTfTCGwHolKnrnjyEGHRT5pPxENJVVh0OOsZzi/HSxHj1kytM5DA7yQoEk4fQx3sHdYXkaRwCr5DeWuFA7ViVnPYTQO3skF0GRbBQueigOkUEeGmCX3nnJyB0rStAgGHETEDoAA8hAM4u7iEIAiaWKVXC6mk933hv8RDSCD75LLepK5le2aZVHJEyZhcQkpXcVh8vj0X/4xF7d7zLIVjIASEElxpJU+3SvrZtqls/jvA8b9iZKLivjdh3YlVkp3oPCseSykJzr4wAOMhHLQSCzW8dpcvFYwbGclyNIHiqHkKPPFdRFWp5VB6zNQQEIQTDOMLH1x4+3BojCbVPbw3XnfFCWly5CqEZtA0E6k7MEaClffzkl9/YnxwTeQcAIKdJUsbClJ3nHT+dcpKddkqVHJ8eQpfGj6n0OGLoR5y8Qqh19FIz9Z3pRrwEb5MHH1HZIzKcvumY/gHvFgZ9Tm2DYassFCzKZO9YQkohWiFkbSrNvDfdsgF3jBK2kDC2fSpjfh6C2EHxELSagQ2dwLYJ3aA3/znSM9EE+w3LgBw0TqwG4+d5D3yQWGvIHwR4SRkzLeN90VSB6BCkDGrhOFhvHs38507aye87W5LS9DNffWd+jpzv3AWEOjLxDlHQkO8hFK2TZyW5rgMulngIcdSAtwvvWNtzutWVvakip4qh1vVxfIwAQk5KOR5COjv3nMlIozVxWD/Xi1JnTgd1rxJa7Ms4tMoYA8bTvCAbDyEm6NNUSHrf7n2Cu82CdtmNGJ7uc8qYwbhEDOZ5fmKreX2AdyGT96uQPudIGWNTn7BgzBV4TFpdO+edV22+LlVV/a3rrBEoqx5jrgIcg/HQc9A8eZDWcBwIph2oj/dOLyBEp/AgBE2FHFLGVu4pl4EtGMPd3fbQyZOqMD/zNduA0BYy+C0pY+1cdv5ID6FEdmKsy84zKD2ESEBItDtNqyQaGwWVeAFkc31H7s6sVffsnoO0Kgv3/ctBBcI1ETr8LMKTjnSf9EnpplXJ4S6kNubtot3y9K63HzF8MRf9+KDyeOQpzJb5Gc+83uN8qwSCMsOlX2V/L8yzjxkUD4UyZYx5h62vnbPhZ6JMSi340KYWxA+uVo6lFs6HhFrDSUAIRozhRy+9hmHhHrsOCy/HJixlUFN64QvW6A7k3YANKgdg+OaPvYc//y//JPzSv/jjjmc4cW2Ih9D096NNpS8uChhbBAOZdY62MAedXyZ88RDiKoS22O+QbVk8ykwUUMuOWaAyrLwAjU1g3KEqS5dTJakHg/SLMUylySZPk8N4LKjeKLtvamPXiCzv8+whNGD44UsHAABffnvR/hpyfbydfvKzx6naU5CSSipuJn42+og2QfUCaD4V5N0wCtL0fSqTfLKvgptEBglCcLTnVrA5aOYk0BrOxdS0EfzhUwsf3qgmG7yOA5WUscIEZ3eSn//MegipKy4QfvxLN/hv/v1/1TGla7+AJuUcSUDoTVBTaVcFTBhsq3CxqCTHS5UxjQXZpa4WRZELLjvidYWgQgd5CLlWGTM0H+wHkXy9EJtLdWD5X4xhqyCZ2k+jUf0xRZoaCSrtTUy79Hlc2zCOVgGh7abMtj9wUUwWzobtIhMz//dP/gGhPSZKl2uNvG0YmhxXlXIbgzbJQgAHeQg1xUOIRxm9PIPmzvz7H+/wwVoh5G/Blt4LX9CD8RDSVCpIZaw8CbqrLF3gD0PGvtfZVDqIh5BgJzuVTv7uHBCabiJXcsz4+VzrCireYnOXO+3Sv+x9i/QmRIIdMw+psSrIJEzHcJsHCSJI79lGmTlmkDJG4CvOtNi0qzyu19Qg3Ce9Y2AyNptUXI76QZ0+EaONiDyEQC8gxI5tEv8Vs/7QlDzer0L6nCJljHkvhb6ZvPmhxyq0JgrwaxN7XiryQMpzPDKneAjp8Cit4TBIVbE/fGrhSzed3afti6qSZutSJMzngpjiqlPG/FS2soW0N7LWfyZVxg5qh2lIcyd8mvbJruveiaTLhLXNyEoniz/On0yQ77PhWlfh5LpMgAbAR5Ux3rly5OsjziMtiOWMfiVCY/4YKWN4cw7Jw7zzDbc6z/Y90ynBnAL9rBBqKqRRZYzqO9mUMaafqSsEtVcFWHr3riDBZcON/T2fjx7v23AqcyQftAPWVsVbewgxik9yXPn8hzluTFNpDumZSgcel6mvP6NSzgcptYZTQM8Hvvrhavx5q5SXpcPnVAFK6oUv2MJXCFmU/w3Mta4AAWUq3Q1wravDFslxTKX5x7OvMibKQeeXCb/3g/ZxfJkb+kjF8j8Y898HAHulJFnA6+7u9ZEDslokuEg/gr0p8HEs7TBTf6mmkvervgz0w7BVNhEPoS+/vSwbeDpcFQqhdK+/cDypKHDYoHjmC2Hu5tSg/e41FfKm1BEqbATK9ej3XFdZdTjocM+tEhDik0JrOBUIIcBzJ/y194qAEK/jqPxIC8nu++NIAs8I7Y0jUSpQzajrFbsldMfrqxPmeB2RgNBrN8AbDbNjf8dPzEPIo0JI1C/ce5VCaPs9Pk2lzVKxjn9Odqkc+x1V3TSjYiqdLscaSW6PQfrtPBRC5qkW3YCzSU+nA0ImbM39t+R0/YXH5RQpYwwm5shXzyljV03ftGm+k9Y975TKqggctFm1VLotptIbEmsN+UM356/ZKIQ8egj5lzAXYqFriNqqSnDucG0fcg+hl3YI4x/EOfZ0/MqLKbsVnB0Yp5TNjdSbNUPc+vnchFXc9lVGrE2l0faYAJopYxG9npZqi5bPoZEsAlfW6+uzKDs/g/mKMz0yusYZYmbeH6UQ8pi6GJtLhaj7ZqFMjWIhxD/ogAF+94ev8IkyICSp0MS8N5Nf3DGVNAuJY9unks8FVEucMWWsE5ojh1XqmKRcRVcICdL+cx2PjGG9NBcLjvO8Bz54kNZwHJuUsfc3489fGuTHQyhJB/mCNoJyoSqlgtxDKHx7WBVC09+fuwHeKn0aPB4/SCqSOeOIp50rXwohyXXd+0Gu1KEDS57uDwmQuHjzHLFjRq5Vq7oTN3A1Txw0gwjd+ICpIxmloDWRTOfX9yWfe0XTKHa47dNj4/Ddz5/h03caCiFGfQoAVEB9G/DznipK3quM3q/HRi8oofdZryZCy5+WDY7E/GxcUAY2dgpym3kH3r2HJsf1qUzyRWugcDqE4Glj+0yG1J5JbPIZwTOB7jO++bV36g8wL4Hdzjk/+vtwC5MTwzVE5XSgsT2ELnUFCDCMc0TopR3gjVC94p8oHkKCqjsAlsoUYQ46vyqUSTUzX/enHSazcNtF4MVj+dcFwfsAANYeVhfZBJp3vD4HhZDLwiUn9tcU1MxcAlHX5JpOyN3hRmxAJNFrW97T9bn/3o/uxiljMnVFOn4chYIYEtQkJvdnQF3hcMW3QkhXAX7UBpgcjodQCn02QnC051YJCPFJoDWcC7pp//RXVAEhvoeQdSNlJmgPIwc8KxxvHpVSwSwv2FcnvN9FxbB6CL0N7SEUwZtGBTmHQzyElFXGtt8zjBgGxwnhkjJmcn1Mez7iOZGJr5Eyg/Pe6U6g+3HMs8rYg9DU6LiUMYouJw8hQUBaplxWetcliLGHkMRUusy3ChsONskVwrzKi3dKAnMkO/ibPrrzn6vHeYd4052vNE7tnndJpowd7CEUPUiXFqHMPR6aP/cv/QR8+Nmfh8rCv+fSIHjt3DuOrLwsCkrkhqhbbxejTt5Vji5IbSNj30vIlDHOuTdRc7UtPXY0uLKBLura773Ep4l9PlTudF2ZPhe6iiHx5tFpPwLF0wG7lETZY6vMWJV5em0qP2Xm2SdEewXuIf0DU6aYpPqmNwHXYxNoFylTpX1BFBMh7k9J0Q9lQIj1ndjsKm/vwUP5cRQU2PapeyWbb4411g/EzqtREIwWKa09XbtJmuylruDjvfdyXF+Y+42eh6UvP1HqpA8eszWEBGP4iU/ewF/8175h9fFpJ86Dx8dYqt3kzXYw0zVElVY+OMCLgAyQi4dQO8Dby3Fx52tMU2kKVzNjFtlCtlXukO2rZhntVgkMCafvswv2Tak7gZ4TrZQ0UgjxU4wAFKbS1PH6Eev5FSWBw8R405fkcr2TOXIM6T5RmOXTNraoAu05BkR+4stvNH5rrxbkVaZpB/ywi6tC4nCM9VNQUfuAqJ21U8ZsvXyYAD+ATmXB7bwr7j3f9lkY4wSrjAUel2nldzGV5pJSa3hQeB5C7i+GSSnGQh7sDVH53jWxPYQA8Kbs/KGm0lHKe4oDJnbPQrKzxQnO3DuznSoA952RJSXO1kOoOSa1rx9GaCoEyDIYKlcI7Z/FpMxMvN/l3YszGtdyLimW6byrl9XhsN6GFeKkTdKKQYEyLol2xa/e9PVP3xp9y64yDR147ke4BXu2KdzDghKXfpX9vYDvzaTOPcdCeKneKFUnbi0FfKRukUCU7oaYT2WSNczGFcBavTguaO2bDzqd4iHEJ5PZyeNg5YGyTHS2EvVcdyMLfK4KHweAaWKqnbLiK8+dU4mFrB1e2gM8hCJ403Chy7I7mi4DALOjwQYD6ZQxmUKILbepU0Zd9FX7lDizqkmhnxMnQDO6pc7qVvcDmHbd2hDVhgreUHnhhKLPvMrYRaHoS9JUWbCoJk//G19RBYQkZecZ1ClzhccilZSssGlTh8PMWZeAkGY2hJNHK+e43LkFL1UtsSpj/eJpl1ifHQqBtUXxENryIK3hYGx3BcDfQqnLYae6oGDbWTWiYOHGTFwhXfdudijwEJqP89IN8DZYlTGB+em4VjmLBUkr8OUns/MQopjKzuvvVAG474y4eiQ1lR8lpIpuGLUniytU4KrSnzgQo+7S76ZLUx1l7omZHdmcqowJvNkEfeowYhixQg2ZhFJo4qc+fQvvrjX80R/7YPS5NWVs5C5M00q/KETDdo5FPhfYkDqVwhs+6A3N+idlso8MjOn+6RfzSMtUmijEct2gcEVWMfKRKabS3nHrbHztYPbj+LAv+1lRla4kCgWzianfNkKOTRTJL+0Ab0WGxwFYFDDjCDdj02R/rGXn/dxfqYdQP8JNpsLiKLjMJif81ERyXko4i8HrYSljjgqh+fnJFULbUr7ZmPljfgqiHhldI8W18ePRZ0qXvUIIUfdNsEufSUDk3/v2T8Nf/DO/pFf0g1fpk9N+7hl6KBUehb0tRYxKi/7YVlMGEPU9InWUzbWzfZ6GJxzVd0T3EGLGwV7n/I8meGW+/Ty4mEpvKSNYbNhcfUl5aRP6ISdz08IOnnqski+iyYJUnLISvj3UFQIEACPG0A8jtMMYUCG0h3T0h6aF+DZdFioHMfCCM/KUsS1XepfbAauy8xRhU/u2ygxtxQ6val0l2UliHsUSBMxyYejQNySk/tjCe56xTKUz8xBi4G5GoG1wmR/sSqBtLP3zGpSz8bVbDfn3KfqdyEPJBfJeJft+Fba4tH/298J6CPGM0XNEq2gEG5ixDYbRik9DTzjVZu7RrN5LCYxHEQpU1BUCdCJzdV8k0BoKNHZyTmLItZ2g5Dr5LPC5sLvbonxq7efua4Dafs8UEAJ4nYMGb6+h26HeLm549jtXbr4a2+vaBHE2Zecl/h2C3GnnlLFhUiBq7bITmN1270E7bjls7GScuOwkaZxrkrtuXFI/v3DE8hgjnh35KIQ005XBPX00HI7VNtmy84247yxFPAobgqsdNOFuLuWcMsYqXcxScX0FZuSBKJ/KJJ9wlFXJpDCHfl+2zwQh5K2A05lIpTWcDNtdAX8dRz+qSiIWkoeZVIh3t4n8kQxSeiXIvSCYXA94ShcDgHAKIYGSBsA0Jco/dqbLYkQpVv0wwjBihYcQtUMmWdSIP84JtDimSPhSQqrobQLj1PUihOb3Tn2upruGheM5LCDEButH02B9RHgpnpL7prcRketcRC/NoBtKin4hD/JPGVvpTD2E6mqpEOZCb7j5mpqHkKn30jEcV3YeIIE0vgTJYHaSGY47AxeJeaMJxeTwfKgWM4t032SnMoAcHSGAccTw3PYAAMd6CC0KmLiR/7vnXXPRsyfH0Tc3JCljbvenNaoqxFtgHrNL2TlWGQNYjcpV5JkyZtkOskljYVOyeeXTQ0GnGOSiHuNzqSsY8Wyczga7PBvop8o2zaCYShdE2PYvmPm/J9iNxdxTxjhm/dx3j1vtiyh+bSs5w+bz8s2fRKrfMucCkH8Ksw/yVsqF4XFbQ6KQhRJ2DCx1A86kokmBj3kFuiVNSbQAPmgtQjyEPt6ngNCH24EeQo14FzccvFQls0CNikXqzPQLrWFA6Go7IWJw9cwIKqFmcv31ZdH8F6SpkdxzCW19q9LadZOR8aLAkoZNvTwIvQVEushSTa02Ig7FT/UmWZpBGFPpXPqRQk4ctRlzBKaqeLfqUttqvgCgnY6+CagngOn5H8aBG03xg3TpkeoI/kDso/fYtONYJjrMQiibhUlBh/3utqOHkK88d87XDBjg6T6ljH24XfwcR3j8RDyEqMFsTRlz6GKZ69pKjqdjrUokUdCN7yFktShmfIvMr426niaEyeL++1yrjAHIpMX8XbfklQK8SZfTRCzRccYw9SkkvY75aUpwqrMBsKm42+AydyMippJMeGz7lP5N+2E8G8N5CGXSZh4dl36V/T1v7w1/YzHblDFBKq5JyhiAu4J83fwJrEzyyWajTHL+h4PCe24J7CWyVsoFIIXWcD5sBwFYOyxXWXs/4kRe9oIvVLvbccr/7tswMZV+mhVC74MphBL2EPJiKr2yKAeZn9/7KegmVQg5B8z4VdRc1E+Xasqpd1VCqug89IMyQ12aNjvj4MejOTRlbMXU/DQ1SLCHN/5ojTvZpBgy7KrA8tWC3YBPnzJXMCHdhWZTo0VZkzv9onTRe/dI4MhHUQ0A/bH+msi8lGB6/odwYNl5gGluXhRCW8oIlhjkBXXtOLphTE8OWHDiKvIyIeV/deSzu043QBuZPYS+WFLGHs9DaN0199PFEuXgyDz/xUPoopsy5mdiYrQjHjDwraK36gfZRaBeRbQlLz+nRb/17c9kbNkt6I+q9oIZBUnewcJtIJmftpq8Ms4DS5oB1a6ISe0jXH9BA9uFLflc4IXxUQUdwsEr/65f7QvAUiFNYW4qPc93Ys1LmTZlev5npHgI7Xnc1hAMtxd+qQLk3GG5p0oUIsJZRKu8TFZViqtU3g3iIbQqhI4LCEVJGeNM4JZFko2KRhpA2V7X6iGkp8LyFTBr+9Gpf7nYVDvTZiuNdk4Zayq4S89zW5o+XS8VhlRKIx+Ir6INpvTjOJsS5zkmr/MSTl+X+uJiaefuz/3a7BfTi0JKOO5akmlbKaTGvrpStiljDPLy7zPUOGfvIbQN8JONLGmRgFSsDDhonX8UiodQTBIdwR+IXWlxmwUb39Q22QlawYqd/4Wg8oH+c/e1MNp+D4JpwCEBoQ9vQgeE9EoDh4fyEPKiENpf17LDhIiHkCJljFlUuE1MthMi42vjTpA8Ls55gTmTflCwALuKVCWi9y+5SZYODuec7MKVF1iNsyvYDzjBybc+ayrusP4QbQOh/I2ImNcsKq5gn9J/rWnvs62aO1jKWLLvV2GLS/tnf8/TMxdsLp0nZcxMletr3iEvEsAfdwDclUlubD3PABLxEEIIwqdYCjyEImcSpEYCraFAc3HJcWVMDktA6FxcOLuTNGTRemjKCmfCgdAUMCBVxt6HKjvPNe9LI1d7yW339A6SHXp2Z+/e6XgV7QNLRhMTgQLKZTJx9ZTLr6If3ftBniqAe6ykjBplPO4C87iUsS3dkFtK0T7VDgC4Jpzkfl5rmUox1zanTjnsHihlrqBJwupLXU+8NHHbBHVaX22OOwfBNY97DaqINqdPMoU58DvDFkqoK2j7QfDLj0kZwYLgUMXCU8fRj3nvSBZg14HdhKbS8yA3kp1aveDA9NEwbYQohN5eaqgPbIcpeQghBN6uXeS5s3gIaaYp+UrVMlMIiVPgggeEbJQZnImDznl2S5Wx0u+myqUOUd2OAwbYKurcUiwPhTMm3CSB9mApUwlyafaL6VUhVabThfS51tUyVmWJrjebZMPQef4j9S7aE0QR7UCWfoeemfryNJ5HKjxuawiFY/uySxnbM3lnlMd7JlQ5r+tOZdyJeQWT0ebHe3+ofxBArFxtXsqmiyJAHEAZmOu6W3oI+TCVdvIQIs8psHTdh1Lyoqjut6TOZKkUsLz/2aSx7CuLHFHdjqUbxqwn35t+lbl3yXsIkTbg4Zmvhrzrd4Wr7pnLO1bYYtvO/Hldbb+Wp3I7x0J4CWzoKnVsAzMYwyblSse7aKPMjq0QYvtsWZpvRA6cVxQPoT2pjuAPxL6sKYAHSeM4Rg8MFFzgq8dkC9N+VExM2c7W16KI/R40BSQ/3gf4EKzkPP/4vt4fK9iUTVd1EPe6tv4Vi4eQsMqYwEPIZkLI7My5LIAaT9UUuTDmj/qBK4GHUCPKNWcqdxCjxmz6Xer8swnwGMD1gGHfo2OYypLne4/53myMhxCvP4jZroTHtu8PNsFhtO3DynzrweG1N1u/qoDvTd4pY1tI36OrAvYVmDH3Lgo439GFnhcZnn9YUPgUS4GXVlxPp/RIoTUUKKwkjXi7SBxGDBin8rIXfLFPd+BH/WNPTGlT6bCG0mIlTeyOvh9Gr9WmyGJrYD2EFv8OybGoZlJXCOrKdEIoMq23b2f21T5k7M9zShkLrBCaCacU8Iz3xUY+C+FrJI+xfnDz3DocNm1yvm+8antaZedzDThqpI+2On2wE5neu4cmFQWOwFQ614DQrny6mVejr8DMmiarFwRf5juJBCBWv8OE+pbgql07K4BHIqMZSkY4VrEAcFvQrg7yCb3sBQs46Q4yhZDOgnTX6fpvIwiolLFQhtICVg+uAydk3OpW7oEIGlHZ+SVlTKgQ4n0Xcp4Q9Y7Vu46SEU4AXQAAIABJREFUULsGrgAm/xQtDyEyOS2B+GS5HDYx35Yp7oyUaukhu29d6goZj2Xnpyp1rtU9C4V46BZJyIHe0LfPV/+/jvWax40xL5XQqbIJHgBeX/7oPG5rCIZbA/NhPkYmKOF2rAoxuM4L+Z3/xVL+N41SkkuVsdcePhzsIUSkw7Ej//0w2qeISMwQdwqhjpSd10/N8yGV7RwDXkdNkMxSxgj7NF6d+2VqNJkE1rtymVyjQOkSQyGUT6BQbCo9va/8gMgjmCpfSXCYugXBrj9XVdWjY9unLnFL32PifjzLeyG8vhdkXJaWf9+k3tvOOzA/5Upz8zW6h5BAWZVe4aHiIRST84/gmbFOWO3L4SUpByyYwVOPKRbR0ooL05cyf/c1Kdh/DwYEf/B0P95UOmp5z62HkHtgjuchRA61lT4Lq4wJVIh292d7fcaGhId5PTHPwXEhfhUphMj1MAHZbHbdEi6NHAofClwbpiIP+Y7HSyouZ16Srqm04H47KLhXU+mVtjdLWykUYnKtaxhGDMOYf//fa5k7r/iad5j6BSbhIUSRyuYxAMz9bOi2yN9kLQGhLQm0hgKN3YSVWZgYOu8X8mC3y4D5O7WH7kRzJs3kJ7/3ozt88jZgQEiWchm5o+9G7DWFYvEQYquMdTpqwL1Kws6jbMU10BLEQ4hznv1g8BwEC0V9D6E0PLzscDjnZJUM4qD60f3DlDKW73i8BNppM/qlup4sIIKY/+eGzHdiu8hLrmJP4WB4z9/WwNxTW5JsLMb2WbSDVbqYmjv76f+Xd17a562E8Uw0ZVsUBCC1LJLAQSGOH1ye70A4UmoNJ8K+isVi3ujQUHvD/NZCHqgG8uW5m0jXAyzm6K/86vub9++XcVQ58y2cgEnvXu6cRuYhdK0rqAze9b05uTm9UZWxeB5C/eiu1NL1XDAtgRuPxx0X4imEPFQdPJT95BmAbyrdDeNiVp8mfsvOs2NLp2Oq7UKyAdeCkITVl3kHhLaYbsJcPSnITVOuoqeMMWSZ3u6Z/FMn/ZP6zDU/HAcCHxPWNWWsPN6sEfhf7AeV7U6lfJAK3wHSR//a+2vw49HUFYIKxR94+9GlLLs4gMLKvO/9IE4XE6DriSOjdTRrPqIaHMYYugGbL8Qtd5LyVAhZ9gfZLFJZ/4yDJuYYA/0e554ytqmSw7wfrn1BTlwaYsi/3oNwqaKPcU/Ph+0cy5/5+fZr2fEsrfQlY2gvn3EKRiPeeOSzaM+uP58+Lw+Cr/ed9P2pBOG60SygdRgHziuKh9CeEjFIDB9VkjpD5/1CHpAJp0g9ZlqC09suFvM9dJ/+lSMCQni/6IvS0dNVhYbRfQFIXRdJR+iXgNA6wZBXGNPzwdBiMxGzCXjtr6cNsUMzn+cw+gmM66bYRUnZdOLxdsdi7Y53o1/FYFC4pvZi743WsxrSG8LFhb2Ce1IIMR5CD2SqXcifWGmzIei0UsLpeYef/r8bMVzrih+I4rDe8zTG3H4YoREF0g7ngHMQ+MH1I4bxBF5avlCOYAihNwih/xkh9L8hhP4PhNB/Pv/8Wwihf4QQ+g5C6G8jhK7zz2/z378z//s3w17CCcC8DsvAVBpvF4mrnLFMUPJFVumFP5h1wwgVkuxaBOn8OR5C1HHCKoT413P1kBLlSmdSll2DVSG0TxlTVhjjKM3MAmbbz4+zIaVLwOtaT+fsd2HO+AsYGj+K2tOlrmDE666g8HgDhgqBUfpeMmR4ykpk/hmHVxnD6e3GGsDfWV+DRCam9lnBUwuStrOYyafox1E4HE2VitZnvb03J/MQ4vhmmmzAeAsI9ZINP86zuwWZ7xjCbOglpVjFcECapSDjYszwPQiEzpt0B4BfwBj/PAD8KQD4txFC/zoA/A0A+HWM8R8HgM8B4Ffm3/8VAPh8/vmvz7/3WDhUsfAxYV0lzAm98AVn9oZ4zOBotQvtv41U1Hn9zFffef9+FYdXD+CaLmOvC4TFQ4g1le5H451p+ypjE6sCUfO4kYwtW0+KHV3Vpu8gYDC8L9LzGWeOSFXk4afq4IEwfdqUlsHfjNBq9zEDQ3j3B2t4wfQ2tIdQRu9XIX3CbMbEwTQVt5n7MdcNgX40C/CnFoQzDaSdkbXiXFEIEZQtAk98nP96mf/DAPALAPB35p//JgD88vznvzD/HeZ//0WUhi4tC3x4CGVX/rgggG/suTNMpqq9KJ/5wWaHX//07aHHA/DjkeNK75IyJpC3AtApYxP3zsZDaG+MaoIPrxwrJaQhvSdPH90gQmdktF2Iwe2wiTnepZDmrCBBCM2ppvzg96O0e6IWHKlxtGzAFdKGTakXp3/mwdZDyCTQjhCyrC7FUSYZzLWjB4R4yqokU1yP9RACgF0K8COj1SIQQjVC6H8FgN8HgP8RAP4vAPgBxriff+W7APD1+c9fB4DfAQCY//2HAPA1nyedNm4L7osH8zHzVIlCDlwW9Rh/Ed2Pscw9GQ8hQPBnf/aPwK/90s8elD6zl4LGyY9fr7X1skiiJhTs4IVWY0hpQIhz+y9Nxa0WpIakpLoHnI9I3Vkradh7HQGozpWdnGbW51oHiDO5To6/GMDxu4LJSfSl8M/ztiykcjGVdkydlgTmMS8g5HuBVfZR88TaU9pfNTwZPioZp4JW0QhOyrzVtTMBfpP5D6nCeA+4AWZC7inM5oj78jN4aflCq0VjjAeM8Z8CgG8AwJ8GgD/pemCE0K8ihH4bIfTb3//+912/Lm+oDmuJYLuYSi/VplKMABe04PTVq3qM3zbUaQnsl3qYeAgmrX/ssw/wH/7in3D/fotj2+0A+aXzvEgik7i9QkjDQ2inNENmuyKcqkIAFoEWvA9whXlO8w7o6EchJKzKwtt1y0kpsTn/M04OZSnZx07Mpwl4Rm2Dwy4VF62bV+J2n3u74qsr6GqPi6l0Tu9+4TGIlK4dDta3zzwV9+Zhw1CeqpbqvJQOaCWk6kQIZhOhsMcRVG3O8z0Ig1GLwBj/AAD+AQD8GwDwKUKomf/pGwDwvfnP3wOAnwYAmP/9ywDwB5zv+psY429jjL/92WefWZ7+Obk2lh0H2ppKp7lrV7Blrcw0t43dgtTCtybILmTcnNxrUx+8+7W/3t5zcGAJCO08hAZjDyHXKmz9YqJq33Z8KCF34P1kEcAgMC4KMBIPIZ2UsYfadZvJSMmwpEs4pEza4DtAHINLjYQeQuo+KOa1+1NekOscqTLUpC0ls8AqRIJq40yhF6fv8sztAHXuUehVGdviIzDTWVRZjadc35OlmtkzZ3oPfKFTZewzhNCn85/fAsAvAcA/gSkw9JfmX/urAPB35z//vfnvMP/7b2F8sHFJNogXIC47mP1omypRSBmVGaCXUucnIIWBt3NSBOyfYV0haCrENZU29RAyrzK2xYcCESE0SbcDPieSFuT6TuhKi/tk8/JZHrePWNIlQvcPbHAyq5QxAF6QW7RRlZ0yzgFyneNGITQsKSFByCjgWkif7E2ldVO3JBs8rtdOyrab4OO4vkg2ZezAvi6sSj1PGvWvwE8CwG8ihGqYAkj/Pcb47yOE/k8A+FsIof8SAP4xAPzG/Pu/AQD/HULoOwDwhwDwVwKcd7r42IVyjGC3fVEInQLW/6IRmQGuyjD1xDxAbJbX5o+cxDLHn7wuIuRqsyayjeM94JaL3+483vsRbheTNMFpUeOiQFxMpR2DH7fAEuretBoagVNqGkDPVDrJSZYUy/4gm0Uq2zdMi6GjjSS7Ycw/ZYyUXGeaTNf7rajoDWEb1W27nHSbmlYITdioFGyPX8gBaxMhx8+LvlZ3HpkfeoF2jsekaf9PKQIB7ILgcVPG3M8/a06XOhkGZUAIY/y/A8C/wvn5P4XJT4j9+SsA/GUvZ/cw7DtsN1PpUmUsf8STUVHbaFW7Fmyn6EW4l96k9dpU8NT26l8MSIgF4LWplneb0PYaHkKcwJKZgmrvlQMAFulRHiZmOjCps65BmotQWryvJJVXn/t4wt0Lm3Z7EH2woEEAlN5s239vhxE+uV4OOLEICHwnNgqhPu8KcoUzYz6PzAmbeZYPBbk0CCzoP2+BFdFKkp2roKmfDZ5IxGwOzfPmM5ir+yKVFlGguNa0EkADJl/Z10KokBb71JW9Z4qpn0yQgE7kDNHDpbmc69VTa5lxrSu+h5CFqaLLILh6lLl78/h9ToLAlfb9EVdXAlCrSnoLX4FkyEbxYwBvVzDSYsi0PHKK7FNNV6WB0E+MPIOY7Qv7U16Q9NwB46W70PNQKpweXhu3rWgX8H3JWhmx8wmM4yFk058nlTKWXQqzf7J+DwJRRrEQOJQ1BXA3xTVfCBVy4KbowOKlrMRWGrCR//gDr1MpZkkApWcCxeqUMf73uPQva5Uxx+pdgb2eSEU2Zw8hTfPBbDy8vC82MrjmmbpCgNCx6RIY41MYjl8EG1Vp7TaHhacQCn/9ebebxyT2nEjMYT5qwdBVuoTbiLKZa6cUEOqGES5JpjAf6CEUqeJoyqTYIjLHTyULl4WSr4VQITZsydtZqcC2DbTu1Conpoepd+K1vXim0us1+1GL7ANdHUkZmw81lZ2XHIez+CfePeZe/0SB6KfMcuiceh/m1wDrdaqCCOlOsiScvd4Dc30IoYO8HNZ0wmEZjzNqG5x2calJKvv23+Rl52NiltKh83vXRSG0/izY9Z9RtVeIAN8T72gftRB0I1b3q7u0z9oiGLZPD1eqAjlehNECQhxl1WOtD9NRC6dMiqP4w+NqimtcbrmQBSqJo/lOpYfFYMxJq9LrIg7jiGEIIMm9NvVuh74dNDyE2MDSpV4+qwUzmVirdzmaNQfbMdumzportfiBWP65UkHAAS8eNYV0OTpg7Kva3XGIlcu8+9YFSI9NB77vxEh5uYUzlS5kiW2QnXzOZ5BeZqabrUJopR9GY+Wll5QxqUJIVj06jXuuFUg7CgQw9bOBN6cEfnDFQ2glkRbx4HCqAJh1WPPn2SpAZZKSL5yBXKgQmplUKbJn7pDjbsSDeQix3jWBTN2vTQUDtRDphxGGEZuXnXfcGVmvz75aD0CACdKu1DdJbXP3OgIAaNl0mV2gLLNKUpvzP+NYkUbAmLwvuRsPX2u0HXvQatAt3i1HzP9j4G+hTfrakao6NF2/KihfOD8ubZz9bEAPoayVEe5Kl8lSwC1NaEoPz9hDyCKQdggHbjCrLDgekbxnKMniuFAyNZVmKB5C54QswJcOjJngtn0k49LYaSeHKU/0cA/Iis2NaVNpsrNhamhKPIesA0K9n/7lWrt5GanoPJnrm5Sdf8ggfGapLUf3D2cp8nARBNKkptIn47oJCE080vUXNIk9J5LQ1BVU6BwL4c7W3NlxI0q9+co5bmTlOs0j+b6JuJ1IKeeLx24RIfAwELhOWIuH0DlBCM2DGdvG5hSZEaezCx1xkXiNVd5zKXfuKSDLCXStO/RoCabIFULi3GnjYAxa2xmAh4BQ4IW5cTU0AWQRqPIQ6sdcJll0m7Acr7IJAvG8cNhqWSEOuypIlhTuLNoGYX/flv6HVcal6iEkbKP2G3ZLlTGq+YRbXKHN/woFKzhrkpTSl4yh3uteZta//J4HLx8MoG9mvfnQetyo9/zBPYQkqZP3LtP3IAAJjuIFUa6+LotCKKf0hcIe3kAuGcz0qhxh7h/tiTmoiJU0dqbJfmgDLQCvTbUEYwCmkvMAqyeQEEHutP6kiF/O3XxCcWw1OOvzZFN4WWUezcZoMqG8/IKQwz2ERlfFYBqIFUInbvfMELJWGVvvQ7qm2oU42M47MPP/cIje5dywCcb62IiaPITyTRnrktq8msfF4PN1wTw418BoAFJpEQ+OYwSb4yBfIYAqc4n6YyP2MxF6CKnMPY/a1Y8sl14VHQedB3MYokzxnUZwrSsYhu1CBEClENpDjFFtB8JuuT4PCiGvgzHrpeapGppQIbQPlCWZl69DNoofA6Sm88f1UdkVeZB4aW3VqQgwxnIPIfJdMdsX9rfQJn0JnTLWDhguhn1woXAMgk2zXBVCnAIXMbx8pkCUWRXDWxM2RV4Jo6xKKoV5ea4Hlp3P2ksrDGUUS5Brg5w6Dpu82kIerKV/AXblf2WDlBAfHXDsfHlR9QA340BbnMudiyYUl20w0NZDyFUqa6y8iVQNzjx1lv97F80Uu5KXnweXUmXMiisn1a4LFPxOFaLGHMe1DHXXj+mkahfisXkFtpsT6s8i+d89k5KfjQtTmrahl483DyHzYh6uZta+OLWqU5MzeWn54rFbRCi0BwH+j3kTLxOkebWFrJErhMqCFCBm5J+p8ue8a7xXDhL1ESC0BHSkZec5fdFqpmc6OfHrkRTeQ8iPMoO0J6WHUC55+d4XGxlcM8XtkIn5GjDoA1UdPJrtZsTEosJLUSEjaue2i3RY+4KBNZVuArwDZ1TtFSIg8BDKdiHMKl1UFQ63kKI942iykYk372PXa9gz8IqdxFJlcSqw5p7CbMZBlW4zJ8FRPHcSMJW2KIlYSBG+IerOMBmtgQjl4nczMHhQ9sSctApTG9xSolxZPbw8p4w11eJHAkB5CCkXYwIFla5CiK1mRwJepoGWgydI7tXeJqoKQVPtF8P742UYkLVO78xkMsm5vkuDjksnhUyrjAmMaKe+bRsMAcg/2CWG7yeGx+09CHv9GbWbgn2fSj53QNd0iLH+AUhTtwgiD0UXn1YLD55bUycThEuqEIDA/Ns7nPcypWeSAom0iAfH80KpVXnJFLJFZSp9CbFTaUzcFLJFAXNYR7/38ALwv0i6Nn48hJaAkGUfsyiEHNvatQ41GG+VGebBcdlimP61tZIUwAOXnc+Mo9Ml8guaiFMn+3G7s95qX1vM94IstN3HJYQQ3JpqoxAqptKFZJH5gWW5EGaVLuZrHR/lxm08eK5NBSNe50/HQ81VxtTUzIYplp64NlU0a4kUKaNYglzrGoYRw6Atady+TL2Vl0whKSQD+bIw3RnsjQrVBuc7fXTAkU2k3ato+WVZJFmnUfCrLkwKob2HkLLKGINrwMzcI+mgSSnHcBLAQJkheRd0qrL0uXm3be7XGccLcWDjyN1xcy+rNCELr6UPovwXxB46eV8zj2tTzUGxufpg2YArAAC/rdv61wX2EDpBqsw4r5FM+1XX+SHG2M5DKKGqVr1yrfAYXOvIRt+JUVpEEBxNTBtJmWMN+uSivwVfiHa3hxHDiBPahY6YSraaSh/c0VMBWQD/KWM32kMI0LKzITc0RbtAyc34/rDVu/ykYpFJKQ4UUPSp1NpXWNqCMZ4UQlmkBXk+x8y8To7eHe/6zKqMCSD9Rr9JlyJ+aTm1AYP5GTfNoAK6K+iG0VilqX38Qp7E3iSj4aV/5rwQJvMQlTebpJgFgOH6CnP6POX8Z+8BaXzcAIzzWiG5NWLIVwYh7gFu2SrlwpD3DCVFPAwES8dhXRa6RH9PAc//gru7jQwqP1Hf6WXSEnNQEStPAGJ6CPkyld7CGreuCiHLsvMOCqGmQoCMAwH8wFSo59SPIyAEUHsI0qjSjMgiOZmArDaWfUA2QSB++l/wxRCVTkh8v4IYDweDP/YAAPSUxH4xla7NVIo5c2vqTdl5LR8TF7J51wq5kG/K2Iq+N5tfBbltKnpc5TrVX52kyIEPzvAe+KS0iARxTenIptpNwRhRB0YWpEmUv428O3Y7eieGTVUiEwbfCqGmBkQN7EtAyNZDyDJ32kYuzT2PUM+JUjJZBcalhrr8Yy2T0xTev4IU1yqepviqdhebJSBE3Try7iadou55PFpTxiayNJMvBMS2vWHm/z6QVdrKeyFsO+a6bhgqU9Eldg8AEZTrBHaukoyamVZiHntOtxOkTvqkjGJJ4OiCz7xM/ThmP/ksiP0veKkrS1qCbGLOHah8dMCx5dFpeQh1veMOjKDqglAhJCs7z8E1ENPqlFylUUyQ/D0n1tzb8Dwl8Epu83fdUplk6UDdrzOqEGSGqgdOApcFRC5tQ/G+9ouP2apMFaohT9iurnW1KITGcU4VLQGhQkZkq4ygUzUtx1zX+c+iijTciAutiNYlvyIH4cj2PQhEaREh0J0EBVootQP2nq5SSIMp55UoO1KWgcb3EDq+oycB2XBVxtZDIbh3w/7nu1Pa506TFDPt+8MJOPtQooVO7etH00og4t8VKoRmnIOAhcO41NXyvI6gT65vtoO8r/S9I8GuJJSpXBwKKSAAru/EZVYIIbSMu6aLQ73jny+Ydmo2z8uwahL7e4Gffd6m0kQBrOpXfVoKYI7CRvHOs0VBEvEQ8uUB6Z+gJkLcry8BoS2pjuIZ496oL44dx+Qgn9rLXvDBpUZLh76AkGUn76EDjjlpVQVUo3kIhVGLsIsu65Sx2k263PWWKakHT5B8pnJMyjy1h1AWKhD6vbFOp8ngOgGE6X/h+wbeAiKTeyZg9TZcU03bBwyE0gqh9ANihcIelSdeDmj3q56r0Or7dW6Jqlyn7oGtB9IZydpcPQClRSQImVzY5vgWD6GzIFjM8DyEdGWgh/j7xE0hWwMedh455vDLnYdQCPnwEGrqCuqKlwKlRzf6CbSEniD57AfFE2jd3cpCKkz+GThYdTuWLNsGr7rWhcxLtv45ACnuNtPM5+vpeV8bKiB0iIdSyve2sMO2nWG/7RQAhJtmF4XiNQds+1Vnj9ZRtfma5kYlIbkNClrFfvAGc1EIbclohnJiBBFs67LQnhZshYiIBnKBGeC6a6EoQa7xI2Nil1j1vAPkivsCcH4ogusitP04BYksBtGrQvEiw9wz4yCTxeV2rUEaX15q3MpUVqVoUyXX85aRxsQ8K/WYBLKQWsYfhJZ7KE6ZyvuaedyogFCr8lAqFBIkX2UErXSx61dJRURXhZDp/C5+yljZvGK5NnX0AF1KlBaRIM5lEQdT74xCLmwi2rTB3iJdT+S5R0wlcy2rbs18yb2lpFjFdtGF4N4PS0U16UkJdvuJB5GabcC591RmOfTCvBuxmbeHpM3emlpedj7HSlKxA7mROHpinl/b4L8HpF/lKYSSTZlyKqTA7zvXKmPoIVPmCiIc25rV5+y4nUAZ0Vn2q+5VxhyPG/m+J7tBEXI+wvHSBDjHe+CTMooFwXYQmHB1o++GseSHnpRbU8NdphAyGaS8dMAxB5U0y3u2gVLG2NSwez8uaRymuCmEbJU3jLl14IV5P4zeAuO3SyVNQQyVJljwDwlm7rzYfIKngAF9nHzVYxOLQoh6Dx5xt/na1ECqzocNiOXdXh6alILtEh+1o9JmvcJ4swn7VUW1VqN5B9WfK4+7foh73Djz0vVcShB7hav8fmBKi/CNhw7W2VR6xNlPPgvAV3bMEe3tQE6ZSisVEQdMACJPMg4v78lc7+rn5PcdvDXVZolw70bjkvOEa1PBvXNIGfOQIhF6x6wbsLfA+E0xccjDS4WH5btKKy8yq4Z0nd+ZozzG8jTx5I89ANsqY8viIuWUqaV/9jMu3ZoKhnGbMhakylih4Ixg06yuAONVLZIj1qlbzh5Civ5cMB6mUnZe7YF0NEjw5/BM3pBHeY2mTxnFkoAfSTY3fVtzREv095wQRQi7OF0GR2NFhBcTIQ/f4e/4sXO1nXfNRTtb9Tb40w6jsaE04dZUXKWZDp1pFUNVNTjfz4nsII6WqW2CQOw+IIQ3xwLIbdH/mLiaiprSpWbiacmaMkYFhE6ifjLh2lSA8XQPSFDdth/WIrOAa8F2PuQ3cCnjYr3GiAzHt8/cQ2ieQ1tee9vb9Xmu3kXOUCn/AImlMEfaRFZt9D0aCbWIAsF4QbtTKOC0XvaCBXIfh6kTowz2dBRCh04s401iqwpB41BFy55tiojvBeBmFxohuHeDemdakDt9VXjibGDMmntjU2k+/j2EeP2gp5SxppZ6LuWZFkTdrzMuOgXX9OZC96HhyS6tShHAXVPt0KIWutUCpWL0dsUrpKB5TqK+s65gwHhjqm2r1CycCF67MmprFp+zJPammQ/IJkwshdDRx/WFbSAtPCGDQmI/uGxTJwOQyQwlM2wHgRnXjqPzZPpaSA+yE8mmO6weQibP3UMnGHPCLzl2zHKS5P2zqf4lgw3+3HtHhZDl/WmH0W4ywVZNCzwpNfdSk5lK66aMlSE1dZY+1DJlUhvK66JCAHX2CqF9AHdp903e12bC7VLBvCZc2lCQlLHowbSCPSktMPkLYYD4wQkX1OXT+dVa7RSiGFjvIuVcW1j9NkKKEnUutgGtM3KG1EmflBbhHfeGdXP0OOhHnGD0t+AD7mIGocQWpPE7VxL5PwZGmTKGUehNE4r1WPd+cPIQsp2Y9OPoxUQ19KTUp5caCQiJdpK0J4kpQC80rXfG4uX9GyGosAcQ2kOISm0Yz1Hkgdy3fsjNVHp+Fp52gW/1VHYew9qGgqaMFQqeiV14wwexyr87exfF9hBKba6y8SM89tBkTMs5MOqTMoqlABvBFvjE6GJfBaiQFrzFDD/dQbvKETsp9rELGVtuyZOC1vEUQm3vqtAjO1vbn/qsMuaiEOp604Czohqc9wnSmivvqx8k792mMtWmklQOC+MCAJt2G55+wBbebpER9KkA23eA9LHJLC4O4NpUgAAD4PX6bfthPR7n3p4C2/kQ9hu4BIBk0mb9sfcQMh1zF0uBwW5DQH1csZE3QMzgw3RebZJzlThriPjPJC1SahGFmZtx9H5+mShJY0kZyxxFpQJ2d3uVgcqe+4FtIrLcPUo5yY2ZcSiF0HIwaLVSxvi50zejlLpt/9J5ur6bb5NF5jo7635QXGFJpCrJv+z8GccLsz40FL1x6mKaNHUFNb2QQgjaAcO1riTpsZHbldM4JOo7p34LA17GmDBl5wuFMJA+8FXii5c6ayEH83fcpcqq7XHJXCR28KFPda4ScmMZAYi8NAHiq7ZSIbEWcRZ0OwpxJBkhkJqYyjD3zijkwiZYiPc7tUYhze2qAAAgAElEQVSdvJcOOOaEP1EPod6P6TILu+i496O1d4XL/bGvYijKqQ+VMmaoEJIsHlVBepfJaTRiK/sisShwQ3sIzXQeUxePQf4edD29S/94foWk38KA1pSxSzGVLvBScd3WAl4QbAgB5KgQgnVjigQ2RGO8oForwKSQMrr2jRpYcdz1Q8zpoDgblQxZzlUCcQYvLZ+UqIFvPEyyEUJwayp4tWikGOPZO6M82jOypDswixliipbEc09goXmtDxx4WWXKOAYxWb3W1Wba+NoN8MbSQ+jWGE6IKHwpEK+BlRr94M9LTZVm1GalFKDvieW7mo3hrVjhEbR/oBYQPlMXY3NrqmVBATAFhIIYKvtk6Z/9jEtLQIhOGUv9HhQKFK4+pSmwlE+3GOOnlHlLD0UHI/030QJCnFS7ZMakeH6EoeeguZFKiygwqMoci1gCAw+U039aBGUSAdgObDWVNh8cfbST2AEgvtdSLBloN2C3wVawszXlvq9/f+1G651pF48lYwWiIHhQVwguNQo2QfKV2gZAq0qYPnlJE0y1lOsBZBMcmjg+Zew8RR6uTbWUmgdwUQvmy62ZAvMYUyljJSBUWLCdD/kNXMp44+hTGg1qTtw5jLlvLjW8WipE14q+KmXSntuljhd8QOsGBUBic5VIm8hkAy+79yAQZRRLAp6k0SCSTMlTFwf5B5ukPQoiqW/aUf/judX2VbTsWQfcUIukZRKCENy7YZnYiU8JgajsrHbAjJG/d7NviA/eNLVHHwPidTT9rx+wndmttsSeKuWqmiSmTmZBHS2UPmxHpozl1i74E/RbQwfaEbSq9NjozYpzArptXdJ3wvwv9yMUQmd8NwtRWZXm+SojFqWOxZjrohBa/QJtAlGVdSDKF11K2QQbQgaFBH5wpcrYhtRaRGHGNqWj0zIXLqSPqDoEvZjZejkAKGSsISaWMSer0p2YeLnak4ImzH1pqIXHSzcs1UJMuTWVm0eZJwViyOfk00tNVaa3nSeJeaSMEWIr++KwVGo8aGLee3xfDkHhpdVRgeQ2h5Qxz5B3fIRV3RjkvS+BoLzgPS+j4KPF57QQL4TzVEYwlT2F/Y9cqWMWmMGboiEAGpvuAkP62OlJROFZ1ojTxjFACQgRHmskPwRsPwhQ3JrKaud8UQjlNAEtaCPa2bFTKIRaEMZfaN4cqkiYw6tuFaZrvc5f2w8j9COGt7YpYyYKIYZ+wJJJmATBBClUpRP7KmN7VDuqScqwg5LvdR6TMrbOA85U5OF2qZY5BsC0uEh/YeG3nDcJKE4pY8NUhr4Ebwob4s+BZCx9YGS1igud7lpHoPh9KIUQdQ+0A1oxOLgfzTsw6p8EW0QBwH7nfF2YlEebP5qpK2hVKCgHKXZw9NEB8+Y+R3bsXCloDa9H78RQC0C3RdL8Wc51kUBTOz9+ZcqYsPR2Dd2AYRwNJq4IAcYY2mE09CiLo+TyaearrjKWkYcQ/W5mX2lQAef6mgpBhQ5MGfMYmIzNtd4qhJI3leaOQ+5VYAGmpnXvxmIoXUgXwTyMKItjq1VccCmfPm24u3kIiVPVZBv9CSiEUhMNCFJz/R9jD9noC7UpmRtlJEsBzqT1jVHHQbwz0LLrf5YJaGGLqEIOSUsoO5UTb5o62u5XH1AhRL6XKFW0Usak5uQ692j9/OA5//yNpXm+nDkwN2Jv1d647x1VSYpIjtPx8CqImKp42lfZM6X3aG5+GIL5+RRIXt/X9uym0kLfCQwYpuB4+IBQGdOzwjbIjv0q2WSQNhvbz8acrdIFoak4hSnGZecp+gFDhaYiH+bHjekhRDwuE/UQCt7u+V69AGBV0fuMJNYiCoSbZcexpoyVR5s1ooj2hZ/uoFftJcTEMuZkVaU8iRP1bwOmiJBAL5nM2JadfyNoRyo6z6b1XhVCzITC3rtF5rkgSBkbR6grZDVJjMbmfmV03h64Xew9tEzpBmy1aEmR26Va+gBASGPcOcd105A+d1UI2fXBas537woREKRqA2SqEFqU2IpqrgpLDqP+n9r86Ubd+V2aHkIugbSzcQZzdZ+UqEEQ3CTJAPYdx5ofWl72M7KUSezGbQlOm9LGoSLykUpI0kzVqw6K+jPX2w0jXAO9fyQgRCqo3ZQpY3yIssj0HrmZ1vOVkCHkuuOIYcSGgXHFBBJAbD5oXdGsEIXJQyJ0/zC1h2HMLWXMwFS6H9M3Ul/6Z18eQmuVsWMUQoU8+P/Ze/NoW7K7vu+7z3zu8O59c79+PQ9Sd6s1t0BCDLYQIIQwtiE4LBYGlldkJzbLU5ZDnDj2P17xikOIbTwRhxgTjIfYGGzLIbIQCBASkkBIaqFWt1rd6vH13O/1m86pUzt/nKo6de+tYc9D1e+zVq/ufu/eqtq7fvXbv/3bv6EqFVd1L2BXX4yHDMxh2qwNEuHU/KrIEPUIoWWi3mXVX4TQZg7WKf+h6iy362RhB0f8HZgkVKmIF1OFCxWL4i5DDQckjDAYMEyGhzczLHNCqLxzEwq4soiQgeuq33+98PpqO8/1IvSKqauvIXQ9Wf9da8pYbQ2BPGRcYo4YK3WoMORosVRDKClS2yymjJVYKH9/PijPieJ6FU1qan37dKv6oVzE02AtK99MRsNCBwAx1Ecy2PkpI9e5KV+fLAddQ4noOdUyzBjLylLEuxHWKdavU1Q6SVu6yDYeLPmPEFqpHB5bpbolvPF7VFBEylOEEAByCAWCKQ82oy5jXaJGSVYtZomwkvcfveOC6WiIJOVFkXWXLFepWhcuATYOofW4xLqMVUfmABArvH2gQ4X5GkLGN+aMGe+ksenKcuhZWckJGJSR5ZBonEMb3EQIrQnfaSLO4QghsVTlmKlu7MDA1yljCUUIEYdRtbHMRrK1MfVyaKZJOSpeI/JyJt12fsNS48DPaw2h3FZJQ41mtiz3DamT0X0HlqCVLFCk2s4fUJIKJ/hEVEzHgyOpK94Mc5+bwYZ7zzy2k1ymsl24xNmkjGU1hBTbzqumjOX3NeX8MBshdDCFElB0jNcWkm3qMhb5xjhCp04rHiLTqkjSGCOE6g8jNk72dROLxgiZDsrVrNR2fpFYrCGUz10H55Dwj2oWQijoRF4qRQgVhz/qXWSnVppoyNF9J74446HbjqOhQ1JhA82QZED9BLOIEOrIiWR/aQs7TXFgA5wKdLY6Im+2PPL+o5A2Dg8Xi++hGkKJbpex+nefO5qKotKKNYSmKilj2EQIKaVH1XRTtPGOkpVZx/hk2OwQWmi/cx/4/0594TJ0v0vRY5PRAItV2fEaQ6qk2e5Ns8wBloLhekIpY0TGAfuKV/yZ6O9K/J4Q9WmzvtOX1BCNys3+rsruGA+xXPGia2o7B6OkhfR5zcGSl3o1B1KYA16PHDu/GWNZtFiM34F5aCUzjqnChYpFpbONUHwnkoQoVc7CZaJ4amFCAVcZ2i4Ve2UoqId2kkVIbkuOuSgV48rbqF9fiUYINdfBkF0I83QRufH5qyFk6jlHwwGGA9bYZSxYI6sJ5U1yJGNtSru1ejrON91wYosea6mBUU7DFXZ++4pyqbyvXtOP0XAAxhg455QyRoRNSxR1zJERQoegNRQp4Ap7rGVrUeaGOR8NsUhSpMKOKPMsQ4tYdbE2NH4HDhvQBE5AUkGUmWWbfi5psC8NF1MlfFL97ieHW2ZmNVOElHwAHcBckDs8fITnLhJ7G8DcEMkXMKG28zWROeXrtFyg+K+l4cgbW6czxXMaNHyqnQjlQuKR6FzjBlgk4y7hsoZQVLLRwnQ0KOpzAVnHmpHaaXkU1Dz3MKuDukhS5U6PBOET64X1rXC4WL96DSFAPmV+fV/1CJtcVyw81LYs2yrh7Q+5gzWi/nAovu/ADrSShUBlaOFwbXAIKY5NeGoRIRTTiSQhxbSi4Phixa0VMm7G58LSXkPIh+c/sdhmOn/Hi5VeytimxpJshJDp7l1qju9KSq1+9VJnm4q5l+Sp9MyL6PPyQzMQTRBGt5flise3HtdFVpX1TdZ1sFnuuyhXwJAxpFhHCNlLmevm3HUe1bUs/z1HzlOXTnEb6OhVtQihLOJTY62fBVDEeB3NHOp65F7nzcZDajufEapURI5eSDJQVlhygqpVTJUIh9b6UqsjrY2lCxnbMjwCOA2eynTR0uXQeLWL9jW8+/wd5wbFbKJXVPrqQrKGkFaEUHUuv7jjWxzTXcaAZidC0lZcN0QC+E594bqodHgnsmocdn60FpUOgULOzcn7cABwvk4htVZUuqAbstN9TKYnGnznDc7dKB1CpdT8Rr1a2FHVdgcgcWB4qLuZmF0h35zCBcsORayaYHo446LHBL6S95dpkfIipzgS6jLWeSprCHmNUKgyOFwuOA0Lr9MIoXVdCXMRARU1hDID6NK1BIBAyliNc0m+hlDW3cxwjbKi1pPh91TUEJIxfFpSqZoM6N4ZWZF3PnLTYWc9R6sVx7AjsjE9VLNMuKi0N3mpuK+Bph8DxpBmXcaCd4gR7onA2T6Ltqj0Gp1UXJ0aQkqHrxn+IoQOHR4HtT90sTa01BCK0TFqgZCkgiihqrCoy1j3WbeuLCswljkhVN55N+WkcHg4NnhWRRcuW23n13rh0rUEA6aeulWk1IkshIc6VADARKRuSE5j++/M8W34PSUWIiWnowEWtQ6h0IysJrr5zVfjv8NOdEWlGygXUF6lHCkXPHyKYINcTfVzDwYMnANXFivMFaM0CcI+zQ0doiumW47UWamnPk01Sgq01hBqtHc8RggVkVUBdhnjHNa7njY0mKAaQmu6YaVET0OXJBGFVaqdYaOYKuGJGv24jlQ4qMDE2/8aVrpeSwgJpFw6VvRFyqatotLZdS9eSzAbD8EUT95VT6qWhiOEZiOTkVybWmqmi18DuROh/JylTlJROYQME2G0kMsOO10rKp2TFPXE+if3I4aiy9i8tdMj0S9UbSzzqY1NxNt2fo1Orcbc/lGxD3Uc/CHUEGrvkuYRD7bEbDyklLGMQKWCyPPSpSOElNotE+HRVkPo4GZGqPPBEWVry/DwfxpcdBlzsukr55fnDhOd76+hhlC297h8PRFoOV/PYMAwGcqfEC51NoE1xfMBCxFCOnqwqVV5bdv5EDt3SBChU6eVlvbpLnQD5zw7kY3N1Kr/BnKWXKDAfBByZb5Y72DAkKlCbNmKEMrnLog5JOKlYT2LLUIIwKZbVltn3ey7qRj+VCZCOr9IHmEj6uCvtHf81xDS6ZLWRVweDoVObFZK2PDNCbUQDT83U1Qcmy5j9MF3lc1m5mD4rLeT2ipD26URW9VWXbpGjgGyrjsAzNSVqBhX/o5fvbYUPJluCxkXnJ9SJMz6Ocy835mlGkKmI5mALDKv5jkXScidO+rw77j1xXQ0wCrlxXppBcYKx6RqzYnQKBdQziOEpiK6zpdTo/K++k0/BqV3a80hRMTFAVnT3As4+F5mFd1qY2Kp0T59qhMhpJGq5sUuBRSLYjvCxdrQcjhEKWNrApIKosxUOaUjN0Dp1XaVI1XxGVMPY+3o6aOtYsVtbIoZ20oZW7+vVy8vsTMdif1Szcn42iAU0S+lGkJKRev91RCSMxhbiko3RJUkKbfYftoiylETkeiNlvbpVjZEFTW3gjLA22hJxWWZPlj1oYFFjfwMB6yoF6cTqUkQVmn5lmPeCCdpW4RQPVI1FI/ct80RJVLKwGeEkHpRbHtwB3Xm6uzgCGtpWaLDK3lEmAotZKzUbjm0D56QR7xd6DIRPC0xrnS9FhGq/ZuZJUdDJYcKHQL2vr98IX/56hLbU72NiMpCuEzMpqTaiRBiVtrOT4aHUsY4Ry6D61PDSHSucSdwJOMusUnJtmsILlOzEXW+KUc+SqWP+iwqbaPtPAPSImVM0DFP9ANVWS/E1FUNobUdyaMq+H7Q2a665upECLWnqtXjq9nJmlLKW7DrkZ8aQn7eR3iQQyhQ5DzJ5Q1pfws99oXp8OhCnqQKYaxRGQJy2EpFaiP//rSiRRo27Pl3vVyl2JmN1e+BdYFD6QjEbINrKhrGaIQQP6oH1Wo5iTtic5IVj1znhmog6iBwUmvZELTR7c4n5WiY/BvLD6+q6ca4D1PeD9pLGevm3BGOqY2SXMvtwmbarA3y9PW2qHiBbl/CEUIHDn9E1/r6ZkE+I4SWKnuFDjMbU8pYDkmFFfRz1FUjHLpmgPYWgVbdeRQEsK5h0r5JtyUTVQaHX/kbDQcYDphjRc8M1/CqryHEwbAjEiHEqq8DSCyEZWMoMRt5M9No/9qEjQ5ITUU4F6s0viiQDjuE2ygicK0a5mV9EJmpVZtmuhlHvpEs1xWqxVtqcsV9peq61KSMMVb8jf2Uscj0Sm8pvadS51/p35X6PXU2TvHIHEIZ4t0b62tMqtYQUu5u5i1C6GBkVXgpY4DdmoaspVlIbJFydojMSgkcgwKl6knOnQTDID94wgS5bOQntMA6r1nNCdhdOZlVdGOzzSYyxY5qLTuahGsI1aCSMlYUyZUxiFoK+gE2uowp6MGWjeJ0VB9anETbdl5xzYqm9lidEWgxZexQAU8gtgOa5oOq3BWSp8fOGiOEusmgJP9UVJoIl/aDxVijI5Yr9eLIOs6wdddItc6KIUQIJRpFse3AvPa3mI2H4DzCSDkLhCQVRAm1jRIrKu+zaAx2opYGjzYALArZYOtTC6HOVoY172E5c+llb5Fxd6GgR2sITUZ2vr/y5mtb2yEknzu9KLqMxRIhZO49zCcV8pRdfrni1pyA5qG1wVXK2CrGotINzKtSxkQihEI4fTX4DGVH886MaggR8RGCc0KaQ2USVNf3yXAAxjS6jMVYQ6hItVPvzmYdD/tWXw1oQqQbVkr0aBaVPtDVRF1ZEXGQLyr5ppeDrx2B0qfQARjpFlk7hFx3GTPR7rz+PW6XCpjuajqE1l1GJCOEdFJSq4rnaxR3rLjB+l/MTlHpXJ7SvJrsIUfg2JIT0Ak6hliohw9CkWlUVFqGeSkaJp+7xrbzocqGJmX1tzfXq+VWS0fnrvuo2lXmi583Xa8bKWNq6ztjbG3/CI+dF99jItruvsLeycs6eI8Qoj1igU76YNcgqbCBVI56NTNFg7U1nJGIhPp3mBvleYRQLiKtURu2DMyqk1eXxmxjbrBDJc/YJmXMxDdYMa7cEOFgOLU7FXmoxqKSYovgxhharlIwZi4lVaf9axNFcX1DjitgEx1RpZOXqxRjMrKiIZe7qwtXRaVjk42aGkIj2aLSGb6cG5X3lajrUqMLyhtCaw6h4jHIniPM47QTq1E2tohQ+/faemii9s9BlBq4ZAwGDJPRwH2EUGkKgu0yZjOKtKEeXOyOUZPEZqUEjjmBXqd9yef3LqOtZUGIcnhjusrrVKi89w4bm1MPEUJFypilb5CVNjNnhBxC9Sh1GVtxjAcDyZRUgUgN4ylj5iOE5rkT4dCcrVKOlJsqJO4SnfUqtrEeJN8MHX6XZthEqi2NFpn3z2DASp0O13M3E0kZ6xhr/b5+p2RvEQAO2VK84s9Ef1fi92SvWyL2VBndw2+VCGnORaLx22oR1jencEFrdzbXeN6DzCKvpWWSgKSCKFOENEoKqXjlfSJW5ofahS5VCv0aw2MNoZaFdzZ2FCF0IGXTds2Qzb1O7860rjQby4RMr9HpsFHFcMAwHjLjJ2ZJqhKp1fyzeWTeYSfC0nBdJeuUDbAQarvYpC7aa+LGCFQqwu6btuLq2VgWiUSEkE85K+5t8MBOqF4fQYSL2XRtV2RlEjjHKtWr2zcdDaXtQ63D14zZWP6+ZtikvIW3R5TtymeOjUMoTseoSWhVC4EaY2lrMhI8wSydSIbm/SU0aN7MLPIIIZkNaVnWOr4Z9HESsym6bKImS/P7uen4XP0eUCu6rdehor7jk5H3VGr1u0kZMxghlNVvKtKMOAfA4tz0GyW+ceedoeynjJmoKRYWmy6XEm3nO4atCNCDxPddEVC3q/Lfc2SXFU7x6FLGSinhGmuufJdVpnjQdBCVyCRTcM6zyKrurEe6bMoWxPcdmIakImDm4yGuLuSLvnYlPJ2oJl/Il5lDSLiGkDWqDBiXMlifI+5WybNSdyu77+L/+pF34OwxgQihhtzptUG0Am8zQDPHBwAsVtz42NaRSoYjhLRSdZprCB12ouXfITniQ6OhDlv2Lq9YdggZrSkWCJOivmHeZaxJ7n2Pu+L+Umk8dbWU1mP+wJvOKT4XQTiipSae7B7DO6JNI4rPvN4+FE4ZzuZQriRAg13qKSqrOLwKLkIIsNvkpqGWpqWyBTFCFqxJSifUYrSnKFxdJlKPkKRpgOGAhDQNr7BIGcuLSgtvfnUKbMaHSo0cXTZdxuzO622ndrSvMRsNkfLNplWERCVlrC0FxcJ7ytMoTb6HeU3dmaWFjmZO0DmJjrz2mN0aQjmbjUt0zsIG2ZgciRAKuKi0JcbDAe44vYP//U+8xcHdujV33aWcilvxZ6K/K/V7stfdkEdJXlnI7TFCYKnT7TRjLuMQyhDqstqi6+ZeHELr57Zf0kAF3zWE8lpaFCEUklQQh1hHCCkUfQ3qYydMc7iodO71dxPGfogji5/DNLQ2R8N44KhzwGbMC9vRIgbDyYvcaYnonOUqNR7tMJ+YN5DWrVWZXPHrNkNuUt2ZKjdOJ1FGgXQ7bbSps4hK0waxW1bUFIvqkEashtAy+8bC2lxUYScVZzoaRjB2gqgmymK6PHds6NtZ84nK/kr/8Gc+HlqPTK0kKykCBJjenutmDwcHKnZwV6HVLGDmE1nFwbIaH4F97IQade0y85SxbHFKuExaAq/57+7hJ0LIRMpYc7tUU6icjCxTru54rK2VZthAYgyrlGNoeBN+NKqEr8PXo6sTQ+sDYwxbCgcushQ1J6KRjXbyItKLJBWLDgI6X6/OCh2LqiLCYhMhFN9G2EQtn62JZIQQY0YauMxl72uQOA8o7JJ3yaSi0uQQChphhVU+kdSsvE+EzyZlLOsylniMEAKqjX2XxmxDJyHbG74DlJ0DwThlG3Knx6K507x4n8vEfNH6mbETs804bURK1tYQyk8rY+48pPO9hrpxbY34GuKK7S5jwekDffJ1ZpFudEgtvmWj8v4SaTy1fixycBF1qMqG+W54TddzkzZrA7ZZcxv3Os0Ha/PxSMI+PBiZJLTHqnmNxg/AJEgMRDhZw+ahQUM9uCmljBUEKBUxk4e9Cf64QK6pSkhjcOGAhAL173A8HGA8ZEUNoaLQXduGtErefBvsFvFxErNwVFTaBCoh40lq3tGyZcFxl6QakZItrcrrUsbCLNRI1DEbD3HNpmHOYORE2Q/1xnnu+Fwkq6K4citdXGc6OCRCgwMyLpkCc6SEkCHharjOcMAwHQ3cHpoZIjFQrH/tmJGrnyTWJECgNqzrOc+LYoe4HnleG4pue+QQIodQyMinjGVdxihCqPPMxsOixbmwQ8gKh5R5QKkBW+MhlitezI81DtQMsZ2jbW5+64okN2GjhpCKYdbG0oIerC0qbaCegR+4xvcakEHZRMP4pFMGxG9a/Fd86YTtbNrO8/YIoRDgtiIvHNFFZxoRBCp7DL9kkToGGjko7a8MNAmwt+60k69Hw+DWI9mGTObYytawy9dj+g7sEJpU9JOGtpBSXsssx7VL4elENfOyQyjzdwjXc8iJ1D4WZe4hR95IzZBiA2D3BW0rzM86AlF1bPWh26bzt5W6obUYI0dC7DnHOnw9stbitMEEoNZlRhYTJ9nOaSvWn6eMraiGkF0ikhkiSrYc6EAbmIjKnY+HuJ6kWKWiuokZqcGzNRl56+xWzFtM65FlRsMBJsMBrkh29O4i5BAKGJVc00Rrw0aERf1CNZ8MsVjmNYSyCKGhwGmtFcO86pouF5y6YsUjAC7bqrJSlzHL4xcORa/Pna5LgTpC5vgA8to8gUYI8c0pU2LBMV50pjqSMpZ/fzHrXZ25CtW4bHfw2XYWL2NtO99AHom6WHEBh5Bv2dBIk27QneTgImpRLiFkpxte0/VmPtKXdGFMrH16y8HalkyqUHYJuWjgpoP+FKmwI8ocRWRVcBFCgN0D0PpamoCnNL4ACVEqOoBE0cIG5pMRri5X4BILxDplzLcRRmgjUF9KuoZQpbx1V1Z8dNFIUoV2557IHWaXJZwxSg7nlrmwUWRxuUpbCk42Ua1vGWOVUSVCxikRHPOJ/S6EXezqMinVEJqOBFPGItCHBNE3fKYv6bBM9Yv1q9iHJrrI5vd1O+95UewII1YdsB1d6qQdyII1iWHP/qarjXg6xTKlCKE+cKCGUOqxhtARQ9/hqYdAFyFAIAJGm4PdrawutgZ1jIpBtLBRm2ciG7rdTmLpPVQ5hJbW60ZZQkeWOrDBt1HMHMCBSLVlyF1dmmiQjclo/e5fu74qOrSEjaXIC2eHKfF/a/2g9J5KOkD6d6V+T/a6B5mPzdfvc0FSpIyp658iBVwyZR5ocagI2qU+HBDB2ir59+LJrqAIoTWtXxNj7GbG2EcZY19kjD3IGPvz2Z+fYIx9mDH2cPbv49mfM8bY32WMPcIY+xxj7G22B9FVNhs2UYXNrG2EiLDYmgwL5Z7nBfspKh0u20XKmDtFr1djJ6e5XaopCv1yXS5CKN8Qmn4Okydm60gts5FMwNqIvLo46KCPt6g00PlCYg3jm7moIRRiVxdN8hpCl68l4hFClGIlTwecrkTYrLMQLDfdMAk/1P5dK0Iosw9Fa8eUUtV09PlcwRFlBMaKQ7cwU8b84bOuU0iISEUC4C9zzu8D8E4Af5Yxdh+AHwfwEc753QA+kv0/AHwngLuzfz4I4B8af+rOUZ9rCghslA51OaKPvSM05byOh0W9mk0NIdm8ZkNGetVzOi0h1Fwjx5mizwwGN44BiZPHmvnZnuYGUZthwovNyVJHvzQUzwdMvKfNKZONWkfA4TQjXtwLiMkhpFFXJSYE0m5tG7v15eYAACAASURBVOVxdhlrnrf84OHi9aQ9Qsi3XFXeXl93dt+RSqijXERI8/fbrnuUrfEQV6PbCDPB9unNB2tbUhHkWdt2GX1ee9+13eUjVS/oBhg2Dw2a6sFhbdddpgihdocQ5/wZzvnvZv99CcAfADgP4HsA/Gz2Yz8L4I9m//09AP4ZX/MJAPuMsXPGn7wHqKS8LFM7GyEiLGaTjUNooRMh5Ntgt4jcgm+GtcMkjjnNiyTLhUyrOLzaa6UBwLWFuZPKVcoxtPAeKmsIGahnEDWR6hAX9TM2XQfjnKMq8kLS15cSXcYilRGC6DLzWGsIJfqOdqX9lQGHinzmhzmSDjY5MIG19PHIkJIKxthtAN4K4JMAznLOn8n+6lkAZ7P/Pg/gidKvPZn9WQ+QzIMUKLYKyHmSk1Xa341Jp2g/3d6kjK3/3WqcWzHKD13TaWqA2PdjPWWsNGQ1h4nizTRhjGF7MsLl67Jt5+2kjJls+7lcpep1W1oi8w4bDgupCD0iFHLnnkzTBimyiMEBAwbROYSaaght5DyvwxE03FLkBTm5iDIH5CHfC6j8bsX/qyJQzybGjbCJQxi1otIiDpVQaluWKFLtAjygOCCjfp7LWKfbyBG2YBljOwD+DYC/wDm/WP47vraopFZbxtgHGWOfZox9+vnnn5f51d6wSaWQcQiZL/pKhMe8XFS6E22vzVOkjDk8AUtSAw6TlnapJlmfEErUEErNO7xsFFlMLEVKzipOVIsokOgc8bz7tV1aWi5zDlxPTNfQKBWZTzUck4FSXmd2s7RTwiLkeCIs4SJt1ix56pZ+mnaxv5I6cNd3qPjofruGBVzv0K8dsjUZRfYd2EFIKhhjY6ydQT/POf+32R9fyFPBsn8/l/35UwBuLv36TdmfHYBz/tOc8wc45w+cPn1a9fm7QUsNlHZBLdXOMLEhJQKhOef1+jJPGVsvUEKn0GVZM7YZrLqO0yJClX9a5Go78/wzvcgUqVvJRCE21BCYDNsjhDhH/j6XiU4EYs17MlVksdTZRa2WWvu4tsalGkLZvCTBGlk10AYTgJvinsmKYxzSaawILfJRdgjtBO8Q0qiX1aI77ROZ3BBh0mDnbU2GuGIzStISRYSQhm7d7K8E7MNsrZdzqDTXLnJ5UJkT7+GVLqxRleffQd8R6TLGAPyfAP6Ac/6/lf7qlwH8cPbfPwzgl0p//iezbmPvBPBqKbWMkGCulDLGO3ciSRzlcMqYeoex7i4MKhF2uthPGTPLuruCTI2yVD4SrTU11nw3OJtFpQ8/58JAC1z/dFEPmE/JliVx5SB2SL7WcLCiMH09XZQrdD+yjlBHVTY8yNRsbCtK0iKijRxaIq03B4YyKWP6DpW584PKDVJFsXtElV3XR0SOd94N4IcAfJ4x9tnsz/4qgL8F4F8xxv4UgMcBfH/2dx8C8H4AjwC4AuBHjT5xyJROqE2wNZbbKHHO16kSsZ1IEkcRyP3Of0LcIWRBLo48p0OjpmWOhgOG6WjgIBT0YJc/qxF6ho1G2dzp5YobP12y0Q1Oqe18gVyr8iJCaNRXvRvnuGeiXTyVYZ1s8jAtyfnOLPQIIaD4nsmJQ7giGFkTdIovVnHUA8tITBZ3lqzRCrQ5olru66Xt/MEaQmGtSaVn8RS9vDUeYZGknTzAkaF1Neec/ybqRfxbK36eA/izms9FQD5CaOO97q9A94V5afFerjjVD6phazLEZYcnMcsVN1Cwr7ldqkm2JkNcuiY2P2nKsbJQQ8hGN7jEguMKWH931w49Z7SnbvKl/zZEk3bWXCAcsGCY84MO4ujkAmjUPVRDKCeWb4BwQ5U8CFeVVvw9PeYlp/hxJ3fUJFNLm5Qxdd2q2mUV0IsG9lHbMmdTjDuwNcmzA7XsHDwW2tw4pL8jD4q2GkJiG7Y86rN/+aH944hDSCllzJASrlLmLjeMjTnycilRWjCWdeFyoVYlDM22GgKt+oUX9ckAjVo5Nc9hI3VnXUvNbGobAMwng9Jz8kPh66R3Y6JIGbCaMmbHMemT8loTfIRQ5Tct8T58blSicboSYdNcixLwUeBYj3zN1TkIZYxhPhZNFeIAk+xuVqM7VBxRRijbKiFmkdjUtS314Lx0fgsQcggFzOYEsyW/N/uQig1bjCeSxFFaOuTkLKRqCIUSyuyGLcdtVd05hMywLeEwsxVuPDNW62lTXD8xEql1lPl4iCTlRVQQkLW4HzCwaDZwGoV2Y6I17Xb9ndrUD0sLEXX2EWubzAHszsYtl+qgXAHo2zpKyKAqG+67ProorG+eTSOHZsdMe6T1VkXX0CaWBlLVGGPYEnZEmWUzb7GtSXbZnsbpGDUNSYVRNhsSIQRqoExGA1wRbAu9MqCsiDiYj4dgRQtOhUK/OdoG+6Hfd2rQCHSFclEsrpwiYr1miNn5nUs4zNRTo5rnYzoaYGD4xMxGrSOg2nmVRLnpN0ikm37rNYTYeuMyDPE0VoPygdMtJ7Y8PokgwdRzIQjHCDZ0sBklaQNT3bJk7B+gdCjWaAOJRBo7jFwHCh0YZJexAzLq57nmRb1e94W+Q6LHVmwcyEQ4kPe3S4gVAwTWm9+pSIRQpBs3HWQXfF2WJrr8tXTHMMn2dCRcY2lRFE82q18YY8ZT+1Y6RaVbUhCBg86rRZKGZWA5If7x2jsdP+ggthGpFgrHt1oihLpMD9dTooED4qB5OOxItvIoyXg2wptDUEA/G2KdMibTVCPFgAEDTZ2+3tf56DIWakfUMGoIxRUpZ57QpII4xNZY4gQ/+6aCzA8ljFJ2CC0SxRpCxk5Oq67jUgZbaggJRtjpk9cQcjB2KUOzubDutWWKVdogC5xjHaatm3/enMN91eB7Uqvd0v7zec2U165vnjVJNSL0fNP16AkB557Nk9okshTSDU1ysf677337TRGkSWqkR7boTvuw0nMQhHnyyIhrsUUIrbgRx4xcpM66jqKJA3cnketHEE2184VNXdteSxMALpNDiPBOS62Y9mr0699fUYRQx6iXi51Sd5dlKlFDqOsbwEPMHS+8sW0AZQo6L0Varmo8h/Z7KmSbqRWVFmAnyzV/7XpSOMqWSWSFg3UL7UZDS5RlUTfAnsM4SSOTDUDYAfEn33WbyMW0HoUgCE0aTL4oi0ozQceMQKT11ngo5gzLU65WXOJArO0AzP2cL/OUsVCDBjw9lmwDp64Sz84lKlRbTR5FJmWMut30h+2yQyjxGKFwZPPg0OEksHGRibBTZzPmddt5i+/CsENvayqeO61cUFGke5fh92SrqPR2FlVyuRQhZMv5RNglf5flaC/TLFdpgOH5fSPTmSZ1Z88OVggJVGWD2ygqLVZ6ICqHEGQdM/XIHkQlKxlHVPN93aYn5Q6tEBtg+H8WF9HCMUCWikksGAk705GwwZpw1aKvRHC0KOyd2ahUVFo0Zcy/4nWN69BcMylj7d0xTLGVF0m+Lh4hZMP5KNvtowkOnkVmqD5n/bznjtiyTl6uelhUOoBCkLoMBwxbkyFeu2bLIcQ62XaeyKH3SpQpyUMpWlX6d6V+T49tiQOhkBByzAggG0FuqmukeLt7swQbserZwb4dqWPUND2zYuNjZzoWNliTTJaD/OAJo+Sn20CeMjZs+Ok6DCnhKmXu8gSiKTd4OnJ3EsNcdpySMDQb5ke43Sbb1BBSNsRa6rmYWozzekg2aqntzo5GCCWu6kaZhnP4LuZon+bx7UgUVRe/ZSliUMsx6ZEmAz2m6Bit9Mhm3WmdoE7xiS6yNR6CMVh0ihsm+x6Xhrq5yhy4510jTdxXppmHMRgLO2LVpq5tq6VJRaUBkEMoEOoFdXcmoLD4JhwQoJSxPjAcsCIqaJHweIvaWmZrPMRilRbfhm2Wq7g6Ts0n4ieEC4v6ZXs6POBkUSOrpZa9aumNuMAG7GCEEC+MLIrKjJOd6QiXLG6GklVKTR4ITUh+CB3q9xeDAcPOZIRLFtNmzcOy1Ke2Nbc90np3NhJ0hm1qCAnbdw333ZmODNg78oQfsern2fKUMedOusAgKzZwpFLG8o0QbU66QYvHfJql+yxWEkWlDxsH2qeQh37f6YmqQG0aF90DyhEB1otKm51fmVDZRLmotED3runY2MY84fZqqe3UpYypdPnzhkbnpZgQGNPOzK5hHr4BXoW5GohByFWhn03qzoiipAjHqMqGn4jNHWGnSDiY0qs70zGuLlfCB4bLlBuJsNmZjXDRw5wnhrqkGSWANSI/YKcIIcIgmTKXamvajIyyXqYhtxQk5BBwdmSb0OUqxVRkQxqA4nXNbkWbcJskKwOhzALdMeRoD5VtjBDKw7SLovXml43d2QiXri2NXGuVp7ZZiMyYjgYYDtjBotK9jAJhlf8ZG9sTiZQBWUS74RABQ44fQoVA5EZkjyGTNhUIpmr57BQp4GKOgEQoArx9zo/NxlgkKa4njhwQJRsuTFvFgTO05bA6xu/ANGSpBM7OdITFSkxxJBY3bER4zMZ5yliK6VjhnRuL5qm6jstFpykkeAwAxpwNzU/B1kX7XEToGXI6byJe2vQLKxzO6g6v9tRYbkAm8+L6Q+VaR/V/xRjLwr0385VEW1TaRlebwGgZ3s7MRsrY5qar1E63O7/EJDMa0XBtP2f9gKVrctNxquRBVdYcHt7tiJSlCIaD3bJ02c3sn4tC9iEz1kU2t7tEHVFmYMaKcXcRcgiRQygMWnJcAbGib0lq72ScCI88Kmix4tgaj1p+up8UDg8H4bkbh2w835+Mw2yZqKaMtbMzHSHlMNJpbJVdQv4kTOzni7ozmd5eRFY3itiwa7m4Z2JoA+GemJw+HaeHkb2EQToYGbFcyRTrF9hfCdZplesi21xDCHBzUFlmGWqXsRyPum7XyuFQXMRoqfSKqpoVR8m85ilFCHWL5oV8VnICzSeC7/yIcWAqvam4gd71tO59lHzBt6vos2LG3J7DZHMrs/MrMz/K+kUwNVb0OWrJpibzy1k7CTtYAJshSdO4irprdV6KCbEi4TadxXIbiEAwmPIehlxlCsGk7ux6ZB2hjqpscD8Rm+KFlQOBrWvhtOpVCbtDvE6rmQgbN3bpUZIgu4yFsEaYLVsQK6FJRtwUytxcUcaNJ1lgw1a0hQ7jAyPsMiulic0nFCFURR4BIxYSrEeSaLZlL8i7Y2heJqfByBwPB5iPh7h4tWl+NqdjgB39solU0jeQEu3UtmYOt4xdJBQhFCvrotK2wvazFFKSjXghxw/RcWKMEFpHXpppOw+IR5AvRe5rwRGlj0KXNNdY17XN19+dmWtsEivkEAocGcWRb4TiDFEnDiCwqExHm5+ZZx3HWi566P8NKeDKEkIOFx2RlEsHC+8yeww3EQHmnM5CobKMFUWllaNhmt6TwRDqVZE6q6oH5ULsF0mKmdD31yEOfN+BGpgCyNToE+ZQ10Fajz2itQ7pF4/VIt7PqqeUXpj24bDDGkLTcTwRQqXULTOROhIHhmxdg0e8o2/DfafmDsBkWKeMBbge2XYGCWYTkEOICJpccYgo7Lz8RnQh6oQS+SaUg2Fr0rMNqSAuQ3NXkaZs7s5GuHRdoIaQxQghkydmtiMldw6lGV1PIksZy+F+2hy7pd25B9gr7mmk66APmgx0ipohiIho0YGzEV5bJEjTeL7rJDWjV8UPDDdlOYQjk4QOKh2mKGUOrTC7jOX4e7ZjszGljPl+AAJoLD4morD4pvI+YK92BuGYFsN7Nto4gcQihIAjsqYdyXPo951uFtqffT4eYjhgdhV9/v0VEXo2FzXz8ysaKpvrF3mHl3itJ72TyryW0/r/pA1GwW9hezrKaghxgDH1Ln8h0cXCtQJj2rZcdN5UzYkwEZCZEOSqWJPi2fAeJIA5JDrL7nQEzoErBho6uCHrltUaeZmn3rcXd5ZKGTPY7t51ZFbQKWOeySO/TXS6jZWuWip+MViUsaghJJIylgly2B5gwhQHawhRhFAVeZtwt13GNNVqoReMFRFq/Nvd2QgXm+an6KaV6xc7XcYAM5FcK8ups4dTxq4nKaYj+v5iZLO+mncYc6w3EPGtx7E9r03qdGd/Nw1EG6qyYSFiU6aeTUTpMktDkZdbkyEGTKKotFCTAPF9XaPdZZI81S5Nw4tgN27v1tDi6NmdrTvdXl7E4hg1T2CSETs2Tu8l2s5ThFCHEKkhVHIIiUQIHTYOjHnCK4sIGbq26v03uMoN3kSmOPj+DDqdxUJl2SZCaKT6bptqCGU59UZSxtb/tpkydnmxKj6f68nKSF2BuCjNbQhRIIrk66uNlLE8A4PWY59UyKYp3Wld7uP9rnrJAXngFX8m+rtw+uo3nYxjSJfZRGObOPDJDwzF7MOsSYCB+05HA4yHzHkx72TFMQzygMK2g10kSj2v6xTDd2AHslQCZzoaYDRgQsra9kaICItyIdvtKUUo1LE7Gzs5ibFZY8cmx+ZiBlExPhsRQgZPKVdc8zlbHKXb0xFWKceK8yIKZBqlQ8hPm2OnCLxLwPRmaH3PNLt3mAa4Dh2XGYLoEi06cMdTC3QdTKY+CaXMHyhmLXrf+nlnjGF35rqYNwu/yYHHwyUzZQviJmDJ6BENCpsxti761iikm3BAwE5KBxEes2wTygEc35ooXkVTAR9R4A43C4KLx+50ZPn0K6tdUxSVtrioWdjA7woW01uuFMcn8J6Gg3VhdK3TmbyWUxGZIfsexH5+J3O+LpO0+DNKGYuTzem4vQihKItKd8rpk42l685PglBgd+q6BbomjImlPgmmIu1I2IfJihvbX60jk9xGo5gqxt1FcoeQszS+ACHPQQTsTEdiNYQsd9chXNO8kE1LEUJ787GJS3YSZyljxrqMtRdDlKItd3o6wrVlikXJwXHoAgDyFtoMzNIpzuHaPKqsLNY6AjZRJUmaFlMbVcpY5fvr55ohW1RUhrxrT9AnsjpEnCooTJ3udOlc6sM8dwlV2eA2Ijb9F9a3QbKS6PbVws5M3O4QihAS/F5N2TtibBoPhZfCbNjeraWttASljIUmGXFTCLSoopJQHCI1hJx0OSJCYTbevGc1JW/R4eDSiBUoFufCIbR0ugE0p2N2i5DxhoWQ5adLGmMTeU8mHEKpXcd47kRYrnjxBcWZMqZBRzapO0UNIfP6QbnbnW868m4BaDo/OzQPBFGDTOMa7xSpW2a6fQFru0O8y5i5osyu7NIywTY5sO0MEljTdiNMnTRNz6zYONkV9GAvU1g9wSccIvAOZ9JpKv2Ui52Zm9DcPEJvolx02Q+bk5FmHbNIZPLn5dkRyeUXINFO3ROrubBcbSKE4nUImTDEQpb35vFtZVGWRjdD/GANofBOZDWh1KsMy3JPdlxklN6X9uGwu3cfY+2UJBXp9iWGcFFpxoymXDl3CDGGVRp623n/NYTIIUR4RiTHtb0tdPgfO2GS/a31Rv4995xVv4i20Wmrc5nCvWvYnY3x2vUE3Naz5bVrnEQI2etk2LYQJmmKidLmVqLWk5bjLteDdtvO5+mZy2RV3DOqlLEc2thjMGDWajkUkWohnsi20SXRKOS8S4MiCFFEC+vHshFmWcpY25orlookFpmcp1xJRCa1FfN2mjK2JklDTBkLg/xgNI5ue3YgyYiAndkYF6+KFH1NqaB0l2hZUFi24P21D9wnc1GNB5IhnE3Q7myE5Yrjem2NHDPkl9d2yha/3vCuTLadn7fkTnMOgGGZ2HU4i0ZCtqH8HgTnNHcILZIUPHtZcRWV1mjFHRMS7/PiVQs1hLipmmKhIjC/3uVKt+18CE4k33NIRIuArI+HA8zGg6hqp6xTt8xFCAmnjKWpQMqV3EGlS4JMGRMs/q15k/aOo5MhBowihAhjZAJncLMGAPvzMV4VcAitDLZiJOJB+Y0bixDwbTSLFYu7aNngySNT1KJo/CHaXUGos0cj7SdmZlPG7EYILVbppobQOK53TmzYm4/x6tWF8euu+Fozx7cmx/a8PvC95hHhoiobXON39ThmKF3bFYnBbIjd2RhXlyssV80HhmlW89tUhE1eysBa5HqZPIo9yKLSQAj6lDFmzAaNlRAlgzjE/tbaIZR3LamDwgG7hMhiJ6lEvZ/W+mHXUReNoqh7ZN/gMcHuCsuVZlHpFnYFIyHbsJ2qszMdYThg6wihvIZQZO9cn9LcRq5X9gQPXMTJagh1vctY37Eu93F/V73jgDxoHg471qn7W2O8ciWGCKF1I4dVKpIyJkZeeqFtDci3X6YcUXvzMZYrjiuLlZHrtcOwDL2siGdbYnc2tn5wHDJkqYRAi4d4bz5GytsLXy5ThBcOSNhHS4nqpjcd/v3w2vGKRsCok5++rP/PmCOiSi9YOE0SjhBKFMO0RSMht8a4vGg/qWujqOWk6qRpS9VkLDPmUuTvPs4IIRttjgNDYHy2NkOrImWsa2tybDKTPW/XZZ0gqhDRgfMJXrEQJWmD3DHTqlcFU5H2tyYAgFeuNIyf8yKSR7wsR/N9j2eOqFeMHkY0k1BZkUZ8dH4LCZKMCMgV1qu1Rmvo4YBE7wgoaiA/AWpc8A2Q167RLzAsMncS3UsEihsCaIjO4VmHjdRqtMPmPSkaSEVx/fX/SjvmJGR2bz7GIkmRRllDqIpwvldziNcQMhshtCavITSM8pBGwHki9L14Hrtu23lyIhE9YC+aCKFypI6hCKG5mN1hPkJova97+bIbRxzHegzhRQiJFf/Wu4VYPbhjlmyBWCDvgUm0W01WUyisFg9+8OGAhCQWFKQNpevbaG65/+YEyK6iL1LGItsAjoYDHJuNWhfCxYpjbLGbltBJnQB5ZIZNXXgsjxDKRC+qLmMBOWtDYG9rjFeumq/lkFquZUV4xOWaR99rXKjKBrcRsWm2TmkI5I52U3bWcUH7sLhvmz4X/F6PC6aqmYH3oMmBPse3xtYPjkOGJCMCRE/OF4lqW2giOEQWFW3joR+nn/mC/7LtCKFVnDWEAOD49gQvtZxUrcONNYywFnk1FUKdvwebodF5hBDPU8ZicgiZoEOb1PxdXlua7UJYFJWOzEHcpXcbNfQe4sX3IZkk0dQQKqdumYoQyuyONvuwSFUzpM/3HdmlOdywI80o0sEUdji+NcHLMXwHluiZFRsn+4IbpUXC+7cxIfQwbXQ6NYTEU0IYgz1Fn6cq5dEixhxCVXNpZ37XC2FLBOJKtcuY6CmlmRDqZMUxGjAMlA2f9jnem4/XXcbyotLRpoyZkKcADcwCsfoZgMGT2iJ1Mes62LU1WUnHe5SR4nn9RF8QfUEzPVHp90QQqaM2wdXlCteWrgocqyOdutVWo1UoUmcTYSOsz0UPwBw5INKi62XH1iOD7G9N8MqVhZvObwFCkhEEbUWlW1IpMuFV37ARcROiYRrOMw0HDMdm9kNBlytDqUoiTjqp7iXti9uJ7QaHEOcAGJYruympoo7verJaaql9Pbg3H2GZrMAzOY9y099To+cwe4Ip2bKsKGWshC9Zq9BXhnUnQcROrgNNdPm0TSp88CbYdCTrGtrmmMkP/Ezp8z1HtS1zpB1arhAs/q15EyF758T2uvPbZWed38IiMMkgqhBOGVul4X3shDpWNmyxXNPs/Y9vja2HgiaW253bZH9rjJcvt6ek2oxANFX8e5naN3r25xMsV2lxkhRXZKbGJjkmJLrbAU1NG9RYRSkbGUJrj0HHdXSEFwlLhIKqbHCN361BUgfGkC6zMuzYYIxhfz5uTxlLRe8rNufT0RBbk6GbOee8cKRN6YCiliKNz1Gh79AgyTBK9sVJnUC1Mx4OsDMdtTqEKEKob2gaDz2KEMhDQW2SrFKMBgwswk3QCYGUMX2Hc3u3s9GAaRtIi8S+Hjy+PQHnm9PKKDf9WsQn43Xkp+OmDfO862B8a3J33m3c0Hsg3LDfloUQDBxp3s3VoF7NGws0UZQEMLjW78/d1W4qikqPQtQrkntnS4gWGO8qsVkqvWVPwIN9PeEUIdQZLChGrRa8otcMs/vKOkLIlrGTp2wa/v6qHHaWnHjHtye4smiuIXA9WanVyhF8T4wxI8Utk9R+LbWT22vDYZVyDFjEefkm5ClkB6hQmLjp4p55DaHcAI9UNmqJrIZQ/rymdWfIck+454A8aB4Om5QtAbmPKUJIvluWSAT5pDlClPNCn4tH2IjVbnKXMrb+92QYa71D+xwXLDDeVbpmqcSJgMI+tTPBi7VhbJsaQuQQ6iEhGqaBPdPxrUlrSpQu5hxCInMnMb8C+qX5ZIQDjDnpYqhlIPFyDSF73dCAtROBsXXh4H4XlI6f3CH04mvXjV4330BQ50/Am6xpHYKI1Z0giNgx7xS3h3CkjtSBYdP+ak3RZczgHuv49hgvOZrzTU27sGzzApu6VrAenOvOb6FBlkoknNqZ4oVLzQbrYkVt54kWbChd30azwO1dnMQsI/7+RE5GFol9h/MJAcOsjWXCNdJ0xIyl3IBOHNQrckOgRqIWYmOajYfYnY605e4wcTuEBJSq0Iari3JFELEgt56ZdorbwEZx5FM7E7zQMvaUZ10j2/S5hCPq5PYUL77mwvmwqSEUnr0SzhpR2MFUQ4jQptgYq7aarOfkzgQvXm5WWEnCIzU+CT/05/Tz5M4El1tSonSJOULveGYQvtSwEGo7hATE7dRuu2HWRpLaT509tTMFsC5wuT2JLELIRPReYBGAupzYmRg3zJPiRLlbc0XAzSFI/o117FvrPKqywTV+V5PcKf6CE+eEBqXULZO12U7tTPHS5UVROLqKVV67yKgjaqpt74hSONKC3iP61XX7W+vI75ciSJ20QciSQZQ4ubP2JPOGBeP6ipPx2RVEjEBp48GGbBy6plODRnw8p7MN/PMtUXZK8E0NIbO1a9zN5end9fw0GScuuhiKREK2sUx1IoTEOL49Lv57Zzayei/CPie32w9chMn0wWoVgwFegRUTwqNdwo/8hyHI1iLKlORB+3DYS6B+8QAAIABJREFUpGyJyf2pXXfOCR1yp41JW+vUzgSrlDdESCs4okRKgeyuazdeWSSCT6pOuBFC4TAcMJzc1j+UjBWSjCAQqSE0RZJyvNpQCX+dMhbZaTVhAA3jwdrpY1jG8qnddQTM8xYV/fUVx8REPRmRdyJVrLJdv+QOoecuVswP5+BgGg4vcVk4tTPFxWsJrifqkVzLZGW1Gxqwbhk7zm6xM+27Qyisb/0gYpuh/MDFJCvOwdjayOwUSk5/X9GoFXNvWHcSRBc4uW0+StIGKV9/v+2Omew7F4pMzg/E6sdvo6NoHmn8wiX78y5fjNs1NnWteD24UztTOwfHERCqZBCHOLWz3tBWKqxM0BerlCKECA/4NpoFHB47MwDQjj5pIuaUsd3pCNPRoNVh5ioVS80wzYtKu4nKmI6H4JxhZzZu/+EQKRtIXUxLkRjTuoaE6RpC65pirItzC0DIEdjZsRNEBEjpwKm5KEmLrPJaPjYcMw32z0q4dpFC5LqDiJSipl1oNmpZRgNYL07vkkOIMIlqq8kGRBTWMkklWiISwWMl/ap0Td/FoB2SR8DYWXg3Xf6sf3+W3hljrHEhzO+q52gR66YINOu5NpYrrt5JQ0In5yeFu9FFCPk3ukLjxPYEL19priEhiyvHpBU6tTZYajtvHfpO40RVzrjG7+pz0oJT3AZpVsvHZLcsEbtjVdzXrSPKCLzUJS3UNSkQ/XyaIoSI0Dm5k3cBaFbYwX7shAUCUKA+Pfoy3Ryy78emok9MRwg5XiDP7E7x3KVrNc+y/pfZGklHOSVQy6gN4++hhnwu+pky1q3N6sntKVYpxysNKdmyrFJutEVx3HRLXtxCcxcfAdhmkpzcmeLlKwskuecjSLhEpI44G8dMU8qYhfvu6h+AiWIj5a2LnN6d4vnXrjfW6+0qJBmRkIcWXrhYs2EDwMHCCwckFLFgBFY6UCwZmwGEfpYZDwc4vjW2uvAuVqa6W5ls5yyeO10fIcSRpe2rjU9CFk4byKnXLiotOF/z8bpe1HbMDqGuGz2C4ztzrH19lWWVxtr1s+17jUhmtNa8lp8LbI0jPFMlD6rZAiZlS3T935mAc+Cl2sLKYVA4NtrqpRZz2D7+vfkY4yGrPzDkXKFLV/t9T25TDaENFtcViXpwp3enWCQpLl6zX+g7NEKVjH4hoLBPbE8wHQ3wzKtXqy5Q/Bc5hHqIb8PU96ZS2+FhhuUq1g3gmsb5KU6X7BatP6WTU190e3NVy4mDA7hhb+rgXjYw9N361j8GOLe3rjH2rGGHENX0y4nIiUQQnUFc/5w5ttaBlY0lAiKPEDKpWxljOHtshmcr91fZfUVT1STWw8logJPbE6PrTh1pqDWEDsio//VSpONuVwlNMuJGu9Vkw08yhhv353j61WbFEa73l5DHghF9wHli+/phcXp3igs2jB2njgh783t6Z4aXrywrO3wVNYR0xicgG/PJEHvzcY3jWww9x5y4Th5kht+tJ7cV7xUS/g0x84iP6Wy2GbrQsr7KkOhGqnlFQM8IbXwCkKtC74S7NlXSAUcrEQ+5U/wZgzrQBnkNIdOHbzfuz/H0K/VjzyM+TTcJWN9X3d4Rg5dqCJFeaUIkG6erxGqt9JJzezM806I4wvP+EtYIwvnic3GRu/e5vTmetWjsLBPTDiG37/fG/cwgrDCKclFzEQG11nPq78nVRvzec8ewvzXBe+89a/1eRjFh0HZss3pm10aEUMRFpY3TLXlxuvZ27FvrPKqywblXm+6GPEpS4zDGOlnqFmPAcGD2uzi/P8fTTRFC3M7+6sb9mQOHkEyXNE8EsZ8pfwfkECIC5txeswebg5EB2hWs2IAaOe4m7uWZG/fnuHDpGpaWiiYuUkM1hETeiVRtArGF9qbjWwCApw4bJ6WFWm18crJw4/5c65RyaayWUzOT4QDHZiPjhqlbwjDC7CE2vslogFM7UzNGYPa9JKmr1EXDtOmWQAx3MQzUdakdb8zfPWGekjxoZwuYlC2x7/XU9hSjAQs+Qkg6UkdQX53bm+HZV68V7dkPXQSprH0neN88Qsh2EeMiQmgQ4ZqkjXgtzRv35wDgxEkXGn2UjAARE9Tz+zM8d+na0S4A2hs2Im58G6a+NwiCin5vBs7thYIuIq8hdNPx9UL45MtXjvxdPsMuOlSc25sppoxlG/FVqhkhJCrPvuVeE2MGqG/9Y4Yb9qZmI4R4zCljpon8WyGIGJE48BsM8jo6gTuEuGjkpfxBVJLy2jqKa31uPk32/P4clxcrXLxqt4hxmj3/ILQDrLKMBhANORsPcXJ7gqc0otRjhayViDi3P0fKgQsNhXHJACX8Eq7hv/H8m1b0WQ2hJLXvMLF4inTD3gwDBjz58lFnjJEaQqKOu/05Xr6yxNXF0VpGIixTjomrPPkADBgjdGUcZSTHdIPhzdBq1fWUMYORjFbJ9E5UkU0E4Z71YUzYG2HpSB1Bzmf24ZEIacv3vbHlvkbgHGlK+0NRzu2rHkrGDUmHUTKDQ7XVZAt50bemUDaKEOoQVgxYw9f0afBL3ruokWNJ0S9Np4iYev+C1xkPBzi3N8dTRxxCvBAbF/plU9xS7T1xnVz/IDawtjExxu7N0/n9teybCt1POMc41vWYnCcbKufCxfx07xvrB6qywTV+1ww3KEfnuoJjxe06ZurGnwhHJqnd13aKkq15M4b1NUf8+jfuuSj0HR4BSwdxmPMtioOj6yeSfULEGJRUoJWbXUtGZ4Ab63N7dk9iUm6qhpfJuZMMXz4+b4wQUoqAkhyOiRMzOgkjZLn5xBYuXU/w6tWl5pXWX8sq8hRSs3haDypva74LrF1CeQ5CnIAcqhIb7ZtPbOGpV67W1NEJg1S6aYRoZHLDgTvPI4SG9u5r2RGX8tDtIosyJ3143Fyvt6uELB39QbToWeHBrhfUyYiMB8Ixvk+TBe+/PR3hxPYET7x0tEaOKYI+gRHgpuPzxhpCk6GMQaTGrSfXxa0fe1H9PWm9B1F59i33JujCGJqQGN8tJ9Zy97iG3JVJOI90PW57ZhWZ6aCcBXjoQXikSh5UswU8ydatJ7awXPGgoyOEu31JTuHubIzd6ajWEbDidlLRT21PMRkO7KaMIX/+EO1TVvPf/rjp+ByvXU/w8uWF70dxSojSQdSwMx1hf2uMrx3Z0JaKSjvYsBGBoWI82NoIBr7BvO3kFr76wmWzF3Va1N3u/N60P8ezF+s7sU3H9h0tZ3dnmI0HeFz2PWXX52AOT8LCMGDUMCRLwW6M5Z7rlswReXR9VWMlfZIdGULvPQDZKPRO2GsTQZhH7vu79eQ2AHNOcRusLEa63LhfHSENSNQQklwPBwOGc/uzilR9k6xrCMV+YOmK27Lv4KsvGt4rBA5Jh0mkN8PyxtLtp7bx6POv1f79OMoTSaIaCwascYdNPDWEAOD2UzvmHUIlzC64pt6V+HVuOr6FlAPPlE/JOC/ExsUJ02DAcOuJbb0IIeXnJP0pRLBOIHXyCCGTDqEwT2QJOSr0Z+AHH4RHVGWDc+9ylUfnPv5SoBthrtD+XYLbT23jqy9U768SndqELdx6chuPWXY+pMFGCGUEpFNvP505hJ4P9DuwRMDSQVRxx6kdPFojpBymapgQUWBCgXZwY9fE7ae2cOHidVxZ2GnxaeT7E3knlgrX5wbh4ZMRvS5j8jJ268ktLQOJTsIa6Nk3L8rWZIRTO1M8bsgwT9KIi0obP4zwJXMG03h8EcpzEOKEs7eFzMPccGyGyWiArwUcIZTKpm5J2Ml3nN7G4y9eqYiQ5vK1iyRk4M7T2/jKc5eRWqzdlPLQAwZsfjRy4775+BaGA2bdSRcasVorveWO09t47tJ1XLpWXfiy0yHqfcKKEWhrMfBt/Yjf/7ZTa8//Yy/YMXhid0TcfXYXAPDwhUuVf+9qfLed2sbXXryiXNyS9KAovr9d28iN787T23jkufoIXLFbZkWlY40Qalt7AjrJJYhwMNm0w8/GfR2du4WvBBwZkQinPsnP4Z2nd5CkvDJK1GYNnjtP7+DqcoVnLtorZBzFehSI83syGuCm43M8ajGbIEQClw7iMHfmoWxlQS3ZZ0pdgIjICaiGUOAbzDtO7QAAHmlIu9TB+vdneTN2YnuCk9sTPHzh4Pxwvl6rRwOdBVv82e86s4PFKpWM1jBVy0n0OXkwBowSxmQp4jk4xOvO7uLhC68ZaT2/spjaEB8+14Xs3uTIIvqGwvr0uht28dCFixYexgwpt1eb7c4za/uwKgtDPFVNzREFAF/RPYxowOa8aRGo+XDHqW2r7yNEApSOmMkMDoshyXdlCuvLF6oFlQxQwimRbYjvPLON0YDhD56xY/AY/f5MbWAkr3P32R08/Fw5QoiDY50Oxxy973tvOAYA+NKz1ZFKTXAAYwvdQDpNZN+xEIqboUvXk8ZOnqIkKe+4HBpMbQ0ZX46kLswdER33nN3FEy9dxeXrdtLq9chqCFlybNyRHbgftH/WJKm9Gop3nlnfVzs6tQ7OkVqsgWQEF3pW4h73nDuGrzz/GhZJdYOVLhKwdBBV3H5qB7PxAA8+/Wrl388n1GWsM1hRkIevacnoDNSYnY6GuOvMjhWH0LqGl4nvz2T3Hvn3cPeZXXz5wmsH8tk5NHSLgizcfXYHAwZ8SfE9KRtugcqtWUyMsZvz9LrswOWhmpRJGVKL3XCs05XmA5XfswndGdbmhegCHCE0Enn9Deu08S8b0IE2WHHZyEvx8R+bjXHT8TkefPqQ3cE5VimX7LIqft/TO1Oc2pkcva9BVjz0DBKL+k7Brrv33DEsV9yeky5AQpYOooLhgOHec8cqFQdjVFS6O4goMEkFamuz69twlbz/veeOxREh5In7zx/Da9eTA4WlOQe2xu6czbPxEHec3sEXn1EzSrvwHpzg+9u1jeT47ski076oZZibSl0kgsaZ87ibztfOcUAeNLMFPB5M5A6hP1Bce22zEo3UUZzDN57fw4NPHT1wT1KO+XikdM02GGO4//wevlBxX1NIF8X2Qji67r5zmS1gaa8QIqFLRz+QNFrvv3EPX3z6YukEf/3v+XjoLKWDCAild25pIxjBBvO+c8dw4eJ1PH/pupkLcpcbQPvz++ab9wEAn3vylc1duYHoQ0nZeMONx/D5p14Rr+dS+rmZjvNK4X5EN9jbGuP2U9v47BOvtP+wAC6dqOawYUN4/Fb4QTuJIPqD/Ld8y4kt7G+N8dknXrbwPPokKcfMYibE/ef38NiLV3Cx1LiHA0hWKbZE7qvhiHr4uUu4ulgp/X4ba4dWiOtRmHvW209tYzYeWHXShQY5hEziaIPwxpv28Nr1BF85VBh3LhXOSBAm8K3M5e//tluPAwA+8/hLph/G8IJrSp9I1hA6s4utyRC//0S2EHIODo6tiZ3TsTrefutxXLh4HU++fFXq9ziYw9RZ3/KvQ1kuNMYR7CGE2nO95eZ9fPYJCUdkA1qOydAReu+hyoYMVEOI6A+MMbz15n387tfMOMVNs7Ls2Lj//B4A4HNPlB0BHKllu+L+83tIOWrLgeixTnmz6UjTJrAaQsMBw1tu3sdnHg/TMWoD8iBYwV59DwD4uttOAAA+8dWDG1qqH9Q1HOSTWzM6wzVm7z9/DNPRAJ96zKyi52BiJ0htiLwTi4XrhwOGN57fw6ce2+iXlOsYQ2qy8PbCcSf/ntQNxnDlNig6vFl96y37eP6SvCPyMG4dk6HjS14q7iusOxv+jqIDiTpUZYNzR3Uj23nbLcfxyHOv4dUry/Yfdgjn3Hqky9tu2cdwwPA7X32xfGMAknaF5Lt8R7av++RXzR9UAvYdafrY1Klq6887bjuBB59+Fa8FWWDdPOQQipBbT27h7LEpPvnoiwf+fGYpv5UIlGCMUt/PIXf/6WiIN9+8j9+xsPB2ZQP47rtO4YvPXMRLlxcA1n3Gth2P7Z4bjmF3OsInv/pi+w8fotORGbp02Jljgq+//SQA4ONfeUHtAqZSF73iW6cT4azvhDSRv7sHikNn+bXXNqlw+nq2zkm+i93ZGPef38MnHt3Yh/kVbNp3J7YnuOeGXXziUfNzzhFyyliJwGyTd9x2AilXO5SMEXIIBYGcwmKM4V13nMTHv/IiVilX814TYWNFMZauac1gicMQ+qa7TuHzT72K5y7pt5cuj9n6N+jI0PzGu0+B882mmHMYSBmTe/bhgOHdd53Crz30vGD6jsP3cOh+8dKFMTQhP77Xnd3B2WNTfOzLig6hElGuyW1rj5IO8iln3NIjONq8BLZJIuow2dHO0DtXlJ2333oc25Mhfu2h5808h2FsO9rfeccJ/N4TL+PStYMRUmIR4Orv7p13nMSnHnsJ15bm6wiJO9IcE7B+e8dtJzAdDfDRLz3n+1GcQA6hSHnvfWfx0uXFAc9lvKeRhB4eFapvZa54/2+99ywAGFf0ZjaAaidbtShc503n97C/NcaHv3gBAAfn3Isx8Z57zuCZV69JdTzhYJrtVSXmy7f861CWC61xBDoHimNijOGb7j6N33j4eSySVOsRur0mG0xtDRlf0R5dmDsiSiajAd591yn8+kPPGamlZooiUseyXv22e89iueL4aOYQ41jbFbb1+bfeewbXlik+9mWzjjjO1+tY0OuREzmTu8d8MsS77zqFj3zpQlDfgS3IIWQUzVaTEnzL605jMhzgQ59/pvizIL2/hDpO8sktGZ2BG7P3ntvF+f05/sPnnmn/YUEmowEGg9BaEas9z2g4wPvecAP+8xcvIEnXBRWV6yNpyMIfvucMhgOGf/+5p4V/ZzJkDt9DTwn8+9bl/W+8ARevJfh1DcOcg8UZIWQFT/JSKad2dSdBdIHveMMNePrVa/h0SOkyeTbExO7W9W23HMfp3Sn+U76/ysxmIzUiG3jnHSexNx/j//3Cs0avm28lwl6PLDpcNOyVb733DJ546Soee/GKwQcKE3IIRcrubIz33ncG/+6zT2GZptmfTTw/FeGWQDzWvj3nCvdnjOF7334TfvORF/D0K3rFY3O6VsPru998Iy4vVnjipStIOcf+lnv9cnp3im953Wn84u8+tU6PFUDrFKzjjo41fRijHt9092kc3xrj3/7uk1rXsb1xsYZvnR48YXXEIUKCQ00+VH9P4LIKfMf9N2A2HmjrQJOoRQjJT8BgwPCBN53Df/6DC3jhtevFNeQcQvL3HQ8HeP8bz+FDX3gGr141V9Cb8/XTRLseeeQDb7oRv/Xj78Htp7Z9P4p1SDoi5ge//la8cmWJh55dp1Oc2CaHUHewsGlzsdmNyIj9L95+ExiAn/34Y3oXysY800pTEr6Zg3usedcdJ3HH6W18/qmLAIDTO370y594x8149uI1/MfPi0Vz6aWL9YyIvlclFMc3Hg7w/Q/cjF958Fk88ZLsyWAXiko3EZnMFDJg+LmdOY/JgRsFVfIgHIzGmv9fGfXr7ExH+CNvvhG/+HtP4cXXrht6Hk24ROqW5hz+4NffguWK4xc++bUiXUjoUEzz1f3g19+Ca8sU/+pTT+hdqARHyHVmw9Zve/Mxzu/PfT+GE8hyDgFFo/Ub7jyJN9x4DL+dFX497uEEnwgApYXPlHHsW5mr3//mE1v47jffiJ/7xONGiksb2/wV79PUO1K7zmDA8F990x24tli33Dy5M9V8DLXn+LZ7z+LO09v4ex95GMtVQ02X3DGn+x5En5Nz+Jd/whY/8u7bMBww/L1ffVj5GvqF2H1AMn0QqiFE9JMPfvOduJ6k+Mcfe9T3owBw69i468wu3nPPGfwfv/Eo0izC5qSDQ/f7z+/hG+48iX/0618x1+7clG1kExeHU10/ANOEHEImKYTNzULOGMN/97578Nq1tdI4sT12cl+iQ1gT1TiM2b/w3tchSTn++i89qF00bmfm8PtzUKcMWEdRAetTOXVjSO8ZBoO1nnv4udfw0wKGqdP3EDVledd4R8FuXPWe69zeHD/yDbfhX3/mSaVWwBzAsVmMDiFBhN67b9moitpwozsJInbuOrOD73vbTfiZ3/wqHnz6Vd+PIxepY4D/9ttfj8uLFZarFAPGcMyRbfFX3ncPXrqywN/8j180cr08295H2r84Np01pMtFaHUIMcZ+hjH2HGPsC6U/O8EY+zBj7OHs38ezP2eMsb/LGHuEMfY5xtjbbD48AXzz607j1pNbAICdOW2EuoWNfHIXhapdo37/209t4y++93X4T194Fj/1q49oPcVeB7+/0XCA99xzBpPhAK+/Ydfbc3zbfWfxXW86h5/4/x7Cr37pQuPP6hltPTAcaKMrzJ9/7+tw+6lt/Ll//rt48mW51LHZeIjRkM7cOgmdNBN1qMoG50HaZz/+nffg1M4UH/xnn8Hzl/ymjuXTc3xLYo3XmNP7bjyGH3vPXWAAUs7lmlVo3PctN+/jT3/znfiF33nCSOpYmkUzS82bD8g28YqItfJPAbzv0J/9OICPcM7vBvCR7P8B4DsB3J3980EA/9DMYxJNfODNNwIA3nPPWc9PQhhDRDFKLzgulG18hvKf+ZY78Mfeeh4/8eEv42/88oO4tlxJXmE95r25g2gADxuRUztTnN6Zej1dYozhb3/fm3Dfjcfwp3/uM/i/P/E40poi07tdjsowTnzfqxyaUX/TEX76h96O60mKP/4PPo7PPP6SwC3zTjgBh+e30jBv0TlDsueN7blje15iQ4fe3cmdKf7xD70dL16+jj/2D34LX3jKb6QQB7An5NgwY+/+uT98FwYMzuvI/OVvfx2+8a5T+Cv/5nP4+x99BElTunwLuTjuzwOMECInUDC0OoQ45x8DcNgK+h4AP5v9988C+KOlP/9nfM0nAOwzxs6Zetjuord4TDKvdRcjFAgRFBSqKYPFtzI3cH/GGP6X73sTfvTdt+Gffvwx/OH/9dfw9z7yMB569hKuLdfhwk3/5Au1mJEi9ETrf5l6R9rX8VvLKGdrMsLP/6l34p13nMT/+O++gD/y938T//yTX8OTL1/BIkmRFN0WXenB7hj9RD13ndnF//NnvgHj4QDf+w9/G//Nz38Gv/bQc7i6qNENmRxuR+0QIgpq9aejtY/2SxEjXFVa8ffaLmvmOm++eR//4oPvwiJJ8d0/9Zv4sV/4PXzsy8/X60BL/3DOMRoOMB25062j4QDT8RDf9UbRrayZOR8PB/gnP/wAvutN5/C3f+UhvO/v/AZ+7rcfw1OvXJWet5RzgAV+WObEiUo2WxOq0nGWc563fHkWQB6ach5AOb7tyezPxNrDRA8JGxEblqxN344iScbDAf76d78B3/GGG/CTH/4yfiL7R4Q/NvgcfnIC7Dk9fVE1NFVvF0aNmb2tMX72R78O//b3nsI/+vWv4K/+4ueLv/vA4LP4qYkJx5yEHo9Mzg9woISQzjgCnQOD7+b1N+ziV/7iN+MffPQR/PPf+Ro+9Plna3/2DF7G78xiLSgNszLt+/uovL9j3amK77kjiBJvuXkfH/5L34Kf+tWH8S8/9QT+/e8/7fwZvjBNHXVzPQgDvHyPs/EQP/UDb8V3v+kc/s5HHsFf+6UH8dd+6UHp63xqmmA6GsqlvDnH4v455GEHhLbFwjnnjDHpN8kY+yDWaWW45ZZbdB8jLKhoIWGCAPPJg8TgPL3zjpP4l3/6XXjqlav45KMv4plXr7UWm379hYeBh4D33X+Dsecg6hkMGL7v7Tfhe992Hg8+fRG//+QrePnyAnc99xjwJeB9b6D3QJhnZzrCX3nfPfjz770bH/vyC/jyhUuVumH7+vPAJ4FvJznsMB1cRwlDqMqGJZkyaB/tzcf4H77rPvzlb389fuPheh1oi8lvDfCNd5+S/C0Dz6c0RjPzwhjD++4/h+94ww145LnX8JuPvIDLkt3Htn57hHfectLI89iF9sQ+UXUIXWCMneOcP5OlhD2X/flTAG4u/dxN2Z8dgXP+0wB+GgAeeOABWl0JQpoAP5uO5M6f35/jj7/tJrEf/uxZ4CHguJMaO92YXxMwxnD/+T3cf35v/Qdf+BzwpXXNA42Lmnm4oOnDGO0xHQ3xbfedxbfdV1Oz7+I28En3NSeM0hE9DqA0lsjG1KV30Du6/e5m4xYdaItPDHDT8W2xn+3YWs4Yw91nd3H3WYXmHp8ZYjvm9Yhwgmrs3S8D+OHsv38YwC+V/vxPZt3G3gng1VJqGVFHMDU+iHCwsJgdWCBNyYzvRdf3/S1g3JDRfNfB1DLyfH3f97NBF8bQRMeH54e+TWrNeDu24SQ0qZIH1WwBY7JFMuoc0guSGK6Z2UTX7R1NWiOEGGO/AOAPATjFGHsSwF8H8LcA/CvG2J8C8DiA789+/EMA3g/gEQBXAPyohWcmCKJMkAtQiM9kG4djdp6WGtP71HxWKaMhpnlpoivjKNPFMbnApG7x/Q6qNumivxpKDSHfc0gQhHd9oEUMjhCbzxjzu3NHq0OIc/4DNX/1rRU/ywH8Wd2HipZiIxFJ0UKCMLXIce5xwYxhseszpOfCpCOdBoOF5oUgCN90yD5SifAwEhXi6749g2wJr7gv105Ygj6k7tGyoHhbHBtvYPn6IeKwGCQZGZbpgR4lo4voFZnONKk7SQ8TdXCubpuRXBmE1rkD0LpPtEAOoSCgRYA4hBXlXbqmKcPD9yLj+/5WMDymYGqUdU3PdWE8XRhDEx7G14lNXcMYOjE+CWrH28W1h1CnSh5UswUMyRaJqAdo0qXIbXgny0rP1i5JyCHUBfpmoBGHCHAB6qSjpgWnY3acltrD1ylEH+WcIGKh8vuMLKWfdAxBBEDE32E0e0RLc0w6VAhyCBkl++icF3wlCFUM1hDyRTSLXU9xredIr4pR/m66OGe+x+T7/qoIP7fAz8U6BwTRFTplH3kai9IcdmneXUDz5RtyCHUFMry6h5WFvGOtv0PA2pirrtvD+SUIglCl0M8mdSfpYaIGMoB3AAAbgklEQVQOVdngGr9L1ENzuob2iEQz5BAiiF5iapH0vcj4vr8FjDt3Nd+1KYdXDM7CXjjWLdQSCxUv4+v4nHZ+fIfp23gJfUKRmQ6uZ6FnYPTChjBJXkPIwTfTdXtHE3IIhUAwRV+JcJBYVIJcIPu4KDocs/N3HsH7dL3Yk3FBdJXOyHaF3jKlO2njR5SpkgdVWSPZCo+o30kk+tzaHMf87txBDiGTFEZUZEULif5iTAH7XHAiWex6i2s9p3k/qc1wV3R4V8ZRpotjcoFJZzO9A31oDgkCQIcc1cRR6N36hhxCnYGMhu5hQUFa17l9VOqWxlxl/JBBRBjDkCxFfXJqE5qXMMjk3KTuJD1M1KEqG5zHWTcydIzMqcI1QtMRtE4TLZBDiCBiRWfBMbVY+V5kfN8/BrRftSnDJjADqZIeyFMPhuiV0DYChB70PglZQpGZTtpHoWdgdHHOLZLLqJNvJpDvMlDIIRQEgRR9JcLBykIeYI2bLhFkjSZDzxTF+3StB7ugd7swhia6Pj5bNMxbTPZGpd4ypTtj0ImEO2zKGuEfekf2sTTHUdiv/iGHkFEiMpSIDqGj7AwpSp+bhJg2KH3E9WLs8n5dMTS6Mo4yHRxSWAhMcBflyjU0hwSRQbaeElHYyDE8Y7chh5ANguz6RBAOiGLhMYy1MVddV+VeFIEoR0/Ga+y90jpWSazre6zPXUch56ry3hN9QHST3q3fhzEwfqU5DG3eA9fr1HbeO+QQ6gpdM+IIAeWls0h1ZTPo+/4W6Oq3HMNi3NW5JwjCIBHoMsITOo5HkqtW6MC9Y9B7CgVyCIWA9kaJFhEiNPqo5ANMVTJmFEXwPl07nGJwcFVSepfRjkEQL+Pr+JxGNb4KvRXNhtL3/Ql1QvlGSIaMEY3eiBhrc0fvRARyCBFEkEgoMJ2FypgC9mkAhWJ8EdV0eTHu8tiI3kIqtR3rGz96CVGhY18d/jlyKlTT9QMMa8QwbzE8Y7chh5BJCmVFnQWIvtJHpW5pzFXGj4pBRBGIcvTG6CyPs4trke8x+b6/Kl07Cc/kXPW79q4PYplnIkx8y69njHy/PuyunkFt571DDqHOQEZD97CgvLimcXyYOrFztVkIaVPiND0rxs5dtBh3kpC+QYIoY6vtPG32iDp0HI8kVwLQgbsSfV6n+zx2CcghRBCxQsYDQRimB4bDAeOo6zrEw/i6rpe7Pr5g6IEu6iqhfCO0ETYIOaKscEBGae58Qg6hIKC20MQhrCzkOqelLfiUQZL/sHFmlJIcEIQZ6FtqhzYvRBmT0WgkW4RByEYmBCCHkFGyj46q0RN9pY8Lj7UxV11X5V49czi71Ktd0eFdGUcZ32PyfX/rCIwvhDko9JdOO3AfRKZ3iTCJbf1uxIP9AyjOYZfm3QUO5qtT34J5yCHUFUIwvIiIMKUYa4sIGbq+6v19YOhZRL7lGNvOR7MYx/KcmkTzPgindMqWiLntfEYoz0EQIRDb9xsMgc+HVXsk8LEHAjmECCJUWhUknVoQhFHIblCAJu0gpGO7D71jog4OddvMtFyRbnYOOaIkKc0XzZ1XyCEUAtQWmjiCBcVYmeJu6j4kg0QdjhZ51xEv0UbYlN5HtGMQpOvj8wLNKQAHmxfaHEVFlTyoRrPQxjg8on4npLOJdsghZBIyPgkfBLVQ9fEbsDTmKn2idPDYN4ezy+8hpG9Ph66Mo0wXx+QIEZ0htO4E9A502oF7ITa9S4RJh+RI5Vs08v36um+fcDFf9E6aIIeQFXy0JwzI8CL6Q92mwJWTKiRnmNN6PY51TEjzbJveGHJ9GacvevTNhEql3oqtfXQoz0EQRLSEbsPZtLtCH3sgkEOIIPpErnR7s+klwoFkrpOQsdUx6H22QusnUYeObJiWq07q5tAdul2cc4sckFGaO5+QQygIetYWmjCDkfduSAH7lEGS/7BxZpS6/h4ilbsD7yPSMQjjYXxd10cq4yM7XwGatGjpug7oJfQ9Et2GHEJGoUWAMITUJjqgE5M+GkLWxlx1XQ+d5fr4TkUhG5HoJIa/eZ8qpNBfqg9R93v08RNlTKYnkmxVQ7aIElFMm4OHJFu2EXII2UC1swBBREedDLuS7ZC+IYf1epzrmJDmuQXSq/J0cc58j8n3/a1jUE9Zw2DnJ+fQ5oUwAG2C9VGaw9Dm3bc+a8GqnAY+9kAgh1BX8G68EMaxoiB1T0sJQpFoRC6aB9WDNgpEFWRLCODw26H3ERk6smFarjooO6E7dOl7laQ0XzR3XiGHEEFEiwHjwZgC9poT4PHeju8f5SY+okW+bwaJEXEKeM68fC8xfqMyxFZDKNZGCgF/V0QLirIWnYz2iL7ZBkTvIIdQCGgvArSIdA8Liw8taHFhyjjsi35xbUxHa7yTHiAcE+unAtR/57SeEmWq5EE1moVkq5po11zfxDBvLp4xhnnwBzmETFIoq4CK/BLdx6fxUHdvV88UkuHktF6PYx0T0jy34vJZY5qXJroyjjK+x+T7/hqIbLyEdILvOk7Cf6jxc7agzQthgp7LkREnksI1yHklh9USQr51eRyQQ6gzkMATEtBiRTgnEpnrzbfRl3ESRMyQbUcQG2Jx6AZGqE6RA88V6DP2BHIIEUSwtGzYVDauR37HkALmHus0+N7AO71/hJv4UA0Rwgz0fg/iWx+FiE8R0W47X3VNc5eqh76rKOFcQwcYFizSzQYhRxTRbcghFASaiwAZoATRQUx9133RLyaeU8aYi2VeDnFgkxDpGITp+vg8oHQQYf4xCKJzRLPWElERhVw5eMYo5sEf5BAiiBCROtnxeXJRd01XpyQhncY4rNfjvPVqSPPcgstT0a6cwHZlHGW6OKagMKinrGGw0K/ItYkeUyUPqraZQdnq1CZYZSy+xt+leXeAVTklXS0COYRs4HyzZvhaRA+gxYpwTKcMU4JoIeo1mb5VgiACxMf+qhNEMB/0zrxCDiGCCBUrG2hbhr6FOg3S9/aFw/uTU8UBPZljkiWiChtGuVc730Z9O4ffDm2SIkNHNkzLFcmOMcgRZQmar1Agh1AIaBsqZNj3EwPvnRavcDG1gfGuXxzJmIn5kvkeonWolMZoZAwB65Bo31HI9GxOSYYIaRRlhmSN6C0uZJ++rybIIWQSUuaED3yeXNRd05WjKaS9qMt6PcL3cljXqJfQvBBEK75Mo0q9ZUh3kk4kylTJg6ptZlS2OrQvMdJZ19HvB7UfDOlZarA5X6SrhSCHkBV8FPklgSckCGqxIvqBK5kjXShPF+fM95h8318DkfVByMiOeA4IgiC6QhROkRiesbuQQ4ggCH3yDYQPR5Nv55bT+5Mjzzq+5ckZfRkn0WuK75nknegZUTgBZPE1Jp/dfDtMJ2U0TsghFASeQhqJwGl5r0bCZ0kZh4up79qzfnEmYq71YBf0btfrkPXQQa1Ny/tUGV/IItJKxXidvuOoJ69/cK4hH7HrDoJQxYHsR78224UcQkYhYSMMYWWTFcs1Y7p/mQDr9bisa9QJejDOoB04RCfxZhoZrOtCEI0YqFdVbFgNyl6nNsEqY6ED9yjGwLlFnUu6XARyCNmADA7CKSRHRAS4Mkpc6tWu6PCujKOM7zH5vr91TBa/JwiCIAjCF+QQ6gpkeBFe8VmnIYLTDxWqHCgxnPQQBBE5XdIzHuvbEYRXOrgv8HXgTgf9RMchhxBBhEqrAWsgfJYWr+6jvRHS/f3YZKwnG0faIFuC5vUIsamAMpXficN3TGt0ZOjIBukOoq+Q7PuGHEIhQN8BESt1xqorIzYkY9lZvR6Z+wRY18gWJhwcMuOM1qFSGqORMQQsG9G+I4+0fgMqzQyUnkSfyrEELK9EvyF9RVghArmyKfsx2K8BQA4hk0gLNAkpUYeEbKiEspLhQTjHlcy51Ksd0eGdNJi6OKaAEJKZDr+DTn4zhDJV8iBsmyn+nhA9t/V8RUiTjU1EBjmEOgMZJ4RHuMc6Db4XXmv3N5WqQF02CIKQoEvffDEWagVO9IxOOi0lO7c5J9A5D1UWiueitvO+IYcQQQSLBeV1RCEGukgQ3SFUQ6SO3hgNfRkn4Z3IVEArvdERhDQkGx2la0qMIA5CDqEgoBN8QoEg3nvdIulq8QxpkXZUr0fGweKsrlEIuP4eQvj+dHFcd8k5PYxY1KblfUY1Pp00nlDkOpTnIIRR/Uai+rYcQ3OjTgxzx7lFnUs6VARyCBklgo+OiAMpxagSQkuySjgmBqOkD4ZDMBtdojfE8OlLQ98RUUangPmRIkKaz1Kik9+eDL4O3Hs/8URkkEPIBj5OoMjIJ7yiW6fBxL19Yen+VYaIinEShSPGIC51IendcPH9bnzf3zoC4wtiDviBf8n/um/96fv+RLyE8P0ZJvQIvyB0XhWhP5cLPUe6tAlyCBFEnwl28SK6Q2wyRkYD0XcMfwOxqQCCIIgypMOIjkMOoRDw1RaRCJtWufDQceowdQ4lV46mkBxazur12EgnbLtMQPNch+uTfO+RA5pwHv8Y2vAyvo7PaUxU6i0TXYroHRNNUJoRQchjyc6MwX4NAHIImaTrxjXRHUhWCedEIHO9MBxMj7EPc9YjbHwDEXz64ULfV2+wYpd15OPzVqi7CwXCQ3qWGoKar35CDiErmDiBsnVPIg4sdJKyudnNlbkPpe57IbF2/6rreogK8z2/0rjUhV3Qu10YQxW+x+X7/pYRWk8CmINCfxmO2nDmPI5N//aUKnlQtc16cTChio/9VQcIXqYc6LnobFm3kEOIIAiCsEfwhghBEFYhFSAP6U1CF5Ihg9BcWoFkNBjIIUQQwdLizQ6i41SdMnel5Du4mLQtkDILqLO6Rh2iN6dIfRmnY7ogP10YA4BKvWUiotbF/HTmHfQMrdps9M6JHkPOIa+QQygI+pbSQfiHZIZwjHU95Vqmu/ANGRhD0EZcF96Ra1rep9JBhNqTEECvnPF9x8Ya2Zn9gScnWxecezHIQAzP2HHIIWSUTKCFT6DsPQkROVKbrBByqnXrNJi4ty8s3b9qgfQSFeZ7fiXRclBI/m7QzpAa+lKvwve4fN/fOgLjC2IONOvb1f4e1RAiyphcd0L4bgjCJS70HOnSJsgh1BVo/SBUCMJgJ7oNyRhB9BpSAfLQ2kxo00EZ8tVEJYTmLVrQcxHNkEOIIAh16hY/V4tiSIuvs3o9NqLH3FwmDnpyikQh2kQtHZGNSp0cQkQtQRAEcRBLOjekfULAkEMoBPqW0kGI0SoXHlqQE0SouHZwdMGh0oUxNOFleB2f086PTwQqKk3UweGt3o2za3rAeMqn8AU83dckIT1LDRE8Ytchh5BJglIARNxY8GiXveSmZTW/no9vwPd35/T+HpyAxsYXgX7sxUmS6TH2Yc56RNe+gUJ/Gd6kd22eCD2q5EE2zSiXVZItoneQk9035BCygo+QZFpAeovLIroEIYtz49bl/brw/XRhDFX4Hpfv+1tG6Lvu+BzYhJwChC6dlCFfKZ+Rp5qGKguhPlcPIYcQQRAa1ClzV0o+pMXEVL2eluvILKDO6hoRBEGERlXUhuivks4jCIJwhjWdS7pcBHIIBUEoKR1EWLS8Vy8tyAkiVAzJtvA3Evu3pFPrIhZ6mMJqgqYxdGF8uriYA5rnOOFc491ZeOedkSNPcxrSu1QlBhmI4Rk7DjmEjEICTcSCLVn18Q34/u4s3b9qgVRyAmo/iO4Fssv4fk8EQbTTtdNUzfp2pLcIV5CsEb3FhezT99UEOYRsIFtIzuU9iTiQep8auc0kN4R1HMuYy5paMX4/h59ZdwyhzoHv5/J9/xDo9BxYHlun564vqNpm9O5r8ZXy6WNfZxR6LqIZcggRBKFO3eLnalEMafF1Vq/HhrOw7TIBzTNBEIQIlXor8uKwBEEQncSSziX7VQhyCBFEqLSGD3toQU4Qnacn30hPhkmoQMLRDM0PUYdObTaSK4Ig/EAOIZN4y1GnRYSQxHSuOtes02Di3r6wdv+q63pwAhobn+X35FoOfMudCbowhia8jK/jcxrb+AoZoE06EThW9FVH5De2/VVQa2tIz1IDFer3jhWHEGPsfYyxhxhjjzDGftzGPcLGR0gyhcR1CwutxbXC5wlCEefhuhr3k37WGL8f0/UqQp0D38/l+/6KGK2VEekciEBpCESZKnlQ/ZZMyVYnZdRXymfkqaahykLxXOSs8Y1xhxBjbAjg7wP4TgD3AfgBxth9pu9DEEQI1C0yrhafkBY5R0aczMLurK4RQRBEaBjcpBMEQRD2sKZzSZeLYCNC6OsAPMI5f5RzvgDwLwB8j4X7EARBEARBEARBEARBEAqMLFzzPIAnSv//JICvt3Cf8Ph3/7Xa7330bwLbp9Tv+8zvq/8uES4vPQr86x+p//vnH5K/5uLy+prLa6pPVc0v/xgwmgBXXzF7XREe/bXmebLNC4/Yue7v/Rzwtd8++GfPfkH+Opdf0JufZz4L7J5T//2cj/8U8OAv6l+njhe/YuY6X/2Y2Hw990XgxJ1m7umD3/gJ4MoLvp/CLssr7nXDtVfd3s8GPK2ftxcV9B3zWK7ywoPrsVx8Ru33/8NfAiZbB//s0rPAuTdrPxrRQT7788BgrPa7H/6fzD4LACTX/NpHpkhXar/36Z8BHv2oxn0Ttd/71D8BHvmw+n1NojoGlySG9yRV/Ie/AIznar/7gZ8E5sfNPk9g2HAICcEY+yCADwLALbfc4usxzHPDm4CzbxD72dOvB258K/DahfU/qgxGwD3vV/99Ijzuei/wyuNrY7aJ179//f5FuO0bga98dHPNs/cDN75F7znPvx048wbgxYc3f3buzcBZR1mi93wX8NhvtM+Tbe56LzDZNnOt6THgzvcArz55dFyMAfd8QPxat38L8OSn9OZntgfc/e3qv79/C3Dz1wNXXwauWXYY3v4twHxf/fdf/37gq78uNl/z48Dd71W/ly9O3AGcf2DjDLr3u9Wu89YfAp7+LHDDG809m0luezfwlY/40Q0ydkBo3PJO4PS9zfN253vWeqqNM/cA594CfMOPmXs+Ge7+NuDqS5ux3PQO4PhtYr977i3A2TcCL3/16N8duxG44w8Zesga7vku4PGPAzd/nd37EGaYHgPu/Fbg1ScAXAdufTewc1bsd296B3DmPuDiU2t9akqn3vpu4OEP+7ePTHH6XvHvYfs0cNs3rfdWuuM/c9/6HYmwcxa49RuBy8+FM++n7wFueZfvp6gm17Or6/Z06o1vXe93XnpU/RqqDsmIYNxw1W3G2LsA/A3O+Xdk///fAwDn/H+u+50HHniAf/rTnzb6HARBEARBEARBEARBEH2GMfYZzvkDVX9nI473UwDuZozdzhibAPgvAfyyhfsQBEEQBEEQBEEQBEEQChhPGeOcJ4yxPwfgVwAMAfwM5zyQuDmCIAiCIAiCIAiCIAjCSg0hzvmHAHzIxrUJgiAIgiAIgiAIgiAIPTy2fiAIgiAIgiAIgiAIgiB8QA4hgiAIgiAIgiAIgiCInkEOIYIgCIIgCIIgCIIgiJ5BDiGCIAiCIAiCIAiCIIieQQ4hgiAIgiAIgiD+//buJ1Szso4D+PeHVgsLGjEGMykJW0QLs0GDItw0mpupTeiipghqoVG7/myS2khUUJugaEChFKEkF1FNELSqZpQhnRFzKKUZJieZqCQotF+L90xchrmDN9/x+J7n89m85z7n5fJc+D7v79wf53kPAIPREAIAAAAYjIYQAAAAwGA0hAAAAAAGoyEEAAAAMBgNIQAAAIDBaAgBAAAADEZDCAAAAGAwGkIAAAAAg9EQAgAAABiMhhAAAADAYDSEAAAAAAajIQQAAAAwGA0hAAAAgMFoCAEAAAAMRkMIAAAAYDDV3XPPIVX1lyRPzz2PNbkiybNzTwJmIv+MTP4ZmfwzOmuAkcn/K9ubu/sN5zvximgILUlVHe7uPXPPA+Yg/4xM/hmZ/DM6a4CRyf/msmUMAAAAYDAaQgAAAACD0RBav+/MPQGYkfwzMvlnZPLP6KwBRib/G8p3CAEAAAAMxh1CAAAAAIPREFqTqrqlqp6oquNV9fm55wMXQ1U9VVWPVtWRqjo8jV1eVQer6snpddc0XlX1rWlN/K6qrp939rBzVXWgqk5X1WNbxnac+araP73/yaraP8ffAju1Tf7vqqqTUx04UlW3bjn3hSn/T1TVzVvGXSOxcarq6qr6ZVUdq6qjVfWZaVwNYPEukH81YGFsGVuDqrokye+TvD/JiSSHktze3cdmnRisWVU9lWRPdz+7ZeyrSc50993Th/yu7v7cVCA+neTWJDcm+WZ33zjHvOH/VVXvS/Jcknu7+x3T2I4yX1WXJzmcZE+STvJwknd1919n+JPgRdsm/3clea67v3bOe9+e5L4kNyR5Y5JfJHnbdNo1Ehunqq5McmV3P1JVr8vqs/uDST4WNYCFu0D+Pxw1YFHcIbQeNyQ53t1/6O5/J7k/yb6Z5wQvl31J7pmO78mqWJwdv7dXfp3k9VNxgY3R3b9Kcuac4Z1m/uYkB7v7zPQPwMEkt1z82cNLs03+t7Mvyf3d/a/u/mOS41ldH7lGYiN196nufmQ6/keSx5NcFTWAAVwg/9tRAzaUhtB6XJXkT1t+PpELLxjYVJ3k51X1cFV9chrb3d2npuM/J9k9HVsXLNVOM28tsDR3TltiDpzdLhP5Z8Gq6i1J3pnkN1EDGMw5+U/UgEXREAJ24r3dfX2SDyS5Y9pO8D+92oNqHyrDkHkG9O0kb01yXZJTSb4+73Tg4qqq1yb5YZLPdvfft55TA1i68+RfDVgYDaH1OJnk6i0/v2kag0Xp7pPT6+kkD2Z1G+gzZ7eCTa+np7dbFyzVTjNvLbAY3f1Md7/Q3f9J8t2s6kAi/yxQVb0qq3+Gv9/dP5qG1QCGcL78qwHLoyG0HoeSXFtV11TVq5PcluShmecEa1VVl01fKpequizJ3iSPZZX1s0/M2J/kx9PxQ0k+Oj11491J/rblFmvYZDvN/M+S7K2qXdOt1XunMdg453wX3IeyqgPJKv+3VdVrquqaJNcm+W1cI7GhqqqSfC/J4939jS2n1AAWb7v8qwHLc+ncE1iC7n6+qu7M6sP9kiQHuvvozNOCddud5MFVfcilSX7Q3T+tqkNJHqiqTyR5OqunDyTJT7J60sbxJP9M8vGXf8rw0lTVfUluSnJFVZ1I8qUkd2cHme/uM1X1lawuipLky939Yr+oF2azTf5vqqrrstom81SSTyVJdx+tqgeSHEvyfJI7uvuF6fe4RmITvSfJR5I8WlVHprEvRg1gDNvl/3Y1YFk8dh4AAABgMLaMAQAAAAxGQwgAAABgMBpCAAAAAIPREAIAAAAYjIYQAAAAwGA0hAAAAAAGoyEEAAAAMBgNIQAAAIDB/BdusfxMkVWKKwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "####You Try"
      ],
      "metadata": {
        "id": "qT554Llc-N1m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ComparePredictions(\"Shark\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "id": "yBQXaysL-QJ-",
        "outputId": "c28e7b2e-5cce-45a3-f3e5-4a6579e66b1e"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83/83 [==============================] - 1s 15ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}