gemini/orchestration/intro_langchain_gemini.ipynb

{
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      "source": [
        "# Copyright 2024 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "359697d5"
      },
      "source": [
        "# Getting Started with LangChain 🦜️🔗 + Gemini API in Vertex AI\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/intro_langchain_gemini.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fgenerative-ai%2Fmain%2Fgemini%2Forchestration%2Fintro_langchain_gemini.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://lh3.googleusercontent.com/JmcxdQi-qOpctIvWKgPtrzZdJJK-J3sWE1RsfjZNwshCFgE_9fULcNpuXYTilIR2hjwN\" alt=\"Google Cloud Colab Enterprise logo\"><br> Run in Colab Enterprise\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/orchestration/intro_langchain_gemini.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/generative-ai/main/gemini/orchestration/intro_langchain_gemini.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>\n",
        "</table>\n",
        "\n",
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        "\n",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b24d7ab7a7a5"
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      "source": [
        "| | |\n",
        "|-|-|\n",
        "|Author(s) | [Rajesh Thallam](https://github.com/RajeshThallam), [Holt Skinner](https://github.com/holtskinner) |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11d788b0"
      },
      "source": [
        "### **What is LangChain?**\n",
        "\n",
        "> LangChain is a framework for developing applications powered by large language models (LLMs).\n",
        "\n",
        "**TL;DR** LangChain makes the complicated parts of working & building with language models easier. It helps do this in two ways:\n",
        "\n",
        "1. **Integration** - Bring external data, such as your files, other applications, and API data, to LLMs\n",
        "2. **Agents** - Allows LLMs to interact with its environment via decision making and use LLMs to help decide which action to take next"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fpilrb5XVT_k"
      },
      "source": [
        "To build effective Generative AI applications, it is key to enable LLMs to interact with external systems. This makes models data-aware and agentic, meaning they can understand, reason, and use data to take action in a meaningful way. The external systems could be public data corpus, private knowledge repositories, databases, applications, APIs, or access to the public internet via Google Search.\n",
        "\n",
        "Here are a few patterns where LLMs can be augmented with other systems:\n",
        "\n",
        "- Convert natural language to SQL, executing the SQL on database, analyze and present the results\n",
        "- Calling an external webhook or API based on the user query\n",
        "- Synthesize outputs from multiple models, or chain the models in a specific order\n",
        "\n",
        "It may look trivial to plumb these calls together and orchestrate them but it becomes a mundane task to write glue code again and again e.g. for every different data connector or a new model. That's where LangChain comes in!\n",
        "\n",
        "![Augmenting LLMs](https://storage.googleapis.com/gweb-cloudblog-publish/images/Patterns_augmenting_LLMs_with_external_syste.max-900x900.jpg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aATSgLjXVVY0"
      },
      "source": [
        "### **Why LangChain?**\n",
        "\n",
        "LangChain's modular implementation of components and common patterns combining these components makes it easier to build complex applications based on LLMs. LangChain enables these models to connect to data sources and systems as agents to take action.\n",
        "\n",
        "1. **Components** are abstractions that works to bring external data, such as your documents, databases, applications,APIs to language models. LangChain makes it easy to swap out abstractions and components necessary to work with LLMs.\n",
        "\n",
        "2. **Agents** enable language models to communicate with its environment, where the model then decides the next action to take. LangChain provides out of the box support for using and customizing 'chains' - a series of actions strung together.\n",
        "\n",
        "Though LLMs can be straightforward (text-in, text-out) you'll quickly run into friction points that LangChain helps with once you develop more complicated applications.\n",
        "\n",
        "### LangChain & Vertex AI\n",
        "\n",
        "[Vertex AI Generative AI models](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview) — Gemini and Embeddings — are officially integrated with the [LangChain Python SDK](https://python.langchain.com/en/latest/index.html), making it convenient to build applications using Gemini models with the ease of use and flexibility of LangChain.\n",
        "\n",
        "- [LangChain Google Integrations](https://python.langchain.com/v0.2/docs/integrations/platforms/google/)\n",
        "\n",
        "---\n",
        "\n",
        "_Note: This notebook does not cover all aspects of LangChain. Its contents have been curated to get you to building & impact as quick as possible. For more, please check out [LangChain Conceptual Documentation](https://docs.langchain.com/docs/)_\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50b19d12"
      },
      "source": [
        "## Objectives\n",
        "\n",
        "This notebook provides an introductory understanding of [LangChain](https://langchain.com/) components and use cases of LangChain with the Gemini API in Vertex AI.\n",
        "\n",
        "- Introduce LangChain components\n",
        "- Showcase LangChain + Gemini API in Vertex AI - Text, Chat and Embedding\n",
        "- Summarizing a large text\n",
        "- Question/Answering from PDF (retrieval based)\n",
        "- Chain LLMs with Google Search\n",
        "\n",
        "---\n",
        "\n",
        "**References:**\n",
        "\n",
        "- Adapted from [LangChain Cookbook](https://github.com/gkamradt/langchain-tutorials) from [Greg Kamradt](https://twitter.com/GregKamradt)\n",
        "- [LangChain Conceptual Documentation](https://docs.langchain.com/docs/)\n",
        "- [LangChain Python Documentation](https://python.langchain.com/en/latest/)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e985f332"
      },
      "source": [
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "- Vertex AI\n",
        "\n",
        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
        "to generate a cost estimate based on your projected usage.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ohPUPez8imvE"
      },
      "outputs": [],
      "source": [
        "# Install Vertex AI SDK, LangChain and dependencies\n",
        "%pip install --upgrade --quiet google-cloud-aiplatform langchain langchain-core langchain-text-splitters langchain-google-vertexai langchain-community faiss-cpu langchain-chroma pypdf"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3fd2805"
      },
      "source": [
        "**Colab only:** Run the following cell to restart the kernel or use the button to restart the kernel. For Vertex AI Workbench you can restart the terminal using the button on top.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c49ae51e03fe"
      },
      "outputs": [],
      "source": [
        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
        "import IPython\n",
        "\n",
        "app = IPython.Application.instance()\n",
        "app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "034fe628"
      },
      "source": [
        "### Authenticating your notebook environment\n",
        "\n",
        "- If you are using **Colab** to run this notebook, run the cell below and continue.\n",
        "- If you are using **Vertex AI Workbench**, check out the setup instructions [here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/setup-env)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6d83f50a3d3f"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "    from google.colab import auth\n",
        "\n",
        "    auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5f33c406cb7c"
      },
      "source": [
        "- If you are running this notebook in a local development environment:\n",
        "  - Install the [Google Cloud SDK](https://cloud.google.com/sdk).\n",
        "  - Obtain authentication credentials. Create local credentials by running the following command and following the oauth2 flow (read more about the command [here](https://cloud.google.com/sdk/gcloud/reference/beta/auth/application-default/login)):\n",
        "\n",
        "    ```bash\n",
        "    gcloud auth application-default login\n",
        "    ```"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a92ac8ea"
      },
      "source": [
        "### Import libraries\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2e3e35049739"
      },
      "source": [
        "**Colab only:** Run the following cell to initialize the Vertex AI SDK. For Vertex AI Workbench, you don't need to run this."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "4f6ab09a187c"
      },
      "outputs": [],
      "source": [
        "import vertexai\n",
        "\n",
        "PROJECT_ID = \"YOUR_PROJECT_ID\"  # @param {type:\"string\"}\n",
        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
        "\n",
        "# Initialize Vertex AI SDK\n",
        "vertexai.init(project=PROJECT_ID, location=REGION)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Smo-7TpE4B22"
      },
      "outputs": [],
      "source": [
        "from langchain.chains import (\n",
        "    ConversationChain,\n",
        "    LLMChain,\n",
        "    RetrievalQA,\n",
        "    SimpleSequentialChain,\n",
        ")\n",
        "from langchain.chains.summarize import load_summarize_chain\n",
        "from langchain.memory import ConversationBufferMemory\n",
        "from langchain.output_parsers import ResponseSchema, StructuredOutputParser\n",
        "from langchain_chroma import Chroma\n",
        "from langchain_community.document_loaders import PyPDFLoader, WebBaseLoader\n",
        "from langchain_community.vectorstores import FAISS\n",
        "from langchain_core.documents import Document\n",
        "from langchain_core.example_selectors import SemanticSimilarityExampleSelector\n",
        "from langchain_core.messages import HumanMessage, SystemMessage\n",
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_core.prompts.few_shot import FewShotPromptTemplate\n",
        "from langchain_google_vertexai import ChatVertexAI, VertexAI, VertexAIEmbeddings\n",
        "from langchain_text_splitters import RecursiveCharacterTextSplitter"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3dd3a375d3b6"
      },
      "source": [
        "Define LangChain Models using the Gemini API in Vertex AI for Text, Chat and Vertex AI Embeddings for Text\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "eVpPcvsrkzCk"
      },
      "outputs": [],
      "source": [
        "# LLM model\n",
        "llm = VertexAI(\n",
        "    model_name=\"gemini-1.5-flash\",\n",
        "    verbose=True,\n",
        ")\n",
        "\n",
        "# Chat\n",
        "chat = ChatVertexAI(model=\"gemini-1.5-pro\")\n",
        "\n",
        "# Embedding\n",
        "embeddings = VertexAIEmbeddings(\"text-embedding-004\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "05bb564d"
      },
      "source": [
        "## LangChain Components\n",
        "\n",
        "Let's take a quick tour of LangChain framework and concepts to be aware of. LangChain offers a variety of modules that can be used to create language model applications. These modules can be combined to create more complex applications, or can be used individually for simpler applications.\n",
        "\n",
        "![LangChain Components](https://storage.googleapis.com/gweb-cloudblog-publish/images/Figure-3-LangChain_Concepts.max-1300x1300.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lyGZZEWlZOe0"
      },
      "source": [
        "- **Models** are the building block of LangChain providing an interface to different types of AI models. Large Language Models (LLMs), Chat and Text Embeddings models are supported model types.\n",
        "- **Prompts** refers to the input to the model, which is typically constructed from multiple components. LangChain provides interfaces to construct and work with prompts easily - Prompt Templates, Example Selectors and Output Parsers.\n",
        "- **Memory** provides a construct for storing and retrieving messages during a conversation which can be either short term or long term.\n",
        "- **Indexes** help LLMs interact with documents by providing a way to structure them. LangChain provides Document Loaders to load documents, Text Splitters to split documents into smaller chunks, Vector Stores to store documents as embeddings, and Retrievers to fetch relevant documents.\n",
        "- **Chains** let you combine modular components (or other chains) in a specific order to complete a task.\n",
        "- **Agents** are a powerful construct in LangChain allowing LLMs to communicate with external systems via Tools and observe and decide on the best course of action to complete a given task.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EZfIhJSMjDLV"
      },
      "source": [
        "## Schema - Nuts and Bolts of working with LLMs\n",
        "\n",
        "### Text\n",
        "\n",
        "Text is the natural language way to interact with LLMs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "8e0dc06c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Saturday \\n'"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# You'll be working with simple strings (that'll soon grow in complexity!)\n",
        "my_text = \"What day comes after Friday?\"\n",
        "\n",
        "llm.invoke(my_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2f39eb39"
      },
      "source": [
        "### Chat Messages\n",
        "\n",
        "Chat is like text, but specified with a message type (System, Human, AI)\n",
        "\n",
        "- **System** - Helpful context that tells the AI what to do\n",
        "- **Human** - Messages intended to represent the user\n",
        "- **AI** - Messages showing what the AI responded with\n",
        "\n",
        "For more information, see [LangChain Documentation for Chat Models](https://python.langchain.com/docs/modules/model_io/chat).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "qhQOijKAt1ta"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AIMessage(content='Hello! 👋  How can I help you today? 😊 \\n', response_metadata={'is_blocked': False, 'safety_ratings': [{'category': 'HARM_CATEGORY_HATE_SPEECH', 'probability_label': 'NEGLIGIBLE', 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE'}, {'category': 'HARM_CATEGORY_DANGEROUS_CONTENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE'}, {'category': 'HARM_CATEGORY_HARASSMENT', 'probability_label': 'NEGLIGIBLE', 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE'}, {'category': 'HARM_CATEGORY_SEXUALLY_EXPLICIT', 'probability_label': 'NEGLIGIBLE', 'blocked': False, 'severity': 'HARM_SEVERITY_NEGLIGIBLE'}], 'usage_metadata': {'prompt_token_count': 1, 'candidates_token_count': 14, 'total_token_count': 15}}, id='run-415901f6-c09e-4790-9629-bdba79abe14a-0', usage_metadata={'input_tokens': 1, 'output_tokens': 14, 'total_tokens': 15})"
            ]
          },
          "execution_count": 31,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chat.invoke([HumanMessage(content=\"Hello\")])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "878d6a36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "A delicious Caprese salad highlights juicy tomatoes perfectly! \n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = chat.invoke(\n",
        "    [\n",
        "        SystemMessage(\n",
        "            content=\"You are a nice AI bot that helps a user figure out what to eat in one short sentence\"\n",
        "        ),\n",
        "        HumanMessage(content=\"I like tomatoes, what should I eat?\"),\n",
        "    ]\n",
        ")\n",
        "\n",
        "print(res.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0a425aaa"
      },
      "source": [
        "You can also pass more chat history w/ responses from the AI\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "3bNQxPln7wC0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Here are the ingredients for a classic tomato sandwich:\n",
            "\n",
            "* **Bread:** Soft, white bread is traditional, but feel free to use sourdough, wheat, or whatever you prefer. \n",
            "* **Tomatoes:** Choose ripe, flavorful tomatoes. Beefsteak or heirloom varieties are great choices.\n",
            "* **Mayonnaise:** This is essential for flavor and moisture!\n",
            "* **Salt & Pepper:** To taste.\n",
            "* **Optional additions:** Some people like to add a sprinkle of sugar to balance the acidity of the tomatoes, or a few basil leaves for freshness. \n",
            "\n",
            "Let me know if you'd like me to elaborate on any of these ingredients or want tips for making a truly fantastic tomato sandwich! 🍅🍞 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "res = chat.invoke(\n",
        "    [\n",
        "        HumanMessage(\n",
        "            content=\"What are the ingredients required for making a tomato sandwich?\"\n",
        "        )\n",
        "    ]\n",
        ")\n",
        "print(res.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "66bf9634"
      },
      "source": [
        "### Documents\n",
        "\n",
        "Document in LangChain refers to an unstructured text consisting of `page_content` referring to the content of the data and `metadata` (data describing attributes of page content).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "150e8759"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Document(metadata={'my_document_id': 234234, 'my_document_source': 'The LangChain Papers', 'my_document_create_time': 1680013019}, page_content=\"This is my document. It is full of text that I've gathered from other places\")"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "Document(\n",
        "    page_content=\"This is my document. It is full of text that I've gathered from other places\",\n",
        "    metadata={\n",
        "        \"my_document_id\": 234234,\n",
        "        \"my_document_source\": \"The LangChain Papers\",\n",
        "        \"my_document_create_time\": 1680013019,\n",
        "    },\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c2b70f23"
      },
      "source": [
        "### Text Embedding Model\n",
        "\n",
        "[Embeddings](https://cloud.google.com/blog/topics/developers-practitioners/meet-ais-multitool-vector-embeddings) are a way of representing data–almost any kind of data, like text, images, videos, users, music, whatever–as points in space where the locations of those points in space are semantically meaningful. Embeddings transform your text into a vector (a series of numbers that hold the semantic 'meaning' of your text). Vectors are often used when comparing two pieces of text together. An [embedding](https://developers.google.com/machine-learning/crash-course/embeddings/video-lecture) is a relatively low-dimensional space into which you can translate high-dimensional vectors.\n",
        "\n",
        "[LangChain Text Embedding Model](https://python.langchain.com/v0.2/docs/how_to/embed_text) is integrated with [Vertex AI Embedding API for Text](https://cloud.google.com/vertex-ai/docs/generative-ai/embeddings/get-text-embeddings).\n",
        "\n",
        "_BTW: Semantic means 'relating to meaning in language or logic.'_\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "a2c85e7e"
      },
      "outputs": [],
      "source": [
        "text = \"Hi! It's time for the beach\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ddc5a368"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Your embedding is length 768\n",
            "Here's a sample: [-0.02856108359992504, -0.007161829620599747, 0.001575448433868587, -0.010381614789366722, 0.0047200522385537624]...\n"
          ]
        }
      ],
      "source": [
        "text_embedding = embeddings.embed_query(text)\n",
        "print(f\"Your embedding is length {len(text_embedding)}\")\n",
        "print(f\"Here's a sample: {text_embedding[:5]}...\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c38fe99f"
      },
      "source": [
        "## Prompts\n",
        "\n",
        "Prompts are text used as instructions to your model. For more details have a look at the notebook [Intro to Prompt Design](https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/prompts/intro_prompt_design.ipynb).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "PrvHxWMidmTU"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'The statement is wrong because **tomorrow after Monday is Tuesday, not Wednesday**. \\n'"
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prompt = \"\"\"\n",
        "Today is Monday, tomorrow is Wednesday.\n",
        "\n",
        "What is wrong with that statement?\n",
        "\"\"\"\n",
        "\n",
        "llm.invoke(prompt)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "74988254"
      },
      "source": [
        "### **Prompt Template**\n",
        "\n",
        "[Prompt Template](https://python.langchain.com/v0.1/docs/modules/model_io/#prompt-templates) is an object that helps to create prompts based on a combination of user input, other non-static information and a fixed template string.\n",
        "\n",
        "Think of it as an [`f-string`](https://realpython.com/python-f-strings/) in Python but for prompts\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "abcc212d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Final Prompt: \n",
            "I really want to travel to Rome. What should I do there?\n",
            "\n",
            "Respond in one short sentence\n",
            "\n",
            "-----------\n",
            "LLM Output: Explore ancient ruins, savor delicious pasta, and soak in the vibrant culture. \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Notice \"location\" below, that is a placeholder for another value later\n",
        "template = \"\"\"\n",
        "I really want to travel to {location}. What should I do there?\n",
        "\n",
        "Respond in one short sentence\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"location\"],\n",
        "    template=template,\n",
        ")\n",
        "\n",
        "final_prompt = prompt.format(location=\"Rome\")\n",
        "\n",
        "output = llm.invoke(final_prompt)\n",
        "\n",
        "print(f\"Final Prompt: {final_prompt}\")\n",
        "print(\"-----------\")\n",
        "print(f\"LLM Output: {output}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ed40bac2"
      },
      "source": [
        "### **Example Selectors**\n",
        "\n",
        "[Example selectors](https://python.langchain.com/v0.1/docs/modules/model_io/prompts/example_selectors/) are an easy way to select from a series of examples to dynamically place in-context information into your prompt. Often used when the task is nuanced or has a large list of examples.\n",
        "\n",
        "Check out different types of example selectors [here](https://python.langchain.com/docs/how_to/example_selectors/)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "aaf36cd9"
      },
      "outputs": [],
      "source": [
        "example_prompt = PromptTemplate(\n",
        "    input_variables=[\"input\", \"output\"],\n",
        "    template=\"Example Input: {input}\\nExample Output: {output}\",\n",
        ")\n",
        "\n",
        "# Examples of locations that nouns are found\n",
        "examples = [\n",
        "    {\"input\": \"pirate\", \"output\": \"ship\"},\n",
        "    {\"input\": \"pilot\", \"output\": \"plane\"},\n",
        "    {\"input\": \"driver\", \"output\": \"car\"},\n",
        "    {\"input\": \"tree\", \"output\": \"ground\"},\n",
        "    {\"input\": \"bird\", \"output\": \"nest\"},\n",
        "]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "12b4798b"
      },
      "outputs": [],
      "source": [
        "# SemanticSimilarityExampleSelector will select examples that are similar to your input by semantic meaning\n",
        "\n",
        "example_selector = SemanticSimilarityExampleSelector.from_examples(\n",
        "    # This is the list of examples available to select from.\n",
        "    examples,\n",
        "    # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n",
        "    embeddings,\n",
        "    # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n",
        "    FAISS,\n",
        "    # This is the number of examples to produce.\n",
        "    k=2,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "2cf30107"
      },
      "outputs": [],
      "source": [
        "similar_prompt = FewShotPromptTemplate(\n",
        "    # The object that will help select examples\n",
        "    example_selector=example_selector,\n",
        "    # Your prompt\n",
        "    example_prompt=example_prompt,\n",
        "    # Customizations that will be added to the top and bottom of your prompt\n",
        "    prefix=\"Give the location an item is usually found in\",\n",
        "    suffix=\"Input: {noun}\\nOutput:\",\n",
        "    # What inputs your prompt will receive\n",
        "    input_variables=[\"noun\"],\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "369442bb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Give the location an item is usually found in\n",
            "\n",
            "Example Input: driver\n",
            "Example Output: car\n",
            "\n",
            "Example Input: pilot\n",
            "Example Output: plane\n",
            "\n",
            "Input: student\n",
            "Output:\n"
          ]
        }
      ],
      "source": [
        "# Select a noun!\n",
        "my_noun = \"student\"\n",
        "\n",
        "print(similar_prompt.format(noun=my_noun))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "9bb910f2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'School \\n'"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "llm.invoke(similar_prompt.format(noun=my_noun))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8474c91d"
      },
      "source": [
        "### **Output Parsers**\n",
        "\n",
        "[Output Parsers](https://python.langchain.com/docs/modules/model_io/output_parsers/) help to format the output of a model. Usually used for structured output.\n",
        "\n",
        "Two main ideas:\n",
        "\n",
        "**1. Format Instructions**: An autogenerated prompt that tells the LLM how to format it's response based off desired result\n",
        "\n",
        "**2. Parser**: A method to extract model's text output into a desired structure (usually json)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fa59be3f"
      },
      "outputs": [],
      "source": [
        "# How you would like your response structured. This is basically a fancy prompt template\n",
        "response_schemas = [\n",
        "    ResponseSchema(\n",
        "        name=\"bad_string\", description=\"This a poorly formatted user input string\"\n",
        "    ),\n",
        "    ResponseSchema(\n",
        "        name=\"good_string\", description=\"This is your response, a reformatted response\"\n",
        "    ),\n",
        "]\n",
        "\n",
        "# How you would like to parse your output\n",
        "output_parser = StructuredOutputParser.from_response_schemas(response_schemas)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "d1079f0a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The output should be a markdown code snippet formatted in the following schema, including the leading and trailing \"```json\" and \"```\":\n",
            "\n",
            "```json\n",
            "{\n",
            "\t\"bad_string\": string  // This a poorly formatted user input string\n",
            "\t\"good_string\": string  // This is your response, a reformatted response\n",
            "}\n",
            "```\n"
          ]
        }
      ],
      "source": [
        "# See the prompt template you created for formatting\n",
        "format_instructions = output_parser.get_format_instructions()\n",
        "print(format_instructions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "9aaae5be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "You will be given a poorly formatted string from a user.\n",
            "Reformat it and make sure all the words are spelled correctly including country, city and state names\n",
            "\n",
            "The output should be a markdown code snippet formatted in the following schema, including the leading and trailing \"```json\" and \"```\":\n",
            "\n",
            "```json\n",
            "{\n",
            "\t\"bad_string\": string  // This a poorly formatted user input string\n",
            "\t\"good_string\": string  // This is your response, a reformatted response\n",
            "}\n",
            "```\n",
            "\n",
            "% USER INPUT:\n",
            "welcom to dbln!\n",
            "\n",
            "YOUR RESPONSE:\n",
            "\n"
          ]
        }
      ],
      "source": [
        "template = \"\"\"\n",
        "You will be given a poorly formatted string from a user.\n",
        "Reformat it and make sure all the words are spelled correctly including country, city and state names\n",
        "\n",
        "{format_instructions}\n",
        "\n",
        "% USER INPUT:\n",
        "{user_input}\n",
        "\n",
        "YOUR RESPONSE:\n",
        "\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(\n",
        "    input_variables=[\"user_input\"],\n",
        "    partial_variables={\"format_instructions\": format_instructions},\n",
        "    template=template,\n",
        ")\n",
        "\n",
        "prompt_value = prompt.format(user_input=\"welcom to dbln!\")\n",
        "\n",
        "print(prompt_value)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "b116bb23"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'```json\\n{\\n\\t\"bad_string\": \"welcom to dbln!\",\\n\\t\"good_string\": \"Welcome to Dublin!\"\\n}\\n```'"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "llm_output = llm.invoke(prompt_value)\n",
        "llm_output"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "985aa814"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'bad_string': 'welcom to dbln!', 'good_string': 'Welcome to Dublin!'}"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "output_parser.parse(llm_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7b43cec2"
      },
      "source": [
        "## Indexes\n",
        "\n",
        "[Indexes](https://docs.langchain.com/docs/components/indexing/) refer to ways to structure documents for LLMs to work with them.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d3f904e9"
      },
      "source": [
        "### **Document Loaders**\n",
        "\n",
        "Document loaders are ways to import data from other sources. See the [growing list](https://python.langchain.com/en/latest/modules/indexes/document_loaders.html) of document loaders here. There are more on [LlamaIndex](https://llamahub.ai/) as well that work with LangChain Document Loaders.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "ee693520"
      },
      "outputs": [],
      "source": [
        "loader = WebBaseLoader(\"http://www.paulgraham.com/worked.html\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "88d89ad7"
      },
      "outputs": [],
      "source": [
        "data = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "e814f930"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 1 comments\n",
            "Here's a sample:\n",
            "\n",
            "What I Worked On\n",
            "\n",
            "February 2021Before college the two main things I worked on, outside of school,\n",
            "were writing and programming. I didn't write essays.\n"
          ]
        }
      ],
      "source": [
        "print(f\"Found {len(data)} comments\")\n",
        "print(f\"Here's a sample:\\n\\n{''.join([x.page_content[:150] for x in data[:2]])}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e9601db"
      },
      "source": [
        "### **Text Splitters**\n",
        "\n",
        "[Text Splitters](https://python.langchain.com/docs/modules/data_connection/document_transformers/) are a way to deal with input token limits of LLMs by splitting text into chunks.\n",
        "\n",
        "There are many ways you could split your text into chunks, experiment with [different ones](https://python.langchain.com/docs/modules/data_connection/document_transformers/) to see which is best for your use case.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "CbA6yXonidz9"
      },
      "outputs": [],
      "source": [
        "loader = WebBaseLoader(\"http://www.paulgraham.com/worked.html\")\n",
        "pg_work = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d19acb18"
      },
      "outputs": [],
      "source": [
        "text_splitter = RecursiveCharacterTextSplitter(\n",
        "    # Set a really small chunk size, just to show.\n",
        "    chunk_size=1000,\n",
        "    chunk_overlap=20,\n",
        ")\n",
        "\n",
        "texts = text_splitter.split_documents(pg_work)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e3090f05"
      },
      "outputs": [],
      "source": [
        "print(f\"You have {len(texts)} documents\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "87a0f45a"
      },
      "outputs": [],
      "source": [
        "print(\"Preview:\")\n",
        "print(texts[0].page_content, \"\\n\")\n",
        "print(texts[1].page_content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1f85defb"
      },
      "source": [
        "### **Retrievers**\n",
        "\n",
        "[Retrievers](https://python.langchain.com/docs/modules/data_connection/retrievers) are a way of storing data such that it can be queried by a language model. Easy way to combine documents with language models.\n",
        "\n",
        "There are [many different types of retrievers](https://python.langchain.com/docs/modules/data_connection/retrievers.html#advanced-retrieval-types), the most widely supported is the `VectorStoreRetriever`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Vu_uBr4rKHvP"
      },
      "outputs": [],
      "source": [
        "loader = WebBaseLoader(\"http://www.paulgraham.com/worked.html\")\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DnifR4tZkV5P"
      },
      "source": [
        "Here we use [Facebook AI Similarity Search (FAISS)](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/), a library and a vector database for similarity search and clustering of dense vectors. To generate dense vectors, a.k.a. embeddings, we use [LangChain text embeddings model with Vertex AI Embeddings for Text](https://python.langchain.com/docs/integrations/text_embedding/google_vertex_ai_palm) .\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1dab1c20"
      },
      "outputs": [],
      "source": [
        "# Get your splitter ready\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50)\n",
        "\n",
        "# Split your docs into texts\n",
        "texts = text_splitter.split_documents(documents)\n",
        "\n",
        "# Embed your texts\n",
        "db = FAISS.from_documents(texts, embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e62372be"
      },
      "outputs": [],
      "source": [
        "# Init your retriever. Asking for just 1 document back\n",
        "retriever = db.as_retriever()\n",
        "retriever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3846a3b5"
      },
      "outputs": [],
      "source": [
        "docs = retriever.get_relevant_documents(\n",
        "    \"what types of things did the author want to develop or build?\"\n",
        ")\n",
        "\n",
        "print(\"\\n\\n\".join([x.page_content[:200] for x in docs[:2]]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "24193139"
      },
      "source": [
        "### Vector Stores\n",
        "\n",
        "[Vector Store](https://python.langchain.com/docs/modules/data_connection/vectorstores) is a common type of index or a database to store vectors (numerical embeddings). Conceptually, think of them as tables with a column for embeddings (vectors) and a column for metadata.\n",
        "\n",
        "Example\n",
        "\n",
        "| Embedding                                             | Metadata           |\n",
        "| ----------------------------------------------------- | ------------------ |\n",
        "| `[-0.00015641732898075134, -0.003165106289088726, ...]` | `{'date' : '1/2/23}` |\n",
        "| `[-0.00035465431654651654, 1.4654131651654516546, ...]` | `{'date' : '1/3/23}` |\n",
        "\n",
        "- [Chroma](https://www.trychroma.com/) & [FAISS](https://engineering.fb.com/2017/03/29/data-infrastructure/faiss-a-library-for-efficient-similarity-search/) are easy to work with locally.\n",
        "- [Vertex AI Vector Search (Matching Engine)](https://cloud.google.com/blog/products/ai-machine-learning/vertex-matching-engine-blazing-fast-and-massively-scalable-nearest-neighbor-search) is fully managed vector store on Google Cloud, developers can just add the embeddings to its index and issue a search query with a key embedding for the blazingly fast vector search.\n",
        "\n",
        "<br/>\n",
        "\n",
        "LangChain VectorStore is [integrated with Vertex AI Vector Search](https://python.langchain.com/v0.2/docs/integrations/vectorstores/google_vertex_ai_vector_search/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3c5533ad"
      },
      "outputs": [],
      "source": [
        "loader = WebBaseLoader(\"http://www.paulgraham.com/worked.html\")\n",
        "documents = loader.load()\n",
        "\n",
        "# Get your splitter ready\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50)\n",
        "\n",
        "# Split your docs into texts\n",
        "texts = text_splitter.split_documents(documents)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "661fdf19"
      },
      "outputs": [],
      "source": [
        "print(f\"You have {len(texts)} documents\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e99ac0ea"
      },
      "outputs": [],
      "source": [
        "embedding_list = embeddings.embed_documents([text.page_content for text in texts])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "89e7758c"
      },
      "outputs": [],
      "source": [
        "print(f\"You have {len(embedding_list)} embeddings\")\n",
        "print(f\"Here's a sample of one: {embedding_list[0][:3]}...\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8ac358c5"
      },
      "source": [
        "VectorStore stores your embeddings (☝️) and makes them easily searchable.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f9b9b79b"
      },
      "source": [
        "## Memory\n",
        "\n",
        "[Memory](https://python.langchain.com/docs/modules/memory/) is the concept of storing and retrieving data in the process of a conversation. Memory helps LLMs remember information you've chatted about in the past or more complicated information retrieval.\n",
        "\n",
        "There are many types of memory, explore [the documentation](https://python.langchain.com/docs/modules/memory/) to see which one fits your use case.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f43b49da"
      },
      "source": [
        "### ConversationBufferMemory\n",
        "\n",
        "Memory keeps conversation state throughout a user's interactions with a language model. `ConversationBufferMemory` memory allows for storing of messages and then extracts the messages in a variable.\n",
        "\n",
        "We'll use `ConversationChain` to have a conversation and load context from memory. We will look into Chains in the next section.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "893a18c1"
      },
      "outputs": [],
      "source": [
        "conversation = ConversationChain(\n",
        "    llm=llm, verbose=True, memory=ConversationBufferMemory()\n",
        ")\n",
        "\n",
        "conversation.predict(input=\"Hi there!\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iG0y5VXL8R6Y"
      },
      "outputs": [],
      "source": [
        "conversation.predict(input=\"What is the capital of France?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R7TfAEQDoPK_"
      },
      "outputs": [],
      "source": [
        "conversation.predict(input=\"What are some popular places I can see in France?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HoJ9HnR6oKbT"
      },
      "outputs": [],
      "source": [
        "conversation.predict(input=\"What question did I ask first?\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f29fc79c"
      },
      "source": [
        "## Chains ⛓️⛓️⛓️\n",
        "\n",
        "Chains are a generic concept in LangChain allowing to combine different LLM calls and action automatically.\n",
        "\n",
        "Ex:\n",
        "\n",
        "```\n",
        "Summary #1, Summary #2, Summary #3 --> Final Summary\n",
        "```\n",
        "\n",
        "There are [many applications of chains](https://python.langchain.com/docs/modules/chains) search to see which are best for your use case.\n",
        "\n",
        "We'll cover a few of them:\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c34ba415"
      },
      "source": [
        "### 1. Simple Sequential Chains\n",
        "\n",
        "[Sequential chains](https://python.langchain.com/en/latest/modules/chains/generic/sequential_chains.html) are a series of chains, called in deterministic order. `SimpleSequentialChain` are easy chains where each step uses the output of an LLM as an input into another. Good for breaking up tasks (and keeping the LLM focused).\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "43d4494a"
      },
      "outputs": [],
      "source": [
        "template = \"\"\"Your job is to come up with a classic dish from the area that the users suggests.\n",
        "% USER LOCATION\n",
        "{user_location}\n",
        "\n",
        "YOUR RESPONSE:\n",
        "\"\"\"\n",
        "prompt_template = PromptTemplate(input_variables=[\"user_location\"], template=template)\n",
        "\n",
        "# Holds my 'location' chain\n",
        "location_chain = LLMChain(llm=llm, prompt=prompt_template)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "b6c8e00f"
      },
      "outputs": [],
      "source": [
        "template = \"\"\"Given a meal, give a short and simple recipe on how to make that dish at home.\n",
        "% MEAL\n",
        "{user_meal}\n",
        "\n",
        "YOUR RESPONSE:\n",
        "\"\"\"\n",
        "prompt_template = PromptTemplate(input_variables=[\"user_meal\"], template=template)\n",
        "\n",
        "# Holds my 'meal' chain\n",
        "meal_chain = LLMChain(llm=llm, prompt=prompt_template)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7e0b83f2"
      },
      "outputs": [],
      "source": [
        "overall_chain = SimpleSequentialChain(chains=[location_chain, meal_chain], verbose=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7d19c64d"
      },
      "outputs": [],
      "source": [
        "review = overall_chain.run(\"Rome\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f6191bf5"
      },
      "source": [
        "### 2. Summarization Chain\n",
        "\n",
        "[Summarization Chain](https://python.langchain.com/docs/modules/chains/popular/summarize) easily runs through a long numerous documents and get a summary.\n",
        "\n",
        "There are multiple chain types such as Stuffing, Map-Reduce, Refine, Map-Rerank. Check out [documentation](https://python.langchain.com/docs/modules/chains/how_to/) for other chain types besides `map-reduce`.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6f218c3e"
      },
      "outputs": [],
      "source": [
        "loader = WebBaseLoader(\n",
        "    \"https://cloud.google.com/blog/products/ai-machine-learning/how-to-use-grounding-for-your-llms-with-text-embeddings\"\n",
        ")\n",
        "documents = loader.load()\n",
        "\n",
        "print(f\"# of words in the document = {len(documents[0].page_content)}\")\n",
        "\n",
        "# Get your splitter ready\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=50)\n",
        "\n",
        "# Split your docs into texts\n",
        "texts = text_splitter.split_documents(documents)\n",
        "\n",
        "# There is a lot of complexity hidden in this one line. I encourage you to check out the video above for more detail\n",
        "chain = load_summarize_chain(llm, chain_type=\"map_reduce\", verbose=True)\n",
        "chain.run(texts)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ta-Vt4t3wTQ7"
      },
      "source": [
        "### 3. Question/Answering Chain\n",
        "\n",
        "[Question Answering Chains](https://python.langchain.com/v0.1/docs/use_cases/question_answering/) easily do QA over a set of documents using QA chain. There are multiple ways to do this with LangChain. We use [**RetrievalQA** chain](https://python.langchain.com/docs/modules/chains/popular/chat_vector_db) which uses `load_qa_chain` under the hood.\n",
        "\n",
        "![QA Process](https://miro.medium.com/v2/resize:fit:2000/format:webp/0*x2f4Es8-NO6zUmks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3QjgeWSMw0Bb"
      },
      "outputs": [],
      "source": [
        "# Load GOOG's 10K annual report (92 pages).\n",
        "url = \"https://abc.xyz/assets/investor/static/pdf/20230203_alphabet_10K.pdf\"\n",
        "loader = PyPDFLoader(url)\n",
        "documents = loader.load()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OJecKul0xgWT"
      },
      "outputs": [],
      "source": [
        "# split the documents into chunks\n",
        "\n",
        "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=0)\n",
        "docs = text_splitter.split_documents(documents)\n",
        "print(f\"# of documents = {len(docs)}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8V8AoFiQyMSx"
      },
      "outputs": [],
      "source": [
        "# select embedding engine - we use Vertex AI Embeddings API\n",
        "embeddings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "st9_gVXmyGf6"
      },
      "outputs": [],
      "source": [
        "# Store docs in local VectorStore as index\n",
        "# it may take a while since API is rate limited\n",
        "db = Chroma.from_documents(docs, embeddings)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "29At6L691XZr"
      },
      "outputs": [],
      "source": [
        "# Expose index to the retriever\n",
        "retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": 2})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ibQdjXjw1foF"
      },
      "outputs": [],
      "source": [
        "# Create chain to answer questions\n",
        "\n",
        "# Uses LLM to synthesize results from the search index.\n",
        "qa = RetrievalQA.from_chain_type(\n",
        "    llm=llm, chain_type=\"stuff\", retriever=retriever, return_source_documents=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zvjxHX6a105H"
      },
      "outputs": [],
      "source": [
        "query = \"What was Alphabet's net income in 2022?\"\n",
        "result = qa({\"query\": query})\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XLQPrNUq2aiF"
      },
      "source": [
        "![image.png](data:image/png;base64,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)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Of9XWLw9C653"
      },
      "outputs": [],
      "source": [
        "query = \"How much office space reduction took place in 2023?\"\n",
        "result = qa({\"query\": query})\n",
        "print(result)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hb9RHMw5DKjQ"
      },
      "source": [
        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6zIK2KuCpVVpZfTlVdW1BloO+gwcNFO6/QfNus7G5mHBmO7Dx48qr/ArICAgICAgICAg8M9CQHdPrCIGbNQ58sffYTsiEnfu3sGdO9z/u7h/Yir87m7Hpt8KYOTkBAdKR0qysuMI1uV9kZnJuiksGDnByYGQnpKsnosqf5GJTEVkxcK4e264vEIwcoSDLSEtuYwGZE+RlEqwdXCokLjCreweVo0djT2mI7Hreiqy0+7hzKKucKy09sZsnnRf+Fw6gP2HIhEl7Y6BnVUdkgplaN0awdHBFpSWDDUkrPZPk1JBtg5wcmZzpykNyWWRrDpJSCVbODgoOy2iFtOx97dd+LTeEUyZHIlk/YP0WqXD2A21nYqR/CS1DPOsJ3j6QhcRtovNqrEYvccUI3ddR2p2Gu6dWYSuFQHSIRYY2cPR1gQtZpzB4ydP8ETxPw7njh/G+hF1+em+tF6wapbyYORYOX7aoGg/KWCdeOP2s3Dw6lNkJP2JXeNrIXrGJKy5z+RgR8hk8SqNRnY01n6xCWcvf4exo/fAdOQuXE/NRtq9M1jU1VH9YqJdisYT46YY2NcHlw7sx6HIKEi7DwSnUkb2jrA1aYEZZx6XYsiwjDuH44fXY0TdB1XQXwMY6JKPomgpHu0cwUbSD6LOvFM4Nr8Taql13wS+vvWRGX8P2SrVYC8B9xMt4eXrynLz5C2+gg0T/oON19k2QaXBvH59uBq8QFa2GjVVlPArICAgICAgICAgICDAEFA3wXxoSO/8iB8vuCB8aDc2+uoCl9L/bm1HYECLNOzd+DNe1H8fH7SMx9aF+5FQzLre6b9hyYazKOKGAo3q4/0PWiJ+60LsV0bityUbcFYRWaFkAxOYGOcj/Wkysos0GnCjegjp3hj3tszH7ngJyyTBg8j52HKnIUJDPCsQqXDLRlifJktR278L/MWWbDQ8Fce/P4R4tjNEMeOVLxj59MeAplex/LNDEPUYhHZstF6edQNROw7hqmIvNn25jVAvpDsa39uC+bvjGbeM4weRmL/lDhqGhsDHIwTdG9/Dlvm7oazOA0TO34I7DUMR4qnsfBuY1YC52Tv4z3cRqH14CiZHJpdOAdFXpsZzIx/06u+P22tnYNPVTBQwnrdNX47fdb7wsFF6hre0tj+6+IthaSRH6vHvcSheyhblVQKQcUN07+6Na2wP6T0POOIypB6ehq4tw7E0poif7svoBaNsYGIC4/x0JstslIgrx08DDT2XMqTsHIfgsJlsv3kZzJz/hXYBXrA2Zjpo6o5uIY0Qt/0bHE7i9DAXV1fPwme7YlGcw16opLXh38UfYksjyFOP4/tD8ZBKi9nkpMqCEXz6D0DTq8vx2SERegxqp/g6ZNywO7p7X8O6L/dACWMqDk/ripbhSxFT9Or6y3EjjV2OYR9HwW3yGswOMsHTu3dxl/2/l5TNpGXMFtaGwfvSeiz+PYPpVz5iVi3Hz8ah6NO+Bn9eo5rIu7kFX8zZhnhO9MWJ+GXhJlx1exdd3jEBt3f44R0HcUW1sroyaIR4AQEBAQEBAQEBgX8AAmVzHvRWVooYNu3ink9vrGpVYVtBIy/0G9QeC2dux77U3vhk40rcGzAejV0nsS31LNG6XXPYsekVBmyyie8nG7Hy3gCMb+yKSWzLO8vW7dDcLgVGFUfrRE3QuYsdho3xQeCDaOy1VTFmjEaTN2NVwlBENHPBVDZaWGDUEAPWbMW0xqwaGv10VQ71r4k/ho8Pxp6ZbdH4Zx9YZD2DRbtAtLU6hfgHrLtUusuDOr3mhVFd9B7YBrM+SsSgAf8Gmz0N6eO9+GxkJDqdeg/NHStgopHXuNFkbF6VgKERzeCiZBgNB6zB1mmN2XZwwOTNq5AwNALNXKayjQbZ14OGA7Bm6zRw1XmgQce0yRR8+5+DCJ4yGbsD5mjE8F0yzCdsxsbkMZjeqTYmkBsCGf//Et2EqUnFdy4T+A8fj+A9M9G28c/wscjCM4t2CGxrhVPxD1iHsh5PQSZoOWMblicPx8SmLoiwNUVunj26zP8BswOsYGLFT7fKesE4EDXpjC52wzDGJxAPzl3DbB78eBjWiDKCx5jFmHN2EAb5sB1wnETIy7VBh3mbMdbbFFZTNuObR0Mxyq8WRFYyFNZoh+lbZiG4eSLGB+/BzLaN8bOPBbKeWaBdYFtYnYoHp06uGiXoujSq2xsD28zCR4mDMODfnEaxYNISM7YtR/LwiWjqEgFb01zk2XfB/B9mI8DKBFavqr9Mepe+34Dz2ZmQzuqEhrOUxSmK7LgSCcfHwYXN91/z5U0M7OmJbWaGKDRqhv+wl9yu1sW4MIc/77h1q3F30GQ0qz0LNSkbEnEYFuychw7sJVX6YD9mjdyOwONd0cJJv52UcSRcCQgICAgICAgICPz9ETDgVpVWbzWleJGSgiIrVzizUcGKQfoiBSlFVnB1ZiPQFSM17qVFRZCbmEGrn8jSyPJS8TSLTetg2+JVaevqUrpFqXG4lSiBracfPOzYrsTcfuQmVrDQVYiaFznSvg9H442t8Vv0p/BVMF2EY58MxJ8fR2KGH18tSonI8pD6NAtGjmxrOi2GZchLfYosNq3G1VHfvuVqZl7iQoq7J/YgoXY3dGlgrcgnu/s12rY4jn6xpzCxTsUOOEtSlIq4W4mQ2HrCz4MtWpVwe7Kzjp+FSZU+kcjy2AhthgzWbq6w0exrVYFuVfWCmwZRVCSHiZmKp+rBrzibnaqabQC72i5s5Ls8zBxvSS+M4Vib6ZsatiKkxt1iC05t4ennATtjCeu4S9nLhoVOnS1HUZ6G78MbY2Pr3xD9qW8FO2D1YYuEM2TWcHO1UWzZp8r7avqryl35r7woE0/TSmBbuxasqqDWZRSZzSc9Zoss7dgaBttyPJelEa4EBAQEBAQEBAQEBAQ4BP6CzvffB1jZo9PYeSIa+xevg/z/rmD/cFdFJ1Seth+fLy7G6IX9UO+lOilvEhs2avlpM3Q52gYrVoxDixpPEDV3HBZkfYJzp9lc/beW7zeJ0ZsuS4ZHp3fiRPR+LF4nx/9d2Y/hrure/JtmRihPQEBAQEBAQEBAQEDgv4CA0PnmAV168zv0G70Dxe2m4duvw9/ijraeSuTdxPavvsSGE7fxjOzg2aY3Jn3+Cdrz7oqhh5bwuBoQkOLmd/0wekcx2k37Fl+H16sw6l0NRQgkBAQEBAQEBAQEBAQE3moEhM73Wy0egTkBAQEBAQEBAQEBAQEBAQGBvxMCwjfvv5M0hboICAgICAgICAgICAgICAgIvNUICJ3vt1o8AnMCAgICAgICAgICAgICAgICfycEhM7330maQl0EBAQEBAQEBAQEBAQEBAQE3moEhM73Wy0egTkBAQEBAQEBAQEBAQEBAQGBvxMCQuf77yRNoS4CAgICAgICAgICAgICAgICbzUCQuf7rRaPwJyAgICAgICAgICAgICAgIDA3wkBofP9d5KmUBcBAQEBAQEBAQEBAQEBAQGBtxoBofP9VotHYE5AQEBAQEBAQEBAQEBAQEDg74SA0Pn+O0lTqIuAgICAgICAgICAgICAgIDAW42A0Pl+q8UjMCcgICAgICAgICAgICAgICDwd0LgH9L5luP5r99g8ryDeCyrDvFJcWvnl5j94w1Iq4Pcf5VGAR6cWIcvIz7BuEmzsPKXOORq8CPPvom9i6dj/PgpmLf1ItI08St4gBPrvkTEJ+MwadZK/BKnmVOK5LObMYfRnTBjCSJjnkOuQVe4/O8gkHf2O0xccASpb5kwqouv/OQkPOfqJs/AsQXjsOjk8/8O0AU3sGnse2jTsg0GrLn93+HhLypVnnEKyyKW47QC6IqFFODGprF4r01LtBmwpmLk23X/d5DRX6jnaluqRGqyhIOYG7Eel4orSVhZdNF1fD9jNf6oQKfgwQms+zICn4ybhFkrf0G5ZqYCTd72Sp6Nm3sXY/r48ZgybysulmvMmMvga+sqlPPSt7IEHJwbgfWvDRJ/yfL0E1g6aQ4OPNJoqGWPcGDOBEyc/zOeaD6+uxuzI9bgfD4/zf+V2Crpa34yknT6rTdfy39I55vw4toBrN15oXzn8ZXxLsadn5dj+cE4VPATr0zxv5OxGDeWhKLVoI14aC6G2CIBW0cG4N3Z56Gwx4JLmNs1GFNPSuDsJMO5OV3Q7pMoZHCdm+IbWBLaCoM2PoS5WAyLhK0YGfAuZissWY6kXUMR8P5SXDepAzfpGXzesS0mHMkUOuD/HUGrSy2MjcLmfVfxnNSP3oqL6uCr+PxMBAbPweUSrkrFyHhwGw8z/jsW+nzPLETskyDwo4kYEVTnrcC4upignD+xf90BxOTooPh8D2ZF7IMk8CNMHBGkI8Hb8+jvIaO/Rs/L2xK/zGRp57Fj3WHcfZ2RKEk8dozti4+XH0e8Bp3iG0sQ2moQNj40h1hsgYStIxHw7mzdHUa+9goFuDS3K4KnnoTE2Qmyc3PQpd0niFI0Zqx+vHn561+lWFkazu9Yh8OvBVLlJRlaA/f3LcKGk2VtrTzpZ3y3dCPWf80GyNJUoy4yJP6yCktOpcDctHK6b3uKKulr8XnMDAzGHGUD8d+vEv0jgpQeLgkk84af0kVJhQpLCykvv6TCw8pu82lPfxuq2XsX5WsmLcmjrMxsKpJpPtS8llJhXj69bGnEcuRlZVK2PsIlJa9Ak/FVdJTGiB0ofGsaqVh+HtmfnB0G0F5WsfSt4WRfbwwdyVXWoeTWXPp3zdb0dZyUZR1DYodw2pqmzkmR/Z3JYcBeypfG0lx/C2rx1c1Svoro9HgPqtl9M2VqwlGd19J8ysrKJ+nL0HxNeUkL80i36kgpPyuL4fAyzGik5dFJaWE2ZeXpIsxfpiQ3h/Ejo/S1XciyxRyK1UVCgwXusiQvizKzi9S6USFa962WLlY/X8qCmS1lZ5EmFIU/DSFn8Rg6WqSbNe6pND+LsngEo1+m+mjqqp+U7i9sQxbtltIjlXnoy65k6uV1V0VPC29VxKv86qqLBp3SsqTxiyjAoh0t01E56f2F1IbFLa0Qx48781/lnKK2bNVcvIqdM6+gbY8vKaPKfIVMxmMnLE6vHlSse+U6qsaC96KECvM1GzsZFelpe3TZuT5b0iVHyYVp1MAijLaWawx5mdOIlFHW5fU02t+J7MRisjHXpFNER8eIySF8K5U1M5HU39mBBnANVIXA116xxozC7evRmLLGjOb+uya1/jpO0Wbw5q1QjvZtRRnq0DfJBZrWwILCKoCkC08VfWl+NmUXVsFRqzIofnNod18Hchl+qLRvwnz+5u5kHziWxjSxZ229qgV+TlvDbchj/G9UpiU6+C5HW9fNq+RR0eGxC1471/YP+vRVVZLit/AnGuIspjEVGwjesspRqNabt2fkW5qBuAsXEZ+l8V3kL3o3kSWsQnef3vh0Zjd42teEjbU1xO2nIypF9Vb4cgXLM6OxsHdTuDqK4e3lBlvHhui57IJy+gb73LSquw96fzoT3TztUdPGGtbi9pgelcJGgaW4Nrc9PIfvZe/lyiBP34K+nt2w4h7DQZ6J6IW90dTVEWJvL7jZOqJhz2W4wM3ukF7FV0EtMHh8X3jbmsM+mL3tftQIvqP2I1vFvuwOlnXxRp8tXFk6gnFTjN92APNCHaBSBBNzcxhLi1Eil+Bq9CWg1bsItFTmNfbqhA7iWzhzNhvGTcdj24F5CHVQ54S5uTGkxSWQG/nhs+gnOD65EYwVWQuRnVMEU0tLmOhgQ+uRNAbzgtzR54c0Jd+S3zC1qRv8Z/6h/NIgT8TaHl7otSkZcmkCfprWCR4OznD3cEOtBmGY+2tpPi3CygevLi8ZElZ1h0/vTzGzmyfsa9rA2lqM9tOjoFQdKRJ+moZOTM4O7GuAg70nOn96SDnVSXoNc9t7YvhetaSRvqUvPLutACfqynRScncnJgS7w9HFA+4uTvANX4SzWZxUS8v0cICzuwfcajVA2NxfoRrgKIrbig9bM510doWruDWmHEvXrQtqrOTIjF6I3k1d4Sj2hpebLRwb9sQyhdKpE5W/0NJFrk6vylclNgEJ7u6cgGB3R7h4uMPFyRfhi87iWSwbIRsViWfJ29DPowfWJz7EqtB6CFv7RMGrNOEnTOvEZObAvvA42MOz86c4pJiDVplMy1e17E6frGWIW9IFbedeRuGFL9CizvtYk6jT+pjolLrrae/ARvUcYO/ZGZ8eegzOA0qvzUV7z+EoU5d0bOnriW4r7kGmE+8yzpRXUsTMC4J7nx9KdUGC36Y2hZv/zNLP+nIkru0Br16bkCzXVxeOkYp+Rqmv6tLkqTg8OQDegVPwS/QidGk7F5cLL+CLFnXw/ppEVkV9uEtx9asgtBg8Hn29bWFuH8x8XoFO2SrV/OXtXG0bWvZYdRnx+Qr547Xo6dcHU6Z3gruTLWxtPRA8eS8ecR9b5I+xtqcf+kyZjk7uTizOFh7Bk7FXEamr7jL9WMnisf4Db/j02QzFbAJ5EnYNawSfXhvxoJi1Lyo9V7Q1DdB3OiuzPmtrrK3g3GYa9h9bgT4NHRVtj533B1h9U8IxqNfOZXEVbCmJtVQKOXrAwdkdHm610CBsLn5VORm1MuTh8LiXbIMYH/G//or80E04t70/XFTNiYKmMZqO34YD80JR1syYw9xYiuKSijbF315JrkbjElrh3bLGDJ06iHHrzFnWXvLnVVev3IUuGUr0+/9yeTnT5+xCN55FNzdgSDPmf+v6wLOWE7w6f4YjVe6bWKBdh+bIv3IBtxVfEPLx+4nLqBc8Cv3b2+PCid+Rx/EiuYgzV2og8N2WrE3m99UVWC+95cnDp68lfHbBSPO251X0/UxftYIsjn2pH4XIZ8nY1s8DPdYnqfsOuvyvVv6/4kG1duVflZg0ntZ1r0XGBgYkEg+gHUl6hwlesYTyI9+KkRsTEXkN3EwxmSVUlLCXRniZUbPZ16s4gqw58i2hcxG+ZN16Nl3M4dgrpLurQ8jBphft5EaMWd0WBZiQyGsgbY7JpJKiBNo7wovMms2m6yUSujCtAVmEbVWPoMuSV1JHixY0hw1NSs5FkK91a5qtJEyFd1dTiIMN9eIIc2/SfiIyazSCNh4/RUf+eEwvokaS2L4nbXumhKkkZhY1swuhDclVxDP/Gs0PtCG3AZFspCGbNodakPfks2VvxrJEWt7egpp/eVNZgMbf/GvzKdDGjQZElo2icyP2sbtm0ZjujcjFuy9tus0zJKlBi1WOfp/oRY59d1M2e15yaQY1MjehGv5zFSO2spT11NWuLS26L6HYxYFk49GftsTmsZR5FLsunGq79KLtKfrq/DryklL8ogAyEXnRwM0xlFlSRAl7R5CXWTOafb2EpHFLKchGTD1WX6McNtSVE7OGwuvaUPult0mqNfIho+SVHcmidBSaVyfZl4QFbazJY9CPFF9IJMuMpunNa1KDqeepIHYxw92D+m+JZbVnCMSuo/DaLtRrewrJuHwB1uTJ8t1n+Qrv76Sh3iIS8Y18S85RhK81tZ59kZTqfJdWhziQTa+dVPoBpJykFDc6dFHyynzx24Q0dgG1sfagQT/GM0uTUWb0dGpeswFNPS+hcqMfCruzoHbLHjEbjKOlQTYk7rGaruWw0ZacGFoTXpds2i+l21J+mWpXVvmEV9ZsLO3+gtZkEfQNPdanhixN3NIgshH3oNXXctjIaA7FrAmnujbtaeltZvsVRxNlybSyI/uaNCdWqUsVbF/X+Jjk94nk5diXdiuNiGY0MieTGv40l/vsIUuh9V3tqO2i+ySpTG8rlCVRjXw/TKYjk5qTc8ORFPlIOXQtvb+AWlsE0TdcxXlx5+TsRyKzRjRi43E6deQPenhDn2wLXsHOueJ57LFKMuL3FQqbFRmTOHwt3ciVUWHcFurnbkvBy++TVKF/IjIWh9PaG7kkK4yjLf3cyTZ4Od2Xatf9sYRPR5kPvL2C3nWoRWGbHtD9je+TS61QWh/P5FhOz1VtzRDafiePpGkHaWR9Zu91e9LqP59TSW4MfR1kTeIxR5mL5bfz8rYUS4tZu+DRfwsp3WwsrQuvTS69thPnZjV1Ne812iBNOrrtLp+uzWf+3m0ARaqHwlUp+dur7M2hZOE9mc6qh3lllLi8PVk0/5JulvDnVZVQ/ldbhg9v8eibpv9nflk/ntm0o5cd+X5yirK4AnOu0JeBzhQw/0b54nnupHe+plZWwfTdUyacwiM0uq4vTWYVz/9lJLnVG0snWFtQ8udMamwfTtueMxXi89V6yqksD7++6rMLKa+dV9n36+GZNRAaI9/8/lcfiep8Xu4986/o3FeJpuQyTvyWBuZKUJJ0GqeuvYF5msZ+6DdlMBrbGcO0bgjCAmyQ9vTJKyygNEbjiAO4sGcq/K3kKEhPxDORDWpKspCZq3oDM4ZfvykY3NgOxqZ1ERIWAJu0p3iiMbdNF07GjSNw4MIeTPW3grwgHYnPRLCpKUFWZq5y9JKM0WzwDAzrFIz3WtVBzY5D0NPuNCIPprL4YlzduQ9JHQcivPxwgq6igLwYrBoQhkU5Q7Hhmw/gZMhGYtgQnLFIOXatzCSCyAgoKVFMqlXTyYtZhQFhi5AzdAO++cBJPYrOzbvNLzGBa4MmqJsfjY1bo5XzxdU59V2YwD+0Iyz/OIHzRTI8/PUMCkL7olV8NKLTZcg+cQRXvLuhe5372Lf7Cur3/hBBlplITMyE5buD0dX8OHYfydJD/PXlZezXD1MGN4adsSnqhoQhwCYNT59I8DjqAC67j8BXo5vAytAQVo1HY85IT1zacxAPuOHMyoIenZQkROHnGC8Mm9EXHmaAoV0gZuw6hR8+8kb8vt24Ur83PgyyRGZiIjIt38XgruY4vvsInnH5rnljKMvnyfKZefbBF2Nbw8SAhxHjxog4cAF7pvrDSl6A9MRnENnUhCQrE2p11pW9nC66sXmH1cyXokw2Sh31M2K8hmFGXw+YMU2zC5yBXad+wEcNNPW0PIOyx1E4cNkdI74ajSZWhjC0aozRc0bC89IeHCwVjG6Z6jNQ2evLWvYYUQcuw33EVxjdxAqGhlZoPHoORnpewp6DD8pXQNddObzrgJmlVjDxD0VHyz9w4nwRZA9/xZmCUPRtFY/o6HTIsk/gyBVvdOteB08r01tdZVE2zn4eiv676mLh4XXoXU8b/8pxJxg3G4wZwzoh+L2WkB/VI1vv+Few82qQEftuV6lvFzXFmLmj8I6lIcx8B+Hz4Z64uOcAEhT2LkLTMXMx6h1LGJr5YtDnw+F5cQ8OKCI1694Kbk/5ddS4wTis+7olLs3ojPem30DwinUY6aFL6kbw7c2+JvhYwMipAwIaiuAaMg4jmtrC2LIBOrRyQ3ZGBnPsVbdz2Z192H2lPnp/GATLzEQkZlri3cFdYX58Nyq6WYvXaYO0NFjzQR5iVg1A2KIcDN3wDT5wqth14W+vZMrGrPRLrJKuSNmYoYR9a6pqW6fJEaApw5ago1Xz//x4itjXVHMknVyFZRujcDWnAWZFp+LsjHfKF81zZ1S/A9qJY/HHhQIUXz2BMwhEp+YmqBHQEW3yonHiugQpZ8/jcdN30cFahjs8vlp3K1p5Hn591WMXD2J57PzZK/l+vTC9rv/VS7jqEdoes+p5qy+lWUeMGuuP35ZfgyhgLIa2ZT2FvzoYWMPWRmXABhAZG0EuU3WWX6ZwQxhlx2DD1AHYe/4enotc4edjihxyAnuXKA0GsLa1UXdKDViH1kguU3agVUl0/BoaZSNmw1QM2Hse954zJ+rnA9McgpOasCEcarmUNbymbTCotxiddh9AUr+G2HkgG+8tDIW9Dtqaj2RPfsH0PsOx1Wg0Io/ORSeFYzNDTSsRCvMKmIspDZSHXDZrwsLSqvSBDE9+mY4+w7fCaHQkjs7txDrtqsTcbw20HPQZWrKrKd3+g2ZdZ2PzsGBM99HVaGjmYx3FNqEIpqk4dTUJib8noOXgpWj0+H38diYNDkcvoG7Xr+BJKUhOL8HdjYMQuF0zvz2a5HFuQ1fNX19eBta2KFMdEYyN5JAxeaalPoOBSx2I1VZlhDpiFxhkpoO9M1Qe9OikLC0NGQbOcHUrw83aswXDVYKTyekoubsRg8oDAPsmeWwKBpfPSSOfIWqJ3ZhUeIKhEbJjNmDqgL04f+85RK5+8DHNATlRmR7ozK6pi1KkVDdfijIZxmkZMGBTaMqgsIZnC07DgCLFX+0/srRUPDNwYbJQCwZGdcRwMchEeqlgdMtUm5bySeWyrqUvq+o5W4CV+swALowPNVdGTHdcDJCZnq5KxfOribeeZGZtEBpMmHrqKpISf0dCy8FY2ugx3v/tDNIcjuJC3a74yhNV0FsdZZXcwolrzSEuvIRjf6RjcB0XtX9TcVMV3A0dasFFodY8spWcfAU7rwYZsRpV6ttNxHCvp7JLI4hrM8mnJyNZYe8mELvXU/tnI3Ft1EI6kpWRKKs7m3ZWmY56m8B9wESEz++C9SYTsOV9Vy28lbgbwoZNY1K6YSPmmwxQw9ICyvdt9uLJBgUUzcdL2Lk0JRnpJXexcVAgtquEy/3aN0FexWmir9gGaZLVupY9wS/T+2D4ViOMjjyKuZ00B3hUqfnbK7OaVhAV5qGgrDFDnrIxg5UBf15VCbp+y2RYub6p8vPjaYqhC/di0RfzsOGLfpg/FnBtGY7JS5bjPwF2KhL8vyaN0SHADJP++BM3LX9HVuvpYLeASXt0bP4xtv1+B02u3oJv8CrUMpQijsdXc+K1V6m3ulR+/67MY8Sjr3rs4kkSivS2589eyferWa54Uan/9a6Yo9rvy3WVqp16VQkaOqLLIrYTSWEBnp6ehUDrqmZ8U+nY/Mnv+iNk1q+su8MFNkJfLIepORt7k93DqrGjscd0JHZdT0V22j2cWdQVjlVC1hAGzBlCWqKcy8xRzslBnsJByHBv1ViM3mOKkbuuIzU7DffOLELXCoQNDDSHMdkcuYF94XPpAPYfikSUtDsGdlZ1lBWMa/2RPtqJEWy0+GCdeTh1bD461VIxbgJf3/rIjL+HbNU7CXPA9xMt4eXryuhI8WjnCDYCchB15p3CsfmdmCGXki++gg0T/oON18tGDs3r14erwQtkZVelF8ro1AhCSFAOfju0FSdvNEKHDo0RHGiHy8dW4tAZF3R735c1LPZwtDVBixln8PjJEzxR/I/DueOHsX6Eu1ZdFQ9eS166SSqfGsHRwRaUpmp4uacyPE1KBdk6wEFkwBo+TtSqrzqEnJy8Sjq0pZQdHGBHmayTVIZddvRafLHpEkSsTJMWM3Dmsar+TxB37jgOrx8BLzY33IHSWUdYlU+OF5mZejupCo7vrcLY0XtgOnIXrqdmI+3eGSzq6qinoVfyp/pbpotGsHd8Vb74bMIIDg52oEzWcVVVic3YjF77BTb9kaV8mdU0h1LGjBwdYEtp6k6Pop5Pk5BKtoyeVsuiqg7PbyWyrgpJI0c42BLSkpOZlpQG2VMkpRJsmbwNDVhHidlYmbrkIEfpGFSpUYa3+lGFixoICglCzm+HsPXkDTTq0AGNgwNhd/kYVh46A5du78OXDTrw6m1pXbTKErXA9L2/Yden9XBkymREJqucRBkLVcKd+S+lyHhke0nEsHpJO2dd3qrUq4xbHVdV8RXSbDxXO0jWAUvPBNk5wUmBmxTZz7PVgyyytHRkkh2clJFgAiytO1A5VgW4uPBT7KrRFV3wPSZ9fVG9Tqgi5+WahIqRpfeyqth5qS0Z2TvC1qQFZpx5XOpjma+JO4fjh9djhHtFZX/5NkgPi8rH0kfYOYJ9zTtYB/NOHcP8TrX0+CL+9srE1xf1M+NxTy0rNmhzPxGWXr5wNeLPy8ufWoZV1zd+PCXMzxuj/ayDuPo0A0l/7sL4WtGYMelltu00Q+tgf2RdPYK9v7GBq05BsOAqYeiEjsENcfePzTj1Zy207+jJrITfV2uJVwFGVfLw6aseu6jlyNOee72S79eWnUqp+f2vdr7qf6LqLlU/5VegaGhkpMewXoFYtWYxgGXRE/y2YweOsb1BpcnHcJxNjanv4w0Re4N6mixFbf8u8BdbshHtVBz//hDbLoktClH1s/TywkYi2YIKij2L37ktj9gCy7Pf78NNxawO5sifJkNa2x9d/MWwZKOrqce/x6F4KVvUqJ+wkU9/DGh6Fcs/OwRRj0FoxzfMKY3F8mEfI8ptMtbMDoLJ07u4e5f9v5eEbJkxW9wZBu9L67H49wzWgOSzz37L8bNxKPq0rwFp7HIM+zgKbpPXYHaQCZ5y+dj/e0nZkBnVRN7NLfhizjbEc8ORxYn4ZeEmXHV7F13eYUsu5Vm4EbUDh65ydPUFS3QICUDiphWIrhuE9o6maNK+NYr2fIdD1l0Q2oiNFxo3RPfu3rjG9hrf84ArSIbUw9PQlY0ULI0pPzVGXcpryUtNRceFEeqFdEfje1swf3e84iVN8iAS87fcQcPQEHiKaqEWGz2OPfu7YuqNPPMsvt93k33yrDwYuXdDSKM4bP/mMJK4nlruVaye9Rl2xQLN3u8O72tsn/Y9DxSdalnqYUzr2hLhS2Mgr/8+PmgZj60L9yOBqYw8/Tcs2XAWRerRH+2yZWw6VLK0Nvy7+ENsyb4GpR7H94fiIWWLcPVrXUU6THe6vypffDbBRlO6haBR3HZ8czhJ0WnNvboasz7bhVhpDRiZmMA4P53ZYzbYbCV1MKoXgu6N72HL/N2I596eJQ8QOX8L7jQMRYhnxc6DOhvPRSWyrgpJo3oI6d4Y97bMx24lU3gQOR9b7jREaIgnDGvVYl+4YnFWYXtscdzZ77FP6Rh4+NKOsuwQgoDETVgRXRdB7R1h2qQ9WhftwXeHrNEllFsQ/Yp1YaOFNdjgwzv/+Q4RtQ9jyuRItnCzfPkvhzuPbNEM77+snb9qvTSrUBVfUXwBP6w8p9hbXp52Et9uj4Uf01HljJBiXPhhJc5x+wrL03Dy2+2I9WO2rGO6SGVY5UbPxphvizFy7U5sXz0E+cs/xJdnNM9V0GS88uvK7NxAw5akvt3R3fsa22t7D5Ruli2yndYVLcOXQpebfak2iJdVKWKXD8PHUW6YvGY2gkyeKtunu/eQxA3ilGtH+NsrY7ZoPMz7EtYvVvrffDZVcvnPxgjt0x41mBXwtXW8LKojq25Hxg158CxKwc5xwQibyc5ikJnB+V/tEOBlDWMmD1ZhZN88jB1suprWWlc1H8oLq3Yd0CTue3x/41/o2LHsS0i9jh1Q+8Iu/CxjWwo3Zm0oV3ceX627fao8D7++6rELn3/xtOfyV/L95WAxMIGJcT7SWb8qu0TM63/L5furbqpzAvnbS0vHgstyW2UV0eFRbortedhaBJ1Bln6EIlrYkrHInMxFIrL3j6CoVG41VRFdX9GN3KycqWFAO/L386MOY0dRB/v69NFJRk1jQQyXmgtFh0eRm8twOsSiZam/0OSWDmRu7Ub13GpT8+GjqbOLv2LBZdH1FdTNzYotaAqgdv5+5NdhLI3qYE/1PzpJhZoLOJRkS/+yhXwbQ8la9C+aeaXc/l3lUnE33IJOb2PF4CvXHSv7b9KRVioWaebS5W8/IG+bmuRUy4as3ILp8xPcgkpuIZI3mwmqkaf02qTjSuKySu7+SB+2dCYr+9rkZmdJDo0H0qorpUv2Sq7R543NyGfKubLFnFrcsQfPfqBwGxE1mHZBme75Ngq3FpXPl3+TNo1oQc5WbKFoHWeqaedHvVeULhbURfO15KVcnGfRblnZFnJFh2mUmwsN54TJaMdtG0P+tazI1tWVbNniy7Yfb6c4xTpTGaX+MplaOpiTtVs9cqvdnIaP7kwu/spt/xSLt3h0svDWZhrR3IlqOriRm60deYUtpHOKFTn5dHPTCGrhbMUWItUh55p25Nd7ReniXyaH+0o51LR3o1pOPhTeK5CcA+azhYY6wWFVuE4rurmRlXNDCmjnT35+HWjsqA5kX/8j4tRZZ9Cpi6/OF59NcAuab20eQc2dapKDmxvZ2nlR2MJzisVJsuRItnC6BhmKGtGMP+6whc6lCy4Z00Vx22iMfy2ysnUlV1u2+LLtx7RdIZjKZKqzxhxFHllXZcGlginaNsafalnZkqurLVt82ZY+3h7HKLMgS6VfJrckB3NrtkjKjWo3H06jO7uQv2rBpY5ty7hs2uEZ/cC2FBM1mEYXFIvNntO2cGsS+Uyhc+rFZzx10SHbirpaeGkWtbB0o747kqj4vsaCS8aMfty1F9byyZZe2s45JHjqVaUFl/y+XYGDeT1q0cqdXD19qb69Pfn2Xkl/ciufFX7fnOq1aEXurp7kW9+e7H1700pFpK6682D1/CiN87Gid6ZGly56zqaTE3zJssF4OvGMW2SpWlhcdq1sa/Jpa5hFmf9kC+AvzWhElj22scL47bycLV0qYfBvohEtmD9ni+rrONckO7/etKJ0IwDthZJVb4M4KamCFh1uUai3cVm7pG5vTKjjymS2arBiO6KvvVKWkHv5W/rAm20R7FSLbKzcKPjzE2VbGDJkdbd1Ku4q/uqSIY++VbAjPjxzLy2ncI5P5/rkWduRnBv1oRWXuOXvJXTji+ZkWv8T+rWyvQvYYud5/tzi+q/olqavLzpN491F5Dhwn8YCen5fXbHmynuePJXqqz67YJR57byKvp/pq87AFq1Hss0uahiKqNGMS2zzizj9/lcngep9yOZ/CaHKCEhz6PGNS3Tldop6dxJV3sKU23T54jWKz+RaNLanam425UlU3W1VKn2/UspOvEN3ktiqePZPWiJlf0tDYQrdvnyRrsVnKjqgsqJcys6TlMWr0ql/WSdv8/vk3OZrYttxV0uQFT6jxwkplPPS9Eoo+2k83U98rtXJLjw6jnrOZzs3VAuHrK3LTaWEh0mUpe5Q8BN+PXnx02bMUEpCIqXr2k9amk2Jd+5QEtsdgW1HQiVs7+2qB4ZncgI9Ts/Xlj8rMzXhISXpBIDLl0ipuVVFu5BSbl+mi9fiSanORZSbnUdVVmfNCr0yXzw2wdFnOxQkJzxmGFfEj+1vXKjPPqSUm5JAiQy/qiKhWRWd13yy1plB+6E0N4USEtN17gsvzU6kO3eSSKkubEeditXVJvfqT6qhLroLf0nc9cr25e1cwc9r1kufryh7Ccmj1DvXKfYp27VGBYDGoEte6h26HvuU7YCkiuT7fUms+EhVGleZnVe0JcZbagI9TMrS8ufli6r+Nqg8/bI7Xe0Ib3slK6RnjxMoRU9jxpu3rFj+qyrrGx+eEspKekiPkrNZl/sNBca3/jZEDw8vm6eKdsHbnuv1DxX1VTfPJYWF5doyPv+rm0L1PDXgyPxVo+oC3TeLgOzRaew8EY39i9dB/n9XsH+4vkU5b5YvrdLYJ9j9ny9G8eiF6FevKt/otSgIDwQEBAQEBP6rCMgeLEZQ41/wwa3TmFSvwgxO2QMsDmqMXz64hdOT6r2l0ymrH7432gYJ7Uj1C/CvpvgPtQtdsHKTfoTwN0GA8m7hp81HIQ1fhW+HvKUdbw5rQ2f0nLfkb4K6UA0BAQGBfyICBjXc0apzK9TTta7GoAbcW3VGK52Rf1+03mgbJLQj/3uK9A+1C12CEka+daEiPBMQEBAQEBAQEBAQEBAQEBAQEPgLEKjwrewvKEEgKSAgICAgICAgICAgICAgICAgICCgQEDofAuKICAgICAg8DdCQBp7CAdvVX1jyL9R1d9cVdg2rYcO3nqJ7TffHGtCSQICAgJvPwL/jM63PAPHFozDopPP336JVBeHsgQcnBuB9ZeERri6IH376eTh7HcTseBIKs/+6S9Zi5fUo/zkJMWexy9ZCpB3Ft9NXIAjqRU2i64iIXnGKSyLWI7T3J7Kf7dQcAObxr6HNi3bYMCa23prJ8+6zA63eh8BIUMwNCQQYZO24kaedvKi699jxuo/tCM0nhQns33o507B+P/8H5btvV65TJmeHPhyItZc1OVv5Mg4tgDjFp1kJciQcHAuItZfqnCtmUaDkWq9zEdy0vPXsw22t/TlDRPwfkAIhgwNQWDYJGzVBLngAU6wcwciPhmHSbNW4pc4vr24pUg+uxlzIj7BhBlLEBlTGW8MuwNfYuKai7o7/ZrtnIbdyhIOYm7EenBNgTzjGBaMW4STb9BO1D6B8XdqWQSWn66sntUq9CoTkz3ah9njVuEPXSrMS0Vbd9V15s33qpEFuLFpLN5r0xJtBrzM4Tva5fH7Te16aVPgdOov9L16dBoa+q2LJ8Uzlka/T9Kb641F/DM638xVZTy4jYcZL21Vb0wQ1V4QOyDi/I51OHy37JTJai9DIPiWIVCI2KjN2Hf1eZVOzqwS8y+hR8XnZyIweA4u6z6Zgb+4wlhEbd6Hq89fbfMlyvkT+9cdQEwOfzH/i7HP98xCxD4JAj+aiBFBdXRXgTVS+8eHY8ZlD4wZ4I9mvQfA7vg41gGPwguNHJL4HRjb92MsPx6v8bT8pezRDxjYuiu+OJ3PTioFri7oitYjIvGE772G6cmF3Ztw7J5uf1Oc8QC3H2awgtjhYed3YN3huxWu2Vlc6jTl+ameu2KcnxmI4DmXq3Swle4yWWdk/3iEz7gMjzED4N+sNwbYHcc41gGP4kAuvoEloa0waONDmIvFsEjYipEB72L2+Xwd5ORI2jUUAe8vxXWTOnCTnsHnHdtiwpFMnpcDht2F3dh07B47+1RX0GjnNOxWlnYeO9YdhqIpKM7Ag9sP8aaawnI+gXLw5/51OPCWGinlJ+H2jYeVv2jqgF5Td8vVWUfa1370fA9mReyDJPAjTBwR9FrkKvObmvXSV1BlNPTlq9pz3ToNDf3WS6cSn6Q33xuK+GfsdmLohkEbTmOQFqgyFOVLYGzBnXFVIciK8CJXBksbC3ZW2ssFKTvd0thYi6IWEVlBNnJhBZsaekqQFqFAzk6S4w634oJcgvwiI1jU0EFbLoecnV9e2dtUpWUqS9L6y59PhoJsNsJjZQN9VdEiyBrhghd5MLS0hplW9V+FnrIEfj7Z6aBSYyYbFTdS5GfnQGpuA2tTXcjx6Afjv+hFdGALVAAAQABJREFULmSWNrCoyL+sAEo42KmLqqIq/X0dXoqRlyuFueIM4UoLKkvA9FTKwFDDwcmkinKU5mcjR2oOG2tTtc7JM54gucCsjH7pFa9MivOQy+i8LOuyohfIlVnCRgt8Viiz3fwSESy0FQu6+NZkuKLtFuflMv2w0q3XfHJmcS/yDGFpbfYSOqALfxmep2VA6vcBPhrWFxV3tFPzXnwRh38Femz4Gn2fDcFh+VAsezcXU88U4hk7DNDaIBtXNk3DuJn7kGJqpj7WXJ1ffVGMcyvm4qjb/+HCsRloyJRDPrYJBv5rJhafCcO3QabqlHovZPl4USiCtaXKcRnCbdAGnFY4YH0DIJppNCjzYcwl42QtMdbtEzXIsFog40kydKgnePWzHI1iXFSCjK/7PsOQw3IMXfYucqeeQSEDWRLPdpm6GYglsd9jsBPnTz5E4wEN8PGqo5je5gN2kqJGkN3Blm8OwmHSBez6nDtpVIJ/SxoibM0hfNV1OOw0kuq+lCH/RSFE1pZQoQzNdq74sc5shm6DsEEpCI14XXqnEc35On3tpGYyHdf6fAL3ElaUXwKRhS77qIwfHQUpHklRVCCHWVmDCUl+EYx0te/SfGTnMJ9pYw2V6zduNAF7zlSkXQkvpT60TL+5kWDdfrAi5arc69JN2fM0ZEj98MFHw9BXr0OoCvUKabT8pm6bfFXfW6G0qt1WQaerREjLJ2nkqszHaCStzktdPY7qpP920GKfH1aF1kPY2ifM5tl1dx/0/nQmunnaoyYzPmtxe0yPSlGOOEjuYueEYLg7usDD3QVOvuFYdDYLcuk1zG3vieF7C0rrJEf6lr7w7LYC91jjJr36FYJaDMb4vt6wNbdH8Ip7zL3oDtKEnzCtkwccnN3h4VYLDcLm4tfS82JlCavQvUFfTJ/eCfXta8Layhltpu3HsRV90NCxJuvs2MH7g9W4yY7Jlj9ei55+fTCFpXV3soWtrQeCJ+/FIx3tm7JMT9g7iCF2sIdn509x6DHjMO8wxjXyxaj92WpmZXeWoYt3H2xJkYOPV1ZrJPw0DZ08HODs7gG3Wg0QNvfXSo6+LcLNDUPQzNURdX082ZHrXuj82RGwoljgofd8L0b4NMfU37hj5JWh+NJsBPgMwa4MPj6luPpVEFoMHo++3rYwtw/GijvpiF7YG00ZD2JvL7jZOrLjhZfhAveFuDL9YI3k3Z0TEOzuCBcPd7g4+SJ80VlkcfxLE/DTtE7wcHCGu4cbajUIw9xf03hGslgeeeZr8FKEuK0forWbLZxdXSFuPQXH0vmGJzker+KroBYYPL4vvG3NYR/M6S8P7ixLWWDHnEcvRO+mrnAUe8OLlevIjm1exoCTxS1B6KhIPEvehn4ePbA+iU8mjGJRHLZ+2Jph7wxXVzFaTzmGyljn+JDc3YkJwe5wdPGAu4sTfMMX4awCfBZJWTj3dTd4OTBbsKqJOsGf4ajCrvTzzdHUZbv5t3/AaH8X2Dq7wsWlKQaODkOD1rMVn+555Vx0ExuGNIOrY134eLIj4r0647Mjpb6FK0xnKMWf+SMHNmLqYO+Jzp8ewmOZDHFLuqDt3MsovPAFWtR5H2sS9cjX0B721i8QG3OffefjggFs35uJjfN6KY86l8fj11/zEbrpHLb3d1G/MGmzU4D791Pg1iYIXqVvZYY2zdGsfhLO/35fO3m5J3I8/WkMmrnVRl0XB4jbTUDkQ26Mlk2XWBWKemFry6Uuf1MhTaktedozOmIH2Ht2xqeHHit8qsJH+vTGpzO7wZP5SBtra4jbT0eU0omUJ6u443AMxajIZ0je1g8ePdaDqWepb9PhE3VQUD4yhL29NV7ExuB+qY81sH0PMzfOQy92ZLxx0/HYdmAeQh1UzaoJzM2NIS0u0fYBRn74LPoJjk/mOt5cKGSdwSKYWmp0ppWFav2VP/0JY5q5oXZdF6Yv7TAh8qFyJJzzXap2TiuX8gGHXWi9MKxVfMbQp3csbaV+UE8BGo91+QTukMqsc1+jm5cDa6+sULNOMD47qvKRpfy8THui4LMB+k6fjk71WXtubQXnNtOw/9gK9GnoqGjf7bw/wGquwWRBnhmNhb2bMvsUw9vLDbaODdFz2QU2CMb04fLnaOHUFzsUTTwPNlo+9Bv8+p1SvyvW+dutH6OR7yiUNa8y3FnWBd59tpS2eQq2tP7oa685+l3azsXlwgv4okUdvL8mUSsvqwli5gXBvc8PpW2xBL9NbQo3/5mlU2rkSFzbA169NiGZcyd6/WZ5m3w136uDPfZIGjMPQe598ENpv0fy21Q0dfPHzNI5P/LEtejh1QubnjysVKd1l6B6qs8ncUy8QnutIlsdv9VzVs9bTkXjVCXlsb8mJPIaSJtjMtkRowm0lx05atZsNl1nJ0vGLmhD1h6D6Md47uz3TIqe3pxqNphK53Mv0LRyRzqzI3RXdiSLFsrjwbmjcf1EZtRoxEY6fuoI/fFYzxl60lhaHGhDHv23UCx3DHFeLK0Lr00uvbZTCjsBTXFqmomIvIZspzt5Uko7OJLqs+Ps6/ZcTX8+L6HcmK8pyFpMY44WKdOKjEkcvpZusCPwCuO2UD93Wwpefp+kmsfZsqNmlwaxI7V7rKZr7Jg1WU4MrQmvSzbtl7JjxvMoaqSY7Htuo2cKMZZQzKxmZBeygZKLK+E1djEF2nhQ/y2xpKzKOgqv7UK9tqeUnfRWUTWyd1AvO1/65JTibHTKufIlBToH0Pwb7PQ+XnrPKbJ/Laoz+kjp6aJF9PtEL3Lq9SM948WUOwbYj0RmjWjExuN06sgfdD86gnytW9Ps0uORC++uphAHG+q1M7f0WGh9+sGiYxdQG2sPGvRjPDvoXEaZ0dOpec0GNPV8AcUuDiQbj/60RSlYil0XTrVdetF2TrB6guTc6/ESYO3JeLnPeCmk+zuHkjfTlRbcEeR6yiNOL/xEZNZoBG08foqO/PGYJHy4a+oRd9yzrzW1nn2RuMOOqfAurQ5xIJteOxVHFRf+NIScxWOIqSYHFI+ec3YWQNaezM7uMzsrvE87h3qTSNSC5sTq5VxBc0Eba/IY9CMpzTOapjevSQ2mnqeC+EUUwGyhfp8NdD1LRpLEAzTS24xafHWL2QI/31q2++BPmuNvQz7Dd1E8q0vRw300ppFZ6ZHsjHceOWfv6EV2vp+QUr1z6MqXgeQcMJ+YeusN0rilFGQjph6rr7FTEGWUE7OGwuvaUPult5kcq3hUPUv3aAeTPzuq3svTjd4ZsoaibmXq0IOyY7F1M1TE/IEb2XRbT09Vapt1gIa6MR/08UndWbinCj0RkanfaNqfwI6Zzb9F68PFZNtpJT2QSil+UQBZtFvGJWT22IAswrZWuNZMI6W4pUFkI+5Bq6+xUyNlORSzJpzq2rSnpbcZIpysOR85cDPFZJZQUcJeGuFlRs1mX+c5DbCQfhriTOIxR9mh8yzw+kQuge4gfbSDhnpbka2XJ7m9M4TWRN2iTD0qm39tPvOPbjQgMk2/P2Qcx+6aRWO6NyIX77606TbfueFK7ESmfjR6fwJDMp9urQ8nsW0nWvmAMaHZzmnYreax7WWncrKTlPn0TkFLvx/UjY720/I+IZ4WBYjIuH4f2nA9i2SSRDow0pvMSo9A5/f/2rQVT9R8DqHtd/JImnaQRtZnx6rX7Umr/3xOJbkx9HWQtULunO6di/Al69azSen6C+nu6hBysOlFnOsvhxMfNlo+9CHdVes3c2eafjAvikaK7annNmXrSiUxNKuZHYVsSNavE5XopvT+AmptEUTfPFYZqDY2kt8nkpdjX9qdzeJKLtGMRuZkUsOf5nL+VZZC67vaUdtF90nC5zeZ91DbLfPnr+R7tVlTPpH8ThO9HKmvkkG6NKMRmZvUIP+5XNslo5T1Xcmu7SK6L+F0xoLaLXtU6mMsKGxrfvlrvWVwfTb9PonPj+sjWZ3PVa/o1dGP/x+iYQy/flMwuLEdjE3rIiQsADZpT/FEkoCon2PgNWwG+nqwz+eGdgicsQunfvgIDcq+zeutJxk3w+AZw9Ap+D20qqN7woHszj7svlIfvT8MgmVmIhIzLfHu4K4wP74bR7JKSRv5ojcbmfRhn9SdOgSgocgVIeNGoOn/s3cdcFEdW/8Pu0vvvYgFERQ12OCpiIixY4mJPRq70WeML9bwTNTYokYTTSyxBktiQSWaWGJJwa4xlgQrKiBdEaSXXc43d5ddtt4FNO8zyR1/uPdOOXPOf86cOXfuzFxHMWyaRKCtdy4eP66cV5e0xIRF49DcxhQWjYfjw9F+uBgTi0S1aXdZ0mHEXvbFmAXj0cKWLU2xDcL4hWPhdykGB+9b4NW3XofTT3txkNvsVnYFu/an4tU3+8PtLh+v7Al+/x782mAg3g63QXZyMrJtumBET0sc33MUSlF0wJKwNw2WqTi59lNsPnwFeU3mIi7jDKKamxih54heI3oDR/bgJLeJrOhnfHOwiMX1gYNRTAniViMQNaorOvdoC99W0xF7IQYzQ2xRUZSF5CcSONiVIic7v3KGyoB+SNlMwOHvcK3RKEQNbggLNn/oFBaF3ae2YZJ/Avbv+RUNBr6NcJtsJCdnw6bLCPS0PI49qobVQQPioOfj5ar/SMaLH+PFAn6D5mNiOzOeJQWV9ZMYrUZEYVTXzujR1hv3qtuO4iBMj72AmJkhsK0oQlbyE0gc7FCak418bgZFLfDq+ROun12F/0jWz/xYP7Pww6D5E9HOzESNgu6lLPEwvrvWCKOiBkPRPcMQtfsUtk1qopg9FAdi2KxReMXBFGY+PfBaqCPSk5LZ0hrjfKv33TYFBxEb3wKT5g9EQ7bKwrzBa1gwpRNDmAW2XICvnSVsFtYy9STWfroZh6/kocncOGSciUJzg/ZDhqTDsbjsOwYLxreALVs6Zhs0HgvH+uFSzEHcV+vHuoiox4hQf2g0rt6IxewwZzw5swJvtm6CVxefh549l+oFta7N8erbbyPg7Cz0GDQbSz+Zh7F95+GsKQPC2HJ8E0tETF2IfvXYQgirphj93xHwPh+Lo5WzW1oVGb6VJeFw7GX4jlmA8S1sYWpqi6DxCzHW7xJiDt5XlGNtPWTGCAQ5iWFeLxL9Qh2QmfJIMQNsmLIqhd8mGgZdVH8ooq/eQOzsMDg/OYMVb7ZGk1cX47wWyAXX1mJYv+XIG7kJn73hxvOmoYwtkTKDV5MWqFcYh83b48Be5PEEE1hGTMXCfvXYchMrNB39X4zwPo/Yo5k8ZfQlVUfvDNlBffSqG8doDpuFUa84wNTMBz1eC4VjehKSmW2t1Xgir1aExgPZm80AtkTULQKhTSXwipyMMS0dIbZpgghm43Ifc/sNxAiaHosLMTMRYluBoqxkPJE4wK40B9kaBqwa2GjY0LqGl5ZZv4q3XnfCT3sPQjG87sL+1FfxZn/Db59qq5vqLWAW0huv2pzHiXMlkD34EaeLemNw2wTExWVBlnsCR3/1R68+DRR8G7KbagRrbXvVaGhcmoWg96s2OH/iHEpkD/Dj6SL0HtwWCXFxyJLl4sTRX+Hfqw8a6HejNEjx3hiySWk3ee04L80XlGhwSHhB9F9SMiawd2Sdv5I7E4kYogoZKtgC/czHJuwVvndVZ7L3Q5tglrEsy7gspi7w8OTXFml6GrLK72Dz8DDsVKfo3AIFOczoc/6HqQNbHlLJnYjxxr4KZWNd6Zhw67pNqWocZAbMt76yThF86ngAWWlIUxs/ZJkZeGLiibo+Vc0tqusDT5NsZGXJYN5+OAb6dMWe2FQMaboLsbk9sKy3M6QX+XgtRXpaFsrvbMbwMA1J4NyiAJwozkq21OW0YrT3Lcf8xZswf8gSTIQXgvtPw4pVb6PYCD2/ziPwuvVg7D6ai3CzXfhe1A/butlCeoaPTwUQpi4eUDaNqSgX1zbNxLB953D3KTPUgQEwzyO4kdK7MKAf7KV3Jlt/a8KWInirZLOHH6cgpSeRllWOO5uHQxMOZ7Qo4B5FnNVRUF0/Ny9uaryYesDHW2NlqaoezQtTuHh4Vuq41Gg7qsqaipB7bRNmDtuHc3efQuIViADzPJCbmj5WZubV8ydp8n7mptbPTD18YIx1WSZb62jClqlUgQ97vzbguif7arG83zg5q3o1RCJT1qeloOrwrdZ35f1F4goPZR9klsLeyxN2HGlpOm87W41chn3L52PxpvkYsmQi4BWM/tNWYNW7oXBSslaJkeKH6VTGE5h41kVV9xSxvuoJk+wsNhABrEdXO1jVD8fg8AAcCfsSn4omoN2URdg19juM99BbuV665sFzcOREXXy24Siu3HDHv97fjrYbwrHWxchKZBM7eNexV9lVU2+2bI84G8PrTerywOxwxhMTeDIbpbJYIoaPpwmysyrtsIk9HNlDliKYQCIWsbaufj3GbCL8VR1clz+r+ggfHI6AI2H48lMRJrSbgkW7xuLweK6lZHj0/WwMGr0dovF7cWxRV8iXf+tSqYyxQvDwOXIdntHrP2jVcx62juqM2QGG6jeBHVvWY68Una2J9WT9T4GLrcFadBOM6x03GOkdJ3WJ1SDGFA5OziodMRGJYMrGXvY+w6gd0jueyGtmNNnyJAUkbPmPyARWNtaVkxDceGkKhWk3hSj3GjbNHIZ95+7iKZvUCgwwRx65VaYrxagONuo2VFlO36852g8fCJ+uexCbOgRNd8Uit8cysOHVYDCqm9UxCBbt0bszYeapK0hN/gWJwSOwslkS+v58Gpkux3ChXk8s8GM6xm0NYP6GXrupxmGtba8aDc1LC7Tv3Rk08xSupCbjl8RgjFjZDEl9f8bpTBccu1APPRf4sTGKY/A5giGbxPlhtRivn4MTnaIq26aT8k+MELnAxYkZMuasymCvcE5y4/DlqnsImtyUdWI29paXMWQ4B4eQl1dQ5QTL8TKBSaWPbAg+ETs+wNGsDaae/gnT6lda0IJk3EqRoK4v6wwPuZKMjiEC2vHSXDzNZYOOFUeLGY2sbJBTO7ip2W6RK1tfR5lI4zxyB0WCLCUVGeSIti7sXszWtA4OwLrYAzgUfxjSPuvAfFrw82qGu66OMGszFad/mqbaCFaQfAspkrrgRNEbitjAKu6EuQcn4ouyTNw4tZ0d7xSF99Z3xgZj9ETtMXygOyL37cM+kx/g8Ma3CGPTkaZGMP2DY4Q1jAJTGe6unYjxMS5Yuuc6Rob4wEZ2AbNavIorehlWjxTBhTkglM1kYFDay2XMRdyXq3CvaQRcHM3QZupp/DStfuVAUIDkWymQ1PVVJ6J2/Xy8uLm5gOKYM6jkpeIZsrNL1OgbvjRRKaqInWjB145y9OSEZHfXYuL4GLgs3YPrI0PgYyPDhVkt8Ko6cJWKy6s7DQuZI0eIYwZQ2c8qnmXDGOsiFxc4MWcuswp85MZ9iVX3gvBOR4Ws+vpNtfjm+lxlYTFzbtzK/sAjrh65Ilcg51EKnlWwEVPkDFeedi7KvAVxp7k4OPELlGXewKnt8zA56j2s73YecwL1dQoRXNkadcpUPDAruqcMKakZIMe24LpndUJF6h68M/x7tNgajWHyAiLU6dQB/tIdSOWmUqvtfFfg6Y2jOF4QhvmbRypsYMlJ/PsdczQd0YifFSpA9hNO/+TvCFCRms4elthDjHulneMvXZUqcmV9iZCZxumHg4IHWQpSMwiObV2q8tXiSqkfRm2iPtoVqdjzznB832IrohUgQ1SnEzr4S7EjlZtZdcFD5oT3+Pc5tFh2ClsnNDe8kbjsV2yasRMmY1dgXJBiGLZs0ABeJieQk8v0TiG1Hi4IBdlPoEKZ8ZTOJoxcPdxZXuV+JD3FdKJejN7pkNUXoQRdmaZ9L483ZoeUhfX/Kvuu/tTKWNldrJ04HjEuS7Hn+kiE+NhAdmEWWmgYMC5v9bCpsqF6alWTUdzyTTYrvw6xBw6x06ik6LOuGztmwXColW7qkLNCeGQ48hYfwvakG2jWJQJBza/A6asfsMbsNDx7zURjNe9PjV0dSlxEbW2vXmKVkVbhkQjPW4xD25Nwo1kXRAQ1xxWnr/DDGjOc9uyFmeoM8hHiSzNkk9ieFOKx43wkX1RaDa3ii6r2JaUj8kWvyGa4tfMzHEnlDGA+rqybizm74yG18WKbAwnxZ36RvxasyGbn4O7/HeU1FEXctA/6+F/Fho9icJ+zoLIMHJnVk83+rsS1mhLj6i67gG1rzsqPR6rIPInPd8YjsFekYpNVJW+i+pHoE3QX0Uv2IIHbd1J6H3uXRON2096I5J5+mbEJGDoMLa+swpxDErw2vKP88YKfVzGa9ukD/6sb8FHMfflgIMs4glk92UzfymsGcZGl78Lkzv3wATuLWmbhjlc6hqKRvRhmZhbVoCdGizcHw/fn+Zh30geDhrcGt9Ofn89KEFQ/7AElJQ3SOiHozjneogpkHP8KhxLYyR9l3IMVXxDBl2Hb7NZOfHYklTkGTEOurMPcObsRj1bo28cfV9kZvzGKhkXGkVnoGdwfKw027PPx0qDvGwhO2I5lBxLZJju2AfjnFdh0pkTrgZBPHi6t+u0oy0xBmrQOQrpzjjebacw4jq8OJbATZMrkm/xMzMwgLsxCSloupI159LyiAfq+EYyE7ctwIJFhXpGFn1dswpkS5ZsH/TyLfHshstkt7PzsCBTd8wrWzZ2D3fFSxbOn/mLsVCp+vrWLiQIGYGjITXwZtQVXsouQc2MHZq/6Ra7jTNnQx2A7lyB912R07vcBO69cBgv3V9AxtBHsxWYwUxvoNOtjy0Ui+yDobjSW7Elg23m57rkXS6Jvo2nvSMi7p2YBvXemzm4QJe/HF5/9iCccjGzQubozFjfcgtGuocHKFbQqcvH7kW/Y0jTFxjfptS14Z/Q8HJC/PsvFxRWLECMegNE9bPTWrYqkQvy4cTUucsfuldzGjmU7kdZxAHrV2Pmuj8g+QbgbvQR7FAYL9/cuQfTtpugd6aeqrmYXJszGiFGYxXQhlxleozZRD3W2qdVNlIz9X7ANdgqQUXB1J2JvuCG4XUNI41dh1L8Pw3vaeswLN0PKnTu4w/7upubKbUVF7u848g1bYsQtwxHZoeD3aMxfuAMJ3DhQlozvlzF98+6C7s05q2YoEAp/3IjVCpBxe8cy7EzriAG9OOe7JuE59Y6dd37j8Dc4dOWx7mZSNTbUbUIJZzANhurbIYMkjCWwNyopaVLUCekud7xFFRk4/tUhJEil0DT9z4eNjsyiAAwd1hJXVs3BIclrGN6R/+2k8fHamKCKdJuISIQmb8HquHoI7+QK8xad0K4kBl8cskf33sqNvtWjVVvby0vdJgKRocnYsjoO9cI7wdW8BTq1K0HMF4dg3703mhkxW7y0lYmGbJJ3cx47XhtHTFlh9X9fhHjVr+2lzynGKzO24rOHIzEu0AMSWxmKrTpidvRchFnawX/6RHw9aiga1XWHjYkzuvXsAJfrNRTKLBhRO1YhbfRUtPScDkfzfBQ4d8eSbezkDrasktc+6atK5Ab8NArNA8xgnvMY5p0X45vprzCX6mJVbnEzTNu6Fokjp6OV50z21F0EUdNhWL99FionXSCqNxBvtp+LScnDMexflceJGeEVwVHYsSoNo6e2hOd0R5jnF8C5+xJsmxeKSgpVPFReiRpOwCcLz2D48AB20oUbJOwoN4eIxdg60R9mtsbpiQKGYkizxXhfOg1vBlaqrxE+1ZBgXJghZPQUdI75AB2CvmPr6nPwxLojwjrY4lTCfeZE1tfhWT1C/MoMbP3sIUaOC4SHxBayYit0nB2NuWHsyMjWO7AqbTSmtvTEdEdz5Bc4o/uSbZjHNaze8Hy8iBq/g81r7mLYlCB4vceONbRph46tnZAuMjaPocmMGW87VqFnFjIaUzrH4IMOQfiOra/MeWKNjmEdYHsqAfeZD123RTd0dxqFCQFhuH/2Kj4wqOfsge+dzVhzdximBHnhPXY+pU27jmjtlM6WWGnypnEnfgUztn6GhyPHIdBDAltZMaw6zkb03DCYZ1/QyKp+Y4xvL/XM3DXbc/Hu1s1Im8BOUKjzLsg7jPWJVyD53RxmpuztRpShdrZi67U/wcIzwzE8gJ2k4CZhR0A6IGLxVkzkWcYgbjYNW9cmYuT0VvCcyebEikRoyj6esX1WEOvH1bQIFuGYu3EG4ke+gZY7TFAhegUnLYLwbvQCdOUf69nDzyMcmDsG29odRa/gCLix5WArfxqMSU19MNO6FKVuvfDRN0vRhc/3Zq8FxexhurX3rxgZWAcFJfmgwHHY/PVY1GNTPPe1Mea9F6PZtK1YmzgS01t5QgFJU/aBoe2YxRmsmhGrrEmCFt26w2nUBASE3cfZq0uM2kRdFi0QPncjZsSPxBstd8CkQoRXTlog6N1oLOgqxqVZm3AuNxvSuV3RdG5VabNX1yCRnbnu+ugA5o7ZhnZHeyE4wh+TN6zDneHT0KrOXNhRLkp9+mHprsWIMNhebAmF2ALurb3x68hA1CkoQT4FYtzmrzGWA7maqqLkjF/vjIBckYR9c8Zib9dT6NHaVT4JoqSr/itRtwlx++Conqh1zW+HtDLX5patMx49pTNiPuiAoO8CYJ3zBNYdw9DB9hQSOAOmpt/82FTZRH1saMjM7OCSYDHqDXwT7edOQvLwYVAOr/rKyuOMjNfVbmaHrojsIEPs7Q6I4BZPm4Ui4l+m2HGzB3orB36DTGgl1NL2alHRunVA18gOkMXeRocIbv25GUIj/gXTHTfRozdn+54z8NokEeoZtOOGxuvn5Ee7+Ivcvfl3olWem0aJSVlUqL2hWJpLybdvUyo7XYRkUiqXameoLgpSys9IpAepOWwPdu1C1c71Asq4fZ3iU9jJALykWJ3piZScVah7CoIsg7b2daf2H9/STWMxvLxK8ykj8QGl5tRAktIcSn3wkNJy9RwDURt6crmN8KmOTXE63bx8ka4mZMvxl5XkU25BqRH81AiU51JaYhJl6SgIO3QgP4MSH6RSteF4EbwkZ1C+gVMX1Ljmv6wW7sWUfvMyXbyaQNlcc8tKKD+3gEpVildOxcXqOPK3CdfPkjPy9egcH6vllJuWSElMj1XV8mWXpxnjW41A+W06/s0xusmdFCAPUrq9pC3ZdFpF6gcM8LVzaU4qPXiYRvrUW0lV55fhn56YzHTqORqyPIduLutGXZfcoCc16I46vLCIkqcplFTjtmH6n5dOSWm5PCeP6KtNf5w0P50Sk5kdfg5INCiXs3OBqpSVJfHYRI2C6jfllHNzGXXryk6xeV6QGUq5KQl0L/lpzcYBaR6lJ9VQv9RFUL+upd4VH5tMry/hOVlJVYe2TVAl6L+olh3SX7Q6scXpN+nyxauUoDBgVJKfSwVMJ7iTpwLs36Bv2GEaqlBLbNgRIxp2UJaxlfq6t6ePb9VEkWujmyrO/6SL2tjeP4mVGpDls0l8drwGVdQ4qwlXQtshF+7/GgjI7n+C8KDv8cYfP+E95frxGrMuw8OfduFE3AF8sqEC//31AEZ7CauRagyjUODvgwBbyvV+q+441n41Vk9uA6tHh7Fo8lLkvHMWP80MZDM0L3eQ3jmBUxSO7o35li+83DK89NxJ7+DEKUJ498YGZ31fehmeh8GKTBz48BOUjV+GIfVf9h5hTFD2HYBfD2LXxsX4b1wnHLuxAu1fVNeRPcRPu04g7sAn2FDxX/x6YDSE4dVYe/wz0p97Zv+fAdPLKaWJlS/admuL+gZfU1aHb7aB549vsfWYFP3Xfo63BMtQHdCEPH9nBMza4oNda1Hx0Sb8Z+gnbAOzH9pPi8WX77z8jjfXLOKAruj+d26fl0E2cQC6/pNBNnXH64tXvAwt8QJ4YB9iuXIYJx83x8xPp+JfL8rx5jhjey/++HYrjkn7Y+3nbwmO9wtorb8LCWHm++/SkoIcAgICAgICAgICAgICAgICAi89AsL6gpe+iQQGBQQEBAQEBAQEBAQEBAQEBP4uCAjO99+lJQU5BAQEBAQEBAQEBAQEBAQEBF56BATn+2VpoorH+GHpZCw/+fSFclRw5gtMXXpU/mlbDcLPVV8h0lKfys93rXh8Cp9OX4WfnrKza587FOHGlono0T4Y7Ycufw48qvh7bpaei8D/jo+iG1swsUd7BLdnR0jelGpxXYT7J9h57NPfweT35mLN97fYCfZqgTvned8nmD1lCmYs3o6LmeqHWfGXlaadwdaF0/HOu1FYsfea/Lx5NcrCpYCAgICAgICAgICAgBYCgvOtBcj/320ZHt+/iQeP2XmjLzAUsy9Wbt1/BU91zrSpbX1lOPdBGDovvCz/kA7l/YYDG2JxLe8FMP00BnOn70dp2CRMHR1cSzw0+XsBXNWSxP+Sj6eImTsd+0vDMGnqGITXVd9HXYYbK3qj7fDNeGDpAx/rRGwfG4ou886hUC5ZES4t6onOM0+i1N0NsrML0b3jOzjMfRWRnXrOV7YidTdGhvbFyutmqOstxekPX0WHd48i+0U8h9USdaGYgICAgICAgICAwMuOgPoo/bLz+mL4Y1+zkorFage4y1CUy+YBbR3AvvWhE2Qlz5Avs4GDtXYif7ky9vEYqaWtXppcJbKSQpSKrWGlbAFTbwzf9BOG63BgIEJWBAXb7OMqerIo6rfWk1IZZbA+GUoKSyG2tlLDSJ1MBR4/SkOR4gvS6gmcUCgsl8DaQg9HRvjlCMmeZuKxNBBvTBqFwdzRid0idPHQaj9Z0TMUmNrAXlUnD38a3FagooJ9tKI2j5/SQuTmSWHpYA9zPeWljEex2BAf/HoDMP2UMv1U6oUGzxxIetpd9hSZj6UIfGMSRg1Wftq+smDpT2yX/e8IWxGPr0a4yT97/3bQMDT591ocm90ebxTux9LPM/D6zhNYxX3BUPo6XDq0x+ItU9HjvSSesv9Ck+jPcNDlPVzY/aH8a2Sl/ypF037rcWhBT4x20mZcuBcQEBAQEBAQEBAQEOAQ0OM6/A2BkV7BgvA2GDFlMPwdLeHceTXuyqRI/HYWujZ0gbtvQ3h7NEG/RT+C+/IvF0rv7MK7nX3h6tkQvp5uaNx/Oc7kcImV5fyc4eLjAxdnP3R7/xCSKt/Ul9zchvEhnnB094KnZ0u8Ob4fmrSbh0tlMiSu7YOAge/jg15+cLZzgL29DzrNPox0jqwsEWt710e/L+/jwof/gqebG9zU/7y7Y9VdVok0Ed/O6oqGLu7wbegNjyb9sOhHxWehOb5Lbm3H2+3Y1/VY/V4+7TDjhyz9n/9V1fdIUXefAAx8/wP0YnLZMafS3qcTZh9O1yorwy02izpu7xOk7RiChq9tRCrHO+Xg7Me90MjFEY62dqjbeQ6OKYE0wi/HMxdkt1age4dFuFx8AfPb1EXftWcr8WD86Wm/369twlutvOBaLwB+Hm5o1G0OjqaX6+dPUYXif46f2V3RyInx6uaLzu9F4a02ffG5HNurWNTJD6P3FVWWYJ9sjx4Mv16cvrAPAWbHYdnAlvBy9YF/I4axa1O8/ukF+RIO6ZUFCG8zAlMG+8PR0gn/6tcZYzVw4tM3Ka4sCEebEVMw2N8Rls6dsZqrUD1U4ujn7AIfHxc4+3XD+4eSIJPdworuHbDocjEuzG+Dun3XI7lSh+XFxS0xZUcsFvd2UXV2M0tLiNkn4ctZvtIrcbiEtugSVvl5N3EjdI3wwR+nzyCXt6wIgXPi8Oj4NNVngItz81BibgObF3lUlzoGwrWAgICAgICAgIDA3wGBGn+W569YoPQCzQqUkEWzMbT5+Ck6ej6JSuM/oTCHhjQ0Op4KmEwF8Ruofx1PGrAznX24Mp6WtrenhsO/poRi9hG/7Dia3dqOmsw8R0W3VlK4gw+9tu4q5clklHdtPfWv50CdVt4kafl1WhjiQAGjd1NCCftC3IP9NKGZBUkCZtDZUiklLA8lM0kjenPrNcouL6HEfWOokUUrmnedfeVRmkDLQ62p46cPSVZaTIUFBVRQUEgF6SdoVmtnajzuW0pjX9SM/ySMHBoOpeh4OdcUv6E/1fEcQDvT2bf+OL5D7cmP8X2P8V18bxeN9JeQpM1Citf+sJZafYq6zUjS6E3aei2byksSad+YRmTRah5xrGmGYvr2LXfymXCMmIis6HIKlYipwaBNdD2HfSUsOZbG+ltQmwV/sG/HGeFXkzBJ7y2ldtbh9Bn3GUF1/nTa73faOcCJGr9zinI4Gnm/0kdh7hTKvurHpNbgT7MKKd1Z3ZkcfYdQ9K1ikhXepK+G+JJE0oYWcgBx9TSxpn7blZ84k1HamlfJWo5fKZ2d3pjs282ji3kc1WK6sy6SXBwG0K58rugsCpRYULMxm+n4qaN0PqlAgw8pn76xb9tdmBVIEotmNGbzcTp19DwlabSXlG6tDCcHn9do3VX2FVNZHl1b35/qOXSilTdZRuk9WtrOmsI/SzL+xcfCq7QkzIG8h+2lTAZz7tbeZO0/jc6ovoYoo+RVnci69Uf0u3bba5VVYlsev5vmTuhDzTz9afCWm3K9UKYJvwICAgICAgICAgICApoI/DNmvrmnJBKj1YgojOraGT3aeuPe/j34tcFAvB1ug+zkZGTbdMGInpY4vuconiQexnfXGmFU1GA0ZMsrTJ3CELX7FLZN8kfq4Vhc9h2DBeNbwJatWbANGo+FY/1wKeYg7vx+ALHxLTBp/kA0NAfMG7yGBVM6QX2FhjhwCGaMCIKT2Bz1Ivsh1CETKY80N8iZmlnAytoa1uIk7Bg/CtudP8TeL/rBk25j/55f0WDg2wi3yUZycjZsuoxAT8vj2HM0h02eM76v+mMk49uPVWrhNwjzJ7aDmUl1HhPFCBwyAyOCnCA2r4fIfqFwyEyBFmv6CYkDMWzWKLziYAoznx54LdQR6UnJkMr4+dVPzECsRvv5wtHeEqkn1+LTzYdxJa8J5sZl4ExUcwOFK6NlSTgUcxG+I+fgzcYWMLVqghFLJqNttQASI2h6LC7EzESIbQWKspLxROIAu9IcZOcrpppJ3Aojokaha+ceaFtXfemNDLd59C1Hzh5B3GoEokZ1RecebaFZPAmHYy/Dd8wCjG9hy5bK2CJo/EKM9buEmIP3+WVWTy24hrXD+mF53khs+uwNuLHeL5OyGXaxRGOJkUTCeC8vl6/pVxXXU1aZVlZYDjOvJmhRrxBxm7cjTr5eXJkq/AoICAgICAgICAgICKgjYGhlqXqev8m1KVw8PCvXR0uRnpaF8jubMTxsp4Z8zi0K8CSNrT02cYeXd5UDZe/XBsFsA9r5jCcw8awLHxVyItT18YRJdhYy0ouYQ+YKDxflM40p7L08Yae8ZTWZ2DuC+aiKYMKcHlEFZOrLBJTcVGTg0Ltv4L/JA7Dr5BQ05zz40nSkZZXjzubh0GTbGS0KmPOdyfHtpsa3KTx8vFG9D2CawN7RQbU0wUQihqhCprXsRMmc1q+pA5ycVUJBJDJFBVvWQ1J+fgFnLUJ8t+rtZ4Uey/Zh+fzF2DR/CJZMBLyC+2PailX4TyiPtLIMZDwGPOr4qJxNkacv6ttW5+nEFKLca9g0cxj2nbuLpxIvBAaYI4/cQMrNrKYu8PCs0pkqafj1LadyhYmpiwf0FpdlIuOJCTzrVvENEdNBTxNkZ2WxajyqqjJwJXv0PWYPGo3tovHYe2wRunKeNwsWdraQFBegSCkD2BdP89myG2sbKGExVFZZlVXwcMwJZnczeuE/rXpi3lb2ADI7QJks/AoICAgICAgICAgICKghoPSY1KL+vpcmJkonSwRnV0eYtYnC6aRHePRI8Xfr7HEc2TgGjdxd4ETZyMyo9IoYJLlxX2L+lkswY2uFKTMNaaokGVJSM0CObO24Tx24laXhkapcBXIepeCZPueaF+YCXPp4EMYe9seyvcvR3bWymUTOcHU0Q5uo00iq5PnRo1s4e/wINo7xhYitEXehLPZgoWSuAs+ys1HCW1ftEpVIKktr38vjjfCrLFvd36r2K2JtI0anuQdxJeUxUn/bjSkecYh6b72KlH5+3OHmTHiSkQ4lQijKQU5xpedpaiLfgCktV544Q8jLK2DuKAuyu1g7cTxizMdi9/UM5GbexenlPaFsGkXFJlCpWCUnCj749c1X6a+zwvr5doWLIyEzLa2Kb1kKUjMIji4ulTUZ/pE+3IUx7A3JwbqLceqHJejqUdXtzRo3RoPsBNzNVSope7i7lwybRo3hxfgyXLYMv256F//ZfJ3tgqgMlg3QwMsEz3JylTHCr4CAgICAgICAgICAgBYCVaOwVsLf+1aMpn36wP8qO/s45r7cOZVlHMGsnsHov/IaKnx7IbLZLez87AhSOS8t/wrWzZ2D3fGExqxc0N1oLNmTgFKWVHp/L5ZE30bT3pEICByAoSE38WXUFlzJLkLOjR2YveqXGjq/UjzYPhaDPi3DxG+2YnRDU5SVlaGMnaAhM22KPn38cXXDR4i5z7nUMmQcmYWebNZ35bVyiBr0xRvBCdi+7AASmf9YkfUzVmw6gxLVrOaLaFUTmJmJUZiVgrTckipnUB9pMT+/+opUK06Wjl2TO6PfB+z8cpkF3F/piNBG9hCbcTv9ePgT1Ufvvi1wZ8enOCxv2Ge4tHYr4lTOtwc83AjxZ34Bt3KiIvsMvtr/u2L5BZt9TkmTok5Id4T42LC3Ahk4/tUhJLB2Yc2jJ6jzIZXrjSF9K9dTWiOK8R3ZJwh3o5dgT4Jc63B/7xJE326K3pF+Gll1bqTxWDXq3zjsPQ3r54XDLOUO7txhf3dTkct0W9z0dfTzv4SNnyhkLry2Fqu+E6P3oE6w4i0rgl3B74ievxA7EjhdLEPy98uw5Yo3unRny3+4s8OPfIPYy1WbgXV4EyIEBAQEBAQEBAQE/oEIqBZP/NNkNwuOwo5VaRg9tSU8pzvCPL8Azt2XYNu8UJgzVGZs/QwPR45DoIcEtrJiWHWcjei5YbB2CMHWtYkYOb0VPGfaAkUiNB22HttnBbElJMC7WzcjbcJsdK3zLsg7DMP+9Qokv5vDrLqPObL72Pl5LJKfAcsiXLCootJzNrHAa9uzERu1A6vSRmNqS09MdzRHfoEzui/ZhnmhbJE5GuOdzWtwd9gUBHm9x445tEG7jq3hlC7SP6Naq0aXoEW37nAaNQEBYfcRt8+Rh4oZgnn55SnKlyRqiAmfLMSZ4cMR4O0IN0kB8h0isHgrW38CTf7OXl2CYJWWsxM6/rMVax+8iXHNfdnbD4LMzbtq9tq0DoZMn4ivRw1Fo7rusDFxRreeHeBynZE1C8HoKZ0R80EHBH0XAOucJ7DuGIYOtqeQcJ9535WHhVSxrc3HB4b1jTmu/EGMZtMY34kjMb2VJxRq1xTD1m/HrCAmnGoaX5dK2aWvsOlcLrKlc9G16dyqDGavYk3icUz2DML09R/h9zdfh98Otg6+WIRW/2EPlz3tUXZhIX/ZyRuw7s5wTGtVB3PtCLmlPui3dBcWR7ClP9L7ODB3DLa1O4pewe7gtFMIAgICAgICAgICAgICbJqQ23/5jwZCVoBMtnRBZu8NLwftM9KkeJaeimdiV9RxtVKth5bjxcplpORA5MqOu1MeEC69gxMxiajTqzua2HO5ZLjzcQe0OT4E8aemom51HfBqNIisgM3EPpbB3tsLOmyzhQDP0tNRYusFdxvlmoZqEK1JFmkJSirMYFHNpwp+fmtSsXreMuSmsRlcEyfU8WQz3+pJRvgrzX6AxEJnNJTsRI9G0eh06QI+CKzESvYMjxIyIPJuBC8rgpRM2IOVovFKMm7hj+RSOPoFoqGTGKXcee5mtrA2hIM2H7z6pi6A/mtZQQZSckRw9XI1eIa8/pJGYitKkJ2SiXLHOvCwrZnOSJ+lIom9KnBia9LZqighCAgICAgICAgICAgI8CAgON884NQ4qewC3m/VHcfar8bqyW1g9egwFk1eipx3zuKnmYF6P4ZT4zqEAi8UgYr0tejWSMv5fqE1CMQEBAQEBAQEBAQEBAQEBKoQ+Gs73yXFQEpylTQvwVVRwlFs2XIQlx7mgux88ErnIRgzsDVcajaZ+BJI8g9hIe8M1i++jIbvTUU3tY2I/xDpBTEFBAQEBAQEBAQEBARqikCduuy4MMuallLl/0s733TvNkzmzVAJI1wICAgICAgICAgICAgICAgICAj8mQjQRytgwk4Fq23QWCZbWyL/H+UqKtj52OykCQmrvOjNcZB6ev9/sCHUKSAgICAgICAgICAgICAgIPAPQEDM9gFafb0ZUuZ/ipgfaso+tlib8Jd0vrk9opzg+Xl58s+0JMrYh0GkxM5Y1ntKcm1wEcoICAgICAgICAgICAgICAgICAjIEeB8Txvmbwayuzzmf9pyE8ASSa18z7+s882dff3s2TO5852SkiJ3vsXiv6Q4gloLCAgICAgICAgICAgICAgIvMQIcJO+Nhmpcueb8z/NmR/K+Z21mfj9y3qrHAglJYpvN3LCOzs7w9LSslYgvMRtLbAmICAgICAgICAgICAgICAg8P+IADfrXVxcDNmTTDkXnP/J+aG1DX9Z51v9eHIHBwc41asHOzs7wfmurSYI5QQEBAQEBAQEBAQEBAQEBAR0EOB8Tm6pydPMNFWauh+qiqzmxV/W+VaXz9zcXO54c853bRe/q9MTrgUEBAQEBAQEBAQEBAQEBAQEBDgEuEM+uFDI/M0XEf4Wzje33ob74xxvwfl+EWoh0BAQEBAQEBAQEBAQEBAQEBBQIqD0NZX3z/NbuzNSnqdGoayAgICAgICAgICAgICAgICAgMA/FAHB+f6HNrwgtoCAgICAgICAgICAgICAgMD/HgHB+f7fYy7UKCAgICAgICAgICAgICAgIPAPReAf4nxX4OmPn2Ha4oNIkv35LV1w5gtMXXoUGYr1+ZoVFpzBF1OX4iiXWPEYPyydjOUnnyryFKYh9SlXqAKPf1iKyctPapYV7lQIFKalQg6VKkb/RcXjU/h0+ir8pJP55cRYJZe2bugX70+ILURa6lOmgf/78Nyy/79h9udhJUs8iEXTN+JSmZE6ZImI/Wgq1l80llGXTrXr0C0qjym6sQUTe7RHcPthWH/lN2yZ2APtg9tj6PKNWDp5OU7q9D0DhP7G0RVph7Fk8mQsO5rx/9K3/kxodfRHNY79mbW+5LSNYWAs3aB4L+e4ZZBdrYSKxz9o2oRa46BFuAa3qnGmBmX+jKz/EOeb8OxqLL7cdQGZ/wPnuzj+MLbuv4KnpKfJiuNxeOt+XJEnluHx/Zt48JgNmGXn8EFYZyy8XC4vVPb4Pm4+eKyHgBBVdu4DhHVeiEqoeAGhvN9wYEMsruXpZnvZMNaUS003dFn/k2LKcO6DMHReeBkKLfyTqtFD9sXI/v+BmR5hXmCULPMcvtlwBHeMHScry8SFPVvww11jGXWZq3YdukVZzFPEzJ2O/aVhmDR1DIJ+XYDp+0sRNmkqRgeb4v7NB+DM2z87yHB3x8f4LGYvli7ahrv/gzHof4m3hv5ojWP/Sz5emrqMYWAs3YggL9u4ZYRdzeSyx1U24Tlx0CRcvTvNcaZ6Zf6sXP8Q55sHPlkJCouqO2BJUZj7FM9K9c8LlhXko0imPw1lBcgvkmnOeph6Y/imn/DlUA/5LPijtCIo/HVTeA/fhJ++HKrJuKwIublFMGy7ZSgpLEJ1pdEkXr07WVEucpkc1Qv8eIFxqnlGvQxFubkMQ37qFY8fIa1I98lGWpiLp89KNTE2SMoAxiy/rKQQ+lWiDAX5fPhrVSYtRO7TZ9CvLqytnuWiUE1WDbnUdUONrFH8Deozw5Z9katErT41spWXbFblURp0oa1eu6joGdXTCnZskyq3/KI6snP6UlKk7slVoFRd3w1gplmT+t2Llkudtva1tq5rp7Pnb7n90I3XjTHCt6wQzwrUcVKjwKuTavn0XOrVPdlTZD6WIrDPJIwaHAG3Z48hDeyDSaMGo1vEOGz66UsM9VAfZnT1XlWVUb1R5WQXRvSZKZiWilUV5sXgRfFXVR2k1/D1rpsImT4D7W5uw1a9rzGMyKNGjrusma1j+TUNLYOPZyxhac+elfCMM1rMqN+yt09V45h6Qi2v+fjUQ1KvjurJpy9KeZScbhp/f5OVPEOupiHnx8AQRtWStTbjFo9O6wqrEaOjNxqp3A0/NtrZTb2HV9kEQzhoF2J1aI+Vqix8mOnRY41xpopINcZGVeYXd8EOCf/LBZlMRk+fPqX4gweIhkbKf7l7Ll5/kNKDFWFk2fR9ulhKJH24hnr7D6DZc3pSQ3sJicVWVCd8Fn2fZqC87An9snQAtfB0IEdXJ7K2dKLA/ivpfJ6ituKb22hCWy+ysrIhe88Qeqt/S7Jqs5DipSy9+CZtm9CWvKysyMbek0Le6k8trdrQQi5R+pDWRNajvmtP0SevupCFSESWDt7Ub0MS3V8TSfX6rldUUP6QYmd2Ybxako2NJdk37EqzDyYSR15Oo7c/DZg9h3o2tCeJWExWdcJp1vdpZEAaBU32f/nVRdSxwUCKzlDkLPlpBrXwCqY55xhILMiS1lM/vzdoc6qMyh/G0swuvuRg40hODi7UuO9COlVZTp5Z/T9evMrp1486Uuvh79CgRjYktougVXdK6GHsTOri60A2jk7k4NKY+i48RfrIS29+Qq+6WJBIZEkO3v1oQ0o5PfllKQ1o4UkOjq7kZG1JToH9aWVl40gTllOodUf69CGTUZZOh99rT406TKcj6WX0UIWxlF33Jv8Bs2lOz4ZkLxGT2KoOhc/6nhQqUUzx0eMo2MOKrGzsyLXFMBrXtzG1nXuRFEipC8+qefILLR3QgjwdHMnVyZosnQKp/8rzpFCXErr9zRSKaOBIDs6OZOsUQK8tO02P/9CSK+m+QjfWJ8uJK/BnvFnakI2lPTXsOpsOJso1gKkAnz4X042NI6ilpyO5uLuQvZMfdf3vkUq51PmW0s1PXiUXCxGJLB3Iu98GSpGVK9qF6ZWljQ1Z2jekrrMPUmW16oUV13x6Kkuk9f2b0MDps6hLA2eys3Mg34j3KOZBKem0qbrsXB/p3ZgGzWLl6tsx/TYjt3Yzaf+xVTQw0InMmL7bNnqd1t4oqepPDLPS8x9QiIcrubqq/3lRt8/uMF4r5aqGvskF45Or/DdaGN6QRsUUVuIho8yvBlHDnqvojlSfrivarDKz/Kc4PprGBXvI7YedawsaNq4vNW47V26rSi/MoibW/Wi7nDxPe5ReoFlNLKn1629SS3cHsrexpTphU2jP/XJ5HXw6qVmHOmeKa4O6J73J7JY72UpMyczWidyaBJCLjYRMzWzJ2bsPrTnzOUXW60vrkzn7ol/vn3JJlfj6OtiQo5MDuTTuSwtPZRiwX4b1WZa4nvo3GUjTZ3WhBs52ZOfgSxHvxRBTMXngw+DF8aeoS/3/kl/+Q/6OvZkdzaDt/Z3JZ+z3lK/KYFgeVRbVhYzX1qmyVV6U//oRdWw9nN4Z1IhsxHYUseoOSfmwLr5BG0e0JE9HF3J3sScnv6703yPcOFJOvy0Mp4ajYkil5Zlf0aCGPZntlpJKf/I4fVAfx1I027D8Ki3q2IAGRle2bclPNKOFFwXPOaewo7IkWt/Pj97YnEoyPj61BWX3hscnKd3b8Do18h9IWx5wfU9GKbtGUlP/N2hTQhklru9PTQZOp1ldGpCznR05+EbQezEPKu06v50ouf0NTYloQI4OzuTI9D/gtWV0+vEf/BjI+4wWRpWy6h3fdWTlxiqlb2Bs3DLU5/jbU6/e6PBhGBvpvQ30eiN/GrjlgdxHkaXsopFN/emNTQlUysYquU14aAQneX2G+GeJfJgZ0OMyHd+hsJpjo0L4mvudOqBpREDj7i9yU3MQtJxvziEzk1CjN7fStf8P1LUAAEAASURBVOxyKkncR2MaWVCredeZmdENpWenU2P7djTvosJ9Kr6zjiJdHGjALmZCpfG0NNSe/IZ/TfeKma99jymav4QkcudbSvFLQ8nebzh9rUikXSP9SSJROt8JtDzUmjp++pAV/JbecvehCceYE8FUNmF5KFl3/FR+fWtlODn4vEbrruaxB4w8usYMRj2HTrTyJjMmUo6GGUkavUlbr2VTeUki7RvTiCxazaPr+oRRF6/0F5rayJUG78llseV0KaoZWZpZUciieMaBjNI39iSnDsvpXmk8fRLmQA2HRlN8ActaEE8b+tchzwE7KZ0bPLUCL17MrF2YFUgSi2Y0ZvNxOnX0PD248QmFOTSkodHxpCC/gfrX8aQBO9M1jXdlPcXfvkXuPhNIDlXpWZre2J7azbuocG6L79C6SBdyGLBLPsCpnO8HaXT0vdbk3nQs7X3IAaOJMYe3maQRvbn1GmWXl1DivjHUyKIVzWMgll9fSCEOATR6dwJzIUrowf4J1MxCQgEzzupxvkvp7PTGZN9uHinUpZjurIskF4cBpFCXpdSeObHDv06gYiZddtxsam3XhGayBx4NueTtWqkb0lu0MtyBfF5bR1fzZCTLu8Yc2Xrk0GklKVSAR59zv6EBTo3pnVM5cvTy2IAc5h5KS27oU45i+vYtd/KZcIxJyRC6tZLCHXzotXVXKY892OZdY85NPQfqtPKm3KhWNkflj5SM6yl70PXpT1/eyCdZ8S2KHuJLjp1X0T2mxoZlV+r3W7TzdgFJMw/S2Aasf9V7ndb99pTK86/Rx+H2cp4VfaESMxnDs7CACgoKqLAgnU7Mak3OjcfRt+xpShpfE30zIpfc6bWmfgrvmGEho7Q1r5K1vP/r6nqStu9dfp0WhjhQwOjdlMBAL3mwnyY0syBJwAw6y5xGlWPDvB7e9pDzISHzwPF0IJEVLPyDNvb3Iceua+i+lF8n1evQbFN2Z0T3SHqPlrazpvDPkpjkzNFZ2o6swz+jJGYXVH3vIYe5Ib0vovhPwsih4VCKVhgXit/Qn+p4DqCd+owLjz7L62MPzj79v6Qb+TIqvhVNQ3wdqfOqe4wzfgxeGH86AObT92N9yHXA1/SEpeXse5M8nPvTNuXMAo88OqSM2Drt/Fy7BkosqNmYzXT81FE6n1TKi3XuNwPIqfE7pDAVeWySJIzcQ5fQjXJOj5uQdb/tVc532hp61Voxjmnoj8Y4psMR/TK1EbkO3kPyEedSFDWzNCOrkEXyiSpZ+kbq6dSBlt/j51ObKjcG845P5TdpdRcX8ui3he7f20x9PT2o98YEphOKMUAi9qH+X96gfFkx3YoeQr6OnWkVM0q8doIb99uzSRA27iewcV+WHUezW9tRk5nsQYIXA8a9RroR+6IrrIZvwDduGdbpfKPtqak32kaL9W1eG1pON1d3IRePfrTl/j3a3NeTPHpvpAS5u6I2GaaBg46gvDaDb6wxrMda40xN+h5jr+Z+p65M6jHq7wNf3HT6X4GSOBBDZoxAkJMY5vUi0S/UAZkpj/Qu2RAHTUfshRjMDLFFRVEWkp9I4GBXipzsfJQnHsZ3V/0xMmow/CwAC79BmD+xHcxMGAiyRBz+7ir8R0ZhsCIRg+ZPRDt5YjVBkiXhcOxl+I5ZgPEtbNlHhGwRNH4hxvpdQszB+5VExAgcMgMjgpwgNq+HyH6hcMhMwSNj60/MQtD7VRucP3GOLUd4gB9PF6H34LZIiItDliwXJ47+Cv9efVD33n7s+bUBBr4dDpvsZCRn26DLiJ6wPL4HR3N05eDDS/EqmCBuNQJRo7qic49gFH27B782GIi3w22QnZyMbJsuGNHTEsf3HIUe8poVioMwPfYCYmaGwLaiCFnJTyBxsENpTjbyle+dKRdnPuyNobvrYdmRDRhYX/+3pcSBQzBjRBCcxOaoF9kPoQ6ZSHlUgpsHYhHfYhLmD2wIc/avwWsLMKUTa2y9QYyg6bG4EDMTIbYVKMpKxhOJA+xKc5CdX47Ew9/hWqNRiBrcEBYwhVNYFHaf2oZJTfTzxFUhSzqM2Mu+GLNgPFrYsg9J2QZh/MKx8LsUg4P3K9eRGNJniT3sLVNxcu2n2Hz4CvKazEVcxhlENTdcn0IsGZIOx+Ky7xgsGN8CtuwDVrZB47FwrB8uxRyEsloVBNXSUwlaTliEcc1tYGrRGMM/HA2/izGITeRdC8OqEKHxwCkYHGANkVsEQptK4BU5GWNaOkJs0wQRbb2R+1hrf4SpGSysrGFtLUbSjvEYtd0ZH+79Av08Cbf310DfqiWXCgU9F+q63hZ1RZpZpDcPIDa+BSbNH4iG7MNp5g1ew4IpnZhuaIdqtIeJJSKmLkS/emaAVVOM/u8IeJ+PxdFMUx6dVHYS7foU99XSPf1F1WJlhvXePwH79/yKBgPfRrhNNpKTs2HTZQR6Wh7HHn3GxZg+S1piwqJxaG5jCovGw/HhaD9cjIlFoqyW/bKm/KlJLb/MOYwd38vQY1gknFmEQ4830cfqBL765qFiWYcxedTpVcfWqedn1yRuhRFRo9C1cw+09b7Hi7XE3h6WqSex9tPNOHwlD03mxiHjTBSMmgqtOg3fmiGk96uwOX8C59j6twc/nkZR78FomxCHuCwZck8cxa/+vdCnLj+f2vRlt42MT+ImmLzhYwRfikK3HrNxo/NqbBjbkFkVRZC0nIBF45rDxtQCjYd/iNF+FxETe5/XTjzhxv1rjTCKjfsNWWc1dQpD1O5T2DapCYxZVg3+n9u+APrHrVLDfY5nrFHypqE32kaLaS6/DRWjyeQN+Dj4EqK69cDsG52xesNYNFQCrqyE95fPZqTy+kTV1uOa9D1eXmuXWCM9qV0VL2kpE3s4OiifPUwgEYtQYWC9tqkoF9c2zcSwfedw9ykb+AMDYJ5HcCOCLDMTj03c4OWt1CxTePh4w4oTm22CynxsAjcvb1VHN/Xwgbc8sZq4MBoZT0zgWdenqlOL6sLH0wTZWVmMiCf7M4G9owNz5RTBRCKGqEJrfXllmuaPBdr37gyaeQpXUpPxS2IwRqxshqS+P+N0pguOXaiHngv8QOlpyCq/g83Dw7BTnYBzCxTkMMfJWSm7IpEPL2VxUxcPeMqLSZGeloXyO5sxPEyDOpxbFEAPeSUJZWXIvbYJM4ftw7m7TyHxCkSAeR7IjSrXz7Ns5X/gxNXW8Cm+hB/OZ2FEXU8VVurETOwdUaUSEohFFZAxHDMzmEPv6gEXJcCm9vDytNNLg5lhiHKvYdPMYdh37i6eSrwQGGCOPHJjb5kYrczHMHH3gkpdYA+/NsFyNkrUmVG7lmVm4ImJJ+r6VHVXEdMHT5NsZLFBS6ECBvTZKhLL9i3H/MWbMH/IEkyEF4L7T8OKVe8i1EkpkFplqkuF3CaeTNdU1YoYD54wyc5iD2eAv3qzV0tPzeDjW1/VF0Q+deCBLKSlGXO+TeHg7FKJt4i1iwmsbKyZ1nNB8VVb1hX1hApkHHoXb/w3GQN2ncSU5pxLW1ozfTMql0RPvZpRVbquGc/dydtW4gqPKuWCvZcn7HSaxnh7wMQO3nXsVXpp6u3JbBSnI+zxxdyQTurypB5jVPcaquc2dM2j96UnkZZVjjubh0Oz+zujRQH36M25rGrBqodBfW7LZTPzgW99pWKK4FOH7afJSkOajK2VrU2/rCl/aqxyp1alH9iJY+zBsGxkXRwcxSUSyosLIY2OxrUpC9GaRx6d/mkqMm7rNOpnN6Yu8FAYWrbwO50Xa6uRy7Bv+Xws3jQfQ5ZMBLyC0X/aCqx6V2GftEnX5t6ifW90ppk4dSUVyb8kInjESjRL6oufT2fC5dgF1Ou5AH7Ez6e2TkirMT6JfIdhav8l6L7RDO9G94WXWv8y8/FFlcr4QKEyj5BeYnhcepLGjfvuauM+YO/XBnKkDBlyfYAZtS/++kppxBkctwyONWXM6hoJ6nqjk7U6Y7Yvhk3tjyXdN8Ls3Wj0VQdch56+CB6bUXae1yey6mFIj0MVfpmyupr0PWWZF/irpoIvkOrfihTbqb52IsbHmGPs7uvIyM3E3dPL0dNVAZ3IzQ0ulMUGdKUDUYFn2dmQ9z+RG9xcCFnMOKhSn2Uj22DnVLgTGvCJXOHiSMhMq6IBWQrY8kE4urhoZK3NjVV4JMLzfsah7Sdxo1kEIoI6I8zpMn5YcwinPXuhb2PmyDu7wtGsDaJOJ+HRo0eKv1tncfzIRozxVQ50ytr58VLmgolJpfMkgrOrI8zaROF0UiVtVsets8dxZOMY6JBXEqiESnZ3LSaOj4H52N24npGLzLunsbynq8oBkWeXtMHsfT9j9/v1cXTGNOxN45/tU1ah+BUzh8YNZWmPkKFqxBw8Snmmf0OX7C7WThyPGPOx2H09A7mZd3F6eU8o1EUEFxcnUDZ7oFLSQi7ivpyPLedzFPT0qoALHClTw0mVpaQigxwZPW38NblHEatL3AlzD15ByuNU/LZ7CjziovDe+ttaGatuFSyI4OriCMrkHBdlmgwpqRkgRxfoVFstPZWyDai5KtxkmVnIJie4uVXKoEd2Zc1MXWocCi59jEFjD8N/2V4s767UiRrqmzG5TE3Y2yjm15QrNzgS8vIKqh78OK5Vuq4rgti7DtzK0vBIpRAVyHmUgmc6KlqN9qACZD+pMi4VqenMQeAc+wQendTlST1G5PocuqcixKP3lyTMvpmhTdRpJClty6NbOHv8CDaO8VVRUF0Y02cp23SdqwSPDeBZ2SAnZodRy35ZU/5UjLILNqu59+tf4DhmL27fuY3bt7m/O7h3YiYC7+zElp+LUJP+WS1bp16//JrZWWXfETnDlQfrIvaQL+40FwevpOBx6m/YPcUDcVHvYf1tgolCyaHS8rw8FOh94OUqVVaowwx7IxOOyPA8/HxoO07eaIaIiCB0DnPC5R/W4NBpT/Tq25g9XPPzqU21OuNT0cVleH+3FXp2B75672NcZNArg5QdolClMpnIyiY4uXnwjkuN3F3gxB5sM1X9FsiN+xLzt5xHjlz9eDCQV1yZbsy+KJms8S9Pnzv/DGS0PdX0RqfuatjQootY9v5uWCkAx8fqgGvQM4QTD/+XzODE4xMZ1uPKgUxZpTFbosHni78RnG+jmDIDnpIGaZ0QdA/xgQ2bDc04/hUOJbDTC8rKIGrQF28EJ2D7sgNIZJapIutnrNh0BiWcYRI1QN83gpGwfRkOKBLx84pNOCNP1KrYxAxm4kJksbpy1Y+kENVHZJ8g3I1egj0JpaxQKe7vXYLo203RO9JPi4jubUXODRz+5hCuPFYOSFp5bCIQGZqMLavjUC+8E1zNW6BTuxLEfHEI9t17oxmb9RQ37YM+/lex4aMY3OfGdlkGjszqyWZQV+Kazpl0/Hhp1c5uxWjapw/8r27ARzH35Q8tsowjmNWTzbqsvKb3yDsTMzOIC7OQksZOC0lLQZq0DkK6h8DHhr29yDiOrw4lsN39ZaqBAiYWsLK0QPP/fIHpdY5gxrS9qL7/LULAgKEIufklorZcQXZRDm7smI1Vv1Q5ORoysZmMlDQp6oR0R4iPDXsDkYHjXx1CAjttoKxMBN9ekWh2ayc+O5IqfyDLv7IOc+fsRrzUCiI1uTRVIBJ9gu4ieskeKFTgPvYuicbtpr0R6cfvfMvSd2Fy5374gJ0vLLNwxysdQ9HIXgwzM9V0thr7JvL4wiyGaW45fCL7IOhuNJbsSWBaxzTv/l4sib6Npr0joVNttfS0DBe2rcFZ7tznikyc/Hwn4gN7IZK9j1RvU3XZ1Zir0aX0wXaMHfQpyiZ+g62jG8KU9dWyMtZn2QxojfTNmFymHvBgb1niz/wCrotVZJ/BV/t/16u3+gQQBQzA0JCb+DJqC65kFyHnxg7MXvWL4uFdo4AI9Y21BxXix42rcfEZK1hyGzuW7URaxwHo5fKYRyc1KtG5EdWvve5VEePRe7RC3z7+uLrhI8QojAsyjsxCz+D+WKlrXGBUn8suYNuas/JvAFRknsTnO+MRyPpcQ9SyXxrjryIXvx/5hi0Ly1Q9VCrllt7+Gl9f8ET/kb1Q19MTnpV/3h3GYFibTOzb/B0e16B/ythSQqO2Tlm5vl9xU/QxiHUJ0ndNRud+H7BvUMhg4f4KOoY2gr2YG5fY21wP9uYu/gx+USg5zny1H7/r2H5WqaFxTMWPDSIiQ5G8ZTXi6oWjk6s5WnRqh5KYL3DIvjt6KwYcHj51KzU6PuXHYd6Ez1E29kvs2rkObxWuwtsfnUZ+JU9lF7ZhzVnu2wYVyDz5OXbGB6JXZACvnajwZXar2S3s/OwIUjmfLv8K1s2dg93xUliJDIzlSgzUMSr3ea7xXUlS95enz0lt4FXd9tQlzGKMjdn5iJs3AZ+XjWXHO+/EurcKsertj3BaCbiSpjoOOkafh39qzPTDkE/UgEePOfVU8x1SajI2Kpl+gb/qC8D/Ktc1X/iuZ8Ol8gQMudAldGScN3mOPsQ2wemGkuurqZe3LdusF0odQwIpMGIijYtwpgaTTsrzl977mt4Odic7Z2/ycAug/gMUG1W4zXBUeo++fjuY3O2cyZuN0gH9Byg2vKk2Syo3iKXRXrZR0spUQs2iztNt1YZLRqPkFu2YEEIeto7k5cVOyfDpQP/eeUu+KU59k5ly72PJkXHk7TmaDjFhyq9+SEEWATSD271lIDzZ1p8cJE1o1gVFnqc7+rMTPzTLFP6+hca0YScbsNNY6rrbsRNFBtLqyg2o2mT58dLdvMN2h9HvW8ZQG3dbdoJJXYYVO01m4OrKDYva1NnijbS9bIOsFZlKmlHUmSu0upc32bo3pdCOIRQYGEETx0WQc4NJdJLJr77pi6NUfGkutbHxpsHfJNNdFcZVmy/Z3jBFKDlC47w9aTQHItuM+mD/dOri50gW5mzjaZd/07A2ltR8zmU9G3RL6PrqXuRt605NQztSSGAgRUwcRxHODWgSxxDTmD+2jqHWbnbk4u3NTndoRP2WnSVuO6SGXOdvV23GZWklt3bQhBAPsnX0Ii9Htvmyw79p5y1uW6SujNzJDVX6nE+XVvUnfwc7cm/gR3Vc3anZoNV0qfKkHjkB1X9ss+BettHUypQkzaLoEtt4emvHBHZqiC05enmxXf0+1OHfO6myWlUp1YVRPbWk+m3akq+XHzVmJ544Nx5Ia37jttjyyK628VTRNIW0vZ81NZl1QXUiAbdR2Oa1HRwQlZjdo98/ak0SExHb3GxKbKKDexRmfyZkweWrob7x9j+2zTDj+2kU7MJOIfKuT951WtPo8d3IM4Q77UifrsvF1fivnG2ynN7FjxwtzNnGwy7072FtyLL5HLrM9sRqbGZj7WqwPcov0X9b1qGIQb0pwMubvJ3syKvDNIqV7/Dk10nNOjRYk9/w6V51N1zy6T0V/k5bxrQhd1t2yk5dZkfZ6UADV1duoNZhx7A+y/u6ZX1q09aXvPwasxNPnKnxwDWkUDF+DGrNX/kNmt/anBq886PCHqv4LafLH7Yi+xZzWD9SRVZeyChpPdvM7tybNqY8q37/LLnOa+u0a9HbrnxY51+iVf39ycHOnRr41SFX92Y0aPUl+UZ2Wcb3NC3YhZ145E31vetQ69HjqRs72Ys7tUujHpn6OHZJj31kXD7ZRv0dJFV9+OkO6s9OHdPYwM7Hp7ag7N7w+PSUjk0OINvmMymu8oiZ3JPvUmObJjTlxBP55kXL+m2ora8X+TVmJ544N6aBa36Tb/w3ZieK/9hKY1q7kZ0L62/slK5G/ZbRWYUhVxvL9WCgjRGf3dSRtWqsYgZPtflS/7jFM9ZUtz116ldGGB6znx6bTAG2zWlmFeD0bmMbajLlBD3hDrtQ+l7aOChJq34N889rk/n0WN13uJRT/b7HeKq536kSRO/FP+S0E72y1yyyOJ1uXr5IVxOy5YO+rCSfcgtK2dCrDOWUm5ZMGfm6O4O5HOW5aZSckc+6C38oLy6m0iqiGpml+emUmJxFhcaIaJQqZsbndVoiP/dQI6EWN1LKz0ikB6k5ek750CJnFC+t/NytNJ8yEh9Qao7hB4WqUuVUXKzEv5jSb16mi1cTKJsrKiuh/NwCgzhW0ajeVfnt4/TNsZvyHfryEtLbtKStDXVaxZ3woD8Up9+kyxevUoKCISrJz6UC9YYtz6W0xCTKKtSmoC6XNm2Gf3oiJWcVGtUj7ZJUmkOpDx4Sm9HWSdKJKC+mYnVeWbukJyYzXquneHr1VOUYP6SCjNt0PT6FnaCiXTOf7Np5X8B9jfSNU0+e/ifNpeTbtymVnbJBMimVS3WE089w+W06/s0xuskd/yAPUrq9pC3ZdFolPzFEGavxa6Q9pHnplJSWq+P4GNVJjUq0b55D99RJGdR7Dt8MSnyQStXq/nr0uepBu4Aybl+n+BR2OpR63ezaKAYvij+teo3e6pFHf5kXY+v4sC7NSaUHD/+PveuAj6rY+v9kN71tCglJCCWFhICG/oAQAqFLE6QTuiA+QIQIiPKBGECkaPSBSEfk0QJGRIoID6UJKNIMPRAC6aSQXnZzvrm72Zrdu5sQRPQOv7D3zp05c+Z/zpw59965c1KoqqmQUm7STbqZzHYrYv+k5dyeWPoT3zymv4b+XD4+q9aoxvwkr6zhvBak0c0r8fSoqlEyMi9x834iPWA2WRcLYxjoXue1L1U7a3qOQZ02XZ4GG6umDdVHRxeHKmUM8s9vkw3rsc48Y+LYq23n24zraC0+SP9TSHGb4T9hAUNST/2E4F2bcH34RHiGdYYT+1rbnFvLJCQVAhXp3+D/VpRh0sfD1R+VqK4KB6YgUHbuXbTseQQdPvsMU1vb4uHBxZi6LAfTzpzA7GD+ZR+m0P9HlJElYEV4CL5/7Q+cmNlQe03+PwIAA51kyyTebdkTRzp8hs+mtobtw4NYPHUZcqadwYnZwaqPUw3UFrI1EJAlrEB4yPd47Y8TmNlQmAc0oBEO9SIgQ8KKcIR8/xr+ODETgsroBUnIrESgtv1OfQs/BbD/RgiYewzCkpV/ow49h65YtpuPnWsqsGjD2xixgn2M498Bs+K+xDTB8TZdGma28G3XA+0a2ppe559Q0rId5u9cg4pFG/D2iBXs40B/dJgVhy+nCY53dcVvZuuLdj3aQVCx6iL3Ty3Pdk3ybYce7Rpq74LxT4VD6PefioDw5PtPhVtoTEBAQEBAQEBAQEBAQEBAQOBFQqC2n3wL7+ZeJOkLvAoICAgICAgICAgICAgICAi80AgIzvcLLT6BeQEBAQEBgeogIEX8d/vxh3LD5upUFcoKCAgICAgICNQKAoLzrQ9GWSL2L47C+gumzVCFKcnyfWVRkYkflk3F8mPZ+qjWUl4hUpK5PUmfXSq6uglTenVAmw4jsfbi79g0pRc6tOmAEcvXY9nU5TjG7dMspJohUHAa/5mxjO2l+7QYqvWgIvOHv6xctHTpurRmmD33WhXI/GEZpi4/VvucFKYg2ZTxVE2bVJVRFrzn1w14q38o+owZiz5hAzBz21UUqApWIPfaXqyYOx3T31mCbefTVYHBVEWUByV/IO4/Mfj0009Vf5/vuQhue3FDSZYYh0Uz1uK8PpOqaTc1+6l5rFnGUCNPk19L9DXnguOfRCHmRC3b6qKrtWOPTdW7p8G0mnVV2FWzXs2LF+Hqpino1aENOoxcy0umKOFHFuciCtOmzsSC1d/jhuae1dxe73tXYO706XhnyTacT1dFJWM0i5DwI4thETUNU2cuwOrvb6j2F+calKacxuZoRveteVi557LCj+DlRONiRQZ+XDUT0XH3NcaqDPfjovHWjKU48FCDD9kt7F4YhbVnCzUIvCCHtTQ2/2q9FZxvfRJhgVLO7liHQ7eMOwtlZ+cjLCIav8r3/i9DZsJ13MvUN8Poa6i6eWU4Oz8MEdG/mhzEo7otANmIXRCFfaVheHPGBIT89iGi9pUi7M0ZGN/GHAnX7+GZda/6zL54NYrjcXDzPlzMfppNhnT0oCzzLyoXbV0Kr//ift9dlpmA6/cya1ffys5iflgEohXGg592NWySPkIVmd9g+sB5+NVvMka2bYkhI11wdCpzwA8qXOaiC4vRO2I2jpV6wF12BtE9O2HawUy9N/nSazuw4L1P8N9Dh3H4sOLvh3P3UcxzPylLP4fdm37Abb0mVcNuavZT85iFzHrWtvVp6WvNBZSH379Zh7jLefrEUeO87NgFT2+Pq6N3Nea0ehW1sKte1ZqXzo7Fgqh9KA17EzMmhBukU3Z1Jfq2i8TGezbw8bFD4raJCO22EAo/tggXFvdGxOxjKPVwh+xMNHp2moaD8qB2Zbi6si/aRW7EPRsf+NglYtvEUHRbeBacC1yRvAtjQ/tj1RVL1PeW4tT/dUXHtw4ji2ccaTFp7gTc2YflG46p61Qk48B/VmHj+o+Yo68O/CRL+h5rVh5Hqo2VFokX4+RZj/3ng8KLOxvWFC8WaVAqFrMYTcokQ1Euu411kLDIVMo8/b/SwlzkSW0gcbJSbZVWkfkQKUXWigrm3ojccAKRVarLUFJYCrGdrUa7lYXKCpDPaDoYa1xenD2Be5gCZXPqZkzvg7oOO5IVQdF1Fl1ReUGWjfRMKYJfexPjhvng3vKFkAa/hjfHDZNvxdSjy+vKkpW/rG9P8iGzl8BORUR5SQ99ndpVT1lfnhTA3N4J1rr0uMJsm8kKtp2kvrtGffJR0ZeV4Em+DPYSO3VfKy/KinLZ0wgHSEySgYoii6IphZjpkjLx0qmUs52ycHV+q/CurQfm3pHYcEJX64zphGGdlBU9QYG5PZz0CsAA40Z1yYTtBaWFyM2TwkbiBCt9AuZi0FUw2eu9xqlGhcGtRnVlpVf3DXQNTNu8IzegCsSsvLSkCBXWtrCsrFtRWogSkR1s1WqhuKKvb+yJzsOUIlRaD3Xr+rBUX63RUdn5Q/gfXsWGj4bh8ZhDqBj7Cbrlz8ap4sfsqVkZ9i37HGmDtuPHmF6whxSD3Dqiw5JNmNHrXQTpjMO8a9fwMGgSzhz5PzTVuWYKc7LCJyi2cIK9EjRNu1n2QD8JzTIaJXjHHCsnKylEqViPPDRoyA8N0GcUDNtuHRpac4HWNY5GOSzsrKvYnurpoQzZ6ZlG7DHTSS27pMeeGtI7LZ61T/jGlnZJnTN9eq9ZpHI+NoQdv3zZXC5lc7nuWNOkX3msj44sOx2Z0mC89uY4DGtowKiw2L4n1nyOa2ErEb9lNNy5Ym+EYGSTf2PNkbno0Gkfln2ehkHbf0RML3sG/iC4deyAJZtmoNfMB1jz+TWErYzHltHu8jnrjZCRaPLvNTgy919osvVT7HebiXO7/k8eSbr0X6VoOmAtvvuwN8a76OlElSwrtIvoAOu3f8H5kknoxzaSqsg6jmNXX8bYMfmI/fE4ct8cDY5U3ukzuObZCdFN1WDpw6RKE/oypCUoqmBRo5Xjt6IUhSUi2FU1eijMzYPURgIn/QZdH/WqeTUZm1XmzKpk9eeYrlP661cjt8qG5i9ARrU3Oy//jRZ1akWR04ZSgL2YHLvE0C1pOd2Pm03dfCVkz6JTSdyCqH/0cUrjdsovPUdzmtjRgG2F7ERGj39eRoObe5LEuQ652Nmw6I4DadUveSS9voK6ulmTSGTDIjMOoHUPEmh1nwbUf20S2/39Pq3u25gGz32fevs5kYVYTLb1wmnO9ymKzfiL42nr622orq0t2TvWoeYjX6f+Qe1owXlDAWakdH1FV3KzFpHIhkWCG7COHskq+8Do29jbs+hjftR97n5KNBYLpfw+xc3uRn5ONmRvzyLz+XWnufsTSSq9Tiu6siiWLCqgpYMLuTcJJDd7CzK3dCBX7360+vTn1KdBf1qbxIFUQjd3TKcujVjETVdncnAJpFc/PkXZ3KVK+r4SexbBUUJuQf0p+nhalSAErKRGKqar60dTC09ncvNwIycXf+r+3iFKYfRkiWtpYJMhFDWnG4ta50iOEl/qMjOW7smhMiwfOfGSm7RjehdqxKJCujo7kEvgq/TxqWw5L+X342h2N1+S2DuTi8SNgvpH03G5AmiwpXNY/tsi6tQqkqYNDSB7sSN1iblFJXx0iq/TV5PbkRcnZydPajtmILWwbS2PDEflv1N0uB+Ni+X0jEsySt8ylPx6c/rJTvXynkl/6OjBg4TVGnLh0QkjOll8dT2NbuFJzm4e5ObkQv7d36NDnAD4kkm6xKK79vvCYNAY2eOfadng5uQpcaY6LnZkwyIcDlz1izyyHhM+rR3YhIZEzaFuLCqmo6OEfLvMpFi58GWUuHYgNRkSRXO6seh0jo4k8e1CM2PvyYNA6ZOV1BC/LGzRnXWDKKDxENp0jwNfRo92jqWmjV+jDXdL6f7qPtSg/1qWL2XHfSlo6BzWZkNytBCTpXt7mr3vCMUMCSYXSzGJHQJo0JqrctQM9k0+1tzIWiQiGxY1dsC6RyTjGzcqm5ROB//dlAIn7pNHRZU3Ir1Bq3oE0JAtlbZFnqn9X+nZd1hUv3a0+GoWbR0yiDargvqwciWHWTRXVxq+RxFtlKtZev5daurwCm14rE2HXaGfZwSQ59h9lHL3Ap08+TslcoGFjCR5FESbVjRoVAvykDiRvUM9Cpu+mxK4mE+cXirtpqqfbExoHmuWYVUUY9ePnGzsyd7Gify6z6X9csOnkE/jwXPp/d7sOpOP2LYehc/5Xm5LDLKpSZ875rPdeohUnQtus2irtvTSa5Opp7+ErBkftj5d6L3DlXaQT9Z66HN6x9l/Dwf99riqrl+lS+v12NMyzsbr6J3e9rhMzpawccci+zqyCM2NurxN745uRf0+u8UC7PDbLoN6LyerPR87tO1P7V015tFHskr56rPN5fTbok7UKnIaDQ2wJ7FjF4qRG0v9nTCkJ3J5sYjKFuaW5MAiU/f74oF+Agz39Gs/0anrijlDXqjgAE3wltDgnfls6LBo0q7DST10Sun8u03J4ZUN9FiaTtd+OkXX5ZOignzBgQnkLRlMrCpLJZSdrR5zOXFjybvOCNpTGYlTUYP/f1nql9TT8SV6jwuHy1Je7Aiq22oh/XbibWrsOZq+qWzn8CQf8hq3n8X2VY4dfdjKSej9T3p/NfUNGkpz2Bzc0NGCxJbu1H72PjoSM4SCXSxJLHaggEFr6CoXdFn2mH5eNpiae0rIuY4L2dmwiNUDVxFzmSj/4L+paeBE2sdFApUnKd1Y1YMChmwxPD6rMzb1zplMdrz6appOVdvvVHbRwO8/I8IlZ8SDLci62QTaePQ4Hf7lAZXGr6AwiR+N2BovDyVbEL+OBtbzpMHbU0mmafRLz1BUkBO1X1gZ7rj4Fn3Rx40kg3cSp9fF344hD5/JdIRTOo0ofopjS7IIGEWbL2dReUki7WXh461bLqQr5eV0JbotSQLH0667rGLJPdo3uRkz0Noh3avKrJi+HeNBPpOPyEMZS2+sonAW8vvVLy6xaIEyyrvMnNQGEuq86jozGYYSp+zhLET9q/TFJRYBTpZHl5kD00DSmVbJQ97foWXt7Sj8Uy56I3NIlrUnu/BP5Y6TOoIcuxK/jDowZz/yv3dZwHQZZZ2cS60cm9Dss0UUvyKMhckeQVvjOcNSQPHrBlI9z8G0PVVmiCmi3B002CWIph1XjMo85uSGeYTS0qvlDEoWkpZNXj4Dv6SrbKIvvrGVhvs6U0TMHZLyykdK8cs6sJuLSPrvXRbaXZZFJ+e2Iscms+lsUTytCGOh4kdsJQWb8bRuYD3yHLyd+NjknIhgC2tqNmEjHT1+mH65d5WHDtd+KDn5s/bvsPaL79DOsY1ZyPNK51tTz+TIsPDuq7uSXWsuNDkP76U6eqARspdXJ+T6aUgnc2nHYBcKmnZc4dTlsQkyzINClyqcSP2Cq44u6afAOXNnooLIqf1COi8PeV9Mt77oQ27KCUrOMzP2PgPpy6sssl7xDdo63JecI2LoDsPo7vJQdmPrQwO/vEr5smK6sXU4+TpHUMwdRdhrLVk9KOXX/fLr9Fk3N6o7YBMl3NlI/T3rUt/1d9koUEfC45wgrk1LiwAas/0mFbAJdv/ERkymDWjQF79Tdnk+Xf4onJyYTTDat+JvaYyHD01WGA/+caOhKwUHJ5KP6yD6utIxLr+8gFq69KENfDdKbALbwXTPwTmA/L1fojFrD9IfWZVWIncz9bVrTLNOq2/8ZUkx1NmuFS26phMRVZZMq7tak0NdH2rg35Sa1HMi6zodaPZBZjcNiZhDgo2bJhZWFDzpG0pkzRT+sZ4G+jhT99UJ7Kb/LnNU7ajTJ/e1HW6NPmvbVnazES4hn1e/oEssIqEs7zK7QWtAks6r6HqlTnDyGbX5MmWVl1Di3gkUYN2SFl7R6Ysmv5o88I4TzUrax1XnAqa3jYbShis5bE5JoriJjcm69Yf0Bze2a2IjeexxFbt0bbtBe8omLQ290+6D5pn01mcU4exLw7feoGJZIV3fwsaWhQW1jo5ndlfzARVXS9N2GRnTXF2d+bhAax7ls82ldG5OMFlYN6MJG4/S8cO/0ANDkx13U2pQT9hIvrOM2tuF06cP+DRXExHuuJAuLWXzm/dI2pMuo9zNfcmu8SxSDx0ZJcV0JrtWi0h36FDhJVrK5hzvkXuIVVWl8vhdtGByP2rm2ZiGbboun9tVF40dsGjLH7VzoIj/sJt3NhMfntSAgmadptLC72mid0Oa8iObd5jjOT/ElQZ+nc06zYet4cbkc7ClBQWM2U43C9gNyf6J1IjpQoNBX9Dv2eWUf/kjCndS2LLSM1EU5NSeFioMOhXf+oL6uCluVqjgIE30caVBauNFC1q6UJ8Nhh8caI99zlYYmsN45sx8fn01Radq2/k29K6lGs/OX5CiJEbL0fMwrnsEerXzZkulduO3RkPwRrg9spKSkGXfDaN72+Do7sPI0eySOARRcecQO7stHCqKkJH0GBYSR5TmZCHf6NosMYKHv4PRIS4QWzVAnwGhkKQ/wsOS6/gmLh7N3/wAQ/zYGiyrRnj1w+noXOX9syYjuscyPDgYh199J+DDSc3hwN7FO4RMQvREf1yI3Y8EjW8ttGrKHuBg3K/wnfAhJjV3YK/pHRAyKRoT/S8gdn+CVlHDJzIkHjyAywHjMG+YH3ttbg6XsHnYdfwrvNn4Lvbt/g2NhryBcPssJCVlwb7baPS2OYrdh7WQ1SbPXkM72STj2JpPsPHgReQ1WYCTaacx76XK12QWLTB58et4yd4c1kGR+L/x/jgfG4dEMx75lCfi4IHLCBg3D8P8GLjmLgibtwvHv3oTje/uw+7fGmHIG+Gwz0pCUpY9uo3uDZuju8HHJsc0iVti9Lxx6B7RC22KvjVM5zHX/iU0Hsva92ftW/tj6AdT0N7STLvv+s7Yx2aGeG+ifnOoU9MUnTCgk1ILFiHWBsnH1uCTjQdxMa8JFpxMw+l5L+m0oXFaK7okRkhUHM7FzkZbhwoUZSThsYUEjqU5yFINMAu0mLwYr79kD3PrIET+33j4n49FXKJCyS1aTMbi11+Cvbk1giL/D+P9zyM2LlHOqKas2nk/4td9cRNMXfcR2lyYhx695uJqxGdYN9Gv6lIBRlkUNATThwWypVbu6BLaFBZefTB1Qgs4i+3RpEs7eOdy68NN6ZucTba64abJ48au6xgMcjmBPfvT2EKcMlzcuQ/JXUdhoCePORc1xIitl3A1bi7CXB/j9MpRaNWkK5b8wj65lEnZ4goxLDT1ysKC9bsc5fJvWSp55H6oCC7NB2PCkoP4484fuP7gNg6MKsSaye/hoOZHaBpVlIdmNl0wI3oAGrDX1bZNx+O90d74Je4w0o3aUSUFxa/swUHE/eqLCR9OQnMHtgzJIQSToifC/0Is9lcaPnHwcLwzOgQuYis06DMAoZJ0PHqod8G5NnHVmaFxoipgwgGjMXIOxr0sgbmlD3q9Ggrn1AdIKjVd1iY0oiqipeu+zvz2VFXL0AGzJd/F4rzvWLw/KgjW5rZoMnopprazhHHrZYLea83H9bXGmOymMdtMELccjXnjuiOiVzvUN7D0yRQ9MdR7/fkFuLxmJAYsz8PYDZ/iNbYGRSZlNkhsobWc1MKCMcQGjtbQKbiMNSMHYHneWGz49DXF8pXKRsrYkiRLryZo3qAQJzduw0n5enH9HFTJFTVCl04+iP/lHIrKLuLHU0BY91awtA1F1w4FOPnjFZSmnsbZBy3QrYsTMzPGsK3SgjpDFIQh04chkK0vde8SiqYWXugzdQJaOIth36QL2nnnIjOTWZKQKMSdi8Xstg6oKMpA0mMLSBxLkZOVjwq7rhgzyAUn9uwHt+dA2cWd2JfcFaMGesqX5agb4zsyMDZLazJnKtsxTaeUpWvjV9Pc1ga9vzANc7jV9awc5FKkpmSg/NZGRIZt1+LZtXkBcjQdV3MRci9vwOyRe3H2djabZIMRaJUHcicY/2TODE7OzPBWtmDGZjdRhQwV7EOiNKaQdeq6qa6ZO3nB01FZUoslAycypKc9hplnffiopChCfR9PmGVlIIP1obE+oyRv2wye9X3UBkPEaHiaISsjg7VV10B7mtmsbbb20MzDC96qNpzg37oNUHoMKRnluLUxEtrQuqJ5Aed8u2oSUh/b9sLHe5fjgyUb8MHwpZgCL7QZOAsrY95CO64Um7x8GyobE8GnHuMzIwUpJIKbIfmwvqZnmsHDy1tt3J38oWAzBRnlt7AxMgxaGuDaHAWcArgq21KzqDoyd0NdT8V1aSoPnccp8vbdNdo3r+sDb1OCPPLwDpSoWNE+MK4TYFOnXp1kMd76fLwXyz9Ygg0fDMfSKYBXm4GYtTIGb4caWIBYK7pkDlHuZWyYPRJ7z95GNjPowYFWyCN39lZO2TtL+Pg2VMlQ5FOPaWkGUlIUA9XSxxdq1fCBQjVSFJU1ZAWj/DaGyHckZgxcip7rLfHW1v7wMjAkzSWucKu8JhKLYGZrD7tKr8Sc+yZBzrwpfavsozTVyLhRYsF+rTogcogPuu+OQ/LwptgZl4teH/c1NLI0KtqiYfgwhAceQtiXn0A0uT2mL96Jibsd4WBRjIIiFeCggny2T4Md7B10XC2RP0as/BojlFTN2c3H9Ei0XLcJp+LL0I85Z4aSmaM36jkpAWXr6D2ZjDlbVV3nOz0Nj808ma1TGT6ImD3zNMtCBmf4WDJzcoZE2ZQZc45EFZBVqx1D48RQ7/Tlm0Pi4qqy8WYiEcyZ/ZeWG5O1ARuprwnNPE1d57GnoabYH3Y7lpbGbiDr1lPPLyJPZoMdTHC+TdF7zflYsxNs2TSfTa2cnM3d6qLSBGtX1jiTGdMTU6Y6JT3ZQ3w/dyjGbxNh0p4jWNxdsYbb2tEBFsUFUA8dQkF+EWBnD+XQkT38HnOHjsc20STsObIY3eULx5WE2Y1om0i8z6ZOvPMK3m7ZGws3swc7cwPVBXiPLBHCHGHrmb/g92v2+DmnPeaGsgc97CuUzl1b4d9f/4ybzS/ij6AIrKlrDukNnvnK6Lwngava6EHEohbbq40ee5Cn8InMRbm4vGE2Ru49i9vZFvAKDoRVHsFdbhOt0CFyCHy670Zc8nA03RmH3F4fo2+1VN7A2OSbM8s434Y/maJT/BSqd1VpnqpX6wUtbWamnEhEcK3jDMvW83DqwUM8fKj4u3HmKA6tnwBfDb9LdnsNpkyKhdXEXbiSlov026ewvHcdlUGVQ6EkayouYjYJuZch5WEaM3GKVJHzEI+emDY7KJoToY6bMyidOaBKIozao+Q0sEW7cNPogxZbojpwcyakp6So2obsEZLTCM5ublpFDZ8wh9fNhU2c7CZC1XYuTn75ATZdsGD0LdF63ik8qMT14cMbOHP0ENZP8DVMsojREnfGgv0X8SgzGb/vmo66J+dh5tqbijrSXGTnKvFhTmZGFgvF7Q7XBB75iBgOLoQsZoTVbJ7Elx9swgULNzhbtsa8Uw9U8n944wyOHlqPCZoKoJdjMyhVSeRaxzCdAE+4uxEy2GSibL/iSRaylL6zuRkzWGyyKVfujkPIyytQ3NTx8P5LjgKHqmpXQ52Q97GI3cyJ0XnBflx8lInk33dhet2TmDeTZwuu2tAl2W2smTIJsVYTsetKGnLTb+PU8t6oo2WZpMjNzlXtvCFLz0AWucDdvfIGKDcbatVIR0YWwcXdvVJyalnBBH6Lzn+Md3fZondPYMvMj3CezaN6k1IB9F6szDSpb5VSFLmijsnjRowWo9gTqAtx+Oa7PTgo7YdRPRx4OKlA8u5/o8vo9bivVERRPXTu2BjStGRkioMQ1CgLd2+rMS6/dQdJ9gEI8tIxJLmnsOadRdh7T0mIPQwvLkYx+0zTwUGnrA5HVJCFx0rdZ9JMTmU38HXqwkNL1jqV9JyK6rCxS+mqmy+uiOxRMtLImdklfh70kKvdLN1BqXvOtVYtWVeHPQ1dN2ZPjbrQIni4u4IepyFVJeoi5OQUK+wTn+0ySe/ZDZLuGFINBR6bqrTNrK4+aDXRqjU9kd7Hzgnszfj++lhy/Acs7V5XNf9bBgWhUdZd3FYZIPbg6U4S7AOCwA0d6f2dmMDe/O6vvwTHf1iK7swBVqQy/LbhLby98Qr7vLky2TRCIy8zPMnJVeaY9GvdPgJtcy7i8N6fkNimO8LlX/Wbw71rBJre+gWbj/+Oup27wp/xwztfKbE12KpxzLkPlG+vmYJJsVaYuOsK0nLTcfvUcvTWMOjiFqMwLPAC4r75DnsOStFvVA+23UEtJL458wkZnmuVTZugU8qitfGr1ITaoPUC0RCjab9+aHyJ7b8ZmyB/jihLO4Q5vdtg4KrLWq+LZGyZSIq0Htr2bAsfexEq0o5iy3d32VfWZeyFLzMglpYQF2bgUUouSlRGyggUokAMHtEW17+ch00Xs1CUcxVfz43Bz6qJyVB9M1hailGYwXjKLYdPn34Iub0VS3ffZd9ks4fOCXuwdOtNNO3bRz7Q9FIRNUSffiG4vXUpdt+V10LCnqXYerMp+vbx11ulaqYIvq/0QbMb2/HpoWS5Y5l/8QsseH8X4tES/fs1xqV1ixCbwHWIPUE5NAe92VPUVZe1XsRpkZWl7sTUiAGYf5g5ytYeeLlTKAKcxPL+yguWncNXq8/I90GtSD+Gz7fHI5jx0PAxj3xEvnilTzPc2P4pDiVzwsnHxS8W4P1d8UDL/ujX+BLbuzUWCjbTcGhOb/a0fRV42NTimTsRN+1nmE5FI/R/rQ3ubvsY3yQybWH7sv60cgNOl1Q+YTSvi7rsDUr86Z/BvWmsyDqNLfuuKfSPh3cp25VFrQclKseemVY0rIlOcB2RpWLn1AgMmH+Y3VBZw+PlTggNcIKY6bfBVBu6xJ5WPEqRol7bnmjrY8/eDKXh6JbvcJftglCmvCdhI+3cV6txhtsPuyIdxz7fjvjgV9DHT+FolZ37CqvPcPspVyD92OfYHh/M5O5XlW1j/OafxMLJn6Ns4pfYuf0LjCmMwRuLTmnty1uVKE+Osb6ZWcJSXIiMRynIlQahXzXGjShwBEa2uIiY97+DxauR6MT7NNMcruxGJWnff/Dp/x7LnScquITtcVfh3qY9/KybYtCAxriwfgV+5hSxkL0ijzkAcd+h6MzoVuRew6EdbIkbtz6E7Wjw8MhyvP/RAchXcRTdwPboLUhoOQSDAhXyMIQIFf4P6z87L98PvOTm1/h4ewo6DX6l+s53wz7oF3IbW5fuhsKEJWDP0q242bQv+nAexnNKJs8F4qbVknVNusNrTzX1zuCkxWxJ3/5ofutrfHJQYeOfXFiDzSeVzjeP7TKm93o6pImdNIjHphqeQqpQFdWKnrCgVDHj8O+D3pi1diHCLR/h1q1b7O82knPZEoumgzCg8QWsX6Gw4YWX1yDmgBh9h3aGrTQeMeP+jYPes7B2YTgsH3H12N/tZOTKRHAsuIatH0Tj67vcPFmGpO8/Zv6AN7r15Jb6cfvuH8IOtkTU6LIsh07o0vwGtmy5ipe7dlW/kWvYFV3qncOuAzK2NWKI/E0373xVDWyrgK3KYA/GmD2T1muLnm19YM/eOKUd3YLv7rJdRJQGnfk/I0a2wMWY9/GdxauI5DdeKspGD/jmTHsvw3OtUcLPpoD6vd2zof+XpWrZZh6+jknB+Bkt4BnlDKv8Arj2XIqvFobCCudVfFu2HY/pEbGY3zEEBwLtkPPYDp3COsLh+F0kMOegfvMe6OkyDpMDw5Bwci+cVTX5DkQIemszNqZMxtzu9fAWeSNs5L/wssU1WFny3Q9ZoHmPnnAZNxmBYQk4c2khNq9JxNiolvCcze4di0RoyoIFbJujGGj6ORCj2azNWJM4FlEtPaGo1hQj127DnBCmDibeQIhffgebP72Psa8Ho66FA2TFtug0dysWhLEt+1p9jZiU8ZjRwhNRzlbIL3BFz6VfYWGo4T1GRX6TsSL6NCIjA+Ht7A4L9tpb0mUJNk9pzJaXsJ6wtbU4MQ4vBVrCKicTVhFLsCPqZeYL8MlHjK7vbMan98fi9eC6sHCQodi2E+ZuXYAwtrVkq69jkDJ+Blp4RsHZKh8Frj2x9KuF4GGzKqSWbTDPIB3mCEzbiNW3R2J6iBdmMqfZvn0ntHJJZa/sGCnzehgeNQX/HTcCAfU9YG/mih69O8LtCteMGC8b4t3KAqkaenByr1rrxM1m8ehEQlX+lTkiP0xeEY3TkZEI9HaGuwXbAlPSBUs2s/UnBlMt6JJlW4yfHoHY+R0RcoCtoc55DLtOYejocBx3uQHmwzXO1hjiBMa9FAhLqxxkWkVgyY4ovMzUleuRQjVeQqClFXIyrRCxZAeiuIvqYVzZAz5+8/HDtMlYR5NwcEFH2LNdwz78zzi0e/UNvN/9FGZUUqjWj7G+1W+OHj1dMG5yIMISzuDSfJ5xo7oRqeRA1ABDRnXAgjeTEMlsh+GRpShvHb4A69+Jx9jXWuBrswqIXj4G65C3sPXD7mzBkTlbd78Wi66NwiD/r9n63mKIWr7NnNvecGLVpQ+/wYIJX6H94VfQpkt7zP9qOZLGv45mXtNhX54P85AJ2LR9FjR2MasCk7m5GNYereD921gE1ytAST4h+PWN+O/EBqx1Hr2sQolliJth1uY1SBwbhZaes9lTsyKImrKAYNvmgDNh1aSmr4Ua5VmYPBdYos08HlnXqHXtSrz2lNlrLb1b2ka9BFGDjCj4bWZL7mEU+57Cl73hI5k7vJVPMPlslzG999JopPJQC7szlzDfoE3VM6yrklPkGNETmaF6mvllF7Blw1nkZkmxoHtTLFBds0TX1Yk4NpV9c7R2Ea6NGgT/r9nXT8UitHybPRDr7YSyc9HYcDYXWdIF6N5UXROWXbE68SimTl2HL25FYlbLeljgSMgt9cGAZTuxpAt3Jy3Fw28WYMJX7XH4lTbw4Bvg5h5s3XcDzPhfBbowf0LlQbD+dw2zxce72qPbvyoJ8M5Xqs49xYEl2o6fjojY+egYcoCtD8/BY7tOCOvogON3E9gtRkP2jZgIDYaMQocFbyIpciSUrD1Fo5VVeeZMG0c0NjjXPn3LNaJg+PvWv+6VWv3qVJpPaYn3KDlH/aV/1Z4XU+r1X+n8pbuUxRWTlVB+bgGVqr5YLmdvXkt5v/bXpllON4/uoCPX1ft9SW8upXb2nSnGlC+vy4upWN04+4I5n1ITkyij0NBn39qtK8+k+amUmJRB1aymrK74Lc+llMQHrG0VGKrr0vw0SryXTLzQqkpXHpTmUPK9+8Se7KuuqHdZKaC0m1co/hHbpUV1lTswJh/2wXduCiU+YH3VrsjqSik/LZHuJefIt6fTIlutE346XPtJafmsNT1JmktJN29SMrdlm0xK5VJtJg3yrqsHmqRrqBNqPVDWAABAAElEQVTcDh05yffofkou22jM9PS0ulScep1+PX+J7ioGGJXk51IBp+Mau1AUpN2kK/GP2M4+Sr7Uu5DcL0ijm1fi6ZH6orKQ3t+n5VcvUQOZBvtWWb68uFjDlnDD2ZRxI6O0zf3Jo8NHdEOvUulnpjznOn3cozvbReixHn1n+yU8fkCJqWwbVf3VNXI5PblPDzIKdcaiRhG9h1LKS32gNb71FjMpk4251ERKYjwY59ckgrVQqHpzgWmyfgq29NhTJTVdvVPmV/kteUwJNx9QbnkK2+nGTrHbibIQj+0ypvdKEupfXez4baq6nrGjP0FP2E5Ljx8kUmpe9TWxPPcR3b2TRNl8LoixLlb7em1ha6Dh4lS6/ut5unQ3S25nZOxuO7dA7SPJ0jZTf48O9FF1jJeBpvRlG5wzefRVHx3NvFr1Oxlh9pzgH55E9vBowB5z8SZr1G3SWuNTRCvYs0A76sSe6nDfOJicWKjn40sw9MgJfPbZVLS2fYiDi7/EzRbT0N1bdd9qmJrYmt09aiTWh7pG+6BRvvJQZF8XNaimTUjsBM8G3POxqklk71F9+pYSeDWSVCUmz7GBR+DL8Khy1Zh82MMyJ0/oZ5M9jfZowFasPm3ip8O176MfJvbo1gk+geqLuoPSIO+6eqDZhRrqBPehjsSrEQxJQLMJzeOn1SXruk3Quq6aopU9C7TDnWo8nrLxCMTLVYWvqGTjgUCDF9V0lUdPy6+Sjim/BvtWWVmsYzyMjRvZ/RPY+eNJfLPiV7R7b63+D6sNMCaWBGDAO1GgQFcmad3EdhJyrY8Gutl6zzk9aVhtPeHeYjjUrV87azwZLfu6tTF29XawhpnVmwuMybqGTKir8dhTXb1TV9I5snKFL9MXVKTqXGCnPLbLmN5XJaaLHb9NrVrfUM6foCdspyXX+qaNHF0uxU7e8FObf93Lz+i8trA1wJ51XTTRNugs0A4rK7uPEzt/xMlvVuDXdu9hrd5dIQzQrEa2wTmTR1+rQb5WiurO87VCVCBiDAFLtJu/E2sqFmHD2yOwgn085t9hFuK+nIbg57dk0RjTz+26ma0v2vVoh4a861qfG3tCw88SAfZFvW+7HminV/hmsPVthx7tGrKlE/+cRAV/4NvNRyAduAafj/FSv2Y2CQIxArv3NKmkUEhAQAsB5mA2+FcPhPrbG/3YUauecCIgoESACvDHt5txRDqQRf8cY3A3KWXxv/OvGfdY/UXrIBfq9smTJ0g99ROCd23C9eET4RnWme1T7MS+aDXhyfGL1mGBXwEBAQEBAQEBAQEBAQEBAYHngkBt+52Cp/pcxCg0KiAgICAgICAgICAgICAgIPBPREBwvv+JUhf6LCAgICAgICAgICAgICAgIPBcEBCcb72wF+D0f2ZgGdtzmu1sWyUVnP4PZixj+yGz5S+ZPyzD1OXHVGUKU5Lle1GjIhM/LJuK5ceyVddq/4CFed+/GFHrL1SLdEXmcXwSFYMT3L7JL0J6KiwLkZLM7QHNvheq1X4X4eqmKejVoQ06sO0djSWVXhgr+De+XnR1E6b06oA2Hdi2cNdVoSVM7PGzkqOJzT9VsZqNU/1NqnHQf/0Fz5UlYv/iKKy/oLu34lP2qzAFySbbu78+xuo56ClxEaoLCAgIPBcEBOdbL+zFiD+4GfsuZiuieemUKY4/iM37LiKbrZYvy0zA9XssDC9LZWfnIywiGr/KN6svQ2bCddzLrOVJRIsXtqH92R1Yd+iWVq6xE8r7Hd+si8PlPGMl/yrXa4plGc7OD0NE9K/ywDW12u/sWCyI2ofSsDcxY0I4L1DaesFb9G98MRuxC6KwrzQMb86YgPD61fnW+xnK8U9BvGbjtCpr2jhUvf43yGEBWs7uWIdDt6p7c8bT97KzmB8WgWiFYeYpyF16MTDWnIOMdEi4LCAgIPAXROAf53xLC1mY8ielep9oc4a3IL8IMgMPhMtY4JcirYvm8I7cgBNfjpCLtiLzIVKKKr9fNfdG5IYT+HKExv5prJSsKBe5RRr7p+kohaykEEV65x0pClkY7SelBpjToaN5Kit5gtxCA23KSlBoIMoZP1YsYpUWn5XYaTaseSwrQm4uw1YzT3nMrj15ohmpUXmh8tcAltw+dCWFRerwvDrVuChhmQ9ToBSJ1mWefhuTEUdHlp2OTGkw+r05DsO6BWuR1j3R0gvdiwbPdfE1rjuapPhkJyt6gicGZK5Jw+RjPtkqiciykZ4pRXC/NzFuWDcEm7i3o1SuZM9GjgraSgZ1f2Uoys1l410339h5zcepNmV9dAzhUB1eq1NWmyPujB8zPdd5dUNfH6u2WTWHrw/sGvsYX0u92ZuzhylFVR+kSAuRm/0E2ia1NjBmHHP2Rb8h558DePFiM1SVOUiNTq2PazVp4UhAQECglhH4hzjfFcg6+TGGtPBCHZ/GCGAR/OqwsLCfnMuvhLMEN7a9gfYs38PLCz7t38EPGWont+TGNrzR3hvOHl7w8mmPd37IqHTe2evkNX3RcMCXkN1Yib6v78HjlK8x3O9VrE+6hzV9G2LAlw/lbUgTv8Wc7v5wdfOBj5sr/Hu8i+8ecDM7R6MfAoe8i/mvsOuOErZriw86zz2IVPlaiSyc/HgIWnjVgU/jABb9sQ6aDvoEKtZ5FKL01k68FeGLOp5+8PV0R9DA5TidU9kvysGZj15BgJsznB0cUT/ifRyRx7Hlw0qKix+Go/Xo6RjW2Bk2rhH47HYhrn81CW09Oew84dliFCYNaIL2Cy+wWxmWpIn4dk53+Ll5wNfPm+2XPgCL/5euwK/kGjaMaQmvOg0Q6F8X7gE98P7h1Ko3RuxVtApL7rhfIIa8Ox+v+LvCUeIEJ5/OmHtQt54MN1b2xet7HiPl6+Hwe3U9krmuG+w3xyonIz+4efjCz5vtUzpgMf6nJ7YvJ+ueHRfj1+Jz+KB1ffRffQiLO/tj/N6iSmlUIGPrMPi/8hlu/KGjFw8uGix7W6YPX5nJfHE3G3x6XnJtA8a0ZGOgQSD867ojoMf7OCxXskq2uR/pZSwJ98XQryplVPoTZrfwRtv5vyjkWZGEL18NwOBNKajgk60GSTY4sLJnRyz+tRjnPmiN+v1X49CHneE/fi+LTahIFRlbMcz/FaZPrL8XP0R469GYPqwxnG1c8K8BEZhYS3LUpu2KiM9u69wQSpH47Rx093ODh68fvNn+4wMW/08e4jl77wQEtpqNn7ho0PJUhgssIm7gmF3sRszUcSrFpcWG+44KQ3T06bNhXpUcqn8ry7Ix4+bjAzdXf/R49zvITRC7feXjyRhmeq/z6YbBPqq51X/E198SXNswBi2ZnWwQ6M9CSQegx/uHkVrOdK/v69jzOAVfD/fDq+uTIc06iY+HtGB2xweNA5hdr9MUgz45h3ymCVVtBl+b2lzKEtegX+AQvDv/Ffi7OkLCdt/y6TwXByvHGK994cOLNWN4DmLXTBnX2qwKZwICAgLPGwHNCD4vynG1Iw2VnqGoICdqv/A85XGdLL5FX/RxI8ngnZTPTqXxyyjUyZ8i/3uHxUospjs7x1JjCwtFJC9pPC0LdSL/yP/SnWJW9c5OGtvYgixaR1O8VB1hT0722zHk4TOZjpRwRO/S8lA76vTJfXZ8g1aFS8jn1S/oEovAJ8u7TGsHNiBJ51V0vZKGpUUAjdp8mbLKSyhx7wQKsG5JC6+UU+mZKApyak8Lz8s5Z6x/QX3cJDR4J8d5KZ2b04TsBmzjmtdOHN8dnMiP8X2X8S3LOklzWzlSk9lnqejucgq1EFOjoRvoSo6MSpPiaGJja2r94R8k5cWKay+YLKyb0YSNR+n44V8o4fdoaisJpPG77lIJ+3dv32RqZm1Bge+cYdxJKX5FGEn8RtDW+ALGXwHFrxtI9TwH0/ZUGeXuGEwuQdPoeA7Heh79tiiMPEKX0lXd0IpaWHK4WpJFwCjafDmLyksSae+EALJuuZAYXDqpmL4d40E+k48wzjiR8PSb4bUiTEJ+I7aSgtV4WjewHnkO3k6M1SpJemcZtbcLp0+5iKSl52hOEzsasK2wspyMUlZ3JTu5jjCd0dQL3rJV8X1QWg2+eGWXSzsGu1DQtOOkgPs3WhTmQaFLr+r0rZR+nhFAdYbtJi7+avmFedTMxpJs2y5m+s70KHU99XbpSMvvlPLKVocoA/8OLWtvR+GfPmAREdV6q0IsZTV1tWtN0ayR0nNzKNjCmppN2EhHjx+mXx4U1Jocq9LWjkgnjV9BYRI/GrE1nmkr09j4dTSwnicN3p5Ksuw9NKJufZp0uJLrkp9pRoA7Df4vixZp8jg10ndeOjr6zMerjgCkN1ZRuMSHXv3iEosQKqO8y2tpYAMJdV51nY1SIzxVkYc2ZlUx5dcNXqyqjA91R3hlk7uDBrsE0TSFMaG83xZRmEcoi+TJjELxtzTGw4cmyw1zKZ2JCiKn9gtJYVKL6dYXfchNMpjkJpXZfy2bUR2MOftiaUEBozbT5axyKkncSxMCrKnlwissai3fOOa3k2yC4pmDTB3XahyFIwEBAYHqI1Btv9NIE/+MJ9/iEETFnUPs7LZwqChCRtJjWEgcUZqThfwK9uT54AFcajwW84b5s8iR1vAf+gGmtLeUBxKQJR7EgUuNMXYee5LJwkpa+w/FB1Paw9LM9Nsm2YODiPvVFxM+nITmDuYwdwjBpOiJ8L8Qi/0Jivfa4uDheGd0CFzEVmjQZwBCJel49FAKcUgU4s7FYnZbB1QUZSDpsQUkjqXIycqv+oRYgyU535cDMI7x7cf4NncJw7xdx/HVm00gX20rDsbIOePwssQclj698GqoM1IfJEHKixXXAEHccjTmjeuOiF6tUbA/DvHN38QHQ/xYREIrNHr1Q0zvXBl/U3YT+3b/hkZD3kC4fRaSkrJg3200etscxe7DObBgT4Zsko9hzScbcfBiHposOIm00/PwktHlwGIED38Ho0NcILZqgD4DQiFJfwQGl/FkoN+lN/dh92+NMOSNcNhnJSEpyx7dRveGzdHdYKz+iUkT33bwvlMNvnhlZ8HeqNgg+dgafLLxIC7mNcGCk2k4Pe8lnb5Zom3frrD/5UecZe/u7/3vFIr6DkO7uydxMkOG3B8P47fGr6Bf/Tu8stUhWu1TErfE6Hnj0D2iF9rV1xN56inkaJi2DDf37cZvjYbgjXB7ZCUlIcu+G0b3tsHR3YeR4/wKRvcFDu0+hgLWo6KfdmB/Ecvr51rjcaoLjOnj3QivWoRleHAwDr/6TsCHk5rDgcVCcAiZhOiJ/rgQux+VJkirhu6JYcwUJbWue/Prhul91OTCSH8t2Bswm2QcW/MJNh68iLwmC3Ay7TTmVTEmYoRExeFc7Gy0dahAUUYSHltI4Fiag6x89dtORctG2tRkT3nM9HL4O6MR4iKGVYM+GBAqQfqjh+C1L4/57ST/HGTquFYyKPwKCAgI/BUQMOrm/BWYfGoezEXIvbwBs0fuxdnb2bDwCkagVR7InZgryT6GSs+EmbsXvJVzvHld+HgrYubJ0tnaXjN3eKkvoq6Pd7Ui6snS0/DYzBP1fdRwi+r7wNMsCxnMoeGSmZMzmB+sSGYWEIsq5GvPzUW5uLxhNkbuPYvb2RbwCg6EVR7B3UhsJAXfHhp8A07+rdGGtSBLYP+ZS+DiqmoQIpE5KtiyB+LFSsGeuVtdeMqxYtilsRuZOnXhpiRl7gQvT0dF1D1pKlIyynFrYyTCtivqKv53RfOCHNiO/Rh7l3+AJRs+wPClUwCvNhg4ayVi3gqFi5KeZjXVsRmcnCWqyH5mFmKI2E2U7tSpKq55YKDf5akpyCi/hY2RYdBmtTkKcpiMXJXKoUns2Ryr8WWrQKrDF6/sbNHr471Y/sESbPhgOBRwD8SslTF4O9RFqyPWHfoigmbj+MVkJP2ciDajV6HZg/746VQ63I6cQ4PeH8Kf+GXLANOiWe0TczfUVSiZ/qo1lSNHzSBtKVJTMlB+ayMitRUWrs0LkCOzQ8ToQbAbtguHc8NhufN7iAZ8hR4OjGRJzcapbudMH+/GeNVUWcU4NfOsD7UJEjF75AmzrAxwJshClxHdc4OYVRbUvG5k3JuLymtg04z012osPt67HB8s2YAPhi/FFHihzcBZWBnzFkIVplzJKES5l7Fh9kjsPXsb2RZeCA60Qh65o6pJNdKmPrNg5gRntSGHhVjE7GoF/zjOfMRrJ/nnINPHta5IhXMBAQGB54cAr4vz/Niq3ZZlt9dgyqRYWE3chStpuUi/fQrLe9epdN5EcHd3A2UwZ4IZU3mqeIKsLMXCTpG7O9wog03Kqot4kpUF1bJPXVb1PBEX1XGDM6UjRUWDOcCPkpFGznBz43PqZLi9ZgomxVph4q4rSMtNx+1Ty9G7jnGxidzc4EJZzDlW8g3knvwSH2z6Bcpl33pYBT9WlZ01M6sMLyyGdz13lKU8hKqZihw8fPRE4QiLXFHH2RKt553Cg4cP8VD+dwNnjh7C+gm+KGI3JeLOC7D/4iNkJv+OXdPr4uS8mVh7U82zLrw1Odftp+45R1PkWgfOlq0x79SDSj4ZvzfO4Oih9ZjgyycjVtncjEVWZY5yuXyVO8sg5OUVsP81krJRk8oq8a0eX/yyK2K6IEbnBftx8VEmkn/fhel1T2LeTD3bJNqGo094Hn76bhuOXW2GLl1CEBHmgl9/WI3vTnnilf5B7OaQX7YaPddzaA4zBWCKdeSsBOXloUAHMKZmWknnVG+Ia9PkyPDVJSZvSQTXOs6wbD0Ppx4o9fUhbpw5ikPrJ4BTA6sOkRji8T/s3bsXO3+Q4LUxYexdWXXGKV/fTaOjYN04r2rwRKjDvu2g9BS1jWM8P0pOAzm7wY3deNdEHmr63JEGpry60aCGNs1If0vTkSbujAX7L+JRZjJ+3zUddU/Ow8y1NyvZrBS47DbWTJmEWKuJ2HUlDbnpt3FqeW/omtTqY6yNhu4Zr14GuPPaSf45qBrjWpcp4VxAQEDguSFg3It7bqzVXsMytiQhRVoPbXu2hY89exKRdhRbvrvLvtwvY5O/CI36v4Y2d7fh428S2Tn7WO6nldhwukTuPIka9cdrbe5i28ffIJH5VhUZP2HlhtMo0XIUFLyaWVpCXJiBRym5Wl/bixr2Qb+Q29i6dDfulrKypQnYs3Qrbjbtiz7+fI4de2L1KAXSem3Rs60P7NnT8LSjW/DdXbYTRpnS0dOPk8j3FfRpdgPbPz2EZM6Xzb+ILxa8j13xUtjySJ0fK922RAgcPAJtr3+JeZsuIqsoB1e/nouYnytvTcRN0a9fY1xatwixCVyeDGmH5qB3m4FYdbkEqTunImLAfBxmnru1x8voFBoAJ7ElLNUvCHQbrOa5GSwZscIMJv9cnt1UGFVx037o1/gS1i2KhYLVNBya05s9PVuFy+VGmmVvSuqytyjxp39GJnv8XpF1Glv2XZNvb8jV1NIL4i+r21J1+OKVnSwVO6dGYMB8tj+9zBoeL3dCaIATxExnqyZ7dOkTiqRNn+Fkg3B0rmOF5p3boyT2P/jOqSf6NmMC4pWtUcBQl33wSfGn8bMCMJzesg/XDFb7k+TIFmQ17dcPjS+tw6LYBPkNtiztEOb0Zm9kVl1WyFPcHKOG+eKnDxbimM9QRLbi8KvOODXn6bsxOpo4SBFkjFeVYEVo2KcfQm5vxdLdd6EwQXuwdOtNNO3bB/7M+a6ePFSE9R/w6kZJDW0av2xKUndiasQAzGexGWTWHni5UygCnMTy8c8GILMphchgtjSX7ff9KEWKem17oq2PPXtjloajW77DXbarjsKk1hRj/VAoc3nHMfHZyXLwzkG847oCudcOYUfcr/IPhpW8CL8CAgICzx+BWnNznn9XDHNg2XY8pkfEYn7HEBwItEPOYzt0CusIh+N3kcB82IZB07Bx9W2MnB4Cr5m2ENm3R6dWLkgVsecfoiBM27gat0dOR4jXTNiK7NG+Uyu4pIqqPHmzaN4DPV3GYXJgGBJO7oWzkiVxM8zavAaJY6PQ0nM2HNgeD6KmLNDItjkIYRJIUJar8svW346fjojY+egYcgCBdjl4bNcJYR0dcPxuArtR8KpSQ5UhfhnvbP4U98e+juC6FnCQFcO201xsXRAGq6xzqmK6B8aw0t0hThT0FjZvTMHkud1R7y2Cd9hI/OtlC1yzsmRvFizRZt7XiEkZjxktPBHlbIX8Alf0XPoVFrJ3wQ4hKxB9OhKRgWzHAXcLts2jBF2WbMaUxnw3JLoc851boHmPnnAZNxmBYQk4uVclkaqVLNtg3tcxSBk/Ay08o+BslY8C155Y+tVChFpVLa6VY14Pw6Om4L/jRiCgvgfszVzRo3dHuF1RlNLSizMXMYOnrBZd7qQafPHKTuaHySuicToyEoFsVx93iwLkS7pgyWa23EdPknTvg46yONzs2AWNmDgsQ7vgX+Zf43qvvnKdZTk8sjUKGOoNj8KU/47DiID68LA3g2uP3uioBKwKP7UnR/5bVg7uefg6JgXjZ7SAZ5QzrPIL4NpzKb5iu5ooesVuOEcMR7Ml70I6axSC5Ra0OuPUnKfvxujU19LnM5fmG+FVDaS42SxsXpOIsVEt4TmbrZMpEqEpCw61bU6I/BuQ6slDTVf/EZ9uOMDSgc+m6VoYdQt8srFl39GsiD6NyMhAtiOUOyzYlnySLkuweUpjtqbGAT16umDc5ECEJZzAV9MjEDu/I0IOBMIu5zHsOoWho8Nx3JVPBpY1xljNqZ4j3nHMbuAM2klO63jmIBHfuK7Aw28WYMJX7XH4lTbwMDYs9bAtZAkICAg8GwTMuA8ynw3pZ0e1gkWWfML2ck099ROCd23C9eET4RnWmX1Q5sRe/xt6rFuCtBt/IKnUGf7BfuzDxlLm7EnZRGAHS2UVKaOZWgIHL+ZAVfH/pHiSmooSBy/mLFS5qNFZKUpKKmBpzTmfukmGgrRHyBHVYdtcMSdf97Kh85I0tmVdEkqd/RHsxz4yLGWOk9QSDnb62tAlwvGdjCfiOqjH2qzKk2557twErCqrSW/9iNjEenilZxM4cXmyW/ioY2scHR6P4zPqq9qTFbAPSDNlcPL2goR7WKiRynJZ9LlcM7jU82RPvjUu1NahtAQlFZawVgmajzCTEXtTkilzgreXhLmY1UiyJ3h4Nw0i7wB42RKkZMaWZygR19EL3rL62jSVL2OyK0Mui8Kaa+aCep7sybe+pqqZxydbflIyPHl4F2kibwR42YJtuwEzsUilM1Xq/lly5BqWFbAntJmQOXnDS1dhqzBWmVGtccrTd2N0dHGoDq+sbNqjHIjqsC0nbXUtEA9PhvpsJN+gbhjrIx9dvv6W5bKItrkwc6kHTx1jIi0pQYWltdzel6TdwB9JpXD2D4Yf+ziylDnrUksH2CltxNNgzMc7e0vCZ18M4iWnyTcH1f645u2GcFFA4B+GQM38TsMg/YOcb8MgCFdqjkDZuXfRsucRdPjsM0xtbYuHBxdj6rIcTDtzArODdSf3mrcj1BQQEBAQEBAQEBAQEBAQeB4I1LbzXRsPvp4HDkKbfxEELNvNx841FVi04W2MWEFw8e+AWXFfYprgeP9FJCSwISAgICAgICAgICAg8FdC4G/hfNs8zoDo/l3Anq0XNLjs5K8E+9+Ll5faTcRe9qdOecBd9ickAQEBAQEBAQEBAQEBAYEXHQG23FlUUADO36yN9LdwvhsdOwBwf0ISEBAQEBAQEBAQEBAQEBAQEBCoRQS4r7ccK/9qg+wL7XwXs72Z/9e5DyQSCdsqqy7s7OzY/r2V+7nWBjoCDQEBAQEBAQEBAQEBAQEBAYF/NALc3iSFhYVIS2PxAXJz4cT8z6dJL6zzzTnZFZZWyHRyYbuHiJFP5rBh+1kLvvfTqINQV0BAQEBAQEBAQEBAQEBAQEATAW5fwGLmZz4WWULK/E4H5n8+zcPeF9b5FovFsLW1hY2NDYqKilhEyixweUISEBAQEBAQEBAQEBAQEBAQEBCoTQSkLBhXeXm53Pfk/M+n8TlfSG+Vu9uwZJH53FgIdS5xzjf3SuBp7kJqU0ACLQEBAQEBAQEBAQEBAQEBAYG/DwJKP5NzvDn/k/NDa+p3vpD7fHOi5PZc5O5CylhMYO73BYwV9PfRSKEnAgICAgICAgICAgICAgJ/cwQ4Z5t74s053tyv4cCO/EC8sM431y3O4Racbn4BC1cFBAQEBAQEBAQEBAQEBAQEag8Bzgmv6VNvjosX2vmuPRgFSgICAgICAgICAgICAgICAgICAs8eAW7rQiEJCAgICAgICAgICAgICAgICAgICPwJCAjO958AstCEgICAgICAgICAgICAgICAgIAAh8A/xPmuQPb/PsWsJfvxgO0FXitJ+gd2LlqI/16V1gq550qkKAE/rluEqGlTMXPBanx/I1+DnQrkXtuLFXOnY/o7S7DtfDo0ISxK+BHrFkVh2tSZWLD6e2hVlabg9OZoRvctzFu5B5ezKzToCoeyxP1YHLUeF8pMwaIQKcnZqAmCBaf/gxnLDiOtJpVNYU0oYxoChSlIfsoxUHR1E6b06oA2HUZi7XXDtqcwJRnypioycfyTKMScqJnumNax2ipVhKubpqBXhzboMHItD1EZEvcvRtT6CyaWqUDmD8swdfkxnvLP45Kp/X0evAltPnu7WXObbpp0mN4f/wRRMScUtsC0Ss+mFLNDPyybiuXHsp8N/RpSVdnJGtZ/mmr/EOeb8ORSHL7ceQ7pmp7j0yBXdhMHYmKw/4ZJntPTtPRs65Zdxcq+7RC58R5sfHxgl7gNE0O7YeHZQnm7RRcWo3fEbBwr9YC77Ayie3bCtIOZciew7OpK9G0XiY33bODjY4fEbRMR2m0h5FUrkrFrbCj6r7oCy/rekJ76P3Tt+BYOZwkeoFKgsvSz2LHuEG4Z9qEqi5bh7PwwRET/inJl5Wr8FscfxOZ9F5HNggQI6TkhUHYW88MiEP1rTSSo5DkbsQuisK80DG/OmIDw+vp3ii07Ox9hEdGQN0V5+P2bdYi7nKck8tf9zY7Fgqh9KA17EzMmhPPwKUP62R1Yd+iWyWXKMhNw/V4mT/nncMnk/j4H3oQm8Wzt5tPZdNPEQ8j7/Rusi7uM5z/6y5CZcB33Mv86/pKWnTQN0Fotpd9612oTf3FishIUlophZ1s7UEgLc5EntYHEyUrvawVZSSFKxXaobnPG6HLbLdZkw/fSE2vw+bUwrIzfgtHu3L3YGwgZ2QT/XnMEczt0wr5lnyNt0Hb8GNML9pBikFtHdFiyCTN6zcSDNZ/jWthKxG8ZDUXVEIxs8m+sOTIX/2qyFZ/ud8PMc7vwf80YtqX/QmnTAVj73YfoPd7lGSmFDEW57Km9gwS2ItObkJU8Qb7MHhI73UrG6ElRUlQBa1vLysYqUFpYApEd23y/SvMVbHtMc7YtUZUL2hmyIii6YAs1N+wJxsMUFFlrFwV7B8HX37KCfEht7HQr8Z9LC5GbJ4WNxAlW+nityXjho8n6+6TAHPZO1hr9VbAoK3qCAnN7OFmrkVAyLyvKRT4cINEjaMP1GF5PCmBu7wQ9JAFpCYoqrKEWZykKS0R6bAM/7hyPVcY5e/LzMKUIVUSo7JDurz49kGUjPVOK4NfexLhhDfXaF45MReZDpFRVFnZFhpLCcljYVcUa+trT5ama54blwDjRIz9ZdjoypcF47c1xGNZQn/JVkwFVcXN4R27AiUhVhtEDffwpK/H1S1lG91cfver2Vx8N3XYMnrP5Qcq2Ratql7Rr8PWNv31GX8roG2tA3pyR8aPDKx9P2tyrz4zNl1XGp7oqamQ3WX3DNPX115BN12CE79DIeFX4A2Z8FIxe45e3ifOZshVzb0RuOIGqQ5CfjrSkCBXWtlDNsKWFKBHp85/0YaxsnPvlbF8pxBpzs2E7qVnvGR6zrfr+Gqk8g67/co7uZEufAT9SurcyjGyavkvnS4mk91dT38aDae77vcnPyYLEYluqFz6Hvk+Rmd52YSyNkDjSkF2FrI6MHv+8jAY39ySJcx1ysbMhl+CBtOqXPHZNSvdX96XGg+fS+739yMlCTGLbehQ+53vimiv/PZrC/cZRLEeGS7J02jLUj3rH3GI1+eiyur8tok6tImna0ACyFztS2OSRFBw4kfblKEhxbd9Y1YMChmyRt6XM1fyVpl+jn05dp2xV1wvowARvkgzeSfklh+l1b1cavqdAVaX0/LvU1OEV2vBYSunXfqJT17MZl5Wp4ABN8JbQ4J358oyS7GxS1cyJo7HedWjEHsU1ZRXDv+V0aXEnajRkK6XJGyihE+80J68279NZJkMO8wdrB5D/axspWVZO9+NmUzdfCdk7u5DELYj6Rx+vrGe4hZKbO2h6l0bkLHElZwcXCnz1YzolB6KSnp8T2djbk42TH3Wfu58SOdWU3qfVfYNo6Jw51K2hI1mILcm9/WzadySGhgS7kKVYTA4Bg2jN1RLGYiKtHdiEhkSxso1cydFRQr5dZlLsPXkHqPTcHGpiN4C2cbIvv09xs7uRr8SenF0k5BbUn6KPp7FeSun6iq7kZi0ikY2EvAeso0dG+lt8/Sua3M6LbG3tycmzLY0Z2IJsW0dTPM/Qkj3+mZYNbk6eEmeq42JHNi7BNHDVLyTX4BqOFz6aVHyV1o9uQZ7ObuTh5kQu/t3pvUMpcl0qvrqeRrfwJGc3D3JzciH/7u/RocqxWX4/jmZ38yWJvTO5SNwoqH80HVcoCCNpqF4xXV0/mlp4OpObhxs5ufhT9/cOyceE3BYEDaU5c7pRQ0dmCyzdqf3sfXQkZggFu1gy2+BAAYPWECdOJiQePeMZ52XXaUVXN7IWichG4k0D1j2S95OjWCVV6oGfkw3Z29uQk193mrs/kaRSjoYHOViYk6WDK3n3+4IeqAaemor0+grq6mZNIpENSbwH0LoHt2l5qC299Npk6ukvIWtmf2x9utB7hzndYsmg3qlpVjkq/52iw/1onNpoUfqWoeTXO4ZuMR0zLAeuOf3yk/Pt4UAW5pbk4OpN/f5zkKeNUjo3pwnZDdhWhTV1hmYZTjZ9qEH/tURZsTS+cUt650Sxqmjp+QXUofFo2pkhM8gfV5ivXypiOgeK/jK7b2NP9jZO5Nd9Lu1nhqRKf794oFNTfWqIBleCd/4o/40WdWpFkdOGUoC9mBy7KOSjpqw+4uubIZlx4+G3RZ2oVeQ0GhpgT2LHLrTks7HUuOU7pIa3lM4v6ECNR++kDD67pYfXq5cMjWc139pHfPMlz/isHEfVt5vGaBqyFzo2vd98mhPWiIZsrRyTJSfoneZe1Ob9s6SY6h7Q2gH+9NrGZJIZsg8MCF1/oEvMdbq1PJTsOn1C91kfZakHaWaHAOoYdYhS9dgOJZaG5c01wuYpZiv9nR3J0bURdXn7XRrdqh99xgY+ry5K2bzZpwH1X5tkZF5UYBo0dA7N6daQHJm9snRvT7P3HaGYIf/P3nfARXVs/39hi1RZitLEQhPQxO5TERGsBBv2AhaMxkRNsz1eEjUxMUZjoi+W2E1ibGiIJqgx+oy9RCOaYG+gIEUEkb67nP/cXZYt7N7dJeLz9397/cjeMnPm9Dl37sycEHIRs/jJMYAGr7xcha4hHrPHXJv9AmnonPcoivXlItY32zUKp9k/Z1KFrp98wMMQFWOe8S+3T/Z//5DdojX9PUhoZUUin9G0NeNZM0In+L61mELFIgoYs5FS8qRUdm8XxQfYUNt5l5g7MfHQDL7LT9KMICfqPO+sIlih0uu0KtpNGcCy4OkWMwCxKIDGbEyhPGkZ3dsVTwE2bWneJal2AMY1Lc+kFT3sqf2CVJLxwiVF3RCRDbWMX08HD++n01f30kQfVxr83SMlEdIUmtvWhaLXKYMaUygrvriQwliAMHpnNskLNlI/+0B694QyWFSgl76Mutu3ow//1OVUMV1cGMY6/NG0M1tDftJU2j53MvVv6UmBIzbQFUUQYwomjL6jb1FAgxG0o4CVl56jhJa2JLbrSB9zUaT8Ia2NcqGui29SeeoShrMfjdqcqgj2i1LXUEwjTxq65aHhIEeWSou6sM4w9nu6xfphed4xmtOuPgXPOkUlV5dSuMSHBq26SIVyORWmrKaYJhLqvvQKC4JusWBGTKKAsbTlWhHJsvfQxGYiEjUZTKv+eEzSpyn0abgT+Uw+wIyfK8sCOp8Y+vryU5KXXqXNI33JOXIZ3WQkqINvGaUuYbzzG0WbU7nXlSJKXRNDjTyH0haFlyylH8e6K2By7JPx0cvRFepE/oyum4yu0pvbaFwgw483+C6nkzOCyKnzPDrLRdtUStdXRZObZChx71GyWtkLP8yCrUPJJWgaHVa8KBayTjyM3EMX0mVpAW0d6kJB0w6T8hELIMLcKXQhc7aMtiVhEvIbtZmUbEqlNTGNyHPoFtaZ8NQr2EpDXYJomrIxKmQvrWHuobTwslRN29gtdK2IvVDumUjNRCJqMngV/fFYSk9TPqVwJx+afKCMn+9G7JxKf6Sx7ko4HIf1H9zLcjhJfAbRqouFJJcXUsrqGGoi6U5LrzCFkd2kRZ3tKfzLNMN6zQCX/jiW3H0mE0OZE55SB5sNp3WX8klenk5JEwPJpv1H9JfMmN7px5IpLs0OtqeBirdGroycMlf0IHuFjvHIgVd+HHmLqLN9OH3JvVXwtqEZWBvAkYUt6gBd6YO5IIToMe0c5UGNJ+0n5XhHGR19K4AaDv2eHvHix0OXIRRkV2lpuIR8Bq2ii4VykhemsJfxJiTpvpSU4tSgt5Yw1D6kCoBW/8HkFCIim5bxtP7gYdp/Oo1pqb6DhzZennA8DiGRTUuKX3+QDu8/TWm5O2mUR2OatL9qNKnsKL0V0JCGfv+I3344eWvh+idtMeQH9JHA3ePtL/n7Yc63mO83+WHy+mnmY9U+vVyhgw1G7CBlV5dALW3FZNfxY8WAifzhWopy6UqLb5bz+gdOF7TigbRyRezB6f2dzP30Tjt3ajFxJ93V7bo1+ckrbxldXx5Jzr4jafPVUpIXX6FNrD8TidrTAtYn8+qiwg/ZU7cv7qp9kt5+Uc3TsVuuUZEsm/ZMbMbaaEKDV/1Bj6VPKeXTcHJi/o07eHlc3VePoY0peSQtu0e74gPIpu08YqGXtp9UQHu+f57lt73aj8+X/45ff8sGcwyQZhzB4YvPYV6QMAQjZ8ahlYsQ9ZpEY2CoBNkP7rOJFbU4hK0wI+kMEmd1hGNlCXLSH0EkqY/y/Dw8rZriLAwZiZlxreAirIcm0QMRKsnGg/tGWjMBLgnbIi5hPHpF9kWnoN4YO9gFR3buUSyuq7iwDbszemBMjKfBT9Sa1BalrMTogYtROG4dvhzSENZyGftYI4RI81OiSMSmB0gh1Zq6WoSUlaMxcHEhxq37EkMUc1CqIFcUQyr2QnDrJig+th7fHlPOF9ds19C5uGM/9HA4jV9PlUF+5z84XtIPIzrdwrFjOZAX/Ir95wPxSv/GuLl7B843G4bXwh2Ql56OPIeeiIuyxcEd+5FvALj8XjJ+SgnA+IQR8GNzAaxdwpCw/TC+eT0QGclJ+N03Hh9Nag1HNk/EsdUkLJjoj3OJe3BbsWZAgKBh0zGiuT0EDSMQ2kIEr+ipiG/jDKFDMCI6eaMgVzW/VIQ2kz/Gqy85wNomCLEfTID/2UQk3dNYfCC/ht07zqPZsNcQ7pCH9PQ8OPSMQ5TtQezYr0uBHNd46H3E0XUxEOMYXf6MLhv/4Zg/pTPEvF8ghWg1IwlnEmeho2MlSnLS8UgkQf3yfOSpFdhMe+GHKXJygm3GIaz8Yj2SLxQieO4xZJ1IwEtCEZycbJFxaCW+WJ+MC4XBmHssCycSXoL82m7sON8Mw14Lh0NeOtLzHNAzLgq2B3dgf77hehA5wck2A4dWfoH1yRdQGDwXx7JOIOGlKsUWBGHY9BFozqYdNYwIRQuRF6KnxqONsxAOwRHo5F2AXDZXkY/vKinVys5VOipPQ3LS7/CN/wiTWjuyKUqOaDVpASb6n0PintuqUrX4FSJk9GyMf1kCa7EP+g4KhfPDNKSXm6N3pjZrWA788jMV/t8t54xX4voB+3bgUBGDVfIbtu4pYff6Q1Jb/TKAkjwtGUm/+yL+o0lo7cimnDm2wqQFE+F/LhF7lI7EQE317b8Ng4RoG5eA8b0i0bdT4xpTu5Qt/R2ZEYRt45Awvhci+3ZCY7dXoGTvISjZuxV7Sti9/hLj9qOFqy+cDfgBNXd0zkzoLw3Zp6I/MNtvKtvXD7PcOL3V6IvRsV8POJz+FafK5Ljzn+Mo6TcCnW4dw7EcOQp+3Y/zga+gf+MHRv2DVjzQWDldjwpO4IN+o7C9yWfYt2YYmmr259U4KE94bfRRGvYmnoXvuPcwJsgG1nbBiFs4FZ34OxedFlSX/P2iIGgYpo9oDntBQ0SEtoDIKxpT49vAWeiA4IhO8C7g+lf+vlDpk5nvGzkTca1cIKzXBNEDQyHJfgBjoZcKy7r85RFDXTarA9umB16d0hG/LbsIUegUjOtq8sxIHUBmXFo5wVmievewYgGmAJXyWi4GtBagIGUdZo3ehVM3HjNFCUHzeoWghiwDZxVKVk7OUDcnglBQCaPNmQAX1m7w8FTNia2HLrHD4NNrB5IyRqLFtiQU9P0M/VyN8UWO+z/PwfAJ30IwaScOfNxLOYfbpj4cRaUoKlFRwbKKsnnEJbCHg2NVNCe/j5/nDMeEbwWYtPMAPu7FgnbN5uw6IPa9DuzOTLzydltEzduI8ZFz0FyFsmZZ3XObLugXSZh1+AIy0o/iXoc4LG2ZhgG/HUe22wGcaRKFj/wJDzNzIL2+HrFhW7QguLYuQj6LcV31tCXPZvNLrdzh5a1+6OTfHh1QgdNZj2Dl2Rg+1dYhQGMfT1jl5YD5QnZYQ+LqVkWngMnSCnYO9lByhJvXbc2+KKlQEcPHt2l1pyfwaQQP5CAzkwFSTWSTPURmjhTX18dCmwRXtC7iXIjmvG0ZL72PMjm6GmrQZQ0PH2/YqdDR+2sNQUEK1s0ajV2nbuAxCz5DmtdDITVU02G2vfDDtGN6uWvxfHyybj5GLpwCeHVAzLufY9mboej72S4snv8J1s0fCeWjGLz7+TK8XpqJHOl1rI8Ng5akXVujKL8exhmo93ZoX3y2azHmf7IO80cuxBR4oUPMu/h82ZvoxPHDWgJXtyqtFQghsLKDg32VfjNZWltzdszPd07PuBq1snOVTOTZyHpkBc/GPuq5uQKmh55WyMvJYaU8VCXN/GX66uJabZdWAgGsK+WQSY3pnVHHoQcPu1rKT2FYeuA9+1v2kXEYbD8C2/cXIFy8DT8LBuKb3o6QnaitfulfwyLPzsIjK0/mO6odCQRMtp5WecjhHIkJ4jQKQ2SMP9Zw8/Cs9j/6S/89mVm7eaC6C2K+KjJuMOxHbMf+gnCIt/0MwcBv0NtRhhNG/DTnV9W4Gsbp7VD9/IYJ/aUh+1T2B+b6TSU39cM07i80ZWHTpR8iaRYOX8hA+tF76BC3FC3TBuC349lwO3AGTaI+gj+M+QcGUSseULYg/etXXGzng9Jzv+B0Thwae2r10JpoQPaQxwYeZSCLxbwejdT+SeDpi6aqWEALkrELnn6RVbWWuELtkgWwsnOA2iUzn6zoYE3hsRWcnNmgQxU6VmwkUcB8Xy0jPWNEmfVchZNZlZ55YesG6LOY7URSWoIHR+YizOmZt/D3AcpS8NWoaMz9T7kSFklRUVkPtjbWkN9YiSmTElFv4nZcyipA9o3jWBzVoFrgfI1bWzFFYh27VDXYz3YnKCxSRm6mweVSnKpbELYZw0ZkzyHph73YmSxD/zG92bI0vkOGu9vi2QjiHjT+5DB+WdgLHiqtEAchqFkebt0oqFZW6fWbSHcIQJAXC1pld7Etno0y72mMTw7/goW9PKpprji/Dm++vR6Xqgf3bdGsmResnuSjwOR+1g7h0eEo/G0vvj10GS0jItAqMgwuv/+CFXuPw/OVAQhiL02uDZwhbp+A42n3cf++8v/Vkwexb208fNWxtRYTBG5ucKE8ZGepkSk49jXmbzgHsYszKDsTXHysPOR4kJEFNgkZblXwNHmuKqX/V4aCx2r+ybNzkEcuaNhQAzGBKxo4i9E+4TjSqvC/f/8qTh7ch7XxvtVglWLmpzfAsyHcKIcF6CrkK/EkLw9l1VD0nMhvYOWUSUisNxHbL2WhIPsGji+OQgOVHuipYvSWEZglLDARdp+LPRceIDfjD2yf7oFjCe9g9bWnTCZCdJ+7Bxce5CLjj+2Y7nEMCe+shsC1AZzF7ZFwPK1azvevnsTBfWsR71tusB5KWKcl7I65ey7gQW4G/tg+HR7HEvDO6mtVZDAbMkoQP98N6Zk2WCOtCBrAzZmQnZnJxnSqDvkDZGQRnJm+mnXoNqV7zQEzUe9qtGttpVg4LFM7LRQWFlUNNJQYlAO//DTsgWuQt40aGJl3o14XxA5zx3927cKubb9AMmQswth4Dz9+PPploHVBAzc4U7byRbuqjPwBC2DIGW4qR2Kgruq2MRh8/YcKhvEU2H9TZlyabVVj7Ldel1gMc/8PdjH+bvtFgiFjw9hCY9PsR42rYZw0mtI6Na2/1KpSfSFoWAu/WV1b34mJ9Kqq2oUjOrwQv+39Focut0RERCtEhrng919WYO9xT7wyIIgN8pjiH7TjAQ68qP0c7PptO/7ZdD9mvrsTmTyRJ68NBHihoSvhUdZDtX8qyUd+qTJeMUUXVeSy4IG/XzSpgzWNx+o29ZxpKq6ex3V56+90r88cL2tuROaZQ31GAK0cUHaffaLc+gvbrlCGzF8O4mJFMzQPFEHOPmNkyhqhY5+O8HFgI+hZB7Fp7y22+ruCjaPyH9YeHmhIqThxlJuOUYm8E5uw+0/lnI5awRU0x6jRbXBh2XvYKxqE2G78Y56y1GUY/0YyvN9djXnhYjy4fh3X2f8bGQWQC1tg8MBAnFu7BEdzmcUWp2Dlsp8g7Dcc3e1kSF02Hm8ke+Pd1fPYCNIDRb3r128gg0XXgvpF+HPzfCz47pYi8KtI/xmfbbgA75598BIb8a3Mv4zkrXtxgYPLczhERCM0fQOWH2uC8O4NUK91d3QuS8RXe53Qp19LNkIoRIv+/RF4cQ0+TLytaEuetQ+zo9hI6tIUg1vzCXxfQXTLq9jy5T5kcJHO0wtYNfc9bE8lBDF4rW5sxsIdt8C9apXf3omFm6+hRb9o+OvECDyoVz2qwJlvVuAkt+lyZTYO/XsLUkNY234agBif+/cPxEW213ribS5MliNr32xEdYjB0hROF6wgFgtRnMP0rECmwM8QvZXNBmBIh1v49rMfcI8pX2XOb/h83QmUKf2jfnTZiOuDTBkadeyDjj4ObGQgCwc37cUttutAhTEF1g+RkcAHU46H26YicuD72M9efmzcX0a30AA4CcVsesxDbJsaiYHvs33J5TZwf7kbQgOcIBSLIWzRH/0DL7J95ROhZFMW9s2OYqPYS5FSZrie/OE2TI0ciPf3Z0Fu446Xu4UiwEmo4Kkh9Gver52eVcOxYrQJi5HzIBMF7NOy3kPQFNH9W+HG5oXYcUuhebi9cyE2X2uBftH+eqvou2nF8ao4h8m0AIaaUtQzqnf6oLN71h7wYF/1Uk8cBWe+lXknsGn3n0pbkxuWA6/8tKaxGWnDAFqm3xai9ZgR8P1tPuYd8sHw2HaKj1C8+PHol6F2BU2j0b/VDWxeuANKcd7GzoWbca1FP0Sb6EiMweDrPwzhVeP+s5KZCrCwNcaM8MVv8+fhkM9wxLbjPvGZaT88OKma0f2tVX9ZBURQG7+pi4DWtTF6NX06m1bJ9hKLiA5F+oblONYkHN0b1EPr7p1RlvgV9jr1QT9ux7Ba+gcrtluIrc1LePurGWi0bybe3ZlZPZimhTK74LWByqboN6A1rn/3BZIVneYTnFu5EcdUwTdPLKPbDlhkZLRfrFlJ544xHusU17nU9pOVyL+cjK17Lyh8mk7Rurl8vlPM/1ut6Vlwad+NvrirWhhYRvte9SbPCXvZMghDh5xy9s+g9s5CEtnasgUArtRxRrJyR42yS7T8FW9ydG9Bod06UkhIBE15NYJcm71Oh0rVi33Uze1ju4h40oS93Eq/LPr53Q7kxlbCezf1pkbtJtCk3p7UkVtwyQtXc8GeNs7yzPXUj+3i8vL7540sIOUWxQWymXaKQSsuPKv+L+6xQrlDytPf6d9DAklSvyF5SBzJO/ID+pVbUMktbgkUVpdX1xVTjxWZDKFyuv79a9SB7WDg2sibXBzcqNWYlXS+arMT6cUPqJVNc5p5Ur2YU5sK1dUj+iZGQqLg2XRGUfQxfRfDVi43n0nqqsX054Z4as/akng3Jvf6LhQybHnV4kEVnJq/pX9tpPh2Dam+mzd5s11SAgZ+RicVq/zK6Op3k6mjhyM5e3mx3VB8qOsbW+hq9QI25cIRpfYU07cD7Sl49hnlynTG8XMJLclh0HdVC0tsqWn7TuTr5U9BbMcT16BhtOIP5R4wWgtUiv+kDfHtyd2R7WjS2J3qs91Ghi2vWsDLLWjbyRbp2lmTqGUCnZPy01t+83t6rQODwXaN8GjYnGKGKhczcou89B9ldGn5K+TtyBbkhHajjiEhFDHlVYpgq9lfP1SqXJRotr3ww6Sn52hZDKdX7tTMvxE1cG9Jw5efUyxYfnpuGcUESqi+ezPyb9SA3FsOp+XnFCtBqfjPDRTfnu36wRYFN3avz3YVGkbLlatEGUhD9Z7SuWUxFMh2J3Jv5k+NGrhTy+HLiQOpWEyqSVvxtzTQPphmK5WtaqGvAw36jltAxsd3Y3aeSTvZYh87axG1TDhn2C7LrtJ3kzuSh6MzeXk5s8WXXemNLVeJUz1TF1zKM3eyBeR2ZC1qSQmnr7EFl5r6SlS271Xy9pxAnPthDOXRO65RfYecsn5+lzq4sd1YvJuSd6N2NGFSb/LsqNxRx7AcuOYMy09rwSXTecNtaC6m1Icfd0+zjFo21aXld2l5JNvVp9tSuqVhF3z48dFVDVfnpOzqdzS5owc5OnuRlzNbfNn1DdqicCScOE1YcMng8cHg7T9qLFrVQU7jko82wzxR87hqeWU1RPnd5RTJdv3qtvSWxiJPHvvRgysfTtUNaZ7w9pdqHdDbDzM45vtNYzB56K3h0xkCj76hGIlI3Zc8/o5iWD/efObJqr6FleHxD1r9iYIvuviV0rm57cnBewTvphaG5c21f42+n9yOGjg3pqBAHwro2on8bJQLLnl1scaCS0P9oi7OzGd8O5Dsq/t/ziWzBakOgxQU8vpkjTarIz0N36flJ8+V0sUPWpGNVlxR1UQd/VhxcOsmrP//E6r8aTquXMuF0CcYwR6ao8plyLr6F1vE5Az/ED+2sLIcRU9lEDvaQ2zCcL78yX3cyhLAm33asSMZyIrNTVLUMx9uZfYmxLRaj86/HcM/gzRGWGstkkqU5T1AttQZjTwcjcwf1GlE9gQZaWxU34XNXWVTK9RHGX6ZNgZ/vLETCSHPAkcGWV7EFs3mQu7kDS+JZlvqVmueyfDkYQaeCBugUQM77S8vDF7Wg3wIGnihgZ79pGvC0rkjv40l4a3w85C/sG90OW6zBYLNgrzZIk6dchqX8iI2Ypwrh5O3F2qQwPahLqsUw0alULz0cnQ9RJmjF9zZ1xhTjrKsq/grvRzO/iHwYwuRy7l9wsWOsFe1ZwoQnTLGYFYUsKyPBVZwaeTJRr41K1eggGVpLLByQSNPNvKt+YiNExWxr025cid4e0mqp84ri/DUqyhgWUILYOXSCJ7ajWlBN3rBy3f+2rKyMlSKbYz6BHlRFh7kNu9SbwAAQABJREFUC9DAq4FZe9arW2d70JdVQmwj1tZpdQGtM1690yqpcSF/gvu3siDwDoCXHZsVT1bs07hKuXnkwCs/DfjcKW8bOmWf2WUt9ctg+wxe1gPks2kDXszHmGaNusD4YRjuP3Th8F0/I5nxNcE9M8t++HDS15D5/aU2FPP9pnZ9PVd89Or6dD3V9d36+/5BH1TNe3w2wL4G593BvWJX+Im2oG/AZnQ/dwbvV/XjRnWxFv2iJmZ6z/l4rLeC6qaGnyz7BdPG/IE3dibgWYUkqlb0/VqCb31c+b96T34XR7b9imM/LMGayn/h/A8T4KXqC18wmiqzf8AHSyow6bORaFq73ugFo0gPOhpO5sg7TU0KgvRAsdyycMDCAQsHLBywcOCF40Dlw5XoHaAdfBtF8oXsFyuR/cMHWFIxCZ+NVG+QYJSWv1FAe0DpbwCyVH0BOEBF+OvHjTggi8HKf499YQNvjlPW7oPxyecvAM/qEgW2a4Zvp97o1FTzC0ldNmiBbeGAhQMWDlg4YOHA8+GAtU0T/KN3KPwdzFi5+EL2i9ZwH/wJnmdIYhn5fj46amnFwgELBywcsHDAwgELBywcsHDAwgHLl3CLDlg4YOGAhQMWDlg4YOGAhQMWDlg48Lw48ILOCH5e5FvasXDAwgELBywcsHDAwgELBywcsHDg+XHAEnw/S17L72HPxzOw9pxqc+RitrvCY8WempW5v2DR1MU4xO33/IyPuoRtHqqVyD38BWYsO4I6INM8VIyU/vs805TtYXwxYxmOvBBEa+JVVzqnbsMIm2s8LjrxFd5axPbwfvZmUKMt7RsluLxhCvp26YAuo1drP9K6kuPeno8xY+05rbvqC83nTN9/WYSpiw9VPTa1DTU0s85KLmPDlL7o0qELRq++YlZVS+G64cDz1WcT7a4yF78smorFhx7XDdFGoBazXYrq0hVW5r4Y/vbv9yFGGFmrxyboSDHbYaoOBPRi8qNWTHwulSzB97NkM0sscmrrGuy7zqV1rMCp98MQueB3ZfKJilzcvnIHuaq4/Fm2W5ewzcKTUPjHD1iTlIJCs+r9Fwr/LZ5py5YK/8APa5KQ8l8nWhsv/C0aDclEpw1DxQzcL01NxsbdF/D4eW9w+jgRc2fsRnnY63grPtwAdtxtObJPbcWafdcNlNF+XpF7G1fusJzL3GFyG8ri5v59nDgXM3aXI+z1txAf3tjc6pbydcCB56fP5thdBXJvX8GdOuls+JlYcep9hEUuwO+6SZP4q5n19IXxt3XiX81ihU5hE3Sk4hTeD4vEgroQ0AvHDx32vGCX/3vBN8vaV53xXCEMOUoKClCiN+kce/bkCX+WOIMCZaNi9zNRUhVkWHvHYt2RrzGqOnd7VUV5GYpLtDFSgaxg+yzrx0tVQvlbG9jaEJRXsuICPH5SbjD7Vc06cpQ9KUCxXt6xMKas2ADvZCgueIwn5YaHP7m6+tlSwfZPL2Ehkv5DXlKAAhOY9vd4pi1bLUw4eRpKKygvQUGBYdy14Cgu+HST8bfkCZ5otaWNl0Ea2d6yJZovgZXlBnRQn5y021DjzI+rUpcNy1sNx5QzPrvUj4f8cTZyZSHo//p4jOgZYkojJpSxhnfsOhz5epSirKE2TNVJdYP6aJDjcXYuZCH98fr4EegZ4qAuXuNMX/0ahQzekDEfaeyoqXvqGvz0Mv9rHLwSmBF70cWTDyc1duozY/7OsA9iQysK32yGPjO/8KSgWK/f4ucXS8StYJghu9Njo9beiF13BF+P8lATqzhj/rq4RKf/0ymie8nTP+nDuzL3PjJVnZ4uLKPXxvTWDN3R15YRfUIll2Pa9MOgf+WFw/aV1na+KDcgE3381cKuhmwM6YhGLfZV5H5miSKbnsZddmqM99qlFeV1YiOD/NCtWttrXvnpsQPNdnTiPnN9hSaoZ3ZeR8l7Xiyw0vP0Ybd2FDttOAU4CKl+xDK6LpPS3aRZ1NNXQg4su6HELYgGLDiszFjJ8lxeXhtHbTydyc3djZxc/KnXv/YpMj5K/1hA4X7jKVGV0kueTZuG+1HUsuskq87SVUhXlvQgNxsBCWxZxsKBayjt9gqKbjKAVqfLSXZ3BfULHEpz3osiP5bBSii0o0bhs+nnTGUeptLUzfRqBw+ys3Og+g1a0+hXB1BQp7l01kAySA6eqbBrCkZOj44uoqGtPUni3IBcWFYyl5AYWnpamVGwZnnuThld2zqdIpqxDHyuzuTo0pwGfXacHsuV2ansXhpCk/v4k8SG0WbnQxH/2q/kq/wRHV00lFp7Ssi5gQvZ27JMlDFLSdmUjO6u6EeBQ+fQe1F+5CQSsrqNKHz2z8pMm0wmqZtfpQ4edmTnUJ8atB5Nrw4Iok5zzyqyf0nvJtGsnr4kcXAmF4kbBQ1YQIezVHmtalJRe57Jasr2xmIKtXuJhkzuQ/4SGxIxefpE/ItY6nRlw9K7lDSrJ/lKHMjZRUJuQQNoweEsluPM0MGnm0Sll9dSXBtPcnZzJzcnF/Lv9S/al1lREy9dnQsaTrNn96Sm9ZlcxA2p86zddGDZMApxETMddKSAwSvpMpdK0aCcatL+QG4E1yvf0OROXgpddvLsSGNj2pBde2UmRH3U89oXj12yvGcG7Vl2ZQn1YNlPRdZicmRZP/t/lUwLwv1ovNqIKXvTcPKL4vyCOmufPvx0MyfeXRFNTQasphptrEojc3WymgY/J7J1cCBbJz/qNWcP3ZMp+e7uKCJrMcsY692fVqXp054qHtSoz1EipT8WhJPf+ESWp1N5yLM30XC/KFp2XUbS8x9St3axNG14ADkI61ME58+qymn+6Nc9JS6G6ZXS+Q+7UbvYaTQ8wIGE9bvS+MEB1HbmEXVG4fKzNLdLIMVtyyE5j73ow7NIrz3o4w9HCZ+/M+aDmO2Zqc9cRsCt0yOoGctu6ersSC7NB9Fnxx8rbN8wv5i0tOThSB0HdCZXjf7kgZTHl8ru0oroJjRgdTpLo8nO+wXS0DnvURTTC5FQSHaNwmn2z5kG/Y+x/skQ3gobcLMhgcCWZRruT+/PDqNmwzZX9alldGRma/Lq8B6dUvRjckpbPZD8h6ynDJUPMaC32roTQV/8uohCVVlp5Q8p+Z0uLNPiDNr30IDMefSJ0wiOntk9/cm5fn2WlTqC3v5nHLXrv1wRI/DZjGYfIr+3mmKCh9EM5l+budan+hJfingnke5wtCpkEETDZ8+mnk3rMxmIqWHnWbT7wDIaFuJCYiYTx4DBtFLhfJX4GOrLDMumpv9/oMsO2RVa0sONbAQCsmUZggeuecB0gM9ncNzRPQzHRmp+VNDNNYMpIHAYbbjDeRE5Pdg2jloEDqF1mulkq0E/peQ3WlDzibtJkWCauy+7Skt7B9CwTUxP+eRnsK9iMPTEfZcv6us7dRlVjVidnqBOob8owLmgOERENi3jaf3Bw7T/dBqVpy6hMIkfjdqcSlyy76LUNRTTyJOGbnlI8oKtNNQliKYdVqpCIXOEYe6htPCylGqkcJVn0ooe9tSeSwdfHXxz3Vsp/TjWnXwmH1CkhtZMY604F4soYMxGSsmTUtm9XSwdtA21nXeJpNJLtKCjhJpP2E63WCBUdmc3TW7JAjqetKcmw9YnDy5NfJATdZ5Xlcq89DqtinYjydBtVJUJvkYtWeoi6sICg9jvbzEq5ZR3bA61qx9Ms06V0K3Focy5NKPh6y5Rvryc0pMmUiBLP/vRXzKWkX4GBTl1pnlV6cBLr6+iaDcJDd3GtaQM3MWiABqzMYXypGV0bxdLqW7TluZdkpL00gLqKGlOE7bfYvwsozu7J1NLFtwrUu/KUmlJmIT8Rm2mVKUwaU1MI/IcuoUM+eO/xTN9smUvC82Gr6NL+XIqT0+iiYE21P6jvxhVMkpdEkYSv1G0WYkcpa6JoUaeQ2mLAeRkfLpJBbR1qAsFTTusdFSF7MUyzJ1CF142TefGbqFrRTLK3jORmolE1GTwKvrjsZSepnxK4U4+NPlAmRE56eg1H65MLotCncg/9nu6yVKZl97cRuMCRSTiCb557YvHLvl5xrTrpkYqby075dRbTpkrepC9Ai/zgm9O3+27fcEB0W6jNjp5dSmFS3xo0KqLVCiXU2EK69CbSKj70isKPbq5qDPZh39JeuNurn3e+mq6qoPvzBXUw16ZGprje4jIhlrGr6eDh/fT6TR9oTeP7vHSy7UdQiKblhS//iAd3n+aLm8fRR6NJ9H+KmTKjr5FAQ2H0veP+O2lJp55PPagEIv2H15/x++DyGx9ZrQs6kJOfrH0/S1mAPI8OjanHdUPnkWnSvh9Vk06i7T6E15fqpFWmxTnYhIFjKGNKXkkLbtHu+IDyKbtPGJuVe/B2z/xypnZ+I9jyd1nMjE3QuWcTBuMoB0FrBnpOUpoaUtiu470cSrTLRY0r41yoa6Lb1K5Ub3V1p0719lgBxd838mk/e+0I/cWE2nnXQPEGPO/suu0PNKZfEdupqulciq+solG+qp8FL/N1OhDWB/gE/M1XX4qp9KrmxkcZ4pcdpNk1TIYS1uuFZEsew9NbMbaaDKYVv3xmKRPU+jTcCdFrMDpGF9fxisbnX5Jr3BLf6Sx7ko/zz3n9xl6IPD54FtVcrnLglnpFVre0408Bm6g2zfX0wBPD+q39haThv6jKHki+bgOpu8eKZ9LU+ZSW5doWscGlfj6T147qBH3/UlbDPad+vGqy7v/O9NOSIi2cQkY3ysSfTt54+buHTjfbBheC3dAXno68hx6Ii7KFgd37Ee+yAlOthk4tPILrE++gMLguTiWdQIJLz3DnETCEIycGYdWLI13vSbRGBgqYanR76Psyg9ISm2N1+cPg189oF6zQfhoenfYmPOtwwBsvV97ha0wI+kMEmd1hGNlCXLSH0EkqY/y/Dw81fsNji04S/4JKQHjkTDCj+FlDZewBGw//A1eD1byRxgyGrPHvwyJtRg+fQch1Pkh0tJlELaagaQziZjV0RGVJTlIfySCpH458vOeVn/uE4aMxMy4VnAR1kOT6IEIlbB06/fLcOWHJKS2fh3zh/mhHvvXbNBHmN5dyRX5td3Ycb4Zhr0WDoe8dKSzNO4946Jge3AH9uebyDhzeKYPJKs/evZ4vCyxhtinLwaFOuNhWjpk8mvYveM8mg17DeEOeUhPz4NDzzhE2R7EDr3IyXGNTzchgpOTLTIOrcQX65NxoTAYc49l4UTCS/qw0r4nCMKw6SPQ3F6AhhGhaCHyQvTUeLRxFsIhOAKdvAuQy1LbmyInJWB+XB/dS8ZPFwMxLmEE/JmobPyHY/6UzhCbkY9BiwCDdmllhGdaUJ7Lhfk6KUdachJ+943HR5Naw9HaGo6tJmHBRH+cS9yD24bmWVVT83frAyRsi7iE8egV2RedGutLO2tY94zTSxC2jUPC+F6I7NsJL/WLQz/sw45DRYyCEvy2dQ9KXolDf4lxe9HG08Y8ezDB3+n3QTLIzdVn+T0k/5SCgPEJGOHHDMDaBWEJ23H4m9cReMu4z9KmU1septsopyBChIycibhWLhDWa4LogaGQZD/Afb0dQpVCGfCH5Wb4WnHHfujhcBq/niqD/M5/cLykH0Z0uoVjx3IgL/gV+88H4pX+jfHAqN5r645CNakAJz7oh1Hbm+CzfWswrKmBvtmI/5Wn7UXiWV+Me28MgmysYRcch4VTO9XOR4naYPLHr+IlB2vYBMXigwn+OJuYhHsK2xUgaNh0jGhuD0HDCIS2EMEreiri2zhD6BCMCBaTFOTmwrgdceLUHzvwibNKqjo/tfAZBn2wDv+FwZi65lN0OJeA3n3n4HLkcqyZ6AdtLVajY99jLAa7HMHOPVksFqjAhW27kdFjDGIa3uDtP43agVbc5wvn2vadalSf2ZkOx54Z3BcQkDXcPDyrhC/Dw8wcSK+vR2zYFi1cXVsXIb/eOHy2azHmf7IO80cuxBR4oUPMu/h82ZvooFX6b1xYOcGZBWrKwwoioQCV8krIs7PwSNQAHm6qZ9Zw8vJEfdWlKU0agK23qrUABSnrMGv0Lpy68RgirxA0r1cIakh65oVxENiCMzb31MrdC97VluQE//YcZ+S4zf5aS1zgqsLXSgCBNaNLRrAWFCBl3SyM3nUKNx4z5xPSHPUKCQ1JvfrOysmZBe1cO+ywEkEoYHUrWZtZ7KWggQfUbHGCl2d9xUb1soeZyJFex/rYMGhJ07U1ivKZ53OtRlQJV99fc3imr761BC5qoiEQWDN5ykCyh8jMkeL6+lhoq5orWhdxbwauOtCM6KbcDn0/24XF8z/BuvkjsXAK4NUhBu9+vgxvhxrJpMlwdFUxUCCEgGUac7CvioRZsGdtrZS5KXJSIs2P66NMNs/aqiG8qhXFGh4+3jCCpQ4/NC7t+hqwy9dQymfPTAUkGmCex6n5OqnUcSvPxvCp9soCNPbxhFVeDnIYDbozeLXpMF5fpF2h5pW1Gzw8+WzFsO69XmrEBllr1m4eqAZvH4m4wfYYsX0/CsLF2PazAAO/6Q1H2Qkj9qIApIGnYZzeDnXRQ6Nxf6ffBzHvlm2mPrMF+Nm5VnD38lYHHU7+4Fxl+SHj/GIM06BTmxTTbZSrZwUnZ0l1Ug8rEbN95lP1jq2omjHgD43qtao+92vTBf0iCbMOX0BG+lHc6xCHpS3TMOC348h2O4AzTaLwkT8Uvp1P7zm91dIdDrb0L/x6sR18Ss/hl9M5iGvsWU0f97j6MOJ/5VlZyGWW1aja6ATw9G0Kx9oMEIh94NtUZT8C+DRiFpuTiUxmu4wCSFzdqnAUsH7NCnYO9kwyymfWzP9y3aBR/nIVDMhGAcqsP8Z9RqCKHBVcgz74TXRSlan6FfiOxlsxC9FnrRhvbh7An3G7XhfEDvNBrx1JyBjZAtuSClg/1w+usrO8/sBaIDUSU2jGfWb6Ch16nvWlKsx51nBfSHhWViqLEsC1gTPE7RNwPO0+7t9X/r968iD2rY2Hb3k2soTdMXfPBTzIzcAf26fD41gC3ll9DdZWLEhhS1akqgVrVIjCInXwqEu4qkXd+4auhd6N0LAiE/ezFBbLilUi//4DPOH1lIagGb8vv7ESUyYlot7E7biUVYDsG8exOKqBfkemACeAm5sLKI/xSIUiCnDs6/nYcDq/yqHro1qOGyunYFJiPUzcfglZBdm4cXwxohqYooJCeDdqiIrM++o2K/Nx/8ETRXsC1wZwFrdHwvG0alnev3oSB/etRbyvrvcwzhNTS+hSqXutZJcrGjiL0T7hONKq9Oz+/as4eXAf1sb76mnKiG4KSlhnJUT3uXtw4UEuMv7Yjukex5Dwjnr7PL14KFqyqnL2epqtvmWanJRt8OMa4NkQbpTDXnRVilKJJ3l5KKtuq+YJr32VGLLLm/z2rKsC1lbsRYN1dGojRmFhkYGXzZo4mnLHfJ0UoIGbMyhb1VlzrcjxICMLbHI/3HRpqIGEsfrWsFISzcaVlAcVFkLbdTH9MKw8rJJh3TOJXgZcDb4eusQOg/t/dmHXrm34RTIEY8PY6LDAFHvRxNMwTjVYxG6Y7+/UUAQNzdRnAZObCyGPDaioLAAFx/D1/A04J3IzwWdp0qnEQ8k/02xUjfmzOzNNzqr27BAeHY7C3/bi20OX0TIiAq0iw+Dy+y9Ysfc4PF8ZgCA26GSS3mvpDoMvao85u37D9n82xf6Z72JnpoEO0og+CdwbwpUeIethtYRQkp+PUkWXborNqGhlvzK2YUGBCg8W2ObkgVwaomGV7fLblhKOSfzVaFLfqdrG9D3l7qlKmMh7TTAGffA1zVKK85Kzn+Gf2+0Q1QfY9M6nOFtSo4jGDSHajGFfZc8l4Ye9O5Es648xvR2N+IMmJsUU6rjPPF+hgVydnJoS+dRJw/9doEK06N8fgRfX4MPE24pgQJ61D7OjOiBmaQrKHm7D1MiBeH8/c5o27ni5WygCnIQQi4Ww9vBgI7WpOHE0lwV+lcg7sQm7/9S3r5KVonxxzgNkFrDPbiYSLGg+FKM6XsHXCRtwIa8E+Ze/w5xlR3kDFhNB6y0mZ58fM2WN0LFPR/g4sNH3rIPYtPcWW2FfUd1Ja1cUwPeVaLS8ugVf7stQ0PX0wirMfW87UmV2PEE7c0YPMiFr1BF9OvrAgY1oZx3chL232Ar2ClU4oN2S+kqA5kNHoeOVr5Gw4QLySvJx+bs5WHZUGcYJW/RH/8CLWPNhIm5zt+RZ2Dc7in2tWIoUfaJRA67lmRmyFbZA//6BuLjmQyQqkUPWvtmIYqPVS/Uix6+bUvlDbJsaiYHvs72y5TZwf7kbQgOcIBSLGS1m4GWQcmNy0mxDhiAeO6psNgBDOtzCt5/9gHtMxJU5v+HzdSdQZvhdlde+5Abt0obXnmuogLUHPNiXndQTR5HL+srKvBPYtPtP5ZagBvli3gPzdVKAptH90erGZizccQvlrLny2zuxcPM1tOgXDX8Tgm/++uyrg0dDUOoJHFUSjRObdkOv6zJEKo/umU8v+3reegxG+P6G+fMOwWd4LNpxKmyuvfDgpI8M8/2dGorAXH0W+OKV6Ja4uuVL7MvgeoCnuLBqLt7bngq0HWCmz9K0O7aXc619qZqe2pwZk7MV80PC4hw8yCxQ7HTlEBGN0PQNWH6sCcK7N0C91t3RuSwRX+11Qp9+LdmEmFrqvZUN7Gxt8NLbX2FGo32Y+e5O6I2/jeiToGk/DGh9Hd99kQyFiJ6cw8qNx6qDb7NspuIMvllxUrHHeWX2Ifx7SypCWF/pZ9R21ZIwxl91SX1nmjpiIOawEkMsLEYO0x8Wlpjtcwz74OrPdUrEnh7DvMn/RsXEr7FtyyqMLV6G1z48zizA8CFoPgqj21zAsvf2QjQoFt24T6S88iszL6bg9RVskPNyMrbuvaDoEwxj+eye6HDs2QF+0SGJOyTgu2WZmPBWG3jOcEa9p0Vw7bMQ38wLhZ1jKyxZcAKxsc3h7dwQIratlCTiE2ycEghreyfMmPI9xo8KQGN3B1i59kZUVzdcqkGwCK1794HL+MloHnYbx3Y51yih9wabl/vmxvXInDwHvRq9CfIOw+h/vAzRn/UgroNXJXHHCZgemYj3u7bCT2w+Wv4je3QL6wrHw7dwmwVMTdlglO4hfHkmNn55F+NeDYGHyBHyUjt0m7MZc8PqIe+MbmnVtRgdJ0xHZOL76NrqJzbvOB+P7LshrKsjDt+6zQJ9H1VBvb+CoDexcX0mJs/phUZvErzDRuMfL4vwZz0xrLlR7++WIXPCW2jjOQPO9Z6iyLUPFn4zD6Fs3vyzP8yRrRgdEr7DsswJeKuNJ2Y418PTIlf0WfgN5hlAjk836zFHPnnJApyIjUVzb2c0FBXhqSQCn2xk80/YfPBa6ZwWg4zJqbFWGycvvm/Qjjhcp61fgRujp6OV1zuwEzigc7d2cHkoqB570WqaXVg3GmnQvgR+kw3apdjRsD1zKqD18mvdCCNnTMH340choLE7HKxc0TuqK9xqGrEueqZfizuYrZPClu9i48p7GDejLTxnsVGfEgFasIRA385uxYIULQr04sFfH2g0cgamfD8eowIaw93BCq69o9DVHKIFfoZ1T+zIS+9ZfRgLmmPUyJb45J8yvDsmhNHIHUbsRRcQH0562jTq7/TUqb7FfLN5+izEyzM34su74/BqiAdEjnKU2nXDnM1zEeYoQTsen1VzOELbto98w+dLm1aj/MxPjOh1Zeve6OMyHpObh+H2yYtY2KEXorvKkXStKyKaMYcgDkXEP6zx3ZW+6NdKKXF+va3JCS2a6rXGzH+/jT2RM/Hujm7YOspLZwDIiD4hBG9vXIk7Y9hcbV/21YXkaOit+vJrbZ7NCBoCR8bjpeZi1MvPRb3IT7B1xstMr29rocx7YYS//F5AW0dOXlyIDroRnqg1evdxwfjJzRF2+yQuLuTzOTUx5fPByFGVz8cvCZOxhiYheW5XOLBdUT/6ajw6DXoN7/U6hX/3NDAJUNAEw8Z0wdzX0xE7+h9sZRd38MnPEWJHPjvQ2Y6V11dUIm3Xe5i4sxcO922HBtxAQF0fdbma8/8EbNlTyrp3hzLy9ezjV55PGXfuUmZBzZXUsoJ0unYtg9jCZpKzbQtlhnarkZZSabmhh3o4JL1GB7ceoCvcCnHFIaNrCzuRQ/dlBnc5UJWs/W8pPbzyO529eIvyODbIy+hpQREZRVtaQJn30iin2Az6Sh/Sld/P0sVbeYotAuVlT6mgqJztN8F/SK8dpK0HrrC9PqoO2TVa2MmBui9L06gro6dZ9+hORr4Ctqponf2aKVvZ0yy6dyeD9KmaXhz5dJNRmJ9xh+5mFrDNonQOM/HSqa28NCYn3TZ4cZVSQWY6ZT01tNa9Jga89sVjl8SLh047sgJKv3aNMpRGTFKDRqxTz6zLWugko+HhvXRmV6bzSwsl3voyKki/RtcynjK7YdueSmUa9qMFheeCR/fYfgbPygbNsxc+nHRJqaW/qwZjvj5LCzLpXloO1XSVZvJL0+6M2Wg1vnVxwoe3lEpLjfv0Gljx6m2N0mbf4NenMnp0+xqlsb5evfORqgnjNqPe+aSIsq5dotQHhbWwK1V73C8ffzXL6TnX1BE9j7lb0tJS7f7dXN7z+WADbRq/LaesjQPIvcundFWP6zMoP7PtwICvKD1AUwcvJG4znudxWHGN1HWAb4FvBgfYp6t/tu2DA12WY/nU9rC7n4yPpy5C/rSTODIrRL1wxwyQ/z8UrTjzT7TtcwBdli/H1PZ2uJ/8MaYuyse0k0cwK8SM73r/PzDDQoOFAxYOWDhg4UAdcKASD1f2RsDm7jh35n2Y2rXIby9BeKufMeSvI3inaR18oq4DSl8kkPK7R7Dt12P4YckaVP7rPH6YoPsFo66xrUT2Dx9gScUkfDay6XOJs3Q/StQ1hRb4xjgg7oT3t61E5Yfr8PaoJWzBhj+6vJuEr6f97wbeHMvEnd7HtpWV+HDd2xi1hODi3wXvJn2NaaZ6R2N8tzy3cMDCAQsHLBz4H+cA2yawyT/QO9SfTUcznRVWdr7o1LsTmtZ6KyfT2/r/sSQV/YUfNx6ALGYl/j32eQfeHEet4T74E3z+HJlrGfl+jsy2NGXhgIUDFg5YOGDhgIUDFg5YOPC/zQHL95H/bflbqLdwwMIBCwcsHLBwwMIBCwcsHHiOHLAE38+R2ZamLBywcMDCAQsHLBwwjwMypO7dg7+MbPxhHkxLaQsHLBz4b3LAEnxrcL8y9zC+mLEMRx6rNsrXeohfFk3F4kOPNW7WPOWFUbP4/8Sd4swMxd6neoktzkSGPn7rLfz8b8rv7cHHM9biXFXHZ5CWylyT9OP5U6DdYtGJr/DWIrZHuB4V1y5p5KpabpXI/WURpi4+ZKTCs35cgssbpqBvlw7owrbj4z+KcOKrt7CI7dv/d8nmb8fUp2by7P8Ur03lwfMrV5n7CxZNXYxDz9zPsP22Mx6boFNy3NvzMWasPWcm0Wzv4d/X4c0BoYgeOw7RYQPxzreXUVQNRYbMExuxYMY0vJnwOXammIIL28Hy4ma893ES7urdt47hmvQh3lp9Vm+eB01eavpGzXPNMtWoPouTksvYMKUvunTogtGrr/xtiNW+nPnuw1/MwLIjpvHPWMMllzdgSt8u6NBlNFZfMZz0XZNnmuc14Wv4ulGL67Sf4cejJmbP607J7V9ZHo8ZmDb1Hcxd8TOuam4YXlmAP3ctwZzp0zHzk29xNltTsUtw+1eWz4XZyNR35mLFz1e19hqXZZ7AxgUM7psJ+HxniuE4pQ4ItQTfGkylwj/ww5okpBRq3Kw+rUDu7Su4k8s//MAPoxrY/8xJxan3ERa5AL/XyHTCWFBxCu+HRWKB3ocvBovk2aewdc0+XGc+lJcW1lWZoh//bapKU5OxcfcFPP47exzpyK0i9zau3Ml9vqQ9TsTcGbtRHvY63ooPN9J2KVKTN2L3hcfPNIulkUZ5H5vMs/9zvOYl+7/zsCIXt6/cgRHXbSZuFTj1fhgiF/xuQnImlrjq1Fas2XfdrDYqc3/A9JgE/M72tx/dsS2GjXbBwaksAE9+wuBUImP7OIQOWIpL4sbwlh3HBz264s39efwvA0+P48MJb2DJllPQSOqogRfD9cwObPjlBsvjrOfQ4KWmb9Q8h0YZPRBqfetx4lzM2F2OsNffQnx441rD4Spq+XKWpfqPH9YgSX/Hb2Y7j5E4dwZ2l4fh9bfiEd7Y8J4WmjzTPK/RoKavm9ChTvsZXjxqIPZ8blRc/hz9OsVi/R1b+PjY4963ExHacx5OFXPtl+Dcx1GInHUI5SxbqfzkAvTpNg3JXCIx1idf/rwfOsWuxx1bH/jY38O3E0PRc94pcFUrM7ZjXOgALL0kRmNvGY5/0ANd39yPvOc0QmNYMzi6/pcPeRmKpSLY21RtY2ftjdh1RxCrwxN52RM8lTtAYq9nuztdGDp1zb+Uo6SAvfKxBA12eprThmdOWe2aqGS5O1kqar1vZvISKFGw07MdjxxlT55C7iCBih2VufeRWaInUw/XJBtxuJ9ZghpPedvQwZVdymQyCIVqVZaXFLC3W0dI9DBJXvIERdYOcFLJtSY4g3d4aTGgH1x6l7Licgjt7aqSiBgED1lxAQpltpA41dPPe62qLDOoTMjoVt3kl3cFSxQls7VXFTbrt4aOa8nNGt6x63CkhmHw6QlrnrONciHs7aoJMICTfrrkj7ORKwvBkNfHY0RTvZrK4FWg6KkMhsjm0xMOmUpmB9ZcSnZ9B9M5GWN+Nfa8OqtrFwZ4JitGQSHDV+KEeqpmnyuvmVj02I5pvJahuKCQ6ZgETtXIazOOl598PkeX18ymTPeDbB8D71isq6GgDDdZGUoqWaZEVUKNynIUlwn06KQ+2tjXi/uZqOnazMNNm0PaVxVn9+E/GIR1n47Ao7H7UDnuC/R8OgvHSx9BLs/A5i/3wO2dM9j+AZcpshz/KG+Bgav34qOoCXDRBlV1lY9D701FUlEDCKoVV2/BqptyFD8phcjJgaU6UR6avKxI019Xs0x1CV774EoZ85NyPM7OhSxkCF4fPwJqkzfC7xq6o8TIsC/n8JBCZG9Ts38zSgNHxmNk58oQMuR1jB/R1AQ/rsSH728N++sdoROHMB48KYK1gxNqdmu6vkejJX3+RuNxbU71+Q81HH12pH7KOjRtn6p4VI4jK/+NP8M+R+qmODTk/OJrrTA6+A2sPDAHXbrtxqJ/Z2Hwll+xrC9LqiMbDLeuXfDJhrfQ9500rPz3nwj7PBWb4hoqZPFaq9EIfmMlDsz5B4I3f4k9bu/gzPYP0JLZQ/k/ytFi4Grs/SgKE/QbkAayz+D0eWwm/t9tQ0oXP+5GzYZtpixFJpcyOjKzNXl1eI9OKfLqyClt9UDyH7Ke0m8splC7l2jI5D7kL7EhkdCOfCL+Rfu5irK7tCK6CQ1Yna4gp+zaVpoe0YycJa7k7OhCzQd9Rscfs6QVt3hgaDHCdLwy5FK6mzSLevpKyMHZhSRuQTRgweEqerSAsgvDZWU319DggEAatuEO28Kf5dJ5sI3GtQikIetuUcW91RQTPIxmzO5JzVzrU32JL0W8k0h3VLmHpHcpaVZP8pU4kLOLhNyCBtCCw1lViQTK6NrW6RTRzJkkrs7k6NKcBn12nHL/WkI93GxIILAlifdAWvNAI5WO7Aot6eFGNgIB2Uq8aeCaBySvasPPyZYcHGzJya8XzdlzT4FrDSrPf0jd2sXStOEB5CCsTxHLrlPZ3SSa1dOXJA7O5CJxo6ABC+iwUuhUenktxbXxJGc3d3JzciH/Xv+ifZkMH+kftCDcj8YnFlc1IafsTcPJL2oZXWdMKj8zm4LtB9LmCzy0cDU19YM77xdIQ+e8R1F+TkyPhGTXKJxm/5ypJ/GCnB4dXURDW3uSxLkBudjbkktIDC09XahLMruW0vkPu1G72Gk0PMCBhPUjaNn1Ml7dKL3yDU3u5EV2dg7k5NmRxsa0Ibv2C1giASn9sSCc/MYnUjXl2ZtouF8Ug6nMMqBXx3P/0pFbGt1eEU1NBqxW4ssjQ9ndFdQvcCjNeS+K/JxEJGT21Sh8Nv3MyaHGUaXHjH+2Dg5k6+RHvebsoXsMNdkVJgt3RxJZi8nR1Zv6r0rTqV1KV76ZTJ287MjOwYk8O46lmDZ21H5BqkKXpAb1RE73VsdQ8LAZNLtnM3KtX58kvhH0TuIdZdIm6Xn6sFs7ip02nAIchFQ/gulImfl28Vguo7saPJM/OkqLhrYmT4kzNXCxJ1uXEIpZepoKa9hIXfGaaZaCJ37kZOtADrZO5NdrDu1hzDbKa/kjOrpoKLX2lJBzAxeyt3WhkJilpFRffn7K+XyOPl4znVX4QX06wePbypneRTcZQKvTmX/mdDBoOM1mfq5pfaaD4obUedZuOrBsGIW4iJlOOlLA4JV0uYyplEHaZHRlSQ9ysxGQwFZC3gPX0ANeH11OZ2YHk/3Ab+lp8hvUovlE2p2vUlkZXV3amwKGbSJdMyg/NZOCHDrRx5fzaPOwwbSxOsNYVd2yx/S4SAUnn5LGeVODUTvpqeqW1q+ccva+SsEvvUk/MR13bD6TTqp8u1Y5Ja627QbTmDbuJHFyIMdGYTR9x21FMi+Ofypeqnzjt8yBaJ5rlmGKpeg39Pp0k/2kkt/ujiKyFjuSq3d/WpVWblAXSK/uqIlU6LRmv5R2gxaH2tFLQyZTH38J2YiYv/aJoH/tr+rfqmjQ3/ep4TJjYb7RnRxF1iR2dCXv/l9R8oeGfawmzzTPNSDWtL8VJzTikFK6vDaO2ng6k5u7Gzm5+FOvf+2r0iP9fTILUZhaG/A3rGFDeDDFpTdaNKeJasUl2dWl1DtgGG1iimvYpzKgBu2IPTMiKy7ZUPafv9HxK4/VfWfRTxTvLaGh255S2f5Xydt1JO2stoNyOvvPFuT4yjp6JMumP387Tlc4oquOop/iyVsylFhVdpTRY7UBUX7SOPJuMIp26jcgFYhn9otnBukFBlR+9C0KaDCCdnDOS3qOElraktiuI33MpTKSP6S1US7UdfFNKucCZ2Z4zYavo0v5cipPT6KJgTbU/qO/SCa7xQzUnrp9cZfpQyot6sI6qNjv6VYpA5F3jOa0q0/Bs05RCR8MHR6ZjFfqEgqT+NGozanE6VhR6hqKaeRJQ7c8VCtkFWwZb1kpXVnek9w8BtKG2zdp/QBP8ui3lm5xAU0V3j4xX9NllvGv9OpmGunrTJHLbjL1l1HqkjCS+I2izakKDCh1TQw18hxKWx6yDi11EXVhwVHs97eolGGUd2wOtasfTLPY203pj2PJ3WcyHeA6M92j9Eca6+5DkxUPuU4onCQ+g2jVRZYdTF5IKayTaCLpTkuvKINBzeqckwgR2VDL+PV08PB+On3nMi0Jk5DfqM2kRDGV1sQ0Is+hW+ihvIC2DnWhoGmHSdHnFbIgKsydQhde5rwNzQ62p4FcD6I45FoZzjSdES8tWvrB6YqYRAFjaGNKHknL7tGu+ACyaTuPLummoyw/STOCnKjzvLOkCLdLr9OqaDeSDN2mpxPlOsYQEtm0pPj1B+nw/tN05zKPbnB6GupE/kxPbzI9Lb3JXrYCRSRSBN/qgKCa8swV1MO+PS3g7IJHx8t15HZrcSjZd/uCcY9fhgodE4soYMxGSsmTUtm9XRQfYENt512qkaWTc+zhEh8atOoiFcrlVJjCXg6bSKj70iusFdbSzUXU2T6cvkxTO1al/DjUF1Gokz/Tx5tMH0vp5rZxFCgSKYNvRpdhPZERR4tI6EMxX19m2WtL6ermkeTrHEnLbrJWOV0JEZFNy3haf/Aw7T99hy7Xyi5KFO0oeVZOJ2cEkVPneXRWqQB0fVU0uak6iOfAa9aL0tJwCfkMWkUXC1ney8IUWh3ThCTdlxJneny8Lj85g4KcOtM8JfJUen0VRbspO0ZOH/j4yetzavA6jcp5dcKIb7PvRl/crRoc4XRw7Ba6VsQ69j0TqRnTjSaDV9Efj6X0NOVTCndS+iR+2krpx7Hu5DP5AOvCOZ3jsUP26qYKvqkomSb6uNLg7x4p1VWaQnPbulD0Oj0v5rK7tHVcIDk6B5C/90s0dnUy/ZXHab/mIaXU7XNpcv+W5Bk4gjZc0edoWR+VuZNiA9vQrKOFVLTFePAtqhdCk364xzAvpr/WxpCPcy9acZv1BFw/UcVLTd+oea4uU8Hv0xU+00Q/yXTp5qLOZB/+pSLTM69/0KM7ulzT8uUKPNiLWLPhtO5SPsnL0ylpYiDZtP+I/pLx932aklCcy27Sos72FP4ll3lZLXd9PlaTZ5rnujC17E+znynYSkNdgmjaYeWbXCEbkApzD6WFl1nGbYN98lNef2MYjyJKnuhDroO/I6XmSillbltyiV5HmRV8PpW5TT4fYYKstPlRTBcXsljEezTtzJZTwcZ+ZB/4Lp2ofpGUU/qy7mTf7kP6U7evLb5IC1mM4D16J7Gq1Yc0dTvNndyfWnoG0ogNVxT2XP2wDk9UHzefwRj6iwtC3LEfejicxq+nyiC/8x8cL+mHEZ1u4dixHMgLfsX+84F4pX8z5WcmYQhGzx6PlyXWEPv0xaBQZzxMS9ea/ya/l4yfUgIwPmEE/NicCWuXMCRsP4xvXg9WfoY2AQbHLdPwaoybu3fgfLNheC3cAXnp6chz6Im4KFsc3LEf+Vpsl+Mab1khgqeuwacdziGhd1/MuRyJ5Wsmwk81hUXUBpM/fhUvObBEA0Gx+GCCP84mJuFexTXs3nEezYa9hnCHPKSn58GhZxyibA9ix/5HuJf8E1ICxiNhhB+bQmINl7AEbD/8DV4PNunbppICeRqSk36Hb/xHmNTakX3ud0SrSQsw0f8cEvfc1qJSdUHCtohLGI9ekX3RoeRH7DjfDMNeC4dDXjrS8xzQMy4Ktgd3YH++CE5Otsg4tBJfrE/GhcJgzD2WhRMJL6lA1cGvECEjZyKulQuE9ZogemAoJNkPcF93IqWwFWYknUHirI5wrCxBTvojiCT1UZ6fh6d6554RhG3jkDC+FyL7dkDJj4Z14xGnpxcDMY7pqT/TUxv/4Zg/pTPEJiSPMKrj+jhmigyZbYycGYdWLkLUaxKNgaESZD+4r2Vf3CfotOQk/O4bj48mtYYjm/rh2GoSFkz0x7nEPbituZ6mBh5swRjTx4uB45g++jN9tIH/8PmY0lkMjmz5td08eqIEJmozGR+/+hIcrG0QFPsBJvifRWLSPeVDEqJtXALG94pE3w4l+PFv24UQrWYk4UziLHR0rERJTjoeiSSoX56PPP0KoMTjmfGa8SQtGUm/+yL+o0lo7cimmzm2wqQFE+F/LhF7+JkNYasZSDqTiFkdHVFZkoP0RyJI6pcjP+9p9dxjXn4a8jmcjDV53ckbD3h1wohvU3JN+VcQhGHTR6A5mxvXMCIULUReiJ4ajzbOQjgER6CTdwFyc+Um0VYlDCN+V6Nx+x4YO9gFR3buUSx6rriwDbszemBMjGfNKQqCphi1+SIuJ81BmOsjnPh8DNoF98Anp9VLLrmpVcVSMbyCW6NJ8TGs//YYFNNdNZpkAsaW6Qm4Nmwl5nVzVNiB5uOa51awjXgLCwY2YdNN7NBiwr8Q530aSfuzaxblu2OKjrIe0yQ/qdWOCf5BS3ca15xCogWPu2B4jJ6N8S9LYC32Qd9BoXB+mIb0cr6+T7v3rQGyrm+InOBkm4FDK7/A+uQLKAyei2NZJ5DwkhVPn2xTO38De/QYOxguR3ZiD7dav+ICtu3OQI8xMWh4g9+nGvURJsuqCCkrR2Pg4kKMW/clhrA5KHIZcxRCkTLuquK3SMQCGqlUey1GUQpWjh6IxYXjsO7LIcrpK1XlK9g0I7FXMFo3Kcax9d/iWA0DqhtBmhEd1Q0CzwWqTRf0iyTMOnwBGelHca9DHJa2TMOA344j2+0AzjSJwkf+TGDcHDZrCVxcVe8kVhAIrFEpl2kt1JJns/mmVu7w8lZFrYCTf3t0YNXlXJxoAgwF3SbhRXiYmQPp9fWIDduiqKb649q6CPlM91yr0ZCZUNYXo9+KwcI+ayF+czMGeKloZVCZ0/FtqgImgE8jDyAnE5nlD5GZI8X19bHQRsEVrYseIZvNxbNy94KaHU7wb89xAyhT/DXhjzwbWY+s4NnYR21Igsbw8bRCXk4OAxBYE4i1Gzw8lfjKHmYiR3od62PDoMUl19Yoyq+Hcf+PveuAj6rY+v/NbkJ6Nr1sAgRICAFFpHwIQgBBpBOkKE0MgljQZwEeooCiaEAUfQhSRVroPBACIohSlQ4SIPSSDiFLCmm7Od+523ezezeh+FD38gt7y8yZM/9Tb5mZxLWYNvkTzJ/8HKaOAsKax+Ptz2fiX1o2K9O+5zMS+PiyI9fRkTjLIK1QGxISA3knKZTH52PMwLXYf+4WnMNiUb9GPiiIzHTOUJ53nAJCoO22uLxvZgh6GmSip04IiVBwOLW/iem4TaHalSG3K/GBL9/YajcJnGVSti/Luwwe9JV1E5JQlr/BQ0lRMyIUktwc5LDO17XZBa4r6GOQiT46hSBCoe21uJ4wYd5cIurAaAYR0JpBhnCF/5wQEBKqDeaqu7ULNQQ3od2cIFUex/wxA7F2/znc4kQwtn4N5FMQv5XUl7Hye9+wZp+VnYWbklDG1wA2pGyHoZJc5AhgsxuwtTlJlTg+fwwGrt2Pc7ecERZbHzXyCUEmzNvGk6na8jkaUZhgzTdk9nQi2kXEt5l2gP2zf4BOB/njZ6nEHZ4eujtSYayLk9b2qtI3LVlxOxR8tHGrgVaD+yGi0ypsSH8ODZM2QPlMIrr7G0uY77mjdtwAxNVPRptvv4B05BMY/XEShv8wAiGaLrij+eAJmtjzbtd/4fEuk7BoWAeMq6/34xXIWvkOxu2OwMD2p7Bi/imojlxDeUEJti5ejxp9e6Opn94e9S1L4K0Ih4/+NI9nCWV/pPXD+jJV+LWro6FMpIp+0qw5+7pgZqdmdW0dOEHu52/011IpnNhfq8rFbFxIvm0KzlZD9++8+zNIXDsNkz+Zj8nPTcUohKF5/Nv4fOYoSMRicuZd+BvmukarwegX0QmrNqTjuYZJ2KDk9llxVb+LxV41nMLt+QhTO7cBj/o6No/rjxeXSDFi9TZ83En7Dbertxeciwtxx+Aricf53AE8POGlM2n19c0Y1/9FLJGOwOptH6OT5sNxYzvuzQdjgpAHvNsV/3q8CyYt4gd64+obCzygPb15PSDyDwtZd8R1i0P+L5uwZMdJNGrfHo07tIHfoR8xa9MehHbtiRhj3LH7ZEAaEAA/yuVgYPSqyt3fYvLCA8jT5RE6udsBoCp8SeEf6AuXZuOx5+p1XL+u/TuzbzuS5yWgjt7HalqqQtk7vyPx3yvh3qUz8N1b+PR3VlT9plLillKfCLGDy8nl5e2DEOTij0BfFzQbvwdXde1fv34G+7YnY15CFAIC/EC5nDwb4FBi97eTsfBAnjbZFAVDd1EaiABfQnZGBodZ3aZOQ3oWwZfxtr5JINFX9w+Er0szjN9z1YDR9TP7sD15HhLqlLKsZGg3cSOOpN1A+tGVGB2yG+Pf4inqnCQcbHmcRrl+FhtCfn6hzcTXrnJYZ9TmWfW5bzBqxBrUGL4SJ7KUyD63B9O6BBqCgNWK3Gltt8XlHRUahADK4RsyPaIVuJ2bq8udnSDRdpyfnWk3ys9Hoc6J2ddxK0K9Kxla66EUgQG+oGy+8dOzzlqRlp4F/mgfAWY6b1mfn2YGBYByOGjq61bcRm6u9jZQKqonWsIq5S0YzSAbObkEv6AgQ0MSo9Ldm10IFNXn8M2oEVhTYzhWnsiCMvsc9kzrgkAzz/wgsQakgQHwpWxkGABjttLSkUW+bNtiYKtx7ptRGLGmBoavPIEsZTbO7ZmGLubMQxRPWz5H16wBa77dsasTYr7NID1hR28/ZictDqrWt6rYobmP5gd1TQZhQP2D2LB+E1ZvUaHHoKd5eLjlxrOZrHoV7YfMM04JKA1HuyejocpKx42Sw5j/xr+w4ITxVZpbZCTCJLeRp9QrvkCTUFwRgEcaSXBsXRKSkpKwen8aVLfP4Mc12/BHniFrMWGAE5jcm8Z77Ip0ZN6QIDAk2KRMFXbvmz+wbKsKusBVjLpjWV93bGlWlsdCMalY7Ktjg7D+tLiP1Ze66987HHNl7TBx4xGk3UjH0ZWjEbJ7PN6ac952TN57CP+x629scCRrgkED6uPghvXYtHoLVD0G4WlWXHGfiir5CFFZqS4jKYHf9m+siU92/oipnUIM8dElJgaRuRdwzuCw+SHh+WvwjIpBGPsQ1eUkJHQcgo01P8HOH6eik/aOlTtYhsPz38C/FpwwvnV1i0RkmAS385Q2ALi/p81c/P0l/XBR82zfDa2vLcRXu2shrl0gajzWDk+UrMF/Nvmgc3dhtHjVN2mdrujW6AyWfZmMdMHPFRzB7IkTsDJFBfdqImqfLxka9uiB6GM8V+WaixqHqM5KxtguzRE/47j5qxXuhXjZAuyeNBJflw3Ht0nLMHtoEWa+/CH26OfMLPsN38/ap5nrsiJ7B75eloLYrt1Qt0ZD9OgRjWNzP8Sai0ISo0ZW8lh04afHM45XoA6XaXRmGb5MTtckzgVHZmPihJVIUfGMKC4ukBXlIC1DiRLTmCDALXGBi6wIOWkZUJZHoFuPxji3eCpWXSjli6W4uHoqFp9tiO7d6gmlRTdZwx7oEX2M5wJdAy2LWUge24WfBMzA8ZJMJL3WAb3e5/mt1a4IfrQtWkf5QMa8gZ+KhvBTnZS9v2pe11bk7sV36/6wwFXbtESsL6Lc2b6o5k9RMlThaNG5BSI8+Slw1nZ8t+kCD/wuMyTFtmuLy7sisieebX4BSxLX86dDPHtHzi/4fP5elGjiLT8FD+Gnqyl78avwmq0iF3u/W4c/dFNCiuq41ERupkKV1r4nGRr7KUXtbj3Q+NxiTF11gTWBteHiakxdfBYNu3dDPbF8kJO0yJ7PovmFJUhcf4UxrEDOL59j/t4SzQ2VqJ7o+l722/eYtU+Y87cC2Tu+xrKUWHTtVtfInn5Pdvd2YXAT/HQwLUOF8Bad0SLCk9+OZGH7d5twgUf+lwl3RaY28kCw5uBZuxt6ND6HxVNXQWt6F7F66mKcbdgd3UTB5ht0tl1VeAt0bhEBTyk/Zd3+HTZd4FkLNMxrgRLF05bPqSRjezphx7fpZVblX3t9k8DFRYaiHLZfpQoxVfbRzIC0Pp4f2ARHZk7AJufeGNzW2rsoJ/gHSXFt3X/w5c83NbpLhcewbMNJBDV/gn2yNwr/WIzJU5biguCSy65hc+JCHFF0ROdHXFCh/APJK/jTrWwJIod8i+0//4yfdX9bxrWCW3hfzNwyD8MM3xyaAkMo+nkevvpdmNKwBGeXJmJZRlv07Vrd5Pt++QNT3oR9e7pgWb7ycZV9uaiNW5s/17QtcR9rWvJu9tWZSXitQy+8z+sXqF2D8Wjb1ojykbFe1rAdk8vy+SZbxN+IMiJF/ecHosmRmZiwyRm9B7fVvEUV96n27Ei0Qb7IC0zNHIZXtyjw9pxJiHNJQ2pqKv+dQzrfZMoa9kGv6IOYN10bv4uOf4OZP8jQvX87uKtSMHPYq9iieBtzJsXBJU2ox3/n0qFUS+Fd+AcWT56CpVoDwrXNiVh4RIGOnR/k56gm/X2A35M/ZKRv0vfxcnJuMJZ+03ycf4uWxvNMFCajvo0DRfRf45dQ8ksKCn1xExWbDnTgnhWfWkQJTYPIO0BBCp6BJKpXIu3jcQ+iNKwiYp8v4kEvfyxMoGY8w4NcUZOCvXlGgX5f6QZoWRK1XfbWtteovtcjNEFDDYkAAEAASURBVGa3bjivcge9EeNJDUb/RDeFgTRutalZyzoUVi+GZzzxp5h+s+iofhRx0R+0MKEZBXvxgIWaweTNMzL0+0o3SJAHtZ1alEBNg7wpQKHg2VCiqFfiPs3gRmGgT0KUOzk5N6LxBy1GQKgzaDUPRHR3cqZG4w/ywMQztHRkCwrx8qWwMJ45JeJJenXZGasDIKwNDCn6YyElNOPR5jx7Ss1gb541pB99pRsIVnBwJsVHy8k7OJLqhQdScKP+9NVBYYSbmrI2v03NA3h2FUVtUoQ3pRdHPM0zZAgzgpiP/hbti6l+mOwbNCmZR2WHvkibeOCj2VZygr7qqiCv4IbUum0Lio1tT6Neak/+ka/QDsuyVgbw2NON0vPL6eXmLC+eFSQkqD7F921Dwa2nagbSqbM209vNA3gmEQXVVoRT0xdH0NM8I4pmwCUzaUvHefSWidwO0FnDgEuuJCLD6tlGCZ1ZOpJahHiRb1gYzyoUQU++uozO6MaTmQ1CMgNUOCil88tfpuasA/6KEAqqH099NQNstYM1beuJdoCgW+1m1LJOGNWL4RlP/GOo36yjmoHOlQfnclN3ZRfadrQDLkvoxFddSeEVTA1bt6UWsbHUftRL1N4/kl4RFOBPwVoQ21Ia2SKEB/eFUZgvD7588lVapgNbDOuSE19RV4UXBTdsTW1bxFJs+1H0Unt/inxlB3sFcTw1+mDL5wgDscwGQgtyta0Tdn2b6YBL3b5AkYqWUC+PBjRWGxR0A/I9qffSIhLvGw/MXp1AUe5O5NxoPB0st+13BX00DLjUNCqIdQF15xl/Hn3/cKXBxroiXCiHdnzQnnVDTnKeecffz5sU7d+nn3SjxUpTtbbt5R9OCj9PCmg8iL45rPXt5ScnU9MakfT6z5UHYBaJDrgsp4PvNaHw9v2pe/0wUij8yDvsSXp7w1XtQOdqDbhk7yfiDzhYGiYxsOsnLQZciumCVTs1gKrdMfPlB86K8yFq4xaEzQZcsghFfKxpDDPdt6BoPuDZBDPiIfkHZ8ZTtNybgiPrUXhgMDXq/xVpwprNmCzub8T40PDF/mhBd86bHn2fDpuEc9s+lSUl5iOs2rkJAsKEBNEy4XGRxZ8LPTUrQ1Ow4NDX9KwQ24NCSO6loA4f/KQZUCkM9IyWWdbjY5enaJYwtVBpKi1/uTnnNP4UznruGdCYBn1z2MpEByb83Mfdf8RsJ/cRLwtS5aTMuEJXc4o4hfsTNlUBZV25ROl5hqG9thutTlmmYkyMCinr7AlKSeMZR6xQVxVk0ZVL6WSVhXIlZVy5SjlFljXLqbi41Co9oYny4mIqNamiKsikK9dyqMhyiLoVfiqfUlFB1hW6lJ7HIc9yK6W89Et0OUNZOeCplHTt7FlK55leSK2icpUJQ2ZkxPtiVrTKB8WUefoQ/X7sAuUKTKtLqEBZaIaJXVKi8hb09BplFVgDVEXKa2fpbHoBy4dngyhXWcjJto5bys2Ux3uToQkl7lfmlWusU9Z4NylnbVfQx2tZZLXbHMwr64kxKb5cmEVnT6RQGs/+UZXt7uzCSLk48zQd+v0YXdAqAJUUKKnQxCj+FKwFTDKv0DX2Z9VCuziTTh/6nY5dyNXYnLqkgJSFgr2L41lVn2NESbd3LzpRiZidEzb7pqtXzvPpmMiJRO3Q2JY6axH1DG5Fn56xj3R53mlKfLoTz2Jx04pPY/tMu0Dnr92ycs3Y3l3tqfIp82oGKU2SrLuiw5Xumz+wZOCedKF6vlzUxi35Mju252PNClf/oDSP0i9dpgxrgrIRk+35G5tMqLNoUc9gavXpGSs+wppP1VGyZ0c2G6ziBZ6Z6ubVK5SZb9+eLCmWK9PowvlrdKtywmBZ9L4eSwRqJg/CHbv/UATUF6cjrvFmPHtqF96qbXgp/g9Fw9HtfyYCPBByehwab34Wp3a9ZbKQxz8TjXvvtTie/0ifo76MXUk/Yff66Zhb8R4Or38RpmPerWOuQupPO0FxnREjjPl1bA4E/nQE1Li8Kwk/7V6P6XMr8N7h9XjRvuL+6Vz+lRqszqfOf6V+OXitJgIS9zpo+XRL1Lb2+WE1aTmKOxD4ayIggXudlni6ZW3Nt4x/zT48TFyL4/mP9DlUiFP/XYRtqnhefW9oFRJvQZ4y1O/EA+QdmwOB/xkCPAj31H+xaJsK8bzi5FBH4n3PknA8+b5nCB0EHAg4EHAg4EDAgYADAQcCDgQcCFQNAcf3BVXDyVHKgYADAQcCDgQcCDgQcCDgQMCBwD0j4Ei+7xlCBwEHAg4EHAg4EHAg4EDAgYADAQcCVUPgn/PN952TWPj2WJ7HMR+1ExZgxSuxVUPoYSlVlIH00hAoKq1GZpvBihs/8qqOJ9BsyrvoWI16tinevysPM2/2ennn5EK8PZbn1M2vjQRejvaVWL0Z8cT9c9/B3EPFPJ8RL+TBi9jIXLwQFPMk+g7siUceGhnwEuwbPsaXWc9g+iv/p1m30V6fq3tdlbEXSxZuwNEbLqj95AAk9H0MlbpfcgLffbgPMR++Cl79vWrbnYv4aekybPsjBxV+DfDU8y+gewP9EiUVUP6xHvOX/Ypr6hA07/cSBv1fsHY1SqZ+5+JPWLqMFxbJqYAfL9P9/AvdYagqSrdqrP2vSxXu/Q8m7K2H8Ty/vWEtiSoxVYi9/5mAvfXG8/oBxgUsqlTVotCfZdem7bTP/wGf/icbT386Ei2qqkcVN/DjtMk40WwKxnb0s+jF/+DwLvy7nkvb/khfwvFbLQQK9+I/E/ai3nhey8LCkFTHFuCd2b+hSDdNhUTCK9TynOshMXEY8EIPoz8RGiw8hZWJn2PlkTz4NB2Ef4/vjwaGMVV/lVhRLeQchauBwD/myfetNRPxzrpStHnlTSTE1awGRA9B0bL9eL9NB0w5ZG9Sfwtey27g4ulLuKFfwtDi8v/08GHmTRSYW1gz8R2sK22DV95MQFxNfeItVFLh0s9LsXRvFpxda6AGL8JBd67h1y+HoPljfTDnlLBczMOw8cIHv63Cwh/PGVf3uo9sVaSvxAute2LGCRfUVKiw54On8OQbW5GrXzxVaKv0AlaMGoBXZ27nBWWq2HjZSXzevSUGL7gEt4gIeFxZguGtO2LS/iINgTsHP0aXDmOwozQYQep9mNK5LV7fcoOXyuE1SE5+ju4tB2PBJTdERHjgypLhaN1xEjRV7dCtInf/82LFKVuwaN0R3Kr2/FXFSNmyCOuO3NJMintPHfmz7NqkHXX2fqyYm4zUquqRpoNluHHxNC49DM7xbv27ph9i/uieJPnPrVycgi2L1uGIFUNSXdqFpcsPoShUAYVCgbCwUPg7Z2NnYn+06j4NJw2xVokf/tUDb+zyQ4+hneC04SXEf7BHs2CYFti/Sqz456rBA+/5fZ248KElpqLzia3Io+0Mumw2ba+KivLyROaTLqdysTlOywspL1dJJWY0dSCoikipFJaZqLypipSkLLZ2hedDLcqjPMs5jYv/S0ODI2jkNvMFE8ToVG7V5IyqmAqLxDqmKyvSB2aU8vJszwdcLgqcCS9muyoqVuZRoTVo7LRnRoZn8S7MyyWldcFQkVLJiyaZ17B6ZK1NXkQhsZUHtZ1x2WI+bIFCEa3q70PyAat5z2QrOEhTnuRFANrPpPNm7Yrrn6pYSXnWwbCjt8Lc6UVm8/6qSwrJKHLjoh+CBAuVBWZljZyL88etWLEPFaV83II8mn1Ef+hUrGTXaKrr3YMW5QqU1ZR3aB6NaBFEfhERJHfrRUvMwDK2brlXsm0kRQTE0xLdIiNEt2j188EUMHAt451DS+L9qfbIrbpFEsrp1Mf/R95PfEpnVCW0bWQEBcQv0Sy+oKF7azU9HxxAA9fyYiqidC25sH1sqfNWbVlfnW1QmVdowz9Y8QH6elYwLy3IZx+mppxvO5NnM+3iUIbi1nTYcLGUCvJZA3gxl287e1KzKSlW+TEUZ9mprfk6TQFrumCsyQpJRabz6PJc9tZ9kJjtCvQqt2N3YRATNuzvsg8qLOJW7G93Z59sU5b+x4Z/t+TAqj6J+iMTCuyPzfokqhcm9Sx37darOn7mpMV8jRXMzCsbj8RisqaUCH+lBZTPsVed8y119mxmWGzMSJwXHls7kPzkA2i1hc8qOTCWYmtE0zv7dEpefogmPOJObT6/xDZVSvvfjSGPp781+p9qxwpTLnjfrhwsypscqm0asZgMBAIi2DE/tvIdUV7F6pnw/Hfc/QcssqOi09Of4lWMnMnJxYtXvOtBs6+W0uUNY6hjXR9y8/TkFf7qUqdxG+mKJjEqp8MftqWmg1+n/lGeJPNuTzNTzTImUt/8lT7r+xiFyn0p0M+D3Hi1x/gZB0hYL5GKT9K8IU0o1DeAggN8yK9eJ3ovOUOTqBWfnEdDmoSSbwAnDD5+VK/Te5QsrLTEW/nlDTSmYx2Se/qycQdQTM8ptDOLr6lO0/SnAshVKiU3Xrmx19w0KhKhoyGm+091eRZ1q9WT5lzj5S54v3t0Xxo3oQvV5ZXVZDJ3Co8bS5t17ZvWE+sDM0obxnSkOnJPXslSTgExPWnKzixN/8oPf0htmw6m1/tHkafMm54c1oeiHn+XdhlWaiyl3ye2oughSZR50cibsFrZ2RWjqX0kr2rp70tefvWpd+IeuiVAI9KeGc/Cgfom/fpZX3osVE6+gX7k4cYrgcbPoANawdDJeUOoSagvBXDS5eNXjzq9l0zWuq9vs66PG3l68sqXdTvRuI1XSKWRBa+g6exELrwqlqLHbLpqlozYSL6ZtaLtL1NN19Y0/YKgS+Va/asjJ09eHVUeEEM9p+wkQdzCVnJ2BY1uH8mrOvqTr5cf1e+dSHu0YIjUU9HlWd0ppv9YGtuxNnk7y8gl6Akas24bzewXS34uMpJ5RVGfb05yC9rk261pHxrUJJjkPp7kFd6GRq+6qAvQYvzZtw9Bnrdu6ZdGJcrb8AIpAp+n1ZrF98rpUOJzNPCjH+jMr2OpgUfVk29V9h/0y57TWr3QIFVIPyQoSN43iQpKttJLCn96brWx3dLf/00NvbrS/Jsqyv7jF9pz+pZGT7VVf6AEhZz6JhWQKF1NYdv/Wep8+5mpVGLLlgUyJWdpxej2FMmrSPr7epFf/d6UuEfLl9YH1CUfN0/ydPOhup3G0UaNU7KOefHp72lkyzByd/ckH16ZdGh8E3LXJ9+idlNMp78fSS3D3Mnd04dXcx1K8U3crSff6is0J74B9XtnLHWM9CdvbznVaf8WrbkkJBmV+fpi59fmPiemP40d25Fqe7PPcQmiJ8aso20z+1Gsnwv7IC+K6vMNnRSeKYjarng7xuS7gLa82pDqD1+nWV1XgJtUZ2jG01HU7zutD9ac05y/TLO61aKec65xGd7vHk19x02gLhwTnGUycg+Po7GbLeroKtu1T6txpdi6/ymr7N/NXAq3aVMv7Pmj8sP0YdumNPj1/hTlKdPc/KeW2PLfubTmxWh6/N1dvDaibiv9nSa2iqYhSTmkFtMnUfzK6eiUOKo7bI3hgYQ6+zvqX7eLLq6K+RobmFkCxOyKxmRR/rhy8Wn6fmRLCnN3J0+fUGoxNJ6auFcv+S4/9RGvKFqHRv+iv8Msol/eqk+u4fE04/tJ1JFXfo5fzDFEj62t5Juvm8cKQwXtjk058APGuRxvo/vRwktCK2pKS3qBGkY/S/MvlNGVOfHUoN87HBt41V5eLVVepz29teaS7qGLiAzsYmc739HHUWu5gmiOYdHlv+vhPyD5FkTHivnZE+QR96UmWVKdmUFxvFx179nHKJ/vAvOPz6H4WnJqN0NYflpITGLJ2bURJSzYTju3HqCrRothWqW0750Y8nlikm5592JKnd2NAuR9ieM4KVf0Jb+Y12knLzVPnI4f/lC7nPdJXmlqRV8/inl9pzYw5LNj1Cx5zcmQKoWmt5FT3ecXU4qQOxSm0Nz4cArtu4wyBUdj9mREhI7QpMlmXEGOk29hWWAXZ4oatIiO55ZTyZW1vOy7Kz0+6YT5ExGub7sP/FRzehuS132eFmsZpZS58RQe2peWMaNCEIx1dqVGCQto+86tdODkSno+pCaN2Kp7TFDyK70ZFUR9l980WVGTeUv5jFrxDdDg5RfY8aspd/c4aurdgMbsvyPanklXNbvCcrIxPk/QJN2S8sWps6lbgDbB4k5RX78Yel0rGMrnG4U2wa151Tiz50FMR0VnZsTx0va9afYxXuVTnU/H2XHVkrejGadZESyWDzbnwXbyrc78hjq6htHwLSXc3+nURl6Xnl+colm2vDBlLsWHh1LfZZm8uGYKfdaKE6/By+mCsLp47m4a19SbGozZT3fE6jHfF3iZdxfnKBq67Cy/PcimjcMjydm5FvWZfZRulRfQ8U/jyCdiJLOsTb6da8TSiPVX+KiITs2LpwjfTjTrokqcP7v2YUSkPGUlTRzZgxqFRtOAhac5JTffjEmT+fmqHhUdm8o4Kmjg6mxSKxdRd49oenuvPvgxdtdmUjuPpvSh/hG8gXARHZvKeqwYSKsNT9ENF8mMrvG01b1KOn/ppIgts/181opv5gbTcq1wafe4puTdYAztv8NJYhwv6957Nh3jVTXV+cc56a1F8nYz6LTKik8qZT1p7UP1WE/Os54Un0+iF6KdyVmTfIvbqWBvrX3qsb2dZ3srpvMcpKOdna0n35rlrDlxjoinb0/yKqjFZ2jxc3XIt4PwFqcyX5dS2c+YLuUu+Jyhy+gsv8HJ3jicIrmdWn1m09Fb5VRw/FOK89G+0RO1XSs6Z9qOqR4VbhlOEf59aOlNrbjKj0+kx/260XzLu2xNv/gN1heX2aaFZc5dyDlqEC06nkvlJVdobUIUuT4+iU5Ucg8i9ikWV8T8j5l/t1Az4ebBpl5wWTF/JCzdHetMro0SaMH2nbT1wCU6KeK/b61+nkJqjiCju36TooL60nK+eRXz++L4aX2NR68lxuQ7YxY95aFNbkV9oRhmZjCJx2Rx/gSbbE0+9dgmtYZESS9Es98USb4929GEdZto0ybhbyOtXZxIw5v7k2eTCbTfcOfCSfTJRGrnLSGJLJL6LTyl8fVGtqsWK4zlhT07cig/TV91DKCQXgvp4vkF1DM0hLrPu8C1tLHBWRZB8d+epAJeDfLM4ueojm8HmsmvYkVlYMc27jZXsF3PvMd/56N/YPKtooufP0nu7FiNeZeKTn3UjNxbfkqpmoDSgNxaJVp8IqBXAzUVZpyhM2lClqymouxU2jd/ENVxe4pmsYMvYuev8IqhPh/Mp82H+Sm1vhrvbRmuIK+YPvTB/M10OM14RcV3zc3cmtC/f7lMV69e1fxdXD+C6giv6oUgYuacbdMxNKXbqZR8uzemD47po0kxbRwWQorhmzn8mm82+6A6RR81c6Mm//6FLuv4vHpxPY2o4009mFFNEHRrRYmGbysKKfmlmqQYtlHzKUDRtpeptmIYbeQn0UbeyjRJo+GJnYYVJZ0/dJAu3Twp2p451yyNwgw6cyZN4+TURdmUum8+DarjRk/N4idYRVtouMKLYvp8QPM3HyYT+M3JqC7S50+6803JSeNNiabf7tTy01TxYMcytvrZidDCzbnU2TWIhm0q1OiaW5N/0y+XtbK+evUirR9Rh7x7LKJs4SbJ4qmL8vwhOnjpJp1kHbVVj29nNDi6PfIeHdKJOH9Rd3Kv9Srt1GW9pb+No1iveGZGmzR5PzPX8LSd+DXpe408GatrovwJC1z/NlbMPoxwFh1cSh9PHkuDWyoo9Il/0/Yc4U7SuJkmTcazVdsrODaLetWUU+M3k7WvcnPnURePWBr3m0nyzTc8T3k8Su8f1eu8QLuAjs3qRTXljenNZE7aLZqrRNfiuuWhpc6L2nK2kORZPGFWnqdDBy/RzfOf05Puj9Mko1MiDS33lvRp6p1KmGvsx725yatxNV3+oi15NOfPTkrF7DRba29COf1DBfVl+qKtBzW39tmJJvi6m11TpUyh5h6tadoFG3yZJt9uj9B7RoWk7u616FWjQtK4WC+KX1okbrtWdM7oP7Q3/YY3KJobfF/qsZBvZIV64xpScL8VpMvFjeKrlHy7U+MPjhlsvnjjMApRDKfNFs5Ri7t5Yqa3z/NicSVfxP+Y+Xcji8Ke6qKYXth5GCAk3w3cqFXiefYOAjExvWCECpPppZoKGrZReEVVRNterq3x3fn26ul0xDp+Ysl3qbivqarPZkmLxWRt8m1DvoVamzTVffXlL6ith6ltCeBpN+GzE1+ZDymi61P96Nrk7yohJ/mj9Oz4hXTAcCOvpuyt71LzgFBqM+o16hzmSlHD/0tZpWdo88LVdFDzRK0qscJS+ezIj1lUXZxPPUKDqG5d/gRv0CpK1zg4bWxwN7V5fsgzpbkHtZ6WKi4DUdmyltjKd+zojM16eqD/Ab+mo8Ue+PflD0cDPNgs6yYkoTURYei9FDUjQiHJzUGOGnBmRp0CQhAqtcYxj25WHsf8MQOxdv853HIOQ2z9GsinIL6RAdyfScRaHkX/yfzJeG7qKCCsOeLf/hwz32iNZxLX8uwjn2D+5OegvRSPtz+fiVeKM5BTnooFg9tgmWmT/o+hMI8Z8jA96W6Tzr9a2xm1L/GBr1w/xlYCZ5kUFWrTUXDadmz24eViZOSUI3XBYLQxZxSPFeZpKzsFIMQAnAc6DOkDjwErsVUZB5ekzZD2+h5PC5NT5Oj7xPLIvgFJcBgUBrx9UK9Zcx6Ut8NOe/56IppfJ6kSx+ePwcC1+3HuljPCYuujRj4hSCsYJK6dhsmfzMfk56ZiFMLQPP5tfD7zDbQ2nYZDnY2smxKE1ozgdeV0m5R1JVSC3ByB6RD92Wr9qq5cQyaC0SwMyEzOQXnqAgw2BxH+jxXiZkY2bkiCEWYEAz71mqE5D9XZkWG7nqAmEubISe6PAJ2IpSxfibsnPIQLwjWefcVJwEKzSeCtCIePXh2cFGCfzX3MRCb30xZ/QjvCZts+tNeF/92bD8YEFiPe7Yp/Pd4FkxYNQ6dx9Y0F7mpPjeubx6H/i0sgHbEa2z7uhCChD67e8HIuRuEdff8AKizAHTYeTy8dAOrr2DyuP15cIsWI1dvwcacg6LsP2KBbFR5NdF6VKWLLNzOQfUOC4DCFYQYWFi4EVS87kIWbklD2Qwatg5R1MFSSixzBKfFmirk6W9CTIBM9cUJIhEK7MqcqU8RubmrtLcjE3pxCEKEwTMNgpccuiKhT28CzNCKcrSAHGRmV+apU2UkOf6NCQipxh6dRIVknSTPIU9R2dURN+1+pHf2JGq0wuF8EOq3agPTnGiJpg5L9ZXeYewp9YdNfCXx85QZ9kDjLIK1QawbrmpbS4m7NPstwQCyu1HjGtv8RgV6dbUcv6ppyZ23fCQEhoVrZieoF+2+PDhjSxwMDVm6FMs4FSZul6PX90/BS7RXRJ8HvC/ZVNfzMOVQhU8yn1XjBNmamPpulJhaTtW3a4I/9vWCTQSY26RQSATFzkHg+gy+PrUQ/llvJhRV4uesr+P2GL2rp9bx0Lz4Z/Q1UI3dh2yfNUdxHirjeL2OopBUurLiBV9r2YX9uezPGCqMv0JS2Jz/WcmmdgXgzfio6z3PBG4t7mq2g6hJRB7X1MVYagXAOZTkZ15FZYs/f28COmbrbXMH9Bdt5kplobcP0l79ijD1/+a5UtQNSBAb4grIzoIsdXFGNtPQs8MfYCNArp4SnirNGUn0O34wagTU1hmPliSwos89hz7QuCNQheYedpazdRGw8koYb6UexcnQIdo9/C3POFnDSL0O7iRtxJO0G0o+uxOiQ3Rj/1hxI/QPh69IM4/dcxfXr17V/Z/Zhe/I8JNQxMKTj5o5NOtbYvZtzNvtwXo5AXxc0G78HV/V8Xj+DfduTMS+hjq4pxs0EuBqtBqNf8M9Yu3Ytkn6U49mhbeBqxpQUAQF+oFxOenWJHaDE7m8nY+FBZwTYbU9PTI1z34zCiDU1MHzlCWQps3FuzzR0MQoGWbJ2mLjxCNJupOPoytEI2T0eb805qyeg/ZUGcpuE7IwM1grdpk5DehbBNyBAf6aavyU4vGYTUsPi8FTDGvAP9IVLs/HYc1Una8byzL7tSJ6XgKjgAPhRLsvY0DqUu7/F5IUH4cx6a6ueUU1MwBflkpcLzr2JEn2ZinRkchAK5EAtxp9pO9Zb4im05r+Bfy04YZxJxS0SkWES3M5T6lu7y18VLicloOOQjaj5yU78OLWTcUo9lxjERObiwjmlIWEqTz2Pa55RiAljG1JdRlJCRwzZWBOf7PwRUzuZTqknQrdKnBp1XtSWo4IR4Mc3OOwjDNJV7sa3kxfioIsffCnbkNAKzarT0pFFvmwfOh9g4pOkQUEIoBxOXvSUKnA7N1crT6m/iJ1GISgoAMQ3Wcaqt5Gba9AEKz1WQXnLiKs6Owe55Md0KvNVubINP2pW0I7t6sua9F9/qvKvDE0GDUD9gxuwftNqbFH1wCDN3X7lkndzRso+wJZ9uviJxJVS9m+i/se6NUkDA+zrhZ2OSPQOWVQvBP9dA60G90Pwz2vZXyfhR/mzGNqGvbXdemIMOGmmXIWqHPqJQCg/H4Wae2SpuK+xi5muXTsxWYw7SIMQFEDI4ZtmgyXdzoWoOZgQdK03ELMWjoTL8hF4YfZZrc8rPIvUdDkeb9WYb4ad4N8pESs+isXhRRuR12k4BhmcqAkhw65prLBIvqsghzu/J+LfK93RpTPw3Vuf4vc7BsJQKW9BqX/WxjcdObkEv6AQcRno0w8jGbO9u80VbNY7q5eCWTN/y4N/ZPJdu1sPND63GFNXXdBM/VN6cTWmLj6Lht27oZ4dZQMrbVqGCuEtOqNFhCc/HcnC9u828XRpKpSVqZGZ9Bo69HofWzl5cg1+FG1bR8FH5gIXSSaSXuuAXu9v5STTFcGPtkXrKB+eB9oFsoY90CP6GOZ+uAYXhRiozkIyz9XbPH4GjguzC0q4vqwIOWkZUBal2aRzfzRUpA+uDdGjRzSOzf0Qa7SMIiuZ50JtHo8ZGkatcCB7DIMG1MEvkydhR0R/DG5qORGvFHW6dkOjM8vwZXK6xgEWHJmNiRNWIgWPo2eV2+Mn6IyPKrwFOreIgKe0Alnbv8MmnsdOVVYGdWYSXuvQC+9v5cTHNRiPtm2NKB8ZXHg6QLNNWhvdejTGucVTseqCMDVgKS6unorFZxuie7d6ZkVtHVTcycHF1FSkpp5FyuGfsWzSAAyedQtd338LbV1laNijB6KPzcWHay5qkiV1VjLPr8xvSGYcR0WdrujW6AyWfZmMdMEPFRzB7IkTsDIFeLyn7XrVnISSCROKfp6Hr36/zfslOLs0Ecsy2qJvV4Uof/bbkcK78A8snjwFSy8IylyGa5sTeX59BTp2foSPxbYK5J3cghWbjuCGPkiYFFelzMSwV7dA8fYcTOInc2kajFNxLl0Jtawh+vSKxsF50/GrULnoOL6Z+QNk3fujnbsKKTOH4dUtCrw9ZxLiXNJYNoJ8ziFdqYYoXW6/Iu8ktqzYhCPWmDLhT9gVteWKOujarRHOLPsSyVrh4sjsiZjAwqUY9gGNz2Hx1FXQqt1FrJ66GGcbdkc3K05JGtkTzza/gCWJ63GFs5qKnF/w+fy9KBGSGsbCtp1WILLns2h+YQkS119h6VQg55fPMX9vicg0g2X47ftZ2HeLca3Ixo6vlyEllvW0rj1naQGOzUNx27VZzcYFaf3nMbDJEcycsAnOvQejrciTZRskbJ6W2rRPQgzbta24Epkj4n9M/XuJefIhrd2tWnphk3HhgqheaC1b9tggDKjzCyZP2oGI/oOhcddVqGe7XX4jE8JvhVP2au2yIhd7v1uHPzTNifvCkqr6bNGYbJszzRVpJHo+2xwXliRivdaQ8Mvn87FXY0h26uoue7WZiK9fDsTuSW9h/kWWn09LtG50G8kLl+tsOQtXM4pQ4SRB4cG1WJdSZCAsHisMxbQ79uRQsBuTRn6NsuHfImnZbAwtmomXP9yDAh2Zst++x6x9t9jiK5C942ssS4llf1T/Hvz93eYKJbbzJA7HFco/kLxiAw5lWwkCFpD8pQ//AZ/WCF9CmQ24FGZjOLN0JLUI8SLfsDCeVSKCnnx1GZ3RfBtb+Rs1c4xK6MRXXfm77mBq2LottYiNpfajXqL2/pH0yg7+RounlZsZH01y72CKrBdOgcGNqP9XBzUzoRQcnEnx0TzlXHAk1QsPpOBG/emrg5qpOKjoj4WU0Ixn0uABZDWDvckvth99pRs4SOoMWs0DgNydnKnR+IOUJ0LHlFfT7yJN97VlSvh7bP4W98VNlb75FusDM0oLE5rx7DFyUtQMJm+e6aXfV79r+mfrG1715a+og4cbT88nDP7Qbub8FNOpRQnUNMibAnhUuK9fFPVK3KcdmCrSnmlfhf2SE19RV/6uO7hha2rbIpZi24+il9r7U+QrO7iPBXRwZjxFy70pOLIehQcGU6P+X5EOfnNSJWdo6cgWFOLlS2FhPANLxJP06rIz2gGDYgOcNN98exM/wxJSIF5nx4lkbr5Uq2kPenvZKb6q34roj4UJ1CzYiwf91aRgb56Vpd9XugG8/In/qUWU0DSIvAMUpODZUKJ6JdI+zQBesXra7/o82n5hmE6zaEkv8mgwlvSfQZcfHE+NPHszE+V08L0mFN6+P3WvH0YKhR95hz1Jb2+4qpOPWDv27IPJl6bS8pebs474UzjT9gxoTIO+OaybAlCPARfjAbqGb3U1p8vp2AeNybX+u6SfscukNA90jib2zVpsTX5dnpqlnbWm4BB9/axge0EUIvciRYcP6CfhO8zSffROtKxSPcCFv3G/Ypdu+bEPqLFrfXq3MlNW+sDfQYrZcvEpWpTQlIK8Axh3ntkoqhclaoVLJWeW0sgWIeTlG0ZhPBtKxJOv0jKNU7KOeen55fRyc7ZBfwWFBNWn+L7awd3CuGAxOxW++T+//GVqzn7GXxFCQfXjqa9m8Lcw4Nxi03zz6Ua1m7WkOmH1KIZnPPGP6UezjgpjXirzZWrXpvsaqkVLqJdHAxprVEgaz+MMevM33+K2K95OZT1SU8aC7uTjzN/7Hzb93t+kb5W++dYOvtSPAShJfokUoS/SJovPbgUKtu1TLK6I+B8L/27JsW29YGbE/JHmm28P6mU6l6ddf8pjB77qwDNFtaUZmpmZdJiJ1TPB0hp+6qzN9HbzAJ5VTMFjfsKp6Ysj6GmenWeKZtCBmK8RwUzHlvbHTky2wx+Vntf6K2+ewSokiOrH99UOxtcYkllDNqcapNzNPPbJmYJ6L6IrDELRiTk0oL43eQTWogg/d5LHcqz/dTfN7KqggGdm02WeYm9V/6rECvP2bdv1Ldr2Wn3yemQM7dbMKsUTJ+x4g2I8G9Don25qxwPVbkYt64RRvRie8cQ/hvrNOqobBCoiA3vYieQ7tnnlPonUKz85mWeOiaTXf7Ycom+BxV/8UCLw/5e+e7gX5tWFyErLgzQwDIHu1XuKU5J1BqeulcK3Xizq+slQyt+Xqly84OGifZlQpuQVKZUS+IWH8pNvUybLoMxIh1Lih/BQfvJteomf+xZm8+cqah8owuSVVh5UlZSgwsUV2ibE6JgRvesD233gh/OF/Abghho+ijDILR9m322LqtvITL8NWWA4y8P8pUyV2yvJwplT11DqWw+xdf0gKy1EgcoFXh4u2u85y5TI4CelEr9whJoLphLX6sIspOXxZ0phgaimelSiZfUE6182f4Kk9uHFGiqBqMLtzHTclgUiPFB4eWmyidYzKVeVXXUBstIL4caLRVSC4x7bUd1Ox1V+WuzH3y7z10NV20p+xOuDjuLV1eMRWz2T1NGvQEluGrLLffmbRi/Dd8pVa9xWqRL8+PogHH11NcZXmSk7tnw7E+m3ZQgMZ90yFy4Ks9KQx58/hbHc7UMg6EkmSrzCEOxZubSo3Qj2llkCr7BgflNko+/qi5ge1xibnz2F5IH8FijXE5ExCniZ8WyjbnVP27PdKtPjJ3vfxaPxgifwy+5/I8ZW36pMz1pBcfu0GVdE/I+5f7dsk/WpWnphWd/8WFQvzIuaHd1tPeHTztvXLyBLqkBUmDv4Lg8SmdTo18R8jQhmpszZi8mmZa3tq9gmM0u8EBbMb7StFRDOlSqRzV/PyYPl/JGO+VZ2Owe3ip0hD/HVflrJvvXamVTccApDTEwYPASbUd1A9m0/BPvbbMGcqI2j6slBjYvT49B487M4lTwQpRdz4RkZA4WlEYvJwAYf+tN3myuI1dPT/rv+/rOT77+rVB39ciDwl0SAk6b1H2B62QgkPmcc4Pe/7kpF9np8ML0MIxKfMw5Y+l8z9We1b5J873qrtjFZ+rPar2Y76su7kPTTbqyfPhcV7x3G+hfDHnqeq9lFR3EHAtVEwCT53vUWaj+IG+dqcuQozl+BOUBwIOBAwIHAw4GAE4L7fILPHw5mDFw4BffBJw8bUwbuHvAOz05Sp+XTaFn7Pn44/QBZpsJT+O+ibVDFf4OvhzoS7wcItYP0XwYBCdzrtMTTLWvzAFDH9rAg4Hjy/bBIwsGHAwEHAg4EHAg4EHAg4EDAgcDfHgHHC4i/vYgdHXQg4EDAgYADAQcCDgQcCDgQeFgQcCTfD4skHHw4EHAg4EDAgYADAQcCDgQcCPztEXAk3397ETs66EDAgYADAQcCDgQcCDgQcCDwsCDgSL4fFkk4+HAg4EDAgYADAQcCDgQcCDgQ+Nsj4Ei+//YidnTQgYADAQcCDgQcCDgQcCDgQOBhQcCRfD8sknDw4UDAgYADAQcCDgQcCDgQcCDwt0fAkXz/7UXs6KADAQcCDgQcCDgQcCDgQMCBwMOCgCP5flgk4eDDgYADAQcCDgQcCDgQcCDgQOBvj4Aj+f7bi9jRQQcCDgQcCDgQcCDgQMCBgAOBhwUBR/L9sEjCwYcDAQcCDgQcCDgQcCDgQMCBwN8egX9c8l2RsQVTX3sNiVuzUHFP4q1AxpapeO21RGzNsqBUcQs/f/k2Ptl4FepKbVTgxo+f4bVpOypduW8nKm7gx89ew7Qdt7QkizKQfkvLY8WNH/HZa9OwQ3d8N22qr2zEx+/Mw8Ey67WLMtJxD+SNRAv34j9vflYZX2MJ3Z4aVzZ+jHfmHeTjB4Pv/cCtEtsP0Yl77p+lzv1ZfbtzEgtHPYNWzVth4JzTZq2qr2zAh2/Owe829NSs8P3Sm/8VDuadqd6R+go2fPgm5lQNqOrRtihdcWMnvnhnJnbZcBAG38E47vziHczcdese/bQFA9U9rBYfD8b3VJdl0fKGWPDn81o9H1OEjPS7lb2xrj19E8XqAV60F0Pve9Ns4xs/fgfzbATtwr3/wZufbYVlKlOJjwfmK+6TPv6F/O8/LPlW49zST/HlmtX47OPvca5yZlxJ12yeUJ/D0k+/xJrVn+Hj78+ZJ9l0G8c2fIuk37LNz+uIld24iNOXbtgkfe8XynDj4mlcusFZR9l+vN+mA6YcKteSLbuBi6cvQbh0t5s6ez9WzE1GqqoyhbL976NNhynQN1e5RDXOFKdgy6J1OHKL7FRSI3v/CsxNTtWUeyD43gfc7HTif3v5nvtnonN/Yk9urZmId9aVos0rbyIhrqZZy+rs37Bq4Y84Z0VPzQrqDu6P3vxvcLDWnyqfU2fjt1UL8WNVgaoy4coFKf8o1s/dgOP5la+Z+Q7Kx9H1c7HBWsHKVR/cmWrycX906AF1xyIW/Om8VtnHlGH/+23QYcoh6KJWNQAxryumb9Uget+LisXQ+96YQJBtfP+KuUi2FrT5cnHKFixadwT2Q+2D8xX3Rx//Ov73n5V8q45jedJptHjnXTxx+nsssnoXqMad27dRYicxVx1fjqTTLfDOu0/g9PeLbD4FrmxITlAMno9d3z5vdkl95zZu22tUU0ONkttKFFnlTwWVkGg4KTB4/i58+3wIPwi+gesZd6BPX50UgzF/17d4PsRE9OoS3FYWWb1RUBUpcet2aZWfPlXcuI6MO/rWjF1U31FCeccq08ZC+r2yQhRwWYv3CbqrLB+lErZJWcdXT7ryrz16Wkyt4iYQY+yK7ljP7soKC0T4tOBEfQdK5R2rMrAoWfmQhW6dA2NRVckdmN5vVZQWwZRtq/1jnm7fLrHKUyV9NdU5Q7P2sBXgM+fDULXSjjVaatzKvgFVbA+8MmwAOsZ6VqqlP6Euuo1CUwD0F4RfDX629cY2j2UoLLCQmVUchEbYbovu2JCTFTpCFaubNRysFrR6UqVxEFYvaU+qi3DbNlBa/2KobocXVRGUt26j1LohG6jod2z5Di121vWQhWfBE5eujq/RN675tScHQYa2+BAI3I0OcTURH8KduTu/YA17s1hgg1d77dnklXXBTty06mME2CrR5Keg1zNQOYzY0TeBlvAGy2pdXTs2Ymz1dKaaOmdNFhpe79N/dmQmZvPaGFVFA7VkV8xXiPJkzc5s6KNGbCIxwlJ37sb/6vINy+498GN6WLbyHDp94Dc6f0v1wDgq+fVfFO3bnRakZ9GSeH+KGL6ZCgytFdPJeUOoSagvBQQHkI9fPer0XjJlqA0FTHZK6Nd/RZNv9wWUnrWE4v0jaPhmIyVSXaLP27hRw3//TqUmtbS7Kro8qxvV6jlHc1h8ch4NaRJKvgHBFODjR/U6vUfJVhstobMrRlP7SF+S+/uSl1996p24h26py+nwh22p6eDXqX+UJ8m829PM0xdoVrda1PObnTT9qQBylUrJTa6gXnPTqOzyLOpWqyfNucYdKzlLK0a3p0hfOfn7epFf/d6UuOcWqfnfzV8/o76PhZLcN5D8PNzILzaeZhzI1/Bc+ttYauDRi5YUmXdOdXo6PRXgSlKpG8kVvWhumprKL2+gMR3rko+bJ3m6+VDdTuNo4xUbMi4+Td+PbElh7u7k6RNKLYbGUxP3ZjQlRShfTpc3jKGOdeTk6etH8oAY6jllJ2Vp5FNKv41tQB69lnA5I765a16k6MffpV3Fej5L6feJrSh6SBLlMG626VXG9IudXxtwUzGG3aP70rgJXaiujzPJZO4UHjeWNuvkVpyymF5qHkLu7p7kHfgYDXypJ8W0nEi/V1YG7tZl2jCmI9NxI09PN/Kp24nGbbzCveBLR6dQXN1htEaPszqbvutfl7rMTCVV+WH6sG1TGvx6f4rylJF3+5mUagargEN3iuk/lsZ2rE3ezjJyCXqCxqzbRjP7xZKfi4xkXlHU55uTGnCEPhn0ovgkzRvShEJ9Ayg4wIf86nWi95IzWCuIbOqr6rJW5+ZcEzjXYlvXh9w8PcnNpy51GreRtGLX8hXddxxN6MJ6wXzJ3MMpbuxmG7Zmi5aKTk9/ioK9nMnJxYv8FT1o9lWNMmj6I/yn0VO3ptRnUBMKlvuQp1c4tRm9ii6WCyxa4vcF7fxab5f2eCymlMUvUfMQd3L39KbAxwbSSz1jqOVEtndTHIT97tHUd9wE6sJYOMtk5B4eR2M3a7FkNG3TMfRCv2MLB+F6OR2dEkd1h60ho6p8R/3rdqGZrBTlhz+ktk0H0+v9o8hT5k3tBf3RkxV+S3+jsQ3cqGmfQdQkWE4+nl4U3mY0rdICVdm/pJaI2A6R+uav9FnfxyhU7kuBfh7k5hdL8TMOkOA9VBemUWuPtvTFZZaVOpO2vNWKop58h374NdHcd1w9R9Nau9Mjz46kzvXk5Mp64h7Rnt7bmsV6WNk+Nf206WvE8RGVg+qCCB+mIAr7gt5UTYfs+RDbfiGX1rwYTY+/u4u1R7eV/k4TW0XTkKQcKreFveq0RSy4ShcNvDIdET8kzmvV46apj7FNs0xj1wGuUpK6yUnRay6l6X21VX+iB0H41foE07pXz7G+uT9Cz47sTPXkrmyD7hTR/j3aqg0cuvhUh+SevuQnD6CYnlNop+6aKWXBxirFWcG2NDpnvb6YHdiKoVR+jD5uG0n9Fgt6zlvJLnr3sTBqPmG/NpdQX6U5verRswvSSS0is0o2//lqereBB/XSBe3i09/TyJZhmhjlE9qChsY3IfdmU0gTas07bn4k6iu4qI6nOnJP8vWTU0BMT5qyU9cXUX93n2ynOv63OIUWv9ScQoR8wzuQHhv4EvWMaUkTrQZqcxjuxxHuB5F7psEObm6PEJJJJOQcMZBWpGvU7p7JmhMooM3DIyiw73K6yRfy1g6iEP94+l5vaMoV1Ncvhl7fmaepls8Bq01wa5p6UojUFlvBZhoeEUh9l2so0dpBIeQf/70uEeSydpLvC9Nak0fbL7igklb09aOY13eSptV8TgjaBFPrqdqEyLRVVcpn1IqTmMHLL7AKqyl39zhq6t2Axuwv4MQzlpxdG1HCgu20c+sBuloqBAwPavvFZY4r/6WhwRE0cluJhpwx+JVRymetONkbTMsvsBtX59LucU3Ju8EY2l+wj96J8aEnJv2uCZhUnEqzuwWQvG+S5mbFpuPgFor/O5SCI0aSpjnVGZoRJ6eI3rPpWD6n9PnHaU58LZK3m0GnzaK/wJqK+WlNPvWYn/PMT/F5Snohmpydtcm3KmU6tZHXpecXp1Ahly5MmUvx4aHUd1kmo2GefBvwvbWang+pSSO26lKSkl/pzaggjdzs0zPH9FKqMWnQYOjiTFGDFtHx3HIqubKWEqJc6fFJJ6i8/ARNaSGn+i+upAsMecmldTSyETv9+u/SvkrJt4rOzIgjeURvmn0sn9TqfDo+J55qydvRDAaoEs7qDJr1lAc1m5JCKsEJxjqTa6MEWrB9J209cNU8oeIjAQcX5ygauuwsFaqyaePwSMazFvWZfZRulRfQ8U/jyIdlpUHfJClSruhLfjGvk9YU8jnotKHg1lPpZLmIvmqSFK3Oqc7MoDh5BPWefYzy1WrKPz6H4mvJqd2M08yVka9Bi45TbnkJXVmbQFGuj9OkE5VtzR6t8589QR5xX5JF3q3pkwY/5xoUO2I9XWHsi07No/gIX+o066IV/C5RqsEuxXksPzGFWsjr04srL1AJ/7u0biQ1cnWm+u/u4+TbxPY0+y7kHDWIFh3PpfKSK7Q2IYpcH59EQldF6Wh6YPxPHAej/huS74xZ9JSH1nYEHGKdXalRwgLavnMrHbhqYXyagOpMNWJH0HotUDQvPoJ8O82iiyqBtoUtnBS3xX3vxJDPE5Pod829ejGlzu5GAfK+lMTPJwz+51IGbX2rKQU3HE6rL2vlbu47BBz5xjayP80/kUfq0mu0YXg0uTb7iE5Z4elqqZivEcdHVA4aGdriwygf7Z5Wb7S+XVyHRH0I24iYX7i1+nkKqTmCjG7tTYoK6kvLb5aSGPbmscCcV7H2RHmtRtw0yP6yWqsHtnwoR7j/Dg2miJHb2LpYZ0T9iaUMLOoKfo1v3CL7z6cTeRwprm2g4dGu1OyjU6RSpdD0NnKq+/xiStEGFZobH06hfZdRZqUUpLIdXC0Vqy8ui0q+3dCNUvr1zSgKHLCKswP2EQfHUyM3F3Jv8bEmMVZnzqMufk/StPOlojpSyebP7+MbbF3yzf3+rLUP1Ru8nLShNoleiHYm5yon37Z8Bcfw6W1IXvd5WqwFlFLmxlN4aF9axoCK2pkuLtyz7VTZ/5bTiSktSF7/RVqpDdS0bmQjvsmvT+9WDtQG6dzPHZNvDx74Q3bbDZQewk+/ZINDAsrTd2HnMVvvh22TsHslbwuWblbjmYHd4M+F5c8MQg/3n/Ddisva1+rOPvBxS8eOb77Agi1HkN9gInZn7cX4R2SVSOdtWYrN6mcwsJuGEp4Z1APuP32HFZer+FmFgaIzfHzckL7jG3yxYAuO5DfAxN1Z2Dv+EUMJ7Q4PKNzyA45HDcP4AXXhyq83/dqMx8qd3+OVBgJ/BNnjQzB+WCd0eKYlakotqls7VF/Blh+OI2rYeAyo68pvTP3QZvxK7Pz+FTRwbYx3NvyGNWNawKviDnKu3YSz3BulebkoqMYbKvXVLdhwqA4SPhqBx7yc4OTVGCOmDEe9g2uw8aIFVhp+jiH6BeanHvPjWg/9J4/CEy4S5l6Ns+tW4XBkP7wc54nca9eQ69kRQ7q4Yfuqrciz1j/hnG9XDOkOJK/agUI+vPPLCmy8w+d6yKtAzw6mslg89+4QNPaToUatbujVWo7stOsoOb0eG1IewyuT+6FuDaBGZG98NLody8zKpr6KLRsOoU7CRxjxmBecnLzQeMQUDK93EGs2XrRSweIUyfD4kPEY1qkDnmlZE9bELo3ph9ED6sNDGoT2rRvCOawbXktoAl+ZJxq0bwmFsvLYA2cfH7il78A3XyzAliP5aDBxN7L2jscjsqroqxpXt2zAoToJ+GjEY/BycoJX4xGYMrweDq7ZCL3YZbHP4d0hjeEnq4Fa3XqhtTwbadctP56pGi0LVMwOJW7t8eaUXqjlArg3fBHvDVHgwIatyBb02A5+1nkswen1G5Dy2CuY3K8uavC/yN4fYXQ7qxLmRmSIfe5dDGnsB1mNWujWqzXk2Wm4zp9/VJ3OveNAsscxZPwwdOrwDFpacxASN7R/cwp6aYHCi+8NgeLABmzVAmXiX5rjzn/FbFGGxu9swG9rxqCFVwXu5FzDTWc5vEvzkKt3HqTE3g+64/mVtZCYPBf9alf2sVohMnYDx2LYo3I4uUTgmd6t4Zt5Fdc0amJun4q0avgaMw2pihzE+DAjVunAug7p9NyGD1HZ8Qu+XYegO5KxaofGq+GXFRtxh8/18K8C9pU45BN22tNUscVrNeJmpaZt0TQreO+6D25n4NhheFTuBJeIZ9C7tS8yr15D6dl1WHU4Ev1ejoNn7jVcy/VExyFd4LZ9FbZaDSoWOnderP5dygIuaNH9KXge+An7+ROZSz/vwZ3uA9Dywm7szlFD+dNWHI7uih410+zGDls2r76yBT8ci8YL4wdAG2r7Y/KoJ6AJtWbY2ziw5SsyTmPdqsOI7Pcy4jxzce1aLjw7DkEXt+1YtfVGNfydtt27sp1KLNvwvyWnsX5DCh57ZTL6aQM1en80GjbdeCW6937i4Ui+XZ/CS6NaIMDZBaFtR+GFJ20FsrvtcAUy1y/Dths3sPaFmpzwcqIdNgDLsoqwb/FiHBd8ofszSFw7DR3LtmDyc/+HWsE18cTQmdhnOSq/IhPrl23DjRtr8UJNpsO0wgYsQ1bRPixefNzGN522+HbHM4lrMa1jGbZMfg7/VysYNZ8Yipn7dLOUGKrxgEL+tlUSHAaFIcPyQb1mzRHJDkXYnAJCEGq4Zqhoe4cHYGTfkCA4TGFM2nzqoVnzSMhlUiiPz8fLbWrBzzsYj3R/C0tO5AtvSQzfjtsmbLyizs7CTUkoakYYg6u0ZgRCJbnIYUditun4CTLhxykkAgp3oZQKmRk5KE9dgMFtWqN1a+EvDm9sLYM/FSLPgpSRrgc6DOkDj20rsVV5Gz8lbYa012A87VU1eqKYSnzgq8MekMCZMauoqOBxLdxn50CEBOhNy4l1LRTe+kMjcxz0spF1U4JQxsSAkLQmIkIlyM3JMS1pY98JASGhRvlZKeUk94eeFSnzKHH3hIdwP8ObEyfGTixTy839mUSsndYRZVsm47n/q4Xgmk9g6Mx9PINNVfSVdTXrJiSh3A9jp1gHQiHJzYFe7BIfXxjhc4ZMWsH4WXJSNVqWtUyPJd4KhPvowefvCkODQAIfmrbE8bPOo5Yn58AQA65wYh8Q6s23xNY2CXx8OXnUXZI4yyDljrKmaHCqGp17x4EdBELEHITEG4pwHwOfTopQBJFgp1qhGG3Bnu04Qao8jvkvt0EtP28EP9Idby05AX4Izv5DB0L5Kfx0TIqw4oP48UCOjbEdQlknyP38DTxJpFI4MXYqHR0jT4K31ih+AABAAElEQVQpVcPX6NjQ/lRFDuJ8mJGzOLCuQ7pCNnyIXb/g0QFD+nhg28qtUN7+CUmbpeg1+Gnw7bt97C340xxWxQ/Z4rWqcdNau7ZompW9H7ovh5+/wQIhlTqxr1GhPDMDOeWpWDC4jS6mtEbcG1tR5k8otBFUTHVOJVqf7k4W3HfXVt3RgfZh55F07Pr1CprHv4729U7glz3Z+Hnbb6jVpQfqoQqxw4bNq7OzcUMShDBDMuGEkAgFNKHWDHsbB7Z8BeORkVOO1AWD0UYTozlOx72BrWX+oMKb1fB32nbvynYqsWzD/2p03hmBIQEG/+LkE4ZQq4G6EtH7ckKvkfeF2F0TcQpE52m/Ibv4DtJ2TUQbn7umZL0i39mvXv4rfBNW42zqWZw9K/yl4vxPYxCbugwLf7nDj0VZmWXtMHHjEaTdSMfRlaMRsns83ppz1oym+upqLP/VFwmrzyJVQ4dppZ7HT2NikbpsIQRSVd/usELK0G7iRhxJu4H0oysxOmQ3xr81x4KEFAEBfpw0MI+GRFOJ3d9OxsIDedrgJZFwCmhrs3JFGoAAP0IuBy0jyd34dvJC7D30H4wasQY1hq/EiSwlss/twbQugQYltdWK4byuOWlgAHwpGxkZhhagTktHFvlyfyzuFPjJbFAAIYcNWF+64nYucksEqlL4B/rCpdl47Ll6Hdeva//O7NuO5HkJqGNBysAH79RoNRj9gn/G2rVrkfSjHM8ObcNPoatITxRT01aM+zJFOILKMnDdIKgK5F1Pw+1KiSXXkQYiwJeQnWHsMwMEHpIA3wB2ChJOjvnGo1z/IohnXsgv1Gcw2jYlzKPoZu+6lcp3WCdk7SZi45E03Eg/ipWjQ7B7/FuYc7agCvoqRWCALyibHbFekCzRtPQs8MAGWIrdSvMmp+6dFhXm4qZGhwSyFUjP5JtYTpyDdZ7PLn4m3Gh3ZZykBqEs47rRFivycD3ttkgSWYkIn6gOHXs4OEHCN1JQlRsG1VJ+PsxVhf2DmKrwTWyuEShUpGdygOabSCNQOv9ix3ZwDt+MGoE1NYZj5YksKLPPYc+0Lgg0jTTOzTBu7S9Y+e/a2Pru21idYWIcljxaHptCaWKf4r7GWQSfKspBjA9Tnu7Hvh2/wF4NrQb3Q/DPa9mvJeFH+bMY2oYfWPEMXHaxtxYl7LYn0qkqxk0RCjYvaSG3p/vWq1uKy/JYqCX1D4SvSzOM33PVEFOun9mH7cnzkGArqJjqnFj9WherIAvrvMM9Dt3i8vHLpiXYcbIR2rdvjA5t/HDox1nYtCcUXXvG8MMK8dihpWzd5qVBQQigHH6gpXfQFbidmwuDm7TBluG0LV8RGoBAXxc0G78HV3Ux+vr1M9i3PRnzEqLvk980cHFvOzJ+KBNUhozrxvynIu860qwG6ntrylZtU5doq8yfdt5JeLLxAFpTnV2O5b+FIv6FrqgZGopQ3Z/iyQQMbJaNtQt+wI3MJLzWoRfe5/m/1a7BeLRta0T5yODiYnh8x5ypcHb5cvwWGo8XutY00AkNVeDJhIFolr0WC36wfGot0iF1JpJe64Be7/P8mmpXBD/aFq2jfCBz4XfkZpsUdbp2Q6Mzy/BlcromOS04MhsTJ6xEispdHDOJC1xkRchJy4DSdKS3tA66dmuEM8u+RHK6YIQFODJ7IiasTEFZPidOqnC06NwCEZ78RDdrO77bdIFnFSgzBHcz9kwOJMy7rCgHaRlKlEd0Q4/G57B46ipcKOVCpRexeupinG3YHd3qWWTM0kj0fPb/2bsOuCiurf+H3aWXpSggYEEERBPsz4igEo0FG8Yuxm6Sp8ZnUHkkPkssn5qYkDx7xV5Qid0YTewtGlEDYkEBpSqC0mGX893dZfvu7IKaZ95j/OHOztx77rn/U2d25ty2eLBlCfansGyzMgenv1mH86WSZJOPZn36wOfGGsyLTZY6CXHWUczs2RZhy+K5y1HxW2DEEC+cnjsHJz0HI7y1BNtXoKcyV127PN+BGNYuEaujNuB6bjHybm1FZPQZ3Y6N1xChfQJwL2YRdssAQvKeRYhJaobeod4wdXVldx8TcP7MU5bYVSL3/Cbsu1394lu6+NR/TIzMnZMQ0m8Wq68uhoXLuwgObAJ7PtMjE2P0lYeGoX0QcC8Gi3Y/gEzse7AoJgnNeodCU+z6+ZCceXVaVPQL1n5/BS8YtdKkrViyLQPBA3spkm/u8XWd5cF34DC0S1yNqA3XkVuch1tbIxF9xujQVUW0OnQM4cDuXLmyO/oJ53HmKUtkK3NxftM+VEtVqAi/rP0eV2RAYeuSbcgIHohe8uRbAYUB22F3lJ5kiODRrjvaedqwu/xZOLHpIB6wx2zK5ReRJhawsrTAO//4NyI8jmL653sgyb9VfYeqq1IMzbHDa8jlawQc+FRHDhwMvM5TBvyCZCh+ixEY4nUac+echOfgcEjdmiHs9cYCbj/ENTWxUXGTi4KucybSuFuU8wQZ+RXwrJY/Ue3LVZmGYdisD/r43MCaebFIlpivOAtHZ/Zkd5qXId4IN8vZv9QIO9A1dekxG3QJDUTahu9xtkEndK5jjhad30Np7L9x0L47ejdnOYkROqKPPK9RX3zY9gG2LNkPWag9jW/WnYc01LJOlXm3cGTHQVyX+BJdmz5f4f4O+vTxwY018xArAxRZR2eiZ9swLIuvfE1+UxdDNTjG88XAYe2QuDoKG67nopjNeWtkNKrtxmswtLyLamYpP/Zf9ilC/O443PMdhBXtNZJaXhMMDe+MJbO24cdl2/H1/PMID/eFu0NdCFiZOGGXhdj4iY8SD1aqcHfcPfgOWgFtUkMR3nkJZm3bh/QBXZV9uPZ4jTHx6/k4Hx4OX3cH1BWwEnvCLli48ROtXvx3p2Pjd48warw/XAW2EJdYITgyBrODzHH3sFZz5QFBC3zQ3RGjJ/oiKPkCro2Tn+Lj3ekb8d2jURjv7wqBrRglVsGIjJmNkNZpmBISi1kdA3DI1xp5z6wRHNQRtqceIJkF0HpyEjo+BS0+QHfH0ZjoG4TkCzcwZ+MKpIyKQCu3Gexn0WLwmg3Hqi0zEaCleTz4TV6P5feGY0pAPUyz4sHmvWC0dswEj922MGsbha3RGRgztSXcIhxgXlAIp+6LsHlOILsPJI/qOhhiCZzvsKFovvCfEH0+Av5V49acnq4xVI7x/PDZxvXImBiJbh6fgdyDMPxv70Jw2xxmWleWfDT/fCNWpIxCRCs3zLBlP8DwmrHFYrZgJgPItHIoIj7ZjtHDmqC+iw1MnD5Az47OuKky3Ovf5aHxxK8x/3w4wn3d4VBXwMrpCdFl4UZ80tQHJnr1VfmYDL/559i4IgWjIlrBTTYpNBu+CltmBrDLHvndFuM4fxVapqZ8dvHQGu7XRsHfoxClBQT/8euxfVwDmLJAW9ON5/cZNq7PwMTIbvD4jOAeNBx/e1eA2+Zm3BfCGgNWhw43DoDH0Ah8sn00hjWpDxcbEzh90BMdnY3UFHbXnM9uOLR2v4ZR/h4oLC0A+Y/H+u3j0MBUDE2kOG3HrAxjpoQgdlZHBBxi7xrkPYN1cBA62p7CA4nzaKgCgnkLTP/hHzgQMh2f7w7Gts4qvuPsXjioNDW4y2+Oz/X6GvaYAQc+1ZGDQT5eSwNuvyAdgiUPw4Y2x8J/ivD5CH9mV2wza8eNfX3VWHAWexUAGxgvWf+keI0nGo6b+rvrOSNAiw+6w3H0RPgGJePCjTkc/kSThHrfs8pJajaUBBVEbY1GxpipaOkWAQfzAhQ6dceizXMQyN7XMbhx9bc1gy2XHeiviiodVtgtFB3FcUjq2AWN2H0qs8Au+JvpViT26F0VOw3I7AoH9yxGTV6/HPeGT0FAvWmw4tngveDWcMxkjyWybpWpe/HluD3odqoHWtfRyJk4fQUPDaK2IjpjDKa2dEOEgzkKCp3QfdFmzGGA8vB6/CbHzKpxiuUbn23E+oyJiOzmgc/IHUHD/4Z3Bbdhrh2oq0G3Gk1f59ub/xW0yvIo/eEjYlfcf+J0yigv/SE9yshnBY0MbKziREZKKuUUab2OzdmxoqSEyvR0qcjPoJTUHFInWUKZib/RlRsPKFdSpUNcSgX5hXppqA9eQSUlZbJSSdITIirITKG0nCL2TrPhTcJPWlaB7raiAspKeUjpeVqlQwwT1tXiddOrSKITO45TouRVdekmoqRF7cmmc7TOihyKVgWZlJLGZKADIFF+GiUlpVMBk59YVEEiPXKU03pdn2V56fTwUQZpm4KR+sqwzUxJY7qqY1LVZfKVaInoZWbqa7PpiqQTtON4orQagXQaoiRa1N6GOkenqui84QnWiA4nDiLKT0uipPQCxgerKFEhqhY/Eo5FLzMp1Rg/JG2s3xZLMhPptys36IHMeVBpQT4V6nNAalBp+g61k0Z84fI1uvGpkRyM4OR1NBFx+AV99A1hzxULajKelI83ETcrSqhEVWc4dV8DDc2+GqfVvzKdyUqhh+l5OkoDq7fU/U1/f0Oy0E2vekdrLDOWbeRnpFFWgaZ/LqHjkwbQIgN1B7l8haggi1IeppNqmH677KyCkk7soOPKQE2ipEXU3qYzResqnVU9kRjV+u0oNWgUq7WNahH4CyBQdokim9lRwIRNdDr+Nl09tJj61hdS0FJWHvAvwH4ti4YRKLsUSc3sAmjCptMUf/sqHVrcl+oLg2ipgWClSfl10dGkW/u9egjUyqF6eNW2/u9GQJy1j76I2EmPXnPAervsrIwuRTYju4AJtOl0PN2+eogW961PwqClhmudvybxm0joVONGeW3TWgRqETCAQOHtbfhq3jr8nPgMbIUadBg0Df+a3Ll61WgMjFF7+j+JQCFub/sK89b9jMRnBEfvDhg07V+Y3Jm78ow2x6+Ljjbl2iPVQaBWDtVBq7ZtLQI1Q+Ats7PC29j21Tys+zkRz8gR3h0GYdq/JqMzV1Womk1cZ6/a5FsnLLUHaxGoRaAWgVoEahGoRaAWgVoEahF4/QhovQL2+oeopViLQC0CtQjUIlCLQC0CtQjUIlCLQC0CEgRqk+9aPahFoBaBWgRqEahFoBaBWgRqEahF4E9C4H8j+a58ip8WT8LSk9Wowa1LAKI/sHPeHGy/VbU8sK42b/2xYiT/zOplR0zGpGmzsfzwHVbhW7lV5t/G3q8jMWXKdCzccgXZqpXhipPxM6vhGTF5EqbNXo7Dd1R7siroGeexcX4EJn8WhW/2xLMVEZV038o9Vb0Qp+DAggisvcrKoanuv1HGC3H+31OxmNWWNwqqogykv/WgvlHA3gjxooz0t1ZXFbyp6uobQcE4ouKUA1gQsRYSMzG8FSEj/blUtyuf/oTFk5bi5BvQXwVGhhl6gy2Uc+Uc5E3KsfgWNnzSAx3admDlShN1slGZcQSLJk3CEg2fY7RcGf+nvo1A9K8yueocRN9B5lfj5k3FqitGKY82Fbn/k2C45FNMj7mt3caII/owYEX28PyX7/D5wgNIVY17RtA02ETOOxvj6U+LMWnpSe4uWnqi1K83aUtypsqvrcGUCeMxfvx4TJgwER9/OhnT5yzHj7drIHc50Wp8im5uRuSUKZi24rxafiIlUR6PmMgpmLrgAPtqJJ46x65m/NVJo+YH/zeSb1YH+mlyIh4+raHRy/EtT8Kh6GgcuPOKdOT0/vTPctz6pjfah6/HQ0tPeFqnYMu4QHSdcxFFEl6Kr2JBzxDMOFkGl7piXJjfHcGTj0Baa7/8Fr7p3R7h6x/C0tMT1ilbMC6wK+ZclPZkK+LtwqjAvlh20wz13UU496/30ZEt1ZtrVFb5pwNRNaCKXrAFKi7uWIOjd9mFler+G2WtBAlHNmLf9ecw+NZz+UXMCgrB/N+MWP3hjfL830W8/OIsBIXMx9sIqzpvKrr6HxSBOPsidqw5ComZcG/luDgrCCHzf5MtglX+FMmJD/GqLlhzTHWMNM/+Wd815so57JuT4/PY2YjYV4agT6dibKf6OrgQ497W/8N3sXuweMFm3FNJMI2WK1tl9/f9axAX/1IHfQOHmF+9vHsDfrpnUHm0Can6PyrA7cObsP1cmnY7g0f0YwDmhV/ciMPqnWy1bRVsDJI01ECVd9a2/GkyEh8+NdBLVU809OsN2ZIqQ6KHv2Dr1vPIEljAnK1fwKdipJ35DiPbtsCAVX9IF09Tbf+690UPTmDdyhX4gSX8R/PVqZeeW4d5y5Zj5Y6L0hPG4alOQ/atGvFXV/dXPKa11Mkr0ns7u5u6I3zdrwjX4k6M0qIy8K2tZIsUaJ037oCoKB8vRZYQ2pvrfo5HXIqiMj6sraoJt6gI+S9FsBTaw1zHZZKIrRrH51eDZtmvWPHDbQR9k4BNI+tKef04YDia/n0Fjkd2QPC+xfghawC2/RyNHmwRANEAZ3TssBAbpvbAtFRmCLeD8E3CJoysK2HmYwQMb4q/rziOyA79kRLzHQ44T8PlXf+CZAGusr+VoVm/VTj4VU+McTQOR2NbiYvz2dWwLYRsIZ5X2lT1ojz1lUjp6lxZWQlTybLfWls5W7iGydVa64T0gE59YndCHmcUgy0irb6Ji5HPfoCwFVqxRQwMbWIUyxqzhRUMtdU8L2IrnPKZvmkel39ntF8UwtTGHhZatLnHLWcLWoksbfXyJCotRqWFFeTLPVSWFaGUZw1tc+Ieh11Vadl75dPHyCjWQlU+KeUnszURm7xi+gZxZ2uSVpoy+StJKPYM9JXbtRpvqrqqIMR2uHxLOVu0i/klWyOFLR9XTt5oO9Ppp9gdqccZkENr6h6Odb9qe2Au/g3phYRPNYzkjLNPo3mX9GEYvigQw0ZorWVDhujIMFOfq5INEYryXzLdFsJe7sD1yVGHbirpaO7p0nMxnmc/hcj/Q3w6egga6tI7tkjc9p2JaBcRCVq8GRuvTsPS9+RWpTlG1XfmwyqZEusiJ7OnCgisLbRwk6wGrTV31SHERXhRIoC9jY7xddmHqv/jOaKOkwWc6jirUOTyPyrNaoKBSnfZLvfcxKUvUCC2gdC6yhGq8s6QdA9fB12moDaMmp6UGmFLunRCTtFIbOTN5Z+WLTCKJbmDrOQHCvHbgp744J+fYXW3nzFVZbliTjvRJU85ScmnHh0zFb6LAPvT2P9THoYMka8IVYpzscdh5t24Suf04ykuLUIZX1ec4I6/qqy90f3XVLLw7SYjekTLQxtQ31VpbBUJtt/bhwZGfkk9G9uTgM8nK49ONPNwhuEFKYpiaZjQjgbtKmLzFdOzM4tpYAs3EjrUIUdrS3L0D6Nll15KsRA9Wk69fQZS5Jc9qbG9gPh8K/LoNJMOZ7AVUip+p/mdGtPoWAkdySam7E2DqXHPaLrLamuKn52hxQNbkJvQgeo4WpOloz+FLbtEEsoV1+ZRcOtwmjy4Cdnw7ajLkrX0STNfGrcvT0pJ8p/ozjL6oMkg2iQZS3UTZdPt0+co8bnyeOGhseQuHEg7C0rp2Hh3chq6hwrlfcqu0D+b2VKvdc9IlH2bTp9LJGXXQjo01p2EA3dSgbR9KT1/ruhJeXGjyL3OMNojOymnqPVZcWMBBTcaRDFZMp5Kf51OLeq1pS8vyhbREaeuon7eH9L6dDFVPIqjGV29SGjjQI5CZ/LrO59OVfVTJWw0zbRkpV6UXaaZTa2p3xYmE9V9VcKSfU7ZiSllVRg1HRRBM7s2Iic7OxJ6daFpsQ+rFm8oocTNE6l9PSuysrEnt3YfUVhLK2ozX1IDnEOfRIn09fvOZMHjkaXQnfqteULiikcUN6MreQltyMFRSM5+fWn+qSw9OlxBj+JmUFem75Y2NmRp35i6RR6gFEkdV875VNC1ecHUOnwyDW5iQ3y7LhQtUVC1rYRurR1JLd0cyNnFmewdvanbF0dJpnpV43oJycbBkYTOftR3/imSi6wkIYbGt3UlKysbsqvTgoaP70t+7WfTlTIRPVrem/wGz2Q4NiQ7AZ/M6r5HM/Ydp+hB/uRoxie+bRMasOJWFScc44j023t54tf0vrMF8XiWJHTvR2ueKO1CSrjiGs0Lbk3hkwdTExs+2XVh9lnKgbs4hVaFNaVBETOpayMnsrMTkleXaRT7sGpBqCqZNba3JBsbS7Jv3I0iD6RI679r2nXnf86kEFXeUpW6yulbJIyXJFDM+LbkamVFNnZ1qMXw8dTXrz3NvqK9MJXmuF2i71Iph52VXZ5JTa37kcRM9PspESV+/T45W/CIZykk935rKDV5OYU26Eur0tjiP1y+Ucq+Pr2QSkXxn0iH/GQ+ojHZW9qQjaU9Ne4WSQekiq7optwpTaIdU7pQIwchOTnYkqNvf1py7rnUhrh8jTpmttSu73vkpDLXJxXP6MzigdTCTUgOdRzJ2tKR/MOWkTQ0SPSxxrFInx3L8HaxFZCpmS05ufehlToWCik98w/ycejNfGkWbQlzIs9xh6t8t8Tlqcg1ZRWFNR1EETO7UiMnO7ITelGXabEkVWPRA1oaaEXvfDiRunsLyYLZppVnF/riWJXvEXPMXepXLan1gBHU0kVI9ja25BE0hXYnVy0tp8+nafm/FLrx7VCa9qMkInL5H6Wo5XtcGLDISQ+/CSLLZv9kPkjeQ+WTa26sWWnSDprSpRE5CJ3IwdaRfPsvoXNP/9Dw3amUvDyUGvRdRZQbS2N8WtH0X0sUg5RdmU0dfEbSzky5vT/itCXmwGW+XaePrR42CibYTtHuwWQvHEJ75CmK/GTRCfq4vgUFfv1AeoTLTljA1hujxFw6xiiX7B1OTi4jaM5nTanu4J30XDH+UZrQuAPNmBlCVk1nsqOSWFGFp3S/N/kMjKQvezIfwHSTb+VBnWYeropHXPFXPsCf9/m/sciO1GFYU/C3j5isJM7DjARNRtDG+FyqKE2hvWObkEWrOXTT0PKSqsl32QWK8LOn9+ZckSbFVHKXVoY6K5JR0YOlFGgmoCYjNlJ8bgWVpuylsU0sqNWcm1ShldyJKWP5+2TdZj4r8F5GFyL8yP69OXRFmseX0N2VoeQsTZBlTtJfYEHNx66nE6eO0aXUF3RknCc5DdhKz6R6U0Hxs1uRY+i6KoXjUKaiG7QoiAXH4XsoW5xPG3tbk8/n55WrfInTKLqzNbWed1uLSNGNRRTEEsHhe7LVEr6KhF00e2Ifau7mQ0M2JFKpVk+NA2VnaGqTOjRkt2RJyAq6GtWcLM2sqN0CWUKaubYnOXZcSvfLEuhrxmvjYTGUIMnxCxNoTZgHuQ3cRpkaORMZTVOiC1V6oSoT1X0NdrUTc1XZiejB0kB2QedJYatvsRUpS+hOzFDycgih6PsiEiUspkB7bwrffp+FjBK6v3MU+QgEsuTbgD5RyY/0kYsnTTwuQVRECV8HkbDxMIqRgUEJa8LIw20gbdMCg7VmF2OdhJ7Uf+UNeikW08t4FlwbCKnzskQSac1VdT5ldHmmPwksmtPY9Sfo1LFLlKqZe+fvoIGOfjT5lOzi7yW7OAxyCaRFt9hKnAlfMx1pTMNiEqQXdIUJayjMw40GbstkFw83aX47IfmO2UUP2JRKH+6jic0tSOA7nS6w5FuCo5mgCX20LYkK2UXjgXGNSCBoQANW/k7PKwoo/v86kb3nRKl0OMcxYO8lP35ELoyOFFZdsvYXkEXzsbT+xCk6dukh3eLCXToWu9D2DKPVt9gqkyV3KGaoFzmERNN9kYjuLOtEQs/+tPLGSxKLX1I8u1BrIOxMyxJF0uRH3a5FTOQqvKn4ME7fwmzo5vx2JPQdQ7tkwNK+ic1ZkuRL0y9oZxSSpEtt3Ie3OO1MmaRx+ylJUvTjRy7kOfG41AdIebYOpm8fseSbyzdy6oWmgNgoahjdoWWdhOTZfyXdeMnW+HwZzy6GGpCw8zJiEGtszIYWd2AXQOG0/QFLfsS5dDayNdk1nUEXi7l9jRZmqYVqcy27EEF+9u/RHJkDZ6FhJYU6C2ngTnYnQkWO1Y1FnHbMfML9xe+Rdafv9KykW0CHWZyoM3C7NE7k7R1Brk5htLnqSlgpVwmLLHaxxMUzbDXdYsvqltyJoaFeDhQSfZ9Ech1vNJjW3cwjcVkaxY3zIYs2X9EfDGPOuUt9jYDM/SfQ/hSmi0V/0NowT3LotpySmX1w+jQ1/6ciSg7/o9KqapcbA4lf5Uq+OecmSqDFHdjFXvh2kqnTWYpsbUdNZ1ykMjXeZb7NOvhbxtNz2jPMlepPOEayHLeUzkxtQnUlMlLVEy5b4vKx1cJGHS29ybc4k1Z0taB6444wuLjshFuenDrGWJEl36No3xnmn+oOpp1V2Xfh4XHkFbyULv67qyL5lsQKGZ7KuDFiYzzlVpRSyt6x1MSiFc1hyR1n/FWf/p/yTfevSW/0XvvbQJwP/6HTMTLAEXzzBgjtFwhh9hM8rs6jaPwARMRdRuyMdrCtLEZO2jMIhHYoy8tFgfw5Z74/hk4fiQBHPswbhKJfoBDZTx6zH+S4Nj4CIuJwOXYG2tlWojgnDc8EQtiV5SG3ijDxW2Fk1Gh0C+mB9vXt8P5HA+D46x4cyGIDl1/Hzn3peH9EGNy4pFsYjxXD+2Hpy1FY992HqGsqhog958YXKH5YZ0wKIGC/nFVUqD9nXBi/AsP7LcXLUevw3Yeyx1fkMyovqoBZvaZo0aAIZ9dvwVnpA+Pyszo+zdqh9/s2uPTzRZSKH+KXc8XoPaQ9Hpw9ixxxPn4+dg0+vfqg/v192H2tEQZ93Ak2uWlIy7VB15E9YXliN47ladA1kmYjrccjNOjU8Kug5UQsGP8ObEwt4Bf+L4zxvoLYuGSkHDmEGz6jEDXEmz0+YgHvwXPxCfvZ10QyjjH6JOdHnIR9u6+h0aCP0ckmF2lpubDpOhI9LU9gtxYYYqQeicNvXmPx1YQWsGU/IdsGTMD8cd64GnsAyQafbSTwW41E1OhuCOnRHvU1MRPYw94yHSdXfIv1R67jZdPZOJt1HlHvmCBp325cazQIH3eyQW5aGnJtumJkT0uc2H0MTxP3Iy6hBT6dOwiNzQHzRv3x1ZTOao/V8PwGYcoQX1jz6qJLYDMI6oVi0tiWcODboGmX9nDPlzw3KeYcR6Yar2DvxEerkVEY3S0EPdoW40eDuAvQcuICjH/HBqYWfgj/1xh4X4lFXPJDHIn7DV5jv8KEFrbscRRbBEyYj3HeVxF7IFkqWXW71gRaLvyqT32+RZSI/XEJaPHpXAySAYv+X01BZ44na1THbVv8o5F2ZthPaXCs/lUP/6VG6IU6IeU3ceoRxP3mhbFfTUALW/aohG0AJswfB++rsTigqejiFBw5FI8mo6MwpDEDx9QRQVG7cGrzp/B5YNjXqGLWXsMo+AERiLscixntbFFZnIO0ZwII7cqQl1sgfflUybFkz1jdfEU7zjuCrYfF6DE8FE5sVGGPEehj9TM27XjELEjHJmiJiQvG4x0bU1j4heNfY7xxJTYOKdLGjOfhMzH6XSFMzTzRo38gHDJTkcYCm8G5m1iiy9T56NeAPW5i1QxjvhgJ90txOJaRWA2fpsKvXv+jGseq2lcXA5VhJLtcc6tIOYJD8U0wOmoIZOoUhKhdp7D506bKR9U06AEO6DWyN3B0N04WspPFp7HjQDE71kcqI63mWgcM+L7qYKNFW88BUwH4zDWJxGKIkzjs5JkRMYpTx2Tj81t+iD512aMnxyWevBCn9v6KBmEfoiGHe+T7D8X0kQFw5JujQWg/BAqz8eRxGXf81TPdN3lYh4a+yeHeFtomsHdgjqOKHROWcPIqxTocIwe/pjzkx6/DjOF7cfHec5YY+MPX/CWoLilfnjOxh4NQMQoETGsrxfLMXB9tU/Dy47FuxnDsvXgPzwX14O9rjpdUl/1KUdXH1BmuKqswmXcIxyDPbtgdl46hzXYiLr8HlvSWuFjdm/jxYUQOHoMtvAnYc3wBukmf4baAna0AJYXFSv6JPTNaDFjb2FYREuPx4UgMHrMFvAl7cHxBN5a0q49h1TYcX7Zlx6b3wj9a9cScjewiIdJXvZHaNwt06B0CmnEK19PTcCalLUYua47UvqdxLtsZxy83QM+vvEGZGcipuIv14UHYptrfqQUK81hEcFK1RuNo8vD6n/OWsGbm6aV0DjxPeLgCORlPkM17CpO69eAuZ9XUFZ7uVQ/UGaNP8nmLMpGRU4G768MRpA4GWhRKnJSq7MXIznoGE7f68FRYOw/1Pd1gkpvDLnDkRPV/mjq76l+d04rp2t6lmLtwHeYOXYRPUA9twz7HN9EfoyQjBxV31yNcnUk4tSjEs4wsdlFZB67OcgUyhX09N9jJvzJ2TIVOkJ/mMdsxsbKBtfRKhZ2TPIcqNQgRMjnGkagG2OVNze3dFM6uVStXGsRdwpwZPL0aKp6B5Xl6wBU5yHiSCf4zE7jV91QGYx6TiZsJcnNyJEyySanbteygnv/1+Rb2UlsWS/bquDor/JupfT24qQKrSVJlXJEhO1P0NcJPKdrq2NHDvzjbsF7ooCY9JO1r4sZ0W6Ho4DG83UxykSNRdB+54bHmDKfspyZwqeeukBXsvdGG+a6ykwZ8jWQ0Fcykg6v8Z8rLR/y6GRi+9yLuPRegnr8vzF8S6iocuEpjo3XTsB0zN6Nnq0Tm/m04/vQpykfVx4HRkmaEipIiiGJiED9lPt7R7MmSai9FhsODp8yJIUNqT6YQOjop9MuEx4Mpi58iFp8Mzt3EDu4e9oq+pu5uDBcmH4necfo0TQarvuv1P58h0FHFmbDobgiD1kq10TkY19zE2dl4auKCegrnDqZObSAJhSjVSU560DpkJAZYD8GuY/noZLYTh3n9sPkDSbyt8gn6u7IzBnyf+Sg9vlkTG85B1E+KUpCWCbi0qQdR5lH9MfnpEwMxipHl0DF3+aiCVhjY1wWhccfxvJcl9p3xQtjcBjA9Km+g/Wli7wBl2iW5WKiEmOlnNnsnQm/81Sbzxo+oaucbH+wvNwB7OePfw0Ix+5cyGetUgfJKc1hamEJ8bwU+mRAL83G7cDMrH9n3zmFpzzoKp8I5V1MT6YtYogp51RTCy5eFsqRXfA8rPpmAWPNx2HUzC/nZ93BuaU/UUZOUCUyqkhDpOPyWGMHuEF6N24+De45A1GcEpPargwnRo50Yy+6SHqi/EKd+WoRurnLCZvDza4TcB/eQL78+YMnu/TQbNPGrxyiJ8GjnWHa3+QDqLzyFnxZ1g6IrqyZzbd1n+Mf6m8q7+paN0KieCV7kabyqrIMnq06h6PTyNA5uOYlbzbugS0AIghx/w0/LD+KcWy/09WMXR0514GDWBlHnUvH48WPZ350LOHF0LcZ6qQTVKvrG0NTBiuFDXLKr6i3Kf67EkAX5nFy2BHldV9St6wzKYYmzPOGtfIHcXJlnNk6fqoTOc0IdBzO0iTqHVDkWj+/gwomjWDvWS2MOPNRxdgBlZyjHZfe6nqRngRyc4Szg0EU5JaZsquomPyz9LGbJHr8zZh+4jidP0/H7rilwPRuFaavusxeiHGDWJgrnUqvkxXi9c+EEjq4dC5/6HqhbnoHHWQowkPf4CV7IdU9CXE3J1UZV+cLjHEeHaqj0rdrVOznZeRM5H0bhLkL+83zFhbw4Owe5bOniuq4ucHYgZGdkKO80ip+APX4LB2f5i2Madi0Z3gBvVTNQfvDd4VG3HBmPsxTjVOY9xhM1YJXNZXvKcY22M6P8VE3YN0IvtNmXHuGxF/AcKBsZCgNjOfaTdGSRA5ydNXwEj+m+IyGXJftyDUT+WayeuwFXBYyOQV+jxEzOjkxUrJrGik8wIdYc43bdRFZ+Nu6dW4qe6g5c3qUanwbsWGN6aoTFqdiz/Qwcxu5B0t0kJCVJ/phv/3kG/O9uw4bT7A6L5ibKx3NFIGCJS04uyLEu6srH0amXRsyd3dDJfabMRivTM1nSyi7C3ZyN8Gk6BtXrf5LUZ1QTDNQocM+Nx2zYkV1EZCv8GZg6rcbcDZeQJ/VpOniX0DfvgPBBLvhl717s3fkThB9+hCA9v1JpUzDg+8r0+WYNbNTmyf2l9FosDt6th07vN+OOyU3qGpanIR2TssJHiwF94XZmP/bv34tzvgMwwFMbCW6uJWd5nPHXcP/X30Keeb1+yv8NFE1sUPqY/RS04ydWekiEjJ9O4EZ5I/j6CNiNE3ZlJ/JAu+7t4GnD7mhnncCmgw9YRYhylooa2NgdT1d2hzzh/BlpGb/K3PPYtO+2rCQXS9aeZIjg0a472nnasDvyWTix6SAesGoL5XoJ8+A7bDhaXo/GlwcF6B8eDMULyqqsiBIQPfrvOOL+OVbN6QSzJ3dx9y77u5eOfDEfzQb0g8/Vtfj6zFOWPBQhfkU0DvF7Y3BnK4gSojH670fg/vkqzOlkhieSfuzvXno+C1482BXeRszc+dj6QOJYy5F2eAk2XHdH1+7snkplHm4d2YGD1yV0dWw2XRAamIYN359Fg06dUce8BTq/V4rYfx+Efffe0uop/GZ90MfnBtbMi0WyZAhxFo7O7Mnusi5DvPpTMbIBjKCpgxPDh7hkV9W7/PJmLL8gqYdaieyTP2Bbgj96hfqgUd8P0fbBFizZn8IQqkTO6W+w7nyp9KLLoD6ZmMGMX4ScJxnIF/mhTx8f3GA112NlYCDr6Ez0bBuGZVpg8NAwtA8C7sVg0e4H0hJRZcl7sCgmCc16h8JbwKGLhtGAOHMnJoX0wyxWN1hs4YJ3gwPRxJ4PMzMLNOvTBz43WE352GTpzR9x1lHM7NkWYcviUek7EMPaJWJ11AZczy1G3q2tiIw+w3WTSA83TG85xtGlGqqETMxYGa2iHGZz+eyxJ9UzOvb5zYzAvRyXNy/HBUk968psnPxhGxL8eyHUpzFC+wTgXswi7H4guZgvQ/KeRYhJaobeod46BmOJa3V4k1Pg+WLgsHZIXB3F7C8XxXm3sDUyGmckNmPEZrSdGfRTJkwH+CjKYX4yv1SZ4BrggVdNvVDFqMIzFH0C7iFm0W7IIE7GnkUxSGrWG6He8qyxigGeF7PJ5riz7TscTZcIvgDXV87Gl7sSgFZ9q+dr2BWScq6sHjOzUZFHO3Rv5wkbdtct68QmHHzAquXod+AGUJGcNmDHGtNTJShK2o7tl90QNqoX6ru5wa3qz73jWAxvk4296w9BawWM8svYvPyCtP59ZfZJ/LAtAf69QtGYYxzJI2DZhuZORfhl7fe48oJxWJqErUu2ISN4IHq5v8NtW6r+T8VQ9fsf9dvYNcJAFUQDc+N5MRtvfgfbvjsKmTpdx8rZX2JXgohVcVLx3Sq8y8iz5HLEEHidnos5Jz0xOLw1++1Mc1PVL1Vb4vZ9pXp9M8OmMh+3j+5gj2ll647JEhYkj9Mmy+J8UsI1/LJtDoaEL2d3oGdhWrAFOH0FGeErjdQxfsBA9HX/BXNnn4b/gP7cj9NqQqf4zuOMv2yyLAYdwY6D12WllRX93tyOuoa+uXH+mpSZgx4961PsGf4hPHcI2M1fK7T8jNW39uXBzGsMpoTEYlbHABzytUbeM2sEB3WE7akHSGZJsifXjE09MDTiE2wfPQxN6rvAxsQJH/TsCOebrBN7XnnMlBDEzuqIgEPsede8Z7AODkJH21N4ICHMSgDq2ngNBmFEh9n4NC0cw//GHqLVsZVf3YR1F/ORK5qNbs1mK1uYvY/lKScwiT2ruGrebYwY4I2t7O5+Ca8V/sGShZ72LKGYvw4X83Mhmt0N6l2XI+XEJPhMWoOVd8PxeSsPzLYj5Jd5ot/inVjYhV0GiO5h75fjsKfbKfRoXUeHcxGiW2hHiOOS0LFLIxZmzBDY5W8w3ZqIHr0DZD/Tm7VF1NZoZIyZipZuEXAwL0ChU3cs2jwHgTqnawRNJQLG73HJrooKe0QZv45+B75m5sh7ao6QhTsQ8S67e4/JWL/8HoZPCUC9aaw0oM17CG7tiEwec67tuPWpoVkLfNDdEaMn+iIo+QJuzNqK6IwxmNrSDREO5igodEL3RZsxRwcY/OafY+OKFIyKaAW3GewnzWIemg1fhS0zGbamlfp10QhUeI0n4uv55xEe7gt3h7oQsLKBwi4LsfETH5jZRmFrdAbGTG0JtwgHmBcUwqn7ImyeEwhzFsQ/27geGRMj0c3jM5B7ENPbdyG4bQ6zat4SMGvLMQ6SOWchaPEBujuOxkTfICRfuIFFbblcohnaRnHgLk3e2R0W/IrR7/jCzDwPT81DsHBHBN6V1Gj8fCNWpIxCRCs3yMTQjC2GsgUzA9i5K9psqvF2di97QtSYjQe/zzZifcZERHbzwGfkjqDhf8O7gtswNwZYA3amuP435KcamqHFB93hOHoifIOScXavcdyD51ctvVDDiMlvzsYVSBkVgVZuM1gx0mLwmg3Hqi0zIYFYfePj3ekb8d2jURjv7wqBrRglVsGIjJmNIFshWnP4GgUGCoICtbn+unkKQmJnoWPAIfha5+GZdTCCOtri1INkdtHdUNGrujucdqz38kaE+N1xuOc7CCvaa6R1vCYYGt4ZS2ZtQ9yIpursyJwY3vE1g3neU5iHLMSOiHeZL+ayJzO0G8Mxd1M78NkFemv3axjl74HC0gKQ/3is3z4ODdhjdw24bKtSw/8taiuNC1z+Rzkh4zDYlz4AXZWdNPYMzI3/PqZv/A6PRo2Hv6sAtuISWAVHImZ2EMwFmSq++yw0TYHnOwxDmy/EP0WfY4S/lqIyPtT1S9WWuHyflW2AXt+MytvYP3ssNr93DL3aukBXCKWCQ5j87iE2Pvt1lG8Be3d/dPxwDX6aPwLSXxR5XDGZ6RqXPCVqpFfHJL+zq2z85viwTwMs/t4DA9gjKNUMDwpCPD/98VeSfKfu/RLj9nTDqR6tUUfDVBREXufOn/Ja5198ENHLVLp19RolZmrW3SmhzMTf6MqNB5QrKSQgLqWC/EIq06y+oW/+onxKS0qidPZGOYlFVCFSdizJTKTfrtygBzLCVFqQT4VchMVZtLGvC3X4vzvsne1X28Qlzyg1JZNe1oBQRf4TenA/jZ5rFFYoOT6JBiySVDB51U1EBVkp9DA9T1mV5VVJ1qS/Ttkp32R/VJhFSTcT6AmruqC1VeRTRloWFWiBYVifKkpK1PRLVJBFKQ/TKU8Db60xJQdEBZSZkkY5RVoDs3P6dVEnLc2DZXmU/vARZeTrKBnExs1KeUjpqkxWJNGJHccpUVLkRrqJKGlRe7LpHK2nWoO8HcenrnE4mitPVVBJSZla1R7lOd17OnFXqVBQmJVENxOesOoy2v1FBZmUkpZDusSg3bq6vFVQ0okddFwJLImSFlF7m84UraP8nPZ48iPG2ZlBP1XB6vpw+S35cPLPGumFJkaM98wUSsspMsrfVORnUEoqk4eWrIzDQM46qc61JJMSf7tCNx7kSv2UuLSA8gurp2MKupo7XHas2bYG35WVaQopK+kmJTxh1XmqQ8eIuYteZlJqRj6rzaO96bStqmaa/k/Rm8v/KBq9hh2Dc6ug/IwUSmW6p4mZXt6NZUtVvzT7cPm+N44Nt53okucr65jm/KvzXV/8LTlOkwYsYhXnqkOs5m3/N0oN1hyfv0BPVh7pl620Zm4Yebv1o42sHvZbt7ELg31fRNDOR3+SVv/HAFBJvt9CMfzHYNE1cNklimxmRwETNtHp+Nt09dBi6ltfSEFLX8cFmq4B/4RjKsn3f078ZXQpshnZBUygTafj6fbVQ7S4b30SBi3904LKKyH936gXrwTIn99ZmRj957T4z5917Yh/JgJvn46JKWvfFxSx85FRF+yvAytdv3G8zhvrtbTeOAKEwj9+xMbjIoSt+AEf1avpjzJvkFFTFwxY+M0bHOBtIW0CK6/2+KB9Q93P3L8tbL4NfJi1x6ydK1A5bx3+Mexr9jKXNzp8zpZ2nuzPHs/5i24mVvBq/wHaN9T5xsWfNCkztJ+1Eysq52HdP4bha/ayp3eHzxG3ejL8/wrA/jfqxZ8k+dc1jImVF9p/0B7/UTV+XZOppfNWIvD26ZgpXAYsxJ+ZpZhIMvi3Ujq1TNUiUItALQK1CNQiUItALQK1CNQi8F+GwFt4m/S/DOHa6dQiUItALQK1CNQiUItALQK1CNQiUIVAbfJdqwq1CNQiUItALQK1CNQiUItALQK1CPxJCNQm368T6Mqn+GnxJCw9qVU19TWNIkbKgQWIWHv1NdH7D5ApykC6pAZytbdCnP/3VCxm9aRr0lttOC05sdq86ZK63Kzg0NNT+DYiGr/q4lGrnxrVan2pzL2O7XPHo39IB/wtsCv6j5uNmEuZ6iWWWK11o/iq1sjcjcuvrcGUCeMxfjz7mzABEz+dii++2c1qcVcD9RrLmJs3rrOVT3/C4klLcZLJTZxyAAsi1uIqqwmnuq+3vzgFcfOmYtUV7SJyevu89SeUulNtVgvP499TF+NYVjVkXu1B/oQOr0MPi29hwyc90KFtB1YWMpGb6coMHFk0CZOWHIMadEy/DiyIwFqJQnJulXh66ltERP8qra/N2VTrJIsNcfMwddUVVs6wJptSXyS2tOTT6Yi5rVbwrSZE/1p9Xoe+vIkZ/6f5UrGBoQuWK3zrK01VMSem8z8txqSlJ1+JnP7O2vSLMtJrYF/6R6jpmdrku6bI6exXjqfJiXj4tGbuTydJtYNsEYOLO7Dm6F21o3+ZL+UXMSsoBPN/M7T0ia4ZlSDhyEbsu/5cthKoriZGH1OVUzkuzgpCyPzfpIsc0cvfsX9NHOJf6iKm2k/XeeOOidhCO+HtO2Ha4RI06z+RJbv9EUCnMKtrG/T+9jqrTizZqsOXceMa00r08Bds3XoeWQILWJibgy/KxY1Nn7Ia9tPws+HFShnbryJjYzjU06b8KZITH0JieuLsi9ix5ijustxBdV9PT0kjXN69AT/d+29JNtR1R++89Z0oScCRjftw/flf+HWg16SHz2NnI2JfGYLYRejYTvX1ISY9Lr63Ff/3XSz2LF6AzfekRd9l7Zl+XdyxBkclCsm5sZWOf9+PNXHx0Ol+OPuy2HB5Nzb8dE/jAp6zU9VJdX2hgts4vGk7zqUZ4tcY2n+RNq9JX177bN8CvlRtYJR3ssK31niuGnMqf5qMxIdPa0zOUEdV+uUXZyEoZD5qlIIYGqia5/8Hk2+27iDHDR0RW0lSdRMX5yO/WMWRqp7U3Dd1R/i6X7F6mKvamWrRUO0pLsaLF6orWqmefNV9tuKa+lRhiM9KvcCJUZyfD70wsYGkQ7E7x48zirWTZzbP/PxiPctElKOwgJ3jkFm1kVCTE7syfpyBYl15hrgURaorkqn1Ux1VjNKiYuOCnjgZq1kg/8VnKc5c2I6Fn41G+OjJmLfxLM6v7oA//jUBS65JLt6qwZcaK1xYssULNIWu2le+b9kCo5Ytx/Lly7Fy3TYcOf09umdtwdpjhfIWsk9dctMjY3HxC7xQxVKd0it/M3UPx7pfV2OY6yu6NHERXhTquXjWNV9jOJfrv7ytITrMzvSqu4G+Mvnq0x0DdlpeiAJmxHrHlvOv8SkqYkuRvyjT2a+mctfva0Qoyn+OF2V6uJRjrUcPNVhX+aoLGzGeZz+FyL8PPh09BF399axwJqXCFnLZvhOJ7SIw/b1EbN541Yg70NyxSFxapHfVVS7M2dUkil4U6hlf1zzV9YXnWAdOFk6o46y0pRrJkfnPF/lFOv06d6zRjk0Sf6g//FTP5+nETp++GLA3FQVS2xWXvkB+ka68QRf+8q7s3IsX6jLXw1eN5CEfRv4ptxX5dyYp7TiubgPvN9K1+oyufgqi0MJCbU6mcA9fh19XD1N2qNqT6H+xRo4iO1WVE2j10HVAnX7l08fI0Ar2OnDXRep1H3sd9QrfehriFFoV1pQGRcykro2cyM5OSF5dplHsQ9nKJBXX5lFw63CaPLgJ2fDtqEv0XSp9FEczunqR0MaBHIXO5Nd3Pp3KYnVPc2NpjE8rmv5riWLaZVdmUwefkbQzM5mWhzagvqvSpOcqpDQak72lDdlY2lPjbpF0IEVS67qCfp/fiRqPjiX5sj3i7E00uHFPir7LzpfcorUjW5KbgzO5ONuzKmzd6IujGaxofxldntmUrPttISo4Qn9v5kvj9uUp+BDdWUYfNBlEmzL01WetoGvzgql1+GQa3MSG+HZdpOPJ+NQxVzZiyqowajoogmZ2bUROdnYk9OpC02IfSheOkMzjUdwM6trYnixtbMjSvjF1izxAsileo3nBrSl88mBqYsMnu87/pJkhzmTB45Gl0J36rXlC4opHFDejK3kJbcjBUUjOfn1p/qmsqsUJSihx80RqX8+KrGzsya3dRxTW0orazNdfB7rixgIKbjSIYiRyYlvpr9OpRb229OVFmZzFqauon/eHtD5NLqdHlPj1++RswSOepZDc+62h1HtLKdDqHfpwYnfyFlqQgG9Fnl2+oGMSmqJHSvlK9nv70MDIL6knm7+Azycrj04087BETvq3ij++ojYWPhRxXseqOKI/6Ks2FtRw0gmKrw5fkuE4sNSl3/oqrhftHkz2wiG0R66YEtp5u2loXXcad6RK5/WNJUqkr99Xl3HRrbU0sqUbOTi7kLO9I3l3+4KO6tVPyWCqWwXdWBBMjQbFkEykpfTr9BZUr+2XJBOpmFJX9SPvD9dTWvJyCm3Ql1alMSu5PJOaWvejLWwOqvuqlNX2yy7TzKaW1HrACGrpIiR7G1vyCJpCu5OrlgDRN181IhpfKjT0v0s03S3Vr+/ilFUU1nQQRczsSo2c7MhO6EVdpsVSlYtSyLexvSXZ2FiSfeNuFHkgRVqTVl2+ttSu73vkpKLTT8RVduolJBsHRxI6+1Hf+aeqMGV8lyTS5ontqZ6VFdnYu1G7j8KopVUbms+52oSYnp1ZTANbuJHQoQ45WluSo38YLbv0UgpESbXlbsDXiJ/RmcUDqYWbkBzqOJK1pSP5hy0j6XCaWOvyNRriUX7V58NEUt/gYisgUzNbcnLvQyu5FikqPUP/8HGg3uvTKWtLGDl5jqPDBVWjSPXLmvpJFJIzFsnWCbB650Oa2N2bhBYC4lt5UpcvjlXJigtzWWywbD2ARrR0IaG9Ddl6BNGU3clVC9no0wHZPFV94JOym/Tt0Gn0IxNl9eXI5lyaRDumdKFGDkJycrAlR9/+tOTcc6lf5IqJWrHpTjJn3Jb7PMM2IYnpdyhbn77q8FvcsUmpPZp7pUk7aEqXRuQgdCIHW0fy7b+Ezj2XRAR9eiahUEK31o6klm4O5OziTPaO3tTti6OUUf66/amEDR1+ScStG6o28MOPMxS+VTEnPX5FJxZP/9CIEamUvDyUGvRdxZgT0aPlvclnYCR92ZPlTQI+038P6jTzMMlCRgklxIyntq6SnMCO6rQYTuP7+lH72Veq8hEJlpqbhKaMvijxa3rf2YJ4PEsSuvejNU+KdOPOFcA1yb/C9/+NRXaki18wR+YZRqtvFZC45A7FDPUih5Bous+yEEmA9hdYUPOx6+nEqWN06eEt+jpISI2HxVBCIUO3MIHWhHmQ28BtlCl+TnuGuVL9CceqEudSOjO1CdUduJ2eqSyyQaI7tKyTkDz7r6QbbJk78ct45kgakLDzMkoUKZNoeY4jzlhO71vLAl7+joHk6DeZTknz6pcsYQ4il8BFdKtC2Y8xRUfGeZLTgK30TKoAFRQ/uxU5hq6rUlRdWiHp708Ci+Y0dv0JOnXsEqWWJXDMVRYMBHxPClt9iwrEJXQnZih5OYRQNANOkux3EnpS/5U32Ep+YnoZzxKIBkLqvCyRRJKA4y8gi+Zjaf2JU3TsUiqJSn6kj1w8aeLxUsaciBK+DiJh42EUIwOZEtaEkYfbQNqWKSZRwmIKtPem8O33mWsqofs7R5GPQMCZfFPZGZraaKbYUgAAQABJREFUpA4N2S1ZNrGCrkY1J0szK2q3QJKwiylzbU9y7LiU7pc9oKWB1hT87SPWroR+/MiFPCceJylXD1jyzYy+0eB1dDOPJXJpcTTOx4LafPUHidTkK6FhRoImI2hjfC5VlKbQ3rFNyKLVHLqpa9k2NpJkK9o5kGztPqQdEr3S2krpxMceZBH8LT1iWBvNlwEstfQ7VV/qzfiTJN8WzWjA9EiKjIykmRGf0sA2Dajp8E10VzovbrmRmozzacdAR/KbfIpkqswcf5ALBS66pTVzfQfKzkylJnWGkEykVymquSWZWbWjBZLEUJxJa3s6Usel96lMIjfrYPr2UU2TbwGZ+0+g/SnsoqjoD1ob5kkO3ZZTssjAfPUyrqn/D+kWl75X6Z1n2Gq6xVa8LbkTQ0O9HCgk+j6TrojuLOtEQs/+tPIGW21Q/JLi2UVxA2FnWpYo0vZfqYXqupPwNQUJG9OwmATmNSTubA2FebjRwG2ZzCrY/BYHkr13OG2/zy6uSu7TzlE+JBAYSL7LLlCEnz29N+cKSdPtkru0MtSZhAN3ElvLsQZy5/Y1ZRciyM/+PZpzpSq5v7uSQp2FNHAny3AN+hp9QmJeiMuHMWzuL36PrDt9Z3DV1YLD48izzkDaLnHGeXtphKsThW2uupGgmnxzxiI5Bo1o8LqblCcuo7S4ceRj0Ya++kMSqLgwl8UGgbk/TdifwpKRIvpjbRh5OnSj5clMgzh1QN3XKNGqmRwTFndgF4dMnx4wfRLn0tnI1mTXdAZdLDYUEzVjk8TH6ovb1bSJ+2c59JXNWM1v1dDmRQm0uAO7yRa+nWRTP0uRre2o6YyLVMylZ/k7aKCjH02WBXx6yW4GBrkE0qJbzOGq8VUTeSilKd3TYStlnLqhbgOqNzM4dYoDizK1Ocl03prFPElO8GBpIJkJmtCIjfGUW1FKKXvHUhOLVjSHBdWKm/OpndCXxux6wGJ1KT3cN5GaswtU3+kXOJNvCU0ZfQmcH5GL50SSpiBcuGvA9ia+Kn9Xet231N86egK0nLgA49+xgamFH8L/NQbeV2IRlyL7aYj4rTAyajS6hfRA2+IfsftaIwz6uBNsctOQlmuDriN7wvLEbhzLc0Cvkb2Bo7txUvIrfPFp7DhQzI71gZPKnMWpRxD3mxfGfjUBLWxNYWobgAnzx8H7aiwOJOv6OUrZWWBvD8v0k1jx7Xocuf4STWefRdb5KLyjtiSSNd7/aAAcf92DA5K3e8qvY+e+dLw/IgxunFIl8FuNRNTobgjp0R7u9/dxzFXGk6DlRCwY/w5sTC3gF/4vjPG+gti4ZKQeicNvXmPx1YQWsDU1hW3ABMwf542rsQcgnSLx0WpkFEZ3C0GP9vXVF08RJ2Hf7mtoNOhjdLLJRVpaLmy6jkRPyxPYfewZUo4cwg2fUYga4g329DG8B8/FJ++ZwUQJk/aeWTv0ft8Gl36+yH66e4hfzhWj95D2eHD2LHLE+fj52DX49OqDRoYWG+H7Y/jM0XhXaAozzx7oH+iAzNQ0HY+V8OE/dDpGBjiCb94Aof0CIcx+gsc6fyqTsVtWWIQKM2tY6eTBBObsOWuUlqBU12Mw+vjixDJPOrCqfrevr3NwJZ6VRXiWlorU1FQmlww8LytDzvWjOJnAnkY3YiwlIQHs7S2RfnIFvl1/BNdfNsXss1k4H/WOsomBPbN2vfG+zSX8fJE9fsWeRz9X3BtD2j/A2bM5EOf/jGPXfNCrTyN13TJAU+dpE0t0mTof/Rqwn1WtmmHMFyPhfikOxzISOfRUhq1OepKDqvrfthg/6tX3KjqClpi4YDzesTGFhV84/jXGG1di45BSnoojcb/Ba+xXmNDCFqamtgiYMB/jvK8i9kCydHj98hUjad9uXGs0CB93skFuWhpybbpiZE9LnNh9DHniFBw5dAM+o6IwxNsCsPDG4Lmf4D0zTksD+AGIiLuM2BntYFtZjJy0ZxAI7VCWl4uCyprLXbevSWHDRSDucixmtLNFZXEO0p4JILQrQ15ugexxF1WsNX2NXgGJDfswvX1VT+ThyNbDEPcYjlBJEBD2wIg+Vvh50w480unquWMR3384Zo5+F0JTM3j26I9Ah0ykSp6/5sRcwo8JLLtMxfx+DWDGlvlqNuYLjHS/hLhjGdw6oDoVtf0ayFGqT/FoMprpU2OmT6aOCIrahVObP4VPuqGYqB6bZG5KD1bJD6tnE16tOPRVbdLV9HHKvuKUIzgU3wSjo4ZANvUgRO06hc2f+iCdK1aa2sPeMh0nV3yL9Ueu42XT2TibdR5R6gGfDVQDeSjZU+6p2Yo77nP5B2UvjT1uv/JMLxZNoZbGaFCVfOX7D8X0kQFw5JujQWg/BAqz8eRxKRL3xyGhxaeYO6gxzNm/Rv2/wpTOTMdqugmMxb2mA3D3M4QDd++/1FnmyLwaKoI0z9MDrshBRgbzjpLHmEyd4eomS0pEmRnIqbiL9eFB2KY6R6cWKMwTwzpkJAZYD8GuY/noZLYTh3n9sPkDW9YyR9FanJ2FZyZuqO+phJhX3xNuJrnIydHpkRV9rXoswd6lc7Fw3VwMXfQJUK8twj7/BtGftVW0keyYdwjHIM9u2B2XjqHNdiIuvweW9Fa9BFBrrvhi6uyKqqnC0Fwlncw8vdBQnq/xPOHBHmnPyXiCbP4zmLjVh3KKPDZfN5jk5rBkV9LTFM6ubgrMJUcUmygTGTkVuLs+HEHqIKNF4TNks2ctTerWg7t8XFNXeLobWjnQAh16h4BmnML19DScSWmLkcuaI7XvaZzLdsbxyw3Q8ytvxk+qgg2dO6ZCODrJr2BMwOOZolIs0n5WnQU7ewchm6VsMxHwwavkfmbWysMDTkXJeCypHuIu7ynnogipj5/CzL0+XNm8tV6r1ccXJ5ZViZ2KfstH0/tp9TdM3rQTg+Rwi1Oxtm9LzPjnVvz9x8YcctNMRq3QY8leLJ27EOvmDoVMlcPw+TfR+Eego97h1U5YdEDvEMKMU9eRnnYGKW1HYlnzVPQ9fQ7ZzsdxuUFPfOXNwDIgUjWaur6Y2MHdw14hS1N3N9QlZqsSX6BXTyXz5bI3Ff03RkZmnvBSGho8ZYaGjPJsZD0zgRvzHwpvwmN252aC3Jwqn6NXviJkZuSg4u56hKsbGpxaFCKP0c5+aoK69dwVdmrq6gmDpmbKQ378OswYvhcX7z2HoJ4/fM1fguoSs5Oay123r8mAKc8Z8etmYPjei7j3XIB6/r4wf0lMRvKrVBWsdclX5zH2kmIWtw9Tf3tHJxFUZu7HtuNP2cu+o1D/wGhpI6ooQZEoBjHxUzBf61qTIxax3qZCRyjdDw88U1bBR8TmyYm5ZFgT2Ll7wF7uVtg7Km5MHrk5mchkeqJXB/SGoxrIkb1cKtEnFxV9gr032rDQVX7JcExUjU1SIFlw1hm3n2SCXx2bMIidbDTp/wZtVbfNi7Oz8dTEBfUUQQts6m3Qlj19f4lLz8xZ3N67FHMXrsPcoYvwCeqhbdjn+Cb6MwTKfbCUsRrIQ2Vayl1VWzHgH/TqBne/Zxn6sGBclCo50bVnYu/ALjyrzpgIwOcx/WdxVWKrgjquULyKwC5a6rnZKXy2Llqcx6w4cHeUM8BJ4ZVOvvkRXom919lZhPzn+YqXgsTZOchlSy/XrSvP7kxgUnWzh+dUBw5mbRB1LhWPHz+W/d25gBNH12KsF2tv3gHhg1zwy9692LvzJwg//AhBGhdgvDrOcKBsWXJfNQ3xk3RkkQOcnQUwYXeKIapQvBRDL1+isCqOFLPEnd95Ng5cf4Kn6b9j1xRXnI2ahlVJGpbAb4kRQ3xxNW4/Du45AlGfEZBeAxiCjU1Ufl/L4FwZLRF7wSlf/n4Tc645uQTHuq7shRwHUHYGJNcvsk2MJ+lZYA/4wrkKVhM5qPIm8pF57IUeBzO0iTrHEs4qjB/fwYUTR7F2bBMmF2cQCxoK2pUvkJtrwGrZGFadQtHp5Wkc3HISt5p3QZeAEAQ5/oaflh/EObde6OunSF8UHEl25HjID2p+lx9/1U/zwD7o7ngVO7YkKmQvpyl+tAvbfzVFcGgI7KoOavKh+V3ajBNLLwUlLVHIBzb0yXNDM786KGfygJFjyUgWM4fJR+fZB3D9yVOk/74LU1zPImraKkMjqpy3QqfQTnh5+iC2nLyF5l26ICAkCI6//YTlB8/BrVdf6BGpCg0jdqkQuc+U+lWZnskCaR12Qe7MoadybPXTV+i/MbiJ2IuLSkNDdk4uyLEu6prVgbMDITsjQ/nymvgJ0rMIDs7OVYMr/ZecG5mu8OBUxwFmbaJwLlVuZ49x58IJHF07Fl5mjL4zSS8y5GZc+SIXhkxNfG8FPpkQC/Nxu3AzKx/Z985hac86VYGw5nLX7WuccG/FJ5gQa45xu24iKz8b984tRc866uFLgbUcAC2rVpyo2uEZ5cM0e6l/Z3fP92zHGYex2JN0F0lJSdK/u/d/xgz/u9i24XRV9SLVXoZjkWpr+T435pJWhMLcZ8rcpjIdmSwRrsNugHDqgNxXywdSfNZAjuwiydmRJfwshsn1CflnsXruBlw1c+SIiXImlLFJxoYerFxdqmUThrGTjFblXY2xVQVGyh0es0VHdsGenaWYOZv6aszdcBVmjhyxsoxdXPM7Y/aB63jyNB2/75oC17NRmLYqqYq43OvXQB5K9tT2lLZiwD9UiUWts/QLd78mLvqwuIQ8aS4hn5M2Zd1H+OzmSF2UZzyGAt7KPDx+8kKR0+nup+OofOhiQ7jr6PsaD6l7r9dI+O0jVY7Lm5fjgqR+c2U2Tv6wDQn+vRDaWFu7+M36oI/PDayZF4tkSTwWZ+HozJ7sanQZ4qVV8vhoMWIIvE7PxZyTnhgc3ppdn6tvvIah6BNwDzGLduNBGTtXlow9i2KQ1Kw3Qr0FcHWtC0o4jzNPJfzk4vymfbgtpS1G5s5JCOk3i9XZFcPC5V0EBzaBPd8MZlp5Iw++w4aj5fVofHlQgP7hweyeU/U2w3Nldywub8byC5I62JXIPvkDtiX4o1eoDxqG9kHAvRgs2v0AsinuwaKYJDTrHQrJzUitzUQyhyLkPMlAvsgPffr44MaaeYiVgYysozPRs20YlsVXolHfD9GWleRbsj+FJamVyDn9DdadL9Vx91ljFJsuCA1Mw4bvz6JBp86oY94Cnd8rRey/D8K+e28018LQBGYM2KKcJ8jIf1OVZVR4tO2FL+aFIOX/BmHsyovIlBbWYG+Ux2/F5EFf4EqzGZgbXo8lMdXgi9+MA8salHWUPEaQfBd377K/pNu4uHch5u1MRYPOXdlvggbGUpVx0RPsnBSCfrNY3WOxBVzeDUZgE3vwzSTWUom8W0ew4+B1SEyAa7PpEorAtA34/mwDdOpcB+YtOuO90lj8+6A9uvdurrwbzEXE0Dkqwi9rv8eVF6xhaRK2LtmGjOCB6OX+zuvB1hBuEv7KL2Pz8gvSGrSV2Sfxw7YE+PcKRWOzhgjtE4B7MYuwW+ZMkLxnEWKSmqF3qLeOmanqjgh+ffrA58YazItNliZm4qyjmNmT/Zq2LB4VvEbo+2FbPNiyBPtTmDJW5uD0N+twXudzT8qhxOzxqgyRB9p1bwdPGx4qs05g08EHrJpOOcrFmRxyV9LQtafb1zRENvMZIo926N7OEzbsTljWiU04+IBVxSiXGpA2KVU91Ftlh1d9H6Y5kigJ27dfhlvYKPSq7wY3t6o/944YO7wNsveuxyGtZR+Mj0Wqw3FiLm1IKPplLb6XKTGSti7BtoxgDOzljmZcOqDP19REjjwvFhua486273A0XZKEFuD6ytn4clcCyI/FVb0xUVfAkExKD1Y+jatlEwaxU9UXztik35/yvFg+0fwOtn13FLKpX8fK2V9iVwJJbVBfrGyUsxOTQvphFlvDQmzhgneDA9HEni+NS1Dl6zX5U6mqKP7jG9ANRUONHe5+lXqxELFHLlXyAL22qTEc+13Od+AwtEtcjagN15FbnIdbWyMRfUZ5w0Szh67vJiz28Ity8CQjH0VPOHDX1fl1H3sTD5K/dTSlL7lYUsM27cmrnjf5sYonTn6DaPnvsrfeVF8ikPNedHsDjW3jQrasMkd9Fzv2Jv8g+r7qZR9pG/Ej+j7EmiyDl9ED+ftr0nHkL/Kxl77vbKWJ7VzJ1qEe1WNvfnt2/DttuyN5rY+9h5J1mD5v68wqhLhTQ3cPaj1mAn3g1k5WYaDgKkWH+ZDQzoUaeXtQHZfmNPj7q+zFJtUXLqVkGKEMWt+bVdt4dxZd43jRT9Za2V/+oqfkuP65yl6AsGzYhtp71SNvP1bxxMmPBi3/XfriFpsh3dk6kdq52pJDvXrsDW9P6vj3bf/P3nWAR1U17TfZ9N4roYQUCChIiUCkCipNiNJEUAyEIiKfImAEAUURUBAUUEAUBOmIoIAiWOhSpAmEngRSISSkl93MP3d7vbsJQfF37/Mke9uZmfOemTnn3nvmDMmrqB1kpBRVkHUjByW62NpT06SjVFl8llYktKJAd15ppG4gefjEUP+FygAuruvlb0ZRa8beNzSIAqLjqZ88WI+DOVX0TPzeXhVPXvaNadIRxYoid1bHc+R0NL1xUHFMOu0ko4yNHNThYkv2TZPocLImcE9Bvox2jgil4Je2U6l2Oa19VXB02c4RFBr8Em1XLgpiQjw+XUhnvh5P3SI8yMHNj4L8PcjRNYziXlpIB2+rqFVDLoGRCJbG9NuUbELApYeN/BmHv8PYkI3EgTyCm1DXMV/QSdXqDSK89Ns47+gCio/yIo/ABhRRx58Cmw6ghUeFoDleyeTtZuQU/QapmsWUTMQhxavivci+8SRSNOkdWh3POq9VVnovAZeVR+mtR+pQ5wG9KDoklEJ9PCjksddpqyowVay+poQ2pv8idOTyO9enVm3CKSSiEa944kuN+i8ipYsSnAmtHhlLQe7eFBLizcGXj9HLay7Ig4QN21dXd45WFtPZFQnUKtCdo/zrUqAHrxTSfyGp3Vn5ZfpmVGs+70uhQQEUHd9PEfDFwZwmt7LTtLBHKLkHNqG4DrEUE9OZRo/oTL4NxtAe1v9Ck+1uiqK4ryk7vZB6hLpTYJM46hAbQzGdR9OIzr7UYMweKjWGtb6vMcVWzIdZEHBZeextauHZnKYcNXS+wupK3X18qdeS7byajnK1E7nfMNUXaYLPOG5YsZXtpBGhwfSS4FREMecA87ceoTqdB1Cv6BAKDfUhj5DH6PWtHOgupySmA/r6ogGr+u3IZUv/oi8TWlKAhx/LwauFRfahOQcVq3KZ7hON9E2iWDGf6tiEKHZMS19fRGxVg47hXulfX1JCywDy8GM/wisLRfaZQ4qqi/SV3B8cXRBPUV4eFNgggur4B1LTAQtJ7ib15Lpnf2rMVjg417R/MB1wyZ2OSDlBDUxgoVOnw5SsDog0o//cZ1zbMoG6RniTkyMviNH1ZRrcypkemnKMr5jaNDSFO2QZGykh0oVs7ZtS0tE807ibIleL5/9Dq50oBsVFWcl0+txNXp3DEhSlVJiVQtfS80Siac3RYRqZKZSWU2xk0Cil/LRkSk7nFViEdQcqhTU5NFt5Xjpdu55B+aY1SxjF05dPB1K7Dy4Yoa+hZX7PWF01inu9KIuST5+jm8aAkxZSZkoa5RSLdNZaAlSWllK5VkWlhVmUci2d8pRjY61beYyWTxlpWVRoGWmdotU6qOQ1VbSFqlbhGt4sK6acq2fo+KlkyjBVwWrKJYplDcU0VUyMl24bl1Ne+jW6npGv6yRLf6Sxz8wi0RXtTDG/T+elBZmUqi+nkpdYfasjjjE6moeHIspKPk3nbvKqJkaISgszKSUthywyNX3dYTvNSrlG6UYNTTC1DErLKqyGHymlzPPH6I+TVyhXsF1ZGRXmF2nZtol2N1Iv1UoHwqoEJn1NaSadP/YHnbySK/fHsjJeV6Wo3ChOKha6eqg6a+S3mj7MCAXLTmk9tFevL1KRN4c53yctoMxUE/2GmA7o64uKJaNt1H7V143vCPqUksq6aqDIYn2iFi0LsbLcJsxjp68vxmxVS0ITu5WUn5FCqdznG1ZdpK8sz6P0a9cpw0iHryuXifa4V38qphsmaio/LVrONBa6dRJjoLhWmbyb1v54ntdSUm7SZJrVxo06LUg1xFl1j9HfSiot1fIbIrgbLV5LJw0+wtf2m/UHjZ5zYDQeDrRUKgncAutBLLWCeUpMI8gUDQk8w6LhqSKi1xoOXiFo4KW6qP8rw/Vf1+Hnfd/iw2Nt8NZnUeqAKf07LTs2U1fnQESbAk7ihqB6lqNk56Q7QV7iFgiTxe08ERymRsiyqtTkLjthTZW/ebN1gX/4Q/AXY1tNuUSxFONTg2tivHTb2AFeIQ2gq8o8hWnnPjj1T0S0qS/ONZDpXotI3INQV4idNrKJ1dfI7SZPidNxRmD0wzDloiRuQaZtRZ+jvu6wnQaaNDSeUeQZjOqZmhOCGrfiwHXV5gg3T16tR70Za3f1RdM7pnyNUxAat9Jwg6MbdNgZoairh0ZuUJ2qpg9TFbuX3+r1RSpO5jDn+yTuCDKtxKZ1QF9fVCx5UqWh/aovmtwR9KmeUdct1icaJyeGleU2YR47fX0Rt1XjsrIlwTO4nqZf175NTM8cvBBiosPXlctYe9SCPzXjH7SrobMvWs40Frp10qFo9KAqby/eH/Ajfl24EGNbueDGjvfwefIjeKVbaDWDLu2gMwQRwd2oILV0Um+4V0tUHzQyNi4Ib/ME2tSv7ozoB60i2vJwcM1f3+HLH6WIX/wJXgi5H9P3beAS3gZPtKlf7bnk2pJa960IGCJgi8Bn3sdHhhf+k2dsXMLR5ok2+H/loqrVkv8RX/P/si+qVkNbfrMVK8ux4uHn/3d/6tBmKtYtrsI7y/+H5z7kRR8i2uH1rZ/jlZgH6O1NNVrMRniDXo37rbdaEbAiYEXAioAVASsCVgSsCFgRsCJQQwTux+vSGopiLWZFwIqAFQErAlYErAhYEbAiYEXg/zcC1sH3/+/2tdbOioAVASsCVgSsCFgRsCJgReABQuC/MfiuuoWfZo/F3D0GC64+QE2hFOVBlJVTBm97bwKWHeU1dbX3RdGTIWXrOxj/2R+8UuvfsRUjI11Yi5yXKr71E2aPnYs9wpru/8Rm0Ibasu3F/AkL8GsNZCtJ3Y/Vc5PwauJLSBg1Ae+t2IOrxfe/grKUbXhvwjIIzV/TreTaXiybPg7DBg/CkBGvY9bqQ5y5sabUTJf7x9teS7R/c3txYgJk/LEeH701DiOGJWDslE+w/bywCLpiqw2dUNHS+S06gE/Hz+YcB/+A7VrIuyr3BL6ZMQJ9u7TDo3Fd0Xf4NKw8nAlOAK+11Y7NaxEU2a3A8aXjkDhiBEbwX2LiSIwZ/xY+2sDrIVcDxuKMdPka8yKMLLpUcXwpxiUqZBmRmIiRo8bg1cmc5fbnKzDlrh4ku9Wv5L3IVnTgU4yfzXkOqtEO+vzFjrVl07ZJ7X1j5WUpW/HO+M/wx33wwcb4/T3nNDZnkl9xBtJr0PeapHcPF/4bg28e/t26eh7Xbv0bNO0BlJWzWh5auxQ7L3L3or0vqnicDvbIBqz46ZJepyRaqIYXK3Boant0mXkM8hQIFbdw9fw1TvdcQ3L3XEy7DXVlo4I/8e3SrThVUB0mZTjzWT88zMmf3vs5E5KAMIQ438Ivs+LR/NGXsf1+eXaliLLsQ1i7dCeE5q/JVnH6I/SMfRbzjpYiIDIa9d2y8UNSN7Tut7TGNE3K8Y+3vSDZv7u9IEvFljFt0OSp6did5cCrDfmi/OQSvPjoo0jYkCLPXHivOmGy/UrPYceXW3Dizj8QimQBbykn/hrSpiNe+6EUTfqO5EFmXzSjvZjatRV6zT+hzGZZGzZvEiEjF6S49stqrD6QBXtexsHR0Q7S3JP4akwHtH/tZ+QbKaF/quLQVLTvMhPHTOeQ0S9i8lh67ResXn0AWfa8gpSjIxzsgZKr2zGjdyx6zD1lvNwDYbfGRcM9yFZ6bge+3HIC902dtWTTtkntfWO1kmUfwYYVP+FSDX26MZr/7DldmzMqS8UhTG3fBTNrQ8mNMqjeyf/Gaie2oRiy/FcMMcBGhrLicti5uhhmyZOV4W6hDG5eroZL+Ek5s5qdnVYZGUryCwF3L87eZMAE0uJ8FEid4cXrYuk/7chK7qLIlpfMclIWrImsFUUoZPruxpgbiiM/Iyu7i0KZG7xc9QUWr4sJcqJ15BE7iu+Wwt7TzSATqHBNDDtOmaeHtRTF+QWQOnvxMmMqNKtw60YGSpRrBdqGDsHyX/Vb2wwflsOkLpiqtKnzOm1YpiObThHWseJKe7iq2l7nouag/I/38MIbR/DQgiNYl9hIsyTijH2Y2KkHJn34PJ6aF6fBVlYChTq6GOouk5WV5HPeOXd4GdMXi3XJHJ4q+ctxYPli/NnyA5zfMQahyiarGPEe2j/6AT75fRgWP65Znk5UNn6Mk0rZ7kS8ltG2F7FlUX6M490iW14+z8kojqoa6v/WbnuJ41xRVMi24G7U7+jIZUYnNPfKcG3ZKIzc5IMpvx7AG81dlZemYtibnfHE+Ml4oss6PKMpoNmzmIemiHpPqXcqburzlu5Ii5FfIIWzlyfUbkG7rGBr5XZwdTGiPJbyll3F52PG45eoufj925fRWKW2w8bgpc6D8NjoRMzpcATvtNL1R9piQMTmRXWRiUjZF9qJKL9z8xcxb1F/5cpULMPqfoh+dRl2vd8Nz6lXgjWuT1W3biBD5UDVAvO9d4tg6+YJMy5KXUK949wcL85bhP7qBcbKcXzKo3hsyXJg0mL1bVwrhU3XxGcb9A1aZLV2q6qqYGur6iu0LvCuOOY1l01hl8a12aDP1xWpWkfa/q4itVpF1TfLiu+i1N4TbvppuvkOcXzUJLR2DH20KA0R31ytcRN/89YeA2gJpNnlL9I3Mko0/afqyr34LRWNGvwa18gaEHqgi8hSsLhXffT5/AZrE+/3jkb/N6eiR4QvPNhZe4Z1wuQdmfIpCyi/iHWvdkG4fzAahgcjoFE85h7IQ5X0BN7t2ApDxw1ElLczfLssxCWZFCnfTUK3hn4IDG+I0KDG6PPeL8iWf2KqQu6+Oej/SAj8w6IQGeoN/ybPYP4RHqTzVnZ2OV5owdfqRSOCU81HPjEFuzK5YHVkLTuPVYmxCPYORAinNX7k+UT0adwW00XmB5RfXIdXu4TDP7ghwoMD0Ch+Lg7kCQIr68KY+IWFwc83Ak+8uR2pQoZgk5t4HYViVTe/w8gWoahTL5jpdsCrG68p34SL8DOGdWUu9s3pj0dC/BEWFYlQb380eWY+jvAD0oWPemHExtvIWD0IDfsuQ9q1xehVvw8+v2GmXuZ0Qa/e0lPvo2P4AKxSNDDKf5uIR0JjMfVwhfzOqrTP0TeyH1bcuKbUtxQD2dIFkSgPBz/ogUg/b3i7e6Bulyn4UUlTjyUfFuPnZV/hSusJmJegNfAWbvR6DBMXf4GpfesoiklT8N2kbojw9UNYmB98I57Am9tT5W8qhRukKd9hUrcI+PqFIczPFxFPvIntqga2WJfEdF4hhu5/G34T5wDp7VRczxcqr9gcYkZhxY4vMbopvxbjTSFbQ/gFhqNhKK/n3Oc9/CLHRIoT73ZEq6HjMDDKG86+XTDrk2GIbjkRv6kzC1fg6PQ4RL+wHlnabW/KlkX58cWys1j+QguE+NdDdEQQv61/AlN2Kf2DIKzoVlvtJWIfzL/s/CokxgbDOzCEU5o/gucT+6Bx2+mGU4OUOtHQLxDhDUN5Xe4+eO+XbIWvM1YP6XmsXvE7vAe/jXHqgbdwoyfaTVqKtQvHoJWTXrchpnfSk3ivUwRe2lyi5FaFnJUDEdFD8J98quwCvh7Vlu2ZfVhIGNq+8RNyNGqiLGP6pyp3H+b0f4TbKgxRkaHw9m+CZ+Yf4YdLwZUuRu/o/nhzag+2CQ9++eGJsE6TsUPws8JWTd7S5LVYdSAAQ94aoRl4ywlxmvrBMzAqhuuy8nec1vNH5mzetO6zXZx4Fx1bDcW4gVHwdvZFl4WX1PYsZ23yny3s+a2zvSv3b/LnDdN2K7vwEXqN2IjbGasxqGFfLEsvwdnlL6AF+9p60REICojEE1N2QQWbSZaiFxwRGVkXkrvC1E9Dm/74l08t89nG+gaDPqoKqZ8/g5gBb2Byt3AEeLOfbdgFr2++zt/AFZtpzO9BNiZdduFrjGrLesh2GRLWFm/8lKO2NZN9vlImzY8Up97viPABq5RjiXL8NvERhMZOhaKrqULa530R2W8Fbmj7Ow0By/aqbuK7kS0QWqcegrlP6PDqRlxTvgk3jY8x0oaYLWTjFqVh0jeLjymMY1hpvJ/VFlV2AR/1GoGNtzOwelBD9F2WzmM68f5Su/h92a+lZD0PNhmtTFmK1OIOZB/5PH15Kpcqy1JoM6c8d2oxnU5zhslzs9uRZ8Mh9M0VTucry6V9k1uSR+OJdKjwCE2KsSenpgn0xe69tOtwKpWf+5DaezWk51aek6dbLzq3lOLrBFO/NZkkKz9IExp5UtvpynTppRdpSU8/8uq3jpPJ5tPafj7U6JW9JE+6W3Cc3pGnTj/D2cmu0Nw4ZYp6+b4pWSvp9MxY8op+idZf4ZT1Zddoy8im5KSdRl2/VaTnaHY7T2o45BtSVG8fTW7pQY0nHqKSC/OoI6eH77vkJGf/lFHBqc8ovp4XdZrH6dy1U9Lq7IvVUZEu2N4xhhK/TeEcacX017J4CvPuRouuSklqjp8e1iUHJ1Ajz7Y0XZkTu/TiEurp50X91gk5z0vpuxcCKWzkj/J025psgZw1VIyPKL764PFx+e80PtKfBm4QcmxxOuekpuTs4EKx753jrIAyylzWnXwem0uXy7Xa0Jhs9nbUYMByOp0no/K0rTQ8yolavfuX8cyClX/S1GZO1HzaSd3skAbiSenCvI6cdrwvLTnJ2RFlBXTqs3iq59WJ5glpwqUXaF5HLwrru4ROcpZSWcEp+iy+Hnl1mkfnpeK6pJ2+XCqm8wYyKU7wp3p6nlP62nnUp0d7J9DEOV/SjtNZ8iyF8jtYLz9sz+mCn1tJ54r4TNE5Whpfh4L7raFMmaBHMWTv1JQSvthNe3cdptRbG+m5oLqUuKtYwaBMaJcA6vfNbTafuRTn2oHmX68wbcslYvyI8tf2I59Gr9BehXHS8XfaU2DcLDojlmlWIQmrRe20l6jeVp6mmbFeFP3SelKY/hYa2dSJ7KPfoIOcaVLTXuzPPmxPXg2fo5UKYOnc0niqE9yP1mQa5N5T1KBwNcW7udMz3wh2ZXrT5iGqd9r+Qk6OU5kvepxcW83kzKaCv40jzwj2t5fZ35ZepnUvRpG9fSuaaVHa03I6OKERebadTgq3UEoXl/QkP69+JLgFuS442FPk81/SqdxKKkvZzKmlnajF9NNsS9XnXbyuH7l7PEtrBR012Mpo96g65CRk6JQZ8UembF5U9xVtGWPvRE0TvqDde3fR4VRjqX6LacMAT3Jq8gy9MXkyTZ48iSaM6Uet6jWmwV9dlPsNc3Zb+t0LFBg2kn7kroQNgPr5NKJXFAZABcffofaBcTTLIgPgpOMbBpCn10DaqDRPYjyyz35Lrz/qSV5PfcYMDG362kWV3Zrx2YI+6fUNhogoMjPb24VR/OdnqJD5X1g5iMK9u9CCy4IvFLP/e5BN6F/jPCmC+1eFOq+jF6PsyV6u6yJ9voEuCV3NeIr0H0iKruYoJTV1JgeXWHpPsAtZJi3r7kOPzb1M5Wp/x33JkUnU2LUPfc24a+8bIa+4bu9IMYnfUgr7jOK/llF8mDd1W3SVpKL4GKVm6KPLxTAWG2eJjSnEMNS1OWNSUul39EJgGI2UK7mZ/tIogdo9qfcK476M7x9AonaIGfQGhjbzgZ1jPfTsEwev7Ju4UZ6CHd+fQuSwJAxsyHMYbH3QPmk99q4ag8bC2wOyQ4uhSRjWrQueahOKy1s24HiD/hjV0Q25aWnIdeuKod2dsXvDLuTZNcOErUewaWIs3KtKkJN2G/ZeHijPy0VhlT08PZ2Rvmcx5n+xAycKGmPaviwcSHrICFYmZOU3ld9uPYfmY2agf0P+/unYAH3fHYdOImkaZSk78P2pSAxLGghF9dojaf1erBoThfQdW3EsPAHvJjaHO3+ic2+WiJnDI3B00zZcNXizoBRTtI7CPTZw7jweM/vU4ykRLmjy0lsYGnoYW3dlINUcPx2s68Kx2QRsPbIJE2PdUVWSg7Tb9vDyKEdebqH6zYIheDLzfHjykFFdUL4B0KHpEItej7vh8M+HUCa7hl/2l6DXwDa4sm8fcmT5+HnXcUT16I0G+jN5dIjwgV0MBk8ahoe9bOEQ9hT6xnkjMzVN+UVA7+aqQhQUAa5u7oymYpNdWYS+UQ3QoH591K9XDw1aT8ZvJanYsfUYwhPeRWJzd/7M6o5miTMxPOIoNm27ytN4d2DrsXAkvJuI5u62sHVvhsSZwxFxdBO2XTxroS7JkCym83qiqw4lDYdizcmz+PmTkWjjcRO/LP4f+jwSzm+35+MPjuGTJW/BhuMN0H9UR7jlpiEt1w1dh3aH8+4N2JUnUCHYtRiKpGHd0OWpNqjr1wNDewE7N+wBQ4OS39ZiWwmf6+2rYin/gmTKlqOuiPOz5zekzul7sHj+F9hxogCNp+1D1oEkPGRkxoKGoXKvVtrrkqjeXjz7Lbaea44xM/pDYfp98e64TkY+pyZjy4bjaNB/FDq65SItLRduXYeiu/NubFAAayA+KstQJnWCm6slleXiPD9cTO8MGWid4S9PO74/iagX2d9GsONyisCAGaPR1kGl6Vr3Gt21Q7MJW3Fk00TEulehJCcNt+294FGeh9xC5dtttrVBbwxFMx87ONbriT5xXsi+eQPSGvAuLypGpYOriSk+whce9sNlpSgzNl3dhM2Xm9V9odtpgaFJw9Cty1NoU9e0c6kqvo201FSksi9Jy7iD8vIcnNi5B+dKqmm3PAXB0zkdexbPxxc7TqCg8TTsyzqAJIsMQNFQdHcDBrjawMaG/yTOCGo+FJschuDzT0coW1LPptXVssBn6/UN6qJ6OmL/yEi8N+IhuNk6odGQt/FSxB/YtDWl+v5GzUBctktXuX89GYUXuX9VqPMAzBjdFgp1rk6fDzjE9sLjbofx86EyyHgO/f6SXhjY5gr27cuBLP9n7DoehR69G1RrOpwePLBx7ozxM/ugHk83cWnyEt4aGorDW3ch47y4f9SnozjWbc/QyyI0bouMs5xqa9xkXEr12XvxW2oi97ZjoYe9NyYPXmkbeHp7qedf29jbQVIlQxUHE2bfskFgSKhGqT0j0Ko116Aih//Zwi8oWHlNisyMHFRe/AJD2q/RqaJv8yLkkQSVp5Zj4uDNOHTpDuxDYhDtWAAKIB5KuOCpOZsxdwZHgM8YhFmjgZDW8Xj9owX4XxsdUnxgWtYsHoD6B/mp62HryZ+gPUw/T8mys3HLhj/vhqq9CTwjWqE1f4w7nHUbNsF1EabWCAnqhgXDJjeHB5b6MimPbSXIN1lH4R4beITWgadKJJ4LHcz1z83JQnaJOX7aWDPyknycWj4RgzcfwqU79giJiYZjASFANEcUB32arZcJfI1W2QntenUBTdyLE+lp+D2lNYbOa4rUp3/D/mw//HikHrq/G8H6kWq0tPqkrRd8fFWg2EAisWXdk7JeGNkkoQjlXOPbuVOVoqFc9yTBPTD504YQZnFUHvgAg1bdQSnrbtZtGwTXDdPEIki4PYNtGG922NlZuG0TzG2qbmBI+N5gm1zkZGWixCJdMqPzrCe+GtVSVobnjd65jXLO4NiJB1nCH49QkPbLfIx8fipeWdgNB9plIKfyIr4Y0h46luTbHEV5CuWz9QtCsJq2K7oMfQauA9djV35HOKz7AZI+q/CEkBZeMFNhE7Hl8j3i/FyemoPNc2fg/eUzMEhhnIh//SMseDUOPqpmU3Ax/F8r7cX2ITFtH1mZJTzI9EeQn0oYW3iGBMPA9KWZyMipxMUvhkDXRfmieZHwVKP1sKKqiUsdhPoWISWdp9ohWO1b5Jd5zmTymUIENQ1Xzivms2b0DlBMK1KR1/lVtlGAlr+1DQpDqHqesM7dRg5sIck/heUTB2PzoUu4Yx+CmGhHFFAA1G7BxhPe/JCr2GxgbydhW2PDqQFvlzp14Ft8FTeEJURUwQtqqYqReuMWHELrIoj19KL6vHLHhM1XZorrory0rR+CNMqvT1l97PLoK/hqnWrON1cxdRmefmQi3lzdHRPE+io2MZ3ZyS5PYc7muZjx/nLMGDQLoxGC1vGv46MFryLOrAEoxLFx741FBz5EVx552tg5wsOPpzwp5r/wDYrJH7o2raqGJT5bt29QldT/dQgLR32Vz5CEoU4Qu4eMDEgtwLwmsmVl2nL/GqDVv9oiKCxUaSsifX6cj77o/CDaDr26ECbuPYH0tN+R0noo5jVNxdO/7Ue23484Uq873o3gypnpagwJa87YePB0UE3njFCehkrc3wvjGlF/bOjk5US1MRPFmKd+mBxncd+QaXJMIYahxU5DAYBZvxWlAeo+7am80n0i/y8jK/GDnw8PDnmgoh5v5u/D5zNW4LB8XjQPJ/lJXrFJ4OvvDYdWSdifegM3bij+LhzcjZ3LElDv6mKMTtwEx+HrcTorH9mX9mNud39lZ1bCg0I7dJq2DSdu3kL6n+sxLmgfkl77zHLA7NhwAiqQcUMja1XeDdy8q3zjY4SSxM8PPpTLvNW1Q/6+zzFjxVE4+HiDsjOQob4kw830LJA3Y6JyYHo0ZZfE6ijcTCjKvc3mpNyq0pHJDzf+/ADjz/OdzfHTYC3DpcWjkbjJEcPXn0ZWfjYu7Z+L7v666qtqGRU78FDVEj6a+83vuXTsiY4Fv2H713twpmlndG7WBe19juGnRduxnwfFTzfSDG61qenLpn+sfa/OvoQH9E82Rfb3q/Gjat0w13C0fbI7unfvjq4xfvIBOST+8PMmZHPnomnCm0jPInhzu0v8/eBN2cjQNDBkN9ORRd48z5o7Jot0SVznw43pSdkujGsSicGrtOdMO/E89/9heEcnpF29BomvP7wdWiFpf6rajm5cOIjdO5chQUVUeIOmBYxjuyHoH/gLNm/ejHU/eeHZF9rrvvkVseWj9oyFCL8Stn+7TtOw7cRN3Er/E+vHBWFf0mv4LFmNrJYkeru10l4BonobGFYHARUZuKG24yrk3bgJA9OX+MLf2wGtkvbzwFDloy7g4O6dWJYQrie48tCxDZ7o4I7j276DPGRC666K05/iuTYcH7NXNX+bL5rRO9ja8FcYnuVbqRhwCT6hgD/lyB80JQEI8CPk8GBIhWzV3Vzkqh2GFnNju7JLWMxBjpsch2P96SzkZ1/C/rndoecWjJVkuavP2zGuN570OYq1X59Xzx1WEZddX49vfrVFh55d4KE8qa2vwin9Y+GcRbrPJdXdjlDIwk0S3ASN/CsY3zzRvkplYmoBS/hB3q4Tpm07gZu30vHn+nEI2peE1z5LtpAz32brgoCG0YiOjkJUw3paA28tEno2rbhimc/W9A1a9PR2pfl35C8o5Kd5sJWTyynJAwIsw7wGsgVw7JYfCYNXtTbjbm6usv+rbp/vgo49O6Lgt+34es8ZNO3cGc26tIfPsZ+waPt+BPd4Gia6Gj0UTB9SUS5uq22tCumZt2Djzy85/Czwx8bIamEmqteRgSbHWQeOfXpP4yZjNqYrqvIOc35Lt9B9OdIdvdwXFv8iopJw9OjZFBfWfIyd6YIBFeLEkmmYsv4cpC76UNmhSe/eiDq5FO9suio3MFnWTkzq3hrx806hjKexZEjrIPbJWIS58duWrN34avsVjuyuQIUsE+vGdkGfqbz+p8wJgQ93QFykJ+wcjIQbm4JPEo1+z8Xi/OdJWHEiFyV5Z7B68gL8rjYmw4KS8B7o2fQC1ny8E4rqncCSaVOw/hyhEdel2aWVmLXhCq/wy6v8Xt2IWSuT0aRXT0QYG1TxPTKxOsrZE4p/WYaFwtwCRih59RysyeiAfj1CUL9ndfjx25Cb/MaiTiyejA2Dm6QKWbu/wvYrHFldIXTqNnBwsENxDmOez5/p5LyFfxwIVS0+6oKmd9w6o2dcGlYs3Id6HTvB37E5OrUtw6ZPt8PzyV5oajD2NiWbaRa6V+zw0LhZSHTfhBG9/4cV+66jSHi+4i8xZ3/4GOM+/h0Vrh5ws6uPnr2b4dLKWdhwRd6CuLpxFlYmN0Gvnvw2vn5P9G52CStnbYDi8lVsnLUSyU16oWd0jIW6JK7zlbqCK46cOmJAfAB+ez8RH+xIhvxFNk/NuPLD+1i01x5xj8fCjpdQ7B11Ekvf2YSrgv7KsrBzUnd+2zYPp4wS5XvsmuP5geH4bcZ07AkbgCEt9WxHxJbR4mkRfjJkrhuLLn2m8lrTMjgFPowOcZEctOYAVjFUsZ3tWLsdJ26Zesitjfbi5RhF9DY6ph+eiz2Pz5NW4ERuCfLOrMbkBb9rHnJV7WDXBL17R+Hk0newSQEssnZOQnf+yjbPJLBeeHrCOEQfmYYhb27GeXmQLA/uT3+NccMXIqXNeLz6uNZbJom43sE2iAP2COcO/A4BsqrcA/hqy1nFkqCSBnj62da48vUcfJvCdlyVg98+Wo4DqnkbVXk4s2Mttp+4ZXxqGQ+obmZIUSf2ScSGufHXyyzs/mo7rvAqGHK3oMLB2K853sbKuPfAW+90QcoH/ZGw5BAy5c8TMtw5tRqv9H8LfzSZiBlDQvgFi+U2XyPdNyYbnxOm4129eBEX+S/57CFsfv8drEuth05dm4j2VYKJ2XDfY1ecw3jmo/jmOozt0gdTd/GLHadAPNwhDpH81lrwsajKx9mda3kKm0jQrgn5LDtdez674sgqLDoo5H6oQvaeT7DmXAz37w1r5m/kwovLFh3xNJ5tfQVfz/kWCnX+DR8tP6CYhlSDPt+tc0/Epa3Awn310LGTPxybd0Lbsk34dLsnnuzVVPOF0zJgDe6i4l+wbOEfkPfOyasxZ00GOvTrgdCHauCP9aiL6nWV6XFWRQG/AKzRuMkCm7MRfHgxcngskV8ZJtpf6lXn/hzW7hTyB5SaQRCjIqBRFXJUtnMEhQa/RNs55odK/6IvE1pSgIcfhYZ6k09kH5pzkCOvDAKHhLoW09kVCdQq0J28QutSoIcPxfRfqAj+KTtNC3uEkntgE4rrEEsxMZ1p9IjO5NtgDO1hPoVHF1B8lBd5BDagiDr+FNh0AC08WmAk4FJE1koOspzQlSK8nciRAz+7vjyYWjk/RFOOmY4MK/3rS0poGUD8GZBCvX0oss8cEqrHEZt0YfVIig1yJ++QEPLm4MvHXl5DF4QAHO26a++L1pEDEt96hOp0HkC9okMYSx/yCHmMXt+aqgwstJCfIBpvZacXUo9QdwpsEkcdYmMopvNoGtHZlxqM2cMhjRzEtTGBIl1syb5pEh1O1gTviNZLSy+M6oKCtcH/26viycu+MU06wpEqvN1ZHU+e2oGuWnQ5OkZENqF0Ge0cEUrBL23nepjepOl7afaQthTmaku2HIDlaGdDdu71qUPCR7QnTdneZRdo9chYCnL3ppAQbw6+fIxeXnNBHoQq53RhNY2MDSJ37xAK8ebgy8depjXyBuarIrqkG7wjovOmxC88ScsSWC5HG5I4upG7MwcheUZRr+m7KUMJfPHZFZTQKpDcvUKpbqAH+cT0p4XyKDpF4K5rn6/Z2nQ32fWF1MXVmTrMu6IOVtUEXDJhU7bMZEzz44uFR2lBfBR5eQRSg4g65B/YlAYsPEpsnVR58m1q5hRNbwiRjSLbvbeXiH0IclzbQhO6RpC3kyMHVHallwe3IueHppBg+jrtVXyWViS0okB3LwqtG0gePjHUf6EyCNyk/FK6ufNt6hHpQfYOHuTn60b2dp4U1edd2qMM1NThIap3Msr64XVq7edMnqH1KbROS3op8QkKjhUCLlmA8sv0zajW7Dt9KZRH6dHx/RTBfUKQcOVJepuDjaPfOKgJztWRuYxOL+xBoe6B1CSuA8XGxFDn0SOos28DGsNOVkcX5OX0bE2Mtw4f7YNCOvP1eOoW4UEObn4U5O9Bjq5hFPfSQjp4W+VFqmfzYrqog7O2GDr7QsClB/E7PeGDAvFrcpJwuwU36UpjvjjJAf7CJm63soyNHIzqwr6lKSUdzaOjC+IpysuDAhtEUB3/QGo6YCEJ3RNVnqEZLR2pwSu/qP2KnLzWP4OAS61ril1Dm9ZtKxHd1+5/DOiqTigCLp3rt6I24SEU0agB+fo2ov6L/pQvjCDcZRrze5CN6ZZf/oZGtWY78w2loIBoiu+nCNYW1Nlkn68S2+D3Nq2K9yL7xpNI0dXcodXxnurAauF2bdy0dUV734Asn6g8+hY9UqczDegVTSGhoeTjEUKPvb6VVPG8pvExRs0QM+EuURqmfLPomEIMQ12bO2psCCTLoI28uIaLrT01TTrKi22I95fGalqb53hunHUzhkBlfgalpOZQscqfGrtJdU5aSFkp1yg9T79DLqXM88foj5NXKFe4JCujwvwiKlfTLKe89Gt0PSPfzEoWKkbav5WUvHst/XheWHlDsUmTZ1Ebt060IFXNQHVJ77eS8jNSKDWnmIeGehvXJTMljXKKhZ7Rks1cHZmGtIAyUzMo35hBVIdfaSadP/YHnbySK++MZWW8bkxRuaYOlaVUqgFXV/jq8NEtWTtHYrJVg4OsKJuunDlGx05foRzhwcjIJi3MpJQ01l2jTSilwswUSuO211yugS6Z1HkjAilPSQtu0vnjR9geLlK20ScNli0rha6l55kYbJmmLXbFtC2L8yvPS6dr1/X1tpR+HPsMzZKPHMW4Kq7dc3sZ09vKZNq99kfSmL6Ukme1IbdOC8iU6UsLsyjlWjoZuCjRKvAqFZdP0R9HjtOFTP1HH8OCononzae05GRKL2SPI5NSpVTX8whtlJZVqKWTCvqlP46lZ2YJqwmZ3kozz9OxP07SFYWTpbLCfCoy5QeMkDHF28itmlOyYsq5eoaOn0qmjEIT0lXL5sV1UcP4HvdE7baSSku1/Gl5HqVfu04ZRh33PcphSXFjum9JOdaWK3PjyFVYeaYoi5JPn6ObvMKT4XYPmIvKJvSvaZRlVC/upc83rMG9n5FSQWaqiTa+B3zUgonTMO6bzY0pRDC0wOYqS0u1xmA8PBHtL9UVqfUdG4Hi/XmnbqV6fxGowJE3W+DJH9th4cKxaOVyAzveG4vZea/g4K8TEWNiqsj9lclK/d+JgFWXLG23quxv8faHFUicM0gTzGVp4dq6r+II3mzxJH5stxALx7aCy40deG/sbOS9chC/ToxRxADUFq9/kk5VNr59+0NUJM7BoPpWh/ZPNsW/i7cMVz/siGY/PIu/fn0N9fVnjP67KmOV9v8pAtbB97+5YYvOYs2772D5z+dxm3wQ0a4/Xnv7FXSyIDL+31xtq+z3AQGrLt0HUO8fyaKza/DuO8vx8/nbIJ8ItOv/Gt5+pZPWijD3j7eVshWBBxuBKmRumYSxR+Pw+QfxCLAOvh/s5vqPSmcdfP9HG95abSsCVgSsCFgRsCJgRcCKgBWBvx8B6zPh34+5laMVASsCVgSsCFgRsCJgRcCKwH8UAevg+z/a8NZqWxGwImBFwIqAFQErAlYErAj8/QhYB9/3iHnJmRUY/VQ7tG43GJ+dN5aT/B4ZiBUvzkD6HVPrDYsVFK4VIyNdWAOVl269tRfzJyzArzWmZY5XTa5X4dZPszF27p6aFDZdpugAPh0/m9dwriluplP7/P8AAD+3SURBVEn/bVeq1e7a7fwTZo+diz1/RzuXXMPeZdMxbthgDBoyAq/PWo1DGapEK7WLlPT0Kkwe/y6+vaZZ4V3OoeIPLH19OjZd0jtvgn1xRjruHZoynP4qCUsOa9e1DH9t/RQLPv4YH6v+PtmIE8ICu9XeSnBmxWg81a412g02npRLXQ/OSLl3/gQs+FVh59VmpVfgwcJZTzg+VNfb8JJlZ8oz8Mf6j/DWuBEYljAWUz7ZjvM1aiPL2Jm/S4aUbe9hwrKjfKsZf1gtv6bxCeZl0LtDlI8UJ78Yj8QRIzBC72/UjO+QVnkLP80ei7l77ugR/ZsO2R4s46+NuynZSnBt7zJMHzcMgwcNwYjXZ2H1oQyd5EvV0sdq+XRTMtXy+RrJZEZPLRLREvxNESrCgU/HYzavUV/tHl5Ut03xq/l56+C75thxyTvYNG0CtpS3x5jxCehY1yDDyj1RFy1ccQhT23fBzGOmspCIla7Aoant0WXmMXmyCyr4E98u3YpTBWJl/v5rFbeu4vy1W7XLuPQcdny5BSfu/EsX+alWu+u2Mypu4er5a7ilPS6sXXQV1CpO46OesXh23lGUBkQiur4bsn9IQrfW/bD0Yu0/oEqv7MbyRe9g5NhluKo9zq5Ixs6lq7Ev3TzPikNT0b7LTNTInNQYluPK2tEY+PIC7OYEUOpNehZrp72F+d/sxK5duxR/Px3B9dJqdw/scjZh2oQtKG8/BuMTOqpZqHZ06kEF+PPbpdhaS4b94OCsqq3mV6femtMW78lSt2BMmyZ4avpuZDkEI8y3HCeXvIhHH03AhhRtpbKYZC3cyMnFDq3F0p2KZPWi/tBiv6bnE6orpSgfKa79uhprDmTB3skJTtp/jna8/HgFbl09j2v33QGZqpSl/HVxN6RWgdMf9UTss/NwtDQAkdH14Zb9A5K6tUa/pRchWH619LFaPt1Qmvty5h5kEtVTi4Q1h78YkVKc2/Eltpy4o8iiK3ar/jVR3da/+d6P/8bR4r0LWxsUZGV3UShzg5er/tJVMpTkFwLuXnDRv6RkLCsrRrmdK1xUqMnuIPuWFDHPjsGwgfWVqeP5ZlkJFKRcjC77JeUMbHZ2KiICcSnKSqrg5KLK0leF8uIySFxdTGex4qf4Gxkluim1BVJmeAu3yN+i3MhAiZPiSOe/rAzFlfZwdTIEQVaSzzk/3eFlCiAdQlwr/XpaJJuKiC1ChyzHr0NUx4pfWcldFNm6wbO68lUUoVDqDFddciJHnBetylaeGtvwJs6sKbXjNtRcEcOmxjIzeQOdM9XurEPF+QWQOnvB01H1TM1vIbTa2TZ0CJbrA8r5QMX1Xoay4nLYiemiBgb5XvmB5Vj8Z0t8cH4HxoQqZakYgffaP4oPPvkdoxY/rikhqhPmZNOQsXHyAh2YhnHLn8D3oxsatTvV3cbaqurWDWQYMwhR+VQUhcR/x7Fi0lhM3ZIJRye9JMcFZ3H2RiMkHvwRbzcxtCsNFd09Y3LK7mTjljQGz44ZhoFG1lAzWQ9u57LiSti7OhnB5p/H2VhdVWiI2Y/qHlP1FqOrKgvZNSwbNRKbfKbg1wNvoLnKSUwdhjc7P4Hxk59Al3WDNGnrzeqEaZsRq4u4rMb9IUT9mnmfoMbAnB8Q5aOhImQbdmr+IuYt6g+tPKjqG4Ys/xV6Lp2vmcYL0mLkF0jh7OUJtVtTUxOKcn9VbgdXdaesdZGv3S2Uwc3LVaHztqEwxl9anI8C7hu8PB01fbgWGYPd8gNYvvhPtPzgPHaMCVWWqcCI99rj0Q8+we/DFiPOhD8xysuUTzenZ9IylFQ5QTNsKEdxmcQIFubs2wj+JmQS018FTib0lC8a9GVqYJn/3ULI3LxgMCxT32NupwJFhawnKtvVu13UtizWbT2i93pY6yuHP6AEy5LX0rjODThzoy95u/tQdN85tP+OsPB+JV3fOpG6NvQkZzc3cvZsSN0mb6MUed4EKV1f1Iui+k2mKd0bchZDO7JzqUMdJ/1AGRXn6cPHOSOfvS05uHN2tt5LKLX8Om2d2JUaejqTmxtndGvYjSZvS5EniKg8/g51aDmEXhkQSW52HtR5/l76pFcjGjBpEnWtz5nk7BwooO1E2vLjAuof40MOdnbkHvkMLT5jJJOKVODtR04SCTlzRsA+S2+SrNI0b90mkdL5Dx8nPycJSZw5612fpZR6iTNCujxEz458kiK8nFgWFwrr/BZxem150crrW2li13DycuOMn15+1OjpmbRXeU2XNqOpX88FF0mqlC3cy428fbzIr9HTNHNvFifHyaVNL0VRizd+1WR3LP+DprWLoqHrMunqop5U7+nP5CxKzyyjoY8Ek7dfIPl5+lBEt7dopzI9oqh8pedp1cg2FOLiQm6ewRT7Qjw94tKKZhpLkiJLoc/iG1P/CdwmDXzJw8OLwju/RpuuCRmSKun4Ox2o5ZBXaECkG9l5dKYFFzlZiAg2NZPZnM7ptbvsNv0+ux81D/Yib38fcnXmLKvx8+hwgZF2vrqIetZ7mj5LM6P30uu0qFcU9Zs8hbqzXdizLrrU6UiTfsjQJDTSb3it4/IDr1OUSwuavD9X634Z5Zz9lfaeyVLcaVInhMtKmwz3IjfOwurl14ienrmXTKgclW4eTL4hL9LH73fke7vTZ1eUSU8KV9LTLg3olV8UNqRoK7ZjZzdyc/akht0m0zY2dOn5D+lxPyeSSJw5U20fWnqT8RGVT1EF7f+Vx+bQoMHv0vcXfqdJjV2pz9eapDTlv4+nyOAXaUvGFTq6bx/9mVKohYs2FcW+qJycTdfe1oHcOYNe7yWpOoUN6pF6iebGudBDz46kJyO8yIn9l0tYZ3prl2B7wvbP41xT+9GuuEG9uf1MYahdTrVfeWYGtXRqSON+1fe17KGObqX1v16WZzdV6YQx/05mbMa8LzDUS86XSEcmNSYhuyunAuG+SOMPScyvVcMn3JSZ0QExPioA1b+ltHkw2+vAjQbZaOW3CBj1rEdPf5bG1RH3MbLbv9Psfs0p2Mub/H1cyZkzs8bPOyxvB+n1RdQrqh9NntKd+1p7suP+qk7HSfSDKl1uWTKtHdeZGnAWX19vd/KJ7ktz9t/h3E5a/NkCbv8+m/o1DyYvb3/y4Uy5PjHxNO+wkMZTG3d15TQ75Qfo9SgXajF5P+XKNKdlOWfp171nKP0vfX9SaZqXSF9u2F8KsHHdGw2gSZO6Un0PrrtDALWduIV+XNCfYnwcGAt3inxmMSmGDSJtK4a/EZmKRfpfDQLCnraeivRlctzKKHntOOrcgLMx+3qTu0809Z2zn+7ItPGvpD9ndqSGwzapdUqW/RUNaNhd3v9yGmM6v2oktQlxIRc3T86c+wLFP+JCrWYqEnOJ+RdRG9Kt1H05+m9kuJSeo9ntuLMd8g1d4cx6stx9NLmlBzWeeIhKLsyjjpxKve+Sk1Qgk1HBqc8ovp4XdZp3ntVIkSnLwT6Snv/yFOVWllHKZk5j7tSCpp/mdI3SyzS7rSt1/DiVTVlKF+Zx5x/Wl5acLCCZrIBOfRZP9bw60TzOLSuke43htOBNE76g3Xt30eFrF7ljdCD7yBdoTXIRSbO30fAGnHa73jO05M87VFl4ij7o6ElhI3803vCl39ELgWE08kehwxDnbUiglL57IVBOW176Cg++uWNuMGA5nc6TUXnaVhoe5USt3v2LpIzdh+29qOFzK+lcEVMqOkdL4+tQcL81pMw0rUPeoJ6p5XTuw/acAvs5WqkgQOeWxlOd4H60hgnc2fgcBdVNpF3KsUqZMFAJ6Eff3NZkKSPKp7X9fKjRK3spT+BWcJzeaR9IcbPOcNXF5JPSudlx5BkxhL65zA1fepnWvRhF9vYmBt/ytPDs0MLi6fMzPEAqvUArB4WTd5cFdFkqOIQYsndqSglf7Ka9uw7zw5YY75rLLGRnM6lzOu3O3cTBCdTIsy1Nl6di5ypeXEI9/byo3zohqbSRdnbtQPOvs7aK6b0cB0E3n6cvT+VyGt4U2sxpeZ1aTCdB7c1uXP7r5zmNLz9k1n+0NyVMnENf7jhNWeoEsNwuIjohPfchtfdqSM+tPCdPB110binF1wmmfmsy2c4MN/ngO3Q47Sg4Sx929CL/Hp+TfPytPfiWXqB5fC2s7xI6ydnuZAWn+EGrHnl1mkdC6ufS716gwLCRJDcnticx+Qwl0DpjkP5aRumLHicn9yAKqxdBTRrXIU8nf2o3cYdR++GGEZVTenk2tXXtSB+bSGWpUw+VPjcYQMtP55GsPI22Do8ip1bv0l9c538cZ1HbFbEfLbhVu7r1FsdQVUb1W7g6ntzcn6FvFHnYVaf1fs34WFGbEamLaHtrD0K0/aG4X6uWTxC1NXE+egDxoTD49ibnhwfR1GnTaJrq751ltC+HLVeOkSt1mH9duW/Kx5TTwQmNyLPtdFK4tVK6uKQn+Xn1I8GtyVOqO9hT5PNf0qncSipL2UwJkU7UYvppfpwUZG7HL77Y5ys6e9o3uSV5NJ5Ih0qucJ+r5F9+kCY08qS20/9QPFiVXqQlPf3Iq986KjQ3+GYeV75+niJd7Mij/qPUO2EizflyB53WODhdfyLKS4BNty8X8z3qur+whpKLpJS9bTg1sLenes8soT/vVFLhqQ+oo6diXCBq36L6qi+TiP7yrbqbrp6K9WXSc7OpHb/sHPLNFdYcftDdN5laejSmiYcKtR46NTagep0hy1hEj7sq+nCBRpxnBNO4zDRK6fK6FymK8ZAPvkX9S3V1W7eWtXGk+j59ry/QH+jyspQd+P5UJIYlDURDnmph69MeSev3YtWYKKTv2Ipj4Ql4N7E53G1t4d4sETOHR+Dopm3q+aN2MYPwxtBm8LFzRL2efRDnlY2bN7TmdAq1l6Vix9ZjCE94F4nN3Xm6gjuaJc7E8Iij2LTtqhwfsmuBoUnD0K3LU2hTV/j8LEGj/uMwMJo/iwV0RlwTe4T0HIuER7xh59YYnduEIv+WBXOeLeAtF0Dsn10MBk8ahoe9bOEQ9hT6xnkjMzUN5clbsOF4A/Qf1RFuuWlIy3VD16Hd4bx7A3blGSeoU8/Qy9iy4Tga9B+Fjm65SEvLhVvXoejuvBsbmIB3j6HohZ3YsKeIiZXgt7XbUMLnevtq07aHp6cz0vcsxvwvduBEQWNM25eFA0kPQSYm3+0U7Pj+JKJeTMLACG54pwgMmDEabR30pgVos4I9Hhn5HkY85AZbp0YY8vZLiPhjE7amCPM+CXYthiJpWDd0eaoNQi+LYVNDmZWYWqRzLJFdswnYemQTJsa6o6okB2m37eHlUY683EKRgBMZUs3qvR1iBr2Boc18YOdYDz37xMEr+yb01V4HOtWBpCGGrjmJsz9/gpFtPHDzl8X4X59HEN64D+b/wRFssmQRnbiN5C0bcLxBf4zq6IbctDTkunXF0O7O2L1hF0yonIKzfVO8umgKYg69jVdXXOOP2ZpNlroDW4+FI+HdRDR35+lE7s2QOHM4Io5uwjadieJcRlQ+UQk0DNV7hBKf5uiX8D52/HUZf51PxaXvn0fx4pF4awdPc9PbqiWnXlnjh9yOgydh2MNesHUIw1N94+CdmYo0qewfx1nUdvNM24/xemrOVhfDyrIySJ3c4Ko1jUxDTblnkY81ZTOm61JdWeXSyMT9muU+wYwOmOFjgJEKqjvXcPLkSc3fqYvIKjN2tym87NBswlYc2TQRse5VKMlJw217L3iU5yG3UBknwf3VoDeGopmPHRzr9USfOC9k37wBqVzmU4gcxj5f0dmjfdJ67F01Bo2129euGSZsPYJNE2PhXlWCnLTbsPfyQHleLlQsjEmsOCdBw6FrcPLsz/hkZBt43PwFi//XB4+EN0af+X/AIEa3Orws8T2SRug/biCieY5GQOc4NLEPQc+xCXjE2w5ujTujTWg+bvG8evN+1BT++jU3rb/6dxo7Nt6XlSNlx/c4FTkMSQMb8vRZW/i0T8L6vaswRqehjFFUnePATKZxMupFphHBNJwQMWAGRrd14MlPghsX6Z9rNDZQ8a2dX211rB2KDyAVWTbPk7QJREioZr6lZ0QrtObY5MNZt2ETXBdhaiQkqBsWDJvcHORw7x3M9bHx9AaPSRWbjT3sJFWQ6cdKybKRddsGwXXDNPO0JUw32Aa5OTmKsrZ+CNLJPmkLL18/5ZwxCdO1gYubq1xx+BGBB/C2/GVCyVfsxyzvKLHSStm84OOrriQkEltUyaSozMxATuVFfDGkPdZoU/FtjqI8BshXg6n6snY9pZnIyKnExS+GoL0uATQv4oGMaxcMfcYVA9fvQn5HB6z7QYI+q57gmeWAEjXec8FTczZj7oz3sXzGIMwaDYS0jsfrHy3AmFIR+W5n8Jx8GwSEhKrnudoGhSHU2GREtfAOCAuvr75fElYHQSxJRoZiKGfrF6TOIigVxcYRL9ZEZgFT3izSOb7PVpKPU8snYvDmQ7h0hx/eYqLhWEAIEFUcDmgxo/fC3E1Pbx6wyaXhI3s7SKpkIgN65Y38Iyu5g9vlLgjvxE5R+ONzZWm/YP7I5zH1lYV4/UA7EZ24hcyMHFRe/AJDdBUGvs2LYErlVNwdmr6KRVN+QPupr2JFu2dVpyHLzsJtm2C2bbWhQ8K2GmyTixzB0LU3czoLnSdD7ZJG9iWIeO4jrH5OdcmWO8xxGNJiKVbsPwf0bqO6IP81K2eQzu0WHLCP8fHVtKNEAltuR/6q94/jXFP7+V+cj2i9zWIYpeuzXOqEwrcoBel57NSDVRqvYFF1KxlnCoPQtI45/y7vKUzYjAt6mvAFL9tWQy9VtWZ/L+bXLPcJZnSgQpyPShzdX+7D4t7AxvVG5nzrmZlpH2MLSf4pLJ84GJsPXcIdHlzGRDuigAI0/aGNJ7w1nTLs7STcX3H7KbEJ1PL58IxAq9YspeyqRlRbCfJPLcfEwZtx6NId2IfEINqxABRA5gP1eC72ndvlcAnvhBeThD8mW5aGX+aPxPNTX8HCbocxScNJcNKW8zLre5iwrRd8/ZR6KmG/bOMCN1flCyUeM9jaCnUw07bytrDUx5vuf83ZogCD8b6M+6DsW7AJDIFmWOaJCHlDVWj1/dpA6u8raQRo0bANQpiygxf1LzUaG+jzv7djXU9zb7Qe2NISPz/4UC4PODTWn7/vc8xYcRQOPt6g7Awox1ZcBxlupmeBJxfDT9dHi9dP4g8/b0J2RobmjZvsJtKzCN7MX7HZcMS3Lhn9Y92r5o6UxCzibUhLTxTloF/3PomvP7wdWiFpfypu3Lih+LtwELt3LkNCuCmAtOop8YW/twNaJe1Hqqr8jQs4uHsnliWEMzNHtBvSH4G/bMbmzevwk9ezeKG9fiRoCbedHTpN24YTN28h/c/1GBe0D0mvfQZR+SKDEeBHyOFBsqrlq+7mItfoWxhVvaXIv5OvHmTKsnOQSz4ICFDWlRtMhZso7/DymslsElOVfMKvSgIZLi0ejcRNjhi+/jSy8rNxaf9cdPfXNWvV3RoKEvj71ZLea4gq98qwa1wTRA5ehUytB1Snul3wv+Ed4ZTGHaCoTkTC198bDq2SsD9VqW+sNxcO7sbOZQkwD48Dmr66CFNiDmLq+A1IV8og8feDN2WrH6IEYWU305FF3vBTGboKKFH5BJ2tzpaP/YvfwDubtd7EUylKSwE3d+ERU3ezSE7dIoZHqnqorugfy89L/nGca2o/qmoZ/CrrWV0MHds8gQ7ux7Htuxtqu1fQ5lUtPn0ObTpNxt5yS/y7gUTKEyL+yxK91CcrCRDxa9XxCWZ0wEGMj75QtXgsu4TFoxOxyXE41p/OQn72Jeyf210T8CrGSsL9tg8hlx+2VT4f+fvw+YwVOCw8XCk32aXFGJ24CY7D1+N0Vj6yL+3H3O7+6odU1X3Gfst2jUOTyMFYpevg0OV/w9HRKQ1Xrym/iiv10TJeKuU1118KEmn6IGPyKc6ZaVtTXbcOQZXjMK2/OrdX60DCftcHlMsPtZqGwr7PZ2DF4TwtO7SFDT9QQFqpXsaRCgpQJH8pyW/+A/xAOfyCT0Wj6i5ylR28qH+p0digWhU0e7NuL2329n/nDZLwHujZ9ALWfLwT6UIjFZ7AkmlTsP4coVHv3mh2aSVmbbiCcr5UfnUjZq1MRpNePRFhkYIqMZHUR8/ezXBp5SxsuCKnhKsbZ2FlchP06hlR+8DZOMDBrhg5NzOQXxlWTd42cHCwQ3HOTWTkl2mclBEp7Zr0Ru+ok1j6ziZcFQatsizsnNQdrePn4ZQlqxzaNUHv3lE4ufQdbFIQQNbOSejOb67nKQnYNX8eA8N/w4zpexA2YAhaqhZ9Uckjy8S6sV3QZ+ouNlQnBD7cAXGRnrBzcICofFUN8PSzrXHl6zn4NqWCl6TIwW8fLceBMrHPCRU4smoRDgoLPldlY88na3AuhvWnoaEyiPIuq6HM5jDVbveyCv7UmgFpnVg8GRsGN/4ik7X7K2znZe6kFVxfdtLG21mC+j1rrvdV+Wexcy1P18rWdGaqpuK5Peg4IB4Bv72PxA92IFn+Jr8KhVd+wPuL9sI+7nGeKyOmE4QmbJNRJ5finU1XoVC5nZjUvTXi552SL42p4WViz0E5/eTEzzihxFNSvyd6N7uElbM2QGGeV7Fx1kokN+mFnmzoNoIuFefgZkY+ymzE5DPXQPoy8YpFN37E3Ckf4Hv5nJ0SXFgzE19dbYH+z0Tr3wxzchoU0DuhUw9Vh6R3j+LQ7h/HWdpIxLeI2I+x6mjXuzJMvK0Nyns9jQnjonFk2hC8ufk88gW1rsrD6a/HYfjCFLQZ/yoed78H/y7iv2rU3hIxv8ZvAy32CVJ5/2fS1kT5GKBYeyf47fXNDCnqxD6J2DA3/uKWhd1fbccVXj1L7tbEOEnC0aNnU7axj7FT0dnjxJJpmLL+HKQumuGOjKfQZUjrIPbJWIS58VvzrN34avsVXsWqQj3IM8XGqeMAxAf8hvcTP8CO5Dx5/1lVeAU/vL8Ie+3j8HgsT3vQ8ifFGWZ4aft0aSOz/aUpuXTP36N9a8tUfNNk/6vLszpHEoT36ImmF9bg453pcgwLTyzBtCnrcU7qovUQZIugIP7ice4Afr8l9Mm5OPDVFpyVu2EJGjz9LFpf+Rpzvk3hdqtCzm8fYfmBMvnXC9H+uUZjg+rUz4J7a2Pi+L+BRulfX1JCywDy8AulUF5BIbLPHDooj94rowurR1JskDt5h4Twaihh9NjLa+iCPPBdEzzAMWqKrWwnjQgNppe2cwCfTsAlXy67QKtHxlKQuzeFhHAEb9hj9PKaCySQEgIRG7v2IfUiCFrBJwrSxfR1H1dqPOkIh3sIWyUdTWpKbn1Xy48M/skyaCMHwbnY2lPTpKMcFGeat0FZDm7I2MiBoy62ZN80iQ4nc8ClMhBPcW8Z7RwRSsEvbecQBqLisysooRWv7MIrq9QN9OCo8P60UBngp0/boJ7CDcVnaUVCKwp059VV6gaSB0eu91+oDHSRE5DR9YVdeKWODjRPtVKFENTCgYeuHebL7yg8uoDio7zII7ABRdTxp8CmA2jhUSEy3Yx85Zfpm1GtKdCDV6QJCqDo+H7UPjCOZglRdvqbvE2cqX6rNhQeEkGNeMUT30b9adGfQqSpYeCHUFwMm5rJrKm3UZ3Ta/ei0wupR6g7BTaJow6xMRTTeTSN6OxLDcbskQexmG5nEb030E1W7Z0jKDT4JRLUXr46hKNmFRF9GIlDlk4uS2CbciQbiSMHszlzkKsnRfWaTrtVKxKI6kQxnV2RQK14ZQ+v0LrcdryCS/+FyuArQ27qgEtBWdVbOZ39sCN52mnkLLuwmkbGBpG7dwiF8EoIYY+9TGsUhk6yjI0ctOVCtvZNKekoR5WKyqdmYrhjEHDJtxQep0XPPUS+Hv5UJ8SLPHnlmNe2XGcLN76JyWku4FKnHoeT1QFmGvelaUeu5D+Oc03tRx85nXpz+4lhqF9Wfiy9STvf7kGRvIKEg4cf+bpx4LVnFPV5d48mMFbMx5qxGTFfYFpWjc/hzkbHH5KIXyurhk84WmlGB0T4GOJobrUTrYBHUbzK6PTCHhTqHkhN4jpQbEwMdR49gjr7NqAxe0oVAZci/RWV/kVfJrSkAG7H0FBeoSuyD80ROnstnlR2mhb2CCX3wCYU1yGWYmI60+gRncm3wRjaU6qNu2EthTOFJ5dRAvsSRxsJObq5kzMH+XlG9aLpuxUrQuno44ETIryYmJ5PrxTxPfKAS+26F39NfVwb06Qjymj2yqOU1NSN+q4WwhNF2lYLC6O+QU+mPJH+V8BDs2nrqWbfaF/GPdRfXyZQywAP8gsN5ZXQIqnPnIO8qIIu/rKsH+j11n68El0o1Q+tQy1fSqQngmOVK5aV0+VvRlFrHpf4hgZRQHQ89ZMvxiAsmCHeP4vZkKY+92/PRiBtwRj9/8ktUtzNTMddO3/U8dd+uuLqyYqQdTMPEv8Q+Fu4jrUpUGRFWbiZx5/2Q/xNrhluqmx1z0s5WKjKwQkOyof6avHmdULLqhzgpCosylyGIn5bcEvmidAQL+i/nBYtqrwoK+I3Grdk8AwNgVdNCPCzbT5nIcy38UGdYH7zrcNUXD7p3UxklrkjJJDfpOiU0zrgOYEfdmyGH579CzsHl+MqB5c2aBTKgbha9xjdFeNdc5mNslKe1Gn3sixc+IuDY70jENOQAyTLhTXNHeDu6qB4gyDWzrWo9wbyygqRfukSbhZK4F2/EaIC9KcTCWYnohMsWzZPM5J5hiKkZgpjIBJzRFHWTeTxVK0Q9gG6usDr7ZdVwcFJiRuXFpXPCHWxUxX5PL2twB4BddgvWKJTJuUU4yJcM6yHaIl/HOea2o9+rfTrLdbW+mWVx2U5uHIxBblSD9Rr3AhBRuJDquVjddiY8QU1aG+Tfq26PsGMDpjko1O/2j0oy7qAv9LK4R0Rg4YcWFleVAipgztcLeqv2ArY56fftYO/SXsrQ9aFv5BW7o2ImIa8oEK5fK1oB3dXdX8qXiMZCtMv4dLNQki866NRVAB/99PetPXRPC8dn85kas33mGlbbYn193VlEtNf/ZLVOJbeRWb6Xdj51+GxlynHKMPdG1eQJQlFZIgLeGQNGzuOX1GxEWhklsE9JJC/AKtOqn7F/ItCT8yODVSkavH3Pzb4rkXkrKT+/yGgNfj+9bX6GsP+/1dTa42sCFgRsCJgRcCKgBWBfwgB9YPDP8TfytaKwIODAEeNh3PwVZv6Rl53PThSWiWxImBFwIqAFQErAlYE/sUIWN98/4sbzyq6FQErAlYErAhYEbAiYEXAisC/CwHrm+9/V3tZpbUiYEXAioAVASsCVgSsCFgR+BcjYB18/4sbzyq6FQErAlYErAhYEbAiYEXAisC/C4H/xuC76hZ+mj0Wc/fcqXbrVN35BR+//j62paoWzS1GRvodrUXgq03SsgJl17Hn8+l4fewrmPTBGvyhn4XPMip8VwnOrBiNp9q1RrvBnxmWKs5AurCmNdfo1t75mLDgV8gP9e6suvUTZo+diz3GLurda3BYlYEds8Zi7Bxep1tg9U9vnIJ423sTsOyosBa23iY9iS/GJ2LEiBGKv8REjBz9Ct6YsQjfXzBMB65X+j4d/g06p19vVf1HjMKM79JE14K/T5VWkC07ja+SluCwTlNVIf/sZnw4eRzGvfE+vv4j27R8XK+vJk3CV6eUiS+UwpZdWI/p41/H3J03jJQ1YzPVqHC17KbkDFaMfgrtWrfD4M/OV4NLbd+q0bdqyV/bYqjoidmr6h6t32JeEUnhptin/TQbY+fu0bpaS7tqv1lL9KxkACumVi34DyHw3xh88xJ1t66ex7VbOj24Rc1Md09i6+frcCRbGHxX4NDU9ugy85hlyT4s4mDkJtkVLOsfhwGfXYRzvVBID8zAU+1fxvfCIvPV3e5swrQJW1DefgzGJ3TULV1xCFPbd8HMY8KK9YSCP7/F0q2nUKB7l+Ko4haunr+GGkAI2aXV+ODjTdg4+z2suqR6iDHG5G86x0kc/q+9K4GLqtr/X5iFbXAGBGUERNkFFTTlmYK4K+GGoalpuaTpU1tcMKrnmmaaRX1ScyeXUtHIcnuYz6xMLVP0iYo7i8giQijIMsPvf2ZjVmYGzefyv9cPzl3OOb/f+f6W87v3nnt+v329Gvsy9QMyJXXZNRzevBV/lEvh6enJllVsBmljAQoOfYRhnftj6dmG69DD9ep/pHPKfm/Br/kC2Nvb6/3Z8a3JqPZwvTRZu+oKvp70Ev6ZlMYSbGhLVPz+AWJ6zMKPVU3RRH4UC/t2xdS9RaZviKsv48Catfj3Za3cKs+vwch+r2FzXgj69/A2WG6Q0TFnM1o2rNtrgN3cSZmDGbuqEDX5TYyLbm5d+397KQN9awD/fzsrmgbN2aumjPq3+rf3EdVjIZQujZ2rLrqK89eKDEo95KGe33zItrjqKgQ4TDlN+H+GgP5Syc9q5209MWrtYYwy6p8cleVV4DuxLHRG10ydYE9ScvJQob+YJysoR0UpeyrqLKlnXW+23mdFLewdNYtb16KqvBK8eujKr+zExv80w9u/b8G/Qhlnb/wD1SFxWLPnQwwY62qKMeU5eUUp7sIZEp11yuV3ClAkC8GLk8fgpRYG91rsjUBOXoXB2qSq5uWV5agROMFevWamrecorD1sgKC8An/ds4VIbG8cwNRxKUP61m9wPmIGZtMSfLXhd7y99HmT64QraFbxneBoShjySpRX8eFk8mIdMeMdWTlKy2RwkIhhZ9B948LqMzZ2aDUkEfOH6qx6UvUSmrXvhvWbTuKtjztr+WcYqERvuGa0uq3aWtSy9LimSJuSl5YnlqVSxgefX5/OaUsq9yzxYQ1+NvYIf3U5vtDttwEZxaF1fOtUZOuMV9TaQ6v+VSiv5JmRJXuyfXI9Eqa8j1237GCvyXKsbLIIu5Z8jvwhW3AwqR9EbE3rIW6R6LxoPd7s9w6CjdZ41eGD7SoD75iZyOi6CgfWj0agxiR1itVnM+b7zdaLZRn4+Hx95TVpNwpaRvKQ405BEWQhL2LymJegNVULvoXRlDGaelStxluG8tIyyBwkENcZh76+mebfAk/MH1rvVzV6rhGApbY15Rje5aUokzlAIrars6/aohzk1TloW3iOWgtDt6Xw1/XzV61c69nBuR57VpCvz29askEt6wojMu83dMsa7Rtipl9AXvEX7tmKINY4b53LZnW4WpEfwAHOOuOHTlW2a/04ZpYOa8nI15vB1PIYo88ld8Qh8FQg8Ojy9zxBLcuu0xexPjRwVTbLcsX2+wdS/Oz3KMZPTAI+nxxZxrmEParMVIZcy659TFEOofTOiQo6v6wnudnziOfAMjUOWk258hq6njqLevlKSMSyZkrcgmngwkOUr0gZpaQTTMMSEqhXi0aMjpCaPD+Ldh1IoqEhriRkdJ0DhtCKs8pUmnpk5bkrqXejAJryoyqDo7xwO41o1piGbSvVK6c5qLmeSrN6+ZHYQUQiBzH59Z5Nu2/ISHZ+GfVkWQIFtkJybuxJA1Zmaaow/s7Tsp5uZM/jkQPLXDlodRZdYhklHdu8SBP7+pPEnmV4c/Sm7u/uV/ZHdv0LivUZSKuyWefun6U1o9uR1MWNmrqJydW/N727zzR+VHmE3gp0of7rblL+pjhq7D2e9tzVsCGj61/0p8D42fReDONfwGc0vSg6YQ8pEiEqaPYPjKfZ78WQn5jxw3ckr+gE2qO4WHOKFkb70ZgURSYvxSango3DyC8miTJZaiv57SO0JD6cpBIXcnd1IgeWVTNu+TFSImoqC6GqEda3nTTSVUIv7dC0q75Qc44WPGdHvtN+UmUgrblOqbN6ka9ExDJzScgteCAtPJTPuGC0b6yiuFZDaUZCL2rZuBE1kvhS97dT6JomCVk98lJkNT05vys9N2oqDQsQEb9RN3onoYeBzmkY1fCl4sNP7EAikQOJ/XrT7N03lNm9zOJn0Ey9/dYpV5+eGfPdnT459Dn1Dx5GCQyDFixrIF/YhJ6ftYsOJA2lEFchk6UzBQxZQSbUn1GsoT8+Gk4jF/xAF44YZIat3M8yzDam4TsUWUdVW9WJdyjU+QVae1tzRue3fDsNE6vkeT9jNcX5iClk/Ha6Xk96SVM2U3+/Gacn51PX50bR1GEBJOI3ou5JmUrsNRzo2k398qhW+pamzgKyFTqzTG0DaGVWlcq3MB/lIBKx7G5+1Hv2bmJmrSBK87s+R6OmDqMAEZ8adU+i81eYrViLt/w2HVkST+FSCbm4u7LMsiyDaNxyOlYmM/JxWVd17J7JRenvTPEka4hfNdTz7pSUWVm/L9WzVzndPrKE4sOlJHFxJ1cnB5ZxN46WHytT+Ts3e+LxHFhm1EG0Orea+ZdY8hm4ygq/f58ykl+jjh6O5ChqRO7hI+m1gcHUac4Jlb3XCdTQb+aSXO0LTNmgplrdr7myZn2aKcz0s/TeP7uGRreTkotbU3ITu5J/73dpnzqjrEqHfUkiYhkfJW4UPHAhHVIOVIyz+xmU/FpH8nB0JBHLwho+8jUaGNyJ5pxgDkspV+vHsfrpmPH11SYwbcgYUwcut8Mh8PQggKeH1YfgVCeVqirFrJAEAS/ThvRilpb9Bu1kadrt28+lMyYGZG3wrYic7tN3rzQl74kHlCnjZRnLKEriRyOSM0gRCtxTDO5eUorfcovkSpoKOq/Qlov3SFawm8a3FJDAZwitPHWHau6m04fRYmVbxj0rpzOrh1GgRyB1G9CfOrf0oLbjt1KmOnjTKy+7QMujWarswSvpdJmc5GXptCrOhyTdlpMig7rZdNT3v6NXmnrTxAOKGwBVKlgBS8c9bO0ZKpFXUXbqeAq070ALzrFA/oo2BX3p1/HkGjyVDrGMvcTC2ZPzo6hpl8V01gR+d/eMJ2/3eNqqCIxKdtLLHo0p7itVkKqhKRQE0Msb0qm4ppJu7GRp7+3b01wmDCVNoYACXt5A6cUsZfSNnSwFuD21n3uGavQGZAUfcsr7oic5dVhIGbIqOjojmMTPz1WnJL9PmStjyU0ST98oAn+juor66k0ZfIuo23u76Pvvv1f+7d6ZTB+N78hSTrej935T5DCXUcayKJL4jaDkDKXkKWN1HHlJ42nLLbmKb3Yj4R33JZ29K6f7F5JpuK8L9Ui6TDKz8lKk1Q0hgX1rGrcujQ7tP0ZZMn2d07Cp+pXRheXRJPEeTCtPl5FcXkbpq+LIR9KNljPhm8VPvyHVTYeLA7Ud/j7NmTOn7m/+mp+pUHFH0UC+r2UyfVHI7pUtdPGejAp2j6eWLAWzz5CVdOpODd1N/5CixRrdM2RGe1x13CD4Lt1A/Z0CafqvWmOQZydRN6fnaP5/TSigOviOW/w5xTUXEM9jJKUUa9s3tadnM2b7rVClBAoR2FPrceso7dB+OpalHxDp2o15ecjo8pLnySn6U8pieMsuLKdoiTcNXnmayuRyKktnN3Q+Euq2nKVNVuhviIDsW4+jdWmHaP+xLKpS2KeVeFcdnUHB4udp7gnVzf39zJUU6yaheKVx6OubHv/meKrzd9b4VWM9v3bWjC/VtdeqozQjWEzPzz2hupG+n0krY91IEv8NKUz7/nevUFPviaR0aWqf5tT1EwaoIrV5/X6/5sxCipAE0dhtV5hvr6RruyZSa/YAImjmUf3gW6EwBn7TnA3q65d5ezX2S/o+zdg36LZeSl/Hu1Lw1EMsPTfbytgNmjLN9lnW9wxaFiUhvxHJpHJXGbQ6zouk8VvoFnuAdGZhBEmCxtK2K2wcqLxGuya2JntBEM08qgi+NbhZMY6ZpaMaX+rz9fqYEjVkjNFFgdvnEHhaEDD1NvypeGL/cEzyETJ8JkaHsVTcdj6IHdQFEpY6PUdnXqnl9uW4uGs7TrYcitejRSjOzkaxqBdGxzggbft+lCgb4CF46DS8FOQEXpPu6BIqQLPYKRjXzgV8USt07+SJ0iLj+Yi1hT9h4+qfUNM2BrEx/fBCL3+UHNyEH85WGLElz9qL1D98MW7BBISzPOi2zmGYsHA8/H9Pwe6rDZ9fzQ8ZiYQxbSGxFcK732B0cbmFrGx9YARiMRxu/ogVn6zD3j/L0GrOz8j/NRFt9N59K1gtwd7NeyDvNxKxjdmhpB9eHuCIgxu/xnUd1vghwzFzdBhL72sHn9hB6CJhKcc1wuCHYPjM0Qhj6YXtfGIxqIuEpRzPYS9AzW18hM1IxfGUWYhwrkVFYTZuCyRoVFWC4rtWzJunSpxOTsSsWTPxxthhGDzsLWwt7YWlBw9gwfNszpH8InZtP4mWQ19HtKgY2dnFEPUajRiHNGzfr5I8BO0w8YPX0EZkC/vgUfjXWH+cSEnF1WuW5EXgtx+NxDG90aNfJzQ3N41CnoW9qX/Ad9wCTAh3hq2tM8ImLMR4/9+RsvuqCqAG4cemPlw7jdOntX/pmfmoZC1Z1jMTfPOCMXTaSwhy4qFJ9y4IFTRD7JRxaOfCh6hVd3TyLEVRkY4imBOp5ppcxiYN8CHQ1TWBgE17qkFNTRVK8nKYPLKVfzlF5epad/HD3LnIjk3AMKdUzJ75HQqsUANFZcv9Zl9L8NtjdOIY9O7RD53MCow1aJU85Mjam4o/fMdhwYRwOLNpS85hE7BwvD9+T9kNpVkTH+1HJ2JM7x7o16m5atqXlXjzw2Yg9XgKZkU4o7aiENm3BZA0YtgV3zU9b16JohU8MblY71d19aUjKr6z5EuVTDD8wjAj9ThSZkXAubYChdm3IZA0QlVJMSybdn38yXD+21RkhE/GvKF+sGP/Wg5egGndjOYXqpnQ+bHGBjXFG1JWU0fvVxczQ98ggFjsgJs/rsAn6/biz7JWmPNzPn5NbMPc1S5sP9kSQ1+PhqiY2UaxCL1Gx8AhbTv2F53Ht6kZCJ88D0P97AC7lhi8YBr0u27dOGaWjtotmvX1On21fozRqcTtcgg8RQjoDmFPEdsPy6oNxC6SunmCNmwk59XKzQw8pujJcCuvEDWZ6zAqaotegcbh91CijClsIWnspqbDA59nA0eRE1RTWFmgzAZVIr2q7ECOa18txpqqMTj0/UfoxPwh8Co6TGqL4Qt34JXdY+CuU0VekI/bNlI099aKktfcG1KbYhQqVkjx0Clsxa6txBWNNbdkNjzwbGshl+kz6djvI+xcOg+L1s7D8MWTgGYdETf9YyS90QWumrqMVu2tb7HlQBH7SPNVNGd8KzaquY9yWTKS06dh4XMqJGzELizYV14GbAQMJ0ZTExzZiOGivciCLh5q6y6q6xj92IJXmo61s0Zi52+XcIcFfSFBdiijJibwNqrMeBCh36ensU0x97mSffT3+guYfKIILj5qWcpuIa+wBpnrRkFf9I0Rfk89ygi94dtCEznz4O3FBFGYh9xbfPPyYuzYunlAqqlqgr26U+xDtPzbNpAyeddJn9cc3lIbFBcWqoo1BD+W4bPLzB2qftcRUe1UW9IzVsyIb1sJGrupBctjNsbaFzmpJ3Ar5sHbEvvMt4GbfSM4C+7jXoW2Jt27y9b0cYJIeBgJEYOx7qYqoLfrvwGV250YAT5aTd6B/Z/2guA/d9F5wD8xOSoMKWNbqoJWMyxYtC8Bq2zrBg+rBMbKWiUPOQryb8NGymSpFSyzcSlsiguhWvjIFm4eUn3+rcTblleK9LWzMHLnb7h0hz0QCAmCXRmhibEz0kHGGp4a5le1+mKNL1WzYstDafpazBq5E79dugNBsxAE2ZWBmlijS/Xxp+qbwN0DGnWFrRjNpI3qxggdIPR3LdpgoLa8xbIKZTK/aTEzLOeIfh/txNJ5i7B23nCo3HIcpn+chMn381BYk4l1o6KgN1I1Dse923nMhwjg7qEZp5g6i9lH5o00DllBx7pxTHbLDB3VgMjU34yv1+mStWOMThVul0PgqUJA18KeKsYfJ7Oq8IGHxu4uEHZIxC9ZOcjJUf1dOJqGfWvGwVcdPNmoYw3r+SXcuVPKgmYftKjzxQ7w95Wi5nYh/jJ4UMhzd4MLFSAvT3tBnnsT+eQCNzdrIjhDBg2PjTmvYIEYv9sc7P4zF0U3T2HbNA/8nPg2Vl3U8qC4icjasRVHXMZhx8VMXLx4UfmXefkgZoVkYsv6n1jA9BCbrQ0L3tgnQDWaVSzYai1l91TBnPwSVkyagBS78dh2Jh+lBZfwy9IYuD+Ittv7Y+QX6zFRuBUTXl2Ji4pH7rzGcHcRokPiL8hSyz0n5wKOpu3DmnG+qk7JSnGnVHMHwQb3wmKQaxN4NLVCXkxpDKVgeKwkwnOHmwuhIC+Poa3e5Lm4mU9wcXPTnPlbfq3SMyO+jfvx0MwIgxHcshhXLpXW3SzXZF5GtiiAne+Dj0/lMlvIU/7d2DRCTc4BIZGdlfKX9PoAq99ogn/PHI/Pzml0p36urOo3k1bD7bx+mkzB4O7mAipg/dAKFrk388Em9EJj1jZGRK3BW45LKyZhQoodxm87g/zSAlz6ZSliDIzDWN+s48lcr4yu1emLdb5UUV9+aQUmTUiB3fhtOJNfioJLv2BpjLt+kGzMvBFp/RN8eHo1QTV7a5Kvwbu2BDm5f9XpmH55xZGaSENs0FJZcz5Nw0AdZpoTmt8KdsPGR7c5u/FnbhFuntqGaR4/I/HtVcxducNF2AGJv2TVjVM5F44ibd8ajAtsDq8m1cjLya/zIbUlOcj9S+O7VO0bqZqGrM6vWTqaAVGnvPGuVnDWjTHGLXBnOASeFgQeJBx5Wvr2CPi0gVDIR3khG+BLZQgeMACBp1djfspV1av5/H1IiGFPgZenP8RShHyE9oqG9GQyko4UKKdXyHIOIOnrDPh17466h6nq3vFaxGJA2CUkL96OK1XsZNVV7FicjIuh/RHrbyH4thFCyC9HYW4eSis1o44l2OS49c0U9Bj0Pvazkcq+aVt07RIAMV/Rlk5d2UVs3Xoc0rhX8UJzKaRS9Z9nJMaN7ICCnevwQ8OXXdcSsPWAB3valfHrEShWYKwt/hUbd/1XhTt7wpSbJ4NXRF9EeIvYW418pG38ni1XJ0O15XhLS0Oz5xyFOZ+/Dvef5+LttVch54diwIBAnF49HylXlZMykL8vATEd47A8XbFsI9uqj+OrL44q1xuuLfgRn2/JQMgLsQj0a6i8dHWusm6AVNLgtUDsgDBcSl6M7Srh4+qOxUi+GIr+sf7KIn/Xfw+lZ38XE4p2GPZDBgXi9zXLcEQh+PJ0rEj6Afz+w9CNLacibsLeGqh1zcPF1LQBZ0TNXYOZfn9g7tgPcEwzM6UeHh9Pv3loETsAYZeSsXj7FajMegcWJ19EaP9YWDLrerqiPs1uBJm9y7wi0DfCGyL2lik/bSO+Z2s5ypTGUZ++PUqemM+z0pfK2fTAPJkXIvpGwFvE3oLlp2Hj91fYSjPVbCFYFhILheCXFzL7L4XVLo3d7ATFj0DE+S+RuP5PFFeU4Ozm2cz/KmzbxKbrN2u8rbdBS/ZqzqeZYEPvlPwWvpnSA4PeZ7kU5PZo2rYrugSIwVfgEToAAwJPY/X8FKjcVT72JcSgY9xypNcGIX5EBM5/mYj1fxajouQsNs9OQn1d16NpcGCWjtotGlTRHupiWlltdoypLf0v9n3NpmVZO3dMS4Xb4xB4YhDggu8GiUKA8D594Zo2EUFRC3CuXSI2J0Xi3JvtIPXygWfQaBwJXYyv5nZhswYffHPquQhb57XAnhdbwt3TCx6tx+NY++XY8m5H7fQCTfP81pi+YQV6Xp6B9lK2LrW0A2bf6INVmxIQphsMa8rr/grC0aevK9ImBiFqwSkL86g1FXnwm7gMC0N/wqggT/gGeMNvyH4EL/oUkwK1wb4sfTtSLwVh6IhO2mX5lE3wEDB8FLrJf8SWXTc1jTb819YLw2dMgvsPIxDQvCWah89AYftI1WtjYQTGTuuB7EWRCIuMxj/a9MQntlGIdM7BlasPEn2zVSS7/QsfvyLBoXkzsSmLj46Jm5EUeQ5vtpPCi63FHjT6CEIXf4W5XdSS5zUBDo9Bm6BW8A99GQeDF2HNjLZsKbqGyktf507pTXbno/X0DVjR8zJmtFesSy5Fh9k30GfVJiRYFH4DIW8w3w1s3+riivn8qzC/5Q8Y4s9u6Ly64tPa17FucQzE1rbhGIF3176H8MtLMW72v00mlapr6jH1m996Ojas6InLM9pDytabl3aYjRt9VmFTQpixD6hj1podISLGTkOP7EWIDItE9D/aoOcntoiKdEbOlassgK1f3x4dT4Cwo3W+VBgxFtN6ZGNRZBgio/+BNj0/gW1UJJxzrkBh2oLwPujrmoaJQVFYoG8sZsHhBb+BDesG486y3vBy9cWQLc7o0FYAoZ1Q/6m6ohU9v3kWwVbboAV7NefTzHLPLvL8MHHZQoT+NApBnr4I8PbDkP3BWPQpmxYo7IjEzUmIPPcm2km94OMZhNFHQrH4q7noYsfmc7+xAesG38Gy3l5w9R2CLc4d0FYghJ2wgeGBWToWOqCH6Rn4mBljanO+xZxxM7DpvKWI3gJN7jKHwGNEwEbxZehjpP90kmbr6FbWCmGvcU7ye+xpUhHkYk80k5hYOPhBe1n9F5tXXsqmlHqhKXvKY36T415+LkrYq81m7mbWqDXRiKyyErVCe2i6Y6KIyVPVpSw7ZqkNXL2k7Mm3ySKP/qT8LxY05IPnGYBmjgQZ2bA546pBozL/As5lV8HFPwR+7IPNKjY3WCZ0hpPZjlaxaSps2o+kKSSGd1BMHoV37rMPvDygeagqv8eesrOPBsWezaARvfzqMkSH7cGL5/ZhJHsTUSxqiWBP9kGkHhoNlJehzum1xV7H38tHbgmbGtDMvZ615g0qPPBhA/l+YDqWKtaisjgXBTUu8PJw1p/7bKnqA11/TP1mviU/twQ892Zwr3f95QfoUGU+LpzLRpWLP0L82IfnVYo1noVwdlIHm+b07VHxpOiGVb60EvkXziG7ygX+IX7sQ+0q5drcQmcntQ9j61FX1kJobyJwrgcqWeZBpNzwwgt9W6lu4uSZ+DCyA9KGZ+DQm80NbFfViKHfbIgNmi1rxqfVw77O6WqUsgyfpTau8JKyJ986VxRTAe+xNwdFcjFLHiZRPxSRIfNgCm54vYC+rVS3r/LMDxHZIQ3DMw7hzeb6XkuvuXoPTNGpt7DeBUNMn4gxRo9D7oBD4O9BgAu+/x4cuVY4BOoQ0Abfh/F2iwcZvOqa4nY4BDgE/gcIVB9/B+37HkDnzz7DlA6OyNn7AaYsKcHUo4fZNyqWHnz8Dxh8ZCSqcfyd9uh7oDM++2wKOjjmYO8HU7CkZCqOHp6FZ7rrjwxTrmEOAcsI6N8YWy7PleAQ4BCwgICNoy869emEFjoJMi1U4S5zCHAIPEYEhJ3exzcrajF/7VsYsYzg6t8Z01O/xNRnPvoUotP732BF7XysfWsElpEr/DtPR+qXU7nA+zHqI0f62UeAe/L97MuY6yGHAIcAhwCHAIcAhwCHAIfAE4IA9078CREExwaHAIcAhwCHAIcAhwCHAIfAs48AF3w/+zLmesghwCHAIcAhwCHAIcAhwCHwhCDABd9PiCA4NjgEOAQ4BDgEOAQ4BDgEOASefQS44PvZlzHXQw4BDgEOAQ4BDgEOAQ4BDoEnBAEu+H5CBMGxwSHAIcAhwCHAIcAhwCHAIfDsI8AF38++jLkecghwCHAIcAhwCHAIcAhwCDwhCHDB9xMiCI4NDgEOAQ4BDgEOAQ4BDgEOgWcfgf8DpL4QvTDBNDYAAAAASUVORK5CYII=)\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "intro_langchain_gemini.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}