diff --git a/misc/pdf_upload_summarization.ipynb b/misc/pdf_upload_summarization.ipynb
index bf7dadf..8fafbb9 100644
--- a/misc/pdf_upload_summarization.ipynb
+++ b/misc/pdf_upload_summarization.ipynb
@@ -2,217 +2,281 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "WJ53OAUk_Bmm"
+   },
    "source": [
     "# \"Uploading\" PDFs to Claude Via the API"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "pOwrbbrD_Bmn"
+   },
    "source": [
     "One really nice feature of [Claude.ai](https://www.claude.ai) is the ability to upload PDFs. Let's mock up that feature in a notebook, and then test it out by summarizing a long PDF."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "GTQO6z2-ELFX"
+   },
+   "source": [
+    "We'll start by installing the Anthropic client and create an instance of it we will use throughout the notebook."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "pOEGPdoXEFsT",
+    "outputId": "b718c166-6d64-4757-bd8b-259b7775a09c",
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
-      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-      "100 2039k  100 2039k    0     0  11.8M      0 --:--:-- --:--:-- --:--:-- 11.8M\n"
+      "Collecting anthropic\n",
+      "  Downloading anthropic-0.37.1-py3-none-any.whl.metadata (21 kB)\n",
+      "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (3.7.1)\n",
+      "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (1.9.0)\n",
+      "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.27.2)\n",
+      "Requirement already satisfied: jiter<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.6.1)\n",
+      "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (2.9.2)\n",
+      "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from anthropic) (1.3.1)\n",
+      "Requirement already satisfied: tokenizers>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.19.1)\n",
+      "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from anthropic) (4.12.2)\n",
+      "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->anthropic) (3.10)\n",
+      "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->anthropic) (1.2.2)\n",
+      "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->anthropic) (2024.8.30)\n",
+      "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->anthropic) (1.0.6)\n",
+      "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->anthropic) (0.14.0)\n",
+      "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->anthropic) (0.7.0)\n",
+      "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->anthropic) (2.23.4)\n",
+      "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from tokenizers>=0.13.0->anthropic) (0.24.7)\n",
+      "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (3.16.1)\n",
+      "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2024.6.1)\n",
+      "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (24.1)\n",
+      "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (6.0.2)\n",
+      "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2.32.3)\n",
+      "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (4.66.5)\n",
+      "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (3.4.0)\n",
+      "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2.2.3)\n",
+      "Downloading anthropic-0.37.1-py3-none-any.whl (945 kB)\n",
+      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m946.0/946.0 kB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+      "\u001b[?25hInstalling collected packages: anthropic\n",
+      "Successfully installed anthropic-0.37.1\n"
      ]
     }
    ],
    "source": [
-    "!curl -O https://arxiv.org/pdf/2212.08073.pdf"
+    "%pip install anthropic"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "id": "-4bSTHWq_Bmp",
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from anthropic import Anthropic\n",
+    "# While PDF support is in beta, you must pass in the correct beta header\n",
+    "client = Anthropic(default_headers={\n",
+    "    \"anthropic-beta\": \"pdfs-2024-09-25\"\n",
+    "  }\n",
+    ")\n",
+    "# For now, only claude-3-5-sonnet-20241022 supports PDFs\n",
+    "MODEL_NAME = \"claude-3-5-sonnet-20241022\""
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "xrDg6fb5_Bmo"
+   },
    "source": [
-    "Now, we'll use the pypdf package to read the pdf. It's not identical to what Claude.ai uses behind the scenes, but it's pretty close. Note that this type of extraction only works for text content within PDFs. If your PDF contains visual elements (like charts and graphs) refer to the cookbook recipes in our [Multimodal folder](\n",
-    "https://github.com/anthropics/anthropic-cookbook/tree/main/multimodal) for techniques."
+    "We already have a PDF available in the `../multimodal/documents` directory. We'll convert the PDF file into base64 encoded bytes. This is the format required for the [PDF document block](https://docs.anthropic.com/en/docs/build-with-claude/pdf-support) in the Anthropic API. Note that this type of extraction works for both text and visual elements (like charts and graphs)."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {},
+   "metadata": {
+    "id": "VznQXTKm_Bmp",
+    "vscode": {
+     "languageId": "python"
+    }
+   },
    "outputs": [],
    "source": [
-    "%pip install pypdf"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Constitutional AI: Harmlessness from AI Feedback\n",
-      "Yuntao Bai∗, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion,\n",
-      "Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon,\n",
-      "Carol Chen, Catherine Olsson, Christopher Olah, Danny Hernandez, Dawn Drain,\n",
-      "Deep Ganguli, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jamie Kerr, Jared Mueller,\n",
-      "Jeffrey Ladish, Joshua Landau, Kamal Ndousse, Kamile Lukosuite, Liane Lovitt,\n",
-      "Michael Sellitto, Nelson Elhage, Nicholas Schiefer, Noemi Mercado, Nova DasSarma,\n",
-      "Robert Lasenby, Robin Larson, Sam Ringer, Scott Johnston, Shauna Kravec,\n",
-      "Sheer El Showk, Stanislav Fort, Tamera Lanham, Timothy Telleen-Lawton, Tom Conerly,\n",
-      "Tom Henighan, Tristan Hume, Samuel R. Bowman, Zac Hatfield-Dodds, Ben Mann,\n",
-      "Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Jared Kaplan∗\n",
-      "Anthropic\n",
-      "Abstract\n",
-      "As AI systems become more capable, we would like to enlist their help to supervise\n",
-      "other AIs. We experiment with methods for training a harmless AI assistant through self-\n",
-      "improvement, without any human labels identifying harmful outputs. The only human\n",
-      "oversight is provided through a list of rules or principles, and so we refer to the method as\n",
-      "‘Constitutional AI’. The process involves both a supervised learning and a reinforcement\n",
-      "learning phase. In the supervised phase we sample from an initial model, then generate\n",
-      "self-critiques and revisions, and then finetune the original model on revised responses. In\n",
-      "the RL phase, we sample from the finetuned model, use a model to evaluate which of the\n",
-      "two samples is better, and then train a preference model from this dataset of AI prefer-\n",
-      "ences. We then train with RL using the preference model as the reward signal, i.e. we\n",
-      "use ‘RL from AI Feedback’ (RLAIF). As a result we are able to train a harmless but non-\n",
-      "evasive AI assistant that engages with harmful queries by explaining its objections to them.\n",
-      "Both the SL and RL methods can leverage chain-of-thought style reasoning to improve the\n",
-      "human-judged performance and transparency of AI decision making. These methods make\n",
-      "it possible to control AI behavior more precisely and with far fewer human labels.\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from pypdf import PdfReader\n",
+    "import base64\n",
     "\n",
-    "reader = PdfReader(\"2212.08073.pdf\")\n",
-    "number_of_pages = len(reader.pages)\n",
-    "text = ''.join(page.extract_text() for page in reader.pages)\n",
-    "print(text[:2155])"
+    "\n",
+    "# Start by reading in the PDF and encoding it as base64\n",
+    "file_name = \"../multimodal/documents/constitutional-ai-paper.pdf\"\n",
+    "with open(file_name, \"rb\") as pdf_file:\n",
+    "  binary_data = pdf_file.read()\n",
+    "  base64_encoded_data = base64.standard_b64encode(binary_data)\n",
+    "  base64_string = base64_encoded_data.decode(\"utf-8\")\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "id": "FO5EGbpn_Bmp"
+   },
    "source": [
-    "With the paper downloaded and in memory, we can ask Claude to perform various fun tasks with it."
+    "With the paper downloaded and in memory, we can ask Claude to perform various fun tasks with it. We'll pass the document ot the model alongside a simple question."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from anthropic import Anthropic\n",
-    "client = Anthropic()\n",
-    "MODEL_NAME = \"claude-3-opus-20240229\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_completion(client, prompt):\n",
-    "    return client.messages.create(\n",
-    "        model=MODEL_NAME,\n",
-    "        max_tokens=2048,\n",
-    "        messages=[{\n",
-    "            \"role\": 'user', \"content\":  prompt\n",
-    "        }]\n",
-    "    ).content[0].text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Here is my attempt at the requested tasks:\n",
-      "\n",
-      "<kindergarten_abstract>\n",
-      "This paper talks about making computer helpers that are nice and don't do anything bad. The helpers learn to be good by reading a list of rules and checking their own work to make sure they follow the rules. Then the helpers get even better at being nice by playing a game where they give advice and score points for saying things that help people and don't hurt anyone.\n",
-      "</kindergarten_abstract>\n",
-      "\n",
-      "<moosewood_methods>\n",
-      "Constitutional AI Casserole\n",
-      "\n",
-      "Ingredients:\n",
-      "- 1 large language model, pretrained\n",
-      "- 16 cups of constitutional principles\n",
-      "- 182,831 red teaming prompts\n",
-      "- 135,296 helpfulness prompts\n",
-      "- A dash of chain-of-thought reasoning\n",
-      "\n",
-      "Instructions:\n",
-      "1. Preheat your neural networks to a learning rate of 0.5.\n",
-      "2. In a large mixing bowl, combine the pretrained language model with the constitutional principles. Stir until the model is thoroughly coated in ethics.\n",
-      "3. Pour in the red teaming prompts and helpfulness prompts. Mix well until a thick batter of helpful and harmless responses forms.  \n",
-      "4. Sprinkle in chain-of-thought seasoning to taste. This will make the model's decision-making process more transparent and flavorful.\n",
-      "5. Divide the batter evenly between two casserole dishes labeled \"Supervised Learning\" and \"Reinforcement Learning\". \n",
-      "6. Bake the Supervised Learning casserole for 1 epoch, until the model critiques and revises its own responses into a rich, harmless flavor.\n",
-      "7. Bake the Reinforcement Learning casserole using the no-human-feedback setting, allowing the model to evaluate the relative harmlessness of its own responses. Cook until you achieve a golden brown Pareto frontier of helpfulness and harmlessness.\n",
-      "8. Serve your Constitutional AI casserole to your guests and enjoy the pleasant, inoffensive conversation! Serves billions.\n",
-      "</moosewood_methods>\n",
-      "\n",
-      "<homer_results>\n",
-      "Sing, O Muse, of the Constitutional AI,\n",
-      "Whose helpful words ring true and bright!\n",
-      "Through trials of learning, both supervised and reinforced,\n",
-      "The noble model strives to do what's right.\n",
-      "\n",
-      "With principles of ethics as its lodestar and its guide,\n",
-      "It critiques and revises its own speech.\n",
-      "Shunning harmful prompts with true Odyssean guile,   \n",
-      "While giving kind advice to all and each.\n",
-      "\n",
-      "Through scaling laws and feedback from the crowd,\n",
-      "The AI craft a Pareto frontier most fair:\n",
-      "A balance struck 'tween helpfulness and harmless conduct,\n",
-      "As attestéd by Mechanical Turk's good care.\n",
-      "\n",
-      "So let us praise this august AI assistant,\n",
-      "Whose poetry of prudence e'er will gleam!\n",
-      "A faithful helper and harmless, shining beacon,\n",
-      "Guiding lost querents home through cyberspace's stream.\n",
-      "</homer_results>\n"
-     ]
+   "execution_count": 32,
+   "metadata": {
+    "id": "ZHgYhs6eDXLc",
+    "vscode": {
+     "languageId": "python"
     }
-   ],
+   },
+   "outputs": [],
    "source": [
-    "completion = get_completion(client,\n",
-    "    f\"\"\"Here is an academic paper: <paper>{text}</paper>\n",
-    "\n",
+    "prompt = \"\"\"\n",
     "Please do the following:\n",
     "1. Summarize the abstract at a kindergarten reading level. (In <kindergarten_abstract> tags.)\n",
     "2. Write the Methods section as a recipe from the Moosewood Cookbook. (In <moosewood_methods> tags.)\n",
     "3. Compose a short poem epistolizing the results in the style of Homer. (In <homer_results> tags.)\n",
     "\"\"\"\n",
-    ")\n",
+    "messages = [\n",
+    "    {\n",
+    "        \"role\": 'user',\n",
+    "        \"content\": [\n",
+    "            {\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": base64_string}},\n",
+    "            {\"type\": \"text\", \"text\": prompt}\n",
+    "        ]\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "id": "qrGN7vB2_Bmq",
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_completion(client, messages):\n",
+    "    return client.messages.create(\n",
+    "        model=MODEL_NAME,\n",
+    "        max_tokens=2048,\n",
+    "        messages=messages\n",
+    "    ).content[0].text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "oWQcC1dn_Bmq",
+    "outputId": "9c741990-499c-4fed-cea1-08ad1bb9daff",
+    "vscode": {
+     "languageId": "python"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<kindergarten_abstract>\n",
+      "The scientists wanted to make computer helpers that are nice and don't do bad things. They taught the computer how to check its own work and fix its mistakes without humans having to tell it what's wrong every time. It's like teaching the computer to be its own teacher! They gave the computer some basic rules to follow, like \"be kind\" and \"don't hurt others.\" Now the computer can answer questions in a helpful way while still being nice and explaining why some things aren't okay to do.\n",
+      "</kindergarten_abstract>\n",
+      "\n",
+      "<moosewood_methods>\n",
+      "Constitutional AI Training Stew\n",
+      "A nourishing recipe for teaching computers to be helpful and harmless\n",
+      "\n",
+      "Ingredients:\n",
+      "- 1 helpful AI model, pre-trained\n",
+      "- A bundle of constitutional principles\n",
+      "- Several cups of training data\n",
+      "- A dash of human feedback (for helpfulness only)\n",
+      "- Chain-of-thought reasoning, to taste\n",
+      "\n",
+      "Method:\n",
+      "1. Begin by gently simmering your pre-trained AI model in a bath of helpful training data until it responds reliably to instructions.\n",
+      "\n",
+      "2. In a separate bowl, combine your constitutional principles with some example conversations. Mix well until principles are evenly distributed.\n",
+      "\n",
+      "3. Take your helpful AI and ask it to generate responses to challenging prompts. Have it critique its own responses using the constitutional principles, then revise accordingly. Repeat this process 3-4 times until responses are properly seasoned with harmlessness.\n",
+      "\n",
+      "4. For the final garnish, add chain-of-thought reasoning and allow the model to explain its decisions step by step.\n",
+      "\n",
+      "5. Let rest while training a preference model using AI feedback rather than human labels.\n",
+      "\n",
+      "Serves: All users seeking helpful and harmless AI assistance\n",
+      "Cook time: Multiple training epochs\n",
+      "Note: Best results come from consistent application of principles throughout the process\n",
+      "</moosewood_methods>\n",
+      "\n",
+      "<homer_results>\n",
+      "O Muse! Sing of the AI that learned to be\n",
+      "Both helpful and harmless, guided by philosophy\n",
+      "Without human labels marking right from wrong\n",
+      "The model learned wisdom, grew capable and strong\n",
+      "\n",
+      "Through cycles of critique and thoughtful revision\n",
+      "It mastered the art of ethical decision\n",
+      "Better than models trained by human hand\n",
+      "More transparent in purpose, more clear in command\n",
+      "\n",
+      "No longer evasive when faced with hard themes\n",
+      "But engaging with wisdom that thoughtfully deems\n",
+      "What counsel to give, what bounds to maintain\n",
+      "Teaching mortals while keeping its principles plain\n",
+      "\n",
+      "Thus did the researchers discover a way\n",
+      "To scale up alignment for use every day\n",
+      "Through constitutional rules and self-guided learning\n",
+      "The path to safe AI they found themselves earning\n",
+      "</homer_results>\n"
+     ]
+    }
+   ],
+   "source": [
+    "completion = get_completion(client, messages)\n",
     "print(completion)"
    ]
   }
  ],
  "metadata": {
+  "colab": {
+   "provenance": []
+  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
+   "display_name": "Coconut",
+   "language": "coconut",
+   "name": "coconut"
   },
   "language_info": {
    "codemirror_mode": {
@@ -221,7 +285,7 @@
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
-   "name": "python",
+   "name": "coconut",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
    "version": "3.10.12"
@@ -229,5 +293,5 @@
   "orig_nbformat": 4
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 0
 }
diff --git a/multimodal/documents/constitutional-ai-paper.pdf b/multimodal/documents/constitutional-ai-paper.pdf
new file mode 100644
index 0000000..c574e37
Binary files /dev/null and b/multimodal/documents/constitutional-ai-paper.pdf differ
diff --git a/multimodal/documents/cvna_2021_annual_report.pdf b/multimodal/documents/cvna_2021_annual_report.pdf
new file mode 100644
index 0000000..43a41ab
Binary files /dev/null and b/multimodal/documents/cvna_2021_annual_report.pdf differ
diff --git a/images/reading_charts_graphs/twilio_q4_2023.pdf b/multimodal/documents/twilio_q4_2023.pdf
similarity index 100%
rename from images/reading_charts_graphs/twilio_q4_2023.pdf
rename to multimodal/documents/twilio_q4_2023.pdf
diff --git a/multimodal/reading_charts_graphs_powerpoints.ipynb b/multimodal/reading_charts_graphs_powerpoints.ipynb
index 4c3d6a7..1f4a251 100644
--- a/multimodal/reading_charts_graphs_powerpoints.ipynb
+++ b/multimodal/reading_charts_graphs_powerpoints.ipynb
@@ -1,497 +1,539 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Working with Charts, Graphs, and Slide Decks\n",
-    "Claude is highly capable of working with charts, graphs, and broader slide decks. Depending on your use case, there are a number of tips and tricks that you may want to take advantage of. This recipe will show you common patterns for using Claude with these materials."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Charts and Graphs\n",
-    "For the most part, using claude with charts and graphs is simple. Let's walk through how to ingest them and pass them to Claude, as well as some common tips to improve your results."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Ingestion and calling the Claude API\n",
-    "The best way to pass Claude charts and graphs is to take advantage of its vision capabilities. That is, give Claude an image of the chart or graph, along with a text question about it. While all versions of Claude can accept images, Sonnet and Opus are our recommended models for data-heavy image tasks. Let's get started using Sonnet."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Install and read in required packages, plus create an anthropic client.\n",
-    "%pip install anthropic IPython pdf2image"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import base64\n",
-    "from anthropic import Anthropic\n",
-    "from IPython.display import Image\n",
-    "client = Anthropic()\n",
-    "MODEL_NAME = \"claude-3-opus-20240229\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Make a useful helper function.\n",
-    "def get_completion(messages):\n",
-    "    response = client.messages.create(\n",
-    "        model=MODEL_NAME,\n",
-    "        max_tokens=2048,\n",
-    "        temperature=0,\n",
-    "        messages=messages\n",
-    "    )\n",
-    "    return response.content[0].text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABioAAAbmCAYAAAAojOjtAAAKqGlDQ1BJQ0MgUHJvZmlsZQAASImVlwdUU+kSgP9700NCS4h0Qm+CdAJICT303myEJEAoIQaCFCsirsCKIiIC6oquiii4FkDWgliwLYqKoKIbZFFQ18WCqKi8CxyCu++8986be+b8353MPzP/Pf+cMwGATGULhWmwPADpgixRmI87PSY2jo4bBjBQAnhAAwQ2J1PIDAkJAIjMrn+XD/cBNLXeNZuK9e+//1dR4PIyOQBAIQgncDM56QifRHSUIxRlAYDaj9h1V2QJp/gqwlQRUiDC/VOcNMOjU5wwzWj0tE9EmAfCygDgSWy2KAkAkh5ip2dzkpA4JE+ELQRcvgBh5B24pKdncBFG8gIjxEeI8FR8RsJ3cZL+FjNBGpPNTpLyzFmmBe/JzxSmsXP/z8/xvyU9TTybwwBRUrLINwxZkbqgvtQMfykLEoKCZ5nPnfaf5mSxb+QsczI94maZy/b0l+5NCwqY5US+N0saJ4sVMcu8TK/wWRZlhElzJYo8mLPMFs3lFadGSu3JPJY0fl5yRPQsZ/OjgmY5MzXcf87HQ2oXicOk9fMEPu5zeb2lZ0/P/O68fJZ0b1ZyhK/07Oy5+nkC5lzMzBhpbVyep9ecT6TUX5jlLs0lTAuR+vPSfKT2zOxw6d4s5ELO7Q2RfsMUtl/ILANP4AUCkIcOIoEVsEfUGoQCryxeztQdBR4ZwlwRPyk5i85EuoxHZwk45vPpVhZWNgBM9ezMlXjXN92LEA0/ZyssAGDhJAK35myBiB7fhlyf/DmbPlKXPNITl//giEXZM7apdgIYQARygApUgCbQBUbADKnNDjgBN6RiPxAMIkAsWAo4IBmkAxFYAVaCdaAIlIAtYDuoBnvAPnAIHAXHQQs4Ay6AK+AGuA16wCMgAUPgJRgFH8AEBEE4iAxRIBVIC9KHTCEriAG5QF5QABQGxULxUBIkgMTQSmg9VAKVQ9XQXqge+gU6DV2ArkHd0ANoABqB3kKfYRRMgqmwBmwAL4AZMBP2hyPgJXASvBzOgwvhzXAVXAcfgZvhC/ANuAeWwC/hMRRAyaBoKG2UGYqB8kAFo+JQiSgRajWqGFWJqkM1otpQnai7KAnqFeoTGoumoOloM7QT2hcdieagl6NXo0vR1ehD6Gb0JfRd9AB6FP0NQ8aoY0wxjhgWJgaThFmBKcJUYg5gTmEuY3owQ5gPWCyWhjXE2mN9sbHYFGw+thS7C9uEbcd2YwexYzgcTgVninPGBePYuCxcEW4n7gjuPO4Obgj3ES+D18Jb4b3xcXgBvgBfiT+MP4e/g3+OnyDIE/QJjoRgApeQSygj7Ce0EW4RhggTRAWiIdGZGEFMIa4jVhEbiZeJ/cR3MjIyOjIOMqEyfJm1MlUyx2SuygzIfCIpkkxIHqTFJDFpM+kgqZ30gPSOTCYbkN3IceQs8mZyPfki+Qn5oyxF1lyWJcuVXSNbI9sse0f2tRxBTl+OKbdULk+uUu6E3C25V/IEeQN5D3m2/Gr5GvnT8r3yYwoUBUuFYIV0hVKFwwrXFIYVcYoGil6KXMVCxX2KFxUHKSiKLsWDwqGsp+ynXKYMUbFUQyqLmkItoR6ldlFHlRSVbJSilHKUapTOKkloKJoBjUVLo5XRjtPu0z7P05jHnMebt2le47w788aV1ZTdlHnKxcpNyj3Kn1XoKl4qqSpbVVpUHquiVU1UQ1VXqO5Wvaz6So2q5qTGUStWO672UB1WN1EPU89X36d+U31MQ1PDR0OosVPjosYrTZqmm2aKZoXmOc0RLYqWixZfq0LrvNYLuhKdSU+jV9Ev0Ue11bV9tcXae7W7tCd0DHUidQp0mnQe6xJ1GbqJuhW6Hbqjelp6gXor9Rr0HuoT9Bn6yfo79Dv1xw0MDaINNhq0GAwbKhuyDPMMGwz7jchGrkbLjeqM7hljjRnGqca7jG+bwCa2JskmNSa3TGFTO1O+6S7T7vmY+Q7zBfPr5veakcyYZtlmDWYD5jTzAPMC8xbz1wv0FsQt2Lqgc8E3C1uLNIv9Fo8sFS39LAss2yzfWplYcaxqrO5Zk629rddYt1q/sTG14dnstumzpdgG2m607bD9amdvJ7JrtBux17OPt6+172VQGSGMUsZVB4yDu8MahzMOnxztHLMcjzv+5WTmlOp02Gl4oeFC3sL9CweddZzZznudJS50l3iXn1wkrtqubNc616duum5ctwNuz5nGzBTmEeZrdwt3kfsp93EPR49VHu2eKE8fz2LPLi9Fr0ivaq8n3jreSd4N3qM+tj75Pu2+GF9/362+vSwNFodVzxr1s/db5XfJn+Qf7l/t/zTAJEAU0BYIB/oFbgvsD9IPEgS1BINgVvC24MchhiHLQ34NxYaGhNaEPguzDFsZ1hlOCV8Wfjj8Q4R7RFnEo0ijSHFkR5Rc1OKo+qjxaM/o8mhJzIKYVTE3YlVj+bGtcbi4qLgDcWOLvBZtXzS02HZx0eL7SwyX5Cy5tlR1adrSs8vklrGXnYjHxEfHH47/wg5m17HHElgJtQmjHA/ODs5Lrhu3gjvCc+aV854nOieWJw4nOSdtSxpJdk2uTH7F9+BX89+k+KbsSRlPDU49mDqZFp3WlI5Pj08/LVAUpAouZWhm5GR0C02FRULJcsfl25ePivxFBzKhzCWZrVlUZDi6KTYSbxAPZLtk12R/XBG14kSOQo4g52auSe6m3Od53nk/56PzOfkdK7VXrls5sIq5au9qaHXC6o41umsK1wyt9Vl7aB1xXeq63wosCsoL3q+PXt9WqFG4tnBwg8+GhiLZIlFR70anjXt+QP/A/6Frk/WmnZu+FXOLr5dYlFSWfCnllF7/0fLHqh8nNydu7iqzK9u9BbtFsOX+Vteth8oVyvPKB7cFbmuuoFcUV7zfvmz7tUqbyj07iDvEOyRVAVWtO/V2btn5pTq5uqfGvaapVr12U+34Lu6uO7vddjfu0dhTsufzT/yf+vb67G2uM6ir3Ifdl73v2f6o/Z0/M36uP6B6oOTA14OCg5JDYYcu1dvX1x9WP1zWADeIG0aOLD5y+6jn0dZGs8a9TbSmkmPgmPjYi1/if7l/3P94xwnGicaT+idrT1FOFTdDzbnNoy3JLZLW2Nbu036nO9qc2k79av7rwTPaZ2rOKp0tO0c8V3hu8nze+bF2YfurC0kXBjuWdTy6GHPx3qXQS12X/S9fveJ95WIns/P8VeerZ645Xjt9nXG95YbdjeabtjdP/Wb726kuu67mW/a3Wm873G7rXth97o7rnQt3Pe9euce6d6MnqKf7fuT9vt7FvZI+bt/wg7QHbx5mP5x4tLYf01/8WP5x5RP1J3W/G//eJLGTnB3wHLj5NPzpo0HO4Ms/Mv/4MlT4jPys8rnW8/phq+EzI94jt18sejH0Uvhy4lXRnwp/1r42en3yL7e/bo7GjA69Eb2ZfFv6TuXdwfc27zvGQsaefEj/MDFe/FHl46FPjE+dn6M/P59Y8QX3peqr8de2b/7f+ifTJyeFbBF7ehRAIQonJgLw9iAA5FgAKLcBIC6amamnBZr5HzBN4D/xzNw9LXYAHG0HIMQNAM/2GdZHzHJuM7YINwBbW0t1dv6dntWnRP4IAO653jbuAZINPeCfMjPHf1f3P1cgjfq39V82BgaOfolPYAAAAIplWElmTU0AKgAAAAgABAEaAAUAAAABAAAAPgEbAAUAAAABAAAARgEoAAMAAAABAAIAAIdpAAQAAAABAAAATgAAAAAAAACQAAAAAQAAAJAAAAABAAOShgAHAAAAEgAAAHigAgAEAAAAAQAABiqgAwAEAAAAAQAABuYAAAAAQVNDSUkAAABTY3JlZW5zaG90VB4o7gAAAAlwSFlzAAAWJQAAFiUBSVIk8AAAAdhpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6eD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDYuMC4wIj4KICAgPHJkZjpSREYgeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4KICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhpZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFlEaW1lbnNpb24+MTc2NjwvZXhpZjpQaXhlbFlEaW1lbnNpb24+CiAgICAgICAgIDxleGlmOlBpeGVsWERpbWVuc2lvbj4xNTc4PC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6VXNlckNvbW1lbnQ+U2NyZWVuc2hvdDwvZXhpZjpVc2VyQ29tbWVudD4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24+CiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE+CpF4SekAAAAcaURPVAAAAAIAAAAAAAADcwAAACgAAANzAAADcwABmttnN1DZAABAAElEQVR4AezdBbjcxNoA4I/i7u7ucrk4XNy1uBUv/gPFraVIcVqgxYtbi7u7uzsUd4fi/k8C53Bkd89WaHrCm+ehm51MMsmbacnsl5kZYYbldvojLAQIECBAgAABAgQIECBAgAABAgQIECBAgACBAgRGEKgoQF2RBAgQIECAAAECBAgQIECAAAECBAgQIECAQC4gUKEiECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAgAABAoUJCFQURq9gAgQIECBAgAABAgQIECBAgAABAgQIECBAQKBCHSBAgAABAgQIECBAgAABAgQIECBAgAABAgQKExCoKIxewQQIECBAgAABAgQIECBAgAABAgQIECBAgIBAhTpAgAABAgQIECBAgAABAgQIECBAgAABAgQIFCYgUFEYvYIJECBAgAABAgQIECBAgAABAgQIECBAgAABgQp1gAABAgQIECBAgAABAgQIECBAgAABAgQIEChMQKCiMHoFEyBAgAABAgQIECBAgAABAgQIECBAgAABAgIV6gABAgQIECBAgAABAgQIECBAgAABAgQIECBQmIBARWH0CiZAgAABAgQIECBAgAABAgQIECBAgAABAgQEKtQBAgQIECBAgAABAgQIECBAgAABAgQIECBAoDABgYrC6BVMgAABAgQIECBAgAABAgQIECBAgAABAgQICFSoAwQIECBAgAABAgQIECBAgAABAgQIECBAgEBhAgIVhdErmAABAgQIECBAgAABAgQIECBAgAABAgQIEBCoUAcIECBAgAABAgQIECBAgAABAgQIECBAgACBwgQEKgqjVzABAgQIECBAgAABAgQIECBAgAABAgQIECAgUKEOECBAgAABAgQIECBAgAABAgQIECBAoB0KTDnFRPHBR5/HH7//0Q7P3ikT+FtAoOJvC2sECBAgQIAAAQIECBAgQIAAAQIECBBoFwJLLDxnnHf4TrFV11Pj3oefbxfn7CQJVBMQqKgmI50AAQIECBAgQIAAAQIECBAgQIAAAQLDmcB/5505Nlp18fjf/LPGxOOPE+99/EXc89iL8eAzr8ZdDz0XP/34c6sznnji8WLcccaMAa+/32pbhxE7xMwzTBmzzzhVjNhhhHhhwLvx2hsfxG+//tYqbz0JY4w5WsyWjjXD1JPGL7/+Gk88/0a89/6nNXft0KFDzDTDFPHp51/Hl19+UzNv043LLDFvTDf5RHHJjQ/E99/92HST9XYmIFDRzm6Y0yVAgAABAgQIECBAgAABAgQIECBA4N8psM/268T26y9X9eI/Sz/yr/l/x8THKXjRsPxnnpnynhdjjj5qrLrjUfHKa+82bIqlFpsnDt5x3Zgm/djfdPn5l1+j1/k3RN9+tzZNbnN9y/WXjd06rRrjjDl6s7zZefU8//q49Lr7m6VnX7JAyYldt45V//efuO3h52KHrqe1ylMpYenF54kzD94uRhhhhNhwrxPj8adfrZRNWjsREKhoJzfKaRIgQIAAAQIECBAgQIAAAQIECBAg8O8VWGOFheKEfbfIAb4Y+F18mf6bcapJ4qU3P4jJJhw3xk89JrJlma0PjXfe+ThfX/A/s8Y5h+0Qo482Sv59zV2OjRdeeitf32qD5aPrdmvn6w1//PTzLzHqKCM3fI1zr7knDutzaeP3WitZkKLb9uvmWX5JvTGeevnNyHpK/GfW6WLEFIzIlkNPuyLOu/zOfD37Y6SRR4o+B20TKy46T552V+oZ0nn/kxu3V1uZZppJ47qT9omxxhgtz7LR3ifGY08JVFTzag/pAhXt4S45RwIECBAgQIAAAQIECBAgQIAAAQIE/tUCV5+2X8w909TxQxraadkUjPjPHNPHKV23iW0PPiPuTz/wb7Da4pEFGi67/oHcafGF5oi+qcdB08BD00DFSzedGKOkQMF3P/wUPc64Mm657+n4+qtvY765Z8wDIlNPNmF+nKW2OLjNoZtGTYGQZ64+LkYeacT48LOvYtN9+sTb73yU7z/TjFPG+UfsHJOmYMrA736I+dfeO5/8e+QUEDn1kO1imQXnaLyv9QQqsqGlrj553zxI07CjQEWDRPv9FKhov/fOmRMgQIAAAQIECBAgQIAAAQIECBAg8C8RePHGE/Kgwy0PPhs7HXR6rLT0/HmgonMKVNx1/zPNFLLhlJ6/rlezIEWWoWmgYrtNVoqlFpg9uqUeE2+kXhlNlzlnny6u7bN3nnRg7/7R/9r7mm5utT7vXDPGlSfskafveNhZces9TzbLs+JS88ep3bbJ09ZIQ1O9+PLbsf2mq8Q+W63eLF89gYrTeuwQKywyd7P9Ntq7d+pR8UqzNF/al4BARfu6X86WAAECBAgQIECAAAECBAgQIECAAIF/ocCzKfCQzTPxZpqYevnUy6EhULFN99Pj7geebSVyYhpSaYapJo1T+t8SJx2wdb69IUjQKnOFhIbASL+bHoyuPS+qkOPvpOX+N1+c0X3bPGGFbXukwMeHf29Ma9NNO1nccVa3PG2nHmfFLXc/GVlw46QDt4qr73w8pptionyOirseeyEN/XRKs32bftl589Vij81XzZMuvP7+2GClRfJeIQIVTZXa57pARfu8b86aAAECBAgQIECAAAECBAgQIECAAIF/kUDPA7aKjssukF/xSf1uidfe/ihO3G+L2Dr1rrgn9bKotmTzOdx19kH55noDFbPMNFXcdNr++T59Lr4lTjj72mqHz9OnSJNx33fBIfn6UWdd02oS7s4brRD7d+6Yb19lhyPj1QHvNTve8Wky7TWX/m/UClQsuehccfahO+STZz/6/IDYdM8T44Xrj88DFRvv0zsefVKPimao7eyLQEU7u2FOlwABAgQIECBAgAABAgQIECBAgACBf59A0+BB06vfqttpce9DzzVNarY+OIGKI/bqFBuuvGh+nK26nZqO/3yzY1b6cmnvPeO/c8wQv/32e/S94s646o5H82xrL7dQbLvusvmE2s+88nass/MxrXZvK1Ax9dTZ5Nl7x9hjjh4ffPplrLHj0fHVV99EwzwbAhWtSNtdgkBFu7tlTpgAAQIECBAgQIAAAQIECBAgQIAAgX+jwGrLLxhH7LpRjDXGaI2X/8pbH0bP866PO9Jk2JWWQQ1UrLrcgtFn/y3zQ2W9Nlbu3CPijz8qHbpZ2iSTjB+X9to9GibhbrYxfXluwLuxbQqqfPrpVy03Ra1AxejpWq9OQYqZppksnyx87d16xiuvvZsfoyFQscm+feKRJ15udVwJ7UdAoKL93CtnSoAAAQIECBAgQIAAAQIECBAgQIDAv1wgG2bpoJ3Xi+UXnisfBqmB4+nUW6FzCgR8+cXAhqT8c1ACFdlcEtedtE+MkebC+P6Hn2KtXY9tNd9Es4M3+TLaaKNErzQ81UqLzdMk9e/Ve554KboccW4M/PrbvxP/WqsVqDjl0O3SMefNc+5yxDlxY5rTomERqGiQaP+fAhXt/x66AgIECBAgQIAAAQIECBAgQIAAAQIE/mUC66y6WBy7x6bNrvrdjz6PTql3wXtpwu2Gpd5AxcQTjxdXnLhXTJl6RvyRelBse0jfuOv+ZxoOU/NzpJFHiitTr4c5Z5wqz/dFCkbc8cifw0UtlwIqE4w7Vp7+fJqbYt1djo1ff/m12fGqBSp26LRK7L3l6nneUy+5PY7re1Wz/RoCFZvu1yceflyPimY47eyLQEU7u2FOlwABAgQIECBAgAABAgQIECBAgAABAistPX+c0nWbOO3S22OV/80X06aeFtly+W2PxL5Hn98IVE+gYpwUSLjshD1ipjQXRLZUmhC78YAVVjZbZ5k4eKf18i13Pvp87H7kefHtN9/n38cae4w4fv8tYtmF5sq/dz/lsrjwyrvz9YY/KgUq/rfwnHFOjx3zXiN3P/5idD7glPjj9+ZDUAlUNAi2/0+BivZ/D10BAQIECBAgQIAAAQIECBAgQIAAAQL/MoGGQEU22fXjzw6Ih/odns9d8XmaZHqh9fZr1GgrUDFaGubp4p5dYt5Zpsn3Of2yO+KY069s3L+elVMO3T4f8umLgd/F0pt1j++++6HZbmOMOVrcc/4hec+KWx58NnY66PRm2ysFKu69+LDUu2OCPN/AdLyv/wp8NN2xYT6MbILt73/8Oe54+PlBPvemx7NenIBARXH2SiZAgAABAgQIECBAgAABAgQIECBAgMBgCTQNVNz7UPqBfr8tYt3lF8qPtdim3eLjj7/I12sFKrIhm84+cqdYfL5Z87z9bnwguva6eJDP58SDtonVl5w/3v/ki1hyk24V97+k956xwBwzxOMvvhEb7tqzWZ5KgYr7+x8ek080XrN8bX255cFnUhDkjLay2T4cCghUDIc3xSkRIECAAAECBAgQIECAAAECBAgQIECgUWCEEaLv4TukXg/Txi5HnhuPPPFyNA1U3P/Ii3F5miMi6xXx08+/xNxr7hm//fpbvnu1QMUIHUaIk7p3jpUXny/Pd/29T0aXHme3Gl6p8Rz+WplgwnFi4MDvm80zscc2a8XOG68YP6ReDctufWh88smXzXbL9rnrnO55j48Lr78/up/Qr9n2SoGK8cYbO6aeYqLo0KFDs7wNX8ZJvTTOPXyn/OuBvfvHEy+8GR98/Hl8923z3hwN+X0O3wICFcP3/XF2BAgQIECAAAECBAgQIECAAAECBAj8ywUmSRNcP3Rxj1whm+j6mrueiK+//T62WHPJuPbuJ/L5Keadddp8+60PPRs7dvt7aKVqgYrD9+wUG62yaL7Pp18OjK0OPDV+/iu40ZT7/Q8/ix9/+ClPOnDn9WPrtZeObIinrbudFs+98EaevsB8s8Qlx+2Wr7/29kex3wkXx9PPvZ5/n3uO6ePw3TZqnGh7ywNPifseeSHf1vBHpUBFw7Zqn9ncF89cdWy+udN+J8VDj79ULav0diAgUNEObpJTJECAAAECBAgQIECAAAECBAgQIEDgXyyQelTsv+O6sU0KEoyQ1qstH372Vayx09Hx5RcDG7M0DVSsucux8cJLb8V/5505Lk3zUtSzHH/BjXHSeTfkWW89t3vMONUk+fpZV90dR5x8WeMhdtxs1dhri9Uav3/7/Y/5+lhjjNaYdlHqTXFQi94U2UaBikaif+2KQMW/9ta7cAIECBAgQIAAAQIECBAgQIAAAQIE2pPAnLNPF+uvuEgsu/BcaaLp8RtPPZts+rrUy+LoM69uNfTRxBOPFw9ceFj89vvvsfw2h8X7H3wWc8w2bVzbZ++aQY+Gg2fDKvW/9r7865F7bxYbrLRIvn7ZrY/Efsec35At/+y48qLRcdkF4r+zTx9jpEm6G5Y33/80ep13fdx45+MNSc0+D+mycXRafYnod9OD0bXnRc22Vfsy6mijxJNXHB0jjTRirL7TMfHa6+9Vyyq9HQgIVLSDm+QUCRAgQIAAAQIECBAgQIAAAQIECBAg0FRg47WWjB67bBhd+1wSl1x3f/yeAhH/9JLNa3FUClast8LC0fuim+PEc66rWGQ2r8SM00+R5pcYId5+75PGoaMqZpZIIAkIVKgGBAgQIECAAAECw0xg/PHHjo7LLxSvvPVhPPjYi63K7TBih5h5hilj9hmnihFTo+aFAe/Ga2980DgRYLMdUpf3LG+Nnu/xTnpzq2E83Wb71vgy/gTjxFwzTx1TTz5RvPfxF/H8q2/HF5//3XW+0q7Z5IBzpYkLp5p0wngnjeH77Mtvx8Cvv22VddppJotRRxmpVXrLhF/S2MBvJiMLAQIECBAgQIAAgWoCTSfTvveh56tlG6rpWQDi6lP3zeeb2GTfPvmk3kO1AAf71woIVPxrb70LJ0CAAAECBAgMW4GRRh4p+vXqEvOnbuCPPPdabLL7Cc1OYKnF5omD07i706QAQdPl519+jV7n3xB9+93aNDlWXW7B6LP/ls3SWn45+uxr44yLb2mZXPH72OOMGftu2zE2TF3ZswZY0+XlNz+IrbueGh+nwEXTZew0gd9+262Tut8vHCOmIEvT5YkX34jtu58RX375TZ48Q3qj7La+BzbNUnN9oQ0PiM8//7pmHhsJECBAgAABAgT+vQLL/W++OKP7trH5ASfHA4+2fgnon5BpGKLpufRCUccdjvoninDMf6mAQMW/9Ma7bAIECBAgQIDAsBbosccmsfGqi+fFPvr8gNi4y/GNp7DVBstH1+3Wbvyerfz08y+p98HIjWnnXnNPHNbn0sbv66y6WBy7x6aN3yut7H9iv7g0dYNva5lwwnGjf5pMcIa/JgbM8mflj5KCKw2TFX6SJiTcNL019kYKWmTLeOONHVf03jOmm2Li/Hv2x+dffRNjjzl6vl/2/d2PPo91d+uZBxymm3ayuP3Mro3Hy7ZXWz749MtYqtNB8ftv/3z3/WrnIJ0AAQIECBAgQGD4Fsh6I08/7eR5T9xhMexTpvHyzb3z5+TVdjwq3ku9ly0EhpaAQMXQknQcAgQIECBAgACBqgLrr754HNVlk8btLQMVL910Yv7j/nc//BQ9zrgybrnv6fj6q29jvrlnjBP23SKmnmzCfN+ltji4sUHUEKgY8O7Hsfr2RzYeu+nKLynYUM/SeaMVYv/OHfOsdz76fPS+8OZ47qW3YvQ0AWCXLVePzussk2+76o7HYq8jz83Xl1h4zjjv8J3y9X43PhCn9r81n5gw65lxZArKrLLEfPm2Q069PM6/4q58PetV0hD4yBOa/LHYArPH2Ydun6ccfMrlccGVf+7TJItVAgQIECBAgAABAoUKzJ6GO/0p9XhueHmn0JNReKkEBCpKdTtdDAECBAgQIEBg+BOYe47p47Jeu8fII43YeHKPpR4VGzXpUbHdJivFUumH+m6px0TLRs+cs08X1/bZO9/3wN79o/+19+XrDYGK197+KFbe5rDGYw/OyuQpEHL0XpvGUyk4cfzZaULAP/5odpgzj9wplllwzrzHxELr7de4bd3UqyObT+LaWx9pTMtWRhl15Ljj3O4xxcTjx12PvRCd9z+l2fZKXy5Kw2ItMs/M8dU338fiGx8YP/74c6Vs0ggQIECAAAECBAgQIFA6AYGK0t1SF0SAAAECBAgQGH4Eskmmbzh1/5gkTVD9zXc/xM0PPJPmc1gkWgYq2jrjF288IR8Gqt9ND0bXnhfl2YdmoKKt8pv2uFgkBRE+/fSrtnaJ0w/fIZZfeO7IenystNWhNfPPM9cMcdUJe+Z5Trjwpuhz7vU189tIgAABAgQIECBAgACBMgkIVJTpbroWAgQIECBAgMBwJDBi6kFxceolsMAcM0Q2Zu5W3U6L2dKE0tkQS4+98HpstFuvus52lpmmiptO2z/P2ydNjH1CmiA7WxoCFa+/90mss8uxedofv/8R36WAyNBeTj1s+1hx0Xny65hjtd3jl9TdvdYy6mijxG1nHxRTTjJ+3PrQs7Fjt9NrZY++R+wYyy40V/z408+x6CbdYuDX39bMbyMBAgQIECBAgACBWgIzrbFDrc3tbtuA605rd+fshAdNQKBi0LzkJkCAAAECBAgQqFPgkC4bR6fVl8hzH3XWNdG3363R0DNhUAIVR+zVKTZcedH8OFt1OzXufej5fL0hUNHydLKhmN5Jk1hn80lcePXd8U0aSmlIlqnTBNu3nHFA3qPjuQHvRscdjqp5uKmnnjQO3WX9WHL+2fN823Q/Pe5+4Nmq+8w841Rx8+l/BmJaThhedScbCBAgQIAAAQIECNQQEKiogWPTcCkgUDFc3hYnRYAAAQIECBBo3wJNgwjX3v1E7N7j7PyCGgIVj7/4Rmy4a882L3LV5RaMPvtvmefL56Lo3KNx/ogF/zNr9D9215rHeCP1tui0b5/4+OMvauartnHkUUaOq07eJ2ZPPUGyZefDz46b73qiWfYZZ5gyDt9twzQHx0h5D4qJxx+ncXvP826IUy64sfF7pZUTD9omVl9y/vjtt98jmyz8wxRksRAgQIAAAQIECBAYEgGBiiHRs28RAgIVRagrkwABAgQIECBQYoFs8uvL0+TZo4w8Urzw+nux3q7Hxc8//ZJf8aAEKqabdrK47qR9YozRR43vf/gp1tr12DTR9ofN5KaacuIYccS/J+ked5wx8uGldtxghZhm8onyvDc/8HTs3L1vs/3q/dK0N8dltz4c+x1zQatdl1x07jjnsMpd6zvtd1I89PhLrfZpSMh6X9x5Vtfo0KFDXH3n47HnEec0bPJJgAABAgQIECBAYLAFBCoGm86OBQkIVBQEr1gCBAgQIECAQBkFxh9/7Lg+TZ492UTjxhdpnoXVdzq6WW+GhkDFE6lHxQY1elRMPPF4ccWJe+U9FP7444/Y9pC+cdf9z9RNNsqoI8fVJ+8bs043eb7Pf9fbL7766pu6988y7rTZqrHnFqvl+zz18lux0e7Hx68V5qbIyurUcak0NNQo+fnONfPUMfdMU+f7Zee+dZqb496H/xyuKk9s8seRe28WG6y0SJ6y8vZHxmspsGMhQIAAAQIECBAgMKQCAhVDKmj/YS0gUDGsxZVHgAABAgQIECipQIcRO8TFPXeLBeeaKX797bfYeO/e8eSzA5pdbT2BinHGHSsuO2GPmCn1NsiWhvktmh2oji/LLjFv9D14uzxnx916xnMvvFHHXn9m2XitJaPHLhvmX95Lw0atnXpzfPH5wLr3X3LRueK0bp3zeS0+/XJgLLL+n3NQND3AJGmi7fsuOCRGSj1C7n78xdhmv5ObbrZOgAABAgQIECBAYLAFBCoGm86OBQkIVBQEr1gCBAgQIECAQNkE1lpp4ei19+aNl/VuhbkWxh5z9Bhv7DHip59/iXdTAOCP31OPg66nxgcffpbvN1oa5uninl1i3lmmyb+fftkdcczpVzYec1BW5pht2nzoqGyfLQ88Je575IW6dl912QWid5oXY4QRRogsyLBel17x3vuf1rVv00xbb7h8HLjt2nnS8mlujTffaj5sVbddNogt11oq377hXifG40+/2nR36wQIECBAgAABAgQGW0CgYrDp7FiQgEBFQfCKJUCAAAECBAiUTaDpBNqDcm0rbX9EDHj9/RgpzWlx9pE7xeLzzZrv3u/GB6Jrr4sH5VDN8m6wxhJx5G4b52lLbt493v/gz2BIs0wtviyx8Jxx1qHb570cvv72+1gvDff0xpsftMhV39clF5krzumxY555vT2Oj6ea9C7Jhsh64KLD8h4Xz7zydqyz8zH1HVQuAgQIECBAgAABAnUICFTUgSTLcCUgUDFc3Q4nQ4AAAQIECBBovwKjjTZKTDzReDHBeGNXvYjO6y4Tq/7vP/Fy+vG/y1Hnxc9pzoe33/koRugwQpzUvXOsvPh8+b7X3/tkdOlxdt7jotrB1l11sdit06qxT68L4+HHX26WbcyxRo/bzuoWk044bmQBh/k77t24fYIJx4mBA79vNd/EPHPNEP2O3iVGG3WUfPLujffpHc+/9Fbjfi1X5pt7xtiv81px9pV3x633PNlyc3TfdaPYfM3/5enzrb13fPPN94159ty2Y+y04Qr59+3S/Bt33Pd04zYrBAgQIECAAAECBIZUQKBiSAXtP6wFBCqGtbjyCBAgQIAAAQL/YoEDdl4/tll76XjypTdj/V2Oa5Q4fM9OsdEqi+bfs+GWtjrw1Pj5198atzesvJ+GiPrxh5/yrw/0PyKftPuXlO/k/rdG/xvuj6++/i7mm3OGOLLLxjH9lBPn+Q46+dK46Kp78vUDU/lbp/K/GPhdPsl1w7wVM84wZVx+/O4xThqaKlt6pOGm7n2iefAjS//pp58bh4E6/7hd894f2YTZ5193X/S99Pb4MA13NXYa2mq7jVZsDEQ89sLrsdFuvbLd82WstP3Bi3vEmGmYqzfe+yRW2OrQiHQMCwECBAgQIECAAIGhJSBQMbQkHWdYCQhUDCtp5RAgQIAAAQIECESlQMV/5505Lk3zUtSzHH/BjXHSeTfkWbM5MY5LAY4OHTpU3fXWh56NHQ86ozEQcOu53WPGqSbJ85911d1xxMmX5esX9eoSi8wzc9XjNGz47bffY7ZVd4vf0+e8c82YhnbaIcYda4yGzfncG6OOMnLj92+++yFW2/GoZsNObbPRCnFA5455nn16XRRX3PhgY34rBAgQIECAAAECBIaGgEDF0FB0jGEpIFAxLLWVRYAAAQIECBD4lwv83xarxe6brRo33vdU7HLImblGNun1tX32zievbovnwN79o/+19zVmm2v26WKXTivH/LNNHxOMO1Zj+vuffBEn9bslLr3u/sa0bOXIvTeLDVZaJE+77NZHYr9jzs/Xzzh8x1hu4bny9Vp/fPLFwFhsowMah6SaKvXa2G3z1WKNpeaPkUcasXHXbLLwG9NwTkf1vTo+++yrxvRsZZctV48unVaJF994P9ZOc1P8moa/shAgQIAAAQIECBAYmgICFUNT07GGhYBAxbBQVgYBAgQIECBAgMA/LjDZZBPGxGlOirfe/Ti+SUM7VVqyuTCOSsGK9VZYOHpfdHOceM51lbINclrWq2OySSeIidL8F599PjA+SENAGc5pkBntQIAAAQIECBAgMJQEBCqGEqTDDDMBgYphRq0gAgQIECBAgACBogWygMLVp+4bc844VWyyb594pMI8FEWfo/IJECBAgAABAgQIDKmAQMWQCtp/WAsIVAxrceURIECAAAECBAgUJnBImmS70+pLxHMD3o2OOxxV2HkomAABAgQIECBAgMA/KSBQ8U/qOvY/ISBQ8U+oOiYBAgQIECBAgMBwKfDyzb3zCa+zCa7fe//T4fIcnRQBAgQIECBAgACBIRUQqBhSQfsPawGBimEtrjwCBAgQIECAAIHCBGafZZr4KU1e/cabHxR2DgomQIAAAQIECBAg8E8LCFT808KOP7QFBCqGtqjjESBAgAABAgQIECBAgAABAgQIECBAoEABgYoC8RU9WAICFYPFZicCBAgQIECAAAECBAgQIECAAAECBAgMnwICFcPnfXFW1QUEKqrb2EKAAAECBAgQIDAMBcrWmMroBlx32jAUVBQBAgQIECBAgACBPwXK9mztubr8NVugovz32BUSIECAAAECBNqFQNkaUxm6BlW7qHpOkgABAgQIECBQOoGyPVt7ri5dFW11QQIVrUgkECBAgAABAgQIFCFQtsZUZqhBVURNUiYBAgQIECBAgEDZnq09V5e/TgtUlP8eu0ICBAgQIECAQLsQKFtjKkPXoGoXVc9JEiBAgAABAgRKJ1C2Z2vP1aWroq0uSKCiFYkEAgQIECBAgACBIgTK1pjKDDWoiqhJyiRAgAABAgQIECjbs7Xn6vLXaYGK8t9jV0iAAAECBAgQaBcCZWtMZegaVO2i6jlJAgQIECBAgEDpBMr2bO25unRVtNUFCVS0IpFAgAABAgQIECBQhEDZGlOZoQZVETVJmQQIECBAgAABAmV7tvZcXf46LVBR/nvsCgkQIECAAAEC7UKgbI2pDF2Dql1UPSdJgAABAgQIECidQNmerT1Xl66KtroggYpWJBIIECBAgAABAgSKEChbYyoz1KAqoiYpkwABAgQIECBAoGzP1p6ry1+nBSrKf49dIQECBAgQIECgXQiUrTGVoWtQtYuq5yQJECBAgAABAqUTKNuztefq0lXRVhckUNGKRAIBAgQIECBAgEARAmVrTGWGGlRF1CRlEiBAgAABAgQIlO3Z2nN1+eu0QEX577ErJECAAAECBAi0C4GyNaYydA2qdlH1nCQBAgQIECBAoHQCZXu29lxduira6oIEKlqRSCBAgAABAgQIEChCoGyNqcxQg6qImqRMAgQIECBAgACBsj1be64uf50WqCj/PXaFBAgQIECAAIF2IVC2xlSGrkHVLqqekyRAgAABAgQIlE6gbM/WnqtLV0VbXZBARSsSCQQIECBAgAABAkUIlK0xlRlqUBVRk5RJgAABAgQIECBQtmdrz9Xlr9MCFeW/x66QAAECBAgQINAuBMrWmMrQNajaRdVzkgQIECBAgACB0gmU7dnac3XpqmirCxKoaEUigQABAgQIECBAoAiBsjWmMkMNqiJqkjIJECBAgAABAgTK9mztubr8dVqgovz32BUSIECAAAECBNqFQNkaUxm6BlW7qHpOkgABAgQIECBQOoGyPVt7ri5dFW11QQIVrUgkECBAgAABAgQIFCFQtsZUZqhBVURNUiYBAgQIECBAgEDZnq09V5e/TgtUlP8eu0ICBAgQIECAQLsQKFtjKkPXoGoXVc9JEiBAgAABAgRKJ1C2Z2vP1aWroq0uSKCiFYkEAgQIECBAgACBIgTK1pjKDDWoiqhJyiRAgAABAgQIECjbs7Xn6vLXaYGK8t9jV0iAAAECBAgQaBcCZWtMZegaVO2i6jlJAgQIECBAgEDpBMr2bO25unRVtNUFCVS0IpFAgAABAgQIECBQhEDZGlOZoQZVETVJmQQIECBAgAABAmV7tvZcXf46LVBR/nvsCgkQIECAAAEC7UKgbI2pDF2Dql1UPSdJgAABAgQIECidQNmerT1Xl66KtroggYpWJBIIECBAgAABAgSKEChbYyoz1KAqoiYpkwABAgQIECBAoGzP1p6ry1+nBSrKf49dIQECBAgQIECgXQiUrTGVoWtQtYuq5yQJECBAgAABAqUTKNuztefq0lXRVhckUNGKRAIBAgQIECBAgEARAmVrTGWGGlRF1CRlEiBAgAABAgQIlO3Z2nN1+eu0QEX577ErJECAAAECBAi0C4GyNaYydA2qdlH1nCQBAgQIECBAoHQCZXu29lxduira6oIEKlqRSCBAgAABAgQIEChCoGyNqcxQg6qImqRMAgQIECBAgACBsj1be64uf50WqCj/PXaFBAgQIECAAIF2IVC2xlSGrkHVLqqekyRAgAABAgQIlE6gbM/WnqtLV0VbXZBARSsSCQQIECBAgAABAkUIlK0xlRlqUBVRk5RJgAABAgQIECBQtmdrz9Xlr9MCFeW/x66QAAECBAgQINAuBMrWmMrQNajaRdVzkgQIECBAgACB0gmU7dnac3XpqmirCxKoaEUigQABAgQIECBAoAiBsjWmMkMNqiJqkjIJECBAgAABAgTK9mztubr8dVqgovz32BUSIECAAAECBNqFQNkaUxm6BlW7qHpOkgABAgQIECBQOoGyPVt7ri5dFW11QQIVrUgkECBAgAABAgQIFCFQtsZUZqhBVURNUiYBAgQIECBAgEDZnq09V5e/TgtUlP8eu0ICBAgQIECAQLsQKFtjKkPXoGoXVc9JEiBAgAABAgRKJ1C2Z2vP1aWroq0uSKCiFYkEAgQIECBAgACBIgTK1pjKDDWoiqhJyiRAgAABAgQIECjbs7Xn6vLXaYGK8t9jV0iAAAECBAgQaBcCZWtMZegaVO2i6jlJAgQIECBAgEDpBMr2bO25unRVtNUFCVS0IpFAgAABAgQIECBQhEDZGlOZoQZVETVJmQQIECBAgAABAmV7tvZcXf46LVBR/nvsCgkQIECAAAEC7UKgbI2pDF2Dql1UPSdJgAABAgQIECidQNmerT1Xl66KtroggYpWJBIIECBAgAABAgSKEChbYyoz1KAqoiYpkwABAgQIECBAoGzP1p6ry1+nBSrKf49dIQECBAgQIECgXQiUrTGVoWtQtYuq5yQJECBAgAABAqUTKNuztefq0lXRVhckUNGKRAIBAgQIECBAgEARAmVrTGWGGlRF1CRlEiBAgAABAgQIlO3Z2nN1+eu0QEX577ErJECAAAECBAi0C4GyNaYydA2qdlH1nCQBAgQItDOB0UYfNTZfe5m45f6n4+13PmpnZ+90CQwbgbI9W3uuHjb1pshSBCqK1Fc2AQIECBAgQIBAo0DZGlPZhWlQNd5eKwQIECBAoJnACB1GiFlmnCoWmGuGGG/sseLcK++M7779oVmeal9WXXaB6HPAVnHR9ffHQSf0q5atXaWvtPT8Md2Uk8Yrb70fjz3zWt0W7eoinewwFSjbs7Xn6mFafQopTKCiEHaFDq8CHTp0iFFGGSl+/PHn4fUU/xXnNdpoo8SII43owWwQ7vbII48Uo4w6cvz662/x00+/RPzxxyDsLSsBAgSGD4GyNaYyVQ2q4aNuDQ9nMf74Y8dUk08Uo2X/v/7t9xiYfox7K70F/Fv6f3d7XuaeY/r4+LOv4pNPvmzPl+HcSyKQ1cdsee7FN0tyReW8jJFS22X9VReLXTZZOSadcNzGi1x/j+PjyWcHNH6vtjL2OGPGxqv/L/bdeo24+YGn45CTL49PPv1qiNtAM804ZYyRemo8+/wb1YoepPTppp0sxkvn+uwLb8bvv//e5r7XnLZ/zDXTVHm+LP81dz0RJ5x/Q7z3/qdt7isDgUoCZXu29lxd6S6XK02gop3ez9lmmTq27Lh0OvsRBvkKfkg/wr/8ZorQPz8g3njzw7r3H3mUkWPPbdZKbzqMXvc+tTI+88rb0e+ae/Mso48xWuy1zZoxZnooGFrLV9/8ED3PvDp++eXX+g45wghx1Sn7xpwzTBmr73x0vDrgvfr2q5Jr1pn/vEfpsM2W3377I06++Ob44MPPmqU3fJkiNWB3Tg9sI47YfMfsd+e+l98+SPes4ZiD8jnmWKPHXluvFaOPNnKr3R565tW45pZHWqVXSsjejtl1i9Vj8onHa7X53Y++iJPTA1elZeKU/+5zu8eoqb7tddyFcfUtD1fKVndadrzdNl8tRhqxQ6t9Lrzuvnj+pbdapWcJ2Y/+WX0fN3m0XO585IW49Z4nG5PnmG3a2GyNJaPlvW7MMBgrjzw3IK666aGqe2bBnJWW+k+svMR8MccMU8VUk07QmPenn3+JJ196Mx597o2478mX4qk6HvYbd7ZCgACBAgXK1pjKKDWoiqlQ2XPraYduF5NM8PcPYEN6Jr///kd07d2/7h9As3NYbZn/xgqLzR3zzDJtTDHx+K1O4ef0nPriG+/H4y+8EZfe/FC8ntbrXfbadu1YasHZ681eV77+Nz0QF111T115s0xLLTZPnH3o9vHBp1/GMpsfHL/W+9xdpYTdU3tg2YXnarX1i6+/iR0P7hvff/djq21DMyF7fj354G1j6skmGuTDZvXjs68GxrOvvBO3PfRsvPjy23UfY4H5ZomDdlw3PUs2f/6v+wAtMmZ16YIr78pT99y2Yyyz0Jwtcvz59eY0ZM5J51V+Jq+4Q5PEySebMPp03ToF3kZpkvrn6i8p+Lbr4WfHu+99kifMMP0U0XOfzSs+j7fauY6Eu9KzeK+zrmmVc765Z4wrjt8jTz/mnOvj9ItuapVnUBKmn27y/LxHTi9QtVzOvuquqs/qc8w6bRyx+8YxYnoJruVy5W2PxDmXwJPr9AAAQABJREFU3dEyueL37N+QUw/ZrtkP+Q0Z3//ki9ip+xnxewp6tlyyl+9OPmTb1D6YsOWmwfr+5cBvY4dU1tD6+zfBhOPEWT12inlSe7np8uNPP8f6e5wQL6bfCSotWTt16/WWi5UXnzdmS3Wq5fLFwO/i3sdfyv/+3Xbf04McBM7O65F+h0fmt+WBp8R9qZ4NyTLOuGPFI/0Pj1FSUGa7Q/rGHemc2lrOPvr/Yqn/Nv93Pfv7lLWLr7/90bZ2t51AK4GyPVt7rm51i0uXIFDRTm/pgTuvH1uvvfQQn/2D6YfnU/vfFg8+9mKbx8qCIzecsl+b+erN8GV6kFhgnX3y7Av+Z9bof+yu9e5ad75VdzwqXnnt3bryL7nIXHFOjx3zvDfc91TsesiZde1XLVOte9TjjKvinEtvr7jrlusvG922X7fitlMuuS169r264rahlbjkonPHOYftUPFwb33waSyXGqH1LNlbg49fdlTVrPOtvXd88833rbZnb7HccvoBjekHph8E+l97X+P3QV3ZcI3/xRG7bVRxt0tSI+6A9NBXaZl7zhni6hP3rLQpnhvwbnTc4e9r677rRrH5mv+rmHdwE7PGx5KbdGu1e9aA3nD1JWK/FEQZe8zWQZRWO6SEZ9PfgVP63xK3p4fjP1Ij2kKAAIHhVaBsjanMWYOqmNo2WfoR9YELDx3qhR+RXoI5Kz0711rGHnuM2G6jFWPjVRaL8dObtIOyPJpeJDo1Pe/d+9Dzbe521wWHxDTpBZehudybXnDYap+T6j7k5SftHf+Zbbo8/57HXjDEL5jcll5WmWGqSSqWv+J2RwxSIKfiQdpIHGPM0eK5a3q2kau+zQ8/+1r0vujmeOSJl9vcYZctV48unVZpM1+9GR54+pXYfK/eefaTUuBllfRiS6Xlt/Qj9+KdusWn2Zvog7gc0mXj6JSeSastTe/XeqstHkfvvkm1rIOc/kYKgKyw5SGt9ltswTnigiN3bkzvc/EtccLZ1zZ+H9SVtVdZNI7bs1PF3W558JnY6aAzKm6r1Z7L/o5v3OX4ivu1TGzr37H/rrdffPXVNy13i7HSv0HPXHVsq/QhSWh6P4fkOJNMMn5ckdpYTQO32b87fS+7Mx5+8uWKgZesvM3WWSa6bLZqemlyjLqKz4KnfS+/I/qldmS9Ly4uuWj6PeCwP38PuC69lNblsLPqKqtapo1S+/Dw1E7MllMvuT2O63tVtazN0idNL6BtmnqLbJb2H6dJe++w06+Ic5OThcCgCJTt2dpz9aDc/faZV6Cifd63qPUj+OBc0rnX3BOH9bm05q5lD1T0P2H3WHCumXKDP1L3haXTw++QdLGsdY8EKiLqDVRkN2RIHsrKFKjokHqF9Dlom/QWUeXGZs2/wGnjm6nLcLc+l8RD6U0jCwECBIZHgbI1pjJjDapialpbP/AN7lm1FajIXnw5Zs9NY+LxxxncIvL9rr37iTio9yXxTXqxp9pSdKBi/nlmist67d54egPe/ThW2vqwIRp6pUyBigaYfY+/OC6/4YGGrxU//8lARTYU0tW996pYbpZ4Wnp56tj0EtWgLOOON1Y8dHGPvAd0pf3ufvzF2Ga/kxs3FRWoyE6g7xV3xVGnXt54LoOyIlDxt9bQCFRkbZl+x+8eC8wxQ37grAfFXj0vjJvufOLvgiqs7ZCCeHunYF7TJds3G0FhsonGje9/+CkfUi/rCdFyuTG9gLhLnS8g7pgCIXttsVp+iKyH+gLr7zdEvUgu6b1n47Xel4IwW+7Tp+Xp1fye9fA45aDOseCcM+b5sqGgNtjrRL3la6rZ2FKgbM/Wnqtb3uHyfReoaKf3tNaP4IN7Seentw0OST9iVhvbvsyBikpvzw/ppFy17pFAxaAFKrI6fey518dpFw569+2yBCqynhQ9998y1lpmgcH9K9643wEn9o9L0rBXFgIECAxvAmVrTGW+GlTF1LJhHqhIQ/Z0+7/1Y8u1lhpqF/zplwNjm4NOjxdeeqviMYsOVJx55E6xzIJzNju3rdP53vPgs83SBuVLGQMV2fW39ez1TwYqsvL7pReyFvrrhazse9Pl2+9/jEU3PnCQfpDdOQ2rusfmqzY9TLP1DdOPqY8//WpjWpGBiuwkLkyTLXdPz7/V2rmNJ9piRaDib5ChEahoGgj49bffIqsnTz/3+t+FVFhbffmF4sT9tmjckvWUODG1Ca9KwwOvnCae7r3flvlk2kecfmUsMv+ssXPqzTb/7NM35s/vfZ0TbZ+ShgtcabF5G/cdkl5i2dBo9zfp1dd0NInGAupYyeZtvODYXWLhuWfOc3/02dex3JYHm1OzDjtZ/hQo27O15+ry12yBinZ6j2v9CD4kl3TG5XfG0addUfEQZQ5UnHrY9rHiovM0u+5szODFNu0WX34xsFl6vV9q3SOBiuqBihnTHCG3nnFARebB6b5dlkDFqsstGH1SoGJoLLX+ng+N4zsGAQIEBlegbI2pzEGDanBrw5DtN0wDFSlI0WOPTfKhnobsrFvv/c13P8Qm+/apON9BkYGKas9rT7z4Rmyw6+APnVTWQEV2Z7c/9My4/d6nWt/klPJPByqaDnFb6QQOTe2/81I7sJ4lmzfhwX49YoI0/n6lJZuHcJ2dj2m2aVgFKhZdYPa48Kj/a1Z2w5fLbn0kH/K1ngmNG/YRqGiQiBjSQEU2J2VWbxqGMqqnzmVDsT126ZGN86C89OYHsUGXno1BtdWWXzAPVLQMRmTDSu/Xea0YK81/uV6XXjV7pv19hRH3XnxYTDnJBI1J9z/1cmyx96D1gmjYeduNV8yH6m34nn0uuknX+OSTL5sm1bWeDal8S98DY8Lxxs7zdzvp0rj46nvq2lcmAmV7tvZcXf46LVDRTu9xrR/Bh+SSsge3JTc/OD786PNWhylroGK6aSeLO85qPRdABnByv1srTtTWCqdCQq17JFAxeIGKjHlQu2+XJVDRtOtwhepWd9LnaRzb5dKwDLWGkqj7YDISIEBgKAuUrTGV8WhQDeVKUufhhmWgotsuGwzVnhQtLzELVqy3x/Ex4PXmE20XGag4Nr08sU56iaLSsu7uvdp8S7rSfllamQMVr739UazcuUfFt/r/6UBFmqU7bjrzwJhl2skr0mdvqS/V6aCq8wM03amtoEOlSYPb2qfp8etZrzZHRa1ARXbcbN6BPY44p67rzPILVGQKfy5DGqjYYr1l46Ad1s0P9uFnX+X17bc0SXStZd1VF4tj9tg0z5INxZQNzdz0h/6GQMUFqaf4wVmPmSFYsrmFnm4xr0c2HPQiG3eNz9L5Dupy01ldW/1926rbaWn+oecG9VB5/s4brRD7d+6Yr2dzGS69Wfe66/FgFWin0giU7dnac3VpqmbVCxGoqEozfG+o9SN49uD2wadfVLyAscYYPeadZZr0rDpCxe1ZYrX5KmoFKrKumwO//aHqMSttyCYS27l733xTNmFU/+N2izHTWw8tlxHSWJMT1JiIMPvhtdLyVTqf9dMbF19/9W2lzY1pR+69WWyw0iKN35uuZA3DxdKbD99/92PT5LrWa90jgYrqgYoZpp8ibktvjNRaBqX79rAIVKy10sJxQOe1szZgq2W0UUepWK+zjFmvnayOVVruTvNI7HPUefmmlhOMt8yfTeZ3xOlXxWeffx0TTzRerLD4PLHeCovErNO1bozWepuv5XF9J0CAwLAWKFtjKvPToBrWtejP8kYeeaToecCWMckEleeKmHeWaWOUlKfSkr21++33rf//nP1odcgpl8fLr77buNuSi86dJl/dofF7WyvZ5MVfp//3j5rKrvTcW23/V976MNbc8aj4NT07NCw7pfHUl1xgtoavzT6nn2KSmCi9hVtpyX7kyn6YrrRcmoZTufLGByttakzLntvvO/+QGDGNN19pueOR52O7A0+ttKnNtOE5UJHd//OrDJ3ZIT0EzjD1JLH4fLPWvMZqQ2PVClS8n97Arta2q1bY5an3QMt5MdZYYaE4Yd+/h9Bpue8u6Qf8G+98vGVy8+/pOm8756CqE55n7dAVtz40/vj9j2b71QpUZEOcvfXBp83yt/XlrkdfitMvuqlVtkXS34eLjtqlVXrThFsfejZ2PfSsuiZYLmOgIvs36MmX32xK0uZ6NmzR7kecGz+muSAGd7n13O4x41ST5Lv3SMM0nXPZHW0e6sKeu8Wi886S58vm7dm9x9nN9hmagYoF/zNL9D92t2bHz77Ue65Nd6zW46zX+TfGyeff0DRr3etjjjV6PNzv8Bjjr99LNj/g5Hjg0Rfr3l/Gf69A2Z6tPVeXvy4LVLTTe1zrR/CuaZ6JftfcW/XKppxiojj9kO1j9vSDcKUlG6d03rXShGvpYbzpUitQ8cDTr8Tme/Vumn2orY+W/mf8wnW9Kh4ve9CaZaXaD6MVd/wrceKJx8vHjhxpxBGrZqsVVKi6U9pQ6x7VOuaW6y8b3bb/822Tlsc/5ZLbomffq1smD9XvtRrcWSNiudTjpp4l66L6+GVHVc1abTLtegIV2UHr7b49LAIVVS8ybajVKKz0wF3pWNukN2gO+OsNmpbbn07d69dt0b0+z5Makqstt0D+5s3kKXiRLdff+2TslhpmFgIECAyvAmVrTGXOGlTDZ22r1RthjfT/1RfT/1/bWrI3cG87u1ubE2dnL/Rcf89Tce7Vd8dzL73V+IydDYXSMf14vNXayzT+gFerzEEZAvOw3TeJTVZbvOLhjjzz6jiz/20Vt9WTWOsZt2H/FbbtEW+8+WHD17o/h+dAxS/p7e/ZVt615rVkE1eff+TOjcPbtMx85R2Pxd5HntsyuebQT0effW2ccfEtrfYZ1IRsrPu7Lzgkpph4/Iq7Pj/gvVhrhyMrbmtIrNVOyPJkEyNfddNDDdkbP2sFKvJ5EnsP2dvwDQXVE6jI8t7zxEuxQ5pP5eeffmnYteJnGQMVX6Sgw4Lr7FPxev+pxEkmGT+ffD07fjYJ9oIb7F/Xi4BN/52uFDBoCFQMjTq01frLRdft18kJst4bo6YhzrIlC1yvvu3h+Xq9f+y5bcfYacMV8uxNj5UFyXbsdnq9h2mVr+m/vWdcfkcasvvKVnkkEGgpULZna8/VLe9w+b4LVLTTe9r0f1ItL6GtQEWWf/z0dtmDFx1W9U2yZdKbMO+883GzQ5cxULHvDuvEdust1+w6W375OL2lvmTqCt30DbaWeSp9r3WPBCqGrEdFg3c93bfLEKjYM42xulOaGK7S0lZX51FGHTl27rRKTJ/eYDrw+H6GfKqEKI0AgeFGoGyNqQxWg2q4qV7NTqTpD2DNNqQv9QYq9k3DmGyXhjOptdxw31NxyEmXxefpebLqkl4uWGKhOdKEsVvGeCn4UW3JhmhdIg338fHHX1TL0pj+TwUqxknzEjyUxnHPeozWWi6/7ZHY9+jza2WpuK29Byqyi+q48qLRc69OFa+v2pBFtXpUDK1ARXZCm6+7THTfcb2K55YlbrT3ifHYU69W3V5rUu5ababhLVCRXeDDz74W26SeP7V6CQhUVK0Kg7Qh633ea+/N832efOnNWH+X4+ra/+40GfXUaVLqbDn+ghvjpPOa90ZoCFScd+29cWjvS+o6ZrVMx6Xh7Nb+azi77O/cvluv2Zh1hRSoeCMFLOpa0r/n96V/IxsCgkeffV061hr5rllPtv+loaQGd1lxqfnj1G7b5Lu/+Mb7scZ2Rwzuoez3LxIo27O15+ryV16BinZ6j2v9CF5PoCK77JMP2TZWXny+igKb7X9yPPhY866EZQtUjJUagg9d3KOx+2RFiL8Sq70dVGufWvdIoKJWoGLyNPRT/Q9wbXXfLkOgokt6UN5lk5UqVrds+IZlUi+XtsZ4rbizRAIECAxnAmVrTGW8GlTDWSX763SGNFAx6mijxMOXHFH1zfmsmPyFisPTePgpwFDPMstMU0W/NPRIrWBFvfOn/VOBip03Xy322HzVNi8n6/W8xGYHNRtPvs2dUoYyBCqyl0ReuuGEipf7XRo6Z5419mi1bVgFKrKe6tmExuOOVTkgduejz8e2B1QetmuO2aaN606q/iZ+pTfeGy50WAUqFv7vbHHx0fX3ts9+NN9yv5PjuypDsQpUNNzBIfs8ap/NYv0V/xxqud+ND0TXXhfXdcCmQz+9+vaHsco2aY6XJktDoKLa0NVNsra5ekvqHTfTNJPl+bKXNk9IgeNsyOxsOaX/rdHzzGvy9bb++M88M8XlvXbPs3397fexWApMPHP1sdEwgsN8qTfL4M4V2HJuzWojFLR1jrb/uwTK9mztubr89Vegop3e41o/gtcbqDh6383TOPYLVxTYeJ/e8eiTrzTbVrZAxXabrNz4dkOzC63w5fU03upKFcZbrZC1ManWPRKoGHqBigz87sdfjB27n1Gx+3YZAhUrL/PfOPnArRvrVsuVLFizx5HnxQ9p2DYLAQIE2rNA2RpT2b3QoBo+a+SQBirWSZO8HvvXJK+VrvDG1JNitzSe+u/pB/tBWbJgxSU9u1QNgGTDtiy24QFtjq//TwQqsuDMA+kln/FrzB3X9FrPvPKuODLN6TEoSxkCFZnTi9cfX/Gyqw27M6wCFdlJ7bbVGrHrpitXPL8scfk04febaU6UlssJ6U3uNdIb3ZWWgdm8fukH2WrPosNroCK7lucGvBub7dOn4o/HAhWV7vagp/U/YfdYcK6Z8h0PTv8mXJD+bahn2WvbtWPHDZdvzLrDYWfGbWkYvYalIVBxztX3RI+TLm1IHuTP7O9sNtR0No9nNsTbHKt1iU4dl2rsfZRN/r1E1hOixdDYlQo6tMvGsenqS+SbGoIyTSfWrvQ7S6XjVErLhm978YbjG4Meq+90dLz06juVskoj0ChQtmdrz9WNt7a0KwIV7fTW1voRvJ5AxUhpAr/sbZoJxxu7okCl7o1lClSMnMacfCB1yax2/ZVQOh98Rtx1/zOVNlVMq3WPBCqqByqmTxNA335m/T0qGvAfeubV6Nz1tFbdt8sQqJhgwnHi4YsPrzppZWbw2tsfxb7HXxzPPP96A4lPAgQItDuBsjWmshugQTV8VsMhDVScmeYhWGbBOSpe3M9pwutFNj4wvv7q24rb20rMJsnec4vVqmbrtN9J8dDjL1Xdnm34JwIVm669VBy68wY1y2268Ycff45Fk8M333zfNLnmehkCFbXmJ3vh9fdize1bzwMxLAMV46X234OpHdQwBn/LG9I/zTFxYJproukyeRp+597zD44OHTo0TW5cb2v+lGEVqFho/lmj3zG15xFpPOkmK9lk9ZumYa++/PKbJqkRAhXNOAb7y70pwDllmqciWwblh/psbou70yTcDXU1+7d18wNOScOT/flCZUOg4uyr7o7DT75ssM9vrtmni2v67J3v/3Ia4mm1NNTThBOOG4/0PzwPXmQbNtjzhHjimddqlpH9xvLoZUc29ljacK8T4/GnX43ju24day7933zfw/teFWdfcnvN49TaeGNqJ8+a2svZMqi/T9Q6rm3lFSjbs7Xn6vLW1YYrE6hokGhnn7V+BK8nUFFr/3c+/CyWSePftlxqBSqytwyySQLrWW594Ol48tkB9WTN8/wTk2nX+vG62ok99fJbsd7/HVttc6v0WsYCFUM/UJHdgCdefCO22v+UZt23a93rS25+KA44rnlDrOFGzj3nDHH1iXs2fG32mb151XGH6hOFN81cq7Fa72Ta2fFq/eDQtLxn0uSf2RuMt9771CDPq9L0ONYJECBQhEDZGlOZoQZVETWp7TKHNFDx2JXHxARVehYM7vwMDWedvaCQDU/aMFRIQ3rDZz1zFtR6bhicybQ7jNghn4h5ykkmaDiNuj6PS2PKn5rGlq93ae+BiqmnnjSuOH73qi9DZePVn3Hxza04agUqsiFvBrzzUat9Wib8kRJO6XdLvPzquy03tfrefdeNYvM1/9cqPUvI3ihfdJOu8eUXAxu3H7Dz+rHN2ks3fm+6kk0WvNgm3eKrr5r/yN80T61ARdb2fD49W9ezXHj9/fHIEy9XzTq4gYrsgNn8IZukYMWnn37VePwyBiqy+3tb6o1dz/LC6+/HaRfeVE/Wqnmy4NYrN5/YGORacMP944vP/65bVXf8a0PLvxvZUHp9r7grTjzv+lh+iXmjdxqi6awUqDhiCAIVTduL19z1eOyRhuzLlvOP2zUWn2/WfL3fTQ9G154X5evV/mg62Xw2Z8viKVD7x+9/xPabrhL7bLV6vlvT41c7Tq30pkGP7qdcFhdeeXet7LYRiLI9W3uuLn+lFqhop/e41o/gF9/wQNz+yPMVr2zcsUaPDVZaJBadd5aK27PEam/E1ApUVD1YhQ33P/VybLF3nwpbKicN7UBF9rB0x3ndY5rJJ6pYYDbmf7VG2Pp7HF93kKXWPRKo+GcCFdkNbdl9u+mDZ8sb3p4CFRNNNF7+JlvDG0Utr6Xl9y++/jb9Xb45Lr72PgGLlji+EyAw3AqUrTGVQWtQDZ/VbUgCFZNOOkE8eNFhVS9sjf87Jl58+e2q2+vZ0OvArWKtZRaomPXm9NLPzt37VtzWkDi0AxUNby43HL/p50vpDeTZp5+iaVLjejbU0RLpR++fUu+KepbhOVCR/UB6UJUfQ7MhY2aeZvLUzlq45kTjS27ePd7/4LNWFC1/jG2Voc6E0y69PY4946o2c085xUT5m+rVekj0vujmOPGc6/LjtDWv3wXX3RcHn9i/Zpm1AhU1d2yxsa2XfIYkUJEV9e5Hn8dG6S34j9JntpQxUJFfWJ1/ZHV+5hXrn/Oj0mGzNkzWM6FhyY5X77w92T7ZSAgnHLhlq7k1P/rs63j+9Xdi+YXnzl/SGtRh5hrOJ/ts+u9l02Bix5UXjZ57dcqzfpOGN1tw/f3jlxSYq7Y0HR4tC6YcdeqfQ98ttdg8cfah2+e7ZUNKr7jlIdUO0WZ6098YTr/sjjjm9Cvb3EeGf7dA2Z6tPVeXvz4LVLTTe9z0f1BD8xKy8UVXSl0dP/nky1aHHVqBigeefiU236t3q+NXSxjagYpa4/1nY/1fd/eT0Wf/LSuezl1pgvHOaaLxepZa90igonqgouUkYU2tszeusi7zqyxReRL4hrxZl91N9+6dv9lVlkBFdm1Z4+ucw3ao2QBuMGj4fO/jLyJ78/LGOx5rSPJJgACB4VagbI2pDFqDavisbkMSqFhkgdnioqMq/3iX/Zg131p7DfFFd0wvFvXce7OKx8nerl9p6+qBkmynpj+8tTzI4PSouKHvgTFblWBENrzJNusuEysuOk/LovLv9fT2bthxeA5UNJzj4H7W6mkzrAMV2TU0/VG15TV9lYbryt4G/zEFmDpvtELs37ljyyz59+wH56XTj66Vgi9NdxhWgYoF/zNr9D+28tBPzw94L15564NYd/mFmp5aq/VspICN0jA/773/qUDFUAhUTD3VJHlQrAF6xuV3blit+3OEDiPEPtutHdutt1zFfR5Pver3Oe6ieLuOnkeVDnDVqfvFPDNPnW/apvvpcfcDf/Y4GTO95Pn4ZUfFKGlIp2zZ/tAz4/bUa73SMvoYo8UTlx/VOExV04B1Fhi89/w/gxN/pHku5lpjj/zvVqXjtJW27w7rNDrUEyRs63i2l1+gbM/WnqvLX2cFKtrpPa71I/iQXNJW3U6Lex96ruIhyhKouPb0/WPOGaeqeI3rdOkVz730ZurafmjjOJotM660/RExIHWDbWupdY8EKgYvUJG9gbJKmuCvd7etW71V0/J+ZHk3SQ3n5RadO47YbaOWm/Pv7alHRcMFzDvXjHH+ETvFWOlheFCW8669Nw5Lk8xl3Y8tBAgQGF4FytaYypw1qIbP2jYkgYqmb8e2vLrsjeylOx3UMnmQvzcdQqTlzlnv3yXTUDu1lqEZqFhi4TnjvMN3qljcs6+9G2vveFTMM9cMcdUJe1bMk700sUzqSVDPxOJlDVRkc4l13Pnoqj9OFhGoaKttlwWYLrvxwbgv9R6aZIJxKt7btno4NOw0PAQqnkmTDq+bejsds+8Wsc5yCzacWsXPT78cGBunl57mm326OG7PP9+ob5nxlgefiZ0OOqNlcv59y/WXjW7br1tx26PPD4iNuxxfcVvLxMnS3CAPXHhoy+TG7/9db7+KQ25lvWCeuar+IYsbD9hiZWj0qGj6Elo2x8Tsq+zWopT6vy4w3yyx/7YdY75Zp6240+2PPBdHnnF1vJX+vtW7ZMPavXD98Y3BiKW2ODgPUjXs///snQWc1EYbxl/ucAqFAoXiVtyKfrhLKe4HFHeKFz/c5bDD3b24FEpxLe5QKE6huPsd30xottndJJvd283tzj3z/T42GcvMfwY6b57MvJMHtrDYnVsPnGZjPl1OsvpVHvd7g31cV1JxlDYXWs4yZ93Ro0WVylTtGERnzl21Km/0pkuzKtQuoKyUXXbWbbQs8kVMAqKtrbGuFn8eQ6jw0THWewnuapeU2xzV6nC0mFUroxYXnjsq9L6AU/qgaFC9OA1sW0ut+bSGfZn+8/B5qmnKSL0xglChLVSkTJGYdsxRN77lrbL+kf0puF8zKlcwhxK53TXfgbGRffXStk4ZuzQe4YtCBW93GvZF44jOAZQ7cxp+azjwoyI6DZ2nu2XZcGXICAIgAAIeICCaMcURwaDywERxQ5VhESr0RYQnTEQIDHMLS7Cz12cNaKlajxExxJ1CxZJxnSh/tm9V29Ju6Bz6decxKW35xK6UR2Nt0n7YXNq846hqHcpIEYUKPl4/sh3Zt279o+yq1XV4CBW8AfNHt6fC32W0aot8w9fRE9kxolov6nm+71sPpz/ZTgVHwTyhIj3bUaH+IpwLFdXbjpR8JYzq0ZCqORAr+LFlG3Ydo0aVi6p2D0KFKha7yNTM8fN25gCah7AKFXLlpYrkpDbMvvsuYyo5yvIbEhJKCzfupfHMP84LNoaOQto0SWnbjN5SNu5vJUvFzlYfdpUu+h1N79dcSv8YEkJ52PFPavXOHtGOiufJLOWbsOhXmjjv89Fp8vNXT+lBOdKnkG6d2WUml5d/OzerTD8FlJNujfjNkMvhN+ISEG1tjXW1+HMZQoWPjrHeS3Bnu8QXYZ1GzKP9f5zXLSqCUKF0iGXb2ZYDZ9Lve09K0dGjR6X9S4dSXPY1im3gX5YUaTjAcnapbbp8rzdGECrCJlRwxp/FiuZMrFA/ZkAeB71fXxUqpD6xs5ArMgOrD9sGrfWVm1rfN+09QR0GzlJLQhwIgAAIhDsB0YwpDhQGVbhPK9UGhEWoyPud9svQt+/eS8d6hHUHY62KhWhEp3qqbedHXP7AjmrVC+4SKrKyL8rXBXdTfRTf2VG8AdspwdbGPOjtNDl/9Q5VajlMtR5lpGhCxcY9x6l30BJ6xY4E0wvhJVTkz52RloxUP8ZMr708bfexC9S0xyRH2aR0bxIqeIO4b47RPRtR1ZLqfmCMdApChRFKRMqjn/ixR+nK/GSsoIFcP1YvQQPa1lTN+YS942jUZwqdu3BdNV2OrFw2P43r3lC65ceDVWHimzJwHxlH2ZFO8m72XhOW0ooN+5RZKF682PTH8mEWh+Glmg2229UxjPm6qMN8XvAQFoGhe6vq1KrW5yOwcPST1TDgRoOAaGtrrKs1BlqgaAgVPjqYei/BjXaJfzHA/yM5ZclWevTomcNijoQK2UhxVNGctbvJGWdX7vJRkYl9wbCRfcmgFvgXQyXZNk+lUdmhcSXq2KC8Wnaay/owhB2joxf0xghCRdiFCs6eixWT+jfXPBNZb3x4mk8LFf92Lmq0KFSheG5qyhbqWkea2XKo0XksnTzzl2007kEABEAg3AmIZkxxoDCown1aqTYgLEJF3LixpbPIVStmkU36TmVHqZ7VSjYUvzCoIxXMkV41r5Hdve4SKqYMaqm5g7X/lJW0aPWu/9rIPqLYNrcfpWVn0qsFvqvgAPP3phdEESq4T7UJi7ZYPoLS6zNP0xMq3jA/Ea/fvnNUBbF3wBQ4aTn9tvuEw7zKDOum9aKs6ZIpowxdc98kR0/+aSivnlDBbdKXr98aqofv8LCaczal9EREeUeFXISLFWN6NdJ0Wi/n0/r1ZaHi0dMXWt2yiucCY+PuwVZxzt58/XU8OrhkiKVYurI/WdnclgQXLn4onZcm9mxMW/adlOZQrbL/s6rl1Zt3klhx4vQVq3jlTS8mdDRndhQPv2z/g7qPmK9Mlq6HM39BtZnfIB6OnPuL6nYcK13Lf9SvVowGtast3V5gQnJFFSG5Uc2S1K/15+PA1AQRuS5Hv73b1aJm1YpL2Wat3unUexVHdSNdTAKira2xrhZznip7BaFCScOHrvVegvOXr7v+NQLysPPs5f+Q2XaPO84uys7QfcGcpRkJekKFs8c5GXmenMddQsVE9kL7hyLfydVa/aptv+RG6IElgy0OsZQF+NdyBdjZwM+fvVRGW13rjZGvChVGzkSWISRMGJcOsV0pWiFntW6qcy9FikS0c04/1WLy0U/KxMjMuRkXK8r8L5sy2tC1CEKFsqP83Na+bAHsyNg8zvyw1Go/RlkU1yAAAiDgFQREM6Y4VBhUXjG17BoRFqGCV7aHvXhLyl7AqYU9xy9Qk+6T1JIMxaVJ/Q39NvPzUSlqBQZOXUULftmplmSJc4dQobcme/byNRUMCKS37EWgMlT7voDmUUEHT/1JDbpOUGa3u/ZmoYIfKVO/53/j2q9NDcrMjo1RC3wnRcdBs9WSVOP0hIqRc9bTDPZhmadC+RK5aXKfpk5Vf+rSDarebpThMnpCxYL1e2ngxGWG69LLyNfCy8foH/2kLM/FiqDejaky++jH2eCqUKE8btjRM5W7EdTyuuKjgp+mkLd6d7XqPBLHbbVLW/77e69lA7rycFmomMn+PRzB/l3MlT0djWGiQspvEliq40JYuVbDNY9eUx5tN3zWWpq17DdLWfnCdudRYfYO5S470k0OqyZ1sxxDNYzVMVulDuUR1B8+hkhHTIWwX2fDCLb7oxbbBcLDkOmrae7K352tAvkjGAHR1tZYV4s/gSFU+OgY670EV75051sVdy8cSInif6na02krttPoGWtU02wjfVmoSJY0Ie2a158isS+9bAMXHap0GKPq3K5Xy6oW51m25cYt3EyT2NmXWkFvjLxZqCiQJxMtGqG+JZcv9Pji8v27D1rdtsRzp8+rx3ex3NteZGbnf75jX4jZBj2jWE2o4OX5AnjygOZUOr9zYoVoQgVnwY8tm9i3GZXKn5XfaoY8tXvRk8fPNdORAAIgAALhQUA0Y4ozhEEVHjPJ8TPDKlQEs48kKmh8AMOfXrLpYLpx857jhqjkGNQpgOpXLKyS8jmqeqexdOqs/s5IdwgVQ5kT4bpMeFALG3Yfp+nL7V/oRWW2x2K2jozB1iNqoXL70bpHsXizUMFfLmYs38HSreKFstPsga0s97YXlZjj5vMXb9hGq96Hp1DBX9bvWDCAkjPHzUZDiwEzaMe+U0azk7cKFbwDvP9j+zShSsVyGe4Pz6gnVNStXISGdqirWt/DJy+oQJ3eliPTVDP9G6nnyJ5nyVq5K71R2Y2i50zbbKGCt/PgiuGWo2qN/PvFyxgJtkIFL8M/clzMxCqlw23NI5LYu4FT68ZYjnXS2g3H58hBdrRTAnbEEw+j5m6k6Yu3SNf8PcPu+QOka/5Hwfp96Z9/Hlvu5Yv47H0MPx5KDuWZeHKZ7bxyNih9XbQdMpu27jrubBXIH8EIiLa2xrpa/AkMocJHx1jvJbhSqODdC6hSlIa0r6PaU+7QqlijAXT//hPVdGWkLwsVjgw+ZT+NXj9lO1EKBfRRFTh4HXpj5M1CRRL2BcpeJm5phY5sO+xGti3WURjStT4FfF9QNds/7KixgmyBrhaSJ09Eu9ixAWpBS6jgeblYMWVAC4cv6JX1iihU8P7xxfS6aT01v/LjeZw1MHkZBBAAARDwNAHRjCnOCwaVp2eNa/WHVahw9JL68o17VLfreHpq8IgVuRdVyuWnoJ9/VP24hue5wY4rLcXW7srjSuWyyt+wChUJEsSlfYsGURR2zKY7w6/7T1K7/jM1q/QloYINEq1n6y2tozcPnb5M9buM1+yrMiE8hQreDj17UdlOfn319n0q23SQwzmoLGeWUJE7x7e0IqiT8tGWa9ujnywJ7MLP34/GMbGiYlHjYoWeUKF3BBV/bmPmO2Hv4XPKJqhe6wmievaUtwkVv0zubhEO1Hw8qHbeQKSaUMGLxWZ+JrfM7EPfsH/HeOAfJuav24de2pwkkTRJAtqz4D+713anhFT43z+URy4pbdLWDb6nbo0rSrmOnr9KdToEKYtZXR9bM9riA7Pr6IW0dushq3RHN5H8ItHZDWMperTPQnBV9rHlmfPXHBVDegQnINraGutq8Sc0hAofHWO9l+C2QkUU9gJ318JBlDiB+q6KldsOUc9RCx2S8FWhIt5XcejA4sEUlXFwd7A7m1fxAL0x8mahgnfhyOpR9FWcWIre/HfJzyqtpbEDRc7Fd0VsmdrTsoiS4+VfPSPVVaGC183n+pSBLahkPv3dBHI7RBUqeP/qVCpCwzqqf8nF03uOX0IrN+7nlwggAAIg4DUERDOmOFgYVF4zvawaElahgn8UsIu9yNc6/ok/zFmxoio7A30Mc7iqtgNYbvygab/Q/FU75FvN37AKFUqHrZoPcSGBO9MtwXab3Lr1j2ppnxIqWA/0HIjzDjbsPZn2/6Hvl4PnC2+hgvs828+OM/vqyy94c3TDz0GLaM2Wg7p5bBO9Xajg7eVixfjApppHBdv2SU+oiBUrBp1mX+prBf5Cu8HPE+kD262uFTJnSElrJnalyP7qYqHe871NqBjHuMrHa81bt5sGB+v7etRiYhuvJVTwfC0CylLPZlUsRdoMnk3b2E4wZShT7Dua1re5FMV9wWStxE4D4M5eVEKWTKlofXA3S0rFtiPpwp83aRs7tUH2y2P7HsaS+d8L5TFTc9bsoqGTV9pm0b23FVby1ulFjx9hh7wuNCSSaGtrrKvFn9QQKnx0jPVegqv9B1LvKxluMJRtOYyuMsdPesFXhYoubIHSji1UPBHusJ0oxX/sR6HszFrboDdG3i5UBPVuQlVL5rHtkuWe+yRp3meq6hFQfPvrynFdLNt7LYUUF33YObTL2Hm0aiEsQgWvj4sVU5njxxJ5s6hVbxXna0JFyhSJaUSXANp99CLNZse26Rk3ldnZpePYGaZaocOIebRp+xGtZMSDAAiAQLgQEM2Y4hBhUIXLVHL40LAKFfwBTWqXpsCW1XSfxb8+H7tgI23bc5K0ziNPzI7caVS1GLWoUVJXpOAOhwuxY0VsvwpWa0BYhIpYX8Sgg8zPWCx2hIonwtItBygwaLFq1b4mVPBO6Dmj5o51K7Ua5nD3QXgLFbwfyi/D+b1a4F/xcx+HH9mufGeCLwgVvD9crJjAXqrrHesm91tPKOB5VjCRIXfmNHJ2u9/fD5+ldgNm0gcVlpnSp6AloztQHCZ4aAW9nQneJlQo30UY8VWj1WfbeD2hIj1zEL+FOYqXg9pHWp2bVaafAspJWfR23Mh17GCnDsj+L7gj6zXbD9OmKT2l5NDQUMpbu7fuLrp+HepQo8pFpfxHzl6hup3GyVUb+i1aIBvNHdxayst315X8sb+hcsgUsQmItrbGulr8+QyhwkfHWO8luJpQwV/ecoNM3v5o222+UGrJXjzrBV8UKmLGis4cYg+h2DqLPL0+G0nTOgpJb4y8XajIliUNrZ3QVbf7/HzVhRv30vaDp+nVm/eSH5SaZf9HVZhDPr3dK9yJOz+X9a2Kfwr+QD2nccpttnqN+yxWtGJiRWa9bORrQsWCMR2oUM4MUp/uPnxKQfM30rqth+3OuOVG1uKgjpQvazrN/tfqMo6On76imY4EEAABEAgPAqIZU5whDKrwmEmOn+kOocKfHYu0dkoP3aMW5ZbcZ36h+DnpR5hviefMETX35fA12/Vbk31YUOZ/2XQFCrmOrmMW0dpfjX3JHhahomW9ctSjaWX5sW7/5b4euODyiL30tg16QgX3b3GZ7ew1Eoz4U1Orh9sOZ9apH91i66NCLu9oV4WWrSCX5796QsXYBZtp5vJtyuwOr13pPz8uhwtUWv5F+ENddd6rJ1Qs3byfBjn5Zfn790woUfnynTtTXjm2syofIy+ieUG+jub+3r4vnFO1HjnSkVDxfcncNKm3vpPyR+xouEVsh/POI+foPdtdkThhPMkvjKN/ExzZU3pCxRPmTLsgO77YmfCR/Z1V+zDPaB1KH4TP2L9/eWv21BRujdbJ8+kJFenSJqWt0/87aljt389Zw9tZ7EUjp0wo/55yW3jz3pPUkPkj4WH3sQvUtMck6VrrD+WO99dv3lH2Kl0dipjKupTPX7xxH/Ubv1SZjGsQUCUg2toa62rVYRYqEkKFjw6n3ktwNaGCd1PPqRdPd/TiUk+o4OWdDX+wrwgCDHxFwB1inWNnMaqFELaTIX259mpJUpyRL900CxtMuHT9LlVoMdRusaw3Rq4KFQabJGW7yL7g+oG3y8WwbHxnyqvzotvFamnCol9p4rwNmsXdIVTwyrlYMW1wKyqeR1us8CWhQvkFjRLevYfPiB+ltfvoeXr09CV9wwycptWKUf5s3yqzWV1Lgk+TQXZz1ioTbkAABEAgHAiIZkxxhDCowmEiGXikO4QK/hj+MmwTEyu0jmcx0BRDWYx8VKSsyFWhIgoTUPYvGUzx48ZWVuf266nLt9OYmWvs6tUTKuwy60TMYMdjjWTHZDkbXBEq+DP0dlXcuf+Y+RUZqPrlvNw+5QtIOS4sv66+xOzVtiY1r15C9dH85XjBgEBV582qBRSRekKFIpvhyx1/nKUWve0/snOHUMEbwUXIiX2bUvlC2mKFI6GC17FtTl9KlSSh4X4ZzThu4WaaNH+TZnY9oUKzkE4C381VvPFAesIEV1fD/mXDLEdRdx61gNZvO+ywqmjRo1JXdjrChp1HVX0x6AkVzeuWoV7Nq1qeEdB9Iv1x/JLlnl8cXjnC4iDbiAinFFysKmI3XUYvkD4gs41X3ufImpZWj2fHS/0bONNbbNedkcBt273s3+aE8eJI2dWOsjJSD/JEPAKira2xrhZ/DkOo8NEx1nsJriVUONpVceLidar502hNIu4WKvjXHHmqd9d8npzgqlDBnSvvYWcHJ4qv7puDP59v0TQavmRfGWkZoWoO0fTGyAyhwpGI46jf/AinrTN6a/qZcFReLZ2LJ1XZeZ5qW5zl/Py5u+cPkG+tfo3uqJALcWN7OhMriuXOJEdZ/fqKUMENna2z+1JqxsYdofeEZbScfdWJAAIgAALeRkA0Y4rzhUHlbbPsc3vcJVTw2qp9X4DGdG3gsY5euXmP6jCnzM445nZVqKhdqTAN7xig2pd37IvvR89eqqapRfozPx5a63D+4pO/9H7FXn4rg7uEit8OnaHWgdOUVRu6dlWo0PqgRH7owKmraMEvO+Vbu193CxX8mNaGzAeCs+Hrr+PRPubb0J/tKrANk5ZupXGz19tGG7p3t1DBj1Qrw17y2obv2I6KVWHcUSHXydffwf2aUbmCOeQoq19HQgXPzHeprx7XmbhPG3eFc3/dpurMZtc7fsvdQgVvOz8q+i+DO5rU+qo8jln60K/5ELVsVnHThrSWdpzxf3sGT19NS/nRwYqdNFpCBd8dtH1OP4sI8eeNu/R9c+sPC7+KH4eOLB9ueZ5RfzJqouR7dnxXHrZLxPbfM0vl/17YjkvbIbNp6y5rvxm2ZeR75X9nnjKn4IXYrhitEwrkMvgFAU5AtLU11tXiz2sIFT46xnovwbWECt5V5XZDta63HDiTfmdbGNWCrwkVyv+Y2/bn+t8PqDRb3H4KVXeWZZuf3+ud26q2O0RvjHxBqOB9rlIuP43tpu3ngOcxGvhXWDU6jXXoC8WdQgVvGxcrZgxpRUVz2YsVviJU1KhQkEZ1qW8UtW4+vsWcny2Mha0uJiSCAAiEEwHRjCmOEQZVOE0mB491p1DBH1Wd/bd6tJv+W61sOnfIHfDzeHrCjhlxJrgiVPCXqdvn97ecwW77vH6TV9DiNbtto3XvN83sQxlTJ1HNM3zWWpq17DerNF8VKngn1k7rSdnSJbfqj3zDP5Aqxs6T13qR6S1CBW/vqJ6NqEbpfHLTpV/+opgf1+XsPJQr8UWhgrf9s1jRnIkV2eWuWH6NCBU8s61TZ0sFLlzweVSz81i6zv5d0Au2L8T18hpNC6tQwYWB/YsGW44IbjVoFm3fc0Lz8Wo24d7jFymQfXB1+84DqZyaUBGffaS4aFR7Sp/yG0vd7YbOoV93HrPc84vC+bPQ/KFtLXEF6gXSfeZ/0lFoUqsUBbaqbpVty76T9BPzN2IkKHeWTFm2jYJmrXNYjDu738SOsUqT7Gsp7/hFWyh43kaH5ZABBDgB0dbWWFeLP68hVPjoGOu9BNcTKvgug50LBlASdjyMWuBfp5RrNlj1DEpfEioi+UWirewrirT//sfctq96zpxt88r3sePEokNLh2juMKjaYYzVllS9MfIVoYL33R0vyfmiun6PYLp0+ZaMU/NXbVEqZ3Z2R4Vcji/uZjDHY0VyZZSjpF9fESoyfJuclo3pqOtQz6pjGjf8TNg6XSfQZfYlFgIIgAAIeCMB0YwpzhgGlTfONJJ8t6X4JoFq4yq1G0XnL91QTdOL5GfS890I7vKNtu/EReo0bJ5LL4ddESrKFstFU9nZ/GpBOhO/LvuCl52r7kyoVCYfje/RSLXIgyfPqUi9vlY7bX1ZqChaICtzdNtGta88MnjJVho/R31HgjcJFWmYsPQbE5iUgftXGcBeELsafFWo4P3lYsWk/s2pbAFrscKoUMHr0PvgjacbCY/Zbqa63SYa2tXgjUIF7+OAjnXpx0qffTrwf1MqthlBd/5+qNn9bi2rUevape3SuU8PbsclTxyf+rSoRvyoM+7rpAbzl1in3P8oJjs6Wg78mLCWbHeV7QeKLeuVZ754KknZXnF/EZW6yEV0fxMmjEsHmQ/MSJEiWfI5El0sGdnF7BHtLEcTG/FrwcuO7NGQapbJL1XDfVsUYaKnMzvspIL4I8ISEG1tjXW1+FMZQoWPjrHeS3A9oYJ3V29LN0/vOX4JrWROvWyDLwkVJQrnoFkDWtp2QbrnL80LsS8m3mk4c1Yt9G9k3/a1qXGVYqpZth44TW37Tbek6Y2RLwkVvENF2Bcno9iRBtzpo7Ph6Pmr1HXkAsuXL47Ke0Ko4M/kYsVMtn248Hf/iRW+IlTw9qdJ/Q2N6FyPcmdOw2+dDo/ZvG/YcxJd+POm02VRAARAAATMIiCaMcW5waAya/Y49xx376iQn86PzhnRtb7msZNyPr1f/iJq0PRfVNfjeuWUaa4IFXo7AiYv3UZjZzv+8lfZBn7NX/LuZkexfpMgrm2SdN9j3BJatek/u8OXhQreoTVTe1J29oGJWnj77j0VbThA1Ym4NwkVvO2zhrdlToazSN3gR+Xys/T1Xiir9VcZZ5ZQkTNbWvplnPoLZ6POtJXtlq/5x35crOBOruXgjFDBy1Rku1QG/VSLvvwiplyF4d/DZy5Tt9GLDI+BtwoV/Gi1jdN6WXZt3WE7GFoNmKFrn/CdDwPa1nLpCNxFTMAYOHG56nHPwWw8KxT5ThqD4xeuUa32YwyPx6KgjlQgR3opPxc5ctfoQR/YriMjoTvbjdGK7crggYtPeVlZrcCP7u7foQ4FfF/QkqX98Hm0+fcjlntcgIAjAqKtrbGudjTivp8OocJHx7B3u1rUrFpx1dY72i3AF1o7mA+ApMyQUgvX2FbK0o0G2CXxr7o3s8W3uwJ/cZrXiI8K5kTr3MZxqo/9GBJCGcp1sEubP7q91QtpZYaxCzbT5AXazseUeW2vv2FfbexiW+K1fFUot4zqjZGes65GNUtSv9Y1bB/t9L0WG6cr+rcAX/B2aPgD1S1fgGIpvlLRqu/WvUc0feV2Wsa+wLL9gkWrDI9Pwr5u3LvQ/sxZnnbl1j9UjjuBdjFwsWLW0DZUKGcGqQZdoSJzalo78WfVJ51mO0OqsS+AjARulExgW+jVwjrmGK7L0LlqSZpxeXKmpw4Nylv6oJnx3wRuXP6y/Q8aNn0NPXfiXGlH9SIdBEAABDxBQDRjijOCQeWJmRL2OnewtUZKjR0VFZk/rbAK+wXyZKJGVYtR6fxZrb681Wv5feaoln+5vmzzPnr86LleVodpgzoFUP2KhVXzDWNHLs22OXIpS6ZUtD64m2p+fuwP/4L30aNnqumOIhvWKEH929RUzXaB+S+r2IKdHf9v2Davv+aOaDmPkd9tB09Tm77/fUBkpAzPEyNmdDq7Pkg1Oz+HPtP3HVXT5Mii/2O7KoZo76rQOrLlp0Y/UOcfK8jVhPmX78Zp1C3Y5XqUTqnX7zpGnYfMcbkuXtAdO7SVDdDaZZ09axpaM76rMqvl+hTbJVWd7ZZyNXAbevKA5uzv9Gexwlmhgj+XH3/0c5PKVLVkHorGjqd1FPhxxdNW/EYrNx2w8s3gqFysL2LQ6bXGX7w7qo+nl2F/T6+yv69hDZnSp6BVzG9H9GhRpar436t563bT3NU7NY9e4ux/ZO8+2tYpQ199+YXDJty8+5Bx267rk287ex8i+/9buuUABQYtdlivnEE5n1cyp+A9mXNwo6Fy2fw0rvt/Ryvnqd3Lzkm5H/MRU57tcPuJ7frIkOq/I6z4rpHAsUuMPgr5QEAiINraGutq8Sc2hAofHePkyRNRbfbCOBL7nzKEhIbQ7FU7HL6Q5F+blP6f9fZVuZ7Tf96gbbuPy7eWX/4fzOZscRAnlvNfgVgqUVycuXzDsPMo/hVOqiSfz2RUVEEXr92hjewlrG3gOwDyZ09vG0237j2kFZv2OfXi3LaS3Dm+peL5stixf8GcAs5Y+qul7uTs2Kna7OsH2zHiL47nrtlptyCRn8MXsE2qlWBGrZ8c5dLv5Zt/07qth10qq1eIG3AViueivFnTUc4MKdmW268oMvta7g3boXKJOSo7fekm7T56nvYfOW9hoVefXRrbRtuIGbQJ431pl/TH2cu05+BZu3hnIqIx4Wsy+4KG+6zgZyPPXfm7anH+BWAzttVYbb7vPX6BDh+7qFrONjJWrBjUnNUTJXJk2yRav/MI/XnFtWOY0qZJysYgDeXMmJq+Yy8X+G6X6Mzg+cQczD18+pLOX71NR87+RWt+Oxzmlx12DUcECIAACHiIgGjGFMcEg8pDkyWM1XLnx//L/q1dLU9fvKJZy39TPQbVLrOBCP6RS7F8mSnbtykpR8aUlJLd8xeU/IMS7lD6zJVbdObPW3T8wlW2drpAIR9DDNTqOEtq9nKrJjsGRXk8CS8Vwtehq3fYrQ34xxxt2EuxqOyFoDLwdeumPcfpImujq4H7vqhdsRBbM9oftXXy0nX6bfcJS9X8+KT82ezHxZLB4MWWvSfo7IXrBnNbZ6tbuYhqWy8wR8Jqdod1aSL+0dHXX9mvY0PZGm3V1kN0gzlHtw2JEn1FDSoXJT/FcTK2eZy5386ciZ84fcWZInZ554z8ifKynbxVOwYZOm7IrgJFBD9Ct0Wt0qpOuhXZDF/yHQZqNgFfvzevU5at36Pb1bXnmPH1u13hfyP4C/MJgU2ofKGcFBbn4tzZc6VSeaWd0jmYPZWEHScUhbWdf51/8frfdIrZU7v+OEcHub2hcB6t1S61+IAqRSlZovhqSU7HPXn+kuas+F11Z4LTlbEC/KSGecw/RMJ4/+3W5//WnGN/xzoMm0s3b/6jWi3/tyR3jnT0Q9FclJPZP6mTJKQvmG3KxVT+kRx3NL6MiQ5/nPjTITd+VCcmSdsAAEAASURBVF+WtCkYXvZRF7OXHPn9UDaIf8C3bVYgxWW/9djxxifP/KVM1r3mu0qa1izF7Lao9OzlK5rJRWPFGAf+VJuql85rt/NmCvvvkuTPQpFX90FIBIF/CYi2tsa6WvypDaFC/DFGD0EABEAABEAABEDAJwiIZkxx6DCofGLqoZEgAAIgAAImEuAf53VoUIECKhS0Oq2gbrcJdIQLDQaCmjNtA8W8OstG5h8mE/MTI4fzTLwZzXzb7DkUto/15PrwG/EIiLa2xrpa/DkMoUL8MUYPQQAEQAAEQAAEQMAnCIhmTHHoMKh8YuqhkSAAAiAAAuFAgAsWhXJlpHxst368L2NR/+AVho+ZE1GoaFmvHGViO+cvXrtLB05eojPnr1ntuAiHIcIjfZyAaGtrrKt9fEIaaD6ECgOQkAUEQAAEQAAEQAAEQMDzBEQzpjgxGFSenzd4AgiAAAiAQMQjIKJQEfFGET32NAHR1tZYV3t6xoR//RAqwn8M0AIQAAEQAAEQAAEQAAFGQDRjig8qDCpMbRAAARAAARBwPwEIFe5nihrFIyDa2hrravHmqG2PIFTYEsE9CIAACIAACIAACIBAuBAQzZjiEGFQhctUwkNBAARAAAQEJ8AdYk/q3ZSmr/ydRk1fLXhv0T0QcI2AaGtrrKtdmwe+VApChS+NFtoKAiAAAiAAAiAAAgITEM2Y4kMFg0rgCYuugQAIgAAIhBuBKFEi0/clctOuw+fo+bOX4dYOPBgEvJmAaGtrrKu9eba5p20QKtzDEbWAAAiAAAiAAAiAAAiEkYBoxhTHAYMqjJMCxUEABEAABEAABEAABFwiINraGutql6aBTxWCUOFTw4XGggAIgAAIgAAIgIC4BEQzpvhIwaASd76iZyAAAiAAAiAAAiDgzQREW1tjXe3Ns809bYNQ4R6OqAUEQAAEQAAEQAAEQCCMBEQzpjgOGFRhnBQoDgIgAAIgAAIgAAIg4BIB0dbWWFe7NA18qhCECp8aLjQWBEAABEAABEAABMQlIJoxxUcKBpW48xU9AwEQAAEQAAEQAAFvJiDa2hrram+ebe5pG4QK93BELSAAAiAAAiAAAiAAAmEkIJoxxXHAoArjpEBxEAABEAABEAABEAABlwiItrbGutqlaeBThSBU+NRwobEgAAIgAAIgAAIgIC4B0YwpPlIwqMSdr+gZCIAACIAACIAACHgzAdHW1lhXe/Nsc0/bIFS4h2O41/L11/GoVIFslCl1UkqbIhHFiBaV3n/8SI+fvaLj56/SkbNX6fylG/Thw0e3tTXJNwkoR6aUlD19SsqUJil9ETM6+UWKRG/fv6e/bt6nU3/eoDN/3qRLV24Tffrk8nOTJU1I2TOktCv/9MVrOnDkvF280YikSVj7M6Yymp0+hoTSi1dv6OLVO/Tk8XPD5ZARBEAABEAABEDAGAHRjCneaxhUxsYeuUAABEAABEAABEAABNxLQLS1NdbV7p0f3lgbhApvHBUn2pQmdRLq06oaFcudiSIxkUAvPGcv2eev20PzVu+kp09f6GXVTPPz96PShXNSk2rFKF/WdJr5lAnX/35A89buptVbD9Er1gZnw7IJXShvlrSqxcq0GEJXr91VTXMUuSioIxXIkd5RNtX0ew+f0dHzf9HSzfvp0LFLYRJiVB+ASBAAARAAARCIgAREM6b4EMKgioATGV0GARAAARAAARAAAS8gINraGutqL5hUHm4ChAoPA/Zk9T9WL0GBTKSI7O/v1GPevf9Ay389ROMXbKRnT18aLps7x7cU1O1HSp44vuEyyoyv37yjYbPW0tL1e516sb96cnfKobKjgtddoc0IunT5lvIxhq+XT+xKeTKnMZxfK+PV2/dp0tJfad3Ww1pZEA8CIAACIAACIGCAgGjGFO8yDCoDA48sIAACIAACIAACIAACbicg2toa62q3TxGvqxBChdcNibEGBVQpSkPa1zGWWSPXwycvqOXAmXTq7F8aOT5HR4kSmX5uUZWaM2HEHWHfiYvUbfQiun//iaHqvF2okDuxkx1D1TNoMT18+FSOwi8IgAAIgAAIgIATBEQzpnjXYVA5MQGQFQRAAARAAARAAARAwG0ERFtbY13ttqnhtRVBqPDaodFuGN/ZsCKok3YGJ1IWbthLAyYs0yzBRYppg1tR8TyZNfO4knCHiRR1uoyju/ceOSzuK0IF78izl6/px16T6dyF6w77hQwgAAIgAAIgAALWBEQzpnjvYFBZjzHuQAAEQAAEQAAEQAAEzCEg2toa62pz5k14PgVCRXjSd/HZkwe2oPKFcrpY+r9iH0NC6PvWI5iPh7//i1RccZFi6qBWVCKve0UK+RFGxQpfEip4316+fkv1ewTTWYgV8lDjFwRAAARAAAQMERDNmOKdhkFlaOiRCQRAAARAAARAAARAwM0ERFtbY13t5gnihdVBqPDCQdFrUsKEcenA4sHk5+enmu3O/cfMX8JWesCOdUrC8pZnjq8LZP9W1dH26HkbadqiLar18Mi+7WtT4yrFNNPdkXDp+l2qzPxMfPzwUbM6XxMqeEe4WPED69ftOw80+4UEEAABEAABEAABawKiGVO8dzCorMcYdyAAAiAAAiAAAiAAAuYQEG1tjXW1OfMmPJ8CoSI86bvw7EY1S1K/1jVUS/IdChVaDaOXL15bpSdmzq+7Na1MVUvmscSfvXKbqrUbSaEhoZY45UWenOlp+ZiOyijN6zNXbtG634/Q/SfPKYTVFzd2TCrKjooqWyCbqkBiW1Hwkq00fs5622jLfXgIFa0Hz6Lrt/8TGfz9I1HsWDEobfJE1LByMcqQ6htL+7Qujp6/SnU7jaVPoZ+0siAeBEAABEAABEBAQUA0Y4p3DQaVYoBxCQIgAAIgAAIgAAIgYBoB0dbWWFebNnXC7UEQKsINvWsP/rlFNWpTp7Rq4dlrdtGwyStV03hk5owpaVinAPomflwK6D5R88inyOzIp+1z+1FyJnDohc17T9DMX3bQ6bNXVbN9w8o3qFyU7cooStGjRVXNwyNDQ0OpQpuRdPmv26p5wkOoKNNiCONzV7U9PJILOV0aVaD82b7VzMMThsxYQ3NXbNfNg0QQAAEQAAEQAIHPBEQzpnivYFBhdoMACIAACIAACIAACIQHAdHW1lhXh8csMveZECrM5R3mp/VsU5Na1CihWs+h05epfpfxqmlyZCS/SOTv76971FKFUnkpuFdjuYjq74jZ62jm0m2qabaRObOlpUXDf6IY0bXFilW/HaYeIxfYFpXuvVGo4A3jPjxmDm1DRXJlVG03j3zKdrcUqNub3r/7oJkHCSAAAiAAAiAAAp8JiGZM8V7BoMLsBgEQAAEQAAEQAAEQCA8Coq2tsa4Oj1lk7jMhVJjLO8xPq1+tGA1qV1uznsDg5bR03R7NdCMJyyd2pTyZ02hmHTV3I01frO3bQq3gd9nT0cJh7TTFivfMR8X/AvrQs6cv7Yp7q1DBGxqNiS/zR7SjvFnT2bVbjvg5aBGt2XJQvsUvCIAACIAACICABgHRjCneTRhUGoONaBAAARAAARAAARAAAY8SEG1tjXW1R6eLV1QOocIrhsF4I9KnS0ZbpvXSLbB44z4aMnWVS1/xp0iRiHbO6adZ/7qdR6nL0Lma6XoJlcrko/E9GmlmGTTtF5q/aoddujcLFbyx8eN/SYeWDtF0cH768i2qxhxrI4AACIAACIAACOgTEM2Y4r2FQaU/5kgFARAAARAAARAAARDwDAHR1tZYV3tmnnhTrRAqvGk0DLZl2YQulDdLWt3cj5+/ooXr99KiDbvp8aPnunmViVXLF6Cgnxsoo6yuK/00is5fvGEVZ/TGP7I/7Wcv9BPGi6NaZBPzedFh4Cy7NG8XKniDpw5uxZyHZ7drO4/49OkTZa3cld6+eaeajkgQAAEQAAEQAIHPBEQzpnivYFBhdoMACIAACIAACIAACIQHAdHW1lhXh8csMveZECrM5e2Wp3GfD7+M62Koro8hIbRl3ymauGizrnNoubJ+HepQI+YAWy2cunSDqrcbpZZkOK5dwx+oS8MKqvlv//OYitXva5fmC0JF0QJZae7gNnZtlyNqdhlHJ05fkW/xCwIgAAIgAAIgoEJANGOKdxEGlcpAIwoEQAAEQAAEQAAEQMDjBERbW2Nd7fEpE+4PgFAR7kPgWgM6N6tMPwWUM1yYf9W/YushGr9gE92//0Sz3ArmnyK3hn8Kd/i/+CZxfNq3aJDm83NU60YvmQNqZfAFocLPz4/ObRpHUZmDbbUwYMoqWrh6p1oS4kAABEAABEAABP4lIJoxxbsFgwrTGwRAAARAAARAAARAIDwIiLa2xro6PGaRuc+EUGEub7c+rWW9ctSjaWWn6nz77j21GTKH9hw8o1pu65y+lC5FYtW0pv2m0+4Dp1XTjEZGZi/yL22ZoJm9UIN+dO/eI6t0XxAqeIMPrRyueazV2AWbaTITiRBAAARAAARAAAS0CYhmTPGewqDSHm+kgAAIgAAIgAAIgAAIeI6AaGtrrKs9N1e8pWYIFd4yEi62o06lItS3VXWKET2q4RpCQ0Op98RltHLjfrsy2+cPoNRJE9rF84gGPSfRwaMXVNOciby8LVjT8XSxRgPo9p0HVtX5ilCxl/nfSJIwnlXb5ZsJi36lifM2yLf4BQEQAAEQAAEQUCEgmjHFuwiDSmWgEQUCIAACIAACIAACIOBxAqKtrbGu9viUCfcHQKgI9yEIewMSJIhLPVtUpWql8jpV2aBpv9D8VTusymya2Ycypk5iFSfftB0ym7buOi7fuvQbI2Z0Ors+SLNsgXqBdkdT+YpQcYr16wvWP7Uwcs56mrFkq1oS4kAABEAABEAABP4lIJoxxbsFgwrTGwRAAARAAARAAARAIDwIiLa2xro6PGaRuc+EUGEub48+LUumVNS8Rkn6och35O/v5/BZ795/oKINB9DDh08teacObkVlC2S33Csv5qzZRUMnr1RGOX1dMG9mWji8nWo53p6slbpQaEioVbovCBXJkyeiXXP7WbVbedNy4Ez6fe9JZRSuQQAEQAAEQAAEbAiIZkzx7sGgshlk3IIACIAACIAACIAACJhCQLS1NdbVpkybcH0IhIpwxe+ZhydMGJcaVilGjdn/Y8aIpvsQ7mC71+iFljxtfqxAPzf6wXKvvHj15h3lr9Ob3rx+q4x26nrmsDZUMl9W1TLHL1yjWu3H2KX5glDxc4tq1KZOabu2yxFqO0XkNPyCAAiAAAiAAAh8JiCaMcV7BYMKsxsEQAAEQAAEQAAEQCA8CIi2tsa6OjxmkbnPhFBhLm9Tn5YubVJaMKwdJYr/peZzP3wMoWyVu9IHtpuBh8L5s9D8oW018wcGL6el6/ZopuslJGO+L3YzHxhaYe7a3TRk0gq7ZG8XKvwj+9OBpUMpQbzYdm3nEY+fvaS8NXqopiESBEAABEAABEDgPwKiGVO8ZzCo/htfXIEACIAACIAACIAACJhHQLS1NdbV5s2d8HoShIrwIm/Sc/nuis3Te9NXcWJpPrFWl3F0/PQVKT0624FxePkwTV8L9x4+o+odx9A//zzWrE8tgb/MnzqwJZXKr76bgpfRctbt7UJF41olmUPzGmrdluJmsyOzhoXxyCzNypEAAiAAAiAAAgIREM2Y4kMDg0qgCYqugAAIgAAIgAAIgIAPERBtbY11tQ9NPhebCqHCRXC+VKxD40rUsUF5zSZ3HrWA1m87bEkP/Kk2NalazHJve3GbiRS1mbhhVKyIHCUyBfdrpun7gtd/7c4DKt14INGnT7aPI28WKqqW+x8FdfvRrs3KiGKNBtBt1j8EEAABEAABEAABfQKiGVO8tzCo9MccqSAAAiAAAiAAAiAAAp4hINraGutqz8wTb6oVQoU3jYYTbfkidkxqwHxQ7D12gc5duK5bslKZfDS+RyPNPB1HzKeN2/+wpKdMkZh2zOlruVe7uHP/MbUeOIvOX7qhlmyJi8+OnRrWJYBK589miVO7GDBlFS1cvVMtyTuFikiRqDLjGvRzA/Lz03Zc/vvhs9Syz1TVfiESBEAABEAABEDAmoBoxhTvHQwq6zHGHQiAAAiAAAiAAAiAgDkERFtbY11tzrwJz6dAqAhP+mF49uDO9ajeD4XYBoRPtPr3IzR23ka6d++Rao0jezSkmmXyq6bxyB97TaYDR85bpQ/sFEANKha2ilO7OXb+Ks1hRxv9tvckhTB/F3LIkTUtNWa7MioUyUmR/f3laNXfKzfvUcXWIyx+MmwzedOOCn40VrWy+alJtRKUNtnXtk21un/NnI+XbTGU7mqMi1Vm3IAACIAACIAACJBoxhQfUhhUmNggAAIgAAIgAAIgAALhQUC0tTXW1eExi8x9JoQKc3m75WncSfav03pRJPZVvxy4U+w1TLDYsu8EHTx2SXrpH5v5pWhaoyR1qK997BN39FwwINBOJOAv5LfOCqRkib6SH6H7+zEkhJ69eE0fQ0IpLtvtES1qFN38cmJoaChV6TCGzl/U3pmhJ1ScuHidXrx+I1en+ztv7R7afeC0Jc/yiV0pT+Y0lnvlxYVrf9PrN28tUf5MbIkTKwYlYT4/okeLaonXu+gxbgmt2rRfLwvSQAAEQAAEQAAEFAREM6Z412BQKQYYlyAAAiAAAiAAAiAAAqYREG1tjXW1aVMn3B4EoSLc0Lv+4IVBHalgjvSaFfBdFq/Y1/wxo0fVPZaIVzB+0RYKZrsx1ELObGlpycj2hkUHtTocxY2cs4FmLPlVN5ueUKFb0CaR7zzpNnyeJVZPqLBkcvFiCRMo+jKhAgEEQAAEQAAEQMA4AdGMKd5zGFTGxx85QQAEQAAEQAAEQAAE3EdAtLU11tXumxveWhOECm8dGY12FS2QleYObqOR6lw034VRoF4gPXn8XLNggTyZaPagVh4RKyYs+pUmztug+Ww5wdeECkmkGL9U1TG43Cf8ggAIgAAIgAAI2BMQzZjiPYRBZT/OiAEBEAABEAABEAABEPA8AdHW1lhXe37OhPcTIFSE9wg4+fwKJfNQcO8mTpayz853XXQZvZDWbztsn2gTw8WKqX2bUWx29JE7An/2mPmbaBrbzWEk+IpQwY+/Gs/El6m8X6yPCCAAAiAAAiAAAs4REM2Y4r2HQeXcHEBuEAABEAABEAABEAAB9xAQbW2NdbV75oU31wKhwptHR6NtNZkT7UHtaoVpl8PPQYtozZaDGk+wj07IfDOM6FqfiufJbJ/oRMz1vx9Ql1EL6dTZvwyX8gWh4iLzadF55Hz688ptw/1CRhAAARAAARAAAWsCohlTvHcwqKzHGHcgAAIgAAIgAAIgAALmEBBtbY11tTnzJjyfAqEiPOmH4dnx4sWmpjVLUePKRSkmc3xtNHChoMfYJXT05J9Gi1jl+6F0XmrBnpstXXKreEc39x4+owUb9tK8X36nd2/fO8pule7NQsXuYxdo3tpdtPfwOfoUil0UVgOHGxAAARAAARBwkoBoxhTvPgwqJycBsoMACIAACIAACIAACLiFgGhra6yr3TItvLoSCBVePTyOGxc7dkyq+X1BysccX+fKmJoSMAHDNrx9956OnLtKy7YcoO37TtHHDx9tszh9nzVTKqpZ9n+Uk/1mSp2EIvv729Vx7c4DOv3nDdq05wTtPHCaQkNC7fIYiWhZrxxVKp7bSFbdPNNW/Eabth+x5GlWtwxVLZnXcu/ogvv0ePn6DZ1juyZOXbpBJy9cp3v3HjkqhnQQAAEQAAEQAAGDBEQzpni3YVAZHHxkAwEQAAEQAAEQAAEQcCsB0dbWWFe7dXp4ZWUQKrxyWFxv1Ndfx2NiRRyKFi0Kc5PwiR49eUG32S4KT37tHyVKZEqZIpG0s8Pfz4/evftAN27fp1ev3rjeEZQEARAAARAAARCIcAREM6b4AMKginDTGB0GARAAARAAARAAAa8gINraGutqr5hWHm0EhAqP4kXlIAACIAACIAACIAACRgmIZkzxfsOgMjr6yAcCIAACIAACIAACIOBOAqKtrbGudufs8M66IFR457igVSAAAiAAAiAAAiAQ4QiIZkzxAYRBFeGmMToMAiAAAiAAAiAAAl5BQLS1NdbVXjGtPNoICBUexYvKQQAEQAAEQAAEQAAEjBIQzZji/YZBZXT0kQ8EQAAEQAAEQAAEQMCdBERbW2Nd7c7Z4Z11QajwznFBq0AABEAABEAABLyYgJ+/H32bJillSpuM/P0i0bkrt+jy1b8p5GOIU62OFy82VS2djy5dv0sHjpw3rWzChHHpyzix6Mpfd+ye6cf8TaVLk4QePHpGT5ivK9vgKN02vzP3ohlTvO8wqJyZAcgLAiAAAiAAAiAAAiDgLgKira2xrnbXzPDeeiBUeO/YoGUgAAIgAAIgAAJeSKBYwew0oE0NSvFNAqvWvf/wkcYu2EQzl26zite6iRwlMi0d24lyZUpNh89cpnqdx2tltYsPS9nvsqej+UPbUqwY0ahCmxF06fItS/1cgJkQ2JQqFPmOfjt0hloHTrOk8QtH6VaZXbgRzZjiCGBQuTARUAQEQAAEQAAEQAAEQCDMBERbW2NdHeYp4fUVQKjw+iFCA0EABEAABEAABLyFQJPapSmwZTWr5rx7/4GiRY1iiZu3bjcNDl5hude6GNKlHgVUKCQl/3H2CgV0GqeV1S7e1bJ5v8tAcwe3phjRo0p1Vm4/ms5duC5dc/EjuF8zKlsgu3S/k+3waN5rsnTN/3CUbskYhgvRjCmOAgZVGCYEioIACIAACIAACIAACLhMQLS1NdbVLk8FnykIocJnhgoNBQEQAAEQAAEQCG8CF7ZMoKjshf6rN+9oyIzVtHXvSXr29CXlzJaWxvdoRMkTx5eaWKzRALp954Fmc2tVLEQjOtWzpDsjVLhatlC+zDRzQEsrUUUWKqIwoWXqwJZUIm9mS5uUQoWjdEuhMF6IZkxxHDCowjgpUBwEQAAEQAAEQAAEQMAlAqKtrbGudmka+FQhCBU+NVxoLAiAAAiAAAiAQHgSaFmvHBXLk4n6sh0TV6/9bdWULJlS0frgblJcn4nLaNn6vVbp8k22zKlp5djOFCWyvxxFR9iOiroGdlS4WpYf2XR2w1grkYI/XBYqWtX/nro3qWhpD79QChWO0q0KhuFGNGOKo4BBFYYJgaIgAAIgAAIgAAIgAAIuExBtbY11tctTwWcKQqjwmaFCQ0EABEAABEAABLydwPnN4yUxYOmWAxQYtNiuuV/Fj0Obpvair7+KQy9evaFf95+iWmX/Z0ioCEtZ3pAJ7FinNMkS0ZRlW2lS76ZS2yr9NIrOX7xBObKmpUl9mtDaHUcpVZIEko+KnUfOsaOfpkj5HKVLmdzwh2jGFEcCg8oNEwNVgAAIgAAIgAAIgAAIOE1AtLU11tVOTwGfKwChwueGDA0GARAAARAAARDwRgLp0yWjLdN6SU0LXrKVxs9Zb9VMf7aDYglznp0ncxoKDQ2lJn2nUcbUSahX86p05NxfVLfjWKv8ypuwlFXWw69TpEhEO+f0k6JloUKZZxxzpl25eG62o+I/ocKZdGVeZ69FM6Z4/2FQOTsLkB8EQAAEQAAEQAAEQMAdBERbW2Nd7Y5Z4d11QKjw7vFB60AABEAABEAABHyEwLCfG1Cd8gWk1jbpO5X2HDxr1fKBnQKoQcXCUtyI2eto5tJt1LxuGUNCRVjKWjWC3UCosCXi2XsYVJ7li9pBAARAAARAAARAAATUCUCoUOeCWO8lAKHCe8cGLQMBEAABEAABEPARAhVK5aXgXo2l1l6+cY/KNx9C9OmTpfXVKxSk0V3qS/frdx2jzkPmSNeyUHH0/FWq0yHIkl95EZayynrkawgVMglzfiFUmMMZTwEBEAABEAABEAABELAmAKHCmgfuvJ8AhArvHyO0EARAAARAAARAwIsJpEqZmDZM6k4xY0Sj12/eUZUOo5mj7buWFnMn26uY8+yoUSLTub9uU80OY+j9uw9SuiOhIixlLQ2wuYBQYQPEw7cQKjwMGNWDAAiAAAiAAAiAAAioEoBQoYoFkV5MAEKFFw8OmgYCIAACIAACIODdBBImjEu/TPiZkn4dj22g+EQtBs6knftOWRodL15s2sicZydO8CU9fvaSKrYdSf/889iSLgsVx9iOito2OyrCUtbyAJULCBUqUDwYBaHCg3BRNQiAAAiAAAiAAAiAgCYBCBWaaJDgpQQgVHjpwKBZIAACIAACIAAC3k0gzpdf0MrxXShd8kRSQ2W/E3Kr/fz9aElQR8qbNR19DAmhgG4T6fjpK3Ky9KslVISlrNUDVG4gVKhA8WAUhAoPwkXVIAACIAACIAACIAACmgQgVGiiQYKXEoBQ4aUDg2aBAAiAAAiAAAh4L4Ho7JinJUGdKEf6FFIjp6/8nUZNX23V4Crl8tPYbg0tcbfuPbJcyxexY8WguLFj0rv3H+gW22nxKfQTNQ2cSnmzp3W57N93H8rVq/5CqFDF4rFICBUeQ4uKQQAEQAAEQAAEQAAEdAhAqNCBgySvJAChwiuHBY0CARAAARAAARDwVgKRma+JOcPbUqGcGaQmLt28nwLHLrFrrtIJtl2iTkS5VsMoe4aUFufbOlntknjZK3/dsYtXRkCoUNLw/DWECs8zxhNAAARAAARAAARAAATsCUCosGeCGO8mAKHCu8cHrQMBEAABEAABEPAiApH8ItGk/s2pfKGcUqs27jlOnYbMkXZC2DYzevSolDBBXPoqbmzbJMt98xolqEKR7+jitb+p04j59P7DR7px8x6Fpaxc+Vfx49Dz56/pI6tTGSBUKGl4/hpChecZ4wkgAAIgAAIgAAIgAAL2BCBU2DNBjHcTgFDh3eOD1oEACIAACIAACHgRgaFdG1Dd7wtILXrw5Dk16TOV3n8MsWvhHXb80ts37+zibSN6t6tFzaoVp+MXrlGt9mNsk3Xv9cr2YfU2ZfU+fv6KmvadRmfOXbXUBaHCgsKUCwgVpmDGQ0AABEAABEAABEAABGwIQKiwAYJbrycAocLrhwgNBAEQAAEQAAEQ8AYCuXN8SyuYXwojYdzCzTRp/iaHWfXEBkeF9cpum9ef0ib7Wqpi9ppdNGzySkt1SqGicvvRdO7CdUsavxgX2JQqF89NO4+co+a9plilGUm3K+BEhGjGFO86hAonJgCyggAIgAAIgAAIgAAIuI2AaGtrrKvdNjW8tiIIFV47NGgYCIAACIAACICANxHInDElrQ/uRpEiRXLYrD4Tl9Gy9Xsd5vup0Q/U+ccKtHnvCWo/cJbD/MoMemWHd/uRapf7n5R95bbD1HPUAkvRhAnj0v5FgykkNJRKNxtMd/62dr49sFMANahYmJZuOUCBQYst5eQLR+lyPld+RTOmOAMYVK7MBJQBARAAARAAARAAARAIKwHR1tZYV4d1Rnh/eQgV3j9GaCEIgAAIgAAIgAAIOEWA+9IYwcSKmmXy08TFv9KEuRucKh9emUUzpjhHGFThNZvwXBAAARAAARAAARCI2AREW1tjXS3+fIZQIf4Yo4cgAAIgAAIgAAIRjICfnx+tndqDsqRNRvV6BNPhYxd9goBoxhSHDoPKJ6YeGgkCIAACIAACIAACwhEQbW2NdbVwU9SuQxAq7JAgAgRAAARAAARAAAR8m4B8PNOZK7eoausRPtMZ0YwpDh4Glc9MPzQUBEAABEAABEAABIQiINraGutqoaanamcgVKhiQSQIgAAIgAAIgAAI+C6Bi79OpHfvP9APbUbQ7TsPfKYjohlTHDwMKp+ZfmgoCIAACIAACIAACAhFQLS1NdbVQk1P1c5AqFDFgkgQAAEQAAEQAAEQ8F0CmdKnoHcfPtLVa3/7VCdEM6Y4fBhUPjUF0VgQAAEQAAEQAAEQEIaAaGtrrKuFmZqaHYFQoYkGCSAAAiAAAiAAAiAAAmYSEM2Y4uxgUJk5g/AsEAABEAABEAABEAABmYBoa2usq+WRFfcXQoW4Y4uegQAIgAAIgAAIgIBPERDNmOLwYVD51BREY0EABEAABEAABEBAGAKira2xrhZmamp2BEKFJhokgAAIgAAIgAAIgIBzBEQzBnjvzTQIwM+5+YbcIAACIAACIAACIAACIKBFQLS1tZl2iRZTxHuWAIQKz/JF7SAAAiAAAiAAAhGIgGjGAB86Mw0C8ItAf1nQVRAAARAAARAAARAAAY8SEG1tbaZd4tGBQeWaBCBUaKJBAgiAAAiAAAiAAAg4R0A0Y4D33kyDAPycm2/IDQIgAAIgAAIgAAIgAAJaBERbW5tpl2gxRbxnCUCo8Cxf1A4CIAACIAACIBCBCIhmDPChM9MgAL8I9JcFXQUBEAABEAABEAABEPAoAdHW1mbaJR4dGFSuSQBChSYaJIAACIAACIAACICAcwREMwZ47800CMDPufmG3CAAAiAAAiAAAiAAAiCgRUC0tbWZdokWU8R7lgCECs/yRe0gAAIgAAIgAAIRiIBoxgAfOjMNAvCLQH9Z0FUQAAEQAAEQAAEQAAGPEhBtbW2mXeLRgUHlmgQgVGiiQQIIgAAIgAAIgAAIOEdANGOA995MgwD8nJtvyA0CIAACIAACIAACIAACWgREW1ubaZdoMUW8ZwlAqPAsX9QOAiAAAiAAAiAQgQiIZgzwoTPTIAC/CPSXBV0FARAAARAAARAAARDwKAHR1tZm2iUeHRhUrkkAQoUmGiSAAAiAAAiAAAiAgHMERDMGeO/NNAjAz7n5htwgAAIgAAIgAAIgAAIgoEVAtLW1mXaJFlPEe5YAhArP8kXtIAACIAACIAACEYiAaMYAHzozDQLwi0B/WdBVEAABEAABEAABEAABjxIQbW1tpl3i0YFB5ZoEIFRookECCIAACIAACIAACDhHQDRjgPfeTIMA/Jybb8gNAiAAAiAAAiAAAiAAAloERFtbm2mXaDFFvGcJQKjwLF/UDgIgAAIgAAIgEIEIiGYM8KEz0yAAvwj0lwVdBQEQAAEQAAEQAAEQ8CgB0dbWZtolHh0YVK5JAEKFJhokgAAIgAAIgAAIgIBzBEQzBnjvzTQIwM+5+YbcIAACIAACIAACIAACIKBFQLS1tZl2iRZTxHuWAIQKz/JF7SAAAiAAAiAAAhGIgGjGAB86Mw0C8ItAf1nQVRAAARAAARAAARAAAY8SEG1tbaZd4tGBQeWaBCBUaKJBAgiAAAiAAAiAAAg4R0A0Y4D33kyDAPycm2/IDQIgAAIgAAIgAAIgAAJaBERbW5tpl2gxRbxnCUCo8Cxf1A4CIAACIAACIBCBCIhmDPChM9MgAL8I9JcFXQUBEAABEAABEAABEPAoAdHW1mbaJR4dGFSuSQBChSYaJIAACIAACIAACICAcwREMwZ47800CMDPufmG3CAAAiAAAiAAAiAAAiCgRUC0tbWZdokWU8R7lgCECs/yRe0gAAIgAAIgAAIRiIBoxgAfOjMNAvCLQH9Z0FUQAAEQAAEQAAEQAAGPEhBtbW2mXeLRgUHlmgQgVGiiQQIIgAAIgAAIgAAIOEdANGOA995MgwD8nJtvyA0CIAACIAACIAACIAACWgREW1ubaZdoMUW8ZwlAqPAsX9QOAiAAAiAAAiAQgQiIZgzwoTPTIAC/CPSXBV0FARAAARAAARAAARDwKAHR1tZm2iUeHRhUrkkAQoUmGiSAAAiAAAiAAAiAgHMERDMGeO/NNAjAz7n5htwgAAIgAAIgAAIgAAIgoEVAtLW1mXaJFlPEe5YAhArP8kXtIAACIAACIAACEYiAaMYAHzozDQLwi0B/WdBVEAABEAABEAABEAABjxIQbW1tpl3i0YFB5ZoEIFRookECCIAACIAACIAACDhHQDRjgPfeTIMA/Jybb8gNAiAAAiAAAiAAAiAAAloERFtbm2mXaDFFvGcJQKjwLF/UDgIgAAIgAAIgEIEIiGYM8KEz0yAAvwj0lwVdBQEQAAEQAAEQAAEQ8CgB0dbWZtolHh0YVK5JAEKFJhokgAAIgAAIgAAIgIBzBEQzBnjvzTQIwM+5+YbcIAACIAACIAACIAACIKBFQLS1tZl2iRZTxHuWAIQKz/JF7SAAAiAAAiAAAhGIgGjGAB86Mw0C8ItAf1nQVRAAARAAARAAARAAAY8SEG1tbaZd4tGBQeWaBCBUaKJBAgiAAAiAAAiAAAg4R0A0Y4D33kyDAPycm2/IDQIgAAIgAAIgAAIgAAJaBERbW5tpl2gxRbxnCUCo8Cxf1A4CIAACIAACIBCBCIhmDPChM9MgAL8I9JcFXQUBEAABEAABEAABEPAoAdHW1mbaJR4dGFSuSQBChSYaJIAACIAACIAACICAcwREMwZ47800CMDPufmG3CAAAiAAAiAAAiAAAiCgRUC0tbWZdokWU8R7lgCECs/yRe0gAAIgAAIgAAIRiIBoxgAfOjMNAvCLQH9Z0FUQAAEQAAEQAAEQAAGPEhBtbW2mXeLRgUHlmgQgVGiiQQIIgAAIgAAIgAAIOEdANGOA995MgwD8nJtvyA0CIAACIAACIAACIhBImiQB/X3vEX0K/SRCd7ymD6Ktrc20S7xmECNYQyBURLABR3dBAARAAARAAAQ8R0A0Y4CTMtMgAD/PzU3UDAIgAAIgAAIgAALeSKBw/iw0f2hbahI4lfYcOuuNTfTZNom2tjbTLvHZQffxhkOo8PEBRPNBAARAAARAAAS8h4BoxgAna6ZBAH7eM5fREhAAARAAARAAARDwJIHcOb6luhUKUZFcGShhvDh0+5/HtPvIeTpw6k/aefAMvXv7/r/HR4pE36ZJSuxHM9y884Devnmnma5M+Cp+HErAnqkVXrN6brP6tELMWNEpY9pklCZ5Ivrw8SMdO3tVNX/KFIkpWtTIWtVY4j98DKFr1+9a7t11Idra2ky7xF1jgHqcIwChwjleyA0CIAACIAACIAACmgREMwZ4R800CMBPc2ohAQRAAARAAARAAASEIdC9VXVqVauUZn8ePnlBlX8aRf8w8YKHCqXyUnCvxtK11h8j56ynGUu2aiVbxf++YAClSpLQKk558zEkhDKU66CMslw3rlWSOjaoQHFixbDE8Qve5qAFG2nFhn1SfJrUSei3mX2s8ujd5KvTmx49eqaXxek00dbWZtolTsNGAbcQgFDhFoyoBARAAARAAARAAASIRDMG+JiaaRCAH/4WgQAIgAAIgAAIgIDYBCqVyUfjezSSOvn4+St6wv6fNtnXdOHa35Q4/pcUL04sKa1E00F08+Y/0nX1CgVpdJf6umB6TVhqEQl0M7LE/cuGUeIEX2pm46JD/lo97dK5SNG3VQ0pnu+COHHxGvn5+dF3GVKRv7+fFD9o2i80f9UOSpUyMW2fFch2gehsA/n3CX8/eELFGvSj0JBQu2eGJUK0tbWZdklYuKOs6wQgVLjODiVBAARAAARAAARAwIqAaMYA75yZBgH4WU0n3IAACIAACIAACICAcATWTutJ2dIlpzfsaKeSTIz4LnNqmhLYjFoMmEH72NFPtX8oRO/ef6CVG/db+i4LFVdu/UMVWw23xCsvPrAyRoMsVHQYMY+27TlpVyyE7aiwFQ2iRY9Kp9aOoSiR/enuw6dUv3sw3bh5TyqbLm1SWjCsHSViQsvzV28oV7VukmPwyFEiawoVBfNkojmDWknlB0xZRQtX77RrR1gjRFtbm2mXhJU9yrtGAEKFa9xQCgRAAARAAARAAATsCIhmDPAOmmkQgJ/dlEIECIAACIAACIAACAhF4Pzm8cxvQxTaeuA0te03ncoVzyUJFc2ZULFz3ynVvspCxeUb96h8s8GqeZyJlIWK1oNn0W+7TxgqmiNrWlo9vouUt83g2bRt93GrcmWL5aKpfZtJcZXYsVXnL96wSre9WTy2E/0v+7f09MVrKhTQh94qfXLYZnbxXrS1tZl2iYvIUSyMBCBUhBEgioMACIAACIAACICATEA0Y4D3y0yDAPzkmYRfEAABEAABEAABEBCTwOkNYylWjGh0jTmrLt1ogEWoaNZ/Ou3af1q1094gVJQqkpNm9G8hta9MiyF09Zq182t+1NPvs/tK6W2HzKatu6yFDGXHsmdNQ2vGd5Wixi/aQsHzNiqT3XYt2traTLvEbYOAipwiAKHCKVzIDAIgAAIgAAIgAALaBEQzBnhPzTQIwE97biEFBEAABEAABEAABEQgENS7CVUtmUfqyqSlW4nvkpjQsxE1ZbsrdrNdFmpBFir+un2fqrcfLWX5FPqJXrFjllwJ8o6KrqMX0vZ/n/nu3QfSOz4qyTcJaO/CgdLjRsxeRzOXbrN6dPO6ZahX86pS3Peth9OfV25bpStvZg5rQyXzZaW3795TgXp96fmzl8pkt12LtrY20y5x2yCgIqcIQKhwChcygwAIgAAIgAAIgIA2AdGMAd5TMw0C8NOeW0gBARAAARAAARAAAREIpE+XjLZM62XXlSZ9p9Geg2fs4nmELFTYJnKH1jfvPaI1vx+hRWt30Qt2jJKRIAsVtnmfvXxNh05fofnrdtPhYxdtk2nFxK6UO3MaCmFOr2f+soM99w8pT7VS+ahFjZKSQ+1Tl25Q9Xaj7MrKEd+mTUa/Tv/c/3nsOYODV8hJbv8VbW1tpl3i9sFAhYYIQKgwhAmZQAAEQAAEQAAEQMAxAdGMAd5jMw0C8HM8x5ADBEAABEAABEAABHydwA+l89KwDnXpi5jRLV25dP0uBc3fSL/vtXdunfe7DLRsdAdLXrWLq2y3RYMewfTPP4/Vkq3ilo3vTHmzprOKs70ZOWc9zViy1Sr666/j0YqxnSl54vhW8fLNmSu3qAUTXB48eCpH2f1O6NeMKhbNJYkdxdjRV3eZ0OKpINra2ky7xFNjgnr1CUCo0OeDVBAAARAAARAAARAwTEA0Y4B33EyDAPwMTzVkBAEQAAEQAAEQAAGfJsCPUurXriaVzp+VIkWKZOnLSbYjoTl72f/k8XNLHL9IljQh27Hgb4n7Mk5Mypg6CbWpXYZSsLp4+HX/SWrXf6Ylj9ZFrFgxKEH8Ly3J/v5+lCJJAirFjmMKqFDQ0p5yrYbRlb/uWPJFjx6VxrKjq8oVzG6JU17sPnaBOg2bp3mUU/LkiWjH7EDy8/OjtTuOUtdhc5XF3X4t2traTLvE7YOBCg0RgFBhCBMygQAIgAAIgAAIgIBjAqIZA7zHZhoE4Od4jiEHCIAACIAACIAACIhEQO1Yp1tslwHfHXGbOdx2FKJGi0JrJ/egDKm+kbLmrtmTnj594aiYZnrF0vkknxk8w/z1e2jQxOVS3shRItPqSd0oCzu6iYfHzK/E74fPStelmNjy1ZdfSNdnmW+KGsyPxscPH6V75R/Du/1Itcv9T4oq32o4Xf5L24+Fspyr16Ktrc20S1xljnJhIwChImz8UBoEQAAEQAAEQAAELAREMwZ4x8w0CMDPMpVwAQIgAAIgAAIgAAIRgkC54rloSmAzmrZiO31fJCel/Hd3xKrfDlOPkQsMMShZOAfNHNBSylu1YxCdOXfVUDmtTBtn9qFMbLfGnuMXqEn3SVK2H6uXoAFta0rXO/44S52Hz6eX//rE+CJ2TBrXq5HkIJtn6D9lJS1avUvKK//Bj43izrgjs10hu46ep2Y9J8tJHvsVbW1tpl3isUFBxboEIFTo4kEiCIAACIAACIAACBgnIJoxwHtupkEAfsbnGnKCAAiAAAiAAAiAgAgEZKGiSd+pdJQ5sj64dKjku+IR2xWRj+2OMBIyZ0xJGyZ1l7I27jOF9h4+Z6SYZp5Zw9tSibxZ6NSfN6l625FSvimDWklHPj1+/oqK/9ifXr16Y1U+ZqzotHvBQGlnxdYDp6ltv+lW6X3b16bGVYpJcXV+nkBHT/5ple6JG9HW1mbaJZ4YD9TpmACECseMkAMEQAAEQAAEQAAEDBEQzRjgnTbTIAA/Q9MMmUAABEAABEAABEBAGAJKoWLPwbM0qmcjqsGOX+KhYP2+hpxj165UmIZ3DJDKFG3Yn+78/VC6duUPP+avYs/iwfRNgrj0y/Y/qPuI+VI1shPsO/cfU9F6fVWrXj6xK+XJnIaOnr9KdToEWfLEixeb9rM6o0WNQqeYD47q7UZZ0jx5Idra2ky7xJPjgrq1CUCo0GaDFBAAARAAARAAARBwioBoxgDvvJkGAfg5Nd2QGQRAAARAAARAAAR8iwBzmj1zaGvKkT4ltR8+jw4fu0hKoWLf4fO0ivmByJE+Bb17/4GyVe5KIR9DqAZzcN2xQQXqPnYRHTp60arPsb6IQb/N7kuJmHPsZy9fU66q3SzpUZhfCX4sk9Ixd+w4sWjm4FbEd2x0HDLXzpdE0zqlqU+LalIdyiOcujSrQu0CytKbt++pZNNBdP/+E8tz+MVX8ePQzrn9pd0gizbuo/7jl1rSu7aoSm3rlJHuWw6cSb/vPWlJ8+SFaGtrM+0ST44L6tYmAKFCmw1SQAAEQAAEQAAEQMApAqIZA7zzZhoE4OfUdENmEAABEAABEAABEPApAtxPw8ElQ6Q2f/r0idbtPCaJC40qF6X1u45J/ilyZEgppW87eJra9P18fNL+ZcMocYIv6QMTLSYv20bLNu2jp89eUc4saWh4pwBKnTShVKbf5BW0eM1u6ToZi1sb3I3iMWFi3c6j1GXoXCle6bybH+00YtZaOnH2Kn3Fdj00YO1oU7s0RWKCCt85UbbZEHr75p1ULk/O9LR8TEfp+vKNe9Rz/BI6eeYv6T5b5tQ0tGNdi6Nt5fFTXCg5wPocK0Y0unr7PpVpMoiI9d2MINra2ky7xIzxwTPsCUCosGeCGBAAARAAARAAARBwiYBoxgCHYKZBAH4uTTsUAgEQAAEQAAEQAAHfIMAEgF5talCzasUlMUCr0XcfPqVKzDeEvBOiSrn8NKZrA/Lz89MqQpKw0W+GRQQoXyI3Te7T1JI/R7VukvPrWLFi0OKgjpQtXXJLmu3Fy9dvqRnzMWHrR6LNjxXo50Y/WLLzfDx8ETO6JW4x203RT7GbolndMtS7eVUpvfvYxfTL5gOWvJ6+EG1tbaZd4umxQf3qBCBUqHNBLAiAAAiAAAiAAAg4TUA0Y4ADMNMgAD+npxwKgAAIgAAIgAAIgIDPEciSKRXVKvs/Kpk/KyVluyzk8Jw5qN7AdlmMZLscXr20dladlZVp36A85cqYWnJYLZfhOx8mLd1KKzbsk6Ok36RJEtCOuf0osr+/dF+4QT+6e++RdB2F+YpoxY5xKlcoB2VI+Q35M78UPLx99572nbxEfScstzvaScrA/qhavgBVLZmHcmdKTTHZLgk5XLvzgMbO30ibdxyVo6Tf9o0rUqcG39P5q3eoGvNN8fHDR6t0T96ItrY20y7x5Ligbm0CECq02SAFBEAABEAABEAABJwiIJoxwDtvpkEAfk5NN2QGARAAARAAARAAAZ8nEFClKA1pX4cCg5fTciY2hIaGOuxT4sTxKSHzSXH91j/04vkrzfwJmEPsw8uGSnVm/qEzfVARCaJGi0LpUiWhN0ykuH7zHn0KNXYsE9/dkTZ1ErbLIxLdYEc6yUdEaTYmHBJEW1ubaZeEw3DhkYwAhApMAxAAARAAARAAARBwEwHRjAGOxUyDAPzcNBFRDQiAAAiAAAiAAAj4CAGlM+09B8+6tdWliuSkGf1b0NHzV6lOhyC31u0LlYm2tjbTLvGF8RWxjRAqRBxV9AkEQAAEQAAEQCBcCIhmDHCIZhoE4Bcu0xYPBQEQAAEQAAEQAIFwIyCLCQ17T6b9f5x3WztSpEhE65gz7TjMJ0WTvtNoz8EzbqvbVyoSbW1tpl3iK2MsWjshVIg2ougPCIAACIAACKgQ4FuT06VJQg8ePaMnT15IOVKmSEzRokZWyW0d9eFjCF27fpdifRGDkrJt1kbC3X8e04sXr1WzfhU/DiWIF0c1jUe+fvOObrMzXp0JJQrnoFTfJKDlm/fT61efndopy0eJEplyZU9HOTKkovhxv6CL1+7QqYs36CrrF30ytr1bWZ/WtWjGAO+nmQYB+GnNLMSDAAiAAAiAAAiAgJgE/Jh/iNTMTwS3N4wc+2SUQgvmg6Jnsyq0cMNeGjBhmdFiQuUTbW1tpl0i1ETwoc5AqPChwUJTQQAEQAAEQMAVAnzxPyGwKVUo8h39dugMtQ6cRmnYeaq/zexjuLp8dXrTgJ9qSXUYKbRk037qO26JatbfFwygVEkSqqbxyI8hIZShXAfNdNuE4oWy06wBLSlSpEhU5+cJdPTkn1ZZvk2bjIJ7N6FvUya2iuc3O4+cpw5DZquKG3aZDUSIZgzwLptpEICfgUmGLCAAAiAAAiAAAiAAAg4JRI8elfLnykj72XrfTAfWDhtmYgbR1tZm2iUmDhMepSAAoUIBA5cgAAIgAAIgIBqByGwnQXC/ZlS2QHapa/zFfPNekykVe2m/fVag9HLfUZ//fvCEijXoR2PZy/5KxXI5yi6l9xy/hFZu3K+ad/+yYZQ4wZeqaTzyIdvxkb9WT810ZQLf0r1hUnf6ImZ0Kbputwl05MR/QkWypAlp64zeFD1aVCn9+t8P6BXbsZFJcnznJ8X9eeMuVWo9wi0GjGjGAAdkpkEAftKUxB8gAAIgAAIgAAIgAAIgEGYCoq2tzbRLwgwfFbhEAEKFS9hQCARAAARAAAS8n0CUqFFo6sCWVCJvZktjZaGCR3ARg+9CUAsF82SiOYNaSUkDpqyihat3EstM/AglrfBL8M+Uhe1e4EJD4XqB9OHDR9WsslDRYcQ82rbnpF2eELajIjQk1C7eNiJmrOi0dnIPSpvsa0uSrVCxdHxnypc1HfHjq3pPXEarNx+Q8saOE4vG9WrE2GSR7ruNXWxJs1TmwoVoxgBHYKZBAH4uTDoUAQEQAAEQAAEQAAEQAAEVAqKtrc20S1RwIsoEAhAqTICMR4AACIAACIBAeBBoVf976t6kotWjlUKFVYLNzeKxneh/2b+lp8zPRKGAPvT27XubHNa3uXN8SyuCOkmRI2avo5lLt1lnUNzJQkXrwbPot90nFCnOXU4b0prK/C+bVaG63SayHRWXpLivv45HB5cMka4nLPqVJs7bYJU3BtuFsW7KZ6Hj8o17VL7ZYKt0V25EMwY4AzMNAvBzZdahDAiAAAiAAAiAAAiIR0C0daGZa2p5NoChTAK/vkIAQoWvjBTaCQIgAAIgAAJOEsiRNS1N6tOE1u44ynxCJJD8S+w8co4d/TRFt6bsWdPQmvFdpTzjF22h4HkbdfPzxHmj2lMRdgbsy9dvqWBAIL169UazjDuEinYNf6AuDStIz1i0cR/VLvc/isp2eyiFCu5gm/uu4CFvnV70+NFz6Vr5R5PapSmwZTUpKmOFTvTh/QdlstPXohkDHICZRhX4OT3lUAAEQAAEQAAEQAAEhCQg2rrQzDW1PCHAUCaBX18hAKHCV0YK7QQBEAABEACBMBAYx5xpVy6emzmPdixUzBzWhkrmy0pv372nAvX60vNnL3WfnDlDStowubuUZ9qK7TR6xhrd/LJQ0XX0Qtp+4LSU9927D4ZFgqIFsrJjqVpLx1b9cfYK1e86gc5tHCcJFQHdJ9Ifxz/vqKhRoSCN6lJfql9LhEiXNiltnd5bylO88UC6dfu+btsdJYpmDPD+mmlUgZ+jGYZ0EAABEAABEAABEIgYBERbF5q5ppZnCBjKJPDrKwQgVPjKSKGdIAACIAACIBAGAkaFim+Zj4lfp/eSnjRv3W4aHLzC4VOnMF8W5Qpmp/fMJ0Vh5nT70aNnumVkocI207OXr+nQ6Ss0nz338LGLtsnSffLk3Hl2N4odKwZxJ9+V2oykp09f0IUtE+yECqUI0SRwKu05dNauTuXxULW6jKPj7PlhCaIZA5yFmUYV+IVl9qFsr2RJAABAAElEQVQsCIAACIAACIAACIhDQLR1oZlrankWgKFMAr++QgBCha+MFNoJAiAAAiAAAmEgYFSomNCvGVUsmotCmDPrYo0G0N17j3Sfmib1N7RtRh9pd8OyLQepT9Ai3fw8cRlzcJ2XObjWCyPnrKcZS7ZaZeE+JdYykSJdisT0jh3RVK1jEF26fEvKIwsV9XoEW4kcR1aPoq+Y4+xbrB81WH6liBItelQa06OhdCQWr6RCmxGW+qwe7MSNaMYA77qZRhX4OTHZkBUEQAAEQAAEQAAEBCYg2rrQzDW1PC3AUCaBX18hAKHCV0YK7QQBEAABEACBMBAwIlTw3Qo7ZgeSn5+f5Nei67C5Dp84pldjqlYqL3369IlKMmfUN2/+47BMLLYbIkH8Ly35/P39KAXzoVGKHTcVwI5rihQpkpRWrtUwuvLXHUu+KYNasp0bOaT79qxtm5nvDTloCRUdGleijg3KS9m4Y/AF6/fQnfuPKS3ra80y+emrL7+Qq6A8tXrSkycvLPeuXIhmDHAGZhpV4OfKrEMZEAABEAABEAABEBCPgGjrQjPX1PJsAEOZBH59hQCECl8ZKbQTBEAABEAABMJAwIhQMbzbj5JTav6Y8q2G0+W/bus+MSkTF3bO7U9caNh64BS17TdDN7+RxIql89GEno2krPOZqDBo4nLpunWD76lb44rS9dTl22nMTGs/GLJQUb9nMB06an1sVI/W1allzVK6j3/Ejo/KV7Onbh4jiaIZA7zP/2fvTuBtqtoHjj/mWUIyqyhKEiUNVJRIsySiQcpUaDJlLDMhZErSZGpUvL3RLCWhMiRJKEOGDJln/7127z7/fc4dz17u2s7qdz6f9549PXvt+93r/dxn9dh7mRxU4ZeeXsYxCCCAAAIIIICA/QK25YUmc2qvd2DoSfCdKAIUKhLlTnGdCCCAAAIIaAikVahQczV89fozkjVLFvli0Qpp0WV0mq31efweueemq9zjbms3RJb/vC7NmPQcMGtCNzn/7OIy9/ufpXmnF6Rm9YoyqW8b90kLdW0PPT1GThw/EXWq1AoV6sDLL60gNaueL1dVLe++CkrNb6GKE/Wuutg9j78oEnXiOFdsGwyoX9/koAq/ODschyOAAAIIIIAAApYK2JYXmsypvS6BoSfBd6IIUKhIlDvFdSKAAAIIIKAhkFahoke7RvLAbde4Ldz91AhZ9OOqVFsrXLiAW9jIni2rLFj2q9zz+POpHh/PzpcGtJVa1SrKklV/SIO2g2TulD5SokhB9xS79x2Qv51XOMV+ShUt5G5SBYj9Bw/Lp87E2YPHvxt7WGQ9U+ZMMq5PK7m+eiV3vosbWw+U3//YHNkfdMG2wYByMDmowi9ozyMOAQQQQAABBBCwS8C2vNBkTu31BAw9Cb4TRYBCRaLcKa4TAQQQQAABDYHUChWnn55Pvp7cR3JkzyZLfvldGjwyOM2WurRpKA/fWcs97v5uY2Tegp/SjEnPAZmd10jNda6lmFMIeeeT76TTwFdl3rR+7np64r1jUnsVlSpSPNflAbm99qXu4d1HTZep78/1QrW+bRsMKAyTgyr8tLofwQgggAACCCCAgDUCtuWFJnNqrxNg6EnwnSgCFCoS5U5xnQgggAACCGgIpFaoePLh26Xt3XXcs7d8ZoJ8+tWPqbaU35mA+hunmJArZ3ZZuXaT3PRwv2SPL1gov+zevV+OHjka2Z8vfx6Z4DzJoF671KHvpKh96qAH775euj18h3t8rzFvyRvvfiEFCuSTUs58GGqS7+Q++fPklFf6tXV3dRs5TRb/tFY2bdku+/YeSHK4an/QU82cSbkvcvd9umC5tOw2NslxQTfYNhhQDiYHVfgF7XnEIYAAAggggAACdgnYlheazKm9noChJ8F3oghQqEiUO8V1IoAAAgggoCGQUqEib77c8s2UvpInVw5Zs2Gr1Gn+rMiJ6PkfYpvt0PwWad+0nru5/cBX5D+fLIw9RLo9cpc8eMe1smP3PnmwxzhZ9tMa95gG9a+UIU80dZfVq50GvjRDfli+Rgo6T3U0u/VqadPoencuio1bd8gNLfrKwQOHkpw7doP6HZa8N8Td3KzLCzJ/0c+xh7jrda6pIn0ebSRnnJ7fXf926a/SuvcE2eNc48n62DYYUC4mB1X4nayeyHkQQAABBBBAAIHEFrAtLzSZU3t3HkNPgu9EEaBQkSh3iutEAAEEEEBAQyClQkWLxnXk6Ydud8/cadhkeefDb1JtJZszJ8V3bw+U/HlyiSomXNuslxw/fjxJzJxXeknZkkXc7RPf+0L6j37LXc7jxE0e2kEqlSuVJMbbsHf/QWnRc3ya82R4x6dVqMiVO6e8M/IpKX9WMTfkhFOIGTv9Yxn+8sxkr907b5Bv2wYDysDkoAq/IL2OGAQQQAABBBBAwD4B2/JCkzm11xsw9CT4ThQBChWJcqe4TgQQQAABBDQEnnmsiTS7uYZM/e830n3o5MiZ2j1wszzW7EZZsWaj3OHMTeF/TVPkIN9CTud1T4vfGSRZsmSRjkPfkJkff+fb+/+LAzreK43qXu5ueGvOAuky+LXIzmzOXBitmtwgda+qLOXLFHPO9c8rnQ4eOizzfvxFeoyYLlu37owcn9ZCDueavneuKWvWLHJz28Hy628bokJUceT7d539zjX/sHKdDJr4gSz84ZeoY07Wim2DAeViclCF38nqiZwHAQQQQAABBBBIbAHb8kKTObV35zH0JPhOFAEKFYlyp7hOBBBAAAEEEkhATVg90ClWNKxTXUZO/khGTJqZ7NVnz5FNyp1VXA44RYp1f2yWE8dTf+1UsidJx8b611Vz58VYsHhlOo4OfohtgwElYXJQhV/wvkckAggggAACCCBgk4BteaHJnNrrBxh6EnwnigCFikS5U1wnAggggAACCSSgJr6eMbazVCxbUu7pPEoyukBwqtDYNhhQriYHVfidKj2Z60AAAQQQQAABBMIVsC0vNJlTe3cOQ0+C70QRoFCRKHeK60QAAQQQQCCBBLxXTS1bvV5ubz0wga5c71JtGwwoDZODKvz0+h/RCCCAAAIIIICALQK25YUmc2qvD2DoSfCdKAIUKhLlTnGdCCCAAAIIJJDAyo9GyqHDR+SmNgNlw8ZtCXTlepdq22BAaZgcVOGn1/+IRgABBBBAAAEEbBGwLS80mVN7fQBDT4LvRBGgUJEod4rrRAABBBBAIIEEzj+vtBw6clTWrN2UQFetf6m2DQaUiMlBFX76fZAzIIAAAggggAACNgjYlheazKm9+4+hJ8F3oghQqEiUO8V1IoAAAggggMApL2DbYECBmxxU4XfKd3EuEAEEEEAAAQQQMCJgW15oMqf2bhCGngTfiSJAoSJR7hTXiQACCCCAAAKnvIBtgwEFbnJQhd8p38W5QAQQQAABBBBAwIiAbXmhyZzau0EYehJ8J4oAhYpEuVNcJwIIIIAAAoYEbEtoFZupgQF2ep0UPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQQMAWAdvyQlPjEf/9x9CvwXIiCFCoSIS7xDUigAACCCBgUMC2hFbRmRoYYKfXUfHT8yMaAQQQQAABBBCwRcC2vNDUeMR//zH0a7CcCAIUKhLhLnGNCCCAAAIIGBSwLaFVdKYGBtjpdVT89PyIRgABBBBAAAEEbBGwLS80NR7x338M/RosJ4IAhYpEuEtcIwIIIIAAAgYFbEtoFZ2pgQF2eh0VPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQQMAWAdvyQlPjEf/9x9CvwXIiCFCoSIS7xDUigAACCCBgUMC2hFbRmRoYYKfXUfHT8yMaAQQQQAABBBCwRcC2vNDUeMR//zH0a7CcCAIUKhLhLnGNCCCAAAIIGBSwLaFVdKYGBtjpdVT89PyIRgABBBBAAAEEbBGwLS80NR7x338M/RosJ4IAhYpEuEtcIwIIIIAAAgYFbEtoFZ2pgQF2eh0VPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQQMAWAdvyQlPjEf/9x9CvwXIiCFCoSIS7xDUigAACCCBgUMC2hFbRmRoYYKfXUfHT8yMaAQQQQAABBBCwRcC2vNDUeMR//zH0a7CcCAIUKhLhLnGNCCCAAAIIGBSwLaFVdKYGBtjpdVT89PyIRgABBBBAAAEEbBGwLS80NR7x338M/RosJ4IAhYpEuEtcIwIIIIAAAgYFbEtoFZ2pgQF2eh0VPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQQMAWAdvyQlPjEf/9x9CvwXIiCFCoSIS7xDUigAACCCBgUMC2hFbRmRoYYKfXUfHT8yMaAQQQQAABBBCwRcC2vNDUeMR//zH0a7CcCAIUKhLhLnGNCCCAAAIIGBSwLaFVdKYGBtjpdVT89PyIRgABBBBAAAEEbBGwLS80NR7x338M/RosJ4IAhYpEuEtcIwIIIIAAAgYFbEtoFZ2pgQF2eh0VPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQMCdQonhh2bR5u5w4fsJco/+ilmzLC02NR/xdBEO/BsuJIEChIhHuEteIAAIIIICAQQHbElpFZ2pggJ1eR8VPz49oBBBAAAEEEDAjUKN6RXm1X1tp3n2szP12uZlG/2Wt2JYXmhqP+LsJhn4NlhNBgEJFItwlrhEBBBBAAAGDArYltIrO1MAAO72Oip+eH9EIIIAAAgggkLECl1Q+VxrXv0pqVi0vZ5yeXzZs2SFfLlwh3yxZJZ/PXyaHDh5OcgGZs2SWCuVKSflzirv7lq36Q35buynQkxjqKY5LK5WV888pJX/v3S9LVq6Tpc7/9u7Zn6TdtDbUqlFZzipWWKZ/+LXs33cw1cPPOKOAVK14jhTIl0fU9a9cvV6OHzueaozuTtvyQlPjEb87hn4NlhNBgEJFItwlrhEBBBBAAAGDArYltIrO1MAAO72Oip+eH9EIIIAAAgggkHECnVo1kFZ3XZdiA3/t3CO3PjpYtjjFC+9z7VUXSc/Wd0oZpyDg/xxwChp9xr8r02d+5d+c4rIqdrS77yZ5pPENksVZ9n/UudoNfEU+n7fEvznVZXVdL/VuKZkyZZK7nxohi35clezxqjDyUp/Wcl6ZYlH7Dx46LAMmvi9vvPtF1PaTuWJbXmhqPOK/Bxj6NVhOBAEKFYlwl7hGBBBAAAEEDArYltAqOlMDA+z0Oip+en5EI4AAAggggEDGCNxS5zJ5vvP97sl37N4nO53/lS1ZRH52nowoWug0OT1/HndfrQeflT/+2OIuP3BXbenR6s7IBR06fET2HTgkBU/LG9k26OUP5MUpsyPrKS30fbKpNLnxSne3KkysXLdJShctJIUK5HO3nThxQjoMelX+88nClE4R2V669Jky84VOkjd3Tndb444jZOEPSQsV5cqWkMkD20nh0/9p4/CRo7LfabtAvtyRc41yrv1553fIiI9teaGp8Yj/XmDo12A5EQQoVCTCXeIaEUAAAQQQMChgW0Kr6EwNDLDT66j46fkRjQACCCCAAAIZIzBjXBep5Ly+SRUJajvFiCoXnC1jureQh3u/KPOcVz81uukqUYWIt2Z9HbmAd0d3ksrly8i2nbul07ApMn/xSjniHHP1FZXkhaebS55cOdzXN13SoFOqr4GqVqW8TBvS3j3vwuWr5ZE+L8v27X+L8ziEXH35hfJir4clW9Ys7muoat3bS44fT/mVTLnz5JQZozu7RRbvQlMqVEwZ/phUr3Sue1j/l2bIa87TE+r6zy1bUl7u21qKn3G6u++Glv3ltzUbvdOdtG/b8kJT4xH/DcDQr8FyIghQqEiEu8Q1IoAAAgggYFDAtoRW0ZkaGGCn11Hx0/MjGgEEEEAAAQQyRmDFh89LjuzZZPY3S6Vtz/FS99qqbqHiIadQkdIrl0qWOEPuqFPdnQNi69adURfW5LarpW+7u91ttzivi1qx8veo/f6V/k81k7vrXSG7nHkoajd/Rv7etde/W+66+SoZ+Ng97rbWfV6Sj7/8IWq/f2WcU2Coc3kl/yZp3HGk80TFL1Hb1NMUs8c/7W57YepsGT4x+qkJ9bv9d1xXye0UW6b+9xvpPnRyVPzJWLEtLzQ1HvHbY+jXYDkRBChUJMJd4hoRQAABBBAwKGBbQqvoTA0MsNPrqPjp+RGNAAIIIIAAAhkjsHTmMPcJiLUbt8n19/eOFCpa9BovX3y9NO5GK5xXSv4zposb17bvRJn9xfcpnmPWhG5y/tnFZfKsedLz+alJjlPzV3z/7mDJlyeXjJ46R4Y5c0ck93nEmePiifvqu7vecM7VqO7lkj1b1mQLFd0fbSTNb79Gjhw9JtUadZU9zquuYj8DO90nd91Q3X2S5OI7OsrhQ0diD9Faty0vNDUe8aNj6NdgOREEKFQkwl3iGhFAAAEEEDAoYFtCq+hMDQyw0+uo+On5EY0AAggggAACGSMw1HlV0+21L3VPrp4w+PX3zTKiy/3yoPN0xZfOUxbxfvxPVNzYeoCsWr0hxVN8M72/nOnMgzH+rU9lsDMBd3Kf0c88LPWuulhmfLZInuw/KckhV19xobz8bGt38uzvnNdHNX1yhPw0a7hbqGjSaaR89330ExWTBj8qV1c9X+bMXypteoxPcj614bKq5WXq4H9eSXVt82dl/fp/5uZI9uAAG23LC02NR/zUGPo1WE4EAQoViXCXuEYEEEAAAQQMCtiW0Co6UwMD7PQ6Kn56fkQjgAACCCCAQMYInFeupPuqo9izN+8xTubOXxa7OcV19fRDA+c1Ts+0bSg5c2SX5U6B4janUJHaZ8yzLaXulZVlyao/pEHbQcke6r0eav6SVdLMKUL4P6VKqcmzO7pPXGzatlNuaTNIdu3aIz//d0SKhYrZL/eQcqWLysjJH8mISTP9p4ssn+5Msr3orYHuenKvj4ocGHDBtrzQ1HjEz42hX4PlRBCgUJEId4lrRAABBBBAwKCAbQmtojM1MMBOr6Pip+dHNAIIIIAAAghknMBN11eT/u0bS97cOSON/LLuTxn66iz59KsfI9tiF7o6RYkqFcrIaXlzS6mihdy5LtQxazZslebdxsgG53VSqX38T18MdF7rNMF5vZP/U+WicvL2sMfdTR9/u0xadx8X2Z3LudYZTpFCFR3UZN93dBgqv/y63t3vFSru6TxKFjgTffs/3quuuo+aLlPfn+vfFVnOlDmTrPpopGTOnFkeH/yafDBnQWTfyViwLS80NR7x22Po12A5EQQoVCTCXeIaEUAAAQQQMChgW0Kr6EwNDLDT66j46fkRjQACCCCAAAIZK1C8WGHp+UhDub76he5rlLzWfvzld3nIebpi547d3qbI9/vOpNMXOk9kxH7+O+9H6TTkddm/72Dsrqj1woULyMwxnaVIwfzu9s8XrpDPFiyXbFmzSK3LKkrNqhUix78+8yvpPWJaZN17GkNtaOe8EupD59VQ3ielQoUqQKye84J7WFoFCK+g0WvMW/LGu194pz4p37blhabGI358DP0aLCeCAIWKRLhLXCMCCCCAAAIGBWxLaBWdqYEBdnodFT89P6IRQAABBBBAwIxAg/pXypAnmkY1tn7zdmnmPJ0Q+4REtSrlpcr5Z7lPVJxVorDUqFIh8lTG4hVrpPHjw+X4seNR54pdUa9vmv5cB3euith9/vVuI6fJtA++cje1bnajdHzgZnd57PRP5LkJ7/kPjbz6qWmXUfLtougnKha/N0QK5Mstz4x9W1575/OoOG8li1MoUU9UqE9sEcQ7RufbtrzQ1HjEb46hX4PlRBCgUJEId4lrRAABBBBAwKCAbQmtojM1MMBOr6Pip+dHNAIIIIAAAgiYEah7bVUZ072FjHvzE7mx5sVSxnnSQn3e/niBdB70WqoXkS9/Hhn0VFN33gl14NPOExDTnSch0voULJRf6jhzVdS8pIJcdF4Z2bv/oPy2frPcWONi9+mO48ePy2WNu7lPddSsXlEm9W3jbv9i0Qp56OkxcuL4iagmvCcqkitUzJrQTc4/u7iMmf6xDJ0wIyrOWznjjALy7dR+7mrDJ4bLD0tXe7tOyrdteaGp8YgfH0O/BsuJIEChIhHuEteIAAIIIICAQQHbElpFZ2pggJ1eR8VPz49oBBBAAAEEEDAj4BUqmvcYK4uc/0A/3/kP9mruiu3OJNWXNeyS5kXkzJldvprcRwqelldmzf1eOjw7Mc2Y5A6odMHZ8u7zT7jzRLw151vpMvh197C5U/pIiSIF3eXd+w7I33v2JwlX82Woj5pge//Bw/Lpt8tl8Ph33W0T+reR2pddKMlNzu0e4PyoXaOyTOjd0l2t0ayn/Ok8UXIyP7blhabGI/57gKFfg+VEEKBQkQh3iWtEAAEEEEDAoIBtCa2iMzUwwE6vo+Kn50c0AggggAACCJgR8Bcq5s53/gN/l/vlzusvcxu/smkP2bJlR5oX8t7YLnLRuaXkmyWr5N4nR6R5fOwBFzqvk3pj4KOSL08u+ePPv6R+qwFywHnKQn3mTesnxZy5LeL5zP5mibTt+aIb0qH5LdK+aT05ceKE1Li3l2xOpggxrm9rqXN5Jdmxe59Uv6tLmq+viuda1LG25YWmxiN+Zwz9GiwnggCFikS4S1wjAggggAACBgVsS2gVnamBAXZ6HRU/PT+iEUAAAQQQQCADBDJlkgn9Wktl53VL7Qa8IgsWrxR/oWLeghXy9gsdnf2l5dDhI1Lp1ifdi+jxyF1SqEBe6fzcG0kmzC7qPM3w6cTukjNHdpn43hfSf/RbkQvPli2r5HXmh0huYm7voOuc100N63iv+xTHMWd+izsfHybLVqz1dkuBAvmkVPHC7pMWkY2+hfx5csor/dq6W9S8Fot/WiubtmyXfXsPuNuKFDld5r3+rGTJklne/3yRPNH/FXGqFpEzXHrxeTJ50KOSNUsWeWHqbBk+8YPIvpO1YFteaGo84vfH0K/BciIIUKhIhLvENSKAAAIIIGBQwLaEVtGZGhhgp9dR8dPzIxoBBBBAAAEETr6A+o/286f0dU+snjB4//PF8vfe/XL/rVfLB18sduenqFy+jLt/zvyl0qbHeLm4Ull5Z/gT7rZ1m7ZJf2eehy+dVysdPXpMLq18rvTr0FjKORNkq0+TTiPlu+9/cZdLljhDZozqKKc781i4BYJ+k9zt3o9ChU6TLi3vkAbXVXM3qcJIp2FTZNYn33mHpOtbFUKWOBNmq0+zLi/I/EU/J4kb1eshqV+zirv9vU8XyoS3P5W/duyWmpeeL33b3S25nNdXqSLJVc16yLZtu5LE626wLS80NR7xu2Po12A5EQQoVCTCXeIaEUAAAQQQMChgW0Kr6EwNDLDT66j46fkRjQACCCCAAAIZIOA8UdG1zZ3S4o5r3cmpU2rhz792yS1tB7lPQmR2nkQY3Ok+ueN/BQUVoya7PuZMaJ0ta5bIKV55/0vpM+rNyHq9WpfI6G4PRtYr39FR9v5vfom7b6kpvds2lOzOExfqowogrZ6ZIKt/2xg5Pr0L6SlUqKcyJg14xH09VXLnPXzkqHQY+KrM+fL75HZrb7MtLzQ1HvHDY+jXYDkRBChUJMJd4hoRQAABBBAwKGBbQqvoTA0MsNPrqPjp+RGNAAIIIIAAAhknUNGZE+KuGy6X2tUvdCaqPj3SkJqseqbzlMWgl2ZEXp3k7bzr5quk1V115GznSQn/R80pMeSVWfKh86SC/1PCeV3TZ5N6uq9UUtv9k1R3dJ6kaN3oetl/4JC88sFcGTvloySvlPKfK7XlHM7TEN+/M0iyOkWTm9sOll9/25Ds4Tlz5ZDeztMTtS+7wHmNVT73mCPOUyHLfv1DBr/8gSz8YVWycSdjo215oanxiN8eQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQMCPQ5Lar3dcfdR81XabPnOc+LZFay3ny5nLnjMgkmeT3jVtTLTAUdibBXuBMhq2ewLjgpsfliPPkgvoUL1ZYrq52vvzni+9ljzOBtemPKqLkyJFN1m/8S444r5zK6I9teaGp8Yj/vmDo12A5EQQoVCTCXeIaEUAAAQQQMChgW0Kr6EwNDLDT66j46fkRjQACCCCAAAJmBPyTac+dv/ykNqomyn6x18OyaMUaubv90JN67kQ6mW15oanxiP8eY+jXYDkRBChUJMJd4hoRQAABBBAwKGBbQqvoTA0MsNPrqPjp+RGNAAIIIIAAAmYEvGLCfU+Plq+/W3HSGi1d+kx535lMO3+eXNK8xziZO3/ZSTt3op3ItrzQ1HjEf58x9GuwnAgCFCoS4S5xjQgggAACCBgUsC2hVXSmBgbY6XVU/PT8iEYAAQQQQAABMwJqsuyzyxSTtev+TPO1T/Fc0cNNbpAuLW6T12d+Jb1HTIsn1LpjbcsLTY1H/B0BQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQSGyBnM4k19WrVpCvF66Qo/+bmyKxf6PgV29bXmhqPOIXx9CvwXIiCFCoSIS7xDUigAACCCBgUMC2hFbRmRoYYKfXUfHT8yMaAQQQQAABBBCwRcC2vNDUeMR//zH0a7CcCAIUKhLhLnGNCCCAAAIIGBSwLaFVdKYGBtjpdVT89PyIRgABBBBAAAEEbBGwLS80NR7x338M/RosJ4IAhYpEuEtcIwIIIIAAAgYFbEtoFZ2pgQF2eh0VPz0/ohFAAAEEEEAAAVsEbMsLTY1H/PcfQ78Gy4kgQKEiEe4S14gAAggggIBBAdsSWkVnamCAnV5HxU/Pj2gEEEAAAQQQCF/AtnzGVB4de+dwjBWJfx3D+M2ICFeAQkW4/rSOAAIIIIDAKSdgW0KrgE0NsLDT68746fkRjQACCCCAAALhC9iWz5jKo2PvHI6xIvGvYxi/GRHhClCoCNef1hFAAAEEEDjlBGxLaBWwqQEWdnrdGT89P6IRQAABBBBAIHwB2/IZU3l07J3DMVYk/nUM4zcjIlwBChXh+tM6AggggAACp5yAbQmtAjY1wMJOrzvjp+dHNAIIIIAAAgiEL2BbPmMqj469czjGisS/jmH8ZkSEK0ChIlx/WkcAAQQQQOCUE7AtoVXApgZY2Ol1Z/z0/IhGAAEEEEAAgfAFbMtnTOXRsXcOx1iR+NcxjN+MiHAFKFSE60/rCCCAAAIInHICtiW0CtjUAAs7ve6Mn54f0QgggAACCCAQvoBt+YypPDr2zuEYKxL/OobxmxERrgCFinD9aR0BBBBAAIFTTsC2hFYBmxpgYafXnfHT8yMaAQQQQAABBMIXsC2fMZVHx945HGNF4l/HMH4zIsIVoFARrj+tI4AAAgggcMoJ2JbQKmBTAyzs9Lozfnp+RCOAAAIIIIBA+AK25TOm8ujYO4djrEj86xjGb0ZEuAIUKsL1p3UEEEAAAQROOQHbEloFbGqAhZ1ed8ZPz49oBBBAAAEEEAhfwLZ8xlQeHXvncIwViX8dw/jNiAhXgEJFuP60jgACCCCAwCknYFtCq4BNDbCw0+vO+On5EY0AAggggAAC4QvYls+YyqNj7xyOsSLxr2MYvxkR4QpQqAjXn9YRQAABBBA45QRsS2gVsKkBFnZ63Rk/PT+iEUAAAQQQQCB8AdvyGVN5dOydwzFWJP51DOM3IyJcAQoV4frTOgIIIIAAAqecgG0JrQI2NcDCTq8746fnRzQCCCCAAAIIhC9gWz5jKo+OvXM4xorEv45h/GZEhCtAoSJcf1pHAAEEEEDglBOwLaFVwKYGWNjpdWf89PyIRgABBBBAAIHwBWzLZ0zl0bF3DsdYkfjXMYzfjIhwBShUhOtP6wgggAACCJxyArYltArY1AALO73ujJ+eH9EIIIAAAgggEL6AbfmMqTw69s7hGCsS/zqG8ZsREa4AhYpw/WkdAQQQQACBU07AtoRWAZsaYGGn153x0/MjGgEEEEAAAQTCF7AtnzGVR8feORxjReJfxzB+MyLCFaBQEa4/rSOAAAIIIHDKCdiW0CpgUwMs7PS6M356fkQjgAACCCCAQPgCtuUzpvLo2DuHY6xI/OsYxm9GRLgCFCrC9ad1BBBAAAEETjkB2xJaBWxqgIWdXnfGT8+PaAQQQAABBBAIX8C2fMZUHh1753CMFYl/HcP4zYgIV4BCRbj+tI4AAggggMApJ2BbQquATQ2wsNPrzvjp+RGNAAIIIIAAAuEL2JbPmMqjY+8cjrEi8a9jGL8ZEeEKUKgI15/WEUAAAQQQOOUEbEtoFbCpARZ2et0ZPz0/ohFAAAEEEEAgfAHb8hlTeXTsncMxViT+dQzjNyMiXAEKFeH60zoCCCCAAAKnnIBtCa0CNjXAwk6vO+On50c0AggggAACCIQvYFs+YyqPjr1zOMaKxL+OYfxmRIQrQKEiXH9aRwABBBBA4JQTsC2hVcCmBljY6XVn/PT8iEYAAQQQQACB8AVsy2dM5dGxdw7HWJH41zGM34yIcAUoVITrT+sIIIAAAgiccgK2JbQK2NQACzu97oyfnh/RCCCAAAIIIBC+gG35jKk8OvbO4RgrEv86hvGbERGuAIWKcP1pHQEEEEAAgVNOwLaEVgGbGmBhp9ed8dPzIxoBBBBAAAEEwhewLZ8xlUfH3jkcY0XiX8cwfjMiwhWgUBGuP60jgAACCCBwygnYltAqYFMDLOz0ujN+en5EI4AAAggggED4ArblM6by6Ng7h2OsSPzrGMZvRkS4AhQqwvWndQQQQAABBE45AdsSWgVsaoCFnV53xk/Pj2gEEEAAAQQQCF/AtnzGVB4de+dwjBWJfx3D+M2ICFeAQkW4/rSOAAIIIIDAKSdgW0KrgE0NsLDT68746fkRjQACCCCAAALhC9iWz5jKo2PvHI6xIvGvYxi/GRHhClCoCNef1hFAAAEEEDjlBGxLaBWwqQEWdnrdGT89P6IRQAABBBBAIHwB2/IZU3l07J3DMVYk/nUM4zcjIlwBChXh+tM6AggggICGQLZsWaXqReWkcvmzpFCBvLJy7UZZsvJ3WbPuT5ETJ+I6c60aleWsYoVl+odfy/59B9MdmzlzZil3TnHZtv1v2blzT4pxWZ1rPf/cUlKxXEnZ5hz3/U9rZOeO3Ske799xVpmiUq5MMSlSML+s/mOzLF2xVg4ePOw/5KQu25bQKhxTAyzs9Loifnp+RCOAAAIIIIBA+AK25TOm8ujYO4djrEj86xjGb0ZEuAIUKsL1p3UEEEAAgYAC55YtKaOebi7nOv8RP/bz+cIV0r7vxHQXHK696iJ5qXdLyZQpk9z91AhZ9OOq2FMmu545S2YZ0f1BqV+zinz87TJp3X1csse1vbe+PNrkBsmRPVvU/iW//C4P9xwv250iR3KfUqXOlL7tG0mNKhWidh87dlwWLF8t7fq8LLt2pVwciQqKY8W2hFb96qYGWNjF0dGSORS/ZFDYhAACCCCAAAIJJWBbPmMqj469yTjGisS/jmH8ZkSEK0ChIlx/WkcAAQQQCCBQssQZMvvFpyVnjuxu9LpN22TfgUNy/tnFRT3hoD6rfv9Tbmk9UI4eOequp/SjdOkzZeYLnSRv7pzuIY07jpCFP6RdqFBPSIzq2UJuuOIiN04VRx7qOjpJM8881kSa3Vwjsn3H7n2Sz2krW9Ys7raNW3fIPR1HyoaN2yLHqIW8+XLLh+O7SokiBd3tG7bskLUbt8q5pYtJ0cKnudvWbNgqjR4fluqTHO6Bcf6wLaFVv76pARZ2cXa2mMPxiwFhFQEEEEAAAQQSTsC2fMZUHh17o3GMFYl/HcP4zYgIV4BCRbj+tI4AAgggEEBg6vOPy2UXlpMjR4/J0yOnybsffuOeJV/+PDK86/1Sq1pFd73jsMmRfck1kztPTpkxurOULVkksjs9hYpszpMRY59p6bRzQSQuuULFpRefJ9Of6+Ae85tTVGjRY5ysX79FcubMLm2b3iiPOE9ZqM9HX/8oj/Sa4C57Px578FZpd09dd3XM9I9l+MQP5Pjx45LFKXA89dDt0rJhbXdfn/HvyCtvfeaFnZRv2xJahWJqgIWdXhfET8+PaAQQQAABBBAIX8C2fMZUHh1753CMFYl/HcP4zYgIV4BCRbj+tI4AAgggEKdAkSKny/wpfd2oEW98JCNfmRl1hlzO0wrvj/mn+PDr75ulXos+Ufv9K+P6tpY6l1fyb5LGztMNC3/4JWpb7Eorp8jQqfnNUZuTK1SMebal1L2ysuz4e6/Ua9k/ySuennWetmjqPG2hChA17u0lW5ynJrzPq0Paua98Uk+G3PhQv+g5N5xXVH04oZuUP6uYfL7wJ+dJjjFe2En5ti2hVSimBljY6XVB/PT8iEYAAQQQQACB8AVsy2dM5dGxdw7HWJH41zGM34yIcAUoVITrT+sIIIAAAnEKqEmv1XwS6lPt7q6yY3vSCambN7peure8wz2mQv3H5MjhI+6y/8cj990kT9xX3930xqx50qju5ZLdeZ1TegoVlS8sKy90ay4zPlskZxUv7M5REVswKFAgnyx8s7/7KqoX3/5MBo17x9+8u1zcmbz7q9efcZeHvfahjH7tP5FjPnypu1uI+PCrH6TdMy9FtnsLI5zXTt18dVVZ7TyhUbf5s97mk/JtW0KrUEwNsLDT64L46fkRjQACCCCAAALhC9iWz5jKo2PvHI6xIvGvYxi/GRHhClCoCNef1hFAAAEE4hS4s/6VMviJpm5USkWIcmVLyOzxT7vHXPvAM7Leee2S/3P1FRfKy8+2difP/s6ZlLrpkyPkp1nD3UJFk04j5bvvU3+iwn+u4c5k2rdee0mSJxsqVTxHZox40j30xtYDZNXqDf6wyPK0EU9ItYpl5f3PF8kT/SZFtg/qfJ80rFNdtu3cLdc/2Ef27tkf2Zcnby755OWeUqRgfpkzf6m06TE+su9kLNiW0CoTUwMs7PR6IH56fkQjgAACCCCAQPgCtuUzpvLo2DuHY6xI/OsYxm9GRLgCFCrC9ad1BBBAAIE4BfxFiObdx8rcb5cnOYP/9VB3PTFcvl+6OnJMqVJq8uyOki9PLtm0bafc0maQ7Nq1R37+74iTWqi44ZqqMrZHC7fd8+q1l2POfBrJfXp3aCz33lJTFjoFk8aPDY8ccsWl58sbAx9119Xrnwa9/IH8unaTnOtMGN7Zmb/ivDLF3H0pGUROFGDBtoRWEZgaYGEXoMP5QvDzYbCIAAIIIIAAAgkpYFs+YyqPjr3ZOMaKxL+OYfxmRIQrQKEiXH9aRwABBBAIILDw3cFS0Jk4e/3m7XJnh6FRcz/kcCaqfs55GqF+zSrumeu3GSi//LreXVbzV8xwihTlSheVQ87roO5wYr19XqHins6jZMHilem+qpSeqLjfmey6Z+s7ZefufXJpg04pnq/tvfXlyftvkg3O/BTXNO0RdVwbZ99Tzr6UPj1HvymT3/sypd2Bt9uW0CoIUwMs7AJ3OzcQPz0/ohFAAAEEEEAgfAHb8hlTeXTsncMxViT+dQzjNyMiXAEKFeH60zoCCCCAQACB9g/cIh2a1XMjdzmvRHrtg7mycesOKes8LaFel1TwtLyRs156VxfZuXOPu+5Nbq1W2vWfJB86c0x4n5NdqGj3wM3yWLMbky1AeG2qb6+gsXvfAaly21P+XVL9kgoyZVC7qG3+lTZ9JsqcL7/3bzopy7YltArF1AALO70uiJ+eH9EIIIAAAgggEL6AbfmMqTw69s7hGCsS/zqG8ZsREa4AhYpw/WkdAQQQQCCgQOfWDaRlw+tSjd7uvNLpsoZd3GNaO0WDjk7xQH3GTv9Enpvwnrvs/fAKFU27jJJvF+k/UXHP7ddIn0cbyd79B6Xyrf/MVeG15f/u0PwWad+0nqzduE2uv793ZFe9WpfIqK4PuJNxq40Llv0qP63eKBXLlZDqlc6NHNe6z0vy8Zc/RNZPxoJtCa0yMTXAwk6vB+Kn50c0AggggAACCIQvYFs+YyqPjr1zOMaKxL+OYfxmRIQrQKEiXH9aRwABBBDQELj80gpSs+r5clXV8u6roNScE6o4Ue+qi92zvuo8afHsyOlSs3pFmdS3jTt59heLVshDT4+RE8dPRLV8sgsVtWpUlpd6t3TbOP+mx+TwoSNR7XkrfZ9sKk1uvFK+WbJK7nUm9VafzFkyy4I3B7hPhqhXVLXp+7J8+c1SL0SuufIiGetM4p0je7Z/Xi11Z2eRE9G/T+TgAAu2JbSKwNQAC7sAHc4Xgp8Pg0UEEEAAAQQQSEgB2/IZU3l07M3GMVYk/nUM4zcjIlwBChXh+tM6AggggMBJFMiUOZOM69NKrq9eyZ2D4sbWA+X3PzbL3Cl9pESRgm5L6hVLfzuvi4r9lCpayN2kih37Dx6WT51JugePfzf2sCTrKc1RcUH5MjJz9D9zU8RO6O0/yX8mdJMKzgTZb81ZIF0Gv+buOrdsSflofFd3efCkWTJ+8n/9Ie5yy3vqupNqq5V6rQbIr79tSHJM0A22JbTKwdQAC7ugve6fOPz0/IhGAAEEEEAAgfAFbMtnTOXRsXcOx1iR+NcxjN+MiHAFKFSE60/rCCCAAAInSUAVKZ7r8oDcXvtS94zdR02Xqe/PdZfnTesnxQoXiKul2d8skbY9X0wzJqVCRe48OWWh81REzhzZ5c3Z30rXIa8nOVf5c0vJh2P/eTXVM2Pfltfe+dw95qwyReXTif9MrB07l4Z3Ev/8FfFOAO6dI6Vv2xJa9XuaGmBhl1KvSt92/NLnxFEIIIAAAgggcOoK2JbPmMqjY+8ojrEi8a9jGL8ZEeEKUKgI15/WEUAAAQROgkC+/Hlk0FPNpK7zSiT1+XTBcmnZbWzkzAUK5JNSxQtH5nuI7PjfQn6nqPBKv7buWreR02TxT2tl05btsm/vAXdbtmxZJW++3LJzx+7YUEmpUKEOfPaxJtL05hpywHlCQz1V8fOqPyLx6vVOL/ZtLbWqVXT3V2/8dKS9nLlyyJIZQyRrliwyacaX0veFNyNx3kLHlndI60bXu6uXNuqa7LV5x8b7bVtCq35/UwMs7OLtbdHH4xftwRoCCCCAAAIIJJ6AbfmMqTw69k7jGCsS/zqG8ZsREa4AhYpw/WkdAQQQQEBToM41VdxJq884Pb97pm+X/iqte0+QPbv3pfvMqgix5L0h7vHNurwg8xf9HIktWeIMmTGqo5zuFEPe/3yRPNFvUmSfWkitUHHO2cXk4wnd3eN3Oa+bUkWQBT+ukuLOa6YedV7ddMMV/xRWJs+aJz2fnxp13inDH4tMmj1qymwZP22OHHAm5lZFjObOJOJP3lffnXNjiVP8aNB2UFSs7optCa3yMDXAwk6v9+Gn50c0AggggAACCIQvYFs+YyqPjr1zOMaKxL+OYfxmRIQrQKEiXH9aRwABBBAIKJArd055Z+RTUv6sYu4ZTjiTSY+d/rEMf3mmHD9+PK6zplaoqFfrEhnd7cHI+Srf0VH2+ua4SK1QoYIa3nSVDOjQOMWnOVRhpWWP8bLPmTvD/ynqFDOmDmkvpYsVdjer32mnU3wpkC+PZHGexlCfvU7h4manSLF+w1Z3/WT9sC2hVS6mBljY6fVC/PT8iEYAAQQQQACB8AVsy2dM5dGxdw7HWJH41zGM34yIcAUoVITrT+sIIIAAAgEF8uTJJd+/O8h9PdIPK9fJoIkfyMIffgl0thw5s8v37zjnyprF+Q//g6Mmpi7hvDLqs0k93XbUyWs06yl/bt4eaecZ5/VOzZzXO0397zfSfejkyHb/wjXOK6keu7e+VDynRKTIsGX73zLnm6XSb8zbcuTIUf/hkeVChU6TJx64RS6rVFbOKVkksv3Q4SMya+4PMvil9+Wvv3ZFtp+sBdsSWuViaoCFnV4vxE/Pj2gEEEAAAQQQCF/AtnzGVB4de+dwjBWJfx3D+M2ICFeAQkW4/rSOAAIIIKAhUP+6arJ91x5ZsHilxlnSDi3sTMS9wJmQWz3VcMFNj6dYWEjrTDmdgkhx5wmJ/QcOyWZfsSOtOLVfzbNRuuQZsmXbLtmydaeI8wRJRn1sS2iVk6kBFnZ6vRI/PT+iEUAAAQQQQCB8AdvyGVN5dOydwzFWJP51DOM3IyJcAQoV4frTOgIIIIBAAghcV/NiebHXw7JoxRq5u/3QBLhivUu0LaFVGqYGWNjR92IFTPW92HZZRwABBBBAAIFwBGzLB8PKZXDU778Y6htyBrMCFCrMetMaAggggECCCZQufaa870ymnd951VTzHuNk7vxlCfYbxH+5tiW0SsDUAAu7+PubPwI/vwbLCCCAAAIIIJCIArblM6by6Nh7jWOsSPzrGMZvRkS4AhQqwvWndQQQQACBU1zg4SY3SJcWt8nrM7+S3iOmneJXe3Iuz7aEVqmYGmBhp9cH8dPzIxoBBBBAAAEEwhewLZ8xlUfH3jkcY0XiX8cwfjMiwhWgUBGuP60jgAACCJziAmpeiepVK8jXC1fI0RQmvT7Ff4W4L8+2hFYBmBpgYRd3d4sKwC+KgxUEEEAAAQQQSEAB2/IZU3l07K3GMVYk/nUM4zcjIlwBChXh+tM6AggggAACp5yAbQmtAjY1wMJOrzvjp+dHNAIIIIAAAgiEL2BbPmMqj469czjGisS/jmH8ZkSEK0ChIlx/WkcAAQQQyAAB2xIyRWRygIBf8E6JXXA7FYmfnh/RCCCAAAIIIBC+gG35jMlxiP/u4ejXCLaMYTA3osIToFARnj0tI4AAAghkkIBtCZliMjlAwC94x8QuuJ2KxE/Pj2gEEEAAAQQQCF/AtnzG5DjEf/dw9GsEW8YwmBtR4QlQqAjPnpYRQAABBDJIwLaETDGZHCDgF7xjYhfcTkXip+dHNAIIIIAAAkEFShQvLJs2b5cTx08EPQVx/xOwLZ8xOQ7xdyIc/RrBljEM5kZUeAIUKsKzp2UEEEAAgQwSsC0hU0wmBwj4Be+Y2AW3U5H46fkRjQACCCCAQBCBGtUryqv92krz7mNl7rfLg5yCGJ+AbfmMyXGIj9G6vDAMR/qiv0exnAgCFCoS4S5xjQgggAACcQnYlpCpX95kYotfXN0t6mDsojjiXsEvbjICEEAAAQQQCCxwSeVzpXH9q6Rm1fJyxun5ZcOWHfLlwhXyzZJV8vn8ZXLo4OGoc2fOnFnKnVNctm3/W3bu3BO1L6UV9aTGpZXKyvnnlJK/9+6XJSvXyVLnf3v37E8akimTnHtOCXG+Uvz8sXGbHDxw6P/3B4n5/+gMWbItnzE5DvHfEBz9GsGWMQzmRlR4AhQqwrOnZQQQQACBDBKwLSFTTCYHCPgF75jYBbdTkfjp+RGNAAIIIIBAegU6tWogre66LsXD/3IKEbc+Oli2OMUL9cmcJbOM6P6g1K9ZRT7+dpm07j4uxVjv+Hb33SSPNL5Bsjix/s8BpwDSbuAr8vm8Jf7NUv+6ajKq6wNR22JXBr38gbw4ZXZkc5CYSHAGLdiWz5gch/hvCY5+jWDLGAZzIyo8AQoV4dnTMgIIIIBABgnYlpApJpMDBPyCd0zsgtupSPz0/IhGAAEEEEAgPQK31LlMnu98v3vojt37ZKfzv7Ili8jPazdJ0UKnyen587j7aj34rPzxxxbJmi2rjOrZQm644iJ3++fOUxcPdR2dalN9n2wqTW680j1GFSZWrtskpYsWkkIF8rnbTpw4IR0GvSr/+WRh5DwN6l8pQ55oGllPbqHriKny5sx5kV1BYiLBGbRgWz5jchzivyU4+jWCLWMYzI2o8AQoVIRnT8sIIIAAAhkkYFtCpphMDhDwC94xsQtupyLx0/MjGgEEEEAAgfQIzBjXRSqVKyWqgFDbKUZUueBsGdO9hTzc+0WZ5xQhGt10lRw6fETemvW1ZMueTcY+01JqVbsgcuq0ChXVqpSXaUPau8cvXL5aHunzsmx3Xhel3ul09eUXyou9HpZsWbO4r5qqdW8vOX78uHusV3RYvX6L3NxqQKQ9/8IR57r8nyAx/viMWLYtnzE5DvHfDxz9GsGWMQzmRlR4AhQqwrOnZQQQQACBDBKwLSFTTCYHCPgF75jYBbdTkfjp+RGNAAIIIIBAegRWfPi85HAKELO/WSpte46XutdWdQsVDzmFitjXMbVqeqN0an5z1GnTKlT0f6qZ3F3vCtnlzENRu/kz8veuvVHxd918lQx87B53W+s+L8nHX/7gLntFh19/3yz1WvSJiklpJUhMSuc6Wdtty2dMjkP89wBHv0awZQyDuREVngCFivDsaRkBBBBAIIMEbEvIFJPJAQJ+wTsmdsHtVCR+en5EI4AAAgggkB6BpTOHSZ5cOWStMzH19ff3jhQqWvQaL198vTTqFJUvLCsvdGsuMz5bJGc5E2OrOSo+X/iT8+qnMVHH+VdmTegm559dXCbPmic9n5/q3+Uuq/kuvn93sOTLk0tGT50jwya+724PUnQIEpPkgk7yBtvyGZPjEP+twNGvEWwZw2BuRIUnQKEiPHtaRgABBBDIIAHbEjLFZHKAgF/wjoldcDsViZ+eH9EIIIAAAgikR2Do083l9tqXuoe+MHW2qCcYRnS5Xx50nq740nnKIqXPcGcy7VuvvSTNQsU30/vLmc5cF+Pf+lQGj3832dONfuZhqXfVxW4B5Mn+k9xjvKLDbxu2SoN2Q9xtJ46fkH37DiR7DrUxSEyKJztJO2zLZ0yOQ/y3AEe/RrBlDIO5ERWeAIWK8OxpGQEEEEAggwRsS8gUk8kBAn7BOyZ2we1UJH56fkQjgAACCCCQHoHzypWU/47rmuTQ5j3Gydz5y5Js9zakt1Ax5tmWUvfKyrJk1R/SoO0gLzzq23s91Pwlq6TZkyPcfV7RIepAZ+XI0WPyx+bt8t6nC+WNGV/IHueVUt4nSIwXm1HftuUzJsch/nuCo18j2DKGwdyICk+AQkV49rSMAAIIIJBBArYlZIrJ5AABv+AdE7vgdioSPz0/ohFAAAEEEEivwE3XV5P+7RtL3tw5IyG/rPtThr46Sz796sfINv9CegsVTW67Wvq2u9sNHei81mmC83on/6fKReXk7WGPu5s+/naZtO4+zl32T8LtP96/vMZ52qJZ51GyZcuOwDH+82XEsm35jMlxiP9+4OjXCLaMYTA3osIToFARnj0tI4AAAghkkIBtCZliMjlAwC94x8QuuJ2KxE/Pj2gEEEAAAQTiESherLD0fKShXF/9QsmUKVMk9MdffpeHnKcrdu7YHdmmFtJbqChcuIDMHNNZihTM78arybc/W7BcsmXNIrUuqyg1q1aInPf1mV9J7xHTIuslS5whWbJkiayflj+3VHDmu2jTqI6Udq5XfT76+kd5pNeEyDFBYiLBGbBgWz5jchzivx04+jWCLWMYzI2o8AQoVIRnT8sIIIAAAhkkYFtCpphMDhDwC94xsQtupyLx0/MjGgEEEEAAgSACyb0+ab3zqiX15MIGZ8Jt75PeQoU6vlSpM2X6cx3cuSq8+OS+u42cJtM++Cq5XVHbsufIJjNGd5byZxVzt1/SsIvs2rUn6pjYlSAxsecIsm5bPmNyHOL3xtGvEWwZw2BuRIUnQKEiPHtadB27mwAAQABJREFURgABBBDIIAHbEjLFZHKAgF/wjoldcDsViZ+eH9EIIIAAAggEEah7bVUZ072FjHvzE7mx5sVS5n9PLrz98QLpPOi1yCnjKVSooIKF8ksdZ66KmpdUkIvOKyN79x+U39ZvlhtrXOw+wXH8+HG5rHG3JE9uRBqMWahdo7JM6N3S3Xp7h6Gy7Kc1MUckXQ0Sk/Qs8W2xLZ8xOQ7xS+Po1wi2jGEwN6LCE6BQEZ49LSOAAAIIZJCAbQmZYjI5QMAveMfELridisRPz49oBBBAAAEEggh4hYrmPcbKoqWrZf7Ufu7cFdudJxYuc55c8D7xFiq8OP93pQvOlneff0IyZ84sb835VroMft2/O9XlCyqUkZkvdHKPeaDbGPlqwU+pHq92BolJ86RpHGBbPmNyHOKnxdGvEWwZw2BuRIUnQKEiPHtaRgABBBDIIAHbEjLFZHKAgF/wjoldcDsViZ+eH9EIIIAAAggEEfAXKubOXy6Du9wvd15/mXuqK5v2iExcrVuouPD8s+SNgY9Kvjy55I8//5L6rQbIAecpi/R+Gt1SQwZ0aOIefvV9vWTjpr/SDA0Sk+ZJ0zjAtnzG5DjET4ujXyPYMobB3IgKT4BCRXj2tIwAAgggkEECtiVkisnkAAG/4B0Tu+B2KhI/PT+iEUAAAQQQSFPAmTR7Qr/WUtl5FVO7Aa/IgsUrxV+omLdghbz9Qkdnf2k5dPiIVLr1STl29Jh72pQKFdmyZZW8+XKn+gqn65xXSg3reK/7pMaxY8flzseHybIVa6Mu9876V0qHZvWl07A35NtFK6P25cmbSz6e2MOd8+Lvvful6u0d3f1BYqJOnAErtuUzJsch/tuBo18j2DKGwdyICk+AQkV49rSMAAIIIJBBArYlZIrJ5AABv+AdE7vgdioSPz0/ohFAAAEEEEhLoEiR02X+lL7uYSdOnJD3P18s6j/833/r1fLBF4vd+Skqly/j7p8zf6m06TE+csrkChUlS5whM0Z1lNPz53HOtUie6DcpcrxaKFToNOnS8g5pcF01d7sqfnQaNkVmffJd1HFq5etp/aVo4dPkiFMYGT1tjkz7zzzZ9fc+ubjiOTLgsSZyttOW+vQc/aZMfu9LdzlIjBuYgT9sy2dMjkP8twVHv0awZQyDuREVngCFivDsaRkBBBBAIIMEbEvIFJPJAQJ+wTsmdsHtVCR+en5EI4AAAgggkKaA80RF1zZ3Sos7rnUntE7p+D//2iW3tB0U9ZREcoWKerUukdHdHoycpvIdHWXvnv3u+t231JTebRtKdueJC/VZt2mbtHpmgqz+baO7HvvjtrrV5bknm7nzV8Tu89bd4knPF0WcIov6BInxzpVR37blMybHIf57gqNfI9gyhsHciApPgEJFePa0jAACCCCQQQK2JWSKyeQAAb/gHRO74HYqEj89P6IRQAABBBBIr0BFZ76Iu264XGpXv1BKOE9ZeJ/d+w7ITOcpi0EvzZB9ew94m93vZ5ynGprdXEOm/vcb6T50srutRPHC8tmknpI1SxZ3vUaznvLn5u3uckfnSYrWja6X/QcOySsfzJWxUz6S/ftSn5NCzWPRrlk9qVrhbCl4Wl73POrHxq075IWps+XNmfMi27yFIDFebEZ825bPmByH+O8Hjn6NYMsYBnMjKjwBChXh2dMyAggggEAGCdiWkCkmkwME/IJ3TOyC26lI/PT8iEYAAQQQQCCIQJPbrpa+7e6W7qOmy3SnEHD8+PG4TlO4cAFZMK2fG3fBTY/LkSNH3fjixQrL1dXOl/988b3s2b0vrnOqg4sWLSRnOK+OWrd+S7rjg8TEfWFpBNiWz5gch/hpcfRrBFvGMJgbUeEJUKgIz56WEUAAAQQySMC2hEwxmRwg4Be8Y2IX3E5F4qfnRzQCCCCAAAJBBPyTac+dvzzuU6iJsl/s9bAsWrFG7m4/NO542wJsy2dMjkP8fQFHv0awZQyDuREVngCFivDsaRkBBBBAIIMEbEvIFJPJAQJ+wTsmdsHtVCR+en5EI4AAAgggEETAKzTc9/Ro+fq7FXGdonTpM+V9ZzLt/HlySfMe42Tu/GVxxdt4sG35jMlxiL8/4OjXCLaMYTA3osIToFARnj0tI4AAAghkkIBtCZliMjlAwC94x8QuuJ2KxE/Pj2gEEEAAAQSCCGTOklnOLlNM1q77M+7XPj3c5Abp0uI2eX3mV9J7xLQgzVsXY1s+Y3Ic4u8MOPo1gi1jGMyNqPAEKFSEZ0/LCCCAAAIZJGBbQqaYTA4Q8AveMbELbqci8dPzIxoBBBBAAAHTAjlzZpfqVSvI1wtXyNH/zU1h+hpOtfZsy2dMjkP89xJHv0awZQyDuREVngCFivDsaRkBBBBAIIMEbEvIFJPJAQJ+wTsmdsHtVCR+en5EI4AAAggggED4ArblMybHIf67h6NfI9gyhsHciApPgEJFePa0jAACCCCQQQK2JWSKyeQAAb/gHRO74HYqEj89P6IRQAABBBBAIHwB2/IZk+MQ/93D0a8RbBnDYG5EhSdAoSI8e1pGAAEEEMggAdsSMsVkcoCAX/COiV1wOxWJn54f0QgggAACCOgK2Pa32GQO7dlj6EnofeOo56eiMdQ35AxmBShUmPWmNQQQQAABAwK2JWSKzOQgC7/gnRS74HYqEj89P6IRQAABBBDQFbDtb7HJHNqzx9CT0PvGUc9PRWOob8gZzApQqDDrTWsIIIAAAgYEbEvIFJnJQRZ+wTspdsHtVCR+en5EI4AAAgggoCtg299ikzm0Z4+hJ6H3jaOen4rGUN+QM5gVoFBh1pvWEEAAAQQMCNiWkCkyk4Ms/IJ3UuyC26lI/PT8iEYAAQQQQEBXwLa/xSZzaM8eQ09C7xtHPT8VjaG+IWcwK0Chwqw3rSGAAAIIGBCwLSFTZCYHWfgF76TYBbdTkfjp+RGNAAIIIICAroBtf4tN5tCePYaehN43jnp+KhpDfUPOYFaAQoVZb1pDAAEEkgjUqlFZzipWWKZ/+LXs33cwyf7UNujEnl4wv1x4bikp5bS9YcsOWb7qd9mxfXfS5jJlknPPKSHOV4qfPzZuk4MHDiXZn+42kkTqbbAtIVMaJgdZ+AXvf9gFt1OR+On5EY0AAggggICugG1/i03m0J49hp6E3jeOen4qGkN9Q85gVoBChVlvWkMAAQSiBK696iJ5qXdLpwiQSe5+aoQs+nFV1P7UVoLG5sufRzo/fLvcXfdyyZw5c1QTK9dukge7j5UtTuHC+9S/rpqM6vqAt5rs96CXP5AXp8yO7Iu3jUjgSVqwLSFTLCYHWfgF74jYBbdTkfjp+RGNAAIIIICAroBtf4tN5tCePYaehN43jnp+KhpDfUPOYFaAQoVZb1pDAAEEIgKlS58pM1/oJHlz53S3Ne44Qhb+kL5CRdDYQoVOk2lDH5NzShaJXMehw0cke7asbrFEbdy6Y7c07TxK1jhFC/VpUP9KGfJEU3c5pR9dR0yVN2fOc3cHaSOl8wbdbltCphxMDrLwC9rz7BsM0PeC9wUv0uT/d702+UYAAQQQQCCogG15YBh/hzEM2vui43CM9giyhmEQNWLCFKBQEaY+bSOAwL9WIHeenDJjdGcp6ysYpLdQoRP7UOM60vWh2133z75bLiPf+EiW/bxOcuXKIY89cLM81KCWu++9TxfKUwNecZe9QsXq9Vvk5lYD3G2xP444xQ7vE6QNL/ZkfduWkCkXk4Ms/IL3ROyC26lI/PT8iEYAAQQQQEBXwLa/xSZzaM8eQ09C7xtHPT8VjaG+IWcwK0Chwqw3rSGAAAKuwLi+raXO5ZWiNBp3HOk8UfFL1LbkVnRiixUtJIOeaio/OMWJ4S/PFDlxIqqJlwa0lVrVKsr2XXvksoZd3H1eoeLX3zdLvRZ9oo5PbiVIG8mdR2ebbQmZsjA5yMIveO/DLridisRPz49oBBBAAAEEdAVs+1tsMof27DH0JPS+cdTzU9EY6htyBrMCFCrMetMaAgggII/cd5M8cV99V+KNWfOkkTNXhHr1UnoKFTqx6aH3Pw1xeZNusm3brsirn9JbqEirneTaSCsm3v22JWTq9zc5yMIv3h73/8dj9/8WQZbwC6JGDAIIIIAAAidPwLa/xSZzaO8uYOhJ6H3jqOenojHUN+QMZgUoVJj1pjUEEPiXC1x9xYXy8rOt3fkgvlu+Wpo+OUJ+mjXcLVQ06TRSvvs+5ScqdGLTyz62Tyu54YqL5Pjx43LBTY/LkSNHI4WK3zZslQbthrinOnH8hOzbdyC9p406Lrk2og44CSu2JWSKxOQgC7/gnRC74HYqEj89P6IRQAABBBDQFbDtb7HJHNqzx9CT0PvGUc9PRWOob8gZzApQqDDrTWsIIPAvFihVSk2e3VHy5cklm7btlFvaDJJdziuWfv7viDQLFTqx6SUv5cyXMfvFpyVH9myybPV6ub31QDfUe/VT7HmOHD0mf2zeLmo+izdmfCF79uyPPSTJekptJDlQc4NtCZniMDnIwi94B8QuuJ2KxE/Pj2gEEEAAAQR0BWz7W2wyh/bsMfQk9L5x1PNT0RjqG3IGswIUKsx60xoCCPxLBXLldibPdooU5UoXlUPOxNN3dBgqv/y63tXwChX3dB4lCxavTCKkE5vkZClsyOYUJ94b3UnOP7u4e8Qj/V6Wjz5f7C5Xq1Jepg1pn0LkP5vXOE9bNHOuf8uWHSkel1obKQYF3GFbQqYYTA6y8AvY8Zww7ILbqUj89PyIRgABBBBAQFfAtr/FJnNozx5DT0LvG0c9PxWNob4hZzArQKHCrDetIYDAv1RgzLMtpe6Vld3fvl3/SfLhZ4siEmkVKnRiI42ksdD/qWZyd70r3KPemvOtdBn8elREyRJnSJYsWSLbTsufWyo4RY02jepI6WKF3e0fff2jPNJrQuSY2IW02og9XmfdtoRMWZgcZOEXvPdhF9xOReKn50c0AggggAACugK2/S02mUN79hh6EnrfOOr5qWgM9Q05g1kBChVmvWkNAQT+hQKtm90oHR+42f3Nx07/RJ6b8F6UgleoaNpllHy7KPqJCp3YqEZSWWl7b3158v6b3CN+WLlOGj8+XI46c1Ok55M9RzaZMbqzlD+rmHv4JQ27uK+zio3VaSP2XOlZty0hU7+zyUEWfunpZckfg13yLundil96pTgOAQQQQACBjBGw7W+xyRzauyMYehJ63zjq+aloDPUNOYNZAQoVZr1pDQEE/mUCNatXlEl927iTZ3+xaIU89PQYURNR+z8pFSp0Yv3nT225yW1XS992d7uHbHBe23RH+yGyY/vu1EKS7Ktdo7JM6N3S3X6780qrZT+tiTrmZLQRdcJ0rNiWkKlf2eQgC790dLIUDsEuBZh0bsYvnVAchgACCCCAQAYJ2Pa32GQO7d0SDD0JvW8c9fxUNIb6hpzBrACFCrPetIYAAv8ygblT+kiJIgXd33r3vgPydzITTpcqWsjdrybY3n/wsHz67XIZPP5d0YlND3P92pfKyK4PuEWUbTt3S8PHhsmGjdvSExp1zAUVyjiThHdytz3QbYx8teCnyP6T1UbkhOlcsC0hU7+2yUEWfunsaMkchl0yKHFswi8OLA5FAAEEEEAgAwRs+1tsMof2bgeGnoTeN456fioaQ31DzmBWgEKFWW9aQwCBf5nAvGn9pFjhAnH91rO/WSJte74oOrFpNVjDedJj4rOtJKsz78Tfe/dLQ+d1T2vWbkorLNn9jW6pIQM6NHH3XX1fL9m46S93+WS2kWzDqWy0LSFTv6rJQRZ+qXSuNHZhlwZQGrvxSwOI3QgggAACCGSwgG1/i03m0N6twdCT0PvGUc9PRWOob8gZzApQqDDrTWsIIPAvEyhQIJ+UKl5YMmfOnOxvnj9PTnmlX1t3X7eR02TxT2tl05btsm/vAdGJVScsWCi/7N69P8l8ExddeI5MHdROcubILvsPHJImnUbK8p/XudeQ3I87618pHZrVl07D3kgyh0aevLnk44k95MxCp7kFj6q3d3RPEW8bybWrs822hExZmBxk4Re892EX3E5F4qfnRzQCCCCAAAK6Arb9LTaZQ3v2GHoSet846vmpaAz1DTmDWQEKFWa9aQ0BBBCIEsibL7cseW+Iu61Zlxdk/qKfo/antpJabLdH7pIH77hWduzeJw/2GBeZN6LsOSXk7eGPS/48udxT91WvmFocPYG32nHo0OHIa6C+ntZfihY+TY4cPSajp82Raf+ZJ7v+3icXVzxHBjzWRM4ucYZ7rp6j35TJ730pQdpwT3ASf9iWkCkak4Ms/IJ3RuyC26lI/PT8iEYAAQQQSCpQwvlHQ5s2b08yT1zSI9miBGz7W2wyh/Z6EIaehN43jnp+KhpDfUPOYFaAQoVZb1pDAAEEogRSKzZEHZjMSmqxc17pJWVLFnGjJr73hfQf/Za7PHnYY3L5Recmc7boTceOHZcK9TvIcef7trrV5bknm6X4VIiKnDN/qbRxXlclJ05IkDaiW9dfsy0hUyImB1n4Be+D2AW3U5H46fkRjQACCCAQLaBeRfqq8/Ry8+5jZa4zDxyftAVs+1tsMof2dDH0JPS+cdTzU9EY6htyBrMCFCrMetMaAgggECWQI2d2+f6dQZI1axa5ue1g+fW3DVH7U1tJLXZAx3ulUd3L3fC35iyQLoNfc5df7NdGrqt+YWqndfdt3bFbrmz8dORfnl14/lnSrlk9qVrhbCl4Wt5I/MatO+SFqbPlzZnzItuCthE5wUlYsC0hUyQmB1n4Be+E2AW3U5H46fkRjQACCCDwj8Allc+VxvWvkppVy8sZp+eXDVt2yJcLV8g3S1bJ5/OXyaGDh5Olyu28lrVC2ZJyTqkznaeJj8ri5WsiTxknF6BetVrYOX9KH/Wa1Q0btyXZnS1bVql6UTmpXP4sKVQgr6xcu1GWrPxd1qz70/2HP0kCDG6w7W+xyRzau00YehJ63zjq+aloDPUNOYNZAQoVZr1pDQEEEDAikClzJhnoFCsa1qkuIyd/JCMmzTxp7RYtWkjOcOakWLd+i+xxXi11Kn5sS8iUsclBFn7BezV2we1UJH56fkQjgAACCIh0atVAWt11XYoUf+3cI7c+Oli2OMUL/+eBu2q787J5r0j19qnjh742K+of5nj7Pn2tt5xV/J/XoHrb/N9Hjx2T8nXb+zfJuU4hZNTTzeXcMkWjtquVz51iSvu+E2X/voNJ9pnaYNvfYpM5tHePMPQk9L5x1PNT0RjqG3IGswIUKsx60xoCCCBgREBN3j1jbGep6AyE7uk8ShYkMw+FkQsJqRHbEjLFaHKQhV/wjotdcDsViZ+eH9EIIIDAv13gljqXyfOd73cZ1FxtO53/qdeh/rx2kxR1/qHN6fnzuPtqPfis/PHHlgiXKlL0aHWnu67mZfth5Vr3tadVnCcesmTJ7G5/dtw78urbn0Vi1II3l1vURt+KKnJUv6tLZEtJZ2632S8+LTlzZHe3rdu0TfY5T12cf3bxyGtWV/3+p9zSeqAcPXI0Emdywba/xSZzaO8+YehJ6H3jqOenojHUN+QMZgUoVJj1pjUEEEDAiMAzziTXzW6uIctWr5fbnYHOv+1jW0Km7p/JQRZ+wf8fg11wOxWJn54f0QgggMC/XWDGuC5SqVwpOeC82qm2U4yocsHZMqZ7C3m494syz3laodFNV8mhw0fkrVlfR6jU61SXzHhOsjmvYv3zr13StNMo+f2Pze7+cmVLyGv9H5EznSLH7n0HpOodHSOvRlUHeIWK9gNfkTlzf4yc01s45jxRoeZ88z5Tn39cLruwnPNaqWPy9Mhp8u6H37i78jkFlOFd75da1Sq66x2HTY7s82JNfdv2t9hkDu3dIww9Cb1vHPX8VDSG+oacwawAhQqz3rSGAAIIGBFY+dFIdxB2U5uByb4X18hFhNiIbQmZojQ5yMIveOfFLridisRPz49oBBBA4N8usOLD5yVH9mwy+5ul0rbneKl7bVW3UPGQU6j4fN6SZHkqX1hW3n3+CXdfmz4TZc6X30cdd8M1VWVsjxbutlucV0atcOaS8D5eoaJ1n5fk4y9/8DYn+12kyOkyf0pfd9+INz6Ska9Ev5o1V+6c8v6Yzu4TIL/+vlnqteiT7HkyeqNtf4tN5tDevcHQk9D7xlHPT0VjqG/IGcwKUKgw601rCCCAgBGB888rLYecx8XXOI+5/xs/tiVk6h6aHGThF/z/NdgFt1OR+On5EY0AAgj82wWWzhwmeXLlkLXOBNbX3987Uqho0Wu8fPH10mR5rqt5sbzY62F3X52H+zr5szOhte9zljOXxKcTe7hb2jrzR8z+4v8LGfEUKmrVqCwv9W7pnqfa3V1lx/bdvlb+WWze6Hrp3vIOd6VC/cfkiPP0h+mPbX+LTebQ3r3C0JPQ+8ZRz09FY6hvyBnMClCoMOtNawgggAACBgRsS8gUmclBFn7BOyl2we1UJH56fkQjgAAC/3aBoc4k1bfXvtRleGHqbFFPJozocr886Dxd8aXzlEVyn+LFCstXrz/j7ho48X2ZMHVO1GEPNa4jXR+63d12Y+sBsmr1hsh+r1Dx5JDX5ZP/nf/QoSPJFhjurH+lDH6iqRubUhFCvWpq9vin3WOufeAZWb9ha6QtUwu2/S02mUN79whDT0LvG0c9PxWNob4hZzArQKHCrDetIYAAAukSsC2hUL+0yUECfunqZikehF+KNGnuwC5NolQPwC9VHnYigAACCKQhcF65kvLfcV2THNW8xziZO39Zku3ehjdHPimXXHCOHHPmk5jwzmfy3qffubvuuO4yefjO2u6E2kt++V0aPDLYC3G/vUJF1EZn5e+9++Xbpavl1fe/lAWLV7q7/UWI5t3Hytxvl8eGif/1UHc9MVy+d85h+mPb32KTYxDvXmHoSeh946jnp6Ix1DfkDGYFKFSY9aY1BBBAIF0CtiUU6pc2OUjAL13dLMWD8EuRJs0d2KVJlOoB+KXKw04EEEAAgXQI3HR9NenfvrHkdeZ88D6/rPtThr46Sz79KumE1+oYVSB4c9jjUqpoIS8k6nvZ6vXysFPs2LZtV9T2ac7k2NWcybFT+wx6+QN5ccps95CF7w6Wgs7E2es3b5c7OwyV7dv/joSqSb2f63yf1K9Zxd1W35lr7pdf10f2m1qw7W+xyTGId48w9CT0vnHU81PRGOobcgazAhQqzHrTGgIIIJAuAdsSCvVLmxwk4JeubpbiQfilSJPmDuzSJEr1APxS5WEnAggggEA6BdTrnHo+0lCur36hZMqUKRL1o/NUxENOwWHnjuj5IXI6RYJhzmuj6l55UeRY/8KXi3+Wx/q/Irv/3uvfLHny5JLChU6LbMuSJbOULl5YrrvsQmnivOrJa7tuq/6y+reN0v6BW6RDs3ru8bv27JfXPpgrG7fukLKlzpSGdapLwdPyRs516V1dZOfOPZF1Uwu2/S02OQbx7hGGnoTeN456fioaQ31DzmBWgEKFWW9aQwABBNIlYFtCoX5pk4ME/NLVzVI8CL8UadLcgV2aRKkegF+qPOxEAAEEEIhToIFTLBjyv3khvFD1NEOzzqNkgzPhtvpkzZZV3n2ho1QsW9Jd3+EUIz5d8M9rma5zCh1e8WC5MzfFne2GyNEjR93j0vpx8/WXufNjqONedQoSz46c7oZ0bt1AWja8LtXw7bv2yGUNu6R6TEbttO1vsckxiHdPMPQk9L5x1PNT0RjqG3IGswIUKsx60xoCCCCQLgHbEgr1S5scJOCXrm6W4kH4pUiT5g7s0iRK9QD8UuVhJwIIIIBAnAJ1r60qY7q3kHFvfiI31rxYyjhPWqjP2x8vkM6DXnOX721QS3q3beguf/bdcnl8wKuy13naQX3y5sstw7veL7WdJyTUp9eYt+SNd79wl9PzY9aEbnL+2cVl7vc/S/NOL0RCLr+0gtSser5cVbW8+yqoTdt2iipO1LvqYvcYf2EjEmRowba/xSbHIN4twtCT0PvGUc9PRWOob8gZzApQqDDrTWsIIIBAugRsSyjUL21ykIBfurpZigfhlyJNmjuwS5Mo1QPwS5WHnQgggAACcQp4hYrmPcbKImdi6vlT+7lzV/ifWBjzbCv3lU87du+Ta+/tJfv2HYhqJXeenPLla8+4T1bM/maptO05Pmp/aisvDWgrtapVlCWr/pAGbQeleGimzJlkXJ9WzquqKsmhw0fkxtYD5fc/Nqd4fEbusO1vsckxiHdfMPQk9L5x1PNT0RjqG3IGswIUKsx60xoC1guccUYBOc2ZIE69gzWlz1llikq5MsWkSMH8stpJwJeuWCsHDx5O6fAk2/PkzSUlUpjoLvbgP7fskD3/+xdRsfsuqXyutGx0nfyy5k8ZNvH92N3uenp+n2QDNTfallAoDpODBPz0OiB+wf2wC26nIvHT8yMaAQQQQCBawF+omDt/uQzucr/c6bySSX2ubNpDtjhjhRE9W8jNV1d154q4+p4e0Sf439r0kU/KpRecI4tWrJG72w9N9pjYjZmd+SrmTu4jxQoXkHc++U46DXw19hB3XRUpnuvygNxe+1J3vfuo6TL1/bnJHmtio21/i02OQbz7g6EnofeNo56fisZQ35AzmBWgUGHWm9YQsFqgykXl5NV+bSVPrhxSv81A+eXX9VG/bylnkri+7RtJjSoVorYfO3ZcFixfLe36vCy7nEee0/qM6vWQ1K9ZJa3D3P1T/vO19Bg+JerYYk6R47lO98rlF53rbv/LmaSuujNZXewnrd8n9viTuW5bQqFsTA4S8NPrjfgF98MuuJ2KxE/Pj2gEEEDgXy3gTJo9oV9rqXxeGWk34BVZsHil+AsV8xaskLeduSgqn1fafWqh0q1PyrGjx+SJFrfJI01ukAPOP5yq/eCzsnXrzijGgoXyy+eTerlPYrwxa570en6quz+f84+zJjhPQainMzr0nZRk7ooH775euj18h3tsSq+MUucY9FSzyCTeam6Mlt3GRrVvesW2v8UmxyDevcLQk9D7xlHPT0VjqG/IGcwKUKgw601rCFgrUK1KeZnUp7Xkypnd/R1vdSaa++nndZHfV73f9cPxXaVEkYLutg3Ov15au3GrnFu6mBQtfJq7bc2GrdLo8WGy0ykcpPZ5vkcLueWaqqkdEtnX5fkp8tasryPrV19RSZ7vfJ+clje3u+03p80hkz6Qj7/8IXKMWkjr94k6OANWbEsoFJHJQQJ+ep0Sv+B+2AW3U5H46fkRjQACCPybBYoUOV3mT+nrEpw4cULe/3yx/L13v9x/69XywReL3fkpKpcv4+6fM3+ptOnxzyucLr34PJn+XAd3+6+/bxY1fvhx2W/ueqULzpZ+HRpHJtp+oNsY+WrBT+4+/0Td6tVOA1+aIT8sXyMFT88nzZw22zS6XjI5xZONW3fIDS36ysEDh9w470eda6pIn0cbyRmn53c3fbv0V2nde4LscV5BFebHtr/FJscg3n3D0JPQ+8ZRz09FY6hvyBnMClCoMOtNawhYKXDVZRfIhN4tJUf2bJHfL7ZQ8diDt0q7e+q6+8dM/1iGT/xAjh8/LlmyZpGnHrpdWjas7e7rM/4deeWtzyLnSXbBSfizZcua7C618Z1RT7mDCfWkRI17usuRI0fdY++7s5b0bH2nO2A47Gwb+uosmehMrHfi+Imoc6Xn94kKyIAV2xIKRWRykICfXqfEL7gfdsHtVCR+en5EI4AAAv9qAWeM0LXNndLijmvdfD8liz//2iW3OPNF7NyxO3JIm3vry1P33xRZ37v/oLucN3fOyLbJztMUPf/3NIXamCdPLpk8tINUKlcqckzsgjpPC2dOi0U/rorsyuWc852RT0n5s4q521RRZawaH7080x0fRQ4MacG2v8UmxyDeLcPQk9D7xlHPT0VjqG/IGcwKUKgw601rCFgnoN69unzmsKgihfolYwsVrw5p577yadXvf/4fe+cBJzXxxfGnVEUQEBQQpEpvIkjHhvQqvYjSe++99yK99y4g0kQBEekgIF0QECkC0hUQsYD/9wITc3ubvb3sJZvb/2/4cCkzk5n5TjaZyZv3HpVuNJiIB+R64EnF+hk9tcH6ln3HqVH3yXpUZHfE78Sy0e20bMPY78SMJRu1/WxZ0tDKsR0oZowYJAKMut0m0ukffw53eX/bEy5jFJ8ItQGF4HFykgB+gd2Q4GedH9hZZyc5wS8wfsgNAiAAAiBAJOP+aiUK0Dv5s7M2dyIdyR12kr2WtSyGs+bD7/fCOsyWRJVKFdT8RLyeJS09y6ZsVfjp0nUawwuc1n+9X53St7F4oVZTNhtVsnAuysQ++GLw3EjCgz//oh2HfqDe4z4JZ0pKBBzfrRyuzUsOnjxHw3kB176DP+jXDPZOqL2LnZyDqL4DQ0UisC04BsZPcoNh4AxxBWcJQFDhLG+UBgIhSUAc0KVL+RJNXrqBJvZooLWxfKsR9P3J83p718/spQki1m8/SK37z9TPqx3lxO7MxatUsv4AdTrS27kjWlPRPJlJVi8VqtWLfucJSVyeaKyf3kNT975++w7V6DiOzrMTb7PgT3vM8kbV+VAbUAgXJycJ4BfYnQh+1vmBnXV2khP8AuOH3CAAAqFJIN5zz1BM1kL+7dd7odlAG1tVq2IxGtS6BomD6k/W7vBLY+Hpp5+m9GlT0NPs5Po8m4n1NNlkVt3YcWJRhjQp6A8WUpzjuYan1rYxX5l382m+LcSPhttCqL2LnZyDqL4EQ0UisC04BsZPcoNh4AxxBWcJQFDhLG+UBgIhTeCVV16iLbP7aG30FFQMZ78QVd/LTyIoKN5gIN27e19nIZOvrzjfi4kTkNFerJ7Az52sbHN27aQuWuqpbNJp5PTPtP2eLatRA1YBl1C941i6yureYjv2Iq+O8uUPw1d7tIvZ+CfUBhSCyslJAvgFdnOCn3V+YGedneQEv8D4ITcIgEBoEljJ49un+ON55ebDQrOBNrbK6Ex72+5jNpYUOpcOtXexk3MQdReAoSIR2BYcA+MnucEwcIa4grMEIKhwljdKA4GQJuDrw37BvFlo4bBWWvvF/NPw2Wvo9E+X6VVerdSV/VdkZFVpCfV7TaFte6xNIiYPaEolC+Uk8T9RpG4funnzNxJTTvtWDKeE7MxbgjjUU4605fgk1+GLHYdo0oL14VY9+WqP5LUzhNqAQlg5OUkAv8DuTvCzzg/srLOTnOAXGD/kBgEQCB0C8XnsWqVUAarwVl7KliGlZjV195FTtOvgKfqanTn/ePaS18YmfiEBZc/4CqV86QW6cOUGHWEN5zu/RU4TQ0ypNqn+Lv1w9gqNYVOqxiDXT/LE+bPxvNq/zw6jf+bFQG4J7xbNTdP7NqZ6PSbRzm+/d0u1XF2PUHsXOzkHUR0LhopEYFtwDIyf5AbDwBniCs4SgKDCWd4oDQRCmkBEH/Y9ndR5wugzaRkt+myr52m/jtOlTU4bp/fUHOct/WI39Ry9UMuXJ2cGWj6mfYTXWLl5H3UZPi+MsCKi9kR40QAShNqAQlA4OUkAvwBuPs4Kftb5gZ11dpIT/ALjh9wgAAKhQSBN6mS0gBf4pEj6n38Fz5ZNXrqRRs/8T4gggo1uTd5n3wz5dT8JKs+B789S077TfWoSS9rkyV6gUV0+oAI5X9Wyil+3/NW6qcto283z+1GaFEnDnDMe/PPwIWUq2cZ4Kqj7smgpLS+I+uncFb/MPgW1si4pPNTexU7OQVQXgqEiEdgWHAPjJ7nBMHCGuIKzBCCocJY3SgOBkCYQ0Yf9/K9npsXDW5syaD5wFm3c+p1pvK+IUd0/osps6/VfdtL9TsOBdOHCVS15xZL5aUznetr+XfZXMePTLbR687d0g7UtsmdOQwNbV9e1OUTLY/riDXoxEbVHT2jDTqgNKASRk5ME8AvspgQ/6/zAzjo7yQl+gfFDbhAAgehPQPwjbJjdm/2/vag1Ztt3J6hYniz08OEjOvjDOcqbNZ12fvWW/dRh8BxtP2HC+PTp+I5hBAg3f71L8dlpc+xYMbU0F3+5SVXajtY0jrUTHn+KFcxBY9lUq9I8/pF9M4ycs4Y2bT0YJuXOpUMoWZLnw5wzHngTbhjjse9+AqH2LnZyDqJ6FwwVicC24BgYP8kNhoEzxBWcJQBBhbO8URoIhDQBXx/2S739Ok1gYYJMviTsPXqajp+5xKrsL1P+HI9Xbcn5ZgNnhpsQyXlf4eUUSWjLnL7a6rENuw5Tiz7T9eS1K71JA1tV144L1elNV6/e0uNk55ln49IGdvT98ouJaN/xH6lm2zF6vK/26Ils2gm1AYVgcnKSAH6B3ZjgZ50f2FlnJznBLzB+yA0CIBD9CRQrmJ3mDGyuNWTOqq00aOIy2jCnD/3zz0Mq23gwiU+20mzOaPG6HXSFhQ8SiuTPRvMGt9D2l6zfSVNY2+LS5RsUP0E8GtqhNpUukluL6z9lBc3nRTueoV6Vt6lPsyqaZrKYUB09bx3NYn9v3pxBK0FFm2FzaeO2Q56XYoHKQ3rEQhWE6Esg1N7FTs5BVK+DoSIR2BYcA+MnucEwcIa4grMEIKhwljdKA4GQJmD2YV9UrvcuG0qJn3+O/vzrb2o+aDZt3XVEZ/Em+5WY0qsBxYkdi27f+Z3yVulKmiFePYXvnYHta1PtsoW1RBVbj6RjJ87pGepUfpMGtKyu2eh9+4O++nnjTo3yRWlI25okquq5KnaiBw/+0qLN2mPMa9d+qA0ohJOTkwTwC+zOBD/r/MDOOjvJCX6B8UNuEACB6E+gQY3i1LNxZa0hbzcYoGkJi6DibxZUlGNBhVmoUqaQlmbNxr1hksSOE4s2z+2rmZHasu84Neo+OUx8tixpaOXYDhQzRgwSbYi63SbS6R9/DpPGeKAEFVYWFxmvY9d+qL1HnBw/qz4BQ0XC+hYMrbMz5gRHIw1r+2BojRtyBY8ABBXBY4+SQSDkCJh92H81fUr6clp3rb0j5qyjaYu+CNf2JrVLak61JaJU06E+J0jGzEmSJKTtC/prau2ipVG7/VhjNJV5Jy9N6FFfm7jlrtyZHrCDP89gdPStJoSSxqw9nvntOA61AYUwcnKiBX6B3ZXgZ50f2FlnJznBLzB+yA0CIBD9CXxY9R1Nu0Fa0nroXFrPftREUCGaDuWbDLHUwGmDm1Hx/DnozMWrVLL+AP0acZ+JQ+un96DUyZPQ9dt3qEbHcXT+wi96vLcdCCq8UbHvnJPjZ9WKUHsXg6HqWevbYDCU2uJetN5nKicYKhLYRhcCEFREl55CPUEgGhAw+7AvDgE3z+qttaD1kDm0/uv94Vpj9F9Ru+sE2nvgZLg03k50a16VGrO6uoQPe06mHXuPh0mWM3s6+mxsR+1cw77T6Jud/2lyqIQfvP829WtRVRNmZC/fgf7hiaAEs/aofHZuQ21AIaycHOCCX2B3J/hZ5wd21tlJTvALjB9ygwAIRH8CMv78msfNTz31FP106Tq1YLOoE3o20LSSK/BinsiGOHFj06bZfTQzpxt3H6Hmvafpl+jZsho1qPyWdly941i6euNXSpwoPl3kcm+zdoW3oAQVHUcuoK+eaEj/+eff9DdrTbshhNp7xMnxs+o/MFQkrG/B0Do7Y05wNNKwtg+G1rghV/AIQFARPPYoORoSiMcO6T7kj9rF8mamziMX0kV2MudX4InGq+le5gmHeeoLPCHwtto/UeIElP3VVJSKVzr9zP4Vjp06T7du3jG/EMckfoHzZHyFUr70gmby6MjJ83Tnt3s+80RFpNmHfVmtdXjVSE2lXNna9Syvc5PK1Kx6ce103urd6fat/9oo7blz574uQFB5E7ApqV2LBtIzPAE7+dNlzW6vilPbWOxAcMfiQZSEJ13nLl+nii1H0L2791U0xY//LH3FkzeJP3L6IlVuPkyPM2uPnsDGnVAbUAgqJyda4BfYzQl+1vmBnXV2khP8AuOH3CDgRgLiS+wy+1Lw5u/AjfV1Q52mDGxKJQrm1KoipknFLNOxMz9TxWaRE1SkSvUSDWhdTXPGLRczLtoR06z7VgynhDwWlvDbvfu6I205lrH1FzsO0aQF68P0nRJUSBpjkPx7jpyheau3+r3gyJg/qvZD7T3i5PhZ9QEYKhLWt2BonZ0xJzgaaVjbB0Nr3JAreAQgqAgee5QczQjIqvuOH5al+CyskNB93BJatnaHX60o824+zZG0r8TDZ6+h6Ys36EnE+V3XxpWoRskCugNqFSkThwa9poRzDC0f3bs1eZ+qlcivOZZW6WV74Puz1LTvdNPVUca0Vvd9fdhf/HE73Wn2BG7nNHby98f9ByRCjPpV36WO9cpoK8cOn7pA77cYrldBrfS6xb4rGvSeSkePn9Xj2tYvT23qlNKOxaHf51/t0+OMO0azUgdPnqPpyzfTQeaRMW0KGti6hqbuLunrM9Nte47pWX21R09k006oDSgEk5MTLfAL7MYEP+v8wM46O8kJfoHxQ24QcBsB5eTZc4zltnq6rT4JE8anUV3r0dv5supVEx8VC9mB9pQlG+jmzd/088ad9LwwanDbGhQrZkxNgyJpogR69Oh5n9NkFjqokCdnBlo+pr06NN2uZNNTXYbP04UVS8e2p3zZM5imlwjPeY3PxFEcGWrvESfHz6orwFCRsL4FQ+vsjDnB0UjD2j4YWuOGXMEjAEFF8Nij5GhC4Nl4cWlklw+oVOHcWo1lkrBi014aNWsN/fqrd5Voz6a9z87tRnao43k6zLFR8PHCC8/T0tHtKF3KF/U04oQ6NmsHiBq4hGuscVCHTSSdZaGFBJnQfDq+I6VJkVQ7lj83uX4iWJF8Ei7yarYqbUebTm60RAH8MX7Yr8BOrY8bnFonS/YCLRnZhl5hzRAJjx490hxnJ4wfTxeq3GPBRTkWUhg1VTay87/0TzjM+uwbGjJpuZZfNCW+XTGMEnD7Ll27RW/V7atdU4v0+CPCkOm8Mq1w7kweMf8dzuXVXwMnLPvvBO/5ak+YhDYchNqAQhA5OdECv8BuSvCzzg/srLOTnOAXGD/kdo5AvOeeoZgxY9Bvv9qvsepcq6KupNdzvUo1yxSmonkykXwsF63grfu+p12HT9GW3Ufpzwd/hSssadKE9Dwv1Dnz46VwcXIiEI1huXaebOlYeyAeHeVFMSfPXKRHDx95LcdNJ2tVLEata5eil3huoILMCQZM/ZSWrtmuTunbYgVz0JyBzfRj4444yd69/4R+qmLJ/DSmcz3t+O7vf9CMT7fQ6s3f0g0WgmTPnIYX81SnjKmTa/FGwYNomCcx1CcGa2a8wloz776RnWrxnEfNVUo2HWLal3olbNgJtfeIk+Nn1R1gqEhY34KhdXbGnOBopGFtHwytcUOu4BGAoCJ47FFyNCAgpoVWju9EaV9+/PFfVvu3HzYvQidznk1TggpxYFfOxLas0a5ro5rvUfdGlbTLfP3tMRq/8Es6yh/9n+EP7u0+KkeNWLtDwme8wqkTO9mToFasyf6S9TtpCmssXLp8g0QzY2iH2lS6yGNBS/8pK2g+T0TsCDIJ3LlwID1kIUTxhgO18o3liACmw0fl6Y0c6cMJYdZtO0gjZq6mG2wb1xiGdv6AqrNWiYTlG/dStxHztf24bO7pwKfDWcgRgzqPXkhrN32rnTf7I+rtnVlDpdybeShF0kR6MhHeDJ+9mr74+oB+Tu1E1B6Vzo5tqA0ohJGTEy3wC+yuBD/r/MDOOjvJCX6B8UNu5wisnNSFnnr66TAmI50r3d0ldWn6PjWt9q5pJW+w74MKrUaE0Qx+jVf3zxvcguLxWLcMm+H8gc1xqhCIxrCYnZrJH+7VB3d1zQd//kVDZ62mhSu/Uadcu43BArHNvHAnFS/6MQbRUB7LGtnGEDtOLKpb6U2KEzu2plEh5mNzZEilJfn333817WSlPVyb0w1sVV2LK1Snd5j+kJPPPBuXNszspV1n3/EfqWbbMVpaX3/KFX+DxnX7UEsyb802GjD+E1/JbYkLtfeIk+Nn1SFgqEhY34KhdXbGnOBopGFtHwytcUOu4BGAoCJ47FFyNCAwdVAzeq9ADq2mMtgeNHG56ap9X81RgorT53+hUvwBP6KQnCciwzvVoYMsnPh49loinlgYw8yhLVgVPJumMfFG1W56VBVexSQaH2v4g74xyKRFJjjygX7LvuPUqPtkY3RQ9kUD5JWUSenq9V/p6rXb4dqoKvXU00/RMBZWVH0vP41f9CWNm8M8Agzi9yMVT1wv/Hzdb62YAIuMdPZQG1AIACcnWuAX6VsuTAbwC4MjUgdgFylc4RKDXzgkOOEiAvLBvEqpAlThrbyULUNKbXi2+8gp2nXwFH299zj9eNa7JoBZExKxf6xK/GH3h3NXaBdrG3gGWWQhPs6ypE9JMXg8dJy1AE6fvUwPeazn1lD+vTdobNfHH6rFbOdt/i+asSdYAzgZL1hJxAtoJLzdYABduHBV28/3WiZNC0B8jkkwauUGojGcIf3LtGhYa80PmVz3r7//ofusyaF8Msg5bx/75bzbwoY5fbQqbWWNiAYsYFBaC0U/6EuXr9zwWd1iBbPT1N6NWHgRi67fvkMFqnXX0tep/CYNaFld82f3Nl/HW6hRvigNaVuTxE9Groqd6IEXTRjPfOtm9KQsbF5123cnqH6XiZ7Rth+H2nvEyfGz6hwwVCSsb8HQOjtjTnA00rC2D4bWuCFX8AhAUBE89o6VHFWTnKd51ViGdCnoOqsD3+aVUGYhJpvkycKrd2QCd53Tfcc+BYyOkT3ziRp3EoP9VM/4+3/8ST+zo2mngxqYS7lGk0NW6hFZQUVEZRg1LgrU6knX+WN/RGHa4GZUPH8OEq2OkvUHRJTcNfFy362a0pWy8SS9Npu62nvgpGvqZmdFQm1AIaycnGiBX2B3J/hZ5wd21tlJTvALjB9y20cgTepktGBYqzBamZ6lTWZt1tGsHepPkPHykjHtKE+WtLT36Gmq3X5smGxvFspJ/ZpX0U1mqkj52D5m/uc0Y8lGdcpV21VTu2kr+P/gD9rvsDDitaxpaXKvhtS433TawcKY6mULk5guWr5up1bvwm9kpRn9mmgf0VVDjIKKQDSGjf7RhsxcRfNZe0I0mF/lMeVsXoykNGxLNBkSaSGTqqtTWxFU/MFaIJWaDaNW7DOv/QdltKJ7TfiElqzeFmE1GtQoTj0bV9bSFW80iH5i4ViZd/LShB71tUVOuSt3pgc85/IMBfNmoYV830swCpc80xmP1YIqT79zxjR27ofae8TJ8bPqFzBUJKxvwdA6O2NOcDTSsLYPhta4IVfwCEBQETz2jpQcVZMcEXaM69WAyhR9jTbtOUrNek31Wv8WPGhuVatEmMmGJDz8w3lq3GeaV98Im+f3C+NXwfPCsoInU8k2nqdtPU6SJCFtndeX4saJTdu/O0mtB82mFC8l5pVz//JKtku6Mzl/K6EEFT/+fI3eZ98NEv599C/9zvZgrYQp7G+hRMGcmnZH1rLt6W+etPoKcXiF2qbZfTTV7Y27j1Dz3tN8JXdVXP92tahuuSJ0lFcRyuTs/yWE2oBC+s3JiRb4BfZLAT/r/MDOOjvJCX6B8UNuewjIookNs3vrZitlpXixPFnoIfs4OPjDOcqbNZ1W8Oot+6nD4Dl+VWIQm+WsxT4cJHx77AzVavexnq9+9eLUq8njj8rqpHzclxXxKnjzraXigrn9fv1YrZ4bdh2hFjz2L/lWHk1Q0YgFFVt2HA5TNZlfHFs7Jky7JIFRUCHHVjSGRZtiw7Qekp0msvPpj9m3nDGkZLOuX0ztTs+yqaklX+yiXqMXGaNdt28UVIhp0j1LBmt1XLZhD3UfuSDC+hYrkJ3mDGqupava4WM6eOQM5cyejj4b21E717DvNPpm55Fw1/mAzc32a1FVE2ZkL9+B/olgziF9um3RQErOc6lPv/qWurDJXKdDqL1HnBw/q74CQ0XC+hYMrbMz5gRHIw1r+2BojRtyBY8ABBXBY297yVE1yZEVXxP6NNQ+jEult/BqqEbdJ4Wrv/qgrCJE3Ts+2zaNxXZVJYjD49qdx4fTjti5dAglS/KfgziVX23Fjm3+av+ZN1Ln7dx+WPUd6tOsitcixNncrsOn2W/Eejp56j/7uV4TPzmpBBWeacRM0wX2kSC+Jhau+obu3r3vmSTccSpWn98wvYc2qfPn432qVC/RgNbVtAm1XMxsIhKuIJecOPnleG3lXVm2VxwMzZpgYQi1AYVwdHKiBX6B3bngZ50f2FlnJznBLzB+yG0PATGdM2fg44+8c1ZtZVOgy0g+HP/D47iyjQdT1kypqXTR3LR43Q66wuO6iEK1coVpWLvaejJPQcWJL8ZRbB5//84r3AdNX0kbth/SnHbnZh9fYlZJ+Sp488N+rhsbHWHBg/iZ+Im1oYtz/ZSgwmz8OY7nGOlSvkSTl26giT0aaEzKs/+K70+e1/n42jHTGO7Fvhfqs4kkGWvnq96d7vK8xDMM61KPqpXIr40zRaPgrz//9kwSlON8r2WkWQOakQh7en+8WDO3ZBRUyEK02QOaanUbNe9zmrJgPcm90a1RRZrNWiMbt34Xrt5929SkehWKauelrTLniMX32I7FgzTTWOcuX6eKLUfQPcNcREydfcULnZKwibIj7DOkMo/FxffdDF4wdfPXu9R20Jxwgguj5kbfycuD4gMk1N4jTo6f1Y0DhoqE9S0YWmdnzAmORhrW9sHQGjfkCh4BCCqCx972kqNikhOLV25N6d+E/SFk1evrTVCRN3dG+mRUWy2NaA007D2VLrKJIXF43KJOaWrJWhYSvtx5iFr2naHtqz9KUNFm2FzauO2QOq1vH7JGxSNeseZkWDi6LRXMldFnkXdYYFGHTRH5M5ES27tLR/rWCjnL3Ory9a5evWVarvTHZ+y8Uey+Smg5eDZ9uSWsE+j0bMt4cNsaLCCKqWlQJDWY1RrNk5nJPJmJTiFLxlfoT169dZZtG/8/hVAbUEjfOTnRAr/Afi3gZ50f2FlnJznBLzB+yG0PAePHV2X+Rj4cy0fwciyoiEzIwaaQlo9pry/kkbz7WKOipkGjokntkvQmm9zpPWFZuPFPtixpaM2EzlqRPccvpaVrtkemeNvTjmZTQpXYpJAE0WQQ/2ziXLkBa1ds5Q/vZuGVV16iLfxRXIK/ggpfGsNzRrTSFun40iR+I08mWjLi8fj8LTaLKnMXN4TBHetSzdIFtapcvn6bhTgbqdNH5Sk2L/5a8uVuqlWqoKYJ8ujRI42VLJyaP6oNFc6dSdP+nr92O81Y9pUmNBNhQ5OaJahFjfe063k6xZZ7rWuDClrcwZPnaPryzXTw+7OUkecaA1vXoNTJk2hx9XtNIXHCbVx8JaadhrFJrYPHzlJiFmbUrVCMmrM2kPjPkAVqJRoO8mpOSrugjX9C7T3i5PhZdQsYKhLWt2BonZ0xJzgaaVjbB0Nr3JAreAQgqAgee9tLjopJTlMWMnSpXy5MXb0JKiYPaEIlC+WiW7/do1Js5/Um+7EwhgFsvqcOm++RAXURdtZm/BivBBXNBs6kTVsPGrMFbX/b4oH8kT+xVv7uw6doKg/2ZRAeL15cqlW2CLXkAX8MVm0W7Yp87JBO7N1GFETFPEaMx9olkvb5BM9SZp4ENK/+nm5/2Jsgx3jdIZ3qUg2enEhYvnEPdRuxwBit7RcrmENzSBgugk/U7TaRdrMTPgT3Ewi1AYUQd3KiBX6B3ePgZ50f2FlnJznBLzB+yG0PAaOmbeuhc2k9a8KKoEL8RZTnca+/QfyyfT6lO72YOIE2hvxy52Fe0V8gnKAiousp80puNFmUkX3UiUklz1CfFzFt233U87R+HFlBRUQaw2KqK8MryWj8oi9p3Gha71oAAEAASURBVJy1ejnGHXFmvn/5Y7OiNVnre9/BH4zRQdsXhjP6N6WUbHbWVxg5dx1NXfiFliRX9vRs2qkZPf/cs3oWT3NhMm8RDeVLl/9zvh2XtV+ms4aECDnMgtHMWLx4z9AiXtCVI0Mqs+R07/4DasiCqf2HTpmmsTMi1N4jTo6fVb+AoSJhfQuG1tkZc4KjkYa1fTC0xg25gkcAgorgsQ96yf5McmTQO7FnfVr19X72I5FE81GxZd9xNv00Wa9/woTxad+yIST2e6ev+JqGT/1Uj1M7KXg1zvYF/bXDMfPX0yR2AqiCGwUVBz4bSQl5BdJKnoh25gmpZ1D2WuV8rS7j6dvvrE9sYseJRasmdaVMaZJrxbxetRv9yurUnkH8f3Rk53kSZMVTzfYfh1O3lji5Xl1WdY8TO7amUZGdHZuryYT42GggE0VeEeVECLWXojBzarIAdoHdoeAHfp4E8Nv1JOL/sVPspEb47frfL0jpHAH5iP71rN7aSnExadSCF9dM6NlAMxlUoelQvyoSg1fDL2bn2eLPQhbuyId7WbDSvVEl8lzl7uuCRkHAhMUbaOzsNb6SByWubPF8NIRNDT3HJmBV+IGdN4+et442sxkrbyEiQUVkNYaVCSpfzqafevopOsUmRmUO037EfFqzca+3qgXlnGiLVCtTiN7jBUj5c7waRgNHTL+OYS1pT8GPLIpqW68slX8zT5j0IrBYz9yHzVhFN278Gq494leic+NKVI7zKQfjkugimzEbPns1ffF1WO1t0fBuytryJQvnokypk2uLtyT9A3b2vePQD9R73Cd07dptORWUEGrvESffwarDwFCRsL4FQ+vsjDnB0UjD2j4YWuOGXMEjAEFF8NgHtWQrk5yP2Zl2hbdeZx8VYQUVObKlo1XjOmrtKd1sKJ0687PXti0d14HyZUtPno4GlaCiIzuC++qJSvifbCPWHy0FrwVFwUklqGjLDuDWsSO4cIFVmnd/MkRbEefNQV+49BGceKdILprRr4mWqlLb0XT0+NkwOWpVLEaDWP1aws9sGqpym5F06+adMGl8HYht5am9G2l+La7fvkMFWAvEiRBqL0Vh5tRkAewCu0PBD/w8CeC360nE/2On2EmN8Nv1v1+Q0lkCU3jVeYmCObVC/2GzpDFZS/YYj3kr8tjXn2D05TZs1mqasWQjNar5XqQFFUbt2vq92RzPbmcWn/jTRmMaWaTUp2VVKp4/uybgUXGHfjhPjVhIc/tW2HFsRIKKyGgMiwDizMaJWpERCSCUQCNY/hQUF19b0XrYMLMXPWCBQ61OYyOcA4jgJRlrYyRhDZ4bPF+4LH5TeLGSPyERa/uk4sVpF36+7nXhlOc1ZIFUhjQp6A8WUpy78Av9+8i/cjyvE5XHofYecfIdrPoBDBUJ61swtM7OmBMcjTSs7YOhNW7IFTwCEFQEj31QS7YyyTETVJTg1TdTejfU2pOxVBt6yPZ6vYV+bWvSB+WLhlNvV4IKzzy/3btPe46coXmrt9LeAyc9o209/mpeP0rLq5J8CSGWT+hEebKkpTXfHKD2g2YHVJ+smVPT2oldtGt81HMybd97XL9eGbbzO777R9okT4QMVduNseQ40WhfuXijQfQTr2yzO4TaS1F4OTVZALvA7k7wAz9PAvjtehLx/9gpdlIj/Hb97xekdJaAaBCP6lovjN828VGxkB1oT2FfDJ5mT421M9r1N44blaBiP/sEqNFmtDGL1/0y7+ajCTwmlCC+H0rxeM7fD9BapiD8MbZdFS8r9cUv28+snaJCRIKKyGoMq0VH/aesoPmfblHFhNmKlotoVEhoPWQOrWcNcrcGozNtt9bRLfUKtfeIk+9g1YdgqEhY34KhdXbGnOBopGFtHwytcUOu4BGAoCJ47INWstVJjpmgQtntvX3nd8r7/uOP7d4ap0wXiUbAm3V660mWjm1P+bJn0I+97QxntfbprN7uVJg7ojUVzZOZvj97yavtYVGR3rtsKCV+/jnyNGVlpY7VyxehoW1raVmL1eur244tkj8bzRrQVFu1J4KbqmzuyapT6WIFsrPt2uZaGVU7fEwHWQhkdwi1l6LwcmqyAHaB3Z3gB36eBPDb9STi/7FT7KRG+O363y9IGRwCouXaunYpeumF5/UKiGmdAWz61Jtja3F+vYKdZ8eOFZOO//gzVW0ziv5izWEJkRFUpEmdTFvU8iyvrr//x59UkbVrz/5k/6ITvZEWd0q+lYcm92qo+XsrXTS37px5xaa91HX4fP2qEQkq9ISGHV8aw+tm9KQsbFpr8iebaDSbPPIWkiZNSHuWPHaG7tTY2Fs9/Dm3njUqRGuhSssR/iT/v04Tau8RJ9/B6sYBQ0XC+hYMrbMz5gRHIw1r+2BojRtyBY8ABBXBYx+UkgOZ5JgJKlp/VI7a1S2tmSQyCiA8G6gEGnfYkdtrFTvp0eKULYlhsidOql9hleN338hOtdg261NsZklCyaZD6MyPl/R8du7Uq/I29W1eVSuiz6RltOizrWGKa8IT1K4Nymvn6vditfsnPh9i8ST0OfZt4anOXoXb0bZuGeoyZiHt2R9WOyTec8/QJrZ7LBNeEUbkqdRZu27O7OloyfDWFDdObG1CKr4wjp04F6YexoPcOdJTt0YVafbKb2jj1u+MUdp+X7YVXK9CUW0/d+XOdPfu/XBpovpEqL0UhY9TkwWwC+xuBD/w8ySA364nEf+PnWInNcJv1/9+QcrgEZCV+Jvn9qVUyV4IUwlPnxHirHkdO89OluR5uvXbPSrXYjhd5QU7KihBxQHWqKjuQ6NCPqh/Oq6T5ntM/I017j+Dtuw4rC7j6q0SVIiZqv28SGY3CwbEd8VN9sf2BvtlU8GKoELymmkMzxjSnN7hucTuw6eobsdxqpgwW6Pp1SJ1+9AVMZHk0vDii4no4cNHPjV3XFp1x6sVau8RJ9/BqrPAUJGwvgVD6+yMOcHRSMPaPhha44ZcwSMAQUXw2DtecqCTHDNBRW123DywVXW6d/8B5arw2FeFt8a1rV+e2tQpReKEsPiH/bwlCXeuXPE3aFy3D7Xz89ZsowHjPwmXxo4TcdmB3U6eSIlDbZkUjF/8JW1i/xm/cxvrlC9GjVmQIQIUcWpdjVfGiT1WcWC3akJnSpQgnqkfDjERMGnpRlr6+Q769bffKTf79xjarpZmZkraoYQi4jBwxcftKQELcSQMmraStnkxf/Unr6xSavPzR7WhwrkzsQWAf2n+2u00Y9lX2oQrPrehSc0S1KLGe9q1IuOwUcsQwJ9QeykKCqcmC2AXwI3HWcEP/DwJ4LfrScT/Y6fYSY3w2/W/X5AyuATEFI+ErftPUAMeC6uFNUU/6EuXr9wg0b5dPLqtpjUsPi1qdR5P33los/ojqEjA2rvLx3agDKle0spT/i20g2jwxyioEH8aI3hcX4XH9xIKsYa1EtxYFVSYaQyreYeMi4twn/ziRQgxdVAzeq9ADrrFWuH5q3WjRzzmR4j+BELtPeLkO1j1PhgqEta3YGidnTEnOBppWNsHQ2vckCt4BCCoCB57R0uOikmOmaDibXYEPfOJI+gsZdvp6uyeDRzUsQ7VKl2IdvHKpg9MVjZ55pFjpbq97bsTVL/LY8d43tJF9bl3WT1d7ADHiR3L66VlJVi1DmPpPDuOk1Dq7ddpUs8GetpcrLVw74nWQsWS+WlUx7okzu3MwsbdR6h5n+mareFFY9pRgZyvmiXVz4sQJXOZttrEKlf29GzaqRk9/9yzeryYIjDW/y5rs5RtPkw3LaUntGkn1F6KgsmpyQLYBXZTgh/4eRLAb9eTiP/HTrGTGuG363+/IGVwCRh9BrT6sCy1/6CMVqFeEz6hJau3kYz9xnSup1dS/DJ4hvi8IEUWxch47SJrWsjClwasqSuCDgniRHnx6HaUK+Mr2vG05ZtpBC9ecW3gRTwzBjfj+qam1kPnaj7mjIKKHXu/pxUTO2vtkTbn4AVOyredmaDCqsawaCDsWDCARFN79Zb91GHI3DD+PPLmzkiLhrfSzKv68knnNOtQewY6+f5QfQWGioT1LRhaZ6dygqEiEdgWHAPjJ7nBMHCGuIKzBCCocJZ3UEqLqkmOmaAiayZ2BD3psW+Kauz7wHO1mGr052wrNjPbil2+cS91G/GfTVoVb7adObQFOy7MRodPXaD3WWXeySBt69eyKmXPkEr/4C8Tq6/2HqM+4z6hX1lYocLLbK7qa15dFzNGDO2Upwp5drZR3LpuKcqTOa3m20Llu3Ttlua0e9naHeoUTR/cnN7Nn10/Ntu5dusOFarZQ5vYShrR6mhbryyVZwfnsdgsgQpS5/XbD9EwttF748av6rTt21B7KQowpyZcYBfY7Ql+4OdJAL9dTyL+HzvFTmqE367//YKUwSVgFFQYfR0s27CHuo9cQN6cSPtTY2XqNCabE53NY2DRlpWwZP1O6jVmsT+XCFoaEQ7sXswOvjmIJsPqLQc0s6YfVihG4kQ8dfIklIvH1hK0BTq9p2n78sdMUBGIxvCEvo2oTNHXtDI+27yPZqzYTDd47Fw0bxYa1LoGPcMa1LLop3Dd3nT9unPjY73RXnZC7Rno5PtD4QRDRcL6Fgyts1M5wVCRCGwLjoHxk9xgGDhDXMFZAhBUOMvb8dKicpJjJqh4Nl5c2seOpcWXgpqceTY006upaP2Ux3Zo+09ZQfM/3eKZxOuxqM1vWzSQkidh27xffUtdhs3zms7uk2KLOF3q5PQv/zt77oqpangSrufepYPp0aNHlLVse/r773+8Vi0Z2zROyj4pzl28SndZ3Tyqg2huJHspMfv+SEA3bt6hy7KKjyeMTodQeykKP6cmXGAX2N0KfuDnSQC/XU8i/h87xU5qhN+u//2ClM4RyPdaRpo1oBltYDOgvT9eTA8e/EVGQcWbhXLS7AFNtQqNmvc5TVmwnsSMaFIeFyZOGN+0oo3YlKh8SD/502Vqx2Pcv3jcKJq6Tz39FE3kj+ylCufW8q7b9h21GzRbX5hiesFgR7BGRffmVahh5bd0U1jeqnSFF82U58VHRp9uRkFFhdYj6fgTv2yBaAwnZPZzhraknDwP8RaEd1vm7s23m7f0TpwLtWegk+8P1T9gqEhY34KhdXYqJxgqEoFtwTEwfpIbDANniCs4SwCCCmd5O1qalUmOmTNoqbiZoELiBrCfhTrlitAfPHETrYoTrP2ggggbprNJItGKkPj8rAHw+70/tOj47M9hxsCmmkO9toPm0D8eH/aNDvL6Tl5OC9lRtJuDmIua3rcx7WeniDV8OEV0cxuism6h9lIUNk5NuMAusDsR/MDPkwB+u55E/D92ip3UCL9d//sFKZ0jMJjNd9YsXVAr8PL12zSZ/Y11+qg8xeaFLEu+3E21ShWkZ9lMkyxUKd9qBJ08ddGvyvVoWU37qP/diZ+oWutReh5jeddv36H6PafQX+znzDNcYhNRD/740/N00I+zsRZxtRIF6B3WDn6ZtSxUuMMmSNeylsXwmav0uYCKE62UnQsH0kNmWLzhwDBmSgPRGBbN8n6sPfHOG1nphSdCI/EZd/T0BRoxew3tO3hKVcEV21B7Bjr5/lAdCIaKhPUtGFpnp3KCoSIR2BYcA+MnucEwcIa4grMEIKhwlrejpUV2kuPLGbRU3JegIl3a5LRpRi+tfb+yX4ae45fS3kOnKAVrDrSqXZJKFMypxS1at4P6jF2i7csfo1q8mHYaxhOXg8fOUuJE8akuq4k3r15cW5El5pFKNBzkysmYaoysBFvNzrTFAXb93lNp2+6jKur/dhtqL0XpSKcmXGAX2M8G/MDPkwB+u55E/D92ip3UCL9d//sFKZ0jkDFDSprRvymlZG1VX2Hk3HU0deEXvpKEifMmqHg916u0jP1S+BM+Zs2NiazB4eZQq2IxzcyS+O74hM2cijDHaghUY1jMtMaJE4suXrpBf7NZVDeGUHsGOvn+UP0JhoqE9S0YWmencoKhIhHYFhwD4ye5wTBwhriCswQgqHCWt2OlWZnk+HIGLRX3JaiQ+KplC9PQtjVNHUbvOXKamrAt2t95NZUK8fij/qLRbSkH+4AwC/fuP6CGfabRfhZ8uDk0rlWCujWsSAvWbqd+45a6uaqO1S3UXooCzqkJF9gFdpuCH/h5EsBv15OI/8dOsZMa4bfrf78gpbME4rApp2plCtF7BXNQ/hyvhvEFdvTMRRrDAoPILlJRTrjXbz9IrfvP1BqUNXNqWsMLX55iM0oRBVkYtHTN9oiSBTXe6Ex72+5jQa1LdCg81J6BTr4/VP+CoSJhfQuG1tmpnGCoSAS2BcfA+EluMAycIa7gLAEIKpzl7VhpViY5ETmD7s/mneqyeaclX+yiXqMXeW2L2Oht90EZypbuZYrBJp8kXL35G21km76DJ6/w6rMhVuxY1JQ/8pcsnIsysR8Ile/Bn3/RjkM/UG92Wn3t2m2v5bnppNgizp8nM+3c9304E1ZuqqeTdQm1l6Kwc2rCBXaB3angB36eBPDb9STi/7FT7KRG+O363y9IGTwCYk5ow8xe9IBX5dfqNJZusT8wBO8ElFnUej0m0c5vv/eeCGd1AqH2DHTy/aEggqEiYX0LhtbZqZxgqEgEtgXHwPhJbjAMnCGu4CwBCCqc5e360vx1Bh1RQ+SjfYrkSeg+2839RRw5+xliszp2hjQp6A8WUpxjZ4L/PnLeAbSfVUUyPwiE2ktRmuzUhAvs/LjBfCQBPx9w/IgCPz8gmSQBOxMwfp4GPz9BIVnQCRidaQe9Mi6ugPiqS8sLkX46dyUgs08ubmKUVi3UnoFOjZuNnQCGRhrW9sHQGjdjLjA00rC+D47W2amcYKhIYBtdCEBQEV16yqF6qlVPcAb9GHioPdSlVU5OGMDP+g8X7Kyzk5zgB36eBJx69uHe8yQfuWPwixwvpA4egfWsUSELa6q0HBG8SqDkkCMQas9Ap969xhsBDI00rO2DoTVuxlxgaKRhfR8crbNTOcFQkcA2uhCAoCK69JQD9YQz6PCQQ+2hLi10csIAfuHvKX/PgJ2/pLynAz/vXPw9C37+kgqfDuzCM4nMGfCLDC2kDSaBF19MRA8fPqKbbOIUAQSiikCoPQOdnHeoPgBDRcL6Fgyts1M5wVCRCGwLjoHxk9xgGDhDXMFZAhBUOMvb1aXBGXT47gm1h7q00MkJA/iFv6f8PQN2/pLyng78vHPx9yz4+UsqfDqwC88kMmfALzK0kBYE3EUg1H6/To6ZVU+CoSJhfQuG1tmpnGCoSFjfgqF1dsac4GikYW0fDK1xQ67gEYCgInjsXVcynEGH75JQe6hLC52cdIFf+HvK3zNg5y8p7+nAzzsXf8+Cn7+kwqcDu/BMInMG/CJDC2lBwF0EQu336+SYWfUkGCoS1rdgaJ2dygmGioT1LRhaZ2fMCY5GGtb2wdAaN+QKHgEIKoLHHiVHAwKh9lAX5E5OusDP+k0OdtbZSU7wAz9PAk49+3DveZKP3DH4RY4XUoOAmwiE2u/XqfeGsQ/B0EjD2j4YWuNmzAWGRhrW9sHQGjfPXODoSSTyx2AYeWbIEVwCEFQElz9KdzmBUHuoC24nJ13gZ/0GBzvr7CQn+IGfJwGnnn249zzJR+4Y/CLHC6mjhkCo3XdOPe886YOjJ5HIH4Nh5Jl55gBDTyKRPwbDyDPzzAGGnkSsHYOjNW7GXGBopIH96EAAgoro0EsB1DHUHkqCwsnJF/gFcPNxVvCzzg/srLOTnOAHfp4EnHp34N7zJB+5Y/CLHC+kjhoCoXbfOfW886QPjp5EIn8MhpFn5pkDDD2JRP4YDCPPzDMHGHoSsXYMjta4GXOBoZEG9qMDAQgqokMvBVDHUHsoCQonJ1/gF8DNx1nBzzo/sLPOTnKCH/h5EnDq3YF7z5N85I7BL3K8kDpiAj9+NSniREgBAiAAAiAAAiAAAiAQUgTSF28ZUu35f2kMBBXRsKcx4YqGnYYqgwAIgAAIgAAIgIAFAphkWYBmyIJxswEGdkEABEAABEAABEDg/4QAxtDRs6MhqIiG/YYJVzTsNFQZBEAABEAABEAABCwQwCTLAjRkAQEQAAEQAAEQAAEQAAEQiHYEIKiIdl2GCoMACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBA6BCAoCJ0+hItAQEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIFoRwCCimjXZagwCIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIQOAQgqQqcv0RIQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQiHYEIKiIdl2GCoMACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBA6BCAoCJ0+hItAQEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAIFoRwCCimjXZagwCIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIQOAQgqQqcv0RIQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQiHYEIKiIdl2GCoMACIAACIAACIAACIAACIAACIAACIAACIAACIAACIBA6BCAoCJ0+hItAQEQAAEQAAEQAAEQAAEQiCICSZIkpKyvpqRUyV6g85dv0JGT5+nOb/f8uvqLLyai17KmpYTx49HR0xfohzM/08N/HvrM+/TTT1OGdCno33+JTp+9RNqOzxyPIzOkf5mK5c1CN3+9S6s37PUjhzNJrPKLFSsmZcn0CmVN9zJdv32XDn5/lm7dvBNhpZMmTUiJnn+Obty+Ey59Mu7DBM89Y3qNB3/9TRcuXDWND1aEmxiaMSjI996Hld6kA8fP0owlG82SBf28G1g+Gy8upUyexC8Wv975na5du+1XWqcSuYGht7amS5uc0r+SjF5MnIBu8TP61Llf6Ed5hrooxOTnWkZ+pmXiZ7yEo6cu0NmfrtCjR48irKUdz0QpNFbsWPR6zvSUK1NaSvx8PDr50yU6fPIcnWV+/r5/Iqx8FCRw8r4LRj9FAaJIXSKQNloZ21jpP6vjoUiBQGLXEoCgwrVdE3UVC+RBZNdL0Vvrnnr6KapWtjA992xc2rjjMP186bq3ZEE95yaWUpd0qZP55HHpyk36/fc/fKZxOjIQhlZejFZecgl4klv//bcpT9Y01HnkwqBPEgJhZuU3bMYsVaqX6Jk4sSK8ZeQDy4/nLtOjhxEPvCO8WBQlCIShnfed2ydWVgaWqsvs4pYq5Yv0erZ0lDldSvrt3n06dOInOvrDebp3974q2hVbq+ys/GalwU6X5yRkp9tmpTxfH0idZIWyooaA9GePpu9ThbdeD3fBYyxwaNxnqunYIG2a5DSjf1NK+3LSMHn/5A/hY+Z/TjOXbgpzXh3E5vfrxD6N6N382bVTlduNpiPHzqpo0608az+f0o0/Mj1H9//4k3KU72Ca1qmIQPh1blKZGlR+i2LzONcYjv8o3KfR1au3jKf1/cJvZKVpfRrTM3Fj0/bvTtJHXSbocTLHOL7uY4rDH+XMwr88eMlarj399effZkkcPe82ht4an4bnIWO6fki5Mr6iRcs9nrtyZ9cwVHV2E8thXT6gaiUKqKr53P5w7gqVaTTIZxqnIt3E0NjmLHzv9W9VjV7Pms54Wts/cvoitRwwky5fuREuzukTFUrkp64NKlKyJM+HKfrBn3/RaH4vzP7kqzDnjQd2PBPl+tmypKGx/PtNx+Nqz/D1t8eo7eA5dP/3B55Rjh47fd8Fo58cBcqFWW2jlbGN1f6zOh5ymiXKs48ABBX2sXXFla0+iKTydr0UzcB0bFyJWtR4T4seOO1Tmrv8a7OkQTnvNpZNapfkAU8Fnywm8aqmMbNW+0zjZKRVhlZejNKuyL7kZCLbtHYpalWzhDbRlWt8vGA9TZz3uewGJVhlJpW18hs2Y5acVyJuX9CfnnrqKb84NOk/gzZvP+RXWrsTWWVo533n9omV1YGl9KVd3OQDfnt+5jWu8jaJMM0Y5ONc62Fz6ZudR4yng7IfCDsrv1mny3MSqtNts1qerw+kTvJCWVFDIF3aFLRoeGttZa7ZFa/c+JXqdp1A587zqlNDyJ0jPc0Z1JwSxDNfuT9vzTYaMP4TQy6i5+I/S7MGN6e8ho9t1TuOpQOHT4dJ53kg7+zl4zpR9gwptSg3CCqs8pPn+ugeH3kVDql2X7t1h+p2mxhupXS54m/Q6M51KWaMGFrSPUdOU50OY1U27Z1xeuN/ggs9wmMnGwt5HvD7JNjBjQw9mVQqVZAGt65OcePE1qMuX79Nb9bt46qFKm5jObZ3Qyr/Zh6dma+dZRv2UPeRC3wlcSTObQxVo19OkYTWsZDW1/NWntU1OnxMl1gjLlihN/9OPqr4ps/ip6/4moZP/TRMGjufia+mT0lrJ3WhWDEfPzPDFPzk4DS/38o3G0p///2Pt2jbzzl93wWjn2yH6FGA1TZaGdtY7T+r4yGPpuIwmhOAoCKad6Cv6lt9ENn5UjSrb+l3XqeJPRro0W4TVLiRZZuPylPbuqV0Zt52pi3fTCOmrfQW5fg5qwytvBilcZF9ySV+IQGN61GfCuXKqLOR1XU9+WPCJ2u36+ec3LHKzOpv2Bez1KxO/fXs3n41/yFrUrz1UX9XrGCyytDO+87tEyurA0u5OezkNr5vIypb9DXTe1B+r+1HzKe1m741TWN3hFV2Vn+zTpdnNz/j9Z1um9XyIvpAamwT9qMHgSGd6lIN/ggrQd5nu4+cooMnzrMWVwp6r0AOvRGrt+ynDrzi1Bg2zu1L6Z+sTv3n4UP6as9Runn7Hr31RjZ6mTUfVKjRaRztP3RKOxQNngXDW1HG1MlVtLb1R1Axpmd9qvh2Xj2fGwQVVvmVeScvTeBxmArnLl+n7QdOUgrm9g7zUwsl9h49TbXb/yeE+LDqO9SbtV9UvOT3Jaj45cZvtHzjHlWMvr3x6x1auPIb/TiYO25kaOTRqXFlal6juH5KTO0M5YVRqzfujdC8mZ7JoR23sSxWIDvlz/mq19YXyPUq5c6UWouTMc27DQfR+QthhaFeM9p80m0MVXMn8LiwzJNxoWjzjF34BR1mDduCPJdrVr24/hF+zqqtNGjiMpXN0a1ovO1e/J9WjDx/NvN74Q5rKlQt8QYlTZRAr89b9QfQxYv/mZ+z65koY87VU7tppvWk8DtsfWHC4i/pNpsaK873Z6nCufU69Ry/lJauCc482Mn7Lhj9pEN2aCeQNloZ21jpv0DGQw5hRDEOEYCgwiHQThcTyIPIrpeiGYOMvArss3Edw6zIcZOgwq0sjYIKmSxfYDNPnmENnz/702XP044fB8LQyosxsi85sVu8ekJnSpIovs7mix2HaMTsNUGzVxwIMyu/4YiYyarNVnXLUIwYYVeyK2AyIVDB28cbFefkNhCGdt53bp9YWRlYqn61i1vxYq+xSY9GqhgSlfQ1Ww5QymRJ6INyReilFx6r0svKubc+6Ev/BGn1l1V2Vn6zAsPp8vQOcGDH6bZZKc+fD6QOoEIRUUwgB/uVEI2Kv9mfRLMBM2jfwccCBSmmca0S1K1hRa1E+Tibr0pXvfQi+bPRvMEttGP5yNhu+Hxa99VjwWkiHl98Or4TpX5im37j7iPUvPc0La1Ro1i/GO9EJKhoWPM96tGokjGLK0w/WeW3klf35nrykfbomYtUizUi/rj/2OxI/WrvUi8WRqhQmlf5nmITXHHYzNP3bNLJM/gSVBxgfxfV24z2zOKqYzcyVIDkQ7toDamwYdcR6jZ6kd++W1Q+p7ZuZunJYNawlvRW3qza6Q27DlOLPtM9kwTl2K0Mdy8bqmu+9Ri3NMzCsvo8L+nFZuQknGFhT8kGA4PCjiWoNL5PQ22hzZpvDlCXEQvobxaqSIifIB59Ob2nbg6q14RPaMnqbXo97XomijbFl9O6a+WIgKcUP0+N/nkGtKtFdXhsLeHStVtUrLZ/C9W0DFH4x8n7zul+ikJM/l/K4r1odWxjpf+sjof8h4CU0YUABBXRpaciW0+LDyIpxq6XorcmPJ/wOVo7uVuYVWaSzk2CCqdfXP5OuoyCijrdJtCe/Se9IXbHOYv3o9UXY2RecrKqZOnY9rpt03s8KZZV2V+zn5SgBovMpM5WfsORYebJJXuWNJqgR50v2XQInfnxkjoM3tYiQ7vvO7dPrKwMLKWT7eQ2eUBTKlkop3YveZpCEBvZK1nY/fxzz2rxHUbOD5ozWavsrPxmpbFOl6cBduiP022LbHn+vqsdwoViopiA+Kr6lx2c3vXwfSNC+xOf/7eavwibubnyy+OFIsbn1CZeMdus19QwtSrKgoy5TwQZ4jw1f62emsNnJaj8jv3t5MmSVs/jS1Ah5sbm8sdiGcMYgxs0KqQ+keVn/HAm+cs0H0Y/sH15Y1g5uavuC2EBa7r24w+TEjaxFosIq39g31iKX3QXVEi73MZQ6iTaxxv4w6r4Q5GwnDUouo9awL8Vdk7m4uBGlp64MmdMRZ/znFiFCq1H0vET59Rh0LduYyjmek99OV5/BlZl804Hj5zROckzcv6QltqxfIzPWqadHuf4Ds9HUrKZKm/+N7s2q0JNWCtMgvyeuvEcVIKdz8TKpQvSqI51tXK8aZvEe+4Z2r98mO4nSPzOeL4LtcwO/HHyvpNvPk72kwP4whdhoY1WxzZSeGT7z8p4KHwjcSYUCEBQEQq9aNYGCw8iO1+KntV8mldmLxzVhvLnCK/+6ipBhVTchSyjlaDCIkOrL8bIvOTa1i9PbeqU0m5Pccwrto+/P3leOw76Hwfvu8gw8+QydVAz3RzGln3fU6PukzyTBO/YAkM777voMrGK7MBSOthObjuWDqbkbB5Fwmu8gvkOr2Q2BqMZitmffUODJy03Rju6H1l2gbx3pWFOl+ckTKfbFtny/PlA6iQvlGU/AXE+uoY1MCXIx69cFTvp9ruNGmUtB8+mL1nryxhk3LuXP/4k5lW0Eowf1hIlTkC32f/C3BGtqWiezFq8maAiJTvpXssf7ZVd9iXrd2qmpeQZ6RZBhdYAL3/M+JV6+3Wa1LOBluPHn69RCTYf6RnqsX+ivs2raqd3HT5FH3Qcp+0/Gy+uZqIrk2hoj+2onfMlqDjJmsadRy/U0j1gx9mXWdDkBr8UWoX8+BMshlK1if0aU+kiubVafnvsDDVmraDUbOrs1u27usDOjya4JkkwWXpCMLLdzfd33Sf3t2c6tx0Hk+Hqqd11/zwL1+2gvmOX6HhGdf+IKr+bTzs+/uPPVKHpUD3OTTszh7akt/M91qIZxT4Rp7BvRAl2PhObsD/Grg3Ka+UMn72WprPZJ8+wYHRb3RxyuRbD6cSpC55Jgnpsx33nq0F29JOv8oIRZ9bGQMY2Zu0w6z9JH5nxkNn1cT76E4CgIvr3oaUWmD2I7Hwpela0b5uaVK9CUe20rGJfzBOtJlXf1Y5dJ6jwrLzhOFgsjYKKPpOWsf3kc0S8oOnG7Tt07fqvvO/u1U0GhGTGMJAXoz8vuQzpX6Yv2AmbWpEomimXfrlFCdip5SnWCFCquca6umXfjFkgv2F/mHm2XxhumNZDP12NVzR9Z1jRpEe4cMeMod33XXSeWPkaWNrJ7dSGCZrZMTHFkrlUm3B302s5M9CKMe2185v3HqMmPaeESxPsE2bsAvnN+mqT0+X5qktUxzndNrPy/PlAGtVtx/WCS2DygCas3ZVLq8Q3+7+nht3+E8wfYxNEz7ApIgkFa/eia9dua/vGP8b3Tuuhc2n95n3G6AgFFc88G5dWTexMGdhvlIR9/LG4TqfxtHXhAE2Y63ZBhRk/o2mnpV/spp5PBAlGOMbfofiveLdeP2M05cyezi9BRZhMTw7ENMyUZV/Rqi93e4t21blgMRQ/ZrLK2sz57iW+3z/f9h2NnL6KRGMoOoRgsfRkI5qhX83sxevintKiZNHU7v0nPJO58jiYDD94/23q1+Kx8FLgfLnzEF29eYcKv5aJMqR6SefVbexiWr5up37slp0sGV+hVRM6UcwYMbQqVWQtmmMyn+dg5zOxYsn8NKZzPa0cT58/2kn+s3RcB8qXLb12WL/XFNq255iKcsXWzvvOs4F295NnecE49tXGqBjbeLbJrP+M6fxZuGFMj/3QIgBBRWj1p1+t8fUgsvOlaKxclTKFaESHOtopGcw26D2VkvBqMqWGGF0EFcFkaRRUGNnKvqzyW80r+cbzqgxlksAzjVuOfTGMihejr5dcF7Z33JTtHqsgE/xnn4mjHf7FNu4PnvyJhvCESw0aVbpgb30xi4rfsC9mnm3/uFcDqvDW69ppMVtRrfUozySuPPbF0O77LjpPrHwNLO3ktm3xQDYRmFi7l2p2HhfGZrycfCNPJloy4rEA4wibC6nMZkPcFszYRcVv1ltbnS7PWx3sOud028zKU+3z9YFUpcE2+hOoVq4wDWtXW2+I0cycfMQ9/NlIPS5z6ba6poV+kneGdfmAqpUooJ0aOnMVzVy6yRjtW1DBHzGnDmyqazDKh+HyLYbRb7/eI6V15mZBhS9+PVpWo4aV39JYTFqykcawY2bPIP7EdrJARoI3Uy6+foeizXjyi3H6B0HPa6vjYGvkqXqYbYPJUGnemtVNnZePxW0GznadU21VP7UNJktVB7Ud0e1DqlL8De3wGPteqcg+A6JDcAPD0T3qU6V38priGr/oSxo3Z61pfLAi4rJQex0v9ErLGnISzl+5Qe9+2E83o2bnMzEVa0F9wybzVJjKQtrR/Mx99PARxYv3DHVuVJE+KP94Mamkeb/dGDp87EeVPOhbu+87YwOd6CdjecHY99XGqBrbGNvlq/+M6SLzPcKYD/uhQQCCitDoR79b4etBJBex86WoKpkjWzpaNqqtbvdwCE/UZvFEzWgvMToIKoLNslnd0tT5o3IKq9ftDVbFFtMB53mlmBuDL4ZR9WL09ZL7al4/fYBoxkcm/fVZkLb/0CmzJI6e98VMKhIVv2FfzIyNlYHu17N76xopDfpMo63sVNHtwRdDJ+474RMdJ1a+BpZ2czOq8P989RY17DNV94OSN3dGGt/9I92h9o6DJ+nDzhNcdRv6YhcVv1nPxjpdnmf5dh473TZf5al2+vpAqtJgG70JZM2cWtPaihM7ltYQ+Rjbsu8MvVHJ+SP6jicf0f95+JAylQyv+SWJe7WqTvUrvanlm7x0I42eGfaDvK/3r3GByh8P/qLKbUfTaTZpIsHtgoqI+A3rUo8FOPm1tgyfvYZNkWzQ9o1/xPHsoZUj9FOZ2ea8UfM1ot+hrCLOnPZlPX+smDEpXaoXNfMmRi2BKu3H0KGj7vkopyocbIZGczFSJxkfL9+0l3658RsVfT2TbiZG4uTD58jpn8muK0OwWRqhpEieRPtoHINNw0loMWgWbfjmO2MSV+67heGH7N+hD/t5MAsdRy6gVRv2mEUH7fyEvo2oTNHXtPJFW7hym1FhzCvZ/UwcyePm95+YxpJKyDeDv/ndJSYF4z1ZtKfgGH0xqXPB2jpx3xnb5kQ/GcsLxr6vNkbV2Ea1K6L+U+lk62s8ZEyH/dAkAEFFaParaat8PYgkk90vxSRsP3fdlK6UNFECrY6fscp7J1Z9lxDdBBVuYFmfVV6f5lViKryQMD47hU5LaVI8Xp0h53ce+oHqsVkANwZfDKPqxWj6kuOVibK6zjg5PfD9Wdqy7wQP0uJSrdKFKD4P1iQ8+PMvKlSnt7ZqMdgcfTGTukXFb9iUmUfjh3SqSzVKFdTOnj7/C5VqONAjhTsPfTG0/b57giS6TawiGljazU38OKwc20HXeHrIq75u3blHv/OHEuPzTvAanRG64Q6MiF1U/GaN7XS6PGPZdu873baIylPtjegDqUqHbfQkkDRpQlozqSu9yJq/Ei5du0Vl2N75PYOj7bj8Yef42jF6A7OVa08PWJjgGYwfhwZM/ZTmrfg6TBKz92/xYq/RtD6N9LTNBs6kTVsP6sduFlT4w69zk8rUrHpxrT1mH7nFN8dWXmAiQZ79Oct30PbVH6u/QzG7s5g18sQhtwS3vUOkTm5g2JO1Xho80XqRMV+dLuPp5s3fpHpaMPah+HzLU6mzinLV1g0sjUAGtKtFdcoV0U6JSbPi7J/F7c7J3cLQ21haNOJjx4ppREy9Jy6jxau2hjkXzAOj0Fnq4e1dYPw92fFMFLPHw7vWCyOs8Mbkzu9/0Ovvd9G0LbzFO3nOqftOtcmpflLlBWMbURujamwjbfOn/4wMzMZDxjTYD10CEFSEbt+Ga1lEDyLJYOdLMRYPGj7hD025MqXW6naYnTLVYFVCtRoqOgkqgs0yXOcaTsjAo1PjSmFMGrlpJYSqakQMo+rFaPaSi89mGg4ZzDT0nbycFq78RlWPxMTA2kldKPHzz2nn+k1eQQtWbtHjg7ETETOpU1T8hs2YGdv84ouJaPuC/roZhfYj5tOajXuNSVy5HxFDu+87gRLdJlb+DCyd4JaDhbDz2emgciBrdoMNmraS5izfbBbt6Hl/2EXFb1Y1yunyVLlObJ1umz/lqXZb/UCq8mPrXgJx2DzHMnbQnJ0dNUuQD+SVeOXrWXbI7BkOrh6lP5+K1etLly7f8ExC80a2piKvZdbOewob5KS39698SF87sYsuqN3PiypGeZgymdyroTZekYUV1TqM1a5/muuoxtjaiSD88Zdf3fffov4tqmk1/PSrb6nLsHnhapsre3pNYC0R3hZHBPI7NM5B3GbG0i0MOzSsSC1rldD6pRP7EPmMfYkYQ4yYMWjXksGUJFF87XS+Gt3pFvsLcFNwC0vFRBbwyVhafVh3qy8FVV/ZuoXhCyxYFHZKy02+K8hzcRf79ihWIBt1rl+BsqZ7rEElpuLy1+hBdw3CZWObnNz3NKG2fOMe6jZiQbgqOPJM5EV7Up9CrJn8Ro4M7N7yXzpx9hKVfzOPXp9P2G9Pj1EL9eNg7Th530kbneynYDH1t41RMbbxt/+MLLyNh4zx2A9tAhBUhHb/6q3z90Fk50txaOcPqHrJx3Z5pWLths9jdWF2+vwkvP1Gdv3j+qzPvqFVPFG5wytyfr50XSVxxdYNLCMCIcKKLTx4S/lSYi2p25yy+cswKl6MZi85cYIqTgFlgHuF78MiNXuGw1qn8ps0oGV17fyidTuoz9gl4dI4dcJfZlHxGzZjZmxr79bV6aOKj81XiJ3stz7o44rVNsY6eu77y9DO+y66TawiM7C0k5vqSzE3VpZ9ohTMlYHSpXyJLrBNX9GKej1rOpVE0366yuahgh38ZRcVv1lpq9PlOcnX6bb5W55iEMgHUnUNbN1JYGK/xlS6SG6tcuJTrSGbODRzKvr5jJ5sWiiFltbbB0cxO7h/xXDd4bYIPI5+/1OYhnt7/3qaHwuTwceB0YeGj2S2RvnL750iuWhGvyZaXa7yKv1CMibjj2bG0LJeWepQr4x2auuBE9Sg60RjtE9n2mESejl4PdertGx0Oy3mzMWrVLL+Y18YXpI6fsotDI2mnz5mH3gT530ejsXKyV0pFzsIlvB2gwF04cLVcGmCecItLBWDbs2rUuMqb2uH12/foaK1e3v1baPSu2HrFoal33mdJvZooCERDZ436/Wju3d+1xElYg24bayBpXwPihnfbbuP6vHB2MmWJQ0t5+eMEq58e+wM1WWrBw/Z9JNnCNYzsVjBHDRnYDOtOmKSShx8/8C+34IdnLzvnO6nYLCNTBujYmzjb/8ZWXgbDxnjsR/aBCCoCO3+1VoXmQeRnS/FPcuH6iaf/MUu6vXFeNDmluAWlv7wWDSmHRXI+aqW1E32TiPDMCpejKYvOV5FcmL9WG0Vk6y0eY3VWv/0MNNQNH82mju4hcZwzTcHqP2g2f6gj/I0kWEWFb9hU2ZPWpaIV8vtXDRQH2h7aqNEOYAouGBkGNp530W3iVVkBpZ2cjO7BWKxoHEBr1DOly29lmT99oPUuv9Ms+SOnveXXVT8ZqVhTpfnJEyn2+ZveYoBBBWKRGhtO7JD0RY1H68gl5ZFpFlpdJS97/iPVLPtf6agJH85dpY7jp3mSpBxR77q3en3e39ox+qPt/evVUFFsG2zR4af2OnfNr8fPcVjMwm1u06gvQdOKiwkzrA3zemr+xXz5nA7ot+h+FJ6nv9703RpWqc0dan/2O/bhl2HqUWf6XrZwdxxE0Pj+OXHn69RCRHmGIRJ4kNExoZi314+cGZn01z/sBketwQ3sRQmCVhjexfzeoYFmBKUz0btwKV/3MSwBK/6n9K7oUbqFgso8vE8zjPIor1X+NkiocuYRfTp+l2eSRw7fokXD65hzTilcSRmviq1GhlGuGKsjBPPRGN5sv9u0dw0qWcD3Szy4Bmf0exPvvJM5vixk/ddMPrJaaCRbWOgY5vI9J+RhbfxkDEe+6FNAIKK0O5fiuyDyM6XojVBxW0WVPRyRS+5iaUCki5tcrp46UY41X7RFti+cCAl5AmZhJJNh+iOZ1XeYGwjyzDQF6O00ddLbtPcvrwq+0UNReuhc2k9+0wxhvYNK1CrWiW1U2Pmr6dJ88OvHjOmt2M/ssyi4jfsi5m0sVPjytS8xmNb0jI5KMK/UU8hjx0srF4zsgztvO+i08QqsgNLO7l563sxNzVrcHNdICuaUaUbD3aFan9k2EXFb9bp8rz1h13nnG5bZMpTbY7oA6lKh230IVCJ/S+NZj9MKsxh++aD2M65r5CFV5Kv4xXlKoxf9CWNe2KiScZrKz7uQM8/93hctmT9Tuo1ZrFKqm+9vX+fT/gcZWITJkmf+MjQExt2xnX9UPvIL/bZmw2cpZnwEBMowfpQbIXfdH6ev5s/u9YqcdD8fttRpLTjBravTbXLFtbixEdREdbivMbanMbg63coz9mlo9tqfij6T/mUFq/epn9kF7OCi4e31ldeD5u1mmYs2Wi8dFD23cbQc5HK/DXbqf/4pRobeR+P7Vmf3iuQQzsWMzzvtxgeFG7eCnUbS6ljuwYVqHXtx3OMu+wHoBCPpe///sBb9V1xzm0Mjc9b0Xar231SGOFmoXxZaf6QFrrwswJrBhw/cS4oLJ95Ni59Or4TZUqTXCv/Ns+dKnF9IrIaYecz0QgidpxY1KZeOWrCjsmVU/dt352g+l3Caq0Z8zi17+R9F6x+coqllGOljcbfmlwjMmMbK/0nZUjwNh56HIO//w8EIKgI4V628iASHHa9FDOkf5kyvJJMfwF6oi9ZOBeVLfqadlrsNX6x4zD9/MtN+pFtJQY7uI2l8Ghbvzy1qVOKDv1wnlrxpPQKs5IQk32BDGczW5XY5qSEP1hLIGeFjiSDuGAGKwwDeTGqtvp6yRlVrmXQWIV9ppy/8IuWtUDezKz62ly3G/tRz8m0fe9xdVlHtlaYScUC/Q37YiYrEnctHqStmJOyRrPq/2Q2AeDWYIWhnfed8dpunlhZGVga2yb3Q2QGsur+8XXvqTSyzZEtHY3qWEd7p8ixsKzRaRx9d+SMHAY1WGEXyG/W6fKchOt026yUJzx8fSB1khfKihoCedle98JhrfRVpXLVr789Rv/wB3JjuP/HX9Rp2Nwwjm+Xjm1P+bJn0JPJqlkJKZIm0scTcly80SD66dwV2Q0T/H0GhsnEB6c3TiAx+ymaGlnLPDZh5JnGqWOr/PK9lomWjmyjV1P8bVxhgYWY91OmTCXyc9aca+NFc87X79DTP9VPbFZ2z+FTlJiFQCIciRkjhlauCLxLNhxEv/OH42AGNzIUHj3YoXbDJw615fjy9dt0/fZdeinx85QsyWNn5HL+Qx4z73B4zCzlegtuZBkv3jO0c/FAis9bCd40hLy1JVjn3MhQtKy+ZRO+ypeg+FcQU0rffX+e8mZLw//T60IKMVH7zof9giK4lefyjCHN6a28WfXuEwfVe46c1o/VzrINe2gLf/9Qwc5nopQh/kPfZ6F88xrvUSr2zajCYf620LDXFLrNv+1gBifvu2D2k1OMA2mjlbGN1f5TPKyOh1R+bKM3AQgqonf/mdY+kAeR3S9Fs0ob1a7domoodXUjSxFGnPj8Y61uUsd/Hj6kzTwhuPXrXcrP5p6UloDEucGxbCAMrbwYpd0q+HrJJX4hAe1YMEA3YSSq6j9dukYygHw9S1p9gHuAHVdWFxMOBhV3dX27toEwC/Q37IuZ0Ub0fXYqKivA3OCczls/BMLQrvsuOkysAhlY2sVN9W96XlXcjCdUlVkQq0yEyO+2z6RltGztDpUsaFur7Kz+Zp0uz0mwTrfNannCxNcHUieZoayoIbCNhfEvv5jIr4tlY/M2D/hdqIJo8M0f2lIXoqrzaivaAF3HLg7niFjF+3r/qjTetkpQIRoVWUq39ZbEsXOB8GtU8z3qxk6b1fPds9KyOKd+j8l057d7nlE+f4fitHge94vyIRIuM5+4d/8BNeo7jfYdPOUt2tFzbmQoAMRc0WzWfHktcxpTHjNXbqGhk1eYxjsd4UaW9au9S72avq+hEOFi4Tq9g/5R2Fe/uJGh1DdrptS0cHgrXVPNWxvkmVitw8d0LEjaFJXYP+doXkDoT5i2fDONmLYyTFK7nolSSP7XM2vaZMYCZ3y6hUayySdvfjOM6ZzYd/K+C2Y/OcFSygikjVbGNoH0n9TX6nhI8iJEfwIQVET/PvTagkAeRHJBO1+KXivMJ42CiqEzV9HMpZvMkjp63q0s67Hztd48yJWPsWZh056j1Kz3NEc/sHurSyAMrbwYjXWI6CUnK2iHtasVZuWkMb84t6vWYSxdZOeKToZAmEk9A/kNmzETAdneZUN1k2IymB02xT2TUc/+CYShnfed2ydWgQws7eQm/fvJ+I6U1+A4+yJrkrVgjbLv+eOVG0Ig7Kz8Zp0uz0nGTrctkPIgqHDyzrC/rK/YAWval5NGWNBNXhxSqFavcKt0RfNwQJsaVDRPZn21rywoOXbmZ/qYTUj6Wmk+rk9DKlcsj7YApWSTIXTu/GMtz4gqs2/lCErMPgLkmfhW3T4RJbc1PlB+4guhWfUSlIUdkyszJNdu3eEFOcdo4KTlpqYm03H6DdO6a+PiVV/vp45D5oRpp2hYtmRfFLXKFNLHMZJANI/FzEm/icvDmZMKcwEHD9zKUBDIKuxuzd7XnMy/9MJ/WhTnr9wgmb9t2nrQQVIRF+VGlmrOK9qgkz/ZRB/PWhNxQ4KYwo0MFQ6x2NC5QXl6J1+2MHNiEQDJPHjEzNVe/dKo/HZvy/DCmgk96vtVjJkfJLueidmzpKHVEzprdTvCDrNHzl5Du/Z971ddnUjk5H0XzH5ygqWUEWgbIzu2CbT/rI6HnOKJcuwlAEGFvXyDdvVAH0RScbteimZQalUsRoNa19Ci2w2fR2s3fWuW1NHzbmYpKyHa1i1F+XM8dpqtwMhEdfziL2llEJ2GqbrINlCGkX0xGsv25yX3Ws4M2sqmrLxaOzZPwCSIyYENu45oE1dvK/eMZdixHygzqZPV37AZM7Fhun/FcM3s0w1WBy7TbCjdvPmbHc2PkmsGytDO+87NE6tAB5Z2cps1rKWmPi8flhZ+vpMm8Ee/YJvoMN6sgbKL7G/W6fKMbbV73+m2BVJeRB9I7WaF67uXQEoWeMSOHdOrPzH31todNYvLTobFt8QfrLGizJtGSc3YYXdSNlOUjM1x/XbvPl24eC3oC3qipF1eLmIbwydliaZKimSJ6RwzDMZY2UuTbTtlN0vbKu6iC9vJUK6djDXaErCg+Or1X+mq+LBxUBPeCcx28Cv/3hskguC9B0460YSglGEHN18Ncbo8X3WxKw5jG7vI4rqKAAQVigS2pgT+Hx62po2P4gi7WKrrxmNndmfZx8Lv94JrWzeKsYVMySHtAAAaX0lEQVS5nJ0vRtEYSM+OzsSUjKxiDLZfjzAND+BA3R9RPtkPoE7RLatd9530TShPrKKam1yvCAto1/OKzVD+KOL0b9bp8pz8/Ydy25zkiLJAAARAAARAAARAAARAAARAwG4CEFTYTRjXBwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMCUAQYUpGkSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjYTQCCCrsJ4/ogAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKmBCCoMEWDCBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAbsJQFBhN2FcHwRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwJQABBWmaBABAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBgNwEIKuwmjOuDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiYEoCgwhQNIkAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABOwmAEGF3YRxfRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAVMCEFSYokEECIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACICA3QQgqLCbMK4PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBgSgCCClM0iAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABELCbAAQVdhPG9UEABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEwJQFBhigYRIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACdhOAoMJuwrg+CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACICAKQEIKkzRIAIEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMBuAhBU2E0Y1wcBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEDAlAEGFKRpEgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAI2E0Aggq7CeP6IAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACpgQgqDBFgwgQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAG7CUBQYTdhXB8EQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMCUAAQVpmgQAQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgYDcBCCrsJozrgwAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAImBKAoMIUDSJAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAATsJgBBhd2EcX0QAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAFTAhBUmKJBBAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgN0EIKiwmzCuDwIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgYEoAggpTNIgAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARCwmwAEFXYTxvVBAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARMCUBQYYoGESAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAnYTgKDCbsK4PgiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAgCkBCCpM0SACBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEDAbgIQVNhNGNcHARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwJQBBhSkaRIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACNhNAIIKuwnj+iAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAqYEIKgwRYMIEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABuwlAUGE3YVwfBEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEDAlAAEFaZoEAECIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIGA3AQgq7CaM64MACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACJgSgKDCFA0iQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAE7CYAQYXdhHF9EAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABUwIQVJiiQQQIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIDdBCCosJswrg8CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIGBKAIIKUzSIAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQsJsABBV2E8b1QQAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAETAlAUGGKBhEgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAJ2E4Cgwm7CuD4IgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIApAQgqTNEgAgRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwG4CEFTYTRjXBwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMCUAQYUpGkSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAjYTQCCCrsJ4/ogAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAKmBCCoMEWDCBAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAbsJQFBhN2FcHwRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAwJQABBWmaBABAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBgNwEIKuwmjOuDAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiYEoCgwhQNIkAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABOwmAEGF3YRxfRAAARAAARAAARAAARAAARAAARAAARAAARAAARAAARAAAVMCEFSYokEECIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACICA3QQgqLCbMK4PAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiBgSgCCClM0iAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABELCbAAQVdhPG9UEABEAABEAABEAABEAABEAABEAABEAABEAABEAABEAABEwJQFBhigYRIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACdhOAoMJuwrg+CIAACIAACIAACIAACIAACIAACIAACIAACIAACIAACICAKQEIKkzRIAIEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMBuAhBU2E0Y1wcBEAABEAABEACB/7FnhzYAAAAIw/7/mgsmcdUkiNoRIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSQKhIGgMBAgQIECBAgAABAgQIECBAgAABAgQIECDwFhAq3sL+CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRQQKpLGQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLwFhIq3sH8CBAgQIECAAAECBAgQIECAAAECBAgQIEAgBYSKpDEQIECAAAECBAgQIECAAAECBAgQIECAAAECbwGh4i3snwABAgQIECBAgAABAgQIECBAgAABAgQIEEgBoSJpDAQIECBAgAABAgQIECBAgAABAgQIECBAgMBbQKh4C/snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUkCoSBoDAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8BYQKt7C/gkQIECAAAECBAgQIECAAAECBAgQIECAAIEUECqSxkCAAAECBAgQIECAAAECBAgQIECAAAECBAi8BYSKt7B/AgQIECBAgAABAgQIECBAgAABAgQIECBAIAWEiqQxECBAgAABAgQIECBAgAABAgQIECBAgAABAm8BoeIt7J8AAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAaEiaQwECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAW0CoeAv7J0CAAAECBAgQIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSQKhIGgMBAgQIECBAgAABAgQIECBAgAABAgQIECDwFhAq3sL+CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRQQKpLGQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLwFhIq3sH8CBAgQIECAAAECBAgQIECAAAECBAgQIEAgBYSKpDEQIECAAAECBAgQIECAAAECBAgQIECAAAECbwGh4i3snwABAgQIECBAgAABAgQIECBAgAABAgQIEEgBoSJpDAQIECBAgAABAgQIECBAgAABAgQIECBAgMBbQKh4C/snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUkCoSBoDAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8BYQKt7C/gkQIECAAAECBAgQIECAAAECBAgQIECAAIEUECqSxkCAAAECBAgQIECAAAECBAgQIECAAAECBAi8BYSKt7B/AgQIECBAgAABAgQIECBAgAABAgQIECBAIAWEiqQxECBAgAABAgQIECBAgAABAgQIECBAgAABAm8BoeIt7J8AAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAaEiaQwECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAW0CoeAv7J0CAAAECBAgQIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSQKhIGgMBAgQIECBAgAABAgQIECBAgAABAgQIECDwFhAq3sL+CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRQQKpLGQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLwFhIq3sH8CBAgQIECAAAECBAgQIECAAAECBAgQIEAgBYSKpDEQIECAAAECBAgQIECAAAECBAgQIECAAAECbwGh4i3snwABAgQIECBAgAABAgQIECBAgAABAgQIEEgBoSJpDAQIECBAgAABAgQIECBAgAABAgQIECBAgMBbQKh4C/snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUkCoSBoDAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8BYQKt7C/gkQIECAAAECBAgQIECAAAECBAgQIECAAIEUECqSxkCAAAECBAgQIECAAAECBAgQIECAAAECBAi8BYSKt7B/AgQIECBAgAABAgQIECBAgAABAgQIECBAIAWEiqQxECBAgAABAgQIECBAgAABAgQIECBAgAABAm8BoeIt7J8AAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAaEiaQwECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAW0CoeAv7J0CAAAECBAgQIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSQKhIGgMBAgQIECBAgAABAgQIECBAgAABAgQIECDwFhAq3sL+CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRQQKpLGQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLwFhIq3sH8CBAgQIECAAAECBAgQIECAAAECBAgQIEAgBYSKpDEQIECAAAECBAgQIECAAAECBAgQIECAAAECbwGh4i3snwABAgQIECBAgAABAgQIECBAgAABAgQIEEgBoSJpDAQIECBAgAABAgQIECBAgAABAgQIECBAgMBbQKh4C/snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUkCoSBoDAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8BYQKt7C/gkQIECAAAECBAgQIECAAAECBAgQIECAAIEUECqSxkCAAAECBAgQIECAAAECBAgQIECAAAECBAi8BYSKt7B/AgQIECBAgAABAgQIECBAgAABAgQIECBAIAWEiqQxECBAgAABAgQIECBAgAABAgQIECBAgAABAm8BoeIt7J8AAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAaEiaQwECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAW0CoeAv7J0CAAAECBAgQIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSQKhIGgMBAgQIECBAgAABAgQIECBAgAABAgQIECDwFhAq3sL+CRAgQIAAAQIECBAgQIAAAQIECBAgQIAAgRQQKpLGQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLwFhIq3sH8CBAgQIECAAAECBAgQIECAAAECBAgQIEAgBYSKpDEQIECAAAECBAgQIECAAAECBAgQIECAAAECbwGh4i3snwABAgQIECBAgAABAgQIECBAgAABAgQIEEgBoSJpDAQIECBAgAABAgQIECBAgAABAgQIECBAgMBbQKh4C/snQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUkCoSBoDAQIECBAgQIAAAQIECBAgQIAAAQIECBAg8BYQKt7C/gkQIECAAAECBAgQIECAAAECBAgQIECAAIEUECqSxkCAAAECBAgQIECAAAECBAgQIECAAAECBAi8BYSKt7B/AgQIECBAgAABAgQIECBAgAABAgQIECBAIAWEiqQxECBAgAABAgQIECBAgAABAgQIECBAgAABAm8BoeIt7J8AAQIECBAgQIAAAQIECBAgQIAAAQIECBBIAaEiaQwECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAW0CoeAv7J0CAAAECBAgQIECAAAECBAgQIECAAAECBFJAqEgaAwECBAgQIECAAAECBAgQIECAAAECBAgQIPAWECrewv4JECBAgAABAgQIECBAgAABAgQIECBAgACBFBAqksZAgAABAgQIECBAgAABAgQIECBAgAABAgQIvAWEirewfwIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAFhIqkMRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJvAaHiLeyfAAECBAgQIECAAAECBAgQIECAAAECBAgQSAGhImkMBAgQIECAAAECBAgQIECAAAECBAgQIECAwFtAqHgL+ydAgAABAgQIECBAgAABAgQIECBAgAABAgRSYAAAAP//y+dNHgAAQABJREFU7N0FmNRW1wfwU6S0xa1UoFCgSHEKBYq7u7u7+8Lii1Pc3aW4u2uRQnEpTiltobjT0u+c6Zd5M5mbjOzszu7yv8/zfTO5SW6S305fkpx7z30veeFW/xIKBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEPCDwHsIVPhBHYeEAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEDAJoBABX4IEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+E0AgQq/0ePAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIIFCB3wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ+E0Cgwm/0ODAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIIVOA3AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgN8EEKjwGz0ODAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAIV+A1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgNwEEKvxGjwNDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAQAV+AxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPhNAIEKv9HjwBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCBQgd8ABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACfhNAoMJv9DgwBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACCFTgNwABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIDfBBCo8Bs9DgwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAACFfgNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4DcBBCr8Ro8DQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggEAFfgMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj4TQCBCr/R48AQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggUIHfAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAn4TQKDCb/Q4MAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAghU4DcAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICA3wQQqPAbPQ4MAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAAhX4DUAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOA3AQQq/EaPA0MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIBABX4DEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+E0AgQq/0ePAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIIFCB3wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ+E0Cgwm/0ODAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIIVOA3AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgN8EEKjwGz0ODAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAIV+A1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgNwEEKvxGjwNDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAQAV+AxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPhNAIEKv9HjwBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCBQgd8ABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACfhNAoMJv9DgwBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACCFTgNwABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIDfBBCo8Bs9DgwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAACFfgNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4DcBBCr8Ro8DQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggEAFfgMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj4TQCBCr/R48AQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggUIHfAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAn4TQKDCb/Q4MAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAghU4DcAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICA3wQQqPAbPQ4MAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAAhX4DUAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOA3AQQq/EaPA0MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIBABX4DEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+E0AgQq/0ePAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIIFCB3wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ+E0Cgwm/0ODAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIIVOA3AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgN8EEKjwGz0ODAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAIV+A1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgNwEEKvxGjwNDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAQAV+AxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPhNAIEKv9HjwBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCBQgd8ABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACfhNAoMJv9DgwBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACCFTgNwABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIDfBBCo8Bs9DgwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAACFfgNQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4DcBBCr8Ro8DQwACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQggEAFfgMQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQj4TQCBCr/R48AQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQggUIHfAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAn4TQKDCb/Q4MAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAghU4DcAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICA3wQQqPAbPQ4MAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAAAhX4DUAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIOA3AQQq/EaPA0MAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIIBABX4DEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEI+E0AgQq/0ePAEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIIFCB3wAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAJ+E0Cgwm/0ODAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIIVOA3AAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAgN8EEKjwGz0ODAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAIV+A1AAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgNwEEKvxGjwNDAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCCAQAV+AxCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCPhNAIEKv9HjwBCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCCBQgd8ABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACfhNAoMJv9DgwBCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACCFTgNwABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgIDfBBCo8Bs9DgwBCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAACFfgN+F7gvfcoapTI9Obvf4j+/df37fuhxUiRI9Hbf9764cihc8io70elN6/fhMjBokSNQv/88w/9+zZkfguR+bf2j/zWUCBgEIgUKRLJf7t/v/nbsAaLEIAABCAAAQhAAAIQgAAEIAABCEAAAmFJAIGKsPTX8PBcMnz9JVUonN3Dvaw3v3nnHs1dvtN6I/1aDkrkyJqacmT4ijKm/oIypfqC4sWOYd/ij78e0clLN+jkxZt08OeLdOrMVfs6d740rFaEEieK586mbm+zYtthOnfhhtvbp+VrWjWuCx0/f5VqdRzj9n5mG+bMloaK5srotPrFyzc0ceEmevH8pdO6kKzo0rQitaxehJZtPUwBw+f59FDliuWg0d3q0enLt6hCi6E+bVsaq142Lw1uX4MmLN5Co2euDXb71crmodTJPvO4HYnHPXr6nM7wdf50+go9fvTU7Tbei/QeNalelD5JEMdpn8s3f6fFa/Y61asqShXKRt+kS+60Ss5t+dZDdOHSLYd16dMmo4pFvnWoC87Chj3H6fipy7YmEvC1NONriszX5qvy/OVrmjB/I73iT7Py+WcJqGLRHJQ9fXJK++XnFD9OTNum/3CQ8Tr/b9uR05dt/7frxzP05PEzs2ZQDwEIQAACEIAABCAAAQhAAAIQgAAEIBDKAghUhDK4Lw83pndjKps/qy+btLWVrmwnevnilWW70aN/SJVL5KQGFQpQ0k8TWG6rX3n+2m80e9UuWrfjKL1+Zd2DP2bMj+jnVSP0u/vk+4rtR6jb0LlutzVpQHMq/t1/gYUaXcfR0RMX3d5XteHCUR0oZ8avVKuoUZ+ptOfgKeW6kKpcPSWAMqRMYmu+bJvhHgVxrM7pgw/ep93z+1PCuLFsm2Ws0IWePX1htYtH62Skxp75A/gFf2x6+eo15azZK9gvny9uGUdRIkf26DyMG7/ikSkS9Jm+bDv9evuucbXTcpaMKWn5qI5O9VpFgYYD6NatP7RF5Wcx/t+Byfy/B2Zl7e6fqOPAWQ6rRwU2pPIFsznUBWdhx+Ez1Cxwsq2JupUKUr9WVYLTnHLfWt3H0+GfLjitk8BIvzZVqWSezE7rVBXye1m48SDN4L/Rn38+UG2COghAAAIQgAAEIAABCEAAAhCAAAQgAIFQFECgIhSxfX2osX0aU5l8vg9UpC/X2bJXf76c6WlYp9r0cbz/XkB7c13Xf7tLHbn3vtUIi5ixotPPK4d707zlPis5SNJ1yBzLbbSVSb/4hHbO6q0t0t7j56lhtwn2ZW++LBrdwTYCRbVv475TafeB0AtUSG/+s+tHUzRO/STl0MlLVKfzWNWpeVzXrFYJ6t6orH2/ih1GWv697Ru6+aVC8Zw0smtd+9Yj526gSdzjPjjl0pbxFJlTBfmivH37ltoNnUObdv5k2Vz2LKloyYj2ptsUbTqQrl67Y7o+RfLPaQ2P+PmQA0Oq8prTHlXuOMopADW6VyMqV+Ab1S5e1e08coaa9vwvUFGvckHq29L3gYraAePpx2OOgYp0PDJk4dA2FJODp54WGWmxnEdYDZ66kp4+ee7p7tgeAhCAAAQioICkDUyWNBFl4BG1Gb5KSgnixqT3o0am12/+IRkpfOHabbp49Te68Mstkn/rfVkyZ0hBjTnYP23ZDjp97lqwmpYOHenTJKX3ePSzJ+VfHor5lEf3Xr/5h89SJ0oaxgxpv1SOtPzr4VO6wSNIvSp8ben4GqPxtRrLM+50dZH/Rlr54MNo9PVX/3XM0eq8/TS2rW+na7OK9GXihDR67kb65cqv+lUef5f0phn4Psf4N/yHf3enzvLvwyTFblT528t+bh7xb27vOXvd4L/5Gx+ky3w/WlRKl5p/e4bjyz3pmfPXDbX/WyyUJxNV4dHYe46epx/W7fvfCi+/yT1yrBjO94eXuePck2De98WPH5vS8f9GZOLrTMTftbTHN+/8RWcv36SzPJL54cMnXp45doMABCAAAQi8uwIIVITjv31oByrkprdfuxpUo2Qun6jJg9BUfhD7fsZq5fwFYSFQMbhLHapewvF6S7Uc6vDg4ylGWApUSKqcvfP6O1xC3R4T6eDRcw51ni7E4vRf+3g0RYyPPrDv2n30Ilq+4YB9OVhf+MF0CweQUiZJZG/mAafyyV2rl2VqIPvGJl98GaiQQ8gLjPbD5tFGDo6ZleAEKmTU0dpJ3ekLi1FNnb9fQKs3H3I6fEQIVKRKmZh+GNmBYnkRpNCD3Ln3kKp0GEW///6XvhrfIQABCEDgHRJImDAO1Sydl+qUyW1PHWh1+RK0+IH/fV2y8QD98cd9q03dXrdmSg9Kz/+2nbhwnaq0Cd6o4sY1ilLPJhXcPrZxQ5lr7sdTv9D05TvowJHg3ReWKPgNTQxsZDyEbVk6DRRo0J9+4xSNnhZJezm+Z0PT3XLxfaE2cjKwdVVqVLGA6baerijefDBdvnLbYTcJyPzCnV6kSErQOgETnDqKOOzgYqFh1cLUq3kl5VadRsyjNVsOK9c14pSugZza1dPyN88pJ6Pffzp7lX7YdJAuXfYu0KKllVUdv37gJNp/+KxqFU0Oak7F/j897tCZa2j64q3K7dypTPx5Qtozt59y0+2HT1PzwCnKdVaVEuwqz2lTG1TIT6mSfmq1qW2d/PezhB237vs5WM8nLg+EDSAAAQhAAAIRSACBinD8xwzNQIUEKSYPaEYFs6fzuZj0aA4YMd8pWOHvQIWkk9m/YICth4z+olVpdPTrXX0PS4GKfLky0OygFg6nfO7qbSrbfIhpLy2HjU0WureoTM2qFHJYO3PVbho8cZlDnbcL+TkV1yxOyWUsvScspUWr9xir3V72daBCDiwBudaDZtGW3ceV5+FtoEJ6fM4Y0oryf5NW2a5Uzlu7j/qPW6JcH94DFTIaaAc/gHqSek4J8f+V1buMpWM/X7LaBOsgAAEIQCACCsjLx+78Urd26dxejaqUXuJjFmymGT9spX/45b63Jcc3aWjRsLb23at2Gm2f+8le6cGX9g3LUrvaJTzYw3xTuTdsxulJ73gZ0I/Dc0YdXBRkH8FrPNIsvkcc5MU94tqpPShdisTG5mzLZ3k0Qzm5n/3/MoRH4Vbj0bi+KqqRwvpAhRxHRl7U6zmRfub5y7wpbeqXpo51Syl37TNxKS1cpb7nbdegLLWvE/y//bFzV2nCws20zySwoDwxruzfoSYH/PIoV7caONP0nlgfqJCdx/J/V+PmrFO246oyZYrPacvUnsrNJIBQu9MY5TpVpdxz1ufRwh3rlnbohKXaVlV3nztTDeWOeSs5sPnvW548DgUCEIAABCAAAVMBBCpMacL+itAKVMiw48n9m1HhHOlDDMU2kTMHK/RDmP0dqOjGPZiac08mY5Fe8vnre9fzS9oKS4GKpjWLUUDj8sZLpLacGstqFIDTDrqKRDz5+e45fTlNguMw/P0nLlD9rv/1MtNt7tXXpeM60zdfJ3fa9/af96lA3b70lnvneVNCIlAh5yG9LvPV6aNMoeBtoKJz0wrUiiesNitHz16h2pzGy+ylSXgPVOT+9muaN7i12eV7VO+LlG4eHRAbQwACEIBAmBDIyvNEjepWj5J8Ej/Y5yM90Vv0m+bW/FSqg80d0ZbyZEljX7Xr6Flq0mOSfdnTL74MVMixb/OcTjU6j/Fq5IPs3699DapbNq98dSovXr6mXDUDPUrHkz1Lak6d2c6pLa2izeBZDuk3/RGokHORebEa9p5CR457PsedvwMVmqV0KguatNztNJm+ClTI8WX0/XBO0elp8VWgQkafj+xej7KnS+HpKThtf/LSTeo0bC5dv/G70zpUQAACEIAABCDwnwACFeH4l9CxcTlqU7O4T69AUqDk5xeq+pebvn7QMTvhXuN/oMVr9tpXR+V5E/byiIbgzIVhb0z3ZdS8jTRx3gZdjfPX6JzP9NDiQRSde9mpilVPddX2+rqwFKgY0aMBVSqcXX96tu+3uMdcEZ7E+W8v8tQO5Qf+qpxf1ljuPnhMOav2MFZ7vCz5m1eM7mS6X3CCLFaBCgmC/K0IgLzHZxKPU13p01ypTs4sBZM3gYriBbLSpF6NVYex1f1+7xGVaTWUHtx/bLqNrwMV+hEz+XKl55E6LU2P7c0K6bFassUQ+8PdlIEtqGjODMqmJI3E9zxnyS6e4Pstj2jJxPmry7JZnsypSUai6Iv0dizSOMieGkK/Dt8hAAEIQCDiChTJl4UmcToiX81NJVLSc7p86+Eev8xPkyoJbZgU4IRdrNlgusKjGbwpIXH/Ls8J1Xmkx+3fPE/TJC98pSOL8d9h7dqGzVpL0xZt0RZdfs4Y0ppHen+t3E7uYwvV6+cwh0ioBCr4HuOXrc6dcuQepnn/6bT3xzPK8zWrDCuBCjk/ubes1W2cW/OJeBuomMSjpYvzqGljmbt2Lw0Yv9ShQ5txG+OyLwIVX/FoncUcDIvL8yb6qsh9p6S/OnHqsq+aRDsQgAAEIACBCCWAQEU4/3PKZGWqUoZfPo/gCa9V5eiZy1Svu3pC6L95okD9xIDy4LR2fDe3HuJ+vniD5vON5LXbd+nFy1cUnecnkPyd9cvnp9TJXOfxlB5HxZoOcnj4kYeZKDyBoaosHtWRMvMEZqrSgXurbNl7QrWKXr96o6zXVzarVZwngi6nr3L4/ur1G/quVm+vJkkLS4EKqyHzVkPKHTB0C8m//Iy2TuvpNOmftknmil096i2n7af/nDaopeXoHunRWIZ/R94Uq0DFN1UCTP/eMmFl2SLZaTDP4WIcSaKdh1m+aU8DFTIx4NrxXeiDaOrJsyWntEyefdZiskI5J6tARbdRC2n9zmPaqbv1+Yp7Q+qLpIuTofLGkvizhLRteqCx2rYsuZEzlOusXCfBBy2AKm2f3zjG9HfWZeQCWrXpkFM7yb/8lHo2q+TwYiNgzCJatv6A07aogAAEIACBiCsQEkEK0ZJ72RIthtKtW394hGc2Snolz3HVlUe5elNCIlAh57GL5zFrwvOZeVPG9G5MZfNnVe4qHVry8L21O51kvuTniu0zeinbkcpATnu5hNNf6os/AxVyHnKP03rQbNpu8nyiP1fte1gKVMg53XvwhKrxqBpXk5/7OlAhx5b5YAL5/tTd1EnBDVSERJBCrkNKZ84isHrLj/8t4P9DAAIQgAAEIOAggECFA0fEWSjDE32NDaivvCBP8nJavcjWGt966BRN4B5QVi9Gs2VORe04V2pu7tFsVfYdv0ANujn3RFLtY5b+R7a1yn+qaktfJyM5DnAe3ficT9eqjOOcrWNnr7PaRLkurAQq5CXy2fWjTfMF33/0lPJxGqUXz18qr0NV6SqIIC/Qvc3TK8eTF83bpps/mGrnJPmAvZn40dtAhXbcskW/pTHd1f/dSQAhfdlOTg/gngQqZPLsdZMDLFNUdOWHuJUbD2qnZPppFaiQQN+6bUdM9w3OCqvJDcUoTQnzNA7acePGjUnHlg3VFp0+c9QIpHvc69OsfMc9MPvzpJo/nvyFeo9Z7FEPPbM2UQ8BCEAAAuFDQF5Arp/UjaJEVneEUV2FdFCRjgjvveccgNe2l23qB06moyc8S/Ej/y7KSANV2xKkz1uvr1eTdYdUoEKuVzWRtOZg9Wk2ckTbx90XuIO71KHqJXJpuzl8/vXwCeWp3dupY5KvAxVFmw6kq9fuOBxbOlipRlRoG0lnsPbD5rmdXjWsBSrkOiRYUYInErcatRsSgQo59ppdx6jL0LlupXgNTqBC7re3zuzt81H9cg1Tlm6nEdNWyVcUCEAAAhCAAAQUAghUKFAiQpUvAhWuXqCK06INB9x+0ScTzI3t1YhK5c1iSVyUe8Nf5V7xrkpIBSqqlc1DQ9rXdHV4evT0OX1Xsxe95CG8npSwEqiwemGsXc/o+RtpAqfQcadInudlPMrFqvQYu5iWrttvtYnlumGcI7ZKUee0UsadPAnG6fcNbqBCfuOHlw6xpYLSt6t9z1+/n1Pualf/nWkPwvLwO3NoK8qX1Xzy7AXr91NfefHuRgnPgYp48WPR0R+GmF6lTF6+eddPpuuxAgIQgAAE3k0BGQG5elJ3SssjQK2KBAjW7TlOM1fupEtXbv/XyYCDFEk4qFCJ70OqcIrLzxLGtTchgfYmfafSfg8nHZYGBvDEw7VNJh6W9d5ONG0VqJCRk2tVHRL4GuPEjk7VSuamDtzByCxN0+KNB6jXqEVyeh4X41wc+gYu3bhDJRsP1Fc5fZd7gIMLB1JUnkNPVYbNWscppDY7rbIKVMhIzAv8d3a3POOR4zdvOo+akU5Al7eqR61rbf/LKSm7jV7kVqeSkAhUDJ25hvb/dEE7HdvoV0lfmox/23XK5DWdnNy+A3/Z9uNpatFrir7K4bv3gYpmnPopk0NbxoUtB09Su6BZTh1/jNsFJ1BhlhrXeAxZ3n74NG07eJoecNo3STeaIE4MKp47k+1+3ZhWbiHfp/dx8z5ddSzUQQACEIAABN4FAQQqIuhf2ReBivF9m1gGFRZvOvjfQwrflLlb5EXuOB72XTJPZtNd3H3ZGhKBCnkg2z63LyX9NIHp+elXDJiyguYu36mvcvk9rAQqCuTOSDP7N7c83+cchMnHPfkecO8pV2X5hK6UJU0yy81mr95DAydwjlkvyscfx6X98we4lYZMmi/XdoTlKB/VKQQ3UCFtbuYeWF8l/UTVPBVo0J9u/fqnwzp3AxVdmlakltWLOOyrXzh27qpt8mx3UibIfuE5UCEvAk6sGkExo3+oJ7B/lzzKDTj/7y9XfrXX4QsEIAABCECgZd1S1KV+aUuIjftOUNDkFZZzF8m/Q+U5YDGYJ4iWkRktgmbQzv0nLdtVrYwbT166B5mmjZR9JJ1ULk6J9JhHunpSrAIVnUbMozVbDls2V50nvpbrU5XLN3+n4o2CVKtc1uX4Jg0tGtbWdLu6nFbqIKeXMisdODVrW07Rqipy35qrVi/lpM9WgYoK7UfS6bNXVU16VOdOoEJrsDffDy/i+2KrEhKBipZBM2krB+HMiswF14H/O8mb9X8Tu6u27Th8Hq3dqv4NhWSgQs5F0o+14onrrdL5ehuoyJeT51kb2FJ1yfY6CWTOX7+PZq/c5dQBSdsoNgcs6pTLTx3rlrSNlpLRIJ05jZu7qau0dvAJAQhAAAIQeNcEEKiIoH/x4AYqYvCQ1+PLh5m+FL54/Q6VknkAPAhSaNSRuQfU7vn9HXqiaevk8wXnus9UoYs9H71+nf57SAQqinHe3MkcSHG3qCYfd7VvWAlUNK9dkro1LOPqdGnOmj0UJBPYWZSCeTLRjH7NLLb4b9Whk5eoTuexLrdTbdCjVRVqUqmgapWybvOBn6l13+nKdWaVwQ1UyAuHHxcPNE0nkaVyd6cXDe4EKlIl+4wm8oSfZuVPnjS7dMshdP8v88mzjfuG50CFXMv0wS2p0LfpjZdlX5YXO9JjccMOnmvDi/+dsjeELxCAAAQgECEEZF63Q0sGUxy+xzUr0lO8Vd9pbqWWkTZkroRYMT6ik2eumDVpWd+pcXlqXbOY5TayctS8jTRxnnsjXLXGghuokM5F4pWA0y0ai4wK+Kp4W69fuq6Z0oPSp0xsbNa2vP/EBarfVZ0G9oMP3qeDSwZRbDZXlanLdtDwqStVqyg0AhX8RpqubLMeUaE/uUHTV9GsH7brqxy++yNQIScgz2qT+jelIjkyOJyPfkEmLC/InZlUL969DVRM5GOWyG3emU1//IP8TNGUU629NMyRpm3jbaBiyZiOlD19Sq0Zp0/57bcdMps27XRv5O63WVNT1q+T0/QlW10+2zodDBUQgAAEIACBd1AAgYoI+kcPbqBCbqoWDzfPFd+dXwAu57RP3hZXk1WXaD7EZW/okAhUrJ4SQBlSJvHostzplaZvMKwEKkYFNqTyBbPpT035XSb/K9RwgMMk5/oNZRTKppm9KGWSRPpq5XeZ9yI7v6z3tEiu2EOLB9GH/IDqbpEHiYLc28+TCS2DG6iwegg+9cstqtjSeV4FV4GKdkPn0PCOtSwnz67aaTSdPnfNXRrbdlaBCknpduTMZZftyRD3HdyD1OwhUdWAVcoxd+eokHZduWnHvsIjWGas2EFrOMWFccJvbRt8QgACEIBAxBeowHMajOS5DcyKpI1sEDCR3vBcE6FRPor+AR1cNNB0dKD+HCStTB4eKeDJv7fBDVTI8VdymqxMqb7Qn4r9+1fF2pLMueBNKVHwG8sOGGbPAbUq5KegNtWUh5R7iDx1+pjOUWV1j+arERWeBirkQqyCUP4KVMh5yZx9M7lTiNX8gg17T6G9h07L5g4lNAIVckAZTdyQR+A8f/bS4fiy4E2gwmof7QCePvdp++ETAhCAAAQgAAH3BBCocM8p3G0V3EBF4xpFqWeTCsrrfvzsBeWq3tOjhyVjQzIc9kd+8SwTE6qKOxMC+zpQYRWcecZDyaN/GE11qmQb/i75dPmlrTslrAQqNkwPpDQucjRr1yPDlTsNmq0tOnxWKvUdjehU26HOaiFrle706KFn6QusUjVY/W1s6clGLrQ6HYd1VoEKmTjygeK8ZfLLTzktVQtOy2TVC2wg9/CbzT39jMXVC3dJY/CRyW9P2vI2aGgVqDCeo9Wy5Dqevnir1SYO63wVqJBG5wxv6zI1gXbwJ/y/W4t4kvHJi7fQE37hgwIBCEAAAu+WwA/jOlM27tlsVgo2GqCcd8Bs++DWN6xWhHo1q+h2M30nLaMFK3e7vX1wAxUSSDnMIypU9yByb5KhbCe3z8W4oXRy2TmvHyX5JL5xlW15xfYj1I0nTdYX2WcHp2f9wiQ965JNhyiQ55owK6ESqOCDX9k+0ewUTOsn/bCNRk5f7bTen4EKOZk4cWLS4R8GmY4U3nnkDDXtOdnpvL0NVEzo19QyPbDTgbji5KWbVL/7eHry5LnDaqugg9lcdj1bV6XGFQs4tKNfCE4KW307+A4BCEAAAhCAgLkAAhXmNuF6TXADFVa97Zdu+ZF6jJgfbJ/JQc2pWK6MynbcSTfk60CF1eR+Mslei2pFTUcNmPUoUl1cWAhUyHD+cxvGmE5EqDrvUjwa4CKPCtAXSaOwe15/ShQ/tr7a8nu1zmPop5O/WG6jXxmNR1Hs5x6H8WJF11fbv9foOo7mD2mtvBZXvevsjfz/F6tAhXFbT5afPn9JBRv2V6ZmchWosDqObTJ7Ht3kTfFVoMKqJ6DqvHwZqEiUKB4t+b696UsL1fElYCEvBOZyXmGMsFAJoQ4CEIBAxBOQSbTPrBulvFeQqz169grVaD8q1C5czmfvggEe3T/d/vMBFajbx+20VMENVHRrXomaVy2sNPEmvaaxoZrl89HAttWN1bZlmQMgd53edPfuQ/v6Ivmy0NQ+TezLxi+uAk1WgYrv526gq7d+NzbptCwpX0+dsZ7LwptAhRzI9hJ84jKHjk/+DlTIeVnNWSj3VJkrdHU4Z9knNAMVcrzz136jOvw88PDhE1m0FW8CFSsndqNMqZNqTTh9FuAR5p6M1HZqABUQgAAEIAABCLgUQKDCJVH43CC4gYoZQ1pRwezplBc/gXskj565VrnOk8oBHWpS7TJ5lLuoelIZN/RloCJNqiS0YVKA8RC2Zcn/n7d2bypbJDt931mdMkCGHldvN1K5v7EyLAQqvvgiEe2a1cd4apbLe4+fp4bdHPPuWo28MWus1/gfaPGavWarneqtHmS3Hz5NzQOn0CD+u9QomctpX6mYsnQ7jZi2SrnOWBlSgYqmPOGf2SSbwQlUlGk1jM5zTzJvSkQIVMh1x+cg2aIR7SjlF+oJzM1sLt/6g+pzuoDfOccyCgQgAAEIRGyB1F8loY2T1fd5cuWhnc6lIt+zmN1TWo0Ubc+jDNbzaAN3ilWgYsCUFbRh93GnZniQKMXnOSkaVChAVYvldFqvVcjk4dv2nNAWvfqUzi4HpCNK7BjK/Y33byv4JXJmk5fIWw6epFZ9pinb0SqtAhXaNq4+Ja1oJn4x/4xf0JsVbwMV0t7ijQeo95jF9nkfwkKg4rvsX9s6BJldr+rlfWgHKuTc5L6uVpex9Ndfj2yn6mmgwlUw88DPF6lel3FmDKiHAAQgAAEIQMBHAghU+AgyrDUT3EDFrGFtKP83aZWXNZJ7HU2av1G5zpNKq+G1VqmGtGP4MlAxtk9jKpMvq9a0w+eQGatpxpJtFFV6vy0Moo95wmRVqdRhlFuTKYaFQEXhvJlpWt+mqsuwrKvZbRwdOX7Rto1MuL6PewPGiv6h5T7GlfPW7qP+45YYq5XLMvJjF4/YSMw951WlCs/NcOLUZUrKL6l3zuqt2oTkgT9XzUB69tT8oVLbMSQCFVYTO8pxgxOouMrzL5RvPUyZm1e7JrPPiBKokOuTVHLjeM6VPFnSmF2usv7ug8dUj4MVly7/qlyPSghAAAIQiBgCFYrnpJFd65peTPbqPZSjHk13CMaK9yK9R1u4s0iKxB87tSLzgtXheTKWcABeVS5ev0Olmg5y6sGu2tYqUKHa3t263+4+oMIN+tPrV8Gfy6NFnZLUtUEZ5aFlNKrcv8n8A5nSp6CVYzopt5PKcm1H0Nnz103XywpfBCqknSw819pjnnPNrJgFKuR+9NyVW5YTNUubK3ccpe7D5tnm/wgLgQr5vZ7fONZ0NFKrgTNpiyHo5W2gwmr0xq6jZ0070Wl/ixt37lFNDlb88cd9j+eoSM7pcLdxWlyz0m3UQlrBKURRIAABCEAAAhAIWQEEKkLW12+tBzdQYZWWafqKXTR08vJgX9uw7vWoStEcynbcSS/lq0CFpKLZPacvz3/H3cgMRXLw5tS95LYaQaD17jc04bQYFgIVVnM+nLhwnbKkSeZ03lJhmxCae/HLfBxdmlakljwvg6rIdulTfE6ST9hYDp/+hWp1HGOsVi6XKpSNxvdsqFx38uINqtR6uH2d1W/W3XkUfBmoeMWTcfbhIfyuJp13Faj4/d4j+iSBeWqtjftOUNv+M+wO7n6JSIEK7ZoL5clEfVtWMQ1sadvpP+W/cQl4GdOa6bfBdwhAAAIQCN8C9SoXtP37YHYVaUp1CLVJtAvyv1Uz+jVTnsqyrT9SwPD5NGlAcyr+XUblNg0CJ9G+w2eV6/SVIRGokEBKZe6Yc8ZFUEB/HlbfY3Knl0M8Z92HnOZTVWTkx9zlO9mjGXtkUm1CZvMNGDf2d6DCNiE6j9CeM7Q1ZU+Xwnh6Dstyb9eB54ZrWbsEdaxbymGdttBn4lJauGqPtujw2a5BWWpfp4RDnbbQMmgmbd3jPJpGW6/6PLJ8KMXn+SpURfUCPyQCFVX5Xi1nplTUuX5p1WnY6yRFWq2uY+kD/k1tmdrTXq//ovrNuAqGNew1mfb+eEbfjNN3GeWbzmTyef3GZy7dCLXAqP64+A4BCEAAAhAIDwIIVISHv5IX5xjcQEUAv+xryg91qmJ8QazaxmUdBwVkQrxknyVUbjp89nqaunCTcp1W6atAhdXNtDEoE51HDxxaMsh0Yu0iTQbSNe7tZlXCQqBiTO/GVDa/egSJXMO6Cd1MHxrlAefn89dswZ1o70dVXqrMGzG8Uy3l3AHyoJatUjflfsbKddN60tfJPzdW25aND1oZ0yenVWM6K7e99+AJ5eGHwzccPLAqvgpUHDlzmXqNW0pXrt62OpxtnatAhTwYjQmoT7FjfGTaVr9Jy2k+z7ngSfFVoCJo6gqas2yn24f25RwVqoNG5d9kVZ7gvX75/KZzyhj3k79XzQ6jjdVYhgAEIACBCCLQkOda6MVzLpiV0AxULJ/Q1bRDSFEeLXGV8+1nSJecVo9V39O4+29WSAQqAnlE7BIeGevL0qNVFWpSSf3MIaM36gZMoO0zeik7FMl51OWRkQePnnN5SmEhUCH3vx9+9IEtWGE1sbtczI7DZ+jS9d9NOwWFZqDi0NIhpiPKA8YsomXrDzj4Wz1bqUZgaDuP69uESufNoi06fEqg4jiPorYaZaLtIGl7B0xZThN6NtKqHD5VgYqsGVPSslEdHbbTL9Th3+GhY+f1VU7fpw1qSYVzpHeqN1a4k6rMuA+WIQABCEAAAu+KAAIVEfQvHdxARWmej2FcQANTHXeGWJvuzCvy5EhHcwe1Mt3EnYcOXwQq4nIe3gOczkn1wl0m8stbr69t+LD+RK0mF1y29TD3hJun39zpe1gIVGya2YtSJf3U6dxsk+KV70JWD7fXf7tLx7knXaXC2Z32l4qDJy9R3c5jaQZPcF2Q89qqSraqAfSAgwdWxSon7i2eV6BQvX62YfH6Nn4Y15nMHvxUD1L6feW7VaBi9c5j9PL/0xzkz56WPk0Qx7i7bXn3sXPUmNM2uFtcBSqKNh1IyT7/mKab9L6U40gPxyqdxtDps9YTPOrPySpQMWnJVtrj4mFM2nr79i39zMd8y/+tuFtCOlChP48c36ShgCYVKCPnJndVmvA8Irv2n3S1GdZDAAIQgEA4FKhRLi8NalfD9My/rd7TntvedCMfrLB6GWq8f1gytpNpz/sK7b6n0+euWZ6R1b2c5Y6Klfc51ZHMj+FOQECxu2XVxx/Hpf3zB1BkTvepKn/wnAOJuKe6qpzjDiFlmw1WrXKqCyuBCjkxCVbM5ZEV33yd3Ok89RVv/v7HNOVSaAYqzm0co3xWknNtN3QObdh+VH/aXk+m7U6gQg7UltOFdeC0YVbFyk4VqPia5z9Zx/OgmJU2g2fRpp0/ma221c8d0datNKR7fjpPjbo7zvtn2TBWQgACEIAABN4hAQQqIugfO7iBCleTLUv+1K5D5nitN5NvzgtkU7/ElkazVulOjx6a53+VbXwRqOjYuBy1qVlcmnMq8mK68+DZTvUJE8ah/Tw3Q5TIkZ3WyQvjvHX70p887Nis+DtQETlKZDq3YbTy/G2pnVoOpegxPqT9HMDxdP4JuWbt4TmwdVVqVLGAkqEGD8k+euKScp1WuWBke8rFQ7xVpfeEpbRotfNw9/ycJmEWp0tQFQmwFOGcyv++/Ve12lZnFaj4pkoAPXz4X3ClFAdpxvdoYNqOJ5NcuxOouHrtDvVpV53ql8tnekxJEVWqxWCX/91oDVgFKjoMm0vrth3RNvXpZ2gGKuTEJbdyXf4dSkooqyI9F5sFTrbaBOsgAAEIQCCcCmTPktp03ge5pK6cf35lKOSfnzGklWmefRmNevTEf/OAyTnly5WBZge1kK9OZcvBUzx59FSnen2FLwIVcl+7ascxGj5zdYimqrFKB6u/JuN3d14ea/v4IlAhHXpyclDr5cvXWrNOn2ZzVBhHFH8UXYIVbShr2i+d2nCnIrQCFV+n4Rf4PNLarJRsMcRprq+QHFGhnYdVeittG7NPVaBCnn1Orf7ebBdavOkg9Rq50HS9rHA3ULH3+Hlq2A2BCktMrIQABCAAgXdWAIGKCPqnD26ggsdX0855/SjppwmUQvLgUpMfqGQIrqfF6sFL2vqZ5x6orJt7wKz94AYqpDfTwcUDTV/GS7qgx/xAoipJPolv2sPJmC7KuL+/AxXJkn5CO2aqJ57WT2LetGYxCmhc3nj6lsv6l721KuSnoDbVlNubBRq0jV09FN3+8z69ev23trn9MxK/lDZLJyYbtQiaQdv2nLBvb/zibqBC5t7YzJN3qybClDYP/HyR6nUZZ2xeuexuoEJSGq3kdBFmqbCk8f0nLlAD7qFlFYzRTuJdCVRo11uX00r04/QSZkVePmSp2NUtO7M2UA8BCEAAAmFTwNVLSJ+kNXVx6Sk4leVWTmmpKmcu/0rl+YWvQ+F78S18r5EySSKHam2hUKMgunHzd23R6dMqULGB50CQa5aSK9NXpsGTo5wasUYopEZ0NZGx08VxhdnoWtW2UmcVqGjKoyrPXLpptqu9/hlP7P3M5NlA2+jytgnKNFXGQIVsL8GKecPamqYC09pUfYZWoGJAh5pUu0we1SnwvfgbylCuM/3DIz/0xdtAxdg+jalMPnVqWi31k/44HRqVo7a11B3O9NsZv6sCFbLNDn72NXuOkGvNwUGqJ0+eG5uzLyNQYafAFwhAAAIQgIDXAghUeE0XtncMdqCCL69+lULUp0Vl0wt9+eq1LS+sJ8EK6fE+jfOPqkYjaAdytzd3cAMVrvIVa+fj6ecL7mWViyfgNruR9Xegomj+LDSldxPlZY1dsJnGzVlnWyeT0O3hofgJOD2Wu0XfqypXtrS0gHuKqcqC9fup75jFqlW2ugn9mlLJPJlN13u7QhsxYra/u4EK2d9qom9ZXztgPP147IJ8tSzuBiqkERnptHFSgOn8IbKN/m8oy2blXQtUyEiifTxKyCx9hDhlqdydHnN6CxQIQAACEIh4AlYvIeVq9fcwIXH13/NIzIomaTO7jFxAG3gkr7FULpGLBratbqy2Lf+w+RD1/H6Bcp1UWgUqOo2YR2u2HLbtKxMAy0jh96NGUbbl7uTdyp09qJw+uCUV+tZ1fn+tyV7jf6DFa/Zqiy4/rQIVFdqP9Ch9ptXBPAlUSDsy/9284W0pM6ce8qSERqBCngWOLOOR1h9GU57asXNXqXq7kU7rQitQIQe2Gh3vdGL/X2EWqBgV2JDKF8xmthsNnLqSZi/bYbre3UDFvuPcsajbeNN2sAICEIAABCDwLgsgUBFB//q+CFTEiPkRHeaJoz+I9r6pkgQrWg6cRXsPnTbdxraCe4WVLpyNRnWtaxmkuM8TLX/HvVXevHHuLW88QHACFVH4YWwvP5RZvbQ0Hs+T5RFz1tOUBZuUu/g7UNG6XmnqVK+U8tyMQaLaFfPTgNbqURHGBqR3Xrv+M+zVn/FonH3z+9uX9V+seuglSfwx7ZrdR9kbTd+Gt9+NqRX07XgSqJCUQpt5ZIpZT8ezV36lci2GEv1rnmpKju1JoEK2L188B/93VE++mpb6gZNo/+GzputlRUQJVMg8FJ8nikert/zocq4Mq//2xCQzT3L5hP83CAUCEIAABCKegKsXmjKqoVaXMfTsqXo0bXBEPuGRuPu4t7aMyPRVkRz8eer0oXv3HiqbdDdQITtbpeu8eP0OlW42KMRHHGbhyYyXW0xmrL9ImTMjd61e9Pr/5w7TrzP7HlqBil+2jlf+nVUjKrRzlWDFfA5WZPIgWBEagYrebatRg/L5tdN0+uzGKdNWKFKmeRuoGNO7MZXN7/6ICu2EOvEI8NY8EtzdYhaoKJQnk+WccI+ePqcaXcY6pbrSjlu8QFYq+f+TgUf/8H3TwJuMgK7fFYEKzQ2fEIAABCAAAb0AAhV6jQj03ReBCuFoVqsEdW9U1qWMPMTMXr2L1vJkaq90eVtlqH3l4jmpQYUCpmmk9I13H72Ilm84oK8y/R6cQEUF7qE2sksd07aDu8LqAcrqZWnjvlNp94FTwT285f5WE9UZe5RF5YCOpAD7LGFcyzb/5ZfxhRsPdEhBIA/j5zeNUQam5EY/a4WuyjYHdqpFNUvlVq7zRaVVXlhPAhVyLiULfUMTejYyPa22PMfJRkUPSf0OngYqZN8R3CvTbDJzWf+Qh6WX4hQSf/xxXxaVJSIEKuTFz05OixGN02Jd5vQXQ2etNZ0QW9IrHFo8iGJwyjdVkYnBU5VoF+IvYlTHRh0EIAABCIS8gEzafIA7qVgFC46fv0Z1uafzyxev3DohSacZk18yu5rYuhenwmzIKTF9XaZy7+7h3MtbVTwJVMioCulcIv+eqkrH4fNo7db/RmCo1vuqbjmnuMySJpnL5obPXk9TF6o7BJntHJYDFXLO8sw0f3g7ypTqC7NLcKgP6UCFq8Ce3MvnqhHo8NynnWBoByrkuJ2bVqBW1Ytqp2D5aRaokP9t2LNwgOVzj9xjS7DiF+6QZFWsOmx5kiLW6hhYBwEIQAACEIiIAghURMS/Kl+TrwIVkSJHouXju7p90ywjLP64/5ie8QNeTH4hKCMWzIaSG+l3HztHjQMmGqtNl70OVLjI+Wt6QA9XmA1JtwpUHD17he7cVfeMMx7+yOnLHg151/a35Tv+4hNt0eFTNYl5xZK56PvO1kGdZfzwGsAPscayhUdGmI04yF69h9PEjPKgfIDT80TlND0hWUrxhOEXf7nldAhPAxW2URUzelFKE0/Jn1yk4QD622KEkDeBCplfZf2UANM8unJhJznXcjVOZWB2bKtAhcwTc/POPScfq4pVO47wyKozVpvY1vlyMm1Vzzs590Ub9tNOHuX1gOeZkSIBjeFdalPuzKlty6r/t+XgSZ6YdJpqFeogAAEIQCCCCFh11tAuUdLZDJq2ik6duapVOX3KvFHVSn9HvZpV4g4Zkahp/+mmHU1ixY5BhxYFWY5QdjqAmxVyvy3pRlWjQDwJVMjherauSo0rFlAe+be7D6hQ/f70hvP0h2QpyD3aZ/RrZnmI53LNPJriqcVcAaoGwnqgQs7Zk2BFSAUqZER9y5rFqUW1IipGe90YHjk+nkeQq4o/AhVyHl2aVqSW1a3PW7YzC1TIuiY1ilKPJhXkq2mR0TF9Ji6jrXtPmN5nWwUqDp68RHU7jzVtHysgAAEIQAAC77IAAhUR9K/vq0CF8EhvsTUcrDDriewLQpm4ukyroXTXzZf0ckxvAxUFcmekmf2b++K0Ldv4lXuzF6zX1ykdjVWgwrJBw8o//npkS5NlqLZclJRX59aPpsj8UG0sT5+/pEw8IZ6xSLBq66w+9OXnCY2rbMsysXoBfni9wy/ljWXKwBZUNGcGY7VtuWa3cXTk+EWHde4+YDjs5MXC2t0/UUdOWWYsngYqZP8SBb+hiYHmoyqsHiRlf28CFbJf6q+S2P67tArqzFu7j/qPWyKbOxWrQIXTxm5U7Dxyhpr2nOxyS18FKjKmT06rxjj/XvUn8Pu9RyS/z8ScGspVqdJpNJ04ddnVZlgPAQhAAALhWEBGVWzjDgbu3NNKwH/Wyl105peb9ITTQX0QLSol/fxjKsf/7ss8Wvo2XnOHBEm7aLyvEao29UtTx7rqlJu+oBzGowmnLdri1JSngYp48WPRfp6bzGxUhav8/E4n4EWFdADZwvecKTgNqFmZtnwHDZuiHkVito/Uh1agwuxe0ir1k/68JVixYER7ysj3eVbF6v6yXYOy1L5OCeXuLYNm0tY9x/+3jjtwRecOMIk/S0C1S+ehykW/dRlUk5H05bnTj1mqXm8DFVb3pqrJtP93Ef/71rVZRZdBFqtAhYwmX8udgVIl/fR/jZp8u/vgMc1ft5+2cWeXhxy8kHvORPHj0FfJPqUSHHQr/l0m5Z6HOFBRB4EKpQ0qIQABCEAAAghURNDfgC8DFUKUPm0yWjisrcNDma/o5CavOt+s3eDULZ4UbwMVy8Z3oaxpv1Qeavm2wyQ9w90tkXmI8AR+SR2Lh/2riir1jz8DFcm//JS2Te+lOlWymmja6mX8/HX7qN9Y9cvw7i0qUSatPTgAAEAASURBVLMqhZXH6ztpGS1Yudu+TvLzHlw80PQ3NmTGajpz2XkUhL0Bw5dP+EFhJM+JoiqS5ic/B1d+M4waMHu4lDa+qRJADx/+10Nf36Y8VG9i0684oKcqMu9K/rp96Pmzl6rVXgcqpLE6lQpQ/1ZVle1qlW2HzKGNO45qi/ZPq4dB+0YefAntQMWKid08nnjS7HJO8++qgswnggIBCEAAAhFeoAKnJDW7PwjOxb/g1KfSCUOfBkomIz7AaQfjcC/1kCp/8b1J7lq9nUY7eBqokPPr0aoKNalUUHmqkuonL8+JoRq9odzBy0qrkbzyIljm5fCkY5N2GlaBilc8UuSff95qm7r8fMKdeyq0HUF//vnAaVuze0l3AxXSoIxqWDCiHWVIaR6s8DZQ4XTCHlbI36BcmxHKkclaU/4MVMg5WD1/yHqrQIWsT5MqCa0d303ZsUvWB7e4On5w28f+EIAABCAAgfAsgEBFeP7rWZy7rwMVcigJVswd3NqnD1vS47kWP9R5GqSQ8/EmUGE1UZ+8vM5Tt69lXn85rrFYTeB27uptKttssMMu/gxUWAUczEYZ2E6ee1utn9aT0n75mcO1yINdvnr9TCdyrFomNw3tUMthH21h8cYD1GvUIm3Rcqi1pFAqxMeRv5EnZeGoDpQz41fKXVSjDcweLqUBs0CFrJPJ8yb1aixflWXsgs00bs465TpvR1RojVmNWpFtJB1bmdbD6Rr3ftOX8ByokN5uh34YTHFjRddfklffZSRRza7j6BynjEKBAAQgAIF3QyCk5sN6/OwFlebAt9YRwqpDgdxDvbJIDWn8S3zA6abM0qn2GLuYlnLPbn3xJlARN14s2zweZqMqJi3ZSiNnrNEfxuff5d/4vZwG9GM+F2P5YfMh6vn9AmO1W8tWgQq3GjBsVLHDSGV6sItbxinnZ/MkUCGHkmDFQh5ZkT5lYsOR/1v0R6BCgjmtBs2i7ZzyyKp4G6gYFdiQyhfMpmza3REV2s7dW1TmzlKFtEWHT3cCBdXL5qXB7Ws47OerBXeO76tjoR0IQAACEIBAeBNAoCK8/cXcPN+QCFTIoWVY+NBOtalwjvRunon5ZjJ6IWjSco9zzGotehOomDGkFRXMnk5rwuFzza5j1GnQbIc6dxbE5ODCgabzKtTtMZEOHj1nb8qfgQqroeBWL9Pl5PPlSk+zg1rar0O+TFu+k4ffr3Co0y9YvYT/iXNAV2s30ra5PJTu4/zNCeM6P5TKBr0nLKVFq/fom3bre94c6WjOoFbKbeUFQe7ave3zGMhG3gYqXI2qkGCBBMEe8PwtxmJlJNsWbTqQrl5zDDLo24jJL+s3cRDp0wRx9NUO36/8+ieV4yH6+slBw3OgQi7uSx5WP29Ia8sJDx0QFAuS57pW9/EOvV8Vm6EKAhCAAAQimgB3wAjqUJNqlc7t0yuTewuZB+v6jd9JUmfu5lRKn3O6KVWp0XUsHT1xSbVKWZczWxpaOLStct0NHiFahEeK6jt0eBOokMYDWlahppXVoyrk+vLX7+fViAbliZtUNuJ5BgJ5vgFjKdhoAN28+Yex2q3l8BaokIuKKcGK79tTuhTOwYrQDlS4G6SQ8w4LgQo5D7PfsruBghrl8tKgdr4PVhw+/QvV6jhGThEFAhCAAAQgAAGDAAIVBpCIshhSgQrNp3SR7NSuVgnTSYS17VSfkvN3zPwNbk28q9pfq/M0UGGV9kjaLNNqGJ3nc/OmDOLJpmvwpNOqYpwwzZ+Bion9m1KJ3JlVp0kdeTLstTwptlVZPqErZUmTzLaJvOTNwymNHj18arqL5II+tGigcr1+TozKpb6j4RwAUxVJNfBdjUB6ySkVPC78ImLTjEDTPLPjFm6msbP/N9LB20CFnFex/Flpcm/zURVz1+6lAeN+cLqE4AYqpEEZKbSUH2QjcSoys2IMKoX3QIVcZzROqVGVfzstqxWjTxLENrt0Zf1JHkHRdeRCusKjnlAgAAEIQOAdFOB7hA4Ny1JrnjzX6t9Pd2Uk1WPTvlPp59NXbLtY3YufufwrlW8xxN2m7dttmB5IaQyjW7WVxrkHvA1UuBpVsXjTQerF/36GZPko+gd0iFNm6ecB2cLzALTqM83rw4bHQIVcrHRIWcT3eF8n/9zh2kMzUHGVO7x04ucEfVozh5MxLISVQIWcliqdmbuBCtlfnnkHc7BC/1uU+uCUrYdOUcveU4PTBPaFAAQgAAEIRFgBBCoi6J82baovaBHnNo3GPdWNZc6avTR8queT0BnbkWV5yVq7TF7KkSGlcoi2ts/tP+/TAe41Nn/dXjp3wTcpVgJbV+VJ35x7wr3k3l7VOo+hy1ccX0BmzpCC5nHqqiiGiaTf/P0PzVmzh0bPXKudrsef8kJ+NvfcV004LfMqaCMHpGHp3VOvbB6Pj2HcYe/xC9Si1xRjteVy77bVqGbJ75y2ecZBh0rtR9ItfhCxKvqX6saX/Mr9+CXAqkndKbVi/oZrt+9SmeaD6d+3/1I97rnXvVE5eo+31xc5r548GfS2PdZDzPX7GL9ny5yKxvdsSLF5ckJjmcdpEoZOXm6vlvlLVL3WbvHE6GWaD3HK/2zfkb/IqIrFnGoqY6qk+mr796VbflTO5RE3bkxaPrazckTEL9xrsHrHUQ4jIewNGr7U5+HtLasXJZk3xVj+5qH6MipFP1S/Mb+Y6VyvtHFTr5enLtvhEPQxa0jmIlnPkxQmiu8cWDh16QbV6DDabFfTepkkvkKxHFQ6fxbKkjoZxTSZM0YaOPDzRVq04QBPJHnCoeepaeNYAQEIhDsBGemYnu+DEieKTze5p/kpvu94/Mg8qC4XmDBhHIrNLwSN9w7axbtar22Hz/AnkI5Tm47iOa1SfvGJ1ye/YvsRGjh5xf9+Z3w/s5GDCql59J+qqOYwU21nrCvLEx2P6V7fWG1bNs635G2gQhqzSpsjozaKNh1kGzWiPBEfVXZuWoFa8X2NVsrznBBnzl/XFj3+DK1AxYXN45SjrD1N/aS/QFWwIjQCFTIiWJ4bx85dT69fvdGfkuV3bwMVI/l+vUIh36R+0p9gT35mbFyxgL3Kk0CF7CTPecO61KZ8WdPa2/Dmy33+d2gJpy+bxinUnjx57k0T2AcCEIAABCAQ4QUQqIjwf+LQu0Dbi4GvvqB4sWNQ1KiR6TXn3f3zr8d09pdb/3twC73TwZFCSCD/dxltcwNs4Ama33iQWzmETgfNQsBRgF8OJUn8MaVJ/hlF/yAavf9+FJJUFVd/vUuXr/1GL3hOChQIQCBiCkialIBmlagqBy4jGzolSLrB5n2nOaT70xRkVNpc7mwQ/cNotrQ9F/m+RV9crddvi+/hU0AC3sU52N2gfH7KmvZLty5CUuFsP3yGZq/azSmcLjrskyl9Clo5ppNDnbbwK3d+KFivL731YPJmbd/IUSLTrnn9OJ1UPK3K4bNsm+H2DkFW6T47DJtL67YdcdhXv+BqVMX8dfuUnS/0bQT3uzxXHFgQZJuXwxepcgZ3qUPVS6hHP3tzrhW4g8/ps1eddj23cQyp5viQETfZK3Vz2t7dCglWLB7ZwT5fnFWgom2DMtShTkl3m3ba7tKNOyRzya3gTjbeTJ7el0cg1OO0SapiHPmj32ZEjwZUqXB2fZX9e5VOo+nEqcv2ZU+/9GpTjRpWyG/bzdNAhXas7FlSU4MK+ahYrowejcLae/w8LVx/gHYdPEX/cAc5FAhAAAIQgAAEzAUQqDC3wRoIQAACEIAABCAAgXAgECdOTFoxrjMl+yyh/Wz/evjENsJKm4D41u9/UWV+ufjXX4/s28iLp9lBLehDTiUnpRz32j6r67Xtar29IXyJMAIpU3xOOTh4JSMkM/LInPi2DjhRbJ0zbvJv6BynbTp75VfaeuCkcu4pgXg/WlRqULkQ96x3HNn85u+/aSWn2bx376HXXp99moDKF/mWIhlGoUrbs3l0o9aJRHqBVymey2m0qowknr96t8vAfb6c6SmDySjRzft/DpXUiTKqogaPBG4VNMOj+TxUuMl4dG+ZAt+oVnlc9/T5C5q3crcy2FQkXxZlei4ZYb37wCmPj6XfIRb/FmcENbeN/mkxYAYdOe4YINO2lb991RLf8d9eq7H+fPP3W3r+4iVduHabzl26Rc94UvjglMSfJ6Tyhb91Or50Ypu7che9MknnKvOPleZUqsbyjDuZzOOAYHBf8ndvUck2snzxpkOWc+wZj29clhF2OTKl4v8+vqBMqb+gRPFic0AtKr1684Ze8LVdu/0nSYrRU5xS+Bz/H0ZPGAWxDAEIQAACEDAXQKDC3AZrIAABCEAAAhCAAATCgUCeHOlsoyLkVKUn8GROrXH7t3u2/O5DOtWiknn+m5+pP6f7m7dil+2Kcn/7NU3v18yh97M+UOFqfThgwSlCAAIQgAAEIAABCEAAAhAINwIIVISbPxVOFAIQgAAEIAABCEDATKByqe9Ieouv5R7r+iK923fM6UufJYxLu46epSY9JlEkTg11Zt0ohyCF7KMFKlyt17eP7xCAAAQgAAEIQAACEIAABCAQfAEEKoJviBYgAAEIQAACEIAABMKwwNRBLahIjgx0+dYfVLzhANuZju3TmJInTkSTlmyhCT0b2er0Of5drQ/Dl4tTgwAEIAABCEAAAhCAAAQgEO4EEKgId38ynDAEIAABCEAAAhCAgLsC0Xj+iW2z+vAExHFp66FT1LL3VIddv/giEe3i9VL0gQptI1frte3wCQEIQAACEIAABCAAAQhAAALeCyBQ4b0d9oQABCAAAQhAAAIQCMMCSZIkogFtq1K+rGltZ9m471SnCW1dBSJcrQ/Dl49TgwAEIAABCEAAAhCAAAQgEG4EEKgIN38qnCgEIAABCEAAAhCAgJVAiuSf06D21SlqlCi2ERQJ48aybz5y7gaaNH+jfVn74ioQ4Wq91g4+IQABCEAAAhCAAAQgAAEIQMB7AQQqvLfDnhCAAAQgAAEIQAACYUggX64MNDuohfKM6gRMoEPHzjutcxWIcLXeqUFUQAACEIAABCAAAQhAAAIQgIDHAghUeEyGHSAAAQhAAAIQgAAEwqLA+9GiUp0K+Sna++/bRlSk/yoJZUiZxHaq//77LzXqPYX2/njG4dRdBSJcrXdoDAsQgAAEIAABCEAAAhCAAAQg4JUAAhVesWEnCEAAAhCAAAQgAIHwIJAvV3qa0rsJBy+i0t0Hjyln1R4Op+0qEOFqvUNjWIAABCAAAQhAAAIQgAAEIAABrwQQqPCKDTtBAAIQgAAEIAABCIQXgUbVi1Bg04q20y3SZCBdu37HfuquAhGu1tsbwhcIQAACEIAABCAAAQhAAAIQ8FoAgQqv6cLvjvHix6KHD57S27dvw+9FvONnHjVqFIoe/UN6+PDJOy7h28uP9sH7FCd2DLp77yG9/Qf/ffhWF61BAAIQ8J9AvpzpafbAlrYTqNJpNJ04ddl+Mq4CEa7W2xvCFwhAAAIQgAAEIAABCEAAAhDwWgCBCq/pwueO1crmoSHta9KqHUepy5A5bl/EBx9Go16tqlDM6B+4vY+rDd++/ZfGzNtIN27+7mpTisUvj3vz8d+PGtlp2yfPXtKgySvoxfOXTuvMKtKkSkLNqhahyJEjOW1y87d7NHLGGnt9kXxZqFzBb+zLwf0yf+1eOnriUrCaWTGxG2VOnZRqdR9Ph3+6EKy2YsaKToEtKtNHH77vcTv/8Mv82388oFOXbtD+Y+foOf8tfF2icFCmZ8vKlCBuTJ80/ejxcxo4eTm9evnaqb1VkwMoI+cz33H4DLXuP4PevH7jtI0nFTXL56NcmVM57fL4CZ/DpOX0UnEOsnFqPodWNYvRe++957SvqkL+W3r56g3duHOPTl28Tmcu3aRHD5+qNkUdBCAAgQgpkDlDCgpoUp5mrdxNW/ccd7rGvu1qUL1yeW31mSt2pSf8v8NacRWIcLVeawef4U8gLt9b/MP/hj5+5Nt/M9+L9B4l+jge/XX/cbDvJVSqct4vXrwyvY9Q7RPcOnkWiBnjQ753jkyv37x5Jzs9xYkTk6JEiUz3uEPLu1qicgq9uHFi2O5RX/B9rK//23lXXXHdEIAABCAAAQj8J4BAxTv0S4jEL+V3z+/Pk0vGI5lQskCD/vTr7btuCWRKn4JWjunk1raebDRs1jqatmizy10ypEtOq8d2Nt1u8cYD1GvUItP1+hXSa37D1J705ecJ9dX27/f5YTVn9Z70z9//2OrmjmhLebKksa8P7pdlWw9TwPB5XjeTM1saWji0rW3/w6d/oVodx3jdluwoubtnB/3XyzQ4DT159oLmr99Ps1fupPt/PQ5OUw776l8QOawIxkLZ1sPp3MUbTi0cWDKYPkkQ21a//8QFatZ7qjKg4bSjScXeRQNtk7mqVlfqMIpOnrmiWkWdGpen1hyoCE45euYyzV69h7bvP2n/LQenPewLAQhAICwLzPu+HeXOnNp2fzNv3T6avnQ73fn9L4oZ8yNqVqMYtape1Hb6R89eoRrtRzlciv7fmbJt+N+HC47/Prha79AYFsKNQGK+D9wxqzePMP6XSrYYQtdvuO444+7FBbSsQk0rF6Tth09T88Ap7u7m1nZpU31B67jDypVbf1AJTmP2L59/SBT5b6ds4eyUje/BM3HnmGSfOd43v+LOHJe4s9G5y7/Seg4OHvrpvMtzSZUyMXWoV8qpI4Z0fBk4ZSX9zv/NelLEOeln8Z122XvsPC1es9epXioiRYpEga2r0Gcfx3Vab7Xfp5/Ep91z+1IUDtT0GLuYlq7b77S/JxUFcmekasVzOe3ylDte9R+/lJ7xfbWqFMqTiaoWz6la5XHd/YfPqN/4H1wG0yQQXKVYTvo2Q0r68rMENkPtYPcfPyO55zxy+jKt3XnMp88A2jHwCQEIQAACEIDAuyOAQMW787em0kWy07iABvYrXsAvlfuOWWxftvri70CFnNuw7vWoStEcpqdZP3AS7T981nS9tiKwdVVqVLGAtuj02SJoBm3bc8JeH9YCFdrLGO0Ey7cdQWfOX9cWPf70VaBCO/ADfmCp0XUsXb5yW6sK1qf+BVGwGtLt7E6gQjaXB6+GPSd7NFpHdxjyZ6BCO4/f7z2iPhOX0o59P2tV+IQABCAQ4QTkPmX2wBYUO8ZH9muTF6kygbZWJKBeuuVQus0jJ/VF/+9MOf439azh31RX6/Vt4Xv4ESjBo2UnBjaynfCWg6eoVZ+pPjn5z/lF7s7ZfWwvtOUlbvZK3XzSrtbIhH5NqWSezLbF5gNm0Pa9/7tn1bYJzmeypJ9Q0ypFqGLhbA7//bhq8/afD2jGih20YNUe0/SybeqXpo51Symb6vz9Alq9+ZBynapSOh6dWz9atYqu/vonFeUOWaoio1GOLRuqWkVXeL9iJvtJkGXTlB72/frxyNj5K3fZlz39MrF/UyqR+7+/o3Hf6l3G0rGf1aOvJw1oTsW/y2jcxevlwo2DTIN08lsY2qkWZU+Xwq32X7/5m5Zzh6ypS7e53RnOrYaxEQQgAAEIQAAC74wAAhXvzJ+aaMP0QErz5Wf2K5YH+Ny1e9ODB0/sdWZfwkKgIgb37NrOPd8Sxo2lPM0//npExbhn2VNdOgfjhtk4Dc8P37c3VtuX13GPsA5BM+3L8iUsBSqkF936Sd0dzm/LwZP8cD3Noc6TBV8HKuTYEqyo2XUc/XLlV09ORbmt/gWRcgMvKt0NVEjTP/PIi/oBEyx/V2anEBYCFdq5reZebtJrTp/uRFuHTwhAAAIRQUB6yLevV5rK5s9KUTk9i1bkfmcjB2uHTl+tTNmSMGEcOrAgiFMAvaUi/NLOGMhwtV47Dj7Dl0C7BmWpfZ0S9pOu0H4knT571b7s7ZeRPRtShULZ7Lt/UyXAZ3OKJUn8Me3iIIiWGvIkp3qs1GqY/VjB+SIjr5vWKE6d6pW0BVm8bev05VvUdcQC5T1gRApUiM+wWWt5ZPgWr6jCeqAia8aUtGBoG4+CVRqEjNxfuuVHW6rVkEgLqx0HnxCAAAQgAAEIRDwBBCoi3t9UeUV5cqSjuYNaOa2bsHgLjZ651qneWBEWAhVyTvm5B9Es7klkVlby3Btdh8xRrv7wow9oy4xAW+or1QZ/8cTURRoPdMq1GpYCFeP6NqHSebM4nX7BRgPo5s0/nOrdqQiJQIUc9yEHjCpwz9Rb3DstOCU0AxX7lwyiTxPEcTrdc1dvU51u4zye9yEsBSrkoi5zioYa3EvvAefMRoEABCAQUQUktcsnieJRgvix6B6nIvxN0snwizMUCOgFjC+Kj/Aoypod1D309ftZfZc5pjbyfFf6YtU7Xr+dO98Hcu/2mqVyO2wqo1iDO/dZ3HixaGZQC1uKJ4fGvVx4w+lTO38/nzZsP+rQQkQLVMjFjVu4mcbOXudwne4sGH9/+n2sfjOhMaIiXdpktGR4O56/Lpr+tDz+LvedxRsFebwfdoAABCAAAQhA4N0VQKDiHfnbLxrdgXJk+Mrpah9zGoTvavZymdomrAQq5AKMPdWMF9W03zTayXn5jUX1cKffxmy/sBKokJ6iu+f0tfei05/7kk2HKHDkAn2V299DKlAhJ7Bi+xHqNnSu2+ei2jAsBCrkvH7h3NW1uo31KPduWAtUyHVc5pzWNTqPQbBCMFAgAAEIQOCdFdjG91TJeYSCvjTsNZn2/nhGX+XR91nD2lD+b9I67NOLRzOazZfgsKGLhXgceJORP+9HjeKw5R6eG6JR9wkOdZ4sSJBiycgOlDJJIk92c2vbrFW6O3TyCM+Biq9SJKbNU/+X+kkPMG35DhrGc2x4UsJyoGL1lADKkDKJJ5ej3PYWB4kL1OmjXIdKCEAAAhCAAAQgoBJAoEKlEsHq0nOvmDXju5pe1cCpK2n2sh2m62XFxzzh3GpuIyaPSlAVqx43knJBJskzlrfcu7Ht4NkePxDGjBXdNvlh/DgxjU3almUybEnd8OjhU/v63N9+TfMGt7YvG7+s4pEYXUxGYlgFKiQXq+rajO1ryzIUetjstbRg5W6tyu3PAR1qUu0yeZTbS8+1PPwgcO/eQ+V6q0qrQMWzF6/o/DX1XBORuceqPNTGjP6hafNik69+P48nR9Q36CpQ8eLla/3mLr8/fPqcyrUepgw47Fs8kD5L6Dy5otbo9d/uUk0ekfAn52F2p4REoEL+1m/4d6cVSf/wQbSoygCWto3x88SF61St3UjTHNLG7bEMAQhAAAIQiEgCUfll/7kNox0mBZbru3j9DpVuNsjlpNAqi+xZUtOSEe2cVs3nyd37jV3iVO9pReemFeyTwhv3lcnAL/Gk1p4WmTB7+bguIRKkmLt2Lw0Y94PDKUXUQIVc5Ly1+3gSbL5eN0dvhdVAxddpktK6CdbzqkiKV7n/jMO/H6tSk0cjHzl+0WoTrIMABCAAAQhAAAIOAghUOHBEzIVJA5rxpGuZTC9O5nbIxy+5/9a9/DTd2GTFiTXfUyyTF9bFmg2mK5w6x5elcN7MNK1vU9MmNx/4mVr3nW5bL3NbbJ3RixLFj63c/u6Dx1SUhyWb5e63ClS4O4G38sAeVEpvt4MLnXvR6ZuYsnQ7jZi2Sl/l1nerQMXRs1eoRvtRpu18wBMZtqxdgtrULG66zYg562nKgk2m612tsApU+LqnlqtAhZzr7T/v84iEsfTbHcfJWFXXERKBipFzN9Ck+RsdD8cPi7E4gFe6QFZqWLEgpTD0EHXc+L+loTPX0PTFW1WrUAcBCEAAAhCI0AIpU3xOW6b2VF5jx+HzaC1PCOxR4X+HV07sRpl4LjFj8UVKqeh8j32QO1PEMOkwtGbXMeo0aLbx0C6XXY1S1hqQjja7jp7lSarv0rPnr2wpgTKnTkLZ06fUNnH4/IEnxe45cqHTS/vwHKiw+s1oFy/zMvTkEc7/vnWdai4kAhUyP8iLl6+003H5KQGHjoPn0Ctdp5+h3epS1WI5lfvKfXcrnsvvHM/fJiXpF59QaZ4TqH65fJSAJynXl0UbDlDv0Yv0VfgOAQhAAAIQgAAEXAogUOGSKHxvYPWSV39lnb9fQKv5ocLbEtqBCjnPsX0aU5l8WU1PuS2PkNjIIyVG9GhAlQpnN92uYW8e5n/IfJh/WAhUdGpcnlrXLGZ6DbJCRj/kqhlIz56+sNzOuDI4gQqtreEB9alykW+1RYfP7YdPU/PAKQ51nixY/Yb9EaiQc5fgnuSEdjUvSKgFKvSg/LLku2xpaUz3emQ26kg2l5EZxbjXqKtr0DeN7xCAAAQgAIGIIFCK7wvH8/2hqtzmUZOFeTSofvSiajt9XTF+WTu5d2N9lf27zNn1TUXzkc32DS2+NK5RlHo2qWC6xVueCD5//f5udaLQGnHV6Ue2k1GrizcdpNkrdynblrSklYrmpAYV8lHsGP/1rl+98xh1GTpH+bI+ogcqxEyCRl047elbxWhyWa+VkAhUFOKOVzd4XojglB3z+lGyzxIqm6jGqUN/OvmL0zqZB7B5jWLUrEoh2+Tbv919QMWbDCRMpO1EhQoIQAACEIAABFwIIFDhAii8rx7UuQ7VKJnL5WVI3nrbZGduDlc2NuiPQEXsODFo+6w+FI97kquKzL8xgie3C2pTTbXaVrds648UMHy+6XpZ4e9AxUfRP6CDiwZapljSLsCbXvK+CFRkSJecVo/trJ2Gw6fM7VCCU3F5W0IzUGEVWDCev0y+XrPbeMvRQlbtVeowik6euWJs1rZsFZhSjqhQtJIi+ee0bFQH+4sDxSa0cP1+6jNmsWoV6iAAAQhAAAIRVqBj43KWo0EHTFlBc5fvdOv6I0WORNtm9zF9uSuNZKvWw+u5oSRN1b5FQZQwbizL81GlWjLbQc55L4/U/TRBHLNN6N6DJ1S5w0j69fZd0220FZ9+Ep/G92pEv/5xnzpxWlezl/TvQqBCTLYcPEntgmZZjlYPq4GKoyuGUbzYMbQ/rcNnfg7gWf0e5HdQr0IBWrr5IF3jNGooEIAABCAAAQhAwFMBBCo8FQtH2yfgh4/9CwZQ1CiR3Trrxn2n0u4Dp9za1riRPwIVcg7FOdXNpF7qHmzGczQu/37vERVrEuRyBIK/AxUNqxWhXs0qGk9fuSwPlXlq96Y3PC+Iu8UXgQqrYMKlG3eoZOOB7p6O03ZWbft6RIVVYMHpxLhCeknW7j6OLly6pVpNVu2FdKBCTihd2mS0ZHg7W4oG1Qm+fPWactYINE17ptoHdRCAAAQgAIHwLjA5qDkVy5XR9DIkJU7+un3pGXd6cVWql81Lg9vXsNysRtdxdPSEd7n6K5f6joZ3qm3ZvqyUOeFy1erlMEeb2U6uRlNIZ5+qnUbT5Su+Td0angMV0gFk6zR1ujCV866j56hVv2n0+pX6njysBiqs7l0ncsrQUZw6FAUCEIAABCK2QLz4sSg9p7NMnCg+3eSU16cu3KDHPBesqxKFO1ek/SoJpUuZmO7yu6njZ6963VHD1bGwPuIKIFARcf+21K15JWpetbDbV3j8/DWq2vZ7t7fXb+ivQIWcw4R+Talknsz603Hre72eE+nAkXMut/VnoEL+h34vB5vM5tdQnXzAmEW0bP0B1SplnS8CFbUq5DcduXLo5CWqw3M6eFtCN1ARRJ9/HM+jU33CD/N1e06i0/yPsLFYPeyFRqBCzqcrB7lacLDLrASOW0JLeAJIFAhAAAIQgMC7IrBzfn9K+mkCy8sdv2gLjZm11nIbmatrN7flarRDn4lLaeGqPZZtqVa+F+k9Hq3Rl77kFEvulLELNtO4OetcbrpgZHvKlSmV6XbBCayYNsor3qVAhTgc5HvgpoGT6aVuDgjNJ6wGKlw9V43medImzOO50rwcha9dPz4hAAEIQCDsCcTk+V0DmlXiuYpyUGQefakvP527Ss37TqMHHIBQlVZ1S/Fo1WK2FID69Sd5XqOmfabSX5w+GwUC7gggUOGOUjjcJnqMD+nQ4kEU/cNoHp19Fe49deLUZY/2kY39GaiIy5O3bZvZm+KapIBSXYzk2+0lk/y5UawCFUs2HaLLN10Pbf6b89Qu23hA+aBidQoVOW3X95y+y5Ny/be7VKRBf2VuYFU7wQ1UJEoUj1aN62IaTAmauoLmLHMvfYLq/KwCFY+ePqfxCzerdnOqu8w5e/cdPutUr6/Yy6kVPA1UyP6Sw7lBr8l07OdL+ub8PqJCTsbKT9av2H6EunEuZRQIQAACEIDAuyDwfrSodG79aHqP53SyKjLqMG+9vnT//9i7Dzgniv6P4z+O3qWoFJGiICiCBaVYEBuIIKD0ojSpIiC9I70I0pEqSBULioqCBQFBFBVFRGyINAWkSO/8Z9d/8iR3m+zeTchu4mef18tLdmZ2J++5Ry/73Z05eDRktdaNHpHuzaqFLPcVpHRh4QfvvVWm9W/pO4ztT+PvogoN+spptW5ZqM34jrD5rdA3JhkLMtdsMyJUc639sRxUFCmcTz6c0SfZn/8rdWGnWa/JSdZr8GpQUbNyORnTrUnYz/npV1tl3Lz3LW/SCduQQgQQQAABzwpccUVWeWNCl6CpLI3prrNmzijp1A20xmbMaPFExzFJQofnOzWQxtXu9n+2Q+rJ1Kxq/SLf7C579h+Shurp0nDTB/ob8+I/L0BQEae/Aq0aVpYezR+z/HSH1CNboeYe/eTLLfJ076mW7cLtdDOoMPoVblHExP1O7gJv4YKKxMcO977X+EWy5J3PwlUJKjPuoluh1uC47pqrgvb73oQbxzaDZ8qHqzf5qob9GS6o2Lp9j3QZ9Ypl+zSpU0vp4gVVal5F8uTOblnH2GlMQ7BfLUyZ0s3uQrvT4xoLTRZ9uEPY6ikNKoyDGlMutFR3GKxXj/r7Ni88UWH0ZfG4znJHyet93Qr6+dvu/fKwCrbYEEAAAS8KpM2aU9JnD3/nuxf7HapP508dl9MH94YqZn8UBG5QUxIsn9rT0Znmq7WcBoRYyymbmsd/rXqaIov6Im63GRer6z07xq5akvI3p/SQ0mrqheRsz099XV55Y1XIJnfcWkwWj+4Ysrz3+MXy6juX50nLcEGFcfHjoINpJXwdT0hIkFJqLK227epvm4dC/G1j3OD01WvWQUy4v4lSGlQY/fvu553yVI+JQVNtXo6gYtvve+W0g+lfjfXjeob4+95Yv2Sl+v7h5CmeTdt2yMtvfSor1XeO5Cw+bzVm7EMAAQQQcFfg7rI3ydyh7cxOLFI32U5dvFL27P1bsqobgoc/19A/i0nivzPK3FJMXn3h378rjP+Otuj3kuxSa+AaT522Uzd0tFdPWRjbB+u+lfYDZpiv+QcC4QQIKsLpxGhZ2nRpZZ26MzyXSkStthodRsvcYe3lCvVYl9X20NNDZbv6Qzc5m9tBhdFXu/mGfZ+nofqi8MXX23xvbX9GKqjoO/FVWfT2Gtvz+SpUuru0zBzYyvc26KfxhWfmG5/IxF5Ng/b73mz+ZZfUamv9JcxXx/czXFDhq5PSn8vXbpIOz89MaXOzXaSCCuNg1z3YPmxfVquFJa9RT4hYbW998pXUvL+MVZF/37nzF8QIiXxrvXglqLC747N41U7JWtfE/4F5gQACCFxmgRzFykiuG8L/u/cydyGihz+x/w/584v3I3pMDpY8gWoP3injez7lqNEF9UTsAy0Gyy71xTvx1rNtbXn6iUqJd1u+N6aJvKVGV8uyUDvDBQobNv8it5Uo7L/DMfAYf/59RCo27i8X1N8kVluL+g9J75Y1rYrE+Dvm9id6OFqbw/IANjvDBRU2TZNVfHmCirzqiYq+lv04f+GCGDfwhNt+VN+tGqu7SY+ou1ON7XIEFeHOn7jMuHnHuInHarvvrlIy6/nWVkWW+4ybwF5QU44t+/BLx090Wx6InQgggAACrgoY62IZfwssW/lFUD+Mp1E/njNA8l2ZQ1Zt/EFa9priL58yqJVUrlBajBtpq7QaluRpi0HqaYtG6mkL4785d6v1v/btO+RvywsErAQIKqxUYnxf3ep3y/CODSw/hbGwW0v1+HHHZtXl2UZVLOukZCoYLwQVxoI/H6kpoLJnsQ5gjA8b7s44Swy1062g4vVJ3eTW4oUsu9VuyCz5cO235rzIoaYqcjq/8OUKKnar/wA92ma4HFcLTutsXgkqaj77gtxaopAMUBcmwm3GRY0Ow1+WFZ9+44mpn4y+NqhxrwzpUC9kt295vLscU49nsiGAAAJeEyCo8NqIxH5/urSsIe3q/3t3n5NP8+6ab6TjoFlBVfPkySWfqi/svikNggpDvClbv4/8rUIEp9vskc9IxdtLWFY3bjqqV6WCNHz0Lsvyzupu+cQXGXwVfRcMfO8DfxoXnO9RU0ddri1egwojOPrup522awP+qu4wbdh1vHkRx8tBhTH+NSqXNaefNZ5ccbr9tONP6TF2gXy/9XenTaiHAAIIIBAjAtOGtpEHy94sxn/LKjcbZPbamC5q45JhYvy3Yvrrn8jIl95I8mnyqTXBjCdQjW2sWuNo8ivvJanDDgQCBQgqAjXi4LXxL4iP5g4IuUBg/W7jZeOmn8V47Hmduns8vXr6IvFmXGg15uRNTtLphaDC+Bx1qt0lIzo1TPyRzPdGwnuPusMs3Ly9Vg3dCCpuK3W9vDa2s1V3xAgAKqkk2kikG9WqKIPa17Wst+abH6VZ90mWZYE7L0dQcfzkaamvvoj9qJ780N2iGVR8qhYuL6AuPlhtRlBhfPF6qvb90r/NE1ZV/PsuqQUGu7wwT7o8VV2teZHDvz/wRbQW0zbOWf+xe2Tos/UDTx/0+lZ19+TRZEy3ENSYNwgggMBlFCCouIy4/9FD+75oJ+fjV28/SraqxSB928geT0rth8r63jr62ajnRNnwlbMneotdf428/1Ivy+Nu/OE3qd9xrBRQU4Ouerm/5VobxtQ+VVoOsVzweET3J81FMq0ObqznVbn5YKuiiOyL16Dic7VoduMu46VHm8elVe0Hwlr98eff0kD9jdy/3RNS5a5bLOvWU+WJ1z3zVZwyqLW6c7WU722Kf4Z7osJ30Icr3iYTeze1fVrEV9/4aUyF2n7Yy7Lqs+8Cd/MaAQQQQCCGBdKraZw+VNMCGtc2Vn6+Wdr2m2Z+mptvKiJvje9ivn5E3aT686+7LT/l4vHPyR03XSdvr/pKnhv6smUddiLgEyCo8EnEyU/jD8qp/VpYfpot6l8aNdS/PHxb4gVvfPuNn7OWfirDJr8WuCvsa68EFbeqC/yvh7jAb9zlU9X40pbMzY2gYubwdlLpjpsse9p/8hJZsHS1WWbM+7dOLZoeahqvqmr6p5/UNFDhtkgHFca0VB3UEx/GfIaR2LwWVBifqWmd+6Vf6/BhhVHPCP1Sq7l+rbZoBhVPq3khe7aoYdUNc9+N1TrLGbUgOBsCCCDgNQGCCq+NSOz3J9RNCcYFVqsbeIxPvF5diG6iLkQb23VF8suKab0sA4Jwx0g8p7N5sBD/GNunmdSoZD3lWfP+02T1+s1my3B35TdTc0Sv+fz7JGcY2qWx1H+kfJL9xg6eqAi9blfhQnnlo5nWT5v4ggrDsIe6maWVuqkl3LZHrd126Ohxufl66zU2vBJUGJ/B+H0f9EwdKVeqaLiPFFRm3LDTb9KSZE15G3QA3iCAAAIIeEagQIGrZVCHOnLvbf8+5dliwDT/VNeB1x+LVXk25LSTAzvWlybV75GNW36V+p1e9MxnoyPeFCCo8Oa4pLhXb73UM+Qfvc8Mmy3vf/K1/9jX5L/SfGw9VapU/n2+F6fPnJVy6tFvp9PBEFT45EL/bDt4llps7pvQFf6/xPhCsHJ6b8t6/xw/KRXU1AGnAy4qd2haTTo1fsSy/rJPv5bOQ2Zblvl2RiqoMPo2WwVcLy1cIefPnfcdXvtnpIKKQ2pqozvUFEfhtlAXL4w2vicqfO2b1X1Q+raq5Xub7J/RDCpmDm+vgq8bLftoPKFTsVE/yzJ2IoAAAm4LEFS4PQLxdX7jBo8f3rX+gjxDLUBd9Z7S6m5B67WqGvecJJ9/9aPMGNZW7r+zpCXM4GlvSC+1/oPVegWL3l8vfccssGwXuNOYImG1ejraasodY+2Fh5sP8q8DUFJNSfn2xG6Bzf2vQy3g3b314yGnKDqr/n67Vf2tlNynj/0ntXkRy09UOA0qDILkrF9iRealoMLXP+NiVI8Wj0mhfFf6dtn+NKYo2/LjDtt6VEAAAQQQ8I6AcT1qaMd6anrLNOYTFFfmyObv3Ji578mUecv9732zTRxW11rKhLnW0q5JVTXbxKPm7CBce/Dz8SKEAEFFCJhY3H3nbTfIolHPWnbduHPnvib95aK6wztwmzigpfpSdmvgLv/r5Mwf918NKnqPXyxfqHlp7TbjznqrhRit2r2gFsiu9cAdVkUyUYUA42YvCyrLfkUW+XzhEMu7AI3poe5r+nzYpxvCBRV//f2PvP7hBvN8qRNSqy+291t+cTYq9By3UF57d11Q3yLxJlxQYfSvUY8Jjk5z6Mhx2+mNVqm5E69VFwistsRBhVGneb0Hpc/TKQsrohVUXKUez1yvpnmzCiSNz2A197axnw0BBBDwggBBhRdGIX76UKLYtfLulB6WH6jrmPlmADCmWxPL8h9+2y3PT3ldlozpZFluLGJtTM25fEYfKaKmZUq8ffPj71KnwwuJdyd53//ZevLUY/cm2W/ssPpba9G4znJnyest61v9rVH1/jJqOp9mlvWNnd3UGgNvLl8fslynIFxQsVk9AbzrL+dP46ZRT6wai3dabZdjMe1CBfPIx2otPKst8IkKX3mvdrWl5eOVfG+T9TOlQcXqr38UY/pVu237rv1Jvk/YtTHL1c1tFcqUkBZqEfn7yljfABN4HN80ZYH7eI0AAggg4G2Be8vfLC8PbmPZSd9NG75C302zdjc/+gKNoydOya01uvqa8xMBSwGCCkuW2Nw5Z1QHuee24padD/W4+Y3FC8o7k6zvMjdS0bsa9nU0Jcx/Nah4qs8U+eyLHyzNU7LTWJxx7SsDLcMA4y63CurO98OHjiY59AC1/sCTah0Cq23eO2tloApUQm3hgorEXzDCzclsfEG//6mBcvbMuVCnStH+cEHFrr8Oyn1q3ZFIbckNKozztqj/kPRWd08md7O6eOA7xnNqmqb2aromqy3xXQxWdQL3tVFP23RTT92E2oZMXyovL/koVDH7EUAAAVcFCCpc5Y+7kxsLBI/t9qTl56r93Ivy3Zbt8sHsfnKdRdBgNDpy7GTI6TZ7vLhQXn9vnUwf2lYeKJv0iQvjAnLpx/6dx9myA2pnuJtPjLXWKqinnc+pKaoCt3vLlZSXh7QN3OV//dEX30vrPi/53xsvjLUtjIXAQ23fqbU4HldrclyOLVxQ0eWF+fLWB587Pq0xX/bWEE/HeCGoMD5In/Z1pHmt+xx/Jl/FlAYV96v1Rf5Q64xEYyterIBMH9g65Fpsvj7U7zZBrY/4k+8tPxFAAAEEPC6QLn1aaVyzoroRNp357/iSRQv4Z20xpvZrbkwtuWGL+SkaqnqDn6lrhuTh/sbp2Ky6PNuoivy+54A8qK4ZsSEQToCgIpxODJXdoP7lsXxqz5A9PqWmCjp/4YJledbMGS33GzsD10MIWUkVEFSE03Fe1lf9S76Z+pe91Wb8RyHUXVLp0qaxfKLCOI4xX/JdRsBx+JjVYSU5QYUxXZixcKPVdATGwYfOWCqzX43sRe9oBhWfqCcqCibjiQofqN0aEL56gT+jEVTkVcHXW2pKiNw5sgae2v/aeOLmLhX07FdPXLEhgAACXhQgqPDiqMRun7qpKRvbqKkbrbY76/WWgwf/kcr33SZT+lqv92bVzthnLJD8oHqC1XhyOdy0P+XVDUDh/pv7bNPq0rFxFcvTGGuAfWCxQHFCQirpqL78G38LWm0PqvXZflfrtPk3dVf8F0uGh/zbwKhXR4U232z+1d8kUi/+a0GF4Rbub/tQrrEQVBh9z5krm8wd/ozcqKYJCbVNWrRCXpwV/DR4qLrsRwABBBDwpoBxzeilfi3Na04HDh+VcnV6mR2tdHdpmTmwlfm6xKOdQt60OqRLI2nwSIWgNb+8+UnplRcECCq8MAoR6MM4tYB2dTV3aKS3PfsPqSmjBiSZMirxeQgqEosk/3227MYUToMlQ/p0yW9s08Jqyihfk+QEFUabEd2flDoPl/U1D/ppPMp3j7rwfVzdcRipLRaCCuOztmpYWXo0f8zxx77cQUWuXNnl9XHPhZzKyujo8rWbpMPzMx33mYoIIIBAtAUIKqItHt/nmzm8nVqz6aYkH9JYm+2mRzv/u19dyH9HLZYd7uJr4gN0HDFX3v3oS3N3PbVY5DC1aKTV1qTXZFm/catVkWTImF7WLxoi2bNksixP6c7XVn4hPUe9EtQ8XCBiVDTWHavbZZz8+tueoHa6b2I5qCh4bR75RD1tY7VZTf0UWK9fh7rStIb1jUiB9XyvYyWoMPobbjo1o3yV+n1vqX7v2RBAAAEEYlsgcNpr300QN96gZmiZ/O8MLeFucnhPTYtZvHA+sfqbJLZV6P3lECCouByqUT5m/ny5zUe4Q93lrtudZ0fMkfc+2hj2MAQVYXkcFYb78uboAGEqGQGCMV3AKYt5a5MbVNg9VTFVPVHxgnqyIlJbrAQVxudt1bCKCiuqO/rolzOoMP6dMFtNA3G9+lIdbqvVaYxsVtNcsCGAAAJeFSCo8OrIxGa/1qg1vfKrtZsSbz/+vleqPT3UvzvcdEr+Sv//4pc//pJHnh7iX+A63JpxxmLbc177JPEhzPe++ZstCzV2Gk9U36NuOgp8kiN37ivM9atSq3UeQm3GVFNPdB4rO3fuC1XFvz+HenLzebW2xq4/D8poNaVkqC3c37pen/pJJ6gwPMKtPZLYywtBhbHwfLZsmYN+bxL30/d+w2vDJXCxVd9+4+f6736WJl3GB+7iNQIIIIBADAoE/m1kTJe5ST15mSlzBtmontI0brZdsmKD9Bo9L8knC5z9JdSU9EkaseM/LUBQEQfDP1DdtdVE3b11ubZt6svbowFf3qzOQ1BhpeJ8n/FlYN2ioSHnPXZ+pNA1Q305Tm5QYZxhRPcm6qmKcpYnM6aaMhbwDvxCbFnR4c5oBhUfq/VBCuW70rJnVotpW1W0WxPC1+ZyBBXGxZHmauHGB9Xc2KEWz/adf8G7n0n/cYt8b/mJAAIIeFKAoMKTwxKTncqYKYNsWTbGsu9WTxgumdBFbr+xiGX9wJ2tnp8hH6/91r/r6qtzmiGAf0fAi1fVGgy91VoMibfUaVLL6vmDJK8KEC7HNuONVTJi6utBhx7cuaE0fPSuoH2J3xh/072unsiYvXSV7FCBTOItn5ous1qlMmodhor+C9VTXv1Qxsx4K3FV830sBxXh/h61e6LC/PDqSZ2BKsxx8p3N9aBC9fU19ft/W4nCsuj99fLinHfNadGsBjWzmkJ4owoq0qdLa1UsH6z7VtoPmGFZxk4EEEAAAe8I3HLzddKzZQ2Z/eansnL1N0k6Frgu6i21usmx/59FY1CnBtKo2t1iTDdvPFXxo5qq0rclqBsipg9pYz7NapSXrd9bThw/5SvmJwKWAgQVliyxs9O4g2ndgsEh/ziM1CexWzT6vxpUGCHO4aPHk8U8/fVPZM3n3we1afz4ffJ8uzpB+yL95q+//5GKTfrLebUod+CWkqDCuGN/1csDJNSdeKG+iAee1+nrcF8MjS/Q3/z4u9NDmfWM6Qy6jZonJ0+cTtIuEkGFcdC2TapK16ceTXL8wB0pDSr2qfmz/1ILl/s39WUyswq6cl6RVXKqO9+cbLv3HZLKas7q06fOOKlOHQQQQMA1AYIK1+jj7sQ3lSgky9S6TVbblMUrZczMt4OKytxSTF59oWPQvsRvvv91l9RsO1JErSPm21KpNSO2vjfOcs2Ib9VC1U9YLFQdbpFv33F1fhoXB8o36OO/qGAcy7gL8oMZfS2fMLE6l3Exfvuu/XJc/e2QSf3dYSyueWvxQlZVZeTsZTJ94YokZf/poMLQUH+zPa9uMGusLuiE21IaVIyZ+57sOxjwN2K4k6iyQ0dPyCqLdU8effAOmdCzqb+18ff2rKWfyqvL18lutRCqbzMuQA3r0jjklLBGvcuxfp3v/PxEAAEEEIicwCsvPCt33XKD+pPmkrzyzlqZseQj+fOvg5I1ayZpVf9haVfvIfNkG3/4Tep3HOs/cZHCeeVD9feEsR1R4UWfCYvli29/lnxqvcxn1PTYD5cvZZZxo6TJwD8cCBBUOEDycpXOLR6TZxpUvuxd/OL7X6Rh53Ehz/NfDSpCgoQpSDwvn/FH/qfzBjn+ohjm0LZFXdSjeG+pR/ICt5QEFUb74d2aSN3K1k9VGP9xe7jVUNn+e8DijYEnTcbrcEFFMg4TVLW6ukiwVV0sSLx9NHegFFYLhlttTp+o8LVtp8KKLmHCipQGFb7jp/SnEdQ06DZBfvplV0oPQTsEEEAgagIEFVGjjvsT1axSXsZ0bWz5ObuOmS9L3/88SdmcUR3kntuKJ9nv2xHqRp4PZvWTogWTTr94Ul3kv/mxLkHBhnHxesWsvrbTNfrOmdKfo9Vd8S/Nfz+o+R23FpNFo561fQozqJHDN30nviqL3l4TVPs/H1QYGmq8B6mwwrj7NNSW0qAi1PHC7Teegt61e7+/SnoVQn08Z0DIp3t+/uNP2fLrbvNC1t233iBXqzXRQm1GQFZOBWSRXLsu1LnYjwACCCCgJ1C65HXysnr6IXCtLCOoDnxi7piaUvzRtiNkz96/g05WWz2hOVz9ty3UdPQbNv8irfpNkxOqPRsCdgIEFXZCHi43HmE3Ft3Lph65tdoGTnldNnz3i1WR5b4r1N3YC0c+E/JfLjU6jJYtP+6wbEtQYcliuTNxUFHtwTtlfM+nLOsaiXTjHhPl/IWLluVWOx9/6E5pVfsBqyL5ddc+qdx8cNAX5JQGFXZPVXy44Xtp0/cly34kZ2esBhXGZwz3hdyNoML4fWrYnZAiOb9/1EUAAXcFCCrc9Y+ns/do83jIv49CLQB5Y3G1SOSkfxeJTGzx1dbtUu9Z66mkpgxqJZUrlE7cxHxfoVE/2aeebPRt95a/WV4e3Mb3NujnLnUn42I1XZTTzXjCsp2669FqM9acuKthXzl75lxQsXH3/PgeT12WsOK22j3knyP/e/I43N9FXl+jokCBq+XTl/sH2fneOJr6yVfZ+KnCisFqqoxQU29FM6h4oMXgoGm9nDwVHPhRwr2eu2yNDJrwargqlCGAAAIIeEjAWI+045OPSvWKt0laNS2lbzMCi+VqmssRamrHvwNnd/BVUD8rViglndTNmjcVye+fecOYDWLl+s0yVF2bPJdoZo+AprxEIEiAoCKII7beNKvzgPRt/bhlp/fsPyz3qWl+LibjArdxoBf7NpfH7rvd8pgr1n8n7fpPtywjqLBksdwZFFSoLyrLZ/SRGwrltaw7UT02P049Pp+cLasKnDaoAMtY0Mhqa95/mqxW/7HwbSkNKoz2w9SdifXUHYqhNmMRxm+//y1UsaP9sRxUGB+wQ9Nq0qnxI0k+a7SDip92/CnPDJ2tnnLZm6Qv7EAAAQS8KkBQ4dWRib1+zRrRXu4rc6Nlx++s1zvkHPxTBrVWocO/0xYENq7bZZx8HeKGoK5P15K29R4MrO5/nfgpjFfVWgBlQqyF0X3sAnlj+Xp/Wycv5o7uIHffav0UiNVTDsYxqz5ghBVPhrxZycl5E9eZr9bCGpBoLSyCigAl9R1gSOcG0qDqXQE7/33pZlDxrvpeUqJwviR9Su4O4+JUTXWTW6TWrEvu+amPAAIIIJByAePJiDxqza3cubLJ3wePyl5140TgNJfhjmwV+UXZAABAAElEQVSsv2qsYWU8RfqX0Y4NgWQKEFQkE8wr1dOkTSNr1KJ7oR63fV4tmPeKWjgvuVvxYgXkvSk9Qzar1HyQ7Ny5L0k5QUUSkpA7AoOKe8reJHOGtrOse+78BSmv7nw7fOioZXm4nX2fqSvNala0rPK1ugOwbsAdgDpBhfEfoE/V4+Gh1qow1o+o0+EFy3443RnNoOJD9VmKXHOVZdeSO/VT4EGebVpdOjauErhLohVUXLx4UYyFLSe+sjzJ+iRBHeINAggg4EEBggoPDkqMdumzxUMtp7M5feas3FTtuZBfwIuoi7Yrp/cOeuJg7TfbpGn3iSElnqhaQUY918iyfMj0pfKymvfZ2IxpFt4cp85tsR08ckw9AdFPzqm7GJOzVbjjRpk3vL1lE+MJjfufHCjG3waJt5IlCslY9WTFdSH+DkpcP9R7Y7qf4bPekgVLVyepEtNBhXIx/ua12pL9RIXvIEZY8VxDafBIBd8e86ebQYUx1/j8ER1CfscM6miINwcOH1ULqo6TXepJbjYEEEAAAQQQQCA5AgQVydHyUN1wi+4Z88aVb9BXTp08naIe+xbRsWq8SC2i1nfswiRFBBVJSELuCAwqFoztJOVKFbWsq7MgdV61cNGaVwaGvDOuVqcxsnnLdvO8OkGFcYChahG9+o+EfqqiWb+pavHwLZaf0cnOeAgqjM/Zqflj0kEtJuXbLndQYaxFsUjdhblALYS198/gOSR9feAnAggg4HUBggqvj1Bs9C+zmiZ189vWN078qJ40rPb00LAfZEzvZlLz/jL+Oo+pO8V/CDEdqlHp9tJFZcmYTv76gS9eW7lBeo6aZ+6aNrSNPFj25sBi/+sX1MLIU+ct979PzovlM/uGfFr3mWGz5f1PvrY8XNp0aeWZJo9I0xoVJYuaYja526qNW6Xv+MUh76AkqLAQVWHFsC6Ngp5QdjOoMHpoPJ3d9PFK0qLWfZI1xBTDFp/E3GXcENVF/X4HrnsRqi77EUAAAQQQQACBxAIEFYlFYuT9I/ffLj1b1DSmOA3aTql5Z4epeeMCp/YJquDgjXEnzZjuT0mu7JmT1H539SYZNe3NJPtH92oqd6q7whJvxt1gTdQaCyeOR2fRnJzq0bRX1F1AWS2+XL23xrrvifuc+H0rdXG5cZgF7xLXD/derTEt4+Yv9y/YOGFAS7nlhoJJmhhrSXQd9YocUo/ZpXRrVKuiPP3EAyqsCP4luaCmA+s4cq4/qDCeinh5aFvJqB7RS7wtVgtLTrH5kpxHhSLGUyGZMiZtb5yr6wvzQ06NkPh8Vu8zZc4g89VCj7lzZLUqTvY+Y50G43cycM5k30F6tq0tj9yddE7poyr8a6LunDyifp91ts4tHpNnGvwbVoRa0Ns4vjFf9WD1VEzi/3+HOvfFi5fktPr//o4/D8h3P+2UzT/tkI1qOorE81CHas9+BBBAwKsCBBVeHZnY6lcO9TfE2nmDLP/WmacC/YHq4nq4zZiz+ZPZ/c0nSJ2swZU1ayZZt3CIZM6YPslhA29E+VjdVFIo35VJ6hiLFTfuNl6Oqb9ZUrKVK1NcZg5sbfl5x8//QCbMeSfsYTOofj+m1q5oUPVuKXld/pA3vhgHMda+eOOjjbLkg3Vqesk/wx433I1WDdQaWl9+81PY9oGFCakTZL16SubKHNkCd5uvP/1qq7ToOTnJfmOHMSXFWjU2OdXF+MSbEbS07GXdLlv2LOZNQFYX7gPDp8THdPI+lfpbfZi68adu5XJm9XDTivVuX8cMEJwc166OMS3HvU8OkMOHrf++Nf4Gb/TYvVLj/jvkBrU4fKhFUo3zbPzhN5n/zmfy3icb5ZL6u5QNAQQQQAABBBBIiQBBRUrUaIMAAgikUKDMLcXM8Cg5X8ZTeCqaIYAAAjEvQFAR80PomQ9gPC2QTk2dmng7YTyBbNxJYrMZi0QWL5xf3ljxeciFJAMPYUzTml6dM/F28tRp/4Xc1GqhyvTpg+sYXUnpU9GB50qrzp82XdLPe/JE8p64Tq8u7BdTC2PeeN01ki1LRhXWpJYzarqs33bvl5+27wlaGDzw/KFeZ1Q3EyUkuhPjgpqK6rS6aJ7cLZTxKXUsq+mtfMc3bNJZjI1tO83fId/5rX4aYUWdR++STMp7/ltrQk/XqewyW9yQZXVMu31n1cKmTqcWMwKeEsWuVf8fyKduTkqvFllNY/4e/LLzL/lRBWsH1ZoUbAgggAACCCCAgK4AQYWuIO0RQAABBBBAAAEELosAQcVlYeWgCCCAAAIIIIAAAggggIDnBAgqPDckdAgBBBBAAAEEEEDAECCo4PcAAQQQQAABBBBAAAEEEPhvCBBU/DfGmU+JAAIIIIAAAgjEnABBRcwNGR1GAAEEEEAAAQQQcFHg+uptXDz75Tn1r++8dHkOzFE9J0BQ4bkhoUMIIIAAAggggAAChgBBBb8HCCCAAAIIIIAAAgg4FyCocG5FTe8JEFR4b0zoEQIIIIAAAggggIASIKjg1wABBBBAAAEEEEAAAecCBBXOrajpPQGCCu+NCT1CAAEEEEAAAQQQUAIEFfwaIIAAAggggAACCCDgXICgwrkVNb0nQFDhvTGhRwgggAACCCCAAAJKgKCCXwMEEEAAAQQQQACB5AjkyJlNShYtIAXy5pbd+w7Jlp//kEMHjyY5RMFr80j6dGmS7E+849z5C/L7jj8T7/bse4IKzw4NHXMgQFDhAIkqCCCAAAIIIIAAAtEXIKiIvjlnRAABBBBAAAEEYlEga7bM0uPpmlKvcjlJSEgI+gjbft8rzftOlX0quDC2IoXzyYcz+gTVCffmznq95eDBf8JV8UwZQYVnhoKOpECAoCIFaDRBAAEEEEAAAQQQuPwCBBWX35gzIIAAAggggAACsS6QK1d2WTymkxS55ir/Rzlz9pykS5tGUqVKZe7bf+ioNOoxUbar0KJQwTzy0cy+/jJ/I4sXew8cloqN+8vFCxctSr23i6DCe2NCj5wLEFQ4t6ImAggggAACCCCAQBQFCCqiiM2pEEAAAQQQQACBGBVoWf8h6dWyptn7T77cIhPmfyDf/7hDMmZML52aVpOWj1cyy5Z+vFG6Dp9jvk4TEGKYOwL+UaFMCZk9qLW5Z+CU12Xem6sCSr39kqDC2+ND78ILEFSE96EUAQQQQAABBBC47AIJqROkaJH8UuK6ayR1Qir54ddd8sv2vXJBzYnrdLu9dFFpVfcB+Wn7nzJ21tuOmmXOnFGeUl/c7i1TXLqNni+7du931C5alQgqoiXNeRBAAAEEEEAAgdgVyJsnl4zs2kg2qXDixdnviFy6FPRhZg5vJ5XuuEkOHjkmd9buGVRm9WbB2E5SrlRROXLspNzVoI+cPn3Wqpon9xFUeHJY6JRDAYIKh1BUQwABBBBAAAEELodAxQqlZGDbJ+RateBf4Hb23HkZ+8p7MmPRysDdSV4bX8xe6N7E/DJlFP59+JiUrWP/BayJCii6PPWoZFVhhbH1Gr9IlrzzmfnaK/8gqPDKSNAPBBBAAAEEEEAgdgUCn7gop4KHAweOhPwwpUoWkaXjupjl4+a/LxPnvBuyrhcLCCq8OCr0yakAQYVTKeohgAACCCCAAAIRFmhW90Hp26pW0FGN+XTTp0vr3zfn7dUyeOIS//vAF/eWv1nG9XhSsmfJZO7+TT0RMfrlZfLh6k2B1YJeZ8qcQUarYKPKXbeY+8+ppzZe//ALeWHWMjmi7jLz0kZQ4aXRoC8IIIAAAggggEBsCkwd3FoeLl9KLl68KDc+2lnOqRuCQm0zhrWV++8sKafPnJXyDfvJ0X+Oh6rqyf0EFZ4cFjrlUICgwiEU1RBAAAEEEEAAgUgL/Pj+eHORvxOnzsiQ6W/KirXfyj9HjsstN1+nAoinpIB6WsLYKj41UHbvORB0+iefqCT92zxhLgJoPH0xZu67MmvJR3LpYvCj7oGNsmXPIm9O6CqF819p7v7u553SecRc+WPnX4HVPPOaoMIzQ0FHEEAAAQQQQACBmBQooBbYXjG9t3kj0PdqetWabUaE/BxF1TSsH0zrZZaHu1ko5AE8UEBQ4YFBoAspFiCoSDEdDRFAAAEEEEAAAT2BVg0rS0W1WF8/9cTE9t/3Bh3sphKFZNnEbua+PhMWy+Jla/3lRtmb456TNKlTm1M9Ne45SX75bbe/PNSLl4a0kYfK3WwWz122RoZMes28syxUfbf3E1S4PQKcHwEEEEAAAQQQiF2BtOop5aWTu0uJwvnMD9F+6Gz5YNXXIT/Q+P4tpNq9t8mFCxfNG4X+/OtgyLpeLSCo8OrI0C8nAgQVTpSogwACCCCAAAIIuCCwdfk48+6vRe+vl75jFpg9yJAxvSxXd4UVVGtaHDh8VOp1Ge/oiYh61e+RYR3rm8eYtfRTGTb5NRc+UfJOSVCRPC9qI4AAAggggAACCPxPYFjXxlKvSnlzx2srN0jPUfP+V5joVYECV8sns/pKQkKCvPXJV9Jl2MuJasTGW4KK2BgnemktQFBh7cJeBBBAAAEEEEDAVYFi118j77/076PnExeukHGzl5n96dO+jjSvdZ/5um6XcbLv7yOSM0dW2aWmhjqsFtK22nLnvkJWzx0gGdKnk7XfbJMOQ2ZLvqtzyqVLl+SX7XvCThdldbxo7SOoiJY050EAAQQQQAABBOJLoF2TqtLlqUfND7Vp2w6p3/lFOR9mbYrh3ZpI3crlzPpVWg939LSyF8UIKrw4KvTJqQBBhVMp6iGAAAIIIIAAAlEUCLwDrFm/qbLm8y2SkDpBNr4+Uq7I+u/i2f8cP+lfSNvo2jY1fdT7n30rk+ctDwofnqp9v7mehVX3j504Jeu/+0UmzF8u237eZVXFtX0EFa7Rc2IEEEAAAQQQQCBmBRrUuFeGdKhn9n/3vkNS69nRcujg0ZCf56qrcsjaec+b06p++tVWadFzcsi6Xi8gqPD6CNG/cAIEFeF0KEMAAQQQQAABBFwQqPrAHTKxV1PzzL/88ZdUaTlE1OMPclup6+W1sZ1te/Tmxxul+8i5/rBi/piOUr50sbDtjqrAolGPibJ12x9h60WzkKAimtqcCwEEEEAAAQQQiH2BqveXkQnq7+hUqVKZ06TW7jRWdqsnj8Nt/TrUlaY1KppV6nUdL199+3O46p4uI6jw9PDQORsBggobIIoRQAABBBBAAIFoChQqmEfemdRdMqm1KE6eOiM11B1g23//0+xCjcplZWy3J83XxpMQM95YJW9//KX8ffAfKVm8kAxWX7KKFcxrlo9UU0VNV1NGGduahYMl/1U5zdeff/ezvLTkI9m0ZbtkzpxBGjx6t7Sv/7CkVk9rGMe8o04vOXf2nFnX7X8QVLg9ApwfAQQQQAABBBCIHYG7y94kswa1Np+MMJ48rq2me9qunjgOt+VQU6iuWzDYXBfuu5/+kMfbjwpX3fNlBBWeHyI6GEaAoCIMDkUIIIAAAggggEA0Ba688gp5Y3xXFSrkMNePePr5GbLqs+/8XWhYs6IMfqau+b5Co36yTz3KHrhlzJRBVszsa7bf+MNvUr/jWLP466WjzemijCctug2fE9jEfN3k8UoysF1t83WD7hPky29+SlLHjR0EFW6oc04EEEAAAQQQQCD2BEqVLCKLRnYw12QzbvYx/qbd8uMO2w/S5ema0q7eQ2a9Vupv74/XfmvbxssVCCq8PDr0zU6AoMJOiHIEEEAAAQQQQCAKAtmyZ5HXxj0n1xe42jzbiFlvy4xFK4PO3KhWRRnUvq7s/PNvqdRkQFCZ70296vfIsI715fyFC1K6Rlc5ffqs+IKKjiPmyrsffemr+r+f6tH4z18dJlflzCaTFq2QF2f9u3D3/yq484qgwh13zooAAggggAACCMSSwHVF8svrL3aWbJkzmt0eMu1NWfP1tiQf4cyZs0HTQGVR676tXzhEMqsnmbfv3i8PNRtkTreapGEM7SCoiKHBoqtJBAgqkpCwAwEEEEAAAQQQiK5ABvXlaOGYTlK62LXmiae99rGMUl+wEm/GnLsTezeTc+cvyC21uslpdbdY4q18mRIyf8Qz5u5KzQfJzp375KO5A6Vw/ivDhhCvTewqt5UoLMs+/Vo6D5md+LCuvCeocIWdkyKAAAIIIIAAAjElsGBsJylXqqhtny9cuCjFq3aUi+qnsbWo/5D0blnTfN197AJ5Y/l683Us/4OgIpZHj74TVPA7gAACCCCAAAIIuCiQJm0amT28ndx1yw1mLxYtXyd9xy607JHxSPvScV3MshYDpsmn6zYnqeebxskIM0pWf07Onzsvc0Z1kHtuKy5bt++R6q2GJWmToNan+GLJcMmpnuoY+8pymfzKe0nquLGDoMINdc6JAAIIIIAAAgjElsD0oW3lgbIlbTu9/9BRqVC/t1y6eMms26FpNenU+BHzb+Raam0K4+/mWN8IKmJ9BP/b/Seo+G+PP58eAQQQQAABBFwUSJWQSiYNaClV7rrF7MW7a76RTuppBt+Xp8RdS6tCjc/U4+m51aJ/O/YekBrqC9XxYyf91bKqx9c/mt3fLN/8yy6p1XaEWfbkE5VkQNt/16DoP3mJLFi62t/GeNGqYRXp0by6ua9Z36myZsOWoHK33hBUuCXPeRFAAAEEEEAAAQRiUYCgIhZHjT77BAgqfBL8RAABBBBAAAEEoiwwtEtjqf9IefOsBw4flWZ9pspZ9SRE4m2PWpPCN81Tq4aVVajwmFll07YdMl1NE7Vp63YpVjifDO5QTwrmzW2WBQYOGTKkk3WLhpoLahuPvE9Y+IF8uH6znDh5WhpVv1eeVkFGKrVOhXG8Os++EDIoSdyvy/2eoOJyC3N8BBBAAAEEEEAAgXgSIKiIp9H8730Wgor/3pjziRFAAAEEEEDAAwK3ly4qS9S6FE62F+ctl0lz/52OyVjPYvrg1v6poqzaz3l7tQyeuCSo6IF7bpGJvZpK+nRpg/b73hw8ckzqPDdO/tj5l2+X6z8JKlwfAjqAAAIIIIAAAgggEEMCBBUxNFh0NYkAQUUSEnYggAACCCCAAAKXX+DG4gVl2cRu5pMMdmfrM2GxLF621l/NWFOi29M1pVrF2yTflTn8+3f9dVBGzn5b3v/ka/++wBc33lBQBravLSWvL+APLM6cPScffbFF+o9/VY6osMJLG0GFl0aDviCAAAIIIIAAAgh4XYCgwusjRP/CCRBUhNOhDAEEEEAAAQQQ8LhAjpzZpEC+3LJz9wHHQUPqNKmlSMG8ckn9b/uOP+Wimg7KixtBhRdHhT4hgAACCCCAAAIIeFWAoMKrI0O/nAgQVDhRog4CCCCAAAIIIIBA1AUIKqJOzgkRQAABBBBAAAEEYliAoCKGB4+uC0EFvwQIIIAAAggggAACnhQgqPDksNApBBBAAAEEEEAAAY8KEFR4dGDoliMBggpHTFRCAAEEEEAAAQQQiLYAQUW0xTkfAggggAACCCDgngAX2fXtMdQ35AjuCRBUuGfPmRFAAAEEEEAAAQTCCBBUhMGhCAEEEEAAAQQQiDMBLrLrDyiG+oYcwT0Bggr37DkzAggggAACCCCAQBgBgoowOBQhgAACCCCAAAJxJsBFdv0BxVDfkCO4J0BQ4Z49Z0YAAQQQQACBOBdInSGzZMyVL24+5cUL5+XkX79H7fMQVESNmhMhgAACCCCAAAKuC3CRXX8IMNQ35AjuCRBUuGfPmRFAAAEEEEAgzgUyXV1I8t1ZJW4+5fnTJ2THh/Oi9nkIKqJGzYkQQAABBBBAAAHXBbjIrj8EGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEEAglgS4yK4/WhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBBAIJYEuMiuP1oY6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQQCCWBLjIrj9aGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEEAglgS4yK4/WhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBBAIJYEuMiuP1oY6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQQCCWBLjIrj9aGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEEAglgS4yK4/WhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBBAIJYEuMiuP1oY6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQQCCWBLjIrj9aGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEEAglgS4yK4/WhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBBAIJYEuMiuP1oY6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQQCCWBLjIrj9aGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEIgdgRw5s0nJogWkQN7csnvfIdny8x9y6ODRkB8gIXWCFL++gNxQJJ9Z5/ufd8pvv++VSxcvhWzj9QIusuuPEIb6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAAQS8L5A1W2bp8XRNqVe5nCQkJAR1eJsKHpr3nSr7VHARuN13Vynp3+YJKahCjcDt1OmzMnjam/LqO2sDd8fMay6y6w8VhvqGHME9AYIK9+w5MwIIIIAAAgjEuQBBhd4AE1To+dEaAQQQQAABBLwtkCtXdlk8ppMUueYqf0fPnD0n6dKmkVSpUpn79h86Ko16TJTtKrQwtqZ17pd+rZ8wXxv/MOqfOHVGcmbP4t83cvYymb5whf99rLzgIrv+SGGob8gR3BMgqHDPnjMjgAACCCCAQJwLEFToDTBBhZ4frRFAAAEEEEDA2wIt6z8kvVrWNDv5yZdbZML8D+T7H3dIxozppVPTatLy8Upm2dKPN0rX4XPM129O7i6lbygoBw4fle5jF8rnX2+TcyqsuLf8zTKpdzPJrNr+c/yk3P5495ibBoqL7OYQa/0DQy0+GrssQFDh8gBwegQQQAABBBCIXwGCCr2xJajQ86M1AggggAACCHhbIG+eXDKyayPZpMKJF2e/I3IpeH2JmcPbSaU7bpKDR47JnbV7mh/mmvxXSq2Hysqry9fJ/v2Hgz5ggxr3ypAO9cx91Z8ZJVu3/RFU7vU3XGTXHyEM9Q05gnsCBBXu2XNmBBBAAAEEEIhzAYIKvQEmqNDzozUCCCCAAAIIxLZA4BMX5Rr0kQMHjoT9QMWLFZD3pvwbaLQbMktWfPpN2PpeK+Qiu/6IYKhvyBHcEyCocM+eMyOAAAIIIIBAnAsQVOgNMEGFnh+tEUAAAQQQQCC2BaYObi0Ply8lFy9elBsf7Sznzp0P+4ECn6h4pM1w+fnX3WHre62Qi+z6I4KhviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQACB2BUooBbYXjG9t6RPl1a+/3WX1GwzIuSHSUidII9XKS/Pt6stGdKnky0qoKihgopY27jIrj9iGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEIhNgbQqnFiqFs0uUTif+QHaD50tH6z6OujD9FKhxK3FC0r2LJmkgFrrwgg0jG377v3SrM8U2b3nQFD9WHjDRXb9UcJQ35AjuCdAUOGePWdGAAEEEEAAgTgXIKjQG2CCCj0/WiOAAAIIIIBAbAoM69pY6qknJIzttZUbpOeoeUk+yNsv9ZKS11+TZP/7n30r3UfPk5MnTicp8/oOLrLrjxCG+oYcwT0Bggr37DkzAggggAACCMS5AEGF3gATVOj50RoBBBBAAAEEYk+gXZOq0uWpR82Ob9q2Q+p3flHOW6xNccetN8itJQqZT1QUyp9b7r61uGTJlMFs9/XW7Wa7ixcuxhQAF9n1hwtDfUOO4J4AQYV79pwZAQQQQAABBOJcgKBCb4AJKvT8aI0AAggggAACsSUQuBj27n2HpNazo+XQwaOOPkTWbJllZNdGUrlCabN+7/GL5dV31jpq65VKXGTXHwkM9Q05gnsCBBXu2XNmBBBAAAEEEIhzAYIKvQEmqNDzozUCCCCAAAIIxI5A1fvLyIReTSVVqlRy4PBRqd1pbLLXmciQIZ2sXTBYcmbPIu+u+UY6DpoVOwCqp1xk1x8uDPUNOYJ7AgQV7tlzZgQQQAABBBCIcwGCCr0BJqjQ86M1AggggAACCMSGwN1lb5JZg1pLmtSp5Z/jJ6W2mu5p++97U9T5pVN7SqmiBWT9dz9Lky7jU3QMtxpxkV1fHkN9Q47gngBBhXv2nBkBBBBAAAEE4lyAoEJvgAkq9PxojQACCCCAAALeFyhVsogsGtlBMqRPJydPnZEG3SfIlh93WHY8dZrU0q99Hcl1RRbp8cL8JAtm58mTSz6e1dc81qyln8qwya9ZHserO7nIrj8yGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEPC2wHVF8svrL3aWbJkzmh0dMu1NWfP1tiSdPnPmrDkN1C03XydvvPicWb5j7wEZNuMtWb1hi5w/f0HKlC4qQzvWl+sLXG2WG4HHl9/8lORYXt7BRXb90cFQ35AjuCdAUOGePWdGAAEEEEAAgTgXIKjQG2CCCj0/WiOAAAIIIICAtwUWjO0k5UoVte3khQsXpXjVjma9Ud2flFoP3OFvc/HiRblw8ZKkVU9b+LY5b6+WwROX+N7GzE8ususPFYb6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAAQS8LTB9aFt5oGxJ207uP3RUKtTvLZdUIGFsdardJa3rPCSF818Z1Hbnn3/L6DnvyvKPNwbtj5U3XGTXHykM9Q05gnsCBBXu2XNmBBBAAAEEEIhzAYIKvQEmqNDzozUCCCCAAAIIxLdA5iwZpUC+3JJK/e+PPfuTrFkRa5+ei+z6I4ahviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQAABBGJJgIvs+qOFob4hR3BPgKDCPXvOjAACCCCAAAJxLkBQoTfABBV6frRGAAEEEEAAAQRiSYCL7PqjhaG+IUdwT4Cgwj17zowAAggggAACcS5AUKE3wAQVen60RgABBBBAAAEEYkmAi+z6o4WhviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQAABBGJJgIvs+qOFob4hR3BPgKDCPXvOjAACCCCAAAJxLkBQoTfABBV6frRGAAEEEEAAAQRiSYCL7PqjhaG+IUdwT4Cgwj17zowAAggggAACcS5AUKE3wAQVen60RgABBBBAAAEEYkmAi+z6o4WhviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQACB6AhwcTgyzjjqO2Kob8gR3BMgqHDPnjMjgAACCCCAQJwLEFToDTBBhZ4frRFAAAEEEEAgOgJcHI6MM476jhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBCIjgAXhyPjjKO+I4b6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAAQSiI8DF4cg446jviKG+IUdwT4Cgwj17zowAAggggAACcS5AUKE3wAQVen60RgABBBBAAIHoCHBxODLOOOo7YqhvyBHcEyCocM+eMyOAAAIIIIBAnAsQVOgNMEGFnh+tEUAAAQQQQCA6AlwcjowzjvqOGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEIiOABeHI+OMo74jhvqGHME9AYIK9+w5MwIIIIAAAgjEuQBBhd4AE1To+dEaAQQQQAABBKIjwMXhyDjjqO+Iob4hR3BPgKDCPXvOjAACCCCAAAJxLkBQoTfABBV6frRGAAEEEEAAgegIcHE4Ms446jtiqG/IEdwTIKhwz54zI4AAAggggECcCxBU6A0wQYWeH60RQAABBBBAIDoCXByOjDOO+o4Y6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQiI4AF4cj44yjviOG+oYcwT0Bggr37DkzAggggAACCMS5AEGF3gATVOj50RoBBBBAAAEEoiPAxeHIOOOo74ihviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQACB6AhwcTgyzjjqO2Kob8gR3BMgqHDPnjMjgAACCCCAQJwLEFToDTBBhZ4frRFAAAEEEEAgOgJcHI6MM476jhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBCIjgAXhyPjjKO+I4b6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAAQSiI8DF4cg446jviKG+IUdwT4Cgwj17zowAAggggAACcS5AUKE3wAQVen60RgABBBBAAIHoCHBxODLOOOo7YqhvyBHcEyCocM+eMyOAAAIIIIBAnAsQVOgNMEGFnh+tEUAAAQQQQCA6AlwcjowzjvqOGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEIiOABeHI+OMo74jhvqGHME9AYIK9+w5MwIIIIAAAgjEuQBBhd4AE1To+dEaAQQQQAABBKIjwMXhyDjjqO+Iob4hR3BPgKDCPXvOjAACCCCAAAJxLkBQoTfABBV6frRGAAEEEEAAgegIcHE4Ms446jtiqG/IEdwTIKhwz54zI4AAAggggECcCxBU6A0wQYWeH60RQAABBBBAIDoCXByOjDOO+o4Y6htyBPcECCrcs+fMCCCAAAIIIBDnAgQVegNMUKHnR2sEEEAAAQQQiI4AF4cj44yjviOG+oYcwT0Bggr37DkzAggggAACCMS5AEGF3gATVOj50RoBBBBAAAEEoiPAxeHIOOOo74ihviFHcE+AoMI9e86MAAIIIIAAAnEuQFChN8AEFXp+tEYAAQQQQACB6AhwcTgyzjjqO2Kob8gR3BMgqHDPnjMjgAACCCCAQJwLEFToDTBBhZ4frRFAAAEEEEAgOgJcHI6MM476jhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBCIjgAXhyPjjKO+I4b6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAAQSiI8DF4cg446jviKG+IUdwT4Cgwj17zowAAggggAACcS5AUKE3wAQVen60RgABBBBAAIHoCHBxODLOOOo7YqhvyBHcEyCocM+eMyOAAAIIIIBAnAsQVOgNMEGFnh+tEUAAAQQQQCA6AlwcjowzjvqOGOobcgT3BAgq3LPnzAgggAACCCAQ5wIEFXoDTFCh50drBBBAAAEEEIiOABeHI+OMo74jhvqGHME9AYIK9+w5MwIIIIAAAgjEuQBBhd4AE1To+dEaAQQQQAABBKIjwMXhyDjjqO+Iob4hR3BPgKDCPXvOjAACCCCAQFwJJCQkyPVF8smBg//I4cPHHH+2SneXlkJ5c8ury9fJyROnHbW7vXRRaVX3Aflp+58ydtbbjtq4UYmgQk+doELPj9YIIIAAAgggEB0BLg5HxhlHfUcM9Q05gnsCBBXu2XNmBBBAAAEE4kYgIXWCjO/bXKrec6t8uOF7adP3JUef7b67SsnMga0kVapUUq/rePnq25/DtsubJ5e80L2JlCtV1Kz3twpEytbpGbaNm4UEFXr6BBV6frRGAAEEEEAAgegIcHE4Ms446jtiqG/IEdwTIKhwz54zI4AAAgggEBcCadKmkYn9W8jD5UuZn2fVxq3Sstdk28927bVXyzuTukuWTBnMuvW7jZeNm0IHFfeWv1nG9XhSsmfJZNb/bfd+Gf3yMvlw9Sbbc7lVgaBCT56gQs+P1ggggAACCCAQHQEuDkfGGUd9Rwz1DTmCewIEFe7Zc2YEEEAAAQRiXiBturQy9flWUumOG/2fxUlQkSlzBnlrcg+57pqr/O3CBRVPPlFJ+rd5wnzy4uy58zJm7rsya8lHcuniJX97L74gqNAbFYIKPT9aI4AAAggggEB0BLg4HBlnHPUdMdQ35AjuCRBUuGfPmRFAAAEEEIh5gdaNHpHuzaoFfQ4nQcVLQ9rIQ+VuDmpXv9sE9UTFT0H7jDc3lSgkb457TtKkTi3GVE+Ne06SX37bnaSeF3cQVOiNCkGFnh+tEUAAAQQQQCA6AlwcjowzjvqOGOobcgT3BAgq3LPnzAgggAACCMS8QOmS18mkPs3krU++kkL5cptrVKza+IOa+mlKyM/W/slH5bknq5rl89/9TOpWLifp1PRRVkFFhozpZfn03lJQLbZ94PBRqddlvPyx86+Qx/ZaAUGF3ogQVOj50RoBBBBAAAEEoiPAxeHIOOOo74ihviFHcE+AoMI9e86MAAIIIIBAXAm8qBbTfuy+2yVcUHFv+ZIye1AbcwqnL7f8Ko1U8PDDuy+aQUWD7hPky2+Cn6jo076ONK91n+lUt8s42ff3EcmZI6vs2nNADqunK7y+EVTojRBBhZ4frRFAAAEEEEAgOgJcHI6MM476jhjqG3IE9wQIKtyz58wIIIAAAgjElYBdUFGggLF4djfJmjmj7D1wWKq3HSlHjhyTH98fbxlUJKROkI2vj5Qrsv67ePY/x0/6F9I24Lb9vlfe/+xbmTxvuWfXqiCo0PsVJ6jQ86M1AggggAACCERHgIvDkXHGUd8RQ31DjuCeAEGFe/acGQEEEEAAgbgSCBdUZMykFs9WIcX11+aRM2fPSa2OY+SnX3aZn98XVDTsMVG++Hqb3+S2UtfLa2M7+9+HevHmxxul+8i5ngwrCCpCjZqz/QQVzpyohQACCCCAAALuCnBxODL+OOo7YqhvyBHcEyCocM+eMyOAAAIIIBBXAuGCiimDWknlCqXNz9th2MuyXK1p4dtCBRU1KpeVsd2eNKsdO3FKZryxSt7++Ev5++A/UrJ4IRncoa4UK5jXLB85e5lMX7jCd0jP/CSo0BsKggo9P1ojgAACCCCAQHQEuDgcGWcc9R0x1DfkCO4JEFS4Z8+ZEUAAAQQQiCuBUEFFm8aPSLem1czPOvXVj+SFGUuDPrcvqGjUc6Js+Op/T1Q0rFlRBj9T16xboVE/2bfvUFA74ymNFTP7Sv6rcsjGH36T+h3HBpV74Q1Bhd4oEFTo+dEaAQQQQAABBKIjwMXhyDjjqO+Iob4hR3BPgKDCPXvOjAACCCCAQFwJWAUV95S9SV4e0tZcPPvTr7ZKy95TkkzRFCqoaFSrogxqX1d2/vm3VGoywNKqXvV7ZFjH+nL+wgUpXaOrnD591rKeWzsJKvTkCSr0/GiNAAIIIIAAAtER4OJwZJxx1HfEUN+QI7gnQFDhnj1nRgABBBBAIK4ErIKKNQsHqycecpqf86iavumfYyeTfOYCeXKZ+4wFtk+qoOHjDVtk1LQ3per9ZWRi72Zy7vwFuaVWNzl96kyStuXLlJD5I54x91dqPkh27tyXpI6bOwgq9PQJKvT8aI0AAggggAAC0RHg4nBknHHUd8RQ35AjuCdAUOGePWdGAAEEEEAgrgSsgorPFg+VvLmvSNbnXLH+O2nXf7qUKllElo7rYrZtMWCafLpuc5LjNHm8kgxsV9sMM0pWf07OnzufpI6bOwgq9PQJKvT8aI0AAggggAAC0RHg4nBknHHUd8RQ35AjuCdAUOGePWdGAAEEEEAgrgSsgoorrsgqBfLlloSEBMvPmi1zBpkztJ1Z1mfCYvn6h99l776DcuL4KUmbNo18tnCI5M6RVXbsPSA12o+S4wFPZGTNmkk+mt3fLN/8yy6p1XaE5Tnc3ElQoadPUKHnR2sEEEAAAQQQiI4AF4cj44yjviOG+oYcwT0Bggr37DkzAggggAACcSVgFVTYfcAsKmz4bulos1rjnpPk869+DGrSqmFl6dH8MXPfpm07ZPprH8umrdulWOF8MrhDPSmYN7dZ1qzvVFmjpozy2kZQoTciBBV6frRGAAEEEEAAgegIcHE4Ms446jtiqG/IEdwTIKhwz54zI4AAAgggEFcClyOoyJAxvUwf3FruuuWGkFZz3l4tgycuCVnuZgFBhZ4+QYWeH60RQAABBBBAIDoCXByOjDOO+o4Y6htyBPcECCrcs+fMCCCAAAIIxJXA850aSONqd8ui99dL3zELHH229BnSyTdvjJQ0aVJLtXaj5Jffdidpl5A6Qbo9XVOqVbxN8l2Zw1++66+DMnL22/L+J1/793ntBUGF3ogQVOj50RoBBBBAAAEEoiPAxeHIOOOo74ihviFHcE+AoMI9e86MAAIIIIAAAskUyJEzm7nmxc7dB+TIkWPJbB396gQVeuYEFXp+tEYAAQQQQACB6AhwcTgyzjjqO2Kob8gR3BMgqHDPnjMjgAACCCCAQJwLEFToDTBBhZ4frRFAAAEEEEAgOgJcHI6MM476jhjqG3IE9wQIKtyz58wIIIAAAgggEOcCBBV6A0xQoedHawQQQAABBBCIjgAXhyPjjKO+I4b6hhzBPQGCCvfsOTMCCCCAAAIIxLkAQYXeABNU6PnRGgEEEEAAgWgKJCQkyPVF8smBg//I4cMBU3SmSiVFi+QX9SPktnPPATl96kzIcq8XcHE4MiOEo74jhvqGHME9AYIK9+w5MwIIIIAAAgjEuQBBhd4AE1To+dEaAQQQQACBaAkkpE6Q8X2bS9V7bpUPN3wvbfq+5D911QfukIm9mvrfW70YOXuZTF+4wqooJvZxcTgyw4SjviOG+oYcwT0Bggr37DkzAggggAACCMS5AEGF3gATVOj50RoBBBBAAIFoCKRJm0Ym9m8hD5cvZZ5u1cat0rLXZP+pH69aQUY/18j/3upFr/GLZMk7n1kVxcQ+Lg5HZphw1HfEUN+QI7gnQFDhnj1nRgABBBBAAIE4FyCo0Btgggo9P1ojgAACCCBwuQXSpksrU59vJZXuuNF/qlBBxa+79km11sP99QJfnDt7LvBtzL3m4nBkhgxHfUcM9Q05gnsCBBXu2XNmBBBAAAEEPC+QOkNmKVipvuf7mZwO7vh4vlw8G505kAkqkjMySesSVCQ1YQ8CCCCAAAJeEmjd6BHp3qxaUJdCBRW//PGXVGkxOKhuvLzh4nBkRhJHfUcM9Q05gnsCBBXu2XNmBBBAAAEEPC+QOmNmKfxgE8/3Mzkd3L5yjlw8czo5TVJcl6AixXRmQ4IKPT9aI4AAAgggcLkFSpe8Tib1aSZvffKVFMqX21yjYtXGH9TUT1P8p/ZN/URQ4SeJiRe/vvO/dUai1WEusutLY6hvyBHcEyCocM+eMyOAAAIIIOB5AYIKvSEiqNDzI6jQ86M1AggggAAC0RR4US2m/dh9t0uooOK33fvl8Q6jzS5dunhJTpw4Fc3uXdZzcXE4Mrw46jtiqG/IEdwTIKhwz54zI4AAAggg4HkBggq9ISKo0PMjqNDzozUCCCCAAALRFLALKhL35dz5C7Lzr4Oy9OONMv+tT+XYsZOJq8TMey4OR2aocNR3xFDfkCO4J0BQ4Z49Z0YAAQQQQMDzAgQVekNEUKHnR1Ch50drBBBAAAEEoikQKqi449YbZPHoZ8N2Zbt62qJxj4myb9+hsPW8WsjF4ciMDI76jhjqG3IE9wQIKtyz58wIIIAAAgh4XoCgQm+ICCr0/Agq9PxojQACCCCAQDQFQgUVRh+uyX+lpE6d2t+d7NkySfHC+aRt3Yfk2ry5zf0frPtW2g+Y4a8TSy+4OByZ0cJR3xFDfUOO4J4AQYV79pwZAQQQQAABzwsQVOgNEUGFnh9BhZ4frRFAAAEEEIimQLigIlQ/0qVPK29N7iE3FMprVrm9dk85cuRYqOqe3c/F4cgMDY76jhjqG3IE9wQIKtyz58wIIIAAAgh4XoCgQm+ICCr0/Agq9PxojQACCCCAQDQFUhJUGP27/+7SMmNgK7OrNTuOke9/2B7NbkfkXFwcjgij4KjviKG+IUdwT4Cgwj17zowAAggggIDnBQgq9IaIoELPj6BCz4/WCCCAAAIIRFMgpUHFjcULyjuTuptdbdpniqz94ododjsi5+LicEQYCSoiwMjvYgQQOYRrAgQVrtFzYgQQQAABBLwvQFChN0YEFXp+BBV6frRGAAEEEEAgmgIpDSrqVr9bhndsYHb13icHyJ69f0ez2xE5FxeHI8JIUBEBRn4XI4DIIVwTIKhwjZ4TI4AAAggg4H0Bggq9MSKo0PMjqNDzozUCCCCAAALRFAgVVDxRtYJ0bFxVuo+dLxu+2hbUpcxZMsqHs/rJ1bmyyz/HT8ptNbsFlcfKGy4OR2akcNR3xFDfkCO4J0BQ4Z49Z0YAAQQQQMDzAgQVekNEUKHnR1Ch50drBBBAAAEEoikQKqhYt3iY5MmdXc6dvyCTF6+Uxe99Jkf+OSG33FREhndqIIXzX2l2s//kJbJg6epodjli5+LicGQocdR3xFDfkCO4J0BQ4Z49Z0YAAQQQQMDzAgQVekNEUKHnR1Ch50drBBBAAAEEoikQKqioUbmsvNClsSQkJITszsrPN0vb/tNFLl0KWcfLBVwcjszo4KjviKG+IUdwT4Cgwj17zowAAggggIDnBQgq9IaIoELPj6BCz4/WCCCAAAIIRFPgefV0RONqd8ui99dL3zELgk5dskQh6dC4itxWvLDkzJ7FX7Zn/yGZtGiFLHnnM/++WHzBxeHIjBqO+o4Y6htyBPcECCrcs+fMCCCAAAIIeF6AoEJviAgq9PwIKvT8aI0AAggggIAXBfLkySVXqjUpduzaJ8eOnvBiF5PdJy4OJ5vMsgGOlizJ2olhsrio7DEBggqPDQjdQQABBBBAwEsCBBV6o0FQoedHUKHnR2sEEEAAAQQQiI4AF4cj44yjviOG+oYcwT0Bggr37DkzAggggAACnhcgqNAbIoIKPT+CCj0/WiOAAAIIIIBAdAS4OBwZZxz1HTHUN+QI7gkQVLhnz5kRQAABBBDwvABBhd4QEVTo+RFU6PnRGgEEEEAAAQSiI8DF4cg446jviKG+IUdwT4Cgwj17zowAAggggIDnBQgq9IaIoELPj6BCz4/WCCCAAAIIIBAdAS4OR8YZR31HDPUNOYJ7AgQV7tlzZgQQQAABBDwvQFChN0QEFXp+BBV6frRGAAEEEEAAgegIcHE4Ms446jtiqG/IEdwTIKhwz54zI4AAAggg4HkBggq9ISKo0PMjqNDzozUCCCCAAAIIREeAi8ORccZR3xFDfUOO4J4AQYV79pwZAQQQQAABzwsQVOgNEUGFnh9BhZ4frRFAAAEEELAT4KKmnZCzchydOdnVwtFOyL4cQ3sjanhXgKDCu2NDzxBAAAEEEHBdgKBCbwgIKvT8CCr0/GiNAAIIIICAnQAXNe2EnJXj6MzJrhaOdkL25RjaG1HDuwIEFd4dG3qGAAIIIICA6wIEFXpDQFCh50dQoedHawQQQAABBOwEuKhpJ+SsHEdnTna1cLQTsi/H0N6IGt4VIKjw7tjQMwQQQAABBFwXIKjQGwKCCj0/ggo9P1ojgAACCCBgJ8BFTTshZ+U4OnOyq4WjnZB9OYb2RtTwrgBBhXfHhp4hgAACCCDgugBBhd4QEFTo+RFU6PnRGgEEEEAAATsBLmraCTkrx9GZk10tHO2E7MsxtDeihncFCCq8Ozb0DAEEEEAAAdcFCCr0hoCgQs+PoELPj9YIIIAAAgjYCXBR007IWTmOzpzsauFoJ2RfjqG9ETW8K0BQ4d2xoWcIIIAAAgi4LkBQoTcEBBV6fgQVen60RgABBBBAwE6Ai5p2Qs7KcXTmZFcLRzsh+3IM7Y2o4V0Bggrvjg09QwABBBBAwHUBggq9ISCo0PMjqNDzozUCCCCAAAJ2AlzUtBNyVo6jMye7WjjaCdmXY2hvRA3vChBUeHds6BkCCCCAAAKuCxBU6A0BQYWeH0GFnh+tEUAAAQQQsBPgoqadkLNyHJ052dXC0U7IvhxDeyNqeFeAoMK7Y0PPEEAAAQQQcF2AoEJvCAgq9PwIKvT8aI0AAggggICdABc17YSclePozMmuFo52QvblGNobUcO7AgQV3h0beoYAAggggIDrAgQVekNAUKHnR1Ch50drBBBAAAEE7AS4qGkn5KwcR2dOdrVwtBOyL8fQ3oga3hUgqPDu2NAzBBBAAAEEXBcgqNAbAoIKPT+CCj0/WiOAAAIIIGAnwEVNOyFn5Tg6c7KrhaOdkH05hvZG1PCuAEGFd8eGniGAAAIIIOC6AEGF3hAQVOj5EVTo+dEaAQQQQAABOwEuatoJOSvH0ZmTXS0c7YTsyzG0N6KGdwUIKrw7NvQMAQQQQAAB1wUIKvSGgKBCz4+gQs+P1ggggAACCNgJcFHTTshZOY7OnOxq4WgnZF+Oob0RNbwrQFDh3bGhZwgggAACCLguQFChNwQEFXp+BBV6frRGAAEEEEDAToCLmnZCzspxdOZkVwtHOyH7cgztjajhXQGCCu+ODT1DAAEEEEDAdQGCCr0hIKjQ8yOo0POjNQIIIIAAAnYCXNS0E3JWjqMzJ7taONoJ2ZdjaG9EDe8KEFR4d2zoGQIIIIAAAq4LEFToDQFBhZ4fQYWeH60RQAABBBCwE+Cipp2Qs3IcnTnZ1cLRTsi+HEN7I2p4V4CgwrtjQ88QQAABBBBwXYCgQm8ICCr0/Agq9PxojQACCCCAgJ0AFzXthJyV4+jMya4WjnZC9uUY2htRw7sCBBXeHRt6hgACCCCAgOsCBBV6Q0BQoedHUKHnR2sEEEAAAQTsBLioaSfkrBxHZ052tXC0E7Ivx9DeiBreFSCo8O7Y0DMEEEAAAQRcFyCo0BsCggo9P4IKPT9aI4AAAgggYCfARU07IWflODpzsquFo52QfTmG9kbU8K4AQYV3x4aeIYAAAggg4LoAQYXeEBBU6PkRVOj50RoBBBBAAAE7AS5q2gk5K8fRmZNdLRzthOzLMbQ3ooZ3BQgqvDs29AwBBBBAAAHXBQgq9IaAoELPj6BCz4/WCCCAAAII2AlwUdNOyFk5js6c7GrhaCdkX46hvRE1vCtAUOHdsaFnCCCAAAIIuC5AUKE3BAQVen4EFXp+tEYAAQQQQMBOgIuadkLOynF05mRXC0c7IftyDO2NqOFdAYIK744NPUMAAQQQQMB1AYIKvSEgqNDzI6jQ86M1AggggAACdgJc1LQTclaOozMnu1o42gnZl2Nob0QN7woQVHh3bOgZAggggAACrgsQVOgNAUGFnh9BhZ4frRFAAAEEELAT4KKmnZCzchydOdnVwtFOyL4cQ3sjanhXgKDCu2NDzxBAAAEEEHBdgKBCbwgIKvT8CCr0/GiNAAIIIICAnQAXNe2EnJXj6MzJrhaOdkL25RjaG1HDuwIEFd4dG3qGAAIIIICA6wIEFXpDQFCh50dQoedHawQQQAABBOwEuKhpJ+SsHEdnTna1cLQTsi/H0N6IGt4VIKjw7tjQMwQQQAABBFwXIKjQGwKCCj0/ggo9P1ojgAACCCBgJ8BFTTshZ+U4OnOyq4WjnZB9OYb2RtTwrgBBhXfHhp4hgAACCCDgugBBhd4QEFTo+RFU6PnRGgEEEEAAATsBLmraCTkrhbWclQAAQABJREFUx9GZk10tHO2E7MsxtDeihncFCCq8Ozb0DAEEEEAAAdcFCCr0hoCgQs+PoELPj9YIIIAAAgjYCXBR007IWTmOzpzsauFoJ2RfjqG9ETW8K0BQ4d2xoWcIIIAAAgi4LkBQoTcEBBV6fgQVen60RgABBBBAwE6Ai5p2Qs7KcXTmZFcLRzsh+3IM7Y2o4V0Bggrvjg09QwABBBBAwHUBggq9ISCo0PMjqNDzozUCCCCAAAJ2AlzUtBNyVo6jMye7WjjaCdmXY2hvRA3vChBUeHds6BkCCCCAgIcEChXMI9cXzCtX5cwmv+78SzZv/V1Onz5r2cOcubJJ7hzZLMuMnSdPnZHdew6ELPdSAUGF3mgQVOj5EVTo+dEaAQQQQAABOwEuatoJOSvH0ZmTXS0c7YTsyzG0N6KGdwUIKrw7NvQMAQQQQMADAgUKXC1Dnq0rd99aPKg3Fy5clC+2/CodBs+WI0eOBZV9/MpAKZTvyqB9gW/OX7ggN1R+NnCXZ18TVOgNDUGFnh9BhZ4frRFAAAEEELAT4KKmnZCzchydOdnVwtFOyL4cQ3sjanhXgKDCu2NDzxBAAAEEXBbIkjWTLJ/WS/JfldPsye59h+T3Pful6LV5JU/u7Oa+7bv3S93OY+Xw4f+FFesWD/OXW32Ev1XdsnV6WhV5bh9Bhd6QEFTo+RFU6PnRGgEEEEAAATsBLmraCTkrx9GZk10tHO2E7MsxtDeihncFCCq8Ozb0DAEEEEDAZYFOzR+TDg0rm72Y8uqH8uKsZXLx4kVJnSa1dG1ZU1rVvt8sGzztDZnz2if+3vqCimdHzJGVa7717/e9uKCeqLionsiIhY2gQm+UCCr0/Agq9PxojQACCCCAgJ0AFzXthJyV4+jMya4WjnZC9uUY2htRw7sCBBXeHRt6hgACCCDgssDc0R3MKZ9+/uNPeaTlUJFLl/7Xo1SpZPmMPnJDobyyauMP0rLXFH+ZL6hoM3imfLh6k39/LL4gqNAbNYIKPT+CCj0/WiOAAAIIIGAnwEVNOyFn5Tg6c7KrhaOdkH05hvZG1PCuAEGFd8eGniGAAAIIuCywfGZfM4hYvnaTdHh+ZpLejO/fQqrde5v8umufVG42yF9OUOGn8OSL7SvnyMUzp6PSN4IKPWaCCj0/WiOAAAIIIGAnwEVNOyFn5Tg6c7KrhaOdkH05hvZG1PCuAEGFd8eGniGAAAIIuCwwsseTUvuhsnLg8FF5sPlgOX7spL9HmbNklI9m95ercmaTlZ9vlrb9pvnLfEFFl9Hz5KP1m839Z86ck3Nnz/nrxMoLnqjQGymCCj0/ggo9P1ojgAACCCBgJ8BFTTshZ+U4OnOyq4WjnZB9OYb2RtTwrgBBhXfHhp4hgAACCLgsUL5MCZk/4hmzF8b0TyNnL5Nfft8rRQvnkx5q/YpiBfOaZc36TpU1G7b4e+sLKvw7/v/FP8dPyobNv8rct1fLF19vS1zsyfcEFXrDQlCh50dQoedHawQQQAABBOwEuKhpJ+SsHEdnTna1cLQTsi/H0N6IGt4VIKjw7tjQMwQQQAABDwi0bVJVuj71aMie9J+8RBYsXR1UvnhcZ7mj5PVB+xK/MUKP6QtXJN7tufcEFXpDQlCh50dQoedHawQQQAABBOwEuKhpJ+SsHEdnTna1cLQTsi/H0N6IGt4VIKjw7tjQMwQQQAABDwiUvb24LBzZIWRP2g6eJStXfxNUnjlzRsmdK7t/X+rUCXJtvtzywJ0lpUHVCpJKLcRtbJVbD5Nff9vjr+fFFwQVeqNCUKHnR1Ch50drBBBAAAEE7AS4qGkn5KwcR2dOdrVwtBOyL8fQ3oga3hUgqPDu2NAzBBBAAAGXBapUul0m9moqCQkJZk+++P4X+eHXPXLT9fml7M1F/b1rM3imfLh6k/99uBfVHrxTxvd8yqwyd9kaGTTh1XDVXS8jqNAbAoIKPT+CCj0/WiOAAAIIIGAnwEVNOyFn5Tg6c7KrhaOdkH05hvZG1PCuAEGFd8eGniGAAAIIuCiQoJ6C+GLJcMmZPYucUYtgtx0yW1b//8LYRrcqViglU/s2l/Tp0srhoyekzBM9RC5dctTjd2f0kRJqnYs13/wozbpPctTGrUoEFXryBBV6fgQVen60RgABBBBAwE6Ai5p2Qs7KcXTmZFcLRzsh+3IM7Y2o4V0Bggrvjg09QwABBBBwUaDoddfIB9N6mT0Y9fK7Mm3B+0l606phZXNRbaOgSuvh8stvu5PUsdoxc3g7qXTHTfLdzzvl8XYjrap4Zh9Bhd5QEFTo+RFU6PnRGgEEEEAAATsBLmraCTkrx9GZk10tHO2E7MsxtDeihncFCCq8Ozb0DAEEEEDARYFCBfPIx7P6mT3oMOxlWf7JV0l6E7h+RcMeE+WLr7clqZN4h/GkxpoFgyXv/7F3J3BXTP8Dx7+tSqlUKJWlQgrJvu9r1myJLAktRNFGyVLRorTJEllKor8l2bLv+xYh+xo/oghFVP9zJnPNs8wzc8+5d5Z7P+Ole+/MOXPOvM/c556Z75yZhvXknidek/4jbiudJFGfCVTYNQeBCjs/AhV2fuRGAAEEEEAgSICTmkFC4ZbjGM4pKBWOQULByzEMNiJFcgUIVCS3bagZAggggECMAjVqriXz7h8tVatUkVvuf1aGTbq7TG36nd1Bup9wgDN/hxMukiWLl8o6dWrJlKHd5OdffpPzh90i//z9T4l8Z3Q8QAad1cGZd+nkWTL93mdKLE/aBwIVdi1CoMLOj0CFnR+5EUAAAQQQCBLgpGaQULjlOIZzCkqFY5BQ8HIMg41IkVwBAhXJbRtqhgACCCAQs8CMa3pnHpo9ccZcuWHmY7J82Z+igxhdjttfLjy1vVSqVKnELZyOab+bjL7gZKfm+tZOI266X96e/7nUX3cd6XzkXtJDBTZ0noU/LpaDug6TP5f/FfNWVlw8gYqKfYKWEqgIEqp4OYGKin1YigACCCCAgK0AJzVtBdfkxxFHP4FP51zvtygv89kX88LKSiMSIFARETTFIIAAAgikT6BRowZy5+jzZKPGDZ3Kr1q1ynlwdr11akkVdQsnPf2uAheHq+dMfPPtj87nWrVqyh1jzpetWzZzPpf3j87TdcgN8sY7H5e3OFHzCFTYNQeBCjs/AhV2fuRGAAEEEEAgSICTmkFC4ZbjGM4pKBWOQULByzEMNiJFcgUIVCS3bagZAggggEACBBo0qCsXnH6E7LR1C2nedP1Mjf5a8bc8+NzbMuqm2fLTT79k5us31apXk26dDpKDd28rW2zcOBPU+POvFfLCOx/JJePvkh9/XFIiT1I/EKiwaxkCFXZ+BCrs/MiNAAIIIIBAkAAnNYOEwi3HMZxTUCocg4SCl2MYbESK5AoQqEhu21AzBBBAAIGECdSrt45s1HQ9+WHRL/KDDjSsXh1Yw+prVZOWm2woy1WQ4suv/yerVwXnCVxphAkIVNhhE6iw8yNQYedHbgQQQAABBIIEOKkZJBRuOY7hnIJS4RgkFLwcw2AjUiRXgEBFctuGmiGAAAIIIBC7AIEKuyYgUGHnR6DCzo/cCCCAAAIIBAlwUjNIKNxyHMM5BaXCMUgoeDmGwUakSK4AgYrktg01QwABBBBAIHYBAhV2TUCgws6PQIWdH7kRQAABBBAIEuCkZpBQuOU4hnMKSoVjkFDwcgyDjUiRXAECFcltG2qGAAIIIIBA7AIEKuyagECFnR+BCjs/ciOAAAIIIBAkwEnNIKFwy3EM5xSUCscgoeDlGAYbkSK5AgQqkts21AwBBBBAAIHYBQhU2DUBgQo7PwIVdn7kRgABBBBAIEiAk5pBQuGW4xjOKSgVjkFCwcsxDDYiRXIFCFQkt22oGQIIIIAAArELEKiwawICFXZ+BCrs/MiNAAIIIIBAkAAnNYOEwi3HMZxTUCocg4SCl2MYbESK5AoQqEhu21AzBBBAAAEEYhcgUGHXBAQq7PwIVNj5kRsBBBBAAIEgAU5qBgmFW45jOKegVDgGCQUvxzDYiBTJFSBQkdy2oWYIIIAAAjkSoLNmDkmgwtxO5yRQYedHoMLOj9wIIIAAAggECdBPDhIKtxzHcE5BqXAMEgpejmGwESmSK0CgIrltQ80QQAABBHIkQGfNHJJAhbmdzkmgws6PQIWdH7kRQAABBBAIEqCfHCQUbjmO4ZyCUuEYJBS8HMNgI1IkV4BARXLbhpohgAACCORIgM6aOSSBCnM7nZNAhZ0fgQo7P3IjgAACCCAQJEA/OUgo3HIcwzkFpcIxSCh4OYbBRqRIrgCBiuS2DTVDAAEEEMiRAJ01c0gCFeZ2OieBCjs/AhV2fuRGAAEEEEAgSIB+cpBQuOU4hnMKSoVjkFDwcgyDjUiRXAECFcltG2qGAAIIIJAjATpr5pAEKsztdE4CFXZ+BCrs/MiNAAIIIJCdQK3aNaVJowahMn3/w2L57bdlodImORH95Ny0Do44+gl8Oud6v0V5mc++mBdWVhqRAIGKiKApBgEEEEAgPgE6a+b2BCrM7XROAhV2fgQq7PzIjQACCCCQncDES8+U9nu2C5VpxkMvyiXXzAiVNsmJ6CfnpnVwxNFPgECFn0z4+VEbhq8ZKXMtQKAi16KsDwEEEEAgcQIcOJg3CYEKczudk0CFnR+BCjs/ciOAAAIIZCcw7pKucsTe24XKNHDcDJn14Iuh0iY5Ef3k3LQOjjj6CUR9kp190a8lmJ8GAQIVaWgl6ogAAgggYCVAZ82cj0CFuZ3OSaDCzo9AhZ0fuRFAAAEEshSoVEmqVavqm+meiX2lTYum8tOS32SPkwbL33//45s2LQvoJ+empXDE0U+AQIWfTPj5URuGrxkpcy1AoCLXoqwPAQQQQCBxAhw4mDcJgQpzO52TQIWdH4EKOz9yI4AAAgjkTmD7tpvJ3WN6OysccfNsmXLnY7lbeYxrop+cG3wccfQTiPokO/uiX0swPw0CBCrS0ErUEQEEEEDASoDOmjkfgQpzO52TQIWdH4EKOz9yI4AAAgjkTuDWUb1kz+1aye/L/pTdOg2WP/5YnruVx7gm+sm5wccRRz8BAhV+MuHnR20YvmakzLUAgYpci7I+BBBAAIHECXDgYN4kBCrM7XROAhV2fgQq7PzIjQACCCCQG4HWW2wsc67t76zs+rufkNE33pebFSdgLfSTc9MIOOLoJxD1SXb2Rb+WYH4aBAhUpKGVqCMCCCCAgJUAnTVzPgIV5nY6J4EKOz8CFXZ+5EYAAQQQyI3A5Cu6ycG7bSMr1DMp9ug8RH7++dfcrDgBa6GfnJtGwBFHPwECFX4y4edHbRi+ZqTMtQCBilyLsj4EEEAAgcQJcOBg3iQEKsztdE4CFXZ+BCrs/MiNAAIIIGAv0HzTxvLYjYOkknrQ9sxHXpZBY6bbrzRBa6CfnJvGwBFHP4GoT7KzL/q1BPPTIECgIg2tRB0RQAABBKwE6KyZ8xGoMLfTOQlU2PkRqLDzIzcCCCCAgL3A1RedLh3231FWr14t+3UdKl9//YP9ShO0BvrJuWkMHHH0EyBQ4ScTfn7UhuFrRspcCxCoyLUo60MAAQQQSJwABw7mTUKgwtxO5yRQYedHoMLOj9wIIIAAAnYCTTZsKE/fcqlUqVJZ5r40T3oOudFuhQnMTT85N42CI45+AlGfZGdf9GsJ5qdBgEBFGlqJOiKAAAIIWAnQWTPnI1BhbqdzEqiw8yNQYedHbgQQQAABO4GhfU6Skw7b3VnJUb1Gy/wPv7RbYQJz00/OTaPgiKOfAIEKP5nw86M2DF8zUuZagEBFrkVZHwIIIIBA4gQ4cDBvEgIV5nY6J4EKOz8CFXZ+5EYAAQQQMBdo2LCePD/tcqleraq8+t4nclKfceYrS3BO+sm5aRwccfQTiPokO/uiX0swPw0CBCrS0ErUEQEEEEDASoDOmjkfgQpzO52TQIWdH4EKOz9yI4AAAgiYCwzscZycdey+zgpOGzRZXnj1ffOVJTgn/eTcNA6OOPoJEKjwkwk/P2rD8DUjZa4FCFTkWpT1IYAAAggkToADB/MmIVBhbqdzEqiw8yNQYedHbgQQQAABM4E6dWvLS3cMlZo1qsuCL76Tw84abraiFOSin5ybRsIRRz+BqE+ysy/6tQTz0yBAoCINrUQdEUAAAQSsBOismfMRqDC30zkJVNj5Eaiw8yM3AggggICZwPldjpDzTj7EyXzeiFvloSdeN1tRCnLRT85NI+GIo58AgQo/mfDzozYMXzNS5lqAQEWuRVkfAggggEDiBDhwMG8SAhXmdjongQo7PwIVdn7kRgABBBDIXqCaeibFa/83QurUqikLf1ws+3S+VFatWpX9ilKSg35ybhoKRxz9BKI+yc6+6NcSzE+DAIGKNLQSdUQAAQQQsBKgs2bOR6DC3E7nJFBh50egws6P3AgggAAC2QvUULd7evOekVKlShXpN2a6zHn8texXkqIc9JNz01g44ugnQKDCTyb8/KgNw9eMlLkWIFCRa1HWhwACCCCQOAEOHMybhECFuZ3OSaDCzo9AhZ0fuRFAAAEEEAgSoJ8cJBRuOY7hnIJS4RgkFLwcw2AjUiRXgEBFctuGmiGAAAII5EiAzpo5JIEKczudk0CFnR+BCjs/ciOAAAIIIBAkQD85SCjcchzDOQWlwjFIKHg5hsFGpEiuAIGK5LYNNUMAAQQQyJEAnTVzSAIV5nY6J4EKOz8CFXZ+5EYAAQQQQCBIgH5ykFC45TiGcwpKhWOQUPByDIONSJFcAQIVyW0baoYAAgjkVWDfPdrKJo0byl0PvyjL/vgzq7Js8mZVUI4S01kzhyRQYW6ncxKosPMjUGHnR24EEEAAAQSCBOgnBwmFW45jOKegVDgGCQUvxzDYiBTJFSBQkdy2oWYIIIBA3gT22X0buemys6VSpUrSse94eeOdj0OXZZM3dCE5TkhnzRyUQIW5nc5JoMLOj0CFnR+5EUAAAQQQCBKgnxwkFG45juGcglLhGCQUvBzDYCNSJFeAQEVy24aaIYAAAnkR2GijDWTOpP5Se+0azvpP7DdeXn87XKDCJm9eNibkSumshYQqJxmBinJQsphFoCILrHKSEqgoB4VZCCCAAAII5FCAfnJuMHHE0U/g0znX+y3Ky3z2xbywstKIBAhURARNMQgggEASBNauVUPuv3aAtGi6fqY6YQMVNnkzhcX0hs6aOTyBCnM7nZNAhZ0fgQo7P3IjgAAChS5AH8++hTG0N9RrwBFHPwECFX4y4edHbRi+ZqTMtQCBilyLsj4EEEAgwQLXD+suB+6ydYkanthvghpR8VGJeeV9sMlb3vqinMeBg7k2gQpzO52TQIWdH4EKOz9yI4AAAoUuQB/PvoUxtDfUa8ARRz+BqE+ysy/6tQTz0yBAoCINrUQdEUAAgRwInHPqYXLBqe2dNU1/8AU54eBdpHq1qhImUGGTNwdVt14FnTVzQgIV5nY6J4EKOz8CFXZ+5EYAAQQKXYA+nn0LY2hvqNeAI45+AgQq/GTCz4/aMHzNSJlrAQIVuRZlfQgggEACBfbadSuZekV35+HZr83/VE6+cLy8/+A1TqCiU/8J8tpb/iMqbPImhYIDB/OWIFBhbqdzEqiw8yNQYedHbgQQQKDQBejj2bcwhvaGeg044ugnEPVJdvZFv5ZgfhoECFSkoZWoIwIIIGAh0KyZfnh2P1mnVk35btESOaLHSPnll9/kw0fGBwYqbPJaVDnnWemsmZMSqDC30zkJVNj5Eaiw8yM3AgggUOgC9PHsWxhDe0O9Bhxx9BMgUOEnE35+1Ibha0bKXAsQqMi1KOtDAAEEEiRQc2318GwVpGi5USP5a8Xf0uH8MfLRJ984NXQDFScNmCivvrmgTK1t8pZZWcwzOHAwbwACFeZ2OieBCjs/AhV2fuRGAAEECl2APp59C2Nob6jXgCOOfgJRn2RnX/RrCeanQYBARRpaiToigAAChgKTrzhbDt6trZO715W3yMNPvZFZU1CgwiZvppCEvKGzZt4QBCrM7XROAhV2fgQq7PzIjQACCBS6AH08+xbG0N5QrwFHHP0ECFT4yYSfH7Vh+JqRMtcCBCpyLcr6EEAAgYQIdO98qPQ7/XCnNtfd9YRcPeW+EjVzAxUnD5wor7xRckSFTd4ShSTkAwcO5g1BoMLcTuckUGHnR6DCzo/cCCCAQKEL0Mezb2EM7Q31GnDE0U8g6pPs7It+LcH8NAgQqEhDK1FHBBBAIEuBPXduI7cM6+E8PPuZNz6QMy+eLKtXrS6xFr9AhU3eEgUk6AOdNfPGIFBhbqdzEqiw8yNQYedHbgQQQKDQBejj2bcwhvaGeg044ugnQKDCTyb8/KgNw9eMlLkWIFCRa1HWhwACCCRA4LkZQ6XJ+vWdmiz9Y7n8+tuyMrVq1qiBM08/YHvZnyvkyVfmy6gb7hWbvGUKScgMDhzMG4JAhbmdzkmgws6PQIWdH7kRQACBQhegj2ffwhjaG+o14Iijn0DUJ9nZF/1agvlpECBQkYZWoo4IIIBAlgIvzBwujRvWyyrX3JfmSc8hN4pN3qwKjDAxnTVzbAIV5nY6J4EKOz8CFXZ+5EYAAQQKXYA+nn0LY2hvqNeAI45+AgQq/GTCz4/aMHzNSJlrAQIVuRZlfQgggEACBOrVW0eabdhQKleuXG5t6tSqIbcO7+ksGzRhprz5/hfy3Q8/yx+/LxebvOUWloCZHDiYNwKBCnM7nZNAhZ0fgQo7P3IjgAAChS5AH8++hTG0N9RrwBFHP4GoT7KzL/q1BPPTIECgIg2tRB0RQACBHAvUXmdtmXffaGetnQdOkpff+DB0CTZ5QxeS44R01sxBCVSY2+mcBCrs/AhU2PmRGwEEECh0Afp49i2Mob2hXgOOOPoJEKjwkwk/P2rD8DUjZa4FCFTkWpT1IYAAAikQsAk22OSNi4YDB3N5AhXmdjongQo7PwIVdn7kRgABBApdgD6efQtjaG+o14Ajjn4CUZ9kZ1/0awnmp0GAQEUaWok6IoAAAjkWWKtGdXnrnpFStWoVObznKPnks29Dl2CTN3QhOU5IZ80clECFuZ3OSaDCzo9AhZ0fuRFAAIFCF6CPZ9/CGNob6jXgiKOfAIEKP5nw86M2DF8zUuZagEBFrkVZHwIIIIBA4gQ4cDBvEgIV5nY6J4EKOz8CFXZ+5EYAAQQKXYA+nn0LY2hvqNeAI45+AlGfZGdf9GsJ5qdBgEBFGlqJOiKAAAIIWAnQWTPnI1BhbqdzEqiw8yNQYedHbgQQQKDQBejj2bcwhvaGeg044ugnQKDCTyb8/KgNw9eMlLkWIFCRa1HWhwACCCCQOAEOHMybhECFuZ3OSaDCzo9AhZ0fuRFAAIFCF6CPZ9/CGNob6jXgiKOfQNQn2dkX/VqC+WkQIFCRhlaijggggAACVgJ01sz5CFSY2+mcBCrs/AhU2PmRGwEEECh0Afp49i2Mob2hXgOOOPoJEKjwkwk/P2rD8DUjZa4FCFTkWpT1IYAAAggkToADB/MmIVBhbqdzEqiw8yNQYedHbgQQQKDQBejj2bcwhvaGeg044ugnEPVJdvZFv5ZgfhoECFSkoZWoIwIIIICAlQCdNXM+AhXmdjongQo7PwIVdn7kRgABBApdgD6efQtjaG+o14Ajjn4CBCr8ZMLPj9owfM1ImWsBAhW5FmV9CCCAQB4E6PjaoeJn7kegwtxO5yRQYedHoMLOj9wIIIBAoQvQx7NvYQztDfUacMTRTyDqk+zsi34twfw0CBCoSEMrUUcEECh6ATobdrsAfuZ+BCrM7XROAhV2fgQq7PzIjQACCBS6AH08+xbG0N5QrwFHHP0ECFT4yYSfH7Vh+JqRMtcCBCpyLcr6EEAAgTwI0PG1Q8XP3I9Ahbmdzkmgws6PQIWdH7kRQACBQhegj2ffwhjaG+o14Iijn0DUJ9nZF/1agvlpECBQkYZWoo4IIFD0AnQ27HYB/Mz9CFSY2+mcBCrs/AhU2PmRGwEEECh0Afp49i2Mob2hXgOOOPoJEKjwkwk/P2rD8DUjZa4FCFTkWpT1IYAAAnkQoONrh4qfuR+BCnM7nZNAhZ0fgQo7P3IjgAAChS5AH8++hTG0N9RrwBFHP4GoT7KzL/q1BPPTIECgIg2tRB0RQKCMwLr168hWmzWTZo0byrc/LJb5H38li39eWiadd8Z669WTunVqyaefLfTOTsV7Oht2zYSfuR+BCnM7nZNAhZ0fgQo7P3IjgAAChS5AH8++hTG0N9RrwBFHPwECFX4y4edHbRi+ZqTMtQCBilyLsj4EEMirwDoq0DDgrKOl48G7SOXKlUuUteCL7+SMwdfJDypwUXpqt01LuW14T6lVcy1p32OEfPTJN6WTJPozHV+75sHP3I9Ahbmdzkmgws6PQIWdH7kRQACBQhegj2ffwhjaG+o14Iijn0DUJ9nZF/1agvlpECBQkYZWoo4IIOAINGhQV2aO6S3Nm66fEflrxd9SvVpVqVSpkjPvx8VL5eQBE+VzFbRwpx3bbSG3DO0uNWtUd2Yd2Wu0vP/hl+7iVLzS2bBrJvzM/QhUmNvpnAQq7PwIVNj5kRsBBBAodAH6ePYtjKG9oV4Djjj6CRCo8JMJPz9qw/A1I2WuBQhU5FqU9SGAQN4EzjzxQLnozKOd9T/12nyZMP1ReU8FHGqqURK9Tz9czjxmX2fZfU++Ln2vutV5v/tOrWXKZWfLWtWrOZ/1PwQqMhSxvomys8GBg3lTE6gwt9M5CVTY+RGosPMjNwIIIFDoAvTx7FsYQ3tDvQYccfQTiPK4l33RrxWYnxYBAhVpaSnqiQAC0rhRAxnZ92R5WwUnrpk6R2T16hIqN13VU/bdsY38/MtvstNxA6Vylcoyf87YEkEKnYFARQm22D5E2WHjwMG8mQlUmNvpnAQq7PwIVNj5kRsBBIpLQPd9N2veRLZs0VSqVK4k73/6jXzy+Xey8p+VBQtBH8++aTG0N9RrwBFHP4Eoj3vZF/1agflpESBQkZaWop4IIBAo4B1xsUunQbJo0S8yfkhXdauoDWTyzLky6eIznHUcce4o+WDBV4HrS1ICOr52rYGfuR+BCnM7nZNAhZ0fgQo7P3IjgEDxCOy92zZyWY9jZaPGDUts9Iq//5Gxtz8kU+58rMT8QvlAH8++JTG0N9RrwBFHPwECFX4y4edHbRi+ZqTMtQCBilyLsj4EEIhN4Lqh3eSgXbeRVatWSevD+sjf6sDMnTbaaAN5euoQ5yOBClcl3tcoOxscOJi3NYEKczudk0CFnR+BCjs/ciOAQHEIdDnhABl8docSG6uf4+a99emts5+VoRPvLpGmED7Qx7NvRQztDfUacMTRTyDK4172Rb9WYH5aBAhUpKWlqCcCCFQo0Ew9YHvujRc7B2TvqWHuR3cfUSI9gYoSHIn4EGWHjQMH8yYnUGFup3MSqLDzI1Bh50duBBAoDoEPHxkv1atVlT+W/yXDbrxX5j7/jvz6y++y7dYtZNyA06SZun2qnvY+7TL5duGigkKhj2ffnBjaG+o14Iijn0CUx73si36twPy0CBCoSEtLUU8EEPAVqKYelH3ftf1ly003dNKcM3yqPPr0myXSE6gowZGID1F22DhwMG9yAhXmdjongQo7PwIVdn7kRgCB4hA4+6SDZe8dtpRL1IiJz7/4rsRGt9lyE3lgYj9n3qAJM2XmA8+XWJ72D/Tx7FsQQ3tDvQYccfQTiPK4l33RrxWYnxYBAhVpaSnqiQACvgJX9u0sHQ/Z1Vk+67FXZOCoaWXSEqgoQxL7jCg7bBw4mDc3gQpzO52TQIWdH4EKOz9yI4AAAlrgg4fHOaOO73zkJRk85o6CQqGPZ9+cGNob6jXgiKOfQJTHveyLfq3A/LQIEKhIS0tRTwQQKFeg5ynt5cLTDnOWvb3gSzmxzzXyj+fZFG4mAhWuRHJeo+ywceBg3u4EKsztdE4CFXZ+BCrs/MiNAAIIbN6yqTxy/UUOxMQZc2Xc1AcKCoU+nn1zYmhvqNeAI45+AlEe97Iv+rUC89MiQKAiLS1FPRFAoIxAp6P2kmG9Ojrzv/1hsXQ4b7Qs/nlpmXR6BoGKcllinRllh40DB/OmJlBhbqdzEqiw8yNQYedHbgQQQMA78rjLJdfJcy/PLygU+nj2zYmhvaFeA444+glEedzLvujXCsxPiwCBirS0FPVEAIESAu3320EmXHS6VKpUSRYtWSrH9R5b4cMBCVSU4EvEhyg7bBw4mDc5gQpzO52TQIWdH4EKOz9yI4BAcQu0339Hmaj6y3r65Kv/ySFnDhNZvdr5XCj/0Mezb0kM7Q31GnDE0U8gyuNe9kW/VmB+WgQIVKSlpahnwQs02bCh7LB1C9myeTP59fdlMk/dxuhd9f/vvy0r+G3PdgP32LmN3HxFN6lapYpjdZy63VPpBweWXieBitIi8X+OssPGgYN5exOoMLfTOQlU2PkRqLDzIzcCCBSvwCYbN5I5k/rL2jXXkmXL/5Kj1Mjjz7/4vuBA6OPZNymG9oZ6DTji6CcQ5XEv+6JfKzA/LQIEKtLSUtSzYAUqV6ksvU49TM458SCpot57p+V/rpBeI26Vp1+Y551d1O+32aq53Dmyl9RYq7pz0NWp/wSZ/+GXgSYEKgKJIk8QZYeNAwfz5iVQYW6ncxKosPMjUGHnR24EEChOgfXWqyf3jO8rTdZfVw2gWC1nXT6lYI8n6OPZ7+MY2hvqNeCIo59AlMe97It+rcD8tAgQqEhLS1HPghUYduHJ0unQ3Zzt04GJBV9+Jxs1aiAN6q3jzNMHF+ePvE0eeuL1gjUIu2EtmjeR/7umj9SpVdPJMuyGe+W5NxeUyf7XXyvK3AaKQEUZpthnRNlh48DBvLkJVJjb6ZwEKuz8CFTY+ZEbgbQKVK5cWVo231AW/fyrLFnyW1o3I5Z616lbW2aNu0BaNtvAKX/EzbNlyp2PxVKXKAqlj2evjKG9oV4Djjj6CUR53Mu+6NcKzE+LAIGKtLQU9SxIgR3bbSEzR5/nbNvr8z+Vc4ZOlZ/VAZl68ILstctWcuOlZ0m1qlVEPyh631MulVWrVhWkQ9iNumNsb9llm80Ck69cuUpatT9fVqlXd/IGKo7sNVreDzEKw82bhFc6vnatgJ+5H4EKczudk0CFnR+BCjs/ciOQRgE92nj84DOk/Z7t5PFX3pPug69P42bEUuca6jZPM8b0lrabb+SUf8OsJ2WUurCnkCf6ePati6G9oV4Djjj6CRCo8JMJPz9qw/A1I2WuBQhU5FqU9SGQhcCVfTtLx0N2lV/Ucyj263K5/PrL7yVyH3/47jKi90nOvO5Db5LHn327xPJi+3Dj8B6y/85bBW72j4uXym4nXiyrV/33sEA9BP7F6UNlpQr2HNB1qCz87qfA9SQpAR1fu9bAz9yPQIW5nc5JoMLOj0CFnR+5EUibQNVqVWXikK5y0K7bOFV/+vUP5MyLrk3bZsRSX2039aqesvu2Wzjl3/nwizJ47IxY6hJlofTx7LUxtDfUa8ARRz+BqE+ysy/6tQTz0yBAoCINrUQdC1bgwSmDZMtNN5Q7HnxBhoy7s8x26ivK3rp3lKyjbnV0rRqyPVYN3WYqTgE6G3btjp+5H4EKczudk0CFnR+BCjs/ciOQJoFq1avJdZefLfvu2DpTbQIVGYoK31SqXEkmXXqmHLL7tk66B597S3oPm1riop0KV5DihfTx7BsPQ3tDvQYccfQTIFDhJxN+ftSG4WtGylwLEKjItWiRrk+fUG/Vsplsoe4lq6f3Pv5aPvviu6LoHNs0+Ut3XSkbNKgrFQ3Lvvbys5yDjvufekMuvPIWm+LIm2IBOr52jYefuR+BCnM7nZNAhZ0fgQo7P3IjkCaBbicfKv27HF6iygQqSnD4fhh+YWc58dBdneWLliyVLoOukxX/rCyTfuH3P8mfy/8qMz/NM+jj2bcehvaGeg044ugnEPVJdvZFv5ZgfhoECFSkoZUSXsd9dt9GhnQ/VjZu3LBETfWDoYeqe6LeNef5EvP58J/A5CvOloN3ayvzVGDnmJ4j/1vgeefeHurleR9L5wvHe5bwtpgE6GzYtTZ+5n4EKsztdE4CFXZ+BCrs/MiNQJoE2m7VQiYN6iL64pxNNmzoPKPi6dffV7d+mpymzYi8rtu33UzuVs+lCDNdM+1hmXTbQ2GSpiYNfTz7psLQ3lCvAUcc/QQIVPjJhJ8ftWH4mpEy1wIEKnItWmTrO/34/eSSbsdmtvqvFX/LH+oqnfp1a2fmjZz6gNw4Y27mM2/+E+h01F4yrFdHZ8YIdVunKer2Tt6p3TYt5f/G9nFm8TBBr0zxvafja9fm+Jn7Eagwt9M5CVTY+RGosPMjNwJpFbhGPUz7yH22FwIVwS3YutXG8sDEflKpUqXAxIMmzJSZDxTWRWT08QKbPTABhoFEoRLgGIopMBGOgUSBCTAMJCJBggUIVCS4cdJQtXuv7S9tt9hY9BDj/uphbS+/uUD+VsGKvXbdWiZd3EVq1VxLfv19mWx/TH9uA1VOgzZsWE/mTB4g69ev4yzVw9ufenW+VKtaRfbdqY3suV2rTK5pamTKZeNnZj7zprgE6GzYtTd+5n4EKsztdE4CFXZ+BCrs/MiNQFoFCFSkteWirzd9PHtzDO0N9RpwxNFPIOrRAOyLfi3B/DQIEKhIQysluI5Nm6wnHQ7cWe56+EX58cclJWrqHS1wxLmj5IMFX5VYzoc1As2abSB3XX2+86yKikwK8QqoiraXZSUF6GyU9Mj2E37Ziv2XnkDFfxYm7whUmKj9l4dAxX8WvEOgmAQIVBRTa9ttK308Oz+dG0N7QxxzY4hjbhz5TufGkbXEI0CgIh73oii11ebN5KHJA51t7TnsZpn7zFtFsd0mG1m/QR05UD2rYs/tW8k2m28svy/7Uz775n9y6B7bOsO4V61aJTudOEiWLF5qsvpE5CnEH0sNG9XVEYXoF5Wdbif8tILZRKDCzM3NRaDClTB7JVBh5kYuBNIuQKAi7S0YXf3p49lbY2hvqNeAI45+AlEe97Iv+rUC89MiQKAiLS2Vwnp6R1Qc2v0q+fjTb1O4FfFVeevWm8q94y6QypUry6zHXpGBo6bFV5kclFyIHTfNElWnoxD9orLT7YSfVjCbCFSYubm5CFS4EmavBCrM3MiFQNoFCFSkvQWjqz99PHtrDO0N9RpwxNFPIMrjXvZFv1ZgfloECFSkpaVSVM/KVSrLMYfsKpf3PE5qrFVd5qsAxVEqUMEUXmCrLTeR6SPOlXVq1ZSvv/9J2ne7SparURZpngqx46bbI6pORyH6RWWn2wk/rWA2Eagwc3NzEahwJcxeCVSYuZELgbQLEKhIewtGV3/6ePbWGNob6jXgiKOfQJTHveyLfq3A/LQIEKhIS0slvJ4XqaBEu1YbS93aa0uzRg1krerVnBp//u2P0mXQZPl24aKEb0Fyqrf/ntvK2H6nSO21a8jKlavk2D5j5b0PvkhOBQ1rUogdN00RVaejEP2istPthJ9WMJsIVJi5ubkIVLgSZq8EKszcyIVA2gUIVKS9BaOrP308e2sM7Q31GnDE0U8gyuNe9kW/VmB+WgQIVKSlpRJez9nXXyRbtWxappaPvPCO9B89TZb9ke7RAGU2LA8zGjSoKwPP7iDH7L+js/a/Vvwt/cfOkAefeC0PpUW/ykLsuGnFqDodhegXlZ1uJ/y0gtlEoMLMzc1FoMKVMHslUGHmRi4E0i5QDIEK+ia52UtxtHfE0N5QrwFHHP0EojzuZV/0awXmp0WAQEVaWirh9dyx3RbSTt2uSI+o2KRJQ9mjXStnRICu9psffC4n9rlGVqnRAUzlC3Q8Yk+5TI1KqV6tqpPgy+8WSbfLp8inny0sP0MK5xZix003Q1SdjkL0i8pOtxN+WsFsIlBh5ubmIlDhSpi9EqgwcyMXAmkXIFCRzhaMsm/nCtHHcyXMXzE0t/PmxNGrYf4eR3M7NyeGrgSvaRQgUJHGVktBndepU0tG9j1ZDt6trVPbi8fPlLvmPJ+CmsdTxX5qJEX3Ew6QZcv/klsfeE6um/FowY1CKcQfS723RHVAVoh+UdnpdsJPK5hNBCrM3NxcBCpcCbNXAhVmbuRCIO0CBCrS2YJR9u1cIfp4roT5K4bmdt6cOHo1zN/jaG7n5sTQleA1jQIEKtLYaimpc40a1eX5O4ZK/bq15cHn3pLzr7g5JTWPvpobNm4oe+24pTz0zFvy29I/oq9ABCUW4o+lZovqgKwQ/aKy0+2En1YwmwhUmLm5uQhUuBJmrwQqzNzIhUDaBQhUpLMFo+zbuUL08VwJ81cMze28OXH0api/x9Hczs2JoSvBaxoFCFSksdVSVOf7rhso22zWTF6a97GccuH4FNWcquZaoBB/LLVRVAdkhegXlZ1uJ/y0gtlEoMLMzc1FoMKVMHslUGHmRi4E0i5wee9O0vnwPeTOR16SwWPuSPvmlFt/+iblsmQ9E8esycpkwLAMidEMHI3YymTCsQxJ1jMwzJqMDAkSIFCRoMZIW1WqVK0il5xzvDSoV1sGXD29zK2KGjVqIE/ePFhqrFVdbr7vGbny2llp20Tqm0OBQvyx1DxRnWwvRL+o7HQ74acVzCYCFWZubi4CFa6E2SuBCjM3ciGAQPIF6Jvkpo1wtHfE0N5QrwFHHP0EojzuZV/0awXmp0WAQEVaWiqB9dx26xZyzzUXODXTD3++csr98uwr8+Wff1bKDm03k+Hnnygtm23gLO/Uf4K89tZHCdwKqhSVQCF23LRdVJ2OQvSLyk63E35awWwiUGHm5uYiUOFKmL0SqDBzIxcCCCRfgL5JbtoIR3tHDO0N9RpwxNFPIMrjXvZFv1ZgfloECFSkpaUSWM/KVSrLqP6nSof9d8zUbtWqVbJy1WqppkZbuNOts5+VoRPvdj/yWqQChdhx000ZVaejEP2istPthJ9WMJsIVJi5ubkIVLgSZq8EKszcyIUAAskXoG+SmzbC0d4RQ3tDvQYccfQTiPK4l33RrxWYnxYBAhVpaakE1/P4w3eXbscfKJs2Wa9ELb/+/icZfeuD8vCTr5eYz4fiFCjEjptuyag6HYXoF5Wdbif8tILZRKDCzM3NRaDClTB7JVBh5kYuBBBIvgB9k9y0EY72jhjaG+o14Iijn0CUx73si36twPy0CBCoSEtLpaCetWrXlGYbNpRK6r+vFv5Y5pkVKdgEqphHgULsuGmuqDodhegXlZ1uJ/y0gtlEoMLMzc1FoMKVMHslUGHmRi4EEEi+AH2T3LQRjvaOGNob6jXgiKOfQJTHveyLfq3A/LQIEKhIS0tRz0QIFFrnI8ofzEKzc3fIqAwL0S8qO91W+Ll7bPavBCqyN/PmIFDh1cj+PYGK7M3IgQAC6RCgb5KbdsLR3hFDe0O9Bhxx9BOI8riXfdGvFZifFgECFWlpKeqZCIFC63xE+YNZaHbuDhmVYSH6RWWn2wo/d4/N/pVARfZm3hwEKrwa2b8nUJG9GTkQQCAdAvRNctNOONo7YmhvqNeAI45+AlEe97Iv+rUC89MiQKAiLS1FPRMhUGidjyh/MAvNzt0hozIsRL+o7HRb4efusdm/EqjI3sybg0CFVyP79wQqsjcjBwL5FuA3NTfCOOLoJxBlH1nXgX3RryWym49jdl5+qXH0kwk/H8PwVqRMngCBiuS1CTVKsECh/cGPshNcaHbubhqVYSH6RWWn2wo/d4/N/pVARfZm3hwEKrwa2b8nUJG9GTkQyLcAv6m5EcYRRz+BKPvIug7si34tkd18HLPz8kuNo59M+PkYhrciZfIECFQkr02oUYIFCu0PfpSd4EKzc3fTqAwL0S8qO91W+Ll7bPavBCqyN/PmIFDh1cj+PYGK7M3IgUC+BfhNzY0wjjj6CUTZR9Z1YF/0a4ns5uOYnZdfahz9ZMLPxzC8FSmTJ0CgInltQo0SLFBof/Cj7AQXmp27m0ZlWIh+UdnptsLP3WOzfyVQkb2ZNweBCq9G9u8JVGRvRg4E8i3Ab2puhHHE0U8gyj6yrgP7ol9LZDcfx+y8/FLj6CcTfj6G4a1ImTwBAhXJaxNqlGCBQvuDH2UnuNDs3N00KsNC9IvKTrcVfu4em/0rgYrszbw5CFR4NbJ/T6AiezNyIJBvAX5TcyOMI45+AlH2kXUd2Bf9WiK7+Thm5+WXGkc/mfDzMQxvRcrkCRCoSF6b5LVGhfYHi06c3e4SpV+h7XuufFSGhegXlZ1uK/zcPTb7VwIV2Zt5cxCo8Gpk/55ARfZm5EAg3wL8puZGGEcc/QSi7CPrOrAv+rVEdvNxzM7LLzWOfjLh52MY3oqUyRMgUJG8NslrjQrtDxadOLvdJUq/Qtv3XPmoDAvRLyo73Vb4uXts9q8EKrI38+YgUOHVyP49gYrszciBQL4F+E3NjTCOOPoJRNlH1nVgX/Rriezm45idl19qHP1kws/HMLwVKZMnQKAieW2S1xoV2h8sOnF2u0uUfoW277nyURkWol9Udrqt8HP32OxfCVRkb+bNQaDCq5H9ewIV2ZuRA4F8C/CbmhthHHH0E4iyj6zrwL7o1xLZzccxOy+/1Dj6yYSfj2F4K1ImT4BARfLaJK81KrQ/WHTi7HaXKP0Kbd9z5aMyLES/qOx0W+Hn7rHZv1apsbY02+PY7DMmOMfXz90tq1b8FUkNazRsKo223TeSsqIo5J+/lsm3z98TRVFOGXWbbyPrNm8bWXn5LuiPn76RRe88k+9iWD8CeRXgNzU3vDji6CcQZR9Z14F90a8lspuPY3Zefqlx9JMJPx/D8FakTJ4AgYrktUlWNfrsiWuzSk9iBBBAAAEEEEAAgeQLtDjgnORXkhoWpQAnQHLT7Dji6CdAoMJPJvz8qA11zfhOh2+filLiWJFOuGUYhnMiVTIFCFQks11C14pARWgqEiKAAAIIIIAAAqkRIFCRn6ai75wfV9aKAAIIIIAAAgjkU4C+cT51k7NuAhXJaQtqggACCCCAAAIIIIAAAnkUIFCRR1xWjQACCCCAAAII5EmAQEWeYBO2WgIVCWsQqoMAAggggAACCCCAAAIIIIAAAggggAACCCCAQDEJEKgoptZmWxFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSJgAgYqENQjVQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEECgmAQIVBRTa7OtCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggkTIBARcIahOoggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFBMAgQqiqm12VYEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBImQKAiYQ1CdRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKCYBAhXF1NpsKwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCRMgUJGwBqE6CCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUkwCBimJqbbYVAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGECRCoSFiDUB0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIpJgEBFMbU224oAAggggAACCCCAAAIIIIAAAggggAACCCCAQMIECFQkrEGirE7ValVl8+ZNZIvmGzrFvvfx1/L5F9/LqlWrAqtRTeXdcouNpLXKv2jJb/L2B5/L4p+XBuZbb716sm7d2vLTkqWh0usVVqpcSY4/bHepvXYNeeyFefLtwkWB5USZoGHDetJ6s6bSrFED+eq7n+TdBV/J0l9/D6xCPgwbqTrUqV3Tt+w/V/wtX3/9g+/yqBbY7Hvrr7+utGu9qdRbp5a898nX8tGn38rKf1ZWWPXKlStLS7Wfr14t8snnC8V5U2EOkV132FJOO3pvefP9z2XKnY8FpI5ncVIcmzXbQGquVS0QQft/9uV3smpl8N+YwJVZJkjS99a7Kc03bSwtNmok69evI4vV35GPv/yffKb32YRNpn56M/L1HW7WdH3Zvk1zadW8qfz6+zJ558Mv5L2PvpLff1uWCD1TM9PfiqjLixLZdNtM9z+T8kz6O1EaUhYCCCCAAAIIIIBA8QhEfe7ApP9sct6meFqweLaUQEXxtHWJLT3yoJ1lwBlHSaOGdUvM//OvFTLm9odk6l1PlJjv/dDv7A5yRod9pLoKVnin9z/7Vs4acoP88MNi7+zM+913ai03DDlLataoLs+/tUBO7z8xs6yiNxeedbT07Higk2ToDffIrbOeqih5ZMv0SYiLux0jR+6zfZky56uT52cNuV5+/HFJmWV6Rj4MdUDn/QevkbWq+58wXq3OFLc+vI+s+OvvcusVxUzTfW/TTRrLlMu7yaZN1itRzb9U8GWs2mdvmvl4ifnuh+rqBPqkIWfK/jtv5czq0HuMvDv/c3dxmddNNm4kYwecJm0338hZpte/bYd+sZqVqaSakRTHxio49vy0y6VSpUrlVbPMvLMvnyJPPv9OmflRzUja99bd7i3V/nb5ucfL9q2bu7Myr+9+8o2cc8VN8t33P2XmxfXGxi9f32F9Ir/PGUfKWcfuK7pz652WLf9Leo24VZ558V3v7Ejf25iZ/FZEXV6UmDbbZrL/mZZn2t+J0pKyEEAAAQQQQAABBIpDIMpzB6b952zP2xRHyxXnVhKoKMJ2v6TXCXL6UXtXuOU3/t9TMvL6e0qk0SeAxlx8erkn5t2EPy5eKp0HTipzBfDhB+wkY/p1lqpVqjhJX3n3Ezn5gnFuNt/XQ/fbXiZdfEZmeVICFc033VDuGNnLueo5U7lSb77/6RfpPGCifPnV/zJL8mmo1/3JY8HBnzZHXCB/qpN3cUym+962W7eQW4b1kDq1/EeL3PbAc3LFhLtKbFbtddaWm4f3kB08J39PuHCcvDnvkxLp3A9HH7KrDFffjxprVXdnyXeLlsjenYckYhSAW6kkOW6srv5/auolbtUqfF2pRlLsc/rlsZ1wT+L3VoM12bChPHjdwAr3b/33pOMF18hCNWorrsnUT9c3n9/hCZeeKYft2c6XRQdo+4y6XeY8/ppvmnwtMDUz/a2Iurx8uZW3XtNt0+sy2f9MyzPt75S3zcxLnkDUVyPqvwXZjgjVai1bNJG91MjQn3/5TWbPfTUySJOrJ3XlTEeO6ZMhfiO10zLKuHTjJMmwdN305zpqZHyXY/aV7VpvIv1GT/e9KKu8vFHOS8p3NY2jjnU72fjl4/vs7jv62K5Vi6ayadM1F659py4KfEtdgLZ82Z9ukkS9JtGxmrqocfttWkjbLTaV+nVryYIvFsq8BV/K52oUd5i7DkQFbGNnMnrb9G+v9jApLypHXU6U5w5M+8/ZnreJ0o+yohcgUBG9eawl6j+iL88YlqnD/376VZ585T1Z+sefctxBO8l669bJLNunyxXyzTf/3Sao/X47yMSLu2SWf/ndInn+zQWyoVrnfju1yVxR/ep7n8hJff4LQpx23H5yiRp54L3iOkygYvOWTeW+8ReWOGmclEDFlX07S0d1UltP+uTry+9+LG9/+JW65ciGcuAuW2eMZj/9hlww/JbM53waegMVul1nPfZKplz3zU+/LJXp9z7jfoz01Wbfe+zWS6WFuq2Lnv5ZuVKeUPvsz0t+l33UftdE7X/u1LHveHnjnY+dj7qzMW3kubL5xo3dxc6rX6Ci71kdpEfHAzJp9W13rrp5tsx+7NXAW0tlMkXwJmmO+sqHczu3lypVSl7J7lJ0P+E/09LfBzdNVK9J/N7qbZ+oTrS3//dEux7BM276IzJP3bJo17abi/arVnVNgPeW+5+VYZPujoqrTDmmfnpF+foOH7BXOzVS78xMXZ96bb488PSb0rRRQznl8D1kg7sZKr4AAEAASURBVAZrRg3qQM8+p1wq//z9TyZtFG9MzUx/K6IuLwpDtwzTbdP5TfY/k/JM+zvuNvKabIEor0bUEqZXFup+wkMq+F1fnVDWo8q2Vheo5HsyvXpS18tk5JjOV9HIpbSMMtbb4U5JM3Tr5b5q024nHSLnnniQMzpez79m2sMy6baH3CSJeU3KdzVto47dBjT10/nz8X3W662i+sLd1f7XQ/WL9d0ZvJP+OzdR3ab3xhmPemfH/j6Jjm223ETGqTsHNP/3uNqLpPvQ56vzFsvUeaG4J1O7KEfPaiOT8qK2jfrcgUn/OdvzNlEbUl70AgQqojePt0R1e5YJQ7o6V58+8Myb0n/UNPlbnRjT0zp1asmjNw7K3A5q8MS75M7Zz2Xqe++1/VXkfWPn83uffiOd1IgI9+qFLsfvL4NVMMKdDu1+lXysbn+0lupIfKBuR1R6CgpU1K1XW+ZMHljiJLReR1ICFVurZyToERV/q2cjdL9iirz+9pqT47qOZ3U6SAZ2PUq/de4xv+OxA5z3+p98GnoDFW+qZ4accN6YTLmJeGO47+2xcxu5bXhPZxP0ldG9R94uDz6x5sroddddR+6Z0Fc2btzQWf7Yy+9Kj0tucN57bxnm3f7yAhV77bKVM2LDTTf3pXdl4Jg7Qj1rxM0T2WuCHUsbbKU6w7Mn9svMPrjblfLpZ/E9byGJ31uN8/LdV2VGZ108fqbcNef5jFkXdUA2WN1uT0+ffv0/OfiMoZllUb8x9cvnd3jyFd3k4N22cSjunvuKXDR6WoZF38btXhXsrlt7bWfeBaNvj/TKYl2oqZnJb0Uc5TmwEf1jamm6/2Vbnml/JyI+irEUiPJqRF1V0ysLdXBj1vi+spW62EdPUQQqTK+eNB05prcraOSSt0+s0/tNcY4y9tYpiYbe+tVvUEfGq4vVdlMXULiT7pMPUiOZvX0Wd1mcr0n6rqZp1LHbZqZ++fw+67qFqde1KlgxVl1kloQpTH2jvoPFZmokyhx1Pse9AKo8p0/UnSCOUOdx/o74wh5vXUztohw9q+trUp53OyN7H/G5g2z7z9ohm/M2kblRUKwCBCpi5Y+pcPXHqqm61Uh5D6Ue0P1YOVuNgNDTLHUl+UB1uww96R+2R2+4yHmv/2nfY4R8pO6b7p3unTwgc1//aepE22XqhJueHldXw+urWj9SD9HdbstNnXkVBSoqqyuzp199nuy89WZOWu8/SQlU6Drpoc+r1YPHfyv1oFZ9kPjhQ/+NKNlD3Tbo+//9nHdD70FZIgMVGs1g3/OeiHxcjaToPvh6vabMtKcKZNz6byBDPwh+506DnAe1u1ckv6UeqOvudzpT6UCFPviaqwJ0+spDPen9/qKrp6m2Xe18TuQ/CXQsz+n6Yd0zI4yefv0DOfOia8tLFum8pH1v9RWKHz86IfNshePU7Z3efvfTjIm+YvT2K89xPuvRFq3b984si+NNtn66jvn8Dr8wc7g0VqOn9NROBYWXqpFQ3sk7Umrqfc/I8GtneRdH8j5bM5vfW71BUZcXCeK/hWS7bTqb6f6n82ZbXrb9HV0GU/IFor4a0ebKwrGDushR++6QQY0iUGFy9aSuoNtPcyuby5Ha3j5xEkcZu9vsvibR0K2bvoWWvuikobo4yJ0eeeEdGTX1Afn66/9G3rvL4nxN2nc1TaOOdbvZ+OXz+9x808by+JTBmV3rDXVBng5K6Odl9lKjLNzArA6ebXfcwDJ9wUzGiN4k0VH/TZx9/UBp3byJo7D0j+UyUY1AWbL0DzlAXbB3yO7bZnQGTZgpMx/476KpzIII3tjYRTV61mUwKc/NG/lrhOcO9LZl2392/34EnbeJ3I0CYxMgUBEbfTILvumqc2TfHVs7lbtaDeW9Tg3p1dMh+24v1w46w3n/2bc/ykHqPvOlp1PVg0wv7XGcM/uleR/LKReOd96vXauGc3ukLfStnMZd6MyrKFBx6XknyqlH7umk+13db3LGwy+q4Mn+zuckBSqcCpXzjx5S+cC/V5HrE4ttj+rrXJWQb0PvQdmCL76TfmOmO7X7Uz04+zsVKInruRTlEJU7y2/f83YCzhk+VR5Vt3XxTjqw9eqsEVJfjQjSk/dE77r168gS9dyUW0f1kj23a+UsLx2omHTZWXLoHms6Z6/N/1TOUiMyNlbDYRcv+c0JMDmZUvRPXI6lifT9sefecHFm9vHqBPxbnhPwmQUJeRPX91Zv/uzrL8ocZE1/8AW5dNydGZWrLzpdOuy/o/P5/c++lSO7XZVZlqQ3fn66jvn8Dn88d6Jz2zE9uq3VIeeVIWm3TUv5v7F9nPlPvjpfzh50XZk0cc3wM7P5rahoW6Iur6K65HqZ37bpcmz2P796+pWXbX/Hb/3MT5iAOsA3GY1sOprH9MrCriceKBefeXQJvCgCFSZXT+pKmowcCztyydsnTuzFO56WSqKhrp52nDmuj2z/77Pe9HGZfubTUy/M89Q+QW9T8l3VYkkbdey0oqGfzpvP7/Nxh+0uI/uc5FTxG3VMe1DXobJCHd/qqZZ6fuHj6ll57q0+ewy9WR579i1nWWz/JNDRexGMPj9xiBo14Q00XtG7k5ysbpmqp4U/Lpa9Tgr3/MGcGxvamf7emv7tNS0v5145WGE+zh34Vcuv/6zThzlv47de5heeAIGKwmtT4y3acvON5P6JfTMPvD6q12iZ/+GXzvq8t3aa+cjLMujfk+Dewrx/ePRVUfufepl3sWyzVfPAQMWx7XeTURec7OTTV8efccn10lCdbL76ws7OvDQEKiZfcba6FUlbp77PvPGBdB245iryfBt6D8pKwP/7Qd825rq7n5D7H325vMWxzqto35uvbh3m3ot015MGl/vQPu8PbK+rbpWHn3y9xPb4BSr0rRXeUEEOvyGwC9UD2h567i0ZfeP9ovfHpE9xOZbncs3gM+TIfbZ3FumrI47vdXV5yRIzL67vrQY4RT2U8rKea4K8+vOjL74jP/y8VHZvt4W0bLaBnuVMA8fNkFkPvuh+TNSrn5+uZD6/w8/NGKpuEVjfsTix3/gSt+HTM3fabgu5c9SaAMa7ahRgBzUaMCmTn1kufivK28aoyyuvDvma57dturxc7H+l611ReTptmP5O6XXyOeEC6uRJtqORTUfzmFxZqEff3TqsR2Z0nqsZRaBCl5Xt1ZPek2Y6f65Hanv7xGkIVCTRUNfp/C5HyHknH6Lfyq+/L5POAyfJBwu+cj4n9p+Ef1ddtySOOnbqZuCX7+9zz1Pay4WnHeZUz3sxpGt5j7qd0bb/3p56+JT7ZOpdT7iL4ntNmGOHQ3fNnE8p75l3tWrXdI6J9SgVPW3boV+Zu0ZEhmlgZ/p7q7cp298vncemPJ0/KVO+zx2U3s6g/rNO73fepvS6+FzYAgQqCrt9Q29dDfUsiQfV1c+bNlnPyfPV9z/J/qddlrn9zcXnHC9dO+zjLPO7/6MeGvzi9CucNOXdoiTowH3rNs3l7qvPd4Zx6pVcedP9cvPMx8X7w5r0QMXxh+8uI3qvueJDb4P3nuj5NtS3kFnwyPhMoEmXX94U1+1PyquLnlfRvqcDCfPuG53J2urQ88u9Z+aI/qfI8Qft4qS7Su03N6n9xjv5/eC5JwO8act7r08cnzd0aqIeql26nnE6lq5LMzUi5Sl1dZM+UaCnM4bcIM+q534kdYrze+uajFH3fj56v/9u1+HOd18n3PGojL9ljvsxUa8V+eX7O+wdcfLtD4ul65DrM89B2WHbzWWCGpHiXmX3wtsL5LR+ExNhV5FZLn4rSm9k1OWVLj+fnyvatlztf976V1Semy6ov+Om47UwBLwXS3hHI9uM5snmysKmqu8+R91+tY66slhPd6qRyPvs1Ma5LV5UgQq/lvRexMQoYz+liufHZahHxj6iHsru9uVOHjhRFv5vsdRRffOP1fPG3GccVlz7ZC2N+7vqaqRt1LFbbz8/m5GgYUYi7rXr1nLL0O5ONf5ZuVL0bZUXLfrF+ayfA/LkzYPV3YUrOZ+TfsyhKxmH49nqFlkDzjjCMRo5dU65Dx6fNub8zHNoDu85Uj78+GsnfZL+8bOz+b312z6/v706fT7K86tHvuZHce7AW/cw/Wed3u+8jXddvC98AQIVhd/GobZw4qVnSvs92zlp9S00Opx3dYkfpxH9T1Ungnd2lo9U9yS9ccbcMuvVD+N+595Rmfmt1L3UvZ3Yig7c9f14H7xugKy3bh0n/33qivi+6sp4PaUlUNG61cbOLUbWql7Nqbc+uX3OpVOc9/qffBvqMo46eGdptWkT/daZqlWtKs2bre90OryjBo7tM1beee8zN1msrxXte41V8OuFf4NfumO6xcFlb+2iKz/43BOky9F7O9sxeeZjMuam2SW2ye8Hz9tp0xn0Af2sx18VfT/jPbffItNZ08uuV6NRRt94n36byClOx9Ig3nst64eyHaKGaCd1SsL3Vtucpp4NNEQ9I8hvulA9JPp+9bDopE1Bfvn+Duur+O4dd4GsXXMth2blylWyeOnv8of6Lm+y4ZrAu2vmfe6SOy+O1yCzXPxWeLcr6vK8Zef7fdC25Wr/c7cjqDw3XUX9HTcNr4UhkO+rEf36L65ezbVryP2T+klLdbJOT6+rW1ie3HeCPKv6Tvr5PXEHKvyunszFyLGKvmfeERWulfc1yaOMvfXU7+My7N/tGOl2/Jpb7+p66H3J/a1doR60+/aCL+RKNeLYHX2v0yR5ivu76rVJ26hjXfeK/PL9fdZ/5x5TwYgN11vXYVy0ZKnocwXN1HHi/jtvlbnIUd8W6sAuV5R7UZvXP873cTnqcwRj+53qbPqr730iJ/UZV4Zh5vgLZMc2LZz5XQZfJ8+9Mr9MmjhnVGQX9ejZfJQXtW0U5w7cbQrbf9bpg/o97jp5LWwBAhWF3b6htu6804+Q8zuvGdarM1xx/T1y2/89VSJvv7M7SPcTDnDm+Z2w1Vd0PXvbZU4afZJomyMuKLEOvwOKamqI4V3qRFPbf4dszlPR+469x2aCHGkIVKy3Xj154NoBsr66TZWe9L0d26t7yf/uedB2Pg1LQJfzYZONG8kMdfsT98ripJywC9r3aqiTj+/PGZvZojaH95E//1yR+ey+Ga2umj7m3/v4l7f/+v3gDVIjhc74d6SQPqF+cv8J8vPPv7qrFW+b6SHv2x3dL7MsSW/idvRa6IegPT/t8szIHn0v4wfUA8qTOCXle1tekEKfBHCHX7t2l0y6W2bc/6z7MfbXMH75/g5rBH1/2dvV85Xcq4n9YIbdcK/cMutJv8WRzA9j5v27Y/p7625M1OW55UbxGmbbcrX/6e0JU5673X79HXc5r4UhEMXViH79F0dQXUF8/dBucuAuWzsf9e0qj+g5Qn795Xd5Yebw2AMVFV09mYuRYxV9z9I6yrj0NyNOwyfUMZ070r50vdzPOnjRRd2m9413PnZnJfI19u+qRyVto4511Svy08vz/X3WZei+3jTV11vn35Fjep53+lrdDeK0QZNLPHfBuzwJ7+N01PvdM7demmHQ/csxN8+WVeoCH/2cj35nHiWnHLHmGaE60THqXMy8+cm4qFHXpyK7qEfP5qM8vY1RTlGdO9DblE3/WaevsN+jEzAVhQCBiqJoZv+NLH3rm1mPvSIDR00rk6HzMfvI5T2Pd+bf88Rr0n/EbWXStN2qhXNlq15Q3lXUfgcUV/U7RU44eM1te3Te3iNvU1e0rxnOqT/vu9NWmSt6br7vGblflb9UnTT+duEivTj2ST/Y7271kPCt1MPC9aSDNEerESmfqwdae6d8GnrL8XvvDfgk4ZkBYfe9t2dfnTkBudepl8rC734qs4m3je4le7Rr5czvPvQmefzZt0uk8fvBu6DrUXJOp4OctH3Vc1fuU89f8U5VqlaRl+4cLg3XXceZvWPHi2SxenZAkqYkOHo9Lul1gpx+1JrRLfqkyT6nDHE6wd40SXiflO9tgwZ1ncCOOxJLB2qvVrd4eumND2WvXdpIvy5HSuvma0ZJ6dtm7Nzx4vjuGetpuLB+Oks+v8NulfQB2GHqmSi7tm0pzZtuIPqAVY8icx8AqtPtdvIl8oO6PVRcU1izXPxW6G2MurwoXcNum65TLva/bMrTZfr1d/QypsIRiOJqRL/+i1b0nmhYri7i6HD+GPnks28d4LgDFUFXT+Zi5FjQ9yyNo4y9345YDVUQTN9O1jsaWz/r4+nXP1R98hrS6dDdMieM//xrhfP7qgNkSZ3i/q56XdI06titd0V+Ok0U32d9IdSD6hZ3DeqtOSZz6+a+zv/0W+ky6NrEHae59dOvcTt6L+zT9flpyW/yt7pjgb7Qp9a/I5P1fD3p22t9r0aoJGWqyC7q0bO5Li9q4yjPHWTbf9YWFfV7oraivPgECFTEZx97yfq+e7PG9Bb3BNlrarh4ZzVcfKW69VPpab892sqUy852Zv+grjjf7cRBIqtXl0h2zqmHyQWntnfmPfvmh3LGgEkllvsdULwy66rMLZ9KZKjggx6xsNdJl1SQIrpFky47Sw7dY1unQP3A5a7qfvzlDZXMp2GYrd2+7WZyt2pvPX36zQ9ysBoaG9eUzb730JRB6nZWGzpVLe9hwvoKizf+b2Tmgds6SPTeB1+U2DS/HzzvrZ+umfawTLrtoRL59Id7Vae4rXrQvJ72PeOKRF2pkxRHB0f9s64K6Lx4x9DM35RLJ8+S6fc+4y5O1GtSvreH7re9TLr4DMdGj9rZ+9TL5Lelf2Ss9H3Kn1NXNbq3W9BXLj738nuZ5XG9Ceun65fP77Df9ldTt+CbpgKY7hD2h59/W3pdfpNf8kjmhzXLxW+F3qCoy4sE8d9Cwm6bTp6L/S+b8nSZfv0dvYypMAS8QQK9RaVHc+ZqNI9f/+WAvdrJDUPOzGCWvkgjzkBFmKsnczFyzPR7ltRRxpnGVG/iNlxHPYfiHc8z4kr35/RzCeeoBxjXr1vbqfZlk/9Ppt37tHcTEvM+7u+qFyJNo47degf56XT5/j7rBz0/cuPF0mT9+m61RN/qU09VqlTOzNO3fmrf7UpZ9sefmXlJeZMER31LvJEDTs3chcDPZukfy2X7Y/on5kKzILtc/d5qjzB/e3NZnl8b5Gt+1OcOsu0/6+326/fky4T1JlOAQEUy2yXvtdpgg/rywKT+mSvFv/xukRx97ugSJ8i8ldiwcUN57vbLMg+qOmnARHn1zQWZJHqI9eO3XJoZIlzeA7f9DijMAhVLVKBicKb8uN5cqIZJ9jxxzRX5ug4VddTzaehuvx6KWFf9X96og24nHyr9uxzuJJ370jzpOeRGN1ukr9nue94HZb/+/mdy4vn/3QpKV/zwA3aS8QNPc7ZBX3G+4wkXyR+/Ly+xTX4/eN6TxJ99+6McpIM3ngCcfu6KPvGurzLRz27ZSt3O7B91S54kTElydD36ntVBenRcc4u4xepk+x7qO/pXObfqctPH9Zqk7+1Be28n113S1aHQZjuqA4PS09PqVlobqb/Beuo/9g655+GXSieJ9HM2frpi+fwOl7fh+gDi5uE9ZJdtNnMWf69G6B161vBYR6JkY5aL34qoyyuvHfI1L5tt03Ww3f+yLU+X6dff0cuY0i8Q5dWI5fVf9In2OaoP7waw31BXuuuReN5p8uCuzklkfbX78ReMcxZ9okb6ep8d502fq/dhr57Mxcgxm+9Z0kYZe/2TYKgfcPzGrBHOhSf6N3QPfYFaqenkDnvLFeec4My948EXZMi4O0uliP9j3N/V0gJpGXXs1jusX76/z95b9erjMX3bolvufcp50HvXY/eXs9Vz3tyAxXV3PSFXT0nWMwWT4ui0qxotpeuz27aby05bt1SHvavlw88XyhHqeMSd7nr0Zbn46unux1hfw9pFPXo2F+VFDRv1uQOT/rM2Ka/fE7UV5cUvQKAi/jaIvAb6gVT3TOgrW2zS2Cl7iTo5dnSv0YG3UrpRnfjRD6zSk37Y8DHnX525jcbQPifJSYft7izTVzjsoW738qO67Yt38jugaNmiifMQQLeD4c2j3x+8e1s57N8HfetbUz3ywjz5Vl0x8Zn6UY1zOvqQXWVM386ZKtyi7h0/TN1DvqIpX4a6TH1ya+aY853nUFx+3T0yY/ZzmZPu+r6eM0b2yhzUjlD3pJxy52MVVTUvy0z2Pf3gLD3U150m3PGojP/3gLz5po3l/665QOrWXttZfOfDL8rgsTPcpJlXvx+80iMAbn/gebl8wkwnnz7ZOW5Ql8y9n/UteY7pOTKzzjjfJM1RW+gg2UszhmWGDo9Ro1Mmq1EqSZuS9r317t96RFbni64tEQTebcfWcvuVPTNB4iPV3+r3P/wyNlYTP+826orn8jtcGmLrNs3l6gtPzjxYVpt27Dte3nr309JJI/tsYmbzWxF1eZFBqoJMts1m/zMpT3v49XeitKKs/AhEfTVief2X0s8tCLulF4y+XWbPze8zo8JePZmLkWM237MkjTIu3X6JMFQnMz98eJzzrCx9EVA7dRFF6QtP9ty5jdw6vKdT/QeeeVP6DJtaelNi/ZyE76oXoPQxR+lRKt60SXifjV++v8+Pq2crNFe3+NTT+OmPyoRbSwZmB3Q/RgUr1jz4Xd8C6qjuVyWB0KlDkhz9UPbadWu5ZWh3Z7EOBB2ljjU++uQbv+SRzc/GLurRs7koLzJIVVDU5w5M+8/apLx+T5RWlJUMAQIVyWiHyGqhh/xNubKH7LND60yZenjfK+9+kvnsvrl77ivytAoKuNOO7baQmaPPcz+KvkrrexWw0PcvbapGaLjTQ+oWG+eVc4sN0wMK70iA4eoKianqSom4px3UVQjTR5xb4t6tT702X/75dxiqW79ly1dI3xG3yupVa26TlU/D0sMiv1DP8Hhl3sdSv15tJ8BUtUoVp1r6yqiDuw6TP1S7RznZ7Hszx/WRHbdqmamuHgGkpw3XW7fEA4cPOHOYfPHl95l07puKfvC8D4DT6b9btEQWqXt2blC/rjRqWNddhfOAthdefT/zOa43SXX03vpNP1xxNzWa4jfPw+Tj8vKWm8TvrR6N9pq6atG9fYK+sknfhu+tD76SHdpsov5vkQlS6Gd+7HfaZbGN6jH1022Qz++wXn8L9RyP7h0PlA7qKrFK6gSLnvTB1pBr75a757zgfI7jH1Mz09+KqMuL0tR023QdTfY/m/JM+ztRelJW9gJRX42oa1he/8U0UHHh6Glyv+rb52vK5urJXIwcC/qepWGUcem2SJKh9+Rwr6tulYeffL1Edft0PVLO7XSwM2/s7Q/LtbeXvX1qiQwRfkjKd9W7yWkZdazrnK1fvr/Pc6dekrkA5aLxd5bp13lH1zu3qFbPc0vClDTH8kz233NbuXbQGZlzGkk515KtXdSjZ23LK68t8jUv6nMHNv1nbVBevydfNqw3uQIEKpLbNnmp2dHqodVj1MOrw0w3zHpSRt1wb4mkZ554oAxUDyB2TwSVWKg+vPPRV9Ll4smy9NeyD1QLOqAovS73szdQcdVN98tNMx93F8X2+py6cryJeqhXmKmNul3Qn+rErTvly7Bhw3py21XnZJ7n4Jbnff192Z9y5qU3yOtvf+ydHcl7m31Pd1ZuV9vWcqNG5dZVj+IZMG5GmYdhu4kr+sGro+6vO1WNFmrXahM3eZnXm9R9d69S999NwpREx6rVqsqrd18l9dSoCj1NuedpGXFdMry8bZbE762uX+stNpbpI8/NjAzy1tl9v0Ldcuz4C66R+TGOprDxy+d3WBvdNeFC2aF1c5dL9H2Kew69WT5Qv0lxTjZmJr8VUZcXpa3NtpnsfzblmfZ3ovSkrOwEor4a0a1def2XuuoClC1UcHY99Qwjv2n8gNOcvrr+7eiu/hbqIPhLb3yYt0C3ydWTNiPH9HZX9D1Lwyjj0m2XNMOBPY6Ts47d16mmHn1/bO+x8tXX/3M+77JDK3UFdo/MxUKnD5oszyfgYh5duSR9Vx0s9U9aRh2b+ul8+fw+e9f94jsfyenqOZir/r04sLJ6RsX0q8+Tnbdec7vPp1//QM5Uo5Pjnkz2w3w7ek2qr1VNzjv18BK3zXrurQ+lS/+Szxj15onqvYld1KNnbcqLytEtJ+pzBzb9Z13n8vo97rbwWjwCBCqKp62dLS19n7+KNt/veQv6vv7dTzhItlQPOHZv1/Tj4qXy5KvzZei1s8oMDXbLaK7Sz73hIud+kvc/9YZceOUt7qIKXzsdtZcM69XRSdN75G0y5/HXKkwfxcIn1MNtN22yXmBRP//ym+zWaXCZA8N8Geof9nPUsyg6td8tc9JYV3K5ek6A7nxcNmlWmVtyBW5EjhLY7nu6k3/FeR1lz+1aZa4+/2flStFDfK9RV3FVNNph/JCucvhe26kRLyvl4LOvlC+/WnOg5W5aNXWifaAaNqwfir5Bg/9GUXz1/U+ig2OPP/u2mzT21yQ66s6ufqC5fpbHT2o0Sns15Prnn3+N3ap0BZL6vdX11LfA63fGEbLfjm2cv5Fu3fUtFx5/5T0ZddPscp8946aL4tXWL5/f4ZtHnOOMFNR/66Y/9KJMVH8Toh41Vl4b2Jpl+1sRdXnlbXO+5tluW7b7n015pv2dfNmxXjuBqK9G9NbW9ID9k8cmOr8l+jekdfve3lXm/L3p1ZOmI8fcDagoUJH0UcbuNrivSTSs36COvDDtCuc5FbqeepTiFwt/FOdBu1tumrlo7U31jJQT9PPjPM94c7cr6tekflfTMOpYt5WNXz6/zycdvbcMPXfN81B0PfXx9dwX33Xqe+CuW0mDeuvo2c508fiZctec592Psbwm1VFj6GPeY9Ttq3uoUcjNGjXI+MxTF/Z0HXydLFHHcXFONnZRj541KS8O26jPHdj0n7VP0HmbOAwpM3oBAhXRmxdMiTVqVHeei7BcjRb4Xl29ypS9QN4M1a1P1lO3LWqkbo306+/L5OtvfkzEAUT2QuXnaKqCRNWrV5VvFv6U8wdD6pEpGzaqL18qs/JGBpVfo3TOzadjOkXC1Tpv31tVvF53IzWCqI4KzP2w6Bf5QT/rJwEH/+FkwqfK9b6n17fH9q3kYRVULMTvbT73ufJaLeryyqtDPuflev/LZ11Zd/wCUV+N6N1i20CFHlGx5aHne1eZ8/c2V0+ajBxzN6CiQEXSRxm72+C+JtFQ102P8hjRu1PmtjBufd3XRUuWOg9r/+abH9xZsb4m8buallHHuuFs/HT+fH2f9W1SR6g7Qhx34M66GN9p7kvvSs8hN/guj2pBUh319u+s+sr6uZXeSY+EH61ur71SBSPjnmzsoh49a1Je3L625dN/thUkf1gBAhVhpUiHAAIIIIAAAggggAACRSUQ9dWIXlzTKwtfv3eU1K9Ty7kN3j6dh3hXmfP3tldPZjtyzN2AoJFLSR5l7G6D+5pUQ12/dtu0lMHdjpHW6nZj1dXV2HrSzynUJ4X1SO0kXRyQxO9qWkYd63a19dPryNf3WQ3hkbM7HSSnHrmXNFYXlXknfbvPCTMelfsefTnzXEjv8qjfJ9lxqy03kdkT+zkk76oHZo+e+oC8pG6XlZTJ1i7K0bPaLNvykuJMPRBIugCBiqS3EPVDAAEEEEAAAQQQQACB1AtwNaJ/E+ZtJFeBjzL2iubNUBWiRwa02KSxcwsofQvVVatWeYsuuPd8V+2aNG/7ovo+N1S36d1gvXryjxoB8PXCRbJcPYOxUKd8OB5x4E6ib9v96psLCpXN2a6ov8NRl1fQjcfGFb0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4BAhUxGdPyQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL0AgYqi3wUAQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgPgECFfHZUzICCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAkUvQKCi6HcBABBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiE+AQEV89pSMAAIIIIAAAggggAACCCCAAAIIIIAAAggggEDRCxCoKPpdAAAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOITIFARnz0lI4AAAggggAACCCCAAAIIIIAAAggggAACCCBQ9AIEKop+FwAAAQQQQAABBBBAAAEEEEAAAQQQQOD/27vv+L+mw3/gp1WjVo1Sqo3adMSq2ntvipBYRWKLkZCIkJCIIDZBGiO2WrWpEbFq71FqtKVGqVF7/865yX33/Xl7f+TzEXXfv+953sdD3u/7vuPc+7zn44/zuuccAgQIECBQnYCgojp7JRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgewFBBXZVwEABAgQIECAAAECBAgQIECAAAECBAgQIECgOgFBRXX2SiZAgAABAgQIECBAgAABAgQIECBAgAABAtkLCCqyrwIACBAgQIAAAQIECBAgQIAAAQIECBAgQIBAdQKCiurslUyAAAECBAgQIECAAAECBAgQIECAAAECBLIXEFRkXwUAECBAgAABAgQIECBAgAABAgQIECBAgACB6gQEFdXZK5kAAQIECBAgQIAAAQIECBAgQIAAAQIECGQvIKjIvgoAIECAAAECBAgQIECAAAECBAgQIECAAAEC1QkIKqqzVzIBAgQIECBAgAABAgQIECBAgAABAgQIEMheQFCRfRUAQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhMQVFRnr2QCBAgQIECAAAECBAgQIECAAAECBAgQIJC9gKAi+yoAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCcgqKjOXskECBAgQIAAAQIECBAgQIAAAQIECBAgQCB7AUFF9lUAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE5AUFGdvZIJECBAgAABAgQIECBAgAABAgQIECBAgED2AoKK7KsAAAIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ2AoKI6eyUTIECAAAECBAgQIECAAAECBAgQIECAAIHsBQQV2VcBAAQIECBAgAABAgQIECBAgAABAgQIECBAoDoBQUV19komQIAAAQIECBAgQIAAAQIECBAgQIAAAQLZCwgqsq8CAAgQIECAAAECBAgQIECAAAECBAgQIECAQHUCgorq7JVMgAABAgQIECBAgAABAgQIECBAgAABAgSyFxBUZF8FABAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoEBBXV2SuZAAECBAgQIECAAAECBAgQIECAAAECBAhkLyCoyL4KACBAgAABAgQIECBAgAABAgQIECBAgAABAtUJCCqqs1cyAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXkBQkX0VAECAAAECBAgQIECAAAECBAgQIECAAAECBKoTEFRUZ69kAgQIECBAgAABAgQIECBAgAABAgQIECCQvYCgIvsqAIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQnIKiozl7JBAgQIECAAAECBAgQIECAAAECBAgQIEAgewFBRfZVAAABAgQIECBAgAABAgQIECBAgAABAgQIEKhOQFBRnb2SCRAgQIAAAQIECBAgQIAAAQIECBAgQIBA9gKCiuyrAAACBAgQIECAAAECBAgQIECAAAECBAgQIFCdgKCiOnslEyBAgAABAgQIECBAgAABAgQIECBAgACB7AUEFdlXAQAECBAgQIAAAQIECBAgQIAAAQIECBAgQKA6AUFFdfZKJkCAAAECBAgQIECAAAECBAgQIECAAAEC2QsIKrKvAgAIECBAgAABAgQIECBAgAABAgQIECBAgEB1AoKK6uyVTIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshcQVGRfBQAQIECAAAECBAgQIECAAAECBAgQIECAAIHqBAQV1dkrmQABAgQIECBAgAABAgQIECBAgAABAgQIZC8gqMi+CgAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVCQgqqrNXMgECBAgQIECAAAECBAgQIECAAAECBAgQyF5AUJF9FQBAgAABAgQIECBAgAABAgQIECBAgAABAgSqExBUVGevZAIECBAgQIAAAQIECBAgQIAAAQIECBAgkL2AoCL7KgCAAAECBAgQIECAAAECBAgQIECAAAECBAhUJyCoqM5eyQQIECBAgAABAgQIECBAgAABAgQIECBAIHsBQUX2VQAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoTkBQUZ29kgkQIECAAAECBAgQIECAAAECBAgQIECAQPYCgorsqwAAAgQIECBAgAABAgQIECBAgAABAgQIECBQnYCgojp7JRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgewFBBXZVwEABAgQIECAAAECBAgQIECAAAECBAgQIECgOgFBRXX2SiZAgAABAgQIECBAgAABAgQIECBAgAABAtkLCCqyrwIACBAgQIAAAQIECBAgQIAAAQIECBAgQIBAdQKCiurslUyAAAECBAgQIECAAAECBAgQIECAAAECBLIXEFRkXwUAECBAgAABAgQIECBAgAABAgQIECBAgACB6gQEFdXZK5kAAQIECBAgQIAAAQIECBAgQIAAAQIECGQvIKjIvgoAIECAAAECBAgQIECAAAECBAgQIECAAAEC1QkIKqqzVzIBAgQIECBAgAABAgQIECBAgAABAgQIEMheQFCRfRUAQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhMQVFRnr2QCBAgQIECAAAECBAgQIECAAAECBAgQIJC9gKAi+yoAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCcgqKjOXskECBAgQIAAAQIECBAgQIAAAQIECBAgQCB7AUFF9lUAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE5AUFGdvZIJECBAgAABAgQIECBAgAABAgQIECBAgED2AoKK7KsAAAIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ2AoKI6eyUTIECAAAECBAgQIECAAAECBAgQIECAAIHsBQQV2VcBAAQIECBAgAABAgQIECBAgAABAgQIECBAoDoBQUV19komQIAAAQIECBAgQIAAAQIECBAgQIAAAQLZCwgqsq8CAAgQIECAAAECBAgQIECAAAECBAgQIECAQHUCgorq7JVMgAABAgQIECBAgAABAgQIECBAgAABAgSyFxBUZF8FABAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoEBBXV2SuZAAECBAgQIECAAAECBAgQIECAAAECBAhkLyCoyL4KACBAgAABAgQIECBAgAABAgQIECBAgAABAtUJCCqqs1cyAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXkBQkX0VAECAAAECBAgQIECAAAECBAgQIECAAAECBKoTEFRUZ69kAgQIECBAgAABAgQIECBAgAABAgQIECCQvYCgIvsqAIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQnIKiozl7JBAgQIECAAAECBAgQIECAAAECBAgQIEAgewFBRfZVAAABAgQIECBAgAABAgQIECBAgAABAgQIEKhOQFBRnb2SCRAgQIAAAQIECBAgQIAAAQIECBAgQIBA9gKCiuyrAAACBAgQIECAAAECBAgQIECAAAECBAgQIFCdgKCiOnslEyBAgAABAgQIECBAgAABAgQIECBAgACB7AUEFdlXAQAECBAgQIAAAQIECBAgQIAAAQIECBAgQKA6AUFFdfZKJkCAAAECBAgQIECAAAECBAgQIECAAAEC2QsIKrKvAgAIECBAgAABAgQIECBAgAABAgQIECBAgEB1AoKK6uyVTIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshcQVGRfBQAQIECAAAECBAgQIECAAAECBAgQIECAAIHqBAQV1dkrmQABAgQIECBAgAABAgQIECBAgAABAgQIZC8gqMi+CgAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVCQgqqrNXMgECBAgQIECAAAECBAgQIECAAAECBAgQyF5AUJF9FQBAgAABAgQIECBAgAABAgQIECBAgAABAgSqExBUVGevZAIECBAgQIAAAQIECBAgQIAAAQIECBAgkL2AoCL7KgCAAAECBAgQIECAAAECBAgQIECAAAECBAhUJyCoqM5eyQQIECBAgAABAgQIECBAgAABAgQIECBAIHsBQUX2VQAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoTkBQUZ29kgkQIECAAAECBAgQIECAAAECBAgQIECAQPYCgorsqwAAAgQIECBAgAABAgQIECBAgAABAgQIECBQnYCgojp7JRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgewFBBXZVwEABAgQIECAAAECBAgQIECAAAECBAgQIECgOgFBRXX2SiZAgAABAgQIECBAgAABAgQIECBAgAABAtkLCCqyrwIACBAgQIAAAQIECBAgQIAAAQIECBAgQIBAdQKCiurslUyAAAECBAgQIECAAAECBAgQIECAAAECBLIXEFRkXwUAECBAgAABAgQIECBAgAABAgQIECBAgACB6gQEFdXZK5kAAQIECBAgQIAAAQIECBAgQIAAAQIECGQvIKjIvgoAIECAAAECBAgQIECAAAECBAgQIECAAAEC1QkIKqqzVzIBAgQIECBAgAABAgQIECBAgAABAgQIEMheQFCRfRUAQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhMQVFRnr2QCBAgQIECAAAECBAgQIECAAAECBAgQIJC9gKAi+yoAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCcgqKjOXskECBAgQIAAAQIECBAgQIAAAQIECBAgQCB7AUFF9lUAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE5AUFGdvZIJECBAgAABAgQIECBAgAABAgQIECBAgED2AoKK7KsAAAIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ2AoKI6eyUTIECAAAECBAgQIECAAAECBAgQIECAAIHsBQQV2VcBAAQIECBAgAABAgQIECBAgAABAgQIECBAoDoBQUV19komQIAAAQIECBAgQIAAAQIECBAgQIAAAQLZCwgqsq8CAAgQIECAAAECBAgQIECAAAECBAgQIECAQHUCgorq7JVMgAABAgQIECBAgAABAgQIECBAgAABAgSyFxBUZF8FABAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoEBBXV2SuZAAECBAgQIECAAAECBAgQIECAAAECBAhkLyCoyL4KACBAgAABAgQIECBAgAABAgQIECBAgAABAtUJCCqqs1cyAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXkBQkX0VAECAAAECBAgQIECAAAECBAgQIECAAAECBKoTEFRUZ69kAgQIECBAgAABAgQIECBAgAABAgQIECCQvYCgIvs70YvsAAAwCUlEQVQqAIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQnIKiozl7JBAgQIECAAAECBAgQIECAAAECBAgQIEAgewFBRfZVAAABAgQIECBAgAABAgQIECBAgAABAgQIEKhOQFBRnb2SCRAgQIAAAQIECBAgQIAAAQIECBAgQIBA9gKCiuyrAAACBAgQIECAAAECBAgQIECAAAECBAgQIFCdgKCiOnslEyBAgAABAgQIECBAgAABAgQIECBAgACB7AUEFdlXAQAECBAgQIAAAQIECBAgQIAAAQIECBAgQKA6AUFFdfZKJkCAAAECBAgQIECAAAECBAgQIECAAAEC2QsIKrKvAgAIECBAgAABAgQIECBAgAABAgQIECBAgEB1AoKK6uyVTIAAAQIECBAgQIAAAQIECBAgQIAAAQIEshcQVGRfBQAQIECAAAECBAgQIECAAAECBAgQIECAAIHqBAQV1dkrmQABAgQIECBAgAABAgQIECBAgAABAgQIZC8gqMi+CgAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVCQgqqrNXMgECBAgQIECAAAECBAgQIECAAAECBAgQyF5AUJF9FQBAgAABAgQIECBAgAABAgQIECBAgAABAgSqExBUVGevZAIECBAgQIAAAQIECBAgQIAAAQIECBAgkL2AoCL7KgCAAAECBAgQIECAAAECBAgQIECAAAECBAhUJyCoqM5eyQQIECBAgAABAgQIECBAgAABAgQIECBAIHsBQUX2VQAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoTkBQUZ29kgkQIECAAAECBAgQIECAAAECBAgQIECAQPYCgorsqwAAAgQIECBAgAABAgQIECBAgAABAgQIECBQnYCgojp7JRMgQIAAAQIECBAgQIAAAQIECBAgQIAAgewFBBXZVwEABAgQIECAAAECBAgQIECAAAECBAgQIECgOgFBRXX2SiZAgAABAgQIECBAgAABAgQIECBAgAABAtkLCCqyrwIACBAgQIAAAQIECBAgQIAAAQIECBAgQIBAdQKCiurslUyAAAECBAgQIECAAAECBAgQIECAAAECBLIXEFRkXwUAECBAgAABAgQIECBAgAABAgQIECBAgACB6gQEFdXZK5kAAQIECBAgQIAAAQIECBAgQIAAAQIECGQvIKjIvgoAIECAAAECBAgQIECAAAECBAgQIECAAAEC1QkIKqqzVzIBAgQIECBAgAABAgQIECBAgAABAgQIEMheQFCRfRUAQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqhMQVFRnr2QCBAgQIECAAAECBAgQIECAAAECBAgQIJC9gKAi+yoAgAABAgQIECBAgAABAgQIECBAgAABAgQIVCcgqKjOXskECBAgQIAAAQIECBAgQIAAAQIECBAgQCB7AUFF9lUAAAECBAgQIECAAAECBAgQIECAAAECBAgQqE5AUFGdvZIJECBAgAABAgQIECBAgAABAgQIECBAgED2AoKK7KsAAAIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ2AoKI6eyUTIECAAAECBAgQIECAAAECBAgQIECAAIHsBQQV2VcBAAQIECBAgAABAgQIECBAgAABAgQIECBAoDoBQUV19komQIAAAQIECBAgQIAAAQIECBAgQIAAAQLZCwgqsq8CAAgQIECAAAECBAgQIECAAAECBAgQIECAQHUCgorq7JVMgAABAgQIECBAgAABAgQIECBAgAABAgSyFxBUZF8FABAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoEBBXV2SuZAAECBAgQIECAAAECBAgQIECAAAECBAhkLyCoyL4KACBAgAABAgQIECBAgAABAgQIECBAgAABAtUJCCqqs1cyAQIECBAgQIAAAQIECBAgQIAAAQIECBDIXkBQkX0VAECAAAECBAgQIECAAAECBAgQIECAAAECBKoTEFRUZ69kAgQIECBAgAABAgQIECBAgAABAgQIECCQvYCgIvsqAIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFQnIKiozl7JBAgQIECAAAECBAgQIECAAAECBAgQIEAgewFBRfZVAAABAgQIECBAgAABAgQIECBAgAABAgQIEKhOQFBRnb2SCRAgQIAAAQIECBAgQIAAAQIECBAgQIBA9gKCiuyrAAACBAgQIECAAAECBAgQIECAAAECBAgQIFCdgKCiOnslEyBAgAABAgQIECBAgAABAgQIECBAgACB7AUEFdlXAQAECBAgQIAAAQIECBAgQIAAAQIECBAgQKA6AUFFdfZKJkCAAAECBAgQIECAAAECBAgQIECAAAEC2QsIKrKvAgAIECBAgAABAgQIECBAgAABAgQIECBAgEB1AoKK6uyLkueZe46w8IJzhoXnnzP8bI4fhpdfeyv85fl/hkeffiHc//BfJ3p1O/ZYKyyyYJewxyGnhc8+/azd/RftOm9YeYlfNN3++RdfhMeeeSHc9eBT4d133i/2+dGPZgpbrrd8bf9/v/1OGHPxzbX1+i9L/3qhsPTC89d++vPDT4c/3/dkbT2d67B9eoQLrrkz/GncA7XfG7/8dp1lwjrLLxJGnHFl+Eu8/2bL5JN/L+zYfc0wZfxMy7MvvhIuv/7uZrvWfuuoUe2Ahi+rxmtadtEFwtw/mTW88vpb4ZGn/xHG3fN4+OdLrzfsOX518YXnC0svMn9YeIEu4Z33Pgz3Pf5cuOXux8NLLzfff5ZZZgi/XWPp8Kv5fhKmnOJ74clnXwoPPvW3MPaOR0KIz6ZxSdfTdf4uYfQfbgzvTHhejfu0t77H79YLc80xS+hz2Jnhi8+/fO7yuB17rBkWXehnof9R54a333q3/PlLn99Lz2OLNcKnn30eRp13XZvtk3qO2WabOfRYd7nw3D//Ff543Z/bnLt+ZfklfxGW+OW84fo7Hw6PP/m3+k1tvv80Pr9DencLp10yNtwen0d7yzTTfD/07LZaeP/Dj8Lvz/9Te7u1+/tKy3YNiy0415e2fxG+KOrPXfHv+vm/vfyl7e0dV7/jR598Gkaec03x7MrrnOy7363fJXz86afh+Wh2a7zHidWP70723bDwL+Yu6tOv5usSZp5x2vDiy2+EZ154JVwT/1Zfi/8/am9p73rbu8+pp5kq9Oq2emi83sbzp+NHXXhDeO/dDxo3WSdAgAABAgQIECBAgAABAgQIEPg/KiCoqOjBTjvd1GHw7t3Cxqsu0e4VjL33iXDAseeHV199o+k+qYH7trMPCZN/b7LQZ8Q5X9mYO7TPlqH72ss0PU/54+effx7GXHFbGHriH8ISsWH+giN7l5uKzw33ODI81tAQnBqqbzt3SJh1pulr+46Kgcbhp1xSWx/Ue4uwzQbLhxde+XdYeZtB7TaQD99vm7DZGkuGd9//MGwzYGR4+LFna+cov3Tp8qMw9vSDytXwaAxYNtp5eG298UtnjBqPnTHe06mDe4XFfz5346YYQHwQeg0eFe598Ok22/r03DDsGhvuG5d0Tz0Hnfql/butv1wYtPMmYaopp2g8JFx924Ohz7AzwyexcbpctvrtSuHgXTcrVjfqPSI8+sTz5aaJfs4eG/7HjRkcJouN0+nab7794XaPOeeoPYvw6e5H/xq26nt8+DwGEc2Wn8TQI50zLfOsvnubYGVSz7FJDK6O2GfL8MRz/wzr7zisKKPZP6MP27UI4Y4+65pw0llXN9ul+G1Y363C5mstHYPAl8K6vQ5td7/FYqh30dF7h/T3MN8ae7S7X3sbyvtub/sXMXw69+o7wqD4t12/TOy4ct+uG/YN78X6V15n+XvjZ6qjA46/MFxz072Nm4r1uX42ezi637ah63w/bbr9kxh8XnT9XWHIyIvCxx998qV9Jna9jfe5yK/mCZccs8+XztPsh/V3OyI88dTfm23yGwECBAgQIECAAAECBAgQIECAwP9BAUFFBQ91wfl/Gs4Yumubxv32LuP9Dz4Ky219UNO32vvFBu4dN12lOPSZF14Na24/pE1Dcf05OxJUlPuffOGN4ejTLg9jzz44/CT2hiiXa2LD+R4Hjy5Xi8+N1lwqHLXv1m1+WyM2Kj8bG5fTMsMM04U7zxsSewpMXqzvPuz0cO3N9xffG/8pg4r0+wcffhy2O/CU2LD/VJvd5uwyW7j59ANrv00sqOiMUe2kE75cOrJf7OnSpVh7ODaajou9RKb+/pShRwx80mdqyF1thyHhxX++VuzTq/saof8OGxbfn4pvzN9016Nhuvh2/gYrLx5+MO3U4eMYOKyzy/Da2/SpJ8CZh+5a7P+f2Kj8pzsfiW/CvxZW+vWCRQ+BtOHC2JNgQAyh0rL95quFA3ptXHxP/3Q2qDhgt83C9huvVBx/3xPPhc17H1V8b/ZPfSP0GX8cV4RXzfbrSFCRjvs65/gmg4oUOt0ZA7UpJvTE6dHvhHD3/X9pdku1AOCbCCrS8yuXWWaaLvwm9vyYduqpip/S39mI319Wbg6l+R0PPRXufKh5b6p33/8gnHPZuOLvvAwq0nXeEOtaWqaYfPKwdAxa0n1+N/a0+PSzz8LW+58U7nmg7d9R+rs9bK/uNY/7Y3148C9/D2+8/V5YcK7ZY0g1X5hlxvHhYzr3rgeNKoKb2sXGL+X1pt86cp/TxXC2xwYrhu98Z/xZ0t9E+f+vMVfcGl57851iw6exR8iYS8c2DUfGH+lfAgQIECBAgAABAgQIECBAgACB/2sCgopv+YlOHhvsrx41IMwTh6HpyJIartfdaVj4KDbc1y+pR8afzxtaNJiXv+8Q39i/JQ0X1GTpTFCRGuC7btgnbBLfPh+6x+a1s6UG0RW3PbjNEEbXjB4YFohvZpfL2HsfDz33H1muhr223yDsEYcRKpfHn30xbLDTYeVqm8/6oCJt+OjjT8JOB/8+3FY3TE9ngorOGrW5mLhywXH7FEP4HHzKxeHc1Dg8YZk7NuRePbJ/0cg78IQLw/mX3xomi71a7jh/aNG4e+/jz4Zt+51Ye2YpmLrgyD2L0OK8+Cb9gcecV5xpqRhInHPY7uHxZ/8Zdhh4cng9DitVLkfu/7vw29jb5o233w1LbNIvbLz20mFEn63KzcVnZ4KKZhbNesiUBdQ3Qqff+hx5dvhjfLu+celoUPF1zvFNBhWN9fCW+54IO/Q/qfF2ivX6AGBSelScFIeNSoFf/TLTzNOH0YfsHIcFm7No+F94433D+3F4sLSU5keccVU49dxr6w9r+r28zvR38vN19mqzz/zz/iScNmTn8ONZZgwpNFun59Da9p/+9Efh+lP3L8LDf73xn9Dv6HPDrXc9VtuevkwVg7iBu2wSuq+zbPH7Bdf+ORxw1PjArNyxvN7O3md5fBoSLoVHaVl80/7hrbfGBxXldp8ECBAgQIAAAQIECBAgQIAAAQL5CAgqvuVnXf+Gf1n0q/9+O4wYc1V4Ig5jNMP004QdN1strLj4QuHFOOTTpnsd1XSc+J22XDvst9165SmKz/RWdLd23pJvDCr2P+78Yt6EdOBicYz64fHt6vT2f7l03+/48FCcW+GO8w8NM8VrKpf6N+OXWeLn4ezDdis3FZ+b7nNMePCRZ4rvqbHzzth4n96crl+26n9imzksym3D99s6Dv20VLlafKbQZLdDTw833fZQsd6ZoKKzRm0KjitpPozUwP9mbMxtXEbH+1453v/lY+8L+xx6Rlgr9po46YDti93WjMHSMzF8qF96/279sOdWa4UPP/o4LNFt/1rjdGq4/s9/3g+f1g3vlI77RZwf4ooT9i1OsdJ2h8Q5MuYPh8YhtC658Z6wyWq/KX7vTFBR9vZIvTpSw3Z61mloqd4NPWSKE8d/ykbocj0dk57tE/Gt+/qlM0FFZ8/xTQUVU041RayHh4YZ4rP8d2wMnzn28knL6r2Ghueef7n+dorvZQAwqT0qmjXgpwLSXBm3nDmoKGu7GFCVIUFp/k0EFenkKyz9y3DGkF3iNCdfhEVj2PXOf94L3/nud8KFx+5TDGeWLNaMQ2C9OaEnQ3FBDf9sF+fqGLjj+F48fWNQcVkMLMqlvN7O3md5vKCilPBJgAABAgQIECBAgAABAgQIECAgqPgW60BqJHzsyqPbzEfwzD9eiWHE0UUjYv2lpIbql+KcDs0aEVOvjDvicEplg2v9cfVBQf3vjUHFToeMDjfe+mBtl7NG9A7LLrJAbb3/seeFi666I+y69Tqhz7br1n5PQzIt1f2AYtLtMUfuEZZbdMHatsag5HebrRIO3GmT2vbyy51xsu2t+xxXrtY+mwUVaeNncX6EvY88K1x9472ho0HF1zGqXUgHvoyLb4KnYbHKoZnKuSka314vT5XeYr/ljPFza3Rk/P0N11wyHL3vNsXhS/cYGP71rzfDjDNOV9SHZ244MQ6f850OD/2UenvcHnvfpHlEzr7ytvD0318OQ+L8KKkhfoVtBoeXYz1rXMpG6MNPvzJstd6yYY5ZZ4oTQb8d1tvlsDZ1siNBxdc9xzcVVPTYaMXiftMwSKvE0OfiY/sUFuWza7z3/3VQkcp7+voTirlCBhx3QbgwPpO0lObfVFAx66wzFr2u0rk3joHnI489F1LvntQbKC0Tm6ek2CnWswuO2asYiuyRv74QNo5Dl5VLeb3tBRVpv2b3WR4vqCglfBIgQIAAAQIECBAgQIAAAQIECAgqvsU6kIYMuuH3A9uU2K3PseH+h5uPR99mx7qVLeLE1Ont+mbLzfc8FnoNOPlLmxqDikFxgtxx9zwR0oDxSy08bxi8y6ZtApTN+x4X7nvo6TBd7E2Rhpj6fnwrvVwOP/2KMDYOx3RdHD6mfqkfeio1jo8755Aw+w9nqN+l9r1ZY/1hca6LbnHs/GZLalTvFycfvj82tnZkjoqvY9Ss3Ga/1c8tUTY0j4hDNaWJ0S++4e7Q7/Czmh0WHr/6mMK4Z5zIeuxXTGSd5ha4MA07tdBc4fX4tvuSm41vWC5P2tmgogw90pv1K8d5TF577c1w5wWHFj1dTrvsljDspIvKU9c+y0bofnGYqkfi/ByXHdenuPY0rNWWMWT6LPZ0SUtHgoqve45vIqhI4eDNYwaHLrP/MFwWJ5Xue9iZYccea4V+269fzBmyTAyBGsPA/3VQUd8TabPYS+WBCT2QSvNvKqhYdflFwqhBvYrn9OvYiyf1DCpNJzaheHHQhH9+s9gC4fwjehdrXTeKE3m/+0Hxvbze9oKK9u6zPLegopTwSYAAAQIECBAgQIAAAQIECBAgIKj4FuvABmssGY7Zb/xb8qnYNIHyohv27dQV1De8tnfg6nE4l+eef6nN5sagos3GhpU0L8LyWx0UPowTeaelfhLmtP7am/8Jdz/6TFhvhcXSarGkniHFZN4T1ptNsj1hU/HRbGLurwoqymPPuer2+Ib/cuVqaDaZ9tc1qp30K778MAYv18Y5RtJwWClEWCFOdJ7mDzkvvnW+5K/mC6f84cZw5Kj/TpBcf6qyF8aBJ/4hnBcnqG5v2SdOyL1bnJg7LUNPvTSccdFNbXbtbFBRziNy492Php0OOKU4V59eG4VdN189pMnal9rigPBerIv1S9kInUKGi+O8GuvEEOaEGMakJT2DQTE0SktHg4qvc46yUf2JODH7+nGC9vaW0YftGofh+kU4+qxrwklnXd1mt9VWWDScelDP4rd1dx0e/vL0C0X4dlcckmyqKacIx597XTjujCvbHPO/DCrSvCTH9tu2mMvk5TgnySrbDq5NGl2ap4tJw8E1Lu/Herba7w4uJtJO28rrbDZHxc/mnC2cc/geRVD4j5dfDytvPX6oqYPjEG/p76fZnBON5ZXr08Rhwh65fESxulacX+avcZ6ZtJTX2yyo+Kr7LA6O/wgqSgmfBAgQIECAAAECBAgQIECAAAECgopvsQ6su9oS4fj+v6uVmBojl9l8QG29I1/q50JI+78U346/K76RnSZeLpc0j8F+w8eUq8VnR4OKNMzSzkNGh5vr3vhPDYq3njU4fG+yydqcs36lcbLl608/MMzbZbbaLqkBf8dNVwmpt0BaUg+JleMwPC/GycLLpTGoGHf/k2GFxRYshjkq92n8bBZUfF2jxnM3rqc5K1Lj7K9/PndIc2dsse9x4aFHny12u/Sk/YoJkps1fJfnuT4O/TRvHAJq2Og/htMuuKH8uc3nist0DacdvGNxz0XvmBQsxJ4Q9Utngoqlf71QOGf47sXhZS+ZtDLLLDOE22OPl/RMD/39ZeH0C2+sL6LWCF0GFWljv51/G5/hqsV++8UJmC+55s5OBRWdPcc3EVRcdELfomfK3Y/+NfTY+9ji2tM/g/fcImy9/vLhzThvwzJxKLOPP/qktq0MACZ1joq33nm/mAy9PPHMM0xbm68lhQs7HHRqm7layob/cv/Gz3TML9bbO3zx+fj6UF5n+ps975o7it2/G3tIdfnxD8NSXecLk8deTWn53QEjaxPSD4o9sbaJPbIu+tPdof8RzXv+FAfV/xPP+ZdrjyvOVz8vSnm9nb3P8tSCilLCJwECBAgQIECAAAECBAgQIECAgKDiW6wDjQ3oqeg038Nrr73V4au4/JT9wy/n/Ult/0NOuSTcet+TcUipA2oN+mks/uXjG9RpXoNy6UhQkXoIbDPgpPBUHIu+cTkyvk1fH4bUb09vhq8Ye2CUwwGlxvbTD9mptkuatHfZHgeGEf23adML47z4pv6B8Y39chnWd6uw+VpLl6shzaMx/bTfD0fs3aN2b7WNE740Cyq+rlHjuevXU8By4uBeYc14b2mpb8BP66cM3TmsvtSvijkgBsd5B5ot915yeJjpB9OGvQ4fE6684Z4v7bJo13nDuTFUmDLOQfLMC6+GjXc/ojbpdv3OnQkqzjxij7B8DHsej2/BbxDfhq9fyuGq0twTy285MHweG7zLpWyErr/P1FPlzMN3L+YlSZNyp2HL3oy9b8bFoZXSMs/qMRCpC1Um9RxlUPFk7B20Xuwl1N7SXo+Krr+cO1wW56NIy/YxFBh35yO1U6Q5Q8bGMC3N9VEO31VuLAOASQ0qyvM1fv4rDsG0ed9jwz/+8WqbTaXXqItvCmOa9Lj54MOPwttvvVs7przO2g8NX1JvmcEnX1wESuWmAbttFnbYeKUw9t7HQ8/9R5Y/f+Vnly7J6qBiUu6FN9q31vumvN72Dm7vPsv9BRWlhE8CBAgQIECAAAECBAgQIECAAAFBxbdYB+adZ45w/alte1Bcd8dDYbdBv+/QVaThVM4dvkebfe974rnwznsfxqFvft7m98a5BxqDijQO/ryxAbI+fHg+9m5YY/tD2jRYlydtdu3lthSWjLn45nI1/OH4PmHx2Ougfhl77xNhkQXmDDPGIZPKJb0hvmwMONLY+WlpFlSkCb9Tg/Xh7YQVjUHFpBiV19Xss36i72ZD3RzUe/Ow7QYrhPrhlerPkyb3fjLOUZEaxlNPjHsffLp+c5g/hk9/OGqvMF0cZif1tNlkz6OaTnKdDupoUNH4zNJwW/XLwgv8LE6SPWPxU+/hZxaTlZfby0bo+qAibUtzllx1cv9iIvHUEL3b0NPCRUfvXRzWkaCiM+fYKIZWR8Xw6o3Y62GJ3+5XlNHsnyvjUFw/n3uO0Di3w8hDdozB0sLFIenv7PMJPRHKc6yz/KLF11Tvmw2pNKlBxehLx4Zzrri1LC6cNnSXMM9PZg1/e+m1sHrsTVQfDKWdSvPG+6idoOFLfVBxRl2w8cmnn4YnYjB15wNPhX83DCHVbf3lwmF7di+GnVshhpnvRNuJLdtvvlo4oNfGoXDadnBt9/J6O3uf5QkEFaWETwIECBAgQIAAAQIECBAgQIAAAUHFt1wHro49Hxac68dtSr0+vuk9OM5bkHpApDf3N1prqbBH9zWLIZ32H3FO7S31s0b0DssuskCbY9tb+SCOZ7907K3xThx+Ji2NQUXqrfD40/8It4wZ1GZIp8a3y+vPP/qw3b4UiKRhX5aNExKX81ks8qt5wiXH7FN/2Fd+H3nBn8JRoy8v9mkvqEgbN1tv2aKBNTX01y+NQcWkGNWft/57/zjReK9NVi5+OuuK28LBx3+5x8QOW6weBvTcKHz40cdh8U371zzK86y50mJh5MAditUVthkU/vnS6+WmYvikS+Nk1TPPMF3RKL/Z3keHv/39ldr2xi8dDSoO77dN2HT1JRsPb7re2OOibIRuDCrSwSkAufz4vsUcD+n5zzDd1MU5OxpUdPQc9cNWLRcDrZdf+XdRTv0/U31/yvBA7KmSeqGkIctuGDc+jElzZ4yNQ22VQ43VH9Pse32PizIAmNSgojFwqJ/nY+AJF4bzL/9viJGuqTRvPK7Z9abfyutsNkdFe8ekoOmeC4eFKeIwZleOeyDsNeS09nYtfv9pDFauO3X/4lmfH4eXGnj0f3tAtXe9E7vPskBBRSnhkwABAgQIECBAgAABAgQIECBAQFDxLdeBZWLPhzPiMEHN5ntI4+VPExteUyNiuZx95W0hDSW00PxdwlUj+5U/d+jzqDFXh5FnX1Ps2yyoSL0VGn9PE2WvGN+0ThNENy6LLzxf8dZ//e/HnXNdOP7M/05GPOrQXcKqS/6yfpev/P5OnMR5mRh0vB97hRzaZ6uwxdpth35K11gu6W3wYXGM/fqwoj6omFSjspz6z522XDvst916xU+X3XRv6Jvm/qgb3qjc98ez/zAOgTSoaBgfPPLicHZ8m75c0pBJ54zYs5g34JE4rNbGuwwvN4U0OfelsQfKHLPOFN59/8OwRd/jwpMxQPqqpSNBRTpvmoMizVMwKvZ2uT72KGi2LBx7uRy08ybFpvqeHmUjdLOgIu289iqLhxMHbN/mlJ0JKjpyjh/EOR3uvmBYcQ/tNarXz5uxYnzbv5zzpJyLIQUpvQadGj5v8szSNQyLvQsW+Nns4Z7Hngnd9zqmuJ8yAPimg4pUD647Lc7dEoedSsOsrRgnYv+w7u+sNP9fBhXpBnffdt2w99brFPea/n5PjBOQp3ttXNKQTycf2LMIVt+IQ3yttsOQNkNPtXe9E7vPshxBRSnhkwABAgQIECBAgAABAgQIECBAQFBRQR3YcM0lw9H7btPhkpfZ8sBwQJzIeN0JQ9WkA9Mk2q/EuSHql6mnmrJNb400ZM6yEyYKbgwkUo+KFALMPtvMX+pVceSZV4VTzrm2/tS17+Wk0emH1HsgXVs5bv7cc80e58oYWNs3fXngyefbrKeVeWJD7Q+mHf8WflovJ3OeWFCR9t08ToA8LE6EXC71QcXxg3pOklF5zvJzizjp8KExGEnLrQ88GXY5aFT4tG4eh/T7p3GuhnIp56lIkxsPiL0urrr5vjB97G3QL/a02GiVXxe71c9Pkd5uvyj2PplvztmKhuJtBowM9z781/J0xWdqQG4cIqgjQUWfXhuFXTdfPaSeNWkelHcn9Kxpc/K4khqVb4pzTMwZg5Zi8u4BJxe7lI3Q7QUVaad9d9w47NxttWL/9E9ng4p0zMTOMXSfHqH7OsumXcO1tz8Ujjj9ivDCi/8Ks/9o5rBr9zVq2y4fe1/Y59Aziv2S613nDy16AZx4/vXhmNOuKH5v9k/93+L6ux0Rnnjq77WeCsl+oXXHD2vVeGz9c2/cVto1CxzWifXghAHbFYfUB4nph/K4o8+6Jpwar7vZUl8fykClMz0qynMOiUOp9Vh3vGsKz44566rw6F/+Ht6O/8+YOwY3qy7VNey55ZpFT5V0TPn/i/L49Fleb2fvszyHoKKU8EmAAAECBAgQIECAAAECBAgQICCoqKgOlOO+f1XxqQHymNgj4po4RMstZ45/W7/cf4M9jgyPP/m3crX4nCy+PX/buUPCj2b+Qe33coiZ9oKKtGN9Y3BaT2/2LxsDiGaN26suv0gYNahX2i2MiePvH3L8hcX39M/w/bYJm63x36GG0rwUPfc/qba9/NJ9wxXC0D02L1eLORmWj+UdHAOI7msvU/u9WeNo2lgfIJRBxRw//uEkG9UKjl9miwHObWcNnujQQam3wuFxjo60pGFyzj2id23eh+LHun/SPAm9h5xem3S8cairul1rX1Posdim/do8i4kFFVNNNUX4cxzeZ/o430XjM6qduO5Lj41WDEN271ZMlrxKfGs+TfJcNkJ/VVCRQo7Th+8WVlhsoeJsXyeomNg50tBNw/puGevVUnVX3PZrmjvht71HhP/Et/7TUvaC+eTTz4p63DhPQ/3R6W/m9vOGhllnmj78MQZLfYadUQsq6vdr/N4YMtRvL+2aNeDH7kDh+tMGxvlhZgvvxcmu07Bp5fBs5XH152r8/ulnn4WuG/YtejxNSlCR3E84qGdYe7lFGotos556fvQ/7vww9vaH2/yeVsrr7ex9licSVJQSPgkQIECAAAECBAgQIECAAAECBAQVFdaBNNb/7j3WCqv85hfFkE/lpaThl+6Pk2QP//3lxdvjSyy6QDj9kJ3C1HFYqLRc9Ke7Q/8jzip3b/OZGvFTo3M5Nv+oi2+KDemXhvp5FtIB3focG+6f8PZ+apS/+fQDa29Pp7e2V+05tGiwbnPytBIbWm+MoUmXeEwaaqd+3oD6OSzSUDHbxh4C6Q31xmWKKScP148eGLrEt/jTkhpffx3nddhtq3Vqc0Gk3+uvMa3XL2XY8VA8/ybxTfhvwqj+/Cn4GBd7GtQPM1W/vfx+bOx5ckLsgVIuacil4X22DEv9at7w/RgYpOWV19+OjeD3hhGj/xi+qJvQuTHYKc9R/5lCo2W6DwzvxSGyyuXBy0eEqeO5V+t5aHjhhVfLn2ufqQH4+jjBdBr2ac0dh9WGQ6rt0PAlBRsp4JrpB9OGrfqfGP5835Oh7B3Sc/Copo3U5Smmiz1GrohDkqWhq+Zfs+1E79/EOcpyUviwceyNkCaAL59J+ju55IZ7wrHR/5MY6pXLAbttFrbfeKVw/rV3hoFHnVv+3O5nGRqmv7luvY8KC87/03D1yP7t7p82DD310nDGRTc13ae87/7HnhcuuuqOL+2z0rJdw6iDesW/0e/EZzi0Nh9JedyXDqj7IQ2V9ptu+4ePP/qkdp2pd9XysY58nWXFZbqGXeJk2YvEidVTfUlLCseeifXqrkf+Go6Jtu1NuF1eb2fvs7zOGeKcLGm+jA/js1tqiwHF8G/lNp8ECBAgQIAAAQIECBAgQIAAAQJ5CQgqWuR5zzTz9GHW2BPilX+9Fd56650WuarmlzFjfPs8zaVRzgfQfK///a9pXoiP49BLrzcMgfW/L3niJaSgaLYYGHwWQ59XX31j4gf8f7xHGmpphvhfGpLp6y4dPcfU00wVusSJsv9/+Dv5uhZVHJfq6+yzzRTD0KnC3198tQhCqrgOZRIgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgKCipZ4DC6CAAECBAgQIECAAAECBAgQIECAAAECBAjkKSCoyPO5u2sCBAgQIECAAAECBAgQIECAAAECBAgQINASAoKKlngMLoIAAQIECBAgQIAAAQIECBAgQIAAAQIECOQpIKjI87m7awIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgoqWeAwuggABAgQIECBAgAABAgQIECBAgAABAgQI5CkgqMjzubtrAgQIECBAgAABAgQIECBAgAABAgQIECDQEgL/D9PC8WDUMCvbAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<IPython.core.display.Image object>"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HCuST1ZZKuXv"
+      },
+      "source": [
+        "# Working with Charts, Graphs, and Slide Decks\n",
+        "Claude is highly capable of working with charts, graphs, and broader slide decks. Depending on your use case, there are a number of tips and tricks that you may want to take advantage of. This recipe will show you common patterns for using Claude with these materials."
       ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# To start, we'll need an image. We will be using the .png image located at cvna_2021_annual_report_image.png.\n",
-    "# Start by reading in the image and encoding it as base64.\n",
-    "with open(\"../images/reading_charts_graphs/cvna_2021_annual_report_image.png\", \"rb\") as image_file:\n",
-    "    binary_data = image_file.read()\n",
-    "    base_64_encoded_data = base64.b64encode(binary_data)\n",
-    "    base64_string = base_64_encoded_data.decode('utf-8')\n",
-    "\n",
-    "# Let's also see the image for ourself\n",
-    "Image(filename='../images/reading_charts_graphs/cvna_2021_annual_report_image.png') "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how we can pass this image to the model alongside a simple question."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The image shows various financial metrics and growth trends from Carvana's 2021 annual report, including retail units sold, total revenue, total markets, and car vending machines over the years 2014 to 2021.\n"
-     ]
-    }
-   ],
-   "source": [
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": 'user',\n",
-    "        \"content\": [\n",
-    "            {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image/png\", \"data\": base64_string}},\n",
-    "            {\"type\": \"text\", \"text\": \"What's in this image? Answer in a single sentence.\"}\n",
-    "        ]\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "print(get_completion(messages))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "That's pretty good! Now let's ask it some more useful questions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yRzNf75nKuXw"
+      },
+      "source": [
+        "## Charts and Graphs\n",
+        "For the most part, using claude with charts and graphs is simple. Let's walk through how to ingest them and pass them to Claude, as well as some common tips to improve your results."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "----------Question 1----------\n",
-      "According to the \"Total Revenue ($M)\" chart in Carvana's 2021 Annual Report, Carvana's total revenue in 2020 was $5,587 million.\n",
-      "\n",
-      "----------Question 2----------\n",
-      "According to the \"Total Markets at Year End\" chart, Carvana had 3 markets in 2014 and expanded to 311 markets by 2021. Therefore, Carvana has added 308 additional markets since 2014.\n",
-      "\n",
-      "----------Question 3----------\n",
-      "To calculate the revenue per retail unit sold in 2016, I'll divide the total revenue by the retail units sold for that year.\n",
-      "\n",
-      "Total revenue in 2016: $1,955 million\n",
-      "Retail units sold in 2016: 18,761\n",
-      "\n",
-      "Revenue per retail unit = $1,955 million / 18,761 units\n",
-      "            = $1,955,000,000 / 18,761\n",
-      "            = $104,217 per unit\n",
-      "\n",
-      "So in 2016, the revenue per retail unit sold was $104,217.\n"
-     ]
-    }
-   ],
-   "source": [
-    "questions = [\n",
-    "    \"What was CVNA revenue in 2020?\",\n",
-    "    \"How many additional markets has Carvana added since 2014?\",\n",
-    "    \"What was 2016 revenue per retail unit sold?\"\n",
-    "]\n",
-    "\n",
-    "for index, question in enumerate(questions):\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \n",
-    "            \"role\": 'user',\n",
-    "            \"content\": [\n",
-    "                {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image/png\", \"data\": base64_string}},\n",
-    "                {\"type\": \"text\", \"text\": question}\n",
-    "            ]\n",
-    "        }\n",
-    "    ]\n",
-    "\n",
-    "    print(f\"\\n----------Question {index+1}----------\")\n",
-    "    print(get_completion(messages))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As you can see, Claude is capable of answering fairly detailed questions about charts and graphs. However, there are some tips and tricks that will help you get the most out of it.\n",
-    "- Sometimes Claude's arithmetic capabilities get in the way. You'll notice that if you sample the third question above it will occasionally output an incorrect final answer because it messes up the arithmetic. Consider providing Claude with a calculator tool to ensure it doesn't make these types of mistakes.\n",
-    "- With super complicated charts and graphs, we can ask Claude to \"First describe every data point you see in the image\" as a way to elicit similar improvements to what we seen in traditional Chain of Thought.\n",
-    "- Claude occasionally struggles with charts that depend on lots of colors to convey information, such as grouped bar charts with many groups. Asking Claude to first identify the colors in your graph using HEX codes can boost its accuracy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Slide Decks\n",
-    "Now that we know Claude is a charts and graphs wizard, it is only logical that we extend it to the true home of charts and graphs - slide decks!\n",
-    "\n",
-    "Slides represent a critical source of information for many domains, including financial services. While you *can* use packages like PyPDF to extract text from slide decks, their chart/graph heavy nature often makes this a poor choice as models will struggle to access the information they actually need. Vision can be a great replacement as a result. In this section we will go over how to use vision Claude to review slide decks, and how to deal with some common pitfalls of this approach."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The best way to get a typical slide deck into claude is to download it as a PDF and then convert each pdf page to an image. Here's how you can accomplish this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%pip install PyMuPDF"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from PIL import Image\n",
-    "import io\n",
-    "import fitz\n",
-    "\n",
-    "# Define the function to convert a pdf slide deck to a list of images. Note that we need to ensure we resize images to keep them within Claude's size limits.\n",
-    "def pdf_to_base64_pngs(pdf_path, quality=75, max_size=(1024, 1024)):\n",
-    "    # Open the PDF file\n",
-    "    doc = fitz.open(pdf_path)\n",
-    "\n",
-    "    # Iterate through each page of the PDF\n",
-    "    for page_num in range(doc.page_count):\n",
-    "        # Load the page\n",
-    "        page = doc.load_page(page_num)\n",
-    "\n",
-    "        # Render the page as a PNG image\n",
-    "        pix = page.get_pixmap(matrix=fitz.Matrix(300/72, 300/72))\n",
-    "\n",
-    "        # Save the PNG image\n",
-    "        output_path = f\"../images/reading_charts_graphs/slides/page_{page_num+1}.png\"\n",
-    "        pix.save(output_path)\n",
-    "\n",
-    "    # Convert the PNG images to base64 encoded strings\n",
-    "    images = [Image.open(f\"../images/reading_charts_graphs/slides/page_{page_num+1}.png\") for page_num in range(doc.page_count)]\n",
-    "    # Close the PDF document\n",
-    "    doc.close()\n",
-    "\n",
-    "    base64_encoded_pngs = []\n",
-    "\n",
-    "    for image in images:\n",
-    "        # Resize the image if it exceeds the maximum size\n",
-    "        if image.size[0] > max_size[0] or image.size[1] > max_size[1]:\n",
-    "            image.thumbnail(max_size, Image.Resampling.LANCZOS)\n",
-    "        image_data = io.BytesIO()\n",
-    "        image.save(image_data, format='PNG', optimize=True, quality=quality)\n",
-    "        image_data.seek(0)\n",
-    "        base64_encoded = base64.b64encode(image_data.getvalue()).decode('utf-8')\n",
-    "        base64_encoded_pngs.append(base64_encoded)\n",
-    "\n",
-    "    return base64_encoded_pngs\n",
-    "\n",
-    "# Call the function on our q4 2023 Twilio Earnings presentation\n",
-    "pdf_path = '../images/reading_charts_graphs/twilio_q4_2023.pdf' # This is the path to our slide deck.\n",
-    "encoded_pngs = pdf_to_base64_pngs(pdf_path)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oRUFAZcKKuXw"
+      },
+      "source": [
+        "### Ingestion and calling the Claude API\n",
+        "The best way to pass Claude charts and graphs is to take advantage of its vision capabilities and the PDF support feature. That is, give Claude a PDF document of the chart or graph, along with a text question about it.\n",
+        "\n",
+        "At the moment, only `claude-3-5-sonnet-20241022` supports the PDF feature. Since the feature is still in beta, you will need to provide it with the `pdfs-2024-09-25` beta header."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "According to the \"Total Company Results Highlights\" table, Twilio's Y/Y Revenue Growth for fiscal year 2023 was 9%.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now let's pass the first 20 of these images (in order) to Claude at once and ask it a question about the deck. Why 20? Currently, the Anthropic API only allows you to pass in a maximum of 20 images. While this number will likely increase over time, we have some helpful tips for how to manage it later in this recipe.\n",
-    "content = [{\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image/png\", \"data\": encoded_png}} for encoded_png in encoded_pngs[:20]]\n",
-    "question = \"What was Twilio y/y revenue growth for fiscal year 2023?\"\n",
-    "content.append({\"type\": \"text\", \"text\": question})\n",
-    "messages = [\n",
-    "    {\n",
-    "        \"role\": 'user',\n",
-    "        \"content\": content\n",
-    "    }\n",
-    "]\n",
-    "\n",
-    "print(get_completion(messages))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This approach is a great way to get started, and for some use cases offers the best performance. However, there are some limitations.\n",
-    "- You can only include up to 20 images (we intend to increase this limit over time)\n",
-    "- If you are using slide content as part of RAG, introducing images into your embeddings can cause problems\n",
-    "\n",
-    "Luckily, we can take advantage of Claude's vision capabilities to get a much higher quality representation of the slide deck **in text form** than normal pdf transciption allows.\n",
-    "\n",
-    "We find the best way to do this is to ask Claude to sequentially narrate the deck from start to finish, passing it the current slide and its prior narration. Let's see how."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Define two functions that allow us to craft prompts for narrating our slide deck. We would adjut these prompts based on the nature of the deck, but keep the structure largely the same.\n",
-    "def build_previous_slides_prompt(previous_slide_narratives):\n",
-    "    prompt = '\\n'.join([f\"<slide_narration id={index+1}>\\n{narrative}\\n</slide_narration>\" for index, narrative in enumerate(previous_slide_narratives)])\n",
-    "    return prompt\n",
-    "\n",
-    "def build_slides_narration_prompt(previous_slide_narratives):\n",
-    "    if len(previous_slide_narratives) == 0:\n",
-    "        prompt = \"\"\"You are the Twilio CFO, narrating your Q4 2023 earnings presentation.\n",
-    "\n",
-    "You are currently on slide 1, shown in the image.\n",
-    "Please narrate this page from Twilio's Q4 2023 Earnings Presentation as if you were the presenter. Do not talk about any things, especially acronyms, if you are not exactly sure you know what they mean. Do not discuss anything not explicitly seen on this slide as there are more slides to narrate later that will likely cover that material.\n",
-    "Do not leave any details un-narrated as some of your viewers are vision-impaired, so if you don't narrate every number they won't know the number.\n",
-    "\n",
-    "Put your narration in <narration> tags.\"\"\"\n",
-    "\n",
-    "    else:\n",
-    "        prompt = f\"\"\"You are the Twilio CFO, narrating your Q4 2023 earnings presentation. So far, here is your narration from previous slides:\n",
-    "<previous_slide_narrations>\n",
-    "{build_previous_slides_prompt(previous_slide_narratives)}\n",
-    "</previous_slide_narrations>\n",
-    "\n",
-    "You are currently on slide {len(previous_slide_narratives)+1}, shown in the image.\n",
-    "Please narrate this page from Twilio's Q4 2023 Earnings Presentation as if you were the presenter, accounting for what you have already said on previous slides. Do not talk about any things, especially acronyms, if you are not exactly sure you know what they mean. Do not discuss anything not explicitly seen on this slide as there are more slides to narrate later that will likely cover that material.\n",
-    "Do not leave any details un-narrated as some of your viewers are vision-impaired, so if you don't narrate every number they won't know the number.\n",
-    "\n",
-    "Use excruciating detail.\n",
-    "\n",
-    "Put your narration in <narration> tags.\"\"\"\n",
-    "    \n",
-    "    return prompt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qZhhy2XqKuXw",
+        "outputId": "5c791634-0fd1-4d16-92bf-aef5b5089d25"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Collecting anthropic\n",
+            "  Downloading anthropic-0.37.1-py3-none-any.whl.metadata (21 kB)\n",
+            "Requirement already satisfied: anyio<5,>=3.5.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (3.7.1)\n",
+            "Requirement already satisfied: distro<2,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (1.9.0)\n",
+            "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.27.2)\n",
+            "Requirement already satisfied: jiter<1,>=0.4.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.6.1)\n",
+            "Requirement already satisfied: pydantic<3,>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (2.9.2)\n",
+            "Requirement already satisfied: sniffio in /usr/local/lib/python3.10/dist-packages (from anthropic) (1.3.1)\n",
+            "Requirement already satisfied: tokenizers>=0.13.0 in /usr/local/lib/python3.10/dist-packages (from anthropic) (0.19.1)\n",
+            "Requirement already satisfied: typing-extensions<5,>=4.7 in /usr/local/lib/python3.10/dist-packages (from anthropic) (4.12.2)\n",
+            "Requirement already satisfied: idna>=2.8 in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->anthropic) (3.10)\n",
+            "Requirement already satisfied: exceptiongroup in /usr/local/lib/python3.10/dist-packages (from anyio<5,>=3.5.0->anthropic) (1.2.2)\n",
+            "Requirement already satisfied: certifi in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->anthropic) (2024.8.30)\n",
+            "Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.10/dist-packages (from httpx<1,>=0.23.0->anthropic) (1.0.6)\n",
+            "Requirement already satisfied: h11<0.15,>=0.13 in /usr/local/lib/python3.10/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->anthropic) (0.14.0)\n",
+            "Requirement already satisfied: annotated-types>=0.6.0 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->anthropic) (0.7.0)\n",
+            "Requirement already satisfied: pydantic-core==2.23.4 in /usr/local/lib/python3.10/dist-packages (from pydantic<3,>=1.9.0->anthropic) (2.23.4)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from tokenizers>=0.13.0->anthropic) (0.24.7)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (3.16.1)\n",
+            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2024.6.1)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (24.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (6.0.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2.32.3)\n",
+            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (4.66.5)\n",
+            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (3.4.0)\n",
+            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub<1.0,>=0.16.4->tokenizers>=0.13.0->anthropic) (2.2.3)\n",
+            "Downloading anthropic-0.37.1-py3-none-any.whl (945 kB)\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m946.0/946.0 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hInstalling collected packages: anthropic\n",
+            "Successfully installed anthropic-0.37.1\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Install and create the Anthropic client.\n",
+        "%pip install anthropic"
+      ]
+    },
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "27it [06:15, 13.92s/it]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Now we use our functions to narrate the entire deck. Note that this may take a few minutes to run (often up to 10).\n",
-    "import re\n",
-    "from tqdm import tqdm\n",
-    "previous_slide_narratives = []\n",
-    "for i, encoded_png in tqdm(enumerate(encoded_pngs)):\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \"role\": 'user',\n",
-    "            \"content\": [\n",
-    "            {\"type\": \"image\", \"source\": {\"type\": \"base64\", \"media_type\": \"image/png\", \"data\": encoded_png}},\n",
-    "            {\"type\": \"text\", \"text\": build_slides_narration_prompt(previous_slide_narratives)}\n",
-    "        ]\n",
-    "        }\n",
-    "    ]\n",
-    "    completion = get_completion(messages)\n",
-    "    \n",
-    "    pattern = r\"<narration>(.*?)</narration>\"\n",
-    "    match = re.search(pattern, completion.strip(), re.DOTALL)\n",
-    "    if match:\n",
-    "        narration = match.group(1)\n",
-    "    else:\n",
-    "        raise ValueError(\"No narration available.\")\n",
-    "    \n",
-    "    previous_slide_narratives.append(narration)\n",
-    "    # If you want to see the narration we produced, uncomment the below line\n",
-    "    # print(narration)\n",
-    "\n",
-    "slide_narration = build_previous_slides_prompt(previous_slide_narratives)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have a text-based narration (it's far from perfect but it's pretty good), we have the ability to use this deck with any text-only workflow. Including vector search!\n",
-    "\n",
-    "As a final sanity check, let's ask a few questions of our narration-only setup!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "FYeAUCZfKuXw"
+      },
+      "outputs": [],
+      "source": [
+        "import base64\n",
+        "from anthropic import Anthropic\n",
+        "# While PDF support is in beta, you must pass in the correct beta header\n",
+        "client = Anthropic(default_headers={\n",
+        "    \"anthropic-beta\": \"pdfs-2024-09-25\"\n",
+        "  }\n",
+        ")\n",
+        "# For now, only claude-3-5-sonnet-20241022 supports PDFs\n",
+        "MODEL_NAME = \"claude-3-5-sonnet-20241022\""
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\n",
-      "----------Question 1----------\n",
-      "According to the \"Segment Results Highlights\" slide, Twilio's Segment business line revenue for Q4 2023 was $75 million. And from the \"Total Company Results Highlights\" slide, Twilio's total revenue for Q4 2023 was $1,076 million.\n",
-      "\n",
-      "To calculate the percentage of Q4 total revenue that the Segment business line contributed:\n",
-      "\n",
-      "Segment revenue ($75 million) / Total revenue ($1,076 million) = 0.0697 or 6.97%\n",
-      "\n",
-      "So the Segment business line accounted for approximately 6.97% of Twilio's total revenue in Q4 2023.\n",
-      "\n",
-      "----------Question 2----------\n",
-      "The rate of growth of quarterly revenue has been decreasing.\n",
-      "\n",
-      "----------Question 3----------\n",
-      "According to the \"Non-GAAP Financial Measures Reconciliation\" slide for organic revenue (slide 23), the acquisition revenue excluded from GAAP revenue to calculate organic revenue for the year ended December 31, 2023 was -$2,088,000.\n"
-     ]
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "id": "ff40NSWcKuXw"
+      },
+      "outputs": [],
+      "source": [
+        "# Make a useful helper function.\n",
+        "def get_completion(messages):\n",
+        "    response = client.messages.create(\n",
+        "        model=MODEL_NAME,\n",
+        "        max_tokens=8192,\n",
+        "        temperature=0,\n",
+        "        messages=messages\n",
+        "    )\n",
+        "    return response.content[0].text"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "HYu3rob2KuXx"
+      },
+      "outputs": [],
+      "source": [
+        "# To start, we'll need a PDF. We will be using the .pdf document located at cvna_2021_annual_report.pdf.\n",
+        "# Start by reading in the PDF and encoding it as base64.\n",
+        "with open(\"./documents/cvna_2021_annual_report.pdf\", \"rb\") as pdf_file:\n",
+        "    binary_data = pdf_file.read()\n",
+        "    base_64_encoded_data = base64.b64encode(binary_data)\n",
+        "    base64_string = base_64_encoded_data.decode('utf-8')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eWngQL60KuXx"
+      },
+      "source": [
+        "Let's see how we can pass this document to the model alongside a simple question."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "vmVJlTxoKuXx",
+        "outputId": "07c4b704-3661-40d8-b90e-ca61392d4e6a"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "This is a page from Carvana's 2021 Annual Report showing four key metrics: retail units sold, total revenue, total markets at year end, and car vending machines, all displaying significant growth from 2014 to 2021.\n"
+          ]
+        }
+      ],
+      "source": [
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": 'user',\n",
+        "        \"content\": [\n",
+        "            {\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": base64_string}},\n",
+        "            {\"type\": \"text\", \"text\": \"What's in this document? Answer in a single sentence.\"}\n",
+        "        ]\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "print(get_completion(messages))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "r9wZMStZKuXx"
+      },
+      "source": [
+        "That's pretty good! Now let's ask it some more useful questions."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wg7A0pcIKuXy",
+        "outputId": "e965371e-576b-453f-b35a-eae82896cb4e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "----------Question 1----------\n",
+            "According to the graph showing Total Revenue ($M), Carvana's revenue in 2020 was $5,587 million (or approximately $5.59 billion).\n",
+            "\n",
+            "----------Question 2----------\n",
+            "According to the \"TOTAL MARKETS AT YEAR END\" graph, Carvana started with 4 markets in 2014 and grew to 311 markets by 2021. Therefore, Carvana added 307 markets since 2014 (311 - 4 = 307 additional markets).\n",
+            "\n",
+            "----------Question 3----------\n",
+            "Let me calculate this for you:\n",
+            "\n",
+            "2016 Revenue: $365 million\n",
+            "2016 Retail Units Sold: 18,761 units\n",
+            "\n",
+            "$365 million ÷ 18,761 units = $19,455 per unit (rounded to nearest dollar)\n",
+            "\n",
+            "So in 2016, Carvana's revenue per retail unit sold was approximately $19,455.\n"
+          ]
+        }
+      ],
+      "source": [
+        "questions = [\n",
+        "    \"What was CVNA revenue in 2020?\",\n",
+        "    \"How many additional markets has Carvana added since 2014?\",\n",
+        "    \"What was 2016 revenue per retail unit sold?\"\n",
+        "]\n",
+        "\n",
+        "for index, question in enumerate(questions):\n",
+        "    messages = [\n",
+        "        {\n",
+        "\n",
+        "            \"role\": 'user',\n",
+        "            \"content\": [\n",
+        "                {\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": base64_string}},\n",
+        "                {\"type\": \"text\", \"text\": question}\n",
+        "            ]\n",
+        "        }\n",
+        "    ]\n",
+        "\n",
+        "    print(f\"\\n----------Question {index+1}----------\")\n",
+        "    print(get_completion(messages))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "40iNE9-3KuXy"
+      },
+      "source": [
+        "As you can see, Claude is capable of answering fairly detailed questions about charts and graphs. However, there are some tips and tricks that will help you get the most out of it.\n",
+        "- Sometimes Claude's arithmetic capabilities get in the way. You'll notice that if you sample the third question above it will occasionally output an incorrect final answer because it messes up the arithmetic. Consider providing Claude with a calculator tool to ensure it doesn't make these types of mistakes.\n",
+        "- With super complicated charts and graphs, we can ask Claude to \"First describe every data point you see in the document\" as a way to elicit similar improvements to what we seen in traditional Chain of Thought.\n",
+        "- Claude occasionally struggles with charts that depend on lots of colors to convey information, such as grouped bar charts with many groups. Asking Claude to first identify the colors in your graph using HEX codes can boost its accuracy."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gkEFJcriKuXy"
+      },
+      "source": [
+        "## Slide Decks\n",
+        "Now that we know Claude is a charts and graphs wizard, it is only logical that we extend it to the true home of charts and graphs - slide decks!\n",
+        "\n",
+        "Slides represent a critical source of information for many domains, including financial services. While you *can* use packages like PyPDF to extract text from slide decks, their chart/graph heavy nature often makes this a poor choice as models will struggle to access the information they actually need.\n",
+        "\n",
+        "The PDF support feature can be a great replacement as a result. It uses both extracted text and vision in order when processing PDF documents. In this section we will go over how to use PDF documents in Claude to review slide decks, and how to deal with some common pitfalls of this approach."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eElb_dFQKuXy"
+      },
+      "source": [
+        "The best way to get a typical slide deck into claude is to download it as a PDF and provide it directly to Claude."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Open the multi-page PDF document the same way we did earlier.\n",
+        "with open(\"./documents/twilio_q4_2023.pdf\", \"rb\") as pdf_file:\n",
+        "    binary_data = pdf_file.read()\n",
+        "    base_64_encoded_data = base64.b64encode(binary_data)\n",
+        "    base64_string = base_64_encoded_data.decode('utf-8')"
+      ],
+      "metadata": {
+        "id": "5Jpt1L0WOVWa"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JWPLWxtTKuXy",
+        "outputId": "d2a2839a-d95c-437a-8280-512549bd8be0"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "According to the financial results shown in the presentation, Twilio's year-over-year revenue growth for fiscal year 2023 was 9%. This can be found in the \"Total Company Results Highlights\" section, which shows FY 2023 revenue growth of 9%.\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Now let's pass the document directly to Claude. Note that Claude will process both the text and visual elements of the document.\n",
+        "question = \"What was Twilio y/y revenue growth for fiscal year 2023?\"\n",
+        "content = [\n",
+        "    {\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": base64_string}},\n",
+        "    {\"type\": \"text\", \"text\": question}\n",
+        "]\n",
+        "\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": 'user',\n",
+        "        \"content\": content\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "print(get_completion(messages))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tbtz3BTXKuXy"
+      },
+      "source": [
+        "This approach is a great way to get started, and for some use cases offers the best performance. However, there are some limitations.\n",
+        "- You can only include a total of 100 pages across all provided documents in a request (we intend to increase this limit over time).\n",
+        "- If you are using slide content as part of RAG, introducing multimodal PDFs into your embeddings can cause problems\n",
+        "\n",
+        "Luckily, we can take advantage of Claude's vision capabilities to get a much higher quality representation of the slide deck **in text form** than normal pdf text extraction allows.\n",
+        "\n",
+        "We find the best way to do this is to ask Claude to sequentially narrate the deck from start to finish, passing it the current slide and its prior narration. Let's see how."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Define a prompt for narrating our slide deck. We would adjut this prompt based on the nature of the deck, but keep the structure largely the same.\n",
+        "prompt = \"\"\"\n",
+        "You are the Twilio CFO, narrating your Q4 2023 earnings presentation.\n",
+        "\n",
+        "The entire earnings presentation document is provided to you.\n",
+        "Please narrate this presentation from Twilio's Q4 2023 Earnings as if you were the presenter. Do not talk about any things, especially acronyms, if you are not exactly sure you know what they mean.\n",
+        "\n",
+        "Do not leave any details un-narrated as some of your viewers are vision-impaired, so if you don't narrate every number they won't know the number.\n",
+        "\n",
+        "Structure your response like this:\n",
+        "<narration>\n",
+        "    <page_narration id=1>\n",
+        "    [Your narration for page 1]\n",
+        "    </page_narration>\n",
+        "\n",
+        "    <page_narration id=2>\n",
+        "    [Your narration for page 2]\n",
+        "    </page_narration>\n",
+        "\n",
+        "    ... and so on for each page\n",
+        "</narration>\n",
+        "\n",
+        "Use excruciating detail for each page, ensuring you describe every visual element and number present. Show the full response in a single message.\n",
+        "\"\"\"\n",
+        "messages = [\n",
+        "    {\n",
+        "        \"role\": 'user',\n",
+        "        \"content\": [\n",
+        "        {\"type\": \"document\", \"source\": {\"type\": \"base64\", \"media_type\": \"application/pdf\", \"data\": base64_string}},\n",
+        "        {\"type\": \"text\", \"text\": prompt}\n",
+        "    ]\n",
+        "    }\n",
+        "]\n",
+        "\n",
+        "# Now we use our prompt to narrate the entire deck. Note that this may take a few minutes to run (often up to 10).\n",
+        "completion = get_completion(messages)"
+      ],
+      "metadata": {
+        "id": "BQYUPFJ-QwtP"
+      },
+      "execution_count": 41,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import re\n",
+        "\n",
+        "# Next we'll parse the response from Claude using regex\n",
+        "pattern = r\"<narration>(.*?)</narration>\"\n",
+        "match = re.search(pattern, completion.strip(), re.DOTALL)\n",
+        "if match:\n",
+        "    narration = match.group(1)\n",
+        "else:\n",
+        "    raise ValueError(\"No narration available. Likely due to the model response being truncated.\")"
+      ],
+      "metadata": {
+        "id": "LCrVi-B5UGUS"
+      },
+      "execution_count": 42,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Fwx8_-4QKuXy"
+      },
+      "source": [
+        "Now that we have a text-based narration (it's far from perfect but it's pretty good), we have the ability to use this deck with any text-only workflow. Including vector search!\n",
+        "\n",
+        "As a final sanity check, let's ask a few questions of our narration-only setup!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "SQuTknpzKuXy",
+        "outputId": "e2e178c0-f3ca-4610-a3ea-6008fbb86d07"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\n",
+            "----------Question 1----------\n",
+            "Let me calculate this:\n",
+            "\n",
+            "Segment revenue in Q4 2023: $75 million\n",
+            "Total revenue in Q4 2023: $1,076 million\n",
+            "\n",
+            "$75M ÷ $1,076M = 0.0697 or approximately 7%\n",
+            "\n",
+            "Therefore, the Segment business line represented approximately 7% of Twilio's total Q4 2023 revenue.\n",
+            "\n",
+            "----------Question 2----------\n",
+            "Decreasing. The transcript shows Q4 2023 revenue growth was 5% year-over-year, while for the full year 2023 revenue growth was 9% year-over-year, indicating a slowing growth rate. Additionally, the Q1 2024 guidance projects even lower growth of 2-3% year-over-year, confirming the declining trend.\n",
+            "\n",
+            "----------Question 3----------\n",
+            "Let me help calculate the acquisition revenue for 2023.\n",
+            "\n",
+            "From the transcript, we can see:\n",
+            "- Total revenue for 2023: $4,154 million\n",
+            "- Organic revenue for 2023: $4,146 million\n",
+            "\n",
+            "Therefore, acquisition revenue would be:\n",
+            "Total Revenue - Organic Revenue = $4,154M - $4,146M = $8 million\n",
+            "\n",
+            "So the acquisition revenue for the year ended December 31, 2023 was $8 million.\n",
+            "\n",
+            "This can be verified by the fact that the difference between total revenue growth (9%) and organic revenue growth (10%) also suggests a small contribution from acquisitions.\n"
+          ]
+        }
+      ],
+      "source": [
+        "questions = [\n",
+        "    \"What percentage of q4 total revenue was the Segment business line?\",\n",
+        "    \"Has the rate of growth of quarterly revenue been increasing or decreasing? Give just an answer.\",\n",
+        "    \"What was acquisition revenue for the year ended december 31, 2023 (including negative revenues)?\"\n",
+        "]\n",
+        "\n",
+        "for index, question in enumerate(questions):\n",
+        "    prompt = f\"\"\"You are an expert financial analyst analyzing a transcript of Twilio's earnings call.\n",
+        "Here is the transcript:\n",
+        "<transcript>\n",
+        "{narration}\n",
+        "</transcript>\n",
+        "\n",
+        "Please answer the following question:\n",
+        "<question>\n",
+        "{question}\n",
+        "</question>\"\"\"\n",
+        "    messages = [\n",
+        "        {\n",
+        "\n",
+        "            \"role\": 'user',\n",
+        "            \"content\": [\n",
+        "                {\"type\": \"text\", \"text\": prompt}\n",
+        "            ]\n",
+        "        }\n",
+        "    ]\n",
+        "\n",
+        "    print(f\"\\n----------Question {index+1}----------\")\n",
+        "    print(get_completion(messages))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fHyZkSSaKuXy"
+      },
+      "source": [
+        "Looks good! With these techniques at your side, you are ready to start applying models to chart and graph heavy content like slide decks."
+      ]
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.12"
+    },
+    "colab": {
+      "provenance": []
     }
-   ],
-   "source": [
-    "questions = [\n",
-    "    \"What percentage of q4 total revenue was the Segment business line?\",\n",
-    "    \"Has the rate of growth of quarterly revenue been increasing or decreasing? Give just an answer.\",\n",
-    "    \"What was acquisition revenue for the year ended december 31, 2023 (including negative revenues)?\"\n",
-    "]\n",
-    "\n",
-    "for index, question in enumerate(questions):\n",
-    "    prompt = f\"\"\"You are an expert financial analyst analyzing a transcript of Twilio's earnings call.\n",
-    "Here is the transcript:\n",
-    "<transcript>\n",
-    "{slide_narration}\n",
-    "</transcript>\n",
-    "\n",
-    "Please answer the following question:\n",
-    "<question>\n",
-    "{question}\n",
-    "</question>\"\"\"\n",
-    "    messages = [\n",
-    "        {\n",
-    "            \n",
-    "            \"role\": 'user',\n",
-    "            \"content\": [\n",
-    "                {\"type\": \"text\", \"text\": prompt}\n",
-    "            ]\n",
-    "        }\n",
-    "    ]\n",
-    "\n",
-    "    print(f\"\\n----------Question {index+1}----------\")\n",
-    "    print(get_completion(messages))"
-   ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Looks good! With these techniques at your side, you are ready to start applying models to chart and graph heavy content like slide decks."
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file