Homework 0701.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ny6jCBCr4Zc-"
      },
      "source": [
        "# Project 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T_FPbe90uN8O",
        "outputId": "bb14d4e9-17cf-4733-953b-b87ef873d1f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pgmpy\n",
            "  Downloading pgmpy-0.1.25-py3-none-any.whl (2.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from pgmpy) (3.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pgmpy) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from pgmpy) (1.11.4)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from pgmpy) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pgmpy) (2.0.3)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from pgmpy) (3.1.2)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from pgmpy) (2.3.0+cu121)\n",
            "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from pgmpy) (0.14.2)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from pgmpy) (4.66.4)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from pgmpy) (1.4.2)\n",
            "Requirement already satisfied: opt-einsum in /usr/local/lib/python3.10/dist-packages (from pgmpy) (3.3.0)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->pgmpy) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pgmpy) (2023.4)\n",
            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->pgmpy) (2024.1)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->pgmpy) (3.5.0)\n",
            "Requirement already satisfied: patsy>=0.5.6 in /usr/local/lib/python3.10/dist-packages (from statsmodels->pgmpy) (0.5.6)\n",
            "Requirement already satisfied: packaging>=21.3 in /usr/local/lib/python3.10/dist-packages (from statsmodels->pgmpy) (24.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (3.14.0)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (4.11.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (1.12)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (2023.6.0)\n",
            "Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->pgmpy)\n",
            "  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
            "Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->pgmpy)\n",
            "  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
            "Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->pgmpy)\n",
            "  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
            "Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->pgmpy)\n",
            "  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
            "Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->pgmpy)\n",
            "  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
            "Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->pgmpy)\n",
            "  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
            "Collecting nvidia-curand-cu12==10.3.2.106 (from torch->pgmpy)\n",
            "  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
            "Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->pgmpy)\n",
            "  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
            "Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->pgmpy)\n",
            "  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
            "Collecting nvidia-nccl-cu12==2.20.5 (from torch->pgmpy)\n",
            "  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n",
            "Collecting nvidia-nvtx-cu12==12.1.105 (from torch->pgmpy)\n",
            "  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
            "Requirement already satisfied: triton==2.3.0 in /usr/local/lib/python3.10/dist-packages (from torch->pgmpy) (2.3.0)\n",
            "Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->pgmpy)\n",
            "  Downloading nvidia_nvjitlink_cu12-12.5.40-py3-none-manylinux2014_x86_64.whl (21.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m63.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from patsy>=0.5.6->statsmodels->pgmpy) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->pgmpy) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->pgmpy) (1.3.0)\n",
            "Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, pgmpy\n",
            "Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 pgmpy-0.1.25\n"
          ]
        }
      ],
      "source": [
        "!pip install pgmpy\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(action = \"ignore\", category = FutureWarning)\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pymc as pm\n",
        "import arviz as az\n",
        "\n",
        "from scipy import stats\n",
        "from scipy.stats import binom, gamma, beta, poisson\n",
        "from sklearn import metrics\n",
        "from pgmpy.models import BayesianModel\n",
        "from pgmpy.factors.discrete import TabularCPD\n",
        "\n",
        "%config InlineBackend.figure_format = 'retina'\n",
        "az.style.use(\"arviz-darkgrid\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wERSs-y44ZdC"
      },
      "source": [
        "## Q1 [10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7i4YipQ64ZdC"
      },
      "source": [
        "A drug company would like to introduce a drug to help patients with Alzheimer's. It is desirable to estimate $θ$, the proportion of the market share that this drug will capture.\n",
        "- The company interviews 100 people and 15 of them say that they will buy the drug. (This the observed data: n=100, observed=15) $->$ likelihood\n",
        "- If in the past new drugs tend to capture a proportion between say .10 and .40 of the market, and if all values in between are assumed equally likely, then $θ ∼ Unif(0.10, 0.40)$ $->$ prior"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4zEIxwLb4ZdD"
      },
      "source": [
        "**What is the traceplot of $\\theta$ using Bayesian analysis to estimate the market share for the new drug?**\n",
        "- Sample 10000 draws\n",
        "- Plot the traceplot\n",
        "- Plot the posterior distribution plot of $ \\theta $\n",
        "- What is the mean posterior value?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 152
        },
        "id": "wufusf_X4ZdE",
        "outputId": "8f97730a-01eb-4cc6-8a89-d5ee6fa56dab"
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      "outputs": [
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              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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              "      100.00% [11000/11000 00:05&lt;00:00 Sampling chain 0, 0 divergences]\n",
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              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
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              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
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              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
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          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/arviz/utils.py:184: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
            "  numba_fn = numba.jit(**self.kwargs)(self.function)\n"
          ]
        }
      ],
      "source": [
        "with pm.Model() as model:\n",
        "    theta = pm.Uniform(\"theta\", lower = 0.10, upper = 0.40)\n",
        "    x = pm.Binomial(name = \"observations\", n = 100, p = theta, observed = 15)\n",
        "    trace = pm.sample(draws = 10000, tune = 1000, chains = 4)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "FE9glClOuN8T",
        "outputId": "19f73adc-13d9-43a1-9d3b-1fc8dfd2cff7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/arviz/utils.py:184: NumbaDeprecationWarning: The 'nopython' keyword argument was not supplied to the 'numba.jit' decorator. The implicit default value for this argument is currently False, but it will be changed to True in Numba 0.59.0. See https://numba.readthedocs.io/en/stable/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit for details.\n",
            "  numba_fn = numba.jit(**self.kwargs)(self.function)\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 1200x200 with 2 Axes>"
            ],
            "image/png": 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ERERERERERERERERERETikIK7RERERERERERERERERERERERE4pCCu0REREREREREREREREREREREROKQgrtERERERERERERERERERERERETikIK7RERERERERERERERERERERERE4lBirBdARIa33cWGbdshNQXGFMC4cU6sF0lERERERERERERE4khTkyEQgLQ0tR+LiIiIiHSk4C4R6Ze33jb86c+GvXthwclw4QWhCQHffAt+/RsDwBf+H1x2Sef3v/6GYfx4OHq+w8EHDc7FeyBgKNkDmzfDlq1QXm6orILKSqirs+M4jn2kpUF2FowrhEkTHSZPhEmT1NAgIiIiIiIiIiIi0l91dYYPPgRj4Ii5hrw8tbuKiIiIiHgpuEtEelRebnh/Ifh88JnTQy+sW1pg9Ro7XFLS+b0pye5wRkbn11evMbz2hh1OSoKDDwp9vaLCkJEBycn9u6AvLjYsXQ4rVxo2b7EBXQ0NfZmSDVRLSIA5BxqOmAtHHuEw50BITFSjg4iIiIiIiIiIiEhvrFkLAdvsytJl9iZiERERERFxKbhLRLq1dJnhop8ZjIHJkzsHd02Z4g7vK+38/vHj4bhjobHRZr3qqNgTEDZlcuf33/+g4d334NBDDOd9x2HOgZEFUJWXGxYvgaXLDUuXhs4nGlpbYcVK+3j8CcOokfDZzxjO+IzDmDEK8hIRERERERERERGJRFOTOxwM8hIREREREZeCu0Sknd9vOmWfOvAASE62F9hbt0JVlSEnxx1n4gT49YMOY8dAfn7nac6f5zB/XtfBTv/3bYcTjoNt2w2zZ4e+Zoxh2TI770WL4Qff6375d+02vP0OvP2OYdVqm8a7O9nZUDgOpk6FGdMdcnMgNxcyM4MLYBsTGhqgogK277DLuXWbLefonf6+UvjDk/DEU4aj5xu+/S2HQw5WkJeIiIiIiIiIiIiIiIiIiIj0nYK7RITiYsNfXzD85xV47HdQMNoNSkpNdTjuWENtLRw938Hxhb43KcnhkIP7Pu/p0xymTwPoHAhVWQXZOVBeAWMKYOaM0NdbWw2XX2VISYEdO2HLlt7Nu7raPmbMgDO/FDr/lasMjY02uC0jw/uaHa6uMSxbDks+MrzzLpS2ZS0zBhZ+AAs/MMw7yvDTHztMDJOxTERERERERERERETAUfOpiIiIiEi3FNwlItx6h2H5x3b4xZcMP/hu6NX0jdc7ODG4ws7LdfjjHxxKywwlJbQvQ3294c234M/PGTb3MqArnNGjOn+2Pz1jeOc9SEyEq6+A004NHSc7y+HEE+DEExwuutDwwSJ46R+GDz6EQMCOs2gxnH2u4ZtfN5x9lkNqqlopRERERERERERERLwmjId1G+zw5ImxXRYRERERkXik4C4R4VvfdFj+sa0xuGpV59djEdjlNXKEw4h8w6rVhr+/ZAO7Ghp7N43ERMjPg7y2R0ICtLTYx/Tpncdfu87+9fvDv26Maf9eEhMdjj8Wjj/WobjY8NSfDP96GVpb7fuf/CP891XDVVfAEXMV4CUiIiIiIiIiIiIiIiIiIiKRUXCXyH6kocFmljrpk6EBRkfPgzO/BJ84weHww2KzbF1pbDT873V44W+G9Rsie09+Hhx8sA3cmjwJzvgsFI5zSEiILLDK7zd88hOweg3U1cGUyaGv791r+NFFhs9/Dr70BUhPd6c7dqzD5Zc4nPklw733uxnRSvbATy8xnHO24ZyzI18WERERERERERERERERERER2X85xhgTzQlWVFREc3Ix4TgOubm5AFRWVhLlr0gkJl75r+E3jxj2lcJvfuVw0JzYBBdFun3t22d44UXDiy9BTU3n16dMgS1tJRnTUuHII+CYYxyOOBzGjYP6evjiVwz19TBtGtx9u8Po0b3/zE1NhpSU0Pc99gfDY3+wy52TDV//msOXvxga5AU2u9er/4OHfm0o9+waDzsUbrjWYVSYcpAi/aHjl8jA0fYlMnC0fYkMnL5uX3l5eQO4VBIp7RNFYk/nKTLcrVtvMAE7PGtW7Co4VFcbgl1L2dmQlxd+ObRNisQPbY8i8UXbpEj8iXb7mjJ3iewnPlhkA7sAHnjI8Ntfx77cYjhriwzP/dXw+hu2rKFXWip86lPw+c85zJju8NTThhnTYe7hkJwc+ln+/YoN7AJo9cOIEX1bnvoGWLPW0NQETU0QCMCr/3NPiKqq4bePGv78LJx3Lvy/M2yZRrDf72mnwlFHwS23GT5cZN+z/GP4zvcMd9xKzILsREREREREREREZP+2c5dt7wQb3BUrSUmQkWGHk5NjtxwiIiIiIvFKwV0i+4nzv+/w9juG7Cw480vxFVDk9xveeRee+6th5arw4xxyMNx1u0Nmprvs3/5W15/j06dBVRU891f4zjmhZRBbWw37Sg3FxQ7FxbC72LB7N6SkwBWX+UKms+QjuPHmnqPbq6rh3vsNL/4dLvoxHDHXnV9ersPdd8Azz8IjjxpaA1BZCT/5qeGaq2HBSfH1e4iIiIiIiIiIiIgMln2lsG69HZ40EWbOiO3yiIiIiIjEGwV3iQxDZWWGESNCA4YKRtsAowNmQ1pafAQT1dcb/vGvAH98mpCyhUGzZ0HROpgxHT77mdDArp5kZjqc9x2Hz34mwBtvGu6937BrN+wuhpISaGkBCA3ays2FKy4LnU5qSu8+0+YtcNHPDAtONlz8E4dNm2HNWph3FHzz63DIwQ7XXGfLNDa3wA032sCys74Zn5nUREREREREREREZHgKZu0KDickxG5ZRERERESkawruEhlmXvmv4Rf3Gi79GXzqtNBgobmHx0fw0L7SAL/7fQPP/TVAc3Poa8lJcNqp8NUzHaZMscFS06a6y22MoaEBKqts9qvKSqiohJISGyT1g+85jB7tjp+Z4fCrhyOrK11ZaQPO0tPd948YAUfMtVm9UlPBcaC5CZqaobHRvqe4GFr8kJgIfr9932uvw0dLDVMmw7Ll8NtHYexYOGUBXHcNPPgr+9nAlnXcuw8u/gn4fPHxG4mIiIiIiIiIiMj+w0TWhCoiIiIiIjGg4C6RYeTfrxhuvd1ehf/iXsPsWTBpUvwEC23ZEuCXD8FHSyvCNhZMmAA//D584gS3NOK0qXD/gwGWfAR1tbbUYnNL1/P49Kdg9Gj3/8xMh+xsQ3V1+PELCuCzpzuMGwdjx0BycujrBx7g8Mt7u/8OAwFDWRlUVRn+8gL862X7fGWlDewKKi6Gp/5oH1OnwKRJsG2bfe1vL9pykZderAAvERERERERERER2X+UlLjDO3aoLKOIiIiISEcK7hIZRk46Ef70DGzZCmPGxGYZ/H5DYqIbnNTaGuCW2+GNN92sVl3ZsQM+XgGfOCH0+X37YOvWyOa/azcc1eG5b3/LISkJCsfBuLFQMMbw0UcOj/zOcOdtDmPG9C+YyudzGDUKRo1yuOpyOOVkw513G0r2uOM4Tujdb8GsXUlJwRKR8NI/bIDXFZcqwEtERERERERERET2D97qDgFlEBMRERER6UTBXSLDSGqqw00/h+f/Zrjwhw6pqQMbINTQYFi3HlavgdVrDOvXw/jxcP89DuvXB3jk97Dko56Durxq6zo/l5He4f8MyM2B3Fz7yMmBgtEwbpzDIQd3fv83vtbxe3A47lg49hhwnNDXAgHDXb8wnPFZh4Pm9O37O+pIhycfh1//xvDiS/Y5Y2yA15TJsHs3NDbZ51s6ZCH718sQaDVcdYUCvERERERERERERERERERERPZ3Cu4SGaLq6w2LP4ITTwgNAJoy2eHSiwcmKMjvN6xeA4uXGBYthnXroDUQOk5lFZzxhQCVlV1PJzUVxhT4yM4OkJ5ug7UyMiAzw5ZB7Oi8cx3O+hakp0NONiQlRefzdQzsAps9658vw7/+bfjWNwznf98X5p09S093uPRnDscdZ7jtDkNFhQ3w2rwFDpoDxx9r57Nzlx1/3Dgb9AXw71cgNc3ws4vCL6OIiIiIiIiIiIiIiIiIiIjsHxTcJTIEbdxkuOFGw/YdcN8v4MgjBjYAqLTUcP8DhkVLoL6++3EbG+2jo/Hj4WtnwufOcBg1Kh+AyspKjOk5z3bB6MEJcPL7DU8+bZfHGFsysb+Ome/wxO/htjsNH3xon1u1Gr7yZYenn4R334dnn7OlGF94EZ7/mx3nby8CGH76Y0hIUICXiIiIiIiIiIiIiIiIiIjI/qhvKWlEJKYe+4Nh23YbgHTTLYb6+p4DpPojK4uwgV2OY7NpdSUzE778Rfj3P+DPf/TxxS/4SEqK391OYqLDb3/lcOwxMGM6nH1WdIKq8vMd7r7D4cc/ckhIgC9+ARac7JCQ4HDiCQ6/ftDHpEk+fvoTh8+c7r7vby/CV75hWLd+YH9fERERERERERERERERERERiU/K3CUyBF12icOatYb6erjoJw7p6dEJQiouNvzjX4Z5Rzkcdqg7zQ0bYcQIG9yVngY5OVBWDs3N4QO+5hwI533HZhQbamUFR41yuPM2qKnpXP6xqclQWdW3TGKO4/C1r8ChB8PUqV2Pc/klUFNjeOdd+9zevfDdHxi+/CXD9851yMgYWt+niIiIiIiIiIiIiIiIiIiI9J2Cu0SGoLxch1tvgpxsGD++f8E+xhiWfATP/dWw8AP73PbthgNmw9ZttoTgv/9j2LHDvlbfYB8dTZ4EX/h/cOopDjk5QzsAyXEcsrM7P//Qw4ZX/wdXXgafPLFvn3H27M7vq6oy3HCT4Uc/dJgx3eHn18HZ3zHs2m1fNwb++jy8/Y7h2qtg7uFD+/sVERERERERERGR2EtKhBZ/rJcCUlKgodEOJ6nXSkRERESkE50mi8QxYwx/+zskJ8MZnwkN6JlzYN8CfAIBQ3U1lJYZ/vca/Oe/UFoaOs6bb8Nb7xhMD9UAJ0+CUxY4nHoKFI4b3gFHb79j+NuLdvjaGwwPPwQHH9T/zxwIGG661QbY/eACw9VX2O/08d/BD35k2LLFHXfvXrjoZ4avfcXw/e86JCcP7+9cREREREREREREhr/Ro6Cyyg4XFsZ2WURERERE4pGCu0TiVH294bY7DW++ZYO7Zs+C6dPCB/PU1xvKyqGszJZLrKyAyipDRSVUBh9VUFEBVVWRzT9cYFdaKhxxBBw932HeUTBu7P4TXDR6FBSOg1274dRT4KA50Znu9h2werUdbmmBjEw7nJ7ucN/d8L3zDfs8wXfGwJ+fg8VLDNdfC9Om7j+/gYiIiIiIiIiIiETPIQe77cAJCbFdFhERERER6ZqCu0TiVEIC7G4ry9fcDE8/Y7jyMihaB2uLYNs2w7btsH27e1dTtOc/cQLMmA6HHOxw8MEwZTL4fPtnMNHs2Tab1uNPGM4528FxovM9TJ7k8PtH4OrrDSed6HDMfHe6I0c63HUHXPBjQ0OHUpibNtvArysuhU+dtn/+JiIiIiIiIiIiItJ3+flqVxQRERERGQoU3CUSp1JSHK6/xvDd82HiRNi2DU77jKG1NbrzSUiA0aNh0kQbyDVtGkyZ7DBxAiQl6eLeKz3d4Uc/7PydBAKGl/8Nn/4UJCb2/jsrLHT47a9shraOJk6Am25wuOJqQyBgn0tIgNZWG/SXmNTr2YmIiIiIiIiIiIiwaLHb5jh/HlG7obW3xoyBnBw7nJISk0UQEREREYlrCu4SiRONjYbUVIemJsN7C+G11w2LFkNjI6xf3/1709IgI8OW9aupof2CvMvxU+ETn4BvfcMGcsXqon24+NOf4TePGP71b7jpBhg1qvffZ2pq5/fs2Ws4/wLD2d92+MmP4P4H7fOtrZCfB6csgAUn6bcTERERERERERGR3qupgUBbWcZYthE3NEBpmR3OzbHt3SIiIiIi4lJwl0gcWPKR4ec3G2bNMqxeDbW14cfz+WDqFJhzIMyY7jBxos24lZ8Pj/3B8PgTnd+TlQXzjoR58xzmHga7iw2HHuL0KcOUdLZps+GR39kWkJWr4F//hnPO7v90m5oM115v2FcK99xn+Myn4YtfgL+9aF8vr4CZMzu/z+83+m1FRERERERERERkyKiugS1b7fDECTByZEwXR0REREQk7ii4SySGWlsNv/6t4dnn7P8ffth5nEkT4ZijYf48hzkHwq5dsGw5fOHzoQE8J50Ijz9hQp5bcDJcd3VoINfYsQr8iaapU+DCCxx+9bDh0EPgrG9GZ7r19eC9We7l/8CsmTB3Lixdap+7624YX2g4aI4dsbLScOFFhm9+HT5zun5nERERERERERER6VrA05zc0mJISlKbooiIiIhIPFJwl0gM+P2Gf/8HnvqTYffuzq9PnACnf9rh5JOgcJy9oG5tNVzwY8PqNXacY4+B8ePta5s32yCxjlattiX8ErWlDxjHcfjqmXDgATBuLFHLmpWX5/DQL+H+Bw0v/cM+t2495GTD2LFQXAzNLXD1tYZHfgMj8uHaGwxbt8Ftdxq27zB8/7sOPp8aZERERERERERERKR7MazKKCIiIiIiPVDIh8ggCgQMr70Bv3/MsHNX6GuOYzNtnfklm6HL6XA1nZDgMGqkG8D1r/8YvvR5+N3jNlAsEHDHTUuFb3zd4etfhZQUXZUPhmD2rI7+8KTh+ONg+rTe/w7JyQ6XX+JwwCzDvb80tLRAVTXU1EBaGjQ02PKMV1xtuP0WqG9w3/vHP8H2HYbrr4HUVK0DIiIiIiIiIiIi0jXT+d7hQbN9u2d4h61gICIiIiIiLl+sF0Bkf7H8Y8PZ3zHceHNoYFduLpx7jsNLLzj8/DofB81x2L0b3l/Y+Wr6s591SE2FU0+BslL4+lmGf73sBnYl+OAL/w/+/LTDuec4pKcrqCeW/v2K4XePGX5wgeGV//a9deRzZzj86gGH0aPs/wFjA7uCSbk2bYJfPggP3AsnfsJ939vvwMWXGqqrY9gyIyIiIiIiIiIiIiIiIiIiIn2m4C6RAbZvn+GGmwJceJEtmReUmwM/+qHD88/aQKy8PIdduw233Rngm9823HK7oa4uNChn3pHw9+cdKivh5f9AU5P72gnHwZOPO1z6Mx8jRiioK9aamgy/ecS0DcOixf0LsDrwAIffP+Jw+GHucwHPJN97H554Cm7+ucO3v+U+v3IVXPATw569CvASEREREREREREREREREREZahTcJTJAWlsNz/7F8M1vG157PfS1/Dz489Pwja85pKQ4lJQY7rjLBnW9/G9oDUB1Nfz1hdD3JSQ4ZGQ4fPPrbvDWgQfArx5wuP1WH5MmKagrXqSkOPzqlw7TpsG0aXDZJf3/bfLyHO77hS23Gc4zz8LL/4YffM/HpRc7BCt7bt0KP/yRYfMWBXiJiIiIiIiIiIiIiIiIiIgMJYmxXgCR4WjLVsMddxlWrwl9PiEBCkbDtVc7ZGY61NUZ/vgnw7PPQXNL6LhHz4f5R8Gq1YbZsyAx0Q0OOupIh89/znDkEQ6fPBEcR0Fd8Wj8eIff/soG6qWmRuc3Skx0uPACh9mzDHfcbWhsDH39rnsMhYXwhc875OXBjTcbmltg7z644MeGe++2WcBEREREREREREREREREREQk/im4SySK/H7Dn/4Mj/3B4Pe7z8+YDj/7qc2kNGM6JCXBS/80PPp7Q0VF6DTmHQXnnuOQnQUP/9bwzns269PnPxc63mWXKPHeUJCa6pCa2vn5p542JCbC17/at+C8UxY4TJ0C198YWu4zEIArrzH8/rdw4icc7rsHrrjaUFsLtbXwj38aBXeJiIiIiIiIiIhI3DAqOCAiIiIi0i0Fd4lESUmJ4cZbDCtXuc8lJ8F55zp87Stu5q2Nmwx339M5q9fBB8EF5zuMKYDHnzT861+2PCPAY48bTl0A6ekKyhkOFi02PPI7gzGwZi1cc2XfMntNnerw6G/gvgdsOc+gujq44CeGPz4Bhx7i8KsH4OJLDAcdBJdcrHVIREREREREREREQinASkREREQkfim4SyQKXn/TcNfdhto69zmfD+79BRx2qA2maWgwPP6ELcEYDNoCGDsWLviBw2GHGp5+xvDCi9Dc7L7uODB/PrR0KNsoQ9ff/2HaG0tqamwmt75KS3O4+gqHuYfboMGmJvt8eTn8+CLD7x+BaVMdfvtrGDkytLyniIiIiIiIiIiI7L+Sk0PbomMlOxsa97UNZ8V2WURERERE4pHquon0Q0OD4fa7Alz/89DALoBJEyE7ywbSvL/Q8O1zbMnGYGBXcjJ87zyHRx+GLVvha9+CPz8XejE9fx48/juHq6/wkZOjoJzh4sbrHb72VSgogJ9f55CQ0P/f9tOnOTz2iM38FrRpM9z/oMEYw9ixDklJofMJBAwbNuqWPBEREREREREREYmdnBx3eMSI2C2HiIiIiEi8UuYukT7auMlw7Q2GnTvd58aPh298DaqrHb7+VWhqgltvD/DvV0LfO+8ouOSnDstXwFn/B5VVoQE2Bx8E3/+uw+GHKaBrOEpMdPjxBQ7fOduQmRn6GxtjaGjoWwnOSZMcnn4SfnaZ4eMV9rkX/w6TJ8GZXwod1xjDvb80/OtluPnncPxxWtdEREREREQkNurqDCtWQUoyHHoIUbkJSkREenbcMe6wMv6LiIhIf+3cadiyFSZOsP2WIhI9Cu4S6YMX/hbg/gch4Cmv+JnT4ac/dtqDcpYuM9x6h2HPHnec/Dz4yYUOC04Gx3FY/JGhssp9ffo0G9R1zNH2dRneOgZ2Afzt7/D0M4brrnZLevZGSorDQ7+E2+4w7UGFDzxkGFNgWLQYzvqmw+jRDn/8kw38ArjmesOtNynAS0REREREho9du3bx1FNP8eabb1JSUkJycjITJkzg9NNP51vf+hZpaWl9nvamTZtYuHAhK1euZP369ZSVlVFRUUFCQgIjRozg4IMP5owzzmDBggW6to/Qxyugrh5qsdnNp0+L9RKJiOwfdJgSERGRaFq7zv5dvxEmTDD4fDrZEIkWxxgT1ZpcFRUV0ZxcTDiOQ25uLgCVlZVE+SuSIaylxXD3PYaX/+M+l5oKV13usOBk9+Dk9xu+dbZh1253vM+cDhdeANlZvk7jOQ5891yHk09i2B/ktH11bctWw3nfNzQ324aVX9zpMH9e39aH5mbDxZe6GbySkqClBbKy4MrLHeYcABf9zLBtu/v67bc4HD1/eK9/w522L5GBo+1LZOBo+xIZOH3dvvLy8gZwqQbe66+/zmWXXUZtbW3Y1ydPnswjjzzCpEmT+jT9Sy+9lH/84x89jjdv3jweeOCBPn+f+9M+8dXX3M+ZmwNHHalr0/1Bfb3B54PU1Pj9vXWeIsPd/14zGMABTlkQv9tikLZJkfih7VEkvsTLNum9tjvpRGUGlf1btNvXlLlLJEKlpYbrfm5YuSr0+c9/jpDALrAHqquvhAsvMuTmwv87A957H9atczjqyNDx7r0bCgp0cBOoqID0dGhuhpkzYO7hfZ9WcrLDrTfB935oKC62gV0ANTVQVgYjRzo8eD/85GLD1m329auvM9x9BxwxV+uiiIiIiIgMTWvWrOHiiy+msbGR9PR0fvCDHzB//nwaGxt5+eWXee6559i6dSvf//73ef7558nMzOz1PBITEzn00EOZO3cuM2fOZOTIkeTn51NdXc3mzZt59tlnWb9+PYsWLeKHP/whf/rTn/D5fD1PWGQ/UlFhWLLUBpQcPd+EzW4usr+rq7NVIQoKICNjeG8je/caduy0w6NHw4Txw/vzioiIiIj0loK7RCKwYqXhuhsMZeXuc9nZcP01cPT88A20hx7icPZZthTeE0/Z5377qOHII0JLLhYW6kJVrLmHOzz5GNz7S8P3z3NISurfupGb63D3HfDDCw01Nfa5tDRYcJK9Hy8/3+H+e2wQ4s5dNqjsiqsN99xl118REREREZGh5tZbb6WxsZHExEQee+wxDj/cvWvmmGOOYdKkSdx9991s3bqVxx9/nB//+Me9nsctt9xCYmL4JrVjjz2Wb3zjG/z0pz/lv//9L8uWLeONN95gwYIFff5MIsPR0uX2rwFWrYaj58dyaUTi0+Il0OKHHTvhxE8MzDyM528gELvSSU3NUN5WFKYPcdciIiIiIsOebhsU6UbROsOPfxrgJxe7gV0JCXDRjx3++aIN7AoEDL97LMD7C900k+vWG352WYAnnoK1Re70tu+A7dsH9zPI0JKf73DLjT4mTuzckPLwbwMsXtK7NKqTJznccatDcrL9v6EBrroWmprsdEaOdLjhOhhTYF9vbITLrjSsXqMUyiIiIiIiMrSsWLGCJUuWAPDlL385JLAr6Nxzz2XatGkAPPnkk7QE0xz3QleBXUEJCQmcd9557f8Hl0lEXIGAO9yHzVBkv9Dit3+bB2kb0bYoIiIiIhK/FNwl0oXfPhrge+cbli0Hf9uF9Ih8eOA+h6982cHn81Ffb7jmOsMfnoQbbzEsXhLgxlsCnPd9m7ErKDkJvv5VeO5PDpMmKSOS9N77Cw1PPwMXX2q49oZAr2plH3qIww3XOQQTxq1YCT+/yeD3GxobDbfcBiNGQH5b2d/6erjkcsP6DQrwEhERERGRoeN///tf+/CXv/zlsOP4fD6+8IUvAFBdXc2HH344IMuSkZHRPtzc3Dwg8xARERkuGhvDD4uIiMjQ5ahLXCSqFNwlEkZTk+Hd98EbPzNtGjz6GyekXJ3fD5u22OG6OrjkcnjVbUsmwQf/73Pw56cdLrzAR26ujmLSe8YYfveYuzKOHBla2jMSJ57g8NOfuO955z247Q7DA78ybNsOq9fY9OeZbf0PtbVw+52mV0FkIiIiIiIisfTRRx8BkJ6ezpw5c7oc76ijjmofXrp06YAsy7/+9a/24alTpw7IPERERIaLkhJ3eO++2C2HiIiIiEi86j6PvMh+qKLScPW1hi1b3OeOmAu33+KQnh4aUJOd7XDHrXDu9wx+f2hK+ZNPgu+d5zBhvAK6pH8cx+Geu+GRRw1vvQPfObvzOuX3GxITu1/XvvxFh8pKw+NP2P//+z84YLYNQmwN2ABFsJnm8vLh1pudXgeRiYiIiIiIxMqmTZsAmDhxYrelE73BVsH3REN5eTnbtm3jL3/5Cy+88AIAeXl5fO5zn+vT9Pan6zEH98Yix9G16P5gqPzm3uWK12WU4avjdjIY84jVeu44kX1ebZMi8UPbo0h8iZdtMh7OLaqrDeXlMHYspKRo/yTDh4K7RIDdxYbcHKiogIsvM+ze7b721TPhRz90SEgIv/OfOsXh8UcN519og2MOOxQuON/hwAN0sJDoyct1uOIyhx/90JCZGbpu1dYazj7XcOoCw9e+4pCf3/W6d+45DnV1huf+av9fWwSnnQqrVtO+3je32ICv6moYN3agPpGIiIiIiEj0NDU1UVFRAcCYMWO6HTcnJ4f09HTq6+sp8aYK6YNvf/vbLFq0KOxreXl5PPTQQ2RnZ/dp2jk5Of1ZtCElPaOpfTgz0yE3NzmGSyODwfubp6UOjd98f9omJT54t5Pc3JQBn0dOTjKpqbFp087IaMaX4HYGR/J5tU2KxA9tjyLxJZbbZOj5S3KX/esDpaXF8N7CZgCamn3Mn5c0qPMXGUgK7pL93ptvGW6/y3DEXFi5ygZ4gQ1uufgihy983h50AgHDo7835OfDKSc75OW5B6MpU3xcdKEhNxeOOVp3KcjA6RjYBfC3v8PevfD0M7B0meHR33S9/jmOw49/BPX1hn++bJ/776vwlS9DTS385xX73O5iOP9Hhu+dB9/4Gvh8DsYYrdsiIiIiIhKX6oKpiLFlGXuSlpZGfX099fX1A7I83/72t7ngggvIz88fkOmLiMjwZozpeSQRERERCVFR4Z5DlZYFuhlTZOhRcJfs11atNlx7g93Jv/2O+3xaKtx8o8PR820gS3Oz4ZbbDa+/YV9f+IHh3rtDg1w+c7qCXiQ2li5zT1S+8uWe10PHcbjsEmhoMLzWtk7/5Xkb4PXz6+AX90JtHfj98PBvDR8ugtM/bfj7S3DXHZCdpXVdRERERETiS1OTe3dwUlLPd+YmJ9ssQY2Njf2a72233UZDQwPGGGpqali1ahXPPPMMTz/9NDt37uSWW25h5MiRfZp2VVXVftO5X1/nNronJzlUVg5M0J3ED+9vHmiN39/ccZz2zAf70zYpsWeMob7OXd8qKxsGZD7ebbGqqp7Gxti0+9XVGRobe/682iZF4oe2R5H4Ei/bpPfcorKyftAzd1VVD845lEgkcnNzozo9BXfJfm3OgXDIwbBipftcbg7cdYdbVrG+3nDxpYbVa9xxFi2GZcsNhx+mIBeJvXvvdlj4AbzyX8PJJ3V+/cprAhSOg9M/7TBtqj3BS0hwuO4agNAAr5YWeOxRuPUO+HiFfX7pMvsAuPxKw713Q3q61n0REREREYkfKSlu+aaWlpYex29utmUaUlNT+zXfCRMmhPx/5JFH8o1vfIOLLrqIN954gzPPPJM///nPPZaKDMcYs990knk/pf3cMVsUGSRD8Tffn7ZJiT1jTKftZEDm03GeMVrF+/J5tU2KhGpuNpSXw4gRkJQ0uO332h5F4ksst8mYn1sM0jmUSCz4Yr0AIrH04kuhgV1jCuDXD7qBXeXlAc46JzSwC2xQWHb2IC6oSDccx+HYYxxuvMFHYmLoRdvmLYZ334Nn/wI//JGhrf8CgMREh+uucfjUae5zL74ET/wR7vsFfPdch4QOR4lVq+HyqwxNTToZEhERERGR+JGRkdE+HEmpxYYGe/duJCUceyslJYXbbruNtLQ0iouLufvuu6M+DxERkWhI61+Mc1Q0NRkamzo/39ho+HiFfaxbr7ZIkZ4s/xhWrnZv2hYR2R8lJMR6CUQGjoK7ZL/h9xte+qchELAXgn/8k+Ge+9yLwqlT4OGHHCZOtMExa9YG+Mo3YO9edxqJiXDxRQ4PP+QwbaoyF0n8e+ttd/j44yElJXS9raqCc8+Bz37Gfe5fL8Odd8NZ34RfPegwblzoNJd/DJdeYfD71agiIiIiIiLxISUlpT3dfUlJSbfjVlVVtQeA9SWjViTy8/OZO3cuAK+99lpE2cREhqJAwFBebmhtVRuBiPTNuvWh/xeMtn9bW2HvPvsorxj85ZKha388JhljqKq2wxWVMV0UEZGY8iT1JiP693KJxJSCu2S/UFlpSyve9QvDU08bHvuD4TePuCf4hxwMDz3gMGqUDXx5+T8Bzv8RNHnuGCoshD/+weHLX3Tw+RTYJUPD/30bHrjP4dOfgk+f1nm9/dvfDV/7JmzdCscc4z7/yqtw9XWGaVPh8Uft+72WLYcLL3KDJUVERERERGJt+vTpAGzfvh2/39/leJs3b24fnjZt2oAtT15eHmCzhFVUqFdahqeVq+CjZbB0WayXRESCamsNtbWRtdmlp0Nmpn3ESseEm55knC41QUqENmwwvPEmbNg4sCtNdbWhuFjBzSIiIjJ4FNwl+4UXX7LBKAC/ewwe+4N7wj3vKLj3bofsLIdAwHDP/QFuuwMCAff9nzoN/vSkw/jxCuqSocXnc5h7uMO1V/mYPy90/TXG8L/X7fDqNfD5z8FXvuy+/v5C+PFPDU1NcO1VPn5+nUNykvv6qtVwznmGFn8AERERERGRWDviiCMAW5Zx9erVXY63ePHi9uFgdq2BsNeTCnwgyj+KxIO9++zfyiqU4VskikwfN6eKCsPCD2Hhh/aG557MPdzhmPn20THjf6y1trrDtXWxWw4ZWrZut7GAW7cN3DyamgyLFsOqNQM7H+m7hgadk4iIyPCj4C7ZL5z1TTjqSDvsvTA+7li441aH1FR74bp+g+Gf/wp970UXwnVX+0hIiK+LW5H+amiAKZMgKcnenTf/KIefXOhw3nfcdb1onQ3g2rIlwCkLHJ54HHJy3Gls3gK/fcQGiomIiIiIiMTSKaec0j78/PPPhx0nEAjw4osvApCdnc38+fMHZFlKSkpYtsymMiosLCQzlilRRAZQYoI77KjpTCTmln/sDq9c1fP45eWm/REPEhMgta2cUmNjbJdloDQ1GcrKjNpTh7B9+9xkcpu3xHRRJIyPVxjefR+KirSNyf4pEDDs3GnYtSv2xxod6kSiS8Fdsl/w+WDUyNDnPnki3HKjQ3KybXlaucpw0c+gpcW+npAAd90OXzlTm4kMT+npDrff6uOlFxzuut0hKcnBcRy+838OV1/h4Gtb9csr4P/Og6XLAkwY7+Mvz8C4ce50/vwcPPFUbD6DiIiIiIhI0CGHHMKRR9o7u55//vn24Cqvxx57jE2bNgFw9tlnk5SUFPL6hx9+yKxZs5g1axZXXnllp/dv2bKFhQsXdrscNTU1XHLJJbS0NTB8/vOf79PnERkKcnIgt+0hIrHn92S76qZCcbuPltlHsOrFYGptNVRXG+rrDU2Ntvf3qCOhsHD4Ror6/Yb3FsLS5bBla6yXRvoqLc0dHjkidsshnbW0mPasojt2xXZZRGJlx84Aa4sMa4qgpCTWSzP4mpvd4br6rscTGYoSY70AItFWW2t482044zP2IrC11XDn3YaX/+OOc9opcPWVDomJdpyPVxguvcLQ0GBfz86Ge+6CA2YrsEuGv6wsh0MODn3uM6c7vPWO4b337f+BAFx6OVx1peHUBT7+9GSA626Ad96zgZATxtvxWluNstyJiIiIiEjMXHPNNXzjG9+gsbGRc889l/PPP5/58+fT2NjIyy+/zLPPPgvA5MmT+c53vtPr6e/du5dzzjmH2bNnc8oppzBnzhxGjhxJYmIi+/btY+nSpTz//PPs22d7lWbOnMn3v//9qH5GkXgy93C1AYhI39TXw4eLbYaxtFSYPt2Wk5w00TBzhkNScqyXMPp27HTLTW7aDFOnxHZ5pG/S0mD6NHd4KDPGtAeCOg7tfWZDlbKIisCq1W50d9E6GDs2hgsTA5EEt4sMVQrukmFl1y7DFVcbtm4DnwOnnQq33G7432vuOGd8Bi67xCEhwWHxEsO/XzG88w40tKV5HjkSfnmPw6RJOguU/dv533fIzDD89382dWpzC9x4s2HDRsMPvuvwve8aHB989nSH446128t9vzQkJRkuvMBRkJeIiIiIiAy6Aw88kPvuu4/LLruM2tpa7r333k7jTJ48mUceeaRfpRKLioooKirqdpxPfvKT3H777aQN4V6/sjLD+g12eMQImDlD13kiIkNFbyohBYzNKhUPgR2BgP2bluo+N4QPpSFMINZLEF+27zDs3AmTJ8O4sbFf9yKVnu4wZXKslyI66upsUCVAViYcPTAVy2PCWzpaRESkv8rKDLW1djg/3yZPGWwK7pJh5amnbWAXwC/uM7zzPrzzjvv6F78AF//EwedzePMtw/U3mvaLRYDRo+CB+xzGjx86FxIiA2XKZIfrrnH4v/8LcN319m4ygD89Axs2GEr2QEMDHH+sreH98n/gxZfsOLuLDTfdACkp2pZERERERGRwnXzyybz00ks8+eSTvPnmm+zZs4ekpCQmTpzIpz/9ac4666w+B1zNnTuX3//+97z//vusWrWKPXv2UFpaSmNjI5mZmRQWFnLYYYfx2c9+liOOOCLKn2zwtbZCbZ0dzsiI7bLsD1pabLtWWipDpm2qaJ1pz0Izexb42pLgO0qdITLktLRA4iD2GHnb5ZuaOr8eshvpTaRaHMvKcodThmFmst4IBAzr1tvh1Wtg3H6WWSaWGhsNlZU20cFwMxinH01NhoSEoZ/lTGS4Sk6J9RLIcLV+g9s+Mmli6HndYFFwlwwrP/2Jw4aNtiFszoGhgV1f+wpceIGD4zi8/qbh5zcaAp6LwlGj4MH7HQoLdUIm4jVxvI+HHzLceofhrbftc4uXuK8/8CvD8cc5rFzlblANDZCUNMgLKiIiIiIi0qawsJCrrrqKq666qlfvmz9/PuvWrevy9aSkJI4//niOP/74/i6iSCfrN8DuYjuclmYYMSL+26hKSqClrfRJcNkBTl0Qm+URGS7MMAlm6k6CJ6tOiqcjtrKy87jD5fvwBs8paNqVMIQyLBlj2L7DsK8U0tMcMjNh4oT4P14HtbYaPlxkq3SMHgXTpsZ6iYaW0jLD8uV2Wz7maKOb2yUuGWMIBAwGCDgA+9d6muQ51qYPk8yfEh+CgV0AVVWxWQZfbGYrMjBSUx3uuBWOORqWLnOf/+qZbmDXa68bbrwpNLBr9Cj41QMK7BLpSnq6wy03OnzvPKfT3S8nHAfZ2Q5XXuZw5pcgORlWroLlH8dmWUVERERERCT6+pMJoabGUFsbec98ba3hvfcNi5cY/P5h0qMfAW9w1M5dsVuO3ggGdomIREtNbefnhktwl0TO7zfs2Wtobo6vH3/nLvhoqU0ssHWbobw81kvUO/X1MGE8fOJ4SE+P9dIMPevX20SCLX7YFcG5mjGG+vr4Wodl+KupMaxeY7Mibt8e66URkWhScJcMWU1Nhg8XhZ4UGWN45ll48y33uS98Hn78IxvY9eprhhtvMbS2pXzOyICCAvj1g86QqukuEguO4/B/33a441Yn5M6yf78Cv3ssgDGwZSs0N9s07ldcbVizVhcuIiIiIiIiQ5X3iq6vHevl5YYPFsHCD6GqKrKJLF8B9Q1QWQVbtvRtvjJ0qIKjyNC1Z6/h7XcMRet6f5BISoKJE2DkCMjN6fx6U3P4YemaMYbW1qHRHus4cNCB9nHg7M6vr1hpH96b+ONB0TpobrLnSCUlsK801kvUe5u22O+2rMy25QeFC6wcaoKlogH8rV2P11c5nn1VJFXely2H9xZCUdHQ2C4lvrW2GtatN6wt6v4GGL/n5ou6+kFYMBEZNArukiGpvNzwk4sNl11pWPiBPYAZY/jto4bn/uqO95nT4WcX2cCuN9403HyrIdAW2DV9Gjz9BDzya4cxY9SKJBKp4451ePRhh4kT3Of+8CRcebXhJz+CUSPtcw0NcMnlho2bAtxzf4A33tQFjIiIiIiIyP5m2XJ3eOWqyN7T0OAOV8ao3EFQTY3NIrbko6HTYT6YEj2ltEbk2zaBsWNitzwi0nd9CeJdsdIGXu3YSa8yNAKkpDjMmukwdqzDyFGd2+f9Lb1fnqEk2tnIWloM7y2Ed96NPJg6lhzH/vZjx4bvnylry4hVU0vcZe8KlpE0DL1iZ01NhsZGQ8kee17TrMDJAdPUZNrX4x1DJCOrxLdt22H7DptBcOPGrsdrbHT3ma0DEOQY71JSYN6R9nHIwbFeGpHoSux5FJH489CvbUpJgOtvNDzzR/jHP+GPf3LHOfUUuOJSB5/PYekym7ErGNg1Yzrcf49DTs5QO/UWiQ8TJzo88jDcfJvhvfftc+9/ANt2wIgR7h1LNTVw/o+gsRFe/Lvh0hr4/Oe03YmIiIiIiAwFI/LhuGPscEJC9+N2JeDpj/UPwRJ+yz+GxiabSWzrNpg2dXDmO1RKkBUUuJ1Gcw4En0/X/EPJnr2GoiLIz4eDD9JvJ/3T2ASZmZGP7/fbknaBVkNDI2RkhK6DwzGrX0WFZ7gyutPesNENjl66DE76ZHSnH23GGDZussM+H0ybOnR+8MQkdzg/P3bL0Rebt9h1BeCAMBnTJHr2x6AaGVjea6nuMtNlZTme4QFcoDjV2Eh7DEFGBhx6SGyXRySaFNwlQ9JPLnRYvcZQUgIXXuDwn1fg94+7rV6fPBGuudIhIcFhw0bD5VeZ9oPelMnwy3sdsrOHzsWCSDzKzHS4/RZ4/AnD40/Y53bt6tzw0tho/xoDd99jqKyEs8+yd2eJiIiIiIhI/EpMdEjsZ+uhz3EDvCKd1qiR7k1Do0f1b/5Bxhi2b4fWAEyaCAkJkV2TNja5w1UxziIWjw48QNf2Q9mKlfZvyR4YX2jIy4uv37OlxeDzRb69ytDS0AAfr4QtW2274fTpsV6i4WMoNLsaY4OmIRjcFdvl6Y3UFJhQCCNHwuRJsV6a3vHGjgfb7YcT780Ifb0xQSRepSSHH+4oMdFpLyGakTGwyxSPWlvdcpQ+1bCTYUbBXTIk5eY63H0H7Nlr00/+5hH3lPT44+Dn1zkkJtormF27DU2ehrhDD0GBXSJR4vM5nPcdh5kzDDffZqivd+8unjoViotDy2kAPPp7Q1UVXHiB7ugVERERERGJZ6Wlpr2U4qhRcNCc3l/DTZpkO+4Bxo+P7D0pKe6wNztGJKprDNVVMGYM7W1DALt3w/pg+RJjr1lFxBVvnfwVFYaly2zn/DFHG1JS9q82pGhk7xsqGQC75PnJAwHD7mJIT4fc3JgtUb95l31ElDM+zZzhBhr1NzA73sRbsFpikkNuHowdC/n5cbZwPfD2lbUMw9KnA72ulJS4w1VVdh0QGSxjxthsgdXVsKYIyisMRx7ROQg+Lc1h4gQHQ2gJdREZ+hSvKHHPGMO2bZ2vRCdOdKiugXvvd1+bPw9uusEJabw74TiH44+zw5mZ8KUvDK2TbZGh4ITjHR592GHSRPe5zZttprzU1M7jP/dXuOMuQ2vrUG9lEhERERERGb4CAVvyw986NErL+P2GRYtg7TpYtz70tU2bPcNbBne5hrOPVxiWLjN8tNRQW2toaDA0NupavzutrYbKSoOJs8ib+Foa+GipzfrX4u+8PUvXetvynZE+IIsRkUDAHfYGnIRTWmpLLC1ZYqiri7e1NT4kJjqkp9tHcnL894F4f3/vcDiRBuxEsl9tbbX9TTt29m0/PKbAHc7L6/XbYy7JE/jnc+j9TmM/5y03HswMJDJYUlIcsrIc1m+w/1fX2FKr8STuzm/ja3FE+k3BXRLX/H7DHXcbzv2+Yc3a0D3w4iWGm2817TvmQw6GW2/qfOGSkOBw600OX/8aPHCfw9QhVLtdZCiZNMnhkYcdTjjOfW7NWsjMsKmqO3r5P3DzbQa/X2dXIiIiIiIi0n979rgBKruLY7oo+42KCigrh/IKWPghvPs+vPNerJcqvi1aDIs/UsBSTwo8ARQ52bFbDumeNyl/WpgbPLsTLJkWMKEZG8OVuduz1x3esNHfuxnJkOTNNhPNbEzbd9hMnkXrQrMw9UZDvWHrVsOqVYbNm9W2LSKDK1hyEcInV2htNTQ2GZqaDM3N7j6qvt6wabOhunp477e8AeO1dbFbjnjQ3GxoaRnev/f+RsFdEtfuf9Dwr5ftjvjyqwx79tod0Jq1hquvNfjbruOmTYM7b3NITXUwxlBWFrqj8vkcLvyhj5kzFNglMpAyMhxuvdnhu+c67RfdpWXQGght7An632tw/Y06uRAREREREYknW7fZTExVnoZ/3yC2InqzhJkesnkMV0PlLvMWxVj0SnW1ae9k2rEztssS77zZZRJUUmjAFBTA+EL76Espv7Q0dzhaATjhjjd5uXadSExyGDtG3Vr7g9RUe8NwuJuG+2PjpvDDveFvhZpaqKi0mXOGkqwsGzCbk21LX3uz92VnxW65osV7/tSXrLOBQPc3o3u/r6FcIlaGpl27DG+/Yygrt/+PHmWTK3RUV2fYsAHWb7Cl6YOWLbeZvj5cbNd1Gd6qqgzvvAtvv2sD+6T/vNcnGWG2vcEwzCpvy3Dzja85vPW2oaICTjzB1qHfus1w2RWGhkY7zrhxcM9dNhUlwG8eMfz1ebj7TsPcw3WhJzLYfD6Hc86GGdPhplsNdXXQ0hI6zmGHwvKP7fDb78DV1xluudGmlRUREREREZHYqa01bNhoh7dsDb0zvG/TCz/cnWJPJo2ychg/vn/LIBIvWvfTYEWJvXXrDXv2wMyZMKbAbX+b1s8qF/0JRE1OhpnTob4Omps7v57gadofP97hkyc6OI5Dbu7QjfbzBsTWRHhMjNS+fYYtW+3w6NEweVLob7u2yFBZaYcPPABycuK7HfaYo+N3+Rob3eF9pbFbjr4oLHSYODHWSzFw+lNGvKnJ8MGH9lh9xOEm7DaSk+OWY0xP6/SyyIBqbYWmtuPl5Ikwo4uEJt5jqjcAtb7BHW5shPQYlmaWgbd8RVspWWNLWx91ZKyXaOibNRMqq+zw2DGxWQZFvkhcKxzncNftDj/8gcOlP3MoK4efXWqoqrav5+fBfXc7jBxhD2D/fDnA08/Yg9tFP4PlH6vFRCRWjjvW4dHfOEyeHPr8uLFwy41w9lnucws/sNn5GhoUPS4iIiIiIhJLkQZgRaovmVwKx7nDo0ZF/j7v3bPJSaGveYPUMjN7v0yxVF8fWlIlnngz7IwvhAmF4UuqiQwlO3caitbZckbDQV2dYfsO22a+cpX7vDG2pNzmzYYtWwf/syYnO0ya5DC6wCE3zx4sRo7w7K89x4/hkFEIYPUaw+7d9tHUGN3vfOs2qKq2j2CQttfOXbY8VW2drbSwvxqR7xke0fv379kDKcl2uLGx8/lGvKurNdTU2Idf1TRCFK2D5hYbQLN0WayXRgAqKmxFJzNUUtoOsO2erK9bt3c9XnfZ54KGc1bUZE/Gx4E4f2htHRrrpDfIr6Gh6/EkcmPHOhww2z5yc2MThK7MXRJXGhsNqamhG4PdSKCy0vCzSw1799nnMzJsxq7CQjv+kqUB7rw7dHrJyYOx1CLSlYkTHB75Ndx6h+Gtt+1zu4vhe+fDzTfCO+/SfkfZR0vhiqsNd91Op/2AiIiIiIiIDA5vWa6cbFhwkh3ua7mtzEza23IG+u5wn6eTIjU19LW8XHc5RuYT94L9Bfv2GZavsEFUxx9r4i7j9aSJ7rJOnwZOtOqyyaCLtz4qb4aj/mRi6a2KSsPadXa4vh7mHj548x4oTU1dv7Zpi/3r88GUyb2ftr8fpVmbmw0le+x+rrnZZpIaUxC+xNRwUVyMW05rdGyXJd69/qZp3/YXnGSrRURDVqb7G/RlXTNAQtu5UlPT0AsYLy2zgYAAUybB1u2GstJgoNv+fQyvr3eH/YN43JHwamoMS5ba4QNnQ2FhbJcnHowdY8sqAkyc0PV4GRnutpzl2UelJLuZvwbrlL0/55dNTYaEBEhMjJ99U0WFYfnHNv5g/jzT62VraTG8+bb7/6kL4uezydCgzF0SN/77quHrZxl27Oy8p6+vN1x2lWFbWyRychLccavTnnJy584Al14eepC44Vo48ACt4iKxlp7ucMuNDt//rtN+wlhcAj+4wA3sClq6zJZoHC53RoqIiIiIiAw1KZ47nTMzIRBwH33Rl+bqgQgyGTcOjj/WPjpmmC4tM6xZax/79sXX9ejyFfZvIADr1sd2WcKZPs1hxnT76HNgl/o0JIySPeGHB9oez7yCASDSteYWd7i+l1khGpvsfm3jJihtK223ao0beDLsDfLhxhuElJM9uPMeblJTYOoUW55p5oxYL03flZXDtm1QXmEDPjoGxg9FijEfPorWucNrimK3HPHEmzF3+w548y1DyZ7OB5OkZIeRI2HUyNDsxYMhWtdx5eWGd96Ft9+1iWHixfYdNvizvsG9cag3+npNLRKkyBeJCy+8aLjpVkNpKVx6uaGi0t1RNzcbrrnesHat/d/ngxtvcDj8MHuWVlUV4Lzvh94l9LOL4JQFWr1F4oXjOJx9lsNddzjtDQld3XW5aDFcd4OhRWmhRUREREREemX9BsPiJYbq6uhcT1VXwxtv2ceq1VGZZK9Fq48uMdEhLc0+kpJCp7pyJezabR/BYKp41NLS8zhD0n5y+R8IGOob4vfDRikpjgxRgQBxfbPl2iLDq68ZXv1fgO07hkdKncEOQjlmvsOpC+xj5Mj43+C9bcfxlFkwMQESEh0yMhzGjXPIyorNd1lTYygr631pMG8/WjAwobHJBlV6M1dJeLuL3eG+BHaI9MfECfDJT7glFVv8UFfXebzUFIexYxzGjHHIz4///X04Hy2zlwitrf27wSXax1pvOUudO+9/du02fLzCPkrLYnNyougXiQuzZ0Fa210BGRnuiXsgYLj1dsPiJe64V1zmcMLxdo9ZX28457tQ5znpPOub8KUvatUWiUfHzHf43W+cblPGArz/AVx/o4moNriIiIiIiIjYu5u3bYfKKlj8UayXpn/KPdlyamojf1+CD9LT7GM4ZJ8YChYvMby/0PDu+4bSUnvDZlVV/FzL+/02o0Fzc2yXyRjDh4tg8WLiKjvcyZ+Ek060jzFjYr00XfOWix1o3vJFiQldjzcc9bf8ZW87OY0ne0VjY/evl1d4hsuHbtqLeUfBxPH2YcvgSSTiKdOJ93fLiFEJ0bo6wweLYOlyWyEDIg+AS052h4djXII3kCM5aWDn1Z+ytCK91dpqSxnX14cer6uqIp9GsCQjxFfQbE/CnSN0J+D9fqqjuyyyf1u33gb27t0Hu3cb6usHf0NSBIzEhQMPcLjtFoej58NDv3QYOcKegf3qN4bX3nDHu+B8h8+ebl9rajL88ELDPk90/IknwPnf12otEs/Gj3f4za8djpjb/XjvvGvLNIqIiIiIiEjPvA30af0IbIr23c1bPOW1du2K7D2NTe5wdS8a5FvbSmTUN0Bzc+hre/Yali6zj93FoY2wo0a5w6NHEbe8HbIle9qy2LxmP1Os1Nfbmy4bGmDZx7DkI1i0pOf3DZaVq+zjo6WxXY7SUqhty6wwmCUGe5KQ4JCYaB99Lqs5QCaMd4cLRg/efL2lafPzB2++HUWl03UQdg3pae5wWlrX44XjzX6R7Pne98TRNhJto0Y65OTaR7xtc/uLrdvd4V27e//+sWOgoMBQX2fLORetG/xjsDeLzeo1vXuvNyikNY6C5mT/YIzNONcQx5lM49XmLbBmrT3Pzs9zv79wwfl+v6Gm1lBb2/V3PZyDE4fzZ4vUrt3axgaC9xi6chW8txDWrR/c73oQ73kR6d5RRzoceQTtFzV/ed7w7HPu69/4Gnzz6/a1lhbDpVcYNm12X586FW6+URdEIkNBdpbDPXfBvb80vPSP8ON88fMw7yht0yIiIiIiIpHweTrJvcEJveW9M7qmFnJy2qbfx3vpvNk2+psVpj9qa6GsLSNYehqMG+u+dtAch4PmxGa5ejJurFsCaKQnW0hpqTtcVk7MNPexVGQwwM4Bpk4duGv/0jL7t7bO3iiakuK2LSYmMmjBFbFc94cDdU/Fr9RU9/eJ1uYUbr8ypgBGjbTb7KSJ+1lKNYmqEfnucTN4jtMbo0Y5OI4NQvT7oamp5/dEW38ymWVnuecTFcqTIINs23bYsNFeV5xwnCE5Wf0/kdq6DUpLDcUlMGoEjBlryzRmhskgWFdv2LrVHp/zcgd5QXtp1y5DWTlMngTZ2VofomXN2v5Po2SPYcMGew42Y8bQ+W2qqgx79tpg7IEqndzcbKipgdGjYfsOmDXTPt/SYmhsHLj5goK7JAbq6w0PPWz43nkOebmhK3ewQeWtdwwPPOReti84GX74A/taa6vhplsNy5a778vJhl/90sGnArciQ0ZiosNlP4NJE+GhX5tOdyT+/R9w4AGG0z+t7VpERERERKQnuTm2tBr0r4O9pY/BOpGIZfmP3Z7sHDt2wezZsVsWgbK2oCsb3DU48wxmCdq3z7BiJSQkGqZMMowY4ZCZqbYHsYZSmaJ4cMTcyLedhgbD+g02AHnWTBsYdvBBtqRUuABix/PchPEOhx1qs13l5sY2IiVYgic9fWjtNxYvMVS2le+aexiMGDF0lj+acbhZWW5wV0Z636ZRXeMO793X9XjR0NRkSE4ODUbuz35q9GiHsW0B7nX1JiTTaTDDpMhA2bDR/g0EYNs2mDEjtsszlE2cALNmht85NvUxC/Jgq683rCkKDsPR88OPF0+JLoOlcMEeS+K5rHl/rVxl/27dDoWFps/nPc3N9trLGDjkYNpvthkIxhg+WmZvrNm7F44/bmDmE26d9PsN770PLX6YPcswYfzAfE4Fd8mgqqw0XHaVYe1aWLfO8OD9nS+CVq023HizG+hx2KFwzZU2cCsQMNxxl+GNN93x09Phl/c5AxoFKSIDw3EcvvYVKCyEn99kQu4QDwTg+b8ZTj3FBoL5/YbERG3nIiIiIiIi4TiOQ2IUWvq80/A5binAaEx7f7F7d4Cly2HECJhzgDMgd6GPGQMVFXZ41MioTz5iPp+bQWTaVNvQnRBHSXVOXdD5u1++wv7dsgl27oSxY+GTnzAkJLjjluwx7N1rP8/Mma2MKYijDxVFO3a6bTETJwxsZ4sMDseJ3wC1VatpDy7KyoLCcQ5jCiLLntSfjJTRVFVl2kvPHnmE6XTzek9WrjJsaytXbPeVg7fNBb97gIpKe4yKlvJyQ0IC5OQMz33IxyvcfWV2tmHnTti42ZaMLRjd+TMbY8dPS+vf97Fjp6FoHWRlwvx5JirZJisqDeVtAdb+lv5lAYtH3v1fX7OLRqqvWW3FiteyoBUVdvstKKDHhCJ+v2m/MSYhgQHPRObN3OvNFtRRJOcB8bD+1nkCSmtqY7ccfeUNjh3umpttPEZfrN9gzzvAlhKee3jUFqsTY9yMyQN5DCgpsaWdx4wx5OfZ7X77dhvYBVC0LrTEfDSpWUYG1eo1sG6dHV63HhZ+CAtOcl/fudNw5dXu3QKTJ8FttzgkJzsYY7jvAcO/X3HH/9Y34Nxz1PAgMtQdf6zDL++Fy680VHnuJEhOtifFwcDQz58BZ3xW27uIiIiIiEhvNDcbqto6dZOSILebzuiUVHd4/HiHeUdF7xqsL32SvXmPt4Hd24k9mKprDB8ugi3bgA3Q0mz4xAl9/w5zst2O19Q09/mRIxxOOL5/yxoNs2a4JdnGF/ahzGEML/GrqyEh136/FZWhZS9rqmHPXnAwjBljGFPQv3mlerareGrVKFrnDqenQ+G42C1LR1Webbi+fvDmW1npDg90Rp7hoLTM4G/rPBs5km5vzPTul0tLYUS+YccOqKs1NDYNjUxSK1Z6hlfAiZ/o3fu373CzPsWinN9A2LPXtH8v848y/QpoLikx7Cu1/UJ9MVCBjXV1UNe2H0pMtMMNDbYDOVzg4bLlNqPLxAmmy8w6kQjuo2tq7f6oYLT9vz8xXrt3w662cs/DLbCrvwpGQ+0WOzx6VPhxCsfZDn2w5bikH/qwvX68wuBvC5447FBCAvOjobbWsGSpHW5pgYkTux9//QZ3fQA4dUFUF6eT/Hx7fgq2Io7fb/D5OgehebMSZma6w8lJbsDLQN24MxD74aZmw9a2wOi0tPBBtdFUWWnaM3Tl5sDYse78Nm827ceDA2ZBfJ3Zx6dgxmZwM2cOlMG4waGuzpYSbW6GzVsgP88+728d+HmDgrtkkB13rMO1V8Ftdxouu8RhwUnuTq+y0nDpFW5q4BH58Is7HbKzbGDXw781/O1Fd1pf+gKc/30nKncriEjszTnQ4eGH4Ec/Me1R3CtWwpXXGnbusPXY160zZGXDif1oGBcRERERERmOKioNS5fZ4bxcmHu4e91UW+tmKxqRH/ndsq2thtq2u6gTEyE1tffXYulpUN9ghzMyInvPhEJbOhHoVVBNXzvKd+0ybN9hh8ePp88lFIrWGXbspL3zAaCpn3d0p6a631tiHCaPGt/PchPxmmFo63bP8LZWZs7oXzO6N5uZN0hPuuYte+YNuBpoWdnucF9Ltu1PNmyk/Thx7NG96yxubrbbWrCjumMmqZRkmDndDvsSbIe+40AgYHrMpjJQvFkg+pIRIiRTyiDv/8YUuBkfo5m1yxvwtmo1HHtM1+PW1Zn2IKVgZtCgpibDytV2eF9p5/f2hTHRyXblVVJigwtmzbCZH+ccGPp6Y6Np77zuLrNOb7V4zif6c+z0vtdbRWO46M/PPXIU+NqO11mZ4cdRd2T09GU1rqhwM+MMxDnkxk3u8LoNPQd3DWQ5+3Acx2ZWBttftm273QeNGxs6XkqKW341NcaZL/vyOzU2hr6pqdEt6Tki3w10HShVVbCz7Vq0oYH27xLcQF8gJFFFvOvv5hKnl2zdGqjddWPDAE04QgrukkF32qkOhx4aGlnb0mK45nrTvrNMS4W77nAYM8aO88RT8Kc/u9P4zKfhpz9RYJfIcDNxosNjjxrOPhdq2hrx3nsv9O7Z2lqDouFFRERERERCmYCbgaG1P3eNelpua2tt1nWwGQwOPsjgOL3LzjR6tBvs1FUWhFhbv8G907Y/JRSK2zJhNDRAUlsm6qoqW4br4IP6dh27r9TtXEhJsaXMJPriMXBusMVL2btYS/C5WUFs8El02qCamgzrN9jgp1kzey71FK+Bjx3Vesoo9evYE0ZKisOoUfaLeOsdu392MBxxRICpU4bmRuvN4jdykEvq9vU41BuBbtZbYwyLFtvj7e7dMH9e6Os1A7gueXkDsHcXw6QIsoTl5bnlqIqL7fabnGKP8x2rygxUNqze7qO7CmwLBsbYcfq5UHGut2t8dpZDdh/Ps1pbTXsGp4QEW3ZWutfT+jfc18++GDXKYdQo2LfP/XJqaoAOwV1JSQ4jRzgYht457uo1ht3FtixtX2+46a/SCDNNaR2VWFBwlwyo0jJDdlbnOsPewC5jDHffa/i47Q5Snw9u+rnTnq722b8YfveYu4dMSoJPndbzBbCIDE2jRvn405MBzr8QdrU1YJeW2caPxkZYtAhOO8WQlKR9gIiIiIiISJD3zuGOJQmTktzAqr4GB9XXG9551yEhEeYdaTq19XRpP2r0TkqyndaOL/T5kj1w8EHRnZcxpr3z23GiX5YmUu++Z2hugUArzJoFSYm2bW90N+VSzCD2hLz6mjuvk04MLRnnzWTQMYOMz+d20PenCbK+3paRyck2HHmEQ1VVh8w9MXbqArWtdFRRCWuL7HDteDjyiOhMt2idW+YxLa3vZeeiyXEcMtJNSBaK/uhPBpMGTxYE7zby3kL7t6gIDmrbj9bUBICh1Vvt9xuWf2yHxxfafU7C0PoI/dba6gZS10eQ9SIxIXyJo9ZWE3LMC363fj8cdFDvbgqONIisvNxd5u4C2KItN8c9p+t4nOqr5CR3eLjnTkhK6nmcaPH7Yd16O5yaEl9ljuOJd53Oyen9+2fPtje0QHycT8UquMfvCdJsiHEWofp6Q1MT5OVFZ4eyu+1mmcpK94abhAQ3M2gk+8L0fmZezcmB8rZMlx2zuHqvEfoyn8REGDvGDu9v5wGDbSDLJHoDufubqbu3FNwlA6Zkj+EnFxsmT4Jbb6LLQIxnnoWX/+3+/+MfORxztDvuqFH2JDN4kGxtDW2IEZHhJy/Px5OPGW6+zfDmW/a5YJro196AmlrDLTdCerr2BSIiIiIiImBLMQbl5tjSR8HrqNZWW/4KoNXf6a0hvKUNa2rcEmXrN8C4cUCLHT5oTu+XMV47EcePdzN59DVrVzSsX2/a27+8bWPh7N4Na9oCUDLSuy+DNZBaW93O8aJ17vOnLoh8GjbYa+BXDtumaLeVikr7XFdll6ZMhk2b7fDEiX3veVm63Ha67drtcNwxhuzs0Mw9Q11joy1FmpcHI0fE6QYeJ4KBXQDFJfER3BVrwU7N1gAke4Itp/Tw3XiPJcYY1q23x7hZMztnUYoXGzfa/Y4xUFYG06c7cRGYEC+SwvRUhjtnKC0zrFgJaWmG+UfZBADB7xZg9eoBXUzAnmPt2WvYsQMqx9qOfm9WNL8/NlUn+poxLF7PzWLBGNP+PTpO+AQTVZ4bKAa6rGVFhaG+HsaMGbwg/oYG056JNi0Nxo4Nne+2baY9kGHSxN7vc486sn+fY906tyTuqFF9D/CKZXlfgIpKw5YttrxgYWFvAlLtOprtKSEdroR9c7OhvMJeVKSlQnCf5C0n7Pf3PwCyqcnw/kJ7L88Bs0y/y7V3xRhobLtGbY4gkMYbNJXSh8DY3Bz7gM5ZNgvHwo62pBTjC+3fxkbD1q32Jqqefs+EBKdP19H91d9fRoeK+KHgLhkQtbWGH19kKC6xjU233G648frOm/677xse/q0b2vz5z8GZXwodx+8PjX6+8AKHQw/RbkRkuEtJcbjxevjlg4YXXgx9bdFi+OklhhuuNeTnO6SlaZ8gIiIiIiL7B7/fsGatHU5KggNm2+shn8/tHE1MhB073Ibn0aPcu+QTe2gN9Jbr8Wan8Ga38JbhGgje0kzeYLOBMmO6w4zp0ZteRjpkZGA75ApgxozI3hfurt/du93hmmqgLRNEsCMbiFrWnXAqKk37b5CX27kTr7kfmXrAZsXqTZnPaCgcZ+/Ir6oKzd7lFa1FCmZTaGo0/OvfdpvdXUxU17dYWrnK7lu2boNPHG+iGliTmuJ25OVkdz9uNHn3OZWV0ZvuiHy3tE9fStR2VWKt+/f0PM70aW5mg/5mBupzBooIYmEmjIeJE+z+YvIkd0a7dsGOnXbY74e5h/dxGQZYMAMIQMMAB4QMFQbT9tvbHz/NE/iakGDLOnszVAIsW27/1tbC9h02SNJbMquqqnP2zGiYNNE9P6qpgT17bVAiTmhQVVGRYdNmKK8wjBkT3WNbd9vzxysMpaWRTWfUKBjbFgyyfacnuMMM7r52MEW6/9yx082+NWkizAxz/uY9R/UeIxIS7HsgOmXw6uoMS5ba4cYmmDa1/9OMRGMjbNpih0fkw9gO5f52l7jXAYXjhmZJ55ISw+q1kJdrmHt4bPqVlnxk/5aVw8iRkZ8/VVba88iRI+y1xszp4bMyNzQYdu2yu1jvDUBe0Sghu3GTm6R57Tp7w8xwMHKk02XpZO86H9x/rlrtXptlZhpycuKvv7IvSebSUt1zlrS0qC7OkJWVCWVtx9tY3bCj4C4ZEJmZDp/9DPzuMUNyMnz29M47so2bDDfe7N6ReMRcuPgiJ+Qka+Mmw513u7ucU0+Br3x5wBdfROJEQoLDxRdBRqbhqT+GvrZmLZx1Dhx2qOEXd8auBIWIiIiIiMhgMsZ2KoItrXPAbDucne3wyRPd8YqKolsnpKEBamo63wHeE2+na2kZTJzY83uqa9zhXgUPxfiyMNjGlZgI48ba72x8YeeyfpFkC9i1y3DEXCckwK6+i7JlA2nLFvc3POLwodeJl5PtlooLBp4Es1CUlkJtXc/TiFXJnaHAWwK2tCy6ZahGjIBdbcGNWYMYcOANloxm3GF6urst9SWLxEDproRqJDIz3Y7+3mYASU2Fo44ATPgMOHV17saXk+swa6btO8jOdneAweMhhB5v4pW3lNa+CINxouXNt0x7gNIhB0OB57f/eIVpz0h02KH2nGIgNTcZPvjABmIdfpghNcUJ2d5SU+DAA7pfhsEsRebNRrN6jS1DFhTMymeMaQ+q31dqsy0NFm9mwJ7k5zuMbAswLd4TeoAbTlm8/C2GsjJ4732Dv9XhkINMj2Xj9nm+x23bwwd3daW11f0dUpJhaj+DsbzB/Zu3DF5wV7xLTe35RpWerGzL8FdWDqWlhpEjY7vi1zdEfn7tvdlm3FiYNMkue0uLYc8eyM+H3Fw3OB6gujp6y9pRuACxrs6ba2sNNW3XeJmZkJXlRHyOnZQEBflt783oefyB3JeFm7b3ppu9e/tWclSiY6Cv22bNsvuggIGdbcf8wc7EquAuGTDnnG33cAcfBEfMDd3blZcbrrjatJ+Ajx8PN//cITHRYdVqw5gx9gTomuvcuwOnToHLL3EG/W4+EYktx3GYexg89cfOR+WWFli8BG67w3DdNdo3iIiIiIjI8FNaZu+8Lhxn7yL2Nlh2F/iUkWnv6gYoKHBLgPWmQyTBB9ltd4M3NbulCyNpVA8ake9mOciI8H0Fo23ZMnBLYkQi23Pnesdye6NH2ywfwWUaCMGOlNZW20FYWm5LjaWmwCltZQqLigy7imH6VNPeIRNOT3fTjx3jBkPk5UVh4fvI57gZ3g460HZ4DETWlL6ad1TX37E3sKup2QbfBHmbH0vLAqRsbSUz0/Q9M1GcWviBaf8e5h818AEdEl+MMe2ZkBISGPRqGQkJDrm5tpM3GJw7fZqb2aw1gqwi/S0pNdg2b4GKckhLNW3BGoP3nXszc9bU2GNt0Jq1NvPTyJEwa5YTUvJrIOzYCdOm2c++fTscfpgTEjAFUFnp/p+bG73vaewY9xxjXIQBqYuXmPYA68wMQ1amG8gTzHblPT+LVrU3bwBtXb0N2uiv8jIbBALg72f2zXi2Y6c9zu/dZ8vFLVnq9FgyuqWX30fHrC3B/k4ThYxI3uAQ77Y60FJT3UCy9DjM1DN/XnT3mR2PM/F+E4M3W1xJCcw50A6vWWvX9aQk+PxY02WwiTcTU3+D5Hpr+w43aD83B446MvL3pqY6zDkw8t/euz/WTRquvmSBHcrClXzur7xch9GjYF+poWTPwM2nOwrukgEVDPDyamkxXHuDewKZmQl33e6Qne1QVGT42WWG3Bx7Z0NwR5+RAbferNJrIvurI+bCCcfBO++Ff/2VVyE3N8CPfxRHLcgiIiIiIiJREOx437sPTl3QdQN1IGDaM4L4fLbRPJgdJinZzYaVmtJ9kJW3LFJmltPeibJqtaGqD3d+J3o63ge7E8ErM9P9PjreTW2M/e6SkqLT7tTcYsvWdCxx1tTkZvVYvxEmTer7PPLynEEJ6srPd4MnwpVsO+Rgd3jUqMi+v1h3sjQ3GwIBaGk2JCSCz+eEZEIANxNRfT0sW+6nstJQMLrvmTO8WYnCZSiKFW+AW00N/QroiFYww7DVy/V+MLYTY9xsV7EIXKyvtyXstm4ztLZCQYFDUxM0N0ceDDxqpJu9a9zY7seNBw2N9hjR0Ein/U4sBYOdikugsYeMWPX1hi1bbaBR4bgOG36E621wP7h1q2HHDhtQ2zFL0kdL3eDh7oJyUlLczJaR9Fkn9eG8pKXFrpcAgTTaY/KMCZ1nQoI9BxuILB7RWl+qqt3AtGiUZevOho2G+rZsiDOmQ3r64B0ogsc3v9+W202JoHzWmDFQs7H7ccYXutlaohFsF298PnedHojj0LvvmfbgogNmhWbFi8SHi9zrnaPn962aS0427dc0HUvN5ea4v288HlNyc6Fkj92Gs7MNFZWGzAyn/Vjub7FJVdJS3e+lq5ty/H67febk0GNG4WjwXkcGA1cjjTNqbDQUrbPDmRk9rzc1fc0C3aa83LBtux0ekQ8TJ7rz85ZZLloHCQmG5mZDcnJkH6apyfD2u+7/py7o/L7mZsO+fTaLbWpqlK5Pm+HNtyA/3wx6ML9El4K7JCoaGgyvvgaf+yw9Rn0++CvDipV2OMEHt9zoMHGCQ1OT4cpr7cGkvt7WDQ667hqHCb08yIvI8OHzOVxzFTz1tCE/Hx78Vedxnv0LZGQEOPccBXiJiIiIiMjw5e0w9AbbVFXBkqV2eES+zUK0Y6f9f0wB7XeWjsiHUaO6nr630zM1xVBa5v4fzIyVMARbFAvHOWFLxgUChr/93bB9B0wYbxu7Z0zvXxtUU5P7O7W22qxhweFIBeLkLvOcHBsQCOEz5EQa0NWVgLHBdYOZvaZonQ0GKVoPEyeEL50SvCl1Xyk0NQeABLZsMUybqvZJr+wsN3A0I7P7caMhELCZphoabLWInJyB/T3iZDPsHYchs+B+vz02FZdARlvmvB07AWMzEmZ4sum1NBtqa21HcEaGiVow7nAyYoTbYd4xeyXYrGjB8lyjO2QCSk11A66ysujWsuU2mGp3MeTm9GFlM4aycti40QaNp6XZMrlzDoSZ0+0oiUlQVNT5rYkJ4G87lmZmup8lWBJr/Hg3MGOg7N1rMxoddKCd96GH2Od9PoeTP2n7thKHUEa5hAR7nE9ODh/EHYmWlvDrQTDjK9hzT2+GzIGSmOjg9HEnOHoUbOghuKsrycn25nQIDVipqzPs3RvcruJ/v9XYCBs32eHcHBjbIcApGPwO9lgcaSBu+3s8Ae4VlXab7Uq44LKGBjcLYaxvFoiFhAR7ndHYaDNBlpXD6Z+yN7B4g1xTU2HiRPt/SClozyq4/GP7nuwsOOpIt2R8dY1h40b7+0+N4nlvlqeMczCgPNLfsKkZduwwtLTAuHFOt+tNNFRW0X4N3NTkfpdAe8Aq2ADZ3cWwbRvMaAtQTopkP2pMe2br0jIYOSL0e1612v62Gelw7DF9/xwd+dvKx5aVGUaM6Pm39W6vfn90M9uVlRmMsecu+1M2sWgYgk0xEm+amw1XX2dYvAQ2b4afXNh1lO+//m144UX3/x9d4HDkEXbclBSHqy6Hq6417XdBAJxzNhx/rDZskf1dZqbDD3/gtA0b7rjLdDr5++PTcPR8w4EHaJ8hIiIiIiLDk+O4HZrJns7D2jp7J3tiokN9Q3Q60GzWIvf/YBnBweicGyw7d9pgAmNsuZCcHJtdol8816otLbZToLf27evnMkTJtm02wAng8MM6N+r7/fbavKmprZyRYzuRepOdYyA751pb3YkHszsEs/wANLRlLEgeoI745CQ3Y8BQKx/Xnfp6W4qkocEG5lVXG5YutUEao0fDlMn9b5cJVnQAuz0UjoMdO9yMCUuWwvHHGqqrbYa5vmTvCKfF0y7t7cDrrx27QocHumPSK5I+s9ZW+7v2NrOOt6O/uQV62c/frYQEB59jCBgbjJmUDA6Gww9rZfr0zl1bg9HR39/+x5Bukyg3X06d4jB1Stevd7ddjsy3nefQOZNNR/WezF592UbKy+37GhqhdB9MmGgPmy0tNqsl2Mws3iDn1lZDQoJDfr6bdSotgkxMIctdb4PKmpt7H1Ccl+ee+xQXA45jyxA70e+UNsZmRrPndAa/304/I0rnXt5sXcHhxib78G7Pkdq9u5WPV/hxHMO8o7ou+RXMthTPOmZy6002ntZWe4wCexNEcFvcvMX+ra3r2z52sPVmdS5aD4G1BgPMngUFo+P7sw2EgcjS150RIxzKyk17MG5VlX1kZIAvARwcfD57PZiTbcMcExOgtMxQXg67dhuSk2zwV1W1PTetroEPPoRjjrbb75KP7PpcVm6zPPW3LG5trbHZYbPcLJGju7nRqCubNtvjj+MzHHZoD8vUz1WxwpOdq6bDfjE1TGlL7zl+uODqjoxxg19zcmDBSaGvBzOx1dXbTF8pKdHdtvqSRbg3WVV7UlFhWLrcDh96cOeA83i2eYth2TLD9p1QV2tvbgmePzU0mraMngO7L1Rwl/Tb83+DxUvs8F9fgE+cAHMP7zze2iLDPfe6Z+SfOhW+8uXQcebPc7jvF4b/vQYvvmTTap57zv53QiAi3fvs6Q7JyXDLrSakLnpziw0Q/f1vYeRI7TtERERERGRoqq4O8PEKgzGGvXsNoz2dJYmJDsfMDx1/717De+9DSQnMmmlowCErCwraGkrzct1sDOECs7ZsdW+0GzUqfKdnIAD79tl2HVumJHSc5mZbBqRjJh2/pxRGpB17wYZ/sI3bY8bY4YpKW5IjLxdmz+q8jI5js2lAz53TQY19CLzqSceOnmAmlWjy+92OncTE6JXs6K33FhJykybYz9+xk2IgNTYakpPD32z6+pvu8OGHmZA741NT3QwwnUrSebIf9SdgJD0dmtt+f59v4EtgDZaPltptZ8sWmDLFdgLW1NhsGv0p7diV4Lpe7Smz09pqWLzEoaHR7pPmHOgd37BylR1OS4OD5kS+fXi339QoZijYH7Q09zxOVxp6KAUINujFcRxq69yNcrCTPXS3P/D7TfvnSEyEtDR34YLLedAc20E9a6bNpBkv8vLdZfUudzi5Oe5xrS9ZPIKd4oEAIe263enuey8Y7XamJyWFz9xljGHpMjvvqio3Y2OkGTXLyt111BgwAduB29pqH8Hg0l27DY0NhooKWz65L3YX23UEoLYGMtsyqfU2iKRjycigkN8sCtvPso/tyV19rWF3MWGzpMLQuDGg43q2eAkcd2xk721tha1tZdxSkgkbaNmbmy+8gXbeoPSBlpICUya3ZX/qYfv2t7hZtAarzOzs2WDa9hsDEVgVLMUXqVkz7XlGQ4P97hoaTI/70P7yXuOkJNvrq2Cwk+M4Ya8JqqrsZ9u1y2Z0Tk0JXd/r6m0g/ejRob9ldXX/yo8aY1i0uOusz5Eew2tr7bqWlBR6ndiVbE8Gyr5kJMzLCy2/+NobhgUnRe939e5q4vnaICHBXR+ieZNKsLobwMcruy+93B8tfvfcMVo2bXZLNdfVu5mLN2wwfNyWDW/a1KjNLiwFd0m/feXLdmX+zytwwfkOcw/vvJGUlxuuvta036k2cwZcfqkTdoM69BAfhx4CnzjBMGvW4NT6FZGh59QFDlu3GZ54MvT5sjK46jrDQ/cT9Yh2ERERERGRwVBaZoO6DLZDp6e7WT9eaTuLAsZ2Ch5ycIcMD8bNBJUfpjO5uNg2TgLk54W8rV1yslva8eAOnV9+vw0u87fCzOmmPcMXuB1dALt3206Q3vDeWbzkI/u3thYKRptOHad+v5tRpGOH8/btpr2zdNIkm+EEbAfvmAL7/aSmhmbtqqkxbNhgA4dsOY7eX2N6g4Z6265cW2vw+w2JicEs1u5rxSW2vCDY7FAnfqLXizZgBrOTorjYsGqN7dw67ljTbfam7dth5IjBW7aOhlP5oGBQZGvABjnU1LpZdHbuggNm9226zc2GDRttYIwJGJxu2oXr691tandxaHBXa6sbfOL3233UuvX2/xEjDMXF9o2jRsL48aHz8HYWd8ziEi2D0VrV3Gyoq7UduU19yFTUF1u29i77gnf/6O183bHLduB3JbhPBJsNJyiY6SJWKqvcbJejRsJhh3YeJynJrlfJyc6gB6Z1NBBZhCLZz2VluseJYKBLUj+2tZQUp/2Y39Xxp7nZDSrbvgMObgvu2rnTBrL0Vm2tPb/J2WXX40MPsR3Ia9ZCc5PNPJiX1/N0wtm+wx3eV+oGd0VLiyfoPhrH6wSfG6TXMQvm0fPcIOpIstlEy/YdoStipwDuLnTMtFofJuh0tyerZHV118Fs4XjX8y1bDXv2wPTpnUuydTXvweD3u0H7PW2X/dqHGdOe3dbnI+JSu+vWuRlRR4605Zodp3fZO6uq3eGGBjcQqL7ehGRpKikJPbcIJyXFoaHBtAfg5eVGfoNJX7S0GBzcQJusLFvytKPGRsOevXY7SE+DiRPD37jj5e8iQK+52Z5D+Xw2Q1tvvuvGxu7Xo96cGwf3XYORBTA3xwYOB39X08O+0gZ02+GOQYeBgImbOAdjwgcmd2VCoT3nd5yhm4G4q0Dn7uzb58azjBpJewZHb0bo4LSDgm0e9fV2+xvIG68U3CX9lpjocPUVcNKJcFyY8ol+v+H6G017CvecbLjtZjvezbcFOPf/HAoLO7/vqCPjY2cnIvHp1dcMTz4V/rW1a+Ha6w2TJxt++AMnbk6eREREREREIrG2qOtW69ZWm30CbGdV8CY7x7HBSSkptnG5pMQtAzZ6lNup2THLUkfeTAG1tW52i+5s3+F2CKzfaIOnBlptXe86Tmvr3GWs8mTSSkywJU5GjLCBP5M9gWnLlrulisrKeg4MCt7dnJ5uvze/32Y1mtKH76O+ARZ+aH/HkSNtA7E3I5L3MzS3dH5/tATb8wDq64AO30FP61M40QxyWrXG/m1qtuthXzrpwwl2AkTaIby/CgZfNjfZDrDs7M5Bl72xYaMN1AK7/SS1BfwUFHQe19vJ1LEjzbuONbfYG5OD0y0vg8YIy89F04RCd588YRBKMtbV2QCTNUUDV3a0I29mtZDnq23QSziBgKGyClKSOneAGmPaS/NNngSzZoDjcxg1yse+fQGqqg2lpW7gWl/2Rz2przeUltqgtdTUwQ3I2rvX7VMpGB29KgW7iw3vvWdITrHZJbrKbFddbec/dkzvSu32RoLPdvz3p9O4utq0B/ZlZvQcyOgNch87NrJ5zJrhZi/5eEXoPsbvh7IyE1LGOtLjXH29obbWBqkE1/2R+e65WHY3gV319bYcd1ZW70pAFRTAxAl2OJIMOD3JzfVRVm4jHxKT7Hbb0mI7w7OyBr9NvrXVhA1ay4ygnFgkGbJyc92MPt4SZYmJdj2B0HOH1BQ3IDoYMNzYaNi4yQ4vWz5wWWv6oqnJLYs8Ij94c0PPentu19gIyz62QbnZWTB/XuTvbWiw2015OaxZa/fL847qW7CqN8NwxzKzg3mOEKnqaltO3u+3+87p02wg0sIPjF1+x3D6pw1NTYa9e+2NOvm5/Zvn+g3uDT4pKXaekUpKsjccJSa6NxL1ZOoUuz7t2dP7ZQ3q77E6P9+WAX71tchW7PHjHY4PEx9RUWF44UUDGGZMh3lH2fXV57hlmvsT3NxbNTU2g1tlhQ2iP/SQ7sefMWNg9uGDecNLX+a1eYt7Tnv0PEhIMHy4yO4f6+p6nuBAfz4Fd0lU+HxOl+lJf/WwYfnHdjjBBzf93GHkSLj6OsP7C2HJEsPZ3zac8RlHWXZEJGJHHmHvSti71zbo19SERu0v/NA+9pUarrky8rs/RERERERE4snkiXDALPd/Y9yMNN7G4JSUts7fEbajduHCAOvW2U6/0aO6vx7KynI7VrtriwzOr+PU4rmcRFC5J6NLaVlk72nyBAp07PAJJznZZgBISLQdtTU1NptRdU1kQXJee/bY8l0Aq1bZcpnTp0Lw2x+ojELdCVfC0lO9MERFhS3PNG4c5OUOzvV4Sw9Bbr3JghFc4sxMtzNq7Ng+dBp6siD01FFVUWHaO4zz8/pe0mswBTs+KyrAl2DX9aTEvmeJCgZggZ3WiLZgwnCl35KTHU5dEH4+3hJ/zc2ww5MJp3hP3zPqeBljAyv6294U7XIxQTU1Nhhl+3ZDUxNMm9Y5c0RfO5/KyuwbR4TJNhPOR0vDZwRJS7NBJitW2I7YYBCV9zMEZWU5TJxo+yGWLfdT32Cor7NZVjKjlBGovt6wZau9OX38eAdjTHsJ0pI9MO+o0PH37TOsXmMDpFJTnZCsP8FtubLSsHMXVNf0PnNHWbm7TSS2HVe6s3WbzXwHNqj70ENC5/fqazYb5OrVNuA7I8MGIx80BzZvNu37uqOOsOVVP1xs/y8ugeO76PvpTwemz+dm0jzuWIfycndi3oxsXnv3ucNl5fY7qaykPVBmfGHP8030BN5Eeiwd4Qls9iU4NDa5y1peYYNTevtVtLQYFn5oz6GmTHYDJlI9ASXJnn2f97tuaHCzkRaOC5+VtStZWU77OUlpqQnJkBrpcbKiwrB9R5isVQYWLbbb7uxZplNmRK+6OtP+e2ZmwKgezlU7qqlxS4pnZroVPMKekzqhgVhd8R6DIPz65D0eeQNn/X7at7/UVCjsZl1saITSfYbSsmDWpfg/3ofjvbmgvLx3Wcz6KivLzcT58n9gfFuw9MpVvQsQi0S0ji3R5D3XTU+3x+HUVKc9GNpp2080NLo7jKpqGxQWVFllM4xFyhsEunt374K7EhMd5s+z2+eq1aZ9WsFtP9zpz7Sp9sktW93nkpLscQ0iKwfc2mozVGVn9a90/THzOz9XUekZrrDXeOVlhvUb3LKeaWlw/LEOa4tsIGNTs82U3Z5FynGY03ad98kTh+b2vz/ZstW9Hty8BcaNieniKLhLem/ZcsO+fXDaqT3vcN58y/CX593/L/ihw9zD4fY7bWAX2JPw+x+Af//HcMtNMKZAOzIR6VlersMtN8KTfzRcdbnDho1w+VWm0116/3vN3sF1y40Dd5eZiIiIiIjIQDnpJCckOMbbadXiucGlstKWQysvh8MPt53JmW2djbk5htmz3AxfHdXWuh2pYzwZchIT3Yb0Xbvd+QW6KNsxmAbjLucxBbazCCILzgp+tePH2Q7B3cW2kd/n9D5TT3o6YAzVNfa3SUiEpR+Dv9WQkw3jxrpZRHJ7GTg20HJzYMlSO7y7OLYZKQ4+yP0NO35PjY1uKcHGxvAdvznZUDA6kdNOSaa6OsKUAx7eTHgVFd1nLaiotB0GYNelaAQgdbRps83kMGN611mAjLGZihISbOaC7lRVtd3dXut+v+GCAPvEE8hQXg5jx0TnNvisLJh7mB1OTQ0zW89sWrvY1wUC9g7++nqYM8f0uT179RpbymnmdMO4cW7mqWjEeiUn2+OCL8FmGti7F8ZEoTOq1JOhaO5hkf0mXZV68vkcdu2ChEQ7nb37ui/rGPxt6hvc+XqPiR0zuPXWxyvtNru7GLJzDCnJ7vrsLeMFNrCkZA+MKrb7jyPm2mxpQcF1Z3FbOeFNm2D6dMPatbYMcGKCYfYs6C6oY+cud3hXBGWNSz2ZFr1BUF5NTXbT6ljWyrtvamwMLZPZ0CHgJxhgDlBT230w52uvu9nXRnQIQOoYGNbbQLGuts+O04zG9vThIvc8qbnJkJXpZnkKdwxOiGBdLC52198tW8MHTNTW2n1WcDgYxLVjpzvOrt29C+7aucuws+39Tc0dvncTWXBu8Bi/b5+hsNCdwL5SN9vJ2nVu4E04NbVuUN7YMeFLy3Vn8xZ3PT/skK7fP3mSW8p1/QbTvk7MHICMNMF1PeQ7DTMbYwzFbcHeHQPKYi0jwy1FmJLc/bhe3WVODFeizLsP6irrYziTJzntmfpWrjLt61htnQ3QbWiw20NPQdO1taZtO+16vHERZPaz5Q/d/3s6jvWX97zSe+OJdx8eaDUkJYZ+Lu+NLcGAzsHOTusteRncf3e136+oMOzZY1/MzISMDCdsFteurGg7nmP6XiocbPIIsNdyC07uerzqGjewC6Cx7biZ5cm+mJAY2fGotjb0SxnIjKGVlYbERMjMDD+TJk8gc3Jy77JExkp/zy3AbsPBjNlJSaHnd0Edzzk3bzZUVA7ODVgK7pJe2bLVcNW1NuVlyR749re63ph37TbccZe71Sw4Cb56JjzxlI2oDkpOtgf+onXw698Ybroh/ncOIhIfDjzA4Y5b7T7jyCPgzttsgFfHu3UXLYaLfma4647Bu2NYRERERESkv3y+ztcwXTVQ7thp7+ZuaoIX/27vjM7Ps43KtXW2ZB1Aelr32QS8Df3jC532zB8fr3Bn7O3YHWjeDubcHHfe6emdx/Vmn/DeVd2Rv8W0Z1gJBNyOzI4dxR2bvCJtHA4EbKd8cnLoBHrTHl4wGj5xgsPmLbYjsrzcdk5s3WZfP+4YmHNg7K9v57VlSdixw+2gzM7ufh3p9D0MUmmO7n6/5C46MB0HEhIdkpIcEhJsZoS+CGZ1A7sNgu2EXPiB3Ua7KosWTfX1pj14bNnHXQfd7d1nO8UAjjzCdNmO0txsM40lJUJTI9AW4JAaQUaFSHi/6eKS8AGWmzbb7WPKZEhI6Po7dHxg2oIoUlKcbjNOhZSm7SJ70O7d7msrV4UGxXal45pTX2/at5m162znZTAQKDcHDjm452l2JyPDBm4kJ9uSSJWV0Qnu8mZBDGan6qu6OsOmzYaaahsA1psAlSDvsWB0L4ND/H7Tvq9KSLC/vd9vaGywgVITusk65M0qFun3EAhAfSO0Buzn7RhgFS+aehGguWlT968HPCt+Q4NdF+vrbUYqx4Gi9XY9CBgboNMX3kCn7Tv6H+QXCcexHbiJSfbviBGw1dOxnxzJfrCXu/1olXmqqvScI0Vhmt5zp44BPqvXmPZ96pwDbZBkU9u52kB1gPt8ocFCFRWQlWnP9bZvdz/yzBmd3+tv6fzcQKuqgqXLDI5jA/yCpSzzct2MSd2V52xqMlGviFTdtl9MTgnNWgc2y1q4Y2NX6+e69TbL2+hRJiSbYILn98/JDvPGLiz80LBhQ+d5BgKw8AO7z5k1o/tykg0Npj0r1OGHudcBHTNCRVIKN5hFbKBs32GTrUydav/PyrL7uaQke3wvKjJMnmQzRQXPMxMTQ6tkeW9eCJaQh8HZV/ZFXZ1hx87Q7F4+n5vpz55Hd//b7C52r5lycgzz53U/fl2dPScrGA3Z2Q6lpW4mzEh0lxnQmwm6tbXnfZ/3mAbRLfHnnda+fYa9++z3cvQ8E7aM7qLFbpD7J46PLGvacFAw2t1Ouiob3fF6tq7eDbgc6AAvBXdJrzzyqHsy9uLfDV/4f0579KJXc7PhhhtN+0F+fCFcfqnDK6/C7x5z9x4ZGW7EY0EB/Oyi2DdKicjQddSRDnfdHj7Aa20R/PBCwz13QeE47WtERERERCR+febTyVRVNYQNJPE2JHrvqDcBW/KtLNF2CDW2ZWopLLSBB8GsW/l5nYO7Ro1y7071NkY2Nxt27XLfF1ycjtkRusuilZfrdiJ27CDqyoTxbsN2b8qGRJLFA2D9RtshC/b7XLnK/Z5TU/seaBPsVAlmFEhKMowaBdOnRTa9cWPdAKncXPu3psbNDrJ3r9tZtXefzUbRE7/flgkB25GZmBjd6+Hsto6ASRNNe+dcZqYbTBiOz+eQkGAi/r264+1kDJc9JS8XDj/UDnfsbPcGdHXVcD8g2n6CzVtsYEptnS27mZ5my6pC+ODF3ihaZ9pLQM6eDQWjnYjLbQUDu8CWBD3h+PDjDXQ5Vu/er6HBsGYNjOvQntOe6cyxpfG6MmUSbGobNzPD8M67dnjs2M7bpzdjWlYXJZn6kp3Mm/2oqalzEIT3+9xdbNu1qqttJ2NfZGU5jB1rmDXTsR2lbdtkWZlhe1sQyrhxvS+h6Q0A7m/WD7/fdoQ1NnYfYxII2GPR0mV2X+NvMSSGKYfZ2w7Qxkbas5BlZoIJGDZutMfL3FxbMrIrI0bYjvHJk+x+CDpnuSktM5SXGzIybMCB40SW0SkoJdntFI4kg87kydDaFmw1ciSsLbJZ+A6YbYMUZs807Nln1yvTYfvNyrRZVRwgO8y+1O83YY8fxSWRH9sTE2H+PIdVq21gpp2u/d06Lk9XvEHekc63I++xJ9LjkM8Xur6npjkcMDu0/GUwYLa+Ht5b6I7r99uA++wsyMnxBLdEsC54z8nC/S594d3XNDR2PV6kyisC7euGN/gTQrNSVVbaQNagaVNh6hQ7HM3ydwkJDo5j2vcHVdV9P16F26d4p+UNXkxJccIGTXtvPAiXJbSx0T1vnDABgnFciZ7zkq7OCTZvseU58/MMR8yNzvmd3w872s79MzPd36ivgueDe/fZUqT9FWgN/d5DXmt7ft2G7oO7vOvl+g32e4fQbTyzm2Adr2gG3nTU0OCWoCxvC/5OTnbPWevq7blNaVnnI3lamg0WNNjyoakpfcys2sNqFe7GFe93YoyhstKeg3uD57vzwaLO26zfH1pasifeKsiR3FyzZKk9L9u6DU452WZg8gYx9rSPqq0JvT4OfgVzDnTXN59j55GYCBjTvr/ctQsKC92F9GbtjDZvBqoVq2yGY4DVa+DoMCUovetMU3M/grsGsTu2L8nFAgHDzl2Gqip7c9uGjW7G1kjLvQbbQxobBzbbGii4S3rpumscrr3B1pK/+06ny4vLh39rKGo7UUtOgpt+7rBuPSGZvHKy3Y0jLRXuuNUhVxl1RKSfsrJsw8qWLZ3Tvu/cCT/8keEXdw1M6mUREREREZFo8GZJ95ZDSElx8PncABBvQEqap+Mn2LkR7BRMSAgt4diRN7ihstJ9vqER1hQFFwrGt2Ux6djA7b3xr2PZpexsN7grGLAUT3bvDv1/+w4bZNOXDDK+trvha2rsX1+CDRSxZfDsj1JZaYOabABJ6HWp9w76YAfTnr3Q0uwua3edVeFs3mJLNoFdH8JlquiPujpjs9E02M/rc+zfwZKS4na+hAs0aW11O0XT0iKfbl6eLS/V2AipaYZNm/0EAoH232LMmMhKkxx6sO0Mqa212X28nSTezq/qKrv8wbI5kZQB7U59PTS33fTWXuqvh/eE66DsrtPS54Nmz+eJdrnW8jIYNdJmFijZY9dfb+ap5ma303NtEfzkR5G187T63d8hkiwttbWmU7mavnTalHmCHvbs67yPCa7HgYDtrE9OsiV+gh1v0VKyx3Z8Q/dZHONJRYUN6Ckrt/vQquoAY8f2fkfT1GSoqbXHqXDbb02Ne6wsLgkNdIRgpkcrJcVhzBiYMd2THcVzbMzPMyxb7rSXNg2WVExKcqfRUxm6ceNozy4zblz34wKMHOG0nx9UVpr2THDLPoZ5RxrWrXdo8Ruysuy+JjnZLU07It+2qTqOzYriDXqqqDC89bZDdrbhqCO7X/k77jMKx7nHoPGFYcYNlrHr+eMBoUG5fQ0uLCt3v8/iku4DQ4MyMtxzqsREN7tZx3OecNZvcL+DTxzvZllyugjuCp6XbNtmSzMH15NoBERDaGBMtPfb3t+/rs5QXGzPGceMcToF5DgOTJ3S9/Z5b+nRsvKet6fduw0JicGA9MjmURUmC6m3BF9xsVu6z+83rG07Z05KhNmzO3+2YMBKUsiNFO5wZaV7jh+JTZvt3/IKqK4x7QH3UdOLwKVIgpy8u92kJJg8EWbP6jp7ajjRyDbl/Za6CvyrrbNZx2bNjF0fUrhS3nl5DoXjTPs+BewxMi/P7qccx6GiIsC69a0kJdqg+MQEG/DbMWioL+cyvY1la262gdmJEdxE4ffboMxAwJ7jVZRDq7HXFr29/jnmaHe98vl6/qDe7dDv77ztH3Zo9+8Ptxpt3WaDhVqaIaVDWdKAgV1tQV9riro/HxuIQKGOv2Mkx5fW/mQbjWIQZHOz6ZQdG2zQePBGhppqQ1Zb/Eok+6Zt2+H1N2wA4axZwRuW4rf/WMFd0ivp6TYrzvbtMHVq+BX7rXcMf3ne/f/HFzokJcHV15r2VMM5Oe7O0XHghuuckAshEZG++M9/DXfdbWhugQMPgHXrbKpzr/IKuPAiw203w5FHaL8jIiIiIiLxp7IqwIeLDIFAgHXr3Ww1py6wmZcOP6zze7yNsiZgM0Q0JdtOonGFkNjWGZIaJsClaJ1bpnDsmPCZXFJSHArbOrB6k02rt/pzB3yk701Ksh3aDWHurA2WOtm0JfSufb+/58b11lZDTU3ouMa4mTHKy017RrJwnUkhJbqMXY6ZM21wkAOMGu2+3NJi2gOBkpPpskPP25GzY2f0grv8fpv9Z+lyQ3a2GygRCBjq6mzpLV83ZfIGizd7ysFzOpel66pzPSMd9mE79/wBQ9G6Vurr3L4JxxdZKb7Ro4PfgUNiYqA9UKOjaCd+8AYS7dtns+F5g9sy+pkZLJyO7S/9FQzc2LzFbUdetdq0f6fejO0DWd5u4Yddl7Dsq3BbRlZbupbWVvv7DFQ2kNB9j9Nlia2+6G2wTfv254QGCnbMmNLYIbtQx2z9kWhtNXzwoQ16nDwRZsywQTqjRtrXU1Npz1QZ5N2OoHe/SVfLOHOGzcJ18JzOpXu709vO3dpa20ke3Dcv/9juZ4zp/DkBpnmCvFNSHOrr3Q+7c5cNHKisssGWfV6uDuP29H0mJ7lBqtG2a7chIw3S0iL7AJVVbva93BxoqLftz35/WxBwW8f95s2GhkbD3r3u/t8bhLFnjxuk0NWcg0FQHT970To4/tiIFrdbyd4giwE8TG/eYoOY9pVCTrYhIz10ZnV1thwh2PVryuS+L0xPmbkCARvYOn68/T9c0FY4vdk3BucBdhufPbvrcZOS3f1clucGif5kMItW8F9yMhwwq630aBSymiYlht5gMmumLXsLDhMn2qDU3phzkFv6zBvg59WbALnudkPR+k570ts1Pzvb3a+MbMsieeABbUGjjsN7C/02mLkasrMNuTlOzMJUAoHIshRu32EDoaa3BdvW1dmAG5/PlnItKHBvaookwM/nc9pLf7aXoY2QMfbcwPu+d96FU9rOBUeOcG/GcN/UNq8KQ3V18MYSmzl13XqYOtmQnm4zCyYn22xQHTO4xqNgVk/ofZbjTZttFuHp06K3PFu32XKZI/INcw8Pn80XbOC2d9/ak42b3GREe/fam4eC5XB9jsGYnreg8YX2vNLQ/8y2PVFwl/RaYqLTXt+3o93FhtvvdA+HJ58EJxxvOP8C90QoKzP05On87zscf1zsG3tEZOjLynQvfHfugssugbvv6dzAWF8Pl19p+NMfYUyB9j8iIiIiIhJfSksDVFcbAsZ2LuflGuobbGdxUphSVBBaJiEl1XbK+Hy2Qb2hHkpK7GujRvH/2fvveEuSvDoQP5HXm+f9e+V9dVV193jXY6AZjCSQfvrJIISQtEgCwaARZmAMaBjGM8OAkOCHEavVLpJWErsyLIvQMH6m3bSvLm9e1fP2em8yfn+cjBuRefPed1919Qhm8nw+1X3fNZmRkRHfiIzviXMwM+3+7e4uOpZtimAAALms2wZI2bRMTvVOQL2S9iRmkuXlWMKdPMG1qM9+TiKdZuJxKE3SmpnIMpN65T0SfO027Q/rdZZTKXyYCf6bt/VrlQA0Yb63sgZ87gtAsUDFs9ERN6luc5PWIQq9yCdHDmk7uqMD2DgOirtLXERfX+f1KhWilRUujifiwIkT9+98vXD0iFbN2CspurZOctf0lE4IDu/xm6vXJNKpFra3LBQKEg869luXLg9G7lJot6kYVKmwX3pVxGwbiKdpfwpoNZ17xcy0VheZ2Uc5Xxacvt+V8LpHqDYlbU3e8iq03xMsreZwPxLX91wMT3IyFGKyv9lk+fqpLQ6Cep3WjpubVNdLG7G9VOImbMbR/a2LmTHNq+Kw33YbDvG6W07MVAlgb5v1EojCRtKsWNKx0VTU8WJjU68Z3lkiuSsU0uSuQZKW+xnf7twBJia73xcCrPJX0LOnUJB46mmujZ44LhGJCBdRyK9tKWvBclnia49JtG2qTIZCAnFjfhGNupO9+1FEFAJYWpIol5n8Tqfdc5dBqmRhQatlDWqb5odMBsgAGB/fvzUpwDnBnSUm508cBx56kO/fWiRRcWsbmJ7u/t3LmSOZ1q73E/czCR2Pu0lpJ04wzjxwhnOZ06dYdwIkOK07c9P9KDfdC9ptrbYGaGvPl4vBSCZ6zhpzYmQsKnDUsTsMR2THdtXsD2ZbsW29CcOyej8L3A9Ua8Ycabj7mSEc1jH4XsbkQwcFDh3kvKhcYbyyLB7XstykV7/+cv26fh7pZQu/131xkbb69MnVNQoI/M9C2Kdv1uvcSKIgBOdLjYYi9AKQEo0G45xlAe15ibk51qt57c3my7DZu49otWSHM3Bnyf2ZbfNfs6nnH4MQ+l+udfjICBX+7i51f3bokKG06xCIKlVeh9pUo1QXl5f5DB6JAIcOMNYJITA8xHj5ZwX3e0pSrcoO2eqFix5S8cvAjZv8/24GKBYlhnpsbuo11pbL+h6lU247zA4E10Mmx3lPn3wKuLUoAaktXIFu8mcyyTnRfklw94KA3BWgL559TiIU0pP7fmi1JD74S7Ljm7swD7z7XcDPvk8vTsXj7oWx7/ku4Ae+/xUoeIAAAb4l8ZY3C/zdvyPx+JPARz8kMDcnMDFh42ffpwf0cJgTwH/wwyIgdgUIECBAgAABAgQIEODPJK5d16uFdluTgq5cBc49IPGYo0YUDgNveiOfa0IhqnJFQlxYzOWYfI3FaQk3aD7RXAw3f5PJyM6aztEj+99pvx+YCXpzHel+JSCzWYmVFf23AHDkiMDEOBU4/GxQgP5JWWXFCPC+HDhAUsqpk0xmUj3FfYByWXYSm6mk+zOVsMo7aiGjI6wLRXi5cROIRiSm9/Ncex9vmVqwb7aYBFTlUrue02lgYV4iEu4+abst75sawtAQbWgATajbC4oQVixoYkK57J9kabeBZkui0Xx5rMV6Hbh8mXUVi/koqElgfk50iGovF6kUCRgAOkljE68gr+S+Ya8iRqO6n0T2IAYkUyT1AYxxav3aL0Fokpeqte7P7xXzcyQYAiR9mGSGRILqe296I8tUbwCVsrwn+yl1b8tlxrKtbQBSq9a1mtrqb32DVkMqtvVKNNq2RK1GV49lI3Yurbi/tx+1FIXxca0wONHj9yMjvG/jYxx77i7rimkOqHwhfRK99bq2Hh7ag+jpRbtN8lyxyNhjKkCVihLbu0Amx3pTRNBIhHmS3V2gkJeYnuJvSiUqegxiHdUPrRYT+k88RSJIq0WLv8OH9efNJuPa7AzJeEpF6gtfsmE7BJhwmP3g2g3g7BnpstpVxHGFcrk/wcscN6XNY+bz/JdKucf1pKEomEiQkKw27KpzDg9p5cFBY34/FIr9P5dSoloFHjyvy3LnjtvasFIFNreky8ZzvxZ1JmamdIzab7s00asMBw8Cx525x42b7vt5L/bZ5j2cmQGOOyotlQqFIgDGlnyeiigA+33yFVCQBLS1m8L5c8Bb3kR3oS995eUff25Wk9LmDDXQUIgWg+q1QiyqxxK/Ln7wgMDb3tr9AdWtiMU7mnA1Ngq89jX3XPwuNJvM6Y6Ocq7aanarFppotzWpS6ngAug5f+nVzqtV4Mmn1F8SAgJCAG96o0QyeX8mKe02CSZCcO4XchRlTYtsk/Tn3cgx5UPQ3Qv3c5OLOU9QKpvlsm5/iThw7izHxqef4ZgvwJx+q0Ul3WgMGCm98mV9OfCW47WvBp56Wv/dbu2fjN9syQ4BPeTzLLIXRkcFRkdJkO+Q5hz0atNKiavR0NcUMsYp2ziOZQkkEvKV5nrvCTXGkcTdvyB+7UUpNntJVl3ffQWu0TuHHx2VeMaxox7uodpVrVGlG2D/VnaY5vx1KM1NVPk8lU5rda0WuLauFb0AN8n85i2uYYRCnLe+ksTJgNwVoCdW1yR+/oPsmD/5buCvfF//3vd7/5vElSt8HYkAv/jPgF/+FeDadb6nJjSqUz94AXjPTwtff/sAAQIEuFf8L39P4Id+kBLiAPCmN1r4pV+08cEPcSGo1aLH+F/8nnvbmRUgQIAAAQIECBAgQIAArzRWV9sdNSTbRiexurEpce4B0UlKhI1FzUKBi5QhC0g65C4z6W0+/dTrEhsbtMEZHhYYGdEJhF655VoNqJT1axMmASKTdX+2bVimrK0Bhw6iL4QgYUMtkO9nYbTvwr9xXevrwJVrfP3mN96fBKMQepHZCnEn984Orzkelzh1srtiKxVNsvAmkLz1CJDopxAKAZvbwPQ3SpWpB3o9Vc/OCpw47m87ptYGi0W2qZejoHHpsk54vuohPu+bCId0ElIl7YaHWSbTrkMCuL2oE8ISzprBy0x87exKx7ZL9lURSCZpd6auZWrStHTcP44fEzjew3lhL5h1BjAxOjuDrmTrvVjj7QfqXiiCZyRCUoxKIEcioqftlZmsBXjvVd2mUt3fN1E0kqD30+7R3MkfDr888scgKJV1/6wbY0E8DhxxyD7XrgscOrhXMk/iya+TbHL8aP9Cm8qPgyCZJKGtUJCuZLsXqZTAwjzwutcyl7C+qcscNZWfBjxvsylRqVBZwrQuNLFX3e/uMonXbFI54p2PAmWn7dTrwO3bWuFBbXyfnKCSSqWi1XFu3WYfSyVJajDLYrqfDGIjd/0GlWZeuqSsAt194cpV9tvtbRJEkknOFwDgxg3d104c07mc9fX+51zf6I67Jsz5Aq0ePRXbp579FDWuXdf1+eCFblWh/WIvAuXXnyZpeWFe4shhXZ6WEf9KJbiUdF4u1LynVgNS4/7fSac1EX6/CeREQiDpxEEr9PLZHdNTFrZ3pHNsTf73zq0aTW1zFo9znqQS42oOqhSqBrXL9EOv2BqNCgg9ut8XlCsSV686irYpWmcCrAc/slNnjjFAtZtqpI26HrtMqzhzvI7vox1IyblJOAw88STJC4cP7d+62yRY9tqAYSq59brumzep8iYlcPlKf/LagQUSJwCtStwLd+9qlVvLAo4d7f99L+5lDH6l1ZjMmGpZJNVwrq0rd2zMQqPBv8NhxjDzeewbgStXJcoljtGnTvZ/RotEBC6c5+t3PspKn5mSCIUdhVDB9r0wr76/9/mfeprjMACcO3vvvf7UKRJ6haAloDkO+KHRIDEI4NzGJAx6n58febP7WPW6xKXLwPKyxMLCyyd8D4L1dZI5d4aBtz6y/98/8yyf4w4ekDhzWpc3FNLk5Hhcb/5ReCWIhdNT4LNPn45rEv3N55hec4Fajc8FKr7l88DZM8CxI2zbphXkN5LqEpC7AvTEr/wq5XEB4H//fYnv/I7uh3iF51+Q+P1/q//+sR8F/vi/A197TL/30/8U+PozwBe+SEb7Rz8s9uUrHyBAgACDIBQSXQ8T3/Z2C+KDEh/8kETb5gL7T/8s8Gu/IpFOMw4VCtwlF74HJn+AAAECBAgQIECAAAEC3E9sbdto1CUiEWB+XpMSALcFgJ8dirKuGBnmQuXhQ8CRI7Skv37dRr0B/Pc/4cJ4JCrwbW+nXZxSiTBVG6JR4OABvl5c1FZOFY+yVT+roAMLwHXHQqGXKosJKUkQUOpZ+yFembtnVVJMSontbWB8VGK1yue90TH9PWH1Xjw3FTNUIskPuZzEU09L7O4yed9oAHeXWU/bu7yWUye70wommcGrkqCSAY0mk/PVqlYrAUjMU4mqvewI7xd6LcSHPAvivXZLm7BtEhABJk1eKZx7gHYgwN7lunNX9y+VaD5wAGi3LZw/F8bTz/Rhn/TAc8/z/9WqJk4osoV539JpJkeUslMi7m/p9XJgxo5SmaoOflhY0DY0G5skBq2tAY+8xf29V1rxQR1enWc/NidmssWLyQngoQt87af841WVeznWb4NCgPejVKLSiDcB2A/NpoQQ3etZZqLfDHHRGJAw1vg/93ltifXWR7pjYTarx4dbPvUaj+lYNjbW/fl+8drX7E+tKGFcp1Jg2AtXr7E9tZocoyIRrXKpFB32shtUScFCUZPaFImuV9+QUis+bW0DU1MStxdZ5+UKxwFTPcIk+Q5id2qOzZ127Lml6v1lx0J3aAh49Nuk67e1uh5vSqWXZ4eqrmF7W+LWLSCdlgOTH5aWtGLKoUP+ROFCQeKlyyQEpZKM2XthYoLkPEAr2vkhl5OdhPQXvwycdKyG6zV0yFG5HNuQF352an5oNJj85/g0+Jq02b/3S1BfX5dYdPpyY//DWjfucSm91dbjYjpNYtdjT3C8PHNa4uCBe1+jTyX9VVhHR+/v2HXxEs+1vAq88fWDE0jqdYmbztyYypPdvzPnfL0U1SJRoOXMwQclFti2xBNPMl4cPCBRq/OHd5c4Vx0aAl7zKuf4e4y7haLEXYc8NT5OYn//c2tltVJJk2vNTSKNRu/fA9wkcssh7cQ95F51a1U8M+cCi3f8yV332xLUO37VahK3bnMucXgPYpAXfm21VuP4Y1kkoCuyn9nebVsi4sm9q3E6FtVjVbPJeKAUh+8H7bFaldje1jEGAM6ccn8nm6GC7927QCTi329Mq/pajW2xlwWfL4y6u9e5iZRuRcZazSHHe+7L2dPocCkyxvPc3SX2C0XEVbFaStkZg4QAJhxC7OUrnAfk8myX99tWPZeT2Nxyq3Sr589ajf152FPH5oYH7yaVel12NuisrWvlQoDCG298g/77S18ZPPDW6xLXb5BQderk4PnalRWxZxM2Ce/9SPOzM5wrVCtU5DIRDgskEkAqdW8Ku/cDAbkrQE+8/+cE3vcLErdvA5/4qOhJ7CoWJX7po3pnm5KP/r//i/7O3/+7wPd9r4W/9Bcljh8D3vYIMDYaECgCBAjwjcHqmsTyCvDzHxD48Ee5W/bqNeAH/o7Ev/997or7yZ+RGBkBPvKh3kTWAAECBAgQIECAAAECBPhGQG1aqdW6kw7mQr/55GK3ncV6JxHaduyVymUuON+8ZeOprzPJ32gykZ9KSpw87j6SmyAlOrtwL17Utoz3y1LvG4HVVap0lUpU3ojFBOy2tlQwk0iFwr1d29efYXJqYoI7rVfXmPBV927ZIQuY6gWA+176ndduS2QyTHTmckDtMHDMUWOKxwXicSo09UPFIN55SXkvB4cPMWlx5owmrinix17JwFDIrUJ2v+CXBNvZkbhzhwv0ygIt51ilFYsS8RhJjkkPkUNZuVSrTL4XCi8vG9yo+9vCvVKwbb1Wa1lsn2Zb31iX+MKXBNbWJObn/duQbWv7zEHsCa0ByQwKJokpFGKbNpHNDUYI9UO/JHc4LJBK9f7CN1rdQqFcZiyxbZLpZgdI6hUK0ok/JBY0GsDuLu+bZZGglEoC4T7ERlv27497qZe57K0a+1MRKhQlnn1WYnmZ5Z2ZBq5fZ5JZKXQAvKalu4xhhw5xHFMkKb/EfCYjEY2is6GzFyyLydZ43G2d1AtmjNkroTc/T0te5WoCdBNM2y3AMst/H0gnjToPstfKpvn5F77IJP/EJMcaM4G7s+smlrTtbusyE70uYWOT9by17SZDSbv3b67d0GVV9pGqb5eKEpubVHyJhFmmVovE2Poe5BAT/WJFP7XFkMVzJZNUVJqZ5mZilQjfSyEQAGzJ3FuzBYz2qdP7jXrdsLze5xL41asS2zvA1WsSC/O0kzXJo95x+M5dWn6Vy8DbHhGYmuS4WqsBNc/GgPUNvVng6jW9ueBe0Kt/5vNuS00v9kv8ajYAOISNSoUxudWiTZ5qP22jHanDN5p6XF1bAx5/QgKCFntKXdREKqXnc2Oj+yujF2trmgh0/aZWmlKEc9vWJNd4DF0KmSbZs1HXbb464BzzC1/S388XSLBM7YOg+LXHgEuOg9S5s8ADTvlWVrXNasmnP7VbFDMZHiYxQ7U1c846SL/dL55/QeLKVdbrX/wLErMzL48NkkrBUbfj33fuSJw/B9c1hUIC4ZBuR73qN5PVdeUdn4pFEk+p9NndJlstxuBSyU3OKhSB517wfNnz860dPn9dv9lN/AKA5RXOY1IJxsdwmP1rZUV26uCVdOSRkla7ly+731djizmuh8LAgQMCV65RndIbQ0IWAJuEX0vo506zjt75KP9vkrgLhftL7mq1mB9ttThWHTlMIqJJ3trrOaXffLDd5nPD/VAbu3NXk/tSSW0tvRdmZrQithcrK1RHNjdr9VMdTSRYP/EEfJuaL/HSmQ9XKq/8WklA7grQE5OTAv/y1+i77ScdD5Bh+ulflR2v7NFR4O1vBT75af2d7/4u2qQB3An5937olS13gAABApj42mMSH/4Ydz++92cF3vse4GOf5GeZLPC//ENgdlZ2JmX/5CclPvUJYGwsIHgFCBAgQIAAAQIECBDgfw4evBBGpUyy0I2bvb9nJrUluFhrhZjUsiVtgnZ2uYv4qa/ze7akEsvUFJM77TYTrX42iJaQnd3xiaQmG4x7bIK8xJhXCqZ9X6XaW8nAhLJfTKcFwmHgoQcFcjmdANp9Ebh6Xa/QppLAMYcw5bV26wcpmYig8o77M1VvXoUMU21j0ofE4k1A3otFnLIGAWhfc/7c/o/RD+GwQCRCG8HhYba5SIT1YduOrahnoV8IgXDk/khnmKp2+QLbtUK7zUSGqv/TTgJpZZVJ5LV1fjYSZZ8x75tatN/ZBeoNG3eX2/eDd9ETQrAfqXbwcq1CL1/hNQLAhXNMnppKRDsZYHaOO/bn52RPhkM/4kM0CkQ9VoP7gWmPCujkFkByTnMfBA0v4nH0tPnb2tJx7eACcObMN2b9Ryk9NRpUghgdIRF0YV6gUtXKiFLqpP9eyaHnXtAEgkuXGRNX1/je3CwVOo4dEyiXJVZX2T+yWSp1AGz/wumfrTb7rdeicD82M/u9Z9JmLK7VOfbU6vy7WAAw7/5es01CxNIy8JpXhVGpShSLAo26xMqa/u76usRLTkL2LW8iyauX6kOrBVy9zrbsTX7vRfKYmODvZmeA8+e7Pw+FaAM3PCQ75A5T0UMhndbKaHuphQ0CFRP9in/sKGO0EG5Cgy1ZNipZarLH1GQ3walSvnclr5DFBLcih0npVv4MhYBTjjpWOMI45oWKa4t3gUKJ1nfJpJ4fTE6yD3jRbMrOmH4vY6nZRhp1IBoTOHGCNlDnz7F9KZWSSgX42uP+x7l+Uyeqi0Xd75WSpYmIJ6b2JBS+zMEpEtEKSHuRFhtN9tNm07+PFIvAdWdOdXdJduwqmw1gawvY3tHfHR/TxJxEQveDe72GXrhxEwhZEkJQZcay9iZ+9oNJhjBtvXI5NzHrpUvAiePuTQT1GoAhTSAHgKpBtuulWpVIUIkU2P9Y68Ve5Md2W98nP1KQ2WdHR/kMIWX/eUulLLGzq+KTriO/6xX74D4lEtq+tVCUnbm9F9KmEpCwgOPHBu8w+1HR7IUbN7S61jPPAH/xLwz+W5MYqNpIIsFxo+1s6llZc1t1KkSjJBkfPUo1Z7+rNsk87ba+M82m7NhZXr/hT6K+dZvj8coKc2vmOGuOG8Jyq4cOghs3GWOTKYlmU29aUnOjyABqa4pMBewd1/yQzfYe60zXoL02tMzMMAd59qyea0XBNqnG69u39bOnQq9W2m5LhEJmHyKxP5Vif4rF/Cvb7GuvlPLu2tpg6pl7oV4HhtISKyt8XhlU8S4Slp22J6Dnt7WaxJVrfL2b4dwYcN87M67ZNglm6xu8NyZxcmRYb05pNNRzrrscr7SyMRCQuwLsgVhM9F30+eyfAp/7vP77B74f+Mw/14336FHgPT/l71sfIECAAN8IfOWrsvNw+Bv/P4k/+D8F/uiPJV54ke+trrkH3KvXgH/8Lolf+RQX2AIECBAgQIAAAQIECBDgzxJCIa16YSZ4Ek4ytlFnUiaV6r3gbNroVWvA8eP62cdU5Kg3DEsTCUw5ykfqXApxIyH9chUFgMEWRe/Xjliv1ZWfjc8gqNW4qF0sMjFuLvSub5A04UU8pu/lyyXzDIJXYnmuVJJMgDTVAjrPc+Mm71E47Cbt+JVlP3Z7/eAlIahEkKp6RVxU5AA4BEi1i9uy9DFmZ7VF4shId8UNWpfmMeOJ/knVckW3x0GIi/1gJskVocEssyJlJeLo2J6ZNpG7u0zK97MrerntqVcSu91mm7Jt9A0GyrZF4Z2P9i7Qyqp+vbEBpPrY/u3nuhQZqlJh4npmundiTbUn22YytN12K52oxJEiRXqvXN0nE2YdqiS0+RXzGCpJXCwAO05fmJlxf79S2Vu5JBTS8Tdkufvd6tr+1NZsqYm27ba2NeqU37mATJZ9p1aTEGDcOXs2jFxO0IbHIPMoYpeUEv/1vwHT0wJnzw6YaTMqo1SiCsXQkH9uIxoVmJgguWtyonejMZOSyyvSRdjxHvZ+JAS9SX6zD6dSAtEo7aBs6SZnjoyQ7BiJMFbabf7WLylutoH7CaXkArjjkQRVR7wkPdX2FuZJlhSidx1ub8MVL1wH90E2J9FsakWadlsfe3pak8lMhblBsbklMTPdu80srfD/obBDwHXO4R03lf3boDaXflAkI1NxZL9YW7MRcu7N6qomrHn7s5d4025rYg6gFIAGx+iIxNVrjFn9XIKUtfXdJa0ueOqk/3f7tSFdTvdrRSq/dZt9HFDkD9FFJPI7tkn0yhf81WR2M7y+cw+4xxgvIWG/eLnjeLut53DeZwOFaAS46DxLXLoMCEhYIdG5Dw+cdbe7qT5qOgDjkoqj5jX3I73l8rpdKnL3IOhFFjPhVav0qi2NjLEvWxYGFptS1pXmeJFMcGPL8LDA5ITsWP0B3NwzN+sQrIXA1rYNIdgOpaSSlyIq9bvnqnmabbxY9B/XlbobQHKwshJPxN3Ko9ImoSlsjDfjYyzHkcPclNRu681MqSTbVa2myaHh8N52nQBja9axAj53DojFlejM3r/1wo9sqkhBZh2qY09PkRDkvcdmue22RD7P96o1Tca+tUhl5mqFyn/Vqn97fvEihXaOHpU4fkygVrPxR39MwvLYGOcqJ09IHPEhQ5nHC4fZVra32S+UtXQ/VKvSIUb3bkAmGa5el3jmWb7e7xhlWbToBThXUTaxe2HxDmMMwPm4apNe5ey3vbX7GkzSbqXiJtvHojrWxmK02cxmgNV193PkNxIBuStABzduSoSsboZoL2xvS3zm1/Rs5C9/HyUxVbAaGQEWF4Ff/jTw3p+ViEQCkkSAAAG+8fjJdwtcvyFRLAEf+yVK7//qp4G//Xcl1p2FtbV17l67c9dZZFsFfvTHJT79SeD0qSB2BQgQIECAAAECBAgQ4BuLclni6lWJdJqJHhPhsMDZM92/MRNGtRoAyYXN+Vkuss/NUmEgEgEyBqFpeAh44UUqWjSaEqMjgN/CbTbHhVqAShnmd2JRJoWBbpKSd7F0EJgJi/V1XscgkFKriKU9BIVWi9ZApRKQSEokIZDL9ycWmYvB+0lke3eoq1vjXTxPp/W9ZILKnfULWVRJK5a670izyaTH6ioQi/cmFySTerf/oASyzS0+L/ezRFLtbWWFiVOV+FE2Gq8EbFt27DYsCzh6pP/zupQGORHA8gpw6KD/d4Vg0kUlksQcMDHOtpdIhJBOC6z5qMHshXhMWymZ7aLRkCgVmbyKxwUy2f0fux/MfpfN9bY0OXFC4NFvc6urra9LrK2z/vI5TSzohXtVWzGVD0zcvUvLG95j3SsqFXeCsVJh2/OzAfSqbZiKUs1m//xqMglAkexG/O2dAK6Nb24Bo6MSzz4vUK1Smea1r+lzcAAtI64oFYpqRSKXY4LWJG3sB1JSUWBigkSuXsm0hBkHPESDYhFo2xLhEJBM+tfSkcM6OX7oIJOS0qZ908gwsB+rpEhY3z9p68SpNxGXTJKUQYcRiUZTF9xs6yaJr1hkYn5yism+s6fd5wXYhiYnmeicndE2gLUa40U8zmtUiTs/YsZ+6Cghi9drWTzu+LjoaxH3cjEyDBw61H0//M555AjwmtcAi3f4/UZbYjfTrUrRaunxcG2N1sHLKxIHFgbY4O98fC/KQ966bzWB9TVACIn5WY6DoyMkiygi5SAwyTEKt29L3FpkfIlEaDPVbOhYHo0CraZEtUbiYTRGe0IAuHxFolajUp5JXDJRKnFs2S+xxiS9NJuauLtfxTfXvOc+L3u3PQQjU/FkaMg9py1XgMce542dmtyfpSvAdfztHf57/evc1nBTU+7YALzyaipS6g0Cqo17xyI/kol5P8zY750X7GY04WV0tFuRsN64PxsFYjHgVQ/zdcii1ZxSHfQeX0odD6gAKHHpMpVcH3rQKZ+njbVaQDQEWCGB4WFgYkLsq9yTU3ouYBLmx0bRUZuadgh3oRAJJ7bt3tjiwsvsA2dOs50XCuzTpZJ7vh+LAnczPH8/4v7WFtVmx8Y4D7IlcOG8u10rDA/rZ6V0Ci6bdiEEMln9G69SoSJyStl7/DLnD73iiyL5ttsepS6BzlxawfsMNTUlcPwYcOyowNYWyV1KBU/1oWpFbziKJ3g+1Vd6kbWyWY7dgPvZcddHNXMvTE93k6XVfPihB3medpvn2diQyOf9azOTIal+a4tzq+dfYB/ze6ao1kiS39lhO1AQgn1f3fPbi8DxY8Azz5G0qjA+znnSkT42hkKwLW5vs64rFSqhxaK9VfN2dyTWNtgSv+e7u4nWCmFjXt9q6XtZrvTfLOLFiRP6PvYj5nmfI8z1g60tTe4Kh7Q65SCxptVm2yuXJcLh7vPs7gJzDoGxVusmd70S9q5eBOSuAACAbFbi597PicEHfx545C17LY5IfOJTsvNwe/Ag8BM/JtBoAO//Be6ayju7F/7ks2TJ/tW/8speQ4AAAQL4IRYT+PhHORFVD9rRqMC/+T3gv/0/Ev/yN/m924vA2bMkpdZqnAy+690SH/sw8LrXBgSvAAECBAgQIECAAAECfOOQy9lotbgwWiwOtkgYNZJi4QhQawCVGpNbG1tarWhkmP8UCSoW43eqVeDmTeDwIW130Wy6k21qBzvteLR9VzIpcMaHcAYYKkm4t8X1/SgRtFo6+eolNmQyJCJVqsDxowLHjgl89nOys9g8M8VkYMUn0Qt0Jwm9UIkLv+SheqIc3efOXmEJpFKykxw0rymb5SL25av8++1vlZ0ETiSCzibL6Wmg5BCcZmf3PqeUtIwD3PZJgMcG1LkvEvemFjExrpMmgya4bVuTtSJhEn/2g50dN7mr6SHvvfpVAs8+z79rdf794ktc3K9n5H0lYdRqwPom2+zCvMTcrMDUFEmYQDc5cb9YmNfWZNPT/b/rrX/zvhdLwJWr0kk4+98oPzugQTA7q+OL2bbKFar2VCq9lRoaDVorbm0xyetN1kxPu1UlvFCJ99DLyI4oIuPODjA7w2TXIHURCgEQ7vaXyQHiDpPCw8P3rsSzm2HSqW2zj2YzwIqjJDPuJApTKSa5avXue3/xEmPA8JDoWMwNguUVkjbyBeD1rxv8d5ZFUtXaGuOWUjwxE3Tqewqrq8AbXq8L3ss+dxDrveVVID0EhNsc91QCL5PR5IGlZeDCiDqmRKXSW53NRLutCdGRCAmU8/MCY2MSoZC/IqAXAvt33AuH9bXvJ5maTpMcYfBhUauRsGASEm7e0q+zOSZAczmtQqlw46Z0Ebo7/FHhntPsl5g0Mc52PjLC5Hc0ShLMxISAbfceT3oRe/zsEG85ldBs8vq8VtQPXhDI54EXLjKxXq2ShNFqMe426hyjFxbcZVa41/5t9oO2rUmQ+1VRq9VJjstmgaRnrNmLAGX2zUKR88nhYdFJ4s9Mu+d9x4/x/+cfYF/LZDjPSqdJgFHjzXBj/+Qukyi2m3G3wV2PKqvlECvDYZ6/UvEnzbwc9CIsm/Cbz8fjen5o1oE5RthtbsL4ytcASImZGYHXvdb/HPU6vzs8LNBqkWgYiwGzs93Xa87zFVGb1pp8PTbGsikLZW7u0CgW9Vg7PqaJlbVtYGNTYnamdx3bbZKat7ao2qasMvdqz0tLevxtNoHPfZ6N1oxTqh0fOqTnjaYimknqNF/fi5KsEAL5vLbYm5t1k7sW77CcTdBq9uQJ6ZTRbYP8gjP3Nkl9Fy8a1+T8v1aTLlJ9LMZ4WCrJTgy0bbaB7W0VNyQmJgRWViR2nT5nn+ju750w7aNM5cXIMLCzK7G0zDgeCklMTQkS/mzWpdmGTaysSDTqjL8z04ONckJoO9hsFvjaYxIXzrutUM05Y3OAOUA/jI0Cp09q0jcAnDiuc4upFHkVd5eoDLe7Q5J0LEYyUTTKuWmhyM83t9hHYk7891XFNN7zkqD9nrVuGyp0q2vdY5UJdU8jYfaFkVHH7rrBPjAyBLx4EZiZkjh50t1v15yY3mqTRK/IY945uqmK6yXp7wfhkOhJAk8lu0m0CuYmGrMNJxICb32EbxSLEi+8yDY3NESCIeAmT29saiXCWIz3UT1nuCytbbxsW+R7RUDuCgAA+PgvU84PAD7+SYn/+O8p09sLf/hHwJNP8bVlAR94r0A8LhCPA3/l+yR+8cP6u+94G/CXv/cVLHyAAAEC7AE/qetkUuD7/4ZAsynx27/LUfjKFeDVDwM3b3PxoloF3vNeife/F/jO7wgIXgECBAgQIECAAAECBPjGYGtbr+B6n0RsW+JzX+DrUAj49nfwGyGLybJWi4uN2Syfay4XNGFEYWISOHiQv1MJCCllR0VGoVbVSR+T8PH4E/z7rW+h6tArBbstnSTPyz+Hmbx5/kVN1ImEuTg8PSMwPaUtMvaDu0tUOsrnaT8zOsKNkNEo70s8IZwEjnsFuFCUWHJ2XKfT/qvDZnLeXMT2Lpov3tEJtlMneis19YOUEmUj4RkKMXmjEk1m4iwUllhcZJIt5aPk4JdgV4msZpPrkMUC7VjuxS5lkKSNtwxdFmjG60rFv8yxmFYOuJ8tvVplcl1KYNNpczPTYmCVur2QTutEhdqtbibxWn2Sp8kEE0TSBkpFxpRsDsjnJUZGRMdiVEomhhRU4mkQexeABJlehEPVJrwqMApra+y7yQTVHbwqczN9yF0Suv3cCzERcLcFITTZEaCqnp+DxeFDJIJYFguxtKxJHpUKsDAvcOQIY7xlCZTLck+yQDSiY0Ei4b7Hm1tUHcvmmCxXxAfRI/kFUHVlfZ1qZG98w94tXp1OkSwSCRILBiE/qfNPTQrMTEvEYu4krYnxMapKtNrA5KRAMiFw/UYLxYJEpeomaSgiTTg8eFsESBrrByklnniS5KbJCYnxcfbjXJJjrbfsa2tussfEhEQ8xqTh5hZwIEsSnZmkf7nJaIAxWxGJvOSuWo0J/0ql20LtyhWgXgPCYekiH3XNQczAKf3JoxubsqOg4/ptuz/pzuyP9Zr/d9Q1lSs6kb2xQWJDJqMUe3Sp9yIrKUu9vcp07BjwyJt1GUwCSi4PbG5qS1DAPb68+lUCzz4nO/clEVfkIu+52PdnZ4CbJdZVv2R9B/scnI4eZqJaWCQSu+q9B1nSD+p3oZCe33jHc0VeEYJEAAnGifGx7rnDfrEwvzchWspO0XD4kL5Ht25TLYhlv3e4NlaENfmpV+xWMddsl6Oje28sr9ZIUqpWHZWzeLfdtyU4/nztcbbP06cktrckrl5nO3pjvNu+0o9I1W5r8kQ87m4f96KKJ3uMs60W42mjSZXekyd7l6kXMlmtpmSSohoNCpAU8nuT+MyYtp/2Pyi8BBhFNus1VzZRb3QTzioVPoMptc5XPcw28dTXOeYLSExMWGi3Jeo1tp1ald+9mNObWGwbKOQ1yf1eLFkrFd0+cjkdT23Hll0IxgSvalq5DOSi7D+9YrAQmqzcbulnoFKJ11MoSjzzrMC3vUP/xiSfrq5Q2RFQc6399fWhIYGhIaoZe6E23SgbbQCYmKQa841bEhsOGSqZAMo+NumAs5HBiVuKfF82SYf3cD8GQb3Be1Wp8HUqpftotQrcWdJ90Q+D9s+YMf94JbOq29vSpRSnlFlHRoB2Sz+vqFI0GvpZ37yWXnOFep1zyakpfRWra/+TGF0GAnJXAADAj/+owPKKxNoq8Iv/TPQldq2vS/yL39CN9we+Hzh/jt//+tMSH/2E/u5DDwK/8AHxsidJAQIECHC/sbMrcesW8Hf+tkCzCfyv/xvj2rPPA298A2V8NzY4gfylj3CB4Pv/RhDLAgQIECBAgAABAgQI8I2DZdHiS9kk9LM8KhS5w75S6V4QvrtEEgDARXzz83qdZKRohIk2CeDOHdlRqrowqr8bCTP5rBZAqzUumBcKTKIAXEw9ecK/nPuxxKlUaME3lAampyTWN4DNTaoV3Atqdb3QvLSkk/5vfAOQ6GE/ZqIfyaJQAODYm7TavF+ppE4yjI36J5fqNa1wMTnR9XFfRDyruiaRZXn13shdzz5HtYuNTYm5OdH3fm1uMonUbjt2h4f6H/vSZYmNTeD0SYnpaXfiu9fO/kJRwhIYWM3BtmlZEosCo6MCoRCT6NUa+1KhyMRntSqRSHTfj5s3JVYcG5CRYSZvknGJcpTJa1NxY9BW2OqRODAT4Ik4E68bG7KTOJ2dBebn7n0N4tBB0WVBaRIc+9XlhQu0oLRt6VKi8ibtGw0gFtEWPwr3qkqjkEppR4hEnMmxQlMrCwGOVVyIRFUAmJ9311Uvxa/9ol/i9YGz/H+7rZOvUUM1r+tYzv8jEd7fdlsnPHslv/fC/LzbhqpSZaJawG0H1mqRFAXQQiqVdEiLngtUam+ZrLYM3QuvepjqZcD+rWioptDdhryIJwTijsLT6IjAixdbkAAqZZKVFMEinXZs4CpUPFRxcnTEX1whFiXJcm1NYmQYOHqUil2FYnfMLxQ0GUUlBre2mWguVYB3Ptp/nNjdZZyvVqnspEijJpr3qd32wo2bjLdb21TpGDOIQ9kssGixn584pq+lXKbCx8CQwO6uMyAKAbtNq7Gtbaq09foN4B4LBiW6JZPA8eOOHZTQpBQv7tUyWHWRVkv3qWZTIptl3ZBsJtyqn0a3qdWoMlir6XnA1WsS5YroGkd2d0l0UHFBkRqkLSEst3Xd+Bj7MrB/gnQkItBokjwqLHes2Jd1ofNdM4z0+327rcehhkNaUeSG/ajMKZTLmixRM+pflaHV4lxFCODYUSASFRBOoZV9ntdOKxJxW/2F9yDYHjsCXLjA1888o8lP0ahDiB9wGPeqOHlh3iPTKljNcQCOvXfu6vH90mWJK466a7UK3LkDjD08WHlMmMQcy3KTvVotJ75KlmN2xtgQMAI8+5x09eVe7cN8388q1cTCAuMVgI5giRc3b5HYs70DnDjWv1Hfj2yP2f/rnjhu2yQ7Afvvq62Wnl9UqyStmQSmRgOIRoRzX9zPGko5EiAB27T5U2jbQKjz/b3Ls7goYVncNGBZ0tX3OxtQhFaDUrAGULXzIuIhB8XjVOwtFPjsdOUqcORwb/Le9Zu6vs+d3f/5AfbLmWmt3nX1msSZ0wI5Z44aCpEoNTHBecQzz1JNK5djjJmZdrcN8/6nksBRRzXqNa/2uQbPW/eiKmfCnPebJOvhYfRVnyqX9YdScp41CMJhvckkGu1Wg+7XK+8uSVy6xNdTHsK2F5uGOnkiAZw5w+/evClx2VFwfOMb9GY187msV6zpGpszXOsA3Apf3pBdq/LZ3quI+UogIHcFAAAcPizwO79Jv9d+LHHblvj4L8tOox8d0bszXrwo8b6f1964R44AH/+oGHinToAAAQJ8o3D1msT7PiCRLwC/+evA3/qbEp//gl4Qe+JJ4NvewQdMJTf+0iVK21pWENMCBAgQIECAAAECBAjwjcMDZ4FrN/RziLk460fQkJILl7GYVnwYG+OO081NibqzY/XQQYloTKBtM8FVLAmUKxJ2W1sQRsJcuD5+FLh8uXeytdnU1myD2NIMgo0NJkTaNhfoASchPULCgdfKqdWSyGZpDzI+BoyPDfrs5v5eIqEXeyeMxPegNnltm4n7O3d1gkeRymIe0ou5S9676K1QrerjmMmvsTEu9D9whsntp5/du2yNhsQNpy5jMRKAdFkkMlkuVIdCtFKyrG5rI2/Zhehe3J4Yd5J7TnmZ3JbI55iM/e7v7E8cA7gh67nn+foNr6fCzV7Y3iHRDADe/Eaqz8zM8F4IAOmUxNceE7h5C5if6y7A5pZux5kM7Y9SKYHXvjaO8+fC+E9/UN+3+0aHZCS6k3/hCO9DNMo11s0trbjjTTi/Urj4koS0meB/1UMkAB08IHDwAOPL8y/KPROt94LlFSq/HTwIHD3ivrfTU0yEZh1VB8tifQwPd1uw3AvGxoBveztf+yVazaSXqarkhd7MPID8hgErJDA64k4wnThBhYFSmXEvEnGSpp4G125rYoJlCcRiOtEWiQJnDwusOApUN27IjuVc1EgKhsO0XJSS9/hrj+vPTCWwXpfUMBLAjQYwOSFcdlf7Ioc4MAkKvpCyc9y5WWBpRX9kqq6MjQCVqsTN2yzbxLibwKSgxjLLYsyo1R31CikwP0flkx0n9inFQq/Km2on5uWOj2lVFm+8B1jfKyskZ1y5BszPy1d0nXF1jfFXxZWtLZ3gb7bcRMxaA52LqVY1qTyyB+nGa9+8skpS6LYzz7j4EokF1SrHheHh3seiEtH+6qNaBa5eZTlTyW6lUoV7VelTMWJ1VY+zE+P8+/Yi1aMOH+qOJYpYeOsW63llVavnbW6x3+bzJLCrOZfZlqWUKBRIEqjVgFMnJSYndN00GhynpOS8xW/M6NUXr9+gQh+grSMHxdgo8NzzEsvLQGWC84o5w6qvSyHTKYRt02raRLutiUrKLnY/yBc0Edhrqd1q0ZbWtmlBtrIKnHHmS6pewpHu9haPu8k55rUpmPFqdlZ01LAsi3GqWGR79Cpr7YXdXYlCkW0qGnWXa9BeYT4XFAokW5VKrIfDe5DwFUZHgbe8ia9DIWde4owrc7OaBAzwuIp45iUHFQoSuxl3yc0qUUpugLvOFXEL8CeATIwxjrRabtWnUEhfv0nm7DeO3w9kMm6bxEyG91BhZUWXy4x/gzQPcz7SakncuKHjhFLFWl7lv1u3SHQFAFu6ydmKhGRCKWP5wavmp8aKao1tUVmyug/I/xXye19XPM7YPZRm/dy9yzFjaAjY2ZYIhT1W8OC1JhICti07n5lt5X5DSlrtmraMm1sk1Var0rFe1p/FYjqWm0il2CZqjurePqaLLpj36l4Uj71rBoOGJ6/apndzUS9EowIPP6T//tJXBo+Htq3buZTc7KOeA/dbf5EI0GoLvHiRBzTbTC8V49kZxqd2m2sprZauh15ziVKJZNJyBa+sVJmDgNwVoIOhIe6U6Yf//F+4iw9gJ8rlgc/8GnD1mo0vfkkP5HOzwGd+WQy08BIgQIAA30hIKfGZX5OdhfOf/6DEv/lfu6Xav/BF4Lu/i+9LCfzC+0VA7AoQIECAAAECBAgQIMA3BKmkQMhiEsCrAmMuKpqJMKWIwJ3dJBkpJYNSCchkJba3mQSslLlYHQlLHDsK9FqFjMWBQwe5KTCdlh3rLbU8q85vJtV2epCBgMEXZGNRrb5hJo0TCRIL4nFNmLJtiduLtCXMZpjoy+f77yxOJIBh5xmwUORGHmnTFobKGPt79hse0smjWIT3qFzpTpp4Eythg3ARCQtYlvQoIkg0Glo1wayLaFRgfBxYWDAIJg5KJYmrV4H5BSYUFLa2tEJOOg2XslPn3giB6WngmLObPB6XHVUxl42X8/0DB7gz/expkhXMz8xjq41Tqg3t9XitiF0A8NJLwJvfBLTaUqsf+LgE3LmDjs3ftevAuQeYmGm3WaRsVteSn4qL2Y6XV4Ejh5lskrKJeMxJ1N5jVsZMhqRSJNHMzdLqK5EQSCZ9mDwvA/W67CRyolESevaCH7nm6BH9etDd+oPg6jUSlZ5/gZaksZheR06nBVIpiUZTlZsqTCopurJCy5t8HhifgK+VpZeQlkwaNlMxnaQJ+diCmgmw+2GT54d43K2+YlkC1RrJqVKSTDrjUTmq1yW++hjrKBEHHnlLt0Lb7q7ExgbbkUm0bTSpvpBKCayuSXzpK4O3Y9Xk83mq2qtxZnycJNuXLkncvMVz0h5z8GPnchKf/yKVH20bOHtGK/WZ2NkB1p0+m0pp4pEf7tzRrzc2uQG9F+p1bXvVagG3F6kElsuRtBSJaGKct+uHQv2To9OTwPlzwEuX9HvpNNuU3aYiU6vV23oXIHFBqYTZ9t7qPn7YNcYAMxEtJVXLGnWJkRES01RfqNa0+s7UlL8aX6NBe+NcVlv7hUIkBE9PC7TbnDNsbWtFLtWPTfXQ/ZBHOSaSkFMscswOR9i3R5163Ny8N5UYE6Mj6CizxHwIR+UyzzEyzH/j44xDhYK+XyMjvF+2ZLlNMrcx3LrIADs7PI5tS1y/7pC/hthGvaSBWk2fa3R0f4Tget0gJ+6T9BaJ6LFCCJajWtUdwavEeeMmz5fPAY9+O8m7pRKJa97cYatF0m8mA6SHqFzrtREcFOWKjt+ZjJ6/VKuaNOA3D5md0fOUXpic4BwBoLKkghC8/mqNKnx+xJNGs7s/be8Af/JZiWvXOe8oFgUevMDzKEUa9RshtIKgEALlso1sVpFQuy9oakp0CFC5PFCt7T3PaLWAFy/ydSIBnD5F8jfA/pDL6fG0bet5pffe79UPzc9Ni05z7Gq3STA31VafewG4dBnY3gLmDwDf/g6JVpuxv1Tid72KZpWqWyEyldLqiebc1r6HaZjXAnpjk2TMWEygVpNIJtGxjbtXkinA9qTKVyrxXlSrVDVWZDeFcMhx0xKaBJbLAxfO8Zmi7cRrWinzN0oZcGdHusaKXla62Sw3QBw/RrLV9DTvUbXW21JboVbns2qxxOc6qoVy/rXu3I8DC4wDrQbbSqlEZelKhfOngSxrXwY2N4GLl9zvqQ0b6pkGIEGtHyJhziPyeeDUKeChC+wrV64CS0vSNW8yN9QU94hD9wr1HCUE733n+TXGsSefdxQIpcT2jsDqKn8zyLh68aJEOMy53NjAm6y6cfu2fp3NAU8+BRw/JnHsKFWUlYpZOMwxJZkAIIDFRd1HwmH9r9VyW3YqhEMST32dpGWToDk0xPrpjJMDxIVMRs+hsrmXr2K8FwJy17co1tclrBAwMz14B1tekfjN39ateHpKd4j/8Vnd0CcngV/7jOgE8wABAgT4swQhBH7xnwH/6Ee5m+b976UV7Uc+JPEPfkS6dkv/9z8B/vL3Av/4RxCoEAYIECBAgAABAgQIEOAbhlOnwqiURUctfRBEI8DaGhdqw2EmjhVZpVrTCe9CQRNNlMXi0aNM8mYzXDA1lSQ2NrS1x8EF0k+mJoF3vF3bRBYKEpubPBYTofr5aWZarx9xAXtvTE/p9adYtPf3btyUWFoC1tYlZmf3VoNSqDe46xygAoFScQaYqN3LYtCL8XEmJDbW/XfBq+N5bbhMct7oqEQqyeSLmdiqVnVZB0mCSylx8xaPsbzqTl6urtE6BOhWMugQXJyd6rmc857UyYymkZTM59xlnJ5mcrnmWCBmMujL8VDkDLu996K5WnP88lfQsRg69wAT/ONjmoTg3bS1tExFAAESZS5c0AnLVEon2xTMxKSqn61tIJNrI5W2sH9Nmf0hEdd2H/u1t/Pixk2dFAaA8w/IgW2vlOVY2yapKpVSu+Xv79XnckwgJRNMGr3zUf3Z7q5OJKVSJJ4cP862tbbO9qOajZ+ykCI9KMzN6WRcOERVNoDJw7Nn3N9NpwDF/TMTzveCVkvC8afDxibVckIWsFRnX4nHgUOHSLgacu65lO7E+X6QyWoVwNERN3FtN8O6XFmhShhAcqa5kbFfV1xf55hRKDBmzkxLTE0C2zuiE5tMAsug2NwimULaTIqfMBKlIyMCr3pI4rN/qt9bXZWYmpKIKVUSU+0ItBT2UxT0gy2ZbG802OaSTlwoFtiuTBsk7/E2NxlzTp8Azp3zObjgscfHdUI/m3NbnClbOkX08yYDR0fcbXl1lfesH0wylxdzs7TsbTZ5H1dWqVzxjrdyrFbj/s6ubjtrqyQKZzMSQ0NA2CGcK5XEXm1meJjfm5yQHTW4dBKw0W1bNggyGYmXLgtcvgycPEFVunJZkyAtcW9qJqbdXT8kklrNc32DBOtDh5gjO3WS1/rQg/y8WgW++phWbrpy1T0WT00yJmWywJ2l7nPlcppkqmJAP1JILgfEohKTk/6OExubrK9DB4Fk0j1Xqla7FTqzWbmvxHy1KhFyCMRegr9JEllbZ1mu3+CcMxaVeNXD/F00Cjz9NOtExYSnn3GPC16YRNx+JDXbZjutVOSe5J35ebdSz1548SXpmkNGo5pw7EcmqFWBobRWUQVo45XZ5fW0mv6/SySAM6c4N1FznUaDqnxtZx61l3LuLYcwMTGuZzN+fca2dSxot4F4XHRik21LVCqyY7t6L9hrzqcseKWUuHqNsWpmWrryzdvbjpoTgEtXOD9ZXaMSaVeMdH6Wy/k/h1y/AZw/LzEzLVAfMB7shbV19ovVNc+46Kmycllid5dzjd1dYGKid52a44OqQ1tyjDDVOQEgl7MRjfJ65+dJwuX59UYCKTn/qtd5z1ttoFSWuHO3dxmOHeUz5BtfT+ebXjDDUCTiP3dW4+vmlo5B5jw8m+V4GYo7xMkWy9i2WeZKhe1XEde9iMd0E/XbENIPUvYfS/1QKDgbqKr6GDu7wPycJpqOjPDZ/ulneA/yBSB/GTh4UOLsGc9mrvu33wOAfj5VZUklOScpFLWip93mvODSZWBnG5h07L5LJf382AvFokS2xWM9/ayO3dmc3Leq99wc+zWg4+mt22x/+bwmakpJUqFaL1lakqiWWHFTU2yHsVg3kXfUIUNfvQrMznUTeisVrp2ovms+P/WyfjaJviND/t+5nwjIXd+CqFQkfu79nNB99MPAhfN7Bzbblvj4JzVj99Qp4F/8KvCxTwJff1o38tER4Nd+RWBhPiBBBAgQ4M8u5ucEPvExTjZUvJqYEPjILwHverd0TST/6x9y8vyuH9O746SU+Nf/Bvi+74VLEjtAgAABAgQIECBAgAAB7hekpPLOnbskWAlBAk0oJHD8GL9jEonMJLR393zYs6gaMr5bqwEnjvO5RtlXHTqolV+qNaC6zoX/USfZF4u5iR7Nhk5AmzuYt7akK0k16C5Wr8JVL6iE2vaOVmxSGNTeyauG4adeVCiQdBAOS5w/1735Z2hIoNGQSCRJSBGWO8l25y7wljfLrutPJNC5l1tbJF8Vi0zE6OvYn7LGXqoAUkqUy6rtdNePLYGbt6mGEg5xE6dSuRoa1klFRUaoVNiGrl7jNa+vo8syMxYTDhlrcBw+BNx1kt4HD/b/bi8UCiyfBOvXm1ww+0U06lYDUomAWk2imrOxuNiGFUJHxWy/sCwgleA5YzHWfz7HfrMwx7ZaKmtizsu1ZfQqhly6DLzmNd3fW5inOpewNLEsnwdeuMgkz0uXgZPHee8aDbaF5WWSv6JRtxKGSrRve4hVveBnKwuQTNpoAqE216SVukCjwRhixofdXf67e1figTNayU6p7uwHLYfwNCihKpORHWvDyUln3VyyrLGYwM6OxAsXSY6ZnpH48leYbIzG2KeV5SRAMlHSiQ/9Et+qzaq2vLsrO9dq2sgqmPFAERYrFZ3IOnAPyUPbdohQSeDyFU3+BdCxhBwUUmpyTaup1SZNXL/RTRjZ3rFx4EB3tjCfYwyTkuOgGqZy+e4xAgAOHyQJByAJK9mHVKnGWxU1rRAJYGNjokNk8rs+M0+yuCg7B7Btkha947WJjKcfX7m2N7nLJGd3XUOE9y3mtD2VPL5+g/3r2FG+f+eOtitSd3dljcngY86Y1YvIp+qpUKDCSjiiVSpHRrX1rR96KcQAVOsJhZiYVvOKZpMku7lZ4NwDfG+/yjg7GeDWbYmxUarRAf6clXJJl30/xg6HfMav2TmBY8cEhneAL39V95+nn5Gw2xIbxrypUmbfPnqEcw2FaJRtNueo+Wxusb15Ff8qFVpjAoydb3yD+/NWuztZbSbm/XD0MPva7IzbFq8fJBinsznd5i9fBV77Wn1NK6t6Tb7X+KBQr7ut8PZS2wJIbDVhrvOr1/ttP/U6UFVzauko3oCkFD/LSwluxDDLa4qCVmvAsM/4LyyBSJRWkopgubXFub9l+V9/L+7V+jrHH8D9HDE8ACHh6WdIfksleX0jw5oEvb0LPPY4x8UjR/YmccbjVJI6f86wsAYw6Yxlm1v6/c0tdAgcoyPuZx5lG5hK3rsF44sX+7f5/UIIbV85O9Nt067QaPDebe90E00bDenqB4m4tnaNhN3E33yB8/UTx9mulw01MSn3Vrna3GJMf/oZuGyWvVi8wyEsHGZfqdYkbt/m3+WK7LLZBtjfWy1NxH/gDOfS4TAV58x+ac7TYzGWqd1mv2k19fW0WpyHlEqsO78NEadPAy9nS4bXZhTY2zrWtt3W1Z332+ynrRY3Q3lj7spq90aDvdBqSZey5F7quiaR06WO12PczeVJ7hKC98GP3LW8TMXB2RkeX5UnHiPZ6398VuLuMvvnyRPue9FvrmtZepzdi0O6sqLnwTdv6Q1RCtGIJl4rvO61+3tWqBhq3GaMMa8hndYKqPu1GL4XBOSub0F88lOy4/3+np+T+IP/4C93bOI//1e9sy0SAX7eUbr5pQ9KrK5K/PwHOZn4zKcFjhwOiA4BAgT4s4/z57pj1bkHBH7mp4CPf9I9u/gP/wmIRCR+5B9ygvy7vyfxv/8+8Ed/DHzqE9quIkCAAAECBAgQIECAAAHuB0oliUuXuFBYLKKzNj09TZsyWil64HksSTmLjAAX0VVyPBQCCnkSX6JRJmae+joVeq5elThxXCtzWJbsEB2KJeDmTT4rnTjhPqFJ5umQFYokNtwLbtzq/dmgQgHeBI/5s+FhrShm2rHEYiSqKahd2/m8o0oWZYLGz6ap17kATdgxk66RCBU01L28ddtRlbB10jwc4u5llWw0j9tokGBz67Z7t/Be2N4Cbt/h61e/ygakwOJdpZamz9BqSayusv3F4v5qIICzMC51sv3mLSZyTEJWPi9x86a2+RxEYc28z/tJpJvY2tb3MJsFljwKKVFTmUdQAaJcZnsedSwAow6RMRTifbxXclc8IXDylES16iRltyUKRdpwNRpUn7sfaLUkFu/QgWFy0khew63soIiG4+PCUcTSlfzVr0ncvkMC0u6Obl+1Gu0upc1E1KmT7nPv196r107+1VWSyCwBnDxJckMmy/cunO+2TwFIWri96J9M90JYehe+SfB7+hnGOVOFpR9U4ta2WZ5yhcSaN72RMeK5F/j50jJV9CoVkjAUaTYa02TSZsudrPaz+YnFBL7tHe7OsJvRJMhImLFtYsJJvkbcdawSlf3ICybRqNVyx7qO2p7n9/3IOr2wvU1rzWRSYHiIeQVh6eTktEEYKVd4XaovKxtFhVJZK70sG0li295byalQZN8Ihx1CmBP/Q2GtKLebARIJ2Tmn2VO9ROS9VKCUzWMoTDJlJCJcpAYvvLFyzoegtl8oskCjoY9fqbJeE3EqnFiWBHzINb0UKwASzxUqFYm7S93xskOQ86qgbQG2pMrNgkFujkbQUf0yYdaZInhJsN/4kan6YX0NuD3K1297RPZ0blAqb7oMEuUS+10mo4lhzz4nUa9L3LkDHDkiuoi6zYbE2ppjaVh13+DdDPDYE1RX85JMF+8ARw7LjrJPpeK2XQY45nnJXbduUf0IIHnn9a/jb1st3g/vvWi1aFddLsNXDafV4niWStMKLpnoP0CPjnBc7RA/jalGrQZ8/osStZpELktS88ICiRDRGBWuGCfgIlCurklcvtL3tF3wi3vtNi25qjXgwQsSY6PdCrBr68CZ072Pa7bFet0gSvQY36IRuIg3AAn9akxW1niDQEqJrW0d2xsN9/3staFir9lGLAa8ySEBWhbn85tbJLsvLTtksjxJ+KWqVqWtVbUqkJRAxYdoZVnAa1+tyiCQTrGdmco4qny9xqqxMT2GC0sRgEiE6je+7TV3V8e73xCCZY6EexNvvcjnWdeNBvDwQxKhkEA4IjoKS62WdClLJRIULjh6REAIgbV1d7+cnBSdGGa2cSF4DkWcymRI7lIWu5UK5zcnpcTkpAAc0TazDUk49Sb1Mc0xGnCTNRcWhGEpz/bl97163VAsFprEZdpl+xGwFJJJ9/PUfjE11a3epWyeBTgHTCT4HA1wLlLwIVneXiRhOhxmfL50hfO5C+e7vzs2qudUyopcwVRvbjUlvvaYwMoKiX0jI/dAKDKaSGf+LjgPGEr37i/5AsmClYpuo0vLJBYmE7xHw8NUEL7rxLpBFTIVzpwWLvVhE2fP6nYSDvM5VH3X1R4kx9EGqOo6OsILHmSjWaPhbPoBn1296pZ+iCcEXv1qjlnRaO/n5vuFgNz1LYi//tcEnnteIpMFfvqnxJ7Ero0Nid/6HR1g/94PcWcBwEn7oUMC/+LXJNY3tARtgAABAvx5RLkscegg8Ff/CvB//xf3Z7//70jw+u7vBP7d/8n3NjeBH3uXxMc+Arz6VUH8CxAgQIAAAQIECBAgwP3B1pbOThSLWonkyhXg6GEJCYlDBwWE0CpSirhhWVxkbzR0wqVQ1EnoZAJotICIk8iNxvjdWl26FswBEirMxKQiItSqtBuJx0keSae5a1cAmHYSi0q94J7QK/NkvN9qAw+c5WuvvR7gKJ5tSuTzsivJsDDPRfXNz2vyw6EDXMhX1iReNJtMEjzxhMSRI+41MFdSyafsikBmElKaTbdaBKRWrOgkXoRAyJIdgoJ56GyWiVy1gbMXxsZ0cmJ4GHjmWZ0QfvJJqnEBJIlZluys9a+vc8F8eBgYS1Lhxk/VQSUyq06S2pVYcwr81NMkDtQbJH+9/a3uY1gWd16rej/Qg6Dz0IM6uaEWzc+f04v8ymqvF1T9jwxrxRozkRqL6d3/iiySyQCRqMRuxka9znIeOCD6kgyllHjpkqNqVpWIJwTVWDYc+6qQO4FVqzEhNDUJ4AjfGx3xO7LGxgatoA4edCsD3bxFgkujyT4/NkaizH4ID52yOYctl5nQr9YGU8MbFKOjTLK85tXuJJJJIDEtkvZSchkUrbZOyN8v25t8gUmmYkkpgOl62tnVpCUBEntU+zHJV5GIY/Ha0u2w2QR2dljIUkkTPkIWyQ55Q3WgUARaWZ2EGh5y11nIJ2mtyuFHoukQvZzvjI+RYFev66RjrU9itReklNjYZD+8c5d9UPV9lXTzJjVNlEokDbWa2lqqc+x9lmVrm0SSNnj/lBWobfPaMhmWqd2mctJeCWIzxtfrJECEwxKpFInZs7OOrajQMaYf+ql63StUfcUM5b2uJK7RzXuRJSJhdM0ZFMpl1muryfas5iUKpspKKqUT8PvJhSoCcb1O5bu94GeZDLjjzW6GFlomYdHvHhw4AFy/TjUzCRKXhoclKlUeo9FgLFhfl5idBc6cEh2rv9U1hwTgEKj6lceL4h7WWGkf9ZpaXV9DuUyVnNuLjFl+48zyCsvw9aeBt71VdiWoTTW55RVACOkihw+ltYpUJAIcPMj3L74kO78fHyeJIRpl37p1izFyKikghOzMV28vApmsRDQi8MhbJOKOFet+iV0AyQvJpFtN6uZtXVZlAWnGVMB/3Fk2FApLZbcqUjhCQnIsxvm2N2Z4lU0BEhqnplg3Zjs1+1erJVEpc75rWbShe+bZvcfFq1f13H56CpiZ6d3JOkqHOeD5F/l6YpybIRbvcDxW8+22rVXf1hxihfAcy0+Jz7JI5kqlBFotibV1YDcjkcvKjjrw5StU/bNC3RH92ef4GxVrQ2H2t0iY9ScADA1JF8lDEVeUOinQbZOunqH6WcHvByrO5vPMm8/MACdPDE7uWlIEQGfzxPyc/qxSIanPvPfxODcGbW2x7tstkiY3N5XtosTMjMCNG7JDTD953JkL+QyckYiOy/E463ZyEoBgHQuw3atyhsNuAk84zGP0GsOeeVbfo16W814V2FaLx/MqTWUywJHD/scAgMwuny+UquqgmBgHHjTs3AHg4AGB1VVd/6WSm8jm1+bv3NV1s7urn6tHR7qVo0xCer/njGKJ8TOd4pxiELVf7/HM276762zkkHzWL5b8iXNexbxGg5tAwhEgkRB43WtpPQlQxW8QSKlVcC3L3wK+WpWoVrn5485diYZTz696CPB9LpEkfIUcxeVEXKt1FQoSTz7Fsq2tScz7uNAtr+rxsVJhDBE95n9644NEw7Fct22uDYT2aQe6HwTkrm9BnD8n8K9+G3jsceCdj/ZvXFJKfOozshNgx8aA7/8b3Q/yIyPduxACBAgQ4M8TVtdoWbu5Afz6r/IB8vkX3N/51/+Gk4xf+WXgA7/AB7hSGfip90i8/+eA73xnQPAKECBAgAABAgQIECDAy0e+oLOpUuqdwNUaFzUvXqKCjWUBj34bP7NCXNxtNrlAWsgbdleexeZ0yrGgSgJpX2UGCWmjp6LI0gqtrB55MxNVsbjoWPMoazA/daZBFJsA907pShVQuUyv0s/CvECxKF1WRYCzi78FfP4LEiur3eohKysSly7zN8kkF31HRrkjvlzpbetiSy54310CpiYlxpxE1PIyF5EzWS52SwDnj9DC0BJM6jzxJJNjVPjg8bI5iUWHnLW56X9Ocxe4aRnZK6luolqReNZRIUokgIMeOy+T2NJuc7XPCgs0m7S4EGAiL5djwkspBnRg/P7mLX0chQcfZN1/+av6PdUmVbItFGK7WLyjiQa91GkmxkXHFlKhXjcUidr9lXrKZSYgDizwnMPD7ntt2oz5JZukpC3a9rbE+Djwjrf7rwHcvi07CkJ37gJnzrBeLl1mXYbDOlEnHUJgq0XVhX6JexMXL/H/Tz4l8YbXSczNCViW6Jw3nWR5Dy7Qjm50VKBcHqwDJpNM5kpJEqEiA165OljZzASqH5SNXygkkEqR5BUO+9el9KjltG0SScNhJmOrNW3f0i8RlohrNSjb7p1M9KIf0UJBCHdC3lvLw0O0wh0dAyIh9iulOmImymJxgYV52tnW62wr+Tyw7nwnk6GqYbksEY0C3/Nd7sRgJgNX/zCtaYQwCGbCsLuxBC6cA2ZnBS6+RPu2VpN1VCxJl82Pit8mmbbgSUwOAu84MDQExKP+RDtllTk1yfHNtploXly0sb1j97VsVbZRg6JSBbzOVdITIwGdFWm1+N7mpsTUFDqKSoC2gg2H9bg1PCwRjwGbG7w3Y6N830VmrLsJp157oZeLWo3HrNWARNLdVmsOMSkalQMRKVNpttFmi7G7XJadstfqHCsLRUf9McZ+0E+lDGAsUIiEJaanOV8ZGRF44qnev7NCexPDvIpWCqZikB85Ss2fTp8CvuPb+fradYG20WA3t0i68pKOdnb5b2aaJI+REYFajQnqXI7n9iP7mPArUy/4KZKYY1ytTku92VnO1VKpbjsz1R6bLY6Z3jHXi+Vl4Mxpq9NW5+aAV7+Kr83+p5RPFZQaqjqXCaW6t7FJot2pk1ynV4T+e4EEcOCAQDbH+2YJkrB0eZjz9FrA7gWTaCIEMDIElIps64kE1fmiUelq+9PTmoQ7MgxMTQmcPSNRrbkbsUk0q9epglOvs75SKb7uZ2UKaLXIdluPe4lE/9ioiBZ2W2J9XZWDpPZszt1mzXg1NuYm//ltlDDRbFINFCDRdnSMpN3nnic5qW37K4IKY7wvlwDZpuWfImx57YFVu+tHGu4HZaUHsC5nZwfP/yjy09oa68Y7PzJjbcVnXiIs3cbiMSqoZbN6w874uCb437hJxWQBCcu5560m70O1yrnvCy/qNmPb7vtV6EH4BNjWrl6jTfb0FPAdjwo8+5ze2AG473fLmY8r29CZGda/IjtWq/qZpqd9p6ea1XzJO9czLalTSR3DBDg+Ly3RcvnGTW4IGQRSUlk0mQRmZ6RrLnDZMxf2Gy9jcf2+GSOqNf1+Ls9ngV7Pxr3I5Jld6bRxiVBY2112X0P/OX/Z86y7te1vDWnOb8NhPXcB2NcqVQBVoJB380b8rDL9cOs224NlAadPAocO6U1TUir1QI6zl68Ct2/re3zqBPukmmeNjXLukS9w3hGL8thm22y39dyq15zEqyTYtjX5H+Bmh0iE92h4iHX53HOsi2KRn01O7k+Jcb8IyF3fopieFvgrf3nv7/3JZ4EnjYlzNgv8te8H3vVjNr7rna+ARmWAAAEC/E+AlBIf+RglswHgAx8EfuPXgR//Cbd/OQD83r+W+MEfAH7zXwA/814u1rRawC99lDsP/87f9meYBwgQIECAAAECBAgQIMB+ISUT8kpZoJ8sSS7HhfNGo9sqKhTSyhzhCJO4AJ9l6nVtcZJKcrF/bV3vLlekLaBbqaNWY5KnVNLPU406E3Gbm9Kx/dHPR6urgykIpVNAoaSva36u9+7XF140yucsxpbLLNvGpk7Am8SwbI5kLAGqMPdS9DChVG+07aQmO6m6kjbPVy6jkxB74Cz/32qzTOYif7OpEyv7VcAxLfYsyz9R1zIWsNVjaiTsJIuFO2nbKwdQq9OqLF9QikdUoBrEYkNKtUveffArV+WeCjvmjnG1+O5n43fnrraELFfcu9dDIe5Er1SZyM3ngYlJ2o4BTHp21GQcdYSOyplTN+EwEA0LxKIC5TLrYn2j94K9lBJf/irbmBDuenVZejXZlzY2aaVy965EMiGxscmyHTgAHDrYf22hWqV611Ca7e6woVwwMkpFvTNn+h/DttlPLYv1JYTAqVMCtxeZ2L1ikBU2N5lI3K86UqVCWzBlc+K1udndlSiWNPnQJDPE4txUp5DLUTkhFpOIxqg8qGCSH11EAkk1i60tAEJgOM1+ALjbVK+lnIQPYQICuHDeUEy7KV3nNzEyyuSj7ZDlpATmp4CJMR1PUkZ7yuXZ/svl7qRntSpxe5H1uLnFJJ9KCE9NKSIBAOkmBkmp+8aRw+i4cgCK0KrJB8re5oUXgJAlEe2jdLFXW3CpE/ogHAJOnRO4e5c2s4t3qAR05jR/o5L1kajoxHfeR557aclDOpU6NpnKMX4J62JR7svS1otSiee4s0Q1zHc+2l+Nq1AAwmPs+yTPda853g+lrn6ExO1ttrlMhm1/ZBg44FjC5QtM1IbDcFnn7YVSEdgK8xgPnGVcU11GtY96Hdh2SClq3BFg/R0/zn4xMuzuj2vrQLPFQrztkd5rrdEoE60HFva24fSDq3/3ueZSCdhw+karJbvIZP3swba2gXyOfVDCmQu0OR75wS/hHArtrejoB7N/mGTN+4lEQqDgWKuFQ3reZ96PU6cE8nnZIQtLSdWldIpjoLr3yyvA2irramhYEZj6x5H9IOpDENnephLP0SNa1Ujh9a8d/NjtNjc/1KqOBXoPRbmQQXCpVYGhYY7D3ng5NqbjcaWilZB6qT95iV6JhOgQ4tbXtVJSLKaJXuZv8p7ybm5xDIrHgJFhqo56lXATcWBqgu3abvN+lso8/vBQ9zH3wto66+3uMklE3rktCagCBw6QLFtziNBmf6xU7i+polp1E3pm+9njSunY2Ov7RBUf3jvbBhYXJRaceFWr8R6srnFsO3mi96FrNUPRy4GXoKMwPibQaCgiPecDfiQkc57abrMcx4/zjdFRxr1Y1LGRdtrM5ibwpa9IpJK9ZwDq+RIgyefBC0A6LbCyIvHEkxLXrpM8ruxs/WCSm4aHSDxSRB+ljuslx6pNOgDr6tp1XsOhBPvSoOQuBSlJplf1/sKLg82ABfRzt1nvZnlHR4BzD7ivf3KC7T8U6m7Diry7uq7JmpGIVtwqV1SsBB5/ks+b9bq2GjbnF35tIRFnwZOO1aQqq6n6KoQ7rpsxLl/gvCqVQocEqNBv7DE3W2SyJHetrGqy9NSkP/ERLG6nfwEcM6amBTY3SUxVc8gXXgAedcjZ/cbqQbGwIHDqBJBM8flmfZ2xr1jkfYh8A5hXAbnrWwC7uxLhsJbDGxTZrMSv/8vuYJXNAh/5GAPD9/2lgMAQIECAP/8QQuD97wV+5Mf4oPPudwnMzQp87MMSP/YTskua/vf/HR8efvs3gJ97P3Ddkdb+nX9Fqd2f/qe9d50GCBAgQIAAAQIECBAgwH5w8AATJUptxUzc9tp5L8EFTrWwOjXFY+zucjE859jiTE5wUb/k7HItV7g71UwcJRLAieNcIO8kHCWwti5x+Srw2ldzZ7j6TalM6z9l7XPsqD7WoKpElofEs77R26rPJFSopF29rhNQ7TbLdmAB2Nnhe+NeBaoeUCSTcEQn7dVxK30S6AAT+LLCczfqIBnFs7u6ZJAvvAkwpWBSruiFb1OVY2yMC/+vfpgJhMef5Pu1ml54NxU78gW2i1bbba+Z9NlZfWBBJz7NXf0AF9u3d3RC0I8YZyoE+alFqWSliYMH9TFDIfd3NjaBkyfdif9Bk/jpIZ0U8F6rmVBoNNhWFDFEEWEOLAikh0I4eCCExx/X3++VtO0FM6nSarFuqlXHmirG+3P1um5fg5A8zPIPYvVjJnHVdV66zP9bFvDAGWBhoTe/wUyGW6I7/hw/zu/YEqjVtIXWixd7l6/ZlHj+BfZ5VadzcySfWJabeKESTR0b1DbJeX45f/O9pWUnSV1n0mh6RuDoke4fMfm5X+qaD/Y4hBC8rqShmjjmKJbkco6iRYV9z4yFEkyqRsL8d/Elxgel3lUsMRaMj1EJ5uZN2bE18iWoObi9qO1dR0eYYI5G+V65QtvM7ovYqxJIGHvueSAUkmi1ZGedTAjGmIlxEoErFd7PnR1geFRbiZpot7SalNn3m03pIqmGQu5+0Y+EeueuO2k55JN8TCYYz0ZGGDfNxKyfTc9e5JlaTTePzU2g2ZCIRAdfPxyknw+qVhYOU1libFxASreyUL3hY9XooJuuS3gJwpboVsgrV9ykYiEYe5tNKly1WsCxY/xsd1erwnzxy+iyB1RoNoClDI+dSva35/KDy27Y58LUGLe5CbzkxMuJcfd4nE67SS9+JTUPvZeKqV/yud12j7eD2ruZakWpJJUPH3ucMbbR4DzCT/GrH27elFh2yA5+/cZEs6lVb6RJqAb7UKnMNfZYjPnIfJ51lctr22g/YrcXxSJccaan4pngeaXUx91wVJnu3O3+ul9OdWqS8yDA0+c9X1XX3WwC2YybjAzoOWk2x/FsdRV48ILsDGCmReD0NOO6srf0Q9vT99ttifk5XlehSJJWoy5dccyvbsfHqQr8p5/n37mcVqAbGnL3a3OTQjisFW+k7CaCKeTytF3MZqWz+UJ0iMzmXNivjU+MA5tb3GQwPEzyRiTC9jM9ze8MYu/qtcfuhbHRvRXITEjJ+g6HdJ/PZBiXFRGHVuTAkSP8fDfD+5/Pw1eNOOT0e6/KYzjs7rve8S6ZEFBc+koZHeKi14Y5ZMSVVotk1Ilx3uuhIeDECfYv75w5NSCBbnxct7P1DYlCgfWxmwEOHxrsGJbFf9GoQ+gd57+hIc7rFCIRc66o3/f2jb0gJeNJteom1Kn51iBtJ+aUM5HQJCNVZ7Yt8ezzVEM7dBB4+9sYGA8dZEy27W6lOXNeG4lSQW5ri2SqbJbP3YUCf3/yBDdF3V3iPEtdE+Bv0Q3wHoXDAhfOA69/nUClQkK2GSOqVVpqPngBWF6Wnc01ANvfl7/KNYdWU+LsWYHZGcfO07lmdR17kXXN5xXbdo+ZzSZjmfpsL95vOAzYUuDrT/MgpiKi97lc4cC8fp4ZSlOxux+EcK9H3Itl+X4RkLu+yVGvS7z3AwyYv/xx4PDhwR8Wfu3XZc+FCiWHFyBAgADfLDh4QODjH+Ek8IGzjJVnzgi852eAj368+8HpD/4vTmR//VeBD/6SVjn8w/+HEvof/kXKtwYIECBAgAABAgQIECDAfjE6aqFWJcGn2WCCQEoAUifhAb24CZBgVS5z0bNe5YKkSj6NjHBXqVosbdlMZpWc5IrKSDWbThKiTVJBPAbMzwKzMwLJhE7+1htAfReYHCc5xFz4LRScBfc0uuxtBrVl9KLXQvSzz0ksLvKgR4+KzgKv6zzO60SCpIGRYYmwsSN6d1fStqQF3HUSe5Eok01+yQB1juVV2u0B3fZ+Jtot4PpNkjG8i73mrmzvLt+xUSYWCgWdsDGtMCIRgUhEW4EdOshk6+KitvwzCR3q+MmktgMaGtIJj0qF9Twyio7dJAAkEiSlveH1TEqpNjU8zMTMnHOuA/PAikfxAiDRrxeaLS6GN5tMHKnr9NpHKpTLstMWWHfCTYLyaV9zs45ym8Vkb9sGclmtdjQ2CiScZ3czWZPNsv9t70hsbLY7xzZtx3ohFmPyTDgKA82G7CJrRSIkFUxOMOGrEo29kqF+MBMa3uRGrUZr1RcvMvkci+6tZOFni9f3+57+IQRJeUNpEo9e5yie9CN2LS9TISAW03EoHqcylxVSKmvugq2tMUm5vQ1H+aF7ndqMSfkCyUIqadxoyI66ocsWzVH48sIbtyoViVxeolRSicYB1n56MWIcpFOMpdkciQMqWW4bbdKytOKbgmnLqLCxCdTqUteBpAqKaXnnh0gYOHlCIJdn/dQb2jIVcKsJNRt7X9aLF3U/Wd/Q/VoIWuBOTlLR4z//V56jWKJ95bXrwIEFiYce1KqKuRyw5hA+YzEq0ijiz/hYbwLnXqga/biQ1/2vXnfUzkYd295hJnhNYjAThr2PffAglUZMO1Ovsk6+oBOf6piFIhXzhoZItjPvca9xRmHQfI0E+2+tRhKeIkbzb567F7lLel5EwpwrJJPuOJRKUXlva0v3IaXE0mqRUGBZtFhSbUvd71iUr1WfTWW7yeHZrGNR1+S5so4a2eioVq2r1ajEpMbEvZDLSxSL7OM7Ow6JW7iuGgCvKRQiAWJslISp6SnOwZZXGBfmZlnmly6xjr02qS8X7dbe3wGcuOEQQczzSrm38l6vcqoYFHfmVSTe8WhsV3zdakncuKn705HDHPMiYYcoIoBMTnb6xeZWdx+hwpu7ILYtu+Jytcr6Hxnhtfaab7aa/v220dBk9ERcK3z6wSTUuNQfwXsubb6vyBlbW878psw5JyBc5ctkGCcKBRJp/MbqRJxxTqD3WP70Mxzf1JjUaACbW1RGbLb498gw45qCSRhUxJN6nXmOq1c5Hz56hKSTY0eBp58Frl7Tv2m19DzS8swJvBs1AM5znnuOsaFS5jzy4EF/olA0wvsFwKWyKCX7/e4uyx9x5lu9YlbIUbctFvU4GI0AVWN8VWQnL5Erne5NLqxWZc9z9oKymSxXdL8xx1b1emREk1AUUdF8FqIysqO4uCIxNuZ+jgiFBayQQMixsC4713fhvEAiTqVRv/sjLM7hzDlrschzKSVcBVOF1RuPzH6s+j9AZbZbt41jl9xKxCbCYd2HhoZYjvUNPjdUyuxrlocodOK4wInjVNwy7e7vJebu7HIua0LdH7N+/AjV7TbjxOwMy6m+n0rxeeq55xmvymV3+0okBVpbQNuWXSRfXqc7eKnnPDU32NhgO5mfY935jROqzcbiQKzmJhOnkpLj8iKwsyMxMmoOGrx3p04Czz7vJkmpa1a4co3E/vUNjhHmPGhhnvN2PyiylVIDB0gAHh3Rm6oaTT2XqFTVc6jE0rImdipLbLNMfvPlgwf9iXpDQ2zDjQbHFtvem2QcDuu6jEXvfc1jUATkrm9yfPJTstNxfvzdEv/x3w1GNvjKVyU+94Xen7/7JwT+0l8ISAsBAgT45sJDD3bHte/5LoFr1yX+4P/q/v5/+0MO8h/9MPDPfx34wz/i+08+BfzKr0n8wvuDOBkgQIAAAQIECBAgQID94+iRECplgfFxiUuXmHQGnIRYrVvVBGACprChVatsWy8yNpvdCTMFCeD0KX732We5mF2raXWNxTtANsdE2sS4XswfGQaiMYHdDBevVVLBTC4Psnt+EKjF3lKZiUGAC9m7GZa1XKGdjkI8rhdr1Y70eFx0rKfCYZ1ovXVbqzYAJMEtzAMnTkhEDEXmapUqC60WMDkhcdCwzJueYuJga5MJmYSRpHrueWB0jAkqpdKgEDYSGr4Jon0sDJ866Vjg5IFhR2nC3I2fSPD8ZiIvHAbaTkJTqXAcFdoyUalL1eu838eP9z7/6CgTCeYu6rU1YGNTushigJNUtnW7Xl3jfVGqAFIyMa7UuxSB7PEn9XEUcchUFjMTnjMzwG5GYGEBGB8naWdkRCCTJYFGWf3FY8798rRVdQ3FItBo2tjZ5RsquRDqsaouhMCDF1j2fF7i5k3gWpTl6QcpmQyYcogeg9hn+SWrlE2RUmwSlratfNsj+mRmMm17212+ZlNid9dJWBnnmJjQSSxb8p4pmyhAx4ZiicTLVz3cX9U8l3NIpiWW88L57u/YPZS02m1tFWpZ3YmiaLRb5bDRAG7dlshmgDHn+wcW2E5tCVy81HsXv4m1deDSJZZ9YR6YnNSK77u7wFNPA0Np2VEva7dJriiVtX3MlavA+BgVVI4dIzG1Vncngw4eBHaNmOF3v71WuQq5HPssoJN+KiYy3vFERw5rEgOPIzEyTLLB6KhbZalokFM6fbVPPZn1XzVeSyk7sRQQXQoNgE6gplLdycxsBjj6aguWRau7SNggNPQpkF+irWUkaCtVQPGH6g3dthsNpz9Kd1FSaRJ7Lpwj0VfKbpvHbJZqN+q6TJULhaG0JkBGIiRoKEvdZFKTHl3lbklHdcn92elTOolaq3UrQi7MA0ePciwQYFuQYLyNx/l+KDSYUlInUSo0Ad2s43JZfycec2z2hli3XgUYBaV8MTNDWyb1t5k8twTLaRLdpATqTZJo0mnWnW1L3L7NPl0ocJ4y68S5bFZbnqrz16oSzz7L+7qxybhgCZJv63Um5VV8XlujVej8PMd/pQR4+hTtRZU9tiKkLd7Rc6NWi2PV0BDbhtmvFMz5gEk22Yu8t7Orx0uF1XW27VBIx8lOfRr3rFxinUo4lpHjbGM7OxyjFxaAyQnR+R3rjPGwXtc3KJvTFnY3bvBY6+v8O5/n/RgeBooFIFkDzpwmiXVuzk36UBCea67XJa5d8ydoqRjpZ2spQPvFzU3OvVx2rmBcub1IEmooBMQH6ANehCMkKZUrLF+lAiwtS+zskpzQanHOYtpHA84YKozXZqEdpFICb3mz2IsnjN1dYHhYolbjvblwnrE9HKJ188EDJOWtrnb/tlKRHSJOo8m2F3KUtZJJU6FOl2B6SvfTclmPM167PAVvHO5FkN3NkMBTb7jr6+QJ95y9VNLKqxPj8JXNi0RYBzu7e5Ocmz5tx7QHVDF+aUni6WdZX/PzcJ1YWcy+6mGW9UXDPt7bh9ttIGH0934E/14KYtmcf31HIiT5HD0KF9FpEHgJL62WJnYNpYE3vQF4/An+Xat1q/ia15nPM57H47KzuQTQc/ZexCvLsQFVZTHHjXqDMaTdBnI5WjxubTsbNhI84Ngo75dSy81mJWy7twqkF35kfz+o+XQsyrkewH68tsZxI53Wc6lUEhhKu3vxxibw/AsSDz80+EOzbfO67Xa3QmYspmO8SeT0wk95+cQJ4IUXRcc2emRUrx8owr86dskzdm1uuUnYaxtUqDOFgppNjifRqMSwo1A3NkZFcUBvYpqbAy45tozTU90xXfUFKYGxUYGnn2GfazUlSkUShhOOrbufcu2Mo/I3MiywudU9V2o2OX9Rz3xUGOTrWIwE5u1ttm0Vs6NRrdC2340G94KA3PVNjke/XeArX6N84I/8AzEQsatYlPjkp3tPEf7hDwv89f9vQFgIECDAtwbqdYnXvga4dYuL8oB78ey//wknGL/wfmBuTuB3/pXE9BTwo/8wiJMBAgQIECBAgAABAgS4N6yvt1GvSyRTQNOzwN5u6wX2U6f0+2YyttnqT6xyfbfJxEkm6xAbBBclH3hAoFSUyBck8o6FRirl3qmtIKV+RlILqrbdO7mzF9otLpBvOQloKbtVOwQAaUvsZpi43dzSi8LxGBOCY6NcWM5kgJcuSaRTwJEjAteuAseOSQhLdCkqtVpMNq2sAmdU/Up3cjmXp8LHOx9lJScSAumURCTKZIRZv5UqEy8HD7B+TduUdAo4/wBfFwtaDUElOrrUoTz3tFiU+Nzn1V8SthS4uwwctSTSQwLpFHDurP5+ochzLszz76NHaAW4vKwqFLizxGRKq8Xd38PDXNwuFN1EiE4ZCqyvm7eYKMhkeN9TKSaFV1aBsdHudUaT0OGnkGaSbKIREgx3dqhOEAqJrmSQbTO5c+QIy70wL/DSJRvXrzPZKQRw+JAEhOgQuwDen3HnHP2UsAZFuy1x+zaTubcWmbyScCcj+T2H1CSYKJufY4JUKaOZKm17wUyWTk9TYahY7CaUrW+Q7GB7rFe3toHpKU0i2dllUljabLutFhANa/ULBTNJBzARqwiL1SrbxJnTvcvdyz6uUnFUJQTL0blO5/+5PPu8tJlcKhWVgoCuCJfVmlPW7R22q50d4MxpiZERgXKZ6ngdMp3Pkri3HzYaEot3SN5otZjMUcnDVpv9xbK0QsfOLstbKjGxHI87CTIjrtRqjlUY3GStVEqrDg2lGVsFdFIuldIEmnAPMoJKgmVztC+qVjXxqkuVrMok1/g4iW8mOU991SSeRKP+FnKAe+3M7M9SArfvAKMZd3Kx3dYn8Sb8tzzE2EJB4uBBC7duUX1ibZ1Jf9d1exTOzP6gFB2URZZ6zwtFQmu1ugm4lsW62s3wmr72uHPcljtpe+G8wMaG7CRgI2HAES1y7Incxx0Z0eOsX5kaDYmvPcb7eva0xIED+gCplOhYk2WzHLtTKZ1wHBrmdbfb2upZYXKC8T4S3p9jS7PFNlAuO0lc45jDQxw7olHg4YdZj9dv8DM1tptQJMClZXTU9RTabdpMbW4D8MSO0RH2K2FpRdNiUY8t6xscl7xxGNCJ2edeYB86dUoXqlLRVnr5vI7P/caHUpkEkXKZxNmREaqcQpCsWlE2247t7F7kLjVnOHLk3hwi1lY1oUWR8E3lKaUOWKu7rdp2HbJTNse/t7aBdz7K1ydPaEJPyAI2NmxEY2w0JoHRS0apVt11l4yTYLq6xraeTJJ4B+h699b1lau9SS65PJAvSFy9RmKF2b4kqKJWqwNwbCDNY6trTyYdgoZTR5tbEjPTg9V7uwWsLDvXaZGUcOUK23ouz3E9HvcnjcQcO7l6DcBI9+fbO8DnvyjRbEqEI73Lk8+z/2QybpJ4q806CYeAufnu3+3ucj4tLOB1r6Ft4s4O50bj433qQBhqWFFt/SfE3upyySTw6oPAgxe65wN1Z0xUamMKQ0MCU5OaSJvJsp9blkPu6oNatf/n+8Hlq7Jj4xmPG3bklsChQ+xrDz0osP05ifExKqclk7QKn5tleXM5iTtLwE6mN6HfVOsaHeW8XUH1FYBx5MEL3OghhMDN27pC/ciO3nPMzjrld97rF+OKJVrBqzHbT/nI7Hv1Oi3dUyk3KTccAh5+0K0EZ6JW5dxJEXTX1llP0mbbYn8HMlmJ5RWBjU0+F83PcT57+BCVFGPOeLqyyjFgUCVHvzmq2kDhh7hPPK/VSBxVaDaBu8vdE83VNYnRUeCll6g2mc91E1BtY2LUbvHahtIcZ6NR9u/9YGaasW7buR7veNxuA2vrskN8ajUZ99fWgLm5vXcf+RElFQF78Q5w/pzsac8YjWpb2r3mI7cXtYp3tcZy12p6E48t4bKejsUEHrzA866uyS5iF6DJ336wLJIlS0WqeKs2HY+TAB7N79/q+F4QkLu+yfGWNwv89m/w4eJ7/9Jgk5BP/6rsmjwr/L0fAv7u3wkICwECBPjWQC4n8b6fl3jpEvCen+YkcnOTCxfplJ4kfP4LnCj/0gfpd33sKDA1FcTKAAECBAgQIECAAAEC3Bvu3G1jdaVbnUJYwPgok9RvfD2THAreJHAkonemjo3q5Iu55lMqcvH4q18j+WF5hcQf06rjzl0u8o+NMmGk0E/h5pG3CKgUwdPPSCyvSEQiQC432HNSueLYeDhkh3LZ/3v5vE4qbG07VlJRIDbJJMqBAwI3bkoIweMVi1Q0iDoWUlWf3eaqHpsNN2Gt2XSSGtvuLAABAABJREFUVW3/3c4mvGvVKqnvvUfRqMDcHL+8sKAXysNhR3mtxntdKjHprlRDAG5EWl2jOlQ8BqxvAmfP8Bir68DpISaZzKKEQ7QHLJW4uK4sNcwF/XabCaNelhleFMtMMoyMAKjzWqtVJnH6IW8kQMKWk9xrq3pxf7fekPjCH7L9pZJAOi1xccpQ35a01ryzyQX5aIQqI5bQNqYhi8/08z5JTYVkCoh7EjNDQ0CjITAxbrmULoRPXuPKVYm1NeCmoz5SLbtV3EwoZb16HftSaDNhklcyGZI+FBSpoVjU9lVVR+UOkkmzapXJjmjE3QZWPQmiWhVoRw3CjWRfstvsm5FId5tPphhPwuHeF+enUJHJSNxeJCnCstzfqZQBTFB1y0xihULdSTg/NRDbSUQKcA1HSjpebO/4J/vsdjepFKDtTSIOZCUTVZblEK6csqq6VGVv1LWtUq3ujh+KIFEs8TpqVQBjmhibTmvr1ZERkuXyeYl8nsn4dJqJYoBqLKdO6LqJx912R6qOTOWSRgMItWnDE44I3LrF+BAOM+b5xTrz98NDOjHoxdycHjNMBbBslopk1bI77pkKPWWP/eCwoyYGOApn0GpsBw/qft5oMAkcj/E+7DXibGw69oCih5rOAFhbB77+tESzRavITEarPQC05FOkoHKZY0+txnvspyLTL5E5N8v2B+gE6YEeNrYAr+nQQXctbG2xzjMZXvfMjCajhCx/q65+MI9uxpFQGIgIIFrn61BIoN2WaNRZZzu7gPCcy4/o2wvtth7DhGA9WIJzgY0Nqa+jRwhS96hSAWaKEuUKyX7XbwDHjgChAySNtW3WdSbj/n2txn7YaLAOZ2dYEzduSpRLHAe2d9z3oNEggaZWZ/s9fGjw671zB5iddpP5BkG7pckd2axb4dK8X171F4Br4IpkpTAzzfdVfPOWJhx2k0kUGVWNV5UK42oozMT4f/hPjnJljGSocIh9QMUeWmNJNBuOelAFvlCXoogisTjcVphwE2drtd4E45VVoOmQNC9dBv7pT7iv0vydlxChBP6Ec60d1RpB5bihIdrNmkquas4j4Z4zqL6u0G47dnx91J22d3hvpHTmzpKb0NX9tW233a/C2rpW/FlbJ+nm2nX2qWyOBIpGo7sflMpaFUoIfe9v3fa32HbNFYRw7rVwxb14nHUWi3XH4XqdxLNWy4lVUhOgtrcBCI57/RSLgP3FGj+YKo0hi2ToxTucvxQLHJf+x59KlMssZxi8HkWYO36cBHhI3o+udu0Tt8Ih0SGBLcxL1/PhmTN8Thsb44aT23d0RXvb+d0ltiXL4njvt5kgHidRs+FY305NkoipCG0A46JSifWSn71/qzJMTmpy4/wc82eppL5YUxW31ea5m009JxLC3Sak5JynXJawQgLlMsf1VAodorOJGzcHJ3eNj2tCr8LpU8ALFwf7vUKxpPvF9Rv+zwX1Osu2vcPnmEKx24bZjNFNIw40W5w/WmoDhuU/v1bPoJZFwuTkpPt+FoqMv0NOG6tUHPK2YUVZKADbMd4Pvw0F5pzRa8k+N8tnBzUGNRvd44tqCSeOCyyvGNde67biBdgeSiW4Nu140W4zxq44x3vwgt7MolQlATd5ruGooKeSbEvrnlhcKGorWdVHwmH+q9V7qzvfTwTkrm8BHDsmcOzYYN/96tdsY8efG3/7bwE//PcDskKAAAG+dfCbvyU73tq/+s+Bj38Y+IUPcTJVKjsPtM6E9KtfA37m5yQ+9mGBdLo7Vi4ty64FlQABAgQIECBAgAABAgTwQybLBcatLSCT07tXazU+g9TrwBNPcXf96CifM9QiayjkKNqUAbHNv1OpbhUMgIudF1/ytywAjESYZ0F1KM0F6ExG4uwZJgEOOUmcUJiqKuWKRDbHBM/qGpdRQyG9oArQWsq749tr7aTe88MDZ1kOpVzTbutFaDNZJkASRSHPssxMM+l0/aZeZD5+tL/SgCKQmEmSWk06BB2JWp3JJr+yDrKD10uOWVtnAjSR0OQutbMeYIJtawtojjEx1LaBq1d7E+EA4NQpgbEx2VHt8SYtAO4Wrzl12LbdJBm/132VGfo8AlvQdT88wmtRyV+vgsGNG8ALLzp169zQfF4fvFwhuUNZdm5vS3zlq0wutVpsk+mhvckaD5zRZMGOVVUZqNUldjO2O0HmubZ6XWLFIX9lMt3kOK+Snt1m8rleY+K0UmWy5ayjdOUlmvjBVAio1ZmwU1DnX1khgWRiHNjZZtmG0kDZUdba3NQ2Xuqa/Qg9zQaVFw4sSFqdlLRNz1AYCPW414t3epd/dJR2QWMjTvK/KfHCi5owNTSkVaHqdZ4zGuXvQiFNGBKiu99NTXYnxhRsm7FteVknwNbWnVvqXMeWk9QMRwBvfjqZFJiflx1VsY0NTbCanABe/zpgfIz1pVRvQiEmeC2Lih5KwE/F9kyWdTAyAsxM6esuV4D1dV5cLsfvb25pO1JLAOfOsdCWxX6gVJ9mpqlGE4vrMnihiIgAcO6sRC5PAogQvYkPg6xsNZsSW9sk8kRj7l+YamxdNoXOVzuWfz4IWUAiLjoKfjdu6M+uXSfhJw8mdlUuNNIjC1YqM9FuOwRgk/hSqXA8iUXRIUH2wpVrJKCk0yQhNlu6j2xuOoSMMGN6PN4/Fp0/p+v++g13P5eS6lvhsGM35SmTSfAEeo/tAI9RrfqokjnkTQn2sV5kM2U/NzrKuj521B07olH2bZOoo0CFM8bBeFyrUw3StuJRTYqMxd2qi5tbTvJ5hyojSmHFTxUnk9EWU/XjbCM7u5xLRaM8bjisx/5wWPfXlqNWtrXNsm9tUaEym5VanVNquzgvQhaP4Ueo6gc/C1YT42Pd74UjJJsArB9ls2WFOI4rUmU63a06c+YMrxHQY5jffGZ42OrU9fQUOmMhwHakxmIFS3BMUuo7CvE47chNhdhymWpdOzs6lvnBVEUFugmiAPtMweh75r1RY8Hyimeu4/RFKSVu3OR5VlYkIlFWiKniEwq5SaWhEJAraOIOVR4lbt5yt/RolGO2FeodZ5oN2okXisDEmOzYb++Fi5f06/FxIJ4QMCf1tZqeL6txJ5cDXlol8SGeIAFjd5d9pOwhIVUr7DeAJjsArAevwlqtRvLdScfi225LrKzwd9vbsrNRfmqK8WRkGHj2eYco5/SVly5xXG00ATj3Ut07dS9qNX9yV8qIA3upWSksrwCHDuq/VayJRvW4IgTPubrK+L+1TeJqqewoCjq/yTrj/MYm1SgnxzXxW80PFMy+6FXHLJU4/1DjyOwMAMl+cuOmxGtebSMcpgrq+hrrJZ8HYnGSj+t1xu1Gg+271dZEwnCEY8yBBW0xCPD6hoYEEgkJAY4tZlz3tsaQ5bbtVpiaEpiaAq5fl1jfBD73hd5CM15YllaeNHH1miLCO2q8zvzXjwjqpybVCzPTbINPPW2Wv/t7qjeNjTnPwEXed/Vc3m7r9rab0cqhagw1YRKi/FQdFSLO+J3JsC8cOsD6rte5YabzTNnj+TniKGOFQrznSpn5iSeBlTWJo0doYz8zQ/KflMDEmIcw67npkbCbVOa1qZ6cFFjf6C7QXpGs1ZK4fIXPSyePS89n/r8RgvcilWAMq9WAgwcF7DYV97I5RXKXGB9nCUzC7fauVgEtVzzPGANuyLlXBehBEZC7vsmwuyuRSNybRGuhYOOffcj/s7/5N4Af/Ueip0xegAABAnwz4id+XODqNcrtv/tdAm98o8Av/gLVvCIR4Cd+nImKf/1v+P1nnwPe9W6JT/8yMDmh4+WXviLxCx+U+OG/D/zQDyKIpQECBAgQIECAAAECBOgLlaD1SwqaeOZZ4NFv5+tQiAvbjQYTLfUaVb7GxoETx7WyiYnxcT6f1OoSoZBeiGw1gVu3qEIkQsDosPt3apGzbfM3kYhwJVKe/DqTGOsbBlkG7p2vjYbEY49zYbZQkBge5skLxW5rsl5KDSMjAokkiVW5HHAHVL7ZyaiEPBVpIOFKuL50iYvVkSgTjJkMkEzx/KEeSkONhlZDaDRI4vjaY0wCXrvGxfVSmQvrjQZw4TwTLreTTJoUS90ElN2M7BATTLsoCZ0QUQlVZeOkYKoObO/ASeQ4v+8QkzQBJZ0CjhwWrsXr3V0jAWaoO6ljtFoke6nF+oKRhO0Qre4VQu/EH0pz570i5h094iaWLC3zu5UKSS2HD7stM8ykrpQSxRKv69ZNZwd1E4hMDK7EY8K0YOlH7tKJFImRYSq+SKlVLlIpTfQB3FWn6jIcEQOpsrRae1f8sGP9Y9usX2ExSams8rykvKvXgGRCYmyUvxkfc0hWToIkFuU1rq8z6aoSKrYN2C0gFPVXFJGS6jaWhU4fB0iGq1bZX1spJpwbDb7XaGgiUibrkCBq6tpJgDp8WJNovIosyQTL349YxrJ1vxcOs52rZKrZXxQqFYmbN93vKYJErQpkdjVpBmBbSKUMBRfoelLtxrb53nCEVrzFAv+JkCbe5PJAJidQq+mCKxJCPs+cQDSq63h9nUQX9XuViM7lqHLkTW6r9t1s8TV/K7U144D9XToxdnGR5zp9Wv+wUpHIZklysITTPyRjTCTSfQ51rXMzwKyjwrGbAbI5Gzs7NqR0E2SqVSZ5CwXeu1GHJDkIwdYv2V+tsu23Wujq882moybnjAnRGBPoipyhLLMyCWB7i+/H44wDuZyOf2aytNFwK3KqMVN22CaaUFLbdpN5NjYknnhS4pZD2Bsf77ZGzuXYH6s1o/07/6+UgXoTaNTY/oWlrSP9oBL76SF38lqhXOK/QtGx7N2V+7J77GV9NT5OYkUuz7GVcwiOidWKJiMBbtuyVlOTGQD+Xwht4SptXRfb24z9u7tUWQmHgRMnBN72VoEXL3IjcLGo+/3mFucSl64YBXXqLZMBbt7ktR865Cbbr627Ve384LWY7QdhAZubVDihao3A/LxW0FP3bHqacyDmEfleLObYfhvzrUgEeNMbuhuAn1pVP7TbPI86tu30mZBDnqvXFXFDYnOT8w7LIsnEC69VmUKhaMxhe4yRI8NsK0A3oWBkBDi4wNfbO3ruGosB165LrK/rcaZSBUYcQtS0oahqWY69ZIr3olYjqToSYR3lC/6k41iMRBJLKHIu+7E5Z647ineVCsfv4RH/vjk8zPMK0a2gqogtLrvKAeJ6qcjz1mqcR4yN8ZqyWelSvvHGAC+pdnubY48ECXLKzjgWZz8xiTOtFkkVbYeg/3IyKWosVuRMhWZT2yh7P1OYGHcTSBQZ51UPiw4RR/22WGJ8LxTYb719V83nazVHnSzEucztRcaqRoPHHx52EwzVHLdWo+V3Nqfb0fgY8PBDAqtrwPYOyaXhMOd/yhpatZNSH7KQia2tbhJUrQ5cfIn3zBLA/BzLUu5BUK07hDIJXs/Z02yzu7uMl7ZjrdiLRP7AWWB5Cbh1S9sXh3qMH6kUFU7f+ajAZz+n31+8wzKUS06f3GcjisUEYjHgyCHZN8ap+eLQkEAoJDsxVf3GVMqzbeMZreKQ/evckFCpUI1NPXPt9cxy9y7jajLJzR2qndbq+hz5fLdiccNR4Hr6Wc7FxkY4nqrfeGOUKs/QMO/l2CjnWYr8pOBV/eyn8A2g06mVAqd57kaDbVlKzuVqVb5evOu5lgbLoeYWoyOM3Upt27I49yoWgNExPjsuLQMXRvS5FRmwa7OBCaMBJBL6mcREpcI5SLHkfhaybQnLu8PnPiAgd30ToVyW+OmfpeTsL3/cTSzYC62WxD/6MfcArORS/9pfBd71jwNiV4AAAb71MDQk8Cu/DFy7ATzyZsbAt7xZ4D0/BZw8CZw5LfCOt9MS4l/8Bkf5m7eAH/0xic98Cjh0iOSwD31YwraB3/09iY0N4Kd/EnpxLECAAAECBAgQIECAAAF6wLKoJtRoOuoQm91WFwoFx+ogFCKxC+DCZjTKxdiRES52xmJcqDVVoIbSDknLsQPc2XEnlUZ8EmwdSFq/KZJSMgUszGsywNioJrWYiY7/948l1jccS4Uy8NCD7sOau397JT+VyoxSGhkd5cKtSmjUGyR6NJuOXWOUC7kbG1SCOn1K4jVvFy6ikIK3nsNh1mMqyTXeTA447Cz2rqy67T+aTaq15PP8fzbH6zFVYQAmNpQaweiIVmvys7roKl9UJ5Zt252QVWSHdksnhSJh1lMsTmUggPWl1qvbtjtxouwrkglgwkleLq9y/TGVeuWfZ007jmaTCTa1AD8+xnJks1R7Urhz11FeCXOts1Kj/UqhwKRDPxuets3NWypRmXKUJcbHgVg8hKG05VICsQCsrUvMO7aaiowkJRO4jRb7obKg8iawVOJVKbgAwO6OxOe+wNdHD9ONwQ83b/W+DqWYMjQkkEzqZGk+L5HLulVSkgnGByF4/avrQDwhO0SA6WnuXm802Sabze7rCIehs64+xc1mtc3jsaO67ZTLfL9YZN9ID6HTGCMRXScqhlkW0Ab7oGXYx8WceheGOpAVMlUHZVfZLMuJC2Ee//QpXR+bW92Erocu8P+2TaXBeEL3m9ERNwGhXGE9hsMkKS4sMJk5PKxjASRwfM7f8jES0UoMAGANYB313PNMsI+NAW94HUsWi9HGrdkkQc5UVJmeBk6fElhdlR0FsIpjZWS3NYlzda23deBeUEourbZjNemgVmPiq1ZzFJ3yTELvZNxlNL8PAJYhDbe1LbG23qKloGm7JCWGhvib7R2PXaDPcTMZXq8M63a2H5RK/GdL5/fOmJFM6PtnotF0J/z8yGSNpsTjT3IMowqnRLMF3L7NaxgbkzCvzCRm7OwCL11mewN4/177Gvfxl1dY5kqZY8jwEAlH6RT7YtVJyI8MAyH0V1DrqKw5ajSxGOOlS93SucZWC3jxJbanWo02vNvbVHlSbXBQNFu8VyrxHQrx/Eq10pesIpXaqP6dbbvV7KxQf6JLNkvLtcU7JKHatluR0XvPzXG8WuN3w2GqZypyV8ixlfPGTj8FzkLBURkZYzzxQy6nSUk7O8DIsMSGQb5RxBS7zTZWKQP2GK89kdAkagU/ZU7hFFl1qkE53hMTnAOp+Col60SptSqC4M6uJjwODTt9K8L2Ggn3tyTsBUWkAfTcLu4hPiYSwOgYb0QySes/CSbrTUtyQLfxRl1iYgI4cQIYH5P43Of1vVPt8sABKgoCJCldugzs7MiOYqZC09kgUHLq//p1iXrDiYtCdMgEtRpJXocO+tf89BSvZX0DLitpE5tbvZ8lwNOhXHbGWcG5dMgCyoLtRvUTU6FN2XWmUmxn444qlZ9CLGT3c4HXSnl9nefc2eU1iZBSsdSdpR9pNxQCTp2kmpW5ycTElkMyKhaB0RGJV78KWF6VWFzk9SRTojM/Ajjf3N5h/SnisIKy461WeMxcQZNqWo416sgon4lU3Gm3eR/qdUfdrk1i45zxfGYSy4olf3u4XI5zYq/CV9NDcjRjVC4HnHsAePoZd8VIm5+ZdTs2wv5TKPJ+tFvAf/lDNRdHFxIJtlH1LCJAm8p0mvMOBWWlNzamY1OlClw4z3s8MyM78/5ojEqC8Tjb9d27+twmbaHV1JuV7i45xOIsy+S1AR4Eti1RqWrSsJ/KmHqOW1ykMpoagwFaH5pt26V2JrSl4vYOx3lFSG7b/taKCqUSOtfpJU2bbcarnmdCxfd4AojVgViEc+mRYSeOePpXocD54IEDfMZo1KWLtOu1mR8ddeyZI/3ViNu27tXqnm5u0aYScI+z2Wz3+CehY3IyCYyNCZTLEtev87O4Q1wuFGVXv+1n59gL5pzF1facZ/paHYgY9f75LwDf8ej+z7MXAnLXNwmklPiFX5Sdh/t3vVvi//jX3Dm5F2xb4r0fkJ1FGwD47u8E/sL3CHztMYl3/VhA7AoQIMC3LpRcrInv/UvumPg3/7rAxDjwkY9zB8PGJvCP3yXxyY9zR+urHgaefIrf/cM/Aja3JD7yoXtTWQwQIECAAAECBAgQIMC3FmZngKUVZxHX4m5ZtSDrTYZ4UW8AcBJow8NMllQqTNgODzNZphauKxUu/NuSvzMXi2dn3IlotZB75DAwO8cFTaWyoRajlVVgr+32Sj2kF0w1lmzW2flqHGt4mFZYgDth0yE2OQuruRwX0yNRLu7vbPOzcpk7zv1sJZqtbkJZ22YiqOAkbPI59+dC8DzhCK1fZma4WN9s8Xx+i/Sm/YaZABOC5DvbBrJ5Lj5vbDDZpMo1Ps6d8ENpHn9trfv45qI1CRUS164zuRoOs5xeZQel+CEdwsLWFjAB2udAUGHgzGltPeXduQ1oC6xiEYD0T5jbUqtaRcLA3/hr+p752QJC6KaUyTLBFItSQSUeZxs3ExrKYiYe5z2Tdm9rNsCx9Kx3W3ycPCGQTPHmbTm7uptO/3jsMYm/8D0SyaSFUKiT8u5AJaxrjkKVqcQXCgFDKZZbkV9saajKOIeq1yViHls7RXzzVuvqqptYUal6vmAcZmQEOH5coFJ1H8W8/lSKCUxl12L2FZWQ9L6voHbfrxrtcnWNyVbAbUWTyThKUs550mm34o5XwafR4LVtb+sEeiKBgVkGqRSTh8pWzbwm70HKZWB6mqpiX/qKLm8/tNss0+SEwOio6JC4OtoPgvEo7KzbN+oS5TJ/J4SbBGoqPljC3d/SKfbxYpF1FgprS8b5Od0HAP8keDrN+LS9zf4UT3STefzUI00yrNne8jke7+JLEo2GthS6tcj2puphKMU2lUxyDCkUmcSvN/2tIwFakqnzKpKlUv8ZHdVEgZMnGB+2d0iCyOWpEHPoEGB2gFyev63VGZsty518TKccG6sYcOQIf9loDGbN0y+NUq/psbTdZmwylYikdKtmlMt6HAGYiD95ovfxzfqLOPZfmazsnHN5WZNkrRDbYTrFxLmZ+G80gYRzrF5EInWZbds/yRgOA8Izb2g2+a9Y5PXvNYcZBGo8V+hHFuulhqoIRAsLvOeJBBX44nGOufE46/LWbRIGVB9XpI90in3TO9aFLP0dW7Idm0pHlsV74FVo8StnLg986SvAxqbEoYP+jSyXY3mzWca2ep0qo0oZZShN4sgXrup6isU5X1GYnGS/PrDQrfy2vS1x85bElat6HNprZbvR4JhZd9RkQmG2zWyOBDjbZjy3bX+iSCoFnD4NHD4ocOyYwNLyYI3GJLokk3pOq/qx7zzDQTQmOmRNy+o+XywG1KoSN24Bh2pUYY1GSOJSlmsA+5i5OaJRZ6wtVzRxAWAbKRRZD8kk5yvXbrBOZmdI0laKgIpYeHeZtn794J3f2TZQrcguhR277b7G4WGBBy9QmUmA90W17WhME2HNWc/mFueZ6pzSh7So4DcnNOceuZxW3PESWM+ekS5Sz+go58YddS9nrsg+yhIKC4APUVpbbjImffHLJEiVykBpkQq8r3pYdCzcllf42YsXgYU5qswB3HwQjoiO4mG7zTGuWuU/RbpsOCQ5b8yXkn004kMwVsQvgMQxb9mH0tqafH1d22xzLmHB4BlhKO1+7lhf5zhpbnxQbbczH49QcavZBERRz38Bjl/qFpuxPGS51ZhqdbeqXqPu3C/BMimCmnldgCbkAiyDbbOd2DbjnFIennBEbtbXJa5cY/s8edwZ4/s8Z/aDlJx/FwrueeJ+rXR3dtzkJDMu5fJAucj2UK24FZxbrf7kxVqdnycTvFfDQ/oelHyUotU1KTSb7rmc6sjhsMChQ8BDDwp88cvc0JDP87cbG5yL1OvA3JzEt7+d/f7iS/5lHBoSmJ+TOH3KsT+uS7Sa+t77QZXRJBnbUlveSpv3V81pkon+G3dM5Av85yqjj2UzwA026pyJBFxqvaYVvLlW4tpkVdOfSQBbW7Kn6uS9IiB3fZNACIHvfCfw7HMkFvzw3xcDEbuklPiVX5V44kn93vw88IH3kdD16lcFxIMAAQIE8KLdlvgP/wn4//xlIJFgnDz3APBPfhz4rd/lYJ4vAO/+KYkP/TOBT35M4Fd+VeIP/4i/f+rrwI//E4lPfYJ+0wECBAgQIECAAAECBAhgYnhYoNlwE4yU1Uu9oZWXzF21zSYXyL1qIMsrTDasLLtz0Wpn8fHjTP4B6JmIiceZ+O4kTZ2MTrXCxX2vIhUAXzUs8/jxBADneObiaDarFJn0e8kUCQhRg9gQCgE3bjBRai5Yh0I6gQxwgbzZ4v+jES4SR6KOmhOAXF4y4e58v9n0r4eSY8EkhNteD9AKSGOjTMKMjmpigyqTgplQNJN+ykZH2XUl4nwdsvi6VmMiRimEhUIC0ahELE4bEBPNJgkWClIy2fvcCyQy1OtMMk6Ma+KMeQ+8ig6NOtUPLCdppdTSIIAppzwHFrhTPxzW7XJltXshHeBm1GhEdu6bBNUI1ML45GT3b1QdKZU2dfxancknpTwTi7IM2RzrQSWuLKu/Mo9KMCkogsXdJSAWc2faK1W2oaUVXp+3/auES6moVYQAJiXKVbbDWpVJ9VKZtl9e7OxIrKxIJ4kCPHih99oBVVSoGDA0rJSxSOi4fZskHkViUcjngduLVKEYH/Mn1WQy/raEAH9jOwly0xLRxPKyhN1mXU5M0nZGQYB9Up03n5ed15bl7ifxGMkAElRIKZYcRbBVxshkspuEoKy3VBtLG0nuSITJ+EadiTp13n7kP1OJAXBUisbcMQlgXUcjwKkTwJ27ErkXgMtXJA4eML7ktOFYTCIRpyqMSmDG4+72ZCbAQiGgXGG/iYSBo0cFLr7kThip5ODdJY+CUq07JufztDwq5ElC8KqySbjvg4ooZluJRnnsRkP3n+0d3nPzfCohms9rJYemo2S1uekQMGVvIlGhiI7FrAnLcscuYbxutRhTsjl0bdqs+SRo8zkSXs1rTsQZJ2Mxt5og4CTi+6g+HDzI/5skZlu6x2GvqoafNZhJ9GnbbmKk31jUUbAzyCx+YLKVCeYzp4GFeWBlzf1boHcSVrWXVovJaaXwpq4vFncUSYxkdijEPqKs5XL5e0++7+66+2U0xrGqUub8wiSLlMq8l2YbH3fu69hI9zUqAk0ux9hpEqXVcS1H4eTAAtXPJidJvLvt2GKOjfGfKaigyE2K8G1Zjv3ZPpaGM5neY9nUJGNiPsf4tLnF+lXx0BsngW4imeqboRDJSsWSdK5bYmVV4Mo1NwkBAPI5G/EEG43XRntzk/23bbMfhkPA+XMC//1PZNfYubPbTXSrVRkjGw3ZV/3FRMij1JlRyoBeMr+yxwXved4h8ZAsyy/PztA6r9ViG49FaWP8+BP6OCsrjhpglnEh5dxbpUymYh2glXVNlEokXioF3mJRq5ZtbLLeYs44GAo5dtED9Jtkgm1SEcnKFc5f0j0IDQDruVIFnn+B16Us/mZm+Lubt1hfuRxcduwm7DZw8wb7hrQlhKUU0djezPmxgqncWCySrBKNMd+yY/Rz1YeVhWgoxHFTkVa95M7REdZXqeUm2aRT/J2KV80WcP0GOlbuEKy/el3i2WepAlQq6zZz46Ym/QwP8/6Ojeq5oyK5maSnhqEsrBAKAUePMXaZqsojw7zufEGTfMyxqVTSNr+tlqRtHdjWl1ckRoZJaguF9TzIJHYBji2gTyxRhCFA35e7S+74YVnuuV+j4SYjeQk3Sjxmawu4fUe/XyyxvXjnH2vrErm8Jl2PjPCat3cchbQqCeOmsuhLl3VZdjMOmVGph3nUrZZXqM535DC6NlCwvKxbL3Gp0QTe+ajA//vHgzOTIxF9fiFYvnCY7bFtMz60nDGhQwzdY0zws2zsPFNJ97hfKEgMDwtX31FQ8bpSAWLjJHqub/KZT5GFiyW223pDK3xLqZW+TdQbgAovrSbJXPU6OnbR6xtU6h0ecodj7xhsblhKJDg/UX0pZpC7QiGSso8e4d/mBohIxF9F1YRX9axUYnmnHJJz3bFKbbV1/0sm3PMw1RLicX1fImEgHtcXtbMLnO5flH0jIHd9E+G7v1NgapIPZe98dDBi12/9jsR//UP9XigE/PPPIFDqChAgQIAeqNclPvQRiS9/BXj2OeATH6WH98/8nESzCXzgvcBnfo2TyHodeN/PS/yjfyDwnp8G5uYEfudfcci/cRP4Rz8m8elP9LZaCBAgQIAAAQIECBAgwLcmxscsNOpMpKxvANkMFyCVNcaEz2794SHuqm21uFhvJja3t9wJZXPRuNlgYte2uZgbDjMpYe5OvnMXOHOKSa6NTb0Iv+kkNh55pDcxwkxkHDusX8/P6ySIWbaVVQDCnRiwBJM9hYJWKpBSolZnkrNc0YvEimyg0G4zOTI+5n6v2eLvvvwVrXYDUF1oYb77OlR9xmNMHiedxE2rKTEyyuRuschkQyHPpJg6zsiIXkB3WXIYi8qKFJUzzikEz6V2tPvddy/i8e5kfqnI+7DrtCO16/jrzwDlkkQ4wvOre64Sb2p5cHtHL6xns2wHXjWlsTGBsTFaBaXTwEuX+L5KKnthLj2227xnigRhO2Qsb7Jn22lv8YS2xwyFSATc2qYq1MMPCywtcwOsUi4KWSQXHTjgfva22566MhqiKl+xKFFv2F274PN5Td4olyV2d9kWwhGBhXkmoaV0kw9ajlWjbbt3dStQ7YnnXF1jMm16in2+1ZKOCoWG+ftcjrHiAYcck8lo4lU2x/Z36oRbSaZcZuJcjurjKNJFscS4U6v5qweoRDUtMLs/B0jcsEJUXDhz2l32SFRbYB5YYPJeJWfU8UMhXmM6rQlPszMCd5e0cpx06lVKndwtlWhxubSs+72pYCIE188VgSGXY9uOxYBxoy4ATd7yKgw1HFLM6KibNFcqse2HI471o2ONWiwCG+u87nYbuHyVJAfzmr04eLA7cba9zbbDBBttCHd2ugmZlYpb9QvQio+bG0AkTGJfswm0bCBiGZakKbbRmelu5Rcv2m3dL70YG9MKbMoKp93mvVLKKzs9ftsFo/7LFSCZEIjFRZcCjYIQoIKL0+w67UVKFItsM2FH6UV9p1Tmv1BIX3fLUYVqNLuVMNIptsuJMcZyP8WoSBhIjOq6X1l1jwGjo2wnSmXPq/BYKgMnjrvfy+X8JXHWN6hwdOQI/y4Uu+/NwQPA0aOMD+UK2+vaGnD+HMuyts5xqNWiokg67Z9EVscHtAJZqeSOBfWarhN1jdGY059TJH8Nuhpq1r1ts8xesmI4BJhvefuslJq4BDBhHImwjscnOKYlw4ztjbomxHptAIcdZSwrBIyNAwcPio4V9ZHDJKA2G7KrPZi2x2psO3TIrbDnB+91mnVRKnHcVnakAjz/9o7ue6urju0tGBOEcI+tSqWzkHcU1ZzP7ib4t0qOF4t6w4G3bm8ttjGUpkWhn5Vn07mGYsFRyHRU22zJ+B+JsG3euNndz1qOckw0wnGpn5qNak/MbXaTL8o9FG0AzhOXHCLeyeMkxajrVgpNkxN04SgUgJajdlV1lG8lGCuSCZINBBw7aCcOqw0L5TLbvusaPX0sHidhyCRBxRMCZ84whvWzWgN0HLFCimCgL7pe1zaqgJusW6mQ0Le15SYZj46yfY2OaBtn77gfDulni11n7rC5yfeVtbe6QY0G20KxBGCOcdI7H6o3AFngfR8dAQ4f5o8ffohWxLmcQ5oRwJrFOeDUlHtueeMm5wrKvjeX18pWSpFHWWQrNBr8l0wyZl18SaLokJUVAQtwP6N456pxx+J0fNxNTJOS1zzubKjJ50kU3N5mPY2NuuvBqxLqbTcKxZKOa+WybjeRMOukp9pUj74wMcE2bNp62rYmqsZinLvcutVbMNW8D9Eo8IUvSjSaEpcu63rvBSk5pxbC2bDTYHszN2DUag6xrU1LzTt3+f7du+w/7TafmdUzqHm+TEbi6jV9nIce9C+HnyJyvzGrl8VfLA6oKVoy4Wy4qGliXb5A8njCIGfnfeKoCSl5DGnrNqvaYSLhjnPZnJs435kbOt8x1cSUqrJpuWo+x7da6FTCpctum9BikeNZsUhS18YmP7dC7nZdKvLZ49zZ3tc3PQ3AeY6cnOhtMQsAqZToEFYrFYliifM89YxdqzOGqc1GpiW6uYmu0dDj5p27fP4yrYwVm8okdpkw53ZeZch+ls/3ioDc9U2G17xa4DWvHuy7/8e/Bf7tv3e/94M/AMzNWv4/CBAgQIAA+PrTXPwHgCeeBD77pxL/9t/rBdvf/l3gX/468LPv464bKYHf/l2JW7eB9/0sFyI/9kmqLG5tAf/4JyQ+9mHG7wABAgQIECBAgAABAgQAuECrbCxu3uKCaSgEPHDWURRwdu6fP6d/0/YsYJsJMG8iyFxkVapGSmVAgEn58TEmBFSCMpF01Hpst0UFwOSL2sFt5h4yGSYtkgkqopi7vDMZvdBqLq76LfgX8qBSWI3HGhvv/f1EggmYZos739XitzqnSxEo7l5wzef4bBeJMAHUaOgPQ2GdoAe4ALyxwQRuOAwsLXExvtXSiSKVFM0Xuhd6Gw0m9I4fkxhKMxn2mLND2JvkUvAmmf0gZXfSbWHBSIrYTkLbIdap8itFMAlNJlPlMBNXXpWmXI6f37gpaQO2oUk2AHzJF2tr0lXGsM93Dh/iRiqAib1bi+7Pp6dZvlyOCUQJYHUdePghWuWo4ysSydIScOSQdCVaNzaBRB6YniJZwmxLSi2gVAZiLYlkUn8YjbIOx0ZZ9qefpeXl0jJw7BgJgrk8n/kPHKDqxe1Fh+An2F+UXU+zqZPIdcey6c4drayxtU3FDL/EwPCQW8XMTNLm8rTmM1Es7W1fUi4ziVksahJOs0lSRrHYrVJmEinLFR13TNtGPzQbrAe/XfX1BhC2eN9TaR5na0vZ1DBZq9pzqQiUwP40N6uPcd1RKlnrYU3oVw+ppKOm44kpN266FWZU/1CviyWeW5EmbdshMTnf2dllv9ndYYItkWBMatrd5fJieIh9AXDapKTKXDhMUtfdu0yeTYyzT4QsntePHKnGgUZTqxl60Wrr8kxPwU0olLy+QkETFnpZ65q2OYDRNh3SlWo3NeM7/QgLWwbJTUoeb2RYoOFpYwK8t9Ih640MAwcXOG4AjuLbGtv0yCjHHkVAVcRHpQgDsH0pRcJ4nEpI5skswRhnJmLNBG/CSfKrejJjeyjUrdTlN55t7zBJPzomsLoqsb4BhMMSU1Pdg2UoJDpttV73CRrCTVhS5blylX0qEuE5y2W2IwnWhZkMVZgY1+oo+ZxWXwuFAOncy7ijOmmFSFY8dpTv72Ycq2nPMW3JxHA/ZaHdTLf6TYegIfrb7fnBVACxHTs1k7zsvScFR1VJtZFZw9KwVNLEO2WzOj9PcrxwyIbDac5RhofdakW94I2jaixoNHUi2ozDihDai2hQr0uXuol0+kut3h0XM1nOH7xQY0vZUVEslSQaDRuW5d9WFLyk8HabcUgIvk6ndHyN+6iMscC9j9+L7KngVS5S7X9ujvd1fo79vd5gHwM4By9X+HpyilZjzRaVXTY23cQZ6RAjikUe+/RpTSJS3yv5kLtMBcLZGfap6WlNkjeh4qSUJCBUa+459G6GY8LGhh6vLaEJeYkE75ma47hIFyX+plQiyavZ0NbirueGNolPE9BjXirJe1+uaKKREO4Y36knaBLgbgZ4x9tF19yy1dIKdSapJxYTiIRlp30oJdp2Gx3FLQWvupk5hy8UdP+xLLcqlRlf7t7Vc7RyhXMSwE169RKp1VwiHNaWxK2mYyldYRtJJhxbQ8Hyt0J729QmEtraemiI89rOtda5+UWCc4ZeFsfmsQ4f7lZ/BPQmEAVlqWr+fes277tXEQvg/PWBs8D16/w7ZLHvra075NmGVtx6w+uBq1f1b0Mho91LxoN4nG0ym+W8sOmoR6mNK2YfLJU0AXhSzYuMMWF83K0C6n2eNeE3lihSpN8caxAkk7x39Tpcsaxe5zOFar+9rBUVEgm2o3aLBH1zHum99yPDfF4tlZ1NPkZZ8sZ8zkvkjnqeOUOWXgtIp7r7V9MZO22pFQv7QYIKv+vr3ZuXUimtuKuse5WdbiSi504iBFy9JnH1KpXq1Hiez7vHzulprYoXiwLf4YgjffZz+jvm9/cquxdqQ0fdiUXeDRevBAJy159jXLosceK4v2zgIBga6o4O5sNpgAABAgToxiNvEfiHPwz87u9J/NAPAt/9XQInTgDvereEZQHv+zmBgwcEfvs3JT74IYmvP83f/ennOGH5xEcFPvMpgff/PCdV5TLw0z8r8YH3Daa6GCBAgAABAgQIECBAgG9+1OsS21skVKhFUrXoPz4GHDsCHDoMjI4ahBPPIuzQMHerCzhJO2fx1rv2k0oCzzzLROHaulabKpbcqkutlkCxRaJJrS5RKHAhOh73J2RtbJCQAQDVFBMie+1EBuCbuGvbTqJZ6oXbzqK359zRqLYKPHRI4Ikn9QGl1JZ56TSTS6a90bazU7xeBzK7OvEDMLluJoYsi0n3fK67TKpIfqQlhWyWdh9WSOANr+fxVteoRpZIaOWAAweYPJmaAg4d1L+v1SQW7wAjw9J1T2MxtwpEOqUT+akEkMk5ChhGsqxj39VkvZQ9CdBhI3GY9iSjq1Um4U6e4FXfuEXCm1K1mp0Bzp7RdimAWylN4TWv1goiXsupRrO77Sze4XkVuUAlmO4sudWyVPIhmwMuXuK92dnRCe1oBFi8y/POzvE4hTyPcWCBtnnJlMDYqIVlJ1muCBtzs8CLF4GtbYm3PUJSljqnsNiOpqeBI0cEiiXaJAqLbdCy3PZVEiTtAIPv8O71PQFN/PSzspPQdZxIdJOZvMdtNHQcUp/ncuyXpTLblbCoMqcSNHtZyoTDjFHmvVKkhJrTpjIZJvjrNZJEm03+PTfjf8z1dRIp7DbtdmIxd5JLtQUBtv8Rx/ZKxdZane+7EqxOAv1tj/B+r290EyDaLf7zki8ty7EolVq1p9F0t+9qDThxQiCbo62SsvBUieZojDa9AJNdx48Dm1u0rr18xVF4ceyrDjgkjHqddVWtaJWRUpl1Uyj4Wx11yuy5b1euyk7CfGSE/c52yE+2QxRV+fRolOPDt79D4KuPKaKLG2OjHBeUTao6n5Tucx/wEEqGh7tVTAAm/EzliBcucp1td5fJ40SCpCiehMQuVb92G5CC902AbVeRt0Ih/otE3G1IEZLLZcakVIq/M4lpprWvtwZiUV53q8l7500ee8fS3R1aufIzxtaJCW3V1gUpXf3UD7bN87dbWvmuWgWOH2PbyWZ1uRvN3iQBLxHBvD93V3itoyNAQbjnJ7bNWKlIPSYadfY3r/3e9rYmDYnOf1yXTSJqS49nQvRWugH4/VBIExeE4LzF7J/TUyQD+KlRNepse48/KXHqJPvWV78G2FKi3dK2sWNjQCTKAjcbsmOfrazFvJCy2wLUD73sprykLlMNzrKAJ54CQj7xuR8Z18TUpI5Tmaz7Vthth0hkvDc6QhKGF0Np9t/lVf5uc4tzVhW3VbuLRmh5OTfH+NGvnEtL/iqXgD+ZdWcX2NhkQ47FgIkJgbV1ibU13aerVU1qz+eBkyckNtalizDiRbPJut7ZYX2Y9zmf7ybATU7pWBCPd1tb+mFrS5PhUind7rNZ/XelQrJkwrHZSziqp2o+tbkpsbXdraBTb/AYipC+vuE8SzgWn+kU66vd4sb2RkNb5IXCHJMqVV5LwqetDaU5VlQdi0jZI1j5vV0sSmRznCcodU/1tY1NPYccHXHsT3tYmJqEYWWHrmASlg4fFjh+nPVx+7bsqPYMD+vxxGwLHSIK2A5On6Ka4vYWy1eva9VU77W2HTXZZpP3LJXSSmsAYFmiQxpJJqSLQJJIaKXRVEpAWKIzrh46yLnN6Kje8FCtUonLl6DkkGTX1lmm5RU+N83O9lYaNMcyy2I7FJYmyqm53pCzKWFygrE1EXfbt5uWgQnHxrNc1vdHkdNNtL3jr4PVdbgtscFx0/s8kcvJbltqyfq6vag3haRSwIXzbNAT4zoOep+/vajVNLG13WZcCIUEzp6RHVKW+bypkM2yvQ8N9Y9naiw3YbeBYkG/WyjyeJUyny29KqGAVhBNxNlHFdldkbTbNpDLAlfrJBDOTnsOYBRiYpL1kh7S4/zMNDeiSFvPO3LOpigvye7IYYGjigy+K/ncJtl+5uag56VDwJVVEsTaNufpfiTJw4eAhGOTGI4YMccZ/He2OTdWFs7VKvt1Ksl2sLzcfUz1c4VaXVvF+s7P7jMCctefUzz/gsRPvUfiwnng4x+Ba/fYoDB9oQEuovzkuwNiQYAAAQLshR/6QeDcAwKvfQ1j5skTwCc/xsWHo0f43vCQwKc+Afzmb0n8xz/g765dB/7Bj0h89MMCv/kvBd7zXu4otgQXDAIECBAgQIAAAQIECBAAAHZ23CuT6TQAAWxsMRm1vAyczwIXzkvMzvAZxJtoyue44CscpQ6vVR/AxfpOItOzGOrdtdpu68XvobSz6z3HJHwsrhU54jEuRm9tazKFUk1Z39CJtwMLmmBkqjmMjJCA5MXGpmOn2AAeeghoNgVOneSCbibLax0e4gK2JbSSgV/KKBbjgm0upxP7588BS3eB6Ci/U60BEDqRGIl071IGmGgolnUCw7b5u1CI/xcg+UWCyW5vUoK/kbhzRyfH220mjU6e0JZM0ajXVoQJCstyE7WGhri4nEqyDAcWSN7IZJgoqax1k6sOHqTFyrXrrDBF7rLt7kS6qz6lTjDs7vITbwI1FHLbgSgIwXbdanEh/smnJEZGeKJzD7i/60eSU7v4my3WvTeJ77fCqXbah0JAwyHjqPbfbAJve0Tgqa+z7WZzTDbn80CjacO2dRKsUOS/y1eBA/Ns19euO/aANskV6vz5PHD1Kttps0mikoBWLQAciyqbVj0PnCERqFpzk1b2AysEHJgVWF4FAInZGdahV7UEYAJDjrnf81Mr8NpGKVJfvcak2x4iEb5IJhkbzp5hP7p12zmX0Yb8+pyw/MljSt1jdY2JxFAYmJ8FFZYs/c92lEZKJbfiSbHAPuIHy9LtcHiIiVo/zEzT5vbRbwOefQ4o1Jx24VxXtQIMGRZQpnJTJMJk54EFHX9LJWBpiX1rZ4cqdcsrbDOVijtO5wsqMaz7lVKGa7VIkLi7xFipkpvVKvtiL7VAlTQU4H0uFtk+rBATruEQ/4VCWv0PAPJ52i81Gvx+NApYFjdFKvsgKXneWMxtJwR0J+TN251MAsWShLRtZLPupHzTGOdKZcDaAR57nDkUQGB0xLELtXgck1xo9g9ps03WHcJfq+UowzltQMXqQoFJ1HRKk2qG0hwTqGjkqMFZjtqEY0vYaDhKbzZcnScUEq6Nn595SUfcjS3/WGpia0vbJSf6EJsAx1LVsS0+coRtpd12lPoi7NuNuh6/e0GRf+fmnHEKDuEx5KistNzJ75cusf5KZRKF0mk3wRDoTr4rcjdAgmOzAbz4Eu9hONxt/1ss8D4r5aLhoe5ktlLNaTa04qU6RySq25JXnS6Z4DW2HDvKZpP96uJL3WVut93EAzh1Y1k8n/e6ge645IdehLt4nDGlIrSSYCyuj2eFHHu9YUO9zYn3w8Pd9T46yvVqRYTuRShTiMVJZqtWjf4wrG0hFa5dp4UjMpyPKPvcSEQgmXSTVppNxj4BklTSKRJjanXWcSKmVb6KJc4VF+a7Z34SbGsmiWJ3l/+2t3lfbt3Wv2u1tHXz+Qc4J2i3OSfrkH+c+i2WdIxS90bZF6r5rfpN3CGzmOppY049t9vO/asw7nTGP0ly+G6G81avDbqysh4ekhgbdZNNlV0dwPjjIpV7qkmN/cUCSRhqfLFbnJsrJU9ls7a+wfvcagGRop7XpIf474GzwMqqjmeVMknV0RivdcixOP3Sl0mom5/X352e4jlKJdbdxZckLpwXuHZdjxftNsc3dR1q7tBsOlaZkqSzbsVZ6ZrTmHOedlv/vb0tUS4Dx493TzhGDOU9c4OElIydlgXEImyP0ai2mvarbwmgUWM7v3WbY7U6fq1mjI9xxqNiUeLGTZY1meT31FjVbpOwODIsUalKbKxRcaxaJTnJjGdSdj87AnqebMauRlOTsoXQ1676uXdOZlmcy7Y8pKl0mv+EIKHni1/ubs+Ao1rpIfyoZyCzXMrmdWSY9zkc0WT6nnaUBsplia8/wwN7iVTzc1Ru+/oz/PvkCcbJZ56VLpJ/P5WmRsPZYOW0QTWvSCYZAxp1TcrL5nSfV232zl048xd3f+23CaTdpmqtEGyLpRKPa853/H6vLHurNSCTZZ+MORsUFGo1lteM2wpN475MTQIL8wI7u/rHY2M8Vq3Ge+ZHmu51PZ3XLWBxURPRzpzm89ekk1cdHnbP5YaG+JtUiu0uEuY6yp9+np/fXqTicqnM9lKvMQYXi3peUXY2M5jKkKqV2DbnC40G5/cK1SrVv6PRV45vE5C7/hxiZ1fiZ9/HAf2ZZ4FPfUbigz+/v0byO//KxmOP678PHQI+9Qnhy2oPECBAgABuCCHw2te433v4oe74GQ4L/JN3CRw7JvHpz8jOruKf+KcS736XwG/9BvC+DwA/8LcEHnowiL8BAgQIECBAgAABAgQg2p5tp9UaF84jYaD9/2fvv8NsWc7qYHxVd++89+yJJ59zo66uEkEgZIQMxsYEP/7ZGGyMsT/AgBFBwiJIIBRAIJIQIEQwBgwYh88mOYCxP3K0UL6Srm4OJ86ZPDunDvX7Y9W7q7p37zlHASzJvZ5nnpnZobvCW29V17tqvZ7dcHzwQZsSSJs0LlHEoIybuqXZ5KbxnIBkLt9uA0FJoVHXGI1NoMFsBmc3xXd2bIrGbMqkUqDmqQgVNC5fsaQkwKo4xAkDF8+8jwcVT5uyv98JYDca+SoP881l8+iklKjZa5RKDNw2mwykyAZ2t6fnm+cuOh0Gbk+dBtZX+f+TT6ZP62twQ1jUhHw/TbCSwMZkwg1dCYJJUGm1TTWEG9sMZO/usU3cTepujykKb95Mk+8ODhgs8Lz06X63T6KcoLFShnARAHffrTCdUunl8mVuhG9tsrwSUG6vALjI1+en1lU63YhGmlQmaTGy2L5p0+MAtLWzZxmkylMhYf/ZTfNuD7i5w05myieV+qyc9gdIJrmxbVQ8DJml4qTYXFnJTzVzz93A408wwCCBb8FoxEDe05cN8SRhAEDuvwxhSDJHpcJg1EMP2/dWWoZgE9kx4/sMprVXqOzi1rRc4phsNKwCjfTDiSl2MkEaranS1G7zlH29zrF3q8C8oFziqXM3EFSppseHS8CKIsDPkHEkCLJ/IDahsbkBnD3L2lZr1o899TTmvkAgqknHx2x/8WdRSHs9dy5dvk2TDgqwAaM4YtmCklHzMCnnZJwkCeByDo6OgXs30uqGMi539zSeeIrqYHkBQ2mbXp+2+dDDlhi6vkZ1gX2f9Tg4AjtdW9vyPQAB0AhIsJCgZlBiAP24Q+WB7Z0clShQbe6RPnDxkimPShPy4sgGULOpNVstEg4uXgQefdQqNBweWtPSYHtVK5yPFBgAS7QlsPT7lnz01NO2/qK4eGPbEIEy9nrKKGlEJqgex4tp986dUzhnVCUfeECjPwAGI31iGqskps13OsBTTwGf+nwqIV64QAWS8Zi+UMqZp+zj+WyHrJKUKD5NJpxz6g5hT0gzgwHJDqJ243v0V4MBf2o14JFHmZpXDMFVKBGIXxVywEmkrW7PztOtRpokF0W0q1mYn7Lv4Uesr65WbeA2T/1qNNTo9wzJZmKDwqnPjOjHBn3rw4+P7ZhSyE/pJQgC4Fn355MqhkPOE4eHtt+EqDib5QeN19fTZMgoWiR8ZX19t0+7LBvFNR7MVbh8hWR3pYCDfc5dk3E6laBc6viYCngKTKnWagFT47sODnOUKjNE/EYDC4hjoJQzJwyHVNcbDGkzJZMqyiUTAyQSt81YXTW2XS6n51mA35X96oND4D0PpK+ztcV+f+GnBYgihdl0hq0tBvWffEqnCG/tNssVR6xjHFlyaKlk1rcx1497exwvG5vp+x0cAsOBnhNZAH7Obffj43SqTBfNhiV3KVibnc1Yd/ne7q5NCy337fZsm81mHIeDAddWx8c2hZj0p/I458gaTkiwcUx/PTC232wZhcFV+sGdXcYOxmM7F0WRTdssadrvuMS+K1cs8Xh/n30yMORSmasaDdrG/j7rseyAuagIiQ8RzEJDYFEc16dOsb3GRj0rMnNts8n5YW+PY+097wFWVjQq1czA0pxL63V+7omnuI4WHw+QEBN4nItqhvxy9arGSottK75NCMPZsSzrO1nvm9vi6af1AtlrMrHqfXHC+wLsi2YDmE1JcR465I3VVZJiPYfVNMz41UaThMTdXT4HzGaYO4Zqjf2nYcs3ndr5SFSAJlPr45KEzwudTnoaPXOa65/OMfvq6MiSEwG7ZnfLL7h5c3FO6XbSqRKlbA8/zHFcr5GEnj2AIOqzgFmDmHJ7JxDIswgCzj3nzyuUA40nnrDvNRqLc00Usfybm2zYK1f0nCClNcdSt8u1Sd5a/tp1EoSANJGKZVEolYBzZzWggXvv5jWy4+Mk9DPPs9MJU70qMKOPAsfN+bPAlWvOOiSxNn3tusb+AYUkxLDduTM7h2jYMQjQpl1lzSS2hw5S33Ne2NlxUp1Ke/jA6hrX27JWc+HOSZ6Xn3K8XGZ/+D7t+L5ncK2atY/JRM+fEdz5fDzJIaWW7Pd9n8+GOjGEK3MQ5ZFHAd/XaDUN8f8W8sK3SvF7+jSfJTtd2uPhEcsmBOlKhX7lne/SqJSB66eBF3/Gydf8YFGQuz4Gsbmh8FVfCfzkT2uc2gJe8jW3JgRorfFrv8FTQ7//hxq//O/te/U68HM/g79UFmGBAgUKfLxDa42f/wUy/L/0S+hPH31M47d+W+PlL1O4dFHh1a+jdH4YAm/6MY0HHwJ+/Ecpl1ugQIECBQoUKFCgQIECyxBHAHxuAmcD84Je36RJie1GqWzol0vASpMboqUyN4zPnLHfrVa5AS2pEo6P+bdsHp+0Ka88pq146CF7rSzk6406cPUayV3vepfGUcdwHJyN2r4JersEippR2cpLr+Ke0K3V0sotUvd6nQEJCTbEMTe7lWI7jMaLaRyaTUvsAixRQlKluGSbrMoZwA3fDzwMHB8xoJINkEsAN9Ewm/aL15Cgax6S2LZR2ZwaLlfSSkeS3i5xFLZ839rQapuBvhvbrMNgyE1xweamSRWTCYRJwCEbhD46soSd3T0Gpa5dY5Dg0LRle5V2ubLCoL+QOI6OLVnnyafSBLLxhIE0CVC3WiSZNJtGhcL03fXrRqnOCTo0W4YsqBmkk5Qqjz+hF1Sh/uLtJIEk2qbUqDeAasXD+po3VzSTmEDgW5Wcm7skVR4fs40bRslnOLJkxRQpztkG8E0wa/+AKgZPP718nLtwA1wKNsCbJFSNeuZ9TEmnE43p0AYeXZWESiWjcqaotKIGTDsz/04pXw0tU5U5hOQ3GVv/0eubdCrmdQmCxDED1YKSSddXr/M7Qlj0TapLIX6l4NlxWC4bVaLI+rAwJMl0MKQvnE6BtXXWX1JYPvww7SkboAU4/q8Y0mpKUQUmdapphKkJ7jz6mMaLXmTvNZlQpaLXo81L2tJnPINfbDZtaiI3aBdHDAz7PgPNWaLqYGB9musfNzesAqBAgsY1E1SWgNl4zHE+GdN2JBi5vZNWl+n1OfYaTbZD2XkPWPS9edjZTQeYPY9+Zvsm67mXQ4LNwlV6lLmjXAKgFDY3qZC/tmoVZSplXpPBe7a3TjQCoyq4u2ts1JkDXCJkHukLsIH4wRAYTdK+UxAnVp0tFtVAc+3xmGmjkgS44w4qPISRxu/+vv1+ktAv6IQ2PZ4AG07w+bxDhgAYqBbb7BwBd99j3wtD1nU6NeoUVZJVzp9jkHLQt0SUShlQxk/k9cXjT9DfDoeWyC12JEQQ1zdrTdt//An+3tzkOHV9jIuT4q6SElP8qai0CDkjD3mvi+3UjZpNEDgqkY4tKEUiWWL6cntbo9e1ap7bO4a41gXO1havDxhfBKZhbrf1nDgcJ7DSeB8kJP20iyhjt3mpt4YDEvkOD2n/Mo9Uq3YtKMj7vudZwqakwNvc8HFwqBf8k4uVFV7PXVcBJM+FIdtS0lWKr9oAg+yNOu8rqeT6A/4tBLvsmuwkiMpOVpEmCOz+/KWLJMZrbVRSjW8WYrykHZxOuT5ttSxhWNZloohE1ULzmm/W575td1HIk/Tr3Z4ZV05bJkYBzPf53TgmcbJS4Xw+MUqzT1+hDV+4wO/fvMnv33kHfevDjwCbleVtNR5z3lVeel2rHRsNAqa/lLlc0GyyLcplm8LMD/JTpCXaKksCJLtlx4DYYZJY8l63R4Kb75FI1GiwH7KpxF1kU8cPhmli+K2wfwC87e3AvlFUnk2BGzc0PEVbvHQp/XnPMyS+iEqe7bbC5ctcb5ZKtGWZ/7PQmjZ2YxvYXOdrbrPMZvbQgYs8hVOAKaoBu6bIzmXj8SKRGuBc7xImV1f5WVnfT6bAo4+nyVyAJcTU6wovfKHGn7/Vvt5qWtWwlRb7XoP24q5lk4TELiBtO55PIlCppLC9TRLoaGiVEwcDjTvuSNu2pN4dDHj/O++icM6NG3w9CFTq83nPulprdDq0s0bTpmJ0kSRA51hjNLREx7xUiQDmHTqZcC10aov9ev4utqkgTixpq9MB3vs+4FOer+d9qBNLzB+PkEpFPhmn04RHmTlc2tVVmhL4HtePraadW9kO9OEyr1UqOQd9FNeBVfNccemSJTDWa2zfe++1aqaeOawWlBbn/IND9iuQHr95ZGdo50CS5mEAz9O4es2Wd3+fz5nVCq8hRFJpi60tPiOKSu9syboPoG/1PI6LM2do10lsSXMAffSVq5agm03F+5FAQe76GMWXfolCu02p7jNnTl79xbHGm39C47/8V+A//MfFCeDNPwI06rdJnS1QoECBAgtIEo23/JTGr/06ADAv+jPuBb7lFZqnACca3/FKhZ//1wqv+S6Nh81J3v/1/3Fj4/ter3HhgvXlT1/W+NE3a7zu1QpbWwXxq0CBAgUKFChQoECBAoTy0sG2E0RL5mmGtra4edls2kC/pCuqVoA44mZxGGKeZiKKbAoPz+fms0u2kgDX3XcB62sKe/s2RYVspKY2ajPbTrt7mgohygbatNZ48ql8VZzRkEoTQuhx71Ov2zQzgW+JA7Kx3e1z03c0YgDbVXSanhCIdEkNADA2aTUmJs3aaATcd9/i91InqDMB4sxL87qutGzw0IUGg1CjCTeP3futrQHnz1MdTBsVhJYhMpVKQDjT6HRteaZTXuvoyN43q94iZK3Tp3nzCxeoaOYGhOLYtv0yJahqle3UbpPMUSrZFBrVmgkI6PTp90GfCgey2e8SXFwSmdZU1BiNWZ8wZNAg8IFZJkglBJ952XOCWLwo0OnylHiiGTTxDBFwa1Nhf1/PU8gBrE9QIkHh8JBB1pvbto+7XUueDHxL0pvNbNDHJdn5RtGj32fbjUygxg1sJYleOGmesjVlCWkAyzSZAuWSxvs/wCCX26ZCxNncsAE5rVmfShkYgIFaV0Xo4IBkueHQ9jGQT5zISz/qwiX4uKnJlMf0d0dHdqzMQqPyo/gzHtv0WKVgMSDXqFPJIYwygVEz3htNoDmkX6vX7Se2b2qcPm3TnWaJDa4a4fQWRKYrV4EXfTptTlIQunBtczjUGI8ZfJpMOO4EWrM85TIDbaWSDYqur6WD1K6tnzlDxT4Xjz1h28Mlxc5C2truviF41PJToW5vGxKwJgEQYN2kLQZ9/v2nf05Vo02juqMyAUKlaFdxzO/3eun2XG1bZUTBdKLnPsSF1rRHSae7tkb1jYYJJOvEBiinU8tQ2DVpNQ8ObEqz1TUSIFzUagAStvWtlByy8H1LPpmXN/OZQR84LJFglVXfAPLTKZ+E1TawY8ZlNggqRAp3jhZFyiBQKFWYDm86NemtjI1mlcQGA439fWA4Xj4OKhUGOiVt7pxcENv5M5wtEqO1IXwoBQQJU9oC7J/77nMa0vnT9evZsro+dFkaq1KJ/nswIOFzPOacPBiy31fb9NXb28C1G1ToCCPalaSx3Nyk8orcr1TiOJLxCpjUa1ON646S5Yez45tHgHPTBcrF+wM7/662+boQDns9Kpq5OH2adnvXnYvtCdCuZqEdKwpAt5dgMtHo9mgb5ZL1IRNjJ6ORVXCrVoCJQ+6RtcRgsEgmbLWAe+9VqFY0JlO2v9a00zi2Sj5ZwlI5R7UVyqTOM2RJ8YNJkibIBCWFSoWdmUrZZ2zN840KXQhIE7n+V4gLSpF0O08HPWOaumrJ1jMMSfzd2GAZZI3rrk+V4rzoeTwg4WI8SRPmqlXeT1L2ASRj9Q0R2K1n3sEEgLadPWThmfm33+d7nW6GNDVgOkeXmD4eA9CLdqS19QG1mlFFyznMID7UxcY6XxPS3d13cryutu0YnzjKbm5K2MmE88Uoh9Ti1t1Fr8f26/RYnu1tzh+Hhxxv169rnD4FlMpUeVLKpIv0Wf9HH2OKYunvqGrnrWWE1Nkkf73qrpvGY47jWpUkkxs302typYC6E/P3FNd7B4d8bzI1BG+VtvFsOsagxDFYLp/8zOR5tq0PDjTe9jYSF+Wagtg8X8oaUSc6dT+3v90UqlPz7DWb6blyXqtFImG5TPs+PmabaABIFp8n3/OAnX8SzWcOl5S9kfGFo5HGlavWf27fBKJIk1To9MVKiwcp/IDEXVGMWoCyh2USTaW1m9tcV991V3q+aGbIVZMx8Gd/zkMGAMd5HNOXHuyzzpMxn5WzED8oY7pUYhmzU6JSgB8onD3LdqnV9bw/osgSsWXsnjmdVhheX2fa5K0thatXNZ5+mjZ+6ZLCZMJ1bhDQTpSfJrtn4aYI1tquxwOf5RD/I/2ws2PLcf784vVkvE6m/BFylzyfdTp8JtUJ4DdZt2fcy/dKJcwPsAH0PZJOXPYUAj/9LNPtpgmweevXDxcFuetjGF/webe3/Hv6MvBb/4N/Z4ld/+zLgGc/qyB2FShQoMCHgzhOb5q94x08KSiT+m//L6BU0vi2b1H4qR9XeMtPafzX/8b3nnwS+OqXaLzmVcBffzFzUb/i2zV2doGXfKPGm36Q6TQKFChQoECBAgUKFCjwfw/W1zwGPQxJQTbeJdAvm5FuwGQ24+ZkdvN9fz+9YcwL8TpdcMNxQylkt3nd//b2mQpNCEiNut2s393V89PYLk6dskEL94nG9zTe936FxKS9u3iRm9Td7pLNcDCYODWpSSSgmSTAww8zRcl0xg39cpm/s5uogc/6b2yYdGIl3svzuLk+nXKj+uiI9VpZWUwXFUfp12JHOUuUYQD2SVCyqlnAcjWRVpOBypUVklQkTZ97j8icBh4Ngb1dAM8h0ef6dW5m332XDZhubrAe5RLwyGP2OkLkuX59eRsrMABwcMBA9WwKvOOd3BBvtqhuUqnYwJgQgASlgEG9SjWdvvJ2n2Y1GLRU4IZ6XiqwwyO2Q6PB+mpDnFGeUV85NKfNfQD7tMFr1xhcrVYYlDt7Ftjb1bi5zaBCe4Ub/U88md6Y3z+gzRzsaxx3YwyGen4CfTIB1IzEtWqFQaokYWDpwgXuD6yvMbVNv28VknRig8mrbTt+hZy0tkaS48Eh27O1wmDJ8bHGn/058ImfqLG2mt+i2cBot6vx3vcyGCVBkjxVlZ1dtpP4mN09licKWZfDw7SyzqDPveaNdQBqMTg5HFhCSqPJ8k8mtGXXX7kBV8DuWbdXTN9qqnkoj7YmqVc9j2U+OGBZJOgI2PF24YIJJC1Rs5jNqEzxwHuB5z5HIwisEo1gNAJW2tZ+89RHymWjCuK81mwxCB8EDNodHtO23KChNtdvmHRPN2+agJMEQj37W8OSAvzAptIpBbymI/SQwqOPLao1HB7SfzYaVChwCaUK9FmSRlCwssL7a23VWKA5DtdWaSeiTFcxwcZwRkWInqNYJ0EuGWPihyYT2lxHgriKpJHr14E779TYP9DQCYk220aFJkmARl1hOGJ6tqOjtG1FEea2mWgGyYUMkiQkaiZC4HTa6ODABAW1JUILCaaRUSiBNgRi01aBO18Ygzh3jva/7ainRUsCmYLJGLh5k76mVlfQ2qTClLmmsvy7gc+2ls9EIVIknyhOEyFlDut0SDAcOikqPWVUyQzZZqVlldqevmxJsKJKJIQ9Sc9bqQBbl4B3PwD0u0CjxbYMfCEDsa71RpoUM5txXqtW+dk1M27ccsv8vrOTTjUF8Htnz1l1FVEi6/bsuNTgfF6r20D72hpVj8LQKLqUbNC830+TwmUN4vvAHRdJcjt7hmutVov1uccE6idTpMhcbj1dQoUQDPOwjCibx1VbXeU87Pt2LTQamXVJYAiv3iJhBuA6oz+wJNqB+VvSZvf7NKb9favEKXjooQj9gcagT2Jrvabx4s9gQ0lq3sNDri2mE7bL0bEJ8isgnLJch4f56T2ffEqjXltUiwLy11c3tm1KupPgrukmE6bXvLlt08sCnFvvvEQfUq8Dn/M3gfc/SH/YchRXWy22Wb1hiVi9nv2MEI8Gg7R6JsAxePUa33PTnsu87ge07bVV2qVrK+78PBql1XObDR4A+IA5ZB5GhtShgV6PBwCiiGqpu3t2LnUVyADa8r338rPizw4O+Hzi+SyfzM8utM4ngHa7HN/TKX2J7y+qEJbLVnlpby+/7+OIc+7GOlM+39jWODpiG5RLHPdxbIi5Ma9XN6T1ZepdMt8K17bT4bUGQ9quHBw57nKdd9yhXZeQTvtZLpPgmaeMpRP7Od/ns0kcp1NCA9Y3zon0mWsMBuy7TpefcYlZvm/WT8ZvJ9qus7TmvB2s0me4xOI4JolMyJsr2bnPoFJOq/ydOWNFaMJwMR2hoD+wbQZwjsgjsj3+hIbnpclKoxHnmOGQPmRj3aZDlXqdP29T5QU9R6VqYr8HmFTQZ6nK1Who1GpqgQDcH9h1x3RKXybjLaWSfALprVaz/aLM2vra9fTBijiyth6GtN9WK62+deUqn5fz/L7O/C6XOL81GpZcrZR9Fsn6Hxfb2xo7O1y79AeWaJ9Hjk6S/LkkCjW6PeDaNZJ9V1f1XIXrxg1zwKwG1O9a/K6Qrtzx2WwAOG3/Pz62/fDU03yG2d11PguzH+CoWSfJInEToKrhwCjASUpwOcxzdGy/HzpKmPNne6SfG2pV6zezvvAWWSA/JBSsno8BHBxo/OzPJ4jjJccLboF771H4rtcsWs+ddwBf/c8LwkCBAgUKfLgolRR+4A0Kz3su8LmfA3z36xT+xVcrfNEX2s/8t98EfvwnNEol4Nu+2cNrvlPNF9jDIfCq12j81L9K8PDDGodmc2JvD/iGl2m8+z0fmv8vUKBAgQIFChQoUKDAxyZWVhRaLRuAHw7z0ye4aNStkkGlenKKGtl0rNe5MfnJnwR8xou4KV4uZVIEanuti+Y0rJAjnnqahJtKmZvFbjo837fqBS7W1vi7vWrSq5mg0O2k1HFPd4/GmsQncPN+MmW5smo7buB4MmF7HndskOuJp/nsddyxamcAg2jZa1WcAIjn2zI3WtwIXzeBhkZGpaBeA+om4JZHsMnbHAeWk8I6Hda5VrNpd1ot4O67FM6dXUyXJtepZZRcDo8YoBwMbAqhkQn2i7INwDbZ27NBKrePy2XazPo6N9ibDdtuh4c2mOAiCoFphuQg5Ax5+r0kaWu0nj87j8xm/vq6JThKqphOh/17550Kp7bmXyVZrwVsbDL4B3DDfjozp+snDEIdHNjUpIDtEwZCNWYzKnMfH6VtStRxGg1LdDp1Crh4Uc2JCYKZUcgLQ+QTj7RJpxLY/mibVFazEHjnu3K+Y5AN1nS7wFOXSTjoD1g/IaisrVrbjWOkokW9Hu1i/4DEiZNS7XlGuWY4tGmkjo4ZKBmY1xoNowbgs0yDwaIaCmADiaLsA9COyxUbAOv1TIBt3xIeQpMCR0gz04lR+jHfkVQp8wCMpu/qdmgze3sk4z31dLo8UQQ6FwNJ97mzw+9UK4Zw5SMVce11gZs7VPkQ4tBsyrIfHZqUsBEDUVevMsgHpANnRx3+Pnc+X83JRTZVbRxbX5hNpxqaNGrHnUWlQCE8TSdUgNvaJKHhjksKF84rXDi/+HkgTTzJS+F2K2xvp8m4CvRllQrw1rcBDz1MX+TWZTwG7r3Xx/lzdnJxiZnLkIBEm4ceJuHu6cvpdF5CsHEJH9MZ/cpgkO4jSbe4sQHce7dVMgOsjSQx29FFv4/UePM9S5AAgPe9nwH4J54CoKlQsrnJoO4ddywqmrlzxMYG2+7UKY6HvX0SIaXcClbxcWODdR2NGCidjOm7RUUwig1Rpc6+yAoGxDE/32zws5OJvda+GcvHx6bdwPJIgNn308SbvKBxVrGM6nf84M2bwNUrrFBWdWo8YZ8upIuCITxVrJ22VxhIvuPSYgrkmzuYE1+y2No0JDWPJKDNDaDVUvA8UetQ6PbY9tn7A2kiw8ULHI83tp3Ae2YczabLSdlZjMf0Tetr1szqdTtnD4ckUl53CIdHRyz3408wJfBTT3Hufvd7gPe/n/WQH+kDP9O+wxFfF38wmbINs+j1rFLLdErbOb1F1bxLF1WuOl4cp1VysmMAWLSh4+PFvuv1WM88sg3Aezz8EOvp+sfhkKk3b+6KqpzC7p5Cs0kf0KhTBbJWYwC/FHAOPTgwxBJTNilPHuksivKJB90u7ebyZbbpc56tcPo0x3GlzLZ019hlQ6AU5a5EMxUoQNu/cJ6+xPdJFhHV1/GYdjZZYmdBsJg+VUinScI6L0uNmWe72b45fXpxrtvetv0eRZi343Oenf7ctWtUa3z0UQ3ttG23yz7v9S3BPQzZTx/MXBWGlvSaaEvyc/14Vm2uXqdv+Dufp3DvvemFfHbNEMdmTTq1CqXyvvStjBmXZO6u4fb2WFe3HJIGWHxk3vPEZEK1tXBm/LO5X6MuZC37POEeEPJ99pk77AYDqmoJXLuslu1nh8OTVacBXmcyoW9UoJLZdJIm/IxHHGNjM/dcuwY8+TTXcsMB7VoprndX24tEYIDErf0DEuTyuBdXr9q/hXiXPZgAcF4/fUrjjkuWNC3k//GYbTOZ8lmwUuGcHztjfjxmytRGg2VdX1ucw7IHDJKEbSkkesCaVckQI7e36YdHQ/aHrOdd/50ds/v7XDv0B+k+Vx7tZWJU5bIHoACOsytXgA88xHvtmWs9/Ej6c+HMEseHQ9Z/YsomdurO7dl5PnUQZ4/Ko4FRVB4MgCefJMlT1hG12mI6YMCkibxHoW3Wb64PjmP6Q3m+ceeNctkcTNjnXDMndDltKX8rZQ8xfKRRKHd9lOPgQOOlL+dpvP19je94JeD7Hzwh6298lsJXfrnGL/0y/1cKePWrFEqlgtxVoECBAh8J1GoKP/JGLtwkVcLLvwkIQ43fNOqJv/YbQBBofOPXA5//uQr33gO85nUa183p7P/3P3Mj5zXfCfzwj3BBMhgC3/pKjVd/B/A5f6vw2QUKFChQoECBAgUK/N+AZlPhmglOTYyqlOcBUAym1hsMJt33DH5ea71AEBJCjAQL/MAJkKeOfwM7Oxoa3MitlKmWkSQ8dR5FDOJoLE+fANig3kkkLVdVbH+PAYk4Bi5dYpDz/DkGObOB3cHg9oMxolqhPLZRtWKJOtvbRqUr5GZr4APttcVrKADIKBK56mlAjjqVqKNEVjkhTrgh3+0Cd94JaCfItbLCDednPsOkMzkFvPf96XK47SBqGoBVSgl81leUEa7fYHqTKEwTvJ79bODBD9iT9PVGOojX7zspvPTyE/cS8AxDngo/7tgyXrnCIMnhIet/770m/UYFeOYz2T5JDFy/saheI5vfAolhXLuqsbdPJQs3PQuQDgTK5+OYJ8XnCkMwgQ9zCntvl+lD9w9soESCBDu7aSKTu5FfrbLhOx3b/5US7UsCBq59djpMxXPzJsehKHGMnM396dQGiyQYO5tRMWE6tX399GX+fvazFseWa5+r7bRiR7fH9hayVbNh+6rnBK7PnaP6W9UoBMYOEWowZIBnMmE93OuLvcdOOqtKJaMsZNpKgiMAFV/+xt+w5EQJioSRCcz0qZZz6hTbZaXN9uj1WK5ajf8LSXIy5k9iVISOjqk8cNylnUlavDix/eDWXyNNtnBJBIkGAqeNb+7Q9kfmftUqiTxhCNx996KqkgTGDg6Z1rXbo0+tmPRGkvau2UwrA/V7tq/X1mwq2EbdtLtme3VHhijjs6zlMu1R2ikvgFoq5QfbxUeOJlYpo9m0ZA4X1RrJaZsb+YQEuU8ucra15N7KY5uurlLxrNXifTod9lEpsAGzwFe4cN7Hk0/Sd1UrTO80v6ZRiiuXSOwU8qP4sPEYGCW81zxNoanL1PFF4xHHxPGxVeDI1uf+ZwJvfXv65emUyi69HtV3XN/hBmnjhMFpaYNUKlZNol12PnT/v34DeNb9i20qKW+HQ9rExYskJWX7RZl1RZKwvUVlSMinUh431RpVf2w5lLJp1taNoh9gFG/Kdq45d4a2deUy+zX2bcpVQRjZdEeDIVIE2Tg2qfT61u5cMkOtxuD5+jqJK1Dp1JSuH5K6S9o3UbqqVpenqhOMJiTcDYesu9xjd8+QjKcaMGRJBa5tnvdc1jKJ9VxZZGXFKmQBHPerbWOrmWE3HueknExI2HUJiu5wnROIVVoBpt8nmWr+/2DRL0yn7HM3dbKL1TbLLmop0wkJBaWA9r6MmO7abrvN8dVsGWLRRKPZtASOemNe/DnuuETVvt09rnl7fa5/jo+wQKbOYjRatH+Z58+c4RhfXTM+fYZ5H0wmttzu+KxUWOdGw6q0aVgikeeJGudiikOpmILts5JZU547k05JFoVcF1y5YojMZq197ToJK3v7tFvfY332D9n3ooAHsF+mU6veKuM5S9hyijYngHie9XtuXwyG7LfAHNCo1Thn7DlqweXy7R3cKJeB8+fTRuOSUl14nsLmpp6nZJc17ywENqoAuuy/7Zs2bXenw/LdblQllaYw4BiVQzNhyPuurtrPJAnvI6jV+LlHjbTm5qZJd29ISru76XR2oha0vpFOy5w9HNNsWHJbrQY85ij0Xrxo0qCbsj/0cIQrV2NsrLF92m3Oi52uXeckCRvY8zEncO/v0ZZ3HHJmVjUtjqk056bcPTxiv999N33w6VP285OZo0zqkHsbdT7/vevd6es/auolCk1iC8dde+BBa9pyv59eew1HlpSlFP1Ca4X13tqi2tf+Pt+XcSZpMkdj4OhIm75VuWlp88jInsc5x13HzWb0bXDWE3LgJIqAQYbgGIVAc9P6mL09HkypShlyyF2TifVhrj1K3bPITfN8m3oSsl6djCWV8+Jn4oT+WGv6RZlL3XnaJXYOBuzD4+PF8q627WGwOLbz5MLaUgFXr2ls36RdSf8EQfrgVOiUoVwG/raJsb7z3QlubHP/ob1qlP68RcKmi0aDPiWcsb+EsFitOkQ0Z25PkluvKz4UFOSuj3L8j/9ppVt/7w+Af/jFwDPvO/k7b3+HxkoLuP9+OyKOjjR+47/az3zplwDPuv92p7MCBQoUKHA7qNfTftXzFF7xrcBgqPGHf8TX/tOvAFGs8S9fSnb4z/9r4AffqPFHf8L3P/AQ8KYfBb72q4H/8J+44A9D4Lu/V2P/gP5bLXtKLlCgQIECBQoUKFCgwMcFtjY9PPpIDslGM0jXqDNYUKsp7O1pPPzwIvklCLhZKcFDITBkr1mrA+//gDllu0dVDwUG/ERR5ckp8NznLp48XVvlCeQ8dDoMvPkegx7dbjo4KZvqccQ0Dfc9A1hfZ1qV7KaqUnajNRvsiEKmJpD96aDEjdtWCzh9WiFxduKFXNZomBP4QEqdRzAa57RnKZ1WwVO3VtU5OmIblAJeb3OTwa9LF4G33gD+4u1M3xNFwD33LH7f9xggGZgAnBskOzpicE9IeZLWZWsrHURsNthmrSZw5jT/zgZDXGxspE+4K88GxIT0cPkKN9hdW9o/sGQll6h1aot1391jILDbMyfWnbpIFz3n2XyO3t3VeM97NN7/Ab7ueemT2tevGzJQTtCw0+U9Gg2e3BfC1tExA22rq0ahC7Ze0o+1mknhBktI2NwExhOFSkXhyCEnVqoMlu3tL5YhNqpwUcRrN5u0PTnFD9gARRzTvg8OgfVVfkdO5AtcgsKNG9ae3X6+co0kgnqdJJdSAOwe2ZSjxzPahtveAMdwvc6T72wLBs2jiESr0cio5iBN7jo6MgQM158oBnaGfQYLB8OcVGOm0X2P9ZI6uGS6iSFzyWeg2aau4kk2/SzAcbCzwyCjG0DSmuNwNLQKPUISypKTXOIXFH1tONM4tUWfIuWVFDpil0eHTAfn+SbYbca8m6oQsGlfJH3XLOQYHZpga7nCcSLjMwjsWAlKJCtJCqCjQ9NGq/a7taq152wAbmOD7enWcbXNe4tN9PuGoGnu/+AH5k2BF7yASj7tNv13aIi/0pbVMnDhIvDZf0PhwYeskYWzxbRzhqMGgPU/2KdPlTmi32d6QvGh1Qr7dMtJ93vmjIdSkFZ1ePxxk37SY3+VSrYtXbv3PI71TocB9SCw4+ODQeBT9SMPkzEJgRvrVOIArAJYve74Sadc2e2+PPLcSURnITqI/ff7tIMwZPA+csib83lWm6C0IXmNxta3Tsa05WzgOAXTme6cMh4bUqkhkQQBffP6hlH1DIDYqC5urNvyunVz/V4ekph2qAypZTxmMHi1zTJdOM/2jGKg7C/GsPtd1u9Kn+RZ8XVuPe68k37PDdrfeYfCM+7VODgA3vUe2lCppPHU02x7mRcTQ3jb2HDKbEhro5FJs5oTQN4/ZJNKmwRB2i4OD4HDI43RkHvGzUw7RbG5rpAJjSJfs2nVy9zGkPTGy3BwoKlcKOnoOo7SYmTJJEop3HnnosrK+tqiiuD83qBfHpi6XLhAm9+uWn/heSSSnjtL37O/z/mtUgX8Ie15NAbat5GGcaFuh7Qz8bPiL4+7+QpfTz0N/O7va2xv69QaJk+hVLCzs0R5TdtuqJTtHLtsXXl0DLSat2BiKJOu2BCZJxNgb4+paEuBVUUUZMkebp1FgVYZxZ+rV02qxBHnRddVjcdsS0k/3Gpxjm6vcJ546imdSju+2qbdToI0KUpQrQHlEd9THlLr9IMDjQfem1b1kjSA/T7Ho7wG0DaEnHRSWtt5E2Z8cLNBG240ScCUNH0ufM/OXaJGpDzgL/5Cp1J8ivpsniKgBttqMjEHPhw7qDpzmAgKrLSsLbi+MvD5Oa01tNaIIj5znDlN1cowtPYo5NYktiTRvBSDooJVraZtplq1h0vcto1jjhU5VOQSpoVALKl/s4c9XFUp1iP9vqjfutg/yLcjuY+4hoODRUKjrM0mEyoXjk7bOq607FqlbNYgd99lSXsuufed71z055I6XFSBj475u505jAFwvhCyHWCUuM382etlDuHA2pI0j+uPfI8+RdbMWZUtpTgvbawDR+VFVavA51rSXT8qWEJiFFn141QdYrsGrTfMXKgsuf2OS2xjwKbGnq8LnX4+PNQol0lInxlioDyTu6lwAZNeW7P/XZXH0Zj1FALqWptziNRlNNLodDXe8Q6SLmtVKu8C5tCUsdlqhWtDSSsp7RuG3CcZDi2RdW0t7T9SKe1vQ132g0VB7voox5f/Mz4s//ffAr7vexSeed/ygH6SaPynXwF+5mc1NtaBl71U4zM+nZsPP/rjev7gduEC8DVfVRADChQoUOCvAr/128Bb3wp8yvPtSYRf+3VgOtV4xbfwRP73vh74jf8C/MRPc9Hd7wM/+uPA3/97wIMP2o2in/pXGnt7wEu/4UNTcSxQoECBAgUKFChQoMDHBt729hnGJvWBTqjwAKOO0ekA0Nykj0KNx54w6jlTbrZKUPb40AZfRFkji/aKDSBMJgyUVqoMpCWx3SyeTtOKNqLSddxhwLBUAu6/z7735FMsSzgDQjBQ0+vZdBZaa6o1GAJMUOYp8HvvyT8NDFjikAS7PaXwnGczaKUTKn4JEicgUC5xA7bpXKtUMoG0CtMDep4hRZnNXFEwctFqsm7zzXJl00mOxjYoVq/lB4dmM5LY4sQoiM0syclVhnChQfKdkLvqdaDb1bhy1ZBrTAq70Yib/sfHgLqT1zp1im0gKii+z03xRj0dyFWe3SxfXeX7xx1bHyEluAij5f0k2Nuz6WeGw8V0lHFMu44iu7n/xBN8Vk50Om3h/qFV86jVLDFCUkBOJ5bEJsiWr1Qy6VJGVsGjVLJqLg2jiCf9rsCN+moV6PaBWaixtm7ISyYY/fgTRoXJ6bu9PZMmdGJSEfYYMJDPSIBc7h/HtInBgJ87d45jcDiwNi8qG2Go8Xt/yCDE2pqaE4IA9le3S/s4OmIZhAC10lpM3yfo9YFzjm84e8bY6jZ9ithKNjg/m1rCaK3Gcen5DmkSoCqgkxau3rABUFHuuniR/587a4OmccS6rK9ZAkw2JY0EJV1MTfqyvMCpm7ZSm2CmRprwkU3JJ38eHQv5LV0IN8Ao/bu1xYPZW1sqRQhRhgzqGWILQLsdDplKSAJ1vvFF4nt7PUu27HaN6p7p1zCi76lWGUTU/XTgq2aUF0W1p16nnbnuSUgJYvex8SeuvxdsbSpsbWlsbVrFnsmUBEHAEHdEUcn0TRSxDp7P4DJgArbmmpUq/cDqKussSg2Hps69vvERdWDiBMoOjxJMpouKlS4GAzMPRZZwsrFury2II7bJ0dEiaTCOMW8w8aMugoDzy7OfxXrt7FrFk17PEGvMNStlq2QzC4FyYtJ3te287XlW5elvfTbwC9d1yhdurKdVtFrOxPas+4G/+Iv8dHh5RJVSyQajgxID+L2eJRFKufOUd5Rn/e3KCj+zsWHTX41GhvRUNipAngl06rTPlzbMG7PLcPEC6+h5XH94yqammk6N+pdRkYpiqsCc2tILqWuHQ6AUcmzu7dmUTom2gff+gD8pkuS6xulTJCGc2rIB3HlqWKO8KN+JIywswHyfr1+5sryeMi9l0R/YcS5zn0CIxIBJy+bxM9Mp3wsC2k+zaVPpAsvJXZ5n1yblMsf9009z3MohgPVVEl9mM43HH+ffngKe8xx+r9E0KdtylEvGY1v+RHOvezLRqfYOQ0ui1gAee5xrqOFocd2UlzLNRbudJjJMxmxjuZ7YugdLCAG4vj085FoEsL4gie08LTY9JygavyG+MC+NmaBaAy6c5VqjViOpfySkWzP1hBHw7geMItAS4uPGxqJalnsoI8wQ67NjO+W3HeJ2qWzVNXMVgMH1vozjep1r4Tvv4Fi96RAItTaERaPUOJpIemSNO+9U6Pdsmthn3mdSRob2fu98N9WYZM4aj4Edo6Q1V9h1yuUbosp4wuv0+zmkcwdJYkixpUUiCcBxL+t8T9H/aQ0cHWuUyhwbnQ7X7uOJTYd9K0yNqlcU0U5F+UphkdDb7eq5qquLWtWkr55xjjw81NjfZf91exxHWUXg9XW2x36G4CPqa267yJzqGdJMJbOuzI5HMRF3zm80+Oy5v89rRU8uV1TLPrZKvy2oKmn2w8Ya1WFrNTO3mXWXPLNmDzYcd+x6av/All/a4tIlhec+F/jAQxoKQBKl+0JrtsnxMf1Xu21URLVZ6yn7nJEk6Xk4ivgZIScD9DFjWd8540kOQ+0f2Pk/OmHt4/ls55W2TSnoEo1mhphVKnHecvs6SThOxpPlpFyB+9wNsA3On7cHilIkYs3nprkCtZ9OY6gBdDsae3sKO7u892OGROeO1yyxT64nBHRPmYMlis8PraZV0YamP+gPgD9/K/DIo5wP2m0elpJr64TrxNYKbenadfp6KYer5C3PcHl7LIJabZHM95FAQe76KIdSCi//JuDv//+Au+9eHsg/Ptb43u/XePs7+P/+AfC67wZe+Gkan/u3Nf7oj+1nv+MVJHwVKFCgQIG/XPyv39F445s4uz/4AeCvfRpPZgPAb/4WMJtqvOrbuUn4xV/EU8qvfT1TNwDAf/vv3Jx53nOB9z/I137113lK6bWvRuHLCxQoUKBAgQIFChT4OMXVawkUuFkt6lmeR5KGi8eeAI4O+cyhNQO/1SqVjTTSKTNcCEGJKjMKtZp5DlEMqhwdkfTkEkIG5gRuFrIZKkGi2ASzR85GsmzwDoYMII/GLEPFbHZqo6AyGmmcPWNP9grcFIMuPE9BKW1ZGLAb7bUa0Otr1t/dYAZT+EAzXdfmJjfRy2WrQhZGwM4eCTahbHxHQDkAponcG7jrTr7/9NNA32zex7HtM4GcYB6ZFHIbG2zP9TV+fzrNV4ACMkFvTRLcZOJsQpu69XrcvH7qKQa1L15U6HU1rlxjkHA8Immk03UCmB7VTWZT4No1Blom43Rg0/cZJFFg4FEnZhM9xxbcgIuknVMKGDn94wYLJOgxD1TPWI9mK32aP45s+skgoI1PTNqoMLQb7aIcUKsvBi3LZY4nUdEol9nvkwm/f3CYVmaTAPjYIblk03oBlqy0smLKNcGciHn+HNvbDXiJikOraUkmvsd7J+bvJKOkEptUI9evW6JftZohGhnFLzeN1jOfATx12ZZZTpdvbS4GZdz6rKwwsPqEo0g0Tw1jECXWBKQ9AxiClPM5V7EkjkngBBhMFwJRnprGPA1PxxkD5sJbW1bhajbj2FKL3Ks5fD9NuHRJXa5aS5JwjJfLrL/4Tq2pFiIkq2oFGGaCXnL9Tod++e3v0Dh9BrjnLo73VpPB2idMINPzGSQeDul3JFjfbtNGY8fPSP1nMwZF+/10EArg2MgGj7IKHEls/NgJQSbPM2oacTot094B8PDDGoMBcPE87VUp+tqVFaNkWFokEQ6GVn3DJbu57XbhvEmvZdRWwhmv43tsj8ND/m63SXz6sz/X2N9PcPmKXiD9AWwHrS25JRYVIw2cO6dw7hxTEYehJY0C+ebjqohkXV44Y52OO7ThixcVAt9exfVBonQ0nVpSc61m1NRqLMfGJsvkKlyGM0u2CwKWwVVGcst8/Qawm5lHRDmwvcr/Dw/pozodlk/s58D5XhQCftPeJ4/IW60AJVO/sVHI6Bpy6sYGia+C8Zg/JaMQosFxNlelcypRLtNvAlga8V9dZRuOxySguPNOp0O7rddZfplX9/YXicriYiVYL4Hb0dCOpdHIksgaDZJQJxOFP/vfVBcRPz2ZpFNfuz6n06U66mqbgW9X8TGlFGgg65Eoog0HSLeR/B2UFtdG3a5VCOn1SGAolx2inqljw8xvFaNWohT9XeCng+dhCFy6qBDFwNWrGg9+wKagqlW5JnXT3S0l6Xmcd4ZLiKNBSUh+VECbjNme2XRwAH2grDMFrRa/f1LqcCCtNJudMtbWLKFHxofgylVLOB8MNK5fB+64g20hBFkh+Ygd5anlAGly5rz+RlVrFlIZtdO1bdnrsa8kHeDhITBuA3fdQXs8PLLXnk4ssV9Ili6yJM9lpG/AEBDN2IpCQ4JV1j7qNUtGAtieYci5bW+XdXnfg2Z9l+NcRc3HDywxG7DrZ61JTFPgmk4wm3LMil8KQ7teyFXE0vZ1edY4CZJidiSVcqAUCU3rG3x2OGWI2E88CZQO6OdF9dVVHBZUa2ll2zy4a99ss40njAl1c/zGxoYlUycaqFUXHaiktXaxt4eUuhhgU3m7iGNLMgrMOunOO4CDI65N45gEsqee1tjcMAqCTn/cSul4DqfSWUJus26eR5yqzWaGyLTC36KMKe91u3x2ce05D7kpCw3W1zivTEOkDlW4OLVF3w7N54kkAbRZv/kBkORcfzalTxailvvsqJFOGQ4gZY/ZR3tZ5zcaVk1qWdpVgHY4GhlF1K30em8ZRP1vfc3cJ9MWMr8oZQnO4mfFF6202dZ+wHnowgXgmlnzXL1u1yrTqZOy2CGaZsldGvTbkkoxilhG8R/HXesrtnfsc9qFC2QcTqf8yRKcZzO7PtzYyBCHl6xNRiOrMOau45Q6mVD6oaIgd32UYX9fY3MznXJLKYW7717+nXe8U+MN368XTp0AwNveDrznAfv/P/hC4JM+sSADFChQoMBfBT7xecDp0zzZdN8zgO96LfCvfhb477/J9/+/32Ww4fWvY0rH++9X+IWfBX7whzX++E/4mYcf4aLkkz/J+vM/+hPgsz+bp/gKFChQoECBAgUKFCjw8Yk44kalBCQTQ9QSgtfRkWYaQ3M4pD/ge0liN6mPjhkE2driZnccM3jneQzCSqArCnmKOgr5mb39RfWpZQhM+rXLVywJRUgzAtnUFHWw4y4PJkqQYWODAfK3/gUDUy5aLW7G7u+lX8+WbzCkMlZ2g7ZcYmBsfd2kvztIE6LuuZvEiCS2G/+zGQMes5Cvl0omoOSkRyLhg9/xPAafJK1JFgf7Vunig8Hurk0ZAtjyJYlVeun1DAlwRIJBFDtqS6KUkDBocO2aUccw13v2/cCDD9mN7dlskRByeMT6l00btNvcKL/7ThKEXHKEi1nIdp1OgXNnFgMrQckJCqrFfnNJQdqoPEmqmihy1B+UJWCI/U6n1rbbbdpdNt1XEKSDBIFvUw0C7K/tbQby6jWF0Vin1Md8j4GJJ55kWfsDEh4mExLCxoaAl6eYw0o7ZMKQ9+vuAk9fJgHSJXJKgMYlsmTTDp7aWgza1WpsLzdoGk7TSh7lsqRP1Wi1gN7AEhQ2N6x6R14KTBezGf2T7wHKt4oELqLQEujC0ElTkgl8VCrpugQ+fxp1pz0MYcj36R9WVoCuXvz+uXOWaCGBqJLxCQCveecdLNTVaxqeb/xndrAq9qkbeJ0rF7Xs32HIcRbHDJyvtul726skUQhhYG2V+0XlMusiBMdmC4i3MU8bepIylUBIZy4ZYWVlMQXveAKMhwyuSWpFF2uGbCqEk44TFItjjvUgYL0SbYNY8nk/sOmjBOUSMDb25KYldVGpsD+uXQPglHl1NT/9pvKA4ZCdHcVpRYVz59jP9boh1Jo+jxPgoUeATkfj0kXn3g7ZYzbleO0POO7d9/Ig80NiUlAFgUans2g67lh1VSXHY/rQJ8dG+Wqdakfvf9AQtg1Z+OjI+rudXfZTtwPs3NSG/KLm18sSB4S8WDF+czjkx8U+t7aAS5f42YND2kavx/aSNs0jTEwmi30zGABXr/D60gbuZ8IZ55NujwSddnvxukJ8FGjN8egSA8KI6wcZG16mwTsdOyZEXRNYDAoL3HEzV1ExA1D66vw5IeZpPPEkr9Xt0qcKgWClZcdEEKTrIfBUeoyIMlyqCs4/eWk5Nzd5/ZUVQxSMmS4OSKc8nquMOt8djW1K1Z4p8+lTHC+RWR+65JfVVTPnju3/k4lZJ5g2mk6tcuFobIPzWVRr6SB/FLG9O13bz3PSKqzd+YFNpwvY+rl22e3eOo2nQMjEFccfJQn7pd8nyUGDqpLtFUNONOOqUuYcPRpxvLjrpUTblIBSbgA4c8akDzWfW7a2nk7ZDuOcsaV1mhjf65r5NkNcfugR9umz7ud9sykEF+xJ2+trQ4CVZw0hSwCGTG3mbyH9ZMkK7pwqJDvf5zrqZqYccWKvLcNECISifAfQHiaTNLl84BBWVlasKpwQSoC0zZfKS3nft0R2jgTYPpcvc75qt4FPeB7XBeJ7fd/4XYe4OpnY99srnKcOjFJU9iBCqWTXWy45p9/TePjRNGlEMBwCDzxg/1cA1tc8HB0n6Js65JF9tE7/FmQVATc2SHqV9ogT4Po28KJPBwYje43xmPfo9XDLRi9XLElUxmE9qyzsdGQQkFAZx1bRCTCKqMYW3e+Kr+ma581ajXPd2hpfrxuVYiHyHhzkp3acTTXe9yCf76IIePs7FlM4T2fA/tMsW7liUnE7vsx9jsumnVym6OemTZ7XtWTJUkHJpnfc3bX21WiYMXzCA2d27Iq9avP32iqf25560imreY5MEpLIG0PeM07s2jyO2Ze+z3a/eBFoXjFripi2X6/ZfQhZ+7tlDWcapbLCeGznU7cqtdpiikMN+sz9fdppvcHnl6lZ03nOHCvPcbJunU4t+X5lJZ+IdXiYJhrLOGo2ab8TQ9h3Uxy7Y2o8LtIyftzjwQ9ovPJVGl/0hbeXNrHf1/jpn9H4zf9hX8vK7NfrdsF0/hzw9V9bELsKFChQ4K8KZ88q/MSbgX/9cxrf/m0K9brCK75Fo1zW+LVf52fe+hfAy16u8cYfADY2FFothTe8Hvj1/wL8pJOm8T0PUOb9oYeBL/snwN/67MKfFyhQoECBAgUKFCjw8Q5JZaHATdxalYFrz2NQ2FXwGA7TQW5Bpcr9IUmpA3CjstcFNreASkXjnrvTgZEkTm9ALwua3X0X0Ggo9PvcjDrY5/eyhBZ34/b6DQYn/QCAE8CSjdEs+iYt18YG/9/Y4OZvvW5VKUaj9KYqYIMF3Z5NnSPXcO8p3xkObVBTIa3wADCIt75uPz+dAe94p71/UMKJUIpB1J29/PfzSAxRzM9Pzaa9EP3CiP0qKc3abQa3NjYYcNSa5AcJcI8mtg6DvqQ/YvpMF9L+W5u8hxvkdUltoxGDK8sUCKrVtOLVYGjToijF4Gqtxo37eWBLA5cuAod1kivyAkNhxH5dXeX73R5fW1lhsKDXWwwi2sotBvclCCtkMRfTCRA3eL8Vl7CibB3L5XS6SXfM+D7fK5fzSQWBT3uSFI95ATu37EA6MJsN3DQaJE5IelTABkZcHB/TXqSskzGDrwBTMrqoVBbT7qTKlLMtoWEJL1518T2BazuDfvpSbmoxgLZdrzvKOkaJbmQUg9ZWLYkPYKBJFB/SQSP+rlZsgD+lWKEdH2jGwmBIAt9nfxZ9Zh6yKocuiVEIVpLmJQ+9HuZKHEwByX7KpoQStQzB5gYDaFrbwLqg3V5UoogiwDfKNBJoclWZRqM0USaK0u8L0e8d77SvuWkHt29yfvrDP9IIRSkooJ9qNNgnY6T7pFohMUrIelm7ltRBM0MY3tvTqJZtcG3Y51wo5I9Gw5CuTFrTSoXjptujnU0m9F0yJqMIgLGFcoWfCUNTbnO9wKdP9E+IpPX7PJzpKZuGq1xeVLaYTtK+5viIZVhpWwUgF1k3qMAUQb0+U8SRlKHnQgGtZlqtRwKXwyEJEkdHVJ0RlExgXOY7CYJGkWlzIfW0maLoJCSJJY5evMhg+XEnHdiWSg1NYLhaTZOdksTe0w9Y3tksrXaUtRFX3aTZtCm19vdpvxKUvXo9/b28XVV3DSIpy5oN3v/wCHjwQba7tPNgQBJXyZC5sj53ruZoMJuZNnI6NokNGdZpgywGQ6aiAzhXXDjPNnzf+9Pzgksom5Pqc9YW8/qC9lCp2PG9tsY6C3HgM16k8Lu/r+F5htBm0gWOItp4a4V1UJ61bZeIPBzS7vtmvh6OTLDbpAtzVVP6/TQBNIlZhgsXjJJmDjlU6nFbUBzfSWJs4zT9N8A+7Pbttbodo9jiKpIa9dBVsxaT+gYBx1Be32XJO8qQptzXZzPa1dFhPjlO0oCfhDixY+3wiO1Vrzp+XHPOLPdpz3O13Zi+R9aLtZqpTy1DgC/Rt506zT6NonQsejAgqcKdtx5+hHY4yJCKoE2qx1n6tcce5xhyFb2yhJu1Va5z5Z533cXfd99N+9Gafnw8NkpqWfLkLZAktk4KhshpbDcKaR/drlGsU8CjjxoSqbPmiGOu32XdOxg46wLFtq9UOE6inPVhas1oCn/5KteUslY8SWmqWqOdlctqru4naV4XlMuELXQC6nX7/CjQCdfblTL73D2QkKfSKeTEKOQBIiGMVypA1axrDo/o/8PQXk/IT0pZAmilwjo26ov3sQV0rjlhu90wz58A/duN7TT5113vag30egmuXrVlTzT7Nft82OmYlIOgH62Ul/uqxCFpeh4Qms+FszTRK08x07VjUcYqGcXUuiHPnj7FdeRtpTp2+93YgfJ4yOC4w7Wb+LQoss+iIaz64+6u7YdyiX0bGRXWe+5RaK9Q4XIy4bPd0aFR+DN+MHsI4dHHgPvu03wOdIopdl8usa7ztPANErp2dmhPzYYdf3nVlLkpL41ir7dI7lpfN/VzGl8U1oZD+qLA5/rRVSwfjW7xXPcRQEHu+ijB409ovPxbNSYT4Jd+GdjY0PgHf3/5tPPHf6rxo2/WqdNxzWaaAXjqlGUdeh7w2leTWFCgQIECBf7qcO6swutfZ32vUgr/8qXcGPn5X+BK4tHHgJd8g8ab3sgTo0op/MMvYprG1zlpGh96mKcUvvDv/Z+oSYECBQoUKFCgQIECBf6q0GopxIZwsrHGTexulyf6h0MbyHY3DlPEKGf7Z9AHupmgkwQl+n0GUqJo8fSsi+GQ74/HDKzIKfr9fSCJNTeZfasG4gfAHZdyAjoGN7bNRrZzvyS2Ck7ZPa5ul/cX0sMjj3Gzdn/fpBOcLo9NhDMTRDH1l7QqsSGDSSDJDawHJQYABgMbnE+cQI1A7lkq8TvLTmCXqyx7nFCdRU59SxoVYFGtSO55uwh8SygKAkOYyKDTdVS9IlN+bVI7KZbl6IjBXcFKy6bdAiwBI0vskpRs5ZJJoTi1m+dxbDe9m410WjhRe6tUmXKy12WKy/FoMV2fBBZELQ3gdePIBvBFsadSsYdePY99d3jI4NBsthh0bzaXq5ANhxqTiUbJZ/20UbfY32ffx6bfDg9NipGrQDRju2YDLJ0O26nfx1z1pFxmANL3GSTb3ODvQ0PemExZ1rU1S7RcluKmVGZHHR1q7O4BOyYNqu/T7jQYYFld5edvbJPUJeTEp560bXvmzMLl51hZsQSbUhmoVUgscAMqWftdWXGU0TLBr3e+m0pWAjfI1myaFGWOKp0boDk6Zr3GY5IeSmWb7lRO57s2Xa0CN00Kmk4XqFb0Irlvys9JusdbpXJyy1p3glB50IZoKESfnhPcmk7ZTru79NNumjHfs+l2gDRpo1Kx6jkA0ySVy2kf2+3yf1F2kAC9e++xMyaGQ7an52VS9jhtFSeWTNRs2SBfo8G2rZRNGitDTBmN034ujm0qvemUY1bmtWfeZ9L1GtWDbjetOufC9c3jkQ0ulo2vj0wKRc+z6XOzROiZkwZoMrYkwTg2yhCOgoxvfIH4kOl0UT1kY32RsJW1s3kZzOvZcX32jFWDAyzhQb7z5FPAn/wZsLGW4OauMkqDwKFz2XIZVKFKFkmIgx6D32dO0z/s7xtCBNIpojrdNLmrVE7Pd15GcKBcoppgv89AcBaTiVHkKqXtOAxJzvI82rQQGFzCmhCo8vy1kFhkDeTa93jM8sRO/5yUpsv3ge0bQHuN5I7jY+C4QlsT4u7Zs1Y15PjIqpE1GrTjRoOEAoC2dfpUOk2iNr6l2bI+b6XF64ThIjFoNOLn+wPer3PMOVVU5lZWLBHOVSJ14ZLhxI/6RrFMyAvDkSUXvOOdGk89pefEmWbTBuSr1bTCT57S2NGR8cNCzpiR+OeSJT2PtpAlwN7c4Xr3kUdtWss8iHrPaHTbNC8A6XXjYGiJhZLy8u67qNIkWZUqZcDzFW7e1Dg4pI+QFM4ugtIiKcm1teEwTSwej3k4YjBMk7tEcdbzzDXrhlCY084ptSoz9zSMau3Rke2zToe+9MIF/u+mP5M2SZJ0ylBRiz1ziuut4ZDfySNIuAhngJfDQDjuLBKuRAVPaxIOJeWjBklEUl7Pp4Jhx/iBZpPtcsclhRvbPNRQLltVxzAE3vNePpPcDrZvWiL35ib7RJR9JxPrQ2VsDkdUJMpiqWqrgdt+Kysce4N+/mfF/5Urdh5fRh4CjAKiouoUYHz9cJFINh5z3ZUlDrpkWnkv29fVKtcuohDbXrUqkPfcTeKpIEkcxaQ+7UdIVoAzL2k7P8shFDed7mhs1/G+Z0i3Zh5QirZy7hxfG404BueHdgz5dGpUmPPSw7okWoDPJHuGLCzEsqND4HP+Zlo9DLCqiUmc7ptKlWTg4dCkZ3TsIo/AlQfPYzufRDCfTunbu322X6Vs1/C+x3GTpx41GtlU0aUS7VbWWb0u5yYg/xk7u2cQxcbGDIl2NNTo9e19p1P7bCp+M2tXa2sc724b1mppe3GVQ0sB10kHFxbPnJRL+Qen5tfNIQdKeeT5U+avjQ27Piv57O9EG9+gACRcH8qzbkr5VS3fX/lwsGTLocBfNe66E3jBp/DvjXXgec/N/9xTT2t86ysTvPq1aWLXc56dHpx33pl+6PynXwY89zkFsatAgQIFPhqglMJzng3886+wDwM7u8DXfaPGA++1q5pn3a/wCz+r8Jl/3X73yhXga14C/Pn/tp8bDDTe+74P7gG2QIECBQoUKFCgQIECH72oVBikLZUYbL18mZud09ny77hpS5I4HcTNotHkZmmpRNWe4Qh45jMZBAoCbq66gYk45oZ8u21VMQAGGEdjbngmJjA4m3GDNTLBkHhJgMPzzYljQ5aRoLtLJBIoZdOBTCeL1zoJQgqbzWxQOIqtatf17cUAfGTSR5SCtJKNqzKQJPa9M2eAO+9gEOL0KROQy5S/aTb3h0MexgxnltiTh7ygWZ4Cx2jEjefxxAayTwpYp643oQrMZGLLoTWDgLKvOBphrjSw0l68hqgJVWskJF24wIC35zvpaZyAxGDI4IibYihJgIvnGdRuNNj22TSAbnvkKRb0++xrUVqJIxu8qVUtOajfd4JVzvd9L1/dIdFAHGsMBvz0LLQpuQ6PbJBVAqfKKPecOmVTfAjkpHocp/sLoH2EEceTpCNRyhJunnqKwZCtLYWtLYVyWc3VqfIwGKbHahzbdssGovsD9oHvp5WG9vYWlYfimIGfrkMUDGfAeGrTp62v8SerbiBp5YDFYGI26BSUGHgpG6WpVisdFMtTMVnJBFhnM/ZLv28DwZJ+Sl4fDklwu5ZR9RHSoYu8e2YVVbpdks2Ojm15J2Oe7t/ZsXY4mfAzEqh1Id/b3KQPcgN6riKGUukAkuuzxadKqi37JeCRR5anZ0qRyXwq0p8+rXLV59rtNIFPJ9ZPr6/np93LBjOn00yQVZF4eOEC1e2DgMHC4cBWYzAEWs10o7n/CQHVVaME+PfREfuh0zFtuWQrTcb10JBpDg7seEqMKoRnAs+nT6eJ1uKnZXznqZgIKhXaupvW08VsRr948SIPe7Zadt4ZmDYZjTR+5/f5WqOZVqiU1I7SDCst4NIFfsb36FskpV+rRRWtRoNphk5CuWyJe66vdTGe2ACtpL66bQmdnM/FMYm2V69a5Z7sd/pGnS1vDvSUSQcX8afVZLtubZJImCWjSv9NxvTL/T4D+tOJsWPFepUCkuNglFQqFb4u+71+YJUEZyE/I6Qq1++JDTSb5rqlRd8ghN+bNy0p1CXRi1pIrbbo5912EAwHJE7t7NIfdTpW9ajXZzt3uhwHcp9q1RLRpjOS18RfLlt3RhEATeJltm9qNa6hzp5dnPdlXXntOtMh9vsaa2tpcpOk4KpUFtVIszg+Rq66FmCJmjJvR5EhnB0C73sf8Na3pudUgPc8exa4cC5T39Cxpxwf4/lI2XgU2f/dtcjRkRm/xrY2NiyZaTJhv1QqwDlDbpfvjceGHBWSBAik17ni77Pkc0mtnEXJEFmvXGVfyIEQF2fPAis56mp567VOZ/H1JKG/kkMKLrk00aZ/NOPoSWzn8k6HffTgg3pBEU8wnZLwfNIzlMCdR4XQmlL+SQzxw+m/7HNOs0U7ElKz21Z567ZezxK7NNgGQmJyr+8bhdpu99bKTKFZq1ar/L1svbi7u/hsUavRL25t2oMAqXSJ4Pru4IDE48NDEmGffIq+ZHc3k1KxZH3eeGxTrNdq9Ee5ysVO/UYj/rz//dZnDoe8T7dLW792jc/K29u8nut/my3bXfL9ZUqqLlxS+1zNTS0qNCpFIlnWt9TrRrWwzzaumPXxM+7Nf56q18gN2Vi3vlCDa8H19Yx6r8frHh2Z58kZSeyTMft8zxx+2tmxqaqbTf4skLKctpbDB2EIRE598gilR8fpvYfRCHOlOK25ts5LbRrHHN9xQr/U79k1RMWktXTXVC7ZcAHa+g4YJcjhkDYpdhWFaWU5gM+J586mD37ModL1lflL6pX3LK68tHJXaj2hly41PywUyl0fJQgChe9+HfAjP6bxlV+hcO5seoQdH2v8m1/S+M3fTE8Wp7aAz/xM4Nd/w752n5HfFud/3zOAr/qKgthVoECBAh8teNvbNV71ao1yBXj5y4Cf+Tl76uWbv03jld8KfMHn02+3Wgrf9z3Ar/0G8FP/imkaez3g279T4yu/XOP/+afAq18HPPBe4NXfkeAff8n/4coVKFCgQIECBQoUKFDgw4ZOuPGpdTpQ0u/yRPv6Bjd/X/TXgLe/Y/H7cczNXdlcHI3sa1klCA2bFkzIRu02N0vlxL+kDTqJWCWpTGRj+PDQnnLPbqoCJjWOE/xKEv5/3EkrQQAZksWSo6rZQMfaGglDh4d83hLlgtXVxeCiTtIqJDrhhvhxl2Wq1xncElUrz2NwWJR5siSQcjm9kRvO7P2DwARozN/PfhbVH+J4kWAiimiRIZll1dWmM/ZtFDFoKpvorhIQkB+sczEaARWjvKX1YlvOg8GaQY2aIXI1m/aeJZMq7eiQf686gQulGFxxVQlcpZ04ZuA2CEiKOjyy6TQFLhllmVpEkpCQ1O/b1GhHx+xPIQ/O61uxQbxSCTg8zr/mbAqMp4s3LBllDkmn55Ib9vdtWsqSQ9yQ15KE+7Z7eyaYGXCsSNtHEYM5zSbTmOpEUsQA6+vatKkiYcK0v9jFcKipctRbDJjFjj9YWeEexMYm61GtmHK6qkwRA0Uzo2JVq5nxPVsMDoUzaycSnEyygWMlex+aii8ZhT7p19HInIjXTH8Vx8DVK/RBEuTJI0RNJvzM9WtArNMEwmUkUyHbAaxfpWr7y7WzPJMTfzfoA+VMgEjIkrMZCQGVik0TValYIlqc0N+5Y1bDEro2NozyXGjsYdt+bmWFH75+g3bue5ZEIyp8WZTLaT9+knJEEADTHNsHbJpbNwAWhlYZTPopSVjH6XT5vVxVxcDjtTsdoN3WVPXaNSmCTJuOhoDyNLa2PPS6abWixLTnzKhonD5FkuDuLv931fp0AmTNQvpc5slJhqgAkOTSMSSReh3Imd5YTvPdZQpuUt4otPfNkg/EboQ4qRTVUWp1BolPIqsIOh1LtNUwAfEeg92ukmO/Rzv0AyDKkP9WWjZFspAjhewsqQBTSpx6Md3S6iqVf6aG9FWtYWE+H42N0mZM3+CuVzodk85rSX0rRk3MJRhlCU5ZnyBKLlK+4056fEynDIwLCV1UPoVAp7VVAd3ZsSSnOGY9ymWqipw/D7z1L5hareRbQqiQat2+CAL27eUry+duWdNVqjlpLw2CEpgHNYPs/KmTHLKn+R1GwM6OnpdVg/3dbAHPumgV5QQnRiBVPulubMgIvR6JWnlIYhZqOABe+AKF971fYzazalt7eyTrlcrGDzrEgOmM9ZMUZi4uXbJKtlIXzzOEZcXxMBpbYtvNm8D6GvfmhRR50fibrL0PhuyfbNu6baXMukR5wN13AgdHJIu65JFlCq7dDttze5tztOfx2UDa8MY2bfDUqXTqUxdRTMKhECmCgOSMdmDr7KZtdeGSs6o1EiTf/d70+JGUym766tEovTbxPRLDxiPOY8Ai2Xs4APbAeoiyl/hFgQbLrpP8lMR7+2a8eRlVnQzc4XF4kPbfoggm89lgyDW6zpBPKmXWW+aljQ3Oj8MR63btGsd93nhIEvbh1assjEvuLpc537mqalnI89bN3RizUM9Joisry9W+srYLpImMp06l39Ogr6zXrd/fvslxfPr0os1HoSUul0q83nOfI2nLFT7wIFtd0oEC9jCMO2f7hlQral6iOBU7feSmPA1KhnCbQzrNIyu5uHqNZalmbEWUe10oRcJPtj+lznOlRtjDUK0mlbH8wLZho2F9l8pZv4UOqbJWAQ6MErHyuCeQLVe/76imjmza+hPJUksQBJxPRQlY1l1ZYreUz/OA7Z30e+LrYNpp+wbX+EMz3mtVlrFcJrFQ1slJQn8HmHTxVatIWilzPN3Ytv4DWFyzZ9NK1+pAs6FQr+m5v/U8PuNHke2nLFzf1GpyTSHzijuOXOVprbG42PwIoCB3/R/CbCaO1S55KhWF7/yO9BLo4FDj//3PGv/tv2ccmQf8gy/kwvAtP2kXZc+6H/jUTwH+3X/g/+US0zGWSgW5q0CBAgU+GjCbabzxRzRmIR9y/sf/BH7qLcArvp0L5zAE3vUejc//PDtHKKXwj76YC9/Xfbeepw/4pV8Gfvf3uIABgO/5Po1ef4yv+aoTnlIKFChQoECBAgUKFCjwUY9ZqDEepTcnSyUG1YZDu+HoeQryqVqVm8uy1VQuOwFEkyphFgI136QiTPj+PfcwKNk1Sja1GlMvDowChrux7SpEeIrl6PdJ9FmGcJbebAfy05UoxY34QX+R3CUYT/jemdNU3Njf5yZs4Kev6fskTJXLi2lIRJGqWmNQUH6yQb/sxnAQGEJaYjevhawgaW7yvjcv+9imCXODNU9f5gb/qdOL3/F8BoXmgRLTt57itQZ9GwgZj53AQqYQ9z2DbRWGRgVntNgH7dXFNG1SR7GpUokb8dKn7ons9TUGwqawp8jHRvUrzlwTSJ9wltR0rRaJXZUKA1RuVgI3GJhtYzdYnU3RKfXMBrRdcpkf8HNuYHk8ZgDNy2yptkxKqqBkvrekw8dTBiL9wJYnKJu2y6QY1OCYdIMBx8ckkPQGdgzeuMH0Qr4HPP+T7Y01TF9oBqSi0BIX5FN5gSwJaPinGKC5vs1gTTZFqBCn3JP0yxAatSzfz6iWOeNf1OAkxRRAO5PAyGRqCK5DoNVgXyUamI5tUGqWGddCPNjc4PeyCitu0E3DkiZS5CSTutMlGwmqVQlEOtdxVULM3xJIlcDTgiJYkrEZzYCekLuiiP0u5fU84Pw5tonbhgBtdDikj9CgMkeieU3fZx2z6VNPnzJBY9j7AZYEoxMbUPO8xdRDgrJRbPJ8m9KzWqUCz6e9QOF3fo+VnBgij6iGhGG6zeXv2PjVesNpY815SetMGiXFgGajrtCoW4WwOAEef5zfSRJbPlGZyfNDGuwv3xAjpL3EtyuVP8ZvRagC2K+7u6yPBFlbLTsmx6PF70ynhtAb83OTCbCzx5RtpYB1nauSeVhg04RRvqpEt0t/NBnb9YCLw2OOGSEgZ9FeJTliTtxw2sRTjFu6/drrWT8Z+Gbe8zl/HB2xL6WtoyrHdRwvElnc/pAxvyzKpZRNp5mHSiVN8h0MeP052XW4SGBfabMOon4EWOLIZAw8dWRVKoU4oUACmO+xPzc2gPe+1xDjEuCgY9qlZn2quz7qdlnWzrHt78nEEoBlvVGvsy6u8sitUj/5fpr4JEgSBszFLoTQMhpZX+r7ph2UTQ8MpMl9ZWcOE2xumnSvmXHke4zxa5BseOUq+39zk4pAAlkPtNu0g/FEwzNKVp4CwoTjZTQGYNZybh3HZr0TzhaV8S5f5nV2dxbfm07pv930mDd3OKcfHwIDM36ffJJ+q9XKb3/X3wJct62uWrIgQAWZbp+2cxIZdBmi2M65wwGv4Rn7OzwgiVsgKTCB/PtNpiR8V0r287MZx0uS0CaaTZNiTrNto5CqTW71V1rs43IlTUYajfheq2WJjEFgifJ5mE5J2J9O6Ye6HTsOp4ZUc3BA35K3jhUEpRMOO2g2iLvmiyLWdXXVHGbo0E/0YZRVJ0DfS6swiULsdMZ2kbbf2EiTd5cp7IYhiYPii0qZz8laY9nclCVR9nts32fca0gyS5SUXB9Ur6WV0/b26B8qFfofKYObOhfg+imKSLgpG2JVti9aTT5jVioKW5uATjQefNDWXUh7q6v569Y4WlzXiBLz7i77X3yFTpaPJ5kTt4eLqq8A8NjjJP3IGrVc5n3E35YradJiXl9kfcqRaR9ZT7qkOZ0YQlH28JFzXVEThErbkhwAEdVSgG0n6aOlHed9sexBFfagVqmUo/amzGEvzx5EyYMoNg/6iz5Rxldsvi/ErjAiOUva+Z57nLVdxGfD6RRYAfDM+7nncfUq16jRkrHk4tIFVvvadV7noYcApUgS3tqyRHl57oxjS+JeX7frXPe5dDDg9/wgPSf+VaEgd/0fwM2bGq/+Lo3P/9sKX/KP8j/z9GWNX/8vGr/924sPqy/6a8A3fr3ChQvAv/wWbYldzwK+/l8A3/xt9rNf97UKd91ZELsKFChQ4KMF5bLCD/8g8E0v12g0gDd8j8KZ0wr/+qc1vv07NZQHvOJbVIr8K3jW/Qq/8HPAG35A48//N1+7sc2Fjyym3/yWEXZ2Y3zDS/QtTyEUKFCgQIECBQoUKFDgoxP9fnrnVYhIYcTTuRL8f/QxPQ9ItFrcsJRHCffkvzwzqNCewE8SbkaWSiYQYG45HhvVHCwnrgAMcBwe8r6TCdUpJLKzv7+4+e6i1zdkAiFRZFJNLNswHg2p7LK1pXD+LBC8ADg6ZiEbTQaRe30GJnZ2ea29g/x0JF7mno2mk9YwsmlvZANdAt7SJu73Jyb9jedz0z5LCJKUOn7AwENWVSCMFoPtS6HZh3INUQhqNi0hR16bjLmvKMGCUtlJX2FQqZi+0JgrYbhpvS6cZ2qmOLYqWHkkn2vX0qf1XZWuZVhdZUpAz6O97pqUfefP3/q7kwmDK/VGmih2u8hT5Mk7oT8eAZ7p+3odKFUYuBsNGUyslNN7t9evW3WraoWBCzfAIyRNNzCTxPx/tU3iYjijKoIQdnxzmvypp20w6egoffpeVKHiiOWRtgSMGkB1eTCx17ftISo/WbhEtCwaDX7H8xiYDwITrO2a1IDmi8ZVzcdSpUKSJsA2lSCaa5/TqU1n6aJetyRDIVRNJlS72d9fDAqWy3y/1bJqO3J9ge8B2vULzjh+/4OLAaS8QL6MHQk87e9z3FcqJuDlp0lad94B3HGJpIHp1LaBKIt0OjZFztkMAXQ4pJ1sbKSVlATZVJYuJHVNuQQoP5M2ygSxBkO2m6SaO3WK43w8JgHguMM2a5vAqOsXjo8Z0M/68igyRA9pQ1FJc8opzfrU05YwkyK2aSrUXb+epPrk6lWmDA1n1oYvXzFjsbZ8XhF72N+3PkyUQbQx2o1Nzj1Aejzn1c/z+LO/b0ktccz/Wy1rR6Wy7aM5Ydinis2165wT3vEuSwK7cg34hDZOxHDI+2Qh5RRFirw2uBVcc3eJrc3W4px282Y6JdKc9KOt+pN7YZfsNX/ZKP35AVDFYsq+UpnqJVmlSvEzLhoNji2X3BWGttzZNFuC4ZC+2V1D+IFRXMpZC8j9AUsgFoKjFCqKnTFmxnu9njOvms93exwf168vKhplx3y5ZIPP0p6yfhwOjQJjleSa2cz6UK3zSfVK0e9oU1ZJ8+r2xVwVTqfH8bFZm9VqvGfWxIQYMp1wHj86yk/PFoa2jxsN4F3vBp540qr23Aru/CHqbKJ4Winz3rPQls8N3osSzMWLNh0eANzc5diNE/qcs2cMUWndEplkfpDruqR+d45VINllfY19VK9z3TWZOgRtsz477tIeggAou77AEH0GfaqIyUECgCQdWdKVKyT4CvKInMq0gXuYw1Wuk7bM9ncUce0N8DOyLsimxtWa40cUbi9fMf41pywrK2wLdx7b36ePtGqlvN4yVSrBcEj1npWV9NgHOMavb9NO8+b19TUSl6+Cdtsf2HX9Siu9fpc2krWYzBeSetG9vigdZ5H1aS6WkZWkbXu9xcMqccI6drv5aTJdfzwY5PfFbGbJQ4EhDrll9x0iVzizxGmV8Y+1OnDhgoKCiBWoedrzeXljXnuZKlQWYcgyNxr2GbJUMinjq8ufRw+P8knfANcf8plK2fabUny+dIld8tx8O5hNzRpqkrEFcy8p62xmCUOi1JjyG+7hCc+qnFUqfPaolO3hktFwkWOyDHv79MWlMpaSwIQgmbUlKV+5xPYfjdJrC/dvz0vbXWiUqEfjRaW1MLRzw2QGPPAAfdnuLvCMe6xv9QP6rzix7dNq0R4GI+B5z2E77e1zHjg44Pxz773W5uN40c6OjtI+R+4l/k/GZKVi13fuGMqd3z8CKMhdf8V4+rLGN7xMo98HnnhC4+Il4NNfuDhj/Mqvafzmb6Vfe9azgH/xVQqf9gL7+R/6fhK8fB94w+uBl73cPpx+yvOBf/jFf4mVKVCgQIECHxLuvkvhLT/GDfRTp+jTT51S+Fc/aWSbK+l5QWs9J3u1Wgo/8Abg535B49/9e74/m9mTfQDwn/7zFNs3gO96rVq4VoECBQoUKFCgQIECBT72MCeiZDZar14zKTpMYP/iBW5s93v5e7KiDCSb2Z0OySS9PoMVgvGY18sjRblYX+cm6NEx5qmabivdgzKbpzkb9c16OkgTmFPHeWlDXEiQ7ug4HRgRhbHVVf4/HJlNZBO4ahrVMd8DfLOhHMKknnPKFwRpZSffJ7lNNpUDo9xxcJgfSNCw7e4GRORvN51Es5kfgDkJrupTo8HT8YMBgzndLjelz5xJt60CCRuTCVO7ucE8wf6BDerNN7JNCsYwokpKNpAkWFmhXeUptQHpQEW/b9uivcJrukHWZgOYmvfrVSAx6T7jaPHEdO2EYM687ibo5G7WuwSiapXlFmKFUgw6uIoigAkmRDZo4QYvVlYW1Sj8gG1WqzFYD9gT4dMpA52uskK1yuDEpUsKD37AXvzhR3itdp5qnvnY+rolNbj1O0nVQiDpdsQuJTChsEiecFNeeZ4lBLlECg2qXqyu0lYnY6Bv7K1eTQdC5B4AFXAAk+bRXEvU7KI6A9mTCdD0afMb6+lUPkI8EDLCYJi21/E4rfwkAcGsCst0uqjq4/k2zWrLKF8dHxmVLY++RtSEsMbPVaqsn6TAHY1IDlhfSyu8TCckUimwnRSY9igbKAWsKk0WeWS+QT+d1mk6Y51X2lQQrFRISIoj2k0ck7ggAev1NfqmoUmd6S/ZctreZt2ygWtRLxIo0EanRtEuihmAr1Y5/9ww/qpmSCn338eAXqyBKE7PcnlzhLTLZGyU9Hz+3tigitT2jfTnXZsFrPKITgBlbLRcYj9I+q/dXdpOpWR9qJAIXVuOTQo0mXtqNZPe1ZkztWbbxiZFcRJbnz0L6RvOneP/R0eGIOE0w7JANUAutTv2fZ996Hlp246itOKKzJGlklXwC0PrA4Sw0GoBWxu83qOPpe/d67LNDg/52Wo5TcoNTyh3q0m73NykXezvWQKp1MNNrxpHi+SE8ZjlrVYXCY955AqB2GUp4LiZTKxvTBIgShhgzRI/XRsfDunH4oh212ikVTcX7umQBmJDxvb9RWJXHiQjBMD2Xl9j2Sdg2ZOEY71U5ngql6wvCAK2j3sf3wM2t8gacVX8hiNe69w51iUomfRoTjtcd8ZWs8l+nE7YHv1+WmEt8POJXVmIkmiW9NEwipNBsFyZyQ84Xo4OzQva9KdZPwhJ2MWN63x9eztdN1mfzab0l4GxD504yrqOopk7RstlS9QIZyxXt2fH42OPc5xLe8xmlhQ6m5JIFIXAnZ9Ie5L0u0liSeNa8zviE09t2ZRsqTbxac9nz9C/yNyX9aV5hNZmAzg280cYUv3vnrs4x928ScJyEKSV5VwcHdl2iSPAKy2m7+71gP1Kes0RGfKFqC1Np4vpbLOQMlcqHA/ZuXHQ51hIrY89J0XgCHjbOwAYf7m2ynmk1wdWVvNTZ0qabUnX6vu0NdfO3XVvHpotu3aZGlLQsmchOeAhttmsKxx5igd1EvuZnd3F77pmH8X5a0SdpH3DaEwS8sY6/8+2gVLputVqbEcqtmoMQTITlJ0jZW02HiP3IfYZ93BNGoY2DbfA93kPUZTSmuNIVBVbTUP8Cvn+wcGJAlZzTByykYJdB7kpaSXV+3RK37osVS7Ach8dGTXoyD5nywEEF3LgZDDgz9aWfS+VBjmx351OOW9MpixXKeAzLTLzCrA4brS29hPObH/0+lYdbKXFOaTRMIdzlFMWh7wZh3aduwyVsj3g0e2auch0ymRsUzG6qZiThP0wHHA+iWP7HL7aZvlKZY6T/dCOl/19qkPe2E4rUg+Giwq7fgAkOX3o+ofVNu89HJJoFs4MkVj8dkildIBtd9La7ENFQe76K8ali5RCf9e76Vi6HY1HHgHuvz+9cviiv6/wm79FS/6E5wFf+eUKL/hULCi5NBoKP/JGIChp/PCP2EVTswm86tuVkecvUKBAgQIfbbj77kX/XK0qBEF6aam1xvd8n8ZznwN80RdyHvA8hZd8jcI9d2l8/w9RQjSK0w+Mf/JnJP/+0PcD7XYxFxQoUKBAgQIFChQo8LEO5XEzUYgIWXT73FhU3uKGdbnEjedKheSKQxPcKpeZbqNcsZuogKP8oe29854qJKgEMNjRuY1T4fPyHttybmyYNH89BjKEcARwc7rvEGTyrgVwg9YlpwCsW2jSqpw5w2em3R3gyJTR8+ymfZzY4MQsBDo9BhCGA6aNUoob1dn2j02KDT/ID05urjMIsizgKEGUbMB5WXolDQDKpJeJeF+XrAFws70/SF9Tawbc3CDj+jqDK8uCS+fPk0A4maSD5VHEutZqvK+r9JUqqwmExcnie6XyonKCBEQGAwaBXFSqJCeEIU9uB4FNH7KykkkNc2RJGnlpYQStliVvVSrplHwSoJFuyEvrVq+TcAAwgOe24yzkpv9tb81qkkTqDdreikPakrplFQjiyKoFSPquoUOoqVVZd7ElaQfXBvp9fufeuzlWDg4scaJWtemGfKPUsKwtRRFIJ05wLfMZN6AaxZa06oFjZK7oVUsrq40nNsWMlLnXsypLpRKDbX7Admg46gdnTlufIcpz1UzqsHvvYQNdvqKtsl6m8KXSYvuXAqDSpv0LQSdK0qlvNjboQyYT/j1X3KvQ1p56moSM1TZtfCHYBgaw8vyBEGJcYqigXM5XuRhPcgi4mj52c5NjteNcz1Uk6RzT7yjv1iTeTm+RgLHaNsS+iZ07NExK0jJw2Lck0n4/f67TMOPStMdsxp+SCdZVa6bemmRQKb+oaQE2ZVuryTSeLhlYrpttp24P2NrMr6vYhBvQFaLzSWNgPOYcJSo3gVHZeOQR+ldJoTgvG+jbmk2TKk8DFy6k/fxJ6hwK6c+WygzMX7pIm3UJ3qn5yASOJUg6HhtCRWa+i0J+L4zy1f90Qls9OuJYardZpkuX2N5PPmXr65I545jrglJJYW0NqNc1FSnN+ysrtKG8eUz6RoLfLglaVNbcsbUsMC9E5fGYPxsb9CN7+xxrQhxz75dLxMTiWkpU/QRJYogD2ij95SgXAlTAyZLYUulnc5RRZzOu9UKj4lOpcs5hu/J+LoFDynp8nCbgRRHttF5jGU6f4euDIVN45mEW2nVQGHHekfKPc1JjA2yXUik9H+b1s5DN6jVAZ2yv0aBfOXXKKj0C5uBARkGrVAZiM/c0W/RLSbLY/nINl+TY69nUXgDgm8G/tpYmMs3MwYFmwxI5ZzN+d3ML8NuL9xNygGu/48lyoo9SXOvHMf3w1hb7d9n87Xns+zhiX5xEJBT/Kf5UfpdLVu1I4PtppcZ5fXR6HRKGbNM8xdfJxJBrzX2i0JDIK8DpLTP/OWNWUgYLlCFjJQnX49UaMDtECu44WltLq/5ImsDYqLsliVmv+LTpjc3Fa/k+53I5eNA3qndJnPY3eenkPC+duk6Gr0v4kzZ0kbWFlbYHXKcx+oGdd/PWEi6pOW/JeuqUSUnp2MWgD1Q2LAHfxWSWVveTNcNwZFK97rMuOzv2eUJ5JhVgic8Wm5sAPBKDpQ7PfCb5D+99b1opWIHXEYKNtM9kyvuKgpUyr+/sLFd6SxK+V62agwFmrEuq5E4nf80l5K6TiF3KSz8LhjP2tRxCcMdnHJOIFwQkpG5sWCU8beohCqPZ8txKpUtMJ++Z1fMtoVvUShMhTyumio8M6bxzzHYSO9dmnl1pLab/XF/nenze9sbQrl0zB8Uyhjed2rWZu2avlu06uVpbJLh7Htdrkp5e0Ovx8MzBgT00A6QVFQH7LNXJ9KNSab8sz4RCAK2tpBVAXYL3ZJq2148UlmwrFPjLgu8rfNdrFD7hecBffzHwkz8NfM3XaezspL3xM56h8M+/Avjpn1D46Z/w8GkvUEgS4M/+9yKftN1W+MM/VPi937evveqVTPNVoECBAgU+dvDUUxr/7Cs1HnnE+vp//x+B3/094Md+XOO1362RODqvn/O3FH76JxROGeZ+dmH/4AeAr3+pxvbN2zmLUKBAgQIFChQoUKBAgY9GyEZ1o2E2LZ0N0/19jc1N4Jn3MQg1nqRJPgADd0GJm7mDYXrzUTaJs5iTQG5RtlOnbNDtpJPz2R2qUrC4oSrqKJUM8UKIXbUasL5BhYHRUKPX0zg+1vPgfh6mU1Zi0OeG7mjEIIvcOxX0nLLtej274S3lnk64OZsk+YGRZej1uMnuedygX12zKhHZy7gBS0kfpU3Kn8NDQzxyNpYrZZIlTp/iZrQbGE20qWsm2JMlWUkg2lXIEoi6kDxnugEJORWfJW/My1bNfx2wShGtBssn7ZAkDHY2Ggyc5ZUpjm0wJAppz1HMoGKzmf6sqIi4wZJK2baThrWbyXixbfICNHl9v4y0J4MnG3yXYIkofaysUHUkSfIDMuMxx/wTT+gT1cjqdRI1zp2xr91K7Q5g27WaHBO1GpXAtGY/zEKO1ZJR4VpdY7nPn0MqtaGgWkN+ZDAHbkBwdy/9XqOeJiaJ6oWM19ksHZQKAt5bfMfaKsdEu71IsIgTq4bQaKbv4xJcsyl2TkqbemI6KE3CgFKLhBcJhIUhyTpTkzYya8uS8uzwMB1crZ6giJAkiwF638e8f1z/G5qx0umQiJYLo1znpiYD2J7DoUl1s8dA3R/+kZ6TJs1XsbJiUvOpxXE0Glsyp9hUdu4JI95D1GfkGklCUmSvb1ORra6adFjOGK5UGRi+5x6mwRRyqKus53mWkDpPOdjk9bY2TdpOzXo0G7c29UQvqgFl/cqcCOmMJde2XTv0PNrL7h591mRiUzRrzfXAstSrt0J23q1WTVom0M9euWLfy0vZBbAcu3uca9fXactZEqCUb2IUnFZWOUanUyr4VY2KX6vJ8Vuv8zsuQSTrJ09S4nL9f5YUlE2xPBmn+ycI2O8b66YcjiLYeGS/1+saoqfThuUy/VgzM98n8aJfrpTTKqlZAk67TcLJ6hoD/BsbwH33AUxPmlbcdH2MjPG8tMXy3nTCe9dqtu03NoDTp0kcFBu4vp22d1Gt6/X5WaUU4jh/juz3WOerV9kQbhvO04UusVvfKEG2V61qTaeL3MGXJGyLUcYf+z7LOhpx/RuZlG/hzKSLdMbYLIfsMBimiS+1qlVqPGk9OCfXyWdyFtQyHoZD+uusulYUmZTFI/qduYKmWR8+8qhzvwxp++YOx02pBDz0MH33aESCj1vPKOK47XT4vX4/n0xYKht1NCHPmPrJuMpbswGct1xiuZCcXMjzg0vCdZFk7jWbATs3gbe9Pa1cWa3SHk+dos8vmYMt5QrtaDCkAuaytaurXDQv24RkpE7XEM1gVSbDmVHmyozZvPaTOt8qBW65ROImYMikk/yUqbeC1pjHkDyP/V9vYMEOhVQl8P3FtV25fHKqyCz6fbbzvCyJve1sSlvp9qiYNAvtPDce016OjszhCI++yg9od9euA48/zmdadx7S5rtJYtdPkqa8XEqrBAInP68eHfFnb88QMeU7E3uvLLI2e1IKPrdtfZ9tIQeuXMiaQ8GKObj+plw2B68y997Y4HeWHSyZzU54bgG/WymbeSm7xtT8kTYQNU+Bq26dnW/HY5LVDkxd3dSLUZi+V61K5dJ5mUMSzpotjute3x76OH3KtpXvcS2Zt153DwxNJsvHYbebnqM3NqgulkdYdzGZpH3HrdLEfiRQKHf9JSKKNP7wjzSe91xgNlO4dIkjan1d4afeArzu9Xr+IPt7fwD8sy9Lf/+r/7kdZWGo8Ybv1/j9PwRe9o3AP/5HdnQ+9bTGj73FWuM/+ELgsz6zIHYVKFCgwMcSnnpa45u+RaPTAV7+rRo/+iYqPf65Q+q94xIWFBnvf6bCz/0M8OrXaTz4gcXrXr0GfN03aPz0TzCneYECBQoUKFCgQIECBT76sdr2EEXcAQ4CE/jpA9NyWsHg+Jgbw894hgLAwG42vdFoCMTmRHF1yYbzsmDtrdA55mbrqVM8pS0EjLK/XM0J4Iaw7ygquRvbyzZQx2MGWA+OgCefZj0HAwb2NXiy+aTNVFFzklQuh4fc+Jc0Ju6mbLnME9FuCr681IIVE2ybhfb7EsyfTu3mdrnMuvqeTS8GUDVGNvrl1LcgjnIUrzIKTIBVI1lZYaCpZ5SYchUaMpvZrhpLNliqYepvNvNvl9QmAeJlGZbKZaZyEhJiFNnAnQSp9/YsacuFBFdWV9k20yntey+xClorK5YIkN28F0JVp2vSTzkb+IG/SBLIIhvAGY9pV1HIgNUsAs6dZVsFRkUqm5ZxMGT/aNiAmjKEFyEduWi1jJLAhEFLIYKMJ7S7gUlxlSVnALSF0ciOi3Z7cYwMhsDWKZtCRxQAuh2g1sgJApmyrjp1q1RtwLvbtdfI+hVXickdT3HC8dhu2+/UnGCcpFKSgIy0r9j4eMwAa71B4tnaGoN3k7FRiNLW9+3ssOwAx3ilDBweaqpxjGxQbjQy6lfGH23v3Dr1E8AgYt0ElXd28j+jEypblIJFmxtPLHFgZYW+X+x4NLbBet+7vfK4EBJQFDHw58afQ6NilCVZrK2xTSWFaBZJZIN7y+aXVsv2fbmUTgEoPk+U2ubkGm1IbTCKU8ZPCDl5bU2lAnDDgVHO6XCOFH/sEngGRs0wDKl0ubPHskjZksSmEDs8NIRq3xCpTGCyWrVj1fOszwoCpmUU3yNp7+Ra8/bK9HeplFGDyPjZes2S9KoVaxtZ6IQph6rVRfLQ+ropY4l9JMoyQuirVIAL59PfUSpNdJqMHXW6yKqaQGGeis71t5L2Ns7aqCjSxNaXiW1VqgAmfL3T5f1kPIqvjCI9T+24jLy6TN0TMi9HtI8zZ5AiIQK2XaBZJ1FIk8Cw+JyRUe86Y0gkW5ucC9bXaLPNpiVMDYbsu5HT925Avte3KlEA1RLX2pbUmofpjH5T0lEK+aPVtEQJL2e9AOQTTwDg8mWbtrpeJ0l2OgEefVTj5k22nSg5ipqO+JPpVOPGjRjHx2m1RIB9KUp9a2uOglWGoBvFHEPuuic0abU6HY7rc8ZOS0GaPFDOzDVuu40nVo0qS/xykSX8uAcr1tbYn0EA3HuvwvZNjcGApKFl7Zkl5Lhjf2oIO1ll0YXUv54Z0z36k80NphXzPc7/qb51fIfvW8LE1qb1h5UK23N3l+uwUsmoueWoL2XLsdLinHRwSIVKmcMlBfPtQubavHvIGiirutrt2meK/oBzq5TZXTeLzYsyrDb+fmeX9+z2DHkqM1etrfHniSfS/dRe5fitVCwRd1VIKMaOo3D5QZjsuj4LacNaza7N5PnBRdkQ68Lw1utUwc2dtGHGCTAdLqriNk2qSHeemk7Z3nJIotdd9EVyiGN+AMRnG547R/u8fDX9+evXWI9+n2VxSezV6iIZqN8DxlNz+MDc++ZN2m6rtejXRiPg7Fn60X6f/e/59Pc6sekBB8OTidnSD1G0OD8AfFbY2aFfq9V4v3ZGbc9dZ4samdZWuVSwbNxUzbOlrC8kFeitCEYCIUW7zwWNhj3kpBTrkasuqS2xyjMKgPX64qGKPEifzMJ0f1Yqlvg1T++p8w8CZZWGAa6D1lbt/3FsfeVkymcjmRuOjuy9KybVpR+YPncGqoyjVee5JA9CNjy1lUkf7xiRrCuZcpSvuc9zSgHqNvvug0FB7voIYTrVuLlDB7O9Dbz3/Rp/8TY7Uf21F2q86Ydsjyul8KK/xpMsp7YWNytcjEYar/kujbe/g///xE9p3P9M4BM/QWE81viu1+v5wLr3HuClX18E7wsUKFDgYw7uJohJNxAECj/xZuBnf17j4UeAf/4V+f59Y0Phx38U+N7v1/ijP3auY5j9d9/NhU6BAgUKFChQoECBAgU+NhCUTEBb21RiABYiCEHAE/ftNg8Q5hGQXIQzADnptNbXDYnAfH8W2g3KWs2kJIm52S8BgDi2waVKxQYZp1NuakowSVSrsrjdAIWLOLEn5GUjvdlKp23Mg1I21VdW/Sgv/cx0agOkcyJJnCGkxDadUs23gfxGg/XfvmE/OxyaNC1ToxhlUoWMRgxS+P7iJn+eipOkOZGg++EhryFEqYsXjYLNNH0qehnixKotLAsyTCY2JV6zRULUshPPlYohPajF4Lsf2JQ/167b9gJ47UoZGDmb+adPW/JVNqBVqxmlq6l9/+YOr3Phgm2PZXXKqiyc9FnBMnu9sc2gZ3/AcgxabIckZh1z05NpkiWCEgODSuX3d6VibGSF9/d8oF42wbaBrUcY8rOez1RQ5RyiF7D8tH6/zwCaG2yfzvhTcgilx4bItn0zTeaYGkLGZEpyk6SKCaM0QUTaYjKx+xUAx1dsCBfiK1aaNvAjQdzEqERlla2kTcdG8U36Mort3ryrVHXFBB3HEwZ29vby27/TselphoN8/zqdGlWLHgPfkg5G0ohq05ahIeJVjIrXaLg8xa60Sa/H8ZwdbwcHbItO16RyPEHRLQsF+rwFQkLOmK7X2dbr6wp6yaB3x4WbhqndtumM3J2sBSW1jNpPs8UAcaXCfjk+po9cXUsTAu6+M8DhUTJXW4sTmxJtmYIMYFRrZunUk1niapxYGxqYIHGnS9urVs1cZ8Zeq5UeVwtKF7dAqUw7FTJMlnwZJ9Z2qzWSDeLYktwazliIIs4/Fy/a/7tdq2hWLtEnra5aJaPZjAFsBQZPJZi8oBKY6TdXiW4w5tjIklOT2AaDq1WjzlUjcVTG2/FxOgDtWtlggHlK6DAEZjONpy/b15chb+dUFHnCkPfWCVUK19f42n33AtdumPR3ZmyctEYplXijbg8427BtIeUdmfRj58/TH3se04RmiRV58D2TdvmEYPAyZRblATDrpBiWcO/6ruxI7nZZH3fcJAn9+WDIuS2K2J+BpKB0UuceHgKHx/HcJjpdrinziC07O9afCdlintZL8UdsU8jzboGHQxKcfN8G8IU0EEX0O1nijpRhMFyc+13VoIV0j4m9Z2Tm2K0tEgxl3VgOgPIqyySKNPNrR4v+QEhuMqbrDaM45qyVymXMjfj4mH64VOLnKlWrQjToW59Yq6V9WhwbAmDM+Xpq1nFbW5xTplOWJU4WfU6lYtd6AiHudrvGtqL0nBon+QRzID0vnATfs31VqXCsVMppe9nYSNczCyHnAovztaj55I0rpRjXz475g/3F+TeRtLMOUTVrVyttjs9nPwt4zwPLyytw+2Aw4PfPneMYE8K1KBkuI42fCHNIYzpbLGu/TzsUWw1ndmxKWu9ef5FMJWs4N31urw+U9qgqmvVP17fpbyfTtFKYkI1l/Llrw3AGRBWOQaWMHzrhsMnuniE+mnWnpBN0+3wwsGkE8zAn+AIY5ayt5CBEHBm/YBS02g2+7vZls0G/E4b8jKsqloeGUS+u1y1ZKEmMwm8zXe/hkGtEuZQ7J3q+TZ0sKZDbbT6vyP37QzsmXL8C2LGjzTVHo/SaUWxIeWl/K3NC1lbkmWc8sqmPx1POc6OR6XtlVYIBs9YyxPHjLm33zBnkQg5p3bhhVBnl2cjYge+x/DedsVOt8dmmUqUyX7PJuczz2H5uW0t/1Wr0jbMpr3f5qrHLkN939xOiyJL9J+O0OudHCgW56wTMZhpv/QuN6ZSy15MpJ8L+QOO4YyXzbu6kT/Ll4X3vA+JYw/etVbz4M4Bf/HmFe+8h2SsPB4car/wOjccet6992ZcCn/A8QGuNN/4IF7UAF+ff890KlUpB7ipQoECBjzXcfbfCW34MeO13abz21QrPvI++vFRS+MavV4gijSBI+/cbNzT8ADhzmr7/e75L4Wf/TRn//j9w9aA1F4Kv/FZep0CBAgUKFChQoECB/1tw48YN/Lt/9+/wR3/0R9jZ2UG5XMbFixfxBV/wBfin//SfonZSzoRbYDwe40//9E/x53/+53jwwQdx9epVjEYjNJtN3HnnnXjxi1+ML/3SL8WW5JD5EJDE3BDWyCcUnDnDVEHtVYWnntYLac0EsoHu+/mBtiThewMTvAtDcxLZUSPQCcujkU7ZtSytCWDVFtxyeMoSJZCTlgWwSgfL0s9F4WJgZtmTTqViAqA+N3aFIFSpOClGTkiPIgEkrXOCfuC1zpyxZDed2KDFSsteep5OKk6nORwaoscnfgI3mZOEKc1ceMqSoDxlg5iBExhxFY4ECylVSovklbITdFSKbT+8RdoVac88Igxggwl+YNViAKPs4QSVppPFNI9ZxbnQ2ci/edOqQQGWpCe2IIEEBeCxx3jqPHvKPdvV2bFwkj0Di8GYcplqYRKsFRuZTtm3kwmDcqJolyXTjMeANvdUivd3y9tqkYRx7RqJQKttkvZQZoDVTTuyswO88NOA8USh39cpkoVb7gUlODA4M5kC12/kE1PCGW28WqOawGRMRS/PA3Qm2BP4JhVObBSJcoKBgCVGyjiXeqfGxjqDNUI8FeLBdGoCZJmBH0VAOAb29tkeWRfvBpsGA97zYB84vcV+OlGRwBAOlAempHHIAIMBf1bXrCogK8Of2TQdJJ9ObIAvDDE3TG0CrxLYks9I8On4yF5Dgqm+B5T8tEqe59O3JppBwmEmJVujvqgqEYb2vu58E5iUO4NB2vibDRI0mw3Ay4kuTab0WxIQjGKbajaObHpbrTPEL0U/56YO1glVQxqNdLCt17dl0jo/nVq9kSaRRpEN+roBZCGvzkk9sW2P2WxRkWw6tf7CVZdahpMIeElilF/EpiJL+gQ4TkRZYm2NY3BlxY4ZsYVEmIQ56PdJipD0ewcHi+mbZya4Kmoe2fK67QjQrvLmRcHB4aLqyXOeA5w9TTK2hlE8nFmi2HRK2xT/pTWgEsyJZQcHpv9O2N6sVNNzm6DXZxu543x3j/0sgf5qZdEX1GsM/KbGNmyfhxHXRYcHhviSWKKbkAC2Nuycvb9v6mb6TdZoQoQYDBzlT+NTR2PapNhivW7S4wXpawj6veVqUstwkv/bc9eXCQP+jTp/jo4WPy/3rhlCn9td7nwk42oyAX3slAQEWTsKwctN4bu1CZw5o7C2xnjt8THnx9HQprcMgvwxmUfqDh3VqihKk7yHI6BsCMpClp/NgKeeNmRrkxItCNjfa2tp3zUZs43cvpD6S1kk4C9L66AEnNpMx5qjiNfd2EgrbHX71pdniVW1GueHwYBtOpkaJa+SnY+WqXWVgjQxx/M5NnQCJM7rUodE82/PWzxUEMdWgTKFzBg+tUky3JxU46b1NZ8VciawnHi5u0cbya7nqhVAXF6equ1sln/NyTTdf0rxtfl6APlkMSGpXMsoXrpqZHnrMcFa26yJlU3bfOPG7Sk3rawsEvbCcFElzkUck+wK8Nnz1Cmbbnl+jcx3J2OOcekfSbcZR0C4JL2mKGi5bX36tLXFwKcPnzjlTxEfK+C66gTV0qyiqe8tJ9YMR7bcglOn7N/9vrUbgdsO8rcC+yg7BQ+deoiK3Gy26LMB9sFq26oBbm/zGaDZ5Pzleey/wOeYE9VEwCh7GWJjktg1IMA+XFuz6tFSVQ+OPz7hWXgZmk1L1FoGsVdRx47j9OdF9Vr601X8Xl2lLQ2GRmHQPF/XauxPZdYJlTLV3HZ3T1YgzB4M0THJ8jK/DQb0dWur7Kesiq2GWQ/3acNHR0B7hX9PpotkxlIpPyXyRxIFuesEjEYa3/btH8IxwgyqVS5ee/20fFyrpdBqLf0aLl/R+LZXauzs2tde8i8U/tmXkQz2a7+h8bu/Z997xbcqXLpYBO8LFChQ4GMVd9+l8Mu/iBQRWJAldkWRxnd/r8bVa8DLXwZ8/ucBvu/hVa9s4NxZD298E59kRiPgla8CfuxNGltbav5drQvCV4ECBQoUKFCgQIGPT/zBH/wBXvGKV2DgHKEcj8fodrt48MEH8au/+qv42Z/9Wdxxxx0f9LUfeeQR/JN/8k8wymGCdDodPPDAA3jggQfwS7/0S/je7/1e/J2/83c+pDqEocZ0srjfKoGN4ZAbnO1VGyBoNmxQTHncdJQN60RbVRw3iDedkqCyv2/UCSKTLqPBzfVKzQYySmYDEzDKEoFVExgO7Sa/pNLKK/eSjFJz+LdI7zIYAOsbwKULQHiL4KXUM4ktkUheazRI6hiO04GD20EU2Y15N4g8GJrN6yStHjAakmQQRXYjvd3mJvTWFnD1Osko584t3svzGaip1YB77qE6xOUr5n4nlDsyG+KiHHX6DDfGt2/y/WotraZQCrhnmSUNASSGSX96XjrtxDKIQtd4BKbtygkcZIdQFFoyShQtBtPdgMDOjt2Ez6t73mn9bHAnpSiVea/VYjkaDUPCyqmj56dJGfPrijqEXHPJY3elYtTH3HJ4vK8HjuV+n8SquXJWdzFA7Qes6yOPAu0VjWs30iRMIVCVyvlqB9pRDVnfWHxfMJ0a/zEjuSsPyrPEziBIt5vbxhKIms3SqYAkUDaZkNAgCgzZlG9Afl1OSh0q74mygtjXrYKljTpw790M0kqKF0EqaG/8cLUKxIZgMBkzAL8MMga2NplqFrApqzyTwjVOgGhAm8wjbISRJSo1THol36jr5KkbNVpphRmxV1ddcXWVv9urnBuyikzy2UQDJW+ROHx0ZFXXZjPMSRiCFHkLVEIBTvb9pYA+TPpx+2aCakUjMmpIeeSp0dAEAT0T7F5CggbYtuvGJl2SnRDOXPNzbVEI0cswGNi2abZsYBbIITzAEnqHxn+JkgdAolGpbP7ft995+rLt62WKIMfH6Xkp7/3jY5YpT2nzdvjwbsDeDZZXq5aM1F7lHDKZWfJXnNBe84gHwxHrVi7TJ4ThySm1JN1dFr2uLUMUsQye8Vc3bnBuFZK7i/Pn8lOhNYzy5tEhf7IQP7G3R4LqSil9DVk7yFqiP2C9jo9tOsdel2uF2cy2p6QIPT7me6XS4lx5EjzPkMRz7GRlJa2m6ELsS0jWbhpJF0EOaeHUKfb3SWuWyThf+UlIVIKjI6bRPTo2Y9Oz7TqdAbq/2CZ5pK7bQRxbkoyoYXa77NvJxKYpBawqj+/b8oid5ikJut8F0j50bz/9nptidhmpRexD1tCeZ9Im6/QcC7BvhXi4cJ2Ea0eX/CkqQv2+nRukznOFH50/9nRir+XOtb5vlSuVYvtmDw00W/R1oWkXuU6jsZyLEkUUYsnOlRNnfspbJ4zH+X4vjrBAlp/3xxLaQBSxrEpx/nQJK1Fkx1Jeyj/BzR1+bmbWXYMB076utG4/NZ+LXs8eKMkjeVUqduxWa5xr3PlpMKB9t5q051uRR+V5xyUqAyCB2AMSZ0zfuEG7FYXTE5VITccnGQNw/YDWGbL2EmOZTCyJTNIQAmk7qBi7dIerS8xyfaDWWJicsjYivqDZZPncOUFrs4YW1THzXhja6yQJoH0AOv0MkyT0jXM13RnbUdJ5Hh2ZQ1Ye21oOIXV79nlO0itHsU1jKM/OgW+V06RMQiLLIghs24c5B7IEcQQgx+eLGly9Tn8jc2Wnyzk3CLjuFTu5cjV/3smi0eB+hxBnR2NAjekbzp5mGyUx53Nl7ElI7WtrNqUoYFOcj8Z8LQzt4atlOElt9ENFQe46AdXq7Qe9gwA4d5YbMefOAmfP8qnqkz4JuP+Zaqky1zI88F6NV71Gzw0mCIBXvVLh8z6X13nf+zV+4qesZ/rifwB87t8ugvQFChQo8LGOPGLXwaHGzZvA855r3/v3/xF4+BH+/X0/qPEnfwZ8+7dprK4CX/H/1FApj/H9P6gRJ9z8/4aXafzYm4ALFxR+8qc1nngSeMPrgdXVYu4oUKBAgQIFChQo8PGDhx56CN/8zd+MyWSCer2Ol7zkJXjhC1+IyWSC3/7t38av/Mqv4PLly/jar/1a/Pqv/zqaefm9TsBgMJgTu57//Ofjsz/7s/Hc5z4Xq6urODo6wu/8zu/gV3/1VzEYDPBt3/ZtaDQa+KzP+qwPuh79jFKKq/yhtdlgVCSBSeCqVreb8dnUVOWy3ZR1iTlC0NLaBtCmE26CLmyWOhvXjSYQ3UwHmmpGuaDRsASkZZjNSFaIYhvw843ClucvD1ADQDSjYtnNm3rpKd08paos3BSPymNQPRuw0JrlKZf4+bwTykmSJisA6VPnAEkGrvqLBA80uGHuphjMw8Y6cNedTI3mmaCcnE4fDbkJHgQ86Q2QADCd2FQ8zRbTpADc6HfTb9TrNtAYhiYA5ASzAhMQiBPnVHTOY2Q2+D0nb53Ql25qJ63TwaReF1hz1L+yqaWSmMGZWo2BuXY73eduSj2pW69/8ilzCXqVyyz22bPAlSv2BPfKCskG4zH767izeAK/17MBHz/gWE0pP3hUkQBsKjaA120ZpaFJJvArpCcP9tT96ioJAZMx090EQVoNTSeWYCCpQj+UVKhzuN/NKC7VGxwjbvBHKdZTgjCJCYYoxbIEhgCVJMDYkA7dtux16U8ODzOpaQ3aLQYNJUDd77M9lwUd5dq9Xvo+riqMBMVdiPkmOl99LQuXcNhfog7o3kMCX65dTqe068nMnvx3A8QuXFJVliyURwTon0CwyJap1bT3V4opT69epd25dVldNfONMYqRGSPjiSWquShXTvB3yhID3cCp+EmA9nDcjeevy9zoBngF7hxRr51MIDs6tkQIUR505yIhI7h24BIkOg75Mk+tbzigD5Fgd6kErK4ASjtjV7E9V1eB82eBx5+iKpTcq9nMJ8EIOermTf6TbQdZEywjaZ005+ZB1DhdlZR+36QtyiyrJhP6h8GAdc+qhsVRfuBTFKzkM3u76fFar9NXZ8kz4mdyYV4fTyxpo1Ri2fPKsCP3dMZ/vUEbvdUaQzAYmraaWqU8sRuX5CxdkLWbvAC6TmhP2X6OovT3azXWS8axpEBeXbWprERN5qTl8F13AI8+vvi61qxDvblYVqlPucJ7nsQJDwLe/yQFsV6f6ercMb8Mrj27ZBYh1ufBD5YTds6eNimw+7Q7UWCLY157aNRlVldvTX4HFsewpGIrl5fb1e4u69Lp8l5yDT+wCkGVsh3jeeTRUoltECdUp83zmVkM+osE+EbDqFoli8vBwCh/iRpgHsaGvK3B9XMcL6oDVsqcT2V9LWPmg/VVyBRDOWt9T3F8iJrosi+nDgqUrGKR53NuXlnhz/GxEe3U7M+8PnAJccugNTDLsfHpdJEYKBA7z65xXIxy1iwAv+eme0zixWuEJiUiFOsvaz73/i7kgIXUc2ON7aHBH3cu2tvFbWHZ2EoSjr31dZJ0opL1RXlpQaMobRPL1gX1+iI5VJT6qjX7nDwYsp6tpvHBtyCULqvH8bH1gUdHlkQqhKdaFUAJRkY5/d2UPZm/5RlEIOvIRoN2EkZ83gsCcxAhs87Vpk/lWWpqSLpyr0bdqGDD3i8M7bOHpLLOg+eR6CcQW5KUwI8/Tp977lyaoJpdd89CJwW4UWGVNZbnMTVykpAc6bbFZMIDTwpp35PD0cPxcboetVp+H9Zqdq7xM/2ztZnTCB8mCnLXCahUgM94ER9EK1XzuwI0GgqrbeDt79L4kz/hZ//25wCv/o6sF/jQAub//bc0fuTH9HwgNRrA932Pwqd+Cq93eKjx2u+27z/vucBLv6EIzhcoUKDAxyMODzX+5TczzcobfwB4/ifT3/+1TwN+9/fIUAeAP/0z4MEPJPie75rhb352GZ//eR7q9QSve71GGPLUxze8TOPv/l2NX/sNfudrv0Hjh76fQYICBQoUKFCgQIECBT4e8H3f932YTCYIggC/8Au/gE/+5E+ev/fpn/7puOOOO/DDP/zDuHz5Mn7xF38RL3vZyz6o63uehy/4gi/AS1/6Utx7770L77/4xS/GZ37mZ+KlL30p4jjGG97wBnzmZ37mB33oT9BqpTdKs+j2uJkMcDN5bS2tFHIrRBFJFMNRmggwGadP3N5OLCVJLNllWYBsft+EQQJJa6NNusRanXWWwBmQc/Jb7qd5And3DwtbcCcFXSXVmARdVlrkreSpSgQBUK9yXzAbkAa4MZwldgHpze75tXKCB08+ZYL73qJag4vBEDg+1lhZoYJX/Dg3midG3W00YgBV1KlE6Wk6pT1cubycHLKxwc3yK1fzA1HhjG12q4DaSWlSPtjvnDmTVkmKIu6Puhv2MqQqFf4/D5I7AaNsn2aJXX6wPFVTpcz28H1zkt1nX2cDC3mqAKUyUDfpQ/oZu9GJJeMNjbIZwPR6vT7/HY5sH7qYzBYDBjPTPze2GRCRAER/wPtsOIHAk4htQDoIVqky8Cn1rdWNGlTO90ZDYAQGu8oltlUQ0LYGA7Zlr8f2K5eBRx9joNqF59m0Jp5RJnDLFcMGoYMAUIak4PkM9GvNvtzf5/0nU5bF89gP8xRm5fS1x2Pg9Cl+Jknov9w+lvpOJ8vT0G1uWKWYwwN+P3LSD7qoVlkmCQ7HMVW7hKTolstFp2vT8y1DXurdLIYDqqsMlqSLqVQ4bmZRJq2ptsTHZsMoipiAWqnEtJRa0wc1W1TjyEtJE0VpYpenqDQGbUirU5Ic63XaU6djgn0Tkoqf82zg4UetLeSNXyHQZJFVAcq+lzfPitpl4KhKZpUoDg8XyVx5JEOtGUwVX1Wr0X9UjC+fzYDHH+McWKuRgDQe2cBmOGMw9fz5xWt7vk3fBHCsZes6V4Xzbd00DBnY8Q1RJjVkM8cXeX5+WrFmk2pX2fmy16cfbbVYz2w6T9/jumAZ4oRzQKls0oj61hdm7V7STeehXgMSk/ZrNKI9K5PuyvWPk6klxccR7ysB4EoVJNDdZurD2RTY7tMXbmzwPnlrhGX1XoY8VZQsef/OO0nakLEYhlYNUgjBYbiYci2L06eAJ560/49H9L8bG07qaSyuE8TmAH52NssnmAoJJC9gvrFhx0C/R4LXMrRW+P5czWu6PBWd65u1pnJfVuVKcGM73dbiX2Yz6x/aKyb158zWCVgkUAmp2fPYdpMJ55lqNT81skDmkyQGpjFw9130t+OxbePxxJJm8gimnY5RvsrY1eqq7buVlUWflvWzVbMuPjpc9LXtFcYgfJ9lmYVIz+eGhOSm2lYKODymr3HXbZcv23lZbHcZuel2IAdlOjN73/X15TZSrdHn1mtGFdOn76kYf7C6Sp92+hRw6pTClSu2osuWzJL2/iRSnbzXbtu1S7VyMjm5VrOETQBoryh0Ogqz21Cuu500rpGjqhXDqGI6Yyh1DbFTxzeMHBXcRn0xjZ1g2fwNsO+XpVD3fZN60Ldjr9ezc8PKinOIaclaWCdAt8Nx6DkHMdIfYt3cQzJSz6Njk5L6Q1QLzK7f4tiQlzzafhDQx62tmvW4acOs351MLBE8jhfJ9DIGpdyqyjG+bN7sdNk2GtZXjEaW7LW+YdbKms8vsXlmW+ZPBaVy/rPpbGbTgsqhJYFS5lklZ72rE6DkHGAaDJnaPgi41ppO7TpzNGZ5Naiw6ao9lkqL13bTOs+mrPtknE6tHMHabvZQzzKb+3DwlyAG9vEDpRRqVYX+gIugb3m5h2/8eg9f+eUKX/j3Fb7y/7Ee6Pd/H+j1PgTacAb/8T9pvPFNlri1tQn81FsssWs61fjO12p7imQN+N7vVkVqrQIFChT4OMX3/5DGlatcmH3nazX6fc4199+v8As/p/Al/9B+9vgYeNnL+3jN6wYYDjX++osVfvgH1XwD4ugY+M+/4sjYbwNf940ab3v7hz9/FShQoECBAgUKFCjwfxrve9/78M53vhMA8MVf/MUpYpfgq77qq3DPPfcAAH75l38Z4bId5CV4/vOfjze/+c25xC7B53zO5+BzP/dzAQBXr17FQw899EHdw4XybEomgBv9jXp6I1dUbYYjk54qsyHpmbRZ7TYVJ7KBke2bi+Qp2TyfTE5OpZMNdvW6DOpnnzCazXSAWIGf7ZkNY3nv5k2bMkGQLZuoa8hO2GRy6yBrtcoAzOoqsNK0JCbPZ1Azr45xzA3mwdAoF+RsemeJOwLPT/cbkE456RITpjPWJS91D8D+7PeB69f5mSee5D6lEBFOInPkBWwk2BlFVATT2qQeXPJYOBjyXm6Q2TepPWo15LJ9bofLWCmn+22eBs3Ya29Aoo4Q/YR8MTFqQKMRgx5RbNMGufCNMpS3JFihVLqPej1rt70+1T1u3GC/ry1RvG40lpPDPKOWkpfqByDxqFK2QQi5d143SN3cQJPbX55R6ul2jRKaUXfL9sNJ5InVNbZtqcwfCcyurvGnWjWpSJZfArEhNMWxCfibD7sBqDheJHbFMYmpEpSuVtKBT99Hys7299lfgyGDU5VKOigexyQx9U2KrkbDpKGKSGoQBSZBvW4Dt+54ktQstwp8tlct4UA7PwDvt7pq20DGbcuxvTzyahaxIQX1evyZ5JDNbofUO50uJ3IIqXI6o1/O1vvwgAResROp0+4usLtjyI8T+qnpjGPQD9K+89w5q4oFgLY6s3WZTkisOTJESiHiDodU93jo4XR7lY2CRrNl03gJAa1ctmpsriJktcYybGzQ5iTFjkuocxVO4jif3OvidoLjQLrPxmPa8vEx6yyKjL0+ScvXri2SDjo9BiJv3ODPSSpBC+QBZYkkAAlcq22SsTYzAdbYKaebFmr+2ixfPWk6S5OzXYxGLLuLlRaDqlkCWbm0+L/nGwJMKU1ErWXWNMvScGltFI/KnMNcMl+W2DwakTwvNlOp0CYGQwasB4PFtZSrzpmFm1rvJD8MsFwSCA78fEJHqUw/lk3J6M4LUcT2ztqBEGm6XWA8Zduvry2XqfB9YG1dpQjHcWxTeY6dtnNVSw8PgZvb6bSV7j1mM0tGXua7/IB9v7bGctyKhDab2nEchnZ8VCqso6jVlkq0/3mK1NEi6WylRR+RN4cLsdU9SNA1BOYkNj+m3bPpUKOI4z1JuEaQNNIuMUcgPrbecJS6MmsalzSgE6OaOM73SZI23V0biI9OkjSBYZlPq9bMwYKMXYm617XrbMuuUbxrNdJpbs+epQqkawy9Huc4dy0uthzH9OsbG4tEEJnbU+VzFHbctmo0OC/k2bmrTNZuk6yyumbXZ5Uqn2NqNZKajo7Mz7Fd18QxC1wKlpNkALavkJaXkbWk7SdT279h6Lw+4b1dolO/n7Yfpji/vXiPUnZcyHddX7K6uqjst4xkBSz6b8Hp0/Z+eXUPAqscHARpNcX5Mwfy+zBLmNWJbbPs2rVq0lA2W6yX9PPRMdMcHx5yHtNYHP+DgVXzzUInJ7eL1CMPefPWdMrn5KNj3leeK6KQ65v19cx6ymB1zabydp+zBkOu4Q72lxMaAfqbdjt9eCxJWL9Ox6ofNjJzIBTmDS3za69nVc2qVatyl0cadyF9pnXaFkslXqfZtIqHLjzPKgPnHTQTwt+ytKjttrXBbtfOJ7u71j/NQvu3jMlyJaMu7fwt7faRRkHuugX+4u0ab38H8O73kFjl4hn3Ai/6dOCrvlLh3/6iwsrKh0+w+hufRXYzADzrWcDP/YzCvffwulprfP8PaXzA7An6HvD671LY3CyIXQUKFCjw8YpvebnC6dNclLzmVQqtlvX5lYrCN73Uw1t+TM0XyADwX/7bFF/+VQne/R6NT/0UhTf/qELLObldLtuHneEQeMV3aPzab2joD0XbuECBAgUKFChQoECBjxL83u/93vzvL/7iL879jOd5+MIv/EIAQK/Xw9ve9ra/lLK88IUvnP999erVD/k6s1l6I7jbNYETJ6BbqXADPPAX0wEC3HBUnlEMGS5uPN8qKD2dLW4C93rcGN/YsIGrw0OWdzJePJFdqaRTbLkBSaWMAo8hUjQzAUQX1SqDA93u8pSMC1AMevT7JB50nSD9SQGYJLGBhdkS9bQ8bmC1wnZxU5oos9m82mb9+pk0N62WSUs4SgdVEkOEOTwE3vt+4P0fIMkrjBiolB8JugjpaTqzAb48dLsMFEwm7MtsYDsMF4MnrmKUUtzsH4+tWpiLcmU5watUYnndAPc83VWdtjAeL268Z4kEWpNgkCS0xewG/9kzLMcy4l8QMGgCmBPrwWJgXOoqBDGX4FGv096X1VPGTd772pT51CkGEprN/ACwQIJgkppFyqmQtrUoYqBH+qTTYQDy8HC5alezyTL4niVNhCYd4NGR8UFeuh7TaT4ZMAydoJfm3xIwPglZ/5JNHafBfQxBEGSUCJZsZUQRP3twwB+xKTdYFwRUGblyJe0P6g2OrSRhsO8k5CnQSXtJmphl6fCARWLRMozHNqVbNn2q2z63Qh6RIo5JrnUDpNMZUo5gZpQ7XNUvl2QWzkjkuH7dpOyKFtUgsmNiGflxGXo9pPp7NqHPjSLafaXCMo0NUULsr1YFzpwmucxTHBOHhycQWx0VGwmgSj0qlZPnDkEzJ/CYtfX5+HDV5EwbS1DaxXQC7B/Y7819t16eckvQd3z96mr6vWwKLrdJppPbV5oaDtj3yzCZMIgtBL3E3Nv3SKYRlb2yIaTfcQd/Ll5cvJZO0umVBXlkSWWCzhIoXuC95aWFBNu43rB/C7LjVYhnWchaR/yiEHry7F4Ih36QJrmsrVlii/zUKpzHj4/TaQbddYX0qbtezCI0/r3dtgpmojp4+jTvde5c/ndd3y6/54ps2pCzD0hMkDUYPFuurCptFC22YThjH9dqOUSCJZhO0yk95bXBkD9RbPyomwp3srjGk+7O+qtW086xt1pDLn0vZ10SRRw/LqStymUbM77VfWUd6AeL64/hkH5R5qxy2ZCqBraPlhHtKlWO3UY9n1ws41DGhqxpe33rx7TmXPz007ilJPDRkXNt8/3sXJslIdVq6dTIrv9006EL3LWUoNvlPNYxhJrhiHOjzBmjsfUjcUTV28cfJ/E4777Z689fu42UmNNJeq0j/T4cWiJf3jXKFSCKFxs477CDHxhCdJM/pRLL7/avHKq4XYgioovZjITk0JBOswSpUonfE99Xq/GZSQ4clIzaVJ5dA+nxrJOTSbSTiU3fN53xudBtRzlMcnBw8twQJ/nqzctSEQLLD9Jk563bXRvpxCpgbWxwfXJ8RJvtdNJrjiSh74kSrvWlz7NrmnLJHEi6xTonm8q0XmM/j0e2nmJH167zYFKvZ9drAG1YDga46PVp/0KkBLhuqRuV3U7XHAQ4Ag4O7Rolr7+AW6tPC7pd64flGUKQN5eK3cymizatFJ8jPhy1wZNQpGW8BdwHoxvblLsUKKXwxh/4yBKrzp1V+N7XA//rdzS+9eUKlYq9/i/8ksbv/4H97De/XOGTP6kgdhUoUKDAxzPOn1P4yTdzM/FFn57v85//yQr/9t8Ab/lJ4Lf/F1crOzvAN32zxpf8Q42X/AuFn/xxhW/+Vo2jYz6o1Wo8mXdwyIXIm9+i8djjwLe+HKm5p0CBAgUKFChQoECBjxW8613vAgDU63U85znPWfq5F7zgBfO/3/3ud+PFL37xR7wsMycC4d9OFDiDjXUPUZhAg6feu5n0Awf7JjWTtsoGy0hakzE3f09SdQmC5WkwgPzTr6XAqnBsrPNvzwOguEm8LA3g/Psm9YbW6dQNsUndlJe6B+Bnr15Lv3ZiSkBnQzfJBMvDGRCd8PjjqfzUNnLZZoNBlFnIPuj12Ba+nw64CrFNefxb/o8i9otskLvkvEZj8eTvoG9OswOpI7uSMm08NiS5JUF+93pxnK/0BrAe7RWrTgHcWo1LgpD1ulFAWpJKlBdLB+T7fV5fgkdHRwwiuuosy/blR8N0UGd9jcGaODLpUZakn8uiUs4PXs1mVKqoVBQGTlsJySYbxGm3ea2ZUYnazSjVAEbtQQHJEa9Rr6eVDtbaVC6ToMJsxsCIMqQEpRkIEVJQGNo97G6P+wECCWj0+gyMZAmgg4FVspJgoYvRkGNdVNBKZRvwyI47P0Mw3d1NB5ZWV606Qblsv1+p2FRhosDlBo0qZQZVJIWrnyF3pdKdaJIxaibINBrlKJg547leQ24a19HQEt6A5enRsu69XqdN6IQKCe7nRNlHa2B0GypbkrpxGdy2/XCP6uWliQpnwIVzwJ130P7ybrIsZfBsaoNsk4lVybvd8QiY9D5xJj1YTj8MhyZFnZefWhZgWTyTtilvnOelNZIyi/qP8ujzfR/Y2DQqQab+vkebFn/qB9bPu/fLzqWS/qda5Zg6dxZ4+BG+1+kAI1ddagScOr2k/KDPW121ChFi9+vrNn2SoFZnW3W63G+8++7Fa0o9fG9xrNdMCrlZjtqQfA/TxZR0gjy7KZX52ciQTdz0XEHJpuYVcuGyOSYvIN1sAdMx/aPMPa5yTF4quySh38m7z2TC+2TXOa2WHSaVMolALil4rhpq7l1vcEwoYM4AWW3ba4jr0olR+VRWhVAQRaxXo877Z7+cJRLl+YpGg0QGWbdUq/Tf4zFw/RrQ72kcd6kIAzB1mTtm+n2NVUfhUtYkQhQRG1m21qxWzV51PW0b/T4V7MpGKXBZkNxNIxpFVBnMrm0W7NQpf16KZiG0S13W10mEWFmx5SgtSRsO5KdWFixbb4ch21aICittq0IJbUk4rh+s1Yx6b8b3rbSsnblr7NCkkpzP+/rWqo+e8X29HoC6UefMWWcuqCflGNt4fGslxFrNpGqN2TfjMW3kVo9TkpovD3IoZXWVpJDJ2I7706fznzmkXbJKvIIwBMpL1sa3UhT+YCMvclA/26Z5bbws5WFemaoVkyo205/uOknSIrt2K+scmeflue/iRdreYUaFudenT4wTezDDRaWaLl+/DwzHtDtZC08NaVzlpUuEIZSbZxt37aSwmNJcw6qUTibpdhTidBznr8FGo+WprYG0D/N8q7pbrrCMy55vXeTZsdYmNavTdjNDxu31+R13jSHpFwH212zmqN0poCzsIHNALNaAr+wawvNILs5Lh6mdeoq5SZpaIP3cnFXAHY0sUVSuOxra1MfA4rp9Pr/J9Uy9pkYFMZxxnncPkpUr9pnkpJSmJ2E2S5PfZU2axWBg5nOn35Qya88lSnUfLgpy1y3wuld70FpjcxO4eOEje22tNR78APC856Yt9fmfrPD8T06/9ju/q/GL/9b+/4//EfCFf68IvhcoUKDA/w04e1bh7NnF15NEwzMr9mZT4dWvUvj8z6vju79ngKNjrkp/5deAt71d47XfqfATb1Z42cv1XN5eKeDee5jWAwB++38CTzyp8X2v5z0LFChQoECBAgUKFPhYwpNPcmF76dIlBCcceb3biWLKdz7SePvb3z7/W9JAfjCQQHSigZu7JlDuvB+UGHg7PuZme6tpNn1PuuYJ7zXqaVWE2Syz+ewEiY+PzL/O/TyTNk2bwGycANHMvifElaFJdRRFy8s7m3AjWYEbotnPRJHzmmJwRjZvl52OFwmGKM75zJJyKLDc5dV0YCyJSQhLIsCrsoyntqyaUH9g1TPkutMJN5nHY94vCkn0SGL2pWwcu59vNviCWzYFE0wx/48nabKEgkm7kxNMVzDPgZnXXNty3+v32d8KfHYMfJZpOGSdZYj5Hj8TmACGkIACJ1galPid2Yx1j3P6IQic4GxoAi/SDgB0Xt/Bfl7u02wC1WMGv+No+Xey9jce5X82jlnncpkNpEwAaDbjmBGVM/nuPCVPuNivLqYT2nI4Y4Bkc8N+7rjL8dOsp/umP2DgP4qpLNFq0oY0bBl6XdqWylETcMeT+3swZDAur18Ak7LQA6omnVmva9vc/XyS+X5QSgfMPcXAdaUCqKbjP8x1wsiS1Cpl+76ksJyalHJKL95rDkW/ub5qUnc6gbl5/ZwxVKuxD6VtUu3St0qC7nsutGZQJ0noB7pd1lmUPTzF/o0MsVTUtqbjJeU38AOjVHECuUubtE7a+Nultr6k7C4UjFqh81rJpEFaaSmUfJ32veY7eX5cgbYpPiKMgNOn+He3u9jO7vfrhjyVxEA4Zd3c8ktQslJWmM5488iMQTfloEDIRcM+sH3DkEQy9wx8zlnua57P/10yEACoBn1jFNG+a9WMElwO+UT6KA+e56SJ8zlHzgOlZozJmCgbslCScBwBDHYq0OZFFXHFvBbHDNJOJraNBIO+o+IRMq3mSovEUzevaL1qA6fu96MEQIIFmwDMvaP0fFWp2jaeq9oh/RumHvL/cEDCl6eYJjKOmUZ6NsHC3Jhq08w1Zb0ynnDelvdW2lR4PD4mKfvK5cXyZP8XEtFgSCW07GfdwDJMXet13nsc2X7I/gA29WoQpNdjcUIVIYFvUgu6945DSy4PHTJVKchvJ/e+vS4JVL1u+rM64bxYrwHXb9BWo4i/azVHXVNZsoHMeVrzM3I9WUOl5lnT19Uq5/nDQ46nbHmHAyBoG2J7xk8DljQbh9beptP8epcrDjFY2euVAtqGtA3AdtehteVel2Ow5/R7pcwxsrlpDzvIe5OJTWsmynEufI9EI62tiihgyO7yd5f+Uyc23WM4Y1scHdn5plxerK/YTJCxFWk/DUPOdNqqVKa9RlG6TLImj2KWSXxO9p4L85B8Ri2WIYty1az9tFFOGtNv6ITrnrNn2aYzQ8zKG6ftFes3F+6h7fhz518FrgGWrimQtrko4hpGyuF7XJtFMX1CFJMQ02ym+9K9dvYZIYtK1R6uKJsxXKvyut1Ofn8uhcJ83Zr3udGQbZ+ah+tWMVXITVm/Op3Qz6yv8f8wsj5kdRWLPlpIhMrOEZ55fqjVOEenyqg4JqOIxCVP2T5aVufjjvVL8n65kvZFbvllLSjvra7a9hW7yLtfsuT+WchBpfEIUB596cZWmmjtYqVFEp3W6XS2gsGA5FJJOdjpOmsLD6m1LWB9V60GVA0p0/c4dqdT2+aVmkOc0nzOFyzzp5JG1W0b15fMHF+Q11/TcXqdj8xngszcNRqxXJKGct6/zrNBZOZYl5AtttZoIvW83zDrJN/nM56UUdKJB76ZR3W6HsMR0G5ZW3GJweNhusySpnMy/uDUdW8XBbnrFnjBp6rblmz7YDAYaPzAGzX++E+AH/4h4NNfuNwdvOvd/KzgM14EfMPX3Y77KFCgQIECH68YjzVe8R0an/95wN/9O3ZO+Ft/s4xP+sRVvOZ1x/iTP+NrV64CL/kGja/4coUfexPwzd9mc7Nv3wT++ouBPzWffewx4KtfovGjbwTuv7+YawoUKFCgQIECBQp8bGA6neLY5EY4c+bMiZ9tt9uo1+sYjUbYcSVuPkJ45JFH8Md//McAgPvuu+9DInclMTAc+tAAwlDbVDfgRmS5rNDpAisrCsoD1td9bG9H8AK7f6SUIqlKMXAlKhtxkt7oqtUUJjOjYhVrrK15qDX4uclUz68VlIDh0EO9yfuNxjH8Et+vVHyUy7y/7ykoAOOpvc/YUyYwqDEee4hjzYC3U17PA+6608dwqFEqK/glDZXoVJ0qFYVqVaFc4bHZUimBHyTzz/glezS2XuXzzHE3Tqlc1eoK48mtN/vGU56IDgKVKsN4CmAKwAMqFc+ohCh0exrjaQINhdOnffh+nPqep5QhKrA/JQC10fagPA++H6NW54ZyECj4AVCr+mi1I/N9YH3Dw/UbCZKYG8WTado2Gg0F31fo9SyToNVSVHgrKSQ6SX2+XC7BL8UoBQr1OpAkCRLN501pTy9I4AcKfkmhnGhEsaiopJ8XZzONMNYIR4DnKWxs2D3VWahJCnPtM3OKOdE2pdM0NHY50fM+9YJb5K8B4PsKN3cUjrsJNjfYN94w/3t+oKBvQ+8o0cBkopEkCp5z9L9aUbh4yceDH4jm7QSwzbp9jelUw/MVmg2FkdKYhel7hea0vWcCGHHio1ZnWwW+RnsTaDY92i9oExoaUaxMajO2TQKNIGH/eor1UgngBYsSDZ6fLqfbrr0+28/tI7ess0ihWlHG5jSCEiN07ufLJTX3WYAhTbnElzLvH8bpsaonvKbyaGfdrkYYatQa/Ew40wgja+tekF9OAKjXPXieRn+oTD3te6Wyxizk2Gu3+X65EqBc0ShXYkRJAr/kIYit3wkChVLZY7CnnCBJFu/b6wN+4KFc8VEqJfNI6nCgMYnZp6USzNiibS8rv2B1zUMS6zkRZjLN/3wQKESR+Gn2gZBoQvN6FNl+aDQUhkPHpxqSlF8Su3Heq3oIQw+TaYCgNINWtJcoVOgPNBoNhXLF9rfAL3lItIaGtDEQhoqqbUojgYZf4sF3P1AoV/RcJSQIFO3AmEe5mqQULEolH54fzxVE3DFZKqlUWTxFP3RwQBVMv0T7AoCt0+m5sgRg4PiKwPg8jbTPhPIQJXwtioFE22sCDMp2O7Y/2m2Fek3PU1xm7cfzFZJQYzJl2aPY2kaz6aHT1VhbN+0YKJQrHs6e8VCtpf1aFAGdWYLJoRl7sUa1qhCUE0ym7GfP3Ft5CqOxNuOX/TWd0S4bjQSeh7mNiF3U6vz74CB9X+Ut+vJu3/o2ge8rrKwonDvr47iTYP8goaLTVBTyFMZjjelMo7XCC6pI0ycZlYzZTGE84Xyfw5UBwOvUMnNTueIjCBJMpknKvvt9zlVRBHS63txnpnyWrxAbWwXsPFWpKAyGCgn0nBTZ69trB4Fi+uLYR7ms0WqRMTc149gz9pWMra8RJZhZCKiJQrnM8XzcTdugBvuKaW8Xx16vr+frvFnGBwO0dz9Ij/XpLO2Twoj9KPYRxQkgimYx0F710FpByh6U4pqlVPIwMIToo+NkYQ07L6vHa4dRgv6Q9090uhylEm2pVFHwPIWVlodqVcELwnldZG71ApIOT1ozBIGap6vzfYVy2UMYaZw7p3DlakwCibFnUabyAttfk4mCUgrwkrl6SzlQqNU9nDun0e3H8z6JzDwXhhqev2itnu9hMuLrrg9050Yv0PA1kHjazHvANOT8G0XWP0XJYj8LtAYqVTWfD7xAwYu1VZHLjNMg8HDqFDAYxvA8hVIA1OuKY9pLUutpZMZ+FlGicGrLw3CUee5o2LEAAJsbHg4OkznhLopZrsDnM45fUnj8Ca4L7rjoA4gX6uuXPOzuA2urCkGgU/1fCrj2KJeVmcd471qV/TqdaNPP+fPseGrXakGgUusRrZQhLSmOO0U/VC4rAAn8ksLqKnDcseXRABp1tdAugpUVD8cdZx3lB+gPmNOYY9deyy9x3S9YayvEmr4SsHOkbw7h5KEUKOjy/5+9/463b6nr+/HXrLr7PoVLuRfpxdgVFTVW1BgT/UWxxQ4IgnRQpGhEUBREaSICEbHHAomxfo0xMT2KsSVSRJFyKbd8Ttt9tfn98V5zVptZa9bae5/P517m+Xgc+Nxz9l5l1rQ179e83nRdrkvisk36vtg477Ksy2NbDs2hRmOaCy6W2TyUAej1qB5tAo7p1MFN96RrDMKk8jwd18J4yrFY8EvXo6ZrKb+vAfQs/F71xp2Yw/Go/+73bepHGLlj3udmCx/+UILFkiPY0Fx7OOaX70aW1TyHA8h5LH9fCRg2QW6sScXnwyH159Opc+lgJvq4PAmne/R6DmwnBrOSy/HX9xkcC3A9eh/lOVGL6zG4LoPrcixWHP0+g2MDiZjn2tk8xuKA44rxjuYkYNU+3PMYNqmz8eV3Fe9ptisvL47i7/N93z2OLSwWMWZzDs9jSGKOdWBhtWI4OgSGQ5ofHx/RmLfa5OaFkr5wOrURRtkcIAwYju/BEIVI3zUZLCfB0ZGFIKD+OEo4wpjqG2P0dzstK9sFbrkvQxAmuHatem/DPsNixS/HYMtWNL4tMOKu68Cfvo3jpS/nl2ruH34Jx8+9CbjppuoDfuc7OZ73ffyyYj7kwcALv7/YYRsMBoPho4sg4HjBv+L4y78C/vKvOFYr4Ou+JhsXjo8t/MgPW/j9P+B41WvohS1OKL3v//xfwPO+F3jpj2UCrz//C+Bbvxn4tV+nF//JBLjvjt0qDQaDwWAwGAyGfbLI5SMZqPK25Oj3+1gul1jK8httQRAE+L7v+z7Eaf6BZz3rWZ2Os1pXxSCCyYTWhe68M6F0Tn12+fv1HWnwFgyTSbablEOddmU8tnDb7dkC/OkpCWPygRcKmFPg4l5HFiYTC/hgfHnw01Na0GWMQWaaliS8kEYiqupO0sARatP/zRcco5GFhz3EwQdujWt3Ti8VAi4h7BoNGeaL+sV5eozqz4ig82LJL4OcHPTOViYIIRUTLZYc/gUFY4MNw/AouyvLpiAuANxys4WTk+RSvLBaVw6FxYJfptoRzOccD36QjeHAqgTmBTzhcGwLnscqIhLfp+BePtVTFPHLxWzHYZhOWSkFB8edksXuJgZ9Eh1ss9FWfPXOawl6vrqGCEFM4fwDhqUi2CZ7prLPBhteqDJn5/Iyl6UqGw4ALBg8j0QvMhHibC6CS/TfjsMuj5Uk1BZ9yX0P+mUbhSq8puBXK56meMl9pvRxCu4D55KUU45Dok8hJuKcIwqB84vsIKIOlcVC+ZQodJ3FlG2Cfo/hXve0ECf8MjBaZrHkcHNiHFczOjLos8sUejKSmCMIiv1c/vnlr1UECAWHB1Yh8AtQPfJ9CsYlHCQolZ6X/r/XI+Hr2VkC22KNQXfBJlevy2ljVmuO2Zzjfe8vdtiXwl0OjEeMnAlK/QY9Vw7HYamYlw4eRRzn5zkRjA2oMgJPJ6zgHgNkVTiR1NUkLv4u4RxBIO/BWWmgiWMKeNq5QKkMcV7xnEUqOSB11+G4FI9YLBUqpm1mNGDwfStLlXqhbm8AjZNi7K1D3MrJKc0JJmNW6V/yzzmQ1CVK70rBaycXd5rNKLA+HunHosrn9lyGwYBEGn6PnPiSOOtL5gsR5C4J3yy6J99j6PWAJGGVfpghE+lSn1Asr0E6fnoePVtZXVssOO6hSCN9dMgQpGn+8uN3z6d+bDaTP0Nxb3dcSy5TpHmS/kM2rgDAZsOx2QAHU3m5i37TYuwyZZxov03DJ/Xz3WOLnseofyoJJhhjl05ViyUF3jcbjvGI5qzlsTAf3xTlVRa7TMYk9hSsVhzDfF0s3axoW7YqL52CjeI5lFmtOVZrjijkl9c/myUIIo7JiOZKhTTBQKG/E4xHJH4XY93xMZMKBPL1I9/lRXF1XFTBkQl9AeGkywpz/DzLFd0fkGYMsandibmHIN/26jg5TUgsm0tlNh4xDAdUfsLlUnXtPKF+SNVWykjH/rQ6WAy4+T4WrnnFz6zXxTJSUS7LPOfnNOcMQ+DaSYLligSrNI5XPy/RiV9y7STXVjjDrR+MkfBU1K/TdlvOoX0/cwpzbIZ73Yvhne8qjvu+x9C7R1Xge3qa4PCgOHmgtIYcrsMK5eU41O7z7e3m+6jfTVQpT5tw7EzE6SjaSRDSe+DFjNPYk7NnSmL6f8/JRJ6i/vb89Ll62X3ovE8CaV8gsfEMg6z/JucxjoMp9X0MaUrpEGCpmHg5Tyrz5M2G0zRxVb0O8V7u2AB3GXo+sOaAFbHLexaOs7GYR6UK6nK7F4xGllS0uisGAyGQzL0jRBwf+jBHnM4fPI8hCklwKoSDqne4m2+2EEcWrl2jShXFHElMQrh8i7p2ksBirDDHFKmzASpDQRRlc36x+eWSUjPV7a/bYMRdV8h8zvHa13H8zu8Vf//PvpwsFcu8730c3/NcftmB3PtewMtfSpNhg8FgMHz0stkAudiVNHjCGMOXfxnwqZ8C/OjLOP7Pn9Pv3/ku4PtfSOl9f+d3ybJ2sQD+3W8C3/s9wC/9CvDiFzKMWizcGAwGg8FgMBgM15tNLmLmaqygeak//notUchswYtf/GL8v//3/wAAX/3VX41HPepRnY6zWnEkcXK5pun3KYUBQKlnkFC6iotzcuxYraLL1AEctEh7mkvp4Hrq9F5BkFymoxFEEa1W5n8fpanLPvIR4KZ7BFgsgDvvoL+Nx/Q3z6W0E0GEyjEBSrk1GiZYLindWRJTihux1v3BDyaYTLJ0FElSPU4QJGAsojSIIUecO1ccpgdiaRq3jfw6AGC5Uv8NIDcSztM0Toq0WuEmoRRZOQFC7ADrdVy4LgBI3Cw9RJ4kAj7w/phS7oBS7QDAeAgEm4ieN9L0OUH1mkVKCDdNn8LjNJWZKAqLyvXe965+dxNs4DmUemyzqaZ5jMMEnksOG2FA6auANN2eSH3D05RZimfeBpGqhDlUN3r97Jmqjt0fZGlYYkbXIj67bLieQoompIEcnomMen0K+gnng34/QRxRio1lBFy7M4ZtAScnWRs6OyW3qCQClnPJdaeN1LIzYQ4YBcgGg8wFR5Sv+H6/DyR3ZIdJQGWzXNKxen4qLInStCul8/oe/U78/tod1NYKdbSmrFiakkt8nna7F7/fm9DvxyMKgOTTmQYRsOml/UNA6UyCTfH5MYf6tHJ7Xpfa8XpJgZaYFYN/izlwcRHj9Iw2svXS84nrF+UZW1n5vve9EXw/TcsU0Tnz9xmGQBjE4JxSvSQSYSpAbei9742RM5LSag/DIfW35c+GYdq3N+iPxTNbr6iPT6L65zg7l/++XN8AwB0AR4dx1vfExc9EEfX55X4pDrP7CUrPstxPxGm6yiSiejw9oHYD0DiQfxa2A8QxOReKNpkfJ7mkn86nF4pDqjPS+4+zFEUAcHgMWHHxWidTar753wURpY8Eo3EgXz8SFOvnxTkwnmYp8yyL0tHZFgWakyh1bUrLabUkYdDJNbop1wWmUwo25/uthzwYeNffpimsAhpD+v20PsTVOju7KN17mFDqt/TeIlT7hSQp9q11iNSFgnVEz67nAatlhNtur47tSS6GG4cJlZ2ozy7NE44O42xsY5RmKYmzNrIRbXZD7mNxQse8OKeMBp5bHN8sO7un1araBgESZ23WKIglhmOqh1FUXx7Mons5X1CZJDGJSTdrcofSKc9Q0jcUsIvB5tWqONdzUheefN0GqH4N0pS0mzRlpIo4TDDoZ+NzzIDIBhxx/Sxrj3dei+izC+Be96K/i9St5fvgbnMZhAEKoqAworqv+g5naX2WzB0BoDfN6kBsU98eh8DZSXqtirne8RHVMyHSXS3TtF0Rrak7Ds2PRiOApcdnKPaFeSwrde8U/eSGnsd8TvWX8zQ9baFfpbErieh55VNJlo8tE48DWboxxiiFWBkxNp6dpfNIXq07AM35V+ts3MgjxDRRLqUldS7ZZ+aLYuo0Ff0Dion/zd9kv4ui5DJVYOHawwRRkqUfL76/0HNYRsC73hWjL4QaDv2ItIzSaxiQYE8cz/FIDBVF9N4zndB8w3aAYZ8cAReLbOyKoqQyVwIARzJPk8GtbI69XgLWoFovkGTz2TvvTJ8Bp2DR5RjJ1fe4KD8PXr22xTxBzIvvF4LZPMFomM2hgg0uHTcZqL4NR3TPANXPO68FdO2Ka1ssEkrBKyGSvAsB2TPppWmAC2VUgiFLXbrZJORKnPY1qyV9x7Jy95Q+Q/FeseHU3t30HUr3/Sc/J0HaVtcR9dO33R5f1m3xHmzZwOEBtckkHdOiKL6cN+kgjnV8nL57JOnzSedDSULHL7+f1rFeJ5fzEJFeU1XHZP08s4rt1HZy7/AAHvCAGO9+N6c5gEXdh0h76rpZSlnHSd0/bQA2wAbFOc7hIfXTH/7Qht6jYlyO5yfXEoRh9k4rrqU8DlycAe4xfXcxp/sVzuhiThvZ9DuRGrI8z5pM9MtWFyPuugI45/gf/xN4xas4bs+9hB8dAt/7HIbP/ZzqW8Vtt3M86zkcZ+kL18EB8MqfYFJ3L4PBYDB8dDEeM7zqJ4DnvoDjsz+L4av/hXpsuPe9KBXjv/1N4HWvp12LQQD84i8DT3kS8G9+jRy85gvg1a8FXvnjwIMeVDwe5xwf+jBwy81mDDIYDAaDwWAw3Jj4vn/577Bs5yIhSKOrPbEVcwe84Q1vwG/8xm8AAD7xEz8RP/ADP7DdAXkWT1ynwi7GgNNzCiyIvy0WwO23U3BiOqUAc3nfqusVA8r5wM/ZGS2q5lNf5UUZlzDhSkVCoCDMRDCzGS1cLpfp5hNWvQZKyUGL4RezzN2A5+4zitJ0g+mCfsKrx8nKhf4hNkWmv7n8x3pT+l2Kl5aFLDhUOE1C5S2cA4CqyOvkVP5dsRknf27LKpaxIEqKTmbiuvjwMuZxeazhAHBLgiTxmSANgi5XtPA9GlGwZr6ggOHpabUsxHNOOHCn5JlzUP3wPBKhiO8zVrwuDnqe5eOLOhFLgkF1POhBVAcWi+yY4zGVU95sbzSmwApjxTLX3UvuekUnlfNZmkrJITGU42ZtD6CgcCFoytKyKNfT9L/XG1RJP+h5ubqbBnLFPSxXwGScfXwwAI6OgPe/v3ioMBXyWKi684nrcRy6H3E9l20tBhbL3DO16JpEgM/vUZBMXKMIhiJ3v8tc2QAkKhkMKa2LbefKhFFwPAqBkxlS1570XpfZdQZBsXyjhNrDohSEFv0CYxRozbfla9eQpr/LrnM0Bvp+rg1w6kfH4+xYl/8Pai958vehqluWReUpnGvimD5r2yi42gEUXHOdTFBpBdS+5rn7XJXKtgkhkuoKL/2/+Lf4b3HsaS5AxRhdc3nIjeJqOZX7i/zvnbQNhyEF0excxKrfBy7S449GwP3vT0Kd/AEu6zDUz6d8biGeEPdVvq6zU2p35bHxnvekcexDH876tJPTrB9oIoqyPqXXp3oqXOgsmy7CsoDemO5nOMoC0EkC3Poh6gvyp3r339N3LSer38sl/YxG1fosxjE3DU76PWB5mrp6oBrgHY2z9uz3svFZxsEB9TPlOjGfAXO6NERhtbwdJxOEcVBfK87Z84HzNfUt970vCb0sRvdxVp7vMLrexSITI6/XVBZ8UPysaKM8fS6yxydchXq9TLTiif0DSX19E/fCeSaMOj6m+dtqhUtBXe33Nc8hmM9K7SudI+V/x6zsvh23/nmK46/X2TGCkNr4aAgcHVMfJsYe0ecOhjQv5KD53tEhfTYM6fkBNI8Rz4RBXrfiJBXGJcXjq8okCDNRh2zuF+f6JuF4F8XZfFTVh9x+Oz23y/MEWV3O90FBWByvYsnzE3NBx6E2G0c0zxoMSNx6ckrP6Oiw2leSkI7u4+ab6RjLZXG8iBvmWhzp+Jj7nTgXY8DhEY2Pdc5SFxLBl8DzsjIozxXL11FmNM7aCkB914cSwOtlZaYahzlIUCEbB0Wd6feprFabrJ/r9evHzn6f6qCYZzgO9Z2iHxiPqf5bCf1NzIXzdW2zoWcq6rHfo+fY1LaFIQxPr/PyncH5+10AAQAASURBVCT/IZbNNcr923DAMLvIrkdFuS+StbEgUr83ibHU76X1yCJB0p13Zp9xrOyYR4f0HET/nZ9Di40qMvGfGM+k74bpNVt2KtoDHV+IjaYHKIjvC/OB9BqimMYvy6Z+rdfLPpefPwOp21k65ubHO9drfr8UlNtqkPtefu4VRnTPjKX3zottSxcOYDansT7/vqB6PwXSFJ25Z2HbVOfn8/T9LqY5Sf7doELpWicT+r7jANPDy4+AI3Wl5uTWlyS0uc336diLRfp807mLmEuJY/s+vZfkzyXe010nc7zO99f5sskfJ//uNxxlf+egMayfbhwZT3D5/tXrV+c+ALWHtnN6HYy4a8/87bvJrevP/6L4+y/7UuDpTyXb8jIfuY3j6c/K0jYOBsBP/BjDx9zXBNUNBoPBQAwGJNpynOaxwbIYvvbRwGc8AvjhH+V4xzvJ0evrv47hsz4LeNozOc7O6MX7u58DvOZVHA/OCbz+/W8Br3ktx9OfBvyLr6za1xsMBoPBYDAYDNebochLA2ilWlylq2w6KRx1+NVf/VW84hWvAAA86EEPwhvf+MadHTvPaETBvkral4s0pQKqC7Rip/7RIS0cx0lxkTGKaDG6DrHDNv+dYJMt6IvFZ4AWgf1+8fvC8eTkhK6/LIqaTum65jNyFxYL8Cr3ASANPGm+moxToYzr0gKxWECXCS/ylIP1bQRKZTwXiLzi4j2g4cyTClw+/OEspdXhAXDbbdXPDgYUXBdNIGq43ihKBSaKRefRmMQyQZCloATo34MBBWKjKK1zkhX9IFQHDByn+jfLorr2kdvSnfNxFtwKouJiO0DXXTJVaUU5RZZ4vnFudz6T/F1weJAFiM/O9Xe9y9y3bYsccwSLBS7Tba3XkKYiFHXXtpGmeCoGoIGs7nNUg7UsF+g6mGauAOJzbupQYdlUDstls4BFuKLk3VYEHACXtDfGMmFqQdwVVYVdl99B1pZ6fTq4ECCIZ7Za0f27TiqgzD3MOKId+Lh3em3pefPDh+1kz5cxKn/VM3bsNJDPqV6JchqPqV7ng4yDQRoE3qSuVZxconaJrtioLUlCbXM0pmuXCUPOL6p1VaTdvflmqhvCoRAoHiOMMnHXYADcdnv2t7Mz4G/eTuVopZ9xPaqfnkv/P5sVn5FlZXViMMj6nLwg2fOqKSmThH4YK7YT38uJe3LolnVemLtOHbhE2SRJ6hQXZnV7NC5+f7MGPvyRYvnddFOWMqiMLI2oCArbNvUx40nmFCdjtaL6bdvZeVSCoMWyPrC9WVPwVTzjgwNxUZmIDaDrEf3kZpM+X0uk9co+V2mPnOpJ3sBVCFHq+uemqYTrUp0RDAYkOkOufxLCkTp4kt6PV+xrAHoux8dFQYRtp+LcpNg3Dgap2KPhuqOoOHbTRVBZXpzTsScT+szFhdrZJwyLbTpJyMUVoGsQ7XG9or52PKa5XBPjdI7Bkc0hy/g+1dNyilYVcUwizNPSPPMhD6b+xPUAcKrTCc/EZmUmY/rsueS8ov6W53Pla5TVhyTJxPj5OrkJyH0JyMajw0PqW46OSLgkE1yp2q0MKxV25pf1HYf6OCHgOD1t5+BTJj9mr9b07M7P6RyTCc11VN1lXtg1GlE7OU+F2GKek//ycEh9kZgLCYFRWRiUJHQMz6M6mm9L5Xml7H44p7rgpCnKRf/LLGqzm4CufbnK2sjlRo3cNQiEkFU476oQKWtHQzqe6BPzG03EsyzMXSzgjrOGF4Acjl1/HUeHwFqxKcb3iqIpIBUtl94rZc9cnDNfNuIcLO3vw9z7mu9Xxcp5oih1cEqPKY4vM/T2vaKTI0BjB0D3s1hQfbm8fs0xvknY1fTeKUP0QZd1KX0fkKUnB6jfWK2LddvzqK+Lo+rzKjMZZ3UzQbE9xXFWTp5fvBfbARLJRrJ82TEL+JiPAT74IfpvMe4zRhuYxDN5+zuARTr38FNhrhi7fC+bi43TDWNBQGOPSty6XFE7anqM43EmdBXX3fOrnxN9+803A7en7+OqZ8+TekFsV4y4a4/8z//F8dwX8ELlPT4GnvNshs/9x/Kp14c/TMKuD6eTI88FXvoShoc/zATSDQaDwVBEJuyKIo7feOsGX/rFvLKYev/7M/z0a4Gf/0WOr/znDLbN8MAHAK9+BfD0Z3KcX9Ai3DOezfGTrwIe+ACGd/0tx6tfyxGGwI+/guOv/hp4zrNhUgQbDAaDwWAwGG4ofN/HwcEBzs7O8BERcVJwfn5+KQC7973vvfW5f+d3fgcvetGLAAC33HIL3vzmN+Po6Gjr48qYKRYH6wRCYoF1EzQHMVSUZ/+rFQkm+n3642JRDMpYpS+4YgVSsas3v2t4mXO/KeP3SARy5x2cgiu8mN5ukbrI9Ae08CsQ5eZ7RWGcZZcclHbAZEwL3vnFcGbRgv5oRAvUm3QHeF1aGsbovi4DlKmTievR+qIorzjJFsc9LxORxEn2vMVO67Ij2513UvBEFtRx3OIzLaQbiynQI5jNquIIQF7f/B49B1mZu04qPlymu9MlAZ882wQgy8d0XdSKCQX5Bf/ZrCqiYQ1BE9leqX4/dc3KXc96nYm7kgS49dZiEMf3qZ4zlgk7HbfaVkX9KWtNB4Mstcl0Ss/PSlORinIR9XM0pOfWBllAnDFIrVHEbn/HLQaqVQEwzyseQhbcEkFQz8/KgIHajueSQCYMSeB1x+1Z28jX2ek0K8+6wJ7rkrNZEFIASRxjOMyEKBbLslKVA2s80UtRVWYyUQsTuoi7yp8Xjkf5urNaUz0dQS4cAuh+otKxhCvjclltA6pg52IhEU+U7jcUjoGcynvQL46Tok0Px8AD7g/83d+n3wuzZ+556rIqp9J5z3vln1MxGJZS3Zb6mHJZlOuyrE/KC7sA4I47gFtuKQrH8sdTCgjSc7/vveRKcngg+Qzou/N5KsJWCPouz9cQ2BbudOJ6opjGg/JzBrJ7d13AkgRXVf1DFKXfSecGPKE6FoaZoE3UI9E+HUnw33GA6RiYW9XnFkYkdLNy7bs/SIVwDtUpz6seM+Hq8js8pJ/z8+K9jYaZ8MlxaJy8mAHL3Hg+HtPY47rVdlkWbHGe9UFxRHXQTuco+c8eHRUFI3VOTgCQpB8o1wGeZOPBcJgJEpKE6q5AVkcvUkGTKGudvcaWTWOq7xfL4m/fnYqcYyoDMU9VjfsJz6UvS+n16BlcOsBo9LEy97wwAsrL6nFUneOLedV8TmOwmDc5TrNARGxmyF9/EAAzXhT8Rel5L8XSirG+12//DsFz4o68sFsnouD7VPcvZjS29u3qhoHFgvroMKJNJIsFXWd5PJ3NSEh6qQ1rsVFDtFdRl/Llw1ORo87cMY8QBzbNYcOg6GR5KVJqqHfTKTBbMCwWxQ+qxoL8vJWx6gYilUvx0VHWJvs9qq926golBECCs7PU7cqm8UZsPFDR8+mYSyu7LjGeWVYptXnKwZT60NtvL9YT2QYmy8rm/Y4jf4bn55ngskyQCpBlczBPMabRjSh+34IgoD67/K4tKAtbgeKcUFzbYEjPw7KorokyYFaaqnpVX0fL5+/7uf5fcW08oTTSnlusZ5zTOCuu8+w0bcc5AdlwRIed5+aGnkfXvVxSquBej9KjinK4dOkKAD5AY9uZzXJCUhSFgbJbytdzVZ/c5A7YFSPu2iOP+DTgXvek3WaOA3zNVwPf/m0Mk7G8Zn/wQyTsEjvvPA/40R9m+LRPNQF0g8FgMDSTJBwvfNECv/lbG/y3/w688PsZPK84hjgOw3c8tvi7Bz+I4cdfxvHkp9NE+ewMeMazOF77appsP+iBNPECgD/8j8C73sXxQy9Cwd3LYDAYDAaDwWC43jzkIQ/Bn/3Zn+H9738/oiiCI7PHAfCe97zn8t8PfvCDtzrnH/3RH+G5z30ukiTBTTfdhJ/7uZ/biWDsHvewEIfJZSqN+SLb6S2oXTzOEQa0AKsKxAPALTcD732v/vVZFgVTRMBqNC4GsG2bRBRlSpvuL8kvROcFPTLB0J13Vt1MBCIIs1gUxV2C0RhgyyxAFQZAcxJPNcLZYbNO03msaaF8OADOc/ckdv0ylqYLcbLfn5/LxQ0isJgnjgEXuRRI6e/vde9sgXm1qtaVOJYLA0WwWwZDaUe2XU1LeXmtqfMMYxS8tWy1y0YUUn0sixjyASVZ09XRqYQhBTzErvNNUN2ZLyinJ9XI5gqAAgciwBqnTjuzeVYuk3EWvGYsFcPkAo0i2JoPPNm2XAggyAe0D3IpTJKY6rrjUn0f9EmIM5c86+WSvrtO03aKlCLCociyio5BeURQX7jmtA1kCm66ic4hM1c8uZY6cOXEXyrRarlfKAefhBPYJiDxVvnzeYeY1bqaXjJ/TZZFxxn01IHtS9Eass8IcYFApOC7PG+zwWQjunW2K0FAATjXzZz2RF3mkLdTgUz0EMcUdC2IxVbVzwDUX4VpyjOBcGiTsV6n7a1GfCALeNbR86ldn6Z9mWWlYpi0Po3GWd2zLUoRGoVFIUe5/yn3n45D7UuILTmvDxaKYLRwUhJ1O4nlbZdZFPA+Oc2dm1NQN9iUfl+D6K/auAQB1X5WRfmxWhYF1UU/JQhDeo51xxRlkherCWfAszN5u5EFyaOIUvXKhJdhmp768IA+N59TnT06omu+dkJ/7w+K5xPjsG1nfa7479WKUu9aVlEYuloX69FoVBVQXQrYUwF8vp8uiyxUFALzIBFhXhQRRyS28Xw6Zu1z5UUdr6g3m1LKTuHypHKgAWg8uyN18RGC53zqMzsVvIn539kp9fuVdLGSupt3zSsjBIF5jo9ofiPaeByr51Be6ioo+3scyYUCl/2JEB2m/W4cU/2wHRIxxVEm6h8M6PrzczzLylLKOm42JxRj+HpNbkwAsCw5/KjmWiJ9rqxvHw7p+qR1glf/3ZQK1E6FfOcX1D8tF5moqtzHWxbNfaYTSuu7UdQj0Q7G42JZOQ6VoUooLaNcZyxkAvk8fg/KAq17J8u/E4g64feonHXe+xYLYLWqnlhnQ0Q+jWyds5goA1FXhyOaY4rNP+X7Ey7PQVh9R5GxWpEIbDIuujjGERBA7TL4kQ9Tn5lvu5tNLrW4uM8k+4wqLa9w75SVeRRmKaPLlD/fT1NqCnfHpaRM8+Ow7xfbbZn1iuZmTe9F+Xra6xXfy2ybUmUyRj9C5J5w+eaMOsQ7yirXrlXPB6D7kt2bcMjNl99mQ2PedJp7f1jnnJZz5xkOivPS/oA+7/vFFIxN5I9ZeVctvQc3pcEVx2tyS+vCHg750cdsxvH7/x/H//6TYnPyfYYnfSfDF34B8Ms/z/C0p1hKYdd73sPxtGdkwi7fB172IwyP/EwTODcYDAaDHv/lvwG/+Vs0e/4v/xX4D/9R/7v/+b8UJywnp8DTn82RxMBPv5bh0V+V/e39HwC+87s4fvf3W24DNRgMBoPBYDAY9sgjHvEIAJSW8W/+5m+Un3vb2952+e9P+7RP63y+//W//hee+cxnIooiHBwc4M1vfjPud7/7dT6eDA4KSKgWQXWpCyIAtGiqEkwBaVAdWTAoTorvD2IBOUmAMF3AXy7pp+yU1Ujuvsr3yFAKHqXRw/Ln8guv+V3vi4We84BMVFaH38uu6+SMBGj5Bd/VigKRIrh87RoFB4XIReb2tFpVA2B+GtC8404q15NrFOA7PaEAdFkMoYMqQBuG2QK/41BgQATaxY53gShjEfRjoDIRu+v7/SxILIKUZfIL8lG6sO7kAst1gYLCdUfZzn5Z+jSBVl2UUL72xbJY34QDQJyk96HxPOZzuu68kCFfp3lO6GcxOp8sOHp0RJuzVIhD5p0nLCtN1RLXO+HEMQV/4jj3nHXbSSpYufOaXNgliDXLq24HPEOaniWq9puiTPPB6fPzeqGVcG6Jas7puLnnJYJcKxJHLJaZs+FyQT8Xs+Y+WYc43okJhJLBkO6t51cfS5KmPtPFsqgMy0KHsrDIttO0v0FVRDDMicI8t3rnsgCs6FfmM1xmStElTsgJkSf0kw9y84SO6XokDPB7FLzzfXKImU7lqaBkCLcToHrPeWFRf0DP/OKC+oy69FSCwYDqoJMr94RTPYyiYvmv16lQpkZo2nYVUDa+iHLJ9x/lrkSk/xRCNuEEU+eSI4Q0svF7NKK+pzz+OKlYpksK08WC6ojoP4Xgi7GiO2WSZHOofPq4/P0LR7TNhn7y/U157Mv3/XZJYLlZ099Pz6i8VMKufFs5P6c1YFnaqLKIbL6gfm0wqBd3lt3lZNeep99vP+8SuE61XuoKX8MA+IRPkNf5KMrqhTj+JqD/CNP+qa6NB6lbrir4b9k0pxapp2VcOrSmjq+X4lFOAsJr1+j5CYGy6EeS1Lm1LKJwXFwOGlFEwvTynLjfy+b6eeazkktprgteLOrbZfYl+Wdsu7ghI45pHl2oL2k/XOcg1x80j4n5Majfb9dXA9U6HMfUXvMimck0fa8ayo9xz3vWz2OGQzqGYBPQcUcjuctgnihqFsCW2aypLMpi2rqyTBLqXxZLam+33Qb8/XvSfudc/uUgrN53XVvlqev0tWvZexOgFhnfcQf1Y/lj9tJnLLuWy/Ok51L1aV02NDAre4arVTbPlKX4A4rvylFc378C6s0YQE70LZxLh9TH5V3ILJvK6vw8S586GjU7quWpOFpuueHAcWme1+8Xx8f5nJ6/7P0hirL7PT/P6orq84KDAxIjHtT0vzLKc1addMFhoCcCa4tx7urAZsPxnn8A/uIvgbf9Gcdf/CVVooc9FHjkZwIsN7J9yRczfMkX1w8pf/4XHC/4fn5pS9vrAT/2o8axy2AwGAzt+MLPB77z8X288WdWePRXMfzzL9f73h13cPzGW7P/Fjum7ryTBF6vfTXDs59p4ZM/meNlL+cUoNkAP/oyjr/6K45nP5Oh1zNjlsFgMBgMBoPh+vIlX/IleMMb3gAAeOtb34pP/uRPrnwmSRL85m/+JgBgMpngkY98ZKdz/fmf/zme/OQnIwgCjMdjvOlNb8JDH/rQztdexnOBWz8CMIUAxe/puW30+jS3r9ut3e+Tm0rZ7Uc4BgC0KN3zi6k56hZxo1IAdQ6161MZEQyJouo5bLsU2BCBN0mwR7jmFALyaXqfJtcA3YX8fGBVLLJHIYmtKmKI9Fp4GgAqiDsk178R6c/ShXaL0e/iuLiY7Peync4qwUivL//95X3YFKwVu7aFS0g+LYVl0S5vcc9+L/d8OC4DOSL4wiz61WRM19aUrqvwrNPztnWJATJ3ntFIHiyWpXLpwsEBcJ/7VFNdAjnXofRzzGoWFc7m5LAj2k2/n6ZdSYWTnNO/hQDALbWDa9eobrz//eRiIUM8z80mcwoTgeWzM3WwMQhSsVwu+MpSIUv+voS7m0yMGsfNu8yTWN+RUAZLU6Mtr2XCxHKwcj4vtm/banbvG/QyhxkZQrQ1GqXXn2uHsueu0w/adpaaNO9Skyfh1a6jn3MIk/UH5f6vrrwH6diwXNI95YP0yyWJCcupz1Tw1MlnuQIJchXjAbP0hDZBKHElKdVfxii4epq6Uy2XWR0uOwXJAqkidVzlXnK/i8Jc2mHQtYu6pysYiiNq90313rGAxTq7zzAkEYbKOQhIXSWjknhFNl726LhO2obLbWIwSEWlmmnZ6vrZB9wfeO/703SPMyDuF8swTxhlc5PFvF5clsSpOK0kfuAJ8MEPykXFUXp8lavGZAycRuqxaL0qPufFQt5XDNPxVYj1GKum0SwcZw4kqftfeb4SRVk7lq3E6rTHspNnXZ8kux/PpWvLj3+MUd0Z9Ov7Sxm6rkmDAdWV/H03zedUhBEQJVTv73EMfPjDxb/n78FKHav+4b307/U6S/HZdK+qlLuc07hfN98M0lRp5XP4brG/E/Nc1wPimja6XmXibpmYD6B2tEyvOf8e0JW865x4bsLBSHBwIJ9L5RHvH2VX4OUyd08XzQ49eVGa6CtWLetQ/pklCS5dlmMh8kr/FobysWW5qBdOhWHR+ZOBNnJEYebwmD9/fgwIQsBtuXnBcbK5oJjvn53pPfv1itqE66bzlD79W/bdfo9ELvk20dT226YClZEvay5SrZYc5Zar7JoZo/mcEHN2gSfFdzUO6ptV7qKuR32Sm7qlqe7b70mzm5Pbl0N9sMy9t9LH78CvwWJZ27t83rKLU+D3inVBzAMGA8qId3GRibs5z1LPux49I9H323Z9XyXm0nkuLqjO67zvX7umvq/BQE9ovw+Mc5cGr31dgh94UYJnPDvBN35rgn/yzzie8CSO172e421/llWav3038Nf/t92x/8Mfcjz7OZmwazQCfuLHjLDLYDAYDO1hjOEZTxvgp14zxrOewQpi4zpuuonhJ1/FcM+bgC/8AuBVP5HZ1d5+O/D0Z3J85CMcX/xFDG96I8NDcplrfu//Ixev973PuHgZDAaDwWAwGK4vn/RJn4RP//RPB0Dirr/4i7+ofOZnf/Zn8fd///cAgG/7tm+DW4oC/smf/Ake/vCH4+EPfzie97znSc/zjne8A0984hOxXC4xGAzwhje8AZ/wCZ+w03sJQgpSqoKjmzUFfVyPdiRPxmn6jxK5bGFKer1qUPnosLjLlPNiMIHzYmqzyxQettotSUfQII6RJKkbQWnX7WJBf3/4w2iX/3pDny0Hc5mVOWDk2WyyxeDBEJhM9K5JhUw8FMfV9V/HSV0PAICTKCKPakHaSh1AXIeCgBtF8F+4soj0FWWGA+A+NdlCLTtLIVQWAvb6WfmGEf09jtOAeLoQLxMF8STb2V+3e7pwrlwdVgX7dZnP5UEj0aZ00sL4PbWLzXJZDQYD1dREZ2fqIE3+2CJ42+tRG1ouqQ3mg3ZJkrnPrNfpeVhWD2Vub4LRMAtqBgEFStsExdc5UYkIXpZTn1oW0JOUFxNOb6xeaBgrUsuV4ZALZzyXUjOpqBNAquj1geN7VB1yZKzW+sLQPLIUsr5P99LrqwM4sjq8Wsn7PkF5maiuvMMQ+NCHKLC13tAztyzqY0RqRZEmrQnxvESa4UJam1z/MZH0X3nqhEyyc65L6XtU9xtFxTSH+WuuIy8KEI6W5fGyjO+TQKHJmaOM6KcFgwG14yiGcqAXLjdN4lrwrN7IhNvLZVHc3UR+7iLczUZD+rEsuh7RVlYrudAknx4svcQK5TLUSTsmKKemlXFwUEwjennedExs6kPPz7M2l58bibRbKjjPxgAZ4rxJQgLGwYD6ijoH1q7I7jGQlDNjDINhcRwH6Jrq3Kny5AUtQNUdcrkszj1VqFzA8sdLEhLMfOQjlP6vDiFWCXJzyDjWcwCVtafRmNptKHEyyjObycVjstSD4wn1DzIHpjzCTVXFJsjmLHXiHl3xapSmsnVyorx+L3PNmUzaCcjK5REENNf70IfSMb6mTfb7VD+Pj+nncm7bUVAuI4qo3Z+fp3NQSTnN5/VziiAspsbzPPrRdWxuHaFhmRukqi90HCoz2TXI6rAQogmstA6U0yPug/y4UB4jzi5ovJnn6lEQFuuASL3K040V+XmHbA5bl1Y9z3yubu+ODYDT9Y7H9I4q3KWOjzNHrUGf/sZLx9lsMpfYPKLcgeJ7Vb5fWq9SEVXQLmW4eKdeLLIxXJbiWPn9mraaT1sM0FjopcLGKKyOS8Oh3KENyJ7tvXPvwUlCc1vtdN2KRiXaS12fCsjn+dtinLs0+O//A7j1g/Wf+YSPB77oCxkecH+9Y8Yxx5t/nuPnfiH73b3uBfz4yxge+AAj7DIYDAZDd77wCzycnS3Bc29anHN8+CPAzfeRjzGf8PEMb/rXNFEaDhle/jKO73kux3oNfOS21MHrVcDH3JfhDa8DXvWTHL/9O/Td9/wD8CMv43j9T0FbUGYwGAwGg8FgMOyD7/u+78M3fuM3Yr1e43GPexye9KQn4ZGPfCTW6zV+7/d+D7/2a78GAHjAAx6Axz72sa2P//73vx/f8R3fgYtUNfKMZzwD4/EYf/u3f6v8zvHxMY7LW80buPPOBEku2KFymwoDYGPRouLQqy6UqnYI54kiCtznYRZqowNlsY5Y3HQ9EhKpXL1GY+D4kM7pe3SK+YwCMiKod3gI3Hpr/TVv1sBHbs8CQuXz6QjJNmtguaWDUxDQgvuHPlT8veMWhRdR1M0Bod+nAMRkrF6AZigeVwQ+GcuCEqtVMZCRJ04yd6hQIn4Ri/7DflbO4rUvH0jf1t0BKNbxOC6mBxHXeJVMxySqkwXYg4CEHWVXpeWyhQAl18ZWy6xd5Z0n/B4FjqbTaootkYazsGNegmWlbn/5Uyd0D+VAfB1NDiW2I3fN4AkQJuSM0Cu5fek6+uUJA7nb1iao9mX58+TPNRhQW9ERuC2XRfcRFW2EJXlkos1lmpKuqyuNCp0xQZAPinqp8GkwpDKcpf12PpA3mWbBQ5kLi8pRRRzCsoHDoyzFWOV6XKAHehauyxBK3LvKlJ9bXUqvcion4YgogrHTKfV9+XYoXECYRcH4pro8GlMfO5vR8Q+m1I+I85QDv/l+7/wCuM+9soC5KPvTM+CW+8jPl1+iq21ruXJpU49l/XKvX2zjYUDX7HnUl/7t3+kduyweKV//ZErHLo89PEndx1LHrSSRB5xtu+iCJuOOO6oi4cmY+n0R1K5DlOVqRXVvMKC2rb102lDFGaM2FwRZGsDy3/t9ucB6OqX71hVfF1DUoyQC4KXCZZ6WPfRTLrpucQ4iq6+LRbPASzVXGI+rabTiuLl/PzgArt1Z/X1ZLNrrNZen7dD8Nz+vsB0SJ6yWmeCrbr4TBlV3PJ6Q2+9gUBQGlemnzr+qsaDJ+Qqg8lCl/PR7dF35NjWdFOdFyxohep7hkFz1br8j+52qSQjRex2rFbURL3UMEo6kdU7Eo7F8bgxQ2zo8oufW79P7z/mF3DkpD0e9uDffBwtBYhipRUTC6atOKFhHqOGuG0U0bjGrKiwCS8VqLLtWzqmOOpz6oeEoc0h0nPbzPl1cl+pNkvY/jp0bE0LFpqmk5OaZcy1erYrz5HIfa9nd3WZl1y7KebkAlqB2c3CQ9SuMAYcHmbtzE2KzFFAVz+ZZLLrXn82G2lKMtL522eTQo7IW42O5fsRR1ueUhVLiuckEp4eH9HwWC+A2RWruKGrvNjmZFPvKIKg6wuZRCc+2wYi7NCir7iwLuP/9gH/0scAjPo3h0x8BHB/rB7PPzjhe/BKOP31b9ruHPgR4+UsZ7nEPExQ3GAwGw+75mZ/l+PW3AC95MfCZnyEfaw4Pst9/yiczvOxHgO95Lr/crSkEXve4B8Nzv4fhUz6J4+Wv4GAAXvBcfacwg8FgMBgMBoNhX3zcx30cXvnKV+I5z3kO5vM5XvGKV1Q+84AHPABvfOMbMZJZHDXwZ3/2Z7iWi1r/6I/+aON3nvrUp+JpT3ta63MBtEg6nWQpdPLBSME6XeA9OqTAVTndThOzWdVBoik9iioFwXpFi/aqdHieQ4vLrpM6zARZEIOnqbrGoyzAJwtunZ1TSqc2i8fiepIkE8oJ14U2KfrK5R/F5N7U69HfxOKwzFEn6iCEuvPOzKmpjnxAa71KRRIHWfB5uSR3ChVhAJzWBAniiO41imgHeRTtLpgh4+gIlRQY43GaxmtR/N1y1U1UYzu0A17VXnp9Sk1U95qrI/ipI44zcZjjyIOHIogxGkGd7iS9RlWwTLgrlXfkC6GLCMJ5Xhbw9XsUiLt0CMvBcz95mtLnrFfVz4hr9j29QFWZsmjyTCKC3GzoeVuMgk9Aut6/1nPsaHJ1qUOVUrHMZFIVkexa2NUV4YAlnJ08L+2TWLE9XpxTH64ScohyKIt0LDtzabjtI2pHmNWC+p+TU9o4T1/G5VigIxRUpd8rs17RZ5mFrNKA+vlySrE6IUWZKMrKwfcy1604qY5FrksBcVGn44jqogjwX8xSxzEHiO4pF1pNxlmwcbMhkaXAcTPXxzqxTK+f9fflayyf7+BALhoRqUu7whhw872BVa6PZJDPRXp9ute8iKfJJU3Xhci2qbotlu3ShYnA/PEx/buNyLKOoyNqP5zL5zKMqQWvYaTnPAVU5z7LFY0Rx8fZXA7IxoTJOBVhhSSQPjqiz4ahfgpGFbff3k0YYttFkZ/oL1QipTx3SoRdjAFxaa6nU55xROXluVm5xumcajjMrqfJUebwgP5f1MX5PHUbddRzRuEWFSnKz/P0nHdkZSb6fYtVncXOzqgNCoGDrrPUxYzqdb+ftdFt8obYaco7sRFGR0Dte9RP5udno3HWd7oOsGZ0nZxXRSJnkvEhifUEj0dH2Rw0L2g5OEjnj6kY6OKC6lKTWL9OfNKErRAxDQZ0jUmcibtF+eY3HOXH3ukUODlpfw2TaVWcWSZJN7XYKKYbPzioL/P+AOijeHzRXmT99WCYuZA1vTPrsFrRfLHsWuw4NGcQ7wssTYWo4yZbps65tpLWGtTWyvMdgBw483M/zukd5fxc/j6WH1/F+2m+zGybNn7o1s1NQOUv5hR1wrR8P8ohbwNijtuG2Yy+J5w9ZeL8PG3FYzoYcZcG3/kEhiC1Nb/pJuCeNwG9XrcA9tvfwfH9L+S4/fbsd5/zWcAP/gDDYGCC4gaDwWDYPb/9Oxw//4v07+c8j+OnXkNOXU182qeSM+Vf/CX99623Ak9/FsdrXw0cHTF82T9hePjDyd3y/vc3Y5jBYDAYDAaD4cbgUY96FH7rt34Lv/ALv4A//uM/xm233QbXdXG/+90P//Sf/lN8y7d8C/pN/vk3EPmgWr9HAamyIEUEqLruuu26T0ME+fPnDYNqisiDQ1ooPjmlhXdZYEgEDU7O6Hi9frqbvLQImw/SNjk5iQBgklAg++CQAhBiEbftbY/HxWsXYpn1mtYNhVuWCiH00A1uclDgJr8g77q0sC/qwHqdifNEqi8RqM8/F9WiviNJxSgjv7jfpZ45bpqyMn0mnlcNFDkO1e/T06wchendelN1pmsrZMwTR/UBdhG4n0wA22ZYrrYJKRYRgiQ3DRoKZ5s6Hvxgevc+Pyv+Pp9yRRV4s6wsvUyZkxMADDicUtvkSF1DYrq20UhSXzk9o12uAozGQDJrnx5JuOLFsfz+xDUGm2pgryn1p52mkQo0UjmqRFzDAWCPKeglRGgyF79Vy8CS36tPabNPgoDqlSodq8zpLv/ZvEbRzgUuPb8+cH8xS50Scmn0HBuwfQoM2jbV1fyYIZ45Y8VrKDt4yfoCDjom51n7iaJ6968m8oKgTZoeNU6dv8qHDcOqUFi4zV26KKb3d9tt8nH87Ew9Jjk2iUkWy/oxIInJYYylIrT1Wi1sahLKlAOrw6E86BmXxA8ibfDBtCimkwmaZOJpZerj9BxN6VpFgFukJJaRDzarqJuz2DaVcf5arTSlrah74hnbdpZGtK7PTBK1oGG5oPpzcEDHO7+QC9OtdA6ad5AVKa2ANEVvOqVer+g6JyWnpl0QBMDsQs9ZCshc0gCqP8IJVLiKAUWxY5nJhMpYJs4RQqVr14rlqytgjELa7FCYO4fUj8kYDqspnwWFed66OdVZnKjHbuEA14V8mlUZUZrS27b15w5hQJ/30/6dJ8Aid/x+PxXMhlkblolRBHmBty6zWXVMy/cBwyHVyfmMrqEsPJQ9M9E2hNhMxfk5tT8h0s2nreecBEmiP43jLHW9LUklC3QXdk2nNAeXjfnDIY1l+fsUz7nuXaFJUCtc1USfY9vt0rWrxDajdLMDs3KbQxhdv3iuwu12OqHfSdPj5jZHtKHtpqJ8mu0woH7adqgsRmP5eDSZpt/L9QUqB3CgWg8PDkmMF4apAL2EzAnacYrnUN0n58DNN2dzxfNzek4Xs3apqv1UCDceFd8Fe72svGTPpt+vbwf9fprOu+G5ir/rpno34q7rxGc/cvtX1TDk+Plf5PjFX8o6A8aA73gsw7d9C2C1SUZqMBgMBkMLPuVTgPvch3aUf85nk/OkDmdnKIiRAeD9HyCB10++mpy+HnB/eUri3/z3HJMp8KgvNOObwWAwGAwGg+HqueWWW/D85z8fz3/+81t975GPfCTe9a53Kf/+6Ec/Go9+9KO3vTxteFJ01JgvSPQko6uwqwvCOUTstl2U3JMK7ghM3y0FoHeQugCZ51IaqvmiWdyVdwKKQgoMFgL/dppeSdNFY9tgpe9nQZqEN6duAaqL0GFIQUchetqsqSzEovjJCQUky6mBVAvVKmFfWahm21mKqy7pEUXQ2HXT4JskyMA5ECkCi7Kg87boOKhxDqw3zdEbEciyNdLNiKMlMRBxKo+bbqJ38HLaGOHy8l//q0TYZWfBTRWiHsgcHUSbPTqgQJcNCuyK84u6Nxhk6aZE4IYn2zlolElEWpwOgeXy/TMrSz+zXKXONTmxoOdqCgTSXfmLRb1w0/NIBJd3onBSZzjRZzQ597W9736PglsyoWi/T9fUxlFKRl0fokr1piIvGMj3H7adpdiMwuY2uV4XhQFRSPWU81RI56T1NlWQiXONRvRMxH+XA+oqQYMIBJ+eFsta5pK1bepYmTFfuZ8UQjjB0WH2b6nYjquFhyKlZNP4FwRUt5uECCpUhoOAOgXn5XcZCQssRvUtn9pIFaCdL6h9yESUQigi0p7Wjel2zk1Rx2lLpw3XBYynE+q78mICkVYxCEkg7Tn0PK9do3sT1zVQiDkAeZ0UdYIn1D/2eyT2y4+zR8c0F+v3qf7LhAGHqQjAdq1LN70wLKUA5pl7ynisrkd+Ty2c5LydmFs4USbpOLVZ58RoDSJ8gZjrxFFRJDcYVt2t4lgtvpI5xWw2NI8rHCNSu0SWhQEsdU1sEoUDRUfDMADOUjG5qhxUz0fXhVIHmVimTgy02VAdD8qi6NKmA8/L0rvJ6CJuahK7XbvW4d1AktpchhAoJUlRTCL+vZKkAa97F2wSXKlQzSWOj0mokyQUcxLvISI1Yn7MPDnJHKd0+tMkoefJWPbT5O56cACA0XOuPGtG9Ve0Jd+jxyAE0oX3sfT5zC7ScV1SZqs0fWAb0Y5lUX/ZRtxVJgqB22+j/kMlhpK5m9U5U03H9F4t6tV8rvfOlRdzrddFMV6+b8k/C54A//f/UjvNb5zgSeZEB66eMwjR2OlpJnIWMEbXINyKT06r72MqJ0vBKnW/FqlHm9rMVa69lDHirivg3e/m+OGXcvz932e/m07IreszPt0EvQ0Gg8GwXz7mvgyvfy3wsz/H8fSnMti23thzeMjwxp8Gvv+F/NK9CwDe+z7gac/k+KlXA9Np9Vj/7284XvkajjgG/uqrOZ7yXQyeZ8Y7g8FgMBgMBoOhC+XgpO6CYx3loOcD7ge8++/0vy/EWqsVsAIJlsROXp6mxBDio7Y0OR+cn+vv7m0S2YRBJizQobxbeTKmgJdlUQCIcwr8RWE1ICRcmoBsMdo5qHc6aTKY4wkFSqbToiCnTQBLJaYoB/0cJ3WwkbkjWVkwHaDyUN1XFFGguhwklAXiWU4UqLs73rJIVOP3KBCkEtDpCjHmM8DK1TdV4FO0xbEQkcSU1lEmnBDfF8+p36cd6E2ObradBq9AwZckJtcdy6I6UCfmWa9JCCIL8EQJ4Fqp+4KiT2EsFUUF3QUsfo++KwsQn54213ft8/gk9BHP3/cp4CMcOIKQ/t2UQiuOu6fbsSyqNxzVMq8Tu+iiSo0LUFvp6tCRJwwzoVi5PxOiId30cjpCyiQh0U2dUEVFxbFGUsBtxVf9tL7K0qNxkGtGPiWkcJBrk3au5wNrUJ/leRQozteXuuC/juOmZdE4VQ5yWml7b5MKddfCLoCuqZ+mfSzPZ/qDolNLHAMf+nCzGE0MG9MptZPNhi5kNCCxnBAlLpd07rzoL08cAUmL6GkYpmKsgXoeoxoXXY/GC9FOBsNcirNNds9ryNtHWWhp25kQTpb+zMmJKtcrCrKX+9+Ta/K5lu8BYeruuFzSfHAwKj3lpOhoIsp3rUij7PeoDkyn8rJrckR1nWIZJLG6rHXnEiIdmviOuAdxffk+eDZXt4/BoDo2cw6pfZVOukkx1os+bzoF0K+ew+/lrr90jDCkOXp5fGMsdfeSnHdXwq62x+Kcxhmd+YHjqDcIqPDSOfw2aZDzc1UdbKtdWsLFInVKljiwtnkPbPvOWJfqmKXOTrffkR1bCCCTpDoOck6fCUL9seTiInPQA5rdSi8drkpCccelMl/k6kbd2McTOlewVo9fHO2EXb0+zUtX693Mzy4uimLnJmTvWIKEk1CLA0CHFIVA6hqXGy/y9UbmbiV9lul1yMZ4vwcM+5kAehNUHXuHA3rW4nc9vzhH5VzPiTf/HnQ9xVtNGHHXHjk/J7eut/67YiV4xKcBL3gew73uaQLdBoPBYLgajo8ZnvPd8nFnvebKdMPTKcMrfxx45as5/v1vZ79/73uBpzyd43WvBSbj4nd/9uf45bj31n8H/M3bOV78g8DN9zHjnsFgMBgMBoPBoMPhgYUwTMBRDXRtgtS9i3dfdCwHBxJeTGGj4uiQgsS33V5duBWBryhO3UDSBVTbogXWXbr8CBrTJpT+e9sF9fJi9MWMFqKn02z3/DJ1+SkHteOoGGx0nOYyUS3En51l7iN5Ro13oH+OPL5H4oO1RQGFcsB3NCSHORHcdtxi2jbPy1xGOJcv3MuCY3nHN11RRpKQCC+frlCG5+s7tuVpCkqtN4CV1jPddB2rlTz1k/jb5bnjzElks0kDXqkDnI6Dh6y+MStNL9pX31uQOn047nbORP0e1d3JRC6A2SbdXZ4wpH5SBHEcN3MbDAK6jq6pp2SIdGp5RHorWfrEXdxmXbvdJkhdPoftqEUS970FePuW7mBAUahcTum7Lb6XivxSIUZ/IG8rMuFXGFAfr2I+ywS++eMIAeZi0TzmsPS8Ih2fcLSRCeqA4pgv3IwsC7jPzXIhYpLQZzyvWA/r2nFdWrO2lOu6bM4yGFAZlt1GVksg1HR5AVJXFKcoxhUCGs+Vp5IC6H53lTKJc5oDiHonGKRBfZVQoiw0TyJcRm7DUlvXEeTFcb14pCzMTBJ5Gcj6mU1AfXgQZE56m5AX2tXJqTwdpOrafZ/ayplESNmExapltAuCQD9led28QJrSDVQ+YhNEGxFxWdirEnU3CWFk5+wPMkFEY7vroFJWlafvAa5f72bLebPjkWNf6m216KdiEd10mirKghHfpzY/m8nb0H3uDXzgg/oiNzGfkLnA7ZM6sSFPBVybdfbsXI/GRNXcN4raz3/Wa33BtKzOClEVkAr6NcpP1NNdvreuV3QdvbTv1RFyNiFr+zJB3mhIc6tLsXP52jZ0bTwBsIXQUdUP6r43CEdB2bvZZg2Ap65clvw+5wsAmm1ZpCOOk27jzo1AS02pQYfNhuOXfoXjG76J49ffkk1a+z3ge57F8KqfMMIug8FgMNwYvOXfcjz2CRwfuFU9ZXUchu95NsOzns4KL2LvfR/wxCdznJ4Vv/vDL2L4ki/O/vud7wIe9wSO//bf9xHOMRgMBoPBYDAY7n7M5xwX58DsXB4Y6PcysUIdg6H89+UA69lZMTjGmFxsc5IugJZFVeWA3XyRuQosl/S9XS2e2iUnDxXzxe4C1HWs11W3FbBqUKC8uB1FzUKhfBDHyQVO41guxFMJPlT1IM90SgFbmSuIZWfONLZdFR3MZrQYv1rRdV2c0/9vNvQzmzU/C1mQSxUU1WG+rA+cOVuuih8dyt3p1isKFC4XaZoRjXYKyMVd+SCMaHPLpVxwUkh7ojiH7PeDPqXKurhQPyORuq0pqGjbDfebrieohGhBoNev1cKoXc1nWT1dzIFrqdBhuSgGCnWfTx2rlTo43xRg3zWDYbGvKDMq9QWuR8FPVVqpzVoe6JvN6Pc7cVvLrTO1SeGrw7CkeFUJgmV1spwCTEZU6qN4QuOpSLGqwrLS2+apS8iGArWiz+r1UmewBoTA5rRGyCNEAbpCFVkg1te8HoGtENZyDhwcFn937Zo6+NtGyJAkAHjR7UsIrVW0TfenS3lO1NOcswnmC3JlvLiojmNxROKRtm5BXZE511l2lvpKEIYkrGtTTwQX53qpynq96n1vNrt1lRIsl5ngTSZU0yWJ5f3rekUCvDYOTgDN/7Z59k39QD7dJ6Aer7ukKZ8v6Kc8Jx2N1fMPcZ7T0/oNKKsVzY3qhCl+qf60ScuuwnGqfUwc1wuwT067OSFepbCrjKrebAK6l8k0J7ZRfHYwJCGfLoPB9kJ8BqoT8zmUk2NZXR4ohODbsFrJUwU2odPe+31qR/0OAvn1itrX6ames1VbVO8k5fmj7VD9UM3TNpusv/TcLP16F87OaIzdZsPK9caIu3bI6SnHz/0Cx9d/I8fr38gLuwE+/RHAz7+Z4av+BQPTnUkbDAaDwbBH/tf/5njNazk+8AESab3vfWrxFWMMX/Nohpe+hBUmtx/4APC4x3Pcfnv23cGA4YXfT4Iw8dn5HHj+93P85OsSRJEReRkMBoPBYDAYDHWE6ZxZNXM+PS0GQmxbHjjs6ux1dFQfEK2j58uDdOJedN2MVMQR8DH3Te9XcZGWdbWiCpFOTNAl6NVEpBHUUQW1xiPg8FD+N4FIu1hxAxvmXI9apnxsyy6ENoIk3p0bi4yLmZ7bhm7QWQT6bDsTRuQdOsSz1Wk/siDpeLxdIGATNLujOA7gS4JhzKIgmcUouKfql3hSDeSNRi0FRG2UbdDr5+5KS/nLRX1fUW4TYUCBPcdRO93JhA39PgWv+5opFH2f6mCZcn32csG63g6EY01ulAKZYKIu6C+Cs3nHlygid6Y6mEX3dXhI7VwVxNRNHafLfFYVuqmQujWt6111yuSFC65Lz34yprEIvNqmZMduEkgA1XlPGFK/PJ+TOHg4oLIOJG3C87oHdvPnbeofbAcAax+IrhPUbjZUBy2bxHKTMfWVuufYdh6mei79XlW84Xl6gq86Eboo43WN+1keK3V2KYvgVKLDJiaT7gKv5bI65om+UJXquomu12JZzc+iMkYo6mAncVJAfYnF6DomU5pjbgJo2yQ1zanqxr/Nms53000kznedHQjKZdcQqV27gBtPVOK4RZG3bOy1bSozmdAoiumZ1rmvOg6Jt9vM57r2U/m4lUifKDabyCi3z0ixgQaQ37/uPenOR2ybnsFoTBtvjg6rYtoyqxWNo+VzXKYI5/XvWElCZVWX8ruMEDE3jTsq0b7sXUVnU89mTc8yCNv1n+U57npVdQy9K2HEXVsSRRxv+zOOH3lZgq/5eo6f+Vl+uRMIAB74AODlL2V45Y8zk47KYDAYDDcUSZK9dD/ogcAttzR/5x9/DsNPv5YVdnvecSfw7Y/j+MAHshkYYwxf9f9jeP1PMdx8c/bZX/t14KnP4LjtdiPwMhgMBoPBYDAYdJEFH/IBl8lELrzQFThdu1YMmCRJfeChLuDa76sXW6dTCmpNp2ohgU5wkqf/o1ppu+qgSXkxfR/irrYIpxbbJkcYVUoqQRTJd8jPFyRkCoMsmFeX7nAbRB20nTTd5ST7W1NQdjKp/3uZxZZuDTrpLLsQx/KgqSrgwXnVqaks8rNFAHPLSECTw0XC5Wm3eEL91cWMHNPyaf7qhBEsFUTUBQx1g9w9hZuBjivEroU21xMOtQuEqt86OKiOQRcXlJ5XN1gJKMSnnH5sm/qsvOtSub7ZttxZsM05PZUTByuKRurOU65zQUD9wfl5s0PhcEjig/NzKtObbyYxtXBFFMxmlJZQ5nJydJQ9Ey91XtNRKeqKs8qB7jbiRsetusOFqSiYWfS4VWk+yzSN4weHaZotCZsNuYWenVO/o7qHLnOF6bQ4T2nqHuKIRICbTTdXqzoYo8C5SD1aFi+4rlwUEMckGCg/KxXl8VcI+MttQfw+L3YIAj0X1S6OMyqSJHPEy7frrs6AdcKKfN1SjTN5fL/anzKrnbg9jqhvcJz2Qi/PVff1DHrtvW4OKNKs1jGfU390kc5NVX2T36tuTNh2fj+bkXvStWv005SCbjqVi52EUO4+9wGGY+qL9j1VqBOi1Tl2ClROVFFI463o+y1JHYgi2lgkE1faFt2/yn1PCHxFfdUVoXYZi2yHRMQHh3ROrflb6bjiHmX1PH//lk3lviuXaJGuMY7pecxnqVA57p4qMUmonZ2c7C5lt0CURRjW9wk674z9PrCYAXfe2e78dRuOmEXtV4xHu3xvG43bCRXHmuL6NhhxV0uiiFJX/cF/4PihlyT4iq/ieNb3cPze7xd3ANzrXsDznsPw5p9h+OzPMm5dBoPBYLjx+Mefw/C6n2T4xE8AfugHGRxHb6z62Icz/NybGO55z+x3sznwmMcDf/vu4iz/4Q9j+Nk3Mnzh52e/+39/Azz28Rx//hd3oxVSg8FgMBgMBoNhjwgBQp1oqG2ahzrKaXiYVUplUhNEkKXwAbJrD9L0U6pFVp1du0FA16MrolItLO9ShDXKLdzu2jUsX/466Tkch8RO/UEWFGhyOzg/V+9q5wntZBeL6NukS9RhPKoGScejamB8MqFgj2XRO2kbLKu9IKxMV2ehLimVVEGzJAGchnosAtNLhcBP7Oj3c2UuBHZtVrTDoD7QEoXVlJIi8CYrS84p+LptexqN6V72JUqso9/XS+1zcLD3S7mkPFZ07Qdtq11KKxGozT8Hzun3HJnrUt33Vc/QcfRceWQhmvW66vayXFbLZTAAPvbhwP3vVzzObEb9pw7hhsa+KKJzfODWrG8tB7IdR+FmxOj8YSrIXa92l/ZYht+i7QwGwFoy1qzXVEYiBWLb1UCG1KFqQi5VAN1zU/2L07JWzY8aXbckfXX+PgBo3cw6TVPdJiWZDk19fxjK+/0oBJZrAOn42SQQKo8/Yq5weFjsu4UQVyaOPD7uPuYyhu52simTSbMwqPwoHZf6bx2XTkBPfLLZpO5VdjYX4AnVtTbjQBDQXKyN2En0kSpRJGPFuawK2fzd9ageRZF6vtl2DN6s6Z1iNKTr0rm2OoQAqk1Vcpyq8HA4pB8hguJJuxR+ug64k3Gxffq+WkCYH8MWErFc3RxIZ+x0XXVd4wCCmnlaWHJY6vocpwd6n2MWtaf5rDh/9DxFf5ek7ofltNU1z3Q4BA4PgAc/WO+adAgDYFbqr+OYBNFdnbjz6DhAC2ybfnQEwElSL1zWEdgFoXxziIomJzOA2mWT6L7r/Hc+ayfqW+5IAJjnOrzS3HX44/8S4Ld/N0EQcJyeAh/5CHD7HfUN6ZM+Efj6r2X43H8M7SC5wWAwGAzXi4c+lOF1PwmpCPnigmMykY9l9743w8+/CXj6szje/Xf0u80GeMKTgFf+eIJP+9RspjwaMfzQi4C3/jvgta/jlwtZ9zjeyy0ZDAaDwWAwGAx3W+KYFqDLAonyf+8aztNF0ovmRXnZ4v9xOve/dq05BYrO4vOdd7bbMavcrctSN5EbKD2KjMJOcYsWtWczeUzZdbMAape0OSryDj27KC/LUgeK8s4uR8cUiFssqwHDi4v0ejrsG4qjdulHZHguHadtqkrbAqKWZTga0zMotw/bUYu2AGonSaIW5C0WxTRMtkMByzhN0TIcNru+7YLhIHNaaUNTYJsxKjeeUD2xHf0gmeuRGGOxaK5jvb5caOJ5JHS4uKivJ7M99+F11DmjqRiN2qU9DcPsWcn6Y5V4sczFhTww3+9TWc9n9QFCEdxmLHumKvc2v5e2ndRNz7IzQVVXytcm0mLK7mmzqQbjmUUuHPtyDpQRR1S2OufUTnFUqnOeV1+utktjhq7zUt340gbPBVZMb4wZjZrHlLq+x3EBm8nrr+sVxz/G6POiftahunTRXw2HFOBeldJAimVikTrQdrI5RRxngicGSvsFkJAiisgtJb/M7HvNGjhdAZUOLJ0nCUco1wNuvg8u1691mExoPiUL4NsOcDCtXrNusD+KAS8pzqXCCFAZfw0GwHpT6ie7zF05PR/HBmTD0TZzIlE/y+14MMjmj103Bthp2mCd749q5ixxetNt+s+z8+pcujBnSseFzbookK8lbQz5NiVD9m7XZb4rnNRU9dN1i9dRnquMxvV123Ob5zBnZyRebCOmyY/Tlt3OeU+IxvNwZK5htpO6SgbU5/p+Vm/E9+pErxx0bW3SFQN0/3V9tk4q99Ew7f+5fMwdDSlFtPZc1y0+88NDuvdNUN9W8uOsbRXbet7JrTwWHx3Rs833n+V2kC8H2fzAtjM38Hzd6/Wz73YRxPk9Oteu1wV27ZoGGHGXkjvuTPC0Z860JoH3vQX4/M8DHvVFDB/7cCPoMhgMBsNdC5mw6z/8R45Xvprjhd8PfNYj5WPbeMzwxp8GnvsCjj99G/0ujoFnPBv4/ucn+LJ/ks2CGWP42kcDH/ePgBe+mONpT2a43/3MmGkwGAwGg8FgMLQlkERkdinikcKz1BCrFeBpptQA2qeM0SGOKbjSZpevDMuiNGDbLLo2BWfKHB/T4nfXNB5JTIEaryd3NBJls+1CMisFtB2nYZG/JiVLnn6fXBC0nx8HwPaTanMX6X3KgbZykER6Xklwrt+vrxOyAI5lA6N+vbgrf0wdZ4k4AsRj3MTABuQe1O+RwE4IAnq93QYr4rRetxV3yXCc1E2GZ0FXwXBAQSGVw2AekYq0KZjqesCgVxV3DQaZOKepTuzClaErIrVNG1cVz28nBNBta0Gol9qszHpDzgiqvng0yvowx9ELkDNQfQGovsxn9JzEElpTn9gG1eWU+wSetBeGbttWRVrFLlhpKsZtA6VRSM/WcZpFQq5LbbKuXwQogCvmJ7JjWjaw2uiLKXTaQ92xxiO6Hrag9iITu7kuBboXiyyAfXamFrvriNeFUKXfLz7n6QGwyo3TuvOcKKqW53JJPyIl977FiQyUskqIbTZr4J3vaneMKM4E5EB2zaNxO+ensjAPoPY4KD2zOFKnLJWlv9VtU/nzh2lKWOHKIxt3fMXcsiur3LG69gPrDTDSqDe+V5+esEv+kKZ6f3raQtTV8thlZjUpZusIA7mYT1Dul8r9vUrAdHRI89ny91UirNUy7Zs10zkzRvMo4dynMz8c1ojpo1I52Db1tYkivd+q5jqXC3KiPjlpviYgm19ZVvN8r6l85gvqhzzFnK3thozxBDgRYl1Ggkadtuq62TNZLIubt/Lvg+V2qyoz38+Ol78H4bYm5hNxlPXNvV72/mTZ1Afk521Nc0/fL5b3Zl3s38vvwjcSRtylYDhgmEwYzs6KT86ygJtuAh72EODjPo7hH3828MAHygPjBoPBYDDcFXn3uzle9nKOzQZ4zvM4fuSHgM/7XPk457oMP/FjwI/9OMdv/y79jnPgh34EuPWDCb7jscUZ/cf9I4Zf+jnA96vHm885RiMznhoMBoPBYDAYDHXrTPsKijWJUsTfgqAq7rLtbPEzv5Aqdv8KmLV1Zp0CbVKCAdUgW1PAo4nJmI55cq0Y2K0ryy7uRDLCINs1zUDOJlHqjrML0U15MdtxKMgShnIR0mCQpYhiTC2eWa3o2DrCLtvBZYVpEwQbDEjcEwTqoJSuGK1yTbmAqGzB3/Uo2OB5ahGf7LxdxH5JnNUlHaeaur6jNuCe3qeXiq94xzrW61MdlZ2nTqDQFtelAM8mqAapk6SdU1Vdmfp+fZkul/SZ1R527G+DzGVMJSYB5IG4fQmKuwb/G90ucuOTrtCs16Pnm0+5uFqmghkO9HzAEnXWApAACdo5eTTVxfK1NrXzwZD6pyRN9SicHvfhGqGDTln3+/IxUaTWEm0sjEjA1/Ss/TTFbL8nd93Jsy4FcvNjdxIDcNWufF1Qpe/0fRoDhHh/NKD7Fe0hL86RihAk1ydzR6mjLDjYJtWnuO7yXGi5lPeZTeJmgJ6TrLn0ejT3yIsBkmR7R9v8OJ0P/rd16hn0gXNZne248eHoEDhRPJu8EEEIJfLtxXbovuKYXFFPNOvGNuzCAWezBoapYLsOx70+fd0uxHD9Qb2YSLAPoYnq+ps2r5zP5H9XOaxFCRAu9cfgJKE5t3i3WGu8O+UFmQJmkXh2Pi/WxyaRVdN7Spv3cdsmkflm0+z+qfM+MJ+Ra5Uude6YcZSlOeRcX3yXZ7Omvngb10yVq5tl0d+Ey+Rslt1LFGXjRy9dmwiDdP7gAElDWff71fvNNzFZe3Pcdiku94URdykYDBh+998f4D//l3MwxjEeAfe+N3DPm0y6RYPBYDDczWGUN/wjtwEPfADw6Y9o+DhjeO5zGO517wQ/86bs92/+eWCxSPCU72Kw7WzslAm7/uqvOZ77fI5nPwv4J19ixlmDwWAwGAwGw0c3R4fscuGyvJM/ioqpD3aFbZdS8kgcH/p9Cj6WF27H4+w6LStLCTGf03/3ertx78jTJcChk+6iC+VLiWJ1IFIVlNNBCIvKwVqO/S409/rZTmgweeBBCOUYqzoLTKe0EC++Jwu+yeob5+3FgCINn22TK5sK3+smqBLBIFUAQ7jFbCsI0AmQJEnmuqLTHOrajHhuss+s1xRcc5zt2rDv1ZcJ57tJkyoCgNs6+zXBOdUH24ayos4X++l3LCsThLRF9Qza9Knn5xRoa5OaUYfRUP23bQJqQvghRKo6bNZyBwxRP4V7hu9RPTjLiT4ODtQuPLJjlen3qd3ln4nrqYPRB4ckxtmsSXwtgvVJQgKnG9l9wvPouZTTepVdypqcuBjo3u1cv98kQmQgF8y6YHsbRzsVwyH1S6rrKYvbogQ4nG43X9B1lBFsk260zOlpdU4JqEXvbio8UNVvkRpSRpyQEG7XdHWrK6MaxxlIqAW0e86nZ+q/5ctIJpjM1z+VsGuc9sG7dO8SHB3SGC8THA4H9YK8hGukYUY7cdc+3qWATOSiOrZsftcmXeFV0dR/thV5M5AAug2LZfU8bZ6b61HayCSG9gvFLjbgyI45GpHgVYzHOk6/deim8x4NSfA8X8jb9WKZuq1xgLWoh/n+hnNgMe+W5lvgKsTU4lnn51R54aH4fZu23OvRuNy2/VvWjSHsAoy4q5aDAwtf9AXshp34GgwGg8GwDx76EIY3vRF4+Ss4nvQEhn5fb/b7mG+1cMvNCX7oh7PJ3K+/BfjIbRw/8H1Aryc/zp3XOH7gBznmC+DFP8zx9ndwPPW7mBFTGwwGg8FgMBg+arl2kiCRpLZpy2is7zKQD4io0rWsVrRAXBYrlIPYs0UxaL3LhXLXa/7MVRHFQLKppr3qEsBoYjIhwZzsuex7FzFT/DtPKBH9CVRuJXl4Ulyst216r5zN2qWsE+u4s1l9ejdV2hhdDg7qXVHy6QvDSD8Ixhg9T9uiYHddMDMflHBdShHTlboAU5JQerKmbKy2AziWWlTVlPhivU6DXVs2Hp6kbVNSb9qkEhSohHZCDBEEaiHFvgSlSdJN2OW6adq0tJ04DjAcARbL7kc3cBrF5BTVJLppQwLAF459pQFwF4lTokgeELVtes75dqCT2kjUi6jsenRW/z3HoX5NFXeSPVsnTaUmFeelx4njqvtLnDpbuJ48vasOvT5ds+58os7xpfxodcYHHTiy57FZ6znm8fSzLB0Pyv3gwRS47bbtr00I83THgWWDKNRx6VhsTymLdZA9Y9ejunZ+3k5M2FQvhfBF5jApK6ddiIR3lSo3iqrXw1gqCD1rf7x9x6qZVRT65VOlbcv5jMYa2bvJomHM0SmrNunoGGg8bJtaXYdNQI5tozE9f5lTZpm2jnBXje1k89Ku9Pry8qhD9mzaCHLiGFg0XHO5DsRROncPd5sye7Eovivm23Jd+uT8O55qfiZcrWTOZU1EIbCLJhCGmbNpF2wH8HixfqiE6RbLUsh3Od96TX2yr+EaeaOy5WuswWAwGAyGuyPTKcMPv8jCfe9bXT17259xRJF85vSlX2zhJ18NfMonZ7/7r/8NePqzOO68Jv/OZkM7uQVveWv95w0Gg8FgMBgMho92dBe2u+68PzgoplMs0+QAtEuHrjJhAHzMx9S7aejsgLcsCjRsw3JJaaLK91t2jNqFIG2hSGfU6+VctfaA75GziiAf5Ms7pOwiwJx/+xQBlTjpHlzZZ3qeszO9dFfrdbvAIedUx22bAo+61Am7REqTbephGDQH5FyXglB11zFqSCOzK7eUcpt03O73Pxxtfz27oE19UCGCb4Ioomcbx1k7bxM47dcIKLtQN+7sCtn4Fcfd6p4Qtlms2B+qsFPnsOmUxjBm0Y9OStLVitqg67Uvp9Vqu6D8etVOfJDv8xjLUj4B2GlE0i/VvzAkYdF80W7cGCjKfxf9kXhedWLjtueOwszp8PiY5myT8fZzGl0cV972hQsbkM3DbIdEoLtANw1aPi1kfj7Y1R1p0iL9WZmVZI7YJp3armk6dxjub3NGHNHxo1Q8YzvUP1hX6FrletRmjo6prtY5vOpSnvfw1FV1PlO346sY63YJA9Wdba7bsfWdM7ehMNfTCC1V5ugsTdmrKSgfDvVMwcrvJKI/82t2Tvi9LN0goJ6fLRaKlJTp/0RR/Xm6IJtTbCPCj8KqKHuoGDsu0ydv8e65CdoL3q+XmFrGXawLMRgMBoPBcD3507dxPOt7OJ72TI5bb5XPkD/5kyy85pUM3/ot2e/e/g7g2x/H8Zd/VZ0F3XIzwxtex/DFj8p+99f/F3j8Ezn+5u1G4GUwGAwGg8FgMOgEjWV0DUw2OQaxjiuKh4fA8T3o//MbPPLoBECXy3qBmU5QN0m2S5t3eZy46lQUl66tq3tPPhAQKxbm1+vtnU+YVQ2SCzYBHV/UpU3pmnaJCHSIdBmHh7s/h6CNw4OMfS/wtxECNImWDqYU/K9LeadDU2B9vaoGnvIitdMTuZvWNqj6xvK1ejUp7Zq4UTzFrV1dSKl/Wi4pgF8O2k4mzcepS6OlA7OofrguCUlFoF12q7t0QXPdesFJXQC63A+LYKROX9VP3TlOT0lYcHRIPzKxskooORlTm+71qcz8nl4qpK7jXRtBTK9XrTecF9N3dhV/l9u6KujbhTZ9A7No/qJbLmFAIo/FEpgo5j3bslpRWyw/Y7+n7vd9X19kVB5jYoWjjejfx+MsyB9H+0nxp0PZeaer8ODoqFmYDOiN271+NiZu26V3eTfwPPV8D6BnuI0QfDQubgiQsV5lwnfOdz8vqCMMSJgvfpo2ATS970ymwHSiLjPVuGBZ9WL4G40oovLqOvf1feozGfTEzF2xbRojj49pLOp0vel4qiMmdV0a37aJXm026nq4WXdznBVw0PfPz7c7ji5d64fjkDit7Tyv6RntS0Q5Hrerx/sY+424y2AwGAwGgxZnZxw/9CM0Xf2//w/4hV9ST10ti+GJj7fw3O9hlwse5+fA054J/NpvJOClSEy/z/CD/4rh6U9hl+kx7rwTeOozOH73943Ay2AwGAwGg8Hw0clgQLvLrxpZ2qo8dSIDVZBUpNM5PaUflSBJldItz7Vr7YRrqiBsV9FcE7tKkbgoCZAcR38xud/XFwjxpDn4ukkDYlfxduY49MyioBhY2+WOf9tRCwxvBKJSWtY6p7o6LDsrwzapR3yfgrR+yyBv/lV/OExFZanzFYfadaCrs4uOoGYypfvo6tqyjQPccKjXDnWcS0TfuK3LiUwYG0j6rCYXNuEy15a8AxlPqC8PQ7q/09OWB0sds5rEBDLqrn25rLog9XpUT8tj3FLhmCFjvabgp0iree1a6vAiEZuqBMw8IUfD9YrKbLPunm5RhzZCj/W6W1qoJhiA6Zj6EvFcFovdiYba1GOe0PyljSAsCOgcu3AJytPk5rdZq4XMcUJ1TGdsKZcPRzae5OtHkKZn9rxi/d02leDpKY1JR0fdj+H3um9MODuHcvKTF1bq3OZ6RWVn2frzKZlA4fiYRJ51Qq0ydo14FqBxklndBBFiXFrMt3MJlOGk87XynK3t82w7nwGaxahJ2h+3FV/7fnN/MBymzmY3gIqjjeuTrChGOQfUrnN5nTl7HGfCvTZj0biD86HtAOMbxNlVB10RJWOp6N4rPrcy0jm1Rqcmq88q4a3jyDcaqMS25cPsayPObNbunWofqR9vgG7BYDAYDAbDXYHJBPjmb2RwXXqJfOqTm99cvvIrGF70wmySxjnwkz8F/MhLOTab4oyPMYav/zqGV72C4eCAfheGwI++jOOVr06UqSANBoPBYDAYDIa7K8slcHJtf+5FbRBz+s26feDm+JgW5XUWQ/eR0lEVhLVYd8HH9WA+ox3eTTuAR6NMTLcrVEH0rkKTuu/N5xSUuZgVF+rbpItrIo62dzzbJ+fnxWDhYCAPwrtuvTDBdSi4v9m0C3JsNsBipn5OOu2mjftYv9c+bZkuF+ckDgiDNE2XpsCi16N+S0eYoHIRcl36W5NARjfoZtnbpb0B5LE3ab9bc9/DYfcAfpMwN0laiEE4teVer50ISQjK6rBYJpK1bRIuLRfb9auy767X8vKXjfvjMXB+cbV9VxjeAOIClopOGPaiMFY5n4mUdDp9ho5goSzY1kXVNwqBX9s+wbJIhD6fq4vT94v3rRLS5MefOJK7w3QRX5ZpIxCzneI8ybapLXcV3p+dFe/LdmheC5RSGGqKDZfLduOxSF+YZ7NunzKRMSCMKD2eDOHU2kU0KcawbYV8Mgb9NI1tqQzK/aZl1wvFdTaPtGU+o/lq22exWDS/Dy0WqbPZDZAKru7+dMbeMKDxL24xvpfnmZtgu3c22Xdtm36EYLoN/X41jeBVMJmQ0PXgcDfHKwv3plNKR27b9fdX9yzq3vEOpuTMnEc1H9ts5O/wPKFyKAv+/H7mBnsjsUvnWcH1npYZDAaDwWC4i2BZDP/y6xne9AZy2ZpMim9VUcQrjlwA8Hn/mOGff3nxd7//B8B3PZXjQx+ufv5TP4XhZ97A8LCHZb97678DXvCvjLjLYDAYDAaDwWDYRZCsC/lF3DbBm7Y7oXXZSQCJXX1alOPj7RyoeNK8238+JyHdNiIE3WCx57ZLTTEaURnoOpj0ert17BJsK5BR0cZFI4+sDPPBidW6+jx1hD6bTZbWpa0YR7izDAb0zPICA53AyWJJwcmFhigvinbbt/X7FPgpH7M/0E9FJlKeNomAXFd+7b1+1m/uKrCTxDuouypHqNLvVQ6OlrVb4WiZLukzV6vmMm6bFme5pDrguHLxT9e2vg2LZbV8mtxrtu0/46i7uKCrU1IZUTeDcDsnPfUJqr9iuXquI7DXER/XXbufikl7/aqQV/RBrlsNqM9nJEyQoRKl5X+vujfXK4qrdYX3YVj97F6eWQ1xRM/O9+g+4hg4O203b1QJGkdjEhupyryM0m2oxbWEQXbt4pnMFzSuq4RYvT6Ng/l+KgpJ7LxYqMfwfc2NtmGVCouaxh3X2b1rmA5XmVbyRmI8phSIZXGQbCPCxYzmU2enwMmJ3vHLY9161T299mgsr/NxTD/LDsLbeQv3pi6ucSqEmHAXqbqZVRTuMYvErGenaXl3nOvVtYmTU7lLq8wdbrWi/spxSLjZzzl3XlwAs4usK3UcoOfT3H+f/diN0kcacZfBYDAYDIZWPOhBDJ/6KdWZzC/+MvC0Z3K8733FN1TbZnje91p4yncBX/B52e//9t3AYx/P8Yd/VH2jvfe9GH76Jxm+7Euz333lP79BZk8Gg8FgMBgMBsOemU4ZhmN52oF9BS6aBDp1QRXPo0BYORg2mQDDXGB5X2kQdSkLMMKg3qHA8+tTUqio280cRXpBYNuR74bnoIX4ctC8axBdtUgtC4LKPrsJMvHPUe5HhRCe6bgcuC4JitqIxy6/6wGjGgeHfTnRWIwCEMfH7a67KUA0kBwribP+oI1rkYy67wthi+9ndUAnWL9e0eeC9Fn3++q2cX4uTw3YldWK6rCsv9y1MElVl9YrOtdFQ5rbtuwq7WuZcptUidpESsEbCZ00vV2ClJxTAFcmbLEYpR09OKC+ppdLhSvq+a7F2LLuuk50w1i3/nNX6AiC6px28mPOJmgnEmoStU2m6tROwH5cRFU4LgBO/aDoN/IIQUMYysUNKmHjgUJY5GmIc+dbCMRVTmhKwZTGPEuVFs6y5M96uaA601VYKxM0uh6Vy2xG4gcdVHMzxrqJBMqONyo4p35aNsflkJe5613/ebqMMEifR4MgzvXqx8fr7kB4F6HXqwq0ZHV1NqP5zbw0x9mn+LsLvT6Jq3TmCfsi4fReUDfmtGGxyObWTTBLvdGqPM6pXGh12Notq6Y/TBJqv71e8Txxrk+IolSYdiaf++9iUxgDnf9G6EtuwK7aYDAYDAbDXY33f4DjF36JIwyBxzye46dfC3zsw4uzsm/8Bgv4BuB3fo/jFa/kCEKajL7ohzj+9E85nvUMhsEg+47vM3z/C4CHPYxeSD/vc424y2AwGAwGg8Hw0cFikaWauir6A1r4Fovytl0NIvq+XJQzHGa7cC2rGHwSC6ycNwfXZefMw5h6cZZZzcHYtsK4YNO829r1qsFDy6aA/7Vr1c/rptOKI0BVFLK0R10D0TqL3YMBCRmimAQ4+R3ZPCGx2iagz1ymzsoxmRZTCMmeg+z5RVu4FEUBMB4CUOzG9/3dpnkUCNFLHLVP0wNQqhXGKGjWJv3Mts5Qdd9fp65Ik8l2wRHXqxcFMVBgeVdpaMvOU0KEuusUR5uafmK+2E9aXcehsuqSPksK6x74VI0LXRgM9+eK0DUlnorVCrAtcsYJgiylk+OQoGq52q0Y2/fVAkjHlYsaOCeXlKaxNc8+2kjt+Ri5G4kUg4W/2Vn7KQsImmjq322LnH4WuXlFpZz2vAwpzheFwD70ELI5iDjvPjk7k/9eVa902vxsJndatayrE5t2GWcTxYMNQxpTgeK8UNWWBScaojLHaXaTkjnnuC4538zm+0kjVkd/UN/G44jmhHU0jbVX1a/5PUp9qRpzxLvdVTva6bJZS3R0LVLiijFwX+1S530vj9hk5Lj64+DxMXB2vrv5WxiSEG6X88G55pxmOKD5A+fNc0aZ65qKclnquhmq8Fx1u0mSYv8ge/dWXZcM1yPXuU3Qbm7Bkc3rHOf6ChlvAH2ZwWAwGAyGuzp/8/ZsMeRjHw487KHqz37FP2N4w+sY7n+/7He//wfA457A8c53Ft8UGGP4hq9jeMy3Vd/0P/hBjigyqRoNBoPBYDAYDHc/oohvlQoJoKCRLB1M2UVAuCGcXMsWKV3FArgI4JddfvJBIs4poCh+ZqnLgUyQVObgoP7v/dQZhfOqyGRfLhtNi+eqxWVZ4Gwbrjp9ZJkwTYclCxgGAf0+ilI3tJLQ4+K8Wfyhen7nF/opXArHQ30QdBcCEmW6JWT321agmU8vl98Zvm3QZFviuFmo4jj1qeouGoSNqxVapapqS69HbiuyQE6dy49OVVG5c+1D2AVQW+si7JIF7sSYIJ5v23SDKkedrrQJLu4K2+nmcCVrllFEY17ds/e89m6LnKvT2dalZxLjZZ1DVh5V2tI2LmRt3H/mcxK3CGFXXuClm763Lf0+pbpdLItzia4pv5pQtamu5xPjl9VRpLULUYnMCcbv6ztL5VGlgC0jG7ejaEdpuvdEqOgHRL0vC/53UeejqLtYl7H9CxYm46rzzapBjHkxa663OmIi8f5i27QxZR9i4s2a5myjsXxusV5fn/SRusiak2Prp/i17RvL3XO1qk/d7ErSy1/Mdp9u89KBriU6LlGjcfMYrVPV1y2eW3nut215uY7+3KHL2Jlv62FAfe82qS2vd3pGI+4yGAwGg8GwNV/+ZQw/9yaGR3wa8JzvZrBKs6Ok5Mn90IcyPP+5xUW7Wz8IPPEpHL/0KxxxXP9mfuedHE9+Gsd3fy/H+fkN/BZvMBgMBoPBYDBswSZQp1JoYrUGLi6qv/c9CvKqAuiuR8KwOmeHXl0wf4vFTpXLhGC5pKDIve9dn/5BRxygm8aui2jMd3fvELCvVGxNOA4tYJcdVfLsKyBupU4B+wjc7uKQOi5sbYPoqxUFlVyvKIjbxkWjSTSpS5PzkedTALAN5UCOrC5tkyYmz7ImaCVz+WEWBcxG470b+GixixR/sqB9uY9rKxrbZdpJWfrRqyCOugWjl2mbcF29lES9PqVwHI+zQKbt6NXxIKAfzyOxkO+TwPTwsLk/S5LsWuuoE521ESNsI2rMiy+b+k+VEK2J1SpNG7uFAEE3sOv7qYhkh5HYS6cxq1uAeVthievK26qTS88trqsuJVhbtnWTqksZvQvKz8Jx2vcr+xbFHB3VCyiCYDebJereIZZrEiZ61ykN5PExzYt6vfZiZhkygfN6RX3ZPhxirweckwBql/1Y52tpqJ/la1wu6Uf1XhaG1XoYBvri2X2nPtZ5n5zP1P16FNO8rtdrfn5t3llWW4iEGau2vSAAZpJ1CxkFIZnmC51VGi+jSL5OooPrXf/UjDdAUzQYDAaDwXB34AH3Z3j1Kyw86IHFt1nOOZ73Ao7XvT7BYpHNuO5zb4YHPah4jDgGXv9GEm69//3y2VkYcrzgX3FcOwH+z58Dj38ix7v/zgi8DAaDwWAwGAx3P+KIAgRNKXRkC4xhIBfFrNcU5FXtzA8DWrCtE+zUBQbr4ox+j4Jrh4cU4Jahs0h+cVF/fTquA/tMObO4iwVzLFstdoui3acz08F1Kfi2LwcJmdBhF+KZXZJ33qhzPqsLTloW9R/Tqb5zD9DNPWm5aF9XdEQgdcds4xrVVnDCE/qZL/ZqKKbNVabKkzEaKf6ww8KZTnZ3rDp6/Xph0GCgL5pZLMmlRccpx3OpPl1LnTKHfXKxkQVcVWNhGFFdDlLnieVyd+OZ524n7BgM6h0Nt0GMUaKs/J5+WqpdChIsK71HzXq/2ZBroU656ggE29Dvq+dankfCVR3K1xWG8nmO2Nd7cJgJuniynYhul/RzY+U+XFfKc+6mMWdbcUBbZ1DXpftW9X2DPl1Tfk7RVnw1ndK4XDtHD+knCPbnbqkiDIruwk11s6nvmExobOz32wmjbUd/k8mNQBRRedX1Y03zMSFmls21B8PdpItlDDjsMgbxqghVp276DenGbwTWKxorz85263I91HRyU1K6lvmiw2ae/OevcBfGeKx3/8yiPuKog6NlE0bcZTAYDAaDYa/8j/8J/M//DfzKrwLf8u0cmw3NvI6PGV77KobP+azqd/7m7cBjHs/x67/BK65fjgN8/uexyxfxD38E+K6ncvzRf74RllsNBoPBYDAYDIbds60zUl0QS3bsJoePusXXOgHCZk3pH09P1UHZpvR9AKXwaSN0UAVQ/D0FVq53iiCRbkbXkSCJ24kDfH83QZg6PI/qSz74ucvd8bZTdbS6kdLkBJtiPaoTnkVhjfAmxXHa9SO7Drg2Bf10RQZldPqL0ZgENLtOH9jEYEgih8GgWTioI6bba6osjT4rikhM0Ovrp2eSUffdj9ze/bhtCIN60eByCfRL19nrAfe9b1WQIRxadMifM0nI9exUkSpUJTrgSTEVnU4b0GW+0HMkVLFcbvd9FYxRG55OgfGIYribtX6gepcB7SShe2wzzOu23bap9HwfOKxxYlqt1HOtKNJPoyW7LiFkyZ97taJxxraa0+xdNbfcTBsXhMveVaS6rqsjtr2dYPf4mOZ5bQRenkfPx2L0fZnDUZwU64XuXEDUg3ONNNxtcV0SR5QFEm3noUKk2EZE2dR3JJzKtO17mhDe1yGe7VU7BMmuS8d9T3yPMXKIy3N8DBxM6UcmLlwtmwWXE415Iuf1KdlVzOftRaiu127TxI2M7Jkzq37e3KWdM4vKzXP1x9BduOvtmpNrwOlZ8+d4Qn3DWUeHsDqMuMtgMBgMBsNe+a//PZuuPeqLAN/PZuuDAcOPvoThW7+5+r0gAF7zUxxPeybHBz+YHYMxhm/5JoaXv5RdLmCv18ALX8Tx+jcmjSkdDQaDwWAwGAyGuxt1AaLBoD4w0EUwsE8RjE6wq21ATCVcYtbunTKa2IXApM5JwbJIlGBZ+3MnyweyuopymsQsi0V1F/cud8fHEe1iv1EJw2Lw2XHkz104ZMzn1b8xRt85OyPHhTauEk1v1W0dJ6KGwKfv7S+1zXxGIpr1mgKUuoGiXo/K3evYR1iMRA791CVqNKz2xeIZ6/bDXRzVdsV6TT9hAKw7Bu/9Xv1zjsJuYubRmILH+QB0Xd8ex83jiBA5szQIv14Dt966nSCjzWqVWdnKEONAnKRp467w3Myi/qlJDN41RWQe1diuajPzOY2VXfqFJKlPGTca6fXzBfEPl4smr9KhaDisCrcZA2DRxoAwTH+C9mOOZVE/I350UDo/bekys1y2T4u2SN17gpr+ez5rLzIE9ufAFcU0p2UWEJX63rZpnMV9dbk/FfMZcH7R/r1os5aLifJ9gEiRd5Wuna4nL9c64ZMQbDOWipYvqqLS1YruOQwU6aF58zxovdluY4lMZL/N8Ry7Pt33vphOqf8pC+jytHGr7PXk7cax6wVcdWsLqpSWPCGHwP5Av15rv7ukE4O6Of4uN1/pHmux0BdSt8GIuwwGg8FgMOyV538vw0tezPCP/hHwmG+rvr1++MPAE76D4SU/xKQ7OP/qr4FvexzHr/wqRxRlM6fPeiTDv349wwMekH32l34F+N7nc1zMzDKYwWAwGAwGg+HuQTnY13YH63K5vfPXNngeLUTvMlUT5xS4Hza4FekcZx+peeoYjtQ7vXWDDAfTYr3IB3aThIJNbd3NytSVS/5tK9iQsELXWWA0oqCEbqqfwaC7gKyOfTmPDQbdAu2yIHPe+WS5rAZPrQbnD84pkLZt+x8M6Jnl+x6rZbvJX7usbm02egKAxnqWO7bss55bTM1Vx3qt50KiqsvlYK/fq9aNqGWQea/uXZokvHugarMGNjVB8DDs1m/NZ9UUW+V6Nuk4BgnnhTLl9SudsWQfAb6PBkTRRlEmeNj7OdP+gydUZ1X11nWpXc803dtU9HpqIamo136vWo83a8B35cKFbURVUdytvwklAs19psEuEwRZn+169Gw4Bz54a/U6yoLxJpcmDkqLugn0xeaqua+OC5EM0c+sViRAXa/lY91kQoJi2dziUjgvud+2qR73DU8ywaBO/9lVeNwVz9utMyB4VZyow67mtD1ffz4v3mWWSxK39npZP1gWEC6XVO8uZnJ3R8tqFlsGW8xnp1O54Gmb+bEQrF0163UqUKx7TyvVSdX8hFl0vAuJs1STCLJuzlPXVs/PSWTaZnxxXXoXVKUZzjNQ1KNdiCSdK3J91MGIuwwGg8FgMOwVxhi+4PMZ3vg6hvG4OPNbLjme9BSOb38cx2oFvPF1KIi1BJsN8LrXczz+iRxvf0cWSviY+9JxP//zss/+yZ8C3/kkjvf8gxF4GQwGg8FgMBjumhweWBiOaeG8LOjYx0LyZLqdgKbXp4V5ERBwHFr8PDqiBX/HUTsPdaFripJy8HOzufpUfLMLddrLvPDDq3EyWixIoJIPeO+a8o5ky8oCxfk6GUXkaHJ4SIvuvX59+pb5nFwOykHW8mK5SJ3k++3TZzKLhBeqwDZj2No1Q0Wc0L2UXYSa6OJKlsQUfGFsfylGAQrKLZdFUVSXdCxiJ79st/t8ricAUAVnZC5Nqs/uWuzqOnJHviCgezo7J+HRtWs3RupPVbvQDepvG8iuS2G4S4eSclnveuyMIhrjjo5ItOp5VA+EIySzmtNxXgWue/VpvdqgM/fgoD6oTdqsptSh03TeY9tyYYRuPWds+zHYcegeVeOA6DJVbj8XM3nf4m4x51qvurf1RLEcqxKgdBVeCvJ9Vxhm5ahyCeqK5wAX59SH1bmeFVCUBWP03Ns47ADVFMxeOv8CiscKAhLTqOpUklQvTeeeDg7ofFfpehtHabrIhmX+wWB/DmIq2j4/vYO2/8qu5jWzGc0RdN7X8u8ySUI/UdTeGY0xGjv3JcwbDqmttRGZ7uMVYTQk0aX0by02K202QBDqj4e2XU0zLSj38duk3N5WGFonUAxDatu2xlrCqSIt5y5cu6J0A8K+Ngi14Qae1hkMBoPBYLg7wSSS/l9/Cy10/sN7gV/6FY773Y/EWl/8RfJj/N3fA098MserXpNgsaBZ2WDA8MMvYnj847Lj3/pB4ElP4Tg9NQIvg8FgMBgMBsNdj+WCYzFTi4B2TkLpF/JpFNoIsXwvW9i3rNSpa0IL9iKVl8x5qC1i0TlOF6PbBjP2Iaxo64xRF/TI309QcobILySv1ySGuaq3nV6Pnm1dYGS9pkDiekU/vR4FzofDatAikrjzlN8W8+4fbQPMPE03NVQFM/hu0mPKEMH384t2QgTB4SEJw9oEljivd0PaBbtIianjutHVaUYnmOh61D/tOsXReq12MJjN9hNsdpzugTRVOx4Or78Yye/tNm1Oni6CxDqCND0lY9RXbTb0wxiVI8Nux5xev5vTZBiSIEPXDUYl2tiXQCwMac4gE6jmr0XVB6nEFU1CFQ4S6eTTDHdhF/1JFOmLD1V1SvZ8tAVILTk8BI5q0hJeSJx5AHU5b9tHXpWbW5dxNknk7Xa9JgG/jhNNnrJDXBAAp2f0bzFXsp3mMumaltq203FU0k9vI4hpctQMg+Zx/qrTrAPyduv7atEqY+qUdQC1kZ06gXVgs27fJhepK1cXOKf+Xfe+68pPhpgr1Yld823U71H/tmvh3krhkAWQI+DltVhqMVYe3f59OKT2pTNn3KYNbdsP93r1m2JWK3L9SiL67DbXatv0rtVlY1uSzhmut2jeiLsMBoPBYDBcNzwve5H9lm9isCyGwYDhB3+A4bufxaSW5JwDb/m3wLd8O8cf/WcOzjksi+Ex38bw0h/JUjt+w9cBh4dXnGPFYDAYDAaDwWDYAeMpa714LaMvCQbLRFsXM9p0IcQXfq/dwn4+xUaSACcnmVPNYkE/uxCHcE73FAZ0ni7imV2zy3RDdQ4T1zO1Zt2CvQhsl4Nu6zUFzhcLEqI1Uf6+EF9dXAAnil3YTdQGL3cgIFHtwAeydHtt06ienpJAhKNbesd90hRYdZx2bmVlNhtKVbWvHfG2TXVi0UI0yyy99FmqNrIrnVK5TKKIAlttXRYYKwakHKd67F05LHbhRnBDaIXkAScx1bE60U+XdrJedRe+nZxQYFkHVZ1qMydp89mNSH8qGUt1hFNdhBCDgVqA1JauzlDb9O+9NFCfF2PuWrQqQ4zLcSwX8gyGFDAv09Sn6PbJusF88blt0lLuiiCUt9vlkoRa26bzBKrC6X26V4l7kZ2DQ0/wMJlWBfaLhvn8YtE835al+5Nhp+7Cvd5+UqxtNnStw2FVvMc10hrr3sdV4vvbO+ztCln51bV18R6qEmfaNjlwij5ts6Z34l2L7Ore4/LjSNlcWNamyu+/o7F8TLGddiKosEXf0SV9aB1xAmw03us5stSUbaZE+fWQOE7fLbd4xlcx5tZhxF0Gg8FgMBiuG9/0Lxne+usMz3w6w5c8Kvs9Ywxf/S8YPv7j6WVIxh13Ai98Ecczv5vjve+j6dznfg7Dv/5phq/7GuCx326EXQaDwWAwGAyGuyabDddyuWliJQkGuy45KQ0GpQBz+jk/3YDRdUcqs7ZzXqlboF+t6GcyKbqwlNMX6ixk93p7SqeC7VJc7pqr2FncNd1c3XPabPYr9GhK76ODagd+ni6p4IQYblfppI6OdnOcpgB8zwfslvUtH+BNEqpL+xIytg14M4vWI8Y1Ir6rIo6rwfDNpr2TjGUX+70oqpa3KCcR9PZ96pe3SdeTp8417/AGCR7rslzS0On7eq5nvk91qtfLytX12qVkArLUs0fHwMGh3nd0+qJ+Xz1mRJqptpil59KnQ1NfUCewraPseKJ6dqrgvmVvL4Lcpn8XjqjA1bqHiHG+7fi5q9VZ3XRvwxEJDwZbiI13gevdGJsQBJMJie/ygqaC2NdtFjstlvXzPXGOuno5uwCCDbU7IUi5Krcq1wWmY+rP1mv9fq0tm026uWFHDsyuxrPZljrBqXAqqntHu56OY/n3hfJ95PtLGXFcFdSJFIA67Pq5JKn7sChrHRHRfCYfU+KI3lXmcz3H4jaO4V2cRPPfLW9+Wa86bIho8YVyHeBJ9/a5bQrKXWDEXQaDwWAwGK4rwyHD1z6awXGKs8L3vY/jL/6SXobq5ov/58+Bb38cx0/9dILlkuP+92d4xtMsWFbxWx/+MMd73mPSNBoMBoPBYDAYbnxms/3NW9crWuRdLuVBp01ADh9dd6TWLe67LqXzmUzVC6M6i8/zRTHoW3YF0wkArtf7C0TMd+DEsCt0nmM5gLHr3djK89aIt8ZjChLuSuBUZr2qnn8b16nrie/Xp1JjjAQgbdM/tWW+aOeKBeinyssLklyP0rjtG57Qz43ioiH6NeGKFATtBWtxpP8dIZwZDMiFRFcQsw1XFTDr9ev7uTZOF7MLejY6AlcR8D9LHfpsKxUnSxa96tbBROrZk2t0rF0SbzEu9npVsfWuKAeCXU9PYKuDSpCueqZJ3K7t6QqwuqQLbhKpqwQKnqcvRBfHEOU0n8nLXvz9+LjYlnXH8W3EAgLLIjess7P6vnsX52qiyWlqW4HgoOVGjDhORR5empJsVHTZcxz6W74NM6vYH27WNOdW1jtGbsB1c0/OSZMRBPub46kIw3ZusE2bDHyPBLv7dp0Mw/0J0QRN7sFnp/V1um4O7brUZ3vefuba+ffZLmNQFFV1Qjp9fK+3m+eSP5dtU9tuu1lCRRjSfGOXc1nbvpo+tAnGaF3hUEPkvgsHcUG/r+fMbDt0bTJHy20x4i6DwWAwGAw3JH/yp9m/v/ALgVe8nOGe98x+l08nEMfAv/k14Ju+jeM//hGlaswThhw/8CKOJ3wXx+/8bvXvBoPBYDAYDAbD3RnLaudS1WbnbhvCkNLOXZyrd1HrCKM263buYKrd6P4NkLJnH/g+iaN0A8Xl8lGlM3QcOu6+cRwKtCyX+3NXs6yqW46uK8j1oC4YttnIxSiMZeVnW/X319Y56KrJCzHDoH2Q6vCwuxhJV4BWZjQEjg6pbJuCmTpB/stg+Z6WM6KoJBRIz1OuN/0+9QNd3O9ctz4g9r73tzveeJI6TbYMNK5X9WlbZW3FVwiXwrC9qPHyOtY0HsrGveuxarVabZeucL3ez/yBWVl7Ojyk9rLLlMj77vt1xfJt+xrfBw6m9WJElUAhCEiUoNNsZMeQldlqmf2+rasgsJtxKEmaRRnCBel6p23cJn3i8THQ7wFeC0HgYpEJTOOY5lh5YUoUkjNXvh7ypPqso6i6OUI4ES7mu22b4tjHx1WBRNvUpp7XXpDSH9S3kZhTf9TWQc+y68dCZmVz+Kt051Pdq0qEDNB1NgldJhNKcT0eA8OOLqCqzC5ldpHqVAfVnGBb4pjG0Tq3savA75HITAbn5LSrQlWPmJU6l/Z24+go5q26bWRX7zqnp3puqHH6LltXVl0x4i6DwWAwGAw3JF//dQw//7MMX/kVwNd/LcNnfgbDL76Z/hsAHvFpwCd8PPCgB2XfufNO4Ad/iOMZz+Z4zz9kS2H/+mc53vFOekF96cs5fvhHOJZLI/AyGAwGg8FgMNz9GY0omLGtS9W+FrG70iYNoGo3OmsIrtyoNIlUXIcCnF1FKSrKKdx6/e4OSnXB6CjKUnDuy10tkTgy7SqVWB2qAESTiE1Vh0WbLLsqOQ6lpkIagLl2TR1sd5z2QdJdpegT1LmPtUVWlqenFNAeDPT7Md9LnRNY5oDW5Vp8n4KZo3E1fY8I3uoG+ctpFXdJOXVRlLYHkSZUEGyRzspxqH9SIdq+LrMLcowb9Cj96L7c92wnc6wx7AbH0R8/xDgQBKlz5xW7/VwVbcVGsxn17V0doNZrtYhwMu0miF2kY1F+7HY96t+bjrdPUUZ+7A1DEiC1FuRYmcioretZmW2FZfPFdv1RJJnvRBEJX2VixPx95MdC8fvNhsp0H0JJMU8vi8baun4FQfu08fNZvdA2Cqnetr3vJJaLQw4O6Ifn5vBdnZTbwgD4ijF0uSQhaRnXTftxXnSaKtfvixnNwVbrbsJPgJ7fNg5pO58f8Or86CqYTuVix10TBGqxdpKQSEv1LqCap/IkFdY626cW9rzimNL0HmM718dtbNfv4gIj7jIYDAaDwXDD8uAHMTz3eyx84ifQ7Gs4pP9+zSuBj9wG/PX/Bd7zHuBrvwa4503Z9/78L4DHfgfHa16b4GLG8ZX/nOGhD8n+/gd/CDz+iRx/b9I0GgwGg8FgMBhucPLuS36vfcq8+bwoyOmKCEK0CSIeHVLAXTdlwlUThbspmzb4/vbCmPL3Lav4XNqmx2sjasqLiNarNOBzoC+SOzgEjo4pQJMXkqgCnaMxBZl3jawe78LgeTSkslAFGVRBOqsh4CAT0lg2CW1UzGea9ZuRi1GbwFfYUVyRd+AuX4OuK0MjNc/RcerLLM8moIBkGNBzU9UPVRBrVQrc+h4wnZTO0TLok8QUHGMtnArakBcsLBRBS5mTiy5rDYFUU8De6wHDfrHBzBckcpGlGt4FTWIiZlH93ZVAcR8MBjeWmNm29cUpoo1Fye5dgZTn1AwCtxXGKs9npYLSmuP1+jSGlrGs3V2HYLloJ6IXxFG1DQvRT5fj7QrZ2KvjvJKHg/rs2Uw//Xa5zxeMNRxk6oS8m3WWXl327Hv9enda29J3dwUAJ3cteYH0VQgtxVxmtidX4W3Z1SaEfn93qWZ1YRb1xf0+zc2HA7UI0y6Jy3t9cvBcr4vjX79f7afDIHN27Jq6Ptzine3ocPd95GZzfRx/F4v2AjnVeFbXxzTV67hmXlwnSJzPikJeXfHiZErub4IgyOrcZtM8d+TJ9gJi399PSvIuGHGXwWAwGAyGuxzTKcOHPkT/Ho+B7/pOhpe+pLi4GSfAr78F+MZv5vjTtwGvfQ3w1V+V/f39HwCe8CSO3/odk6bRYDAYDAaDwXBjcXRoYTSmdAj5XcqbNXAuFv53vPvU9ZodYVYrSIUTZWGO46RBIysTc1jWdjuuARKzbEP5OqOom/tMmTaCt82m6rLUlnI6MZ00RHVsE5yIY3quoyH99Pr1IqGLc+D8rBrkHQ+zVB0ALdi7LuA1OP2oYEzt0MQ6ujDpsAkpgDCdthNLdQlYJTE9dyEKyNPG0SIKqQ43CVN6OWFIF4EFAwWCVEH2nQX+G17vdxmMFs5co3G1vsUxcHItc04TP9uyaxcPIc4cj6mtCecQVQCr6/kti55/Xf+nM0YEa2CxurHWcAZ9CraW+1LXu9qUWjJcj56vZbV3utknmw21+YmGAxJPSPTQRrzX1P8eHFC7VT0f3WXCXZSp61EfPp/X90/rlVy4vVzIr2MbseE293XRMoDe1QEUoHFJ10VrWzyXnlGbsUrVXzJG117n4KUjGrIVDnjrVb0AhFmU1lF3s4E41tERjXW9KxSKJgnNe5vKo9+/urqwD1ardPpyhe5CwklptaL5yfm5egpVTse5XlF/dXFRHP9uNIfL0bi94+k+BDwHB9v1dYIoFdDqbuLp9dSpMMttatRik8V8tttn3TRXWq+ApPQ5zlNHzw4Oal3eA8W85UbAiLsMBoPBYDDc5Xjwgxje8qsMj38cwzf9SwbPA97wM9lLc34H1PkF8MpXczzxu4DP+WyGF72QXb68BgHwYz/O8eKXmDSNBoPBYDAYDIYbh5PTBPMZBezKgXCxELvrtGg8QWFFX7XDWSZCKQs9Bv10IZlTip75nBb/2wpYyovxvRYuAzL25fixb+cCxvTTb+wrZZsKUWVWK3LPWa/ox/PkC+dJQvWgHPCMExJ2CCcNsUM/4d2CzJyrXSl4sr/AZBjQe+bJqb4QwUpT/wEU/BHpVnSDSzzpnuZGIJ5LHdsK4nTeuHfxXFTn6fWx86ApT+g584T65FHJjWVfqwy2BXhb9oeXx7KpziYJ1UXPo3WUXQdodURhbZxkbiSSRC4UFq5vu6Br2wgDGo8dR2+sumr3sdkiFRZKzpsXrbYVITf1v5xTcPqqUp41sdZ0kYoCfUH5Pl1ljmrmJG2djDYdUiQK1uvuTkBt6ZJeK+Hyezs7y8Tx2xBH3UTDZ6c0Tym/Y4jNGCrEPKA852j6Xh6Zg+C2aSoBGruuytmvDt8HJpPqs9WZ0/EESK5YhJt3sIsidRmWU5nnWcy337iiS9uNPmIc8Wr6zXyf6ro0Ju36fer8rL4MVaiuQ3fMWK/pGDpzCEtRRleRzrDXy95/ZCK4IMjSpAphbBjqu3GV5+OTCT3nLjSNwVcxjzLiLoPBYDAYDHdJ7nlPhsd8G8O3fjPNML/6qxjucx+acH7WZ1U//973Ac95Lsfv/h7Hi34AeNhDs7/94X8EHvedHO94pxF4GQwGg8FgMBjuGugEMGTOFbZDjmBloihzqrDt7V0bTk6Bk5PUPaBj6opygDAMaUH3KhaZbyQ41w/e7So9jC5cIdYKgnrnk3IQ8Pxcvgt9vugWDAGqDmd5GLZffK/b4S6eg04amCTJNEdnZySqWS4pKJxnnw5APGlOUdXkZGPb24lzlstqPRJBrV2Ifjgn57g29Pt6QlqRJqcpvWZXZK6DrrMbke98lorU1nQfi8XV9yOX3AX79n6/vYivjaufoBzIFWIKnfHw5EQ/ALqLoGSbMVqkSpLNEZpEq13nAoOB3rgyGKrb2C6rahtBCkeaUq931xBD2nazqGC90hPZ6Y6Brre90+uuCBQCzzimOr+vVLJdGAwohXrdWM/TVGzlMSJJ6EdnztPzq2mYdyHKOj/vJsDbNZsNsFxR+xRilYNDfcefts53V8FgQKkN67iqiEqbVPIAlefFLOd83UAY0jvXrudBXctH1qbK/cZk0s5FWoWqjsr6hG1dyMpj6HJJ5X5xUZ/6XcxXw6BdmsXy8zw/3z5N4/XEiLsMBoPBYDDc5WGM4XM/h+GXf57hx34UeO97s789+quKL41/+jbguc8HHv4w4J99efb7W28F3v6Oq7pig8FgMBgMBoNhO4TQqQ5Z0ChJKLXMdKr+fpcUcW3pkvIiDGlBtylop+MAsOugKGO0UD0aA5MdpN3YFbqL/dsIh87O9HeQC8oORzJ24eSgYhd1fK6REkVXJJkPnCyXJPAqOwF1dZg5PqZ62ZSuqKlMmp5xHLcLrLpeNSVbJV1n+neRNlI4YXSpr02BOsepthfb0k/PNp/tT1AgC3zPZrtxyhACGiGuK4ssez0aL2T1h1ntA3x1fe+NEJhvi+sAjkXBbx1hlOcBmy0FHYMBjTOTKbn96aDb523TN/q9VOx2RVF+nXEEqDqf6LabKJR/djQuCvSuWmQVBCTGLQuArwqVONHxSGSQJ46BYEcOYrpj4KBP4uvyPFOVqrkOx0lTOXcYc3z/xhJv+b1sc4dImV4gVVvUtd/ZAghq+unhkFzd6oSXQswv0pFeJa7bLvVcV0TfIdIxN7nLDUfbu7htS92cO4qAKKn/TFvR1VURBvRTN7Zt48C8r40XvX7qTqUhCry4qL+H2UzvvbtNf7XNfTOox5EwJOdyQz070PIZDAaDwWAw3Bh4HsNnfxbDZ34Gx5/8KfA//ifHs57B0O9z/PK/yT6XcOC3f5cmto/6IuBP/hT4+I8jIZjBYDAYDAaDwXBXwfPai2p4Uu9otG/GY9q9nMT6u9jLNC3C6yyE71pEIIJhV5UeSBfdNFx+72qvvaneTqcUVO2SbkgHWcB+MLxrBhQ8jwLPMuetS+crb/vUjbtGBNzqEE5bSZw5+zgOBYWk9bWFW1wZmRBvtWkX9GvbH18ven39IJ4I0pfL2+9RnWsrBqoTaLQNsDpu99RzjtPeodKWpDZs67KiO+7VPaPlcn8psLYZG10HiPYQ4GeMRIb5VKGO0yIdU0dhrOpZrZbFen9XFCU6qWBWJ/2qZRXb7XqV9d2Om4nMogC4CEh8mK+fuqKP4VDu4NkGy6JU4LJ+qYtwUaSq7dLe9lkvXA/o94DVWt/1Kj9HGE9IgLTZZOUinlP5Obi5fjYMqD0xJheBzWYkUtIReHZ16yrXxzaEYbsxw7J3I1pqGmsW8+ZjMGs/rpqDAQBGx1YVTRS1dz7N43uUft1x6fndFefaMvr9Yh+6Td0UMAb0B7S5oIs4VDW3udB0LtM9x9ap2u/CyXPGY2qzTX2JcP/dx70acZfBYDAYDIa7HbbN8DmfDXzOZzNEEcf/9wfyzwUB8J/+M+10/IxH0Att3u729JTj4ICcwQwGg8FgMBgMhhsNXbFGOdB2PckHYwcDSmOm44AE7GbR/KOBtiKltsGuXVAncGAA1hsAG/r3Va3/N6UlrEMV6LwKgkDuwmfb1GZWq2IAN4/jAoMeBQ27pr+Uoes2AOiVXV40FUVVodFoXC9OPDok1wnLbtc2yoFUWXDVtqkcy58fDQHPp/RMTQG6G7lvW62KdWcwoHsWfXnbar/Lvsb3KWgchHQdbcQCbYVdzKJ0YostHD7acCM5/ugy1xAo5NEVK9g21TuRInE+v76CqqtwN9VBOIZ1EclEkX4bUKUVBEggZJWizDKRpkwYWfnMDpyLVP3oeNw+BZfr0vfy9dpxSMzGcH3rgXC9TOJuzz+JaFzNl1cQZOki85T/u67eiPSM+8BJ3TvFXK3NPKMrg377fu2uRt7RLlLUp16v/j2ySQQn3lcHqRtVW3GX71MfYls3Tio9xlJ3vk02ju2i7nOuVz77fj8qC9fyRBGle1ah6u8ti8qMsRtvzttGyHl22vCBNIy43mT96q4xaRkNBoPBYDDcrXEchje9keHrv1ZtQXt2BvzU64HHfAfHH/8XDs45NhuOpz6D4znP47j99rvwdgKDwWAwGAwGw92KfGqYzbq4KH94WE0LZdsUkN42bdBopJ8CSSDS2dTSYnXyRlsIvl7UORAx7Gdnv4zBQD8lWB7Lqk/twkF1e7O+OmEXsF2KK939QKq0bU7HLdhC7FB2jHI96hvCNDWQKrDQ69Fnd1Fn8n1Rm4Ar5+RS1Ot3S30FXMZRlPXl5JRcJ1ynXdrU8vXIyokxEnKNhgCzq38bpKnqZHVElJlu32ZZO0oBpCgolegy/3uRHvdGYDEncbDjktihrj/aVjjCJSkrDd3xJWlZVURRlt7s2rW7plOWDm3nafPZ/sUtXZBuPNAYX3fpLFNOkbuW1BmnIY2q6OtEfbPSNLSHB8BwoHcdOqlau3B6CiyWW8yRLGQDZ0qSqMW3+TlKvmz3lYpYRhQWRfi7rPuqOdxVC7sm02xedxWUx8WBIlVe0wahsijGl2w4AMjpctVh/rDZ0M8274Fd0rLWwTnFk5rmz7vuA6ZTSrd+dCz/e1vhugodV0cVKiFvklBfEkY3zjxSoCPssu1Ktykn7Zh5sj8RsBF3GQwGg8FguNtzj3swPP2pFn7j3zB84zeoF0ze+z7g+1/I8RVfxfHcF3C87/3A//4T4Fsfy/Gbv8WRbLPabzAYDAaDwWAw7IC6YOjpaTUwE8cUANo2GLrZtHdcsW1yzFEFKpbLGy+N4b4RopttKAc3PC8LpHBstyCfp0lwtFwCszlwdESiCltDoHR8TCLEckBJ5jwFkGvGwWF30U8bdAROqoCVeCajEYmUVOI1VRvqErCybHWQLIr0gpLzGXB2rp9aru456Dwj25aLRNepu1VXkVmb9GyzFg5lOmkCo4jET5uAxGMC4Ui4WFGblLmTtW2rwhWFse5tok5AMtIQ25Sd/qIrdv3zfGA4KIbXlgsS/dSlHI5jar9dhZR3NSyrGFQeDKlv2gZm7U54kIDmJrrn3TWupx532lAeE7Yp47p2LcTUh4fdj389uWqXq3KgXiYE0um7CoJORn326an+mNm1v2EN300SGjNXa3k9dr164ZVlUVpH4U7WhO1k9bOXG0N2kbLwRuBGSRE3n13tRpbJhP5fzC1sa/t3lOGw3gm3a0rGOFILrAfD+u8eHV2/sX/XzsizWXun4V24Iu6KWUdRct271VXgejQG+zsWCXbFiLsMBoPBYDB81HB8zPCU77Lwll9jeOy3Zy8xZc7PgT/7P9l/LxbAj7+C4+nP4nj/B26QNz6DwWAwGAwGw92Ww0NLGZQpp07TEQfoCLtUohVBGNJxdBaIXZcWYYWIQydQoONG08UlKo+O+CjPsCFYIKNp4TmK9J6Znx5Hp7yDYLuUgip0dn/zVGhiMQpGep48uG1Z5GwkCEp1stej4LWoK45LZem69N22oh/L1gtYtqWpTFapSKlt4KJLMC+J0/K3svoiaFNeOgImgJ6R6rijMfDQh2icK746JwyVyGJXb/T5PnM2o6BsuXyuXdtP2wTQ+UY2m8wxwfeLAqAopCDo0ZF+/6crHN6Vw0uwARbLbjfvursR9MiOK8Pzrl9Q1bKqQeVt6yJPdieCCAOqbzqOoF1En03P2fd344JXLlNVxgDpNZSucb1W3+tySeJF3TTWdyWmLdwUywwG+xnrZdg2PYc247Vqvnd0TH2san7MoTcHS2J5Xx0GDcIrLnfQYZZcBLNZU9s/OqRr3lYoapAjxNsy9uHKK4SKp6fpz/n2G0Su2uHSdgCvxh1LpAGsYzBIBY+pgEjXjbcNu3LwShJKM94kMBUC5l4P6GuKsrftS3fiLKsgDKgM889mG8Ge67abn4UBzelvlJTZRtxlMBgMBoPho47DA4bveKyFt/4awzOfznDve+l97y//CnjM4zh+8Zc5osiIvAwGg8FgMBgM++H0NLny3fBlsY1KCKXjvuB6JDaJYwpE6qTa0XHm6OI+ll8E1hWxCPKCGZ1F+cEA8Bs+Z2uuxoq0RnFMgYljRfqNMlfhcJVHBDlPTjMHo/WqusCfJHRPIq1WOTh6dpZ+N33GUUjPK4q7CRKSGAi23C0vC1LEUfZ7mZBvF+4kB4f0vHWfJU/Lto2ooAt1KVQsCzg52d81dKnX5QBMv79b5waZyOWqVgksezciJdsuCoDWa2qbJye7D9DeCA4v89nuxYWMAeNRNl7k62oQbNcn5NtT2+ddFobEoZ4wq0novVrJ0xK5LrUxy6Z6VRacqqh7Hk33XBewXq/rBd3zWXOqsS5ctHAG7DK2hVv09bsKvDNGAtBdsVx1F0Eul/rOjduyyzSAJ9eoj92F0+q1a+2/c3GRlhvPxgDHIfFWU9IKjur42mZslQnadiF82YMeR0pT+lTZvTCmf327ct/VJV+vryqt+zb0etUylqVdFYi5ultTR3t96h+DgOb5Qw3RcVt0HbzK6WJVm4aihv4oDGgcFHN3HUHmti5j27rOHR9nP2XiOHV9Y5lobZt+Iwqvbs6+D4y4y2AwGAwGw0ct/T7D1z6a4Vd/meGF/4rhIQ9u/k4QAm/41xxPeBLH299xV54GGgwGg8FgMBju7qiCq6NxNciXD/radnshVJ7lghzGzs4oeLmrlBSq3bJ1gU7H2U1As+keJhMKWHk9Eueo0HFiKLPZ6AfvugZmmgL6ecpBPFnZyBb4m8owiqrXf3HeXWSyrUuNKoC375Q5qxUJD8rB7ibnuUAR6LmqgGdbR5M2zh9NwVQdohg41xCaCmyb2vJkKg9clwWxwP5cC8p9eRxtL14E5OIWESRtupdduVDc1en3SdA1GtNPUx/MWDH9ZV07yLfpsrjScdqNbxtNYYpOfyFbCQtDEjgfHpDLZn+wvVtbnaAUaB5Pt5nHAEU3yTJXLaTOE8fU/tqmqNp27BLna+Pe5vvNLoDhliLIMuK53UipyHbCngdzMZeuE+ImCSinaok2c1vfrzoE7eI9oVwtu7jv6tC00aR8L8IN864eQZhI3h2vB8NhcWxpahYi/fjJqfozp+kGlTimOj6/IsGojHK6WNn9rVfF+jSZNo9JOt1H05ibp+5dtyvXrtGzqEvZzJNMtLaNEJLjxth00JUboCkaDAaDwWAwXF8ch+FLv5jhzT/D8IqXMzzi05q/8+6/A574ZI43vfkusK3FYDAYDAaDwfBRhQgWq4K5PKHPqNJO7DLIpmJSSsOjE4SVBUZsWy1qASjIIgtE7tph6OKCnBiQ7D74sY2wRTe4OdRM2QFURUY6zmxd2LcTVRNNQYNdunjk2ayp7ZYDPF2FCkfHlPZqm9RXjXAKjIkAr6zeuV4xuJTE+ilgHCdLIahKG9WEbdf3bY5DjglCzBGnrnHBRj9wvS8xgawv31a4okKIaMv92KgUKB8MyF2hbb3ahVBvWxyXrnsnfQzLAo2e25w+eDoBwNOUQA7Q61AevT4wGNLPrtu1bnDXcYrllw8sxzGJu/cZOO31mp0yt8Hz1MKuXh/o7yG9py5xvDvRfBvCIKt7MiyH+oU8m81+HNLqsO3UJZW1F+H1+1Xh6o2SgnCbOsesLGU6QGWUdwhiVr2zkWA2q3dJOjhsFn2cndFxxLjeNgWe7jO96vSAKk5OdpfOdh/YDgmOmwTbYXT9BZOeV0137bjNqfLEfEk15t+IjmXCmaruHVdwcV5/D/NZ8waR1uypTifJ7lIf1rl/7iP1pqDc3vfRbnb9OA0Gg8FgMBjusjDG8JmfAXzmZzC8810c/+ZXOf7TH6tfwjgHDvewU8FgMBgMBoPBYOiK75NQJwrVgoTrHfDw/d0JYxynWYzW71eDizqL5W1dEjjqd4Z3pUs6SoGuUO/kJPv38THtcpeJR5gFWHt2jzg6okV3sTv7oxFmtQs2WbZaSCGEQfsUA5QFfv0epWT1e/Tv9aYarAkCvXZoWXRvoh2IIHW+f7Od7cVO4nh5UV1bV4DV3ai+lsePeWnccBxKqRe1FPCEOxSl+f1uQcAkJnfJXbDMlcti0Szush2qJ9u0x/Vqd8HPrpTrB09onN02FWUdvR71dQxU78p1cpeI+5OtB17vss/jut3qkuuRGLHtfJAnlEZxlX7P62VllUTAMiIhaP7Z6ApbBsNie+oCs+jcXcUasj6/36Px5XoI6vLky7Hfp7m0bGyVfjcBwiS7h+EAiBOai3Oe/j0mocxorHYuiiKa6zJWfa4sPY+qTvl+cT4rBOH7FFlsi2Xt3631enOQCoSTpL6Ob+OS1NMYqytlzVAREHl+dd6o+44kHDYvzuXvx4zRTysH2l7xPUXnPnUYDKk8dvkOtI0bWbntAtu7x7re/jbJCOrKr63g0rJobHMcIErd3ZrqSq/fbvNUq+vZz2ENBoPBYDAY7tp87MMZXvRCC7/6Swxf9S+Ab/9W4B73qH7ula8CXvjiBO993w28DcdgMBgMBoPBcLelsoOdUWA1v3Bddly53mw2FCQYDPSde8q4Hv3oLOpf753md0VUQiGe0LNjFi10d3FQqsPzKHB8MQNmOw7cN7n0dEmz1fX+BwPUigfzgWkddyHZ83I9ClTNZ/QjS1PZ65MYZVebloQzgLh8100dQmraYFMZJkkmzhSit3KQ8WBa35dMUpemyaToVtKW0Uj9N5UrQtnJ5irZR9B8MqHA48UF9b9tRXU6bk66fbYFGt8GA/rRdU3cZ7BeJxgrUjm2/duNzmqlL+yajNu1xcEQACOhTRRvn3a3CVFHuqRS7krb8UQn5aGKMOgm9BdzLrECGkiew6Zj0H8X8wme7MeFJ9+X+n67VNa7u4jsn4MB9ZM6blsyLmb0/PMCh82aXJGaRCBxJBdGcJBoVtVmVPP1tiIL8Xyvop/c1fid30Chwnb24K7Ugn5PnepV5danS17wpLpHr+xiKakXsj5Lt71PxvV7djhvPzcoixN3JfxdLprb4b5TY+edK2Xjep0QV5WS2bJozBqN73rv5oN+1j4uzvXqynpFfeI+3CvvotNEg8FgMBgMhqvhllsYvudZFp7wHRZ+8y0WfuD7gE/9lOzvHMAf/SfgW76d49sfl+CXfiXBj78iwempEXsZDAaDwWAwGHaP3ysGNHq9LKB9fCwXcjnu9qmwJuNqKsUmXLc5ANY1VUkY6O/4jeMbxxlAleapDVexIN70XHhCC92TafsAm+3Iy+HggAQ6UdTu+eoi6oAygNyhLrZ9FuLzy6X++bRc5kr4acoy6XNkOZESJye2bdzh8ohgR08ECHlzXYoi/TrkKYJZYSA1ebjk4px+wnC7XfRBoBZ4DRSpbr0aIUAXIU+btK8irc8uYFaaEjN1CtrGxabpmnQFQqsVufSI9F51QuarSvOq017rBI+rFQC+G/HIvoO/28As/bZo2xRIXq+qKbn2SZ1QZR+4LqXR1WU+oxR3u6CrSExG17H7YkdueoCeSKbJZU+QJMW21MbNSSWW0W2btpOlFsv3uUKEpboOv9deyJzUjNX5671RxKdXkUpvPt//OQT9Ps0j2swrddIp1lH+rqpPbuuoV5cOT3W9OkLFrs+cgfq4sMbVuguRQui4T6ZTet+fTPb7PpjfRFE3J5P1ByoRf5JQeS3mN5YDpg5CtLzUmH/kq0QUbed6p+IG6YYNBoPBYDAY7hr8ky+18JOvsvD4x1X/9vfvAV7/RuA3fwv4um/k+OV/wxEERuRlMBgMBoPBYGgHq1EiDQe0u/rys8h2O1+7Rj/ldFOLxfbijfkSWDXsPBVXLXbs2jYF248lwUrHIYFLm8BJV2FUHAPTg6sJSDUFFAcDckvahvIi+2CwO3c2ZmWBiiYXjfkCODsFjg7172kwINeHcjqPfj+rN/naP0mDGLtwM9hsqE6qHAh0AjRlwUU5sDoa072oymNf6crKbNY1Kct41u7Wa3Lw2NWu8stnl7a1+ZwcK8T5bEcujpIF7MpB8cFAHRS8mNFPHlmdWa22S8cXBHQvsi46CAFPVk+5WoyyWrUPViaJfsbY1aqdCGs8JuGCTEAmHHEuzrcXXm4jDLMdYNhncN2sFC4ugNPTah0ofM/O+t9dBERlji7CTacOMU7ny6B8LF5TZ3SxnWoQe9sxkLHdCJQBake6438bQWOb70zG9WOXjgigXB7bjMVxLHd+Y+l5JmMK7O8axtQipLsCll1tdzqOgk0CLVGHlqXA/Gq1fR+oLe6yss0ZZcHEJqC0jLK+iLF6sYw4Zn6eVzsWsWzc618H17IbRVC2a/JziXnqptamLUZhvSivicGAhCgiRab43TZMxtV+LH/M9QrSScw+oyeHx9Sey235rsj5eSYWUj173TGzToSXp+79qO0cdrm8ekFcHbp9cZLOgbdNAb8r7qZdosFgMBgMBsN++eZvZPi+5zHcfB/gppuqf1+vgZ9+A8fX/UuO//THCfiNNHM1GAwGg8FgMNzQTCfAeFL8nQjElO39l8vq7lfOiwFGnUC63yPRhWqRM4npOHULxmLG2/NJQJN3gig7h3UJ1nbd+RrFJEjYZpe/TrBjNKZd93WEgd5uZRFw0xEhLJc1Qp6W8CRLH9Nmd3vTPbkuBXw2GwpmlRfHg5AEGmJXt+1QgMux6b/bLqbbNonOysSxnjuISsAQrItBxvJr3nxG9fSutCN9ly4Gop3n43aum5VZHOkLOsrORm3FoF3TVekgnnu+LmzWQCTpY3blrLMPBqn4ttcDfA8INuRmU5eCqWt9Kff5XQVWcQQsVrx1cHC1ov7FcXaTakuMw70+9Re2Q/fU68kFPsoxj8nFgtvCk2r/xBNy++h8TF4V5graio7iOHXV7H45tbhuc+B6vdlecFuel7RxSyrPt4JA7pDD0/NczLqLE+rEMZwDSXR9U8J1RaRl7SJwubio/7sQf3VNYSm+W8Z1gfvcm8Tro4bU5MzK9bmSxhJHcoHielU/ZxYbPnT78zCgenJ8TP3dVad7v0HMdwHs1wk4SdrNH9fr7cQm5+f0/fPz9Odie7G/TGRdrouDQbXuN6Uh7IrvU/1ZrUgMJ2M0pnnPaEw/26TvlsHYbvvXhNPzUm2MGI9TMXvTGFgS4amEhX5PXwh2lXR9TpZF/djBIa1V6IhH4wg4Od1uc8IuMeIug8FgMBgMhg64LsOX/1OGX/1lhl/5BeCxj6HF0DLXToAf+EHgm76V46/++go8ow0Gg8FgMBgMd3nOzjlmadCp18t+AL1AJAMtXDY5L+XZrCmoMJ7Iv3d4SAEpnVQ0lkVuA2FEi/wnJ1XnsC6p5rpgWbQgq5tCR0VT8MTz6H2gaYF4oRk0EQG3OE6FSkd631Oxr0V53XJNEmC2oPuRiTLiiIIup6d073GamvHkJBObtSGOgdkW6XSYJRd4cWTBlF5vO8eEOix7Pw4t+0YqimMk+NwGx2kvBrqKdGqVwNoV7emynHoBlopycFH09UFALjCbgNrqPoKs5b6iTuCr86yjqH1hr9d0b7Md3l8cU9A4jui4npe5EOZxPfV96dxJmz6cob5vrhunmpy5VGOhk7ry+X67NND7ajKbTbNjabCH9L8XF/pOll2CxGFAz6htKjae1Le5i5m+SOTwUC6evh7j1nxBbfoqUvTtiigCPvwREpo39bWbdSYsU6VyPdWcIx0c1juhWlaz2Mz3aQ633nG7qevzbXu3gqptU/QOBySCVvWjst8r38f2IBTztphv7asdlef9ywWJybtg2+3SFgvBUt2Y6DnkSOe51M52nca5yyYVgVSMzotz3Hz7CYNsI5jMCbKMkxNCq8bDJNm94E3GwSG978ocvwWWRWOs76v7xCaShOZtZ6fdHHQbhXqSDSC7xoi7DAaDwWAwGLbAshj6fQvf8RgLv/tbDL/4ZuBzPrv6uQ/cCjzl6cA3f1uCvzYiL4PBYDAYDAaDBr1e5oCl6xpgO1kKQtXi9HQq/9tyScFpmSiCMXnAWsZ6TccR6bv2aWJrO/WL/LYkXY+gzaLrpiGQJQKtrkOL0qqF6S4uIXHcTeCUp2mBf9xip3q5vHXSh8Xx1Qdft9ldvVw0BxPX62qaJBldgkS+Vz1/113/u0hBp8u5xAlFFSxqEpG4XprmzqEgju1oOP7l+hqd+24SSPh+5lRSvi4ZXQNNTZTPF0fd3OHKwUXZs1Gl391VOj5Rreva51XUWd/fPkUuIL9WmXh5sybx02RKP23J9+GMUb+i6luahtxd6wlE/fR9ukdZ6lXZ57el6fltM/dgFs29VPVeNX9Ikt0LxsrkXehk16caK7YVuQvCQF7H+r1qe+j1mgVDu6afuundqKkm22y8yKMa/3Y1x55M5JuW8/T7tAlE5YDUFsaoPOrmxl3d9Xp9EmGV61+YGwf7/fb9cZhuupgo6nV+rms7dA0q0eOu5sWuS+eYTtuPn4zpiUVH492OzV03+Ywn1XKrmxvOZ5QK+fRU/ZmTUyBO6JrOzuQOittQHi98T94/le/DsuTlVJf2UsZopN4QEIXN74hhUJ2z7UPIe3aq56i4XpF4W0e81kSTCHwb9vnea8RdBoPBYDAYDDui12N44AMtPOOpDF/w+fJgwPveDzz56cAL/lWC2243qRoNBoPBYDAYDGrWa+DkGi1K67rQOKmYabVSp9aIo/ZOOkGgJ+IB6gMxXiqMODreTcCPof66wlAe+HKc5iBWGxYLek5BkJ2z198uDccudo7rBhFdD7jpHnqfLb/mNKWE7OIwdCNQbj/DBsGCii6OE71eNZ1fl13/jFFg8eiQ3Fb2jbjGcl2SBaeSNEWcqnySmNrAsE9OgNMpcN/7ktjK9+VORvmm3hSw8f3iM7XtNBB7SCmnRkPqI84viimFwkDdt1p7SmsmO98uhBqrFd1fvp9SCVntHUWStnEV2RViHOoqshDYdrE/FHVZFXB0HBIAuw4Jaj0fsFh7Iarv03d1yrKp/ykHndvUK9uhcbwsMIprjmFt6YoigtSDIZXdvrCsere0ujnUtqkeZZRFFSKtpawsdUTHQPf5yXwhF0okvFp/1mtgsY2LZstnLJxjgarITsftZyIRsOhubLjRSZKi0F4IqwRHR81194476seCXo/Gat3NE+ORnkhEuKS6Hj0jnXnlekVz8bLouiy46NqN6IwfcURzuV0JK1XYDqVsXa6aN6NUvmvTXKzpfjZrXPf8mJ5HAqDynLJc31yXxvg2ovT1en+Or/k65zhy18jRGBjkrtdPxbI619TkRjqf648LZfoDKvfyu/a+Nm5F0e7cvXe1KaHMVW6aUWHEXQaDwWAwGAw75pZbGF7yYgu/9isMn/xJ8s/81/8GfO03cLzohxL87buNyMtgMBgMBoPBsDs4ijvTy8wXlIJHhspxYtXCJaZuwdeyKEizXGapFbrCLL0FVlmAdheOLTJmM9p1fHJCqVtUO/t12MXitm6g4uSa3jMeDDTck0p03bm867Qo29I1YN9lVzhjaBVEUwVSOafnut6o2/w+WJWCQLKy22yoraja8HxOQdHVGohiElDecUcqemPy76mEnjJXOtspBqnjOA3EnpIzRxRTmeUdSpoC1qsduz1si07QPAqp36+7N9tBpT7Kgmb9Ponj6kQjgYaQqdz32XZ63NIzHw6rv9Nx6xmPqW/cNv2kSMko4Ly+v9us6TsXFzRWBBtq622dnkR6SZ3rr6TDWmVCA8aqos82/VUcZZ+PE7qna9eoDalIYmrTXVMF+z5VxTqR5S4QQlXVfKbNnAjYz3jGOaXfkv1eB28HabbKcymZgGAbEYDOd207679sh8YK2fPRmVNdzKrjVVdRhIquc4m80G04pD5xMtEXMGw2JOYV7Z+XUrvdfntVVC6jrhyThN4xdOd9Ufq5JmGROF6/T+84umLj8/P6exICN5XbrmwsCwKan1y7pncNgm3FxHWsV6m7UgAkuWda7t/r3n2a+uQw7C5Sa0rV20sd1EbD+utQ1b2KK2lI40ObfjrY1L87yyiXZ5NrJUBtTtWtuV4mXnbd7RyIywjR+WSi/x3GSHAmeyZ1c7mm8a7p/X/bdIZ+j47fdpyuw3XTzTJH7cfzfYjB9tidGAwGg8FgMHx0c/N9GH7qNQx/9n8SvOzlwO13FF/iOQf+8I+AP/wjjgc+gOOffhnw9V/L4LrXeSuMwWAwGAwGg+EuC7No8T0fWBmPm3f11mHZ1WAUs7qJdvKOJkHQfcFTBEl0AtFl1wXb3j6or8Pp6dW71DDWPZCq870uwfSui+v7Tm3Vln2mFy1zfkFCOt3UMLK22OtRoEr1zETaMc8jsUTbQGUd8y2cWsqEIf0MhpnzSBwD5abf66tTFaqeneqRqkRiB1N1ah/PkwekRiN6Bvt27pDBQX2ljsiTJxRYlLW7YZrizPPpoCK4XWa1onKoc5nrMm7EMQXpeVLUmIUx1XPLSlMHW3SvsnuwLHoWoi4sdxj0y1N33CaXQ4DcOxbzPbpitBAr6iDKenahL1rZrLNUaGWXM8uqbyuck/Byl0HvOsoBbN32VDnOluPZPtzA2o7NMrHBepU5Fq6WzSlBu9Dr1aff8v3U0eeM+h7ZNYzGxXmf69FcsG16W2aR2IFZqdNPh3rY76vbt+fp1RUhtrAtAEzvWTa672osRW+C6rns3DtC23q+XNAz0B0fLyRiRl1kbXe5pB/Vu4jvAcvSd3jSTgTrefUpAfOfc5zdiFZHQwZmVTdj1NVX36fnKxs/p1O5kFQXUV62Te9k5XOsV7QhZhmQUJhhP31JhdyJkgSApB76PXXbKc9ntnl2bd5LXZeEh13Sc3d575Y5KK5X9Hu/V7wOv0fP8uSk+h3Lorn8ZFzfJtq288mkmM7RYpnbny6OW98+xJgRJ+3XEPaRst04dxkMBoPBYDDsmU9/hIXf+FULv/3vGB73GPmuqn94L/DTbwC+9Ms5fvRlJmWjwWAwGAwGg0HOaCjf/e26tEO735OkHLH03ExUJDEFYg8OaGHTcWg93JIEDvI4Tr2jQBJ3F/BEkX6ANYmLu+/3ESSVnjepBvCadnX7vl4aLc/LFprzbCMIKJ9XN2VTGzcCx6W6Y9tUZ/McHJJ7gvi9uJUuaQ3rKJ9XxbjkurZZ07X0+t3ak3huOsQRuQh0pddLRZkNIpvlgtrHvlLRXCV1QS5VObS97/m8Wi8EPb+YUkfgOPXBNN+jY+q6/LV1/2jlUqjoP2bzottTXdon3QBwW+cmMa5ZNmCn+fiCNbn0JAkJDs7O1MLCe92LnpFog13SnOrQ1vmjzHwGgFP5yBzn2lB2Syn/d9sx2PXk/fFM4nbUBIO8DiRJ/dhzcqKfInpbrl2rujY1tScdt7yu7EKM15Xp9OoEdXlcr36eORgC97gH/XzMx6j72kVJWDkayvvrJnja12w23YRdQP08bTBQ/y3fxpZLartRJC+fwaA+DaXsO6qplkiFDJB4q9wGtp1TbyN8bjM9rDuP6h52IbTSFbw5LvWvunPUo3TOLBsnXJec+cpi43LdK9eRkSLdZVthl2qOHMfqdLrCSS4M2gm7HJFOu4tTssaJ6sabssjvKkT80ymJmYYdnJTLHBymcw3JmGs72Xim6lc5p+vIf5+xan8rjpckNPeazeg706leitU6fD9rM+I6Vqv284Sm/lz8vcv8o22qVB2MuMtgMBgMBoPhiphMGB73GAv//q0MT34Sw1AygY0i4Hd/H/iar+d46jMS/J8/5+BXuUXcYDAYDAaDwXDdcV0mDeKNx7QQLxOIhCHtgj09re543ay3d0G6OKcgx8FBtniaJMWFbLGYL/7uuhRg2nbhdls4B6Yt0lB0oddTp3XJ4/v1ATfXpZQPdfCEgkVhQOI5sdC/LZZVXKD3NYVI02n9PRU+O6HPxqnziu2ku7gn6iCF7RT/tk1qmybBoeDoSB40ZBYArk7TNBlnQp1y+Ynnpss2gqv1Ws/1K0koCCMLxOwDx9FLWyMjjorpt5qoC5J3TQkXhnKHlF4fAAP6pXOKelsnihBOKK5HwckmMaNuvXBccnWpSylWbkvTqbzcOKex5eQa/X/X8cR2mtMzlakEuhk5iOUdzxeL+jIejehe4x0FXn1fLfJRBa4FOmI7ju6uQALXlQSeJUtLZVfPcj/sunTNol+TjU9dXKkuLsihUMYuhHc6bXwyprrRRiBS/mx57G0jwNIdYwF6DsN+t7Fex6mlbbu8KprSbzoWiVwe/jDqp1T9QHnTQzklYRuSRL8PVNUHlXDTstRC3/zcZbWiFJLLpTzIH8dQCldcjzZsTKfF565y+F2vqawYqB89P6dr6ZLevNfTFy/p0Ka9XQ/3zDYsFyTG0p3jnpzSM5O5YZ6ecSxX1flKWcTmudQWNgH9xBHNAUS5VuYjGuGJ6bS+fahczzabbgJSzum6uoyXuptHXG+/wt02nJ8DiwbRoePQ+HJwWP1bvv0tFln7BorlYTF6JstlvXMiUOxL16t6Z7vViurtbEZzsl2kBxYixi5zvH6/6vAtI0jFtGGEy4nANhvntsWIuwwGg8FgMBiumMGA4Zv+JcNzvhtSgZfgL/8KeMazOb7mGzje8m8TLBZG5GUwGAwGg8Hw0cB0ynBYWpA9OqbFy5OT5p3kHO0FFL6vdqQBiovBqkVesZgvjiVEArqp5fLs0qEiiuTpIdowmVQDsfngsVhQLl93P/eZwYAWvNvs+hWBdlX8YT6nhf4LRZA8f24d8oE+Uc/8Xn0AhIOefd27jeDsvFh/4ojqx2IBnKWikSgulmMUFoNy24ieoqi5rAC6X1mMIInp+lUuDxdpwMJySKizzcK/SuxWDgoLt4fryWSqFgfk608UVZ2VdFPEBGmQX5nWr1QGKgelXi9ru11EKQVBa/os1qtUrHJW/exZ6Xcyoojqznq9u3R8DCTEUo0X0ymNE8fHWTqoszNyt6qja4Dc9Sit5WhEAiFdJ4NyoDuJgNWKt3KLsSx6Prpp6CwrdQhTBNk3G7lTnBA0q8ZS162m59mX4EAWJA+D+nEeqIqHwpDudzbLhKAycYau85yA8zTN5hZ9V51Ti0j/VMdsTkHybZpcpZ5wclHV6deiFnU4DCmtZ1OQXTZ30hE0u179eLXLMaZ8jdvM98KInuN739cu1er5hd5cYFtk45XnA/e7L7kJltvjyQmNBXkmU3qGsmcQhsA9bqr+frORCyxsm9yZ7FTMrzMGLhY05+HIhOuDgf68Ms96vVsHOI52YrFtqzGzml1tbYf6gPJ7nA7CGU6XIGhwTuP0nGRtm1nUrydpmrn5jNpSfp5VFqzrzE+6pm8sbzLQ6beANG3zhbpvrBNlifsRm2SmqeixfO4o3C4t5TZI21kquJfNH2wnFS6P07StJfLtLy/Cs6yieNvzqC6uVlQnhOuyjK7i4PmMjt9LxaJivtcktipfhxD26ab99n2aH0ynVF663zs/p3OJfuR6vnoZcZfBYDAYDAbDdeJLHmXht/4tw9OeAjz1ycDN95F/7vbbgVe9BviKr+L4kZcleOc7jZuXwWAwGAwGw90ZBgp8j8YkmBmNSAQTR3oL6ywNTIvF/DBsTs0oAkGqYGmvl4krgOqCMksDRY6TpgxMd4PrCBtk59JdaK2DWXQd0sVvTUGJYLmsihHygbYwoPPkBWC2TT9i0buncF3LM59TKihBkqQL7hquYHXoCghkwqkwLNa7ilsDpwBbXWBVlJXKkSWO6RovUheCXTz/beCod7OynfpnIgINw0H7uiYQz/7ynHbqqJD7jOdRaqltXg/DcPu0jI4jdwUC6Nh1ThS6qSob3SzK51dcz3qdBbS7lFs+6FQOUm/zlh4G+s4TttOcgqgpgL5eZ3UzirNUt6rnuC0ihViSCnr6fT1Rh0p8kNRcaCUwyEhEsVxmz3wwzD4nc9FJ4m4OUo6jrgdhWK3v+yjuLuIZURayNiH64/VaLc5wvTRV1FBP6Ht5bI0CkLmh2akziWpeI9LW1YlQhMBsGzbrYj05P0v7PJngpHQfQpQhEzGo7isIModIWUC9nGrQ9/TKeD6rd9thjPqcpjRgOoKfsoB0m2cg0rG25XrOMWyLrpsxmvPVCedGo6zMVc/x9tuL/6063MEhue9eHq90zPGYhGTalMpQzP1VdBGDNcGTdmIxDmoTXcWKPJ3nHx/L72c4pHYp0tb3+yRw93vVlIed0ghqUB5jhNNvGVlbrtQxXhSzWXazu/BVoOsWDOg504p0mJZFZWA7xQ08XeaKXUWr5fFANv5xyDcaAOmmmZzQzfer40n+3vxe+m7BSDAmKL+7WHZRSOi6dFwhAG9CNZ/abOi4XuqsfHAAHB7U9yX5OcZmU/N8FO18s6GNRefnNPa1fb6i7XRxL9wVRtxlMBgMBoPBcB3xfYZv+DoL//LrLfzqLzO85MUMD3qg/CUrDIHf+33g8U/ieNwTOP7tb3LM50bkZTAYDAaDwXB34/yCY7Eghw/XpZQFs3nz94Bc6ogk27GbCLEMp4Dj4aE8KBEEwEKRlkU4TKh2lE8ntOA/ndJC+skJ/SSJevHT96vBDqDZmSJP3Q76y+BdaXF3NGqfKlIWqF2XFrPjmBacj4/p5+CAFuDzi+FdRDRJUhR8lRkO1QFX8ftymZbXu/upQEi2QF+X1g2gumRb9cFS2cK5rqjnqhG7uCv3kxaa3wPA610/NhsK2p6dyV0VdOOKt9ycXlMq7AqjYiBTVi/LAZFdpkC63FWfq2+eR/ejEgaoAjoiHafq+vL1w3XbB8rqRAjLZdVBSZAP+rpeVcQ3HlFQtuyOOBrrl3XbIHevlDYmjiggtQ2bjbyvVaXKayPYUXF+DqzSNMGOVX0GsrGijUOXYDioPgtxr6dn9P/9HnAwoSAnAz13cf4kyYQzQrQsoyyuES48ZVcv0X57fdrU18XRRZfppL1whVkUTPV7+m52+fooxlPXLTrjlRkMaGxs6/Il0/GJ9GFhqBZqiL6yS5DdTlPIqtxjxP2X50RphjA4tkR8pVi+kzoy1TyHixm5eEVRVSBSPtamydlHE2aRqMXzqn2rXRIh5/H9VBSW+/2+3OpY+j/jGvHb9Ub0JRzUp3LeICxgqWOohnhCYDlyUfnZKfV/snkos1JxVoOIOp/2rHxNQUDt5uCw2mf6vr4Tk4wubdjzqK8tOzdFSU7kO2ifbk+UgWwO6/eoTxCPdDCg95zRsNj8Pa95bt0G38/cwsR70WpFZ2x672BMLZZa58Sro5F63nSVuG5VCF8n/td57xJz9TCkuf18pv8uqqqbdeOwarPPcJj2tX4mQJdtnCk7QI9L89H1KkvdOBqlTl65OVwYZfVks6Y5sdhkIyj30+VxeDKhcWoXDohhkDkfi/UOVQpix6Fz5sc62TM+OsR+1POgMhXvPrL5/FUIIDWztxoMBoPBYDAY9o1lMXzB5wOf/3nAr7+F4yd/Sv3Zd/8d8IpXcfzUTwOP+kKOr/wKhk/8BIBd73wcBoPBYDAYDIatCQIOaKbzKmNZFGhbbqpBWhHoXK/lqRoA9TpoU5oQkUbGYsVFVs6LgcWjo+KCbFOKySYcC6iLj4RhNRAp0o+1oRysH6WpaaIoS9VxcUGLvJzTwr2VCp5Eeexrqr5cpqmhLHLtmOVEH7ZN560Edy0UXBfEbnWWHqNup3v5Nm69td6dajwuXhNAaYnGo3rR2vXAsim4KRO59fvUDoJ12k5qKl6Tk4RoZ4zVB3bnCwq6bdYkFit/drmoBogcp3h+26H7apMOVMV0SkGvvItdr1/tN266R/HZysojjqj9yuj16D7iVKTK0n6NpQFIi1HZ1fUf5WfIQMcRASvXLd4HS495MM3aLAdwkrsP16PnUL4fIXwQ/24S17QV3MlSAO4CkXLHcYAgrc+qa99V98V5Ne0YQGUyHND5y8/OtqkO1wleynheGrzk1L9durXl7i8IiwHcfDnnnRlUrhSB5PcyJ7x+n4K1DNS3Wha1Jd3UlP9/9v40SJZkvQ4Dj8eWkXtm7VV3X3p/3W/vt2N7eFiGJAACAilqsbGxGZs/I1LSkBLNRsYxk5lsxjQajWQkSEozFIfLcAG0kCOKAEEABEAsxFu7+/Xrfbt993trr6zcM8Pnx/Ev3SMysqpuv8YbgIhjdrurKiMjfPn8cw//jp8vC89bTJDZ3Jwn6JXLNih/mpLgWQnIniKJJI7nSTTTnLIFgSV3nJZyaRFKJT5P7r+9jZly5Fz5PMfPfoBnJRM+L6/vlcd2XETOOO7QH0j/xuWzjWHx9WeBTuwUdNb7u4jLJEictb+lDbOKMUvLQBB62NnhH7OE++GQ48wdd2dReRH4/tnIaWLXgbl+mjw6iSwISK4QBdFHLUMWtSrncBeyDhwMzr7+9byTbXiUmY+mE9pfXpmTKX2QpOItl007KdprnkpQCu5gW3BtMvnwCXyVsiWNTg35/rQ0eaOR8buZdcY0846SXZufti5b9D1+wLJJGrtyJb+ZptMPlhZ6IRTTngtOIrfXqsB4yrEyCXhtrUpfkCXju83gecDa6qOlP50V75Q2DcOzK7DlXXf1CvDGG/b3OObhh8nk7L6x38cHmiwelUxdijlms/NH3mGH8ehs7dIxfsYPWAWp99BJ9S0EPlGsPE2FMZtC/XuldjidnPxu6Pus3yJ7Kpv0jsqssz7sdJq+8Zuz99ocm9nbn//bh42C3FWgQIECBQoUKPAHDEop/OzPAJsbwN//hxqf+yxPX/4P/wi4cSN97XAI/NIvA7/0yxoXLwA/+RPAj34FaLUKkleBAgUKFChQoMAfVWikgwbZTfPTiFoLb3oCkik3iic5wSQ3ID8c2mDiWVUVlEfCTTYQ4gdzWWFyUSrN1zmKzhBYUfb52U3tyZiB/WzwTIJ1/T43ld3gjz5F7emDQmsTkFHmJLFT3vGYhJRsObP16XZZ3maTJJrxOL994jg/oHWSeeQRIM6iWPAowZ4PC8kUmHoLyAeK/76bFIhZnOlW5qJFz832RzZg82ERg0S5qVYHykbhYDplEHl7O31t9wxBa0kFu7QEtq1Tv/HEkm7EVFy7bp2RGOP6GFFIWRR0ls/39hkYcglBglp1PuAlihGSfrIc8+eT/Ivbly4J5MNEuUyy6XC0WOVL2uI0XxjHwMYm8Pbb6fvn+VXlnUxQyfMHAO2p08kvSxCy7bMBv1p9Pk3gjJSVUSLJ+pzB4GQSq9TtJIW0uQxWC3y8Uratd/eB7rH1yYtUrk7CIhsOw3z1M1EBBU6uc7lMwuhZIPP4YMhxNjWkkUU+e2ICtp5PAkmt/ujqc+MJUAuAcd+mclqEMKL9C8EjO25Pg/RtVAJqKl1WnZxONHTHdJ4P9oM0kaJW+2Dz3XGHamgn+fm8qWM6pb9KkbtU/sUyxwxH8+NzOk0rSCY5ZDF3vfGo/u5MxK7Ajns/SJOmsu28CMqQAfOIAIvK4J1A8CtF80S3ue97OX2QxQKGYr3OdXGi89P5Li0B9+7n31JS8GbxqATwPHKIp4DBaN5HDYf5CnxBwHHa7dp2zluf9/v0lYvKvgjZgwV5dfBNqsG9vfRnQiqeTtP9PBrSpk+aN2U8XrjAnzvHrGutDqwss1wnEViW2ny/OTqi/44iAHqeLChw+04BuTnbSiXWKYqAkmfIT47tVMvzxGsZuyVzYMVVLXsUcpocOsiz9zCk3TSb5kBMAvS76e/G5ZPnizt3078PBgBMm1Sqth4nvf8u8r8n+aw4ThPEz/Lesmic5Y2PMxPeTPmmE6pGHx4ZBWLnnsOBHbNJ8sHTRgKs50nk1LMQjvPWkGeBSw7Oez8XJa0khwB6lsMXixA76Svj+BQCa6YvF6VZ/m5QkLsKFChQoECBAgX+AMLzFL7vS8D3fUlhOtXwfYWbtxLcvcsXqGtXgXfeTX/n5i3gr/xVjb/+3wLf9yWNH/8xhU9/EgiCguhVoECBAgUKFCjwrwNaLW6EnqR64KbHq1Z5wrpa/WAqYN8tRPHE3Uh1A8t+wADBaZu7CtwYdQMJskE7PmPQzoXWjxaUiML5NtfaqCWcwM45PEynoXE32ZUJ6j2KEs2p0CYdTJaIdsZA2OYWsP2QPy9q18Eg/2D7SWStD0pc+f0idp2keiPPzSMeeiaN3Ph7dHpdnnkahsO0KkIYMtjf7y1OyRWGJCEddR4h+O387PsnKwA9iiLfeIK5ceTlKPK54+UgJwCfp3pTivmdRw0gnTRmsgG4mcqXsYtaHdjdOfuzwkckO5xEKHAxU2uIgOkHDKIJooi+bNmkqJS0o1mcxa+eRI7M+77n0VYBEmC6znMHQ7afmzJSKWB/n98LQ5s+U+y11bLf/V5hMATGZyQyNer5ymaPglot3T+DgSV3nbZ+qNf53bOqKwk5YDI+m89OpiQtBD77LUs2O0klqRzzs8nkdNJPuUwlOE99dyqRve53l07ORaNhyX/TyTzR4YOmTzytbnn+YjwC3O7yfZPC2y1v05DjTGB6OKR9HJ2ghHKa7X6Q9UClcvY5JTsH+B6AnDmuXOY6TMiMOslXnMsijKgipRTbbKZkmSHGDUecK/LU30TJr9c3KeZE3dWkRnTrMDZ2EmfWJKLY6SPfp4oq13fj9wWy3i6X02uSUgSUlm0qO4A/R9ECVcOcvp9MuJZ2bT/3AIGet3MhlZ51jZu31ul20z7IffZ0asg6brnV4neIvHXjdEK72s8o+egkTQTyPEtK0dqmpkumVNUcj4Dyd5kaWewwSeh7s6TAMEqTtpQHQFvFxMmU/ewe0jkNs3e1Mclb5TJTg6Zs3JSjalR+895hTiJ2BeHJ5Sk7BGpRfM3OkUD+e0GtZtTfuvlpGrM+Yzx+dKU/scvjDiBFEv+ShyCg7fhePtnP93kAImtzgCUnAeyLbveDkZ3GznxfKtHXhYFR/vP43pxH7vI8k6bXqBF+t/4pj5B+3OX6Im+MVqsfPKW5qPtG4emk0Sx+P1IRfxfcvAIFChQoUKBAgQLfC/g+V95/7t/z8I/+e4V//88Ct+8svn4yAf7FrwP/0V/U+Omf1firfz3Bu+/+PiUaL1CgQIECBQoUKPA9QRiRLJRNvyJQ5tRzGNpNZaWAdosB+coHDApI0NzzuIkam39hyA3mYMHRUd9nYLUcL1Yv0MnZN5WzsR79KCl39HzqukfBwiD3GZbY2UsaDfP3hEF8IR6chcQjUIrtH4ZpxQwgP93bqWl2DCLnuzpZfNLYy/TnoyjP1Ou0nyCYD+C12rTV0044P0pbZSHKCachygnSKQArqxxLpdKjBfwrFaOqY9INhkbhKPAXjyGAgbRHTd0jp8qVtzjANB7bILxCfhDppNSBkVFSOn/OpnsTnFSfPGSDwLV62hYFp5lxNqgfLyB2naX/F+HggESRZpN9mqqrKaCn0mSKWjU/WB2XqRbSatHul9r8twgSOD7r0S1XRfGsKomlkk3d4+LoyKSZUgxmZoOij5pm8qzwA/oFgPscvg80W8DysocwUJiMGNhLtEnVM7XEv8Skd/Q8qqdcugRcOG/Lela/JYS2s0B5+fPdo5B4ZVyGIW3su5m7BEL+qH9A2xf7ieP8tcThEdN8nfl+pu3z7GY6Xezju12OwUU+0R2Pg74d+3k2fRbs7jKgnxckz4NSi8ewKO+4EDKH4CSCQhjl+9ZSifdp1E+Yk05xGsozpMfMdWHAdVwQ0IbGo3lCiPi2MPz9OVQanEI+bjRIZKhW5j+T9Nju993xH58ytvLaezyiL+z2aIezLs1bDyrrv9zLplPOE9NJen4KfKonpexVc67w/fS4OO5wzhuN5g8wyO9RtNjPnaYqJvA82vTycpoYIhgO0mqFfnA6sWVtnXWqVHn/s6iL5hHY/ODR1p95dpT1r1lfMUfoOWHdnx3PAAl8c/NlwNtUq1YdLEk4f0nbjTNkWY15EkuzyX555DXXCfNRLevfFdvg4IA21+2ejdiVZ1+i3rXQPhL20cQoEZ+WQnhWRLXYxWXHhoyh4Yhtd9ozuses+2BAHxxG6XGYN7eXH3G+ybNLrRe3c6XCdJ+lOF99a/8gnwxbrabtPQy4RyCqbFlkx3rWzuQ7tRqzzZSN79LJYvJTYtaPwwHTt7fbi9fklUqOPRqUSpyzwhw7k7VfHtn8u5mlxG5399JjyD/DO+lZDtA8KgrlrgIFChQoUKBAgT9EaDYVfvqnuIn+i/9M484d4Md+ROFv/12Nwxz5/7194B/8PPAPfl7j8cc1fvxHFX74y0C7SNtYoECBAgUKFCjwBxblksJQpU90N+oMNJZK+SkdtE6nH5DVnlLcmC7Hi9MtVaqLPxPlk+NjbjQ3Ggw+522a+gGQyIanczLY8/JVXvp9YHjGzfvvBtMpg9ofVLkDOF3tKYryn+Guuj2P7Scp246ObNC8XGbw4LSgmCh/SPtXqgxquqe8syfSz3qK3PPSKdV8H5iYE9h+YNJ4wAQnnY3qahVYXwfuPzg9rU8YGhWbEevreYZQUjrbBjnAcpyaysjAVbSS38+Sy1PBBE2ca33fqFcYwmIn5/2rVud3JaWfBG6jiHWXMRpGDJhKXyUJ2yNLRPL909Of5OHo6OT0a6m6LkhTchKJbjTmv+PuvBLLSaQ33yiQuGUrl9PqG8cd2rmb7uaDEPp8fzEZtlY7m+pfVkVJa/4u7VWuUMUFYGBew6hC1K0iSRDmB6YHff7rdqmAIqTGZpPKOO5XZJy4qeiiiNdmU2K69Qd4H0/lK1WEoVEi07ZdgPxx/M475pqcQFxeOqE8lEqsy3B0NvUxgGXzfetblQLKlfQD85SERCGi2bR/W17h/W7dJomib8aVNopFeb5Snuvao6SBcwO7okSRpzp3GpaWgb3d+ZS6H1S90A3CukH2s6hxlcvptKphRLuQoHZo5gIhZNRqlvTn4qS0XcPByXPFB1W5cMeZdsqQR64EOGcrZVKfnXVOyfzeaFr705rPzVNrEt/hQkjzgX+64pVn0gXHZRIOpK6JIbq7dc+2/Wn+U2vO43tnILEtUt5rNhW6/qOnvzwNkwlwUrdEIcldeSTMPKWdeo31TJLT10WTSf78KOPyNNLmeATsZPzzZEwypEsM84RcZ+ynUmb5xiMSHNot/uyOi+GI/8IQWFkBDpxnaKMUNRzZ9KSyhvB9S0zuD0wbneC/q1Wu2SYjoFIDNteBBw/t59kUzEkCLC8B9+7Zv2VTtEWhJeO5vuQk5I3PIDi57GdB1kd5Oam3PZ/rlmnyaKkrFfJ9z/4++0HSLWrY9YBSZz84cHhI/37WVOGSXlDWm7O/l9jHOuE6vFw2RLPJB2ve09Qn89KIAiyD2HitxrY5iz8ZjwDPEKqyvi8M7HtAHjG90VhM3pX3vumU46hcYnuMDdEyTykZALofUir0RTg6skRrpeb7KJkCSc67tRAhtea6VN5xhs46u91e/K6cHX+uP5pM8hXCw2jeTyZTro1394wyoRAcM/64P1h8AEzSuudBJ4+WujWbWnMRRiOWaTpNX/9hEPA/CApyV4ECBQoUKFCgwB8y+L7C5z4LfO6zCkmi4XkK/YHG3/n/nLxZ9+abwJtvavzcXwM+/1mNH/sxhc9/9vfvhF2BAgUKFChQoECBD4ZqXSEani3QtvAetbNf2+tSQWYyZbA7FVBW86lPFgW03E3YwLfqBCdBIT9o45Kpvtt0Bkny6MSu7On9RiOdErNsCFWywbsouBLH/J4EeKaJDbJ5ng3Cd7v5J8h9H4BR6hr00woJQJqUJwSRvGBIrc6N84UkIVO+0dCeuJaAmx+kn7NIueUsQa+Dg3R/JgnbR0hNcflDTlWZwaMQJnSSDuTX67b9FPL7XNJ9tFoMoE4m9nku4SKZmsCrE1SpVDhmJDDlB1Ypo9+3zyuX+bMbXHDLMp3mp2pZhHJMm3FThgEM/pyFDJINyFSr6TYul23gcDoBujljPQWT+sRt/HZ7Poh8GibT+bLV6iZN4RlTOQ1H84Qf7RApgiCtHKBhlWEk+Jd3CCuLo45NgZRnV1nyBkA7EQJTXpqZLIGmXLZEJ53QvoTEeHjIZ+zvW3XBRchrt7OoEghJSMqytzd/TZZEO53QdlIpyTQwHJ4eck4SkmBuDUmW8Dz+f3mFn6+tk1Q1mfCzyZgK6TPlS0nDmuMvphNL4PI8E2Q1v1ergC6nydZyv7xApWvq7ucnkaMWYTxme7lzSeDbNj3L/fp9tpP4//Fofv6MDfkgKlmCsptaLjAEkkct/4eNSpllCHzO2f2MDxlPrP85jegTm7Svi5RgBBqW8Ob66Dw7Ehvxz6CsKmM5G0h27zse22e7WERucwu9t0/feFqQu99fnCbxLCRyNxWd4KypgfOgPODhNhWS5PeTFGGVR/t1x2cYkdSSV/cwoJ08auoyqWe2TUrxPEHQMyqbbpl8n+typU5PIz0azxMKZa3jtnUQcMyKDU1zUiJnoRQVzpKQ63kvu07NpKP0PX6nXp9fS2bLnTjzXSqtsnO/eoNE+jzbGg3pb7PvEGcla5wZ2pDQDeHOXRPk9U0Y2nlUebw+itJrkunUru1mZR6yPR6FzH+WcdNosm2rFUvG73ZtO4Vh/gGfvDShZ8GjpBV2x75bb7FR8TWnplM39coj7Q+GtBXXhqYT1j2OOc6A+YMgk4mTbnSUfjdZWgJg1uHKqANKe7q+IgzpV901eXatfRJqNdpPdj0BcL226N2zWgEOFsy/nllrZobuHOr1dDmrVfZXHpl+0Ryz6H1uNm9qtk1cSvd/s2lU4j7A+/+jELuAR1unyDgJQ6OEeoZw2lmVcx8VRVrGAgUKFChQoECBP8TwTLTsf/3vevgn/0jhP/rzCu3Wyd+ZToHf+h3gP/lLGj/x0xr/xX+Z4IUXNZKzHrctUKBAgQIFChQo8PuKXldjMOSGfKPB4H1WHeckeP48UejcufyUIYL9PRIE8tIXiUrPeHwyaaTqBCiHQ26o7x+csnGq5lPgxfF3T+hy8UFO1WaDGZ6yQYMgnFeZkiCeBL0Bq1ITl+0GvBtAOUsdp1OmpaiUT79WLVDNUIrEhURTLaqdkzZKKW6kd3K+P9d/Oa8NZw3Mnlbnsyg4fC+QFzA5OLBVj2OOqUWQgGrdKN4kSbodp1Og21msLAWwTet1EjWrVQZ5ajWO42qVwY9GM+d7j5DuFGDwJknSwZlHTfNzEgb9k8kFwxwyilLpMTad5qdfORE6rYBRrTLINpnYwO9p6QSn03SKnUolTX46OiRJSYi4bjWlDU/tC2WvO0mBw1U78wP2/fVrQDvHBvLQ7zNAeHjI4GunY4kqWlsFoNOIIIvG6Gk2Mx5xDEhQu1Gfb/9qNe1DAfquU8kpCzCd0pdrbYLEfZveMQgYTNzfJ3kpq0imk7Olm0wSzp3ie31vPrhYq9P3PlL6Xe/ktIyL1F3OrIjmIDufuUqQeRj0jeKU6XPPo59rtYHLl4DHrtvve75RJYK9tlw+e4pIIb6eBe78MhqyfyVNYt4cOujbOfk09ZtFZJGzKiTmQRtyi5AeXSzq99HIzAfVtH26JB5XrS0vbVdeyrTjztkJMYvGxVnm70VkyUqFa5NGPb/Mi5BkUoA18uwq4z+ytnDSmFEeU449KtbW5v/mB5mxZux1MslJY2ZIzltbpz9rd2cxEVvUejyPawZ3PitnxlWzyTGc1/6zFGzOOn86nSeyyHUy/hf5qV7PppY8d24xIUOUY8tl+hH3foMB57MUsats55FF/jaMHi2VcPZAQhDQVrNz1ez+zr1l/qrX5/2Ye9/TFNTykE3LPnt+xneMjMpRtl/zxrvbZqLkFUX2u0FgVfLOOp8pdTJp/CzpF7N1mrOrBWsEDdqZrD0ESULf3e1yHS79JOlnR05av7z1R79PG04SS97JphNXypIY3bZ6FOKhUvnjUWsz9oQcmWmfXs8oIruHSvLWouaAQK1uD+54hqDpljkusw+yxC6lOAYlHfajrtVKJfqU7HrA9xbbVxTN1zcPuWNcnTwOzwppo2x1Zd0mflZ59t3tw0ZB7ipQoECBAgUKFPjXBJWKwk/+CYV/8o89/Ff/JfDJT5x+6rbTAf6//wT4s/+Bxs/8KY2/8tcSvP66hj6rrnOBAgUKFChQoECBDx29vkbf2ej3lFVmOe0EdSkGWg1DtnF2HbW2m/vZTXKYRyUJrylF3BCeqUodW/JO3vOX2gwglUrA5obdOB30Tw+Yet78ifHsxnc2APUoCKN04CAuf/AsLnFmAz9bzumUp6WXl/mvVuOzRsP0NS5cFYRFqheHh/kqN2dBq23LOjL97+VsmteM2lIeESWbQiXbp9PkdALdh60V/EFeV84SQAIsGSsLSTs2HlGN6eHD+Wuy5ZMUlKNxOngrgSkJhDSb+YGI/X2ma5Pg5WBAwuT+gUnZmEeoybSNS9g8K9FRI91neaTPk+C2tUZ+8LxSYb3H43RgWnl8eBxbgqsovJ0El1wqCAKWvRTbMh0enhxcc4NGGungmJBSJBgmKmpum5+FmOAGwBalKHIh6g0C8ceBUXer1RYHehdhMslXCBmP84k3tZz2FcTls6kldDomnc7AkslcKG/eD2qcPU3VQijbPvfu2baU9DqDAdt0ZcV+xTNB7SB4NLJjr5dD+DH20W7b+eG0IKiMibwx63n5BJ1HRaNpCGR5yhKnlK8ck/TSbBplOUMEKEW8n4xHN0hcq7MNNjbO3qdJYuqb47/dlJtAJi2jpt91bexRAs9hZMf47PumHJIiEUgrk8hnj/qsLCkAOJkUf3xMov1knCb4+j7XYS6B51EVp86CRWN9TtUJbMdG/fT5NypZBcGltkkDeoY52+1fUeVrGxJ7vGDeUiq9DhbC6dKyXSMJRkMAyhCfWqeXR3BwyOvd+aDVTNvTNEMaSuG72JJ1fYPMdUlCe2407EGFrI0Fhnzm2kx2rbW5AVy+zPVKdu4aj9nPV68yVXerRVvMjgWZy6tVtnkULj4g0De+OQh4b99Pkyomk7SPjEs8DCMHK5ba834iSSxxp1pN9+tZxq2ci46j/DTFC32b065xmW1Yqear4Z4FVefdKC7buYX9qBCFKvcwh0DG10xhWc2PGSGmCcHRXafG5dPbq2rIdovIdNl31UXIXpJdD8oaU2y3FBsVyVPmycGA79dHHavoetzlP3mGOwZEFTt3eGYKqbVJRV5Oj++oZMlSWQhxt25SSvf6wGGOineWzOmuf6tVU2bFAybSBu54jyKuU/Z2DTld9hhM2k7fZ1vI+uf8uRwCqqnjxFE2XFqiv1/ke7NlBeyzUtfU8tecopilfI69vDYUNBqW3C1QWDw+H2Wduah+2qhzy76FTuZTVn9YKNIyFihQoECBAgUK/GuIT3/Sw6c/CSSJxsvfAX7lVzX+xW+cLP27vQP8/C8AP/8LGlubwFd+WOOHv6xw5XKRtrFAgQIFChQoUOB7juRsaRvy0jsojxu02dOp0yk3Qk9Ke+d+FphT+CcRaUoRNy6rVeDKZSrYjMbcRJ5kAkiSai0vcOT53JzPS9nzqEEuzzMnln1uzruBOlFNKJctmS2LbFqfShVnOiLb6dhN9EqFdckjMKSedcL9sml2PggG/fxAbKPB8krwYTo92+nlIJgnryTTeeWX1LOa+Wk8vhtMp/Pp8oD8vwHsi7NssCuPdckLrEemb6cJ0DmYD1C4djMYcPy4aRxd9Pv842CQTsPjPtcz5fB9m1rMtafhMD/dx1ywLbHjqtGkAlkeoSj1PZ0OmJ6WXjWLZuNk8htgU6Jlh3fgM6DuKQZ2g4DlyQpN1+pGhckjiW4OyvqBRYjCdL8pD6jWgXhi/dXhgb1+aNSA+ka5qBSdTjrLg9vWefaaTXt2fGzKVrXEoaND4O136ZryFPtcLEpTJSlRo8iW4/iY80SQCd6XSvPKjdJ2othzWko0wChp5KgdhSHJiiMPcM2zZNI5xuWzKQP5AX3SLHVmaNR8soamOa76pl22dzg2BElif2+1TlfbEBWmXHvIGT/NllFK8xak4VqQnqvRNPPConlRpefs0KhcLC3Z1KblskmfhnTfPwoODoEVo2Lp2pfWaTLndEJychhZHzoes25nSZ8mSl+z9ImJVbnL2kMYGuKKfnSyYxbJlO3Wm9g5Ly6TUNHt5RPdhSTS7X7IaeEWQCN/fn/UtFSCbHpE19+krsuZ7zsdjWRKO3PJ6GOjmHOaotzhgRk/AxIzRIFrNMonFQh6PSfda0ybOjyya5pqlfPDaeqikwnHQ72eXq8EIXD1MvDa6yd/34VSgK84h53kDx+VpH5WhZnpBAhySA/dLj87OrRrHRf9/vxcL+SpyYTku7feZpssTG9txmWvZ9cozRZwkCGoTCZ2/t7Opk13yiCHP8Zjk+LOSQUr6WJn7xhg+d11r8L8mlVswffn1Sp7PZyar07BrsU8j7Y6GNJPhRHw4IHt2xMPXKvTFUkbDa6JDg7tHCNwU/VNp7Y9FAAv4IWlEtDL+KJanW3ge/SnyrPvkr2eVbByIcTVet22V14qxyw2N5k29STUa4ZUdco65iRk55K4xLE3OuUdanmZ/uqs6TBLEdAF29jPkuNyypR36MBT1i+6aDStPdTqLH9WmXAR3PTfccy+6nTyUwnDlD07hocD9kOrxbWPpGtfXze2kvGheXYCGGW8E1L8hiH/1WqL+3xvL3/umTht1u2yjdyUnaUS+2hGDF7QN5VqOsXiePxohPmxUYD1vfxUoO4c2OmQ2PthoyB3FShQoECBAgUK/GsMz1P46HPAR59T+Pf/rMa/+j2N//EfAy+9dPKGz917wN/+u8Df/rsaV65ofPkHgR/9isLmZkH0KlCgQIECBQoU+F7C87nx7W7iu8g7Na1hiByNdEoWQTaAtwiJCY5lN29dgthwxH+NOq+9f5+B+7yAVr3BNWi/yzJKSpPJ2Ab+KpX8uubVcxGRoF63JCshkGRjNZPJ4uBUvZYmzEUhvx+XT9/8lzX20RE354NTlCdOiu19EGJX2VHQ0Zqb67KxH0WssyggSCD64AAYTk8PrFSrtKfpFMhrhiwhBTAEtymDtaepzmURl/MDIIJSKf28k1LEjMdnSyEjwZTAp/KHa4ueY29aWzUZKV+taoMwkqrtpLL5nlEMWlC/lWUGPStGHWE8SQd3u90T7McxeDelqwSxFkHUFSQV2iLFuFwSpjxaUW3gNIxH/JenrOCSQ6V93DasVBjAEZWtahWATgeD4lL+O68bkFImuCrBYZ1QycBNS+e2l6eAiaviULVEJVE5aLfYr3m+wvWd7TaV2fIg33UJTXGJ4280sm0/HC4mTQgRRcomgbZmk/dItB2TWTKj1mmfW68zQL/r2EO5PE9k8n2TQqvCFK9588wigpbYdrbPKsbvbGwwfbBLvKjW2Z4p5Tej0iXt3GxwLhTbUIop04KAz6pVzTyXX6xZvVxE0QJlpRNuIvXKBmuXl9PteBpB7rjDe5Rb+Z8nU6pVyNg97gBDM66FBDOd8pl+wKBjHgFy0RpBxn6ieZ/OMX+fTMzPo3w1jfEI2DNtVqly3J0lmB+G6THoe0B/mD++PB8oh/Y6gP4vq9wTx5Zcchpm6a9A2+1Ei+ey8YT1Oi2NdmRI8dLGHzTtKJBPsMiSXT2V9j2L7Kscp/tkUcrJPAXY4ZCdlDcvuD5q0QED6eM5ddNHzD3leWkfE1SB88tUu8w7MCHk8/HYKJxmyhZFaRUfwI7RRp1Ev7w5vD84PZ3oSXVLjcmMfch7QRAYgt+IykOCUgysrQC3bqe/1+sZJS1T/kky//ki9HNs3subuycsy9ER20yIHIuI95Ly/TTIgZXxKJ9MAdCfZYksp6VNjSL2gxBa8lx4ySgAa/D/gwHHr9hxHFtVTz9jf+ES19cT8y5SEyUlxXHhrjFFOagUW//VN4QbIews8pmLDu3UaryHp2ivwyHr3FxNz6VhSD95fMyf84g5fkASkauo1KiTcLPIdnZ2WUf3nSMLzxDx+/18GylF/Pwkwmz2e9KucRmYnpGALypUWVSqDoHdjMVynB7fUYljzvNoR7X6fOpAGXdysCaLZAJoQ+JvNznH3rx1trKfiaDt+BFRJnXHXpKkyd4lo8hdrc63vaiHunbnKl9lCeaBb32o79Oe263F9pwlWtWq84cCZB5z34uCID03ufePy5b05b53CFlVfEGSpMlfgkqFddzfp80eHnIdf9p+ShienlXng6AgdxUoUKBAgQIFCvwRQRgqfN+XFL7vS1T0euFFjX/4C8C3Xjg5cPTee8DfeA/4G39T48J5jT/5U8CXf1BhebkgehUoUKBAgQIFCvx+oxzbYEOjCUCnA1TZIEfo2w3PcpmbkcMhU1EBDPaHIbCz88FS2wHzQYRGkwH08dgGQj0faNQYhPV9bpJm1Zs8xc1UNxCyqEiSHks23peXee1e9sQ/bFBsPDbpWxQ3dd3gnLuhLWSAZJreIJfV7vExN7cfJTUXwKD6acG97KawbKZLW3ke+yu7Xl9E0Ftftwo477yXDlRI+jH3dPhwZFWwJBWHtEFW0WiWhiLzGjALdpRtUEjK6/vzm/e+b4LDQT45RJ4/6Fsy2SKUy7xXYtSD5kgKOQbl2lEeRiMSafb306oPU6PwEIYMNIRG2U7KN52mCW7u+Ioio5rzCGNOwypUTKdsCzedEJAf2BeCVqVq20UgQfb1DWA8ZJagyYj2r1Q6ADEe24Bnlnyw1GbA0PVFoqhRLi8mowpOI7DkpWkRG62UgQsX6MME5TLQzwQXpczTKcspAaCZWoAy7ZljC4uCfEJ2ldRnk2maTAnQVyxSWtDapnGS9F8StMzzx9J3QWCV1KpV2piQDc9CDJm7RgG1Csvu+hEhYQY5gfgwot8F2Pd5tjcZAxOQdOUp4FEEoVzlkdkzQ0uQLceAt5z2J4M+bbfVNkQJkyKzk/E5gfFLClQSiUu068QoZEo/PEpZzwLxZwokPJymalaKgVKYJmpk4SpUuFCuPSiOUVEwclNiKXVyyr9FmFPZ0JYsDBjyaaZdFqqtGfJgnqpjVjFO0rJOp6zXSSqCvpcOMNdqXJs8qpKV5+cTb5LEpJ4szftigHVZXl7cz80mfaO0/yJlrLMiNgps3a6Zlhe0jR+kCV9ZBSh3ThTiYl46UCEDuuNUOek3gVNUZtTZDhasLJMUMntmi7aVNy6GQ0taBKge6yIIaGdZYkCrST/WPYHMFob0Fa7/nAvUZ5SOwpCkDCGLiQ/IU/CRNYT0RxDQRqbTtG1l1Z0U7LjLU6aamFR3WQLuWZW/XLSXQDWugVEt6lmyZBjQ3l11tYlR5RP7ySPtyQEDSYt7Fuhkfn6IzfuRKBlOJpYMlQe3raVMx8en+/ME9CN9k064202/B8naWlJBpsqt+Z28NZFLdpIUywDLf3Bkyzir7xnTxnk+581qVSEIWO7hgOPn+BhYXjDXyTrDfd9oNIFKTOWwxBDaXHJrf8DxMJmk55XsO4qszfPS2Xm+URH2rULr8rLt78mC/pFxVyoZotEZSIKCvFTWeXYjJC2dXSsq9o30j7zXeD7HnaxbPJ9zVqXC749Hi9+9j7sAuvRhvb7xOx/gPX124MTM2Xnzkag2l0o2nWpg3smk/4cDoOdzbPt++v0mSYBkRJssG4Wyep3XZt/rAvPdfh9YXTUpW5v82+07+e2RXRfM7XUEQJ7rmCassyiTZf1onuqkXKPAtaSsY7J8RfEdUcmuv09TpJT7n0WJ/VFRkLsKFChQoECBAgX+CMLzFD75CYVPfoK/v/TtBP/g54EXXzr59OSt28Bf/jngL/+cxuOPafzYjyrsH2ic21J4/DGm4gnDgvRVoECBAgUKFCjwYSLwubmcl9pOgRu4s3REsCfEBZUysKtNGhaf10Ulu0G/tJxPkpL7A4bcU7KnaScTG7T1PJOuz1kGrixTaaXbZRBdUsq5a83pdH4zPXuSfDxmwLtcTqed6EpejBxIgGE8YR09xfKX4vy0gs0W/z9LG5OTlsbd2O/10gHkUsyydo9PJ8ytrbENEp2/4T6dAHACL5Iy0YVv0tT5PoNsnQ7vF/g8RS8nk8+06WzaSgIvtRqDCmMT3M1TKMmS+1otqpsdmuCeawdZBZNm06QDM30wGtsAUKvNz4479nt5gb9GHVhZJflKSBx5aQazkBRNfnB6msgwMmXM6U+XQOYGDkYjtoUEP7KEwa1Nfnf/wH7X92wqqjijTJCnlFGK7TNPSttZjjFLj5SHo0N+NwgYkBkO8pRYWN6lJZJCXZWpJOEzyjGDMwDbNQzPRoLM2n4pZv2Xlvj9+/fzyRMAbf3BA5syb6lt0yW6kH7q99MBXdc+J9PTCTepcpuAuW/GWp5C2XCYLnejbkkJUWj6xqh2tNvA8lJaEWt/fz4Iv7RE39HrMXiVJTFmg9bAycpq0gaex/RdwxGDrpUq55ogTKcoFOLVeGTSCGZIEKUooxC3YD55VMyIniaoLuSHMPJweKAxGgIDSadWA46O520+ioCnnkBqLO/usR9lvInNZgOYpciS8WZQi1VotrY47736Gn9vNBjUjyIwlxNOJjbKmF5eZtlOIv5kXZPv0T6lb4U8OJ3avy0ZtbgPFGg0AWpRLJQ+mUzo0yITKA9Cq85UqeT7IA2jDif1dpQdxZcrxb+3miT6iD3GcZqstgiXL7Ed3ZRgQgRoNlnuRUqGiUkjmCIxOWnsltqcuyRFaxZ54xGYD1BHEQPdK8tMC3qaMmjJqBGK7cUlzjkry7Tnhw9NuunM3KHAddBZXN0sZVvWjxti1kx9JfO5551O5mzUbd9liTqpRzk+ejoh6WERKWxlmQQkUR1yCSbLy2yznZ15lUTfZ5rQ4wWFllSEx900qUbWV6L8lvUDE6fttObcmUdKkzVzo2Ft23f8UJ7/nqUmNYTlwJAV8lIwKqOGpBR9ynTK/4ua4ywVmW/vmYdGnWMuji0x0V3HLyL6umqYWUQR77t/MJ/aLQhsGtzhyPqPco5yrudZZV7x5SelCjwLua2S85zxiO8fUcT2DoP5dTBgiLPD+b9lfYxSti9lPIzHRu1VsQ3y+sP1pZLybzyZX88mUyCuKSTaqBRmyrq/bw4L5aBS4fdlPu8c2TpliZ2zcnfnCcMXzwPv3Zh/TRsM5t85l5Z437oz17rrD88/Oa26O5fkIU8tcDji+Di3xfG5SPnsuAOUlo1KnmnPRt2OK/GHCuzLzhGVS33nPWzvkP6r2QSSEvvsLCmmP0i6ZIBtF0XA5lZ6rEobhaLi7FHZcPYeNaX/d1WH+33eb3XV3COzfhn06VNqNZO6W563oEOSBHj4AIBmO54/R5vY3pm/tlIx/jax5Fx5dquZTySWsTYczc8ZkoI8a8NS1N1dzhmyvskrO8D6niWF5+83CnJXgQIFChQoUKBAAXz0OQ8ffY6KXt9+Gfif/4nGb/8uF6xbm0zTmMWbbwFvviVvqfy/7wOXL2tcuwJcvapw7Rpw7QpfBNR3ozVfoECBAgUKFCjwRwiVssJgaE/4+j6gFqT38zwGk996K/33pSV+f39kSUdCSPI9Kg/NAoDJ/IYu4KSqUobcNOZm6/5Bhjhk1MQiJ+B6ZBRDRMkHmFdUGA4Xp1dRihv9e3sm9UGLG+PdLjeTTzrtnz2sMJ1y43l3l5vvkZ9WIZlkNtqDIBO4NMvYuYCp2eQeDkwqrKX5TWNRGZHUFtOpPa2vE6awyd5T1BAE7on/JfN3CYyMxkx3qcD2ckl9QWCVVSYjGwDMUz6R0/RBwA33vT3WSXkkOIShVfTIfr1aNZ+N2aYuYc8NAvjBPPGn2TQb5iY4I0omi4Ljvs9AjOcbcptR0DgpUOxCqfzDLHlpBnWSrmuzYQMMUTgfbJLUOZIWVHkM9HheOsDqgcGVUgScOwfcuWMVG5LEBsl8D/Aj2qoyAT9RMJD0Q7O/OcFDrfMJBy7kcwmE9Ifz5C7f5zgej00KN8e23EDMdMrgd2xIiSs54yALz7c+pFymzSvPpDM19hQG8z4jD4lm23YzdfaMYl9ef4s6yklczJIhr01M8LJSmT/1325Zgp0E/R8+tJ/7gf07wPLU62yfY+OXq1VgZcX6yeVl4J130oE9ScfUHwC+UWY6TdEhDNNpdUVVwfOAjXXa1fh9tnM8AdbW6Vck2Nyo2wDraGSDWXkKNKMzKmbkpXI8CcqoYxwc2PG5vwfEZVZqZr999tXMjyubWnV1Zf6+h4cmJWONZTp/jsHA7FgaTWzAb/ZnnZ/G0PeNYkpIdbDDwxx/bFQB89rADwBIqq/qGQhMmsQQIWqVy+xfpaxqjucDcPrmwYPT75tV6JKUSu22JU5EEQnioxG5yJ6ySiXNBttxnDNXzIqeWHJMo7EgjaEhxlQqwPSB/fM0AdbbpyhE8ev4xMeB3/iXdr0gCpRxTMJCu0X/MZmalGATS94E0u2wucH+vHQRePc9/k3IsdmyRKX84H022KzBdqtVz6ZoJQpVLilt5svBdnTJ65LKyvOsOuMi0qcQmFxVqIXlqNuUYwrA+pqHoyONM7qBE1XysuT7UU6QPv0Fjv2suhXAsaE82lF2PZEk8+krAbZRtUIi3Gg0r7o0GVvSUZ7Sj9aWoDgazZNemk36k/U14LU3rK15nplvJuzLVpPlU2oBUXBs13eX1p3DCWB77exwbpJ5TNYuQm70AHS69PMAfewiMknHqA72+/y+S5TLOwDgpvX1clQ45e9yrUvuFPWoySTtF7J+KzBE8nKZ9T2LAtgiUm627HmYTtlvt2/nf573LIC+ZjhME4xqdetLG84adjhiu1Uqpyv6iVrYIsRlx/t6SDE7kwTYfmjXk5U4vdbyvHlCVJIsTjfr2oBn1BO9gAc2lpfn13FRyDbodun/4pJNbyrjwEW5fPKBjNFocYpTBfrBniEL97ppe9w/YBkm5n0uL708QJuU95lpAmSnUfG/4zEQTXhPNwQShLSF3b3TD96UzfuA5/G98uBw8XcWkcWVZ9+Pspjdy3zXPeTTalIFNztOlHOwITvfNZscn2K7vR7bXd5js+05GgPvvEvS9rpJkR3nzAn9/uLxOEc+NhgODDnyEdQ6owiAWkzwm5V7RJstlRbvXXwvUZC7ChQoUKBAgQIFCszgeQof+yjwsY8qDIcaX/8G8IXPc1Pkq1/T+K/+8smbaNMpN6LfeQdwd0XrdeDaVY2rhvR1/Rpw9QpQqRSErwIFChQoUKBAgSwqVQXfBw7MRvEiZdVqzaZqdNFocFNVKUuyECKIi9mmsGKgzjfBJHejfmXZpMNxlD4WbTK7ylqADTxJsC5vA9pTJB1lA2BaM4Ap3+t0MEsD9aiYZtK5KAVcu8r0490+ZulcZp8jE1zQaYKGoFE/Pf1cVALQtd8/PEqXJasKE5fSn1cqDLoKASqrPuES8no9Kunu7QH37nNjPIy4Fh+NbDDWvX5Re4ah7RPP5+b3rA2cIIpvTrOvLFtFh9xAnkdlHcHSEsupwL6dTky6w5pNp5lFrcq+7PdtwHM0PvnEfhaDwbz9+oFJ+eYxCOeqsrlwFY9UJlgm2N1jYKTVYjNJqkIXRx3g8ceA23fnA3jlcjpQxUMyThBPsR1E3SGqsx+zPiJPLeIkLAqEzO5xSiNfvMDUrysrDPQIgSmLRp02GYVU1JlODanMaUshS4nS4FnUC/LIMssrtNvt7fTffd+mfTwpXdtwwCB9vQGgy3sliU1fKmUUsmReE00naVUWuUZrW6/jbtoO3M8Ats/RIX1wr8d+r9V4CAvg+Mn6BYH797hsFSdEfWl9lYHX42P+vdvlvOJ7i9s9L6XfIltLqZZF9Gf1Ots1j7gn9u+mOj0+NuojjkrOaDz/QJcU4imjoONzTL7yKu2tap5fr3OejA0x4PJljqvf/t30fkc5ng9M1uqYm3Nr1fTzV1dt/SQo3qjzb2GYttdZ2rKJIa6a+yw6Gyck2zBM99Gs3EZZrFabV+iS4LwE7vNII1lfMh6RH3awb59RLqfT9klqY/F1eYp2WQQmJVS/n08QsTfP9K1nCUuNphlfen59MxwATz6hsLcHfO0btsN6PUuwHgxJ7nKJWP2+LZuLaoXKcDu7GQIzaE/KlFWuBeYJXlkCwsE+x0K1atORLYKkwstCyqIU6+POX8dd2qakN5byuOMSsHVdXyOx4OGDxUo5bvsBVM0ql9WZ0sMC+eke4zLtTIjQrj85jQwq65ud3XnS5Xhs1mAORGm217NjO4yAetWmQivljHtBkswfhBCEhpR8kqrLLH3lMWZ+pNWmLaTUEif5KeyymExINHDJOJ7H/ptOuX72Qq7/qlXa5oyQvIAQM1dmkGDS65HkeRqkH/yAX86mqAZoa4dHNqXbwYFDwsyxpck0TQ6ejIFRQD9+4bxN4wlQgTJPwavuEJY97+wZ74SANRxy7aK1TUMv4y+MbLkldefmJtv78JBjbjLhz5KOMw86Z53/3aLZnFfTdP181s4qFSddqbLlOkkVTSBpK3d3+f+L51l3l0g5NuRIz2M77e6mfWUpTI/l05R2gcVruYkhjInCtdRhMrHj9NIlu05cXwPeejv9bjQcsR+Pp5Y0GUa0H88zBEbzLqI8+nKxhVKJ7z7lMtUkhwN+fpJ6phBVJ2Zcu0R7gayNdWJ/9gMqz8khoUUGLimO8z5fWuKzxD5kjSAqw7lKfCWWIY5ZV0mZPhqRtJd99xyPjd8ybby1xXY8NO/5SrGvjo7y+7ViyO+L1ItPWtfXanYcCpRiux2dgbAlpMpsSs/TsLT0aNefBQW5q0CBAgUKFChQoEAuSiWFL36BPzebwI98ReHLP6Txc39N47//Hx/tXp0OUz6++BLgvkFsbmhcuwZcvwY8+aTCFz5XKHwVKFCgQIECBQp0Oho6IUGr2eTmpRCdXJQio2LlbIorxeBcKUqrqWxtUkVoMrXpCsplbnQfHNrN1JKTrrFeBx67bgMNEyf47AaFXbKDYDDgBnK/bwMjfs5pWlHo6vdPDohNk3nFqUXqTllEETedJSC3vmY2vyOggnliQhBy/Voq2UC6l6N4USrZU8qyWe2e+i6XHTKQ+c5p6QOzhIpez6pkKDjBSEdpSinaSRQC33nFEJ9MQKNeJ8koClh/l0w2XBDcB9JktsHABi0APqdSMapyJi3Q/Qf5QVuA/V+ppNsvrx2SJE1YCML0dRJwVSYAPJkwAHXS64N0bTZtnAshVSwvM8ioVH4A48E266FMWVZzCEyuYke5zCC4H1BtaOS0dxCSBJYN2DQatPXI9Hetlib++B7gm0DcygoDrZICrlZj0NrzTid2tVq8JknOpnoW+AvImcYmdnc5HlpN2uLaKoOtx0JWEZs0dlWtAYGpe1xi8K4Us+39wDkV7/St2N94nA5GBiaw5aYRLUUcsxLUcxUuBsN5Ba5FcNOnip25vu7hQ0viabfPFiwXAoFgZTndB0nC4NV4bHxMbFL0mbE/mRgyllHg8n1ge5fXu/41DACXMzPoA0MPWIpIeBqP531qo2HTRLqkCtcvjceL1SKycAkiQlpU5l+rlVYEA+wzg8AG6KS/RS3nLLsFlYpVJZmlXvQ53poNjqlGg/6j12Nq0YsXaCunKUINB/PBSlHFGY9Zp0X38DxLpKlU6AfarXQ6ObGhlRWrGuSSHGVOWkTMrZZpt4uUD6dTh2R4Qh+66npAut1FwUfGkeeTyPDwIdcMD3eoSJhXBvFRAD9fRN4RpUkF2//QVuVQKT4/MkTkySQ9B2kAv/U7wKWLGl/7Bv9Wr9HXb2/T9hetOSZTYJIhMty6bdTdxrQp8UHHHRtUF/XSVtOOV8CqhSUZVcUo4jg4PJonU2XnLFepRMhjF86zLHv7i+2hVifZsGz8iFwXBLQHSc97eMg5LmvbAgVL7Flqc934qNCafZUlXMkatl5nmR6Bl0yluY5VnpzjLGg7H3q+rYPbD5Mx2ymrGib3S/2eQ5wWYkNoxmbeWnc4tDaTHRcXz6eVt4D0PHfafD6d0P4mJiWqS8IZmXmkWqddlssAzLPEjqSiMoeJsiHAtKH3nEwO7pyRTUsZmLTDGxtG6XLb1gVIK/murnKMhGatI4SQRahVbYpNGbeDPn3nQ99RJg7MYRXPHgYQPydrKUltniTA1jmq+D14OPfImT2KvxICUGBIZf1BPpFExtBoaBUN5XuxUVITv6XA1PAnpQY8DeJDAEtedPGoO+ylEtfi3w3JbDKx9Qkj2sa5Lf7+xpu23brd+fKeZfwvUtjKItumqys8/CJKdPI+Mbt+bBVXPcVrjjsOycn053Bg/VZcTs+pgz7HI8Bx3znmfH79usK7NzSSKQlb3f68n5D02KLcPRjkqxK6a4J2m+NgMLBrj7icHm9ZElQp4pg67NhDYisri0lLvkkje9yd/+zObZtieG3NKjBPp/OHafYPuOb3fUOKVrR9l+Ttvgtkx5fvc41arQIP7i9OTQ/Yd/SjQ/oCSSGdJQtqpP1ECs6E4gd87tGh/VulkiaKCTE8zKQ2z9vD+W5xxteYAgUKFChQoECBAgUA31f4d/4thf/wzyk89+x3f79794Hf/h3gb/0d4K/8nJ4jdh0daXS7j7K1U6BAgQIFChQo8Icfw6HGYMCg1WjEQPvE2SCNIgaiJM3b/gE3EiVwsLJsN/plI7ock/TVbgFbG9yEXV7ixmQyTasENBrcLA5DBnVcpZbsRnmtzkBOuz2fSmM8NhviZokXhfkpoXZ3T96kL5ftBq+b+vEsxK61dabcSm2OK56irZjN7ywxbeIoR7nBYgnKS2ByOk2rZEwT/r68zH9CgJp9Pk0HESSo5SKvHY6NMo+rdOOSW6o1tkvJKHxJujlpn8HQkDHmbz1DvZ5WKnHLlUy5US9BA2WC4p5RLZLUoXnwTWqSrJKLBFGnUxvcmE7Tm+4K+WQZbeoYhsCFC5Y8uAi+P6+Q5tqh59nfpX8mObYlAbvxiGStmzdPeGimY6dTBl5kLE0m6botLTHgcXzM/pYg5ElBOWXuMwtce7a9XWSVyHyf/uHwkL4iD1GJ7Sr3CgKStsplBmVdckbVKLJpzTGwtkaFglVDTtWwAR4J4lUMAbXVMrbgmdR3omJk2r9e5zWtlh3D2fSRGxtpwgiQ7r9sewz6bF8hS2R9kqQ+dceDa9+L+kTSW512Vsn1L3K/8YRB+PGY46zXpZ31+4Y86yggAjaoXq2aPse8P4wiBu2ETDWri1F16PdNUC9TKSGqumg20+kY3YC1MiTSKEo/R3nz4/fwkKRGSVWllKmD458b9bRKUcPY3YnqTgZi60ql+0kp2mHNpMZ1A8tdQ7I8PJpP4SjzqltnrdP1iiKOX1G82D9Ip+YEMFOmazUZ4F5etqlHy2Wbbg9g2UT1RJ7tEiiEiJ2nrNZuk2T12DXze4v/F3KFa+u+T8WgD6oo4Sn6j6Ullj9b52bDBrgFDUP+dOfQRRiP2A5JwjpLQFfGTqvFe+UpyQGWSASoWd+FIcudaAaQU/Xx2d7r61RxioxiSqNOex0MF4/96SRN6JNUyYLRiO0dOuM4ioD1DeC5Zw3RNtMWWTJy3yhNdXsMsB93Oac8++zi+RdgUPnuXZJtUwTriUM2NgpLAt8ngSsLz7PKacfH/L8CcOWKj+UlRZXDDIKQdhnH1q+PFgTkDw5YR3fOOqluAOdhd31cKtkUk7J+E5tJpvS/u0blS9YOopa4tmYJyK5PikK2x9Jy/rqzbNLoHezTd5dLHONLS7Ys8r0omp8j8tZm7trxtDaQeygwBXdUAkLzHVGPHBiV2tt3rH9xx9+SQ04W/3jlirGZlDO1P86lph7T/ywtpQk7Mv6EUNjv076jEtcKuzv2mYMB17v9PvtrlmLWsypMor45axttU7iVIpKuxsa+W007z7n2P51a/xs4awq3Tp0O2y6bdljWgnFM264768sosr4ljwhTrXINtbHBd5PNLaMkWU8r4gkqFZJ9XOSt6bY2gGc/Qp9fz0mdLBB7dA9wuIQ9Wfdk5+48guHq6uL0eFnVQqUMYSr7vjWZJ5nnphnMXCMqpoKsoly5PE/W9Lx5EmzPqIyJCtZ4bIjwg3nisau4lm2OvLW3i4MD4GBfA5pKVXKYJVVH0AetrXKdsLFuDio49ajV5+c9z7OkZ8Ggz7FUMamNsz7kuEuy4t4u37sSzbVh93gx4fq01KedIxKOxK9k+xXgmNbmmmaDP7/9tiUoj8d8BxqN5vcUANbj1i36r6vXTk7xW6tRlS122ybHjk8kuTt1yCtPOebhObG/yYTvu0GOrX/YKMhdBQoUKFCgQIECBR4Jy8sKP/PTCn/tr3j4n35B4c/+HxSefurR75N98Xnyyflr/qd/DPz4n9D43/7vE/yL3yhIXgUKFChQoECBP0LQNh1VXMo50a25uZkk3CyVgLzWwP37DDC7m839AYlad+6aoIpJjSabj3GZG7tHh7yvpKh778b8hq4b3BqaFAW1GoMVWUyn9vowmifiZAMGElB0N+VFYajfP1uQSxDH3GCtVHhfpWw7fv6zCltbDKjkEYiEQNLvcyPdPd1dreRv8B93uEG9t2+Vh9y2GgysUpmkcXqU+rhwg3LHHfZjNmVKNgDk1nNlhe1Sq/Kf580Hu7PIqh11u0ybcefu4iC9EM2yJ8/l937Gttwgmh+kg7NuAE2UJWKXkOblB1ekbGtrDJJVq+m2UJ4lKAmyymLNukKpxIBLEOXX13MUHnpd2sDeHvsn+yZTMuQpIc3du0/1MyEIDAckRmxuzj9Hgk7v3wRe/rYlBC4iOYj9l2L+CyMAmv2+iBgXhbT7OCZRQYJHX/oCbadWszHeUoYoR1tSCI16hhs4cVOISfq9fpcBLCGCRKV0KhjfKAj5viVnLC8zYNtqkzyQDSJNnbQriwIs0lfZ8Z8khvSybEiuEYPcQuBxfVNkgn1Ly/OkMxeBo+SiddpO45h+V0hfjQbttBRxDCeJCWw7Nul5DMy9+RbJHnmqA1pznC21jQ8zf19etootUg63DY46NnAaCiE3074SIPU8+lgheC212WbttlEvcpVSDPoDG3TsdhkISymOOe1YrTLA2WySyHlasKxcZhni2KoFthyiWLYegyGDoDIGs8SNjpNmNo7Zz6JgKahUMEvZOh7Pz5eVClJRuF2j4LC25pDJ8oKgSb5yigLb5bHraVIYYPvx/Hn2c1aFQ/xurUpfMJnkExCAeZJdFnv7/K7W6blhMuU8WypRkeb8eeDcORLBsilMz5I6NjtHAPSnOplXO3IRLLCVcmX+b35AO6nVjHqRb4LvhqhRq59O2szCJc1JqjJtyhUYBZC4xOdllWvy0O/zn0ua2Nu3xAQXtRr9iKse6hI2ZTyW4jRxJAxMut9o8XpgaYlz29Ck8PY8oNX00F7yEIW0OxeTsU27XK9aYvqi+8s6ya3LSRgMrB8WAom00WRC3+Sqkbnp4VxyxeEh/w0HbFNReh1PONaieD6NMpBP+Jspk+l5kku3m7b7MEynFAQM6dWpt+/Pr+dcSGprIf2Nx5akLBgMrEpvKWa7uDYa+OlUlY0Gx7+nSEKSVK9ZYutZ1CpnqdnN75UKy+MrEkKyqVdHY5ZD0ooDafUvIXwDHFtZMvJoaFWNs/5pqW0JurWaVUAGuNbJIx5GGT/q+SSBBr5VyqpXjQ9xxtNkfPKhCs8hqdVqXHeIglwYWdXh7LyUd0imc7w4XaiLSpltWqvRB9Ucwmy1miYJheH8ut/zbfuvrGTU3xxMJvZgh/v+urLM8SFzeakEfO5z6bXocZfzo4t2i74qj/gp63oXkVFvzR7kkINPLg4PDbG0nz/fCGRNJveS9ZGo0wL5fVMp0+Ze+o5dL/QH876hbg5USKpH5c+n6Y6CnBTEQ6t05WI84ZgXhTx559swB7zGY7veGg5I/Hz//fTzGg2THh7zvi77/ur5wMc/zutFmUypeVLU3Ht/DsFciJUu4gwRvt+zypMu2oa0Lr7D9+01i/xVvT4/dwF27CnPHgZz6y2+yj3ApBPuwWQJ2x82irSMBQoUKFCgQIECBT4w1tYU/vTPAn/6ZxW2tzV+53eB3/wtjRdf4ovCf/wXgNu3gd/9V8CN99PfdRf0ccyNuV//DY1PfhJo1Pna8NrrGklC2ea8YMWrr2lcvgRUKo+401agQIECBQoUKPCHBErZjUnZUJSNYYGbog9wlHwySgzufururlUJAtKbttOpTXWQl4LGxXjMjfE4XpzORyQFwiCtTCXKVm5gRnlWlSSKLWHNU0CnZzd2pS1OUv6QYJQUodXixu6Tjy9WLMqekBfVEMCksexxE7jZJPEtG0wRgsXRoUmJmNlIrlZZn4MDk9IwsznuB0wd5fsnp8uTDee4zABBXLKEGQlyjE3KFzm5PZkYYl/AMrSaDHR2jlivjQ2e2gbmA9mlEoP0iwgHgrzARjLB3C60qJCdpMaTDXy0WukgaLPJAOiFkg2IuOkxsqjXGagajdNB2lo1P4BlP1eo1T2MxzS2cgmzlD4u5k7Gd3hNrzdvV5LqZzymDY/H8+NsmuQHjvuDTLAnAqZYTBCYTk061kxbx0bZoNFgv6VSrZr7i90AABTw2hu8T61KW5G0R7u7tqwKwFe+rPDSt/VMBcNNsyeQtCWtNoAko74SsTwaADQ/k/JVKiYFlpumzIyZfo7y3eoy/Zk2Y/noKENQzQRzhTDS61qFtbjEsrhkQqmXHzDIK2lZqxUGesTG8sYEAFy6aILtxvbi2JC4Jmzj6RRoLVmlukVQTmBc/E+1liaRRSHQbAPQJPw8doHjQIJnecoFYpfyeXaMlMtWWSyc2P4QnzcYpNs5jvmdrP84STUhiqxdLy3RD739lv28Wqet7O3atlhbZ7mEKFlv0Kfevk27FcKomyYVoHpM1ud4aia6BYDqKHuOD6qaoHKeooWMh7jMMh4fG8UKQ0oajvj3TUOKDgISFuMSU4S56osuJglQCvjZ8XH6s+Nj4J1303OH79vCXL7Ezw+PaN9xebGKUuCbtGEZgmZ2vdHpGFuZcN576y0b6IxjzkPThAH74YDEEhlH2flRCOmurxgM5tcJAyeNs9hhFkL+WF4CnnpKYTLV6B5z7sl+Zzqx6lrZNtOJJZO55JQkSftsGYOzuTPjzweGsBuGVEkJAvqK8fhk4s5pODy0abAEsmYMwzSZCbBkbinjjEBtyr65ybbN86WDAUkXu3uWnCVrLKl2XvrjBLT15RV7n1qVbZ5HSj0NCunmrdZo2+/fTNuz8tg2Kytsh4ODxb50MKB/STLPDwISZ8ZOKlQhUchaDQDcoTidmlTizt9mCjOZNbWo1bjPO39+PrVrHNNmazVbTlnjLy8BD7fT1+e1o6RQr1WB9hLtr1QiUXg0toRrwNrIcRdYM3Ou1kg1vALXkX1H5ejoKK0KJ2g0DDEdVuUmLtvUbO122rdm6zAc2fSsLlloOHSUAHP8ZVQCHj4w5TXEtrapWxBwPhTfC21S02fGzMoKx9L2Ntvn+JjXLS3xbwrAaGLS+bmLNjW/lnbnVXdtsLnJfvz2y1yjiz/p9sy85Bj9UQ5BSQ5btNpAkKO6VTPkFU8B2zu0o/HY1jWMLDlf4Jl+6XTS6neybihX2H95c5E8d2+XZGgZJ1cuU63w5Zc5rrpd4MYNq5I5mbC9XUUmUeEqxzaFnrSfKDxliUCHh+zbOZJkTJ/gXi9mliVniy20WlSZ6nRt30l6zdm1Zi7f3SVRfOqsmaKIbV+v5RNE3fZykUypEix/rtXYDqsrZs1t5uYk4VqyXE6/k8Yx5zYhz3uK9jcaG+K2IsHSizk25SCBpJWtVNnGLiG3ZPyQvJ8EoVXT+8gzzPYCaB5iqhqSWg6hSsbJ+fNM63i4YJ0saLXyD0MFIf/uHhxRHssmhMFalevTiaNQ7kJ5rNNkYglh8k7p+rHxBOkFIWjD00nOu+z3IERVkLsKFChQoECBAgUKfChYXVX4qZ8EfuonFXo9jW9+C/jiFwClFH7mpzV+5k8t3qUZDIBvvgB88wUNpYBnntZ4/tMKR0d8IZomwMc+mv7OZKLxH/x5jdEIePYjGp95XuEzzwPXr2EuvWOBAgUKFChQoMAfRnS73FyWNFKiojGZ2A3oyYQbvqEJQAUBAM0NaDlt3moyACGYTBi8k834Rh3oZ4IZ8tnmpk3/GATzKS2y5XXRbpvTwSNuPGfJSktLfI4bZB2PrQrKZMK6lOJ5ZR43fckiEtR4ZFMHabNpLSdvv/kCTygfdWyay8AHVtcYePc9PmNvjwGTRiO9sez786f5BbLh3unMB22FaLUovYWcBk8Sc5pZM1DuXu95wMWLDProBLjbt20yGmKWGsntDzko0e8Bvgl21OqGtCLKak59hkNu8JciXiPKTdqQu1qt+SCo1M/3bYCwFDPwJxvwck1e/UWZ6STEJQYvSjEDYRosp9a0/zy4SkBxzPHS6xsCl1OHWs0ELabs/1IJsw364UhjOmb9r12jylbW7kSt5pEgBIIFr0rZP0s6PhezQzCKzxeygG+CGceO+pDYZqNJNZ2PPM0UJ1kMR+yjlWXWNYtpwiDI4ZEdv8021ZVW14AXX9K4fccSMKJontw1u5dJaSaKEp/5FHDvAfDuu+mUrUImcH2QuIVSNK+cJQSwnkmXJYSOet0Ezk8hEUi7KmXr6BKchHwzHrFue/sMAMblfJKhG1TSmkFYUV4MnVRdvlHG6HZNmhvfBHtziLYrywzqC6Gzb1SwSkYlUQgCngcstaxqxTvvMMCaDYgKJLjnIjQByuGAPtX125MxMPQwC3xFceZwlk4razSbJxMxBVGmTysVBpTHY4VE65nCpdheMmW9X32Nz5Sg4qVL9PXtFlVoHm6zbdz+VKB/dLcSTko5C7BdXcKy78+nylGgXTx4kA4UZjgAs1SSogy5SBHF8+iDR+O0Uocf0CcrRdWIaoXBZze1ZLXGcZ/BbjJYAADRxklEQVRNk5uHXp/BTJ2YwHrFKpbV6vTDwyFVOh5uW2W57LAajc2cYOxpOGC7r6zQl3V76dS8rRbHeLdrCY9Axn9k+mj2Z2cMbW3a9rpzV2Nnh/dfXmZw/K23+fmMzGHarlIhISQw/TpYoIhYrdl5rNGw5Vxb5f/zlPyCEBgc0U4vXgCuXgU++izn7XfemV9DuWO+YZTw3HE5ndJXP3adpJBcgm+mQ1zFrMiZ/90U1QeHfE4U2XGswfp2Omx+UShaXzdlBXDYyVGZBcs9MiSJOOaztM4ndrZa9GsyLwWBXQt1+/NB9PGY/m7R2lTBKuEArOP6uiUM7u6ybbPKp4JsalFReUv9zTepO03ZxLeVYmtfYh8y1l00GkYtMQSuXuHastOZVyMC7BojjtKkZ/eeU7N2Xlrm/+OYPmMwsPf0zDo7j+yQhUscy5K2qjVLPAPSar0uwpC2Ohqmn5lLthNSuM/x2DmmQujhUZq8ImnQ5dk6oT9x1zvTjN9wf69WmU5WaxJTGw3OFXfupMfi/Qdcb5ZK1t8fHTmp2BKWayecXwO2W+k11uGhHWcXLvD/V6/QzkslhWpFp+aUGdEsM449n+XNHoI+Pua6p+HY6HTK8u0anyTEb9dmVpb47jAZ23WCpH1ehF7XpnTPku6yENKMpKxbWnIIY6Zucghpqc02kXYbjqyiWHanX5StJhk7qlTySbO1OttUVJr6A1vurC16Hq/3fa4xjjq0LenjsUOcCkNDsnUOIXl++qBEo8nPgXR31mr2XbVSMQd2cspeKtFHiDJ0KeJaPI7pL/KU26KSQ3g1jSc2U61yfo4CsxZS7NMwBEJD+Mp7z3WJ+6UYuHCe68lmU+HiRXtd3pq8WgXW1+zBhuUlPjtPUS0PUWTfcTXYXkfOWlJr1kEp4JVXuM4QNdFFmEysAvoi1KrcKxkNqag2Owg1+08aYcA/ywGR3w8U5K4CBQoUKFCgQIECHzoqFYUvfdH+vr6m8Lf+O+D/9d9R3eskaA185xXgO6/wdadaJWHr618Hnn9eY32NK+fvvGJfrF94EXjhRY3/5v/Jl4PnP02y16c/xReMAgUKFChQoECBP0yolhU6HUvC0tqmmKpU0kF1pRiM2NsDjnvc6NWSMsvnBmytBoRDm35pz6SFko3sIARaOak/AJMKaGDTGsp3hDQimE4BOJvAblC26wQqXEwm84EJOZEuQU0hrpXL3AwWyEnyvBPBgvGU7ef7liQQBAAUMBrpWWBBKUNiirmRvbFhlMNMarqFSg8ZQlwYzW8gZ4N4a2skELhEIhf1GjeRj+SkvJonxWxtsT2EXBTHNs1KnoJJHHOTuV63z4tNWpTdbDDAUQeQTX3lzafn8n1AYj5LS9zc7xxTcUbIM0oxMKoq6Xq6AeggMAoXHr/XaFiFF/dZUDZI0mySaBAb4pgEv8dGecU39jI1qm8S4PF94NpV/r69Y+snQRbXniVtVt6YkHGVB0n52O87CnTG9oRkEUbAtStso9FoXqFDoDySQgAGEff355UpohJtRtpAUtncuGFTzuXh6JBKRvv77CchUgimUxKL2m0SPIVgCvDaet0oIjUwU8NxSRjbO2mSX17QxDNkPqVIiLh6hcSTz39O4atf13PKzwBmqe/u3aMfU4rj4bgLDI1fiwxRamWVai4vfdsGEvPGRxZCapR3zcQoI1SrDMyKmmIetOb3Ro4PFrjqbXEMPP0Uf37vPQaNBFFkSEUB29bzABi7kvaU56+tUc1OgnpC9nF9NWDIGDUG4oKAZbl9x37eaAJrK/y+UmxfDdrmeER/FBg/2uvTR7np/IIgE3RPsFC5wA+ML0pOVicEbIC91zMpXjXw/POATgK8/e4UhwcJg9+K43c4YB/tH7APhciglIJSmspQnkLga/RMSqIKGIAPAqPCYvyuqLhEmWC9ECePOyyPKJEpZRRH4nTb+H5+oHaueWQ+MuN+kY9xsdS2qjSeM1dMp1TEkHlTCAKXLs7bxSK4BINGnQFZz/iKMAJQZ/23t9kGrnKnECllvgXS81hcpt8ajcxhPue5QjhUiiQa8d8pZRhX0WfIZ49GJLRNjdJdVLK1c0nkpRLnpZVl2p9LCvB9zo3JlAFsl4ScVeVZahtFuwwpOTKKPnmk2TgmsUzSki4t0XfWG87E68DzMEtLDQDNOokkbjB7bFRYzp0DvPtMc5u6xwJfVa3a6d73ze+Z9ZS7PvM8jv933mb/CaHi+BgYDDSWV7yFKnw6ISGh0+HYl3kqS3iEKcvmpsILL7I9RBEuBae5hHgvc2DN2FW3C7SbJJpub1NBSIhtUUgfeLBPewgdFdUotAqrQ6NcJ7YnKWilXLLe29pie8Qx7VFsbTIBquWMElSGVANwPdNsmpS8Ri3zySfoy1rNdBpYwci8D+zu8p7unDRb24K+PZnSRt21uLsefCRkyr68TELQ3Xv8SMZjHtF9aNTRzm1xrQjkk0emiekXk+ZzOuU65O5druvENynFdc76OtXkJpPMejkzpHRCYsbqCmZp0eXwSqtFZblymf3okrvGI76buGQ7UVvVSZqkOBxx3SV+KwrNODfrE5eMKiSr998n2VsIqVlklfmiEq9ttVj+hw/tZ5IG1SV3ZXFwMJ/utNE05fEsqVPSLWdJ4M2mIUcHLJcoN7nLPJcYClj7fP99rvc6nXkyqpTp4kXz7mY+T6YL/IBBr5eep+v1+RSFAp2k23OpnT78lMVxBygtA8tt4HCfc7uo2I1G6TX2zi59hfhKGV9RiXPT009SiQvgGqrXR8pGdeKknI+tuq5rX+7wk3eRUom+wJ2zZ/d0yc/O/8X2Ap8E5uGI5Raia6UM7A/z1+4p5dsBy+gHCq0Ws7BcuqhnaWClndttPkNr+kffHES7fhX4xrfS9w8C2th4ZIWy5P1iNGLZZP9gUepDUeJMEq6jZS7/2Me4vtreTRObZS0ahXb9LgjN4aR+n+uVctmSgZWaV7WtVDg2w+hsqVI/KApyV4ECBQoUKFCgQIHvCa5fU/jP/y8KBwdU9fr6NzS+/k1u6JyEbpcb8i99m28lq6saz3+am3GPP8YTl+4Ly+4e8Eu/DPzSLzNg99STGp/9jMJnP8MNEi8r+1CgQIECBQoUKPAHDOWqgvJsigzP50bl0RE3Wt0N88CkOrx3P00sWl1lgCRJGMSv1UxKFmcTMoyA0cAGUdfWgG+9ME8ea7csqUw2OMcZwlHnmAET93vAyWSK0chuuFbKaTURWd8dH/NZQpARQlVeyu4stEMwkPu5JKVmi58HPtsqjEw6uIwSiNb2+67qRXZZ2aizDzrd9PddHB0Cd+7y5zyVjSCYD7C6aLVIIAAM0UfbYHKjAdy8NR+wpyIAN6PXVnl9szmvdOVu4sdGeWd1hSSBrU3gxs38Mkm6tVnKIj+trAawL9w+LpVoG80mAzHlMgN3koLTxXTKNplMLKmtPwCeeoL/FyKLjBdRfQHSqTEDH1hfV3jnXY1e355mr9WRi8MjppRsmrqsb3Cc5SmWCQZDxmpE6UxIaw+3bfqlMAIqVYV6XVOdKWfzv1pl0E7sp1bjv7t3GCxo1A0B0FyftTNJlXYatOb43tubV/AYDIGHO1aZ4Llnge/7IvCb/9Km1cyDFEUC6EB+gCMMabMXLxhliS7wxpskfXiewsXzGu/fsP0nCi3Z+kqwSHxStWoUSUpAFCnEJZ26XimjQGXUerJoNTFLwSjqXKJWlw2grazQBre2SIgC0qmlfJ/3Oz627bJ1Dugd0853dmnPgwHbf2XVBpymCW1WlHJS/WluVqmkUwNVyjbYWq+nyypphWrV+bEvz1QwCoYrJAIo2HlnrNlfly/x+ylyV4ZAkmi2n8xJEkhcWrJEhbO8lUswvXNkA267O8DaukK9pnB0wL8NB5ZkVa2adIrJPLElO058k+pJfEDFpNSsmBS8gyH/Lyofq6tmrh3Tfl3z8TxbqXJ5XmUtihYrNgLpgGkYAk89SZsSPxCG9N1hMD8OfKPi4qqhyfzS7dqf3zc+vFZfTKxrtRjML1dIRI4i/ltZYd9J2r3AqOKJ+mMQAjD1W1pi0DUI6WdHY6tisrLC9rz/wNjVAkU/UXsT0nUqhZhDCNGa6Tlrpl5uG/zQDzBN1GhkO0PIH1GJdZVPxDRu3uJYa7eN4pSpb8sQvVeWHfJmJo2ZIArz1yi+T0X6d94F1lfZzlT/03MB5TwoDwi9tKLXwQHJvJJGMdFAvcLnb246JDOVXnMIqc3zuPYbjWy7ZlX7AM4TOrFkhm6P4/vuXaBcTlCOFeI4Pz2bYDpNE5qzxK68tc+itKGSMktIWDMip2dTxI4nQLNk7OLIpgNttTjG37+RJsTKfePYEEonJMvs7aftWQ49iN8UgoFLaAaMKmWmPbrHwPJFkyrW9GE55lpoeZlkkyAAvv9LwEsvG0Jlj8psWfR68wSa7Lx/dET/2O3xGZUKbXtrE/jUJ4Bf/Rcc34eHOcpFij5PiFjSPnNw/GrHrNmffILrvLfeSl9aLls7a9SNKpv7TKOa6vqF0UjWb/y51yPxJQgMGTNJEy4EWqfnKQA4v2VTyokanbSd7y1WUQVYVllbKlOu1FrI3HQ4suSuIFis3CNpMqcOQSw7Z60ahcNel8Q0sbVOJ712jErWB0QRUKspDIesTHbeSxKuSeOY9l2tGUK3IZIutYH22mJx0yDgGFLATKoyDAAIoS2yY3B9Ld1GvZ5N8X7UscS0wcD65l4f+IEvcY54y4yfPGKXhlEoPeZ6R4aaq/gpdZd+FbU1If8stelDJ1Pr9yStrTtf1+t8/0vMOuj+/fnyuKmeR0NLaFtqA1evKnzmeYVvfDPB0PjaphkH2TSg0h6liOWdTrkWyVNqSzQAZe3NTVesdb5ilQbbXcjDU1N3FbLfksQe6skjd5XinDSEsGqjjYYyKRE1HjzkHLG/z/eAzQ2uQ6OQ5Q5C+r+85Cu9vrUJScsofds95ufZOaRmFDXjklV4dsdJKbbjX+ab5WWbCr5i3r0GA8z82nhs1hXarIfMO7Tsb7iqkNMp275aYarW308U5K4CBQoUKFCgQIEC31O0Wgpf/iHgyz+koLXG3XvAf/5faHzrhfR1clLtXualaXsb+Ke/aH9fXuJLfhRxs3LfeXHUmikZXn1N42/+LW7IPf9pkr2e/zTLUqBAgQIFChQo8AcVW1vcQM4Gzt1N0DBkUCB7enVtlWufnR1LyirH8ypQdYfstL5OFbDXX7d/8wM+393IdBULBFNHyctNh3F8zADOopRsnm9OXJ+wLBsO+Gw5bfwokJjKZMIglatuEMc2ZcLuLjd0r1zm5vadu7x2NOYmsgQEgsCWod5wFB3MJn0YAVVwkzgM7Ml2+c6t23a9mpfeR8gEct/lZbanEIoOD6m4BE3CgwTjbt4ESmVg2LfkCUknCfA5olAxntCGDg7S6TjHE268Bz6Vb7Z32I8ry47qUKafAkfxYmd3cTcmSAduRdkFYDk6Hdat2cwnQLhpNdfWGKiUQLVSrGe3Z9tjdtI9Yy/d7tkNaO5EuPNVCf7I86MSg2RhkA44tFsMLLtpjY4OgeFA4+CAgcfsyfxa3abD1EiPd+XTHvJSfgHGntViAloU2kCS5xkinVqsEnR4YNUUjo+Bbk9hPNW5ajDyyDgG3n1Pz4JTSs2Tu5R55nRCm1cAIueeDx9q7O5yjISBVc0SckCeKkipBPQmHF9C8AL4nuhe7wb7smQCSf2zv2/7xCWE1utp9ZPpNK1qJn7K/XwwSAcbq2Xg4nl+9uAh6yXkrsAHHuwY1cIqgArH32SSDlBVq5wfvvSFdJDR86hO12jOB+qVR/9244ZNdyWpcI46NgXh5gYD53fv0o4kuNdomFSXQiyCIcHFQJC1RxMEExIizHWVCt/fH78OfPUbmIMovoiNim/JEl+OjzUODjRKJQZlXRJxt2vmTMV5kKQ7jVu3bGooKWMW1VqaZBwGwNBpR+lHaZc4tsG+cmzvWY4Z2FtassHWatUoVywYa9MJlS5EKa1Sse0gPkZIXTL+xAcJmdb1Fe++RyIcVJoM1e3RL91U88Qq37fKi5WyTcmbJFxLPHzI+oQRg5cpQlpAX3Fui6QiUd7b27c+pFwBfviHgJdf4e+LUvPBNOXunpmDVjkOb7yfr04y6NsgdVbBSGcWDbt7LJ/vmdTGpn9FuaoUce4cDNKB9PGY/b6z6xA3TDDXU2lfolQ+QSou8buNBv3eg3fYz2dJjdeo0x4SQ1qXrtaa5ZJaukpk9+5ZG/U8oO+xX10VQMCQymH/VqnOE2Vmqe4k4O20TX+gUa+rubWqixlpdeooL5lnuapE0hahIfQtIkRWypw/RbVL+j0284CUVSsSqUSJM/BJdN3bt8QfeWZkVNsmE9ueQkztGaLj0pIlRjRbhtDhlCswxD5XuagU2zWg1ibttWev9zzaxHhsiVQvv6JmpKBqhXOu+B/XLstGxSYu2UMcqXaqWJKGkCNEaenKFQ/PfkTj5i09U2qSfqpWue5ttehfd3b4vdWVeQJfEACXL3Oe933aahAoPHYdODrS6HS4pgRYDlFKOneO1z35BEkgohQ5GHDuF9KtKLy6GMhc6xy+EIgPk3R32bJOR/nEpZPeL0RhzF0PCEEk5cMcH7y6miYBt1onpIcz31OKPkq+9PjjHB9RbNPW58H11wrAyrKHMAQm48lCFbVybFIONoyCmeOzRAmrXs9/pvyt2eCcvb1tFKYnfDfpgQRCIcK4SBKOvV7PjsHJ1M53oyHw+ps2ZeYijEdAfYnj4cIFkyo4076igjqdAFCcnw4ODelScS5dWrZp9tbXmF52ZztdzzDgu4ooaNfrVqXLD4DJyPrPMLQKUEvt9JzkBwCcMo5Gi9UVXVtS4DyjNe8dhvRrh0f2PbfZtO+i7Rbw3LMKt29rTKfz6mvjsV0XC1FLHieEV99Pv0vIgYxymXGa6YTP9D1gY92mGRU0GvRn7lxTqabLohOqbDabemYnefY2GtFPNxr0Qb4HYG/+3ULWAXGZ/bS2Ctx/aOeRzhHn/eOutfdWC2gvWZLtvfu00Tt30vcFaN/DkUl9Wmff9c29qxX2ebvNth1N8ufSDwsFuatAgQIFChQoUKDA/9+glMK5LeAv/1cKDx9q/MqvAT/0AxpvvKlw6RJPBd+6DfyHf0EvVPja3TMvvwbVCgNtUcTFuPsicXAI/PNfBf75r/L09pNPanz2eeCHflDhyuWC6FWgQIECBQoU+IOBw0NG6p55ygZvRY3CJVAADHiMxgyayiZnEJAQMDDpFPsxNxgbDQZEDjtMMXH/QXrjvFTipukbjirE1gY3Qt3gsAsJAHU66XsliT3FXK+TiJBoBilkI3kw5Hfi2Jy+lyBNYtPh6IR1y1Mh8h1FqrzPssSpPIJHFj1DthFlFlGNCEOT4rJK5Y3BIK18Mhnb78QmyD9LE1TnifpKxSpHAM4p8cD+rJDe6J8mvLcEAF0VsVTQQzEo2RXVmb61nd1dE9CaWJWEdptraCGM+IFDTlAMIpTL7Lu799lXErCsVnlPsRdXOSgI8k+hZ4ULJG1Jkti+LZdZ90XBnERbBavdXZZte0eUTxjMcFVblGKQXshSStkAnZCzRK3BxcoKAxJam/4wnz9wxouoT7ikxd1dm8ZzPMoQ3TJ10rCpwnTC78m94hKfs9QGLl8EXv6O/V6tSnuY3Sdz34cmkOJ7TN+VhWdUX65eMSSxMN1mJ6EUs10WpXrUCe1kOp1XWxHVHoGkTuz3SUCJy8BFY5utFtO0iNqDTthXmxu0627XaU5lT8u74yYu299bTSrlzMqp2Q5LSzbgDDBQkyUuATZYXq2yzwLHXrZ32H6S/sX354PGkp5T8OAhA+FzkkFZKKZuFFKNC9+TQL3C6qqekS8rFRKU5vylos9674YhjpWBixcVXv7O4sipjHmAKaPccaLANu73+YvvMbB31GHAtmrIPI0mg2FBwGtFIeO9GxyvB/tsU2nj4Yj+Q8jEEuxzSWJBAFTKaqaqllWH9D3a6uqKHaMry/TtnQ5w3GGdJzlzymmQlI0SzPd9+qPJJE0sUh7n2M98GvjOK/b7gQ9oHwslUdzgurSJzHNJYoOKEnAVMpY2fntjnQFfGTtTo7QkihpHR47ahJf2JYC9bmU5nc5R6i7FHo8AVWU/t1osT8kE8ZtN9uloyMC+G9guhUAYMUUmcDpZW9YQF84rbG/rmS/OkjLrddp3dl7/tV8nkdDFdAK89y73jMZj2+blKlMN1+pMcT00hEsJCh8dWQU38W/TxM6htWpa2TOIAJi2FmKjUlTSup9JR7i3N0/6KJXsnCBkaBmDS4ZsLrYn5dvbNcqQFUfJCsD1a5zz37/J9dygb9dmu3skaJw/R+La8jLw7rv0mW4ZL19m0Hvb+CKxS6WAcqxQrarFJAVTjnomFZwG/Xm5bIlLrpKT8izJK3sz9/mA/X+jbslivs++KpU4rw6GTE3aXgLeec+WS9JkLi+zHd05cTC05RZyBUA79zyWvd3i/NbrGcXIkGu2nW2j4ll2fLIhWz75pMJozH3JC+e577m48RQqZY2KIRW6KT+jiP0YtoHxbda12zX2Y8hstSrfAYRYBrBcw6HGsx8Bvv0d2vtoQts6OrRrxFaLPvfcFm2w0UjPj4Jmk/UMQ7tmShI9K5+UuRxb//Xa68CzHwGuXGW7i2pr4PMe7Za9f7tl+ufQrr37fZI0tOl3T/F7Ychyb24aVbu+tYesz5EUoQ8fLn4vqNVsmsNGA7h0AXj/Vj7RVIFtmBh1IGms2UGNhPdbWqK/zir4riyb9jPljEv83t07s+X5XCpLIUQHAefqKADaLQ9JorG9QElPFE/Fb7oko14faJs11dXLbJtulzbupgiNY/qZu/dIminFlqA9GBiCOlguIf4IiXF3l+0n74Pu+sLz6Zfd+Ud8cRACFWNDQsCVd7tS2ab+cwmwgbmfAvvh1detX19eQsqYpwntSNTHopgkveGQbSBE+9GIa4gkAWJDjs2zn2rG5127QnJzEPBe/b4lMVaraRKjtImkej9w/JLW9FNJwjYNQ/O5tkpfgwEr5vvzqcTFH8XltA+dJlwHrK+xjK7qmgJt8c4dO292eySY/d7XNOKSwmOPzbdBu8W1tuenffnKMss5mfCwy+Ehn1GucE5y37F6PTPXlIGNDa4hOsfzba7Me/JoZH3ASy/bz/f22K5haG1PnvGn/g2F3/uqxltvcQyIanSWgB6G5pCUB1Sdgw2eDzQr6QN5i1T7PgwU5K4CBQoUKFCgQIECfyCwtqbwb/8ZAFDY3LR/14kldvk+T//cubv4dHm3Z09TK8XFfDnmRtXRkXNfDbz2Gv+1mjzFXKBAgQIFChQo8AcB47FGMmVQv9PhRqJszA763ECXwB3AE8hLbW5EK8V/OybI5/ncXC5FvM7zGfSepdYyG/+J5lopirhRPE1IFtvdZYDHPZHrps3bMGpft++QFOP7DLbs7NgA2/6+SS0FbvZ7ygnIawb4pY61qvlsZNMgbWyYE7QmcCMxsukEiBskTom6gpw2b7cym8gr6U3gZoMby5JmUiCnvodDcwp9LH3CAJwGy7S9Mx/YmU65IT4cMXizs8Pg+WTMTeByeV59IA8uoajfI3lPLyDUzL6jGODY2mSb9m6zL4PAEOSMcoTnAxvLTJvhRjTW12gTN2/xuqOOKYhps51dBs8k9eZxx5CnOrQjuVMUpVU4ROHEJUFp8/dymQEWUTZSirZwGnwT+JIgXZIw4LOxwn5+/yY37et1m1KuFFl1j16PQUiUSRjKKgXXasC9u/nPrlZYl91d/r/bs4SIWZ0rHFca8wFQQbeL1AVBYIN8YcSAtOdTGcQN/p0/l1Yfiku8vtOZVzyKIrbLdGL7RMOQ4DoMBPkBA3bZAEmjYYgoTgWYlkQjLqf7GDCn1Ed81sOHlnxaq5mgX8mmeBP1rcAQU4KQz3eV9IKAAfXRGPjI00zj88ILliySSk3j2Klg3znRr5xASxAyqFqrmXRJim0makmlHPUTDfap56VVIaJo3s8AohbEn6VtXTvoHNNGL5yzfytXbBWqFRusPneOfiQbmHMRBArlWBif5n/GH4piTKPG9twV0k82/ZYTgDrq2KCsYHPDkDmXgW0TWHebXFJdLi+niQdC9plO6DM7Hd7/5i0+c2RUMGQeEn8hqZaE8NFsAYMRx8UTjwN7+3x6VqEuCEzKzYFVn1hdATY3FXZ2efF77xuy7gnkrtCkFI1Ltn+rVZZ3fc0qz0i/pNIEGhJWtwt851XOh6JcdeWKJSZIHywK/knQulrhPcTm63WSrgGOF1H4imNgeYVjpVy2qmzSpgBtX2uWf3WFQWUh3gF2/oyMQqIo9gDzhCrPAy5fIOnnjTfsd28JwaRv+s8xlMcf5/+DkHaRJS1K/VyVOvElS2279vA9S3Da22e/uEokWbjKeYJF+0lKcX0wI74prrvCkPf3PWtbLpnruGt/v3cfiCNAtp8mE/rpk1Iuu3DHe5KYuaaLGbEwDNmvtRrw7DP8/f59jofIEMzDkPW4fRu494D/XMRl9tnmJomeAFCpaPg+A/S7u3ousO/i2lXaZLsNrK97GI+ppBdGaaKakKsBjuMksWlxZSyEkZ3nXFWXPDKOi+GQ/zY22G/PfkTh+JgqV0FgbUdrqvOMx8CnPgm89B377EWEHimPkBiWlmx6ciDt0ysVPqNapa3JWmMysetT8RGVsqyxNFZXaFOL7PYkFU4XSpl01D2Tut2kf354w5Lml5Y4Vu4bO2jUSTzd22d/lSsABuaAQsX6pdt36CsmU/q1SgX4ge8Hfv03LeFJyrBIgS4ILEmv2Uq3+foa8PVvkCA0S7sape+llFWu8wPbluOxTaN5/hwVUh88MEQk8w5Tq1lFTt4sXTZpe3fM1evpwxuXL5kyldhejabCY5FGucz3m9HIEgJLRsXx4JDXiv0Oh4YMGPO9aWMj3/A8n+uK2HnPm04dJTlwbnKJwFL2apXpyltNhUrVJ7lre145WYHjUohwYmd57qlWJSHp1h3afr1OP1YqcU0hpFKANjaZWJtuNtLqwHmQ+fX8ebZ5ZMqTVZWsVmnL588Dr79Bmw0Cto30ocwPe3ucv65d5bvLbC2o0gfDpW2HQ2tLydSuZz3PvoO7ip57e5ipMwbmvXtpaV5lsFI1pM8+sL2t8drrGom2KSuzCENgtWWVOMXXzPkoxXaWfq1WWdaDQ96Dim1W1bVatSqHAN+ty2X62mlC5anhwPqxwZDXtlsZ8qEph0t2imN72EXmC7Enrbm2k0MUUZh+dxiPAWiSPOU5GnxHaza5dzEY2ue5ZZE099l36sCnffq+XVe69t/pAB/9KPDVr9m/yVpgeZnZZY46dn3oKUB76fVC5MxXcWzJnNB81v6BUTjssHxRmF6HfVgoyF0FChQoUKBAgQIF/sDjyz8E/NZvA595Hvi//mcehkONd98DXnlF49d/E/j2y/mbPlpLGoaTsbKqMRwCpZLZJE40/sJf1Lh2Ffjocwpf/MKHXKECBQoUKFCgQIETUC4rdDvAQ6MKc/58eq2Ts887S7UiGA7TqduGI8wI86ur3LCMY24eh2bD9eYtbiIHAaANIak/5Ia1pGQBrAKF55M4EUXcuBQCwJ276U3Yao2bs6K+IhvVrgqCi1KJG6L1OjdoK4bsoM0m6ThD7CjFDPo3munT3zqxwdTIpJOAporFlcvKqFtp3HZSLwiShAGNGzcwI5V5PtVf7pr15dJSOn1K13zW7wOPXWc737nD9vM8k+onscoUexJkcPvWYyC0VrcKSW6Qya2323QrhljhEpWWl01ajAe8rwIDS6ur7IBWi4GNJLFKaxKQDAKb3kXD9lmrxY1rP+C9V1ZMar0FqZPCMJ3SM4vDI5IXKhUS0771Qv5JaRdToxDVcQI2o5EoiAArS0YNpEJikTTUZGrS2yxIaSioVmxqE5ivlyKFsTIBemWVetxyNpu051LEts8GbIX0EwS0Ew2j7qDYfhL0CCOrWOGmnAfYJ3Jafzxi4Li9zODNUjut/uH5DCKFgQnMDkwgysMsj5QCy5RtE2mDzrENvg0GwBNPsM9S5C7FQFqSGMLOyJIfjo/TwWmxMcAEao3q1tioIQxHtOEf/1HgX32V5IPz5wDfVyjFGspj8MYPMFOMAhh0dYM8SQLcucfy1qtUaxmaZz/zNPD++yxLtcZAl5B1xB+51Effo2rJ5UsMOolqj+8BTz5BksTRIckspSh/vLqEWFf1CTBEIjDgm2iqygBUlLhwXuGll/RMaU6CVMMBx/Xv/is9C6xKwXd3SPKR5/o+/XMQ2FR62fHo2nG3a9XS3Pa4dMHayd4e7UjUnxaN/7ffoS2vLNu/jY1CyHiUVoETVQhBMrVqaADT6wipdGPTw0sv06/X6vx7s0mfVCkDvRDomTLFZfa/KOCtrbKPDg7Zx1n1KjdtHAxho9kkSUAnbIOVFRKTfENCFEJKOeac0O3yuYOBUeZwlH8EG+u879UrrHy9TtIfwDktCPi99XXgnbfTZWy1DKnqTf4uynHQrH/zCvDCS6ZMZasy1GgweHnlMhXkZE6XtMNZSECy4gQmw8hJgVcGajWFqGQH+XQCwJDSBgO2txAmZU3iEqfc9HVZNBvA5z5HFdOH27Snow7Hgauu1OuaNU+IObLcwSF9oKSXlOcCwIZJwQWk+8ZF4FliRymyxEvnNrnwnTWQ55HE4nlUDHvwkHaj1HwaMcCme5V2lkC21GE4pO8aGX9Zr1s/7a6pLl5UmEz07PDjlcs2XabM6S5Z2P2upJ1OYALmimN5+6EZZ2bevniBa9ZyWWFjnfZ64wbHRBim5xal7JpOVCTDME12cEnzk7FNyQtYEvRc6jXTr6sr82nMXYJEFAEvfpvPnpFHVLqMw8G8Dy9XqMI1GqYJfVImqYsQkhp1ttF775FoJWQ2gGNXa85B+/uctzc20n1w+zZw8YKepRT2jHF2uwtUbM1a/bhjUqmb9qw37JxxcGiUPSdWAUfIGNUa8OQS1xO9Pp+5smzSvPlU3v3mC/Z5pdiS3c4Cz6OdVSpcfwcB3xMEeUQe1yesr5u034qfSbrx3V2HXK3sGkCIOeMJ21P+7mFeqVUgpCWl0uRSgP16+bJCr6fxcBsYDfWMqJ4kvGetzjnb9+07ysQ8v1JhWY+Pua46d27++YKSSf0phOlebz71XLebVgEWDIfpMSx+NnYU7gDaRaNBW5F7j8cch9lXMiGFyvhZWuI8d/06n+cqy0rq0lm7lWmXotCrQLJYEFhfPCM9Vtjunsc5tNEwCrvG99RqmK35pH1FKVnUkl0kCefId96x/a9gbUMg77fTqVUHW16xarDKtON4YtIvTtJziaiLPvE45+MkYR9Oppy/Hjzkd+4/IKFodeVkNSfXv8j6OEvwFIU6uUYq56a7L5e5fj86Mu1XZXpjUVpeidiWx13O1cttzrGTiZ3foEgYFbLZTF24wnmh3aQtxYaw+MzTtkyTkcbrb6TfYQaD9MG0asWuCaoV++4XBHyu57ENez2OG3eeuPE+1+2DvimvWY9ubfJ9ZG8vrY4tczfAd+BWU8/K5q4Bg4DrISG1i6qkvK83GrTrwBwyKZeNn0nsGHPJZHIYadEa67tBQe4qUKBAgQIFChQo8Acaly4p/Kf/Z4VuV89On5ZKCk89CTz1pMJxV+Olb3OZ/qUvcuP9zbf44vDGm/mbH1n8J38JUEpjZUXj8ccYjPva1/nvN39L40tfTL99jcfanNA5aTuvQIECBQoUKFDgg+HaVR/v35jCD5iyxPcZ7JHT2wo2PUMQpFVWGg2ug1ZWbcD98iWkU1ybm4h6EmAVmyRAJUE3QX9gFYo8jxu03S5/fuVVBl9u3eYp//19PkNO6rdbLFe/D9y+azfmm02Wo99jHWekL491ctPzZFEyCj4K3NyN43TQpt83gUTz+2hkVVzfeQdYXtZU1ZosVobo9riJ7qptDEeYpUATQs2MpGUQx0aZKLT3LsVsi3J5Ph1Yvc7N+0adgbW1NdbNVcDJot1ifeISy7fUtv21ukoCjpCLNjdIRhiP2TcbGxphqFCtsA/lOeOxTdEymTBQ1enYlCGjEQClcOUKcHyssbpK9YGbN+06PYujI6va4UJSEu5sA94aAzerq6zDUpvPmpG3FpC9JCCoE/58xyHpzQLrynm+Nun6jJ3VaqZuGeWuvb2MUpJ2yFxZW3F+b7V43SzF3NCq2QCWeJBSMROFp5z6nd8C3norHeQHnACvUTcQgowo14yNGtLSEtt4ODSBCJXuCwmUTyZsCwkkuWi1bRquZhN4402Fm7f0jADoYpaKSZOU9PbbOqXQl4WQXqQcUYSZgvO5LYU/86c0/vmvCrGBaoYHh4CvGHyToA/AMTSdplO0SmCnFJsxdsRrJpM0CdKts7zducU9PiZ56+H2fPv0+piNo0rM/l5f53eOj217+z4DtUIaEEWB2XMNWTaZpP926SKVEZOE5BBRQEg0x8jOLklskiqp3eLfl517CFExjtmfojznIs8FTic2cJ5oqpUAwEYZ2N5l34pCirTLlcvWnwyMGsT2dtrulDJpEnuZcuQEPFOkZgU89RR/rpQVltse9g94775vx0ipZFP4CK5fA9ZXWY/VFWB9XaHR0Oh20+reAH2H1DvwgUbbkgnkljLXbBl7PTyiH6o3GCyV729vW8LHYMC+6hxhRkrW2pKPWi3OKcttKn7JeOoczQd3fR+4e5d2JPUURcnvvGrLJe0m5efvCnv7wMqynqUrdZU5Xbjt2GzQBrPEBxeTSdqWul2barPTIfHw6hXbfgok6IlKWbmcDnz7Ae9Rqyns7gKNus5N/bu2zrHvplEUhCHwqU8qdHvsb5mvmw1J08nfazWjXoS0Py7F/Hdui0S1/oBz9d4+ScZuWVPIkFrv3qVvENK055G4GpU4ZuLYpBRLbKrmKAT6C/azRkOgvsX5UepcivjdReSBTof33z8gqfXaVaBSsQNF+npnR8P3SEwCGPy+dYckv3Pn0vff2FBz5JzLl416ouOPZdzvmDFfKsEohOWXVcaQqNFCpclwqSFhbPz+fY3X3+RawJ2ftjY5t7z+Ouv++GOG7G7UzwLf1jXPF/aNIpaoIYYRx8bFCyZlX9369PGYZLSJ8Z+iSgrwu+48OxrNz0fnz3FuLMfc13z6afvZcReYGrKYp7ieAYCnn1b41gssuagJac35oHPMtpEU6GHIdwJ3fb23Rx957z7rcuUyEJmDr1ub/O54zPvLWqzdXkzKdNO8TQz5v9HgGmJz0xI+ltrAdKqhzfx2dGRTsbk+CzDkn8y8GQSWmBH482qMCuwX1ydIKmGBKJ56niWPLDoAIGmY33iLypuyvpNDGdUK7+37AMw72njMtpqts42qY6ejUavN7ymLqmvFqBjHsVkDBnZNk0fsAmwaW4Dvka++yi80W5yjlpboe7d3jJKn8+6iYA+BbG7aFI0KHHNHR5iluzs44Ofrayxbo8Fr3IMccYll0QnXJ3cNme/iRaaTHg7nR9rqqvUt9RrTUvd6bINLl0waaedrbvrK8Ripfj08oiKUELvKZfvO1mxaInXXKC72urQT8WWztaC5f60KXDxPNaz1ddbJfUdbWzNrHjOmO0e0z5UV+t3HrhsyYsZXVgxRqFK1qm+CKLJreBdhmCYL7e8DewdAs27T0Mrn4zHbwvdor6MB7aHRsCp+SrH8ns+1rhCufC89ppKE74WTCZ+pFPv5x38MuHBOzeaSWg0Ih/Pvfn5g7ELTB/0bP6Owu2dPEMi7nxx8WVs1KqzOQYjhEHj/Ztp25KCBHHqJzTylTBvGMcf2yko+udNNJ+wHClev8B0nmdp5vVbn+H72Gf78zW/R9m/fnldWk/IINNL7KR8WCnJXgQIFChQoUKBAgT8UqFZV6pSH4K237cL+h7+s8OUftCvrfl/j//SXNF54EbNNwEXBO0lPsL0N/O7v2r83GsDvfVVjmgxRKgEXzmv8o3+s8b/8IvD4YxpPPA488Til6zfW7am6AgUKFChQoECBD4o4Vjh3Hvixr1ARAdC4e8+mMBpPzAaont8wbDa5gVl2gpzVajrFiagtTcY28B+a9BaSEq1WJXGhXmVgq1qdX0dJYGk65Sbs3XsMFhx3uIY6d44B1HPn+NmtW5bY5fkAjMpRKUqnlAvCeTUV6LQSWblsN1R9H6lNfWUIWYeH3ODOO43+jW8ytUfniJvqGxvpNCcAN7739i15CrCKBhKgzOP6V8q2vV1Vh1qVdXBVcxomNUrXBDA8z25kS8C93WLb1epWHWA6BZ54jG1165ZVhbpwDmi1FbZ3bI03N0gg6PZMCp4x7WZnN61+IgFHCVI+/jjbyG1XwU/8CeD8OYXpFLh5M/8awfbOfFoYCfiNx/a5S4ZEcXRk+0sUh/p9BgXKZQYh3GBHopHq4DhmkKhc5v1ndqto+90u+7ZWY3B2by8TYFaWiAdwrEUlkouqFW72r6/zPqOxVTorxyQ3uuox/R6D6EKWFGU0gRAgzWNTZWgvKXQ6mqoVO/x8fY1kyjyMRvMkDa1tyrbhkP0tPiIIaM+i5OKO76OOPY2/s22In859T1IekODiaedgRM0I4Dj96Z9SmEytklgQeEiSBG+9w2sk6BkYPyCpHgHWJZseUdDvAW+9zfofHJCckfq8b/taCAFZQuHIEB/zgtnNBolfgjDk9asr1ixrtXSga28PCEKFZ5+hInU2zaXc1/MUvv9LfAf9hf/BITTqtA+UdDeT6byakJB2JxN7+KnXy0m1awq71MbcTWZ9qTV296iO7ZZ5ZAgNd+9bG5dxPZ0yHWSWWNhopO1f4CreuSTCVpO2e/mSQhh62NryMB4DeyMSo558nL4PAAYDjYHTV56nsLaefs7587z25e9YwxdytNhVGNJHTCZsl4sXgc5L/ExIrwB9U79vg7lKWT/faLB9p1M7X5ci9m+rRX/+1tsmIBoCy6s21RMb36RVOqbvkVSHwxHbXfxcGLIcFaM0UYo4L4uanxs0BugjhdyVRWRsWNLLAXy2UgyWS4B0PAKGQz2zMdcPC4m532d5JhMbcG61jGrahPfKqiEJdGLXAkqR4LG5SVtzCZLLSyYNXzRP7pqpmiG9DpommTWNS/iILIF99rFnfHjAezYaXDc8fGAVGH2f3x2O0sRemdeXlwy5Dwy4b23ys81NhUpFc+0zob30JmlSb8mo70j9lMd2vXBeYWtTQyng3fc4tt3vSRUHA6olCfEoS86VcgJcxwmRdPaZIpnNVU9ZXzPE9WOq39+6zXsrxfG6sUHCUF56WqXS7SvkH5nbRenV9y0RaTgCvJFN5wXYlGCrK0Cvp7C3Z5+SaI4H5SkoAFPDkiyVFNbX9Yxs7XmLSWZA2k6CwCrbjCfsb5cMmEyBsWb71Wtcrxx3LdkVsIovvs9xMiMQVIGlJYW1NT0jJcj+4nhsVSN3d1mOlpRN6zkirLRhKbLzY6tFM798mRc8/RT3P7/vC8D+IQfwxoYldn3ly/z/r/yaJdU83CZJ+NJF+q2T2gowc67xhdMpSS5f/wZ/39tn+1+8yAMoD3eceTdzn/GYSmiDgV3HNJv0w48/xjl1PEr7BUlX6SJrh3dusx8nE0suj0ok0B0f0x4fPACCUKNSoULTwQGg89Y3KvU/KEXbcNXo9vfpd+/dp/pVq6kRx/Zm3V66fJcuss1Dh9wl6/2NDdqQa7vvv8998D/5U5bcNR6zLkJsFVUql6zebPJQ0HSSVr8TnyOHejY30wTf9hLbaGuT74RCeKqbd5vJlONAIO9lQkB2iXSVsp0jPZ/vLXEJM+XBPMzUICcZfnjGfvp9krNWV6mmpZy+Wl8H3jNzocyT8nU35eNHnyPxsdXmIQeX3NXvc/wOR2kVyDCk/2If65mPlbYPzaGGPNI9wHVBtWpJ6FkFqDhmG+vEHvqq1hzCkuK4GIP+ZzQErl6lX4pjo8474lohDB1yXmzIzk6ZPI/vk+/ftPNQ0gRqlTRJ+FOfVAAUDg+T2fsFQDtYXePP164xpTgAnN/SuG3If8dGObZSMb5b2ZSYUYk+I6uKfHxs21OD8+zSEueU69eBX/4Vfrazw/VcGOr0O6kpererzWE5hQcP2FdCRC1FtOmLF0lmVsoa2Hhs3rNKtOfz56zfGU84z128iA8dBbmrQIECBQoUKFCgwB9q/Gf/qcLNm5QJ/+TH05+Vy1yUSz76/+bnACiFF17U+OY3gbfftSkUXLjvga+/Dvz5/zgBYHcJ5GX6q1+TXO2ySUT59IsXuenM/3NxLykfCxQoUKBAgQIFzoJ6DXjmae4CcyPdrlAUmP7gzbfsJvxoxLSEyRRoLHEzWIJzo1E6FcJx16Zo2XLSgzQbdhPV87lhKwFYUQFygxRSpEqF10hQLjLBpLjEz+SeQDpgmmgblNYJN461KYenuAl702yerywDO84pc1nDLS8zmC4btXJieTwGDvYxSz8XLtgF1aY9wzB9otcNOLgkDwaXFXZ2nf4wRJBYguolKmLIpnazyU3hVlsCkNzwfe5ZE0zlPjjVbPLSFyoS0OKYxLmVZa5H9w95v0aDG8dBABx15tecS0t20z+10NVWrQLIJ+O0m8DYbJq7QQXPU7OA1DNPa2zvcNNd0vycBtn4jiLaWhyzzZrGhsWWfJ/XfPQ5BrJIdgSefFxjPHHIaSawLv2cTIGtDeDeXav2UK0C/b5Cs8H06xKI3txk0MsNSkrqKAWkSCJRCfjRr1Dh91sv8LR/ucy0RaWI3xsOTQoXj4EkgPeuGjW6/X0SUZaWTFo1zUB/VOLPbkpIAIgCvlNEoSFkRew3IUqKEogb8Dg4sKpKArF1diDwYJtqU4J63QSgh0YdbAKoDaaFde/TbFIh4P4DljVLTKpUGEQOA9bx8MhRRzIFKEVWZWcwsCQfrWkPEvS5dpXtJWTKqMQ2uHWL5ez1bEor30+ny5FArYZV3/N8Pm911aZijI2C0nGX10YRUC0zzSJAEoiQuqSuQij1FFCrK5w/p3FwaP1jKWIQqzcANtcZ8HPHlyUcKkBp2n1CW63XWF5Jl9ZsKqytAZOJnvX9pYucF0oR27tUYtlCk+5odRnY3qVdiN24fZglzzaMKlNUAj7xMaa/u33bqoU8eAi88opGq2WDa26ANwzYx4OBeWbEhs9N1+i0QxTNB99TKRzHlpATBrT59XVgcyPAzZtTPHwITLbZP9s7gPI0mk2VsoNFvl8gZByAY7bZtM+cTmlHH/8YsLGujJ1oDAbAuVE6tVgQGgJSORUPxf4+cOXj6ZSLorwiac0ODizZsWTUCyUNsKTWlPRPgK1bu2VIOoY8LMH/el1hbZX7IFub9P8Xzs/XXSGdlk/wxBMKN27omb2XK7x355hzXaPJv92+y8Dygwfp1HIAg+Q6ASa7nL8kvaXUu9836fmUVZ4TiD+IY87vqQKD4zyK6Gc9j4FpYAH5smn/Lsp+vmeI28oQaHz65K0tq5Zy5+68b6vWqKg4nVKFLQjSqqMbG8Djj/OXX/plZ42gaEdf/iHOGy45WXyB1lZFTPyVdupaqdKWhVS/tmrXKXfvKXzx8xprq/zO4SHHQwqagfnjYztPZSGErywZe1YP8HurK7Rr32cf93oaSaKxu2OXGJMJcNnMKb0uv3x4SCKO4O7d+Wesmv7e2gKO3rD36hxzXm+1aDe+L8F2Q6TNkIJ2d4EnnwS6DtmnWkkTRZKE7dFo2DEfmzVkvU7iehSlU/7GJeDYHLDY3+N6deQo4/YGHJePXSf5SdaC3WOuAa9d5dxTq5Fo0Tm2c7eMj4sXgI8+p2ZkNGB+bTiZ2NRhh0ccb3HMsor6rvKolHfrjiX8NZvAFz7Hn0slqmQCCvv79Gt5+5bra+yrvkmr1utiRsY4DSllTMU+dP+2s8s5LoqoyCNp5LLQ4LMHg7R9HnfYDq+/KWRT9t1ozH7d3U0/r1ZLK4CNp/Z+npnTA7OmkjEy1bT3j38MmEzUbG3R72muofViwrsQ7N3fJxPOlw8e8DnntuznR4eYmyObQnw0yn7lCmbqyPU67XDWHl0g3LdrKGBeGbbbFaU7+7d2m3UfTgyBV94txxwzjYZN2yf9ozVJnL5Hm711m3OXpEQUEqbvc0zsH3AtcfmyJR+57ba5SZVGAHj4gLavvIxiqkqv30olp0x5ayyDSoXjr9lUePDQfriySn9/5SrHaLttlTKTKf/JMzxf4YknNOfrapqU/N4NYNAzCmnL6XfIRoMHJXo9ux5ZWea1FUPylPVrFs2GVd/b2sqoCoPjIavkFpdIZLt5G6kU425beJ5CqwX4vsYrr3BsJ4kl3UkfP/sMcGDW76urbL9GQ6PTseMmO14nE43RiMSqb71oyyc+Y3cPuPk+8O2XNdbW9Fxf+QHtXTnKbIKSeT9yycliH6Lk1qgDTz/FQoVB+gbr68B4rBCXNZbNWlMOb/3u75mfjTpy9l3MPaCwspI+qEXfyeevmsNujQb3WcpxWmH9w0JB7ipQoECBAgUKFCjwhxpKKVy6xHQVWQyHerbQj0JuUoahwkeeUfh3/21gNNL4yo/baz76HDdeXAWDPCxS/xoOuYHDE2zpi+p1brZtbfHF9/w5bgo8/VT6lEuBAgUKFChQoEAWvX56XeH7lqTQaHAT8v59BmC8BjdOn/+0JUa99bbdCBUFhHNbVF9xEYQAnM3KacKNzm6X319epnKDm6ZBgeQWCRCvrTIAMBoyuLiymn5GGBpyigluiJpJHNsA79GRDXKNTGq7VgsYjoHDg/n2WV5mcO/WbeDTn2TKFElzJmi30wGdWXAi4aZ2p8M13vnzDFS0Wvnp2gRLbUtkiiKeiK6aVHj1WlohxPMMUUgZRZ9VPuujzwIvv2LLU6vln1CXE+HLK9woPr8FvH8LuHqZz1OKSgxBALz4IgP6UxP0JnlMod3WqFa5eb8oPYQfAMohWrz7HnD5IjexWy0bSATSwbVyWSGOgbi0YJGM9Mo4LlFh7PYdc7L6GgOQRx2bBkMCGFHJpD3KpOip1RWefhL4xX9GJSFJHzL1GFC6c5f2+9h14N0b3HS/cpmBlDfeYABsd5dtW60wGCCpJbOBio11D+2Wh+FogmTKALkbhGk0bCq4ixdI4iiVeI2rBicqbr4HhJHCuS1AJxr7B/zsfTnh7nxPgeSJ5ToLlUx1ioCpPAaiSiWbwqbX59+yAb1SCQgMeaUUMYhTjhkAqlYYOC2VrELgYOiQPT0qVnzqE/xOtws0WyzT9vZ8v1+7yiBKf8BnTSeWbKoU+/v4GLg/MOO7acdCFNKXAcCduwraWM9x16jgmbGVaKoebG2S+NDt2rIDdgw+/RTJeKKeN52kx6f4xmbDqnrV6jZwlyRplR/3XfCJJxSefkphMgF+/TetClQppq1trtOn3LjBzhS/5gboRiPg6hNsy/GYKVOBNBlnMNAzAl/JqDPVqsBnnmebvHeDz2w26ZeWDblrODQKMQHQ6Zq+yBABBUrx79evKXzrRT1LdSnEvERbnyekJIEEJ12bUx797jRJ/z2rICWIQpNuTJSivPRY7A+A7beBKNK4csUOrGnCcnaOabOXPT2rD3/g/y6cI8lBEPjzKXL3D2gbYxPQ9hXw8nfoM998C3j6SY0gUNBazwiHs/o6P7Sa6cAfwD7r9VmvycSk2xRFI22VND2PBJ6rV2ibb77Fe7kEkxvvARcvcd5aWQHWVkgq2t/T2Nunj/3cZxV8X+FXfk1jeQW5WFtLK0wBHAdXLmOWtthTJBOIogZJxBwjQi6X/g0CKsK1W1blKwhIRn/tdavg8vhjwNtGzSpv76VcZqD/scdIfPva1zX29uy+TRDYNYfnWeU9d2xGEfD9X2KvyLj0A5Z7ZZXqbJUyv7O0BDz1lDW2wWDepymARIWQZA+OF4WNDZ1LUnJtXoLEG+sK3/99tqCuHxBiaLVKezs4SKcaVTBEP8/Wb2+XRE8AePtdZYgrVH3a3Ejf98IFznmTqR0v2ztMddluK5w/B9y+o2bt66Ji2ldS+rWaXOcFAUmgh50EzWZ64ux06I83NpiOE6A/cslDvq/TxA3Yvoxjo6g6JQEomXKuFV/zoz+i8PLLVLZcW2M/3LxNHyBPGAzSa4+LF2hvtao9aCB1rdVMt6i0KqKkqhN0+yQVdjqG+DWlvR8ecR0xGPCeoxGf9a0XOKa2zhnfXVFotUhCvXvXls7zqNrFdlFYW1P4xMesGugiRBGfFUbpQxzKtOHSMv8uKdg++Yn8/cfbd7jG2z/Q8H3O91Lzp5+iatZoxLYbDBcoZSqrFAlwrzYI9IxcWYqAf/XV9Fc8BTz/KeCFFw3BVadVegH6mWYduGfGgxAcBTLupVahUTiMy0yVO3TWruIvFOYJc48/Ttv6+jcya3Fz/wvnOdfKfS5e5LwlqqiZy3N+ma97Fhq0dfG9ypFEDI1dKdAnj0ZMy/2zPwt89at893HXyjImfT+tlCqEYUEcW4WigSH5yLui+743HBgCvil3wxwIUgpotRSU0jg+ZmlXVxXu37dKVbU659fsXrrYUecIePNNoNXSWF3huBNF3MNDqpElRnHMM4RapSwRK4pY5jA0RJuI340icziixHEKpMvgeZyX6jWW+9wm21HUsmStKPbgeQqf+yzt4OXvpAnAa+tAydjmwLGtjXXg2Y8o+L4QwjXi8rwdS3u44/1Tn6KC8h2HoJkleLnwzRz89NOs562bPAiUTLlWzh7A0RqzNIj1GueN/X2uH+t1ErmF3HXlMvCZ59nPv/pr9nlZovCDB8Crr/M+7lpR5q6+IcE9eMg9g3od2NLpNJfNJt/fXnopfe/2Ej8LfJb97j1L2m41AXjAsTt2Fe1U7Ht9jTazSFn40iX214MHVNfb2GC5BgPO4zu79N3VKmM7gPPuZp63vKxw9x4PbUzGbN+LFz/8mE9B7ipQoECBAgUKFCjwry1KJYWf//s8hXbzFmYb5YL3b9qg3fnzwF/9y3zb6PWYHuN3flfj7/49fu6e/v0g6HT4z6Y+4Bvl9evA+XMaS22g3VZoNjW++lVuZl68CPzYj6iC/FWgQIECBQr8EceLmc1NCWpWqvaEue8zMP7M09y0fPwx4J/9so0rPPG4PRnc73OjXzYmARNYX2IQzw1SlA2JIC8IIQQfCbZNp9yYTqa8v/K4me773OCMSwzaTCbAxJAlZJO9XGbwOEkYOJa0eA8ecqN6MmEZZynfPHsStt83gbWGpJPhTZsNQJlAQKuVT+6qVEkgG495/0aDJHwAgNbY27OkEsCeQD53TmFpyablmKVzU+kgikDKfeuWJUtkT2lL+qIrlxl0FXLAYGCDq5OJlJHBOKnT8rLG5UsMur3zrlX7OLcF3L6tZ8SaMLBr4p1dYJgJbn30OUN82jFEFwU8+YTC+fMkCQiYhkKnArU7u9xcH/RZXzeAsrlhggamHBcukFjgeSQZvvseg48DEySbtSdoQ3fvkQQmkEMTS0sMLjWbaSUhQakEfPyjtJc4Vtg/0Dh208mNbDB5acmmrakYhY/AB65e8XH/foLRyFH3iDkO+31LlJFyj0ZGkcxjHQG2RZYQc+sW01gNBlbtShTpJHijsfhgydoag9VVo7DXbDKyOh7zBHoW5Zht9uTjNlVbq8k2F2SDRp2OrbPvy/tKOrVh3pvKv/zt+fSHSnH8SDq7zU0GUJp1BnHc8Smom2AvYNWp+j0gNmqBjRoJRPcfMph39apRo3BIEMOhJYLpIcmVnY5V3FtdpX/rD4Da1AaK5P0vSazagjKqHoYDgE5HY9ukrUymTkdpo4I2ol9cWmLfZkmKAPD8J0mCWV8H3n3PtqZLTh1kgmei+Kc8hU9/imlDf+d3rX9yTaZc5h9qFaDU5mdCnnMhdZpT/MlBEJh0Q22rMgTYQKv4VyGYlmPb754HPPWEUa1z4KZaA0jAcZWYdnYYDPU8he2dBK++NkGvz7lF1HzGY/a/71u7lLJk/bIQu9wgXxCQkHJ8TFJMVrVOFL6SxAY0Wy3ONUIOBTivRJFR5TC2E0YA+gwyl0qANipEUAqbGxr37rOMr7zK9GQKJOyVIn536ownsQeZI6Rc7SWFzU2Nz3+OBBFgvmwAbfjxxxgwzmJ5meUKQoXr14HxiAH7LPHY92hH08T6UFFnGo9Z/pVV/v7OuxzrTz/Fy6ZT+uw7d+fJZa0W/766yuB5pcxnHBw6ilaOnSSJnUtl7bLUTituCiplBm6vX2WQ+5svMOg7HgMb6xqlEv36O++QJO9impAof/OWJdi3mhq9niXoRU5QvlG3ZPS1Vc5j5bLCwYFNCaUxv7YSfytkS1m3KdO+Qkoqx7bfLagqc9cQ95/9iPOJ5hgc3bRtcWRUFX/6p6j0c/sOG/byJas8GpsUddeu0j4PDmgb1arGnbvAhXMaw4FGtOKh3mBbil0eHNoUwKb42N8nmQxgXydJmjju+pM4TpPshfQgSivu5FOpqJmyipdjJwDLfe0a18njCQklrxs/5CmS9iYT1nFm00iTjcKQBOFy2RLxlCJpamDW1geHnM9krVcpcyyHIfDa6xpJAnzy47SdGcE0Z55fW6Nq486OXkgSOn8+7e8AzMhZ1RrJQfW6xkef4xUuAczFaMQxeXzMttEaeOnbGtevAdWqwhNP0K91jjSmjt1JKu1FaLXYVru7JCTV61RdmqkV+QpRpHD9msa773ItOE1sam0A+OEfVLh3n2tOl5CbJFzDPPE4x8T+gSFsOTaUXdP4Xpp4KyqWy8uss9hqHHM8+745wGLmj2ee5v8V+H4icAmRCvRrLjFK3pHcdb9GWg1I6vTwoU3T/uyz7PhOh5/FMQ+zDIac68plYHtb4ZmnufYbjYELF9jX5TL3kn1fwz+fTh/tzq3ZfW7Xd7rzY2iIbE8/JW3F961kCkzGGhVz0GeprXBwaAm1rqqjO64AO74nU/rkMALaRtn49h3g5Zc5d0QR3w1bLfbL0pJJn9nmmsUzBLbNDY49WdONx1xrVqsKF87Rtt94k3OFUlxDi9oiQML8tWsa//AXWFYhx7r9FoYKnq9TyqRPP8X3u8NDdqz4sSuXTyZnuqjX+Uz38EyWhBSFJD3fuOFcY551+RLwxBPA859S6PepVHb+vEatxvZ75mnr71y467VSzBTB2KU/0zlr1nqdyleLyijIS7l9/3763b/eALa2FEolPVN9k9vVqmnfe3xMxbOLFziPSlsL2madnVUyE5+XJJwzj4409nYt4ateY1sBwMoyn95uaTzcZh3Ev20/5Pvq5z/HNUq23r5nlUbLMe1PFIp/P1CQuwoUKFCgQIECBQr8a492W83lpgf4YvAX/wI3sVwCVaWi8JFnJLjDt4Uf/P4Q//f/Wx2vvLKPV17VeOll4FvfsifgPijefpv/iPkdm//Hf60RxxrtNjexJIVAu80ND3mBe/pJOS1VEMEKFChQoECBP+x4+CCZpUYAGMy+eIEpkjxDatpYZ/Dw3n1gcmBSWSleu7Zqgo1Ib74+/hjXCVprfOJj3OiUU+hRiRugstEum9FXr5BUs73DZ7sKFRJkabcYeNjd5cZpvc6g71GHQZTJhKSS465VYZhOAERIRaRKJZIDur38VFEAg0vjsT2RzQqlr7l8EXjnPaOMYpRD3CXS2polAoUmSDCZ5ijKKIVPfEzjjTfTQREAePBAzwLsUQiMnftvbKSDOgDrXqnkp0Iyj2KbmzQrneN0AEqUP6gswQMBt53A/IMHwGikqGpzmX+7cQOoNxRu3HRIWRklKfmk2QTaDQa4bt7iOlgCBq+9QTWHblejWmVF794jaeC8oy40HNJGNjdZzgcPbJBidSWdvkopRz1CaSjFei8iMgHptJlPPM6g5ZXLDC5+81s6l9x1+y5Pqy+bDftxJhiuNZ8bhOyfuMRxtbXJAFAQALt7CR5sJ5iOLSGy0QCefIJ9JIGEycTYmscgRK3O9g5Dq3bgBs0ODvn5wKRxWl9jm44nNgXmaJghpShLEltdYYrQzz4P/N7X5HOFMGJwam/PBu8Blu34mP3bajLw+8UvAH/n72I2DoOAY0wITsfHlmwg9ri+ln4HOu31Q2x5eQn44udZn8GAhKnz59mOtZqtpxskDUOW1SWcej7Thf7A9/E9KgytndVqlmynFN+fAp/9rjU/E1W+5z5C/1CrAT/2I8Df/NuG1GPqvrXJvhwMbAogz2MASUiA79+0ioNusLTbnSfUrK5aNUXXv3/qU2pGxHnn3WSWlur4GAAUHj7UeOnbmbFh2nwypkLGUlvP7KJaBaBJKBn02Z5ra7S/G4bYcf0alQPfzAn0Zf1gKbZuWtSujo9NarYDe93zn2ZgfW+PQTNpjygice3pp3mXO3c0gpDB6MEIeOvN+SCg73McJolNHWsJG2wIDZJX9/YYnBuP6ccOD9nvMzuYr+JCTEy6tfHYjmc3ECk+yC2vpBZ1IWogzaYtt7SHbz6jrbJ06xvscz/gOIGyxM4f+YrC3/l7GnCeIUS5jXWnHH2NuKwwGFp7AubtEDBjcKiwtqbn5jal0qojYaSMWobGW29bVZcrV9jGx8fAU0+yz3Z3gXfftYo4pdi2X6Vix/bUIWRNJvQPUWTniGqFY3Q6tYRBF26wXHm0+U98DPjmt2w6pfEE+O3f1Xj2GXvtZMoxeHjIZ4lCW6lEBapqlfOBqFu56HRYZjc1WrdLEthkbFN3jcd6Rmpstvhd8T0A8ObbTOkIUBXlwgWmQZUUpUIQlzTVojRVb5LIcOUyg8Yry2lyCUCFl7z+FmysUclFI000PThgUFrU5UolhUtmzSmKOE88obC6wjRbgKN8p4ByRaFa5dpz/0DawdZ9dZV95vls73abSjxCjHUhanDdrlVLazU5F66tGbKj6f/LFy2RKQwVPvYxjX/6i7SJUon3ys5PtRq/H4VWAciF+ByXhKLAMUcSi1Vjlbmx0QA++qzC178JxLHGmpnzK2VeW61RYe2tt+09j7sKlQrw3LMnLHoMXngpTcCp1WR+oA/s9QyZ1MwBnkd1U8Fzz6qZKtgicA2m8OSTwLvvck32cJtzVYoQlrnN1qZdY8hHLhEm8IEktH8fDml/Mr51Qlrxt18G7j1gnywtcX556kmqnJ3bUnjtDY3Q1HHXqCYeHBiFR0/hz/xpjVdfA37xn6WL6Hm0v65Zg9fqwN6B/Xxz0/heT4iUCstLGkeHgCpxfF44L+8RClub9u77B/l9N5nQXvNIPXGZ9ZtOaddZ0vHWllUg3dgwBHNtSUa+D5w7x/mk37c+dXlZ4UtfJHG03ZJ3PZIyNZhmV3x9ELJ8gmw6214fWEo4jsKI74Fy3bktpOYXgOtBIaOvrrDND/bt52FI5SpojacMwffcVjqtMYBUxzXqfE6vZ9vYJW0mRrFzmqTH8MWLTK+4vWMJydmU2EJABuYJso26QuMJhaeeTHDnrlWidtMsApwDXCXJj38MeP11uxYolzk2JM3kWVCtcu2yvp7+jlJMIXjRkM2z6YKbTdrF+rrtG6mXMmtW+dlti+mUKo+XLvLdLwyAblfjtTf4eSla/M6aLl/6d+nXrDJqqTSvaFqteLh6lfX7579q+7heA77/+xRe/HZ6jImPEyK1PLvV5tx9cMC+feddpjGXA28yzl78tnnPa1o1W/cJO7tUkzzsYJZCVZAkhiy4AXz2edZlb4/2lkw5VqQp5J6lEueHjzx9SiN+ABTkrgIFChQoUKBAgQJ/ZNFuK/yJPw4s2vJ97DrwF/6PCts7wFNPlBBFCleueLh8WeOP/a+Av/cPNP76f8tl+8/+DE/53LwFPPG4ws4O8I//Z40XXvzuyzkY8ISImzrJDeYJlNIolTQaDW7OXTjPOjx2nSkAVleBKCrIXwUKFChQoMAfdKytcwc2STQ8z562X1pi4PfoCPji5xWOOsB7NzTeeguA4ub5V77M67/6tfRR7HpN4d59PTv5XSoplMvpTVPf5+lZUcJYWSUBZdMEnkmQsd85fw744R8Czp9XONhnaqJe36h3JQyG7uza07KCUgQkFW7SzkhQZomyusIN5lYr3SY7O0AU25SJlapJo6EZBBiN7SZvvaGwvqYxHJJYspqTkuraNQZQlGKwpdMh4Sx7ir7eUHj2WY3vfIe/S+339u2m72jEDfBqRdSJFB5/XOP1N5wbmS/mprLJwE17Inj6KaZgWl5ONdcMM/JFwNRM3a7GaiYlJkDCh/Riu80gR6NOcstzzyrs7XH9OnRUqgAGb0ajTNqfBctKIaotLzO44imrlpT97sEBVWEqZaTSzwA2MDYYsI/cQFk2aPbYdQZfRHmn27XEKlf1KJtKTdQjLpxnkN43qRO3d2hn2SpeOM/A+quvsahhyHW3kBlExSNJGBAa9Pk3Ccwed1me/X3WU+4/mbD8UYRUSlGt08oTnqewvKyxvMTT7gADstevabz9jr1uc53lS6b2HuUyA1LDEUlJcRmGrGfbvGrUj6pl2sfdezZVyngM/PE/RjWR9TU9IxasrTJIsgidDgOTWjPwf/M2bakUmqB8pOAZEtXUpKmbTJj+7pOf4L8XX9L4J/8LlU5EhSAMFZ54QuP7vqTwyqsa04TPGQ5s262tUemrXOYzKxWSmu7dVzNi0v6+xle/rrCzo2eBLAU+5959+tt5hRxzXcZArl1lcGl5yf4tDOnTSiWWRWsG5tfXMYdLF4F/+kvsL0nh+M67Jn2fx65KKYSYQNjDbduPzQbw5JP8/je/yTZvNtIBd99nPw5Htu+Pj9n+h0faFT5BKWKA+9mPKHSONG7cZHBMApuNOtvnymUGJn/va7Sj27cZ3JMUfQAwGjGd4Z6x/+OOJbUAHIMa9DNPP0UFbnnvLRvCj5BBNjY8HB+xrcUvndsi8e+xx4Bf+xcMiue1s4usKprWRrUlYp12d12FSYXPfJrv4vcf8O8ry/QXYjuiYLm9K0QZBhivXqE9QTGNYrUKJFrPDmfNFL5C217DIdDragQZlccoZKD3/kObAvLmLaYVOwtKJWDSMymCE5bVRR5ByPMVVlbShPEwTB+iW1kBPKWxvWNIeZrKKJWKmVvN1+MScHxMXzYeAa8YtUeXDDd7rkMIluEWhmkyeqPOOfJbLzL1bNkhpGbJYULSapnx5RJu5VKXSFOp0H+MR0Yxps3fu8aXX71sSTvSvwCvEVJqucw5Kg/i38+d4/Vir5MJx1HXkHhEYWhnl+TMj3xE4Tvf0bnkt2aT5bt0kQSDGzdk/Cg0W+kvXDhv7fujzzH4HRgl2BkZXM23y/nz9LXXrgK7e+y0ukm95vkAxizz+XPz5HhBlthVjm3/y7icTICdbfrhSoX93u8D4wm/vL5mSY6NusL583pW7o11q4YpuHhB4zf+pcJ771nCDACsrnEsv3fDKroAJkXoBsuTJXKurrAdGg1LdBUC/OWLgOdrPHjAvnXVrVZXgK9+nUQcKmo+2h5dvcY5RdSKul2T0taj7zlnSOgkttt0j/cfaLxs1rKbG8BHnsl/bqVi++HNt+gn3LXraJj7tVlZUhkTzHpQSBmHh0b1tMc2vW7GhRBBSiWusUSBrtGw95pTmHJVe9pUF3vrHY333rN/X12lHSaJTbvu4rHrJEk26pZsu7Fh3ykajXQZXFw4r3DhgsbRkSWtlCJrI3GMuUVkOabvGU8sca5SsfazuQHo59gOsoYfOjaXaLtODEPrP27fAdZWuV7KO/DreXae1AkVk1xUK/nqqRcuAHt7vN+nPqmhNX92FSFdImynw/a7c9e+r8ygRO3LQtYz9ZpdRw9HzrycIXwBPDgyHrM+2w/t4RcA+OTHgRde4rusHJ7IpqFut/me4AfsC1mP8zquN3f3qGxZKnGsyXukOz+JYmOtCpzbUrh2lUeqhiaFXxyTxFoun318Z+c/eU6vRxvJ66NKmX0gBDStLQHShdhl55jvN/cfkJzkKc4rzz7D78l3l5fzydVCkBWfkFUlXkQwXkQU++rXWL+9favIvej98tw584NifSdTq3In81YUmRT1PSG7zVciLtlnuX7l1Vdpf/fu2b9LXatV3l8phSdNKvV339Xo9XWKuAvYMSt2sLn5aD7+LCjIXQUKFChQoECBAgUKLMDmpsJP/QQX761Wae7zn/mTwA/+gMLBAV+OtjYVPvsZ+/nhEeB5Gvfv88X84BC4efPk9I5bm3YjcG//0VJBam03nB4+5IvJL//z2acAgEqZp2LOnQN+5IeBH/lKzttjgQIFChQoUOAPBNw0cBLEWF5mIOjiRYWbNzU39nP2DLOnY4+7Ns3O5oYJHDgb77I5GwQ2mFqOucYZjpiCB+BGv6xn1tcYiKzXuaF/7hwVKDqd9Gn0PDUmUchwFXkAG0xZM0ENCWxOExuUkU3cfUOw6nSAT3zcbvwD3Kh+6kmpk8Jkomenzy9fAtbWPLz1ti3YhXMa/QEJdFnMVLUWQDasd3dZnm9/h+pZQWBTscime97J5bw2ck+JX7nMTer+ME24ypYRYLutrJAMd//+/HXnzwFf+Dx//tQnjEpajQQhz1OYTjW2Nml72XVoXmpOgP34cNuSxgCqMQGYKX8EAXDhHPDtV0wqN1PgbBmzKXRYuflnZzf+RXnAPWXdOWLQw01Bl9f2Ss0THYeDdDqQakVhnDMelWIgvWfSMUWhHY4SYH78MfZHuUK77HTmD2p0uzawVS5jpr5WqeTU1aQRvXlTG+U0hSuXmaZEyGt+oHD+PBAEGtM7NvWZBGMWBU7WVoGf/AngpZeoIPErv2YVQsKAaQiVmicKLUJilF0qVaNGoxhM05rEHgns3bnrKMHk4GMfVfA8jV//DT77+jXgi5/XCEIGiafGVg8OadNaU1GrVuP4lCDjyjIJm/ccu5O+FNUnz5Couj3MgrYzJaLMK6Gbrmk4ACplNRfoYhtpjEY2oOq2f89RKjy3Rf9UjjELoo7HHEPnz2EO4hPDgHXb2WXQ9dpVfrC1pR2ioGaqWjWvFgJYxav9A/ZXqcSySZqZzQ3a3tYmg57NBtt7MACefRb49Kf4zHdvaPT7eqYWATBQ/sYbGv0+02499hj/fnyMVBq2qLQ4JdTqig1qszw+Ln0a+L2vAu/fknZW+OIXeM0TT2hMJySxnYQoSqtqTaesF8D0fI2mUddr0458X+HphsLDhwmOOpL6SuPePaMWFDBo2qjboLfnA5//nMKdu8Ct20yd1+vRJ1TKJDo+9QTLsbFhlfGiCDMVDRfNBtBsKdy8rREE9Et5Qd88lCLgI88AX/s6yaIbm0B/kE5Xu0hBUdIBA+mA/uoKCW6BL2sG2uL9ByRYLi9TLX00YrCz2ZT5hXPOpUv0gfW6IdUqjoGltvXD1WpaUUzUS4KA5Ll63cOP/HCCV19Lk5Cz9hSFVuGv1aQNLi+zj8QOXAJlZIgpR2MSYptNYGVF4flPa7zwAu+jFPd+XAJzGDppK2PMAvxbm8D0OabGjUskIL3xJvuwWmUbHxzyX63GNValzHbzFP3SeAK89x5VYYTE+s67dmwcd/jc928C62sKn/wkCSjv32RdhNR07SpSCva1msIXPkfC4eGhRr1uCbdJok37WFLxtas82PitF+09qlU+u1pm37Vai8ldWfQHhozaXHyNpOye9U9kVM40yVJLS7Q7pUgCyfpsqW9/QOLClUvAe+9TlfaBITt0u+YQREwFomolva4NDNnjaqb9XFy4ALzymlnLGbUgSWHo+/z//r6shyy5JE9h6xMfA37rt/iz77Fc57bYzq0m1+TtFg9auFhq85Dn8TFVrVzSxb37wPVrGnE8/7zHHwOOOvy7jCX3u7t7mC10lOL8EwS07azrEF+itVU5VYqkriQBLl082T+//gbw7nsaN27kEDRiYDDinmu9rsy6X+G999LXiiogQMXB9963n/k+x2nWTmYqs87fhkON/X3WxbVBN6XwxQscz1FEP30SglDhEx/nuJO05+UyU75L6tLz5zRu3+Y81O8bMvWQ/qNcTqeCPTzkOMgjd4lKMUDbiTLvNBqGtO28a5VKwOPX6RdrNa5J3rvBMnQ6aZvo9QyRKgI2N0k6DUPeeDQCej2de7ilbNZLvm/9rpsaejJNp71/4gngtdeBsiHMh2b8J1Pa4WuvA7u7GsvLXDPu7JjDGNC4fFFBjzj/vV+zxDqZ04SoPJ3SD7/7Dvua8z4/E5KXqwIs7RCZ+d8rcy//3ffYBvI+5KLV4txTqXAOkBToWYWwoyONfo993zlmn5dKXN8em/mmXE7P/xok0L/1NklKlYr1qZ6yZNh2E/AV+3xvn+mgv/NK+vnunCZKaHGJpHp538qWWZC1sZPgkreBtNKzYG3NEke//TJ99nBg1ZYfe4y+UMrp7glkIe8w7lwv5QBYx17fHCAz4/jcVlotFTBqaAvqI74v773zw0BB7ipQoECBAgUKFChQ4AMijhW2NtNpNVz89E8p/PRPpZf6/b7Gezd4IvCFFzTefY+bT3KS76d+UuHf+jf5nf/N/y7Bu+/aYMWHgV4f6N3mZsDXvwH8/X+Y4Pp1bspdvQL8/C9ovPc+Nzj+3L+nZumbBG46ngIFChQoUKDA7w+eeSpAf7BAKgacj3/9N4CdHY3DDk+fHndJngGYamHkbKI/+TgJA8cdbppfuUw1z8QJw6ytAoMBVQaWl7mZ6W6sXr3CjXtPcYN1NGTw6q23eVGnww3kVoMbvtOpPRULyAlm+/v6mk05BHBTfjCwKkGNOnD1qsLDbVtGz2OAs1Yh+UBOIZdKwE/8cYVXXzNBJ4Mb7wPXrymT/kPNNnFTKWYMWm2F2lin0o20mgywTiZssyShUsqs/NqWNwh4nbRZtwcqtKxadY+HO1ZlJYsoNKk1MX9yWxSPXMhzLl6wqd4A4LlnSVaqVGxA9eIFbvCPJ7zPdMqAZKk0n7q8XvewvKzROTYqRk6M7PLlTDlMGS5dJLGtWkuTApRSs2Do668zzfjBgVVMASwBSlo9u8oU4sf58yzKolWoq4pRKZtgYwhcu5JWCnCJSI2GJe255a5U0gGNek0hCBQUqDSThdhwu80+FGJGYIJPpZJCo6Hhm1Rd587Z4JkENOKyJRC55alUFTbWdSoN4toag9KTCfD5z85/x0X2b2Jbksal3Up/vrcPbKx72PgR/v7qqwnu3eOzWk2b/nFt1fZGNjjiIkl4VTmmMt59E2x/6imFyYqG54w3lywi/XR8rGfjPAqpSOUpAErNFPO+/k1+Xi4D0CRQ9bqsW7ulcPeuxsOHRpXqkOpRwyG/K0S1KDTpA9dokw8M+WtlxQT+zPtYpWrbtF5nYKlzzICq+8728KFVAVtbZeonSVWztppWhnG7aDxWC1PSZiEqhgDwyU8A4wnTSS0KtO0fWJJbNm0sYAkvSpFg1O0CN3s26P3M0wycJwnwa79Okt/RMa9//tP2Pl/4HPC+EzwfDul/RMHOVUFTXjowODIKiLPPMwOe5eMfy2WF1VUPjabGuQnT5F44b5R9Bhz3QWDv7/nIxZVLJCLVqiR2PHgIQNMPSF9Pp8D1q+l0VLUa8OJLVN/yPapmBQHTaAK0q+nUpgV+7DpVyGZjUgHPPqOwsaHw9W9wrglCIIocdSxYhSgX9TqQTBcwsE5BGPIg2Plz+cSxk9Ksttsket+5w2C+BD2vXTWpohvA179pab6iqiEQX1GOFa5+QuFXf41zbqtFQnSlwkqvLgNLywpvvZWpY07ZlGJKYgD4yDMePvIM8NZbVJgLfFvGK5c4lh9/HLh4kYSX92/SVnZ3uca5cIHX5pGosyTWZsPDD3w/VQbv3tPw76f9ba0KuHw78b2PXVd47LpCp6NRNmn7rl2xSqKsD6saRSToPPUk57jDDoPqvq8wHFkyWqsJPP8pkt/abZtuG6A9P3jI1K15bQeQKCAqQTK+PF8h9oHJmGm6plNDZHX64KPPqTk1ljC089vS0nzKUiHblctpBUhBNkWcUrSblRWTQs8jIWF11ZKrPY/EtbfeZn0nRrUzb4SIQqjMN6KYNxxyDolju+4IfAAl1mEyNfP3liEnXbNtIWRsF3GsUK3SHw0H9E+PPabwxpvaUVrlfCOk7Hot/4DB8rLCpUskFikPeOwaFS8Bqq0tUsrpm7YfT/KvWTTWWy3bDzOyskNod+F5wGc/AxwcWjKS+Lz3btAWHjzkdZcvcaz5PvCJjyvUqtanXrzAlK5JwnIfOWuw0cgouGbGZdWQxj/6nOnXnEWQ77Hoa2ucH555mocKuj2uR0TxVFTOAD5HDgW4z3zwwM7jj13nWNxYJ3lf3rnissK1azqlvgrY9c1w6KjJabuOunaF6eR9X8/WyJ4CnnxC4fBAMwV1wPpmU8YKhiM7fufawfFfksLTTcFdLgOf+Dz9zMOHtPeqIXnfeJ/fD0OuAVgPzme9LlNcHx6R5Ob71v9IqtoHD0l8ykudvrzEOvV7SPmWC+f57+XvAIlZE2mQ3HPxAsdCVOJ3Je37w2326917Rjm4a8eTp/gO8+prHJtPGMLV5oae9akQtvb26Z82NjTJZSbNqoLtT5krAJZ7OEzb3kmHHkSB8IJDxsySCwXv33QUFEEfsb5O3/ntb3N+kwM2Ap3wPXM0Yvsfmb75+Me4xhGC1JUrnBsePGDZs/3j++khJXbbbAJf+bLiO6+HhWvW7LtBpWLff23KYo3EyINNE47zuvNutmg9mCIKKpuucmmJduIbX511Cc0mMBrY+bhSYUrHOKKq72tvmFSyCb8rRP9KhWvI7Pt5VgkwD4nGLJ34h4mC3FWgQIECBQoUKFCgwPcQ5bLC008xbcAf+3H7dtLrcXPR3cz5f/8ND9Opxp/5dzTu3su5WQ7+zT/Fzd833gRee+3ka6dTbma8/Q7w/2vvzsPkqOr98b9PrzM9Pfs+k9mTmckyWSbLEAhLCCCgXkEWr8qioAGV+0NEFEX8qijXR1S46NevhAto0HuVRXBDvCyicIEAWchG9oTsySSzT8/SPX1+f3y6uqrX6QkzyTS+X8+TJz3dVdXVVXXqnKrzqc959q+RFxuHDgH33a8xb67GnNkS/JWRAXzggxoej0ZpidwgKS2V9N9FhXLDrahILq6lQ5hBYEREdGocOHAAjz76KF566SUcPnwYLpcLVVVVuOiii/DJT34SmdZHrscoGAxi165dWL9+PdavX48NGzZg69at8IfuNK5cuRJtbW2jLCU5l1tFBAoB5hOwDofc1AyMyI3QYFBuWA8PA82hm+7R9xDd7lDwUJFCTY2ZAea45enozEy5eVlcJDdIu7plWJm6WrmhXVKiYLcDr60KDTPmjFy/gF86e7t6JIAiN1duvJcUS8dVZ6cEQhlDPpaVScDH8JD8rWzSmeIbkO83nrwF5PuNDtosjyzXeuPcGALNGFrPMDAQylaTq8I37wFzvY2sWokYN3UByfYDAC63bFwju0JtrWSzcDrlprmR7cr4DocT8DrNYUMyMqRDRSGUJSY0fUFh9LANkTtRRb+jFLxZOrxdEApSidf+ys1VOH5chzv4rDfFN20225m5OcCihaHfVSP/d3WZ31pcFBncZewDu11h4QKgZRbw8v/GfD2AyBvicYemVBIkdvBgZCeFgjxBbbdLRgB/QIK3jOFK6+tknzudcqwdbZdAqYzQsIKeqIcS8vOlI3xkRP5lZEiwT1mZHMMulxy7vb2hDkkFNDTYsG9/UIbVUgoF+UBdrQwROBKQp+9tdnk6PzgibWSXS5adny/bNS9POi8DI7L+NTXSEW93mBkgjI4Quy2yDE+pVPD5zM7Onl5g5gz5XdYsUfGO5d7eyONmSqWcC4yOxymVOqJDbdgPvPpqEB6PHOPNzeYwWxmW0+bUqWaH9jPPxtmf1h0YJTAiHaPHj0snmuG0NnO/GMfx/gPyUAognWE2m4IOaoyMhLJAKPNLbDYpf06H7MPyMpmvv9/MrtfTI/tr46bQUGpagu0u/QiQl6ewZp10GhrbNSNDjktAsgPu3gNs2SLHm3XY0+hzbn8/wpmQCgokmPbcc7QMF2eL7DDev1+uDYHkgTXRGhrM7ZSdrVBfp7F3r0JdnTnNjOlmBsODB4EnnpLXLTPlf2tg1bQGyQjTMgvYtFnB5TKztBnl125X4aFMAWB6k3TkT5tqrrjHo7BoIfCPlyXIWGu5Pg2fLyznguJCoLNblm+UA4/lOLPb5XgNd5y7JXN2bq6seEfofF9QKN9vDJUzOGjuEGPfWM9BZaWW7FhuhYrQMGbdPRqzW4Dt20OZe1wy7fTm2GxnTY3AP16R1yNBOTamTTW/xyiPBQVy3PX0KGzcpLFvn5SlqQ3AK69qzGmR4Ney0Pm/t9fMDmFs68ICybQTDMo+a6iXzuvSklBWqZ7EWSpmzQA2bjb/7uuX9kK84Z9KSzDqEFIulwpnGjLY7UBZWagcRn1WXCTnwf37zfOOkaHvvGWx39XTo5GTHz8gN1H2SN8AsOYFjQGfxr79EgTqcspxKMG9Gt5sOZ8ZjFgQoxO5psb87UrpcAZMQ6LsnQ6HQnkZ4PfrmMAkQ5YndntnZxv7WyPLK+tz9KgEixrHdV2ttHf6Qxn08izbxWEHrF+Xmytl/9BhGXrQGvQNJM7GBkiGmYb6xJ+HKQkgHRqK3JaG/fvNNpzHI3Xfxk3SJszIkN9gHebLGtDsTpCVdNo0YHro/Oh2KwwPa+zcKQFPFeUSRGezxWbfNNc58s9jx3TcsuLNMttj1gcTckPtBWP/KZuco5qbFfJy5dxWUW7WU1bt7RLU5R82zwc11VLGc3MAaAlmCAdBJCl6+/ebbdLGaUBgQLKxbd8B9HQDObmxMxvLTTQcmpVSQGeHxv6DUkcWF8vw7wanU4KubKFgO2uWTb/fnG7/AXN4+MxMyR6Uny9BVU6nQl6+ZFjbtBmY0WzWhU2NKnzMtrdHBrQDkUPvWbndwOCAwpq1Gnv3SrCgMeR4bk4oyMUv65yXp+B0Ksybp7F+g9yDbQ9tGyMgCJDgn3gBntYjbPsOoHyJrL8RIGgwhvd0OGKHrOv3me2eYOjBDa9XHmYpKNBwOoFXX1fh36yUQnGJRnaObH+XS+oBo+3fOC1y/778ilwXnHN27HYCzKHEo9vh5WWyf2w2y68MPURgzSRpDC/rdiuc1qZl+w8Cwf2xQ/h5vQolpTrcprdm6TSyPtntQHWVwuCgZLQcHpb2ssulYLdLUKqxvYzr4Hlz5X63UqEhSp2yPQYGYofhKyyQ8mkM5R19bZWXq1BZruEbMDNGt8yUrHs5OVK+AwH5bdZTuFKRAeDRQawGv1/qBmvhLiuTZWa4YwO3o5WVmoGZ+XkSBNjnk+1TXy+Bae5QO27Pu0DjNI2gVigrlfO60S4eGJDjxBo02VCvMLtF4chRyYS1d6+GxyO/xemUYybeSCIul4po/6YiI0PKY0GBnK/qakLBZUfkWuDwEdm+wSAALe0ZpaT9sCUUhG4NFquvA9rbpew5nVKujPNHZbnZJoyO8HU5gKIKyT6L0PcV5sv1d0mJDOe5bbvUBQ6HOb9xbogeVlSuu82/naHs53n5Uo8PDsh16/MvAldePrZtNhoGdxERERERTQIejwrfeLey2xV++bDcpDt8WIKuDh3W6OiQjqzjHXIxZDxl85F/UaiaIsM0PfSIPE21dduJrdP6DfJP6PCNVZ8P4exjxmfRXE6gsEij2BLwVVgoF4FFhXLhXFnJ4C8iIhp/L774Im677Tb0GWOJARgYGEB3dzc2btyIxx9/HCtWrEBNTc0JLf/3v/89br/99vFa3YScLhW+GT+1Adi1W2HuHI3MDMnkYwxb0dcnN/8HBzVefkXq5OibroGAdHbseVc6hxpDQ2I1NACbt8gt56opcrNebuTKcjq7pL63hTLERHd2WDsrg0EJrDEyUWR55Hu7us1hfayM4Jfq0JP88puBggy5YW4dgmzGdPmd6zeZGZoqKmT5ZWUy/dCQxsCgCmfbMiTr0LQpwBoPk2g4bE+mmR1tSmVkAJnNZg4V5g0NNVlUCNTXKSxbKpnQjh1HOIOMJ9PsFGuoN18bwyTFpSWgfmgoNoMXIE8nT5tqZv5KJjpjj7VDorsH4SeoDQ67HB/KJuuwbYf5WfQTy9ZAkcwMOaaM6c85C1izzhy2y1BYIB1v+XkS/LRli5kFLXx8Kbl5P2um7FvrsBh5eQqzWyTry9695nb09UunT3AkMjtUbq7CSEA6i3z9ZqdHfz9wpF0Cv/LyFRoagNISjT3vArm5EtyllPnUeX+/HO82uwynZnTC5RZqeL1mIEhzkwTM7XlXY8tW6cgqKgI+dLENXV0ab64GNmyULE/GU/K2qOCuiIwbWiPLYwY0WsugtbPVGGouOntASbGs+45d8ndvjzmcHyDHx+tvSmfY1AbZvg0NoX0a6jyvqwWyPOYXOxyJC5ndbnaKGOXc5ZLld3REnhS83thrA+vx7jCyefXLuczvl3JXWCDfb7fJd3V3SyCY0RFkzXoASOcLYHa2G1nQmpuAPXtkmUagIGAGddhsCuVlOpy9ID9fzruSWUBheCjJyQaA12vD+edJZkXrtZQ1i1e8gJtEovdtQ70EeFkDPK2ZpkpLgfo6uaYzglhqayQowRHaT54shYpyhU2bNWqqpXOwt0cCTB5/UqNxmgyRWVgg50GHU4Zmiw4qnd2i8NZqjeFuMyDHOCbDAakAausA96HQkEpO6ey0ZtjyehUyMiVIbGhIhlyy2TQaGwOY3eJMen43WIdQtUrUsVpaKp3v9lAQ3ry5wPTm2B3jcklnfEdH5PtGkFpvr+XBLR15xaxC/44eAfbkAu3HgbIyDSgJnisuNle2qVGydRrbJcsTOldrqQ+Li+W8l2jI3pycyOEUjW1SVibrNzIiy+zpHdvxZ92e1rpzzmwjsFq2RVWVQnVVZIB1dHDNnndlAymFiGySkmHQ8kUJbh8Y90KCWrLX+HxyjjGO83j7OitLIS9Xo79fAhuif3vM8ZIhy/d6zfNvMjU1EsAChLJIJThWDx6STukdO+V1Zmg/FheZ7acMt2QV2bVHPnM5JVufEfRoBD0Y65ybq9CUqdE4Vc6Xg4OxASijMcp4tNIShcpKhSNHNbZtl0Adn0/D41HILwB8oePM55NzqFEvDQ5GHifNTcA/Xjb/Ns4TxrBngJyTsrIUjrUbGb40pk6VzI/nRwUGlpaYgRsjI3LvrKwsdsiwvftkf1iHegPMwC6bTR6YUJChLvv6ZZ8Y7Z26Ghn+MDrDfWmJDmeBkiHLzAClgB/who75jAzJPHS0XWHDRmnLG1mACvKRkPV43L5d6rh+n2T26e2TYfuMZXR0yu+urDC3qdslZaO5ycxoYy1bSpllROvYDLMNDRKU0dcv7cG+PqnTmxple+/abU4bCGg4HAqVFQrz5mgZ2jn0A4x6e3gYWLceOH+ZOZ/xwEVurqz/7BYJahv2y/CARrslI8Nsu5aWAIERjeMdCp1dsk2ysqQ9Xl2tsHVrbMEzhnRNFLDZ26vDQTDZXiAn9HBHWSkizmNut7QJNr8DOELbvzx0TXLsuJmxuKnRzPIbcdRoc9hrQNpbw8OyD4NBc/0K8hWKi3T4oZesLODCC5QMJZ8vx92RI3L0Dw4BQ+3JK8YplXLcGOvi8Ujm3f5+OR8OD5v1sPUaITp41etV4QCo7u7I7zSG4MzKNM9p1qy94WBJY/oMhYsuBIoKI8tVQz3QtUZ+W3Uo0N7tVpgxXdrNWksAjWTK09BBjUv+RcquyyXB6hkZKuEQmTm5EghUALNeKitTKC7WCAYV3tkivys6sLjfJ+cb45rDWr/Y7JH7Ofra0uFQaG4Caqo1Xn5FRWTajlZYCOQflQehhoflAarojFjRe1sHpd51vpVatqjcHNlOnky5Ptm5y/ws2ysB+O9VZobUCY2NsmXmzJYHzo4d1+G60Rii0+s1M3stO1dh0xYNmzIftMj2Apf8i8Ku3drMNpZgOG+t5VrGODfPnQ2sfVvuIeTlybnT+mBVbq48rPTOFhnm3hjG2MjcFq2kxGyL5+VJ2Vp6jgRoDlQAR47ocCDjeGNwFxERERHRJJeZKUMm1oefwo69rBgelk4qo1Ovulrh2/9HobNT49775aZzV5d0HiRK224MHWPc7IiWLLNFzPr4Q4FoERnHzIvL/Hzgj09F/o5t2zX+9pJGUZFc7BoZCYiIiFK1efNm3HLLLRgcHITH48ENN9yAtrY2DA4O4plnnsFjjz2GPXv2YPny5XjyySfhjU7FkQJran2n04nGxkb4/X5s23aC0dSJvsdyo7KuVqG2RkOFHns+dkyGV3S7zSCSo0elQwSQzvrTFsl7R9ulQ7yyElhyhjEMmQr9FoXGqRrKBvh8CkVFZmeedSgEIPaJbECGhDNuor67Fzh4WP4uLQEuOE/eszvk6VUjGM1gdLjlZJvBDdnZ0iHrD2UBO3ZMS5Ymu0JOLuCwmzeBEeo4tg6rFx3QXlFu/tZ4VIod2UpJBhDJRqHwt79Hfl5aEtofGdLpUl6ukJUlQQ9VU4B9BzQOHpSb29abzu4MhdmNcky98ZZ0kALyHdahy2yWzv7hYcsT6YWybgUFsg5G5hRj+xiCIxr5eWbAizVxXXQHtvH3/gMa3V2yDVtajI5Tearf6KiIHt4yEABa52kcOybDjXg8kikOkP0TDEpWCOs+qahQ6OnRyM2V43V42AzucrlCwVCF0pnj8UhnQLTSEoXSEoW6Wo3WeRq/eQw43in3+murZYgPq/ZQ+TE63I1On4gh00K/z9imhQWRB4s1YCQQkA7sw4c1ptZLm9ubJQFf8YZaMbZxTo65P3VQglecTlmeNaNJMCiZS1aFhkTMzZVyGp2pzdpJWFcTyjDiAV59Td5zueIEbyhZnhHcZfD5JLDK6TQzDlRVKtTUjK19bmQ3AGTf5udLh7sxnJZxvkk0lKDbZQ5tlp8XCmqxXJNkZCj0++Tc0NMrx7ZxfBvl29qhqGzSeePx6PC5w24zh/trapLzZ3+/HOcR2yXOOXBagwpnTHzuBfN9lwtwhzrsFCI74+x2hYpyHQ7uspbBeJm7GqchJosJED8DXrLMyUqZwwAZ27uyQob3cdhluxmZKxTk/FRepsPDHFrP49ZziDvBvjMyrbhcQFmJdNYFAnLON4YL82RKp31dLbB7NyICpWa3SNDZ/v1SRoNBKWdaA/v2BzG7xTz/Hz+uMTAgwTTVVZG7KlHWmqyseAE/5j7IygJqalTM0KXh3+1WmNOiw9kKjaDBzEzZZ50d0mFuLMtapxujcRnraQ1YbZymcPw4sC8UJDOlMrJ8uELX67m58l35eXKMd/fIiluDYwAZ1qo3VIhmTJd61u0yh6001s+aLTNRebSuvzUjjfW8mZMjw/Ea2dC6umSfZGZaA0Y09u6VdkWG28x0A0g7JZwFyVqPBc27INbARg0z6MA4NzidowRMhyxYoFBxUKOiIk65Cb1lBCcVF0uHtNutIoLPATMLmJXUczr8eaKsXsY26emRIIXMDCMwRQJpfD7pzLa2V4b9kedz+X4dme3VFXm+3rVLMp0MWuqWZPd3ZkyXTJwOpwQxRtuwwdz2Pb1B1FTbMaUi+v5PHKFFVVbIOcIZFZiWn68wb460c41Av9xc4FCcc2A04/xlnBuHBiPP/4Cci2e3qIjAtaJCHT7XFxdJEEFfv2wzY786HJJp6iP/Ev+7PR45H3T3yPFurcMzMiKHQE7ULDWCiOL+thJZJ28WwsOQGYx1lKA/c8hPI3jLblc443QJhvF44n9HdyhQtKNDgqc/FpVlpqhQsvX39UlW08xMmX5KpQRXWoO7jOFogVCmG8tXjgTkQY54CgvkmsXlUsjyGG07+ezNt6SeN6br75d6e8oUyzYqNgPCEw1zZ5WfL8erhhmABEhAk1EXlRRLwFZODlBRbgapGsHm06Yq7NuvI4JnbTZlyfQYWS6N3+50yrVN9N5wuRTmt0a9GTWRwyGZVQ8d0jh0SNrGc+dI0Ip8v/xfXW3HwdD62m0SKGWzSVDWwvkSdLdjpxxTOij70Mh0agQmZ2cr5GRL8G+84UezsgCEAuHz8yS4Pqgl02lvX2j473x5CMJ6zBYWSjk1zkHt7RpvvCH1U32d+SCw263Q3CzXftasSUb91d+vw+fGkhLjvrk8BGW3y/DzybiccgxrHRkgbbcrTG3Q2L5dziHGQyLWNoP1nG8E2Bqs+9x6njbs2Cntr2TZ+mRehbo6DbtDypvDWvcliN3SWs5jzU3S1ursTJxZDJB2dFmGTCfnLVnw1Hqpm9eskzqnuSn5uo7G5ZLAT2hZL63lvNI4TbLN+UMPpGRasjPn5iqctkiuH202KS8zZ8hxmeE2g7usD7dYt4vHI/0p05s1giNAR7cs/+Ah+X1GO8YIHDMYdUhenpwbN2wwHo4xr4nkNyksWqjR2SXTOh1K9lmNxuYt0na1ZigeTwzuIiIiIiJ6H3C5VMRNYUN+vsJ3/k/kFWMgoNHVLRdvRsBXR6fcZLricnkC7zePabz2ujzxOJTgRuR70dkJLD0/CJdLLpJyc+Ui7O31AKAxby5wxeUa3iwFT2iYqN//UYauLC2RDs7iYrloLSoEiopDNz05FCQR0T+1733vexgcHITD4cDDDz+MefPmhT9bvHgxampqcM8992DPnj145JFH8G//9m9j/o6pU6fiG9/4BlpaWjB9+nS43W785Cc/Gf/grqibthF1XOil1yvDI3lCQzxZZWcruF2ykKCWG+gRGTAgN+HdGSo8TUO93Lw3OqE7OqXT2W6Xjg+3O/I+dHe31Mdud+SQOC4XMGeODV09MkxRWRnQfkyjqVGWOTQk6+d0aPgDCjNnRK67328OFdjdjXDmIOtN7chtE5ntBZCO9sLQE+DxsoYBsZ2tLrd0KPX7IjP3ALLtzIwz5m8tKwX2+c3tYg3iMDIR7dwl75eXSUCHTUlmCECGI9EaePIp8+nwmTOlU9Jg7aSyPgEs2QskO5h/GGhsBKY2hPanpRPl2HGEsl7I+rpcMs3rq3S40x8AmqYZx5mGDsoxEdSRx2LrPOCt1TI0SHQn4f++CgRG5L3o41EphRkzpOMtum3Z2SUdD76BUOaB0G/PyUG4vQeMHnBgtyvk5SnU1QUjOlYjvqtThx9kyMgE2hYCGZkKa9dBfnfot9pswOLFMixMv88Buz0Q0Snu9crNf6OD3Z0hWZyM7HX+UHCa0RliZHjT2jyObTYVEawHJA44zMlWWNAqGYykvYy4wUaG8nIZgqawQGPLFumYmVIp/7Ztl3YzEDtkkNMp+8fIWnHosBlI0dkFnGDCQwDS8Te1wRjWTbJGdXZKZ9nslvjzNDVK0Ed+vpxn9uw1j+1g0MwQUlVlzSosKsrlf5tNOoADAQlq8XoVzlqiYbPJe9OmmufX+jrJzJGVpZCVBRw7LhvZ6PSz2xHu6IxmzRro98v5xeWWY9h8SEdYy5T1/JSVJcdHYMQMBigrU5g2NbLzGBjbgzdA/E7jmTMkKNabBSibQmnoetJYvfp6OSa275DyaQznmIqAEXym5HhcuEC23/4DoSExQxoaFF5+RSMwIt+RNSBlc3BAgqs+8mFg9VpgeEiHzwFNjXJQ2u0SUPHKq7KPDh6Sa0Xrw0rG8JhFhcCZZ8hrm02mNQI8bDZg2VKF516QzGpWybKDebNis2/bbJJ1052hUV8XGtbSLuczI6jDCI6rmiLBbdMaZBqnw7zOtQ5753TpcFCH1G0AtNSn+w/IMWMMm5ooAyUgx64xHKBVdZVZ302plHbAoUNyHM+cYQaO94ey5hUVRS7Huok2bdbw+TR27TKWFft9fX3A1u2W7ehVaKiTgMoplQq9vXLc9cUpZ0BU4IYGMjKkw9XjkW1st6tw1qLR1NXGv9/hdknw0fCQtAdqqhVmzUx8r8HI6GcYGDC3SrJbFMYxmJEh29EIfC0qkmCsqQ3SOX38eOSB6PcDr79htmPOPjP59xQXyzF2rF3qp2PHEwecyfoYbTTJvlNQINvBEwrqzsuTOsFhBzJD7UivVzLfDQ7K+h1rB3LyZHrJbmkOjayUZCOzBosbHfpFRQq1tWbAcvRI6ol+ZnMT8OZqM5NbcXFk0CJgtiesw3JHB6seOizzuUJDhxvztbQAtiTp7UpKFEpCwUjW80gwKL93125pf+zPA7xZsSeWgQGdcFjUxmmRAWPGuTArS5YfHNEYhkIwqOOWcbtdxdT3VhJYr9DUKEN6HjgIHDio0dwUGXQW7+fH7J8kx2GyDHK1NeZ5yBj+z/q9ZWUSaGGzS9D+tKmyzsYwsS63wpQKadca+yEe48EEm01h1izgjMXyG55/MXZatxvhcq+1xvx5sb8xXlZIK4dDsrMNDwHFQckSZ9Tvff0YVX+/DBc9EBouz2jDW4fbbaiXuqSjE5gaGiZ5SqUN9XXSzqyvV+H5AAkYLy+Xc2d3d+Q+NNrgHZ1ybC1aKEGo8QIDtQYCfslgGgxKW0fZgOnNCm+8FWrTKgkSdTk1qqulbXD4iDwA43RK0GFujmyXwaHItk0wGHvtmowxZaIgxmgZGfFH0ACk7vrEv2q8udpclpE92uWU8+Gx0IM9brcE9OwIDUPqdMg5LzfHGA7yxLlcALTUuS4XMG8OsOpNa+CpXO8PDcuxmJEhx0hJCZCZoeEokuOmbWHybRmdDTbTo6ChMW2aXGOWl43tdwwNyXYYCco5eWoDsHVrKOtor7QPzjgdWLjABp9PY+dOHc6+GQiYD7QBZt1wxulmRk7rGTQ3V+5TOBwyJKL5vlzTOhwKcMg1c0lJKKPXVFkHny8243l+vpxr7KF2WUGBHIvRWdMAOT8WRQXVGw/EZWQqNBZplJWNadOlhMFdRERERET/ZBwOeeou0dA9RUXATZ9XuOnzcgPD5wM6OjV275anufbtk6fCurrMJ1pSGRIjmt8v/3bHuaGxdh3CnWzxxb6vFOByabjdckHr9crFdF6e3DzJz5en/PLypMMmM9N4kkeeGs/MjHwajIiI0sv69evx1ltvAQAuu+yyiMAuw3XXXYff/e532LlzJ1auXIkbb7wRzkTjciQwe/ZszJ49e1zWORltpPaII9trdrK73Qr5+Qq795jDDlRNMZZhzmPN6pOIzSYdQyp0E7WwEJgzO3IdqqbI8DjFRcA7W6X+dLtl3oY6GTrKYZenbK0dh6UloaeqtYbfL09xB0YiO/mdDsn2Yc1Sk50tw2L19sqN2/99VW6wDg2ZHTRKAQoqPHSiMZ+hsABYtECygZSWmPV9eZl04hkcdmDBfOmwLCwAjv49tB0hnStZWTJf41QZbtBmk31RUS4B8Vne+AFIRke0M/SEb2YGcOSoQl1d7LTGDWFr50ZlJcJDELmc5lBMnZ1yQ7unVzKtHTtmBlVZb9Jbs0RUV5mvrU86ty2M7MBzOGR/KER2XOXnqYhhdKysy4vXuWdkj2hvBzZsMt8fHgYyQ9souk05b46062pqUm+nWYcTMjqiw+ufH5mNyeGMHF7SmFcyTikopVBaaseePUEMDshxMTIigTZ+v2z/oSHp3AmMyG9xu+XYys83OyFyc1U4QCLba35faYm0rV0ueV1YJBl/rNNYM5oA0oEEFbuN62olwKmi3OxMstlUeCgbQIKc9uwF8vPl6XlvVmRgg3HcGPvB6TQ736M7LBMKrZcRINnVJWWoogxoW6QiOq8XzE++qKwshVmhc11Xl6xUfoHC4SOSmaS+ToJNrOuWmSlluSk0/IvPp2BTkqnLKF/V1QoLh2V/WTuRXS6FM8+Q/btzl2zL48dl6KiDByUILDtbxw3usnZ6NzZKh5F0liMme8Sed83X1s5dm01h4QKN48cR0RlUXKSQmyudsIbW2CpuzFwuhQvOk4xXOTlAWWnkenq9sjubpsl5qCzFzr0jRyMLcmEBsGC+bKADB4LIcMu+qKsBZkxXePZ/zOwmOnRcGoEIBQXGEEwKOdkagYBCfZ15gispkcwixjcODMa/ggwGzcBIlwuY3gzs2CHTTq03pzPWwygLyYKlkmV/LC4yyqxwuxUaG6WODARku86cZWRXSr5d8/PMYaWGhqSjMhgKHhkZic0aYmUN4E0Yl6KAygqNwSE5jzidNiw5Q+rqjAxz3Twe6QQNBqMCHC0bXAKzFcrLNaY3xw8mi1ZWKp395WVyvjeCJI+2m8PZGesJhDJ+FMm5panRHE7L7lCYNk1j3hxzOFjjd3syQwGXrsggmY7O2CyUgCyzr09+2/HjwPTmxG0yj8fMRmdTcmxHDGmoELe+B6T+CQal87ivX845zU2SfXUkIAErAOD3y0NvxroHg5HtBMnKGv87ANkP2dnS5urt1RH1YHR2K4MxpG1evuwb45wKSNDfO1vkuC4rc+LQ4SDqahS2bNNwOEL1Y565jgqyfvV1kqXd5zOH/3K75JwZjw5qTGuSY0Ip+b5Ed4jcboVPfEzjmb9IO7K2RtqpVjY70Nen0dyo8c5WMyvpvv2hhyGCkvkTAKoqZaiwkiIJJhpLkIbTKceQ3W4GdxlBpwqxQWeABPNZ62urs86UQE5jvy1cCBw+JIE43T2hTLV2OWatQ99ajYzo8P07CTiP/T0Op1wHGNn+e3sjlxd9yaRUbFBIsuMwywNMm2YEvUR+lpursPRs2Q/R7T0ZalLqAr9f2qjGNFlZwOI2M5vpK6/Grot1KNiSqEAOMwuqfHd2thnEYR0q08iUO5rSUrne6OmVrKv790tmWUDaG2++ZQafxRtqPdrAgBz/knnNfN8aaF5UJA8B1NSYwepKKTQ0KPh8OtTmkHaTkcETkDpqaFCH96EnNOwvtGwXh0OWkygwsKtLHhgwhn+VYHoFf0C2ndtlZkEb9qtwsO/UqTKE9LvvyvVUR6d53Wa9DigvA3p3ICG/HxH1z3g/mJyXZ4PLqcPXXUY2wexsOX7zC8zjv6lJYf9ByTAVCGhUVyc/X8i16+jsdjkvLJwv9df/hrLxZmVJue/u1igtlYDsKRVmu6OywszunZMt9WMyNdXAmrUavT2hzG1agq1sNgV3Ruw1x2jCQzNPAaBC9xi0Ub7NfdXerhEIaAwOygMRxryjqasJZbv0yHYxghqjzZgObNsmZe7godD1f7mURadTxT1fWr+/qMh8wGPBgtH3afT8hYUqbpD7e8XgLiIiIiIiSkgpeWo9K0uhaorcVIqmtUZXF3C0XaP9mMKxduAvf5VOic4uuXAbHo4/dMh40lpueAwNyRNVR48mnTruu3a73IzMyED4aX6XU54wtIcyLiglHYEup3Qyu9zSkXr6YhURLLZmrWQas9tV+KaXUrIcZQs9xR36N28OYi7+X1ulMTQo09ptZlYS47XdLjcU3G65EHe7FbTWGBmRJ53kYjryzlpfn+yrkRG50Dcymhj279c4dFg6J6urQqnCLawd20REk83zzz8ffn3ZZZfFncZms+GSSy7Bj370I/T09GDVqlVYsmTJyVrFMbFmpYkmnWmR79XVqpghM/LzNfp9Ug/PnRP7HaWlZieqMcyQzWZ2Tvb3afzjZfPz+nqFvHwVEzADSJ3kSVJHdHfLjVWl5MnjKVMU/H7g3XclWGnWDBnOEJB6bc9ujb5+4AMXyLANmzdrDA0rtIYyzxw6pDESlACn7FA2koULpJ7Ly5M6/OBBee12SyaV3KgOzOnNctN2w0bzPZfLHAbG2l6wtmOqq6Wzy+MB/vGKQmGhdIyPBON3kjY1SueM1kDLLIWMDIUZoSEmANkmDrtkrjE6lIqLpXNoSiXQaLlh7XKbwV1H2xHR0WRdR+tNeHsoUMtmQzgzT7Q1a4Fzzo6c38g+Ze3cSlWiTgAZJsXcrkeOyA19Y0iM6KG0AiMK8+ZGBhiMpqgQ2LvXbCPZbMDeffKZwxE5PInHoyKahf6AdCZrS0ScUgptixzYtVuhpFgefDC2dXc3cNpCCRJsmiZttcFhc8jM0UhHtQR8NDVI+yzLEzmEU/QQqdOmxV/u1AYZvtV6zkg07Oa+fZbsTJagg+gOlUULgHVvy3arr0dclRXm9gXk/DG7BVgPhdwcjb/9XX7D8c4TeyDEYA2cnD5d4fxlCv39oU5Ly+9sqAfsNhXeDp2dlk40ZQxfptA4LdH3yHoD0lYuLJShlw4eTL7y1m1tzZAXr1PUmoEvmtdrHdIpNP0IIgK7LjgPqKlOcVzZURQUKJyZoBpUSuG0RbK9jEyEqVi/QY5lo36JyIyToTAttO2NwJ1pDbKfDh2SusRuR0QmBPOaRAIuo4fbnWYE3CoJkuztRQxrQJLbJeeUtkUSqDc8DPT0aFRXmYF30cN7xpNk1N+I4CKD0ylDPb27VwJPjexyo7JmerO8bQR8GYFBQGwZMzLBAZHDThlGRjTeWm0u2QxuUzFBG+Y8Uatn7cgskMCmzEwVMWSaVWGhQnGR7PMZMyTApqFew+ORDIgGa5mvqQE6jkuHttttDsFaU6NQUqKxfafsx6ZGFTN8b16eBJ15sySD3Nvrkw+VNHOGBOLsOyDX3jk5QEdn4p1dXS2BRHvelePv8JHI4WNLiyXwIh5jWxYWAr09cj52Z0iHs8sStKqU7OfsYfMegnU/2Gzm8ZCMN8ssH0ZQW3TbyNBoKVfR7cvMTIXWeXKOyMtzYGoD0NVlg90+Anto/TIzzWB8DWlLzJtrBN3K/aKBASnz5eXxg5JGRoB16yVw2emUwLfo4Bwrj8eGiy7UePV1+buiPLJA2G2S9SYYlPWYOUPq73DgvGU3ebKkDdbUqBAckeMoVU6nOSy1f1iySVWUh4IflZlh0AhoycmOH2QYXm+7Qnk5kJsj96taZiocPSIZuxQsAVZJzkmdXfIAZVGhZEha3GZ+VlMtQ6kDZqAiINu90jIMqdMZG+BvZM4yJAvEt9nlQdNEQ1AmmtcY1tU4L02plIcqCgqAosLok7S5PmWl8nsKC82HH6yBXEBsAG9GhpL7gUoCQ0ZjfaDg0GHJ9LVwgZxbtVY4eEDq9eIi+ezQYfO7mxIENVrZbHLfcWhI1skYCnl2i+yf3JzE5xdA5jP2bX6eBNuUlSp0d0smuelNwDtbFaQ1KtusqEjK62hZs7q6pVwqJZnJjPoj2ytDhAIyrOuRo9JGNhjt4+pqOc+3zDSDkqz1TlmZXOvEBBSH+P2Rh3x0/We09xLp69PhbHHZXsmcGa2hHuFgR+uDF1BSpxoBug6HtCv37Yt8kCaeGdOlLM2aqbF6bfJplZIy5/EodHaZPzAzUx5YPnpUo6REHiBpsgydWFQk823ZGrpO9sZZuIXLJe22TI95jZOTbV4zjTW4SymZ3xjSckYzsHGjnOeys836a/0GIKgV2o/p8LkmWYCoYXYLcDh0z7+mWjLw7d1nGdY5pLJChkJXSuHgIXP7xcvCZSguMstMZoYcpwrSvknGeIDkiOUhNSPb23hjcBcREREREb0nSink58vFuHFz4pKPxF75aa0xMAAcOKDx3AtyY6urEygolCe+pjbIBdaRo8A/Xp74YLB4Rkbk39DQ2OZ77XXg0V+nnmUsWlmZDEGlIDdb7aGOx2RPils1NACeTI3e3shMAEoFYQsFhdlCNx2M7ZqdDZSWaHg8CHdubdwkF/+A3DhtapKsDt09csPTHwDy8yQ7msslWSqyPHIj1uGQGzGOUCBcxN+hLC52B+DJVFA28+ndkRGN3t4R2O2yDaB0uKMkEJDOiIDffO33y1PM/kDoveh/I/K/sS+Nf7Hv6Yj3cnIUPnRx/Bs6RJQeVq9eDQDweDyYOTPx2FELFy4Mv16zZs2kDe4CjGFSTnx+l1vJEBkKcYNzKyskmGZoyAyusdkUFsyXm91+vwzNApg3LK1ZjlxOs/PVGoxiU7E3geMHhEkmp0AgMnjH7Vb4xMfl/GxkvIl+GruuTjpUCvOB88+T9xwOc2gEh0MlDEYx2O0KxUWh4a4Qm33AytrJbLR9RCh7SqgDs6sb6OrWqKkGGkNBOG63QtuiyOVFBydc/lHg1delg3NBq2z3xmlyM9/aqVpXY2a9cjpCw71MkXmih4Uw5OebQ1RZl3XaIuBvf5fX/oBkhDG2d2mp3OSfOyf1bBXz58lN9cqK5IFN1qwpRqeKMcS4JxPYvFmOt4oK6QAZS2AXAMyeLU/N2+0qnNnJUFkuQ30dPgy0tUmHWF+f7MPKCtnuSpnDKRq8Xlt4CE3ZRmYjLTtHMlE4HDL8zcv/a66vcUxZh4CycjjM92U4VBUzPEhJSeq/P7pjNN4xXVIc2To1Hl7Q2jJ96HV2tmTwSbY/a6qB11aZfyslwRrTmzQ2bpLlDvul3fj3f2h4vcCZS8be3vJ4JHjtyFEz25Y1W46VMZQrAMyfD+hQ95+cm0b/7oIC6dAZGIyfCXE0RYUq5iGK90Jrc30AxASvjLfGqcDO3RKMkJ0d28tmHSLywMHIzn9DXp6U9eFhKR+BgE4YFJSRqTBwSMPlNOu8VPaTobo61CmZKeeSwkLJMtnVZZZl63cb35GdLcFEO3dKJ3lJsRF0a16PJFoLrTUCI+ZvstsVqqukczHLA5y5BHj7bbm2mzdXppFMKwrNjfJ5qtkIe3p1OMtkhluG+LLZJStfVaUELqwLZSVLFrQUjy10XWYc3snqQUN1VWRGJGtH7MwZckzk50dmBooOgJg7R0V0vBvDaGktndCBEWDWTGBhOFOGBCMYgWodHWaBtNsVTj9NAtziBVg3TpNlZufINp/erPHa6/Jds2cDO3dGTl9XC7y7V2H2LI2eHglGT9ZZn5+ncO45wJatGoODCllejWPHzM+N/R+PtW2Vlw9kh4JejOMwTAHNTeb2PGwJEHE6Qu2ZYh0OgomuwwxzZsvvtravEhU1o1zZbJI5r9+nkZ83+jHrzpB/Uxukk1sHAZsjdrhmh8N8iG40uTlm8HcqATGGKZWRwUrGMIYG495ITY08EFBZKQ8idHXJ8TQ8JMEuiYKRUuF0SVC7xyOBifV1wNF2hZpqua+y7FzZFlu2Jv4OIyDJeIhiZESGYh0aCgXXhE5YblfCRcATClg1sn9Zy581ANcaPJcoC5iVzSbBmfv2xwYBxp9+9GmiGUPRjYxIGZ82VY7xePVEQ53UX4CUp9Z5GkGtwnU5IBmh9u6TjJE9PQpDUUNk22wS/GbNutnfL+eDYCi7ZG6uPKwynOC+pREYtXChRlen+XDFWUskW6bbjZSGjy0vU5g7R+OdLXLtY5xPMjNVSsFniZouRia+/n45FjxZEpQ6b65cs6Q69HNVlZRNp8v8jdbdMmumZFyLvu6R6SRoMVFN63ZLoBwggVhvrZZzks+H8DCE1gcgrA9vdHYZgcuyBc5fJoGFxnWHkZ3RyCBYXhY7PB8g5//oOGWHQ0VkfTW2sfGg1dvrze9JpqBAYd4cjbVvx362cL5k3szNMetHaz0KSJbseA4clCHZAaC0VGPWTBUT1BjvvO92S/144ABQWho5ffTQw4lkuCV4NLotUVmp8KlrNN5aYwZ7AWYdOKVS2ttBndo5orlZ4d29MnTwrJkKHo9c0xvBXdbDzThPNE41/k7exqyvl4eeglqOCxnecfR2qVFmjAcbgPhZGscDg7uIiIiIiOikMNJ5T5tmPq2dTCAQxO7dkr67p1dujPf2AgcPamzdJq+1Dg2DMADc9Hm5UfXqa8Drr8vFfqrBUUaH1qly+HDUG2NMJx59Q9qgNTCipcM5mrE9AXlaKtqWrWag1/iKt6G7JuKLxkhj2zbgW9+UzAjKCExQ5pBUxt8Ghz12qJGBAR1xUym89Dg/OyPDvFFj6OnRsTcIExyb2dmxw/wcO6ZjjvtEh3ZRYeTTmIFAZEdEsnW322M7m30+HX9ogdAxqIOh/7XclCuNmn/ffo2ODim3xj9tmTeoQ/8HpZOwojxy/r//QwIcg9qcNzhizmd8dzAIfOhiIM/SOTEwoLHy1zr8PcERc9qg9bu1BLN88f8bn2wZNL52hk6G1dXVcCSJiKq3RPzsTHQCnQyUZJsaSwd3tHPOSj6vdHLGvp+Xp5CXB+zYqWEMgONwyI3hwUEdPhXOmaPCHV5ZWQqNUyXwoqFBOntqqjX27tXIyJDhgKxrY/wup1PFDM0S732vVzKJAcDi0xTsdhXqnBzbUDnxtsHpi+N/Zh38JzNTxd0Xs1skeOH4MRnKzZjCZos/fSKVlXZcYUk415AgMK28XKGnV7brokUKTofC1HrJqmYlHfay/saQgF3dwOtvSCeZ16vgdCooS5CSyxXKiqM0NJQ8xV6U+u8wshyNprQEeOcdWTez2x6orVU4sF9h2TKN4IgcX9FZRFORm6Nw1pk6PNTOyIh5HHuybLjoQjnHG4EHDodsq/x8ABqYPt2GinKZ1/rbra8XtCp0dGh4vSo0DKLUC5mZEgjf1aVRU63Cx2ZdLbBzp6xD4zRzuQ67kZFBIxhUcbu33ss5wG6PPI5tNsmYl58nwxzm5CjMma0wMBDEvn3SNujqAqqrlATkq9H3v9NpQ2VFMDz0jssp81RVKWzZGoTbrTE8BEBLJraR4NjKhlXjtMiMW26X/D6bXYV/p02Fju3QdxTkK7TMlCwx9fWpbU+lFE5rk3ZFfr4Z0Nnebh63Tqd8o7G8vj5zW+95NzZ7kFW211yW8X3JlJYCJaUa+/dJhrbSkhPfhqmorVWoqUkc1JebI21WQMp99HRVU4D9+2WIIAB49TWN/fulLZ2ZYWZnKiwIHWPQqCiXAIieHvn+RN8dr0wqpWKCW6c3R86fmQksOT2UxUqb827YaLadg0GF6c0KAwOhQEBIIF28dfH7Jevj3r3yW2pqNJqb7Kgo18jKAux2G05ri5xnzmwJfCosGFu9pYPycIvHI4G41jkbGhQcDgncGBjQaGyMXN+6ulDgqLIOP2ZSSqGpSWPvXumkT2W9KislkAmQoBdr4EtGhopbf1nPQwF/4nOLx6Ow+LT432s9n9XWAFrL+ctmkyCLggSBLS6XmUUJkPbKWWfKfh8YVNhlWbcF8xUOH5ZjMi9XYeZ0CRApL0teTvPzzfV+7vkgKsp1aOg4oLw8ccRc41Rg+w55oKq81KwDlFLIcEvdAEggsvX7gxqWek0+czgUzlgsw9ZFXyNaf3t9nYbPZ81gEn9fGOXqhReDsDsU/MMa5y6NvH6MLo91tXK8ach2v+YqhdVrJIhm3lzze7KyFOpqR8IBA1oj4rf39+nQw2DSfikODcO5cH7k0L7xSKZGo+1qw6IFwK7dErjh8US2e4zzT2bouJ3eLAEgq97U2LRJHkxYs05h6dljP9+2zgO2bDGySyls2SrnxuxsOf9nZCjU1gD1dTZorXHwINDXrzFrRvz9UVstAUk1NVLHlRZrHD6iMGe2xtCQnIuTBcNnZcnQtr29GoWFkRkQS0skyCXg12iynENyclKrMxvq45d7ILLsG+sx1vrL61VYtjS1aWtrJSukMQxrSXHs982aKZ/t3w/s3afC7UBDUaGKeSimo1NGByjIB470y7VSbq5CaXEQfb0aFRUqdN8ocr7cHBWRVS8jQ2F+a+q/PStL4ZyzNdraNIqL5JhOJrpMZrhDx3imiptV1m6X+q6hTv5efJosP5UAm5zQtV1Tk0ZXl4LTYSwzcpuPlgEsFdnZCkvPkdf9/Ro7dso679ptncr83pFA5D5VSqGyUjKADQxI9ubOLnOao0cBNSv19bQuO/oc6nbHfrcKzaNskdcXxcWx12Jnh67fzQeJRFmpDK3rH5ZrjIOHjO8y51VK4ehRCdgDgCNHFGa3yM6sKJfM19nZsce3UgpVU6SttmypQmOjwqo3zOsntzu1cjt/vpQ9AFi9Ws7tDQ0yb0GBHRecBwwP6/A1mHF9X1eLiAyx5n0COS/Ja2V5X2HZuRp9fWa25IICCU71+aSMR69vbYrDI5YU2/Dxf9XYvDl6+OPY+Z1Oud+xf79GQ72sn7WNnOj+wXvF4C4iIiIiIpqUHA5bgiCw5BdGF33AfD08HMTQkMLgoNwIHxkB3t0rN84GByU7RmmJwrBfY+NGhd5ejV275cZ5R4fcDM/LlQvo1WvkBkG8QCl6f8jOkexlN38ptUi/uXOAn/5H5PH42BPAgw+lNv8nPw587obI+e+6W54kT8X3v6ew5IzI9676lNzgSMWfnlYRw0vsPwBcdW1q6z5lCvCbX0Wu+4t/A75/T2rzX3wR8PWvRs7/6K80nnk2pdnxlS8r/MuHIt/7+YMa+/bFnz7aGadH/na/H3j0V6nNm5UFfPH/S21aOnmGhobQ2SnpKsqixyuMkpubC4/HA5/Ph8Mx0bWTQ3GRDeXlOSgqSiGFxgSa36oxZ7bUnza7BG1cfplGd7cEtURnHZk7N3L+tkUIZ6x6d+8INm6SscimVNqQlxcnoiuJs5YEsW9fEKWlNhQUnJwAy0WLRrB79wjqau0oKIi/L4xzic/nR35BALt2j8DhUJg7x/Oegs6SaVsELFqYPJtSMBiEJ0uihZ1OhWPHQ4EvNkApB/Ly5PdccXnsvMvODWLrthEUFSlUVU3M7eO6Oj+OHA2ieXoQBw4EMXOmG9VVDhQWaDQ2yrp6vSphB/VYDA4Ow5Mly/T5bCgujjz2srM1cnKHEQhIJ2DLrPgRArmWsas+eLHG0aNB5OXZYgK1l52rMTwcmXFszmyN/PwgMjOBslLzWCoqDqCqahgupw0uN3DhBW7s2TOCXXvM3oS8PDfeC0+WmZI2v8CFutoRDA8HQm1hG2bNdKGqKgi3Czh8OIja2pFwEEheXpI0ICGDQyOoqgqgKpTZZsYMJ3JypIw2Nwewe88gfD7AZtcoLnKFOkSd49LZkZcHbNgkv8/l9od+rxNlZU7k5dkipjsR1iwOdXUj6PcFsP9gAI2NNrhcNhQV2ZGXJ2WkosKPo+1yoeDOSL7tZs3SON45jIAfWLggcl3jycsDrrwsiL5+jdIS27h0lL4XS5ZorFvnh8ulMG+uI2Z9GqcF0dFpPq1QVWVmnFl8mgM7dwaRm6tQXy/b7vTFI1i3PoCKCqChwYEsjxp1mwCRZfJE5eYOhTNDlpba0dzkQOM0jb17R1BQYENhzJBfYnBQo6R4CMePB+APAA67HXl5mUmPtbw8hLKUjI3XO4ii4iCcDnmYwpNlnpdLSuT8cPFFkiU7usM2WdYo63rNbhnbOl1xuWyDVDMrWs9DOblmuRkLr1fDkzUcWh5QXWVHU+N7q6M6O826EgBqa10oKNSorpYgqcJCFTd7XTJl5cPo6dHIyZX2XLL2zsyZGpmeETQ2auzfPwIohbJSmeess4J4800/7A6FRQudEZnQ+vpGUFg4grIyGzIzVbhOT0VursbQcAC9vRotLQ7k5Sb/fVlZQ6ittWPXrhEAic9Xubm5qK4egtYj6OnRaJ3nQn29E6WlOjRMY+SxsuT0Ybzw0jCUUigttYfPI3l5bixdGsSWLQGUltgwbdrY9rHDYe5Tr1ehttaF2lrz8/PPD2LnzhGUlNhQUmKPODazshwoK7PjA+drOBzmE3e5ua4x11l5eZHZyhpDGcf6fRp798myrcfHhR+QTOaJ2o6LFgELFpgZkBZZMsL290tmsNHW8fzzpA2flxc7vO0F52v4+mMDv94Lr3cY/oB5f6C8zIaCgrG1/09EKg8aVFeP4NjxgGR+HwEWLnBg0ztynbJwgSvmXNrVNYJDhwPo7ZNMX0bb7F8/pvHi34YxEgRa5znGVBZTdaJtmNzcXOTmAvPnj6CzM4jGaXZ4vZHlNyfHPK9mjtJ2iXbmEo016/yABrq6guEh+AoKXON2DMWTl2dmDB0aHg4PV3ham7nfcnM1Dh/xo6dXY9pUs86xPtCTkxPEnnflXNHUOLZ6yXreKC52wWUpt63zNLq75ZgwlpudE4C3ewTzW53welXEtUNOzlA4K2VhQfI64+KLNPr7NPLzbdi4yQ+HYyScua+wUI7JObODeP2N2N91xukaHR0aubmx1/AAsPi0yHNQS8sI1q6TMjFjRmrHtvVYrSjX6O/XSa/bzzwjiAMHg/B4gA0bY699zlgcxK7dIygttaG4OPL745WLc84edRVTUloqGWDffMuPYBA4fbEzYXunbRGw0HJunjtnBG+tCcBuA85Y7Iwpc+OBwV1ERERERPS+5XLZ4HLJ05GGqilxcyJgQav8n4pAQG72+v2Sht0/LK/9AXkKKeCXp2ntdnlqaXBAhgMYGpLpCwvk/2wvEAgoHDmi0X5chh+024AsrwSRZboVglqe0BwYCA0vGFq28XSrkWXI+v9IUF6PBKOGIgzIU69Op9zAMp5COnI09IStlhT3vX3AsWMy//CQ/G08gau1mcnJGL7H4Qi9Jw8dIxBIPWua2y3byG6XJ0O1Bnq6Uw+imzlDnmgzhn1UCnj+hdTmtdmAmz4vT2oW5ANnnC4p1GnixTs+1BjuecSbP5UhPQw6av6x3KtP9dimk6u/vz/82uPxJJlSZGZmwufzweeboFz579GihRPf8ZEKpSR7ljWDlgR8jP2GfWWFDd1dNgQ1MGP6iXTq2jB9+snNmldTbUdNdWqdNM3NdhzvCGLWTIWzz3ROWGCXYbQOvNxc2Xd+P1BWaoNvQOPwERmuubg4+XbMz7fhtLaJ3dYN9XYcORpEbo4N1VPs4WMiKyv2afL3au4cB1a94YdSwKxZscee3a4wv9WJw4eDmDIltd/tcChUVMQ/Nmw2hYyM2O+oq42dvqbajq4uJ3RQY+FCZ2g4UTsOHQ5iYFBj2dLUO9kSmTHdgd27R1BZaYPLqVBTY8fxDo2REY15c6Vw54aCsWprbXA4FI4cDaK+LrVjv7AguqPQ/Lu5yQ6X042ePqC7O4ihIWmDjudT7LNbHFi/IYCmaQ4cPDSCslIbCgvGv/xVV9ngdDgwvcmOPXulLFnPD2VltnBw18xRznFut8K557gQDEYOXZdMTo4tpSGyToYsj8IZpyc+NgsLbWicase2HSOY3uzA9u2BcMdlXq4NCxdEHluVlXZUVp6aYOYF85144y0/nKGyB8j+GS2gxOUC7A6F5mYHRkbkOJwo05vt+NNfAggGgTOXuGC3Ad09Gg315jaz2RSy4gx/PJHGMmRu6zwH1qwNwOFA3HNhKhwOySjoG5CL0fEYnjQ726wrCwtsoawbkZl2xqqm2o4NGwNQAOpGOY86nSocoFZcZENPrw5vn7xcG85d6oqbca2kxIYNGwPYvWcEtTVj255KKcydk3o7Mz/fhp27RkLrm3za6ipZF5cTaJll1uvxLFrkwsxZDhxrD2LjZll+VaXUH0WFNiw548Tqv6wshfOXheaN89V5uTbMb41f1xt1R0aGgjdLoa9f45yz3ns9HLF+HoVFC5w4diyImqh9N1rbMVHATKrtJrtdoSBB/ZjlUcjyjG/dWV9vx+o1EhxSXmZD67zJcX0DSD2Vmakk4+FUO6qrbXBnOJCZEb8dWlxsw9btkl3Neg5zuRSWnevCsB/jvv3GS32dHUhwLrLZFBa3ObF//wimTBnbuSQrS+HMUDld9YYfx44HUV1ln9DArminL3ahtzeIjAwVUX6UUjjjdCd8Pp0wuMbrtWFxmxODgxrl5WO77jHaNtVV9ojALkDaEGcuccHn0+F6qrTUhuIihZKS2O+ZNdOBdesDsCnzvJmIy6ngyjcC2GxQNml3GudeQI7t1nkODA/LA1UGpUYfMty6DSvK7VBK7iNXVo79utDtHv0hnexsG5qbbPD7NTZvHsFIEPBayp8E4Z6arPmSNS+1839EJsRSO84+UwLoxtJOGgsGdxEREREREY2RwyEBQZmZ8T49kYu30eaZnDeJEhkelqEJh4ZC/4bl/8FBeT08ZH7W0qIwb67kG+/q6oLWGr/6L42jR3U4KC0wEhWkNiIBZCMjwK1fVJhiCdgbGpKhqowgNA1I0Fmcv+124MrLI7ety6VRXmZOGw5as/xt3FuP18Hm8Ugq8LiidqM17bghL1eeEkswS0QQkjtOIo+yUqA/O/b9eH2o0an2HXZgSmXsdPFWpDxOYqSsLKC6Kv7sNrsEXyklr8tKY6eprVGY3yrDdthsoX+h6aPfq6yInX/pOZJxzxYaWkAZ89uMTDXmZ9GJHtxu4PM3qvD3KBVaX8s6GMt08k7KpDQ0ZD496xyt5wmAyyU36gbjjaM6SXR3d4eHVHi/qK6W/y2xeO8rp58m+0vrwfBQQ6fS7FkyrFxhodSbdpuc/3y+AZzquEalgCmVGp2dMrxWV9fErZDdLlnobDbAPzyArjjDX4frQI2IfaeUCmcHmqgy2dwk/1szb7bOk/8HBwfiDvc8Fvl5QH5oecZvawplEDHaY1Zer/yzTj+a1rkax45L+6CrayDis9JS+RcIaHR1S1tnPPd3ZoaGN0v2Yes8oLDQj+7ucVt8BI9H/uXnS+a8gQEZ3geQhzbaFspQbzabSnnbneqyOFGKi83MZzOmaxw4KEN/9fQMJJ9xFBNRJheE2p++foWx7I7pTRqr12iUlanQcT0x12ylpRqnLZKLkZnT/bDbAd+ABBJMhromFZkZwLw5Gk4nMDAwEC43YzW9WWPHDsDlBjLc47PNZ82QoeVLS8ZneTnZQMssDZsCnI7U95FxfrGeV5JpnafhG8CEHnsAUFsj+y03R4YuTVZHTqkMwuuVYUL7+lLbydnZGvl5EmBXWjr+v2W0Mt04TWPdOo3KShXx+6qmSFb3oSEf/P7xXSenU7L4DQ+bQ5i9H2W4geoqDf+w3CeYyOP0RMxp0QgEJAilp0fOU0Dits/c2XKPypPg3BuvfXmynUgdabOZ12gnWqc0TtOorpIMfaeiXkp2bZlsfWw2Oe+Otd1YkG9mUU60fKfTXK7TAcARf12ysoCWmTKUr98/kPL2y/YCDXUaA4NG+9ssX5kZ8q+3dww/Kg6jTExUu9pq2lS5lphSObHXhSdLIIDwNVzeiabfS0Dpcb4iNVLgpzN5AjEPgNm5QETjg+WLaOKwfBFNHJYvoonD8kU0cU60fOXn50/gWk2Mjo4OLF4s4yxcfPHFuPfee5NOf/rpp+P48eNobGzEH//4x/f8/T/5yU/w05/+FACwcuVKtLW1vedl8pxIdOqxnUI0ubBMEk0eLI9EkwvLJNHkM973105NLjMiIiIiIiIiIqJxkmUZiyiVoRYHQikRUhnCkYiIiIiIiIiI6FRicBcREREREREREaU1t9sdfkr58OHDSaft7u4OB4CVlcUZY5SIiIiIiIiIiGgSYXAXERERERERERGlvalTpwIA9u7di0AgkHC6Xbt2hV83NDRM+HoRERERERERERG9FwzuIiIiIiIiIiKitDd//nwAMizjpk2bEk735ptvhl+3trZO+HoRERERERERERG9FwzuIiIiIiIiIiKitHfeeeeFXz/55JNxpwkGg3j66acBADk5OWhrazsZq0ZERERERERERHTCGNxFRERERERERERpb/bs2ViwYAEACe5au3ZtzDQPP/wwdu7cCQC45ppr4HQ6Iz5ftWoVmpqa0NTUhNtvv33iV5qIiIiIiIiIiGgUjlO9AkREREREREREROPhjjvuwMc//nEMDg7iuuuuw4033oi2tjYMDg7imWeewW9/+1sAQG1tLT796U+f8Pf87ne/i/j7nXfeCb9++eWXceDAgfDf1dXV4aAzIiIiIiIiIiKisWJwFxERERERERERvS/MmDED9957L2677Tb09fXhxz/+ccw0tbW1WLFiBbxe7wl/z9e+9rWEnz344IMRf1966aUM7iIiIiIiIiIiohPG4C4iIiIiIiIiInrfOPfcc/GHP/wBK1euxEsvvYQjR47A6XSiuroaF154Ia666ipkZmae6tUkIiIiIiIiIiJKCYO7iIiIiIiIiIjofaWyshJf+9rXkmbYiqetrQ1bt24ddbpUpiEiIiIiIiIiIhoPtlO9AkRERERERERERERERERERERERBRLaa31qV4JIiIiIiIiIiIiIiIiIiIiIiIiisTMXURERERERERERERERERERERERJMQg7uIiIiIiIiIiIiIiIiIiIiIiIgmIQZ3ERERERERERERERERERERERERTUIM7iIiIiIiIiIiIiIiIiIiIiIiIpqEGNxFREREREREREREREREREREREQ0CTG4i4iIiIiIiIiIiIiIiIiIiIiIaBJicBcREREREREREREREREREREREdEkxOAuIiIiIiIiIiIiIiIiIiIiIiKiSYjBXURERERERERERERERERERERERJMQg7uIiIiIiIiIiIiIiIiIiIiIiIgmIcepXoETdeDAATz66KN46aWXcPjwYbhcLlRVVeGiiy7CJz/5SWRmZp7wsoPBIHbt2oX169dj/fr12LBhA7Zu3Qq/3w8AWLlyJdra2lJe3sDAAH71q1/h2Wefxb59+zA8PIyysjKcc845uPrqq1FZWXnC60o0EdKhfF199dV44403UvrOrVu3nvD6Eo23iSxfAwMDePnll/G///u/2LhxI/bu3Qufzwev14va2losWbIE//qv/4ri4uKUl8f6i9JJOpQv1l+UriayfO3cuROvvfYaNmzYgG3btuH48ePo7OyE3W5HYWEhWlpa8KEPfQjLli2DUmrU5QUCATz++OP44x//iF27dsHn86GkpASnn346rr76akybNu2E15VoIqRD+br99tvx1FNPpfSdL7zwAqZMmXLC6/zPbCKPBaJ0tmHDBvz973/HmjVrsGPHDnR0dMDpdKKkpAStra247LLLsGDBgpSX9/e//x2PPfYYNmzYgI6ODhQUFKClpQVXXnklzj777JSWMV7tjY6ODjz66KN4/vnnceDAAQBAZWUlzjvvPFxzzTXIz89P+XcRnWr33HMP/vM//zP8dyr3eVkeicbfwYMH8cQTT+Cll17CwYMH0d/fj4KCAlRWVqKtrQ0XXXQRGhsbE87Pckn03g0PD+P3v/89nn32WWzduhVdXV0R7dcrrrgCra2toy6H5ZGIrJTWWp/qlRirF198Ebfddhv6+vrifl5bW4sVK1agpqbmhJb/1FNP4fbbb0/4+ViCu959910sX74ce/bsifu51+vFD3/4QyxduvREVpVo3KVL+WLnOKWjiSxfW7Zswcc//nH4fL6k03m9Xtx11124+OKLk07H+ovSTbqUL9ZflI4mun345S9/GX/84x9HnW7RokW4//77k94s6ujowPLly7Fhw4a4n7tcLnzzm9/EFVdccULrSjTe0qV8Mbhr4k30sUCUrj75yU/irbfeGnW6Sy65BHfddRdcLlfCaYLBIO6880488cQTCae54oor8J3vfAc2W+IBL8arvfH222/jC1/4Atrb2+N+XlxcjJ/97GeYPXt20uUQTQbvvPMOLr/8cgQCgfB7ye7zsjwSTYxHH30UP/7xj5Pew7rmmmtwxx13xLzPckk0Pg4cOIAbbrgB27dvTzrd1VdfjTvuuCPug1Ysj0QUT9pl7tq8eTNuueUWDA4OwuPx4IYbbkBbWxsGBwfxzDPP4LHHHsOePXuwfPlyPPnkk/B6vWP+Dmu8m9PpRGNjI/x+P7Zt2zam5fT19UV0jF955ZW4+OKLkZGRgVWrVuGBBx5AX18fbrnlFvz3f/83pk+fPuZ1JRpP6VS+DLNmzcK///u/n9C8RCfTRJevvr6+8EV7a2srli5dilmzZiEvLw8dHR34n//5Hzz++OPo6+vDl7/8ZWRlZSV8soP1F6WbdCpfBtZflC5ORvvQ4XBgzpw5aG1tRWNjI4qKilBQUICenh7s2rULv/3tb7Ft2za88cYb+NznPof/+q//invjamRkBDfddFP4htUFF1yAK664Anl5eXj77bfx//7f/8Px48fxzW9+EyUlJSk/4Ug0UdKpfBlKSkrw0EMPJf3O0tLSMa/nP7uTcSwQpaujR48CkPPPhRdeiAULFqC8vBzBYBDr1q3Dww8/jCNHjuDpp59GIBDAj370o4TLuvfee8MdZDNmzMBnPvMZVFVVYd++ffjP//xPbN68GY8//jgKCgrwpS99Ke4yxqu9cejQIdx4443o6OiAw+HApz71qfDDU3/729/wi1/8Au3t7bjxxhvxu9/9DmVlZSe8DYkmmtEBHQgEUFhYiOPHj486D8sj0fj72c9+hv/4j/8AIA8GXHnllWhpaUF2dja6urqwefNmPPfccwnb+yyXRO+d3++PCOxqamrCpz/9adTV1aG/vx+rV6/GI488Ap/Ph0cffRQlJSVYvnx5zHJYHokoLp1mPvGJT+jGxkY9Y8YMvWbNmpjPH3zwQd3Y2KgbGxv1/ffff0Lf8fbbb+uVK1fqtWvX6sHBQa211vfff394ua+//npKy7nvvvvC8zz44IMxn69evVrPmDFDNzY26quuuuqE1pVoPKVT+brqqqtYdiitTHT5Wr16tb755pv19u3bE07z3HPP6aamJt3Y2KjPO+88HQwG407H+ovSTTqVL9ZflG5ORvvQ7/cn/TwQCOibbrop/D3PP/983Okef/zx8DTf+ta3Yj7fs2ePbm1t1Y2Njfr8888f9XuJJlo6la+vfvWrurGxUS9duvSE1oOSOxnHAlG6Wr58uf7zn/+sA4FA3M+PHz+uL7jggnAZeeONN+JOt2vXrvB17Ec/+lE9MDAQ8bnP59Mf/ehHw2Vxz549cZczXu2N2267LbycZ555JubzP//5z+HPv/rVr8ZdBtFk8cgjj+jGxkZ94YUX6h/96Eej3udleSQaf6+++mr4OP3KV76ih4eHE047NDQU8x7LJdH4+Mtf/hI+Nj/2sY/FbcNu2LBBz5w5Uzc2NuoFCxbElAOWRyJKJPHjmJPQ+vXrw2m4L7vsMsybNy9mmuuuuw4NDQ0AJO2v3+8f8/fMnj0bV199NebOnQu3231C6+r3+/Hoo48CABoaGnDdddfFTNPa2orLLrsMAPDGG29g/fr1J/RdROMhncoXUbo5GeWrtbUV9913H6ZOnZpwmvPOOw8XXHABAGDv3r3YvHlzzDSsvyjdpFP5Iko3J6t96HAkTyhtt9tx/fXXh/9ONDTTww8/DADIy8vDV77ylZjPa2pqcMMNNwCQ4Yefe+65Ma8r0XhJt/JFE+dkHQtE6eqBBx7AxRdfDLvdHvfzgoIC3H777eG///rXv8ad7pe//GV4uLg777wTGRkZEZ9nZmbizjvvBAAEAgH84he/iLuc8WhvtLe3h4fMXbJkCS666KKYaS6++GIsWbIEAPD73/8+4RA4RKfawYMHw5mCvv3tb8PpdI46D8sj0fgKBoP41re+BQBobm7G9773vaRlMd4QxiyXRONj7dq14dfLly+P24adNWsWzjnnHABAT08Pdu7cGfE5yyMRJZJWwV3PP/98+LXRqRzNZrPhkksuASAnxFWrVp2MVYuxatUq9Pb2AgAuueSShGlOP/rRj4ZfW38f0cmWTuWLKN1MpvLV1tYWfr13796Yz1l/UbpJp/JFlG4mU/nKysoKvx4eHo75fPfu3eGbYRdeeCEyMzPjLufSSy8Nv2b9RadSOpUvmliT6VggSlejtcO11njhhRcAAPX19Zg7d27c5cydOxd1dXUAgBdeeAFa64jPx6u98eKLLyIYDAJIXO4B87o7GAzixRdfTDgd0an0ne98Bz6fD5deeikWLVo06vQsj0Tj75VXXsGePXsAAJ/97GdHfcAjGssl0fixPohTVVWVcDrrZ9Z5WB6JKJm0Cu5avXo1AMDj8WDmzJkJp1u4cGH49Zo1ayZ8veIx1hVA0ouaWbNmhU+op2pdiYD0Kl9E6WYylS9rh128p0ZYf1G6SafyRZRuJlP5+vOf/xx+XV9fH/N5qvVXcXExamtrAbD+olMrncoXTazJdCwQpStrOzzeA0r79+/H0aNHAUSWpXiMdsSRI0ewf//+iM/Gq71hXU6y9WG5p8numWeewd/+9reEGUHiYXkkGn/PPvssAEApFc4GBABdXV3Ys2cPurq6ks7Pckk0foyAKwDYt29fwumMz5RS4fIAsDwSUXJpFdxlRJhWV1cnjTy33oyMTmV4sli/N9nNUYfDgerq6ph5iE62dCpfVrt27cIVV1yBBQsWoKWlBWeddRY+97nP4emnn+ZQFTRpTKby9cYbb4RfG0O7WLH+onSTTuXLivUXpYNTXb46Ojqwdu1afP3rX8fPf/5zAEB+fj4+/OEPJ1zX6PWJx/j80KFD8Pl847a+RGORTuXLqrOzE1dddRXa2towa9YsLFmyBNdffz1+9atfYWBgYNzW75/JqT4WiN4P3nzzzfDreO3wHTt2hF+n2k4ApM1uNV7tDWN9srOzUVxcnHAZJSUl8Hq9Md9NNBn09PTg7rvvBgB8+ctfRkFBQUrzsTwSjb+3334bAFBZWQmv14s//vGP+PCHP4y2tjZ84AMfCP//0EMPxc3Uy3JJNH4++MEPho/LBx98ECMjIzHTbN68GS+99BIA4EMf+lB4eoDlkYiSG1tuzlNoaGgInZ2dAICysrKk0+bm5sLj8cDn8+Hw4cMnY/ViGN/r8XiQk5OTdNry8nJs3boVHR0dGB4ejjveNdFESrfyZXXs2DEcO3Ys/PeRI0dw5MgRvPjii3jwwQdx//33j9rBTjSRJlP52rJlC/7+978DABobG+OWDdZflE7SrXxZsf6iye5Ula+rr746IlDSKj8/Hz/96U/j1k/W7y0tLU36HeXl5QAk1f3hw4eZqYhOunQrX1Y+ny8iiKK9vR3t7e145ZVXsGLFCtx3331obW19T+v5z2QytWWI0lUwGMSKFSvCf1900UUx01jLzGhlzfr5oUOHEi7nvbQ3jhw5ktK6GMvZvn07yz1NOvfccw/a29vR2tqKyy+/POX5WB6JxlcwGAwHdeTn5+O73/0uHn300Zjp9uzZgx/84Ad47rnnsGLFioh2P8sl0fgpKCjAD37wA9x6661Ys2YNLr/8clx77bWora2Fz+fDmjVr8PDDD8Pv92PmzJm4/fbbI+ZneSSiZNImuKu/vz/82uPxjDp9ZmYmfD7fKXsS21jfVNfVOh87x+lkS7fyBUiq0sWLF+Pss89Gc3Mz8vLy0N/fj02bNuG3v/0tdu7ciR07duCaa67B448/joqKilO2rvTPbbKUr+HhYdxxxx3hJ0VuueWWuNOx/qJ0km7lC2D9ReljspQvw9VXX43Pf/7zCTMCWNc3Kysr6bKs9Rczd9GpkG7lC5D6a+7cuVi6dClmzJiBoqIiDA0NYdu2bXjiiSewfv16HDlyBNdffz1+/etfY8aMGROyru83k+1YIEpHv/jFL7B+/XoAwAUXXIBZs2bFTDOWspasnTBe7Y0Tue62fjfRqfbWW2/h8ccfh8PhwLe//W0opVKel+WRaHz19vYiGAwCALZt24YNGzaguLgYX/nKV3D22WfD7XZjw4YN+OEPf4h169aFs/f+9Kc/DS+D5ZJofC1btgxPPvkkHnnkETzxxBP46le/GvF5UVERbr75Zlx55ZURZQFgeSSi5NImuGtoaCj82ul0jjq90cE8ODg4YeuUjLG+Y1lX63xEJ1O6lS8ACZ8qX7BgAT7xiU/gzjvvxFNPPYVjx47h7rvvjrhYITqZJkv5+s53voONGzcCAC699FKce+65cadj/UXpJN3KF8D6i9LHqSpfd999NwYGBqC1Rm9vLzZu3Ij//u//xq9//Wvs378f3/3ud1FUVPSe1tdaf53K9iz980q38gUAX/va1+LWX/PmzcOVV16J++67Dz//+c/h8/nwjW98A08++eSYOnr/WU2WtgxRunrjjTfwox/9CABQWFiIb33rW3GnG692wngvZyzlntfcNFkMDw/jzjvvhNYa1157LRobG8c0P8sj0fiyDo0+NDSEzMxMrFy5MiLzzsKFC/HLX/4SH/vYx7BlyxY899xzePvttzFnzpzwfAaWS6L3bnh4GL///e/xwgsvQGsd8/mxY8fwhz/8AVOmTMGyZcsiPmN5JKJkbKd6BVLldrvDr/1+/6jTG+NGZ2RkTNg6JWOs71jW1Tof0cmUbuULQNLhQpxOJ7773e+irq4OAPDcc8+FU4cSnWyToXw98MADePzxxwEALS0t+OY3v5lwWtZflE7SrXwBrL8ofZyq8lVVVYXGxkY0NTVhwYIF+NSnPoU//OEPOPvss/G3v/0Nl19+edz07mNZX2v9dSrbs/TPK93KF5C8/lJK4ZZbbsHixYsBAJs2bcKaNWve07r+s5gMbRmidLV9+3bcdNNNCAQCcLvd+I//+A8UFhbGnXa82gnjvZyxlHtec9Nk8cADD2DXrl2oqKjATTfdNOb5WR6Jxlf0SAqXX355RGCXISMjIyLT/DPPPBN+zXJJNH58Ph8+/elP44EHHkB3dzc+85nP4JlnnsGGDRuwevVqPPzww5g/fz42btyIL3zhC3jkkUci5md5JKJk0ia4y5oyMJX080a0eippAieCsb5jWVfrfEQnU7qVr1Q4HA5cfvnl4b/feOONU7g29M/sVJev3/zmN/jxj38MAKivr8eKFSuSLpv1F6WTdCtfqWD9RZPFqS5fVm63G3fffTcyMzNx6NAh3HPPPTHTWNd3tPTv1vprMrdn6f0r3cpXqj72sY+FX7/55pvjsXrve5PpWCBKJ/v27cN1112H7u5u2O12/PjHP8bChQsTTj+WspasnTBe7Y0Tue7mNTdNBjt37sQDDzwAAPjGN75xQvURyyPR+PJ6vRF/L1myJOG0ixcvhsMhAzpt2LAh/D7LJdH4+clPfoK33noLAPC9730Pt912GxoaGuByueD1enHGGWdg5cqVaGtrg9YaP/jBD7Bly5bw/CyPRJRM2gR3ud1u5OXlAUDCJ0kN3d3d4ZNMWVnZRK9aXMb3+nw+9PT0JJ320KFDAICCgoKYKHuikyHdyleqpk6dGn7NzCd0qpzK8vWnP/0J3/72twEAlZWVeOSRR1BQUJB0HtZflE7SrXylivUXTQaTrX1YUFCA1tZWAMALL7wQ88Sg9XtHKzdG/aWUmvTtWXp/SrfylSrWX2M32Y4FonRw5MgRfPrTn8bRo0ehlMLdd9+N8847L+k81jIzWlmzfl5eXp5wOe+lvVFaWprSuliXw3JPk8Evf/lL+P1+VFVVYXBwEH/+859j/m3fvj08/euvvx5+P14dxvJI9N65XK6I+1HJjk+32438/HwAQEdHR9x5WC6JTpzWGr/73e8AALW1tbj00kvjTudwOHDzzTcDAILBYHgegOWRiJJLm+AuwLxRuHfvXgQCgYTT7dq1K/y6oaFhwtcrHuv3WtcnWiAQwL59+2LmITrZ0ql8pUopdapXgQjAqSlfL7zwAr761a8iGAyiuLgYv/jFL1JqVLP+onSTTuUrVay/aLKYbO1D4yb0wMAAOjs7Iz5Ltf6yfl5eXs7sN3TKpFP5ShXrrxMz2Y4Fosmso6MD1113Xfha9M4778Qll1wy6nzW4NNU2wkAYoa1Gq/2hrE+vb29aG9vT7iMo0ePoq+vL+a7iU4VY7ilffv24Utf+lLcf3/961/D0//sZz8Lv28EkrA8Eo0/a7kKBoNJpx0ZGQGAcAav6PlZLolO3LFjx9DV1QUAmDFjRtJpZ82aFX5tLS8sj0SUTFoFd82fPx+AZBPZtGlTwums6f+Np09PNmNdgeTD6WzcuDH81MqpWlciIL3KV6p27NgRfl1SUnIK14T+2Z3s8vXaa6/hi1/8IgKBAPLy8vDII4+gurp6TOsKsP6i9JBO5StVrL9osphs7cOjR4+GX0ffbEq1/mpvb8eePXsAsP6iUyudyleqWH+dmMl2LBBNVr29vfjMZz4TPtfceuut+OQnP5nSvFOmTAmfl0YbNtb4vLS0FFOmTIn4bLzaG9blJFsflnt6P2J5JBp/1qGJjQDoePr6+sIPchjZeACWS6LxYrfbw6+NQMpErBmzrcGWLI9ElExaBXdZU2w/+eSTcacJBoN4+umnAQA5OTloa2s7GasWY9GiRcjOzgYAPP3009Bax53OmmpxtBTiRBMpncpXKgKBQMTvsF7gEJ1sJ7N8rVmzBp///OcxPDyM7OxsPPTQQ5g2bVrK87P+onSTTuUrFay/aDKZTO3Dw4cPY+3atQBkKFSv1xvxeV1dXfjJwGeffRYDAwNxl/PUU0+FX7P+olMpncpXqn7729+GX7P+St1kOhaIJquBgQEsX748HAB54403Yvny5SnPr5TCsmXLAEhmgnXr1sWdbt26deHMBcuWLYvJSDhe7Y1zzz0XNpvclk9U7gHzuttms+Hcc89NOB3RyfL9738fW7duTfrvpptuCk+/cuXK8PtGpzPLI9H4u+CCC8Kvn3vuuYTTPffcc+F7vdaADZZLovGRl5cXvp5eu3Zt0szM1mAoa2AWyyMRJZNWwV2zZ8/GggULAMgJxLj5aPXwww9j586dAIBrrrkGTqcz4vNVq1ahqakJTU1NuP322ydsXV0uF66++moAwM6dO/HQQw/FTLN27drwiXDRokWYPXv2hK0P0WjSqXy9/vrr6OnpSfi53+/HN77xjfC6Ll26NGa8aaKT6WSVr3feeQc33HADfD4fPB4PHnjggYj0vqlg/UXpJp3KF+svSjcno3zt3r0br732WtL16O3txa233hp+qvEjH/lI3Omuu+46AEBXVxfuueeemM/37t2LBx54AABQU1OD888/P+n3Ek2kdCpf69ati8jsFU1rjXvvvRevvvoqAKC5uTmis4iSG49jgej9bHh4GDfddBPWrFkDQMrALbfcMublXHvtteFsCnfddRcGBwcjPh8cHMRdd90FQLInXHvttXGXMx7tjeLiYnz4wx8GALzyyit49tlnY6b5y1/+gldeeQWAnJuLi4tT+p1E6YDlkWh8NTc346yzzgIA/PnPf457DdDe3o777rsPAOB0OnHZZZdFfM5ySfTe2Ww2nHPOOQAkO/bPf/7zuNN1d3fjhz/8YfhvYx4DyyMRJeIYfZLJ5Y477sDHP/5xDA4O4rrrrsONN96ItrY2DA4O4plnngk/KVpbW4tPf/rTJ/w91owkgHToGV5++WUcOHAg/Hd1dXX4RpzV9ddfj2eeeQZ79uzBPffcg7179+Liiy9GRkYGVq1ahZ///OcIBALIyMjA17/+9RNeV6Lxki7l6+mnn8bnPvc5nHvuuWhra0NdXR28Xi/6+/uxadMmPPbYY+E0/YWFhbjjjjtOeF2JxstEl6+9e/fi+uuvDweO3HzzzcjOzsa2bdsSzlNYWIjCwsKY91l/UbpJl/LF+ovS0USXr6NHj+JTn/oUmpubcd5552HmzJkoKiqCw+FAe3s71qxZgyeffBLt7e0AgMbGxoSZOi699FI8+eSTWLNmDX7961/j2LFjuOKKK5Cbm4v169fjZz/7Gfr6+mCz2XDHHXdEpL0nOhXSpXy9/PLLWLFiBc4880ycccYZaGhoQE5ODoaHh7F161Y8+eSTePvttwEAmZmZuOuuu2KeGqbkTta1OFE6uvXWW8OdRaeddhouv/zypO1wp9OJurq6mPfr6upw/fXXY8WKFdi4cSM+/vGP47Of/Syqqqqwb98+PPjgg9i8eTMAuSaura2Nu/zxam/ccsstePnll9HR0YFbb70VGzduDHfsvfTSS3jkkUcAAAUFBfjiF7+Y4tYiSg8sj0Tj7+tf/zrWrVuHnp4e3HDDDbj22mtx9tlnw+12Y/369VixYgUOHz4MQO5rWYdlBFguicbL5z//ebzwwgsYGBjAT37yE2zcuBGXXnopqqqqMDQ0hLfffhu//OUvcfDgQQDA4sWLsWTJkohlsDwSUSJKJxpvaRJ78cUXcdttt6Gvry/u57W1tVixYgVqampiPlu1ahWuueYaAHJS+/73vx93GU1NTSmvT7LlvPvuu1i+fHl4vNpoXq8XP/zhD7F06dKUv49oIqVD+br99tsjUoUm0tjYiHvvvRdTp05N+fuIJtJElq/f/e53+NrXvjam9bnpppvwb//2b3E/Y/1F6SYdyhfrL0pXE1m+rJ+P5pxzzsG///u/o6CgIOE0HR0dWL58OTZs2BD3c5fLhW9+85u44oorUvpOoomWDuXrJz/5CX7605+OuoyKigr88Ic/ZNauE/RejgWi97Ox3EMCZHjZF198Me5nwWAQ3/jGN5IOKXP55ZfjrrvuCg8/E894tTfefvttfOELXwgH2UYrLi7G//2//xdz5sxJuhyiycTabli5cmXCoYRZHonG31tvvYWbb74Zx44di/u5Ugo33nhjwuALlkui8fHqq6/iS1/6Ejo7O5NOd9ppp+H+++9Hbm5uzGcsj0QUT1o+qnzuuefiD3/4A1auXImXXnoJR44cgdPpRHV1NS688EJcddVVyMzMPNWrCUDSGD711FP49a9/jWeffRZ79+6F3+9HWVkZzj77bFxzzTWorKw81atJFJYO5euzn/0smpubsW7dOuzcuRMdHR3o7u6G0+lEUVERZs2ahQ984AM4//zzw6lLiSaDdChfBtZflG7SoXyx/qJ0NZHlq7W1FQ899BBeffVVbNy4EUeOHMGxY8cwODgIr9eLyspKzJ07Fx/84AdTChgpKCjAb37zGzz22GP405/+hJ07d2JgYAAlJSVYvHgxrrnmGkybNu2E1pVoIqRD+froRz+KwsJCrFu3Dlu3bsXx48fR1dUFh8OB/Px8zJgxA0uXLsWHP/xhuN3uE90U//TSoS1DlO5sNhvuvvtufOADH8Bvf/tbbNiwAZ2dncjPz0dLSws+9rGP4eyzzx51OePV3pgzZ0643L/wwgvYv38/AGDKlClYtmwZrr32WuTn57/n3000GbE8Eo2/BQsW4E9/+hN+9atf4fnnn8f+/fvh9/tRXFyMRYsW4eqrr8aMGTMSzs9ySTQ+Tj/9dPzlL3/BE088gX/84x/YsWMHent7YbfbUVRUhJaWFnzoQx/CsmXLEma9ZnkkonjSMnMXERERERERERERERERERERERHR+13iPH1ERERERERERERERERERERERER0yjC4i4iIiIiIiIiIiIiIiIiIiIiIaBJicBcREREREREREREREREREREREdEkxOAuIiIiIiIiIiIiIiIiIiIiIiKiSYjBXURERERERERERERERERERERERJMQg7uIiIiIiIiIiIiIiIiIiIiIiIgmIQZ3ERERERERERERERERERERERERTUIM7iIiIiIiIiIiIiIiIiIiIiIiIpqEGNxFREREREREREREREREREREREQ0CTG4i4iIiIiIiIiIiIiIiIiIiIiIaBJicBcREREREREREREREREREREREdEkxOAuIiIiIiIiIiIiIiIiIiIiIiKiSYjBXURERERERERERERERERERERERJMQg7uIiIiIiIiIiIiIiIiIiIiIiIgmIQZ3ERERERERERERERERERERERERTUIM7iIiIiIiIiIiIiIiIiIiIiIiIpqEGNxFREREREREREREREREREREREQ0CTG4i4iIiIiIiIiIiIiIiIiIiIiIaBJicBcREREREREREREREREREREREdEkxOAuIiIiIiIiIiIiIiIiIiIiIiKiSYjBXURERERERERERERERERERERERJMQg7uIiIiIiIiIiIiIiIiIiIiIiIgmIQZ3ERERERERERERERERERERERERTUIM7iIiIiIiIiIiIiIiIiIiIiIiIpqEGNxFREREREREREREREREREREREQ0Cf3/uuiV0bx9GlQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "image/png": {
              "width": 1211,
              "height": 211
            }
          }
        }
      ],
      "source": [
        "with model:\n",
        "      az.plot_trace(trace)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508
        },
        "id": "4FkYrw8p4ZdG",
        "outputId": "fbb57254-4618-4fd5-b015-f1ae1024d9c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x480 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "image/png": {
              "width": 731,
              "height": 491
            }
          }
        }
      ],
      "source": [
        "with model:\n",
        "  az.plot_posterior(trace)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hjyznce67lh9"
      },
      "source": [
        "A. The mean posterior value is 0.16"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SMYWranw4ZdI"
      },
      "source": [
        "## Q2 [10]\n",
        "\n",
        "**Define the model structure.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DuZKyiNE4ZdI",
        "outputId": "84f4d31a-ba69-4b4d-ae75-7071a28581f9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        }
      ],
      "source": [
        "model = BayesianModel([('Smokes', 'LungDisease'), ('LungDisease', 'ShortnessOfBreath'), ('LungDisease', 'ChestPain'), ('LungDisease', 'Cough'), ('Cold', 'Cough'), ('Cold', 'Fever')])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pwm6O-Hh4ZdI"
      },
      "source": [
        "## Q3 [15]\n",
        "\n",
        "**Define individual CPDs**\n",
        "- Define the CPDs using the state names of the variables."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UJUNgseY4ZdI",
        "outputId": "d1918d5c-e47c-452a-adca-3def7ba0d6dc",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+-----+\n",
            "| Smokes(T) | 0.2 |\n",
            "+-----------+-----+\n",
            "| Smokes(F) | 0.8 |\n",
            "+-----------+-----+\n",
            "+----------------+-----------+-----------+\n",
            "| Smokes         | Smokes(T) | Smokes(F) |\n",
            "+----------------+-----------+-----------+\n",
            "| LungDisease(T) | 0.1009    | 0.001     |\n",
            "+----------------+-----------+-----------+\n",
            "| LungDisease(F) | 0.8991    | 0.999     |\n",
            "+----------------+-----------+-----------+\n"
          ]
        }
      ],
      "source": [
        "cpd_smokes = TabularCPD(variable = 'Smokes',\n",
        "                        variable_card = 2,\n",
        "                        values = [[0.2], [0.8]],\n",
        "                        state_names = {'Smokes': ['T', 'F']})\n",
        "print(cpd_smokes)\n",
        "\n",
        "cpd_lungdisease = TabularCPD(variable = 'LungDisease',\n",
        "                             variable_card = 2,\n",
        "                             values = [[0.1009, 0.001],\n",
        "                                       [0.8991, 0.999]],\n",
        "                             evidence = ['Smokes'],\n",
        "                             evidence_card = [2],\n",
        "                             state_names = {'Smokes': ['T', 'F'], 'LungDisease': ['T', 'F']})\n",
        "print(cpd_lungdisease)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LyXKlqQd4ZdJ"
      },
      "outputs": [],
      "source": [
        "cpd_cold = TabularCPD(variable = 'Cold',\n",
        "                      variable_card = 2,\n",
        "                      values = [[0.02], [0.98]],\n",
        "                      state_names = {'Cold': ['T', 'F']})\n",
        "\n",
        "cpd_fever = TabularCPD(variable = 'Fever',\n",
        "                       variable_card = 2,\n",
        "                       values = [[0.307, 0.01],\n",
        "                                 [0.693, 0.99]],\n",
        "                       evidence = ['Cold'],\n",
        "                       evidence_card = [2],\n",
        "                       state_names = {'Cold': ['T', 'F'], 'Fever': ['T', 'F']})\n",
        "\n",
        "cpd_chestpain = TabularCPD(variable = 'ChestPain',\n",
        "                             variable_card = 2,\n",
        "                             values = [[0.208, 0.01],\n",
        "                                       [0.792, 0.99]],\n",
        "                             evidence = ['LungDisease'],\n",
        "                             evidence_card = [2],\n",
        "                             state_names = {'LungDisease': ['T', 'F'], 'ChestPain': ['T', 'F']})\n",
        "\n",
        "cpd_shortnessofbreath = TabularCPD(variable = 'ShortnessOfBreath',\n",
        "                                     variable_card = 2,\n",
        "                                     values = [[0.208, 0.01],\n",
        "                                               [0.792, 0.99]],\n",
        "                                     evidence = ['LungDisease'],\n",
        "                                     evidence_card = [2],\n",
        "                                     state_names = {'LungDisease': ['T', 'F'], 'ShortnessOfBreath': ['T', 'F']})\n",
        "\n",
        "cpd_cough = TabularCPD(variable = 'Cough',\n",
        "                       variable_card = 2,\n",
        "                       values=[[0.7525, 0.505, 0.505, 0.01],\n",
        "                               [0.2475, 0.495, 0.495, 0.99]],\n",
        "                       evidence = ['LungDisease', 'Cold'],\n",
        "                       evidence_card = [2, 2],\n",
        "                       state_names = {'LungDisease': ['T', 'F'], 'Cold': ['T', 'F'], 'Cough': ['T', 'F']})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "50aDWreS4ZdJ"
      },
      "source": [
        "## Q4 [5]\n",
        "**Check that each of the CPDs are correct**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nPDKbpSA4ZdJ",
        "outputId": "d533f000-015c-4f3a-b8d5-b00bcecda2c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<TabularCPD representing P(Smokes:2) at 0x7b88e6fbe8f0>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "cpd_smokes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WftTD8Rj4ZdJ",
        "outputId": "eaefce34-d6d5-4fc3-a383-2b8c1842fae9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+-----+\n",
            "| Smokes(T) | 0.2 |\n",
            "+-----------+-----+\n",
            "| Smokes(F) | 0.8 |\n",
            "+-----------+-----+\n"
          ]
        }
      ],
      "source": [
        "print(cpd_smokes)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m0gYPs5m4ZdK",
        "outputId": "cde5d0f1-685e-4b72-ce7f-bc7c3d7d83ed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+-----------+-----+\n",
            "| Smokes(T) | 0.2 |\n",
            "+-----------+-----+\n",
            "| Smokes(F) | 0.8 |\n",
            "+-----------+-----+\n",
            "+----------------+-----------+-----------+\n",
            "| Smokes         | Smokes(T) | Smokes(F) |\n",
            "+----------------+-----------+-----------+\n",
            "| LungDisease(T) | 0.1009    | 0.001     |\n",
            "+----------------+-----------+-----------+\n",
            "| LungDisease(F) | 0.8991    | 0.999     |\n",
            "+----------------+-----------+-----------+\n",
            "+---------+------+\n",
            "| Cold(T) | 0.02 |\n",
            "+---------+------+\n",
            "| Cold(F) | 0.98 |\n",
            "+---------+------+\n",
            "+----------+---------+---------+\n",
            "| Cold     | Cold(T) | Cold(F) |\n",
            "+----------+---------+---------+\n",
            "| Fever(T) | 0.307   | 0.01    |\n",
            "+----------+---------+---------+\n",
            "| Fever(F) | 0.693   | 0.99    |\n",
            "+----------+---------+---------+\n",
            "+--------------+----------------+----------------+\n",
            "| LungDisease  | LungDisease(T) | LungDisease(F) |\n",
            "+--------------+----------------+----------------+\n",
            "| ChestPain(T) | 0.208          | 0.01           |\n",
            "+--------------+----------------+----------------+\n",
            "| ChestPain(F) | 0.792          | 0.99           |\n",
            "+--------------+----------------+----------------+\n",
            "+----------------------+----------------+----------------+\n",
            "| LungDisease          | LungDisease(T) | LungDisease(F) |\n",
            "+----------------------+----------------+----------------+\n",
            "| ShortnessOfBreath(T) | 0.208          | 0.01           |\n",
            "+----------------------+----------------+----------------+\n",
            "| ShortnessOfBreath(F) | 0.792          | 0.99           |\n",
            "+----------------------+----------------+----------------+\n",
            "+-------------+----------------+----------------+----------------+----------------+\n",
            "| LungDisease | LungDisease(T) | LungDisease(T) | LungDisease(F) | LungDisease(F) |\n",
            "+-------------+----------------+----------------+----------------+----------------+\n",
            "| Cold        | Cold(T)        | Cold(F)        | Cold(T)        | Cold(F)        |\n",
            "+-------------+----------------+----------------+----------------+----------------+\n",
            "| Cough(T)    | 0.7525         | 0.505          | 0.505          | 0.01           |\n",
            "+-------------+----------------+----------------+----------------+----------------+\n",
            "| Cough(F)    | 0.2475         | 0.495          | 0.495          | 0.99           |\n",
            "+-------------+----------------+----------------+----------------+----------------+\n"
          ]
        }
      ],
      "source": [
        "print(cpd_smokes)\n",
        "print(cpd_lungdisease)\n",
        "print(cpd_cold)\n",
        "print(cpd_fever)\n",
        "print(cpd_chestpain)\n",
        "print(cpd_shortnessofbreath)\n",
        "print(cpd_cough)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ZcpGvWf4ZdK"
      },
      "source": [
        "## Q5 [5]\n",
        "**Add the defined CPDs to the model (Associate the CPDs with the network)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqoM70LlBOm3",
        "outputId": "2b892063-e22b-4469-a307-18ef8d5ff81b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "model.add_cpds(cpd_cold, cpd_fever, cpd_lungdisease, cpd_chestpain, cpd_shortnessofbreath, cpd_cough, cpd_smokes)\n",
        "model.check_model()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-u2byYEF4ZdL"
      },
      "source": [
        "## Q6 [5]\n",
        "**The cardinality of each of the nodes is 2 - True or False?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-d--TkB44ZdL",
        "outputId": "877c4898-c75c-4ce6-bb2b-8ebe9e73bed4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "defaultdict(int,\n",
              "            {'Cold': 2,\n",
              "             'Fever': 2,\n",
              "             'LungDisease': 2,\n",
              "             'ChestPain': 2,\n",
              "             'ShortnessOfBreath': 2,\n",
              "             'Cough': 2,\n",
              "             'Smokes': 2})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "model.get_cardinality()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nCErvoDIDmQK"
      },
      "source": [
        "The statement 'The cardinality of each of the nodes is 2' is true."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MgrNYYsq4ZdM"
      },
      "source": [
        "### Q7 [10]\n",
        "**Find the probability of each event hapenning using the variable elimination method.**\n",
        "- $ P(Smokes) $\n",
        "- $ P(Cold) $\n",
        "- $ P(LungDisease) $\n",
        "- $ P(ShortnessBreath) $\n",
        "- $ P(ChestPain) $\n",
        "- $ P(Fever) $\n",
        "- $ P(Cough) $\n",
        "- $ P(LungDisease|Smokes=True) $\n",
        "- $ P(LungDisease|Cough=True) $\n",
        "- $ P(ShortnessBreath|Smokes=True) $\n",
        "- $ P(ChestPain|Fever=True) $"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "D8bG-aew4ZdM"
      },
      "outputs": [],
      "source": [
        "from pgmpy.inference import VariableElimination\n",
        "infer = VariableElimination(model)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SdKw7cT74ZdN",
        "outputId": "b3f96a04-f804-4bcc-bee7-093df996ea82"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "P(Smokes):\n",
            " +-----------+---------------+\n",
            "| Smokes    |   phi(Smokes) |\n",
            "+===========+===============+\n",
            "| Smokes(T) |        0.2000 |\n",
            "+-----------+---------------+\n",
            "| Smokes(F) |        0.8000 |\n",
            "+-----------+---------------+\n",
            "P(Cold):\n",
            " +---------+-------------+\n",
            "| Cold    |   phi(Cold) |\n",
            "+=========+=============+\n",
            "| Cold(T) |      0.0200 |\n",
            "+---------+-------------+\n",
            "| Cold(F) |      0.9800 |\n",
            "+---------+-------------+\n",
            "P(LungDisease):\n",
            " +----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.0210 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.9790 |\n",
            "+----------------+--------------------+\n",
            "P(ShortnessOfBreath):\n",
            " +----------------------+--------------------------+\n",
            "| ShortnessOfBreath    |   phi(ShortnessOfBreath) |\n",
            "+======================+==========================+\n",
            "| ShortnessOfBreath(T) |                   0.0142 |\n",
            "+----------------------+--------------------------+\n",
            "| ShortnessOfBreath(F) |                   0.9858 |\n",
            "+----------------------+--------------------------+\n",
            "P(ChestPain):\n",
            " +--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0142 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9858 |\n",
            "+--------------+------------------+\n",
            "P(Fever):\n",
            " +----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n",
            "P(Cough):\n",
            " +----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.0302 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.9698 |\n",
            "+----------+--------------+\n",
            "P(LungDisease | Smokes = True):\n",
            " +----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.1009 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.8991 |\n",
            "+----------------+--------------------+\n",
            "P(LungDisease | Cough = True):\n",
            " +----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.3545 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.6455 |\n",
            "+----------------+--------------------+\n",
            "P(ShortnessOfBreath | Smokes = True):\n",
            " +----------------------+--------------------------+\n",
            "| ShortnessOfBreath    |   phi(ShortnessOfBreath) |\n",
            "+======================+==========================+\n",
            "| ShortnessOfBreath(T) |                   0.0300 |\n",
            "+----------------------+--------------------------+\n",
            "| ShortnessOfBreath(F) |                   0.9700 |\n",
            "+----------------------+--------------------------+\n",
            "P(ChestPain | Fever = True):\n",
            " +--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0142 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9858 |\n",
            "+--------------+------------------+\n"
          ]
        }
      ],
      "source": [
        "print(\"P(Smokes):\\n\", infer.query(['Smokes']))\n",
        "print(\"P(Cold):\\n\", infer.query(['Cold']))\n",
        "print(\"P(LungDisease):\\n\", infer.query(['LungDisease']))\n",
        "print(\"P(ShortnessOfBreath):\\n\", infer.query(['ShortnessOfBreath']))\n",
        "print(\"P(ChestPain):\\n\", infer.query(['ChestPain']))\n",
        "print(\"P(Fever):\\n\", infer.query(['Fever']))\n",
        "print(\"P(Cough):\\n\", infer.query(['Cough']))\n",
        "print(\"P(LungDisease | Smokes = True):\\n\", infer.query(['LungDisease'], evidence = {'Smokes': 'T'}))\n",
        "print(\"P(LungDisease | Cough = True):\\n\", infer.query(['LungDisease'], evidence = {'Cough': 'T'}))\n",
        "print(\"P(ShortnessOfBreath | Smokes = True):\\n\", infer.query(['ShortnessOfBreath'], evidence = {'Smokes': 'T'}))\n",
        "print(\"P(ChestPain | Fever = True):\\n\", infer.query(['ChestPain'], evidence = {'Fever': 'T'}))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FkryI8sf4ZdN"
      },
      "source": [
        "## Q8 [15]\n",
        "**Are the following  true? If not, can you make them independent by including info on the parent?**\n",
        "1. Cough is independent from Fever. (Having knowledge about Fever does not change the probability of Cough)\n",
        "2. Fever is independent from Smokes.\n",
        "3. ChestPain is independent from Smokes.\n",
        "4. ChestPain is independent from Smokes given LungDisease.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KV3sBXqQ4ZdO"
      },
      "source": [
        "##### 1. Cough is independent from Fever"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ecnHwv_r4ZdO",
        "outputId": "3e0e11f9-7bd4-48c7-9b54-06f32302f69f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.0302 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.9698 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.2091 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.7909 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.0273 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.9727 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.0204 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.9796 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.0204 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.9796 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.5102 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.4898 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Cough    |   phi(Cough) |\n",
            "+==========+==============+\n",
            "| Cough(T) |       0.5102 |\n",
            "+----------+--------------+\n",
            "| Cough(F) |       0.4898 |\n",
            "+----------+--------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['Cough'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Fever': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Fever': 'F'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Cold': 'F', 'Fever': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Cold': 'F', 'Fever': 'F'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Cold': 'T', 'Fever': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cough'], evidence = {'Cold': 'T', 'Fever': 'F'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BSborwGT4ZdO"
      },
      "source": [
        "- Fever does affect Cough -> Not independent\n",
        "- However, given the parent of both Fever and Cough (Cold), they should be independent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OvAYlig4ZdO"
      },
      "source": [
        "##### 2. Fever is independent from Smokes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iUlGrfFS4ZdP",
        "outputId": "2ae7f7ea-54bd-4cbc-81ec-0c7d46892501"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['Fever'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Fever'], evidence = {'Smokes': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Fever'], evidence = {'Smokes': 'F'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a4FCFLEVGPe0"
      },
      "source": [
        "- Smokes does not affect Fever -> Independent"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oEjpmpE04ZdP"
      },
      "source": [
        "##### 3. ChestPain is independent from Smokes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LbS2_iM4ZdP",
        "outputId": "f92a84db-ccc7-42e3-c2a4-763316f277ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0142 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9858 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0300 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9700 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0102 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9898 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.2080 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.7920 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0100 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9900 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.2080 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.7920 |\n",
            "+--------------+------------------+\n",
            "+--------------+------------------+\n",
            "| ChestPain    |   phi(ChestPain) |\n",
            "+==============+==================+\n",
            "| ChestPain(T) |           0.0100 |\n",
            "+--------------+------------------+\n",
            "| ChestPain(F) |           0.9900 |\n",
            "+--------------+------------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['ChestPain'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'F'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'F', 'LungDisease': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'F', 'LungDisease': 'F'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'T', 'LungDisease': 'T'})\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['ChestPain'], evidence = {'Smokes': 'T', 'LungDisease': 'F'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p0ow20LAHQ_f"
      },
      "source": [
        "- Smokes does affect Chast Pain -> Not independent\n",
        "- However, when Lung Disease is given, they are independent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "agC_lezd4ZdP"
      },
      "source": [
        "## Q9 [20]\n",
        "1. Does having the knowledge that the person is coughing increase the probability of lung disease?\n",
        "2. Does having the knowledge that the person is coughing increase the probability that the person has Cold?\n",
        "3. Does having the knowledge that the person is coughing increase the probability that there is Fever?\n",
        "4. Does having the knowledge that the person is coughing increase the probability that there is Fever, given the person has Cold?\n",
        "5. Does having the knowledge that the person has Fever increase the probability that there is Lung Disease?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rbhwlgtY4ZdP"
      },
      "source": [
        "##### 1. Does having the knowledge that the person is coughing increase the probability of lung disease? [5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B90r9CZ54ZdP",
        "outputId": "a77c543b-5030-4cd1-e85f-f4bd8d15c6d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.0210 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.9790 |\n",
            "+----------------+--------------------+\n",
            "+----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.3545 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.6455 |\n",
            "+----------------+--------------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['LungDisease'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['LungDisease'], evidence = {'Cough': 'T'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BcbHQpjB4ZdQ"
      },
      "source": [
        "Having the knowledge that the person is coughing increases the probability of lung disease."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wqU_Qcqg4ZdQ"
      },
      "source": [
        "##### 2. Does having the knowledge that the person is coughing increase the probability that the person has Cold? [5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LiEgyfZw4ZdQ",
        "outputId": "ee94b81d-d296-496a-a503-8badb21d6401"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+---------+-------------+\n",
            "| Cold    |   phi(Cold) |\n",
            "+=========+=============+\n",
            "| Cold(T) |      0.0200 |\n",
            "+---------+-------------+\n",
            "| Cold(F) |      0.9800 |\n",
            "+---------+-------------+\n",
            "+---------+-------------+\n",
            "| Cold    |   phi(Cold) |\n",
            "+=========+=============+\n",
            "| Cold(T) |      0.3381 |\n",
            "+---------+-------------+\n",
            "| Cold(F) |      0.6619 |\n",
            "+---------+-------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['Cold'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Cold'], evidence = {'Cough': 'T'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M3GX04QUnzBx"
      },
      "source": [
        "Having the knowledge that the person is coughing increases the probability of cold."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RSpRq0lF4ZdR"
      },
      "source": [
        "##### 3. Does having the knowledge that the person is coughing increase the probability that there is Fever? [5]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gNUOHyiC4ZdR",
        "outputId": "54ecd05f-91e4-474f-a93b-4f97a9a3c873",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.1104 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.8896 |\n",
            "+----------+--------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['Fever'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Fever'], evidence = {'Cough': 'T'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h0xPRyYOn_YV"
      },
      "source": [
        "Having the knowledge that the person is coughing increases the probability of fever."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4s7Les1-4ZdR"
      },
      "source": [
        "##### 4. Does having the knowledge that the person is coughing increase the probability that there is Fever, given the person has Cold?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lmuxcgfM4ZdR",
        "outputId": "638bc702-8c85-45be-8480-8109e70e4194"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.0159 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.9841 |\n",
            "+----------+--------------+\n",
            "+----------+--------------+\n",
            "| Fever    |   phi(Fever) |\n",
            "+==========+==============+\n",
            "| Fever(T) |       0.3070 |\n",
            "+----------+--------------+\n",
            "| Fever(F) |       0.6930 |\n",
            "+----------+--------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['Fever'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['Fever'], evidence = {'Cough': 'T', 'Cold': 'T'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jNodNm9_oQ9a"
      },
      "source": [
        "Having the knowledge that the person is coughing increases the probability of fever given the person has Cold."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "flMoXFnQ4ZdS"
      },
      "source": [
        "##### 5. Does having the knowledge that the person has Fever increase the probability that there is Lung Disease?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5OFpbufF4ZdS",
        "outputId": "102cb055-4841-47d6-a5d5-b85b37f6ccb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.0210 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.9790 |\n",
            "+----------------+--------------------+\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "+----------------+--------------------+\n",
            "| LungDisease    |   phi(LungDisease) |\n",
            "+================+====================+\n",
            "| LungDisease(T) |             0.0210 |\n",
            "+----------------+--------------------+\n",
            "| LungDisease(F) |             0.9790 |\n",
            "+----------------+--------------------+\n"
          ]
        }
      ],
      "source": [
        "dist = infer.query(['LungDisease'])\n",
        "print(dist)\n",
        "\n",
        "dist = infer.query(['LungDisease'], evidence = {'Fever': 'T'})\n",
        "print(dist)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKT12G-Roc7u"
      },
      "source": [
        "Having the knowledge that the person has fever does not increase the probability of lung disease."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d30u2qCM4ZdS"
      },
      "source": [
        "## Q10 [5]\n",
        "1. What's the most probable state of Cough?\n",
        "2. What's the most probable state of Cough given Cold is True?\n",
        "3. What's the most probable state of Cough given Cold is True and Lung Disease is True?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxoFoKCm4ZdS"
      },
      "source": [
        "##### 1. What's the most probable state of Cough?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 133,
          "referenced_widgets": [
            "38926a9a742c41018b00c3a2db49083e",
            "8b6bea927c4f4ee1bce8d7d44ca18b9d",
            "6fe3cdb3303848bfb9617db5714f430f",
            "6f1b3d4a10f24da29ad80c5db360bcbd",
            "f115b9acab8d441f9fee61e09cc91f4c",
            "55230179df6349d3ada1fe6dc3c5e920",
            "594d131591ef49be8c05e0f56b241c20",
            "80ae1568d5524b93b11407e9daa2b9d3",
            "b435d8a1ae4a42ba8e1be5020603ccdb",
            "37684c4bed694ea28373937c8655cf7a",
            "d701922908444a798cfb198e04b3dafd",
            "2545db9aea2e4e9986f6cf64e4b044ea",
            "61de072ae38246638d3bf6b94ebb0a60",
            "77595932a62648069fca45618ba3cc88",
            "a53e9cb3d28e49a69388d0a06a9b5c00",
            "b5e511fe16884590aa9b5d1c0cda71a6",
            "7d150b53876542b9a323a701787cb879",
            "3a583431b3fb4ba9825be0a790027a64",
            "6ec986d3b3bf4b63b1411bf2831e128a",
            "337224f303984954b49926cdd9567a6e",
            "10882815a9c7459dad18ef117386e9db",
            "6ac1a91d31374425bcc0a3018af914b9"
          ]
        },
        "id": "QlSJWPEm4ZdS",
        "outputId": "cfeb5bf1-1a55-4777-fd18-959aa72d0eb5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38926a9a742c41018b00c3a2db49083e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/3 [00:00<?, ?it/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2545db9aea2e4e9986f6cf64e4b044ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Cough': 'F'}"
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            "WARNING:pgmpy:BayesianModel has been renamed to BayesianNetwork. Please use BayesianNetwork class, BayesianModel will be removed in future.\n",
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