Model_visualizer.ipynb

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      "provenance": [],
      "authorship_tag": "ABX9TyPNzZNpLrU98tiXyCBmNodA",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/galenzo17/AI-personal-test/blob/main/Model_visualizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HFzqrWyws4Os",
        "outputId": "5d0864d9-92cc-48d2-c7f9-18034db1494b"
      },
      "outputs": [
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          "name": "stdout",
          "text": [
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          ]
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        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/core/dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 10ms/step - accuracy: 0.5543 - loss: 0.6863 - val_accuracy: 0.8010 - val_loss: 0.5484\n",
            "Epoch 2/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.8059 - loss: 0.5221 - val_accuracy: 0.8770 - val_loss: 0.4256\n",
            "Epoch 3/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9085 - loss: 0.3864 - val_accuracy: 0.9140 - val_loss: 0.3134\n",
            "Epoch 4/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step - accuracy: 0.9399 - loss: 0.2710 - val_accuracy: 0.9330 - val_loss: 0.2348\n",
            "Epoch 5/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9601 - loss: 0.1952 - val_accuracy: 0.9420 - val_loss: 0.1866\n",
            "Epoch 6/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9767 - loss: 0.1555 - val_accuracy: 0.9480 - val_loss: 0.1582\n",
            "Epoch 7/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9825 - loss: 0.1248 - val_accuracy: 0.9520 - val_loss: 0.1386\n",
            "Epoch 8/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9850 - loss: 0.1008 - val_accuracy: 0.9580 - val_loss: 0.1230\n",
            "Epoch 9/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9947 - loss: 0.0724 - val_accuracy: 0.9620 - val_loss: 0.1138\n",
            "Epoch 10/10\n",
            "\u001b[1m32/32\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9971 - loss: 0.0643 - val_accuracy: 0.9610 - val_loss: 0.1056\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x500 with 2 Axes>"
            ],
            "image/png": 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vjz/+mD59+lC9enXmzJnDmjVrWLduHSVLlkzUce+X2D8XcZElSxb27NnD8uXL7bXP6tatG61OXPXq1Tl69CjTp0+nVKlSTJ06laeffpqpU6c6LE4REZHkRnNSzUnjIjnPSYOCgvjpp584fvw4hQsXtt9KlCjB7du3mTt3rkPntf9tYBcltvdir169GDlyJK+99ho//PADa9euZd26dWTKlCler3u7du0IDg5m2bJlWJbF3LlzadCgAb6+vo99LpHkSI3IRCTeNm7cyNWrV1myZAnVq1e37z9+/LgTo7onS5YseHh4cOTIkRj3xbbvv/bt28e///7LrFmzaNeunX3/wy55f5S8efOyfv16goODo61sOHTo0GOdp3Xr1qxevZpVq1Yxd+5cfHx8aNiwof3+RYsWUaBAAZYsWRLtsqfYLp2KS8wAhw8fpkCBAvb9ly9fjrFSYNGiRdSqVYtp06ZF2x8YGEjmzJnt3z/OpVh58+bl559/5ubNm9FWNkRd6hgVnyPkzZuXv/76i8jIyGirbWOLxc3NjYYNG9KwYUMiIyN58803+eabb/jggw/sq2qeeuopOnbsSMeOHQkODqZ69eoMGzaMzp07O+w5iYiIJHeakz4+zUmNpDgnXbJkCXfv3mXSpEnRYgXz7zN48GA2b97Mc889R8GCBVmzZg3Xrl174GrbggULEhkZyYEDBx7a+C1jxowEBgZG2xcaGsr58+fjHPuiRYto3749X3zxhX3f3bt3Y5y3YMGC/P333488X6lSpShfvjzff/89uXLl4tSpU0yYMCHO8Ygkd1ppKyLxFvXp8f2f9IaGhvL11187K6RoXF1dqV27NsuWLePcuXP2/UeOHIlRc+pBj4foz8+yLMaPHx/vmOrVq0d4eDiTJk2y74uIiHjsyUfjxo3x9PTk66+/ZtWqVbzyyit4eHg8NPZt27axdevWx465du3apE2blgkTJkQ737hx42Ic6+rqGuOT/4ULF3L27Nlo+9KnTw8QYwIXm3r16hEREcFXX30Vbf/YsWOx2WxxrgWXEOrVq8eFCxdYsGCBfV94eDgTJkzAy8vLfpni1atXoz3OxcWFMmXKABASEhLrMV5eXhQqVMh+v4iIiMSN5qSPT3NSIynOSefMmUOBAgXo1q0br776arRb37598fLyspdIaNq0KZZlMXz48BjniXr+jRs3xsXFhREjRsRY7Xr/a1SwYMFo9YkBvv322weutI1NbK/7hAkTYpyjadOm7N27l6VLlz4w7iht27Zl7dq1jBs3jkyZMjl07i/ibFppKyLxVrVqVTJmzEj79u156623sNlszJ4926GX6zzKsGHDWLt2Lc8++yzdu3e3T7RKlSrFnj17HvrYYsWKUbBgQfr27cvZs2fx8fFh8eLFT1QbtWHDhjz77LMMGDCAEydOUKJECZYsWfLYtbW8vLxo3LixvYbY/ZehATRo0IAlS5bQpEkT6tevz/Hjx5k8eTIlSpQgODj4scby8/Ojb9++jBo1igYNGlCvXj12797NqlWrYnz636BBA0aMGEHHjh2pWrUq+/bt4/vvv4+2GgLMpDBDhgxMnjwZb29v0qdPT+XKlWOtjdWwYUNq1arFoEGDOHHiBGXLlmXt2rX8+OOP9O7dO1qDh4Swfv167t69G2N/48aN6dq1K9988w0dOnRg586d5MuXj0WLFrF582bGjRtnX3XRuXNnrl27xvPPP0+uXLk4efIkEyZMoFy5cva6ZyVKlKBmzZr4+/vz1FNP8eeff7Jo0SJ69uyZoM9HREQkpdOc9PFpTmoktTnpuXPn2LBhQ4xmZ1Hc3d0JCAhg4cKFfPnll9SqVYu2bdvy5ZdfcvjwYerUqUNkZCS//fYbtWrVomfPnhQqVIhBgwbx4YcfUq1aNV555RXc3d3ZsWMHOXLkYNSoUYCZv3br1o2mTZvy4osvsnfvXtasWRPjtX2YBg0aMHv2bHx9fSlRogRbt27l559/JlOmTNGOe++991i0aBHNmjXj9ddfx9/fn2vXrrF8+XImT55M2bJl7ce2atWKfv36sXTpUrp3707atGnj8cqKJFOWiMh9evToYf33V0ONGjWskiVLxnr85s2brWeeecZKly6dlSNHDqtfv37WmjVrLMDasGGD/bj27dtbefPmtX9//PhxC7BGjx4d45yANXToUPv3Q4cOjRETYPXo0SPGY/PmzWu1b98+2r7169db5cuXt9zc3KyCBQtaU6dOtd59913Lw8PjAa/CPQcOHLBq165teXl5WZkzZ7a6dOli7d271wKsGTNmRHt+6dOnj/H42GK/evWq1bZtW8vHx8fy9fW12rZta+3evTvGOR9lxYoVFmBlz57dioiIiHZfZGSk9fHHH1t58+a13N3drfLly1v/+9//Yvw7WFbM13vGjBkWYB0/fty+LyIiwho+fLiVPXt2K126dFbNmjWtv//+O8brfffuXevdd9+1H/fss89aW7dutWrUqGHVqFEj2rg//vijVaJECStNmjTRnntsMd68edN65513rBw5clhp06a1ChcubI0ePdqKjIyM8Vzi+nPxX1E/kw+6zZ4927Isy7p48aLVsWNHK3PmzJabm5tVunTpGP9uixYtsl566SUrS5Yslpubm5UnTx7rjTfesM6fP28/5qOPPrIqVapkZciQwUqXLp1VrFgxa+TIkVZoaOhD4xQREUkNNCeNTnNSI6XPSb/44gsLsNavX//AY2bOnGkB1o8//mhZlmWFh4dbo0ePtooVK2a5ublZfn5+Vt26da2dO3dGe9z06dOt8uXLW+7u7lbGjBmtGjVqWOvWrbPfHxERYfXv39/KnDmz5enpaQUEBFhHjhyJEXPUv8uOHTtixHb9+nX7PNnLy8sKCAiw/vnnn1if99WrV62ePXtaOXPmtNzc3KxcuXJZ7du3t65cuRLjvPXq1bMAa8uWLQ98XURSIptlJaGPH0VEHKRx48bs37+fw4cPOzsUEREREUmlNCcVebQmTZqwb9++ONWAFklJVNNWRFK8O3fuRPv+8OHDrFy5kpo1azonIBERERFJdTQnFXl858+fZ8WKFbRt29bZoYg4nFbaikiKlz17djp06ECBAgU4efIkkyZNIiQkhN27d1O4cGFnhyciIiIiqYDmpCJxd/z4cTZv3szUqVPZsWMHR48eJVu2bM4OS8Sh1IhMRFK8OnXqMG/ePC5cuIC7uztVqlTh448/1uRYRERERBxGc1KRuPv111/p2LEjefLkYdasWUrYSqqklbYiIiIiIiIiIiIiSYhq2oqIiIiIiIiIiIgkIUraioiIiIiIiIiIiCQhqa6mbWRkJOfOncPb2xubzebscERERETkASzL4ubNm+TIkQMXF601eBjNcUVERESSh7jOcVNd0vbcuXPkzp3b2WGIiIiISBydPn2aXLlyOTuMJE1zXBEREZHk5VFz3FSXtPX29gbMC+Pj4+PkaERERETkQYKCgsidO7d9/iYPpjmuiIiISPIQ1zluqkvaRl0u5uPjowmtiIiISDKgy/0fTXNcERERkeTlUXNcFQcTERERERERERERSUKUtBURERERERERERFJQpS0FREREREREREREUlCUl1NWxEREXl8kZGRhIaGOjsMSWHSpk2Lq6urs8NIVSIiIggLC3N2GCIJTr9PREQkpVHSVkRERB4qNDSU48ePExkZ6exQJAXKkCED2bJlU7OxRGZZFhcuXCAwMNDZoYgkGv0+ERGRlERJWxEREXkgy7I4f/48rq6u5M6dGxcXVVaShGFZFrdv3+bSpUsAZM+e3ckRpWxRCdssWbLg6emppJakKPp9IiIiKZGStiIiIvJA4eHh3L59mxw5cuDp6enscCSFSZcuHQCXLl0iS5YsurQ5kURERNgTtpkyZXJ2OCKJQr9PREQkpdFyGREREXmgiIgIANzc3JwciaRUUR8GJJc6q5s2baJhw4bkyJEDm83GsmXLHvmYjRs38vTTT+Pu7k6hQoWYOXNmjGMmTpxIvnz58PDwoHLlymzfvj3BYo56bfXBi6R0ye33iYiIyMMoaSsiIiKPpEupJbEkt5+tW7duUbZsWSZOnBin448fP079+vWpVasWe/bsoXfv3nTu3Jk1a9bYj1mwYAF9+vRh6NCh7Nq1i7JlyxIQEGC/1DuhJLfXWuRx6WdcRERSEpVHEBERERGJo7p161K3bt04Hz958mTy58/PF198AUDx4sX5/fffGTt2LAEBAQCMGTOGLl260LFjR/tjVqxYwfTp0xkwYEDCPwkRERERSfK00lZEREQkDvLly8e4cePifPzGjRux2WwEBgYmWkyS9G3dupXatWtH2xcQEMDWrVsBCA0NZefOndGOcXFxoXbt2vZjJGHpvSwiIiLJgZK2IiIikqLYbLaH3oYNGxav8+7YsYOuXbvG+fiqVaty/vx5fH194zVeXCmhlLRduHCBrFmzRtuXNWtWgoKCuHPnDleuXCEiIiLWYy5cuPDA84aEhBAUFBTtltKktvfy/YoVK4a7u/tDfwZEREQkZVN5BBEREUlRzp8/b99esGABQ4YM4dChQ/Z9Xl5e9m3LsoiIiCBNmkdPifz8/B4rDjc3N7Jly/ZYjxGJq1GjRjF8+HBnh5GoUut7+ffff+fOnTu8+uqrzJo1i/79+zts7NiEhYWRNm1ap8YgIiKSGmmlrYiIiKQo2bJls998fX2x2Wz27//55x+8vb1ZtWoV/v7+uLu78/vvv3P06FEaNWpE1qxZ8fLyomLFivz888/RzvvfS6ptNhtTp06lSZMmeHp6UrhwYZYvX26//78rYGfOnEmGDBlYs2YNxYsXx8vLizp16kRLTIWHh/PWW2+RIUMGMmXKRP/+/Wnfvj2NGzeO9+tx/fp12rVrR8aMGfH09KRu3bocPnzYfv/Jkydp2LAhGTNmJH369JQsWZKVK1faH9u6dWv8/PxIly4dhQsXZsaMGfGOJTXKli0bFy9ejLbv4sWL+Pj4kC5dOjJnzoyrq2usxzwsUThw4EBu3Lhhv50+fTpR4nem1PpenjZtGq1ataJt27ZMnz49xv1nzpyhZcuWPPXUU6RPn54KFSqwbds2+/0//fQTFStWxMPDg8yZM9OkSZNoz3XZsmXRzpchQwZmzpwJwIkTJ7DZbCxYsIAaNWrg4eHB999/z9WrV2nZsiU5c+bE09OT0qVLM2/evGjniYyM5LPPPqNQoUK4u7uTJ08eRo4cCcDzzz9Pz549ox1/+fJl3NzcWL9+/SNfExERkdRISdtEFhICo0bB5cvOjkREROTJWRbcuuWcm2Ul3PMYMGAAn3zyCQcPHqRMmTIEBwdTr1491q9fz+7du6lTpw4NGzbk1KlTDz3P8OHDee211/jrr7+oV68erVu35tq1aw88/vbt23z++efMnj2bTZs2cerUKfr27Wu//9NPP+X7779nxowZbN68maCgoBgJlsfVoUMH/vzzT5YvX87WrVuxLIt69eoRFhYGQI8ePQgJCWHTpk3s27ePTz/91L6C8YMPPuDAgQOsWrWKgwcPMmnSJDJnzvxE8aQ2VapUiZGUWrduHVWqVAHMKk5/f/9ox0RGRrJ+/Xr7MbFxd3fHx8cn2u1xOOu9nJDvY0h57+WbN2+ycOFC2rRpw4svvsiNGzf47bff7PcHBwdTo0YNzp49y/Lly9m7dy/9+vUjMjISgBUrVtCkSRPq1avH7t27Wb9+PZUqVXrkuP81YMAA3n77bQ4ePEhAQAB3797F39+fFStW8Pfff9O1a1fatm3L9u3b7Y8ZOHAgn3zyif33xty5c+1lPzp37szcuXMJCQmxHz9nzhxy5szJ888//9jxiYiIPC7Lgrt34coVOH4c9u2DrVth3TpYsgT+8xlv0mClMjdu3LAA68aNGw4Zr3lzywLLat3aIcOJiIgkqDt37lgHDhyw7ty5Y1mWZQUHm//XnHELDn78+GfMmGH5+vrav9+wYYMFWMuWLXvkY0uWLGlNmDDB/n3evHmtsWPH2r8HrMGDB9u/Dw4OtgBr1apV0ca6fv26PRbAOnLkiP0xEydOtLJmzWr/PmvWrNbo0aPt34eHh1t58uSxGjVq9MA4/zvO/f79918LsDZv3mzfd+XKFStdunTWDz/8YFmWZZUuXdoaNmxYrOdu2LCh1bFjxweOnRD++zN2P0fP2+Li5s2b1u7du63du3dbgDVmzBhr9+7d1smTJy3LsqwBAwZYbdu2tR9/7Ngxy9PT03rvvfesgwcPWhMnTrRcXV2t1atX24+ZP3++5e7ubs2cOdM6cOCA1bVrVytDhgzWhQsX4hzXw16r2F5jZ72X4/M+tqzU8V62LMv69ttvrXLlytm/f/vtt6327dvbv//mm28sb29v6+rVq7E+vkqVKlbrh/zhAVhLly6Nts/X19eaMWOGZVmWdfz4cQuwxo0b99A4Lcuy6tevb7377ruWZVlWUFCQ5e7ubk2ZMiXWY+/cuWNlzJjRWrBggX1fmTJlHvi7J74e9vtERESSj8hIy7p717KuXLGs48cta98+y9q61bLWrbOsJUss67vvLOvrry3rs88s64MPLOuddyyrSxfLatnSsho2tKyaNS2rQgXLKlbMsnLlsixfX8tydX34HOXZZx33/OI6x1VN20T27ruwcCF8/z20bg116zo7IhEREalQoUK074ODgxk2bBgrVqzg/PnzhIeHc+fOnUeuzitTpox9O3369Pj4+HDp0qUHHu/p6UnBggXt32fPnt1+/I0bN7h48WK0VXGurq74+/vbV9E9roMHD5ImTRoqV65s35cpUyaKFi3KwYMHAXjrrbfo3r07a9eupXbt2jRt2tT+vLp3707Tpk3ZtWsXL730Eo0bN6Zq1arxiiWl+PPPP6lVq5b9+z59+gDQvn17Zs6cyfnz56P93OTPn58VK1bwzjvvMH78eHLlysXUqVMJCAiwH9O8eXMuX77MkCFDuHDhAuXKlWP16tUxmpNJTCntvTx9+nTatGlj/75NmzbUqFGDCRMm4O3tzZ49eyhfvjxPPfVUrI/fs2cPXbp0eegYcfHf1zUiIoKPP/6YH374gbNnzxIaGkpISAienp6A+V0TEhLCCy+8EOv5PDw87OUeXnvtNXbt2sXff/8drQyFiIgkX6GhEBxsbjdv3tt+ku/DwxMv3nTpwNsbvLzu3UqXTrzx4ktJ20RWsSL07g1jxkC3brB/v/lhEBERSY48Pc0kylljJ5T06dNH+75v376sW7eOzz//nEKFCpEuXTpeffVVQkNDH3qe/zbnsdlsD03KxHa8ldDXiz+mzp07ExAQwIoVK1i7di2jRo3iiy++oFevXtStW5eTJ0+ycuVK1q1bxwsvvECPHj34/PPPnRqzM9WsWfOh/2ZRtUH/+5jdu3c/9Lw9e/aMUfMzMTnrvZyQ72NIWe/lAwcO8Mcff7B9+/ZozcciIiKYP38+Xbp0IV26dA89x6Pujy3OqFIp9/vv6zp69GjGjx/PuHHjKF26NOnTp6d379721/VR44L5XVOuXDnOnDnDjBkzeP7558mbN+8jHyciIonDskyC9PJlUzLgyhUIDIyeQI1rkjWW/0oSjIdHzASrl1fMfXH9Pn16cHVNvHgTkpK2DjBihKmPceIEfPABjB3r7IhERETix2YzE52UZvPmzXTo0MHesCc4OJgTJ044NAZfX1+yZs3Kjh07qF69OmCSNbt27aJcuXLxOmfx4sUJDw9n27Zt9hWyV69e5dChQ5QoUcJ+XO7cuenWrRvdunVj4MCBTJkyhV69egHg5+dH+/btad++PdWqVeO9995L1UnblELv5cQT3/fytGnTqF69OhMnToy2f8aMGUybNo0uXbpQpkwZpk6dyrVr12JdbVumTBnWr19Px44dYx3Dz88vWsO0w4cPc/v27Uc+p82bN9OoUSP7KuDIyEj+/fdf+++RwoULky5dOtavX0/nzp1jPUfp0qWpUKECU6ZMYe7cuXz11VePHFdEROIuNBSuXr2XhL0/GRvbvitXzGMSkrv74ydTH5VgTZOKM5ep+Kk7Tvr0MHky1KkD48dDixZw31WKIiIi4mSFCxdmyZIlNGzYEJvNxgcffBDvkgRPolevXowaNYpChQpRrFgxJkyYwPXr17HZbI987L59+/D29rZ/b7PZKFu2LI0aNaJLly588803eHt7M2DAAHLmzEmjRo0A6N27N3Xr1qVIkSJcv36dDRs2ULx4cQCGDBmCv78/JUuWJCQkhP/973/2+0SSouT6Xg4LC2P27NmMGDGCUqVKRbuvc+fOjBkzhv3799OyZUs+/vhjGjduzKhRo8iePTu7d+8mR44cVKlShaFDh/LCCy9QsGBBWrRoQXh4OCtXrrSv3H3++ef56quvqFKlChEREfTv3z/GquHYFC5cmEWLFrFlyxYyZszImDFjuHjxoj1p6+HhQf/+/enXrx9ubm48++yzXL58mf3799OpU6doz6Vnz56kT5/enlgXEZGYLAtu3Hh04vX+fUFB8RsrfXrInNncMmaMf8I1fXqIw38p8hiUtHWQgABo2xZmz4YuXeDPP8HNzdlRiYiICMCYMWN4/fXXqVq1KpkzZ6Z///4ExXfm+wT69+/PhQsXaNeuHa6urnTt2pWAgABc43ANV9SKviiurq6Eh4czY8YM3n77bRo0aEBoaCjVq1dn5cqV9kRNREQEPXr04MyZM/j4+FCnTh3G/v9lQW5ubgwcOJATJ06QLl06qlWrxvz58xP+iYskkOT6Xl6+fDlXr16NNZFZvHhxihcvzrRp0xgzZgxr167l3XffpV69eoSHh1OiRAn76tyaNWuycOFCPvzwQz755BN8fHyi/W744osv6NixI9WqVSNHjhyMHz+enTt3PvL5DB48mGPHjhEQEICnpyddu3alcePG3Lhxw37MBx98QJo0aRgyZAjnzp0je/bsdOvWLdp5WrZsSe/evWnZsiUeHh5xei1FRFKCkJC4rX69fzs+NV1dXO4lYDNnBj+/6F//uy9TpoQvXSQJx2Y5u5CagwUFBeHr68uNGzfw8fFx6NhXrkDx4ubrRx/BoEEOHV5EROSx3b17l+PHj5M/f379ge0EkZGRFC9enNdee40PP/zQ2eEkiof9jDlz3pbcPOy10vvY+VLDezkuTpw4QcGCBdmxYwdPP/10gp9fP+si4giRkab2a1xWv0btu3kzfmN5eT044RpbMjZDBpO4laQtrnNcrbR1oMyZTXmE1q1NndtXX4WiRZ0dlYiIiCQVJ0+eZO3atdSoUYOQkBC++uorjh8/TqtWrZwdmog8Br2XowsLC+Pq1asMHjyYZ555JlEStiIi8RESYhKwgYFw/br5eu3aw5OwV69CRMTjj+XqGrfVr/evlNXnT6mbkrYO1rIlzJkDq1ZB166wYYM+BRERERHDxcWFmTNn0rdvXyzLolSpUvz888+qIyuSzOi9HN3mzZupVasWRYoUYdGiRc4OR0RSkIgIU/v1v4nXuG7fuRP/sX18Hi8JmyGDaQQqEldK2jqYzQaTJkHJkrBpE0ydapK3IiIiIrlz52bz5s3ODkNEnpDey9HVrFmTVFaVT0TiyLLg1q3oydTHSbwmVNlyX1/ThCtDBvPVz+/hSdhMmcDdPWHGFnkQJW2dIG9eGDkSeveG996DBg0gRw5nRyUiIiIiIiIi8nhiKzEQ1+3AwPg13PovT897CdcMGR5v29vblC4QSWqUtHWSnj1h7lzYvt1sL1ni7IhEREREREREJDW7dQsOHYLjxx1TYiBKmjTRV7o+TuI1QwZwc3vyGESSGiVtncTV1ZRGePppWLrUJG1fecXZUYmIiIiIiIhISnflChw8GPN26lT8z3l/iYHHTb56eqreq8h/KWnrRKVLw4AB8NFHZrXt88+bX1YiIiIiIiIiIk8iMtIkYf/5J2Zy9urVBz8uc2YoVMjUbb0/saoSAyKOpaStkw0aBAsXmssP+veHb75xdkQiIiIiIiIiklyEhsLhwzGTs4cOwe3bD35c3rxQvPi9W7Fi5mvmzI6LXUQeTElbJ/PwgG+/hRo1zNdWrcy2iIiIiIiIiEiUoCCTmP1vcvboUYiIiP0xadNC4cIxk7NFi0L69I6NX0Qej4uzAxCoXh3eeMNsd+kCd+86Nx4RERGBmjVr0rt3b/v3+fLlY9y4cQ99jM1mY9myZU88dkKdR0T0XhaR5MWy4MIF2LABvv4a3noLXnwRcuUyNWMrV4b27eGTT+DHH+Hff03C1tsbKlaEdu1g1ChYtsystL11C/bvh0WL4MMPzUKxp59WwlYkOdBK2yTi009h+XJzScOHH8LIkc6OSEREJHlq2LAhYWFhrF69OsZ9v/32G9WrV2fv3r2UKVPmsc67Y8cO0ifwXzjDhg1j2bJl7NmzJ9r+8+fPkzFjxgQd679mzpxJ7969CQwMTNRxROJL7+XHc+fOHXLmzImLiwtnz57F3d3dIeOKSPxERMCJE/dWy96/evZh/zVnzRp91WzULUcONfISSWmUtE0ifH3Np2hNmsBnn0Hz5vCY808REREBOnXqRNOmTTlz5gy5cuWKdt+MGTOoUKHCYyd5APz8/BIqxEfKli2bw8YSSar0Xn48ixcvpmTJkliWxbJly2jevLnDxv4vy7KIiIggTRr9uSly965ZDXt/OYN//jGrYENCYn+MzQb588dMzBYrZpp+iUjqoPIISUjjxtC0KYSHQ+fOD65JIyIiIg/WoEED/Pz8mDlzZrT9wcHBLFy4kE6dOnH16lVatmxJzpw58fT0pHTp0sybN++h5/3vJdWHDx+mevXqeHh4UKJECdatWxfjMf3796dIkSJ4enpSoEABPvjgA8LCwgCz0nX48OHs3bsXm82GzWazx/zfS6r37dvH888/T7p06ciUKRNdu3YlODjYfn+HDh1o3Lgxn3/+OdmzZydTpkz06NHDPlZ8nDp1ikaNGuHl5YWPjw+vvfYaFy9etN+/d+9eatWqhbe3Nz4+Pvj7+/Pnn38CcPLkSRo2bEjGjBlJnz49JUuWZOXKlfGORVInvZcf7708bdo02rRpQ5s2bZg2bVqM+/fv30+DBg3w8fHB29ubatWqcfToUfv906dPp2TJkri7u5M9e3Z69uwJwIkTJ7DZbNFWEQcGBmKz2di4cSMAGzduxGazsWrVKvz9/XF3d+f333/n6NGjNGrUiKxZs+Ll5UXFihX5+eefo8UVEhJC//79yZ07N+7u7hQqVIhp06ZhWRaFChXi888/j3b8nj17sNlsHDly5JGviYgjBQbC1q0wfTq89x40aACFCpkyBGXLQosWMHw4/PAD/PWXSdi6u5vFWq+9BkOHwvz5sHevaR529Cj8738wejS8/jpUqaKErUhqo48+k5gJE+Dnn2HHDrN9X/ktERER57Osh7chTkyennG67i9NmjS0a9eOmTNnMmjQIGz//5iFCxcSERFBy5YtCQ4Oxt/fn/79++Pj48OKFSto27YtBQsWpFKlSo8cIzIykldeeYWsWbOybds2bty4Ea1mZhRvb29mzpxJjhw52LdvH126dMHb25t+/frRvHlz/v77b1avXm1PYvj6+sY4x61btwgICKBKlSrs2LGDS5cu0blzZ3r27BktmbVhwwayZ8/Ohg0bOHLkCM2bN6dcuXJ06dLlkc8ntucXlbD99ddfCQ8Pp0ePHjRv3tyepGndujXly5dn0qRJuLq6smfPHtKmTQtAjx49CA0NZdOmTaRPn54DBw7g5eX12HFIInLWezmO72PQe/lx3stHjx5l69atLFmyBMuyeOeddzh58iR58+YF4OzZs1SvXp2aNWvyyy+/4OPjw+bNmwkPDwdg0qRJ9OnTh08++YS6dety48YNNm/e/MjX778GDBjA559/ToECBciYMSOnT5+mXr16jBw5End3d7777jsaNmzIoUOHyJMnDwDt2rVj69atfPnll5QtW5bjx49z5coVbDYbr7/+OjNmzKBv3772MWbMmEH16tUpVKjQY8cn8qQsC86di75qNup23+eaMWTIcG+l7P0rZ/PlA1dXR0UvIsmOlcrcuHHDAqwbN244O5QH+vZbywLL8vS0rOPHnR2NiIikZnfu3LEOHDhg3blzx+wIDjb/STnjFhwc57gPHjxoAdaGDRvs+6pVq2a1adPmgY+pX7++9e6779q/r1GjhvX222/bv8+bN681duxYy7Isa82aNVaaNGmss2fP2u9ftWqVBVhLly594BijR4+2/P397d8PHTrUKlu2bIzj7j/Pt99+a2XMmNEKvu/5r1ixwnJxcbEuXLhgWZZltW/f3sqbN68VHh5uP6ZZs2ZW8+bNHxjLjBkzLF9f31jvW7t2reXq6mqdOnXKvm///v0WYG3fvt2yLMvy9va2Zs6cGevjS5cubQ0bNuyBY98vxs/YfZLDvC2peNhrFetr7Kz38mO8jy1L72XLevR72bIs6/3337caN25s/75Ro0bW0KFD7d8PHDjQyp8/vxUaGhrr43PkyGENGjQo1vuOHz9uAdbu3bvt+65fvx7t32XDhg0WYC1btuyhcVqWZZUsWdKaMGGCZVmWdejQIQuw1q1bF+uxZ8+etVxdXa1t27ZZlmVZoaGhVubMmR/4u+dhv09EHkdYmGUdOmRZy5ZZ1qhRltWunWVVrGhZ3t4P/xWXM6dl1a5tWb16WdbEiZb1yy+Wdf68ZUVGOvsZiUhSEtc5rlbaJkGdOsH338Ovv0K3brBqlQqKi4iIPI5ixYpRtWpVpk+fTs2aNTly5Ai//fYbI0aMACAiIoKPP/6YH374gbNnzxIaGkpISAienp5xOv/BgwfJnTs3OXLksO+rUqVKjOMWLFjAl19+ydGjRwkODiY8PBwfH5/Hei4HDx6kbNmy0RonPfvss0RGRnLo0CGyZs0KQMmSJXG9b7lO9uzZ2bdv32ONdf+YuXPnJnfu3PZ9JUqUIEOGDBw8eJCKFSvSp08fOnfuzOzZs6lduzbNmjWjYMGCALz11lt0796dtWvXUrt2bZo2bRqv2qMiei8/+r0cERHBrFmzGD9+vH1fmzZt6Nu3L0OGDMHFxYU9e/ZQrVo1+2r4+126dIlz587xwgsvPNbziU2FChWifR8cHMywYcNYsWIF58+fJzw8nDt37nDq1CnAlDpwdXWlRo0asZ4vR44c1K9fn+nTp1OpUiV++uknQkJCaNas2RPHKgLmgoNDh2Kumj18GB5UlcTVFQoWjF5nNurrY/5aEBF5KKfXtJ04cSL58uXDw8ODypUrs3379oceHxgYSI8ePciePTvu7u4UKVIkxdVIc3GBb7819W3WrDEJXBERkSTB0xOCg51zi2MSJkqnTp1YvHgxN2/eZMaMGRQsWNCeGBg9ejTjx4+nf//+bNiwgT179hAQEEBoaGiCvVRbt26ldevW1KtXj//973/s3r2bQYMGJegY9/tvMsZmsxEZGZkoYwEMGzaM/fv3U79+fX755RdKlCjB0qVLAejcuTPHjh2jbdu27Nu3jwoVKjBhwoREi0XiwVnv5cd8H4Pey496L69Zs4azZ8/SvHlz0qRJQ5o0aWjRogUnT55k/fr1AKRLl+6Bj3/YfQAuLuZPRsuy7PseVGP3/oQ0QN++fVm6dCkff/wxv/32G3v27KF06dL21+5RY4P5fTJ//nzu3LnDjBkzaN68eZyT8iJRrlyB33+HKVOgTx+oW9eUJkifHp5+Glq3ho8+gsWL4cABk7BNlw7Kl4dWreDDD2HRIvj7b7h1yyR6ly2DUaOgfXuoVEkJWxFJeE5dabtgwQL69OnD5MmTqVy5MuPGjSMgIIBDhw6RJUuWGMeHhoby4osvkiVLFhYtWkTOnDk5efIkGTJkcHzwiaxIEVOI/P33TV3bgABwYKNbERGR2Nls5i+cZOC1117j7bffZu7cuXz33Xd0797dXhNz8+bNNGrUiDZt2gCmruW///5LiRIl4nTu4sWLc/r0ac6fP0/27NkB+OOPP6Ids2XLFvLmzcugQYPs+06ePBntGDc3NyIe0Xm0ePHizJw5k1u3btkTIps3b8bFxYWiRYvGKd7HFfX8Tp8+bV9te+DAAQIDA6O9RkWKFKFIkSK88847tGzZkhkzZtCkSRMAcufOTbdu3ejWrRsDBw5kypQp9OrVK1HilXjQexlIGe/ladOm0aJFi2jxAYwcOZJp06bx4osvUqZMGWbNmkVYWFiMpLC3tzf58uVj/fr11KpVK8b5/f7/j5Dz589Tvnx5gGhNyR5m8+bNdOjQwf57ITg4mBMnTtjvL126NJGRkfz666/Url071nPUq1eP9OnTM2nSJFavXs2mTZviNLakPpGRcPo0/PNPzJWzV648+HGZMkWvMxt1y53bLKgSEXEWpyZtx4wZQ5cuXejYsSMAkydPZsWKFUyfPp0BAwbEOH769Olcu3aNLVu22Ccb+fLlc2TIDtW3r+ke+ddf5tPA2bOdHZGIiEjy4eXlRfPmzRk4cCBBQUF06NDBfl/hwoVZtGgRW7ZsIWPGjIwZM4aLFy/GOdFTu3ZtihQpQvv27Rk9ejRBQUExEiaFCxfm1KlTzJ8/n4oVK7JixQr7StQo+fLl4/jx4+zZs4dcuXLh7e2Nu7t7tGNat27N0KFDad++PcOGDePy5cv06tWLtm3b2i+njq+IiIgYyRd3d3dq165N6dKlad26NePGjSM8PJw333yTGjVqUKFCBe7cucN7773Hq6++Sv78+Tlz5gw7duygadOmAPTu3Zu6detSpEgRrl+/zoYNGyhevPgTxSqpl97LD3b58mV++uknli9fTqlSpaLd165dO5o0acK1a9fo2bMnEyZMoEWLFgwcOBBfX1/++OMPKlWqRNGiRRk2bBjdunUjS5Ys1K1bl5s3b7J582Z69epFunTpeOaZZ/jkk0/Inz8/ly5dYvDgwXGKr3DhwixZsoSGDRtis9n44IMPoq0azpcvH+3bt+f111+3NyI7efIkly5d4rXXXgPA1dWVDh06MHDgQAoXLhxr+QpJXUJD4ciRmMnZf/55eH/FPHliT85mzuy42EVEHofTPjcKDQ1l586d0T5RdXFxoXbt2mzdujXWxyxfvpwqVarQo0cPsmbNSqlSpfj4448f+ql2SEgIQUFB0W7JRdq05vINFxeYMwdWr3Z2RCIiIslLp06duH79OgEBAdFqVg4ePJinn36agIAAatasSbZs2WjcuHGcz+vi4sLSpUu5c+cOlSpVonPnzowcOTLaMS+//DLvvPMOPXv2pFy5cmzZsoUPPvgg2jFNmzalTp061KpVCz8/P+bNmxdjLE9PT9asWcO1a9eoWLEir776Ki+88AJfffXV470YsQgODqZ8+fLRblHJlR9//JGMGTNSvXp1ateuTYECBViwYAFgkihXr16lXbt2FClShNdee426desyfPhwwCSDe/ToQfHixalTpw5FihTh66+/fuJ4JfXSezl23333HenTp4+1Hu0LL7xAunTpmDNnDpkyZeKXX34hODiYGjVq4O/vz5QpU+wLYdq3b8+4ceP4+uuvKVmyJA0aNODw4cP2c02fPp3w8HD8/f3p3bs3H330UZziGzNmDBkzZqRq1ao0bNiQgIAAnn766WjHTJo0iVdffZU333yTYsWK0aVLF27duhXtmE6dOhEaGmpf7COpw82bsGOHWbz0/vvQpImpG5s+PZQsCU2bwuDBppzgrl0mYZs2LZQoAa+8AoMGmb+jd+401VlOnjR/U48dC127QrVqStiKSNJms+4vTuRA586dI2fOnGzZsiXap6X9+vXj119/Zdu2bTEeU6xYMU6cOEHr1q158803OXLkCG+++SZvvfUWQ4cOjXWcYcOG2f+AuN+NGzceu3mAs/TpY/5jyZvX1NDx8nJ2RCIiklrcvXuX48ePkz9/fjw8PJwdjqRAD/sZCwoKwtfXN1nN25zlYa+V3seS3P3222+88MILnD59+qGrkvWznvxYFly6dG+l7P0rZ8+cefDjvLxiNgIrXhwKFDCJWxGRpCyuc1ynlkd4XJGRkWTJkoVvv/0WV1dX/P39OXv2LKNHj35g0nbgwIH06dPH/n1QUFC0TsjJwYcfwtKlcOIEfPCBSeCKiIiIiIikZCEhIVy+fJlhw4bRrFmzJy4JI84TGWn+no0tOXv9+oMflzVr7MnZnDlNaW4RkZTMaUnbzJkz4+rqysWLF6Ptv3jxItmyZYv1MdmzZydt2rS4urra9xUvXpwLFy4QGhqKm5tbjMe4u7vHqCeV3KRPD5MnQ506MH48tGxpulOKiIiIiIikVPPmzaNTp06UK1eO7777ztnhSByEhMC//8ZMzh46BHfvxv4Ymw3y549eZzYqQZsxo2PjFxFJSpyWtHVzc8Pf35/169fb605FRkayfv16evbsGetjnn32WebOnUtkZCQu/9/G8d9//yV79uyxJmxTkoAAaNPG1OTp3NnU5dFlHyIiIiIiklJ16NAhWuM5STpu3IjeACxq+9gxs6o2Nu7uUKRIzEZghQtDunSOjV9EJDlwanmEPn360L59eypUqEClSpUYN24ct27dsheYb9euHTlz5mTUqFEAdO/ena+++oq3336bXr16cfjwYT7++GPeeustZz4Nhxk71hRO37cPRo82xdhFREREREREEkNkJGzeDH/9Fb2kwfnzD36Mr2/MxGzx4pAvH9x30ayIiDyCU5O2zZs35/LlywwZMoQLFy5Qrlw5Vq9eba9VdOrUKfuKWoDcuXOzZs0a3nnnHcqUKUPOnDl5++236d+/v7OegkNlzgzjxpkVtyNGmG6ZRYs6OyoRERERERFJSUJD4fvvzWKhgwdjPyZnzuh1ZqNuWbOq3qyISEJweiOynj17PrAcwsaNG2Psq1KlCn/88UciR5V0tWplSiSsXg1du8KGDXBfXltERCRRWJbl7BAkhYp80HW0kuD0WktKp5/xJxcUBN9+axYLnT1r9vn4QM2aMWvOPqThuYgkB5YFN2+aboCBgeYWHg5p0sR+S5v2wffdf1OSKsE4PWkrj8dmM03JSpaETZtg6lSTvBUREUkMadOmxWazcfnyZfz8/LBp6YwkEMuyCA0N5fLly7i4uKT4/gTO5ObmhouLC+fOncPPzw83Nze9lyVF0e+TJ3fhgml6PWmSqVcLkCMHvPOO+XtTCVqRJMiyTIe/wMDoide4bt+48eAi1E/CZnu8JO/jJoXjc3xcjs2YESpWTPjX4wkoaZsM5c0LI0dC797Qrx80aGD+QxUREUlorq6u5MqVizNnznDixAlnhyMpkKenJ3ny5IlWEksSlouLC/nz5+f8+fOcO3fO2eGIJBr9Pnl8//4Ln38Os2aZkghgVtH262eu8nR3d258IileePi9ROr9idW4Jl6j3rhPws3NJCx9fc12ePijb2FhEBER+/ksy9wfFvbksTnSM8/A1q3OjiIaJW2TqZ49Ye5c2L4devWCxYudHZGIiKRUXl5eFC5cmLDkNvGSJM/V1ZU0adJo1acDuLm5kSdPHsLDw4l40B9ZIsmYfp88nm3b4LPPYOlSk18BqFoV+vc3i4KU9xaJo8hICA6O30rXwEDz2Cfl4gIZMty7Zcz4eNseHvErRG1ZJnEblyRvVKI3rsc+6ePi85jChZ/83yKBKWmbTLm6wpQp4O8PS5aY2yuvODsqERFJqVxdXXFVy2eRZM1ms5E2bVrSpk3r7FBExAksC1atMsnaX3+9t//ll83K2mefdV5sIk4XGgoXL8L583DliuNLDHh7Pzq5+qD7vLyc80lLVBmENEotJha9sslYmTLmk9CRI83K2+efN+9XERERERERETALzubNg9Gj4e+/zb60aaFNG+jbF0qUcG58IonGskxS9fx5U7j5v1/v37569cnG8vB4vETr/du+vkp8Sqz0U5HMDR4MCxeaWkT9+8M33zg7IhEREREREXG24GBzdebYsXD6tNnn7Q1vvGH6o+TM6dTwROIvLAwuXXpwMvb+pOzdu3E/b9q0kC0bZMkSvxIDIglMSdtkzsPD/EdcowZ8+y20bg3Vqzs7KhEREREREXGGixdhwgT4+mtz9TZA1qwmUdutm67OlCTKsuDmzYcnYaO+XrlyrxhzXGTIYJKx2bM//OtTT8WvtqtIIlHSNgWoXh26djVJ2y5dYO9efcgjIiIiIiKSmhw5Al98ATNmQEiI2VekCLz3nimFoL8RxSnCw+Hy5YeXJoj6evt23M+bJo35NOJRydisWSFdusR7fiKJSEnbFOLTT+Gnn0yZhI8+MjcRERERSRwTJ05k9OjRXLhwgbJlyzJhwgQqVaoU67FhYWGMGjWKWbNmcfbsWYoWLcqnn35KnTp17McMGzaM4cOHR3tc0aJF+eeffxL1eYhI8vfnn6a52OLF9/ohVa5syue9/LJpYi2S4IKD47Yq9vLlx2vU5e396BWx2bNDpkzOab4l4kBK2qYQGTLAxInwyismgfvaa6ZRmYiIiIgkrAULFtCnTx8mT55M5cqVGTduHAEBARw6dIgsWbLEOH7w4MHMmTOHKVOmUKxYMdasWUOTJk3YsmUL5cuXtx9XsmRJfv75Z/v3adSUREQewLJg7Vrzt9+GDff2168P/fpBtWq6ylseU1iYadp1/ToEBprGXA9LxgYHx/3cLi6mTuyjkrHZskH69In2FEWSG5tlPU4hkOQvKCgIX19fbty4gY+Pj7PDSXBNm8KSJVCxImzdqk9VRUREJPlKqvO2ypUrU7FiRb766isAIiMjyZ07N7169WLAgAExjs+RIweDBg2iR48e9n1NmzYlXbp0zJkzBzArbZctW8aePXviFVNSfa1EJGGFh8MPP5iVtXv3mn1p0kCrVtC3L5Qu7dz4xIkiI01N2MDAe4nXx9m+devxx/T0NAnXRyVj/fyUnBC5T1znbfr4PoWZMAHWr4cdO8x2797OjkhEREQk5QgNDWXnzp0MHDjQvs/FxYXatWuzdevWWB8TEhKCx3+KSaZLl47ff/892r7Dhw+TI0cOPDw8qFKlCqNGjSJPnjwJ/yREJNm5dQumTYMxY+DkSbMvfXrT26R3b9CvihTAsuDOncdPtkZt37jxeGUIHsTb21zKmzHjo5Ox3t5PPp6IPJCStilMjhzmU9c33oDBg6FxY8iXz9lRiYiIiKQMV65cISIigqxZs0bbnzVr1gfWnw0ICGDMmDFUr16dggULsn79epYsWUJERIT9mMqVKzNz5kyKFi3K+fPnGT58ONWqVePvv//GO5Y/ikNCQgiJ6jSEWbEhIinP5cvw1Vfmdu2a2efnB2+/DW++afJqkoSEhd1LosYn8Roa+uQxuLubH4wMGe4lX+O67etrlm6LSJKgd2MK1LkzfP89bNoE3brBqlWqZyQiIiLiLOPHj6dLly4UK1YMm81GwYIF6dixI9OnT7cfU7duXft2mTJlqFy5Mnnz5uWHH36gU6dOMc45atSoGI3LRCTlOH4cvvgCpk83iy8BChY0JRDat4d06ZwbX4pmWWbV6vnzcPHi4yVe41Ni4L9cXB4/2Xr/9n+u7BCR5EtJ2xTIxQW+/RbKloU1a2DuXGjd2tlRiYiIiCR/mTNnxtXVlYsXL0bbf/HiRbJlyxbrY/z8/Fi2bBl3797l6tWr5MiRgwEDBlCgQIEHjpMhQwaKFCnCkSNHYr1/4MCB9OnTx/59UFAQuXPnjsczEpGkZPduc+XkDz/cu9K9QgXo3x+aNFFZ0CcSFgaXLj24sVbU1wsX4O7dJxvr/hIDj5t49fLSqisRAZS0TbGKFoUhQ2DQIHPpzEsvmctoRERERCT+3Nzc8Pf3Z/369TRu3BgwjcjWr19Pz549H/pYDw8PcubMSVhYGIsXL+a111574LHBwcEcPXqUtm3bxnq/u7s77u7u8X4eIpJ0WJbpS/LZZ7Bu3b39AQEmWVuzpnJ4D2RZpvnWw5KwUV+vXDHHx5Wvr6nb+tRTj5d4VYkBEUkg+k2Sgr33HsyfD/v2QZ8+MHu2syMSERERSf769OlD+/btqVChApUqVWLcuHHcunWLjh07AtCuXTty5szJqFGjANi2bRtnz56lXLlynD17lmHDhhEZGUm/fv3s5+zbty8NGzYkb968nDt3jqFDh+Lq6krLli2d8hxFJPGFh8PixSZZu2uX2efqCs2bQ79+5srJVCsiwqyKjUsy9vbtuJ/X1RWyZn10g61s2VSDQkScTknbFCxtWpg6FapUgTlzoE0b82mtiIiIiMRf8+bNuXz5MkOGDOHChQuUK1eO1atX25uTnTp1ChcXF/vxd+/eZfDgwRw7dgwvLy/q1avH7NmzyZAhg/2YM2fO0LJlS65evYqfnx/PPfccf/zxB366VEokxbl9G2bONDVrjx0z+9KlM71J+vRJ4Y2kb916dBL2/HnTgS2qPkRceHtHT7g+KBmbObOpJygikgzYLOtxrg9I/oKCgvD19eXGjRv4+Pg4OxyHeOcdGDcO8uaFv/82JXJEREREkrrUOG+LL71WIknf1avw9dfw5ZfmSn0wOcReveDNN812shQZaZ5QXJKxwcFxP6+LC2TJErdVsenTJ97zExFJYHGdt2mlbSrw4YewdCmcPGnq3I4Z4+yIREREREREUoeTJ83fYFOn3ruSP18+6NsXOnYET0+nhvdglmUyzUeOwLlzD07GXrxoyhnElaenSbg+Khnr56fOayKSqilpmwp4ecHkyVC3LowfDy1aQKVKzo5KREREREQk5frrL1Ovdv78eznN8uVNvdpXX01CvaquXoXDh+/djhy5t33jRtzOYbOZJGtcShR4eyfu8xERSSGSyn8Tksjq1DE1befMMbWSdu40NW9FREREREQkYVgWbNxokrWrV9/bX7u2SdbWrm3ymw537VrMhGzU99evP/yxuXOb28MSsVmyJKEstIhIyqDfqqnI2LFm4rBvH3z+OQwc6OyIREREREREkr+ICFOS7rPPYMcOs8/FBZo1g/feA39/BwRx/XrMxGzU9rVrD39srlxQuLC5FSp0b7tAAdMlTUREHE5J21Qkc2aTuG3bFoYPh6ZNoUgRZ0clIiIiIiKSPN25A999ZxbFHDli9nl4wOuvw7vvmpxnggoMjH217OHDpszBw+TMGTMpG5WYTbKFdUVEUi8lbR1h5054+mknXQcTXevWpkTCmjXQtSv88ov5BFhERERERETi5vp1+Ppr+PJLuHTJ7HvqKejZ09z8/J7g5EFBD64xe+XKwx+bI0fMpGyhQlCwIKRP/wRBiYiIoylpm9g+/dTUIfjkE1PEyMlsNtOUrGRJ+PVXmDYNunRxdlQiIiIiIiJJ3+nT5urFb7+FW7fMvjx5zKra1183TaDj5ObN2JOyhw/D5csPf2y2bDGTslFflZgVEUkxlLRNbN7ephr9wIFQrhy89JKzIyJfPhg5Et55x9RXql/ffCArIiIiIiIiMf39N4weDXPnQni42VemjFmX89prD2jyfPOmScjGVs7g4sWHD5g1a+ylDAoWNH9jiohIiqekbWLr3h127TJLWlu0MFXpCxZ0dlT06mUmHDt2mO3Fi50dkYiIiIiISNJx5w4sWmRW1f7++739tWqZZG1AANhuBcOBo7GXM7hw4eEDZMkSeymDQoXAxydxn5yIiCR5StomNpsNJk40H81u2wZNmsDWrU6/bMXVFaZONV1MlywxnU6bNHFqSCIiIiIiIk73998wZYppMBYYCGBRzOUwnSrto/nTh8kdcgRGHYbXD8P58w8/WebMDy5l4OvrgGcjIiLJlZK2juDubpayVqgA+/aZYkfz5zu9MVnU5Twffww9ephPjDNkcGpIIiIiIiIiDnf7NixcaFbVbtkCaQjjOX6njfdymqT9iaeuHYU/MLf/ypQpZlI2alt/YImISDwpaesoOXOaa2tq1YIffoCnn4b+/Z0dFR98YML6918YMMA0KRMREREREUkN9u0zidrZs4EbgdRhNXNty2mYZhVeYYFw8/8PdHMzPUruXzUblZjNmNF5T0BERFIsJW0d6dln4csvTZ3bgQOhbFmoU8epIXl4mElKzZrwzTfQqhVUr+7UkERERERERBLNrVtmHc2338LFP47RkJ9YzHKqs4m0hIMFhGFKG9SvDy+/DC++qAZgIiLiUEraOtobb8DOnaagbMuWphNYoUJODalGDeja1UxaunSBvXtNMldERERERCSl2LsXpn4TwcFZ23nh9nKm8BOl2B/9oOLFoWFDk6h95hnTDERERMQJlLR1NJsNvvrKVLf/4w9o3Nh89fJyaliffgo//WTKJIwcCR9+6NRwREREREREntitW7D4u1v8PXYdxQ4v5wP+RxYu2++3XF2xVatmkrQNGzp9QY2IiEgUJW2dIaoxmb8/7N8PHTua63Oc2JgsQwaTS27aFD75BF57DUqXdlo4IiIiIiIi8bZ/7Vn+GvkTT23+idci1tOOEPt94Z4+uDaoi63Ry9jq1lVNWhERSZKUtHWWHDlM4rZmTdMJ7JNPTJ1bJ3rlFWjSBJYuhc6dTddUXQ0kIiIiIiJJnmVxe8seDn66HM/1yyl5excl77s78Kn8uDV9Gc/mDUlTrZppLCYiIpKEuTg7gFStalWYMMFsDxoEq1Y5Nx7MalsfH9i+3WyLiIiIiIgkSXfvwqpVXG72Jte88+D53NP4/zSM4rd3EYmNfzNV4Winj4n8628yXDmK57fj4IUXlLAVEZFkQSttnS2qMdmUKdCqldMbk+XIAaNHm7AGDYJGjSBfPqeFIyIiIiIics/ly7BiBWFLf4I1a0gbcgu//7/rFp5sSf8SEfUaUmFofYqUzOrUUEVERJ6EkrZJwYQJpjHZ1q2mMdnWreDt7bRwOneG77+HTZuge3dYudKp5XZFRERERCS1siw4eNB0TV6+HGvrVmyWRdr/v/ssOVjp0pDr1V7mmYG1qP1SOv3tIiIiKYKStklBbI3JFi50WqbUxQW+/RbKloXVq2HePLMIWEREREREJNGFhcHvv9sTtRw9ar/LBuyiPMt5mX15G/Jsr6dp195G5szOC1dERCQxqKZtUpE9u0ncpk1rvo4a5dRwihaFDz4w22+/DVeuODUcERERERFJyQIDYf58s1okSxZ4/nkYOxaOHiUEN1ZRh+58TSG3U4xpvYvnfx3GouP+9HlXCVsREUmZtNI2KalSBSZOhK5dYfBgKFcO6tVzWjjvvQcLFsC+fdCnD3z3ndNCERERERGRlObYsXuraTdtgvBw+13XXTPzY0R9lvMy63iRXMW8eeMN+KgtZMrkxJhFREQcREnbpKZLF9OY7Jtv7jUmK1zYKaG4ucHUqfDMMzB7NrRuDQEBTglFRERERESSu4gI2L7dJGl/+smUhrvPuQzFmRf8MkvCG/JHxDOkdXelWTNY2RWee059NkREJHVR0jYp+vJLs7x1yxbTmOyPP5zWmKxSJVMeYdw46NbN9EtLn94poYiIiIiISHJz6xasW2cStStWwKVL9rssV1fOFqjGvOCX+eZ8Q44GFgKgRAkY0xXatoWnnnJW4CIiIs6lpG1S5OYGixZBhQpw4AC0b2++d3FOCeIPP4SlS+HECRgyBL74wilhiIiIiIhIcnD2LPzvfyZRu349hITY77J8fblaqS6LQxoyfHtdzh/OCICHB7R7zVSKq1pVq2pFRESUtE2qohqT1ahhMqYff2zq3DqBlxdMngx165oVty1aQMWKTglFRERERESSGsuCPXvulT3YuTP6/fnzExLwMivTvMyHG6uxe11a+12lSplEbZs2kDGjY8MWERFJypS0TcqeecY0JuvSxSxxLV8e6td3Sih16piatt9/D507w59/Qtq0j36ciIiIiIikQHfvwoYNJkn7009w5sy9+2w2eOYZrAYN2ZPnZcavK8GCmTbu3jV3p0sHzZubZO0zz2hVrYiISGyUtE3qOnc2n1RPnnyvMVmRIk4JZexYWL0a/voLPv8cBg50ShgiIiIiIuIM167dW027Zo2pVxvF0xNeeglefpnAZ+vz3eosfPtt9F5jpUvDG2+YxSAZMjg8ehERkWRFSdvkYPx405hs8+Z7jcl8fBwehp+fKY/Qti0MHw5NmzotfywiIiIiIo4SFmaaJQ8fDjdv3tufIwc0bAgvv4xV63k27/Tg229h4ZvYV9V6epryal27mibHWlUrIiISN0raJgdRjcn8/eHgQdOYbPFipzQma90a5swxH6x37Qq//OK0/mgiIiIiIpLY1q2Dt96Cf/4x35coAc2amWTt009z7bqN776Db/uaP1WilClzb1Wtr69zQhcREUnOlG5LLrJlgyVLTAJ32TIYOdIpYdhsplKDpyf8+itMn+6UMEREREREJDGdOAGvvGJKHvzzj7nsbupU2LcPa+gwNt3yp01bGzlywDvvmIStpyd06gTbtpm+ZG++qYStiIhIfClpm5xUrgyTJpntoUPhf/9zShj58sFHH5ntvn3h/HmnhCEiIiIiIgntzh0YNgyKF4elS8HV1ay0/fdfrjXpxNjxLpQoATVqmCbFISFQrpz5M+X8eZPXVRkEERGRJ5ckkrYTJ04kX758eHh4ULlyZbZv3/7AY2fOnInNZot28/DwcGC0Tvb66+Yja8sy1xodOuSUMN56CypWhBs3oFcvp4QgIiIiIiIJxbJMkrZ4cVO79u5dqFnTLJkdP56ztzJQpgz06WMW3qZPD126mD7Ju3ZBt25OabshIiKSYjk9abtgwQL69OnD0KFD2bVrF2XLliUgIIBLly498DE+Pj6cP3/efjt58qQDI04Cxo6F556DoCDTmCwoyOEhuLrClCmQJo0pr7t0qcNDEBERERGRhPDPPxAQYMohnDwJuXLBggWmgUWpUty9a+46exYKFIBvvjGrar/9FipU0KpaERGRxOD0pO2YMWPo0qULHTt2pESJEkyePBlPT0+mP6RYqs1mI1u2bPZb1qxZHRhxEhDVmCxnTjPBatsWIiMdHkbZstCvn9nu0cOsuhURERERkWQiKMjUOytd2jQcc3ODQYPM3xivvQY2G5Zl5vrbt0PGjOawrl3B29vZwYuIiKRsTk3ahoaGsnPnTmrXrm3f5+LiQu3atdm6desDHxccHEzevHnJnTs3jRo1Yv/+/Y4IN2nJmtU0JnN3h+XL4cMPnRLGBx9AkSLmk/YBA5wSgoiIiIiIPI7ISPjuOyhaFL74AsLDoWFDOHDANK9In95+6Ndfm+bDLi5m8W2BAk6MW0REJBVxatL2ypUrRERExFgpmzVrVi5cuBDrY4oWLcr06dP58ccfmTNnDpGRkVStWpUzZ87EenxISAhBQUHRbilGpUr3GpMNG2aStw7m4WEuiwKYPBl++83hIYiIiIiISFzt2mVKrbVvDxcuQOHCsHKl+VuiYMFoh/76K/TubbY//RRefNHx4YqIiKRWTi+P8LiqVKlCu3btKFeuHDVq1GDJkiX4+fnxzTffxHr8qFGj8PX1td9y587t4IgTWceO5nolgDZtzKVMDlajhmlCAObr3bsOD0FERERERB7myhV44w1ThHbrVrOa9pNPYN8+qFs3xuGnTkGzZmYRbsuW8O67TohZREQkFXNq0jZz5sy4urpy8eLFaPsvXrxItmzZ4nSOtGnTUr58eY4cORLr/QMHDuTGjRv22+nTp5847iRn7FioVg1u3jSNyZxQXPazzyBbNjh0CEaOdPjwIiIiIg41ceJE8uXLh4eHB5UrV2b79u0PPDYsLIwRI0ZQsGBBPDw8KFu2LKtXr36ic4rEWXg4TJxoapp9+y1YFrRqZSbu/fubcmv/cecONGkCly9DuXIwdaqajYmIiDiaU5O2bm5u+Pv7s379evu+yMhI1q9fT5UqVeJ0joiICPbt20f27Nljvd/d3R0fH59otxQnbVpYuNB0eT10yCmNyTJkMHNBuPeBvYiIiEhKtGDBAvr06cPQoUPZtWsXZcuWJSAggEuXLsV6/ODBg/nmm2+YMGECBw4coFu3bjRp0oTdu3fH+5wicfLbb+DvDz17wvXrppPwpk3w/femqXEsLMs0Gtu1CzJnhmXLwNPTsWGLiIhIEiiP0KdPH6ZMmcKsWbM4ePAg3bt359atW3Ts2BGAdu3aMXDgQPvxI0aMYO3atRw7doxdu3bRpk0bTp48SefOnZ31FJKG+xuT/fQTjBjh8BBeecV8Ih8ebsokREQ4PAQRERGRRDdmzBi6dOlCx44dKVGiBJMnT8bT05Pp06fHevzs2bN5//33qVevHgUKFKB79+7Uq1ePL774It7nFHmos2fNatrq1eGvvyBjRrPC4s8/zRV6DzF+PMyZA66u8MMPkDevg2IWERGRaJyetG3evDmff/45Q4YMoVy5cuzZs4fVq1fbm5OdOnWK8+fP24+/fv06Xbp0oXjx4tSrV4+goCC2bNlCiRIlnPUUko6KFSGqtu/w4fDjjw4P4auvwMcHtm27t/JWREREJKUIDQ1l586d1K5d277PxcWF2rVrs3Xr1lgfExISgoeHR7R96dKl4/fff4/3OUViFRJiLnsrWhTmzTM1Dd54A/79F958E9KkeejD16+Hvn3N9hdfQK1aDohZREREYvXw/7UdpGfPnvTs2TPW+zZu3Bjt+7FjxzJ27FgHRJVMtW8PO3fChAmmTML27VCsmMOGz5HD1Lft1g3efx8aNdKn8yIiIpJyXLlyhYiICPsCgyhZs2blnwc0hA0ICGDMmDFUr16dggULsn79epYsWULE/1+WFJ9zhoSEEBISYv8+KCjoSZ6WpAQrV0Lv3nD4sPm+ShXzN4G/f5wefvw4NG9urpZr1w7eeivxQhUREZFHc/pKW0kEX3wBNWqYxmSNGjm8MVmXLuaqq1u3oHt3UxdLREREJLUaP348hQsXplixYri5udGzZ086duyIi0v8p+KjRo3C19fXfsudO3cCRizJytGj0LAh1K9vErZZs8KsWfD773FO2N6+bcqcXb0KFSrA5MlqPCYiIuJsStqmRGnTmgJUuXObS6HatHFoYzIXF5gyBdzcYNUqmDbNYUOLiIiIJKrMmTPj6urKxYsXo+2/ePEi2bJli/Uxfn5+LFu2jFu3bnHy5En++ecfvLy8KFCgQLzPOXDgQG7cuGG/nT59OgGenSQrt27BoEFQogT873+m9MG775r5f7t2ZlIeB5YFnTrB3r2QJYtpk5EuXSLHLiIiIo+kpG1KFTXjcnc3k7hhwxw6fNGi93qh9expeh6IiIiIJHdubm74+/uzfv16+77IyEjWr19PlSpVHvpYDw8PcubMSXh4OIsXL6ZRo0bxPqe7uzs+Pj7RbpJKWBYsWGBKoH38MYSGwosvmoZjn39uGkw8hs8/h/nzTc530SKz7kNEREScT0nblKxCBfj2W7P94YewdKlDh3/vPXOlVkgING0KV644dHgRERGRRNGnTx+mTJnCrFmzOHjwIN27d+fWrVt07NgRgHbt2jFw4ED78du2bWPJkiUcO3aM3377jTp16hAZGUm/fv3ifE4RAPbtg+efhxYt4MwZyJfPLNRYswaKF3/s061ZAwMGmO3x402JMxEREUkakkQjMklE7dqZxmRffmm2t20zl1A5gIsLfPcdVKwIR45Aq1amXIKrq0OGFxEREUkUzZs35/LlywwZMoQLFy5Qrlw5Vq9ebW8kdurUqWj1au/evcvgwYM5duwYXl5e1KtXj9mzZ5MhQ4Y4n1NSucBAGDoUJk40ncI8PEy2tV+/eNcyOHLE5H4jI015hO7dEzZkEREReTI2y0pdbaKCgoLw9fXlxo0bqecysrAwc8nUr79C4cKwfTvc90dCYtu3D555xjQ4eP99GDnSYUOLiIhIMpYq523xpNcqhYqMhBkzYOBAuHzZ7HvlFdN4OF++eJ82ONjMz/fvN183bjRV1URERCTxxXXepvIIqcH9jckOH3Z4Y7LSpWHqVLP98cfw448OG1pEREREJHnavt1kVDt3NgnbYsVg7VpYvPiJEraWBR06mIRttmzmdErYioiIJD1K2qYWWbKYmrYeHrBihbm8yoFatoS33zbb7dqZprYiIiIiIvIfFy/C669D5cqwYwd4e5uVtX/9Za6ee0KjRplEbdq05muOHAkQs4iIiCQ4JW1TE3//e43JPvrINC1woNGj4bnnICjIXNUVHOzQ4UVEREREkq6wMBg3DooUMSURANq3N6sd+vQxWdYntGIFDB5stidOhKpVn/iUIiIikkiUtE1t2raF3r3Ndvv2cOCAw4aOqtKQLZu5HKtzZ3N5loiIiIhIqvbLL1C+PLzzjlnh4O8PW7bAzJlm8pwADh0yjYEtC7p1gy5dEuS0IiIikkiUtE2NRo+GWrXMUtfGjU03WgfJnh0WLoQ0aWDBArOYQEREREQkVTp1Cpo1gxdeMKsaMmUyV8Zt2wZVqiTYMEFBZtofFATPPgvjxyfYqUVERCSRKGmbGkVlTPPkMY3JWrWCiAiHDf/cc6YsF8B778GmTQ4bWkRERETE+e7eNeXKihWDRYvAxQV69jRz8y5dwNU1wYaKjDQ9Jf75B3LmNMO5uSXY6UVERCSRKGmbWvn53WtMtmoVDBni0OF79bqXK37tNTh3zqHDi4iIiIg4nmXB8uVQsiR88AHcuQPVqsGuXTBhAmTMmOBDfvgh/PgjuLublhYJVG1BREREEpmStqnZ00/D1Klm++OPTftYB7HZzJVfpUubBrmvvgqhoQ4bXkRERETEsf79F+rVg0aN4NgxyJED5s6FX3+FsmUTZcgff4Rhw8z2pElQqVKiDCMiIiKJQEnb1K51a9PwAExjsr//dtjQ6dObT/t9fWHrVnj3XYcNLSIiIiLiGDdvQv/+UKoUrF5tuvMOGGA6g7VsaVYzJIIDB6BNG7Pdqxd07Jgow4iIiEgiUdJW4LPP4Pnn4dYt06Hg+nWHDV2oEMyZY7a/+uretoiIiIhIsmZZ8P33ULSomW+HhZmVtvv3w6hR4OWVaEMHBpppfXAw1Khxr5+EiIiIJB9K2sq9xmR588LRow5vTNaggSnpBdC1K+zd67ChRUREREQS3p49UL26Wep6/jwULAg//QQrVkDhwok6dESEuZju8GHTd3jhQrO4V0RERJIXJW3FyJzZNCZLl85cthWVRXWQoUOhTh3Ti+GVVxy62FdEREREJGFcvQpvvgn+/vD77+DpCSNHmhJkDRo4JIShQ2HlStNveOlS039YREREkh8lbeWe8uXvNSYbNQoWLXLY0K6u5uqxfPlMX4Y2bSAy0mHDi4iIiIjEX0QETJ4MRYqYjl+RkdC8OfzzD7z/vsmgOsCiRSZHDDBliuk7LCIiIsmTkrYSXatW0KeP2e7QwaGNyZ56yjQm8/AwqwM+/NBhQ4uIiIiIxM/mzVCxInTvDteumYZjGzbA/PmQO7fDwvj7bzN9BzOdj2pCJiIiIsmTkrYS06efwgsvOKUxWfnyZpECwPDhJnkrIiIiIpLknD8PbdvCc8/B7t3g6wvjx5vtmjUdGsq1a9CokZm+v/CCmc6LiIhI8qakrcQU1ZgsXz7TmKxlS4c2JmvfHrp1Mw13W7c25RJERERERJKE0FAYPdqUQpgzB2w26NQJ/v0X3nrLzKUdKCLCTNePHTPT9wULHB6CiIiIJAIlbSV2mTLBsmWmMdmaNTB4sEOHHzcOKleGwEDTmOz2bYcOLyIiIiISU2goBARAv34QHAyVKsG2baYvRJYsTgnp/fdh7VozbV+2zEzjRUREJPlT0lYerGxZmD7dbH/yCfzwg8OGdnc3jRSyZIG9e++tvBURERERcQrLgp49YeNG8PY28+StW009WyeZPx8++8xsz5hhpu8iIiKSMihpKw/XogX07Wu2O3aEv/5y2NC5cpnLu1xdYfZs04hXRERERMQpvvoKpkwx5RDmzzdzYxfn/Tm1Zw+8/rrZ7t8fmjd3WigiIiKSCJS0lUcbNQpq1zY1Cho3Np0OHKRmTbPIF6B3b7OYQURERETEodatM5NRMPVs69VzajhXrkCTJnDnjqnWMHKkU8MRERGRRKCkrTxamjRmNUH+/HD8uMMbk737LjRrBmFh8OqrcOGCw4YWERERkdTu33/htdcgMhI6dIA+fZwaTni4WVV74gQULAjz5pkr00RERCRlUdJW4iZTJli61HQ4WLvWdDxwEJsNpk2D4sXh3DkzSQ0Lc9jwIiIiIpJaBQbCyy+br1WqwOTJZnLqRP36wS+/QPr0pvFYxoxODUdEREQSiZK2Enf3Nyb77DNTcNZBvL1hyRLzddMmGDDAYUOLiIiISGoUEWGuMDt0yDRbWLLEdMt1otmzYexYs/3dd1CqlFPDERERkUSkpK08nhYt4L33zPbrr8PevQ4bulgxmDnTbI8Z49CcsYiIiIikNv36werV5kqz5cshWzanhrNzJ3TtarYHD4ZXXnFqOCIiIpLIlLSVxzdqFLz4omlM1qQJXL3qsKFfecV0xwXo1An273fY0CIiIiKSWkyfblYJAMyaBeXLOzWcS5fMtPvuXWjQAIYPd2o4IiIi4gBK2srjc3U1jckKFLjXmCw83GHDf/QRPP883Lplkrg3bjhsaBERERFJ6TZvhm7dzPbQoaYjrhOFhZkQTp+GIkVgzhxw0V9xIiIiKZ7+u5f4eeop0/nA0xPWrXNoY7I0aUzOOHdu08y3QwfTzFdERERE5ImcOmVWBYSFQdOmMGSIsyOiTx/T08Hb20y/fX2dHZGIiIg4gpK2En+lS8OMGWZ79GiTSXUQPz9YvBjc3Mzk9dNPHTa0iIiIiKREwcHw8sumFkG5cqYsgpOXtE6fDl99ZbbnzIHixZ0ajoiIiDiQkrbyZF577V6R2ddfhz17HDZ0xYr3JrGDB5sFvyIiIiIijy0yEtq3N012s2SBH3+E9OmdGtK2bdC9u9kePtzkk0VERCT1UNJWntzIkRAQAHfuOLwxWefOJlccGWlK65486bChRURERCSlGD4cliwxl3EtXQp58jg1nAsXTJWG0FBo3NgsUBAREZHURUlbeXKurjB3rmlMduIENG/usMZkNhtMnAj+/iZX3LSp6aorIiIiIhInCxfCiBFm+5tvoGpVp4YTGmrmtOfOmXII333n9CoNIiIi4gT6718SRlRjsvTpYf16GDDAYUN7eJj6tk89BTt3Qs+eDhtaRERERJKzXbtMWQQwHb86dHBqOABvvQVbtpiGY8uWmQZkIiIikvooaSsJ5/7GZF98YVbfOkjevDBvnll5O20aTJ3qsKFFREREJDm6cAEaNTIlvurUgc8+c3ZEfPONudlsZipdpIizIxIRERFnUdJWElazZvdW2Xbu7NDGZC+9BB99ZLZ79IAdOxw2tIiIiIgkJ3fvml4MZ85AsWIwf74p+eVEmzdDr15me+RIqFfPqeGIiIiIkylpKwnvo4/MaoU7d0znhMuXHTb0gAFmwURULTAHDi0iIiIiyYFlwRtvwB9/QMaMsHy5qUXgRGfPwquvQliY+erASmMiIiKSRClpKwkvqjFZwYJw8uS9y84cwMUFZs2CwoXh9Glo2RIiIhwytIiIiIgkB198Ybp7ubrCDz+YiaMT3b0Lr7xiqjVEVRuz2ZwakoiIiCQBStpK4siYEX76CTJkgK1boU0bh2VPfX1hyRLw9DQ90QYPdsiwIiIiIpLUrVwJ/fqZ7bFjoXZtp4ZjWaas1/btZvq8dCl4eTk1JBEREUkilLSVxFO8OPz4I7i5mSzqu+86bOhSpUxDMoBPPjETYBERERFJxQ4cgBYtTKa0Sxfo2dPZEfH11zB9urlabP58c6GaiIiICChpK4mtenVTrwBg/HgYN85hQ7doAb17m+327eGffxw2tIiIiIgkJVevwssvw82bZn761VdOr0GwadO9ueqnn5qmuiIiIiJRlLSVxNeihZmJAvTpA4sXO2zozz4z8/KbN02tsOBghw0tIiIiKdjEiRPJly8fHh4eVK5cme3btz/0+HHjxlG0aFHSpUtH7ty5eeedd7h79679/mHDhmGz2aLdihUrlthPI3UIC4PXXoOjRyFfPjMXdXNzakinT5uGY+HhpgeDAy9IExERkWRCSVtxjPfegzffNJejtW4Nmzc7ZNi0aWHBAsiRAw4ehNdfNyGIiIiIxNeCBQvo06cPQ4cOZdeuXZQtW5aAgAAuXboU6/Fz585lwIABDB06lIMHDzJt2jQWLFjA+++/H+24kiVLcv78efvt999/d8TTSfneeQd++cUUi12+HDJndmo4d+5AkyZw+TKUKwdTpzp90a+IiIgkQUraimPYbPDll9CwIYSEmMvT/v3XIUNnywYLF0KaNObrmDEOGVZERERSqDFjxtClSxc6duxIiRIlmDx5Mp6enkyfPj3W47ds2cKzzz5Lq1atyJcvHy+99BItW7aMsTo3TZo0ZMuWzX7L7OTkYooweTJMnGjmonPmQOnSTg3HsqBrV9i5EzJlMn0XPD2dGpKIiIgkUUraiuO4usK8eVCxIly7BnXrwgNWpCS0qlVNg2CA/v1h40aHDCsiIiIpTGhoKDt37qR27dr2fS4uLtSuXZutW7fG+piqVauyc+dOe5L22LFjrFy5knr16kU77vDhw+TIkYMCBQrQunVrTp06lXhPJDXYuBF69TLbI0dCo0ZODQdMi4c5c8y0+IcfTLUGERERkdgoaSuOlT49/O9/UKAAHDsGDRrArVsOGbpHD2jTBiIioHlzOHPGIcOKiIhICnLlyhUiIiLImjVrtP1Zs2blwoULsT6mVatWjBgxgueee460adNSsGBBatasGa08QuXKlZk5cyarV69m0qRJHD9+nGrVqnHz5s1YzxkSEkJQUFC0m9zn2DFo2tQUjW3VCgYMcHZE/PIL9O1rtr/4Ap5/3rnxiIiISNKmpK04XpYssGoVPPUU7NhhJtIREYk+rM0G33wDZcqYBb7NmplKDSIiIiKJaePGjXz88cd8/fXX7Nq1iyVLlrBixQo+/PBD+zF169alWbNmlClThoCAAFauXElgYCA//PBDrOccNWoUvr6+9lvu3Lkd9XSSvqAgU4rr2jVzhVcSKBp74oTphRYRAe3awVtvOTUcERERSQaUtBXnKFLENIJwdzdf33rLIR3CPD1hyRLIkAH++AP69En0IUVERCQFyZw5M66urly8eDHa/osXL5ItW7ZYH/PBBx/Qtm1bOnfuTOnSpWnSpAkff/wxo0aNIjIyMtbHZMiQgSJFinDkyJFY7x84cCA3btyw306fPv1kTyyliIgwTW/374fs2U3R2HTpnBrS7dvQuDFcvQr+/qbMrhqPiYiIyKMoaSvO8+yz8P33Ztb69dfw+ecOGbZgQVNLDMyw333nkGFFREQkBXBzc8Pf35/169fb90VGRrJ+/XqqVKkS62Nu376Ni0v0aberqysA1gM+tA4ODubo0aNkz5491vvd3d3x8fGJdhNg0CBTisvdHZYtg5w5nRqOZUGnTrB3L/j5JYkcsoiIiCQTSSJpO3HiRPLly4eHhweVK1eO0Un3QebPn4/NZqNx48aJG6AknqZNTVEvgH79YP58hwxbvz4MHWq233gDdu92yLAiIiKSAvTp04cpU6Ywa9YsDh48SPfu3bl16xYdO3YEoF27dgwcONB+fMOGDZk0aRLz58/n+PHjrFu3jg8++ICGDRvak7d9+/bl119/5cSJE2zZsoUmTZrg6upKy5YtnfIck6U5c+DTT8329OlQqZJz48GsSZg/H9KkgUWLQFUsREREJK7SODuABQsW0KdPHyZPnkzlypUZN24cAQEBHDp0iCxZsjzwcSdOnKBv375Uq1bNgdFKonjnHTh50rTTbd8ecuSA6tUTfdghQ2D7dlNet2lT+PNPU2ZXRERE5GGaN2/O5cuXGTJkCBcuXKBcuXKsXr3a3pzs1KlT0VbWDh48GJvNxuDBgzl79ix+fn40bNiQkSNH2o85c+YMLVu25OrVq/j5+fHcc8/xxx9/4Ofn5/Dnlyxt2wadO5vtgQNNzwQnW7v2Xv+z8eMdMr0VERGRFMRmPeiaLAepXLkyFStW5KuvvgLM5WW5c+emV69eDHhAl9eIiAiqV6/O66+/zm+//UZgYCDLli2L03hBQUH4+vpy48YNXUaWlEREmO4MUQVnt2yB4sUTfdhr16BCBTh+HOrUgRUrwCVJrD8XERERzdviLlW/VmfPmoZj58+bBmRLlzp9Qnf0qAnp+nVTHmHKFNWxFRERESOu8zanzmZCQ0PZuXMntWvXtu9zcXGhdu3abN269YGPGzFiBFmyZKFTp06OCFMcwdXVXNJWpQoEBkLdumbincieesrkiT08YPVqGD480YcUERERkYQS1eXr/HkoVcrMJ52csA0ONiFdvw6VK8PEiUrYioiIyONz6ozmypUrRERE2C8li5I1a1YuXLgQ62N+//13pk2bxpQpU+I0RkhICEFBQdFukkSlSwfLl0OhQqZcQoMGZtabyMqVg2++MdsjRpjeFSIiIiKSxEV1+frzT8iUycwjvb2dHlKHDvD335AtGyxebHqiiYiIiDyuZHUh+M2bN2nbti1Tpkwhc+bMcXrMqFGj8PX1td9yq/p/0pY5sykymzkz7NoFzZtDeHiiD9uuHbz5ptlu0waOHEn0IUVERMRB8uXLx4gRIzh16pSzQ5GE9PHH97p8LV4M+fM7OyJGjTKhpE1rvubM6eyIREREJLlyatI2c+bMuLq6cvHixWj7L168SLZs2WIcf/ToUU6cOEHDhg1JkyYNadKk4bvvvmP58uWkSZOGo0ePxnjMwIEDuXHjhv12+vTpRHs+kkAKFTLLXdOlg5UrTTbVAaWXx4411Rlu3DCNyW7fTvQhRURExAF69+7NkiVLKFCgAC+++CLz588nJCTE2WHJk1i2DAYPNtsTJ0KNGk4NB8y09f6QqlZ1bjwiIiKSvDk1aevm5oa/vz/r16+374uMjGT9+vVUqVIlxvHFihVj37597Nmzx357+eWXqVWrFnv27Il1Fa27uzs+Pj7RbpIMVK4M8+aZAmBTpphlC4nMzQ0WLoQsWeCvv6BrV4fkikVERCSR9e7dmz179rB9+3aKFy9Or169yJ49Oz179mTXrl3ODk8e119/mUujAHr2NJM2J/v3X2jVyswdu3WDLl2cHZGIiIgkd04vj9CnTx+mTJnCrFmzOHjwIN27d+fWrVt07NgRgHbt2jFw4EAAPDw8KFWqVLRbhgwZ8Pb2plSpUri5uTnzqUhCa9QIvvzSbA8aZBpLJLKcOeGHH0xftO+/N6skREREJGV4+umn+fLLLzl37hxDhw5l6tSpVKxYkXLlyjF9+nQsfVqb9F2+DC+/DLduwQsvmEulnCwoyDQeu3EDnn0Wxo93dkQiIiKSEqRxdgDNmzfn8uXLDBkyhAsXLlCuXDlWr15tb0526tQpXJzcAVacqGdP05Ts88/h9dchRw54/vlEHbJGDfjsM3j3XXjnHShf3kzARUREJHkLCwtj6dKlzJgxg3Xr1vHMM8/QqVMnzpw5w/vvv8/PP//M3LlznR2mPEhoqKlhdfKkKaf1ww+mnq0TRUaa3ggHD5pp6qJF5uotERERkSdls1LZkoKgoCB8fX25ceOGSiUkF5GR0LKlmZj7+MDmzVCqVKIOaVnQooUZMnt20xMtljLLIiIikogSat62a9cuZsyYwbx583BxcaFdu3Z07tyZYsWK2Y/5+++/qVixInfu3EmI0B0uxc9xLcuUQZg61cwHt22D+/79nGX4cBg2zCRqf/sNKlVydkQiIiKS1MV13qYlrJL0ubjArFlQrZq5/qxuXTh7NlGHtNlg2jQoUQLOn4fXXoOwsEQdUkRERBJJxYoVOXz4MJMmTeLs2bN8/vnn0RK2APnz56dFixZOilAeacIEk7B1cYH585NEwnb5cpOwBZg8WQlbERERSVhK2kry4OFhugQXLQpnzkD9+iaBm4i8vGDJEvD2Nisn+vVL1OFEREQkkRw7dozVq1fTrFkz0qZNG+sx6dOnZ8aMGQ6OTOJk7VpTswpMDau6dZ0bD6YcQlQvtF694P/bcYiIiIgkGCVtJfl46ilYtQqyZoW9e+HVVxN9+WvRomaRL8C4cTBvXqIOJyIiIong0qVLbNu2Lcb+bdu28eeffzohIomzf/+F5s1NuawOHaBPH2dHRGCg6Zd786bphfDFF86OSERERFIiJW0lecmfH/73P/D0hHXr4I03TI2zRNSkCQwYYLY7d4Z9+xJ1OBEREUlgPXr04PTp0zH2nz17lh49ejghIomTwEBo2NB8rVLF1CCw2ZwdFT17wuHDkDu36X/wgMXbIiIiIk9ESVtJfipUMDNkFxeYMQNGjEj0IT/6CGrXhtu34ZVX4MaNRB9SREREEsiBAwd4+umnY+wvX748Bw4ccEJE8kjh4aYr7L//muzo0qXg7u7sqLh5ExYuNNvz50OWLM6NR0RERFIuJW0leapfH77+2mwPGwYzZybqcK6upjRCnjxw5Ai0a2eu0hMREZGkz93dnYsXL8bYf/78edKkSeOEiOSR+vWDNWvM1VU//mjKYyUBa9ZAaCgUKmQW/4qIiIgkFiVtJfl64w0YONBsd+limlQkosyZYfFicHMz3YJHjUrU4URERCSBvPTSSwwcOJAb910qExgYyPvvv8+LL77oxMgkVtOnw9ixZnvWLChf3rnx3OfHH83XRo2SRKUGERERScGUtJXkbeRIaNXKXEL36qumQVkiqlABJk402x98kOh5YhEREUkAn3/+OadPnyZv3rzUqlWLWrVqkT9/fi5cuMAX6iKVtPz+O3TrZraHDTPzuyQiLAxWrDDbjRo5NxYRERFJ+ZS0leTNZjOrMWrWNEXG6tWDWBqNJKTOnc3NsqBlSzhxIlGHExERkSeUM2dO/vrrLz777DNKlCiBv78/48ePZ9++feTOndvZ4UmUkydN84CwMJOs/eADZ0cUzW+/wfXr5uqrqlWdHY2IiIikdCriJcmfu7tpTvHss3DggEnc/vYbZMiQaENOmAB79sCff0LTpmZRSLp0iTaciIiIPKH06dPTtWtXZ4chDxIcbJavXr4M5cqZfgUuSWt9SVRphAYNTL8DERERkcSkpK2kDBkywKpV8Mwz8PffJpO6apUpQJsIPDxg0SLw94ddu6BHD5g2TbXNREREkrIDBw5w6tQpQkNDo+1/+eWXnRSRAKa7a/v2psxVliwmO5o+vbOjisayotezFREREUlsStpKypEnD6xcCdWqwS+/mBoGs2YlWiY1b16YPx8CAmDGDJMv1gIeERGRpOfYsWM0adKEffv2YbPZsCwLANv/zxEiIiKcGZ4MGwZLlpgP25cuNXO6JOavv0z1Bg8PUO86ERERcYR4XXN0+vRpzpw5Y/9++/bt9O7dm2+//TbBAhOJl3LlzBJYV1eYPTvRa6HVrm16oQH06gXbtyfqcCIiIhIPb7/9Nvnz5+fSpUt4enqyf/9+Nm3aRIUKFdi4caOzw0vdfvgBPvzQbH/zTZItFhu1yvbFF5PcImARERFJoeKVtG3VqhUbNmwA4MKFC7z44ots376dQYMGMWLEiAQNUOSxBQRA1AcII0fClCmJOlz//tC4MYSGmqoMly4l6nAiIiLymLZu3cqIESPInDkzLi4uuLi48NxzzzFq1CjeeustZ4eXeu3aBR06mO133723nQSpNIKIiIg4WryStn///TeVKlUC4IcffqBUqVJs2bKF77//npkzZyZkfCLx8/rrMGSI2e7e3dS3TSQ2m6nCUKQInDkDLVpAeHiiDSciIiKPKSIiAm9vbwAyZ87MuXPnAMibNy+HDh1yZmip14ULJgN65w7UrQuffursiB7o9GmTX7bZTBMyEREREUeIV9I2LCwMd3d3AH7++Wd784ZixYpx/vz5hItO5EkMG2aaWkREQLNmZradSHx8TAm29OlhwwYYNCjRhhIREZHHVKpUKfbu3QtA5cqV+eyzz9i8eTMjRoygQIECTo4uFbp7F5o0MZ92FysG8+aZ0lZJ1PLl5mvVqpA1q3NjERERkdQjXknbkiVLMnnyZH777TfWrVtHnTp1ADh37hyZMmVK0ABF4s1mM2USateGW7egfn04cSLRhitRAqZPN9uffQaLFyfaUCIiIvIYBg8eTGRkJAAjRozg+PHjVKtWjZUrV/Lll186ObpUxrJM59Y//oCMGU1G1NfX2VE9lEojiIiIiDPYrKj2uY9h48aNNGnShKCgINq3b8/0/89Uvf/++/zzzz8sWbIkwQNNKEFBQfj6+nLjxg18fHycHY44QlAQVKtm2v4WLw6bN5s/EhLJu+/CmDHg5QUbN4K/f6INJSIikqIl5rzt2rVrZMyYEZvNlqDndZZkM8cdPRr69TMra1evNh+uJ2E3boCfH4SFwaFDphyWiIiIyJOI67wtXklbMLXBgoKCyHhf8uvEiRN4enqSJUuW+JzSIZLNhFYS1pkz8MwzcPYsVK8Oa9fC/5f4SGjh4aYX2i+/wFNPmXIJZcokylAiIiIpWkLM28LCwkiXLh179uyhVKlSCRxh0pEs5rgrVkDDhma17YQJ0LOnsyN6pPnzoWVLU8Xh4EFnRyMiIiIpQVznbfEqj3Dnzh1CQkLsCduTJ08ybtw4Dh06lKQTtpKK5cplmpH5+MCmTaY78f9fJpnQ0qSBZctMjvjaNbOARJN8ERER50ibNi158uQhIiLC2aGkbgcOmOxnVHmEHj2cHVGcqDSCiIiIOEu8kraNGjXiu+++AyAwMJDKlSvzxRdf0LhxYyZNmpSgAYokmNKlYckSk1WdPx/efz/RhvL2Njnip5+Gy5fhhRfgyJFEG05EREQeYtCgQbz//vtcu3bN2aGkTlevmhW2N2+aK54mTDC9B5K40FBYudJsK2krIiIijhavpO2uXbuoVq0aAIsWLSJr1qycPHmS7777Ts0cJGl74QWYNs1sf/opJOKHDBkymCoMpUvD+fNm6JMnE204EREReYCvvvqKTZs2kSNHDooWLcrTTz8d7SaJKCwMmjWDY8cgXz7TqdXNzdlRxcmvv5rWCFmzQuXKzo5GREREUps08XnQ7du38fb2BmDt2rW88soruLi48Mwzz3BSWSlJ6tq1g1On4IMPTC21nDnh5ZcTZahMmWDdOqhZE/75B55/3lRnyJkzUYYTERGRWDRu3NjZIaRevXubAv9eXvDTT5A5s7MjirNly8zXhg3BJV5LXURERETiL15J20KFCrFs2TKaNGnCmjVreOeddwC4dOlS0m18IHK/QYPMstepU6FFC7OUomLFRBkqa1b4+WdzNeCxY2bF7a+/mv0iIiKS+IYOHersEFKnyZPh669NKYTvv4dk1AjOsmD5crOt0ggiIiLiDPH6zHjIkCH07duXfPnyUalSJapUqQKYVbfly5dP0ABFEoXNZv6IqFMH7tyBBg1MRjWR5MwJv/wCefLAoUOmOdmVK4k2nIiIiIhzbdgAvXqZ7ZEjE+2qpsSyaxecOQOenuYDdxERERFHi1fS9tVXX+XUqVP8+eefrFmzxr7/hRdeYOzYsQkWnEiiSpsWfvgBypeHS5egbl3TKCOR5M0L69dD9uzw99/w0ksQGJhow4mIiMj/c3FxwdXV9YE3SWBHj8Krr0J4OLRqBQMGODuix/bjj+ZrQACkS+fcWERERCR1ild5BIBs2bKRLVs2zpw5A0CuXLmoVKlSggUm4hDe3rBiBTzzDPz7r7n+bd26RJudFypkVtzWqAG7d5s88dq1JgwRERFJHEuXLo32fVhYGLt372bWrFkMHz7cSVGlUEFBZlXttWum9NTUqeYKp2QmKmmr0ggiIiLiLDbLsqzHfVBkZCQfffQRX3zxBcHBwQB4e3vz7rvvMmjQIFyScKX+oKAgfH19uXHjhurvyj0HDsCzz5qlr6++CgsWJGrHiX37THOya9egWjVYtQrSp0+04URERJKlxJ63zZ07lwULFvBjVIYuGUsSc9yICGjcGP73P8iRA3bsMF+TmePHoUABMxW8eDFZ9U4TERGRZCCu87Z4ZaUGDRrEV199xSeffMLu3bvZvXs3H3/8MRMmTOCDDz6Id9AiTlOihGkR7OYGixbBe+8l6nClS5sVtr6+8Ntv5u+bu3cTdUgRERH5j2eeeYb169c7O4yUY9Agk7D18DDzqmSYsIV7DciqVVPCVkRERJwnXknbWbNmMXXqVLp3706ZMmUoU6YMb775JlOmTGHmzJkJHKKIg9SoAVE/v2PGwJdfJupw/v73Vtj+/LNZ4BsamqhDioiIyP+7c+cOX375JTlz5nR2KCnD7Nnw6adme9o0UxohmVJpBBEREUkK4lXT9tq1axQrVizG/mLFinHt2rUnDkrEaVq2hFOnTMOM3r0hd25o0iTRhqtSxZTUrVvXfG3Z0lRmSBPvatMiIiLyXxkzZsR2X11Vy7K4efMmnp6ezJkzx4mRpRDbtkGXLmb7/fdN87Fk6to12LTJbCtpKyIiIs4Ur9RQ2bJl+eqrr/jyPysRv/rqK8qUKZMggYk4Tb9+cPIkTJpk/uj45ReTXU0kNWqYFR0NG8KSJdCunVmsombWIiIiCWPs2LHRkrYuLi74+flRuXJlMmbM6MTIUoAzZ0ydp5AQk+X88ENnR/REVq40pXlLlTJ1bUVEREScJV5J288++4z69evz888/U+X/k1lbt27l9OnTrFy5MkEDFHE4m82URjh92tRla9gQtm6FwoUTbcgXXzSldJs0gXnzwN3dXFmYhHv6iYiIJBsdOnRI8HNOnDiR0aNHc+HCBcqWLcuECROoVKnSA48fN24ckyZN4tSpU2TOnJlXX32VUaNG4eHhEe9zOt3t2yZhe+GCyXLOnp3sJy8qjSAiIiJJRbxmVTVq1ODff/+lSZMmBAYGEhgYyCuvvML+/fuZPXt2Qsco4nhp0sD8+VChAly9auoXXL6cqEM2aGCGdHU1pXV79gTLStQhRUREUoUZM2awcOHCGPsXLlzIrFmzHvt8CxYsoE+fPgwdOpRdu3ZRtmxZAgICuHTpUqzHz/2/9u4+vub6/+P482yzC2Nzuc001xdRri9GJLFyLX1LFLlI+qaUor6hUF9FJPmGElF9K1F9kSTRRCnXclVzrVxuKDaGYTu/P96/bY5tjO2czzk7j/vt9rmds8/5nM95fc6Kt+d5n9d79mwNHTpUo0aNUlxcnGbOnKm5c+dq+PDhN3xOy9nt0iOPSBs3mtW6Fi6Uiha1uqo8SUmRliwx9wltAQCA1Wx2e/7FQlu2bFH9+vWVmpqaX6fMd0lJSQoNDVViYqJCQkKsLgfuLiHBtEbYv1+KjjatEgoXdupLzp4t9exp/i307LPSm2+ayb8AAHib/Bq3VatWTe+9957uvPNOh/0rV67UY489pp07d17X+aKjo9WoUSNNmTJFkpSWlqaoqCg99dRTGjp0aJbjBw4cqLi4OMXGxmbsGzJkiNauXatVq1bd0Dmv5PIx7quvSiNGmA+6v//e9HvycN9+K7VvL0VGmi9cefikYQAA4KZyO25jKAJcTXi4GcGXKGEW2ejRwzQ6c6KHHpLef9/cf+st6aWXnPpyAAAUeAcOHFDFihWz7C9fvrwOHDhwXee6cOGCNm7cqJiYmIx9Pj4+iomJ0erVq7N9zm233aaNGzdq3bp1kqR9+/Zp8eLFat++/Q2fMyUlRUlJSQ6by9jt0q5d5v477xSIwFaSFiwwt507E9gCAADrMRwBrqV6ddPgLCDAjOaffdbpfQseeUSaOtXcHzPGTGYBAAA3JiwsTFu3bs2yf8uWLSpZsuR1nevEiRNKTU1VeHi4w/7w8HDFx8dn+5yHHnpI//73v9W8eXMVKlRIlStXVsuWLTPaI9zIOceOHavQ0NCMLSoq6rquI09sNumjj6SlS6X+/V33uk6UlmY6PEi0RgAAAO6B0BbIjebNzeIaNps0ebI0caLTX/KJJ0xrBMl8+3DCBKe/JAAABdKDDz6op59+Wj/88INSU1OVmpqq5cuXa9CgQerevbvTX3/FihUaM2aM3nnnHW3atEnz5s3TN998o9GjR9/wOYcNG6bExMSM7eDBg/lYcS7YbGYl1QJi/XqznlrRotIVXTQAAAAs4Xc9B//jH/+46uOnTp3KSy2Ae+va1TQ4GzJEeu45qVw5s8+JBg+Wzp0zLRKef14KDDQLlAEAgNwbPXq0/vjjD7Vu3Vp+fmb4m5aWpl69emnMmDHXda5SpUrJ19dXCQkJDvsTEhIUERGR7XNGjBihhx9+WI8++qgkqVatWkpOTtZjjz2mF1988YbOGRAQoICAgOuqHTn76itz27at+XIVAACA1a5rpu3lX8HKbitfvrx69erlrFoB6z37rPTUU+b+ww9L/794iDO9+KLZJPPS6f1uAQBA7vj7+2vu3LnauXOnPv30U82bN0979+7VrFmz5O/vf93natCggcOiYmlpaYqNjVXTpk2zfc7Zs2flc0WTVF9fX0mS3W6/oXMif6WHtrRGAAAA7uK6Ztp+8MEHzqoD8Aw2m1kd7NAhaf58s1LF6tWm760TjR4tnT9v2iU89piZcduzp1NfEgCAAqdq1aqqWrVqns8zePBg9e7dWw0bNlTjxo01adIkJScnq2/fvpKkXr16qWzZsho7dqwkqVOnTpo4caLq1aun6Oho7dmzRyNGjFCnTp0ywttrnRPOs2eP9Pvvkq+v9P9rwwEAAFjuukJbADIj+k8+kVq3ltaskdq1M8HtFYuH5CebTXrjDRPcTp0q9e5tvrrn5O4MAAAUCPfdd58aN26sF154wWH/+PHjtX79en3xxRfXdb5u3brp+PHjGjlypOLj41W3bl0tWbIkYyGxAwcOOMysfemll2Sz2fTSSy/p8OHDKl26tDp16qTXXnst1+eE86TPsm3ZUipe3NJSAAAAMtjsdrvd6iJcKSkpSaGhoUpMTFRISIjV5cCTHT8u3XabmZ7RsKG0YoUUHOzUl0xLMzNtZ86U/Pyk//3PTPYFAKAgyq9xW+nSpbV8+XLVqlXLYf+2bdsUExOTpZesJ2KMe+NatJB++kl6++3MLlgAAADOkttx23X1tAVwmdKlpW+/lUqVkjZskLp3ly5dcupL+vhI770nPfSQeamuXaXvvnPqSwIA4PHOnDmTbe/aQoUKKSkpyYKK4C5OnJB+/tnc54NwAADgTghtgbyoUkX6+mvTZHbRIjM9w8mT1319pY8+ku67T7pwQerSxUzyBQAA2atVq5bmzp2bZf+cOXNUs2ZNCyqCu1i0yHyTqW5dqXx5q6sBAADIRE9bIK+aNJFmzzYp6rRpZsQ/dKhTX9LPL/MlFy2SOnaUli413RoAAICjESNG6B//+If27t2rVq1aSZJiY2M1e/ZsffnllxZXByul97O95x5r6wAAALgSM22B/HDvvdJ//mPuDxtmElUn8/eXvvhCuusuKTnZrIe2YYPTXxYAAI/TqVMnLViwQHv27NETTzyhIUOG6PDhw1q+fLmqVKlidXmwyLlz5kNvidAWAAC4H0JbIL889ZQ0eLC536ePS3oWBAZKCxZId9whJSVJd98tbdni9JcFAMDjdOjQQT///LOSk5O1b98+PfDAA3ruuedUp04dq0uDRb7/Xjp7VipXzrRHAAAAcCeEtkB+euMNszrYxYum2ez33zv9JQsXNm11mzaVTp40M29//93pLwsAgMf58ccf1bt3b0VGRurNN99Uq1attGbNGqvLgkXSWyN07izZbNbWAgAAcCVCWyA/+fhI//2v1KKFlJgotWkjTZjg9MXJihaVFi+W6teXjh+XYmKk3bud+pIAAHiE+Ph4vf7666pataq6du2qkJAQpaSkaMGCBXr99dfVqFEjq0uEBVJTpYULzX1aIwAAAHdEaAvkt8BA6bvvpL59zXLEzz8vPfSQaTzrRMWKmb5stWpJR49KrVtLf/zh1JcEAMCtderUSdWrV9fWrVs1adIkHTlyRJMnT7a6LLiBNWvMB92hoabNFAAAgLshtAWcITBQmjlTmjpV8vOT5syRbrtN2r/fqS9bsqTpyHDzzdLBg1KrVtKhQ059SQAA3Na3336rfv366ZVXXlGHDh3k6+trdUlwE+mtEdq3lwoVsrYWAACA7BDaAs5is0lPPCEtXy6FhUlbt0oNG0rLljn1ZcPCpNhYqXJlkxG3bi3Fxzv1JQEAcEurVq3S6dOn1aBBA0VHR2vKlCk6ceKE1WXBDaSHtrRGAAAA7sotQtupU6eqQoUKCgwMVHR0tNatW5fjsfPmzVPDhg1VrFgxBQcHq27duvr4449dWC1wnW6/Xdq4UWrUSPr7b6ltW7NgmRP73EZGmuC2XDlp1y7T45Z/owIAvE2TJk00Y8YMHT16VP/85z81Z84cRUZGKi0tTcuWLdPp06etLhEW2LHDjI8KFZLatbO6GgAAgOxZHtrOnTtXgwcP1qhRo7Rp0ybVqVNHbdq00bFjx7I9vkSJEnrxxRe1evVqbd26VX379lXfvn313Xffubhy4DrcdJP044+ZfW7/9S/pwQed2ue2fHkzyTcyUvrtN+nuu6WTJ532cgAAuK3g4GA98sgjWrVqlbZt26YhQ4bo9ddfV1hYmDp37mx1eXCx9Fm2rVpJISHW1gIAAJATy0PbiRMnqn///urbt69q1qypadOmqXDhwpo1a1a2x7ds2VL33nuvatSoocqVK2vQoEGqXbu2Vq1a5eLKget0ZZ/buXNNn9t9+5z2kpUrmxm3YWHSr7+aSb5JSU57OQAA3F716tU1fvx4HTp0SJ999pnV5cACtEYAAACewNLQ9sKFC9q4caNiYmIy9vn4+CgmJkarV6++5vPtdrtiY2O1c+dOtWjRwpmlAvkjvc/tDz9I4eEu6XN7881mcbISJaR166QOHZw6wRcAAI/g6+urLl26aOHChVaXAhdKSJDWrDH3mWQNAADcmaWh7YkTJ5Samqrw8HCH/eHh4Yq/yspJiYmJKlKkiPz9/dWhQwdNnjxZd911V7bHpqSkKCkpyWEDLNe8ubRhg9S4selZ0LatNH680/rc1qplcuHQUGnVKjOz5Nw5p7wUAACA2/r6azPcathQKlvW6moAAAByZnl7hBtRtGhRbd68WevXr9drr72mwYMHa8WKFdkeO3bsWIWGhmZsUVFRri0WyEl6n9t+/Uyf2xdekLp3d9o02Pr1pSVLpCJFTMuE+++XUlKc8lIAAABuidYIAADAU1ga2pYqVUq+vr5KSEhw2J+QkKCIiIgcn+fj46MqVaqobt26GjJkiO6//36NHTs222OHDRumxMTEjO3gwYP5eg1AngQESDNmSO+8Y/rcfv651LSp0/rcNmkiffONFBQkLV5sMuKLF53yUgAAAG4lOdm0jJIIbQEAgPuzNLT19/dXgwYNFBsbm7EvLS1NsbGxatq0aa7Pk5aWppQcpgwGBAQoJCTEYQPcis0mDRiQ2ed22zbznb2lS53yci1aSAsXmrx4wQKpVy8pNdUpLwUAAOA2li6Vzp+XKlaUbr3V6moAAACuzvL2CIMHD9aMGTP00UcfKS4uTgMGDFBycrL69u0rSerVq5eGDRuWcfzYsWO1bNky7du3T3FxcXrzzTf18ccfq2fPnlZdApA/mjeXNm6UoqNNn9t27aRx45zS5zYmRvrf/6RChaQ5czI7NAAAABRUl7dGsNmsrQUAAOBa/KwuoFu3bjp+/LhGjhyp+Ph41a1bV0uWLMlYnOzAgQPy8cnMlpOTk/XEE0/o0KFDCgoK0s0336xPPvlE3bp1s+oSgPxTtqy0cqU0cKD0/vvS0KEmyJ01yzSjzUcdOpjA9oEHpI8+Mi0T3nmHf8QAAICC59IlswiZRGsEAADgGWx2u5OWq3dTSUlJCg0NVWJiIq0S4L7sdum996SnnzZNZ2vVkubPlypXzveXmj1b6tnTvOSgQdJbbxHcAgDcA+O23OO9urqVK6WWLaUSJaSEBLOUAAAAgBVyO26zvD0CgGzYbNLjj5s+txERps9to0bSd9/l+0s99JA0c6a5/5//SMOHO6UjAwAAgGXSWyN06EBgCwAAPAOhLeDOmjUz7RGaNDF9btu3d0qf2759TWsESXr9dWn06Hw9PQAAgGXsdsd+tgAAAJ6A0BZwd5GR0ooV0qOPmtXChg6VunWTzpzJ15cZMECaONHcHzVKeuONfD09AACAJX77Tdq3TwoIkNq0sboaAACA3CG0BTxBQIA0Y4bpc1uokPTFF1LTptLevfn6Ms8+K732mrn/r39Jkyfn6+kBAABcLn2WbUxMvq/rCgAA4DSEtoAneewxM+s2IkLavl1q2FBasiRfX2L4cOmll8z9p5+Wpk/P19MDAAC4FK0RAACAJyK0BTzNbbdl9rk9dcr0uR07Nl/73P7739Jzz5n7jz8uffxxvp0aAADAZY4ckdavN2u8dupkdTUAAAC5R2gLeKL0PrePPWbC2uHDpQceyLc+tzabNH68NHCgOX2fPtLnn+fLqQEAAFxm4UJzGx1tvqgEAADgKQhtAU8VEGB63Kb3uf3yS9Pnds+efDm9zSb95z+Z65/16JH59UIAAABPQGsEAADgqQhtAU/32GPSypVSmTKmz22jRtK33+bLqX18pGnTTGB76ZKZzJvPLXQBAACc4vRpaflyc5/QFgAAeBpCW6AgaNpU2rDB3J46JXXoII0Zky99bn19pQ8/lO6/X7pwQbr33sx/AAEAALirJUvM2KVqVenmm62uBgAA4PoQ2gIFRWSk9MMP0j//acLaF1+UunbNlz63fn7Sp5+aBTzOnze3P/+cDzUDAAA4yeWtEWw2a2sBAAC4XoS2QEESEGD6GUyfbvrc/u9/UpMm0u7deT61v79ZjOzuu6WzZ6V27cxqzAAAAO7m4kXpm2/MfVojAAAAT0RoCxRE/ftn9rn97bd863MbGCjNny+1bGn6xLVpI23enOfTAgAA5KsffzQdo0qXNt2jAAAAPA2hLVBQNW0qbdwo3XablJiYb31uCxeWvv7anP7kSemuu0wuDAAA4C7SWyN07Gj68wMAAHgaQlugICtTxvS5ffzxzD63999vpsnmQZEiZuJugwbSiRNSTEy+dGAAAADIM7vdsZ8tAACAJyK0BQo6f3/p3XelGTPM/Xnz8qXPbWio9N13Uu3aUny81KqVtH9/PtUMAABwg7ZskQ4ckIKCzDeCAAAAPBGhLeAtHn3U9LmNjJR+/930uV28OE+nLFlSWrZMqlFDOnRIat3a3AIAAFglfZbt3Xebtk4AAACeiNAW8CZNmpg+t82amT63HTtKr72Wpz63YWHS999LlSubmbatWklHj+ZjzQAAANeB1ggAAKAgILQFvE1EhLR8uTRggAlrX3opz31uIyPNKcuXN10X7rhD2rw5/0oGAMDdTJ06VRUqVFBgYKCio6O1bt26HI9t2bKlbDZblq1Dhw4Zx/Tp0yfL423btnXFpRQoBw5Iv/4q+fiYz6YBAAA8FaEt4I38/aV33pHefz/f+tyWKyfFxko33WRO07ixNGGClJaWj3UDAOAG5s6dq8GDB2vUqFHatGmT6tSpozZt2ujYsWPZHj9v3jwdPXo0Y9u+fbt8fX3VtWtXh+Patm3rcNxnn33misspUBYuNLe33SaVLm1tLQAAAHlBaAt4s379pB9/dOxz+803N3y6ypXN7JZ77pEuXpSef94sAEKfWwBAQTJx4kT1799fffv2Vc2aNTVt2jQVLlxYs2bNyvb4EiVKKCIiImNbtmyZChcunCW0DQgIcDiuePHirricAoXWCAAAoKAgtAW8XXS0Y5/bTp2kV1+94SmypUpJ8+dL06ebxT+WL5dq15a++CKf6wYAwAIXLlzQxo0bFRMTk7HPx8dHMTExWr16da7OMXPmTHXv3l3BwcEO+1esWKGwsDBVr15dAwYM0F9//ZXjOVJSUpSUlOSwebtTp6QVK8x9QlsAAODpCG0BZO1zO2JEnvrc2mxS//5m1m3DhtLJk9IDD0h9++apdS4AAJY7ceKEUlNTFR4e7rA/PDxc8fHx13z+unXrtH37dj366KMO+9u2bav//ve/io2N1bhx47Ry5Uq1a9dOqamp2Z5n7NixCg0NzdiioqJu/KIKiG+/lS5dkmrUkKpWtboaAACAvCG0BWBc2ed2/nwzC3fXrhs+ZbVq0i+/SMOHmyD3ww+lunWlXE5EAgCgwJk5c6Zq1aqlxo0bO+zv3r27OnfurFq1aqlLly5atGiR1q9frxXpU0evMGzYMCUmJmZsBw8edEH17o3WCAAAoCAhtAXgqF8/6aefpLJlpbg40+d20aIbPl2hQtJrr5mvK5YrJ+3bJ91+u/TKK2Y2DAAAnqRUqVLy9fVVQkKCw/6EhARFRERc9bnJycmaM2eO+vXrd83XqVSpkkqVKqU9e/Zk+3hAQIBCQkIcNm+WkiItXmzuE9oCAICCgNAWQFaNG0sbNkjNm0tJSVLnztLo0Tfc51aSWrSQtmyRHnpISk2VXn7Z7Nu3L//KBgDA2fz9/dWgQQPFxsZm7EtLS1NsbKyaNm161ed+8cUXSklJUc+ePa/5OocOHdJff/2lMmXK5Llmb7BihWnBFBFhhjEAAACejtAWQPYiIqTYWOnJJ02f25EjpfvuMyHuDSpWTPr0U+mTT6SQENMmoW5d6aOPzEsAAOAJBg8erBkzZuijjz5SXFycBgwYoOTkZPXt21eS1KtXLw0bNizL82bOnKkuXbqoZMmSDvvPnDmj559/XmvWrNEff/yh2NhY3XPPPapSpYratGnjkmvydOmtETp1knz4Fw4AACgAGNIAyJm/vzRlijRrlrm/YIHpc7tzZ55O26OHmXXbvLmZFdOnj9Stm1mwDAAAd9etWzdNmDBBI0eOVN26dbV582YtWbIkY3GyAwcO6OjRow7P2blzp1atWpVtawRfX19t3bpVnTt3VrVq1dSvXz81aNBAP/30kwICAlxyTZ7MbpcWLjT3aY0AAAAKCpvd7l3z25KSkhQaGqrExESv7/0FXJd166R//EM6fNhMk/30U6ljxzydMjVVGjdOGjXK9Le96Sbpv/+V7rwzn2oGAHg0xm25583v1YYNpgV/cLB04oQUGGh1RQAAADnL7biNmbYAcqdxY2njRrOKWFKS+f7hv/+dpz63vr7S8OHSL79IVatKhw5JrVtLL7wgXbiQj7UDAIACK701Qtu2BLYAAKDgILQFkHvh4dL330sDB5qfR40ys2/z0OdWMrNjNm2S+vc3X3EcP15q0kSKi8uHmgEAQIGWHtrSGgEAABQkhLYAro+/vzR5svTBB1JAgPmXUj70uS1SRJo+XZo3TypZUvr1V6lBA+ndd1mkDAAAZG//fmnbNvPtnQ4drK4GAAAg/xDaArgxffpIP/0klS0r7dhh2id8/XWeT3vvvdLWrdJdd0nnzklPPCF17iwdO5b3kgEAQMGSPsv29tulEiWsrQUAACA/EdoCuHGNGpk+ty1amBYJnTtLr7ySpz63khQZKS1ZIr31lpnYu2iRVKuWtHhxPtUNAAAKBFojAACAgorQFkDepPe5feop8/PLL0vt2pkmtXng4yM984y0fr10661mpm2HDqad7rlzea4aAAB4uL//Nl/6kQhtAQBAwUNoCyDvChWS3n47s8/t0qWmIW3HjtLatXk6de3aJrgdNMj8PHWq1LChtHlz3ssGAACe65tvpNRU822cihWtrgYAACB/EdoCyD99+khbtkg9e5qpst98IzVpIrVpI61adcOnDQyUJk0yLRMiIqTffzctdCdMyHMnBgAA4KFojQAAAAoyQlsA+at6denjj83iZH37muWcly41K4Tceaf0ww+S3X5Dp27TxixSds890sWL0vPPmwXLDh3K52sAAABu7fx582GuRGgLAAAKJkJbAM5Rtao0a5a0e7f02GOmhcKKFVKrVibAXbr0hsLb0qWl+fOl996TCheWli83LRS+/DL/LwEAALin5cul5GSpbFnTkQkAAKCgIbQF4FwVK5qEde9e6cknTc/bn38202abNDEtFK4zvLXZTA7866/mH2onT0pdu0qPPCKdPu2k6wAAAG5jwQJz27mzGRcAAAAUNIS2AFwjKkqaMkXat0965hkpKEhat84sVtawoZk+e50NaqtVk375RRo2zPyD7YMPpHr1pDVrnHMJAADAemlp0tdfm/u0RgAAAAUVoS0A14qMlN56S9q/3zSlDQ6WNm2S/vEPqW5d6fPPzVLQueTvL40ZYzovlCtnJvQ2by79+9/SpUtOuwoAAGCRdeuk+HgpJMS0ywcAACiICG0BWCM8XBo/XvrjD+nFF6WiRaVt26Ru3aRbb5U+/fS6UtcWLaQtW6QHHzSZ76hR0h13mIm9AACg4PjqK3Pbrp358BYAAKAgIrQFYK1SpaRXX5X+/FN6+WWpWDFpxw6pZ0+pRg3pww+lixdzdapixaTZs6VPPjGzb375xUze/e9/b2jNMwAA4IbSQ1taIwAAgIKM0BaAeyhe3EyP/fNP6bXXpJIlpT17pL59TfPaGTOkCxdydaoePcys2+bNzcJkvXtL3bubBcsAAIDn2r1biouT/PzMTFsAAICCitAWgHsJCZGGDzdtE8aPl8LCzP3HHpOqVJGmTpXOn7/maSpUMH1uX31V8vU1rXJr1zb7AACAZ0qfZduypfmGDQAAQEFFaAvAPRUpYhYq27/fLFxWpox08KA0cKBUqZI0aZJ09uxVT+Hra9rl/vKLyXsPHZJatZJeeCHXk3YBAIAboTUCAADwFoS2ANxb4cLSM8+YFcWmTJGioqSjR6Vnn5UqVpTeeEM6c+aqp2jcWPr1V6lfP9Pbdvx4qWlT0zoXAAB4huPHzQexktS5s7W1AAAAOBuhLQDPEBgoPfmk6XM7fboJbI8dk/71L9MLYcwYKSkpx6cXKSK9/740b55UooS0aZNUv740bRqLlAEA4AkWLZLS0qR69aRy5ayuBgAAwLkIbQF4Fn9/qX9/aedO6YMPTN+Dv/4yfRDKl5deeeWqK47de6+0bZsUEyOdOycNGGC+YnnsmAuvAQAAXDdaIwAAAG9CaAvAMxUqJPXpY5aQ/uQTqUYN6dQp6eWXzczbF1+UTpzI9qmRkdJ330kTJ5oM+OuvpVq1pG+/dWH9AAAg186elZYuNfcJbQEAgDcgtAXg2fz8pB49zPTZuXNN+pqUZNolVKhg2ickJGR5mo+PaYu7fr10yy1mpm379tJTT5kZuAAAwH18/735+7l8ealOHaurAQAAcD5CWwAFg6+v9MAD0ubNpnFtvXpScrJZqKxiRZPQHjmS5Wm1a5vg9umnzc9TpkgNG5rTAAAA97Bggbnt3Fmy2SwtBQAAwCXcIrSdOnWqKlSooMDAQEVHR2vdunU5HjtjxgzdfvvtKl68uIoXL66YmJirHg/Ay/j4mMa1GzeaFUsaNzZTcyZNkipVkgYOlA4edHhKUJD0n/9IS5ZIERHS779L0dHSm2+aBU8AAIB1UlPNX+kSrREAAID3sDy0nTt3rgYPHqxRo0Zp06ZNqlOnjtq0aaNjOawKtGLFCj344IP64YcftHr1akVFRenuu+/W4cOHXVw5ALdms0kdOkhr1pgGts2aSSkp0tSpUuXK0j//Ke3f7/CUNm2krVvNLJ4LF6TnnpPuvlvijxcAAKyzerV0/LhUrJjUooXV1QAAALiG5aHtxIkT1b9/f/Xt21c1a9bUtGnTVLhwYc2aNSvb4z/99FM98cQTqlu3rm6++Wa9//77SktLU2xsrIsrB+ARbDaTvP70k7R8uXTnndLFi9L06VLVqtIjj0h79mQcXrq0+QrmtGlmBm5srGmh8L//WXcJAAB4s6++MrcdOph1SAEAALyBpaHthQsXtHHjRsXExGTs8/HxUUxMjFavXp2rc5w9e1YXL15UiRIlsn08JSVFSUlJDhsAL2SzmcB2+XIT4N59t/m+5QcfSNWrSz17SnFxGYf+85/Sr79KDRpIf/8t3X+/1K+fdOaMxdcBAIAXsdszQ1taIwAAAG9iaWh74sQJpaamKjw83GF/eHi44uPjc3WOF154QZGRkQ7B7+XGjh2r0NDQjC0qKirPdQPwcM2bm5YJa9aYaTtpadKnn0q33CJ16yZt2ybJZLm//CING2aC3FmzpLp1pbVrrS0fAABvsWOHtHu35O8vtW1rdTUAAACuY3l7hLx4/fXXNWfOHM2fP1+BgYHZHjNs2DAlJiZmbAevWIAIgBeLjjYrm2zYIHXpYqbzfP656Yfwj39Iv/4qf39pzBjphx+kqChp717THnf0aOnSJasvAACAgi19lm2rVlLRotbWAgAA4EqWhralSpWSr6+vEhISHPYnJCQoIiLiqs+dMGGCXn/9dS1dulS1a9fO8biAgACFhIQ4bADgoEEDaf58acsWqWtXM612/nypfn2pUydp3TrdcYdZpKx7d9NVYeRI6Y47sqxlBgAA8hGtEQAAgLeyNLT19/dXgwYNHBYRS19UrGnTpjk+b/z48Ro9erSWLFmihg0buqJUAN6gdm0z03b7dqlHD8nHx8zEjY6W2rZVsd9+1uzZ0scfm9k+v/wi1aljfrbbrS4eAICCJT4+syVR587W1gIAAOBqlrdHGDx4sGbMmKGPPvpIcXFxGjBggJKTk9W3b19JUq9evTRs2LCM48eNG6cRI0Zo1qxZqlChguLj4xUfH68zrA4EIL/UrCl98olppNenj+Tra3rgNm8uW+tW6nnTCm3ZbFezZtLp01KvXtKDD0onT1pdOAAABcfXX5sPRRs1kiIjra4GAADAtSwPbbt166YJEyZo5MiRqlu3rjZv3qwlS5ZkLE524MABHT16NOP4d999VxcuXND999+vMmXKZGwTJkyw6hIAFFRVq0offCDt2iX17y8VKmSa2955pyr2bqGVLy3T6H/b5esrzZ1rZt1+/bVZ1wwAAOQNrREAAIA3s9nt3vWl3qSkJIWGhioxMZH+tgCuz4ED0rhx0vvvSxcumH3R0dr5wAh1mNpee/fZJEk1akhDhpgOCzmskQgAyAXGbblX0N6rM2ekUqWklBRp2zbp1lutrggAACB/5HbcZvlMWwDwGOXKSVOnSvv2SYMGmUR27VpVH9JRO0Ma6sMuCxRSJE1xcdKjj0rly0ujR0snTlhdOAAAnmXpUhPYVqok3XKL1dUAAAC4HqEtAFyvsmWlSZOkP/6Qnn9eCg6W7+ZN6r3gXp0MLa/NTf6pfiUX6Oyx0xo50mS9AwaYLgsAAODaLm+NYLNZWwsAAIAVCG0B4EaFh0vjx5vwdvhwqWhR+Rw+pDprpuv9v+7VKd+SWlM0Ro+fm6gfpu3QzdXt6tJF+ukns7AKAADI6tIladEic79LF0tLAQAAsAyhLQDkValS0muvSQkJ0rffSk89JVWqJN/Ui4o+HauJGqIdqqE9qqyYrwbq9RbfqEXDs5o71/zDFAAAZFq1Svr7b6lkSem226yuBgAAwBqEtgCQX4KCpLZtpbfflvbskXbuNG0U7r5b8vdXJe3XQE3VN+qoZZtKKKR7O40Om6xZL+7V6dNWFw8AuB5Tp05VhQoVFBgYqOjoaK1bty7HY1u2bCmbzZZl69ChQ8YxdrtdI0eOVJkyZRQUFKSYmBjt3r3bFZfidtJbI3TsKPn5WVsLAACAVQhtAcAZbDapWjWzYNl335kpQwsXSo8/rtSbyilQKWqnJXrl5NN6ZEwVxRerrh8bPqtjny6Vzp+3unoAwFXMnTtXgwcP1qhRo7Rp0ybVqVNHbdq00bFjx7I9ft68eTp69GjGtn37dvn6+qpr164Zx4wfP15vv/22pk2bprVr1yo4OFht2rTReS/7O8Fud+xnCwAA4K1sdrt3dVZMSkpSaGioEhMTFRISYnU5ALyR3S7FxeniV4t17KPFCtv5kwops0/Ced/CSmnWWqEPtpfatZPKl7ewWACwjruO26Kjo9WoUSNNmTJFkpSWlqaoqCg99dRTGjp06DWfP2nSJI0cOVJHjx5VcHCw7Ha7IiMjNWTIED333HOSpMTERIWHh+vDDz9U9+7dr3lOd32vrte2bVLt2lJgoHTihBQcbHVFAAAA+Su34zZm2gKAq9lsUs2aKjTsOZXdsVy+J//ShmH/0zcR/XRYkQpMPavQH7+WBgyQKlSQ/dZbpeefl374QbpwwerqAcCrXbhwQRs3blRMTEzGPh8fH8XExGj16tW5OsfMmTPVvXt3Bf9/Irl//37Fx8c7nDM0NFTR0dE5njMlJUVJSUkOW0GQPss2JobAFgAAeDdCWwCwmE+xEDUc8w91OPq+EjYc0rB2m/WibYx+UnOlyke2336TJkyQWrUyi57dd580c6Z05IjVpQOA1zlx4oRSU1MVHh7usD88PFzx8fHXfP66deu0fft2Pfrooxn70p93PeccO3asQkNDM7aoqKjrvRS3RGsEAAAAg9AWANxI/QY2jV1cR4//OUwLn/tJlYqeUDfN0YfqreO2MOn0aWnePOnRR6WyZaV69aThw81S25cuXfsFAACWmjlzpmrVqqXGjRvn6TzDhg1TYmJixnbw4MF8qtA6hw9LGzaYL6R06mR1NQAAANYitAUANxQVJb3xhrTtUHFFv9lNo8p9qHD7UTXUev3b7xXtC4uW3WaTNm+Wxo6Vbr9dKl1a6t5d+u9/pRwWwwEA5E2pUqXk6+urhIQEh/0JCQmKiIi46nOTk5M1Z84c9evXz2F/+vOu55wBAQEKCQlx2DzdwoXmtkkT6YpJxwAAAF6H0BYA3FhIiDR4sLR3rzT7Mx/ZGjbUqEsjVfnYGoXZEzSx3sdKiHlI9hIlpFOnpLlzpd69zb92GzWSRo2S1q6VUlOtvhQAKBD8/f3VoEEDxcbGZuxLS0tTbGysmjZtetXnfvHFF0pJSVHPnj0d9lesWFEREREO50xKStLatWuvec6ChNYIAAAAmWx2u91udRGuVFBW1gXgnex26aefTIvbr7/O3N+4Qape7bxOrVMWy2fJYmnTJscnlioltW0rtW8v3X23VLKkawsHgBvgruO2uXPnqnfv3nrvvffUuHFjTZo0SZ9//rl27Nih8PBw9erVS2XLltXYsWMdnnf77berbNmymjNnTpZzjhs3Tq+//ro++ugjVaxYUSNGjNDWrVv1+++/KzAw8Jo1uet7lVtJSeavqosXpbg46eabra4IAADAOXI7bvNzYU0AgDyy2aQWLcy2c6f01lvSRx9J6zb66u6NTVWuXFMNGjRa/efEq+jPS6TFi6WlS6UTJ6RPPjGbj48UHW0C3Pbtpbp1zT4AQK5069ZNx48f18iRIxUfH6+6detqyZIlGQuJHThwQD5X/Lm6c+dOrVq1SkuXLs32nP/617+UnJysxx57TKdOnVLz5s21ZMmSXAW2BcGSJSawrVaNwBYAAEBipq3V5QBAnh0/Lr37rjRlirkvmbYK/ftLgwZJUREXpdWrTYC7eLG0bZvjCSIipHbtTIB7111SaKjrLwIAssG4Lfc8/b3q0UOaPVt6/nlp/HirqwEAAHCe3I7bCG0BoIA4d85MpJ04Udqxw+zz9ZUeeEAaMkRq0OD/Dzx40ExpWrxYWrZMSk7OPImvr9SsWeYs3FtvNdN7AcACjNtyz5Pfq4sXzVqaiYnSzz9Lt91mdUUAAADOQ2ibA08e0AJAbqSlSd9+K735pvTDD5n7W7Y04W379pd1Q0hJkVatypyFm572prvppswAt3VrqUgRV10GADBuuw6e/F59/735okdYmHTkiPn8EAAAoKDK7biNJoYAUMD4+EgdOkjLl0sbN5qvnPr5SStWSJ06SbfcIk2fbmbmKiDAhLFvvmlWftm3T5o61ZwgKEg6dMgc3KWLVKKEFBOTOZXXuz7zAwA4yVdfmdtOnQhsAQAA0jHTFgC8wMGD0uTJ0nvvmRW6JfNV1CeeMFtYWDZPOndOWrkycxbu3r2Oj1esmDkLt2VLqXBhZ18GAC/DuC33PPW9stul8uXN31MLF5rgFgAAoCCjPUIOPHVACwD5ISlJmjlTmjRJOnDA7AsIkHr3lp599hordu/enRngrlghXbiQ+VhAgFS/vmmcm77VqGGm+ALADWLclnue+l79+qv566NwYenECfMlDwAAgIKM0DYHnjqgBYD8dOmS9L//ma4I69dn7u/Y0fS9veOOa6w/lpxs+i98+630zTeZCfDlgoKkOnUcg9yaNQlyAeQa47bc89T36uWXpVdeMV145s+3uhoAAADnI7TNgacOaAHAGex26aefTHj79deZbWrr1zfhbdeuUqFCuTjJ7t3Shg2mie7GjdKmTdLp01mPDQzMPsi95osA8EaM23LPU9+revWkzZulDz6Q+vSxuhoAAADnI7TNgacOaAHA2Xbtkt56S/rwQ+n8ebMvKkoaNEh69FEpNPQ6TpaWJu3Zk/sgt3ZtE+A2bEiQCyAD47bc88T36s8/pQoVzAKaCQlSqVJWVwQAAOB8hLY58MQBLQC40okT0rvvSlOmSMeOmX1Fi0r9+5sAt1y5GzxxepCbHuKmB7npK6NdLiAg64zcW24hyAW8DOO23PPE92ryZOnpp6Xbb5d+/NHqagAAAFyD0DYHnjigBQArnD8vffqpaZ0QF2f2+fqalglDhphJsXmWlibt3Zs1yE1MzHpsQEDmjNzLg1x//3woBIA7YtyWe574XsXESLGx0oQJ5u8VAAAAb0BomwNPHNACgJXS0qQlS0x4u3x55v4WLaTnnpM6dDBfbc3XF9y3L2uQe+pU1mP9/bMGubfeSpALFBCM23LP096rU6ek0qXNwpi7d0tVqlhdEQAAgGsQ2ubA0wa0AOBOfv1VmjhRmjPH/ENbkqpXl559VnrwQclpf6za7VmD3I0bcw5ya9XKGuQGBDipOADOwrgt9zztvZo9W+rRw7Qw/+03q6sBAABwHULbHHjagBYA3NGhQ9Lbb0vTp2d2MihUSGrZUurcWerUSSpf3slF2O3S/v1Zg9yTJ7MeW6hQ1iC3Vi2CXMDNMW7LPU97r7p1kz7/XBo2TBozxupqAAAAXIfQNgeeNqAFAHd2+rQ0c6Y0bZq0c6fjY7VrmwC3c2eTkeZrC4Wc2O3SH39IGzbkLsi99VbHILd2bYJcwI0wbss9T3qvUlJMa4TTp6W1a6XGja2uCAAAwHUIbXPgSQNaAPAku3ZJX38tLVworVplWtOmK1NG6tjRBLitW0tBQS4sLD3IvXJG7t9/Zz3Wzy/7IDcw0IUFA0jHuC33POm9WrJEatfO/N1w6JCLPtQDAABwE4S2OfCkAS0AeKq//pIWLzYB7pIl0pkzmY8FBUl3321aKHTsKIWHW1Cg3S79+WfWIPevv7Ie6+cn3XKL1LAhQS7gYozbcs+T3qsBA8w3NP75T3MLAADgTQhtc+BJA1oAKAhSUqSVK02Au3ChdPBg5mM2mxQdndkH95ZbzD5L2O3SgQNZg9wTJ7Iemx7kNmgg1a9v7t98s0mgLbsAoOBh3JZ7nvJepaVJUVHSkSPmw7127ayuCAAAwLUIbXPgKQNaACiI7HZp69bMAHfDBsfHK1bMDHBbtDBtZy1lt5uU+cog9/jx7I8vVkyqUcMEuDVqZN6vWFHy9XVp6UBBwLgt9zzlvVq/3vSwLVLEfCZGG3EAAOBtCG1z4CkDWgDwBkeOSIsWmQD3++/NrNx0oaFmBlbnzlLbtlLx4tbV6cBuN00Y0wPcX3+VduyQ9u93bOR7uYAAqVq1rGFu9eoubvALeBbGbbnnKe/VSy9Jr70m3X+/9MUXVlcDAADgeoS2OfCUAS0AeJvkZBPcLlxoFjS7fDKrr6+ZeZs+C7dyZevqzNH582Y1th07pLg4s+3YIe3caR7Ljs0mVajgGOamB7olS7q0fMAdMW7LPU95r2rVkrZvlz7+WOrZ0+pqAAAAXI/QNgeeMqAFAG+WmiqtW2fC24ULpd9+c3y8Zk0T4HbubL5m69adB1JTzaJn6SFueqAbFyedPJnz80qXzr7VQlQUS63DazBuyz1PeK/27TMfuvn6SseOSSVKWF0RAACA6xHa5sATBrQAAEd792YGuD/+aHLQdGFhUocOJsC96y4pONi6Oq+L3W6mE18Z5u7YYRZEy0nhwia8vTLMrVpV8vd3Xf2ACzBuyz1PeK/eeksaPFi6805p+XKrqwEAALAGoW0OPGFACwDI2cmT0pIlJsD99lspMTHzsYAAKSbGtFDo1EmKjLSuzjw5c8a0VbhyZu7u3dKlS9k/x9fXTGG7MsytUUPi7zt4KMZtuecJ71XLltLKldKkSdKgQVZXAwAAYA1C2xx4woAWAJA7Fy9KP/1kAtyFC81aYJdr2DCzD26dOqaFrEe7eNF8vzi72bmnT+f8vMjI7PvmlilTAN4UFGSM23LP3d+rv/4y34xISzN/VleoYHVFAAAA1iC0zYG7D2gBADfGbje9b9PbKKxda/ali4rK7IN7xx1mVm6BYbdLR45k3zc3Pj7n54WEZN83t1Ilyc/PdfUDOWDclnvu/l79979S795S7drSli1WVwMAAGAdQtscuPuAFgCQP+LjpW++MSHu0qXSuXOZjxUpIrVtawLc9u2lkiWtq9PpTp3KDHIvD3T37TNT3rLj72965F4Z5lav7kFNg1EQMG7LPXd/r+67T5o3TxoxQvr3v62uBgAAwDqEtjlw9wEtACD/nTsnxcaaGbiLFklHj2Y+5uMjNWuWOQu3WjXr6nSplBTTI/fKMHfnTseE+0rly2eGudWqSRERmVt4uFkoDcgnjNtyz53fq3PnpNKlpeRkaeNGqX59qysCAACwDqFtDtx5QAsAcL60NBMapPfB3brV8fFq1TID3KZNvbBLQFqadOCAY7/c9Pt//XXt54eEmPD28jA3PdC9/OewMKlQIedfDzwa47bcc+f3atEi01s8Kkr6809aaQMAAO9GaJsDdx7QAgBc788/M/vgrlhh1vpKV7KkaZ/QubPUpo1UtKhlZbqHEyccw9y9e6WEBNOLIj7ezN69HiVLXj3YTd9XqpSZEg2vw7gt99z5verfX3r/fenJJ6UpU6yuBgAAwFqEtjlw5wEtAMBaiYnSd9+ZEPebb6STJzMf8/eX7rzTzBbr1EkqV866Ot2S3S4lJWUGuPHxjoHu5fsSEqTU1Nyf29fXzMy9WrCbfj80lGl8BQjjttxz1/cqLU2KjDT/2y9dKt11l9UVAQAAWIvQNgfuOqAFALiXS5ekn3/ObKOwZ4/j43XrZga49ep5YRuFvEhLM60Wrhbspt8/ceL6zh0QkLv2DOHhLKrmARi35Z67vlerV0u33WY6pxw/bj4AAwAA8GaEtjlw1wEtAMB92e1mfa70AHf1apM7pgsOlho1Mj1wmzQxt6VLW1dvgXLxonTsWNZwN7uwNynp+s5dpEju2jOEh5M0WYRxW+6563s1dKg0bpzUvbv02WdWVwMAAGA9QtscuOuAFgDgOY4flxYvNgHu999nnxVWrpwZ4DZpItWuzbpbTnfuXO7C3fh46fz56zt3iRKOwW54uEnmw8LM7eX3Q0Jo0ZBPGLflnru+VzVqmBbYn31mglsAAABvR2ibA3cd0AIAPFNqqlmXa/Vqac0acxsXl/W4oCAzGzc9yG3a1OR+sIDdLp0+fe3eu+m3ly5d3/n9/bMGudmFu+n3ixYl5M0B47bcc8f3atcuqXp184HV8eOm5TQAAIC3y+24jQ58AADkga+vdOutZuvf3+w7dUpauzYzyF2zxixy9uOPZktXoUJmgNukiVSnDt/CdwmbzcyGDQmRqlW7+rFpadLffzsGuUePmpYNx4+b7fL7ycnShQvS4cNmyw1//6uHulfuI+SFh/jqK3PbsiWBLQAAwPUitAUAIJ8VKya1aWM2yeR+O3ZkzsRds0b67Tfpjz/Mlt7nMTBQatDAMciNjLToImD4+EilSpnt1luvffzZs1mD3Ozup9+ePWtC3kOHzJYbAQG5m8mbflukCCEvLJEe2t5zj7V1AAAAeCLaIwAAYIHERGndOscg9+TJrMeVK+fYUqFuXZPZoYBITs4MdLMLda/cd+7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          },
          "metadata": {}
        }
      ],
      "source": [
        "# This Colab code block installs dependencies, simulates input data, trains a simple model,\n",
        "# and focuses on measurement and visualization of training metrics.\n",
        "# All code and comments are in English as requested.\n",
        "# Adjust the number of epochs or the complexity of the model as needed.\n",
        "\n",
        "# Install dependencies\n",
        "!pip install matplotlib tensorflow\n",
        "\n",
        "import sys\n",
        "import traceback\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "try:\n",
        "    # Simulate training data\n",
        "    # Here we create random inputs and random labels for a binary classification problem.\n",
        "    # In a real scenario, you would replace this with your actual dataset.\n",
        "    np.random.seed(42)\n",
        "    num_samples = 1000\n",
        "    input_dim = 20\n",
        "    X_train = np.random.randn(num_samples, input_dim).astype(np.float32)\n",
        "    y_train = (np.sum(X_train, axis=1) > 0).astype(np.float32)  # Labels based on sum threshold\n",
        "\n",
        "    X_val = np.random.randn(num_samples, input_dim).astype(np.float32)\n",
        "    y_val = (np.sum(X_val, axis=1) > 0).astype(np.float32)\n",
        "\n",
        "    # Define a simple model\n",
        "    # A small neural network for binary classification\n",
        "    model = keras.Sequential([\n",
        "        keras.layers.Dense(64, activation='relu', input_shape=(input_dim,)),\n",
        "        keras.layers.Dense(32, activation='relu'),\n",
        "        keras.layers.Dense(1, activation='sigmoid')\n",
        "    ])\n",
        "\n",
        "    # Compile the model with binary crossentropy loss and an optimizer\n",
        "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "    # Train the model and store the history\n",
        "    history = model.fit(\n",
        "        X_train, y_train,\n",
        "        validation_data=(X_val, y_val),\n",
        "        epochs=10,\n",
        "        batch_size=32,\n",
        "        verbose=1\n",
        "    )\n",
        "\n",
        "    # Extract metrics from history\n",
        "    loss = history.history['loss']\n",
        "    val_loss = history.history['val_loss']\n",
        "    acc = history.history['accuracy']\n",
        "    val_acc = history.history['val_accuracy']\n",
        "    epochs = range(1, len(loss) + 1)\n",
        "\n",
        "    # Visualization of the training process\n",
        "    plt.figure(figsize=(14, 5))\n",
        "\n",
        "    # Plot loss\n",
        "    plt.subplot(1, 2, 1)\n",
        "    plt.plot(epochs, loss, 'b', label='Training Loss')\n",
        "    plt.plot(epochs, val_loss, 'r', label='Validation Loss')\n",
        "    plt.title('Training and Validation Loss')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "\n",
        "    # Plot accuracy\n",
        "    plt.subplot(1, 2, 2)\n",
        "    plt.plot(epochs, acc, 'b', label='Training Accuracy')\n",
        "    plt.plot(epochs, val_acc, 'r', label='Validation Accuracy')\n",
        "    plt.title('Training and Validation Accuracy')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Accuracy')\n",
        "    plt.legend()\n",
        "\n",
        "    plt.tight_layout()\n",
        "    plt.show()\n",
        "\n",
        "except Exception as e:\n",
        "    print(\"An error occurred:\", e)\n",
        "    traceback.print_exc(file=sys.stdout)"
      ]
    }
  ]
}