DenseNet121_Dataset3_0_epoch_30_16.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Joyfull-Joyal/sign_language_interprter_Main-Project/blob/DenseNet121/DenseNet121_Dataset3_0_epoch_30_16.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "h8N5dMEXGZvK",
        "outputId": "bd134a5f-00cf-4ee5-ac41-491e81afc349"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 2320 images belonging to 29 classes.\n",
            "Found 290 images belonging to 29 classes.\n",
            "Found 290 images belonging to 29 classes.\n",
            "Epoch 1/30\n",
            "145/145 [==============================] - 55s 326ms/step - loss: 3.2132 - accuracy: 0.1276 - val_loss: 2.9028 - val_accuracy: 0.2188\n",
            "Epoch 2/30\n",
            "145/145 [==============================] - 43s 294ms/step - loss: 2.3405 - accuracy: 0.3159 - val_loss: 2.4714 - val_accuracy: 0.3229\n",
            "Epoch 3/30\n",
            "145/145 [==============================] - 42s 293ms/step - loss: 1.9344 - accuracy: 0.4211 - val_loss: 2.1344 - val_accuracy: 0.4757\n",
            "Epoch 4/30\n",
            "145/145 [==============================] - 44s 301ms/step - loss: 1.6533 - accuracy: 0.4897 - val_loss: 2.0581 - val_accuracy: 0.4479\n",
            "Epoch 5/30\n",
            "145/145 [==============================] - 44s 303ms/step - loss: 1.4638 - accuracy: 0.5552 - val_loss: 1.9750 - val_accuracy: 0.4410\n",
            "Epoch 6/30\n",
            "145/145 [==============================] - 44s 302ms/step - loss: 1.3508 - accuracy: 0.5776 - val_loss: 1.8251 - val_accuracy: 0.5312\n",
            "Epoch 7/30\n",
            "145/145 [==============================] - 42s 291ms/step - loss: 1.2701 - accuracy: 0.6060 - val_loss: 1.6511 - val_accuracy: 0.5417\n",
            "Epoch 8/30\n",
            "145/145 [==============================] - 43s 296ms/step - loss: 1.2299 - accuracy: 0.6310 - val_loss: 1.6169 - val_accuracy: 0.5347\n",
            "Epoch 9/30\n",
            "145/145 [==============================] - 45s 310ms/step - loss: 1.2078 - accuracy: 0.6276 - val_loss: 1.5689 - val_accuracy: 0.6319\n",
            "Epoch 10/30\n",
            "145/145 [==============================] - 44s 300ms/step - loss: 1.1379 - accuracy: 0.6427 - val_loss: 1.6337 - val_accuracy: 0.5764\n",
            "Epoch 11/30\n",
            "145/145 [==============================] - 45s 307ms/step - loss: 1.0824 - accuracy: 0.6547 - val_loss: 1.5977 - val_accuracy: 0.5868\n",
            "Epoch 12/30\n",
            "145/145 [==============================] - 44s 307ms/step - loss: 1.1175 - accuracy: 0.6517 - val_loss: 1.4999 - val_accuracy: 0.6076\n",
            "Epoch 13/30\n",
            "145/145 [==============================] - 43s 297ms/step - loss: 1.0846 - accuracy: 0.6556 - val_loss: 1.5526 - val_accuracy: 0.5694\n",
            "Epoch 14/30\n",
            "145/145 [==============================] - 43s 299ms/step - loss: 1.0473 - accuracy: 0.6716 - val_loss: 1.4688 - val_accuracy: 0.6285\n",
            "Epoch 15/30\n",
            "145/145 [==============================] - 44s 304ms/step - loss: 1.0204 - accuracy: 0.6724 - val_loss: 1.5044 - val_accuracy: 0.6562\n",
            "Epoch 16/30\n",
            "145/145 [==============================] - 44s 301ms/step - loss: 1.0127 - accuracy: 0.6694 - val_loss: 1.4570 - val_accuracy: 0.6042\n",
            "Epoch 17/30\n",
            "145/145 [==============================] - 43s 297ms/step - loss: 0.9746 - accuracy: 0.6849 - val_loss: 1.4007 - val_accuracy: 0.6146\n",
            "Epoch 18/30\n",
            "145/145 [==============================] - 43s 296ms/step - loss: 0.9783 - accuracy: 0.6953 - val_loss: 1.4439 - val_accuracy: 0.5556\n",
            "Epoch 19/30\n",
            "145/145 [==============================] - 42s 290ms/step - loss: 0.9390 - accuracy: 0.7047 - val_loss: 1.4830 - val_accuracy: 0.5972\n",
            "Epoch 20/30\n",
            "145/145 [==============================] - 42s 292ms/step - loss: 0.9230 - accuracy: 0.7034 - val_loss: 1.4250 - val_accuracy: 0.6354\n",
            "Epoch 21/30\n",
            "145/145 [==============================] - 43s 294ms/step - loss: 0.8970 - accuracy: 0.7108 - val_loss: 1.3707 - val_accuracy: 0.6215\n",
            "Epoch 22/30\n",
            "145/145 [==============================] - 42s 293ms/step - loss: 0.9311 - accuracy: 0.6935 - val_loss: 1.4461 - val_accuracy: 0.5799\n",
            "Epoch 23/30\n",
            "145/145 [==============================] - 42s 293ms/step - loss: 0.9130 - accuracy: 0.7095 - val_loss: 1.5194 - val_accuracy: 0.5868\n",
            "Epoch 24/30\n",
            "145/145 [==============================] - 44s 302ms/step - loss: 0.8930 - accuracy: 0.7151 - val_loss: 1.4594 - val_accuracy: 0.6319\n",
            "Epoch 25/30\n",
            "145/145 [==============================] - 42s 290ms/step - loss: 0.9033 - accuracy: 0.7233 - val_loss: 1.5021 - val_accuracy: 0.5625\n",
            "Epoch 26/30\n",
            "145/145 [==============================] - 42s 292ms/step - loss: 0.8994 - accuracy: 0.7168 - val_loss: 1.4779 - val_accuracy: 0.6042\n",
            "Epoch 27/30\n",
            "145/145 [==============================] - 43s 293ms/step - loss: 0.8779 - accuracy: 0.7177 - val_loss: 1.3936 - val_accuracy: 0.5972\n",
            "Epoch 28/30\n",
            "145/145 [==============================] - 43s 296ms/step - loss: 0.8563 - accuracy: 0.7250 - val_loss: 1.5282 - val_accuracy: 0.5694\n",
            "Epoch 29/30\n",
            "145/145 [==============================] - 42s 288ms/step - loss: 0.8468 - accuracy: 0.7284 - val_loss: 1.5072 - val_accuracy: 0.5903\n",
            "Epoch 30/30\n",
            "145/145 [==============================] - 42s 292ms/step - loss: 0.8685 - accuracy: 0.7172 - val_loss: 1.5144 - val_accuracy: 0.5729\n",
            "18/18 [==============================] - 1s 75ms/step - loss: 1.1761 - accuracy: 0.6076\n",
            "145/145 [==============================] - 42s 288ms/step - loss: 0.4306 - accuracy: 0.8987\n",
            "1/1 [==============================] - 3s 3s/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAqcAAAJKCAYAAAARP+xYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAADMYklEQVR4nOydeVxUVf/HPzMDzCC7gwqYKyggi2GaiFqKG7gCLlk+KWLlgmGJRrghlpJbixtpGhIulLmVuWvqoyFpgpDirqksKjCCyDAIM78/+jmP02DOMPcOh+H77nVer2fO3Pu+n3Ph5+9wzrn3CFQqlQoEQRAEQRAEwQDCug5AEARBEARBEE+hzilBEARBEATBDNQ5JQiCIAiCIJiBOqcEQRAEQRAEM1DnlCAIgiAIgmAG6pwSBEEQBEEQzECdU4IgCIIgCIIZqHNKEARBEARBMAN1TgmCIAiCIAhmoM4pQRD1lqtXr6J///6ws7ODQCDArl27OPXfunULAoEAGzdu5NRbn+nVqxd69epV1zEIgjBhqHNKEIRBXL9+HRMnTkTbtm0hkUhga2uL7t2746uvvoJcLuf12uPGjUN2djYWLlyIlJQUdO7cmdfrGZPw8HAIBALY2trWeB+vXr0KgUAAgUCAZcuW6e3Py8vD/PnzkZmZyUFagiAI7jCr6wAEQdRffvnlF4wcORJisRhjx46Ft7c3KisrcfLkScycORMXLlzAunXreLm2XC5HWloaZs+ejalTp/JyjVatWkEul8Pc3JwX/4swMzNDeXk5fv75Z4waNUrju82bN0MikaCioqJW7ry8PMTHx6N169Z4+eWXdT7v4MGDtboeQRCErlDnlCCIWnHz5k2MHj0arVq1wtGjR+Hs7Kz+LjIyEteuXcMvv/zC2/UfPHgAALC3t+ftGgKBABKJhDf/ixCLxejevTu2bt2q1TndsmULBg0ahO3btxslS3l5ORo1agQLCwujXI8giIYLTesTBFErlixZgrKyMmzYsEGjY/oUNzc3TJs2Tf25qqoKn3zyCVxdXSEWi9G6dWvMmjULCoVC47zWrVtj8ODBOHnyJF599VVIJBK0bdsW3333nfqY+fPno1WrVgCAmTNnQiAQoHXr1gD+ng5/+r+fZf78+RAIBBp1hw4dQo8ePWBvbw9ra2u4u7tj1qxZ6u+ft+b06NGj6NmzJ6ysrGBvb49hw4YhJyenxutdu3YN4eHhsLe3h52dHcaPH4/y8vLn39h/8NZbb2Hfvn14+PChuu7MmTO4evUq3nrrLa3ji4uLMWPGDPj4+MDa2hq2trYIDg7G+fPn1cccO3YMXbp0AQCMHz9evTzgaTt79eoFb29v/PHHH3jttdfQqFEj9X3555rTcePGQSKRaLV/wIABcHBwQF5ens5tJQiCAKhzShBELfn555/Rtm1bBAQE6HT8O++8g3nz5qFTp0744osv8PrrryMhIQGjR4/WOvbatWsYMWIE+vXrh+XLl8PBwQHh4eG4cOECACAsLAxffPEFAODNN99ESkoKvvzyS73yX7hwAYMHD4ZCocCCBQuwfPlyDB06FKdOnfrX8w4fPowBAwbg/v37mD9/PqZPn47ffvsN3bt3x61bt7SOHzVqFB49eoSEhASMGjUKGzduRHx8vM45w8LCIBAIsGPHDnXdli1b4OHhgU6dOmkdf+PGDezatQuDBw/G559/jpkzZyI7Oxuvv/66uqPo6emJBQsWAADee+89pKSkICUlBa+99praU1RUhODgYLz88sv48ssv0bt37xrzffXVV2jSpAnGjRuH6upqAMDatWtx8OBBrFy5Ei4uLjq3lSAIAgCgIgiC0JOSkhIVANWwYcN0Oj4zM1MFQPXOO+9o1M+YMUMFQHX06FF1XatWrVQAVCdOnFDX3b9/XyUWi1XR0dHqups3b6oAqJYuXarhHDdunKpVq1ZaGeLi4lTP/pP3xRdfqACoHjx48NzcT6+RlJSkrnv55ZdVTZs2VRUVFanrzp8/rxIKhaqxY8dqXS8iIkLDGRoaqpJKpc+95rPtsLKyUqlUKtWIESNUffr0UalUKlV1dbXKyclJFR8fX+M9qKioUFVXV2u1QywWqxYsWKCuO3PmjFbbnvL666+rAKi+/vrrGr97/fXXNeoOHDigAqD69NNPVTdu3FBZW1urQkJCXthGgiCImqCRU4Ig9Ka0tBQAYGNjo9Pxe/fuBQBMnz5doz46OhoAtNamdujQAT179lR/btKkCdzd3XHjxo1aZ/4nT9eq7t69G0qlUqdz8vPzkZmZifDwcDRu3Fhd7+vri379+qnb+SyTJk3S+NyzZ08UFRWp76EuvPXWWzh27BgKCgpw9OhRFBQU1DilD/y9TlUo/Puf9urqahQVFamXLJw7d07na4rFYowfP16nY/v374+JEydiwYIFCAsLg0Qiwdq1a3W+FkEQxLNQ55QgCL2xtbUFADx69Ein4//66y8IhUK4ublp1Ds5OcHe3h5//fWXRn3Lli21HA4ODpDJZLVMrM0bb7yB7t2745133kGzZs0wevRo/PDDD//aUX2a093dXes7T09PFBYW4vHjxxr1/2yLg4MDAOjVloEDB8LGxgbff/89Nm/ejC5dumjdy6colUp88cUXaNeuHcRiMRwdHdGkSRNkZWWhpKRE52s2b95cr4efli1bhsaNGyMzMxMrVqxA06ZNdT6XIAjiWahzShCE3tja2sLFxQV//vmnXuf984Gk5yESiWqsV6lUtb7G0/WQT7G0tMSJEydw+PBhvP3228jKysIbb7yBfv36aR1rCIa05SlisRhhYWFITk7Gzp07nztqCgCLFi3C9OnT8dprr2HTpk04cOAADh06BC8vL51HiIG/748+ZGRk4P79+wCA7Oxsvc4lCIJ4FuqcEgRRKwYPHozr168jLS3thce2atUKSqUSV69e1ai/d+8eHj58qH7yngscHBw0nmx/yj9HZwFAKBSiT58++Pzzz3Hx4kUsXLgQR48exa+//lqj+2nOy5cva3136dIlODo6wsrKyrAGPIe33noLGRkZePToUY0PkT3lxx9/RO/evbFhwwaMHj0a/fv3R9++fbXuia5/KOjC48ePMX78eHTo0AHvvfcelixZgjNnznDmJwiiYUGdU4IgasVHH30EKysrvPPOO7h3757W99evX8dXX30F4O9paQBaT9R//vnnAIBBgwZxlsvV1RUlJSXIyspS1+Xn52Pnzp0axxUXF2ud+/Rl9P98vdVTnJ2d8fLLLyM5OVmjs/fnn3/i4MGD6nbyQe/evfHJJ59g1apVcHJyeu5xIpFIa1R227ZtyM3N1ah72omuqSOvLzExMbh9+zaSk5Px+eefo3Xr1hg3btxz7yNBEMS/QS/hJwiiVri6umLLli1444034OnpqbFD1G+//YZt27YhPDwcANCxY0eMGzcO69atw8OHD/H666/j999/R3JyMkJCQp77mqLaMHr0aMTExCA0NBRRUVEoLy9HYmIi2rdvr/FA0IIFC3DixAkMGjQIrVq1wv3797FmzRq89NJL6NGjx3P9S5cuRXBwMLp164YJEyZALpdj5cqVsLOzw/z58zlrxz8RCoWYM2fOC48bPHgwFixYgPHjxyMgIADZ2dnYvHkz2rZtq3Gcq6sr7O3t8fXXX8PGxgZWVlbo2rUr2rRpo1euo0ePYs2aNYiLi1O/2iopKQm9evXC3LlzsWTJEr18BEEQNHJKEEStGTp0KLKysjBixAjs3r0bkZGR+Pjjj3Hr1i0sX74cK1asUB+7fv16xMfH48yZM/jggw9w9OhRxMbGIjU1ldNMUqkUO3fuRKNGjfDRRx8hOTkZCQkJGDJkiFb2li1b4ttvv0VkZCRWr16N1157DUePHoWdnd1z/X379sX+/fshlUoxb948LFu2DP7+/jh16pTeHTs+mDVrFqKjo3HgwAFMmzYN586dwy+//IIWLVpoHGdubo7k5GSIRCJMmjQJb775Jo4fP67XtR49eoSIiAj4+flh9uzZ6vqePXti2rRpWL58OU6fPs1JuwiCaDgIVPqsyicIgiAIgiAIHqGRU4IgCIIgCIIZqHNKEARBEARBMAN1TgmCIAiCIAhmoM4pQRAEQRAEoRMnTpzAkCFD4OLiAoFAgF27dml8r1KpMG/ePDg7O8PS0hJ9+/bVesf1i6DOKUEQBEEQBKETjx8/RseOHbF69eoav1+yZAlWrFiBr7/+Gunp6bCyssKAAQNQUVGh8zXoaX2CIAiCIAhCbwQCAXbu3ImQkBAAf4+auri4IDo6GjNmzAAAlJSUoFmzZti4ceO/7m73LDRyShAEQRAE0UBRKBQoLS3VKLXd3e3mzZsoKChA37591XV2dnbo2rWrTltdP4V2iDIyloNWvPigFyDbHcVBEoIgCIJgn8JHlQY7HG0sDHZI6rDHZOk3lTd3zDBHxMfHa9TFxcXVase7goICAECzZs006ps1a6b+Theoc0oQBEEQBNFAiY2NxfTp0zXqxGJxHaX5G5rW15O0tDSIRCIMGjSIU6+1pTmWvtsTl5PCUbxjCn5dNhKvtGuqtyd1y2YE9wtEFz8fjBk9EtlZWbXKw4WHHOTg28FSFnKYroOlLOT4H1kZZzE7eipGDQ5EH38fnDx+RO8MXOQwCgIhb0UsFsPW1laj1LZz6uTkBAC4d++eRv29e/fU3+kCdU71ZMOGDXj//fdx4sQJ5OXlceZNjOqDQL+WiFh2EJ0jN+Pwudv4ZWEoXKRWOjv279uLZUsSMHFKJFK37YS7uwcmT5yAoqIivbJw4SEHOfh2sJSFHKbrYCkLOTSRy+VwbdceUTNm63wOHzmMgkDAX+GQNm3awMnJCUeO/O8PhdLSUqSnp6Nbt246e6hzqgdlZWX4/vvvMXnyZAwaNAgbN27kxCuxECGkuxtmJ53CqQt5uJFfgoVb0nE9vwTvDvTR2ZOSnISwEaMQEjocrm5umBMXD4lEgl07tuuVhwsPOcjBt4OlLOQwXQdLWcihSdeAnoiYFIUevfrofA4fORoaZWVlyMzMRGZmJoC/H4LKzMzE7du3IRAI8MEHH+DTTz/FTz/9hOzsbIwdOxYuLi7qJ/p1gTqnevDDDz/Aw8MD7u7u+M9//oNvv/0WXLyJy0wkhJlIiIrKKo36CkUVAjq46OR4UlmJnIsX4N8tQF0nFArh7x+ArPMZOmfhwkMOcvDtYCkLOUzXwVIWcnAPKzl0gsdpfX05e/Ys/Pz84OfnBwCYPn06/Pz8MG/ePADARx99hPfffx/vvfceunTpgrKyMuzfvx8SiUTna1DnVA82bNiA//znPwCAoKAglJSU4Pjx4wZ7y+RPcDonH7GjX4VzYysIhQKM7u2Orh5OcGqs27S+7KEM1dXVkEqlGvVSqRSFhYU6Z+HCQw5y8O1gKQs5TNfBUhZycA8rOeobvXr1gkql0ipPZ5MFAgEWLFiAgoICVFRU4PDhw2jfvr1e16DOqY5cvnwZv//+O958800AgJmZGd544w1s2LDhuefU9O4wVXVVjcdGLDsIgUCAGykTULIrEpFDOuKHE1egpD0SCIIgCKJhU0/WnHIFvUpKRzZs2ICqqiq4uPxvml2lUkEsFmPVqlWws7PTOichIUHr3WEityCYtw/WOvZmQQn6f7wdjcRmsG1kgQJZOVJignCzoESnfA72DhCJRFqLuIuKiuDo6KiTgysPOcjBt4OlLOQwXQdLWcjBPazkILShkVMdqKqqwnfffYfly5erFwFnZmbi/PnzcHFxwdatW2s8LzY2FiUlJRrFzLXfv16rXFGFAlk57K3F6NupFfacvqFTRnMLC3h28EL66f/twKBUKpGengbfjn46t5ULDznIwbeDpSzkMF0HS1nIwT2s5NAJhtacGgMaOdWBPXv2QCaTYcKECVojpMOHD8eGDRswadIkrfPEYrHWu8IEoppved9OLSEQCHDlrgyuznZYNKEHrtyV4btDOTrnfHvceMydFQMvL294+/hiU0oy5HI5QkLDdHZw5SEHOfh2sJSFHKbrYCkLOTSRl5cj9+5t9eeCvFxcu3IJNrZ2aObkbLQcBPdQ51QHNmzYgL59+9Y4dT98+HAsWbIEWVlZ8PX1rfU17BqJsSA8AM0drVH8qAK7T11D3HdpqKpW6uwICh4IWXEx1qxagcLCB3D38MSatesh1XN6ggsPOcjBt4OlLOQwXQdLWcihyeWcC4iOjFB/TvxqKQCg/8ChiJm30Gg5jAKja0P5QqDi4l1IhM5YDlphsEO2O4qDJARBEATBPoWPKg12ONpYGOyQ1OFwnqV/DG9u+enFvLlrC5uLDQiCIAiCIIgGCU3rEwRBEARBsEwDm9ankVOCIAiCIAiCGWjk1Mhc3aT9VH9Dx2EYrcMlCIIgaoaL9aL1HkZf+cQXDau1BEEQBEEQBNNQ51RHwsPDIRAI1EUqlSIoKAhZWVmc+LMyzmJ29FSMGhyIPv4+OHn8SK08qVs2I7hfILr4+WDM6JHIrmU+LjyGOqwtzbH03Z64nBSO4h1T8OuykXilXVOj5yAHuw6WspDDdB0sZSEHmw7eaWDbl1LnVA+CgoKQn5+P/Px8HDlyBGZmZhg8eDAnbrlcDtd27RE1Y3atHfv37cWyJQmYOCUSqdt2wt3dA5MnTtDams0YHi4ciVF9EOjXEhHLDqJz5GYcPncbvywMhYvUqt61hRzcO1jKQg7TdbCUhRxsOgjuoc6pHojFYjg5OcHJyQkvv/wyPv74Y9y5cwcPHjww2N01oCciJkWhR68+tXakJCchbMQohIQOh6ubG+bExUMikWDXju1G9xjqkFiIENLdDbOTTuHUhTzcyC/Bwi3puJ5fgncH+tSrtpCDHwdLWchhug6WspCDTYdRaGDbl7KZqh5QVlaGTZs2wc3NDVKptK7j4EllJXIuXoB/twB1nVAohL9/ALLOZxjVw4XDTCSEmUiIisoqjfoKRRUCOrgYLQc52HSwlIUcputgKQs52HQYDZrWJ57Hnj17YG1tDWtra9jY2OCnn37C999/D6Gw7m+j7KEM1dXVWh1lqVSKwsJCo3q4cJTJn+B0Tj5iR78K58ZWEAoFGN3bHV09nODUWLdpfVbaQg7uHSxlIYfpOljKQg42HQQ/1H2vqh7Ru3dvZGZmIjMzE7///jsGDBiA4OBg/PXXXzUer1AoUFpaqlEUCoWRU9dfIpYdhEAgwI2UCSjZFYnIIR3xw4krUNKOuwRBEERDgqb1iedhZWUFNzc3uLm5oUuXLli/fj0eP36Mb775psbjExISYGdnp1FWf7GEl2wO9g4QiURai7iLiorg6OhoVA9XWW4WlKD/x9shDVuDduO+Rc/pP8BcJMTNghKj5SAHmw6WspDDdB0sZSEHmw6CH6hzagACgQBCoRByubzG72NjY1FSUqJRIj/8iJcs5hYW8OzghfTTaeo6pVKJ9PQ0+Hb0M6qHqyxPKVdUoUBWDntrMfp2aoU9p28YLQc52HSwlIUcputgKQs52HQYjQY2cko7ROmBQqFAQUEBAEAmk2HVqlUoKyvDkCFDajxeLBZDLBZr1JVWV9Z4rLy8HLl3b6s/F+Tl4tqVS7CxtUMzJ2ed8r09bjzmzoqBl5c3vH18sSklGXK5HCGhYTqdz6WHC0ffTi0hEAhw5a4Mrs52WDShB67cleG7Qzn1ri3k4N7BUhZymK6DpSzkYNNBcA91TvVg//79cHb+u6NoY2MDDw8PbNu2Db169TLYfTnnAqIjI9SfE79aCgDoP3AoYuYt1MkRFDwQsuJirFm1AoWFD+Du4Yk1a9dDquf0BBceLhx2jcRYEB6A5o7WKH5Ugd2nriHuuzRUVSvrXVvIwb2DpSzkMF0HS1nIwabDKAjZfKqeLwQqFT1dYkzuymoeOdUHU9tn2GHYCoMdst1RHCQhCIIgiJqR1OFwnmXvT3hzy3+dy5u7ttDIKUEQBEEQBMswujaUL6hzShAEQRAEwTKMviyfLxpWV5wgCIIgCIJgGho5NTKsrBd16DKVE4/szCrDHbRelCAIgiCeTwOb1m9YrSUIgiAIgiCYhjqnelJQUID3338fbdu2hVgsRosWLTBkyBAcOXLEYHfqls0I7heILn4+GDN6JLKzsnh3dO/kih+/nIgbBxdCnrEKQ3r5ah0zd/Ig3Di4EMVpn+OXr6fCtWUTZttDjoblYCkLOUzXwVIWcrDp4B2BgL/CINQ51YNbt27hlVdewdGjR7F06VJkZ2dj//796N27NyIjIw1y79+3F8uWJGDilEikbtsJd3cPTJ44QWtbNa4dVpZiZF/JxQcJ39f4fXR4X0x583VELUrFa2OX4bG8Ej+vjoTY4t9XhNRVe8jRcBwsZSGH6TpYykIONh0ED6gInQkODlY1b95cVVZWpvWdTCbTySF/UnMJGz5CNTcuXv35saJa1b1HD9WqNWufe44hDsnLkVpFpVKpRn6wVqMu7/5D1cfLd6g/N+0RrZJXVKrejvlWJXk5kpn2kKPhOVjKQg7TdbCUhRx166hLJP2W8FZYhEZOdaS4uBj79+9HZGQkrKystL63t7evtftJZSVyLl6Af7cAdZ1QKIS/fwCyzmcYzfFPWjeXwrmJHY6mX1LXlZZV4Myft9DVtzWvWchBjvqShRym62ApCznYdBD8QJ1THbl27RpUKhU8PDw4d8seylBdXQ2pVKpRL5VKUVhYaDTHP3FytAUA3C9+pFF/v+gRmkltec1CDnLUlyzkMF0HS1nIwabDaDSwNaf0KikdUdVil1eFQgGFQqHpEYkhFou5ikUQBEEQhKlDr5IiaqJdu3YQCAS4dOnSiw/+fxISEmBnZ6dRli5O0DrOwd4BIpFIawF2UVERHB0ddboWF45/UlBYCgBo2thGo76p1Ab3ikp5zUIOctSXLOQwXQdLWcjBpoPgB+qc6kjjxo0xYMAArF69Go8fP9b6/uHDh1p1sbGxKCkp0SgzY2K1jjO3sIBnBy+kn05T1ymVSqSnp8G3o59O+bhw/JNbuUXIf1CC3l3d1XU2VhJ08W6N9KxbvGYhBznqSxZymK6DpSzkYNNhNGhan3geq1evRvfu3fHqq69iwYIF8PX1RVVVFQ4dOoTExETk5ORoHC8Wa0/hV1TV7H573HjMnRUDLy9vePv4YlNKMuRyOUJCw3TOVxuHlaUFXFv8772lrZtL4du+OWSl5bhTIMPqLb8i5p0gXLv9ALdyixA3ZRDyH5Tgp1/Pc56FHOTQx8FSFnKYroOlLORg00FwD3VO9aBt27Y4d+4cFi5ciOjoaOTn56NJkyZ45ZVXkJiYaJA7KHggZMXFWLNqBQoLH8DdwxNr1q6HVI+phdo4OnVohYPrp6k/L5kxHACQ8tNpvBe3Ccs3HkYjSzFWzXkT9jaW+C3zOoZGroGi8jm97DpuDzkajoOlLOQwXQdLWcjBpsMoNLA1pwJVbZ70IWrN80ZOjY1Dl6mceGRnVnHiIQiCIAiWkdThcJ7lwK94c8v3TnvxQUaGRk4JgiAIgiBYhtG1oXzRsMaJCYIgCIIgCKahkVOCIAiCIAiWaWBrTqlz2kDhaq1o9M85Lz7oBcT2cjXY4WhjYbCDIBoSG9JvGeyY0LW1wQ6CIHSggXVOG1ZrCYIgCIIgCKahzqkOhIeHQyAQQCAQwNzcHM2aNUO/fv3w7bffQqlUcnad1C2bEdwvEF38fDBm9EhkZ2XVicNQz0APR6wO9dQoc/u21ev6WRlnMTt6KkYNDkQffx+cPH5E3yaoYeW+koN7B0tZTMnxlLO/fI8VEQNwYov+r8pjpS30e0YOYzh4p4G9hJ86pzoSFBSE/Px83Lp1C/v27UPv3r0xbdo0DB48GFVVhr8fav++vVi2JAETp0QiddtOuLt7YPLECVrbqvHt4MqTV1qB2L1X1OXzE3/plUEul8O1XXtEzZit13n/hJX7Sg7uHSxlMSXHU+7dvIw/j/8Cx5fa6H0uK22h3zNyGMNBcA91TnVELBbDyckJzZs3R6dOnTBr1izs3r0b+/btw8aNGw32pyQnIWzEKISEDoermxvmxMVDIpFg147tRnVw5VEqgVJFtbo8rqzWK0PXgJ6ImBSFHr366HXeP2HlvpKDewdLWUzJAQCVFXIcWLcYgeM+gNjKRq9zucrBioOlLORg02EUBEL+CoOwmaqeEBgYiI4dO2LHjh0GeZ5UViLn4gX4dwtQ1wmFQvj7ByDrfIbRHFx6mlhbYGGQG+L7uyK8swscLI3/7B0r95Uc3DtYymJKjqcc27QKrX1fRUuvTnqdx1UOVhwsZSEHmw6CH6hzaiAeHh64detWjd8pFAqUlpZqFIVCoXWc7KEM1dXVkEqlGvVSqRSFhYU65eDCwZXnlkyOlD/ysPq3O0jNLIC0kTmmv9YaYjPj/rqxcl/Jwb2DpSym5ACAK+nH8OCvawgYEaHzOVznYMXBUhZysOkwGrTmlNAHlUoFwXN+uAkJCbCzs9MoSxcnGDmh8bl47zEy8h4hr1SBnPuPsSbtDizNhejUXP/pQYIgjMej4vs4vjURA96LgZk5vZ6NIIi6gd5zaiA5OTlo06bmBwZiY2Mxffp0jTqVSKx1nIO9A0QikdYC7KKiIjg6OuqUgwsHl55nkT9R4n5ZJZpYGff/2bFyX8nBvYOlLKbkuH/rGuSlD7E1PlJdp1IqkXslG+eP/oTIdXsgFIrqRVvo94wcxnAYDUbXhvJFw2otxxw9ehTZ2dkYPnx4jd+LxWLY2tpqFLFYu3NqbmEBzw5eSD+dpq5TKpVIT0+Db0c/nbJw4eDS8yxikQCOVhYorTD8rQb6wMp9JQf3DpaymJKjhefLGLNgLd6an6guTVu3h7t/IN6an/jCjilLbaHfM3IYw2E0Gti0Po2c6ohCoUBBQQGqq6tx79497N+/HwkJCRg8eDDGjh1rsP/tceMxd1YMvLy84e3ji00pyZDL5QgJDTOqgwtPqHdTZOeXoVj+BHYSMwzydIRSpcLZu6U6Z5CXlyP37m3154K8XFy7cgk2tnZo5uRstLaQg10HS1lMxWFh2QjSl1pr1JmLJbC0stGqZ70tXDlYykIONh0E91DnVEf2798PZ2dnmJmZwcHBAR07dsSKFSswbtw4CIWGD0AHBQ+ErLgYa1atQGHhA7h7eGLN2vWQ6jG1wIWDC4+9pRnGd3GBlYUIZZXVuF5UjmXHb6FMj9dJXc65gOjI/z2QkfjVUgBA/4FDETNvodHaQg52HSxlMSUHF7DSFvo9I4cxHMbgec+2mCoClUqlqusQDQkjz2zzTvTPOQY7Ynu5GuxwtKGHNwhCHzak3zLYMaFra4MdBFFfkNThcF6j4d/y5i7fXrs3c/AJjZwSBEEQBEEwTEMbOaUHogiCIAiCIAhmoGl9I2Nq0/pc0HryjwY7biWO4CAJYcoUPqqs6wgAaAkKQdRX6nJa32pkEm/ux9vG8+auLTRyShAEQRAEQTADdU7/Qa9evfDBBx/odOzGjRthb2/Pax6CIAiCIBo2AoGAt8Ii1DlliNQtmxHcLxBd/HwwZvRIZGdl1YmDhSxCAfDRMC/8nhCMm6tDcXphED4c5Kl3BkNzkINth6GerIyzmB09FaMGB6KPvw9OHj+i9/W5cDyFlftKDnazkINNB99Q55SoE/bv24tlSxIwcUokUrfthLu7ByZPnKC1rRrfDlayTA32wLjX22LWlgy8Nu8APt2ejcig9pgQ6Fbv2kIOfhxceORyOVzbtUfUjNl6XZdrB8DOfSUHu1nIwaaD4J4G3Tl9/Pgxxo4dC2trazg7O2P58uUa3ysUCsyYMQPNmzeHlZUVunbtimPHjvGSJSU5CWEjRiEkdDhc3dwwJy4eEokEu3ZsN6qDlSxdXKU4cD4Ph7MLcKeoHHvO5eLYhXvwa+NQ79pCDn4cXHi6BvRExKQo9OjVR6/rcu0A2Lmv5GA3CznYdBgDGjltQMycORPHjx/H7t27cfDgQRw7dgznzp1Tfz916lSkpaUhNTUVWVlZGDlyJIKCgnD16lVOczyprETOxQvw7xagrhMKhfD3D0DW+QyjOVjKcuZ6EXp6NEXbZtYAgA4v2aFrO0cc/bOg3rWFHNw7uPSwACv3lRzsZiEHmw6CHxps57SsrAwbNmzAsmXL0KdPH/j4+CA5ORlVVX+/6+n27dtISkrCtm3b0LNnT7i6umLGjBno0aMHkpJ0e6WDQqFAaWmpRlEoFFrHyR7KUF1dDalUqlEvlUpRWFio07W4cLCUZeW+S9h15g5OLhiAO4lhODy3L9Ydvood6Xd0awhHOcjBpoNLDwuwcl/JwW4WcrDpMBY0ctpAuH79OiorK9G1a1d1XePGjeHu7g4AyM7ORnV1Ndq3bw9ra2t1OX78OK5fv67TNRISEmBnZ6dRli5O4KU9psbQzi8hrGtLTF6fjn6fHkZU0hlM7t8eo7q1qutoBEEQBEHwCG1f+hzKysogEonwxx9/QCQSaXxnbW2tkyM2NhbTp0/XqFOJxFrHOdg7QCQSaS3ALioqgqOjo07X4sLBUpZ5I3yxat9l7D5zFwBwKbcUL0kb4f1gd/yQ9pfRcpCDTQeXHhZg5b6Sg90s5GDTYTTYHODkjQY7curq6gpzc3Okp6er62QyGa5cuQIA8PPzQ3V1Ne7fvw83NzeN4uTkpNM1xGIxbG1tNYpYrN05NbewgGcHL6SfTlPXKZVKpKenwbejn07X4sLBUhZLCxGU/9i8rFqpglCo+/+FstIWcnDv4NLDAqzcV3Kwm4UcbDoIfmiwI6fW1taYMGECZs6cCalUiqZNm2L27NkQCv/ur7dv3x5jxozB2LFjsXz5cvj5+eHBgwc4cuQIfH19MWjQIE7zvD1uPObOioGXlze8fXyxKSUZcrkcIaFhRnWwkuVQVj6mDfJAbnE5LueVwrulPSb1a4+tp27Vu7aQgx8HFx55eTly795Wfy7Iy8W1K5dgY2uHZk7ORnNw0RZy8ONgKQs52HQYA1bXhvJFg+2cAsDSpUtRVlaGIUOGwMbGBtHR0SgpKVF/n5SUhE8//RTR0dHIzc2Fo6Mj/P39MXjwYM6zBAUPhKy4GGtWrUBh4QO4e3hizdr1kOoxtcCFg5Uss7ZkIibEC5+N8YPURoJ7D+X47sQNfP7zxXrXFnLw4+DCcznnAqIjI9SfE79aCgDoP3AoYuYtNJoDYOe+koPdLORg00Fwj0Cl+sfcKcErFVV1nYA9Wk/+0WDHrcQRHCQhTJnCR5V1HQEA4GhjUdcRCIKoBZI6HM5z+M9m3tyyTWN4c9eWBj1yShAEQRAEwToNbVq/wT4QRRAEQRAEQbAHjZwSdQ5NyRPGgIvpdIdhKwx2yHZHGewg2IWL5SO09IP4JzRyShAEQRAEQRB1BI2cEgRBEARBsEzDGjilkVN9CA8Pr3Ff2qCgIE78qVs2I7hfILr4+WDM6JHIzsqqEwdLWchBjvqQxdrSHEvf7YnLSeEo3jEFvy4biVfaNTV6DnLw4zDUk5VxFrOjp2LU4ED08ffByeNHapXB0Bzk4M9BcAt1TvUkKCgI+fn5GmXr1q0Ge/fv24tlSxIwcUokUrfthLu7ByZPnKC1rRrfDpaykIMc9SVLYlQfBPq1RMSyg+gcuRmHz93GLwtD4SK1qndtIQf3HrlcDtd27RE1Y7Ze1+U6Bzn4cRiDmgbGuCosQp1TPRGLxXByctIoDg4OBntTkpMQNmIUQkKHw9XNDXPi4iGRSLBrx3ajOljKQg5y1IcsEgsRQrq7YXbSKZy6kIcb+SVYuCUd1/NL8O5An3rVFnLw4+ka0BMRk6LQo1cfva7LdQ5y8OMguIc6pwzwpLISORcvwL9bgLpOKBTC3z8AWeczjOZgKQs5yFFfspiJhDATCVFRqbnDRoWiCgEdXIyWgxzcO7j0GAor94QcdQONnBL/yp49e2Btba1RFi1aVOOxCoUCpaWlGkWhUGgdJ3soQ3V1NaRSqUa9VCpFYWGhTrm4cLCUhRzkqC9ZyuRPcDonH7GjX4VzYysIhQKM7u2Orh5OcGqs27Q+K20hB38eQ2HlnpCjbqDOKfGv9O7dG5mZmRpl0qRJNR6bkJAAOzs7jbJ0cYKRExMEwTcRyw5CIBDgRsoElOyKROSQjvjhxBUoaXdogiAIvaFXSemJlZUV3NzcdDo2NjYW06dP16hTicRaxznYO0AkEmktwC4qKoKjo6NO1+LCwVIWcpCjPmW5WVCC/h9vRyOxGWwbWaBAVo6UmCDcLCgxWg5ycO/g0mMorNwTctQRjAxwVldXY/78+di0aRMKCgrg4uKC8PBwzJkzh9NRWBo55RGxWAxbW1uNIhZrd07NLSzg2cEL6afT1HVKpRLp6Wnw7ein07W4cLCUhRzkqE9ZnlKuqEKBrBz21mL07dQKe07fMFoOcnDv4NJjKKzcE3I0bBYvXozExESsWrUKOTk5WLx4MZYsWYKVK1dyeh0aOdUThUKBgoICjTozMzOD/8p6e9x4zJ0VAy8vb3j7+GJTSjLkcjlCQsOM6mApCznIUV+y9O3UEgKBAFfuyuDqbIdFE3rgyl0ZvjuUU+/aQg7uPfLycuTeva3+XJCXi2tXLsHG1g7NnJyN2h5ycO8wBqysDf3tt98wbNgwDBo0CADQunVrbN26Fb///jun16HOqZ7s378fzs6a/5i4u7vj0qVLBnmDggdCVlyMNatWoLDwAdw9PLFm7XpI9ej0cuFgKQs5yFFfstg1EmNBeACaO1qj+FEFdp+6hrjv0lBVrax3bSEH957LORcQHRmh/pz41VIAQP+BQxEzb6FR20MO7h31HYVCofWwtlgsrnGmNyAgAOvWrcOVK1fQvn17nD9/HidPnsTnn3/OaSaBSkUr9o1JRdWLjyEIgk0chq0w2CHbHcVBEoJVCh9VGuxwtLHgIAnBNZI6HM5zevdH3tyTmv+J+Ph4jbq4uDjMnz9f61ilUolZs2ZhyZIlEIlEqK6uxsKFCxEbG8tpJho5JQiCIAiCaKDU9PB2TaOmAPDDDz9g8+bN2LJlC7y8vJCZmYkPPvgALi4uGDduHGeZqHNKEARBEATBMHyuOX3eFH5NzJw5Ex9//DFGjx4NAPDx8cFff/2FhIQE6pwSBB9czntksGPWPt0fgHke2ye8arCD4AeakideBCtT8rS8wLRg5YGo8vJyCIWaL3oSiURQKnVfX68L1DklCIIgCIIgXsiQIUOwcOFCtGzZEl5eXsjIyMDnn3+OiIiIF5+sB9Q5JQiCIAiCYBk2Bk6xcuVKzJ07F1OmTMH9+/fh4uKCiRMnYt68eZxeh17Cryfh4eE17k177do1g92pWzYjuF8guvj5YMzokcjOyqoTB0tZ6tqxc2sSYiPHYuzQ1/DOyH5YEheNvDu39M4gbWSOGYFtsXVcJ+yY0BmrR3jDzVG3fdefpa7vB2sOlrKQw3QdLGUxxJGVcRazo6di1OBA9PH3wcnjR/S+Phc5TNHRULCxscGXX36Jv/76C3K5HNevX8enn34KCwtul4BQ57QWBAUFIT8/X6O0adPGIOf+fXuxbEkCJk6JROq2nXB398DkiRO0tlXj28FSFhYcF7POYcDQkVi4IglzPluN6qoqfPrxVFTI5TpnsLYQYWlIB1QpVYjbexmTf8jC+tO3UVap33vFWLgfLDlYykIO03WwlMVQh1wuh2u79oiaMVvna/KRw9QcxqCmQTGuCotQ57QWiMViODk5aRSRSGSQMyU5CWEjRiEkdDhc3dwwJy4eEokEu3ZsN6qDpSwsOGYnrESvAUPQorUrWru2R+TM+Si8X4AbV3V/8GnEy854UFaJL4/dxJUHj3HvUSUy7paioFTx4pM5bIupOVjKQg7TdbCUxVBH14CeiJgUhR69+uh8TT5ymJqD4B7qnDLAk8pK5Fy8AP9uAeo6oVAIf/8AZJ3PMJqDpSysOP5J+eMyAIC1ja3O53Rt7YBrDx4jtq8bNo/1w4rhXhjg0USv67JyP1hxsJSFHKbrYCkLH/+e1QZW2sKKw1jQyCnxQvbs2QNra2t1GTlyZI3HKRQKlJaWapR/bhEGALKHMlRXV0MqlWrUS6VSFBYW6pSJCwdLWVhxPItSqcTGxOVw9+qIlm3cdD7PyUaMgR2aIre0AnN/uYy9F+9jYvdW6NNe9+3xWLkfrDhYykIO03WwlIXrf89qCyttYcVB8AN1TmtB7969kZmZqS4rVtS8pWFCQgLs7Ow0ytLFCUZOS3DFhpWLcefWdXwwe5Fe5wkEwPXCx/ju97u4UVSO/TkPcCDnPoI7NOUpKUEQBGFKNLSRU3qVVC2wsrKCm9uLR85q2hJMJdLehcHB3gEikUhrAXZRUREcHXUbXePCwVIWVhxP2bByMc6ln0T88nWQNmmm17my8ie4LdN8gOrOwwoEtG2ss4OV+8GKg6Us5DBdB0tZuPz3zBBYaQsrDqPBZh+SN2jklEfEYjFsbW01Sk1bhJlbWMCzgxfST6ep65RKJdLT0+Db0U+na3HhYCkLKw6VSoUNKxfj91PHMG9JIpo6N9fpvGe5WFCG5vaWGnXN7SR48Ej3B6JYuR+sOFjKQg7TdbCUhav2GAorbWHFQfADjZwywtvjxmPurBh4eXnD28cXm1KSIZfLERIaZlQHS1lYcGxYuRgnj+7HR/HLYdmoER4W/70OqZGVNSzEEp0cu7ILsGyYJ0b5OeO/14vRvqk1gjybYOWJWzq3g4u2mJqDpSzkMF0HS1kMdcjLy5F797b6c0FeLq5duQQbWzs0c3KuV21hyWEMWJ1+5wvqnDJCUPBAyIqLsWbVChQWPoC7hyfWrF0PqR5TC1w4WMrCguPgzz8CAObPmKhRP2VGHHoNGKKT4+qDx/j04DWEv/oS3uzUHPceKbDut9s4dk2/9+ixcD9YcrCUhRym62Api6GOyzkXEB35v20mE79aCgDoP3AoYuYtrFdtYclBcI9ApVKp6jpEQ6JCv/euE0bkct4jgx2z9un+/tPnsX3CqwY7CIJo2BQ+qjTY4WjD7a4/9R1JHQ7ntYr6mTf3Xyt0G2gxJrTmlCAIgiAIgmAGmtYnCIIgCIJgGFpzShANFHcXG4MdR386bXgQmtYnCMJATGlKnpYoNDyoc0oQBEEQBMEwNHJKEARBEARBsEPD6pvSA1H6UFBQgGnTpsHNzQ0SiQTNmjVD9+7dkZiYiPLycoP9qVs2I7hfILr4+WDM6JHIzsqqEwdLWUzFYW1pjqXv9sTlpHAU75iCX5eNxCvt9N++lIW2sORgKQs5TNfBUhZy/I+sjLOYHT0VowYHoo+/D04eP6J3Bi5yENxDnVMduXHjBvz8/HDw4EEsWrQIGRkZSEtLw0cffYQ9e/bg8OHDBvn379uLZUsSMHFKJFK37YS7uwcmT5ygta0a3w6WspiSIzGqDwL9WiJi2UF0jtyMw+du45eFoXCRWtW7trDiYCkLOUzXwVIWcmgil8vh2q49ombM1vkcPnIYA4FAwFthEeqc6siUKVNgZmaGs2fPYtSoUfD09ETbtm0xbNgw/PLLLxgyxLD3hKUkJyFsxCiEhA6Hq5sb5sTFQyKRYNeO7UZ1sJTFVBwSCxFCurthdtIpnLqQhxv5JVi4JR3X80vw7kCfetUWlhwsZSGH6TpYykIOTboG9ETEpCj06NVH53P4yEFwD3VOdaCoqAgHDx5EZGQkrKxqHuky5K+PJ5WVyLl4Af7dAtR1QqEQ/v4ByDqfYTQHS1lMyWEmEsJMJERFpeYODBWKKgR0cDFaDlNysJSFHKbrYCkLObiHlRy6QCOnhBbXrl2DSqWCu7u7Rr2joyOsra1hbW2NmJgYrfMUCgVKS0s1ikKh0DpO9lCG6upqSKVSjXqpVIrCwkKdMnLhYCmLKTnK5E9wOicfsaNfhXNjKwiFAozu7Y6uHk5waqzbtD4rbWHFwVIWcpiug6Us5OAeVnIQ2lDn1AB+//13ZGZmwsvLq8ZOZ0JCAuzs7DTK0sUJdZCUqGsilh2EQCDAjZQJKNkVicghHfHDiStQ0u7BBEEQxAsQCPgrLEKvktIBNzc3CAQCXL58WaO+bdu2AABLS8saz4uNjcX06dM16lQisdZxDvYOEIlEWguwi4qK4OjoqFNGLhwsZTElBwDcLChB/4+3o5HYDLaNLFAgK0dKTBBuFpQYLYcpOVjKQg7TdbCUhRzcw0oOQhsaOdUBqVSKfv36YdWqVXj8+LHO54nFYtja2moUsVi7c2puYQHPDl5IP52mrlMqlUhPT4NvRz+drsWFg6UspuR4lnJFFQpk5bC3FqNvp1bYc/qG0XKYkoOlLOQwXQdLWcjBPazk0IWGtuaURk51ZM2aNejevTs6d+6M+fPnw9fXF0KhEGfOnMGlS5fwyiuvGOR/e9x4zJ0VAy8vb3j7+GJTSjLkcjlCQsOM6mApiyk5+nZqCYFAgCt3ZXB1tsOiCT1w5a4M3x3KqXdtYcXBUhZymK6DpSzk0EReXo7cu7fVnwvycnHtyiXY2NqhmZOz0XIYA0b7kLxBnVMdcXV1RUZGBhYtWoTY2FjcvXsXYrEYHTp0wIwZMzBlyhSD/EHBAyErLsaaVStQWPgA7h6eWLN2PaR6TC1w4WApiyk57BqJsSA8AM0drVH8qAK7T11D3HdpqKpW1ru2sOJgKQs5TNfBUhZyaHI55wKiIyPUnxO/WgoA6D9wKGLmLTRaDoJ7BCoVPZFhTCqqXnwMUX9xGLbCYIdsdxQHSQiCIEyDwkeVBjscbSwMdkjqcDjPPeYAb+7Liwfw5q4ttOaUIAiCIAiCYAaa1icIgiAIgmAYWnNKEEaGlSkbLqApedOGlm0QhPFh5d93wnhQ55QgCIIgCIJhhMKGNXRKa04JgiAIgiAIZqDOqY6Eh4cjJCREq/7YsWMQCAR4+PChwddI3bIZwf0C0cXPB2NGj0R2VladOFjIkpVxFrOjp2LU4ED08ffByeNH9L4+FznIwbaDlSzWluZY+m5PXE4KR/GOKfh12Ui80q6p0XOQgx8HS1nIwaaDbxra9qXUOWWE/fv2YtmSBEycEonUbTvh7u6ByRMnaG2rxreDlSxyuRyu7dojasZsvbJznYMc7DpYypIY1QeBfi0RsewgOkduxuFzt/HLwlC4SK3qXVvIwW4WcrDpMAYNbYco6pwyQkpyEsJGjEJI6HC4urlhTlw8JBIJdu3YblQHK1m6BvRExKQo9OjVR6/sXOcgB7sOVrJILEQI6e6G2UmncOpCHm7kl2DhlnRczy/BuwN96lVbyMF2FnKw6SC4hzqnDPCkshI5Fy/Av1uAuk4oFMLfPwBZ5zOM5mAti6Gw0hZycO9gKYuZSAgzkRAVlZo7bFQoqhDQwcVoOcjBvYOlLORg02EsaFqfeC579uyBtbW1RgkODn7u8QqFAqWlpRpFoVBoHSd7KEN1dTWkUqlGvVQqRWFhoU7ZuHCwlsVQWGkLObh3sJSlTP4Ep3PyETv6VTg3toJQKMDo3u7o6uEEp8a6Teuz0hZysJuFHGw6CH6gzqke9O7dG5mZmRpl/fr1zz0+ISEBdnZ2GmXp4gQjJiYIwhhELDsIgUCAGykTULIrEpFDOuKHE1egpN2hCYLggIa25pTec6oHVlZWcHNz06i7e/fuc4+PjY3F9OnTNepUIrHWcQ72DhCJRFoLsIuKiuDo6KhTNi4crGUxFFbaQg7uHaxluVlQgv4fb0cjsRlsG1mgQFaOlJgg3CwoMVoOcnDvYCkLOdh0EPxAI6c8IhaLYWtrq1HEYu3OqbmFBTw7eCH9dJq6TqlUIj09Db4d/XS6FhcO1rIYCittIQf3DtayPKVcUYUCWTnsrcXo26kV9py+YbQc5ODewVIWcrDpMBY0ckrUCW+PG4+5s2Lg5eUNbx9fbEpJhlwuR0homFEdrGSRl5cj9+5t9eeCvFxcu3IJNrZ2aObkXK/aQg5+HCxl6dupJQQCAa7clcHV2Q6LJvTAlbsyfHcop961hRzsZiEHmw6Ce6hzyghBwQMhKy7GmlUrUFj4AO4enlizdj2kekwtcOFgJcvlnAuIjoxQf078aikAoP/AoYiZt7BetYUc/DhYymLXSIwF4QFo7miN4kcV2H3qGuK+S0NVtbLetYUc7GYhB5sOY8DoACdvCFQqWrFvTCqqXnxMQ6PwUaXBDkcbCw6SEMS/4zBshcEO2e4oDpIQBGFsJHU4nOcXf5Q3d0ZcIG/u2kJrTgmCIAiCIAhmoGl9giAIgiAIhmlo0/rUOSXqHJqS5x6HLlMNdsjOrOIgiWlBU/IEQRD8Q51TgiAIgiAIhmH1lU98QWtOCYIgCIIgCGagzqmBhIeHIyQkhBNX6pbNCO4XiC5+PhgzeiSys7LqxMFSFnIY5ujeyRU/fjkRNw4uhDxjFYb08tU6Zu7kQbhxcCGK0z7HL19PhWvLJky2hfUs5DBdB0tZyMGmg28EAv4Ki1DnlBH279uLZUsSMHFKJFK37YS7uwcmT5ygta0a3w6WspDDcIeVpRjZV3LxQcL3NX4fHd4XU958HVGLUvHa2GV4LK/Ez6sjIbZ4/oofVu4HS1nIYboOlrKQg00HwT3UOWWElOQkhI0YhZDQ4XB1c8OcuHhIJBLs2rHdqA6WspDDcMfBUxcRv2YPfvq15pGAyLd6Y/E3B7DnWDb+vJqHd+Z+B+cmdhjauyNzbWE5CzlM18FSFnKw6TAGDW37UuqcMsCTykrkXLwA/24B6jqhUAh//wBknc8wmoOlLOTg3vFPWjeXwrmJHY6mX1LXlZZV4Myft9DVtzXzbWElCzlM18FSFnKw6SD4gTqnPKJQKFBaWqpRFAqF1nGyhzJUV1dDKpVq1EulUhQWFup0LS4cLGUhB/eOf+LkaAsAuF/8SKP+ftEjNJPa8paDflfJUV8cLGUhB5sOY0FrTgnOSEhIgJ2dnUZZujihrmMRBEEQBFGPaGjT+vSeUx6JjY3F9OnTNepUIrHWcQ72DhCJRFoLsIuKiuDo6KjTtbhwsJSFHNw7/klBYSkAoGljG/X/BoCmUhtkXb7LWw76XSVHfXGwlIUcbDoIfqCRUx4Ri8WwtbXVKGKxdufU3MICnh28kH46TV2nVCqRnp4G345+Ol2LCwdLWcjBveOf3MotQv6DEvTu6q6us7GSoIt3a6Rn3WK+LaxkIYfpOljKQg42HcaioU3r08gpI7w9bjzmzoqBl5c3vH18sSklGXK5HCGhYUZ1sJSFHIY7rCwt4Nrif+8tbd1cCt/2zSErLcedAhlWb/kVMe8E4drtB7iVW4S4KYOQ/6AEP/16HusYawvLWchhug6WspCDTQfBPdQ5ZYSg4IGQFRdjzaoVKCx8AHcPT6xZux5SPaYWuHCwlIUchjs6dWiFg+unqT8vmTEcAJDy02m8F7cJyzceRiNLMVbNeRP2Npb4LfM6hkaugaKyirm2sJyFHKbrYCkLOdh0GANW14byhUClUqnqOkRDouL5/z+fIDjDoctUgx2yM6s4SEIQBGEaSOpwOK/b4hO8udNiXuPNXVto5JQgCIIgCIJhGtjAKT0QRRAEQRAEQbADjZwShAnCxZR810+OGOxIn9vHYIepUfio0mCHo40FB0mIZ7mc9+jFB+mAu4sNJx6CeJaGtuaUOqcEQRAEQRAM08D6pjStTxAEQRAEQbADdU4NJDw8HCEhIZy4UrdsRnC/QHTx88GY0SORnZVVJw6WspCDPcfeDwJwPr6PVokd5P7ikznMwaWHBUdWxlnMjp6KUYMD0cffByeP125ZBQttMSXHzq1JiI0ci7FDX8M7I/thSVw08u7c0jsHF1nIwa6Dbxra9qXUOWWE/fv2YtmSBEycEonUbTvh7u6ByRMnaG2rxreDpSzkYNMxZt0ZBC79r7q8l3wOAHDowj2j5uDKw4pDLpfDtV17RM2YrfM5fOQghyYXs85hwNCRWLgiCXM+W43qqip8+vFUVMjlOjtYag85uHcQ3EOdU0ZISU5C2IhRCAkdDlc3N8yJi4dEIsGuHduN6mApCznYdMjKn6CorFJdXmvviNtF5Th766FRc3DlYcXRNaAnIiZFoUev2j9ExkpbTMkxO2Eleg0YghatXdHatT0iZ85H4f0C3Liao7ODpfaQg3uHMaCRU8LoPKmsRM7FC/DvFqCuEwqF8PcPQNb5DKM5WMpCDjYd/8RMJMAgXyfsysjT+Rz6XeUHVtpiSo6aKH9cBgCwtrHV+RxW2kMO7h0EP1DnlEcUCgVKS0s1ikKh0DpO9lCG6upqSKVSjXqpVIrCwkKdrsWFg6Us5GDT8U8CPZrARmKGnzLzdT6Hflf5gZW2mJLjnyiVSmxMXA53r45o2cZN5/NYaQ85uHcYC4GAv8Ii1DnlkYSEBNjZ2WmUpYsT6joWQXBGaCcXnLpWhAccvLuTIFhnw8rFuHPrOj6YvaiuoxCESUPvOeWR2NhYTJ8+XaNOJRJrHedg7wCRSKS1ALuoqAiOjo46XYsLB0tZyMGm41mc7STo2rYxpqfq92Qr/a7yAyttMSXHs2xYuRjn0k8ifvk6SJs00+tcVtpDDu4dxoLVtaF8QSOnPCIWi2Fra6tRxGLtzqm5hQU8O3gh/XSauk6pVCI9PQ2+Hf10uhYXDpaykINNx7MM83NG8eNK/Peqfk+10u8qP7DSFlNyAIBKpcKGlYvx+6ljmLckEU2dm+t8LpdZyMGmw1g0tGl9GjllhLfHjcfcWTHw8vKGt48vNqUkQy6XIyQ0zKgOlrKQg00H8Pc/aMP8nPFzZj6qlSq9zuUyByv3hAuHvLwcuXdvqz8X5OXi2pVLsLG1QzMn53rVFlNybFi5GCeP7sdH8cth2agRHhb/vRaxkZU1LMSSetcecnDvILiHOqcGolQqYWZm+G0MCh4IWXEx1qxagcLCB3D38MSatesh1WNqgQsHS1nIwaYDAPzbNoaLvaVeT+nzkYOVe8KF43LOBURHRqg/J361FADQf+BQxMxbWK/aYkqOgz//CACYP2OiRv2UGXHoNWCIUbOQg02HMWho0/oClUql/7AHoSYoKAhubm5YtWqVTsdXVPEciCA4ousntduh6FnS59b+nZ2mSiEHD4852lhwkIR4lst5jzjxuLvYcOIh2ENSh8N5gSvSXnxQLTka1Y03d22hNae1RCaTYc+ePTh27Bj69u1b13EIgiAIgjBRaM0poRMRERE4c+YMoqOjMWzYsLqOQxAEQRAEYRJQ57SW7Ny5s64jEASvcDEl33ryjxwkAW4ljuDEQxDPQ2qj/SYVgmAFIatDnDxB0/oEQRAEQRAEM9DIKUEQBEEQBMM0sIFTGjnVh/DwcISEhGjU/fjjj5BIJFi+fLnB/tQtmxHcLxBd/HwwZvRIZGfpt/MOVw6WspDDNB1CAfDRMC/8nhCMm6tDcXphED4c5Kl3Di6ysOLIyjiL2dFTMWpwIPr4++Dk8dq9LYGFtpiSg6ufCxdZyMGug28EAgFvhUWoc2oA69evx5gxY5CYmIjo6GiDXPv37cWyJQmYOCUSqdt2wt3dA5MnTtDaVo1vB0tZyGG6jqnBHhj3elvM2pKB1+YdwKfbsxEZ1B4TAt10drDUHi4ccrkcru3aI2rGbJ3P4SMHOTTh4ufCVRZysOkguIc6p7VkyZIleP/995Gamorx48cb7EtJTkLYiFEICR0OVzc3zImLh0Qiwa4d243qYCkLOUzX0cVVigPn83A4uwB3isqx51wujl24B782Djo7WGoPF46uAT0RMSkKPXrV/kE0VtpiSg4ufi5cZSEHmw5jIBTwV1iEOqe1ICYmBp988gn27NmD0NBQg31PKiuRc/EC/LsFqOuEQiH8/QOQdT7DaA6WspDDdB0AcOZ6EXp6NEXbZtYAgA4v2aFrO0cc/bNAZwcr7eHqnhgKK20xJQdXsNIecnDvaIjk5ubiP//5D6RSKSwtLeHj44OzZ89yeg3qnOrJvn37sGTJEuzevRt9+vz7X9IKhQKlpaUaRaFQaB0neyhDdXU1pFKpRr1UKkVhYaFOubhwsJSFHKbrAICV+y5h15k7OLlgAO4khuHw3L5Yd/gqdqTf0dnBSnu4uieGwkpbTMnBFay0hxzcO4wFK2tOZTIZunfvDnNzc+zbtw8XL17E8uXL4eCg36zXi6Cn9fXE19cXhYWFiIuLw6uvvgpra+vnHpuQkID4+HiNutlz4zBn3nyeUxIE2wzt/BLCurbE5PXpuJxXCu8W9ljwRkfce1iBH9L+qut4BEEQRA0sXrwYLVq0QFJSkrquTZs2nF+HRk71pHnz5jh27Bhyc3MRFBSER4+evx9zbGwsSkpKNMrMmFit4xzsHSASibQWYBcVFcHR0VGnXFw4WMpCDtN1AMC8Eb5Yte8ydp+5i0u5pfjx9G2sO3wV7we76+xgpT1c3RNDYaUtpuTgClbaQw7uHcaCz+1LdZ3lBYCffvoJnTt3xsiRI9G0aVP4+fnhm2++4by91DmtBa1atcLx48dRUFDwrx1UsVgMW1tbjSIWa+9CYm5hAc8OXkg/naauUyqVSE9Pg29HP50yceFgKQs5TNcBAJYWIihVKo26aqUKQj1W57PSHq7uiaGw0hZTcnAFK+0hB/cOUyAhIQF2dnYaJSEhocZjb9y4gcTERLRr1w4HDhzA5MmTERUVheTkZE4z0bR+LWnRogWOHTuG3r17Y8CAAdi/fz9sbW1r7Xt73HjMnRUDLy9vePv4YlNKMuRyOUJCw4zqYCkLOUzXcSgrH9MGeSC3uPzvaf2W9pjUrz22nrqls4Ol9nDhkJeXI/fubfXngrxcXLtyCTa2dmjm5Fyv2mJKDi5+Liy1hxzcO4yBAPw9Vh8bG4vp06dr1NU0kAb83Xnv3LkzFi1aBADw8/PDn3/+ia+//hrjxo3jLBN1Tg3gpZde0uigHjhwoNYd1KDggZAVF2PNqhUoLHwAdw9PrFm7HlI9pha4cLCUhRym65i1JRMxIV74bIwfpDYS3Hsox3cnbuDzny/q7GCpPVw4LudcQHRkhPpz4ldLAQD9Bw5FzLyF9aotpuTg4ufCUnvIwb3DGPD5yiexWPzczug/cXZ2RocOHTTqPD09sX07t6/eEqhU/5hbI3iloqquExCE8Wg9+UdOPLcSR3DiYYHCR5UGOxxtLDhIQjwLFz8XgH42poykDofzhq47w5v7p/e66HzsW2+9hTt37uC///2vuu7DDz9Eeno6fvvtN84y0cgpQRAEQRAEw7CyzeiHH36IgIAALFq0CKNGjcLvv/+OdevWYd26dZxehx6IIgiCIAiCIF5Ily5dsHPnTmzduhXe3t745JNP8OWXX2LMmDGcXoem9Y0MTetrQ9OcRH3BYdgKgx2y3VEcJCEIwtjU5bR+yHpud2B6ll3vdObNXVto5JQgCIIgCIJgBuqc6kl4eDhCQkJ4cadu2YzgfoHo4ueDMaNHIjsrq04cLGTJyjiL2dFTMWpwIPr4++Dk8SN6X5+LHORg28FKFmtLcyx9tycuJ4WjeMcU/LpsJF5p19ToOcjBj4OlLORg08E3QoGAt8Ii1DllhP379mLZkgRMnBKJ1G074e7ugckTJ2jtXMG3g5Uscrkcru3aI2rGbL2yc52DHOw6WMqSGNUHgX4tEbHsIDpHbsbhc7fxy8JQuEit6l1byMFuFnKw6SC4hzqnjJCSnISwEaMQEjocrm5umBMXD4lEgl07dH93GBcOVrJ0DeiJiElR6NGrj17Zuc5BDnYdrGSRWIgQ0t0Ns5NO4dSFPNzIL8HCLem4nl+Cdwf61Ku2kIPtLORg02EM+Ny+lEWoc8oATyorkXPxAvy7BajrhEIh/P0DkHU+w2gO1rIYCittIQf3DpaymImEMBMJUVGp+bRjhaIKAR1cjJaDHNw7WMpCDjYdxkIgEPBWWIQ6pwwgeyhDdXU1pFKpRr1UKkVhYaHRHKxlMRRW2kIO7h0sZSmTP8HpnHzEjn4Vzo2tIBQKMLq3O7p6OMGpsW7T+qy0hRzsZiEHmw6CH+gl/DyiUCigUCg06lQi3bcJIwiifhCx7CDWftAXN1ImoKpaicxr9/HDiSvwc9P/oSiCIIh/wugAJ2/QyCmPJCQkwM7OTqMsXZygdZyDvQNEIpHWAuyioiI46ri/LxcO1rIYCittIQf3Dtay3CwoQf+Pt0Matgbtxn2LntN/gLlIiJsFJUbLQQ7uHSxlIQebDoIfqHPKI7GxsSgpKdEoM2NitY4zt7CAZwcvpJ9OU9cplUqkp6fBt6OfTtfiwsFaFkNhpS3k4N7BWpanlCuqUCArh721GH07tcKe0zeMloMc3DtYykIONh3GoqG9Soqm9WtBSUkJMjMzNeqkUilatGihUScWa0/hP2+HqLfHjcfcWTHw8vKGt48vNqUkQy6XIyQ0TOdcXDhYySIvL0fu3dvqzwV5ubh25RJsbO3QzMm5XrWFHPw4WMrSt1NLCAQCXLkrg6uzHRZN6IErd2X47lBOvWsLOdjNQg42HQT3UOe0Fhw7dgx+fpp/VU2YMAHr16+vtTMoeCBkxcVYs2oFCgsfwN3DE2vWrodUj6kFLhysZLmccwHRkRHqz4lfLQUA9B84FDHzFtartpCDHwdLWewaibEgPADNHa1R/KgCu09dQ9x3aaiqVta7tpCD3SzkYNNhDNgc3+QPgUqlUtV1iIbE80ZOGzKFjyoNdjjaWHCQhCD+HYdhKwx2yHZHcZCEIAhjI6nD4bzRyfy92ip1HFtLGAAaOSUIgiAIgmAaVt9HyhfUOSUIgiAIgmAYYcPqm9LT+gRBEARBEAQ70MgpQRANgq6fHDHYcTpxPAdJCIIg9KOhTevTyClBEARBEATBDNQ5fQHh4eEQCASYNGmS1neRkZEQCAQIDw/n5FqpWzYjuF8guvj5YMzokcjOyqoTBwtZsjLOYnb0VIwaHIg+/j44ebz2o1513RZy8Ocw1LP3gwCcj++jVWIHuevs2Lk1CbGRYzF26Gt4Z2Q/LImLRt6dW7VoCTv3lRzsZiEHmw6+EQj4KyxCnVMdaNGiBVJTUyGXy9V1FRUV2LJlC1q2bMnJNfbv24tlSxIwcUokUrfthLu7ByZPnKC1rRrfDlayyOVyuLZrj6gZs/XKznUOcrDr4MIzZt0ZBC79r7q8l3wOAHDowj2dM1zMOocBQ0di4YokzPlsNaqrqvDpx1NR8cy/F8ZoCzn4cbCUhRxsOgjuoc6pDnTq1AktWrTAjh071HU7duxAy5YttV7GX1tSkpMQNmIUQkKHw9XNDXPi4iGRSLBrx3ajOljJ0jWgJyImRaFHrz56Zec6BznYdXDhkZU/QVFZpbq81t4Rt4vKcfbWQ50zzE5YiV4DhqBFa1e0dm2PyJnzUXi/ADeu6r47FBdtIQc/DpaykINNhzEQCAS8FRbRqXP6008/6VxMlYiICCQlJak/f/vttxg/npuHI55UViLn4gX4dwtQ1wmFQvj7ByDrvG4v3uXCwVoWQ2GlLeTg3sGl5ylmIgEG+TphV0ae3uc+S/njMgCAtY2tzuewcl/JwW4WcrDpIPhBp6f1Q0JCdJIJBAJUV1cbkodZ/vOf/yA2NhZ//fUXAODUqVNITU3FsWPHDHbLHspQXV0NqVSqUS+VSnHz5g2jOVjLYiistIUc3Du49Dwl0KMJbCRm+CkzX+9zn6JUKrExcTncvTqiZRs3nc9j5b6Sg90s5GDTYSwa2ntOdeqcKpW67w9tqjRp0gSDBg3Cxo0boVKpMGjQIDi+YO9dhUIBhUKhUacSiSEWi/mMShBELQjt5IJT14rwwIDtdDesXIw7t65jwRfrOUxGEERDh9Xpd76gNad6EBERgY0bNyI5ORkREREvPD4hIQF2dnYaZeniBK3jHOwdIBKJtBZgFxUVvbADzKWDtSyGwkpbyMG9g0sPADjbSdC1bWPs+KP2U/obVi7GufSTiFv6NaRNmul1Liv3lRzsZiEHmw6CH2rVOX38+DH27t2Lr7/+GitWrNAopkxQUBAqKyvx5MkTDBgw4IXHx8bGoqSkRKPMjInVOs7cwgKeHbyQfjpNXadUKpGengbfjro9cMWFg7UshsJKW8jBvYNLDwAM83NG8eNK/Peq/k/oqlQqbFi5GL+fOoZ5SxLR1Lm53g5W7is52M1CDjYdxkLAY2ERvXeIysjIwMCBA1FeXo7Hjx+jcePGKCwsRKNGjdC0aVNERUXxkZMJRCIRcnJy1P/7RYjF2lP4FVU1H/v2uPGYOysGXl7e8PbxxaaUZMjlcoSEhumcjwsHK1nk5eXIvXtb/bkgLxfXrlyCja0dmjk516u2kIMfB1cegeDvzunPmfmoVqr0uj7w94jpyaP78VH8clg2aoSHxYUAgEZW1rAQS4zaFnJw72ApCznYdBDco3fn9MMPP8SQIUPw9ddfw87ODqdPn4a5uTn+85//YNq0aXxkZApbW92fwNWHoOCBkBUXY82qFSgsfAB3D0+sWbseUj2mFrhwsJLlcs4FREf+b+lE4ldLAQD9Bw5FzLyF9aot5ODHwZXHv21juNhb1vop/YM//wgAmD9jokb9lBlx6DVgiM4eVu4rOdjNQg42HcZA2MDWnApUKpVeQwX29vZIT0+Hu7s77O3tkZaWBk9PT6Snp2PcuHG4dOkSX1lNgueNnDZkCg14AOUpjjYWHCQhTJmun9R+l7GnfDfhVYMd7i42BjsIgjA+Er2H87jjne//5M29/g1v3ty1Re81p+bm5hAK/z6tadOmuH3776lXOzs73Llzh9t0BEEQBEEQDZyGtn2p3n8H+Pn54cyZM2jXrh1ef/11zJs3D4WFhUhJSYG3N3u9b4IgCIIgCKL+oPfI6aJFi+Ds/PcDKQsXLoSDgwMmT56MBw8eYN26dZwHJAiCIAiCaMg0tO1L9V5zShgGrTnVhtacEvUFh2GGvy5Pttt032hCEKZMXa45fW/bBd7c60Z68eauLXV4qwmCIAiCIIgXwegAJ2/oPa3fpk0btG3b9rnFVAkPD4dAIMBnn32mUb9r1y7OhsVTt2xGcL9AdPHzwZjRI5GdlVUnDhayZGWcxezoqRg1OBB9/H1w8njtn7Su67aQgz8HK1msLc2x9N2euJwUjuIdU/DrspF4pV1To+cgBz8OlrKQg00H3wgFAt4Ki+jdOf3ggw8wbdo0dZkyZQq6deuGkpISvPfee3xkZAaJRILFixdDJpNx7t6/by+WLUnAxCmRSN22E+7uHpg8cYLWtmp8O1jJIpfL4dquPaJmzNYrO9c5yMGug6UsiVF9EOjXEhHLDqJz5GYcPncbvywMhYvUqt61hRzsZiEHmw6Ce/TunD7bMZ02bRpmzJiBzZs3Y8GCBbh8+TIfGZmhb9++cHJyQkJCAufulOQkhI0YhZDQ4XB1c8OcuHhIJBLs2rHdqA5WsnQN6ImISVHo0auPXtm5zkEOdh2sZJFYiBDS3Q2zk07h1IU83MgvwcIt6bieX4J3B/rUq7aQg+0s5GDTYQwa2quk9O6cPo/g4GBs387WD5NrRCIRFi1ahJUrV+Lu3buceZ9UViLn4gX4dwtQ1wmFQvj7ByDrfIbRHKxlMRRW2kIO7h0sZTETCWEmEqKiUvNpxwpFFQI6uBgtBzm4d7CUhRxsOgh+4Kxz+uOPP6Jx48Zc6ZglNDQUL7/8MuLi4jhzyh7KUF1dDalUqlEvlUpRWFhoNAdrWQyFlbaQg3sHS1nK5E9wOicfsaNfhXNjKwiFAozu7Y6uHk5waqzbtD4rbSEHu1nIwabDWDS0V0nV6iX8zzZGpVKhoKAADx48wJo1azgNxyqLFy9GYGAgZsyY8a/HKRQKKBQKjTqVSAyxWMxnPIIgjEzEsoNY+0Ff3EiZgKpqJTKv3ccPJ67Az03/h6IIgiAaOnp3TocNG6bRORUKhWjSpAl69eoFDw8PTsOxymuvvYYBAwYgNjYW4eHhzz0uISEB8fHxGnWz58Zhzrz5GnUO9g4QiURaC7CLiorg6OioUyYuHKxlMRRW2kIO7h2sZblZUIL+H29HI7EZbBtZoEBWjpSYINwsKDFaDnJw72ApCznYdBgLzqa56wl6t3f+/PmIi4tTl7lz52LSpEkNpmP6lM8++ww///wz0tLSnntMbGwsSkpKNMrMmFit48wtLODZwQvpp//nUiqVSE9Pg29HP53ycOFgLYuhsNIWcnDvYC3LU8oVVSiQlcPeWoy+nVphz+kbRstBDu4dLGUhB5sOgh/0HjkViUTIz89H06aa01VFRUVo2rQpqqurOQvHMj4+PhgzZgxWrHj+jjFisfYU/vN2iHp73HjMnRUDLy9vePv4YlNKMuRyOUJCw3TOxIWDlSzy8nLk3r2t/lyQl4trVy7BxtYOzZyc61VbyMGPg6UsfTu1hEAgwJW7Mrg622HRhB64cleG7w7l1Lu2kIPdLORg02EMWF0byhd6d06ft9upQqGAhUXD2kJywYIF+P777zlxBQUPhKy4GGtWrUBh4QO4e3hizdr1kOoxtcCFg5Usl3MuIDoyQv058aulAID+A4ciZt7CetUWcvDjYCmLXSMxFoQHoLmjNYofVWD3qWuI+y4NVdXKetcWcrCbhRxsOoyBsGH1TSFQPa+3+Q+ejhB++OGH+OSTT2Btba3+rrq6GidOnMCtW7eQkUGvX/g3njdy2pApfFRpsMPRpmH9YUTUDQ7Dnj9Toiuy3VEcJCEIwthI6nDD9w92X+LN/eUw9pZl6nyrv/jiCwB/j5x+/fXXEIlE6u8sLCzQunVrfP3119wnJAiCIAiCaMA0tJFTnTunN2/eBAD07t0bO3bsgIODA2+hCIIgCIIgiIaJ3oPUv/76Kx85CIIgCIIgiBqgB6JewPDhw/Hqq68iJiZGo37JkiU4c+YMtm3bxlk4omFA60WJ+gIX60Uv5z0y2DHgkwMGO24ljjDYQRAEwQd6v+f0xIkTGDhwoFZ9cHAwTpw4wUkoQxEIBNi1a9dzvz927BgEAgEePnxotEwEQRAEQRC1QSjgr7CI3p3TsrKyGl8ZZW5ujtLSUk5C6cr8+fPx8ssv631eQEAA8vPzYWdnx30oA0jdshnB/QLRxc8HY0aPRHZWVp04WMpCDnLUlyyGOHZuTUJs5FiMHfoa3hnZD0viopF355Ze1xcKgI+GeeH3hGDcXB2K0wuD8OEgTz1b8Td1fT9Yc7CUhRxsOghu0btz6uPjU+O7PVNTU9GhQwdOQvGNhYUFnJycmFrDsX/fXixbkoCJUyKRum0n3N09MHniBK1t1fh2sJSFHOSoL1kMdVzMOocBQ0di4YokzPlsNaqrqvDpx1NRIZfrnGFqsAfGvd4Ws7Zk4LV5B/Dp9mxEBrXHhEA3nR1ctMXUHCxlIQebDmMgEPBXWETvzuncuXPxySefYNy4cUhOTkZycjLGjh2LTz/9FHPnztXL1atXL0RFReGjjz5C48aN4eTkhPnz56u/v337NoYNGwZra2vY2tpi1KhRuHfvHgBg48aNiI+Px/nz5yEQCCAQCLBx40b1uYWFhQgNDUWjRo3Qrl07/PTTT+rv/jmtv3HjRtjb2+PAgQPw9PSEtbU1goKCkJ+frz6nqqoKUVFRsLe3h1QqRUxMDMaNG4eQkBB9b2GNpCQnIWzEKISEDoermxvmxMVDIpFg147tRnWwlIUc5KgvWQx1zE5YiV4DhqBFa1e0dm2PyJnzUXi/ADeu6r7DVBdXKQ6cz8Ph7ALcKSrHnnO5OHbhHvza6PdmFRbuB0sOlrKQg02HMRAKBLwVFtG7czpkyBDs2rUL165dw5QpUxAdHY3c3FwcPXoUbm76/YUOAMnJybCyskJ6ejqWLFmCBQsW4NChQ1AqlRg2bBiKi4tx/PhxHDp0CDdu3MAbb7wBAHjjjTcQHR0NLy8v5OfnIz8/X/0dAMTHx2PUqFHIysrCwIEDMWbMGBQXFz83R3l5OZYtW4aUlBScOHECt2/fxowZM9TfL168GJs3b0ZSUhJOnTqF0tLSf13Xqg9PKiuRc/EC/LsFqOuEQiH8/QOQdV63TQ24cLCUhRzkqC9ZuGrPs5Q/LgMAWNvY6nzOmetF6OnRFG2b/b1BSoeX7NC1nSOO/lmgs4OV+8GKg6Us5GDTQfCD3p1TABg0aBBOnTqFx48f48aNGxg1ahRmzJiBjh076u3y9fVFXFwc2rVrh7Fjx6Jz5844cuQIjhw5guzsbGzZsgWvvPIKunbtiu+++w7Hjx/HmTNnYGlpCWtra5iZmcHJyQlOTk6wtLRUe8PDw/Hmm2/Czc0NixYtQllZGX7//ffn5njy5Am+/vprdO7cGZ06dcLUqVNx5MgR9fcrV65EbGwsQkND4eHhgVWrVsHe3l7v9taE7KEM1dXVkEqlGvVSqRSFhYVGc7CUhRzkqC9ZuGrPU5RKJTYmLoe7V0e0bKP7H/wr913CrjN3cHLBANxJDMPhuX2x7vBV7Ei/o7ODlfvBioOlLORg02EshDwWFqn1ZlwnTpzAhg0bsH37dri4uCAsLAyrV6/W2+Pr66vx2dnZGffv30dOTg5atGiBFi1aqL/r0KED7O3tkZOTgy5duujstbKygq2tLe7fv//c4xs1agRXV1etHABQUlKCe/fu4dVXX1V/LxKJ8Morr0CpfP7e2QqFAgqFQqNOJRJDLBb/a3aCIBouG1Yuxp1b17Hgi/V6nTe080sI69oSk9en43JeKbxb2GPBGx1x72EFfkj7i6e0BEEQ3KNXp7mgoACfffYZ2rVrh5EjR8LW1hYKhQK7du3CZ5999sIOY02Ym5trfBYIBP/a4ePLW9PxKpXKoAwJCQmws7PTKEsXJ2gd52DvAJFIpLUAu6ioCI6OjjpdiwsHS1nIQY76koWr9gB/d0zPpZ9E3NKvIW3STK9z543wxap9l7H7zF1cyi3Fj6dvY93hq3g/2F1nByv3gxUHS1nIwabDWNADUc9hyJAhcHd3R1ZWFr788kvk5eVh5cqVvAXz9PTEnTt3cOfO/6akLl68iIcPH6rfCmBhYYHq6mreMjzFzs4OzZo1w5kzZ9R11dXVOHfu3L+eFxsbi5KSEo0yMyZW6zhzCwt4dvBC+uk0dZ1SqUR6ehp8O/rplJELB0tZyEGO+pKFC4dKpcKGlYvx+6ljmLckEU2dm+t03rNYWoig/Mcf1NVKFYR6vMiQlfvBioOlLORg00Hwg87T+vv27UNUVBQmT56Mdu3a8ZkJANC3b1/4+PhgzJgx+PLLL1FVVYUpU6bg9ddfR+fOnQEArVu3xs2bN5GZmYmXXnoJNjY2vE2Zv//++0hISICbmxs8PDywcuVKyGSyf30dlVisPYVfUVXzsW+PG4+5s2Lg5eUNbx9fbEpJhlwuR0homM4ZuXCwlIUc5KgvWQx1bFi5GCeP7sdH8cth2agRHhb/vd6tkZU1LMQSnRyHsvIxbZAHcovL/57Wb2mPSf3aY+upWzq3g4u2mJqDpSzkYNNhDFh9qp4vdO6cnjx5Ehs2bMArr7wCT09PvP322xg9ejRvwQQCAXbv3o33338fr732GoRCIYKCgjRGa4cPH44dO3agd+/eePjwIZKSkhAeHs5LnpiYGBQUFGDs2LEQiUR47733MGDAAIhEIk78QcEDISsuxppVK1BY+ADuHp5Ys3Y9pHpMLXDhYCkLOchRX7IY6jj4848AgPkzJmrUT5kRh14DhujkmLUlEzEhXvhsjB+kNhLceyjHdydu4POfL+rcDi7aYmoOlrKQg00HwT0ClZ4LKx8/fozvv/8e3377LX7//XdUV1fj888/R0REBGxsbPjKyRxKpRKenp4YNWoUPvnkE53Pe97IKUEQDYPLeY8Mdgz45IDBjluJIwx2EERDQlLrR8gNZ96Bq7y5FwzgfzZcX/R+i4CVlRUiIiJw8uRJZGdnIzo6Gp999hmaNm2KoUOH8pGRCf766y988803uHLlCrKzszF58mTcvHkTb731Vl1HIwiCIAjChBEK+CssYtArrtzd3bFkyRLcvXsXW7du5SoTkwiFQmzcuBFdunRB9+7dkZ2djcOHD8PTs3Z7VxMEQRAEQRDacDJILRKJEBISwtlWnizSokULnDp1qq5jEARBEATRwKAHogiCIAjecHcxfG1+SV4eB0kIgiDYhDqnBEEQBEEQDNPABk6Z3VaVOe7cuYOIiAi4uLjAwsICrVq1wrRp07R2ljCE1C2bEdwvEF38fDBm9EhkZ2XViYOlLOQgR33JwoLD2tIcS9/tictJ4SjeMQW/LhuJV9o1NXoOU3OwlIUcbDoIbqHOqQ7cuHEDnTt3xtWrV7F161Zcu3YNX3/9NY4cOYJu3bqhuLjY4Gvs37cXy5YkYOKUSKRu2wl3dw9MnjhBr84vFw6WspCDHPUlCyuOxKg+CPRriYhlB9E5cjMOn7uNXxaGwkVqVe/awoqDpSzkYNNhDOhpfUKLyMhIWFhY4ODBg3j99dfRsmVLBAcH4/Dhw8jNzcXs2bMNvkZKchLCRoxCSOhwuLq5YU5cPCQSCXbt2G5UB0tZyEGO+pKFBYfEQoSQ7m6YnXQKpy7k4UZ+CRZuScf1/BK8O9CnXrWFJQdLWcjBpoPgHuqcvoDi4mIcOHAAU6ZMgaWlpcZ3Tk5OGDNmDL7//nvouZeBBk8qK5Fz8QL8uwWo64RCIfz9A5B1PsNoDpaykIMc9SULKw4zkRBmIiEqKjV3+qhQVCGgg4vRcpiSg6Us5GDTYSwEPP7HItQ5fQFXr16FSqV67vtMPT09IZPJ8ODBg1pfQ/ZQhurqakilUo16qVSKwsJCozlYykIOctSXLKw4yuRPcDonH7GjX4VzYysIhQKM7u2Orh5OcGqs27Q+K21hxcFSFnKw6TAWDW1an57W15HajIwqFAooFApNj0gMsVjMVSyCIAg1EcsOYu0HfXEjZQKqqpXIvHYfP5y4Aj83/R+KIgiCqCto5PQFuLm5QSAQICcnp8bvc3Jy4ODggCZNmmh9l5CQADs7O42ydHGC1nEO9g4QiURaC7CLiorg6OioU04uHCxlIQc56ksWVhwAcLOgBP0/3g5p2Bq0G/ctek7/AeYiIW4WlBgthyk5WMpCDjYdxqKhjZxS5/QFSKVS9OvXD2vWrIFcLtf4rqCgAJs3b8Ybb7wBQQ0vIYuNjUVJSYlGmRkTq3WcuYUFPDt4If10mrpOqVQiPT0Nvh39dMrJhYOlLOQgR33JworjWcoVVSiQlcPeWoy+nVphz+kbRsthSg6WspCDTQfBDzStrwOrVq1CQEAABgwYgE8//RRt2rTBhQsXMHPmTDRv3hwLFy6s8TyxWHsKv6KqxkPx9rjxmDsrBl5e3vD28cWmlGTI5XKEhIbpnJMLB0tZyEGO+pKFFUffTi0hEAhw5a4Mrs52WDShB67cleG7QzXP/LDcFlYcLGUhB5sOY1DTAJgpQ51THWjXrh3Onj2LuLg4jBo1CsXFxXByckJISAji4uLQuHFjg68RFDwQsuJirFm1AoWFD+Du4Yk1a9dDqsfUAhcOlrKQgxz1JQsrDrtGYiwID0BzR2sUP6rA7lPXEPddGqqqlfWuLaw4WMpCDjYdBPcIVIa8A4nQm+eNnBIEQeiKw7AVBjtku6M4SEIQDQdJHQ7nLT+u29Kc2hD9elve3LWF1pwSBEEQBEEQzEDT+gRBEARBEAzTwJacUueUIAiCIAiCZYQNrHdKnVOC+H8KH1Ua7HC0seAgCUH8O1ysFz12ufa72j2ll7v2+53rK8M3/M6JZ/uEVznxEERDhjqnBEEQBEEQDMPqy/L5gh6I0pPw8HAIBAIIBAJYWFjAzc0NCxYsQFWV4Y/hp27ZjOB+geji54Mxo0ciOyurThwsZWHBkZVxFrOjp2LU4ED08ffByeNH9M7ARQ5ysJ3FFBwn9+/EZx+Ow0dj+uOjMf3xxccTcfFc2otP5DgHaw5pI3PMCGyLreM6YceEzlg9whtujlZ1koUcbDoaKp999hkEAgE++OADTr3UOa0FQUFByM/Px9WrVxEdHY358+dj6dKlBjn379uLZUsSMHFKJFK37YS7uwcmT5ygta0a3w6WsrDikMvlcG3XHlEzZut8Dh85yMFuFlNx2EubYMh/JmHG0g2YsXQ92vl0wvrPYpF/W7/X2LDQFq4c1hYiLA3pgCqlCnF7L2PyD1lYf/o2yir1G5BgpT3k4N5hDAQC/kptOXPmDNauXQtfX1/uGvr/UOe0FojFYjg5OaFVq1aYPHky+vbti59++skgZ0pyEsJGjEJI6HC4urlhTlw8JBIJdu3YblQHS1lYcXQN6ImISVHo0auPzufwkYMc7GYxFYd3lx7weqUbmrq0QFOXlhg8ZiLEEkvcunJR5wystIUrx4iXnfGgrBJfHruJKw8e496jSmTcLUVBqUJnB0vtIQf3joZIWVkZxowZg2+++QYODg6c+6lzygGWlpaorKz9wzRPKiuRc/EC/LsFqOuEQiH8/QOQdT7DaA6WsrDi4AJW2mJKDpaymJLjWZTV1Th38jAUFRVo4+6l83mstIWr+9G1tQOuPXiM2L5u2DzWDyuGe2GAh34PgrHSHnJw7zAWQgh4KwqFAqWlpRpFofj3P74iIyMxaNAg9O3bl6f2ErVGpVLh8OHDOHDgAAIDA2vtkT2Uobq6GlKpVKNeKpWisLDQaA6WsrDi4AJW2mJKDpaymJIDAPL+uo6Zb/VD9BuB+OHrZZgQswhOLdrofD4rbeHqfjjZiDGwQ1PkllZg7i+XsffifUzs3gp92uu+vSUr7SEH9w5TICEhAXZ2dholISHhucenpqbi3Llz/3qModDT+rVgz549sLa2xpMnT6BUKvHWW29h/vz5WscpFAqtvz5UIjHEYrGRkhIEQehHU5eW+Gh5EirKy5CZdgybVy5E1Ccr9eqgmhICAXDtwWN89/tdAMCNonK0crBEcIemOHKl4XRgiLqFz9ecxsbGYvr06Rp1z+un3LlzB9OmTcOhQ4cgkUh4y0Qjp7Wgd+/eyMzMxNWrVyGXy5GcnAwrK+0nN2v6a2TpYu2/NBzsHSASibQWYBcVFcHRUbe/zrlwsJSFFQcXsNIWU3KwlMWUHABgZm6OJs4voYWrB4b8ZxKat3bF8T3bdD6flbZwdT9k5U9wWybXqLvzsAJNrHV/pzEr7SEH9w5jIRTwV8RiMWxtbTXK8zqnf/zxB+7fv49OnTrBzMwMZmZmOH78OFasWAEzMzNUV1dz015OLA0MKysruLm5oWXLljAze/7gc2xsLEpKSjTKzJhYrePMLSzg2cEL6af/98oWpVKJ9PQ0+Hb00ykTFw6WsrDi4AJW2mJKDpaymJKjJlRKFaqqnuh8PCtt4ep+XCwoQ3N7S4265nYSPHik+wNRrLSHHNw7Ghp9+vRBdnY2MjMz1aVz584YM2YMMjMzIRKJOLkOTevziFisPYVf8Zy3j7w9bjzmzoqBl5c3vH18sSklGXK5HCGhYTpfjwsHS1lYccjLy5F797b6c0FeLq5duQQbWzs0c3KuV20xJQdLWUzF8fOmr+Hp5w+HJs2gkJfjj/8ewrULGZg093OdM7DSFq4cu7ILsGyYJ0b5OeO/14vRvqk1gjybYOWJWzo7WGoPObh3GANWti+1sbGBt7e3Rp2VlRWkUqlWvSFQ55QRgoIHQlZcjDWrVqCw8AHcPTyxZu16SPWYWuDCwVIWVhyXcy4gOjJC/Tnxq7/fadt/4FDEzFtYr9piSg6WspiK41GJDJtXfIoSWREsG1nBpbUrJs39HB4vd9E5Aytt4cpx9cFjfHrwGsJffQlvdmqOe48UWPfbbRy7pt97MFlpDzm4dxDcI1CpVKq6DtGQeN7IKVH3FD6q/evAnuJoo/s6NIKoS45dfmCwo5e7fq9UYpnhG37nxLN9wquceAj2kNThcN436X/x5n63ayve3LWF1pwSBEEQBEEQzEDT+gRBEARBEAzDyppTY0HT+kaGpvUJov4S/XOOwY7lQzw5SMIGG9JvGewY1sHFYActpyGMQV1O62/4/faLD6olE15tyZu7ttDIKUEQBEEQBMM0sIFT6pwSBEEQBEGwTEN7QKihtZdpUrdsRnC/QHTx88GY0SORnZVVJw6WspCDHKxnGejhiNWhnhplbt+2emcwNAdrjqec/eV7rIgYgBNbEvU6LyvjLGZHT8WowYHo4++Dk8eP1Or6pvJ7Rg62HQS3UOdUR+7cuYOIiAi4uLjAwsICrVq1wrRp07S2Past+/ftxbIlCZg4JRKp23bC3d0DkydO0MvPhYOlLOQgR33Jkldagdi9V9Tl8xP6v/aFlbZw9bMBgHs3L+PP47/A8aU2ep8rl8vh2q49ombM1vvcp5ja7xk52HQYA4FAwFthEeqc6sCNGzfQuXNnXL16FVu3bsW1a9fw9ddf48iRI+jWrRuKi4sNvkZKchLCRoxCSOhwuLq5YU5cPCQSCXbt2G5UB0tZyEGO+pJFqQRKFdXq8rhS//2lWWkLVz+bygo5DqxbjMBxH0BsZaPXuQDQNaAnIiZFoUevPnqf+xRT+z0jB5sOgnuoc6oDkZGRsLCwwMGDB/H666+jZcuWCA4OxuHDh5Gbm4vZs2v/lz0APKmsRM7FC/DvFqCuEwqF8PcPQNb5DKM5WMpCDnLUpyxNrC2wMMgN8f1dEd7ZBQ6W+i3nZ6UtXN0PADi2aRVa+76Kll6d9DqPK0zt94wcbDqMhYDHwiLUOX0BxcXFOHDgAKZMmQJLS0uN75ycnDBmzBh8//33qOmNXAqFAqWlpRpFoVBoHSd7KEN1dTWkUqlGvVQqRWFhoU45uXCwlIUc5KgvWW7J5Ej5Iw+rf7uD1MwCSBuZY/prrSE20/2fV1bawtXP5kr6MTz46xoCRkS8+GCeMLXfM3Kw6SD4gTqnL+Dq1atQqVTw9Kz53YSenp6QyWR48EB7K8CEhATY2dlplKWLE/iOTBCEEbl47zEy8h4hr1SBnPuPsSbtDizNhejUXP+pbFPgUfF9HN+aiAHvxcDMnN4/ShBcIBQIeCssQq+S0pEX7VVgYaH9j3BsbCymT5+u6RGJtY5zsHeASCTSWoBdVFQER0dHnfJx4WApCznIUZ+yPIv8iRL3yyrRxEr3jhkrbeHCcf/WNchLH2JrfKS6TqVUIvdKNs4f/QmR6/ZAKBTp5DIEU/s9IwebDoIfaOT0Bbi5uUEgECAnp+adYXJyctCkSRPY29trfScWi2Fra6tRxGLtzqm5hQU8O3gh/XSauk6pVCI9PQ2+Hf10ysmFg6Us5CBHfcryLGKRAI5WFijVYzs4VtrChaOF58sYs2At3pqfqC5NW7eHu38g3pqfaJSOKWB6v2fkYNNhLBramlMaOX0BUqkU/fr1w5o1a/Dhhx9qrDstKCjA5s2bERkZ+S8G3Xh73HjMnRUDLy9vePv4YlNKMuRyOUJCw4zqYCkLOchRH7KEejdFdn4ZiuVPYCcxwyBPRyhVKpy9W1rv2sKFw8KyEaQvtdaoMxdLYGllo1X/b8jLy5F7939bNhbk5eLalUuwsbVDMydnnRym9HtGDnYdxoDR2XfeoM6pDqxatQoBAQEYMGAAPv30U7Rp0wYXLlzAzJkz0b59e8ybN8/gawQFD4SsuBhrVq1AYeEDuHt4Ys3a9ZDqMbXAhYOlLOQgR33IYm9phvFdXGBlIUJZZTWuF5Vj2fFbKNPzdVIstIUrBxdczrmA6Mj/PVCV+NVSAED/gUMRM2+hTg5T+j0jB7sOgnsEqhctpiQAALdu3cL8+fOxf/9+3L9/HyqVCmFhYUhJSUGjRo109ugx00cQBGNE/1zz8h59WD6k5ocr6yMb0m8Z7BjWwcVgh6MNPXhF8I+kDofztmbk8uZ+0685b+7aQmtOdaR169bYuHEjCgoKoFQqMW/ePBw8eBBZtM0ZQRAEQRAEZ9C0fi2Jj49H69atcfr0abz66qsQCqmfTxAEQRAE9zS0HgZN6xsZmtbXpvBRpcEOmtYjCOPDxbT+xftygx2mtFSCYJe6nNb/nsdp/TcYnNankVOCIAiCIAiGETSwx/Ub2kgxQRAEQRAEwTDUOdWR8PBwCAQCCAQCmJubo02bNvjoo49QUVHB2TVSt2xGcL9AdPHzwZjRI5Fdi4etuHCwkCUr4yxmR0/FqMGB6OPvg5PHj+h9fS5ykINtB0tZyFEzZ3/5HisiBuDElkSdzxno4YjVoZ4aZW7ftrW6Pv2ekcMYDr5paC/hp86pHgQFBSE/Px83btzAF198gbVr1yIuLo4T9/59e7FsSQImTolE6radcHf3wOSJE7S2VePbwUoWuVwO13btETVjtl7Zuc5BDnYdLGUhR83cu3kZfx7/BY4vtdH73LzSCsTuvaIun5/4S28H/Z6RwxgOgnuoc6oHYrEYTk5OaNGiBUJCQtC3b18cOnSIE3dKchLCRoxCSOhwuLq5YU5cPCQSCXbt2G5UBytZugb0RMSkKPTo1Uev7FznIAe7DpaykEObygo5DqxbjMBxH0BsZaPXuQCgVAKlimp1eaznpgYA/Z6RwzgOY/B05paPwiLUOa0lf/75J3777TdYWBj+lPiTykrkXLwA/24B6jqhUAh//wBknc8wmoO1LIbCSlvIwb2DpSzkqJljm1ahte+raOnVSa/zntLE2gILg9wQ398V4Z1d4GCp3/O79HtGDmM4jIWQx8IirOZikj179sDa2hoSiQQ+Pj64f/8+Zs6c+dzjFQoFSktLNYpCodA6TvZQhurqakilUo16qVSKwsJCnbJx4WAti6Gw0hZycO9gKQs5tLmSfgwP/rqGgBERLz64Bm7J5Ej5Iw+rf7uD1MwCSBuZY/prrSE20/3/ZdHvGTmM4SD4gTqnetC7d29kZmYiPT0d48aNw/jx4zF8+PDnHp+QkAA7OzuNsnRxghETEwRBGJdHxfdxfGsiBrwXAzPz2s0sXbz3GBl5j5BXqkDO/cdYk3YHluZCdGqu//IAgjAFGtq0Pr3nVA+srKzg5uYGAPj222/RsWNHbNiwARMmTKjx+NjYWEyfPl2jTiUSax3nYO8AkUiktQC7qKgIjo6OOmXjwsFaFkNhpS3k4N7BUhZyaHL/1jXISx9ia3ykuk6lVCL3SjbOH/0Jkev2QCgU6eR6ivyJEvfLKtHESvfOLv2ekcMYDoIfaOS0lgiFQsyaNQtz5syBXF7zDidisRi2trYaRSzW7pyaW1jAs4MX0k+nqeuUSiXS09Pg29FPpzxcOFjLYiistIUc3DtYykIOTVp4vowxC9birfmJ6tK0dXu4+wfirfmJendMAUAsEsDRygKlemyxR79n5DCGw1g0tFdJ0cipAYwcORIzZ87E6tWrMWPGDINcb48bj7mzYuDl5Q1vH19sSkmGXC5HSGiYUR2sZJGXlyP37m3154K8XFy7cgk2tnZo5uRcr9pCDn4cLGUhx/+wsGwE6UutNerMxRJYWtlo1T+PUO+myM4vQ7H8CewkZhjk6QilSoWzd0t1bgcXbeHSQw7TdRDcQ51TAzAzM8PUqVOxZMkSTJ48GVZWVrV2BQUPhKy4GGtWrUBh4QO4e3hizdr1kOoxtcCFg5Usl3MuIDryfw9TJH61FADQf+BQxMxbWK/aQg5+HCxlIQe32FuaYXwXF1hZiFBWWY3rReVYdvwWyvR8nRT9npHDGA5jwOjSUN4QqFQqVV2HaEjoMSvVYCh8VGmww9HG8Fd6EQShHxvSbxnsuHi/5mVR+rB8iKfBDoJ4EZI6HM7bnV3Am3uYjxNv7tpCI6cEQRAEQRAMI2R2dSg/UOeUIAiCIAiCYRratD51Tok6h6bkCaJ+MqFr67qOwBkOw1Zw4rm6aZLBDvo3kWjoUOeUIAiCIAiCYQQNbFqf3nNKEARBEARBMAN1TvXkwYMHmDx5Mlq2bAmxWAwnJycMGDAAp06dMtidumUzgvsFooufD8aMHonsrKw6cbCUhRzkqC9ZyGGaDmtLcyx9tycuJ4WjeMcU/LpsJF5p11QvR1bGWcyOnopRgwPRx98HJ48f0ev8Z2HhnpDD+AgE/BUWoc6pngwfPhwZGRlITk7GlStX8NNPP6FXr15a25/py/59e7FsSQImTolE6radcHf3wOSJE/TycuFgKQs5yFFfspDDdB2JUX0Q6NcSEcsOonPkZhw+dxu/LAyFi1T391rL5XK4tmuPqBmzdT6nJli5J+QgeEdF6IxMJlMBUB07dqzWDvmTmkvY8BGquXHx6s+PFdWq7j16qFatWfvcc/hwsJSFHOSoL1nIUf8dkoFfaRX7kFWqJ1XVqpC43Rr1f1y9p0rYml7jOXeKFf9a2rdvr9q6c++/HsPKPSGH5rF1yb4/7/NWWIRGTvXA2toa1tbW2LVrFxQKBWfeJ5WVyLl4Af7dAtR1QqEQ/v4ByDqfYTQHS1nIQY76koUcpuswEwlhJhKiolJz95QKRRUCOrjo5OAKVu4JOQhjQJ1TPTAzM8PGjRuRnJwMe3t7dO/eHbNmzULWc9anKBQKlJaWapSaOrWyhzJUV1dDKpVq1EulUhQWFuqUjQsHS1nIQY76koUcpusokz/B6Zx8xI5+Fc6NrSAUCjC6tzu6ejjBqXHtt6uuDazcE3LUDbTmlPhXhg8fjry8PPz0008ICgrCsWPH0KlTJ2zcuFHr2ISEBNjZ2WmUpYsTjB+aIAiCqBURyw5CIBDgRsoElOyKROSQjvjhxBUoaedvwog0tM4pvee0FkgkEvTr1w/9+vXD3Llz8c477yAuLg7h4eEax8XGxmL69OkadSqRWMvnYO8AkUiktQC7qKgIjo6OOmXiwsFSFnKQo75kIYfpOgDgZkEJ+n+8HY3EZrBtZIECWTlSYoJws6BEZwcXsHJPyEEYAxo55YAOHTrg8ePHWvVisRi2trYaRSzW7pyaW1jAs4MX0k+nqeuUSiXS09Pg29FPpwxcOFjKQg5y1Jcs5DBdx7OUK6pQICuHvbUYfTu1wp7TN/R2GAIr94QcdYOAx/9YhEZO9aCoqAgjR45EREQEfH19YWNjg7Nnz2LJkiUYNmyYQe63x43H3Fkx8PLyhrePLzalJEMulyMkNMyoDpaykIMc9SULOUzX0bdTSwgEAly5K4Orsx0WTeiBK3dl+O5Qjs4OeXk5cu/eVn8uyMvFtSuXYGNrh2ZOzkZtDzm4dxDcQ51TPbC2tkbXrl3xxRdf4Pr163jy5AlatGiBd999F7NmzTLIHRQ8ELLiYqxZtQKFhQ/g7uGJNWvXQ6rH1AIXDpaykIMc9SULOUzXYddIjAXhAWjuaI3iRxXYfeoa4r5LQ1W1UmfH5ZwLiI6MUH9O/GopAKD/wKGImbfQqO0hB/cOYyBkc4CTNwQqFa3qNiYVVS8+hiAIgjAuDsNWcOK5ummSwQ5HGwsOkhBcI6nD4bwjl/h7e0AfD7Y64gCNnBIEQRAEQTANq2tD+YIeiCIIgiAIgiCYgUZOCYJgnsJHlQY7uJgqjf5Z94dgnsfyIZ4GOwju2bnkzbqOQBDPhdX3kfIFdU4JgiAIgiAYhqb1CYIgCIIgCKKOoM4pQ6Ru2YzgfoHo4ueDMaNHIjsrq04cLGUhBzmeR1bGWcyOnopRgwPRx98HJ48f0TsDF1kGejhidainRpnbt63Rc5CDH8fJ/Tvx2Yfj8NGY/vhoTH988fFEXDyX9uITn4GV31Vy8OfgG6GAv8Ii1DllhP379mLZkgRMnBKJ1G074e7ugckTJ2htq8a3g6Us5CDHvyGXy+Harj2iZszW+Ry+suSVViB27xV1+fzEX3WSgxzcO+ylTTDkP5MwY+kGzFi6Hu18OmH9Z7HIv637DlEs/a6Sg3sHwT31rnP6448/wsfHB5aWlpBKpejbty8eP36M8PBwhISEID4+Hk2aNIGtrS0mTZqEysr/PUixf/9+9OjRA/b29pBKpRg8eDCuX7+u4b979y7efPNNNG7cGFZWVujcuTPS09PV3+/evRudOnWCRCJB27ZtER8fj6oqw19empKchLARoxASOhyubm6YExcPiUSCXTu2G9XBUhZykOPf6BrQExGTotCjVx+dz+Eri1IJlCqq1eVxZXWd5CAH9w7vLj3g9Uo3NHVpgaYuLTF4zESIJZa4deWizg6WflfJwb3DGDS07UvrVec0Pz8fb775JiIiIpCTk4Njx44hLCwMT/cROHLkiLp+69at2LFjB+Lj49XnP378GNOnT8fZs2dx5MgRCIVChIaGQqn8e6ePsrIyvP7668jNzcVPP/2E8+fP46OPPlJ//9///hdjx47FtGnTcPHiRaxduxYbN27EwoW67/BRE08qK5Fz8QL8uwWo64RCIfz9A5B1PsNoDpaykIMcxoCrLE2sLbAwyA3x/V0R3tkFDpb6PWvKyn0lx7+jrK7GuZOHoaioQBt3r1o5agsr94QchDGoV0/r5+fno6qqCmFhYWjVqhUAwMfHR/29hYUFvv32WzRq1AheXl5YsGABZs6ciU8++QRCoRDDhw/X8H377bdo0qQJLl68CG9vb2zZsgUPHjzAmTNn0LhxYwCAm5ub+vj4+Hh8/PHHGDduHACgbdu2+OSTT/DRRx8hLi5OK69CoYBCodCoU4nEEIvFGnWyhzJUV1dDKpVq1EulUty8qdvUERcOlrKQgxzGgIsst2RypPyRh3tllbCTmGGghyOmv9Yanx65AUWVbltcsnJfyVEzeX9dxxexk1BVWQmxxBITYhbBqUUbvRyGwso9IUfd0NBeJVWvRk47duyIPn36wMfHByNHjsQ333wDmUym8X2jRo3Un7t164aysjLcuXMHAHD16lW8+eabaNu2LWxtbdG6dWsAwO3btwEAmZmZ8PPzU3dM/8n58+exYMECWFtbq8u7776L/Px8lJeXax2fkJAAOzs7jbJ0cQJXt4MgCAa4eO8xMvIeIa9UgZz7j7Em7Q4szYXo1NymrqMRHNHUpSU+Wp6E6YvXontQCDavXIiCOzfrOhZBmCz1auRUJBLh0KFD+O2333Dw4EGsXLkSs2fP1lgT+m8MGTIErVq1wjfffAMXFxcolUp4e3ur16VaWlr+6/llZWWIj49HWFiY1ncSiUSrLjY2FtOnT9eoU4nEWsc52DtAJBJpLcAuKiqCo6Nue95y4WApCznIYQz4yCJ/osT9sko0sdL9pf+s3Fdy1IyZuTmaOL8EAGjh6oHb13JwfM82vDH5I708hsDKPSFH3dDABk7r18gpAAgEAnTv3h3x8fHIyMiAhYUFdu7cCeDvkU25XK4+9vTp07C2tkaLFi1QVFSEy5cvY86cOejTpw88PT01Rl0BwNfXF5mZmSguLq7x2p06dcLly5fh5uamVYRC7VspFotha2urUf45pQ8A5hYW8OzghfTT/3s9iVKpRHp6Gnw7+ul0X7hwsJSFHOQwBnxkEYsEcLSyQGmF7g9KsnJfyaEbKqUKVVVPDHLoCyv3hBx1g1Ag4K2wSL0aOU1PT8eRI0fQv39/NG3aFOnp6Xjw4AE8PT2RlZWFyspKTJgwAXPmzMGtW7cQFxeHqVOnQigUwsHBAVKpFOvWrYOzszNu376Njz/+WMP/5ptvYtGiRQgJCUFCQgKcnZ2RkZEBFxcXdOvWDfPmzcPgwYPRsmVLjBgxAkKhEOfPn8eff/6JTz/91KC2vT1uPObOioGXlze8fXyxKSUZcrkcIaHao7R8OljKQg5y/Bvy8nLk3r2t/lyQl4trVy7BxtYOzZycjZYl1LspsvPLUCx/AjuJGQZ5OkKpUuHs3VKdM3CRgxz8OH7e9DU8/fzh0KQZFPJy/PHfQ7h2IQOT5n6us4OV31Vy8OMguKdedU5tbW1x4sQJfPnllygtLUWrVq2wfPlyBAcH4/vvv0efPn3Qrl07vPbaa1AoFHjzzTcxf/58AH8/gZeamoqoqCh4e3vD3d0dK1asQK9evdR+CwsLHDx4ENHR0Rg4cCCqqqrQoUMHrF69GgAwYMAA7NmzBwsWLMDixYthbm4ODw8PvPPOOwa3LSh4IGTFxVizagUKCx/A3cMTa9auh1SPqQUuHCxlIQc5/o3LORcQHRmh/pz41VIAQP+BQxEzT/c3aBiaxd7SDOO7uMDKQoSyympcLyrHsuO3UKbn66RYua/k0ORRiQybV3yKElkRLBtZwaW1KybN/RweL3fR2cHK7yo5+HEYAzbHN/lDoHr6HqZ6Tnh4OB4+fIhdu3bVdZR/RY+ZPoIg/p/CR5UvPugFONrovgb0eUT/nGOwY/kQT4MdBPccu/yAE4+3i53BDi5+VwnukdThcN7paw95c/u72fPmri31auSUIAiCIAiiwdHAhk7r3QNRBEEQBEEQhOliMtP69QWa1ieI+gsrywsIdnEYtsJgh2x3FAdJCK6py2n99OslvLm7uhq+FIVraOSUIAiCIAiCYAZac0oQBEEQBMEwjL6OlDdo5FRHBALBv5anr6wyhNQtmxHcLxBd/HwwZvRIZGdl1YmDpSzkIAfrWbIyzmJ29FSMGhyIPv4+OHn8iN7X5yIHOfhzcOGxtjTH0nd74nJSOIp3TMGvy0bilXZNjZ6DHPw4+EbAY2ER6pzqSH5+vrp8+eWXsLW11aibMWOGQf79+/Zi2ZIETJwSidRtO+Hu7oHJEydobavGt4OlLOQgR33IIpfL4dquPaJmzNYrO9c5yMGPgytPYlQfBPq1RMSyg+gcuRmHz93GLwtD4SK1MmoOcnDvILiHOqc64uTkpC52dnYQCAQaddbW1gb5U5KTEDZiFEJCh8PVzQ1z4uIhkUiwa8d2ozpYykIOctSHLF0DeiJiUhR69OqjV3auc5CDHwcXHomFCCHd3TA76RROXcjDjfwSLNySjuv5JXh3oI9R20MO7h1GoYENnVLnlAGeVFYi5+IF+HcLUNcJhUL4+wcg63yG0RwsZSEHOepTFkNhpS3k4MdjJhLCTCRERaXm61oqFFUI6OBitBzk4N5B8AN1TnlEoVCgtLRUoygUCq3jZA9lqK6uhlQq1aiXSqUoLCzU6VpcOFjKQg5y1KcshsJKW8jBj6dM/gSnc/IRO/pVODe2glAowOje7ujq4QSnxrpN67NyT8hRNwh4/I9FqHPKIwkJCbCzs9MoSxcn1HUsgiAIwshELDsIgUCAGykTULIrEpFDOuKHE1egpFeNE4QW9CopHomNjcX06dM16lQisdZxDvYOEIlEWguwi4qK4OjoqNO1uHCwlIUc5KhPWQyFlbaQgz/PzYIS9P94OxqJzWDbyAIFsnKkxAThZoFuL1dn5Z6Qo26gV0kRnCEWi2Fra6tRxGLtzqm5hQU8O3gh/XSauk6pVCI9PQ2+Hf10uhYXDpaykIMc9SmLobDSFnLw53lKuaIKBbJy2FuL0bdTK+w5fcNoOcjBvYPgBxo5ZYS3x43H3Fkx8PLyhrePLzalJEMulyMkNMyoDpaykIMc9SGLvLwcuXdvqz8X5OXi2pVLsLG1QzMn53rVFnLw5+nbqSUEAgGu3JXB1dkOiyb0wJW7Mnx3KMeoOcjBvcMYNLCBU+qcskJQ8EDIiouxZtUKFBY+gLuHJ9asXQ+pHlMLXDhYykIOctSHLJdzLiA6MkL9OfGrpQCA/gOHImbewnrVFnLw57FrJMaC8AA0d7RG8aMK7D51DXHfpaGqWmnUHOTg3mEUGljvVKBS0WpsY1JR9eJjCIJgk8JHlQY7HG0sOEhCsIrDsBUGO2S7ozhIQnCNpA6H8879Vcqbu1MrW97ctYVGTgmCIAiCIBiG1Vc+8QU9EEUQBEEQBEG8kISEBHTp0gU2NjZo2rQpQkJCcPnyZc6vQ9P6Roam9QmibmBlSp6VHAS7tJ78o8GOW4kjOEhCPEtdTutn3n7Em/vlljY6HxsUFITRo0ejS5cuqKqqwqxZs/Dnn3/i4sWLsLLSbUMJXaBpfYIgCIIgCOKF7N+/X+Pzxo0b0bRpU/zxxx947bXXOLsOdU4JgiAIgiAYhs8VpwqFQmtrdbFYXON72f9JScnfm0g0btyY00y05lRHevXqhQ8++ECrfuPGjbC3t+fkGqlbNiO4XyC6+PlgzOiRyM7KqhMHS1nIQQ4+PVkZZzE7eipGDQ5EH38fnDx+pFYZDM3BZRZWfjam5GAhi1AAfDTMC78nBOPm6lCcXhiEDwd56p3B0BzkMD1q2mo9IeHFW60rlUp88MEH6N69O7y9vTnNRJ1TRti/by+WLUnAxCmRSN22E+7uHpg8cYLWtmp8O1jKQg5y8O2Ry+VwbdceUTNm63VdrnNwlYWVn40pOVjJMjXYA+Neb4tZWzLw2rwD+HR7NiKD2mNCoFu9a4upOYyCgL8SGxuLkpISjRIbG/vCSJGRkfjzzz+RmprKaVMB6pwyQ0pyEsJGjEJI6HC4urlhTlw8JBIJdu3YblQHS1nIQQ6+PV0DeiJiUhR69Oqj13W5zsFVFlZ+NqbkYCVLF1cpDpzPw+HsAtwpKseec7k4duEe/No41Lu2mJrDGAh4/E/XrdafZerUqdizZw9+/fVXvPTSS5y3lzqnDPCkshI5Fy/Av1uAuk4oFMLfPwBZ5zOM5mApCznIYSyPoZhSDnKwm+XM9SL09GiKts2sAQAdXrJD13aOOPpnQb1riyk5GhoqlQpTp07Fzp07cfToUbRp04aX61DnlEcUCgVKS0s1yj8XHQOA7KEM1dXVkEqlGvVSqRSFhYU6XYsLB0tZyEEOY3kMxZRykIPdLCv3XcKuM3dwcsEA3EkMw+G5fbHu8FXsSL+jW0M4ykGOukEg4K/oQ2RkJDZt2oQtW7bAxsYGBQUFKCgogFwu57S91DnlkZoWGS9d/OJFxgRBEATxLEM7v4Swri0xeX06+n16GFFJZzC5f3uM6taqrqMRDYjExESUlJSgV69ecHZ2Vpfvv/+e0+vQq6R0xNbWVv3KhGd5+PAh7OzsajwnNjYW06dP16hTibTXcTjYO0AkEmktwC4qKoKjo6NO+bhwsJSFHOQwlsdQTCkHOdjNMm+EL1btu4zdZ+4CAC7lluIlaSO8H+yOH9L+MloOctQNrGxeaqx9m2jkVEfc3d1x7tw5rfpz586hffv2NZ6j6yJjcwsLeHbwQvrpNHWdUqlEenoafDv66ZSPCwdLWchBDmN5DMWUcpCD3SyWFiIo/9ExqFaqIBTq3m1hpS2m5CD4gUZOdWTy5MlYtWoVoqKi8M4770AsFuOXX37B1q1b8fPPPxvsf3vceMydFQMvL294+/hiU0oy5HI5QkLDjOpgKQs5yMG3R15ejty7t9WfC/Jyce3KJdjY2qGZk7NR28NFFlZ+NqbkYCXLoax8TBvkgdziclzOK4V3S3tM6tceW0/dqndtMTWHUWBl6NRIUOdUR9q2bYsTJ05g9uzZ6Nu3LyorK+Hh4YFt27YhKCjIYH9Q8EDIiouxZtUKFBY+gLuHJ9asXQ+pHlMLXDhYykIOcvDtuZxzAdGREerPiV8tBQD0HzgUMfMWGrU9XGRh5WdjSg5WsszakomYEC98NsYPUhsJ7j2U47sTN/D5zxfrXVtMzUFwj0BlrAUEBACgoqquExBEw6TwUaXBDkcbC5PJQbBL68k/Guy4lTiCgyTEs0jqcDjvQu5j3txeza14c9cWWnNKEARBEARBMANN6xMEQRAEQTCMvu8jre/QtL6RoWl9giAI04WVZRsOw1YY7JDtjjLYYUrU5bR+Th5/0/qeLjStTxAEQRAEQRDPhab1CYIgCIIgWKaBTevTyKkODBky5Lmvi/rvf/8LgUCArKwsg6+TumUzgvsFooufD8aMHonsWji5cLCUhRzkqC9ZyGG6DkM9WRlnMTt6KkYNDkQffx+cPH6kVhkMzQEA1pbmWPpuT1xOCkfxjin4ddlIvNKuqdFzmJqD4BbqnOrAhAkTcOjQIdy9e1fru6SkJHTu3Bm+vr4GXWP/vr1YtiQBE6dEInXbTri7e2DyxAla26rx7WApCznIUV+ykMN0HVx45HI5XNu1R9SM2Xpdl+scAJAY1QeBfi0RsewgOkduxuFzt/HLwlC4SHVfd8jKz4YVhzEQ8Pgfk6iIF/LkyRNVs2bNVJ988olG/aNHj1TW1taqxMREnV3yJzWXsOEjVHPj4tWfHyuqVd179FCtWrP2uefw4WApCznIUV+ykMN0Hfp67hQr/rW0b99etXXn3n89hosckoFfaRX7kFWqJ1XVqpC43Rr1f1y9p0rYmq51POs/G2M76pKcvMe8FRahkVMdMDMzw9ixY7Fx40aonnm5wbZt21BdXY0333zTIP+TykrkXLwA/24B6jqhUAh//wBknc8wmoOlLOQgR33JQg7TdXDpMRQucpiJhDATCVFRqfnamApFFf6vvTsPi6re/wD+HhAGlFUQXEJRTEUU1Nw1AcM1NcXSzKu4lSlYaSqSKS7XyDUzXPAq4pKaPyOvqeUKLoloqbjjSq4oO4IwCPP9/dHjXEe2QWaYw/B+9Zznab7nnPf5DDDw8aydm9etsDoMKaOiyGS6m6SIzamGxowZg1u3buHo0aOqsQ0bNmDw4MGwtrYuch2FQoHMzEy1SaFQFFouLT0NBQUFsLOzUxu3s7NDcnKyRvVpI0NKtTCDGZWlFmYYboY2c8pLG3Vk5TzHqauPEPRhe9SpWQNGRjJ86N0UHZrVRu2amh3Wl8r3RioZpBtsTjXUrFkzdO7cGeHh4QCAmzdv4vjx4xg7dmyx64SEhMDa2lptWrwwpKJKJiIiUjNmyQHIZDLc3jwWGbv84d/fAzuOXYeStzyXNJkOJyniraTKYOzYsZg0aRJWrlyJDRs2wMXFBZ6ensUuHxQUhClTpqiNCWN5oeVsbWxhbGxc6ATslJQU2Nvba1SbNjKkVAszmFFZamGG4WZoM6e8tFXHncQM9JzxM6rLq8GquikS055hc2Bv3EnMqLA6DCmjwki1i9QR7jktgyFDhsDIyAhbt27Fpk2bMGbMGMhKOGFDLpfDyspKbZLLCzenJqamcG3uhthTMaoxpVKJ2NgYuHu01qg2bWRIqRZmMKOy1MIMw83QZk55abuOZ4p8JKY9g42FHD5tGmDPqdsVVochZZBucM9pGVhYWGDo0KEICgpCZmYmRo0apbXsEX6jMeurQLi5tUCLlu7YsnkjcnJyMHCQb4VmSKkWZjCjstTCDMPN0EZOzrNneHD/rup14sMHuHn9GiytrOFYu06Fvh+fNvUhk8lw/X4aXOpY45uxXXH9fho2HbxaoXUYUkZFkOwtn3SEzWkZjR07FuvXr0ffvn1Rt65mVzdqonefvkhLTcWq0BVITk5C02auWBW2DnZlOLSgjQwp1cIMZlSWWphhuBnayIm/ehlf+o9RvV79/WIAQM++AxA4e0GFvh/r6nLMG9UZ9ewtkPo0F//94yaCN8Ugv0BZoXUYUgZpn0wIngVdkXLzS1+GiIgqp+SneeXOsLc0LXeG7Xsryp2R9t/Pyp1hSMz0uDvv5pMcnWU3djDXWfbr4jmnRERERCQZPKxPREREJGFV64xTHtavcDysT0RElYFtu4ByZ6SdCdVCJdKgz8P6t3R4WN9Fgof1ueeUiIiISMqq2K5TNqdEREREElbVbiXFC6KKsGbNGlhaWiI//3/H4LOysmBiYgIvLy+1ZaOjoyGTyXDr1q1yb3f71h/Rp0d3tGvdEsM//AAXL1zQS4aUamEGMypLLcww3Awp1VLRGV3auGDn8vG4fWABcs6For+Xe6FlZk14F7cPLEBqzDLsXRMAl/q1JPledJlB2sXmtAje3t7IysrCn3/+qRo7fvw4ateujdjYWOTm5qrGo6KiUL9+fbi4uJRrm7//tg9LFoVg/ER/bP+/X9C0aTNMGD+20GPVdJ0hpVqYwYzKUgszDDdDSrXoI6OGuRwXrz/AFyE/FTn/y1E+mDjME599sx3dRi5Bdk4efl3pD7lpyQdmK+vXQ19kMt1NkiSoSHXq1BEhISGq19OnTxf+/v7C1dVVREVFqca7desm/Pz8NM7NeV705Dv4fTEreK7qdbaiQHTp2lWErgordh1dZEipFmYwo7LUwgzDzZBSLRWdYdbKX20SQogPvghTG3v4JF3MWBqpeu3Q9UuRk5snRgSGC7NW/pJ5L9rI0KfbSTk6m6SIe06L4e3tjaioKNXrqKgoeHl5wdPTUzWek5OD2NhYeHt7l2tbz/PycPXKZXTs1Fk1ZmRkhI4dO+NC3LkKy5BSLcxgRmWphRmGmyGlWqSS8TLnenaoU8saR2KvqcYys3Jx5lICOrg7S/69aPvroUsyHU5SxOa0GN7e3vjjjz+Qn5+Pp0+f4ty5c/D09ES3bt0QHR0NAIiJiYFCoSi2OVUoFMjMzFSbFApFoeXS0tNQUFAAOzs7tXE7OzskJydrVK82MqRUCzOYUVlqYYbhZkipFqlkvKy2vRUA4EnqU7XxJylP4WhnpdM6pJJBusHmtBheXl7Izs7GmTNncPz4cTRp0gS1atWCp6en6rzT6OhoNGrUCPXr1y8yIyQkBNbW1mrT4oUhFfxOiIiIqFKrYrtOeSupYjRu3BhvvPEGoqKikJaWBk9PTwBA3bp14eTkhJMnTyIqKgrdu3cvNiMoKAhTpkxRGxPG8kLL2drYwtjYuNAJ2CkpKbC3t9eoXm1kSKkWZjCjstTCDMPNkFItUsl4WWJyJgDAoaal6v8BwMHOEhfi7+u0DqlkkG5wz2kJvL29ER0djejoaLVbSHXr1g2//fYbTp8+XeL5pnK5HFZWVmqTXF64OTUxNYVrczfEnopRjSmVSsTGxsDdo7VGtWojQ0q1MIMZlaUWZhhuhpRqkUrGyxIepOBRUga8OzRVjVnWMEO7Fs6IvZAg+fei7a+HLsl0+J8Ucc9pCby9veHv74/nz5+r9pwCgKenJwICApCXl1fui6FeGOE3GrO+CoSbWwu0aOmOLZs3IicnBwMH+VZohpRqYQYzKkstzDDcDCnVoo+MGuamcHH6331LnevZwb1JPaRlPsO9xDSs3BqFwHG9cfNuEhIepCB44rt4lJSB3VFxknsvusqoCJK95ZOOsDktgbe3N3JyctCsWTM4Ojqqxj09PfH06VM0bdoUderU0cq2evfpi7TUVKwKXYHk5CQ0beaKVWHrYFeGQwvayJBSLcxgRmWphRmGmyGlWvSR0aZ5AxxY97nq9aKpgwEAm3efwifBW7A04hCqm8sR+vUw2Fia4+T5WxjgvwqKvPwi8/T5XnSVQdonE0IIfRdRleSW/HklIiKSBNt2AeXOSDsTqoVKpMFMj7vz7qUWvtOPtjjVLHy6ob7xnFMiIiIikgwe1iciIiKSsKp2zikP61cwHtYnIqKqwpBODdDnYf37abo7rP+GrfQO63PPKREREZGkVa1dpzznlIiIiIgkg82pBgoKCtC5c2f4+qrf9ywjIwNOTk6YOXOmVrazfeuP6NOjO9q1bonhH36Aixcu6CVDSrUwgxmVpRZmGG6GlGqpjBld2rhg5/LxuH1gAXLOhaK/l3uhZWZNeBe3DyxAaswy7F0TAJf6tYpI0v970ReZTHeTJAnSSHx8vDA3NxdbtmxRjY0YMUK4u7sLhUKhcU7O86KnX3bvFW5ubmLbjp3i0tUbIuirr8VbbduK+4nJxa6jiwwp1cIMZlSWWphhuBlSqqUyZpi18hcD/FeKkLW/iSGTw4QQQnzwRZgwa+WvmmYu/0WkZWaL979YI9p+sEDsjooTt+8lCev2nwuzVv6SeS/69CBNobNJiticlsH3338vbG1txcOHD8WuXbuEiYmJOH/+fJkyivuA+A5+X8wKnqt6na0oEF26dhWhq8I0/pBpI0NKtTCDGZWlFmYYboaUaqmMGS83oWat/IUQhZvTh0/SxYylkarXDl2/FDm5eWJEYHiJzWlFvxd9qmrNKQ/rl8GkSZPg4eGBESNG4JNPPsHs2bPh4eFR7tzneXm4euUyOnbqrBozMjJCx46dcSHuXIVlSKkWZjCjstTCDMPNkFIthpTxMud6dqhTyxpHYq+pxjKzcnHmUgI6uDtXqveiS1XtsD6b0zKQyWRYvXo1Dh8+DEdHR8yYMaPE5RUKBTIzM9UmhaLw7SDS0tNQUFAAOzs7tXE7OzskJydrVJs2MqRUCzOYUVlqYYbhZkipFkPKeFlteysAwJPUp2rjT1KewtHOSqd1aPu9kPawOS2j8PBwVK9eHXfu3MH9+/dLXDYkJATW1tZq0+KFIRVUKRERERkCmQ7/kyI2p2Vw8uRJfPfdd9izZw/at2+PsWPHQpTwDIOgoCBkZGSoTdMCgwotZ2tjC2NjY6SkpKiNp6SkwN7eXqPatJEhpVqYwYzKUgszDDdDSrUYUsbLEpMzAQAONS3Vxh3sLPE4JVOndWj7vZD2sDnV0LNnzzBq1ChMmDAB3t7eWL9+PU6fPo01a9YUu45cLoeVlZXaJJcXfhKDiakpXJu7IfZUjGpMqVQiNjYG7h6tNapPGxlSqoUZzKgstTDDcDOkVIshZbws4UEKHiVlwLtDU9WYZQ0ztGvhjNgLCZXqveiUTIeTBPEJURoKCgqCEALffvstAMDZ2RlLlizB1KlT0adPHzg7O5crf4TfaMz6KhBubi3QoqU7tmzeiJycHAwc5Fv6ylrMkFItzGBGZamFGYabIaVaKmtGDXNTuDj9776lzvXs4N6kHtIyn+FeYhpWbo1C4LjeuHk3CQkPUhA88V08SsrA7qg4yb0XqhhsTjVw9OhRrFy5EtHR0ahevbpqfPz48YiMjMTYsWNx6NAhyMpx2VvvPn2RlpqKVaErkJychKbNXLEqbB3synBoQRsZUqqFGcyoLLUww3AzpFRLZc1o07wBDqz7XPV60dTBAIDNu0/hk+AtWBpxCNXN5Qj9ehhsLM1x8vwtDPBfBUVevuTei75IdAenzshESSdNktbllvxZIyIiMhi27QLKnZF2JlQLlZSfmR535z15+lxn2Q6WJjrLfl0855SIiIiIJIOH9YmIiIgkTKq3fNIVHtavYDysT0REpDmpnBqgz8P6SU911zzUspTefkrpVURERERE/1O1dpzynFNNCSHg4+ODXr16FZq3atUq2NjYlPrEqNJs3/oj+vTojnatW2L4hx/g4oULesmQUi3MYEZlqYUZhpshpVqqakaXNi7YuXw8bh9YgJxzoejv5V5omVkT3sXtAwuQGrMMe9cEwKV+rSKSdPNeSMsEaezu3bvC2tparFmzRjV2+/ZtUaNGDbFp0yaNMnKeFz39snuvcHNzE9t27BSXrt4QQV99Ld5q21bcT0wudh1dZEipFmYwo7LUwgzDzZBSLVU1w6yVvxjgv1KErP1NDJkcJoQQ4oMvwoRZK3/VNHP5LyItM1u8/8Ua0faDBWJ3VJy4fS9JWLf/XJi18tdKHfqU9PS5ziYpYnNaRhEREcLCwkLcvn1bKJVK4e3tLQYNGqTx+sV9QHwHvy9mBc9Vvc5WFIguXbuK0FVhGn/YtZEhpVqYwYzKUgszDDdDSrVU1YyXm1CzVv5CiMLN6cMn6WLG0kjVa4euX4qc3DwxIjC8xOa0LHXoU1VrTnlYv4z8/PzwzjvvYMyYMQgNDcWlS5cQFhZWrszneXm4euUyOnbqrBozMjJCx46dcSHuXIVlSKkWZjCjstTCDMPNkFItzCiecz071KlljSOx11RjmVm5OHMpAR3cnSusDl2SyXQ3SRGb09ewdu1aXLp0CV988QXWrl2LWrU0O6+lOGnpaSgoKICdnZ3auJ2dHZKTkyssQ0q1MIMZlaUWZhhuhpRqYUbxattbAQCepD5VG3+S8hSOdlYVVocuyXT4nxTxav3X4ODggPHjx2PXrl0YOHBgscspFAooFAq1MWEsh1wu13GFRERERJUT95y+pmrVqqFatZJ7+5CQEFhbW6tNixeGFFrO1sYWxsbGSElJURtPSUmBvYbP99VGhpRqYQYzKkstzDDcDCnVwoziJSZnAgAcalqqjTvYWeJxSmaF1aFLPKxPWhMUFISMjAy1aVpgUKHlTExN4drcDbGnYlRjSqUSsbExcPdordG2tJEhpVqYwYzKUgszDDdDSrUwo3gJD1LwKCkD3h2aqsYsa5ihXQtnxF5IqLA6SHt4WF+H5PLCh/CLe0LUCL/RmPVVINzcWqBFS3ds2bwROTk5GDjIV+PtaSNDSrUwgxmVpRZmGG6GlGqpyhk1zE3h4vS/6zuc69nBvUk9pGU+w73ENKzcGoXAcb1x824SEh6kIHjiu3iUlIHdUXE6fy+kfWxOJaJ3n75IS03FqtAVSE5OQtNmrlgVtg52ZTi0oI0MKdXCDGZUllqYYbgZUqqlKme0ad4AB9Z9rnq9aOpgAMDm3afwSfAWLI04hOrmcoR+PQw2luY4ef4WBvivgiKv5Md+autnhLRLJoQQ+i6iKiluzykREREVZtsuoNwZaWdCy51hpsfdeek5BTrLtjE31ln26+I5p0REREQkGTysT0RERCRhUr0fqa6wOSUiIiKSMKne8klXeM5pBeM5p0RERBVLG+et5pwr/3mrryszV6mzbCsz6Z3hyT2nRERERBJWxXac8oIoTUVHR0MmkxU7eXt7l3sb27f+iD49uqNd65YY/uEHuHjhgl4ypFQLM5hRWWphhuFmSKkWZrx+Rpc2Lti5fDxuH1iAnHOh6O/lXmiZWRPexe0DC5Aaswx71wTApX6tIpJI5wRpRKFQiEePHhWawsLChEwmEzt27NAoJ+d50dMvu/cKNzc3sW3HTnHp6g0R9NXX4q22bcX9xORi19FFhpRqYQYzKkstzDDcDCnVwozXzzBr5S8G+K8UIWt/E0MmhwkhhPjgizBh1spfNc1c/otIy8wW73+xRrT9YIHYHRUnbt9LEtbtPxdmrfx12WKUKjO3QGeTFLE5LYcrV64IS0tLMXPmTI3XKe5D5jv4fTEreK7qdbaiQHTp2lWErgrT+IOqjQwp1cIMZlSWWphhuBlSqoUZr5/xchP6otF8tTl9+CRdzFgaqXrt0PVLkZObJ0YEhrM5rWA8rP+a0tPT8d5778HLywvz588vV9bzvDxcvXIZHTt1Vo0ZGRmhY8fOuBB3rsIypFQLM5hRWWphhuFmSKkWZmg/42XO9exQp5Y1jsReU41lZuXizKUEdHB3LnOetsl0+J8UsTl9DUqlEh999BGqVauGH3/8EbJy3uMhLT0NBQUFsLOzUxu3s7NDcnJyhWVIqRZmMKOy1MIMw82QUi3M0H7Gy2rbWwEAnqQ+VRt/kvIUjnZWZc6j8uHV+q/hq6++QkxMDE6fPg1LS8til1MoFFAoFGpjwlgOuVyu6xKJiIjIQFS1+5xyz2kZbd++HUuWLMH27dvx5ptvlrhsSEgIrK2t1abFC0MKLWdrYwtjY2OkpKSojaekpMDe3l6jurSRIaVamMGMylILMww3Q0q1MEP7GS9LTM4EADjUVN/h5GBniccpmWXOo/Jhc1oG58+fx9ixY/Htt9+iV69epS4fFBSEjIwMtWlaYFCh5UxMTeHa3A2xp2JUY0qlErGxMXD3aK1RbdrIkFItzGBGZamFGYabIaVamKH9jJclPEjBo6QMeHdoqhqzrGGGdi2cEXshocx52ibT4SRFPKyvoeTkZAwcOBBeXl7417/+hcTERLX5xsbGqFVL/X5ocnnhQ/jFPSFqhN9ozPoqEG5uLdCipTu2bN6InJwcDBzkq3GN2siQUi3MYEZlqYUZhpshpVqYUb6MGuamcHH6399p53p2cG9SD2mZz3AvMQ0rt0YhcFxv3LybhIQHKQie+C4eJWVgd1ScxjXpjFS7SB1hc6qhvXv34u+//8bff/+NOnXqFJrfoEEDJCQkvHZ+7z59kZaailWhK5CcnISmzVyxKmwd7MpweEIbGVKqhRnMqCy1MMNwM6RUCzPKl9GmeQMcWPe56vWiqYMBAJt3n8InwVuwNOIQqpvLEfr1MNhYmuPk+VsY4L8Kijw+d7yiyYQQQt9FVCXF7TklIiIi3bBtF1DujJxzoVqo5DW3/Vx32eYmZV9n5cqVWLx4MRITE+Hh4YEffvgB7du311pNPOeUiIiIiDTy008/YcqUKQgODsbZs2fh4eGBXr164cmTJ1rbBptTIiIiIgmTyXQ3ldWyZcvw8ccfY/To0WjevDnWrFmD6tWrIzw8XGvvl80pERERURWlUCiQmZmpNr16j/YX8vLy8Ndff8HHx0c1ZmRkBB8fH8TExBS5zmvR9/NTSV1ubq4IDg4Wubm5zDCwDCnVwgxmVJZamGG4GVKqRVvvpzIKDg4WANSm4ODgIpd98OCBACBOnjypNj5t2jTRvn17rdXE5lRiMjIyBACRkZHBDAPLkFItzGBGZamFGYabIaVatPV+KqPc3FyRkZGhNhXXpFdUc8pbSRERERFVUUXdk7049vb2MDY2xuPHj9XGHz9+jNq1a2utJp5zSkRERESlMjU1xVtvvYXDhw+rxpRKJQ4fPoxOnTppbTvcc0pEREREGpkyZQr8/PzQtm1btG/fHsuXL0d2djZGjx6ttW2wOZUYuVyO4OBgjXexM6PyZEipFmYwo7LUwgzDzZBSLdp6P1XB0KFDkZSUhNmzZyMxMRGtWrXC77//DkdHR61tg0+IIiIiIiLJ4DmnRERERCQZbE6JiIiISDLYnBIREZHkPXz4UN8lUAVhc0pERESS5+bmhq1bt+q7DKoAvCBKwi5duoQWLVrouwyqwnx9fUtdplq1aqhduzZ69OiB/v37l2t7WVlZsLCwKFdGVZacnAzgnxtl61tycjJMTU1hZWWl71Iq3Lx58zB16lRUr15dr3Vo62/InTt30LBhQy1UVD6rVq1CYGAgevfujbCwMNSsWVPfJZGOcM+pxDx9+hRr165F+/bt4eHhUSHbPHLkCJo3b47MzMxC8zIyMuDm5objx49XSC1Scfv2bZTn3205OTnYs2eP6nVQUBCmTJmimqZNm4bc3NxSczIzMzWadMXa2rrUydzcHDdu3MDQoUMxe/bsYrO+++67Erf19OlT9OrVq1z13r9/H5988km5Miqb9PR0+Pv7w97eHo6OjnB0dIS9vT0CAgKQnp6uUYZSqcTChQvRpUsXtGvXDjNmzEBOTk65a7G1tUXt2rURFBSEZ8+elTmvKKXVFRMTo/bZA4BNmzahYcOGcHBwwCeffAKFQlHm7WryeX1h7ty5yMrKKvM2tM3d3R0dOnTAf/7zHzx9+vS1c1xcXNCwYUOMGTMGmzdvxv3798ucMWvWLOTn5xc7/+7du+jRo0eJGRMnTsSFCxeQkpKC5s2b49dffy1zHe+88w4iIyOLnZ+cnIxGjRqVOZe0i3tOJeLYsWNYv349fv75Z9StWxe+vr4YPHgw2rVrp9H6KSkpsLOzAwDcu3cP//nPf5CTk4MBAwbg7bffLnHdAQMGwNvbG5MnTy5y/ooVKxAVFYVffvml1DqUSiUiIiIQGRmJhIQEyGQyNGzYEO+//z5GjBgBmUxW5Hq2trbFzntVampqifP79u2Lbdu2wdraGgDw7bff4tNPP4WNjQ2Af75Wb7/9Nq5cuVJshrGxMR49egQHBwcA/9zXbcWKFRrfx23NmjXYu3ev6penpaUl3NzcYG5uDgC4du0apk+fXuzX/AUjI6MSvy5CCMhkMhQUFBS7jCZ7PwGU+AtbE3v27MHEiRNx9+7dIuebm5sjLCwMI0eOLDQvOzsbPXv2REpKCq5du/baNcTFxaFNmzYlfj0AYMyYMRrlhYeHFzuvtO8NAMhkshL/IJdXamoqOnXqhAcPHmD48OFwdXUFAFy5cgVbt26Fk5MTTp48CVtb2xJz5s+fjzlz5sDHxwfm5ubYv38/hg0bVuL7L2stzZo1w4kTJ3DhwgWcOnUKn332WZneq0KhQGhoKBYvXozExMRil+vTpw+8vLwQGBgIALh48SLatGmDUaNGwdXVFYsXL8b48eMxZ86cUrepVCqxYMECrFmzBo8fP8b169fRqFEjzJo1C87Ozhg7dmyR6xkZGSExMVH1++N1Ffd70draGk2aNMHUqVNLbOiOHz+ODRs2YOfOnVAqlRg8eDDGjRtX6t+EV0VHR6um2NhY5OXloVGjRujevTu8vb3h7e1d6u/G+vXrw87ODps3by60NzcsLAzTpk1Dly5d8Ntvv2lUU2hoKCZPngxXV1dUq6Z+y/azZ88Wu56RkRGMjIwwc+ZMzJ07t9D8x48fo27duqX+DiHd4k349SgxMRERERFYv349MjMzMWTIECgUCuzatQvNmzfXKOPixYvo378/7t27hzfffBPbt29H7969kZ2dDSMjI3z33XfYuXMnBg4cWGxGXFwcFi5cWOz8nj17YsmSJaXWIoTAgAEDsG/fPnh4eKBly5YQQuDq1asYNWoUIiMjsWvXriLXXb58ean5mtq/f7/anpFvvvkGQ4YMUTWn+fn5iI+PLzHj1X+z7du3DyEhIRrX8OOPP2L69OlqY1u3blX9i3zLli1YuXJlqc1pVFSUWk19+/bFunXrUK9ePY1redGk61rXrl3Rtm3bYudv3rwZI0aMgI2NDQYMGKAaz87ORq9evZCUlISjR49WRKmIiIhAgwYN0Lp169feQ17SP9ZiYmKwYsUKKJVKjbIGDRpUZBMik8lgZmaGxo0b46OPPkLTpk3V5s+bNw+mpqa4detWoeZg3rx56NmzJ+bNm1fqXutNmzZh1apVGD9+PADg0KFDePfdd7Fu3ToYGWl2gE2TWkaMGIEDBw5gxYoVRWYoFArMmTMHBw8ehKmpKaZPn46BAwdiw4YNmDlzJoyNjUv9zJw/fx7z589Xvd6+fbtq7yEAODk5ITg4WKPm9N///jc2btyIRYsW4eOPP1aNt2jRAsuXLy+2OQWg8T+2S1Lc78X09HT89ddf6NevH3bu3Fns6TRvv/023n77bfzwww/YsWMHIiIi4OnpicaNG2Ps2LHw8/PT6HnoXl5e8PLyAvDPHuSTJ0+qmtWNGzfi+fPnaNasGS5fvlxsxqVLlxAQEIC2bdsiODgYgYGBuH//PsaMGYMzZ85gyZIlGh/1+PvvvxEZGQlbW1u89957hZrT0qxevRpTp07FhQsXsGXLFtSoUaNM61MFEKQX/fr1E1ZWVmLYsGFiz549Ij8/XwghRLVq1cTly5c1zundu7fo16+fOHHihBg/fryoV6+eGDNmjCgoKBAFBQVi4sSJokOHDiVmyOVycePGjWLn37hxQ5iZmZVaS3h4uLC0tBRHjhwpNO/w4cPC0tJSbNy4sfQ3VU4ymUw8fvxY9drCwkLcunVL9ToxMVEYGRmVK6M0tWvXFnfu3FG9tre3V3sdHx8vrKysNM573Tqk5j//+Y+oXr26iIqKEkIIkZWVJbp27SoaN24sHjx4UO788+fPl/q9FUKIiRMnCltbW9GqVSvx/fffi5SUlHJvWwghrl27JgYOHCiMjY3FyJEjRUJCgkbr+fn5CWtra9GgQQPh6+srfH19hbOzs7CxsRFDhgwRTZs2FXK5XJw4cUJtvQYNGojff/+92NzffvtNNGjQoNTtm5qairt376qNyeVyce/ePY3q17QWmUwm5syZU+wy06dPF9bW1mLw4MGiTp06olq1auLjjz8WLVu2FNu2bVP9niyJXC5Xey9dunQR//73v1Wv79y5IywsLDR6Ty4uLuLQoUNCCPXP3tWrV4WNjU2x68lkMmFjYyNsbW1LnMpr6dKlolOnTmVa58aNG+Krr74STk5OwsTERPTv3/+1tq1QKMSRI0fEtGnThJWVlUafOyGE2LVrl3B0dBQeHh7CyspK+Pj4aPw5EUKItWvXCktLSzFo0CDx5MmTMtf94nf7lStXxJtvvilatGhR5r8PpHtsTvXE2NhYTJ48WVy/fl1tvKzNqZ2dnYiLixNCCPH06VMhk8nEn3/+qZp/9epVYW1tXWJGo0aNxC+//FLs/J9//lk0bNiw1Fp69OghQkJCip2/YMEC0bNnz1JzhBDi5s2bYubMmeLDDz9UNYn79u0Tly5dKnVdbTSnRkZGar/4LCwsxO3btzWqXQghzMzMxLVr14qdf/XqVSGXyzXOe7mOytycCiHEwoULhZWVlYiKihJvv/22aNSoUZmaoJJo2pwKIURubq7YunWr8PHxEdWrVxcffPCB+P3334VSqSzzdh88eCDGjRsnTExMRL9+/cTFixfLtH5gYKCYMGGCKCgoUI0VFBSIgIAAERQUJJRKpfjkk09Ely5d1NYzNTUt8Wt37949jX7OXv15F6LsP/Oa1GJsbFxiRsOGDcV///tfIYQQFy9eFDKZTIwePbpM35P69euLo0ePCiH+aaDMzc1VDaYQQly4cEHjxtDMzEzVOL382bt8+bKoUaNGsevJZDLx/fffi4iIiBKn8oqPj3+tJjcrK0uEhYWJmjVravx5USgU4ujRo2LOnDnCy8tLmJubiyZNmohx48aJTZs2ib///lujnMTEROHj4yNkMpmwsLAQ0dHRGtfdq1cvYWtrW66dHC//fUhPTxd9+vQRNWvWFAcPHlTVx+ZU/9ic6klMTIwYN26csLS0FO3btxc//PCDSEpKKnNzqo1GLCAgQLRo0ULk5OQUmvfs2TPRokULMWnSpFJrcXR0FOfOnSt2/tmzZ4Wjo2OpOdHR0cLc3Fz4+PgIU1NT1fsJCQkRgwcPLnX90hpLTfec9u3bVwwaNEgMGjRIVKtWTfTs2VP1+sVUnMaNG4udO3cWO/+nn34SLi4upb6XVxlCcyrEP82YkZGRaNSoUaE9diV59ev/6uTt7f1af1gSEhLEnDlzRKNGjUT9+vXF06dPNVovPT1dTJ8+XZibm4tOnTqJY8eOlXnbQvyzZz0+Pr7QeHx8vLCzsxNC/NNUvfoPzbp164rjx48Xm3vs2DFRp06dUrf/6s97cT/zJdFGLSYmJuL+/fuq12ZmZuLChQul1v+yTz/9VPW9mDJlirCzsxMKhUI1f8uWLaJt27YaZbVp00Zs3rxZCKH+2Zs7d67o2rVrseu9+ntZVy5cuKDR79QXjh49Kvz8/ISFhYWwsrIS48aNEzExMaWu5+3tLapXry7c3NzExIkTxbZt28TDhw/LXO/WrVtFzZo1Rffu3cW1a9fEtGnThKmpqfjiiy+K/PvzKh8fn3L/Q/bV741SqRSBgYHCxMRELFu2jM2pRPCcUz3p2LEjOnbsiOXLl+Onn35CeHg4pkyZAqVSiYMHD8LJyQmWlpYaZb16blNZz3X6+uuvERkZiSZNmiAgIEB1Xtu1a9ewcuVKFBQUYObMmaXmpKamlnhSvKOjI9LS0krNmTFjBv79739jypQpal+D7t27IzQ0tNT1hRAYNWoU5HI5gH/Okfr0009V5xVpcqWun5+f2ut//etfpa7zsr59+2L27Nl49913YWZmpjYvJycHc+fOxbvvvlumzBe0cS6bPrx6YZaJiQns7e3x+eefq42XdGFWaefPWltbF3nBVWleXNwkhND4QohFixZh4cKFqF27NrZt24b33nuvzNt9IT8/H9euXUOTJk3Uxq9du6aqx8zMrND3vlevXpg5c6bqHM2XKRQKzJo1C7179y51+6/+vANl/5nXRi0FBQVq61arVq3MtxabP38+fH194enpCQsLC2zcuFEtMzw8HD179tQoa/bs2fDz88ODBw+gVCoRGRmJ+Ph4bNq0qdAdAV5WUZ/R9evXo1WrViUu8/DhQ0RERCAiIgI3b95E586dsWLFCgwZMkTjcy2PHz+OOnXqoHv37vDy8oKnp6fqAlxNDR48GPv370dISAgmTZoE4J/P0MCBAzF69Gjs27cPERER6NSpU7EZBw8eLNM2i1LU38tvv/0WrVq1wrhx43DkyJFyb4PKj1frS0h8fDzWr1+PzZs3Iz09HT169MDu3btLXMfIyAh9+vRRNWK//vorunfvrtaI/f7776X+wf37778xYcIE7N+/X3WBiEwmQ69evbBy5UqN7nFnbGyMxMRE1KpVq8j5ml4FaWFhgYsXL6Jhw4awtLREXFwcGjVqhISEBDRr1qzUW7qMHj261FoBYMOGDRot9zoeP36MVq1awdTUFAEBAaqmIz4+HqGhocjPz8e5c+dKvcL11Ybu1e/vC+W90r4iSOH78jKFQoHIyEiEh4fjxIkT6NevH0aPHo3evXtrdBGQkZERzM3N4ePjA2Nj42KX0+R789lnn2Hbtm346quvVHfoOHPmDL755ht89NFH+P7777Fu3TpERETgxIkTqvXu37+Ptm3bQi6Xw9/fH82aNVNdhLhq1SooFAr8+eefcHJy0uArUj6a1HLmzBnUr1+/2IzSfp+9oMnXNCMjAxYWFoW+N6mpqbCwsCjUQBfn+PHjmDdvHuLi4pCVlYU2bdpg9uzZJTa42rpaf8qUKUWOZ2Rk4OzZs7h+/TqOHTuGt956q8jl+vTpg0OHDsHe3h4jR47EmDFjCl1Up4ns7GwcP34c0dHRiIqKwvnz59GkSRN4enqqmtXifu+/0KVLF0RERODNN98sNC8nJwczZszA6tWrkZeXV+b6yqKk78358+cxcOBA3Lt3j1fr6xmbUwkqKCjAr7/+ivDw8FKbU23/wU9LS8PNmzchhMCbb75Z6i1oXvbqH5ZXadoov/HGG9ixYwc6d+6s1pz+8ssvmDp1Km7duqVxTfp0584dTJgwAQcPHlRr+Hv06IFVq1ZpdC89qTV0hmLixInYvn07nJycMGbMGAwfPrzMN64fNWqURnvINPneFBQU4Ntvv0VoaCgeP34M4J8jDZMmTUJgYCCMjY1x9+5dGBkZ4Y033lBb986dO5g4cSIOHDhQ6OcsNDQUjRs3LtP7Ko/y1sKfd3Xe3t5FjltZWaFp06aYMGFCiTsOBgwYgLFjx6Jfv34l/gOqrJ4+fYoTJ04gKioK0dHRiIuLw5tvvolLly4Vu45SqSz1H33Hjh1Dt27dtFZnUY4ePYouXboUe4V/SkoK9u7d+1pHYEh72JyS1mjrD8vUqVMRGxuL//u//0OTJk1w9uxZPH78GCNHjsTIkSMRHBysjXIrTGpqKm7evAkAaNy4MZ9qIgFGRkaoX78+WrduXWKDqY890i8eqlDWJyulpaXhxo0bAPT/cyalWsrjzJkzUCqV6NChg9p4bGwsjI2NS7x1miFTKpU4c+YMoqKiEBUVhRMnTiA3N5d7G0lr2JyS5OTl5cHf3x8REREoKChAtWrVkJ+fj+HDhyMiIkKrewCoatLmXk8yXO3bt8f06dPx/vvvq41HRkZi4cKFiI2N1VNlFUupVOLPP/9UHdb/448/kJ2djXr16qluwu/t7Y0GDRrou1QyEGxOSbLu3buHixcvIisrC61bty7yXCUiQ/D48WNMnToVhw8fxpMnTwo9GIB7pPTDwsICFy5cKHQKzp07d+Du7l6uR4JWJlZWVsjOzkbt2rVVjaiXlxdcXFz0XRoZKF6tT5JQ3In/L5w6dUr1/8uWLdN1OUQVatSoUbh79y5mzZqFOnXqVNo7MhgauVyOx48fF2pOHz16VOanElVmixcvhre3d6G7SRDpCveckiS8euL/2bNnkZ+fr7qy9Pr16zA2NsZbb73FW32QwbG0tMTx48dLvS0QVaxhw4bh0aNH+O9//6u6jVl6ejoGDhwIBwcH7NixQ88VEhmmqvNPP5K0l58hv2zZMlhaWmLjxo2quwWkpaVh9OjRePvtt/VVIpHOODk5FTqUT/q3ZMkSdOvWDQ0aNEDr1q0B/HO7IUdHR2zevFnP1REZLu45JcmpV68eDhw4ADc3N7XxS5cuoWfPnnj48KGeKiPSjQMHDmDp0qUICwuDs7Ozvsuhl2RnZ+PHH39EXFwczM3N4e7ujmHDhsHExETfpREZLO45JcnJzMxEUlJSofGkpKQqcwECVS1Dhw7Fs2fP4OLigurVqxdqfFJTU/VUGdWoUQOffPKJvssgqlLYnJLkDBo0CKNHj8bSpUvRvn17AP/cV3DatGmFnphEZAiWL1+u7xKoBFeuXMHdu3cLPb1owIABeqqIyLDxsD5JzrNnzzB16lSEh4fj+fPnAP55xvbYsWOxePFijZ8HTURUHrdv38agQYNw8eJFyGQytadeAbzFF5GusDklycrOzlY9qtTFxYVNKRmUzMxM1VOgXjwVqjhlfVoUaUf//v1hbGyMdevWoWHDhjh9+jRSUlLw5ZdfYsmSJbxAk0hH2JwSEemBsbExHj16BAcHBxgZGRV5b1MhBGQyGffQ6Ym9vT2OHDkCd3d3WFtb4/Tp02jatCmOHDmCL7/8EufOndN3iUQGieecEhHpwZEjR1TPnH/5VmokHQUFBbC0tATwT6P68OFDNG3aFA0aNEB8fLyeqyMyXGxOiYj0wNPTs8j/J+lo0aIF4uLi0LBhQ3To0AGLFi2Cqakp1q5dW+ipUUSkPTysT0QkAenp6Th9+jSePHkCpVKpNm/kyJF6qqpq279/P7Kzs+Hr64ubN2+iX79+uH79Ouzs7PDTTz+he/fu+i6RyCCxOSUi0rNff/0Vw4cPR1ZWFqysrNTOP5XJZLzPqYSkpqbC1ta2yHOEiUg72JwSEelZkyZN0LdvX3zzzTeoXr26vsuhIty7dw/AP4+aJSLdMtJ3AUREVd2DBw/w2WefsTGVmPz8fMyaNQvW1tZwdnaGs7MzrK2t8fXXX6vuwUxE2scLooiI9KxXr174888/eZGNxEyaNAmRkZFYtGgROnXqBACIiYnBnDlzkJKSgtWrV+u5QiLDxMP6RER6sHv3btX/JyUlYd68eRg9ejRatmwJExMTtWX5mEz9sLa2xvbt29GnTx+18X379mHYsGHIyMjQU2VEho3NKRGRHhgZaXZWFW/Crz8ODg44evQoXF1d1cavXr2Kbt26ISkpSU+VERk2nnNKRKQHSqVSo4mNqf4EBARg/vz5UCgUqjGFQoEFCxYgICBAj5URGTbuOSUi0rNNmzZh6NChkMvlauN5eXnYvn0773OqJ4MGDcLhw4chl8vh4eEBAIiLi0NeXh7eeecdtWUjIyP1USKRQWJzSkSkZ8bGxnj06BEcHBzUxlNSUuDg4MC9p3oyevRojZfdsGGDDishqlp4tT4RkZ4JIYq8qfv9+/dhbW2th4oIAFatWgWlUokaNWoAABISErBr1y64urqiV69eeq6OyHCxOSUi0pPWrVtDJpNBJpPhnXfeQbVq//uVXFBQgDt37qB37956rLBqe++99+Dr64tPP/0U6enp6NixI0xMTJCcnIxly5ZhwoQJ+i6RyCCxOSUi0pOBAwcCAM6fP49evXrBwsJCNc/U1BTOzs4YPHiwnqqjs2fP4rvvvgMA7Ny5E46Ojjh37hx+/vlnzJ49m80pkY6wOSUi0pPg4GAAgLOzM4YOHQozMzM9V0Qve/bsGSwtLQEABw4cgK+vL4yMjNCxY0f8/fffeq6OyHCxOSUi0jM/Pz8AwF9//YWrV68CANzc3NC6dWt9llXlNW7cGLt27cKgQYOwf/9+TJ48GQDw5MkTWFlZ6bk6IsPFq/WJiPTsyZMn+PDDDxEdHQ0bGxsAQHp6Ory9vbF9+3bUqlVLvwVWUTt37sRHH32EgoICvPPOOzhw4AAAICQkBMeOHcNvv/2m5wqJDBObUyIiPRs6dChu376NTZs2qZ5GdOXKFfj5+aFx48bYtm2bniusuhITE/Ho0SN4eHionup1+vRpWFlZoVmzZnqujsgwsTklItIza2trHDp0CO3atVMbP336NHr27In09HT9FEZEpAd8fCkRkZ4plUqYmJgUGjcxMYFSqdRDRURE+sPmlIhIz7p3747PP/8cDx8+VI09ePAAkydPLvSYTCIiQ8fD+kREenbv3j0MGDAAly9fhpOTEwDg7t27aNmyJXbv3o033nhDzxUSEVUcNqdERBIghMDhw4dVt5JydXWFj4+PnqsiIqp4bE6JiCTg8OHDOHz4MJ48eVLoPNPw8HA9VUVEVPF4E34iIj2bO3cu5s2bh7Zt26JOnTqQyWT6LomISG+455SISM/q1KmDRYsWYcSIEfouhYhI73i1PhGRnuXl5aFz5876LoOISBLYnBIR6dm4ceOwdetWfZdBRCQJPOeUiEjPcnNzsXbtWhw6dAju7u6Fbsi/bNkyPVVGRFTxeM4pEZGeeXt7FztPJpPhyJEjFVgNEZF+sTklIiIiIsngOadEREREJBlsTomIiIhIMticEhEREZFksDklIiIiIslgc0pE9BpGjRqFgQMHql57eXnhiy++qPA6oqOjIZPJkJ6eXuHbJiLSBTanRGRQRo0aBZlMBplMBlNTUzRu3Bjz5s1Dfn6+TrcbGRmJ+fPna7QsG0oiouLxJvxEZHB69+6NDRs2QKFQYN++ffD394eJiQmCgoLUlsvLy4OpqalWtlmzZk2t5BARVXXcc0pEBkcul6N27dpo0KABJkyYAB8fH+zevVt1KH7BggWoW7cumjZtCgC4d+8ehgwZAhsbG9SsWRPvvfceEhISVHkFBQWYMmUKbGxsYGdnh+nTp+PVW0S/elhfoVAgMDAQTk5OkMvlaNy4MdavX4+EhATVTfdtbW0hk8kwatQoAIBSqURISAgaNmwIc3NzeHh4YOfOnWrb2bdvH5o0aQJzc3N4e3ur1UlEZAjYnBKRwTM3N0deXh4A4PDhw4iPj8fBgwexZ88ePH/+HL169YKlpSWOHz+OP/74AxYWFujdu7dqnaVLlyIiIgLh4eE4ceIEUlNT8csvv5S4zZEjR2Lbtm1YsWIFrl69irCwMFhYWMDJyQk///wzACA+Ph6PHj3C999/DwAICQnBpk2bsGbNGly+fBmTJ0/Gv/71Lxw9ehTAP020r68v+vfvj/Pnz2PcuHGYMWOGrr5sRER6wcP6RGSwhBA4fPgw9u/fj0mTJiEpKQk1atTAunXrVIfzt2zZAqVSiXXr1kEmkwEANmzYABsbG0RHR6Nnz55Yvnw5goKC4OvrCwBYs2YN9u/fX+x2r1+/jh07duDgwYPw8fEBADRq1Eg1/8UpAA4ODrCxsQHwz57Wb775BocOHUKnTp1U65w4cQJhYWHw9PTE6tWr4eLigqVLlwIAmjZtiosXL2LhwoVa/KoREekXm1MiMjh79uyBhYUFnj9/DqVSiY8++ghz5syBv78/WrZsqXaeaVxcHG7evAlLS0u1jNzcXNy6dQsZGRl49OgROnTooJpXrVo1tG3bttCh/RfOnz8PY2NjeHp6alzzzZs38ezZM/To0UNtPC8vD61btwYAXL16Va0OAKpGlojIULA5JSKD4+3tjdWrV8PU1BR169ZFtWr/+1VXo0YNtWWzsrLw1ltv4ccffyyUU6tWrdfavrm5eZnXycrKAgDs3bsX9erVU5snl8tfqw4iosqIzSkRGZwaNWqgcePGGi3bpk0b/PTTT3BwcICVlVWRy9SpUwexsbHo1q0bACA/Px9//fUX2rRpU+TyLVu2hFKpxNGjR1WH9V/2Ys9tQUGBaqx58+aQy+W4e/dusXtcXV1dsXv3brWxU6dOlf4miYgqEV4QRURV2vDhw2Fvb4/33nsPx48fx507dxAdHY3PPvsM9+/fBwB8/vnn+Pbbb7Fr1y5cu3YNEydOLPEepc7OzvDz88OYMWOwa9cuVeaOHTsAAA0aNIBMJsOePXuQlJSErKwsWFpaYurUqZg8eTI2btyIW7du4ezZs/jhhx+wceNGAMCnn36KGzduYNq0aYiPj8fWrVsRERGh6y8REVGFYnNKRFVa9erVcezYMdSvXx++vr5wdXXF2LFjkZubq9qT+uWXX2LEiBHw8/NDp06dYGlpiUGDBpWYu3r1arz//vuYOHEimjVrho8//hjZ2dkAgHr16mHu3LmYMWMGHB0dERAQAACYP38+Zs2ahZCQELi6uqJ3797Yu3cvGjZsCACoX78+fv75Z+zatQseHh5Ys2YNvvnmGx1+dYiIKp5MFHdGPxERERFRBeOeUyIiIiKSDDanRERERCQZbE6JiIiISDLYnBIRERGRZLA5JSIiIiLJYHNKRERERJLB5pSIiIiIJIPNKRERERFJBptTIiIiIpIMNqdEREREJBlsTomIiIhIMv4fohRgmRRuWDwAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
            "  _warn_prf(average, modifier, msg_start, len(result))\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test loss: 1.1761\n",
            "Test accuracy: 60.76\n",
            "Train loss: 0.4306\n",
            "Train accuracy: 89.87\n",
            "Recall: 0.61\n",
            "Precision: 0.61\n",
            "F1-score: 0.58\n",
            "RMSE: 4.42\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.applications.densenet import DenseNet121, preprocess_input\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D, Dropout\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics import recall_score, precision_score, f1_score, mean_squared_error, confusion_matrix\n",
        "import seaborn as sns  # Added seaborn for visualization\n",
        "\n",
        "# Define your data directories\n",
        "train_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/train'\n",
        "val_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/validation'\n",
        "test_dir = '/content/drive/MyDrive/Dataset/Dataset3.0/test'\n",
        "\n",
        "# Define image size and batch size\n",
        "img_size = (224, 224)\n",
        "batch_size = 16\n",
        "\n",
        "# Create data generators\n",
        "train_datagen = ImageDataGenerator(\n",
        "    preprocessing_function=preprocess_input,\n",
        "    rotation_range=30,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    shear_range=0.2,\n",
        "    zoom_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    fill_mode='nearest'\n",
        ")\n",
        "\n",
        "val_datagen = ImageDataGenerator(rescale=1./255)\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    train_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "val_generator = val_datagen.flow_from_directory(\n",
        "    val_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "test_generator = test_datagen.flow_from_directory(\n",
        "    test_dir,\n",
        "    target_size=img_size,\n",
        "    batch_size=batch_size,\n",
        "    class_mode='categorical'\n",
        ")\n",
        "\n",
        "# Load the pre-trained DenseNet121 model with reduced complexity\n",
        "base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n",
        "\n",
        "# Add a global average pooling layer and a dense layer for classification\n",
        "x = GlobalAveragePooling2D()(base_model.output)\n",
        "x = Dropout(0.5)(x)  # Add a dropout layer with a dropout rate of 0.5\n",
        "x = Dense(128, activation='relu')(x)  # Add a dense layer with fewer units\n",
        "x = Dropout(0.3)(x)  # Add another dropout layer\n",
        "predictions = Dense(29, activation='softmax')(x)  # Assuming 29 classes\n",
        "\n",
        "# Create the model\n",
        "model = Model(inputs=base_model.input, outputs=predictions)\n",
        "\n",
        "# Freeze the layers in the base model\n",
        "for layer in base_model.layers:\n",
        "    layer.trainable = False\n",
        "\n",
        "# Compile the model\n",
        "model.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "# Train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch=train_generator.n // batch_size,\n",
        "    epochs=30,\n",
        "    validation_data=val_generator,\n",
        "    validation_steps=val_generator.n // batch_size\n",
        ")\n",
        "\n",
        "# Evaluate the model on the test data\n",
        "test_loss, test_accuracy = model.evaluate(test_generator, steps=test_generator.n // batch_size)\n",
        "train_loss, train_accuracy = model.evaluate(train_generator, steps=train_generator.n // batch_size)\n",
        "\n",
        "# Calculate recall\n",
        "y_true = []\n",
        "y_pred = []\n",
        "for i in range(test_generator.n // batch_size):\n",
        "    batch_x, batch_y = next(test_generator)\n",
        "    y_true.extend(np.argmax(batch_y, axis=1))\n",
        "    y_pred.extend(np.argmax(model.predict(batch_x), axis=1))\n",
        "\n",
        "# Calculate confusion matrix\n",
        "cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "# Visualize the confusion matrix as a heatmap\n",
        "plt.figure(figsize=(8, 6))\n",
        "sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['A', 'B', 'C', 'D', 'del', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'], yticklabels=['A', 'B', 'C', 'D', 'del', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'nothing', 'O', 'P', 'Q', 'R', 'S', 'space', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z'])\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()\n",
        "\n",
        "# Calculate other metrics\n",
        "recall = recall_score(y_true, y_pred, average='macro')\n",
        "precision = precision_score(y_true, y_pred, average='macro')\n",
        "f1 = f1_score(y_true, y_pred, average='macro')\n",
        "rmse = np.sqrt(mean_squared_error(y_true, y_pred))\n",
        "\n",
        "# Display metrics\n",
        "print(f'Test loss: {test_loss:.4f}')\n",
        "print(f'Test accuracy: {test_accuracy * 100:.2f}')\n",
        "print(f'Train loss: {train_loss:.4f}')\n",
        "print(f'Train accuracy: {train_accuracy * 100:.2f}')\n",
        "print(f'Recall: {recall:.2f}')\n",
        "print(f'Precision: {precision:.2f}')\n",
        "print(f'F1-score: {f1:.2f}')\n",
        "print(f'RMSE: {rmse:.2f}')\n",
        "\n",
        "# Save the trained model\n",
        "model.save('/content/drive/MyDrive/Project/Modeltest/sign_language_interprter_DenseNet121_Dataset3.0_epoch_30_16.h5')"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": [],
      "mount_file_id": "13ZKliajF0ujgVdcv-O6wNPuoKSeWKmPO",
      "authorship_tag": "ABX9TyOHjNmHo2W6i9EPXoVCGLiA",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}