stock_analysis.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
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        "import quandl"
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        "data = quandl.get(\"WIKI/AAPL\")\n",
        "data.head()"
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              "             Open   High    Low  Close     Volume  Ex-Dividend  Split Ratio  \\\n",
              "Date                                                                          \n",
              "1980-12-12  28.75  28.87  28.75  28.75  2093900.0          0.0          1.0   \n",
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              "1980-12-18  26.63  26.75  26.63  26.63   327900.0          0.0          1.0   \n",
              "\n",
              "            Adj. Open  Adj. High  Adj. Low  Adj. Close  Adj. Volume  \n",
              "Date                                                                 \n",
              "1980-12-12   0.422706   0.424470  0.422706    0.422706  117258400.0  \n",
              "1980-12-15   0.402563   0.402563  0.400652    0.400652   43971200.0  \n",
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              "1980-12-17   0.380362   0.382273  0.380362    0.380362   21610400.0  \n",
              "1980-12-18   0.391536   0.393300  0.391536    0.391536   18362400.0  "
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              "      <th>1980-12-12</th>\n",
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              "      <th>1980-12-15</th>\n",
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              "      <th>1980-12-16</th>\n",
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              "      <th>1980-12-17</th>\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 9400,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1980-12-12 00:00:00\",\n        \"max\": \"2018-03-27 00:00:00\",\n        \"num_unique_values\": 9400,\n        \"samples\": [\n          \"1995-10-03 00:00:00\",\n          \"2000-02-02 00:00:00\",\n          \"1998-12-01 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135.16930707269705,\n        \"min\": 11.12,\n        \"max\": 702.41,\n        \"num_unique_values\": 4551,\n        \"samples\": [\n          62.5,\n          145.04,\n          32.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 136.29400536238697,\n        \"min\": 11.12,\n        \"max\": 705.07,\n        \"num_unique_values\": 4471,\n        \"samples\": [\n          566.47,\n          258.14,\n          194.99\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 133.8295281858526,\n        \"min\": 11.0,\n        \"max\": 699.57,\n        \"num_unique_values\": 4474,\n        \"samples\": [\n          185.92,\n          236.21,\n          164.66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 135.08734966638826,\n        \"min\": 11.0,\n        \"max\": 702.1,\n        \"num_unique_values\": 4643,\n        \"samples\": [\n          32.74,\n          72.1,\n          253.35\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16623095.155895837,\n        \"min\": 4471.0,\n        \"max\": 189560600.0,\n        \"num_unique_values\": 8893,\n        \"samples\": [\n          82242690.0,\n          2977700.0,\n          27044500.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ex-Dividend\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08826906713723419,\n        \"min\": 0.0,\n        \"max\": 3.29,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          0.57,\n          0.52,\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Split Ratio\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06440854595034537,\n        \"min\": 1.0,\n        \"max\": 7.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0,\n          2.0,\n          7.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Open\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.272528565206095,\n        \"min\": 0.16349529685522,\n        \"max\": 182.59,\n        \"num_unique_values\": 6152,\n        \"samples\": [\n          2.0934920443276,\n          172.8,\n          95.149510660231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. High\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.584888048251734,\n        \"min\": 0.16349529685522,\n        \"max\": 183.5,\n        \"num_unique_values\": 6142,\n        \"samples\": [\n          2.5220860263922,\n          110.3619798319,\n          1.1725494536155\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 38.942650715619756,\n        \"min\": 0.16173095911937,\n        \"max\": 180.21,\n        \"num_unique_values\": 6113,\n        \"samples\": [\n          25.832586607138,\n          0.51092280267257,\n          4.2441727909096\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39.271265927809914,\n        \"min\": 0.16173095911937,\n        \"max\": 181.72,\n        \"num_unique_values\": 6406,\n        \"samples\": [\n          1.0724488096939,\n          86.668639088546,\n          2.803852834691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adj. Volume\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 87047769.28770934,\n        \"min\": 250376.0,\n        \"max\": 1855410200.0,\n        \"num_unique_values\": 8848,\n        \"samples\": [\n          73488800.0,\n          21754810.0,\n          135615900.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(data.Close, label=\"closing price\")\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 616
        },
        "id": "9-uYGddq3x6i",
        "outputId": "11bf6818-ec43-4e47-e735-bd370de28ceb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e99ac473e80>]"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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VAQAAAACAhOjYJCYYZ4cKxr319oBrsqxmeW/VZE+/EK/e5kj2EICUY43k4l/96lcaMmSInn76aePYiBEjjG2Px6Pf/e53uvvuu3XhhRdKkv7+97+rrKxML7/8si677DKtX79eb775ppYvX64pU6ZIkv74xz/q3HPP1SOPPKKBAwfG4nUBAAAAAIAEsIQoafJ4PDK1JYmOMCoYzWaT8rOsqrc5VWdzqrQwlqOMr0fe2qit+xqNffrMAK0iqnh89dVXNWXKFH3jG99QaWmpJk6cqL/85S/G+W3btqmqqkrTpk0zjhUVFen444/X4sWLJUmLFy9WcXGxETpK0rRp02Q2m7V06dKgz2u321VXV+f3BQAAAAAAki9UxaPvdGmXO7w1GwuzMySlV/Vgi9OtP723OdnDAFJSRMHj1q1b9dhjj2nUqFF66623dMMNN+iHP/yhnn32WUlSVVWVJKmsrMzvcWVlZca5qqoqlZb6LyprtVpVUlJiXNPRgw8+qKKiIuNryJAhkQwbAAAAAADEiW9TGF++DWKcrvBKAAuyWydmplOHaJszuq7eQG8QUfDodrs1adIkPfDAA5o4caKuv/56fe9739Pjjz8er/FJku68807V1tYaXzt37ozr8wEAAAAAgPCEDh7bw8Zwm8WkZfAYRnMdoLeKKHgcMGCAxo0b53ds7NixqqyslCSVl5dLkqqrq/2uqa6uNs6Vl5dr7969fuedTqcOHjxoXNNRVlaWCgsL/b4AAAAAAEDyhZxq7VfxGN5U6+XbD0mSHn5rQ/cHliA2R3ivDeiNIgoeTzzxRG3cuNHv2Jdffqlhw4ZJam00U15ernfeecc4X1dXp6VLl6qiokKSVFFRoZqaGq1YscK45t1335Xb7dbxxx8f9QsBAAAAAACJF6qW0bfKMdyKR68dB5q6MaLEanKkT3UmkGgRBY+33HKLlixZogceeECbN2/W3Llz9eSTT2rWrFmSJJPJpJtvvlm/+MUv9Oqrr2rNmjW66qqrNHDgQF100UWSWiskzz77bH3ve9/TsmXL9Mknn+jGG2/UZZddRkdrAAAAAADSjLeasV9+lt/xaNZ4HD+kWJJ02hH9YzO4BPj5K2uTPQQgZUUUPB577LF66aWX9K9//UtHHXWU7r//fv3ud7/T5Zdfblzzk5/8RDfddJOuv/56HXvssWpoaNCbb76p7Oxs45p//vOfGjNmjM4880yde+65Oumkk/Tkk0/G7lUBAAAAAICE8AaMg/vk+B13RrHG44mH95UkjeiXF6PRxd/SbQcDjnlC1oECvYs10gecd955Ou+880KeN5lMuu+++3TfffeFvKakpERz586N9KkBAAAAAECK8TaRyc7wr21yutsrHlvCXOPRu1ykh9wO6BEiqngEAAAAAADw9cVXtUGP+3a1tjn8Oz+H6Ecjk1pP1NkcuvaZ5VoWpJoQQPogeAQAAAAAAFF744sqSdKSrf4hobOT4DHDEjyOMLcFki+u/Ervbtirbz6xOIYjjY8hJTldXwT0UgSPAAAAAAAg5rzTq99eW6WPNu33O5cZIngMWQqZwsYPLk72EICURfAIAAAAAABi7kCDXZJ0/T9WBJzLsAQPGIMdbXGGtz5ksrjCbJwD9EYEjwAAAAAAIObufW1dyHOhploHK3j8fFdNjEYUHwSPQGgEjwAAAAAAIGqWtoUZ/99ph/sd/6qmOeRjzCGmVC/ZeiDgmDXUtOwU4Rs83nP+OEl05Qa8Uvu7FwAAAAAApLQJQ4olSaPK8sN+TKilHHceDAwrU72i0NWWMj7yjfHqX5Cd5NEAqYXgEQAAAAAARM3TFrxZzP4RQ1lhVtT36upYKvEGoylemAkkBd8WAAAAAAAgat6CRKvZv4zxsmOH6rH3twR9TKje1fYgjWRSvuLRHTx4BSBZkz0AAAAAAACQvryxYMeGMat31ej9jfsiulewDtYpnjvK6Q0eQ80fB3ox4ngAAAAAABA9T/Cpxp2FjqYQIV2wikd3ik+1dhsVjwSPQEcEjwAAAAAAIGreikRTyAnU4SvJywxy/9QOHp0Ej0BIBI8AAAAAACBqHhnJY7c9eumEgGOpvsajNxj1XeMytUcMJA7BIwAAAAAAiJq3INEcxhqHmdbWGOLiSYOCnj+8NC9m40oUp6v1D8BsNollHgF/NJcBAAAAAABRc0dQ8Pj0NcequcWlU4/oH/R8sOnaqV49GKziEUArKh4BAAAAAEDUPG3Bm9lkUnlhdqfX5mdZNW1cWUAHbK+gFYMpnjx613gMp+IT6G0IHgEAAAAAQNS8U61NJmlo39xOr3V10SgmeO6YnOTxkbc26pevr+vyOrcRvMZ7RED6Yao1AAAAAACImjcYNKnr8M3dRaMYU4pUDdqdLv3pvc2SpO+cOEIDi3NCX2wEr6kxdiCVUPEIAAAAAACi5vEJ3jqbbpydYdaRA4s6vVfQiscEFzx+vGm/xt/7trHfYHd2er13eOSOQCAqHgEAAAAAQNS8U41NptDrHD59zbGqOLyvsjMsnd4r2OMTHTxe8dRSv327w93p9d41Lv1GnuLrUgKJQsUjAAAAAACImlHxp9BVf5lWc5eho3GTFGN3ujo971vxmILDB5KK4BEAAAAAAESvLXkzm00h1zl0drG2o1ewhye7eNDu7Kri0btF7Ah0RPAIAAAAAACi5vZ03Vxmza6asO4VfI3H5EaPTS2dVzz6TjUH4I/gEQAAAAAARK19qnHo5jIzJw8O616p2Bl6676GTs97c9HOGusAvRXBIwAAAAAAiJp/c5ng1wwoygnrXkErHqMcV6w8+MaGsK4jdgQCETwCAAAAAICoeSv+TJKurBgecP7hS44J+15B13hMdvLYBQ9TrYGQCB4BAAAAAEDUfKcanzq6vz76yel+588YUxr2vYJPV05s8jj1sJKAYysrD4W8vr2rN8kj0BHBIwAAAAAAiFrHir8hJbnKybAY591hdrROFRuq6gOOXfznRaqqtQW93qj49MkdPUmfIA6kBoJHAAAAAAAQtWAVf31yM4zt/gVZYd8r2VOtPR6PapocQc9d8vii4I/xCRmZbg34I3gEAAAAAABRcwdZ49Ds02Umkk7VwaYrJ7J20NlJdeauQ81BjwereATQiuARAAAAAABELVjwZg3V3roLyQ7vXFFMC3cbzXVIHoGOCB4BAAAAAEDUgk21tkQbPAa7fwJLHt1RPRldrYFQCB4BAAAAAEDU7A6XJCkroz1isJqjixuCTctOZKOWzqZah8JUayA0gkcAAAAAABC15rbgMTezvZN1tBWPwR52sLFFP35htT7dfjCqe0Yimg7c3keYSR6BANZkDwAAAAAAAKSnFqdbDldr9Jab2R4xRD3VOkh4N/uVtZKk/67Ype0PzYjqvuGKZo1Hj7e5jt+xGA0ISHNUPAIAAAAAgKg0tTiN7VhUPCabq4vEcPPeBtnaKjy9jDUuTVLwVSqB3ovgEQAAAAAARGX59kPGdobFd43H9Azguqp4nPbbD3TDcyvU4nTrlVVfaW+9zae6MT1fMxBPTLUGAAAAAABR+d7fPw16PF2XOwxnqvV7G/fpsfe36NGFX2pAUXb7VOs0fc1APFHxCAAAAAAAYipd1zh0u8O7bsH6KknSnlqb0Qmb3BEIRPAIAAAAAABiKk1zR2ONx4Isqz687fSQ1/l2sG5qaV3zMVhjHKC3I3gEAAAAAAAx5UnTkkdXW8mjxWJSiyt0+aM5SMhI7AgEIngEAAAAAAAxFcZSiSnJmzVaTCYd3j8v5HXBeuf4ZpFp+vKBmCN4BAAAAAAAUHtzGbPZFPHUaZNMNJgBOiB4BAAAAAAAMZWuFX/utinilrYEcVBxTtDrVlbWBBwjdAQCETwCAAAAAIDY6sYajwXZ1hgOJDLeDtWWtrnUlmBzqkMgeAQCETwCAAAAAICY6k7F48ShfWI2jki5OgSPzQ5X2I+lqzUQiOARAAAAAABEzNlJ1+fuNLVOZnxnTLVuCx73N9jDfiyxIxCI4BEAAAAAAESspbPgsRs1j8koHHxw/nrd+u9Vcrramsu0jeGqqcPCvgcFj0Cg5C2cAAAAAAAA0laLs+dUPD7x4VZJ0tGDiyRJVnNrndad547VxKF9dPO/V3V5D5PPyD3d+QMAehAqHgEAAAAAQMQ6Cx5PHd0/6vsmeq1Et7s9JHx51W5Jkrmt5DE7w6IzxpaGdx+Ph+nWQAdUPAIAAAAAgIjZfYLHd//vVL9z3z/lcNkcbn37+KER3zfR4Z3D3f46Vu+skSRZfMq0wh2PmypHIADBIwAAAAAAiJh3jceCbKsO65/vd64oN0Ozzx8X1X0TvVaiyx0YGFp8BhFuBaY7dAEo0Gsx1RoAAAAAAETMO9U6yxrbaMHeyRTueHAGCR69U62l8CseXVQ8AgEIHgEAAAAAQMS8wWOmJbbRwsWTBkmShvXNDTj3xVe1MX0uSUYna19W3+AxzOSRqdZAIIJHAAAAAAAQMe9U68wYVzxeNGGQXp51ov7z/YqAcx9t2h/T55IkZ5A50mbfqdZh1jyO6JsXszEBPQXBIwAAAAAAiFj7VGtLTO9rMpk0YUix8rIC21I8u2h7TJ9LCr7GY1ZG+2sKVvE4rG+u3vvxacb+N6cM9pueTe0j0IrgEQAAAAAARMyYah3jikevYHWGdqcr5s+zZlfg9O0V2w92+hizyaQBRdnG/n8+3SUp/EY0QG9B8AgAAAAAACJmj3PwaA4S4rXEofHMJ5sDp283trQHnMGyRJfbo4wYr20J9ER8lwAAAAAAgIgZazzGKYALFvgF60DdXSeO7Nf5OILUXrrcHll8plYXZgdOCwdA8AgAAAAAAKIQ76nWwSoeg63H2F3BpkefNa7MZxzBHtP635K8TEnSxZMGx3xcQE9AJA8AAAAAACIW9zUegwR+5x49IObP43AFTt/2fU0dg8m+eZn647cmSpJevfFELVxXrUuPHRrzcQE9AcEjAAAAAACI2LzPd0uSFm85EJf7B6t4PNBoD5jm3F3B1o3M8e1q7XO8tCBLS396phFGDu6Tq2tOHBGzsQA9DVOtAQAAAABAxBa1BY4Ndmdc7h8sW/xk8wE9/cm2mD5PS5CKxx9PP8LY7ph/htO52hP7GeFAWiJ4BAAAAAAAKSdUwPefT3fG9Hk6TrW+9WujVVaYHXQcdTZHp/eKXR0m0DMQPAIAAAAAgIhNG9vagOX7px6W0OcNNgW7Oxw+U62PKCvQ1ScMD3mtzRFYHQkgNNZ4BAAAAAAAEVu4vlpS6zqHiRTz4NHVOi/64omD9NtLJ3R67eRhfWL63EBPR8UjAAAAAACIyOe7aoztrDh1tZakoSWBoWZ3ckePx6ONVfVy+kyv9q7xaLV0feNYNrUBegOCRwAAAAAAEJF99XZjO57B4zv/d6qW3XWmzj6y3DjWnYrHZxdt1/Tffaif/O9z45i9bap1tk8n61AsMa62BHo6gkcAAAAAABAR7/RkKbzALloZFrNKC7I1vF+ecczcjarD3y74UpL04sqvNPWBd7RlX4PsTpek8ALUcKoiAbQjeAQAAAAAABFxuduDx3hWPHr9d0V7J+vuzHb2bQ5TVWfTL+atk73tWJY1jIrHMJ/c0/UlQK8Q0bvDPffcI5PJ5Pc1ZswY47zNZtOsWbPUt29f5efna+bMmaqurva7R2VlpWbMmKHc3FyVlpbqtttuk9PpjM2rAQAAAAAAced0twd44QR23WXymeKcaYk+6Gxx+XelNplMkVU8dhE8MhMb8BdxV+sjjzxSCxcubL+Btf0Wt9xyi15//XW98MILKioq0o033qiLL75Yn3zyiSTJ5XJpxowZKi8v16JFi7Rnzx5dddVVysjI0AMPPBCDlwMAAAAAAOLNr+IxI/4Vj7mZ7eFmv/ysmN0302Jur3gM43XQXAaITMTBo9VqVXl5ecDx2tpaPfXUU5o7d67OOOMMSdLTTz+tsWPHasmSJZo6darefvttrVu3TgsXLlRZWZkmTJig+++/X7fffrvuueceZWZmdv8VAQAAAACAuHL6rvGYgIrHHJ91JEvyYpcdZFrNRnOZWE61BtAq4l9LbNq0SQMHDtRhhx2myy+/XJWVlZKkFStWyOFwaNq0aca1Y8aM0dChQ7V48WJJ0uLFi3X00UerrKzMuGb69Omqq6vT2rVrQz6n3W5XXV2d3xcAAAAAAEgO3ynLpYWxq0AMxXeqdeyDx/CnWo/waXIDoGsRBY/HH3+8nnnmGb355pt67LHHtG3bNp188smqr69XVVWVMjMzVVxc7PeYsrIyVVVVSZKqqqr8Qkfvee+5UB588EEVFRUZX0OGDIlk2AAAAAAAIIZsDpex3dW6h7EwuE+OsZ2XFbsKy0yr2Wg401l37pGl+ZKkb04hjwAiEdFU63POOcfYPuaYY3T88cdr2LBh+s9//qOcnJxOHtk9d955p2699VZjv66ujvARAAAAAIAk8U5PliRzAoLHeIWbmZbwKh5fnnWiDjW2aEhJblzGAfRU3VoBtri4WKNHj9bmzZtVXl6ulpYW1dTU+F1TXV1trAlZXl4e0OXaux9s3UivrKwsFRYW+n0BAAAAAIDkKM7NMLYLsiJuH9EtHk/X14Qrw2JqX+Oxk+Yy+VnWyELHWA4SSGPdCh4bGhq0ZcsWDRgwQJMnT1ZGRobeeecd4/zGjRtVWVmpiooKSVJFRYXWrFmjvXv3GtcsWLBAhYWFGjduXHeGAgAAAAAAEsTRFtbNOGaA3/qL8RKrpxhU7D9b02wyqcHmlCTlZHQ/QE3AHwWQViL6rvrxj3+s888/X8OGDdPu3bv185//XBaLRd/61rdUVFSk6667TrfeeqtKSkpUWFiom266SRUVFZo6daok6ayzztK4ceN05ZVX6uGHH1ZVVZXuvvtuzZo1S1lZ8V+MFgAAAAAAdJ+jrat1pqVb9UxR6U4tobtDJWJRbob2NdglSf0LyCWAWIsoeNy1a5e+9a1v6cCBA+rfv79OOukkLVmyRP3795ckPfroozKbzZo5c6bsdrumT5+uP//5z8bjLRaL5s2bpxtuuEEVFRXKy8vT1Vdfrfvuuy+2rwoAAAAAAMSNt6t1hiXxJX7dmcXcMXi0tbhU31bxSPAIxF5EwePzzz/f6fns7GzNmTNHc+bMCXnNsGHDNH/+/EieFgAAAAAApBCXuzXAsySgsUxHnm7UPHYMLf+7Ypexnei1KoHeIPE10QAAAAAAIK0lM3jsDneH4HF3rU1S6+tIRHduoLcheAQAAAAAABHxTlm2JKibikntz9OdqdaeEA92dUwkAcQEwSMAAAAAAIiIN6hLtyrBRMWLxJhAK4JHAAAAAAAQEVeCKx5jpWNzmVjzrcwEQPAIAAAAAAAi5E7wGo9TDysxtkNNlw6HmynVQEIRPAIAAAAAgIi43K3/TdRU628fP0yZltYIo1trPLb992vjyro/KABdIngEAAAAAAARSXRzGYvZpJmTB0nq3vqJ3tDSmmZrUwLpiuARAAAAAABEJDnNZbr/XN7A1GohDgESge80AAAAAAAQkWQ0l/E+1W8XfKmnPt4W1T2oeAQSi+ARAAAAAABEpL25TOKe0zcqvH/euqjuYUwR7xA8Pn7F5GiHFVScm2cDaYPgEQAAAAAARMRpBI/JixWi6VAdquJxaEluLIYUi9ngQI9C8AgAAAAAACKSlIrHDqGew+2O+B4eedd49L+ZmxJFIC4IHgEAAAAAQESMNR4TWPFo6lBO6Iqi4tFtVDz6j3vcgMKoxwUgNIJHAAAAAAAQEe9U62Q2aXFGFTwGrvH4l6umJLg7N9B7EDwCAAAAAICIuFzBm7TEU8ep1t4xRCLYGo8dp10DiB2CRwAAAAAAEBFn2/qKiax47PhMkVY8enzWcfQNG5NZtQn0dASPAAAAAAAgIu1drZMX2kW6xqNvUOm7NmXH9R4BxA7fXQAAAAAAICLe0C8jgW2tTR3mWjsj7Go9+5W1xna8p1p7u2cDvR3BIwAAAAAAiIgzCWs8dhRpxeO/llUa2/Gaas2kbcAfwSMAAAAAAIiIKwldrTs2l7E7I6t49OVX8chUayBu+O4CAAAAAAARcbRNc05mxePBxpaoH5tltRjbdLUG4ofgEQAAAAAARMSoeExgaGfqMJHZ5fZoX71d/1pWqUa7U1LrdOo/vLOpy3tlZ/g2lyF4BOLFmuwBAAAAAACA9GJzuCRJmRZLF1fGTsep1k63R9/+yxJt2tug7fsbdee5Y3Xni2skSeceXa6RpQUh75Vp9QkeE9ggB+ht+O4CAAAAAAARqW12SJKKczOSNgaX261NexskSR98uU8eT3uzmXqbs9PH+q7rSMUjED8EjwAAAAAAICI1TYkPHjvGg5vbQkdJKsi2GmMKR4ZvV+s4TBf3RNZwG+ixCB4BAAAAAEDYbA6X0VG6KCeBwWOHfPDzXbV++8u3H/S5tvMwMcNnenUsG+R09bxAb0PwCAAAAAAAwmZ3uI3tnIzErfF4qENFo7tDWWF+VvhtLMw+YWOGmWgEiBe+uwAAAAAAQNgaW9rXT4xltWBXVlYe8tv3dtb2yslsD0G7GpXveUsCO3MDvQ3BIwAAAAAACNuLK3cZ24mcWlyQ7T+t+2BjS/s4ZDKmf0vy2w7Gd9xUPALxw3cXAAAAAAAIm9OdnM4puR2mdS/f7l8B2eITNv7hnU1+5zwdpmX7VTzS1RqIG4JHAAAAAAAQttKCbEnSoOKchD6vR6EDT7fH4xc8frx5v9953wrI7540wu+cleARiBuCRwAAAAAAELamtjUeJw/rk9Dn7azQ0qPA6dV/X7zd2G5ucRnbd5wzxq9DtjkOwaMnOUWhQMoheAQAAAAAAGHzhni5mYnraC1J7k6SxyF9ctTicvkdm/3KWu1vsEuSmh2t5zItZlktZpm6bD8THWonAX8EjwAAAAAAIGxNbSFeToKDR1cnZYRuj/8aj17vbtgrqT14zM5ojUEG90nsNHGgt7ImewAAAAAAACB9JKvi0eEK3ana5fGoxRUYTGZZW4NG75i9Yenwfnl68srJ6pufFYeRAvAieAQAAAAAAGH774pdkhS36cqhBKto9HK7PXIFCSYdbWGkzVul6dMZ+6wjy2M8QgAdMdUaAAAAAACErcHe2lzmWZ/mLYnQsXmML5fbI2eQNSCdbWFk+1TrxFZpAr0dwSMAAAAAAIiY3RE6CIyHTisePe3Vjb4cbWFkx6nWABKD4BEAAAAAAETsZ+eNTejzdR48erSxqi7geMeKx5wEVTyGboMD9C4EjwAAAAAAIGzjBhRKkob2zUvo83YWPDrdHr28anfA8UVbDkgKvsZjPJgSu+wlkPIIHgEAAAAAQNi81XyJztg6W+PREeLcgnXVktoDyD21ttgPDEBIBI8AAAAAACBsHk9r9Jjo6r7BfXKM7TPGlPqdsztdnT72lbZqyHV7AqdjA4gfgkcAAAAAABAxU4JrHp+8aoqmjS3VqzeeKKvZ/7k7q4YEkDwEjwAAAAAAIGxtBY8Jr3gcWZqvv159rI4ZXKwMq3+c0eJ0a/qRZZ0+VpJOHtUvrmME4I/gEQAAAAAAhM3TtspjMvuoZHSoeNy0t0Fvra0Oem2L060JQ4olSRWH94330AD4IHgEAAAAAABh8ySru4yPDEv4cca2/Y3tVZoJGrTH+EMCejeCRwAAAAAAELb23DF5yaM1guDRYvap0ozzkJP5ZwKkIoJHAAAAAAAQtmR1tfaVYQn/yTfvbdSLK7+SJJnJBYGEIngEAAAAAABhS4GZ1tqwpz7sa3/w3Apjm4pEILEIHgEAAAAAQFhsDpe27muUJJmSWPK4bPvBpD03gPARPAIAAAAAgLA88cFWYzuZU63DkZdpkSQNKckxjrlo+gIkFMEjAAAAAAAIy+pdNcZ2o92ZvIF0Ij/Lqhd+UKGrThguSdp5sNk453ITPAKJRPAIAAAAAAAi9mV1+OssJtLvL5ugY4eXyBqkk8zKHYeSMCKg9yJ4BAAAAAAAYWlucRnbWVZL0sbRLz8r5LkMS2vUYQkSPH60aX/cxiSl/vRzINEIHgEAAAAAQFgWbz1gbGdZkxcpHN4/L+Q5b/Do/a+vYwYXxW1MAAIRPAIAAAAAAElSbZNDw+94XcPveF3uDushrtiROp2kzT6lhSeP6ud3LtMauuLRTXMZIKEIHgEAAAAAgCTpmUXbje0t+xr8zr2/cZ/ffjI7RPuGik9eOcXvXGZbpWOwNR7pLQMkFsEjAAAAAACQJNmcoddwLCvM9tsfWpKbkDEF0zc/09jOyfQfZ4a1NXAMVvHooeIRSChrsgcAAAAAAABSw+iyfGPbG+B5OVxuY3tkab5OGuk/xTmR7poxVtV1Nl1+/LCAc961Ha1B1ngc1jf02pAAYo/gEQAAAAAASJLcbp/tDsWB3uDxwgkD9fvLJiZwVIFKC7L1/PUVxv5xw0u0bHvrGpShplqPKs3XPRccmZDxUVgJtGKqNQAAAAAAkCS5fNLGjs1lHK7W/WR2sw7Fp9eMMcW641TrR74xXiV5mYqnwMndQO+Weu8WAAAAAAAgKXwbxnSs2vNWPGYEmcKcbMGCx44VjzaHSwASi6nWAAAAAAD0cjVNLfr+P1ao2Secc3s6VjymbvBo9kkevdsd13icNKxPQscEgOARAAAAAIBe7y8fbdXSbQf9jnUMHptbWoPHrIzUCx59eSseM3wqHr85ZXBKBqZAT8d3HQAAAAAAvVyjPXAacsfmMvU2hySpMDsjEUOKiG9IammrePQNSIN1uAYQf3znAQAAAADQy3WsbpQkT4dj9TanJKkwO/UmT/qGpOa2pCPLajGOZRI8AknBdx4AAAAAAL2cs2N5owIrHuu8FY85qVfx6BuSeqda+3bf7thoJu7jUeCfJ9AbETwCAAAAANDLuVyBQZmrQ/K4fk+dJKkgBSsefcfaHjy2VzwmbKp1YvNNIOURPAIAAAAA0Mu5gky1PvcPH+mB+eslSTsONOpQU+qu8Vjb7DC2vdOqfdd4zLSQCALJQPAIAAAAAEAv17G60evJD7dKkt7bsNc4VpCCweOWfY3GtskUZKo1azwCSdGt77yHHnpIJpNJN998s3HMZrNp1qxZ6tu3r/Lz8zVz5kxVV1f7Pa6yslIzZsxQbm6uSktLddttt8npdHZnKAAAAAAAIExzl1bqwfnrjbURG+2dfyY/2NhibJcXZcd1bNEoCrLupO9U6wyCRyApov7OW758uZ544gkdc8wxfsdvueUWvfbaa3rhhRf0wQcfaPfu3br44ouN8y6XSzNmzFBLS4sWLVqkZ599Vs8884xmz54d/asAAAAAAABh++lLa/TEh1v12c4aSdKhppZOr6+ztQeTqdjVumMHbsm/4pHcEUiOqL71GhoadPnll+svf/mL+vTpYxyvra3VU089pd/+9rc644wzNHnyZD399NNatGiRlixZIkl6++23tW7dOj333HOaMGGCzjnnHN1///2aM2eOWlo6f6MDAAAAAACxU98WKOZkdh4mDinJNba9U5lTSZDc0W+NR5c7gYMBYIgqeJw1a5ZmzJihadOm+R1fsWKFHA6H3/ExY8Zo6NChWrx4sSRp8eLFOvroo1VWVmZcM336dNXV1Wnt2rVBn89ut6uurs7vCwAAAAAAdI83QhzZP7/T65rapmJ/67ghcR5RdNxBksdMnzLHYOfjKcFPB6SsiOujn3/+ea1cuVLLly8POFdVVaXMzEwVFxf7HS8rK1NVVZVxjW/o6D3vPRfMgw8+qHvvvTfSoQIAAAAAgA58G8l4ixcdXZQE1tlau0anYmMZSQrWG8e3oYw7RPOcWDMp9apBgWSKqOJx586d+tGPfqR//vOfys5O3GKyd955p2pra42vnTt3Juy5AQAAAADoSexOl7Hd4nT7/TeUuubWisdUXN9RCt2V2ytBuSOADiIKHlesWKG9e/dq0qRJslqtslqt+uCDD/SHP/xBVqtVZWVlamlpUU1Njd/jqqurVV5eLkkqLy8P6HLt3fde01FWVpYKCwv9vgAAAAAAQOTqfRrF3PLvVZK6rnist6d2xWNLF+N3MfcZSIqIgsczzzxTa9as0apVq4yvKVOm6PLLLze2MzIy9M477xiP2bhxoyorK1VRUSFJqqio0Jo1a7R3717jmgULFqiwsFDjxo2L0csCAAAAAADB1LdNm5bau1V3FdzZHK3nczIs8RsYgB4nohrpgoICHXXUUX7H8vLy1LdvX+P4ddddp1tvvVUlJSUqLCzUTTfdpIqKCk2dOlWSdNZZZ2ncuHG68sor9fDDD6uqqkp33323Zs2apaysrBi9LAAAAAAAEEydT8WjV6iKxyyrWc8vq9S7G1qLhyzm9FzDME2HDaS9qLpad+bRRx/Veeedp5kzZ+qUU05ReXm5XnzxReO8xWLRvHnzZLFYVFFRoSuuuEJXXXWV7rvvvlgPBQAAAAAAdNBoDwweQ63xaHe6dceLa4z9dAseTx3dX5I0c9LgJI8E6J26vSrs+++/77efnZ2tOXPmaM6cOSEfM2zYMM2fP7+7Tw0AAAAAACLkdAWud+gIciyYdAsen77mWDW2OBO+NiUrSgKtYl7xCAAAAAAAUlfHadU/fWmNPt683+/YLdNGB32sNUWDR2+37YFF2X7HzWZTQkNHU2r+8QBJQ/AIAAAAAEAv4nL71+PNXVoZcE2mNXhcYE7R4PHf36/Q18aV6envHJfsoQDw0e2p1gAAAAAAIH043F1PBA4VPKZqxePYAYX6y1VTkj0MAB1Q8QgAAAAAQC/iDNHB2ldWiOAx3dZ4BJBcBI8AAAAAAPQiwZrLdBSq4pHgEUAkCB4BAAAAAOhFHG4qHgEkBsEjAAAAAAC9SDgVj6ECRquZGAFA+HjHAAAAAACgF3GEscZjqCYyoaZgw5/H03W4C/QGvGMAAAAAANCLOMPoam02BQ8ex5QXxHo4PQoT0QF/BI8AAAAAAPQi4XS1tlqCR2jZGZZYDwdAD0bwCAAAAABAL+IIY41Ht1saO6DQ79josvx4DQlAD0XwCAAAAABAL+IKY6p1bbND15wwzNifMKRYr//w5HgOC0APRPAIAAAAAEAvYne6wrpux4EmY3vVzhplWIgQAESGdw0AAAAAAHoRuzP4Go85HdZvDNXZGgDCRfAIAAAAAEAvYncEDx5nnX64sW0ySZlWIoNodT2ZHegdeBcBAAAAAKAXCTbV+uKJg3T++IF+x3yDxws6nENwJhNVooAvgkcAAAAAAHqRYFOty4qyNaxvnt+xLGv71OtJQ4vjPSwAPRDBIwAAAAAAvUiw4LFjnd7gPrl+FY8W1nsEEAVrsgcAAAAAAAASp7Ou1nO/d7y27G3QcSNKNKAo2zhuMVO3BCByBI8AAAAAAPQiwZrLeJcmPOHwfjrh8H6S5Bc8UvAIIBr8ygIAAAAAgF6kxRW8q3VHVkt7ZOB006cZQOQIHgEAAAAASGMtTrc8nvCDwaAVjwGrPPqzOUJPz0YQ5LSAJIJHAAAAAADSVm2zQ1N+sUDf+/uKsB8TbI1HUxdTqQcU5UQ6tF6pqz9HoLdhjUcAAAAAANLU22urVGdzauH66rAfE6yrdSj//O7x+nT7IZ1zVHk0wwPQy1HxCAAAAABAmnL7TLH+x5IdXU65drs92lNrC/v+J47spx9NGyUz3WUARIHgEQAAAACANOVwtQeNP3v5C72zfm+n1/998fagx4kVAcQDwSMAAAAAAGnq7pe/8Ntfu7uu0+ufW1oZz+EAgB+CRwAAAAAAeojmLrpPZ1lDxAB0RQEQBwSPAAAAAAD0EM0tzk7PW0Os1VhemB2P4fRana+0CfQeBI8AAAAAAPQQXU21tgQJHrOsZn1jyuB4DalXoW4U8EfwCAAAAABAD/HpjkNqcbq1ZOsBnffHj7RixyG/833zswIec+c5Y5RhIR4AEHu8swAAAAAAkKZyMy0Bxx6Yv16XPblEX3xVp1v/s8rvnHdK9dgBhbqqYpgGFedo5mSqHQHEhzXZAwAAAAAAAJHzeDxBm8k8s2i7sb3jQJPfOe/1F4wfqBtOO1z3XuCRicYyAOKEikcAAAAAANJMc4tLuw41yxNGF5M7X1xjbNfbHJKkguzWOiRCRwDxRMUjAAAAAABp5tRfv6e99fawrv3XskpJHs06faTqmlu7XnuDRwCIJ95pAAAAAABIM+GGjl7/WrZTq3fWytw277EwOyMOo4KXJ5xSVKAXYKo1AAAAAABpJNpQa92eOtXbWiseC3OoQ4oHZq4D/ggeAQAAAABIIzaHO+rH1jV713ik4hFA/BE8AgAAAACQRhpbnFE/1lvxyBqPABKB4BEAAAAAgDSybNvBqB/rdLdO087JsMRqOAAQEsEjAAAAAABpZNehpm7fw2JmMUIA8UfwCAAAAABAGhlYnNPte1jNxAEA4o93GgAAAAAA0kijPfo1Hr3IHeMrur7jQM/DWw0AAAAAAGmk0e4KODb1sJKI7kHFY7wwhR3wxTsNAAAAAABppKlDV+uLJw3S89dXRHQPlngEkAgEjwAAAAAApJGGDhWPPz13bESPz7SYZTKRPAKIP4JHAAAAAADSSIPd4bffLz8roscX5mTEcjgAEBLBIwAAAAAAaaTB1r3mMkU51hiNBAA6R/AIAAAAAEAa6TjVujPr7puuPrn+FY5b9jXGekgAEBTBIwAAAAAAaaTjVOvO5GZa9endX4vjaBCMx5PsEQCpgeARAAAAAIA00hii4vHn54/T2UeW67gRJX7HLbSwThh69gD+CB4BAAAAAEgjDfbgazx+58QRevzKyeqXn5ngEQFAcKwoCwAAAABAmtjfYNe2/Z2v0XjnOWO1u8am604akaBRAUBwBI8AAAAAAKSJKb9YaGz/8MxRumTS4IBrhpTk6uVZJ/odG1War017GyRJE4cWx3WMAODFVGsAAAAAANLQNScM19C+uWFde++FRxrbhxpb4jUkAPBD8AgAAAAAQBpo7LC2Y16WJezHZlnbP/4X57IGJIDEIHgEAAAAACANHPnzt4ztS6cMUZY1/OAxw9L+8f9H00bFdFwI5JEn2UMAUgJrPAIAAAAAkMI8Ho/21dv9jv3qkmMiukemT8VjCRWPcWNK9gCAFEPwCAAAAABACvrvil3659Id+qyyxu/43TPGRnyv/Kz2j/8WM/EYgMQgeAQAAAAAIMV8snm/fvzC6qDnrjlheMT3K8zJMLYdLne0wwKAiLDGIwAAAAAAKebT7YdCnrNaIv8on5/ZXnfUaHdFNSYAiBTBIwAAAAAAKeZgo73riyJg9plenZVBFAAgMZhqDQAAAABAimlsCV6VWJgd/cf4hy85Rpuq6zVlWJ+o7wEAkSB4BAAAAAAgxYRq/5KdYYn6nt+cMiTqxyIyHk+yRwCkBuqrAQAAAABIMaYQySMdqVObKdT/OKCXIngEAAAAACDFNDuCd55++JJjEjwSAIgewSMAAAAAACkm1FqOJ4/qn+CRAED0CB4BAAAAAEgx/Quykj0EAOg2gkcAAAAAAFKMm+YkAHoAgkcAAAAAAFKMJ0hb5PFDihM/EADohoiCx8cee0zHHHOMCgsLVVhYqIqKCr3xxhvGeZvNplmzZqlv377Kz8/XzJkzVV1d7XePyspKzZgxQ7m5uSotLdVtt90mp9MZm1cDAAAAAEAP4A4SPA4syk7CSBCNIP/7gF4pouBx8ODBeuihh7RixQp9+umnOuOMM3ThhRdq7dq1kqRbbrlFr732ml544QV98MEH2r17ty6++GLj8S6XSzNmzFBLS4sWLVqkZ599Vs8884xmz54d21cFAAAAAEAaCzbV2uEizUp1pmQPAEgxwdtkhXD++ef77f/yl7/UY489piVLlmjw4MF66qmnNHfuXJ1xxhmSpKefflpjx47VkiVLNHXqVL399ttat26dFi5cqLKyMk2YMEH333+/br/9dt1zzz3KzMyM3SsDAAAAACBNOV3uwGPuwGMAkMqiXuPR5XLp+eefV2NjoyoqKrRixQo5HA5NmzbNuGbMmDEaOnSoFi9eLElavHixjj76aJWVlRnXTJ8+XXV1dUbVZDB2u111dXV+XwAAAAAA9FRNLa6AY4OKc5IwEgCIXkQVj5K0Zs0aVVRUyGazKT8/Xy+99JLGjRunVatWKTMzU8XFxX7Xl5WVqaqqSpJUVVXlFzp6z3vPhfLggw/q3nvvjXSoAAAAAACkJW/wePaR5bpgwkC9s36vfjJ9TJJHBQCRiTh4POKII7Rq1SrV1tbqv//9r66++mp98MEH8Rib4c4779Stt95q7NfV1WnIkCFxfU4AAAAAAJKlqaW1CeuJo/rp3KMH6NyjByR5RAAQuYiDx8zMTI0cOVKSNHnyZC1fvly///3vdemll6qlpUU1NTV+VY/V1dUqLy+XJJWXl2vZsmV+9/N2vfZeE0xWVpaysrIiHSoAAAAAAGnJW/GYm2FJ8kgAIHpRr/Ho5Xa7ZbfbNXnyZGVkZOidd94xzm3cuFGVlZWqqKiQJFVUVGjNmjXau3evcc2CBQtUWFiocePGdXcoAAAAAAD0CI321orH3EyCRwDpK6KKxzvvvFPnnHOOhg4dqvr6es2dO1fvv/++3nrrLRUVFem6667TrbfeqpKSEhUWFuqmm25SRUWFpk6dKkk666yzNG7cOF155ZV6+OGHVVVVpbvvvluzZs2iohEAAAAAAElut0cbq+olScP65iV5NAAQvYiCx7179+qqq67Snj17VFRUpGOOOUZvvfWWvva1r0mSHn30UZnNZs2cOVN2u13Tp0/Xn//8Z+PxFotF8+bN0w033KCKigrl5eXp6quv1n333RfbVwUAAAAAQBpyuz266+U1amybaj2sb26SR4RImEzJHgGQWiIKHp966qlOz2dnZ2vOnDmaM2dOyGuGDRum+fPnR/K0AAAAAAD0Cp/trNG/lu009jMs3V4hDQCShncwAAAAAABShNPl9tvPsFBCByB9ETwCAAAAAJAimh0uv30Tc3cBpDGCRwAAAAAAUkRzi6vriwAgTRA8AgAAAACQIpoIHgH0IASPAAAAAACkiCYHwWNP4PF4kj0EICUQPAIAAAAAkCJsVDymNZNYkxPwRfAIAAAAAECKYKo1gJ6E4BEAAAAAgBTR5HAmewgAEDMEjwAAAAAApAi6WgPoSQgeAQAAAABIEUy1BtCTEDwCAAAAAJAifCseywqzkjgSAOg+gkcAAAAAAFLE62v2SJImDS3WK7NOSvJoEC1PsgcApAiCRwAAAAAAUozN4VZ5UXayh4EImUzJHgGQWggeAQAAAABIkAMNdg2/43WN/Ol8eTyh6+KuP+WwBI4KAOKD4BEAAAAAgAT52yfbJElOt0drd9cFnC8vbK1yHFman9BxAUA8EDwCAAAAABAn++rt2rKvwdh3+xQ51tkcAdc72y6wmJmzCyD9ETwCAAAAABAn5/7hI535mw+040CjJCnL2v4x3OUOnGrtcrslSVaCRwA9AMEjAAAAAABxsq/eLkl64sOtkqR++VnGuSC5o2yO1uAx08rHdQDpj3cyAAAAAADibO7SSkn+lYwPvbHB7xqbw6Vmh0uS1CcvM3GDQ8x10jcI6FUIHgEAAAAASBCHy21sr9/j31ympql1zUer2aSCLGtCxwUA8UDwCAAAAABAgjhc7aVwh/XP8zt3qKlFklScmyGTiTUeAaQ/gkcAAAAAABJgza5a3TdvnbF/9KAi3fXSGj0wf70k6VBja/DYJ5dp1gB6Bmq3AQAAAACIA0+Hhf4ueXyR3/6uQ81aseOQJOnw/nnKzrBIIngE0HNQ8QgAAAAAQBy4OrSttjvdfvtOn/Ueb//fGlXV2iRJA4qz4z84AEgAgkcAAAAAAOLA1UVr4y37GjvsN0iScjOZnAigZyB4BAAAAAAgDtzuzs832J1+++9v3CdJymmbco305VHnoTPQWxA8AgAAAAAQB11VPHa0t94uSVq+/WA8hoMEoBk54I/gEQAAAACAOOi4xmO4Tjuif4xHAgDJQfAIAAAAAEAcuKMMHr8xeUiMRwIAyUHwCAAAAABAHEQ61dorK4OP6gB6Bt7NAAAAAACIg2grHq1mFgoE0DMQPAIAAAAAEAfRVjxaLXxUB9Az8G4GAAAAAEAcRNtcJsNCxWO6izJzBnocgkcAAAAAAOJgT60tqsdZzXxUT1cmERoDvng3AwAAAAAgDq56allUj6PiEUBPQfAIAAAAAEAcNDtcUT3OZCJ4BNAzEDwCAAAAAJBEpx3RP9lDAIC4IHgEAAAAACDBPvrJ6TpxZF9J0ndPOkyZdLIG0ANZkz0AAAAAAAB6osnD+mjFjkNBz+VlWfXMd45TVa1NQ0py1eJyJ3h0ABB/BI8AAAAAAMRBo90Z9HiW1ayinAxZzCYNKclN8KiQCJ5kDwBIEdRyAwAAAAAQY06XW9v2NwYcP6x/nlb//CxZzDSQ6YnoCwT4I3gEAAAAACDGth9okt3pVk6Gxe94XqZV2R2OAUBPRfAIAAAAAECMbayqlySNLi/wOx6q0vHeC46UJH142+nxHRgAJBBrPAIAAAAAEGMbquokSWPKCrR6Z41x/NjhfYJef/UJw3X1CcMTMDIASBwqHgEAAAAAiLE/vrtZkjSif57f8Vu/dkQyhgMASUHwCAAAAABAnBxsbNFlxw6RJC289VTlZLK+I4Deg6nWAAAAAADEyTenDNHh/fN0zwVH0lSmF/F4kj0CIDVQ8QgAAAAAQAy53e2pU2GOVSaTidCxlzAF7x0E9FoEjwAAAAAAxNC2A42SpOwMs0pyM5M8GgBIHoJHAAAAAABiaO3u1o7WNodbVgsfuwH0XrwDAgAAAAAQQ3f+7/NkDwEAUgLBIwAAAAAAMWJ3utTY4kr2MAAgJRA8AgAAAAAQI098sDXZQwCAlEHwCAAAAABAjLy86itj++RR/ZI4EiSXp+tLgF6A4BEAAAAAgBhxudsDpywrH7l7G5NMyR4CkFJ4FwQAAAAAIEbGDy42toeU5CZvIACQAggeAQAAAACIEafbbWxfe+KIJI4EAJKP4BEAAAAAgBjZ39AiSZp93jgqHgH0egSPAAAAAADEyLJtByVJffMzkzwSAEg+gkcAAAAAAGLA42lvLHP0oKIkjgQAUgPBIwAAAAAA3WB3uiRJPg2t1SeXisfezCeDBno1gkcAAAAAAKLw6faDmnDf2zpy9ltau7vWr7GMxWJK4siQLCb+twN+CB4BAAAAAIjCJY8vVk2TQ063Rw+9sUE+uaMsJFAAQPAIAAAAAECk6mwOv32L2SSXz/xai5ngEQAIHgEAAAAAiNAx97ztt281m+TyWeTRTMUjABA8AgAAAADQXWaTSQ5X+1xrKxWPAEDwCAAAAABAd+1rsOuLr2qNfTPBIwAQPAIAAAAAEI4Pvtyn55bsCHrus8oa7a6xJXhESFWeri8BegVrsgcAAAAAAEAqsztdMptMuvpvyyRJORkWlRVmqbrO7nfdgYbW/QvGD0z4GJEaqHMF/BE8AgAAAAAQgsPl1um/fl/7G1uMY//3wmr1y88MuPZA2zXFuRkJGx8ApLKIplo/+OCDOvbYY1VQUKDS0lJddNFF2rhxo981NptNs2bNUt++fZWfn6+ZM2equrra75rKykrNmDFDubm5Ki0t1W233San09n9VwMAAAAAQAztrmnW7lqbWpxuv+NOd+Bk2mcWbZckFWYTPAKAFGHw+MEHH2jWrFlasmSJFixYIIfDobPOOkuNjY3GNbfccotee+01vfDCC/rggw+0e/duXXzxxcZ5l8ulGTNmqKWlRYsWLdKzzz6rZ555RrNnz47dqwIAAAAAIAasluAfm11twWO//KyAc7lZlriOCQDSRURTrd98802//WeeeUalpaVasWKFTjnlFNXW1uqpp57S3LlzdcYZZ0iSnn76aY0dO1ZLlizR1KlT9fbbb2vdunVauHChysrKNGHCBN1///26/fbbdc899ygzM7BcHQAAAACAZHC63EGPO9qOu9yB53MyCB4BQOpmV+va2lpJUklJiSRpxYoVcjgcmjZtmnHNmDFjNHToUC1evFiStHjxYh199NEqKyszrpk+fbrq6uq0du3aoM9jt9tVV1fn9wUAAAAASB2/enODHnlrY9cXphmHK3h/YpujNXC0mAPbiRxoaAk4BgC9UdTBo9vt1s0336wTTzxRRx11lCSpqqpKmZmZKi4u9ru2rKxMVVVVxjW+oaP3vPdcMA8++KCKioqMryFDhkQ7bAAAAABAjG2sqtdj72/Rn97brD21zckeTkw5g1Q0eplMktUc+LHa5nDFc0gAkDaiDh5nzZqlL774Qs8//3wsxxPUnXfeqdraWuNr586dcX9OAAAAAEB4Pvxyn7G9ZW9jJ1emH4czeMWjJOVnWYNWPJqDHEPv4vGE/nsD9CZRBY833nij5s2bp/fee0+DBw82jpeXl6ulpUU1NTV+11dXV6u8vNy4pmOXa+++95qOsrKyVFhY6PcFAAAAAEgNmdb2j5YHm3rWNGNHJxWP9Tan/vjtiQHHjxzIZ9beykTmDPiJKHj0eDy68cYb9dJLL+ndd9/ViBEj/M5PnjxZGRkZeuedd4xjGzduVGVlpSoqKiRJFRUVWrNmjfbu3Wtcs2DBAhUWFmrcuHHdeS0AAAAAgARrtDv1WeUhY3/p1gNJHE3sOUOs8eg1aWgfv/1Rpfk6/5iB8RwSAKSNiLpaz5o1S3PnztUrr7yigoICY03GoqIi5eTkqKioSNddd51uvfVWlZSUqLCwUDfddJMqKio0depUSdJZZ52lcePG6corr9TDDz+sqqoq3X333Zo1a5aysrJi/woBAAAAAHFzx4tr9Nrq3ca+u4dNMe3Y1dpqNsnpDv0an732OKZaA0CbiCoeH3vsMdXW1uq0007TgAEDjK9///vfxjWPPvqozjvvPM2cOVOnnHKKysvL9eKLLxrnLRaL5s2bJ4vFooqKCl1xxRW66qqrdN9998XuVQEAAAAA4mrb/ka1ON1+oaMkZVktSRpRfDg6hIz9CzovmBlYnBPP4QBAWomo4jGcxVGzs7M1Z84czZkzJ+Q1w4YN0/z58yN5agAAAABAinhvw15955nlOnZ4n4BzwZqtpLNfzFvnt19WmK09tbYkjQYA0kvUXa0BAAAAAL3Td55ZLklavv1QwLlwZlr/b8UuDb/jdS3avF8rKwPvkSrW7q7Vpr0NfscGFmcb2/+7obWXweNXTFZBllVv3XxKQscHAKkuoopHAAAAAAA6E84aj//3wmpJ0rf/ulSS9I/rjtPJo/rHdVzReGD++oBjpQXtwePkYSWSpLOPKtfZR5UnbFxIfT1rpVMgelQ8AgAAAABiJpwlujpauK46DiPpvjPGlAUcu+6kEcrOMGvmpMFJGBFSX89aagDoLioeAQAAAABh21Pb3On5Tho+S5J2HmwKOGZzuINcmXy/enODsX3WuDL931lHaEhJrlb+7GvKyehZTXQAIB6oeAQAAAAAhO13CzZ1et7TxSTTP7+/JeDYvz/d2a0xxcO5v/9ILc7WQHTG0QP05FVTdER5gSQpN9Mqk4nKNgDoCsEjAAAAACBsXYWEXVU8rtyRus1kfK3bU2ds9y/ISuJIACB9ETwCAAAAAGKmqzUeDzW1JGgk0ev4GrKZVg30OnanK9lD6BEIHgEAAAAAYTt1dOfdp91dLNc4aWifoMcPNqZOIOnsqmwTQI/20me7dOTstzTv893JHkraI3gEAAAAAITNu85hR987eYQkqc7m6PTxxbkZQY9Pun+BdhxolCS1ON1qbkletZHD5Z+eDu6Tk6SRIF1F0dwdSeRye4w1XSXpln+vltPt0Y9fWJ3EUfUMBI8AAAAAgLC5Q1QD5mRaJUlvfFGlr2pCd762OUIHiqf++n253B6d8NC7mvKLBX5BQCI5XO2v8ZoThuvSY4ckZRxIP/QcSk9X/22ZTvrVu6q3OfTa6vYqR5sjOe9BPQnBIwAAAAAgbKFmIQ8syja2v/vspyEfb+8iTKxrdmh/g12NLa5OA8x48q14/Pn545Rh4aMz0FO53R59vHm/9tbbNX/NHt30r8+SPaQehXdPAAAAAEDY3EHmkH5j8mCV+QSPG6vqAq7x6qqKsbreZmz/8vV1UYyw+5xtFY9Ws0kmStiAHuNQY4s+2bxfbrdHX1bX65qnl2n2q18Y5xes2xvwmK4aZqFz1mQPAAAAAACQPoIFjwOLc2T2CeiyrKG7QL+zofWD/VGDCpVpMWtlZY3f+f8s32VsL1wfGAIkgrfikUpHoGe59MnF+rK6QQOKslVVZwtYi7O8KCvgMXvr7SorzA44jvDwLgoAAAAACNuGqvqAY5lW/4+WVkvXVYIDi3J034VHBRxftfOQ37634UwiedehzMrgIzPQk3xZ3SBJ2lMbGDpKwdd0rKq1BV6IsPEuCgAAAAAI27JtBwOO5WRYZPbJGjPDqBQ8aVQ/NQdpNNOxAnL9nsCgM95u+OdKSVJNU+cdugGkj/0N9i6v+e+KXQHH5q/ZE4/h9BoEjwAAAACAbinOzZBJ7cljxeF9Q17rnZF9yqj+6pOb2eW9czNDT9uOl817GxL+nOhZWBcw9ew40BTV4574cGuMR9K7EDwCAAAAAMLidAVvDDNhSLGGluQa+8P75gW9rrbZYUxvLC3M0sjSfN1xzphOn9MVqo12nGzwaYxTnJuR0OdG+qMVUeqyOwMrrBF/BI8AAAAAgLA0BZkaLUl987M0tG+uDu/fGjgGa0AjSXtqmyVJfXIzlJvZ2uv0G5MHd/qciQwLKg806ezffWTsv/t/pyXsuQHE18Yg69OGckInVduIDMEjAAAAACAsT3ywJejxgqzWEPH0I0olSa4QwePzy3ZKkg75rJ3YVUHjvvqu12WLlVN+/Z7ffh8qHoEe497X1gU9bjWb9OtLjvE7NuOYAYkYUq9gTfYAAAAAAADpYc57wYNHc1tnGUvbf90h0sRnFm0PONYvv/N1Hn/2ylpdWTE8/EFGafn2wKY5JhMTZ4GeaMP9Z2vb/kaNKs2X0+1RpsWs2/77uXE+w0ydXqzwJwkAAAAAiAlvABnJsowmk0kLbz1Fx40oidOowvONxxcn9fkBxNeRAwslSUNLcpWdYdHYAYWyWszKzrAY711eOT5NrUoLshI6zp6G4BEAAAAAEBPez+6RNoQZWVqg2eeNC3quJK/rztcA4GtvvU2z/rlSn1UeMo6VFWZLkmadfnjQxxzv88uP3EyLsdTCSSP7xXGkPR9TrQEAAAAAMWExeSseI+9EfdSgoqDHvWFBon1yxxlJeV70DIntxY6O7nrpCy1YV63X1+zR9odmSJJqmlokSUU5wddu3VNrM7ZzM616ZdZJeu3z3bqyYlj8B9yDUfEIAAAAAIjavRccaWx7w5YVOw4FvzgC3uqjRHS1DrYm5aDinLg/L3oe1gVNLo/Ho1v+vUoL1lUbx3bXNEuSmlpa30vysoLX4FUebDK2czMtGto3V7NOH6nCbJpMdQfBIwAAAACgSx6fKsa/XjXF2O7jMxX66U+2S5LW7q7r9vONKsuXJLU43d2+V1d21zbH/TkAxN+LK7/SS5995XfsjN+8r3c3VGtDVb0kyRpG45i8LEuX1yA8BI8AAAAAgC45faoCpwzv037c1R4MNtidMXs+b5VRvS129wxl7tJKv30zRWtAWvrHkh0Bx2wOt6595lNjP8PS9Td4TiYrE8YKwSMAAAAAoEtb9jUY21ZL+0dJp6s9kJzz7Umd3uPw/nmSpD99e2KXzze8b+u1tc0O1dkcEY01Ur6B6ZjyAv37+xVxfT4A8bF2d22X11jC+M1CXiYVj7FChAsAAAAA6NJrq3cb21afD+4Od3vF43Ft6zKaTK2drX0/4Hs8HtU0tQaI/fOzuny+wpwMFWRbVW9zam+dPa7rrHnXfrv97DG64bTgHW8BpIZ6m0Nb9jVqwpDigHMOV9dtfcIJHnOpeIwZKh4BAAAAAF3yXRctw6fi0eGzBqO3W6zH0xoO+Lr3tXU60NjaVTbDGvyjaFlheyDp8XiUk9FadRTvBjM2R+v9szP4iAykul/MW6+L5nyi4Xe8ruF3vK6dPk1hfP3p2xM1pCSwSVS/EL/4+MVFRxnbmSHeoxA5/iQBAAAAAF3674pdxrZvxZDv2o+ZVrMy20JJbxWh1zOLtrdfZwn+UbTAp6qxptmhrLYg0OaIb4MZe1t4mmVleiVipOvCO0Tp35/u9Ns/+eH3JPk3wJKk844ZKHeHt44TDu+rgSE61k89rG/sBgkDwSMAAAAAoEtf1QTv/DygyP9DvLcw0uUOnbxkhAge77+wveKo3uZQdZ1dklTb3BLJUCO2YF21JP8p5EA0+BsUXx3DRV/P+TSWeeEHreu0+r5vjSkv0NzvTQ35+JGl+frfDRX6+PbTYzBSeBE8AgAAAADCdtzw1nUc/3Hdcbp52iidc1S533mzqTV66SQfCDmNseLwvrp44iD1yc3Q1ycOVktbJeLvFm6KwciD8+3KvWz7wbg9D4Duq2sO7HJ/wfiBkqSfvbLWODakT27AdU9dc2yX9588rESDgzwW0WO1TAAAAABAl7KsZtmdbv3fWaMlSSeP6q+TR/UPuM7SFjy6OkkeMyyh68J+e+kEOV1uv87ZoaZmx0JVnc3YPnlUv7g9D4DuC9bh3iOpucPSDsEayAwKMcUa8UXFIwAAAACgS96GDFkZna+DaG77wO871bpjo5mugkRv6Hj6Ea3B5hljSyMbbATsPs1xzj9mYNyeB0D31TYHBo9Ol1trd9f6HfMum/DLr7cu3/DDM0fFf3AIiopHAAAAAECXnG1dGrpaB9F72ncttjfWVPldE2qNx468TSDscWwu4+1oXVqQZYSmAFLTvnq7sf2DUw/X4x9skd3p1iebD/hdV5jT2qjq28cNVcVhfTW8b15Cx4l2BI8AAAAAAD8ut0fNDpfys9o/MjbaWwO63MzOKx69Uxx9p1p7u1N7FWSH91E0u6260rcqMdaMjtYZTAgEUt09r7Wv4zhhSLEk6d0Ne42p1v0LsnT/hUcZ70Mmk0mH9c9P+DjRjndWAAAAAICfb/1liY76+Vuqqm1d/7DF6VaDvbWpQ5/czE4f620u4zvV2lt9JEm3nz3Gb/3GzmS1NaHxViXGkrci01tNmWXtPFAFItFJbyVEwelyq87m0I4DTT5H2/+UF29trXj87kkjdHaHhldILoJHAAAApLQ3v6jSg2+sl9vNxzggETwej5Zta+3uPO/z3dpxoFG3/GeVJMlk8g8RgwnW1fo7Ty83tm847fCwxxKvisdXVn2lsbPf1LsbqmV3toaaWSE6bQORMDFbP+bsTpdG3vWGjrnnbePYj88arcODVDIOKaEjdaphqjUAAABS2g+eWyFJOnJgkS4YT+MHIN4+3LTf2G5qcelbTy7R7rbKx4Isa9Busb4sQZrLRMsbBtpjXPH481fXyuZw69pnPlVh27TvTIJHICW9smp3wLFJw/poVFlBwPFTRvdPxJAQAd5ZAQAAkBa+OtSc7CEAvcKn2w8a29V1NiN0lLruaC1J5rZPmW5P7IJHmzO2waNvZ9w6mzOm9wYQWw+9sSHg2JRhJUGv9V2XFqmB/yMAAABICy1xbC4BoF2Lq/17rbrO5nfOt6NsKN6p1jEJHtuCzvlrquR2e+LadXpjVX3c7g0gOq+u3q2DjS0Bx70Vyj8+a7Tmfb5HF0wYqKMHFSV6eAgDwSMAAADSwmuf79aPpo1K9jCAHu9gQ/uH/GimS1uM5jLdH4vFZ8G8r2qaY7Z+W7BM9KqK4TG5N4DYOO+PH+mLr+oCjv/u0gnG9o1njNKNZ/Bvg1RG8AgAAIC0sHlvQ7KHAPQKL6zYZWx3zOfCKTj0ViX6VjxazCa53B799aopEY2lKLe9kU0sKig7k5tJV2sglXQMHTf/8hztOtSsYX1pIJNOWOMRAAAAABDUjgNNfvszjum6wZM3nPTtRJ/XFuoN7xdZYODxCRudMeps39wSfL1I33Ufge7yxDko7+kONPgv6/DZz74mq8Ws4f3yZKJ1eFoheAQAAAAASPIPCyVp2/5Gv/1rThje5T28azx+trPGuGe9vbWBS2FORqiHBR+Pz3A6ji1aBxqDr1P5WeWhmNwfvZtJhGKxcNmTS4zt+T88WX3yMpM4GnQHwSMAAADSBg1mgPhqbOm8w/OQPjld3mNDW5OWX7+1UZJUb3MaayoWRRw8toeNLbFYNFKhKxtnn39kTO4PoPs2tS2vMnZAocYNLEzyaNAdBI8AAABIG3tqm5M9BKBH211j6/R8aWF2xPc82NTerCbLGtk6ir5FjvYY/OJhza5ard8T2L36T9+eqAlDirt9fyBeNlbV6+E3N6je1h6cO11uLd16QDaHS9v2N/bIX879/rIJyR4CuonmMgAAAEgb6/fUaVjfvGQPA+ixbvn3qpjer97m0F8/2hr1433Xyfviq1pNGton6nvtrbfp/D99bOyPH1ykr08cpD55mTovjLUrgWQ6/08fq8XpVr3NqfsvOkqSNOe9LXp04ZcqyskwKnk3//IcWS1mffFVrWa/8oVuP3uMjj+sbzKHHrFFW/Yb24OKu66yRmqj4hEAAAApqbbJoddW7/Y79oPnViZpNEDvsG5PXdcXRWDnwWa9tbY66sf79ueY/crabo2lY6VjfrZV15w4QhdOGNSt+wKJ4K1mfOOLPcaxRxd+Kcl/+YDXPm/9uXnN08u0srJGl/91aQJHGRsfbNxnbOdlUS+X7vg/CAAAgJR07bPLtWIHzR6AVPHhbaeHdV2GxSSHqzUxPPcPH3XrOd0x7AzcaPdfvzIvk4/DSD/7G1o6Pf/ogk36+sTBxnWx6gafSLtqWpdVuXnaqCSPBLFAxSMAAABSUqjQ8YH56xM8EqD3OGNMqSTp6xMH6ZTR/f3ODe2bG9Y9HvnG+JiNJ5aZSV2HpjL52QSPiJ9Yxn0NHULzSfcvUHVd8PVYKw82afgdr8fw2RPv9c9bqzrHDaCpTE9A8AgAAIC08uSHWwM+hAGIDVdb0nfiyH768Mt9XVwdXCynLpcVZsXsXr5NbqTIG90A4TCZYn/PlR1+EXewsUXHP/BOWI8tSLOA3Xdd11FlBUkcCWKF4BEAAABp55PN+7u+CEBE6m0O7W+wS5IyranxUfGkkf1idq+XVn7lt7+vvvMO3kCqWLrtQNSP7ZuXGcORxJ/Lp8y5T25GEkeCWEmNnyYAAABABBZvif5DGIBAr63eraPveVtrd7c2l8m0+H9UvOG0w7v9HL+aeXTEjzGZTLrx9JGSpK+NK4v6ubfua9CmvQ1+x7IyqHhEeqi3hVflv+mX5wQcS7fKXpdPxaPZHIfyUSQcwSMAAADSzoJ10XfJBRDopn995refaTXpksmDjf0JQ4q7/RwVh0VXvVhelC1J6k4E8ffFOwIPpl/PDfRSDWEEj/N/eLIyLGa9MutEv+Mbq+tlc7iCPsbj8ehQY+fNahLNt+LRSvDYIxA8AgAAIO2UpNnUMSCVuYJ0cMm0WHTB+IHGfoal+wGAOcpPn97woTsdrp9ZtD3gWFYGH4eRHupDrGu88NZTte3Bc7X9oRkaN7C1Ecv4IL8keOrjbUEf//NX12ri/Qu0sjJ4M7dk2N3W0VqSzPFYMBMJxzstAAAA0g7Tr4DYeeHTnQHHPPLI6XYb++WFOd1+HkuU37fe73dnLFtcS7r1a6Njej8gXuptrR3Zr64YJql13cb/3XCCRpbmyxQknDusX57ffqjvPW8l8MV/XhTL4XbLP5dWGttUPPYMBI8AAABIOQ12p0aX5Yc8P24AnS6BWLnjxTUBxyYP6+O3f0R5ZN9zv/3m+IBj0RYsesOHYJWZ4ahtcgQ9PrhPbnQDAsIQzd/3/Q127TrU5HesttmhJVsPSpJOG1OqlT/7mj69e1rA96ivghz/piwPvbFBNR26un/xVa3f/ptf7NHBJE+7/utHW/X0J9uNfauFyKon4P8iAAAAksbmcGnVzhq5OwQKp/36fX1Z3RDiUZLTxeJsQLxYzCblZlr9KqkirVa8eNJgzT5vnN+xaJdIsHRzqvVTH28NOHbyqNh1ywZioaapRVN+sVAn/eo9feUz3Xj8vW8b2wOLclSSlxm0ytHXI5cco5Gl+Zo0tNg49sSH/t8HV/1tmd/+D55bqUn3Lwi5HmSsNLe49MD89Zq/Zk/AuV+8vt7Y7rhWJdIXwSMAAACS5sa5n+miOZ8ErL+2v8He6eMcLnen5wGEL6dDd+e1906X1P311a6sGKZJQ4t1zQnDtfyuacqOsou0dxzR/sLhD+9uNrZ/eMZIPXnlZD12xeSo7gXEy7OL2hsgbaquD3pNZzMBfI0qK9DCW0/V1ScMN449t2SH3G6PDrT9fA1V3bhqZ014A45CU4tTY2e/qSc/3Kr/98+VxvHqOpuG3/G6sf/nyycFXasS6YngEQAAAEmzcH1rd+pnF2+P6HEOKh6BmDnvmAHG9oMXH20EhCNLwws5QsmwmPXi/ztR91xwpPoXZEV9n1g0l/G67uTDdNaR5crPsnb7XkAstbjaKw3f/KIq4Pypo/t3WenYUZa1PfKptzl19ytfaPIvFmrJ1gMhH1MTYmmC7qi3OfTPpTv0zvq9fsc9bd/Tpz/yvt/xc44qj/kYkDy82wIAACDpfKdghqr08NVCxSMQM97vp7tnjNW3jhtqHB9UnKOXZ52o4g7rxSWauZtrPPoqSvJrAUKZ894WY/v55Ts16/SRGlico9Fl+fqyukHXnDg84ntW1/nPHpjb1rjlsieXhHyMt5FNLD30xga/pjFeTS0uZVrNamppD13PHFMaccCK1EbwCAAAgKQbXdrauOKRtzbqT+9t7uJqaXUcp4IBvUlTi1OvrNotSUGrACekwHRHbwSxsrKmW/f5xuTB3R4LEA8tzsBfpp388Hv6xuTBxnrHmVE0WjlyYGHEj6mzOSN+TFcWbQleYbmhqk7r9rT/svHBi4/W1ycOivnzI7mYag0AAICk8PhMm2x2uPTkh1uCho7D++bq4UuO8Tu2t77zNSABhOe/K3YZ2yeOTM2GK+v21HXr8YP75EiSvnX80C6uBJKjY8dprxd8vj+bWyJv+jJleInKCiNb5qCrWQdOl1u/fH2dFq6rDvuexw4P3oH7n0sq9bOXvzD2v3Xc0KjXgkXqIngEAABAUvhOm3x19W49MH9D0Ot+883xGlaSG3DcE4P13oDervJAk7HdnXUY48mk9mmXb62t0vA7Xtev3gz+fhGMt5osmooxIBFqmlunNxd0svaoPUhVZDg+vv2MTs8PKMqWJP3wzFGSWqd5h+J2ezTyrjf0l4+26bt//zTsMWR0+N67dMoQSdKLn31lHJvhs9YsehbeeQEAAJAUzjDXa+ufny1XkJCxrjn208GA3ubTHYeMbd9GFKnEamkPHr//jxWSpMfe3xLqcj9fVtcbFdKp+vrQM3kU/i/HPti4T1Ln4X8k9/PVMfTr6L0fn6aVP/uanGGsnfz2usCmN5156uNt+sE/Vhih6bSxZfr8nrNkDjKkyUODV0Ui/fHOCwAAgKQIJ3j87kkjNLRvrgqzAxtCNLQQPALd0Wh3alXbeqmD++SkbEMHb1fraMz650pjO8vKFE7EX6TfRi63R7+cv16SVFVnC3ldXhw6sc+cNFjZGRaV5GXq0mOHGMcv/NPHQa//6Utf+O131vBp7tJK3T9vnd5cW2Us6VBxeF8VZmfI7ggMOfOy+P7sqQgeAQAAkBQuV9fB493njZMkHTWoSD88Y6Qentm+1uPSrcEXqwcQns17G4zt8YOLkzeQLnRVsdWZTT6vMZOKR6Sgzyrbq45PHd1fI/rlBb3u1FH9Y/J83zquNWC89sQR+s03xxvHC3x+wbd6V23QCsiOXeErDzYFXOP105fWBBzzVh3fcNrhfscHFGXrgvE0lempeOcFAABAUjjcnU/reuLKyX77t551hL7pU5Fx639Wx2VcQG+xv6G9SdPlU1O38UqGJTaVmASPSEW+DZ4eu2Kyxg8uCrjm0ilDZO5G5a+v+y88StsfmqHZ54/zO56d4f/90WD3n1Xw+ud7tG1/Y4dju4M+R7Au3VL79+CosgK/44vvPFM5mVQ89lS88wIAACApOpuiJRESAPHmDR4P65enEw5PzY7WkmLW5Zb3FKSi+Wv2SJLGDymWJM0+/0hdPGmQTjuivcLxoomxqQYcVZova4gK4uwOSxG8uto/VHziw8B1VXcebA56r8c/CL4Gq+8vEU44vK8k6bjhJaEHjB4h4nfeDz/8UOeff74GDhwok8mkl19+2e+8x+PR7NmzNWDAAOXk5GjatGnatGmT3zUHDx7U5ZdfrsLCQhUXF+u6665TQ0ODAAAA0Hs4uljIPiPY6vMAYmZ/Q4skafKw1G7qcP74gUGPR9rZPjdGASbQHW9+UaUPv9xn/P31TnE++8hySVJJXqZ++80JeuY7x+mjn5yu/91wgiraQrruOtjYEvKc2WzSx7efbux73x8kyeZw6fNdtca+twP26l01Qe/lG1r63tN3vebfXzZRPz5rtP707YnhvwCkpYj/NdfY2Kjx48drzpw5Qc8//PDD+sMf/qDHH39cS5cuVV5enqZPny6brX2R1Msvv1xr167VggULNG/ePH344Ye6/vrro38VAAAASDtdVTzGaFYZgBD2tXV77tdJJ91UEKriMdh7iM3h0qVPLNYjb22UJPXLb31tPzn7iJhNVQWitWzbQf3guRW66m/LNOLO+Zrz3mbV2RySpLOOLAu4fkhJbkx+MTCgKFuSdOSgwGncvgb3ydX3Th4hqfV7yeve19YZ209cOVknj+oXcI2vIwcWGtsDi3KM7eE+61f2L8jSjWeMUmlhdrgvA2kq4rZI55xzjs4555yg5zwej373u9/p7rvv1oUXXihJ+vvf/66ysjK9/PLLuuyyy7R+/Xq9+eabWr58uaZMmSJJ+uMf/6hzzz1XjzzyiAYODP7bLAAAAPQsXVU8htP1evv+Rv3o+c/0/04fqelt1SIAwrOvbaq1N5xLNw6XRx0bVb+6ereWbjuopdsOatbpI43p5OccNSAJI0RvFqwg95tPLPbb/3VbQC75VwPG2rPXHqd/LN6hm84Y2eW13u7vT364VZv3Nuhv1xyrfy2rNM6PG1CoA22Vkw6XRw6XW9V1Ng3uk2tck9P2y4L/+9pomc0mPTzzGNXZHDq8f34sXxbSREznr2zbtk1VVVWaNm2acayoqEjHH3+8Fi9u/QZbvHixiouLjdBRkqZNmyaz2aylS5cGva/dblddXZ3fFwAAANJbi7PzYLGrikhJuvU/q7R6V62+/48VsRoW0CscbGzR65+3ri1XmuIVj6FUHmzS2b/7UM98ss04VlXbPtNuT237+nND+uQISASTKXhl7e6a4OshehVkR1wXFrbRZQW6/6Kjwqou9G0y8+6Gvao84N+5ekhJrqxt1cMOl1u3//dznfSr93TKw++p3uZQbZNDzy/fKal9XdVvHjtE3z35sFi9HKSZmP7NrqqqkiSVlfmXCJeVlRnnqqqqVFpa6j8Iq1UlJSXGNR09+OCDuvfee2M5VAAAACRZVxWPXZ2XZFRdAIjM4i0HjO1jgnTRTQf3vLpWG6rqdc9r63TNia3TQ3078W6sqpfUWtEZqqEGEG8Ol1sXzflEa3d3XkAVqyZK3dVxHL9+u70q07seozdQdLo9euOL1hyn8mCTHnpjg2qbHcb1GXzfQWnS1frOO+9UbW2t8bVz585kDwkAAADd1FWweNyI4J0ub/3aaEmtU7maW4KvLwX0dv9YvF13vrgm5PdIo09AN6xvXtBrUt2X1fXGdrD3E28g4lvBBSTa0q0HuwwdU0nH94zXfBrFjGhbo9GoeHS61eyzzuM/l1ZqXlsltSTtbVtHFr1bTCsey8tb19Wprq7WgAHta2hUV1drwoQJxjV79+71e5zT6dTBgweNx3eUlZWlrKz0LP8HAACAP4/HoxvnfqZPtuwPen5Uab5e/H8nGJ0+O7powiD9dsGXana4/D7wAGj1ZXW9fvbKWknSwKJs3dTWgVaSVuw4qDqbU00trcHjjGPSd+1D34rnQ40tKi3Mlu8sV29n3V2HOp/iCsTL+xv36pqnl/sdy8+yqsHuVJbVLLuz68r+RKuut4U8l5/VGiF5KxlbuvgFYqjmM+hdYho8jhgxQuXl5XrnnXeMoLGurk5Lly7VDTfcIEmqqKhQTU2NVqxYocmTJ0uS3n33Xbndbh1//PGxHA4AAABS0IHGFr2+Zk/I8xazKWToKLVP8QIQnO+abCsqDxnbbrdHMx/zb26RmyLTO7vrvnnrdFXFcB1sYPkFpAa70x0QOkrSf2+o0Jjy1q7PL3/2lW7+9ypdMXVooocX0pVTh+u5JZVBzw1payDjDR47C05Hl+Xru20dstG7RRw8NjQ0aPPmzcb+tm3btGrVKpWUlGjo0KG6+eab9Ytf/EKjRo3SiBEj9LOf/UwDBw7URRddJEkaO3aszj77bH3ve9/T448/LofDoRtvvFGXXXYZHa0BAAB6ge5WeGRYgi/cD6BVUW57cD+gqL2pyupdNQHXluRnJmJI3TakJEc7D4auXJz3+R6/KZ6+DuufnlPJkZ6cXVQBji4tMLYvmjhIk4b20eAUan50RHmBNv/yHI286w2/46PL8mVum2Ld1fIF5x0zQH/69qS4jRHpJeJfF3/66aeaOHGiJk5sXVT01ltv1cSJEzV79mxJ0k9+8hPddNNNuv7663XssceqoaFBb775prKz27sn/fOf/9SYMWN05pln6txzz9VJJ52kJ598MkYvCQAAAKnM0SF4PGtcmd79v1ONfU8XzazzsuLX+RNIVzaHS1v3NUiSfKP5Pj4h5Hsb9wU8bnBx6gQenXF34/cV155I1RUSpyQveJifn2XVU1dPMcI7r6F9cwOOJZvVYlZZof9yd89d1z5DtTDIrISHLj7a2L73giPjNziknYj/1XbaaafJ08m/Bk0mk+677z7dd999Ia8pKSnR3LlzI31qAAAA9AAdKx6/ddxQHdY/39j3qPPkMTvDouwMs2yO1FsbC0gGh8utMT97U5J0+hH9df0phxvnfL/fSnIDw4JBKVRp1ZnOPoN2JZPOukigYEuFXDJ5sH59yTEymVIrYOzMf75foXfW79Uxg4s0fkixX4dqs9mkP3xron74r88kSTedMVJjBhQa50OFr+ideAcGAABAQnXVECacfKEklw81iM7KykO6cM4nWrHjYLKHEjNb2iodpdaqRpe7/ZvI7nSpwe7Uuxuqdc9r6wIeOzBNKh4PNTmifmyGNX3CHvQMg3y+r3505ig98o3xaRU6Sq3d7q89aYSmDC/xCx29LhjfvlTegKIcTRhSrLvOHatHL02/14r4IngEAABAQjW3+AePJ4/q57cfTl1TYY5/RclJI/uFuBLwd8Vfl2r1zhrNfGyx9tXbkz2cmDjQoaHKXz7aamzbHW7d8+paXfvMp8ax44aXGNsj+qXH+oe+v7CItJoqWGgCxNOCW09RdoZZxbkZ+uaxQ5I9nLiZOWmwRpXm69yjyyVJ3zvlMH194uAkjwqphgVyAAAAkFA2Z3uA8H9fGy1rh1AgnCmVG6rq/fatNJxBmJp8gu+31lbpiqnDkjia2Ki3Of32P/iyfS3HQ00OLVxf7Xf+yasmK9NqVrbVknJry4Xy9YmD9NJnX0lqXUvu+n+sCHntyaP66aNN+419gkckWm6mVevvO1tOt6dH//37zTfHy+PxUOGIThE8AgAAIKFsbcHPscP76KYzRxnHs6xm2Z1uHTeiJNRDQ+rG8m/oxVq62WE9HPU2hyxmk3Iz4/fRq7a5JeS5jqGjJBWn4VIFv/3meF1ZMUxHlBX4BTnD+uZqx4Emv2vHDy72Cx5Z4xHJYDKZlNELfilG6Iiu8A4MAACAhPJOmczOsPgdf/PmU3Tb9CN014xxEd+T3BHh8p2mm5dl6eTK7qu3OXTyw+/p/D9+LIcrfiHnT1/6Iuxr778wPbvNmkwmTRraR3lZVr8wp9EeuGZs3/xMXTShff25nlxxBgCpjndgAAAAJJQ3eMzpEDyO6JenWaePVH5W15VhD3z9aL/97nS8Re8yzqfzarDus7G0bX+japoc2rKvUSt2HIrLc7jcHqOZzIyjB3R5/czJ6b/+mm+F1bePHxpwPstq0T0XtAesTnf8K1sBAMERPAIAACChbI7WEKBjxWMkZk4eJKvP2nTkjghXvS367siR8q3Gq2uO/fPaHC796PnPjP3bzx7jd37u94732x9SkhPXKd/JUJyToY9+croevXS8cSzLavabTh6sKhIAkBg966cOAAAAUp4tRMVjJLKsFn1+z1mat3qPfvK/z+VhsjXCtHpXrbHtrRSMF5tPJ2ZbHNaTPOlX72l/Q3tn7qF9c5VpNRtrV55weD89fsUkPbekUnfNGJs2HawjYTGbNKQkV40t7Q12Mq2t9TW3TT9Cn2zerzPHliZreADQ6xE8Akg5H3y5T1f/bZkk6eqKYZo5ebCOGVyc3EEBAGLm2UXbJXW/E3VuplVZGa0BAxWPCMfiLQf89t1x/ovjFzw6Yl915xs6Tj+yTJL0nROG64kPtxrHzz5qgM4+qusp2Okqu+09oH9+lnHM3DYVe9bpIzXr9JFJGRcAoBVTrQGknDv+97mx/eziHbrgT5/o/nnrkjgiAEB3uNwefe/vn+rOF9dIkvbWt4Ylb35R1e17e9d6I3hEOL71lyV++/H+e9PsEzba4xA8+rK2NVD58fQjdM0Jw/WXq6bE9fmS7VvHDdVh/fJ03jGtTWT6+EytTuR0egBA56h4BJBycjMDp9499fE2fefE4RrcJzcJIwIAdMfFf/7EmN764MXtTWEONLZ0+97emkmmWiMa8Z9q3T69+n8rv9KVFcNjct+/fLhVS7f5V296m+ZkWMx+jVV6qgcvPloej8f45YPZ3L0KagBAfBA8AkgpHo9HW/Y1Bj3HwuAAkH7cbo/fmnoHfcLG6085rNv39za3peIR0Yj3VOtPtuw3tlftrInJPT0ej345f73fsdOO6K/vnDg8JvdPJ77drSXpZ+eN0/sb9+rCCYOSNCIAQEdMtQaQUl5ZtTvkOapZACD9LFhf7bd//h8/NrZvnjaq2/c3tdU88hMC4SjMbq27GFKSIyl+gfXGqnr9fuEmvf75Hr/jS7YeCPGI8H3uE+R7/e3qY3tct+poXHfSCP3juuOVE2T2DAAgOQgeAaSUm/+9KuQ5d+ybQQIA4mzt7jq//a9qmo3tWAQlpva51kCn7E6X6mytnY/LC7MlSa44JI+1zQ5N/92HenThlwHn7n75i27d2+Px6MI5nwQcZ5oxACBVETwCSClnjikNOJZlbetYyqfKtObxeHT3y2v0p3c3JXsoABLIGudAhDUeEa59bU2NMi1moxFJPKZaz11aGfJcc0vwZWM8Ho8a7M4u772y8lDU4wIAIBkIHgGkjP/3zxV6Z8NeSdJlxw7RKaP767qTRhgfDli/K71tqKrXc0sq9cjbX+p3QapAAPRMjSHClNFl+TG5P12tEa7t+5skSeVF2bK0BeLx6C3zqzc3hDz3VU2zTn/kfW2qrpfL7dFfP9qqlZWHNOLO+Trq52/pwy/3dXrvbz7h35X7yIGF+vj202MybgAA4oHgEUBSfbr9oJ7+ZJs8Ho/mr6kyjrvcHv392uP0s/PG0Tigh1i27aCx/buF8a963L6/Ua+u3i0Pf3GApLI5gld45cRoPTrvz4h4NwlBerM5XLriqaWSpIHF2TK3/cVxxyF59K4j6TVhSLFK8jKN/W37G3Xpk0s07/Pd+sXr63XxnxcZ515bHXyt6x0HGuVye/y6cG9/aIZe/+HJGtwnN8avAACA2GEFYgBJdcnjiyX5dzmVpEuPHWJsez8cMI0ufW3d16Cfv7o2oc952iPvS2qdhnn++IEJfW4A7WyO4Av0xmoGNks8IpTKA02yWkwaWJyjS59srxQ8dXSp1u1pXXs01oF1nc1hrCOZnWHW3TPG6cIJA7Vk60F97++fGtcdbGwxKjB9BRvNq6t364f/+kynjO5vHBtVGpuKYQAA4o2KRwAp4Y/vbvbbP7x/4D+o4zEdColxx//WJO253+nQURdAYtmcwSseD+vHVGvET4PdqVN+/Z5OeOhdOV1urd5ZY5z7wamHGcG3K8b/uFj7VXszpcuPH6Yrpg5TQXaG8rMC6z2C/UL1vyt2BRz74b8+kyS/adiPXzk5FsMFACDuCB4BJM36PXUhzxXmZBjb7VOt+VSZjpZvP6hl2w92fWGMtDjdevOL9mn76zr5ewYg/kJNtb57xtiY3J+KRwSz26d7+si73vA7ZzKZ2mdTxPgvzr4Gu7F97tHlxrbvVGuvUMuONLV03WRmRN+8KEYHAEDiMdUaQNKc8/uPgh5/7rrjjUXfpfap1lQ8pqf32hoGJcKX1fU669EP/Y5t29+opz/ZposnDlZRbkaIR3bPuxuqZTGbdarPNDgArTZW1Qc93idIEBMNk5E88kMC7brqEN3+b4vY/r1ZuK61yn7sgEJNHlZiHD+ivCDsezTaXcrtZA3ULKtZ5jh3iwcAIFaoeASQck4a1c9v3/hQST1LWsqwBP9RE+vpbZ9uPxgQOkqSw+XRva+t06y5K2P6fF47Dzbp2mc+1dV/WyaHK/hadkBv1Wh3avuBwHXsYsmoig9ybseBRr4ve6nqWlun542p1jEOHl9taw4zshtrMB77y4XGtsfjUabV/+foR3SxBgCkEYJHpLUvq+t1x/8+165D8f1Qg+SimCW9lRdlBz2+dOuBmD7PT/77eafnP968X5K0r96uO/73uT7fVROT5739f+3PS8AB+DvQ0NL1Rd1kUvAps8u3H9Spv35fP/jHiriPAalnfYhK2yfb1kaM11Rrr28fNzTg2I/PGh3y+n99b6rfvnd5maYWl1qc7T9bph5WotKC4D9XAQBIRQSPSGvn/fFjPb98p+57bV2yh4IIuSOodmOqdXpyuz167P0t+vviHcaxYp+pzktiHDxu3d8Y1nXH/nKhnl++U9/y6XDaHYu2tL+O55bsUL3NEZP7Aj1Biyv4+o5L7jwzdk9iVDz6/5CYv2aPJOmdDXv13sbELfmA1FDXHPhefNa4Mp11ZOu6i96pypH8e6QzHo9Hlz6x2NgfOyBwavWNZ4zS9odm6O/XHhdwruLwvjpuePvU7Cc+3CpJ2u+zZqQkPX4FTWUAAOmF4BFp6+21VcZvgL+sDv5bbaQuu9O/MmzLA+fqJ2cfoZdnnRh4Mc1l0tL/Vu7Sr97cYDQROmpQoVbNPss4/4cOncy7a5TPtLaKw/rKYjbpp+eO0bHD+xjHh9/xurHd2OLS+90MI25q6zTq9cD8DfrFvPXduifQU7jcHk37bfvyB1afNelCVUJHI1RVvO/01O88vVyLtuyP2XMi9XnXeMy0mvXHb03Usp+eqSevmmKcj/VU63q7U0u3tTdSK8wOvabwKaP768bTRwYc/88PKozth97YoKYWp5Zubb3nkQMLtf2hGSrOjc3aqAAAJArBI9LW9T5Tp5pDdMxE6rI72/+fLbjlFFnMJv2/00ZqwpDigGvDqXj84Mt9uvSJxdp5kGn3qeKjTf4f8seUF8btuTZV12vT3gZJrQv6P/2dY7XlgXN1/SmH68dnHRHycdc8vTyq53O7Paptcui1trW8fP37050hH3egwa6XPtsVsssv0JN8+OU+v/1g7++xYGr7GVFVa9Oc9zarpql1eneDzb+5yPPLQn9voufxVp/PPm+czh8/UKWF/mG3t4ldrGZTHPRZVmB439wum78MKG4fz199AlFf42a/pV/Ob/1l1qShfYJeAwBAqiN4RI9QXWfv+iKkFG/Fo8Vs0qiyzjs9GtUsIZrL2BwuXf23ZVq67aCe+HBLLIeJbvhs5yG/fW/n0KsqhhnHvAFBd/lO537s8knKzrAY+11VVkVaSXvPq2t12E/na/x9bwc9f9Sg0AHrd//+qW7592qN+dmbET0nkI4ONLZ/f185dZi+eewQSf7VybHgzXcONLbo129t1IT7FkiSajpMtT19DF3ne5P6tuC5IDt4d+j2NR5jkzweaGz/t+jz11d0cmWrs9umfEvS5GHtoeLVPj8jJam27e9xbqZFAACkI4JHpK2O/5D8eBNTqNKJdwpUtrXrtyFTe/IY1KMLvjS2a5udwS9Cwu082Oy3X9ZWbfKz88YZx7bsC29dxq7sqW1/Lt8qEkkaWJwTcP0lkwcb2zVN4a/JuOtQk55ZtD3ouccunyRJ+uKrOi1cV63hd7yu4Xe87veh9rPKGmM7WLUk0BN4PB4dbGxRc0v7+/FdM8bqG5MH67nrjtd/vt91KBMJk4JXlm2ubvDbN5s6r0BDz+L9d0ao4NH718EVo5LHmY+1r+8YzlICffOz9NDFR+ue88epT1779Omfn39k0Ouzwvj3EgAAqYifYEhLL67cZfwm2+uKp5YmaTSIVE1Ti878zQeSJJuz6y7AXU21/seS9mq3DAsfLFOB71R6qXWK5U1ntK5nlWExGxVPHa/ztfNgk7Z3aBhT2+Qw1nf9qqbZaAqwcH3rWo1Ws0lZVv+qkAyLWWMH+Fch1tscKmn7oLdln3840ZmTfvVe0OPbH5qhisP7Gvvf/funxnZVnU2S1Nzi/1ppdoGeyOlya8Sd8zXp/gX62StrJUkXTxqk7AyLTCaTThrVzy9kiYVgeaLb7dHmDt/bThfrBPcGLndrk5fPd9VKkvKzgq+1aIlh47pdh6Jb5uWy44bqmhNH+B0zm01+vxzzysqg4hEAkJ4IHpF2vqpp1q3/WZ3sYaAbbv73KmM7kkqDjlOtq2ptqnjwHTX5BDovrvxKTlfXYSbi65XP2qv5Xv/hSXp51onKzWyvOsnKaP3xU3kg+Ic1l9ujkx9+T6c98r4u/vMnxvHfLNio6/+xQqPvfkMnPvSuZvzxY0lS/4IsSdKDFx8d9H6/v2yC3/6OA0062DYN9JLHF3erE3VhWzVNqAX/z3q0tbnG2Nn+06s7/vIE6Ak2Bmn2VhtBVXE0gv26adGWAwE/X+roON8rvPlFlV+Tl775wd+bja7W3Zxq/bOXvwj5S6loPfKN8fr49tP9jmVa+NgGAEhP/ARD2tlfz3qO6czj8ej9jfu6vtCHKUhVQk1Ti6Y++I721NoCrl+/hy7nyfaT/31ubB85sCjgvMXc+uPnjhfX6O21VQHn63zWZltZWaPhd7yuepvDby1HSVq/p04ej0eNbVPqfNfJ8jW6rEDL7jpTJ7RVJX735MN0WL884/ziLQe6fE2rd9YY298+fqiW3zVN15wwXP+94QTj+IAg0+tCBYyxWt8SSCXBMpwjB8avsZTU/jPC17LtrcFTns+6ePe+ti6u40BqaLD7B8yH9w++pqj3r427GyWPP3v5C79ZF5L022+Oj/p+vgb3ydWf25bwkKTcLCoeAQDpieARaeXL6npdOOcTv2Mv/KB9raiOUxmRejpOa80JY+qQt3GA71p5/12xK+T1nU3fRfz5/n+6eOKgoNf4zoj/zdtfBpyvbQ6sTPrwy+DruI686w2j6nVAUeB6jl6lBdl65jvH6e1bTtHMSYP00qwTjXPhrPN418trjO1rTxyh/gVZuueCIzXapznSPRcEX5tr7e5aYzs/q7VC8lCcq8CAZAjWsf2Wr41O+HP+4Z1NcX1OpK4F66qN7W0Pnhvyuu5Ota6zOQJCR0m6eFLgNOlojfD5BdnwvnmdXAkAQOoieERa+dUbG/z2Tx7VT1OG9TE6/XnXUkPqam7xnwbtGxyH4q1K8P1sEOyDppd3Qfl0FqvF7pPhrbXtH/q8XWw7cvistTakJDAsfH3NnoBjs+auDHov3z+rnC66fmZazRpdViCTyaSinAydd8wASeH9nemfn2VsD+ubG/Sa6UeWa+Gtp+iFH1Ro9eyzjOMz/vCxsf2v702VRMUjeoaOHYFtDv/3+EunDAlakRhLIzvpkt3Y4grrF1zoObw/Er513NBO/+61rx8d3c/bB+evDzgWqpFNtHw7WfuGkAAApBOCR6QV73o8Xk9eOUUmk8modnpnfXWwhyGFzF3mXx1w1KDAabgdeTuWej/gNtqdeiRIlZxXSxgNa1LVocYWXfrEYh3+0/l6cWXoqk5J+sfi7Rp91xtBpyon0zKftbXGDy4Oes2ar9orABeu36t7X1trTHdzuT369Vsb4zpGL++Huj++u0nzPt+tnQfb15z8rPKQht/xui7808c60GDXe21LBMz59iRldLLW1sjSAh07vERFucEbGvQraF1vrKbJERDa9GSrd9bo0icW61/LKnXva2sDGgch/fxj8XYd98A7Wr+nTtv3N+p/K3apyaeT9WH983RjW1OpeBpSkqs7zhkT8vxFEwfGfQxIHd6K+VNH9+v0Ou8/KaMNHl/67KuAY5dOCf7LtmgNKMpR/4IslRVmqbyw607ZAACkotj+Wg6IsdmvfCGn26MHvt7aMKLjb5I7Vjf94vX1+u7JhyVsfIic7xTpu2eMDesx7VOtW//75hf+QdvYAYW6YupQvbJqt5ZtO6iWGDeXaXG6ZTKp07ApFppbXJp4/wJj/9b/rA45Zcvj8RgdY6//xwp9cNtpGhbDaVi1zQ45XW719anyC9fhpa3jGFqS22UFotfTn2zXeccM0PjBxX7rdk4cWqw1u2rlDKMCtONC/OHwNrw51OTQjXM/kyStv+9suTweff3PiyRJq3fV+k3bjKTq5KqKYX7rUt517lhjqrXT7ZHd6VZ2L6nG+uXr67Vs+0Gj6cPWfY169trjkjwqdIf3PeiXr6/Xx5v9l0KYeliJnr++64r2WPnBqYfrB6ceruF3vO53/PunHBZ07VX0THvrbVqx45AkqSin8+7p0TaX2Vtv08J1e5WTYZHN4VZxboaxXMf542MbcmdazXr/x6fJYjYF/PIdAIB0QcUjUta/llXq74t3aO7SSi3Z2tr4YVVljXH+d5dOCPq4t1Ks+gv+rj1phLEddkjcYR0mm88ajp/ccYbe+NHJuvz4YUaAY3d0HTy63R5Nun+Bht/xur7wqb7rqMHu1Oi739CEe9/2q+SJh637G7q+SK1BaMc1CU/99fv660dbYzKOFqdb4+99W9N++0HQtRa7ctdLX0iSBvcJvd5iMPsbWnTFU0t18sPt3UH/ctUUXTwp+DqRkjT/hyerODdDv79sggb3CT79uTPBQr+TH35XSzo0m3nWJzwcU17Q8SEh3dUhXL/suCF+AXY4gWoqenHlLv38lS8iasrgcPt/X37w5b6ANV+ROrbsa9B/Pt0ZVlVux9BRCm/93nh4/IrJfvtDSnJ15tiypIwFiXfN35Yb2/0LOv/FmXeqdaS/q/z/7d13WFNnGwbwO2EEkCXIlCVuRVFUEPde1Gq1tbW1tXZop21trXW3to7Wql+H1dplt621jtaNIC7UquCeCE6GsmVD3u+PkJBDEoaEJffvurwucnJyco68nOQ853mfJ3DhHszaeFpTp3fr1N6ImjsY/77eC/6e9pXbWAU0Upg2mBtURET0YGLgkeqklKx8zPy7pJHDE2sOAwCuFk/Na9akEUZrNa3QDgR8EcaC8nXZzuJsxbKCSaWVbi5zMzUHADAx2BtN7UuCW+bFAR19GY8FRUoUai2/lJSJlCxVjb2Hvjigsz6gak7gN38nAFWdsBGf7cexuBS961aVEEJSBxAAWuqpW5aUkYvgxXskmZFqH209j5d/OY5ore7L90M9DTo1uwCnbxoOypZWUKSUdH5OLKPm6idjO+osWx0Rg8NXS/5/nWwUaGKtwPShbdDKxRoD2zjD1sIUu97qg7C3+yJiej+0c7dF9LwhGNWp4uNJm5WejMy79/LxyxHdhgEAEOjjUKmsE4WpCX5/sTuaNWmEn54LhI2FGUy1Xl9QT8sCTPvzJH6MvIZuC0Mr/BpbC92p5wOXRSBwYSi+DLtcr0skPGiEEBi4LALv/nUK+y7rb+pU3u/LtJozxA0Z5ucqeezX1E5TJ1hhyq+9D7osrRuEZdX+BPQ3rrsfrrYWaNzIvEKlY4iIiBoifgOjOmn+lrM6y7SnT31cKmix+bWemov5M7cyqnfnqNIycgtwISEDQgjEJatq6G0/XfHMVHWYJqegCD9HxuHUzTQAgEMjaTaDwkx1Sssr1XgmJ78ILWdvL+5+rLooSb4nbexxJzNP8vjs7XQs3y2tIxmXnI1HV0di3mZVRl9+oRIL/jmHX/R0tdR2L68QlxMzy1znzr08nWWXk+5pMspCzyVi9MqDCFy0B8lZhpuSbD+TgNErD1boQirmzj2d41YqBcauOqR5/PGOCxW+KPvxUJyk67y6RII+47p54ruJXSXLTpYKmKr3zclGgV1v9cV3z3bDqfeHopWLDXydrI0ytdzQ9Pm9xfUcS7ufxkXBzR0R/k4/9GnlBAAw0Qo8htbzurTJWfk4fi1VZxyppecUYPyaw5jx1ylEXNL/f5qUmYdPd13CkBX3l2FLxndVq/5mYrr+Gwh7LyaVuY22brZG3afKiF08AqueCsDqCQHo5GmvaTBSP/OLqTIaFZfP+GFSt3LXVd9EyswrxI2UbJy7nVHmOf5WWg5GfLZfsqypvaXknE5ERES6GHikOumfk7fLfL5DqbvKClMTfKMVxNgcrVvwm2pPx/d3Ydj/9qPl7O2aZSsMTJXXR33R+PvR65i7+SwOXlFNg7WzlNb8VBjIeHzv71Oanx//+jCUSoHnf/xPsk7pMbNit+HM2Z8iryEpIxd/HLuB7w/GYs6mMzhyNVnvuvHpOfCbvxODV+zD1TKmlV5MKAlMLnzET/Pz52GXkZNfhCmVzGQsK4BzKy0Hh67cxeDlERi76hDO3ErXTHkt/brTt9Lxxrroct8vJ78IH22Vdvhs51524KF0YKL0rN3fXggq932rqnfLspsPvNKvueRx6anT90O7y+rc4iB2XXQzNRs+722Fz3tbDY5vABi76hC6LQxFgZ5M48CFoYi8mow/jt3QLFvxuL/e7cQlZ+OQnim7ZBw/HorD098dQV5hUbnrhl8oCSoayvAtL9gyPtC4TTYqQyaTYXgHNwzzU3Wt1+wpI48PvNziG4/qAGRZbIqzsLeeikfvT8Ix4vP9CFigO5tAreeSMJyLL7m5veHlHjj43oAq7jEREdGDj4FHqnfsrcz0Nqzo29JJ8/MnO2qmIy6VTztbTl3PztPBUmc6XFnUGY7a03ABwNZSOnWzJOOxJACSlVeIzdElgezTt9LhO2sbckvVgfxo63n0+jgMPu9txVPfHpZkov05JRhjSzV5CVy0B3M3lQSNZv59WlPQXu2Xw9cQvDhM83jwin0Gj1Gd7TfczxVPBnpplv8v9DKibqSiSCsq10mrhtSiRzpgQnfV+tpxgCQDGWg/R8ah55IwPPntESgFcD0lGw99cQADl0XA572tWH/8hs5rtpRzIwAAFm07r7OsvAs/d3tLbH61J1Y9FaD3+R4tyg4KGoNfUztsf6M3fn+xO358LhAfjfaTPP98r2ZYPaFk/wKbORj1/XMLlMi6jyzK6nbudgZ6fVxSa/PxNYc1GW4ZufqD2vrq6+bpmY47yr8pngn21ruNVK3apfmFSvx1/CbSs5kFWVXZ+YWYv+Us9l++W+ZNFTXtmwgX4jMk5x81Kz1/39pTW/VNra9tgpHHB5468GhhVv4lTnxajs6y/CIlUsuYVaCtczXUcyQiInoQMfBIdc4trS+CAV72Os+Xbqqhpp2VUdFOulT9LiXqZvnJZZWbllRQpP9isZFCeuGrjnFqNzrQ7qJdHnXtSHVGJQA83d0bgc0cMLGHN9zL6Ix69W4Wxq46pKl7lp1fiDmbpNlsRUqBtGzpBU1+oRKPrjqET3eppnX3aeUEmUwGT4eS2pVPfnNE8pofJwUiet5gXF00Ak8GeWHBw37YN70/YhaNQGsXVb3TeAPTI9VdaA1ZtO2C3uWF5VTf/1lrurmNhSlOzB1coVqI/p72CPBurLP866e76Fm7erR1s0Vwc0f0beWECd29Ebt4BC5+NAzR8wbD0VqBYX5uuLJwOGIXjzBaZ/OJWoG3SWv/K2PN2vHZnks6y9Q3dLZE6w9EL952QVL3T19ANWbRCMjlMiwY5Yd1k7sDAN4e3ErzfFpOyd/Hst0X8c76k/BfsAs5+eVn6ZFh2iVI1h/TvbmgrXRG5LcHYjFoeYTOevqCeFP6+MKhkTnGB3rqnJ9rk0xTy69294Oq17nbGbhd/NlXkWYshm4kpecUICUrH/4f7MJzxefnK0nScimLHunALtNEREQVxMAj1TlntToMr30uEPvf7Y8nulVsypZdcQZcRw/VVOxv9l3Fwq3nqlw4nIAbKdk4e7vijUbU9GXCtS9nCm5FNbYylzxe95/qgvpIbAp2nImHUilwW09Gg9qknj4Y19XD4PNAydTajh72ODRzIKYPbV3m+q3mbMdza/9Du3k79T7/XKkg06XETBzTypQM9nUEAHw9QVr/UJudlRnsrcw1Fz1yuQxejlaQyWRwt1cFRw0ddxNrc73Ly9NCa5p8eX59IQgOjSr+Ps42Cgwq7jo7pa8vYhePwND2Fc+INTaZTAaFqQnstcaXqYlcMkW6qib1LOnufjS2ehoWVYWdpW62WnZ+Ic7dztAJqKvdSstBqznbUaQUWB0RI6n3OSekLbZO7SWZntvd1xFxS0Lw+sCWeLG36v9DfWNJCIGvI0q6tLedtwMHDDQ5ofK9sS5K87OdpRkOX03G2FWHsDn6FgqKlIhPz9F8Ti7455zO62PvZkluLgBAfJoqwBPUzAG/vRCE94a3waNdPHBs9iAsHqPbPKo2ycAajw3B6K9KzjkWpuUHHvu2ctK7PL9IieW7LyI9pwBhF5KwOiJGUvc3bkkIngzy0vtaIiIi0sXAI9U5qcUZYT1bOMLWwgyeDlZYMrYjphVnxUwd2NLga98apHour1CJpMxcLNx2Ht/sjzWY/UUVI4RA70/CEfL5AdxIyUZkTLJk6t3JG2kGa8CpM5Wa2lti+xu9MTHYG1+O1z+11pCjswfCr2lJsHJ8oCfmj2yHbj66mXJqL/1yAh/vvKDJftBn/sj2eHdYmzLfu3TWROmaf8420gY3ABB2Qdp0QXvK14nraZIA7q5S01M9HawAqOojfvZEJ8lzPo5W2PFm7zL319VOlSl5JekePt5xAQdL1czzbVJ2l0+1f1/vhbglIZJl3x+I1buustQ0TD/3ynX2lMlk+HZiV8QtCcHM4W2NGuCrq7wdrSSP91/W33ilsjJyC9Bs5lbM3ni6SjdcjmgFQ9WZ53HJ2Rjx+X4DryjRfNY2LNl+AVeSVNnOg9o644XevmhfxriwLJ62u2bfVURcuqNpQqVtwndHeBOpkvILlbiRki35DLx6NwtPrDmM49dS8ca6aLScvR3Bi8Ow/pgqO3xTlP4ayXM3ncFvR64DAHaciceMDarauS2crdGjRRO81Lc5ZDJZncwCK8l45Ph5UO2/fEeScd1IUX7gUSaTaWqGvz+yHZyKP89z8ouQlFFSrmTJ9gua8gPGLrdBRETUEDDwWIc11C/IMzacBgAUlppe+/qAFtj4Sg+dwI82RXGQKK9AiTVa2TI5BZymd7/+/O8Gms3cpnnc+5NwjP/mMJrP2ob8QiXyCoswauVBPL7mMO5k5mkCjRm5Bei3NBxH41QBjOlDW6Otmy0+GOVX6QtTZxsL/DE5WPN4ZEd3TOrZrNwA1dcRV7H1lGpa6JdPdsbJeUPw+oAWaGRugneHqTIXm1gr8NNzgWjjaoPPnuiEsx8MhU3xFEFzU91TpEwmw5kPhmoef/xo2Zk9/77eCxc+HC5ZFvL5ARyNTUFOfhE+D7sCAFCYyvHVUwGSjLBRnZri6OyBmv3c8WYftHEtO1vU3kqVqfbdgVis2hsjyU7Lzi/U/D6+fLIzDszojx7NHbF6QgAOvTcAIR1UjRjeHNQSfsUXY788X9LgZcG/53Sa8Oy9mATfWSXj4+wHQ+tk4KGukclk2PVWH83jp787WqXtbYq6BZ/3tqLj+7sgBPDrkeuS6bUVkZ5TgGvJWfgvLgXXigN/0wa3wppn9Gffjg/0QsT0frjw4bAyt9uvtXO5722mNWYmfn8U/T/dq3c9Q7VLSb/Xfz+B3p+El78igHc3nEJqVj4e6ugOAJjcxxeT+/hK1plVHNB+6ZcTmpq9pWvt1kXq0dUwv1U9+BIzciXnUHNTuSRjvSw/PheIDS8H49mezXCn+PwyauVB7DqXqHd9MxN+vhEREVVW3SnAQxJJmbkYv+YwArwaY+lj+juAPuiOlJp+KJPJ0NnLcIYbUJJZlpqdL2kOksvA4317d8Mpg89tiroleb7bwlA0MjfBnrf74e+om5KsJZ8mjaq0H1bmJgj0ccC9vEJ0q0TGgToZz83OAnZWZnh7SGu8PUQ6XbpPKyf00ZpydXzuYOw+l4i+rfVPw7JWmOLywuG4k5kHd3tLxC0JgRACPZeESTIsf3k+SBPAm/dQOyz4t2QK47ivIyXTnkOn9dVkO2pztrHAyXlDICAqVLOqdEOH2LtZyMwtQGGRQMSlkqw6z8ZW8Ghshd9e7K5ZtmycP+Y81BZudiX1JXu2cJRs74110RjZ0V0TXHz2B+nU8bpU162ua1Vcj1OtoEh5XzUkM3IL8OYf0TrLD19NRgePimWfXk/ORp+lugEqR2tzOOqZNn/mg6Gw1vpdn5w3BP4LdundtnazJEMSMvRnJjd3aoRxXT2xeLuq9mhWXiHi03MkY5QM23lWf/DEkM4flnT0dbZR4IXevliz76pkHe0bUQBgVYHzUq1jjccH1pWke5IapGM6N8VHj/iV23VdzaGRORwaVfw7xaoJNVd/mIiI6EHBjMc66sdDcYi5k4X1x2/iXh3seGpMBUVKnLqZptM10/c+AlXZxdl2pbsLl+5gTBVTXsBWX1AyK78I3Rfv0eks3syxaoFHmUyGdZO7Y+vUXgaDM+ZlBG1cKxGoMDeVI6SjmySwUpqZiRzu9iXblMlkiHi3P/a83RcA4GKrQHDzkqDdc72a4YdJ3STbuHuvpJGGvqCjmrqmY0XYWuruc4f3d6Hzh7slwSl/Pd04LcxMdAI6+rJKY5OzAECn4cfWqb0qtI9UYt/0/pqfU7PL7qR6914eQs8larLhhRC4lpyFju/rD/j9ezq+Qvswa+NpvUFHAHjY3x0ymQyh00qyM+ePbKfzt2FnpZv11tjKrMIZsCEd3fQuD2zmgCl9m2uCnwOWRSB4cRie+b5qGaJUPnXgZv7IdmWuVx8ynGWo+/tI5cvILdBpWlW68dHyxzvp7bhuDJ882rFOdmsnIiKq6xh4rKO078r7zd+J8FI14/SJup6KST8cxfzNZ+pFht/5+Ax8vucypq8/iYe/PIj3t5zF1xExmuc/e6Kz0d7rVhkNRioqr7AIwz/bj9ZztiP2bpYR9qruS9TKQvr39fsPKo3u5K43MFFZcrmszOnVrw1oYfA5fbUYjc3MRI7mTtaIWxKCI7MG6WRc9GvlpAlMavOvYFZaRZhUoD7izOFl17Usbd5D0sDDV+Gqv9Nz8SW1Kj8a7VdmDT/Sz8vRCqbF40RZxv2RGynZ6PpRKF746Rh6fawKEs7aeBp9l+6VrLfzzT5YPUFVQ/XkjTT4vLcVf/x3HV/tvYLsfN2bWF9HxGjq9uljWZzN1sLZBlcXjcDZD4ZKGuNoK12TdMtrvSqcAdujeROc+WAo/nopWLL8neLsZEtzaVbdvkvGqYn5INOud1fakHYu5b5ePcNgUs9miF08wuB69WHqqfZpsaGWsanvDl9NRsf3d6H9/J34Zt9V7DiTgPTiZlRqPz0XWKX30Hfz7PicQYieNxhxS0IwrmvFGh0SERGRFOfE1VHZpTKJJq39D0dnD4SzjYXB1zzy1SHNzwdjkhE6TTfAUZeM+vIg8otKLoxKd8xsdx+djx/t4oHZG3U7rk79PQpFSiUe6Vx2B+Oy7DiTgPPxqppp/T/di6OzBsLZ1vDvo767npwtCWr4NbXDtqm9EXPnHl7/PUqy7uQ+utPx1I7MGgiXGvp/erq7N5bvvgQAmPtQO3yoNbX5fqawGptMJkNzJ2tcXTQCey4k4cWfjgEA1k0OLueVFfdwJ3dsP5OA7r6O+HjHBb3rTOjuXaltPtvDB919HfHtgav4+8QtHIlNRm5BEX49XBKwquw2qYRcLgOUAkUGAiLZ+YWSOn230nLg895Wveu2drXRdDZXU9fN/WTHRfwwqRv6a9VcVE9hBoDfXghCcHNHjFp5EKduqoLKplp/N3K5rMxA4qhOTTHMzxXj1xxGh6Z2ZWbx6mOtMEVXHwdNzUhTuUzz/jdTdW8e/ReXgm4+bPRgyMrwK5LH2uUexgR4aGrYffZEJ6w7egORpRqEtXMr+QzWd8NnbIDq8/TJoLr/t6+990JIA5FUPzyx5rDm54Xbzus8b4zvGu3d7RC3JATJ9/Lw+9HrGNvFA47W1X/TkoiI6EFX+1fipFdGToHOssNXU/SsqRJXKgPvStK9On1XPykjVxJ0LG1sgEeF6/NoU5ia4MKHw7B8nG5dzLf+OFnp7Wm7XqrLauCiPTh1M02yXAiBtHKmS5b28+FrCFwYit+OXMfy3ZeQmav7u68NW07qdjZt526Lkf7umD5UWiMxPbtAUq9Q7c1BLWss6AhIs6KctDIcO+mZVlyb5HIZBrdzQdySEMQtCdHJ5qoKK3NT/PhcIF7u1xybX+2pWf7WoFZ4f2Q7nF8wrNJ1GOVyGdq522LqAFXX+JupOei7NBx/F3e/HRPQ1Gj73xCps1QPXr6LlCzd88ciPRfZ+nz1lCrT0cbCDP0M1Ced9MN/mox47c8Ieysz9GjRBDKZDFte64Utr/XE/nf7691GWRSmJvj7lZ74YJRfpV+rZmFmAgszE0nQ82U9TcUeWx0JIYROV/WGKObOPfi8txXjtYIzfx2/qfk5bkkIng4uCRA625acH2+m5uC3F4N0MqFLN9c6t6CkqdYnYzti2Th/LBvnX2ZJirpCO3DK0VL/VOR7kTG/azhaK/DagJasJUtERGQkdf/bYgNUWKTE/it3dZbH3tGd3nszNRu7zyXCQU/x/092XsSMYZWbUllTvj0QW+bzHo3v/8uehZkJBuuZRjbcz7XM16Vk5SPiUhIe9m+qN+ipb7r2w18eBKC6qCsoUqLl7O0AgJ+fD0Tvlvov/NWUSoG/o25hbnHX4VkbVVlJchnw5qBWZb62Jny665Lm55H+7pLnnu/VDH8dv6mZcv720FaY0tcXh6+m4IlunrVW80tR6kLZr6ktztzKwJD25U8rfBD5e9ojbkmI0bbn7ViSwZaYUdJd2KeK9TsbOvX55t0Np4ANwKWPhkuCPr8cNjwVWtuIDiV1EtdOCsTWU/F49bcTOuudvJGGIF9HvLO+pEardpAaADp62FfmEKrdjGFtMKy9Kx5fEymp2dts5jZ09rLHusndoTCt+01Otp2Oh5udRbmN0iojMSNXU+cu8moy9l5MQt9WTprPLHUQ2sxEjke7eOB6cjb8Pezh5WCF6ynZGNDGGTKZDC/09kUrVxuYm8gR7Ouo8z5W5qa48OEwyGSoF//X2pjgWL+EX0zCrdQcPBXkBZlMhi/DrpT/IiIiIqqzGHisg6aui8KdzDyd5StCL2FSLx9NYev49BxNrS81O0szpBdnS67aG1NnA4+Nyin8rS97rjJsLMww76F2OHE9Ff+eUjVYsLM0XGNwZfgVLN2paoYScfEOVjzeCUBJlkROfhHW/XfD4OszcguwW6t76NPfHUWgjwNik7Ow6dWeaGovDaTm5Beh7bwderf1v9DLtRZ4zMwtQNePQpFXqjbYp491lDy2MDPB7rf6YP3xm+jf2hnONhZwtgF8naxrcnd1SLJahMAX4wNw4loqHunMjDxjMFRf89mePjW7Iw+Y0jc6Ws3ZjhNzB8OhkTnu3pN+FnwxvrNOqQMAaONqo7MsyFf/NOS315/EXy/1wIYTJRlx3vUgeOzvaY/zC4bpdFWOup6GY3Gp6NmiiWZZYkYughbtQYCXPf5+pWfpTdWKCwkZeOVXVSC4qjcEhBD4478byC0owvv/nJM8V7rT/EMdS24cffpYyWyA7W/0xp3MPPgUN3Izkcsk0/D1sagPHaz10K3xyFBkXXMnMw/x6TlYs++q5nubiVwGz8ZW+FqrlEtnL3tEXU+TvFZdnoGIiIjqJgYe66C2rrbYdjoBABAxvR9+O3odX0eovnR1fH8X1r8UjNi7WXj3L92OwoVFSkwd0AKfF98dVipFnew4Wd4utXDWvYiurOd6NcNzaIb8wmPYdS4RGQam6gghNEFHANgUfRubom9rHj/U0U2SQenvaY+TN9Ik29DXVfZonGpq/OyNp/HDs92gFKpi/zIZDAYd1b7ZdxUv9vEt9xiNKfleHv4Xelkn6Hhl4XDJlEc1UxM5xgd61dTuVZqXgxWaNWmEZvfRHZ0M69/aCeEXSxp7bJvam10+qyhdT2mNgA934+B7A3Dwckn2+77p/eHlaIWfI69pzi8A8OFoP73NQppYK6AwlSOvUIk/pwRj9sbTuJx0DzdTc9B98Z7qOZhqJpPJsPedfuj36V7J8rALSZLAY9Ai1fGduJ6GqOupFcowFELgUEwy/D3tIZfB6J1xS5fruF9CCJ3ga1nGGLjx0khhWumyC/WVdlfrqk613nfpDlo4W8PdntNwjUWpFOi2MFRn+cy/T0seh73dF/lFSmyMuoUpfZrDxsK0TtRvJiIiorI1jG+c9Yy91rRpNztLNCuVifLY6kiDr31rcCs8HeytCTz6ztqG/q2d0NrVFjOGtS6zI3BNOXc7A8uKG4CEdHDDSH83dPZqjLTsAizefh5KAXTzMd40tOEdXFWBxxzdjq4AkJmnf7nav6fiNXffAeDXF4IAAF/suSy5C2/I3ot3JBeJ+gJh04e2xriunpov3gu3ncfjgZ41FtC5kZKNgcsidOpuNrW31Bt0rMvWvxSMuLtZRp3KSCU+fcwfXT4quUBUmNWv8VGf9FwShme06vJ5FU91/2ZiV/h/oLrZ8dVTAZIp1qVFTO+Pu/fy4NfUDt9O7KrTBbs+cre3hIutQjLd/7sDsfjOQAmPR746hMsLh5cboPjr+E1M17qh5+lgiaWP+qOTp71RMv0UWtvILSi6723G3tUtu2LIhpeD6+TNxxonyXi8v01k5RWi/fydmsels1ZzC4qQX6Ss0ud2xKU7sLc0Q0cPO6RmF8BaYYrU7HwM/2w/Jgb7oKWLNW6kZGNiD596m32qzz+nbpe7znM9m2lmVcwcXvnmg0RERFR7GHisg6y0vkyam8oxtosHlu68iGQ9TQfUvnmmKxSmcvRppVtXMPziHYRfvIOBbZ3rRAfQ9/85q/l5mJ8rhvmpLppdbC2wdlKg0d9PfRGgL+Mx/GISJpWallaWOSFtNYX0Z45oqzfw6ONoBU8HK+y/rFunE5BeNLZyscaut0q6j/dt5YSIS6pssl8PX9fbUMFYbqXlYObfpzHK3x1vr9dtvNPFu7HeJj11XTcfhzoxzh9UjtYKLB/nj2l/qsaMKYMaRhXo4yDJZvwp8hoAIKRjSXDRztIMsYtHILdAWW5jIlc7C7jaqZoueDs2gkdjS50O0Rtf6WGs3a8R5qZyHJ45EEqhOp+q6xuWpeXs7bi6aIQkCPfX8ZtYsv0CZo1oAyEgCToCwI2UHDyx5jDaudli69ReVb5x10jrdzVr42ksH9dJ8zgztwA2FQxY6Ws2NKG7F17p1wLzNp9F6PlEtHG1wZqnu2qC1Q2dwlQOmUwVdEzPKZA0H6uotYfiJI8PxdxFj+ZNkJlbgAOX7+Ll4mn05xYMrVC2bG5BUfF+yaBUCqyKiJHMvihtRWhJ3eXF2y8gdvGIOnEzWZ/cgiK8+NMxuNhaYOmjHXX2Mye/CDvOxmNYezcUCYGTN9IlzzvbKJCkVXIosJkD5o1sVyP7TkRERMbHwGMd9JC/Gw5cuYs+rVTTxsxM5IicORCt5mzXu/6knj46zVS0A1hqG47fLDcgE5+egzuZedXWWODfU7dxNLbkorpPOQ1YjMG2uLbjqZvp+Ov4Tfzx33UMaeeKF/v4SoKObVxtcCEhU/LaD0e1x9zNJYHS0aWmrG2b2hsjPt8vWdbC2RrfTuyGIqXA+DWHJUGE0ra81kvyePm4kmyyFbsvYXIfX1xIyICNwsxoF5BCCBQqBXouCQOgmjZWWtTcwWisp2EREQA80rkpdpxJQHZ+EbwcGNioqqOzBmLf5bt4qKMbLMxMEHPnHgYukwbTztySXpjLZLL76oZ+YMYAHLpyF2+vP4nZIW0l9f/qE5lMBhOZ6nz77+u98NAXB8p9zdf7rmpu5kz7I1rTlV0dRDfkXHwGms3chpPzh5RZK7g8BUUlqXZ/n7iFdm628HZshOR7eXjv79N4b3gbvNS37JtNyffyEHo+SfP4YX933EjNxryH2sPcVI5vJ3a97/17kFmYmWgyHTecuFnu/7O21Kx8zNtyVqcq5JPfHMHfr/TA7I1ncD4+Q7P86p0s+DW1K3Ob15KzMOx/+xHS0Q1yGRB6PklvR/uybDudILkhUdsycguw8N/zuJSUCWuFqebm61/Hb+KTsR0xrpsnANV3EHW5mbeg+7cX0sENy8b5o6BIifPxmejq3ZhZu0RERPWcTIj7nXRSezIyMmBnZ4f09HTY2jac6RY/R8ZJgmCdveyx8skAvXWGjsamYNzXulOyP33MH6M7uRucPuvz3lbNz9um9kY7d+P+/z77w1HsLa4P92r/5pg+tPqb31xJysSg5ft0lu9+qw8GryhZHjV3MH47el2TcXB+wTBYmpsgMiYZ4785DF+nRtgzra/OnfvcgiIUKgUS0nPx/cFYvNKvOTwaW2meazNXfz3HyJkD4Gan+7vr+lGoTkMJQNUpOyUrHyM6uFWpptGUn49hp1YjHG0zh7fB4HYutd4khqihO3D5LiZ8d0Tz+LEuHlj6WP3LQK4plxIzMaT4fD6orTNCzydh/sh2aO5kjWe+P6pZL2ruYHT+cPd9vUfvlk3w8/NB972P+y7dkeyLPuU1nfnhYCw+0GomY8yu9Q867e83bVxt0KN5E71ZdBcSMhB3NwtD27siNbsAgQtDUajU/1XZ1sIUGbnSci3b3+iNtm5lf3d6f8tZnQzKyrJRmOL0B0OrtI2qSs3KR2p2PhpbmZf7dzVzeBsMbOuCvMIihHyu/0bBD5O6ldvgiIiIiIyrJuJrzHisR54O9oGjtULTFfP3F7sbrPHT3KkR5DJAKYDJfXyxpnhK8DvrTyIrrxATe/gAUN15LigSMDfVDWSN+Hw/mtpbYu2kbmjpUvVmLwDgYKXKolOYyvHOkNZG2WZ5mhsIomkHHde/FIzGjczxav8WkMtk6OrTWJNNFNzcEZEzB8De0lzvtCb176CFszUWPdJB57mfnw/EuqM38OFoPwQUfzEfG+ChN+gIqOqK6Qs8Pv2d6oL11M10zH2oclOOdpxJQCsXaxyKSdYbdHwqyAsLS+07EdWeXi2bYOvUXpoL9I8e8avlParbWrnYGAzC/fVSMB4tro1cXnCkf2snuNpZ4LGungjwaoyfIuMwr/iG3/7Ld5GdX3jfTWcKlcryVyrHn8dKOpGvm9y9yttrqC4kZOJCQibGBDSVZCcKITDsf6pZDG3dbJGdX2gw6AhAJ+gIADkFRWW+d0GRssJBx51v9oG1hSkUpnI8uuoQ4pKzMaWPL77edxWZeYXYdjq+zBqv1enP/27g3Q26TQ4NWbz9AhZvv2Dw+UAfBwYdiYiIHlDMeKxnipQCMzacQhfvxuV2FE7PLoDCTA4LMxPJnX4AiFk0Agu3nsf3B1XF+Fs4W+OTRztizFeH9G4r/J1+Ve4OnJlbgDfXRWPPhSQsHtOhRjsif7v/Kj7aqlsXS62maiXdyyvE5uhbGN2pqcFuotqZO/ooTOW4+NHwCr9nxKU7mKgny2ZMQFN8NNoP5+MzEODVuM7WiiIiqqrSn4EA8GwPHxQUKfHrkesAgD1v99V7o2r3uUS8+NMxAEDE9H7wLtXw7V5eIb4IuwwvByuM6+ppMCN9c/QtvLEuGoBuLU+17r4OmPtQO7R3VwXDhBD44J9zWHsoDj2aO+JQTDIAVb3hF3r7VvDoCVA1titdGgUA/ps9SFPzUXtWhiHudhY4+N6AMjuLT+nri+d7NYOTtULz2XotOQs/R17DtwaaIAGq36uluQlmbzyDH57thv5tdANxpbua11TWa36hEnmFRcjOL9J0jS9P3JIQPPnNYc241bZ6QgCUAkhIz8Xgdi5oYq24r/IRREREVDU1EV9j4LGBaDl7m6S+VFN7S9xKyynjFbpaOFtj8ZgOMDORw8vBCg4VrAF46MpdPPntEcmy8jqxGtvZ2+kGp/b8OSUYgc3qXjOSIqVA27k7dDpNA8CZD4Zi8k/HcD0lGyse72Swdufney5j+e5LOsv9PeywuVR9SSKiB1W3haG4o9Ws4vcXuyOomQNSs/OxaNsFjOvqgSBfR4OvVwcuZ41og8l9muN8fAacbRSwtjBF6znSchqH3huARuamsLOS1oOcvv4k1h+/iZH+7vhifGcAqvP8+fgMnRqVL/drjneHtsayXZfwZfgVnf05MXdwhT+DSaWs0idxS0KQV1ik87vUZ/dbfdDSxQZ9l4bjWnI2ANWNvJSsfL1By3MLhiIpIw/9Pt2r85xHY0tM7uOLbj4OuJWag0Gl6nUbMuHbIzhwpaSBnbejFfa+069abiDmFRZh6Ip9iCs+1rIcnzMIMXeyMO7rSKx8MgAhHd1QpBSYui4KW0/Fl+x/dy98NJqzLIiIiOoCBh4NYOCx8g5euYunSgX/qqqLd2P89VJwmV907+UVwm/+Tp3lx+cMgqN15btKVsWmqFvYHH0L4cUXBk9088S8ke3ue9pcTbiTmYfU7Hycu52Bf0/FI/S8/tqMw/1csWpCFwCqi6vpf52CR2NLrNobI1nP38MOg9q64MU+vgan6RMRPWgKi5RoMbukQVtls9zbzt2hmULbq0UTSdCnLH+/0gOdPe0x/a9T+Ou4apr0Z090wqhOJY3KipQCzWfpZs8teqQDZm08rbO8pm/cPUhK105VC53Wt8zu6P1aO+HRLh4Y2MZFk5WXV1iE7Lwi2FqawUQuw8kbaRi18mCF9+XkvCE6wemKSsrIRWCprMMtr/U02BjwUmImPt5+Aa8NaAF/D/tKNWvZcvI2pv4epfe52SPaYnA7F/x57Aae7eEDZ1uLMreVlVeICwmZ6ORpDxM2jCEiIqoTGHg0gIHH+1d62m1IRzdciM9AzJ0sdGhqh1Gd3NHFuzHyCpXYeOIW5o1sh83Rt/Ve/Ki1drHBjjd7672IO34tFWNXSadv//hcIPq2qv5u1oaEX0jCP6du48NRfganO9dV+qYLqj0T7I2xAR56L3wCvOzx9ys9q3PXiIjqtK8jYjQ15io7PXVl+BVN47HKerSLhyboCAAbXu6BLt6NJevoO7d7OVjhekpJlpmlmQkmdPfC7JDK1fglqay8QqTlFMDSzERTd7m0/e/2x+20HEReTcbYAA94OlhVaNtXku6hsZUZunwUanCdh/3d8dkTnaqUnVh6ujUAjOrkjpSsfGTnF+Hz8Z3RtLjxoL5MzxWP+2NkR2mjway8QtzLK4SLrQWEEJr9+3TnRZ2sWwszOc4vGMYSLURERA8ABh4NYOCxarQvcCp68RV7Nwv99UwTUuvu64BvJ3aDdXEg7+CVu7iXV4iE9FzM36IqzH9l4XCD3bSpYkpf/H77TFe8UFx7zBDtaX1ERA3Z1lPxsLcyQ88WTSr92j3nE/H8j/rPt28MbIk3B7XEvM1n8fPha2VuR99n4b+nbuO136IQ0sENPVs00bnZp57iTcalL+D7UEc3fPlkQJW3XVCkREutLFvAcB3R+/HrkWuYvfGMUbZliJ2lGbp4N0bYhSTMHtEWLZytsetcImaHtNV83yMiIqL6jYFHAxh4rJoBn+7F1btZaO1ig51v9anw69Rf0O0szbDsMX+DAa9BbV10pgTPGNYGL/fjRZMxqIOPQc0c8MeUYKRnF8B/wS6961qYybHp1Z5o48q/EyKiqkrJykfAh7vRydMePz0fCJPijC992fPXk7PRZ2m4ZNnRWQPLnY6qL0NNuwEKGU/pm3k9Wzji1xeM1y08Pj0HwYvDAKimJb/Yx/gNgV759Ti2nU4w+nZL++3FIPRoXvmAPREREdVtDDwawMBj1ew6m4B9l+9g6sCWcLYp+wJI25WkTCzZfgFTB7ZERw97xN7NwoX4DMzceBpp2QVlvnbvO/3gU8Wu2FTiUmImmtpbai52c/KL8OsRVZZNZy97BHg1RqFSoEgpWMuRiKiWRF1PxSNfqcqNDGrrgm8ndq3Q60LPJUpu7nHGQPXIyS9Ct4WhuJdXCAC4vHC4wa7kVaFUikrVVayscasjcTQuBc2dGsHawgwnb6RJnnexVeDAjAEwM5Hj+LUUPLo6EqW//TvZKGBhJoe3QyOdGqbudhbYP2MA6zISERE9gBh4NICBx7rnh4Ox+OCfc3qf8/e0x+ZXWV+QiIgaFiEEVuy+hMiryVg1oQuaVKKpWvjFJCzfdQkv9G4maUZDVFH5hUqkZOXD1a7sm8z6AqN3MvPw2OpDKBICm1/txS7qREREDygGHg1g4LHuOh+fgZFfHICluQn2TOuLG6nZ6OzZuFrv9BMRERERERERUeXURHyNlaHJqNq62WL3tL5opDCBs41FubWsiIiIiIiIiIjowcTAIxldM9ZyJCIiIiIiIiJq8FipnIiIiIiIiIiIiIyOgUciIiIiIiIiIiIyuloNPK5cuRI+Pj6wsLBAUFAQjh49Wpu7Q0REREREREREREZSa4HHP/74A9OmTcP8+fNx4sQJ+Pv7Y+jQoUhKSqqtXSIiIiIiIiIiIiIjqbXA4/Lly/Hiiy9i0qRJaNeuHVavXg0rKyt8//33tbVLREREREREREREZCS1EnjMz8/H8ePHMWjQoJIdkcsxaNAgREZG6qyfl5eHjIwMyT8iIiIiIiIiIiKqu2ol8Hj37l0UFRXBxcVFstzFxQUJCQk66y9evBh2dnaaf56enjW1q0RERERERERERHQf6kVX65kzZyI9PV3z78aNG7W9S0RERERERERERFQG09p40yZNmsDExASJiYmS5YmJiXB1ddVZX6FQQKFQ1NTuERERERERERERURXVSsajubk5unTpgj179miWKZVK7NmzB8HBwbWxS0RERERERERERGREtZLxCADTpk3DxIkT0bVrVwQGBuJ///sfsrKyMGnSpNraJSIiIiIiIiIiIjKSWgs8Pv7447hz5w7mzZuHhIQEdOrUCTt27NBpOENERERERERERET1j0wIIWp7JyorIyMDdnZ2SE9Ph62tbW3vDhERERERERERUb1SE/G1etHVmoiIiIiIiIiIiOoXBh6JiIiIiIiIiIjI6Bh4JCIiIiIiIiIiIqNj4JGIiIiIiIiIiIiMjoFHIiIiIiIiIiIiMjoGHomIiIiIiIiIiMjoGHgkIiIiIiIiIiIio2PgkYiIiIiIiIiIiIyOgUciIiIiIiIiIiIyOgYeiYiIiIiIiIiIyOgYeCQiIiIiIiIiIiKjM63tHbgfQggAQEZGRi3vCRERERERERERUf2jjqup42zVoV4GHjMzMwEAnp6etbwnRERERERERERE9VdmZibs7OyqZdsyUZ1hzWqiVCpx+/Zt2NjYQCaTVfv7ZWRkwNPTEzdu3ICtrW21vx81HBxbVF04tqi6cGxRdeHYourE8UXVhWOLqgvHFlUX7bFlY2ODzMxMuLu7Qy6vnmqM9TLjUS6Xw8PDo8bf19bWln/wVC04tqi6cGxRdeHYourCsUXVieOLqgvHFlUXji2qLuqxVV2ZjmpsLkNERERERERERERGx8AjERERERERERERGR0DjxWgUCgwf/58KBSK2t4VesBwbFF14dii6sKxRdWFY4uqE8cXVReOLaouHFtUXWp6bNXL5jJERERERERERERUtzHjkYiIiIiIiIiIiIyOgUciIiIiIiIiIiIyOgYeiYiIiIiIiIiIyOgYeCQiIiIiIiIiIiKjY+CRiIiIiIiIiIiIjK7BBB737duHkSNHwt3dHTKZDJs2bZI8n5iYiGeffRbu7u6wsrLCsGHDcPnyZck6CQkJePrpp+Hq6opGjRohICAAGzZskKyTkpKCp556Cra2trC3t8fzzz+Pe/fuVffhUS2qqbHl4+MDmUwm+bdkyZLqPjyqRcYYWzExMXjkkUfg5OQEW1tbjBs3DomJiZJ1eN5qeGpqbPG81fAsXrwY3bp1g42NDZydnTF69GhcvHhRsk5ubi5effVVODo6wtraGmPHjtUZO9evX0dISAisrKzg7OyM6dOno7CwULLO3r17ERAQAIVCgRYtWmDt2rXVfXhUi2pqbO3du1fnvCWTyZCQkFAjx0k1z1hja+rUqejSpQsUCgU6deqk971OnTqF3r17w8LCAp6envjkk0+q67CoDqipsRUXF6f3vHX48OHqPDyqZcYYXydPnsT48ePh6ekJS0tLtG3bFp999pnOe1X1O1eDCTxmZWXB398fK1eu1HlOCIHRo0fj6tWr2Lx5M6KiouDt7Y1BgwYhKytLs94zzzyDixcvYsuWLTh9+jTGjBmDcePGISoqSrPOU089hbNnz2L37t34999/sW/fPkyePLlGjpFqR02NLQBYsGAB4uPjNf9ef/31aj8+qj1VHVtZWVkYMmQIZDIZwsLCcPDgQeTn52PkyJFQKpWabfG81fDU1NgCeN5qaCIiIvDqq6/i8OHD2L17NwoKCjBkyBDJZ95bb72Ff/75B+vXr0dERARu376NMWPGaJ4vKipCSEgI8vPzcejQIfz4449Yu3Yt5s2bp1knNjYWISEh6N+/P6Kjo/Hmm2/ihRdewM6dO2v0eKnm1NTYUrt48aLk3OXs7Fwjx0k1zxhjS+25557D448/rvd9MjIyMGTIEHh7e+P48eNYunQp3n//faxZs6bajo1qV02NLbXQ0FDJeatLly5GPyaqO4wxvo4fPw5nZ2f88ssvOHv2LGbPno2ZM2fiyy+/1KxjlO9cogECIDZu3Kh5fPHiRQFAnDlzRrOsqKhIODk5iW+++UazrFGjRuKnn36SbMvBwUGzzrlz5wQA8d9//2me3759u5DJZOLWrVvVdDRUl1TX2BJCCG9vb7FixYpq23eq2+5nbO3cuVPI5XKRnp6uWSctLU3IZDKxe/duIQTPW1R9Y0sInrdIiKSkJAFARERECCFU48TMzEysX79es8758+cFABEZGSmEEGLbtm1CLpeLhIQEzTqrVq0Stra2Ii8vTwghxLvvvivat28vea/HH39cDB06tLoPieqI6hpb4eHhAoBITU2tuYOhOuV+xpa2+fPnC39/f53lX331lWjcuLFmrAkhxIwZM0Tr1q2NfxBUJ1XX2IqNjRUARFRUVHXtOtUDVR1faq+88oro37+/5rExvnM1mIzHsuTl5QEALCwsNMvkcjkUCgUOHDigWdajRw/88ccfSElJgVKpxLp165Cbm4t+/foBACIjI2Fvb4+uXbtqXjNo0CDI5XIcOXKkZg6G6hRjjS21JUuWwNHREZ07d8bSpUt1pp1Rw1GRsZWXlweZTAaFQqFZx8LCAnK5XLMOz1tUmrHGlhrPWw1beno6AMDBwQGA6s56QUEBBg0apFmnTZs28PLyQmRkJADVealDhw5wcXHRrDN06FBkZGTg7NmzmnW0t6FeR70NevBV19hS69SpE9zc3DB48GAcPHiwug+H6pD7GVsVERkZiT59+sDc3FyzbOjQobh48SJSU1ONtPdUl1XX2FJ7+OGH4ezsjF69emHLli3G2WmqN4w1vtLT0zXbAIzznYuBR5T858+cOROpqanIz8/Hxx9/jJs3byI+Pl6z3p9//omCggI4OjpCoVBgypQp2LhxI1q0aAFAVaev9DQMU1NTODg4sC5MA2WssQWoanusW7cO4eHhmDJlChYtWoR33323Ng6L6oCKjK3u3bujUaNGmDFjBrKzs5GVlYV33nkHRUVFmnV43qLSjDW2AJ63GjqlUok333wTPXv2hJ+fHwDVOcfc3Bz29vaSdV1cXDTnnISEBElgSP28+rmy1snIyEBOTk51HA7VIdU5ttzc3LB69Wps2LABGzZsgKenJ/r164cTJ05U81FRXXC/Y6siKjL+6MFVnWPL2toay5Ytw/r167F161b06tULo0ePZvCxATHW+Dp06BD++OMPSdktY3znMq3EsTywzMzM8Pfff+P555+Hg4MDTExMMGjQIAwfPhxCCM16c+fORVpaGkJDQ9GkSRNs2rQJ48aNw/79+9GhQ4daPAKqq4w5tqZNm6ZZv2PHjjA3N8eUKVOwePFiSdYRNQwVGVtOTk5Yv349Xn75ZXz++eeQy+UYP348AgICIJfzvhPpZ8yxxfNWw/bqq6/izJkzOlmwRFVVnWOrdevWaN26teZxjx49EBMTgxUrVuDnn382+vtR3cLzFlWX6hxbTZo0kXzn6tatG27fvo2lS5fi4YcfNvr7Ud1jjPF15swZjBo1CvPnz8eQIUOMuHcMPGp06dIF0dHRSE9PR35+PpycnBAUFKSZfhgTE4Mvv/wSZ86cQfv27QEA/v7+2L9/P1auXInVq1fD1dUVSUlJku0WFhYiJSUFrq6uNX5MVDcYY2zpExQUhMLCQsTFxUm+IFPDUd7YAoAhQ4YgJiYGd+/ehampKezt7eHq6gpfX18A4HmL9DLG2NKH562G47XXXtM0q/Lw8NAsd3V1RX5+PtLS0iR34BMTEzXnHFdXVxw9elSyPXUHRu11Snf9TExMhK2tLSwtLavjkKiOqO6xpU9gYCADUQ1AVcZWRRg6b6mfowdXdY8tfYKCgrB79+4qbYPqB2OMr3PnzmHgwIGYPHky5syZI3nOGN+5mPJSip2dHZycnHD58mUcO3YMo0aNAgBkZ2cDgE6WkImJiaaDZ3BwMNLS0nD8+HHN82FhYVAqlQgKCqqhI6C6qipjS5/o6GjI5XJ2WSSDY0tbkyZNYG9vj7CwMCQlJWnufvK8RWWpytjSh+etB58QAq+99ho2btyIsLAwNGvWTPJ8ly5dYGZmhj179miWXbx4EdevX0dwcDAA1Xnp9OnTkpsiu3fvhq2tLdq1a6dZR3sb6nXU26AHT02NLX2io6Ph5uZm5COiusIYY6sigoODsW/fPhQUFGiW7d69G61bt0bjxo2rfiBU59TU2NKH560Hn7HG19mzZ9G/f39MnDgRCxcu1Hkfo3znqnAbmnouMzNTREVFiaioKAFALF++XERFRYlr164JIYT4888/RXh4uIiJiRGbNm0S3t7eYsyYMZrX5+fnixYtWojevXuLI0eOiCtXrohPP/1UyGQysXXrVs16w4YNE507dxZHjhwRBw4cEC1bthTjx4+v8eOlmlMTY+vQoUNixYoVIjo6WsTExIhffvlFODk5iWeeeaZWjplqRlXHlhBCfP/99yIyMlJcuXJF/Pzzz8LBwUFMmzZNsg7PWw1PTYwtnrcappdfflnY2dmJvXv3ivj4eM2/7OxszTovvfSS8PLyEmFhYeLYsWMiODhYBAcHa54vLCwUfn5+YsiQISI6Olrs2LFDODk5iZkzZ2rWuXr1qrCyshLTp08X58+fFytXrhQmJiZix44dNXq8VHNqamytWLFCbNq0SVy+fFmcPn1avPHGG0Iul4vQ0NAaPV6qOcYYW0IIcfnyZREVFSWmTJkiWrVqpfmcVXexTktLEy4uLuLpp58WZ86cEevWrRNWVlbi66+/rtHjpZpTU2Nr7dq14rfffhPnz58X58+fFwsXLhRyuVx8//33NXq8VLOMMb5Onz4tnJycxIQJEyTbSEpK0qxjjO9cDSbwGB4eLgDo/Js4caIQQojPPvtMeHh4CDMzM+Hl5SXmzJmj+UNWu3TpkhgzZoxwdnYWVlZWomPHjuKnn36SrJOcnCzGjx8vrK2tha2trZg0aZLIzMysqcOkWlATY+v48eMiKChI2NnZCQsLC9G2bVuxaNEikZubW5OHSjXMGGNrxowZwsXFRZiZmYmWLVuKZcuWCaVSKVmH562GpybGFs9bDZO+cQVA/PDDD5p1cnJyxCuvvCIaN24srKysxCOPPCLi4+Ml24mLixPDhw8XlpaWokmTJuLtt98WBQUFknXCw8NFp06dhLm5ufD19ZW8Bz14ampsffzxx6J58+bCwsJCODg4iH79+omwsLCaOkyqBcYaW3379tW7ndjYWM06J0+eFL169RIKhUI0bdpULFmypIaOkmpDTY2ttWvXirZt2worKytha2srAgMDxfr162vwSKk2GGN8zZ8/X+82vL29Je9V1e9csuIdJiIiIiIiIiIiIjIa1ngkIiIiIiIiIiIio2PgkYiIiIiIiIiIiIyOgUciIiIiIiIiIiIyOgYeiYiIiIiIiIiIyOgYeCQiIiIiIiIiIiKjY+CRiIiIiIiIiIiIjI6BRyIiIiIiIiIiIjI6Bh6JiIiIiIiIiIjI6Bh4JCIiIiIiIiIiIqNj4JGIiIiIiIiIiIiMjoFHIiIiIiIiIiIiMrr/AxKTiNcQea0kAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "moving_average100 = data.Close.rolling(100).mean()\n",
        "moving_average200 = data.Close.rolling(200).mean()"
      ],
      "metadata": {
        "id": "i4F2HKI835Ni"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(data['Close'], label=\"closing price\")\n",
        "plt.plot(moving_average100, 'r', label=\"moving average 100\")\n",
        "plt.plot(moving_average200, 'g', label=\"moving average 200\")\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 713
        },
        "id": "oFWXBEc14NRT",
        "outputId": "6e822994-3b7a-4611-a6d1-b50b071f69ba"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7e99abd608e0>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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71PqWW27RlVdeqaeeekpZWVlKTU3VrFmz5HA4zHsOHTqkqVOnKjo6Wn379tWbb76pPn366M9//nO9v2/16tW64IILlJaWpsTERJ1zzjn65ptvzOs33HCDvv/97wd8xuFwKC0tTW+88YYkY5n/k08+qb59+yo6OlojR47UO++8Y96/ePFiWSwWffrppxozZowiIyO1dOlS7dq1S1dccYUyMjIUFxencePGacGCBQHPasxvOnbsmG6//XZ169ZNCQkJmjRpkjZs2FDvb5akX//617r33ns1fPjwkNe/+OILbdmyRf/85z81atQoXXzxxXr88cf13HPPqaamRpL04osvqm/fvvrjH/+ooUOH6q677tK1116rp59+usFnAwAAAMDJzj9srHbS8Qh0Nl0jePR4pPLytv9TT2dofebMmaO0tDStWrVKd999t370ox9p2rRpmjBhgr755htdeOGFuummm1RRUSFJOnjwoC655BKNGzdOGzZs0AsvvKBXX31Vv/nNbyRJ06ZN09GjR7Vo0SLzGUVFRfrss880ffr0eutYtGiRdu3apUWLFmnOnDl6/fXXA5b2zpgxQ3l5eVq8eLHeffddvfzyywEDg0IpKyvTzTffrKVLl+rrr7/WwIEDdckll6isrEySNH36dH300Uey2+3mZz7//HNVVFToqquukiQ9+eSTeuONN/Tiiy9q8+bNuvfee3XjjTdqyZIlAc/6+c9/rt/97nfaunWrRowYIbvdrksuuUQLFy7UunXrdNFFF+myyy5Tbm5uk37TtGnTVFhYqE8//VRr167V6NGjdf7556uoqKjB396QFStWaPjw4crIyDDPTZkyRaWlpdq8ebN5z+TJkwM+N2XKFK1YsaLZzwUAAACAk4HL7783u5v2X6EBdADttwa1LVVUSO2xnNhul2JjG337yJEj9fDDD0uSHnroIf3ud79TWlqaZs6cKUmaPXu2XnjhBW3cuFHjx4/X888/r+zsbD377LOyWCwaMmSI8vLy9LOf/UyzZ89WcnKyLr74Yr355ps6//zzJUnvvPOO0tLSdN5559VbR3Jysp599llZrVYNGTJEU6dO1cKFCzVz5kxt27ZNCxYs0OrVqzV27FhJ0t/+9jcNHDiwwd82adKkgPcvv/yykpKStGTJEl166aWaMmWKYmNj9f777+umm26SJL355pu6/PLLFR8fr+rqaj3xxBNasGCBOSW9X79+Wrp0qV566SWdc8455nc/9thjuuCCC8z3KSkpGjlypPn+8ccf1/vvv6///ve/uuuuuxr1m5YuXapVq1apsLDQHIT01FNP6YMPPtA777yjO+64o8HfX5/8/PyA0FGS+T4/P7/Be0pLS1VZWano6OhmPRsAAAAA2pvbL3isb1s3AB1X1+h47CBGjBhhHlutVqWmpgYs0fWFT75OvK1btyonJ0cWi8W8Z+LEibLb7Tpw4IAko5Pw3XffVXV1tSRp7ty5uu666xQWVv9/9KeccoqsVqv5Pisry3zm9u3bZbPZNHr0aPP6gAEDlJyc3OBvKygo0MyZMzVw4EAlJiYqISFBdrvd7Dq02Wz63ve+p7lz50qSysvL9eGHH5qdmTt37lRFRYUuuOACc1/KuLg4vfHGG9q1a1fAs3zhoY/dbtf999+voUOHKikpSXFxcdq6dav57Mb8pg0bNshutys1NTXg+Xv27Al6PgAAAACgcdx+bY5uWh6BTqdrdDzGxBjdh+3x3CYIDw8PeG+xWALO+QLGuns2NuSyyy6Tx+PRvHnzNG7cOH311VfH3RswVB1NeWYoN998s44ePaq//OUv6t27tyIjI5WTk2PuYygZIek555yjwsJCzZ8/X9HR0broooskyVyCPW/ePPXo0SPgu30diD6xdbpM77//fs2fP19PPfWUBgwYoOjoaF177bUBzz4eu92urKwsLV68OOhafdOqGyMzM1OrVq0KOFdQUGBe8736zvnfk5CQQLcjAAAAgA7NP2skdwQ6n64RPFosTVry3FEMHTpU7777rjwejxlKLlu2TPHx8erZs6ckY9L41Vdfrblz52rnzp0aPHhwQGdfUw0ePFhOp1Pr1q3TmDFjJBndiMXFxQ1+btmyZXr++ed1ySWXSJL279+vI0eOBNwzYcIEZWdn69///rc+/fRTTZs2zQxBhw0bpsjISOXm5gYsq26MZcuW6ZZbbjH3irTb7dq7d2+TftPo0aOVn58vm82mPn36NOn5DcnJydFvf/tbFRYWKj09XZI0f/58JSQkaNiwYeY9dSfBz58/31xyDgAAAAAdlcu/45Gl1kCnw1LrDuzHP/6x9u/fr7vvvlvbtm3Thx9+qEceeUT33XdfwFLq6dOna968efr73//e4FCZxhgyZIgmT56sO+64Q6tWrdK6det0xx13KDo6OmDJd10DBw7UP/7xD23dulUrV67U9OnTQ3br3XDDDXrxxRc1f/78gFrj4+N1//33695779WcOXO0a9cuffPNN3rmmWc0Z86cBmseOHCg3nvvPa1fv14bNmzQDTfcENDB2ZjfNHnyZOXk5OjKK6/UF198ob1792r58uX65S9/qTVr1tT77NzcXK1fv165ublyuVxav3691q9fb3ZwXnjhhRo2bJhuuukmbdiwQZ9//rkefvhhzZo1y+zkvPPOO7V79249+OCD2rZtm55//nn95z//0b333tvg7wYAAACAk50nYI/HdiwEQKsgeOzAevTooU8++USrVq3SyJEjdeedd+q2224zB9T4TJo0SSkpKdq+fbtuuOGGE37uG2+8oYyMDJ199tm66qqrNHPmTMXHxysqKqrez7z66qsqLi7W6NGjddNNN+mee+4xO/z8TZ8+XVu2bFGPHj00ceLEgGuPP/64fvWrX+nJJ5/U0KFDddFFF2nevHnq27dvg/X+6U9/UnJysiZMmKDLLrtMU6ZMCer6PN5vslgs+uSTT3T22Wfr1ltv1aBBg3Tddddp3759QYNf/M2ePVunnXaaHnnkEdntdp122mk67bTTzLDSarXq448/ltVqVU5Ojm688UbNmDFDjz32mPkdffv21bx58zR//nyNHDlSf/zjH/W3v/1NU6ZMafB3AwAAAMDJrrjCYR7P23SoHSsB0Bosng44Nqq0tFSJiYkqKSlRQkJCwLWqqirt2bNHffv2bTAIQ8s5cOCAsrOztWDBAnN6dkfX0X8T/xwAAAAAONlV1rg0dPZnAef2/m5qO1UDdD0N5WstpWvs8YgW9eWXX8put2v48OE6dOiQHnzwQfXp00dnn312e5fWbJ3xNwEAAADAyWxfUXl7lwCglRE8oskcDod+8YtfaPfu3YqPj9eECRM0d+7coGnYHUln/E0AAAAAAADtieARTTZlypROt79gZ/xNAAAAAHAy23ukor1LANDKGC4DAAAAAADalMfj0Z3/XNveZQBoZQSPAAAAAACgTTndHW7OLYBmIHgEAAAAAABtyuFyt3cJANoAwSMAAAAAAGhTDmfojkcngSTQqRA8AgAAAACANlXtcoU8v3TnkTauBEBrIngEAAAAAABtqr6J1tVOOh6BzoTgsYt79NFHNWrUqPYuAwAAAADQhdz/9oaQ590MnQE6FYLHLu7+++/XwoUL27uMTmnv3r267bbb1LdvX0VHR6t///565JFHVFNTE3Dfxo0bddZZZykqKkrZ2dn6/e9/H/Rdb7/9toYMGaKoqCgNHz5cn3zySVv9DAAAAABocXnHKkOed3kIHoHOhOCxi4uLi1Nqamp7l9EqPB6PnE5nuz1/27Ztcrvdeumll7R582Y9/fTTevHFF/WLX/zCvKe0tFQXXnihevfurbVr1+oPf/iDHn30Ub388svmPcuXL9f111+v2267TevWrdOVV16pK6+8Ut9++217/CwAAAAAOGHOejobyR2BzoXg8SRx7rnn6u6779ZPf/pTJScnKyMjQ6+88orKy8t16623Kj4+XgMGDNCnn34a8LklS5bo9NNPV2RkpLKysvTzn//cDNtefvllde/eXW534B4ZV1xxhX7wgx9ICl5qfcstt+jKK6/UU089paysLKWmpmrWrFlyOBzmPYcOHdLUqVMVHR2tvn376s0331SfPn305z//ud7ft3r1al1wwQVKS0tTYmKizjnnHH3zzTfm9RtuuEHf//73Az7jcDiUlpamN954Q5Lkdrv15JNPmh2EI0eO1DvvvGPev3jxYlksFn366acaM2aMIiMjtXTpUu3atUtXXHGFMjIyFBcXp3HjxmnBggUBz2rMbzp27Jhuv/12devWTQkJCZo0aZI2bAi9PECSLrroIr322mu68MIL1a9fP11++eW6//779d5775n3zJ07VzU1Nfr73/+uU045Rdddd53uuece/elPfzLv+ctf/qKLLrpIDzzwgIYOHarHH39co0eP1rPPPlvvswEAAACgI7JY2rsCAC2pSwSPHo9H5TXlbf7H08T/qWbOnDlKS0vTqlWrdPfdd+tHP/qRpk2bpgkTJuibb77RhRdeqJtuukkVFcYmvAcPHtQll1yicePGacOGDXrhhRf06quv6je/+Y0kadq0aTp69KgWLVpkPqOoqEifffaZpk+fXm8dixYt0q5du7Ro0SLNmTNHr7/+ul5//XXz+owZM5SXl6fFixfr3Xff1csvv6zCwsIGf1tZWZluvvlmLV26VF9//bUGDhyoSy65RGVlZZKk6dOn66OPPpLdbjc/8/nnn6uiokJXXXWVJOnJJ5/UG2+8oRdffFGbN2/WvffeqxtvvFFLliwJeNbPf/5z/e53v9PWrVs1YsQI2e12XXLJJVq4cKHWrVuniy66SJdddplyc3Ob9JumTZumwsJCffrpp1q7dq1Gjx6t888/X0VFRQ3+dn8lJSVKSUkx369YsUJnn322IiIizHNTpkzR9u3bVVxcbN4zefLkgO+ZMmWKVqxY0ejnAgAAAEBHYBHJI9CZ2Nq7gLZQ4ahQ3JNxbf5c+0N2xUbENvr+kSNH6uGHH5YkPfTQQ/rd736ntLQ0zZw5U5I0e/ZsvfDCC9q4caPGjx+v559/XtnZ2Xr22WdlsVg0ZMgQ5eXl6Wc/+5lmz56t5ORkXXzxxXrzzTd1/vnnS5LeeecdpaWl6bzzzqu3juTkZD377LOyWq0aMmSIpk6dqoULF2rmzJnatm2bFixYoNWrV2vs2LGSpL/97W8aOHBgg79t0qRJAe9ffvllJSUlacmSJbr00ks1ZcoUxcbG6v3339dNN90kSXrzzTd1+eWXKz4+XtXV1XriiSe0YMEC5eTkSJL69eunpUuX6qWXXtI555xjfvdjjz2mCy64wHyfkpKikSNHmu8ff/xxvf/++/rvf/+ru+66q1G/aenSpVq1apUKCwsVGRkpSXrqqaf0wQcf6J133tEdd9zR4O+XpJ07d+qZZ57RU089ZZ7Lz89X3759A+7LyMgwryUnJys/P988539Pfn7+cZ8JAAAAAB0JHY9A59IlOh47ihEjRpjHVqtVqampGj58uHnOFz75OvG2bt2qnJwcWfz+zTxx4kTZ7XYdOHBAktFJ+O6776q6ulqSsbT3uuuuU1hY/f/Rn3LKKbJareb7rKws85nbt2+XzWbT6NGjzesDBgxQcnJyg7+toKBAM2fO1MCBA5WYmKiEhATZ7Xaz69Bms+l73/ue5s6dK0kqLy/Xhx9+aHZm7ty5UxUVFbrgggsUFxdn/nnjjTe0a9eugGf5wkMfu92u+++/X0OHDlVSUpLi4uK0detW89mN+U0bNmyQ3W5XampqwPP37NkT9PxQDh48qIsuukjTpk0zg2QAAAAAQKAwgkegU+kSHY8x4TGyP2Q//o2t8NymCA8PD3hvsVgCzvkCxrp7Njbksssuk8fj0bx58zRu3Dh99dVXevrpp5tcR1OeGcrNN9+so0eP6i9/+Yt69+6tyMhI5eTkBEx4nj59us455xwVFhZq/vz5io6O1kUXXSRJ5hLsefPmqUePHgHf7etA9ImNDewyvf/++zV//nw99dRTGjBggKKjo3XttdcGTZduiN1uV1ZWlhYvXhx0LSkpqcHP5uXl6bzzztOECRMChsZIUmZmpgoKCgLO+d5nZmY2eI/vOgAAAAB0FlHh1uPfBKDD6BLBo8ViadKS545i6NChevfdd+XxeMxQctmyZYqPj1fPnj0lSVFRUbr66qs1d+5c7dy5U4MHDw7o7GuqwYMHy+l0at26dRozZowkoxvRtx9hfZYtW6bnn39el1xyiSRp//79OnLkSMA9EyZMUHZ2tv7973/r008/1bRp08wQdNiwYYqMjFRubm7AsurGWLZsmW655RZzr0i73a69e/c26TeNHj1a+fn5stls6tOnT6OfffDgQZ133nkaM2aMXnvttaBO05ycHP3yl7+Uw+Ewf+v8+fM1ePBgs+MyJydHCxcu1E9/+lPzc/PnzzeXnAMAAABAR7b7iUvU7xefSFLAij4AHR9LrTuwH//4x9q/f7/uvvtubdu2TR9++KEeeeQR3XfffQEB1/Tp0zVv3jz9/e9/b3CoTGMMGTJEkydP1h133KFVq1Zp3bp1uuOOOxQdHd3g/4MYOHCg/vGPf2jr1q1auXKlpk+frujo6KD7brjhBr344ouaP39+QK3x8fG6//77de+992rOnDnatWuXvvnmGz3zzDOaM2dOgzUPHDhQ7733ntavX68NGzbohhtuCOjgbMxvmjx5snJycnTllVfqiy++0N69e7V8+XL98pe/1Jo1a0I+9+DBgzr33HPVq1cvPfXUUzp8+LDy8/MD9ma84YYbFBERodtuu02bN2/Wv//9b/3lL3/RfffdZ97zk5/8RJ999pn++Mc/atu2bXr00Ue1Zs0a3XXXXQ3+bgAAAADoCMLCLDq1R4Ikyd3EIa0ATm4Ejx1Yjx499Mknn2jVqlUaOXKk7rzzTt12223mgBqfSZMmKSUlRdu3b9cNN9xwws994403lJGRobPPPltXXXWVZs6cqfj4eEVFRdX7mVdffVXFxcUaPXq0brrpJt1zzz1KT08Pum/69OnasmWLevTooYkTJwZce/zxx/WrX/1KTz75pIYOHaqLLrpI8+bNCxrOUtef/vQnJScna8KECbrssss0ZcqUoK7P4/0mi8WiTz75RGeffbZuvfVWDRo0SNddd5327dsXNPjFZ/78+dq5c6cWLlyonj17Kisry/zjk5iYqC+++EJ79uzRmDFj9P/+3//T7NmzA4bVTJgwQW+++aZefvlljRw5Uu+8844++OADnXrqqQ3+bgAAAADoKMJ8jSzkjkCnYvF4Ot7/nFBaWqrExESVlJQoISEh4FpVVZX27Nmjvn37NhiEoeUcOHBA2dnZWrBggTk9u6Pr6L+Jfw4AAAAAnMwuf3apNh4o0d2TBuj/XThYVzy3TBv2H9OrN4/V+UNDN3cAaFkN5WstpUvs8YiW9eWXX8put2v48OE6dOiQHnzwQfXp00dnn312e5fWbJ3xNwEAAADAySopJkKS1CfVmMfgm2bt7nCtUQAaQvCIJnM4HPrFL36h3bt3Kz4+XhMmTNDcuXODpmF3JJ3xNwEAAADAycrl3XffZjUSR99SaxfJI9CpEDyiyaZMmaIpU6a0dxktqjP+JgAAAAA4WTldRsDoCxzDvQHk17uP6qJTM9utLgAti+EyAAAAAACgTfmmV9vCfMGjEU+8vnyv1u8/1l5lAWhhnTZ47IAzc4AWw//9AwAAADiZOb1Lqq3e4DHSVhtPrN5T1C41tYQap1sLtxaotMrR3qUAJ4VOFzz69uSrqKho50qA9uP7v3/2qAQAAABwMvLt5ejb49HX8ShJHnXcRornFu3UbXPW6Oa/r2rvUoCTQqfb49FqtSopKUmFhYWSpJiYGFm8e0YAnZ3H41FFRYUKCwuVlJQkq9Xa3iUBAAAAQBDfHo/WMCNwDAgeO27uqA/WH5Qkrcs91r6FACeJThc8SlJmprERrS98BLqapKQk858DAAAAADjZ+DoerZbgjseOPNjat2clAEOnDB4tFouysrKUnp4uh4N9FdC1hIeH0+kIAAAA4KTmdLsl1e7xGGHrHEutXR05NQVaQacMHn2sVisBDAAAAAAAJxlfPufb4zHCWtsp2JGXWjsJHoEAnW64DAAAAAAAOLnV7Xhcm1tsXvN04OSRjkcgEMEjAAAAAABoUy7vcBnfnojfHiw1r3Xg3JGOR6AOgkcAAAAAANCmfAFdmCV4GEtHju7oeAQCETwCAAAAAIATVl7t1JfbClTtdB33Xl9AZ7MGB4/uDtzy6HS527sE4KRC8AgAAAAAAE7YrDe/0Q9eX6Nnv9x53HtdnsCl1v46ctNgaZWzvUsATioEjwAAAAAA4IQt3n5YkvTeNwePe69vj0drmBFL/Pjc/ua1rYdKQ37mZNeYTk+gqyF4BAAAAAAALaZPWsxx7/Ht8ejreBzRM8m8Nn9LQavU1do68ApxoNUQPAIAAAAAgBYTG2E77j2VDqM70OoNHjtDtyCDZYBgBI8AAAAAAKDFhJpU7e9wWbV57PQuuS7rBHsjduShOEBrIXgEAAAAAAAt5ji5o6octd2N3ZOiJBkTsSVp0s5VevNfv5BuvbXDrV2m4REIRvAIAAAAAABazPE6Hn3LrBOibLJZjVjCXu1UfHW5Xnz/CU3I3Si9/rq0aFFrl9qiPB0sKAXaAsEjAAAAAAA4IW7/dr/jdDxW1hjBY3xUuHlu8tAMjTmwVRFuvyXXr73WkiW2Ov89Hvt3i23HSoCTB8EjAAAAAAA4IU6/0O04uaMqvMFjVHhtJDEyO0kXb18mSSqJ9IZ2//yn9PTTLVpna/L/O8hKjG7HSoCTB8EjAAAAAAA4IU632zw+3lJr3x6PMXWmXw8+sk+S9Msps6RrrjFO/uxn0v79LVhp65m/paC9SwBOOgSPAAAAAADghDhcfh2Px2l5PFpeI0mKDrfWniwt1ZDDeyVJ27r1kf7zH2nQIMnhkBYvbtliW0mElYgFqIt/KgAAAAAAwAlxNWGp9f1vb5AkrdpbVHty3TpFOWuUF5+mnanZUliYdP75xrWVK1u42taRmRjV3iUAJx2CRwAAAAAAcEKcrtql1u7mDHf2Lqfek9K9tmVyyhTjdf78E6yubfiWkAOoRfAIAAAAAABOiP9gFZenGcnjPmN/x0Px3WrPjR9vvO7YIdntJ1Jem6hyuo9/E9DFEDwCAAAAAIATUunX7eduZMvjgxcNrn3z1VeSpK3d+tSey8iQsrIkj0fatKklymxV/h2PHjWn7RPofAgeAQAAAADACXnwnY3msfM4wWNaXIQk6bzB6bUnd+2SJG3KGhh486hRxus335xwja2tmqXWQBCCRwAAAAAAcELW7is2j4/X8VhRYwR0sRE244THIx06JEkqiEsJvHn0aON13bqWKbQVVTlYag3UZWvvAgAAAAAAQOdR3x6Ph0oq9dyinWbwGBXu7YUqKZHKyyVJhbH1BI9r17ZKrS2pko5HIAjBIwAAAAAAaDGuEB2PVQ6Xcp78MuCcNcw7vXrpUknS/sQMVUZEBX5wzBjj9dtvpepqKTKyxettKdVOgkegLpZaAwAAAACAFhMqeDxcVh10zgwevfs7bsgcGHSPevWSkpMlp1Patq1F62xpx9vbEuiKCB4BAAAAAECLsVc75amz3DpUKGcGj3l5kqTCuvs7SpLFIg0YYBxv3Bh8/STichE8AnURPAIAAAAAgBaz8UCJbn19dcC5dbnFQfeZwePBg5KkgvgQwaMkTZlivH72WYvV2Br8w9V6trkEuhyCRwAAAAAA0KIWbz8c8P6h9zYF3RNm8QaPK1dKkoZNOiP0l/n2edyxo8Xqaw2hlpgDXR3BIwAAAAAAaFXVTnfQOWuYRXK5pN27JUlnXn+ReS1gqfYppxiv69eb069PRvVN8wa6MoJHAAAAAADQbJU1zZvmHG4Nkw4fltxuKSxM1owM81pA8+DAgVJSkuRwSPv3n1ixraisytneJQAnHYJHAAAAAADQbG+tzj3uPZOHpoe+kJ9vvHbrJmu4zTztcNXpkPSFkr77T0LHKmrauwTgpNPk4PHgwYO68cYblZqaqujoaA0fPlxr1qwxr3s8Hs2ePVtZWVmKjo7W5MmTtaPOPgxFRUWaPn26EhISlJSUpNtuu012u/3Efw0AAAAAAGhTFY3oeBzTO3BwTHp8pHGwbp3x2qePbGG1EUXQFOzMTOO1oKDZdba2UjoegSBNCh6Li4s1ceJEhYeH69NPP9WWLVv0xz/+UcnJyeY9v//97/XXv/5VL774olauXKnY2FhNmTJFVVVV5j3Tp0/X5s2bNX/+fH388cf63//+pzvuuKPlfhUAAAAAAGgT5pCYBjjrdDAWllUbB5s3G68TJyrSFibfV1VUGyFeXlmeiiuLpe7djQt797ZEya2irMrR3iUAJx3b8W+p9X//93/Kzs7Wa6+9Zp7r27eveezxePTnP/9ZDz/8sK644gpJ0htvvKGMjAx98MEHuu6667R161Z99tlnWr16tcaOHStJeuaZZ3TJJZfoqaeeUnffv0wAAAAAAMBJz1pPS5PH45HFmyQ66pv47Fs6nZmpsDCL4iJtKqtyqrTKqX98+0c9MP8BhVvDdc+w8fo/ixS2fn3L/4AW8NTn27X7cO3gG+bMAIYmdTz+97//1dixYzVt2jSlp6frtNNO0yuvvGJe37Nnj/Lz8zV58mTzXGJios444wytWLFCkrRixQolJSWZoaMkTZ48WWFhYVq5cmXI51ZXV6u0tDTgDwAAAAAAaH/1dTz6L5d2uYOnWkuqHRaTnS1JSogKlyTllRzRrxb9Sh55VOOq0VOu/+nFsZKOHm2xultKjdOtZxftbO8ygJNSk4LH3bt364UXXtDAgQP1+eef60c/+pHuuecezZkzR5KU7/1fKjL8JlH53vuu5efnKz09cFNZm82mlJQU8566nnzySSUmJpp/sr3/QgIAAAAAAO3LGhY6ePQfEON0hWgBLC+XVq0yjvv3lyTFRxkLM/+Xu1iVzkoNTBmo35z3G0nSw5OkitKTL3iscjZvqjfQFTQpeHS73Ro9erSeeOIJnXbaabrjjjs0c+ZMvfjii61VnyTpoYceUklJiflnv+9/EQEAAAAAAO2q/uCxNmwMGhYjSTt2SFVVUkqK5F0V6QseP9/zniTp0kGX6oGJDygrIlXF0dJXkSffVOuqRgzXAbqqJgWPWVlZGjZsWMC5oUOHKjc3V5KU6Z0yVVBnylRBQYF5LTMzU4WFhQHXnU6nioqKzHvqioyMVEJCQsAfAAAAAADQ/updah3Q8RhiqfVnnxmvAwfKN1Vm9d5iuVWtFQe/lCRdd+p1irBG6NLu50qS/tnjJOx4dNSzjBxA04LHiRMnavv27QHnvvvuO/Xu3VuSMWgmMzNTCxcuNK+XlpZq5cqVysnJkSTl5OTo2LFjWrt2rXnPl19+KbfbrTPOOKPZPwQAAAAAALS9+uao+Hc5BnU8ejzSm28axzfcEHCp0rpSLpUrOyFbY7LGSJKuGni5JGlNarVU336R7aTC4WzvEoCTVpOCx3vvvVdff/21nnjiCe3cuVNvvvmmXn75Zc2aNUuSZLFY9NOf/lS/+c1v9N///lebNm3SjBkz1L17d1155ZWSjA7Jiy66SDNnztSqVau0bNky3XXXXbruuuuYaA0AAAAAQAfj62ZMi4sMON/QHo/DCvdImzZJkZHS9Onm+ZHZSaoIWy5JunbYtbKGWSVJpw09T5K0LU36dvn7Lf8jTsAjH25u7xKAk1aTgsdx48bp/fff17/+9S+deuqpevzxx/XnP/9Z0/3+JfHggw/q7rvv1h133KFx48bJbrfrs88+U1RUlHnP3LlzNWTIEJ1//vm65JJLdOaZZ+rll19uuV8FAAAAAADahC9g7JkcHXDe2cAej2fs32QcnH++lJpqnp/QL0WVVmOF5DVDrzHPZyZn67ziREnSVxs/brniW8DKPUVB5zz19oECXYutqR+49NJLdemll9Z73WKx6LHHHtNjjz1W7z0pKSl609dSDQAAAAAAOizfEJmo8MDeJqffkuiaOns8Xrl5sXEwcWLA+TJnoTyWSllk1bge4wKujXR10yKVaJc9t4UqB9DamtTxCAAAAAAA4O/bgyUhz/tPta5y1E5+Pn3/txqZv0MKC5Nuvz3gMztLVkiSUiP76c5/bNAqv27CEWFZkqT/VQbOngBw8iJ4BAAAAAAAzfbpt/mSpK93By45dtYTPN67dK5xcP31Unp6wGfyyrdIkqrLh+nLbYX63ksrzGs5kf0lSds8h+XxnDxLmbNToo9/E9BFETwCAAAAAIAW51te/cXmfH2144gkKbGyTDm53v0dZ88OuN/ldumbI8b+jRHuAUHf1ye+lySpzFKjIxVHWqvsJhvZM6m9SwBOWgSPAAAAAACgxR21V0uS7vjHWvPcsMLdkqSDSRnSoEEB9x+yH1K5s1jyhCnWdaZ5vsZpBJhRKekaeNQ4t2TfktYsvUlc7pOn+xI42RA8AgAAAACAFvfrj7YEnbvou+WSpA19hgddO1B6QJJk9aTKonDz/MYDx4yDpCRdsMs4XH1wdcsWewIIHoH6ETwCAAAAAIBms4ZZJEk/Prd/wPmDxyoDb/R4dPr+zZKkpUNygr4nt8SYVm3zpAWct1m90UVSkk4tNA4X7V10omW3GP/g8dHLhkmSTqItKIF2RfAIAAAAAACabVR2kiRpYEZcg/f1PnZIQw/vlSPMqvX9RgRdX7HfGCQT7ukdcN4M9pKSdNFO43B13mqVVIWept3WXN6U8alpI9UtPqqdqwFOLgSPAAAAAACg2XwTpq1hgRFDRkJkwPvexYckSbtTeqg4Ljnoe/aW7JUkRbj7hfx+JSWp7zEpo9zosNx2ZNuJlt4ifMGolYQFCMI/FgAAAAAAoNl8DYk275Jrn+vG9dILi3eZ73uUHpYkHUjMUOCdhnx7viTJ6kkKOO/f8ShJI/ON9//e/O8TK7yF1AaPRCxAXbb2LgAAAAAAAHRcvu0Mw+u0/G04cEyLtx8235+WZ3Qo7krpGfJ7CuwFkqQwBXZDmlsoeoPH6RulL/pLXx/4+sQKbyFOX/BoCRWnAl0bcTwAAAAAAGg+T+ilxv6hoyQNOmIMj1nbc6gsdUK68ppyc6q1zZMacM3tW2odEyOFh2tMnvF27aG1Kq4sbolfcELcZscjwSNQF8EjAAAAAABoNl9HoiXkAupa6fYiSdKh+LSga98WfiuH26Fwpcjq6Vbn+30PsEhJSRp2WOob00M1rhqtz19/wvWfKCfBI1AvgkcAAAAAANBsHpnJY70sHrcZPBbEpQRdLyg3lln3T+kVFGCaezxKUlKSLJJOjTEmX28q3NT8wluILxj13+PSU9/NQBdD8AgAAAAAAJrN15AY1sAeh0MO75XN41ZFeKSOxCbr6tE9Aq4XlhdKknomZjX8MO8+jxMjBkqSPvruo+YV3YKcLuMvICzMIrZ5BAIRPAIAAAAAgGZzH7/hUUML90iSasaerpduOUP3nD8w4LpvsEx6THrQZwO6B73B4wWW/pKkdYfWyeNp3/7CUB2PAAwEjwAAAAAAoNl8wV+YxaLMhKiQ92QfM4JFV+++mjwsI2gC9vqC9ZKkPsn9QjzA79gbPA6riFWULUpHK4+2+3Rr3x6PDXV8Al0VwSMAAAAAAGg2/9kvvVJjQt7Tp9gYRV3dMzvk9Z1FOyVJI9JHBX+/Avd4lKSoknJd2P9CSdKavDXNqPr4nvp8u347b8tx73ObwWurlAF0aASPAAAAAACg2XzBoEX1h2+n5W2XJNlPHRXy+tGKo5Kk1OjgidcBkpON12PHNCxtmCRpXf66JtXbGNVOl55dtFOvfLVHeccqG77ZDF5JHoG6CB4BAAAAAECzefyCt5DLjT0eZdqNYLHX+FEhv+NopS94DJ547Qmx1FrHjuns3mdLkj7+7uMW3edx6Y4jGvnrL8z39mpng/f7nkzuCAQjeAQAAAAAAM3mW2pssYTe5zCuplJRzhpJUlR296DrVc4qVTgqJEkp0alB1+sLHs/tc67CLGE6XHFYeWV5J/Yj/Nz46kpVOdzm+2q/41B8oWfAL2/feTfASYPgEQAAAAAANJvZ8afQXX/dyouNg/h4KSZ4D0hfaBgeFq7EqMSGH+Zban3kiKLDo3Vq+qmSpPe3vd+Myhun2ulq8Lp/xyNNj0AggkcAAAAAANB83uQtLMwScp/DAUf3GwfZoQfLrDtk7NE4PGO4wkJsEhnQPNinj/G6a5ck6crBV0qSVuetbmrVjVbtPF7Ho++I2BGoi+ARAAAAAAA0m9vT8HCZwYf3Ggennx7y8/n2fElSn6Q+IaO7gP0bfcHjwYOS262c7BxJ0rtb3lWVs6rpxTdCRU3DHY/+S80BBCJ4BAAAAAAAzVa71Dj0cJmUilLjIDMz5OeLKouM+6JSjj8ZOj6+9riiQlP6T1FseKzKHeXaU7ynqaU3yu7D9gav+3LRkIN1gC6O4BEAAAAAADRb4HCZ4OvJld7gMS0t5OfNidYxqaE7Hv3fREfXHpeXy2KxaFDqIEnSd0e/a2LljfPkp9sadR+xIxCM4BEAAAAAADSbr+PPIummnD5B18cneG9IDZ5YLdV2PKZGp4Zcrhww1TosrHZATXm5JOm0zNMkSQv3LGxq6S3Cw1JroF4EjwAAAAAAoNn8lxqfM6ibvnrwvIDr3cqOeg+6hfz8nmPGEums+Kx6lit7At/GxhqvdmMJ9Dl9zpEkbTm8penFhzC+X0rQuW9yi+u9v3aqN8kjUBfBIwAAAAAAaLa6HX/ZKTGKDrdKkiKdNbJu9QaCo0aF/Pzmws2SpJEZIxv3QF/w6O147JfcT5K0q3hXEysPbVt+WdC5q59frvyS0MNrzI5Pv9zRUzcsBboogkcAAAAAANBsoTr+kmPCJUlJlaWyuN2S1Sp17x70WZfbpeIqo5swIy7j+EutpaDgcUjaEIVZwrT32F7tLt59Yr/F49GxCkfIa9e+uDz0Z/xCRpZbA4EIHgEAAAAAQLO5Q+xxGOadMpNY5Z0InZwcMpUrqS4xj5OjkkMuVw7qHayz1DotJk2js0ZLkjbkb2jGL6jldNffqXiguDLkef+OR1tpiX7/yZ91yva1J1QH0FkQPAIAAAAAgGYLtdTY5g0ek/yDxxCKK41ux9jwWIVbwxvXMZidbbzuru1ubKnJ1q4Ggsf6uM3hOhYN/Ovv9L1NC/Ton+85oTqAzoLgEQAAAAAANFuopdbWuh2PKcEDWySZy6yTo5O93xHi++tmgaecYrxuqR0mMyjFCB53FO1ofOEhuIMe1hi1HZ8J2zef0POBzobgEQAAAAAANFu1wyVJigyvjRhsYcZxur3IOJGaGvKz+fZ843K0cd0SouUxaFCLb6/Io0fNUy3V8djQUuv6+Hd8htXUnNDzgc6G4BEAAAAAADRbpTd4jImwmud8HY/DCr3LoUeGnli9qWCTJOmUdKOLMSxEy2NReY3uf3uD1uz1hph1hstILRc8upsTPHpfwywW2SrsJ/R8oLMheAQAAAAAAM1S43TL4TKit5gIm3neFzymlR8zTvTqFfLzBeUFxuUE43qojsfZH27WO2sP6NoXVxgn6gyXkaSBqQPN7yutLm3ej1Hz9nj0+IbreDyK3XtiU7WBzobgEQAAAAAANEtFjdM8DtXxmHi84TJ19nhslLg449Wv4zEhMkGZcZmSpB1Hm7/Po+s4ezzuLLSrytvh6eP7RPiB3MCbm7VfJNC5EDwCAAAAAIBmWb232DwOt/rv8djI4TLeqdbJUU0IHkN0PErSwBSj6/FEllsfr+Nx8p+W6Ef/XKsap1sfrj+owrIqM1+MXrEs8GaHo9l1AJ2F7fi3AAAAAAAABJv5xpqQ530rpo/X8VhUaezb2KSOx8RE47W4OOD0oNRB+ir3q1YNHiVp0fbDemHxLj294DtlJUaZS62jl38VeGNNjRQR0exagM6AjkcAAAAAANCiPB7J5nIqtaLEOJGWFvK+IxVHJDWx49G3X2RxsVRSYp72DZjZfHhz0wv2crsbd9/8rcY07kMlVXK6PYqtrlDcRx8G3sSEa4DgEQAAAAAAtCyPpP5FBxTpckgJCSGHy5TXlGtHkbEf49BuQxv/5fHxUlKScZyXZ56emD1RkvT5rs/NLsSm8u3xGB9p0/8eOK/e+8L8huBU1LiUk7tJYeV2VXXLqL2J4BEgeAQAAAAAAC3L4/Eo1TfROjtbCguOH/Lt+XJ73IoJj1H3+O5Ne0B8vPHqt8/j2O5jZZFFpdWlKiwvbFbdLm/Lo9VqUY2r/vbHsDrTt29c94kk6fDZk1Vt9e5qxx6PAMEjAAAAAABoWW5P4ydap0SHHjzToBCTrSNtkcpOzJYkbTuyrenfKcmXNVotFvXvFlvvfWF+uePoA1t17p618lgs2nvznXJYw40LdDwCBI8AAAAAAKDlHTd4bM5Eax9f8FhnsvWE7AmSpM92ftb071TtcJmwMIssdboa60otP6afLH1TT37+jCSp/IabVNFvgBxh3o5HgkeA4BEAAAAAALQsj6ReJcYAFmVmhrznQOkBSVJqTGrTHxCi41Gq3edx65GtTf9OSW7vHo9Wb+jYIyk65H2bdxVoztuP6N5lb2rwkVzVhNlUet+DkiQHS60BE8EjAAAAAABoWR6Pxhz0LneeMCHkLUv2LTEu9wy8Hh9lO/73x3qXQdfpeBySNkRS85daO70dj1bvWmprWHDXYzd7kRa98kOdWrBLkvTi6Vfr2ht/L3ffvsqz79Hpt5fouXGi4xEQwSMAAAAAAGhhHql2uEyfPiHvySszJlL7wkKf03o1Yul1PUutfd+1q3iXHK6mdxy66gSPlQ5X0D13rHpP3cuOSJJuufYR/e68H2hj1iBZLBa99u2T2pfs1l1TRfAIiOARAAAAAAA0g7OBqc8ej5RY3fAej0crj0oKXmrd8M6KXr6p1iUlAad7xPdQbHisnG6ndhXvasw3BTCXWnuDxyP26oDr4S6Hrt/wuSTpx1f8XIv7jwuou6T6aO3NBI8AwSMAAAAAAGi6moaCR3mUUOXdfzEpKeQ9RZVFkoKnWh9npouhZ0/jdf/+Op+1NGu59ZOfbNV9/14vp8s7XMZbw4zxvQPum7Bvo+JqKnU4NkmfDg5cIm6xSPtKt5vvXTVVjX4+0FkRPAIAAAAAgCarcdYfPIZXVyvSt9S5no5HX/CYGt2MjsdevYzX3NygS8Mzhktq2mTrl/63W++tO6ht+aWSJFuYEZc8dMlQ/fn7o8z7pm77SpL0+cAceSyBkUqVsyqg49FeGdiNCXRFBI8AAAAAAKDJGgoeL8gwBsS4LGG1+zH6Ka4sVmm1EfKlx6YHXLM0puXR10VZWhp06aohV0mSlu9ffvzvkeT27usoSR+sN/adDPO2PEaFWzVpqFFfdE2VrthiDMT5cNg5Qd+z/ciWgPdlxwob9XygMyN4BAAAAAAATVbtFzx++f8Cg7ib4439HV0DBkhhwdHDxoKNkqS+SX2VGJUYcK1JezzWGS4jSQNTBkqS9pXsk8fjCbpel8Nd+zs27D8mSbL6leyr5+w93yjS5VBefJpW9zwl6Hvm7fww4D3BI0DwCAAAAAAAmsG3x2N8lE39ugV2NcYWHJQkRQwdEvQ5SSosN0K5ngk9g641ao/HeqZaS1KfpD6KtEaqtLpUmw9vPu5XudzB4aTVrwhfB+blW41ux/kDzwhZ5K7iHQHvy0oPH/fZQGdH8AgAAAAAAJrMt9Q60hYiWigy9m9UamrwNUlHKo5IktJi0oKuVTewhNvkCx7LyoIuRYdHa0z3MZKkrYe3HvernCGCR99Sa6m243FY4W5J0sL+pwfd75FH248Ghpxl9qNB9wFdDcEjAAAAAABoMl/wGGENES0c9YZuKSnB19Rw8Hj16B6SpN6pMUHXvj3oHdjSwFJrqbaT8mDZwdDF+/FNsvZn8w8eLVJKRYn6Fh+SJG3IGhR0v8tSqN3HdshmCVdmWYIk6bArOBQFuhqCRwAAAAAA0GS+pdYRzeh4zLfnSwodPF45qoc+mDVR//lhTtC1r3YYgaXZ8VhdLTkcQff1iDfCywOlBxr8DZLkdAd3WIb5L7WWRRfs+FqStCM1WyXR8UH311j2SJJ6JQxSdpnxm7bryHGfDXR2BI8AAAAAAKDJapdaW4MvFhQYr/UEj2sPrZUkDU8fHnTNYrFoVHaSYiNtQdfmLN9rHMTH1+6zeCQ44BuSZuwtuTpvdUM/QVLoPR4jw2t/k6UgX//32TNG3T2GSjK6MRfdf655z+DsYklS38Shiq8xQtHDnvLjPhvo7AgeAQAAAABAk5lLrUN1PG717q04eHDIz+4v3W9cTgt9XQo93bra6TIOwsOlvn2N4+3bg+4bk2Xs8bizaGe93++z6UBJ0Lm1e4vMY+sH70uSnJYw/W3clZKMjsisxCjznm/yfFO6h8rqjpQklan6uM8GOjuCRwAAAAAA0GTVDQWPh4z9ENWnT9Alj8djTrVOj02v9/vDQkyOrvEfPJOdbbwWFgbdlxGXYVwqL5Tb0/CwmmU7gzsmy2tctXUsWSxJeuX0q7UzrZcko0sy3G9vS0fYXklGx2O42wgky1TT4HOBroDgEQAAAAAANJm5x2Pd4TI1NVJVlXGcmBj0ueKqYjndTkkNB48hcsfACdQNTLbuFtPNe79TxZXF9T5DkiYOCN5n0lRRobAPP5QkzR94hnna5fbI6h1A41GNHBZjiE3fxKGyerzBo4XgESB4BAAAAAAATVbvUmv/IDA+eBDLvmP7JEnJUcmKsEbU+/2hOh4D9mP0fXeI4DHSFqnkqGRJtcu662MJ8ZwLhxkdk5o3TxanU4Wxyfqm+xC/zxivKbERclj2Sxa3UqJTlBqVKauv49ESPPQG6GoIHgEAAAAAQJPVGzyWePdMjI2VrMGDZ1YdXCVJGtt9bIPfH6rj8ZLhWbVvfMGj3R7y86OzRkuSVh9seMCMwxW8FNv8Tf/4hyRp3pAzzYJSYyP0zPWnSZL+e9dETR1tBIzD04fLYrHIIoJHwIfgEQAAAAAANNnHG/MkSSt2HQ28UFpqvCYkhPxcXpnxuf7J/Rv8/lAdj0fLq2u7HhtYau3//b7n1Sdg30iv6HCrVFQkffKJJOnNkRdJktLjI7Xm4ck6rZfRTdkzOUZJifmSaid02zxGXUfCWWoNEDwCAAAAAIAmW+4NHO3VzsALxwkeC8oLJNUOgKlPWIiOx2U7j+q1ZXuMNw0stZakbrHGPo+HKw43+JyaEB2P908ZLG3ZIrlc8vTurR3depvX6i7N3lhoTLQenmEEj9FOY8/IwgiHSqqCJ2YDXQnBIwAAAAAAaDn7jD0clZkZ8nK+3egQzIwLfd0n1N6LkvSfNd49G33B5rFjIe/zDZg5XvBYd6n1fRcMUkZClLR1q1HH4MHmtdKq4OXTmwo2SZJGZIyQRZJFsUqqNK4dsh9q8NlAZ0fwCAAAAAAAmmzyUKNj8Yfn9Au8sH278Tp0aMjPrctfJ0nql9wv5PXjMZdg9/N+ftu2kPf5JmYfb6m1w2+p9eCMeN08oY/x5ssvjddx48zrVY7AkPJA6QEzXDyl2ynG91ltiveusi6rDt2NCXQVBI8AAAAAAKDJFmw1lkz3TI4JvODrQExLC/pMeU25cktyJUln9DijWc81g8cBA4zX/aGnVvuWPn9z6Bt5PJ6Q90iSw2Vcu/q0Hvr83rOVGB1uXPB2PGrCBPPeMb2TAz77+vrXJUmjMkcpPtJY+u2w2hRfbVwvqyF4RNdG8AgAAAAAAJpk44Fj5nFk3anWvj0XQ+zxeLDsoCQpPiJeiVGJx31Or5SYoHPmCuxkbwhYUiKFCBZ9w2UqHBUqrTb2nfR4PNqeXyan3/Jq3x6PNqvf0m6PR9q50/tFtUNwrHU2nlxxYIUkacaIGeY5Rxgdj4APwSMAAAAAAGiSw2XV5nFQ8NjAcJkDpQckST0SejTqOQv/3zla9cvzddEptftBmh2PSUnGq8MhVVYGfTY6PFrRtmhJ0tFKYxDOnOV7NeXP/9OD724076v2LrWOCrfWfnjzZqm8XIqOlvr2NU9b/fadrHJW6YtdX0iSJvWdZJ6viIii4xHwIngEAAAAAABN4lueLNUJ7KQGg8evD3wtSRqcOjjoWijh1jClx0epT1qseS7M13UYGytZvc+uZ8BMakyqJOlohRE8/mn+d5Kk9745qPFPLNSuw3ZVO12S6gSovn0jTztNiogwT/t3RW49vFVOt1Mp0SkakTHCPF8cFV/b8chUa3RxBI8AAAAAAKBJXO7a4LEpHY/bjhiBXk7PnCY97521tfs4mqudLZbarsf6gsdob/Do7Xj0Hw6TX1ql33y8RdXec5E2vwA1zzuQpnv3gO/zX2o9b8c8SdLIjJEBE7iPRScosco4LjrGVGt0bU0KHh999FFZLJaAP0OGDDGvV1VVadasWUpNTVVcXJyuueYaFRQUBHxHbm6upk6dqpiYGKWnp+uBBx6Q0+lsmV8DAAAAAABandNdG+AFBHaSVFRkvCYG7+HomzDd2KXWPv7BXoTVL8rwDbA5cCDk53om9JQkbT9iTNqucQVOpbZYLKE7Hg95A8M6waPNL3hcmrtUknTN0Gv8vk+qsYWrd4nxd/JdwZYGfxfQ2TW54/GUU07RoUOHzD9Lly41r91777366KOP9Pbbb2vJkiXKy8vT1VdfbV53uVyaOnWqampqtHz5cs2ZM0evv/66Zs+e3TK/BgAAAAAAtLqAjsdwv2ihulrau9c49hvK4uMLHrvHdw+61pCYiNpwMy0usvbCyJHG68aNCmVc93HG5YLQ1yOsYbUdj/6/Y/Nm47V374D7fR2PZdVlWrZ/mSRpbPexQd/bw25Mxj5YTscjujZbkz9gsykzMzPofElJiV599VW9+eabmjTJ2FT1tdde09ChQ/X1119r/Pjx+uKLL7RlyxYtWLBAGRkZGjVqlB5//HH97Gc/06OPPqoIv30TAAAAAADAycnpv8ejf8djQYHkdhv7ItbJDsqqy7SjaIek2onTjRXtt49kSqxfdtDD2zl5+HDIz2UnZkuS8ux5Ia9H2MLM4TIBnZubNhmvp58ecL8veFxxYIXsNXb1TOgZMniMdYZLqlIpU63RxTW543HHjh3q3r27+vXrp+nTpys3N1eStHbtWjkcDk2ePNm8d8iQIerVq5dWrDDGy69YsULDhw9XRkaGec+UKVNUWlqqzb7/NSGE6upqlZaWBvwBAAAAAADtw3/JcnqCXwei3W68xscb64797CzaKafbqW4x3dQ7KbCT8Hj8l1oHBI8pKcarb3l3HT3ijWDyYOnBkNeN4DHEUusjR4zXOuFpX++Qm892fiZJmpg9UdawOkvNJcU6jD6vUoc95HOBrqJJweMZZ5yh119/XZ999pleeOEF7dmzR2eddZbKysqUn5+viIgIJfk2dvXKyMhQfn6+JCk/Pz8gdPRd912rz5NPPqnExETzT3Z2dlPKBgAAAAAALajK4TKP/fc9VJm3wy8uLugzBeXGDIis+KwmP69ncrR5HBvpF/T5gsejR0N+ztfxuOfYHrncrqDrEbYwc+CMOZ27uro2QPXuITkg3fg93xubLY/Ho3e3vitJmjZsWsjnRruNpdYEj+jqmrTU+uKLLzaPR4wYoTPOOEO9e/fWf/7zH0VHRzfwyRPz0EMP6b777jPfl5aWEj4CAAAAANBOfMuTJSnMP3j073iso8BuBI8ZsRlB144nINz059uD0bc0uo5BqYMUbYuWvcau3cW7g65HWEN0PPpCTKvVHJDzwayJKi6vUXZKjObvmq/cklxFWCN0Yf8LQz43zml0gR51lMrhcijcGt6Ynwl0Ok1eau0vKSlJgwYN0s6dO5WZmamamhodqzPCvqCgwNwTMjMzM2jKte99qH0jfSIjI5WQkBDwBwAAAAAAtI+kmNogLT7Sr6fJFzyG6Hg8WGYsd86Ia3rw6M/j8Xtz1lnG686dUklJ0L22MJvZYVlYXhh0Pdxqqd3j0TdcxjchOy3NXC4eF2lTdkqMJOmdLe9IkmaMmKH4yOCAVZK6VUYquVKq8Ti0oWBDk34f0JmcUPBot9u1a9cuZWVlacyYMQoPD9fChQvN69u3b1dubq5ycnIkSTk5Odq0aZMKC2v/YZ8/f74SEhI0bNiwEykFAAAAAAC0EYc3rJs6Iitg/8WGllov379ckjQ6c3STn2epp+FRCQlSjBEImvsy1pEanSpJOlp5VD2SAldrhlksslc5JUnR4d4Add064/W004K+y+1x66PvPpIkXTvs2nrrdNsiNMRbzoHSA/UUD3R+TQoe77//fi1ZskR79+7V8uXLddVVV8lqter6669XYmKibrvtNt13331atGiR1q5dq1tvvVU5OTkaP368JOnCCy/UsGHDdNNNN2nDhg36/PPP9fDDD2vWrFmKjIw8ztMBAAAAAMDJwOGdah1hrRMrNLDUOrfEGE47rNuJNR556p5INYLF+vZ5TI3xBo8VR+X2BH46MSZch+3VkqRu8d5cwjeDonfwAJy1eWt1yH5I8RHxOrfPufXW6LCFK8H4WpVWMyAXXVeT9ng8cOCArr/+eh09elTdunXTmWeeqa+//lrdunWTJD399NMKCwvTNddco+rqak2ZMkXPP/+8+Xmr1aqPP/5YP/rRj5STk6PY2FjdfPPNeuyxx1r2VwEAAAAAgFbjm2odbq3TiphrhIvKCF5OnVeWJ0nqkdDjhJ7tqZs8pqZK+/fXHzz6dTy6PYHzIqpqXCrzdjyawaPve3yBpp8Ptn0gSbpowEWKtNXfQOW0EjwCUhODx7feeqvB61FRUXruuef03HPP1XtP79699cknnzTlsQAAAAAA4CTichvpn7Xu0JedO43XQYMCTlc4KnS00gj0suKaPtXan6duz6N38rTqzJTw6ZPUR5K0+fBmeTyjAq69s7Z2GbS5V6VvyXad4LHKWaWX1r4kSbp88OUN1ui02hTvDR7LqssavBfozE5oj0cAAAAAAND11Bs8+ga81AntluUukyT1iO+hlOiUli3m1FON1zVrQl4elTlKkrT9yHa562SWeSVVkozfYU7nrqfj8d/f/ltHK48qNTo15P6O/hy2CDoeARE8AgAAAACAJvLtlWitO/WlnqnW245skySd3uP0wGE0jWRR7WeCllr7htXu3RvysxmxxrLvwxWH5Qn6sMEXpMrjkTZ4p1D37Rtwz1ubjVWgM0fPVJQtqsF6nTabGTyW1dDxiK6L4BEAAAAAADSJL6gLq9vxWF5uvMbGBpzeWWQswe6f3L/li8nyLt0+dCjk5fTYdElSYXlh8GCaukpKapdsjxljnna6nVqTZ3RUXjHkiuOW5LBFKLXSW5Y9dF1AV0DwCAAAAAAAmsTVxI7HpfuXSpKGZwxv+WKOEzx2izUG4pZWl8rprm74uw5493xMSQkIT5fvX64jFUeUHJVsLt0OxdeZWR4dq8HerSJ93Z5AV0TwCAAAAAAAmsRd3x6Pvo5Hv+DR4/Fo+5HtkqTxPcc363nj+9XuCxm0XLpvX8liMYLH/PygzyZHJZv7SpZ7djT8IF/w2LNnwOl1h9ZJks7qfdZxl1lLkj0mQZneDPZoRehp20BXQPAIAAAAAACaxOU2XoOWWofoeCypLlG5wwgkeyYEBnqNdcMZvRVhNSKMoG0aU1Kk/t4l3N99F/RZi8Wi0VmjJUnVypMkXTAsI/SDVq82XutM5X5367uSpNO7n96oektjExguA4jgEQAAAAAANFHI4TJud8g9Hg+UGl2ESVFJigmPadbzrGEWXTOmhySF3qcxLc14LSoK+XnfgBmn55gkyVY3MPXZbnRm6owzzFNHK45qxYEVkqTrh1/fqHoro2IVX+M9dlbK6XY26nNAZ0PwCAAAAAAAmiTkcJmDB412RJtNSk01Ty/NNfZ3PDX91BN8agPTsFO8S7GPhl7W7AseHSqWJNms9cQhvn0ifftGSvpw+4dyup0alTlK/ZL7NapShy1c8X7bSdpr7I36HNDZEDwCAAAAAIAmCTlcZvdu47VvXyk83Dy9q2iXpMYvU66P71F/mv+dXl26J/CiL+isp+MxMy5TkuRQoaQGOh59e0RmZpqnVuw3uh3P7X1uo2t1WsMV6ZIi3MZzWG6NrorgEQAAAAAANEntcBm/k6XecC05OeDePLuxr2JWfJZOhH9U+PjHWwIv+oLHejoefUNtKizfSgoeivPijWMkp1Pat8840b27JMnldumtzW9Jks7vd36ja3VYbZKkBIdVEsEjui6CRwAAAAAA0CROM3j0ixXKyoxXv/0dJSmvzAgeu8d3b9EafOGnpOMutR7abagkyWUplkeOoI7HXikxxmCa8nIpPt4cLrO7eLfsNXZFWiN18YCLj1+U92udVqPjM73aCB59fwdAV0PwCAAAAAAAmiRkx2OxsX+iGQJKcrqdWpO3RpI0OHXwCT3TUmd1tMPtrn1znKXWqdGpirZFGzVZjshmDfwyt8dTu8w6O1uyGoHh+9velyQNzxgua5i10bU6bEbwOKjUeP3uaPC0baArIHgEAAAAAABNYu7x6N/x6Av9/JZa55XlyV5jV3hYuE7LOu2EnmmpM1zG1YSOR4vFogEpAyRJNWHbZQsLjEOGZSVIhcb+j+rWTZLk8Xj07tZ3JUkzRsxoUq1O71Lr9ArjOcWVxU36PNBZEDwCAAAAAIAm8S21DliyHKLj8UDpAUnGMuswS8tGEE7/4PE4ezxK0rl9zpUk1Vh2B+zx+MqMscZ0bl/wmJ4uSdp2ZJtWHVylCGuErh12bZNqc9giJEnx1UaN7PGIrorgEQAAAAAANInL5et4DBE8+nU87i/ZL0nqmdDzhJ9Zd6m1rwZJx11qLUl9k/pKkpyWwwGBqbnseot3YE1Po9a3vjWGypzb59wmD8Zx2rzDZSqN5eBlNWVN+jzQWRA8AgAAAACAJnF691cM6HgMsdR62f5lkqQRGSNO+Jl1csfAjkf/pdYej0LJTsw2PheWH7DHo/kb1q41Xs88U5I0b8c8SdL1p17f5Fp9w2XiK12S6HhE10XwCAAAAAAAmqR2qnXDS613F++WJJ2WeWL7O4biCrXUurpaqqgIef/IjNGSpBrLLtW4y83z5n6P+43uTPXpo6LKIm0s2ChJOqf3OU2urSbcWGqdYHdKouMRXRfBIwAAAAAAaBJf6BdubXi4zCH7IUlq8lLlUCx11lo7/adax8VJkZHGsW86dR1/X1IuqztdsriVV/Gted5mtRiBZUGBcSI7W1/u+VIOt0ND04aqT1KfJtdaEpsgSYqvMILHkqqSJn8H0BkQPAIAAAAAgCZxNrTHo1/H46Eyb/AYd+LBY10BHY8WizRsmHG8cWPI+/+1KleR7sGSpA1HPzXP28Is0jJjSbi6dZPS0vRtoRFMju85PijwbIjvzurIaCk+Xt29jY65JbmN/g6gMyF4BAAAAAAATeIKNdW6TsfjvmP7dMh+SFaLVf2S+53wM+vmf9VOd+CJgQON19z6Q75Y1yRJ0rdHF8kjhyTvUusNG4wbzj5bsljM/R3Hdh/b/IIzMzXIO2R7X8k+1bhqmv9dQAdF8AgAAAAAAJrE4V3mbHY8VlUZfySz43H70e2SpKHdhio5OjnoO05UUXmdIC/L21V56FC9n4lyD1eYJ0ElNYUqsf1bknep9bZtxg1DhsjhcmhDvhFEXjLwkuYXmJyslMrat2XV7POIrofgEQAAAAAANInZ8eibDu1bZh0WJsXHS5L2lxjDWnom9GyRZ1rqzLV2uT06XFatf63KVXm10wwet63dWu93hClKiQ5jSnWJ7T+qseQaXZvbjZBUgwdrdd5qOdwOxUXEqXdi7+YXHB4um1uKthiDZhgwg66I4BEAAAAAADRJlcMlSYqwWo0T/susvVOiv9z7pSRpcOrgFnlm3aXWTrdHN7zytR56b5P+unCHNGiQJClu1QrtLKw/5It3XapR3c6RLG5VWL+SzRoWEDx+vvNzSUa3Y1P2dwwSYQSO8WHRkqTS6tLmfxfQQRE8AgAAAACAJimpNPZHTIoJN074Oh79Jlp/c+gbSSe4XLkBLrdbOwrtkqQl3x2W5/zzJUk9Sw+rvLCo3s9ZZNHYTGOvx+qwLQq3l9VOwh48WCsOrJAkTeg54cQK9AWPFmPaNkut0RURPAIAAAAAgCY5VlEneKwzWEaS8sryJEm9Enu1yDPr9h7u9IaOkhQfZdMxa5QOxyZJkqJ272jwu87IMoLHqrBNsmxaY5zMyNDWmjzN3z1fkjSp76Rm1+rxKDh4ZKk1uiCCRwAAAAAA0GhVDpc5UToxuk7Ho3ewjL3Gbi4t7hHfo0WeW3fV88YDJQHvV+8t0qaMAZKkhCVfNvhdfRIHKMwTK1ncWrf0DePk5Ml6ZPEjkqTz+56v4RnDm1GjX5G+4FFG8MhSa3RFBI8AAAAAAKDRqh1u8zg63LvHY52l1qsOrpIkdYvppvjI+BZ5brG3y9LH7fEEvI+LtGlNz2GSpMh9exr8Lqs1TJFuI1h8tfJ/kqSdZwzQ21veliQ9cf4TJ15wuBHKZilOUu2wHaArIXgEAAAAAACNVl7jNI+tYd4OP99Sa2/H4/r89ZKkc/uc22LP/Sa3OOC9b7K2T3SEVXkJ3SRJ4YcONvhdFklJ3unWH6QW6PVR0tNx30oyuh1P73H6iRfs7XgcpFRJ0s6inSf+nUAHQ/AIAAAAAAAa7b1vDpjH5tLiOh2PvpBtQMqAFntufFR4wPui8praOmRRtdOtA4npkqSo7Vslp1P1sVgsivD016mHcyRJt14pPZ/7riTpnjPuaZmCvcFjqstYal1SXdLQ3UCnRPAIAAAAAAAazVmn01BSUMejbzL0kLQhLfbcGN+ybq/VewM7IGucbm3IGqTy8CiFHz0i7aztMPTUWZbt24nxZ0sTdOn22vMzRs7QpYMubZmCfXs8uoy6GS6DrsjW3gUAAAAAAICOIz0+SpLUIym69qRfx6PD5dDGgo2SjGXLLcWjEIGnl9vjUY3TLYc1XPuSszSscI+0a5c0xAg+fcNwJOn2M/tKktLKi3Xjhs913Sbps7m/VvKE8zUhe0LggJgT4Q0e45xhUrgxcAfoauh4BAAAAAAAjVbh3eNxTO/k2pN+HY95ZXlye9yKsEYoKz6rxZ4bqtHSx6PacHFfkvHMdf9dZF6vrHGZxz+/eIgsFumG9Z9Jkmy9+ujSq3+uib0mtljo6PHIHC4T5zCil7JqOh7R9RA8AgAAAACARvOFeDERfkuf/ToedxXvkiRlJ2QrzNJysYO7geQxOzlaNS6jriV9R0uSXPM+0RF7tVGzw7gWYQ2TzRomiyy6Ysti48O/+Y3ZnXiiAmJL31Jrh3GWjkd0RQSPAAAAAACg0Sq8IV50PcHj4r2LJUln9DyjRZ/r8jS01NrY41GSNmf0lyRllxToy22FkmqDx6hwIwbpe2iX+hcdlMsSJk2d2qJ1mnxLrb0zcNjjEV0RwSMAAAAAAGi0oI5HjydgqfWeY3skSSPSR7Tocx0ud73XXB6PalxGMJmblClJyrAXKfFIfkDNvrC0x9rlkqTyiWdJSUktWqfJGzwmVRsdj0WVRUFDboDOjuARAAAAAAA02jtrD0iSLL6FxXa75F3mrORk7S/ZL0nKTsxu0efWOOsPHt1uj1zeYLIkOl4bMgdKkhI2fCNJqvJ1afomY+/ebVyfOL5FawzgDTSzjzhktVhV5axSXlle6z0POAkRPAIAAAAAgEazVxvDZeas2Guc8HU7RkZK0dE6UGoEk9kJLRs8VjcQPLrcHjn99oDcmdpTkhSzzwgYa5dae4PHbduM1379WrTGACkpkqTwomPmkJ1D9kOt9zzgJETwCAAAAAAAmqza4Q0C/fZ39Ei1wWNbdjx6JIerNnjclWo8O3O5Mdk6YKm12y0tW2bcOGFCi9YYwBs8qqhI8RHxkhgwg66H4BEAAAAAADTZry4dahz4gseUFB2uOKxqV7Ussqh7fPcWfV7DwaNH2/NLzfcLBpxulPTdZsnjMTseo8OtxjLrykrJZpOGDGnRGn08kpSQYLwpLVVcRJwkgkd0PQSPAAAAAACg0YZlGYFar9RY44RvqbXf/o4ZcRmKsEa06HMbCh6dbo8+WF+7f+KelB5yWsIUXlkh5eUF7vE4d65x08iRRvjYgiwWvzfe4TJyOAge0WURPAIAAAAAgEbzLWg2Mza/jsfW2t9RaniPR0edaw5ruLZ362O8+fxzLd91VJJ0qKRK+vpr4/wtt7R4jQEiI43XmhqCR3RZBI8AAAAAAKDRPB4jejS7+/w6HncU7ZDU8vs7SlLP5GjzeNKQ9IBr1U5X0P2fDJ5oHLz1lj70dkNuOVQqrV9vnB87tsVrDODreCR4RBdG8AgAAAAAAJrM4ut59Bsu8/UBo5twfI/xLf68l2eM1eSh6frvXRNlC7MEXAvVDfnx0LOMgy+/VHJFiSSpm71Yys+XwsKkESNavMYAfsGjb7hMWXVZ6z4TOMkQPAIAAAAAgEbzNjwGdzympCjfni9J6pvct8WfOyA9Tn+7eZxG9ExSuC0wzqhxujXllIyAc/uSu2tnSk/J5dK0oq2SpGnhR4yLgwZJMTEtXmMAv+AxLSZVknTIfqh1nwmcZAgeAQAAAABAo3m8uzwG7fGYnKzDFYclSd1iurVqDeF1Oh53FNr1+eaCoPt8060v3Wl0Yl68d61x4bTTWrU+SbXBo8ejAUn9JEk7i3a2/nOBkwjBIwAAAAAAaDRP3ekyfsNlDpd7g8fYVg4erY2LM74YmCNJGvDtSvU8lq/hH/zTuDB9emuVJsm7D2ZE7VTvjIgUSVJRZVGrPhc42RA8AgAAAACARqvNHb3Jo3epdVlCpIqrjBAyPTY9xCdbjq2RweP67oPkyO6lmPIyffnKncbJ9HRp0qRWqcsiv05Mv+AxzmJMuGa4DLoagkcAAAAAANBoQVOtvR2P31gLJUm9EnspLSatVWsIt1qOf5Mkd5hVB86fKkmKcDuNk3/7mxQd3cCnWojNZh7GyQghy2oYLoOuheARAAAAAAA0WsBKa7tdys2VJB1OsEoygsfWtu1Q4wO8S5PO057kLEnS1ku/L112WWuVFchiMbse40XHI7om2/FvAQAAAAAAkKocLu0+XC5Jslgs0q5dksslpaWpKNroQkyJTmn1OlbtbfxeieWRMbrmxqc09sAWnX7XTRrainUFiYiQamoU5wmXZASPHo/H+LsDugA6HgEAAAAAQKO8tGS3eWyxSDpyxHiTkWEOTmmL4LExYiOMDszslGgVxSTqi0E5clrbuP/K2/HoCx7dHrcqnZVtWwPQjggeAQAAAABAo2w4cMw8Lq92SoeNKdbq1k1HK45KklKi2jd4jIu06e07czRjQh9J0v6i2qDP5fbU86lW4g0eY91Wc/BMSVVJ29YAtCOCRwAAAAAA0GTfFZTVdjympWn70e2SpB4JPdqxKukv143SuD4psoUFL2f+Zl9x2xYTEyNJCiuvUHZitiRpz7E9bVsD0I4IHgEAAAAAQKNU1rjM40ibVdq713iTmamVB1dKks7sdWar15EWF1nvtXCrEXVYQwSPX+040mo1SX6Tvn1SvN2fRUXql9xPkrTv2L5WrQE4mRA8AgAAAACARlmx+6h5HGkLkzZulCR5Ro40l1r3TOjZ6nX07xZb7zVf8Oh79TeiZ2Kr1RRSaqrxWlSkxEjj2aXVpW1bA9COCB4BAAAAAIAkqaTCoT4/n6c+P58nd539ENfuCzFJusg4V5aeKJfH6IZMjkpu9TrD/FoLzxqYFnAtwlZ/x6Pb08Z7PCZ7/y6KixUfGS/JmGwNdBUEjwAAAAAAQJL0+vK95vGuw4EB2eLthwPeuzwe6dgxSVJxtBHyRVojFR0e3ao1SoGh4ss3jQ24FuHtdAy1x2Nbz5ZRtPfvorpaceFxkqSymrI2LgJoPwSPAAAAAABAklTlrLOHo5+MhKiA971SYqQSY0LzPpsRUmbEZbRyhYbUuAjzODoisM5wmxE4hup49LR1x6N3qrWqq82Ox7Jqgkd0Hbb2LgAAAAAAAJwcBmXEmce+AM/H4XKbxwPS43Rm/1Sz43GT44AkaWTGyNYvUtIvpw5VQWmVpp/RO+iab29HW4g9Hnun1r83ZKvwBY81NYqLMP5uWWqNroTgEQAAAAAASJLcbr/jOs2BvuDxilHd9ZfrTpPKyyWnU5J0RBWSpKy4rDapMz0+Sm/dkWO+P71PilbtNfabrG+p9cD0OD16+SltUp/ZWOkXPMZHGINmWGqNroSl1gAAAAAAQJLk8ksb6w6XcbiM95He4S3ats14TUlRkcvo4kuNSW39IkPwmzVjLrGuu9T6qWkjlRIbodYUtLjbL3j0LUPfV7KvVWsATiYEjwAAAAAAQJJ3YIxX3e0QfR2PvqXM2rHDeD3lFB2tNLoNU6NPnuCxbsdjlcOlNucXPJ6WeZokaX3++rbfaxJoJyy1BgAAAACgiztWUaMf/mOtKv3CObenbsdjneDxyBHjNSNDRyuPSpJSolNav9gQwvySR99x3T0eR/dObtOaJAUEjz0TekqSKhwVqnRWKiY8pu3rAdoYwSMAAAAAAF3cK1/t1so9RQHn6gaPlTVG8BgZ7g30Dh82Xrt105bDqyRJfZL6tGqdjeHreAz363j83tietYFpW/ILHmMjagfb2GvsBI/oElhqDQAAAABAF1deHbwMue5wmbIqhyQpISrcOLFmjSSpOru7cktyJUkjM9tmqnVd/iGp1dvxaAakCj3huk34BY9hljDFhhvhI5Ot0VUQPAIAAAAA0MXV7W6UFLQPYVmVMcE6Icq7eHLFCknS0XNPlyRZLVYlR7XDcmYFhqRh3qQj0mY1z0WcBMGjJMVHxkuSyqqZbI2ugeARAAAAAIAuzlm3vVHBHY+lvo7H6HCpslIqLpYkHUkzlgynxqTKYgma69wm/ENS31Jrc/q2ggfNtHo98tYTGWm8eoPHuIg4SVJZDcEjugaCRwAAAAAAujiXKzh4dNVJHrceKpUkxUfZpIMHjZMxMTpirZYkpcWktW6RDfCvtTZ4rO14bLOl1nXzzTodjxmxGZKkg6UH26YeoJ0RPAIAAAAA0MW5Qiy1vuSvX+mJT7ZKkvYdLVdxhd8ej+vWGTf16aN93v0dfaFaeyipdJjHvmXV/ns8RljbpxOzbvA4IGWAJGln0c72qQdoYwSPAAAAAAB0cXW7G31e/t9uSdKibYXmufiocGntWuPNuedq7SHjeEzWmNYtsgG7Dpebx77l3gFLrdtrj0ffUuuqKkm1XaHHqo61Tz1AGzuhf/J+97vfyWKx6Kc//al5rqqqSrNmzVJqaqri4uJ0zTXXqKCgIOBzubm5mjp1qmJiYpSenq4HHnhATqfzREoBAAAAAACN9ObKXD35yVZzb8Ty6ob/O3lReY15nJkYJe3bZ7zp108F5cZ/5++d1Lt1im2ExOjwoHP+S63D2yt4TEoyXo8dkyTFRxjDZZhqja6i2f/krV69Wi+99JJGjBgRcP7ee+/VRx99pLfffltLlixRXl6err76avO6y+XS1KlTVVNTo+XLl2vOnDl6/fXXNXv27Ob/CgAAAAAA0Gi/eH+TXvrfbq3bf0ySVFxR0+D9pVW1wWRClE3ascN407u3CsuNbshuMd1apdbGqDuBWwrseGyv3FEpKcZrUZEkhsug62nWP3p2u13Tp0/XK6+8ouTkZPN8SUmJXn31Vf3pT3/SpEmTNGbMGL322mtavny5vv76a0nSF198oS1btuif//ynRo0apYsvvliPP/64nnvuOdXUNPwvOgAAAAAA0HLKvIFidIStwfuyU2LMY0tJSe1S65wcHS4/LElKj01vnSIbIUTuGLDHo8vdhsX48w8ePR7FRxodjwSP6CqaFTzOmjVLU6dO1eTJkwPOr127Vg6HI+D8kCFD1KtXL61YsUKStGLFCg0fPlwZGbWbzk6ZMkWlpaXavHlzyOdVV1ertLQ04A8AAAAAADgxvpErA7rFNXhfhXcp9vWnZ0s7vYNRMjNVlZGqvcf2SpJ6JPRopSqPzx0ieYzwa3MMdb01mY9LSDBeXS6pqsrseGSpNbqKhv8njRDeeustffPNN1q9enXQtfz8fEVERCjJt4eBV0ZGhvLz8817/ENH33XftVCefPJJ/frXv25qqQAAAAAAoA7/QTLeOSxyHKclsLTKmBodHxUubd9knOzfX9uPbFels1Ip0SkamDKwVeptjFCzcfwHyrjrGZ7T0iyqMz3bN9VakhyO2qXW1XQ8omtoUsfj/v379ZOf/ERz585VVFRUa9UU5KGHHlJJSYn5Z//+/W32bAAAAAAAOpNqp8s8rnG6A17rU1ppdDwmRNmkzz4zTubkKK8sT5LUO7G3OU26PdQ3ldunjXLHYOF+Q29qapQYmShJKq4qbqeCgLbVpOBx7dq1Kiws1OjRo2Wz2WSz2bRkyRL99a9/lc1mU0ZGhmpqanTMO63Jp6CgQJmZmZKkzMzMoCnXvve+e+qKjIxUQkJCwB8AAAAAANB0ZX6DYu7993pJx+94LKv263jcsME4OXGiGTx2j+/e8oU2Qc1x6ne18VJrk8VSGz7W1Khvcl9J0r5j++R0NzxJHOgMmhQ8nn/++dq0aZPWr19v/hk7dqymT59uHoeHh2vhwoXmZ7Zv367c3Fzl5ORIknJycrRp0yYVFhaa98yfP18JCQkaNmxYC/0sAAAAAAAQSpl32bRUO636eMFdlcO4nrn3O2nTJslqlcaO1dYjWyVJPRN6tlK1nYBvuXV1tXom9FSYJUwOt8McygN0Zk3a4zE+Pl6nnnpqwLnY2Filpqaa52+77Tbdd999SklJUUJCgu6++27l5ORo/PjxkqQLL7xQw4YN00033aTf//73ys/P18MPP6xZs2YpMjKyhX4WAAAAAAAIpbQquNOuvo7HSFuY3lqVqy+3Gc1DvRZ+bFyYNEnq2VMr56+UJJ3Z68zWKbaFhLXfKnAjeCwvl2pqFGYJU2x4rMpqylTuKG/HooC20ayp1g15+umndemll+qaa67R2WefrczMTL333nvmdavVqo8//lhWq1U5OTm68cYbNWPGDD322GMtXQoAAAAAAKijvDo4eKxvj8dqp1s/f2+T+T57gTd4/N73JMlcat03qW8LV9kyzhnUTZJ0zeh27Mj0W2otSbERsZKk8hqCR3R+TZ5qXdfixYsD3kdFRem5557Tc889V+9nevfurU8++eREHw0AAAAAAJrI6Qre79AR4lxdyRUlisvdY7y5/HJ5PB4dKjskScqKz2rRGlvKa7eMU3mN09ibsg0F/G36llp7g0ffZGt7jb1NawLaQ4t3PAIAAAAAgJNX3WXVv3h/k5buPBJw7t7Jg4I+d+WWxcbBkCFSerr2l+5XpbNSFlmUFde+wWNClNFX1T0xKuB8WJilTUPHkIO96wSPseHejkeWWqMLIHgEAAAAAKALcbkDuxvfXJkbdE+ErTYuSK4o0Yy1H+mXX75qnPjBDyRJy3KXSZLG9Rin6PDoVqq2cf79wxxdMCxDr916ervWEVLd4NG71JqOR3QFJ7zUGgAAAAAAdBwO9/GXVfuCR4vHrX/++1c6pXC3JMkVHS3rHXdIkvaV7JMkDU4d3EqVNt7QrAS9MmNse5cRWj1LrdnjEV0BHY8AAAAAAHQhznomWPuL9AaPN6/92AwdHWFWfTf7/6TEREnSF7u+kCQNSg1elg0/dYLH+Ih4SVJJdUl7VQS0GYJHAAAAAAC6kFDDZeqKsIWp/5H9evjLv0mSnsn5vkb+5C0dueY6SZLD5dDS3KWSpO+f8v3WK7YzqBM8ZidkS5L2HdvXXhUBbYal1gAAAAAAdCEOd+M6Hq/Yslg2j1vfdB+sP501XR5LmKxhxvSUHUU75HA7FBcRp/4p/Vu75I6tTvDYO6m3JCm3NHhvTaCzoeMRAAAAAIAupDEdj9Ywi87eu06S9N4pk+SxGPGBLcx4/bbwW0nSqemnKsxCtNCgaO/gnXJjT8fESGOpell1WXtVBLQZ/u0AAAAAAEAX4mjEHo/Rx4o06tB3kqQvB4wzz/uGzpjBY7dTW6HCjs/j8Qt3U1KM1+JiSbVTrcsdDJdB50fwCAAAAABAF+JsxFTrxO+2SJJ2J3dXXkK6eX5IpjEYZVPhJklGxyNqWUKd9AWPRUWSmGqNroXgEQAAAACALqQxU60Tv9ssSdqS3i/gfFS4VVXOKs3fNV+SNDprdMsX2NnUCR5jw+l4RNdB8AgAAAAAQBfiaMQej3FbjeBxa3pf89ygDKNTb2PBRpU7ypUSnaIze53ZOkV2JjExxmtVlSS/pdZ0PKILIHgEAAAAAKALcTViqXXMFmMp9ZYMo+NxVHaS5t1zliRpxf4VkqScnjmyWEIuLoa/OlOt6XhEV0LwCAAAAABAF1LtdDV4PdJZo/h9uyRJW7wdj+v3H1O41YgQVhyoDR7RCHWCR98ej/Yae3tVBLQZgkcAAAAAALqQamfoPR6jw62SpIFHchXmcqkiMVkFcakB9zhcDi3Zt0SSlJNN8NgovuCxulqSlBaTJkmqcdWoqLKovaoC2gTBIwAAAAAAXUi1I3TwOOu8/pKk4fk7JUlH+g+V6iylnrdjnvLt+UqOStb4nuNbt9AOLGAxe52Ox+jwaGXFZUmSdhfvbtvCgDZG8AgAAAAAQBcSaqn11af10GUju0uSpnxnLKU+PLx2YvXl3mt/WvEnSdK1w65VTHhMa5fa4YTc87JO8ChJ3WK7SZKKK4vboiyg3RA8AgAAAADQhYRaap2RGKXeqbGSx6Ph+TskSYfOmmxeH90rSd8d/U5f5X4lSbp/wv1tU2xnECJ49A2YYZ9HdHYEjwAAAAAAdCGhgkdfn1638mKlVpbKExamioFDzevWMIueXfWsJOm8PudpUOqgtii1cwgVPEYw2RpdA8EjAAAAAABdSENTreeMNkIyy6BByjm1p3m+uPqQnln1jCTpzrF3tm6BnU2I4JHJ1ugqCB4BAAAAAOhCQg2X8W1NOOzIPuNgxAhlJUZJkjxy6rkNd0uSTk0/VdOGTWuTOjuNBpZal9fQ8YjOzdbeBQAAAAAAgLZT4wo91VqS9NlnxuvIkbJZjV6l4vDXVHZsrcLDwvXypS+HHqCC+tHxiC6MjkcAAAAAADqwGqdbHo+n0feH7HiURdqzR1q82Djxve+pylmlYtvrKrN+JEl646o3lJOd0xIld37+/3E01PHIHo/o5AgeAQAAAADooEoqHRr7m/ma+cbaRn8m1B6PFoukl1+WPB5p4kRpwAD9aN6PVBr+jmRxa3Kv7+v7p3y/BSvvnEI2g4YIHpOjkyVJhysOt0FVQPsheAQAAAAAoIP6YnO+SqucWrC1oNGfCTXVWh6P9PnnxvFtt2lN3hq9vv51SdI1/Wbr0xlzWWLdXL7gsbraPNU3qa8kaXfx7vaoCGgzBI8AAAAAAHRQbr8l1v/4et9xl1y73R4dKqkKOh9/OF9at04KC5MuvlhPfPWEJOnqoVfrnZt+LZvV2rKFdyUJCcZraankNkLfHgk9JEkF9sYHxkBHRPAIAAAAAEAH5XDVBo2/+uBbLdxa2OD9b6zYG/J8t73fGQdDhmib7Zje3/a+JOkXZ/6iRers0pKNZdVyu6WyMkns8Yiug+ARAAAAAIAO6uEPvg14vzmvtMH7/7kyN+T5/isWGgdjx+rJpU9KkqYOnKox3ceceJFdXXS08UeSiook1U61Lq8heETnRvAIAAAAAEAnUekIHhzjL9IWHAOEuV3qvXaZ8flrr9C7W96VJP3yrF+2fIFdVWKi8VpSIkmKjaDjEV0DwSMAAAAAAJ1EZY2zweu2sOABMWMPblViwUEpJkYLs50qd5QrOyFb43uOb60yO72gnTYjI41X72RrX8djjatGDpej7QoD2hjBIwAAAAAAncTxllpbQwSP0zZ/aRxMmaJnvn1VknTlkCuZYt0M9f6N+SZbe4NH3x6PEl2P6NwIHgEAAAAA6CTW7CtWjdOtr3cf1aXPfKW1+4oDrqfGRQZ+wOPRpfvWSpIWXX2avtj1hcIsYfrhmB+2VcldQ53gMcIaIVuYTZJkr7G3V1VAqyN4BAAAAACgg4qJsAade+KTrbru5a/17cFS3fef9QHXMhOiJElDsxI0I6e3JtYUKvrYUSk8XC9HbJIk3TLyFp2Sfkqr196l1AkeLRaL4iPiJUklVSXtVRXQ6ggeAQAAAADogDweT8hhMq8v32se7ztaEXDNd//lI7vrsctP0T83/FOSVHjZJL297T1J0u2jb2+liruwOsGjJPVO6i1J2ntsbzsUBLQNgkcAAAAAADqYyhqXDhRXyhM0xSTYQ+9tMo/LqoxBJvFRNunzz2VZtEiS9MlVp8rlcWlU5ijlZOe0Ss1dWojgsVdiL0nS/tL97VER0CZs7V0AAAAAAABomnP+sEiFZdWNuvdfq3IleTTrvAEqrTSmXsdH2aTnnzdu+OEP9VHEHknS5YMub41yESJ4TIhMkMQej+jcCB4BAAAAAOhgGhs6+vxr1X5t2F+iMO+6x/TDB6WPP5Yk7b9+qj5ccpUkaeqgqS1aZ1flqduKGiJ49E22Lq9hqjU6L5ZaAwAAAADQgQSFWo205VCpyqqMjsc+778peTxSTo7+r/xzuTwujc4arXHdx7VkqV2OxVLPhRDBY1xEnCSp3EHwiM6L4BEAAAAAgA6kyuFu9L1hbpduX/Wevnz5Dr383m+ko0dl8bjV7b1/S5JqfnKX/rnRGDBzf879stSbnOGE0PGILoql1gAAAAAAdCDlNc5G3WfxuPXch/+ni79bLknqV5ynXn9/QL8/e4ZsBflSQoJe71Oskm0lSo1O1bRTprVm2V1bqOAxwgge7Q72eETnRccjAAAAAAAdyKo9RY26b+q2pWbo+MmgCSoPj9KQw3v193cfkyS5Lpuq3yz/P0nSPWfcI1sYvUmtpqGl1nQ8ohMjeAQAAAAAoAM5UFxx3HsinA7du3SuJOnVsVfox1f9QtOm/175cSmSJE94uD6cNkL7S/cr0hqpn5zxk1atucvzBY8Oh3nKt9SaqdbozAgeAQAAAADoQLonRR/3nou+W6b+RQdVFJ2gv0y8XpK0JaOfJs18STd8/zeqWv61fn/4A0nSXaffpcSoxNYsGZGRxmtVlXkqKz5LkrS/dH97VAS0CYJHAAAAAAA6kPLqhvd4jHA6dP///iFJemvkhSqNijOvVUREa3mfUVrbrVorD66UJP1o7I9ar9guKmjueKI32D12zDzVP7m/JGl38e42qQloDwSPAAAAAAB0IOXVrqBz4/ulmMc/WPOhepUU6FhUnF46/ZqQ3/H6hlclSdOGTVP/lP6tU2iXVM9U8BTvfz5FtftzpkQb56qcVapx1YT6FNDhETwCAAAAANCBVNSZan316B56644cSVJClV0zV70nSXp80kyVRMeH/I4l+5ZIkm4ddWsrVgqTL3gsLjZP+aZaSwyYQedF8AgAAAAAQAdir9Px+ItLhprHs1b8R6mVpdqTnKUPTjk35Oc9tlztLNqpMEuYzuh5RmuWCp+EBOO1tNQ8FWGNUITVGDrDgBl0VgSPAAAAAAB0IPZqR8D7tDhjcEmUo0rTNi2QJP3h7JvlCrOG/LwnYrMkaXK/yeZyX7SyEFOtJSkuwth/k+ARnRXBIwAAAAAAHYi9KvRwmSnfrVBKZany41I0f2D9nYyllq8kSTk9c1qlPoTgCx5rAvdyJHhEZ0fwCAAAAABAB1J3qbUkyePRD9b8V5L0z9MukcMaLkna8tgUJceEm7c5LPtV4t4oSbp55M2tXywMBI/ooggeAQAAAADoQOoutZYkLV2qkfk7VGmL1L9GXmSejomwac3DF5jvS2zvSpIuHnCx+ib3bfVauyqPp86J4wSPJdUlbVAV0PYIHgEAAAAA6EDKQ3U8LjD2dvx27Dnqf2q/gEvWMIskyaVilVu/lCTdffrdrVtkF2Wx1HOhnuAxOyFbkrTv2L5WrApoP7b2LgAAAAAAADSevTrEHo9vvSVJGvfD65UWHhHyc6W2/0oWtyLc/XXxwItbs0TUVU/w2C/ZCIn3HtvbxgUBbYPgEQAAAACADuKIvVp7jpQHnqyslHbsMI4vvlgPhccr71iVbjuzdim1W1Wy276QJMU7r2yjamGqJ3hMikqSJJXVlLVxQUDbIHgEAAAAAKCDGPubBebxPecP1LWje0pff21sKpiSIqWnK9ti0QezJgZ8LiVlu/ZXlijMk6gJ3S9p67LBcBl0UezxCAAAAABAB3TLhD7qlRojfWns26iLL653k8GktOWSpBjXBJVW1LcRIVoNwSO6KIJHAAAAAAA6gPI6ezvGRlqNTsf//tc4MXZsyM9VO6u1Jn+JJCnOOVlJMaH3gEQr8gWPTqfkdpunY8NjJUnljvJQnwI6PIJHAAAAAAA6gFMe+dw8/v7YbEXarFJBgbRxo3Fy2rSQn/tw+4cqd9gV5klShGeAfjJ5YFuU26V55Ak8EeEX9joc5iEdj+jsCB4BAAAAADiJeTweFZZWBZz7v2tHGAfbtxuvfftKPXqE/Pzr61+XJMW6zpJFVqXQ8dhq6l3EHh5ee+y33JrgEZ0dw2UAAAAAADgJvbP2gOau3Kd1uccCzj88dWjtm0WLjNfhw0N+x5GKI/p8l9EpGee8QJJkDWOPxzbn3/HoFzzGR8ZLko5VHWvjgoC2QccjAAAAAAAnmWU7j+j+tzcEhY6SMVTGtG6d8XrhhSG/5+/r/i63x62hacMU4eknSXK43CHvRSuyWmu7HisqzNO9E3tLkvLt+apwVIT6JNChETwCAAAAAHCSWbO3uN5rNqvff5Xftct4HTAg6D6Px6O5m+ZKku4c80PzfHm1q2WKRNMkJxuvxbX/2aZEp5jLrQ+WHmyPqoBWRfAIAAAAAMBJpqi8uhE3FdXu8ThoUNDlzYc3a2PBRoWHhWvaKbWDZyLDiQLaRUqK8VpUZJ6yWCxKiEyQxD6P6JzY4xEAAAAAgJNMeU3orsSEKL//Gr9xo+R0GoNl+vYNuvetb9+SJJ3d+2xlxWfp99eO0I6CMo3tndwqNeM4QgSPEgNm0LkRPAIAAAAAcJKpb/xLVLi19s2ePcZriGXWksyhMtOHT5ckfW9sdkuVh+PweEKcjI01XisC93IkeERnRn81AAAAAAAnGUs9yWPAROr1643Xfv2C7ttdvFtr8tZIkib1ndTC1aE+lvr+g5NqJ1v7TbWWaoPHckd5a5UFtBuCRwAAAAAATjKVjtCTp39/7YjaN0uXGq+TgoPFBbsXSJJyeuaod1LvFq8PzVBP8BgbbnRC0vGIzojgEQAAAACAk0zAXo5+zhrYrfZNbq7xOnhw0H3PrX5OknRen/NavDY003E6Hgke0RkRPAIAAAAAcJLpFh/Z8A3l5dKRI8Zx78COxm1HtpnTrO8+4+5WqhBNVk/w6JtqXVxZ3NYVAa2O4BEAAAAAgJOMO9RwEn++ZdY9e0qJiQGX3t3yriTp/H7nKzMusxWqQ7NEesPkOsFj70QjON5zbE9bVwS0OoJHAAAAAABOMp4QY5FHZifVvvnuO+P19NMDJtE43U79ddVfJUlXDbmqNUtEU9XT8ejbg3N/6f62rghodU0KHl944QWNGDFCCQkJSkhIUE5Ojj799FPzelVVlWbNmqXU1FTFxcXpmmuuUUFBQcB35ObmaurUqYqJiVF6eroeeOABOZ3Olvk1AAAAAAB0Au4QwWP3xKjaN3v3Gq/Z2QH3vLPlHRWWFyo5Klk3jbipFStEQ0L8x1dv8JgYaXSsllWXtXJVQNtrUvDYs2dP/e53v9PatWu1Zs0aTZo0SVdccYU2b94sSbr33nv10Ucf6e2339aSJUuUl5enq6++2vy8y+XS1KlTVVNTo+XLl2vOnDl6/fXXNXv27Jb9VQAAAAD+P3t3HSdV9f9x/DUz270LS3d3p5QoIkoKKmKifk3E/Bp8jZ/drdiBgSAqSEp3t3R37JLbu7NTvz/u7OwO27DBwvvpg8e995wzM+euuwvzmc85HxEpw3Jaam1zZGmcPt04tmnjNWbC1gkA3N3qbgJ9A4trepILU16d+ezxmGBNKJ5JiZSinMtk5aJ///5e12+88QZffvklK1eupFq1anz//ff89ttvXHXVVQD8+OOPNG7cmJUrV9KpUydmz57Ntm3bmDt3LhUrVqRVq1a89tprPPvss7z88sv4ZfwQioiIiIiIiFzG7A5n9janu83hgN27jfNevTz9Z1PPMmP3DABuanpTsc9RCimXwGOofygAienKeJRLz3nv8ehwOBg/fjzJycl07tyZdevWYbPZ6JXll16jRo2oUaMGK1asAGDFihU0b96cihUresZce+21JCQkeLImc2K1WklISPD6IyIiIiIiInKpSkl3ZGurGuHOYIyNBacTLBbI8v562eFlWB1W6kTWoWPVjiU1VSmo3AKPfu7Ao5ZayyWoUBmPAJs3b6Zz586kpaUREhLCpEmTaNKkCRs3bsTPz4+IiAiv8RUrViQmJgaAmJgYr6BjRn9GX27eeustXnnllcJOVURERERERKRMygg89mlaiQGtqjBv+wmeubaR0XnkiHGsVMkIPrr9sukXAHrU7IHJlOeiXykN+Sy1TkxPxOVy6f+dXFIKnfHYsGFDNm7cyKpVq3jooYe466672LZtW3HMzWPUqFHEx8d7/hw+rEpPIiIiIiIiculKSTeKsHapX57rm1fmg5tbEh7ka3QuWWIcmzXzjE+0JjJzz0wA7mtzX4nOVQoon8Cj0+XUPo9yySl04NHPz4969erRtm1b3nrrLVq2bMknn3xCpUqVSE9PJy4uzmt8bGwslSpVAqBSpUrZqlxnXGeMyYm/v7+nknbGHxEREREREZFLVUbGY5CvJXvnzp3GsVMnT9PMPTNJsCZQM7wmHap2KIkpSmH5+xvHtDSv5mC/YCoEVwBg95ndJT0rkWJ13ns8ZnA6nVitVtq2bYuvry/z5s3z9O3cuZNDhw7RuXNnADp37szmzZs5ceKEZ8ycOXMICwujSZMmFzoVERERERERkUtCstXIeAzyyyHweOCAcaxZ09O0+uhqAHrX7Y3FnMNjpPRFRRnHU6eyddWJrAPA4Xit8JRLS6H2eBw1ahTXXXcdNWrUIDExkd9++42FCxcya9YswsPDuffee3nyySeJiooiLCyMkSNH0rlzZzq5P4Xp3bs3TZo04Y477uDdd98lJiaGF154gREjRuCfEfkXERERERERuYw5nS52xhiFRmqWC/butNth40bjvG5dT/PEHRMBY39HuUhVrmwcjx/P1pV1n0eRS0mhAo8nTpzgzjvv5Pjx44SHh9OiRQtmzZrFNddcA8BHH32E2WxmyJAhWK1Wrr32Wr744gvP4y0WC9OmTeOhhx6ic+fOBAcHc9ddd/Hqq68W7V2JiIiIiIiIlEFOp4vn/95Msnupdc1yQd4Ddu+GkychOBiuuAKA2KRY9p3dB0C/Bv1KdL7iLc+6MBmBxxyK66qytVyqChV4/P777/PsDwgIYPTo0YwePTrXMTVr1mTGjBmFeVkRERERERGRy8KGw3GMW5253NbXcs4OaRnZjo0bg4/xln78lvEANK/QnPCA8JKYppyPcuWMY0KCkbnqkxmSCfV3Bx6V8SiXmAve41FEREREREREiobd4fS69rWck0I3dqxxdNdScLlc/LDxBwBua35bsc9PLkDWLeZsNq+uMD9jqbWqWsulRoFHERERERERkYtEqs3hdW3KunbX5YING4zzW24B4EDcATbFbsJsMnNnyztLappyPvz8Ms/T0726PBmPWmotlxgFHkVEREREREQuEqnpjtw7N26EY8eMzLlmzQCYuWcmAK0rtaZyaOUSmKGcN1/fzHOr1asro7hMnDWuBCckUvwUeBQRERERERG5SKTkFXhctsw4Xn01hBmBqim7pgAwuPHg4p6aXCiTKTP4eE7GY83wmgCeIkEilwoFHkVEREREREQuEim2PAKPa9YYx/btATiRfIJ5++YB0Kden+KemhSCy+XKuSNjn8dzAo91o+oCcDDuYHFOS6TEKfAoIiIiIiIicpFIyyvj8ZzA47x987A5bTQu35jWlVqXwOwkPyZMeQ/I2OfxnMBjVGAUAGfTzhbHtERKjQKPIiIiIiIiIheJXJdaJybCjh3Gebt2APy25TcArq17rXcRGrl45RJ4jAiIACDFlkK6Ix2RS4UCjyIiIiIiIiIXiRSbPeeOdeuMqtbVq0PFijicDhYfXAzAbS1uK8EZygXJCDyeU1wm3D/ccx6XFleCExIpXgo8ioiIiIiIiFwkcq1qfc4y63/2/EOCNYEw/zBaVmxZQrOTC5ZLxqPFbPEEH8+marm1XDoUeBQRERERERG5SOS61HrpUuPoDjyO3zIegKFNh+Jr8S2JqUlRyCguk5qaratcUDnAKBokcqlQ4FFERERERETkIpE147FimDtIlZgI06cb59ddh81h489tfwJG4FHKkMhI43g2e1Zjncg6AOw5s6ckZyRSrBR4FBEREREREblITN98HIA2NSKYPKKr0bh4MTgcUKcOtGzJ5hObsTqshPiF0LN2z1KcreTGlVtHOSOrkTNnsnVVD6sOwPGk48UzKZFSoMCjiIiIiIiIyEUmzeakUniAcTF7tnHs1QuAidsnAtCjZg/MJr2tv5jkW1w8I/B4+nS2rlC/UACS0pOKeFYipUe/oURERERERERKyOkkK7Wem069/83A5co1L477uxvLbklPh7/+Ms579wZgzTGj0Ezf+n2Lda5SDEJCjGNKSrauUH8FHuXSo8CjiIiIiIiISAn5Ydl+AOxOF1uPJWTrrxRmZDnWq+AOUC1bBkePGnsDXnstZ1LPMG/fPACuqH5FyUxaik4uVa0BQvyM/+cKPMqlRIFHERERERERkWJyMtHK3pOZgSRnliTHhDRbtvF29wCL2b1md7xRvZo+fSAkhKWHluJwOagbWZeWlVoW27ylmOQReMxYap2YnliSMxIpVgo8ioiIiIiIiBST6z9dwtUfLOLg6WQA/H0y34Y7nNmXWjucTgB8MgKPixYZx1tuAeCbdd8Axv6OUgYp41EuMwo8ioiIiIiIiBSTk4lWAL5evA+A8iH+nr4c4o6k2YzAo5+PGY4cgZ07jY4OHUixpbD44GIA7m97fzHOWopNAQKPCdbsS/BFyioFHkVERERERESK2W+rDgFZMhmBt//Z4TUmzeYg1eYAIDLYD+bMMTrat4dKlViwfwGJ6YlUDa1K2yptS2bicl5yrRuUR+CxSmgVAI4kHCmmWYmUPAUeRUREREREREqIzeH0nG8/7p3ZFpdi7PnoYzYR6u8DS5caHV26ALD66GoAetbuiY/ZpwRmK0UuI/BotWbrqhdVD4BD8Yew2rP3i5RFCjyKiIiIiIiIlBCbIzMVrk50sFff2RQjCy4iyBeTywUTJxodvXsDMGnHJAC61+heAjOVYuHvXmqfQ8ZjVGCU51zLreVSocCjiIiIiIiISAnYfCSeV6dt81w3rxrO85M28+aM7QCcTTaCUZFBfrBlC8TFQUAA9OpFuiOd7aeMcX3q9SnxuUsRyWOptcVsIcg3CFBla7l0KDdbREREREREpBi4ztno78avlntdHzmbyrqDZwGoGx1MgK8FcAceM/Z37NULfH2Zv2cmdqedyIBIqoVVK/7JS/HII/AIEOoXSootRZWt5ZKhjEcRERERERGRYuA4p2y11e70urZn2e/x2b82ExOfBkDliABYs8bo6NABgKWHjP0eBzUahMlkQsqofAKPGZWtE63KeJRLgwKPIiIiIiIiIsXAkWtpY8Pek8nnXBtZbiEWYNYso/GqqwBYfHAxAG0qtynaSUrJyi/j0T8U0FJruXQo8CgiIiIiIiJSDJzOvPuTrHav64U7TwLQdtUcY3/HcuWgUydOp5xmyaElAPRr0K84pipFzEUuQec8qlqDsdQa0FJruWQo8CgiIiIiIiJSDPLLeDzXiUQjGFV/8jijYfBgsFiYsXsGAI3KN6JWRK2inKIUsXxXwecTeIwMjATgRPKJIpyVSOlR4FFERERERESkGJy7x2NBdNu/nuZ7NhoX//0vALP2GsuuhzQeUlRTk9ISHm4c4+Jy7K4XWQ+APWf2lNCERIqXqlqLiIiIiIiIFANnIQKP0UlnuGftFO5fPdFouOkmaNAAp8vJ8sNGNewrql9RHNOUklSunHE8fRpcrmwpklXDqgLKeJRLhwKPIiIiIiIiIsWgoEutq8fFMPnnJ4lKTQDA2qMn/qNHA7AxZiP74/YT4hdCl+pdim2uUkIyAo9WK6SmQlCQV3dGVWvt8SiXCi21FhERERERESkGBc14fGneN0SlJpDi68+zfUaSNHUGREcDeLIdu9boSnhAeLHNVUpISEjmeUJC9m534FFVreVSocCjiIiIiIiISDEoSMZjw5MHuGbPagCG3fImv7e8Fh8fi6ffs8y6mpZZXxJMpswCMzZbtm5VtZZLjQKPIiIiIiIiIsWgIMVlBm+ZD8CCOm35t0pDAHwtxr5/LpeLZYeXAdrfsazJM+bs62sccwg8ejIercp4lEuDAo8iIiIiIiIixeB4fFreA1wuT+BxUtOrPM0+ZuOt+oojKzgUfwg/ix8dqnYotnlK0TFhyn9QHoHHUH9lPMqlRYFHERERERERkWJw5/er8+xvd3Qb0SlxWC2+zGrQ2dOekfG4YP8CAAY0HOAJSMklIGOpdXp6tq6MjMcEa/b9H0XKIgUeRURERERERIpBqs2RZ3+PfesBmNWgM1YfP0+7yWQEHsdvHQ9A9xrdi2mGUiryyHisGloVgHhrPPFp8SU5K5FiocCjiIiIiIiISCnotWcVAGfbdc7WdzrlNFtObAFgWPNhJTovKWZ5ZDyG+odSMbgiAPvO7ivJWYkUCwUeRURERERERErYsjsa0fjkAZyYaPTQnfhZvN+e/7HtDwCaRDehfFD50piiFJc8Mh4Bz//vs2lnS2pGIsVGgUcRERERERGRYtC2ZmSufeHLFgLgaNSIjp2bkO5wevVP3z0dgGHNlO14yckj4xEgIiACgLOpCjxK2afAo4iIiIiIiEgxSLbac2z39zETtNCoZu17w6Bs/UnpSczZOweAQY2y98vFz5VXZz4Zj5GBRsBaGY9yKfAp7QmIiIiIiIiIXGrsDif7TyVna68THcyMu1thrn2L0dC1a7Yxs/fOxuqwUieyDk2jmxb3VKUIuesC5a2AGY9xaXFFMieR0qSMRxEREREREZEiduB0Cla7k0Bfi1d7sJ8PAevXQkICVKkCvXple+zfO/4GYFDDQZ4K13IJySfjMSogCoBTKadKakYixUaBRxEREREREZEitjMmEYAGlUK92i1mE6xda1x06+bJfntlgJHZOO/JrkzbNQ2AgY0GltBspUTlk/FYK6IWAHvP7i2hCYkUHwUeRURERERERIrYjpgEABpV9A48tq8VmRl4bNfO037XFbU48HZfDiWv52zaWcoHleeK6leU2HylBOWT8Vg3qi4AB+MOltSMRIqNAo8iIiIiIiIiReyz+XsAqB0d7NX+5DUNYd0646Jt22yPy1hm3b9Bf3zMKstwScon4zEq0FhqreIycilQ4FFERERERESkmJxJTueW9tUBmPtkDwITzsKBA0ZnmzZeYx1OB39t/wuAgQ21zPqSpeIychnRxyciIiIiIiIixeTmdtWpGx3MywOaEuBrgVnLjI769SE83Gvsl2u/5GjiUUL9Qrmm7jWlMFspKi5XHp2h7uX3iYk5dkcGRAJwNvUsLpdLBYakTFPGo4iIiIiIiEgRcjozo05hgT6YTCYj6AiwZo1xzLK/I8DxxOO8MP8FAJ7o9ARBvkElMlcpWgWKEUZEGMe4uBy7IwONwKPD5SDZllwk8xIpLQo8ioiIiIiIiBSh/aeNYFGAr5moID/vzrFjjWOnTl7NI/8ZSbw1nnpR9RjVbVRJTFNKSz6Bx0CfQAJ9AgGITYotmTmJFBMFHkVERERERESK0NZjRkXrNJsTH0uWt91bt8KOHWCxwB13eJrXHlvLX9v/wmwyM37IeAJ8Akp6ylKSMpbY5xJ4NJlM1ImsA8CeM3tKaFIixUOBRxEREREREZEiNOqvTTl3jBtnHHv1gkhjOa3L5eLFBS8CMKjRINpWyV7pWi4x/v7G0WrNdUj1cKMg0fGk4yUxI5Fio8CjiIiIiIiISBGx2h0kpzuyd9jt8NNPxvkNN3ialx9ezsw9M7GYLLzW87USmqWUqoyq1jZbrkNC/EIASE7XHo9StinwKCIiIiIiIlJEvl60L+eORYvgyBEIC4NhwzLHr/sagKHNhtIkuklJTFFKm6+vccwj8BjqZ1S+TkzPufK1SFmhwKOIiIiIiIhIEfl741HPebf65TM7vvnGOA4bZgQfgbi0OP7Y9gcA/2n9nxKbo5QEV+5dGYHH9PRch2RkPCalJxXlpERKnAKPIiIiIiIiIkXE4cwMOPn7uN9yJyfD7NnGeZaiMt+v/540exp1I+tyZa0rS3CWUlxMmPIfVIil1go8SlmnwKOIiIiIiIhIEWlZLcJzXj0qyDhZudKoYFy5MnTsCIDT5eTzNZ8D8EiHRzCZChCwkktDAZZaK/AolwoFHkVERERERESKiN3p9Jzf06W2cbJtm3Fs3x58fACYvXc2B+IOEOwbzPBWw0t4llKqChF41B6PUtYp8CgiIiIiIiJSRE4lGfv2vdSvSWbG49SpxrFNGwBcLhdvLnkTgFub30pEQERJT1NKU8ZS6zz2eIwOigbgeOLxkpiRSLFR4FFERERERESkiKzefwaAciHu4JLLBatWGecDBwKwIWYDSw4tAeDRjo+W+ByllBUg47FeVD0A9pzZUxIzEik2CjyKiIiIiIiIFAGXK7OwTPOq4cbJxo2QkAD+/tCkCQC/bvoVgL71+9KsQrOSnqaUtgIEHisEVwDgbNrZkpiRSLFR4FFERERERETkAljtDgCyFLQmMsid8bh0qXHs1cuzxPafPf8AcFfLu0psjlKyssSgsyvAUuuMPR7T7GnYnfYinJlIyVLgUUREREREROQ8rD1whlavzqbpS7PYeizeq7CMxeKuUr19u3Fs2hSAvWf2suPUDgB61OpRovOV4leg4uSFKC4DkJyefIGzEik9CjyKiIiIiIiInIcbv1pBXIoNu9PF2//sIEvcEUtGBGrhQuPYoQMAs/bOAqBbjW6e5bRymSlA4NHP4oeP2aiAnpSeVBKzEikWCjyKiIiIiIiIFFJCmnfQyGI24ciyvtZiNsGRI0bGo9kMV10FwJSdUwDoUVPZjpetjMBjHkutTSaTJ+sxMT2xJGYlUiwUeBQREREREREppBYvz/a69jGbcGTZ5NFsMsG8ecZFu3YQGUl8Wjxz980FYGizoSU2V7nIZOzxmEfGI0CoXyigjEcp2xR4FBEREREREblAZpMJmyNzrbWP2QSzjGXV9OoFwNjNY3G4HNSLqqdq1pezrMVl8qhCk5HxGJ8WXxKzEikWCjyKiIiIiIiIXKCTSVa2HM0MEJnPnIY//zQu+vTB5XIxfst4AO5ocUdpTFEuFqFGJiMuFyTnXjimTmQdAE8xIpGySIFHERERERERkQJYtOskv648mGPfhkNxHItLy2wYO9ZYStuoEXTtyoG4Ayw5tASAm5veXBLTlVKUex4jEBQEPkbhGOJzz2ZsUbEFAFtPbi26iYmUMJ/SnoCIiIiIiIjIxcxqd2A2mbjrh9UABPpaqBjmT2yC1Wvc6STjekCLyvD8I0bj3XeDycS4LeMA6FStE43KNyq5yUuJMhVokAkiIuDUKYiLg6pVcxxWPqg8APFWLbWWskuBRxEREREREZFc2BxOer63kFPJmRWIn/rjX8qH+GUbe9o9pnHMHjhwAAID4REjADlx+0QA7mxxZ/FPWi5+4eGZgcdcZBSXSbSqqrWUXYVaav3WW2/Rvn17QkNDqVChAoMGDWLnzp1eY9LS0hgxYgTlypUjJCSEIUOGEBsb6zXm0KFD9O3bl6CgICpUqMDTTz+N3W6/8LsRERERERERKULH4lI5Fp9Gut3p1W53Zl9MO2b5AQCunvid0XDddRAUxJYTW1h3fB1mk5kBDQcU95SlLAgIMI5Wa65DQv1V1VrKvkIFHhctWsSIESNYuXIlc+bMwWaz0bt3b5KzbIb6xBNPMHXqVP744w8WLVrEsWPHGDx4sKff4XDQt29f0tPTWb58OT/99BNjxozhpZdeKrq7EhERERERESkCPpac3zY73IHH8iH+Xu0dD22mwbI5xsVjjwGw+OBiAK6sdSVVw3JeViuXmYzK1jZbrkMyqlonpivjUcquQi21njlzptf1mDFjqFChAuvWraN79+7Ex8fz/fff89tvv3HVVVcB8OOPP9K4cWNWrlxJp06dmD17Ntu2bWPu3LlUrFiRVq1a8dprr/Hss8/y8ssv4+eXPV1dREREREREpDTYHc4c223udofTu/+2jf8YJ8OHQ/fuAJ5q1lfWvLJY5ihlkK+vccwj8BjmHwbA2dSzJTEjkWJxQVWt493Vl6KiogBYt24dNpuNXr16ecY0atSIGjVqsGLFCgBWrFhB8+bNqVixomfMtddeS0JCAlu35lypyWq1kpCQ4PVHRERERERELh7vzNzB+7N25j+wjLE5cq5PnGYzAo4Wc2Y5kc4H/2XAdiO7kYcfBsBqt7L6qFGU5pZmtxTjTKVMyQg8pqfnOqR2RG0ADsYfxO7U9nRSNp134NHpdPL444/TpUsXmjVrBkBMTAx+fn5ERER4ja1YsSIxMTGeMVmDjhn9GX05eeuttwgPD/f8qV69+vlOW0RERERERIrYzphEvly4l88X7OF4fGppT6dI2Z05ZzyCUZzYx2y8rW4Su4+vJr0JwLrrhkL79gBM2TkFq8NK1dCq1IuqV/wTlrKhAEutq4ZVxWKyYHfaiU2KzXWcyMXsvAOPI0aMYMuWLYwfP74o55OjUaNGER8f7/lz+PDhYn9NERERERERKZjFu056zveeSM5jZNljs+ec8QgQ4u+DxWyiSsIJfp7wIuHWZHaWr8HCu5/0jFl5ZCUAgxoNwmQy5fZUcolxuXL/vgEKtNTabDJ7Csxon0cpq84r8PjII48wbdo0FixYQLVq1TztlSpVIj09nbhzysHHxsZSqVIlz5hzq1xnXGeMOZe/vz9hYWFef0REREREROTi4OeT+dbyTEruS0fLIlseGY+JaXY+u7U1L8z7jvIp8ewqV4Nbhr1FvfqZBWTWHV8HQPsq7Yt9rlL6ChxbLsBSa4BQP3fg0arAo5RNhQo8ulwuHnnkESZNmsT8+fOpXbu2V3/btm3x9fVl3rx5nradO3dy6NAhOnfuDEDnzp3ZvHkzJ06c8IyZM2cOYWFhNGnS5ELuRUREREREREpYstXOhkOZxS9W7TtdirMpevZc9njM0MaUxLW7jazGUX0eoXytqvRvUQUAp8vpCTy2q9KueCcqZUsBMh4BZTxKmVeoqtYjRozgt99+Y/LkyYSGhnr2ZAwPDycwMJDw8HDuvfdennzySaKioggLC2PkyJF07tyZTp06AdC7d2+aNGnCHXfcwbvvvktMTAwvvPACI0aMwN/fv+jvUERERERERIrNcxM3M/XfY55rZ35LTMuYc6ta+5hN2J1Z7nH0aCwuJxsrN2BdtSYsv6cDZnfBma0ntpKUnkSQbxCNyjcqyWnLxa4AezyCMh6l7CtUxuOXX35JfHw8V155JZUrV/b8+f333z1jPvroI/r168eQIUPo3r07lSpVYuLEiZ5+i8XCtGnTsFgsdO7cmdtvv50777yTV199tejuSkRERERERIrV/lPJpNudXkFHAH8fSynNqHjYnN6B1OjQzISZoPRUGD0agB/b9gegSkSgp3/C1gkAdKvRDYv50vq6yAUq4FLrEL8QAJLSk4p7RiLFolAZj/lujgoEBAQwevRoRrt/+eakZs2azJgxozAvLSIiIiIiIheJBTtOcPeYNbSvFZmtz2K+tAqovD5tm9d1xbAAjsenAXDFwU2QlERsSBTTGnfP9thVR1cBRmEZES8FXGod5m/UuIi3xhf3jESKxXlXtRYREREREZHL091j1gCw5sDZbH0FWWn917oj1HpuOsv3nGL9oezPcbHYeiye3Se8M82qRAR4zt+1bgYgre8AggL9mfV4ZvDxdMpp5u+fD8AV1a8ogdlKmVLApdbVwoyCvgfiDhTzhESKR6EyHkVERERERETyUpA9Hp/6418Abv3OyAj85d4OdKsfXazzOh9vztiera1CqBF4DLClEbVyCQA1H7yLzVde6TVu5ZGVOFwO6kfVp0XFFsU+V7m45PtTUMCl1vWi6gGwP27/hU9KpBQo41FERERERESKTEG26DrX3G2xxTCTC3dVo4rZ2u7tWpsAXzMv2HfD6dNQoQJ07Jht3Pcbvgegc/XOxT5PuZgUcKuBAi61jgiIACDBmnABcxIpPcp4FBERERERkQI7Hp+aZ78zn7jj4TMp2drSbM4cRpa+d2bu8Jz3blKRp3o3pHpUEOtfvIbAq94wOm69FQIDvR6Xaktl3v55ANzX5r4Sm6+UIQUMPKqqtZR1yngUERERERGRAvt4zu48+135LDL9YuHebG2/rz18QXMqDtd/soR0uxEQ7du8Mt/c2Y6GlYwgUNCuHZiWLTMGPvJItsfO3DOTBGsClUIq0bFq9mxIEfzd1dHT0vIcFurvDjymK/AoZZMCjyIiIiIiIlJg+QUJ88t4XH/w4i0mk9W245lLW6ND/b07v/3WOPbpA3XrZnvsH9v+AGBQw0H4WnyLbY5ShoUZ1apJyHsJdUbGY1J6Up7jRC5WCjyKiIiIiIhIkclvj8ezKXkX07gYnHsPAb6WzAubDX74wTh/9NFsj7U5bEzZOQWAYc2HFdscpYyLiDCOcXF5DvNkPGqpdYmz2h2lPYVLggKPIiIiIiIiUmA9GuRdfdqZz3aNbWpE5th+JvniCUja80rbnDMHkpIgKgquvTZb9+Sdk0m2JRMVGEXXGl2LcZZSphU08OinpdalYdKGIzR9aRbTNh0r7amUeQo8ioiIiIiISIFl7HN4rvu61QYgIS2fKr1BOS89bvPaHA6eTgYg3e4kNb30so1sDu/oabXILMVjJk0yjoMHgzn7W+r5++cDcGuzWzGb9Jb7cpVvcffwcOMYH5/nsHJB5QBId6RzJvVMEcxMcuJwujx7ugI88fu/2J0u/vvHv6U4q0uDfguKiIiIiIhIgTlzyQYM9PMB4J8tMRyNy73ydZot94Bij/cW4nC6uOLt+bR7fY5XIKAk2RyZ9zj8iloMbV/duHC5YNUq4zyHbEebw+bZ37FHrR7FPk+5+JhMBRyYUVwmPe9M3yDfICqHVAZg75nshZmkaNz1w2q6vjOfxDQbU//NzHJMs5XO76BLiQKPIiIiIiIiUmC5rUKuEh7gOf/PT2tzfbw1n2BiQqqNU0lWktMdeQYwi1PWjMf/698EX4v7rfO+fbB5M/j6Qvfu2R636OAiTqWcokJwBQY2HFhS05WyyNed+WvLO0MYoEJwBQBlPBYTp9PF0j2nOJFoZcbm44wct6G0p3RJUeBRRERERERECsyZwxrSm9pWo2KWwOPOmNwr9eaXxRibmOY5f2P6tvOY4YWzuzMefcwmTFlT2FasMI5t2kCFCtkeN3nHZAD6N+ivataSt4zAYz4ZjwAhfiGAKlsXhbPJ6Szbcwqn08Wu2ESG/7ial6Zs8fTP2XYi22PyK5glefMp7QmIiIiIiIhI2ZFT4LFKRCDmLAE6fx9LtjEZ5u0w3tg3qxqGn8XM+kNxXv0T1hzxnM/dnj0IUBIyMh49mY4Z/v7bOPbsme0xCdYEft38K4CyHSV/fn7GsQAZj8F+wQAk25KLc0aXhaHfrGBXbBKVwwOISUjLthdnpXD/bI85kWilYlhAtnYpGGU8ioiIiIiISIHtiMleXdfPx/utpY8l/43uqoQH8urAZtnaNx4+C0CoNZn3pn9M4h13w5mSXWKasQ+lv+85b5k3bTKOvXple8xXa78iLi2OGuE1uLZe9v0fRbwUYql1RsZjcroCjxdqV6yRNXo8PnvQEXLe0zEmPi37QCkwZTyKiIiIiIhIga3enz0IGOhrwZwl1uh3bqZgDrrWL09qDoVm1h+Kw+J08O1fr9Hp8BbYAqSnwO+/X8i0C+WhsesBiEvJEhQ6ehT2uot7NPMOmLpcLr5c+yUAIzuMxM/iVyLzlDKsEIHHYF8j41FLrS/MqSRrvmP+XJeZce1vT6ff9iXs+WovLd94tDindklT4FFEREREREQuSESQLyYyI4+d65bLdazJZBSH7l4/GnsulWoGb5lvBB0zTJkCyckQHFxkc87LnhM5BHiWLQOnE9q2hYoVvbr+2fMPB+IOEOATwP1t7y+ROcrFLd99AQuxx2NG4FFLrS/MwdMpBR7rb7Py6+8v0v7oNtZtbAQKPJ43LbUWERERERGRArE7ci4M06p6BDWiggCoe+ow12xflmNAJT7V5lneWCHMn3oVQnjuukbeg1wuHlr5BwDvd7ud1Oo1IS0NZs4suhvJw44shXEigrIUiNm+3Ti2aJHtMWM2jgHgjhZ3EOYfVpzTk4tc/psMuBVij0cttS4aVnv2DOvcPLzyD9of3UaybwBLa7Uix3XZUiAKPIqIiIiIiEiBpOSwNBqgXIg/NcoFcY3tODPGjGTg649CzZqweLHXuOPxqQBEBvkS5GcswLupbTWvMS1idlPn7DGSfQP4od1ATnS9yuhYsqSI7ya7Q6dT6PNx5uvMf+rKzM6MwGerVl6PSbGlMHmnUc16eKvhxTtBuXRkZDw6HPkGtSICIgA4mXKymCd1aduZw/60OWl7ZBsj3B9+PNX3CT7qdruRqi3nRYFHERERERERKZCvF+3NsT3U3wgiPrJxGv4Ou9EYEwN9+2ZmCgLjVx8G4GyWvRPPXW3df7sRrJxftz0pfoEcaNrO6Jg+vShuIU/d31vgdR0ZlGUfvvXr3RPs7zXm9y2/k+5Ip1pYNTpX61zsc5RLhG+WbNp8sh4blTeygrec2JLnOMnbK1O35djuYzbx3o1GJnN4aiLv/PMpPk4HUxt1Y2aDK0pyipck7fEoIiIiIiIiBTJ6Qc6BR7PZ2Lix4ar5AIx75SuGzR1rZCl++CF8+y0AY5YfyPbY8iGZhVhCrcncutHILJzRsAsAT8WEsRaMwi5WK/j7F90NZbHmQPaiOaaMLKd584yl4xERUKuW15hxW8YBcFfLuzLHi+THL0sBovR07+tzNCzfEID9cfuLe1aXjR2v9WH/qWTqVwjB7nTht3sXNX57jo7uvWWtYRG8dM2DynQsAsp4FBERERERkQu3ezcByYlYLT7sa94RXnvNaP/9d6MwTC5MJhNzn+xO+1qRXLH/T2bWS2NjxXLMamBkD54KioCQEGM56v7iC7zc9NWK3DvnzjWOgwd7BSKOJx5nzr45AAxrNqzY5iaXoEJkPIb6hQLa4/FCNa1i7L9aIyqIAF8LjSuH4WMxE3DkEOZeV3uCjsdDyrH68585GxQOQIXQ4vmw43KhwKOIiIiIiIhcuHFG5t+6qk2wWXyhe3eoWxcSE+G77/J8aNVIH075v843rf/gxqHQ9oEzxAS8yVmfMZiD1+CqV9cYuGdPcd9FzmbNMo7dunk1Lzlk7AfZrEIzmlZoWtKzkrLMJ8sC1HwCj8F+RlVrq8OKw1nwAimXsxOJaYwYu54Nh8562iqGBQAwomfdzIFbt0LXrnD8ODHRVbnmntFc8fCPpLfrQGSQLw6SaFUz56JaUjAKPIqIiIiIiMiFmzcPgKmNu+N0uYzMwAceMPpmzMj1YU6Xk4HjB7Lw0D8A1D0DTrOLVMsqEnz/ZL/rVd7t5A62lGDgcdlz7qI2p07BFvfeev36eY35c9ufAPSq3avE5iVlQ741kE2mzOBjDhXgswr2DfacJ9uU9VgQz0/awvTNx7nhi+WetrgU4+scHujONj12zPgw4dgxqFKFR+95j93RNXGZzPj7mOjRbiEngu8hIeCn0riFS4YCjyIiIiIiInLeXhnQ1FhKvXIlAMtqtmTdQXeW0fXXG8fZs+H48Rwf/+qiV5mzbw4B+DLlN/hualsqp42mUfDtBDhaAfBixW3sjqLYAo/OcyvcAFUjAo2TjKBjzZpQvryn3+F0sOKIsTy7b4O+xTIvKXsKtc9nxr6O+WQ8BvgEYDYZ4Rstt86by+Xiid83MmdbrKftWFwqACnpxgcYwe5iWDzxBJw9a2Rmr1zJaoyl2E5SeXHpcD5Z8xbpzhR2n9mBzZH3/yPJnQKPIiIiIiIiki+XKzM4992d7TznkcF+sHQp2GwcDa/AoYhKbD2WYHQ2bQotWxrnc+Zke85NsZt4bbGxF+QXW2vRfxesrdYYP1dNhtR7hgrprxFqaorN5OTL9sDOncVyb8fiU3PvnDrVOLZr59U8d99cjiQcIcg3iPZV2hfLvOQSl7HPYz4ZjyaTyZP1mJSeVNyzKtMmrj/KpA1Hvdqu+mAh83fEsiMmEQAfsxkmToQJE4wBY8dC9eoApJsOcNz/CRYe+gcfsw8/DPiBtfetxdfii5wfBR5FREREREQkX/YsWYHtakVmtjucnmXWy2q0yF4FNiPrMeNNfhb/m/c/nC4n19XpzfC/jcIx0xoZ+yiGBfhiwkSU60YAfmgNjiWLIDWPIOF5+m3VIa9rc9Zb2LjROPb1zmqcu88oODOw4UDCA8KLfE5yGQg1isaQmJjv0IohFQE4lnisOGdU5v2y8mC2tjSbk3vGrPVc+zlsMHKkcfHUU9CxIy6XiwTLFGL8n8RuPkJUYHnm3DGHu1vfrWr1F0iBRxEREREREcnX3pOZmVY+lsy3kna7E2bOBKD+sEHZH5ixL+KcObQLsALw+a2t2XJiC9N3Twfgjbh2mGx2TgZHcCCyCgC1yhkZXq7UNoT5hREfALOr24pluXWS1e45b1QplN8fMCpqk5oK69cb5029i8eM3TwWgOvrX1/k85HLRLg7YB0fn+/Q2hG1Adh3dl9xzqjM23os/69lpTFfG/s6VqwIrxkZ128ueZOzft/gMqXj72jGojtXcmWtK4t5tpcHBR5FREREREQkX1P/zcy08smSEhi2ZQNs3gw+PlS/yQgymkzgyMiQ7NgRGjSA9HSabjL2RIwO8WfEjBEA9K7bm9ZTVgPwa6vrPRmTYYG+hAb4YMJC7zoDAfinPrB7d5HfW8beb8/2acTMx7vTvlaU0bFxI8TFQXQ0tGnjGb/r9C6OJx3HhIkbGt1Q5PORy0QhAo/lg4z9RePS4opxQmVDYpqNjYfjcuyzOfIu6xOddJbKH79rXLz+OgQG8tmqz3hhwQsAhNluomL6WzQoX7sop3xZU+BRRERERERE8uVjznz76Jsl4zF69TLjpF8/wmoZ+6S5XEZwAACLBQYNAqDr5sUAzDowgcUHF2MxWXi3zXMw11i2vKL91Z7ndblcBPpaAGhX2Vh+/U89cO7eVeT3lmYzAo8Bvue8Rd6xwzi2bJlZgRiYvsvI1OxZuyfBfsGInJeICOMYF5fv0BC/EEBVrQFen7adQaOXUeu56dR6bjqHz6TkOO7zW1tTPSrQq+3ZRWMwp6ZAq1YwfDjvL3+fR2c+CsB1te4j0n4XJkz4+ShcVlT0lRQREREREZF8/bnuiOfckiXjscIGo5o1PXvi52PGzx2UzMgiBODuuwG4Zs9quu1dyuh1rwMwqusoWv7jXsrcogVnqtf1PCQu1Ya/OxDYoVIv/FwW9pSD3QfWFfm9We1OAPx9LN4d7iXkngI5bssOG8HWHjV7FPlc5BKRd+KdISDAOKal5TtUxWUy/b72sNd1t3cXAN4FsAD6taiC0wk+DjvV4mN5aOUf3LjF2I+W0aP5actYnp7zNAD3tbmPj/u8X/yTvwz55D9ERERERERELndH47IXdfFx2Km82R0I7GEE4cxmwJFlqTVAo0bMqdeRa/aswuF8hxMpLmqE1+D57s/DW/2NMXfeyWsDmzHsWyOQmZhmIzbB2BPS5QyifWBdlqXt4q+UtfyviO9tzrZY437M5xSRWOkOqvbv72myOWxM2zUNgGvqXFPEM5GyrlBlSDKqWtts+Q71ZDymX94Zj+cGF7P6NUthmT8e7AwuF4NmjOHhlX8QbMsS3B02jOXV4YGfHgDgkfaP8Ol1n2Iymfjroc5UDAsotvlfjpTxKCIiIiIiIgXWwb3/4S/3duCtykn4pKZAuXLQvDkAZvcejefGBx4e9ByPXleDcS1cmJ3w6w/xBFzX31MRm2uuoXPdcgxuXZXIIF9uaF2NdHcm4sdzd3NrnQEA/BV6JPuTXwC7w+k5X33gTGbHrl1wyF3tOkvG44aYDVgdVsL9w+lUrVORzUMuQ4UIPGYs6U+yXd4Zjwmp9mxtA1oaBalenLzV01Y9Mgg++ICnl/xCsC2NdLMP6c1bwGuvEfPJGwyZMASrw0qfen34uM/HnsrVbWtGUS0yqGRu5jKhjEcRERERERHJl7+PGavdyVO9GwDQrX40HFhldF53nTvVESzuN/COc4KDZ/2W8FlHI5D3/DIz3TbHw2Zjb0duvBFatADgw6GtsDucXpWz/SxmBna9j5Fb3md9tJ3VK/+iQ+cbi+S+YhIyM6G61S+f2TFjhnG86qrMvfiAP7f9CcCVta70BCtEzkthAo/updaXe8ZjQlr2r5ULSM26tQMQ+M80eNpYRv1+t9sZ3flm9r/TH4fTwY1jehCTFEP9qPqMHzIei9mS7Tml6CjjUURERERERPJVPsQfAH93wRdSU+H7743zm27yjDO7lytnLLXee2Yv9099mNO+HwMQYu/DI1/tg1tvhQoV4J57Mp/HLSPo2LNhNABXNa5A1coNuOl4JAAvrngDh9M70HC+MvZ3BOjfokpmxwqjAjdXX+01fsURo31AwwFF8vpyGTufpdaXeXGZ+NTsXyu7w8nWY5mVwRuf2EfY/fcAsO/6wXzeeSgjezUE4PsN37Ps8DJC/EKYMmwK4QHhJTPxy5gyHkVERERERCRfdqcRoPPsgzhuHKSkQPXqXnsgZnTvObOTJ+e9wtRdU40GE4TYryXK9jC+lSvD2LH5vmaVCKMirdVmvPYLKe2YZJ/D7MSNvLPsHf7X7cJ3e8yoaF0h1N8TNAUyA4+dO3uaDsYdZOmhpQB0q9Htgl9bLnN+fsaxAIHHiIAIAE6lnCrGCV38TiZaPecP9qjLV4v2YrU7WbbnNAAVEk/z9cQ3MCUmQseO1J7wM/NTXNQqF8zxxOM8M+cZAF7u8TKNyjcqlXu43CjjUURERERERLw4nC6SrN57qSVbjQBdkJ8743HSJOM4fDhkWXJsMZtIM29lyF89PEHHltHdqWB9jXK2kZgwExpQsByYAHd2ZUZWYrO6nflgttH30oKXWHJwyfncnhdPRWvfLG+PDx2Cw4eN5ePt23uaJ++cDEDnap2pX67+Bb+2XOYKkfFYK6IWAPvP7i/GCV38Xp6auY9jq+oRAMzfcYKV+05TKeEUf0x4nhrxsVCjBkyZgik4mDrRIZjNJp6e8zTx1niaRjfl0Y6PltIdXH4UeBQREREREREvw75dSbP/m0VMvLH/Ybrd6QlERgb5walTMHOmMfj6670ea+cMJ/1eI8WeTPsq7dn04CY+uvoPAp2tAXi2TyOv/Rvz4u9jjMvISqRfP0ashgH7/XC4HPT+tbenwnRhZVTHzcim9PfJss/buHHGsWNHCAnxNE/aYQRbhzQecl6vKZePApU/KkTgsWpYVQBOppzE6XLmM/rSY3c4SUizcfB0SpbWzK/y2ZVrmfjrf6l56giEh8OUKcZWDm7jt4xn7OaxmDDxTf9v8LX4luDsL28KPIqIiIjIRW3mlhje+mc7TmfRVbEVkdy5XC5W7zeqO0/bdIyDp5N5YsJGwEhsDAv0hQ0bwG43lll37Oh5rN1pZ7/zbZymJOpFNmHBXQtoXrE5d/+4xjPmoSvrFngu52Y80rQpJpOJcb+lc1XVrqTZ07hxwo2e5c8FNXnjURq/NJP5O2Kx2o2gZkaQE5cL/vrLOB+SGWBMd6Sz/vh6AK6u473vo0iGQtUbOo/iMgAptpQ8Rl56rHYH9Z7/hxYvz/a0/bd3A+pGGx8K3LRpNlN+eoIqiadIrlYTVq/2qkR/OP4wD01/yHjcFf/liupXlOwNXOYUeBQRERGRi9qDv67j60X7mLb5eGlPReSysHh35h5yKekOhn2zkumbjJ+/UH8fLGYTzJ9vDOjUySvSMmn7JBJcG8HlyxvdvyLYL5gLkREMtGZkPAYFQd26BNlgeo1RXF//eqwOK/3H9WfLiS0Fft7/m7KVNJuTe8as5dFxGwDwywg87t0La9YYy6yzBB7n759PgjWB6KBomkY3vaD7EgEKFXgM9A3EhPGzdrlVtp688Vi2tjY1I6lfMZRWx3byzj+f4ee0s6h2G1i+HBo08IyzO+3cOvFW4tLiaFmxJa/1fK0kpy4o8CgiIiIiZcTRs6mlPQWRy8LaA2c857EJaRxzL7eGLBWtl7ozDPv29Xrst+u/BSDUcT31o5pd8Fw8S63tWSpYN28OQMDWnUy4cQKdq3UmLi2Ofr/1I82eltPTZJO1Mm5Cmvdelsx2Z1W1bw+1anmas2Y7apmmFIlCBB7NJjNBvkEAJKUnFeesLjpv/7MjW1u7mlHgcvHa7C8w42Ju3fbcddMrBFev4jXuvWXvsfTQUoJ8gxh/43j8ffxLatripsCjiIiIiJQJ6fbLb08rkdKQ7sj8WYtN8A7knUy0GkuRd7gDAc0yg4unU04zb/88AELtfXC6Lnx7hIxA54zNMZnbLbgDj2zZQrBfMJNvmUxkQCQH4w/y0YqPzvu1dsYkGifT3HtGDhjg1f/3jr8B6Fi1IyJFohCBR8CTQZxsu3wyHqf8e4wzyenZ2v18zPDHHzSP3Uu6jx9H3v6IX/7j/bO558weXltsZDh+2PtDVbEuJQo8ioiIiEiZMHVT9qVWIlL0ziRlvsl35LS36rp1RnGZgABolPlG/tdNv+J0OQky1cTXVR1HEXxWYMmyjPtonDvrOSPYuXkzANHB0bza81UAPl71MQnWhHyfN6eY6J2daxn7Vq5dazT07OnpS7OnsSHGWJJ9Q6MbCnkXIrkobODRvc/j5bLUut9nSzxbIWT18dBWxsno0QD4jXiI4Td2oVv9aK9xI/8ZSao9lR41e3Bf2/uKe7qSCwUeRURERKRM2HPi8lpaJlJa/lh3xHN+bnzObALWG0uO6dEDgjP3cPxty28AVPHtA+CV8WgxGwHE7+5sV6i5hAdlLmn2PF9GxuPWreA0opvDWw2nUkglTiSf4Od/fy7Ua2QI8rPAihVw8iRERkLr1p6+mXtmYnfaiQ6KpkZ4jfN6fpFslPGYpy1HvT9E2PPGdSz875UMbFUFVq6ExYuNPWYfeSTbYydsncDMPTMxm8x8dt1nmE0Kf5UWfeVFREREREQkRwdPe1fP7duiilG8AaBJE0/70YSjrD66GrPJTFU/d+AxS7ZksJ+xZLpW+aBCvb4rS/DSnvF89eqBvz+kpMC+fQCE+IXwbJdnAXhr6Vt57vWYmu7IsT0+1QabNhkXXboYGZ1uiw8uBmBI4yGYClW2WC5XroJsNVDIwGOIn1HF+XLY4/F0ktXresOL1+BjMVOrfLDxM/jpp0ZHv37G74Qs0h3pPDvX+H3wULuHaF6xeYnMWXKmwKOIiIiIiIgA3sFCgP2nvDOrhl9RC2bONC769PG0/7rpVwDaVG5DgCUKgA2H4zzPmWg1CriEBRauKEvW6Xjm5uOTGfR0L7cGeKDtA5QLLMexxGOM2zwu1+c8nWzNsX3DobMwZYpxkSXbEWDOvjkAdKjaoVDzl8tPRuXpAilk4LFySGUAjiQcyWdk2XfLNys95zMe7UZksF9m54kT8NdfxvnTT2d77LvL3uVA3AEqBFfgjaveKO6pSj4UeBQRERGRMkMFZkSKV3K6Pc/+mqlnIDbWWN7YpYunffY+oxL0Pa3uYYe7SMt7s3YCkJhm9+ypGF7owGNm5DFr0ZusBWYyBPoGMqL9CMDY6zE3WStaZ/VSvyawZo1xMXCgpz0mKYYtJ7ZgwkT/hv0LNX+RPPm5g2np2Yun5KR2RG0ADsYdLK4ZXTR2u7dXaVw5jCZVwrw7f//d+Jo1bQpdu3p1LTu0jFcWvQLA21e/TXhAeInMV3KnwKOIiIiIlBnH41NLewoil7RjcbkvUQYoP2eGcdKmjWd/x9Mpp1l+2Fh+fUX1K7I95kxKZlDF38dSqPlkzXi02nMIPGbJeAR4qP1DmE1mNsVuYu2xtdmeb/OReLYfT8zW/vmtrWmVfhrOngWz2QhouC05uASABuUaUD6ofKHmL5KnEGPpNEn5L53eGZPIliPGBwMZBZTsDier9p0mzeZg/6nkS/LDuU9uaZW98ccfjeNddxkfgrgdTTjKkAlDsDvtXFv3Wu5qdVfJTFLy5FPaExARERERKajtxxOoWS44/4Eicl6e+H1j3gPWrTOOfft6mpYeWkqaPY16UfVoUbEFkLkMNDHNxndL9p33fLLuk7flaDxtakQaFxmBxzVrjBLV7uBDpZBK9KnXhxm7Z/Dlmi/5fuD3nsefSEyj/+dLPdctq4VzQ+uqRAb70a9FFU+FXLp189rfcfLOyQD0qZe5tFykSIS5M/ni4/Md2v/zpZwiGXwzi8uMXrCXj+buIjzQ15PJu+eN6/CxmNlyNJ6XJm/h2T6N6FinXLHdQnFYvveU57xqRKB358aNsMFd6fqOOzzNNoeNm/+8mdjkWGpF1OLXwb+qoMxFQv8XREREROSiFJ9iY+q/x7zaHvx1fSnNRuTysO14Qq59ZqcDpk41Ljp29LTP2z8PgK41umYrvHL4TCqztsae93yy1ud4afLWzIsuXYxlqgcOwEHvZacPtXsIgEk7JmG1Z+7neG6mY0iAD8O71GZgq6pGw65dxrFD5j6OTpfTk815bd1rz/s+RHIU7l4GnJD7z12GdLsTk8sIiKfYjKJPH801vmezbh8wdZPx9+bwH1ez/lAct323qihnXCIW7TzpOQ/2PydfbswY4zhgAFSqBBg/pw9Nf4jlh5cT7BvMjFtnKDv5IqLAo4iIiIhclO75aQ0jx20o7WmIiNuyAZXhzBljifW1mUG4qbuMYOSABgMA8LVkBh+v/3QJp5JyLuZSEM7cKgOHhECtWsb5/v1eXdfWvZbIgEjOpp3l39h/Pe3JVu/9K4P9zgloZFTrbtjQ07Th+Ab2x+0nyDeIztU7n9c9iOSqEBmPACb8gcyMx5x8NGc3AKeSjC0O7M4CVNe+yByJM7ZVebxXfe+O1FT44Qfj/K7MZdQjZ4zk+w1GdvP3A76ncXTjEpmnFIwCjyIiIiJyUVp38GyO7W/O2F7CMxG5fFzVqAIAN7SuSvcG0V59ledON07atweLsVfjzlM7ORB3AICetXsC8P5NLYtsPnnGTGobhTY8mYpuvhZfutQwCt/8sfUPT3vCOUVlQgKyBB6t1sxl5FmqdS89ZCzN7lK9CxEBEYWbvFzWChTuCwoyjikpeQ5LcgfNze6Mx2V7jxKbkPN+rIfOpND4yb+oefaYkaVcBk3fdByAJpXPKSrz1VeQmAhVqsCgQQCM2zyOL9Z+AcDo60cztNnQkpyqFIACjyIiIiJSpnyzeJ/nTZiIFC2HO9LXpV55Fu866d25xCiywtDMN/ZTdk4BoGetnp7AnGfpchGoGOafe2enTsZx1qxsXcOaDQNgwYEFnrasRW7gnEI3+/cb67pDQoyghtv4reMB6FWnV2GnLpepc3YbyJuvu8q702n8ycV69wdxGRmPNmcaHd+cl+PY+1f9xcZPh7Hom/vZ8OmtfDntPZg9uxCTKl1Z93WtXzE0syMhAV57zTh/6ikwm9l9ejcPTHsAgP92/i8Pt3+4JKcqBaTAo4iIiIiUOcv2nMp/kIgUSmKazbMs2s/H+61idNJZWLzYuLgis3J1RuGV4tr/sGu9PPZp62JkNbI9exZ01xpdAVh3fB17zuwBYNL6o15jTiZmyRhbudI41qvniRzFJMWw5ugaAG5qctP5TF8kbxmBRwB77h+ordp/GsgMPDrJOdvxjvXT+N/CH/F3GNm94dZkrtu6yNga4f77wXHxZ0A6sqQ5RwZl+fp8951Rdb5SJXjgAZwuJ3dPvpvE9EQ6Vu3IW73eKoXZSkEo8CgiIiIiZc6KvadLewoil5Sp/x6j+cuz2XrMKHLhZ/F+q/h/AUeMwEiLFp6K0ltPbGXZ4WVYTBZuaXZLvq/xzpDmhZ6XyWTikZ71ALimSUXvzkaNjOPu3XDa+3dCjfAanuDj3H1z2Xcyid0nkrzG+PtmyXic584e69fP0zRp+yQcLgctK7akVkStQs9dJF9ZA482W67DEtOMoGRGcRmXKTXbmCax+3h1obHP4RedbqTu05MZfPt7zOpwnTHg229h1KgimnjxcWTJeDSb3emjTqcxf4DnnoPgYN5c8ibLDi8jwCeAnwb9hI/ZJ4dnk4uBAo8iIiIiUubM2Xb+VXJFJLtzCzn5+Zi4sW01z3Xnpe79Hfv29WQEfr3uawD61OtDzYia+b5G5zrnV2W2UrgRbMm2grVaNSP46HBkZmNm0btObwB+/vdnfl5xMFu/1yZ8a9cax4zl28Af24z9IQc1GpStWrdIkShg4DHJHXi0uCIBcBCHi8zsxbC0JCbPegeTzUZi96t4v9sdOMwW1ldtzAM9R2D7yvhZ5b334JFHPJmPLpeLs8neWxCUtqwZjz4ZgceZM2HHDggIgFtvZd/Zfby66FUAXu/5Og3LN8zpqeQiocCjiIiIiJQ5UcF+pT0FkUuGI4cKLn4WCwNaGnsdhlhTiNjoDszdfjsAVruVCVsnADC81fACvY75PN99ZgQfslW4NpmgTRvjfOfObI+7s+WdAKw6uorvl2/I1u/v655QYmLm49u2BSDBmsCig4sAuLX5rec3cZH8FDTj0b2vsYVIcPmAyYnDdJq5T/Zg/6vXsGnLt/gePwqRkYRO+A2nOTOb14WDZyqE8MvrN/F3I5gydzRTn+rHtB1TGPTjG1R75yqu/OF6npr1FJtiNxXbrRbUsbjMbE6zyWTsvfr660bDHXdAdDRPzX4Km9NGz1o9ebLzk6U0Uyko5aKKiIiISJnjWX4lIhfsj7WHs7W5cGF3F7sYuG0hFmuasf+he3nz2mNriU2OJcw/jOvrX1+g17Gc589txs+7PacS102bGsdFi4wlmFnUjKhJy4ot+Tf2X5J8FhBuH+zV/+Q1DYyTjRuN4EbVqsb+cRiVcp0uJ3Ui69CgXIPzmrdIvkwmo0K8w5HPUmujb3jn2ry+IQQncXwwtAH1ygUaezfOmwfBwfDXX1CxInXKB7PvVDJp5i2c9v2UjzcdM57IsyPCTPh9pnFqgUWHYdHhf/ho5Ufc2vxWXuv5GrUjaxfffedh7KpDnnMfswl++QVWrDCCtM89x6ojq/h7x98AfHTtR8pGLgOU8SgiIiIiF50kq50GFUNy7W9SOTTXPhEpnOcmbs7W1rZmpOf8up3LjJP77/ekLU7cPhGAq2pfRZBvULbHf3hzy2xt5yYsFlRGxmNOmZn0728cly7NsSpwRjZmkuUfXHg/vlqke97TphlHd7ajy+Xiuw3fAXBnizvPb9Jy2Svw93tG1qPNKO505GyKV3d8qo2V+84AcGWjClQLDwegVkAaDBkCP/xgBDB//hl69gQgNNCXFPMqYv3+h918DJMrmG41rqRTtU509K1FxyPQ4QhUSqpGmO1motIfpmvVfrhwMXbzWBp+3pBv1n1TJF+HwvhuyT5+XHbAc+1z5jQ88YRxMWoUztq1eHD6g4BR8Kllpey/Z+Tio8CjiIiIiJSaNJuDjYfjcJ4TULjyvYXsik3K5VFgd5xnBENE8mUxmwjy88FkMhGZEk+nQ+7A5JAhnjGz980GYFizYTk+x+A21XipXxOvtvPdIsGS21JrgMaNITAQkpJgc/YA6l0t78LXHIDdfJw083pPe7f67v0mHQ4YP944v8VIB9t8YjNrj63FYrJwR8s7zmvOIgXmDjwmJCTT7vW5dH1nAUezLDdu+cpsz3mV8EBC/Y0P5ZJH3AeTJ4OPD0yYAIONjF6H00H1mlM56f8GmJwEOjpRLe1H+lb+khX3rmDl//azMvgxVn0Huz49zbX72xDquJ7Dex6kUtrH9Kh5JTanjQemPcAL81/Adb6fGOQiNd3BmzO2M2Pz8Wx9r0/PrFA/eUQX+N//4MwZ4+f8+ef5cMWHbIzZSIhfCJ/0+aRI5yXFR4FHERERESk1j/y2gUGjlzFm+QGv9lNJ1jwfZ3Nkz2wSkfMTmLW6M7D1lWsBY3+1/tsX4+NyGnsp1qkDwLaT29hyYgsA3Wp0y/V57+hckzY1Ihh+RS3WPN+LgHNep6DM7qWUOX7g4OMDV19tnM+ala07MjAS//TuAJzx/ZqHr6zBN3e05cvbjexG/voLDh0ygpfXGdV/v1zzJQD9GvSjTmSd85qzSIG5A49/r84sgLQ7NjHHoQ0qhhBkMYotpRzZD1FRsGQJ3HgjADaHjeGThzN63TuAkx7VbiA6/TnMBPHryoM4nS5OJ1nh7bdZV6URoempTPjtOb6e+Dqh1mT8XfV4s9sEHuv4GABvLHmDF+a/UGS3mpJup/FLM/lm8T4eHpv5QUBsQhq1npvuuf7itja03LYKvjMyj/n8c7bF7/HM5dUrX6VyaOUim5cULwUeRURERKTUzN1uVKf+acWBQj3OkpiY47JKESm8fi0y38C/Nbi5J0BYL9TCwyuNys7cdZdnzOy9RgbW1bWvzvPNv6/FzMSHu/DygKZEh/qf9/xyLS6ToX1747htW47d4bZbMbkCsZuPQcg8ejetRIi/u9zBlCnG8YEHICKCBGsCv2/9HYAH2z143nMWKTB34NGZnlldeuaWmGzDejSIxmQyEXTYyBRMiQyBhQs9lditdiuDfh/Er5t+xcfswzf9vuHZDp9jcpf2SEyz88LkLbR9fS4rjyVz900vM65Fb+wmM9fuXsnSL+/hwZV/kpBi5+M+H/PeNe8B8ObSN/l89ecXdIuJaTbGrjrIvO0nvNozsil7vr/Qq/26yr5w773GxdChuHr25JEZj2B1WOlYtSOPdHjkguYjJUuBRxEREREpdVmXYOaW6QFQ9/RhfprwEh/c3wMaNID163MdKyIFk+7OIH6hb2OGdajhaa/6+89USjqDvUJFGD7c0z5281gAetXpVSLzM+e1xyNAq1bGccGCHDfW86E8ETajGvd7K97gTKqxXx4OR2aW5A03APDV2q84m3aWGuE16FmrZ5Hdg0iufIzA4J+rDniaxq85zOEzKTicLs9+x8O71IItWwjebxSKSX7oXmjeHIB0Rzq3/HULM3bPwNfsy/gh47mv7X2cSEz3eqnf3IVbbvlmJQkBIYy67lGG3P4ee6KqEW5N5rlFY2j4/OOQlsZ/r/gv/+38XwBG/jOSL9Z8cd63+PY/O3h+0hZGjvOuLp+S7sDmcJKS7vC0Xd0wGtPw4XD0qJFl/eWX/LrpVxYcWICfxY9fbvgFX4svUnYo8CgiIiIipa5BBaNYzPuzdnLNR4tzHFP31GEm/vJfeux3Bxv37oUOHeCRR5T9KHKeUtLtTN5oBDI8WYAZxo0DwOeF5yEsDIDYpFjWHVsHwB0tSmb/w4yatesPxeU8IGOp9aFDEJfzmFBHXyL9anAq5RSPzTSWkTJtGpw6Zdxb5844XU4+XvkxAP/r+j/8fc4/S1OkwNwZj74Ou1dzt3cX8Nxfmzz7HftZzPDccwS5i1+nNK4PGFmDI2eM5O8df2MxWZg4dCJDmhj7sTatEpbvy/9bpSHX/OcLXrzmQRwmM7WnToDatWHxYt7u9Tb3t7kfgBEzRpx3wZnle0/n2L4jJoHxaw57rt/p14Cv1/0C06cbBXPGj+eoJYUnZhkFZp7t8iz1y9U/rzlI6VHgUURERERKRdYN61NtDr5ZvJfPF+zJNq5WuSDevbEFL8/9mnBrMnujqnHr0NehVy8jY2n0aHjnnZKcusgl4891RzznXeqVz+zYtg1Wrzbe/A8a5Gkeu3ksLly0qNiCqmFVS2SO244n5D0gOBiio43zQ4eydVeLDMSED2/0/BgTJn7d9CvvLnsXZruLdtx2G/j68ue2PzmedJwQvxDuanVXtucRKRbuwKOP056t648sP5++a9fA9OmewGOyLRmAFxe8yDfrv8GEiQk3TaBfg36ex7SrFUXFsPwD6C6TmV/a9OOhQaNIDI+CmBjo1w/Lb+P4st+XPNjW2HbggWkP8PKCV3hl6kbmbost8C22rxWZY/vYlYd48e8t4HJx7a7lDH1oMD6j3cu6330XV7t23DPlHk6nnqZZhWa80L3o9puUkqPAo4iIiIiUiqzLJqf8e4w3Z+zIcdwHN7ekxabldDu4kXSzD3ff+H8sr9UK1+zZ8OSTxqBffslxiaWI5O3Q6RTPudc+jBlBuauugurVPc1/bDP2fLyzxZ0lMj8AkyfnEWZtjaHWc9N5Z+Y5vy9quJeI79qV7fHpdiMjulv1q3jjqjcAeHbus4zb9Jsx4KqrADz72N3f5n4CfAKK8hZEcucJPDoIPTfrOIvq3xnfn1FV6wFwKuUUYzaO4Y0lxvf0+73fZ3Djwdket/TZq/J8+crhxvf6o1fXZ3aDznS6+yvo0gUSE+GOOzB/9TVf9P2Ch9s9DMAri1/mjbUDuf3XHwp+ixbv0NPQdsbvlIkbjoLLxZNLfuXrSW/Cli1Qvjz8+iv897/89O9PzN4727PE2s/il9PTy0VOgUcRERERKRX23PZrO0d0SADRE34FYGzr6zgUaRSzSEi1w7PPGvtjbd9uvGERkUJZe/Cs59zfJ8vbwwkTjOOAAZ6m7Se3s/LISiwmCzc2ubGkpoiPJTPw+MAvxjLvLxfu9R7UpYtxXLjQq3lXbCInEq2AcX/PdX2OO1saQdO7r4xjRW0f6NePtcfWsuTQEvwsfjze6fFiuQ+5vLgo4IdhWZZa51aEqdv+9VSeNwOAGt37A/Dzvz9z39T7AHjmimd4svOTOT+9Je+wz4L/Xsn6F6/B7t7rNdk/CObOhbvvNgY89himTZv4/PrPebLtR5hdYdjMh4j1/x+P/fOY1+qFc32/dD8P/rIOqzv436txRTa93Buze0oml5OX537NoyuMgk489ZSRbX3bbew7u8+zLcLz3Z6nVaVWed6HXLwUeBQRERGRUlGQwON/utamhj2RqHkzAfir2dWevqR0O1SoAJ07Gw3//lss8xS5VCVb7Ww8HAe4lyOb3AG+9HTYuNE479PHM/6TVZ8AcFXtq6gZUbPE5plR1TpPTZsax4MHvZpHjM0sQOXvY8FkMvH9gO+5NqAZVh+4YSgcSIvhnWXGdg03NrmR6uHVETlfpgJ8u2blcgceLS4nMQlp2frD0pJ4+5/PjIubb6ZSY6OKe2xyLHannQENB/BWr7fOa65D2lQjwNdCVLAfQ9tnft8P/G4tfP+98cGDzQbXXINp504WbGhKlbSvCLEbvxc+Xf0pH638KMfn/m3VIV6bto2ZW2M8Wzp0rluOsABfrDYnPg47n09+h+HrpwGw8bHn4f33IToaq93KkAlDSLAm0DS6Kc90eea87k8uDgo8ioiIiEipcDjyDzy+0K8JzJ+PyW7nRJ1G3DkicxnZqn3uzerbtDGO06cXxzRFLll7TiR5zltWi8jsmD0bUlOhUiWoWxeAowlHGbNxDABPdHqiBGeZf8YWkLkc/PBhr+bdWe7Rz53R6WP24ff1dWl8EmID7NT+pDZ/bvsTgIfaPVQ0kxYpoCSM5dUBNis9GkRTu3ywp6/+yYP8Nv55qiaexFWzJnz5JYG+gZ7+5hWa89vg3zCbCh7aGdbB+Fm5p0ttPri5pac9NCCzUvS/R+KNDwe/+Qbq14eTJ6F7d67btxoLYZSzPUKEbTgAT81+ipcXvkxSepLX6/xv0uZsr52RVf1w52p8PuUd+u5chs1s4f9uHkXDd18hwZrAz//+TLtv27ExZiNRgVFMHDpRWx+UcQo8ioiIiEipsOVTifrrO9oaJwsWAFBh0PXcnCUj48kJ7gzHW281jtOmqbq1SCGcSrJ6zm/rVCOzY/ly49i3L1gsADw952msDittKrehd93eJTlNfC0FSCFzB0jZvt2oVJ2DjMAj6emEL1rJjLHQJMjI3LSYLLxy5St0rdG1KKYsUmBHnUbALyQ9lS9vb0vLauHgcnHv6kn88+NImsXuJd0/ANMff0BUFI3LNwagRngN5t05j2C/4LyePpvXBjbjwNt9eal/E6/2AF/v8FCS1Q4VK8KiRdC8OZw8yRtjXuDdGR8TlJ5KmH0IV1YxijC9sugVanxUg+fmPkd8WrxnX9Vz+fmYweGg3sPD6bNrBXaTmcf6P02/D2/g1kk3UvmDytz1911sObGF6KBoJtw4gQblGhTq/uTio8CjiIiIiJQKRz5LrT1BAnfgkZ49cx7Ypg2EhkJSkvEGSUQKJCPwWKd8MFfUzVLResoU49iuHQCz9sxi3JZxAHxx/RdYzJYSnWeAbwFer0EDaNjQWBa6alWOQzy/U+bPh9hYapmj2PTQZnaP3E38c/G81OOlIpy1SMHsTze+LxsGGX8nvtSvCT9s/5MXF3yPj8vJ/Drt2PLPUmhvLLFuWL4he0buYceIHUQHRxfqtepXCMEnlwziAB/vn7Mp/x4zTipXhtWrmXj1MJyYuHnzXP75cSQ3bF1I+4gnGDNwDNXDqnM27SzvLHuHRqMbcc+E93CRvUq3r8UETz8N06dj8/HlxmH3MbnTMjp814G/d/xNii2FhuUaMqrrKLaP2M7Vda7O9hxS9hQ68Lh48WL69+9PlSpVMJlM/P333179LpeLl156icqVKxMYGEivXr3YvXu315gzZ85w2223ERYWRkREBPfeey9JSd5puSIiIiJyabM58s5O9DWbjWWTe/aA2QzduuU80MfHyMyCXAMOIpLdqaR0ANrWjMxsjImBrVuNjepuvBGr3coD0x4A4J5W99CxWscSn2f/llVybM9W1KJhQ+N46FCO44MyApiffmoc+/XDEhJKvah6hc4aEzlfM7fEsHjXSc/3rzUoFICWoYDLRdQzT3DV1J8AiHv5dcLnzaJNz7Zez1E3qq7XkuuCOpOcnmuf2Wxi6bOZH/Bl/H4ASLP48mS727jr5lc4HRhGzbgYPp72AQ1+/pK7Wt3Fvsf28ffQv6kXVY+YpBjG7n6OIwG3cdLvTUb2P4wTY+/KWmuWwEcfsbAW9HqtPpMbfM0x6xLMJjN3tryT9fevZ/uI7bx59ZuUCypX6PuTi1OhA4/Jycm0bNmS0aNH59j/7rvv8umnn/LVV1+xatUqgoODufbaa0lLy9wk9bbbbmPr1q3MmTOHadOmsXjxYu6///7zvwsRERERKXPyy3g0m8jMdmzXDsLDcx/cqpVxnDOnSOYmcjk46a72XD5rJd2MatZNm0L58ry//H0Oxh8kMiCS93q/VwqzzD3jMdvvkEaNAFj64yTen7UTgPIhxr0906chZrPJ2Ktu3jxj/BMlu1elyOr9Z3jw13Xc+cNqao+awegFezjj3r+wfoATxoyBL780Bo8eTcT/PU/bWlEX/LqVw43XaFo1j79HgWqRQdzXrTYAaTaHp/2VqdsAWFK7DRvnrSbmbuPDiP9M+xoWLsTH7MPARgPZ+MBG3rr6LQItUThNyaRYlvPfuQ9xJOA2Tvs8x5hZ99HlHug5HBZbt2E2mbmpyU1seWgLPw36idaVW2cWuZJLRqEDj9dddx2vv/46N9xwQ7Y+l8vFxx9/zAsvvMDAgQNp0aIFP//8M8eOHfNkRm7fvp2ZM2fy3Xff0bFjR7p27cpnn33G+PHjOXbs2AXfkIiIiIiUDfllPNqdLvjtN+PimmtyHHPgVDIDP1/K0iZXGA1LlhhLLUUkXyfdS60zgnM4nfDDD8b53Xez98xe3ljyBgBvXf0WUYEXHgApSrZzC1T16gVApb3b+XzBHlLTHZ7l5Nc1q2yM+f57o2p3s2bQsiUixeXchFyAm79e4XX93qydJLmMwHrQts0wYoTR8fzz8PDDRTaXn+7pwB2davL+jS3yHevvXnL9zeJ93DNmDQDjVmdmETdoWJ2YV97mz2bGMmjXsGHErN0EQLBfMM91fY5HGs2hevLbDIoaTr2g6rhMVpJ8t/Bpi2SW1zAKPD3Y9kH2P7afCTdNoHF04yK7V7n4+BTlk+3fv5+YmBh6uX/hA4SHh9OxY0dWrFjBLbfcwooVK4iIiKCde78QgF69emE2m1m1alWOAU2r1YrVmrnxcUJCQlFOW0RERERKQbo974xHZ1paZsbjHXdk63fhot+Y/7ItaQzzYjtxpFwwEaeTYccOYyN8EcnVmeR0pm86DkCFjIzHNWvg33/Bzw9uuYXHZt5Pqj2VDlU7cE/re0pxtjk7dCaFx8Zv4Jb21RnepbaxzyNQIy4GP7uN4/GpnrHVIwPB4YAPPzQannjCWE4uUsRyy9g7FpeaY7vdYoRl/Ba79yju1QteeaVI59SgYiivDWpWoLFZi8zM33GCQ6dTvPqrRwURn2rjtav+Q8dj26kec4xK7VuS4hdIYJA/Lh9fXkhI5t1043EuYFs0LKwF+8qZqXL7Q9w64Hkqh1YuqtuTi1yRFpeJiYkBoGLFil7tFStW9PTFxMRQoUIFr34fHx+ioqI8Y8711ltvER4e7vlTvXr1HMeJiIiISNmRX8Zj4OZ/jcykChU8AYWsEnz+ZGvyV7hMaST7LKTqw6n8ZwBsmTGmmGYsculYsfe057xFNffyy4wlnv37s8p5mOm7pwPw/YDv8bX4lvQU8/XylK3siEnkZfcyUKpXJymiHH5OO90OrGdnTCJgZHT6WMwwdqyx1DoyEu68sxRnLpcTm8NJ30+XcMXb83Pst2ct1hQUBF995akmXxrO3drgvdk7Peef39oaMAo1xQeG8tBdb/FvFWNv1aD0VExxcZhPnSTMHXR0+PphqlaNpkQzYkcoH9z5K0/d9rmCjpeZIs14LC6jRo3iySef9FwnJCQo+CgiIiJSxuUXeGxxaKtxcsUVXplJT17TgJcWvEK871gA/JwNcZFKiuUQ37eBX1M/YeLuq7m+/vXFNneRi90vKw6w7XgiL/VrQqBf9iBGsjWz4mzNcsGQmAgZhUMfeog3l74JwNCmQ2lWoWCZUiVtV2yi59zmcOJrMbO3fXdazplEq2O7+GeLkdgS4Gs21r1+9pkx+KGHjKJUIiVg1b4zbD2W+6pNP0eW7UE+/BDq1i2BWeUuNd3hdT3138wt8WqXN4ow+ZiNv5MPhlZk4B0fUDHxFAH2dHycDnycDuxmCyeDI7m1dwueu969jNrlUpbxZapIMx4rVaoEQGxsrFd7bGysp69SpUqcOHHCq99ut3PmzBnPmHP5+/sTFhbm9UdEREREyiaXy8WIsev5z89rc+yvXyGEzS/3JnDxQqPhiiu8+qtWOOgJOobZbqSS9X0qW0ezpMoLdDsIVpODG36/gQX7FxTnbYhctHbFJvLi5K2MW32I75bs8+pbd/AMC3aeICXdCDz2beHOPPrhB4iPhxo12Ne8Gv/s/geAF7q/UKJzL4zTWSr0nnWfH2tk7Nt4w9b5TN14BIAjZ1Nh2TJYu9bIKBs5suQnK5elhTtPcPv3q7zaQvyNoLe/jxGOWVy7DQl+QfDyy/DAAyU9xWxiE9Ny7cuYu6/FmHu6+wPE2NDyHIyswt5y1dkZXYu95aqTEBBCmj3LB4wKOl62ijTwWLt2bSpVqsS8jCphGNmJq1atonPnzgB07tyZuLg41q1b5xkzf/58nE4nHTt2LMrpiIiIiMhF6HRyOtM3HycuJeciMBazidDUJJg502jo08fT53A6eGXpMwAEOboQaR+Oyf1f1263Mfdn6LsL0h3pDJ4wmG0ntxX7/YhcbLLuybbu0FnPudPpYsiXK7j7xzWe5clBvhawWuHdd41BI0YwesM32Jw2etbqedFmO57r1WnbWL3/DIs79iHJL5BqCScZvCXLhw/vv28chw6FXBJeRIqS1e5k+I9rsrX/+VBnDrzdl52vX8fHQ1uxvmpj3hu3HP7v/0phltnd0alWrn3VI4OAzMCj1Z77yoUGFUP4j7tCtlzeCh14TEpKYuPGjWzcuBEwCsps3LiRQ4cOYTKZePzxx3n99deZMmUKmzdv5s4776RKlSoMGjQIgMaNG9OnTx/uu+8+Vq9ezbJly3jkkUe45ZZbqFKlSlHem4iIiIhchPJ6o+Lx++/GsU4dr0Ix03ZNY/OJDZhcgUSm3+f9mEaN8CtXgQl/QKuwhsSlxdHtx24cjDtYhLMXufiFB2Xux1g5PNBz/u+RuGxjo0L8jCXIx45BdDRn7hnGl2uNvR4fbPdgsc+1oKpHBebZP23TcW7+egXjNp/ij+ZGsdNnF40hOuksI/YthMmTjYHKdpRiZs9nG5EGFUI954NaV2Xx0z15ZVD+1aZLSsNKoex547ps7Q0qhmB2L7HOWoAmJ/1aVGb2Ez2o5g5UyuWt0IHHtWvX0rp1a1q3NjYVffLJJ2ndujUvvfQSAM888wwjR47k/vvvp3379iQlJTFz5kwCAgI8zzF27FgaNWrE1VdfzfXXX0/Xrl355ptviuiWRERERORiZjsn8Ni7SUXmP9XDc+1yAdOmGRfnFIB4b/l7AITar8OH8tmfvF49gmwws9xjNIluwpnUM9wz5R6crgIEO0XKsDSbg30nkwDIuqAxMksQcsHOk9keV9tig9dfNy6eeYaXVr9Dqj2V+lH1uanJTcU55UJxFuJH+ItON3E2IJQKyWdZM/oOnv7Dne340EPgfh8rUlyigv1ybA/x9+H7u9p5gncZapQLytZW2nwsZiqG+Xu1/Xpv5grVsIDsxabeHpz5IeErA5oW3+SkzCn0jrpXXnklLpcr136TycSrr77Kq6++muuYqKgofvvtt8K+tIiIiIhcAs7NeBzWoQZ1okM81/7WZFi61Ljo1cvTvvroapYdXgZAedMgvLe/d+vQAZYvp+LcFfz90d+0+roV8/fP54+tfzC02dCivhWRi4LN4aTRi8bWBD0bRnN/98ziFFl/3qKCsgcL2s2aYOztWK8e64Z0ZvTPTwPwfu/3MV1Ee7Ll9R70XCdDorjz5ld5e+ZnND2xD6fFB3O3rpnLyUWKUWgOQbkb21bjvRtbXFQ/U/mZ8EBn5m0/QYtq4bSsHuFZXg1gNpv4dFhrHh23AYCRV9WjUeXMWhy5BV/l8lSkezyKiIiIiOQn1ZZjyNCj49YVRiCkWjUjkOj2/nIja6lPvT5UDMpli54uXYzjnj3UL1efxzs+DsD/Lfy/QgUu5NK1/tBZBo5exrqDZ0p7KkVmrzvTEYysRocz83vdaneQZLUzf0esZ1/HDPVOHaLOVx8D4Bo1iqcXvwjAwIYDGdBwQPFPvBDO5rInbG42V65P3+GfcPW9XzJ94WZYsABCQvJ/oEgRqBqRuTXAY1fX5/2bWpapoCMY1e7v6VqbdrWivIKOGQa0zPx7uHJ4IK2qR/D89Y35aGjZu1cpXgo8ioiIiEiJSk33Djx2q59lybTLxcClk4zz4cPB18gcOZVyir+2/wXAyz1eJizQO6Okaz33c9R2b2S/z6jk+2zXZwnyDWLn6Z2sOupdWVQuT7d/t4p/D8cx5MsVnEy0lvZ0isTppHSv62+zVLK22py8PGUr94zJrCJ/m/MYX098nak/PYE5JRm6d2d2l0osOLAAH7MPH/f5uKSmXmBZP7AocDaVycTe8tUxhYXlP1akCM15sjsBvmYigny5uX310p5OsRnSphr1K4RwfXOjYNN93etwQ+tqpTwrudgo8CgiIiIiJSrNnhlAeOqaBvhkyaS4ct9amh3YYgQcH8wsbPHNum9wupy0rNiSjtU6siMm0es5fSzu7IoGDYzHxsbCnj2E+YcxpPEQAH7c8GMx3pWUFSlZAt+ztsaU4kyKTmKa3et60a7MvRzPptj4c90Rz/WAbYt4/YMHuXb3SgLtVmjfHteff/LMglEAPNTuIWpF1CqReRfGDa2res6z7iWXE68PMyDHbC2R4hTk58P2V/uw5vleXtmPl5oPbm7J7Ce6ExGkpdWSO/0GFhEREZESleYO/LSvFcnIq+t72v19zAzeusC4uPdeqJoZaJi6ayoA97U5p5K1m2cVdWgoNGtmnO/YAcDdre4GYPzW8SSnJxfVbcglIL0gFdYvUGKajZR0e/4DL0B8anqufXO3x3rOH1j1J59OfQ+T0wmDBsG6dbBqFT8fncGm2E0E+ATwUo+XinWu5+vDm1sy8eEr2PrKtVzZsIKnvWa57FVzW1aL8Lr2U+BRSoHJZLosgt5aVi35ufR/CkRERETkopKxZDLA1+LVPuu+tvTdtdy4uO02T/uu07tYeWQlJkz0bdA3x+f02r2xuntZ2+HDAPSo1YMa4TVIsCbw66Zfi+QepOzKukw32N+Sx8gLl5hmo9u7C+j/2VJsjuILcv5v0pY8+y1OB6MW/MCohWOMhgcfhD//hDZtOJV6midnPwnAE52eoHxQDtXiLwImk4k2NSIJ9vfB15IZ6Ei2Zt8ztlyIH4NaZe4/dzkEf0RELlb6DSwiIiIiJSoj8Bh4TuCx1l+/YnHYoXLlzCIxwIzdMwDoWqOrZwnomzd4L7X0KhyTEXjcuxcAs8nMPa3uAeDvnX8X1W1IGdUkS+XVnKrPFqX9p5KJS7Gx92Qy6w6eLZbXcDhdnmIyfZtXztZfLS6Gv379Lw+sngiA7fkX4IsvwGLB5XIxYsYIzqSeoWZ4TZ7v9nyxzLGoZc2wurVjjWz9/j4WXh7Q1HNtdxZ/ZquIiORMgUcRERERKVFpNiMI4JXx6HTCt98a5089BVkCC79v/R2AQY0GedqGtK2KjzlzjFfB6k6djOOCBZ6mwY0HAzBzz0yOJx4vgruQsioxrXDVkS9E1my8hNSif900m4PHxm/wXD/bp5FX/+RO/kz76XFaHd9Nqo8/rw19Dt/XX/P8fM3YPYMJWydgwsS4IeMI9gsu8jkWt4hAX5Y805OPhrb0tPn7mL32nMspK1JEREqGAo8iIiIiUqLScsp4XL/e2JMxJMTY39HtcPxhVh0xqlEPbDjQ0+7vY2HTy715d0gLAFxZF1u3aWMc9+zxRCSbV2xO28ptAfhhww9Ffk9Sdvx7JN5znpEpWFzSslRiTiuG/SS7vrOAaZsyA+k1ygXh52O8xYtOOkPLh+4gIi2Jg9XqcXTpap7+5XXP2BRbCo/NfAyARzo8QufqnYt8fiXBYjZRPSqIxlkyWTO+Bk9f25Ar6pbj6sYVcnu4iIgUMwUeReSis2jXSWo9N51az03n/yZvYdORuNKekoiIFKGflh8AslSiBvjVvffitddCRISnedyWcbhw0b5Ke+pE1vF6niA/H/x9jX/OemU81q5tHBMSjOrWbve0NpZbT9s9rUjuQ8qeFXtPe107XSUYeLQVfdbdqSSr5/zaphUBuPuKWuBy8e4/n0BMDNStS83Na6jXsYVXlvFzc59j79m9RAdF8/pVr5/71GVGgPt3QHSIv6fN7M7oHNGzHr/d1ynbfrIiIlJyFHgUkYvOc39t8pz/tOIgAz5fxmvTtpXijERE5EI4nC7u+3ktoyZuBuBEohEsmbklxhjgcsGsWcb5sGGex7lcLj5f/TlgVLPOqXJmRptX/CgwEFq6l13OnOlpvq7edQCsPLKSXad3XfB9Sdkz7NuVXtfFHHf07GcKYC2GwGNWPu4CKv+9tiEfpm+m5751Rsdvv3kF8wE+WfkJn63+DIAv+35JmH8YZc2wDjWoUz6Yfi2MIjKRWZZWl+RyehERyZsCjyJy0Qnyy/6p9PdL93PkbEopzEZERC7U4C+WMWdbLONWH/JqP52cbpwsWmQss7ZYoGtXT//64+s5nHCYYN9gbm9xe47PnRGKdHFOBKl7d+O4daunqXZkbbrV6AYYez2KFP9S68zl1X+tP1pkz/vt4n3856c1Xm0ZRXN8fxrD4I//ZzTeey906OA17o+tf/D4rMcBeL7b8wxpMqTI5lWS3hrcnHlP9SDY3wcAszn7BxMiIlL6FHgUkYuKy+Vi78nkHPu0MbiISNnjdLq89tQ7kxFsBO7v7l46/ddfxnHwYKhY0dM/cbtRhbdPvT4E+gbm+PwZSZDZMteaNMl87iwVbfs16AfAz//+XNhbkUtQcS+1Xrb3lOd84+G4InlOl8vFGzO2M3f7CU/blQ2jubtLLdiwAR54wGi85hr45BOvx646soq7/r4LgHta3cOrPV8tkjmVlnOzoF/s14Ru9cszsFXVUpqRiIicS4FHEbmoTN54LNe+bNksIiJy0ZuzPdbruv9nSz3nj/eqbwQFfzeqVnPrrZ6+FFsKX6z9AoAbGt2Q6/Ob3DmP2f6GuOkmMJth/37Yt8/TnJE5uf74eg7EHSjk3UhZFxZgZMdVjzIC2cUVd9wZk8gnc3czfZN3BfWV+07n8oiC25QlkJ/hh7vaE+TnA6NGgd0O111nbDMQnFmlevbe2fQZ24dUeyq96/bmq35fYTZdWm8H7+1am1/u7UhgDqtnRESkdFxaf9OISJn3+O8bc+1zFn0xSBERKWZbjyV4XR+NS/WcB/n5GEuhT56EoCC4/npP3wfLPyAuLY5KIZW4qelNuT6/KXOttbdy5aBdO+N89mxPc5XQKnSv2R0XLsZtHnde9yRlk9XuICHNDkClsAAAHMUQeYxPtXHtx4v5aG72fURf+HvLBT23y+Vi4Ohl2drNZhMcOZK5V+qHHxqBd+DfmH+5bux1XPvrtcSlxdGhagf+uvkvfC2+FzQXERGRglDgUUQuKlc3qpCtzd/HXbFUGY9lmsvl4oW/N/P5/N2lPRURKUE++e27tmCBcezWDfyM4hDpjnS+2/AdAKO6jsLP4pfbo3Pf4xGgn7GsmpXeBUVua34bAN+s/ybvuckl5aS7qJGfxewpRFIcS61/W3Uo177U9Jy3jXG5XCRZ7fk+9/pDZ3Pv/Ppr49ixIzRqRLojnf/N+x9tv2nLzD0zMWHiwbYPsmj4IkL8QvJ9LRERkaKgwKOIXDQeHruOeTuM/YpuaV+d7g2iubdrbc+bg+KuPCnFa0dMIr+uPMT7s3fxcQ5ZICJyaUrOJZjSoKI78DF/vnG86ipP38w9MzkUf4iIgAiGtxqe5/PnWNU6Q9OmxnGLd5bZLc1uwWwycyDuAIfjD+d7D3JpOHDKKFJXKTwAizsgXhy1Zd6ZuSPXvqNxqfR8fyG7YxNxOF18t2Qf6w+dpfaoGTT7v1ks3nUyz+e++WvvIHrTKmEsfbYnpKXBDz8YjQ89RJo9jb6/9eWtpW/hcDm4odEN7HxkJ1/2+5IAn4ALvkcREZGCUuBRRErV2gNn+HHZflwuFzM2x3jaHU4XP9/TgRf7Ncm9cICUKav3n/Gcfzy3+LMeD5xKZsq/x3DpG0ekVKXZcs7wCvTzAYcDFi40Gnr29PR9uupTAO5ocQdh/mF5Pn/G3xE5Zq61b28M2LDBWIbqFuYfRpvKbQAYt0XLrS8HaTYHt3+/CoAqEQGY3d84zmKIPGbsI5mhVfUIooIzs3b3n0rmti+XsuKHv7A//Qwp3Xvy5aQ3eXrRT6ydvCDH5zx4OhmH0+VVhfvA232Z/mg3qkUGwccfw7FjUKkSU1sGUu/TeszdNxc/ix/jhoxj4tCJ1C9Xv8jvVUREJD8++Q8RESk+N361AvCucgowtH1148Tl4oE5P1Lu+CEsQ+tDtfCSnqIUgX0nk/i/KVtL9DWvfH8hYCzD7N+ySom+tohkSrPlvEGv2QQsWQLx8RAeDq1bA7Dj1A7m7Z8HwMgOI/N9/ty2eASgenVo2BB27IBt26BaNU/X0KZDWXtsLUsPLeWZLs8U4o6krDh0OgUfi4kqEYEM/SYzU7BHgwpsO27sPVrUS60T0myefSQDfM280LcJA1tVYeW+M9z381oAapw9zrcTX6PhqUN0PfcJVv4B+xYYgUR/fwCm/HuMR8dtoHuDaM+w+hWyLJU+fRrefpuTQfDYY1UZN3koANFB0YwZNIbr61+PiIhIaVHgUUQuCp/N3+N1XTfa/Q/quXMZvmAsANYb+sDc2VCvXklPTy7Qc39tLrXXnrc9VoFHkVKUZs8547FO+RD49l3jYsAA8DH+WTpm4xgAetbqWaAMrTyXWgM0bmwEHlesgN69Pc09avYAjGXd8WnxhAfog61LSZLVTvf3jOzBPW9cx7+H4zx9D/ao4ylm5yjijMetRzOLKd3WsSa3d6oJQIi/8f1dNf4Ef459hgrJZ7EGBTOjVntWV2+Gvz2d9ke20nfnMvjqKwgJgffeA+DRcRsAPMuwXaTz0qCKbIzZyOmU05z+5E32N4vnvW5mTlvXYTaZearzU7x85csE+QYV6f2JiIgUlgKPIlJqth9PyLUvLNBdafH11z1t/gf3Q9euxl5d5csX9/SkiKw5cIbVB87kP7CIpNudzHfvFQp4slpEpHTkttT6xQ7l4c6/jIv77wcgxZbCF2u+AOCe1vcU6PnzzHgE6NMHJk2CuXPh//7P09y+anuqh1XncMJhNsZspEetHgV6PSkbjmWpnl7v+X+8+kwmk2epdVHvxnEyyeo5v755Jc95VLAf0Uln+P23Z6mQfJZ9kVW4ZdhbnAgt5xkzpt0A5mxdwMfTPjCqUg8aBF264MKF1byFFMsK0sybsJkO0v2XLBOPAq4BcNKiYgu+H/A97aq0K9obExEROU/a41FESs11nyzJsf3Xezsam75v3AiLFwNw423vkFazNsTGwtNPl+As5UItyBIELG67YhNp8MI/PPjrOk/b/lPJ/LhsP/EptmJ73fk7YlmUT0EAkcvVzpjEHNsjFs4BqxWaN4cuXQD4aMVHJKYnEh0UzS3NbinQ85s8kcdcIkgdOxrHrVuzjWlftT0AU3dNLdBrSdmRX4Vozx6PRRx5nLstFoDGlcNoWzPK094wGMb88TLVEk4SFxDCPTf+n1fQMcPfTXuSOvQWVlZx8vIbvbjhm6s46n8vsf6jSPSZgs18AEwufMw+VPIrR9PTFrofgAGpNfji+i9Yc98aBR1FROSiooxHEbnodK3vzmZ8800AljfqxNpqTTn4zic0vGUAjBkDDzwAnTqV3iSlwHwtOX/G5XC6PFVFi8LaA2c8e4ZmZXO4eGXqNuZtP8Gv/+lYZK+X4fCZFO4ZY+zbtfuN63K9X5HLUbLVzoHTKTl3TnUH+264AUwmktOT+WDFBwC8fOXL+JgL9s9UTwGyHPoOnk6mSr36+FoscPYsHD8OVTK3XhjcaDATt09k6aGlBb0lKSNi49Py7C938igPrvyTVifCofLD0KBBkbzulH+PAVAv6x6MAC+/TNMT+4j3D+bWW97kQFTVbI914SLFvIzy9TaQ0hggDY4vADOYXH4E27tSN64Wv9evS4PJczAtWGg8sGlTWL4cwvIuxCQiIlIa9O5IyrRdsYk899cmjpzN5U2NlF2HD8NfxhK833sYm6QnduwCQ4YY/c8+qzLXZUSl8IAc21ftO12kr/PMn5vy7F+65xQAJxOtPPfXJjYdPF0k30PP/pX5ujZHzkU0RC5Xp5PSc2z3s9tg9mzjon9/AP7Y9gdn085SI7wG97e9v8CvYSLnJbNrDpyhx3sLefCPrZl7A2/1LnKVkfG45tga9p3dV+DXlIvf9lwybb+5oy0Awz57nucWjaHjj59Ao0bw6KNgzztLsjBu7VAj8+LQIfjkEwBG9RnJtop1so0fc3dLTvt+wCn/t0nx3Ymf2Y9bTlXmw5kw7yeY9lsD9n+0jo2jf6Dh488bQUezGe66yyjSpKCjiIhcpBR4lDKt32dLGb/mMK9O3VbaU5FCcua3mfsXX4DTCd26sbleK+MxLuCdd4wCBIsXw/ffF/s85fw5nS6+XLiXn1cc9LRFBPl6zlcWceBx36nkAo275YkfGfjk7TSqW8nIfJo27YJed/nezPv4deVBEtOKb0m3SFmT7sh5f8eVXXwgKQkqV4Y2bXC5XHy44kMAHmz7YIGzHQHPJo+uc3IeZ2w+DsC8HSc4UdNdpGbLFq8x9aPq075Ke5wuJ9N2XdjvArm4JKRm/13cu0lFejetBAcOUHvnRmNcxapG1Pqzz4w9Fc8z+OhyuRj6dWbWfePKoZmdr78ODgd07coXk97k53s6eD3WSRqvrLyDZJ+F4DITZruRlzvMY9ynR7mvxjCu2g/X795C+ZR4XH5+0LKlsSpkzx5jFUhk5HnNWUREpCQo8Chl1uytMaTbjeyiXbE5f6otFy+r3TszbO+b1/NMn4b8PaKLsefXDz8YHQ88kPmm0uWCunXhxReNhpEjjSqlclH6a/0R3pm5w1NEqFnVMDa+lFlR9tNzKplfqPpZlrV1rlMOi9nE/65vRPtaxhuyqvEn+PyKoUz5+Qk6H9qMn8MOMTFGNd1vvjmv1xzprjSa4c0ZO3h92vbzvwmRS4jD6aLXh4s91z5ZtlaIGvOtcdK3L5jNTNw+kc0nNuNj9uHOlncW6nVy2+LRzyfzn7m/JbuDQMuXez/WZGJAwwEA/LDhh0K9rlzcMvZ49PMx89mw1qz+39V8c6d778M//gBgU6V6/PDLPPj1V/D1henTjQDkeUi02lm1P7OQWliA+4O2w4fhxx+N8+efB5OJ7g2ieaSnkYXrJI2Tfq+x8MBCgnyDqJD+KpH24Xy9IJ4Um4N/nniTB274H1/fMBImTcJ05oyxB/aoUVC79nnNVUREpCQp8Chl1v2/ZBaPSM2lYqZcvKz2zP9nc57ojsVs4uEr69GqegTMmwcnTkBUFAwenGUDePcDnn8errkG0tLguutg1y4W7TrJTV8tYfWh3UaAUkrdkt2nvK4bVSq+ZWC7YxPZfSIJMDb0//Hu9ux983ru716XZ66owgfTPmDZV/fwyIoJBNvSWF2tCQPu/JDxLXob0YqHH4Y5cwr8ek6ni/gUG1Pde3ll9fvaw7k+7nSSlUkbjuRa5VfkUrL4nIJLrapHAND4xL7M/R0feACA91e8D8B/Wv+HqmHZ977Li8n9d0RMfBqjF+whLsVY3p2Ulpm5trhWG+NkSfaiZsNbDQdgU+wmEq36IPNSkZF9/lK/JvRvWYUKYVm2/Zg3D4B/GnbBiQluuy0z4Pjqq8Z+oIV0Jsu2ArXKBWHOCLT/+KORRdmyJVx7rWdM5YgAnCQR6/8/0iz/4m/xZ8otUwh0tvKMafLSLF6ftYtZDa7gyJ33GxmZwcGFnpuIiEhpUuBRLgmxCdbSnoIUUkbGo8Vson7FUO/Or74yjjfeCIGBmdksGcvoLBaYOBFat4b4eJIHXMed3zzNpOND6PhjA+p/Vp9v1n2D3Vl0ezVJ4W047P3GLaNy6J2daxoNLhdJM2bB22/DU09ly0QqjKzLub+8rQ0BvhbjYs0aWg3qxZCtCwBYXa0JDw18jqG3vs2myg14rs9IXP37G0vgBg40skjy8fKUrdT53wxavjo7x/5mVd0B1rQ04/n2Ze4b95+f1/LE7//S6MWZ53WfImXJ6eTMQMwdnWpyc/vqAIxa95cR8B84ENq1Y+mhpaw8shKzycyobqMK/ToZ8Z3Tyem8N2snrV41PkSIy7LUdne0e7+92FhI9A4uVgurRq2IWrhwMX339EK/vlycEt2B59CAc5btx8d79hedW7dD5oeVw4cbGYRxcfD554V+vdPJmf8WHX9/Z+Pk2DF4913j/MEHs5Rgh+bVbcT4P0e6eRehfmFMv3U6V9e5mrsy/o7MmK77+zjIz1LoOYmIiFwMFHiUMuvcf0guPSe7Si5uGUugAnzO+TW0f7+x1AngoYeALP9Oz5rIGBLC8fHf8vBNQVQavI9DoT/hMBvZNXvP7uWBaQ/Q7cdubIzZWHw3IXk6fCbV67qiO9vkxX5NqHfqED9PeImQvn2M5WIffgjdusH775/Xax2Pz3ytyhHurJZ166BHD3z37+VIWDSDb3uPm297l+mN2lG7zjJi/V7keMCTNOm1m/4PhvHQVam89urVjF0/hhPJJ3J8nSNnUxiz/ECOfV/eZmRUbTmawOb3v+ZE+SpGcLxuXbjiCpg3jw0HM4OxOWVLilwKXC4XZ5LTSU3P/PDn+b6NualtNSZ1DqL7pkVGo3vbjO/WfwfAXS3vokZ4jWzPlx8Tphzb98Qmec4T/YNJKxdtXOTwIcf19a4HYMPxDdn6pGzK+HdGtsDjhAngcnGmUnV2R9fEkbGcwt8fnnnGOP/ww0JnPQ75MnPrF09Rtf/+F5KToX59uPtuT/+m2E30GdcdrD0Z0wAAbkBJREFUm/kAQT5hzL5jFlfXuRqA/+vfNMfn9z/330siIiJlRCF27ha5eExcf8TzSXaG279fxYG3+5bSjKQw4lLSufoD441n2jl7PfLxx0ZRmc6djWVJkG2p9a7Tu/hizRd8v+F7kpoaFc1rxkGF1L606/E09Wut5+VFL7PyyEqu+P4K5t45lyuqX1EStyZuWZfSg7HEcuRVxn5WvkePMPnX/xJsTcHp64d5yGBITzeyWJ9+2shGefVVDp9NxeF0Uat85rKy+BQbq/af5sqGFTiZZKVyWABms4m5241AoY/ZhL+PBXbvhsGDITUVunXjiT7PsjrxBImWn0nymcnhmARwJ4/sOAs7KgGVAM7A1LthKjQs15DmFZtTI6wGjaMb079Bf7q+szbH+z3wdl/iUtKpFh/L04t+pvl24/s7yS+QYLsV04oV0KsX86KqsrFyA3ZE12Zlg1D6t6xSpF93kdJmdzip9/w/Xm2D21Q1spBTU2n94mNG45Ah0LYtKbYUJm6fCMA9re85r9c05RB3dDpd7DmZ5NUWc0VPak2dAEuXei15BWhesTkAU3ZN4a1eb2E2KchTVjmcLm79diWbjsQDEOLv6z1g8mQANl9l7O3pVevuP/8xsvAPHjSWXr/0UoFe88jZlOyNO3fC+PHG+ejRRmAT2H16N1eOuZKzaWdpUK4B04ZNo365+p6Hmc0mbmxbjT/XHfF6On9fZTyKiEjZpMCjlDlH41J5csK/pT0NuQCP/77Rc+7I+i/+kyczl1mPGOF5N2l3JZNsWchn66fx3JLNLDu8zPOQEFMjRi4J4Y25azkWupprgk7y6c2PcWOTG7l90u0sPriYfr/1Y84dc2hbpW0J3J0ATN6Qmc03/dGuNK0Sntn5+OMEW1PYE1WN7d+No/8NXY32l16C116D11/HeeoUPUKvw2m20KZGBBMf7gLAB3N2ei2rblw5jH8e60Z0qD8nE628Nbi58Wave3djn9Bq1Uge/zNRaz/j2PrPcZmMpZ+B5ioEpPXFx1UJEz481z+KuPWLODr7T9ZXhg2VYefpnew8vdPzWmaTGT+/VoQ4ehHgaIMFo5hNmDubJmL6ZGZ//zBBNisOk5lvOgzmw263UcORzDzrctK++Y66Z45S58xR4gMWsOPodBJ7Lye0Wp1i+X8gUhp25lDsLT7FveT5nntg82aIjjYCMcA7S98hMT2RGuE1zvsDopzyHZfvPe399wtwomY9aoHxO+IcQxoP4ZEZj7Dj1A72n91P3ai65zUXKX0zt8R4FXkpF+KX2XnsGPxjBMb3dOwJxzK3AQHAxwdeeAHuu8/IenzySQjJLFyWkxf/3sIvKw9m73jrLWNLgR49jH2pgQNxB+gxpgdn087SvEJzFty1gHJB5bI99P2bWvJ4r/p0fWeBp83PomC4iIiUTQo8SplzKlH7OZZlLpeLhTtP5tw5erSR+daoEdx6KwAxSTEsS7yLNL+TTNhhDDNhonfd69iwrSMBztZMb5LMk8sfpGriSZ5Z9BPbj19D82rVmTpsKt1/7M6/sf/S6ftOvNbzNZ7t8qynEIEUn2f+2uQ59wo6rl0LkyYB8OiAZ9i2Kh7/BjH0blrJ2NC/alV48EHMX33FMx1O8HbPe1h/KI6az03hu/+E89man0nzW4vddBITfpw4XZlPVw0jzhoBlKNTynEYNBhOnMBVry6Tv3uaR3/vzuGEw2CCaL9WuJKu5fNBD/L1ogPsO5VszDGsLb1HPAIHn4H33uN0ZACr/vyIPcHpHIg7wLLDy1h9dDVplvWkWdYDJlpWbE0obejfpB1/fPYggZ9/jbkW7ImqypQmt7C7wpWYMLHXEkHy2+/S2r82kclzORC+ghPBCUAslm/r0qVWN4Y2u4X7296Pj1l/LUvZllNtr6ZVwmDxYiP7y2yGsWOhYkX2nd3HW0vfAuDlHi+fd5ZhTr/TVx8wAk/BfhaS040M7I9OBTMOjEJSDoexX7BbdHA0rSu3Zu2xtczYPYORHUee11yk9CVZbV7XdaOzBA7HjjVWVbRpw4n6TeDYPpznBKgZPhzeeAMOHDCy8L/8MtfXyino+OHNLY1K1j//bDS88goAJ5NP0vuX3hxPOk7tiNr8c9s/OQYdM1SLDOKL29rw8Nj1AAT5K+NRRETKJr3DkTJlV2wiA0cv82r748HO3PSVsa9OarqDQG2+fVHbe87St8CMpUNOJ/z0k3H+wgtgMuFyubhv6n2kuU5icgVzQ8NhXN+wE9fUvYbZm+zs2LIdgPjAUF7o/TBf/f0Ww9dPY8u2rVDtCsL8w5h751yG/TWMufvmMmreKLaf2s4PA37AYtb3SXHJWlV8cOtzqtO6q4auad6FbRWNTL8PZu8yAo9gVLgNCIDhw3lw9UT2RkXyXVsniT6T6T32DJyzYs7GQR6buRIsUCM9jNFvJNM8yoGzWSXGDKnE/IUPAlA9rDqfXfcZfer24+CZFOpXCKFPs6q0fMUoMBCXkZH1xhuwYgXlli7l+nvehlmzoGNDAHp+PI71pyaRYlmB3XyEf2PXA+tZGmvsT8etGbM6CnwAro8AMybMhLyVDuEYf9wCbJDmC4sPLWHxoSV8ufZLvu73tbYFkDItp4rtT1zTAO537996xx2e7K9R80Zhc9roVK0Td7W6q0hf89N5u7O1ra7eDHx9jeIhR45ATe8iHkMaD2HtsbUsPLhQgccybM62WM/5/reuz+xIS4NPPzXO77oLyznbuHj4+MBHH2EfcgOb//6KY9fXJ6FWZRKsCcQkxZBsS6ZJdBPaVe6aY6bj4DbV4PHHjSh8x47Qowc2h43bJt7G7jO7qRxSmXl3zitQ9fbaWbYaqfX/7d13eBTV18Dx727KpvdKGiV0CKH33kGaIIgioCgo8lpArAg2BERAVH4iKiKgNJEiVQgdQu8QWkJogRTSe7I77x+TbLIkIEgS2vk8Tx7YmbuzM5ub2Z0z957jKtWshRBCPJok8CgeKVPWnzF53LKyGw0CnLGxNCM9W8+N5EyTL2ni4ZORbZrTcdmreZUf//hDHV3g4AB9+gAwc99M1pxbgwYzPLMmMqreQNpW9QAgM8f0onJD1eZsCmxMxwv7cP7tZ+ikBm/cbNz4Z9A/zNw3k7GbxjL/2HwycjL4o+8fD/XoMr1BwUz7aI7M3Hiq4KIvv4otAPHxsGgRACtb9TMu9nOxNt3AkCEc3LSP+L0/8HvNX0jMCzZqFTusDEHoDNWxNFTGM+U6lWM3cdnhAoe8s7nsmMwUY8zuBkTdwNLMkrebvM2E1hOwtlBfp0peFXVHawueCvJmzfHrxiIEWFjAsmVqjtHISKhXTw2Ev/MO5R0rcfHGEJxzh7DtvSB2XN7Kpq2/EHtkFxlaA+keTiiVKpGRk0t4wjmy9BmAwVgTSaPYYGmoyNvNX2DhVn9eOXuAF7ZNZ3kNmNTZlpMxJ2n5a0u+7/o9rzV87f5/EUKUAUVRTEYcZuaYnuMHNPBDk5YGCxaoCwYOBGDDhQ0sPbUUgGmdpt1XTsVAj9tPhU3L1mNtYUZGjh691kytWnzuHISHFwk8NvZpDEBIRAjpOenYWNj8530SD05+IHFgI3/T0bB//aUGnB0cYPBgtPui89qrTzh38xx7ruzhZMxJTqWd4sCHltw0z4bDY+Bw8a9lrvPGPrcHdvoOaLFRC9nExsK8eWqDsWNRFIVhq4exKWITNhY2rB64mgrOFe7qWApXspbvt0IIIR5VD+9VtxDF0N4SiJnzQgM0QI8D6+h0fi+uC0ZCYHl1asxTTz2QfRR39sd+09EBtXwc1VEB33yjLhgxAmxsOB17mvc3vw9AoNVgsjMCjSPp0rJy+fqfc0W2vahOZzpe2IdryAbIyVGDSKjT8N5q8ha+Dr4MXD6QZaeX4WPvw4wuM0rvQP+jhLRsXl14iH0X45nev446cuI2FoRG8vmaML5/rm7BiMGHwP5CubXq+DoVrPjmG/X3UrEiv9sGGhdvDovh079P8XH3Gmi1GtKzM2lfPo6UQFA0UCEBqiS0J8x7FBossMrJpP/xTXywbR7WuWrqhav25kxrHkDGU5U5ZZ1Klj6Llv4teb3R61R0vn0OxfyLuu+2nMfDQUcdXyf8vLxg+3ZSn34Gu0P74cMPydp/kG1VhoJGy6zn6lHe0ZPyKy8y+NOdav9t2RK+W2fMBZatzyY2LZbGkzYBClrFDi1qEOONJu1YtHUL86u142PvDN754QeGnspm+IzOrLi8kZHrRpKancrY5mNL5PfxsDh2JZEv14XRu64P56JTGNK0vEnhIPHoWRAaybdbLjD/pUZYW5hx6FKCSQXhiu62jGoXCN9/C1lZaiqFTp04d/Mczy1Xhwi/Wv/V+x7l6+diw/tdqzH5lpuT+XrXLcei/VfUB7Vrq4HHLVugXTuTdi38W+Bp60l0WjT7r+2nTfk297Vf4sFIylBHsLeu4ma6Ii+vKK+/Dk5OaDXRGMhiX8wf1PtxPUdu3FLR3BwcMiEwHhzrNMLBxRs3GzdsLGw4fP0wuy+Hkqu9ToLlHBKV37DVt6Fr9ZdQxn+MJilJLZDXqxfTQ6ez4PgCNGj4rfdvNCjX4K6PxdvRGnd7HVoNeDlY3c/bIoQQQjwwEngUD7Xxq06Sa1D4so9abbLwBQ2gTqv+5BOmbPiuYGHUZTWX1LPPwsyZ4OFRlrss/kXhKo3juldX/xMaCocOqYHCN97AoBh4dc2rZOmzaO7XHJe0oRxPSDHmDttw8obJNqt7OzCoiT/r99uQsHYGztHXYefOIheV/Wr0Iz0nnSErh/DNvm+o6laVVxu8+q/7nJ1rQKMBi1JO7J6Rrafu55uMj0cvPXbbwKOiKHy86hQAwxccYvvYNgSU4DSspIwccvUGXO109/zcSh7qfvi72BSkPsjOLigcNHYsRJreRPh1dyQdajgRm32Q9zZ/QLKFOo3+qUvlWLwwCtucEHaUjyfWzpn2F/bjlKlO2d8dEMTvwd3YWaEu68c/ha/zvY1QsrFUzykJ6TmM+kO96Az7rAt6z3LUav8x/TxDmLjxe3Qr/+KDhgpfthtG5ax46PaiOg0b1IvY6dPBsqCAgaWZJT4OPrzUpIFJMZyPulXHTqe+Zq4CWTNmYnXqFG47drB8hxfvPTeWqXum8u7md8k15PJByw/u6XgeZhPXhrE/Mt5Y9CEiNo3fXmr0gPdK3I/8c9DEtWHsuhBnsq5JRRcWD2+qVpbPv7H03ntcTbnGU388RUJmAk18m5TYDaBXW1fi1daVKP/+WpPlI1pVxNuxUMCmfXtYvhyOFS1SZ2FmQcuAlvx5+k9WnVklgcdHUExKJocuJQDgaF2oqMypU7Bnj/r//1On0Z9L2kWU1ViuXFPbW2gtaObXjCDPIGp51KKWRy0avjIBi42bYVQjY6qQmJRMNp+OIfnKYa5lbybNchVZmqukmm9k9pmNhAJjgqDf5+P5evckxm9TK2NPaD2BfjUKRvvfDUtzLdveaYOZVlPk5rsQQgjxqJDyaOKhtWj/ZeaHXuKPfZfZG3ETgKOXE43rvxkQDIcPqwUpgN+Du/DcgC+IHDJCTV6/eDFUrgwffqjmDxQPhZdaFEwverll3ki0GXkXnk8/Db6+zDs6j52Xd2Jtbs3cXnPR5OVjzJ8+lZlbkM9r9/vtWP9mS55vHIDW1pYdFeqpK777rtgqB4PrDOadpu8A8Nra1/H6cBInryXddn9Ts3KpMm49wZ/+Q3p27n897LsSEZf6741QA6HGnIR5Wk/dxs87I0pkP7JzDdT59B86TN9uHDlyLz5acRIAX+dCU6jXr1enn7m4wBA1l5uCnkxtGMlmfxNj+SntFwXSZ2lvzsWHoVUccct+h19nnOdQp0EAtIo8Qt+TW3DKTOWyoyfjO4zAedd29tRryxdDmt9z0BHAyqJors+WX21hb/hN0Gj4s3YHxnZ7C4DhB1Ywf8nHVO7QTA06WlqqRQe+/94k6GjyXuQH1/M828jPJICdiwamTgVAM38BUwJeZkzTMQB8uOVDfjn8yz0fU1n46/BVJqw6WbQowx3k3HIe3n4utkjOV/HwCI9NZenBKyY5W2/n1qAjFMrfO3UqREeDhwc3+/eg88LOnI8/j5+DHysGrMDKvGRHcc0eVN/ksZ+LDe2rexYsqFxZ/fdc0VHzAP1r9Adg88XNJbpfomwMnXvA+H93+0I3zr7OyzHasSN4e5OYmcj8s2+j1yRgZ+7OzC4ziRoTxbah2/i267cMrz+cZn7NsBgxUn3ewoXqiH2g0cQQPlxxguQMC+z1XbnwxlnWDNhM14p9sdJrOOYFg58Gm8N9jUHHt5u8zfjW4//TMdnqzIv9rBJCCCEeFRJ4FA+l+LRsPvjrhPHxs3P2Ahgr0FZws6V3XR/4+GNQFPbUasFHnV5nT/lgRtV/HvbvV6dTJSfDpEnw+ecP5DhEURvzRis+XS8vqXpEhJp3CWD0aM7fPM/bG98GYFyrcVRxrUL+Tf78C+CrCRkADGkagI9TQXDL0kzLkiC1aAErV0L58vDeexAVRY7eQK5eDXxM6TiFjhV6AAZiLD+l4/dzi93Xb0POU2uCOqotLVtPt5k7ORgZX2zb+6UoCt2/3WWyrHIxectikjNpOinEZGRkvi/WhvHawkMcvZJ4X/tyIi8Qm5Cew4mrtw/K3ipHb+BYodeOTs4sWPnTT+q/AwdyLv0KvpV+5orVQKJ1Y0mw/JEMswNk6TMxM7hhn9uLcpn/o7xNZ9wcbai+6GfefGMWS/v/H7NbPc+1BUvJPXOWYYunUcPXiaPjO9Er+N+T9BfHpphiVHGp2SzcVzBKcXWNNkxprQZLW0UeQZOcDPXrqyOmXr3ziFmduRmLXmlCBTdb5r/UCHsrC8wLjVrJyTVAo0bQpQsYDGg+/pipHacyusloAEasGcHGCxv/07GVptFLj/Fb6CUaTrz74IyDlUWRZe2nbafRxM18v+U82blyg+hhoSgK7adt590/j7PjfNGgIvCvvy9zMy1cu2YMrGdMmUjX1f05HXsaH3sftg3dhpddyaeI6FLLdJu1fBzJT/OnM9eq3w20WjXwGBlZ5Pkt/FsAcCrmFJcSixYOEQ+3tEI3CI25P8+dK6gw/dZbAMwInUGGPgWtYs/LlVfxRuM3cLO5ZWo2QI8e4OqqFiT6889iX9Pb0Zru1dqz7mYLrn6tMHG7OeWsC4Ld7zZ7l+mdpxdbfV0IIYR4EkjgUTyUJqw+VWRZ4elTU/oGwYYNsG4dAA0WzVYvcoCT1/KCAocPw1dfqU/45JOCL52izCVn5nDmRjKKohB5Mx2A9Sfypkv/9JM6IrVpU2JrlKfr711Jzkom2CvYOPIr/6t6Ro6eBaGRHL+aCICLrek0YJ2Flj0BdTjx/AiwtYXLl9U+4ONDrLMn62q3JePYcbQaLe83moVOXx1Fk0WM7jMOXT1rsq1TUUlM32Q6IibyZjr9ZocyfpU6oi8718Bnf59mYTFVLQtLzcrlfHTKHdvEpmYVWXY+JtU4omzz6Wh6z9pNoy9DuJmWjX1WGrWvn6fL2d30OL2dhldO0ufkFhp98ynXOj6FMnOmetF/B+GxqcSmmL6uwaDQ94c9xsdTNpy5qxFPAL/tiTSpOp+fIoHISOPf6tqe1Qj6IYjdUStRNOloFXus9PVxyhmKT9Z3+GT9ikvOK5jhaNw3d3sdM2eOpP+Sb3l1+0J8Bj1DRS/HEplafrvp89vOxpo8/qHJM/R7fgpze7wKc+aoU/mrVbur12hayZWt77ShVRV3AJOiQZvD8grxTJyo/rt0KZqDB5naaSr9avRDr+h5dvmznL9ZtELvw+BmWjaHLiUU6Uf5kjJyGDhnL+/9eZzt52KLbROTnEnIr6uZ13ME6bNmw/HjpbnL4i7k3+QDiE7KLLbNtrMxd9xGdW8HePttSE1FadiAUU57OBB1AAedA+ufX3/H3Kv36+KkbvzwfD1mD6pHsJ+TMeCjAHh6qrn3oNi+5m3vTVPfpigobI6QUY+PGtu89Bm/vtiwYOH06er3jCZNoGtXEjISmLFXnWnhlDOU7FwdV+LTOR2VXFBoLJ+5OQwdCkDGwj/oNnOnyWofJ2v1nL54MYwejWsGfNhtEhGjL/Fzj5+Z12sekztMLrXjFUIIIR4FkuNRPJT+PhZ1x/W1yznAM+PUB0OHYlmrJj8NcefFX9UpNquOXqNnnXKk/N9wYs7txWbJX3i9OxbtgAGgu/ecdeL+BH3yD4DJSK8ZA4LVYgN5ef8yX32Z3kv7EJ4QjpOVE4v7LkZnrv6u8i8aF+2/zN6IghGHjtampzCdmRY0GkKHj6X2TzNg1Sp1etWhQ5RLiaNn2A6yGjbEMHcuI8+64J4zgRu6d8jVXqXBL9UoZ18OB50D9pb2XIuHFAsvrPX18EvyolP4STxS44m3diD9hDVJ8fU4HJtJ8vbTXEqP43hTX2pXq46mVy9wdDTu0/WkDJpO2gLAljGtqehefPXVszcKApMT+9QyTlf+dst5RrSqxIiFh1Byc+l5Zhc9wnbQJuIgFgZ9ke0kWsEJD1j9w24yfn4LJTiYgDGfUadGO2wt1UDdtcQMLsWlMeiXffg62/C/5+thbWlGJXe7IlOrT1xL4s3FR/l2YN3b/XoBNT/lF2vDTJbVKOeg/ufXX0FR+OPpygzZ9za5hlxa+rdkTONPeWN+ChqKn0L2x8uN7/iaJaFlZTembLj9+pFtKvG/beEAHPStydsvvwSBxYyKuQeFR718vOokzzTwU6tnP/usevE6Zgza7duZ33s+4fHhHLlxhK6/dyV0WCjutu739dr34mpCOi2mbAVgyfAmNK7oWmy7/ED1+YldiwRyG03cTFaugdC8dBkAMwbU4e0lan69elfD+HzTD9SMyUsTsP5n9d/nnlODseXLl+ARPdl+2xPJ5rBofh7SAJ35nadtbj1TEFS8XV45s3/JN/fipT1qhXitlu9Gt2DusW/QarQs6beE2p617/0A7oFGo6Frbe+Cx/n/yb+HUq0aHDmi5hfu2bPI81sFtCL0aigLTyxkWL1hpbqvomRl5qifi/kBSG7eVKdJA3z0EWg0fLb9M1KyU/Cw9scqowNrj19n7fHrgDpz4tzErqYb7dsXpk3Det0abpbvB/bqZ8Dy15pRP8AZ1qxRz1mKAi+/DKNHo9Nqpe8IIYQQeSTwKB45TjYWWM/9SS1GYm1tHNXYurI7Ctmkaw8w/O//MWrrca4mXwVfYAx4pMbQ69uOPN3nQ9pVaIelWfE52UTJKjxaLjdv9J6fi7U6He7rryExkRwPV/qbr2DPxT3YWtiydchWqrpVNT4vf4Rj4aAjgIO16dRNnYUa9MjKMah949lnSevTj6bvLaf2jQu8ufsPGl09DS8M4rVmA5nZ/Fk8sscTbTkOvTaGqJQoolIKBb3NIcX8b+LcwDYd+p+CHhch0gmmz4dTHnCqApx3hXFasD0Lfh9r8XL0watKPVIVb0LPWmOmdUWDBS2/Oc+6/2uLj4MPrtauJgGo/CnKXWt58Vwjf2Pg8ZvN52lU3pmW5/czZudCakerQTCDBvb7OHC6pitHHDI4bZ3AKQ8D1+1vzcl4FFb1RLtKS3X36lR16MTeU9WxUMoBcDk+nae+K5ji/WG3glF8lrk5tA0/gPv+aHAbAG3aGCuF3+rLdWFFltlamkNaGsqs7/msNXwSdB4M0L1yd5b3X47OXEf51xOJSszgtd8PF3l+s/sM8N2NWj6OrH+zJYnpOWTrDVyJT2fcypPG9cNaVCDI15FXF6r716iCS4m+fmaOgbSsXGx15jBlilr0YudOWLAA68GDWfXsKpr80oTwhHA6L+zMliFbcLJyKtF9KM7pqGS6fVswsmfAnL3Me7Ehbap6kJxZfN7Pjadu8FRQOZNlWcVMx+0VVI7kv1biuvR73NPOEuYG6wLNOOjjiYdOi8eVqwQe/4Nm9ZegNO6J61dfYl/r7kaXiuKlZ+caZxLM2HSe97ve+f0sfBPhzPVk9AalSKAxvzBTYYEedlyISaVS3BWcZo3FoIGpH7Tig7MzAZjacSpdArvc7+H8Z0p+5LF7d1i0SB1xP2ECWJnmmRxcZzBTdk9hW+Q2rqdcx9veu5itiYdRfuDRKu/7AL/8AmlpUKUKdOnC8ejjfLv/WwA6+45hR7xpED5bbyAhLRtn20LfEZs2hbp14cgRngrbyS+N+gBQ189JnV3z/PNq0HHwYPVmqlYmlAkhhBCFSeBRPHSuJWYY/1/P34nDhQrKAOgTEuG7vATdEyaAuzoCKCr1Gtd1o8nRRoIBSFab2FrYkpGTToydwk/pO/np965UdK7ILz1/kYqVZeBcdNHiEVqNRp0GPWECOVro/39e/H1+DRZaC5b0W0KwV7BJ+xx98VN9bXWmp7D8GOeuC3H8X3u1gMCfh66SbGXH7vLB7POrxYdb5/LSodW8uWcRVeMi+aT9CEaGdaHCzQNUjjtDuqVCig7irWGPH/xTCS46w47y6s+dpFnCGVcDZ7gCEVfUhbfEt4N/VP+1t7Snnnc9ugR2Z9uRAMKu2aBBQ6sq7iRmJlLVsAfbqweoHBfFxT5XeSEtiVPuMD/InONN/NmriyY5NxljRy/EzOBBFXc/PLValNOnOa9LI8rBwKnYU5yKPQVWoFWcMFfcyB8LpJCFVrHj7c1OmFm4UvFmNs8e30X5pDTqXgel0y9oXFzUKWfvv2/8u8u3oNB0c3src7aPbYtWq+H85HcZ3SmeNXlx5HeavsPkDpMxyysYVMfPCS9H04t+gB9fqF9kWWmp7u1g8vj5xv5k6w1kZOtxsrGkSy1vLkzsiplWU2I5uoY0DeC3vGrXL847wNIRTcHfXx2R88knMHIktGuHn68f659fT6tfW3HkxhE6zO/AhkEbis9FVoJmhhQtvPHVhrO0qerB6qOmI9L9E67T92QIDn9/ib5bc8xGDIfAQNLypiw6ZqRQJU491pkVrvNVv6F8XzGOa88V3ooeyNtuYMEyu6wV+M5egY+9D37BrfB1r4Svgy++Dr4EewXj4/Dfcns+aU5eKzhPLDt45Y6Bx6xc05HUP++6SMiZGLa+08ZkuTGIV8iIFuXZMmsRU1ZP46JFKq+NdOAfi20ADK83nLebvP3fD+I+5P/ZGu+DPfOMmusvLk4NFOXl/ctXw70GwV7BHL1xlAXHF/Bu83fLcnfFf3Q6KpmovNQAVhZm6i/8x7wP3XfeIVmfTt+lfTEoBp6q8hQv1h3AjuMHi2wnKSMHBWj79TbqBzgzd2hD4no/g9uRIzS/dIxfGvXhyz610R48oOaATE6Gli3VQLaZFIERQgghbiWBR/HQOVWowvC8lxqRlJ7DrK0XWHzgCigK09dMUy8WKleGMWoOwFMxp+jyexdytFfRKNYEuz7N5G6DOXvJi+QMc8Y2cmV7Kz9WVMrhz2ZORCRE0H5+e95v/j6ftf3MGAQRt3clPp3kzBxqlnP898aFFDcSrpaXHbz0EoaMdIYNdWKl/hTmWnOWPbOM7lW63/W2nW1Mo3qLD6jBvn0X49lw8jqdangRVSiQnWtmzmftXyHC1ZdPNs2my7lQupwLNdnGjvJ12RbYGrusdIJj4vi291NEtarFX3E7WXxyMSdjzqLP9sLC4I+l4o+FIQALxR+t4oCBWGrd2EPH6zvQJkdy3hXOu8B1e8g2U38yzCHGDlKyU9h+aTvbL20HQGvlgFWuK29uiCN2fQrYAlXzfkzkAhGQCzozHTU9ahLsGUwdrzoEewUze3MOe85n8nGj2gxs5A/p6fDGG0T99AshFWB2QytCfXMwaBLJ1iTe9r095gnHOhY89kjV0PNsPJ02TKf3rz9jMWs2DBxY7HMXDmtEWm40n634glmaOeRWBXO0/NDjR16u93KR9h72OjpU92RzWDQjWlfk/S7VHmgSfo1Gg87czGQ6qvltckH+Vy82r2AMPO6/WGgk7/vvwx9/qMUQBg2CrVsJ8gwiZHAI7ee359D1Q9T7sR6L+i6iuX/zEt2nwhyti45sTc/O5XRUsnFEaP2rp3n22D/0ObUFcyVvZOPpPfD1VK7VbcJJM0fWXzqLeW4k2yrA31WhvBZygtWmWkVDOTsv/JzLk5LmyKVYS4J8nKlezoKj0Uc5dO0wqToDZ9zhDNfgzCI4U7A/GjS0Kd+G52s/z+A6g7EwK340roA3Fx8x/t/R2oK9ETeZuvEsg5sG0K22N3GpWXg5WKHRaPjs79NFnn8xLo0Fey/xQpMA47LriWqAp6WPDeP0F8jZvoOaA/fQ+GYE3wTDlBYa0i2S0ZnpmNF5Bq82ePWB/V1rjDdY8lhaqsXp3nwTJk+GN94oMkptcNBgjt44yl9hf0ng8RHR+38FOYatzM3UdCsREWqKnQEDGLVuFBfiL+Coc+SH7j/gY+9R7Hay9QambzpLUkYOW87EMHt7OF7e1ekNtIs4SOShb+CvS3A+L/durVqwYoXar4QQQghRhEa526oBD5Hk5GQcHR1JSkrCwcHh358gHilLDlzmveUnaB7oyu8vNzEu/zbkPMkTJzNu61z1y11ICLRowfWU69SbU48bqTdws/LFMnE8vWvXZ0KPGjSaGALAnvfbUe7tkTBvHslDB/J/vSyYf0wtNtOrai/m95mPg0760u0oikKFD9TiIDvfbcvVhAwaVXAxTr07diWRzBx9sTng+s8OZX9kPD5O1vw8pAGL91/mkwOLyZ00kWF9tCyobUCDhiX9lvBMzWeKff2YlExemnfAOGpnYCM/qnjaM7RZeZML2cIFiABGtK5IVGJmsTlDu53Zxf9CvofUVC46ezOvfk92BQQT7uZn0i5ysmkgtPB7AWrQLKaYwhrBUWfpdXobdaPOYK3PIc1ch7lBT/WYi+i1eiKcYUsFWFLHkr1e2eTeEvv2SQa/XAeiNToSrM3IsLDAysKZjlWCaVexCY19G1Pbo3aRYMsHf51g0f7LDGtRAUtzLS0C3Wge6KbmWnv9dYiNJcrOhh8bBbPP15cIFx/SLK1pcvkcFRLC8E0MI8rBQIwtJFXyYaO1HRlmEaApmF7rnAHtLkKnxs9R65nXSc/OYODcjWRrLpKtDcfe/iqx6QWFRLrdsGfqp6HU8KhZ5H16Ut3ajxYMa0TLynmjSA8eVIsg6PXw99/w1FMAHLtxjKeXPk1EQgTmWnPmPDWHF+u+aLLd5Mwc6nz6D8818ueL3rX+c6Cn9dStXMorBGUy8lxRqH8tjGeP/cMzJwsKb+wMCGZrpYa0jDxM64jDpOgUfg+C3+rAfl/TbTfRVWJ469EMbPgSVubqaNfpm87xbYh6Ef/bS43wd7Gh9dcb0WviqBK7n65nVpCrjeeqA1yt7kNkeUdOxBUEyII8g/i+6/e0DGj5n473cZWdayA6OZOWX23917Zf9Q2if0M/ao7fQFp2oVGPioJn6k3MDQbe7VSFXj0as+FMLKP+OIJHYjS/bPyEPY6XOeINu/3gaKFZyW3Kt2F299kmqTMehOjkTBp/GYJWAxGT8s7pGRng5qbenAkJgXbtTJ5zKfESlb6thF7Rc3rkaaq7V38Aey7u1s7zsbzwy37j40NjmuPapL4aeBwzhjkDqzBizQg0aNgwaAOdKnUCoMd3uzhxLYlPetRg1rZwYlOyWPV6c2ZtvcA/p6MLXkBRWDV/NHVuFCr0ZW6uTtufMwc8ig9iCiGEEA+7soivSeDxIaYoygMd9fOg5AePGldwYcmIpurChASUl15Cs3Kl+virr2DsWHINubT7rR07L++kknMlRgcv4qt1MXSo7kl5Vxt+3nURgJAxral0bC907Kjmcrp+nfmXVjNs9TByDbl423kzpukYXm3wqrEAh1AtPXCFd5cXX2X23BddUVCoOk6tznHgow7Y6cyxtjQjOTOHnt/tMlax/mZAML3dDPDhhyQvW0jf/rC5kjoSZfZTsxlef/gd9yMtK5eaEzYCatGR4vL/3Rp4BNBqwKDA98/VpWWgOz/vimDurou83i6QkXVc4fBhdnpVZ+KmC7zWphIdqnvS5MsQUrJysTTXcu6LrkW2mZqVS628ffn1xYbGokbFWfN/Lajl42jcN4fMVDpc2MfHWWE47dyKJiuLDHM1X+R1OyjnXoEqvV7CfugIcHcnJiWTRhNDcLPTseu9tur0sTuYsuEMP+QVQwGo4GZrnCKZcfoM11p2JDD+qslz9DorzLIKKtdeqdMIv1nToXlzdp2P4/lfdpGpPUGG9jBmdrtIzIq74z4AaNHS6IqBz7ZCxx83QYcO//qcJ8256BQ6zdhhfGwS5B49GmbMgNq11SIYedP3UrNTeWHFC6w8sxKAD1t8yOftPmf10eu8teSoyfb/HtWC2r53P0I5KSOHxPRsYlKyeGa2OhJ4dMcqPNfYnwZfbKbppWO8t30ewdcLLrzT+vTD/K03qLoumRzNDTK1xzEoW0i2OI1Bq46C1GJGc/9mtK/Qnr41+lLLo1aR1/4u5DzTNhWd3p3POjuTj7f8zHPH8ioBVapE5PJfWJS8h6l7ppKQmQDAsLrDmNZpGo5W9zYy+3E1YsFBNp6K/veGikKTKyeYW1vLhospHI6Mp49lIu6R57APP4dzZkHxK4OdLSv9PNjtn8U5l2g2BOpNbpxoNVqa+zVnRP0RDKw9EK3mwee7i0nOpNGXIWg0cHFSob+zwYNhwQJ45RU1eHSLTgs6sSliEyMbjGRW91lluMfiXuQHlvNZmms5kxmCdto0cHMjZPMcOq3qh0Ex8H7z95nUYZKxbXxaNhfjUqkf4FLsd4jCqsdE8MblXXRtVweCgqBVK3iMr0OEEEI8GSTweBtPQuAxJiWTgXP2Us/fmanP1HnQu1OmCn/xi5zcXR310749bN+uJmr68kt47z3QaPh6z9eM3TQWa3NrDg4/yNmrDry95Bj1A5w5dCnBuJ21b7Sgppc9BAbCxYvqhcagQey8tJMXVrzApSR1yqO3nTdTO07ludrPPZFB3+Lc6Yv4V32DTIKStlnp1EqJ4vvXO7AswYKv/lEDFGYGPTvNDlNuxmTC7LMY8Ayc8FSnCs/vM5/+Nfv/634oisKAH/eSmpXLqlHNi1TP/bd9Xf5aU+oH3F1hkOxcA5tOR9O6qjt2uuIzUuToDcSmZFHOydq4f80nbzHmlwJYOKwxLSqrAdK5uy7y2RrTKYz+Frl4RpzBJieLKa+1x6tJPbUozi2S0nNQUHCy+fdpXD9sC2fKhjMmy0580olcvcL2c7G89/t+2oQfZKJTLG57d6pTeg0G8PMj99mBJLVuj2v3Tsbn3joyT0HPH6+5EvLzB2y5upPLjmCTA2BHlnl13uncg7pnk6g97lus03PgxRdh7tx/3e8nVeE+a1IV+uZNtaJzaqpa6XrAAGM7g2Lg7Q1vGwskdKzYhdOnnsUc02D8R92q80qrine1H5dvptNqatFRcRP71OK5qo5sadCJ9uFqgD3NwgrLXj2wGP02N2pXYPbB2Sw/vYKTsaY3KCwM/tjldubke59SzuHOxTk+WnGC3/ddLrK8krst/Rv4MWm92qf3NDPDY9QIzK9cBm9vWLeOuCq+fBjyIT8d/gmAQJdAlvRbQj3vend17I+zfwukANSIjuDLjd+ZBJQLM2hgj4+G9VU0nPBU2OejEGNn2qauS006VOtGkGcQnSt1LtPq63cj/wYO3BLgX7NGzdHn4ABRUWBreuNxzbk19FjUA3tLey69dQlna+ey3G1xFy7EpNJh+nbj46fr+jCxYi7WjRuBopCwaC61oz7mWso1+lTrw9JnlmKuLf5z/W7+Xo5/0gkHK0nrIIQQ4vFRFvE1yfH4kPptTyThsWmEx6YxoWfN2wY/Hgc5egNh15OpWc7RpGpmRbe8C4D/+z816GhnBxs2QHM1r9nh64cZu2ksANM6TaOGew2ORKgBxMJBR1Arx6LVQv/+auXYn36CQYNoGdCSsNfDWHB8AZN3TeZi4kUGrRjE6nOrmd97PjpzXRm8Aw+v/OqQt5MfdGwbfoBX9q+g8ZWTmCkG+AUGWDsQ6FsDvUZLrehwki2i+aAr/BGkIVer4GbjxsoBK+86T51Go2Hx8CZoNNw2KGxppiVbX7SKLoCXY9GA3u1YmmvpHnTnQImFmdYYdMzfv+3vtuVyfDrtp23H00FH00oFU89falGBCu62JqMjL+eYc9lPHf3l1fb274Ojzd1f5DhYFz1X1P7kn4IH5pZsrNqMH/MvvmNi1Klo9ephbmnJrZPlb32vNZjhZVOH8Z/v4L2Qrex7eQwtIo+iJRU4AN8WGv3ZsaNauEHc1o6xbY0Bv4T0bDzs8wrtuLqqBWa++kr9GTCAuNQsjl5OpH11D2Z2nUl19+q8uf5NNkVsQGO1FYfc3jjkPo0W9dy55sT1uwo8frjiBH8UE/QD6FnRHn2P7rinHmB5NS1rq9Ums3tL/AJs2BX2DqH/hBYUGVG06AzVsDLUxVbfEg/rCuz6sF2RIlDF6R7kXWzgsVEFF0a0rsScHRHcTMum2R497j0nsnzVp/hfvQD16+P20kvM+fxz+lbvy0urX+JC/AWaz23O7O6zGVxnsNxEug1dbjav7P+LN3cvwsKgJ8Ncx9aK9bHJyUKv1aJrWp2djTKZlhTCTf1NCmVIxMxghleaNx7plejW8U2+6NPnwR3IXcjP8VhEly7g6wtXr8LGjfD006arA7tQ0bkiEQkRLA9bXmyOWlF2kjNzMNNoTM4phYOOANN7VYPGjdXCMj178oZuC9dSrlHRuSLzes+7bdDxbnzVL0iCjkIIIcR/8PhGsx5xhceh1pqwkV+HNqRttTvnjzlyOYFvQ87j72LDB92q/+uUzAct7Hoym05HExGbysqjUbzQJABf54JAzsxn68Lq1fDDD+qCuXONQcccfQ4vrlJzmzX3a86rDV6942tdS8ygfoCzOp1qyhTYsQOOH4egIKwtrBlefzhD6gzh6z1fM2HbBJaeWsqVpCusenaVceRGVq6e3rP2EBGbyoa3WlHB7fGfkh2dXDB6b83/teCp73aZrK8eE8GbuxeZFGiJs3HELjsD14xkOp3fS6wNTG8K05pBjhmAQrfK3fi5x8942985uHcrrfbOAYRR7QKZfpvpmh72pR9EtjDTUsndrkheyHxtqrgTMqY17aeZXijVuYfpsP/G7C6CLB8Urmjr4fGvuanGP1XDZLTm/7aGM61/HU4G1mHwgM+pePMqP1/9h4pHdkNKijqyuF8/ePddSbb/L/xdbTDXasg1KBhujZm/9RZMmwaHDxOz5h8a7VLzbPo4WbP7/XZcutwM1/Tp3LT8jmztWZIslqC138yLLoOo+WM4Pslx/DMTqpV3Jyczi/L2FphpgOBgtXBNtWr8uD282KCjgoJiOMnYD19hUYvrJHcAMADH4NoxuFbQtqlvU15r8Br6jFp8srIgn+rqUS3uKugI0KySGyc/7cyZ68n0m11wPnmnk5ob0NrSDNLUZbF2zvR4+nOORfyujlj7+Wf46y86z5rF8VePM+DPAYRcDGHoqqH8fuJ3vmz/JQ3KNbir/XicZOcWfxNGl5vNjHN/03b9H1jnqvlpN1RpyrhOI4mzdSaXONLMt6C1X098vJqr1d7SHn16MDpDdSyViugMVdGYWxDvAP6ut6+Q/bAofFo0SWNjbg7PPVcQ4L8l8GiuNWdw0GA+2f4J/zvwPwk8PkB7I27y7Jy9gDqa28/Fhqa35JWe/2JD9bx5/Di4uzPnzRYs3KkWBvq116//mst77Rst6P6t6fecQ+M6YKbV3NWMAyGEEEIUT6ZaP6Q+WX2KeXsiTZbt/6h9wWiYYhSeIhLoYcfm0a1La/dKRJWP1t92dBpA+MdtMQusBDduqKMev/3WuO7dTe8ydc9UnK2cOfHaCXwcfAA1OJifb/BWMwbUoU9dX+jaVR05+eab8M03Rdr9ffZvnl3+LOk56VR1rcqOF3fgYevBqqPXeHPxUWO7/R+2x8Ph9r+PR92tUy8jJ3fndFQy4bGpTJm9gQmb59Dxwj4AFI2GX+r3ZF79Hlx18sJcn02FuC3EWG/ijOcFslFHTnar3I3xrcbT2LdxqexzQlo2dT/fBMDHT9Xg80LBstsFAx8Eg0Eh5EwMr8w/CEDYZ13UwEoJSM/O5bWFh2lS0bXIlOt8pz7tfNcBofz9PXMjhZ93RfDX4Wv4OluzeXRrPvzrBH8dUSNQD9P7+6ipMm492bkGdr/fDh+nW0bmvvACLFzIpsDGvNL342Kfr6CQrt1DosVv5GrVwF/nC/D5Fqh/HbS3+5Tv14/WTq05655GjuYy3etagFk8a08fJzXrItlmycamDua2VPGojpuNG242brhau1LeqTz9avTD16GgekxWrp6Bc/ZS28eRT3sVzeV4N/JHWptrNcZq4sVNgVw2ogkNr55WPx+OHVMXjhlD7uQv+XznRKbsnkKWPgszjRnjWo1jXKtx9zXa6VEzY9M5ZoYUTJ8e/1QNQmfM5f1t86iUl+c1yt6NG+98yFSvpmyJ3EKK+d9kaPeDRv1sruZWjfeav8eztZ6l2rgQk+33raf+3j/t9fDPyriZmkX9L9RiSBFfdjO9iXXxIlSqpN7xLVTMKV9MWgxeX3uhoBA6LJQmvk0QZe/fpkHvG90cz9Gj4PffAVi3cAI9w79Ar+gZ32o8n7b99K5f62ZqFov2X6ZvfV+872G2hBBCCPEokhyPt/EkBB5HLzlqvKDP9+3AuvSsU67Y9pFxabT5epvJsouTuj20U8zyE73fTt96vkw78Sd88YVadfLiRXWqNbDxwka6/N4FgN+f/p3naj9n8tzMHD3rTlxn9NJjRbYbObk7/PUX9O2r5nS6ds243cJORJ+g44KORKdF07BcQ7YO2crcnVFFih+sHtUcJ2tL/F1tAHUkRVJGzj3dGV+w9xLfhZznrQ5VuJGcySstK2D/EEzl+X7Leb7+p+B4jYGluXPJeuNNdGmp6DVa1lZrQfiwN/g9w4GY1GTSzLaTYr6SHO0V43MblmvIRy0/omfVnqXaJzNz9FT7WA08fzuwLm8sOgJAsJ8TK1+/uyndj5NjVxLpNWs3AG93qIKjtTkDGvr/5yBn4fOMp4OO6GR1tNTT9XyY3j+4JHb5iVT94w1k5Oj5qm8QHWp44mJb6Pxx8CA0bAhAh2H/44Kbf7Hb0OVksfH8Qv68uYKP2kF2XhxIl2tJQJInYE6qZSa52mxszdJJ1WeQagkZdzjV2GRD33Nahjz3FW16vYWZ9sGNor+1aFK+i5O6oWRlo/30E5g8WV3Yrx/8/jsXUi/z3ub3+CvsLwAa+zTml56/UPMxrK4eHptK+2nbaVrRlUXD1cBY88lbuJaYAUDkpG7oPx6P2cQvAMhxc+eN5sNYX7U5L7a25WDidNacX2Pcnk5fgx/6vMPzQc9jaab2x/TsXGqMV4tq5VfAflTEp2VTL++mVPiX3UzSugAwdCj89htUqwanT5sOkQQG/DmApaeW0rZ8W9Y/v/6JT8NS1lIyc0xThtyiVcQh5p/7S70BodEQ+vWbtM/4kYzcDAbUHMCivose2u/DQgghxIMmOR6fULl6AzsvFK0aezE2rciyqwnpbDodbXqhmuerjWd5r8vDOQUqv9r07VQ1JMPUqeqDKVOMwcErSVcYtGIQAIPrDC4SdASwsjCjYw3PIsu71vJS/9O7t1q0ITISPv5YrRyLemGy/VwMPev4UNuzNpsHb6b53OYciDrA0FVDqaD5sMg2e36vBnUiJ3cnR2+g8kfrAVgwrBEtK985ub7BoPDXkWt8vPIkoOZZA7UK81sdqtzxuWWhcNCxR51yagGSsWNh+nR0QJhfNUZ1fpNwVz+2DG1E3OGf+f7gNJKzbwJgY2FDjyo9eLvJ26U2wvFWOnPTgjO1fBw4eS2ZTjWL9ocnQR0/pxIdiRiQF2AHjEFHgPKuj3/agdKUHwR5d/lxWK5Wi7fM78sNGrC5UkM6hB/gl+Wf8VrvDzntWZC30cygp9GVk4zb8gvlYyJ4R6Oha4+3+DwwipVnVpNFBudcrxT3skYeqdDoGpRPBJ8UCEiECkkagqq3wea72VDlwZ+P3utSjS41vRgwJ1TN2ZunwgfrqOvvxOLPv0BXubKaTuPPP8HcnMD58/nzmT9ZfHIxI9aMYN+1fQTNDmJ0k9F82PLDB1IoZN2J63g7WlHXv+ReOzo505jnLjTiJtvOxtC6irsx6NipvB0MH47Zzz+r+9D2GTovn8PJOYdJSf6TKUd+IyM3HXOtOV0rPk//aq/wXN1mRVJb2Fiac+bzLmg0oDN/uFO53OpfQ06TJsGiRXDmjNp/nnnGZPXbTd5m+enlbI3cSp8lfVj57EpjQFaUvK1nY7iWkMHzjf3RaDR8v+VCkTaV4q7Q6cJeep/aStW4vHQRLi6s/vZ1Bl6aRkZuBi39W/Jrr18l6CiEEEI8YDLi8SE08vdDrDtxo9h1havpXU/KoOmkLbSOOEStGxdItrIlx96RG1orknV2JFvZsvmzXgX528wfnjjzzM3nmbG5+Fx8AFsP/UiFzX9Do0awdy9oNKRkpdBufjsORh2kpntN9r+yHxsLm9tuY+6uixy+nMCa49cBeLahH5P7Bqkr165Vp1NpNBAWxqwoM6ZuPAtA7+ByzBgQDMDmiM10+6MbuYZcbHM74pozCg3qBZcuNxvPlJv4JMfwS7/qHL8QzbId5/BNiqb2jfP4kE2cuRW13ngJ51deBIuCoUUZ2Xqqjy9+Sjg8uGmrKZk5NPhiM1m35AY7+0kHdEMGw9Kl6oJ33iH3i4ksORrFTUMIU0LHE5WiTvEs71SeUQ1H8XK9l3G0KrnchXcrfzrWzGeDCfJ14vClBPrU9fnX/JDi7hQ33U2qfN6fOp/+Q1JGjsmywx93xMXWkrjULHq8u4iVC8bgmRoPQLiLLzF2znikxuOdEodNTl4Q2NVVnWbYuTMAN5LTqDtpNtnaC4AGM8UZDdZ42jsys39TBv10lFaR5+h+9jiDzOLUir7160PLltCpU5EKvw+DW6us5/v95cY0D3RT8wL37Qu5uWyp2IDNvV/iy69fJTwhgnc2vcPKMysBcNA5MKL+CN5s/KYxVUdpO3MjmS7f7ATu/xyvKApLDlwhM0fPJ3+fvm0738QbrN30FY4ReZ+3X30FY8eSmJnIS6teYcWZPwFo6d+SH5/6keru1e9rvx5WienZBH+mjni8MLGrcQq/idGj1RuRQUFw5IhakK6Q1WdX88yyZ8jWZzOg5gAWPr3wiZq6X9piU7K4npTBnB0Rxu9tk56ujZ+zDYN+2Wds184xl25/fEe/kwWzZhSdjvgRLzCuZQ6zT/0GqH167XNrsdfZl+2BCCGEEI8YmWp9G4974PG7kPPGKb3bx7bhj/2X+XF7hHH9slebcjEujXf/VCsKf/bPDww+cufcNwBUrAjDh5M+4iXmX1jOS3VfemB37Asf460aXjnJsj/eV4OC+/dDgwZcT7lOp4WdOBlzEgedA6HDQqnhXuOuXmv4/IP8czqabrW9+N/z9QtW5OV6VNq1o2KDt1A0xVyIAIHlDxESPQEAnd6bl8P9eWp/JM0ux+CQXfTPJ80CDpWD/T7qz0kP0JhbYG/viqOLD07lKrH2ZCwKuSiaHBRy0HMFgyYR0GOXbUU9vTc9yzelZd1eBDfogZlF6f+ebqZm8c3m8yzYe8lk+YVxbTDv11et+KnRwK+/wpAhrD23lvdD3udkjDpiM8AxgI9bfcyQ4CEP9GIsPzC2YmSzEh1VJFQv/rqfrWdjjY/XvdGSGuUev/NwWbpd7rLd77dj9/k43l1+HM+UOLZe/BObf9aD3rTafLadPbn9+mPzxafgYxpEqzpuPVm5BpaOaMpHK05wPia12Nd6lHJ0FpdaZFiLCnz8VN5nwp9/ktt/AOaKegMlvVYdbGbPgubNWXNuDR+GfMiJGHWEuZW5Fa83fJ3xrcdjb2nPnvCb1PFzQqtRR/iVpH9O3WD4gkPqMdzH+3274Outat24wJy/vqBcSpx6A/LXX6FbNw5fP0z/Zf0JTwjHTGPGlA5TGN109GM9KiwpPYc6n6lTdc9P7IpFcYHHS5fU70kGA2zdCm3aFGmy6swqnl76NAbFQP+a/fm11693vAEq7o7BoFDxwzv3acvcHEJtTuA0/SvM0tTzmKFzZ2J6d+CHgFi+O/4TCZkJAIxsMJJpnadhZf745uEWQgghSooEHm/jcQ88Lth7yTj99twXXfnr8FXe/+vEbdv3PL2NppeO45iZSh178DZkcuViFPZZ6ThkphovvvJ93tWG8Y3Tcbdx551m7/BKvVfKdMrZ6ahkun2rjvroXtubHnW8qevvTGJ6Dj/8sZ1PPhuKU9JNGDIE5s0jNi2Wtr+15VTsKRx1jqx7fh3N/Jrd9eutOHKVt5cco0WgGwtfLjTl9+xZqF0bcnL4u1pLprUcRKRL8SNfspUtxFvOIss8y2S5WxpUj9NRLsWamzYKF52yiXDOQCnB6zddLlTJsCHIsQpN2wyia60+VHSu+O9PvAdX4tNpP217kWI/la0MbNr5DWzZoo7Y/P13rnVuxvsh77Pw+EIAHHWOvNf8Pd5q8hbWFg8+CfuByHgi49J4psGjk3/sUVK4SANAyJjWVHIvmidV3L07FU0Y3DSA+aHqzYDIyd0hKYm0dRv5dP5uLjl7M/LZ5rTu3gzMip/6eiMpk7jULGr5OHLpZhqtp24rtt2jFHjMzjXQ8qstJtP9b1Un6ixDDq+h+5ld6PR5o0m7d4cvv8RQuxZrz61l0q5JhF5VK2i7WrvSJWA4W49WwEJRcyn7uVgztV8dgv2csLK4/6nF28/FMmTufgDOfN7lP28zIjaVdtO233a9LieL10OXMnLvMvXzPyAAduwgwd2edze9y89H1CnXAY4B/NH3j3v6PH1UJWXkUOdTNfBoksrgVnnFnPK/fxSWlpVLzQkbSdPuJs7yK9DoCfYKZs3ANfg4+JCZoydbb7iv0d/bz8XiZG1BkK8jCek52OnMSUjPpuvMnQxpWp7KnnZciU9nSLPyJdInHxa3Fu8rzDUtkf4nNvHyua24Xr+MAhxrX5M/hzQkJPsM+67uQ0G9lKnlUYuZXWbSrkK7stt5IYQQ4hEngcfbeNwDj8sPXWXMMrUwSn7uwCZfhnAzLfu2z/lpcAN05lpaVVHzCuZfyGoNepwyU9EoCsvKJ1Lxf9P5zT6C9zvAjbzZJ5Zmlrxc92UG1BpAY5/GpZ40vf+Poey/qE4Z/G5gXTV/IEB8PLRoAWFhaoXJQ4e4aZFL29/aciLmBB62Hmwfup1qbveWtzIkLJphvx0kyNeR1aNamKw7M3EGVcaNQZv3pTXUvzbz6vfgn8pNMDMYaH9hP71Pb6Nl5BGyzDL4oSGsrGvNOVdIMWTc9jVtzNxRsgPRGapil+1LvahwKseF4Z9wDr02HYMGLPVgYVD/rZgA/mYuRDh6s9feklPuKcQ5R7PXLYXUYn4d9b3r83rD1+lRtQduNm739H7ku5aYwQd/naBXnXLG/lZYT10yXy/9HMszYWBlBX//zWKvOF5b+xqJmYlo0DC66WjGtRqHk5XTf9oH8Wj66/BVY/Gm7WPbECA5Hu9L4cBjo/Iu7I+ML9Kme5A3s56rZ3ysKAqZOYZ7LhTUYsoWriaYnrsexdHBiqJgUOBiXJoxv2Fx3FMTGLvjN54+uUUNwmk06pTat95i2Q2FD9bPI8N6HlFpBbMKLAx+6Ay1MVe8sDB4UcvBmz+ea4enewU01v/95srByHj6zVYDnbcWZErJzLnromIv/3aAzWExJssGNfFnZJtAln02hz5zJ+GfFK2u6NED5sxhv/4yL6x4gXM31ZkGfav3ZfZTs//z58ejJjNHTW+iKHDgow6429/me87GjdClCzg6wtWrJsXnZm29YEzJkqE9TLrd16TmJFPDrSafNV3BmKXqe3v6s853NVo2M0ePzlyLRqPBYFD4YXu4cfu3pSj4JkXjnJHC6nc7oqlUSf18fshk5uh5Zf5BPB2smNovqMho2oxsPRtOXadLTW/0isL0f84xd3dB7m8Pex0u4WcYdGQdfU9uwUzJYo8fLK9vw5q6NkTmmuZBb+rblDcbv0m/Gv0eaBEsIYQQ4lEkgcfbeNwDj1m5et5ffoJWVdzoU9cXUEd4VBm3vtj2LzYvz4QeplU6h8zdz/ZzsSbLnm3ox+Se1eGTT8id/CW/14bpHWw4bp9ubGNpZkmgcw2quJWnglMFGpZrSKuAViWWA2vN8ShG/XHE+PjY+E442lhAZqaal2zHDnVK2K5dxJRzpMvCLhy5cQRXa1e2DtlKbc/a9/yaByLjeSbvYu/rZ+qw5MBlOtXw4pVWFSn//loaXjnJqNCltIg8ilne6NAbdi6YG/S4pScZt3PVwR3nN0di++H7KDodSVlJ/HP2KK8tXUauJhoznDA3lKN9pcYsGtYdvUFh4Jy9JkEEjWLAOyUOt7REtIrCkjfbonOwU4/Z0dFkNJmlmZaT49uxc9t6oo9t4MK+P9lmf5MdAaDPG6yh1Whp5NOInlV60rta77vKz6UoCrkGxVgI51Yu6Uns0R7E6scf1N+Lhwfpq5fzVsx8fjr8E6AGPr/v9j1NfJvc8+9DPPoURWHEgkOkZ+tZMKzRYz1FsyzEJGey43wcTwV5Y2VhZqxQXFiAqw3bx7YtkdfbcyGOMcuO8VH36jwVVK5EtvkgnbyWxFPf7bpjm0pxV5hzfiWVdmw0Louyd+OGvStXHNz4LVjPHr8YYmwuoWj0xW7DNR1aU56OrYbSs9UrlLO/t/cuNPwmA3/aa3w8rnt1AlxtuZmaxft/neD9rtV4tXWlO27j1hHHPeuU40pCOkuGN8Xypx9h5Eh1RblyZM6YypGmFfj+wPcsOrEIBQVvO28W91tMq4BW97Tvj4P8AP8d3+ecHPXG55Urak7jZ54hIS2b8atPoQFWH4sqaKq5RqbTOOIzY7HNbYNrzhg0aFjzfy2o5XPn/MaXbqbR5ZuddA/yRquBzWExxBd3c1lRqBFzkX4nNlMv6gyBN69gl13oxoG7O7zzjlqVOz+f9wOSnJnDxDVhnItJwU5nzs7zBcHBwlXQ75QqwCs5jjeyztPz4nZCL+1ilz8cLAc7KmpJNS+YkWFlbkW3yt14qvJTdKjYAT9HmeEghBBC/FcSeLyNxz3weDsLQiP5eNUp4+O6/k7Meq4e5ZyKjsDYfzGe/j+GFln+9TN16B1cDvMli+G11yA5mZCWvvw6rB7/xIQSmx5b5DkADco1oHfV3gyrNwwvO6//fAxDf93Ptrz8cK+3rcTYztUgNxf694cVK8DaGnbs4GaNCnRc0JEjN47gqHNk65Ct1PWu+59e80JMCh2m7yiyfNPbreg4o2D50RerEf7Z1wQuX4BjVl4FcQ8PrvV+llHp/iTVqUfImDZFgiyZOXpyDQo3kjKZu/siI9tUwtfZxriu2sfFF5EJ/aAd3o5Ff3cNvthMXGrRKYQLhtTD+acf8Ph1Mr/UymFxEzuO25nma6vqWpXe1XrTNbArLfxbFHvnf8SCg2w8FV1kuZlBz29Zh2g6byZmyXkB13btOPDNWJ7f9gbn48+jQcP7Ld7n0zafYmEmxUSEKC27zseZFFR4pr4vU5+p8wD36OF2LjqFTnnn8w7VPdgcFsOEHjWo5G7H4LzpzQAna6dy+v3PaHA1zDjSvbBEK1gfCKc8IMJZ/bnkCNF2FEmhUc+qAs83e5WeNZ8m0CXwX/dxx7lYk30pzr9Nef9190U+LVRMxtj+zz8JH/EM6wLhcOvKHA7QcepmGHqlIIg6KGgQ0ztNx93W/V/39XFUeGRxNS97mlVyY3yPormi40eMwmXOLJT27UlYtY5GEzeTayjaV7QGPeWytrHbeQZo4KUjFXljnxmVqvphV7sGVKsG9eqpBZssTfM0f7L6FPP2RN52X61yMnnu6EaeP7qeSvFXjcv1GrjoZM5JT1tQMlE0WaRbQLbOHMeuvfF/6W1q+ASXWd7JhLRsEtKzcbaxpO7nm4ptUy45hhrRF3m2piu13a3Qp2ewbPV+3NMScMhKwyY7A5eMZHK5zjGvFNZUgY2BkHHLVwwPWw86V+pM/5r9aVO+DXaWkuJDCCGEKAkSeLyNJzXwCLDuxHVG/n4YuHOOqJupWTScuBmDAsNbVWTOjoJpZJ/2rMmQZuXh0CGUnj3RREWBhwfK998TcCCOTPMo9Jqb5GivYrA4R7pyzpg/R2emY2jwUN5p9s5dXWjdavSSo/x15Bo6cy1nPu+iBvE++gi+/FLNUbZmDceCvRm4fCBhcWG427izZcgWannUuvc3K8/dJOJf9mpTGpZ3AeDndcdoc/EwgTUrQPPmYGHB9aQMnKwt73laI8DO87Es3n+Fz3vXol7eF/O+9XyZ1r/4IEKf/+3myOXE225vgmsiL372KqSmciUogPXjn2NF5hFCIkLIMRRUxvVz8GNY3WG8UOcFzl2zoYqnHXvCbzIuL39oYe+6pjBy0VdqJU9ACarNoXHD+MbsAH+c+AMFBXcbd37p+Qs9qva45/dACHHvTkUl0f1bdSTf2S+6oDOXKYT/ReEpzvmcMpLxT7yBd0oc/gk3CLpxnnrXzuCTot4Y09vYYtakMXvrteX/MsoTa2NFhfittNFsZqNyjtBbBlg5Wznj4+BDOftylLMvh4+9D418GtHCvwUu1upny5Yz0bw07+Ad9zVycnf1ZtyFC+pnYmCgOj08T9eZOwm7ngzA4uFNaBLgxOFp7zDtwEyW1FCMo+Hzudm40TqgNR+1/Og/37x7XBSXS/XW0YmKotB++By2/Pwqeq0Zz727gH2K6fdMrUHPc0c38Pau33HNSOaz1jAhbzDyu7ug9SU1hYq5Qf0xs7bB/LVRmPV7huScVOIzEhmxcA8GslDIRNFkqv/XZGKmTyPw5kVq37iCuZJNpjnE2Wm54e3ABQs9qeZpKBrTXMy30qIl0DWQIM8ggjyCqF+uPq0DWmNrWbIpMZYeuMK7y48Xu84uK51O50MZcHwTja8U/c4BEGsDWyrA6qqwyx8uO5mu97Xxok1gBxr7NKaZXzOCvYLR3qYIoBBCCCH+Owk83saTHHjUGxTeW36c+gHODGzkf8e2Sek56Cy0WFmYFfnCHf5lNyauDWPD+n3MXfYp1eLU4gUJVvaEBDbipGclrjp6km6hQ6PcpGHNyyxW9rKPa4A6xbdv9b48X/t52lVoh73O/l/3PSUzh7cWHyXkTAyTnq6t7v+KFdC3LygKF3+czCTvcH458gsGxYCbjRv/DPqnRC6Wft4ZwRdrw267/uKkbmUyXTQ1K5dVR6/RO9gHW13xOaAKj9wpjs5cy9keLur7du0aaLUwciTJ777J+qRD/H3ub9adX2es7ghgpa+Drb4tNvomaFFHCTxdz4eJ9RzIeGs0zmtXEW8Nkf4O/PNyGxZYnSPs5hnj85+t9Szfd/0eVxvXEnonhBCi7BQXdBrarDw5egO/77sMQMjoVlRytSlSqGfT6Whema8GDLePbUNA4g2if5zO8sML+dM3mZ3+kHuHmHBtj9q09G+JDbVYtNMWM1xoXN6V/ZHxaBQDDa6epuHV01SJu0Td5Gv4xVxGm61Ou1V8fNjWqjdvubegZq3y7Am/CcCoDi6YX5nHb0d/46hLwQj5NgGtaRXQmnre9ajnXQ9fB19JhZCncGG7wgrnfBz66362nYlh+cKx1I86w/+a9OOr1kPVhopChYQopmydQ6MLanXyDHMdJzwr8mmbVNZVvlImx2GmMUOjd0eDDVpFR8tAHyyiY0kMP8V5h1ziiokvWppZUsezDu0rtKdr5a7/KZ93dq6BrFw96dl6Gn8ZUmybwLjLvLf9N1pHHMLSkAuAQaPlmFd5Trmbc8IzizC3TMLcMgl3STIZRWyuMaOxd0PaB3aid7XeBHsFS98VQgghyoAEHm/jSQ48/leVP1pHjr7gV+3jZM21RDVPkE12BqNCl/DMic24pyXedhsKsCMAprSA9ZULlltrLOlbsx+dK3eluV9zyjuVN/myuOdCHM/9vM9kW/97vh7d0i6R27olGwNymde7PCvsrhinhfWv2Z9pnabh6+B7/weP6cihWy0d0ZRGFVxK5HVKkt6gUP3jDUUqTQOc/LQzb/8QwrO/Tqb98W3qQltbNb/XK6+QWcGPFWErmBDyLecT94Em73evmGGueOBu7k3DnBTSLl8g2kYh0okiRWysza3pXa03Y5qOoX65+qV6rEIIUZoaTtxMbEpBgG7RK01oXMGFhPRsvlx3hv4NfGlc8fY3VvIDlx92q8bwVpUIu56Mh5kex+9nkPj1ZKLtcoiyhxOeDiT2aEVkoDMHYg5wJu5MsduzwAw3vQ63xGycU3KxzwaDBjLNId0CUnQabHPAIVPBIUv9/E2ysiLe2ooo+yxibQvy/GkUGGjVgLcGzaKhb6OSecMeQ3dKfRI5uTtZuXqqjlPXDzi2kSkbvgMg3MUHXW4O7mnx6PRqMA0bG77t8BLfVWlPjpkFfep6sy/mT47E/oNBk4RCDmBAQY+fWRb6xHj0GgW7bHDMBJscsM0B22zTf21ywMLeicz2rbCoUw+dpQ0u1i542XnhaeeJt503XnZeDJl7kF0XCnIoBrjasG1oLRj7DtHL53PcE45Xc+J41/rsyLnApaRLJsdrZ2lHp0qd6FGlB50rdcbb3vu271tWrp7OM3YQeTP9tm38Em8wYt9ynj8dgiYvaH66hh9bBgSxyjuZrdf3oVeK5rCs4V6DnlV60qFiBxr7Npbp00IIIcQDIIHH25DA473bfSGO528J/t3KzKCnyeUTNLpykhoxEbinJWKdk0mGhY7r9u4k62ypERNBtdhIwtz1/Fgf/qkEF265VvOy86KGew0qu1SmknNVvl6bglaxRYMNkIuiyeJ/ndwJnfk6CyqlGatrA7Sv0J4JrSfQMqBlib8HK49cY9XRa2zNyzH5bEM/xveocVfVJx+U2JQsEtKzOR2VzJrj19kcVjQ3Y/PIo3xxYBEVIgryf4bVbsLFDj0Yn+PPDdtstLmrSDMPJd66+Bye+bzsvKjpXpPnaj9H3+p9cbS6c4J8IYR4FOTqDQQWKqh1r6Pcq3+8gYwc9cZYi0A3k6CPa1oiLxxZywuH1+KaoU6DTra0IcLVF7taXhytCIs1Nwi1jeW6XUKRXJH/VeOr8IJza/qPnou7V8WS2ehj7tbcqfk2j25tUh1doxhY8sf7NLp6ukhbuneHKVPIqlqN9Cw9DtYWmGk1HLuSSK9Zu4t9XaeMZHqf2kaVuMv4JMdQLjmWwIybaNLT1fyPjRtDq1YFP/9SqTomOZNGt4w6XD2qOUG+TrBlC7z4IlxWR/Iqw18hZORLfLFzE9aOxzgSvYvotILvEho0tCnfhkFBg+ga2LVIEHL1sSjeWHSEW1noc/jB7irNtyzHbPc2wp0h1A92NfNlb0VLwlIiTNrbW9oT5BlMBYcgOlVuQquAlgQ4BdzxOIUQQghR+iTweBsSePzvtp+LZUih5Pbdg7w5cz2Z8Ng0avs40iu4HPUDnMnKNbDi8DXG96jBqqNRfLjihPE55vpcfJJjCEi4Tv2rp6gRt44NFVLZ7Q+HvO885aw4blauDKrzAkODh1LHq/SLJ2w9E8Pfx6P4vFet2053flgVN10QAEVhonkkPUJXYbc1xKRoQpaZOTp9LgpwzQEuuEC4M+gD/LDr3gfX1l2p4FIRf0d/rMzvfLEjhBCPqh+3hzNpvToC8d+KuNxq1tYLTN149o5trHIyefbYP7yyf4UxV+St0i0g1RIya1Qhrm1j4mpXIql2ZV5dug8NGjRYolF0aLDCzR6iUxNQSMc2O5PKSXE0tDLQ0c2NBnZVcOzeF6pWvafjEJCWlUtiRg7WFmbGvMu32vluW6JupnJmxyG6uip4eDiBjw+4uamzC27jQkwqzjYWJpXHb9WzTjlmPhuMBiA+Xt3evwQab1Vc7upeweWIT8smPVvPd90rUe6rz2HWLACuOHqyskYbwjwqcNbNl57dnYizOcmGy5s5GGWad7Slf0v6Vu9L+4rtqepalZmbI/h+6wUUFBzTL+ObeIgKSQdwzb1AuFUGpzzghAcYbkm/qNVoaeHfgh5VetC9cnequlWVHI1CCCHEQ0gCj7chgcf7Uzh4dbcXXxfj0mj79bZi11lnZ/JG/BGGh20i68h+jnvCGTc4Wc2LAz4WhGkzyTTPxkKXjWWOAevULKxzoEqGNYMHTaV761ewNLMsdtvC1K0Xvz8PbsDL800vGvwSb9DvxGZaXzxE8PXz6kILC6hTB2rVgsqV1REbQUEmRQuEEOJxt/b4dZxsLGge6HbPzw0Ji2bYb8UXh3mzfWXe6lCZ8atOsTD0IkHXz+ORloBTRjLOGSk4Z6TglJGMhUFPn6/fxaxTR5Pz75rjUYz64wjda3vTPNDN5GYfFEzxFiWruJt5TwV58/1z9e572zl6A5ULjbIFCBnTmkruJTOd+Pd9l/hoRfGFW/K1uHiEr9fNwCs1/rZtwlx1LAjWsLZyDse99CbrtAZwy7LAKtuceJtMUi1uf8lga2FLXe+6tPRvSVPfpjT3b24srCSEEEKIh5cEHm9DAo/3p93X24iIS6Oqpz0b325118/L/4LuaG3BtGfqFAl4oSi0iTjEO2c2UOvk3jtvrGdP+Okn8PC4191/4uUHHxtXcGHJiKYkpedQ57N/im3rm5XIvOeDCQyqrE7nEkII8Z/Fp2VT7/NNBPs5MX9YI8zygofFjZ6/fDOdVlO3mizb/2F7PBzuPLqtuFyEhQugiJJz68285oGu/P5ykxLb/vWkDJpO2gLAR92q80qrkp8SP/L3Q6w7ceO26+2y0ul9aiu1osOpFhtJxZtXccguPl/jFQdYWlPN473Pp2juZ40C/mnmVDL3ILB8XSrVaEFlt6o0KNdAChkJIYQQjygJPN6GBB7vzz+nbrDjfCxvtK+Mh/3dT++5EJPC5PVneKN9ZYJ8nbgYl8aZ68l8sOIEiek5Jm29kuNocuUEwVFnqXzzMvV9HLDy94XgYOjQAerVk9F29+FcdAo+TtbGi92MbD2/71OTx9f1d6KevzO5BgW9QcHK4h7nvgshhCgRRy4n0Od/ewDoUN2Tn4c0uKvnbT4dbXJz78LErpibyTTVkpaRrafhxM2kZqlFY85P7IpFKbzPBoOCVlt633n6zw5lf2Q8ldxtsbOy4NiVRJP1ng46dr3XDgszLYcuxfPMD7uxzsrEOjcLm+xMbHIycbU2x8oMfOwtOXE5AXNDNnaZUWSb3cRal8XoLq2p1OkZdO63L0QjhBBCiEePBB5vQwKPD59fd1/k07+LScIO1PFzYtXrzct4j4QQQogHS1EUZmw6R2jETX4YVB83u7sftbj1bAzT/znHyy0r0CvYpxT3UjyusnMNxKdl4+V455vMxQVGY1OyeGb2HvSKwqrXW+BiK7MmhBBCiMeRBB5vQwKPD6+w68n0+G4X1pZmhIxuzZWEdOr6OZfqnX4hhBBCCCGEEEIIcW/KIr72aJX0FQ+96t4ObBrdGludGR72Vv+ay0oIIYQQQgghhBBCPJ4k8ChKXAU32we9C0IIIYQQQgghhBDiAZNM5UIIIYQQQgghhBBCiBIngUchhBBCCCGEEEIIIUSJe6CBx1mzZlG+fHmsrKxo3Lgx+/fvf5C7I4QQQgghhBBCCCGEKCEPLPC4ZMkSRo8ezYQJEzh8+DB16tShc+fOxMTEPKhdEkIIIYQQQgghhBBClJAHFnicPn06r7zyCi+++CI1atRg9uzZ2NjYMHfu3Ae1S0IIIYQQQgghhBBCiBLyQAKP2dnZHDp0iA4dOhTsiFZLhw4dCA0NLdI+KyuL5ORkkx8hhBBCCCGEEEIIIcTD64EEHuPi4tDr9Xh6epos9/T05MaNG0XaT5o0CUdHR+OPn59fWe2qEEIIIYQQQgghhBDiP3gkqlp/8MEHJCUlGX+uXLnyoHdJCCGEEEIIIYQQQghxB+YP4kXd3NwwMzMjOjraZHl0dDReXl5F2ut0OnQ6XVntnhBCCCGEEEIIIYQQ4j49kBGPlpaW1K9fn5CQEOMyg8FASEgITZs2fRC7JIQQQgghhBBCCCGEKEEPZMQjwOjRoxkyZAgNGjSgUaNGfPPNN6SlpfHiiy8+qF0SQgghhBBCCCGEEEKUkAcWeBwwYACxsbGMHz+eGzduEBwczIYNG4oUnBFCCCGEEEIIIYQQQjx6NIqiKA96J+5VcnIyjo6OJCUl4eDg8KB3RwghhBBCCCGEEEKIR0pZxNceiarWQgghhBBCCCGEEEKIR4sEHoUQQgghhBBCCCGEECVOAo9CCCGEEEIIIYQQQogSJ4FHIYQQQgghhBBCCCFEiZPAoxBCCCGEEEIIIYQQosRJ4FEIIYQQQgghhBBCCFHiJPAohBBCCCGEEEIIIYQocRJ4FEIIIYQQQgghhBBClDgJPAohhBBCCCGEEEIIIUqcBB6FEEIIIYQQQgghhBAlTgKPQgghhBBCCCGEEEKIEmf+oHfgv1AUBYDk5OQHvCdCCCGEEEIIIYQQQjx68uNq+XG20vBIBh5TUlIA8PPze8B7IoQQQgghhBBCCCHEoyslJQVHR8dS2bZGKc2wZikxGAxERUVhb2+PRqMp9ddLTk7Gz8+PK1eu4ODgUOqvJ54c0rdEaZG+JUqL9C1RWqRvidIk/UuUFulborRI3xKlpXDfsre3JyUlhXLlyqHVlk42xkdyxKNWq8XX17fMX9fBwUH+4EWpkL4lSov0LVFapG+J0iJ9S5Qm6V+itEjfEqVF+pYoLfl9q7RGOuaT4jJCCCGEEEIIIYQQQogSJ4FHIYQQQgghhBBCCCFEiZPA413Q6XRMmDABnU73oHdFPGakb4nSIn1LlBbpW6K0SN8SpUn6lygt0rdEaZG+JUpLWfetR7K4jBBCCCGEEEIIIYQQ4uEmIx6FEEIIIYQQQgghhBAlTgKPQgghhBBCCCGEEEKIEieBRyGEEEIIIYQQQgghRImTwKMQQgghhBBCCCGEEKLESeBRCCGEEEIIIYQQQghR4p6YwOOOHTvo0aMH5cqVQ6PRsHLlSpP10dHRDB06lHLlymFjY0OXLl04f/68SZsbN27wwgsv4OXlha2tLfXq1WP58uUmbeLj43n++edxcHDAycmJYcOGkZqaWtqHJx6gsupb5cuXR6PRmPxMnjy5tA9PPEAl0bfCw8Pp06cP7u7uODg40L9/f6Kjo03ayHnryVNWfUvOW0+eSZMm0bBhQ+zt7fHw8KB3796cPXvWpE1mZiavv/46rq6u2NnZ0bdv3yJ95/Lly3Tv3h0bGxs8PDwYO3Ysubm5Jm22bdtGvXr10Ol0BAYGMm/evNI+PPEAlVXf2rZtW5Hzlkaj4caNG2VynKLslVTfeuONN6hfvz46nY7g4OBiX+v48eO0bNkSKysr/Pz8+Oqrr0rrsMRDoKz6VmRkZLHnrb1795bm4YkHrCT617Fjxxg4cCB+fn5YW1tTvXp1Zs6cWeS17vc71xMTeExLS6NOnTrMmjWryDpFUejduzcRERGsWrWKI0eOEBAQQIcOHUhLSzO2Gzx4MGfPnmX16tWcOHGCp59+mv79+3PkyBFjm+eff55Tp06xadMm1qxZw44dOxg+fHiZHKN4MMqqbwF89tlnXL9+3fjzf//3f6V+fOLBud++lZaWRqdOndBoNGzZsoXdu3eTnZ1Njx49MBgMxm3JeevJU1Z9C+S89aTZvn07r7/+Onv37mXTpk3k5OTQqVMnk8+8t99+m7///ptly5axfft2oqKiePrpp43r9Xo93bt3Jzs7mz179vDbb78xb948xo8fb2xz8eJFunfvTtu2bTl69ChvvfUWL7/8Mhs3bizT4xVlp6z6Vr6zZ8+anLs8PDzK5DhF2SuJvpXvpZdeYsCAAcW+TnJyMp06dSIgIIBDhw4xdepUPvnkE+bMmVNqxyYerLLqW/k2b95sct6qX79+iR+TeHiURP86dOgQHh4eLFy4kFOnTvHRRx/xwQcf8P333xvblMh3LuUJBCgrVqwwPj579qwCKCdPnjQu0+v1iru7u/LTTz8Zl9na2irz58832ZaLi4uxzenTpxVAOXDggHH9+vXrFY1Go1y7dq2UjkY8TEqrbymKogQEBCgzZswotX0XD7f/0rc2btyoaLVaJSkpydgmMTFR0Wg0yqZNmxRFkfOWKL2+pShy3hKKEhMTowDK9u3bFUVR+4mFhYWybNkyY5uwsDAFUEJDQxVFUZR169YpWq1WuXHjhrHNDz/8oDg4OChZWVmKoijKu+++q9SsWdPktQYMGKB07ty5tA9JPCRKq29t3bpVAZSEhISyOxjxUPkvfauwCRMmKHXq1Cmy/H//+5/i7Oxs7GuKoijvvfeeUrVq1ZI/CPFQKq2+dfHiRQVQjhw5Ulq7Lh4B99u/8o0cOVJp27at8XFJfOd6YkY83klWVhYAVlZWxmVarRadTseuXbuMy5o1a8aSJUuIj4/HYDCwePFiMjMzadOmDQChoaE4OTnRoEED43M6dOiAVqtl3759ZXMw4qFSUn0r3+TJk3F1daVu3bpMnTq1yLQz8eS4m76VlZWFRqNBp9MZ21hZWaHVao1t5LwlblVSfSufnLeebElJSQC4uLgA6p31nJwcOnToYGxTrVo1/P39CQ0NBdTzUu3atfH09DS26dy5M8nJyZw6dcrYpvA28tvkb0M8/kqrb+ULDg7G29ubjh07snv37tI+HPEQ+S99626EhobSqlUrLC0tjcs6d+7M2bNnSUhIKKG9Fw+z0upb+Xr27ImHhwctWrRg9erVJbPT4pFRUv0rKSnJuA0ome9cEnik4M3/4IMPSEhIIDs7mylTpnD16lWuX79ubLd06VJycnJwdXVFp9MxYsQIVqxYQWBgIKDm6bt1Goa5uTkuLi6SF+YJVVJ9C9TcHosXL2br1q2MGDGCL7/8knffffdBHJZ4CNxN32rSpAm2tra89957pKenk5aWxjvvvINerze2kfOWuFVJ9S2Q89aTzmAw8NZbb9G8eXNq1aoFqOccS0tLnJycTNp6enoazzk3btwwCQzlr89fd6c2ycnJZGRklMbhiIdIafYtb29vZs+ezfLly1m+fDl+fn60adOGw4cPl/JRiYfBf+1bd+Nu+p94fJVm37Kzs2PatGksW7aMtWvX0qJFC3r37i3BxydISfWvPXv2sGTJEpO0WyXxncv8Ho7lsWVhYcFff/3FsGHDcHFxwczMjA4dOtC1a1cURTG2+/jjj0lMTGTz5s24ubmxcuVK+vfvz86dO6ldu/YDPALxsCrJvjV69Ghj+6CgICwtLRkxYgSTJk0yGXUkngx307fc3d1ZtmwZr732Gt9++y1arZaBAwdSr149tFq57ySKV5J9S85bT7bXX3+dkydPFhkFK8T9Ks2+VbVqVapWrWp83KxZM8LDw5kxYwYLFiwo8dcTDxc5b4nSUpp9y83NzeQ7V8OGDYmKimLq1Kn07NmzxF9PPHxKon+dPHmSXr16MWHCBDp16lSCeyeBR6P69etz9OhRkpKSyM7Oxt3dncaNGxunH4aHh/P9999z8uRJatasCUCdOnXYuXMns2bNYvbs2Xh5eRETE2Oy3dzcXOLj4/Hy8irzYxIPh5LoW8Vp3Lgxubm5REZGmnxBFk+Of+tbAJ06dSI8PJy4uDjMzc1xcnLCy8uLihUrAsh5SxSrJPpWceS89eQYNWqUsViVr6+vcbmXlxfZ2dkkJiaa3IGPjo42nnO8vLzYv3+/yfbyKzAWbnNr1c/o6GgcHBywtrYujUMSD4nS7lvFadSokQSingD307fuxu3OW/nrxOOrtPtWcRo3bsymTZvuaxvi0VAS/ev06dO0b9+e4cOHM27cOJN1JfGdS4a83MLR0RF3d3fOnz/PwYMH6dWrFwDp6ekARUYJmZmZGSt4Nm3alMTERA4dOmRcv2XLFgwGA40bNy6jIxAPq/vpW8U5evQoWq1WqiyK2/atwtzc3HBycmLLli3ExMQY737KeUvcyf30reLIeevxpygKo0aNYsWKFWzZsoUKFSqYrK9fvz4WFhaEhIQYl509e5bLly/TtGlTQD0vnThxwuSmyKZNm3BwcKBGjRrGNoW3kd8mfxvi8VNWfas4R48exdvbu4SPSDwsSqJv3Y2mTZuyY8cOcnJyjMs2bdpE1apVcXZ2vv8DEQ+dsupbxZHz1uOvpPrXqVOnaNu2LUOGDGHixIlFXqdEvnPddRmaR1xKSopy5MgR5ciRIwqgTJ8+XTly5Ihy6dIlRVEUZenSpcrWrVuV8PBwZeXKlUpAQIDy9NNPG5+fnZ2tBAYGKi1btlT27dunXLhwQfn6668VjUajrF271tiuS5cuSt26dZV9+/Ypu3btUipXrqwMHDiwzI9XlJ2y6Ft79uxRZsyYoRw9elQJDw9XFi5cqLi7uyuDBw9+IMcsysb99i1FUZS5c+cqoaGhyoULF5QFCxYoLi4uyujRo03ayHnryVMWfUvOW0+m1157TXF0dFS2bdumXL9+3fiTnp5ubPPqq68q/v7+ypYtW5SDBw8qTZs2VZo2bWpcn5ubq9SqVUvp1KmTcvToUWXDhg2Ku7u78sEHHxjbREREKDY2NsrYsWOVsLAwZdasWYqZmZmyYcOGMj1eUXbKqm/NmDFDWblypXL+/HnlxIkTyptvvqlotVpl8+bNZXq8ouyURN9SFEU5f/68cuTIEWXEiBFKlSpVjJ+z+VWsExMTFU9PT+WFF15QTp48qSxevFixsbFRfvzxxzI9XlF2yqpvzZs3T/njjz+UsLAwJSwsTJk4caKi1WqVuXPnlunxirJVEv3rxIkTiru7uzJo0CCTbcTExBjblMR3ricm8Lh161YFKPIzZMgQRVEUZebMmYqvr69iYWGh+Pv7K+PGjTP+Iec7d+6c8vTTTyseHh6KjY2NEhQUpMyfP9+kzc2bN5WBAwcqdnZ2ioODg/Liiy8qKSkpZXWY4gEoi7516NAhpXHjxoqjo6NiZWWlVK9eXfnyyy+VzMzMsjxUUcZKom+99957iqenp2JhYaFUrlxZmTZtmmIwGEzayHnryVMWfUvOW0+m4voVoPz666/GNhkZGcrIkSMVZ2dnxcbGRunTp49y/fp1k+1ERkYqXbt2VaytrRU3NzdlzJgxSk5OjkmbrVu3KsHBwYqlpaVSsWJFk9cQj5+y6ltTpkxRKlWqpFhZWSkuLi5KmzZtlC1btpTVYYoHoKT6VuvWrYvdzsWLF41tjh07prRo0ULR6XSKj4+PMnny5DI6SvEglFXfmjdvnlK9enXFxsZGcXBwUBo1aqQsW7asDI9UPAgl0b8mTJhQ7DYCAgJMXut+v3Np8nZYCCGEEEIIIYQQQgghSozkeBRCCCGEEEIIIYQQQpQ4CTwKIYQQQgghhBBCCCFKnAQehRBCCCGEEEIIIYQQJU4Cj0IIIYQQQgghhBBCiBIngUchhBBCCCGEEEIIIUSJk8CjEEIIIYQQQgghhBCixEngUQghhBBCCCGEEEIIUeIk8CiEEEIIIYQQQgghhChxEngUQgghhBBCCCGEEEKUOAk8CiGEEEIIIYQQQgghSpwEHoUQQgghhBBCCCGEECXu/wE2aPViuMMz/AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_training = pd.DataFrame(data['Close'][0:int(len(data)*0.70)])\n",
        "data_testing = pd.DataFrame(data['Close'][int(len(data)*0.70): int(len(data))])"
      ],
      "metadata": {
        "id": "DVyYdGYa4SqG"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "ABpYb_py6oTd",
        "outputId": "fc7e16be-ae19-4643-eb1f-224da6b0416f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close\n",
              "Date             \n",
              "2007-01-11  95.80\n",
              "2007-01-12  94.62\n",
              "2007-01-16  97.10\n",
              "2007-01-17  94.95\n",
              "2007-01-18  89.07"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fc97c143-dcc3-4ef1-a58b-e6ca3bf9095a\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2007-01-11</th>\n",
              "      <td>95.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-12</th>\n",
              "      <td>94.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-16</th>\n",
              "      <td>97.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-17</th>\n",
              "      <td>94.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007-01-18</th>\n",
              "      <td>89.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fc97c143-dcc3-4ef1-a58b-e6ca3bf9095a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fc97c143-dcc3-4ef1-a58b-e6ca3bf9095a button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fc97c143-dcc3-4ef1-a58b-e6ca3bf9095a');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-11b0049d-d6d0-4bb0-9f88-7059184f75ae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-11b0049d-d6d0-4bb0-9f88-7059184f75ae')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-11b0049d-d6d0-4bb0-9f88-7059184f75ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_testing",
              "summary": "{\n  \"name\": \"data_testing\",\n  \"rows\": 2820,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2007-01-11 00:00:00\",\n        \"max\": \"2018-03-27 00:00:00\",\n        \"num_unique_values\": 2820,\n        \"samples\": [\n          \"2011-05-10 00:00:00\",\n          \"2016-04-07 00:00:00\",\n          \"2010-02-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.8965839042554,\n        \"min\": 78.2,\n        \"max\": 702.1,\n        \"num_unique_values\": 2672,\n        \"samples\": [\n          113.45,\n          381.02,\n          518.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler(feature_range=(0,1))"
      ],
      "metadata": {
        "id": "I759sqTU6utu"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_training_array = scaler.fit_transform(data_training)\n",
        "data_training_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JA67-fks7LOE",
        "outputId": "bbe1e066-a943-4b96-9331-a3ff1fa92dd3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.13326826],\n",
              "       [0.12200616],\n",
              "       [0.10699001],\n",
              "       ...,\n",
              "       [0.55912606],\n",
              "       [0.61243337],\n",
              "       [0.64569412]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(100, data_training_array.shape[0]):\n",
        "    x_train.append(data_training_array[i-100: i])\n",
        "    y_train.append(data_training_array[i, 0])\n",
        "\n",
        "x_train, y_train = np.array(x_train), np.array(y_train)"
      ],
      "metadata": {
        "id": "iBD8vQHH7UL8"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.layers import Dense, Dropout, LSTM\n",
        "from keras.models import Sequential"
      ],
      "metadata": {
        "id": "7bz2sGmo9YEO"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=50, activation='relu', return_sequences=True, input_shape=(x_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=60, activation='relu', return_sequences=True))\n",
        "model.add(Dropout(0.3))\n",
        "\n",
        "model.add(LSTM(units=80, activation='relu', return_sequences=True))\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(LSTM(units=120, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "\n",
        "model.add(Dense(units=1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXIVJaTX9id9",
        "outputId": "73ba0c02-ac72-469d-a0e1-a3fd7e20483b"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: 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__(**kwargs)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "BvCBKp___fM2",
        "outputId": "d7743a75-9233-4f9d-de6d-15a2c75314a8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │          \u001b[38;5;34m10,400\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │          \u001b[38;5;34m26,640\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m60\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │          \u001b[38;5;34m45,120\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m80\u001b[0m)             │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (\u001b[38;5;33mLSTM\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)                 │          \u001b[38;5;34m96,480\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m120\u001b[0m)                 │               \u001b[38;5;34m0\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │             \u001b[38;5;34m121\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">26,640</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">60</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │          <span style=\"color: #00af00; text-decoration-color: #00af00\">45,120</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">80</span>)             │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ lstm_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)                 │          <span style=\"color: #00af00; text-decoration-color: #00af00\">96,480</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">120</span>)                 │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │             <span style=\"color: #00af00; text-decoration-color: #00af00\">121</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m178,761\u001b[0m (698.29 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">178,761</span> (698.29 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
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            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "model.fit(x_train, y_train, epochs=50)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "udQ-C-xC_sz9",
        "outputId": "64198807-afba-4e4f-c58a-49fbed43f636"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m71s\u001b[0m 315ms/step - loss: 0.0117\n",
            "Epoch 2/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 302ms/step - loss: 0.0033\n",
            "Epoch 3/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 319ms/step - loss: 0.0027\n",
            "Epoch 4/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 302ms/step - loss: 0.0022\n",
            "Epoch 5/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 309ms/step - loss: 0.0018\n",
            "Epoch 6/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 312ms/step - loss: 0.0021\n",
            "Epoch 7/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 319ms/step - loss: 0.0017\n",
            "Epoch 8/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 315ms/step - loss: 0.0016\n",
            "Epoch 9/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 310ms/step - loss: 0.0015\n",
            "Epoch 10/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 303ms/step - loss: 0.0016\n",
            "Epoch 11/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 312ms/step - loss: 0.0014\n",
            "Epoch 12/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 317ms/step - loss: 0.0014\n",
            "Epoch 13/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 314ms/step - loss: 0.0014\n",
            "Epoch 14/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 319ms/step - loss: 0.0012\n",
            "Epoch 15/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 302ms/step - loss: 0.0012\n",
            "Epoch 16/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m86s\u001b[0m 323ms/step - loss: 0.0012\n",
            "Epoch 17/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m78s\u001b[0m 304ms/step - loss: 0.0013\n",
            "Epoch 18/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 320ms/step - loss: 0.0013\n",
            "Epoch 19/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 302ms/step - loss: 0.0013\n",
            "Epoch 20/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 311ms/step - loss: 0.0012\n",
            "Epoch 21/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 304ms/step - loss: 0.0013\n",
            "Epoch 22/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 311ms/step - loss: 0.0013\n",
            "Epoch 23/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 318ms/step - loss: 0.0011\n",
            "Epoch 24/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m61s\u001b[0m 301ms/step - loss: 0.0014\n",
            "Epoch 25/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 304ms/step - loss: 0.0012\n",
            "Epoch 26/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m81s\u001b[0m 300ms/step - loss: 0.0012\n",
            "Epoch 27/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 292ms/step - loss: 0.0012\n",
            "Epoch 28/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m63s\u001b[0m 310ms/step - loss: 0.0012\n",
            "Epoch 29/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 301ms/step - loss: 0.0010\n",
            "Epoch 30/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 299ms/step - loss: 0.0011\n",
            "Epoch 31/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m59s\u001b[0m 292ms/step - loss: 0.0011\n",
            "Epoch 32/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m85s\u001b[0m 307ms/step - loss: 0.0011\n",
            "Epoch 33/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 313ms/step - loss: 0.0010\n",
            "Epoch 34/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m62s\u001b[0m 307ms/step - loss: 8.7622e-04\n",
            "Epoch 35/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m60s\u001b[0m 296ms/step - loss: 9.4941e-04\n",
            "Epoch 36/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m82s\u001b[0m 299ms/step - loss: 0.0011\n",
            "Epoch 37/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m84s\u001b[0m 307ms/step - loss: 8.9182e-04\n",
            "Epoch 38/50\n",
            "\u001b[1m203/203\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m83s\u001b[0m 311ms/step - loss: 9.2365e-04\n",
            "Epoch 39/50\n",
            "\u001b[1m128/203\u001b[0m \u001b[32m━━━━━━━━━━━━\u001b[0m\u001b[37m━━━━━━━━\u001b[0m \u001b[1m23s\u001b[0m 307ms/step - loss: 0.0011"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('keras_model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sgY55pD__pH",
        "outputId": "8a84cd4c-7866-4df5-ce14-7149d21f5e4e"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_testing.head()"
      ],
      "metadata": {
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          "height": 238
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        "id": "yUhMeOazPDLi",
        "outputId": "89062f23-6d0d-4d44-e887-7d8f563aa38c"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Close\n",
              "Date             \n",
              "2007-01-11  95.80\n",
              "2007-01-12  94.62\n",
              "2007-01-16  97.10\n",
              "2007-01-17  94.95\n",
              "2007-01-18  89.07"
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
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              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_testing",
              "summary": "{\n  \"name\": \"data_testing\",\n  \"rows\": 2820,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2007-01-11 00:00:00\",\n        \"max\": \"2018-03-27 00:00:00\",\n        \"num_unique_values\": 2820,\n        \"samples\": [\n          \"2011-05-10 00:00:00\",\n          \"2016-04-07 00:00:00\",\n          \"2010-02-04 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.8965839042554,\n        \"min\": 78.2,\n        \"max\": 702.1,\n        \"num_unique_values\": 2672,\n        \"samples\": [\n          113.45,\n          381.02,\n          518.83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "past100 = data_training.tail(100)\n",
        "\n",
        "final_data = pd.concat([past100, data_testing], ignore_index=True)\n",
        "\n",
        "final_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mR-c9nKHPO9V",
        "outputId": "5ab9d09d-6816-4bb6-c5e3-8a8560bfe2f9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Close\n",
              "0  67.59\n",
              "1  67.91\n",
              "2  66.56\n",
              "3  67.62\n",
              "4  67.31"
            ],
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              "\n",
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              "        document.querySelector('#df-3ea3ad98-0106-4955-9966-435acbc9df7e button.colab-df-convert');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "        const element = document.querySelector('#df-3ea3ad98-0106-4955-9966-435acbc9df7e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fd008dad-2e6a-40d9-9658-a71299b04d70 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_data",
              "summary": "{\n  \"name\": \"final_data\",\n  \"rows\": 2920,\n  \"fields\": [\n    {\n      \"column\": \"Close\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 168.83412738597565,\n        \"min\": 66.48,\n        \"max\": 702.1,\n        \"num_unique_values\": 2769,\n        \"samples\": [\n          665.24,\n          501.07,\n          161.04\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data = scaler.fit_transform(final_data)\n",
        "\n",
        "input_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oa1oqCYZ54-t",
        "outputId": "33d3abc6-25ee-4472-a0ea-4cb8fefef8e7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.74632642e-03],\n",
              "       [2.24977188e-03],\n",
              "       [1.25861364e-04],\n",
              "       ...,\n",
              "       [1.54903873e-01],\n",
              "       [1.67222554e-01],\n",
              "       [1.60252981e-01]])"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "input_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IiOfJAg76ZON",
        "outputId": "c9f88d4c-8922-47b5-a349-23fc5226bd22"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2920, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = []\n",
        "y_test =[]\n",
        "\n",
        "for i in range(100, input_data.shape[0]):\n",
        "    x_test.append(input_data[i-100: i])\n",
        "    y_test.append(input_data[i, 0])\n",
        "\n",
        "x_test, y_test = np.array(x_test), np.array(y_test)\n",
        "\n",
        "x_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNJEiEHa6g--",
        "outputId": "da049594-a4c9-4bd7-fcb4-75ee448f4598"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2820, 100, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict = model.predict(x_test)\n",
        "y_predict.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YAsT-Otj9QFz",
        "outputId": "b3c05738-c061-4ccc-cde0-0a3b9c6eb9d6"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m89/89\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m10s\u001b[0m 107ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2820, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_predict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-_FklNyv9eDS",
        "outputId": "86f68b84-1b74-468c-9ab8-a86f389056f7"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.09480792],\n",
              "       [0.09704803],\n",
              "       [0.09891744],\n",
              "       ...,\n",
              "       [0.18591827],\n",
              "       [0.18386048],\n",
              "       [0.18340611]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.scale_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUs0rmmh9ljh",
        "outputId": "e854df6f-e3e8-43ec-a512-112a14ec6000"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.00157327])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaler_factor = 1/0.00157327\n",
        "y_predict = y_predict * scaler_factor\n",
        "y_test = y_test * scaler_factor"
      ],
      "metadata": {
        "id": "KqpP7pUr902C"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(y_test, 'b', label='original price')\n",
        "plt.plot(y_predict, 'r', label='predicted price')\n",
        "plt.xlabel('time')\n",
        "plt.ylabel('price')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "c1Q2ub8x-McE",
        "outputId": "bf8fa54b-97d6-43c4-a6a0-97aacf5daa02"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "46D9rR5I-rFx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}