{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "JEFYtxdXQP1s" }, "source": [ "# Введение в анализ данных\n", "\n", "\n", "## PyTorch и полносвязные нейронные сети\n", "\n", "![pytorch-logo.png](data:image/png;base64,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)" ] }, { "cell_type": "markdown", "metadata": { "id": "CKuaI37x8hjJ" }, "source": [ "### 1. Введение\n", "\n", "В данном ноутбуке мы будем пользоваться фреймворком **PyTorch**, который предназначен для работы с нейронными сетями. Как установить `pytorch` можно прочитать [на официальном сайте PyTorch](http://pytorch.org/). Для этого выберите свою OS и вам будет показана нужная команда для ввода в терминале. Больше подробностей о том, как `pytorch` работает будет рассказно на 3 курсе.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Wp5-n1308hjM", "outputId": "a3d635a0-dab3-4d02-aca0-a996d3d10f59" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.10.0+cu111\n" ] } ], "source": [ "import numpy as np\n", "from sklearn.datasets import load_boston\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from IPython.display import clear_output\n", "sns.set(palette='Set2', font_scale=1.5)\n", "\n", "import torch\n", "from torch import nn\n", "import torch.nn.functional as F\n", "\n", "print(torch.__version__)" ] }, { "cell_type": "markdown", "metadata": { "id": "Y4yG4djE8hjR" }, "source": [ "#### 1.1 Сравнение NumPy и PyTorch-синтаксиса \n", "Интерфейс `pytorch` написан подобно интерфесу `numpy` для удобства использования. Главное различие между ними, что `numpy` оперрирует `numpy.ndarray` массивами, а `pytorch` — тензорами `pytorch.Tensor`. Напишем одни и те же операции на `numpy` и `pytorch`." ] }, { "cell_type": "markdown", "metadata": { "id": "BW8djI0PNvVE" }, "source": [ "**numpy**" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "qh-uD8hE8hjS", "outputId": "c5692e32-b378-4f24-9ba7-a8d2f49ec3de" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Матрица X:\n", "[[ 0 1 2 3]\n", " [ 4 5 6 7]\n", " [ 8 9 10 11]\n", " [12 13 14 15]]\n", "\n", "Размер: (4, 4)\n", "\n", "Добавление константы:\n", "[[ 5 6 7 8]\n", " [ 9 10 11 12]\n", " [13 14 15 16]\n", " [17 18 19 20]]\n", "\n", "X*X^T:\n", "[[ 14 38 62 86]\n", " [ 38 126 214 302]\n", " [ 62 214 366 518]\n", " [ 86 302 518 734]]\n", "\n", "Среднее по колонкам:\n", "[ 1.5 5.5 9.5 13.5]\n", "\n", "Кумулятивная сумма по колонкам:\n", "[[ 0 1 2 3]\n", " [ 4 6 8 10]\n", " [12 15 18 21]\n", " [24 28 32 36]]\n", "\n" ] } ], "source": [ "x = np.arange(16).reshape(4, 4)\n", "\n", "print(\"Матрица X:\\n{}\\n\".format(x))\n", "print(\"Размер: {}\\n\".format(x.shape))\n", "print(\"Добавление константы:\\n{}\\n\".format(x + 5))\n", "print(\"X*X^T:\\n{}\\n\".format(np.dot(x, x.T)))\n", "print(\"Среднее по колонкам:\\n{}\\n\".format(x.mean(axis=-1)))\n", "print(\"Кумулятивная сумма по колонкам:\\n{}\\n\".format(np.cumsum(x, axis=0)))" ] }, { "cell_type": "markdown", "metadata": { "id": "Fit0yaPPOCwv" }, "source": [ "**pytorch**" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "uZM_OyVQ8hjV", "outputId": "8910db0a-65ae-4b09-93d6-af6757ea891d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Матрица X:\n", "tensor([[ 0., 1., 2., 3.],\n", " [ 4., 5., 6., 7.],\n", " [ 8., 9., 10., 11.],\n", " [12., 13., 14., 15.]])\n", "Размер: torch.Size([4, 4])\n", "\n", "Добавление константы:\n", "tensor([[ 5., 6., 7., 8.],\n", " [ 9., 10., 11., 12.],\n", " [13., 14., 15., 16.],\n", " [17., 18., 19., 20.]])\n", "X*X^T:\n", "tensor([[ 14., 38., 62., 86.],\n", " [ 38., 126., 214., 302.],\n", " [ 62., 214., 366., 518.],\n", " [ 86., 302., 518., 734.]])\n", "Среднее по колонкам:\n", "tensor([ 1.5000, 5.5000, 9.5000, 13.5000])\n", "Кумулятивная сумма по колонкам:\n", "tensor([[ 0., 1., 2., 3.],\n", " [ 4., 6., 8., 10.],\n", " [12., 15., 18., 21.],\n", " [24., 28., 32., 36.]])\n" ] } ], "source": [ "x = np.arange(16).reshape(4, 4)\n", "x = torch.tensor(x, dtype=torch.float32) # или torch.arange(0,16).view(4,4)\n", "\n", "print(\"Матрица X:\\n{}\".format(x))\n", "print(\"Размер: {}\\n\".format(x.shape))\n", "print(\"Добавление константы:\\n{}\".format(x + 5))\n", "print(\"X*X^T:\\n{}\".format(torch.matmul(x, x.transpose(1, 0)))) # кратко: x.mm(x.t())\n", "print(\"Среднее по колонкам:\\n{}\".format(torch.mean(x, dim=-1)))\n", "print(\"Кумулятивная сумма по колонкам:\\n{}\".format(torch.cumsum(x, dim=0)))" ] }, { "cell_type": "markdown", "metadata": { "id": "T4_b7OJL8hjX" }, "source": [ "Всё же некоторые названия методов отличаются от numpy-евских. Полной совместимости с numpy пока нет, но от верчии к версии она увеличвается, и придется сново запоминать новые названия для некоторых методов.\n", "\n", "Например, Pytorch имеет другое написание стандартных типов\n", " * `x.astype('int64') -> x.type(torch.LongTensor)`\n", "\n", "\n", "Для более подробного ознакомления можно посмотреть на [табличку](https://github.com/torch/torch7/wiki/Torch-for-Numpy-users) перевода методов из numpy в pytorch, а также заглянуть в [документацию](http://pytorch.org/docs/master/). Также при возникновении проблем часто помогает зайти на [pytorch forumns](https://discuss.pytorch.org/)." ] }, { "cell_type": "markdown", "metadata": { "id": "23HMyfZUZWFH" }, "source": [ "#### 1.2 NumPy <-> PyTorch\n", "Можно переводить numpy-массив в torch-тензор и наоборот.\n", "Например, чтобы сделать из numpy-массива torch-тензор, можно сделать так: " ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5F1f3nEWZ6tA", "outputId": "4aa979ce-404c-4d20-a297-2ee76694ddf7" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " tensor([2, 5, 7, 1])\n", " tensor([2, 5, 7, 1])\n" ] } ], "source": [ "# зададим numpy массив\n", "x_np = np.array([2, 5, 7, 1])\n", "\n", "# 1-й способ\n", "x_torch = torch.tensor(x_np) \n", "print(type(x_torch), x_torch)\n", "\n", "# 2-й способ\n", "x_torch = torch.from_numpy(x_np)\n", "print(type(x_torch), x_torch)" ] }, { "cell_type": "markdown", "metadata": { "id": "lcFEpTNLa4cP" }, "source": [ "Аналогично и с переводом обратно: функция `x.numpy()` переведет torch-тензор x в numpy-массив, причем типы переведутся соответственно табличке." ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4VsZ0O3Ca8L4", "outputId": "c50e7637-6cc4-49ac-ec34-146bc1721c85" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " [2 5 7 1]\n" ] } ], "source": [ "x_np = x_torch.numpy()\n", "print(type(x_np), x_np)" ] }, { "cell_type": "markdown", "metadata": { "id": "8kzxCLDZ8hjY" }, "source": [ "#### 1.3 Еще один пример\n", "Давайте нарисуем по сетке данную кривую на графике, используя pytorch:\n", "\n", "$$x(t) = 2 \\cos t + \\sin 2t \\cos 60t,$$\n", "\n", "$$y(t) = \\sin 2t + \\sin 60t.$$" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 298 }, "id": "gQ33m_TF8hjY", "outputId": "3a1db043-41fc-41b4-ec92-7d565c431fb7" }, "outputs": [ { "data": { "image/png": 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qKga9I6gbaMeWpv224qRpETH4evaFCCMRYdfwAO6rGrvGCxILsFRSo7GCSDooJTQa38q7eNxd7f+sf8cwZ4h3wmTgo8AvTTkXdQPteIVLwSlRqfD0Nkn3U1pUPI6jnjwEjIocuTjBRoWvn5QWFQtz2bIUDB/K+3uz0e/3t9v8nooXDB7k7Ngphgft7LhsXOnAq3r00E7UEgFKq3MY9o5iXcUmqDBfL7uC/8bGD/VegeWpp+GCRGN/ikxChsfnM8/GGbFTbLebKI6XUantb8Ojh3bZbpcQEonrs+chMijEEAFYOTF8rYBdWz7VSvG5jLMAwKC2MJ7mQFVVUWrRqPuZ9Nk4Jz7Hr31S1PW345FDO/W/nWYHUlJisPrNMr3nbEnKDCxMKsArrZXY2ixntfE4kYYlYFTkyMUJNCq8QVmnrDR5i1Z5VBG/3wlmx07BZ9Jn425Cm/z+tEsRbyHXLYOMHSRLi8nA9+Lwnp+qqvj5ga2mYvq38y7GQ0Qyw45I8GJzOba3VgIw9xAAZlqyDFMjEvG1qRfYbjcRHA+j8q/6dy1lbQDjb8oTJqw89K3N+3UPXIlKxXXZ5wm36x0ZxP0Ht2Fg1Lz4xwSH4YcFSxydC8W7nXV4tuE9/e/ciETclHM+VMD0jDmNMii6RwawuWm/7W/HcGmyggVJBQh2ufXr+r+jH+BNLVJekDgNS1NPA2BORVthPMc+GQgYFTlycQKMCp9qCHK5ce2Ucw0ME8B6YbbTWvIHX5wyB6drtNDxQHQsomZHO/B1G+YB8wsEYFzo+JSCXfqPRiwiwgGlxmaExeLMuCxsahIXWY+lt3gsjYpdMR7wMQ7PS8jV/+adGKtzp3JAlyYruCR5uuV3sXMVMR/9iShEabxV05cb0moAsKejFv85+r7+908KlyLSIhLyqiqetxHK9AfXTjkXM2LSDQX8RcnTsShZASCXY0oMiTQ1gp6IiCVgVOTIxXE2KvxNHxkUgvjgSNQPGuVPRMVDVVXxi6oX0SVRJM4Ii8Wn0s9AVni8mBUz1Ieyxr2okORqk0Oj8J08Mf1SBk9PI/6m9dPwg5KmR6XiyxLvVAa76KukcBmy0xNNiy2/SNo9aJSaKkotUlkb5kXSKIfC3xqWUxwrozLiHTVEqSLwCznfT2H1+1J6MVOStkNycjRu2fmU/vcV6WfgOY5iK5LDp+D7ZD6bcSbO4qRcKPh+F9EzxxNoKIJdbixJmYEz47IQGRSK+w5s1Z9NlkruHhnAa20HpX1RYe5gXJxUiM3N+wGY5We2NpfjFe6eE9VbjrdhCRgVOXJxHI2KE+8QMIsdWnXWMy/M6QL0eN3rOkvqzmmL8X91r6NpyPi5xJBIfHfapbb7onnxgqgUXJ89DwDwYPXLaB7q0bfz94YXpdToAiE7V7rwBbvcuFtZYfk91CsWpRH2ddXjKS0lx2RiZEbvWGg2HQuj0jc6hJ8KGFFnxmbpqRyRHprdb8Wwr7sBT2lTRq1m5fCg+6dRA70GgK+34+Zcc98VX4+gxBF/vvvy1NNxfmK+tG9EJs/P8LuDr+ikBFGNUlVVvD1Qh/8eel/0cQBmvbzukQETKWVZymnY2VZlSAcfT8MSMCpy5OI4GhVRaM8jwh2Cu6Yv0/9+8vBbUtkVKuPgZAF6p6MO/z7qSyPxaR9RkVpUc2CgKa8Z0Wm4Nmuu4f3dbdV4vmmf/rc/N7zogaaftzpX+gDaSX7wRn5eQp6hD4EHkxvhWUkMkx2xTLZRkfVn3FW4TK+TFEWnoSAqBa+0HhBS2QFfr9GSlBkmCZGjA134bY2Pij0vIQ8r02baHhN/DWQ1MeoMAcCPC5boY6Qfqt6uO0ZOm3158GwuiouSCrHYghHJg/ZSiVLY7LoOeUfw08rNQvown44TOaS35V6Ex+peQ++orzt/vPWn8SBgVOTIxXEyKk4MCjC2eIrGld6Rv0gP7/m0gt0CRBdCEfOJge8vAcyeKT22wqgUfEWLUHjwelFODAstHKeGxhiiKPZ5u3Ot6GnS61NWdan2oT78StB74wQLEguEku+T6S1OplERGZSFiQVYnFLkKHq2wtSIRHwp61ydEZYVHo9bchc4+ix9LuxIFvwz8en0MwzOx1UZZ2G2xAmygygbwOoe4wHtL+NZYfx19aoq1ng2mPiMooiQptgAM7nHn+hwIggYFTlycRyMyr2VW4Ry3ICvO5mF96wH4qkjb2MfYX+wm5s+gPziZbcAsc/Gh0Tg+9Musz1m3nNjXjp9+PioSgTqvYqOm+LPtbv17mxm+GhEtChZwaLk6Y4WW+rZ8g8nf0wUCxILcEZsJlLCYuCCL0+/v+foCaF6TpZRERV9V6TOxEc9DdJu+JkxGZgWlaIv2p/LOAuZ4XHw9DRiR9sB9I3KR+46PX96Pz90/ufRxQ3pkuFnlZt175zBX7YhhYze7E9jowiylKHsuooiYNFv+fSRPfiQqFKUFhVL04fHCnZGJejuu+8+pgfwMUY8gNv7+4dwrOzqu5110rB6nbISv9EWt8tTT0deVBJKystM9YiUsGi80lqpN0iKPJioqDD09RkfNIafH9iCIe8oAKBk+nJHx316TAbmJ+Tr3vju9mokh0YbFmO7mgUARAeH4bToDJ06uautWjgc68nDb+lG4LqsuXr9JD4kEn0jQzg80IGDfa04Lz4X8TGR0nNlODMuC9tafCNrX28/iIVJBRhRvVjt2aAfC+ATj7w9/xK9AF/b34ZPp58Bt8sFt8uFqOAw5EYmQYlKw9uacOcFCfmoG2g3f6mGbS0VOotnIrC6pk6hqqqwKF/Z22QYFTAnLgffyF2AS1MULEpWcHpMhp4OnRIej+L0WYgKDsPUyEQsSCrQDLyCM2OzTXNCtrVUICMsDilh0dLjerb+XRzVag8/LFiMlNgYx+e6IKlAv7YM45WK//3BV7GbzPBZq6xARngcPuyu1/pIXOMWUl2UrOjH+XLr2D0hu65hQcFYlKygcbBbXwO2tVTgkqTphud9ZmwmOocH0KCRe8q7G/HDwiX6d+1oq5qU+88KLpcLkZGhAPAggA7+/Y//RJiTFMPeURMVlmGdshIPEln38xJyDd7GFeln6F7KqOrVPeXrs+dJi6TD3lG0DfWhZ2QQXs1KHh3oQveIL0r6SeFSv44/IijE4CnRQUbrFLPmlgzp4bH4klZzGfSOYDNHz93eUqHXjb6cdZ6JirySLBgyzSYR7ir0RVH93mHsbK0yFP/Pi89FaVExbsyZjyCXG2uJgazrNxuMKRHxuirArvZqlBYV42aL3L3TdOexhpPU1uczz8YVGbMN99WDB8fuzW9YpLJo3YUuZE8ceQsl5WXCuszBvlZd7v6mnPlCtQUriH7bkvIy+JNx8aoqSsrLdNbl0pQZKC0qRpDLjdNjMnSh020tHlRadLbLoKoqBkdH8N38McJL2VFn8jdfnDIHFxEJplWeDaa6y2cyZiNKi0bqBzvRMNBpcPJO9P0XSH8do/SX7MKunr4cw95RfYH8bv6lhv4MfmaJLO3FF8OtQNVcxwN6DEpUGq7LnmuxtRhUkO9beRcjLSwGB3qb8X91rwPwGdI58VNtjyEmJBw/nLbY0XeKmvtkBXWqnitKO9BiKZV8/+/RD4RyLxMdtjTR9Beff5eBP9e2oV78WqvdWc3Pob/HhYnTsExr3uOJFjNjMvCFKXMAGGeV0N/H6bnS1NdPCpfCBRi6+500A/IMvu/mL0JiaJRpu+ca3tejU6uZMIDP8XvmyB5Hs4yUuDR8MX2O7ZA8fv0QUd/pNmuVFajoadJTeVdnnoNZDvX0/IVd+isQqRwDPCGZhXJH/iKEuoN1gxIVFGpq+KMPMfX07p5+uS+d4dmIkvIyxwYF8I1MLSkvQ9ew/SLDg3c6PL2N2D2O6X+UDfSbg9sxMDqsG5Q58TmWBgUAvjHV5zF3Dw9Yqg9TfMR1JpcWFUsZWnT2x8tcegXwhfxXpPuGfj2p0WYBX8FYtEC80VGD/d0TG5g2XrzTUSc0KPMS8vClKefqf4siTmZQiqLTLAey0QIxMyiAT+25tKgYGWGxAIAPuxtQUl6GUdVrGH7lr8F9vnGfblC+l38pIoNCEREUavDQV3k2WEYsfaNDBoOyTlkpNCgAcEXGbERqBfZfVr2oR/8ULUM9KCkvwxrPRsfD8TydjfozTFOQPNZz12ZdxSZTxEK3WePZaCAWPFO/x6/obTIRiFQmOVKRTY5bmTYT8xLyTE1aDCLvmHki2REJyAqPN+Wv58Tl4NPpZ5imIdpNjfSnmEz7TrLC43Up+/GO4BVFcOMp7tp9Zq3neQyro359DyUGyLZlx0DFN616kGSCmnYYb6Qiu//uyF+EpNAo/fhFTCGqO2f1WzUP9ugpMlHHOkPjYDd+Q2R0GPiIwu5cqSjkl7POw/ToVMP7Tpoz+R6didxzst/YSg5flrlww4W1ygphhMX36QDm3+6DriN6apqJsvrznIwHgUjlOEM2inRegm+GvFOD0kcYLnX97QaDcnv+JSgtKjblwhnuI81SpUXFJq+npLwMAxYMHobekUHdoPy4YImBKvrH2l0Ykoj1WaGk0MgY8+emp9vuspg9/7uDr+gG5fb8S/TXnzgsFjFkoM1q21vM3fMA9FoK7a53uVzSgWM/P7D1uHqMovtvnbISSaFReLZ+TKRRRD1lBoVGMyIwgzI7dorUoABAWliM6d67OKnQL9WGYe+oblDmxOWYDArgkzpaTUgofIMq6wlh8Oeeo/W2F5t9Q73obzw7dgpKi4p90ZnFfBXKuryrcBnyI31NlF74HJIWQtBhOF2QvuKdFypu2jDYhbr+dnw5a0zJQrTfY42AUZlE1Pa1CV9nN7FIMZV/6BhEnc+fSpuF0qJiJIfKmTVeVdUpzKsKfQ+ay+VCaVEx7shfNHZMlS+gfqBTuA8GlqZLC4vRm83oAynSJ7IDn5bxd8EtzvGlTWR6XC+3VOhdzbfmLkRyaDS+pxVMrcYaM9ykjW+VzUGnSsibmvbhmSN7cG/lFrxGUoKZYcbFxWpk7GRC5A1/UatnvNtZpxfIQ11B2N1WjWHvWCRHC8lFFv0ZNKXnhHLLG5DtrZV4p6PO9nMMdAG/ImO2dLtQd7C+cA96R/A/ra6jqqrhPvXXcw9yuXGD1ou1vbUSWzRJFcBnrJ3SjuNDIhCrRaz3VL6AG3PmYxUxhA9Uv4x3O82/y52C+uH9VUbH9Nt5F+v/f+TQThSS4XwPEELQ8ULAqEwiRDpBlHX180oje2nN9MuFXpsof7tWWeFIR+kPpHmRn/2RFBpleKh+X/MqGiSGpYb0MNyWa5xzwudy/cFDXDrE3+a7lVPHcvGUkQb4+kpe0nSnrs48W1cNoN3fdj0neZFjMhwiJhjga8wEfBTpvd316BkdRBPxCOsHO3ExldqBin1d9ab9TCZq+8UOzVNH3sZqzwYDE3FIHcXzTfuwtuJ5lJSX4Y+HduENTSyRv9Y8WCHY6SjocmKEmK7Vv4++h7cdzLL5NzlmmfNFER8SoRuANzsOobavzXB/OdmHCPSeAHy9Y1b1ORl+QKKV6t4WhLmDUVpUjCnhPlmWZxvew2ttxhR3bMhY6pRFha3DvdhL7ie+R2dtxfOGdee9TmdqypOFgFGZJIhG/k6NSDQ0Ig2RHP83pi6QdhDz9RBGd3QCRpP8sYVkAzUsv6t5FT0j5uZMVlu4NFkxGT6Xy4VbcxcC8OWzD/ebqOpCbGz8UP//j8jxySRBZLhG877pb66qqp5azI1IxCxu5glLW/ECfSJcqCkO0HkZADA4OoKS8jKTXtrZcdkG7xCASXzyqfo9QmdhMqCqqqOZKIBPBSGfWyTp3Pf08FjpZ+nvfa4NsYLh75oRWpk2E1dmnInzE3xpt+eOfgCPxRCw/tEhvKN57t/MXeg4ZTYtKgVz4nximNTJk9Ut7MD6myjGm1JyuVz4TK5vxhAdHPaN3AVYoN1zG5s+1M+bgT3Lg94RXK5JDz1Tv8cQadI074jqRe/IWPqczqY5HggYlUkC7zUDMMzcoCH/6TEZmEJE4yhquC5nf8J1umBGWTB3+P3ee2CLIQ11hBgJmWx5ZnicXoD+g4PhVqqqGiTnKbPIboojj9OIVP/mJl86gsEMgocAACAASURBVPawfFUw64SmrY4OWE8iXJoyxgRjv0vHcD/WV46lUaiRvzLjTHwlex5Ki4rxmXR5isaKPDERWEV7N+XMx4rUMebdV7Ln4cac+XodgE+vWLGS2D3udKTtiyQqZDXFy9NO1yOWvx1+E61DvcLPMqpwdFCY41nwDHya7Dt5F49L8NOrsS0ZWK1ic/P+cdfJlmWPMeVoFLc09TTdmfl3w3uGe5Q+yzSzQFODfEr8wYMvGxxLJ87UZCFgVCYBotrE17mFjYk5AmN5bh59o0OG2ST+5n9pk6QT0P3ThYl1U18o0Qhj+GHB2IIkYvlQ0G58lpai6YjmQf+8v+I0XxpsR9sBNA9268SGuwrl0jFnxmaZjkUE6tG+2nYAg6MjBt2p0qJiQ2GYwm72R9uQMzq0U8gM5KyYTJQWFSMvMhkbm3wR4nVZov4i8+L4y6oXsZeLvOnibyUpT8GiNf5ZuDLjTCRrVN77q7dh1Gukyr7dUav//0eF/oskjnLUW6djkXms5lJnlCQwXt04wFcbBcaiOIZlqadhmhZF/rbmFUNky+om73YdNjw3lLBCnQcAqCYOqj9SQxNFwKhMAn6vzd6gyIkYo9vSvgdZN7qqqobifHSQdaQB+FgtvziwFTfveNJQpC3gUjFWoIvjpsZ96CesMNp/IANLKzUOdpseZgavqurjU2mu1+VyIdTlSwHSLm4noAOkHtQM2uzYKYiwGOnqZLwxAzP8W5vLDREKM8TU8+VrL3FaBBfhNh/LryewGIkgM5BXTzkHAAzXUzQ47T7NWGaFx6O0qBiJ2gTQZ+rfMUQaLLV4WrQzkcU32mv0/9NngeF2Qhq5ddfThvee0wZoyZwvO/B1vs3N+4VqwFagz9M6QuO9S7t/O4b7pfc7g6qqeLmlAiXlZSgpL8Oz9e9i2DtqqI22cZHaDRpRBDAaNVo3aR3u1R2ETU0f6VHT/MQ8w76eqd9jcCSs0o2TiYBRmSBEefIbSKSgqqpePAYgLe7xKYxbBHMj6HeWlJdhXcUmdArqEVZ0Wx6h7mA9T7urvRo/0wyb094KmlaSFe0fIgaDF7ujDJi+0YlpXdkxcehvf2TAug4kmobJR46ztboNX3v5luZV9nuHhcXhA0TGfSLgi7oM1xJKMIsgY2zSoV/NOR8A8N1pl2JBok8mZHtrJV5q9hi2+4LDhV6nJwujIx/ob/Oo9hs+c2SsL2M8E0nfJ0Vp6sCJxl7L8Dyp/d1VuNRw30SQ+9dqn388tAurPBsMz/67XYdx265ncG/lFt04/1rQYkCPmxr26VG+SOmB6pcNDsK9FvJFtFH4b5Km7MlGwKhMEH8UML6mkUiBGgt2U/DY2Wo2ArKZ8Z3D/abc/CWZxrrHpqaPhNGTDOeTngWvlg65gxT+7EANw6Bg1niL5o3dJdAfo6kmEY3aCmumX67//+rMcxx9hhmCh2vs60AUIuLDFZL6CZU67x0dwt3kOAHoSgITBUtr8aCd1YzC/S1CO2WgKS061Gpp6gxdAeHl1gr8tW5s1LUTxhOLSgFfZ74MLpdL18eq7W/Hkf4O7NUUeCn93SlUVcU/taL0ZzPOhNvlMizQRxwQStqG+rBbq/3dmD3fYEQY2H2nwpxqG1G9KCkvM5AfLkoqNChK9IwOGjrw+fqM2+XCjdm+iGV7a6UeZV2bZewfYj1fvaND+nP3TY1Aw7Ch8UNDL5Ho+ZxsBIzKBCGjnQIwNRh+LvMs0zZeVdXnVCdLJCMYekYG8QuS218z/XKUFhXj6vyxBfUsrW5QP9DpVx6VVx3mdYasQG9amioCfKJ8DKIHFIBp0XUKypIJdTjpz6qQbgUR8YGy9/iFgV3LRw7tFE4hrO5tGddxMGyQCBSGkLQcZQeJ5NBZ/S47PMH03ryEPL2mxkZQO/3tWHTEalhWSAyNxMJ0X2REB8Ul2TwLItxHng1W93G7XLhAc5r4QXQisPRkXmSSabwwA73uVBiWn1p657TLUFpUjMUpRZiXkIfSomL8et5nTfuj0QwD/W62T5pybRnqQXhQiC4syZ47EamB0r9/Z1NPnAwEjMoEIEp9LSQKo3QGNiB+sGnUwVg3szk6LOBbtGiYW1pUrN/cuxrHIp3PZp6F8+JzAfgYH/0OU0p2And2oIaB/i5Mkvsmkis2fTd5SP/TIB+1yoOlWADnoX2whSGgqPIzRVXBqdnepKWTWPphDWc4KaV0PHhd6yvhQT18Oyopo3LzHjDDstTTDI7O2Q4L9AxOWWLXFhpTZLf7ESUzDIwOGwZjUdAJoFY9Gw+T6J5dPxkYIaSNpJeoQVmvrESsQIQyKiTMVFfl6ecMtJufETymavWpR2p86UI67ZGPQti2lJna5lA3byIIGJUJ4EnBcJ9LknypKCf1ARrlrJl+uR7migrkNI3G5/b/VulbUFnDHRXru8fPlBLDYYsITAS6WDND2Uv6X/gGMh5XpvsWoD2dtZbbMciaNv0BHXbE4zGHKSqWG+eNIV+TEvUk+dufw0CL4IAx5UEXMjbsjWdf8bASjryG1GecNKrSrnB/+kIWEep6sMtZ1EnBHLjooDBD+pHhaq3eJjO03SMD+hhgymqUgRJC3umskw7lEsHtcjn6jiCXW08fsgiK9Wj1e4f1ffHRCpNukWVRxnvfOUXAqEwAIqoiWzyc1AdYgTc9LNaw6PALEuWY8x4vBe3ipkVQJ153F9eb8Aeu+OwEfB78fi01YJfWA4Cz48e8YCdNgr/TvEoWlQHAiHdUsrUR7KH7nySF1O4H7Xex1tMim+4JAE2DYrFEf/tzGGiEBoz9FjKI2FdOjfJTRJEZgIlqzIN17vP0VjvQwVv8KG07UAfuTsliTZth3xdEK+xaZIXHOyapsEiMdv6XFC5zZExjgsMNRIS9EsUFSnRoG+oVpmF/QM7Zq6o63d4roIsDwGO1k1PTkyFgVI4B6KKYojUl8UX6N4m3eVuetTQGq40sSlYs53jTaMHlcumcdyde9xPa4kHnRjgRnaSgefD3Ow9jQPOmvjF1oewjQvyZ9OqIQNNWNCqTDUXjwbxi5u3xYD0IStRYkXlYYrCsJhwyavFWi2a5fZw8vx1+XWWkJIvqIQBsKbQbG32jE1IsdOQA6IKirL7yTP07jhr/5jmQFGLoHzFfhwo/6K/MgUsJjbYkElyV4atp/pOLVip7xlKXt1gMJePB9+usTJspjJJkoJTpZzg1YgpmfHimGEtt09T1M/V7DFHUnYLx4bwixGTjpDQqiqJkKIpyr6IoLyuK0q0oiqooysXH8xisHto1JE3AHsozuDrJ/zRvc6FG35QtWjSsXiTobrc6Dsp5t5ulwii2X8kao0PzNSEnYPUg+uDyGmQyfEFjcB2S6Fgx/LfxA8PfczXJkL0W6SwKK0FOii+RWgM/m8UJlmhpzP09jXhasmjwkYAVWoZ6TDnxmznqObtfym1mudT0+xrjFmvy/XagKVnZfUHpq/6kvn76ri9tE+EO0b3svzqskdHoVMRwo5gdN0YcoP0hjx/2sdtWkNrLeMBUA/wBS5db4QuE2UjPl0Z3LEvAOynR4xi7MFGclEYFgALghwCyAHxgs+0xAeWyM7B51syPo+kg2o1LJwWy4VCHBXPPacTzvWmXmt4HgA8dChXeV+Us1ZISFm0qdPoDvldE1AAow0yHk+pYx/Ut2uCuy9P8S7VYgcrpuFwuxAf7IjfPOMbKziTpDauIRJSOEYFXnL0oqUCYKtzTUasXf3MjrGesixoiGfiIhFHHB70jhnoZw9OagUwK8Y+51TTgc7zumLbI0NTqRM2YsiGd0J1n6DUKn9e/h3Tvi8YBWMHfhkoRLk0ZG8Mso/tSA/3AwZd1Q0THYdAsAVWnqOlrFRKEDkkU1ScDJ6tR2QMg2ePxFAL4xYk4gDcFKqszotMNoTS90HRxZeNWqQxKjeAiU2ZYgqRvZXurz1vJChdriVnVYBj4xYOG8KJRuf7Arkgsg6yngB5rlqafNlHmGgWT02E9KGzGyiFOk00EfoEX6U3NF3izfDpGBL7zGgAuTS4yiDyykdH/Ofo+jmry/3TeuQhWmlhsHwxh7mCkaxMdfyZouGOF7muynHfC03oIW/xYWohKG8nApkGuksjm8LiGmxXzH617/5pxdO+v9aOh0quqeLO9xjJ1yNPxKRjbrGO4H3MTxGKerKj/4MGXEa6tN3u763GrIKX3D4t020RxUhoVj8fT7fF47J/y44yciEQ9lM6JMOa6mbdBvQiaUmDF3ESB8ZAxRVRV1RsLD0s6xGkNRpanFqV2ztGUXum8cadgSrQAkMLJcjuFrKdgQ624uM7grxKwzNuco2l4sXGzIuUC877siQKXJivC1+0kP0Sd126XSx8rPTMm05RiBSDttXCCd7UIivr/lGkm02tLC5MrHfN4rNZHrabFcVrAtlIfeJ8YVKthYRTU63+UkFFO87N736uqpjK4lVDpas8G/K9xL27Z+ZRf38NA6yQylWRao2GZkYqeJmEjtZP7ebw4KY3KxxW0aPu1HLGHzjSueA+RFY1ZgyB72AC5ZMovq5zpSKVpC7ssT71VG0g1jdB+P2MxEMkO5/lRpOXBtMRk2FDrSzvKPEt/+0s6CetNJOHvT/pONISNIj8yWVrIfdqP2goF8/RZRPIDrjA7HnVeBqYVRRdcl8ulF6j91WsTgQ1U45+XZSk+h8tKfeCfWv+Fvw2trGeqVqPcOq0rUdAmQpYlkNXH7Jh2tFZqRfdlmY0/14p7nOi1ZoSbzhGx4vSxRMCoTCIqSOpLVKikoS+flmKFetYZXtXn67i2Uhx2esPcmmvNvmLRjkyV2N8hPzS6aZbQaWXIjhCzmXjIPMudfuieAcaHmLHOqLSIXQRBYRck8f1H3yd1sv0WbCeRt06jQQB6WipO0HA3XrQO++6LqRwlmTY1MgKIHRHEDomhRm/6wqSxe1FUa6DPkp0yNA++Z+oiQsV3CiqQytKO7PfiwVO++XtqTtxYOos1NYpARzLIwBzDV1sPGF6fGeOsXjkZcK7FcYoiKckZE8gEgQIKowUWxqYiJSXGsF1KSgwe+WgspZOWakwRBNcHAf1ASEgQRqPGbrr5uRbFQ+4Y9O8UQVOCiE+KNNOStf2cl5tn8HYK6lNwoKsZ/2p4F4sL7G9ohuryMQmSBw9uxyMLrnH8WXo8ycnRBuPcOzxWHObPNbYqHF3DA6jqa7H+HbjvcEW69O2Hy32G/eZZCxAZ7IsYw/rHHhHpfrV9pafEIiokTPgeAMzOzjK8lpueDBAbyO+f/S0aE3x10TkIDw7R95VK7qe1USuwZs9G6TF7yaJm+Vtp+z4jYwpSYo3bTW9IRUVnE+6r2opHFlyDN2trnO2ToHVgbBEWfSauOgKdQ/24t2oLfnvB1Yb3nj04Vody+n0GkOvi7+ffaqrR/z81XSNCVFvsi3tOe8KGUBA3pg+Yghj9PugY6Xf0HOuf5bb9duIl+M7uf5q2uSF+Hr73+r8NryckRQolhCaKT7xRaW3tgdc7/ol80UFhpsa3L2Wci+Zmo4fe1NSFd1p9bJarM882vR/m9V3c9v4+fUFwAabtGBoHzflb2bYUv373JdwokUxpazF6WtdlzMWaro2O9y3DeD/7weEj+uwVYCzdEQSXaZ8zotL1kbj+fF9XVz+aYdy+t30QvfBd0yMdvpRYdFCY7X572wfR55IrKfCff72m2vD3joMH9CgpJSXG8vu62wfQjbHogG4bRBIQbx2q0ckGDLTu5OS3UntVU8R5XfpcrOr0EUkam7rwWoOPiVQUneb49/+LlsZJCosSfub23EuwtuJ5DHtHTe9vOewbznZD9rwJ3ZuA//fnnzy+iPbK9DNNn3Wyrw+PHkHckJzqa7WPc+JyDKoTTr5Pts0Hh484zgxQuN0uS2c8kP6aIK4SiESK8ti00Y4fdQuMzUtoJkW4NZzII4WoK9YqVcMKxNV9zoUM6Xn4kwaaLGzkaNusMPt5gcR9HrdwOgUTzpQVPxu08cxWY3YZnPZmhGgyJPy1+PvhNw2pRlUbcTAR/EnQSOrvbHWRbAo91z8e2qXft4USJW4R2PlfV3ie8H0aUcsK9tP8mB2kf+8ExDypQaYqEAy88aWTXBnV2m78thU54VOk2XeiGO/wMjsEjMo4QBdYft63DHayLbw3CVhTZUWyIF0WRb6Lx5E3pvhXvbM51yLKpL8GaYpW+JQ1QYrqKQmhYsq1HRjte4NmwFhfCgOjySp+LJZ2YFTowwMdJoox1aaSNUweb3gl14+xEutIj9UUP0f/AkBRvLxXht23tGBvNwvHDryY55ANwYLiWRv69/ucjA1zjHIiEvQR4jKmJoOVOKoT4gWVLrKCv/p+ThEwKuOAi5Asee/0Aq6AystgfEEy9yOXK4bK+lKsIPO2Af86nCnYDeq0W71WcKPuEMyLscICmzHGonMJdY0vkxunGRHmHa6UeIL+Uk6twCieHcP9BlVrHv5KuMjgz9A2ETokhBARKzF2HB3cVvemiIL9p0O+6Es2n8hf8IPIrMCMxm25RmkllrbkRUoZu21JygxdyaHdRil4olmBpani+ic/TVZGLJgoTlqjoihKiaIoJQCu0l66TnvttuN9LNQ7W8oxfPhFQ9Y1zhfMvjMO+W+nDJx6Pzy9FX52qzPjE+xy6wvMiy3+zccWdXnbqT6buwacgSctyCISGavK374YAAjT0knD3lHH4oWGY/HzM5uaPvL7OygaLPov5sQZmVf+aF85ATU4zLMe1vqBRKlnOxwkKUc2OGtXe7VscwNoFM6nQ0+L9jkdLYImVcCXiRiPwRVhuc2YbzoLKZU4tTxLrm9kYpNWZThpjQqA9do/Ri26Ufv7+8f6i/mcNBWW498TNaQ5gVXqS+bJdAukM0R44rBZsl8Gf/PvrMGyKDptXA89IJaJf51IUojQ4/Dc7eBvRLe/x1pjiyGNNIGypcnqu2RacABQMImpOCtEagZCJCHEQAdAAYAbzn4/u54eCqYnxytny4a+WYH1eMyJz8GceHFnugxWQ++yI8SKFhROGzQBa2fFH40x6qCxMQ0MQw6adceDk9aoeDwel+Rf7vE+FqtZ2jQH6q+HKYNsgbWLCNjs8fF20zqRhGeChzkRiba6U/6AzRCZl5orfL/ZIvVnB5l0hhOV5jJNPn+GhYYWAGQSGR3WxR9kYVTeapbL42Q4IA3wkBVlO4blvU6KtgjttxCn5A3jsMPUjT/Fctrc2O+ncrYMxWmz/Jb3ebXN1/vx5ay5pvdEXetU/LGkvMxSiZiH1YgBUYpZBkqyGPDDkE8EJ61ROZHgpT1EM+ZFsJsmx5BuI3Nhlc6wYguJVI79gV2RkiIrPH7cdRwRmMbTgnRxDaLGgTYXQweX05Y9pJub99vuixEmlgkUbqleV1roWKTCvj8+WFw3G/GOYtdR+T0VF+x/g+PfJcVfq3oLG03da5N6pHWPTgsjRVGrkTCcRDY0NTyR+hD1/vmCtxM5f4bpAgdCVEB/z9IwjJFQ6Hez2sxWCweR/gb8vcyjnETS73cZm5gnUzOPImBUxgGeDUNzslaDojZpOk124IX8/IVMg8hqFosVmNpujY0kPcV4Nb/sMC1WTCF1SiQAxtIYLB3BeP+8JAsT0/yihFxBIZqp/iKZPU4lfFjxNk3yG1X3teBAl5xWKhs/SyFTR2BghuA1i7SiU5o2ZRZapcooWGSZ6ud9ws5dJMxph22CWfAMdkVru8VbZBq3EqdkXkIevk6kaB49tEv/P42y2W9pFUHSqHN7i/W90EDWknc6jarPTkdA+IuAURkHqFfC0xH5B54u8FZSHFY3kRV+UrjU9NpviS7RZOByP6f4Aeb8sT+eoBXsoh++cCwCY/BcodUDWG8IP3SJQcb8smPxUAXhRCIHz0gH0yRij/tsZqE4odSKjAqt09gZHcB5fYlux486loHVwKIc1kV4VqWT4+fBnk2+DgTYM+3+rtUhU0PFRlA0ZZFGeCvTZprIOGz0Mo0gsvxsRnxbMH7bStiSInMc9G8nCBiVcYBSiuk8BsBsVPjmM9ni+ntOH8gJdz4hJNIwK+HM2CyLrY3wh7UUG+J/LYgv8FvRnScTVhRdHkwPiS0IswmpopWkrmSLK9NpynCgyhsn+A1ljYJ7BAuFHcW8n0tRiebO00jZ36hVND9FhAY/o2ynRBA6dwSYmMYZdTyYGGSVTY2HZQ/o4DYKvv5G07HrtNHe/JmySaVMp8tJf4kT5+zpemfipJNFyeYRMCrjAH0QnrdJaQ1wI2tfl3hyPGX26SPioh6VG+cHd9EObTvuvUjufjz0WKeo9FM9eLywW3wpc443FhnEc3vo4HYAZjFFClZPoaKfLzV7UFJeZqpthQi60v2h3/J9TPzick/lZv17+e9nEZloBhBgPeiN5d3LGq1HDtyYLZb+kYE9Q6MO77lQP5hTdqDXPUf7XescpnYTBWlOwJw+o86kneFkvwAV0ZSB74MRQURrFhmj8apQ2CFgVCYI0SNRL5C6ZvOxNzaZJ0ZSfEcbiVohmTRoJTfeNTKgp3Bebq2QbgeI1Xxr/aiZ+Iu6Y9S9y8MuZbPFovhOH35mfL6cLZYQ6SLpyigtKigpL5P+7v/yg+QgQnp4rMHTZXl1O1IHADwnmYnD6ipWnftf0c7/Q5v0kL8LFEuPDnonh81lh1ZJ/wijAlux1mQTGSmqe8ciky5JKltkQN8mmQ7eIRLRyp/R9O9kw9dkkYyo5iiaCDkZCBiVSQRjy/CpLMA4H9uqmE8L3P5w+RnsZkuwnDCVimA3s5POd7smRAb+HJuGxi/6ZxfyO03NAMCuNh+p4ksCWihDLZnCKestuE8bY8saX2lkcJNAsPP9riP4a90blv0nVkgMiTJEUs8dfR9vttcYSB3fyrsIpUXFKC0qxnplpZAIQKNRJ9I9vEy8DNSYy6Z2UrB7rs0BTX0ywIrmfA3HiXKFk+ZdFvnnRSbp9wYPmtK+TDPoz2mTJ0WotBhhvYgw7ujzIeul+YdmjCgmk51JETAqk4jPkDkTgHwxfFBLrTDw86LZQvYAN+3v3w3241XtMEcwe4ItLp5ee4G5vRapEooezvj4s3jwvQh2TVpWg5woRNRNER6t9TFznBSDv5ZzgWHG/E8Kl0oX4oreJvyiaqujY+URERSCQiKe2Ds6hP9xKSk6cdHlcuFbWtRLQYeB0UXFSsSQodyGQMAgm9pJwfTd+v2IVPhpqv6ARVpLU4zd6E68dcaQK06TiznWa8KjVnUR6iQu5Aw6Jf8wlYWPuN+bOjt0e8oeY700NCU7WSQZpwgYlUkETZ+81nbQlAZjTC2eNfRXbQQxA5vex3fIU0qgzMOi6R1Rykk0rMefrn+e6y5DsxaZsD6EYT+6d3kmTq+NnAQrDi9LsZaveKHZXq6EPviyoUibSZ9QsDtInzF/ZfqZiAwKFVJXr9Qcjj7NYPo7bTDUHSy8dpSc4aRZ86Oeo4ZUEOtdsjLMS7Tf4e9HrJUY/OkYH8+YY6firVaYHcff68699XMddODzahf0N6GRCl9nodGtoo0C9nBsUZmzw54X2j9XQBwQkfjssUTAqEwyvjTFxw7Z2PShiQlGvaJ/knCULWRsEaMF3NfaxLpE8xPyMKp6sYorCNNaySOHzFPkREwuJ0wg9nA47eZlBi3Y7f8ttpur9zjNu1+QaDHQDGOpL6uRxesrNgHwSZTI0gM7tOO7ZeqFhgl7TAp9W4uvrkKL12fHZSOEeJdM3cApglxuYf3kc5ln6V7rPwUpjq8KGm7vr96mp8EuSRpriJWlZSmjzsrr/UbuAul7PHg1aCeYDAos36Q44HWuf+WEqfZPrnY26B3RiRP0PT5lm0OIGNMifQaBRnE0bcnWCVbHYhNnHxak3QHgoertAGC4/8ZDyXaKgFEZJ2Szy4tixvR1mC5UJDESt2tCkbxENgBcQC40k4LYqLHLtjQZC8zPN+3DGs9GWxnFkvIy7OBGi1pBVjNxSj9kKQqWThlPDaFJn83he7isVFvpw2mVI6Zim7LBRPR77py2WLgNNSJZEQl6ZMgMCB1PzHvjJdOX6//f76cC8bB3xHR+7F66RTOSHkEOXjRSAQBWe3wDtug+73OQmrNigdFmOruUy3jy+U4lYPyBnQirE8Xg8bAmf3Zgi/S9fME1o4oI7LdjkRsbPcBGHP+oYInhs8w4UbHbiaprWCFgVMYJGSsIMHsBVLiOPnhPcNIZ1BOiUhB7u+r1XKkT8DMtNjfvtx32xKijzzWIC4dZDgTzAGCWlqJhEQ173Jw2uVFcrkmfWM2QsHo4KdiCyVOE6aJxt2ej/n/ZmFVqRKgBZgbk5wd83zMl3Px7bSBDx57SGFdOFySRWChzNGgBX7SYy2oRL2sR1WrN2PWNDkuNwTdzFwKQ05J5PHXEWa/E8YCVgWuzaWB1UkN8y+I3Waus0MkTiwQy/iJECXqMGBuUthHQZ/L5xrHWBlGPEmCsm00mRZtHwKiME1ZjOJdx0tRnxBjzuN/N990Y+3saTTpiFJ/V8vAyIbqSwmUoLSrW/2Z05COklkNTF1aGhY0Y/kiiupse5iz1cLog7w+IZUzswJhwTh6AW7VFTwSa1uH112gNhS09q0lEQfEsIUrkRyXraYVk7dxoVCZKsfGLz7uddbqqsx1ah3pNxXTRtdoiYP/w58wM60stHoyqXsPvK2IuAkbDZdXXYnVsMtT3min4IkSOU1bfSmyUsSBlemqvaCnsAosJk3z0lkmeFeoQsQbVMHewKZqwyibQCai0lppBvme3JhUlew4uSMg/ZpMeeQSMyiSCFtKpyB5P7UwkUwqpd8xDJhsCAKVFxabmOZGWz5KUGbgjf5H+t8yw5Fg0+QFAksPJijESLynToVESIVxiVGhh2irffnfF8/r/+bw4zzRLDY0xLLL9o8P4sKseb7TX6LIa39aMNyuA3jzVhO7F+gAAIABJREFUV0tYa/E9NCJig9qebXgPr2gRqF294PBAh2Ux/QaN7bNDENHykR6dqLnGsxFvdRzSa4ENg13SlA8jG1j1tdA6kl0UxozxkwecjWLgh0w5Bas5iFCpvSczGswgyWpgIu2tr0+9QLDl2L0WERSCe7kIe3Pzfv3+olBVVWef8QZDFP3L7qPlaWOCp/6oTowHAaMyiaCU30tIzlKUulprMX+ewp/ip8vl0runKZJCo/ToyAlEir/RDmX7Zblyp81xogZM2dyM0soXbPdHG9fWTL/c/D5HAvhW3kXYoqULS8rLcE/lC3i6fo/BG33o4HaDcY4ICjHUdu4WfM9f68YYfnRQGyM08FRXHhU2dG86q13UrCfSiGP479EPDMyuNZ6NePTQTlM97Gzi5MhSPvQ6P2nDFvus1hBc2SVf9CnGK83ClCZEcjqs3jDTZrKnqM6xs7VK2MMiS52ySEVWj3224T0T9Z5GjrzB4J81WZQSxDHcLkv2j3noLwJGZQK4WqBeW9tnXhS3NpebvL8glxsrHAg11vb514nODx5i3mJiaCQ+n3m25Wfnap/l9cqA8clkU09VVjDmQdVbGexYN2xWugjrKxmbK1TIchv2jl2XmOBwrPJsMBTjneDpI2/rtZ2MsFjholKlLWzf13Lis7g0oWjRohjSFnir2hTz5O8hxrZvdAgl5WX4aeVmu9MwoLa/HWsrnkdJeRmqSNrtWi2i+a+kS58udHbpFqsUsgjj7QBn1P5MQZ2LYZpFegswL+CVPU2OKOoUTGBSpI/GmH2/rn7J8Drb9sdcukwEanRoynfV9OUGWSZ/B+/5i4BRmQBmCUYDMy45jzWCNNf8RGv57p6RQVtZFxGWkP4KGnXY9aOstGjuGg+oTpGo+GgFERWWgqrhykbyUrVWPofN8EdyvShz62s5F6C0qNgQ3VyVebZedKWgEiZWtR1gbJgTb+BlEV4IZ8xpKhMAmgbHlApYEdcLFaqq4t7KLVJjckf+IsN53J5/CUqLinG+QFL+sbrXUVJehtahXswg7Ma/aFMUrcDLrcvQ7efgOH8YVyxNmRoml3r3Z6HtHx3G41xvGcNlyYr0XPi5NCWFy/T/35Y3NvOeb3qOCgoVPj8NpHa6iqsD0pRvsDsITx5H4kTAqBwDyBgjdgOGXuSKrHze1SlozpQv8tOFhP8++mA5KcbKwNR+J6IAYBfZsHSUFd+ejQCYGz9VuGjw5w/4WE6lRcWYGumrMdE6yWyBUf4O17X+R4lTARibKZ1Savm0YzjXWU+ngNJIbJVng76YJoVEYb2y0nDt79cWLnZOD1T7NOUuTxuLnpNDowwNpfdXb8O9lVt041Xd12JrDOzugUTNyDoxUBTj8bX97Y2RscbusUi7LkwqlNZw+JHXtCbqVVXd8eEFIWUO0e9Iaow2WfITWmmWRImynlA6GQgYlQmCNo8x8As5K4JuavrIUqdqe2ulHqqv8zwv3U4GkUJtr5b+KCkvw38a3jfcYKJhT2zWBF+M9UfqYblGBbZitolw0OH0xsOkAZNn2jE8cXgsn/8pQZ2pcbDbdP6lRcUGltPvD449tOs1+XIevORObX+7lAyxYBwFUvobpmhEDJp25Sm+fL78rsKluGPaIqER+0/D+7iKREyskMzSiS1DvcgMj0NpUbE+MrtndBC/qnpJjw4ZhdoKuyUNvADwVW1wlb/joMcjPBLL1WR4eSQeomF5fBQBGJ2N1Z4N+A/R86LPJFWVWMfdT81D3YgODjO1A1yfPU947e6tlDucv+JSaA/XjMnmXJFhfhYmGwGjMkHwcx4YaGG2KCZd9yR+dmCLcIFm3ufva15F82C3Se+K76/4Z/07Qol1K+zprDWl4XhvnUpRUFmJIT+aGGU8eTv8Wavl2FFH/6ApBcgUeluHevXG0+/kXWJ6f0T14jecMeDxWlu1rud0e/4lhgdblHpZVbjcxLKj1znZhlItM9p0TgqjBssk87e1VBgKx7kRSSaSwzlklsiezlp0DPfrNFXmgccEh+sR4F/qXsOQdwRfnDLHUOynEcofaoxaX6z2EacZHqvxEFThwR9hUH6khBOEcwXyf2iOk0z+ZT9Hi24a7NajCCalBIgbme2w2rPBEMXVaSKct0w1EnNErLR9XfVS6RURNZkaR1mqeDIRMCqTDLaw8NL1NOe5SutmpvgKEYDjvV8AuGbKHMPfohtZiUrDndMuQ2lRsSlVIxtdu7210rRIhmqzP2g+vsvPnPdEYMVU+6h9rH5B89AMqqrqqZ3pUamGMb4MlMYtKv5W97boSgafSptlomrzs2iuSD8DYUHBSAqNMtRg6HW+lGt8axw0qja/0VFjOg7AGKlQLbnscGORe1dbla45xoZQ1fS3mqRX+DG8v6x6Ue/Mp6AR4LqKMbJDaVGxqc5zeKDDoKiraOoLVOr9T4fM5A+G9Aifc+Ak6mFwOmGSgqdWd2r39JUZYmVvlsZiRuch8lz+QqJEfB1Rv7468xxdMdotSNjRelO91i8jWhsoekYG9cZZXutu2DuKzVpzrky37nggYFQmATTnGeoOki7gtKlO5CnzKRZZyoWCKtFelz1XD/F5Zk3jYLewyAz4vKbHSQ/Ejwp95zOienUP2t/JjU4kxSmoyKHV8KoHP/Tl/mUUZfpQilQP+MiOp9q2DvXiL3W+/P6smEzMTcg17WMXl86hjLsQdxDuFtDFezhRTP760257GTYTttFNU8eIDJU9TdikiVxelqzgCrJI0oIt4JvLwoNGr6zLHjDef/R3W6OsMBmnx+ve0KOqPE1doGd0UPfoa/pbTTUFfd9n+54L0UheGV6ymDcvw6hE1DRecq+yxsj8yCS80CRmel2ZPqZM/s3chVCIEsZU7Rms6W8znNt38i42ETre7DgkzDpQp8CrqnqdNcTlNg31ovW/TsFMF9kMlslGwKhMAmi65+cHtgolxwFfZ/i3NO+6cbAb/+EkUfjcKV0gqRwJTfvI0jiiZsPHLIqhlb3Nvryvd9TQ/McovqL8shVoE9xySd2D4n5BrpoiKcSYPuK7xAFgM9FHo8yaIe8IdrZWCR9a+lrrUK9+HBHuEFw9xUwZB8a0lgAIjXSwy21KqcjSFU77lQBjdEMp3oyJdE5cDi7W+qPofmVd+zR6ZnN26GLdOtxr6E2h9YHXCEGA4Z7KzfCqqiGKiguJ0FN/MuIJJRhY1V8mCsq+qrRoiOTRMdIvHGoHjImIAkbVAcCXahr2juppXYbo4DC9ViVqcKbMQJq1WE3WgzXcfUO1wdYrK/G6IPK99Bj3pzAEjMokYS7XH3InybnuJ3MR0sJi9RCZn0VOvUQr3KYNY7JCKomWWEquqq/FQDH+Wo6583dtxfOo62/Xi7V1A+3wqqqhC9sJEkgH/vwEa/VgCplEyjziGYv6e6p6m/Vu8mumzMHOtip9AVxXsclRTwE1bLNiM4Wd5b8kaQ9Zf0z3yIBhtDNgVJumjC0rXTN/8RkSoQS53Lox/+vhN4U1mzB3sD53gw5RY7/bA9UvO6YEM6z2bMBr7UbDcDupNfELLAMTULUbzz0RdJIULjPEFzi4N+l0Ruow8jReHi6XyxA9MFCnTdSQSQdqsTEZ1PlhRX56TVlP0G25F0mZhce6P0X/nuPyLZ8AUIbRr6peMjBNnjjyliH0V6LThAsj8xIvTz1dbzJjEE0T5NNjtDZCU0hJoVHCxkYqS/85rbsZ8Enm01kO9x7Y4miIkwyi+SIULzWPvS/T+aL9Onx/T+tQLx4j6bsnj7wtZLZRfC//UtyWe5F0UuabHYewxrMRJeVlulPwf3Wvo4OkFWRFT1YboAvGkDqqd6izVBerk/kjtilTfRZFx1T1WpSr96oqUgXSPhRBcGF27BRDxBITHIarM8/BBQn5ejGegjLSXmwuh6qqumNzsK9VyLqiAqr7JHR23jD629vSIRCPXCqJokX7/nHBEj0zkBORgDB3sOXwOWoI6LNKHYkjZALr/IQ8oRQN3U9J4TLdOPCTYc9PyBOmNwFnUz4nCwGjcgzQPtxn8nLvPbDF8Nr8xDzDg7qKowE/wUlc/FmQunK5XIabdbVng1S3SUSrDXMH65L2/2p41xD9PHf0A10lmKrxWgnrUdCmPLsFns11lw3Zeq9TPhisb3RImjr7smRk8JrplyMhNNL0AAa73CgtKjak7gCfU1BSXubIsP7iwFgk8828i/SiOeCLAmlt6jSNpntDttlhkIF1svOD3mRpUBr58cXt1Z4N0tG3rP62tmglrso8G1dmnKlH2N0jg9jUtA/L007HDwoWo7SoGLESA7u9tRKrPBtwf/U2/X7/Y+0uA7OQgdUnnpJoi/Ep2KePyDXIKFh9j6UP7brL93cfFZIG3iMD6liUz+5dmT4d4KvbiaKHtzsOGQg3K9Jm4keFS/TnjscPCxYbnEV+CCDrMRLJLF3m51C4iSBgVCYRlLIo6qBf49lo8LZOI3pD1Ad7xWYRtsIaz0b8p+F9o3cjoR4/UP2yydspLSrWPWdRKsJOI4nhIW6Rk3VA0zoBX3gEfI1b/+IGHzEMjo6YOsYLolKwXlmJVdOX46/caAHAZzhYDv/Jw28ZegpYgT0/KllfVK1GyPJ4v+sIOkd8kQwjb6RypA3WZEjVDWTeJQWLVNnC9quql0zbNArqXqHuYF3tuqxxr1BGKC8yCeuUlQbDINJgU6LT9FpW18iA4Z6600Iqh4Gm0tZVbDJFHrQ+IXoGaBoKgOOUbJYmz8LOiXWXi67tjtYDJocOAL6bv0gnQ8xPyMPjh99ASXmZLgI5QIZx8djX3WCKMP939AM8J5G6OV8wbO7OaYsNkbGqqjqhBBir7amqKpRZOp4IGJVJxJ0Fl9luQ9MQVuE71Ybi6yd/Iz0wvPQDYK7VWIE+mOyh+JpEZRWQS9vbQSbfzzxsWQpIZJwbBjrRMNCp63oBQGpoNNYrK3F99jwc7GvVJzgCRm991fTlUFUVJeVljuTZRV33DHRR7Bju1ycvLk2ZoZM3ZL00tBgr8topzk/IN8y9l0Vuvzn4ivD1s+Ky9c51XkaotKgYN+WcD7fLZTAMjx7aJWxGzYtMMvT+lJSX6QwlEesN8DVgimpHopQcM55btbQZBaNd01SOk6ZcpuvVMdxv+K3P45h921o8OiV3Hjdr/tckGn6t/aBf6eD/Hv3AUFspKS/TU4Si5mnRWOidRJRWVVXDb0dJLKIajqhX61giYFQmEVZzP6hyLWuM42XXKfhwlbJ5PL1NugFwIuXCvG4ZpZgH86ZF8GcOOTA2nZCfOw8Y5SREUhQ/J+e2VlmhU7V/V/OqQaLihwWL8W2tQfGpI2+bPDjaXd842G14IG8hc0+oN8kMD1Ox/WrO+SgtKjbIwqzybMCRgQ4Me0f1An5kUKihcz7DQRRiRyc+J97IEGKRm0ggVCSv0z7cZzuMiuH7hGDy59rdBlIBQ0pYNO4i7Lq7NXJHsMutF/4p7qncjG/mLhSqRPOePTWesp4N6sm/6IBaPIPUa1hky/fa+Pp8fKmsxSlFpvQij7SwGEO6trSoWFc8YLguay5mRKfzHzWAV58+1NcmVN/e3X4Qh/vbTQYFAPKifPT697uOCFUsRL1axxIBozLJkHXnBruDTA2QfOqJgq8TNA/a94kwo2F3I08Eq22as4AxOXfAOOOljptvT+Uk+JzzrrYqfdrh9dnzEORyG3oCGL6ddzEO9rXildZKlJSX6cbr7Lhs3YAyheD44AiDlPjdygpkkX4elovuHhkwPLjfn3aZrkU2j+vPeLhmh8E75PteZLL9tP/AjmHFmvAWcjM9aAqNEQ54eZ032muEqTIZ4jkpkw2NHwoHd0UEhRjqeY8c2ok/1+4WspkAXzr0pRYP1isrTc2tN+940vA3jSyreo0sOsBnuNn8GCepYirGyBbdnxCj+FF3g57aAnxRkmg0M2ssLi0qxrfyLjaka0vKy0xSM0p0Gq7NOhelRcX4imRSLB2o94eaHSbtONrP8odDO4WGVolKQ8/IoB4pU1xFCDjHCwGjMsm4QeCpMYS5gw0PDP9AuOFCaVExcjlJltq+Nl0c0QmuzTrXoOlUUl4mbIb6Spb5RmcPjWxGO2DfzfyIJqMSonXmswFH7HXAOCKAZ7Ed7m/XH/KZMRnIi0zCthYPHj5klAMBfIvVP+rfwVYuTfVOZ50px92h1TuYwWG9Hiz6Ku9pxBvtNYYi7VplhWGhpQQEXvXZSbMqA9+UaAXWbLmYRK88meCc+DFCwH3a8f+5drcuvJkeFovSomJD/44dWFqlfqATJeVlel1sxDuKV7QCPMXBvlbL8c4726qwyrPBJO8OaGk0bcEPdQfjIi3F9Vjda/CqqkkZgErVyxhxVmB1tbahXkcKvp9Km2XSDhM5hUu4TnZVVXG3ZyMeJylrJu7JP2Os2bKIRFayoVs01ZgflSzNWMyOyxK+fiwRMCqTDBlH/Iim7RPqDpY2vK0r8i1KX+VqGnwenN2UrADL8Le6scI0L9Pyi6oXTXPDC6NTTb0qLEcdGxKuS0zwKGvc60inifVxLCGLIUv5sXOaEh5v+M26hvt1bS/AJyu/xrNRT01MBpjB2dS4D15V1cUS3+io0RfhKE2ShK8FtA2Pdf5/wEnl2ElsAMY05ojqdaQGzRonq0mNg5d56R8d0qMkVkRnNZHitFm6pE14UIghxSUqLDNR0dbhXoOEy2rPBpSUl+HuiudNRnwycLdG4X6zvQaXEVmb1Z4N2NYqv/6/ldSSZGCGtX24z1ArAYDz4nNNKeLCqBShsgIfGawqXG4QY9nffRSrPBtMKSl2v8eGhGNRsrmmYjeHZmXaTEPzK60fUnzuBEQpQMCoHBOwQUwUDx/aoXs2QRp1leI8rjBoBXZTnhWXbTAent5GlJSX4Z0OYzqFddfv624wLCLvdR5Gw2CnYVTqKs8GA1PL5XIJdYR+dmCLkGhA+3EY/dHlcumF4nsqXzAoCdDJlr0jg1KKqx2uy5prqBudxqUAo4PCTDnvXe3VWO3ZYEo/XZVxFn4smZQoivjowssv0jSyuXPaZQYjdU/FC3q6ysnAtsdIrYifs/NR91FEBoUaaOqA7/flC9J8ios/ZkqDtqqvXZI0HasKlxt+d175APBJBpUWFQsnYormt/yvca/JQLPBabSviBnR1mGjVLwdwtzBWF2+wZAWXJoyw8f2S59l6JsCfLp8qqpi0DuC+oEOVPT4app08S8tKkZYULAhBUZZZHdxEWLHcD9KyssMztJ6ZSVyOHmltQK18g2NHwrZlLyzeuYJiFIAwL+qawCOINMSWl+xCeuUlXC7XCbpjDc6avBGRw3uKlxmmtfw9ZwLDNFKSXkZflywBFHBYcKC4r+Pvod/Hx1TQGVquzxkVF0ndRPA1+R3SdJ0g1KzLAy/PX+Rvl/GTmMzz/tGhxxNJlyWchp2tFcZoqTMsDjcmjeWd97ctF84p53pmTFU9TYbGiYZciMSLYeZ1XJ1obXKCgS53CgpXKYXWEvKy1BaVAxVVQ3UapY+WTP9cqyteN4ghT4/Mc9yIJsoxdM3OoSMsFg0DHbhw+56nBOfY+rkn2Ix7ZCipLwMa5UVcMMlpbryyIlMQFiQcQm5Y9oig9wN4KulNQ92IyUsBqVFxXitrVoX7NzdfhBzU6aiOHGWo0iPGk0qBvpC00fSMQgHyW8S7HKbvoc9k8PeUfzn6PumCNSJErhsmzNip+CqjLMM0bhsW5fLha9PvRCDoyM6s3FYold27wHj83LntMUGJXGRWsbxgsufORmnGHIBHGxt7YHXO/m/QcNAp4GhRLFeWenoAQLG6MSyGzHY5fZ7bslkI8QVhDWKzwtlx/nDgsWmjvP7Dmw1qB2vVVYIKcNfy7nActgVw08KlyIyKBSqquLp+j0mhllUUCh6R4cQFxyBHxC6t6qq+M3B7WiyEMlMD4vFN3MXGhaD9zoPGwzxemWl4f2B0WEhc4eBRqf89SwtKsafa3dLZ8pEB4XpabCU0GjdI74suUiXu58Vk4m93cZ02kVJhYZaDP/9qwqXG6jZItDzFDkAqwqXG4xLz8ig1Ln4dt7FSA2LwajqNV37RcmKrfoC4OsZSdT0xI4MdOjzQkTMRhFbiiI2OPy4KnBT5Ecm48ac+fq1YMe/s7VKKiskul635V6E9PBYwz3lhOU5XrjdLiQlRQNAHoAa0/vH7Js/4eDF5ShEN7lInkGEaZHJxlnUfhiUlNBoaS/BmbFjobJMAViGYXXUNMtcJGFCI4DCqBShQQGspydS7O8+ipLyMqzybDAYlG9MXYDSomK9h+dGInHz17o3sMqzQTcoQS63sG50dLALqzwbdJHKB6tfNhiU/MhkU/0sPCjEoPlmhdtyx2T7b9ZozQsS5SqyzKD8qGCJQfKfFnWZQbkxe74+GO6V1kphhzVDWFCwqbgMGHtB6Hky+XuaEl1fuQlrPBt1NQfK8uO13B46uB0l5WXCa+/EoAC+npGS8jL0jAwaIrEtRFCUwc55c2JQeEbngsRptot2dkSCtEeJgW9y7NMG6lGDwuvLDalmckB6eCyqCUtOlH4/njgpjYqiKGGKovxcUZR6RVH6FUV5XVGUE/tLCsD3XnzfYsGRqdj+vuZVQ4NXSmi0SZ6B4uy4bNPoWIbmoR7DLBE6VIpKUNyQPV8ocWL3ILF0Ep8XBnwPDFV6rZyAlhgD7YZnuCrzbBNjJiEkEn+o2YGS8jLDnJsfFyzBWmWFYdFcp6zEj8l129F2QEgX5fscGGJDIrCIFJnPIsaaSujQtAZr/BTVqPg5OtHBYYa6DN89fVlyEfKjklEUk647Cn+q3S2XnS8vw5Zm82JsJ62zIKkA65WVegqKRR4l5WWGaGmdpIg8Gbj3wBaUlJfpRvnVtgOGZ+UtbiqmP6AFdJpSXKesRJg7WJg5oAY0IyzWVtn774ffNOxHlAKOCQ43OD28fMz8hDxTd70s/X68cFKmvxRFeQrAZwE8AOAAgOsBzAFwkcfjcTrsOhfHMP3FQG8aF6zHoH456zxMj07FoHdEyugYD0qLitE7MmhJ93QKmqK4LfciIdX56sxzMCvW13nfNzqERw/tNM3d9gd35C/Cf49+YKoXjBffn3aZtFj9pay5uvf/08rNBt2z5NAoBLuCcHSwCzOi03CtwPA2D/bgwYPi4vbNUy/U59yI0l/rKzaZaKo0VRobHK53vQ95R0wLthsunUHInxfgq+UwKi3//Tdkz8e0qGSU9zQaZNTZsVmB1gAmgrzIJMxPyHNE8ZWBpTo7h/ulg7QA3+iEcHewNEXtD2hNRtTRDvgiDmYQ1isrsbOtSu/epwhxBWH19OX6NbdLf/OQ3ZeTCbv0l19GRVGUCgB/BvC4x+Ox17g4BlAUZS6ANwDc4fF4HtBeCwfwIYB6j8ez0OrzBLk4DkbFLqfL8INplyFOW+j2dNQKvXAKVrNgN9v5CfmYHTfFMI/6k4AfFSxBdHAYBkaHsbW5XDpBEfCNas2KEBeuH655FUcGOjE1IhFLU2foc2RkUKJScR3X0NY1PID7qnwLR1JIlImVtDilCBclFZoK2YBv8RAtHNSofH/apQYvVGSYRKDb3VW4FPcQjzjCHYK7phuZSSPeUVMfzerpyw2KEV5VxYbGvQZFYhlmRKdhv0ZMOTsuWydo9I8O4/7ql9BnoSxxMuOCxHws18Qh24Z6dfryemUlHqt73ZGTVFpUjI+6GxwbWmbgjiUmu6YyDOBnAGoVRXlOUZSViqIc7xTa57Tj+BN7wePxDMBn7C5UFMWZ4uFxgsvlwhe5FMbpAlFG6lW962CGxc8PbDXIsCeGRmJKeDxKi4r1eQunGr4+9QI8suAalBYV6/nqX1W9iN6RQfyl7jVLgwIAfzi0Q+9P4cGa7Q71txkMyh35i1BaVIzvklQh4JvmR9E+3KcbFMDHguIXeaYXxQwK1XeT1T3onJOXW4wpKdEYYBFo+uQeLsUiUq8OdgeZjn1dxSY8cmgnGrRGyNWeDSaD8rmMs4SGbT9hOr7TWaenASOCQvCTwmV4ZME1+LZksN3HDaFkoJgddrVVY6/Wh0RrN6s8G4QGhW96BoAHqrcZDEpUUKjlMRyvmSlW8Dv9pSjKPAA3Afg8gGgARwH8H4C/eDwe8Xi0SYSiKFsBpHk8njO41y8F8CKAyz0ej5NYPBfHIVJhcBq+ylJKdvhi5jl4s+OQLklyqmJ6XCqWJMzAthaPI0HIdcpKeKHi8brXTcyqKeHxuGXqhegeGTQYBMA3enWxoDuaRp1sm8P97YaGTbqw8lHJTTnz9TEGpUXFKDu619IYUqYXv2+nkQoAbG76CDsE0wtXpJ6O+QJVXABoHuw2TB4U4ZapFxqkbuhxlRYVo2Wox7LX5VRARlgslqTOwCstB1DTLydF8Lhz2mWIDA7T65yMhi7KbORHJuuG6AuZ55jkeACfcOTx0Pmyi1T87lPxeDyvA3hdUZTvALgaPgPzYwA/UhTlFfgiiGc9Ho99y/X4kAHgiOB1Rv8Zn4zuxwS8QWG0WfqgjqpePFb7uuEGls2gYJ9hcGrcjjeywuOxLPU0vfB8VmwW3u0yq/FWdDahotOsyxQdFIabcy/UZ2ew83S7XHDDpUu2UwrqkYEO4QPMU4UZXC4XiqLT9Ka3V1oP4JVWY08Mv7AnhRqbAZlBYR3jK9JmWhoVniDAsOf/2fvu8Diqs/szu+q9N8uSLEtaueFKcTc27hYhEELihCSUHxBCCAkJfPmQwYBCev9CAkkgIQVIB5liim0wpjiUYAxoJBfZcpFVbcnq0s7vj507+86de2dm15JsE53n4cGWV9KWmfu2855z3KpEva2l1rATpnjo4GvSVotI5Zohk5Pt55EcEWvLcgQC2m/3+tZhR9s+V+6bDOvHzTEy9G+WrMBvD75mqtrOFDBaMACUxmcZ193EuAzbBO/K/PNdb10sAAAgAElEQVSQFBnrSpkCMJMFZIrfoy0cKUPYy4+qqnYDeBjAwz6frwzAXQA+DWAxgJ/7fL4/APiRqqruddjdIRaA6JPoJf/uGnrEHVEc7jou/PrVvrl4SOcVXFI0Hf+qN89RChJSUZij03t1VYzMzADHf+iQeypxR3QvJiRmYOsRd5TN04FDvcdNTCZRQLHDyaE+vNFVj8+UnBdoAZD3iyJpKBY5x5LQ2CNn5qRmxJt80ynS2xMAgYzGlNRc3DxV3I66P/1TuHHHY6avXT5pdvAvIXwsRzwnMD09H/+sCVwrN0xaiF99GAiSL7SouNQ3E17i6LnxradwtDvAFry2fD7OzSzEF7c/Cr9OGdnWWodPTz4XPNTjx/Cj98waXTdPXYKMmATc+WYgEJ8Y7DGowV8/5yKUJmeZljQ3te3G601WlWOGxMgYdA6IKb205TMcBJORwr7uFmGi5tQx+INOhsiPD874dnTtwzMN5tbspyfOwaN7zfMUWV+Fv9ZPF06J/eXz+bwALkagWlmFAMFpKwKH/kr9/+tVVX3i1J+q8Tt3AzisqupK7uuTAbwP4FpVVX/r4kcVYZTaX7LqgA4w7VCRPc3QpJLhHt86nBzsxUMNr4XEtMqOTsTyjHI8duSt075EaYerxl+ACXEZyM5KQnNzIGNVTx4zbk4Zqsor0Ds0gEcOvWHZhgeA81IKLbOB81IKhbMGIOCvIqLbpkbG4Vab/QB6DdxSfKFJvfnbdZuNiuGy3Bn4+9H/WL6fLjmen1JkVDdV5RW4q2YThshRw6qln+zbYlwL1xTMNcnK89ckG/DKmIf8MqvToqcIl+bOwD/013ZRRjmWZATmWMlpsfjlrpddtTNPNxIjog31bCewRU8AePzwW3iv8wiyY5MwO3G80ACPx+qsyZifNlHKKludNdmkrlxZusrkDDlSGPb2FwD4fL5yBALJlQCyADQB+AGAX7O5is/nKwHwFwDfAzBsQQWBNpdoGM++5qzQN4qQOR4CsASUyQk5WJ9/ruWGlwWUb5asMLI4j6IgKTIWtxQvlUqQiHCsrxN/FDjdnWlgr2f90LmYHBHQ9fKRxT8ZRAE92hOBrxYvNYy0Ls45B/XdrUaltPP4ATT2deA64rXCwEt4XJE3G48feQvtA934Zf3L+GKRM/lwc9OH+Ex+sDr4ZN5sQ9dLZp2wOL3ECCosoLAFyuL4DNPeT3Xje2gf6DYCyhcLF2Icx3pLjYwzSfzcqW5CVlSiqcW0MnOSQXvd3rrHsKsFAoueLHg91/QhXhZI4wCBZckbixYarLUEbxQeObQTL7TUYG93s1RB4EyF24ACBBY9C2PTcHXBXGNQnxIVi3lpxZibOgG/qH/ZdpelRLf6jvR4sdG31rRjBsAUUACgqu7ZEd2kd4uQgorP57sGwNUAmL77CwAeBPCEqqomgr2qqnt8Pt/PQFhaw4T/APiKz+dLUFWVNpwZv9OeizvKsOPK81ifb25DyGimQECqgvpEaJqGvx/9j2mJkeGagnnGlvxwzFQWp5diWmKeLaHgxqKFyItJOaXflxQRg9VZk/E4UYP9855gAPxk3ixMTczFboEBmAhfK16GtCjxYlhRXDruLFtt7H4c7GlHZU21QVkW7fmw2UtKZCweOPAKDveewBONuwyVXwbeP/7Dk43QNM2Y2/ydbOqLvN4zouKFMx5mQ1wcZw4qdEZzTcE8S0ABAjstAEzvHw0o7LX1DA3g5bY9eLV9vymoAAE1h3vUp41WmgjdQ/34gcTPhQ8oNxYtwiMNb0gXgd3g03mzMUXfkfrzoX9bqp+pibm4Im+2a5kkN1iVOdnwVuGv9wM9bSb1gK7BPtf3RJZezXYO9lqWHmXY1lJnVICnC6HSgX+NQMnzHQATVVVdqarq3/mAQvABgD+cyhMU4G8AIgFcy77g8/miAVwFYIeqqmdUpdLJaV2FAruLj0lVMGxQNwkDChCgKv6+4XXLz0uPjMeGstUoJd4UDLKMOTc6CS+11tkGlIVpE5Gny2dQ06obiSKxG3QM9poCCo+/HHnbNqAwPxeGH+17EW/a7FWInDvZ1raor88O+vGxqfiMLovy7+MH8AF5Tm+faLDI1AMwte0o3fSoQPxzpe4wSK0OrtWJB4BZViebDNfXZE2RSu6wQHBA0BIEAoQCTdMwN82qIqxpGu6r24yN6lOmgPINohhRVV6B6wsXCOV6ZLi//uWwA8ptE5ejqrzCCCgADH0wit2dR00BJS86WZjd31G6Cl8SVJ3JETEWpYxnmz8I0Kxrgj+3qrzC9H4wHOLmqyX6vRfnjbL83A3qJrzevt8xoNBr4YUWqw3zaCPUoHIpgPGqqt6hqmq904NVVd2pqupVYT0z+c98A8BfAXxPl2q5DsAWAIUAbh/O33WqaCN2uWuypgh9uqnYH7MZ5nE9acPwWkoiXJw9DckRQb7CBnWTRRalqrwCK7Mm4d7aZ4SSKTK20FEH6QkAWEnUYqmS7P3124U+MMMJekB8sSigAUYPh3817grIrggOegqRr3d2dKJ0B2hSYo6x6/Lnw2+ia7APLf0njRkCw6266yGTi+ngBtX3C5ZXC+PS9N8f1JKiUjQ55M80gD3d9L70gGEDdVnSU9/Tig3qJjzN7fQ82bgLG9RNJqWBu8rWoKq8wuT/DgTo0CLpmeHG4vRSJEVagxeTBbquUK7Ye6TvhDB5+1bds8Jr/cRgr0ks83PE6I4G2MqaascuxWW5M4yKMSUiFgkR0ZYAx6ymoxT5bgov1cMv1o42Qgoqqqr+S1XV0G3Whh+fA/BT/f8/Q6ByWaOqqjslwlECdbibl1YslKnnLWJpFlWekI2q8gpD2gMIZNNr9C1dGZ489h5ODFp9Pygqa6pPSQ5DBtGhS7/WPdSPmbrzokg9l8c5SeMswXhu6gTDv4O396UHBBuG58YEslGqo/XT/dvwrdpnpYeuSGrlWF+n7XLZ8sxyYzHt23ueM/YzsqKC1QP9XF5p3WvZjxGBOW1SW1+avUZIdMiAwPUkeo20ImDvJ/P5oaKYVMersqbaRGqoLF2FqvIKgyn3kD73yotODsj/c4P8O0pXSo3fRLhUknzwFYDo9dGgVxCbFpIrJwNNCGI9kabPkeGRQ29YvmaHGelBLbi/H/2PkVwkR9qTVvsFEvh8ZcPQNtBt0pkbbZyVgpKqqvaqqvoNVVVzVVWNUVX1PFVVw3N3GiHQDenbS5ajZ2hA6BVu19v9rEDDp7KmWsgcYb7ZI4GK7Gm4x7fO8cb8dN5s4aFLv0ZF8xanlzr+zF0dhzGk+bGhNFihvda+X682TmJd9lRUlVdgJmdOBQD8M5mcmIuq8gpDLbnHP4AN6ibs7jhisR5muLNstel9deqH3ykworq5eIkRGD/obDQsp+nehsjAimGbwPVSZGXLcK9vnanqoIFF0zTTayiNz8RablaSFBmLqvIKk+AoRX5MCu72rbUwjVhVQv17EiOicbdvLarKKxDrjcJzTR9arvmbpiyGCHyVd23BPFSVV1iUhUUkATbHYhpvihKw6pbZ8zqhxz8wLHsyK/Mno6q8AlfkzTZ9/cOTjRjS/PgNJw90pUTH68r885AQES0VqR1JIU8nnJVB5WwALUnjvdEW4y07D3iGyppqvNYm5vl/Tnc6ZPDrHiE8ZiTlo7J0ldRaNDkiFpMTcrA2a4qFEuuLD1RK56cWobaryXIY8G52tJ/Ng0ru08zyzRPu1pjurXsG0xLzTAfQT/dvxYP6JruopbZB3SRUql2W4TMdLqLtZCAQqKM8EciOTsQGF21HwKo0zCR6purSPLs7j5j81RkibKQ3hqDhlVbrNjw7dHuJdtYNhQugKAq8igd3kUDFHD35z1AkGcTgl2S7h3qPG4rElTXV2N1xRCgtdLdvLW4vWQGv4jGcDkUB4P/et1eQYMGkSJ8PPaB/5gvTJhqP2cZJ2DCW1peKzAHrxqJF0nvhmoK5JhfLL08wf29hbBrGxaSgKFY8p6LX4D2+dULSRUpUIMhNS8qztLruUp8yyf/c61sHX0K2MPFizMeUyFjhrHJI89syT0cSY0FlBPB6uzkQiJwURT1gwLqR/VTTblNmmRmVgKryCpRxdFr6OxalB1tq42KSEeWJMHmBMP/1qvIKfKPkIqzPPxdz04otB/CV48/DO7qfO1WuXZ01WW+XuNcZoi0a2lp6QncZvCjD3AoTKQm/13nEcgAxltZ39zwvHEo/oc9Q6H8/3rdFah9AM/zmviC5MNoTYWnt8W2Xgz1tlqHqo4ffhF/TDNtnlmVPJEQINuS3A6tq1o+bg6/olROzQaZtJiqZEunxmgKL6DqUyaT//cg7FomWi7OnmeZ7DI8deUu4W/PAgVfw6OE3UVlTjR+EaRP9lQkXGsEECOwJMazMmmwoJTC6NWCu4mIFexszkvOF7/m7HF08OzrJZINwoKcNV42/ANcWBgfj9Jqg74FHUXBbyXLLDPSb/37C1RZ9amScQQQRMf8ooywvJkXYEn/ooFvB9uHFWFAZAbDhGg+a+bhhaMjopXbfu7Fsjcl0KTM60XKY3FZirZL2dDUbPuAMlTXVlsPi8+PPx3w9Qwy1xGYHkmg5c1F6iandcrT3BL4+8SJUlVe4skbtHOwNaechNTIO1+gZMBVmvEt9Cok6VfvXB18xfY9HUUyHCG0rbTq22yRESZODu2uftlQni4kJ1qTEHNfPe3Jirkk+xakdF+nxWoIhnS2lcZ7yrD3GFA188VkYp1d1uzuPGn7zVeUVWC2x72U40nvC4sYpg1fx4Orxc3FpTjDbv2r8BRbpEbZ4yq4lmkgwwc4/HwrQzu1mTZMSc0xMOQB48/hBVNZUYytpN1LaPhAI4HRe49HbapfkTDc9jvnXRHkiLInit3VGIf/ZnZtSaPy5faDb2Il6SnCeDGl+E7GHN/wCEJIO2XBiLKgMM3bqQ1UeXypahDeJXpNslkIvWJEr3Ycnj2GDuslyUV6YXoaq8gpLG+V3giVIfvDdPdQvfByPc1MKUaovZIUzCKSEA6qwDARuzvSoeIPK/KfD/zZumMK4NOHM4a6yNbh6/FzL1ymWEcMsAJieNA5V5RW4deIy40Bi+lQMrHUikmTnZ0bss2DV6bTEYFuDZalDmh8nOJYXNVU6qLc8RNRuGdwqFAPWCoUSNGg12OcfNF2X1xcuwJXjzzdaLZQRWFlTbVm+AwItmxuLFiIlwjp4zo1OMikzM/xk7uW427cWqZFx+EdjIIlZnlluCcR/aAhWy/RaYi6U7BpmEil864uHbAb5Yotq3F8fCIKiyExrTkoBKkk7+Dt7njMUit3gbt9ay37TX468HWiBc50PqnrutG9DHSFHC2NBZZjxpGD7fVbyeOTGJAtFADeWrTHZ7PIXLBtEO2Fray1+uPdF7OtqEVYy9Gf0DA3gheYa48YR3SRAQK6EITs60XTR/2ivlbYYSg/3B3tfwIkBK0ONWv/SGyZCkHHfXfs01K5jFntbinNTClFVXoG5Okvs3Y7DQuo2G+Smcu2gUF7TtQXzcMW44AA2yhNhGH5RJuBejsLNKpyJcfZB5fyUIuPPVObFDvQg/t/SlSabYABo1IfqHQM9JnmWu8rWGAc3SySAQAZOk5nL82YZfy6MTYOiKPjH0XdxXGe5xeiZelV5BW4oWmhikzFUH9gFTdPwQ/09yoxKMFVyQKCCUrsCChS8Xe5C0u4dJNpjbgQWZQwqhlAYkjxx4fEjb0kJIDz4RG+tDcMzPTLeFMDsfj5NXkYLY0FlGHFU0qdn1EieFniPbx0iPF58ktyYFDdPWGKhzPL2shTtA914qOE1YfZCL7xv1T3raBe7JL3UoI/GeSMtWR2jxtKL+3CvWDiTgvaoXxFIsQNmEgO1wfUoCn4x/wrTY3e07UNlTTUUKMKBJdspWJs9FTeQecAGdZPpAGK4deIyzCJMMtEcQoRlGT5T759BxOBjkjOrMs3to0MO79/qbHsqOQDTrsh7HYeNg/iGwgWI80bhosxyEyX3ft1m+Xtk5nGvb51JUJOSGuiOxobS1abK45qCeaisqTakRyqyp6FSr9a2ttSa5gBrs6YaB+eLR1TTNfsVQRVG27B2drm8uZgTev1ig7CK7GmuKO/8vM4JN09dIqRV01kRYE8xjvR4EeONlAaWGI85uI32MuRYUBlGiKxJZctyd/vWmlopoqXGLIH0OMucsqMTTdUHHc6Hg5u4VgELOqmRcfhfjuX1ats+4880O3NjLkZ71Kys57NnSmL4DrfJHuHxmgITw7PNH+D++u3COdR9+iA7PzbV9D5vrH0aPYIW16W5MzApITjneLE5ICP87olDloODUZlfbFGFlRcAS1uDgUl7AIHguVuQxVPQGcEzEkHCFl0qv3dowFAjWJE5yTTAtzuwbi9ZbhkM81l0lBIw8Yr2RpgqMBqAbym+EOenFmFnez0qa6rxYotq+re5aRNwbmqR5fffJaFWM7UIWdvvdm5OWO5CFw4IzmEY2JJh9bH3DFLMcOppTUkNBGFWGTNsa63Dz/ZtM/5uVyHF6fdcjDcSd5SutPx7JlfFvuWSYTlcGAsqwwRRNvCxnHOMwPGL/UHW0k1Fi0036qB/SDj0PtwjzlxnJxfgyxOWmGTGV2ROEsrA8DdFSXym5WvnpRQKZVemJ40TKu+yPZnPcgyaGheKyyLw1RhgdivkmVrxEdEmFtJ8MqQUzaG6hwYMymmUJ4JzQnxWGFio4OPW1lpU1lTjr4RBx3BZ7gxjKe77e18QXgd0AMvAGFxMxuRRF20W+nnv0AP7jZyUCNuop4ywUBKO7+55HpU11XiicZfR+qPztvlpxbjTFzj4hzS/ZTYGBFpKf2jYicqaaks7mCo084P0y3JnCC0HPuwM6nfJ2n68HAw/OJeBJ3f0a0MGU+/Ph9+0MCKvKZiLz5MtegAoi8+y3FNBWvISV88DgGUP5jzBdQOYRS1jvVGWe7Sht91QbgACKhKjibGgMkz4s0Dplx0mr7TuNUk+UNbJkOaXluy/PLAd1Y3vmSqDyQk5+Hhu4Iah29mappnaCzJcqt9sjzQEN4H5YTYQoLleLmjL0RlDuc5aYje06EAX4RIuc0/gGDZAIJNj8437BRXg+NhULNTVCHa07cNG31rb/vgLLTX63s8+dAz2WgJL91A/WvpPYnPTh7atjMtyZ1ishW8uXkJ+lng+xffIGYOLLUIe4OyJRWAsIHrI5sUkmw6u3R1H8Reil+ZmkzwpIgb3+NahgFQz/z5+AHfqJASaza8kLTvZ9fadPc+hdcDK8LuhcIHtLEi0wAoESBtAgNJshyzys0XXlAhsSXl2coHxtaa+TmPO+UTjLvyu4XXMSw0kLr89+BoGuDZ2bVeTpeJhyI5ONB3w12//s/BxN3BU7SvyZpmqLRqwfrZ/G1oJgzI1Ms7SZk2VCKeOBsaCyjBB5ovS1t9tcbxj7QU+EIgOgDeO15s26OlQn2aJtCdtd5AkRcZiUPMb8hBfK14mFEuU0Vyf16XQKcaFuKU8R5KB8aAZWHu/VeJmZVZwOL9RfUqoncTjqab38f29L1jmTvfVbcZP9m3FdomEO4MvIVsoVMjaEL3+ARwSVJivS1iBohanDGxRlD9kaZKidh0zqKg3T1gi3HE4zskF3VayHB5FwXWFCwwhSBnuFDAPRYjzRuIL4y8w/n5pzgyL7fBewkziD1UGOvc6T9Auo7iaCCu6BfOxyY9NMdqqR/s68IncmQb5Y09XM15tDyZ2NLFaoNPrf9fwupTUwR/wPxIoa1A7agB4/MjbeETfDasQBNMf79tiWrQtT8g2sQeHNL9p4VhUUY4UxoLKMEB2MWmaZvScc4kYIAM92O4RWNjOIdkTA1WxFfXw7yhdhX7BAJqC+jLQnrgbMJ9z2gLgLXNHAj+UPE86s+KXTu/2rXWlXMBjflqxoWvF4766zcIWV6w3yshmf3XALAr5SuteYeYeDui1Rg9ZX7x5hpAZlSANWFSKXvQax8emmvY/ZkkqCIrEiBh8Incm7vGtC+iHlaww2mYl8ZmYlWL9GQ8TZhJvlMbAiyXage7EhEp5z4lOMs37fr5/Gxaml1hmjYB5driK7OrQmZIda7BtoBvVje+hhlScdjhfEky/u+d5U0v08ySI36U+ZWovhrt8Gg7Ggsow4PeCHY8ZSfmmCuBLnOTDr4ga7V1la4zZC20NiSRMXmrdg6raZ+DXNIto5PpxcxDrjbQoDO8mfPkWied5SkQscgSBT4bShCDNNMHrrtUQDu6WyLsweBTF2I7edGy36WZu6utEUmSMSUmW4erxQUkOvrJbnTXFICCk68uBHqIkJtsNWEPYWU/qfewPOxuNSvXjLvv8dvj1QbFmaio3fJd521P9uckJ4mr0uaYPjVnDtQXzTMKOoiB0r28dbi9ZjhnJ+cZ1TBMXWrEw8LIqb0tIHowRxw/iRaAMqs2CHRo7pOmtVmbVwN6/nJgkC9lG1at8BhEZR0ba+PKUJQACHQhqjkeDVyVHjOHBL9bK2F2vtu0zJX+jxQIbCyrDAJEf9ZTEXGORkWdofNB51LhZbim+0DScFOlC8Tdyr38Qd6qbsIVs/noVDybrOk7UmGhI85u0rZhyLo+vl1xkUEF5eRQGmVx8OBamHovcoxiU0CAbZtNWHc0W2ZyiLCHLMix/qOE1vKQz3BRFMbHC6EziJj0Z8EMTMvR4WjI7HHYeP4B3TjQYraqZSfmYnRKsPF8ilO4L08uEr4uCLYU26P4nvNdHokD2h29Rvd9xxKSUTdlnDHu6mg19rivyZllo0mxbnUFUYf/9SJDQIGvFMlkVO8VtSo9248vSQ+jBr3JVqxNidYsGatXAZP/Z1ryIeVZZU42/HHnbwvCjQ3fauv35+9uEvz8+Ivh7+ev8Dw1WJeR7OWUHEZ5uet90nvxNQDQZCYwFlRECO0zmpBQYFywDowsuzyy3DC7juceyLCg/xureR8FkGyprqk3luWyY+jUyPOSHfLJ2B2M/8fIW4WzXX5LrPmv/lK7oaudhTjfuo3WzLdqzF9F6n9cXQDVNQ5QnAjcUBvZcdnUcNga4NOC/1LrHeC4MvEkYc2MEgrsVkxJycFmeWcTweZJVT7UR4mT4LOcKmsvNsaiSA6VcV9ZUw69pGNL8eJQTzhwfY55x9BBlhfNTijCNzO8Y6GdQWbrKojBwcrDPkHj5UtEi4UxnU2OQETZXwPxjePywWOhzJEBfxxf1fSc6RwECzDOR6sHuziOGhh3DHw7tNO5HWeuWIcrjxbOksmJJ6qf1a03tarJUcoqimCqaLQIla8CcZPHaZiOFsaByiqCigyLYURv5rWHA2p5i+whlpN1UVV4h3X+xA2130FkKz+mfLZjlAEFqL6/ySqVl3CIUSRJ66MoCWITHi8LYgJkVExRUOfIEfc/ozGCDugl7u5qRH5tiVDS/Ofiq0Upjg+uXWussAYA/ePhe+tzUCSZ6MgVrR1CxThlErpQUJ8jQNj4i2nTg3KluEiYX/IFPmWsVOfZMq2sL5gkrVLZXlB2daAl8DK/rdsc3FS0WBh0G1mbi1YLdIlyV3nEkgeMXmvlW6tXj59pqjPF4YOF6C/243z9kOfAzouIxJSnPqOR4GwAg0CFgtP4tLappGC+zUnA6r4YDY0HlFEEVd/kPUuQeyCBrC/DOf6+174emaZicYJYov4+T0qeS3QxX5M02PSdRpi96Hk6GQfxhEQ6zJFwToZ/v3ib9t2s59g/vne5RFEORYH93q+ngfVi3W6amaSzLozpTH3Qele7QbGuptWzg84KKdG7FvORF5m2hgCcoAIEDR5R48HYFDHdxVrgMPUP9ljbalMRcoXrAFrLgKNvPoFJFtKqzQ3YIsz4KO+toJ7BqnV9oVhTFmLMBQHF8BjbqfjF8m1p0T7rFLTptfV5aMWI98vZyeWKOQQKiw/gIj9fEFGUQmc8NN8aCyjCCF3Ok2kO0935d4XxhhvZbCdNlg7rJ1HLq9w+il/Sb4yLEF92khGzbpToZ5TRUNDlY84ogsla2A2tffXhc3gJTFMWoVmSYTPxDquqetThQ8q2KWr3aYRXnnw+/CUVRTMPrhw++hsqaarxADlU28OV3d+ii5qZju01eKOFCportURRL0sD7+gCBim5ID8As6PT5B7Ghplq4d/NpgVSQX9OMFsw1NtTefza+G/gZXBuRx0EXezv872eYo8+uZO+LG9iRKm6cEJxnbeeUvWmb9WB34DWEOiDnnS3vKLMf3PMkIAaZ/NNIYyyoDBMqsqeZsm9+qEsXHAsEB1/3UL9lu5f+DLqrwm/fJ0sWnSI8XgtThWFReklIOxJ2h5+TZpUI0Q7tHB6irXQR+GpFBCoF0j7QjcXppagqrzBJszA8ovfGXyU6ZZU11aZ2BCVqMKMnOvBlMi8ALHMKt37i/MHEKiDR/g6FjC0GBHWr/kDab9+qexaVNdW4t/YZiI5CWaVDs2SRrw1gvobsDN0A4DF9nlLisk1K28ZL0wPLvHylGgposrWXU/qlfvGbmz9ED2n/Up+cB/X3XmQFLMPqrMnCTgH9nUcE9xudkVKISCA9YbSrQ8FYUDkF0Ozo/NQifJtkdbQH3ibwD+HBlIKpdlWUJ8LYduflrymYm5xbZEUlmDxXACuLiYdIXZZHkUOVQEErlVAzObsZjpvKK9LjRZmuvEsptp/JP9fiZc/QJKHoMtw6cRmqyiswgRwqLMBtbQ0OUfn+e5fLG5xvXbIKlFVWIiLHkObHQZ0tdnvJ8pDbMDGeSIsWl0hGZVDzGxWZHR32+3rgSXbB5GI/T6abxoNWNjIDvFAxX987ephT+hXNothZwKtcN/d1CmWAZJglmWfSwMS3yAEgLSpOSO1flmlVy/jjIav6x3BiLKicAn7J9Vt7Jb7hP3LIRilVly99L8wocxwEJkuCSt+Q9fkkeKNxs4AauYMbOGa/buMAACAASURBVPNgmbFoiZPBzpqWBw0qoWRygPNgm7KfZBUW9f7mgz7zsqczMV98tqHxBQBejhItMlKic4eTDm5/GQ4LpOznU1IF3YwXtZxowEyMiDE5DvIe9DGeSBTFpmNV5mTcVbYGVeUVqCxbZWmfiqjg360L7mPZ0csZgUJ0/QHi5II/pGVgmmcxIVbAdljpYEJGcadu2MYHnJ/u3xZSi1PUnhThMQEzjn6eB7qDQZavHN1IAp0KxoLKKYDqecn6tyLbWv7mYbatM5PyhZn2RoFQJD04ZZXK9/Y+b/na/5SK9bGe56S3ebDWXHF8hvQxIgqqGzgduAxMEoNl3zLQzeinJWq+iqIYbDhZ0KczsZb+k7i5eIlx4w5BM6nBysQ0mR7VT2wSi/Xj5jgunrLM/aKMcvj0KotuxvOJh1/TLNUD821J1g20WOutND4TlWWrcG3hPCxIn2hUI0NEzoeBpxD7Nc3YDxHt8TDQuZus9cnaVeEs6TEtLF5Cxy0DTFSp09cqq455J1AR6YL/Xk3TcGeNeLcEgEnXiwfbldndecTy2mgbmrY9r3IwshtujAWVYcC4mBQhAwcIiiFSPj5VGaUHKr/LQCGyJGWglQr9eX1c5cRTgUWQPYbNiwq43QYKtyJ+PNxSki8SCF86QbapDZgHzvx7xcAWV1sHuqBpGtKj4g32D7+1ztsxA8B1Oh2ZVrF8AJicmIvUSHdSN9nRiZa9ogtSiizJCG3ZsOqBScV8sWihaX73ecHGO2DeipeBqknY0Z6ZSrfdjIRdY0f6xL5EdmjTXxtvj+x24H9CIobKZhn/Ovqu8N89imJSffihQNeLGmX9dPdWbFA3Cec9rNK3m7PRvbaqOvNslTd/Y7MgPhEAzIrXw42xoBImqJjbBRJtHnpYrs2eavy5jmR/jNefF23l9LvN2B7fF3SY4/1HKKa7qCScHpPnsIQZDtzSi3l2nR34NqIIiqIYh4ZI5A+AaXGVLX9+deJS4WOfE4ht0nYQy4ZFW+S8zIrdc1YUBTOT8o2vrSHXFgMLGrfp70M9CSIJEdEG0/CiDLER1aB/yDj2ZB4nQJCk4PR+s58mYo4xsN/HZP35RWA7sHuNfQ9rT37oUl9LJl/EArjd4q1X8aCqvMJEPZfhg3arPTEQUL+mi7W0VckHAFYR9vuHTKoDdVxVyc+CKHbqdPaRwFhQCRMPHwx+YLLW0ff2BNpPvF83M2OiQeMGgWuhTL8JgGmfwi3cDLGdHpMYZjViB96pbjhAGTT9kioEAG7X24h2A3M2W9lFFtRulbBtRHMsBrZ1LbKcDlXq5mIywOaVo2tPBg+XJP19YMKMyzJ8plbUkgzrAi4QECtkEA3nAWA/Yb3Z7TbR69yO9cfmNex9dsv4A2CIqLLnyuRJRBJKIoiUpQH7di+P6wsXYINNC5Dh6xOXYR2XCMxMHg9FUYw2KO1E8CZbtCKkTFDWjqfkhi5Ja5nXXhtOjAWVMEHZQKx3/RnOtGpQz8CZPSq7oer0MpXOYUQlKt2y5enGK4js+x0zV+G2iRcJe9puDJrsDl0eHq51E842PY8IT+iXYSjb0pubrBUEQ7Q3eIPS4SYFna2wdkpqVJxpV4WBVygGAlvXAPBOxyHsEFgo9/kHLYN/J9BW2/a2vWgj1OJHDgW0otj+Dc3WL8wow8/0GZ5MjkcjcxK7Q/K3emIlUoageMuFIyhg3V2a5kK+hoEFJHZdsK34RjL3FIGx5twMr0WdA/5r0Z4Ik+8L70T6wML1SImMMyUoFNR0jb0WlrRSvxlaPfLPYXJCjrHXJrK1AMw6acONsaAyjKDlL72RWfZ0XkqR6fFso/qLhdYqBTBvH8sWIwEg1huJpMhYYU87SnFmw/C6RXbQuF7wsyGqwYpAVZTd4lCv/bCe4g2HUp857NntdMzTZ2Js2A2ID2VRdUmz3WcE71dDTzv6QmTAUZoyILYwWJRWgkH/kKFDty57qpHoAPIFv5eJp4ybfSKnWde/9IVHp+qaT6wyo9zvUUXrlR7znOf16WRgFQ2v7C2CKEDxSuEATMKhVPgzilR8lGyyktD76XvAlK7ZvG9ZRnDnhFaPj3OabvER0RZH0NHEWFAZRtAeMLuR6bY29UWgmfa4WPOcgulinRySs6KorpXdrIGVznZ0X1YRTXVBCearGjYIdxK8tMN2QfbuBPWkeKmTgr73dljn4CgIBOTwRfi6wG55P3dA8cwiXkRxT1czOgbcuWbyEDEDGbqH+k1LtxekTsCD9UEzKFmrk2XGIsl6hlpy/blVZbgww1mNmUJUvcvAKgI266TVpR3cLFey+/olARGjVnAd0oqLMhEjFfF9upDrJtyot8J564sUjl4dZIJZ5zT0OfznxCHh7x0pjAWVMCAqg6/MP094c9HsjErKb2sVq4oCwAKbjI5d4HQLWnbz3VJ8oUFxFG2L8xC1c3h0Snq0VzjIbthBxryyQ5NDWwOAYTfsBPr+yXrQ9LPdRzas+RsdCLSFGHGisqaaU1NIxdrsqcZiHRDIksM18YrQh8Qi0NYHk2thzCqZPwn9LOwOXOZKGMpsTzabGQ4kcZbWbj1+ClwM1+fp0jq7yQIwm318aDPAB4DDZFbjdtGVkmHsSCyUCdYmUFZgjpGjJXnPMBZUwkCDoPXCNrR5yLI4ppN0hUCfp5hbVqLMlFuKrcwjmY4WvehE0jA8nJRwAbmnymh7YrsRsaTBwolCyVhgTJtKBNbuekjCqnGjtcQObZq1N/V1StlHbiFzuMyJTkJVeQUURTEFTJk/yUN6m5XSnu2W9y5yqAZHyxiKVSaMhOCWKehGg07EiGTznjqJNz3bgfqlYMZGIZNXYbiXk2TiwbTuRO3P80IgOgwnxoJKGNjZblU/FQWPzCjnEly0MMj/LFZ2R3siECtgCbEbg978l3FVh8x466BkOM2DDUIbyIJVuErDwKkfNoMh/u77OfUDHp8bH5A0ly0xAmKvcCB4QL9BPE2oQm1VeYWxwW9sflOqseY3zLfcQBSAOofM7TP2e28iYoOsurWj6h7Wl3XpdjZ/cNLWrVOLyo7BOJxgg3n2zIYzmImqUZECMMXlJMGYlmgmHFA/mTRJMsY8Xdh1PoNQyCn4FiUd5ovOpEmczcVIYCyohIF3O9z1KO04+aGAmW4xHTBeisKrXzxVROJhJjdElt38D9oMpymYnwud5TzZaKXGugXPtuFF+5wQqsqx0+EmknLnQds39NBiNzZ9Tfyh5lYYkUImUSLq48vsaynYxvX1EmIIfc6UIswkehg1/t8hSMq/qZNN3Dp9hgtedy4cdlMogchJPoZeKx+Qmcegf8jwk7HDOG5GuVawi8T/HgBYwVWOfGI7P83q9jncGAsqYYC/9GS7G6GoAMtAqw8WKHilWEVRTP7c4eBrgrYaBauo6OHMSABzUsQieHb4J7eh/Ojh0ETu3O7LhLLrEArqSQARMY14eZNQg2Ckx4vzObYgA6OX0wODyoPITNYYZNnxB4KBLxCcJUxPDmTLTIfMzefOhEhlmf1wbXbHcdVXZxjEBzcMsFDAqpUhcmJ8acfjYf0sUYeCgYZrvq1JDeJK4zMdrSGGA2NBZRhwgY0lKg+6Tc1XEyI82xykoLILi+pWAcBNOx7HNuJ5folLZVcKXjOJxxSbQf/F2aH/PjaYZoeNTIxThlyBAoEIVFPLrWSHHXGADXafdvDqoB70MshaGgAwK2M8fAniOR2riArjggcE1YtaJqD4ummvMbthWS+ezZSYxIjo9/BgbCxZpcYH33DBt3raBVRfGVhSsM3FZxYKnNQphqsV9Ukbkgydq54nkPMZCYwFlWHA1ET3S1qUTmrnz80OwzePHxT++3qJRS0gl8/m8VqbXE6fBz/4pBlmKNRPHnZmSHYoT3R3Q9Jsnu6Y2EG2mAYAS3R/iqMc+4yvDpxELwH7LfoLsiaYDgQKtnCaERn8d7olL5J+Z7tIbqjfMnXedC7xkA37RciTWAszd0Y388dQwGyA3VSIy3WpGn7B2C2O2ZjU3W1D+XbLTnTCOy6XS0slScpwYyyoDAOokCLfC+VBMwU7GflQKLq3TltmopWKDnnRYPuppkC2PdkF3ZgHL/sfLmhPOBQ+vVsNslBkZdjnYbeMOVEi23Gxg6e7CHaOmeUpOY5ZJW2JHBaoYVOwxT2R7hiPUA3U3IDfLGdgJIB5xBHzVDHgHzLUEdxQhn1hVgyMtryVOH7y8CoeU/JIFRzcaIW5gdtqr2UU/OmBszCo+AL4sc/ne9Xn8/X4fD7N5/MVnc7n9B7JbN0MTBnsMiO3y1uB39nrKFsiowIDoRMKjvV1GjI1nx0nr5hk4P1L2EZ2KHx6J48ZBl5WRjTkZmDb1fU2n4ss8w11ZgLYt+NCrf4GXG7kyw4y2ZCav67CZVU57ajYtQJDxdsnGgzavy/eOWCE2xJiy7Wi5UMKOu+h2nB2vzdUdiPDUS65oAzN9zrlFfhw4qwLKgDmArgZQBIAuajTKOIFYhdLt+CdbkDZvkOoaOk96ag6yh+UdIHP7U3FTKr+Sax0yxNDr3J4aW8n7ahTAa9NxnSxRGC99aEwbWhDHYKGc3DQQ95OcUEG2Wct8v0BrK3AcH4n4Mz+EgWdUBwTgWBg2kzmkFND0A8D5O+DCG5mooCczm+Hvx552/R32VnCy92/TmjtALC9NahWsSsMOaRwcDYGlScBpKiqOhXAI6f7yQDyG42XWRgptPSeNMQpeWFClpny2RQLaE6OgxSX634v4XjSU7Dbg9nt0oPOrkoIZy9GZHgkq+pCkVoXwU7WJBSU27Rj9nQ1Gc/TzQKoW8g2wzfpisps16LFhTW2CCL/EKfq2q1sPQOj0/aTeZ/bA521yR4XOCrK4LaadOteSfE+d7/KtvEfJn42APAfbt3hhZYgK7R9oDskIdZwcdYFFVVV21RVlfdyRgFREg0fHqEINYrA24DK0NgdHBpfzdnKztEHyDIV1puJZa4Tcrlhq0gNIBTQ3RAmW/IbG+HMV9vsLY9F4D0mgODWOI9TlREZLhkSqnbN67Ftbv7QGJjbtTQp3ARjtn9UyrG0GCuPaZ+Fu/kvYtTJ9r3YXPLtEJOypDAqAgZG/w1XLsfOrVEU2OzafaLPyymRY8u1Tp/1HokCwHDirAsqZwLclr1uIHOMBMw+6nbY0xG8UCjNFBDLh1MvhVNhboVjH/z2cTFTZZULP/DNum9IKJkfE8uMVLyGlEn9CHp0X5gemmgij7t9a02V26fGzTFVUMf6Oo1g7PZ1uLEnYGy2CXFiIgJjlIUyM6QQDYn/rrdRF+aYWVBst2gkPyce4VQTQNAumlcKphCx5Oyu9983WFu0/BwSMC8Mi1h9ogDjhup+qhgLKmFgbpqVCiwrK53mKjJve8BZi8uNSKToZ7CS2M2ewXDjH42Bg4Rv8dCD9Dkb/xMAuCqMNtOCtIkmqu1bAqr2SYmYZChYlhl8T+1mJrKDXjTw510V+c/d6RoLRQGZUntFz3FI/12h+r/Y+ZV8othsYT0jjGRlOMFXtz02QZlR++1mMaLP1M56my1hfn3iMkTq3yuS2GfOjkvSSy3LnwDw9LH3LV9z4xtzqhh+7mAI8Pl8HgCuGtmqqoanDe6A9PTQ+fEZWgLAdWLe6K7HxUXnANxi+7aTdfhkMUcP5h6TmWmzeU8em54Rb2IzXRx9Dj5819x3Fv4s/WekpcejezB4g3xyUhjKwuT52D5vh++/ZcZSy9D4tujl+N67z+Pltj34zBRBlaZ/r29cCOQA/XsqSqchPjIaNygL8asPt+Ofje9iVamZXnuiK3iJuflMpI/R//27e57DT+ZdDsAsf5+ZmYg/77EqCHxt2jJkpiSaHmeAiFqfkz8OOBB8zPvt5gEs/7w6OnpNj7d7zrnpychMDjzmZ7u3AghQl9n3ZfQmAG0BMoObz7/saBZqTzShtrcJl2aagwf7nTHeSMRkcjs7tQ7PV4LrsAAP1gTk/WdljA/p+5d3luP5wzX4fcMbeGDheuPr244E33z+52Ui0TgL3N7Hdo9tOBncbyrNy0bioRi09XVjKML8ftNE4tOT9Xapav7Zb9TUAwBumBS45nmEdf+6wGkNKgAWAdjq5oE+ny9TVdXh1VEA0Np6En7/qQ+vnmrYjbnxwQpmUkI2Pjx5DC8eVnFhon1L5MW9NVIZi4/lnGPMZt4+2GBqbyXCnO1cUzAXzc3yPvsTte8aLSQAto8Vga+q9h9tsc24eGxtCd6cLS3WdkgSgpXEuw2HTAtzh0lPOdTnDQDdx/vRjX7kI9gm2Lav1uQzs7s5cDgXxaa7+h1Oj+kZGsCxpg54FMVEZW5u7sRLR61tiLSBOONnZmYmSn/+t9/abPpZf9lvZgrx39fe3S39Nx7tx7vQ3B94zPu6n/rV44PXVVR/MKlx8x6VxQSCyv7OVtPj+Z0k2c86cux4SLOqAgQp0ytSJoV0rSxKKMHz+ulPv++FhsDXyuKzbH8efz3JMDUxV/pzqmoCqsTnpxShubkTA0OBZGSgb8j0PY+QFhn/s5qbO80Wzr3iYz6c+wgAPB7FNhk/3e2vGgBXufzvtA7nQ4WbhSrWxvgLRx+koNpVr7bbD6pl/XDmLEcDip25kwz8/Of7xMfcDV7Ul8Ts2m7z9IE9ryr8y3p7CfFQwHxjHj38punrbBg6PTn89gvfKrlH91J5RTcic+tIyIOqBtOhraZpjpa5MV73ueMJQauMEjRCXdiTqU2wnSSnFuzzzeFvDYTazqSVsymJ0ckJsgXNi/SNfP56kkEmq0J1/ir0ZVrmX5QQYW7osIXHWwUmcUBQnw0In3wQLk5rUFFVtVFV1d+5/O/UG94jjEHNjzR94Ecz0y2SjdtPjQteXG6WyniaIRAUgvxmyQrp94mUSd0uDzK8TaQgmD92KPsc9IaxcwBckx1sSbULjIe+f/7HXf/OGgklldoAi6RqQpHdsfxOTjp/UPOjrb/b6JMvTS8TDlCZi58MvESK7PeJINtmF+G4LkwpkwdKjgiNYeVUyS6R7Cgx64ZXbYgsIlCVgv+rfymk7wWCbElREjNRkrQtTneWW2EqBbmxSVJyDFMZTxZ8XllEfYPKsjCCAe8uypSQry9cILS3CEViJ1Sc7krlrIXo4HmgfrvBDPvgZCOW6lnYlhaxy2MdCTw/2SfvAtqxwNKi4vHAwvUm21Ie/EW8oXS19LEy/ENn6lyQOsHUjnDrL09l+Z3AKrgf6sZDTJQQAJKi3B9qf9SVj0VD5c/nB/xTmFQNhZ0irJNLJVs+Gx+TatBzqYHS5MRcIRFBpvNFIdpf+ZMLdecYj/z1MLBkiLHAmK88r802HIKEh4gumuznTQ9zw/7BA684P8gGH7MRY5U9V/r1bZJ7nV03nysTk0zoouetHDEDMBv3Mdbcp0jFQ9XD6f0yPjbV5FjJ4NZqOxycdUHF5/Ml+3y+Sp/PVwmA2d3dpH/tytF6HmwRkOJoX4dJWHApycipACPTmHqlbS++qHtb2JWotJWmushMnRAdQjsEMNMQ1+m+Dpfoh81jNlRKBlqFuQlo64lsTHPfSXw3xDYbj1smWmX9qbheKG08EWuMgrVNLswow+cFLDVFUbCDa2PeVLTY8jgRPmMjiUMDDs/2chMImIrwnq5m0+c1OwxbAxnYe/Mr/eCXSfsD5kTIDSWagVe7PhSC+Rlgfq+ebNyFQz3uFn2vHj8XAPCCjQ4YABQmilUXvqUnXSmRscJKhlUW75JZFFULoJJP7H5hNOO2AWvFP5yyODzOuqACIBXAvfp/a/Sv3ar//ZrRehIyraf4CDGZ7W7iU75cn3HU97RhXGxwcPyy7vBoB+pN7xY72vY6P8gGz+teLWw2A5i9NEQceooN6ibjz24CmqIoRovqp/uDFZxdi48HPRhlOwj/W7oSAHBisFfqJcKDvRezHHaV2CFdyXnfiJbPcmLkwqIUiqKgKFa8ELt+3LlGPSZrt9qBEUX6/IOuqh/AvcIBk6/hZw7rJMZTDKxt92uX6tI022cijr8Ko3JhQX7n8QN4qCGwKOukIVZMhEb32RjOic6NWpIo3upgL/xXfRbFW1e/p3cM6MzrusIF0p9zKvtpTjjrgoqqqvWqqiqS/5ac7uf3nbrnjD9rmob/ERyE/NYyO6yfsxlKUnZYKEFiwD+EZ5o+MH3tuCBzkYG+noVc75gxXX7EaXlR0MPdyY+b4lLODhmAbYuPh93+D0OcN8pQaP6zfuDZKUcDQeHGJQ5Ljuym5eXtf8fJalB1aTe4tnCe8OseRTHeMzt5IJkpVgHRLWNzGqfP622Xkuusqj8+0GNq/TlVUMxS160l8f/tD8xQkiJiTGrMoUqT0CDPJF8+IehM8GD3Ma/p12/TMvVrGh7RE8U1WVNs35PN5L3j2aJMQJP65oxk4LDDWRdUziRcLpApoTam73ceNQ0qf68fKPTC8Wua6bCm2+4UtLf9TNMHrm8UWiGxQeMP9r4oe7gJXYN9hq6ZqEVDe7qioTpgrlJkjoMy0HZPqA6Ob+iDyhWkuhKB96X5jEsVA9FrcZLA4Ns9Kx2emwyrMsXb2G5aGm9JAo7oAJJ9XsyUzq0MEfV4f7ktUI1fb5NFM9BhMp0TyMAWBL88YbHpHgtnzsLPVuzmbAz0Pqbsq802y7x3kvuDZ5dR6Re/pmG7/t7d4OK9u0OvwkVJxKmqPjhhLKicAmTObqxsZ/MGJjRYJzh02O4GOyioABwFz9WnF6MMlTXVxp/v9a0z9fjdsM2+vSdYpYhaNIqiGO51P9xnDVR0qM0kUkIBlf8PxRedvrZFLpg5rA0GmG0MeDhRVFkVQg93+j31PWaxTL7yc4sF6WKfcT5ZoWBujm4qOMA81+LBTK1OBW6pyeN0OrPTXI3OTmL17fLbSwLXXDgCqOHaULMD/7X2/YZ5F0tw+JnYfYS8IjLzYmKeiRExpvs938V7x94DESXbjn05HBgLKsMI1tbq4LIqaqX6h4ZAqcuy/K2tgaBCDwracqLg3fEqa6qFwWHAP2QKKN8sWQFFUUzZ6G8P2svus1YCEAhIMqwnN8p+zuP73tpnjD+L3AidwDPi3IrhhUolpfOUzc0fSq13WaXpRMesIIZdlPlFHQLnuHTnlIF3mtzKsY74uYpTxcZjss0SX6hED8C8j7Lahc4bw7UF8109js1OFhD6PP2cqhvfc/07TwX5sako0luJP9+/zURxnkRsIiprqtGtz4C+WrxUOGthSShNUkT7ZXyycwdJkkSU7JFui40FlVPENUQVOFagv8OGh5/TKaxq1zFomib0eWAtppNDfcKsmPWYKTaom/DDXS8Y+xCVNdWmltftJctNswiWvfFZM8XB7jZjoe4L4y+w7fMqimIcGDRQUeHIe2yCkgxUDZdRs3/X8DqG/M7DYXZ4X1fo7kD6F9fGeeDAK0JXRka3FbVuusjnRZ0T+yUzDDvqqht8PNdM9WWLpazC4P3W6WxHlIjY9f3t4FaenlYmor0pGWiF/mKzmIBAB/S8UCO7p95w8Bviwb8fjwhEHmW4llx3P9u/zfjzgH8ID6uvmhK+m4oWS3eQGFjba132VOF+2T+Iv1Gk4hGeQwynmsy4wVhQOUVQeXqRtznr55YRCusGrnXFvo+2mL6zx1qtUHHIi7OD2XDtiSZsVJ8yZcVAoMLgs2r6978fsTotDviH8ODBANsmxhNpqrJkoOX0HxregKZphnDknJSCsDIjVqVEKh4TNfvGHY/Zfh/9DApcmGbRNtG9vnWI1Xc6frZ/G46QtsmAg0cHMx6j/jSyxcRFaSXDsu/Bz2gqa6pNFYasxSkasN9Dqko3YEuLbplilKDwSmtobMTr9GqFVfU8GB1XxPKj99RvbWwVePBGcrVdTSH511SVV1junbtrn8brTfXG3zeUrZYy/0Rq0BcQW2L+uTHcWbbG+LOIhXbxKSYzbjAWVIYBVKOKZ3tR5gqrEoDAYLtCDwxUpoWygWhGw3BpToDh8+Sx91BVXmEaltPfU1VeIT24vqJ7qLzTcch08GiaZqpyKstWWb5XBrbZr3Y1mYLmJdzynBvQwMBuEtqCe6BePnhl76UT5ZeB0lwVRcEdZauMwHJ//XZDmuYhh3Yh24/4YtEi42t/lNC/V2SFN6DnQdtsDLRPz89PWNvpn/piI8O9goDiRBNfGoLC9TOcWu6zzR9IHilGAdG74zf96dyOStlQsHne/u5Wy+a5DEwepbJ0FcbHBKqsH+x9wf2Thty07caiRagqrzBVtDwe4MgFshY0vX9XZk4y3fMiZ9nRYISNBZVhAFtgBAIVRoLXTH1t1r0kaJXww30v4vzUIuPvtNz+8oQg04rvlc9KCR6WTX2dmJqUhwcWrkdVeYXxn1PPnw7AKR2YBgO7OYoIaVHxht0wA7+j4RYsMMxMHm/cJIqiGMGwobddSKumMx0RJVkE5nj4NXIg3VG2yuiLbzq2G5U11QZlU0Q4oFLp7KCQ+ca4YT2Fgsu419lNWkF8y2duqlW76r66zcbBfNvEi4w9GDuaOGA+nHhxSIp+/6Cx7HkDuU9CkeMHgm29f0kCYrQnQnpg0nneRpI0yUD3xWK8kbi+KPiZiRI9GahiBrs3H1i43pSEykDnsrdNvEiaINJ7lhI/2gRsTLdLtqeKsaAyDLBIuJeYDx66wEcrkZ/s22L4JVTVBjPM7OgkYxD7YotqaaOwf6P92lDBGE/tA93oHOw13SwbylaH1Z753PjzTX/ndzTcgD4P/sDMjE7A+pIAMeCZpg8s7wub6RS59Ip/n0jMpHF97WsL51t+PwAkCnZlmKnSqszJ6B0aQGVNtdH+4xGqIKMTnAzj6GyKHrqvtO7Vh8WBbfUbChcgKTIWn+c+QzswmjsThxSBttXyY1OMdvH39oamkkDbekzhgb62OxwSGDrXc5IWYvtiV+lb8oGfHxx+8/7xMvz+UOC6XbX2jwAAIABJREFUCIWYAATJPADwufzzpI6WlBF5ea55j4ZvhQPul2xPFWNBZZhAL9r76jZbqKy0J39NQeBibenvwpX6AN8PzVTKfjx3Ojz6jvQfD+00HaB0SHswTNOdOG+UkK55e8ly27JcBk3TLO2BUCVlGnuDarsygcXFuaUGlfuPh3Yanva0T3+tywH9ozrlm9e3YpiZPN4Qz2TYoG5CZU01Hj38Jg52txm/Hwi0dew0zsIhLLiBndLAT/ZtxYMHXsG+rha09XcZMx/agrq1eJlBU6WDcZ7Nx4NKuIg00WhVwSrfq8lBHYr8Cv3e55troGmaMXeblpjn2NbxKIoxh3zsyFtSNYDtpEqZSLbkY71Rhjncux2HbXdPAKBvKPh+hEJMePTwm1C7gvdNmUTtvGuwz7QnND05yBDbLlDm+KzL/avhwFhQGSbQi7rXP2Dh8tNZxYS4DEPkj/Y9H+TkKO4pDx5Cfzy008R+YQtM/PeEAp6BdEfpyrDVS2kZvlgf4v7h0E4TK8oJjArsVTy2AouX580yqpHfHHwV+7pajEPSrZslZS3Z6Vu1STTZ3u88igcP7sBvXA5/r8ibNWL97PiIaIuPB6UcH+xpx0MNr+FH+7aghcxKIhQP7vWtQyq35MiuLSfaOQDE6dUoP5fZ19VizD+uK5hvamOywHZf3WaEAiqFQq+3K8aJpeR5nEfazXepTwkfs1lQpTBMjM80ZoTb2/bYWlbcWxd4P/hWuB0qa6pNSuR2c5RvC4g8QECteLNgN0UkRjpSGAsqwwja2tqgbrJkkHRIKBqCN/S2W5bW6M/c2lprtIeobe3mhtAGnwBQVfsM7ufkvcO1KqMtq42+tVieWW5UWd/e85yr7X/6M0SLYDyuLZxvSKrQwOx2sYuxli5xYMP8XN/XOS+l0OiLrx83xxjmM2RGJeCmosVSq+NpI2yR+2luWfGtEwcdD7RBzS9sc9Jry6mauI1c48w+uXOw1/hMZiaPNw3agSBRBHAmBPDg53Qimr0d6EH9e04yhxIraJVCMSelwCDY7Oo4LNwVo12Jb5RYFYd5tPSfFM5q3MxRAPPyrmhmRGe+o4GxoDLMWEhK3UiP15Qh8B+4qB1ytyCDqiqvMNEJK2uqsbO93mCX/KP+P65lW15t24fKmmqDrRSpeI2qKdTMkT0Xhq8VLzN49LTKctr+p22S20vcb95/acJiE41UJhxp9/vm2GxO0yXICkLhnpyYiztIUjAtMQ9fKb4QOTFJeJg7qIDQ9b3CBa8AzSR2AODmCUuMoEivO+pzQ8FIF07XBN2b2Kg+hZ6hfqOd6oEinEspimJorjkRAnjwc7pxMSmSR4qhKIoxsK7rajbmEv3+QaPFLGORMZyfWmTaT9ugbjKRFVhXQoFceBYI0Nk3qk8JbS++IZC/B8REAeZPL1oRAGASrR0NjAWVYcZKMpS7p/YZSy+Tijl6FMXi3DYETZi9rcueiq+TC+3JY++ZuP92B/eg5se36zajsqYaTzcF6Z03FS3GXb41pqrprhpn+RcGeoF/sXChRSuKHl4y1sz+7lajTbI8szzk9ls7eT/bBwLLn3bquYOa3/h9lGUnAqN1XpheZska6ZD/k3mz0O8fFL5GpwHycCLaG2Es2fKgpA6PohhVjGwGRN8bp2plI5k7fYsEIZpY8KCaay8cFksTifCDPea5nayNZYecmCRDFumJxl3Y09VsEAoUyA3RKCbEpZte99+OvhMgaJBFRJm7amt/FyprqnGnusmo7nzx2Sah2WTBcF50fd08YQmAwGz1nQ4rCy8c76RThXfjxo2j/kvPEKQAuKWnpx8hipg6Ym7qBGzXKa/7ulrx1YlLjQ3nV9v3mzj+sd5ITIzLNC2kvcY9hiHGG4mlGT70+4dwUCAlsqWlVvjfttY6Q10XCPi8XFMwzyR2eYH+nANvhWJa6hSBXuDXFy4Q6hEpioJ5qcWGiOCWllrT6+oa7DMOOy8UfKHA2sfmER8fje7ufstzuKloMXbqWee21jokR8QKqZs0+Notgj3RuAtHek8AsCoDa5qGn+nzn0tyzkFz/0n88oDVKXB+WrFJmiMU0NcZCtKj4tHUdxJN/SL37eDnOjdtgnFNTk/KN7Jd45GKgl0dh9E9NIDtbXtt91I8isdiROemOhsXk4JdHYfxQftRLEorgcfBjfSl1jp8oFPAvzxhCXYer4cGDXHeKFd6WBQFcWk41teJ5v6T+A85jO/1rXPNfPQoHizN8CE9Mt54XkeJtfO21jpsa6nD9rY9eL65BltaarHp4HsWW+4NZasxPXmcYb51a/Eyi4AlvdbTIuMM4dqKnGnoGxq0LGsCwMykfEw7BWtsGRRFQVxcFAD8FIBFWG2sUhkBxHqjjJlCfU8r+v2Dpt4vn3EUxqVZqJxP2ijArsqajKryClwh8bqW4YuFC1FVXiE8IOK8UcYuwJYWVcr8YZRZhpsnLLGlycZ4I02zJdaDHvAPmYaNd9tktSJQLaf/KVmBnJgkU2X0z8Z3UVlTbZK72am7MgL2TKzeoQGjLXIjWWZkoG3MfzXuklJqVxP59dHEpySD6y0tqiFh41U8mKoP90UHEmCefdiZXf2co7bLqiUePpvWMI/93a2Gl83lebOQHZ1otNY2Hdtt0lVzi0+Pm2OaO81LLQ6LSj89OR9V5RXCRMwPTSjVUxafhXt864wlSFpxUeLEkOY33W+3FF9omG7dNvEiaJpmkAJ4XOZCrn8kMFapjEClAgQGxix72962F5fkTMehnuOGw6Ommdks6VHxKInLxFt6xXK49wRmJOXb6vhkRydiaYYPV0yag6cP7jYG7QWxqSiOy8DFOdNwSc50LMv0YWmGz1HUMTM6Ea39XTjW14l3ThyyZLDvnGgwbfreUbpKKFnCI8oTYVRCQIBwQN0kQ8kO4+OjUdt6DE8cCwTdizLKDQkcRVGwNMOHzKgEg0XzSttebGmpxdTEPMO34vLcmci1WUBjB1ysJxJrOSMp9eQxU2Yrw6nOUcKtVBiWZviENtY7j9djS0stFqeX4pykccZjBv1+TORkRRRFMariN08ctCQjdV1N+PG+LejS22MeKNAQoNyKWoYiXJheZsiv9PkHURqfZXnMsb5O/EqvBCcn5BhWuLkxydjT1YwTg73Yebwe89MmCrWxZNA0zcSUauhtx6BmfR/coN8/aFB8vzD+AlySOx15MclIjYxDXkwyZiTn4+O50/G5KRfggrgiTE/ON96fxt4Oo8reWLbGqNha+7tMidcdpavwfULbX509xTK0Z9hQuhoRnpGpGcYqldMIymKqrKk2LQduba01WYMCgZKcyrz8aN8WvGsjxS77XQd72nFZ3kwUxKaFnHldnjfLYDb9eN8WQ4OosqbaKM8BXScrhOXGOG8U7iyz9ndDCSgA0DM4YHLzW5JRannMtKRxuNe3zrBTBcwzBd7giOJvZNhJWTVAwA7XjfOmG/baaMAusN2lPoXKmmpcoXsCvdy2R7hrQgUamTzOm8cPorKm2lj6BAJZM52hyA47Hoqi4GvTAnPFHW37LEKeR3tPGJVQYkS0xf+GuhveW/tMSIZc9DkyWZ+XW/eENFdkoEueJfGZiFA8mJyYixVZk7A2eyouSJ0gnRcyKv3s5AJE6HtCTx/bbaog7/GtMzlE3uNbJ51TfjxnelhK0sMFxY2vxkcURQD2t7aehN8/cu/Bm8cPmFRw7/WtM13MF2dPM/HngUAGxd+UdodvZmYimps70TPUbwxKJyXk4DP5ck9zJ8gu2FP5uZ2DvRZfjFWZk6X+IDyGNL+pTeCmGvBrmpTE4IWCtdlTMT05H9GeCBzuPY5f6jTrG4sWQkNAJkcmDPnZcefij5yg4v+WrrTMJ8IB+0xPFXavX4Q7y1abhEuBgGo1ExnlsSZrislcqt8/aBywl+XOcNz4BwKv9XtvPYe9esv1rrI1iPR4sber2WDTRSle3OlbI/x+/n65x7fOcSfooYOvYZ/++zaUrka0NwIfdjaaBDK/MuFCk6SRDL85sAP1+hKyU5LEf64/2PuCIVRZVV6Bk4N9JjHZgthUXFe4AIP+IaOCviTnHIuyNkNGVDxucWCvnSo8HgXp6QkAMAFAPf/vY0FlhIMKYD6gyxOysSZriolKeX5KkUUcsHdowMLMmZmUL+yT0guVHgBL0ktxUWZ4hkrNfSdN8jJAgOYoYqW4wZ6uZhNbbVxMMg7rg3DA+WbkD0e3Fc6TjbuM1sJluTNM1Va4uMe3DnVdTZaq5SsTliAzOlHyXaFhuIIKIL6WgMCBJSJ8uMEn82ZJKz6aSLkJsuy10vtkWYbPkPPPjErAVyQKCwz89cECkwibju02huU3Fi1EHqloaWLGYBekajobjcTiluILbZd2AfPn+n7HEUPV4faS5bi//mVDyBIIWDcwpW23mmOjQV93CipjM5URmqlQ0P4222ielpRnmE4d7j2OV9v2YVF6sJUT4fGiOC7DxApr7OvAlpZanBjoNbGKaP89OTIWSRExqDl5DPU9bVIWlAysTy7ynyiNz3RFt+Tx2OE38VxzkDZaVV6Bc1MKkRmVaMw+trbWIic6SXgo8weGm0wUCLRp2MH0hfEXYEpiLpZmBOZL56cU4eRgn+EbI0NiRDQyIhPQqe98fLV4Kf50eKcxH2K4sWjRsGornepMhSLC48Xs5AK8qgs7MpwY7MU9vnVYnF6CtoFu22G3V/FAI+ux/LIlRV5MCl5u3QM/NGxv2+s4X2Gvlc5X9uvyNzOS8nG1C1agoihYQr7/pdY6nJdSZKm6XmqtMwQjr8ibbZGnj/R4sTTDh6a+TjTp2mJbW2tR19WMWUTgFAA6BnrwS70Vuyi9xNWCK3utvUMDuJ8wBne07TMG+nkxybi9ZIWRwD3S8IYxi7VDqK3kcOE0UxmrVEahUgGsJfp5KYVo6Gk3URAB64WxuekDywFG8dXipSgfl2PJap9v/hAv6TfPpbkzbKXgZZlslOLFhrLV+P7eFwzV1HmpxViT7Y7VxL/mOG+UZU4x4B8ySdgAgYEkm9fQsh8A7p//KbS1Ot9gtJWxNKPMlhLL2y7T9//djsOGgOC81AlCJ72bJyxB1jBVKAzDWakwtA9044d7rUKDLLsNUNADQfi2iRdZhAxpa2taYp6jPAp9X+0yaPZam/o6LSKpoWbe/DX3hfEXGIHjxWbVCDorMyc52jnT10txU9FipEXFGf8W5fGafEzskJmZiKamDunMqbJ0lWnB872Ow3hcIAeTHZ1oSgLcJlrDgbH2lxxFGMWgAlhnAkWxaUYvluLagnkoIvTEX9VvN3y2l6SXWlz9GHKjk7A+/1xjs/xfR9/FmycCi370Juoa7MPfj/7HZO5DMTEuA1dx2SH9WYDzzb6/u8WkHfWxnHNsfb93ttfjyWNBmrAHCm4pXmpSW73btxY5WcmOh23tyWMG02tqYp6UYguYDz6+ZbKvq0XoSUHxzZIVJmfN4cJIBBUgYP70fYEvyFeLlyI9Kh731T1ryOdvLFtjDI4ZDva0GXpzFdnTTPYNPAY1Pza6mIFlZCTgllf/hl6/eLs/nJYO/VzL4rPgVRR8qM/GLsrwYUkIPu2iWSCFm2vgWF8HnmjcJW030kQq+D2dFrp2RlQ8xsekmhYdRXOwkcRYUJGjCKMcVICA9em3bJRsKejN9K3aZ42Fpy8VLUJaZLyUnx4unGYCdV1NJsbP7SXLLYwWEcmAz75kYErHJwatXhssE3M6bN/vPGoYb/nis3HleLk660b1KWOjmc/M67tbHcUiN/rWhkRhDQUjFVQAeWUKBN6H75GgI8qAd7TtxTNNAb25awrmYkKcWCcLgGnwnBQRY7GFePTwmyYRRVYB8Qd5OK2dp4/ttlSWl+ScYyvNYwdN0/DXo+8IHV7DweL0EizPFBu2iQLZxdnTcLCn3URpt5sdjRTGgoocRTgNQQUIVAoylVEel+fNMqTeq2qfNbK5/1cwH4W6UF96RgL+XvMOXmhxL3cBABekFGF19hRbfSIefFA8L6XQ2EynQ3EAyIlOwk0OUigi/Gjvi8aCF8XF2dOwtmya9LB9pXWvoVZcGp+Jz0vEHQFYFjhp++pPh3YaWa0IXsUz4rThkQwqQGisMFFg+fOhfxtb5DcWLbKd29GMOyUiFrdMXIpf1r9smeEwFhYDPzTn/90Jff5Bi3qyiBTjFnyrdv24OXjs8FvwhyHFakd6EZ0Pd5SuxC/rt5vui9FseVGMBRU5inCaggpgny2K8OUJi5EdnWQ6cC/NmYFZKeNdHUAHutvwa0ILFbU2QsFPOBl1HuFQa/n2YH5MCga0IeEAeX5aMVZkTjIC4sMHXzMoqbOTC0yeMzz4gJIZlYBX2vYKJcN5iCjgI4GRDioMv2t43SCM2EFUldFr4PrCBbbKCod6jhsLjDyq5lwMnBTfg/w1QZMpO7x1/KDFNpnic/nnG0uzbnB8oBs/IPOoUA/0e9Sn0a9LJdmxxH5z4FXU9wQ9ehakTcSqrMkW9tdoiZSKMBZU5CjCaQwqgHO2yA/jgIAl65YW1ZiHTE7IwVdmLnV1APEceDrEDAcPHnjF0iN2u5vA4432elSTmcqV+eeZZDx2dRy29a9gWJbhk8rf8weUHSIVr0kvDRjd3vVoBRXA3eyIYmFaCWanjEd6ZLyJxPGpvNmYmpQHv6bhUG87XmqpgyqZ2wEBeZ2EiGjH18q3VMsTsqWmU/wcBwi2zo72nsAv6l82/ZubIMUPy0NtxZlcVWetRnS3+RrSNA0PN7xu7M0wbPSthQKzaGa0JwIbBEvEo4mxoCJHEU5zUAHEMwiK5ZnleK1tv0nGXAS3Fzr/++K9Ufgmx8iyQ/dQv6Mc+pTEXFvKKYXoELjbt1baksvMTMSO/XvxyKE3hP8+EmALaKOJ0QwqgPg6TIyIRml8lonWfqq4PHcm/kq00r5ZsgJFuRmuXus/j76LtwhZhK+Gn2l6HzvagrRpVsnzeL19PzYd2236mmyniy5JJkZE43Ybl00efBLDMzXtCABV5RWWRCuUReGRxFhQkaMIZ0BQYeAtg3lUlVfgZ/u2Gtx5ERiDxw0o5RiwLoFRdAz0mIa3FB/PmY7ZKQXC/vWEuHRcPX6uNNjxlc4FqROwjtPa4sEO22eOvY8dZO9iWmIe3uu09x6nKIhNxYUZZUiJiMVPOYYNxYay1WHZK58qRjuoMDT0tJv03YAAhZZJiYSClIhYrMqajEmJOaYkwdLSKp+PQji3tAArI2pW8niUxmfhcX2JkMFNe4pSqCk+M+5cTIzPMNGJl2eWG46mbtDSf9Lkk3JH6UrEeqMQnRyJ+95+1mTZQJEaGYcvT1hsoTKPFMswHIwFFTmKcAYFFcC5DfG14mVIi4rDex1HLDcRxbzUYqzImuTITJJtDx/uPY7fHHzV1pdENjNp6+8SGi/Rx29v3WOZX9hVJxTxqdH46mt/M/6eG52EL0nIAHx7sTA2Df+P+NfbDeTtWiyjgdMVVBjojIohwRttqpj56phvUToxk+6q2YQhfcgdqXhwVwjkh+/ted5ou1F8sXBhyKZUPP2dx7rsqTg/pch1y2tbS52JNOM24VmYVoL6nlaTORwwekuNbjEWVOQowhkWVADnOcv0pHG4XBcB1DQNzzXXYHvbHunjhxNuZCgYOgZ68b29cm4/gxMlleKxw29hN7k57frh6sljJhkVOj9y2jsQUaVHG6c7qADOrVkgQG+nis/8QNtOzgUAXmvbh6eIcZzTwL/fP4hf7H9ZumH+5QlLkB3mIqrTdTFcyIiKR0X2OXiYJJB8wAaAFZmTsMhhQfN0YCyoyFGEMzCoMLzats/k0siDtrrYARTqwNUJwyFOJ5qZMBTGpmH9uDmOZT0VFgQCc6D/KVkhzN5Ew/i7fWsxpPnxZON7trL1i9NLDVn1040zIagwiFpiFHHeKHyTfB6iYGTXjuKrT/7xff5B/PHQTkO6heLK/PNwtLfDQqd3Ck4Ufk3DfXXPGhbbAPD5/PORE5OEJxp32bal3SBK8eITeTMxKSEHWVlJuOONJ2yZk0CQxHAmYiyoyFGEMzioAO7YStcXLsCsggLTAUQ38IGAkmxRXDrqu1txtK8D/f5BRCpew+uhMC4NsZ5IKIqC5r5Oy4zhgpQirAuR27+/uxW/dVge5JHgjUZF9lRMSsyFR1GEopZ3z14Hb5f4cPrbkXdceZ3wSIqIwdcnXnRaOP8ynElBhYGvFEWYnjQOyzPLkRIZZ1FJkDED2Wt9+tj7Fn0yESIUD74x8SJLMvJE4y7DXI1hdnIBLsk5R5iAaJqGBw/sQENvsN1k105lGPQPoaH3OJr7OtHa32Wa7QHyVq6bhdpzUwrxMRtH0jMBY0FFjiKc4UGFobG3w9WglLYiRCytz4w717W9rUzu3G7/xK5SuqZgnskZr6X/JH59YIdh8OQWCRHR8EBBn39Q6P8RKkZyK/5UcCYGFSCQ6Hy7brMpqw8H8d6okD57DxR8pfhCV0QUnoTCwHaMBvxDuK/uWQxwM8NQSBmapuHn+7eZiDOfyJ2JGcn5lse63Uk7Hdvx4WAsqMhRhLMkqDBQmW0npEbG4brC+Tja22Gh34YiVSHi9gOBoeKM5HyLNhFFVlQibpqw2FUFINOkGglEKV78b9mqMzKYMJypQYVhSPPjbvXpsLbJw8X8tOKQLJqP9J7A/YJrl0coMzRN0/DIoTdQRxZGYz2R+N/SlaZqqM8/iGqHditDKLPKMwFjQUWOIpxlQYVhd8cRPGbD/nKLqYl5uCJvli2zRNM0HO07gZda95g0mmRIjojFLcUXus64uof68eO9WwxdM4a5qROwNnsqNE1D+0A3DvceR1PfSfijNLx01CqoeVnuDMxIysdfjrwtZdosSJuIlZmTzigmjQxnelBh8OuHrN1W/uW5MxHjjURjX4fhM09ROXM1oru9ws9F1Eb1Kh7cXrLclWKDncEYj+zoRMxJLsDUpDxLkBnS/PjZvm0WgsDXJy7DoZ7j+E/HoZBnL27VAc40jAUVOYpwlgYVBrdtsdHGisxJmJ9WLKUID/iH8MdDOy2UVSBwAE2XtBC+u+d5y5b7uuyp2N1x1CRtQaEAuO0MYHOFirMlqFA4DfSzoxNxU1FgXvHDvS/i+GCP6d95FQUK6spJMTu5AB/LOceohnuG+vFk43vSxGJF5iRMSczFX4+8bZo7jibOtsqEx0cuqPh8vmUAPgtgPoB8AEcBvAjgTlVVG0P4UUU4y4MKQ3pGAn76ny2nzFJxQnJEDNZmT0V5Qo5xE7sZPgKBw132LqdFxuHGokUWJWO/puHJY7vw5vGDku+U4+sTL0JKmC6VZwLOxqBC8c6JBkeXzYyoeCELKi0yDl+asFg43+j3D+L/9r8kFByV4fyUIqzNniptw/o1DXu6mvFK216LVMpwoSg2DVcVzHVl3XCm46MYVN4EkAbgrwDqABQDuAnASQAzVFWViw2ZUYSPSFChB1DXYB8eOPCK40139fi5KI7PgKZpeKrpfcNelcei9BIsy/C5Wkwc0vx4rW2/oRQ82jhdqq0jgbM9qFB0DfbhF/UvC5cVRxLpkfH4eO50FMamObY87dpkc1IK8LHsIINswD+Ex4+85SqJYwvLDB+Fz/WjGFQWAXhFVVU/97WXANytqupGlz+qCB/BoELh1zRsbak13O6GA17Fg4VpExHp8eJQz3Hs6WqysGhGC/kxKbhq/NyQ5NDPFnwUDh8ZRNpb4SIxIgZL0ksxIS4djzS8YWmpnQouzp6GY32dONDT5mg7zVAUm47P5p8r9Q/6KHyuH7mgIoPP52sF8IKqqle4/JYifMSDCg+/puH19v22S5XDiRWZkzAvrRgRigd+TcOBnla81rbf8OEIB5+aOBtTInLPimH7qeCjcPi4AW/8Fg7mpxZjfloxkiJj4dc09PsH8cHJRvzDof02HJiRlI9VWZNdLyp+FD7X/4qg4vP5EgC0AviNqqpfcvltRfgvCyoi1He34k+H/m1hX50JSImMxdqsqShPyDaCyEfhpnSD/5bXCQRfq6Zp2NvdgurG96QyLKcTTrMZN/gofK7/LUGlEsC9AJaoquqWDlUEQDxI+C+GX/Njd9tRPN2wG/s7xYyq4UReXDImpeRgVkYBJiSmw+s5c3dHxnB6MOAfwu62I3i39RDebz+KjoGRn80syJmI1eOnICPm7GVpjQLOvKDi8/k8AFzZA6qqKryS9HnKiwD+qqrq+hB+fRHGKpWwMOgfQvtAD7qG+tAzNABFUeCFAo/iQWJENBIjYhDjiRiRFtVHIdNzg/+W1wmM3Gv1axpODvaibaAb3UP9GNI0eBUFCRHRSI2MQ4wnEhGKZ1RbqR+Fz9WpUjndE85FALY6PgqAz+fLVFW1hftaOYB/AngXwP8b/qc3BhEiPF5kRicgE2NZ3BjOXHgUBUmRsUg6i6nlZyNOd1CpAXCVy8eawrvP5xsP4DkAxwGsVVX1zGvCjmEMYxjDfxlOa1DRlxV/F+r3+Xy+dAQCSjSApaqqjuzW3xjGMIYxjMEVTnelEjJ8Pl88gKcBjANwoaqqo+NQNYYxjGEMY3DEWRdUAPwJwHkAHgIwyefzTSL/dkxV1ZG3bhvDGMYwhjEIcTYGlRn6/6/W/6N4CcD/b+/+Q+2u6ziOP23ZJo5ZKOpWufqnt6LgRs5xnS3N/xaSWKL5Ky0XKZhTCQVFKyHqj0pChUxNShDHSlHKyJ+THKKsH1Npb0R0Boqw6WCKWzLxj8/34OlssuPtc+73nO99PuBw7z7fe8f7wznnvs7n8/l+vx9DRZJaMnGhkpmfa7sGSdLeTVyoVDQHyjnXXdCVfgxjtvR1tvQT7Osk6at/r5smdeKK+mk6EdhzgwZJ0jC+BOyxgc5sDpW5wDLKfiy79/GzkqRiDrAQeAbYNXhwNoeKJKky794nSarGUJEkVWOoSJLGoRjFAAAE70lEQVSqMVQkSdUYKpKkagwVSVI1hookqRpDRZJUzWy+91cnRcQpwLnACuAzlDsGPAJc12yK1hkREcD3gOXAUmAe8PnMfLnNuqYrIuYCPwbOAz5F2Sb7msx8pNXCRiAiFgKXUZ6744D5lP2RHm+zrtoiYhlwAXAysBjYBmwAru3qXlCOVLrnZ8CXgXuB7wP3AGcBf4+IQ9ssbASmKH1cAPy75VpquBO4HLiL8gf3PeDBiJhqs6gRCeAqygefTS3XMkpXAacDD1Oe01uBk4B/DOwF1RmOVLrnCuBvmfleryEi/kLZa+YS4Ict1TUK9wOfzMwdEbGGMlqZSBFxPCX8L8/MG5u23wHPUT4orGyxvFHYCBySmdsi4jTKh6Au+gVwdmb+t9cQEfcAz1IC54KW6hoZRyodk5lP9AdKrw14A+jUJ6PMfCMzd7RdRyXfAN4Fbus1ZOZO4HbgxGa6qDMyc0dmbmu7jlHLzA39gdK0vQA8T8fejz2GyiwQEfMpc9Zb265FH2opsDkz3xpofxrYjw92PNWEi4j9gMPo6PvRUJkd1gCfANa2XYg+1ELKSRWDem2LZrAWjdY5wKfp6PvRNZUxFhEfo4TBPjVTJXv7P1YC1wN3Z+b6iuVVVaOvE+4A9rI3BbCz77gmXEQcCdxM2dzq9y2XMxKOVMbbSuCdYR4RccjgLzcv4Hspp6aunqGap+v/6msHvEPZOG7QvL7jmmARcTjwJ+BN4IzBtc+ucKQy3jYDFw75s/+zYB0RnwX+CmwHvpqZb1eurbZp97UjXqNMgQ3qtb06g7Wosog4CHgQOAhY0bVrxvoZKmOseeHd+VF/LyIOpgTKXOArmfl65dKqm25fO+SfwGURMX9gsX558/VfLdSkCiJiHvAA8AXglMzMlksaKae/OiYiDgT+TFkIXNXVq3Y7aB2wP3BRr6G5wv5C4MnMdKQygSJiDuUC5CnKlNdTLZc0cu5R3zERcR/wNeAO4LGBw69n5kMzX9VoNFMKlzb/nAJWAT+nTPltycyJWgiNiLXAacAvgReBbwHLKLcvebLN2kYhIq5tvj0KOJvymn0J2J6ZN7VWWEURcSPlSvoH2PNsr7cy876Zr2q0nP7qnt71DN9uHv3WA50JFcr9sW4YaLuy+bqeyTu75nxKf86n9G0TZbTZuUBpDD53vdfrFqATocIH78dTm0e/LUDnQsWRiiSpGtdUJEnVGCqSpGoMFUlSNYaKJKkaQ0WSVI2hIkmqxlCRJFVjqEiSqjFUJEnVGCqSpGoMFUlSNd5QUhoDEfFxyk0wlwBfzMzNfce+C/wauCEzr2upRGko3lBSGhMRsZiyWdcWYHlm7oqIo4FngI3ASZm5u80apX0xVKQxEhGnA38AbgZ+QAmURcCSzHylzdqkYRgq0piJiFuAi4ENwAnA1zPzj+1WJQ3HhXpp/FxB2fnxBOA3BoomiaEijZ9jgSOa749pFvGliWCoSGMkIhYAdwNbgWuAKeBHrRYlfQSGijRebgUWA+dm5k+AdcDVEXFyu2VJwzFUpDEREd8BzgR+mpmPNs2rgf8Ad0XEwa0VJw3JUJHGQEQcCfyKcsbX9b32zNwOfBM4FPhtO9VJw/OUYklSNY5UJEnVGCqSpGoMFUlSNYaKJKkaQ0WSVI2hIkmqxlCRJFVjqEiSqjFUJEnVGCqSpGreB796Jaho1spYAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = torch.linspace(-10, 10, steps=10000)\n", "x = 2 * torch.cos(t) + torch.sin(2 * t) * torch.cos(60 * t)\n", "y = torch.sin(2 * t) + torch.sin(60 * t)\n", "\n", "plt.plot(x, y)\n", "plt.xlabel('x')\n", "plt.ylabel('y')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": { "id": "8RP7x-YIYvQL" }, "source": [ "Заметим, что `matplotlib` справляется с отображением `pytorch`-тензоров, и дополнительных преобразований делать не нужно.\n", "\n" ] }, { "cell_type": "markdown", "metadata": { "id": "64Rj81Jy8hji" }, "source": [ "### 2. Простой пример обучения нейронной сети" ] }, { "cell_type": "markdown", "metadata": { "id": "2WAHjImo8hjj" }, "source": [ "#### 2.1 Цикл обучения модели\n", "\n", "Пусть у нас есть вход $x \\in \\mathbb{R}^d$. Мы построили нейронную сеть $model$, состоящую из обучаемых параметров $w$ и $b$. На выходе она возвращает некоторый ответ $\\widehat{y} = model(x)$.\n", "Для обучения такой сети мы задаем функцию, которую будем минимизировать. \n", "Тогда процесс обучения задается так:\n", " * **Прямой проход / Forward pass**
\n", " Считаем $\\widehat{y}$ и также запоминаем значения выходов всех слоев;
\n", " * **Вычисление оптимизируемой функции**
\n", " Вычисляем оптимизируемую функцию на текущем наборе объектов;
\n", " * **Обратный проход / Backward pass**
\n", " Считаем градиенты по всем обучаемым параметрам и запоминаем их;
\n", " * **Шаг оптимизации**
\n", " Делаем шаг градиентного спуска, обновляя все обучаемые веса.
\n", " " ] }, { "cell_type": "markdown", "metadata": { "id": "02QbI8x9osL2" }, "source": [ "#### 2.2 Линейная регрессия" ] }, { "cell_type": "markdown", "metadata": { "id": "ouIP4JtE8hjj" }, "source": [ "Сделаем одномерную линейную регрессию на датасете boston.\n", "\n", "Скачиваем данные." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "UkGvfSx8fooK", "outputId": "c545ab13-5082-4ad2-ed8b-2ec5bbf107c5" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", "\n", " The Boston housing prices dataset has an ethical problem. You can refer to\n", " the documentation of this function for further details.\n", "\n", " The scikit-learn maintainers therefore strongly discourage the use of this\n", " dataset unless the purpose of the code is to study and educate about\n", " ethical issues in data science and machine learning.\n", "\n", " In this special case, you can fetch the dataset from the original\n", " source::\n", "\n", " import pandas as pd\n", " import numpy as np\n", "\n", "\n", " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", " target = raw_df.values[1::2, 2]\n", "\n", " Alternative datasets include the California housing dataset (i.e.\n", " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", " dataset. You can load the datasets as follows::\n", "\n", " from sklearn.datasets import fetch_california_housing\n", " housing = fetch_california_housing()\n", "\n", " for the California housing dataset and::\n", "\n", " from sklearn.datasets import fetch_openml\n", " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", "\n", " for the Ames housing dataset.\n", " \n", " warnings.warn(msg, category=FutureWarning)\n" ] } ], "source": [ "boston = load_boston()" ] }, { "cell_type": "markdown", "metadata": { "id": "yUO7AdXQfqv_" }, "source": [ "Будем рассматривать зависимость таргета от последнего признака в данных." ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 461 }, "id": "pL8lyhbX8hjk", "outputId": "d2b6a6ac-1d08-431f-f6e2-6911f0805f9d" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,7))\n", "plt.scatter(boston.data[:, -1], boston.target, alpha=0.7)\n", "plt.xlabel('Признак')\n", "plt.ylabel('Таргет');" ] }, { "cell_type": "markdown", "metadata": { "id": "CCBBKoBo8hjm" }, "source": [ "В данном случае ответ модели задается следующим образом: $$\\widehat{y}(x) = wx + b.$$\n", "\n", "Объявляем обучаемые параметры, в данном случае у нас всего 2 скалярных параметра $w, b$. Также задаем вход $x$ и таргеты $y$ в виде torch-тензоров. " ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "id": "bHEK3Lil8hjs" }, "outputs": [], "source": [ "# создаем два тензора размера 1 с заполнением нулями, \n", "# для которых будут вычисляться градиенты\n", "w = torch.zeros(1, requires_grad=True)\n", "b = torch.zeros(1, requires_grad=True)\n", "\n", "# Данные оборачиваем в тензоры, по которым не требуем вычисления градиента\n", "x = torch.FloatTensor(boston.data[:, -1] / 10)\n", "y = torch.FloatTensor(boston.target)\n", "\n", "# по-другому:\n", "# x = torch.tensor(boston.data[:, -1] / 10, dtype=torch.float32)\n", "# y = torch.tensor(boston.target, dtype=torch.float32)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rDDLGlXkT4YU", "outputId": "560f5506-b90c-4d04-9dba-9dcc1df19268" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "torch.Size([506])\n", "torch.Size([506])\n" ] } ], "source": [ "print(x.shape)\n", "print(y.shape)" ] }, { "cell_type": "markdown", "metadata": { "id": "RnqZEmVa8hjz" }, "source": [ "Задалим оптимизируемую функцию — MSE, и сделаем обратный проход `loss.backward()`:\n", "\n", "$$\n", "MSE(\\widehat{y}, y) = \\frac{1}{n} \\sum_{i=1}^n \\left(\\widehat{y}_i - y_i\\right)^2.\n", "$$" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "id": "mrxgrg8buMLz" }, "outputs": [], "source": [ "def optim_func(y_pred, y_true):\n", " return torch.mean((y_pred - y_true) ** 2)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "id": "NWPb6c438hjw" }, "outputs": [], "source": [ "# Прямой проход\n", "y_pred = w * x + b\n", "\n", "# Подсчет лосса\n", "loss = optim_func(y_pred, y)\n", "\n", "# Вычисление градиентов \n", "# с помощью обратного прохода по сети \n", "# и сохранение их в памяти сети\n", "loss.backward()" ] }, { "cell_type": "markdown", "metadata": { "id": "IC5NHp3I8hj2" }, "source": [ "Здесь `loss` — значение функции MSE, вычисленное на этой итерации." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sM-MGJeE8hj2", "outputId": "60575afd-e273-4a07-a6a2-8237cff5414f" }, "outputs": [ { "data": { "text/plain": [ "tensor(592.1469, grad_fn=)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "loss" ] }, { "cell_type": "markdown", "metadata": { "id": "mGYCu-E08hj3" }, "source": [ "К градиентам для параметров, которые требуют градиента (`requires_grad=True`), теперь можно обратиться следующим образом:" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "beqyJg828hj4", "outputId": "8895dd3d-d60f-419d-82f9-86ca0c293ae2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dL/dw = tensor([-47.3514])\n", "dL/b = tensor([-45.0656])\n" ] } ], "source": [ "print(\"dL/dw =\", w.grad)\n", "print(\"dL/b =\", b.grad)" ] }, { "cell_type": "markdown", "metadata": { "id": "KXcqokN5q0tT" }, "source": [ "Если мы посчитаем градиент $M$ раз, то есть $M$ раз вызовем `loss.backward()`, то градиент будет накапливаться (суммироваться) в параметрах, требующих градиента. Иногда это бывает удобно.\n", "\n", "Убедимся на примере, что именно так все и работает." ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "R34fVF0Pq2Bl", "outputId": "6644ad1c-b9a2-4b7a-ea23-8a61d72ee8ea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dL/dw = tensor([-94.7029])\n", "dL/b = tensor([-90.1312])\n" ] } ], "source": [ "y_pred = w * x + b\n", "loss = optim_func(y_pred, y)\n", "loss.backward()\n", "\n", "print(\"dL/dw =\", w.grad)\n", "print(\"dL/b =\", b.grad)" ] }, { "cell_type": "markdown", "metadata": { "id": "FpdSN_hrSS3b" }, "source": [ "Видим, что значения градиентов стали в 2 раза больше, за счет того, что мы сложили одни и те же градиенты 2 раза." ] }, { "cell_type": "markdown", "metadata": { "id": "OPAohe21sJ7X" }, "source": [ "Если же мы не хотим, чтобы градиенты суммировались, то нужно **занулять\n", "градиенты** между итерациями после того как сделали шаг градиентного спуска.\n", "Это можно сделать с помощью функции `zero_` для градиентов.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "RMhN7wrrsNCD", "outputId": "0418bd4e-ea4e-42c6-d958-1f8017684035" }, "outputs": [ { "data": { "text/plain": [ "(tensor([0.]), tensor([0.]))" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "w.grad.zero_()\n", "b.grad.zero_()\n", "w.grad, b.grad" ] }, { "cell_type": "markdown", "metadata": { "id": "XWXQYM8Z8hj5" }, "source": [ "Напишем код, обучающий нашу модель." ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "id": "bjj8p9BkKMqb" }, "outputs": [], "source": [ "def show_progress(x, y, y_pred, loss):\n", " '''\n", " Визуализация процесса обучения.\n", " x, y -- объекты и таргеты обучающей выборки;\n", " y_pred -- предсказания модели;\n", " loss -- текущее значение ошибки модели.\n", " '''\n", "\n", " # Избавимся от градиентов перед отрисовкой графика\n", " y_pred = y_pred.detach()\n", "\n", " # Превратим тензор размерности 0 в число, для краисивого отображения\n", " loss = loss.item()\n", "\n", " # Стираем предыдущий вывод в тот момент, когда появится следующий\n", " clear_output(wait=True)\n", "\n", " # Строим новый график\n", " plt.figure(figsize=(10, 7))\n", " plt.scatter(x, y, alpha=0.75)\n", " plt.scatter(x, y_pred, color='orange', linewidth=5)\n", " plt.xlabel('Признак')\n", " plt.ylabel('Таргет')\n", " plt.show()\n", "\n", " print(f\"MSE = {loss:.3f}\")" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 495 }, "id": "Lnczgkgp8hj6", "outputId": "9783424f-4609-414c-ff62-32b5d9485997" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MSE = 38.978\n", "Готово!\n" ] } ], "source": [ "# Инициализация параметров\n", "w = torch.zeros(1, requires_grad=True)\n", "b = torch.zeros(1, requires_grad=True)\n", "\n", "# Количество итераций\n", "num_iter = 1000\n", "\n", "# Скорость обучения для параметров\n", "lr_w = 0.01\n", "lr_b = 0.05\n", "\n", "for i in range(num_iter):\n", "\n", " # Forward pass: предсказание модели\n", " y_pred = w * x + b\n", "\n", " # Подсчет оптимизируемой функции (MSE)\n", " loss = optim_func(y_pred, y)\n", "\n", " # Обратный проход: подсчет градиентов\n", " loss.backward()\n", "\n", " # Оптимизация: обновение параметров\n", " w.data -= lr_w * w.grad.data\n", " b.data -= lr_b * b.grad.data\n", "\n", " # Зануление градиентов\n", " w.grad.zero_()\n", " b.grad.zero_()\n", "\n", "\n", " # График + вывод MSE через каждые 5 итераций\n", " if (i + 1) % 5 == 0:\n", " show_progress(x, y, y_pred, loss)\n", " \n", " if loss.item() < 39:\n", " print(\"Готово!\")\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "XUIUOScI1AB_" }, "source": [ "#### 2.3 Улучшение модели" ] }, { "cell_type": "markdown", "metadata": { "id": "HaXNOcCC8hj7" }, "source": [ "Попробуем усложнить модель. Сделаем еще один слой." ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 495 }, "id": "9uyqbJlk8hj7", "outputId": "9a10800e-5795-43b6-b88a-7baf7e147660" }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MSE = 32.994\n", "Готово!\n" ] } ], "source": [ "# Инициализация параметров\n", "w0 = torch.ones(1, requires_grad=True)\n", "b0 = torch.ones(1, requires_grad=True)\n", "w1 = torch.ones(1, requires_grad=True)\n", "b1 = torch.ones(1, requires_grad=True)\n", "\n", "# Функция активации\n", "def act_func(x):\n", " return x * (x >= 0)\n", "\n", "# Количество итераций\n", "num_iter = 1000\n", "\n", "# Скорость обучения для параметров\n", "lr_w = 0.01\n", "lr_b = 0.05\n", "\n", "for i in range(num_iter):\n", "\n", " # Forward pass: предсказание модели\n", " y_pred = w1 * act_func(w0 * x + b0) + b1\n", "\n", " # Подсчет оптимизируемой функции (MSE)\n", " loss = optim_func(y_pred, y)\n", "\n", " # Bakcward pass: подсчет градиентов\n", " loss.backward()\n", "\n", " # Оптимизация: обновление параметров\n", " w0.data -= lr_w * w0.grad.data\n", " b0.data -= lr_b * b0.grad.data\n", " w1.data -= lr_w * w1.grad.data\n", " b1.data -= lr_b * b1.grad.data\n", "\n", " # Зануление градиентов\n", " w0.grad.zero_()\n", " b0.grad.zero_()\n", " w1.grad.zero_()\n", " b1.grad.zero_()\n", "\n", " # График + вывод MSE через каждые 5 итераций\n", " if (i + 1) % 5 == 0:\n", " show_progress(x, y, y_pred, loss)\n", " \n", " if loss.item() < 33:\n", " print(\"Готово!\")\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "4wLWo8ZMFEno" }, "source": [ "Полученная модель лучше описывает данные." ] }, { "cell_type": "markdown", "metadata": { "id": "VFMzz1Z2aKyP" }, "source": [ "### 3. Готовые модули из PyTorch\n", "\n", "На практике нейронные сети так не пишут, пользуются готовыми модулями. Напишем такую же нейросеть, но теперь с помощью pytorch. Для этого будем пользоваться `torch.nn`." ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "l9tVvQ3LVLIR", "outputId": "b9b75365-9c3b-49c0-b386-bcf8276466d3" }, "outputs": [ { "data": { "text/plain": [ "Sequential(\n", " (0): Linear(in_features=1, out_features=1, bias=True)\n", " (1): ReLU()\n", " (2): Linear(in_features=1, out_features=1, bias=True)\n", ")" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = nn.Sequential( # собираем модули в последовательность\n", " nn.Linear(in_features=1, out_features=1), # кол-во признаков во входном слое 1, в выходном тоже 1\n", " nn.ReLU(), # та же ф-ция активации, что и раньше, только из pytorch\n", " nn.Linear(in_features=1, out_features=1) # кол-во признаков во входном слое 1, в выходном тоже 1\n", ")\n", "\n", "model" ] }, { "cell_type": "markdown", "metadata": { "id": "RxS3buhB5CQn" }, "source": [ "Для того, чтобы работать с данной моделью, нам понадобится поменять размерность x и y." ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "id": "JiM1tlA_4O2N" }, "outputs": [], "source": [ "x_new = x.reshape(-1, 1)\n", "y_new = y.reshape(-1, 1)" ] }, { "cell_type": "markdown", "metadata": { "id": "OJTD7Zyh5Kcp" }, "source": [ "Применим модель к нашим данным и посмотрим на результаты для первых 10 элементов." ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OcOrnHOC429Q", "outputId": "b3984de7-eebd-4603-ea47-961d0f189b04" }, "outputs": [ { "data": { "text/plain": [ "tensor([[-0.6562],\n", " [-0.6562],\n", " [-0.6562],\n", " [-0.6562],\n", " [-0.6562],\n", " [-0.6562],\n", " [-0.6562],\n", " [-0.6581],\n", " [-0.6648],\n", " [-0.6568]], grad_fn=)" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model(x_new)[:10]" ] }, { "cell_type": "markdown", "metadata": { "id": "PFc_yRe4VPuI" }, "source": [ "Посмотрим на параметры модели с помощью функции `named_parameters`, которая кроме параметров, выдает также их названия." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "X5nhZ6885WLJ", "outputId": "428d707b-3a80-4e6e-9dfa-d9c5a3d137c6" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.weight\n", "tensor([[0.4398]])\n", "0.bias\n", "tensor([-0.7145])\n", "2.weight\n", "tensor([[-0.0142]])\n", "2.bias\n", "tensor([-0.6562])\n" ] } ], "source": [ "for name, param in model.named_parameters():\n", " print(name)\n", " print(param.data)" ] }, { "cell_type": "markdown", "metadata": { "id": "x9NsssLpfrWR" }, "source": [ "Инициализируем параметры так же, как мы делали для подобной модели ранее. На этот раз воспользуемся функцией `parameters`, она возвращает только параметры." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "36yc9Y9rAVWc", "outputId": "caf48556-d599-4b95-c070-68b1c9ccb015" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tensor([[1.]])\n", "tensor([[1.]])\n", "tensor([[1.]])\n", "tensor([[1.]])\n" ] } ], "source": [ "for p in model.parameters():\n", " p.data = torch.FloatTensor([[1]])\n", " print(p.data)" ] }, { "cell_type": "markdown", "metadata": { "id": "12KQgVSigFqI" }, "source": [ "Ранее мы оптимизацию производили самостоятельно. Теперь же сделаем это с помощью оптимизатора SGD из pytorch. Установим скорость обучения на уровне 0.01 для всех параметров сразу. Также заменим нашу написанную MSE функцию на соответствуюшую из pytorch." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "id": "EfVLPBsb5N83" }, "outputs": [], "source": [ "optimizer = torch.optim.SGD(model.parameters(), lr=0.01)\n", "optim_func = nn.MSELoss()" ] }, { "cell_type": "markdown", "metadata": { "id": "QVvLXf8REE6a" }, "source": [ "Обучим полученную модель на наших данных. Теперь обновления значений параметров происходят с помощью вызова `optimzer.step()`, а зануление градиентов — `optimizer.zero_grad()`." ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 495 }, "id": "lB5b9R3Y5jYL", "outputId": "20ca1686-1333-4b81-b263-57c53bebc22c" }, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "MSE = 34.986\n", "Готово!\n" ] } ], "source": [ "# Количество итераций\n", "num_iter = 10000\n", "\n", "for i in range(num_iter):\n", "\n", " # Forward pass: предсказание модели\n", " y_pred = model(x_new)\n", "\n", " # Подсчет оптимизируемой функции (MSE)\n", " loss = optim_func(y_pred, y_new)\n", "\n", " # Bakcward pass: подсчет градиентов\n", " loss.backward()\n", "\n", " # Оптимизация: обновление параметров\n", " optimizer.step()\n", "\n", " # Зануление градиентов\n", " optimizer.zero_grad()\n", "\n", " # График + вывод MSE через каждые 5 итераций\n", " if (i + 1) % 5 == 0:\n", " show_progress(x, y, y_pred, loss)\n", " \n", " if loss.item() < 35:\n", " print(\"Готово!\")\n", " break" ] }, { "cell_type": "markdown", "metadata": { "id": "6naQC75WFL3d" }, "source": [ "Полученная модель довольно хорошо приближает данные, однако дольше сходится к оптимумум за счет меньшей скорости обучения для параметров сдвига." ] } ], "metadata": { "colab": { "collapsed_sections": [], "name": "nn_simple_examples.ipynb", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 1 }