From a6a08a9a4bd5b5128076f75e8eee537968798f8b Mon Sep 17 00:00:00 2001 From: Xiao Liu Date: Thu, 21 Feb 2019 12:04:01 -0800 Subject: [PATCH 1/3] minor comments modification --- .../2.1.4-pytorch-basics-data-lorder.ipynb | 30 +++++++++---------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb b/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb index c0eebae5..90e16256 100644 --- a/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb +++ b/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb @@ -17,7 +17,7 @@ { "data": { "text/plain": [ - "'1.0.0'" + "'1.0.1.post2'" ] }, "execution_count": 1, @@ -39,7 +39,7 @@ "## Dataset\n", "Dataset是一个抽象类, 为了能够方便的读取,需要将要使用的数据包装为Dataset类。\n", "自定义的Dataset需要继承它并且实现两个成员方法:\n", - "1. `__getitem__()` 该方法定义每次怎么获取数据\n", + "1. `__getitem__()` 该方法定义用索引(`0` 到 `len(self)`)获取一条数据或一个样本\n", "2. `__len__()` 该方法返回数据集的总长度\n", "\n", "下面我们使用kaggle上的一个竞赛[bluebook for bulldozers](https://www.kaggle.com/c/bluebook-for-bulldozers/data)自定义一个数据集,为了方便介绍,我们使用里面的数据字典来做说明(因为条数少)" @@ -75,7 +75,7 @@ " return len(self.df)\n", " def __getitem__(self, idx):\n", " '''\n", - " 根据IDX返回一列数据\n", + " 根据 idx 返回一列数据\n", " '''\n", " return self.df.iloc[idx].SalePrice" ] @@ -120,13 +120,13 @@ } ], "source": [ - "#实现了__len__ 方法所以可以直接使用len获取数据总数\n", + "#实现了 __len__ 方法所以可以直接使用len获取数据总数\n", "len(ds_demo)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -141,7 +141,7 @@ } ], "source": [ - "#用索引可以直接访问对应的数据\n", + "#用索引可以直接访问对应的数据, 对应 __getitem__ 方法\n", "ds_demo[0]" ] }, @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -167,12 +167,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "DataLoader返回的是一个迭代器,我们可以使用迭代器分次获取数据" + "DataLoader返回的是一个可迭代对象,我们可以使用迭代器分次获取数据" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -193,7 +193,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "或者直接使用for循环对其进行遍历" + "常见的用法是使用for循环对其进行遍历" ] }, { @@ -213,7 +213,7 @@ "source": [ "for i, data in enumerate(dl):\n", " print(i,data)\n", - " #这里只循环一遍\n", + " # 为了节约空间, 这里只循环一遍\n", " break" ] }, @@ -300,7 +300,7 @@ "source": [ "from torchvision import transforms as transforms\n", "transform = transforms.Compose([\n", - " transforms.RandomCrop(32, padding=4), #先四周填充0,在吧图像随机裁剪成32*32\n", + " transforms.RandomCrop(32, padding=4), #先四周填充0,在把图像随机裁剪成32*32\n", " transforms.RandomHorizontalFlip(), #图像一半的概率翻转,一半的概率不翻转\n", " transforms.RandomRotation((-45,45)), #随机旋转\n", " transforms.ToTensor(),\n", @@ -336,9 +336,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Pytorch for Deeplearning", + "display_name": "Python 3", "language": "python", - "name": "pytorch" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -350,7 +350,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.8" } }, "nbformat": 4, From af8bfffe9e4331c008ccb4d241819dd8a9f5d634 Mon Sep 17 00:00:00 2001 From: Xiao Liu Date: Sat, 23 Feb 2019 10:47:32 -0800 Subject: [PATCH 2/3] minor comments modification --- chapter2/2.1.4-pytorch-basics-data-lorder.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb b/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb index 90e16256..d7ef578a 100644 --- a/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb +++ b/chapter2/2.1.4-pytorch-basics-data-lorder.ipynb @@ -126,7 +126,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -156,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { From 9b1a7556ca098e326fd5d45a781b3ff1440e15a3 Mon Sep 17 00:00:00 2001 From: Xiao Liu Date: Sat, 23 Feb 2019 12:54:10 -0800 Subject: [PATCH 3/3] add more comments --- .../2.2-deep-learning-basic-mathematics.ipynb | 143 ++++++++---------- 1 file changed, 62 insertions(+), 81 deletions(-) diff --git a/chapter2/2.2-deep-learning-basic-mathematics.ipynb b/chapter2/2.2-deep-learning-basic-mathematics.ipynb index 9e2f13e8..03ba639d 100644 --- a/chapter2/2.2-deep-learning-basic-mathematics.ipynb +++ b/chapter2/2.2-deep-learning-basic-mathematics.ipynb @@ -56,7 +56,7 @@ { "data": { "text/plain": [ - "'1.0.0'" + "'1.0.1.post2'" ] }, "execution_count": 2, @@ -82,7 +82,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "下面定义一个线性函数,这里使用y = 5x + 7,这里的5和7就是上面说到的参数a和b,我们先使用matplot可视化一下这个函数" + "下面定义一个线性函数,这里使用 $y = 5x + 7$,这里的5和7就是上面说到的参数a和b,我们先使用matplot可视化一下这个函数" ] }, { @@ -93,7 +93,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 3, @@ -102,20 +102,18 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "x=np.linspace(0,20,500)\n", - "y=5*x + 7\n", + "x = np.linspace(0,20,500)\n", + "y = 5*x + 7\n", "plt.plot(x,y)" ] }, @@ -152,39 +150,19 @@ "execution_count": 5, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\ProgramData\\Anaconda3\\envs\\pytorch1\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n", - " return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "sns.lmplot(x='x', y='y', data=df)" + "sns.lmplot(x='x', y='y', data=df);" ] }, { @@ -207,7 +185,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "损失函数我们使用均方损失函数:MSELoss,这个后面会详细介绍" + "其中参数(1, 1)代表输入输出的特征(feature)数量都是1. `Linear` 模型的表达式是 $y=w \\cdot x+b$, 其中 $w$ 代表权重, $b$ 代表偏置" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "损失函数我们使用均方损失函数:`MSELoss`,这个后面会详细介绍" ] }, { @@ -223,7 +208,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "优化器我们选择最长见的优化方法 SGD,就是每一次迭代计算mini-batch的梯度,然后对参数进行更新,学习率0.01 ,优化器本章后面也会进行介绍" + "优化器我们选择最常见的优化方法 `SGD`,就是每一次迭代计算 `mini-batch` 的梯度,然后对参数进行更新,学习率 0.01 ,优化器本章后面也会进行介绍" ] }, { @@ -255,7 +240,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "w,b是我们所需要训练的模型参数,就是5和7,记录下来,然后最后显示一下我们这个模型最后训练的结果是多少" + "准备训练数据: `x_train`, `y_train` 的形状是 (256, 1), 代表 `mini-batch` 大小为256, `feature` 为1. `astype('float32')` 是为了下一步可以直接转换为 `torch.float`." ] }, { @@ -264,7 +249,6 @@ "metadata": {}, "outputs": [], "source": [ - "[w, b] = model.parameters()\n", "x_train = x.reshape(-1, 1).astype('float32')\n", "y_train = y.reshape(-1, 1).astype('float32')" ] @@ -285,42 +269,41 @@ "name": "stdout", "output_type": "stream", "text": [ - "epoch 100, loss 0.4731859564781189\n", - "epoch 200, loss 0.06617910414934158\n", - "epoch 300, loss 0.0634840652346611\n", - "epoch 400, loss 0.06299427896738052\n", - "epoch 500, loss 0.06262210011482239\n", - "epoch 600, loss 0.062333136796951294\n", - "epoch 700, loss 0.062108736485242844\n", - "epoch 800, loss 0.06193448230624199\n", - "epoch 900, loss 0.06179914250969887\n", - "epoch 1000, loss 0.06169406324625015\n", - "epoch 1100, loss 0.06161244958639145\n", - "epoch 1200, loss 0.061549071222543716\n", - "epoch 1300, loss 0.06149986386299133\n", - "epoch 1400, loss 0.06146164610981941\n", - "epoch 1500, loss 0.061431970447301865\n", - "epoch 1600, loss 0.06140892207622528\n", - "epoch 1700, loss 0.06139101833105087\n", - "epoch 1800, loss 0.06137711927294731\n", - "epoch 1900, loss 0.06136634573340416\n", - "epoch 2000, loss 0.06135796383023262\n", - "epoch 2100, loss 0.06135144084692001\n", - "epoch 2200, loss 0.06134640425443649\n", - "epoch 2300, loss 0.06134248524904251\n", - "epoch 2400, loss 0.06133943423628807\n", - "epoch 2500, loss 0.06133706122636795\n", - "epoch 2600, loss 0.06133522093296051\n", - "epoch 2700, loss 0.061333801597356796\n", - "epoch 2800, loss 0.06133269891142845\n", - "epoch 2900, loss 0.06133183836936951\n", - "epoch 3000, loss 0.06133117899298668\n" + "epoch 0, loss 105.8649\n", + "epoch 100, loss 0.7534\n", + "epoch 200, loss 0.1216\n", + "epoch 300, loss 0.1029\n", + "epoch 400, loss 0.0915\n", + "epoch 500, loss 0.0828\n", + "epoch 600, loss 0.0763\n", + "epoch 700, loss 0.0713\n", + "epoch 800, loss 0.0675\n", + "epoch 900, loss 0.0647\n", + "epoch 1000, loss 0.0625\n", + "epoch 1100, loss 0.0608\n", + "epoch 1200, loss 0.0596\n", + "epoch 1300, loss 0.0586\n", + "epoch 1400, loss 0.0579\n", + "epoch 1500, loss 0.0574\n", + "epoch 1600, loss 0.0570\n", + "epoch 1700, loss 0.0566\n", + "epoch 1800, loss 0.0564\n", + "epoch 1900, loss 0.0562\n", + "epoch 2000, loss 0.0561\n", + "epoch 2100, loss 0.0560\n", + "epoch 2200, loss 0.0559\n", + "epoch 2300, loss 0.0558\n", + "epoch 2400, loss 0.0558\n", + "epoch 2500, loss 0.0558\n", + "epoch 2600, loss 0.0557\n", + "epoch 2700, loss 0.0557\n", + "epoch 2800, loss 0.0557\n", + "epoch 2900, loss 0.0557\n" ] } ], "source": [ "for i in range(epochs):\n", - " i+=1\n", " # 整理输入和输出的数据,这里输入和输出一定要是torch的Tensor类型\n", " inputs = torch.from_numpy(x_train)\n", " labels = torch.from_numpy(y_train)\n", @@ -336,16 +319,15 @@ " optim.step()\n", " if (i%100==0):\n", " #每 100次打印一下损失函数,看看效果\n", - " print('epoch {}, loss {}'.format(i,loss.data.item()))" + " print('epoch {}, loss {:1.4f}'.format(i,loss.data.item())) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "训练完成了,看一下训练的成果是多少\n", - "\n", - "我们期望的数据 w=5,b=7 可以做一下对比" + "训练完成了,看一下训练的成果是多少. 用 `model.parameters()` 提取模型参数. $w$, $b$ 是我们所需要训练的模型参数\n", + "我们期望的数据 $w=5$,$b=7$ 可以做一下对比" ] }, { @@ -357,12 +339,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "4.924540996551514 7.020828723907471\n" + "4.994358062744141 7.0252156257629395\n" ] } ], "source": [ - "print (w.data.item(),b.data.item())" + "[w, b] = model.parameters()\n", + "print (w.item(),b.item())" ] }, { @@ -379,14 +362,12 @@ "outputs": [ { "data": { - "image/png": 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/27J8fvG51UiSlLZ1rjvoHldA3tOwB3/En/Litj/Uj0FtYEfVjgn/fuNtnytm6IIgzHpDN6TwhDyYtWZ8YR/IcN55nhZXC23uNjz+AN9/KSMlmP/FPZf55ifzku+pJrORznTsqnYtbqiXotdLWVnZnJidC8JcM96l/kMrYDK0GQyEBrjsuYxaqUalVKFRaejv3cK7l7cmv7NxxSlK83wUWZZeVU44vPplvMZ6cTvdboiAPp4KEGFsM5k+E4TpNjT1kWfKwxv2srdxb9rZ8tBAatFacKldzM+ajy/io9djoe/SzuS5tuzz3LK8E3/ER4V1/YjlhON9mKQ7bzLplcm47gFdp9PR29tLdna2COqTIMsyvb296HS66z0UQbjKREoK6zrriMVj1Dvq8QQ9mHVm8o35Iy7OGTqrrrPX8T/+8F2aLjwG8mCYUypkli75T3qD7ejUt7I8bxk2g43+UP9V5YTjfZhcy0NnJlz3gF5cXIzdbsfhmJl+wXOZTqejuHjm/08kCKMZHvTa3G08e/ZZNpZsTHYwHB4E7R47L557kUueS2Tpsyg1lxKKhqjvqefKwBUAGp2N9AX7sOqsVNo+6mi4v+kAu9/y0uP88+QY1i07zLYlFQyEb+as4yxLc5Zi0piSOe5bS29Neei09LVQlFGUfLkZjoVpcjXxxNtPcOfCO5MPoMn2l5lq1z2gq9VqysvLr/cwBEGYJsODXpevi2x9NhdcF+jydeEJelAr1RxoPsBjax5LPgD6Qn0YNUYUkoKm3iYWZS8iEA1w7MoxCjIKaHW3opSUuANu9Go99T312B2ZHDi2IHnvjKz3uKnKgVFj5HTPaSqsFTy+6nHsA/aUHDeQ8tCp7ajFHXCToc0AoNZei0FtQJKkZMnj/Yvuv6rHOjAjK0JHct0DuiAIc9vwoOcJelBICj7o+oCaohosOguBSICXG18G4NiVY+hUOrQKLZFYBGTQqrS0ulsJRoNk6bLo8nZh0pgwqA34I34u9fXy4qF1yXuo1T4WVv4ab8RFXYeXTF0mOpWOzy37HDXFNdSQWnWyp2FPykMnz5iHO+imydUEgFEzuGJ86Dl1nXVT3l9mskRAFwRhRJPZUCJheNAz68wcv3Ici96CQT3YNtob8tIf7ues4ywKFEhIeCNesnXZROQIvrCPOHGy9FnMz5qPJ+QhS5eFLMPR+mrae7KT9yud/wwGQ+/g7D3oJhKPsNa6FqPWyNHOoxRkFFz1Owx/6FRYKzjScYSuga7Bh4tKSyASYFnRYPfUxCz8nop7pry/zGSIgC4IQlojvfBbV7hu3D1S7B47Tr+Tg60HyTXlsjx3OfnGfHr9vazMW4ksywSiAdo8bSzIWkAkFsGitxCKhig1l9LobMSsNxOOhdGpdSglJQWmAiQkLnRkcPTcsuS98vLfx6/ei0afTzASxRf1oVaqkWWZuit1VOdV0+RqSqZ2hhr+0LEZbSzNWUrHQAeekAcZmfVF65O90xOz8OtdpjicWFgkCEJaQ3PfCklBpjaTWDzG7pO7x9XnO/FAMKgNbC0frP9+/eLrGNQGHlryECatib5gH1qlljxTHpnaTMw6MxXWCnxhXzLYh2IhwrEwecY8ijKKuOIO8pvXNyaDuUHvZnX1r5hX2IherUeFijhxBoIDeENe4nKcDHUGxZnFKFDwRusbV4033YIgpULJl9d9mW9t/hYV1go0Kk3axUIj9Ze5HsQMXRCEtNK98Ov0dhKNRcdV1TH0gZCpzWSraWtyGfzdC+9OWV7vv+SnN9DLbXm3YTMMNsR64dwLZGgzWJO/hgprBVa9jR/vj9Hao07eY9uGOrpDHxCOhbGqrGTrs4nKUTRKDWadmQxtBiqFCpPGhEJSIEkSeaa8tAuJRptp30iz8NGIgC4IQlrpXviNp6ojkXd/4dwLlJhLqMyuxGawpZybbnOJHm8PGuXgLFij1JCpzWTH4h3kmnI53AjPvqcgkVT4+x3L+ez6edg91XzjD9+g1FyKUW1Ep9JxznGOxbbFhGNhml3NaFVaKrIq8Ef8+MI+agpr0u5yNNrq0ImuHJ1pIqALgpBWug0lEjnsoYZWdQzNuxebi/EEPdTaa1lfvB6bwZZybrrNJYbOgreUb2EgYOC7z32UGS7PjfD/3iXzwNLBjZrrOuvINeWmzL5h8C+J4sxiqvOrCcfCuENufAM+TBoTZxxnWJpz/fZemE4ihy4IQlrpmlQ9vvrxlH04h+eTh6ZZFlkXEZfjKCQFF3ovjNioyu6xs6dhD/ua9gFwT8U93FtxP8+8NZ8f//6jvw42rt5PxcJ9zBvyEHD4HCzPXY4v7MMf8ROX45g0JjI0GXxr87f45uZvUm4pR6FQMM8yj2xDNn2BPnq8PdO+v+f1IGbogiCMKF2qoSCjYMR88tCUjM1oY33RehpdjbR72llXtO6q3HOdvY7dJ3cTjUXJM+URjAbZc9zL22c/2qJxRWUdNutl8kx5FJgKUkoPc4w5+CN+1hevp8nVhDvoTtaq72vaR44xB41Sg0VnIRwLY9aZuS3vNjRKzXVbzTmdxgzokiT9G7Ad6JFledmHxx4C/haoAtbJsnxs5CsIgjCXjJZPTlf+p1FpWJi1ECAZZBOz9N0ndqNSqMjJyKGrT8Uzr3+0ynPbsnzuWH2ZQLSCTO2a5PH+UH8yGCfSQhadhfVF62lzt3G4/TDVJdXJUsuTXSe5ff7t5Bpzk9eIy/HrtppzOo1nhv408DPgP4YcOwt8Cvg/0zAmQRDSmIpFPtN9z3R590t9l5CR0av1KfXsWqWWmBzDqsnnxbdXEQhpktf5yvYrfHXTvew6dnzUl7CJtNCB5gO8c/kdLrkuUWQuIkObgcvvosnVRI+vhz0Ne3hgyQPJl7PXczXndBozhy7L8tuAa9ixBlmWG6dtVIIgpBi+Z+Zo9d/X857p8u65plzKs8pT6tktOgvHrhyj80oNz765PhnMb1t1jk9seIGy7MGVn0M3rkhIF4xDsRCb521mvnU+Zq2ZN1re4M3WNwnFQlRlV9Eb6OWtS2/R4+uZ8U0nZpLIoQvCLHA9uvpN9J7DUzK7ju3CpDGlnNPdZ+LAu59M/lxe2MHGpZdxBVxEZVUy2Kab8Q9fWj90nBadhVAsRH+kH2QoyyoDYE3BGlRKFXWdddy14K4bto58sqY9oEuStBPYCTBv3rzpvp0gzEkj1X83OBrY07BnWtIwU9VJMDHLDkfD1Hdf5MW3a4h/2KNcpYiz8+42XGE7V7wOlJKSx1c/nvwdxrO0fug4K6wV1HbU4ov40EiaZO35+uL1WPVWur3d123ziZkw7QFdluVdwC4Y3CR6uu8nCHNRukU+be42LrovUmIuGXFzhcnk3UfrJHgt160prOHfTz7NvhOZXOnZkDz+yfVN/GnNRuwDURw+DeuL1qe9zliLeoaOM1FZYx+w44/40aq0LMsdeSOLuUakXARhFkiXejjbc5ZluctGTIlMdjedkdIdldbKEa+buP/QQN/UpeGpA6uS111S1s5D67VolDnYB+yTnjEPH6dGpWFD0QYkJMqyyq7ayGIuk8bah1KSpGeA2wAb0A18h8GXpE8COYAbOCXL8l1j3Wzt2rXysWOiwlEQJmL4rPiC8wKLcxajkD6qbUiU4+1cu5M9DXvwR/wpM+xEL5XxBtF0M/G6zrq01w1EAnR5u3AGnISjYeS4kf3v3ZY8x2yQ+fYDMhrV1WOdrHTjhKsfLrM1by5J0nFZlteOdd6YM3RZlh8e4aM91zwqQRAmbHjqYU/DnlE3V5iKHHi6dMe+pn1pr/tC/Qv0hfpQSiqcnX+E21OW/Pxr2yNYM31oVNOzEcRIaZnZGsAnSiz9F4RZKl3L16HleOMt+btW6a7b5m7jfO95Qr5lNDf8ZTKYL5x3lm0bf8+O5WtHHaswNURAF4RZKl3NdyI/nthY4pULr/BG6xtTWn+d7kFS196C3/5DOtvvBsCgd7Fi+VMYLIeRJXnUsQpTZ8wc+lQSOXRBmH5DX4YGI0HOOM7Q7e1ma/lW7l5495QE0UTO+kj7UV6pnY/XV5j8LKPoX9Bo+ojEIgRjQR5b9Rjf2PSNSd/z42zKcuiCIMwuo20sMVUz4mJzMf/3yEV+e3BT8pjS/Cx9vII/qEIdVmPRWsjWZxOOhbF77GI2PgNEQBeEOWaqFgSNpMXhZes/H0r+nJHRSUHJC/QGHIS8BqLxKFn6LFSSigcXP0h+Zv6kV7Rejz42s5EI6IIwx4y2IGgywtE4n3zyXRq7B5LH7t9UR7OnDqM6E61KTV+wj3gkzuLsxRjVRipzKifd2XCy9fQfJyKgC8IcM57+J9fql4da+P7+88mff/bZVbzT/RMGQgMY1AbCsTAGtQGL1oJSr6TUUopWqQUm/zC5Hn1sZisR0AVhjhialtAoNQQiAXxh36Q2Nb7QPcCd//J28ud7lufz88+uRpIkOL+Zfzj8D0RjUUKxEEa1EaWkJM+QR2+gl80lm6dkheZ0p5DmEhHQBWEOSJeWcAfdE05LRGJxnj58iSf2NSSP/d2nw3xiQR6SJGH32Gnrb+OuBXdxqusUbe42vBEvm0s2s7JgJTIyMTmGQW2YdGfD6UohzUUioAvCHDCVaYl3m5z87cv1NPd4mZ/nZ/uaKAtyDXjD/mTuOnG/eeZ5rCoY7NNyrW0Fxms6UkhzlQjognCDGauiI93nU5GWsPf5eeKVBvaf7SLbJLOmqo6SPA9ZGdUoJFPKQ2Im0yDjaaErDBILiwThBjI0dTJ0Njp0BWi6zzVKDQa1ISUt0drXSsdABwuyFoxa6heMxPinP5ziV4evIMsyZUWN3LNSS3NfAzqVLrkJs81gS1asJDZnnkzjL2H8xMIiQZiFxkqdjPR5IBLAHXQDgzPlxGbJG0s2jljq1+5u5+mjx3nuiEy/X0NFoY+q+Q1E6eHElX56g71E41H0aj3HrxznrgV3JXPXIg1yYxIBXRBmyHgWx4yVyhjpc1/Yl0xLNDgaqLXXolFp6PJ1kaHNSG6OnHgwvN/awjdfOsHFbgOZRj/rlp3AanbgDvsxa81c8V0hFAnhjXiJxCM0OZvINeSSbchOpjsmkwYRC4WmhwjogjADEqmSWDxGp7eT2o5a9jft5/HVj1NT/FGzrLEqOkb7PBEQOwc6sRqs5BnzCEVD1Nprk1uwtbt7+McD5/k/bzejUujZURMnqDmE1WAmGDXi8Dtwh9yoJBVdoS6KMopwB914I17eaXuHv9/69ynbw00kCIuFQtNHdFsUhBlQ11lHLB7jnOMckViEAlMBKoWK3Sd3Y/fYk+eN1RJ3rM8TKZk8Yx6hWAiD2oBRY+RCbxPvNYX419cK+MVbLSwpCfDNT8X4xDLIMmQSjAbRq/QoUNDsaqbeUU8oGkKSJPQqPeXmcvxRP883PJ8y3on+WyTSRgpJkdzcua6zblLXFURAF4QZ4fA56PR2YtQYMagNSJJElj6LaCyaEsjGajOb+DwQCbC/eT/vtr2LRqlJuY9JY6LCWoEv7MMf8RMIZLH/aAW/PWwkJ0PP81+8iS/cpkKpGuxpnjj3sucynqCHHEMOMTmGRqnB7rETlsPEiZNvysfhc7C3ce+kgnpijEOZNCYcPseErykMEikXQZgBOcYcajtqKTAVJI8Fo0HyTHlXBbLxpDJCsRCb521OvpBMpCwSKRmb0UZ1zgZeOh7hQns+alWMr91VxF/cWo1SIZHv+eilplVvZUnOEl6+8DJWg5UcfQ4yMka1kV5/L8FIEFkjk63Pxma0JWfTE02PiIVC00cEdEGYATWFNexv2s9lz2X8UT+eoAeFpGBjycZrDmSjVcLUFNbw0vm9nLio4dVTNnxBWFHu5Yn7NrCsoCx5jeEvNUvMJWwt30pVThUKSUG5pZzXLr5Gf7gfvVLPPPM8FJKCCmvFpOvNRYXM9BEBXRBmQLG5mPsq7+P7h7+PQlKQY8zBqDZS76hn28Jt13St0Sph+gYy2F+7hPpOP0XWMDtvl/jU8k0j7rc59Pju47s5eOkgkVgEs9bMnfPv5ODlg/gjfmxGGxXWCmwGG/2h/knNpsVCoekjArogzJCIHOHTSz9Nl7cLT8iDWWsm35SPfcBODePfFi5dyqK738dpsYCBAAAgAElEQVTBs/n8wwvvkm3U8E8PVfOpVUUoFNK4rmn32Onx9tAX6CNbn00gGuCM8wxV2VVYdBbKssowaUxpm21NpARxohUywuhEQBeEGeLwOSi1lFKeVZ48NlKv8JGCpN1jp6m3id9d+B1alZZFWYsJDtRwuCGLcFRBzUIvf7RWw4YyUCikcQfbus46yrLKKMwspMnVhCfowaKzsMi2iLsX3j3ibFqUIN5YREAXhBky3peBdfY6dp/cTTQWJc+URzAapHOgk3WF63i15VWa+5pZnL2Yiz0Sr9atIhbOQW9oZ8vKdjaWz0eWdOxt3Mu6wnUc7Tw6rmCbSOMoJEVyEVLiYTPabFr0Kr+xiLJFQZgh6WrIW/ta6fX3suvYLvY07BkM5id2o5JUFGQUEI6FOec4Rywe4/mG53EGnBgUBVy8tImLLX8EcROGnGdYsOAVskxB6jrqCMfCWHQWnm94ftz13omHzVDjqTwRJYg3FhHQBWGGDK8x90f8SEjo1XryTHn4I352n9yNJ+TBqreikBTJhUFXvFdod3dy/lIRf6jdwKWubAryTlO1+Dkk7UniH/YeN2qMNLmaMGlMdPR3pARbp9/J2Z6zvHDuBfY07LmmBU0jmeiDQJgeIqALwgwqNhezo2oHO9fuxGawUZZVljKDjsaihGNhAtFA8js6lY6GDpmzDZ/k/KUl6AwdzK/4DWHDC4Tjg1vAqZSq5LmeoAdv2EtRZlEy2Dr9TmrttXiCHorNxfgj/pQFQmMtaBrJRB8EwvQQOXRBuE7SlR/mmfLwRXz4wj4AYhEzRxoK6eotwJYhU1q6D1l7BoPOQm8ozsW+i1RaK8lUZ+KP+JFlGbVSjTvo5sGqBznaeRSAC70XUEgK4nKcRdZFaXPdE6k8ESWINxYR0AXhOkmkK8KxcLKyJBgNopSUVFqXcrBeQX1rMTIyy+dfYtG8duKylgaHkZa+FpDBrDVTkFnAzcU3c8Zxhm5vN1vLt3L3wrspNhdTkFFAXWcd7Z52is3FLLIuwmYcfOkZjAR5t+3dSXc8FCWIN44xA7okSf8GbAd6ZFle9uExK/BboAy4BHxaluW+6RumIMx+w0sI1ZKaV5peobWvFZvBRp4xj2g8hhRawX+8OQ+3T0VedgefXBNleWExv/7gEJ0DnSyxLWFN0RoC0QBdA12Eo2FicoxbS2+9KigPDbZDN6Rw+py80/6OKDecY8YzQ38a+BnwH0OO/Q3whizL35ck6W8+/Pmvp354gjA72T129jfv50TnCWRJZr5lPuFYmPKscvJMeckNKCQkcgw5BKIBWnr6iQ88QFuPiTyzzJfujFBsMyaDcEyOoVPp6Av1kZeRh0FtwKwzk6HNYOfanaOOZ/hy+9M9p5Flmeq86mT+HkS54Ww3ZkCXZfltSZLKhh2+H7jtw//9K+AtREAXBGAwmD996mma+5rJ0mUhIbG/eT82g42ijCIUWgVd3i6y9dlcdF+kOreG+tYSWi4VoFDE+aOaGItKulAqwKQZzLE7/U5cAReXPZdp7mvG6XeSoc0gQ5VBqbl0zDENz3UHo0FuKb0lWXMO07cnqDBzJppDz5Nl+QqALMtXJEnKncIxCcKskW4lZl1nHc6Ak2x9Nga1AYBoPMrFvov89txvWZ2/mo7+DgozihhwV/C7ppUEQjrmF/RQPu8MNQvWYFAPlv0lcuy19lqUCiUKFMjI9Hh7UClVaJVarAbrqONJ99JzT8Me/BF/yu8iyg1nv2kvW5QkaackScckSTrmcIjFBsLckVj27o/4k3Xkexv3csF5gXA0jF6lBwY3T/aGvQQjQRQoCMVCtDnD7Du6kA773ahUIW5ddZzqRR+QoY8ny/4SJYHvXn6XzoFOOjwdeCNeSjJLWJi9EL1KP9hbHWnU8aTrXS7KDeemiQb0bkmSCgA+/G/PSCfKsrxLluW1siyvzckRT39h7hhp5x1X0IVGpUnWkncOdGLRWVAoFEgYqG9ZzKWLn6Xfl8nWagf/fVuALLOLbm83S3OWJl9MFpuLWVe4jgvOC/gjfvRqPRVZFfSH++kP9hOJRril9BbicnzU8aRbGTrRunPhxjbRlMvvgEeA73/4371TNiJBmCVGamObpcsiGo/S3NdMXI7j8rtAlrDK99DUuJ5wRMPCoi5WzG9j8/zVOHyOtBUqAPYBO2uK1qCUlFz2XCYSi2CVrMTlOMtyl6FT6ZJpnbE2mB5OlBvOPeMpW3yGwRegNkmS7MB3GAzk/yVJ0p8Bl4GHpnOQgnAjSeSpT145iVatpTqvGpvBhtPv5HT3aUKREKsLV6NRamjtayUaLqCv5076vdmU5cg8tEHGkqHHoF7Njqodo97L4XOwPHc5dR11ZOmyaPO0IUkSvrCPfGN+SitbsROQMJ4ql4dH+GjrFI9FEG54Q9vFzs+az77mfdTaa1mUvQhv2ItJa2JzyWZ0ah1tLiddV7bQdtGARh3irtU91MyPc9Z5hu4rgwuA7B771bPyIS82W/paKMooYn3xeppcTQSjQVxBF2XmMkrMJSmzerETkCDJsjxjN1u7dq187NixGbufIAw1kY0Yhn8vEWAzNBnUdtQSj8e54rvCJfclbAYb2xduZ2F2Ja+e8fHaaS2xmIpbl8DS8jZOdr+H0+9Eo9Rg1VuT+3c+svKRtP3FTRpTsl59Y8lGSi2lySA9Ur57or+jcGOTJOm4LMtrxzpPLP0XPhYmuhHD8O8dsR/BHXSjltQYNUYMagNatZaLfRdRSkpeO9/Cc84FdLkzsFlcLJl/lgeqNwGlnO89SSQeYXnecnQqHcFokJNdJ+l4p4OV+SvJMebg9DtT+osnNsPoGOhAp9KN2StF5MU/3kRAFz4WJroRw/Dv5Wfk4w64aXY3s7ZgLf2hfuod9WilXJxX7sXtrkSnCVC1oI7FxQH0Gl3yWh3eDpSS8qPa9FgUV8BFX6iPuxbehTfs5WDrQbaWb03Jg5daStGpdGOuBhUEEdCFOSORbmh0NtIX7MOqs1Jpq6SmsCZtBch4mlMN/16FtYIj7UeIxCP4Ij5aXG14etficd5MTJbIzT3B+kVuWj2NuILZ3JZ/W/K74WgYo8aY/LnT24lGpQFIlhnmmnI54zjDVtNHr6jEi01hvERAF+aERGokFo/R6m5FKSlxB9zo1Xo6BzrRKDUpFSDjbU41vHLEZrCxNHcpCoWCxg6JprZPEQlnk291Ulx0hHyLkpgcx6K1UGmtRKPUEJfjeMNe8k35ROKRwZpylR530E0sHmNJzpLk/ZbnLuf1i6/TH+oXLzaFayYCujAnJFIj9T31mDQmDGoD/oifLl8XS3OWEogEuNR3CUfAQTga5nL/ZfRKPbeW3jpqc6p0lSPeoIaA43Oca/Jg1AdYsvg9lhbLVFjXYTPa6A/1Y1Abkm0AEn3Cv7rhq7za8irOwGBfFq1Ki06pY03hmuT9dCodW8u3Jhf8iP7iwrUQAV2YExKpEU/IQ5YuCxgMju6gG5PGhN1jR+ajiq5ANIBZY065RrpFOEObWnV4ujnTmsebZ7OQpAG+dkcl96zUcqClO1mVklhCnwjCQ6tX6jrrUCkG+68UGAtYVbCKHm9Pyix+tAoWQRiLCOjCnJBIjZi1ZgLRAAa1gWA0iFlnxhv24gq6WJG3gur8agAs7RbcQTdNrqZkx8GRctVFmUU0dKh59tA5Lrv83LM8j2/eu4Qiy2CvlvtVo+/YM7RSpiqnKhm4ty3cBiB2+xGmjAjowpyQSI3km/Kpd9QTiASIxWOUmktxB91k6bJSNkyusFZwpOMIXQNdKbPj4bnqVqeP//VyPW81OliYa+LXj61n40JbyjljlQqOVmGzo2qHCODClBEBXZgTEo2snm94nh7vYK+4xbbFydWUdZ11qS83jTaW5iylY6Djqtmx3WPn3bY6flvr4fTFbJRKmfvXynz1E/Mps9pGG0Za19pjRRAmSgR0YU6we+wc7TzKirwV3Fxyc3LGPbQUcfjLTaVCyZfXfTllhtzubucf33ydgx9Y8QZzKMq1U1Faz4KihbzSbJ9Qflv0WBFmigjowpww1sKh0XanT7ywrO/s43d1WnrcuWRleLm1qpF5ORH8ETVd3i7yTfk8efRJFmQtuKZl9aLHijBTREAXZp10/UpGSms0OBrY07BnxMVDdo+d3555meNNeRy5kItCEWHBvDpiurdxRHT4nUYKTAU4/U56/b1E5SiV2ZUcajvEc/XPsaV8C9sWbhu1wVaOMYd1heuwD9jFy09hWonmXMKsMrx5VWK2q1FqMKgNKWmN1r5WzjrOcmvprQSjQc70nKHH25MMwoUZRfyP3/+Ol08o8Aclbl4ERusbnOutIxQNUZhRiCRJ9Af70al0yRa53ogXi85CriEXk9ZEhbUiJRUz0hhFOaIwUaI5lzCrjdQ1cKTUSiASwB100+vv5Yr3Ct3ebtr729k8bzPhWJi6jjqMGiN5pjzqHfVcdsLxxoWc61QN9ii/Q2aeDV5tjqBRaJCV8uCWcYrBTb26vd30h/rRqXWDGz9LEu2edvJN+VgKLSkLkibaN0YQJksEdOGGM1pnxJFSK76wj3WF69h9YjcxOUauKRdfxEdHfwcOvyPZGTEQUlLbmIe9qwCjzsfDG2MsmefFohsMujIy87Pm0xfoS86ubXobroCLcks5rqCLWCxGX6gPd9CNJ+ThltJb8IV9yfGIqhbhehEBXbjhjDbDHa1ixD5g59ayW5OfeUNemvqasHvsLLYtITKwnoZLC4nGVNy6RGZ1RRefWrKNvY17UUiDQVetVOOL+NheuR2bcbBEsT/Uj4yMQWMYnP37u9FIGjQKDUqFknfa32FjycbkeERVi3C9THSTaEGYEnaPnT0Ne9h1bBd7GvZg99hx+Bwpi4BgMNg6fI5Rd6sf+j2nz4k74CYcDSOH5nO24V4+aK5Cq3Nwy8p3UZpfosPbDJCyWfKynGUszFqIRqVJuf5tZbexNGcpaqWacDSMUqkkx5hDtiEbWZaRkJJjHW2MgjCdxAxdmFaj7aAzUmpFq9SOOMMdrfxw6My4ydVEpqaElt5N+B0lKFX92Ar2IulO0RXJIuALAPCrU7/ikZWPpOztmRjz0OvDYB17YUYhC7IWcLn/Mp6gh0pbJavyVxGLx5LfH22MgjCdREAXps1YuwSNlFrxR/y4g24gfd32SEvtE/Xe0RicaLbS3F5BLC6xqcrH5iVBXjrfTqPLSUFmPpX5g61tL7gucKD5AI+teSx5nZGuf/+i+7nsuYzD52BZ7jIqrBXYDB91VxxK7BwkXA8ioAvTZqxqj9FecE5khltsLqZQ8wn+YX8LPR4LeVYHn96goDIvC9ATiodYlrOM6rzq5Hey9dkcu3KMx3jsquul++viy+u+nFKSOLS7oiBcbyKgC9NmrGqP0V4eXusM197n54lXGth/tot5ViM/eDAPZ/wSFp0l2XwrEo2Qa8lN+Z6MjCRLV19vlL8uRDpFuFGJgC5Mm7GqPaZiSXwwEmPX2xf5xVuDLzi/dkclj98yH51aid2TmRJ4ty/aToOzIbljUCAaoC/Yl1KhkiA6JAqzkQjowrSpKazh6VNP4ww4CUfDaFQabHobj658FJjcy0NZlnm9oYfv/j7Rozw/pUd54vrDl/knxuMKuNCoNCzMWpjsSz6UqCUXZiMR0IVpNbScL93PE3l5OJ4e5ekUm4t5dOWjI1bdDCVqyYXZSAR04SqjlRpei7rOOsqyyliRvyJ5rD/UP+El8L5QlJ8dbOapd1rRqBR8694qHrm5DLXyo+UUY5VJjvf3Eh0ShdlIBHQhxVilhqN9b7wdEK81bdHubmf3eyfYUycxEFBy97Is/u6+1eRm6lLu3ehs5KL7IstyllFqKU0ZO3BNv5eoJRdmIxHQhRQTaSw10kNAo9SMmbYYa9b8dnMT33zpNO1OPUVWmYc3DWDOuERYngcUp9zbHXKjklScc5wjQ5uR3Cu0rrMO4Jp/L1FLLsw2kwrokiR9BXgckIDdsiz/eEpGJVw3E5lVj9UBMXGN4WmL0f4ayNDk8ePXL/Cr91rRqrV8+qY4GytBoTDS2teT3Giipa+FoowiMrWZDIQGsOqtBKKB5ObPQ8cuXnIKc92EA7okScsYDObrgDBwQJKkV2RZbpqqwQkzbyIvAye6QCjdgyAuw88OHef1Dwz0+sKsLPfz4DotmfrBPLnT76S+p56oHOXmkps5Yj+CO+gmQ5OBWWsmEA2gU+mSD5KhYxcvOYW5bjIz9CrgiCzLfgBJkg4BO4B/nIqBCdfHRF4GTnSBUOJB4PQ5aXI10eaE8xeX09uvYtU8A//+6DqaB97EH/ECg9ducjWhVCjJ1mWjkBTkZ+TjDrhpcjVRYa2gtqOWQCSAWWe+ahWneMkpzHWT6bZ4FrhFkqRsSZIMwD1AydQMS7heEi8DE90HDWrDmC9EJ9pdMMeYQ5u7jXcuneS9+jIOn7qZgYCWpRUn+Mln57G82HzVtbsGuojJMSqsFQBUWCuIxWN0+7qxGqwsyVlCNB7ForWkjH0iv5cgzDaT2oJOkqQ/A/4b4AXOAQFZlr867JydwE6AefPmrWlra5v4aIUbQroXmcA1lzq29bXz5//1f2lpryIaU7OgqJ3SonqqCyrRq/XYDDYcPgcKSYGMjCzLyZx5eVZ58jqtfa10DHRc8+bNgjBbjHcLuinbU1SSpL8H7LIs/2Kkc8SeorPfRPfLHP4Q0MSq+PkbDs5d6Sc3y83i8g8ozlZSYa0gHo/zRusb3Ft571X3AMR+ncLHzozsKSpJUq4syz2SJM0DPgXcNJnrCTe+yZY16hR57HojQIO9BaMuwk3LmtiwQMt866bk+W+0vkGuKXfEPiqiPlwQ0ptsHfoLkiRlAxHgv8my3DcFYxJuYBMtazSpLdRdyGTfKYjEDCwobmZ1RQ8l5jwOtx9GkkguBur2dnP7/NtHvIeoDxeE9CYV0GVZ3jxVAxFmzmSW9k+krPHoxX7e/CCbbo9EYbaL5QsbsWXKuINuyrM2ANAx0IFOpSPHmMPW8q3oVLqUa4gSQ0EYm1gp+jEz0aX9ie/2+nt5o/UNDGoDOqWOYCyIUlLy+OrHrz4/2aPchkHnZ+2Sc3gV76PVVBKMqjHrzMDgzFyn0rFz7c6UMYIoMRSEayEC+sfMRHLgkPogWJ2/mn3N++gP9pNjysGsNbP7xG4AaoprUnqUy7LMmoorGCyHyTFm0eRSc6r7FPMy57F1wVbg6tn3ePqoTFUDMUGYS0RA/5iZaMOsoQ+C+mA9FdYKWvpa0Cg0VFgrcAVc7Dqxm5YuA794syfZo3xt5WW0Wj3h2C00uZqw6q14w160Ki1WvXXELdxGy5NP5q8MQZjLRED/mJlon++hDwJPyIMr4MKkNhGVo0iShDJeQO25Qv7w3qVkj/LSnCDfe2c3ChRY9BYqrBXcVHwTPb6eSVWpTPSvDEGY60RA/5iZaJ/voQ8Cs9bMhd4LGNQGtFImJ5pKaLhUgEIRZ+sKD7/8zDa6vZ3sbdyLTqVDQiIUDVFrr2V98Xp0Kh13LbiLHVU7JvQ7iN2EBCG9ySz9F2ahiS6BH7oEf0HWAiKxKO09uZxreJD61iLybR184Q4721fpUSsVyVl0dW41gUgAAIPawOnu0+NqCzCaxMNlKFEFIwhihv6xNJE67qEvKlsdIcLOL+LqNWEyuLl9RSMrik0oFcpkoE7MohVaBeuL1tPkasIddCPL8qRz3WI3IUFITwR04SojVZBkaPL4oLmS/3i/DZNWxdfuyqUwN0xvwHZVpcnQFI3NaMNmtNEf6segNkw6zy12ExKE9ERA/xgZHqiLM4qxD9ivarI1vILkpfN78XuW8x/v9eMNwk2Vcb65bQnLCsqAlWnvccF5gZa+FpblfrQd3FTOosVqUUG4mgjoHxPDS/3a3G08e/ZZNpZspNRSSpu7jf1N+wlEAmQbs6nOrUahVeAeyOQ/39XR1TdASXaML94OWZleDrW/gsVw/1W14Yl7LM5ZjF6t56zjLP6In0W2RWIWLQjTTAT0WexaFtcML/Xr8nWRrc+my9tFhiaDc45zqBQqevw92Iw23rl0ErfzFk5e1KHRKFiy4Bh/vnk1CgkSm00MLxMcfo/yrHKyDdn4I34A9jXtE4uABGEaiYA+S421uGZ4sL/gvMDinMXJ73uCHiw6C56QhyZXE0aNcbDE0KWktbOE+ovzicZU3LYUYvr9FFqsHwZzcPqcNLoasXvsAMkAna6cMBgJcrD1IPdW3isWAQnCNBNli7PU0NmwQlKQqc3EorNQ11mXDPb+iJ88Ux7+iJ+Wvhba3B9tLmLWmXEH3Zi1ZjwhD3qVHrtTS2fbn3DqwiIspgE2rXyb26s96LRQaCoEBoN5bUct/cF+Sswl+CN+9jbuxe6xpy0nPOM4k2yFO3ycgiBMLRHQZymHz4FJY0o5ZtKYcPgcaYP9stxlnHWcTW7llm/MpzfQS74pHxVW3jxVwqGTayBu5MGbPNxcfRyTYQCD2sDjqx5HqVDSH+qn0dWIQlIQl+NUZlemBOjh28W19rVyvOM4Lr+L9+3v4/Q7U8YpCMLUEimXWSaRSjnRdQKdSkd1bjU2ow34aHFNutRHKBqi0dHIecd59Go9W8u38pV1X+PZo1fYf6qMaBzWVfbxYE0GEVnGHaxISYsUZBQkZ/8l5hIqsyuxGQbvm1ilObScsMHRwEX3xcGgr8tMWSmqUWrEIiBBmAYioM8iQ/PmNYU1vN32NocuH2JzyWZ0al2yLLCusw5v2Es4GqbJ1cTprtMc7zpOhiaDFXkrMKqNvNnYyd7DLjr6VGxZlMOf35bNleAHOHzp67qHlgn6I37C0TDvt7+PJ+RBrVSzNGdpynl7GvZQYi4hHA1T21GLUWNMrhStsFaIRUCCMA1EQJ9FhqZSMrWZ3FZ2G6e7T3Os8xh3LrwzJQg/feppmvuaUUkqznSfAUCn1OEagAs9axjoX4hR7+WpRzaztSoxm18w5hhqCmuS187SZaFVaukN9NLj7cHusSfv7/A5UCqUtPS14I/4cfgdGNVGjGqjeCEqCNNEBPRZZHgqxWawsaVsC93e7pRGV8XmYvJN+TgDThodjcSJU2QqI+TZzCX7RkCisOA4ZYXNbK168JrGMPTa4VgYs87MbXm3oVFqUsoYFZKCt9vexmawUWIuIRgN4vQ7WVO4RgRzQZgmIqDPEnaPnZa+Fo7Yj2DQGJCQiMtx1Eo1y3KWXXV+XI6zpWwL4WiELlcOffY7iEetaAz1VM1vwB25SIllw4TGkri2QlKkHBva7VBGRpIG6xwlPvyvJCEjT+iegiCMTVS5zAKJ3HlRRhGhWIjTXac523OWUDSEO+imy9uVrAlPyDHmcMnpp65+LX1XPkNUDiJbfkJG7m/xxdqJxWM8eI2z86HXHqvboSzLbC7ZjFappS/Yh1apZXPJZmRZBHRBmC5ihj4LDM2dX3BeYEA/QCASoC/Yx/aK7WhUqekOXyjK8QvF/OZIF0g6rLkH0Zjep9PXTo9fxqAx8Dcb/4aa4om1sB1Pt8McYw7+iJ+bSm5KHks05xIEYXqIgD4LDM2dy5LMitwVyMi4g25sRlsy3SHLMr//4ApPvNJAV3+Q5aVBigrrCMQdeMP5VNoWYDVYKTQVEpEj7Dq2a0JL8cfT7VC0uBWEmScC+iwwfLegQHRwwwizzgwMpjui4Vwe3n2EIxddLCnI5GefXcUJ5/PkmTam5Lp7vD280foGRZlFIy7FH0+PmLG6HYoWt4Iw80RAn0JTtRN9uja3RzuPArAgawHvtL+DLMvcUnoL3QMD7D+p4NRFNRn6Ab73R8t4eN08lAqJU70KDl46SCQWwawzU2GtSFmKD1fvxzmVGzCLFreCMLNEQJ8i4w2EYwX9dNc52nmUdYXrsA/Y8YV9bCzZCLLEiYtaDp3NxBeU+Oz6eXz9zkVkGTXJ63R5u3AH3WTpsghGgrx16S3cATf3Lb4vZexD9+MUGzALwuwlAvoUGU8gHE/QH+k69gF7stb89fMX+O7vG2lzKijLifOThyvZuqjyqvGUZ5VTlFFEk6sJT8hDlj4Lg9qATqVLOXdohYrYgFkQZi8R0KfIWIHQ7rHz5NEncfgc5GfkU2GtSPZCGZrueK35NSRJwqKzDJ5jtCWv4/KF+c7Lx3n5VC8mHXxuU4zFJQOc9/yBRZ7Urd2G7umZ6PUSl+M0OBpwB93J8Q1/WTk0X58gNmAWhNlB1KFPkdFqsxMzc6ffSb4pP9moyul3JjsPJs7RqrXoVDpCsRC1HbU4fU76g14a2/PY8k9v8crpXjYuCvHtB2BDhYRFl74d7UjjWWRbxP2L7segNtDt7cagNqT8hTC8Y2J/qB930J3cnk4QhBuXNJmFHpIkfRV4DJCBM8D/I8tycKTz165dKx87dmzC97uRDU2nDJ35Jio9/BE/9T31hGIhDGoD/ogfrUrL0pylydpsf8RPOBam1j7YzCoux3G6zTS2rqDHo2VNqQm99fdkGn0pM/hE2eLOtTvHNZ6xcuFT9XJXEISpIUnScVmW14553kQDuiRJRcC7wBJZlgOSJP0XsE+W5adH+s5cDugwciDcdWwXeaY8XH5XsvOgVqmly9vFmsI13L/ofvY17RtMkUgKnH4nH1y5xOH6XHp6yzDqIty1cgCr5TLOoANfyEe3rxt3yM2K3BVUZldSYi5J6ecy2ngEQZhdxhvQJ5tDVwF6SZIigAHonOT1ZrWRyvQS6Q+b0cb6ovU0uZro/rBNbWLGnDjHoMrkdIuNfadsRGIylfMu8tjmebzXcZymvj5KM0s53jnYCteitdDsasbhd/D14q+PezyCIMxNE86hy7LcAfwTcBm4AnhkWX5t+HmSJO2UJOmYJEnHHI6P5y41Q/PSVoOVpblLWV2wmi+v+3JK7vpUW5C/f0nmpWMKsjJ7qVnxKsGhmVcAABHPSURBVA/fZEKvURCJRcjWZ1PvrKfKVoVRYyQajyJJEhtLNmIfsI8xCkEQ5roJz9AlScoC7gfKATfwnCRJn5dl+T+HnifL8i5gFwymXCYx1mk3nhTFaOeM9NlYqybtfX6eeKWb/WfzsWXIPHRzLwOKI9QU1iQrYRIrRHv9vSy2LU7uFapVaim1lIqyQkEQJpVyuR1olWXZASBJ0ovAzcB/jvqtG9S4asTtdew+uZtoLEqeKY9gNEjnQCf3L7ofYMTvA2kDfTASY9fbF/nFW80AfP3OSh7bPB+dWsmehgz8EX9yfBXWCg5dPoRJYyIQCSBJEr6wj2VFy0RZoSAI8P+3d+fBcZfnAce/z15aSavDOm1JPkA2xifYWMYMh00gFMMUSgcCZNKGhqOQtJlJMkOTkGnaMhnazJR0miZtTNoJ0EKgMGCnKYZiDJgEX8gYXxjL91q3tdaxh7TH2z9W+lkyknZlrVba1fOZ8Yx+u+/uPq9Xeva37+9535fxJfRTwBoRyQOCwE1Axl7xTDQxyNvp5Zn6Z3DYHJQXlBOKhDjYdpDF5YutksHhHv9Gwxv0RfuGJPrXP91ImX0dP3+nlVMdAW5fNovv376I6uJcK54LF7dyOVzMnzGfxWWL2dO8h0pPJXVVdbgcLl30SikFjCOhG2N2iMgrQD0QAfbQP7SSiRJNDNrVuIuoiVKRW4GIWKWGTT1N1szL4R7/wckPuG7udVaCD4UK2bQ9n8+aTjC/wsN/PXQ1184v+1w8ww3TPHDlA9aHy8AZf54zTxe9UkoB46xyMcb8EPhhimKZVIlmSA4MlwQjQSuZux1umrqbWF292mpfmFNIu7/dqmTxdnlZVb2KHFshb+4Vth4Au93BzcvP8a/3rsdpH/m69EhVKlq9opQajk7975do/e7y/Pgwy64zu+js7SQSixAlSmluqTWLcuPhjZwNnOVA2wHsYschDqo9NbxSfxzvmXK6gzZW1xq+sLyLysK8UZO5UkqN1bRK6KNVqCSqRKmrquNA6wFC0RAGQ1+sj0gsQo1n6ON/uvOn8RLDglKKbJezabebM2fzceW0cPOqZpbXeIiKnbqqG5KOLZn7lVJqXFP/x2oyZ4qOZyr8gF9+9Ev2t+0fsr64y+4iz5lnzdLcsHsDBc5K3txr571D4LCHqZ19EJP7ATVFVdjFzsMrHx6y/Vui2FIRu1Iqc6VrpmjGSKaKJdEZcKLd7mMxw7HGSn5TbyMQgtqaZi6fd4QcZ4Qc+wqumX0NXb1deLu91HE+oSeKTdcoV0olY9ok9NGqWJLdnEJErB2ARITm7mYOtx8mSpR3jxynrXkdR1ocVJWE+Oq6MCd66glF+zjScZpKTyUfnv6Q2hm1+Pv8SceWzP1KKQXTKKGPVsWSzBnwLu8utp7YygnfCWxi41TnKTpCHeTbKimL/Rm/P30lDkcXf3FzJV9atZyPmnaz72w33m4vC0oWUJFfQTASZNvpbSwqW8Rrh16zvg2IyKgVNrpGuVIqGdOmzGK0db7b/G14XJ4h7QfWKQesSUXFOcUsLF3I6a7TtAc68IRvI7fzKfxdyyku2cO8Bc/RZn7LnOL4yodr561lTtEcCnIKMMSvVfT09rC3ZS+BcMCavt/a08px3/ER1yDXNcqVUsmYNmfoo1WxJDoDHjyp6HDgMAWygljwi9ij84g6DpIzYxP2/ABOWwVnus5YzxEzMW6YewNHfUc5FzpHkbuISk8lwXBwyLeBeTPmEQzH69sPtR3CF/JR4i6xZqAmqsBRSinIkoSebEnfSBNyEtWgDzxvo8/BrvoH4g+Ss3S5f0Ke51Nszjya/X7yc/JZXrjcet7y/HIC4QDX1Fxj3fb6p68POx7u7/NTV1VHY3cjs4tmW3EMHsvXBK6UGk3GJ/RkLmgmSviJzoALXeVs2lZJoPf8f1eo6HuEIj48tjKisSgxEyPQG+DuRXdbbYb7oLCLnVmeWUP6MJaxfKWUGknGJ/TNDZs50nFkSG34wB6bF9Zwj1bBMtIZ8FNvHOIX77ms47olO/mo43lKxEOlbQYtgRY6I53UFNSwpmaNVV8+8CHS3dvNqc5TlLhLKMkroSyvjJcPvkyOLYfLyy9nYelC7DY7a+eutXYtGkyrWZRSycrohO7t9LLl+BZmeWZR7C4mFAmxw7uDuuo6qzTwYs96dxw7y70btlvHf7SilLXLm2jzV1PW9Af4Qj6O+44zzzUPj9ODy+miI9SBt9NLU3fTkGV2qwuq6ertouFsA83+ZhaVLqLZ30x9cz2+oI9vrflWUmP5Sik1moxO6Lsad1HpqUREsInNWjRrX+u+IePfYznr7QyGuerJ/yMSi1eluJ02dj5xM4Vup9XG2+nl8bcfx+VwUZJbAgZ6wj2EY2Fe2PcCDR0Nn1tmNxwN0+JvYWHZQvKcecwsmEkgHCBqotZEo0Rj+UopNZqMLlts87exrHwZ/j4/gXAAYwwxE6O1p9Uq6Rs46x1suLNeYwx/9conXPG3b1nJ/JVHr+HTJ9cPSeYQH56xGzvF7mIisQguh4sl5UuoKaxhy/EtRE2UktwS60Mm35XPmZ4zdPd2W0vtAuQ6cumL9FnlkQNj+XnOPFp6Wshz5un0fqVU0jL6DH2gimRg42VfyIfT7uTGS24csqhWorPedz5t4Wu/Or/GzNfX1fL4rZeP+tr57nzKPeXku/Kt2/x9foLh4LDL7PZF+ijIKSAUCVm3ByNBXA7XkA8XrWZRSl2sjE7oA8m62F3M1TVXW8l6/fz1VpvRKljaunup+9HbVtuqIjdbvrOOXJc94WuvmrWKbae2ISK4HW5CkRBng2dZUrGEKk8VB9sOAvGzcF/Qx0zPTGYXzqbZ30xpbikGgy/kY/6M+TpBSCmVEhm/2uLFLCtrjOHPn/+Itw6eH0f/7TevY0lV0Zhe99mPn6Ut2EZfpC9+pp1bzi21t7CzcSfRWJTGnkba/G3WCouzCmaxuWEzu5t2I0ZYWbWS9fPX6xm5UmpUya62mPEJfaw27W3kmy/usY6/f9vlPHJDrXU8lg+Ikdrq2uVKqVTS5XMv4PUFuO4ftlrHi2YVsvEb1+JynL8unGzN+kDb0TbL0ASulEq3rE/o0Zjh/g3b2Xmiw7rtne+s5dJyz+faJluzPpbEr5RS6ZLVCf0/t5/kB6/vt47//o+Xcd/qOSO2T7ZmXafoK6WmoqxM6A2tPdz89HvW8bXzS3nua1djt8mojxttpubgIZb65nrqquqGtNMp+kqpyZbRE4su1BuJ8sWn3xuSzF/9xiLuvraJf69/htcOvYa30zvi40dad7ymoIaNhzdaa5i7HW7eP/k+7YF267E6RV8pNdmyJqH//N0GFv5gM0da47NC71pzlifuaWTzsReHbCax8fDGEZP6SDM1vd1ea4jFJjauqLgCEWFvy17dcEIpNWVk/JDLPm8nf/gvH1jHC6v9/MkNYQpyith6Yiu+oI9cZy4HggfoDHXitDvZ3LCZh656aNjnG65C5cJVEMvyy7h+9vXsbtytG04opaaMjEzo3k4vH5zcxZOv2vH3np/V+eS9fdjsYWtsOxwN47K5ePPomyytWEqxu5hgOMiW41u4df6tSSfg4cbW3U43t8y/hbsW3ZXaziml1EXKuCEXb6eXb7/6Pt99wWUl85WLPuSx2/fze+9bhMIhq21RThHN/maisSh5zjxsYkNEqPRUWtu7JUP39FRKZYKMSug7jp3luqf2svOz+BT9q2oD3Hrtb5hZ2sG53nPkOHPYdnob7f74xcoFJQtoD7aT68zFGEMgHMDf52dZ+TJrhcNk6CqISqlMcNFDLiKyEHhp0E2XAn9tjPmncUc1iLfTy7YTu3ji105iJl526LQbfnSf4eOWPbQHYrQH2jnYdpCllUvp6e1hb+tebpx3Iy6Hi0sKLyHXmYsv5KMop4il1UtxOVzWiofJ0tmfSqmp7qITujHmMHAlgIjYgTPAaymKCzg/I/OTY5XETHwbuLpl27i+topc1yUcbj/MUd9RIiZCgbOAvnAfUROlvrGedn871YXVfHnZlznZdZJid7FuGqGUymqpuih6E3DUGHMyRc8HnJ+R+YXFOSyqilFbCcd9Vexvjc/+POo7SjgWxmV3Uegu5LOOzwiEA1R4KrhnyT309PVwsuskq6tW4+32akWKUiqrpSqh3we8ONwdIvII8AjAnDkjT7sfzsBUfJtAbX/V4NziuQTDQc50n8Fhi4dfkV9BUU4RDf4G+qJ9lOaWYhObVZXi7fZqNYpSKuuNO6GLiAu4A/jecPcbYzYAGyC+fO5YnnukqfiXlV1Gm78NqRU6Ah34Qj78YT8IFLmLqC6ottrrlHyl1HSRiiqX9UC9MSblWXO0csHy/HKqPFXYxMacojmsmLmC8vxyXHYXC0oWWM+hU/KVUtNFKhL6/Yww3DJeo5UL1lXVYbfZWVy+GKfdSVNPE6XuUupm1eFyuLReXCk17YxrxyIRyQNOA5caYzoTtU/1jkXDbTIB6G5BSqmskpYdi4wxAaB0PM9xMS5M5LctuG1I0tYErpSajjJqpiicr01PdgVFpZSaLjIuoQ/eLWigNLHYXTymtVmUUiobZVxCb/O34XEN3Q/U4/KMaW0WpZTKRhmX0Adq0wfT0kSllMrAhK5L2Sql1PAyLqHrUrZKKTW8jNyxSJeyVUqpz8u4M3SllFLD04SulFJZQhO6UkplCU3oSimVJTShK6VUlhjXaotjfjGRNuBitqkrA9pTHM5Up32eHrTP08d4+j3XGJNw9mRaE/rFEpHdySwdmU20z9OD9nn6SEe/dchFKaWyhCZ0pZTKEpmS0DdMdgCTQPs8PWifp48J73dGjKErpZRKLFPO0JVSSiUwpRK6iNwqIodFpEFEvjvM/Tki8lL//TtEZF76o0ytJPr8bRE5KCKfiMgWEZk7GXGmUqI+D2p3t4gYEcn4iohk+iwiX+p/rw+IyAvpjjHVkvjdniMiW0VkT//v922TEWcqich/iEiriOwf4X4RkX/u/z/5RERWpjQAY8yU+AfYgaPApYAL2AssvqDN14F/6//5PuClyY47DX2+Ecjr//mx6dDn/nYFwPvAdmDVZMedhvd5AbAHmNF/XDHZcaehzxuAx/p/XgycmOy4U9DvG4CVwP4R7r8NeAMQYA2wI5WvP5XO0FcDDcaYY8aYPuDXwJ0XtLkTeLb/51eAm0RE0hhjqiXsszFmqzEm0H+4Hcj0dYOTeZ8BngR+DITSGdwESabPDwM/M8b4AIwxrWmOMdWS6bMBCvt/LgIa0xjfhDDGvA90jNLkTuA5E7cdKBaRWal6/amU0KuB04OOvf23DdvGGBMBOoHStEQ3MZLp82APEv90z2QJ+ywiK4DZxpj/SWdgEyiZ9/ky4DIR+Z2IbBeRW9MW3cRIps9/A3xFRLzA/wJ/mZ7QJtVY/+bHZCptcDHcmfaFJTjJtMkkSfdHRL4CrALWTmhEE2/UPouIDfgJ8EC6AkqDZN5nB/Fhl3XEv4VtE5GlxphzExzbREmmz/cDvzLG/KOIXAM839/n2MSHN2kmNIdNpTN0LzB70HENn/8KZrUREQfxr2mjfb2Z6pLpMyJyM/AEcIcxpjdNsU2URH0uAJYC74rICeLjjJsy/MJosr/bG40xYWPMceAw8QSfqZLp84PAywDGmA8BN/H1TrJZUn/zF2sqJfRdwAIRuUREXMQvem66oM0m4Kv9P98NvGP6rzRkqIR97h9++AXxZJ7p46qQoM/GmE5jTJkxZp4xZh7x6wZ3GGN2T064KZHM7/brxC+AIyJlxIdgjqU1ytRKps+ngJsARGQR8YTeltYo028T8Kf91S5rgE5jTFPKnn2yrwoPcwX4M+JXx5/ov+3viP9BQ/wN/2+gAdgJXDrZMaehz28DLcDH/f82TXbME93nC9q+S4ZXuST5PgvwNHAQ2AfcN9kxp6HPi4HfEa+A+Ri4ZbJjTkGfXwSagDDxs/EHgUeBRwe9zz/r/z/Zl+rfbZ0pqpRSWWIqDbkopZQaB03oSimVJTShK6VUltCErpRSWUITulJKZQlN6EoplSU0oSulVJbQhK6UUlni/wE6CjVTC4pXJwAAAABJRU5ErkJggg==\n", 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\n", 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