{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Supervised Learning II\n",
"by Mauricio Araya\n",
"## (A.K.A. Supervised Learning Lab)\n",
"\n",
"Credit: Guillermo Cabrera, Matthew Graham, and Scikit Learn\n",
"\n",
"(Yes... I will force you to code now...)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1.- Regularized Regression... or ridding alone\n",
"\n",
"### Objective\n",
"* Understand the effect of the regularization parameters\n",
"* Compare Ridge and Lasso\n",
"* Warm up!\n",
"\n",
"### Regularization Reminder (slide)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Exercise\n",
"### a) Forging your own Galaxy Photometry/Redshift dataset\n",
"* Use my Tuesday's notebook to load the `sdss_gal.csv`\n",
"* Downsample the data (use $n=10000$ for example)\n",
"* Divide into training and test/validation\n",
"* Select the 'u-g' feature (and hack it to be a matrix)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"galaxy_feat = pd.read_csv('sdss_gal.csv', low_memory=False)"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"n=10000\n",
"gal_sample = galaxy_feat.sample(n=n,random_state=0)\n",
"train_data = gal_sample[:int(len(gal_sample)*0.75)]\n",
"test_data = gal_sample[int(len(gal_sample)*0.75):]\n",
"X=train_data[['u-g','g-r','r-i','i-z']]\n",
"y=train_data['redshift']\n",
"Xp=test_data[['u-g','g-r','r-i','i-z']]\n",
"yp=test_data['redshift']\n",
"dim='r-i'"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [],
"source": [
"y_train = train_data['redshift']\n",
"X_train = train_data[dim]\n",
"# Formatting hack...\n",
"X_train=np.atleast_2d(X_train).T\n",
"#X_train=X_train.values.reshape(len(X_train), 1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"### b) Use the scikit-learn to perform a ridge regression \n",
"* Use now a polynomial model (degree = 10 for example)\n",
"* Plot your data and the curve line (use the `.predict()` function, not manually)\n",
"* Plot the parameters in a bar plot "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import Ridge,Lasso\n",
"from sklearn.preprocessing import PolynomialFeatures\n",
"gen_poly_terms =PolynomialFeatures(degree=10)\n",
"X_train_with_poly = gen_poly_terms.fit_transform(X_train)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Ridge(alpha=1.0, copy_X=True, fit_intercept=True, max_iter=None,\n",
" normalize=False, random_state=None, solver='auto', tol=0.001)"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"poly_regression = Ridge()\n",
"poly_regression.fit(X_train_with_poly, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"x_vals = np.linspace(np.min(X_train), np.max(X_train), 500)\n",
"x_vals = x_vals.reshape(len(x_vals), 1);\n",
"poly_vals=gen_poly_terms.transform(x_vals)\n",
"y_pred=poly_regression.predict(poly_vals)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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0169RyXRKA+/UWj8803k3b96sH354xsMsloYhrpbZpr9qrYlGozgcjgkpqh0dHdOeZ67XawStvPaS5Pzz4YAD4Etfmvs5nngCXv962L4dmpi+rJTaqrXePJv3NK0OQmt94Qyva+CDzbq+xdIo5tqJbqo6hmr3zeRJuJWV09Y4zJKf/hQeewy+9735nefoo6GnB/7rv+CMMxoztgZhHYwWS5Oo5dcvlUrEYrGaLqeZMomaFcvQWpPNZhkdHV30rrBFQyoFV1wB115bkdCYL+94B9x00/zP02CsgbBYmsRkv74YCqfTWdMATJdJ1KxYRqFQYHR0lL6+PmKxGIBNca2Hz3wGXv5yePWrG3O+c8+Fu++GYrEx52sQ1kBYlhRLISOomuo6hra2NpxO55SppFNlEimlmlKjIDuWYrFIJpMhk8mwZ88e83yrU1yX2nNvKk8+Walf+PKXG3fOdetg/Xp48MHGnbMBWANhWTIslYygyUgMQ2oZpkolnSqTqHqylh1GI2oUyuUy5XKZbDaLy+XC7XajlJqwk4Cl+9ybQrkMl11WqXtYsaKx5z7nHLjjjsaec55YA2FZEiy1St9aK+56UklrVU6XSiXi8biZpHO5XENqFBwOhzE0oVCIYrFojEYwGEQptaie+6LYxXznO5X/v/e9jT/32WdX3EyLCNsPwlIXrc5wWUrKptNlItWjGludNaW1JplMEg6HyWQyxlisXbt2xs9hps9MKUVbW5txMwWDQbxeL263G6/XCyye574o+mIMDMAnPwn33gvNKCA89lgYHITdu+dWU9EE7A7CMiOLwcWwVCp961lxz0Y6QyZon89ndhWRSGTGybnez8ztdrNq1SqCwSDBYBCPx0MkEjFjm+tzb+Rqf9HsYv7u7yrZRkcf3ZzzOxxw2mnw3//dnPPPgcX112VZdCyWP86lUunbaE2j6gm6etKeboKu9zMTI5JOp3E6nYRCoX0EAefy3Bu9oFgUOlH33gu//S1cdVVzr3P66dZAWJYOi+KPc5yFUjatl1qr5EbvdOYyQdfzmU02Ik6nk3Q6XfN8LpeLcDhMKBSivb192ufejAVFy3eP2Sx84APwb//W/Fahp59eKZhbJNgYhGVaFlu/5rlWNTcKmWhLpRLJZHIfn7j8O5FImGc1353ObLvdzfSZaa0pFAqUy2Uz2U8VW5DdQCKRACAUCtHW1obX6605jmbELJrxTGfF5z4HRx1VCSI3m5e+FOJx6O+HNTW7Hywo1kBYpqXlf5yLCAmUlstl4vE44XAYn89HqVQikUgYjaVmtC+djWGc7jObfA+RSASv12symKrHqrUmHo+TSqVMNtXAwIC510gkgsvlmnCfzVpQtKwl7LPPwte/XpHUWAiUqjQS+t//hTe9aWGuOQ3WQFhmZDH2a15oql0n8gwymQxer7fmKrnVO51an1n1PbjdbsLhMPF4nEAgQDqdJhAIEI1GTTZTuVwml8uRTCZxOp2kUilzv1prhoeHza6pehfVrAXFgj9TrSuupU9+EtauXbjrnnTSojEQNgZhqYuFaFqzmKl2nUxWZ221220qJn9mk90/Pp/PTN4dHR14PB4SiQT9/f2Mjo6aCmsxAKLZJO61PXv2EI/Hzb1LrGGxxYpmYsqMq5tugmi00rNhIXnZyyoGYhGwuL7RFksLmCklU2ttXhNjEAgE0Fob18xScLvVCvYCpso7mUzidrtxuSqOBdldBINBCoUCpVIJh8NhDI3b7cbtdpNMJicU3cHMC4rqZz6XlNhGpdFOmXE1Ogp///eVRkCuBXa0nHgibN1aaWPaYqyLybJfM1MBVvXrosYqu4i1a9eafy924wC1YxNtbW0kk0kKhYKZrCWOks/ncblctLe3097eTjKZZGhoiEQigcfjwev14nA4KBaLFAqFundRk5+pXNPpdBKJRGbccTSqaK7a5SY7QhNL+vjH4c1vhhNOmPV5501HR6VQ7o9/bF7NRZ1YA2HZb5l2gqjy2cvEJ6tnEd1bCkZhMrViExKLkF1CW1ubeT0UCpFMJo2i7Jo1a4zbqVQqmdRboK5d1ORYjtRhtLW1GYPR09Mz7c5jus9sNkyZcXXffTh//vPKBN0qjj8eHn3UGgjL3Gm1/MVSZ6aUTKk9yOfzZrXq8XiMT36pMjnY63a7CYVCZLNZMpkMY2NjhMNh2tvbcTqdtLe3UxyXoRYpDjEakUjEZELV0yVPdilOp9PEOKoVbhOJBJ2dncbNNZlGptHWzLjK5XC8//2Vmoe2tlmdr6Eccww8/njrrj+ONRBLlEWhTbPEmSklUylFOp3G5XLh8XgoFAqk0+klbRxqkc/n6e/vN64lr9drVupQeQ7BYNDEXCRTqVgs0tXVhVJq2oVK9XcVMMZG3iduusmNkWqdr5FptDVdbl/7GuolL6n0Z2glxxwD11zT2jFgDcSSpJHb7P2ZmWo8tNYEg0FyuRz5fH7CRLlc0FoTi8VQSpmajlwuR6FQMJlN2WyW/v5+AoEAiUSCQCCAx+Ohvb3dfPeqFyrVtRHABDddLpcjGo0aF500T5IYiN/vJxaLGcMxOSYxm7qcenbYE1xuTz+N+va3F8XK3ewgtK7URrSIphoIpdRZwFcBJ/AdrfXnJ72+Hvgu0D5+zCe01j9r5piWA4tFYXM5MF2Nh9QLiFtJ3CONTmddKFdhretIcZy41JxOJ7lczlRZa63JZDJA5VmJUZCYQSwWMy6iUqnE6OjohJhNIBAwbrp8Ps/AwIAJfAPGleRyuXC5XBSLRZLJpHnetWIS9dTlzGaHrZTCCbBlS6XPw2JQUl25siLet3t3Syuq6/qmK6XOr+d3k153Al8HXgu8BLhQKfWSSYd9ErhVa30scAHwjXrGs7/Tcm2aZcZUKZkysVRn28w1nXWqtMyFUsqd6jrVE3mpVCKbzQLQ1tZGqVSiUCiQzWZJp9MkEgmSySS5XI7R0VHTw1oWLIVCgf7+fuLxOOl0mnK5TDKZJJVKUSgUGBwcZHh4mL6+Pnbu3Mno6KiJf2QyGcrlMoODgybW43a7zU5h8vOrjqNMfq5z0oP6xjcq6axbtjTl+c8apWDTppbvZuqdUf6hzt9VcyLwvNZ6m9Y6D9wCvGHSMRqIjP+7Ddhd53j2a2TiWuzKpsuBRhR9TTU5L5RS7uQKapHQqF5di5EIhUKsW7eOcDhMNBplZGSEwcFBvF6viU0MDw/jdDrx+Xw4nU6i0ajpa53JZHC73Ub8r1Qq4fP5jKFQSuH1eo0xSiQS5HI5nE4nbrcbh8NBMpk0O+Lpnt9Uv5+1wOSf/wz/9E+VZkCLaZF19NHwxBMtHcK0Lial1GuBvwbWKKX+teqlCDBTd+01wK6qn/uAl0065irgHqXUFUAQqNkBXCm1BdgCsH79+hkuu39g5S8WjvlIPEwXL1ooV6FcR/SXJNgcDAbx+Xz7fJegEjeIRCLmHJlMBpfLZXpTyHcuEomwY8cOAoEADoeDcDhMKpUyrigJfIvxkeylQqFAoVDA5XKRyWTw+/3muMHBQQqFAg6Hg66uLmNYxAUVj8fp6OiY8rnOKpBdLMIll8CVV8LhhzfsmTeEI4+E++9v6RBmMpf9wMNAFtha9d+dwGtmeG+tGWvy0uhC4Aat9Voqhuh7Sql9xqS1vk5rvVlrvbmnp2eGy+4/LEb5i0Y2ilkOTLeaXShXoZwvHo/jcDhwuVxGW2myy0YpRS6XY2xsjFQqRSwWI5PJkM/nGRsbM6mwslrP5/PmvqTCXLK9ZGcrhsbhcJhrSmzjxRdfZOfOnaarnVKKUChEOBwmGAxSLBYZGxtjbGyMXbt2mX9nMpkpn+usdtif/zyEw/DBDzb0mTeEI4+EZ55p6RBmClJ/RWv9KqXUMVrr787y3H3Auqqf17KvC+ndwFkAWuv/UUr5gG5gcJbXsiwCbOrtvky3mp1NRk41swlqy7GBQICxsTHz+3A4DLDPbkVrTSqVMqmnuVzOaDAppchms3R0dDA6OkooFDKpsDIR7927F5/PRz6fJxKJGDVYcU+FQiHS6bSJP/T29hKJRIjFYrhcLhwOBytWrDCxkJGREYLBoHl++Xwej8fD2NiYGaPL5drHuNa1w37kEfjXf638fzG5loQjj4Q//amlmUwzGYhVSqm/Al6rlPoek3YFWutHpnnvQ8ChSqkDqexELgDeNumYF4FXATcopY4EfMDQLMZvWSS0MvW2FQWD9V5zJiMwW1fhbIzw5PoDn89HNpvF5XKRSCTw+/0TCtiqdZaCwSDJZNLULPj9fpPiq5RicHCQfD7P6Ogo3d3dxnjk83m6urrIZDJks1kikQgej4dSqYTX6zWupGKxaKRLRkZG8Pl8xtU0ODho2p5WGyq32006nSaZTBol3VAoRHd3t6nNqE5RnvaZxuNwwQXor3yF8qpVOMbva1HR0QGBQKU3xEKqyVYxk4H4NPAJKqv/L096TQP/Z6o3aq2LSqnLgV9SSWG9Xmv9R6XUZ4CHtdZ3An8HfFsp9ZHx812qrW9iSdKq1NtW7Fpme82ZjMDkGMdUk9tsjLAEouX6pVLJFP0Bxpcvvw+FQkZfSRRaAaO3JK6hcrlsdiLFYpFYLEY8HmflypVks1mSyaSJIUgW0vr16yekBzscDnw+n9kF5HI5E6gWt5LEQCKRiMkeK5fLjIyMAH8RGhwbG6O9vZ2enh5z/hk/H61hyxbKp55K9KyzKI2M1K0DteAccQQ88wx6zZqWxBunNRBa69uA25RSn9JaXz3bk4/XNPxs0u8+XfXvp4FTZntey+KjFZ3nWrFrmes16w10Tze5TTbCDoeDfD5vnnn1BCJxBJfLhVIKv9+PUor29nbK5TLRaJRSqcTw8LBZ/ff09DA6Oorf7ycajVIsFsnn8wQCAXK5HPF4HJfLZWofJB01mUyya1clH0XSgsfGxoxBSafTrFy5Ep/PRy6XAyq7Gb/fTzqdJp1Os2rVKtrb2/F6veRyOXw+H8Fg0KTKulwuBgYGSCaT5nlLrCuRSNDd3Y3H4wGY+fO59lr0n/7E0O23kxx/RvXoQLWEI4+k9NRTxI4/viWu25mymI7QWv8JuFspddzk12dwMS0+du2q+BvfMDnb1jJf5upPnw+t2LU085ozGZ9qIywZSZPVUCXIK3EE+Z0Un2UyGYaHh4nFYuRyOTo7O4lEIqRSKfbs2UOxWCSdTptucQMDAxQKBUKhENFo1FzX6XQyMjJCd3c3Pp/PtDCt7iEhsQ9xJzkcDlauXEkul2N4eNg0H/L5fMBfajLEBSbv6e3tJZfL0dXVxY4dO0xqbHt7Ox6Ph1QqxdjYGG63G7/fT7FYnHDOCUb0oYdwXHkluf/6L3ZHo6Yft8/nm1EHqhXoI46g8NhjLVNNmOlJ/C2V9NL/V+O1aV1Mi5JcDt79bjj11Ip/z9JQFjr1thW7lmZecybjI0Y3Ho8b90xHR4dxJXV2dlIul410RiQSMX2zy+Uy3d3dbN++HYfDYcT1xsbG8Hg8JvgrO45sNmuCzX6/3xiuoaEhQqEQsViMUqnE4OAgq1evNi6fQCDA9u3bJxQWejwegsEg4XCYzs5O8vk8Xq+XfD5vYgpaa/L5PJlMhlQqRWdnp9ldiFtpZGSEVCpFuVw2RigSiZhU3VKpxO7du9Famw55UouhtcYxOEjneeeR+MIXSHZ14RgaMvebTqfNDmQxUT7kEBw//nHLVBNmcjFtGf//6U0fyUJwyCGV3cP/+3/w2c+2ejTLkvnUDMzlWs3etUyOB8zmmrMNnk9nfORcLpfLVDn7fL4JfaRl0igWi8bn397eblJSxW3j9/vJ5XLs2rXLaCPJar67u5vh4WGy2ayRGZHJWFJcZUzi5hgYGDDB6Fgsht/vN9Lh0vd6cHDQSHNIj+tQKITL5SIUCpHP500PCp/PRyqVwuVymTaoqVTK7FzkO1YqlUilUgwPD5PL5czuSXpX7Nq1i/b2diKRCGN79rD6oouIn38+0dNOIx+PEwqFzPsKhYKR/5Ad2WLAcdhhsGPHgi6Cqql7L6WUejmwofo9WusbmzCm5vKpT1W01j/8YejubvVoLPOkmbuWqeIBc9UCqhaxq/WeqYyPBG3lXDKxyrmqaxlEVFB2D9XnketKoZp2qIvzAAAgAElEQVSI8XV3d5NMJunt7TWxilgsZiTARV4jFovhdrtNjKBYLJqsJJfLZYxCV1cX6XTaBLEzmQylUsnsVIrFIitWrDD3FA6HyefzZqLPZDJmtyHPsVgsksvlTOZVtfy4aEDJzqK6218ymSSfzdLxN39DauVKht71LnzjO4zVq1ebcYs7TnZfiyVFW23YgGPvXsrZLKXx/t8LqZpQl4EYT3E9GHgMkD54Glh6BmLDBnjLW+CLX6z8Z1nyNGPXMlM8YLpr1nqvKJjKeKeagGpVNUejUXMumXQlblAqlYyff3h4GPhLjYMUqFW/z+/3MzAwQLFYJBAIcPDBB+P1es2kk06nSaVS5mepkJaVu6zy5b78fj9dXV0UCgXjMpK6BaUqva6dTqfRbfL5fGitSSaTeL1eEy8QTScpshscHCSdTpvzplIpHA6HKfYTQ9TV1UU2mzVZU4VCgR07dhjD6lCK9quuwjc0xGPXXEMwkyE5Hp8pl8smEC67M7fbTblcXjzqyG43au1aOmKxirtpMWUxVbEZeMmySUH9x3+syOn+7d9WVBMtLaEV9Qv1Ml3G0ExBzFrvTSQSJqg6U6Cx2viI/9zpdFIoFCor4vHq5erOduImkgl8165dZqcRDodJJpNAxc20bt06otEoXV1dE2oU2traGB0dpaOjA5fLZdw0oVDIuLJisRgej8ekpZbL5Qky4RIDEfE+l8tFKpUyhXbi7ioWi3i9Xlwul4mpQMWYSaB5eHjYuLKqjbIEsr1eL9FoFL/fj9frxefzGTkOrSs9szv+5V8IPvIIW7/0JfIOB4HxKm5xiYkLrr+/35xbMrEWjTryIYegXngBZwukQOo1EE8BK4E9TRzLwrF2LVx8caXM/l/+pdWj2S9Z7FXXU2UM1ZMvPzmWIJOivGc2gcZqt1AikTAy3DKBy24DwOPxmDoFWXGL26lYLBKNRo2xCYfDpgZC0l/lei6Xi1wuR39/P6OjoxSLRVKplIkNSLzC7/eTTCbZvn078XicQCBgBPxyuRyhUMjsDJRSpuJZ4gxer9dc0+v1ks1mze5AxAs7OjrI5XJ4PB6TmivjT6VSRtKjo6ODNWvWGHdZW1sb+nOfo/Pee3n4y1/G3dNDfnzsovEkiPqsGNtisUh7e/viUUc++GB44YWWXHqmNNe7qLiSwsDTSqk/ADl5XWt9TnOH10Q+8Ql46Uvhox9tWZXi/korq67rZaqMIdkNTDfWybEEwBR6zSbQKKvYUCjEyMgIo6OjJnArukXFYtG4SGTC3Lt3r+nd4HK5GBsbY2hoyHSLczgcDA8Ps2rVKlKpFIDp8SDumoGBAXM9SV0tlUqEQiHcbjexWMzcq6SKijGU+xLjFwwGTRpstatKMpKgYtykL7YE17PZrAmSJ5NJstms2XHKM3A6nXR2dgKYOERnRwfez3yG8K9+xdZrrsG5ahVaa9Nzore313wOYnjb2trMPcbjcVavXr1ovosccgg8/3xLLj3TDuJLCzKKVrByZSXl9f/+34oW/BJmMbtqarFUGh653e4JGUPybOsZ6+RYgkxE4v5oa2urWf0sxxcKBRMwlePEZZTJZNi9ezdut5vR0VFjcEulkpHzFs0jv99PPB43Qd1cLjehOlrkK2QXIPpKkokkxkT6Qkh6qhS8BQIB03VPdggiwe1wOPD7/cbXD5jsqHQ6bUQAy+WyCXaLF9vj8Zg0VanjqFaljUajBINB2tvbCQaDxpAEvF56rr6awGOP8ei//ivdRxxhgtiZTIahoSGy2SxdXV14vV4KhQLxeJz29nazq5KMqkXDIYfAf/1XSy49U5rrbwCUUkEgo7UuK6UOA44Afr4A42suf//3FYnfj38cDjig1aOZE4vdVVOLVtQvzBWn02kyhuYzVknrlEk/mUxO+KyqP0cJKEtfBak/CIfDDAwMmEyjTCZjahd8Ph/xeJze3l7zbEUoT9xC8j4JELtcLrLZLKVSib1795pgttPpZO/evWYXJJXY1UquEtD2er2Mjo7i9XpNzwdxg8nOQwzAihUrTKFeIpEwsQtxSUmNhBTpdXZ2mhiD7EJES6pQKJhsKimK8+fzHPaxj1GIx3nkmmvIeL34xwPdsViMjo4OU1+xc+dOVq9ebVxae/bsIRKJ4Pf7iUQii2qhwsEHt2wHUe+3/H7Ap5RaA/waeCdwQ7MGtWB0d8MHPlBpFrIEqXbVNLPhTKMRQ7YUGh7NdayTm9nk83mSyeQEN498VtWfo7heRIwOMIViTqeTUChkJmxpsFMul82x1amxoo4qweRMJkM6nQZgxYoVpvI5lUqZ1NdsNmvqDmSXIAVkIo8hYxYZjeHhYSPsJzEIMaRQ2TWMjIyY5kMSZJc2pNWpuqIhJUVykhAgBhEwBkd6Y2cyGQK7dvGyj3yE0UiE333qU8THkwqqmw85HA5GRkYYGhoiGo2SSCRMIDwYDJriwfb29sX1XTzwQNi5s6IhtcDUG6RWWuu0UurdwL9prb+olHqsmQNbMD76UTjsMPjjHysxiSXEUnHV1GIpNTya7Vgnx1iKxSKjo6M4nc6agWp5j2RKyWSZyWSMBlIwGCSbzZoMIVl9S/C3UCgQCATMd0J0juRaIsDn9/vp7u6mo6ODdDptJspsNmuqmcvlsnEFyWScyWRwOp3G2BWLxb9MzuN1B+l02uxkRakVMG6psbExEzfJ5/NG0lu+r0NDFSFnUW91uVymdqK3t9dUb7vdblatWoXD4SASDtNz552s/+Y32bllC1uPOw6fUmQTCbMz6e3tpbOz0zzvZDJpdkSFQoFisUhXV5eJKy26v51AACIRGBhY8KzLug2EUupk4CIqPRygotC69GlrqwSs/+Ef4M47Wz2aWbGUXDW1WJR/jFMwm7FWG25JTZWKXWDC5C6fValUMtlHkq0jongSlJVUVsmKkhWvUhVxvu7ubvx+P36/n23bthGNRo24XjqdNplKqVSKgYEBwuEw7e3tDAwMmFRZCToHAgETMxHtI+npIMJ2gOkVUV2PMTo6aoxTMBg09y0puoVCwewGJAguz0cysQSJEUh6sRQKKqXwDg5y1Ne+hmvXLh758pd53uPBOd6ISHY6kvra1tbGiy++aILl4XDYxDdk1yX1H4vy72fDBti+fdEaiA9T6UH9k3HJ7oOA/27esBaYD3yg0jjkgQfgla9s9WjqZjayD0uN2QbeF1Ogvjo1VeoPJAYgPn3Jt1dKmbHLKj+Xy5lYQG9vL4FAwAR0I5EIXV1d5nXJ2V+zZg0ej8f4/kWsr7pyWoxV9cJCYiFSpCbfJfHxBwIBOjo6GBoaIpPJTGglWl0rIC4zwLQOrQ74OhwOk5IqLiZgQlU0MKHWpL+/3zw3p9NJIBCoaEQpxSE/+QmH33EHA29+M89/9KOMZTLocU0ncVE5HA4OGI8tinsOMNlohUKBzs5OswuTNqyAEQps9XfJsGED7NgBJ5+8oJety0CMB6t/U/XzNuBDzRrUguP1wtVXV4LWDz7Ysu5Nc2EpuWqmYvLkPtvA++TGONX+5FYgYx4bGzMieNXyGHI/shrP5XLGcMgELplBssKVSl+ZYAcGBvB4PKbaeNu2bUaZVVb1otUkz0h0kKT+obu7m3K5bCZkmajld1JAJ7sFeZ8cp5Qyz1lcRpJ26/F4jNifuK8KhcI+8TE59+SfRQFWDFupVMINHP/II2y6+27iBxzAL666isDGjWYHLQV1co1wOGwC6vJMRVZDUl4jkcgEDSatNaOjoxM+x0WR9CEGYoFR0wU0q+ogatKKOojNmzfrhx9+uPEnLpcrGk2f+hS86U2NP7+lJpONQSgUMpNktZ9+qroDrbWRoqguaBORtmb/cU+3c5EGN9JvQMYmLhKn02kMhASQR0ZGzMpd3EniJlGq0sktGo2STqfN6lcSFGSCa29vJ5fLGR+71EaIK0jUT8vlsukPIaqqEkRXqqL0GolETJ1KdcBbJliplpZgMPylP7RM2NVZR/Ui91UqlegslTh261aOeuABxlas4JkLL6R4wgnmuUlwX3YFyWSSQCBAV1eXSfk99NBDjdif1JYUi0UzRqlYTyaTphhSvlOLoj4nk6ksZOfh/lJKbdVab57Ne+qtg3gTlUrqm8Z/vhDYMavRLXYcDvjCF+CKK+Dss2ExrBqWObUK5iQNtN6qY/H3i06PTKSAkbeQ1xv9Rz7TTkcKvqqL7drb240rJhwOMzo6yuDg4ASXjKxwxTB0dnaSy+V4/vnnTSc4l8vF3r17TTOfnp4eU4ewd+9egsEgQ0NDZnciz8rpdBr3S/XnINXOsvoXrSdpHAQYV48E0p1Op0ljFeTzAoxBqF7V14s7m+WwJ55g49NPs37HDp7buJE73/1u9qxcWZH+2LGDUCiE1+slEomYSmhJ3w0GgxNapEqAPBaLmSyy7u5u88yk8E8MXzKZNO60RZH04fe35LL11kFcrbU+teqlu5RS9zd1ZK3gjDNg3Tq4/nq47LJWj2bZUysLSwrJ6g28V7ulqlMlJcd/ZGTEHNNId0G91eCTi+2q5bmlaY/oCsnqX9yGko0kFdIidSFV1LJaFkMlOwfZMXR2dtLW1mZiITLRS/xCjIXoKslELitp2b1Iu1FZrUt6bK0dwWSXkTyrmXCUSqzYu5eDtm/nwG3bWNvXx84DDuCZY47h/ve8h+y4vIdOJk0Wkhgmr9eL1+tl5cqVOBwO+vv7K0J94+nf4laSuI/P5zNxCMB8HrLTk+9ltXtqf6XeIHWPUuqg8dgDSqkDgZ7mDatFKFXZRZx9Nrz97TAesLI0h1pZWA6Hw7iZ6gm8y+vxeNy4VYLBoNEQql4lNlLOYzYpxtXFduIKEuXTVCplcvyrC9pcLhd+v59QKGQyeYaGhkx2UDabNVlShULBuKZE6E58/3IuUV6VFFV55pJqKuJ5EuCuzo6afN/zQmvCiQSdIyN0jYywcu9eVu3ZQ+/gIGPt7Ww/8ED+cOKJ/OgtbyE37goLOBy4xifu6u51UBEsDIVChEIhIpEIuVzOSIZ0dHQY8UKpbRA3VLXRkmI50bqKx+N4vV5CodCySfqYK/UaiI8A9ymlto3/vAGYcYmtlDoL+CqVlNjvaK0/X+OYtwBXUYl1PK61fludY2oOxx8Pp50GX/oSXHllS4ey3JkqC2u2gXc5vjpjploFFRpfIzKbFONqI1bdEEd6JUh8wuv10jHe6TCTyeB2u8lms+zZs8dIasjqXWQvJAYg55FiNWkdKmOVCVV2ULlczhhkeU2MQfXv5oTWhJJJOkdHK4Zg/P+do6N0jo6S93gY6epitLOTgZUreXLjRvauXEmhqtjP4/EQGQ+QV+/S5DVRspUdTygUYnBw0MQ8pE4iHA6b2hH5vKbbFUhm2ORdxv5KvVlMv1BKHUpFYgPgT1rraSNOSikn8HXgDKAPeEgpdafW+umqYw6lkj57itY6qpTqnctNNJzPfQ6OO66i1WSF/JrKVMZgtjUSWmvT2lLcOKIhJMHORroL5pNinMvl2Lt3r9kZiBR2b28vhUJhgnTF2NgYiUTCpMvKc/F4PGaVLy412TXI62NjY6ZXs0iBy65DDESt8dZrHFSpVNkFDAzQOzBA5+goXeOGoOB2GyMw2tnJ0y99KSPj/86PS2NMhUzmRrJ7fNcnq3zpdxEKhejo6GD16tX4/X7jlpNFgOw0urq6GBkZMd+xlStXTrhvOV5attbaZeyv1NswKEClP/UBWuv3KqUOVUodrrX+6TRvOxF4vsotdQvwBuDpqmPeC3xdax0F0FoPzuUmGs4BB8D7318pnvve91o9mmVPIwrmxLhUaybJRCJd1hrtLqh3pyPxCnE19fX1kU6nTWxC+j5Lcdrg4CDBYNAojEpRmYjciX9d/i07JUl7rc5oKpVKRsxOCvaqd1a1YgZTEYrHOeDFF1m/cydr+vvpGRoiEQ6zd+VKBnt7eeYlLzEGITeDEaiFPEOZmCVuIFlK8rPf7zfd7w499FBj6JLJpPkO9PT0EAgE6OzsNDpMcm5xvcnnVeu7s7/HHoR6XUz/AWwFpEqjD/gRMJ2BWAPsqvq5D3jZpGMOA1BK/Y6KG+oqrfUv6hxTc/nEJypCfr//PZx0UqtHY5mBWit6aXzTzBqRqYxbdfprdbxCpCwAI6YnVcrRaNRk03R3d5NKpUiOB2WVUiZYLTUL4jKT/8t5JW1ViMfjuN1u43YDTM+H6XAUixywcyeH//nPHPLcc/gzGV5cv54X16/nqY0bGVixgvy4W6gRVKfFSr9o6fImhiIYDNLV1YXL5TJBeafTSSKRMAq2In2+bt0681lUJyfIZyCf23IuOJ0v9RqIg7XWb1VKXQigtc6omZ9erdcn79lcwKHAacBa4AGl1FFa67EJJ1JqC7AFYP369XUOeZ6EQhUp8A9/GP7nf5ZU8dz+ylQr+oVOUaxV2yEuEln5SwGa1+s1E3+xWCQUCuHxeNizZw8+n8+I1kl8QbKNJBW4urhtqhWv9D2odt1MhSqXOXD7do557DEOfe45Rrq6+PPhh/Ojt7yFgd7eeeXhT7jOpHFIEyKJN3i9XpNuK+mq0pCora3NpOPGYjFWrVpFLBYz6a2BQMAY7npjRcuh4LQZ1Gsg8kopP+MTvFLqYKoaB01BH7Cu6ue1wO4ax/xea10AtiulnqViMB6qPkhrfR1wHVQK5eoc8/y5+GL42tfg+9+Hiy5asMsuJItJoqIRNMJdNR9qCfVJD+lYLGby8KWeoVwuE4lE6O7uNmmnkpmUTqcZGxsz7iJVJZshhkEC1dWupsluo+qYwlTGoS0a5fitWznm8cdJhkI8vmkT95x5JqlxN10znpMg8RSpV5EeE/l8nkAgQG9vr4mhVAv8icsslUrh9/vxeDxGy6n6O13v7qDV353FyIwGYnyn8E3gF8A6pdTNwCnApTO89SHg0PGU2H7gAmByhtLtVIrublBKdVNxOW1jseBwVFqSXnABvPGNyy7tdT69JJabYWkUtYT6pLBMa01HRwcej4eRkZEJ/ZlzuRyFQoF8Pm8UUyVuIc9X5C+qXUnVlcSziScIq/v6OPl//oeDtm3j8WOO4ea3v53BFSsa90DqoLooMhwOm/Rej8djYjMSVxHXmPScCIVCRqdKdhAej2eCEbC7g7kzo4HQWmul1N8AZwInUXEd/Y3WeniG9xWVUpcDv6QSX7h+XOjvM8DDWus7x187Uyn1NFACPqa1HpnfLTWYU06BV7wCrrkGrrqq1aNpGPUWetViKTYpWihqCfWJFEYsFqOnp8fIaYvktFTyplIps3OoXgVLkxwpjhOjITLaUoAnBqce1u/Ywen//d+0j43xvyedxF1nnz1jdlEjcTgqfah9Pp9x97S1tbFixQojH97T00NbW5sRISyVSibjKx6Pm92CPF9xRdXqJ213B3OjXhfT74GDtNZ3z+bkWuufAT+b9LtPV/1bU8mO+tvZnHfB+cIX4NhjK2mv69bNfPwSYK69JOZjWPYHxGDWEuoDzCQuekahUMhIfUsGkuwkqpGUTQl6i7tF2plOPn4qVuzZw6t+/Wu6h4e577TTeHLjRvQCTpwyfuneJvEUqW0Qd1IqlWLVqlWm7wRg+kNIxzcJ3MtCRaqmbXpq46jXQJwOXKaU2gmkqOwitNb66KaNbDGxfn1Fo+lv/xZ+9KNWj6YhzLWXxFJuUjQVjXaXud1uurq6gL9o+0jardaaWCxmOq8lEglGR0fNLiCXy02Y7MVtJC6WamVVYB/57KkIJpOccc89HLRtGw+88pX88IILKLnq/fNvHNL9zu/309PTQzweJxKJUCgUWLlypdlViGGVJkHV31HJYJJe21prIpGIqX+x6amNo95vyGubOoqlwMc/DkcdBffcA2ee2erRzJu5pvYt9SZFk2mWu0yE+hKJhKnD6OjooFAoMDw8THt7uwlIS0Ob7du3k0qlJpxHMpfEMM8WVS5z/MMPc9p99/HYpk187YorGpqaWi8ul8vUgXR3d7N27VocDofpE+12u01jI+kYB0wpuyLPLBAImCyvxdy6dqlSbyX1zmYPZNHj91eaCl1+OTz5ZEV6d4kzl+DdcsoZb7S7bPJORJ5v9ap/z549JsgaCoXIZrOmmnoq94icd7b07t3LOXfeSdHl4rvveAdDCxx8hsr3RaTLZREhEuIiyR0IBIz7yOfz0dXVRTqdNvEG6UonPbZrGfXqlFZL41j4PeZS5nWvg29/uxKw/uQnWz2ahjCX4N1yyQpppLtsqp2ItOwsl8uMjY2Z/gJSAAeVCXP79u2mYrrWOGeDKpc5+cEHefmDD/LrV7+aR489dkHreGSnIBlJ7e3tZDIZ405atWoV3vEFVnXvCqWUKRAUo53NZunv7zf9GWr1C0kmkzYG1iSsgZgtX/1qRdDvoovgwANbPZqWsRyyQqozjiQPfy7usql2IuJikipgyUgKBAK0t7cbd5J0k5u3UirQMTrKG3/yE0pOJ9/esoXYeLe0hUIK1Xw+Hz6fj2AwSDgcJhKJEAqFcLvdJiAtldOStVQtwCf/z2QyZvcAzLpfiGV+WAMxWw44oBKs/pu/gTvvbPVoLPNAKYXP52Pv3r1TCrnVw1Q7keriNcnhlyK5fD5vGv/E43EjJjcfXvLHP/LXd9/Nb1/xCn5/0kkNq3quB2mGJA17AoEAbW1thMNhUqmU6fXs9XpNpXOhUCAQCJjGP9VGutotV+0+mm2/EMv8sAZiLvzd38HRR8Ndd1V6R1iWJCLcNp2Q23TvFaMyVeBeahfk94FAwLS5lMylF198kb6+vindS/XgKBY581e/4rBnn+Xmt7+dPatXz/lcc0ViB7J76OzsxOfzoZRiZGSEtrY2gsEgfr+f4eFhVq9ebeS0RXoEJsa4xPCKQZlLvxDL/LAGYi54vRUJji1b4FWvgkCg1SPaL2h0OqpMQNMJudWiVryhVuBe+hHI7x0OB2vWrCEajdLW1kYymWR4eHifzKXZEInFOP/WW0mGQlx32WVkF7A1pdQeBINBVq9ezVFHHWUCyiKBUSqV6OjoIBQKGSVW+Z0898kuouoYVyQSMdXo1bGd5RADWwpYAzFXzjgDXvYy+Kd/qhTSWZpKM9JR60nZnWyUpst8qp60oDLxSc5+sVg0LS0lG2dsbIxMJmMUZ2dTCQ2wZtcu3nLrrfzhZS/jd6ec0vRAtIjoiWyI9FBob2+nra3NaEoBpgGS9IqWlqta6wkr/qlcRBLjcjqdDekXYpkb1kDMh69+FTZurGg1HXtsq0ezbKk1KcfjcRPUnOsKcqaU3VpGSQrWpsp8Eg0meZ/40mWc1StpmWjl/LPhqCee4Kxf/II73vAGnjv88Dndf704nU4TcFaq0h5VKpzdbrdJ2e3q6jJGOxwOGxkMSQKQz08kRurt02GNQeuwBmI+rFgBX/wivOc98L//Cy2oTN0fmBwElpRRWaGHw+E5932YXKsg15guM2m6XUf1+xwOB4lEAsB0K4vH4/h8PnK5nKkYFv97XTIR5TKn33cfG594ghvf8Y6mC+sFAgFTx1CdOZTJZEzlt/RskB0WQDKZNE1+RGSw2qD7/f59dmbWZbT4sDPafHnHO+Cmmyqqrx/9aKtHsyypdgU5HA7i8bhZ1ZbLZaLR6D7NX2bjfioUCiZ9UuIGU+0UxEUy1a6j2phVt/QUSYz+/n4CgQDJZJKuri4OOeQQnnnmGbPjmA5nscgbf/ITIvE433nPe0iHQnN5nNMiInpSoCYNenw+H/l8npGREZxOJ8FgEJ/Ph9frJRwOUyqVGB0dxe12EwgEzGQ/+VnUchFZ8cfFizUQ80Up+Na3KvGIc8+Fgw9u9YiWHdWuoHw+b4KcMqHLyl56T8+mGjqfz9PX12cmLGn7KTuFWjUS1X5xea369WpjJpO+1prdu3eb+EM0GmXXrl2MjIwwPDxs+jtMhSeb5YJbbiHj93PjJZdQasIEKpO93++nWCwSiUSIRCJGCqO3t5d4PE4sFqO/vx+lFG1tbXR1dU3okx2LxSYUs4lcd61UVCv+uLixBqIRHHxwpUXpZZfBr35lu881gWpXkOTFw1/UUedSOCXCeVIPIa06pXBruhoJKWyb3DnO6XSaNMxisWg6x+3du5d4PE5HRwdDQ0OUy2X27t1LLpeb0TgEk0kuuukm+teu5Wd//dfoJuT8S2xA4iPlchmfz4fL5cLv9+Pz+eju7jad3uQziEQiZLNZI3cBFfeSNPyReNHatWtrTvjLUfxxOWENRKP48IfhBz+A734XLr201aNZlkhtQSQSMS4e+EtAdLaFU7IDkPdJz2iZ8KerkZi88s1ms/T19RlJCI/HQzKZRGvN0NCQ8eFnMhmT2jo8PDxjk5/20VHeftNNPLlxI7857bSGLT7E3SOGIBAI0N3dTSAQML2cE4mEKXALhUKmOrqzs5O1a9eaNNZkMkkkEkEpZZRlJXZRLpeN4GAtlpv443LDGohG4XLBd74Dr3lNRe21BcVK+wuT8+BF72i6wqlaQVBxe/j9fjKZjAkSt7W1GbfRVDUS1SvfyZIQhUKB559/nlAoRDwep1QqGfnq5557jqGhIaLR6IzGoXfvXi66+WYeeOUrefjEE+f93FauXEkwGCSVSpl7kecnMhgipOf3+41aqtvtNs9B7tHlcpldl2hLiXGoNtjVz7kWy0n8cTliDUQjOfZYeN/7KgV0d91lXU1NpDrIOVPh1FRB0OrJqVoPyOPxTJB8qLWyrV75AibmIJlK0tdh9+7d5HI5Y0hGRkaIxWLkctO3dF+1ezdvu/lmfvHa1/LHo46a17MKBoO0t7ezcuVKfD4fiUSCbDbLqlWriMVijIyMmBaf+XyebDZLe3u7qd/o6OigWCzu4/aZqlahHoNdjS18W7yopdZ9afPmzfrhhx9u9TCmJp+vBKyvuALe9a5Wj2bJ0Kw0R6010Wh0QhBUVr1ynb8bzoMAACAASURBVKmuPVN2TT6fNwqt8XjcuKH6+/tJpVKkUincbrcxFslk0qSHTseavj4u+MEP+OnZZ/PsEUfM+d5FFC8UCpmgu9vtxuVy0dHRYVqbFgoF4wYSbSTpZyE1DkqpWQWObdrq4kMptVVrvXk277E7iEbj8cCNN8L/+T8VGY4DDmj1iBac2U4OzUxznE8QdLqVbaFQIBqNEovFTDxieHjY7B601hSLRdLpNMVikbGxsboa/qx78UXeesst3PHGN/LcYYfN6Z4jkQjhcNgElH0+H+3t7eZ+2tra6O7uJpfLkU6nyefzDAwMEAqFyOfzRCIRisXihLoM2QXU+9na4rblQVMNhFLqLOCrgBP4jtb681Mc92bgR8AJWutFvD2ok40bK4J+73pXJatpPwq4zXayb3aa40xB0JnGW2uiK5fLDA0NMTQ0ZAK9u3btMlLXSil27NhhJtN6u8Gt37GDt9x6Kz9505t44ZBDZnWf0nDH4/HQ3d2Nx+MxRlDGBBi5bSluCwQCFAqFCQ19pLhPJLqlLsLWK+x/NM1AKKWcwNeBM4A+4CGl1J1a66cnHRcGPgT8b7PG0hI++lG44w649lr44AdbPZoFYbaTvdaaQqFAuVzeJ021eiKfj6GYLgiqtSYejwMYyYjq8dZaLcvOoa+vz8QuSqWSUWMdHBwkkUiQTqdnNc4Dt23jvNtu47Y3v5kdBx1U13tk8pc+z7JLOPDAA+ns7GRkZIRcLmcmfrfbTSgUMkFkyUCSgLwY0XA4TDAYNIYBbL3C/kozdxAnAs9rrbcBKKVuAd4APD3puKuBLwLLqwzZ5aqkvJ5ySiWr6dBDWz2ipjMbd46sRsV/H4lETM1AqVQiFosBc6uMnsxUrqJcLsfY2JiR5Q6FQhNW/dWS09LgRpr7SBe0RCJhejrEYjHy+fys1Vk3bN/Oebfdxq1veQsvbtgw7bESCPf7/bjdbrq6ukilUjidTiKRCOvWrSMYDBIKhUxGktxzJBLB6XSSTCanfO5T6VvZeoX9k2YaiDXArqqf+4CXVR+glDoWWKe1/qlSankZCIDDDoNPfaoix3H//cteq6nenPbq1ajb7SYcDpvJSiYmyY5p1Ep1sqtIa20mVpkQ4/G42V2IcUin02YMq1atMhOlTKiFQsHsgoaHh+tyJVWz9sUXefOPfsSPzj9/RuOglDL9Fvx+Px6PB4De3l5Wr15Nb2+vkcHQWtPd3W1cYPIZSMBePqt0Om0K3ybLYVRj6xX2T5o5Y9X6azZ/PUopB/AV4NIZT6TUFmALwPr16xs0vAXi8svh7rvh6qsr0uDLjMlumHpy2ievRqWxjBSZxWKxulaq88mUkUwi6Tcgk75MrmIcZDIV11IymSQejzM4OGh2DBKEni2rdu/mgltu4SfnnsvOqva14t6SegN5Rh6PxwgTSt1CMplkzZo19PT04PF4TFZVqVQyshny3ERLqrqtpzxbua6tV7BU00wD0Qesq/p5LbC76ucwcBRw3/iXbCVwp1LqnMmBaq31dcB1UElzbeKYG4/DUXE1HXtsJavp1FNbPaKGMVXQcqac9lqrUdlNAHWtVAuFgilCE/fKbNxQMgYZt1zH6/UaldJsNmsmW/mddEBLpVIkEgkjfT1begYGeNvNN3PX2WfzwqGHmklcDIK06hQDJe08Jeaxdu1aAoEA6XTaiAtKRpWMaffu3axbt87sNCY/d7/fTyKRMHIlbW1ttl7BMoFm7g8fAg5VSh2olPIAFwCmibPWOqa17tZab9BabwB+D+xjHJYFK1fC9dfD298Oo6OtHk1DqHYTeTweI5onxmK6Pg0yKYvCablcNqvR6V6rvnY0GjUTdCKRIBqNzsq9o8b1l6LRKCMjI8TjcVNRvXPnTmKxGDt27DBuKK/Xa643NDREKpUyKayzpXN4mLffdBO/POssnj3yyAn3JcYwn88bCYyOjg4TkBb30uDgIOl0mhUrVqBUpbObuLqkZkGMhhg5YMKzdTqdrBiXC1dKkUwmZ2xaNNNna1leNG0HobUuKqUuB35JJc31eq31H5VSnwEe1lrfOf0ZlhmvfS2cdx68971w221Lvsp6vkHL6VajM61UJSbg8/lM0DaRSNDZ2WlW4DC9C6pWP+p0Ok0sFjOSEz09PaTTaYLBIMlkkpGREfbu3UsymWR0dHTW8QaA9miUS268kf8+/XSe2rhxwj3JLknqETweD4FAwKzsu7u7aWtrI51OMzY2RjQapaenh7Vr15odWCKRMM2IAIrFIiMjIxNcgNVKtGNjYxP6NtjMJEs1TY0waa1/prU+TGt9sNb6n8d/9+laxkFrfdqy3D1U8/nPw7ZtcN11rR7JvKklNTHboOV0q9F6VqrV6ZmTJ2uJGchEOnllLAbO5XLhdDqNhlKpVMLtdpsJV9RIh4eHjdx4Pb0bahGOxbjku9/ld694BY8dd9w+r4sbCyqxkfb2dlavXm3qGOLxuKlRkEldMqaktalkWImekrQ0dbvdE9J6JQ4x2ciLUbVYoMkGwjIJr7ei+PrJT8If/9jq0cyLelxBzUJy9cXdI6t5mdimc38JtQycTLpiTKQXhEhljI6OGqG72RJMJrnkxht5+IQTeGga4T2pDdFak8/nyWQyZLNZPB4PuVyO3bt3E4vF8Hq9dHV14XK5TOzB4/Gwdu1aQqEQgUCgct3xoPvY2BjJZJKxsTFjhBph5C3LG/tNWGiOOAK+8AV461thlvnyiw1xBbW3t9PR0bFgVbVKKeP2EZeW9GAQQzHTyriWgWtra2PVqlUUi0Vz7gPGpVJ2795tYhWzxZ9Oc/GNN/LUUUfx4CmnTHlPXq/X1CoAJvBe3XCnXC6TzWYJhUKEQiFTwyH3JpXUIrbndDpNBz75L5VKTUgsaIWRtywNlndi/mLlne+E3/ymovx6441LOh7RCs0diRdI5TBUXEriJpJJU7J1RIkU/uLrV+PCdZNjHW63mw0bNpBOp3nxxRfZsWMHzz//PGNjYzMGcGvhzWa56KabeP6QQyr9HKoQBdlCoWDqF0Q/qaOjg/Xr1xsFWK01oVCIrq4uo/dULaQn7iKllFFTlXPn83mTABCJRIyxdDqdNjPJMi3WQLQCpSoSHCedVGlX+r73tXpES4rqRj9Q2SGI6JwQCATI5XLkcjlSqRQej4edO3cSDAZNcZ5IftcycKlUimw2a4rhREpjNrjzed528830r1nDvWecMWEh4PV6zeQu1xeBPNk1SCe3RCJhUmyloltW/WNjYwSDQdMZT3ZSIokhabnBYNDoM1UXzoEV1rNMjTUQrSIQqGQznXIKHH88nHBCq0e0ZBB3i8/nM4YhnU7T29tLJpMx2TirV682/aWTySQul4tcLofH49knW0dW1VL0NjIyYqqoq6u668VVKHDBD37ASFcXP3/taycYB1m5r1y5kvb2dgCzK8hms/T29rJq1SoAhoaGJsh3jI2N4fV66ejooKOjg3g8Tj6fJxQKUS6XjaGQnZUU1cluwha4WWaDNRCt5LDDKjuI88+HrVuhq6vVI1oSSA3D3r17TQZTV1cXxWLRZB9ls1mjs+RwOEwQV9wtspKWoLSs0gcGBggGgxSLRWNoJJupXhzFIuffeiupYJC7zjlnHzVfWdmL3HZbWxtr164lnU6Ty+VYsWIF6XQan8+H2+2mvb3dpPRK74aOjg7jRpLYhLjTqoX3JPje3t5ujrXGwVIv1kC0mje9CR58sFJE99Ofgt3qz8jkGoZyuczo6KhpHyqTZXUqpxqXqxZ/vWTrSMaTZCvJpBqJRMjlcrzwwgumorme9E9VKnHef/4nZYeD2889Fz3JOEimlOxmJCYi2k5iCBwOB6lUCpfLZYybz+ejUCgYI6iUIhaLGVeVnEvcTNWSGDYzyTIX7LdmMfC5z0E2C//4j60eyaKnWiJcahjcbjfBYNBk+JRKJQKBAE6nk7a2tgk7Ba/Xi9bauFkk40lW1bJbqBbxq3f3oMpl3nDHHXjyeW47/3zKNYy9nEtafIZCITPGRCJBKpUyLUklzbZ6PBJPkBiEBKGHh4fJ5/OEw2ET5F7o7DLL8sPuIBYDbjf86Edw4omVZkMXXdTqETWduQjtTScR7vV66ezsJB6Pm91BOBw273W73SYwXN3nQK4v7hePx0MsFmNwcJDdu3ebVfiMu4dymdf/9Ke0xWLcfNFFlCYp94ochsPhIBAI4Ha7aWtrM1XS2WzW3GOhUDCyGmLwJA6RTqdJJpM4nU7K5TJdXV10dnYayQ+pJLeBZ0sjsAZisdDdDXfeCaefXolNLKKgdaP7C9fTmUyuWe0SmkoiXIyB2+2mu7t7H3lrp9OJx+OhVCqRTqfxer3mOtUqpR6Ph2w2S7FYNL58yRSa4QHx2l/8gp7BQb538cUUx8XxqvH5fKYVaHVcJBwOs3fvXorFIi6Xi3K5zNjYmDk+GAzidDqJxWLE43FWr16Ny+UyFdXr1683hi2fzzes0ZLFAtZALC6OOgq+8x0491z4wx9g9epWj6jhbSbr6Uwm15TspGAwaPz0wWAQmCgRLv74ydSrFyW1AGIUJDVUdhAz3BCv/tWvWNPXx/9v79yDHL+qO/853VJL6pZaarW6e96DJwNm4ywL9vjBwg62wY5hAdvhYa8DeMDE2FvU7kJRKYpUWIpUqiBbbLIJpGIvdszTmACGgZglYAx2wDYzxsHxA8iM7Rn3w/0aqSW1Xi3p7h/SvfObHqlH/VC/5nyqukY9+klz70+ae+49j+/50nvew5zH+FiCwSB9fX2ufiMYDLrxTE5OujoNm9ZaKBTw+XyUSiXi8bg7Ndk0V68hs7Sj0ZKiaAxivXH11XDrrXDNNbCE3PuV5EySFc10kBai0aJdrVadvIQ3aFwqlVww1/rf7aJoF3NbFAcn9ZeSySRTU1On+PDhVCkJO3ardDo3N0cqlSKTyTA9Pc3x48fdrn0hLv3JT/ido0f58rveRbG+8M/HurTsrt4ax2w2SzAYZPfu3UQiEVKplEtTtYYQoK+vz7UBtacMaywAJwVi72szeRFFWSx6gliPfOxj8PTT8O53wz33rFlm00I7cBtUXezJYn5PAtu20z5nq4vtNd7U1J6eHubm5sjlck5uIpVKObdNoy5wQ0NDLlvIjtNWGs/NzTE7O+tE7UKhENlslrGxMSYmJshmswvKeb/2oYf43aee4q4DByjUtY/mz9VKhUciEecC8vv9lMtl8vk8qVSKLVu2UC6X3bXxeJxSqcTAwIAzwLb5jy3Ys6qsxhinxZTNZl2W1Gq2BF1pF6SyflADsR4RqfWP+P3fh498BP7yL9dkGM3aTHrjAYuVifb6/O1C7Q02z87OUi6XnfR2oVBwMYNAIIDf72d2dha/3+9UV+17WONgM5sKhQK5XI7+/n5ndGwxma1D8Pl8zl0zMTHh6gxyuZwzXI24+OGHeeXjj3PXgQPkwuGG1yQSCdeXwRjjDFA2m3X3MZfLMTIygs/nc8ZxenqagYEB9xkYY5ysiD0N2UU/mUy6+5DP50mn08TjcRe/aXd660q7IJX1hbqY1iuBANx7L/zTP8Ff/dWaDKGZmNtyZaKtz7+3t9cZh0bv461hMPUe0lbUrquryy201t1k3UYdHR3OvWTfp1KpMD09zfT0NKlUyr0GatXJExMTjI2NuTai1pffiAsOHeLiRx/li+95D9ne3obX2IwpG1fo7e1l9+7ddHV14ff7CYVCrk2oHXtvby/bt28nHo+7zCxrFLPZLIBrQ+pN0bXpuL29vVQqFdcJz2ZxLdYN2CqtqOYqGxs9QawxCx7P+/rgvvtqchw7d9YaDq0yjcTcvC6gpTawt3IQNvhsq4BtNo+VoLAuJuuj9+oWlctlpqamAFzHtUwmQzabpVgsOh0j24GtUXFcOp0mn887ddhkMsno6GjTcb/ql7/kPz30EF84cIB0fYyN7pmNF9jFMhAIMDQ0RCAQcD23bYzFxiisi8wu9vZ+NnMXzT/h2dak0WjU3U/baa8du/vlNo1S1j9qINaQlo7nu3fXKqyvvBKGhuC1r131cc7Pqfe6iZbTwN77PoVCgWw2e0qfZbvg2MXPSk1YN1E2m2XLli3E43HnMtq2bRvDw8OEQiFnNF588UWn3WQNi20lOjIy4rKIKpUK4+PjTvdoPucfPsz+Bx/kizfeSDIeb3iN3+93OkrWAESjUTdGK5+Rz+cpFotOpXV4eJhgMOh6T3ulzJsZ4EafQ29vrzNMS3UDtkozF6RWbW8e1ECsEYv6D/zKV8JXvlKT5fj+92vifmvMSslE2922lcool8uEQiEymYwzFrb5zdDQEOPj44gI0WiUUCiE3+8/Rd7bSk0UCgVEan2W7QnBps1OT0+7YjXr87fy2c2ylvb94he85mc/4wsHDixoHGyltm1batuB5vN5Tpw44YQC7UksGAzi8/mIx+MuEN/R0UEikXDG0NZ5NLrHzT6H1djdr9RGQVm/qIFYIxb9H/iKK2qtSt/8ZvjRj+C881Z5xKezEtW6tg1mZ2cnwWDQpbxGIhGi0SiDg4OnCNLZHbLVIbL3zDueXC6Hz+ejXC4zPj5OOp2mv7/fPS8idHd38/zzzzM5OUk6nXbB8UZc9OijXPLww3zhwAFSfX0Nr7HxlGKx6DKJ7OnA9rm2J6BSqcTWrVsJhUJuTP39/S5uYDPErLEJh8MLuoYafQ6rtbvXfhKbm7aeBUXkKhH5jYgcEZGPNnj+wyLytIg8ISL3i8judo5nPbGkdo/XXAOf+Uwtu+no0VUaaXvxZtvYdFYbVLX3yC7q1s1kg7rBYNDVMHiDst3d3VSrVcbHx/H5fAwNDdHZ2UkqlaJUKlEqlTh69ChHjx51BXnNjMMlDz9cMw433tjUOFjNp1gsRk9PD/F4nIGBAfr7+ykUCqcF2201uFcWIxQKufuRyWSIRCLOHWU75S2GZgkG7VjAvZ+Rsrlo2wlCRDqBzwFXAMPAIRE5aIx52nPZ48A+Y0xORG4F/gK4rl1jWk8s+Xh+ww2QzcIb3gAPPlgLXm9g7MnAppZWq1W6urro7u52RWPe2Izf73fpqIVCgUQiAeB22Vb51C66oVDI1UGkUilXbOftytasGdB//NnPuODwYe5aICANuOI12+EtHo+TSCTcqcS6EW3Q3Aalg8Eg6XSaSqWCz+djx44dwMn2o7A819Byd/da36C008V0EXDEGPMsgIh8DbgacAbCGPOA5/pHgHe1cTzrjiX/B7755pqRuPxyuP9+2LWrvQNdBTo7O+nt7WVubs4VyPl8PhebCYfDZDIZUqmUy4Cy0t4+n49sNuviN5FIxLmfpqamnL6R3Z3bXb01QqdhDJfffz8v//WvuevAATL1iuVG2HECDAwM0N3dTSwWc0qtxhii0Sj5fN6dVObm5picnCQcDjM0NEQ0GnVGw540SqWS6xy3HNfQUt2AWt+gQHsNxHbgBc/vw8DFC1x/E/D9No5nXbJkP/6HP1wrqHvd6+DHP4Zzzln5wbUZK93d0dHhirts0NgaS5udlEwmT3Fl2IIw22bU22fZZg7FYjGGh4eZnp52fZ+9PSLsKcSLVKv853/8R7aMjfH3730veY/kxXw6Ojrcv7Vz504GBwc555xzSCaTpFIpTpw44cT2bCW1t69DKBQiHo+7mIoxxsl827oHWzG9mjv41ciAUjYG7TQQjb5JDR2pIvIuYB/wuibP3wzcDDX1SqXOhz4EwWDNSPzoRzUV2A1CM+lu65KxC7itU8jn8/T29rqMJFsDYXfWtgrZFrllMhmOHz/uCsesXpH996z2k5fOcplrv/UtQvk8X7zxRkoNhPfmY11FNmPKVm8XCgUXmB4fHyeZTJLL5YjH4644bmJiwhmt7u5u1wWvs7PTyWjASQnv1ULrGxRLO795w4DXQb4DOK0CSUTeAPwJ8DpjTLHRGxljbgduB9i3b5+WaXq59dZa1fVll9WqrtdBdtOZ8O5QG0l3b9myxekU2SrdSqVCPp/HGONUViORiAvEzszMuMC0Fd2zKbFW9yiXy2GMaRiQ9heLXHfPPZS6uvjqDTdQOYM7xbq/urq6XDZSMpnkhRdecBlU1WqV6elp17THSmtYuRDvnEZGRhgaGnKV0tlsllgs5gLwq7kwa32DYmmngTgEvFREzgFGgOuBG7wXiMirgNuAq4wxE20cy+bmfe+rGYnXvx6+8x24eCFP3tozf4fqle62RV5Wd8jv95NOp4lEIoyOjhIOh13Tn+7ubiKRCMlk0p0afv3rX1MqlZwukc3eKRQKzM3NNRxPTybDf7n7biaGhvjum9+MaWEx9vv9rpbC7/dTLBaZm5vjueeeIxQK0dPTQ3d3t+tBYdNgrRZTMpkEcHLmto2qN0vLtkhd7YVZ6xsUS9sMhDGmLCIfBH4AdAJ3GmOeEpFPAoeNMQeB/wWEgX+of/mOG2Pe2q4xbWr+8A8hGq3VSdx5J7zlLWs9oqY02qHaRTCVSp3m+rG6Qz09Pe7EYGsKCoUCo6OjrofC7Owss7OzLrhrJcCbpYkOjI9zw1e/yuPnn8+D+/fX4jpnwNYtBINBjDHE43FXmV0ul11PBxtXsAFr+7vta2HjKS+++KIbr1d9FlizhVnrGxQA2WjCWvv27TOHDx9e62GsX37xi1pPiU98Aj7wgbUeTVPmZ8nYLCXAZe+USiW36Fp9JbsA299zuRypVMp1aTt27JhryZnL5RYcw+8cOcK13/oW/++qq3jyFa9oadzW5TU4OEg8HqdYLLqTQSKRcP0YSqUSQ0NDdHd3UywWiUajdHR0uCrxjo4OxsbGnKxHPB6nUCi4LKj5rVEVZbmIyGPGmH2LeY1WUm82LroIHnoI3vhGeP55+PM/h3XoO56/Qy0UCkxPT9NVb9dptZhsz+ZEIsHExAT5fN7JgBeLxVO6r9maAts8ZyEuOHyYSx94gHuuu44XdrdWn2nF8ILBIMFgkK6uLnp7e+nr62NycpJKpUJ/fz+hUIh0Ok0sFsPn8zE4OOj0pADy+byLtQwPD7s+EP39/VQqFfc6RVlr9Fu4Gdm7F37+c3jHO2qniS9/ueZ+agPNiqlaKbKyaaulUonR0VGy2Sxzc3OugMxWQNtAszUYNuXVNg+yfSCy2ewZTw2d5TJvvO8+dh0/zp3vex/J/v6W52oX+Lm5OfL5PIFAgK1btxKNRgkGg0xOTjpDcO655xIMBhvO3/r3bdzFyoJbN5NmCinrBTUQm5WBAfjhD2v1EhdfDAcPrngabLNiqlaKrKwBsZpKIuKylYrFIl1dXU5jyaa7WlmMqakpRMRVRtumOfb00IzemRneec89zESjfP6P/qilNFYvdrEPh8OuQC6bzbJ7926q1Sr9/f2uR/ZCgWXv6cm6p2xAWoPBynpCDcRmxu+Hv/kb+PznazLht90G1167Im/drJgqFoudscjKa0CszLZdfGOxmGsnahf9TCbjTg/Hjx933easbPfs7KwLWjfjnKNHufbee3nkkkv4+Wte01Iwej6lUolCoUBPT49TXrX1DzZ2cKZqY+/JytZAaDBYWa+ogTgbeP/74RWvgOuvr50qPvMZqIvDLZVmxVS2U1uzIqv5hsW2HQ2Hw67QzO7S7S7cZv6k02nXWc6OIZvNUqlUXNbPfDrKZS5/4AH+/RNPcO8f/AHP7dmz5Dnbgjbr8komk26R37FjB7317nLN3GvNTlbqVlLWK+sveqm0h4sugscfhxMnai6np58+82sWoJkarbc3g61sttfD6YbF9mK2gehQKER/f7+Tp7BGZGRkhEwmw9TUFMVikdnZWU6cONFUhRUgPjXFTXfcQWJykttuuWVZxsFLNpt1NQIDAwN0dnYyPT3t2pVaqY1kMulONa2257TGcKNlFyqbEz1BnE1Eo3D33bU6if374Y//uBajWELGTLNiKtvcJplMurTVSCRCuVx2vvn5NRBdXV3EYjGMMaf0f/DWDZTLZdehrVgskslkKBYbFt4j1SoXHjrE/p/+lJ9ceimHL7xwSS6l+dj+DIODg/h8Puc2s+O0Mh+2H4TXvdaKfIUK5CnrDTUQZxsicNNNNWmOm2+Gr3+9ZjBarAPw0qyYyufz0dnZ6dI1rby27ZrWzLAArorYumls+momk2FiYsLtzpvFGwYmJnjLwYNUOzr4+/e+l+mBgaXfqzo2DRVOxiGsUKBNte3r63Onp0ZG4EzyFSqQp6xH1ECcrezZU4tH3HFHTaLjppvgYx+Duh+9GfP96438594ObzMzM07/qKenxzXBaWRY7A66VCqRzWYJhUJks1nS6bRLhc3n8w2Ng79U4jX//M/sO3yYBy67jMcuuGBF6j86OjrcYm6L4AAXWK9Wq67vRDQadRpL843AmeQrVCBPWY+ogTibEakFsN/0pppxOPdc+OQna9pODRalVl0gdlecTqddEx8bXLY7cWsc4GT8IpPJOJVWG3coFovMzMwwNTVFPp8/3a1UrfIfnniCy++/n2O7d3PbBz6wYP+GpWI7wNnq5u7ubhKJBENDQ66znQ2u28D5fCOwkHyFCuQp6xE1EAps2wZ33QWHD9ckxD/7WfizP6vpOdUXscW4QOwCavs62IIw2+/AqqraAHdnZ6frKmcb6ySTSSYnJ11zH+vOcRjDnqNHef3991Pp7OQf3vlOhtvQXc+ehqzgHkAoFHINh7q7u53htAt6OBx2BXON7k2jE4H3hGEzwWwV+ckpa4c3ZXVRA6GcZN++WhvTb38bPv7x2s+f/ilccw1VaNkFYiucrXvG6hBVKhUnatfR0eHSVvv7+ykWi4yNjVEulxkbG2NqaopcLsfc3BzlcvmkcTCGl/3mN+x/8EG6SiV+eumlPHXeeSsShF4Iv9/vMqZsM6LZ2VnnDrP9LCqVyind7Rb7b4TDYVc4mM1mF1V8qCgrjRoI5VREasV011wD3/teTcvpIx+h45Zb6HjbbpTNQgAADIpJREFU26jUBemauUDsSaOzs5N4PE46nSaVShGLxejp6XEGolgsks1mXd8E23hndHSUmZkZpqen3YnDGEMgn+eVv/oV+w4dYs7v56H9+3nm5S9fNZ0pKwFimxX5/X4SiYSr11iJHtLGGLLZ7GlZUK0UHypKO1ADcRbjdVkAp7ovRGoupre8BQ4dQj73OWIXXsjc/v0Ur72W6hVXEBkcPG2B8gZbrZEoFApEo1GnsGoluGdmZlyMYXZ2lmAwyMzMjJOeqBYK7H32Wc578kle9tvfcmTvXr771rdyfNeutp8YvPh8PkKhkOtFYY2E7R5nq8GXGztYavGhorQLNRBnKVbfyBZlefs9n+a+uPBCuOsu5MQJ/N/8Jv4vfQk+9CHkssvgyivhiitqWVGejB27YFarVZf2ak8JIyMjjI2NOUNQLBad+qm88AJ7HnmEbc88w94jR5hKJHjq936PH155JbPh8KrfJxFxO/pYLOaCzh0dHUSjUacka6u57f2Dk5lOre7ymwWqvcWHGsBWVhPtB3EWYoxhcnLSKZ+m02l6enoYGBigWq1SrVbP7L4YH6+lydqfSgXOPx/OP5/ynj3MJhLMbdtGRyJBZMsW/PXAbnJqinI2y/iTTzLz1FP4x8fpOX6c7t/+lv7hYXxzczz3kpfw3J49/NvevW3JSGoVa9gGBwdJJBIkEgmq1SqBQIBAIEA0GiUSiZwSUO7o6HDyIUuJFyxHAFFRFmIp/SDUQJyFlMtljh075lI2U6kU1WqVHTt2uEplq0ra0u7XGBgZwTz2GOaxx6geOYJ57jk6hoeRVArJ5WrV2sZAuYwJBCj19pIKh8n29TEVizEyOMjIwADJWGxd9K8IBAIEg0G2bNlCPB5n586dLpOqq6uLvr4+JwMSiURcEaAxhmQyeUq8oCWD62E5EuqK0gxtGKQsCpuCane9AIVCwUlkWNmMhXaqTpF1cJDUq19N+rzzyGQyRCIRent7mZ2dZa5Uoreri0AoxDNHj5KamWFiYoLR0VG3gK4XvM2Aenp62LNnD9Fo1J0eUqkUfX195HI551ayqq6wMgVvC6XCasxBWU3UQJyFWCG8XC7nfOxWEiOTyZySsrlQtox1e1SrVWZmZoBaqqZtuTk6OgrAzMwML9Tf22oopdNpbK/ptcSK5/l8Ppdya3f/27dvZ8eOHa4Wwxt3iMVirqI74OkroQVvymairQZCRK4C/g/QCXzeGPOpec8HgC8CFwDTwHXGmOfbOSalthO1LpJKpUI4HHaBVRFpKWXTWzhnF8RCoeCUWG12kj2R2AI5u3CutXGwBW1+vx+/308sFqNSqbhWn+FwmEQiQSAQcL2mvfGFRpXS0FzEUF1CykakbQZCRDqBzwFXAMPAIRE5aIzx6kzfBCSNMXtF5Hrg08B17RqTchK/3088Hj/Fp213ya3sfq0rpVqtkslknCGwekuZTIYTJ07U0lXrmUz2ve37epVQF4vtQFetVhdsFNTodT09Pa7YLRgM0tfXRzAYdAHgYDBIIBBg+/btBIPBUxb3heQyFnONomwE2nmCuAg4Yox5FkBEvgZcDXgNxNXAJ+qPvwF8VkTEbLTI+QZlvk97Mbvf+XpLvb29TrU1EonQ09PD0NAQyWSSbDZLtVolGAw6VxbgduxW8nt2dtYZi0AgQGdnJ6FQiHQ67cYXjUYpl8t0d3c7EcBCoXDKqWR2dta18LRGpFKpEAwG6e/vZ+/evU46o1qtsmvXLnedPUFEo1EnzHem+7bUaxRlvdNOA7EdeMHz+zBwcbNrjDFlEZkB+oGpNo5LWYBWd792J55KpdwpY/fu3S6rxxa+2VNGoVDA5/M5f7+V0E6n00xNTbk2o/ZkEo1G2bVrF3v37mViYoJjx465ftWJRIJKpUI8HieTyRCPx5mYmMAYQyqVcrIYkUiEQCDgKrWr1Srbtm2jUqkwODhINBqlr6/vlB7SuutXlJO000A0+h82/2TQyjWIyM3AzQC7du1a/siUBWl19xsIBIjFYkDNsHglMwqFghPoCwQCZLNZBgYGiMVi9PX1AbXObFDLnLInllwuR7FYpKenh0QiQUdHBzt37uRlL3sZ09PTZDIZl2GUzWbx+XxEo1G2bt1KOp12JxWrIpvL5QjXC+ysIbPxB5vm60V3/YpyknYaiGHAK6+5Axhtcs2wiPiAKHBi/hsZY24HbodaHURbRqssGqvSmslknEvH21Uuk8m4YrJwOExXV5dzBUFNFXW+1IeIUCqVnLupWq26VNvt27e7dqMAfX19DA0Nubam8Xicnp6eUyTFvbEV+3d6QlCU1mingTgEvFREzgFGgOuBG+ZdcxC4EXgYeDvwY40/bCyauaRacVXNP6nYxzZIPP+13gDy/OB6o3+n0WlATwiK0jptMxD1mMIHgR9QS3O90xjzlIh8EjhsjDkI3AF8SUSOUDs5XN+u8Sjtox2FXQu9tlFwXRd+RVl52loHYYy5D7hv3t993PO4ALyjnWNQFEVRloaWdyqKoigNUQOhKIqiNEQNhKIoitIQNRCKoihKQ9RAKIqiKA1RA6EoiqI0ZMN1lBORSeDYGg4hwdmlFXW2zRfOvjnrfDc/CaDHGDOwmBdtOAOx1ojI4cW27dvInG3zhbNvzjrfzc9S56wuJkVRFKUhaiAURVGUhqiBWDy3r/UAVpmzbb5w9s1Z57v5WdKcNQahKIqiNERPEIqiKEpD1ECcARGJi8gPReTf6n/2NbmuIiL/Uv85uNrjXC4icpWI/EZEjojIRxs8HxCRe+rPPyoiL1n9Ua4cLcz3gIhMej7T96/FOFcKEblTRCZE5Mkmz4uI/HX9fjwhIuev9hhXkhbme6mIzHg+3483um6jICI7ReQBEXlGRJ4Skf/e4JrFf8bGGP1Z4Af4C+Cj9ccfBT7d5LrsWo91GXPsBI4Ce4Au4FfA78675r8Cf1d/fD1wz1qPu83zPQB8dq3HuoJz3g+cDzzZ5Pk3Ad+n1gb4EuDRtR5zm+d7KfC9tR7nCs53K3B+/XEE+G2D7/SiP2M9QZyZq4Ev1B9/AbhmDcfSLi4CjhhjnjXGlICvUZu3F+99+Abwetm4fTtbme+mwhjzIA3a+Xq4GviiqfEIEBORraszupWnhfluKowxY8aYX9YfZ4BngO3zLlv0Z6wG4swMGWPGoPYhAINNrguKyGEReURENpoR2Q684Pl9mNO/XO4aY0wZmAH6V2V0K08r8wV4W/0o/g0R2dng+c1Eq/dkM/FqEfmViHxfRM5b68GsFHX376uAR+c9tejPuK0d5TYKIvIjYEuDp/5kEW+zyxgzKiJ7gB+LyL8aY46uzAjbTqOTwPz0tlau2Si0MpfvAncbY4oicgu109PlbR/Z2rGZPt9W+CWw2xiTFZE3Ad8GXrrGY1o2IhIGvgn8D2NMev7TDV6y4GesBgIwxryh2XMiMi4iW40xY/Xj2EST9xit//msiPyEmgXfKAZiGPDukHcAo02uGRYRHxBl4x7hzzhfY8y059f/C3x6Fca1lrTyHdg0eBdPY8x9IvK3IpIwxmxYjSYR8VMzDl8xxnyrwSWL/ozVxXRmDgI31h/fCHxn/gUi0icigfrjBPAa4OlVG+HyOQS8VETOEZEuakHo+ZlY3vvwduDHph752oCccb7zfLNvpebT3cwcBN5Tz3S5BJixrtXNiIhssTE0EbmI2lo4vfCr1i/1udwBPGOM+d9NLlv0Z6wniDPzKeDrInITcBx4B4CI7ANuMca8H/h3wG0iUqX2RfuUMWbDGAhjTFlEPgj8gFqGz53GmKdE5JPAYWPMQWpfvi+JyBFqJ4fr127Ey6PF+f43EXkrUKY23wNrNuAVQETuppa5kxCRYeB/An4AY8zfAfdRy3I5AuSA967NSFeGFub7duBWESkDeeD6Dbzhgdqm9N3Av4rIv9T/7mPALlj6Z6yV1IqiKEpD1MWkKIqiNEQNhKIoitIQNRCKoihKQ9RAKIqiKA1RA6EoiqI0RA2EorQBEdknIn+91uNQlOWgaa6KsgLUC5XEGFNd67EoykqhJwhFWSIi8pK6/v7fUtP22el57lIR+d7ajU5Rlo8aCEVZHudSk1B+lTHm2FoPRlFWEjUQirI8jtW19RVl06EGQlGWxyyAiFzraV+5b60HpSgrgYr1KcoKYIy5F7jX/i4il67daBRlZdAThKIoitIQTXNVFEVRGqInCEVRFKUhaiAURVGUhqiBUBRFURqiBkJRFEVpiBoIRVEUpSFqIBRFUZSGqIFQFEVRGqIGQlEURWnI/wc+NptjcsTggwAAAABJRU5ErkJggg==\n",
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