From dd17423deda4f8461cd676f67cdb3e3d73fafd2e Mon Sep 17 00:00:00 2001 From: Alexey Grigorev Date: Sat, 26 Sep 2020 22:34:23 +0200 Subject: [PATCH] chapter 7 --- .../07-neural-nets-test.ipynb | 409 ++++ .../07-neural-nets-train.ipynb | 1857 +++++++++++++++++ 2 files changed, 2266 insertions(+) create mode 100644 chapter-07-neural-nets/07-neural-nets-test.ipynb create mode 100644 chapter-07-neural-nets/07-neural-nets-train.ipynb diff --git a/chapter-07-neural-nets/07-neural-nets-test.ipynb b/chapter-07-neural-nets/07-neural-nets-test.ipynb new file mode 100644 index 00000000..99ecfa40 --- /dev/null +++ b/chapter-07-neural-nets/07-neural-nets-test.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import tensorflow as tf\n", + "from tensorflow import keras" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.applications.xception import Xception\n", + "from tensorflow.keras.applications.xception import preprocess_input\n", + "\n", + "from tensorflow.keras.preprocessing.image import load_img \n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "labels = {\n", + " 0: 'dress',\n", + " 1: 'hat',\n", + " 2: 'longsleeve',\n", + " 3: 'outwear',\n", + " 4: 'pants',\n", + " 5: 'shirt',\n", + " 6: 'shoes',\n", + " 7: 'shorts',\n", + " 8: 'skirt',\n", + " 9: 't-shirt'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Big model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we'll test the big model trained on 299x299 pictures. \n", + "\n", + "You can either use your own model, or download the one we trained for the book:\n", + "\n", + "```\n", + "TODO wget\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "image_size = (299, 299)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.models.load_model('xception_v4_large_08_0.894.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path = 'clothing-dataset-small/test/pants/c8d21106-bbdb-4e8d-83e4-bf3d14e54c16.jpg'\n", + "img = load_img(path, target_size=(image_size))\n", + "img" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, pre-process the image:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.array(img)\n", + "X = np.array([x])\n", + "X = preprocess_input(X)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And get the prediction:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-2.8609195, -4.234049 , -1.573255 , -1.9078847, 10.24705 ,\n", + " -2.2489128, -4.297381 , 4.43905 , -4.458805 , -3.9616926],\n", + " dtype=float32)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred = model.predict(X)\n", + "pred[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To the actual class, we need to see what's the biggest value. We do it by using `argmax`:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred[0].argmax()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To convert it to the label, let's use the `labels` dictionary:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'pants'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[pred[0].argmax()]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's apply it to the entire testing dataset\n", + "\n", + "- first, we'll create a generator\n", + "- then use the `evaluate` function to get accuracy " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 372 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "test_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "test_ds = test_gen.flow_from_directory(\n", + " \"clothing-dataset-small/test\",\n", + " shuffle=False,\n", + " target_size=image_size,\n", + " batch_size=32,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 70s 6s/step - loss: 0.2493 - accuracy: 0.9032\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.24929417669773102, 0.9032257795333862]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(test_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The model performs even better than in the validation data: 90% on test vs 89% on validation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Small model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After testing the big model, let's test the small model trained on 150x150 images\n", + "\n", + "Again, you can download a pre-trained model:\n", + "\n", + "```\n", + "TODO wget\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "model = keras.models.load_model('xception_v2_0_5_28_0.845.h5')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "image_size = (150, 150)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 372 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "test_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "test_ds = test_gen.flow_from_directory(\n", + " \"clothing-dataset-small/test\",\n", + " shuffle=False,\n", + " target_size=image_size,\n", + " batch_size=32,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "12/12 [==============================] - 15s 1s/step - loss: 0.6931 - accuracy: 0.8199\n" + ] + }, + { + "data": { + "text/plain": [ + "[0.6931126713752747, 0.8198924660682678]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(test_ds)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This model performed worse than on validation dataset: 82% vs 85%\n", + "\n", + "It could be due to random fluctuations (the test dataset is not large), but also it could be because the model overfit during training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/chapter-07-neural-nets/07-neural-nets-train.ipynb b/chapter-07-neural-nets/07-neural-nets-train.ipynb new file mode 100644 index 00000000..2c139669 --- /dev/null +++ b/chapter-07-neural-nets/07-neural-nets-train.ipynb @@ -0,0 +1,1857 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import tensorflow as tf\n", + "from tensorflow import keras" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Execute\n", + "\n", + "```\n", + "git clone https://github.com/alexeygrigorev/clothing-dataset-small.git\n", + "```\n", + "\n", + "to get the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading Images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.preprocessing.image import load_img " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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BYKeclrolwtETVDmBlv0KSEeDIIvbR3cgR5fdYIDMMgofkLnoDZmORfuthwqaDc8DZTo9qMtqUkgRZo2kmMqsjNpJF3wC+Pz8vnYbA7CKX+cck7RMH0hwBA8mB2mWMunpKc7OF2hAjE656p97zfzLtXWs1NH//EqKkKj7HGEmBLdLX7gCMlFfAtYR+7w3hoitTLiVENZnEyOuszvQ8Hyo/vl36W/tv7zdRQVOtAIwNszXSZz6ojTyr+jWvWSKX3sM4ANmfVUT5O2tVACQpTIJMBghHupsVAOkgCzyR6V2vDIHSRZ11bVH6nenyTB/oJ4LEHD2TKQGGqOQTjl6wiuTetoFpOv8MGxlYpnjOBa58qRyXjjJ8FCH2cw1alaypzZEEmACYizxv3NT9Rv9m2uTBt654011VWst1KR2ux2FONp+a7eIsAzYiVC0vuBgkfOVd+iyZhfKg0EaZJkXDT2zYy7FO96zy5u/AdZfd5vIAgzufkBP/757z7QzNmzlue7R7UGBJUTJ/FYrb5UH8WJJ7EcAPY1kZ9sDgrXWZGQm6qPTWFKiRB0/tYBCUMske6mc7bZzsufOu9ZL3sQsk2plQoVMYWqEUU7QZBkK24Yj6INN4Xq9SwuzAPH6picoDY+iSGtppkWVMA2Uk8UYmHWmYVnFun/99qt8Oz6Mp5S5YKO11vJDTWrt9nGYIVrE0XSgW4FbO27958zVamJy1t7lTSCV/YlExh6F2VNnj4wO64Dqrxiw/rlFv3817UlUIKYeXRJ4B0PKlN89MxWm45XUBrelNc1JaWPpdA5rPipiFQbn8e22J+uOrjnnKRvOPndhykjuR4oAJjWIQUiEzKsBonGYqi1z49Zyme4FmcIUYKayJCGF0TJBKJfE7bjnEElu2/YUsJL/QifgmuPWoo3E85Rcp/VwpD3R+Y7Dk2jMx4lcL/P+/Ota0A9oBSB6RPPSFFL4kMHdnXKjGRslt4rBTwWowWRFzDQMDqZzC9KoQjqQud196LlQKtHzYf0rG8P/ebbBYS0UZULYGUSxlilYIisWBk2Inkfki5VwsnBcPk+/98UNniR823CHDhZDtSH2xyd7FjwAoEVnkTouWU+e1U2BoKVav2cuRltIaHe28r6C4SRlAEVLpRXBnnN9ukotuTdB5o4QRiBRSq4JQGMgTGoRQbopXckkjST01jtCsvODgE4vEJuDPo1sL9o/1Wb7tf5fr6pQvGRS7iXBZLXumJpBwQiwXmotxijG1m4wNmNBHK2NNEQ9HDQxC/ttEA9llnf29P+JU2Kw04M8b2Z9jZlN8+VfIXK96tJfITP4z9sei6TEeiAJWTSkg9Gdfmru0PWHyZZw7N/iXWlN9N04/8Q4BkbAhoBqW8bejZ9Eas6PdjxkmksmxC1FQ8nAzP9NiIjWIrVfIYFCSYErjnyHfi+zXr1h6JjMDGYDTkhudXJSqzVzxrLNZOr5uWSnSH0MpEM5Jj2EqMABmdEALjb49KdPE8YcoUevjFcL9J9uj/3z5q77KgnmnznGXbfY5T5K5u0wN1iz5kxLCQ6BrZ1dVZcBOyOGAUY9Q7+M9AwPJCxycNWHPWuAZA8mt/9vQPD+t9rudFjAonpP+e4LPN2zVGO9pXiXOxe0jlkASlnEpZV8TTZHI617/vn9+Hx1PUnZmi7dkMY1QSE2iHbEJwDRStnYfXNMDW0yDv4+mYjhJQgySikkzGgmd9jI7N4ul9lbScdxdCQuBffbL1trO7uKxOdJRfY/d1HT2C0ku3l1YZ4GeKEH8GCKhP84WvrP7Dj6j2pfYdfMhOjeJx2wAFDHrqEOb5AURISilAJE9GwY1kMtCJwpoXPh9VipiHRJjhGs0yVxc8MQCCdg/c+MafnX2L6mXlhPLiIh7+jiylgld58ZhSfIlDT/P3ngUpZncjcka6njjnfMeSn3VkUJrQGo1SUFmFnfJIlOxfc//z2tuFcrXnwrhe6F5K0r67nKiSRL6RolgkBTZjwVjqMnUz7rSkmS1nidFXBHOSlO10SS7Eqx6XNWTsC9e01NNZYyewScdymnrA3H1CE0zSl6nr3gFee1Jghc7/PrObhXu1t3tx1D5f2tJwR3r/ehNFwfRwn1srX92PcDkcJfSIS4n5QTQMvBAjACDDCsR+kmIq+e6bZoHEUqnWTAzArMrLiVasVpBV5ohaDBMrksZess/8PbPxXn+y8l+v3LiqIrGz72V+7BEd01cGcgUUzFOAAwOLy11fU/M5PdEPUWjgpA8XLmPl/bNjZ8/myKWm/lPS+YPFuSPA0Hivx2prn5fvucoR7KaBspiB9/+D0A9DqsNPNOR33giBkHv0baPgpzDizo8NTQSB57GwxUS43w6gl9xxIOEXhCVX5rVsiD3bNhn4qwOQL9UxPF5q94Tp5MZ5TiXVtz76z9+cJCn50+l0KG+AEAbvvt6eWvolU4lAUnyyxpFOmYHZu9ai1Z1BlOmHMXESEcUpNaaJea0KR23G4tjjh2KAzNQmoROvpCBZDaK2WO7OHw3tvCLc5Azsg56iGHgUjWLSCE2IzWZEfqQJ1OuiNVmwAwEwf+1v5F2oMGpmusANxrpnpjpM4awLwy19uEl37ZaOWHv/lfvXrwCpxz491uwLCaASnvAcDH95/mb21B92/ffpc3Uc8y7GaWOJWu9JBpJAsFcDtmeS6ZiQyzQqrW7QEI8vhonA46ETHwKhIIBm+4QAaHlRA28jgbu/eWk4jIshpdrT6Dt7OrI/Sve+2TO+db9IH2p/1cqYSW4KGPj4+n47/CJQbNQEpZr6//2sw4B2c2jEjJNFasmHW0BxVDX22tNaEBEdGEkCI/I8RAT8uDAjsCYGtHO8aCM2WuUUhEHbxV96FHKgpTt2hAC6Rsnjp7C+zMDB5G0egGetBoxcycblYKzbtXxG+A9S/contTLra/EcH1FLPaMfz1Foy7v+w+RfL/+OfnD86KwSuPnVvirbwBSKV7+syAQeHH3/17dIoZWOTWli7vOA1DZEHYsZSqp7rbFoDL9boyIPN4Px4cF7IP2E83BZKekqmWtA2rJp4khxXjPi2E7/s+/UhXUXSYSuejz2eN751LOegVpPAFRzQsbtNxLF5Vcl48ofqfQZKlj/9je+1OEUAPbp8DMlZPV8rlZz5tiNKTbTyvyS6Tnge5GImSk14oQyhux6c1GjIGanBO0bNkwJUUII2voyyuMkVyJooYqyXNHd0WnFXgMuEszfrdOuaa+5iOp4PwW/uv3FJBzFSxd1fLZNtjYasnTuVST5rWS6i+EmhLu/43z794O0NbRycIwMoPANh5NnhXssSfjoOjXIUtj9u8SVKwQSO5jQO8bG8yAkZ4cvf5CG++SC6nvj8DaDDeZSxNlXLqjCY2TYBC3+pDiUdM92kyRhh2VgFMq7mN2/QCqxE7hk5nPNRIXi6XrmoZlkQAymrRL1oygktt2gDuypGtbe69FXwJ/vpoQmoppYFBylInNUdpzrL16HEbj14ycNCVqakh9CLZYXQAhnALRGgPches1v5eIoJwdb27B84lq+4ED5GZRFS2BBLmWBfSRVpxmNONVmS+H0HSzUspTiuLbP5b+xdsZiMkq/tP5aQELS1UqwOjIzUAd8axvjZaGyFc99Na/tv/zf/26YNnKfMHkbB9DG1ZlkXt7jXxdtmAxIaeECnvc/z8x4axS4NDo2Hb23uv+JDYNKq18GPH8D/on2gAMoXuaCcoFLuzBs5+Tp3OyqcAMxYyv7N5clgirHurKqk9az39zk73615qSICN7zL7Ob8q1ydupno+u6eRNs/9+TZjv2EfTg4e9aUK/cU3mT5oHZw8Xk0KWtqsBzdm/76Eyclmzn+p7WbIoBayLBLO5WeRIZ8CRLVYHW6D1lMWKacjZAsLBojBdEQOdiUkQmBP/mMnYzUX6osR+q3912gZsSA0cm7MGLwVACywlX+duvL8YR6M/fuYj7Qcl+ccVvsi5iTHsV03Mg3L6SGTNcJSM5IO6cFTT4Hfv/0NkrnoD7fMMBkAwNZlDUb3vuf/oqeZv9OgYQD2ONYpc2qf6IYFsF4NqB5FsH5l1uABMExUXaDbtnJfI2dQgJjX/wU/g9kxDd5w+NMnp/NL1sNzE3J24Ne1Fa1+7W/XZr0uYbrhTtU5GUUSFcRBFitSvZA8KSTAEfkcRJ1KQeWZlA2ZSRhIyka1pDQCqHNYdBs6LAvSM78+nbRVfP4NsP4aGuEL8bOZmPNURIz968PrAF1bMiotoNyjVT8uH+0XlLgnNc7wPYLHnqJJt/gZSIrq7g7GLiGOp+KPt5+HPTulkg4qAWbUKzJmEOmZg+8fKYJxkcKCZClZ+HDiVz8ovEs3Oq1pXx0CRtuXC05z1YNkN835wKHTA+AMHvQZYTJHrDtDPveratHG65wa9Bc9XNqXL19d/2qj6sw2cXdBizYZvfUmo2PDsHt68Nrw+mOP+UID6HZFNCjAChyI4sVx5Co8HUczNCeIMjlBdS+8UT3JAQcdRjAT9BNKEdgEwJiF3gQLWobwsCPZ1Oq/lMd/a/81W188XQqB1OO3RvUGnCt7SjxJmzUw7mTC7jYU/58//WcubSqt+IXDyvYWH5NHmNdrZM67uxgkWMQHDmjlL762I3p9Z89cMWBhltbp4dHWLQ8AYEJ6L6xVpxKz2sK5rFzGDTuetYaGKbiNTUnQepmvcaunP84Bf/3VL7Rfyw+8YudepUK1fXnC+rDkgknxpC4YsPvk/st8rcdRXlz/cjyevvG8+NE2VEbw/BoBi9fYVEY6iH7NcKnJXN0a5eGQYfAE7PuiU8vs8YmkHociwqyYM2UaBT62u9C22b6mzM4OX18xBHeqiVNZ/FW3mM0XN6N1EMzG+Nz3aBiL5hP6BVYRgdayxGQKR2hNZq8I4ddTX9PFnJji8dyI9KqFnutwlw4scAKUlilAIN7LDlNUyYutV21HCyOphWnP4WtLYPaJaOAZdT2/vx/0L80jgUN0GNJ1EAjQiAw0ssEdBdEGd9VNqJnOl4w7H5/zwF5UkVh7M5kRnVlRHlfA4897dNR0J/qHtl+rQv/VsMgvx9Nmm5FGPc1eX4ivOLiMDexrZxajJYXnHOWrnr4ARA7lxcPdpMWB+S+tHABQJ2hIK8roiE0ck5C5ZpW6DAj56DRQdvcfgxQMWSbA8bw+COuW8VVLAKCXR7t/03yf571tw5/ugekuZQJQx7CQwMTSEYI3R4NoamM/Y9nXOI672dQIp6U86+MBlKZ00l6vxJXgrQilcZBfTQz9dRvg1fhMnubUlSZg7ftpieNiaJurItEKvc6gtSMVDaejwOjovV52RPulaqgblyYL83rJncq34es9lM6QYAKXrWAat+6vBmjop58ViZBUXvI0tjIF5+toKK1HyPQL4atfb7+WZ/q1CPQyB/yLG6258znGM33XFt5qSo6vPPITuYWeiEdzp73q5wtYuc9ytWyA4bPz+K1Oorh+vhKBj/6tn1G47BrfYdgxdEUGsHKC7Mm7IQKNBg1re0+Eli6Dd4ScZ09e6CJfzZaV6Z6kQQPy+jb347i0n8cZwN9BWRJGnO+6OMmsToW5Ktg/2eIQlFl8Quq2naz197Tdnf8CWF/mq+mVDPC86eUK4tz1eRwBEuX7Z5v2OI4YFxt5/geXZKlKAOxoO5lR9mf9kgmHGjb40BD9MFKgzoHrLPrzF4sh60EQ5G5laJCsayo670ShEU1MQnK3joXntydf6cip4CBrQD8CQIsTpJY0D3cTuZKVX8ti/WrA+kdd31evQARGBqIOWGrqFk7kbn7WUmmXW6UXWw0BWbP7V3VzFRlOtnxwLI961q6F/YexVwBuStE+wa6X8k0tfqClK0bHGllEWL1bNuTcJy23M+nRczYZCN6GLnIyM+nus/RrBdPjxf41uhAjUDdMqUZlRjKs+pPcRxny1eXiE2ThPqw3q6/mF7J68han2XfyLplS8xW0PszkI0v18EP9yljXX57OByaLYPn8OK1sZtZMpcBMkdr6kSAhGjICArFG2A339fPFBsCzVz71ifEpPw7jWnmB6JHVn3MzpLOPo6DngeOodfEk/vVhhnSeuNuzL4cm3X/OjTH2Rl8HU/4dE//iTr8WgP5xqq9/eDt1mpJ1Y2sMNYRmPMQwe/JF5kazu/NT9rDj173AvVX0BCwzG7biu69ecVKvzseqi+kseseWkIWSKhtoEWqSYYEqaoCQHXGwJ7pJ4mlpGt8WqeJpN1a2i69F5lvbV04qRT8zu1vGzITjsTIEfbymfnmm7sx+j59nFoAJfKeWRtNvsd8mv9UXkXa0X6m0eMzp/hda2PPnZoQGul1oUgeVWawJ7IJu6IBM7WR9e0RwMzPz7u411tOpIrybRTNL72oNJeIp7XXx84VsH0MrJQXTsdCTUtpwZlh/2jnqsdBWreo8b3aCdHsxL1NVcq4EUlIbvgg41XzivdFgvns+7fkD/onaa4Hwlc4IANpQYQAmRgJVmCTF2DB9S7wArON2m+t+BsPj1+eOiJMjmxbuOfRfvkJXTs+1i1O18YKHy8SBXf04NzkJNkmgZcZtoKX8eJxLBQCZTrM6QmZu0wwlkTCzd996Zwcv03vcFs5r3FFCe6HMbgJpPFNxD2tPSf/+/ghlaGbECCwbyDXu8+c//fR0XtwL5z5l+lKbwW7xPY3+0xfESKO9Wj+OBxHe+kFO1lniAD3ExX/dingxPC8JUjF8zrQFZrZWAzy3PWgwdp/lS05gLGkJcB98SxAagxL7imiTquh4vvVIVxu+4CRQSEGW108yv2LW0/tnZtGRoMZHUMcLGyEQJ6+7Dhbb0LmslBPLisnxmT8tv1Zk+ydrL3Q649ucyxh6qIZQWB9oNAmRPvEvdBAtznW5Ehur1+e9eaUT8fIFdQNAu1PonmupLVVdVyLxClhb1r4cZHFUSwz11EM0s1pBct/bvu9eYgDGmcWfZMisp6PPm4SZed7/1Blodkwj68BYi12jdLQXhIR1agMzl2trxxR0ssQcM6sAIhTTyflhKI7Q4CLvRjUL0w7m4FT1HLfmnrtVI6qUMr1S0dyTpATNHvUJ5JPnczMz3ovbvGqPCf/OAUoVzVBuDiuh+c+2tLnOHrIdzGbdbT3St3OylMDKClI97xoV3WrYd5Sy0oraixg69yIoFAiBjAz4oO+t2+fvRRNM6tR19KPtbSdpMkdWgnKjEdxf+UnhjEnEAsRTJr93H0X0RDfTAQJ4hRm/2F7t61/dXkhmM1Za4x/gGpzmUPCaRhBXvAAC+kWj/ES23F2Xv/32/PoXL+bpo3J/DkA7zeF3PzxWBfOZ4O2JVa6/ZtOAGI0weEpqh47jiAj3Wo8ErP12u72917G0p+jBruJhF9Ba7ho5xZ+XcDEt/jo9cgDnasz7tBfjmbko8g77vn9+fn5+fh7HsW1brfVyuVwul16+l0Y7SYSWTwDffvjD03n5809/fApYP5TLEDZmID0Be2U9f9b14eu7+IXOL391YeAX11syyFxcxHNU/0//r/9Lvo8tWYZJlrLmpTrjhysuT1nQ5GgeGoDC95UwztZam3cGsCQKJ8myoGd22N1tULZ+ZTJi7L9tgydfYfdr+/F33/Z9//j4uN1uEeHutdazDP0S65et2jvvUzBnUMjtdlsfN904uJ/GVl8235qIekW31rKsGQeiL9aJVG2MPxM4/4HLao74ZyRBHlt9zAsMIUTEEW1llj8+PuYbrSOp+3bCuruZFfchKiDzZWyZQDFfYUyQpL/7uz8nB1Grl1IGK6Hb7WbOsR769RHh1x9ut9vHx8fn52eusVJKzgW+6OAlXYsfx9Fac/fL5a3WamZphDkO3G7Hvu/Hcb7yH/7wo6R933/++eeff/754+MjrU9/+7d/myuqlFJrlZSY8uPbdV0M87mfn58TFzr+SQCOj8+n67wvhmVp5Q+P4zkh37YZo3d+AiAVEcdxpNJq7uW5AR/2ox1rrYa8wxy9c52Y9UhzF9Yxnjq01hoYy4O+KuPnUuwCmZk9mHQEEc/TJb0SUcv/4//+f1tRaQ73HNBzN5IAfv/tD+sQ2AhJ++GHH76ODskfrv+ed6LZuQ2eTmStm+N84pz4S93mTdy9lFSg8OP2mUnYS5nJ3WHGz8/Ppy/89z9/RMTRjoYm6gikciZZaHxh9SNSVUmHO9zU8zMfcXSECprZ1OXdvt9mP/vWghNMkfMrU2ae7mU4umdiPOTzSnJtkxLNhXG/2uKeUM3z1brmWQvzHTng6AosAG3w3lOUaK0lRiRjsu97vlStNfdw37G1CriN1Q6kMZnfB0Qry+0oAiHoMJA64nbbjceNZBZTuV6vDCBAMlPsZ9tvn7khW7RQQLjtN+xngsl1sgD8rl7zjLu3dmzbJftZ64WGbSul2EBDSPoP/+E/rNBzvV5LKXXzUi0z85qDJiK8qIrcUuGhxtbQJoX79vtvJHObz00p4OCDIyg7P7tgFilapKW2bi+stNZvONVb51wDXsy8Fxi14Zh9B1iTTJ5Ko36P5KlrnUYnrutnKuXuG91t8FYCdLQj1CLCtn2yFEk5Ekk/Pz+TUE1pt/dA39d5PDnoeM55lfbxH8lzUOe9Jgl6+Pz7/+G/f4CzPM6sAw9oBWD/vHBQnpWD05JDbnY3Iq7btweIxBADB2Wu62vTzwyoEybMbOZ0f2h/Pr7PO68potY8SutquNa/mSOzIuy3b98egDvv6W3r3VBpKAXF3a2H/ozZXo6P43N9/eTqJU0Odx0HSX9Tf1xvcucZuEzK/PZ7fJzyCzQM/AAMRi/ZsaIRa60YC04H7VDbwSa0f//f/i3vudeIkI4/fv7UWtv3faXw7r6O/8q3/vi7v5knExgjAtIf//w/AdDihZTfftzO+pInc31PWtb2eZycu3stpWzbVnz73e9+515rrddSCRNKAE0N+JtJSrPz1T01zQ1HumU6nFD7Vo84pr9Mcg2tNbWmiIYPCKl2GB6EAvDtWsbxtOYYAMc2p2zlJgLPOayWogeGQnnMwk03GoovomgalwaBnP3JVp4Z8QQJB4fNQFjkjGlQliKm5TF++uknSaFjzv5x7BHxsf/HyVCP86dubm7PBbA+HlZINnuhUyvv5VgBaO6B4Xi2ulACgB5k3fHl509/wjOAa/t1PfNwwQNgSar2uFGzA1kYdV1b+e1xchynYJvQ9vSFP9XmzydGrOTo4XMrf4OFcP1Cy8uu2/vctFN4mSP8ZAKWDKXr+RWn5gqIiHf/4aGHX3/FhfM9RgxjnjQf2lxzM/OyTV4pYwPeLpWkuV0Kr6wYkTGXVKlCB/bb0WW0fd//089/SiqassyDuLRCbZLA/fbz6KvGMm2S9n3PA0np9Z6//dw/1xGeGWXNzpzIK4rFH/+cIw8g67bVWrft8p//01ZrvV7fr9frtm2jDCL+8OPvRwePiEOhI9BMgzu4DcraX+HP+8d8o6QuuSGnKPcwKZfjTgycYLquz/kiAFLV8LXt+57rKhdVfpIkCoeY/EBOcL/j8qCOLCy4199nIkkuhDyH+uOP/+PYJvvQkTVJx3HgxLI2p8D8p/lGc7IiYtu2fP11vwNwO0vVr/15tX+Lx+mo1n+Z434flHjeaJh1H361jZiyB/hD2dehmT/pCsVx2bzA4078xqA/Vs/gIQWWwhjnc+OgmU2K/PSFp+l87fwcSqFTmXn888ik+oBlU2WGe+TFwXm3+fmkG+PpI4c6H66P4Q4y5z6nf/M1RfXd9phnVkDPqDH1rVLcnVaYJeHd3IrXhLA+jG9vFy56oqki+NOf/jR7nsTzdru11o4zn+TdW9Q6ldn9LfK3P/1xFg3p8lToUI9FnbUJTjHqdvvTCkz6wlvFTEOUlHk/3Iq7T4hMPuvj45bybC2XZNJ7gpqxnnPAxmpsn5+fLfbjOCKOMbYB4OfPJxtbi5vew9RYaG5+DEC3nnTsXDbr1njavli9u2plv/GrqJU3fFicvc/H3bqd73673Vawm+vN46eFKhwDeSPrfqbacQ4OgGIfTJaOcMOkKmRPVTz5TOg0bj5seZJ8YT0sb12mfdze5/BN0wzzZU4AWn/j03p3P+z78ee7fkwY2qkHUTzHK2vPs1t8V9Bti8PBPH+5vj/eAVgY8MdWrc71vW6wNJOvc5zXm37GF3QDUN3vhhjdA76lZ3brHdI4NJvpH+7udvu8IxhzbT0YMeY18fYSsHA/8Xmw3ybh6toI0sDkgCxzKKpnSgQA4m4jrUO9os8KH7zX0OcFSZBWtjTvttXLsgGixZEbfkkftFbZROHHigsPs7P2JL+tpbShc4iIESLWs0J+HzGwZmZM40nfaWaW4Nz5BbVkK2Kk2x5DHLzXnOSjUzH/MIkk3X3lsAQTzEZlADMbnljdZ+pVbv5SlzUgRIMOAfjT3x92by7Ly2whhOuqeMV5RZyi35ydiPjxeqTMVcqsIEUgWkv/r4WNQAD4/Px59vlhncxXXgdwPz7XTs7zr1Q65fj5eU7xGJWf10bStvr0fOzPPZy2MtPF3I1dfw0+EqVid4uyv7PUWpuy4vQ2k/T5+ffr6D/tw9oqN6LnA+63C3JYZ5KrWuEuY1HnfpgHn4s1Zz6XJHgXfb5OFe7xV92YUM/RAKYiwoYKu/85dv4fv/9097j7O38dhB+uP/Zcr53GuAjSyvYGIAIN4iLnX6413xKr6VYq7nMuouObBLWf70S2+fTPRUhfL/hpyV6wiBKtVzMaFLm11tou6XdvBJDFuPuUgRCSh+rezn7OC0uXK40s1TBcHN7fvkmSjiVxEAB8e39bkFeSjthbO2p1eIRH6BgXCMAGWxnhucM55KO58fKyP+5/7ld0L1ZGkKK7G035b5gmJLUXoU7zcQ+r8duPP5IiWy9KOwjGGVp3PzsDEO9IegKZ7hVJ+dX+8Xd9TkeFm/yhuyvj6NQ1j/nbtwVw+43SdnECaA5RtzCKWTFPo5MjlfELx/KSLOLs93mMybPx3CHgsX/ifqvk5zqgazv2O0Cct/p6fb+Px3pZXpBrsLNdxu6zlvbXnkvr0ZvhFULH58F7oakPxNOrAfox9uY5kZK21VF2fe6LIhHR7lISzs/96EpHLcHnIKdQM+uG9T5fr+tDl0dr/d8c2M/bGElzkkZnFwBvSTHSLXoIGgWZEjpti92iBYysGxEhaIbpkETM8o6r2asbVXrV226pEADYTJxv5nW8xdTdhLrIGYehtag2391W4fphfuf7Ru0g6O7FzYyt4Ti073/KShl92seLQa5okmjp496Ig9gVTpOlGyD2+XbHccdh9TePWDkjM4N7atC28lDMBeiREs1g3R8bUOZpjeALZfN0WSfpy+q11Qu388gkQJ3pkk43ejBmCat7YFo5QS0b//0HH2CEiH2KhMU2kr3UTTcuHpBuex+B3sP+vqAlKC/MPECyLttlRYOXnObPxz5/jGUDTA7rkXjq5Ly+Aha+YBbt7en999tzWTpGqMTDeZKdSD0Akbe5aO6eex9+NW94uzA1rHPO8iCVgk8G6GPuwyWIEmjPYugkAR/L8eO3X1vZRr6tTr16Iq92byzSSAdQ9j/NAVnbw2vOQfgjb10sylx3ZqkgCl2yjoZZMRjDAIsGpVa+QwyjuyTEvvcQrum+kEzDm38+HedVQgSGJYc8rn8z5jAXUh+l1o08LgnuqLXgvQA/6++WMTjnPiymXJ/aqFwDhYRDRiAQnyMIOin8skLGPrnuXYRRdGdaI7aK6VRLyTlC2wCx6WuAF+9ij48m3QaAvj2v4uPuaFladmwxgoXiI2HT4HbnlbPwOQD3kZByWcwY1YO+xgOYYaLSCkxzAB9OtnanpJcEEPDj9pUhKACYmWbbHdsBIMXqB0iBEPW5kcHs7GEfY5JkOT6eV3LmUI9p+QqAL6lFtFx2PODUBL774OdHDBp/zCcecTy9/pWZ08pz1vHompHHF34FrB8fzwfuvWwPL/vQsdkkcX3BF7r2B8rW9leOgs8B9LGu4pz7Z5MIIL6ng2vXuEUeiNt7ARrosBAcw7Vl1+eEAAwV+8NmwPqJ4+7P0ZkZ4/zQn8/9j1/7TzKelxeD2fPxieNABJLvzkx0EiLavWZiPre9UmnzOW+dHN8DdQFweZGwMHKLjmWA8RblRXD4/v077lfRHPD1svnnKFaSsx8YG9NH5fPHz+OFtTHugq5zS3QmTLIh1U6d5E9//Pun93m1H4tOq9/Xbx8QBkDcnquSVokbC8YV/nxaB9Yb+b0sOo8fUv0+7cf6Zy3l6WVtCa6+uxUmNqc+r3f3IVvAMgQvgKydszIfRHKNlbunAM/vf/vp82k/V4ZibUf7eBiEpwtxtvIiJOLnjz89PT8HZN78oQ8P/fQtRcg0dTmwZ2jFn//8fZzBSO5MAGW78F7kieFEs1La+bgP7A9n+kKMOxo7D27xRyxW1Ck7TKXvw90+b2e9y4c2O2aLlv3bt1NkXgcnXjgi/vnjuYNx6tTWvZ2fP70uZPt0fOzzuY74er0CmJLm09+uM7t//HG9YF52lPLq52s71y3OhJpcdCPrSS6q8XfcaVrO9iJGcjtjP7kO3QS4ZUkAgMXz/TuW36SDY0P9H/73f1ivmLdbJ2wdJsVzERLLElnbdr08vX46mj7c5FLL0+tfyrTbLwWrfEXop9iK1xSD1/q1M0/fPQ+O4/Z1EPBFYjo30gsO6/kqAaZY9PBGDz+cJxedWiJOf83hnpLfnu++63mK3vUp664QG56N88vX0RkM/3V8HnajpFpf1mF8KhJ+//6np/d/pdO8vD0P3l5dKNbP7UV6gVfr8wWdPfvzdYvh2XKdfkkPkPRq3T54/y1L9xEQ8zP9HOf5+ZWOMyX0+vlqPH3oah/096/GB3y+/m342a29JVm+/6e/+9p7LBTjYUDLmQDr7ie3W1oD7yQvkn/6knw5P1cd+Xp+ctxf+/P0xV4B1qsXfqAkf/H+9sPb185wxFo+fEXSyx3vM9vXhdjvtr+IVn/VeGe9fbEoly7d5xTvqQZlw+X5pId9HKrf3eoFLzn/bH63SOZ7vdpgF9+neeoO+F4Alj9ngDD9p0liAaz3Yl3Lex+IrBdpBH76z8852YjnvE+88A96kBhm8xfz+/Hx8XR/PaDG/PNyuawrZ371VXXQL0ur68SdCUyLcnR99MNrzjMfP3/HQJ9VJf+KY/V2ErC1vdpfLZ6LhHyhgy4/1DtP9PWd1xc7O2RYrzkH9K6Q8rkt93Z8vRjAsX/xsE/g+/k5B/fQpdkyY+SrF/5lwHq4/ul9fv6PjwuRgyN4WEB5bH5es55/eKl55urPlbKvPJ6/Rrc/fZF58ltyUrL7K2fo0qNG7/LttZL42f21bU8X1gx1etgJV7/bqK9wan7+9MfnwzAJOJfoOUkfww/oYcO/5qCf6wq/AkH257h9f9Wf+dz1oHz+Uhm39c5Pfz6P9/10D1o9RR5EredEaxkKLYYLLX4Mc37zz4lNl7rNKyds4fV4ptXvKxR+1R3rFzm1V/uX/93/7i+LOXcD0R5DEL7+cD2fBP7rNbPi8eMG2O8A6+md16bX6SyevvBXd4oHFuCh7eX5a2aoxNry29Rhfe35q9dpL5SOr0ITno6G9JyNBaDvq07ttC4NQHkAMnzuP38dkAfkXT/l5ek4+5cqL/lZ/YkVVaOs7PpVR0A9T1+TZnh9tRKWxzfK9mqDfbYXsXsvNlJ9oXSfIsXD5/aCgX7IyvBqWtcLvmLQ7OfX565WxTvs5rH+uQLQw8n89EVHvE7QLwPW1xc5F+Q9cYr2MoHWcw7L+Fwp/nVo8tNKfXrxwyqff54F/O6v//h4oeyfIs9kYZ7df7Z4seDm0M8X7v2/R/r5+RIW9+duHBcr+LIgSLruKPM5Di+MA2/1OUfziuV+8OD/yjA+TmI5g9LXRTk2ZCPvKmu8XWfozN24zY3xsDeOPQdwVb4AUDcTZveSFeodnv183OEzYdrIWpy6j+fjA4CE0tmJPdedGeM4ni7Fl1atF+ev9Un5KZJ4IcIg7tzQ5kvZiwycZUkMuV7/sCzngfs0euSZkWij+5o/fnLJDb38476fIT5zVWiUdOCibo8M2NzPPHGTr8Mv7MfFKLe2FezukehlWTk+y+tbbHuu5Obpl9GrMOSyiDOERQC0OG7cfc4FHW2+Zf6qL6D6yEH02TB/OhIvuSB7QnYwKFgmfV4BZZ+J39jfM/9nz2RsSduLkJ110KDpZ6sEsifdHEriNSpoDuPX5i8zQJ7P/fr55BWc608wlK72op9NQ2nU36x30MqaNAWjGgIKnOQcZNxvvLtHAwA+deoU1s9z4X1516f9BM6KbRoTDfJVIrpX41NfFdF4vgrlr8rE3VOO+bhX67bei5xTNHv0fxyIvw4RlkF5ROd5cg5vAtXIsOxj32XxFgBZ47S1lsN4Jt0nQMrOAFv16RLJtvRz/eQLIPtqRe0i4eA3Hr7tIqrEqT1MHfpxn0jzzEQhAAyc0KNEWH9wCeui5op060Eq/zBe+C+3V+vzxUC0vVPynN25/uvlOl9+BaxXrL6+9D/7Mrf1ilb8ogs7F+iLck8SIEKgDD2ujwT1qj+vBVWOqvdrJsBXCc9up5J+kt9lQfQ7ntxcvEgRuL/o51X2lWJ97f88H37HuXD0/lWO9ha/5Hbwtb0Cplcc6/bCD+vVOrEXkQynju9+H+0vrIq3e9dKjWp1Wbr9XGyDOW0v+v9qlXTgmxUcTv+JJ36OXynxef8siQZgFE/op58xOuSonva1mU3oyQP203fGmXn+yPFfE/knh9XuaysA99Lm5F9GBtn06yQfx7rvoFW+EMisJpYnsb7hyw35oorGqwG9fnsS/MyhpBzPEHtBEfFVpeKx63Q/f/7lznmw+hmt374MCVr8jBb26qV18uX43KvPXw3LbLznQAFkCGtgfAAjhUVgAe4v93lhpcWZ7WA9+AoQg07ec45DZHxVeRgvRIZXxUTMX/TfnwOTXlRmfiWK6jHSYvTn9I+78x88XkEK75YB0LmJWNiZvC4ZgqmKeezPi3WyjvLdItHjis0Df0EALJ7wInio4bBc8IoteaBkS7cfASv/nJEVD1+VWGrqLQT70cj68Oyv7MYjYzU3Noe0PeSgrHODlzvtlSj0/Pr9RdD1Covz+Be29wOpOQ+ep//CLMN5vhEw8jU+aX0Q1rFmnn/ZoZcdHYuCi7n61cIlp3l76gSFBBSeODX6dF8ma73Py1Lm5cWonie1vHvguaPjK7eDjC392uLF+77C+Vei3CvOxV9Wf3nB6QxhbPA1nT96wZDdM/KPojS/rvZXDtKvlskDB/oVIB5m7dXWKNWfXnZvM5lHrwnGmWny/MTyno/nBwXTkArzz3LcnrsX6N5sf76AnfHoS49fYsFMfdCVqSM7/8vrX3KULz3a1z/nOK4yc1dkgQTb8YLV7/fXhO2EoT2e3//BjP0XOZ0ZvDq71InwC1bhpaPdiLNfyTjwErCiL/QkEmf4ns1iEPewNTmmx6e+QNZdz0FiatuBU88KIP3UVv1W/v/V/L6QsFFecEx8wQHpxbzHi+e2Fw/21zqaQbTuCMmrZZFuPY9MVuaJwymmjI2qp7Grv9AmfXmUzW2efL6wv7Qn63Od0McfvsD5VxxWAvHDyXs0vPuqxO14ENbOW4+hX3u5RxZou5MK1189HGj4U2jJRsAzzcWXsXhBwV4hwhrbtb7wvh/rmTWB0dP7vAQIezS3Z3slEr4CrgdMn9r3t/ocIOKVKDFFhfHEX9B2AaCGg+5ZzVzAYO2YBebOOoCvbvaKwq/04mtPpvVwHmDvxTsePl+xmq84o5e6khfnXyrL/xKl+fqLp2fPIiMPS+KF6FqS852qzLkt24Ok0z9f6fheLbjYn6SHyidKmiWm1vPP7zM81x+ufxWqFX+pas5TbPoiJz65uIuEdYHzR737OE64ycD9doAEDGZYTSmznFGO0gmCnkbT3hmzlrTInjpsvM5p/ar9+c+ZYA8Pn9t2JxKust7T++g+vdH8LbYnMvZCH/6hgDWvHIPZ2ytLxC+LeF+f/pKl13pxgqzQPYzzu2k2DQANT8z5+AVdm+npyDykizpp8n1iwjXP3IvnvtA9vRyfp6dRX7iP3F5MwEs3iBf3f0iRPF+nvIi56yIb0TXAo7Ue9cnJYWm1mn1pv8DaP0hInWAPTu0XmJq1he6umcczYeHDfYKvKn8+f2gbnvEP55+6HwEol+1OafpUPMyt1VqT6X073TfWdTY9cR9u0nDn0T+zu77KRmAvrFSvBrRkGo17lFHPET6obS/bSwDtRRmlwQl2G0ImAgAAruk78iBfDeMdz+EC8IIROR0L1TNkqzVI2PfnntMvdPe4N188fn5t2/QL6w470ZGrU8L7USWk5wvupTGhPCSW6+dTBzSRav7bBlNhmQ5q+Pa8eFtAf8Hz/qG98IrB7eN5EPIkSA/tZcbLF7q8VzRseyHKPbzy6nH+MCArTPzF+8y2pvdZt+oUmR62zCvJo5jrCzBJOl7su/aXGI4HANr3z68nJW0jguLh2xLbZy9GBbXWdHQCaBjOyhGxHxK8wgySpwsGi9WtV1Lbj09/89YOddKhlnVM3W03GWQO8qZ9j1u51Ppe9riRMjM3OLIOliSlTuorhUk3tgmgGgLmZysRYdbrDmg/kH24dSuDgXP+Jl7Mnw/4aFwiPGaWbjOzF1bF2cmR0qyn6w79nY308C2z/tHNnCGJTttY23Hcvt/Uwt0PnC+1ph/IslrpvZXnE+s/Y+ZmBIDQgVD0qj+q7kkJkuC7O6KX4bFSmEZMt1JKa+0YZf5EZM0bdye173uttbh/fuy1lLftsu/75/db7AeyIoPQjjCglPLTwJMOS3kQArB5SX/0fd+jBQ3ujl1itIajoWzY3lErAlE3jwY1GLzQSUcwGlSW+y8Hqyf3uk48ur8bGLT0HIxDITWvJSNAA21S0MIze4GNqsAGyqXWjuNoLSQ4O63940/Hv/t3/+7j4yMrHv7pT3+6Xq/uvu97rsAc+VSxHcdhpW/4BzI/U2D3d8kc6i0O/tnM3GouxePIahjKx5VSjOU4jtvtlvNV4gcztP243W5ueL9cabrdblJYNbrBZFu9vL1ZtYgo9i1XOFskc+1gzwIqZdZ2CrPmoEGg0huzoQEINAJWw6zE0dotELqUrVqNiD8ef5+vWa3m/soKJW61sypmbGh9bBuKSim1XmDMnRgNImrN3K4GWNPpuljcYQZ3AvDhZTJTGhqYm7dQAMwY9CwYW6uXaiRpDmYCfAAzNqLQ5G6QRYSMdEO4wcpWrtvliJ0mp5nBmTW5A4C1RwEh12IC1oQYjSphpjLhBi0OJ4DqJudkO0iW4aYbEUGql6OmhCANp3JNUqMmYLH+RcBiDkgCFsLhRnpIVGTedGMxEaFitZgdpI4WTDbEV+BbkLRntizljHEhaaVf3zVRUdRDKAhEKSUrbpKiUErxUhOMSika7jlWS0SkT29us30Clul2s61ei7v7Z/Xytl1uN7vUre07Qu6OUGsyoZQiHlgYhClg1VHDIo627yWTAbh7uRoMLfZDR73Y2w/XsnmwJTSjJRWrjg5YHAzio+hx77g4v60sZ1C0dW/pI6K15rWXgM/iu3kfX8XYCVhkr8Kw95Tzo8ar2XX/w9/88PFREkG8aAJWEvhkzebx6Z4wmg0YXdH2LNqMjWTmL5vMuKRael3OjAOdNYrYNjOLox37zczetippPwoAFsIEY7nU7f2tlNIUpqvUdLTTgRm50kApomoJaSC752ogVVkJcgIiqa+IQ0DoUooZ1ez9cnUwy510wMoxbL1iUKGhojUvzSNiw1kB6IjWWma1xfv7D2YG85zrpqRPXko1d9vcSKpYrrncDynFVGfzc3yDNSIAleI5Z8dxM6skjxZkScBqTUhORxbIiC8UWEO7vF2v37bjsM5hdbthZCIO6k7SnID1wBOdnIjYWmMIwHEocwBvW7ERVW/3xO04lHl/x1MYgdbOIokSPM4LfiF9zbjeJodlZlKnDL1YqfK57u4IGljoO29oJZxOI3yu4NUJfngm955j6ukL/VQyhixFLZJ0q7X65VIlFZekUnyoqOgOkkfAjFYZQWsEWUZ6iQDc01M8tsJSjPDitVaHPDwaDZJbQai1ZkAp9uP1LY6zQjqApA0YLKFMBjoT01WskGqSCdulXK/XerWAcgQGYBWHIRgBuzy3LiUCfl0nF9uyfB7sHLdDse+7117lW0slAVtKJADwMdrHbW+thdtkt5NB2Gu7fDO51Wpmdgu7XM3deGOu/OMIAPN49cqYsjAgszP9i5S1DRHBFquqhIq+/Grt2Roi0BqyrDDJdjsMgQ1qbs5qiGjcZQY4RcFQr3h7Ix1NKMpkx4ygBGY2IZNpLLCujJYZSDJS9LOAAtbQFW598R/mBKQsVxsRQh17gcVNUjNvO1BhZtVGzdpQRImIz0jW1aVM5nrkBZerG4uZNag188FQl2stZuZuzs5fYBgoRml4Vl8sAp4UrJXS56y4H57ipQNIjqA1JEVVWkCyVggRbtf37du36+ex96y9oKXvdVfw32kK/6JIuIm9nkpo33E4Silvl+t300xFZHeAZfFEJLwrNN3V4cngXF8qO2cPVw5LcTEzGRVsCvUKEuZW1cLgThYEjqM1uJl1wSWB76zuVd6uXRhQ5CbOwWGvAH9a3wAKcsCd1+t2uWwRcTNJqrUeOlKtkzQ/mYhSLGAplGSshh/oHJYb2Lbipbjbxd0vpUDHvje5EDJvMInNBDP74XppjfuunAQbhmAdzZ1mbIRCSPbZYGowGcIBL/SSMqk6hxUg3FlMhkAEbDvRaj1Ar12IZZ0AoFE0a4Mfp6kJJXgckXXN3bksMSKg1RWRdPTpIHXAwJj8uBkNzUvkPzN4CfPmhUXyBKceSNCPXXeAOOmiLTpNSbCwjMtpUpyJCqzQrJBJb9Bai6Mlu2AGKRo/lDvZzZkc5QG7ea0yBSSGOcyLVY921LHm+y4YFdUQR0QgU8svoWObX4OQUqlrQsvcrWbmZHjbEZS2wkJGsKXhTnJHJt20UTSmEO5InBlTZrYPTigkKTEORifoYQaKVByYgHXZOAqUkjQwIjhtzIMQAVlRXChsDZK5s1a6M6IcDjCOw3MzYOiMS/FAJnM2FhcB0/Va395r6brdyIcmn2XgdE+Zc/zLHFYcewxRzl3tQK31h7eLs53cYkKAGYDbKKQ3n9Jai/CTaNyH+11e5OSet33QYbUJWFJTCBlKWsxMLQzm4IGmKG1PCsZZdnRWryP5/v52u92yWKnUFUBmfr34iuOzfX5+1mpv13K91ohIBrMUp3trpaFrc1trNCulNChF6QSs4/AhEpZSrNa6ldpaK+7V3C32na1yEQlbioTYrDW5+XF0yTQ3tm+buzvtqFZct1vSLINtMEY0Mer79v7tbbtugQ5YEkzuLIAxFCHU53mpvN7ljJ/8lyvpWRfg4GPBMGqt9bL5Up9Nxsw+cjJcY2Lr7m1UiD2pF21vP1/eTTY4rGaXi5VifvgQA4FTJITHXXYQLm0upVyPuYHVzjJ0AHLlmJmCZnYclALgtqVwc5CREmsxmhMtaGGu69Ua1HSIvFzt+kYWWuNWPCLQEN2h2yzTh8m7ral1+0zfNerK74AiecH041OQDJfLFMelKJPFvw2db4cUsTVG2HG7mckZZp2HSp7OStfWJYtzlE77L9f+4iG2xnoS8rcLgFSxD26t78CuBdcoMpqbpHprjAh3r9XdGQEvjowaZC9I15oBKMXCcEQw5U0G3a/Xcr2eRWcIkHL0WiDYn+uwXgGW9i7KALjduO+81Hp9q8KWql+/WyQs4VN/scLEA2DNNbq94LAmqfwCWDCztDk2RQZg9q42GOhgczei7W5mt9bmyMdUGANv775dtsuVn59M8WfbSq01lYmxCDJ9rFiu18sPP7xdr9fWmnnnsGC6DQV8AhaMA7BsASwOwKpgZLnRCC/mZtbiVjeL3SWVAVhIL8fKCB4HclVMClGsF2GthxmtJH9nZn4hk/eMcq3v7+/1UjXqKkeoc1igxIhgucsRPt96u5Sn64RHSMqAM+8VrTtgla3WWq1MV28ne/aFeduT/tfSWosBWH1+adf2drleBaQ+5Lbvl8ullGIPSvdxnPU65wIGnizv9TM1OxpZX2aln9bk7ua9our1mqUGwkpNQ9lWi9OO45A1x1Yv1XSgmYxl83q90IlGJ00WHgjL3T3KkzDC0CJzKGcnDcRuiVaETGqQA1KrVmmKAxamsOJ0WrTWSk+caQbPMDtGqr0NIsOs7/f+1nJATYFoNBVJpLu5hRUaaYR55l80Myvf3q+aPJ67qSvwk9VChC21auNorKU1a7G7W63m7lK0RlIdpGqOL3PawrBHAyA3EXS7XMvlWmxkqqRAqtiQSctzs+5TwCJJO2wU/vYPlY9wZym4bB3FsVh2Ey9WkRBAa2ztsX79lEBfARbvVUsYOqzoSneGGBHJO3RqH70Dx7abRxzNzT53pXI6YWiWCy2luF9J7vv++fkZEZfL5XK5xOef5wjE8Kgg3Rxvb5cff3y7Xq/HEe5MwNpx84MJWDAmSFkpEdEaE7wAHAcCcnfYJahSymW7RET1YtTRbtX8uO2U3JmARaCUYrVoFCVXG6p3dIVAobVW3Nn20imEX9MedLBtl8v1ei3XLTABKy0khaQaJFl9XpziYfNPllP70RRFMjP3koAlKYy11lovq9mXZESZ6zDPlFF5O4627zuOQ+0s7bW1tl1/EHvt6NuBBCzxo7tW2QlYsGMrj+wwuzrvzIirRY6xI5ZFNWuRGS1qvZgfLWBWrm9vko7QVrjvO4WybcXI227e7YmM1vgJo21vvr2xeOx7sUuoMaoNj1Ab/i4eCgvFYQsnmCFTlCBieJxC9G0rRONN0SxU3Y0KdyeykpeZ+gaVocEsrWC9DXWNEErrVFaEQ65UpxgAwYNM4Ted1VUulzr3fwIWs7B5VjNvMLNSvHPsBDfnERYlz5dSpJbq/OPo+hEgNYsoxQ+AwYDgFpC5l2K1ejAS4CVZWhnNnBb+vIT9Vyth51x4M7N0Y2ttj4PuNO9az0fAylphohaRMB2CfJT81L1bQ32dgvkph7W3FHyYhD16bo9cQKl+Nr+R0VprbsZb5NJ/AKzW2uVaL5dLRHx8fBzHsW3b5XKJLesDSlLrb+Ekxbhcr9fr9fr+dhyHGBJrrZtvx8Jh7a2RrLUe0bo9q558gbvL3sxLKeXteomIzQuFgIp5u+1Qc3cODqt6mcXIu1pkjMalbp3OHe1yqdOq1dA5rAb55fr2/oMPkfAISOplDEEJEVEuZ9D4erC6BcyvJOEYhVTN6G5WlAmSSi2++VbdHbAY/E7oBnQ/e87RJNOSYPte95PDIhnxdv32t/Du1nBrW1oJg9+7lnCxElprpZ4c1gNgPT1fjpskjGGNiKRLkmrdvLWwYizX9/fW2q3h+lbt81PSZbsUM5ZbaZGuKjiOZruc9fpDvfwIN3G/bFcpuvJBA6rAo90ksYWp5ZYc0LJlZyxxX0oPJBrBZpKXgw0sMBgMb1udag0zs3SWiGi3PV3/rGvI08hrF7vma+47S7OA5XY4mtzN0qqbQ2SFZKmXLWljbioz21rkzpmK6JSqOnA49t2P43D3y6UmJUmvq+M4Qp3fIdnafhzH+7e3n37+2d3rZRNxeXu7vF2/3z6TLkGm3EJeAOhoLp/4MvcwpNy6Sb1rrRMptusl1UNpV/7dH/5Q3SXt+36pWzocmToeRQSoWmu6sby9vbXWPj8/r+VtIqOk3MN9AQ2d0QNgdZt9hI6jsBu5W2vOjaSIoyNZS5y9XC5M+VR9v1mLzYt8X3VYjA6mFbXW6tULi2+eQBaIbdsOpSeFXd0xas37dqm1Xn54v16/tdbC/HY7GnB9u/rQwgCoRTlBl+L7vjcpJ7211k11fC/Xt/TzqO5AxNF+9L9Raze/RdtrVkI9bpJo9u3H99ZyqnuS9WnErKWUUtTCapmAJb2J2FsLqFzf3n/4sb69yyhw3/fjOMzKtm3FPCKO47D6vE5fHZ5rc/Pnn/UCs+5Mw+KlbBHxebsVlbe3t+0yq2R6g47jINvgvjGRzmn7vsfR/DiO4+ASVXb81P7mv/lfZ1eP47i+Xd/e3gCUoWubGNrprrWvbNTk5Vfran71//3v/99vb28//PDt7Xo1s4j5Q/v8/Dxut/ff/S7zu39+ftYo29u3Az8DuLy9Oxj8MPL9/f2275+fn81vrNv29s22N5hVj/SfNg92t5tuo9+6A12fRBv9qdb9Zg6tGUfCGQZdf2f/v//h/4PQH37/+9vHz664GW0EPwAE4V4342d6cqiRTN/Y1Dg1mCIr2dJKMfPuW079+LvfRehz3y/bJmnfW5keEF1uST8cMyxeeb5Wi0gd3Zi/UrbBT1kWSowwms5AUJFmLF62WrZ6BOiFXutGL9XcSW/WKJgXkg3H5mfGS0l0qzOxxqK6mqxWac3MwsKiUVa80AxSKaVsdQqznjUjIqigu5UiyUoRaa3ln9JdtoV8/fLCEzqHLiLsOJJn6due3fxsir01B7o9PF1GaC74EbwdRrGUi3WPJQ0X1rx/a83caYUkRRu5jE0sXcXmOYBBRIN5+Hb17b2+fTPFRpPtAC5vtXQHn4m11p/oN5dKKT27YISZNX6zichJM9qOFkTw81NtL+Yk4mg5F/WyecRRDiYsznyHofQhQISsYt/z+lAFDCUOcLu8lcsP5fIOYxNlh8phoNVKd4+QHdvbmd3h4fNhq3c2PNrAL5h3wAo74Pv2/u1yuUAM9jRMx3Ecccv5wXBG9awiXo7WGo+jHG0qNEl6o1//VuXAceA4Kr5v7+94Xbaua4LvRUIulb0f1JG//9uPy/b29vaWsUQRoZRCrMhvrLuxJGCx3lA+3n/4G16+m/D29mZk+djN7O3trXx+2uen7Tcr2+WH9+vbG90iwmb1LU2/nxF1GL1oNpasoc49IqjOIEzA2mpxHVvV9sef2v5z2b4JnegKMZgsGWgOmtXEOwWA7szfu1EYEI0eSQLTzrDHLpoYMBcNBCxgLF67lqyLhEMZNmPJC20ZfYS5mzEFtFq9VkCMCB1qTa2RYjJyrZm56PVyrdtmtegIlOrbxbQloQfgNINbKRTEUuo1921EcDKWZq212c8yGEVJ5TiSufM0i5bOfHm9eL24e9ahmhxWqJVtC1CS1w3WPOTlTEw+90CfsO15GaiSHj0S9p1kSaNRhHDJeFpDyzTBZimdeOodDWjiFlBEKYW3riXM1WlD2q3j3SXBwudCx9ahHE430lvajfbwy1bef6zffiiwZheVT0nb22l10pBPlXnQy94UncPSACz7PQAT3Ok0pFt42xENflM7Useoo0nNzGoaJVvz1jTk2eQi6/B0l33C99z2rk1Ghpy2vX+rb7+rb2+gO8EjmP6lhU6LCCvt8v429/O67VfeZOWwDJp/uhWrJQIqB/bb9vZt264iulsc0Jpu7ftk5yVxUMR0d6zDxWyKGhfUy7f/pe27t1YjWD/e3t60FB5/AKwYxPcBsFKJEfEIZ2W7lNIZ9ohoRy8Tb2a277XJ3VPCqHvU2+1y/dGvnwCu25uB5W138np53xSX2+22N1R/+/ZeL5eM6uUAUEkMpWiWru2S0CKiS4s5INW+S0r2SoPdM+iyGXVztsvff99vf/a3d/nmhlwYPV0Zg6QTNFi5oh2hg0PNn33wKIwAPSIVmDQzeql02y4IOHevNSDCWUpBqUQ3OXcaAkoKdf9vkrYAFs3pJaewVE/ORWitFXAXjuTUzAA66Jny0C9Xq3XHp3nxek2rkJctZ5Rkj3Fly2cCnbEcsMlavY/yUnRAEvzSjbMGAvJioOJgudA289LQKDCdghhAk1cUKkK+gYEGlKL7BHtzZZmfgLUCt3mx5LDSHcSrmYERvGTqKEEeIabqDaVWqrvyNdnRBGnzcuOe/A7J1a1h1XHYUhtmVvCmm7HAaGA0HPxU3VDfbPsRxqJq/GitsRRPh+PWAKSjfIsotQZugLwUM0NrTPuA/06SgWYo5mDo2Nlaa4fpI7jTnVSwUQ2kXd4oMS0XqfsbTJZ7KaVERPCj+S3ZdlMhSVAo5fKeHJZAo6k0Huo5cKjukuvPg7HFY4bUp3YXEVT6ozmAaKJbKVsEGnfDDeVN5QoAdJUCGCy8bhOwulk8vbHtyFCRznsO62dttVx+L9uZUbv2uV2vvwBYbUkfgIUQ1mexeADef/j9nP2I6J7fkiJ9XDFVLjVQj8PK1fcAUEtxsB5hZrXWq5XPY9/aYV63t2vdNriFaDorP0uta5TYtcmKrsmICKkh9Ha5pVIwWk+RJDUiiomxG/by/ve7jNu7caPhrbYOxEgvf5nDoeM4Wuw89lADCSpX8pFsFJ2SSel4RrL4tl2/SYIfCe60vaQ4wEUnnRDn4KHwAXi25G9orGZCtGQbYE6HcRMPKnlA0QtTaGNrgtOtXLyUEmbbxS4XyI5o6LKeDEQplAFdE0xPBbk0vG/f3t6mzqLnPlWEIpmj1lqYOUjzAAKEuZVCdyMRPZKOGYXjhS2MZl6ERnOa25KXh0sLq3MJaqWcZjQPRFi/DGaBkF3Innqrz7CD7nKDAINCdNH3iECpldsELB8vyOkOlppRnOSEPXzeaNbTTwdEUEVlC3sLv7BUNEdzWIMxOdnADsAyacFxoGxUcYm10IzWEhbl7wm39HS3D+AgD7NguPGAw0BxxBXWCokebM2ih3BIMprVaqUwwlDIz/RQKuEyVnij2fbN6ruXt0YHzBRhmRg3QDBkEvx5kDx00KTkfXqQliJnwyw9/szdyhYB6rC6W32zukkEPfUoCDRUmPWsJKZ8KZjRmofQWrqfT5Jll4r6g3SDJW+ycbtGBrJ/QSuSjju/uQlY9Ky5Pd5mcjRL6mGTXF1X2w4NLvu0DhXJtk0tAKRpFZIxGVuoHRaiW9023y40a5DFsQCWvIcOTZaq2VBhp2Tn264IiQggQgGHgCAO6ua8of4e5Qh7a0YUViLiyJsIQcogkGYHjiPwiTiSoQvL3Q1R6ZRFI7NEgLGYW71GhIWz1rwOpRQr1YbXKLtHr1Xz/QgOBRZPMySkIsm8diwrye9AsBCFGwDz4u5CgzXC4titVNaL21Evb3V7awFvLTksQiTNNxPApvicm3MQmRYRtVymwM+e4znAKPUtIqzREkJqjQgaS60sm6UOReoW7oiD4dvmLJL88obWHO6lTAo5V1UHcbs+QNi5nizVUgeAWreuw6pvY8TUdABgond31QUplmC5WAt4bbGDlgmIZQz0Fd+1P+mkO5QsuU8B0EgrMBNNAbXYyjussL6rvrFUU7Uoao2Isl0ANN4wah8c+iz1LbhHRK2VbmwtRcLDfsg1bN7Dg+QHj2MzmG/R9prGsrZHhBnsUlKS4jE0IG3YcGv1WiVVVLEbK4toZgfcYXX7oW7frF4DDphDqLmxA+jRkW5nIdv1wBfvuVy9XUJst8xu3BTG4qVSFA+vqJdrrTUCQnrqlNZaa91BMdkroDvrwwPdMyU4vgJQAn65dK85Mwds2+I4VtXn3QEvuW1wD1i6X07o7gti/SYpbZWdNT4ydqxlgB6AbrZLguoNUia9NbiBpNOMAQ9IQXNtRaXIDaJ0A6AIhAgEu0gYcVgmr6zNWnRnzIjdvguMUDDSNhIMKsjDeNAOv/7B9oPbRhZ3WTQggodaQzQxgnJw296a3QRvsUuZValBMnUWZIpTogMQQduICJNYYRIhlgzBcHe38ZtiXkrxY/dnImFrPSNQH29PhYCLt+iUUVaKpX7Hopg1kFbNSy3lcn2v21VHwJA6Y2MD6CnK8XjzU2WWRCAZwlrr5LAmlkXEttXWWogZlFdrTYJAMi3rEcHVSuiotdKKpMv1LSJAz5s/Byz/NsnmQ0vAyn04lQ6H1THucUSkXO2FiLSt0BRmRQFKpZTP77fJYWmptnS5XKaEuH6bddw4hNAgj6bbEfVyFVgu13L5wWqRtYvV1mI7Pq9vbwDCPgCk02ODX9++2dEBC2vEHH4EYD2RBoFAHHHspZqXLdru7ga12NHCzHQtQMbNxvTGys+ybXXbTDC7wT+9NUkXBMwrrMnK9X27fLPtLXlGGf1M0ZUJPkhd1/1/AtagXpO8dY1B3Nw9o1vMipUtQlYa6Nt2ZbIeQqZDaAodPulBLpX8c/qU4b7k+tXb5f2bzNN3L2hl22T72r0Vsxou87fr6noggfMRtB+T2Tu1rmjSXoqVsnVVfeuRamb2gT/l9lSwkUShWYAZa0EQRnjtkTKEySXR1B1EBWRNk1bSFY0CSusx/RGUIQgJDRAZAsMUQvPSLjUu7384jr1eC71WV2m3XAlWju71zzBou9ZmBebHcYvBZEmiC8BMJJ0uCQAi4NuFTUUstUZEEdxrMSupa+g/EDzdq8rODPRbKkQi5FpKYFt3z3MnsKVrMgC3kumQIoKl+HHAzL2asV6uVjaLAyb3jWJzOnpyGKddi00pmvfYkUoKDI+Y3MBbOVprEQLo7hlQ0kIGlu1SR7nNXlk+RMe2baRJulyurTUJZeGwVjwi6du3r4sMQ4v/FbD28IxdDEaJQB8io4lCMSR6klSLS6lbuZ222gWw0iHjgYNorR1Hr7DNNFOKe5O3qNvlEEq9lO3qtdKOoEdEuXm9vAO4yAFslyuAI3h5+4G324Nbg5kRb8N9j26g0OKAH25Q0MyLGU12lLDDzPxty4VxAtbRJB23vdStbhcCQQZpCVgS6AE0FN8uvl2sblJ3qs18lVKjyYxmpv2u5PpKzB44rFxvBZu7t+QtzEqpLWDemli2q3s5mtIYRTgjwrUCVipzU+WfnMjUS6Ru7pO3er0cCksbBVS2LXgm/HvErNh+AbBO9dmMXuBb5zXSRhwR8RlmZdvMa7cOHQcDGU9X7TAzU7cAlgwCC95uByGKMso9zGgGWPenShdzNaVjFCPdNLMvpkLrmGVwSQjQ0cmAQBzUbqWVDdvl/bh+u1yrmRXHJbaI3JKZqaaRAOOy1RvZepT7FJObPC2zPSMTyTR8K3DZrkc00UopLQJ0dy98Q8Mh69Zc0EXsCG6bAYAnqzmmwXn8aR1lM1mRF9cRxR3lshI9B0rb6/sWKDR6fSu4ut7fr9v3/TCvLZBmu3q5RsT+8fEpenESZSzEz8/vf/r559//4UdkSDsi13I6gP7cfvr245uXn28//7lsl+Km2+1teyNZfAvwdjQr1S/fQvr+eft2/fF2HKp09xuws/HNUEo7Bu9mhcOUBoCXjd13aglek8k93SS50b0es6C0HZIaDaJooosMuglgHIL2W2j3ayMCZpeNrTWZZdkSHUdE5skCKxOy0xEcDphwYWvN3S/XawDtdiDiWst+a9frZds2swLadjEv7fPzsxX9nD6xGyPiezLRP/I7wAvTDJFLBumJ6lvuvaPtoHkljuOIW922Wt7j2CkBcgcjhGbtamY0BMKOvUWTNQPL1rbqxYqkjZ/gLSwkxVZIHqGQY3tT/VHbBjlhQE9j3PUqgrU4vHaWJ9IWpDR7Y3H4wohzp4POABJgrFaWi0WEPovXMILFM9Y1I7Klar+bUDIdWE7PzWFTM4iZgklHiz+0uJA032jfaVutGbXe1cHZpX6v+jOetZmS7CTAJkmmo3hqYtTahxDbBZert3ajHVnjwRkcylwLLxnDkjqH4iEd2g/stdZCRDSjqlcztLbLCkBIUsuk/iYkU5ZGB0hCE+RG0bZmIGQKk9SUooGOdtxKte+3n+ulXOKHMF2+bRERNHe3Fp+fn3HcvNDNpfaJsBJ1a/vxGbe9tT1f/FvpiT/FkLTH3gKUmnCr3+p2vYTtLQjbZE1I2u7s3qHp0eUk3eEklD51M1zGCu5iuNJmMUTrxfVuhpiiWTPRjMW8WnEzg9vGq3ktwnEcZhY5+uaF3QiitC+SpWyXS3OrhIcdc4J7+hYkS+9u6XXlZgfJVD1SVoqbV3eHWKvcqoywbktSGLLueZegXYPu9WHxu0rXOSAg3AtT/k+ReFRmF4XMwiHCXOzDyxBZoAaVpgCRVjgbj5s5fLXkh51DfVJpOx9p9EzwYFbDUcpWykYyY/YApo3OhtvEULqd0/f1/vIyL3OoGKNQrbk7Ec2Rvn/dfzn6ZOVMsWQNoa4Mdc/ILfV+REhqqSR1E4pvW61VZi3DA0YBg5lDygC3IbLZTOPT1ZFYAGsBgu4NlG/drRnjgNY11pxvvFrAx0E3mUmgDcGzf+Wuh9t2h5W+ZkZypFGuES8yo+YdVvFlSBUOWNZDJk9+rdYRBS0BTZpuX55qeOW80AGZoRS659REZ8XMSA/jKX+mqjH1aQ5kYhUN1MqleBoxp2AcpMG9FCeKp4DmKo6IcGT6jfQoDC8s5hJBWYbumAycibI3NwA0hBQ6EMZokoxWa922a2uC5RSbhUpf4kBmsABAGoOlmOnct6nMAxCs64rv3S2FPEhLUetOa64g4SBKtbp52axsYaWWzUslue+tK6FEL5vT6rYx/ebjIK3UjW5lu7TWGN1PNf1XTQJ3enU7Sqm1XhwMa3OGQqDcSi1la2BFhs4RI9pL7YCseJkR6uKdq31u9VyCY231n6cXMkm3atMLIesAprcKXZY0IAsSiyqAGYgRseVuKQZ2wBoDqAcd7QAU0tzC3YtX2YgQNiO91lq8kkzXUOtm2u5Jz6GW5mkuOOdxPkLWOSzEYWaeZLmVWi9hZrYzShLzhKwJWJJSTMmor9ZaLcO/DD3YSOkkXQq80Da/XLdta2QA5uUBsJInzbG17nbX58JYZFoQBvO3NoJKJJXhV1GipdWFbp0eK0MVNaok3d+ra9IkO4XBvuCbkkKbpaq2A9aYrDMiFXJJ9sLK6eaiRt2inly0dTw/efmBgxzj3Cdxwg1knmlvMoaZpqzW6yzdGbuZWU2jE6PZkMWye+pJvUkbHFbL2LVcEiUinZBMLbJmUC5RllLcWLdtU7ttBW5qrRWUUgq8SXJnLiHJLHumVpq1UjM2huTm6ZsSTRHRLJorIkK06/W9bm/R5I2Z165FlJWDYFIqEZ1iZGjhMBTCALjuAMtobtXoxQ06UtW3clhNGW3sLFupm28Xsw3mtMJSjcVxBHZJMNZqFrCymVkTJSqj/LDRacw0Sj3sOS0vRQmUXsrmtjEaWNxLrVUwikZPvg0wlOJeJBCeSWwDJtG9iC2XsiYRTrT2aWZedHkw98KZwHOYb5KnH2OY8nUHfRNoQigzoAFMxbaxoDWz4UYLS5HwIVaOM2yIVkpPBxRETVUXQabq0ElPwAKsFJi6ZzuAGPUmfoGD25MzpRBGqpibEHa4e7c+s1EOhmSOkXaK0PDASgcQphJmZIVNCtRag7m5w0tqt93dzUUYi3KEJ3uVxm+uSvHeeePUOT5ilvXsCB33vRCHFZo5LbPHGYncq9Kog/yIVsnajMSzS+komPVYKxv+QGYGJGfUMWtiEAG9qCxtdJ2mQrbWsh6F+fm+yfKszwIwqN9wtQn3Hn6bskK6QCetzSCB7GROa9DTrYp9vqaiHZHEVoO7ZAhEqZAyqV9JAi0EoGjh7o5SSolai8tJx24spRTCuhHZzA0RHLgepZXmt4gNCDOr3bnhaIrWGnUcrUmE22X7VrdLBHiANAWbUDiKPhAUYRmPnXkdSHFSrK69Z9RJAZTIyWKWIdCdEpr1PG6SvCBCCni5lPpW6lW2IRg0hcFJ27x0m4vXyr3RarqWpXhNK0DQnDigRhKm1GFBMhXCSKdt5jUA0s2rl2trLUDzzUpNUc5dhBtBegZnWE/X6SlpMXMkwYyTw1qVvie3lUt6riSO8n9dL2CmIDLTF+f9M5Gh1ENF0+m69Bx/WaNcyBRZNtb9HOp0yJLOREBE5rbmEc3oZsUSl2GEu1kYYDBz9td5yWFNGSm/dVDe0n1UdIObOUxSkZipnwmFjczfxpQ9cwgml9NJ3URzybdCsgUOCJEeLX41D5wiISaPwzhGLJYkM3WR2GTnxSvaZH1vzDwWBoYFM2N3f2HCNO1Sdl+GY/Jt1gk3Umk0Gd50uZgcEHuqA4AxJFMNFwVJ4ouCrJnSQkpCyPQsJGUsmSgwxSJwZgGJTA6alTNtMGLBLl2O5eor/6Ul6IfDD0nDhhDsNcWoiICRKWlaghjPOE1KQjMwkO7yvkf6elpxb+7ucjLUkjoSXkomLu/ZIMwyiYkNCEt1igoY0YIFsRMOlfSNhpVatlov0UxsUIGbRRRMYot8Z+u6zFHPbirdx58Z2pOThKFXdhghwgCpp4ogQUCEDpjRi6U8iCrI/HIEQvSSiXSNJEtR9NqWYQZsLCaziDD3AAUXIq/vG8AbEWIpzuKXvcGI4le3Go0E3atZbQCDVgqbEmiStTQhGtIlYk7r9M3DUrKcQ6MHJKAng6X+k477Yjrow2gUBmD1+wcBIcwiV4mZ5dDpdD+J9K7CtGx03Vmvf2uS24hM6sWcLVOOmBXSkW7qVnKHa3AEuNdhzbZsod5zh5lRolGFJmsknSMxQ+uCX+BY8BpmPcer0hMYc6Vaonvuk25bgNLv0mnmzqF0v0MrpPdoT6cxWCIloyRqUV2teqjuSzXVUwYmWA0PRgMgRzQCcPuCkoPLU/o0JFaORzjooCmMZp0ABUCFyEB0oEudDyS8LFM29URhZupdc/c6ZyYvSKkzi5IMBDnTGR1H5E+ABIikDVnkwtHL7trQiynhwAR59haI1sk/AqAJqf/OKeupupWySHiSeDSjzDw92ty9mBxo7pZKdzJiI3f2+pFHT9VpTjYjI3rsn4VIB/au6FWjBFBG90vxaxNKd7k2msrCytI53MFHZHa3Dw5XrKSlczOPUfOk4RJyH92LhFQApGyjVdqWU+z1ctxaCM5q1ZDFS1i8lIzKoasU9tiO9kmrBlPOiid8CFK1S0S4HWa1lC1aax6lVPeyN5hYfDPfFAqjmyd3kD2EzA1EmHl35YOrv+Co9bBwoGBnsoDJlgfhxpH7EJDFCKCm0qmBPRUU6YgGd6gBMnhy9P1BqSxL13azRS1yimzJTQ1HBAnqwuMR7p6yOQhY8rxyh45mdB/J/BPjnouEENgdO80sHXqdiFZSz82wgMtpSn+7quG30Rm0ns1ncFg4E3WerioC3QpJ9QyQTrsJxSzuAStHwEd/cmchuV/ziPSxHXAzK7qhm2UiIqP+uw9s9yjESaEtcgRfoVUm95SEmJquB6eEqWsPDZZw4bMCgL+o+yZl2iiBzIAb5HZe1hL6ZKXQdzq+AG0Ct/PoSQeHDguAaDQYLRSp4XJ2JVJqRwNMFgfREtcTDBmStV70AJDkymqVyZUyPU5yr5iZYaTFMzpDaG7FvRhRSuksNjLAaA5at2vNMMYUcoQDjGIlZ7KJxTf3Qli0gBwyKAqHEj2H3qeZYx8FHZVGxzyE0ae4BPQIEWMRlMmbBWWO3LxmV8DyrSlz0AAPqF4ue+ytNWMpXvbYMy9ksZyYMG7uLF4OHdItJTilLz6NvQKlnAG1zHttpXLf3USv9M0MIdIqvFBhNPeiuA2yX5LfHvfk0GEx7zY383zZUyTsG7IPReoI+irvuTWYAZo6RUJjyhuWGf3R+Y5l8OdOWx+9AhYADnuCdAgyFpFmzYf5P4XAKZv7+IehCHsJWFIuFDMr5kIUGnuhEEuuFpauhn3PBSAmKvEBsCbidNAYHEFIjlQn1VpqKSV1XitkzN/OX40Oz3yIC1TdF0dPs0wwjrQTDfOZG3ppJKYj5JCL7/HkLHIzC1MGNDKUIAUQk3mXSJZ0+y2HExyZFfuLvKqzGRAUARI0KBXnvRI1F89vW4zIGsnaptvgQMw7g+NYk8MXclwgadaoZpbv6p0MmjG0ZL5KH8CeGN2JRllPNw8jCs1pOZHhboQbFcMLHYzMvsAEvhJxm70VDimle9E8opllpsADY28pZObGAjNXtxJCUX748ff58rNMTpbaYamWPqhjPfWxcBOQtubW2hHNWbbr5Xa7beXirWSqqTTXR0RrKr6FWgsU30g/9ihl+/5xA2hlaxAk9wIcrbVwr6UacNs/j3TVdfN62Zvcy1YvEdFiT820ux+ft2iq26W4t+NoTe4VsNvtVsqWzFhrAt3IAC0L+wwznHvyhqJl2kOzWpKjdvdaK8aWxj1gAXA/dfNT1drQxgUGOjr2uYM0gYIi08ImYGXFrSHs2Azix4ImWDyhL2WbHt4gFDKz6/WKwZxH9MIm7CkZrLUDiFJKKXYcRxYQa62NCpIHe7EvttaqVYJCuLsh1a1Rqm1eBDTLHOKNw23rIPoSBURrrWWpxKQrA2w0t1+a50FPWPU0kIMwn64A3U4Bkvoc45B0RYgkqWbpspYlajYb6YZq6SXFLl6tulLRaUNE7VJt9MTki6zQ0SqxVYBRLQD2ongDQ7dSnaYWWfojHZoRKnYGzcAgdSIxX3zCEIaWKydoTm6/bCCUvlRFXtfDRKV9P7QEb1lPja+5gCch7Iw54jgOtUils1saB63d9uTRUpMVoxDG4j8BQCaGWrL2ObbuHl5dh4HbtrVIbaYX7+lxDFYupRQzId3rM2UJ0iDQxdic38ox/TBE4Hp9//mnGxBvb98+P/e3t2uZFgcANGPWPe4jBFOKhMlqnUo4GzR85Q7m3l2PSyk0d6Wfqqdxi+7wqsjMRG6Wtp1yr4wwdEenkqYZoHvSTz1jTlW4pVwSpFtN//tk/txdKErpbMgFoyVTiFR3mHvwTGp6VjG4AyxM99EIMX1LuzCSilh1NhQAlHnJyeSN8x7RtbYGdGngXMfrml4eyvtFn7fq2n0OXRZG+ZOMnph3MHTHKAMhUOD4s/PO43ynuqlJYy97kDEekcpmyqFAdB/FkSfk3EiTIZ3y2bK7Oq5rclSYCeBT8ykMSX9VfguDnVa3rgrGCMEQ3f2LCd2AUlUpRaTBMJk+IgXJdNJP7EpkzGDrk7HCYPRy8WGow+akTI6PQyQ0M3nLZSBFp38cfkzZya4g7qKloIzQP2/L9DpEW8Yzb550aCqVHsbTofynURsCCuu+RL0IHLJiTf7ZC24jIvU80V/ZmarZxK80Swjy4ThgQEudnpNhsOhc99gkwKz5MpjKs/J66tGmYVeEK+seIbUxogyMiR3WVXrVvQXc3c0agALbSDGGET0NhUDmsU4RoG8VKwA83TcWhnNM3okFD4BlXgO0JPGlFAW9gB4jl6mnkNetkH0WV7BIb6+ISO7A3cmSyXtoVkqRBAhuVtwoeiorjFZgbmIDM60Ej1wZYrquQMm0pkCccwk40PKYfgLHqXQfo8+uOwBXP/g+v2Ohmsa3QbMuwEdWWBoErrNkgIYmuXvZ9H2Q5wXQQJzIpRPxSBMtX6c7VZpB1rqrZ1KaniYtpYCkQ72qEZBOoQLgEA1GBUXKoY5qllyQIK0i3AQsMwsFjdGzQ/cp7MDUvRx6mPcKyn0oNTDs/F+Ap9PAxO6IRtIKs3odAKABYXClXJFWwbPIgnFUKoSHYjhMYVGYLUBJgpAnS9D3P5JZM6OfEuaZjWtqr+7o7tLWk1MGf8BB9RzlIrNMJLM0WUTCZtZ8w7gsunxqPbjaHIJosq7q7SGZpWaKTLh1M2JiE2kMBdrwJovUZIUrVcnWBugQFA9KMqIRVoyQFVrrof2QZD0WuBsBCELUDNRV50IkzTkFkOoSLSg3F0wpBTYySgUK4VJkqFC3zTMAS7M1EvgGZiHXcigYzJwcRDAOHZiMmg/ybwBR5TRvI+GW07znZ+gpcXrPum4qjf1du58qfgDuxc0bojUQyBxyEZahSTCjmpokmcOyspanYlDprWGjgJMZ2N0R+uIC5vSTWaIjlMuir4n+7qIlD8WuJYRx3ZQ4aSCZnu5pUO2SPJleCUizkhGk0kIoiETWZ1Rn0/oNkyyrE+d7VUXfaokgHEtZiMBC0nn+43BlSkUv+84cCBEB70xT6wadVGRYpk9I/QZa5PYIM5s81lxl1utMAvfswMmZpODTaVW/AYagjVHoKPObjeFVcpCzw1JLU4A7iSd+sBkiR7iDbQg7nfKK6eRHs8U9YkGr7LPQhqGT0XVYTnj3REAChffJIajkeDvrJAOZsucc4fmszFanoWwxy8RldhyB8ycdrebM5lqagEWmcyYGomGu2x47CGWyHPdebIFZDCIBVjQheCSgn2jFmEyyIU4PEKQDXlv40RGBOyRdT3kCBNySfKZxYbjljBWBsTZSF03rGbGGfWPMO8m0WwMQIqUwAr1uhdSAIWHSDEoztdBrcIopV2hwFhAi1BJZhXxwGmYIko25BwxyZqg3JBmt5ZQyZRKQMKGkdkny1L4nTqesrV5WcVgGAAqCORDW2pF0g9PI0n2fEoLHwnCQqfC+ByyT2vSpEa1/auhfgM4/SyDBZER7bi6by7FHkqU2NzldI4nuExkCOz3MVcXuSJq1MVJcmsRm2fMTpDhQDB3cuul6XcoQGGlqZKc3iUTJtcMpWscmhEiUXA+Wlu5mZkalc0Mx7BR4EDSyKdgifRqcnXkcSDAEVOXumq5k6UfiUs9ghZPr6oB5imBjD6cmYnKqkZ6sgJHWPdzgPZGZOstk1snDEIeRteWQHkbGLo6NPRY9QnC4fc2Pbo3pfFCLSfZXOXeRJCNrF6c0vYpvNkhREtb5067a7qo0FmfG3tw0Kg+ZmdHLYJyBCEFB68EU+Vw3lnTC6Xw3kHZrn5ZmFLda3I1DvzYCgwSpkRlaBIkMxYpBQib5CxrUpiIVjGiHVBMTPLVfIGNGlaV2pUhSNAk6lN48gLHvR+VWw1C2jLFO2x4xJPFc9qlZK0NIN3addCqtwtizMnM47+fvbFGyTJycyiyNsJJTC5isFVpXmoBgsyymFIgM5wB7/jxngbVZdyQFi4ECZph7AH3LGZrcPZTlMBu6R636yslSHqDU1QjTZ/gBsJBMmWmKWrn0pzpmCNsduWxwW0Njo0WNg55ztMf5TOMPaOxpktM/iHB0DgvdcWnlsNYJ6ztbNnsNQjD0orwgLXWux6B7YZYhR7lbkPR2JdR5pisRyIj+ygnoRoU1Ul2lThohIUxo4aAZ0E6pJy0ACVgpEs5NzuFl1Cv9CGHdgJrcFnLdT9QQ2J1zl/sr+gIYyqNUG4wYQ5BmCJDi0OiBYT1T+wmsPHXYtqLUIss5LXKsQm1VcSFWzd1ZeZUdoPv7Dauiekhs0q1BETtAINmiZJHc6W4Va5aO7vcOTHZMqb6YyyM5rEmj75Zuhtp0Jiv1Vuc+7UCTHlsIM0OLsFEGRegcSe9wV1+0rqlADPo6pqEiGr31myO1KwUIyIePVPe55cgNBwCDoxQTHK1l1gr3lIGFhtMPNIqN5EFpoJXCAMmcZZTlUS6ROWHJYvTSF+OkTdmnS+BzV7gbihmc5jCM1enUcGvK0XRQQqUjAojqlGyPBoR7zcRFuWolKTozdSh9vrAfnzCO6PBhA7K+IIdA6BwEviMISEvmuFMe854dv4fjnaQV5BlRmKtnsFeJ4Bh01VL6QwqkHOQPJAOmidy0HhKXonHCRA+R6Gh1ChQpTbVVc6Tcw+q2dHbeA23+SjCxi3VJ0DK1o9Lnt59JMSnZxvF+Y0JTGQRL8xxkFBCJC+Y5nnMTWtep9bGau4jz2+jjda+9yj29aB4m2KWmb7zOKSROnelUBvWdEyLp5AwOxVASceibuuzTh3UFoyUwyCDJqWaiRvo9rUA59U4jznm4e6yeFmSMgdVcdTngkNg3l8zhhaXY1tMKNpJr3qHEZS6tq6IGGA02AhOtUnJaN+Oi3EwzV6SyvaV0T+up2AVZSlopSbgBLVkADOtHT/VqxVylQMzMWRrKCrLXKofSEq1R7SKs7xcCCN46sjMkb5mSpQFeMsC4v5TJnbBeaRLsLNJU8ppbj8X5Alic/JQthtu5pB6OCzvhg5mDSE020gzcI7nMrNrw35vc6YD2PL8wcTiOY5AgD5m7DRreRY+Yee/chwRraWcf62b0E0N86IxhP8hjd0aHDEz7IADCWmtd5Zezvrw4ur0rzXG5gFLhF3dxIAyyR4Ys0D9R70mbT0GHs5m6t7NUCw3vilstv1qn5ultO8blK7Jjy/nWHHU0p8Hh8Q6dAx0C7N0F60/WpfLqxcdI5nv5w5hw8SabLL+ZLXkYe0taZd1MOBnUDme66wLWvzgApkOGgMUv7ASsoXyZCqnugfFlEtdXnjC0jgbJWn2AS2e70BUDE3FyXtINLjNEprGlC5JUJuNNOSoPgtYz9GI6QpN9Aafu1x1sGll1TtbebBj11OWnODSG1AzwCVjR83CjD5RnbYfJ5S2ANcehjRgd0aVGkYog4KYjfZI1iQM9U2HGLqludGrfb63tRBzRq56QjhaK3NpURE0Q21ugdd11RK0WR7BFhimzL2//5H58iny7Xv8g/j5UzNTQPvfPFjupy+XNzdQQjYUb7aPX8YkDiMvWkwG4ZTRYCHLrPmmQfrDDFHvbW+wwbGZGRDTSi9EY+94KsG2bdNw+/vReRwnCFEdzibofx0ErLucRQbhVN7foiNKJATlgKxwZ6UGiw3zOQGsGWTHCC8PpllmtAbmZ2r7ve2mRlRi0H3i7xt7SrSwgKbz4pV5//vnn1lqLZma11Lr1hBBZaDOgaJnDKrGYjVFbbMFKMysRUZoqzerbvu8RUeSWOh7JZBQ8hpJP8B6lIZfTcLlcvn//XirNLv/Tn//0w/sbKLYMfLXCLexQ4NjjTabbzZtfLhWGPbkDE3Skx6axUxRIUFiBOUU5G+0gPgHr2fbMEGptr27Vi9COz9vm1TOy0pG+QQDY3NWy4AkAGiWiQYfSSYrGUi40fe773iJgN7WavlVxGGJzF6LddmLL5RVSg1qG1pspk8NSBOwQoawEU4S3wlattcOlC3Ux7GygSSGNTM3o5q5aFNGcvpVqZk2RzPtB3G7HrpYhCpCBXuql0lG3icLz8/v373EcDlzqRrK1JprV8v5WU4WShTtvt1tEZIG1YxSgu1wu3W0KuHCHIyL6t8VE7WqmQxAZWSd5FiWq3UdXnmMfrdfEEL01p9zZqL3tQJjbxsZeEmyP1mq5JDIeR2TiiuM4AiOTRGvIrPImIBRGHAwQB0NvpejzpzdzYNfPf/9OxnG7K7P8QA3W4wdq8fX6ta2aF3ePFgmRHc0lTYvkP7glvR3ais6ERwTtzkL80MNJxNaTX6nf+POkP0uLe1/qp9d8eTRXtcdzdulVG/zdmWHGhlck7ruS8lcmThjUe9oHsyvCl4l7xb6dP1n5DJ4cru6Cfgdb2sYrLz95mNyVlVCudtjsWZ++1gvwRURaY3rNqXabwd49YyI53MfbkruyL4O6XTJ3ynxfkgSd012oPzSrVCsOG2xAxh98Zf1+beuDnMkEXs/+ylhhSKPrutVo6/U+KsJ94Ubx9c/1fefBvDPmfuxMlabve/85vvCr948gh3F9zuN/0cidNz9XWrcK9geV+4ue8PlcWf2lf2un54isXZ9rOpOd5Z+RhSG/5ODHl0F/aO5+jApxM/9ya41+pnnKK/WYh+Cxe8t5IRf6k1W1AtMzkOJzv5u7S+xcai9gIvQFOrOtyouxXSMiyuDikZ4Td68zIGwgywNezysfqNR6ZunN3WKNiOVO8eX6J0JiDosUwCLQRaYxyx3Sct9ILf3PIgIRTXBaqLXWev36cwRSWdDHJ0F8xClCUvEaEUOFEXP0TpDt4NuLmB4ZVBwZjkfA4D0iOvXKDgbZxuy8nOs+4MFF1rs3KU7Kdy7FmZQCw4w8RlQd2RUDm4KUu7l3jfO0hz5QEZxdPY+fr4R8RQnRxJ4PZ/42ZUt0VdcjezGRYX1KRBMXvuSXSePd0I0xSW/q7o2nFbCy+z13ysOted/A+ZtHIHhYo2Y28rSdWI77LUH2NJKMuxd+6Mlc8XmrvH74qZrZTAVps1jDZOLWW3FEPs51f3Yefd8IDad2ZsUp3B/7OH7JHrKzOc5ny2i5bqg/0wFzumEKjG6DcztRMpcuInSuqxyZJkwv2ZUU93Gb3kdrD1+t8hetBz4I6OU25316HulzBw7P1fNXcwPPZ0lKH78c+R5q23sx8gSg9Si2jN5LHXxfiCRVcioJyNINNRDTESFr3qWXKqVMz5Tbvvc/c3qRx22nm4dnxZnhmzZiKgZ+WIZ/9CCCBxiao0Sm2mgBtdMhMi8ftNHGhuzJUcZ9HqZDS3VoDlXOOp7DY7Q/yLvuCpDS6prOYgxN1wcsgk7HxdxW06MCkUVTHtskT6nNHCHfc2YjQva4nH55gZHslShxb3VZ0KeDjwWCjyLhBM67Bb0A1tqJr7/FghT52VonnvPx7gbvoRJfkfieFZpcg54+l0NcSqvKClgTmx5QbPACneb3dRhdPwXgF/Ho/qtThTkU1eccpKfjWWNuOLghMWnc7aR+czNjAvTcxHc12XPXYR02qafnyctJsldzmMa+zs5M6wS69W1q7mMOzuzbyRTwyepdWIYTrcafk1b1bdHXpQkMyrqX5birAezujhbRq3tmf2i8f1Cf94hjhHYGMTOsKhDJN0xLZIJCZA2jjIVWdzwhFTosrKXd0A0BySTLXAvI/EmR8l2IwfQmVHqeZqhTMJ2DxFAKs5pWoxExk/GJMcZyIk53uQCJgbp9Jal7iiGkFj1jD0hBIYQonKkK7mnPCrhzja1/EgG1UAr1xwpfs4T9fRsonO+sZIo7D5bqxzb8tNYn/uXGufBE0/SSuVtmPcoDYJwc1noZ79s/nMPiwqXnllP3NLFuhwZJmxqFdcuNgdZyZ67vz2HMnmORI3WSCI6gs/s5I5m1bdwdMXYCu+1cg6ovALTwWafoh+WrOX8rRzbV8zkNObarti4WgrzecMZY5chp8IsdahcCO989kCQumLrS/voMLPqs7izxTCp8+PMkNv3ma/cwXyTp6GC1cl8OKLHJSmvestubGKvQTUuvjs5fnAXKyfQUAsNRAo3KFNsUM1Y2SYuGo0MMVUOfI6UOpTvQ9Si5FpoZBbtLNMVQ6Ex6kr/tPGyL8I4s8030fBPOP2NEHy6hDifPfnLTwRjxpjCOooA4nStWZOmPvpM5kOR56Eb8fv3E4oE1oxL7n0p4UuOiw0L03A/evfPPcGtkZOJJpO84x4hgEAhnr56VQk/osJGmWWhC657SjL438mB8rjCiUyJOCu29UkaHU0h6onTn4LBWbHqqdH+1E1a+6f/P258G23Zc54HgWitzD2e4974ZDwABCgDBUeIgUhRFa5ZMymOr3JblKLe7flR0dPeP/lE9/OyI6o6o6HBHOaocckVbXa6yVW7Lk2TZsjWLlEhxFAmSIEECIAiCxPjwxnvvmfbemblW/1iZufOce+8DINPO9+LGOfvsIXfmypVr/BYRKXxdHCZ1t5cS3+trxQyBhmLFm+P4rLKfZee3mWwhwUmWcbIoUfKszL8ZEAu2xSmXMF2yxdGUvceAhnT+KS8UOc5p5n8unPOSzO2aUBmc071ft7iUE6HcbVfA0TjmnQdvbUUnBOTixXMfxqGOywdZ9yE12YDmRIzvNY6ziOBY9BMhB3BGZABJe5jmcYGWAQYErZMuUt4ZCvqGxLMkiSNxf7dgOfHdLIqChLpqrTEALAFZOLDjEJxzWggEAJjT0DNrIq3E7M7Ccs4CZmfX2VkYo/VwtPSNI0MkEDDHwez+pLCIGBVfJTxGEkjjLACBObAP7KoqxwKdruvkDyXLK8kNJdqw9IOqgXFlSVE89hSdg0VE0zkhxxygxmm9MQMWjMtkpMDEpkPM5okMi+Uk4PQZe+9odD/jkXjqZ1ChRigrOKwAQAz2DKzrs5qIIKbgQy4FrqR4x92k+LxtaI8jGFl7ZkBbyzKecKpKuMW2OK6vWEVAd48d1hB33WjPwpInjiph1ozyX31TJQlIOz+RJmJTABgNIQUlIWWqZCjynyEm+pz1H077C0UPR+t1VHFS5SsAYUZTMMTCZJ9MvBnZLsUxIopmcpjIVhgJKSTTD4tAIEaNIxAIVuGiCQFIVaZUzcgkgARRXSxmKKGYFDevC1DhMq0lYxCYAgoGCRyCH7wfKpoAqFVQuRugBBY0ZBKwBCMJAKMwAJMYEiBgASYwBEAgmlEocf/RTMs4XhENIk5GFPoAmSDVxRl/YpEA0aOuG2rUJUVU5VTUfBEfkPWhSCmLQlcC5mclpTRbcDEThAgKAwdJn0UEc0GzLVZVEjMmaU6teR5Y1yODRjWTIGPaw15/i4QByCpAZAEws62oxQKIhAiGCZr+IopkSDaGscfgWioUtDI6IZFppJuqqoZhcM5p0If3vmkaLjLwiUgD1UMIIsZaKxKHlIzhgN57a0+VCLYeWgpNCr9VWtOVOWocSo43ERGFBxrWQ76WIb14XHXx/oIBIVYn5IhVH3PclQERkXOhrrU8fWaRwsxkFKsdkv0GAKM0rpZzHXBrSCQwB0G2FTGzDwMRNW2l0TQaVpM9GIAgEHwQ5xziKKEyA7MXQedc1VhECIEDR9UshOB9HBDvvbW2qirv/WKxaNt2x1MhCSo2hOC9n0wmwL7vO61BLRKr4GBMRRBEMsawBwDw3gNwVVVVVQGIc06jsRRIQ5Gd1MykSbYsonzcILEAM1uykFQwQ1gZ6yQAS0iAKyLeexYRY8CYOpodt7B9QNetkCgUo273BhCMCc77wamUOrih22wAeNpOuk1ARFNZY6xP5u3GViKihrYsdOSWNbI0wl6im0Uzn7JlVodX3dkcgqmilAoGiSFUxqJAEAYAm8oRQ0HbKlxr8Y66rrVwjo4tJOhkhOC9q+vaGOq6TQi+qqoQAsbURXFusNZWlTVGAfiYKGY2hRDUgJUJAJOGKEk9ZPAAsYSqxOSIuMqqypAR9o5lUD7FzAjBOQcACs4CwEErgUuwthIR5xT1TmGwOPr6EWylxmgXQiCDxlIqmWAASJKx6I2JOa+zlQLOzmQjqudyjDGJO3FiaTt88HVy65KFadsRr85ooykKk5Se7shJpIr3K3ScrBLmm0iWdERTSGORhFPiIVRZGE+OVpfd/+xD+hV1YUfwiNhnxcTAJD+nPmeJr7TBFf8Fwvg1jd34t5gvAUXpk+zQTC8LSRArhTgoffapDzuq5diSksVRHszRXhAU5RCQo/w1iieAEA1AKKy5Z4XKxYXMCBEqkaL9CJPuJMDIKr2wwjnU1ijKNQAQiI8syEUjhuLySkTX0RNEQuFCSVawMcpk10uY7FMMwFGHBzZgGJO4LMwc1BtorJKWLh8NZcCUciDJSZIN1RxTdIv/kEzyWWYffypMiShMKSIHgQEMFQCkqn7m5ZnF9izppNdEQMURIhIyBuFUk/1rNClHbIwE3HX1CADYUvrCJEnd5d6lqiVFhmepl2Veg+OC37oDQMQXV80C4hsTyM7NR7Z1ipQKAIW5p+RNZd/yEYxm+ExP26bEHAuDp7C5xHzHk1VwiFaOyIOKNV/YwtSCIxKSQ2D0rANAHrTyQahBwIXAuDMCAkFxgdLbScEXkuab7dCn2TJOHczy/gB2S0Pc9RjyaXQixWCmC8cPSRoCU3I4SFoSxOgBBjDA+Y14XBsw8mI1z40ranyvEZ84W7JJQFN5SSDC1UbEalCIV4lpnEFEgvfOubquMd1Th4sEAoAw639EATYoiseQkYGUbSo/Tc9GSYmb+vpU9IpFBIIwKc9CDOOCis4WSDZg7U9Kg9xxpuRLYkza9k9QzH5G9YtrRNGQOXGaE17C7aXHpUSWfyEDBtAYE4KHN9YSuQIAcKYuEUC0Ahx7qtu6sD2tT69B0DvUL6O2GX89Ke8Ux0H/MbMWOMv6ja5O73cXVeR7ZzOscs3n08Y4mtE8v8P1ttwfRAS0Fe+XpkRT5226Nk1UjBsYg84AIMEJJGkL1Geno6+b6yhbSdotIXJJfVbum44HJmAJzpY7KPqRtricrV9QqopUUmweJyb6lIYsEDAzYmXNoDECpwiMqY3SzU5oUn6d+KMk4Uu3oiwIqBUflMMH3Upxd7627l+4FKKoJaIuX0ZFWoWYRIop3ElEOISYM0SoUCLMSbJIiBEcgo5UKi28taMoR0BEJp/2g0IoGGew6HxyBQYAiEUMWd9aIICQxi4IshefH5E3m51J35pNkiQ4o1r9BPRDDMEUYP0pFkBVQ0Ue2FjXi2Nwb+JWIgLCGIUpkFiO8KRtN1OvonFgGL2Wo8Gr+CzFZzxV+s4LUySUKmBcyFt8Mk3bXRjWzk8luy0vz+HFJy/Pv8L2+rn7c89qOwwr34cLj3Xuyc7OUy6tyE6plMvGRZhkYIgi4dYrb3n6Mo/bGVUpGkAhXoloMXu1umA6Uhmj/22O8vY+OJcN2xJRscs3OuFAKCQsLJzZdxnnYkLz+5ZMMItXeQBle2WeKlvttvR4SbLbyIPyByWRVEorFtSKH4BRtCwBI4vGRpKIyRQV3zTay8v7qwWKDFiM6ZMigiykevdJEIjoT9GjkKzjUfzJ3sliTLZXUw432w5f2LHBF1yYk4YYtMpHYseSD6ZqPswcIDAERhb9H2NEEx8rP+jfUs7IQQyRD2YKF8ZE5OW75JeNjHe060SiIjrJ0V5HOyXWJz2xXDXIoPvY1rUnIhh2b35ioy55Fm63k9dGZJe07E6yPzxhupK7diirTumJcew4VVEPI9ZKqq8LMC6ZqJyrJUClsN2nS6H55iPx2ph0ogeMbOmh+dfdNxXQQuFjzEu5hWpXc500KHb1dLKIShPjtbjFRKKwEs0NRCYF44jklx35CBZ/cx9iP+P7FhCUY9uKQdtpp3GrZEpBQSmWbnpoYlUQgGOogUg2t51Mk+IYkJxvH3M6GAIAjbEIiTTi3IXgJLCpyJCJ2GEcQLEODRiJbiFmrzE0iVqjlzBRQgCItctURLr7XjvyrGjSim+RcmsAESkG9WtBSdqRsPSzIs/kNczMAUY18NTdKBNt+RcTML+IUAIwLnfZ/Dn9zQaBLDlyijlQLx6Xl7+BNgZR7+afAkDxKuoHZpt/g+SILQfozKecfcJdGNbWCVEpjZ3T4FkoFrxyn3GIz7ibDlPW8fOCj/hQBQvDZKfT63ZMMJGXQqwkeeqD8u0LyavciDh3QFkSRE1qR80Mo4CR5LWdIT2VP6acDIk3AVRLVuzzXdufScKKlenSL6UGndS63cecatjSn07pYZHpkR+SRkBkS3hM+0rZz9EeDAVolIwzO95f/yKCBrWJ2IScQxhVwkyvJElsZ1HJqvTcUYzJB5EgIapyIIBIANl0dYomW/As3OmVLbG1YvVAZBjnCyThDAeWoHiNCsAZmKDkVpAkHYVgymNVjlvOwdmaC8gWd+HywoJW8x5ZrkdWlzHLjqrxxhsXNhNliwZAHV8jXf2HegnzAij7eup6yHwqSienyTJ4xv4AcDeG9XqOlyzgRM+ykHUmpz55MM4f7UizSao6w2x/VtuRsAC2WKGUZgt4Y0bN/zAa+g9sZ/Ovu5nDXs9tT2l57qRQ0E6SGTNjokBIORgJ9SwZC8/2O52kz7yM79rhXV1pZ71kfsUha+KQxajcc04V4XJTBIudu526WOJSLTojouV2kgL9+lrJGVMId+7pmcrdn63FHT3pEgBgfdVTQ4aQWTgwikEw7I0lA1qGWDggCCVwYTZRFBIR9hQHMCChgBNgFG+IAw6eh+C59/P5fL+ZzNCgkw2gIaqcZ1PVhEZdrcQC0hMCIIQQzXgQJzL6vENw2dKUcdOZ2Vb3TGdtPwyboW8mdbNngkjVNuh9YDUSEQExIADZGoLJkPBohJl98B6DD+yburLGsHNGvFbQDd616gARCCEwgjEWALwLralAADxYpAihoDF6mCrBASWMyWjcdb7nYTDAhkjQAwSsxBp0oUNBsmQIvB8EqKorZi8ZAqXQtaVX27AqTSgi4FFEpm0LXjAEi8gQA2cqoCOzrquagAKIeAYyBIhiIFZUEkVkZkQgBGDjpyhsEEgkBAZxFtlWxvtBAivOqzWGgwkhOMeVMYDIzME5S1DVNRrLbog1EYxYY5jZeweA1lipq8FrWBZCZRjEBw7Ck6qJ6VNIMeaTpSaD1QQQgwAgmLpR5YsBGGxTN8654LlptDOuaZrB1UrYBiphYO/FiAg2Vbvpu8H3prUAsJaAjLYmEzow6k/1KGSJBYMf2DQ1glFgNzDE1gbmgOAH37uhmbRd1wVhsqZ3w3QyR0MiqMxDxWqV+h33aAgRAzGqrgfCwSNiEGEERg7AwmClAkQhB1nCJYAU1qUB+AG8kCCisWQqICOvXj/c39vDqu4GzwzAyIMDAAwehL2wZ0diMUBjGyAkEYbg2AXxghIIQgiOHRON4RcoLBr+KpWL2XvJpBBEQCAAoe+7qqpM1QxDR9gg4Wa9AGBbGc02HwaPWFlTIzCh7TZcN7au281mxcx1U/kgGLwFAGFxQSAYYQSREAgx+MAi1tZ11SLSMPhhGM6QsJLPBrVwH2Guq3gXoILT77Tr4NiSGs6+5G4MXwpT185NMGuShWow/iQQ1CazHUZQ9vDks8bt7hSLQHmrMcrhrC0ubseFyL2zPY5dHRM8sXxZwQCIBbJNlhB152TeTi1S4xWCqMyPwgRGhDWYNwmDOa2H0h4+9gRkV6wo+3nqWMFdjQZntZ25gGRtkRGWWgACyN1EM1K8SjW0Y9RSUeWmMIpdySVkgIMU4pUBDEQa7YnokwgSr8oTUR4pKCTA2aJZlolgO25mh+byI3aoUY/kJK2MEab44OUjmLcsCeVggoCcJLatAyc1hmxpOeN18oc0XwDm9Gz5ojPJ6RTOGi8pbBdSWE62GFZpU9CkUxJEQ1qAQZf6XYO0TmmjaLB9ENIIxpWZ8pxLaoDtkSpWr4wn45gAdeKFI348iABgjPF93S1Pf3bTMUKEwmYmEtCnS4HUHR1SWzhZJ1lnTFhJZlQp4Pq2+z+OQ/FBCSKHICKknEQhRtSJisYmESFgo9kSzEa0alsQgTHYAkAt6sof1YpMYso+n8ZKxq+5Y5mI04wnXHDcWm/xLbZ9Q+UeAMR5aUUmlQp5pYGFU12iAAAYCLTySAw9jRVngmPxIIHEKgi/3i5Fi6g4D0mEJwnMiMxsUlpo/ilPB+Kov+9waqStfomIKRbXDrHiuOrHnA05rcXMk1TGWUQqa4yJABLAIhJ4+7nAWsxLRAQ5ho4m8SMiqej3OOMj9wnl/I40gHF2REQggGTeKsmtWbCz7T3+xFzvNknqfGJStDPCFhJhRq9KAjEX0ex8RCRC1LJ/zAzmjZkediQsOIPlyTYL39mC8sGURzKeVr5PzN3Pc68nMAMjIENEdkgQYelGY8eKfo54mBAUQiPNSVw8mR8BclH8VBdCPJK37pGecRvWHUYJy2wPrHo/y9dPpKM0QaD5sRLlI053RjQSg6cUsyggVEZ94QKIAbXedXxOQuxERMAgAKzWXnUaxMDFlPovKWBVi2uMc4QYjSkamps2ll0xQYrdFQDOCn0QyeDx+fUVY4CSlHw2Hlk0HgeKhYUDgiEQ9o59ABYyEmO0GAyO44xEmnhtkcByCAEIWfFIE0fObAsKagwhJB+LnpNj4lTdHumzpGSlz+yeyzwrW+Izi5dtylRuZa0FAO+9oiEnGgsR0yf4mG8n7POzFZ0ZGDKb1vAqEYjLX0cMchCZcjERST7WEJdK4Q/J3CfpBFCm3O/wrPJdCsYUs53lBLPb5laMI2CTAECK3t8Nk4/PiBhmb7DlCY6dyy7zaEaLHS0krO0Mj613zuwpDtCOTI4q5IOSFiEpeokIhBCExIgxsB0Ev7OcTlldeZNHTqwqhSPkk4mVo+Xz8+0BsmaEKT6LY0Rk/BJEhGKNoojEwuJ9GHMexyZAmsuThJeo7gAKsBbhy31StmUEiLXGmtJZ5HAowkCAASRWG+Exan+kgbgMCgCEHFaaTgg505OZdYUjIhlgPwrzmOzZJwgkh+Dq6IeI+ZO8YttDsGOqL4WsvIk5rdyiJcwJGEFQDAcBDshgGNCmJBSJYol6XQwikEIwI4ujQGK1D6NiGEGMBDBih4QYZJqAfXK1+kQJoysA8yLMIRcp0TiSk4icpoBLBshO3EpTPpm5qqqECOCZGRPvAwAN3NfBVNAcRExMIUQ8DA00YRE1uGauJBJTeUTy/r7ddjabUp/YlrAgxFWAmUON75WiKzRJa4s7Q7Rls4BibwkAW63WPoYlKSoqaiFn7beoHRGR7BtVCFOIeXqHqHqU62FnenbkTykkMkxwfeVXEAHkAGGHJY93lMDeA3Mgg0Bimh0JC3a4TzFe5T0BADImX4LMLSZplLxQE3FiAkrM9ZPdJKtyEwMo5lgS4BGUCnXqD4FPCYNS2J6UbQEKRe4ZoTliyCVG6xQTEERlkMr1L4xCu+KP9oVDAMCqMlkgBbXWISOC+JL+NL5MCtzRtJHgiOoFpzdOW24oMiIB42tCmgjGXBc1XjU+H2IkqmqhQSsrAiIBhxBQtLgeGsHAEoQ1KioASERWUSmLDaDTDkSGEiQKFkyk8XGSESgRfZKDAkak2Xi6Dicq5J6mNktCAkixA5Tsqto4RKUv78RKDCoJVlVV13WGdWdma2oCZPA5jycPY9pI0m6qWyVELIAoiaIkR7eqiwxJStGkodzlSA9QBEVnJJIC71OZDiJE66nOpiZdsGgAthSyVWZM6bb6FGYJhCZHz3Hi7FklRAAhAVVzSYt4g7o99H64Yyx8na2UsKCQPqSQsACzMjXaa8ZxKWSfHckIk3leRDLIVn6W0XIqAhIcB0dkGCyYRrYlrFNZBow9Udk1A3pyqVdHi1UMOAhq5RWAlHuxpeUp+SlZFrbMrVfmBH8o21G4+WQSFs3JTSohRyErIMbCXgy67YBoyGeCokYR1DRvYBEkhBDtFJqCQgKgZQe1PiikVQEAWn5Kko6QOVHB5pkZNIgco48MUjpRKXvidsTN1vikD1G9Tefrs3JdCQGVDdNztwnOQ1S2NF5MEACFAVkZskFl8yxBQIFqWZJwo7BQwJLKPyJjNFUHZi8SiGzSfRDHFZF4MTLG+GFRIXE0m6YlR5AzieIvVNiwtE5SpofMrbz3RJQZlh4MIdRWABmDAAsBoERMwETfrLYtjMwi8tLYGRRk5Zya3RW5lURiliRhjfuD3nTU+rdqygrS1kaSd9k8s8XBAFAlDitZhtFeRQsmlQsTlD+MeZhpNap7xSMFHTHkoPyN4M8aev+6W/liJw+eyi7LxQAAiMaAEUFKZg7U6k0hcBiE3ck7vGZndGIAuPhcSkyFPjJ2K0dXJn1hXEKjBpTkLEkcaUzLwJgTEP+TluhCABDSG+o9x8jjGJDICFL8B9FK0zYCN0kmvlPD/BAL45Rup8ye2Z/IucmILvHVil03D0t5/taQjj9lRXik4ziAaXzyPlSWXN55Fm+xrS1z/hi3iQCEEkv5soAEEMEE0RmRp5T1CGCSOAVCxCkWEZGiwB/k/TF39cTTtwe34FDxCIxHNHumDFvXpl9zadVsbs+7WuR3ECIfkYT+OqrJLBIScJxSL2sBbx0iTCOPqgpgSoFKJ5x8tQi+mLh2/ipZSpItAtgaoq2pyVPJJ06Ip4mERA9CKBEPWyNZQIJRWS547/p+2DjnRETjkkZyUpaamgJgDcOAiHUdsYqstXpPlWYpFlakUpRARJOa/sQRXj2iDqnSjohVVSkU1HK5NMb0fY+Izjlr7XKz9N43TQUA2lsIrFlhrh9EwrRpEHjoNqQ+623o4cTpRkEmb18aGVRQ3LhC8paSroq8ZjxXh8iAsSPYvA4LEQkwotR1nYWsPGhEFN+iGJ9xq0AOISBHItDhJWPImhDCuu/6YQANOSMKgmRaH6iqJ4TV4WLpvG8ns34YiEgI83rQxxkkNYt474lAk++bpkHEzWajPfTeez8QkbWEKDrLItI0ja2o7/u+762NYE2IonBOOlZqmzNGa4WitdY513VdXdc+DBiLeLIOi3N9XdeBfWCfBQ1On3X2FXJLCcxaCsE19WRxvBKRum4XiwUz7O/ve+/X67UOOCI6NzCzMaaubazaqdBSABDU3KPoTj4MjpmNxbqum6q2ZPq+t9Z67xWxpus6ayP6WwguCqQjOytxgQCShsjMFFVMxhS86r3vuk4R5aIsVuD6W2ubplGsK4U2Uzg2A+L94IYOIrQZB3YhBCJUocwYHIbBO4eI7DwZHFyPiG1VhxC8H2yqP8Q+sHhdPirt6SYBka9k5SCSAQB775xzZMB775ybTBpABfByKqfrmGht6ixcqwVN/dHlYtEBUktoOmFUNXQJWBEhIUAhYYg7mKq7GqomAR0RMRo0lghh1xaz1TJf3RZPSovVuJ7vcp/X37iQZVKL8rb6epkDo4A4grjO32DbFqBGQGQuTC2l4KmPMCcy4PLdYNxzCkTdzKEyTy+h9bZkk9MwUY+OFs1kNpvvoa3cEDb9wMwIBuvZYnHoeTh/cP7+6azfrDeDqyftiYpFKl9I6iRvdRuzOysLdKNvVE+QZEaEWD4yiWARjrU0CyQLIG6lT6VHY7nTZjpOLtH4aEquzsjmUqDg4fF6trd/7vxlESFTAVI/uG7oR3rDvLFHK8S4V4Ho/QIKqXEEYl6x0BaI2KgERZSCrPuECOyBolCcY6XllAUtIiiIRCZISOlimW254Jl5GIYcTKMMWney/BblXhsphwWS844kel9QGNWRA5xEJ4NaAh6jJqiyT0bRoGjJyndO5o44aOUY5s95XlLxF8FigbNEl9S2kQT5xB3y+TJ+SL+oQc2SFllS2TIyQpDA4r0ECYEBtIpyVRlEtNva7Gu0zB13juO45SiPk+SSf43U6zNa8vmAVoSP5gcAYfY8DEAMISAwnp3XUnJVSTYsLGK+YHT5ldaTEwDw40+U/uZOxj1KTYwYxyd6grNoaQwCKHSXpMVfbAACIJSxTFKjy5cvdkNYrzvPHdnK2LqqLSIeHruDg3sNyfFqJezYB+Ghaeq4OhGgsE0SQDglZDeucM6VPqKJNACkWjhp3KKdLxcx3Arj4DRQ2/w9cu0T2iKcUK8yiksxvEnHCWpv6T1O5tPFqlutVswymbVA1tp64EHSyZH1IAgAZQskKPJKXLSMgpzNjhYZKNqeFbeDES0RGRvrDIRQVoorvIQglChG1NRtECDC2hgNigMAAEumqqpBHADkTMA87zl8AQolIJ4QOVc0xeWCP5h8IJD03LT3RVN6vlCRqTEG5412d5Csm6f+R24SrRm6UKQwU6qypZY0ABDYZqzbQWgikvzCqveJRB2FxymO1pi4+dk8BKAKCApKRLkPQSQIgwH0hoJYsUj+JD3nZ2+9WGwjXsf2+eVzJfkpkw78htqJ8yWa2rX8tR8cWK3e4VHulohXMlY5ES8XGeuIHmcFIO4esRsSwwN2Ba6t+6TPOhMjCeoTs2qcD5ahAIiYFmriMGoFR7p15wipMlUzm87IWmHs3DAM/bnLDw/d+rsvPB/c5k33X2lndbdZVE3d9x0lCYVUtBaAnXxj2JLmtD9EBDhq9FC4CzKedwiuqiooqsiUdo3syTo57LD1U/YSwjbEWHwERi9SUerKANkJ2YkTDmjbSc1gNr23dTt4RkU9L8wlkm0dkWchgWIWowH0yMCQYzVAI7hH1SGKV2QAQOvCJyYVDYtR0wcAo4difRpVb1THYUogdQrh3VKrSl+OKNamCmAmjOxHFvZqaE5YHFsaHEj068X4icL0niY0AEecLq1mNs4VMOBIESPc0BidoBVGIGVMIAAbQ8zCoDEKoZCtsuk2iAQBFBkRIkvunANWo6s9clZQccFC3qhT0FoIjv0Qgmfl13H7f12p2JlpJV6+VV8vE6jup4hbeKTfKyUxNhbUAtHixQdUd8nZ4mHJI6SUsJDLSMXs4Iu2TRGAILAD/sVwlpcQguh1rNS0XXOp2DnKr/lgseAhdQkllkEAQxbI9M5x54RMVdd7+/OXrh9+7GN/8PnPfuptj37ff/6L/9nB+curbnNnsW4bAxobDADAJIYxYj8UAFWlpgYsHsEQWgDgGERsUsc4Z40gSgis1qL0a9jm2iHRfXKH76BNnGK31iMJ8lQwwZYr6ILNPGtv/9LxYrFer/f29vbOXfTOHR3dQZaqrgQhgBCzgrwwBAOUMGqiOACAhMggiKRufq36hcnqX7zFyLOKNcaKg6qfdQVIdg6mhknaiq+vwFVExphJPVHLrwpZ2fKb0PHjHqY2VoBc9ZolMIKWLxMEBkYRMcCxJgZAjL0SrZqVfH8cEfGUW0nEpxbVjyVF8CZulQGRONMnJp9pNNsn4UTlcdlu23OaD44bW/m3/JCejuqjRYwhW9G0mcx+MTY8XhlA67Wd2k6uOm0RMwNTxcAilne8UOWEXXPG622yLb3pLZgZIKbRColV+YFfv0oYD0pSIorxTvNd9H9cnFt3zeJJFqR3x2dn3LICOEZgb8t9yflHJBQAOcKN4tV77r19vFouN1U9me0fCNDNm7du3LjxT379M69ee/nZZ5557vnvXrh08Rf+2l88f+nStZdeaDjGqaDqeMAkKq+dHJpoz2IGIkIjEA1GbIxJcmV0GugAhBCyfeoEt9p6/cLbmJ+VFavdeRmvjVFOQU0Oyvd1qKt29onf+8MvP/bYI488/NGP/vlH3/IWFrz+6isHbSMQZYwU4iAcUa4AxcRhAMaYQCsk4FVyDAw5+7C0Q4vkMEuBgBDBmnanLBESsjCyMYYQVYAqyYUAgMhouazEoTDZNNUqr4MsMpYm3GYHcX0lCSnG4mn3tllHDOYUEC2yradRYdlA3sIXGscf9esIDVZMQaRhZhbROu0yWi62OpvfnKEIq9ud6CTKqaAgEizFcgbxBxb2YRiG3hhDBESWwEgCJuVTrL1bLXck92mb4HCkwjJrFE7BY3m9bWc3TkViJRfDQ6Toqse7SFgnX2RnqRSR7rmNqvXpNb5O9HOHbYnu4TBaFllYGFnQB58pFcfxVBNAceck+l2/eYvBzud7873znuHpZ575+B9/8otf/OJTL8ClC+cuXL56eOva7/7eH144P/vIz/z4PfdeXR7eiqwOkQBBUATUf3yXMYnDEvm4iIyIQNnCJSnlLY1DeQcGiLXMS5JVSfY0GthhoHnARQ06ZZC98q9PfeLTv/8Hf/SVL3/5yaef2fTDX/joR77vzQ+0k4myUlTU6mhkBoKgNhMiKXgWUPTVa72+lMWy2xOTFkWOydDxSUw1cg1JbwwiQmnj02JOuk2PC1hEO4SImnxTDpQyAlRsjGSJl6zuoQiXgaNjlyLM98gCUiJRtIbL1kyhxHuezq3GCS3SEkZQgyzxcQCt+7vDgApiGG+OCMmAxXpnzXiLgAJZ3MnJz3k9sERp0xqTU0F5LAB3GrTd2U2K6MfXIzr9GbTCs26qr5ctaESq5p+dg3ZCwopH3rDAd7KN8rP+xZifNR4su50nHqKGhTs/FWfrLTTSCg4ODsjU3/rWt/7k05/9wmOPvXztegjhwsW3f/2pJ4z48wft177+5C/9/ZdWx7d+8X/9vwoSCye8/lYyrJM93znztGtHNXNnM7tLG4Xc7btVtkqfsYxd+uVf/h9Xq9VDD7/FGvzjP/7k0HU///N/5U333xu4h0JKRkSBwAAcgtq/KVV1SI47gCKkM36N9ozxHZUdMN9tKEgQaEvlgcTfIcu4UXwW51y2W6X7i6T0+B09CwqqyJ0+hctszcL2np1QczHWNNtFZTjrpfKHnfOLCLL8uCo57OXEBpYo6q7kVDYrvKmr2lR2EDd0LM5jsJWZIBORhqgF4bWAEXEGJ87sC3sJTkJAYAvBaPlEcMREIWg5h2xlDPbchsl43b5EBDg41LpFIymgIAkQC5MBFiciFPcAj4wJSaPqNr6yk76Tys6GHhANQeW6jTWTSXNRmBjQGGHpm6nxtB5wQU0AtOtOILR1ezCEWvdOQhAkQGEMSODAAyIJB+wEnBfhIMw8kQ07JCKLBg2RWDS2IjLQO88gaGyNSM4PIQgiAq1jZJrCYLKwiBep6xpwHWQAMIYq3Xs8w0zG7FQAQEyhamRUwcIgkswWIsLzOTM7L55JwGC7Z8zEmemrNxfPfOFrjz3+9We/89JiOQx+3rmm793m9rcvHsyZeb3phPeXq/m/+/df+fIXX/p//Nf/d9cfC7q2AYBuGJYgrm7slKc2eN9vGAP7wQxuOpmjrZ1zLgRwlqUhspXMmUV6I813CStjwNogTMEjgiGw7IikIgjed2TYWiIEEedhJl6oak3dDkG8EBpyAmAr7wawhgg2rvPsqAKPIazFWosUXBgA2NRIBCy8CZvp/GC1coRtwHrv4MKr12793b/7954/JnbNYeBLFy/UzbmPf+7JWwv4K3/xZ3/kQ+9fL2523Xo2NdOWjo9vDv3qwoXzG3uP6wfmUBm0hATMIQhw8KGpW2NxtekZgmkrY2zXd1Bzx2tqceg7FmtrHNymrltm9H4QNoYqYyrvPQgToq0khEECEJHai4ABiapKQUudsNNYEYEBwJCpVBgMyTertOH6rjKmMsTeBT9YYDRoUBqgjhmCM4gC4oIXCGQoyCBGQhgC+8oa8d51XVNV0ndaHZCZa0LhwH1vCfuhr6oKEUNwkJyYzrsKt7bbrETFVazRJYjCibcyIRAhAzGARlQ5AEAMIaQ1AhC8BAMINmBAFIMak8EgzkBA8SiMwCCWGYlI0DAhANlkGtzdwHcVosxQk8yBiOZ0eUvdLmnzP6O9Toa62x+KOaLpoBb8KBsDIAIzewCvEWhqKtCtzGAM0iUN9QQxyvw5ABJgMIiCZCsyCCLSxuhKYGYfhBX5DPDChUud67puEBysqUxVt9O2bdvBVdnuAIWEjFQjBSREMoBWhFW7C0QZRSsaUwctNxBjsogMERFZpAoRD4+6tm0ns31bNb3Do+PVjduvLhb+N3/79+8cb67fOLx9tOmHEMRowDawDOJRoKnaqkERfu657zz7rW/+nb/zd/72/+YXPvC+H3jpxW8d3775pgeutI194cXv7lV2Vk/a2XQ+nzm/6f3GuUELuwIyiNc0O8mwNkBawkoUdgsIEAWBQXdto8k+AiQogAYZRAg5GpQIkDWhWwViCCr2kYAIoUSzuh5h0lxO9cXyetXt71987ruvfN+Dbwtsfunv/4PHvvTlHu9p29YQLY8XlsAP/dNPP82htwbe+pYHLt9zdegWm80KqarqaT8Ejx4RamuJAKLrIDlACx98Sh3P8iwBBBLkHD8PpDUxAAgEiVGDi081obxR4s+Ntvy4aqDFHbHl9T9oRxYDgBO32r3ziRtuB/RspzeXMuBr9gRPHjmNu4wqYTQuqkkyBu8hcMrdiTFsYwQ2RsUy3zbNGUB8hxE/424ti8R3bZyM6ZKZYVQzMRB4wIDgBU0MMAQJ7EQch8DiSSqIJcDBmoCq8EEQZGRmccjBEFUIwOAD+8GJEwfA7KWN1lbCimpbGQNkiOzNwyNE085m0+kcrdms++V6c+doUTdzFbC05bFxAYMQg0MwQYyAKOLY8bA2xlhbGWsQEZnRCzKf2ztIE13kGIrsT6qu6158+fjaKze/890Xn3nu+edfuHbrzmLwaKqpse2srUG6fggB0JoKbe8HN7jBizAZSwrBY/7pr/6Lp57+xn/xt37xL//lP3/58uXvPPfNqsZH3v4DN154xYewXC02/cr5frNZNecPpk3rnAN0kQkSIwsgElmPFpAYJSCJaHEZCmC9GEBmIsYK0INOFVoDarRHkoikQIQkCIIkCExI2RojJITEhowGRxFRBDhjnM/3u45ffPn6gw88tFx1v/z//cef+vTnvOcBh73ZXCQsl6tZ27ZNc7xYf+UrX335xef/t3/7b/65D71vs95w6C6cPyfiFssj51fGmNoScIR2ANB4S80lFhOH30eDGRgAQ4IkFsACMIoFsRRxzYHYYIw1IwDcCbTeWb070gBIzKtN5oiUaczJCzneQAgFwOyWaMIxDXPrztsmnfTraLeCgkekJ5wwhI/GjdHEPursRaUSOdHy8XxhPi6jIf8UHRa3T7AAAGrn9YGZUThaAuMZAZA09F33fwpMCIhW0EEMj8atXUQQ8Ax/0/bMwUkX2Ol2jSKSQHlWkfqAACPPEkz2exYf2ATF3BAMmpVKRMQeEZFBICAH4Jhj5boBK13PpqonVVVVlgCgk05EQgiOgQfPioeF7p6rb1qtu77vN4u1iPrIyE726ubeLGFJ8V7T6ZRxI9SLyBDCMAwxONDs88Bu5fp+tV5vVqvVZrNxLly7dg0AhFGdts6FYRhCCL73m013fHx8eLzuNgMLAdVkZuvlqp2SoYYIQILvvQsBwE9nUte1Qbter1frTV2ZSxfOnT/Y39/f/9a3nv97/8MvP//iC7/4N/7aW9/1g7dvXf/aE88eTAxOp7ZtprOp85sAXgwxeCQWYlBcFvGAACRoKgZEMKxxZQgIpPzMCxOSoBEiEathLAzBajoTowSgyB3G/0pRIESaiS9oTLyf8guiCITX9WHwOJuft/XsX/7K/+8f/cqvXrp473w2HYa6qqqhY+89ABrTsEgI4ZVXb/2DX/6fPve5d/3Vv/zR97zn7YeHt44Oj86fP1ht1taTH4RQGmvbpjEIIVaxUlxWEOQQnEUEZGJLbFULQrbIDAoOBtZknsCIYlFHQJNdCjEkm9tg5CM7VqHt+BUAiL6RtJ41PTZjmZXcalwyus5FOHMBLqPhthddvCqjuORfpTxz2+o6BnlgxOQ5dZlvMaYTpljZNubu/MV0Qh5DCwACAYKweGZPIESYY5dBlWsUAAPM3nvLbI36lYilkNxyum1kXwR3la8yry2s8trFiBmfvemQxL1UHjKLeJwe7AEDCQfwWoxHw8pBRESLu6mIRGSNsIfkMJIovQgI1XVTGQMiHIJ33rmeAJmZZxaEmLU4jUFTEREac/NwE4KQbaeTeV23Wm6YiF69ZkII3vfDMAzD0Pe9pvt94xvf2Gw2Xddp9txyuez7XkSC5RBEU7E0+iZ4FpH9/XMpIsSEEMZbrX06OQQGRGOsJ+qDGC+uH5YM5D0jGoMgAt26b9t2UjcVmTUZNwyHd47X6/VDb37Q1tWN6y//k1/99RevXf/FX/zr73rn26/c96gJd+rarjfSeV5vhuVmYyqyBi0hADN5QHXiaLFkL1iLYm8hiOJKKR6zCsKEcZ9BEEFBQvWLUsDoRgLUqqeZbSGgMCfICS0QESQAB7QGABjQMwUvxk7uvf/h/88v/8o//Ee/euHCfZuBe+emBwfAAswVVd7znduHEnzb1hcvXFysjz/xqc8fL1ad//n3vPf7wbav3rhxcdYyez84H1xNpBYDcN4gCgRgNGoz8yGgM2RRSF9dATM0dZqESDgAKTQCGAJgoyaGxG4QR2jJHAy8s3QlSViExdrmLUlHIfl21r9yjZ0g8oJbJUSq8dcSJUay5IaYgyK22Qpwwa1yzLDyh5CcSJiOnyJ2jDzohIRV8qbtM7ckLD3dxoB99sr4s98ihw+BxJqxzAGYgQUJkQQNQgBmARQ0ud6xZhJpbMFrBUGUc/lajcUnjq8R2KKXEjCAoDCgB6hARPVXomRMifyURFUuiS5rAAAkIIOEIELWWlsBgEgVKJjsoZ9MxrgSBCJrjCGygEYPLtbDS88+9/Wvf/3JJ59+9dVXBR5kZuU+msiqUcvT6dQ5l93SwzCox+fYLYwxlW3atp1Op7O9WdtOq6q6desOAaGgMHjvuy50HQ0DhmMRoQA1ItrKNvWkrmtb15vNJnjpO0dkTWWn1gYv3nvE2vduuRmstbPJjJvJer0+Plq/ev02i5/tXWAJv/U7n/jcF77y8z//87/wC3/diptjxdhSU1kWrNZiGkYWFEGKcYYYYjyY6uCFfITAqGF9wSOhITBE4gOwwrwASYSTAAnIhEgEGgAmBAIRQkBVM81vZSgjeICEiUO4cPkqc/0r/+Rf/I//46/cOVpfvnxRBh+Q1sulQgZX1hqkdTeEECaT6XLVv+mBh46Obn3mC4+vB//Xh/DOd759fu7ehtYAlSPj+rhOiEWRPBOItCakBjQUE6Qh7oqKvR07jKKoqwSEHLNWz3JxxoOnSVgJ0C6tfIkI4IqmBQn4V88nUcQ+QQkKTyICIqzyqWjlVA4IkGup7ngJIxYRYsoo2vk1pOjo2PMUUZgiWzB/jToQ0ZiWlELzCjfoaRIWQAaYKg9uGbYKCQtjUCwzA7OQaH6V8gVKdaxQyyOJgAQRNEClALptrIpMiuFuNiw5Lc1Qkti53ThRrGoeOfVM7b5AsAsQDkAYfcYGwQpokqQVrBjqyJENRTaODCKHi2VTGWOMqBvR1tZaEnjl9lHfu9VqdXx8vFwuV+tuGAZmuHP7cLlcLpfrdbfZrPvVaqUS02a4XfYDsQasqcJ1zyK1tbZuGmPMVERTMS7ZEPuT5SnA4KiZnhcRDuLZOcYAAsaaOly4f64yWt/33nvfu8ExbYYMjGGMqWqjkA/DMBzsNy44BbeYTGZN08xms6ZpBO3e/kEI7ubN65sBDxf+1//N7/zGv//9n/2J9z7y0JsfeOD+t7314Wk7t7VjQD+sTWtZgojG9SNIEBJEYwIYZF2rImI0uQwCKZEIW2ARjsZiBkAmrSMLXhShS81SaoWEAKh2HyYIgFHr8azIzYbEBoHA+PTT33322y/8t//t379+c3Hp0n0vvnT98j33C+Py+E7btkSkgZnGVNbWtp6ayjz9ze8YC/c/8PCtw83f/e9++f0feN/f/M9/EWExn8+bpkEJHLz3nrPDpEhg0M0JEVMJUl0gTMABk7xJTBxEUJCjHnAiKCZLWDlq4dR1ET8kYFSKeZ9j+QLkQISIRsSPGKGR75Qes1I6G7XI2JPCJpVEgdEgdZoFfUxLji6XURpSAYJy1EU2jGg7+aa7bz0+JY/D7geACJHMyBoXG2EQi+C3QGhYWJBU3EIOJERIIIzCIIEQyrBJNUTcHdShGAJ4Pe8DI2OW+IRMCmIU6Sl+Q9KNHNAiimDFBEwVBySoBOuAUx1K0LRyjuiLNKm7EJZ3jq9du3792quHh4fL5XKz2dxabrz3m96pNqdldwFgtdwMw8AsTdM0TVNV08n0XNM09dBqT/JUlR7DHMGswDXW2sqOCWLe+yEphoaqGH8XREQM1rZBROzXA5GZzvYm07lzfd91XbceuhUzAwdENNZaY4hYmNnw4uioruu96R4zD12/2WystVVVPf/iSw8++Kbz58+fu0T1el1VlpG6zfAv/9Vv7+/Nqsq++cH73vzAfQ8/9MBP/tiHHvq+R5ZHNwWFhURL7qjGDcYIG0EUVmQSI4IgIt4IGCFEYfGcUji1nBhjQDUyAAMSRwxVFgxpfhnQM7ABYCDNNhEGJMNihdFz+Mf/6J/+9u99HLCdzvZv3V6eu3Dlzu3jfvAHe6ax1TAMi8W6rmtbt1VVuSHcOTys65q9f+X6YTuph8H90Sc/+6WvPvH/+r/+F0T2YG9OZP3gBh+ayqCpBAkEgZRLkMQYcgJ0gB4QAT2gLmwPOAAy4AAYADUsXtdBgLBbA+EsUo8UriphEV0cY/ETNyFh5HDaLQqHHRa6SFIJt87FMg852l401DomdchWjPS25KUqDm9fnqPYEFJURF4F5TvuSFunjsMpRzLz+uTv/N+cc67fBDcQiEUwKDFMSYQFNAgCgFzwwUvVnq9r21QVCnNwEhyAGNUZAcjaup0AmmHw3eAFYXblnZhic4moqjQKxKVI5a0GycwMALYiiDmuaAySgc1mHUKYTJqqqlg8Mxtj/GbRd34yPddOzg8OWAwR9X4zmVrnV0O32ayW7GVST/f3Lsyn+0ftvQDgvV8v1zdu3HjxxRdfeOGlw1u3X3zx5RBCcN4NwXvvvVdB3VYzFV6G3hfIxdS2ypgICt6EYMxkKtsgU/qLgnmpGFXify2O70AKt9MiGkSEQMMwAIDCbOqd9YMLzOydc35wzvfeDxy8iBiQwC4EZw01TVPX1gAKhG6VYV6UfQoDIJogzMxozHw+39s7MMasu81qtdqf4DAMBgWJN8sF8PCWRx587/e/82/+zf/sYNZMGuPdWnxnDBEKsz8/pa7rEKFt2xDCcrlExL29Pf2QYbGY2fvgvTetCUFL0hCQRWMZSESIaDKZbFaLyhABD0M/advVamHqe6bT6brr7xwdX7p873z//K//69/8f/43/+9z568er9zR8bDpgkhDtkWqEM2kGpiZGQJrdl5F1hpju6Fr23a2P5tOW1uRD33X987333+f/Rt/42989Gd/Rjjcuv6qIWiaxrt+Npt5ZQtoHMvgfACsqiqIm81ms8lU12QIwfV93/cHBwcSYqyc4jro7K83aK01xopIYAYAa2uqLDN7x55VOEIims5ne3t7vqiaA6oSqvaHEkIIQ58B5jSw2wRZLBYqrQ/DACDGGO99zvsL7HR4q6qy1iJ5JYQULh+/DkNnrSUzlnFiDt77atciHplg2oRGrqTiFIceChMVpnx+tZNohulmszHGNE3jvXfdSuOwEIKIEx6Ag4girFo0lkxDVAsYBgIgCwn/kJL+DKA2b4KURkEAHCHrGMWh5oQLAzvmQKpGgzADBs0jA8+MKEhnltjZUQZL7gsAJyGfrKnbBlk8Inbd0Pc9AFRVNZ/MnBt8MIENoq2qtmpqG9rF4pBMZWq7V+2RkMEKqOmcffno6Lnnnnv88ceffuqZmzdvhhCsrSsyRq3pWAFWHr3jYRic90H8Jhu8iaip2+l02tS1UlrwKW8+zdA6XCv5L6SY9fl8LkXaql4lolETcUA0UjfayOKgZCiCeMMBgs5L1QBZW3nxXlg8CWAQkOBc7/yq6au2rauqMqYSEZEgLAFEIdgZwDnHIEQW0RDZyWRmqZpP9+qajhc318vlZDKZtufZDd965tVvPfPCv/713/qxH/3gz33kZ975tofms31CqNumruvDa1+/cuUKGrxz61YIYX9/z3v/yssvX7p0SRet6536N9X8d3i0ZARDVdW0ddVWTY1kAOjatWtgTEBCMEPXb1ZrMMbYBkx74/YiCNx330N3jpb/wz/473/j3/y2sdM7RyvnUIQMWQHDIsAsAoP0EG3ARjcAg4gIk6Y11ooPfd/7gIhgjTE0ef75Fz/16c/fc+X+H/7A+++5t3n5xedZ/P33PnB0dASE1lZoKgwSwBGCNfX5A4uIg3OKaFjbqm7svJ0z+8ENXdcF5xUpsG3bqqrOt+eYOQTW/NwoXAtrlGaKXx/3vCxfUOGjU/EKU1GJtE7HBZLOzJck2SoVTEkVvWK1tMR6OK27pAaOFeqyBzCvU85qTYLHynYYid3MnYj60O4yfz0S1snjpYRlScBDAA5FFwFSLcSgmcXICvaNiCBOGIQtCjN7YRei9ZCEOYAV1wOawCJI1uxypbPaOI4o2wYpla5puVzzaMjEuprNZrO9vb0b11/uerEstjJADQfrO1h34U0PvOXO4Y2XX3nplZdePrx51HVePALQJ554cbFYLBYL7721Td1Y59ymG4QjPUWfXT/oMptWc0umqafzWUVEAIQs3cZrJmpwvgQDQZFQa1yVzZKFtldevgXJEilFm6VYrSh/oU1SWEQgUE6aBdKqbdW0CWAUknUADAyb1QZRyIAJ4tzQB4/ESKDI2cofmQNDpE7nPTODDMwsPoQhtG1rjL3+6u2mmVy6vO/64fjouO/WwKG25vKlix/7+Bd/53f+6JGH3/yRn/3Jn/yJH7167xUIi0fuffP1a9e89xcunkeUO4e3jDHf99Bbrl27Vtd1Xde2qgAbEFTdOfQLUex0Yz1a55BDcDxcvPcBi3S8OLJVPW0maBswdrFZnJ/vn2vPo6FPf/YL/8s//dVvP/dC1c5ffeGaZ0OmQWqNMcwoLOwHAOjDRsfQGNT4AtHkHQBxPYfeeGrbZjqdTqbTurYdLb/5ze984k8+d/XeNz366FsuDrw4PlxsXD3dBwBA4wX6MAwejTHQtHcO77RtW9f1dGYAwBCJSAh+s9rUdX3u4oW2qpm567r1en20WNqIa4hEZKy11iIaTmnnlEzamHIwGRQvCzIHAgksCWNBRsSRrQ0eQMaSMwVc9ai1ZZOZFNgSkriVIsxELMQTvGMrOTHZtkqI8C1Wm7nV9k1g59ezuNXWabGH+SZoM0qpvgxpSEAsC8UkigdgNF+UBGMmATMDa46OchAhDIERhAMKmCBobG3OZlgnJazU+ISpngDIO6iqpmkalXj7fji8s7pze3np0iXmtTGTdnLeQ+UDdL07XAx//M/+zavXX3ruuW9fe+mVxWKFXNV2Yk19h2tlLsYYtgDg+77vO6eBUerdk1j9rTbGtHVTVZXCpwH7rus2m43rE45tBt5Jo+/7oywG53Fn5s1mkyTnMXodCcV1efK8yGgLAIMRw7vOlZ2ISCggolXIERSxWNlWpELhyhgtWawRFd775WIzbSYKDxSClwg+hwBgMKgr3nfrlRt8txkms7puL1686Jw7Pj5eL1dVVZ2/cIW9Wy2Pr716ePHCpUsX73nplRv//S/9w3/+a//2J3/8xz70Ix8cHj1485sfmJ4/3x3duXHrBnNTW3v99ipgG7BmmqAxIr7v3eKoZ+7pYAJAQEimbttpVTdkKiJ66aWX5vO54LxzsFotunV/39ULl++98sy3bz7//PO/9Vu/89ST35zO56tV+Oa3XpzNKh9CXYmtkQiZAwenoHGB1RtQi7BI4OA0nxgNheAEAiL4roLQk0zBN2273w3D4197CvHfvuNtb21qe3h4eHzn9p07d8DYpmmadlq3k+l87+KlKxcvXmynvA+1reuqqgDYez9suk23mc3mHPjoaHPLL0DEGGya9txsH13NHN2Nmp/S9/1m6C9cuFAshBG4HbAo8BB9XIIcpPgqUUkE0ER5Zo1EIgFAxljiPoCi1HPIAO0kECCo7y7dJwYMwbZ5W0QExkz4kbmMwUbxl3wCkmREA349mbAF29HCF5lJnXFaisOKVWFjjHgAQmQFnsGYXi9anAURGcUTpPRQdgJOayNjMAIg7CGgQAhCZJFMfQZXylOV3jYHy20Fv6UgCaELFy523bBcrofeW2snk9l0rzHG9IMfgrVYHy3dK9deffqbzz3x1JPf+tY3O7/xofPek4CtLxBY743rmdkJs3Nu2fcKoZ3sR5g2HKyaum3ryWRSVRVJ6LvV4Z1N13Xee2AhoorMMAxxZFNKp74Oy6CsJ8+QHm+rahx9DsIRPsbo+0opJ8fPRDaw896XNq9hycoQK+XfTVPXtbXNrJ0lqwS3Tej7frPZ9H1vDCJqbXoIgZl9rpdJAIgQgngfePC+H6y1pqW6rqeTZug3fd9rir/zMJ/tLzd+te7Itucv3d85/7sf+5Pf/fgnPvD2qz/7sz/zUz/9Excvnm/3rwIKEXjvbS1BZOURgViqAapVv9lsupe/+911tzk+Wi5W69Wm73rXe+d9uOeee2az6fn9gw+8/33f933vuXPr5ref/87TTz71ex/7029+85sXL168+sAjn//8n7780tFsVk+nB6vVBtEgi8KoMgcESrXLFL6OQoiSr4jMZjOBMAydc/16GY4PsaqsMaZu22k7uX24fPqb3wYJja0MAjNfvnyZmQUpgAwueIa2nUzns+mEDg4OLl26dPny5Tc/8KaHHnro/vvvvXLPFURBFh+Gvu81OTEEMwzGcvDeO+cBgNICVhx65pS9rqH03jvnxFDe1Ua3YGQDPi1ptUvuBl1LwouVUQorhCkIAlg6KJNzcEQhjgEWmnEkhVK6JVtlhTHnMDEg5iAEKeA6dvRcOSMOa6eNv+YPWSVULksAQQQgpICIrZgDkVjHThABgoBHhbBBX5RjBABBrBUvhUUEOECwp/boNOW2OHgyGALX6w7BTNq96YQq2xhjhsGvlpvrt299/RvffOIbz3772y+/8MKNjQtVMxHi1eqYLFa1sVSBeNeFfu36zrM/nkwm0+nUGtMCICmkHjg3WIO2sqayxiDyMKyGXmQYOjXGKy8wanfAWO1colNsBDbaa9qsBqobJto4He+8rLYOBhwdwNljiApkGVhCiGYrPb+urYgMIhsAY6q6rm3VWGubplW5ChDruq6qdjppJ61wUDhzEXDCEth771h8bawAIJBBQBQU4UG8N08+8dj3PfyWq1fvqyq8du36YnFU1207nQjQ4AaWUKOpKyOAIThh/NOvfvuxbzz37z/26b/6V//Kj/7Yh6bTydHRnfV6fePGjTt37rz80rVr167fuH7nxo2bN27cOjpcbFSJZhY0Vd3WbVNVDRAdHR2hyGw2+Zmf+un3vffdd27d+PJjj336059+8JG3XX3TI899+9uf/NSX3vSmB9793gdeeumVV2/cOX9wAEK5OIUBQAwExKTqVWBBZMjWRR4qDg7YE/jAPvjAThBx05+7eXOBwJUl8c67wVo7n7bffeFlUzWz2Wxv/6BqJ4Bm3bHn/vCOu359+eyzryDidNJcvHjx/Plz07b9wR987/nz56/ec/nixYvtbG4gWgNQVjpxzCwAVVVNJu3UGuccJJRdJaRU9MQQkSaXKIUhj9BX2Z6QF3/JC058COPXhIDEsSDrjtC0g5t0QiBKxZlkW0OExOS2ldPx2pPs6SSfUs406n1nSFgR0/tP/v1/5Vzv+i74AYUtASGQ8GQySaiGpEEmABREkDdVVdmKSMC53ruY2w1oGIhsJcb6AJ6hatpmMqnm78LTvIR1fbrwRcaHEEDI2hqAghdEMqZaHK/Onbs4n+07F65du/7UU0996bGvPPPMM8989zvrzrftuenswuBMHyCwrLs1GEAKIowMzIRcEdZEBlfXNHDcuZ6Bydimqeq6FgiktbQoko6GF3gQW2Fd142tEJF9cM4F7+q6iv5frYGmVIEgQ7XzRjuTVwanIOKCNyXDSucr5GHeCUdwkr1Jq4Ppc4FeJHUmanAnkqnrum2nTdNYa70/BgABz+w59M4NPnTig7EIMRGLDBBCpTvk2nbMYm29v3fhwoVLrZ0sFqvDw2PdkKvKzOYTa41zfWBHBLNpAwCLxVHd0Ic//CMf/nMfvHHj1U9+8pO3bt3qum697oZeoZWsJpBr6CkLEllja324hjg1TRWcP9ibu6FbLo/vvXplf39/CP7FF19cr9ebTX98eARAly9eCiFYJKVPVaFM0k0Go+NMAIBpAJHFVgQsgB5ADApi0NqLzl4+OjqS4C+cPzjYmwNzcAOSOOc4ANmqmUxMVfsgLjAAzCfzuq6ttWpLiiEk7Kuqmk2ac+fOXbp04b6r9z708Jsffvjhe+65Z7/ujDHqJfQxVk4CSFVVwqjwzt6z995Utm1bMWQIcskJZobgmVm9hN774J2kKCciMo4XiwUR1Y0dhkFErCXntJxd3jljTcOqqnrfEQEVhcLIABH1faflqTQ8ZfTrIW5bjTIfTAb7VOArpqOFuHCIqK5rXe/6IOec1qnebDZEpF5C369Pegm1NpJ6CZFqohpQvcloyxUFhV9AoWezYQ0x4X+hhm4aiIioWURk1Pw5lcIKgfPUtiW+vY52331vunPn8Mtf+pPPf/6xJ772jZs3b3ZdPwyDmc1EwmbT9/3RcsNBrG1ax2wBkREQvWPfD8E7Em9MNXUdADSGpvUUrRER5/puuWinDQcfESmBBaGuqKkbh46Zg+uXfQ8MRGAIbAUSnErhBICk+SWACL6P9cQ181lSiIPisUnZdMud1ACwXe0dFBk1OrgiLcT4ifViGQ8aS2iFNMQRg0dTVcYYDtD3Yb061uumM28MGasVRhSFomKjWSNBoh/DxAIMzE2FwxD8sFkt7kDgyWTeNtN7773n1Zs3RAiNGbxf9xvn+qap9uazIZD3Q8BquRn+8I8/9cef+pStcjE3Y+pZReAdey9BQy9ZAMlWprINVTWgcYE5BERaLAfXdxcuXLnnyn13Dm+tVusXnn/mzuoOIlpb7033zh1cWhwdL5brSds6EfaeQ0CU2hBZTKEkJkkBhMKIhgSAhH0QCSgBMAQJCJq+KwteTKfzymDXD+vVzcqSQdHMhE236d26WvXNZGKtRVNZa2/eOKrr2lhSqaetK42/QVndIbr2yk1Esdbu7e1duHjuYG//3W+7dPny5QcffPO99947m88BgFk8h7qu1c+CWo06SygjkDkAAAmHQjDJ+9brWTInWx6ZUcw54Y7fFX9OqVh+xpknRLyzznydXY2y2/Zx/NN/97/XmGk9I8tBWgGtjP7SEwazqW1TUYseQ+eD9wbZGiFiLwMjCNZeKsZJM70wm+730zfphQYhmqIlMHPbtt165b2v67qpjBojQwhybopgREzwANJYmlZ2aqj9nd/5g89+5nOf//wXlsv1bDYjArVAgVg8reUwmZ14KAzrQtvlNDSBiMrIXYAomfqwn7XxfBOIVVJOaXRGJbERuWF3Zvo3NJH0OgAwyoamgjJSLDWN8zrZ2EXrgxcGAFPZum3rtrnvTfcfHh+vuw0jAKFzjkWstR3y+YNzP/7jP04Av/3bv80+PPzm77v2yisE6LreD86g4i8IARpjNAYnx+ZISniYTqe5JqNmgKu8UJX2le0P35OGiKcepzNqBqsYAsmrm+lBqzeWQB36a5g0TdPM5/P9/f29vb3pdKoOHLU/Hhwc3HfffQ888MDVq1cPDg6qqgrDkUol/bABlqapK2sh+PVmqfknEoJzAwqoM5r7pUpeOiwpOgc3mw2hzcoNxNqLhmzvnKtri4j9ZoOIdW394AL7ylhEDMFRKn7jvfdyOp1Akt9yaCEAiEhtN5qGgYjqTtX7aAnRtm21RmRd103TaGiIbiQIASmAePGOxXvvEQzaCqkirAGtRoPaU6f/rFk8q4mkwNmICp0uJ9Sy6Kp/igizBxFmXi6XAGBtLSKL1Ub3tIsHB7f9ZrPpF8eLys4efOBBhPoPfv8Tv/avfuOVV24cH61CCAcHBwCwXi/7vieiXLx6qzMAGqiVv2KO/kASzegBiVuNxltHS+JOhgBAqjqR/+pPdMYInVUb+8zyaGcsvzc6BWe1EMIOq7r7zTNjzWCb3nsY8Ctf+cojjz766KOP3jk+eumVl6uqevTRRx9+5JEf/Zmfete73mWJXnjhhXe87e2/+1u//fUnnrj3nqub5QpMACsEwD70m04Z0Hw+z2EfkIM5iERkvV4rrYdUeRcRIWzB8H9vudVd2lkP0mrBsL0HQII8pu2GiH6orbWr1ero6EgroarvrK7r2WxmjNGEisuXL7/vfe975zvfec+l2YULFy5evLg3P1CUx77bdKv1dDrZrJdd11UGp9OpJdN1m8VisdcaY0zbtipSOOeGwTvnDCgyOGDK8zQoRuNQJWRcqe95SwE0IzIqFBLPzvBioWaVJHmXWR71lHzo7ksFk20FtiWvKJWgFtnCVMCKAD0gRwx5NpBCkxDMZDLx3m82m729/XPnzt++fftLX37i8kMPVFX78EMPElWPffEr/+Y3fuuLX3h8tewXx2tEU9dtCKHruvV6ZYyZTCbdZvTQFQY7GQafyGbLt5rjBtKLx0pERNHwoUIyJYZkstcyhqG9xhAxuruM3skmfDqHO8mI8xPe0P1LGXlHVDz1/CxZRLOx1jQWJqJXXnnl9uGdC5cv/dxHPvrhH/1zV69e3Ww2jz/59fVy+cJ3nw8hvOf7f+BDP/zDL3znuzdu3FgvlsDS1vV0Nm/njRwIqDu9qP6dS78gokpVq9VKyxpnQSaO0rYq9D1kW2eNA58hKesGACeGNGUmjDUj4k9hiogrXKrMpU3PVLGraRpguX7t1T/8/T/444//0WxKly5duv/++69evXrfvfe86U1vunrl8nz/YLNaNvWsbafsfLfZMHNVVefOXVzceTUrdyrDVpWZ1NV6vQZgBCQMmhCsEBNFsKiG136PNwCNSdTRy8oNJLpizjrNFmQFZuP9a91/V8J6zR2YYqGdrUQkLTTG+SIhJAQkBAPiQHNfxcQSugyBoWnsYrWaTub33nflaLl44slnJ5PJD/7Qn1v6QQQ//7kv/etf/80vf/mrIBbErNfdlStXl8u1IrSIiDGVMZirV+7w3PwuY8X0PEDSiMRwEgERMGplxFiYC1ATwtMIWHqDDAJPl6TOXmN3i/w47UavDYCx3bai/hKZnjm/pWdTl2NgFuGmaTRIra7rGzdufPtbz167du34+Pjg3LlvPfn04eHhpG1v3bz5g+99X79c/6P/6X++fOmShGCQmqoGgOCcJof3zmHSqceA2xTzrQHiuTSp957O9oJ9T9pZdzvr+KlSqpJf5sKImPkaDUOWJrLCiIiz2ez2zZs3Xn11f3//ypUrbdsuFos7d+48+OB9y+Xzzz333eA8EZ07t3/PlSvnzx/8hY989NLlC5cvXiJb+00/dAMzWQuz/b1oWXc+jycQkIGUhBgIxYCmbY+GDFFbrWjaYXY+Rtz7tJe/garJ2jLDwiSaZMW55F9lLNHOsN99fvFz//Z/l4VwXdUa42OMOdWGxXUwaAkqCiADc3DADsRVjfHghQxjHaARmk0mF6fzA1832m8QAiIQYqbAAFTNZnsCdHy0EsH98+emkzkzf/2pp3/jN/7NH/3RJ4KXyWS+WvZdN0zaufc8DEPwUlVVVauxXFPnZifJS0SUMsqIc93PPZekFsM+ZRRbSvbEADBLKsmpnP1k6+h0m9SZLMJPTj18lg3FnLHzn9VyJeeyw3nVnXpBnG5NDQOte8lVVSnqMVkjIl7YWts0DYNsNpsH7n/T3t6eRfrABz6wN5v/we/9Xrfe9H3vB4ci3vvNat33PTN33pXFYhFRTVciopFl2T8VQ6jcAP/RuBW8cQlrh1ud/JCbdpVptKBnmozmrSRgqnqoKBqv3Hilbdv5fN42lXbDIFprnXNve/SRD3/4wx/64R966KGHqqrShI1hc6tpq7ZtK2OZPfuBvQNmg4pFE1ACASLFDLyNMbrrkIFutVZfnndDCKEysVcEnG1YAc7UGE61YXXrG8qz1A9Y13VegM45tfSpOWgymehBZp9tWAhBvAvsVAk704aVd93XlLAMjqjKkXEW4hZEfyIJkKABrFAcxoh5IDBxMwUMgnUz9Uy2pb35ft1O/uSTn/61X/u1z3zuC5cvX94/uOfw8HixdG07E6xv3bgNpqqqqmpqQOwGLxKMMW0zH/oUUFBSD4Cta02GiEktzECExmhkGKaiOHlX8WFI71VEr4DasEbnaT6OZzCUs1S5s4b0rPuc1fg1head+5+BA3mX81VjJ4ix+AignuvNes3M7XRaVZX4sOn6YdMNw1BV1SsvvvRy4L7vH3/sS7PZbNK0i6PjYRhQpLaVMQZZGlsZYwz7zK0gMSxjjFokOZXFjDg5VTW4YYdb/acxY501X2f14SynimaWReblPTCrahxCmEwm8709AHDOdet1cK5pGudCVYmlajrZZ+Y7d24dHh4Ofb+/P//CY1/5xtef/o1//ZuPvOXhD3zgAx/60Ife8uijL7+kOAIwBPbOSQiVwdpWwQ/CAdgDBEK1JwURATMDAEDO8cmpqkMqYghBCiTm08jntYeuFHHiOCSVUAcq2oXeuIRlT55xF24Ve6NFBACKyP3ARMKaH0mIhtEKIKCBVLs0vYUFMQymts1LL9+azQ/29y995atf/+f//F9+8UtfRsSLF+67eeM289F0Og9err34aj2ZPfSWt968eVvfWXNijKnUhOndAIXIUHxQ65XROjTGoJbO7ZyNAsS4bJJzcMRaHKtOVsGUNFrKtKeOjznDxnSmirFb0PA1zn+jNqyz7nnWLBORF5ZET7l+5HK5nM1mddv0fX98fMze121b22reTowxh4eHzHywt79arW5dv3Hp4qXaWq3bAQAQ2IcQtYM6kpzKF5KKG1dVpSAZpbIQFZyi8/9puNVdxuesDpx13CSE3HgCS2CPiAcHB33f37pxU4WRuq69dd16s+i61WK9WqzPnTvXtq1zXFUTa+ujw+W5c+dsPV1t3Fcef/Lpb37nY3/0yStXrvwf/w9/q0LBqgIIgCbIAI6ZvEUQCMIBwYtBUFRQCQJBIIiYZBzYee0dDeMNE5suwLy4dK6zyl/Opk59/voGjO6nPvUuHdpGJZMC1gsAUGIdQwKqWIuwav1VMUAVYCVASJalvnjp3Nee+Oav/vO/+7nPP0ZVNZvudd3QB2nqPe993wmAne1fCCG89NIrKhUDQF3XBwcH1tq+71erFVGDJxokxDXFMMq1swDAg8XRMpreFFn9r5p2l+LUWUSMs3KCYYlI9kLuNDqjRN1ZYRByhrvxrFJ35g3asE6WErh7Q2vQx3dkEEyh1W3dENFqsVytVrauLly6xMx37typrcLfm2Hwq8VyNpvNmvbw9u3pdKrcqkqOfmVYnUSvZc6O1A/q4QbFCKsqRFScQvu6e/6fppWS1Km92lESw+B2juuqPbx1u6qqadOOcmXXi0g7mfV9v1n31qy9Y0mRRuuN64fbR8fr+Xy+t7cH6J9//tXnn3/1//Rf/V8eefih97///T/wznfcc+XCrDnHrmPfCfuIWp2ix2VrnQJESIYit2Sr7HYiy7tY5Uu02VTAoFyDZw3RKL6lr69fjMPP/MZ/qRI4Jsu0xmGdZcPCxhAYEmQfxHkJAdAj+bqx674LaOrpBTB7vWtsc34+vxhkXTUtotl0Q/DYtLNmsg9m8vE/+uQXH/vaFx77yp3DFdkawDrPACgDS4yf9PnpAIptpDHcrTpxNbgDoVLrbGnBBQBrbQzkJdKgD7Ud9L7WuLOqqqbTSdu2IuL9AJDqsqS6UnETAMx7Rfmg0t9RNuPX5ddsaFSjABQsL35gV07tjkBRznEikpEBlftKKfGVxykZ0U8SzRkkUYCWFBfxaQcBQMJYa96UVgVJm23WrXVDsKcw3GgnOFWM8m/Q6/oGWdvd9Yk/Q9sxqmRXVKkEAIDio+0sbwDwtlGFEQBms5miEq3Xaz3fWlvX9WQymUwmah4CuyIia/DyxQvvf9+7P/wjP/zwg2+yRtbHd6ZtVVsIw8Cht6qQhRCmB6vVSu8TBqc0P3S990MbkQUGCDHfMIQg5kxvaR7qbMYiInbHmnWrfl4R0Q53Xacye7ZRImJVVczsXA/sK4uAPvie3YAkwzBUtgFjWYiwtlXLQCHIbqT7a89HjLQqGwPApl830xmjXW26LsBkOm3aKVX1crmp22kIHIKcv3TPZDL/1Kc//2u//u9eevn6jZtHy3XfTvaqerbuuvWq995Pq4louguAxturNKRMKhnRdWcmRCNsEEMSo0aGpUlF1hrdsQFoGDwzG51mAOViKlgpqkSsV0gEZXERjLhFWQlXHqQTdvJzw3PY5kr6OUtYO2wLpdthUvpX76aPy9qTiJjCRlZOHI1pPbAlAuNokisXxlmTftb6pTTTOzuu8vLM06OiIaNtTkVItSHk006+b2boO8f/Y8UL/cdpZ7G/nRfP7eQuxcRZOHDOqWkPgGzV6I0Cw+ACUmAJxgCBNI0FpJdevv7ii7/75S8//uEf+aEPvPfdP/i+H7h989rRYjmdNEi4Wi+J4GDv4MhpEgUys/O9OgHq2uqePW5MmGWus7TCAh4r9j86oEv5BpIr73UO4FlkKcmatmt0f/0toiCnFyOkYei8WDDT6WSvaqa9k1V3fOnS/cerZdNMzp2//K1nv/Pbv/sHX/jTLy+Wm9V66B0b21rTeh/CwFXVTKfzbrHRnRiQi7hhqiqrtkDngrAaGg1E/Q6MiUYrKIhDGyJJqr4qopZ3MobqWkFjUAREKGOSjaITsIgMYXRIl9OAxqJC56ZMBx2W6Rmqnzr1T86KiIETaxiSqU5FuYi9pag4CfaoJIidO0jRn91Zf93tVDH9pHTE6c5b3DBWzdH4XNEjEvu2y7L176kSVibT19/eKBl/rySscgRKSerkJqF/z/ICA4/IBMMwEFFdt6jJyoJeJARm5xl7QWMFl8eHbdteunzh0uWr3Wb9nedfeuWVa5/4xJ+88+1v+cmf+NH3vff7xbvl4rCe7BHRzaNlNW1UvEKSoYMQQggu+AwfagCgzNe5O81IsVdJkeGoDBGiC27UM069p6Q4rDE9KTOluBJHetjdwE7KqCcbCeKWX5AFoGnbRbd2DLaqAM2mG2wt873zXqaT2ezVV2989rN//LGP/dGTTz3jHU/ne4SVMPYubNZ3+t4FF6iuZ7M9Rq0phIiEFqky6ukTwhACs4hnIramsRaIbPR9IEKBcqey2DAMmr5DKQ/TEtWVYYMAYA2CuOBDCMG5Xg1kGt+g6QI6ap6rPO5QSFj5WTuRzRsfIyHzYOogpXzUk1N+iuQFADnZQt8ln++GTtK6zwwAoIjQL34SEXPCmvAabCvZ1E5SwKnWjLO2zp2NMH4Fyba5nfVc2l/LTuJuZe/vcfuPrRKCGavIRDOPSo52yzYa7R6oUcuIAiKoWfpItq5r731QaUVQPJORIGwQ7rn6wHq9OjrcCJtpa9vJ/nJ5dOfbLxwvVn/6xS+9993f/1f/yl985zvf3m1Wy/V6sn/R9beZmcXoBq2ccBiGnZqGr79tiYfMhKDWpGyf0X0Xtmd2hxpFBFL9C+Vc5fHyzNFLmO/+Wgzr9J+cc7PZzEl9vOq71fF0f//8+Qvnzl359kvuC1/4wsc+9rGnnny67/umOUD0dw47kQ2isaYyprLWghD7sFkuqrYBMBE805CtyBgkAh8GDsAsHAgxCAcAXcgIEI3oqkUrK1ERTAStDYioGfbGmLrWHEmPGLxnZh5c75zbbDZjWFYBk9bYSWZVUoC1q9Uvs6qRf2kNzhPB0Ge37sTMAaRUj9OawVTgjpkBUvm2bbmSmTFWuPM79PGGZZC78rd8t1yPW78yx4q8kB7NIAKjTTDHE25xtO3Q1jckEv7Z2vdQwgI4Tbai5KQSGD+k0QAAUPTRFB+vmQ+IaDLYgnjEpmqbCsgn24DjQN4Bma4b2nYmInduH98IblJXddNOpvV3Xrh26eL5zz721S89/vX3vu/df+kv/Nzb3vY2H1xd12qz9t47108mE4VyT0pbya1yj1/X6+c9SZcDFxh+OxLWzqCVEtYOU8Py5giieFhZnHu9kwOQNEKRZFRcrRd7zQVmHgIfnD9/z70P3DnsPv/Fj33x8cNPfvKTzz777JUrVy5fuffo6KjrlxfO7202UUUyxjTGQBvxLlgNSQaMISQhAvWAL5drBINIIBZE2BhhwwHaic0qW/6LEavTTiaTrFHHROgwqPE8mYdilZq+X6el7vPoAwDgphzceCwlOQtAAAgALnuFcFRLy785dS7eIUsWfLqEVaoM5dRQ0hdk23JUnpNfGVKydDnL5dI62Xa215PciraP+PRoLN5XMEa6bfUKVPc+nQ3nZOxdCet7LQHttO/V/Xdmdmc6Tj4r5yTp5qq7qTGGgZLFh9RjrZHS0/k+C3rvN4PTSO+hdyxw++bi3nvvPTjYkymuN8vBh+VmvVodnT/Ye/XGYVXbvdn045/43J8+9tUf//Ef+8hHPvLON9fqfeq6zvfd9ksklRCgsE+dFewumaNlgR4AWSLApDKpbHqHE1aL/Dn9PV3Cki2I5BM2zZPDfVrL1VLzZdw0zXK99ly17TlE8+TTT332M1//1Kcf++Ljy3sfeOCRt/7AnTt3rr165+Bg72I7Pzw8nExmqrJtNr1FiBGxABQrK5OxCMAhuGTHGaxprTWEFYIlqqyprLUJ9A5FkBli7UKAAkNDlFspajCEdZkUwqmCrEpVXMhWiIKIoQhfKBeYLRCQx+1FhKsWIIVryjikLFgS7siwZIydK+fVnBGfZbenZmfjOoUhbgN4vyYj2Ak03TmJZPeQJl2WrAoAKBZS22JYOh9GdvsvyaaTOWnJWF//Jn/39/pP004uH87Ht38yScDUGLSqqnQJVFXlXARXUA5F1lZN07YTQfCOhTbMPHjHyMx8+fKVEPjmzTveO5GAJALYtPOjVTebTD27l169MZm09WTvU5/74u9//JP/5//yI1evXn3zm9+sNUqISAWC7XE7Mc1ntzxZOl+Bg5bn0SOqBLmUjwW7BlzZvs+ZMrVko/tZg35WG0k2R40iIMC5Cxdu3Dms65ra9vGvfe03fvMT33j6uK7hwUfev16vb986qqoGbX20WEsIxtih92RoOp0qErQ6iUIIDhhR9YXIuTWSYDKZ1HXb1FNrpoQVQmWMNaZab460z1lP1ncuK8VnbU5EDPRqaCw8FwwAdV1LoRIqEwSACovNQQCStz5GruTBiAoRsNlaZnkCuHCTbYkeUsE2qyo/lCxSvyZo5d1pygLXDs8i2QWTfI1ZPps/0Gk/bbGqdFNEREPla6qYByI2bSR5RrRlQMedoXijYQ1vtP1HZXDljJSTCAmmRsPQ1GGtkTeTyaTvexGsqgoI3RAEwVS194yE1ir0kxYTQUOm7xyLb5pmNpv3fbdYHNmKDg4O1uvV4AdmbtqpD/ziyy83TTObTX/pl37pgx/84Ec/+tFHH32kIs0TgqzBvaFXK8l1nOhka1cVIYtadxlnORGHtUOxWw/6+G/9vHcsHhBq8ZaCbey0bWfDpjPGTNrK+265ObYVGCPL1WK2d+Ng/wKLvXXY9a6ezK543LtzDPP9+51Mn33++h994k8//9jXbh8u63bethO3dDkmIO+llLCQsgsMkxKnb1g2NeBduHABUzJzaaB1m2X+nE/mhIRVftCFYZpWWVsuaKr7gHcOk+G8HKyKo7UlLy3tv7VWxTQoFHVmNieA9+Nzz8TD8lDw2R2yxu3yuYhjIZSd1nUdFRUPi3tulzx5LcMQBV92IPf/LIJzYMpzyg7DCe4JAJZM+b65M5QwDPJQxw0mdfbUzp9k9GeN81n95zMCd/N07FwutJXbmH/K8DJ53KLhhid5WLK9In9VwUpFkhTMbPUrgLrwgpI9AGj9AQDo+365XIYQrLXGsk56Pj+zjJyrWEIA7Dd73WY1nTXvf+8P/PmP/PT73/+u2dT23eHQH1eWhTfMm9pK3Rg/9OvNcg/ewcwBBiRG45lCkN7DgAYEJehMAgpGdJa5b3TLH8eHABG9HyS6EWNaOyJWVQXsh6EX9tYIQfBuE4IjkBACA87n+8eLDVB1cO7SphsA0IJUyEEEUWyRJsJVZfqh86uVgPehM9YYW1dt1TTnXnn1mKGazy9R1R4e9WAnVy6/+Vvfufnlr33rc1/4xndfvBGgunDuMqB1POa4YGFMhWTTUQorh1U/EFEO0dRAVi0LqAxORBQ5IITQmEhemcqzha989OhATIBEKtOp+UCxxDLrNAleLoRACT9TmR+zsJYqAZOAdIARUEuYI3jvMkUCAJLiZ6E/w9uVRVw9PxN06RXeWkVnLLAqFbnIGxqmIL1Tzz9rAZ9c8DtLd6dpoSopRMLsAIHThIvaVicZHBHtaA3jPPrTbXynHrw7Lz613V3COsl20dbl++aW6QvSrKkDjv1YVKL0Jk8mE+VZJTitXqWEh4XpEwr3tC4HRcJDRIGRgcp2AEG53PJPfd+TNetV94lPffprT3zlB9/3ro9+9Kc++IEfuH3r+sG5djrd63tYrg5h49vKtpO9/mglKAJBvBMKQs7UunYCABJAEEIgBEQwSd+4e3ba3WwaUGh/J48joiXXcvAm4mKLIAJ6gaGaVOuhF/FVjZYQrATyZOV4VV+6/DbPePP22lazK1fve/mVxe//4ecf+/JT3/7u9VdvrKieVu3UDeCDQzsa9fNTM7HmJar2Cx3cvu/zfqL5/VraSxELKeWFK1l474duJdvKhf5KNKoemTKIyLazPATK+yaTyWw2m05jER1EVOFcmWNrq7w75WS3TEPlYpYk4mXCLafkrNQcQ6PAjCfCWE45/wwJwtoqy6Q6BvlW8EaWtC0AvGW7/Mmp5+eXzQwot/zryePlzZk5u1nzT3qmJE/aXRjT6+RZZzKms98LTmNYxmxByOW/mJgORh9oJG8PI1M2CRILEVUlLOXx3H/dp0thOXMrTmAqkAQu54dTd+stJls4nV668cp8Pp1NWh/8y6++uv7M8cuvvPCxj1/923/rF/pBjEFjJnt7lYJ/MjPWgzWGDDFj72OZOAPGA4ggiiFAVU8ZCNGIbGVuKLL3WfN11mTF4T2RGg0AlnxrNGKCBEBYvCjuNYcAG1tR3baboR94cAOG4G395lvHYqvJZO+ea9cO//CTn3riiW9fu7F89rlXTb23f3ClafdXPS+XK+e5aczO1OZB1OpVWXnJ6zlPjMpWquGrVZJS3pluMnrmIvhS74Mimyl9jWCyeu1kOoUkMCtDrOt6Op3OZjM1e4mInhyVcIjOR0mpOcpKYtzWiXgC3I7Y4hTweRYDoiISHbZX+KkTXJvTY79DCIys/+PdBIWxqutTKeMsBmph3O1FRAVHZj4V9QFK6/62JsgpF59Oi5DME5TznCaTyc4Ntc+0rZ/CCcHhdXLksxiWnIm6saUM5q/GVPlBZYNtGsgsOBYXKbT1vCGVXcpbSyJjkCKYtmRGzKz0GWKVndiZYrva0t93dtPzFy+EEFabDombqukGfvKZZ598+qknvv71D3/4/X/+Z37ykYffFHhYHx+LhLqxBGsWMkBATJViPHAstCgkgiRGkICNQQ06Hbl55lSq5QPAWLsPQKtDbI0k5NPGqUyH42druAImkUCIHgZAEQoMfe+dBwdoutB3Q88gVVUxGts+yAG6Ibz03Ruf/dxXP/25L9+8uazbg71z9wE0/QaOFsdk6/3Zec9hvd7kocMiyGtHAMnDDQCata9cQ1Nnkm5Peo5yGUjE2k5jdR+fIhUgiSp5HHR3Q0NAo1paRmxp0ycOw6CBDmpf4IBoDGryDhmyMY60LiSpknB3iKw0pZ26MAxuaUB5wko1oeRZp1q+AYDZWzsaPnNTNf+U1XrWQqVYtVB7hYzISEJn9f9UKUqPn/wNAKyt8qJVJBn13g6Dy/OCaZEj4pb7Ak/oC3lkMJ6DZ6jMZzGss87PfPaEkEVQcIGdxszR+4KkZFhVY9hN5llYIPxBGur8uJKMJYnnnDA8M5OKRJWqw0iKIN5ZX6VnCACdZyKsmhpAggQAmdbztjE3bx3/i3/1m7/7ex/70A+//+d+7iPv/v53EcHh4e1Zs/HBd8PAzFrHhIgsGBIURgT9S7EGC6IkAhXgCNI7sp6gPEwRI1ItTn3xItEHWHkUjLXIWLPlEMFiQAyChAQK+cwAHFBMZaxYBloP3gU7mc3bZtp13WrYPz5afuWrX//kJz77/IvX6nq+d+HC8VHX2glg1bt+vR7q1s6mxhizwe4kNes7q8SECVovL8usdmWxSMmaC4M6ZER9IqTRXJLvIyIqNks5h0nlxG3jvaSkLaWPzWazXq9V8mqaZjI9YOAgI8NV9seApbEz72yS5IKSHEWR0U9tHHZYW75EL84rQ28n4QxJzVb5HdVTLd4jcmYzuyIDnYXfVG7OTJTV7TO8dcWclktFx39ntecrdGZ16NQWqaBuUOjF0dcRtowy+W9upx485b3OSoU5C5z/DCFLhBCRxgqSsYUQAHVDTBqZFjy1uwYsbZp1X857IuyRScm25qEfdLgoua1g2x+dSb3c5/K6A4DVpmuapq6bqjYowuJ7N/TON1U7mTYu8Gc++7XHv/qtt7/t0Z/6qZ/8oR/6ocmMmdn7wQ8uZl8IMRIKKm9BTVCTgIpho2OSzFgiAqCyZJGluONIgbRsIWOcKoei/EFEy7gai+IIgoBCHDNQYERGgwapmjJD8EJ11U4vuEFevnb0zEtPf+wPP/7441+r6umknd++sQ68Odi/eOtw1bbTuplUdTsEv1geIWJlMeTC2yfMNFj4g/KEdV2HSbdXfUHt6zpYelVmN8aY4CMelh7JmxVtO9cKUYWYhaK9nEII63XnXMgqnnJMAGCGEMTU05L1RDYKEjgAIQIBAIMEYRHBvL8h5uzfpDKdoZIEIxpCRoHGTkLJ4MrViO41UGslWjpisUKRrVShYuGdoToVCBmK+6aL8KwMGS1cmpWdfDynnuw8iwsLICXPNxFNp9ORTRfTJ9vV0stbncrIznqvN8qw5IzcQAGb37ekK8St+ZJkVy2NSjtSf7k5FadFq1YWpigFjpuEAgoAisBjrXXeYWHroeTxyC7C/Djt8wMP3rtcLI4Xi3Ds2raeNA1AxcF1m8FW0FY1EqxurG7e/sqT33zhym/83i/8tR+4fPnyvfdd3ZudM9YNQ8eBCVCEUVBAUByIdtujAMNYNRoAECVKVTq5yEURs8TcTow8bEvN4wcQi9gDBSABBMbAzICEaDbrIGRRWg/W0iSEi9dv3Pj612/88j/7PVtV8wv3CRtGmp87YIbe+QsXLy+Xy/V62battbDZbABlMpkE3hJwSiYCSawtRScVbdq21S06ewNL6tSTVSZSnTHvyBJBqjWticiMPj5hBhE1fmWTgd4fCgah0p+m8hBR30VGximFUA1q1lp1CwIAB+AAIoAIbduUo58VPWUfpy0MleYhJpCBaK7GDnb7uHh268SN0xxlVWZFSLDGIqKAK5dcvs+ZuY0IqFSGiBBj4UCE5IxwASBENGSyHzZSKmgknaTeRdKsq1pEmCWEscwMIrXtdMcKo12lapfx7ZD4zsGzVNezEWLPZFg7J6QPJhnQTcmAmKGkao6hyMw8Vi0q1UA1mZfqRd5I9GRODnGTIDqzAV5E1CBrrXWORJEWRQMigZAIjSJasUrYSAiGCEXk9u1DRJnN50Sk1kkJjhlNPem7ftN1bd1Y24QAt+70y/X1/+7v/cr73veen/6pn3jHO942m0wDE/AQQI2bDlFAAiADDgBqnmrTEMaY2fHziLelGyCmg2rPypdwGnuOPA4ln2YDrKaz6cb3i82aidBWjBap9cCW9snOKqqWx+HJb3z3i1/46hNPfMO0eyzCggrmqSq7qarNsEGLSNCHNQnUDQqw86sQIn8tXR4aQoIpkzsUlQjyxgUATdPMZhGy/fj4OLOqPLsqhWWZa2vBFBL7DuVpN7LiCYlL6hrOEOOZVrLaotd674dh0NOoCK7JhAiFEZSIsmK7I+7pCQoXQ0Q7/VQjdD4t8/R20mZpFAqOr0H/hIBC5QgAxEyu0twrIoBbRVwKIVfD1HdF4CSv7ToHQpFimbm5MUYBDnfYQRYQ8hKlVASTmROWrMlhJXkMy02u5E07o6rzmCe9pJPch53Ppd26/LsjoecpRqpzz5WGtRs5CQyKPZWZndtkIsdCOSgrn5d0q+5pXRGZxrJ4qKOhFFjXdV3XzllJ7iB9RA4wzKqGNr1/VVUwZvgrrJ8RIGKo6gkACEBgBAAesB+Gi+cufPqzT3318e++9z3f/zM/+xPvefc7pzParI6N8dY0q8Xtrjs62JvWNW02q+Dd/vnv22w2GoQEACE4Y0zTND5EqGvm0f7DHHf8kUQ0FwwZkOt6Mrg+L6u6rpwLdtndcdhXk+ns3H4QclQL1h4nl65e/foT3/nCF7/0zDdfvPnK4uiw8w4NWplokgEBGkAEQAV558AsHsAn6KOYlGfMrJzITBBK1rqYNRld6SAzIJViMkOZTqe652S/kt6NSCGPja40SAZmEUbUI2p0lCiCciIRIAQgNBqi3dStIQspBDmucDRevAAjSbRtIguEwMwulRELW6pQP4zbrKQQsGxly1yjWHhbpp9yNZZLKFPtDhpZeaFsu4e0JboJpaZc0nH+quebbQTX8lZSKO8iwiIq+VMGw0jNGFPaaBRqUQ2RPnnHtLd6K61sntWiPL+Q1AZMs4iIowcBUZVWyAcx4hHHcSuO7wwUbHOoPJL5787XkwOeh1entZTssDBQAPhy0HI7UxJMzJG3sTPzg7AoEtz3vbKnkqj4DKz09CFsvZSQCvRBubCeUxTiXByzrQ5WG/jM55588snvvPMdb/3xH//ge9791tVqc/nC9ODChXoFgbtFt0Jwzay6ffu2ru66rjV1pe97HwbVZ/XJAJBATbNSHEQItlNzRARGao9Mzd77wP3Xb95eLpa2ngXTiK37UB8u+v/5H//qd757/cb1JfvGd3h8JAbs/v5BgCHuOSCI+rYIEcscmRBYUSYFUVhcXqsloYiICim5rmSp0mMyEJaz2DQNJ0t8zgQUEc7MhzTnNr8hCoIgIKFkdrW961KKrmbmtj0lAj6TGQoYJN2RJHC+z86qLplLJs3MF3Ykssgp7FYZO0njo+JGJtlR7nCeCgdWnmCVFDJ959GuahMCaHQnEaZ6i4gxCzkmAzILarI5V/nmcaKLWNadtQcAxqDOow7aZtOLdNs80VSVMaaqa2bm1WJJGJlvZE9ehENVEaKxKc9BAHTAYPSIR5ZVyLMJk23cAEZgVIBxonELU1xS2QUQEa0OBACj/fGklyAmbgMAaAZf7jwmUSvLvNryoBHFshp5UsrRK5+YH0rJtFcyOCj4IBGpa9U5N51OE3cjEVS69Z4RKe9iAKTamggjAhRLQNIqiDZ+XU1FCc5bh/3efDqbTRDCzVvdnz72jedffOkP/mD/5//qR9br2XQi1sJ0OkWhoVtsnJ/NZqkPMbS9quOaBUiWhiIhH0fkeC4t7um/ommGxNSCffXOkqm+fN+9VO9998U73/j6d55+9tq3nrt+88Z6sQwcqkm1hxabtjVAlZ0i91pLpdwniQhQ01NIjEcNjXWB8JTK0nqkTCXJvZdkMsciAzazFUzG+JId9O4UGBYoGMo2Qe86UzI1K+uEJJNzyhmSEPJaiTEmeavcfqmSqiAJhiIiiJIiY0/RIl3YGYeTpg0ioqoCEAQTIAiIYFL6MC/7UL6+smcRiWmZxf6s98e8SPIQid54bKVaVK7PkuFWVdT0+77fbDabzaaM0tbAFPUIRyY1nZaCc2kNyJqgjrwyAs9biKmlVJg7Vs44F/EB5URvMaB0oSQE150TyiMlywAA5j7PC7MF4KQrjNlmAKDSPWIU2Hd4PSZ7Via/nXGmotZs1ovzzXP3OIUr4va+kldQueLy3/xqiAgkuJVtGoWafNr99z246VbHiw0CVxXyxn/r2ZeeeuqpLz722E//9I/87M98+JG33A9WFsddv5HprJ0Ah+A0ALuqKmMRYyXT7O0dvSi6a2wPvJbdk9RbRbsbf7ar3uztX1h11bNPv/jJz3zlK1/79vXb/aa3+wf31m3tBj5a9r7rUcCS8Qsn8/zmyEEQAkbPu742IVgAFgYWExhExtw0KpI28rKE7e0lH5Hk2ij5DhYBWfq+9iRmNiAispqPc71bAUW9VPVC1wgiAgjFonhNtjKEEIiCojWguLJ7Ei2BMd5npOYcdsCcA18wHkdgicncLCIcePSd+tBTCojFwmYEhXhPRCBB2AYin56JIw46M2tvIUlp8QMi+OB0AVibvKgiLMFYk0cV1PavELfB5Ng/BCQ0CERILAl7qwjNRUSAaERTjU9rC6YxjNNX8l/NIsgxDblMzqkbCWx7G0syKCc9L3UiyraFnV9LCbRcH2VuY9mosCrm7umg5IP64jnAKjOLPImIaK3JbyGF67Ck5/zukrTj/DVfpa1kQEo2wzAU6ieUF+YzoeRWkknPQASiCghaK4AEUY0mmd8dLe8ws+fA3nVDqCzWtWkm5xz3v/v7f/LHf/KZ9//gu/7SX/nZt731+9DuLddLgyudU4XZMsYMw9D3ncrgIkFRggEAkFl8Tu1KBobTVcI8FPbcpYdu3T76o0/+8R9+/CvXb4Op22p279WLV5577pXptLJYM3g0pqnQgAxuI2GiPEEEOARhYELdmCMgQY6cZMvMUnhJMs9SiSYbkjMZScJHHxnBiRSTcjFz8vWeJN8dUobT2kiFBYHmfsaH0sgFAEBAa4SA+lyYkxct1XrwPghrSN54ZyIyuvAEmMfIVQAIPRMRG5+fuEtkAIwcWMQHIgp2F4aFk/syjxhkQQ9gcBuMkqlgFHgZgI3JjCAzOCBCrKtyuBARVHEwCesxm5YAEMEieu8VFQNSzZucJ1Byh9jtQvPaocVst9IW36utc6xmPC2OeyQARCQkwkiTQggJXFyUGvUnPsUmhYgWzUlWUq78squ6zHNPMGo3DMBEIwJtehFVS7csmKUF9uT4YIolzD8VDxpr+UV+YwwiaiF0KhyIUOSlwQmela2BkEhUtQAdci0douK3XvXqzVfatt2bzerZRERcP6w3/XLZz+bNtD2/7tZ//MkvP/7EM4++9aGf+Mkf/ckf+9EDvKnGtZIkEFvvfYm3VcZeJSLMSyzGlwIyqBgkIf3K9vrNTT/UL72yevkaXLh86eDiAzcOhztHct8Db+02w2Z97DlwGDj0TU3TWbVwDGgBRVjCELQ+KYglMlrtnYDACLMnY0iMsCuHLH8uEwN1PrLTR5IHIbdM4liYt3SteonygL60WtWSaKWYJ6OApSFomRTKXg3DkP2AnCoPE1EO5MvLO3tskrw1Ep/SVVrXWT5SpcDn+2Q2iojCgYVBWCgW7dFfS9mTEEFYWAKHkCoJUzRHoQALsDGGWbSiYFw4BokosJFtL1WGK4ITDU+IMHkla+BP/nXcgUCGwXvPIWiQWoVIIUjprSs3KvZBxUkRMUZF3Zj2BAAi2VITtSHiuHtod0LwIYzZThLF8Ji2L5LrPKaYOIhBnpUtinTkD0oxCFnQzuaVcgTKIRL0tO1XzQM7qr1Fw6Tt5hDoDPh5cjDztSXNU2HPzawKktVJ3U1JYoCsOjCHlASjvyqbksi5RdK6GSUAiDwaCBBEU/rx/gcudV23Wh8vl2JMZW1F1iLSYjEAVtbMTN2sNvLVJ55/5ZXf+fznvvG3/tLbzp07N5/PkcR7RS6wbTs7PDzMLKnMyAHEgnmNsAXxr8jOVZZo+shbHn7Xu259+fGXWepbt7tbh93+/vzmraXr+6bGe67cC7y8df2F5erYOaD6PmRBRA4cgoQghgQh1LMmTQ+iAQHrvUccXNgNqJNCxMUky3Byz+fNBFLUu/6aKT4bO/RWoR+TLU9dUWkatsnuRA5g13VKARqkpwqOMaapSUDXxkgzkVy0iq/qfYhKEVVSMdSJRogG0CKFEDJjTSt4ROnM3cg/7pSBOnP9pK+lXS93lIiaptGB1eM5TXK1WmFZUwPze0VjPMZAAckLI3KoAmMAEdfLoxwgohXYdL40UkFnsHzE/nyPitxMk5CYdMNIDChuY6WtOvc235OT3zO/71k0gMnZXw7RznBBEkvlNFaem+NepWkBZgkgAJ4FQtNWkOizvLlqYJQM21jIxbC9N+wsk50XkSRG6SMkBS6YImn8ZG/PInsAABaIG1zk+zvXkwCI3Llzs6qqpjUhSN853/mmaZtm4jmsV857R4Yn08q54c6dl2/cXF576t//0A/90I/92I89+OY3TafTEIKiGJSD/DowGbMlfmtMRAR/+5/9NVudn84f+q//m3/4zHMraq9eu728cM/lzbBi2XDYWJCpaWup10fd8Z1lsw9oqKpbNHbTD6tNb+tmvrffttO2bZ3rjw+PWMLebEpE3Wrt7Fg2g1LYrkGy1mb4XVNY1TbrZbZtGWM02CT7E3Wf55TQICJOSHctKWxeIqKRIGX4ezaX5OOQzJb6697eHgBo/rPecG9vb8dwkD9kW0P2AekyZh5KXgxJSLH29KRlcSOgTcm8NCwg8/e8tGzb5I2XiKoUsclJIjOAmbPnHua1nU37GY4iy5V5M9gxlyi/05HXGVGnj5rYV6tFtsFBMkuFELT/AKAZmvlXYKN1Idu23dvbq+u67/v1el2rEzFNRybubFTGZITOKEAnuTwigjXlIs9bo/Y/L1c9KAkS82QruUDJ+ATGLLHyV7XNqXuBi9jXkHL4M5dRCUuhQZRKFWtUt8lJUylpaV073Tgzt80zmxm3Kk35dUp2XJJBHsNNz3mNhAI8yxZFMcr7EHanHi+dmHknQ8TL59vNaj2d1R/8wPs++nM/9d73vN1avzi+DtJxWCH2hjzK4MPGEFSVqTYX+r6HwNZaYOn7HiSYitbrZTtrwXA3rNlIM7FUk/fetk2zWG2M3Xz/O9/+xFOfquywN59qKgwZsNZi8MzshdGadjrZdHcEwQy+aloB1JkIIaRIHGIfBtdzsrNSUmE4VyUAUK6RGVYJHK6Sc748kxcAaClgSqGYdV0jIg9x19psNl3XAUAGb8z8C5ItfyfUsKTFtm310Up2SlglieO2jJNpCAo/dJ65vIRKoj91YVCCizlrq9+hQk4komMSUiRURjXAYn3lbmTyzStZ7cRl+GJ+r3K9SUI7yQhzzLxer/u+V5koewBLDqJNZ6o0aRljjg9XIWXJaZCtrna9ic5OacfMnYFCP83jXxqDtLlttN98Am8HoJZTc1Yrxz/PIJmRP2Yeke1fpX1K30ipNDebICptquqUubOerJHx2Wt8Vsdyr/Kjd6hRCb5k/SfJXk6T43BnJ4B4vCTOnXHLAwUAN67fnExb7/hLX/nKCy889453PPQjH3r/O9/x0GJ5x1YynTRN3Xq3dkO/2WwQLIIb4AABAABJREFUZeIIABpbVTVKkME5Nww+aCnpFCvPAFITEwjZ2hq3Wa7x8D3vfue//nef2awX84v3Ha3Wgb2xxhAAgwsCwsbWzdSA6xiETFW3ranqIChIZOxisdhsNurkttaydwDQNM1gilFLsxuNapJf+JTJyKSgROy977pOYfaISJcBIu5fuJzBZ/IaICKt7rUThl4yoLIh4nQ6rapKr5pMJrqz8dloUMYYZWqSzOppTVYigsiZQUiBKnWyGdx6Zdp2UZ+k1IBb234+bpKkgDK6KbBELtxWWFSbyAsjSw2ZWeTRUxam99ERXq1WWmbRGDOfz7HY9vOiVfyybSxNnQi20ZwUNpuV94NudSJqIjfMmEHsmNViKJD8RwoYAABNM+J/lZOL26oxmSJMF1mi2z4N7Nkg4ogYN4jsuhJAREWqpyL0PAuGkHZiKDR0s13xmwobpQ4+F3Ht1loJ8TTdJ5TaKQWm6ghT8Y4hbNl9yl0qf8iyMxGp7zv3X2T3azl6IkK4u3GWjyspkyO6meDgnHM3b69fePG7L738/EsvvfDww/f95I//yIVLs6adEbj1Zrle8978/KVLF4dbd7xzIXSbbiOBPfcqJ236AaVCIdBShKEWsui9DX0v7Dbr5f783IP3X/7i116YHZwHdrXNZRYMIgIaYAKEg4MDNGSrhmwlSF6THZGOj5ebzabvoa0bY0nRSywZByHv8MVLQ9d1uZq5KTxHqpFByonNa17Fq8Viwc4Bka0qPX687ufzuWbwZLtmudVgYfnKtEiF/qW703w+V4alUkO2RFJh8twhaEk+zezVFhH1vnFCLs0M69SbAICFUcOFpLWNI3bCsWXsmCKThw4KU44ZV24EistCShk5IYUMJYVOsV6vo7V7O6dSedkwDJp4AQDKjHSrKCdLu7FcLjnVQ4NCg5jP5/p26kjSxIasl+WZCgmQoJRkoZAFshWMtz1u1oxRL+VcZ4FdkoNRb1vKmFvzG034CIhSxKBS9JyCIAUICScF2XlkIWs1LFZjWZi9T0bL2HkiQBQAPb8ig0YCBCKqbWWt9RTXv26HzHxSCt7R/qBgH5JEJEwRrTt7dknAO6oAnMaVEE4vtpKdQmWvAMDadnAOJFjbtFW76fmxx7/x2Je/9I0nn3700Tf/0Pt/4O1ve3i2d5Vo2nXrF148ujIZXOjc0AFwZbCqEQBQBkAHWtFdCJmQGwiVhGCXx4c1NYv18erwxUceuf+zX3j26PAGNbPKTgQ4sLBAjRapCRx69tj3k9m0rmsG7J13QdBYJLx8+fJisei6NTMbIEp29FhKdtvrhALDMGRZoKItDwsVNiYpNm3vvbUWq2oymbRtqyR7vO4lJZHt7+9nCqYU82WKyHJJBVBPMiw1tRBRrKE0DM45vbzcUsqNZceKgUXc0A6DyOvnZKuQ0tbEucNZk8WixQE0W7oMJaafbRD5SB6BzP5yrJCkJPDS9pGfrgOuZgFl38aYzWaTMWB1rLJUqzfJO7k+fT6fZ4m4SrsLM89ne0obykQ4GftUAc93oMIwnxdMyZqxsFtzYcD2BSo3FJZ4U4TyZiEFUm3Lk60UisuOheAy5eSpycOYeYQk2x8nPNVS3Nbx179ZqtVHsIuRqDrO+pOSYrnT7IwJnCbyYLI5UgrHDSGghtWRLgeAEhhDBGBLrwdAkN04tbRqBKN8Kvl3ANh0g7XWkPHBOTeQ4Wlbtc3el7701NPffPbJJ5/94Q/+4A++/z3333t5Mtv33i/WTzF7IqkrausKgLvNarleGWoABwHtpBW2EGoIYjfrZdNU69XRKzduH8yaSQ3rxa2WTN02LARiEQiwAqQg7EM4Wh0BobG1IHkfFGMcU+QuM3euC2zbujLGQMh4ZqMlBRFBiQZ2rR5YmDMgIWzoQGtTw3ZpFrlwYaYLTNNB9XJIRQFKgtZHhNMgjEUkG0F1Ta5WK7UEl96l0lKQO5xZQ5ZoyvWfpfezhCyVsPIazl683Mmdv33YwtXKVpm8SVLhh8z9hGLPyAfz8HLhrj04OFAxSkXdLPotl0s9WRdkTgyg5BX1ReVXANDNI+MCZZNK7m1VVQopk5OlKaVnZVYLad/GbQGhnALZ3jN2IjbycT2zlJd3xm2nqRSZz8kMy4fRHSGChpGZJDl5yICxaC2JiEBgXc8ISIQkKUaSBYQMMkvwPrAPHJiZXeR62dcEAGqeX6/XZtvWWdLhzptiMtqWMq+kjd/ahgrfVB5tSaJZOT6RZ53WSuqSYl8BqjxzCOK95+CNFYRK0MwPrgbffeu56zdufuLLj3/zB77/nR/84A+94x3vMO1tkOB8N/SLVd8ZGFwYAALgABgAPQEFDigiDMBo0bODbrM86ldhcciXLtGL14PbLPuqstXEkBUjwhBCYBC0xqWGxhKRtTUaG1iOj4+Vm6y7lQ/OEjZNgxFSKfGpjLCBol7CuJcWo024FeSSKTijIHjvlZvENVy12UCuDEttW7pz4glJ+OQCyBSvhmTvfdM0mZTznpwFvVLDyiI3JU+QSQGWtO2jPOE1ThPPIe/2VOBS8rbZK382sBUom4cuS4IadJJbCkrYskOXLohQBHwhYvamZc+Dtqz92VTyU+9mrZFChMz30Uvm83lOeyY1KovnACKCJE0b8xZ9GMgAAAuAVvkYRa30hnnVMbOWvI9TE5G6EJEgYaXnS8pZPrWfp04KAFTVaPNGVNsZIEJFYxENSXgeOlxSWKwpQVRyAk2SwpSJiMzc933XdTnsQ6mrrmslQkRsUssXljw6cYoRYpe2dXMsMON1RRARUJWnL++stJ0hkEcMkxRWUqAUZq88qnlXsFXjnRt8j2irSYMcjpbrG7dWtbX7B/Nz+9PDY75x89lvPvPSE994/q1vfevP/PDe+XMH82kr4jd9h8KGoG6bEIKgj3HbSCKexIuIJaK+32w2K2vrmzdfvHR++urN5dAvaNXQvG7alhm8eC8BEWwds72ccyRQNa21NghoJvP+/j6zZx/Wm5UKsRAY2yrzb8jCAkrTNNk5mIUCABAe5Sm9SpeQAvvl9Q/JyLXZxEjurL7pZCtHOzmLZyF/tm0LaV+aTqdU+JXz7pSF/MyPlGdFVpt4VimwZLnyLAkLZZQp8j2pgC7IVKIfsgQRKTInA49cbGs15mHMhJ52WpsFmTyAmCKnddi1G2pp2t/f1/1Ax1CHRT+XYk4e7b7vp9PpdDp1zi0WCxWgmqaBAnjHpGojzrmUurHloChFG0xGQ0istly9eYg4BO0GbjsuNWwi5ATGNLBnSVi64Y17A0TfkKZnCwjHvPs4/pVJZd9YgIUIjbGWjHonSuU3qocC7IPrB+993Pks6bw451arlXNuPp9funRJ/dcZkXVHFArpfSFteDqzTdPAduy7nu8Fso2EIzY7EqEPXKp4kEPPJRrV4l9JSMwRAluzwgNCvGIYPJFp2ikzBz8wizWTej6tKtN13bVuOWnryWQ2+OrJp1/61nPXnnvCv/8H3/3BD73nvnvusdW039zx7si5zhgEZIDAxMIC1AAJgti6bo+Oj8/tz68/d31vVt1ZeGGoa+g2i8Fj0/q6bskaYBn8EIKr6/r4+Ljr3aUr91RVtek6Bmzb1nsNdPIA4JxbeEdE+7P5setymAmmcn6uH7LRHQo7MQCYBOScNwHdcPq+z/EKknztAKBqBRR2Sv2q1oEMgZZxmjIcoNZKKu30mayPjo6UZE0qQZZ3lbxCcvcQMZatxkgEtG2z2GE65fIGAFsYm/Ky5BS3VV4eCXTHIJiVzcKqdXLh5fHJfSj7eVIDzcOOiPP5/ODgoGmazFCg4JshjNYrUwSUdl2nEmtVVVrSahiG5XI5ads8mHknw2RepILLUFKxy4WquwKkQFP9mxetyjXl6+ex0g0PkjSUefqOgJkv8X7EFyzNoOfO7Sld5bC+fH5V2fKh+nc6najtT/19qusZYw4PD5umRoRsowj/f9L+POiWLLkLBN39nIi427e/772XmZWVqkW1qBYJCbQhqaU2SkggIXWDBhnGdLOMGgl6GBhgetgMpkHTMIY1S7dg2gYYIRqBGhAUarEaIBmoKAQD2lV7ZWZl5fLyrd9y742Ic9znD4/j1yPulyWpOyzrq+/dL27EWdx/x3fPGUD0hqqqrq+vr66u1uv1wcHBwcHBU0899dprr11fXysZa/2/uq47TEbGUAJidB2k2NE8yfnKruTSp6ykuIxNH6qu+g9toTw97ITuVNxBAauqQgBNYCbgWBFz7hLCVlKWGKXt+PnPrD/ysX/24R//2V/7a7/uK770vc2y3j5MXeobJIqznPPl9ROE+uDooJ5RuryOqh5mkKrCZkbLBS7m0GWo1EGSQAIgSqhCXdWA8Xp7BS7PPoSg7l7VIAB4tVoJ8NXFk+vr6zpEKR5YT0O6PTj+TP9PeGSi5lJQycsgttxK17aORtm2PSZTKK8qxZgpVIqbT+nS3uvRxxs7jSCklHy0ol3mJciJM3NOOzMw7kzFQ0iHD5Ui2AVGkvPmvJFEFhwDi2Vc+xT88f27z8feRnLWDQ+gtvJkfV9KNKn91R4Czq9nK6AyrE/31dkpnNV17Pu+69rtdicaAAizxEhNUymnGYG17daTjb09RoqRqip4bV1Ect8REQrr+3R0AFBVC5uap0bmUdiH/W6SNTNz6lNh18360gOB3e8Bzi9sSkkl01kpRdl3XSeymM9zziBy1bY559lsVldVDGG92cTSC/rg4OD6+vri4uLevXuPHj06PT29c+fOkydPrq6uTL2QXTHincNXRKzkt6dDRMSh6jq71VAhS6kINJSkCFK7TDSjEztOJgQGw2FAfh0s2LLrtpizpAwASMhCfRKA/PoWYjj4+Y89fOmVv/tjP/bvv+pXfuH73vPc3bM7L774sT5fLRazs9vnqc+XmwdVulosF7EXEiQArudx2cnhwez06Mmrr4NA4nabesS2j5GqGqoaKEA5V3PXdbOcQ6gQUESqShPHcbVaIcHm+mqz2VQUwmrOVlHMzVMBawAUcWsKu7AaKeG8Fo2CTh7RjUmw8+JRMQZJMS4Y1uC4IqWdjYZQptpMaI6KEuqpE4tAZAKF8ZIyanbRTKYY2tjsCUTknXpePfFMNQKgvb/aFAYEGX/Pn6IeQD3xSQkX8gOwU1oHlt+g4JwGddgcTWs274d1GzE4U3qwqAhdwNVq1XXder1+8uSJ1qhRHFytVjBlucJ5LkwPi9Jdz0f1vAyDbH8NhvyOeJXfnL82Hf85y9QLqXO/vLxEd+TYK7yDSIptq6qqs7MzlYwU61erlZaxrOdzVejm83mMcb1eP3jw4NGjR/fv36+q6ujoiIoqrTwYqLLJ6qjy2HlN7vIE42cBe3jkaMxAXKnIJh5UCZwgVwXDGWFnGO7O4JRjLiwvOWVmxrC4uLjMafvkii+uPv7yq6+/9Nn3fumXvOfuU2/t24u22/SpD1FiHZtZVc9iFIgSakjdfN5sEixbOT6uXnmtJ+DEzH3PkBLmbexDZKQccy0iOaXNZtPMF808IlEudXk0ITfGqGlEm81mPq9zSaEKE7ax8I0xYE3IURnAu59pFJu3c/P7DfCqwWRXTK3wUq4Wb5ykVuws2WXkk21Al+KAQxxQZWOzl9qLzP5ltI7jJh37Y8axiJRd/It+R79r850AFr6BhIXOvgPFfyfjk3P/FPUPKT93BjLvKrm+vlb51Hh1kNE4hxA0KiK4UINPf/rTpmoh4mw2Wy6X8/k8512FTL9QWHqUpHEtYM2dTCmpSmtS82azgbELxc/dXm3VFPZhfVjeIP6fNqQQgshO0LN7iKDrtvpYAYlh8HFfXj45Pz9fLpdaJJU5bTb91dVVqGcppSdPnjRNs1gsmqY5OTk5ODgIITx58uTi4uL4+Fglr81mQ85L43CBRURDoCdRu4iYSpqB7ZR+K71R0xPLlXaVcPZJwn4Z2tlg0JqiIqKLpJZSI7qcWfOs1hxboYPV6eHxrN8++cmffvH1e48/+YnPfMu3fOCZp48Xq+PHD1/p0/r89kk9m7V9FyHWVT3vmGeLedPxYklnp4eED0KgkEmoyiCsW5a2DFJyl2Sz2dTrNcU6VJgzp7SdzWaIsl6vWfLBwQEiXj5+YksDgxEPdhusHTqdwdgTgScUD2Eyjnj0XjO/1jS2y9iyqn/AgmVM9dB2qrPZjEvqCTl7ij3ESMQDkDjhy6rp6G5xyWGs61pKNa5SAZJEdigle4swWY1hTXA3EiIyozuZrrcnmU2gU38PLvPDGIxLkXvZSw6fPNNGFQJ5bjeA0IaDIrLdbtUvoUs3n8+GheLcda1a3HPO9+69tlgsDg4OFouTWGoobjbrN2qWYeGUGljEzCn1IrK9usqu05JtUCq15Cc/PSBWgerYTGjP/wQAcYWD/Moo5eS9NnTX19eIVNVNGFJNuW3b7fW6RZzXDfKgQ6wvr7bb7cXFhVQ7k4Wi9nK5rOv67t27r7766uXlpfagy6VEhNm8vAgsY9+FJ+O230WrePPWRILeHZ95dFTYGeYxfXSiDP2+EgHBENahX+hRNOFGYKjRJIDy8PrJ8fGhBHrp5fuSNycHp9ct/fi/++S9exf/6dd92Zd/2fvq2bn0F5su4EaYIWJoYp1jTjWFuukWi3jn7q2DxYPLqwzCiBwpAGJGYRABSG3CQIiQum69XjfzJcVKRPo+ab5r27ZIsFrMAaDftskVRQS3gjFGQS7O1N2iWP1ck61gHNtWDq5RiumNND05BvUhFl7kVb+c88HBgcaj+nPbdtGjlclHE1LGYhK2uYgzdoA7jU0+n2y2H7m9ZQJYWnjPhPyIJdXDuv6MsUXGLsL9lTH2s6WejNYfxX4k5ecAWObN0G8dHh5eX19rDQwsZW2aphHuTQLyKetve9vbdEZ9319cXFxeXl5fX3ddp94u2AMOlT7m87k5LvWY4W5qu9FdfqOIdkPwyTp7DderhIgj2dmecHV1ZUeXoVWMcTabKXZbHLLFWCmz2KNUMZRq1+ecS8F7EZnNZk8//fT19fWrr776+uuvq/jJzBZE7Wtq6355Vd3gadOueXx5wphQIyJK2lG+Xx+/F55aYnB10HR9QEQyc2JmrX4lA+4ngDQ/rrd5fbXpQHhZryDMhQmp+ehHX7+6/NcvvPDaF3/xO9769qerugGUWElEClU9i7lFiaG+XmJ1JxyfnH7qyZMetKkEMWCPMRMCIHTCTayRoe96rdFVz4Zl6vsegImIwuAsW61WDzdXtigTMmI2uPHIMqyF6YNSlDgcX+TyVyYrDmOVx95LxeiuuzuxQGm6ohJ3zlln54VqckZZGKf+7MwxEMGdwPZXcfYsP+bJM8EZuTwd7D6pIu5hnMejfduXX3b/oYdLucmuF0s/DnRpHPtLbbvjmV95UqvNYAlq77puu7lCl9VkNh2FKq3coCnB8/l8tVqpKrf/Ug3UtB00PNVgFBq79iYjnyyOjI0PnpMnxCMlQt1I1H5aipJeRjbz+bxt28vLy4uLC41tPj09PT4+vrq6yjmHEObzuaWChRCkqk0W0wXRs/Py8vL4+Hg+n2vRdCrptAbZttEGtbYvUhzoKaVNu7kRrWRsorV1C9DQUKFzKKpVIhwsRAv9fwgl+ZyKxVYEUQSywE7gFU4sWSAvDpcPHjwgxPPz2/PYbJ5cXnTpeLVcHc5efuXevXv/5sUXX/rKr/rCL/qidzz9zHnd1BEwUBQMVUCF5Kaqjw4ODur6oXDFWv4NNKUWtKtGCAEJ+65nJwHFSF3XIUpT10jQ950eC3x9MTnejfJsldEJAtY0MbvORTBmbGV7fuPMZABQIjAZDRxgqbudxxGkWiwllDLkagrR0tToyrN5QLRRaaqKSuabdeepHxzoGEF7gJgwla3DRMLawc3YjbhzEu1YcLoUE+zT3zUQ114KxfaRXMNaz6jk7PH+CmGQnjwbA8D19TURzefz+XwuImp/2W63VUTrr6PakCp92iarqqqDg4PlcmkoeXZ2ZoP3a/X48WMTH7DEWAKAdu716vyELSc/7bZJ+Qc1otu+2+77gFJ7PhQRyXLyFQ5yzipkHRwcrFarx48fc4kmuXv3roYKHh0dqXFdQ0Au226z2VgMs7jwjtdee62qqsPDw8VisV6vAeDg4CDQ8EbvSS9bM6qhpIClGsbEgAUljGP/WjQzIyGP4P6ylQQATV1CxKHAOJSccBAGGOQsfQhn4Pzg8WsHR0dR4uXVxVriyfyQhJ48vqyDHB3e4v7ip3/6o48evd62m6/66l9x96lz/KH/9++qGt70Dx9efLbndcb4ymvrT31q8/d/8JMHq+M+Hz7aXK8WCwnXV9cPZyuQa0DErFVoKc7my9XR8WK56rMIDq0ohIhLi/WaQ2lWylp/BhEFWJtqaHJI12+LvA3HqwPNc27bNvW58HmlKVExDgJzjJEwImInI9Xdn3h+iW2htcSVqSSIaCW3kut9AKXHapeFiJqmWa1WynvX19fX19fKcjaYHXt0acIeRj0wBi8lL07d54BdIwVDOhx7Hs1GZhKoxyARiTQthKa/eLw27mJmErax6SrZIW8HODptV23Dq9VKz3kbsA3p+vr6yZMnbdvqYWAjmczXDoAJpqsYYhb6XHpEMrPmD4UQFouFCsVd14V5M+ErdfUMKiHvOchKVzMRkcwAoL3lG63PlTIzQ96tYZtHaG6rik6s003xvSzV6Nm27ZMnTx4+fHh1dXXnzp3lcqkp9/YQZt52rUYdbrdbewIAmDPBqHG5XDZNQ4AaeqoZIHrWatCi7pGKaZZq3nY7b6k4I6wF7tou62WpcoZ9etmHhukDocZdnrlRqYgoXCZXy0zpp5YY6qqqAhB00mdJTAkR6jput1sKMK+bvu3quv6SL/plX/mVXzn0AtARo9Qd43w+PztbLOafTCkBMgFZL7MbqU0HpFyICIDEJcNGRCRrvvFoaWIV2lbDo4bkPirBCmlc9No4ZJ+Zi8AwUlL24R/HMpHRkGXk6odHR0d+I83We3x8rIiGiEpAVVWdnp765GFVYXTMYViJHePZ5Ylbyslcl7CjyYlHLkQD3AFYu8B6/cUnIRnveezYXw0RUewwqjUyatfXtggmJyqzsTPTckmmVZrW4EZVx5SvdFSqZWsCim6uZhTgntCnj9IhKVuquUeN5bnEANukNMTXzwhLqJdnRdNbNOLcV14b3p0REHOBGO4TlF6yAGDZY1Vp9aypV7oy3qvog3JhjImW/crFnhtCaNtWQxctQtiGjUVgRCfiPXz40EqkL5fLxWKhwaJIN5fNyqXIjx45Kq+Z1L+v81ptMqM9HYz2V/e+SIMhv4+2nsaGnuZhfFR74qRcgMLuBIULQEQEEsGc5fHji5/8yZ9+8ODR0LpWRGazWc/MPa9Ws2eeOTw/n3325ZZCaqqYUqprpAqZxRSz0cZwRkBhEArFipJlKEyhO6QKWg8AFDTKtENEQ2ooeGzOHSj6iN3guWuHkuN1kXEEKY4lDmUhs4lSKTaglKfkDqWaqIpdRweHsRRdI8BcGjV329aoVqzTBFFVuvXawLzEt3/ZjOxcst31mohNatbEonImdie/qXgwliurQY+cIv5qtfK32XvJZfaLy9Exm+5OpC8La/UtNJhISnE+YxV2JRnkJlsJIirr6rtkXFUqlZRGceUQmqaxih32EBXD7TBgNzXSMpG4k4YGekutqNLU97nrNO0RALbbrTiLlogM3oxY2zM9QmnGn4+x8KKHHWAqKBHRZrNRC5SNWUrWhO2F/yWEoAqmuhp0nbuuC83sRqA0BdD6QRQL3Y7khpVxMWI4LqDmt9sEq8kxTKNiZ6AONHIGDT88cmmzSmkErMgx1BQvhuy+TzpAzlDFhoQuLq4+8nMfjSFg6hMgVFUDOW/7bjabrVbnb3r26Zc+++nMXVUvrjebyNQ0Tddvg8dRXaDU55xZQFCABFAQkRG1ySWBDl000BJQcs5tu2GfkVcaJTBz7gYbkN/1AS9KRcphdQKJCISbLdD8BsnDdgMUV6N+8uDBA+UBKmaRgZhCUGBS8so5a84EONEylBoSIQTctVHYxR+JiOpHNww192be9oBifKX/NLZ5+d6rMvZe6TC85dXDujZfmFA/AKzXa487xl2Rpm5B/VzLHIeSlY0WVhKC6i9qolosFmpFtqQZE99017QqLI5PZh2SigxWXMW0GNPfYSytGJpLSSAFAE4gIsAizGrh01dUdWV7gYihQFbfMSJGJAyRqqHrFwCsFkvzZuqlYMRti0WCtvgYLJHMucRwmUgyrGqMemJhqSOk2YLWOsCwowq7s9nMuCorAYBWKCOndTbVrtmHbb19RbfGzGEAoPRiO+sH6Q9IgyooBmVx+iOVSqo3namyT2z2BI/1A0kEHOJ/RAQFCQlIQIQZIUpKXermdbNYHQpz27YxVqHPxIAhhCyRiCPU80Xzlre++Sd/6qXrTRtwJpJEIlLQ1zESOp/uIB3AEEGIAQQCOWjIOYtQCKGqIzN3Xdf3bV3X2jSYaGhNpodAdj0LzOtCxcXLLtRAsy+QdgGlE0SYnIR2j7ioHDufkyvwaIwxm8227WB3Z06bTatEICXQbO9dyiGooyAiGKKu1Gakd4JWGhggu1jNaHgLewLlEtNo8Urddk1O37R5WeCfUe0wOxhFuttPdcPz2DJFRHUctQszcox7kqMRov6uRWk2m81qtdLscZUXsISSKBtb1Q0YXxpJRK78uQGWrZUtODNvNhu115ixUkcSitNGxqLcjgAEhDnjcKRFwEABiSBEjpXRHjNnmEbAM3PKuwNPn2yCiacxu2wYpovpd9WkoBM0uTKE0OfknxlLxfCLi4u6rmezmeryinR2bNimK3ArqVg4hVdZbLtDSbBVzJ2E49ihpUDsJW4bm5etcCwQGJl5DLV/jrZG04BKB2+kgIgCQCQRKUPo226Te+BAKJwkEkGMgZlAGDHEGAUrRHzb295yevbT65cvATutmJJSIiShXTiGlGZWuRSNEYrIWXlECV5yEhEgCgjIiJwl9antmlhRwEqbJHNKSIlF0s6zRkSBRvCvy8Gl9puGsGo7OLvBlskzFTipSkVrzwD6p4ODAz3bpYTVqeOcWVTsur6+fvToUdd1i8Xi6OjIxGMzBuu3lrPZsCZ937lyNDYYw6BB+9iTFFKpoe4R1q6zs7Mb36tVK+wttKstdQMX2WPt65b+ncdhE3a/HuzBJRXqRNbrAUDrulY+Wa/XKSVNUzdBI5dIRRxLVXaV4jxDIWY1PKsug+OT3DbIQNB2TVwqOCGCOOeyAAgEJA5a9FhYGARqARLQcLZkpoMhRgeckoKIiIGCK+LmMasqte1tkHqZaANFklUQXy6XOsGu69T2p5fJ2h41uHSo1XwddrawCSjou1RJ9yY2ceYRKqEk6te2gAz/KBswjU2lExO7p6WB0VzOqacT23p7/rCtQygpICJphIogZM6dhAarUIeKhLlvEwEzc0SSEELAkDkhhKYJWSqR/NznPXN++/jV1y8EuqapGXLf9U1TYV9ioIEABMQ1jEAiypkiYkJE0SCIXo2plJJoLQdEbGZV5h7z4Fzr+11osoxNDEYBxYsx0vukFOTT38mZ52lcYIzHqrgpKeK0J3Z9ChQC2radNfN1uhYNiBVoqrqOFQq0m62eUaKZYiIau7uvR+h7q9LXT5zFOuecC1Lw2AhqNlfviySiahCActf12ZUNCCHArpIvSKmEIak4TFy4DCKSnmMhBtz1WBO4IXzBFsRYDp2Uao4/XQ2DP2Y29a0qtUktf8D43G+Z3qlKt9r4cc+LIkWSUuO6+XmxWN+knOTZDX6YSNhJiLbmqe8hCgFiINIwEUJEVCcMMycZlK8+p5wz4U4Ko9IPhYjSuH6h0Z6VtdDJ2iGBiNqEQvVoc9KFuKv4qiKSkooq2uiCaWRstDLZXJWYNM5bsiGFIQV9EE59mKHRHow9XcZZXrQ3HXwHVcqftINy41Aeu0eMWwFA+3siAlJAIsSQk2aaQ+4kVjir55z73PUAoY4hAnCISIkyA1GoY5Wkans+PTs6Op6HyD13Vb3okkiGGGa53xgd22GLnDdtN9BfaYErGAAARSJGwpiZUx7MilW1vLi4kNJbTY/Srks55xh2p5Yn7jBkOw8mANsDdtFYnrhtdcCdG+IuL7L1ff/kyRMVufWIM9GDiK6urrbb7Xw+Pz09nc1m2+324vKJqoSkNRicefbevYemQYCzthKNAuV36Nm17Iy1Pm3IM4apxjntUitEJBCFUppywp/69b4UePMXALTFFuN/ypCvv1PBzMJqdbJMF1Bt5fj4uG1bTVeGEr2RS9SPLqBKW1oM3pM+FYO0MQAi6l5sNpuqlCSF4sj2vJedT0AlBSUA8oeZcQ4OjpQYI8VRkyvpB0d7wEHnhUA6DH/4xRiBMISggDVBAXFhNPY5FduCFyik+Gf0QFJIUoFan6PezFQuE8/Vyq7nX3RV9insnIzGjya/ewFfBpflYHozqAUfBelQ3h9U4IwGNFaB2am6IoP0is5VLa5ixw0nEGQGQVD/o2oVKCLL+aLv+5yZIGPmpHFRdRMBc069iFRVkyV1CRihbuJm++TXfcs3/usP/wxLDnGetmm+WPR9bqqKmfvEXHQZZXjknIfTT2XpYbOrEHPfdU5oyn23xdDUtZ4wqsbrVANSXe/c9o5RYz+UrCUzphBGItrmPpayn+C6OfWuB6c/OuxkMIu1rrge7JvNZrlcHh0dHR4e6s1d1x0fHys/5Jw1QJmIrq+vPS7Yz4BU1dEOKHGqCnPO/S7F1LBDZZOJuOF/MUQGF98Qwq7Up4yLNXsy8uDuX+3N/H6VUsrGXV7CNf1Oidh4SXUc5UMzIZuaxsxXV1d6OOkzt9utajd2YOhcZrPZ5eXl48ePVQrTolFt25rXjMe6hl1932sasK5ALAOW4rLoUt91XRau65pKNdqqqoAwC8S6un///nw+Pzk5aVNPRMvlsi9xFRAIS5NKVI2yiEu2SuCkjB0funjA4FrV2zpr4qS2INIM5xhj3/cYdiBuJ2sIQb2oWEJS7Xd1Xqo4H0IoNtYB+LwyoTyiSqVCvLEMF5upvXpyecnUjgpx+optDY9NHP7kM16zBQEADMDq96hIRLq2z8KRKqJI0HNKghIjLmcVS0JJEVGGoFQgQtLIekRB4tk8PPOmw0986mImKUZKjCFEkSTFMjLMBsqB7GM4cSDHPu0iqkF2vKeHako7h2BBIpiskV4lLGAXAIEwmHWNkYxubuRST2F2g66v0j0W8YGLTSfnXIcYKKiNA4BZQInk8vLyxlcsZnNDkN3E94ZhlzKkn6xtfCF99rxQhdLy2hnI7I08FrwBIJQcbHY2F2XaoaKkHhWAhCQgm37nBUMXsGqwS84HT0SqGGoISHZeLY1NU3FAg4Z0avq5HlG6d7rvDx48UFkjuMLTb7SP4DqA2KT0/sfX11Tq5AEhMwNhrKv11VWXUuKcUsIYNKOFiB7df3B4eJhAXr3/uhort207m80gUORsdGUS7rYdBQab7JBK63m/48beNkgpvj9bQCnylD5f3JP9USRj34jBgZHxxDnjH6LMpavt+cXu8aDDxctpX/RHsr3d5ms8pVca52Df+Lu/hs9ppw+h7MoQgYjkPnGGnAklBlLAQsUJQiBBILWQp8Wyfu/73vmRj/87lq6qmnadqjiT1E/QRESAE+cEg2FroKjyf6aUOUba/T7o5GQticJud4eOSYiwK1nr7MGAiBjCVM/yyGUEbb9PnGvkqm6rW9Bkdd3FqlHDj/I7A7Dab9Uet//8lDtEJCGSkfF1CAMaolR2DFaXTsV+C0UEbBbOygYAAZ1n1nHsBLlslTxRihPEJpHNtpuUdpK/Lb5ttDFAcD0RzMeUc7bmoKmUhFVxRgOvlS1tQ1X6UHa9vLzUURk0wNh58jnIXZwOcnJyUjX1ZrO5/+ABBjo7O6MYL6+v3vGudz77ec+9413vfPbZZxfL5WazefXVV+/du/fyJz7xoz/6o33f3z4/v//k0Wq1Oj4+/uyrr56f3VJBZsAItd/Dbkknx5KpkLbsOA4OsC9awC247pbbbTGJgkxABPcObw+Ig+/YxVsp6drOGlqpFSzE2kOemS/EXcoUoUQUGTJiOdGLfUYmWAYAmXdLtL9ZnjFt1xR8lNZ328qJADAIYZbcSe6qulrMq6jNPJARiViQCDBUGBAg1034gvd8fvMP/13mLtBMnU7G5/ZoY3gAKM1adxcigBCIaFdCEEQAEQ4hAoBWjEbEABgxRooYXbQ3js5Ym+ywQPo+QX/sSzEQemY2RvUjRydyS2nzNZ/PQ2kaPhgLczJt32gUXYDfZNty1yOiEInTCmGcmkPoO7yONMoJHXCxpJrRCngXEYNOAvIEYYswgUKjfr9c9rn+yXusjOWYWSPLjTT1IeBUbCnOBLXL2ATVRahlGxTCFL9UZNaifdvtVmU0Pf/B20QccfuJxHFrdcOILPzg3mtVVT39pmf6nKum/rqv+7pv+c//s3e++10p581mc73dAKGmtoSqunr93na7/bt/9+/+9e/9a4+uLyVQAjk9O2v7LgvraElAAAbFkEYVX/1m+QH7VfVkwyVKWcevgKUo7xrKTm1hdpyD6zykf1LroWqCPoJB7fpKutbMIsaIZLWth8tMjf5UM33fApInYp0HMj93L23tL4h97hkQyvlXCAwBGBEoYoUIAjn3SGkxrw8P6qiuJUREDAhIoNhFAkwhPfvmO3fuxvuvJyLtnTeEGPl3c3GxAQDuARYjARS7uJ5SO9QLiDhEJ5hJLzjh0KXdFHebQyJgAOh7NuYBdwj4U87/hL3x6+VD8jTcTo+dTep1y+O4bEPwPD+KqQvgcBzHwvw+Nec8jZ9iZyXlveYXmHtxkg6HwK6ZmPGGCIkQIKJE0j5TNJC4rk/qdyI9ImpzEpRBAiWHtp688p7Ar0xif7X4IAtrUPQ3JWi1WqkYy8wat6WyiYatR1cfQn+mcWE5W8MbMRcAXr732pvf/GYAeHJ99aVf+qW/43d+15d9xZdvttvLy0uMoVrM5lXIOW9Tf3n/9a7rnnv6bveYf98f/G9+y2//bf+vP/Wn/+EP//CT9dXLr77y9Pmdyhx8sUIA3RuKO+D26xNcQIy/YXLOme6mtxUayBaMLiUWH109Eix16E0asil7r7RV+NG/hhA8VJnFytOVjSSO26OJE0RMSQ8ux1MNl3bnRJCENwZ0GR9CLBK18i4bkImGpGNISIzcJ2gryrNGVodVBGARFq0cgahOXQAmCsx5tZq/851ve/DgoymlKs44Q6TRgHZjlaHSzeTKbP3dAqKABEQG0Jh1BRosO0tEFIIDbLGjxkI9PWABAGQYNsBQyUsHtt8erW4ELB4iWns9rKycA3dtjFEz6U0eDq7Quzi0klKaecLVMPbi2elERDntKiKZIiAjU/2ovVjtTkJvR/AEh06JqEt9osnKqMPObFU2hpSTaSt+GdVTrthUuQ43eRzBrFClZK3iUnK1p2OMWjxT0eri4qLve00w0NtsVHhTQZt9dpLxAY6I73jXO1944YU3v/nN3/W7/+tv/MZvxECffv75atZkYU6QOHddJwjVrKkWs2oxe+XB69fX1/d/6tFTTz31u37v/+WpZ57+oQ/+g+1m8+T+w4pCSknmjI3UVVVRYPAFYtFQFcZZEx6wJmKprVUqacCGYkp7oYpQzhVwTiSbrGc6jz67yL6yC1VVqalOwW4gpEJl/hTcZw10wpQRraU92nttJHZziKOh7u+dfY7DWT4YTFvOnA3LmDkBJJIMvAVJVHHd4GJGuz7gwihFQwGAGMO621JYfMF73vXjP/7R7SY3TdNuRYCKFWUnWIFkPzgS5/Tdae8ZIABmkAAgxi0go0IxRp8yOsRM6NiRpspfVawm8GQqz4Tc7bYbeUARytLWdoJ37tVupYmQ9vz9BuI4FCgcBU/47bFheAtRKk0WJgeAxeJ70gSA5FI3JgK5DtgOUn1sgFIC1cXFSGkmbPki9ldPo+isMKZumI1Jb/bYbWiuOGW4Y/3u9XO1tmh5P2UDKy1g6gaVkCUPCrZQOG6b5hHkIx/5yK/5Nb/mN/+X/8WXfumXdql/9bXXGOFoMTs5PgPCPqfNZtOmnkU6lfs4L48O501zcXV5fHj0B37//+2rvuqrvut3fCcQqibLKUPKsFjMqjrG2I1fZ5TG48sGxuMyk/Ytjf73RK6CUlMCpaMrMYaIul/oWg3aSqZS68q2gIiWy6Wa2L01Q0RSygZY4mT/yWVj0y1QKVhcMJAvU2PkBztmHxH/jb/43UREABIZDmDRElrCzD1JpsB1xMWsmi+cIWDCxCEib5mRn3nm6RhRJNd13W5bRGSeVhry3/VoBWMX5oAzAgDcNPMYqxACZ1X3bJIj0rRf7Qk7AgVCxNDUnq+giGpeefHsN1lWu8cbAsSHILkYdH9a5nF7KHt+Kl45GZ+0Xl4wtELE7Xrt5zs5r2x37Dld3gUomspgozIotK9w8Wrb87MLOve8JEXo85Bn5zAUx5yPWhCRWYnsl3HEL7o0Iy/HPX78OJWC61AEE61sp1xnqoeeHKYk+t30nOMnjohf/43f8Nu+4//0NV/zNS++9JnPvPTSrVu3Do6PnlxeqD0rVBFjaGIgoi6nruvWVy0Qbruuz+nxxRMiesvb3/Y3/+bf/Pbf8G1926W245QjYEWhohCQMIzMo/ZTDzxj4H3O97vv99fWX8/Lumyld2tAMepHV7BQKXaz2aRSygZdVJp6PL0SNwHWCc0YzZOLdRAn6eecLY8HS9gQuPO4rMbnwib/iewU/4AQ9KQvbA4xRi2poJUzmllsZmE+q/Ef/+BvZAbkGKGRjDkPmY0pS7M8fHTRZpj9D9/z1z/5qdc/+0r31N07l9ca37EL+RcRQHYExH5YjLviJ4SWjzacFYpZiCg8BJjUBwdqptXqsSoCaDUlow9yJiqRXaKproIOzBu5/S9GOp5R7XOjAP1TzrnGafaDjtPLJqYqImKkEe7Yizxw658GZYp3WY12QckNNMKyvfSEZVxqszChxmY3D6Oa957HTCDygGUgBQVklZFiKZpqnfXMeDxs9JgNlsuleq+YWY1Wm83m8ePHKilQiR0xODs7O+O9nDUienJ9pYSFMj4eAl1vNqvDg8vNOtZVQrm8unr729/+Z//q/3R8dBRCuHz85OriMiAt53NLlFseHGy7VmGr67qUc0Pw6NGjxWqJiDnnKsSc86xufvYnf+r//Dt/l6Tcr7ef96ZnX3vp5dvn58vZHOqZ0QYXSyIRaTL5ZCsBQJMWOWl5VYwx1lUVAm63W0m567fb7bZvO0Scz5vZbHZ46zaXJAERUQEKSxw/luIZXdddXFxcXV2tN51ZzWez2Ww207j/6PpaThQ3w0d7YFV6R6JzC9h3PdkYE4Vx7qT9ruP0hld9Qt/vbK+eAZsliCBnSll/AQBA4ABtRW2ETYTrxQyeOq+ffebWrbMjyxyeakkiQgQ59z2Hd7zjHT/9M59dLrBEl93cMs/GjDgS1+0XP2GNCQxhMOYJY0pT1rUv+sQ6vzqwF4ArRV/wnDlZ08ml39INyzlrKTgqATKYdireDqBdOZfJvOyw8noBjuN0xMnS1jh2Mk4qUeAwht0YR4G1dgOPbTp2f1UKaXoMlZsqncIeXaI7Idil2kDRFNTyalDuaVGtfovFQvfC1EmFQuMWLNb6q6sr5RyDQn1yCEFrWmkpq53+QnR4eLgteQItp6qqfs/v+T3z2ezRo0eS8mI2v3V6lnPmlHR/Hz582KV0eHjILFdPLjCG8/PzfnN9fn5+enL6/GdeAICmaT71qU89ffept7zlLb/+1//67/0rf3UWqs985jMHs8V2u13O5jnv+kjaxLGofrr4fjHBueo16T3nDIAelA1BVGhCp7y7vCvwy2Wh8IbvdmxPjjHbIJOYQqkzoeRk3kn7lslc4E7KCUO90TV5oye8CZ2X6YDwYMyy5weQGKLkrQDHKjRVJqJKA/re6MVUJIWU+i/8wvf9/b//L1erRbvdCkYBEARBAMLS0ToIeFYJ7jnFfwfBOM3M7UptIEQ0IH3n9AudpJ7AOU+LCNrz/eWXDOBm6dSe4xkMHDIqn+jYKO9W3y/09fX1jRsQ48hhZ69L42oKdtEbFGBLrjGnF6b0/PRv9KTpxV79U1M6YIM7LXGv7NFkIkavUNST7Cp/cvF5a3A2u9QiY5u+7zXViYs3I5diDLaYdqQz88XFxXK5VEbyE9FNFU3VdIEpXdetDg8vr6+qqqIY1uv2N377t3/gAx/47OXD4+PjikLq+ovHT7brNRHNm9lyvjg9Pc05bzabi6vL+XKBAC+99FJNcHp6enF5wSkvl0tmPlwd1HV999b5b/2tv/XfffjffvRnfo5YmHm73T5+/LheHRq3Y5FHpPhP8aYD0m93SglE1KiCTsjVRUgpaSaALYIXdT2uaRJi27Z6LAZXemGCVjx22oAr1eBzdLJLTsabPB4TwJpM0P9uOJX3Yps93Q67n9XpF+yxhEKAVaQ+ZUSezerFAuq6aprm+OgoQjm7AMDsWAJQVVXbbmLAPvFTd24/99zdF1+8L1kgjKoIuJlYKuMYhoeAhgA7cIk4tMwG1gobxKp723Hqt2fCmRMGm4C3Z2C4Cbz20QqKUdP4TX/RhY60S0ewG+wI2j86CEa2p8mmTkYlIlUcpePY77mk44dx5LeHkgnpmCjuKSMUEN+nlTdaVf9AD+X2rYl6u5MWC9uYc5CLLc+cYlpE1AxYUvLj1IAYY1Tji1bjVE0HnZ3UxqYGrxgjxsggZ2dn3/Vd3/X888/nZT2r6m1K7WbbVPX5s8/mPj158uTll18GACB805vfHOvq4OiQiF59/d7Z4cHZ6uSl+69YFdn3veu9n37hUx95+ZV3vvXt3/Zt3/aHfvy/efapp68ePVktl48ePToMlfpbgmv1VuSmG6yltuPMrPeCiBpeNAxVacmeEPqexkGOdoN+aHFbg7ZIlS672tfBHUsy1gmoxBv6bVLM1SaG4CwqduCFcPOB+kYX33TBWFJDZ9XRwYoIMBASoARAIogISTgSrBazw1VYzWm1mB8dHUSUG2IRACDU1eWTy7qedylzbr/ki9/38Y/9szDO5J9gBwAguiwh/av7xNCKiIIlbfJgEdCONWGxgBKeaxKvRZFM3ivO7OKXw7ZnMjxxJmpxFj4ox04s5YZDqVRHeVD7ZSx4G0H4swsALD9pglA4rhJj9xPuoGSiphlaRdfdZ+KdsQXxI7QZIaIUaIDifQsl1Qb3Dkl0bikbJ5VqGbbOFhOkyTQmW1kNACqVY6+urmgcMKXf9QqvnQHMrJYgbeEVQui6jqWkQ45PHY3bquv6ut0yyK/5lm8+Pz//2Mc+tjpeLRaLeTPjlNvNpt1sReRodfAX/uyf+9mP/PzJycmf/H9+98nZ2WdeeBEIl8vli59+/tX5q88++2zXdY8ePLx9+/ZP/txPfe9f+at379w5+HWLr/7qrz46OtJeJDlnRri+vlaEtVgBKE4YvOmaiBgikkQQtW44mbCg0Iel8qo91iRrKXaG5KpOi0hTEm58pqHfU6Ml20cjdSr99NgZtuzMCyUib0IkE4aa/H4DR4xNEP4Cqwo79OuEAEjIQZgkR+T5rD5arU6Oq8MlHR4eHq0OBnGJindOis4tkgVyrJCQN9dP3vPuzz89+mcXV5MBj7ADEWHX2HrgOtYsIR03BiFEIgFqew3IHOr7dF3SqvsHdY0u6MNqv01f7JaJS4Usvy5+of1obWNszJMVnNyvVRaMT0w781sCzgOixexxbEEwwPK0O9zDU9VPR6KEdWNWnTg53w9sMkc/U3Jtr8iZYPzIcQz0O0jVcybG7BI4ACCVhjdSunX5kJ8YozaJsBBtG6flA9vzq6parVYa8m6W+DgUfkqD0D3eHI3y1Qk+/aZnvuM7vuOVV175FV/8Ra9cXz5++GhzeX1ycjKbzS4eP8ldf3h4+MILL7z46ec101gb1WThxPm7v/u7iehP/ak/pea2tm3/4v/wP/6tv/bX/sh/+98i4jN3n/rVv/pX/90f+F/Oj0/1u5pULKWv14QA9nnSH07DbQAiikcCu/KujLiLqEopGbL7neJSR1T5wmt2VAJBmEcmZt0+QyhlK/sQCtpaZ2wTbO02Gzk6mdEwVMYHsycnP3j/iQcNYEQI2p8ZUXufZsSUUzurw/FBfXK0vHU6Oz5sbp0er5bzSAZRQCCo/2CEruvqOgIwS8dCt05O3v2ut/34v/0kRwFvwBreTCKaeIsw/BcK9xgzGEfaCT9IDSFUVTVYyjVZX89PKKYcM9a+0SUuEVSfyWMjtC2x/4q/wTbDvqjbTzDVnqQ0fbONGdHlrtniyABpIpunYMQhIdwAJbiMFnRuZigeQ/9wHht6DfL8X4M7YD2DkQt98JQXSryi3RlKmzyDRaXpncpWYEgPeQ1HoFIv3w5eeymNM9R0plqk1JZ3Z8ITwmGco+NdzWTK2F/+5V/+zre/9dOf+ezzn/nsP//Qv/qZn/rpF1988fPe/OZf/YGv/5Iv+mUgcu/ePRA5Oz1dNLN2s3304MF8uYwYX37h+ec//elXXnnlB/7W3/qN3/7tZ8cn//CH/+GHP/zh9//yX/51X/OfnJyciMhv+A2/4R//rz+chGNVdV3HODgxVI1CV0HQE9iOvYuoOqHYnLMCltPiRflCc5sMsExbhFIKSQVbO9VsZyeiDTh5wmjMa7JY3J0qLMtYuw9j/7Lnml+QoZRKJ0IDOk3Q4VqltcsQETgjCklPklC61SKeHK9OjxZnx6tbZ8uz08PFrLnB6M4IALzt29VqtWk7zl0V54TdO972ph//0Cch7rkIxWQKDVsf/ivn/Ojkx5KXM5stACClpPo8ES0WC0S89/BBLBW7xRXG9yy0v472uaeY/TvxDSQ13XvvCbYVz/1I1LcbLP7IwGuQO7o0eZfSrrcl+/eGnY4c9lU2j00WriFvLHIalViwVanFM0ChAdlkePaJnbo2Ix2YdnmJpWxeqfazcyNSsaBrKq/mHtoxbo8yo4wBli6OmudVxlGmNZWEBrzbHd0AUNf19uqKmaum/oIv+IIXP/vKm970zHd+53f9nR/64NNPP91tt//sH/3jD/+rH/uvvuM7vvZrv/bo4PBwdfDiiy8CwMFiOaubvm0Xq9Wbnn7m137DN/6jf/SP/vx//2ff9773Pffcc3/4D/2hruv+wO///e9+97svn1y01+v3vOc9zz777POf+vQzd5/67EsvYRwI2CBVLV9eEhnRJNxMgczMqG4nc5IMn+fSyswIMruWQhYuoILV6F0yWiL/UrvsIOHi2E2llqQBFozd09MZ3fSKG0nRyGMyDPDFghARAkDWCBDiFLAnaEm2s3p5sAjLRTxYNkeHi4PlogoQqxjbbWbhEJGIes7AkEVLslYPHz5czOrtuguxft9733Fw8KNtDkR0eXk5ny9ns9mTJ084c9M0znpVBgoBAHrpd2OlQMNRHxEtDQgQUYAyAyKoKyfnvF6vY4zaX1NKysv+2oUw6kYzkbMmqyYu6CGUrgFQsq5sR80qrP/eX3TY5TaOLgDQ3oW28VhkJeVwwyN7TlPHyWj1c21Ezi5cwCoieKKxA9anzmA5yqqqkmLt9vqgRm9wiYTyqqXvvlNK+oCFresVSy0tKjXqlHOslR6WBjbZVTFUuWmxWGiINpVg7r7vNaZBxepHjx5tt1tDxoxDoQy19tgYUkrL5XIm/ODJ4y/7si+7c+fOH/7Df+Tf//t//87P//zv+I7v+Mov/4r/6S/+pR/9kR/5f/yxP37y5/7ct3zTr71z6/z/t9miwPOf/vQv++Iv7jf966+99nmf93nf+uu+5SM/9/OvvfLqH/sjf/To6Cjn/P73v/9bv/nXKQH0fX/n/PY73/nOl1/67MXFRTObMQQTT0zl4ZKLx84TB4NEubNGDdZlEQAkImHRv5aky5RzTtIpqWvNVYu60l1br9fr9ZqZtdBr27bzRZ1KYQwjHnA2WTvyNXwhpaQGL2ZWW5jKa74elmr3moEoe3qfTdm/0cPfPjzhuBaYp5zNVVdXgQRS36P0MTByz/16tQpHq+r8dHl+enB0OF82VV1JU4dhQCQog/kbYJgz59wTcYgQQg7SL2f4ljfHVz/FARBjSMxt3wsiVQFjxcxa6RRgcH+IKlOiB5EPdAwDnKlR3lnlbZ6ePSY7AWNJ6o0uv6CGVjC20ezDzeSfABCLYKwf7ls07S2T56OLI8dxGMFoI4vZ1X8I44QJE5ekuJxjafbLpXSXrc9E/rK5s4sYYlej2aMV7FVw9QSHJdrIw9DEG2jLYkVO/BNCCFa51GIjfYisTVlK7iFzJjQ+H1nWrq6vMdDdu3fPz88//OEP/8RP/ETXdb/jt/zO3/abf/OmT3/+v/8z3/wt/9mn2u7/+5f/ypf98l/Rdd1yNl8eHtR1fX19nTgv54tPfvKTb33LW37lV37lRz/ykaaqX3vttafv3P2vv+t3tm3bbraBSLtSHh4earxS27ayK5c0GPLEKbl+zQci/AUEEUBEGOuStln2TCLSYfu9ptLK6EZqxGIIjq67hEmCan/cZVx7t3KR9D0V+UlNCP6NxDrPU1Akd5ud/TXGWZ965A4hEzJCHzFRxbdODm6frW6frW4dLw5XzXxGs0h1hDiEtzgBRJePEDP3FKCO2EfG3B0sFl/03nd96JOfVsMBc+46CaHCYmMa0ArRjFMgQ8iVA/swnr8vq4AAgAR++WBcysbvjc5ZS57ts+jEawPj8wH2AMvu96tpY7MP7Rcf6e4vBSAam8BxHNjiRwUyss1Ndh0K1qj0pL9QMcYH11rKTsjRYEj7RA4TlHHVWrs8rBhgGcmiUxX9/R7FwJ2x+pXJSetVDD3DreaffletNlDEDf1uCEG7QCnn+43TchoIol1FP/ShD73wwguLxeKXf/GXsMD2er06PvwN//mv/x///F/42Ec++rM//TPPPfvmf/TD//Di4qLbtsdHR23XqRdyVtW/7td+0z/9p//0xRdffPLo8Vue+7wPfOAD6/V6sVhUMR4sllWIt+/ciTFSFUUEaUc/5oWYrDl6NeqNActoHsabrrsMRTxX+NCQN4V7Mws0TQM48oY7sWBUPNpuCKV+hopXvesUSeP4LLzp3PKg7KnI9r3a67iOexKZJ8JQh9RuJLVNJUApp3VTpcN5defWwVO3j56+fXzr1mrWUB0hhkyUoogQEA9DISQEYEYhEuYcI1IQgpRSqpr5u9/91voffWa93mp6fd/lpql1xAgEQ0epoE3klX1ZW8DFSDESWv1pFBrQbXAglhgwQtgXRmzV2JnS7edkTScra2zvf5G9y5sJjCdFJHOe3Gn3eBo1KiEXiWbkCw4ubdvKfHEyMKNaw6nkep/sXjSOgfCBi3480Rm/ZXwkOraZLguVeH1/g5+CQRUWuUxcwKE92YZhX9QwK2MJ40wrnayZBjZCIoLBoDmqkHV9dX1wdJSFletefvnlGOOdO3fe/573IsLx0eH1uv2GD3z99/5//kru00/9xE88+8wzBwcHy+Xy9Vdfe/zw0fVmfe/115977rnU9ZIZMnOf7ty+/eDBgx/9lz/yDd/wDczcbrdKA2dnZ6GKIYTZbJZ459jzlKB+Bot3MVqFm7zVk8UHRw/gXHVYzOpQCodp0mWM0drTskOQMK4DY1KCEbbeqYER6/VaPZ7kUtYtmSG4TDjcO0Rtx3nPW71PWlAOMA/o9ifmlLkHTiKS+3XuN9UMb50cnZw0Z8fN8fHsaNUQ9oBZoAMOg48ThLJG32qxzKE3PVeRCBJC5pyFN8/cOTu/c/v5559Xb79gDlXst1vJGYnUfooUVagaaFp6IgpUEVoZSUtetSB40lYuAKBexeAUMVs1j032E95ACdpXKv2e3bgBN65417V+3Y0hDTXI9fhFREt5mwyAx7Z2+51oZ97ex1ATrLBEP2k0di7lRKzQoK8e4YGenOyMDhxthT3j+eFNfk4i7+2EpLG31J7vQVOcZKrRdliMGrm0q1DnlwKZdQwDAKwijlPZ9fLhZioy4BCNLBGgY1kumusLOjk5uffqq1dXV+fn56fHx1nkz/yZP/N9f+N/phjatv2//8E/+P63v/17v/d7X/j083VTLxaLF1988S/9pb/09re//R3veMdWZLPZzOpGwzVCCAcHB1frVorlW2fk99fv+LDsb0BvIgKWjFK+5bde1zaVRplXV1ebzYZLtQwsOTpczolQwkGtDpJRFLucdl1nizs1Mo6lO6E93/ZxPObhUrL0VgUsms2NJGRSiBHb8F3otQ8OSkr9JggcLOs7d45unayOjuareWxqRhGWRIACXUQWIETYacuMipeqYVLuegKJxKnfLmerd77znRo0bGu0Wbdo+W2oaB2JSOUs5n4sCwzdqHa6IYys2uA6x06EGmMnGcuo+yeA/dUuLx1M/qQXO7+7v8e+6DnQkMvD1vAJjnIbbUhEk2kOF3MyqvJHNzixxXsP9YZcqqfDOKbGyyAmLL2RrDSZoxGAX5YJsYqLdLOAQ/siuRJxflRG3ABgnZ/tOcYVGsNtlM0lLAMcV9t4Dg8PL6+vQxVXq9UAJVdXFxcXDx88OD4+vrq4fOr22axu5lop8Ho9q5urq6v5fB6Qctd/9KMfff3B/Y9/7GOvv/DCP/h7f3+9Xh/X9cPX7x8cHLz0mc/86T/9p//Uf/ff3bp1q6IQALRoRE6ZwuAT9DRjmwUOICar90YXjiUsEbESyViK8+niWN44lvN4WFLlpRLsbtk5dgOXaqK61N7QjsWSqCZ5T8a23WFc+s0+97ZLTyET0czWIZa2QOzKZwNA37cIgigsHaAcLuH2+fFTd8/unK+Oj+bzGdWVrgrFIIR5kLDU3i4FrVMWZsEKCUIvm1hRkJq7Huf8/ve//8d//Me3mw5KPDoRNc0sJUYIJmsEGiJEch5VyShoVbR3MSAbTFoq3CGO5I7JT/8V5umHn5tQ7IYJYPkbxFklVHMhd3nAmjwBjbHKNUFbf0bpld+gUJ9Sqgc+3WPt2GxDNTeQkY7HIwDIheCMJfQVZvOa3O8Jzu3LYPvwcoSJSJObxRkEbUgWz6Gq9wQWEVFLYqn30G90YeGpPVvNpvWsadv24uLi3e9+98HBwf3793Xd7t4+SwkQcb1erxbLq6urD3zgAx/84AdffOkzGMLp6emXnJ48/aZnjo+P/5fv/d7tdnu0Ovjqr/7qtu9+9iM/n1L6l//iX/ztv/23f9Nv+k23T04B4N69eyEETgPz2xhwzxYBLuLsFyRFcIBln0xwIZeOFdfX1yo96Rru5CAkr8rZmWEk4TdFA0Sttk8sLS+JyJu6PBB7l4gRpzibFDn3n+wVsLSttDgvE8qMHgJK5p5zd9CEu3eO3vTs03dvn53fWhwuKVYikhESECOBCKsNCwTQ/K/qB2FmgEBBUZawom3XAstzzz03n88361ZcqkrTNDlvCQlx5w0cjkoe2WiMoYcIDPC8oTLCdKr7v3v6EEvdGP/VL7p/AjmTyo2ABWPs043EcViAB6zJK5CmD/QbvD8eL1l5AXu9XlvVZt1Xq2Hg36tfgV2TDpmce2lPX2PncPSPQpdCsb8awXVXxoLjIQQL1yDnG2FnNoZxgXbcK1Ggf5rP5xrAjWNtmp1W5Qe2Xq+10M3rr7/+2muvvec973nuuec+9rGP/ZN/8k9+47f9HxZ1RIQPfehD19fX8/n8mWeemc1mf/JP/smn3/TMpm3bvjs9P4sEf/CP/rFPfepTt87O1pvNd37nd57eOvs13/xNV1dXb33rW7/ne77nfe9739f8yq/q2+7ll18mIo0U2+Z+gp5YRJ59SQQRpwfUTdeN0GbPsXABizNgZnNNCOwsUP7omiw4u3L7logeXJqq9zZ6XvNQZZQj4wRpdGZ1BSbYQ15ti+eXbph1GIrNEedmVp2dnZyfnqwOFqvVomkyAQuLACMxJ0jAMVbPCCakVFfrzFfcPyaKC2wwz2DdYNWs6lvrzcV194RmcRu3h/ypr//qZ//G33jh5Oxo285zNz8+vbVtM9R9DBQDIHBNWEHMPbftlk4PbeACMMT/cfF0lELjRqaQSe1YDMACIpxz30sG3O2ryC5lKWDWPUHHBgKgteFH7KeAJUEEcs4ZMhE1sSIiyLxYzuxE0hiMYdHjzu+TmUWgilXlumb6jQEAydrwZsBmUEiWIQLe+NmAabvtQghVFRADAqS+32yvu65bLpeIknPPnGwGMWK37dFQH2xBJPelWQAAURFgS6qQvRdKIKiFHXCJShvO58xq3TaqokKadTPDEhrarjd6TzVv9DlZmFMPACwMhM18BgBZSV2YQWt2S2q70DQhxEBBz2sApBC6zZZzBmYBYNylBCAiKF9pGBoMwtpyuby4uAgCleCHf+Rf/V9/3+/+ui/58vuf+sw/++H/9et+5VeeHb5zs13/83/xTx88vn9ycvLt/8dvD7Pw1ne+ZbttT08PNtu2JlivNxf3Xz+4ffbSSy/93t/7e9/1Be9m5t/5O77zL/yFv/D49Qcnq8Pv/qN//PBP/+n3vve9n/7Up2KMGjo760FEOOxKPg0gPvBhEawEMLNkFh6AjIhCHDTflFIIgV2RayGEKgABdh1nBo4hBODct1sOFSKuFsvUcyvdfD6vK02Twrquq6b22aYgQEgUaLPZqIbIzO221bSnnPM29SICiKGqqqaJdY2ImXldqr9Cye9TFOtT56EKANTmoeXqPAvoL3e6AwZO3LbctrxtZdNJy5A2rSACIUSCOlDAQEIAtJD5xZOHkOHObfz8W6tnj+KdFT932hwsgYJUNYWKWqa2zxSpqWYahzU9LnTmN54Ai3l9987Z+W1InAX6lNt+SykzBkJiRsGcM4aAWVAQcXs91DlCl5lJRKCFSZUiOSMiCiNiTqXjrghwBhEkQhnqT03OIhQRrVjPwsiIvqaPns/KvTqjYbntILLZEdHERT3ZCcMs/Wd2tTj8bbaAk9gWLCbS7NLxzNYjpe2lXuops3vAiaU4rofll8KMApOzOtLOtuUvGCu/dj+5qH1wxVE3m00sTectC4eI+nJ22M3iRDwZn9hSzFW21JP7oXi77DZyiUo6UPtEY1BjXf/bf/tvAeDrv/7ru677lx/6kT/xJ/7EF37hF7766qv/4T/8hzt37vzu3/273/bWt+jD+76fzxpETCkvF/Nv+IZv+Gc/8i8+8IEPfNM3fZMKL9/6rd/66U9+6h/8g3/w8ssvv+dd70bEH/7hH764uNCIJxHBOGpSa1Pwi++XlIpKTkQhmLY7JCpPmG6yF/ZYAJjP5ykNRwvhrjnFxPAkrt4ZuH6FJnapJEUllM8IUmX5ydud9D8t/G084u8BgIurh4w5A3fSJkk99kl6QQgBVGbhDIlFEAIKsmRMsYLFCs5vn965c352dnR4eDibzRDThJL1n9GvCyICTJfM1kXlhUD93TunT905/cSnHnZdLwnbdBWreagrFgjIIFmgCnEWIpFQu70mnNcVERJLEs4hhBh25TLKmwAARQBSr8HlIII5IwBpmzCVIArvDgIwEbiy9OA0juyqEXiyAOAYVclSWMmIFCMx7yo62SKIMCIgCpG684BZmJOuinGj50/E4C3iJq7DkIeUbfAqqC8W877v2zZp/j0AUAAi6vtexFBvF6lQu75yHltNIzOiHzaYRlVQPA/cyGD+mW7RdnRpyv4whdzb07wDy07gCaZbPBeVYEtvucNiP7Y4IE+Ktr82nuVyeXF19XM/93M/97Mf++W/4osWi8XsaP6hD33o3/ybf/PgwYO3v/3tX/EVX/GBD3yg7fqm3mVE1HW92WyqOP9Vv+pX/fbf/tvf//73v/vtn680+MxTT33bt33brVu37ty58953f8GXfdmv+IG/9bdM7e26blkvsmtT6k8yP+AdYDm3MgUUkRij5jmTc5RPdgHGZwwizmYzbXAzOWY8ysieack8sIZZSLvYrlwKpdnT7I0Gytx3IoIAWMoP2kF7oynjyfZSEBhAUBKKIDMOtQ8QAIUCIjAOgjuErruoI9w6PXjT07ff9Oydu08dHh2vZvNapAdUrWwI7VRIGElYE8DyJEJEAigiOV2fnsyfeebsY594KNAizIQ5VvMubYiwBw4oRCBYY4gBoe4Qc+KuFUQQCQBBkDjjmHMAQFgAIA64BapJiAiKoEhtnTUBECAABFXOY+0BHoAQg3G0CQg7hpSdh9V2N5TMZD9fT0n7J4xHAa/bb7MYiaAL3oNiy7T6HvpFvdMkMhFh3uFgYeOdFz+Pa7EbnY1BeUeC+yPfZ6r9s33/nNdhYxEVd0wVBxHJYM6vLTioQmcL83dml1+JxcVuFR2mJ3x5jhazftOb3vTKa68dHh5+//d//5/87j/+Be95x7vf9/vu3fsvP/rRjyLie9/73uPjQ31y2/ZNU83rhjRtP2UCOFwu/uDv/wMXV1cA0Ofc9/1yNvviL/7iL/qiL5pVNRG88MJn/uN//I+ayKLp2RGGGvPMbOgMRcK1FbbdVDKaWE4Nc7105lde9i7DIBGRmxLs/VKDi/aYdAAzY4W3foYYh9NY75AhZZtFcuncLiKcM4/lNU//w3GICQhEU5IRBIJOiTMEoogxaAAUMAEFQBaYzeD0dHF++/D81uGt04PlqqYKJKkvwllghUQkDhCA2qmcjIdl1xViON5Zuy5zu2iq595052D58aqLVDVxVi0PV08uLzGACAonIOSAmUAYlvNZSmm7vsbS1hEYUk5hXPVVyvvirnmqsIDmW4lAHXedihGxGLqG3hB62aOIyOcGjlgxswIYA2jOhyJgsUFgKCFkUmpCMnMaB91AcdWzizAYBJC8a8+nwzB+9pSaS1unzaYNIcRIQ/1CREAGgO12y2wOvp1DVpOrPZWLk4b2P9dq4pM77aKxr/PGe3TAPhFXnBmumjW2+CjAgIIMAJKtGSWAAJXjUsZKkB+8UZpd6EKF9aVCu683TaMZhcfHx//8n//zr//6r/+a/+Qruy6dn9+6deuWnjh9n9frtd7ZNFXTVMxABHVdt20fQsBIh6sVAHRd17fdcjarq0gAzHBxcfUXv+d7Hj16dHp6en19XceKU0YZgp5iKRXtAQscVOkvFuukBO0BRW+ZLPtAYDJCInDadC6FW8OQJJ90UWmIX9GHY86Did3n34iaGnmnfKiwVddVCAqdYrzCpVuSsRW7jIs3Mq5zrcMHBGAAFAQIKJGZA8aAIYoIJ9AILMLDE7h1Or99fnjr1urkdHVw2MznVYySpIgnoMWKdUwQjSWMcMFlE0iBdiLiDCICvE0pnZ8cruaYM2GgeYwBYbVaidbKSB1SYJA+p9znZV1xSlpXW8NvVa0SPXY01HsAHxEAtNpJAOgarCIRehUDhsqeEaOQCIqWZTZCr0N9MyvS1JUmzuluuqSBYCjNxA3K9Su+S4oXiQOPJHMoshKW0noabWQxnyEMViE1IohI5qHJGO1iMndHtImH+xYH/7v9k10jUn/5A9+WCM3S56TsyXL5P3HJZJRSVNoYY/9FA8NANjyyO7H0avR3eqiyR0mZaV3Xy+Xy/v37t27dEpH1ev1DP/RD73jHO+48c+vevfu3b98CgJzl4uLi7Oyk77OJzBpSP5vVOtJt12lK9mo+76taAALAdtvNZvVHP/KRD3/4w8vlklPu226D1Pd9iMEGDEX6ZqepibvABVJSCdbXDb1xkf0TJliAiBqRTkQIZLWq+9yTc1DaAqpgpY5Xn6jPMGijirnWt6JUHxsYwU7ivtuKs1UZnXftxsbvaAkyQamoByIIQihBhCIiAQVh5g5SR8BVhFjhm55ZnN86ffOzZ3dvHx2dzFYHTTPDEBJnlZCEcegDrF6bXXkKRPM6DRZ3TyhEhIwAUoV4fbk9WC4OV6vLq23ft0z1er0OVS3DWiNnyADA0nUciHsBJq3cAMBChALIAiSqv5HiEwOKSOJiQUQhV2xMB0NjSRgAgsvtnFw30gQhMxtCaQpF6nvOo7oCwfYslsKnRpQKT+oEkPEFAMi7ymcW6plK1xmjJAsc16I6iEMabc455a6oSLv4QynBTQF3nZn9Xz0o+/HY0nk22O343lr5T8C17cFxDK09JI9rY4rLsrZnOuTFvtRFoRLGJcUh5bVI4xz7ol5WrUVKb1ed13K5/Jmf+Zm/83f+zn/1O79jNpsBQN9nETk7O9Gb6zqKACJYSZYQUATnsb5ou+16s1zMqxg21xtl4Z/8iZ/6vu/7vvv37x8sV/fvvT6fz5UYDIB8ODgW2dl42wbJJd9YRNT/aqs72SwPWPufi7OjDwxc8sk8GdhyGWDZiaKDwTC4LGazmbZZnQSaKrXbdzGtZRyBNfCdApIJ0eVnmwERUFRrCSgxwNBSniSjdMBdIJjP4PCoOlzNn767vHv75Omnj8/PD44P582MKPTMCQKCHrVCgECikILRZgi48wziOH7EEZ8czpdXj9vj1dHx8uB1yjkTCHTb1LYZiRiBmSsirZwhGdbbBEAUZyLCAH3GABRCSMwBA0DQnqoiIigC0mchUBs/aCgDxahiDjqb1G4PUo97tirZSymw5a4CppwlZZChug3yEDuhCCEhSAhQQp+kqnS3dCE4577r1PDkV0YtZgDATib1AGe2G7N0ao+GvleWyyaUqdF9d07s0AOgWPoML+x3A0QjKb2MyifrYEaxCbLfuIyqErKzixsLSc6ISEgRKSCJiLAgC9Egq+NAV0NSvbE3jr3GE4a3AZggM9xQiiX0ff/kyZOnn3764cOHd+/eZeaLi4sf/MEfXB0tfstv+S+Yoa7C9fWmqeNnP/vKM888BQDr9Wa5nDd1VHdVSpmImPDwYAUAAtCut3Vdh4Dr680HP/jBv/f3/t5zz755Nps9ePDgaHXQtm3ftZFqk/rBGafImaUmmOW3Y7JrN26lcZ//bs45BC7C1CDJdl03W9YUAEkAREpMCiKm3Ol/mQcRm4VZuMIqEFSR5rN6tZxrRlfOWn61T31rSLfZbNq2XdS75Aodi8o1PkDUEygLEAMgEEaSJlIkoADAaUvSoyQEqBs4PoGn7tanZ6vDw3B83JwcNqtVbGoIUTQ1OwYQEhYhAI340SvKEGUPsRrkWwCIAYnIKvNpjBNkAIC07SoMm5Tf9tzbfuZn//XVuj+6dXTZbqv5PHFKfZ9z7jO0oa+oChQ5lpMEhJlz3xndz2azxWIxq2pjEiJq20SlV/B2u72+vibmxWIRmkZEUs655JcPtJJ2NcV9xUvNoc0l/8Di4nruVPnSsBQVrbOr6JjzyOjDpaCKHT4mFEhxe+kZxa6DEztzvoGXQVXTNPphLu0XNSBDBQ3zEoI7hI18Z/XMTktPLh6g/chj2LX5sq94QLQnD+OEEawYAqotA4u6YcilhZk8yepq+zAIewiWIEZNFbaJ+ANG9WVTW05PT83JRUQqITOzICwWiwcPHmAIDx48mM/ni8Uixvh93/d9IYRv//ZvJwqz2SxnUbTabruqGgxYxdCjLTByXUUBeHDv/tnZWQj4iY9/8gd+4Ad+8Ad/8Nbp2eXl5cXFxbxuHj58OKx2HUZCU9lojXgyiLFNr5zRzVA4pSETwAxhJskWshlqcHu7RAjFToq7+rFmbdBPlPi14piFiYqIFbBeLBZaeswqben9m83GBKvkein0/dbTjF1qmzMPuGmLi8WybXtJQLGpQ+SUOW2Be6IEDFUDR4dwcgJnp/H27eb4JJ6fnz/99FOnJ6tIKURASAK8WM5z32XuhTHnTIIAEJEIKU6EqV/wQgECqCmsFouj5Wq97VC4CRRwYLqslihgAiHmq3Zt1FxOfxEQZul6gA13/ZZc0G3fDa10qiLapNJVZZ8giGgZMcRBDEl5aP8dIl5cPjY9zvIPACBghp5FpO22zAzILINzfQIQhbZubhmPhOrEFIDMABgEBJChdDMzSCVnbLInOClG5+L6ygVAxLquVSUcrRzc3BMSXTkXjwIi0uXOEM1LpjYk/wsipn6QHC3ph1wY9MRKZbMwVrQhWTsycLFdzKyFA3UAeo/iuEbq2cBM89IINSwVAbnozrBLmRi8HANTYf6fv++vP/+pT//m3/yb3/LWzwOBi8eXi8Vi1gzW4NyzmQgDEQi2m7auazV7/fzPfeQv/+W//MEPfnA5Xwx4lDk7NOmgM9rz62Y7buBrw0PzLXhJWYqlZ/yQ8tfdVkLROnfAhMEEzzRueatVrrQegxG/niuq/c2bGRERIKeceOdDvL6+5puqpEUHU36QSh4+vH5gN1g2MQPlSESZIXcBUjPjOsJiDsfHeHISVys8Ompu31mcn58crE7PTw5WB00VQxUEQHLOmUQQQGhM7gyglT8HjRDxF6w2BhCQUTgSHh0uT44P7j96PW2vI8bcX6MISg7MwEBCkHuA0CwBYNS2U6/1eo3cpbbP3XDUSF1LjERNyl1OwjkiIue+7/uuFbMZQbE0qRFhhjOiKgTtdpdySbNKqcslD6vryNhvFkOinpkHq3nKygntdutxyn5OAlax1D8ISIiQOXPK6jLY3eFpq5y0/sDcoQ9AKSg2mFFDCEgCAPP53O3WDoD6tsebEo98YIT/qYqPmepNKJhw3YQcDaq88OudofbXgLsnsAiIOndlVjeF7jnESIMYq4f24LTSkCtlM6+iIqJ5ObTptzXdyyUXNZWyP1zKaSlkv+nZp15++eUf+qEfunfv3rd+67d++Zd/+eHRAQCkPscYACFECkAg0LZdSmlxsJjPGwC4vt588O///e///u//5Cc/eXJ0PCCgDLkZULZMS7lbKBM4zd2WnZ3LBcbHA0wPxV08SvDp63snnD52eC/uwimqSofBOQ9otdls+r5v2w0N5Qk0hakuBIYAklKnzp+2bRXgNpuNDcwzgtCoMGEIwVJKjIREBEkQCZkxQRAilMAiaUPSzmo4XOLpUXNyWt25c3h0EuuYZwu8fef4zp2zmg6PDhezugJMBKhUnlgC0tD8VF89/OQdYP0iL4Q+UAqUlvNwfDSPkNbXD0O1aPsOSACFAEkQGFGIRTru/BFkz2lC8CkgkIlzzCHE5pByYubUiwo4lvokJQIDGYFJMiFRByy5gromooASIiECAh+uhlK8XdflnFI3BM7G2dzOZJM1tMDQjYDFY6My7pXiM6HD++/8ZRxou+6fUFWNhjVYnzjdNc8Dmi9pYC1jE7s9Fpz1fXdW86iCmh+5AZY9mYhiXYszwxnPoGtiDuWA9ZA3EiXKymgOCjrdM6UiCpXEWl0fs1tR6exi9QC8LENFZJBuF5gqLovgp37qp9797ncvFosPf/jDH/rQh772a7/2m7/5m9/1rnfdfeoOAAgXXSZSM6sbqDfb7uWXX/6J//gff+zHfuwnf/InHzx4cLg6WCwWV1dXkDkD5pyx+MgAwAIO2JVksH23ce5AKpuYDO6vZq7a2a2oRC0gIsvOW2qYZSuvvifQ1mqzoWqroo/WjTH51JKisQReZRzKQE5CtLIreOmpQgBhN0hCikM9lpLqYONEyiJCjJx74L4mrhYyq6ujVTg+jHfuHBwdxltnq+VBBOxjxcv5oq7rGcWaApZkOxOcGUWjamU4ETVSAkYS1s0dCseX4IZCgoRVlY8O69lMrh9nCZcogAwhQkAIAJFQu1lQ0GDx8nUZ/rNzeZDrGKSFLJC31yEE0rTelIQ5AkQAkuLt0qS8PHy5k1t93/V9pxktxbmeZrMmpaBcr8U0ik8wF+JWIuhDoBAo551KBQ4LCkUOnAuAPEDAMC9E0EgxESQKuuDedAXex+dKDulf61oL2pGd2CypkPtoP4yfPZLaFUsTTWOAYeQUzGgKY7PajYBF5Z02eH2UhWJIacZpU/AjtPeaOGYIa68zw5Zl1akrFhzOkvMbootWxThk6iYuKvz4mHnq9p3XXn4FAJbL5Wq1+sRHP/YXPvHnYozvfe9779y585a3vOWpp54KIdy/f/8zn/nM66+/frldv/baa5/4xCdee+21xWLx7DNvur6+fumll06PjlkjYJgtdkxct74bTwgsQciefnZLtLdUlrqPiOb7g7FrxcxYXDqeUAmUDyH0faeClRWNsTPY4lr1MNbtiDAkG+cSAqqjDQWqRgMGyLgb8c5qQaTqvN1pI2xy30EXJK1W8ex4dXpcnxxVh8t4drqIVVrM42xehdBQRU19EGDehCoSgQAKoGh5q4BIIplHOA8AAiADYEGRtBX+P8eVea3hRET98XF9fr7IssZADIwEASESBMQIiNoLrNmlRIizraQ0ZMkNSX/MKUFK0Ka2qSIRZcxMbFY9s2fLLqYOAOAaNP62Z04i2cqEa50pAA4BQ0AjESjHMha5Q2MOJtM0YWEidtkO+eNIXNy5Oij8u7h46AYRxhV1JCIFLKIdQCiY63zLMNDOMYIhdGBPtdxVU5iMx7sRcKzoeWbTz7kfeT9tvuv1Wuk7lNJL5IIb/JpQ8bgbOKLzD9r01c6izl9bahNbcM8NNyQ28ZC4YzYydrWYReTi4iLGeHp6enp6utlstHbb6enpj/zIj3iZ18b8yv178/l8tVrdvXt3u93eu3dPRO7cOtceEOJ8+cgCIqHald+Rsa7n183IzEvTstPxbfGnXy/kNHoIM1dVJbITxFLpj/v48r6KV+YJ8S4mM0iZcSrCTnCbUPJkH4e5hF3uqgfrlEVBQ8bXTK5q6mYN3bk1f/rO6vy0OT6sFnM8PJwxpxBo3szrZl41s9liNauXixpnVZOBU5YQKqQMQsyASDCufkgCJHCzSihvDFo5X1OoAYFCvnW2+LznzmP9UCAmSYBMkEk4AAZWyw31vLXdtdUXgfnxzGtbRUzNSerCe0IU1QnVdR1iZWPz1yvcqHC72V5v27VVXGy7zcAqwNpjYxD0WAPahrDVlJICnq/M6bFJiv0Ti0li+JwZJz2siAJpaYMdYBmsBNd2cCJ5MbN5CRER6YastF0kQSBDK3+Sw7gKkmGWpt5jiRuyMRiAgsNij7+22iag6SysvTMPNWRcMUUA3KWPcChqI+LgoIDSxBtLCo6U6FMtuu09Enrx2CWncjEzL1ZL/Su6zB6dVN/3L7744vPPP394eHh2dkZE6/V6wofGsXfu3NGadlVVEaA6l027tzUhGToMeu6wVUKnC0/+OdITHfXavf6ZUnL0nEYyCFPMLKVUr7BozAEAXK4fcbmGv2o7TfdMoxYAyENoAiKOgWkYs2iGbggDnXRUkwusN0YeuivJ9ELYLhdw+/zwuTedPX334GgV5nNZNDCf1yCBQlPPllW9inExa1Z1tUR8jIhhCA4dwJ2FNZCBtfUWAABrnNMNfQk/95VzF2KFxBTyweH89p2zXihl6fqtSBbuiBlYggAKBgghrGB85ogTdIvmDACUQ0hBQr1UG2oPUtehaULf913aLlcrPwzbgEe5JoKc+81mq/UqV6vVYrFIyXoOMwCL5OGAAhd65k4hT2qe4Gw70R2exki2hQDT7Qd3PEppFEwuSEd/FklzF9ag/dZExPy3ZumAEm/laVQ/V2XNf6iDyX0x3jmjwz4g2v2xqiZ7pIobFtHMi1ciogYOP2u9TH1A50ISERrcI4PhP4SgGs3h4SGWPkAmlNkqUUm1zXkQV6uqsvBUD1iSswZw62i13qHNyO+RXusHD1RYu76+jjREaVxdXdV1TYhcQBOGMrzQpV1MrBdUZe+ML8eGr5tm77WQkeFO/0XcswYoQKhOJiIp5fV6fXV1lXMOdamJVHZEKYHG0SQ22rADrNGWmbyMRQoebIU8ePCVfozGtC+n7ZH9Ps9werJ687Pnb/m8O+dns1mV6jrNam2+HOt6UVUrpIVAg1ATNn2bUtNLmTCqzmUd6PdKTMcUX9cpZSCKFCimlLp8VTd1SomJQjUDAE6blFoAWMDTfScNtKu4bvHx8fxROnx0dXUNFVRVVYUYiepYVYFYUmq7nDYK1cxaCUjNRxBjxQw5Z84CoEFMkjOswybnfrbAGIm56/p1ADg+hara5gQ5IUJNWDNjn6Xv01P9yxnDZYWf2HQHh6uL683jdf/4+sGsDrNITR0DILIAQIIgEGNOASRzTqkXkYhICLlrrTHqEKuAw/IxlbL3ALY3OXNVVYAkgESBykbmPiHmqqoCVkja2gTqase6iIDIgCIlf3HTXqmSBRgACZEJIwaNhxoqE0EhMkCElAmRAIECww4LKiQIVFPYhYylnJkDDLXCAu2EO8+xIKBMBQKQObNobIUKPkM8DoV1n5qmmc1mMUZOuc+diASkPiU7flWHGpYLMVYVaTl2xKqupWisuXSEjSFggNT1qeuvL6+apokUNm3XlmfmnFmYi1MyalWNPnWbbTo4bEKM82UUbGUozpM59xBzL1wahscYY6wo0NXVVcFcNJwBgEoAWgGEmirI0F13IlLHWVM1w4EKKUNmGKbGvDU7jim/sldp054vTkoiCoDAzJmBBxMHAsDAnyIIwLJhToIdhj5gEkEQzrmp4qKOy9RJH8JsXrfdy9vu8uh49rhrgSAQBQHKQkmAARmCBIQKIbLEnkkgANWBqo5e1bENUyhy06Syrg4+iRwv1oFAkHpObQsCNcV5bJrMPadeUhugrWIi7oR7yf1BgPe8df62t50tV/XxwerwYEnESACckXLOKVayWgnLFcDV0cF6yyGHHgUEcpdzVcUqYt8LC9chbtMmCdezRgS7vp8vmkgaHQqIgiQEAiTEzCSk/+kpQ0IBtB5QoMQChIFCXa1WKwFarA6Oj4+rEAhQhLUYXerbtm2BWyJCDCLSd5qUpydGykmNTSqGDMUzQ2xyjgCs2ZhdL5xVISLOwDkIBxHhjAGgCoEj9UBtqznMrJYY4GLo6RMQDSUcmMAZ0exM8Cc57oXwJhiZe/zBxeOOWFQsyqZzqbygz1ksFuBO+AlqmMCILo0RxsnAw7v6nTThT04eG3pNSEGWG19np+vkJzkPFCJa9zoPdvZqm/sIAYtqg06nti96z6CtmMFucMXFdZH9gktpVoiIV1dXi8VC9VMuwXoAgHFUgzyVFh43LvtkR/yHfiU9qQSXkcNjx+tkBfbJzNMb7snyehERAFmQTLl4u93GMCMiLZoQgoqWCTIAAmcmQBYEAaKASIiRKFCogsSIBFhhrEIIi+rYU6xtXyiBXZM1iZCyhMwsHKqqBqyySE6diBAIBdEY+0BABBjDm27D3afunN06nc3qg4PlfN6IZCQWBqIgGWKkEBE5iGSC6Xb8gldEGALqEEkzI0WrQ3JEIBAUjoAq1wALIwoQAg0V1ChUy6NjznJ4eKgKD6eEIojSt127XTMn46iS8JtBSA2uVmoh58EumEKjJvMQgkiucoWIgaqUGDFwxpyw7zilXNUEgJJmPdMW20AgwghSBcxAyDlz7jJUISJVuiymmBj1gAMsHAv5+tfMI+HfFk6L+RrVmnXZCDqXS59vEeH2HCyamlae0GAuQAEWyQyaYYe72DxjGHGwW0h8p34a2A2ESDsShJu1jxGBViGaNUTlo5zzdrudz+fesmOPMj3XcykUvLN/2ovsn1SihXXRNNI6lLQBKW7ByqVGeRzRGufWFgytyeugF+42mkssOO5d+ztiUxOXIQjOljeKltrTrWRPMfQ05pHrjRiVqAIA4syo7gh7e6IAMUSBPmWoaowVbLappka0iCUAAZIGmwGFEEOIoYraXSGEiqoYYwzNbH/f4SY8HfYrzyAxCBBFihVgJSn3fR8QAnFABGbkVAeZNXVdx7e89fSZZ566des0RlrMm7qOKfcICZFCoNwjEcQYmZOwIAFkA61fFHJFgmoYoopawsACLENhKyTFQYKK1OCDiUUgENXVjEI1X8DghsQQAgpLzogQCJqmqZoai+M2DwVVBhNpM5/pJzkPViQFs21KPQEzUmARiKx2k8AcEIIIdC1vg6Q+awPEwPNNhquUCAC4QxEARmEQYU4JECLGKiARMkgGHre38/RkzGB8paQFLkfM9ljrxvhHKX2bOazQH01eIWOLjzGVvRGdgGP0xMXsHUPlv2I3mCDAJYJBX6E2qc9BAfoEj9RUcphVvMqu9bwtgpk8pAhTntz1MvS0RQDHD154jDEqYPliLFCMXN68YkY6KTF6iDjElGpVn5xERkKlTcrmO5n7ZDXgJn5mF58xORX8YYYOo3WyaY/YbJfxZj9jIBSihBwQE4AIZEBqZnMiQMopd5LTbF7NWri6hhVGEREgokAxUhx6SWiIT1WFGIPWoKpjiDFANd+fLwBst9vJwaNzRKgRIUQUDESRBQgkIgeQSBwhMfZE/aIOp8fL48Pl3buz4+PDxbJGlKapQiSBTqv8NlXoqBfJBIKEWYQ5DU2XAUQA3wDE/RWFyygZEREENb5UmPSfPJjFI6FkgY63LIxEsamxokrRCgMzV1rckhMhVBQ496GuAlUesAYRnWSz2aRyGWCl1FHb9z3l3KvUwUwhYDVwHeXESAyUU89EGEKOfRVYmmsUAMksoFYCRmFiDWhhIRBgyQQZMomAWpAGE8Pg/SMt2CSZR4WNuCRU2pGr+6otCewkN+LmvRoD+rTkvO+eXPI44T6EoHkqg8nTncb6IuvjaXBJQzxXnV2NBFNXw7hN04Qt9y/jzK7rtDc6Is7nc61BOCF0GSeZ+yezc1naX21UWIRTfanadJnZ2nzp/cElvZvkRUQpJc3aVTZTr+VQuYEGu14eF7oJrnCgvzy4+DPJ76CIqK/D8Mj/MtlodCqzyEBON668H4CjCnRlSAmGgEOuqpBymxmZu4pguapDdSB4GZ/0DMgEsYpVbEKzgBgEITZ1HShWUEWMkAmlCkKUedzFy9ZEnCtZiiqdUgrUYAwxBGFMKeWcSHKIkLsN5AzYBdlWmA4X8fbp4s756dExzhdVDIBIsUIiAaEQSKCPFbBQ6pMMdRiYOSnCIoBPs5FS8Gr/iub2RtKkEyTAgAQsFAgBObMeIgEpCQgCYMCIQZCAhNRUPMj2kplThwKBkBJk5qZelp3rKcZYAjhDjAWuOh5cUV1K9bxTUUtbLUjOPRGoS0jPfHVQMpOmgGILkKiqiRASAAqQAGjpGABEAiFJogciYhAtZejIy4hDSrVP+5ORF4xlBwAVa4fmcZ7ora6QETS7MOW9lwKMQzRVUtCLXO1NCyvN41Q+IztNqMYi5RlSmLhk3OVfZ5cNhkvzJY2czjlbXrGhpH1ln/9hT9KkkshSAjjQXuR9glVVWXqNyTIhhG3XUol9V8wy/6CXsFRTCyHEps6uy6wUnV0BzvMnOdZ1fHKD+q/jsRA/m7vZsGCMxbbOoCpLeQiVyLJ9wWoHggzMwAzCO9AUgZz7tm2rKoQoLGk2X52d3zk8rh7+5HWXU8+JEwg0FDE0jQQKVaQYQoRIEKAjyYgZYdcQ0MO3uMa3RFRcBEREDCFCJIwZMjMDd4gcUVK6JshVzHVMyzmdn8yeuX1w+/bh6QkuF01VIQBUNQbERBQCpo5DwECYgIc+zciSe6wWiAg3EdKNV2QZYsYBEVCQgIJWs9GYeBHQYnuBAiIBUgUkKEBIgoFihSESRYSAiJL7jIiSA2IGIM4S4iDDMCASBe1YDbMqZu5T6nJuiuu8yTljr3lkqsDnlHtEiTGqQql9g+s6Ft4IiVECNZVQAESgUOBANLdbmVwyZ0HCgCA7Rp2shTjVzDBCYGcWtV30ssME12Cvj9aEsicXAQrurMKCpCq5ZBYBhpwEIIpVdjYY8mTnAXHnsNM7AU2QMXHMRAk/bONkE3sR0dLQsMhEfl4jThvret4mqALjBGQVlO1+rwLr1/Vm7ccBLuBbn5ZLqehiAy09ZV3MpBmSbZC2YvZPO2900ULp0WA32KqaN8amll0IWHKt2I0qZBdXtSM5dPrp/qWxaFoscyi4CYxImfs+dc1sFSJ0/Ya5uXV+/vQzJ689fPHiev3gar3ut9JTThU0FVEthEKEgQSZOarOJTLIMQ4Kh19MgrZJKf1nmQNA3yfmRMCAHLCPkmrsZzGv5nS4nJ0dz5++c/rs3eOzWwcnp7hcNYggIjEAQIacAsaMQkOFoQxIkYJW4BxIAsFaQH3uKzJ3hQGklP/MiD4YL4loWGUCSLGumFkysyCGSLGqqibEOudMgBouihKqEDglJEJqhgOHA2nTB9JdzDmHkHXXUxyKtyRqmxh6NdUzZy3AEAJCxYiboSuF99YFiIQhoJYN0ZBxhQJEJFQtFbMIBkQaqpvCnpijIYm52JvVJhxjnETKGGZ5wco/zfRccIfq59gJwpHxyGJelICMJ1XfIaIIaECgC8AlSRjG6sz+ZSf/BK8nmJVLoqXahmwk/rZ9zLJH2fCoeCHMc2rfMqAxwLJuQLGUP1Nw1NY4CqAqT1kktz7ZlBfbF0NV0y79yL2QBXvJTHanl788EtlE9DL13FRXGavJHqf8L5M9MlRl1iFNE04QBRGrKsRKrtdt129ns/r89vHB2y7vPQR+5To/4Y1cpUSQgGlRxaUICQfBAIKD5QeHxnr7BEnFwaKUZqtH0HTdtu97lBQoVZEht5zW89gvazhaxFvHs7vnB0/dPrp9tjo5mi9XMJtXuU85JxWjJfcZGUE7uUBAIQEsofy/GJDyV8zcxRirOoaAGkZMQSjQdtulzEQUKy1L0CHBbF4xyNCZDAmHQMe4I7IhDDIwM4OEEISqqgoA0HUIIIEiEYjkWAXpMRDGWnLOfWqpiiEEuJoPGI96cmoUHPSpTTlUFVX1PMYoktfr9Xp9HarZsp6H+jJnoAiSICUO1JCACPcWwoOBiVR1KioXexbSf+pQRYRZUspqzESXYuL5zUvUE+IzRQwKbCmzecbQf0YadKUYo/q81EYDpbAUFPtXGOqIozG5jsSkOcMIcNJi3/Umv5hlx+REA8TdOFNvDU3VNgQAaloylLGbJ8KCW8zsmVzTXBSeNELCCEZ1NwUmfYWlK+p3LQxysqpYTMu+jk0IAWB3cx734/Hbp5eJchPpT1yLdhgfPPZ2E9+wyLNUMh/8ItsYsFTssbfb+hSTiCrINUAUSAJROAGgBrhDaXa9bde3bt1arx8hymq1eubzz88eN4cn9Wdef/TSvav7VxebdFktT/puUzcrmh1AiJwhSAgUKcSUribisM7awk2s56ZebbchlHkTEZiEIbfAW5TNfMZP3To6PmzOThdvefbO3TtnR4fLw8N53WROPUCOkVg6AqiqyMwxUtdtA+J8Ptd68/pi0VezakjeMijKAprCVapddrHPHZBgBoZdlgkAJO41MotkHFADcdgnrQsKVBZU6VCQA0gOSCIBI7RpZzohUacPKBAUMhLELCJCEkLgaoHBsKD4Yki6iychUj2rAJgIMqdYQ81EWGNVhyqGCjgCcQCK2vOAIBJAAJWNa9FIS2gFAQMBABZyEa29T0O2AigxhUAhSN5FpfuDOro+VOwSZewcnkCbdZCG8WFeU5Bi0CUAsSoxOespb7ksegXcyUcGDUaCZkkxgvMSgb06lC5BfmdttBNS9jeA0yPsT/vygoxtNP7t/l3+BguYomJfH6YTdsPwWG/z9ViJOKgWXmW2X2RP8Lxx8Lan/smTR03+hCUx2L93EMWHvjk3Q7yNQWfNzCARiAErwARQsWCxOhHigK1aeyel1DTNwRGG+YIWgnPGKoeHmyfX0m4fbuGKuw2lhPVhHRqEOjNxRqnEwyuVCBKjB08tItJ2fSQkzJI2ub+ifD2r06Ki48P61tns9HB+dro4O1kcH9SLeZjPKEOPKAJMgCSk5ikEBtBpM4B2prj5sLf1fCOjVqQYhv8Kjw340ofJ6HVzcqcErZHTkUIgQADq+x4FAEgkCTMIkDYYKflxxBAxajkdkaDDJ9KI5KF8SgghdY2OeTLQq6u+bpq6YcAsklPqAGOIc4ko1MR6HiqAGInrEBrR7kLCgSAgUagkRAkRiFLbQ2Fsz0XBNeyGwrEhhFRyQbzB2O6xJ6RSkZ1LGr2ZnJQ+vHriASu4zsxma7c7Q8k0DqUtWF3tjMfsgi2sIqjnBBEJ42w1z+2TFRCX2WPUjC450U988pb9z/0cJ3+1r9gwoFTsI5e25mUQGZvDJ6Ds5wIFkU1t57GncoJQ+6Csl9nX7MmeZjyR6J/0QJrIm0QUSkHHybJMFhzN9peJsBYA4kQkfWLOQ2Fo0G4rgn2fWajv5fDwaBlnh9XR8fnR4mi+OJgf3n/82Vcf3bufLy47Tg/alGiWqDnGGBgQGSRMba8mANqq+p91yDEAQZdkm/NVHfuTVXNyVJ0c1U/fOTo7Whwfrc7PDg6PllWgKhCLYGbXph0IhRGLbTkMXghBxNFRBHDDSbZ/RaBKMApGVmcqDlMBqlRvVnXTtpbzBrRCKwRCCkgUAiElAURUcUY4iCQYXI1AQbdEQhgyAFQDsn1iZk0aCiGkKoQQQFAD4omiUg/VsxAwVgGAM7dd12JVVzkz9Rmaqp6FCBCbwA1SgywIGACIWMEDYyUhCmGQebIuOErcFAilio2dgbpq5PLF9xfRPCmGd/qJ1qJDd+SaWnQjYMme7uA1OP0WljLqKaXcpwmvek6bQAloUT0HScb8fjDirOm59H+1kRtreVbcx6wJK07ugTGcwRhAPTzZwHQdBKeLP+xOMWZPSFzGThUumcN+5OSCyPZHJU5YtoH599qd9iL/ivFgdlBuT/Zb71d++FwICQUCUgTIzFXGDgA5AwAJI0hIfY517Lt0cnxewaur1epABKowm1W3bx2eHS5eWt574aVt28p2+3jbt9Kl2TxRWAIg9r0lBvpsc+9SsEtEljUjZOEt4XrV5MNVdfd8cXYyOz6o754fHh/ND1fzg4OmqQkABHIA5GEHByu6QAgFcBEYUXt2ZQRA8WenLtfOMXrjFVmqzAFzQEbbBiLKHI3IRjwmhO4TNXYT4LyeIyIKM0cISTiAhgigNv6RgIKI2nYwCQdAHtppREJRgTGG2NVQVRUIdV0vQqTmsJTqZl7VIUYCkJRVbIrMKTF3UoU4owAQZ8Q1hoZYImEECaSIGSAECIgU6vncTCriUj2s3ovtmS5QGAuetiZmyjEGUJFeRa1Uis/pwWs+pglaISL2WeNIkADC0AUrUAhIOeeub3Ofum1rOc95r1aMopu2itmXHdD9c6Io2UjMjuOZx6uEOM4Gn0xhMh6+yZO4T3n+LfY0G57tSJ+Tl5KMyW8MuwcYmoF6SdlGhU5eM5ibfN1+90vhZ2FPNtDkYnS330fPKW/jsStAHZ1+JIMGyYjIhd91cQgBh+qBEhADgASqN5t+MT8I6bxazoD7wz7Pqnj77Oh0VZ8f1gfVS/cfbV95DZ5cbzbplZ63sTpCqoJkNRcysyZdhRDMFesXZxgYX3NOIps6pKNFdfdscff2welxfbyqb5+tVqvZrKmapgpB4W8Y8rD4dkphQBRClCE6PYMQIiH40kwDYO2+ddMVq/rYVCGR0roHsapnE7IeaCv1AMAw+CiZNQJCePDECwEHBIQg3OecsUHlhWFFkJktZRdUyhn87wAAhHUODQEQITFDCAQZhalezmkIuUjEmluNKBT6IJkoNhhqik3EOeAcJIcQSVIkQW1ZiASBBDA2dRKmnFQ0xRC0JhwEgkBanUvzswBAAALurFEevr2xFopTiYh078sE1RUaqDR2vYHheWTXtzVX6reQoom8A85u4q0n/q/6/Ip20adeftwVQXcQPDFg2aPIxRlMiMkYnsrlhwF7mGViqX+7fREcmKo6DITiUm3AYdYEZQbVlYbRUjFvT3AKnURpS417kpo902Zn22cTtyfs3j42HYgMkQnivKIAoDKOgfVkbUWEhZl3Ax54RICzENXCgkhPHl+LhOXRM5Ek99fLJR/O6wD9yQxPF9Vhgy+/9ngW77/4mjxep75/1EkL0MxgqScBa02Uvq9LXjrAjhQNl9P2VcLcRDlcVrdP5nfuLG+fLY6W1fnpwdnpom6qEEJVBSLMQkA4CFbK60CIQAKMCIBaQBMRFXMBEBxgmUpoDsSbAWs+PxqgfexvqusbgmsQsYJ14pyT9CycRLjnAFT6s0PmKtKsqSJSz5z6vp5VAAKSJTMSKajllFAopySCKFkEOQsCCWDGDqhGRAgZMYQ6QGLqMYQokgX08KFQBYYkmapmnhKFqg5VDVVT0wzCvIfcxBAkIyQkFhwCSRlYQ2pU8PG8OsEd+2soqTw3w/eY0NE5WbzYwsyayuN3wtHi0GCOXF8/kwjEWdOp1JCSIoOomyyWtsz74wyl4qjJYjY2D6A2VP9PGyqV/rI2TfudXJyRP/k8uHiC8zKprbaPfpoAhOY2Z1ceU59jXldw5r+cM1W7Kjo2WhrXlWYXhW8LZY/yeOSjtPx27PCoXF4rtK+LiDen6CeTJdLLwXElwpkzc86ci+wVCIUFmCEQiVBO/OD+4+ur9vzurdxviORgVS1jR/m6r+V4Fk4O6vPTo8ViMTt48NK9zf0rvm63bd6Evl6v19bP2dvd7RTxVBqgW8zq48PZ6XFz+2R++2xx63R+uKqOD+erg7laSAVYgZkwDqXZrSAo4NBLhTW4k1AAhhbghLjbF9sR+NyAhaTx86C1Zlk00ZECKQoqVA6dEJFoNpulxFtpe9YQt4QlcjDnLCnHUIUQqhBBcvatXCCrP3GQHQbnLwMQgHAWlSqSdClviaIaGwFY2/3FWOU8xEkBDtzLkqqqiYAhVBgqrGqiiqhixhgjAREDYGYhIe0gO8CEJb4YOhgRm7Qy0CiP8qU9VBl9e+IzhjQo1MsipNFdAFAJmhfcaplbGJRW0eJitg+l/K4d5naZzui5FPdqP4ADUz8YmwW5phJS2AWdNDEhIM/txtXG5xMMnbwR3FmoDON96rp6TTUzwSSVDkYAoFKnX2SN1YphmLXfKZO2bI94z4Xir310M5LwqDQBrM+xOBPUxmI/YqvYXUTIqqLd51zCj5X1kURQWBg4Z7leX6/XG8HbKfcVzRbz+mi2CAla6haVHKzms9mMMSasenqcXt90T9pum5lZ675b4y8lquACbv0WrCIsl82ts+Pz0/nJQTw4qA8P5icH87rBOhIzZ50IggiIiMZz7s4zAEG0SUzWwS8I/GIKtAPETSYSQj3agRmzFpNDyICAFFBrs2BACMCA8EzCdR8uEz/JcAW8JUFhQqiRMVSzZrnCWXOV+pZQ5vVmvV2tVozU5bSYRQzEnKuKRBJR1zQNUW4316HCqqr6/mpeHVVYEwSqRBhJQiSMq4OcMzIHikSRJXTdlkI9q2bbReou8uJo3oQIWwwSU+4W84axpdAjJebECShVAao51ECbiiptZG+UsTu6886aTkSEBBTopkZow9IPjqmdqaiKo1wtIkKUEIYAdM9jShMZBauoX++lDEZzKBIkkF29GoAEUsdobfuUHzSEx9oueTGKmQMDaEAwixXzQwACBBZOma3lDEukYNURsBhiNWIzuJxEcLiTNK6qroebNezQqHAP2TN3iAQoIRAOuRScOccqEolAIu2+NTg3sw4y94lzVlu0VsiqY8XMnIsHk2Uod8E9l0FSCcQlJArALMysRhZm7Lrc9V01a5AAGZBQbxgkL4o2BQFhEeAsQH76gAAaEFMqcMZqMNPsFior32KMFcQBjiFl1iBHCnU9uFOGwI5KUtJYmhgs5kB6wIwhC2IWXK0WV1dXJ8fPvP5q+/53fnx5XBPEWXPw6Gp96/gt635ZL/nqwcuwSM++9fTsqflTd/jf/4f7sw10Aq892vTCEKsMcA18kdrYVE3TQNpWkldVdVRVVd/J9XUNsqhnze2LO7eqW2ebwyWcHi9vnzfHh02gtFjOOW8BoK6rgD1QRMLE11IFBoahHC8KRmBhlTA0OhsEMoikLmfhHOICJalcFQMiiwBLlpqwbVsEqBD6voVIsaraTf9LrjjK4wt4KJcPkhBra9FRGCbXJatjt80wEJOZPewY9Aev+Qv0EyU+ESEC1CiBQRKEGLGIRQQQZEjRiBQECUqvk4gQI8Qso7P9xt/94ZzH1Rfswje4AoHhkeGg3BS4WO7J/nMv7unBO+krRbHyT+MS2WC3mUDEg1dhtMUmO/jbbIXH6/+5TOZ+HaBoQFj0IPuKf6B/ji2yjUrjP/elV+vpws4NN5FovLzZtr1N0xwdMLbW2WXSq7/smX7RTAD3mwXu4PE7O37UqLCff75NRJytwMvCQy3JsXCaSzveruvu3bsH8BRRyJ1AM8Q2hoBN0xwfHzeNXK8xxPi2t31+rJfLxc9/4uOpx7jettu0EckAM4AasnAvMUTJ0ud227fCbUX9rKkOVjQ7PDw4OFitVsvFbDabaYTNL6pdzS/60qUaSGi8RH6L9ZdfOmBJGu0xq3MRQLiKcaj2DTttpZnPiUhbQnijbNDgK0QV6GDPoIsuhwZxSB7S/WAB5siCRBRQYrRUfgw0AJagByYUIgRCGXIJvZWKSrG9ySLCXmea/RvgBoDrwZEjFGrzNiB/g5SgcP3cFFLVkjSDF4qZNsbYC3hrlNlKTYKzqQ12ol3q/4hdC0uwsQ2MudqjxoTP/eUHb8Ahzv1/43MmQwVXTmfy/J3IU/5qq+1hwgnLySZoAGrHhn3FdtbUTI9W6FQ/b7dCl3MHhTz0i6E0QBZ3UAGAR3txBwDvmRE9nRSPM9ttzKy1s1UcJqK2bV944YWcnqJ53bVd+VYKIcznTe6rWB1jAMCrul7UsyVnAvj5l+/B6/c39x6mdrsmOcAIkkLf9s1yDikx9y12MXaruRyfVGcnoT7Shh5Hi1lYLbTkLMkA3FNPhfHs/+Zrt6fjD+1Fv2TAGh6FxahGESQDBMRY13VVNSLSpz6lTjvWVFUwkYFII1+1LuzImWK05akWSQAYYMi3UGcOkqhsBoOclVXyMJZQ6FDCAckiWWTk6fMQY9aNfZoexjD2DYEDIIc5Oyms77bgmMp4MpcuWzDh23FvCI/pKaVJ33ZFMWN1ds27/GVgAQDEo2H7WRha+e/yxMk1psj9y0tJ/rrxjZP1n4CU7F0AkPpdQIBXqG3jzBs7eCriCAJsZ21/oQCW7qBVEwKHqh7RxNV1mAAWOXekeYft0uElFv9wmxePnSo2ZnGJU8wmbyp3DG/MOdd13bbtyy+/fH3Vnp7c3kqvqRk5Z6Sh51vTzCgcM8PlxTURPfPMM/PF6hOfePnTs8u2S48vgfkSc+AcWaSXLmJPUZoqrxZ8ckxn53hynOPi4Oj44OBwNa/DYhaapooRc9oVF/T7+zno5I0uceZC/wRDavnfCViIQgEDBIRAVAELcASgQI1K9V3XtX3HWrQ77vR5hZLyu8BgDhh6gYiI9ukagveHJrAjyrN1EclIinhi1ReYWXKOocyWhuUYSIEIhLS84c5C5VSYqjRfkPGJ8UY/qXj02FnBRAQk23hM2je29ECmv4TBCjb9XEQ08dii+4a3p5FEVpaOtY6Fft3HKJlKOMGdXSCLE6ywSBY2u18QsPzD/c370DxBRoMtu3Of4QFgu+ltSObSYmZdmbTXWn2f6HkcoW5vkfHJNCbyHb35qSGihfL6U+EXyauewMQJYuiUVnJCvZlBpNQpsrFVVdX3/ZMnT15+5f5zz701hAoQiajPrTJZVVVE2NSL4yMSjHJxfXzanN46O1r1B8uN5Fc/+zJcb6DLfZ+2OVft5rpa1ss5HK3iyRGensrBcZ4tt/X81nw+n8/nTZSh0FGpDOaXFJV+IGS42YTyCy1LWfPhf7t2k7ZWOvdfMmDp10IIhFGoHiydEAPNYqxFZNBiAsRIsaJdjlhARFShPUS1aNqYdJNCoemJBYTHtxmKiVptd9pN2WxmjlWQoTY2IQaiKBxASFx1BE9nlesWY2CPiL4Guf9FvXLKLXabiDR1bavsV1zrMRmtm7GJXEYCDx0uBqXAy4CGHdpLAsbigA0DS0z87pSGUbyY/eJlUpuUvsJrap9DKZ480EMV7kG8FCF6H7PsW7YIlqTtH+gVYQBQzcjQ3PZuIsmaJm6pUTJOnfNcd+O8wGEWlNLYXp+1x8JYJSxPGHldPaZLCZvwZ1XmHZBRiWJJKccYt+1aTUi2cW3bfvxjz3/pL//yGGaImJEBOURK3GOsunYLyLGen502VT27uLho2/Ztzx5Jf9peXzV09fp9ePhkfZ36nisCXtXL41VzelSdnNDRCSwPqKrSYjmbz5u6jlWAECREIto1Od873X/JQpa8gYRlMP2/V8ISyYCMKBQCUByKkUsAiQiBebBSaytjAGm7LSICSowVIubcF2EYlKusDd+EfBFRRNveE0AeMo+QEanITEREWJx6NkMiyjAcrSysZWd2q1PwarI0fo6enYzTYCwveDYIQyEaEZEYcPJYcVKM8aTtEO/FeZm653fR+K3CoYejHxI7p6EJWQNVyU5+mUzZBmb2FzvH0MmefuI30cNujp6BbaHsF4+Pk69DKYFvxXkcQNc2L/9dLS44gR5EzC4DwQ/AlsijiVLQG2GT/3Ayfj8Fb0yYXH7iHqf8xg3TLE4VFlA7ACKqcZaZc8a6rtebK0U3rXagjPaRn//0dqu1JRCQiUSNMH3fq3+m7zkEOjg4Yua23SwquHOy3D57exYWR4vtK6+tH1/kvgchPDnFW6fV4YEcHcWTo2q1ohDzwcFyuVrMZjVJDlHzK0X2YjgQAwjB/6ZLlwqd0X3Ckv6fv3TAAgbIUAJSCQmFQDTXSTejsE0Y6maYPXjAEBE1tDOiohWRhmsosuiRzgqE6mTZUYkiHSAxiJDKpUbKw7sBETClniVr4a4QwmCsGOIKdzxsxDTxBnrA8h+WOUIad38xQJnYsOxb+4m4w4x4Z4TyYet+eOCS+yok9aCLizv1z5z8s4qVZxj76TlKrxvG9ouhByeT2pB+wW/5ldQrlebpWkfbTISBRIrLzxJR81A9LZvPYfdY2S2mx7j9EtVGURNMmQCNTVO/OGnR7kkIx+g8fD6WUO1mE5z1893JB9B1XUHhXUanaQBY+rnoPS999kHXymJRMWQAwACxIk6pT/n87AwAXn/99fVme3S8Ojo5zNJXm4vj+QE/Hef1ajF7PGvk8ZO2TZgSHx3lw6M0m4floloslqvlIgSZz2ezWV03EbIQZqJhwbBUlvM08wvu+y/+2j8zdO6xrP6UgsG4DjQgdWC5eV23XZ9TFwMHgpwEIRNWIcTNpm27TFVcLGZUQ5e2XbdtmkZrywEMwZOzeR1CWK/XVaScVb0aakLVdZ1pKOSAiDGGGCsASD2ICAUAoJw13h1EhqCfrusODw/7vm8369VhuL5cLw6W235DAatYabkXSdOZG3cp3LxRZr/d40HEW6ZtrbCoih7sxGlzRKSBoIqPKkcE93XR9OZBRB31PQ2lsN/1kwtEVLaR8WVSFRS1H0sZCXFWAH9BqY9qJGLDk2JszqUExY3YFEuQui2vYY1fOsN0GtpTpsnD7bu71PSiku/U27FjZGKFHMYTdzmwRs9Qarr76nT62CQ3pxxRyRP01i4spklz5lrYp73R85HSh7j4ZL8Fdg+XFpBd10lEAFDTvr1dm3TM53ONKPahnk8etx//2Itf+IVvv76+OjoKkvhqc32wWDZN8/DRk9XiIMTZreOjR09ejxXUTTyiu932fo359AQPDhZnZ82Ln/nsa6+vQwWz+ebwsFktV4vFajFf1PWqruvZvB5KYwYkIOaUxFo0sohqgSTF9KQmCGMrBI0khRACcGIWAEQi4eEUj5WuT6lXoenICgUZU0ogMp/PqbibfgkSVjmp2pz7nDMhIzCIgKAQ5pJ0GmKsqgqjZAkUIHUdwK6Xr0Au1GYHEcPNhhIGRADWMr8whH4woEyOLRGxQ0k/sbDyQrU3mFcmfGs63YTgPADZQ2SvceYE7k28Mva2X+xQ1Xu67QbGB7X9VVOIvEWciOZV7WHUs6spd6OR4E5Z9hPnvVBv3PN+evieOCXsaaq8eFnGP98vndx0ZprRR21SfekEtxvVmCZsAPvXMDBCW1u/axod4skAnal7/7ox/k4pTZnHCGMn4Dvr1e6EgN14DLb8dsAYLt9odkRDY1AR0RpNw3718eVXH/6yL66zEHNmSJEAhqIIhFQhRKQYQhWj9EJRTuvYV01P1SZLK1CJzA6Ouqt1ilVezNN8DrNZ3dSrpjqu6kUImxCRhq6HdunzEYoFFgpg+Q0TseAikLI6AgO/GwHvT3zEa7tHIXwOldCdKuM/IEvBCxHRzrYg1LVJJFApfiDCimvcJTtdmVPhEy6fZBEBZkAmCkgCWFTCX+DiYTBMItB1nZTqKEoZpO3+FBCBcS+rxlOthxgY2yM8kIFzqPmMMI81wVVlgDEt+v2w/FKUnZmWx90r7Db8nDkNWAB0AmGDj4x2J/8bLeVkkDYMGAOcXxa7WYp7fqKCTdbBFtb/tFnjuJKnR7qcRuKSB+jJ+g9zcfnV/p5UekFP/rq/O290STHqG5upY8TqiNoOmtNA2cdOa5PiDbNobAyyqeDYXqHvgmyi+s5Ix6n++Eefp2/6T0OoUrpGyhRARACRMCBWRJGwDiFQJRVUAQ7qebtIXcrIABSbanZ42tX37z9AmofQhFDXdTVrFnVz0NSrqu6rKg4NE2CINDL6RBdCytrmhXYFeSarp3MXYBSxOos87N2UOO0VgDiA4VAi/I2350ZOi5FyjglatR8JgIgq1YmQYjHf9nnbtu22XYe8Y7pcDFgiFEIQLlHdkn2TH0Aexweb65eNMkUGYS3nTFQxc4yxz2gB34LZsBlglyxyI6UaD/jTcrfE4/ImRn9+WYzIQhil5tjDvdEdHFNV4ebwCE0kHAKLy0VEFtbgscB/4lFgf7L2iUoc+1s8Gae919TGidYsRVv0+g44wLqRouw5pk/ZY7FEDAxSSXEZe2y6ETont6FTydkVjw+uiJCIAN4sYdmTJ9hh6+NBx59e063kafkKe6bftRsn5HeBaEgNYTZtA0QkhvmnP/XStsuLed33jyrMQKHnDBAZkIUEIiIBhRAkMIrUVDfVbEU5Zc4stcCyaWpEEq4SzwEbCnWoq1hXoaJmVsdIISBARkkqVIkIBTAxcRgqAFh2iuNTnUbQ5u28i96yidtc/bLvFmq8F59LJZzyZFllKKVacxZOSTIQUghVoFhVFRIy5z71KXc5p4hUqozqmFg13hCJh1zoRJ4sRhykCiAOCd+AgAIi7ILxmEXNFlVV5Z7atp3NVzHGnrvPPS+jaf1QT06zecPYj+PZSYoot1uZ8lNR6Eak80RpvxORuFxTf+RObEk6MG1P4TfFAMLzkvedkezu8bBllvvJ4kxsN1ikNkuungAWu0h9HscQ2Vr51ZadcjCY2HQlLRnYrxgiBqr9k23vvMTnF4THK297odtqwSK7ez5nlolRiL3I1sGenHMOIWjYitnI7dXtduiRYWKX3jMhBtjh3ZRa/ATLP3eGhSouXr//+N5r9z//8w+321xX6hHqkEk4ctYMSpW5WJA3vO4pQ4WxmlMWAQYg5nR2dJRy6BMlmVFcVHWsG6GYq0qjKbWMOCICCIsGWyE7DZAmKzYZM6lzkUgLNkjxv5X1N7QaqcaevOFzA9ZuKcuQcLBxWJWPDJxzSpKJMC3mqxiaUNWM3Jfuu1VV1Tg0ibImdETEkmFQyHc+Mj1uh9DsQTEMNw8OGJCFFf4QS2uDEEJyeeclMxkBBsiXsTfQn/8TlcEwa5Ky4/cAHFr5lfWMbXf6f2Kx3aLzEt74HLPUWgAhy+7tspdUiKMsQiKizDspydOT5xNwyLWvthjD38hCN9ryxPklPPr7e7g0QNQV9pUY/P0yxgu/njAma71YRnEGdo8VFPJ/gjewVU1G4l/tbXzovDGLxcIfeP6EsPn60dpzmKcGWT/OIrfeMLbhaVRvNhcf//gn3/75vyxzT5EAOGeoEEVQBDmDcqzqN61cd7jJMVdYz+o60CzSSjIjYmLpM2YIGBuqawwZqA0hIEKxAjEiyejAGMo3D1wGhJihWO78lXM20xUCOrlMv36DibO4cHcS7ucCLL9nkw9FREUeJMJYAyFhE0KlCqbJwyFgjM2MZhpLwswAHEIEgJxHm4RF3bvxjXaP/6vtWQiRE19dXakdlBMRUdu2QowkQ2wmEMiQi2+V/G9Y0L1gBWMDGufZAAzZD+MpDM+ZUKcd6TBmFf+cfcCCEsrgYSKl1LadjCWdgVHLYQCF6PWfUUaR/eAYGMbY4dkSSwCqjdO8jfZSKHI3OM7062lzoXFeIThcs9AEL7HaagMA7/BhOk7/zN23ZEheya64GxbjpjiNXv8q/IYkN7n2AcsPQ+WsWDpI2Ub0/XC/10MNpPJeZpW4Q8gGOcyOd7Rhb88ZUs8///Mf/fpf/f6ccwgNSM8MUKksGQBoF7yKIk2f+zalFENDYdbAPMJB7hkAAnOMIhGoJgmQKTO3iHMYil7pUIcKX7YnplMTkTi7vCcJEVmv1wRMhBSAcApYnifsizdaF3+xNizUMM7CBhBCJAoIkjMKBZpxVqchJkl932dJGCGEUMXKtAwqmo6Rq9utUWHiGy9F8QnUxBi3HWtNslkdIkQAaNu2mgH6b5VJyVi58PM1IHCjQu2oDM7qJGYEvenk9yECtly05w2x+wl2oON/sYbPUHodelnAM7Db4BHq6YFf4RDgMwGsncg2rkBAziANDlk8WPi3lLSycRKoq0hlXDdZdhlfm83GFtmQGhFFdsmetmjwBsnDWLyifoX1c+sbaPuINzLEmCQmCz6Zpn/Rer1Wr65VTNd71Glg+Gs0hmMTAeLUAD8ZHroT1Lgdh7AVfOmll0yD0YhIIhJNLsHgHxhnSC1Ix8wiFAhIIAIIsCBwoAwBQoUcUuYtYw8wFynNxscewH1i0F/3OQvg/8/b/zRZsuN4otgPIN3PiYjM+6equvrP9My8Jz3JJJmNFpJpIzNtpE+rpbb6Dto8k8z05s08qTUzXdVTde/NzIiMc9ydBLSAEw6nnxOZWVXdtFtZHn7c6SQI/AiAIIjr9ZoZOafBHPjO6rp7rCuRM+1OzgNUfak1sUVmgRLbBbfo07YJfnlJy8yqmXLipMm4nykVoCzLcrlcROQ0jgOdSeiUTy8vL/M05ZSY03ydoJpzYihIq1aVAqKUMkFqmR/SOykiIkyU8wp1LFXrelRcmReoZhpUtVTO9b95PJ3++//n/z2df/WiUnAtC6pM39HjAM01q8gidcGiXOWcBsoSVpc3WWKUUqpUAylP4/n4+Ig9MJm6Ky3RTregZvmw9jynBs0299Zap2my8KKU0uNw2s26tkRAdEq5lHK9XG2937/y8vIyjuPDw8P5fLaZwOIALAWz45TbJgO2NMRo2QGXZXl6enKmd7FHOLfGavaQekftjqVMrXBgdb2GQ9xW3GqTx1Xqpmn6/Pnz6+srEY3j6C8a5e18Rnv3ep2madJ2tNqyLPM82wNMnFJOCTmvm94hyengLXe+t07FXktc7AkYEfHFtWa0w0QcO/wxX6Wl/TlDUWcnopTTMG4eEovVt2+DVCEWPrJO8JsyKKUUTmu2BtVqj03TNJ6fn74bP3x8+fRM5/FfXT8vA405FamvoiI6lfpC8v2AMZdhfsUyDe/Pf5P18/khL8v84fnl/W9+ZB3mCdCBAFupZ74+0EIkoEcASapKkVomKYrKD5IeRizFzlNcRBnEFi3IT9NyqXUZRyZClanKVSHDedJSlVLKD0x5nquIDsM5z6+qqpUt7FtTBgicqlZmnpdJtZzP43T9PF2uQ3r85kh3Z0o3QKxcLpfUTruzXbsm8NJSwTGzGZImErVWCUo7QpbYyD03cddRRlWr1P/0n/+/z8/PIgKRlLmUkhJdr1fNAmabYFbeYobutJKIRDnnx8fH2HJV/fTpU7QN/XmDCd9Ews2hOzw+YD8fWrGIf18Ct18tgNZmZkMcB9M//OEPsg/bsVe+++47n8lNSE6n01EwvIMpr9kyJCQtML53YV5lqa3wOv073PGLOECW70xivoR9GzogqHWZpsmOP7her346mYXCGlRZ9JmR93K5zPNsgGUsFycJe95d6QDsZPII8ZEacR22te02n3cahPc9QlXs4FGt62qzCx9NhFnknnJht4kIMN0Q7QDabQKwINxloev1ehqCFk93ypaGjn1ug6b37x+hg6oqKmhRQG1LHJ9ASqLQCqmkFSpsqTXbNjdVpdROkwyLPJuIwRbrOr/VtrZ+ewxulT9l87O1QULwrux3t0XryZbPW9TVtu3+crlqtSRZ+/29qGqLg6u2SAolVhUBkaqA7D/YNZP8h//xf/jwy09C70rlRKeXl5d359MimqAp58SEgE7kZ3DvE6e5D0JVL5eLSVQNJxhzWP1x+dcWvz6O4+l0yvY53OaWyItovPv+/Xurs9ZquoPpNX70vLGUI5QHcJpMOqndDIlWFTM/PTz57jz7+jiOLjZxsLidtOydtXnF9RTVnTN+/VwI0HflxWHOH/ML03Es0NdY3LD+fD4b/c07brXZqb+ugUqLMrdoDyf70JJEqmoasoikdoyjU9v1tahcqyrS7bCG6J2ITjFH2OOYdvc7gdS2VrBDj32wvkYn0A71bJpnZi6l2IZcE6tatNY6z/r8/Pz9+yeDA6l1SIkIDZd2M1OErTXmCDmnrEoqLG1hR1RU67pHhJRM2yATJ0ogZNiJfgZYVpsKcwtkd2FRCDMLrVhhARqq0vX3Vsf78ieklyEKO0jjujvv8zc2i2nd3d7xeilFq+26WM/giyoAHWzXbob3+6eB/ul3//n15ePwbiwVSvPz88eM7x7HbNtB0U60VlTS1QbwZHfONCbV0+vFVSfj0YeHBxWRdh6Al/Pp5MI2tMLMtpfwOGlo0JJcl7E78zy/vr6+vr7ahl7r/sPDA4WDVE3vSCkZKDiI+PkrvgoWJ08i4qo+ZNYSaXFe3Zgei8E3mskj+zwK1pelhexGxnBX+o1K01qzKZXcLE0J5zxGfc3ByO4YWlkMgSc1TGFDkgb1NgavMu98Ya5kpbGlLW7DZBfm86IGst6eY2S/UzWOtf/b6VBOBj24w6zwToFdH/DnTaMsZfZFbVoys5RFnj995r//TnVWaINFIwV4TTGw0wHRwhSIldrBAiq07sDVKrqoCnQhwGIh2z5xJYgya4WIVBVVZVkHMfHgffRuicowJGqziLYtwLVWXtlGnQTM626em+WbAcv3piJ40NH8rDauDgQ2I+Wc0RL+2vplXGphZve402GVBHu4dVYgn5fqvEyv8wQ+zVq50lLLXGtdFs1KybJqJSZOzEwp6VIpTIAIRqtZVW6SuHvIoZZbjnATGw7h3aZjighh89kjTLPRiW4vWp2///3v3cYZhuHp6clsHD/QMIdiE+wq+FtkVmZm02S3ibpN7Gnbeb521obv48ePkcIOFl5/fAtt01+0Ltc+BgHzEw2OdmWgxrrn8fHx0UDZ+m7El+Y4M4WdmS1cgBr/MLN5GL1hFFZImXmpBTgoFHc8wQAop+NEeEQTH7Lz+XwTsMxo7UikLX7N63SaHJdQuil5D1hbCppxHGs92fUwDJTGMYvIy8ePH4n+TlVBu0MVYwF2LQzZg0uiE0BYdajESIoBVFkZEI+85rWNSVUqNFJVVenWxkzrV0rJPlVrTWRnpvTxHLoD69vlmwHLbBYNGZd4NfEuUUhMiiQsyUlIVm12R23p9IBVYs2tTvtpxz4U+4PAcPPr84/fPTFBygTOVZY8jpTo9fVVBpKShiFRHmjIiU4gGk85ErTNADoO45nH03ngljVtmiZRWkpRKDESc87Z/fHLsqREnp0ZvlW1bkHVXojIDDFLImrHlszzXGtl4HQ6nU6n8/l8Pp9N5ilsVcEeW90kXJbFNDL70yT8aPI8//xBgsvG2lBKeffunTOHYx+HA2/8jr8YKeailZou40brEbDiRZHNykY74aJaSIqINc8ZzOGY2nRI4cjbOPMhgCOCIuPfdes+Dj2AEvYwxH45HVRvhFNI27DtxWegeNPbE1sY2yB72zxKrLZICMPbYRhAnknpbA/knIf0wJjmRW0Ggm66Asx/AoAEdgCEWlBCbwZBBbRATdmsWAOgWCFcSw36Tm0hEpwGgZIMUOV1t1C3TWJnL5u60Plko7BHof5LApbPlrrfpWEHq7gbwhSWaZrW5xv05JyguiwlpaS1TVO6jZytofJ6/GfkAAFIVVSrqmXFUtVayuu//Td/83DCLHNKT9Myj2NW1aUWVpWKPAGJaRjTOHMenpqp1TGTK4bLsjw/P5uTBS3gsFNznAguRTb1EdEyzZHoTiITS9/c6yrq3/zNb2PNEjyD2hyFMa8mN2+0VeVef0vRG4fcyvt37+z5KH4iYj4yx6a4ICDNPxKPFMl5d96fa50IcClhm07kud11Ut3nSKm34rDQENM7EuctW8GIlZNvLG+fiuiDgww49851l9Pd3zLQjAJmRYNp3/VRg48Pe0WDmrXu7/pYI8AfES07oXUfv47juJQJ65S/Tn5YsyCkeZ4/fXpWISSklMyztCq/LfBQVRU1rbEOail9FVWkEHKVCoUqAaKYQQW0KGqdZsBOSCVCEkI7iT4RklKhNa1TcxYFstPWEeLgw2r0hKraZvX4/NvlmwHL1qRq2wLqCUWNlObKMTXY1q3fv39kZglnJUjzTUjAPm2I1rmKcGcNxe9nlL//2988PmB5FVCtSwWdlmViZkEtpZRaKlT4moZMw3h9+RzbmdpOvalerdkvLy/Pz8+11nfv3r179+7p6ckXPX1WF5F3j08GBPN1usoWQ+QSW1uucRshW79PKZ3P58fHxx9++OHh4SHnLFczYJfL5RLzQPmytwGTJ3eXEANhmOIEj8C69a6sTm7jb9cLzudz1CMo7Dr2ys069tme91tbmgmMpZXagjNFem3fr5/eP/hNbSt9OWfT0Kk5nvx+ND/psBzpNN8q5BVPsd9W9fDwEDnNraKqt9PvxalCg5c2mrcRsByy7fVuOsQhZCy1jEb+vJWl9Dsx7DvjOCpWLx6wjoKqQllEylKv16s2r5mZCGgUBgRQRVWtFMx/2vZpSmJAWdWMJFJPS7ce6pGJmRInzsyWokCrSlXbbyICMKmQqubW60ilzVAI/d26qbrONKrbeuLNsktF4O976Z42FjfOjo4Gd4WallFrtcWdp6enl5fn3/72t5fX12maHh+/z5k/ff5s8vDpcjEvxqfnZ1V9fHy8Xq8PP2Sfx1ILto6S4FxoNhGXmXT5P/0f/7f/1//bf//9Ez085Ousr6/Xc066FJKFSFNKFmZVpknHB2ZeltntOHfu2uLgOI4//vjjb37zm/fv3w/DYB2Zpuvra/Vyg+83oKldzcZDv/nNb8xrU2t9eXn54x//qKo5Z8ybKecDQWGzGxqamGfNNR3ebwRxJ7cL8Kp5UbIoDbdZVsFYFlub8+VF/3pnzPps5Hcc6cxpipC9S1XN1HWh9eWClY9ZxnFcluX19fXx8fHdu3eG5p69PqVkp2TP82xBZy63CIEFqQW+diH4YIrs6tfPz8+u/juJiKjWECLTUp46v7k4dNTYC956M6rbjq3RcpSwROgavQ2iA/F+sWJb5rteryBLN1pFJLdchlCe5/Lu3bsPHz6VIg+nNM/TMl/fPz2+vrw+PIgt1MzzzGmc5/nx6T0nMFPO2Zz3IjLkJFUIa3C5e5eYGVKmZVbQ+eld4lxVi9QhjwAqCXPOI9dllroI0TAMteJ0Ok1aReow5mXRUsrpPC7LMnDyozqWZWFOOedElYgUUgUiQiuLsks9sQUqpcQ8z/M3aFg+H6IpscY0KZxM5U/6KJptKG0NuzEl+9BGrkopmelnyGmVtX8Ns8WmC8NlIkW9nMf8qx+/++49lvl1GH88Pbybp5+B1VFoaxyJspJmJKlVRerhUK/XeT6fz7/59a8Np2qtv//d767XawoR4Q6X2txtEblWaU+rFeNSbdc//fTT8/Oz1Wz6jsnPu/HsguH/omlAzjduM5qGi4AvLjnx9WOhfVkDL4PK4ENAwWxBM3ZiYIT3mpnTfi+xA7S/63Jur9SQ+dMgiUNQS9dgbm5QZ6c4ueq+rCx6x7CIjY9VaTsMvIMnDZpRBCxfXaW9nhXHIl54x7tRNlr5BBC5q2skM6/bxsn3JGwJ7wFAWQS//PLLsiylKCf1ZVPAXChZUVd52ehmlZg0CfPQTnmnda8fKyCZZYaIQMuieSRa0/0SkZKtFxrJbTneWPEG/VNKTO3A7dUFBBFpGxlUPaeWG7O3St4Ndggb6Vjfn+G9UxPNA+IkjloxgHEczR9kgZFm14zjYD4v3sclxrX2N8pO8rAMif/mtz/8q799+h//y+fzmb774ftPHz+zqKrprUVEiQpRYobW255an3unaXp5eblcLtfrtZSS821Mj/cpOHcUm7fFcZyZP3/+bAt50hYcKQSmHSlvWoY1z5cmTZON/O3U9oejLuBQ4vWgeVLSftMyBc/Rjrph+vFv+fPMjKZJmWIiIXJYQ/QD9sLsYPT6+mpLGT3utE/U/bmNXo/7vDrMugdYaAudFpfgvUba5UHz4YiaDu+LPxmLq5A1BOj4Y7sWBpo7Vuo+hRGal62Ng9ZaLXiKyKZtd/+vtf3+d/91mqb6kJDglntjlRWbmrg5iba1kaYQEEhYWWBnDUhiJC1zkbLkfDpzGoRSUUk0iOWj3jq062zsSKRkSkmKmQ4aO+6AJSKsq+O7I7KawRkHwIEGYfaIz/iyd3yy9Xlb/PYKc87zPKM5ETy2cFkWD2j2wRvH8UZuhH3D/NNWzplepun7p4d/+2///n/6x/9hXq7MeHr3IIsgkcwoSzWbTVlAlsUHgM8DSgTb67As0y+/bI6kBsTb6lIEAgSciqxcZbdxj1qU//l8lnCMoD+TLdnOfqdxJL5zno3l9XrtRiQ2KWpGWJm+d8O7phBHyn+KzCD7g0J5nxQFq0lVHXDtLYfXiG5ep2nc2vayOGR7fJkEh73h4JETjiKxMXDob7ygpvPK3ilmRydEzPKqIlW5xbX5bNfVY84+CnN2pB72GBcJ4l838pLEHjkBVURoCxDfALHWqoqchl9++aeXl9df/fA+tCr2yLIVwLLIKarLvpNadbUFLTLL9CBGJRHUuZQsIgOzgrRCE6AMrCeptx1Ot4PRVorpOlWj5elnZt+oGDWsVcBBaHuHtf22aVjdl5zpI3G1rQQdeQghU2WscBhyKcW2hJrN7FIxDHaO4eKSwMwW4w74NNBMQtr+IwJIiUGMh4f04eUl8fD3/+qvx/Hf//z86eX9p9PDGblq5oVBKPM8V1lEmUiSDkTEvGVxs4GfXi9OxGEYYem65y2smZmV1/3fztHKyikxgRX2Yz6domuJmpPi6ekpooOTi5YtvcyRs53mnSTToXRg6lziR6l1j8XKuyH2PzWUHDb07vS73TaXLXCMQ6hwbE8KDj5zzFHbWOdcFJ93gjiJOkk4cikCSCHAQVfzOsG0LU0dM3dEc57vdNs4BBo8Ia6N1kPetPgw9lgJoJRt36I3xFrr50shyBqK2Oi8XvSPf/zjf/OvfyCKKx4KtCy+O3tQ4W51kkbAQD2FBcBIWQhCUKlVpBiOVK0JUIIQ0l4NijgbKVlKCduFzJ2HnLO2vGwOWDiIQCybhhXHvruID0SvRxwztwh8VBwptc3Pvk3H9Hw/CzMFJ/G9hh7bZmVMzFQV6fv3T+OY53n5+PHjd+9/nZgpZxpHqrNo0ZISoJyokgOKD7y1x1f0sN/lq83x75ARYcKdEaupxTm2083DDiyc44f9oQmBn270WnU7Ei4W7I96j0LF6E14785R+DWY/Aj+F+zzRklcqgvs1bmrOg3OK8w5+24hs3DNV+h9dN0EB97t2tyN0ZFo3uuOCN2LsZGxho5osUJtWM8htJDa9oCuF/eaFEujfNzpsf+VthwPTltuo8ZM//AP//B/+N/9L/wZ71TgrpCsR1X90FPaoByWUVSriJQ6l2VSFWZWahMnQ1Vly9TKrl7RLQ2rtUTF/d2sHtYjG00Q7UtDxp2nTlU7tbAbreMQxmHz62ixa1s0pH2uXvP4tNPW8jxPzGyn1y7LMjxs7q27Ofv2XLX9K+X90+NzWYjApCnhcvk85McBnAGyFJEYU0owh0UdKBhNCLAb45uY+XQ62aLeLepD9qFGzr6LXjks/1MLdKRmwEezK+fMcTp05TmcM9jxtwOT26H+QId6BiuZdz/Fr3RC6M9ECfexrvvEfvaA+bDcrEtty5FpT9JWzbBL46fv3r2rtX78+NHcAtSOxonD4c2458M6ItT6a7iOPXLm4cM6T4eAEVb20r7OZBp8iNRiPkTEw9as+PJl5B/aa4JH+ktwSsZnRMSd7rJ3XIpAhXLO//E//keR/zMzq2xc3ZhE44s3CEeiSiJKXI3WNieVeSJOQ0qLsYEWbecPGmZ1dekdp3tDJEopybqAs4XUeDVOCv9JW7X2wF2TEAe02gjXHvMonshkFD3Q7aaxsjGfsbIFYEbh1EP8ztvFPlFKeXp6zJ+1lM+qOp7S61wul9eShjNxpjVCPVv+kZyS7vy7TqDn5+fz+Xw6nUTE3O0GqXHCdHHVpup74zetk8VUSL9v0mvFScoti0t53fmk/EMIPrIosRKyX7i3iNrpNQhIutbD25jS3vTz7sev1NrnjPcnZR+oYT0qLTrMR0RElmU5nU6uk0ZGYuanpycien19jRkjOgrEjxL1mBuHr+fPvWu163WE4AhADiU3eT5SzDrrCqbX4Li8BXBEwy04BGN3vFVRUGNpD8Cc7h16AmC2E8iFiH7/+9/XWgHuutzVjCDjXQc1upFUxc4VHWwxeJtoPSeP0gGx7mjBKSWSzmSGiPBew6LtzzsaVtIKEuUiVEGiYGhSHRUjY2BlgkAmrRfFzCiCSxW0jKG0Hu6j/Pz8/N37H87ns2XIafKJyzS/f/99yvz68pyhTKTXywPpd09Prx+fy/X6OD7IzMppHL8nGrUkBYhU16O91gTS2ZbVKiznddIMTRn4zE/TMp6//+s//Pz/KuW3f/3r3/LwbrlOv/zxdwtPaRQaMySVmjg/ns7v5tdnDR7TxCmlRMzvf/UAM+sTzsNDUBg3aw7Bo/T6+oogP+KkldWz655aZ1Nq0VIccqQMKXcyYPWf8gm2iUuFzRFBBGCg9fiv3LJQ1TW/a1qnxP1qXQmJ66KQRGaS4CS2bXoOyt6euMJov5oquv4J2BZT+9bpfK61gojt9BGg+madmpmGlBLTUAvmqeZMtSDbiakKkTVpn0kbp23bIJrRQPeP5+K0QzQEGHV0jgIwl3Lz+XTr1B+vxz1iDta2uEREFndm654i8vLyok3bdW1d24J4nDasDEOqtdp+SoSJLTr7VbcV28/0n9M7mgjj+P73/3T98PH0/Xc/LvN/Hcb6+B3P9SfKsszjMJyWKT2Mf315+cPph+8SKmkZE5GWOl+G01MtZeAhZZRSRWZgSagJcjolQOc6AUq1chFAR0m56MDDdVlqmU6J56Jzmc8POfGZuNZ8rXpVUsGL0pU5iyQUVs5QIuI8QHRa6jzomghMVIiGlAdQvVzn0+kEbaSuAFi1ityPdDedjYgMRplZwQzWsCMf4cy177///nx64BZp5WNppm5UnYgI8cTwvQZ+rz0NGhAehvGH1nGeec2ycLnIVa+fX3UfFkRCFgBlJoDPiu7P0jBj+3yrqpx6rrWHLeIxNKwZyPtI5c5qOxqSlhDOMVHCwkWcbVILs87YohyiilpDYH202hA3it/RSg7jvhEcB3/2vXLUa+KF/zlN088//4yWQC265G4+//YhETdLRB+vM/r4EJFCdooV3SlR6ZC9s9XBncIObf/Ew8ODtA2SaGdt6OFsAYdRTuuRH9SWUKNrNRqDfodqMu+Rilwul1q/65oXB8Y5vHtGRIjVl7pixwEFNp3U/l/vaKPd5xAGVNu78WFgP9wHTot3qM/pruwhGiICiJAk3pYYobBJ2+dkN3weHx8JyfVkpybnpGpLSSY5ArbjUmaRoqq2CUnbkTP1Dn/Kuk9ta7ol0kqULI3358+fRWSpc1Fcr9fHU4asIRRDHkA8V6q1nsbNCqbgCeqE2XkiD/2+a8cXvxl/lbLL+d3V6cRxuqnu9vQ437vTh1qYmPnFUktU3UlRyGBZo0Rx2OJ3D3fiffc6xeKYdbOS+LyDwj0AAvDp0yfz7/g52P7WDTH4Cqy815dY8n5xQ255xLuZw9kj0rmGzUw+CXk9vjrs5r/tLuoE0qfSjkREq87P7TTyqC93UIU2LiIiBCRW1Y8fn0v5NRP5jqOvodgKWHZocVMpglyoW4oi4sc8x8Yj8MaRSY6TGdF2Lgw1H1bXL1/P2SBPNQMEZZDloNmIyMykznN2Lk/ROp+HFCp14eFSip0ziOYY1rAnu4WuYU2s0+IM0WiElowU28SzK43DrNrV2mVmFRWRaZo+fPhgw53zmRXvHvIyiZ1hwMzjOKKyaGLeZM95FMGTEqcgVbXno1ele7IbjzzeOF2GmgvWX+TmgE9jb2ymfUIYDiWlRHXlXfeCd7pVlKgjXzr0RD6joENFJ3fsoBMNt0Ahilz36e7O+Xy+XC5qmQaGQdr+laPucHMC/5pyE4asxJ+6Meoe7mrTvVkXu6bBw+AalqvVzjk266Bp6F06IO9yapnw4kJ2RExvcEOTdV+yCcTPP/9c679OOamWBlg9dnT83P6khk2rfFkvWiN3pHD8OlJeg4YViUlE/hLtpCxoc0Eqj0OjNzSsUFJKEIasG2XW7W7LYgpHm+Gd4jLPswrROrckn+qHlKSuiwJOu5RomrYMpZ5m74s8epO9ACzL8vHjx5xzSuPp8V0ZxjGJ1tf5KqUUSoXTwMxM2ZPTR4nVQ2abAOr9qo0GH+qxwdSWe+NQUcszhYBWa/KWliTIeSi1LAVep/ONiOSAp1Glisag3vLsfiVVNeD4EaSOPIoDWn2xUAuFR9sznHOO/h39Chf42305Qp6PYJT8jp2Or7hqE1XXrlWRDyUU19TQBl2bL0LEEkL1O3IAIOwJteUp48xadxEJXlvixMxMSMxK9NMffy6LnAbbF73O7ooK7HYUNSJv3Wx+w53e9Ab9O+pFGTkySaOPPRDnD8FhX3T8s6tEVTPA2Gk1IV50/1XRUupyvcZAO1dTxfjPUFlELfMJrYnxlioLr1y4DoaP/QqmUL2nXO0bjf0OAJuLRMrnz59zfi+chmEgUdIZgIjM8yyglBMSpzx0VNBb/ovdMGATpJUOt5Yy/a3IfLQvriW5QSci19eLBt3KTD9Hz4gF9gqHPYyR3fs9wPf08INMRuDGgUvusWD3Fd6v9N18xYoluqOgJBpNjkEAK2J+u5rVwZCVaOpG5IpblLrBQhPmSGd3BTSZ33Zc2k3XsLhtdULjmagUx3CKzr3gJqQ2N2scOO8IrYsDmYh4bQb/9NNP8zw/PqwitWIQYd3rt+1G3AALgaVvDiIREciXfYyPOsBCwKzVlGuVx9auDzBtf+5PjYpDT3s2ixqWWYVunTVFVIgVqkIJBMIChAMvVZXIl8NTSkka44qsp2MOw8DcxmDd5SX2XUUFCcHDBaujw82ySuyOKUlVl2URIbMKh/FXs6yJlsa06uHkDeAbgL0jdCjbmGGbIZ2ftvbcmsmxlxk3kyMf15ajypaWsE+HQMG55va1FZ9paytRnGJ7vCteZ8c6scFdib86cd4emo4I9wALLfTJVCrTJWsIB9e9qf6tTvd7341DjKDypJywFwwfjk798RF3HvB6uG0hqvvE0HFJwTWmyHXYL3cSkaZtAwYCZnVurK1aE2FVgKXil18+znORmjQf9+Kt2k34egSvHXvE+wARVqOTeXVpd4gWiR+HILJiZIrulX2rbhdaNazV0d4/JyIqgFIyOCRakwqCXU6IuG3K7T2aqjoMg2WPsVRhaDnDANqwg4QoqboJfbc4D7tcWPeu12vBurg2pETapm5e83NxIiXuZmqno39UfD1xD1ignfxEsna0tou0n4H9fkyAp8HEyA3IHNGsMWYwrhFkYUP/dN1S8UY7paNeYJRIvZ1r6W2C3+zFcVC6P9/GQQCWJcIyr/Ka8Lder9dObr3NIl/Wu98uXcdjs4ko7c+XxEHGvC9u4jmURCBDkHBXsrxC17Yo7PqOzXPeqLSpdZHBPKxEem+9fXpdBLtc7FCCrbO6V3ZujqOqHTm81oZ+3Ff1KjJV17zdK/fHouMKB821nkZt4s331w1cXjUv6Kpk6UaFtWValqVAJsd42qIBUvPF5JwzlGut0zQtS00p2Sky1/nVFq/LvIyZn56eIOXl5YXW86+ZSIm4VrVFQ13j9wEiO17b1LgWGNkbNQ8PDy9X+vDhwziO12mi/Pj6+jrkDJSHhwc6o9RlWoqIqkgpheoSU/F5YJRlj0BDFicoNd9ER7hO84c7R7Fzi3rI6MPDgyVHlpaZxxSN83iKouIqFfYZtSIT15a26cg0zpTetrzbCL353TzOKLKa7k2eyGHmJPaaIwLGf72klg+X9zsfbJMz/AyLZSGi8/k8tz1l0elzj+8R4qqwV5aJyTvi6lvU4BwauG2poVDctnU+R4MJDb7LGFMSlSY0JctnIKeztcpnF0sfFF+3HvGw5mKkfbbCUzv0BIClP1mZU7nW+vBwfv388fvvfvj55w/ff/9jSleiAlBKmRlSME12HugppWSp0EopAI/jWJbCD5xSsj05zCy6NuZ8Pi/PC1ESsHhiZRFLImb+Nal1miabYud5ZohoYeYi+vp6US1pTTC1ywWSUqJCpZTTcDJIZWbSdQXGluYtVBWqUBYVc8mbPsmg234Z2jGieoYK57mokriAEVFu6cBrrbzxiooWrUuYNqOqRWYYruhAzcw5REt35eZkXkpRKqSl1FJKESWRwpoytsZvXN6OyYqz4top3bIFxDbEuB5qRgQAblNmJ9WXyyVqQ86UcaL27vjEICHp8MrcjYNjO3cSGwCLaHM9YI8sbrz4R+XOMgIafHRSHb97fIv2zh3/iuuYsfG0rSav/fW33oYtb4PXaeASG9lVSAGjAbg/xVsYH9ZDeaMlsVoNWnA30PF5/8p2sU9r4w93exU3l5yyikiFiJZSy4Lr9fp4hqpFZG4qDACss+mO5Ta0bW6WNwj+Rvf1TYfjoSXbW9q8abTfiKO39PocauEY1uC8TuuVEiux2mba1uENsERkWdaT6Sxzr8dhK0RVuEWQ1lJqS1va+Gwb1KbH2Udu+56cBGpkDpu8N3BVFRU/CN6DPjQAFoUcTz54EgJwiMigNYq6w1PHf8ZefgiFdSfuCY+zt7QdNgcNfzdgaOto2yLgsgXBYS/zXklsP+sNtMIdlH+jpP2ma++L3FqCwAFojrj8BlXjk/cAq2Nl744PhLfQqYEOGlq/unpobyzH2eI+eXbNkBarQfutTt4vN/x570AAUBfhsOfUWi4ivo3JStlO8E227U+FyiKlyMcPn371ww/ads1sOEnuFOsJ2DHDV/JGrMpE7G3M8re0xZ126vztT+zvmNOdsR7mE1QtEqJsGT4d+bDfLGpOd7RBNTuFQzK8WmuVBVtA/GrOiAiz+fFYIVAiT6t4p6xEIercbd7/ruc5Z0YlTcQApyq5YlAkWRZXwiNm+QqOtvzlzjSxAT7MRodoaa+M2LZcmTLvtltq+dcpZJvceOmOZ8Hb5oCFfWCqNyluJenwCwe08gd8+FxQuyHwV1xdipXjDshGgnQCGZ/UpkV2jYyVfA1gudX8NU3q6snhGK7uYQ3FJ7Y32uM1OGBpiKrr1NKoSzrlVbWoxFcQDFJ/ksM+85QGogpwSoPIXIr8/PPP/7P/9kfdNCkDFd3m7L2QbewUlK+vLJEJdW3P3SfXWLEw9Mxsi21xWCJ7o6l+XszpbgYatzDOsNkKqlCl2oBsN0mGQZIWCLo742vNzL+59LSUuTZflUGYKKAgMoHx6SgK3w0+0125gdDMTEoEYuLMRJKhSZSrJ6IKizi63zfnksbMKfcz+U258l+5saNbxw5SKST2C23fVAn/NT7QrQam3fDtBNsbFivxU0k6zPrW0nU20gF7aY//unDGP2Pjj8T09scHjmXXxxC05YKNfYhJt37XkUuDYqVNS7pJ53sltsddZilsgz92TVpqORfgPVfvrObUsl90PyUemGdb/lJZaq0///yh9WV7Vpuq0vXGvyUiBEaIGfpil+0TML1YVsC6l21F99aPXTCzWkD1SsCtMf7YTQ3r9vQOVNU1O46KtMiDrSdBSbY4oPUcFzcGbSUIta4OsaZcW6zDyiui0DVc90gmCioVHULDmjCE5d7QNlIhtVPv1w/VlmIhun71ULxmADnE1kaj4GjyGAhe2ioews5V83Q6dzpziwjztqm4cwjqPgioiZCTZZP/SK4N5rdxRCeTFA6tiJSMd+4BXDdG3oZO2ex8N9IckUc6O6jFlkccwa1ybG2ssAMsCnpc7F0c09gkBElGUBXfFmPvSKzQxt1b5UVaZCn2ZqnvqZH9srVbkQjA14aEAQzDaZkuIvrTTz9pUJfidN6odHv6x3pygn5NKIkPJbfmyZswF3+yRtlbN0/J6h7ea1hr2TmwsAo8G2CJCFx4drPEFjhaSknr6VmDTSy2mz+fWWoFCTHrmst5y+FHa7TrLm9Gc2OZiK4+rODBVVUFvUUdcot6b7Fri0bzc/pSy6bgkHHkrSMH6z7RXRRCO67K9Xk05kvthFFqO5ljaylYDXbHj41yqNr17hCr6e3fOwcRd2y5+L0teHdJur/wT3s7Y/3WZQoaVieuXf3mTIjAffxWLFF0/bsIXh6ETGe8j8zEfq6N9+O1VxsbU+vtMIs4FrHYfvuOgLHCFDIXElHC1jyrIbXUzB5ny22vu6qK2BEROSWalUopHz58KIvkwXzuPTxFNOi419Dq61VwbR4r79EX+crlXZtqVkKDiKhj1yMD3N2a04ANZhXuLN0w9q2QiCTetpVoW30fedSgJb0hMxRmWhENYLjldbzZzujDirX5/zglsRyzDMiWKMKnLw2+CQ3Gjl/oodSWSsXfsmr9CHuPTkBT/t11FX0Q3tpY9rS9wQSdT8Q/Ee+vUlf6gAwNSB2/Fel/HJp48yZmeVM1eKaiAougqnQKCwWN74tM7yS90852/OXeScTB6a47Pei2Kz22JHb/HmB1BHGy+B7SCMc7EW1qvnGIU6a2PaHcDk+z531CMt9o8GOsy6y2Hp2/6jiXXYO360ZXOtg0X1/P13zijQnJn+xNQqGfiUiJVQfCQJRIzUipDM2sRMW2EBMlpuHp+o9KqJQLuGguSAW5Uv7+r35bRGvOyzhqLa+XqXAd3p9HPl9UtSyUEnEFtNaJQJxQ5SqAauI8EmcRzAXn89B0PUp5jYgAABXbW01gQpK6EDQnuujDxKmmzzWfoCNxZmUmlTIlLFDRiqqiBOQh54GXWut6qOJ1ek3FI6VaJs9tGaGq6FK0AyDjDzcqrTh2eM5JF1ErHseElmPE7qdhdFaOks/M8zxbhIs9oG3bR2vehn3c1vJdtNzZ4af7tIxdDSbMtPS4tvX/mSaRFj+Vc/bs1bRLK7RCDxFpknXKoLBCRJQzE4FImYjV/C+l1gXztqnbQ95rrfEQBw3qWMV2OAWCiZQHP1usBQYyM3MeH2qt87LUanNS5pQ4ocyziHisnw0REdVlq9z7xWsMFwC1PbnNHldsJ80AUGaDtnVNSZVMxDy5EfNYq6hWkVLK7kRe7ObC9RguWQ8uhYcYl2LQvNZYitsBmjOfaXr64eH58vrTdUrvMvLT/+M//sdPineL/vDwlPSKaR6GBK6X+cqZqJbEyZiF03AeTiTgNIMLcSbOANeqUhn8jpCk/E/jeVBgmma5vuTzKVmOWEHOmVTKMkldGMJEDAI/W3ekQmpStYQ5qHUaRiWSWiuEmAdoLrVKKrOCq2kMKnWB1lMiJkbbACSgChYahOStfFhEza/miTXWIAVWqDApmDmnlAmZiDJRAqFKXUqtC1SpZb9lyqoqm1aiKzioR/yv26HqmsIBWJPn23+rGyIqiTa1RJ3CTe82p99IzeO449Nvp5hgrzJEFIg/RVRyRsfOdN0a071OwRaI0tg6tU2bjlP1kCk4FgOmWBXd10ljqzrM7ZoqtyLg6OuUIKek/9sos53nGtvpf3bU7rakxfr9jl1HldN/Wt0Lt8wCW/5elm11GMGZ6IkPvSO+inKkZKvwhsrgr0SVvLN5NWhkKewVvdnxrizLYieV5TQyK6GWcn15eXn/Yybi7ajBA2UiQd6oH0zNOcVvxbADbytjUbKiiyxBVZVEmQAVVmEFSbW1ova8qApUCPdzuq+ARaC2V6DWWpdlrgBQiQSEnDgNaTiBR2o7C6XWZZprKZk4E+b5ej4NKaVlvvh2aKnL+rSl2QIAFriAdQ41NkEG2BR+AhvPqwrtE0iuPQz4Rc7OzCDOlE28O8dQZM1IB3eIdJ+IJkZ8/SZM6N4IcpS0/QAuGE1lkFrr5XKJwbfmEnJ1KTKfS5ruz2ixTxyPyYp9OQIrmV60JhFf8yIB6/Kw/eePYQ9nndBGtPI+suYu9NyhFgG2jg0+AlbUUxxoIn3MV1BrpYAIAZ5EVef5Rlwbt4gE3xrla7VxYsMtaecWAWt/SljwinSOcW2x+4m33AyyD9RCJ/bNlGbbdJEZXM07/NNPP/3dj39HVNdhCjQ58sDNXnhZp1UhJeWN33qX9xeLt1xE6HC0Gu03dcnhaC5/+AsnP2+s2UrBoCClRJSJx5xPPJwoDWqJeYBlnst8pSKUQYpaCj+ccuJFpJaSGWCWCjtoSwVVmZRMfa615oEBtO2NjPUYIhazRJBAaVXHAVkjM9Man6lCPq5ETEzWKGaY24hS3h8UtIO5AwDp3s/VPYDARl6YNnSLlIz+lNTOTE8hk4y2WDZLnWoHvue20w2A/Zrz0Em1/RohIA78vZHVOxoWNmnf/FnR7cL7dYnYSQqyEdsWpgzONHjlCBpWRy4fkXvOjkgECupV3LHUtSQClrRV77i4Efu1GcXh3BBtvst7mLWxwT6u2InpG3eO8Lq+wtt2Rf/oTcLafcvewYnBENSU0jLjl58/Ev0rogTd0PAmGSPz3yyUBoCqVoAISQBS6jO8wMO87hYOp+pxWAdggi3sWVKJNUWCrocwqkU8QAnKBH0DsJiZ4x5tE/iURB8AgDOlnPLIeUxpoGS5w4FapBQplSEZiaXabK0KkSJSwJSYdY1ISqvVqVSEAS0Vo+mdSqB12cJm93WkiQAh4lpUBLVqkVpKCyiHsGf8UGZsgKWOvLqbatw3YT6mOK68d9PuhufWho+Oj28OmL1oaGWANV2uaKFhy7LM82yApS1pemqH95m0IA/Yi6u1zSJI0j53jellbzJSX1x47E9n6A4g/EJvwdNNinnNR1GPRPNKnPI3xaxrjDfJ9yQiarttm1fskRE5fij2NMITh63pDlidCtBBczdVxDrd2MdeMW+f38gVK490kLAgO46pKEAkKqWWh3MG+MOHT0RJdYGqyu1dQbovR06wklJeaikCASlBLbM61gNo90X4Pmg1C0BqrRRIx23F0wXKyO7GRGQSfeOYL2ZDtG1ezTknrIoUcU5p4CETJwKTKImqVqlFpimppJQTQ2o9nQaozvNVWqL3xDwMg6qBciICc2ZOCk6Jq1o+P1pP5KAVc+v6r7IKEYmiqFTdTXQR5Q2f0A6/VpKKCqpVd6fCONPHWRRBGKI79ngR2bRjsu75GL4c5+3mSV2PF7PVAG1rRhocXp3iEL/lc3KMadAWW3BzfDsA2nrU3IKudByn6A6mj8KwG4XQ1KN46J09jEe8637SEG8Vf/KTyfewuI9FDD8xJxfgbqxj26Sl3IuuqCOS0gHru2e8Ql8ExN6N4A+kcHS2V+Uj6x3JKS/zZMv0y1KAQVU/fPikamqK6WsW4CBux2mL70Hg+ZuFc9K6Lh8oJfs/gIU6FesLFiIzq8dm0zYKTEowFxUR0K4V6vnLFFAmFRWmt88lXGeDasFszEw5i46UOKUhrJopULWKkspSpFYGDZkTaFmWx/fvpum6TFeVkplEqiqN4+lyuXAaBEScOI/DeCYeU6lI1iRZPe6rzSGUWGRVx4g4MYMSp1rrMpS2UqbRsaeiAhVVrQRBKcwKSnQj4pmIhmHQEKHuQ9hJ41FIYvGRd8GOgEhtrkbLLKjBDDTdyj8dHd7mOll9WNJjlvU3ai7xi2+Mbwepa22klsOS20ZIBQRK6NcfgN4KOFYYX7F2Vt3lw3GMuNna4wTQgV188ThSjtqKDR06tPWZwKU3Vu4P2zB1eH2vzR2O+4WEfCfa3JoppLq1Of9G+wNCdeBiDWNOugZv67LUX37+oEKqpAIRSIXoVkksxymkK5yGqqUFd7MqqR+XqmxaVaD7fdOS1gzIIqIHTSyCu88Tx1bRGymSTeZNQeF2cA4TRM/EphIlAJAiIlorqaio1EVrSTkPnCAqtZyHYbq8lHniJExkOSpSSlJNiyLmnHgch0dKiTNAJxuIDbMaIaTq6npHAthC5ub5dShrcgiSbf5REajYWnAlVC0LEYgqrzODe9MNQSy/eDzyx+XBCRLl8AhqTm7aO786CcF+KbAupZQyTZMBljaT1oMtsHmv8ul0Wi7bOYaxuC/Mkc73G9xjoO5fu5DgwueQav34but7DyVH3ER0eNOuQr/vknxsVaw2vhjvdET2P3WNP/Y897uB6CA1Fh8IBw7TiXLIn9WNe2x5hOn4b4Szri+0j/iPzvsOi+MrpcyllGEYAVYl2wL98vK6BlhoX46c8DZgKSUlrqbAmM1le3jQn1P/hj0Ym9197kh53IIquEm4CmTz76iuZmCpJRHMjvEFASIaHx5rrUtdliqJkEi1Fq2lljrNF1J5PJ+HIZkjZsjjNF/KPClqAhNTbVt2VFc1UBUyUFUlTVI1nXKVsiaYgR0Itm4eVig4qZCI1lpETIWScXxQ1WmaeEBKqSzKzFItB2EFQEyJUwWLrkeKuxR5ALHHAVmkjAMKDrLdSY6TeN1KSduyjmdEilNxbSnY14tld4wghSj5FI7zAiAil8vlcTw1QN6c1jFINU5Wcfi7f+956HJKrv15f613ngTKdA1bQbtOkzcjSr60TFjeZbu/lMWdODG0LealMgrYtR2+GX09bvxGga/tgJnh9GjeQL21pVak1C3Jj+3TWn2XnrTL+GEcx3VG2SfzKaVEat8keCxWT0iusLqDY7U2M/E68L3p5wMRGTK6eB4eHgogIqfT6XKZfvWrX//ud//08ePz3/36t3/43X99OKda63gepdSck7aIDSJYX06n07wsOeeUiLHFWxhx83jiaQEvAqY05PGklKoIr2u7Ni/WWkRDajmx5VShZSlM+XQ6WQTcKhG0niSUUlqmhW7pucZvftyZb035otNdiXYro1MtsJC8VW6raFUpopVUiC34ra0QJy7zBJIEFa1cNWWi1TG0pmNWylAmDIQEVssOqiCATbFS2EGipnvyGskHUZCGmSqO78pDe5ctERF2ctVNxbH7LjkeLeX/Hr0hu3/x1tTtWOOCMa8BjRsvGt9axLyLrh5MAxfgrhfHLx4H99gwL+LRbDF7xjpkulKQSVUXqctc+eDa8z8dZSLBa9mNF4douJtEOzbVMS5OPE6W40RipeWM3+z9VvONVV2v0Arvl/OO7TkOgYOOr0LGaaabCDmEvBzLsVOHIkS2QmaYW2vR189TKZaOnFpaueJifyz37lcFOHEaNFmcUrI9KD0RFEpKCnd9uoMkAvrK9gSgbVCjZHxlyrBfVwEIJCBCrVoFRrO3fFiOVIYBgIpq1Wq6H5GSFpGqZallVinEOnACzB0unJjSsFxfWCFEdVmUMQ6JmZfF9N6klAAGs4DZgsgMm7YsN1BiJVGCrqm4zKGyQlhnmpngO6YEiduWDv1Jn/E6CcEeEfROCYQKRW/cRAA7GzPPk+Uh6dQWEM28PZ/Pke+d0WWfrusIVXp/H9afUGL99nU/iNhWCR7Pw/HhHT2Cd5yI4MvVB19SeAb7V260ymdgXfP6b3G23VwtIlWqA1ao5K02x/nj2B5qM2U3ylb8XW16d6fPRgGmsFE8et8jHeg+ZhEJkIiIiWwLXS26aPnll4/T9FuiRCBgPVKsioVzb69HZg7/kuq6XVdAxDkNJ+Qx51GZolW4DuUW/b/JhYSdCdLyHTQGFmnLQVVceKHuAyOqosSookSooqKr8/YLcVg2K6iI1gqqqoIkxJpo9edrnWqZalkADJwos5BWc4SkjMzTdBnHkQmLFAJSGomo1jXvKgAFYz28hwWaLMfFmpnLpntPeqNQBqhdALb6Q1trDbBWhG2DQS1/BRMztvWvyBBuEnYsGA/diEPrPOes1jhrx/3eMAQL1AFLw6kt1AArbhXygXCxUdk2x90TA8cs/zXw92ay3Rzxo8x4keZ4dqPmJtYHzlmxI1I7/hrfinS42eDueTQvpH/Ifqr7tDArzcXNwJguUQGk1HfBaYiDboU7iRtv3nTTNdbpvCf7E8O2j3LfX7ypZFFTK5hZQdAqgkXqzz99mKeS05iSVNmybJIqoMeR8msKB0wQUAXgPJwYPHJOAKlACBmJqIYcW9tMwMyigtWayWlNWg2nZ6LkhksJvQRt4VzMLMQWXq4MUSAx3t78rLDFBZFapRRFYZWcayJmUtJa6yxlqWXSWnPmlJkY5usmTkpUic353WQsTESW/G89dyeBWCkRqRDsP6w+LBZlAchMQmJVWJS+GS8ignaavAQNy8KcV7GslUmExDdUy341kMIxUI4+HHJ+O0sdxcnLKr3S753WpltpcLVsvrO0Ba9zWPDazOpW2+q10X6PTuTmDrPuDS72wLQb9/DSDjFzqrUWqVoAppxzHgfOiUPEcxQq3bJubBFPHVk63D9+1P7GQXrjY3H6iX+q2QTmLhTfWR3l01p+O3Kd9sW/GM03ChPVvRZGXoqNjO2ncG6QYPfROBvdHK/1GYKq7eVLKqhVf/7552Upj2NOqWJpG8JXsbZ/dyphnIz9J1WtSswpU9aUlZJiRSZlIiGBZXRRAGxpzo0mSLVK4sScWvBWpaYWJMpELRdA2h2YuBt5ZuJkF9yOpH5Lw1JVbUMutSqqqnBdKDGgVIqUqdRJUYnMIwslEdsIkVlSqlJTsqlGzBh0PjZeARhrCAcDrLCw/do8C/cGyTKNiUevRVHXhvoOWFqrUhUSRc1BW4rMHTUOZzIHMr/jDBothQ5BEMTSueFoDEZfmD+JJurH+tc6edWe9pTcfGp6S8/CQc7da9uVpfYHzVLQgAx6otNdl+lYSRSw6J5DCJRHwKxIt661tqroHfH71v54GAc1IyuM5kZ23BD+zSPp5PXGRB6I2BFHlsIc07Ufe2g7ltQy1mLv0mqp4baPRji+VVZOUKMrAGWAP358LqXwObsviRmqtgp8w83n1+4F2rbiMhEloiyqUlTWLShQAjVFLHbfNCwTkpwZagFiYGbi1emuLUtXGk8+ppFF53kGM9aNQWKaDTO/6XSHkm4nL9qa3DS9JuZMgFZdJpWSoJwoD5w4FUXVopRTYgWWIg85l1KYdEuabEeTb6ZTAizqvY293thL2C4ipdcEiQ4lEvKXa7RKwEq1ooBYaLfpP2KWj5z7iaL832ToI1QducnqNx+Kq1fxW7gFTEck8u/aRfQRxIc7zHqjYW8028noF0ZhX82pLSs/Dp/252M9GrYlOU0i7OrBPurkNtZGbV3JQm039aTXFNb6bdaxr4X6+3yKsandIkCo88YspYc4OO+d34zj5Z6d2Ob1T2ypzWI998aLiCz4XCEgJl4t0Ofn52WpwJr4d1gRXBySfOyOn4jfaltEkhBqlUpAO2gl5hqwC26LIWhARk2rTYlJt5AtaksNpt6sjYmEpQI/nY8YUGLGGxqWjQYTb45FEBTzNKVEwkiiKgu0pkSJU+YEVi0QEWRDWS4yp5SWZVKsoVLmH005qxA8j8w6txiBoHZAGa2aqcabfo313x0DHQLXolQICUiU296dtkZ+xCyrzLkq8qWPTWTZHV7c+rqGVHxRqOLgcYu9cn7d8U0AAt3PujeNO33TNiSie5uizYfSyTCCDutKjU08HL5yhBXduGclKWOzofbggu7dY7NjMSdabefR0i3Z6+hvhHEy2gp4afnC4mh2PHCzhf6TjcXNWAe0nPHOAD6sUbeKLVRas2I5jr+BVl5ERCHUxo4oXa9TKUU1S4sJuEftrujeWgRASG05HqISCdW1IjZYRDw9t4iM46i12qlPILINBimly5p2ydLpwP+TVc6N+ADIIlezlnWeZAIYoqIogAzEREqoVBfRIlqoFEV9OL8yZaIkolQYOiIPlM5C42kYVZbL9Gms6f3pPM/z688vl/eycEokRYWlKGZFFdU0ZB70Mk+V0vlprEN+nWfw8E4WrYUsAlh0WRYCxnRKmqZ5sriMy3RhIDNfp6uevrvMWESz1jJ/Og2ZaSjzpSqBz/Q4JkgCAUyqRItgN08a2xn3H0EE+9Ni4pKiX1CYfolI6jaikVOnafKaY6gED5mIMm+ROJbSudYatvPDEr8C0BAYbbxoVY3jGL/r4GgCE9t8UwI3pluqY6dDoaqOeVizLIjaDKtVii7eKft/J4WImx5s+rwAUiEoSuCc2LN/t7at3wIEqio24VaoQi1Q25pu/2UmZVImHtZITkPS0+kkq9O1xK1OvmWHmQFbhmGARIqPCALGea4uH2Urw3AKsm3NT9aDzRexkgIA/OAmJabE8D0xtULW/kitVUVLBcA5bVOXnby+qTI3VLwZAHjQDFtF0KqcNKV/+Omf8m9+/P3803ePNP7w3et1YtGHh4fyoKWUUmdSGsZ8IgYToGeQhcYTsxBVplqloGY+V7AqETgxJ7NyFGkBVEiqaIVWRVGqIrUtdoF5gJJKFklEdLkUIDNzyqQpWTxmFSFamhi2c+AVqpR4I7GxCilpPTjdVVctTFdbV+Nso9BaVLkyE9Q2x21LWqWUpcycLGFIVa0tKPXGjnxCUoWI2H5y6BogeFOK3ih0r7TfALIkNtTURn/rWBUa7nRzSPd8vI4qjHcwyK24E2dH5APZHd34VpzL8XlviV1HXxuC6eG+MP9K17WudI5/f8xUYzkEUnqE/ZFiR0LFn7wxsbPHAYz6hYsrgHmeXbcy9rMoB2uno1VcWnHVxvUXr81jd7uv3+vLF0vX5q5QCONAiIaFOYZaAzSUu7XdaZSIzPOciFyi7zWma/Y3dPJbyjdR742yA6xIFxElsqgLYPUgMjFNTYMj4pRy4iHnMXEiwnSZpmlKiYYhiZZS55R1DUFdNwnsWEfDxiLV9clvTLNzF7CYCMSwNF0K+9dhC3t27ADiJuPexLgj6ZiilnHbBrxXwypRq4t02+pAuCH2GqwwaosD2mwTupNOwL9yb5XQI8g7AY5+NwRXOvbg6LSKfkAE24f2lmPXi5htIrbT/4z1yz6Szj5haXkcsDqye9e8qpS2oFzajjRnDgENOIDXG4OIL6FVJBqFAAh7eKnl6Jf8ZsBSqlVfX1+/f88ii9LqiXqjPavS0FSWL7LrzdfxdXinwVHwTV8B0A5lA2CpEXZE15hhilMiVi4DExMl5szIzl4ApulSSjmdHvKAZVlqnVNiVfN0QLWqqEm0fXtVCb3DoJTSXwqwjMtMpwq6VdOz9miFEF8T4Sli1k0U8wHYrveWYAdYb/P9qnRoa1ho4b3PxQfo4LSikG2i++49XuF97GXEPgq2mzOJJ2X0150+DiUGK8ddjbExFBx5CHCfctI9CsQSiTzPs20b8NQXEvzcPZHbuKd2GD32iNkB1hsU82pvXhwHK174bqdtJsBOYfdy/NBaD3zTiD/BINSinz69/OqHH2qZLIKJbFa7fwwXImZhRbh7/aXVOv/aQpt7q6vk20qvYSmw4uu+KrJ8eKzD8EBEUGZKbPEUFYr1+CxAUiYiVJmrTMRURWxjstZSayWAOZtUV6kinImYud5xD/85hWg9eDVAzPYBF28EPOr+7CTqXvPiIMgdtOo49QiFXW0+sqp+5vjuV69N26rW1oagHNWQW5lDqMQXqbfr1D6ZVFyjqCEJZyTgESbir37fG7O58PbWkFSJ7XGWNwXKUck2qM7zPI5jXId1KIxmbNdNB8oI+t0qZ/fvPXIdYeuL5PVqrcE3NV/aq6X3C0NN7eBSyqdPn1L6dV3E9B5miJQ3Djpt7qcvn97cmrQOnzPq12hYPWh9Y7mrYRERoPFPAphpyGdVhTIRMWUCicL2lBLpMCYiVJlqnRQLEUsVIBPBAtCJ1pDcWg2wYMBXwaqHueIrit4qNmwagkpcH9ZwpFishL+UkO8e1/ZQIj1a3Xx9A6w1mrYHEW3TnK4s2Dt0vLjA+6+xSccGWOk0Ly83BVtV3bsv+xXVqFLFwre2NN0kr697OORpCJGbpsnfil83eDLAsgfMY2Xbnp1pY/M6IlidzNsGg2h+3nwLeEPnuK1b3WQAHKbnSKLuxbherPtV11aDAGgxiWQhjaXIp4/PiYe5btQQkXRHxG4CFhHdDYW8JQVvIJEL3ZG831TualhEDOxDLcymwtCCWbKtTapWVZ6m1zGn8ZRAZVmWKgugIkutKx1VVQQpU0pJQfNSamXo6jVYKqR+s+WM+4C1QrAqoFhPvt14paPgkXb3AOv4JPacKi0bt3t5bkJVB1i8b5uPhXOwv/5FEsUHOol1rnqDUeohBTACO950XVFz/bg9Rc1vFVF7vd9ax6FQy+wuLcjLX5R9WInft+U/81i5D0vDiZO4nxooNtKOvUHjhOi9chpiBzRfIPsXRderPVL4+Kd/PU4wkYU0JidWBhlyca3y+npNaYhVidzVCTToR9rmyLfb33Xx7S4fWfdPQCu8sTWHVhv1GNlkEA47/83aoSSllPMpDcMgOldZVAvI9qmvJnEDQyZsB/joas4kSxF954y4t8o9wHJtkeirNKx1+G+ZhJGyHZW7SrCPa49v3QQskx5g51Z34nyRae7RIdTdpyJwgL7ndJf95mp/3gI1Y4/c/+ImVUyk02HchhF7CrstFn3P0ZSO4BJ7kQ6ntMZrBFGP/Y1dc/JrUNwAWMCgBzSgx6y3xPIotDenh8gPHQsda7jJhBtgtXHDdkg8AySVl6USJQt4bOxd74m8bt6rbyh/Guj8OSUPw2BZJVNKSrCTuHIeSllslbDxPROUCLXWnAbbGLHtkm88lDOXihBPLONwznkkqsyZsJ4GLIpSRJHO53PO4zRNSqdhGEopqe1TpW1OUwC1Vp+ETXgs2FpBltxumqbzw/fzVIbT41xqkyXKOZFoKcKgYRiK1CO7dCwLwL2/nU2BA0hJ2JaoqseMoFZcaLu4gXacEfwT1Pyj2kQ68y5KKDbGL1ybi+Id1ZOuF/dMQvd5x+0sXnOEJGqmqCNj1KdcU/O4BwTT24lMTTOapmk35bh3H5tCZ/+aVuVFWmi1tDSHx8GikEDKS+tRbzPqrfArHITTOcf/jCO+0flwsnT07nXkNeSXEDviDYjaOiInZIRtIQwFiKG5Vp2n8unTp4eHB2h5fX391d/8lUghgkkQE5ZlGfMwDMPLy8v5zMMwUB5EoaqcWERrKTmtigtA4Ywr1Fq5nadFwDAMFTIvc2qzgjGDR9v4iZBxahURzz1HRIm3ScgwpyMyvpitYRuMdjEOZ5cEY+5SylKKH/i8iS4B254pqyQxZ1WpNZB4K285sCJnxPtxJcgFZmU7qaJClLJt+tGdlHZcmMJRzIF7+tMEEHwcTvR4ETc/d7N9lEYva/oRbI/Z9U22/tYJMAKc7q3LbrGvI0tsrRVPIe0VduB47KxXeGy2U8z/jK6x2FlKO5/OTf9gbLzurb9ulP2m61kpfTl8QXeupe1mvLinsXo+g5uUiWNkpZs4v1hUKzRBddsgolAiKL+8vEJJBEJiM7poSc1tjQM7NTTBt6z+/UuXL6eXceYygM1DKqWIVNLV2q+1zNM0ngYT71o14lHOGYpaF1JqSR1pDTuGs5QP9ldhViwS4n0M0bMtops7AwXIaRiJ1hT11JZ4uwkzinT84nFQtWkQEaeihtXJif3pUtfVXyx+KiCA7i0XBFjRN91POPi84rtdB10T6djXgEn3iVBonxMyil+cpdA8UxHInLwdDR2wNFjQEiKS7JU0bNuncMjX2o1O/IpzRVwbpWaEejs9tvlYQ2x/mLduAP1b46LQOwBx70NOn7fHemuYkmUDJ3UNXYn4p59+FoFAhGQcx8vlopDUPudNcf7UW2t8aq5Uuwyn0twDNQ3l9qwWHiYikZ04vE0lfMmHtXEutYbXugDG9zLP69Kyqo7jyEwiWkpRIdips6o5j8sy1aq5aQ211mWudlA0UTsv6MapQTcI0fWnydu2UXkNWU7twGRZmC3UwhS9mkKKm8gQEXcaKVco1N0EuxY7Fuwoewj5quInYuO7ydw6tkkUrbZPJ/ZvM7HXfMQsHOSwI2PUDny1MS7zAcg5x9jR2MeIOA5bOx6NmpdsNjgFpfiIhv6i1y8h3UWMTfc+do6qLlSCwuJA2HF1A1I7mu8ZT7sHjq8fRa67041RrCpS4I0Kdz/F/1r1zPnnnz4sSx1PDGAYhuvLa8r9tEFkHohtD+A/k4Z17MKO/1efyPbwPT7/Kg1LREiFmVSxLFNKiTkvS5muk6WLGnJOKUFRZbbFPl53bElKaZ5NtSGzB23rrOVKNswissWjt+gUTbN747eRvIGI5YaotRIBosDOvkDAjg6SjpODN8Me8GX+SHFtYZ84MLRX61a9hvipRJt7awOsbQjeHqUdBWivSUWwiEzQrQb6dbcp2rEs+ulif7kFmnYD5L6qjtRHCNB98UrseTkkaPWCoEbFYaKmRkXAsiZZSpz9aubdfF7etr1bgDo6IwjYzRq6P53Z6KDeHqHqOPccRrxCeUWrNY0cKTTz8PLy+vr6+v7htKJz2E2vgaucGx2utGEfEeHrmc9rPqi9DojdnbcpdrN8RcZRERFhqIgSU5XCCapUyjzP11rrOJ7HkzmwRERFwJwTk6LWuu3YMgF0zst7RPgaQtz8M7p7Df1g9lr41QCLFObRpODE8XJcULNiAryfglTDofD36Gv3oxbgYxbhI+UMT9hh+KW7ySfS566jZC8zX+TyjoH8DtpBpB2KAfD4Jn/RvYcRgFxd8iwF2nSuVVZvMXR3ET9da/F9Nl654+NW7b7BNzWsDrAa3foh62h4wNadg/IIMZEa8U4nmffGqOPDSOp7QwkCZFWUABCS6QHTdP3wy6e//au/ZuZ5nsZxvF5fgfNabdh/s2Nv7AJH9a5J+JamH2uO9NGgv8WbetCw7vX3LZOwNXPthlWZcwJ0KVMpBSQ553HMq+NjY6PMTCKw9ISNgRJgOYI3m6I18Ms+rHsSyMyArapozi3Jjoh5Uywtf60V0EScUpa29xUH+fchc/6gkAoSQSA7/sPBrevPe8vjdBq7s0pYOCwrxrX7HX/yjbE8Ylb3U3wg9l1bUlAcZMP7UkPe8Yhc0ZHU4aDufVs2z2daX4/dl/1WZATomZYNsHRfaL+BxkrcStnFhdnuaAOsIxLdHJpu7HQL0+kxKyBakH3gIPw3voIgtDGTh/v13hh0IgJsR4d1kwlClFSpVv3ll1+AvyZKn19f/+pX715ePh3Z44t89a3lpmgcReb48D0Zj+WrVgnXeolUdRzHUkopS5UlpZTTaEeu11qhdqqWK5kee+2+zxuuh61X2nf12IYjZZlZ1XwrmxdD2pEqCiay6PNqqc1WhfGQDdJdJF02gghY0duSviIHeeTmyIjYG57d88fNzxpcWl9DonhHwoEIFDQC91XpHoXT/pQgp1JbU9vCwWsLkcWe6d2Z1QGWrkd79aKCfboCBPQx+nuJ1OuoTYeI+eBW53jHCjZ4hV934xiJ6VInIVKko3w3uB1gdY33Crt6nLw3uf3ri1H+5eXFUkpN0zQMP95LgkZ7DeufwYV144s379/DNS/5SqOwAKiAVCml5mT5oZaBNFMRqkUuKvOgNHB6fPnp87XWWQs/4OE7Pb0vOV2lvBvT/PmXVC/nRIBcn2clPJ3OWJCKZRebRZdKV02V+JHyuzLzKf1wGn9YBErLmMs8P1cBwERi3n1QJZIqM7GJAamSrYRAmYBaZBweLq9zTlkqhuF0XZb3333/cnkBxLzwdrjiUpZpvnAax3GMm07dfDBsMuTy+dzDFJzKBDCxx1utHLkpi+rSjiB73TraFmapTav3+s12luoPgxkuddiARlVFqlfbDb3933hv81joUQff3mavdq9iiPdFVRUmAxvHW+6qcRwBkKCUKiIqRJyZcm15/JWkYLEc5gA0rW7BYRiGcRCRl5eXz58/G2bZTx564ushaX+ER0qJaQjY1GCLPcGeqNZSLMxqTUEeqeEg5fmznD72TEoUMNpOaawt2t4mjKb1AKo6S42g7HS2VVeHaeeHb8WnXJ5AUrEoTbIeO8UFkvPj68L/n//fVPRXP3/4zz/86sepfBwfrqLlOr3+8N13WmsplbjO83w6D6+fr0vRRJryCSRVFqm2U4Sw5iOrElLfVIHlAVNVraxgrRl6kvU4HFUtopUlgZiSKL1yYlWqoigD8wnAsiyJsqidMAo7i5UAQIecaq3dOdgikkmFVCiZxq7QxNzCAmSWZdLlqrXaGl8ClWJUJiKCcQkxke0EXEsb3aAK7ueZP2fqOBZ3yhKEeUtrZ8XFzz8oTclyTuL96pIVd+se+nVjhvfrzkTqmhHb43NsRxNqbjhaz0dqwkdERIk2H8fX0PCYI8GK+aS67hxb6xLlJIo/iQhIfdFNVc3T5BS2n2QfCez1RJA1t5fdv16vy7K4N818T3btSm5HdjoUZvbDSYndBN5GASH27Vhif3WvnEbufXsIusp57+/zQi0uVHU9NUf3RVpw7Bsf2reDzf1yuVwMTEspFonszX6Df6LA/pnlDUK9Tbp7v2aVGaqsiQkgVRKCklYm1Lks14su14RCGayksLwxKpRt5l+ZEiq1QHbfMESLLYjC6c8QrVtRGjN9M5AZQpWiQ1o/5IBl1TdsImZairqaEDUdA4i0zzzpEc+x8UdNJPJlNCfjM53Aezk+330lsqyqHulzT+TeLr6YcKzkiEq6T2lg5Fp/gm2NyL4PAS173DAMlsi/Uy6cIBQskVKKab7Lsry+vlqC1qMFZzjbpYiIT3Jbb/UGG2Dd6uxbgJX2mqkPBzefg9+Mkt+h23GM/MLXEOLrujfIOqLdbKdDVfj0+mep9fn5eVmWU07ztJyGQrirbnf1q6olIf52cVzVTFXtlrdNxiN76/3sDjfhAkAmWVSVWNkOwUIhEaieEl51KfOky5SysmaoSKllWYom4gTmzIkTiIiJZWk2FEjFNhuDOdcwGXYeDW+K9U3XSfjbKEREqnYcqfuwqoh0q7bMayLPpeUj72Zj00Sa4bDRi7THjvX+tvq5O62ga1v8RIQwDotB/kUKJgkHz0t8vZZN7zt+8VjSHZPwuPuk+xN7gYz0oXCAQpV2IEp73b64LIvBinfTf7UaNGx1tn9tp5f711NKtpjjmGUga0CZ9nkKfe5pJ73vKR+TIX4dYMX7EZ682uO81Q3iEbOO7OHE0eYdMxPViflGC1stQmHdLXAmiPjTp0/TtDydx/lSVbYFE3+7w4X1+s/QsCIehTt679c36oly56TIiSpDE5AAUWEpRJoUiZEBhlQtZAc0VlXVUgWcmBnugoGQipQqUhLEdgAJlMFIbA5xatuORMSmQ4tw9bFXvDWNvFGY2WxY23bvcn7EPdpWUraEws4r2vbZuqPEyCRli2iPTHmTmTSsvlGY5+mgYfmQ3GTNWG03xlFO4lh+a/Gvx9fjnxGd43c5RBJ4v5yeK2oAl8vF+u5TlC/PISxxSDv7mojmebbP2XKeKUelFK9Hgxe/g6TN/NzNRL3Bjq8WxEiHPRDcGLs46N3zODDJkXMoaJq+VcifedsYRBOcoGetb+ecX15er9cr/3gmSszaUqfcMAl3/Y03v911o0HD0v1kH+nfaHi3koikfj+PqAWCWkQXEdFaKYElS6mQOUE4EatohdRSa61QUOKcqC0YiYhQgRQKWw5N3yEkPagnBimdtBxb9i3UsdWrjV1SSscFEWuVOztNEuyxyHAAbG63iwUz9s7m1ovbe9CiG5j2+hcOiIA97mjQtjqx9J/4a2bdUO75sLrVQw5ePD0U7CPvdzDBg9djj7kZ6HQzsozjOAyDJQXVduJZVLp9J/MwDHFrKjXvmCtfN2eF1ZUWdmI66dBecenTbdq/Pfl3DOkdPMp5tG2xx6zjSMXXu7nBLnivuCHA2c1xPLaQ2paP03B6ff30+eUCnIlSzjxNffuPoGDkag3Y6POVpSMjdqS+/cC9emJT/SJrudRajKtKlSol50x1kDKXeWIIE5hAWqVWkYWQiJnSoGkAkWglhWgVKSTV46gUEDClrFp0B2S9SfgngFQsIkLEbZeiWOBySgkNr7aeq4qoZQiSFj/tgup76KxJXiE3q7vzOLhAdgSNYQEu2PZTh1b+Oey5zR44amTxvpc/mXpef1eJw0R0tx8fc4zw3fdh7Ywc8f0+hyR5Ub1CS+diRdq+Re+msYoZjHbwBPZHGe3QMyWmmDpmo183Ul8PWN3QRBz3wdWWKrrDrO6t7sL/jHMh1c0V0CHjnZHU9XytHRelZV6ezuPleXl5+VyW75lySrzMZRyB+3BgomrC8ufJZWvcDbJ/FWZ1AuIlz9PzsiyliGBd4sk543yWskAlWeSnQlRUhESlrRUrswCkKlpZK0QtUfJ6kDNYCbJv8b0m+k/UL3Z8uZiNaWMsIqrVnY5xOjW+UKUUktBLOxMYgAmYFYqeI93x1hGwOkbsgCwiTkf9WNsRtjwdSodZ9I3ehXs+rNhZBHlwwPXuRNOsgzZmnpfqbY6q6+Pjoz+jHmIWQrS8fgOy7tPRxWYvWsY+84t1c4BLOBHxDsv8mY3moXdvAZbcirdqPLZxcveMj+DNElnI5wNf91z9ErJlB+uofXcIAd9gZCGjAJZlITyUpU7TUqskECH5IYzHQkQ4SN+fNh3qm073/s5XfCIKTs4kyASVZamEOqQ0DjkngDJDrq+T6nI6n6WUeVnOY54rzilxHsX2ZyWQJq5lqYWrnE7nlOn18pk5PTw8CUhExmFImUWrNk+qLQY9fffjMAy2izi1kwHLNI3jSGSb16pliDcnq6paQIWxhBukwzD8p//0n0Tk3XdPv//9p9/+7b95eb1UqWibDFaHlAJYt/LZVl4DaDNVpmnaZulWgDVJOw6TQ4ShqDKY1B1ZzQBID9kdIvDFT3gcUNyvawCKg3RpCGS9Od5HZLRFPRyAWJviQC0AnUKuvro/DouZ379/76c/mO+JginXoSERWeAxET08PFg3IzvqXuW0sizL5XK5Xq9OEyI6nU7UFKvdhpvgdEfL8S21+riEoREA07R0jTQ6WJ4m73IEMjd7YZHQjSbefj3oBfbdiGVObW4r1LQ3gTkU7Pd47cZUKwDfT2PDAuB0OtlK68ePH4fhvyuTXC7TMJyWZXl6elqWBW37lI3I55dLGnIeMzMrkVg8HbZp3z9uLbfDpjPUCFWLQHUYhrmpCznnKrZer8OYmitmnYds4hmGQWqJ8O3ikNJ2+EhkzgxdSIVRQJVUYXuWVaGVGMSr2oT1tK5UtYqsq6NkkQ5aRUoiJVJFRWVVFWIBc1i1tK8eR+7PLFbbPM+1Qm6FsB+fx56BpB1iHEU6Phk/5NcRsOIz9yalbnbVvboRn/Fru5AWJLk6g9oqkgaH1xsz4ZEa1kjZhzJ1lfidSKhjPRosPjQ4NrBzZ7k/78psh3rWuy7MwsMmzBj0IIBjB2lf4o3Y5hYoL+Gm2bDsdO5kI5LOtUt3wMVBtwkYe9TzlZw4E8T6cWvEnQkjw8QH3hYfNc1RmZAAJUrztMYgq4rtVKOmTHWN+WctRxb90z6agUoQgrBKJbNgKymv2/EoC0RVbUelEoloVUpQtFRzIhWlkJ19Jlq01Co8ECXWEOUYGe4vSKCU0rLU19fXWlEtB+lXAJbLv7QQBw2uXMdWAJm3fWeRV7qH0cyfe9+N8hCp4cBxJE7kaZ94/fhvf6aTzP67ugOmDoNwwOWOUMei+32IHr/mMkbtJG2EicGdhh5HKi0m1tvvfxq4+LnN5m63m9GbTvvkVis+6i6H/U20bV8EAjDdGxfs/Ykebub46xNA/O5WG20adGdOYj9VxHGMU68PfTcKoQZrSW9wAWDKLy+vIkJIUoWag09DPdqno/jnKm0S+bNKZoiYAghbvyfAkhcqOFNaUGwmAigpsYItX9i6DZEEInWZB4uubSo0MxG4BDn3NaBuev/zSynl8+fPIpZGeVh57j5pKChZ7nbpfEabB4ETAlR1JI98E71X99Cna8Mbd2Ij6aD74CCQ98oRmLyDsRL/1e8f64k4vj2v6uHsJsNmo0UAcveTqrpvK9YWhR+Niwynrterm9vuWPQLQ6vNXiaKW222ZkPbuESt1jICbmgVydUBFjUz2TOvenHwjVSCa166TVT+fOypzZ3+elWJDdDDevTNkSWC7lfiiBJQmfnDhw+lyMBcq6wZ6PbEwf3Z7i9Y/Lve8S+y7s2SVy81CeAKc1XNSkgpaR6Xaa4CJhKiqsRDZs7UViWgMH/oMBih1yEhSmCqy+ZKb+E2zMzylyOQMfflcjE/n83tX0RyasGijqSdT8cf67DVa3Z+6n6KaOItRICG+PzNAeMWCent9H9xQMC/OKtp0GJi329+iIgIu1wuNgQc1gSNnn6G4MPDA/OWRNRdY0YcM/1KKdM0TdNk5+IgeNbcXeUl5rc6EnbVa+Da9GJQWEoRKSJyOj1EiHFS+17r+BOFdGaRSqpqXlFHbWqOv1K3lvjEsNePNtMbgOEbBb97nCSOKrwJItacms0khKNh/uWXD7UaYFXbee5tUP3n4qKbhYJX8U/+aBZqRLJJABWw9TJOw1hrBadqahdYRMfhnHMG05YivopKRVWGkK6uMmYGuMhseotu/tpERFL/lCMIbxartpSS8wpYJga2bfJYfDaL3KPBSUxtB9/a7EbVqFngQPROFYpz1z1R7yaZbgrqPuclfjeK5T2MPmocNz935CHrTrzTdaTrArWYW2r2NTWnj2OEhzLEtyQkyfAn3W9l3zJAzDnbdh+7dlj0eNSORJsSBBmGgYjsAM12puHUckDs1GRfFcWtzTGfP3/2Zri5oKoGx+5r45adIgZPxmHt8qx1HjTsT8NFWGKK0Bxq3g+TEhNBmZk/fvxYSsGAWnXIuaU26Qf6JvP8BcuRn1eqfmM9q4YVOVGJBUori4wARMBMApWqj08nypnaWc2IZnD7uE8Fpchg5NAtdhx/UUQvpaiSiIxjUos/FO2kNJboJI4y2e0j2SBgn0LX/+3GuJuZY8EeUCLKICyf+0x7xIhYVefzOgJQV6LT9yg53Yfir/fw1ynQ0GodWY9xj25BtK3pbgoty+J+RvsEh/TW0zT58aiRsBGtDCkMsCJaWZ0WKO4Ua4i15uGILbGap2lysPBvOY54cVLYYkLMq+W4HAm4HY+Y+xOPnCyR+BtmHUIBbs6UcaTWgdjOEN3Fyr88v0oFwfS1VJvWpqrdAsU/d6GmYXm/9NujvbZ8WJZe1dKjrAc7M1NiO1VWQKoElZwzUlJKkbiqKlKwj+bD4YjzmzL5ZxaR9US2lJJFmagqJ8ZtBeu2yqPB2xoHLw6lt9z+dRMScdhvdS3Cjd85tifCFvZxQBrCMlvm5Lvesa4cZwi77paN/X7X/S+OlAblNIWcDb6sEVOMUUjBrE0T1+CnN9jyfYv+q2OTu6u8RLJ3zLYBAfTDhw+qqrod0WofnaZF2vagRmF2DI1DjKAP2pog9oduOIxq88HVWnkcIq2OQ4M9YIF3XNR1jYKFuK/nFswpiFZE9hpkt5J+e/b95yh/qU9kkr/CcjlpHet1un4ez5SRnl+uTz/81VRPmh/HR1w/f6Dl+jTww5g/8SOpJpEBVWutyyVN10GmYTwvUq+XiYgfHx8zSf3886nONAwFM3Q+EQE5FRbNi+R6Os/nd/z4IEplmZKWQZVVkJVogQIqbOmfRLUClFRERVRNPNR8EE/D5WU+JXks+qDp+4mx0OvAnxPNqZ6oPhLOlVXzdR6ela/D9IPLvwmYZ0ThsCtlmzBbeBO1c7CN9Iy0ntW2Ov9UpAJwpykxrRllFX1uwjB4ibYZOF44Clhj3DHk/OrOjtQCL3W/DmWPLdYe2n0U0Fq3MAIKB3PMy0IqVEtEBBClIa/epVK0LC7YOXjNmXkYBt+CQ8HeISLbcONwaWbXy8uL/TpN0+VysXgutKluGIZKYKZKYEJi0sSa2Hb6WMNkFWMABCaiIrWuG19VFaKotdacLfc8wnyQVDVnVqVh2GUZs+9Ti5VvnRBpyZ4QwNFHSgSlzFG6iFCn623BO/hMbWebMreTwfZ2jzAz67q7YHNoXvmBURlzRrGz/6rkonmicSny/unhw8vPv//0/PB+eHdmpktKmVpayh1Q3ilJYLurFUgxlwSRqhQIQ4UJiQWkVYiuRDmlzKy6rEsSdSHGoGBb2WMmJjATQEnEbF1V5bAzSecZIvZnav4jVc3MEEu3QNSyxCVmUYJUKIsSpzTkjME8V63UWqWWWooeQnLVBpJAMDNBfIIli0kT4WEXNh1H7l65+UC845PG9rBqN+Meq3VcuPnYcVbUoNJ7F+Js6Y+9/d2bzbjZMATeOv5kJZokrg588aOxm/6Wt8Rbrqo2UUfdpOEZEHKKRW3LnVM3sRhNnbGv2LabyCdNc1m9YzmUuARB++jzqSwdibTt7MHhWMNIUm+k1xzvx2HFLVa8N8T39nIe6W8X8uYCeqf3rS/SjQZYL8wy/cd//Md/8zd/n85f2PPwp5WbHb/H85GYxxrsrXvca79mMFmSRNNFYVsHOaWUhVBFCWkYzycaUlKVBXZigkqtdZlnXRZGzKtrfJZUFcrMmnMuy2Rca6fsikitGEOy3bU1MO657X76KiyjDZzE4FlVVZT68ymPo+srMrJf13fy6d7B7KGDHX07ZvLyNnw4Rvi/EiKbvUeyT+QWP9qxwhH+7tEtXlPYBW03HVzq4Ywvh5W4pSbGi7qpldJOi/TAUQ2mkwWya0gms9ZM6170cRxPp1NM+XAksqrSvke24Gh+sfgwbvFAHKbuE911rKSpV7eB5h5gxTrjt+T+qEXudTsUTXaODxO4lImZ/8N/+A//+3/31zlnWf5Sa13W8dD+NrdI2xnyNTW8WXk/xHadK6qSCqqYXQMoUFU5j8ojVVEeEumQQLqoaLIMsLouEkMkpRRtXTwAANJ6SURBVPX4XOd4IqpQYLelA2Ebits42I+93gesY1fvcZ4/EzCruYFoG/KIPpEucZKP34oM3eFCxJp7gHVckF5rCPV0F3ERwP71PRw4eDfiv7EqrV+YsXGQhCOdEQ5YjbiGcLqEiLiz3ADdZNW3Cho2OZEd4AxNzBjcjFBg9bLnlFIax9EsTd6Hd3SzBRFlHqStNlq11+vVYiO6Xtu/3d7JKBs4sBbtFWd/5g3Aunf/OFLr83cAK/Je3GakxuK9BekhEXQ+PfyX//I7qZp4WGol/tpjHPy7Vm/8NzRp9/BN+mz17Bm1k6+bwhWXpOzfLBDRAqqCUqFVhVQElPKonEWFU2UlsGiVaoa0VtWqkERm81FKllISRDC7XwVK2hYZpdaag7Kwpim9xR/mlADQ9nOa+SyqUBUL/GujK7bTz+vRhk07Zrql3lNQraO+oMEx6QPgJIt/Rlz7SnXmZonCc6ykg8LY5vh8xxxfX47CeQSsriU3LyK+G5N5WoVxHM1fY0YlhcB3D2KIxqA1wJcF8/nk9qArbhrCJjqVkKSKiAUuxIw03ouuwURbQHmMvYo0jxS4B2f3gOkeY8g+xipcfGGwnA3WlQGIBWC5eWEflQpmMvq/vr7Kui9S83C3/m8qas6WnVOLNAgi9tDTodX2K4miWhyowe5KMQJIQKKwmFgCICpZtVZUe1sgAlWCqAoBnJGEqlJdFCKoisSQKlKXooZeTIlXf66u6nFSSoJiYGaRL2gjJyKqZFtk259rlwjfkKrhJl2Ohda1kr7moyje1YAOtqHXEH/qMOvr8Ss+2Q2nf4L27qqbfHD8ouP3G9/toPx47SVmWI/rdGaqehs2hbr54C0AyuMtnVAe+452BAPtE8uYsA1tkzPd8R91XZguF1OsTN1zwY5bhbwZbZLaOQfj0nasuZs8OlLfJDLuc0I3MYTe3asJ2G/wWrVR3tDKKg7fJeZMKDkPpUgp9U84EMdp3WlYulevqEWSd76XrircJ50eNKwbLTEfFjETCtjStCghKdNShUhASUmEqhKYEw1jEanLPC8TpI6Zc2KSWmvb8WAjQaRKSqA0LNNVQuhTrRWUPHWB08MA6205v0mIA+/uQATryLUg4wM6+DN82DXdwdARuW6WWG0sbzBuN3NirwPS3rdyRKuu/n5yu/nVw5P+fBTdrp0u7Z0DxWMgHRpExMM77YH1lBCiaZosdzuCkWjYhGZgSsuTFW3AGOcRvx7d56q6TJOrbE7SiFaxONm98e6mcCvyJpE7alOwkb+y0IEVv/i8HoqIDOM6L20jretql4hAeZqWh/O7aVrmOfM32oPfVKjFgn0RrZzm/tPN+zffzUggVoBt1wwSKyuv+Y9gKGK4TJwT4VpfSillXjKDlBNIsO46BgCwUgKhqiYwmHzXBbUsyUhbkiZrKgXAUr1nEqrZgO0mwp+tz7QT+5Upm4YVAevIhTcB5QhS/uc92Dq++8VCe93BCof8bfFbxxe1KSlx4LdnviKW+IhcESXtX8sG072lqkzqnnVTnA0pxnE0CIiJ95j5er0CGIbB7LVlWagl/Gt8stE2paSBj3W/+dnbUFsxIUZDELPy7Oun0ym23DtYD7nt7Rk/6dqf78bCCfU1tD2WbiHiazDr+PDaDLOnmspDRFBOeRBZRJZa5mE4LXMtizycBsVbPs2vL4aS3THRtDf9vqYvTYrNeb56dNu/Ud7NtSO5lEJAqQu0WDTKNE0pPRCRotoxGyIy1zllHXKer5NKtbPydPWmiw38vCy/+f7Hp/ff/eGnn0uRh8dzWeowDD//9Id35/Hx8fH508da5eHxdBWBCDf1R0SKVItdtRAeQ0DDzcimR9dSSulyuZRScj4tAiIahxF1DhjC0raSifaJTSKPdrQ2eD2fz9iDhfNx9BBrSCeyH4/NDIlD5fP/XLadj/HJ5nSo3XdTS+bJ7Sgax4WbDDEvy83+dkpHx2FRUO3aI3e8kfbd1F43p7uZgefz2fbWTdP0+vpqA2Ta08vLy+vrq8Grrd+ZJlVrdZXKGpZzXpalli2JYKS2r75pcz+tZ1hcXiPuE9HpdDqdTvM8uw7lJHXHmZuiMczVDFUN+c68+whndjkzdOgWCRsnJP/0Tal2zkdbmbVmdCm/rUkAuBSlSiyJjRRaa1mET+Nj4oFZx4fHf//v//2nT/9uGH4sywux0GGujexHbV632iBixxFi/alpOjmrFCLknCGyTJOIMLbEk8xcW36OcRzfCO/wNtDmJlLjB+u4qvpcmFlFISRqmeoIYofMA1KXWRjMdBpy4qpluiwXiKpWi2jmll3IBjXnjMRLLVU1DZmZ51KxLB6fqW1nrCzCh0ZjNRDr6oQjWh1vZqIDIEWL3QTUHouav6rCzsbxadkYiLchOdJLD1pox3AuonF0o5M1smysKnKkT8hx3t5auH+rw1DcQr03ehSLy3/HoF8zB0Y2SiExYaSDhzs4iqlqKeV6varqsiweEWrY+vDw4KEMbrj5+qDLqtdvLrDo24owgQBYVqH72jqkMI7vBhFAPIvSe+2BWo5fEaqOxI+NwR7ojySNYxp/cg7pGM/x8eYYiQiz5bFRImNCTpRqURGxvCoJPM/FkDkaJfccBqrrMe/4End1ve4smCN9/vySCVBpVIYAiYgSqdSlAFTrmPOQmKHLdF0un5GVAZCSx6up2tCeTg9MeZoWEZzGEZyW5VqvV0NKW7c+P5x5n0/jKNU7EuzpcpRtnwCd9bFuOFURsehhuI1wS1zp4AyKJa4YRveNfy62ylkNez8U7ju5j8ztzOrvumrA4YC/+GJHxlhiw44id69EqPJXImh6Gzg00kDNbL1Pnz75PhWbdZk553w6nS6XyzRNnuJK22xnHfQp2u6P7XRVJ6DzGxq4eBBDXG2MlSCArNNqbT/vnvGBNqVAWyaJjYVuBZpSUPnfEM74WKRzvE7cG6cRTI9FpAhsQ131AUrI13lWhbIlBcDL8yshMaeqcyTOkSHDn19h7dLhzmFGeZsmIFXYCYOymn4E00tW65V21xnKrFxFIQrb+Lwe2zUJKmGoqLPUVKa6vNbyWmkk0kSczbceht+y1l4vF4HmcRBgqbpM08N5ZMZ8mQG0FLpr2vw46t3+lSi0ekvpaAO2U7JMw4KFeq2VrPXTLQ3l3oAhMKKGM74QmCwCZTcxOk1ujpmGQneGM3K/I0K0O7rH7gIWeqD8ImZFjPM/a0umblVJy1F1Hnbp1c1nZEms3Pq2oARbMSztXEht6rajgNOQQ84ZB4vYPGcJN9Y8x8OQtk02aKZlRH/s5cqOnu9cYwDc308h1UwcvkgrHCzEbgQ72h7/3O6nG1rtTda1IkREbuhAdd1BXUoFiBTCoqCPHz+qEqc1Z8EteApAQ7v7N0t4ZfdklNl7RLhZT7zzBpfm9ZsWz6AKUlYBidQCIoUus1SZc5mSTEPCUpaUEg1KxLRuaBM7ZHAcR1G9zjOnMaVhqevZATk/QkopZRwyMy+lqML0HWsf02rDdWuiR4HXMI915YtdfYNYjjVHIsZFqPhYB6Y+DXYz8HE8usb7QjEd9KybbY7dv6mldsWxIDIQvnTaHe0LAop1T/omahNsswHneX56etJwNhdCnpmYAF6DCqmHvUfSEj/4M954Bwg3MA1AecgOphQy4kcOQRh0CnZCHEdqdqjtyvb6IwW84xRWmaOvgEIamTdGc/fv/lzCLwo8M9vU7xFCqrmi7vwihF9++WgngkYwUr1ds9PobZNQdbcryLlQQ0ZDK3InSG2r57aKh+6+qmbbjwhREju+TZhApMwQSNWqpWi9klwT1ZRESmLmBGJmDfhibLHMRQTjKQNYlgWcTK4sCY/lq5+XmejUjUT408JB7SRn9vQ9tC4grkxidPDA0a2SoNWj7kyDjpnip/WgwUXsixPv8SfsM1K+PSqxhg6wYmN2PQrOSOwByz93z4PrlDoO/z3A6nSZqPXEPrqI2mZCDSdxeTi+YZPtYfItNX6WhOlc9q4DlrZdEHE4KKhU3jbz6dpHrdSWGc01X29zCqkEI74T0XrKyN6+i6afvehQ5Vb5kUSxtcdBP5I3ju/GYyH3TkeWm+NlZoqo0LrQBousJBqI0vo15Q8fPtZaNd1exdN9+XM0rPgT9urw2/V0jekEwa8zhFgh5nSHkoIUBOIh1aKlLoQ6ZBqQclm0ziIn0JovAU0zUl2PyVpqUdtkrzqXxeerGs6qmedK4/kgGNB9vrSbVOh6haMXgIja0SnMTEJt77uKiPKNAdA2yePQgF3N+09HIyI+78IWuZBuZe+8CVjxmR0ft7ZtIWytYTdZ0AvfTw12r9CdgsBMXkTWU2fMGLSFQlvgM1WLmc/ns4VZ+QIctw09aEHtNaTQorC5mvbO8thZQ8l5ni03qRluRdczOywQLLVkpL7e10G25fxwSHKh9V1E7nTv7NZIh+hbfGvyCNOP7qfDjVWCgez28hsaSimFURIVaCWCZ4JLKRElhhAYwPPz89GwNf/JEUpU/3Sne8cn/sA9wDLtJIQ1UANhYPVm2PLieu3cT6rKm30i45CnOovIQDqO+UxadZqnWZFJEkLFJnceCmjEqiK11mHIbrYY08xLWZZlPMcdYV9rwR177vxNDfNugoVq7yg9kjv+GTFLQhhEfD7CpbOpiFi8D+3PT/3iR++V7rsiYvFK8YG3a3P6f83n3mhAXJZ2zQWAn6Dl2s0wDH7GlIRNztM0WRBWR0luaXPiR33nFg3Zm6EhNwMaYHWbe4TWiFPbEhTlJ45s4I0NldCQVMOpGb6z554eTU3/jYAVOfP4cGxAB1iuCR5/ullqtVX1SpCWCoiYGMTMiVZpxzRNUiF+4NWXytcIpuptpzv+JH77ylfyCdeFqxBmThWZcWIZuBBPywgM4wAVKeV50elCy/Lwm1+dREQxFwwVdFEuSsx5HN5fdQCfzgNn1QcCoc6fP7x/PJXra2KcHx+naZ4q8vlROc+aitDqxqqlLiXJdEoY9AwkJWUwFKUWAJmzzQ8cfVilSCGZ/0kWlToleqf5SYcs/HmWZx6rlCubM05YqeSSJI/HoOco0i4YaFzuaVK0Wb42710uFwuJMqkwTwcz16WgAas9aa/UUnwbcCkFogQwMWeXI2P0tQ3mK0wpOd8QU2qJd+N+49rOQ1xVkoYvdr/KzOGYBmcOkZAPi4iYCCCiqUV4U3AAMbPZcWslTKJaq2VfmkjI1BwiOj2eRPWXzx9fX18txurh3VM+jdM0XepUqGqiCmJmjJlSEpGFVKQM59EoXBMtpCC1PuZhMCj0PN6ltqD5ebpM12mZqwqYmJOqPjw9Wl6HtB6Et843tVYmys3IWjGu1kyGjFSrKVlCVJstBoPTiHHFQhBT4sYhdtLKJcZJ0RainIhVVapEliBXLRv/wJeP5qK1jsMgVV4+fbSgNlkWHM4QWLn0YdJyluVHkTNDabhieKX8TMRIjyoPVcZFnl7L8l8+/fJv3j08VQW0ahUUEIO5si5UprRQHjhzhUAZnAGuRZEsTCSR9UqZhACQ1iQZWlUqseaUCLmUhWulUhLzaHy4LEQ0pAQRUmUzNWqltrVIhcY0Fi1zmX2xuEoVEUvTxsoiYnsMWTlHpOcWk5JSen5+Xl2eUGZYkOcwtDNpsO2q4X2CkXV20lVm8mEzwBvTBb4Fm4+akbch/mqYpMxICa2px+mLgrFN+zwzHaK5ieEuhjg3+tH2CLMutWycdt8NHGausi0zR8pYAjz3LseWuBz6TQ+briExm72VW1hAXE5F0G5iYwBY8LCEM0TdWFvFfr+gZncsONCB22p7eHiwFAvLsry+vprOldLAIbWxFfcxoTlD081kHvsQlhoSmfqoRf995JBoVNZQmr++elyoWWGu9HlPV+K3fM0UlkRjNoiOvX2CjLMXh5C9qJqJCNV1UcKWVolonufPnz8beY9SIBa7Qzv3iOs+XnNZj0oblXrr7G2J0xvWnDDueTLuqvydVMYx/ab25CixCPNqoboKrQpAwzAMo0UhvzbA8jTtN5yapW752NaWHXp1m0Coirrt5yQhItu3vdqgbuWtN28o/DBBErY4rFUaW8JGb0MUvE4Vl7CZXoML0CXcGOgG8DWyuFy5rRE9EexZyRXxdX8ryq0DSkpJ6ipyEcVcBqIMt5l8CybyV/xfCYe5rl2W3Z5Kv+/wEbESQCnFOmIRnggSOI7j+Xw2bdQCHTxG35RNRyiLITA6e7p0a0BEqAhY7mjXoLlgi5vpvdTcfOcetLUeG3GdIrpxCxKMFr0Tk4gkbyqShsjS6LtEgLmlbhpuHE1nV2uG9zFDzSVn0f/2vB1LfqzcGJXXlq/h1bIeDGx7Ce0OmtUMZSVWU5eiYLp311w9vLpRbHOMuYDEPEft9Bm5layAnDKR2ZyXECZ+DdYSvoRTXrKE4mfwMbOdZ70si23EGXNOBGaGsHVaREqppVRu++yb5FOY4iqQtAVD6Yo29BZgHaDHu+eAchOn25D3Zy4wM4EpJSTbxLgDLKdp2p+MQEHh8gojNnl+KKfYSoEW6OHqiT3WQYl/7nw+e50uTtKOw3JCcTv4QHiNG3IZiyxC+13cqlrKEukZVSqruYacKgCgiYI64BoBAsS7oGpIzOCD7hldNCwdGqKdTqdpWrxme8a/os3aTS03vIR0yV5soG390b/uaUi9JZ3AdGjlCU61FJcc/1f3+rUXIhJoCVqZjYU7LiOV7DoFqyXykrOHU3IVyJy8v0Y301I9PMIBFA2wbCU9io+IQERJqLmiRESVmFm0kCYmFRUIq3nWe7AQAAypq6+7QqFrwIXSdm5r2MwLtkQRzo2yP+Dn7RJlNgr4seSoHgOU9jpwzhnKRObsrKWUh3GAVgFJlVprLUq8rbI1kUBRVd2fQ9M+6eS+1/SITTcfiBzZYRbanymlRGmgIdGQeJRckFCTpjJ4tbF+t2o78aj7PVDOMRaiEWFitWJkneWcHWXvjo2YQkR5CPvy0goWInI+n02T92yZq1YiO+xwmrgJGTFFVYn7Xf7SApQQYN3/tcOhbL2JmVLiMBWt7ay1mtSXUlJW3yccDas1ivh6NT0o52xe8GWpEfc9wN3lkEO2UrMuo3HnXYjZaSjYxdS0vEgchznXrUo7cywzM93wpldbbSRSghIEakm/L9erUxLNp+ky1gEfgDxum64jS8R/JRi2cxFK/Pj4OI7j8/PzPM9KyONgLkLyhTQVItvr39gVqogJqVRUGMps55utTC5zSYkAbce2C4Nb2hkmQCGkDBYoM0RVGUQkBFK15DWsqkyqEG6xFF54b8jflOvjn86B8eJm2TSstctt+GtZDRkmJlLevFYKsIqKoPkpKbFv05eWiNvGQBE0T/1qDevYdO9efFf3aKXcki9achIdBhoyj0yDZFIWSjXT7QxmTmjeR5P73jTsAc5xx1+npqkh6GtOW9f8XVZN3l5e1jgAX4a34oBIzbl+vV6JSOp27FWUduwVDReGlDfLJQKoOdEdJjZUonUNIWo6GiYJE3uX/JHYsaO2za6mTLk6g7YlsLSj6mut1l+XdjqoQgYr3eziMh/5wTmK9pp4NJNLO6LVlynsGWtYtPJ8gvE/I5/YYotpPaktj8o+TjLybWeKOlDWsC0JYQYqZWHmd+/evXv3jpk/ffrkg3uUFG+zoooWUSLLh2ekEIVlW1GqVWtRKKMWSkw5kWWDEdXVsuM+5ocEUFJRgCizL7WjEgAQSCJYKaCtmUf1/6bQdRSLnHbvyZ1LnIItYAeZiaxxpKRqc26bE9RnBhs/l5DgF97Of+8a/SZgVYu80DVGwXYYeQKK1Y5W9fANIMAZ2uZBa6OyigjZ7lBIRcV+17s3rAMj/zcaUPGZjo+d87i5ktMuhY6aAEcM8lzmtD9kwVcAbSzMDWR+DRGpRVwC/XmHG2n+MlNeVDXltbU1bDmOot6ZPIm3zcbeccdxPy60tsC6ed5ysbuce1yVVevsWGvNeWMVAwvd7yJwVcjCuHwUHNpM7H2kYiNxEBXHPiea64ApuDIc5b0BHFZa7BUJvsVunsBBs3BuNw+6E9+l5jjVrcORUm2ZJzr6H2VHVYkt9LXWWpnAcWlI1uNmRGSeYfs3k1aQJoIy2jYekKMWQAolIRUlgJREiJi0KhHDMpivCaHgBzKtC9mWUW/nkKFgBcdedwh1lMd7ZdtZ6mVl1lbdUgogpKq1iMjAoqpVYDlJmVenaSmlSqm1ruu4UhRVQUDWBiLehzcA64vlZg8jYKFpRgRiXoSWSqhpUZ4Lz7xMN0kT2xbFIOcdifwVB6xuDjGfhWPBRuico2pglmY0P41NHSkMqh4fHx8eHs7ns+X8rbVeL4v7uUwYzJvmpis1jWkVyJzRLBdbwjPdrR7yQFkzUhJiJlZOmzsSoJSo1ipaSi2iQqzDmIjyhw8vZX/whH36crlYbIGpVBEcXbNwA/PIzbWFsHsLvarmvtgxks/M0k4/c8p39pc2X97qOLPFeiJzydgB9rXWoYXwN7/1qhM9vX9noGM2ms80Iv3cuYrPNHdaYeSuo950enz4+PHjx5fnonK9Xq/LvKpyrUO6Z10igIyL7ARhdXKVuvagFl0I81yWpZ7JZnrmlhmLFH72qt0kBVgIK5ARlBouqRYADBCoJcP6WlmOo0x/stO9th1S/nKtNYmYSZhzppxECqmZrYo6EdG8LK+XifPw/rsfHp4ebbZ/fvmkUp6efkxMPgNfr9fzOEChtTBzXeoyTe+eWJpouU2a7DDUIs76PrRuWN0EFJFVbjkPk4gsC5EQUVmK1EuilJPoUCumGZdUNqf7kZouV0dU7b7eTfJeYecc7ZAuNts8tZ+e2TfcmcVkAmDz4fV6tT3D5gMiIsIc5bmDKrtwow+A6C71sMNopwu4khVXP2MHqW0VdNXP/GvWNlNeVPV0Oj08PJxOJ1V177sbucxcyppizNRGtz3RNgmY1uk50TzDn33UPnS9Xh3WozUHrEcKoEXeuxWcQjwB2mo4NV+klRrCR2vdkh8YuplXLuUcn7T2OLkklDgzUduDHVn6pkCKyLt370TkcrmoqkczREU1DtyyINUiy6ySiIhSSc0ZX2t993SWqbx79+710/M//MM//K/+u99M+ip1mXJiyvl0TsyiuiyFlSE21eVaay2LQInSmLnWWss2Q6hq3YePMHPOIxFEigXfmSBIWzw1D4BNP+YtcT1Xwg6qMOuIjUt0VpjUZN3DXhRO3IQ9NYhZx8CMR22p5jglWNL3ugBKtMEn7dc47u+GjAqzOxkp2IkcjMRdoqLYiziuZsFWrULrGnCYoDbfh/fdu097jy/2SBTv+HWRHTB1v/rE7prUb06/MWByKXUZA2Dr2dFjNQ4PJuG27K0tAE+Caz8KjCVsq4dEgL6PL8pkB7LbmLfixqDV6Vhg7bE2ezL1CAppH7bqxQHUwxFqyBXjHGzFeJ2IrPEdx9ow2dacbrycRBw2vtgDlqira4+2LVAUZq/joEcuoqYy+PP262kYO37z+fgm95c6uzQdpTJSj9a4M5DYTrRV+zJuX/nclvsAZszzUmulpICQJrCQVGCwhX3/BKuAFOYFQk3EVeda5lorM+fMKSUkElkImaj1vf13N0Br3wXcxJb9kx0020WOg+3UEVm3IN14X9V87cx5HM7mhrB5hpnNHqzVD56IzLBZ10dN+I3u0R0nnNfJIXLV9dPVLKLESHa8RcS16OmI3HbvQ37tz6e009Scq7iFonTFLQj2CKyUiOj9d4+qak5AAO5tccMtKl+2SujaiguVt1+C91fC6TVeXB/xHOoRRokIEFqDmq3v6/xRiizLXBrjMjOgRGoM4KRzrLTPRaiNMBplD4G7vL9uvrlmYa9TiEG5yTOyT6gAD+i9w3K1OWKImcI4dpjo96PiH3+KRFDdTuoc8zoxYD/b1TsZOKcFqpr2s85ag3W5+T0a84OZOYFqS4EpRWpljwFUBdEw4HqZpYKTggyz1iiFZhIqY42sMhMTEAIxEUTrMs/zzIlUR6IxcxYRZCEiUigbBZg5Yd+tOEzd9U0xiQ9o8wvb9TqR+N9OzkajncYBf19QSpGKPNhC9aBm4UtJKWUm1TrPV9FK+1QKPvz0JcDq3tKg7kWe8F8dAb0XIjoOQ+acZCQdoKkmBZJy1rKjBQVPUwTuWDkCw/lP5eC8txIZ9GZ3OgF4vZwQMJSas8afd1PO5Pnl5SUqLA6CJRzsSs23AmCaS6wczfqrIX6KmoVCRD4yDhNGFktzfBSzFJII+/xBLdbRodkedtCJaqa2lUdpq3gSQnjioHTXsb8bYRuxNRR/7DiaBky0L9h7l+IIRrY5Sp0Pio/1CgH7zdL+lWMZx1GDmuz0t/HtmJ+IiM0F58MoEKlQrlV1EBFSENH5PJiNSaTJ3OyGAoq23tF6ak5AqcoECCuxCqpoLWVFsVIlESVSZs60mU6wjNmRbnG8bvb3XonvRtjaAEvDLFFrTS1tCFRERGtVc442e3Acz3bQQGlrKDnnlFHK4n4c0bLauluj++W2vqEQzxtNZFmSodD1PpSIVxsRa15nl3BV+3VngkFsVVFt55GEoLubeNozxH7R3cu9GXJdljuMUAqJ5aIg/fzz54bhHD0yMQc5olaoyb1XwHqUlnn0EVxRqQUlpXYIjTaDxfUdCnqlhqU6b3P07Bi7O91cE/QUxr646bjDbelNmrvHiqtdKQR/WDvjNmbvOAUzqoa4tij8G+mkIih6Tud7Y5rvaFKREyKrRPp0XBEB1CmpdRdEdpNnYjEvzRGzot66b5t9UZuQreRelkUx1lqTZEI6nx8/f77Uuq4ukKhSdXCPJqFYvDcJKYiTWqY8BjNEaykiOuuE8/kxpQQk4pBdYY9WXZfjWMShuVl8iCP1VHXbeos9p5Lna5cqUg2wRITqqvGu6lVQ22xOMp4zCpZSmLfdP2jOrLfLcYB9hI6TjIZjWUXWbQTWDF6TeolUrVoBUV79IGl/xswRkvzPe76GqJfFCx+5rkIJi9lRNiwWzxm0o0MKR9FYyWk7mzaOd6SwL6QASHkXluGr8g4EHFYYVLXWDbbMS+WB3U4rafEB9pVxHB8fH21NcFmWz58/Xy4XxzVqCovszXD/bg1HFrqq5W3wyDIfL2pei1iVd8QDKaO04zDBRHJ1F934Hn91Yh6H/uZMHLnXS1Sidx/CzuR0eI3gHttTFKBN2fRWighIa1XSdZL48OHn6/WK9yYjYvGfqsoAlIlIBRzQhEgZtie8kq6aGrSWIqpqMTWckm1lUcD6d6NHX+GxeqM4nY0UO3XAqaNRDbNRFxFz7K/u9tHUq0VqXKVSNUGSxEyQUkoeRmZW8TnhC3jVif3x/pEoFNQQcutJqkCUdifTRvmkpku6nYIANH5hSxuRPpHhYmtjC507vRe+Qt813oISotZgj8XDFygmtKzrcgeHBS8cTBgXb26O6liwT5biEqWqqot/zv3ftVbb/yFhgSwlyjlbvjxbMcw5l3auhKPPymctrRXaUoYHQ9gDtW3i8bUkByMfKQ/4SimZT9AVUu94aQn8PIzAvtiZwP5KkY1743jVvfO+Y1sKmBVHPIpl5NWjuN6UbQB1O/qYLIcGEVQkDatuQSI2fdGWT98rjF8EM4lIqTpjFpFffsHz8zP91oK6U3NUwfu98kBjhZUpStFSLaJFpYKRQMJUpYiUFalWp5qFOm1FY/v2Gtbx1yN9XEgjrTaTMH5KVT3IH22VzRiRdfV6RhMgehzWpavEUK21jqe2aOIjZ9V+UdHat+fmTWeX2yhGnDhBGEopJUqCrKhxNlZqx/nQrYIDgMafcAuwLL0M9gYCmm8iNlvbzL+BS7PmODjOZe/ltVgteyYaCzFGNwqea203hedWk8QFT8JpNBTwHYDFso/j+Pp6rbX6gV1G2NPpZFT1GtJ+36/fdJf86+vr9Xq1+Fi7Hy19V/SI1uPXY/gImoqKdp6g61b+gJFRwpEi7X6NA+qcHPXBHfUCMTu21L0FsF7Lbf6558a1jI7+OX/eTMXIDE08d4aIN8gqEBGtKCillOdnfP58IWUKa3lRbPw69mul5DruysScwER1m2IdLraDpV0wjxVGWt2kQOx4JPVKBy4fB4XkVNIwgedSE4S1nkhSnVhqIibOi9JFaJorn797fHzMp9O1IClxfmSZXz8/f//9+5GplmkBkSBVJcUD6CG/TNMyz3j6/tefPs/PF/nbH3+7lJyImBdCiyGGVkpQYp036inW4zOATG1zsgBQUti286G8T7WOPGv5+PSQP77MP/zmb14+5zGVxIvqDP4oBKIBdGb5Lg27HNvUZmnP00ZBAaGwyTlOCKoagwAQfEzT9GqRU1jXIoRAqTtXjuAHnA5pl0HUmcQZmogazgNAHipQRVnXxWxazwZkMcWeiHy2BGw/O7zpGkWu8bd3Q1WZLC6s5cNbc/3TNHmerDWsCuBSZBhOwGInoS9LDbuF1sgnALXqPJdx5GEYrtdXi9KapkkkjWNmxjxfX14+qWpKxGw1oMVyxhxea4iGEzMOmelWS20/EawKk2liy8ACgWUiI9u2MsTNHgqt0smWOgtuw3HDQoyoEe+IaWrmaXJ9kOieZ0QWTYnZ0mFKVdHEeRh4miYCMYiIgQqQqKjIg/xaVRVloc+z7erFAymVa/nh/Xi9fmLm09PTf/qvr7/61d/9+9/9t/+Xf/f88cOnp/eDDjrpZ85QygvluQrSWZGpKlM6Z0DnMr3+8stPRMSk58RrOtMKBVGVuV615ofHnHOuopU5jam+QhKpci2iWpIaH6DKRCxMFVUAHTIRVWgZ0sn0YYYKitQZAJMkTmYvpTRIRakigjSkzMyI6zvMWqWUklktEzJWwWM7acI4MvKQbktpey8MbW7dMOK3lGFSFQW+NiXr7tU2/5icWKKSlBJQWo9AIFBWTsTsB9/6JODrXF3NUW+Kze7ud/1ylxPCtHC714d3792J1/bZ21PWoYbjY90rx5vu+nFj2ZySnY6ATXdbN6bUsGGIiCxQQ4L73J1u0zSZSvjw8GDxhNe2oxiHfBIUjsaREGQYe2ElmqtHeh4h5l+meC++6aPeI+fM+Hp0Qa4nT4T6/UFTY50NSikvLy/mz1qZn/3RyPy7kH0f8N30bCkcAZK6alcKwuZBxsp+Xs/dbh7vvE2oLYcsM+c0Us5VllJ1WiaFgkVVi/AiJJSR+HQaDBwaOyrajl93HBBZnKcJNpllHfvftfVtuXq7cAIRnU7DCljjGtgtiyBpSinlJCBFrmuM1OavQViHjk5Zb09njkVVS8J2k8ArZOfZdR6TN4bBb3cP8J1l9VgaiPQPUPBTIpgYR7HpVHcAualFGvY5R/PqYDStoB+d3Mz88PDge32phS+r6rt3Ty8vL24/llIsW5aHd0UPujdS9g77yELu/vN1gO4xavoXHXxe/zLl5hfvjakT3wssp0IIBPdqRTx7WQjXwEoB40NuK8LLsvzxj3+8TO+GYawqqEp2JgXZikQCwO2MAfWEguuoJwv4wmp4chWtrvYG3/Qb0EzBFHXOuUkZu+39NUi0m26PJKY8jiPn06RcyzK/iqJUmNsCQnk4j+P4YL52bCy72kcASim17lzLRCTNqWmEZbZt4luj25YmAShm9vn6olrHcTTlYBgG1cqJlmvVrM11yaI7YkX05JbapfvV57fop3fVqXWHI6ihOdEduH3x617jI63iRRTXeN3+3XmaGx/3PpdIzI6TQlW7jzJvuw5dYBxzOye9zVhrRGvTBSxDsXm4RMT86PZ6zvn9+/d2R0SmVuL8EcPQvE7Tnb0Xte6YikIqMcu7ok3rv0W6f9HS4eYXSwSs+EoH035TVFQVtOXVQKNkDRtXiaiU8oc//OH5Of36r34oMivJwCepYEatlZHM+iAzeJpWS75Utdrpq4bFzHaOvYhkqaCsqpB+l5gVZ1EKXjYEOYroHPsYO2sP5FpFlZQsOcmQTiMRJZXrMiWBymL4ncfT+end+eHRrGslsSO4Sq3MSInMkVpKgVbbWKmqBNNEmMg8xCtAFNUVymnL4QMIfH/BjaLBA7P9aRg3nrKIaF1S5n0KRwY2zUik5qM9GorTRfZnqByft2sJ2TjRPGLu0Y9Cchez0k6LxgFxYlXYC16URr01ZVHQsGIfu1ZpWHNwS1DCll0ES+24MIeW2cZqsN3aqeURnabp8+fPy7JYenKL5zqdTrZh/nK5LMsS0+/5mkNzogkRWVUIaSfi0GgIYvD+HrGpA1zv+O1x+QuVqCl3xL9ZTFeNCN69G29qiyUyCFkpoGp4rVJ9K5+98uHDh//6h/HXv/2rohNpYk5S6zCMFLSzZlyoiBQpzInW/Ghk/1pzcmJbj621pqxAZUqqqxvVmxo5k2g9JsZH7Uj8xtirIdkeNrADgCyWd4EY4ArNlNJApDXhOyqXuiyaqzIP56fT49Pw8LhMRVXB1nypdVHlIQ8AjJMSrbaxtV7F9qNCZZ33mNni94Psyaph4Zs1LCLTsLICVZZMWpZFZc3furIySDSJWhLGftVM9qc6R4J23rqghmyZsHS/dmOe5hjbTfshvDNCGzx1P3WS2f6Nk+3tUI+1AbdiGiIOdjbUNG0R5xbQQC3ctOtIo9X6oseCum7lapFFMNgDv//9Pw7DYN4rJ46GU6CPmOvvokX5WXiXk8U71ZEukjS1NDIrBx5A/5+pfGv92pxxCIMSu+ZMSG11CLCUfutISF3TFmjRtE9VdL1e//Pv/+l/+b/5XysyoTJzi7ZRk09YrjtUYJ2uhm1xCbohiTJzQi1qcZqCxAD4lrbhWANs65jqy4v74q/Ei/ivpcVgolSVyiKc7EyBgU8PnCA5Q5TyQMNZE89LVbEggO2ojyAbRbS0wxttriMRSZzQwvmImZBUJRzI1+YT2paEvr5wAjHGcbT+M1MpC6mto4u7/EREkQC2c/Si6EbdpwMs/2ltfOCVjtAUorFNetGO4etm9eNwxq8joAnQj2g3eB26BUfh7hMdWsVX3C9uvzKzpVSOG7ANf4/HizVqbLnMPRzBmmGqLoDT6WRbI22foEV42LE6RGR/upLVkSW1zfodyHLI2rpv0m2z18/IiGgVe/QvXO592h1/aAs48WHa+7AAWBYcCj1ygFaV1VezrEk1pmn6/e9/LpWqMpGCUlkqEWXiqiBVtnR9zewREaSTGhIRAVoFZjIah6sUEYFWVlYiVeI7nOaY1908kiV0+QZ7ZwULEa052gWlpqQZlBJDMkN4YEqnSnkudS7LQ7LDnCtzyymsBcigtrvVNkiYgLGGTM/saXda8WUFO06CYpbom6MMeDdWvcxOXcrZahZmtoyItphKVIDdGKvsRF33Dl1/rJPwjku6+/FfkTWyUfcR5/cBq+9vrPzm/HOrkrs6dqw2cn+HgGjC4KRwTcRVp2iqeIUp5UhAbguCaPu97YhAC/Wcpundu3fjOC7L8vHjx2VZPBdNCllE4iccX2pLl+zNOAJoBKyOXEd71pv9z1q6SeWLz8fAt6gMdmPX1U9tx5vPpsyszDFvrYHXh0/LUqGcQUyUzBRhoqqV1I4dVl09Y6rmHYMoMamIugKw9YsDA9NBeN/myZsE8Rn35ruZKEkRgZxOD+PTI6VRREQFRTIn4kEBURQRQUJOqsswnJQwTVOt9fwwGpYT0Txfmfl8Pi3Xz2VZhkxELAWL1mF8+DxNy1LfPX1fa1UFsYXabuvlzCDYaulOurxXN5GllPly+fzXf/Pby/X//cNvnz49fxiGpzpPtS4FJZGyhW4ol6q1loTdkDvrR2fnOhItPosOvg8Ap9NJwg6VwExbLKgr5LVW88L4n9RsqLJMXX9dr8EBs+AzajjPuRv1TpiVNtx0+Sei2AYNKhWTenYXNLSyZ47TpqpaiJCIGO5cr1cL7PQjoL3LVufHjx8fHx8pePFNkDp3vrcnJfUGWBbDmH9Kw14Fa08ps2tS0X/kMahxfO/JjD+PA0aUQ9bmpun0256daW/Wfxwv69TlcvGfbH32nqW8UqAqAKYt3au781JbETqfzzY0RPTTB7xO5XymcTxfXi/ff//99TqrbqmiU6JpXi6Xy1JeOdl3EyBC7MeyqqrF91bIPM9I+eExAzotZWgZn2DnvasCSCmoAntBc5+DKkvdQbOqLY/6WVekqnkNo4MdN5VMKSIIOIkKgwFU1cqoygowcctw6FirhDVvpKKuZ39DVNlO62zjvSlZqjEHqalXAg+m/JbCiVKmnHkcoahEiRnKXEswgykEN6t/d3cRmTve6cIdIoz6u8641JzckaviKxE1Yg1/flFdpz/HgmPbooAd/7TnfcnCbzqGdqJrD1g+qdTSnDtwWPoaR6t5nu1oQk/OaWqXWYteobSVWYchYKcTef1R44jdsQy3GjIaH6X9zyl/waqkhYC4e86chji4HTo3a7xo/8Lf2rirsSIFx+vLKz69TMN4rkIrIS2nKIEgZKeoYk0MD6AayCiYbI2PadvtCBOoxsnKKptNuKYe6LP9fH05zsSw0QWgxESJwLY+KloBJmKx1hC3XXmJLRxURKSIaIIYiNnGojW3Q8MpESEaAPKoxiDVotoW46nZIxBFNUpsxG5y2I4m9PERkKREOfMwpIeH3aGVNcKKKpQts+sRqiJwdB7oiGId+eKqP3aIcMO6jF+h4LP36Ieufn/4zwe1I2AdGrwrfgQO7ffxHXvRnp+JyHbqWE5BgzzP5GlKqNmD8zw/PJxs0ep8PnvOqbi44fXb9bJUD1uNkwfvA0pcwWHe5eSiQ7qem/T5cwj7p72OBljm+FNVOz3bNTLrl4RY2W6maY+1ZUK3oJtevwFWgLNPz/jp50/f//BYBIQ14CYRQcEQkqqk2uJImXn1XXFSFUrEUqkJERGxigAkdv4zDMu4JSCzgcHe9dZN9l9T4rsZ2PbQKiewRYElcFVZ0zYXQIgVVGk1Sdrs5+abYdPmFiUiTtCyzdLauAuuqigsjyjZyp0Ree9R/mLfiCglHk/53bvzH58n1bGpbBt1OsP7JnLHabnjj+Mr1DKRerUUZjYEruqUKR9I/xzd6e/x368YVG3E39rpzPrF163UfWpgfzEqWV2xJ2MvzL+u4UQJq9ZPnbBrc2ypqiVN9jZ7bhwAy/Kqh4z1ut9A6q1l5lo3lZD2cRI3y5+GOJE33q7h3tg5J5hzMG789kGPfYya125c1k3aOxRYGewQlwfgdcZPv3z8n/O/VlXmJBVkmfghAIuIJ2zwr4sitbAGZoZHurWI8NUCXUd2y8R91LAiNdbrO8SLINVkhOAaFiG15Z5EWG0roqLK6/lfBGVSSkT/f+L+rVmWJUkPwz73iMysqrX25ZzTtxk0MAMQFAAJvEgymUwCBRol0Wgyvcn4ogeZ6T9SZnwATCLNRBAQIJIgLhQAghhygMF0n76cs/daq6oyI9z14BlenplVtfbe3UOEnd5dqyozLh4en3t4eLhfMixhucwMT+1DYstaLiBWIRUCtDWxOIYjbiEWAHOq2mKE6hXz2zxhUoi177vHN4d/+atnoYcmYEM/HUQwR9KMlfsai5I5BNhaBLTRZXqYLUPEEjHLNTJnhVbJ9fFuJ3j5Tfx+tlxEQ8bVLsXhLGl4CcmCBhkrG1AkQqzZ40bY3pCaUzuFS85+5gWglBEB47RtJ2sIX+Od0RC6E20ZbMW19zmlpLrIe7gl6W9YIkriS/EOgIjYyZ2qeoBsaUHQt8zglruF/kJkMr6dci1n3yVx+8ZW2fcfnsFZtGSy7DmopaacYXcpRUQr7E44X3FUMOeGS4UWL69MlijReZ5bILy2BHW1jl4l0WoVWA1Z9XJwRkggUiYgiVSALdG5UmqHCW1pUTTHzEG/6LLXUzPXqVa+dFrnRpY9QMNOXFsVcQ6uT7wWIur77u3bt7V+oE4VUpfBm4j0omjQoq2LGGlQpcvNWmrReCP5IpsSrTu/Er9xsBpc5xtX3dOwtpN3Z3Zb4sgr+L5FwNVEOF77Sri12r3nVjxuhKfwoabXuADwi0o5Z5E58IOFnTAbVm4pOWwiSiuq6qGitWm+kUQOjg5YNUTjcKlwh4u+DHFWaHVVDGxJd7V4RH+Hofiiv+6ar7YDoqa828OXCxiXxy4RVy59ZsKHj88A1yqUmVlJqda660hlfsevFhKRKjOpgioAhaiF0boEvWAVc26gG0ikaueL60Hdp8xiuW01LLO7ExEoLa0wJETVwyXHcCUMJiaCqtY6NaO71lpFq+oiXFTYRcdQ6J69lYjV4m1hBj5amXQANN8uNICbk1AY37958xBmcb0lRLOUUfD5RFh4Eb98JWg46fMHdKmrb0kcK/dXuF0PplAAiNzTsG5P5FrDAlpcgRtIt/qew900CWW1zv3dFQpHukUgtuIqmwbfDkOxh4eHcRwtI/ThcHh4eCCijx8/vnnzxrdICBiaQqRW3MBZXzwxIk3s8x1AuQVYrz6//fBZhVoIMI8fG5lqO2S4iSoY+GArJ5y0eBWqSs3AsoAJxsePz1MRlXIYmJlJuaX4TCQq7s1nSd4AhW0DISJQUaktPTj5dIhIlqq8jrXv3HGHpLfKinWtonUiVZte31wKQWUOoiAEgxunoyUNdX61/Z2FzlKdY3UnvWyKr3aF5ilR//OzivW365IlHOV2z3G15BZj3KgMEUHicHS5h7qFWUv2WlTrrXAIublixM8a7P1J3473Fg7GQfmQ45cSzuO1bc2wJBct8/FQ0K1sh1hDLgnLVLbb9U9PT09PT+YAcTgcxnH81a9+ZQqXHR2i3ZoopfT9IsZkJLWG4l1NLXdhREluMex/6+XL0AoAM7vdyslLS9bdcst2vDMoB1MQ08WBeTvjKjgeT6UUpQkYmBJR9ZzPVrW/RayQC5Oj/ae68C7c9mrFXUHnWD/wuSV/rwP16bDfcadaPtYJqgQmAYSSqFpO2A7TTgrjzGn/8ePHruuOT88/+MEPXp4+POwPdVI61YduT4pSxkQZwtNIQFdPT4fHNx9fTs8vp/3jV5TkVE953486CYN7ZgPxqSYRyJyccbmfARRSLnko7WdSkGLSnonL6fy779/uyyjy625PpU418UnqpN2e85A70sr6kpCROm3XZb1gmYDXG1LVagF62E4byDtU68UbKMyCWohrEa2yMMaDkDKZe22VyQIJBj1rEQ6B252JVVHV4IiL+EFVQcVYzDR3i5Wl0Y8/VJUuoT6lSqlllFq0uSNsS1rGpJfLcWqS4DgWw/jZgiQikyW11g8fPgCPfd//8Ic/fH5+/vjx48ePHy1f7Lfffptz3u12drPHUKbv+1qnaTob/LXUZCJSLYKkCNU6hz9NyczHMz6u5D82ImS7zGKRG/5TPk0rObcCxFDtFa9a2zuLlGmaAxbyHOK15JSgynMegwhbUECFlFmFVWYjY+r2AASXiJiWwwaJK0kFRMyAg5nB6/7peTifjz98/zR0v5YzTee8G74ZS592CWk8nn9V5KXjbsCb7mUv/XMUPMZzAE1l9uMzlXk6vyjJQ//wUoqrgRQOxC/xOaZFpjjsUKWKiDBIMzeQrXViEIGknlmRABC0LjWsTymNfNUVDcXFwm1XcMjPp+hiTIkM9IVS6Xah2ZgCua1H+IfIQHHzgiUQRHL/JkWvOQ3F76++cmvB+LHjHdkeJfOtYo6dhiPuoHinTgmnohoMJZ5qDEE5BXA6nWROsZkNaEyVeHl5MbvVbrcrpdgFHU9ihmDDstrcw95H5IzEfMl45sb4q0civ/XyBVzhFHOAq/Uzbs66BI1EBkDS0pRdRNfmbiwW1JhVThUVSjlVy9VIi3HNRNZXBrtaa3Ga7rPoJw4ZQcranzcBazPlzWqgBZBalRMUNaXZNDPfW1YoKpM7ZFNUWO4viS8rdq6akkXsTWV0Spk0aH6G7floRVqpDN5DF4aq6j5lm3YXoHN1Uu/PNF271IrX4Ca2GBeAvxXZ5U4HDCNU1TNrWaDUW1unCFixrHQWaSEWzNnK/LMsWI2liZ2ms4jY/UET0SZv3717xy2jj9egIcrVipHQYs5QcJeXdsdQQhSgVT8jub6MG+9P+i36RP2OiDzt0C1ibhu1D6utOrF5ils4ghY9SsTOyDCfMtnNZFMuZktfMxH2zLmUknpSxWqKZ++IG31bPdzIvvAguc+EcVyqF/ejeWiXG/6Xpr8QsEqpKbFITZnqNNU6ASIqFiKBGMTGEAvPrNmA/SegYaVEu30/DMPLKPOxoLucznMIc6qwI1FeOkMhLPgowFW1lOvkviiYN6zaXm49cBWt/Kf7vLvCLCwBy8udShyy3e3A4ePWK9gsv7gIrXVfhOZmZSBolGyneJPFbnebunmQDsMQFSUPgmw3wLaApe08xP9dkTQuJ5/Wq7B1f6Rb4sc671cSq4qtm43PyR5JeqeeFWbNHaASr2ESzbjjfzKzrhJqcAYs0eq8d65F83AxTWgL6SxLfFyhz4rHZhnTNJXl8F9Z8atqb/2qendLqM29E2F9ilaQljrt+kGk9JnH0yRauIVzUVQgmcOxanWJxyHoym+xEKlCUkr7/X7Ydfg4ihai7NrsvKFo4ZdXyzhqWwhMs9J3bhFHNwqOndpcfdEnOIILY92c/co3fB0jsGKzbFaAdYduq9NPhHV19fm4O6ZliT13RWy+zd9UV/dg+P77yb3hqV1mtEDJEXc8aIHHO0XzlYtasDudcIhXt6KABqUMS6h6Vf5vy30qXX1egxSUFmVse2R8X8B4cVLMdc4Chmjp8xFhIt48JeLLbpRZQKoqdjcHKC3KOTMzMepWcZn7sOUxmzXt1n5k98m1mKaV6aZ9Qgim9PkalpiGNab9TrSkNFSZap1yl0lFSaBCxOZzIFq4GWvn1PGfzyL3C7ECkjLtdv0wdETnKkJMmH1QG0y0yXPG9W7MQmZ52nV1nrYkWs3i1WfudF5Vm7Vv8eUd3o0rbYtZ2+V6B4AkJAFcOWRuy0o9obB9RlhFXmxLmFIqpbi21ff9x4/fxz7Y8YLF8DMApaZhlZCwHg37XC+bTc4tTeyKAnHgV1WY2O1b4/2U718VbBHgrPO+B9+CPtHs7bktKwc0/75pQPOegNwYEpqOXj5QPp+n03l63CWLNToVkxNsluhqmQqZQEoSnDQ3W4ptT2qt1C3WRUSPOyWOa0vJiGL3AGshTNz8pgqz4SezgxxEikhl7iGqc3wYAZJoFalEWdoKdMPQ/d5/ZhGLidz3uZ0i1aXUt3k2BYFdz0JY/L5UIvdYuQMcW6ZfDe2WGKfo6ESLVraC607rK6iipe52vxJ3ApKWipmWge1vFdqU2HMHQW3BGj1loe0N464zRrOweFWRQ7zCOGrX4FbeoavuxeGvpin++QXi877me5VckT7uroG2X1udEtwqV0WjqsIMyAKtDccTmhhp7g7tCgSBlNPpPJ5OZ3ydE1OZainoh44WDt6tIVqjz5bbo9ASEb4ma+9I/Tukm7//RA3rdu0VuDgoNVIokYL9jjcs4OcsMMOZPRG9hrafV4js1FZzl1IiS8DDSwI5LiDIukju7fLDZvKulquwdfUZ/TSFf9HnT3vsy1agyXlzOzDr0ie+6w3d+lWD6V2XnujM3PcLz3VTvjx8jTZNLd7pWVXu6BwH7j3n5a1Gn9arbiJfULYD/xSs8U66+ukRUF1e3q8nAp8PPFAswvHKfrToISGN43GaKiEh8VSKCHFKarEaZmlgKeTMAWCtrm4xyz9fHQIFpW9bVq9ELL4avCXXWg+H/TAMtdZyOnHulFItVcSCCtI0jcw8dElrPZ/P1NJM5JzHEWY7tMOaaSoqknNm4nEcAVjqlOPprIq+Xc2fZSwlR+d5zoxW11AjsmP8kpnHqYiUfsfjh9Of+b0//V/9/T86vH18GY/KiZlFpZSSiDgnQjKDfJxLb6U5wlyumDZb5py9ytYYtxKlSqwwXh7W5S4GgYG0KQtTXZxO+khlE1HkDkb4BwkOR9q0WuuANluPAYe5NZjXlduwnQjxKkykj/cnjPESc87GFUNoxaYbJewqVW+/ns9ng8txHI2wEqK2U9ir1hC+1QIKribOKH/LHyou+PiNhp1EFFq34MPnF0skWi3XWJk9PE2ThaywleWHofGVbXPehN8ljHyiqqmx2QXHmU2TVXWIvFj3mIfvvzudp3qedBpzZqbMzCl1w/F4GsvYDf1hGFjSdKos5BE1vKyu2fq9K4vQP3SDzU5kdSKVDdlN16zQ3W5nM96nXKuWUnbDwOagCRBR4tmXWERualiNLpcGmvWaVNVuq/Z9XyZJKdVaWqyrtnrBCglVLXVjaJydV2XUnUJkV6uEiHNmsgsE17BZm7C4W9tay/AT9Lj8GprPrywHct2o4QDk/9rivGWzuAVPn1giimF5mBAHEtfql7Xi3aegRcbmIuwSUd/PQHbVC2n1DbXNXQ1RErmFePea3dweUf4Ti4d+9hq8lavPr6SIF9/ZxdrsQTSDtMfJuXMApUubnV5TzxfIKPPNMwJIFSKwBHss5nzKlsoB84U7y91eisyWeGXC5YDV3Nqrqt2oENw70I9dciSNexdmRruRluwK0RdEvFuWNWAZmviERYHfAEtLKbvdvkyyP+xePn4wP45WAauSzvEYCG0ARh3jCGUFNfhoHrpfjFlEarsZZgzDkDKqlJVtTsVtRnQnavyKMyLd3cQT75q4loGFeWWuyuv0VbdqC3aPROd72hErV5/xafi11Q683XjUYGsyBuGN8vBW5aufvGZ3vMWGgxE0Yv/Sz/UkuErFyptMvuCdbypNJbQcYmmTCiy++Gr/75TtWLYlArFTOPCA6nIqPYirNk32FmBJGBQ188Wd/syrsjlMEZGht0nx1t3AG4UIOL6coI9Vm/cWk6havhJlBcz3qBLWdwMjBeKfvvOwwxZbLyklrXNqd+YrDPYFYvKSCoVokTLMJYZTbZ4MgSUoL+OUU38+T12ykA9QJRVSNsCyiDlUpqmUItQSIG6Wruol4bMGY9MnFmJNmUHKCYfDrutwrpUoKQAS6IJG+poL2FU5Rm1j4ueJRo24N1yV1cqhZWpCF0cABJfYeFiuBCzR6tUltO2D9yTipptRbGMSvXg+q9iKsrRSqz5sh+Cdsa0fGotbZ3DjaMKLLQBz19rtdpaVx5/01aLhzuOqBJl65XtvyCf0loblp8lbARPlBAIPuEkBQGp5Om4QFbqMO7YSBlvJ1+gQZYAfFzagb4lvAFQlIn56OSK9l1qQiVKGsixdWFVVYeE5r5ctYBn1xlrt6DaFNEgiok3Dui9XXi05Lp5II2rKkRmw7bqPMSiBmRIwiUAEypS4My1SFVAGQWcVleYzI26W0Ub9ZmNs2tZmJj69MENUU+LDw67v++NzoTyECwkXbZ/Bt7ZgV4uIXGY9yBBpAepinyPXblcsVpp8KP4rLXdV8fVXJ3j1gE+iLx40ue3bKx9U7MCt+lcdCxVef1c2Cw8NsCwOFDVty9az20oiDQ3RuF2ctijM/laUtfTaGfStocUaVhVefZ6Xsdu9z1vdKtpb0cy+ZrDjz7yMbUDsVa1kmy0rIpq3MKJKwiDi5mQLImZPvgemjx8/En4qMqrMCFDlItvMIQqqcm9DshBU2pIK69LgsDI6qyIO4QvKcutkgNL2aLVWVZlDMgAwtYtS32eA+34YxylxB3Df75iUKSsJERGIm5iyY1ak7G7uDbaiVnXl8ycWVWGer3ccDoe+7/XpbBm+Fo8JKave3ZNjCS6tLIzifppmklmD6j4zh178uRDk8B1gWnEhbqPVF8BWHI6fvkkIBXORTLfDcsaexz8NFW8RcPW9c7OfkV1tIq5wRyJ3mk8tHdHVYWJzCdnLLaVmZcPStl299fyWSqvXVwN32nq5Wq0X5w1f7f5NbGgjESPZdSZRu6jk12WICEpM6fnpSEQCEkJGgrmMkgXQJ9X5MrWt1av99M5cxe520HQRA9do8yUlb/nmslJFVAXh0gMzc5dtjh/2j09PT7vdvpZpt+tlKppFqaSUMpuA1cTMu11VKHe56zil2dlfBESwOaDfCHSjlXS32+WcAKREpfFtYKMrtqRYtgjiH6KJGkvhJksPwCohqkSoljeOf7Enq+mPEv4T0erWY9R2JRoSzNihQQo5ln2R3KLPDbRSkYuHwWYVLU5s7HPf9xyCFkiI9BBp7pUMw2C6CbfzTSdvHJrvcz8daOL3K7jZrsMtnb3DXo9e07M8iK7vvm17eMeoHxGqIc5lK+rtbqkdB1JrJY8a074moqQKguXmERFYFDxOFg2FiGAx0KF8137ixvUVKSic6jjgRsD6DUErX2pc/qCzLXOhHRARpzQMwzRNwzB8//33+6GTUodhP8qLpKSgnDlzC7BP3O92RVS5465TZkthryKcFva8Lx6GNnd2QEzjhnFhXW+/9RMOKW6xqbPgFhf8+wZYi9p8XCuWuoM+V396Vbe62ufYooSiixi71281v9q35cK8kucqwrp9KSLDsNOmZ5mW6hboVR/s3cPhIO1yD4ecYNHmEiuM4eE/pcQLfbzx3d+Wla0nvuuyM8KWZS3wzttXpRSPsLoqcWMb6Vs32ZsiagNXhB8ubi7sMoMVRGmaJiLrDOiaOfwOBVbP3BGQ3kNqzphoZ6ZfwM9W8kvND92bSfN0moZhyJRLKZnz+DLqVPq+T7WbTlPOh+EwjOMoWSvnYzl/OJdj1S7vzkmeJ/14LIfD2+PLU9JEQiLy5vHw4eUlpR8rqyhxzYSOU8eqVYmRqUxEnEEMKaNA0ae+yinSKa46cjcLbbHEgAP93rk8D3lU/sWb9/nx3XP6+aSl49p1GJWflF6ITsAh63vGQ9WXlfC3fy1QgS8hagaUlDIFPyAAbjT13AocMqc6w632JiuLhh/qc1ghzs1YaiiRFagdVqLFUzcMoqVi6LwiUGYGQQlVRVQ4p5SSXfAX2GEJRLVIlUm7lH3sGvRHl9uX9TCPi3wfQESA39rTNkXzBDJfsMnTC5pD1vl8PhwOdvBv9dhpYM656y8ANBWZyqypBbe4pJAqUHA/7KsujtXdiL7yovCFRLJYlivsjuzXJvLygELBgCoZHYQgBObUtaA9VSCzdUlqnez6ZEp5v2854tcKtbsCebvzRKiCiFOabzOLAKgixIur+9qMwgzAtjEqrEiJmImZ8uOviM/58I7zo9aJZDw8HD++/OL0UnaHr/runUyQMhKmrivQE7X03a4hms0x52yLcA7lC3RJE2kqJFW0jgCIOiWo1kkKCKwjNINVyyRVGNSnVKRqmYbELFzrlJhT7qbzmHM2nmPhOQacEqRmn91INW2xtLm57UmLUpS62SlZmjezvxt36dpuIXS7vYgwcaz/Trkl3269K+0qHMKOwNFNfcv5WsuuenidrkGsODjCgX9zWdivjvC1Qrf3ZavHsLR83So+qO1cv0rnFTzdkcOxJ9shrFSzbaMeJtjO0SyMnzkk3xr+CqBtFjq261lR+/htlpVmt/rVu+EaX24OPfGUID5P19TwWNvVbriGxUsbqL+yYlrX/iyLsSqkgrKJIrNdpVU8YR+vSyzzBkdQJ1dSn5llNdE3+GE1xvsMH6k0bwlXWGOAxUsPnXkC+jlip0nIeMobdQ20CxbN1k5k1ykt6KrOKekVFeDF5cwwH3ExbDWOuasixMQpoZRa6263U31ZQ0wQPreKKxQI1ztEhOgyKxHOosU62t2/bGvrXPIpAETh8CXKGGy4DYEhVuEB4q8InOcrIdbpLLttK07Zqt2ro4iIj+BkZJeEmNnuSJuJPecc5yxOa+ySLI8+sbRYReZZQTbFs+RPLEEj857NkkrmaK+k0Cr2mbvr5rktWa5SWDf200gBABzupZqG4XSIHOs0LxOUqUxSCihnIpEKQmKOHQigTCAmEJWpWJ4Rv8aQc852YltnixUnpkJB2GvzAFvf99QwzEgE+78VERBez75vv8r6WDKiNt0qAlY03CIgV2MdAAwL77JZSGZ+IkMuVfMhWI3k1hr2WeJEHgj87du3RL8SEWIitLS0nwBYW56O8BSn3zjD74LpMvjfzQZulNWc3ZFF8RmH1NjbFbvPD+MSDBrBTEPXBLsuFZNYYbSLr36KY7k/cdS0dV+fTjqPgOqeSlFErSZl1aIP0NHWCRIx+haGflmJ3Ys9dEL5QkDY2d2pyv/w2qLNMfbf6aBLTT8Fu3DcvHttAGqB5HR8qceX8atDZpJaiyoxJ/fuBkBzyp2auBORUus01VoUoDKJ1EJETEDieWwVBKKUbCiX8W4mLnZ4yy3+eYaFzVHPJbzsimv99pxPgxWryy5/2a1Aj14YMcs4r12gUw+qBSJdT3PE9StJ2a5Oc1ww3ndV/frrr1P6w1qVydFqBiw/jrzFNE6E+EGXos+7pOGmni+MV7Hm1kBWqxq42VXagD6WHLx6JkpXn5QVBK9q8LLtRvxy22LEiNixFXetAMv4arfbue+CuS9cdSC4OhHOfsxskVS9codpN25gYwD5rHLrdC9yPrVQiF3XleCv5xOBOzHjl6tjZb2OJPWGY7suNS1vmG6AUmqqpT49HZ8+nvDNgUlrnVQpcbehj6pKSr0lIpPmC1prtbW/hWBVZV7eIlzK1y3RV8wWF1Ekmv+Z4z1MCdHRYigilxVu4h2GQVVz15ntcHWEYR8oeAYihhBrtwwVlbBefnRtxcYZiuNp318Q5Ac/+EHXdaVEyYNLMP8vla8rcepdikc/syr+ip/N9RIHeB+w0KZjy8qrmZ4/t7XBzCvNZfU6ArpFaLha+epX/8b3I1s4WMlVe8ZOGHLO+/3eexi9tGQJjrFjjkdx5fiVIyw45NKHSBz6fJtjpEb8dyWt0Y5HpnF0k1bctPqSWRO2tbIl16oP81vBQBGdy50nfSG3hnIt9cP3Lx8+PKs8ptRNJ1SR1A+EpMqqlZqSESuh5lzis2A2IgdKBDb2F0mvHLzO34Sp3JJUb2lYLsp8yWG5Ple0kxabTVVtK2vUjxfoF1ylRQRKQi0sGBETS4tEIwrLW1sBgMQjdtJSa7jDQKqXGElfffVV13XH4+UBtXQjy15tS7RhXV20K6K7aoBgQ1HV/Jpn4KrEOf5Ese/iLyJO5PtYiTQnBt9qactHH5+PLB7X/6pL2xlZLWDvkqs52xFFo4y9yMweD4vDuT5mBWIxI7oBLGcYWuoIvh9cqVevUvheCR1aAkEDF539zq17dn/QTj/j/jQCVqyhLnMLrOi8LdTwImpYRDQMg3fA51RVWftax48f8OH7J9XfTdydBSLIIVFzjNF2nsaqwjkxMyUGESVOXVagqhSpmYmYmJOqFqk999RUyMhFVzlqplejgG52M04f/z67M95KRDhl45sGmdq8eKJ9fRVTwgkkIgpVZVz80HzLIIu+kQBrr784TmzAS/3mUFuWh8PBerXs+Suot+qzk1hEmBcrYdUTDs5H7d3PVrGuruo7JYqTbVW3vnEJLMvzne0sr3ABty1rurRtxYW3nQWrJ7IZNuy4+hUAcdZNQbSSLJnEDnA4xNjgFpj01rL5rBL7uQCCoHpok+IuyWLEHvJAHUu6+VvUXEa3a9A7Hz+vIMD+9YzZaNrcTGphKThNOJ8n77aIxCNCkKWdF0CmSQHO2dx9SRXMue/TOI6qVKsyI+dE856sUmp8FaLu/aZCIkxx7vv+eDzaYExRsu23LX7jMHNtf3l5eXp6+sGPflhrfXl5OR6PfZd9Msyb1MLOGHOcTicLMnk6nUDp8LCnnI/nE1PaPRy+++67Xdd3fSaFTBfPI7+kqsE2odcyTTpIHc/nt4+HB6Jvf/0dJ/7pT3/67bf/4uXlhVMlppQ8LtkFYuLUoi286EiNZvgAqgb/I7Rdj+92nbdmt498sSYgrH+XqCsjyBYs/DOFEtenqxUcPEhWiokzsc2dtvQzqRXbOnmQHNdW4pYqHixeZSNqZy++Djn4PSEsJ1edmLnrOo+AbEv36enJrVfc4lvNvK4Xa1SUKJ7Oz00Zs8lCxRLSREdT2cT28SGs6IYboH/hE5o5M4buwMbI7Z/P5/OW2VZzhOD3n7vOjb9RO45iJtI/plmLepaEkPlxvciY3n/1o1/97H+wDfjxeDydTo/vHjmlIrNJMaWkOudyNzp7AGsHoN1u5zmrbU7RkmtYT0qtOmrPMzMYqozjOHR9SkkM3Yg4JN/lcDovIuYRUmslwDuQI+GioDNVNooCd5KMxbfN82O6UJKJCC0OD0j8np0ubFIVJB5X4+qq2PKQc1Jq6A5gGAak4ZYCom3e4pAdPj7xMmqELVpZE695G3iRG0ZWvmFXo2X59L6tadXAN1a1mqMoBulGudXoCnAdGm71MJbYW48IGiUBYKH5F4Blj7km4j+h+QfdEQmRULFPV77fdNj+TAQVIVH7jz3faanxeQm2BdwALCzBy3ctdwxw98d161f7bITNfSd6fvMGb98+mrxpzVXVFrFHLHIvalVwmv+jed/ngIgqKloVRdQulihd7uJcHcKtcnWBRy71cvHDsmZcesQ9nUdZNEMDhfMmK8I8m0U0cpVjU1sVs8LZjGWk2kxr7bFF4lLvEgVryHbWDbDUwgoitbSgbczhwrOIaFNGfNT2Uw05V5xeAVUXjdpjEc0jWl2dqluYixvT6Z2M+HsfC7BcIY40PmUrpW/ZizV/Rwh2XL7W/esk2sKchs3UFghOp9Mc6a0pF/ZWS/i9NhhRc6mREHAZy+s13oFXMXTb//hr7GrFxS/UgVKbShi7aj8JLjwTP9Am4hC340Us12qk5KePKy6WBVkYp+PT7/zw/Q9++LXIeK7PXWbTaeZ7hfNwipZSS8l5tzoGMUaKEFFDFNbVqrnJ9hv6rzqvuoyJ0ErG0uneic4tq6XrohZO18m94sh574B18xFoVFp+CvE8EQuzEb3mGbDlLeNaojkn++l0enl5qbWmjlTVfa9mLFbhpR6xpUgco6riBsPpJsJJMzNf94S+xVuf/v2KBa8SZzX9qmrHNHGztkKKOAQiQsuSgiVy3Wr0KmDpJi6Vt7hSlPwnix2sIT+Y9S0CVqxwtbB9jJwWtmdqJs6odq1o6Fy3xSwNQDm3FS5lOkghAPFq9zonjr8xWd6BuGXRuCNeMuqrcms7KP/TSqmn55cP3/zg9968OZR60um4e5NBqlrByW49i0itKqXUUvbD4lzOK+Q5CvN8nuA7dITbx9rcVu70U5fl6gPxsYuGFX9Ay2gS++fOmRFc77TU2qvMrASRoqSmdi2ZTwDB/NMVp964ULe8NU5jHhIzLAbt09PThw8frMMW4mdu0sinwksm2HKkBmDaItr2Q+wnM1cJBs5POOW5VVZNxw7ffz4SZ4U1qynWYO2mpQ67Zfdb7Ubla7XUEUjtLa4UJX/Sb3HG+QUgemUPG2umpgbOKQ77ta3T52LFOfbvakseieMlcohMk16zqWltn7U5u4vS8lxpy9jadljRurL1f1wB1raqqyUyjK+4l+P35xHv379JWUs9aT0TJdWKeHhi1nohKKeud7SySCREIJWcM2diBamBNaAgYujFpinthuldfLiOITMpNyJkEcDPyWe/SYtJYtXNindaJDWAW8gsxva1g0kinRMNtXSmEtzHcemrkAcmvSFMtowuIil1BqSqOo7jOI45dxeWBTzCl147h14J/NgBvYSCuHwfu7cc5mLHqp8IWLcVyu00f6KmE9HHilzzJIiL88ID4XrHltr3m8YS+LbDicSPiHMVJuaB4MphwrZw89tgvgJAVxaDN7dpNHKFbrCJAmBtux0/b0F/Sy63kRtaUTBsG1S5m+Tq9c+SfxLK6fycMr76+qHUs6qFoDFQzWi5R2utKkSglDoOt4l9623MQ0R2FmklGnCd02yl3/F/vDpBqwfiYzn+IO20xUz6PjHOVdM0ddyvaqdm0rNasGH0C1NCiemyfYTMl99VoUqNNHFdrbq++klVu5yZudRRRPq+7/ucUhqGwWNjO2DZn25lRLATRZJtmXtFuDjqqw9EYe7sJbeM7jc8TVcTeV+vRojHtLI3UZMQ/mcUUQird/Xn6t9bLHWVTbGJ34SgXvmTsSe89Dy6DP+aYIg1xxmh5onqLIfba9v7uQUp4xP/0gsALkWXVqoVY1wlVESZCD2+E/RBORRyc6NFYKRtJbfKakbsRK/WqsDjI776+l0pJ07a5QQSuy0IIqh1AKqamHNab7y8D/Ec1pNrrMZ7H4m8n7GJBQPc0rCwXB7ROotmnUkpRVfdqyLO26el+k3NIc3t3/eHoRsRfQfIcs5EMJL1fb/bzfdmx9IetmS6LRCzb7ZdSljnoz1i241PoftqAlblFmCl9HqcT1yjyao4H69WKS0jXvn+YsVSV/sfeeg+BWLf7HOMM4VrHI/lhpGueUtGCuA2VMVqp3HyOd2i86ozettYbqeWEa3smRwdDMOgZGlBX03BasgIu5nVTSlHRoezT1n294tVa5g1DHh47B8eDlXOKeUudUCdGQcXvMZsBaJV69TOMc12vLKNXm3a1uCdvt1692rJaOaDeAPLJtL8ZVSVmc1xZr/fZ+b3X715eno69MPx6Vmmwv2gqufzeRiG4/n0zTff/MEf/MHv/d7v/ff//T/75ptvEvXTNPV96rt8Pp+5aEqpnk9d13WpI9WxTFIqM1ehOtaOL8kCVFWan0TT4BoJVABh0g/4bk+7Wrgr+/PTuDvWn77H3//5P+f8pmIY5Y3Wt0OiLknOI/PLc80p9Sl1zKpaaksLKCI0pwiLOpQSXWKNu84MzFbGlGlmeiiRMvPQ9SKiVaqohZ2y+/SZb2RJWboIRY6JLO5s4adOMVYAALBlqCWAjGb2o4dyxhJWuq4zkRs1aPtcq1ikBNR6Pp8hNecMXkPS3B/VVZ/nDudEREoAQVTUAm9BoZU5MZsjuKdmMN+rySyeEWQ4ROFnpiZrGEDfd6r6/HyqtR4Oh6HPiTEMnXtpWkP2Z7yGYd/MiSHq1ah7F7N3/B7AOMZDlcs0MC82K+1XqMxEARYGqQtYC0Ak4RUXqLh91ekC4k2zhnEna06ZGKVMlvFhHE+n08nwtxv4MOFPv+86Pb998/7p/KQPeyTepVRKHbrMkHE6lmkahq47PDJzHc/zcMBgpZl6EynO05i7NOSO+3w8TlomsAhq1UpEOYmK6FQZ2vf9aSoiAlYggRnKpRZVSSCZShUlosyktVappBODVAHSTCCQ1kqkQ0eXrDkrqLuFeZatyDdWvpYs85JBm4hE/8+r5WrlBkWfItKvFlIQUTSjGqMsniHSpvDz8ghcb9j775RP6eT9IV8fyA0t405/tq3cmkptF0c06DWrxy7rYbO9vd/VqDRd7SRfq+QOcWJPVqqThAt6WG45XW1BUyptxerS51NEsNSYoukKG+7FkpluTUf83mxqtDm4jI7Q3n8iostAP8NKtaKYqtqWyJJ725JsfcPjm8MwDJyAlq/AL9t5irAIl7daWQ1ZPz/8MdFn54HP8QzCJ4Y2V/n9Gcuw1hT+S4YlamYa54zZaX4qVwGL2o49LkXVRUwFXyrbJeefmYgV5SLe1UNZ02wdJCJVi2NDbMrUNE0iYlEBor6wJeinTEF790o/40ivvus702Wja6ezT4HOWxix+pOaMoWlFS96M13W5+b2vHdPLzRfdD72POLdfUBc9dD/lHD/zpe9m1lri7x6yasqmpiJSVWlihobEJVxcrTybZchky4NVd7V2HP/nDe3x3wgV79f7Ux9uUXKx7ZA98TStkTC2mja4pVxHE+n0zSdJaSkY+Drr78+HHbebWau45RzLyKlXOgJQKTc6UpcwispFRc1NtaVCDKvjg7BnRMxL6EurxNGll1xpPfAT2Q0XEGotZp/ad/3OWdjlNjROKorfLyk0IoEuMZGUHc3n5UmEISUGWw/zf1npWryxK4U+B2IW4hwB7AaiRZPRvrGIdxCk23TDge0VG1exaytDQVLbo5fotmYfP3UZbwNZ6lbon7b7TuPxbFHQPRhRkTY8PGFII5W1OK32FJ0wRPHa2LJbP9mGI66lfdEwiF4NK6vBAaWc7oi6Z3PaJrUtmwJrmqnUtg+c6us+ibNje7l5dnUK1XpuuyxWxPjq6/eD0OnOpq2m3M+nY5EVGqttRJSzpf7xemG/UmD5NP5TiLVWjNfYnL5EOTG/Yc762uupKkvzhX5Csk2MjaSz9C3HRCwT7bV2A+ZWqBuClfSb/VpC5GrP7FcdTfGPPvQMzOn+bgDJKYAqoCkAPDoh9vWv6BEhm6fbcuzcFC8j1ZYLgO6tjmNk/IpvXLSxTOs1fJAc7h11Th2cjv1q3lZfbgzNFpqWKpKy3snVu5sPRDMz9EGZFLHwv5ZDSaEPFrmNE3n89n0fW5XDleQREtn6chytwBIwsmvXpuy9b9Lo5X/FIccd6Ndd9EhPrHEiUNbjL4NSukSN1hEhh2++uqdeRnljoHL9fVaRESIPSOfqF45pV2RJWpYtZr36ZpuTvBXl8O1oS01LB9wxI4tD3nb7jASubnWutvtTLcya7RZNM/nc2IiBkiJ4f/ZnxZaWiFmKQbU8rav52D5AUsgSJqa+iSJUiKmOcaRgCgxCYF0NmrKbMe9XE6W4Il7g5o37RRx1dk3InJVQ/xcWKQQGjAu+Pt87INCYxFbnKsFg00Y1a1aES1cK8qv+iCb29EXbLphFoxywltcDdMfYF6HprMH7NZuKcWyQI/jqLPQmvtfSnHzjar6JfBo8wIgLZLBVem1FUurXyMG+TcLgiyfWU3B1Ulc13C3rBaINillxyYi8wVe1x+/+l28/+oRUCLt+17kGUunvBYCQGYvrbsakLZNKDPXus6ey8EN2IfjDc20vT+urYYVeddJtjpMXbGjb/XjN6aiU7Nuxkv8VwsCZ3MIOaYbFe/WDM19s3/FXlQiENu+UlrmWxImVAiQdM5m7n3wnVEEwRs0XBRabkCu9jP+eov/VoshrsntHN/vz1VGX7kXeIsSHERXDisIwBH74KOmjZ6FJfSsehtZJUJAFBWr573diFaxCdMguLlW+nFnLcVOAGdVqs5FF3H3L52sDbCu/vspZYVHayZfTsdq6fpjvgbjTEVa3SrxVwNk+2AhNmutpYztdi3nnH/4wzdfffUeOKZMu113fHkSEaJUi4rAXJiIqFZRKCfGazEBooEsCoMV9/LSkfCWDLgzzFnDWg14y/TbBWDFo/d5n4xDTEsHYNkELPzzVcDCjclYTdury5UUqrOHF7GahkUMIjVzO+Z9AoNnMplN0Z1jeXMzCZfF+Rotr1FpNRx9TTm6U9unA6gVDv5l2zUQ6/R1axMXj19WNd/qw1Vs8i8jtH26JNi2vjpf8/4zc0qp73sP+ppSOp6PdoodwdEULlqqadLcd662K5djtSXitD0SAUyXkN/zCmz/MS1CgSNwdWSwBbRtvo/vfjrF/DjFaS4tPV3f9++/enx4HE6nY0rUdd1LmCynNgBV0Rls7wlaLNl72096Ta5/brk4jhKRX5QXEXPCOh6PHBzf/YjNvLROp+Pj4+N3331nhk9T0S2kUc55HE8i8vz88XA41DqVMva9xYd+GYaB5pgNls8aqhXQrkupZXyKt/atM/G01Vm264Y6TokYxEOff/7L7969e9P3v5a+K5QrZxE+l1rHqh33u0MdJ7e1e1XxdHwzJVfmgKiFMaHFnOWctcI76dor3452gLZ4Vux7Pp+5RUqQdiuCmTmnCArepbjw4lGa7ZVcAYkKiy9Xn3qnKjWnATSeW22cEVj8KkdSMHDEJtwrWpu1xa7UeHgZf91xakUf69g4jvaApdux2qZp8mxPnuUQgJ3x41oQoZVyTU3f6boubsndgjaNNc6UI0LMSOaV4K4kWwGTfeC0NvSscNZnwT7I0o/PZ2SaJtMYzBfQA5/+7u/++Onpw+HAfZ+mMu52O2PUcTonykYonRtFrZXasoj9AchXkCkuNn37/f50Oh0OB2O82uKvaUsRRk0LM7KXUqgp+CklC5a5YDNS8yZjZtt7rlPVr5YBL02GzrvO8bH5OMFbKbEZ80316tYEXy1qwRVLHY+jEvou7Yc+ZRQplSblHom7LlXpiOYIhfFEyW096UaK89jVwCXreydXX/wywaLLG50r3vVZiGy9akiDapPS+tRmpb1f7fkXdPvqQK726mqfV4+t4Dt+dmSJzO28V8f5NFBq1VpVzF9V+cZUCC5ktGLVmkaGsG9tEohpU/ClRNuSyOys25Xiraxe8Y3OapZdyEUDjqq+fXcYdjllIRbbMjJ3D4c33333vajphiqisCRlWlkvslZVeG5IrvrTUZCC0X56h9lepYwutwh5uxuKiOMC2W0cDQsvKfkQVsWWF+MCi3MQuxU5L97723Z3W8p57MzfXGtmPD4+/uQnP/r9P/393/6H306oPMju8K7fDdTnAZoTWepgaiqJY5bH3rxKuM2/N3v1m+BUrCEuzmig3Sprt3DfXlnFw0NTdm5teXBt+n7zQa1GdPX7VX+28hLBhOqv+/KY7crjWYOPQpSgt3q45TRq8eCd1b1dzk2wEWHpxervavvXGrjabuT5SFvmRTbTLf1XS4xDbFhVnaMbA6rilInt7vYDcVUVO82wt3KXU+oSZabEKRFXAEIFSISFnbEBFmldnF0Q2VYYFPQYCgaBONgFwT+NrTT6YW1rxBKwItPI5jgpzqtPg7RCpHMAGYhqVa2AGBC37xsJVNHuvqwA4s5qYaRpmqTWRCwi43g+POx+/8/+mf/iH317FqlSeync9TkxqTKBuWyHuZ1XLyJXFtKtjb0/4KDz6oK/iuNYblXiY7F+vXG2tRI8K16/1eiqA1b/FwPWqq3IMFjSHzeUL2969brb1GNtDlja0mpdpcydflLTp+Ks+U7QH44x5led9Eriv7cAyytcdXLLh9sViiU/+OmCAZYrGdEy4J/7ngGpciYaui6NzG7ggzL5BSxqYbgXByxX+u9MY3TwnUrcxvK1GPavlgYIC8PldbUCzfgfJduqmThnKx8ZZyA3GcQ5XnZoIdniE9tfr5bdbvfy8UlKTak7jdN33304nc5v37756U+/+e6cjlMm7oRgFo1JxWwTEu5wcAsifoNql+AeFO5N3AI4WkzwFy7yWNuCOMt1jiU4rii2Irg/c2tJRDpv8fGzBhI7HCtcrb3Ysdjn7UgjX9ldi6h1SihZWt4ahYc1+ZS5cISK8+vmFSvMrC2+yFXkvUrM+/RZAZ/fYYxfUvNtjMRcvTi325ahOX8yc7Q3EdG7928eHg/juVjOl2ZjgohAhYiBSlpBVVAVBZJCoy4DhJckNeJRcMfxmKBxc4ZrfjC3iLP6cDklXDVsH/yiBi2PgX15u/WKlorfdpFsGqarr8Qurr6/w22e8DWlNAz5Yb/f7fRMw+FhN6ak5wzqSRnQRJSwuJEfR3fHxzWyROCNmyWCCF5b8BFr4mOyNNhLOxygunaCu1rntomoJtQQ+2XVSbrm7vsFgHUHqmjD62hnC5HZtninqrPgmaZYpwYb1tWu3gGsOHwKe4Wpxb3CkiHdE92PJmM/V2hFRKI3BVv8TO3+I+frS9InyBnGa/DDGVU1Vx4iCpriYutzPh9z/oYwiJzHcc42mCR1eWAk5kTJQs2oQBRIcvEtB6RpWSLloouseujI4Ky7AoFXhUd4+KJh2Tc5yttICLNP+3OulcQ9M7fgGNqSstS6iNmSUpJKdA2zFkxwTbZ/4qjOL8eUEkGmacr9/v3793/83fmP/uiff/z48VR3oruU+8SJoIl1SGwZcb3duJCuFksH0rrkNpHXjYgrrrpTthSgZiuMC8Mm3k9LV44t/u7qA65ZrFctxgdwA1C+oFzFzRVZnNdjNIXYz1iJ7wftCCw+eennNXl5Z3594E7VFLLVxfpt36QsvsnipkGo73eC9co/vFoo7FFyd7E5xm77Fd2rtA2QMf/qlwrN6dCf/Bt/4z/r0v/mxz/+KhNKHbWUWut0nshiYlkVoecRJZuWMzcViW3vMXPf93YgaN4CTuFVNAR/69bMrEbqbJPjywjcGdklck8U+N4bVTHAErnk1ZiPq8dFD+7DkOqVu4T3FwwRHQ6H0/H5u1993/VSeffttz//O3/nH4zdrqIzi2Gtk4wTlVoJtiWkoOLp3cjT7RaojddvctQ7p4qfCLVXiy43d9GzYe6DXEybPmVXocH7Q016o635Ox24Su3fHLNib6/2kMLZji6Lj9EnayVlEQmyHN2rcxG1A1pervZnPBxNKUVI3HPCbSDY2J5epdhKcjhWmhtKHOZq4WxXMgdXFUtbBcA1LNNQuN2I+ut//fvf+fE/fvfu3xre9FBUmY7H4/npnHhI1BERJQIVxSgYFVNWhOYuGtbQ9VcpaQ6rAMw/johMnfcwR/TJgvxqyYmau03b65kz3sR6fP5wOByGYTidjgw9POwgU8789PScc1KV3W7/8cMLQPv94zid3rx5DyjR6fHx8Mtf/uLrb94/PX3s+nSeTgBSx0WmKjXnrCSCClZmUoVIVVHAol1xXJ9xy4ll1GajUT989Xx+eS7T8JN3v/74vH/zg19NfMZjT79fz2/GkSvXNJxSdxzTt8fy8jj+LjAnLhIpElIJDMNgeRXNocwc7UCTiFSp3qhZCdpxzEV8mf2RmJlJK1QE6iGqUPXiEBz5tTm8AoQ5tE6byiK1SLNoMCW+QKfzurN7hKHIDau0dA4BV90m/Htq59M1JERZVU5NE7/DXj59vgIZFzm3KiaffVxWTMC4YmX+RK51YiNZS+scbiNvHAK3yyjzN8ydXdq3eGfNGdAtJE8vHwGdpnMpo4nknHNKl+BigM4c0rgiIguHiH0U7oeN4zhOp3IsK380L1FAzqMAMLtlCBMDFnxsNpynJar6EN52+//P3zz/1b/6F5S/5f60P8jx5Rf79286DJBHyAAAfBZ+BhMY4zGo8zL70CXCrz98OOz3tZZa65C7ch5LHYdh+O7pebfblVLqx4+JuzpN06kMw7Drc5e7zKgysdRkThVTIeqgCqiIciIQMYhAcxgxvWh9DEDbXcJoMaHgbBVJ5mznhDAL6JwAPQgG585aa9e94i7wqgyMr2y1idx3P//1R878/qsfpWH6R//0j//lH/2yFh0xlnJSdDZ8opQxUJ60XDYmzot+ADyOMxf2fW8UsAS5fmxM7cD74eFBwu1/Z0ra7KesrJJ6rJlvs2tePeBlBQHxsRV9tG1kVg05Yl6t/7dV3HLhdJ5BdhmNIKKhf44/RWsjmn0dzV1jBcSqGrfwsazG643GPWD83hkA4cIpET3y4+VEskXyjHG3KZzQqSrS4ptIbVU170pPK6shOe7qX/OPdVJ4fyRoK7EP3j3fbJpr68APP//5t99++8s/82f6Uvnp+fju7dvxuXTXNwxLc8GFSmhoPvtLA1DUnPPj46NdKy5jyakv41i6OgxDGU9EhM3SWLF7BASX5rHMN1SkhenwHd9qgWnQUZk5pUxEtRb/JtI3ntog6BBzD9REwfUO3Srb1Wjdm1Qe3rytxP/4n/yLf/RP/of/9G/81x8+8MOb33l+qqCaclKukCqlKjPTnkFQ2EGSVpFS61TMQX86j6PqMAwPDw8m6g2OtW2E4/wZ1xqzSnAij4tnQeictz9t4QPBZrma2virhmRwcZ3rckeJ4AC5elj1iiHJaRtrwzUY/cQp287gFpXiZw62YntrmkoMIuyywbcY3lsEW9i2OPBhidEWSCvSRFuIpJV2T/NWcZbTDjGqCtWuy4lmPZeJCaSiUPXwoY597iNWW8CJuJmINwFWNIydv/BDMBlHWPQXbXtrccNTSpS7D9+N/8X/9+/9hb/07/38Z/+87wbmDkRiKszs2L5OmNi4cT4YIWhL001u7DbAyqA5Npkgp0yqJNx1XRlPzgaqF1N6nEG9u1W0X7MT0fNzONKvXnYgi++7quVNxue3RPdvrv56p8zyeaNhff/y9O7tV//iZ7/8f/xH/+nf/0ff/9Ef4ye/+5AAYs4sxEVRJxmlqtZsOzbfYrSDgktkKP+J2klC6npcE/4fPnzwIUQjwlW0AmC+mhdT1BKGVpRBQA37NUJSbGLJT2sTJJYxduMoOBhE408rzQhLaNuWWz+tvvcWfa8UO+M09OJjdGEQh0xE0zRdhblX+7Na8NtthJ+Ar2akdWyGRWrnIfZT13W1xe1EENs5JQlO5/NNbFXDqXhlyjpjceVog+nx7tSKwVaE2r7rjEdERagb9n/zb/2X/5f/8H8P2n3zzftffvtHj7s3qE4HBcCwW9RLqoY/jOtUWdtuzHQuaYESdVmuMRWIbjqObhc7EXR1SuhFPG1XEIz2vQWQcVDr+zks8opMtFxIq75iaaT0n+4PYFWDlUL16Xz+r//rf/x3/973Arx5SPvdj/7gD3/21TffECpzApQhUKj2pADParxp485wfq8NgMXAts6fxucl1eZh2l0/jw62XScrzLqKznewYPWTbhTMFR/EdUhB1fJKVlw+DMN2yvzdbbtbCL5fotKNiF+6OOT25nwgEaqsJl260UTxsCoIW+Cr/cEGubCcFF9stcULW5HIAMta32pz3o1IMdekYqjimN2n1TxnSInLx4e50hAvnV8SzR+w9es44mOZJur6x1/8/Nu/9bf+7r/zV/71vhuPR33/sHMvBXfnXk9cGOAt8aUtKKaqkpJpWKjUdd14WvHhhV0DCrfpaN/QsnLyU8KGl+t7D/45TjMzW/QOjw3m+I1gUvU647q6KiI+pcR34/dv37379a/P/80//u/PZ/TDm5fj9MtfjYT++fkJSXKHrutS6hINJFmVpum7y4lPcCUzAGJm+9U2yF3X2RntttiWwaIOUditrFDGCX3ZPix/XX3evu41bFcCmkaApZYXmYqDcT32f7UU/fvIQ1dfXJXb3Lvedc4LrywuCfnnlUz2Sszo7vtuv2O82+38rVvvbseLu4DlwCEhlbEjbyPv5RZabLGFD1xor755tHSZtVbV+fxuNVOuhcX5Wq0de9hsrJc7Q0GBiqOQYJVG4JOqPaZp//D4H//H/8lf/av/i+8//KzPb6pmaNsMkmU1BivLtRx0aohyreD2Op2ne7UHvM1dekfDorCzc9feFcmcsqaJuL84aL5a2E6L1tfWSC02nwI6f5b24XPKlhfniSQ5juf/4Q+/HSccT9NY8virl8e3+5fTrzSfUtVBhn3/JlFXJ61TmuRoHGnrG63TF9lqpenWSz+sS7GVQ0uDEYIDpP3pglE2t+ojO8Zl78YOLDFrRRBa3s6xHb0uPSFwDZiobcFwAx+xhJJbHYizsC2RcbdjjMTxz75o0VywTUGoIaS1kV1bQD7ZXPFdbfFulRVGa7Nv+KQcj0eL9B23GrVWatoH+ZIDVNW2fvNiseGL1FJeXl7CyQx8a4WlohTWl6y2X/FhW2g1RIttUfpmarNlCqI5q52I1BaZbpbHQqL82L357/7bf/l3/s4//It/4ccp71VYSQViZ4FNwyLW7EnQfFLnwd8oEk6ifFLiitjMQoS5pstf07CsZL2IjstNa4TFGWsnolLKMAx2jjuOkzbIry0thffSO3F1YKuabxJg+cp2If3sF9/+4hfPf/AHkIqv3r8/6OPT6fzhw3dpqFq1KiqfKw2gMh15PEvlMzVVWcP5gB+f+yoyHX738BgJ4r9Ku7rh3ECb4tjtyyDaRLfkxZKJI6EWMiBwufdqBU+rCmmzxYgTfZ/mnz5BqxcjZkXjzrZ+tJOfaD9NcybnpM2RxTULaSFGdXnCo3pzi32rOFpFZUpETqeThJg2l4HQwusiKm5xlZpWNU3T09ML4Di12PmukMg2KzEp6YqGV/u/2+0iWjmJLG5M3EaY+WI6Yrcbnl6eKOE/+X/95//zf/P//t3zB8Kgakbxgvnyr/Xgyk37uAZXqIp26gqAlJgZKSkvfDJWbL+t/NZM2a/ZRBk1vdTn7HQ62bC5JRkFUGs9HA611pS6jx8/Pj6+Gc8lpXQ+n0Xkhz/84c9+9sfv3j+eTqfn5+cf/fgHOefz+cV2T+aoYorAytkkDqAsF5IDhBk1jZt919b3/Tdfvf9//yd/+5uv8e23+PV3v0ipUMqEQijMmAqeznjGh7eH3PHQcUp9H8fvPjienNEpYOV4PNre0Bc5B/9+NNXA+eyWhI9CJgLK6vTQp9ON9A4Z0XzjrRhiiog7uMZ9rnOSNqcq74/fzqGlpdYfWA0k2obiWnXeiNDsvBQNfLL0oYtQSERmNPSHawtfJXI+Ho8istvtLBqybuIdR/65JQhXNrW4NiIx0SzoBovH49G86v1ISoKNfLVcDW5KKefzuUVwrjnz1f5ESsbv5xO3gFNbwIqfj8djBD47i0ATEsw8DIPPbymFu2Eqpx/++Ccfvjv+Z//ZH/07f+Xv/9v/5u8XeWHwBq0ySLpuzt9hFJjV2JDMoZSSiaNzT/T5GMdRyiyBai1qAh5mIUEi7vvOAy46PbRtSqSNgi30tWr20a7oYi720pzC3XqHa2DvItRFh61DEWEFK0iUFaqwPwHcMq7LchN0ta34+fj0/b/7V//Kn/rJL//6X/u7/+Xf+1bq97vdW4zHxJQzuowyQQUylWP5eC7yJruVbRGI4lTbTtZI0QhSRNweT2HX4DopL8MqOLtc7fyKxXWpekTh41v1OOpa636/jzUYKMRNQVyKGlTCO8V7fkv0+a8+v1uA89cdnhBupPq7jjUrmsR15Zs7Zq51ESnUB+VDjiaIbZe23dsSKi742H8bgh8Z20y92e9miim01KKzoCotNLM5uE7ThCpp05MVZtGy+ANbsVdCQtz4bwJB22mVWgwWBfDy8clU1ItDAxETnco0jS8//3bq+/yDH/F/9Xf/4b/2Z3/8w68P5/P3SRUo8JB9YCh/ru3mFuffeT68cle9sq339k0rFgTSJdgF55ZmM3/Xd4UmWs1DX0TSUgeO6/nOgO+PPHJeRwDVv/wXf+/d4w8fdv/53/jbf/D8/MsffX349rsXUlgSORIUHVUAXI7DOZTIK77A/E9XLqJ9fdUlJ8XKaLoagtPB37q1NaOgUmkw1tTNvWWv5+pSvAVYcZXeXypWbIvhW2an4WpC/YOfUvkDs62NF0cH3rrbBONGRjen9Y4Osjk49/H6cCKpnc7eoneYbhTfY8ZbOK4nxmJMIs0nRpuuzcyyNFphA6MruLylu1FYR1E+ueDX5Y5kKjXx7LVj7oSzzWsYvvr6xy8vv/j+Fx/evcHf+pv/7f/qf/kXh+7HfaeKqqhQtRjjAAAGvxbU/W75FPxaLYpIHBfj1GRejip6JNOKD9C2FWaEjtymzT/LtycpkWvvdO0m7W8IWPHXfc5/+If/9OHhh3/5L/2lWv6tb3/5B//kv8M4vgwJUKACFVJBVFJKuy6ZxzBtbDo3222mPeNaU2dqyFyA5dqIYBo/eEPxS1xLEmF/utuBFWmB5+O7tNwnrlYv2iblKgEpgF1cSLcCGQ7D4OtTQt6BWxO0He/cMVksAH/GgXhleVm15SVuBp0b7/CM1e+08nYj/iJgCoejD29LVT+c5kObSI24olbzonoB0NjESkz6dMRQMPFf10x1nQXmiuYIIKeZYqb6eYTi0/T8/DLU8pSA0wm/esI//Af/zb/xP/09qWclgdqWkAFAM8B4NQvFplztz52HI8feq9MAy45vV3Rk5vP57KuUmtybpslUJytx1rucrCpVJWIzOQGwabH2uG3D5LX+vdp7p0V9efnmzb7S8Rc/+/89PEz/3r/3l3eHv//3/gH2DxBB1Z45FVQRqXIUVfd0V5VaRa5pRpHK3HcSzs6JyM/j4vP+wQUAllNlx/DYzF/apCxHAywN1nRT71ePRZNWSutb/vbwrbtp8RlcWzDb4sIs2oNusVoEygg0mddeXVannXyhyUVtt6A05LutIUjsVc2Rbnu6x874u7EbqwXmhwDeQ3uRN1EH5n9NpaKLwm7QQpeLg5eCpWCI07p1ELXP/r0sPWlJl5oX1C6wXmyjoqKXXXPq+19//13H+J0f7T58f6oT/p//6T/9D/79796+IUCECgNQBhhg6CvEvEpebBbFHTxyjqWlLX9b5wxYEX00KGam2Tpard73b+iyRUoyu5uqMVmthe1ycyNonMtVn7zc+j62vuCV8/lh152ml1/+8o9Hwe//3vux/NnvP/6zf/4vMBUANaUucVI6liog7Lt+RcdXSUxNyTJEdhv81ZmImk6Ej2hcjyP1MCm3istYdx+5ugDQBPuq/luV1xtBr7bfW4moHUexItTqrbjw5nYvvLfYEjoTxiEzM9F8u8U0BVd8bjF3NFqvMCV+78d50coR54ubA4E2wTzb+63R5ckDtex2PiOuBHHXITzs473D/3H1xv74BwrKJmNxRhRHTcGqeGGSDu/3D7Wc/uhnpwz8xf8JfvxN+vD89ObxoBBQtTegDAU+4+7czUm/z96rPq/osKhBVVXzyjsuPsHt1MYPlfv+stojIVxz7rpOMakqM9VSmXkV7oPu6u24O5F0beNGZXp+/q7ieNgVGU/jOP74R4d////4b/+1v/YPfv2r7sMTak1EQqS5Ayf4BOMaNunGgOXFbROGWU6iVT99yxxXOILm5bx7dZPoH7anctqu2tLSvBW5czU1dDv59q3vb80LB4cJK/EEM3Z19SG+DoD0+lsr8I0vxpAJV3uyHZfjxerYNJosr/bBZyHOLLXNh4jwMjGtlxhFa8EbC/BdGOOuknqlHKxo4k1fWsFlRcQ6V4nO/MWn59Owo0z6+Ij/2V/6nf/g//C//gv/2g9/+qf20/nbYG4HlJue9XnlU0Bq+/xqyFdf1ghYPlXRcE4tW4EzyjZq2qq6lFKVoiq2tU4p0TILiD98B5g+cahWzqcXlikNNGKq9XkqY9cNb9+8+9/9lf/tf/fPjv/0n/7yV999LPWorCmj36EeryhQC2It74uUYP01U53p5HHBxxrO57ORYqWS+APRdsbM7k+0KjVkCV3ZU1Yw51oAlqzJy4P8OyU+c+fysK83DdmGInzE1bVyX1g9E3F2Cx/aNj6qSnTxilg1d7X/Ts/o2KWqwzC4J2r0SllFs/BigR4jthpt06Zpe8YdWR2trLlRL/F54r/RdLCis5MlDjaSUWNkoRumCQSG8fkiosf3u6fvTo9f9/+3/+t/+H/+P/27f/Df/ldv3x5Op4+p0R6XozrDrN+ofAp+fRaLZpBwYkDLNKlq7twxb9rv9i/Hp8PhoKgPDw/jdHroH37x3Xdff/316XQaHnfPpxcAmXKViXmYpomQ6jQqhCmV84m1z2zGCBUpzPCbD1vdxApfYqgbW899LefRpqjWKqWgShWRqRypjMxcudZHVN4Raj3h5R+9HfDnfnd817989119/oiXI84nTCO+45OllSAFiEl3ooNqyt1urKXWEammBE5S5FylDGPmLnOa7+jjNJ3xjLE8vn0/o9gc9col6hEEj+GvWi1OqcyRmoRITYZURa2Y0k9WrGb/9uYXolpc5DID6JOqKkSrLK71p5zielBVKPS2T0Pc1OMT5ISDxQoyyngmP0eb9SdV01DskKfFBIMqERWZrUKcmtEQQLhqU0ohZaKmlShKsQiWqevsWoxayjzHfT+ec+uy9YGIUpojiwCw5JjnM9leYRiGrutSZ97dF1EBA6DMPZMkFhEp9WJqucG3fjcwhrsCUDkZ0RKRMogZpERkQENaVVWrKGZgZXlEYmbipKoqWsdaFZWYlYRYlQisaHfIs3wFgAiJQaSQscpUy6miECFniKBMKBX9gMMBf+4n5S/8u/nf+jf+8p/66YcPf/Qfvd+V6SV3h51mBg2UBiURqcoj88jL+xLa4tXc4RapypQoU50KseRMFaPgmLKUaVJUZiLVaSoA+tyVeuTExCpSRLPFZ5YisB1pkGeqJNLiYXl7UXSvZKM090iTdS76jG/8eVUFxWWwqOpT0PTVx2JbRJZ+kYkkgYTArd3E3Pf58ZD6nN6+6cpEtepbnFX4POnxpRyPKlW6BE79OI5d4tx1qlx1qrWCOHFW1sbOolr1ElhmvlVP7ZzeECC6tll4NaAS0CWyekSLioniqqrSXfehFb8TsZTz29Mx++D3z9eEusFaXqdubiBfLSv7o2tbEs77Y832bzQ2Wf0W43zVorZEm9o8BtQ91HR+MRYEo7hH3XBVwp4xn1W/4oOQtFxEzuezuaoeHh8QbFJOsJwzazMJJ3HAevVyNZb2OOSUiKCzZ59W01QlpQwoKauqciWdb/WWOpEQKKkyEYlZr2ZXq6QKUqmAlpkrhlRrreN0lmkUnZiUGV1CIqSEocduj5ypz/z4+PDmzZs//9OXH/zga4vmFolpwfOuMsn/aGXF1VfLJWr76rmrSyUCVmr5ZrDJSWXFyeHzRzecmO70fvvlFkwNsyy4cgIJlIA6nbUIQbpMiTtoJzWLKGvHlKeKp/70HV4+Pp/rVFVGqeDc55SLap1qFWWmlHqlY1PIRFAhqPV8LkonSvPlkWzYXU3fqo9EZLGFADTMEijPUlTsAsfsSZRw3g6TiLTM2V/gAcVsFnTtsW0fXMKvCEjp87T6W5jlPKDt8GSGMLk4nUeG4XaxcTWPmRanovMHglYLtIRE82SaIIz+U271IyIDqfP5PI6j22t8I5ZaLNC4+zYGri1L2By8tMs2u94Z2nSPNplWtoWXbiXO5zPcQEQIgMxNCNwMCp3bJwIw6gsTCzpWnoPPElRZRYkslkmmxhiqejz9PGceck6dcuK+7/a7vBuSoiSqfd/thpQz547fHA4PDw/v3nU559Pp1A/d4bDr+y4lj/Zn4RUvrPWq3v2bl6BAXdZ1JHLsBnk8rIgp2OCCM2v1YF3h6MTtmqt+eDPil0I/IVCJP7N9zPkgmntJmCyjNZgUCiVVkgqphJqoMkhpUlVoVdGkkjoautynXUeS6eX7j9P5NBUg4STICoYoKZGySiISM8IKBARVVClScDzW3HcDDSklSkpELABAepgHfiF9BTCNE8CqVc17WDuCEkHLr7BdwPZ6QKvLT3Q93ZOTesUKt6i98vfBbaiKbZlYlhZ1Fi2aQnRbtXpi0oGVcrTtFS3Dp6DtPhA2Ixqu9QB4fn6utdp9L1VY4JB2/ZCjYuXdBmBqlznTGRcdj0c0wJr7D6BpWPM3eunwLRtfHPvy+4VjsLbPtV4iiC5eZNv6TKqJQCptM62kUIDnSMiqBCagjsfHvXz9dXrz5nHoUsrapZQ7dElFKifJCaoVOjKpyvThw7nW/a4fRB5aKJg6jlPukrIFz7mM6P5S/a0Uh5Frisisj2uwzGZnRAkxsLY9dmurn+h7Sya1sJytS/M0A5ZrZJ+iYa26jrA4V5IcwGwaFIUQSGwDt+t7LdPUK0SkaJVaUZmIwKDCkH7Iu292ux32++enZ/n1BxSp5VgpgbjvuFelWqQZI6VJR1RVqqXWKha9m3LH4QyI/TLNvIbNC6ZCgZYzL2zrepPtYeROgfAXTLoSUVnagH0ueXNDaJ6gG3egrq6WO5jl0gJLBapL61vf9sDpdPJu+DmDV+UbW38risDoT28e9rKJAWCB7hAOMVYHr7HnAPx0lTykr6qqzlHzJeR/ayP17PYRzl7181qjM7tQuajGwg6OydQoguEPlK1ZRcs5oqqknFJGhZk0qe1qAPzoK7x7hx980z08MkNLHUUKSk25TzQyUWYwKSA5S5/niBd935ufTSlFpdhGPIFZORJP/+SVrCiinHtX7TpLq13N0XC64QoOhaMZCpHAkFNkL383qusxXA6ae72z4+duCSMLSjh6m2UmZSVVKJHdq0yihaCQSpBEtUsEFpUkSWsVTvtxHM/TCYl3Xe7f8n63O56F0+k04njEJOBUFSpCVIAu9gREVBtVa9HxXFROVeGHrSk/taloWpIUQLveTCJeilEp3hbUNVqt1ZBbGgqWsB6fv89uK9S7I1EjM/ljIsK4hGSJG0C3zVMzyVtxTpMQC8Ex15nK+2b3w6QFlrLdnJt+HaS8P6se+gN+/B2bAHA4HFRVL0go6tHQXcMKdLhzc2A7ECKXX42GzVFA0JJ9LPMGFj2rajHlWlhEIEpau5REK0rVKgTqUu64Syn96d/BMPC+L52+AMJcwZoZmY8VExP1bM60kjt0TMydjeJ8PhNp3+ehz7PKSVAl1Tl8OVpsu7sc9JuWON0+s/GBqJqQbQlXktk++1G6NiuVGQUKLvf+FxMTWMelsYiAL3ey6BO2hN7L2J9Y7QqJZz8DVtIupawqKaXMadSRVJgkJxNeIoJalaGskqBKk3JR5pSw23Hm9HLG90/1+RnHk0zTmbRPwSWEkZRh27gKYmYoT1MtBeMoOfWpy8zc7X+1WIcETjOgqyqpsqrIbAoEkLSFB1GNMyHBhm3sbP95Ys4VqLnwiDspIvLjpFWRzzwlpKCF+fwCONdL5Mz4q3tmUzBc2msaIog6ofxaBQBtmf5U9eV0AhZILyKqi6s/cRQuALxF+2Yb4s0esxsCoBC9UtV6yG1CfEt4h3u3PZm/ubi/EJjIrJkEBgRodSth7vYkbSuqlUQJBNVEqjKSChS7hH7Qx0N++/gwDMMP3r8QUUqVqeZEzEiWDjVRKcKQrkucICKkygI7SDUbVs7c93NIe5GqbNix9nb8ky60tA9GhMJVG9bVnq2EueFRSmmcRmwiha+OcrHYQSwCPKzY5WpZ0Wv18IrnKHekYljIzBZRl5n7vrejlMpExABJRa3QqfS7hIckqFXLWKciwsK7H+3PZ9rt5Jd0rJOUcyFITn2xbigTQcmO6hnQzF0R1FJEykSSknSlSylp90yuU7SLGuR35Qi2aySzLxPhPMxQ5asRqtCKkLLcdDsQgUSWNpEAWL6enUrMTNeCRq5ejB9uTU1UTKLYKGWK2zH/NfqXLZgqZFRCMKV7viIEXCCicXO1KFq1orA0mrs/VCzU7uL56LzD52l0SJrrbMBH0ijcDCxWz1X6rKwiPurJItbOuYJY5kDmXKQCZvZkILqUD9Wu7ymxChMSC0FRFEBm7Ae8fcQ3X+evv9m9OTzk2oE8txgArWWcRukf9oCCkBg5ZeV5naZh6Puu67phGHa7XUppmqbTadofdisQeHWd/naL6nyQumLIVcl2HO7nKfZn3/cvLy9EtNvtTqdTSsn86F5eXgq073vbBj9/fD4cDs9PLxbonZmPx6NNrd06xDJ+kHNh5MjI5QCqXE6FvBgLGjd78Emr89e/+vX79+9rnc7nqe92omWapjdv3v7iF794eHgA+Pn5OAy7lNLxeCIWJqhWgShqzrUbklLW1D89j3k3DMPDV+/ff/ygv/jl089+/uHD8+nw2JUitVYoWAmgKqVW1Qqi1HFHiQAqIuNpUh0xHZmREnL2pHWXDZEVbbdnRaTW2VY1q1+gOUFh11HQm0qttVQR2XcXjxhdFiw1GltC3dD7qnYmUFVLC9QUlostaZomi19WW34EVTXzNgJKXuarZcpd4d1K6lz+3MQFkxBIZ7vh4qW7gH92z3JnoVvWhqhLOqpezrsxm9VXZZombqaraHS/VVa2LacP58X389BJCAKiBABVghrM6He7vk5jmY59BykvKLI/4Ol7DD1+58f48Y8OD/tMKB39WurHXZ+n6TwMQ855nM7MKef+fD5n4pQzgAROSKkFGjGCTNN0Op26LjHv+ubgVmudJlXlPNsl2eFj3lFhfr0U7brueDw+Pj7YYu+6bpxOpiafz2eQks4no/bNkLvvfv3Ld2/eAji9nIzTMqfcZZt037mLSAK5Dwr81A4AkFemUGevaCVdzLdePMdWTHm1bH96de5vIb1udApVpcSUmMCUmMi8FiaQ7IZD13XTVEspuz4TUc1JhDiRahKzahJTYmIWJj10oEw0lNodBuo7GrI+Pb38y19MU0UVEJBzyYlyn3uhYlt+LWK5j4RIxA6FpEILyYSJhGgiCiKBPKAdqSYgnWXWWMN+R7X5E7lpjIhATEzH44sPHwEgPPb8ikpjuYTTiSJrFTjQAcuij1OIVijtPtDVCaJrnUHQg2i5qU9R4/NqbrqLoYYfYv239MGrmIUQG9YBbv7eHFkDJFHrv23NiAiyCDF2o6fXi6od9Pnf8/92u0HagRXhYiP+/uMkTFJGlImYSCYVUMWf/l08HvCDb/qv3ndD1ipTYu26lEpXQQxKxImYiRPlRFNKnWoGhImZsnffomN5PEIXPcyL+Y3jdZ1d3C2YrpiZPpEan0W9bYlejotEAH6o523Yn6zXVbWNkL8I88ives0yFSshrKFwu1Qiz3FKnEkpcZ599FPXk8j+4bFLuVYlSsMwyGxJBZO5iqeKhFmQMFLe7Qjcp7RPvCs1f/XV+Qdf9y8vJ/5nT+M4Ho/n8xmloBSVcRIFJ6gWMFg5M6cuMydQ+v557lidh2Cn0dolM2Wae+RljGN3XiiTbWgpM6CKUpcLkJe3cLYyYwVYuDZZaCrGdhI95pfBpW0ZnGtblRcVyaXaCkF4E9Bi1jjowieRZ1bScVu2XBc1rBUMbUvUsyi4dM1RE+TSJYdgvqDYFdvKrfpXpYZhkcLjeU7no2oVaRZ9gQpqoYFTVtXyIvWECR0j9Xizx5//c18NXXk8pIc9EU7jNBGQU0kymA5vw5k9Zbue2RwAhHKinGcHfbXtUfKQfrZjBpDyDGqqujy1vswRwpWs+BM+AYnomv161danlEXwozgZHhsg9oaZtVxcsNFYNuccJ7VVtT7q0uUBX+SABV0CCVa6VTwEmJdrR8iJmNhClCnlTmqt/UCJud9VQmepaM37gROkLRYiAiVmVmJV4tT1/S51O4DfvUlPb+l07N589ZPz+fz8/Pzhw9OHj8/Pz6fjEecJ4wgVVAVUVKVKkQQi7Pq3Ts55IDKfHKlSWBFtpaWTWaeYifmykFZhRi4YoUMkhRcPV7IiI+dL2qir0I+w5ls/58otNLApXJ4vbw0Qsg4o6P9eBSxL4qDRDH8XCOLX8ZktWq0Qc1XctuXfzASZNYvQjaXRTTdK4o1+Xm+3hucZ4hSYRs0JOSEz7BynVkjVJN9nAQh9h/dv8fjID4f0g68OP/7BI9Ex59KlIrVkAgSZQHa13/SrypQ4dTlJVTsv4pxS5pRzzmZTb1aKhTqyAvwI0whTGYl8VVK+ClsRHz7l+W1ZBOXZ4ogPIkKGBB8Ibl7F0zSumr+DSths9C5fglaPed98cS4YNCfKiYTQpcSdiFDumFMSEHHKA++7/eFQp6KKnLOkSVVVqIIIdjSembkUy33NfQ8iHXLNWc5DffvNW1Ut5evz+fz0cnp+Ph5fxnGqf/iH/2IqOJ9xOuF4xjShThAB0gfMNimoYjHzRhYQgxObVT6/e3C3BrG7O/aCjMLMqe0UfG94GnungwSXtLi1j1RVWugU3o3VOYn/5JOLlrrGHTIjNLhFTMpltxiruip+AJA0bGju5drsRNcLrWte/bsCrFuamp0GRgdXmS+xC9qWsBFf2/Ot52HDeEuTugVYOt8wNb9PYhX7vN+ZSz0Ys48DM5QwCR722O/w+Jh+8qP3bx73D4f0/t1hGp9V7KRGhQAGJ3QZShBRZqEknJWycoekCQQCKRN3nPo+d51PGYfY+fAThqBhzNNDQMgCZRYibv53pqGvZvl+0ZA+h9qWDvi8kFsZSwEb60ottpxPsBsCENDH0WRbOxHVZcJUP15cidbt4Fe/blucK2Ulc1xgBjNR4lRBYvbMxB1nHvr9RFNXpO/7ko+qpEpZiCgTUqJMREwTJ/QZOQknSWlSPSY+S19MyVbtx/P+ND6eT2Mp+OrrXEodz+V0rqfTeRzHaaoi8uuXBusmMwUiUAURVOxPbT6QEEX5eDkCZF5+RiUFWdBUQRGoogw/ctqu7OjY2IwAi6C4iMR0tUT6U8hp7H96WyuMizi44cWFcI7SdfXv/bLCpvjWCq3ujDEOUIOq5UbxSyX+p4vV5e77Tie3xTyP5wvvSkrEbY2WgnqCtdhn7Hp0PX7yZ/GDH/Rv37599+bh/ds3ijKej106pk7qJFKFCIyUUBMoI+kAIeQO3LEwd13qhizJ2mNmzn3Xd33XdUgMIDO6LvuWEI2RzDztmLWi8DxAvTLSSNU7JKKbW8LPKxfX9lXDK+awL2ut3dCnEJEmHlGrKgJThi3xZ2tYq1HFTsa+qSEBERiUGGSzyQB3fYaqCDpO3bCripS6nHsZigpBSJUJXUJmyraoM2vK0mXhLFUKIDyJdufU5cSdqjJNRFNKkKo/7d9AswiKaK1ai/kWoXR2YdCdDEzV0qHf25ngOI7n83Q6newS3Ieflcg3PjQPPWY+fqfT+XjENGG6YQS8umDUIvzQBbP8J1nmsPLiypQb3VNKlpFk1cq2AxoW9pat54fbN3EId8E07lluAlbksau1xNihFO6C2Fsc6jFf71orXRr6DD1i3fv57qQ0q66SEkGlIhOGB+w77Hd4cxgeHvdD1/+lv6zv3r0buj7nPHTd8ViePpTM3O378aR1SgAgMvEEIHPSPYQ5d5wTg7nruNtlTNoNOwWImA2ecm8E3+Wu69LQD5zIAodcyDJ7jLp7gTU1E4pb3hoAK0p8OmXijH8ZSReApWHH56jkj9pPQ7tTSk2Q1lrrp2Wv/KyB+YfVIsFSp1MmIcwHMUyo87Y+d1lEclH2a2WzrgE1jyrJjEzUMRIzd0SJSsqSu8KJkhpZZUolsaakIuBUux45k3Iq352BoqBUuTIK1ZGFRA7vLCS2EjHUFFgG8PDwBvO9ZSqlTNN8D+4d/Vm7jju2Ys7cTmG74ns8Hl9eXsZx/Me/vL4lNDeFyAf2Zwl5NCMlLUtoFCTRxSGFgMsUEotFpLg/p7cBaGXofKVsn7n6li+DW/1xOcch0ayqmrsGI6SMbI9d3Br4dRvWTd5mU63IbrxaSASCHg70Zt9/8+7tD755//VX7969edgPfUrp3df/5GG/q7XWMnYdEvcPu/e74VCmaTxO01RQpVadpkmrMPPYvYikrkspJyVJfer6BNb9w05VBZxSMpdmAFUxDEPXpS53xFCtRFfouVIR3NDOrs/QlWAHn1JWr3wJYKWU3N/BpKt10VyxdrvdNE37/f7Xv/71mzdvDocDp/T9998/PLwppXRd9/LycjweD7uvp2kadt3x5TQMnWI6n4+H/e58PpZSdrtdztnuVbi3xaq7FwEqaqs9CGrjJKhQYppUpbKmjpm73B9UuqmbpinXvMu9JuUudUN/Pp+nOg1Zciapp5zk7cPAzFz6nDNlqrUSlZZvcE7klfOQ0sH0moRJqUAOu35ntmeRs91cqCKHnMxeW6uOpXQpH9o+v2G9ttWOlFLilDtux8mXc4OTpi535+cPD8N+fD7/8o9/tu+YtYyn44xHxFVwGqenl+PpVH//B3PunFqnyW5IVhTFeEbO4ARCrlVPY51GlIJfJahCKkpBGQEgMSXuy0chJPMEmkGKKBP13ZvT999NQDfsu9xNoyh41z+WYY6OAhWRQlIhqlrJ7sqZycOcMS39QbXdr2ipJJV1nsWnTPOuS+f//Kz8Kh8T9Rc1qilCAJRIUUWNW0DtvOIkJ21GKGZAdJqmOo2Pjw8iUuslvUXXMxFNx6+ICBYbuAogiTinVKeRmRMj2bF1lVqraB37bwAwhEg5KXSSOoqeHg59lXMtp/myc7Pm/+kndB32Ozy+wds3ePuIw56Gjt+/e3zY79692T8+cNe/sD6p0ffrn+x2O0vCaklSn5+fReqw73OfjsdjSn3f909PT6fT6XA4jHgo333XDfu3b9+VCafzpMKZueOHlNLL+fT28W3K/Pz8/PDmoKo7PuVMzJOq5ky7vmemOo1D2hNlkMyugFVmLYBPs527VgUSAU0qdH0/ToVTVk6Taur3Y1WLAUBEtgBARSzAJOfdrj8enw8Pu/1+OB6PIoU5Re8ZAyIzmbqkdGawvUJ2d7uVsNVQECwmESlX4jcWF/Kfi6ArlnXV1I75vKsXmS/r8wv7YFd2FBf7C4iJKCFFe7Pv5P30wI/JZi9PtKPi+VcBmFVkgAMWzak3iJnT3LfLlscAaxiG3HHXdX6dyaqX/iEznQ/D4z6PTz3X876npKWWdohBSZROU3k5ns7n6flpMMwwZ1IRKVJVqQhS6pg6AY1nOb6Mx+N5mqZDBhGrUCmYRikTVJioO58mi4qjFydvu2pTSyljKWMRzl1VAqUppF1hO3nUxBCgG8eT2tVugaIqzYeGfe5gES9IiJIComD1LJ3hXEJpPj+0lT5P4MyQU7ls5YIShJQsYgFUVRmQmQ8y7USFiBjcIVHSjFqpjMcJQEI3n5oqUEmBnAsRJZCICCbA7hXADl0Zs66Rsto3xEdWqFbRgjIBtadKSZ6+O3Y9hh65R5/R9djt0tD1f/6r8+PDw9dfv//666/fvX3YDx1TZdUuo8vcJRCklFGmsUoRYel7U36Z2dxxTON2xYKZLatArbXrutz10zT1/e5wOExj5dRZrmyzUvUqOeeU2byvBMq4JBg1ziQiT+3zr7Zc3U45/tiveRUitknv6ucpvkeInmYGhyqXULAR49CsV18AWBGqsIRRDT4NVjScGK4wa47mzJe9rUVcUha30RCRB6iwZL/unGL8kVICZfM2SCm1axlcVQzKANRac+0d4DJfrn2oKtGcg5bTHAw+tfAGxje032dOHcnDLnUop8fDvqekojIlkDIRJyUei5zO0zSVqfSNJqKqddZPME6VOYOzCJ2O5enp5eOHp+N53I+jHe/UgvEs41jLBKnAfpgjdiurapWpVqjqr8b+fD6Ox+N4fFFwJVawcnp8eMPMnBJnu1mkBFHVnrNAs879EM/GTpTsoMly6IqQqKoOfAmU6hyiLTDeVvBwl+M3RGQ6nTRTC1hhIVdUCCRl4FkVRGWzqTHV3HOf+KL8UtsC5/6Fmc0tW2sVqQxKRMfnM4CKORIe0by2+fRdSqbJQgk5Yz+gH/Dmp7vHh+HN28Nh6HOHvu8eDof9fvgzX/N+v3/77vHNw2Pfd5BSpqOUMRERCqSUaeSJ0CUgMeO5z7tdnxKlRLud5cq7JDwH7Cy7K6VXrcPQ0bAvpXTdsNvtEtfcK1OuVVLuc7bUzR0n6vs8DEOtU9L5AoZxZjfvqCoAC5NLRMAFv75Y5/jcEuHpTqNZwtUZXHO5tpfNeWN2Mmu4Rsq+yFfw5Lj2pV1fG3eZWdohhXOzd94lsAZTRUpJEQMNEhEpX5Kno92ocL+BeBbGi6R4YL74nVeFAdasi9WLstmluWkjp1Vut0/dax0QgABSBbQqRFEIBBRQBRhUASGeLakK0i4pIef8xhOU2rpt0XvOY2FOTL2Cx7E+feQPj3h5OfG3L8ysSjXRWctZaiVIRUpdUyAsRbNa1k3ZH1SmcTwVHUUlMylDoMfx2caVakrEgJhexNoBEGiFTRup7bBKTbMHNkFU6xy3lWSMgOWzXMt4Vch3eYDbrUjUaEfU9fNOhWartok3HPq9c68naKAs0zRBwDIBFuinzgpaeiaAGTlBGRAkBjMSkOxQJiMzmMFUAfylb3bD0O33+65LOVHfdw+Pw2E/fPXV28Ou3x+6oeuIlUgzMScMQyUihhT5UF6K1lJlQq19lwiiZZJaRc1OmnLOp3ahEu02glkhU0pu3Oy6zoJYDMNAXZr6PnU5paSZWMCpK6V2Xe6GXlH7nJC47/u+z9OkLLZXmA15zCwhUyRRyyP9aRbG37xEkPFvsEEu/zNHJcWhCptoAdyCZ1N7U0QSscNB9M9ytPpiDSt2lGg+7cLSbmractplYp3/IyUy3gKREikl2E+coFBmYrHY6NqOSKT9a652NmppAbDU9UoAnChxsklVO6BEEhHlqqpMmZkzSzhFZeJZw/ILhXMMP09KxMhMhcGsXaKUqc9MIuZcw0xIouAshJwka8LIDKR51wnAlKyhn68npZRFhzdv0uMjv7z0Oz6papnkdJqOrCdLgSRJZFS9TC6AOc16TlryNGbBWKpyz5T6CjqNE7OmhJTArNQuVWdDbaiIVCWBCimBbb+Z7exW54BTEO1okXTDZU+fF4c/F5rzIqqEe4rLODuywujQ6inlW/vdTOamTyVm4gIPvkza5dz1XUpJ9+g69D12PaVEXcpD16VEu37oMu/7oe/7zEl0RvR//af7/X7/+OYwdD0zpUy7Lg+7zkLYMyNn7RLVKufz8/l8BrKIVrsiwpoS7zKYGFBzlyioolVqAVh1kqbmWNF2Sc7i0JkV2LaEpZS+7zXlvs85dx2zJEkN7+x6c60lpUSkkjnnXGUyh68ASTO1iWctoUH/v4Id4hae/F/yLaGDUdSJoqLhuzDbRnFgsvj9VaQUEf7CTEG06igzC11O4pnZBE5uf3JIpXtRuNpxm/O0q1Fevw9Hl+4n82MpaHBEKTOURGrOed7xCJsvu4nBhFnrBJkaBYOqvu+NFUTYElIQK0hAlUgTCScQSyYhJiZREeLEiZRUVUAgpgRKmIg1caIEEyFQqaRQAgqTdFkpDSkxIXdd98jvatHzeXp+Pn7M8pLMVi+qZJe6ad4XmylAXuSoQzn25UlwOqGeR8U4KYa9+ZERKtsaImYizakKCLWKikoVgcx374iIRW3zZKqgAmA862VzD1NdiahMLiAv/6KZ85lh3kfzho5IirApPrO5vcUU7cfMl8v8KWvHKaX0cHhTSill1Co55/1+//DwMOw6DL/s+36/3x/2+77vhy7vuj7nXKbzkLv9btf3mUHTNE3n8zRNP3n82Pd5t8vMUKm1TqpKgo5SrWYmy4k6Rp3qC6Zj7Q628+pSZp6VgrZnFZoFpxqmpryOxeSsyMGF2G1Yfd8jJ5E+5z7nRMSUupx7oinlPHTd1HUpEUh7yrnjqZhlotlwIBYyxKMoEbXw8Q03pf7Jbgmj9Lr66+rDIs1XPMbG0juGWxj/HBQuNySZgrrdEn6BhrXtaPQHcTx1hFoV7/bct3CJR9X8dOckyQ7ECIG9rFGK3sA5p+Sg2TTNMl/roVnrTC4JGaWRNJktk5lTItUKEEgsWInLMZEizKJFhFWraFWFqhBpYiFSQi0Ki+NGRDnbPlyZWUmhophIAagIVZRakAmJNKfaJXk49Kpa9nnf8S7TU5fOp1KKpNSN4xwDJiXl+ZiGnk7H/WNNNVMZdxkFUIYAuUdVlKoiVaTOYEtAPVk4/WT/plkF4swqsPM0VlBODALQQ1Rnf1rAEEeZNQenWf8PQJeYmXOe1YquN68avHvzYITNOXscHwA/+VOwUCIpJUDSfOqCxDxN0zieYIB1GB4eHoZhkPT79nyfO2bOdqRL/P13v+449UPOnFBlHMfziGniHz0cmZm52NSb2tUidoKZM0mCKLRPgp6OVBMjJ+UkZKrUONVaEzEbV9c5XFrquiEPGOWyUWCommmc7LPZtnLmrku1pq5LlJPKwKlLqYPWrsup60Qkdakfcj8lSjDr2JBzYU6puYzSRaVqwr7ZsC7GrP9RiwY/vpWi7RpVjgs1IrqrGxE7HJUiRmiwVc3NEGKFv5XikglB5jR5q76DA0A07wFZLIE4iOYDKecAbS6uZruTi23E9EG1Z4gIl1tXVdkBjlLKYCIk03FUlc06phpsWAyY6YpFS1uHl0C9KXGh+VpGIhFSZiRGUhCnlChnEiWptvRp3nYyWwJ0VTFPeEjtu0EURClbxidQZsqJKGuilPsudTl1Offd6VimqTDl04nTCXPkvASRXCu9k2Pq8rvD8PWjHKeadw+7wxvKXRp201iO59HyPtQ6mcZUx2cAxFkzc+q465A74jwMe7FQsNXImpg5IREdo/XA59GPxlLLImFUMsQxm/F+P+yGoe9zSlxLsaWbc+7zRb/ev/m5IVsmrjKJCCmIICJlmsbxVEpJoH7Iu93Q9/0o72adW7TMDnKlqO57ZKoJCpmkFsjE5Zxqmc4fvecpJebMCSKqtebU55yJubQ8FxY+m6CoRdUmJeVdMuOvljpNdZRRSoGypq7qxZDq2sB8OBPEsJ9Z55yFYDxDKsTKTJlBpJmNOMbPovZWpkScssWSnNGKyNi1mZv/FaGVlVcRI6tqbUER3dpnb6aUxnE0hyw7YT0cDtr0r2maDm8ePnz4cDgc6jjZEt3v9+N4Eh0tQpaqDsOgzT0v1l9bPosacjpN04Q624CsVzlngO3hWouq2nHeOI7mLEaM8/ls1ndLJZ9zllJsCD79uOzyLneXmNmCw9q7Fi1XRLquszRQfd9PMhFn8wUD0cxLZn8BEWuiZPeZDUm6NLhPQ61VFaIFyqrVeIbItcWqKnk/jOfjftcDcjq99F1SlVLGLjNAIqLzhUeIHd9XJiSuBKBIFRXSlFMuUwU4ZSZKCUlhAFpV+DiObx7fCZdO8dj1mp6/2j8ej6d+Gnbj3mxaTR+uHY+1jlOSt7uOuzf7w9vd42PePXDuatHjeD6dTuPxVKUloK4f377/irvMqRseHiqxKO0f34gSKJ/P08vLiYQeH9/u93tSmL0vnur47Oz3+2majOwicj6fc85MKaXU9bnrOouvL1pI6rDbl/Hc913KhFqGYSCi7z/8+jAI0Ug6AZRITXCQVs78dHrSctx1XSIq04uksd+/LbWTSZQo59wzAbVCMtH5dMxdT0SJFJkzsk7j+TTSYY5TllIiSqpaSqkVxD24E7Cd03TDrhSZTicydzMktZQDahqEkGittUwVSH3fMWUmrgUgTNNkh9fGigBeXl72+33YK6h933UdEo3jGZBht8tFTevvM6vq+XjqMpdaQehyGs9HUqQ8X7rKOVtkaJt3zDFQfUsuvkbcSdOWib0r4e66Fauqy2ygkTidTqdEc+QsO3knu0VfxQS2lGqxtMxPU1ukdd9IaQttWWs1DXoRrWELdXGrNfeJ1u7mq60f828hYeynFN8A+p/x8+3XhJhVBCRzxtP4H2yrI83AJM30Bbu7SkRovjlzi6Qk6rc7MM89LhWGsE/NQDdf2gKR56pAS6XZMoYLIzFEABJTWxkgaAclaLJbw1A2HQYgKKsmaFIxd/4KzZqKpoouQQhZCEpdz0PXAdRl5FRr1ZlTq6oeyllEikCIuNNuV3b7KfXjbsfgBOxq7eo0GEslUgUeH3cKKqA0SJE6Ce0ep6+/+TFSqkXP50rgYdj3KYvgu+PzVcCapmkYUApNE/U9iVDfU840lom55iTMEzEnUtVKUKIppZH5nDkJJHEBwHgh7U2RZZ0PFkmhykyUkDMNrAwBSY/aoc7hQ9m8I9rnecNkn8MDrNB5ybAow45fQRVKSES5IsGsbZQKpBJIJiKC0ByOHGCd5Z2K8QZDLXdJUvPSoFv/gRhE4EQoKlqrlMwMKTDbghBRkRnpErEScVLR+exI3F2SKLDlnKjk4iF3a+nEzdbN9XX7rd9KuQdYGsxVWALW9ntfwykl0uSBByJ2fGLXNxhEKwDydmfVQ2w/SMSqqJYGDqDwLyyC9tI7kSLkxT/jBw9z3HQqg68KZfsNMz6Z0YGJy2zdmf2ErA8zfygqkaVHrnP1AKtASDDn+wEUWhkexWFuuTFLhiaxcalASUGkBNPxLckZTDQxQOAETsSZc005E3PX164fUu5qrd3Uuw+ORZof5GM1hKTMueOeu165G/uOc85d1xEzySCzy7gweH/gSeq5CmWMggLa9wPr96RDYkq9nZ5pLai1HvYW5kXdbcaGdcTUd0yYmGrfcylF6tj3oMxEYBRmTlSYwWbigZQ6suX/pMokgDAduX7VBAkBcywXqJozC8tp5kIR0p1Ou3YmRAA3YaONVWawMrrbc1XNf5QSOlZDFxYIlEkzKKtqQgekSqWS9FpnS8DlGMGddSnER764dEfmjAyJpbHV9I6uI987awIBTGpLM5PJW2NbJYaon4M3DmdRCYuUNODVFZ+kz0UfDQai++UTgeJ1DcsN8BpCyka0irg7gwixbfTuaTp3+x0nadslhEllvlzTpXaplW+3e7VLW+TyYvZOuyokbc9voGVQZrb89qICQsREs1LtdqumcLFqRcxHopc4WWLelUQEJiRzjjTfAzVXAUCQiCiBoaxQRQF0lr+KWdqLwPwpAWe7WZYQcsc5J6Jca02JFdXg3gArT92sW1CilDklykosiUemiTElgdQJ06i1ggR8kkIqKqUy9awpp5RT7agqTUTdjPVapKpIpQqokgi1e+HWvaSV6kR1SqpJa50/p67rVZVmR5M5wWoihShx5eatz1QBYaqih6SEdkhv581QJc0MJUpqWTMAyCCyA43A7F6osM1BnaNhN58XskNOYoDFTD+SlKgRmYQYylWbKdtkJrMwUSELen2ROFoUFsND7FiGiJRK4/AO7aqK83xgsLWi4Olic5e42p4qi/n9AwwLqCoEzMbexeVBj6hh3n8RJhZopUs/gzuAosvyKlrFqnxcuP3aTcBaQUbo60Iz9N4bianF/5GQ+2vVrVdB+qo+FT8vNKCliWqOCc3pTisrYFqNN6KVVzv7bWFxmNKemSNMrChmf829IKGm66mqJ9ckutDHd9fEXg/DjKPS5lBZyRSAWeXSlgVdWKFEWqsKz4/YlAkgINP77PatchKASWvKBp2JGCKsqulwsJ4pJ6YMtpWJYZct4o3WIjoqlZyEQdI2MeYmqszEXc59vz+IctWUkFRSrZoyckeiVVWFSFbOxn3PzQ2l73uptaY09L3MacQIosIzSDfYykyJmaETAJ5ZDzIn+SMLmKez9GAkJqIKUtXctGMlsS6IEZZEyPRjNMyyneD8GbPUFNvBtQ2jCAFEoiOUFbZHF2p3Qygcfs1fiZjPMBGDBO20R7XOLoRzaFY/TfKNAtxPEBAmy5physHs7QUAWkVBsFudM9dZShbMW0IXFtt7OVeiy0a0kk0yrlslQsStB7BZdHd0nZuAtcKauCAjwAe/LTsqqQApxDUsWkYdif++WmKLKyT2yrk5VUWT5Or5ZZ1oOv/lPyOO/7s4WV9ayiKVAWnmKr3o1O1Fbf5489bEWBMALjFXTVdiJCJAK1EiSgkgJFL2UIZqsEVKiVQFSCBVEvPTFJ7PSYWUKavWigpUIRGuqYpoTbOcqUqUIUmryJRU29ESiFRZRUT3e4BBxJRguzGAGNRlIrU0tcxkgeE44XxKlHaEAoXyIEqKrmgea56KVgGYpVKtgFJKqWe14AVEJCq+A0mUoGBYhirzNudEibAjVLHA5iqwDTVACQRJSEZsQgdUaFY+zjptSCJLVIUL8ajpVOtIUOoy94xsoTsAEiVRqBCUBERCtr1WEAmJqBjBSScAJJkJlnSQuUoV0kKaFKyqisxQQBJNpBPN9gEzEOhstYSKWioKBiXM2CbAI5bbhbjidHlSz8ylFPP/qnWqogQkcK0Tc24siqW6tEgCQhCYz+NFvbp5O2WhOn3CM5+iYWGJLfGbqw+/AlgaXJ8cAR10assuZ6hRi11KIkWttUKl6xYhAL03d3rfur7QfVYPxH85wc3kJhGDRBLQ7FRi+oWjj9Mo9s0/L4Ay9pnc10y26cuIF+NaaltNQ9J6gWwSXFxelcCsZoprYTPncDQuCU0j0NlOBTGvVVjcNVaIVFQCMRIRmy0riyhJJgFZxEAkqomq4ReRMiExE0MVQnLCMFurKV0CrUAqkWLW0RQK1AJk4amkLLloqpCEoYJUuypdSg+TCqfMeYDm81RUiHOW0y9V0WxYPjSYg4hU5JQISSpUyCzZipR41rIIBSoqoJSIqlqeOlXWBCRoQnqaTUZEgNC8K65KWdNZ01HoREzoenSKrkjdCUkCqcVCJQuCaVguSjKfqtgmloSlAEASQmcqVtJaqZAqlAkMVcAu/YF5arceSSEKJVWBKFS0GHCDKnHLEE+XC5ULWGmfpeUxQvOrcIhoBtD5HgWRJjLJoKlVKVpXOsdm/S3RitbgdR+trpZP0bB8pPbNHZR7ZUu4cgc1tdOBbCkB5i9tbkSEr4Xa+S2WK/O6DK/86fXEf28Bpdd7kR6qNJulfNktm76RKL6hW/IWk7eoBl6EZbzaVtr5I2hGKxFALCScNG9vO7Y0RMsMZWQGLGohKEETFFRNADLb9ZnZ9FWqxSwmO1oDZheJ+RKRQsFaSatKqQUo9aEKKZg4U+oz5UqJu4fjVKcCMPe5V+JatVZV0F5ZVefAMmF4OWchIaE+9fafsvapf5GOSIigWgnMaqbxQkiEzib9cjKLqGEpZiueQrUQg0fwUfkECKUDZ6YkkB4kYgYpWy9ksYgFgJjZ2maNoJaeC3ZqVzCnma/J4vNBgaqkpOA552Bt8nKeeaCSyCxvqCpA1FxmAVBV8QXVWCXwpM4HBrOWYD6x7svGUNsjNylIxOT3wEDaqHd9CWy+vYJWqw+vlk9UslZ9uFO/3yC96JxWqPlc2E8Wv4GIRMDMx+cXrfLmYZjOH/cD/fxnf/z27SPkZNIvkQ7JcqLllSLGLfS925u8LTv1OAy7cRwFlr6FaxlFlSgBJCTKqqySLp3kqfpVAxE7H2GFlHpOKWVm4iIinJQYRCow5V4UCkrEVEupct7v96KjmdHH6UW05g6KadKTWjK0hETEnQCMSpwUEMt5aX6cYFEIQ3kOUy52dsxEDN9fMwGzGyAIFdO5QJRTETwV/RWlI+VcSyXqRFIRCKhS1VSRTsTa6yMTk10AQhKiWlmqAD1Nk4ikrg7cpUSTKtd6npIqPz2X3W7HmZ+fn1OfkYiITebYfY55OrQ8jk8pdZyTgEVUiKDEyqhm9GbWgTQzDQpRrbudMo9UwEiQLLVSHnpW0ppyVqpSvlelTJWoyiQnsT0mIwT/APDxfO77XnI+Wx5GZvvc08ec8/Hl9Pjw8Pzx48ObN6fTOHS9aOl6Ui2j1q7TyrXUEf0o47s5F2QLMjfzsIBYRZ/O48e+77vhK0r908uJSHpzUhWUUtn22SoZlJU6TfO1RAFXTYVqNxBR4mR5TgEkgFnL+ajz7ffEYDpRJsoYpO9LKZM5GRGIOiVlrYkJmAO6Y5x3ZwnpMGhPk1aSOhGUiCtQVPqUOaVeMrQyVciZyokrlA80a+IqZVIq1CNxIdRS7cISgRIqiDiDqRYA5m4cC7dTdYUqKgCDTq1ElAnQCgIyd6qqWrucj8fjbjecz2ciGobh6elD13W9VNHChZEJIqBM3HHSsTCoo5RRi6D2CZlQWU4itRDlnBhgmqZJm/IBUZEKlWQ7xFpqLYvMBSvAWykd1DYI7nc7x2Oy3GqhrPexoUSQ3v7qOp3jkTe6guHPVaN+k/KJeuLNNAqfXG7RKvbBRYtruJE+6y5thPMXl5U+e3X6Vs+vHqPF/YS19uoZZ33G3a139frV6aCN1fJqD63OGK7jfv+3FFg1gWuzdnU1repZDcR7Gz3U4per4aym40+ifEHlVwny6vOfVeZoDduXb00YE1usKG6ZM0w5AjwV3ay1bnnIe+kD06DxchC5ijAxDchoY7/nFr3zT65cYJiFcLGa26+sEDLlOQF+PeszSiTF6oM/QEyz2KF5F+J3oZx00mK048aaiXW+xovtTuyyCdU5a2Rrwg5kr2MlM4uSimXfDIckbQcdFyQAc3d2ljBkyTnL6WgPeAigq5y5IuaVIbXEtJa3vPHqvXIH67cU3qK51xDkffvsbLXEJcxX0ISCV+0KvICLIF+Q4rKD21jNG3++Wrzbsf+f8sqKb8OQL+dgV+H+c0tGsMlHutwCLKnCLVCUJQR2p3uaRf2cIDb2zAeznVSvHI1Sutz3+rCx4SEi+pMGrARK4RSQm12KYLZnsM7HTJ+bsMjL1fXgQRBXEEgtCpAGy2v000GQFs7Wq5m9w4iXV9qfgFhUD2BhkrsFi6uB+FMRsBxwvcRL+LEn0mLMUtCwiEhFPQOYMW8k5orT/F8L/eoR3O70fIuMVyEpUgw3WB0X9r4A1iyeW6xwVY3EohaOzZu8D5E3SmCe1/hTm6rhld+nTwDQ62XVvYjd/t4XYNasYX36m+alWUqxaDvmtWYZEFqHLLTkIkRGHCfuHlveKl7/Hcz+kyhtmZEZ0YBF/l7jiYBZ8afm69hqas/bZ4pIdJn+wPERrciUK1rsHbbL2xnC1/nVEX3awJWIoEKU250BAIKLT9Ar5eoCs0sh7RjXPmOueTajWIxNu+F4OdValYtYDUIrYlMEaP+S2aJUz7o83RZ43q5IyAW9jBIeielzYbI8Qk+scxYtbdHFGYwcTjEgSvNMfr1cUMk/rDDrZtGmu60ooK8pBCtqx73R6pkV430BWmE+Kd/Yle4UWyfmYmv/zp5+m/AyWO3ywlLEBoAje20HtlqW8Zs/6eLRIIgooUXeUKBl6kHTs76sXB3LimKhMwv1alXDSs6viLki483x8hzXMD55jebzXahb9UTbU+yYd8brp2WuAX/YRuqxCiIRVoIwckXksavEdBcnv092nQ7XxOGWwqturFqMb20Bd1s5NrC1KvH51oQ7kVot243hZ7Dmlqr3H77Fus42/uXVqr5gFS8MCtuGtwUhrLA7vNrnVQ1+JrjtoobdirflPLRdZnFNxnruE/S3Umxj4GuYdcETK8z6Ytiaa7nGAV6oHdeuHovY5PSMO6lI5FjVzULLWaDZtqKoi0XIekfyx01r9OnFcj1TiKrmD/t2zCUihXhtq/5vx7JtJX7vNave6/9qud5nSwS4id9v178VV8Fil+yn7bVwbVpCBLvt6JaqvTX5GZgVm8MnrKwto67644Na8MwnH9rcKq9cfo4DWPFfaqlitGnChDWOMrNc8/tAmL8VS3ELex4nZmVjxhdh85cVpzIBFqHRMGuO8Qlp+ZxafywIxPxhvv42ZyX3xyhEqGub+RXH3Cq3pn/1zG8Byklso2t3R1TFgk0QxBANd5sQERATt0R8zqNUFR4bwxGLahWLYgYQJw/HKillh5XVCr/0tO3s7pBui5grSPr04pVcfX3VxNWe+KTT8ksHLIdmh68LYgaG2Yx3syWkuDd8fVxfQJAtNF8f6W9Jt7iSENHoYpmHLQC+Reeh5tPw8vJiFkE7xzmdTpZxSFpKCIt+ZaHyPQEPtW15rXWaJnOGAGAi1JS1w+FgP1nN1rrvN7mFcteW59WVsnh+ZAdMn0uIldRy/mjBQiWllFMy80omJla2aB9k972JiVLokg3B7bv+r+mnNkxmPp1OZgY+n89GjZUw95N4ETGfl6iGqKrR0xuKBPfDEPNxY+ZhGGymfK7tFT/qzdmqvbjImcDwiHHtmwJYKMQLudwCMAcF3AT799p81lJLU8QtxhkR2WHObrezUftAVNUCz9daLQCWr2r70siCtp1EMEp4c9YNq9A9AS1brfGbsX2sRETGcbRBeWgn65vn4ELD3nia6TLeYgpGBjb6WxPeDWNyC+s2DIOl1/UZ9OXjaTQBnM7Hrs/Mc7h3ZogWZrYJUlXRQkTWuo/LmcTnK7KcP+BB6xwxPdXYdqXEx+xXe9jHaIBgT57PZ4sKG5USf9g5P7WEvtS2F5+0sK/CZ4Q5Cv5TCLnFt+/qUp+6hbu33vrNcTrWQMud9tVSx2meA1OkwDbOVElILJq7ss2TLSrbskF1YcdRhdotOLT4paqqNZ7drIhzvfO6GAUFFdWXq8PHpxpr12V1oqTLE4P42BUrj75CUs/3YbV5zdQqpGaUoatN/CZFlzb4LyhxZcZqb7XlP5k+Ff+8o5uEGm4updgfe2Q+hSQCme9nu+vajFy/+fL5lLId1yytl92+3xnVdl2p/WnPvx4PC0tsir/Gb6ISqy3U51Xz8Ban4gjvTKTPzWtL4vVCdMXqef1JuzgxhyIgAIykAHNiVcmYoxXOEUktnMd6LKtxydI5VtWclS6bnS1AR4EWf4pfEi08bKPoW5W7Y1e0swWraBEiqt1hbN4dehWz7szOq5x6tbeRIF889VdRJlaoGr1JrpfFw+3Dyktj8fpip994oC0FXxL2SJzT1YJfrUTXYubu0OWqLxxSAQOK9pbev9jsAon4yxfXinQLtL3w6trUtcKT1aqJvblnNI3v+wYhVhTxyOMsa7ARAosVtVrA8ZurIOWrGjcQ6st4NypWrypZcwdIAbRLpMpzCCELPFKBEBVys6Ku4siq6VtMP7e3JMK2fiw3GgjbGWzW5Ccg9SdqH9fNSUQ3Pe8RKBBJcadLV3/6gnnfSg5sQOFOi6vHtpvcqy36MpGWQiX+uVpBq05iyQMIy9CfvxjjOZ66mLeH2+kXVX0W0b6gXF2zkYeB9RrRzXHKCg0iVrwewM8f9dUS3/dKRcRA3qGNrsXHiBTXaxJPQ4k/LXmcVmP+9BLfehWtAGgVEFVVyyhIRNKEsEXCVlVhc/BLRLYvJAoaXFyZaFYV97KZ59INPV75LCEJFoZFdfaPxqLP1ExjZobwuaDNyT0ttyefQ7O4s7hNqOXO9NajK2q8WrYI28j7iRWsexhZOtR2BSlu9X/bJQk+caufNOb3DBqWtGgVgU+WXd0s7y0WaNQ+lq6hqmoev8aoipk/bwkjVcU1P6zf3C/bId5v+HsvriIJXB64FvmJW8L4WZZOK1dhxedeWrBEWzPuR+OvrGZ39eW2FdyAlS/GLG/rVcxyuzUlJrI8ciqkKWWd+5805BsromarCnSY22wMYbq3XowOTSGKy0ab/eIytTTbsGKfnW4S7ht4DVsqfSZUoaFVtC5dIaZXbn/eBaz5v9ATDf9h83nR7S+bcQSOXzG2RaT4dBLFSiKpVx3bcjIAz+wg2vTfwN66kd/+U/TzWPV//tO+owpNINKLhUGNhVSVWIj1ltAxftOglyDYTD+9bDHBR6cX6/aFbXxc/rxYKNov0LB0Y8Nq620NivbYyt0GzWno1mCugtT2MWz49VWUuVOixvEp9Vzs4lXElplShQ5DBzW7IDUXBwBwTScCN65pphrEwFUKU1tLBoCrzvuHSJx4DuUYem1Qt9mQBGpGK/PP2NznoODBTwJdxAW7EPZGC1v63y9bZrhf/6u1bWHrqqR8tR5cA6/Vr6sl50rTRbFqq0zdGnUZ8nrZO9vYim9/VYDDfYoaZ8TYabVaXyVObPTLyhazHLBwbUs4fx++0d9Ew7r1DZYrbSXe9ZpdRjeyaFX/q2Sdx/Ynvxu38vLyAsAiVs06vSU3lqbf222BpmSBJSpN3ls3Wzimb1WqbeuuYb1aXLVBmIsvPSX8Tct9wLLyiRP9Wyx6zQSxfeZ+JbdE7K3m4hST44i/1Xj+Dj/f+alxlGWAu3y9fv031k8/vdxi4zu/ftZjubaUDRpcV5hZVLnlDSwidn1eic5yTpQOj/uKMfXp48tTIi1FVGvf95CpjKcupV3fJ9UMEVWeA14T5kQuYCIVyaaR1aoi2WaxlIqx32dmGacXSpRyUi3USanCJETJAg1XQJRY+UB0Hs9d3yfQqRz7xDmn0+mjypS6PmWqtSqKaStTKaCemc/TmZkf9gdVfTmdAOn7LKVqFXMNOpcilsa9vlywWFkAEFQpFR3H8eHweJpGIhry8Px87IehSL3cFzNNx2Ks9b0B1ewVARBROR91+uP946NOp0Eq5weqY61T3z1IGUFFpSilTKmjXpFQkejiIQXOzKwWG1ykqkCTKMq8SUSdKjMJtNZaVZjnqKzaYpbb6lEC2jdSOiKCRUVXKFSpqmoiD3XOQIZaEGAqONeqlHsFjXVEpq7j0/mF0qCWtJHZAgPbPqg0Xx5fpTac/X6v4bjGtrcppfPx128ehkqljh8TT11Xzscn1UF1yqwKowYxp4Re66hhUxMXQLwlFpcEp4EIpJWhQGWI0ASe6lT6/b7WWkW7rqtCx9NEXcdHOZ1O+/1+GIbz+Qw1D8SaUj6dzlqnrutAsydX7jo1Y6JWuwVBgEgptaZEs/5KMsdbBKCQ8djth47keDpN43m323VML+fTMAyJU5e5glmK1IkgPVMa9tM0VeM6ZQVSR30/HI8nEaiFOLWwshYmcg6DJu7KV1pyQPdNUxHPGQouSEqA3XNIPalCoefpnPs8lkmgTDTV0g27cRwfdt34MlaZks5h00kVJETKxOZ1V6WW6TxllDrWlpXKlEFihQiROfoRgJxZSxUpRJQS3/PD8tn1jYalpCdzSiIYSyXSRFzr5BJjq3V/cVnolkGhdkn9m7QSVeWV9ucg5T6BAPRyDL1YDP4vRTt6e8783/wUFVjeZY2X4Daq8m8uEuOIfsOqPrddt76pXugT/PXW2tarmsurfPUFY1RVfJqrlyutFO4JqYUXRZ0z+DJTStwCEKWUymyrIgGI7fIWg8RupqpWUm63qwUABL7oeHMVV4Ou5KRzNiOwH0TSkpe8nqsq930l7lOIg6WE+JMr2cXRAhrClFCwjJRSkC59SimpkgEWtdMufz3Sd1tWAKFX7VkLKFnEGlydEq1wB8uzlTgu2Vy7ix1GU93hJqEwxW5KV10wLjWLKbNpkAsjogGWOalv7eLx8/2V82X4FQe7en375W+R4ZhZQbODmQSVJ3icuS15yyo+F75ooyRYSYvfYs8plKs/cddjnIS4giqI5kjEuVS1u6SiVEWrzX9VASy4LREBFoYCAiJnkLl6cTuob3T8GoDrRJfhN8fgUj3UNVO7uGInME5ep5WI0GZ1fCIk3RIqV1nrt1LiivZJz7HJVdv+nDnIl1KmaRqGw+V5gIgJs6eoLqOafAohIr00KPPxS1WlOWE7QRTc0AFIRLhWQ/ywqtmeXOtBm2DwFwkWejuzLLODHpZrhoh4A/3xRkhEfytdMHg1Z/oL5VVV/cjMTP6fyRh30GrVc3zalH16u7N/JOxwwnnmdS8tnzUJ6aT8pwsfLyOZ/P+bu9olt3EdewBScnc6k9TdunXf/wV3anYz6bQlEtgfEGGIkmW3O8kOKpVSyxQ/QPAQJEFgF2VuknoVbzXH+m4Yn9J5Jk7EmWy/MmVFrSCkzAlYXPAkAMKpapFmigIiJIFCKwqUFm1LWeGHG9RmwahhRbW0q9KCZdkcLi6daLdnnBveubVWE7Gt6N7kUvd/5PYvQitay7px4KqG1R2gOplbSAtAQ42bEKV2Yc1zuyn7nX5hX3E7dFsyWCwE9HJYJaro59iVHF9RvmLr/HO3+LhWSVm7PeDFr8Bdm9nxq063knYNbRwvF+5cajuc7dj1LvI6+J/O7S2Q/QrJ68iwu5uQZOPpxSvpgGUDOIrKT7m0o0GxJSJsTlfj+GTmnMaUB2YmTikPRATOKpTyYBct4yggovM8aVviJYKqllS4zgCARd9Z4lSKwsKdWV/z6sz3Wv3jSXRk3erIni7v27bDaoo66PfYU11BF6a1h58uPxqIulPCToJl4+nFbuEuf1oWfgtfJCUWKVHDkj3z31iVDiXjgDSr8QW2fIDB/AVclmxbKd+ivjd4mXOCMXrEsq2YdpyKycJ1hz63uudY4lJ6k794aBiLi1/Rrob1TqI1xfdd2/FTNSwDAhep8BLdczxO7XLQ4AFmuyRszFk187Gq+mdEFGq9kk8ORO4Vh5MQlyVmW8o5a61iOwbmeWm5ztXSQ9Js97TN1bYqqtbLVa2sS9wWRy6gx9DIH2C5pmcpNeB47F+/J2fvOvm/ybcOraI849dB1faNaVi7hcVlsCVdthXNflqUUxPIdrlPwpLQgebmjuYuLrRKKBJUFe0YxaIUqSqJ7Xau1ggxw4Ysm13bTYdZH69H9WpJEsTX87/YOvkZh6WvUkM+8HxicRHCHMVkfRcKj6pUHd0cyR2KfbA4JxFRYtvjUVxW3xUaG3sNSY2uLQkpaFgfBFkf8937LSusAsvWFUg5KS+VnIuIlpRHYpSqs3KmrGQxcex2hJ1vM1Q1kYqmYVQIidZahQqqqAigiVMFiQhfZODi0ykywQYicabLSvmCUFHSaL1JH+enm9yLw6p76BgVx85PpG4sXMJ8xXr4n1EpNcD6USYRYQUIKdmO43L4cT9ge1W2Kd0Nnl9NXN63Q0KS9W3I0CrcJ74Ry7zC3bDxtuS0WqY1/Qh5TzXbYpy/jAKE1aS0Vjquc+6D+LU7Ah/O7SZ5W2xKg2ugwYVI9MriSIQ9DN3t1o5dx7j8oYaEbbK4/w3AAm2UUngYKQ88jAAnEfPFIyJvzSOQggUK0bmIVBmI1ZxAK6pQ1SUS6mlMta6kJcJNnAUjx0zGaInhTBTsxbEn3rFpeI8kfHB6eC/FQWq04JGHDqTmIGkYBiIy90MIjmXNRY45cT+fz2boAGCeZ/NnNI6j+dIyHS26AfJKmCUOgvbh/nrMzZBhovPdcsi8XPFJIDMbsXyenp5yzuauyKQEYTBEz1m2/+2w4ivcoDCvAMX2nuZ5dtVyHMd5npn57e1tGIbv37+bx6Jpmizx09OTqnLzc28VMM5Yk50/5sLJOe/ckI2duldsUW/D4br7h6KmqXmelon5eLIGWgX8EGCX4lG6I3tcwHILe+ErI//JNuZUdRgGT2yups7ns6qeTid3ExYXVm72oS0Ik/vecj+RpZTX11ebOF9fXykcMjrTTA69Q60yu9pEx97If/cO5v7aiMj8l1n1tGnHxmdmHp6embmKiiLnDErnqbydZ1EaTqe5ViUan05QroI0ZKX0NpVaVYnBCUwEppTTMEau2jGX8dMZHoXTyf2FWd3Ml5zbUnnXmDc03nMk1zHHGaJXJubY737gxi1whgP6xbZL1YR/mibrxB8/fpxOp9Pp5Bmaazx7iD6/iMiCKuHOzeNd2lVJ4riKmv/9wLxC0/bIjU1+aGeK2IFL4t1J44H5wfss9pwPWuuP7vy4q8YCsutjZs/kvfXpWtGNw19H27nuuFzdo92l8W4TIojsYs1uoXRZHL2vXdvmXMuEpNo/vaKeaBtTQiwUxxctK4NmeCXwG5esBCVcbk+sm6mbg8JrStNx/X+PqHyErtXQ3x+FqkfY+qX1Ggphe5IIou2owtBqvajuZOKYaxfrUAAe+A+AKHgpAntz5nE7j3v3gGRzomzAT9CUUi2yO0FtyzJNIdZqASyLxPsQxQwfa90Hy72RRlV0tTEnIrzBK//VB2Hstag9+dzAzFWqYnED5UXsqga3Knx1oeQyHOWWCQRVqaiFVUAMgLUPoiXEx3ORZ972y9lDBO1S5MM9tB0d74Xy30NbTNC1QQOtLJD3DV8RU8d84/sO43e1jCh896CVV0ubMkVEqR3BkpvzNCk76OC4ENgq//eTS4n3d6cZxbEna3cu8dlz60bge+vTVeb3U+zHgzroZnmrm708b4U/dPJG681jNMyypUcsS9c2EPH9nSLXPVxrNZNCK7RCBSqsstghqty6kH1ZH7QH4SVngdTlZHyjQ7loRbG5OQHHJlwbxf8Q2oq06v7BKO7RsLp8EfkVslu8XGt1LjOzeyO5U8r7OjQvCKzNpWIonekoQl6UdQoGmY9FZjaScDIo82XsxSVw2kCVc7Jt2F+WRSLywJpcN8YQv0EOvRwK/Xg8YWh4tgcROVgEO6+cz1jL8Srz+wAu/rrfruY0tUsTMlnNuJmQoCRVpUArgOXZbLCbV8+gcymg5LsZJM0YqC4WoxD7aYvdncK4W8Or7bqW/jdq4vdQhwnLgAV1onWZMK5ltCuLW8zqNKzuKwp7Cneilds4Ldm2onK7JOkFHewcYQO4D2tYWIPOwtC1vhCTXdOwOq3KAeu9lenE+vfTu3oTQSeicKbeyc8uAB0AojO/Ezne2NDfw6goorvp48shc2JA2zaWVpWqZSYIt/UvNcxiFVIE5JaoWwFCKs0aS3i5UA5cGX2RY6or65CDRnWie8yK/xfaZTj2mKAH/rCwHuTbLIgaDmoPkNK8KQIX49Ldml0jRq9emyWeqiYF+HaEBQqbboZwj3XYVkq8pTGBHYtsqxS/xToQyANo5UVvpPCXT5uxJGfswYTBzELNTLedIula1e+UCOwt5GPi2OTYlZf0weALa3m71vW7GtYBbA2JMmMSwyxVThq6klXWG+2NSKhpUtzQagkRR9piD63KXRp+ZaJdRIiudvwx7P4DySoXm4814NhPj4TDWjLS/r16XGiTZlUiTXv9d8C47TAgtRAPQXDXonyzth+hDrC6ictkyo94453B3awcsDrXhh+kf6Agxs4CVvC0C1gH+WyT3TP27kGre3Lovl3UfAtCA4FAGSrVNrMu1Y6RAS+KFS5xgNqtWNbF5PB4AnA+aEtsw+1ga/8fjlBGB6pMnMz8OZ/PZ7NpknZHvNY6TVMXXi3MeKpaRSFSRWYAJLXK9Pn56//+z39D6tPTSepcahkTAyCuIiJqkc2JYDGiJaXFVCelDEDElBcqX/Xvbz+mMz2N/3o6fc00MHNKKJg4FcXESXNOw5BUqcyVWImVWTNBxKb/IlIB5dQ8x5ICSqTMIAKkskhOCSK1nFnr0ylP0+uwGCgV0cpcTTamqXz5+uWvv/7KOb98enl9fR2fTq/zGYmReJqml5cXVf3++vry+ctUy0DZTXikGWpTM0V5eXn59u3b8/Pz29vby8uLiCSm8/RdpOSBqpzNB3OVaWDfARGQqMfotvdmPkaLdVsiEIQEzKpasxQAmZEVVeBWb2FpJr7zRkSAm1xo5eJSIkUBkGoCMmmixMzKiqRF63J5V6uSXUBIRInARIkp11Ip55RSQZnLa60zM6eBRJ49JCWaIiwttF8XwlJVn59Pf/7555c/Pk/zmVhEyjCmcUxvZ5Q6gRIzp8EM/WZAxyo0zwzknAQQ88Pvw1oUsuwoEREr3nQGj4lJSUUqIAQdmKDllMZSUWtR4ZxIMict30Yuf3wu315LlQEDE4nS0/OnmvMba6Fqx0OkGOaSisw0S5k1J9UKKgxRhcg8DoOJJaoAhUhsIq55rKUUkEqtdSaixEo6jTlJfZunH0QkNFZi4YTTiYTmt3kcxzyk1+mNiMZhKNM05kxaErNAp+nvlNIwkMg8z2/LQARUq4071crMUokY5jNa6sIKEKTZcLTxbxplUu2WCNJuG6nITKpVJlBiAuiHIFVFqd8VM3EmhlSISCIVVCy2HkSo3E5LpdgFAeEE84eVE70jLqGub/91oNid2ji23ZHzCmJLEeb86dPzy/PXIX/W2W41gpCIqsrqxmJKyd1M/brJ5M6c72nvzRxw365Q93ytU3apS7PVvbfFPdau3TwNkrgFD0db3RyrWrQ2E22BPx+o1Dtod5F4OegUEQgRKbG0y/nblcdB5rH+x+rVR0hVdbM19rOK07A7sfx/mOt2iXecbUxpL28D1jFaubi7gnYzw938nXJ6Hj4NL5/+6+sf/yGMP/4+l1KGkabz3yBUkiqLcYDHDTbVIGbyQGfEvoxLv+6lJ+4S3Bz212i1xtTbEu9FdDjSdU3otctVx23ia9X2KsX173F9ti3qyjKKk42rV65hbUnb3p826+paK5HGTv8Vc9VunnGpIaqiwsxosRuiCOGOBW832n8pRQjoxjK1Nft7M9x97l7q4QarA5zLjHNY1wea/vJeDSuOh90p2ia9i0m6der1feXdriIipqchP/3x+V9fv/y7llTP35jm5+c0ZFIM50kmKRY2GQCnlZR0cvNechhy2r6JKfGe7ZiYLFJk0Z0aFjYw0f0ZMauj2IQOxa71yM3htGR1ZTLHegD7lCYhNNbN8xMnLBEwxfmtqvSQH4tjat29s9+fUtKcZS4qiy89XBG8m9OP6iWc189twmpcbCJItrL8/ePalm6UrIg+WA3M1VeX6l07D9kT4xt2WLsF7Cb2XRJLY9sl0S/wQZ1iiVJTRaolS81lxnQWBZjHl08syMSzlMlcyC/xQvba9gBmxQ6OxqIIG5zOemzw4mGg9A6jJV8LiHJXVbspPTLT33S24P7rFkDjh/GTm0C8FZUoxB3Qp3YrM25XdS6kt7l1f4poSsk2uX1A/twB74C1nRFzzsi5MJOAmcFMa/3FG8vMuDICVG1fdXUq+hPr7/0SZ5Fr1Bni3kPes9QtvKhPFo5cetCM6eNY8zasUgJE9H8lpx4+CEf8AwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "path = './clothing-dataset-small/train/t-shirt'\n", + "name = '5f0a3fa0-6a3d-4b68-b213-72766a643de7.jpg'\n", + "fullname = path + '/' + name\n", + "load_img(fullname)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Usually we resize images. This is how a network will see these images:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "load_img(fullname, target_size=(150, 150))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Pre-Trained Neural Network" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's apply a pre-trained neural network with imagenet classes.\n", + "\n", + "We'll use Xception, but any other architecture will work as well.\n", + "\n", + "Check here for a list of available models:\n", + "\n", + "* https://keras.io/api/applications/\n", + "* https://www.tensorflow.org/api_docs/python/tf/keras/applications\n", + "\n", + "\n", + "We'll need to import 3 things:\n", + "\n", + "* the model itself (`Xception`)\n", + "* the `preprocess_input` function that takes an image and prepares it\n", + "* the `decode_predictions` that converts the predictions of the model into human-readable classes" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.applications.xception import Xception\n", + "from tensorflow.keras.applications.xception import preprocess_input\n", + "from tensorflow.keras.applications.xception import decode_predictions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's load the model. The pre-trained model expects 299x299 input" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "model = Xception(\n", + " weights='imagenet',\n", + " input_shape=(299, 299, 3)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next,\n", + "\n", + "* we load the image using the `load_img` function\n", + "* convert it to a numpy array\n", + "* make it a batch of one example" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(299, 299, 3)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img = load_img(fullname, target_size=(299, 299))\n", + "x = np.array(img)\n", + "x.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 299, 299, 3)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = np.array([x])\n", + "X.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We're ready!\n", + "\n", + "Next, we will:\n", + "\n", + "* prepare the input\n", + "* do the predictions\n", + "* convert the predictions into a human-readable format" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "X = preprocess_input(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "pred = model.predict(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 1000)" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0.0003238 , 0.00015736, 0.00021406, 0.00015296, 0.00024657,\n", + " 0.00030446, 0.00032349, 0.00014726, 0.00020487, 0.00014866],\n", + " dtype=float32)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pred[0, :10]" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[('n03595614', 'jersey', 0.67924464),\n", + " ('n02916936', 'bulletproof_vest', 0.039600343),\n", + " ('n04370456', 'sweatshirt', 0.035299607),\n", + " ('n03710637', 'maillot', 0.010884146),\n", + " ('n04525038', 'velvet', 0.0018057624)]]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "decode_predictions(pred)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Not quite what we wanted... Let's train our own model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Transfer learning" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of loading each image one-by-one, we can use a data generator. Keras will use it for loading the images and pre-processing them" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from tensorflow.keras.preprocessing.image import ImageDataGenerator" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use smaller images - it'll be faster" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "image_size = (150, 150)\n", + "batch_size = 32" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's get train data:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3068 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "train_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "train_ds = train_gen.flow_from_directory(\n", + " \"clothing-dataset-small/train\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And validation:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 341 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "validation_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "val_ds = validation_gen.flow_from_directory(\n", + " \"clothing-dataset-small/validation\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For fine-tuning, we'll use `Xception` with small images (150x150)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "base_model = Xception(\n", + " weights='imagenet',\n", + " input_shape=(150, 150, 3),\n", + " include_top=False\n", + ")\n", + "\n", + "base_model.trainable = False" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add a small neural net on top of that: just one layer with 10 neurons (there are 10 classes we want to predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "inputs = keras.Input(shape=(150, 150, 3))\n", + "\n", + "x = base_model(inputs, training=False)\n", + "\n", + "# Convert features of shape `base_model.output_shape[1:]` to vectors\n", + "x = keras.layers.GlobalAveragePooling2D()(x)\n", + "\n", + "outputs = keras.layers.Dense(10)(x)\n", + "\n", + "model = keras.Model(inputs, outputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we specify the learning rate and compile the model. After that, it's ready for training" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "learning_rate = 0.01\n", + "\n", + "model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate),\n", + " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", + " metrics=[\"accuracy\"],\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's train now for 10 epochs:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/10\n", + "96/96 [==============================] - 22s 227ms/step - loss: 1.2372 - accuracy: 0.6734 - val_loss: 0.8453 - val_accuracy: 0.7713\n", + "Epoch 2/10\n", + "96/96 [==============================] - 16s 163ms/step - loss: 0.6023 - accuracy: 0.8194 - val_loss: 0.7928 - val_accuracy: 0.7859\n", + "Epoch 3/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.3485 - accuracy: 0.8801 - val_loss: 0.7924 - val_accuracy: 0.7859\n", + "Epoch 4/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.2219 - accuracy: 0.9234 - val_loss: 0.8765 - val_accuracy: 0.7918\n", + "Epoch 5/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2111 - accuracy: 0.9260 - val_loss: 0.9354 - val_accuracy: 0.8065\n", + "Epoch 6/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.1716 - accuracy: 0.9426 - val_loss: 0.9824 - val_accuracy: 0.7889\n", + "Epoch 7/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.1068 - accuracy: 0.9615 - val_loss: 0.9301 - val_accuracy: 0.8182\n", + "Epoch 8/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0514 - accuracy: 0.9866 - val_loss: 1.0111 - val_accuracy: 0.8270\n", + "Epoch 9/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0343 - accuracy: 0.9932 - val_loss: 0.9801 - val_accuracy: 0.8094\n", + "Epoch 10/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0274 - accuracy: 0.9961 - val_loss: 0.9342 - val_accuracy: 0.8065\n" + ] + } + ], + "source": [ + "history = model.fit(train_ds, epochs=10, validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "epochs = history.epoch\n", + "val = history.history['val_accuracy']\n", + "train = history.history['accuracy']\n", + "\n", + "plt.plot(epochs, val, color='black', linestyle='solid', label='validation')\n", + "plt.plot(epochs, train, color='black', linestyle='dashed', label='train')\n", + "\n", + "plt.title('Xception v1, lr=0.01')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "plt.xticks(np.arange(10))\n", + "\n", + "plt.legend()\n", + "\n", + "\n", + "plt.savefig('xception_v1_0_01.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "0.01 is not necessarily the best learning rate, so we should experiment with 0.001 and 0.0001.\n", + "\n", + "To make it easier for us, let's make a function for defining our model:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def make_model(learning_rate):\n", + " base_model = Xception(\n", + " weights='imagenet',\n", + " input_shape=(150, 150, 3),\n", + " include_top=False\n", + " )\n", + "\n", + " base_model.trainable = False\n", + "\n", + " inputs = keras.Input(shape=(150, 150, 3))\n", + " x = base_model(inputs, training=False)\n", + " x = keras.layers.GlobalAveragePooling2D()(x)\n", + " outputs = keras.layers.Dense(10)(x)\n", + "\n", + " model = keras.Model(inputs, outputs)\n", + " \n", + " model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate),\n", + " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", + " metrics=[\"accuracy\"],\n", + " )\n", + " \n", + " return model" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Watching metrics this way is not convenient, so let's create a special callback for that" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/10\n", + "96/96 [==============================] - 19s 196ms/step - loss: 1.1015 - accuracy: 0.6291 - val_loss: 0.7096 - val_accuracy: 0.7830\n", + "Epoch 2/10\n", + "96/96 [==============================] - 16s 163ms/step - loss: 0.6252 - accuracy: 0.7832 - val_loss: 0.6204 - val_accuracy: 0.8123\n", + "Epoch 3/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.5067 - accuracy: 0.8370 - val_loss: 0.6059 - val_accuracy: 0.8065\n", + "Epoch 4/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.4265 - accuracy: 0.8611 - val_loss: 0.5871 - val_accuracy: 0.8123\n", + "Epoch 5/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.3729 - accuracy: 0.8846 - val_loss: 0.5525 - val_accuracy: 0.8270\n", + "Epoch 6/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.3229 - accuracy: 0.9045 - val_loss: 0.5472 - val_accuracy: 0.8152\n", + "Epoch 7/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2879 - accuracy: 0.9228 - val_loss: 0.5437 - val_accuracy: 0.8299\n", + "Epoch 8/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2571 - accuracy: 0.9345 - val_loss: 0.5473 - val_accuracy: 0.8270\n", + "Epoch 9/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2353 - accuracy: 0.9381 - val_loss: 0.5392 - val_accuracy: 0.8270\n", + "Epoch 10/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2103 - accuracy: 0.9544 - val_loss: 0.5536 - val_accuracy: 0.8182\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001)\n", + "history_0_001 = model.fit(train_ds, epochs=10, validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "epochs = history_0_001.epoch\n", + "val = history_0_001.history['val_accuracy']\n", + "train = history_0_001.history['accuracy']\n", + "\n", + "plt.plot(epochs, val, color='black', linestyle='solid', label='validation')\n", + "plt.plot(epochs, train, color='black', linestyle='dashed', label='train')\n", + "\n", + "plt.title('Xception v1, lr=0.001')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "plt.xticks(epochs)\n", + "\n", + "plt.legend()\n", + "\n", + "\n", + "plt.savefig('xception_v1_0_001.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/10\n", + "96/96 [==============================] - 19s 196ms/step - loss: 1.9388 - accuracy: 0.3409 - val_loss: 1.5844 - val_accuracy: 0.5044\n", + "Epoch 2/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 1.3929 - accuracy: 0.5479 - val_loss: 1.2417 - val_accuracy: 0.6217\n", + "Epoch 3/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 1.1497 - accuracy: 0.6317 - val_loss: 1.0638 - val_accuracy: 0.6716\n", + "Epoch 4/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 1.0089 - accuracy: 0.6770 - val_loss: 0.9546 - val_accuracy: 0.7097\n", + "Epoch 5/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.9145 - accuracy: 0.7050 - val_loss: 0.8825 - val_accuracy: 0.7302\n", + "Epoch 6/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.8458 - accuracy: 0.7314 - val_loss: 0.8348 - val_accuracy: 0.7361\n", + "Epoch 7/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7917 - accuracy: 0.7484 - val_loss: 0.7912 - val_accuracy: 0.7625\n", + "Epoch 8/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7487 - accuracy: 0.7647 - val_loss: 0.7618 - val_accuracy: 0.7713\n", + "Epoch 9/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7124 - accuracy: 0.7705 - val_loss: 0.7383 - val_accuracy: 0.7801\n", + "Epoch 10/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6820 - accuracy: 0.7826 - val_loss: 0.7137 - val_accuracy: 0.7801\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.0001)\n", + "history_0_0001 = model.fit(train_ds, epochs=10, validation_data=val_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "epochs = history_0_0001.epoch\n", + "val = history_0_0001.history['val_accuracy']\n", + "train = history_0_0001.history['accuracy']\n", + "\n", + "plt.plot(epochs, val, color='black', linestyle='solid', label='validation')\n", + "plt.plot(epochs, train, color='black', linestyle='dashed', label='train')\n", + "\n", + "plt.title('Xception v1, lr=0.0001')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "plt.xticks(epochs)\n", + "\n", + "plt.legend()\n", + "\n", + "\n", + "plt.savefig('xception_v1_0_0001.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = np.arange(10)\n", + "val_0_01 = history.history['val_accuracy']\n", + "val_0_001 = history_0_001.history['val_accuracy']\n", + "val_0_0001 = history_0_0001.history['val_accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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B8NBDD7F9+3Y6depE586dadOmDSVKlMiTYyYnJ7Nt27aLksCWLVs4d+6cd70KFSpQv379DEmgXr161KxZk8DAwDyJ5YLExER+/vlnpk6dyty5c0lKSqJixYrcfffd9OzZky5dunjbUJx06NAhFixYkOF927p1K0uWLKF169Z88803vPDCCxlKT/Xq1aN9+/Z5Gr8lCFMsHD582FtCiI6O5sCBA4C7ZFC3bl327NlDqVKlHGkz+Oyzz5g2bRorVqzw/oL2Lb0sXLiQ8PBwoqKiCAi4vNuTFi1axNixY1m4cKG3QbVBgwYsXbo0T0svSUlJTJkyhbVr12ZbLVSjRo2LkkC9evUICwtzpAR19uxZ5s6dy9SpU/n55585ceIEpUqVomvXrvTs2ZO77rqLChUq5Htc2bnQgB8YGMiSJUv4/PPPve/1yZMnAUhISKBq1ap89dVXzJ8/n3r16jFw4MAr/qFzqQSBqhaJR4sWLdQUL8eOHdP//Oc/unPnTlVV/c9//qOAhoWFae/evfXTTz/1LisoXC6X7t69W3/44QedOHGid97111+vgJYvX15vv/12HTp0qC5ZsuSi7Q8cOKATJ07Uxx57TDdt2qSqqt9++61Wq1ZN+/Xrp19//bUmJCTkacypqan66aefakREhAIaEhKijRo10gceeECHDRum3377ra5evVrPnj2bp8fNaykpKTpv3jz9y1/+olWrVlVAg4KCtHPnzjpu3Lg8f9/yksvl0gMHDujChQvV5XKpquro0aM1IiJCg4KCNCkp6Yr3DcRoNudVx0/sefWwBFH0paSk6Pz58/Wll17Sli1bakBAgAI6evRoVVU9deqUrl27VtPT0x2O9PK4XC5dt26dfvbZZzpgwABt1qyZBgYG6sCBA1VVNTk5WXv27KmNGjVSQAGtUKGCTp8+XVVV09LSvCeNvJSenq6TJ0/WOnXqKKCtW7fWBQsWaFpaWp4fK7+lp6fr77//ri+++KLWrVvX+762bt1a33rrLd2yZYvTIeba1SbmSyUIq2IyBVZaWhqrVq0iOTmZDh06cPbsWSpUqICq0qZNGzp16kSnTp1o1apVkbs34dy5c5w/f55KlSqxe/duunXrRrVq1ejcuTOdO3emadOmeV53f4GqMmfOHIYOHcqaNWto2LAhr7/+Oj169Ch0l2nmhnraqy5cEbVq1SrAXU13oZE7v66IcoK1QRRwqampHDhwwHs9+dNPP03FihUJDw/3PiIjIwtUXWleSEpKYv/+/Zw+fZomTZoA7ituVqxYQUJCAhs2bODkyZO0bduWpUuXAvDbb79x44035lnDsslo6dKlDBkyhCVLlhAZGcmrr75K3759/ZaMCqK9e/dmuCLK5XJRrVo1b7K4+eab8/2KKH+yBFFAuVwuvvvuO4YPH07ZsmVZuXIlqamp1KpVi4MHD2Zo/Hvuuef45z//yZkzZ+jSpUuG5BEeHk7r1q2pXbu2g6/mf1SVxMREEhISSEhI4MSJE/Tt2xeAf/zjH/z0008kJCSQmJgIuK93v3A37H333cf69esJDw/nhhtu4LbbbuO2224r1DejFQaxsbEMHTqUWbNmUblyZYYNG8bjjz9e5Epml+vo0aPeK6LmzZtHUlISlSpV8l4Rddttt1G6dOlCXbqwBFHAqCozZ85k6NChrFu3jsaNG/PGG2/QvXt37wctLS2NQ4cOkZCQwP79+4mMjKRJkyYcOnSIvn37ek++F+7yHDNmDAMHDmTz5s20adPmogTSq1cvbrzxRpKSkjh58iRhYWGXfbUMuC9r3L9/v/f4F+J7++23CQwMZMiQIfzrX//KcGdpUFAQycnJBAQE8Prrr7N8+fIMsVWvXp1OnTrlzZtrLsu2bdsYPnw4kydPpnz58rz44ov89a9/pXTp0k6HVuCcOXMmwxVRF64qCg4O9vYjdbV/8+ry48thCaKA+f7773nwwQeJiopi5MiRPPjgg1d0sga8/dtUqlSJ6667jj179vDOO+94T97x8fEcOnSI77//nj/84Q/Mnz+fLl26EBwcTJUqVbwn6eHDh3PjjTdy4MABVq1axYEDBzIkgS+++ILrr7+eUaNGMXz48AwxlCxZkj179hAWFsbUqVP57bffLkpQ1apVK3S/si7UTZcuXZoaNWo4HU6eSkhIYOTIkYwfP54SJUowaNAgnn/+ecqXL+90aIVCSkoKixYtYtWqVd7+p7L769tNSE6Cg4NzTCRZzatcuTItWrS4otdiCaIAWL16NYcOHeKOO+4gJSWFSZMm0bdv31zdtXu10tLSUFWCg4PZs2cPM2bMyHDyT0hI4Ntvv6V58+Z88cUXPPbYYwCICNdddx3h4eFMnjyZOnXqEBsby5o1awgPD6dq1aqEh4dTvnz5Qnfyz87evXu9/TFFR0dz6NAhAJo2bertwqFRo0aF9vUmJiby1ltvMXbsWNLT0xkwYABDhw6lcuXKTodWpLlcLs6ePZtjMsnN38x9Q4G7g8Hff//9imJz7D4IoBuwBdgOvJTF8urAQmANsA7o7rNsiGe7LUDXnI5VUC9z3bx5sz7wwAMKaOPGjf1yOWJeOnz4sC5fvlz37NmjKSkpTofjd0ePHtUffvhBBwwYoFFRUd7LHa+//nrt27evjh8/Xt955x1t27atiogCWrt2bR08eLAuXbq00FxSe+rUKX311Vf1mmuuURHRhx9+uMDdI2JyJz09XU+ePKnx8fEaFxeny5cv11WrVl3x/nDiPgggENgB1AJCgLVAg0zrfAL82fO8AbDb5/laoAQQ6dlP4KWOV9ASxN69e7V///4aGBiopUuX1mHDhumJEyecDqvYO3v2rM6ZM0eff/55bd68ufekf8011+jdd9+tY8aM0Q0bNmSZyA8cOKD//ve/tWvXrhocHOxNJE8++aTOnj1bk5OTHXhFl3b+/Hl97733NCwsTAHt2bOnbtiwwemwTAHiVIJoA8z1mR4CDMm0zsfAiz7rL8tqXWAu0OZSxytoCWLGjBkaEhKiAwcO1EOHDjkdTrGVmpqqy5Yt01GjRuktt9yiISEhCmhwcLDecsstOmrUKF22bJmmpqZe1n5PnDih3377rT7wwANaunRpBbRs2bLap08f/f777/X06dN+ekW5k5qaqp999plWq1ZNAe3UqZMuX77c0ZhMweRUgrgf+Mxn+iFgbKZ1qgDrgXjgONDCM38s8Cef9cYD92dxjCeBGCCmevXq/nsHc+HEiRM6bNgwff3111XVfXdsQb51v6hyuVy6YcMGHTNmjN59991atmxZBVREtHnz5vr888/rnDlz9MyZM3l2zPPnz+uMGTP0scce02uvvVYBLVGihN511106fvx4PXz4cJ4dKyfp6en6/fff6w033KCAtmrVShcsWJBvxzeFj1MJ4oEsEsT/y7TO34Hn9H8liDggABiXRYL4w6WO51QJ4ty5c/rOO+9oxYoVFdBHHnnEkTiKsz179ujnn3+uf/zjH7Vy5credoSoqCgdMGCA/vDDD3r06NF8iSU1NVUXLVqkgwYN0ho1aiigAQEBesstt+h7772nu3fv9stxXS6Xzp49W5s3b66ANmjQQKdOnVrg27yM8wpyFdNGoJrP9E7gusJSxTRnzhwNDw9XQLt27aoxMTH5HkNxlJiYqFOmTNGnnnrK208QoNddd523YdlfJ+LL4XK5dPXq1Tps2LAM/Sg1a9ZMR44cqevXr8+TE/jSpUu1Q4cOCmjNmjX1yy+/LBL9JZn84VSCCPKc8CN9GqkbZlpnNtDP87w+sB8QoGGmRuqdBaWROj09XU+dOqWqqrGxsdquXTtdtGhRvhy7uDp79qzOnTtXX3jhBW3RooW3YblMmTJ611136XvvvZdnJ1t/2rZtm44ePVrbtGmToZTz/PPP67Jlyy77iqi1a9fqXXfd5W0sHzt2bIFsKDcFmyMJwn1cugNbcV+FNNQzbyTQw/O8AbDUkwxigS4+2w71bLcFuCOnY/k7QbhcLp05c6Y2bdpU//jHP/r1WMVVenq6Hjp0SFevXq0zZszQ1157TTt27JihYblDhw46cuRIXbp0aaG+DHf//v360UcfaZcuXTQoKEgBrVKlij711FM6d+7cS57ot2/frn379lUR0fLly+sbb7yRp20qpni5VIKwG+VyYcmSJbz88sv8+uuv1K5dm1GjRtGnTx+/HKuoOn/+/EVddGR+7N+/n9TU1AzbNW3aNMPob0WxC4gTJ04wa9Yspk6dyuzZszl79izlypXjzjvvpGfPnnTr1o0yZcqwf/9+793PwcHBDBw4kBdeeKHIdeJo8pfdSX0Vxo0bxzPPPEPVqlUZPnw4jz32WL7c/VxYuFwujh49muOJ/8JIZ75Kly59UZccvo+oqCgqVarkwKtyzvnz51mwYAFTp05l+vTpJCYmEhoaSrt27Vi6dCnp6ek8+eSTDB06lCpVqjgdrikCLEFcpq1bt5KcnOztm+ibb77hmWeeKRDj2OanK/3VLyJUrlw5w8n+Qrccvo+yZcsW2i4r8kNaWhq//vorU6dOZf78+bRs2ZIRI0ZQq1Ytp0MzRYgliFyKj4/n1Vdf5YsvvqBTp07MnTs3j6LL3qlTp1i8eDHR0dGsWLHiopOtEy6M03Alv/rDw8OpXLlykeov35ii7FIJwr7FuPt8f/PNNxk3bhyqytNPP83LL7/sl2MlJyfz+++/ezuEW7FiBenp6YSGhtKqVasCUZ8cHBzMzTffnOXJ3371G1N8WIIAJkyYwJgxY3j44YcZMWJEnnbt7HK5iI2N9fYO+ssvv3D+/HkCAgK46aabeOmll+jUqRNt2rQhNDQ0z45rjDFXy6qYcI//u3v3bho0aHDVcagqO3bs8JYQFi5c6B05rUGDBt5xlG+55Rbre9+YQi45OZmYmBhq165N5cqVSUpKYtWqVdSpU4frrruOc+fOsWbNGurWrUtYWBhnz54lNjaWevXqUalSJU6fPs26deuoX78+FStW5NSpU6xfv56GDRtSvnx5Tpw4wcaNG2nUqBHlypXj+PHjxMXF0bhxY6655hoSExO9g4Rd6ZgyjnX3nZ8PJzvrO3DggE6cOFEfe+wxrV69uvcmqIiICH3kkUf066+/tn6ZjCmE0tLSNDY2Vnft2qWqqkeOHNHGjRvrl19+qaqq+/btU0A//fRTVVXdsWOHAt7lcXFxCujkyZNV1X1zLaA//vijqqouX75cAZ05c6aqqi5ZskQBnT9/vqqqRkdHK6CLFy9WVdVZs2YpoL///ruqqv70008K6Pnz56/4NeLUjXL5+cjPBHHy5EmdPn26Dhw4MEMXCuXLl9f77rtPx40bp1u2bCnwd/YaY9wu3Jjocrl08ODBOnHiRFVVTUlJ0aCgIB0yZIiquhNGjx49dPr06arq7qhx3rx5Gh8fr6ruu/7nzZvn/UF4+vRpnTdvnh44cEBV3eNyzJs3z9vD84kTJ3TevHl65MgRVVU9duyYzps3z9t3WGJios6bN0+PHTumqu4ENW/ePO/QAYcOHdJ58+ZdVdcql0oQVsWUCxcalqOjo1mwYEGGhuX27dt7b+Rq1qwZgYGBfonBGJM3Fi5cSGpqKl26dAGgSZMmNGnShK+++gqARo0a0b17d0aPHg3AzJkzqV+/fpG9vNiuYrpMLpeLtWvXetsRMjcsv/jii3Tq1Im2bdtaw7IxBcz58+c5ePAgkZGRAPzjH//g4MGDfPzxxwCMGDGC9PR0b4Lo169fhpsO169fn+FKvTvvvDMfoy9YLEHwv4blCyUE34bl+vXr079/fzp37mwNy8YUQNHR0fz+++8MHToUgMcff5ylS5eye/duAFJSUkhOTvau//nnn2f4Hj/77LMZ9meXcf9Psa9i2rNnD7fccgt79uwBIDw83FtldNtttxEeHp7XoRpjLsORI0eIiYmha9euBAQE8NFHH/H666+ze/dugoKCGDZsGGPGjOHYsWMEBwfzyy+/cPDgQXr16uV06IXCpaqYruy6qCIkIiKCdu3aMXbsWDZv3sy+ffuYMGECDz30kCUHY/LJ+fPnvb0IrFixgj59+nD48GEAfvzxR7p3786+ffsAiIyMpHv37pw7dw6Al19+mZMnT3r7SOvQoasXpNEAACAASURBVIMlhzxS7KuYAgMDmThxotNhGFNsHD16lJ9++onbb7+dGjVqEB0dze23386SJUto164dp0+fZvny5Rw8eJDrrruOu+++m4YNG3L99dcD0K1bN7p16+bdX3HrIy0/FfsShDEmb7lcLnbv3s2hQ4cASEhIoEOHDsyYMQNwVxk98cQT/PLLL4C7nW/48OHehuJOnTqxc+dOGjduDEDVqlVp3769XRDiAEsQxpgrcqH9Mj09nVdffZVZs2YBcObMGSIjI/niiy8AqFixYobt6tSpw86dO+nbty/gTgDWS23BVOyrmIwxuZOenu69z6dFixa0a9eODz74gMDAQD766CPOnTtH9+7dKVu2LF999RU33XQT4K4CulBaAAgKCvJegmoKNksQxpgcDRo0iG3btjFz5kzA3Q5www03eJfv3buXkJAQ7/RDDz2U7zGavGcJwhiTpdWrV9OsWTNEhKioKIKDg72liNdffz3Dur7JwRQdliCMMReZNm0a9957L3PnzqVLly4888wzTodkHGCN1MYYXC4XU6ZM8Y6i2K1bN8aOHUvbtm0djsw4qdjfSW2McSeIG2+8kbp16zJ16lSnwzH5yLE7qUWkm4hsEZHtIvJSFsvfE5FYz2OriJzwWZbus2y6P+M0pjj68ccfad++PampqQQEBDB79mymTJnidFimAPFbG4SIBALjgNuBeGCliExX1bgL66jqsz7r/xVo5rOL86ra1F/xGVMcHT9+nJCQEEqXLk1oaCiBgYEcPnyY8PBwqlev7nR4poDxZwmiFbBdVXeqagowGbjnEuv3ASb5MR5jirX9+/dTo0YN/v3vfwNwxx13sHjxYutzzGTLnwkiHNjnMx3vmXcREakBRAL/9ZkdKiIxIvK7iNybzXZPetaJOXLkSF7FbUyRsXPnTqZPd9fQVq1alRdeeME7DoJ1a21y4s/LXLP69GXXIt4bmKKq6T7zqqvqfhGpBfxXRNar6o4MO1P9BPgE3I3UeRG0MUXJkCFDWLhwIfHx8YSEhPDKK684HZIpRPxZgogHqvlMRwD7s1m3N5mql1R1v+fvTmARGdsnjDFZ2Lp1K3/4wx/Yv9/9VXv77beJjY21G9nMFfFnglgJ1BGRSBEJwZ0ELroaSURuACoAv/nMqyAiJTzPrwXaAXGZtzXGuDvNuzA2QlBQEL/++isbN24EoGbNmlStWtXJ8Ewh5rcqJlVNE5FngLlAIPC5qm4UkZFAjKpeSBZ9gMma8YaM+sDHIuLCncTe8r36yRjjpqp07NiR2rVr8/nnn1OrVi3i4+O9g+cYczX82tWGqs4CZmWaNzzT9IgstlsG3OjP2IwprFJSUli8eDG33347IkL37t29g+kAlhxMnrG+mIwpZN5//31eeOEF4uLiqF+/Pi+++KLTIZkiyvpiMqaAUVWOHj3K0aNHAUhNTeWFF15g6dKlAPTv359Zs2ZRr149J8M0xYAlCGMc8PvvvxMbG+udfvTRR/nwww+909WqVePtt98G3A3P48aNY/78+YB7hLY77rjD7mMwfmdVTMbkAVXl7NmzlClTBoApU6agqjzwwAMA3HPPPVSuXJmPP/4YgIcffpjmzZszefJkAOLj472jrIkIH374IQ0aNPBOnzx5kqAg+7qa/GWfOGNy4cCBAxw+fJgmTZoA8PHHH5OQkMDIkSMBd7cVp0+f9lYDffjhhyQnJ3sTRKNGjTKMzfzNN99QqVIl7/SF0sEFjz76aIZpSw7GCfapMyYL+/btY+HChTz88MMAvPTSSyxcuJC9e/cCsGbNGjZv3uxd/9FHH+X8+fPe6SlTpnhLE8BFI7C1atXKn+EbkydsPAhjPM6cOUOpUqUICAhg1KhRjBgxgoSEBCpXrsyqVas4fvw4nTt3djpMY/KUY+NBGFNYLFu2jOuvv54lS5YA8NRTT7F9+3YqV64MQIsWLSw5mGLHEoQplpKSkhgyZAj/+c9/AGjatCn9+vXz3nAWFhbmbTQ2priyBGGKjaNHj7J8+XIASpQowbRp01i9ejUApUqVYty4cXZvgTE+rJHaFGkul4uAAPfvoIcffpi4uDh27txJQECA9XJqTA6sBGGKrO+++44aNWpw6tQpAF577TWmT5/uTRiWHIy5NEsQpshITEzkjTfeYPv27QBERkbSvn17b4Jo3rw5jRs3djJEYwoVq2IyhdqZM2c4efIk4eHhJCcnM3z4cCpVqkRUVBStWrVi0iQb5tyYK2UJwhRaLpeLBg0acPPNNzNx4kSqVq1KfHy899JUY8zVsQRhCpUPP/yQ//73v0yZMoWAgADeeuutDJejWnIwJu9YG4Qp0BITE/n4449JS0sD3IPlJCUlkZSUBEDfvn1p06aNkyEaU2RZgjAF2uLFi3nqqadYtmwZAIMGDeLnn38mNDTU4ciMKfosQZgCJSUlhcGDBzNhwgQA7rzzTmJjY+nQoYOzgRlTDFkbhClQgoKCiImJ8d6rUKJECW8X28aY/GUJwjguLS2NDz74gP79+1OuXDnmzp1LiRIlnA7LmGLPr1VMItJNRLaIyHYReSmL5e+JSKznsVVETvgse0REtnkej/gzTuOs9evX8/zzz/Pdd98BWHIwpoDwWwlCRAKBccDtQDywUkSmq2rchXVU9Vmf9f8KNPM8rwj8A2gJKLDKs+1xf8Vr8pfL5SImJoZWrVrRrFkz1q1bR8OGDZ0Oyxjjw58liFbAdlXdqaopwGTgnkus3we4cNtrV2C+qh7zJIX5QDc/xmry2Ztvvkm7du3Ytm0bgCUHYwqgHEsQIvIMMPEKfr2HA/t8puOB1tkcowYQCfz3EtuGZ7Hdk8CTANWrV7/M8Ex+U1XOnTtH6dKleeqpp6hWrRpRUVFOh2WMyUZuShCVcVcPfe9pU5Bc7jur9bIb37Q3MEVV0y9nW1X9RFVbqmrLsLCwXIZlnNK3b1/uv/9+VJVKlSrx8MMPk/uPkzEmv+WYIFT1FaAOMB7oB2wTkTdEpHYOm8YD1XymI4D92azbm/9VL13utqaQuPXWW+natStFZRx0Y4q6XLVBqPsbfdDzSAMqAFNEZPQlNlsJ1BGRSBEJwZ0EpmdeSURu8OzvN5/Zc4EuIlJBRCoAXTzzTCFy6tQp/vSnPzF3rvtf9+STTzJo0CDvPQ7GmIItx2+qiPxNRFYBo4GlwI2q+megBfCH7LZT1TTgGdwn9k3A96q6UURGikgPn1X7AJPV52elqh4DRuFOMiuBkZ55phAJCQlhw4YN3oZoY0zhIjkV90VkJDBeVfdksay+qm7yV3CXo2XLlhoTE+N0GMXemTNneP/993n++ecJCQkhNTWV4OBgp8MyxmRDRFapasusluWmrD8L8P56F5FrRKQ1QEFJDqbgWLx4McOGDWPhwoUAlhyMKcRykyA+As74TJ/1zDMGgKSkJFauXAm4O9fbtGkTXbt2dTgqY8zVyk2CkEztAy6sDyfj469//SudO3fm+HH3rTI33HCDwxEZY/JCbhLETk9DdbDnMRDY6e/ATMGWlpbGuXPnAHj55ZeZNGkSFSpUcDgqY0xeyk2CeApoCyTwv7uhn/RnUKZgS0tL45ZbbuFvf/sbAJGRkXTv3t3hqIwxeS3HqiJVPYz7HgZjAPeYDXfddRe1atVyOhRjjB/lpi+mUKA/0BDwjvOoqo/5MS5TwOzfv5/+/fvz5ptv0rRpU4YMGeJ0SMYYP8tNFdPXuPtj6gosxt3txWl/BmUKnhIlSrBjxw527rTmJ2OKi9wkiChVHQacVdUvgTuBG/0blikIDh06xOuvv+7tXC8uLo777rvP6bCMMfkkNwki1fP3hIg0AsoBNf0WkSkwpk6dyqhRo9iwYQPgbnswxhQfuUkQn3g6zHsFd2d7ccDbfo3KOObkyZPExsYC7s714uLiuPFGKzAaUxxd8iehiAQApzyDBf0C2GUrRdyf/vQnNmzYwNatWwkODrYrlYwpxi6ZIFTV5RlR7vt8isc4bPTo0SQkJFgfSsaYXFUxzReRwSJSTUQqXnj4PTKTb5YuXcq7774LQP369encubPDERljCoLcJIjHgKdxVzGt8jysX+0i5KuvvuKTTz7h7NmzTodijClAcnMndWR+BGLyX1JSEqGhoYwdO5bTp09TunRpp0MyxhQgubmT+uGs5qvqV3kfjskvr732Gj/99BOLFi2iTJkyVKxotYbGmIxyc2H7TT7PQ4FOwGrAEkQh1rRpU/bt20doaGjOKxtjiqUchxy9aAORcsDXqtojx5XzkQ05mrOzZ88SGxtLu3btnA7FGFNAXO2Qo5mdA+pcXUjGCc899xxdu3bl6NGjTodijCkEctMGMQO4UMwIABpg90UUSqNGjeKee+7h2muvdToUY0whkJsSxD+Bdz2PN4EOqvpSbnYuIt1EZIuIbBeRLLcRkV4iEiciG0XkW5/56SIS63lMz83xzMVWr17NoEGDcLlchIWFcccddzgdkjGmkMhNgtgLLFfVxaq6FEgUkZo5bSQigcA44A7cpY4+ItIg0zp1gCFAO1VtCAzyWXxeVZt6HgWqvaMwiY6OZurUqRw6dMjpUIwxhUxuEsQPgMtnOt0zLyetgO2qulNVU4DJwD2Z1nkCGOfp6+nC6HUmD5w5cwaAwYMHs3btWqpUqeJwRMaYwiY3CSLIc4IHwPM8JBfbhQP7fKbjPfN81QXqishSEfldRLr5LAsVkRjP/HuzOoCIPOlZJ+bIkSO5CKl4+Pe//02DBg3Yv38/IkL58uWdDskYUwjlJkEcERFvFY+I3APk5jIYyWJe5mtqg3BfEdUR6AN8JiIXzmbVPZde9QXGiEjti3am+omqtlTVlmFhYbkIqXho06YNt99+O5UqVXI6FGNMIZabBPEU8LKI7BWRvcCLwIBcbBcPVPOZjgD2Z7HONFVNVdVdwBY8l9Cq6n7P353AIqBZLo5ZbCUnJ/Pzzz8D0KRJE8aPH0+JEiUcjsoYU5jlmCBUdYeq/h/uhuaGqtpWVbfnYt8rgToiEikiIUBv3AMO+foJuBVARK7FXeW0U0QqiEgJn/ntcA9UZLLx7rvv0qNHDzZv3ux0KMaYIiLHBCEib4hIeVU9o6qnPSfv13LaTlXTgGeAucAm4HtV3SgiI32qrObivioqDlgIPK+qiUB9IEZE1nrmv6WqliAu4e9//zszZ86kXr16TodijCkicuxqQ0TWqGqzTPNWq2pzv0Z2mYpjVxubNm1i5MiRfP7555QsWdLpcIwxhdDVdrUReKG6x7OzkoBVbhcAmzZtYvHixezZs8fpUIwxRVBuenP9BogWkS88048CX/ovJJOTY8eOUbFiRe677z66du1q4zgYY/wiN43Uo4HXcLcLNADmADX8HJfJxg8//EBkZCRr164FsORgjPGb3PbmehD33dR/wD0exCa/RWQuqX379vTu3ZuoqCinQzHGFHHZJggRqSsiw0VkEzAW913Roqq3qurYfIvQkJaWxtdff42qUqVKFT7++GMrORhj/O5SJYjNuEsLd6tqe1X9f7j7YTL5bPLkyTz88MMsXLjQ6VCMMcXIpRqp/4D75raFIjIHd2d7WXWfYfzsj3/8I1WrVuW2225zOhRjTDGSbQlCVaeq6oNAPdxdXTwLXC8iH4lIl3yKr9javXs33bt35+DBg4iIJQdjTL7LzVVMZ1V1oqrehbs/pVggVwMGmSt35MgR1q9fz969e50OxRhTTOV4J3VhUVTupD5w4IB37Ibk5GTrcM8Y41dXeye1ySeLFi2iVq1azJ49G8CSgzHGUZYgCpBWrVoxYMAA2rRp43QoxhiTq642TD4pVaoUY8aMcToMY4wBrARRIKxYsYL27duzfXtuhtkwxpj8YQmiADh69Chnzpzh+uuvdzoUY4zxsgRRAHTv3p01a9ZwzTXXOB2KMcZ4WYJw2KpVq3C5XIjYTerGmILFEoSDdu/eTevWrXnrrbecDsUYYy5iVzE5KCIigq+//poOHTo4HYoxxlzEEoSDgoKC6NOnj9NhGGNMlqyKySHvv/8+EyZMcDoMY4zJliUIB6gq06ZNY86cOU6HYowx2fJrghCRbiKyRUS2i0iWPcCKSC8RiRORjSLyrc/8R0Rkm+fxiD/jzG8iQnR0NJ999pnToRhjTLb81gYhIoHAOOB2IB5YKSLTVTXOZ506wBCgnaoeF5HrPPMrAv8AWgIKrPJse9xf8eaXpKQkAEJDQylTpozD0RhjTPb8WYJoBWxX1Z2qmoJ7RLp7Mq3zBDDuwolfVQ975ncF5qvqMc+y+UA3P8aabz7++GNq1qzJwYMHnQ7FGGMuyZ8JIhzY5zMd75nnqy5QV0SWisjvItLtMrZFRJ4UkRgRiTly5Egehu4/N910Ew899BCVK1d2OhRjjLkkf17mmtWtwZlHJwoC6gAdcY9Wt0REGuVyW1T1E+ATcA8YdDXB5pe2bdvStm1bp8Mwxpgc+bMEEQ9U85mOAPZnsc40VU1V1V3AFtwJIzfbFirp6em8//77HD9e6JtRjDHFhD8TxEqgjohEikgI0BuYnmmdn4BbAUTkWtxVTjuBuUAXEakgIhWALp55hdbSpUsZNGgQ8+fPdzoUY4zJFb9VMalqmog8g/vEHgh8rqobRWQkEKOq0/lfIogD0oHnVTURQERG4U4yACNV9Zi/Ys0PHTp0YP369dSvX9/pUIwxJldEtVBU3eeoZcuWGhMT43QYWVJV663VGFMgicgqVW2Z1TK7kzof3Hnnnbz55ptOh2GMMZfFEoSfJScnExYWRrly5ZwOxRhjLov15upnJUqU4Msvv3Q6DGOMuWxWgvCjXbt2sWvXLqfDMMaYK2IJwo9eeeUVWrRo4e1/yRhjChOrYvKjt99+mz59+hAaGup0KMYYc9ksQfhRREQEERERTodhjDFXxKqY/CAhIYG+ffuyc+dOp0MxxpgrZgnCD2JjY5k7dy4BAfb2GmMKL6ti8oM777yThIQEa3swxiGpqanEx8fbBSI+QkNDiYiIIDg4ONfbWILIY4cOHeL666+35GCMg+Lj47nmmmuoWbOmdXODu7ufxMRE4uPjiYyMzPV2VgeSh06fPk39+vUZMWKE06EYU6wlJSVRqVIlSw4eIkKlSpUuu0RlJYg8FBgYyMsvv8ytt97qdCjGFHuWHDK6kvfDEkQeKlWqFIMHD3Y6DGOMyRNWxZRHfv75Z2bPnk1R6T7dGHN15syZww033EBUVBRvvfXWRcuTk5N58MEHiYqKonXr1uzevRuAxMREbr31VsqUKcMzzzyTz1FnZAkij/zzn/+0tgdjDOAeYvjpp59m9uzZxMXFMWnSJOLi4jKsM378eCpUqMD27dt59tlnefHFFwH31UajRo3in//8pxOhZ2BVTHlk3rx5HDhwwOo9jSlgBg0aRGxsbJ7us2nTpowZMybb5StWrCAqKopatWoB0Lt3b6ZNm0aDBg2860ybNs37o/L+++/nmWeeQVUpXbo07du3Z/v27Xka85WwEsRVUlVcLhchISHUqFHD6XCMMQVAQkIC1apV805HRESQkJCQ7TpBQUGUK1eOxMTEfI0zJ1aCuEo///wzQ4YMYcaMGZd1fbExJn9c6pe+v2TVFpm5diE36zjNShBXqUSJEtSoUcM65TPGeEVERLBv3z7vdHx8PFWrVs12nbS0NE6ePEnFihXzNc6cWIK4Sl26dGHmzJmXdfu6MaZou+mmm9i2bRu7du0iJSWFyZMn06NHjwzr9OjRwzva5JQpU7jtttuKVwlCRLqJyBYR2S4iL2WxvJ+IHBGRWM/jcZ9l6T7zp/szzis1a9YsUlJSnA7DGFPABAUFMXbsWLp27Ur9+vXp1asXDRs2ZPjw4Uyf7j6d9e/fn8TERKKiovjXv/6V4VLYmjVr8ve//50JEyYQERFx0RVQ+UX8dd2+iAQCW4HbgXhgJdBHVeN81ukHtFTViy72FZEzqlomt8dr2bKlxsTEXHXcubVu3TqaNGnCmDFjGDhwYL4d1xiTs02bNlG/fn2nwyhwsnpfRGSVqrbMan1/NlK3Arar6k5PEJOBewBnUmEeu/HGG5k9ezZt27Z1OhRjjPELf1YxhQP7fKbjPfMy+4OIrBORKSJSzWd+qIjEiMjvInJvVgcQkSc968QcOXIkD0PPmYjQrVs3ypYtm6/HNcaY/OLPBJFVa0vm+qwZQE1VbQwsAL70WVbdU+zpC4wRkdoX7Uz1E1Vtqaotw8LC8iruHL344ouMHTs2345njDFO8GeCiAd8SwQRwH7fFVQ1UVWTPZOfAi18lu33/N0JLAKa+THWXHO5XKxfv75A3OVojDH+5M82iJVAHRGJBBKA3rhLA14iUkVVD3gmewCbPPMrAOdUNVlErgXaAaP9GGuuBQQEMGvWLNLS0pwOxRhj/MpvCUJV00TkGWAuEAh8rqobRWQkEKOq04G/iUgPIA04BvTzbF4f+FhEXLhLOW/5Xv3klBMnTgBQvnx5goLsJnRjTNHm1/sgVHWWqtZV1dqq+rpn3nBPckBVh6hqQ1Vtoqq3qupmz/xlqnqjZ/6Nqjren3Hm1ujRo4mMjPQmCmOMyc6VdvcN8OabbxIVFcUNN9zA3LlzvfMfe+wxrrvuOho1apQfL8HupL4cDz74ICNGjKB8+fJOh2KMKcCuprvvuLg4Jk+ezMaNG5kzZw5/+ctfSE9PB6Bfv37MmTMn316H1ZNchiZNmtCkSROnwzDGXKaOHTteNK9Xr1785S9/4dy5c3Tv3v2i5f369aNfv34cPXqU+++/P8OyRYsWXfJ4V9Pd97Rp0+jduzclSpQgMjKSqKgoVqxYQZs2bejQoUOGkoa/WQkiF86dO8fLL7/M/v37c17ZGFPsXU1337nZNr9YCSIXfv31V0aPHk23bt0u6pHRGFPwXeoXf6lSpS65/Nprr82xxJDZ1XT3XZC6AbcEkQtdunRhz549lhyMMblyOd19R0REZOjuOzfb5herYsrBhd5aw8PDC1xXvMaYgulquvvu0aMHkydPJjk5mV27drFt2zZatWrlxMuwBHEpLpeLli1bMnLkSKdDMcYUIlfT3XfDhg3p1asXDRo0oFu3bowbN47AwEAA+vTpQ5s2bdiyZQsRERGMH+/fOwD81t13fvNHd99nz57llVde4eabb+a+++7L030bY/zHuvvOWkHq7rvQK126NO+9957TYRhjjCOsiikbsbGxrFu3zukwjDHGMZYgsvHKK6/QvXt365TPGFNsWRVTNr7++mu2bt1qnfIZY4otK0Fko0KFCrRu3drpMIwxxjGWIDJZv349Xbt2ZceOHU6HYowxjrIEkcmePXvYtWsXFSpUcDoUY0wh5o/uvrPb59ixY4mKikJEOHr0aN69CFUtEo8WLVpoXnG5XHm2L2NM/ouLi3P0+GlpaVqrVi3dsWOHJicna+PGjXXjxo0Z1hk3bpwOGDBAVVUnTZqkvXr1UlXVjRs3auPGjTUpKUl37typtWrV0rS0tEvuc/Xq1bpr1y6tUaOGHjlyJNu4snpfcA/gluV51UoQPjZt2oSqWpcaxhQxHTt2ZMKECQCkpqbSsWNHvvnmG8DdW3PHjh357rvvADh58iQdO3bkxx9/BODo0aN07NiRGTNmAHDw4MEcj+fb3XdISIi3u29f06ZN45FHHgHc3X1HR0dfsrvvS+2zWbNm1KxZ86rfp8wsQXgcOHCApk2b8tprrzkdijGmkPNHd99OdANu13B6VKpUiQ8//JBbb73V6VCMMXnMt7vu4ODgDNOZu/suV65chunM3X1Xrlw5x+OpH7r7drlcOe4zr1mC8AgJCaF///5Oh2GMKQL81d13fncDblVMuMeGnTRpktNhGGOKCH90952bfeY1vyYIEekmIltEZLuIvJTF8n4ickREYj2Px32WPSIi2zyPR/wVo6ry1VdfWYIwxuQZf3T3nd0+AT744AMiIiKIj4+ncePGPP7449nGdjn81t23iAQCW4HbgXhgJdBHVeN81ukHtFTVZzJtWxGIAVoCCqwCWqjq8eyOdzXdfbtcLk6cOEHFihWvaHtjTMFi3X1n7XK7+/ZnCaIVsF1Vd6pqCjAZuCeX23YF5qvqMU9SmA9081OcBAQEWHIwxphM/JkgwoF9PtPxnnmZ/UFE1onIFBG5cA1Xbrc1xhjjJ/5MEFldf5W5PmsGUFNVGwMLgC8vY1tE5EkRiRGRmCNHjlxVsMaYosVf1eeF1ZW8H/5MEPFANZ/pCGC/7wqqmqiqyZ7JT4EWud3Ws/0nqtpSVVuGhYXlWeDGmMItNDSUxMRESxIeqkpiYiKhoaGXtZ0/74NYCdQRkUggAegN9PVdQUSqqOoBz2QPYJPn+VzgDRG50GNeF2CIH2M1xhQhF67osZqF/wkNDSUiIuKytvFbglDVNBF5BvfJPhD4XFU3ishI3J1DTQf+JiI9gDTgGNDPs+0xERmFO8kAjFTVY/6K1RhTtAQHBxMZGel0GIWe3y5zzW9Xc5mrMcYUV05d5mqMMaYQswRhjDEmS0WmiklEjgB7rmIX1wJ5OBRToY0BLI7MLI6MCkIcBSEGKBpx1FDVLC8DLTIJ4mqJSEx29XDFKQaLw+IoDHEUhBiKQxxWxWSMMSZLliCMMcZkyRLE/3zidAAUjBjA4sjM4sioIMRREGKAIh6HtUEYY4zJkpUgjDHGZMkShDHGmCwV+wSR07Co+RTD5yJyWEQ2OHF8nziqichCEdkkIhtFZKBDcYSKyAoRWeuJ41Un4vDEEigia0TkZwdj2C0i6z3D8jrWn4yIlPeM27LZ8xlp40AMN/gMURwrIqdEZFB+x+GJ5VnP53ODiEwSkcvrKjVvYhjoOf5Gf7wPxboNIjfDouZTHB2AM8BXqtooP4+dKY4qQBVVXS0i1+Ae6vVeB94PAUqr6hkRCQZ+BQaq6u/5GYcnlr/jHvq2rKreld/H98SwG/fQvbhoKwAABMlJREFUvI7ekCUiXwJLVPUzEQkBSqnqCQfjCcTdU3RrVb2am2Sv5NjhuD+XDVT1vIh8D8xS1Qn5GEMj3CN1tgJSgDnAn1V1W14do7iXIK5mWNQ8o6q/4O7N1lGqekBVV3uen8bd/Xq+j+Snbmc8k8GeR77/khGRCOBO4LP8PnZBIyJlgQ7AeABVTXEyOXh0Anbkd3LwEQSUFJEgoBRZjFnjZ/WB31X1nKqmAYuBnnl5gOKeIGxo02yISE2gGbDcoeMHikgscBj3+OROxDEGeAFwOXBsXwrME5FVIvKkQzHUAo4AX3iq3D4TkdIOxXJBb2CSEwdW1QTgn8Be4ABwUlXn5XMYG4AOIlJJREoB3ck40NpVK+4JIldDmxY3IlIG+A8wSFVPORGDqqaralPcowm28hSn842I3AUcVtVV+XncbLRT1ebAHcDTnirJ/BYENAc+UtVm8P/bu38QO6oojuPfn3+QNSJKEoMS4goGC0FBRERBJFHRJoUWMaiFWEgQtRIxTRqbNCmCNsoKiquiSRYsJCgRbZRYRKJFrKLoEqMJKKJIWMLP4t6Nj3UeZMPs3OD7fZqZvbu8OW/Zt+fOmZl7+Atocs0OoJa4tgAfNDr+1ZRqww3AdcAqSY8PGYPto8Au4BNKeekIpbdObyY9QZxTa9NJUmv++4BZ2/tbx1PLGJ8BDw586LuBLbX+/x6wSdLbA8cAgO3jdfsrMEcpjQ5tHpgfOZPbS0kYrTwEHLb9S6Pj3wd8b/uk7QVgP3DX0EHYnrF9m+17KGXq3q4/QBLE2baodUbyKPBh45iaqReHZ4Cjtnc3jGOtpKvq/hTlw/jdkDHYfsn2etvTlL+LT20POkMEkLSq3jBALek8QCktDMr2CeAnSTfVoc3AoDcvLLGNRuWl6kfgTkmX18/NZv5tmTwYSdfU7QbgYXr+naxkT+oL3ri2qEPHIeld4F5gjaR5YKftmaHjoMyanwC+rfV/gB22Pxo4jmuBN+tdKhcB79tudptpY+uAufI/iEuAd2wfaBTLs8BsnUwdA55sEUStt98PPN3i+AC2D0naCxymlHW+ps2yG/skrQYWgGds/9bni0/0ba4RETHepJeYIiJijCSIiIjolAQRERGdkiAiIqJTEkRERHRKgohYBklnlqwm2tvTxJKmW6/oGzFqop+DiDgPf9clQCL+93IGEdGD2rNhV+1j8ZWkG+v49ZIOSvqmbjfU8XWS5mrPiyOSFpdpuFjS63V9/4/rk+QRTSRBRCzP1JIS09aR7/1h+w7gFcpKsNT9t2zfAswCe+r4HuBz27dS1jRafIJ/I/Cq7ZuB34FHVvj9RIyVJ6kjlkHSn7av6Bj/Adhk+1hd8PCE7dWSTlGaMC3U8Z9tr5F0Elhv+/TIa0xTljbfWL9+EbjU9ssr/84i/itnEBH98Zj9cT/T5fTI/hlynTAaSoKI6M/Wke2Xdf8LymqwAI9R2lQCHAS2w9nmSFcOFWTEucrsJGJ5pkZWugU4YHvxVtfLJB2iTLy21bHngDckvUDpyLa4AurzwGuSnqKcKWyndCaLuGDkGkRED+o1iNttn2odS0RfUmKKiIhOOYOIiIhOOYOIiIhOSRAREdEpCSIiIjolQURERKckiIiI6PQPx0aMu/IKNiwAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "plt.plot(epochs, val_0_01, color='black', linestyle='solid', label='0.01')\n", + "plt.plot(epochs, val_0_001, color='black', linestyle='dashed', label='0.001')\n", + "plt.plot(epochs, val_0_0001, color='black', linestyle='dotted', label='0.0001')\n", + "\n", + "\n", + "plt.title('Xception v1, different learning rates')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "plt.xticks(epochs)\n", + "\n", + "plt.legend()\n", + "\n", + "plt.savefig('xception_v1_all_lr.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The best models:\n", + "\n", + "* learning rate 0.01: 0.8270\n", + "* learning rate 0.001: 0.8299\n", + "* learning rate 0.0001: 0.7801\n", + "\n", + "(your results may be slightly different)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To save the best model, we can use a callback. It'll monitor the accuracy, and if it's an improvement over the previous version, it'll save the model to disk" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "model = make_model(learning_rate=0.001)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/10\n", + "96/96 [==============================] - 19s 199ms/step - loss: 1.0845 - accuracy: 0.6340 - val_loss: 0.7188 - val_accuracy: 0.7654\n", + "Epoch 2/10\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.6271 - accuracy: 0.7816 - val_loss: 0.6328 - val_accuracy: 0.7889\n", + "Epoch 3/10\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.4964 - accuracy: 0.8413 - val_loss: 0.5812 - val_accuracy: 0.8094\n", + "Epoch 4/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.4198 - accuracy: 0.8621 - val_loss: 0.5962 - val_accuracy: 0.7889\n", + "Epoch 5/10\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.3713 - accuracy: 0.8840 - val_loss: 0.5565 - val_accuracy: 0.8065\n", + "Epoch 6/10\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.3251 - accuracy: 0.9061 - val_loss: 0.5585 - val_accuracy: 0.8299\n", + "Epoch 7/10\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2868 - accuracy: 0.9228 - val_loss: 0.5488 - val_accuracy: 0.8182\n", + "Epoch 8/10\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2565 - accuracy: 0.9289 - val_loss: 0.5592 - val_accuracy: 0.8065\n", + "Epoch 9/10\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.2290 - accuracy: 0.9426 - val_loss: 0.5506 - val_accuracy: 0.8270\n", + "Epoch 10/10\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.2070 - accuracy: 0.9531 - val_loss: 0.5404 - val_accuracy: 0.8211\n" + ] + } + ], + "source": [ + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v1_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_0_001 = model.fit(train_ds, epochs=10, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's add one more layer - and a dropout between them" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "def make_model(learning_rate, droprate):\n", + " base_model = Xception(\n", + " weights='imagenet',\n", + " input_shape=(150, 150, 3),\n", + " include_top=False\n", + " )\n", + "\n", + " base_model.trainable = False\n", + "\n", + " inputs = keras.Input(shape=(150, 150, 3))\n", + " x = base_model(inputs, training=False)\n", + " x = keras.layers.GlobalAveragePooling2D()(x)\n", + "\n", + " x = keras.layers.Dense(100, activation='relu')(x)\n", + " x = keras.layers.Dropout(droprate)(x)\n", + "\n", + " outputs = keras.layers.Dense(10)(x)\n", + "\n", + " model = keras.Model(inputs, outputs)\n", + " \n", + " model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate),\n", + " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", + " metrics=[\"accuracy\"],\n", + " )\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/30\n", + "96/96 [==============================] - 19s 199ms/step - loss: 0.9504 - accuracy: 0.6744 - val_loss: 0.6143 - val_accuracy: 0.8006\n", + "Epoch 2/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.4989 - accuracy: 0.8217 - val_loss: 0.5565 - val_accuracy: 0.8152\n", + "Epoch 3/30\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.3445 - accuracy: 0.8859 - val_loss: 0.6129 - val_accuracy: 0.7889\n", + "Epoch 4/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2387 - accuracy: 0.9270 - val_loss: 0.5456 - val_accuracy: 0.8123\n", + "Epoch 5/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.1673 - accuracy: 0.9527 - val_loss: 0.5837 - val_accuracy: 0.8065\n", + "Epoch 6/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.1140 - accuracy: 0.9746 - val_loss: 0.5879 - val_accuracy: 0.8152\n", + "Epoch 7/30\n", + "96/96 [==============================] - 16s 170ms/step - loss: 0.0751 - accuracy: 0.9896 - val_loss: 0.5719 - val_accuracy: 0.8416\n", + "Epoch 8/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0517 - accuracy: 0.9951 - val_loss: 0.5812 - val_accuracy: 0.8416\n", + "Epoch 9/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0351 - accuracy: 0.9987 - val_loss: 0.6009 - val_accuracy: 0.8358\n", + "Epoch 10/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0284 - accuracy: 0.9984 - val_loss: 0.6135 - val_accuracy: 0.8182\n", + "Epoch 11/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0224 - accuracy: 0.9993 - val_loss: 0.6396 - val_accuracy: 0.8299\n", + "Epoch 12/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0169 - accuracy: 0.9997 - val_loss: 0.6189 - val_accuracy: 0.8299\n", + "Epoch 13/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0139 - accuracy: 0.9997 - val_loss: 0.6383 - val_accuracy: 0.8211\n", + "Epoch 14/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0113 - accuracy: 0.9997 - val_loss: 0.6285 - val_accuracy: 0.8416\n", + "Epoch 15/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0102 - accuracy: 0.9997 - val_loss: 0.6652 - val_accuracy: 0.8240\n", + "Epoch 16/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0101 - accuracy: 0.9993 - val_loss: 0.6573 - val_accuracy: 0.8416\n", + "Epoch 17/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0099 - accuracy: 0.9993 - val_loss: 0.6710 - val_accuracy: 0.8328\n", + "Epoch 18/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0076 - accuracy: 0.9997 - val_loss: 0.6712 - val_accuracy: 0.8387\n", + "Epoch 19/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0102 - accuracy: 0.9990 - val_loss: 0.6721 - val_accuracy: 0.8387\n", + "Epoch 20/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0067 - accuracy: 0.9997 - val_loss: 0.6818 - val_accuracy: 0.8358\n", + "Epoch 21/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0098 - accuracy: 0.9993 - val_loss: 0.7061 - val_accuracy: 0.8328\n", + "Epoch 22/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0056 - accuracy: 0.9987 - val_loss: 0.7464 - val_accuracy: 0.8152\n", + "Epoch 23/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0170 - accuracy: 0.9971 - val_loss: 0.7673 - val_accuracy: 0.8299\n", + "Epoch 24/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0109 - accuracy: 0.9974 - val_loss: 0.7242 - val_accuracy: 0.8182\n", + "Epoch 25/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0067 - accuracy: 0.9997 - val_loss: 0.7275 - val_accuracy: 0.8211\n", + "Epoch 26/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0091 - accuracy: 0.9990 - val_loss: 0.7288 - val_accuracy: 0.8328\n", + "Epoch 27/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0082 - accuracy: 0.9987 - val_loss: 0.7165 - val_accuracy: 0.8416\n", + "Epoch 28/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.0028 - accuracy: 0.9997 - val_loss: 0.8201 - val_accuracy: 0.8211\n", + "Epoch 29/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0068 - accuracy: 0.9993 - val_loss: 0.8009 - val_accuracy: 0.8270\n", + "Epoch 30/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0093 - accuracy: 0.9984 - val_loss: 0.7504 - val_accuracy: 0.8358\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.0)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v2_0_0_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_0 = model.fit(train_ds, epochs=30, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/30\n", + "96/96 [==============================] - 19s 201ms/step - loss: 1.0704 - accuracy: 0.6408 - val_loss: 0.7279 - val_accuracy: 0.7830\n", + "Epoch 2/30\n", + "96/96 [==============================] - 16s 164ms/step - loss: 0.6188 - accuracy: 0.7846 - val_loss: 0.6079 - val_accuracy: 0.7830\n", + "Epoch 3/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.4696 - accuracy: 0.8357 - val_loss: 0.5803 - val_accuracy: 0.8182\n", + "Epoch 4/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.3710 - accuracy: 0.8732 - val_loss: 0.5724 - val_accuracy: 0.8416\n", + "Epoch 5/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.2964 - accuracy: 0.9009 - val_loss: 0.5414 - val_accuracy: 0.8065\n", + "Epoch 6/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2257 - accuracy: 0.9283 - val_loss: 0.5795 - val_accuracy: 0.8182\n", + "Epoch 7/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.1901 - accuracy: 0.9413 - val_loss: 0.6024 - val_accuracy: 0.8299\n", + "Epoch 8/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.1505 - accuracy: 0.9612 - val_loss: 0.6228 - val_accuracy: 0.8211\n", + "Epoch 9/30\n", + "96/96 [==============================] - 16s 165ms/step - loss: 0.1110 - accuracy: 0.9703 - val_loss: 0.6053 - val_accuracy: 0.8270\n", + "Epoch 10/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0950 - accuracy: 0.9775 - val_loss: 0.6034 - val_accuracy: 0.8270\n", + "Epoch 11/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0778 - accuracy: 0.9817 - val_loss: 0.6340 - val_accuracy: 0.8211\n", + "Epoch 12/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0689 - accuracy: 0.9831 - val_loss: 0.6314 - val_accuracy: 0.8065\n", + "Epoch 13/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0588 - accuracy: 0.9879 - val_loss: 0.6812 - val_accuracy: 0.8387\n", + "Epoch 14/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0459 - accuracy: 0.9919 - val_loss: 0.6525 - val_accuracy: 0.8270\n", + "Epoch 15/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0399 - accuracy: 0.9915 - val_loss: 0.6679 - val_accuracy: 0.8152\n", + "Epoch 16/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0373 - accuracy: 0.9925 - val_loss: 0.6455 - val_accuracy: 0.8328\n", + "Epoch 17/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0338 - accuracy: 0.9945 - val_loss: 0.6521 - val_accuracy: 0.8182\n", + "Epoch 18/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0271 - accuracy: 0.9958 - val_loss: 0.6720 - val_accuracy: 0.8358\n", + "Epoch 19/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0300 - accuracy: 0.9932 - val_loss: 0.6995 - val_accuracy: 0.8416\n", + "Epoch 20/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0308 - accuracy: 0.9919 - val_loss: 0.7371 - val_accuracy: 0.8211\n", + "Epoch 21/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0242 - accuracy: 0.9974 - val_loss: 0.7175 - val_accuracy: 0.8299\n", + "Epoch 22/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.0229 - accuracy: 0.9961 - val_loss: 0.6631 - val_accuracy: 0.8240\n", + "Epoch 23/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0254 - accuracy: 0.9938 - val_loss: 0.7019 - val_accuracy: 0.8211\n", + "Epoch 24/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0195 - accuracy: 0.9967 - val_loss: 0.7576 - val_accuracy: 0.8299\n", + "Epoch 25/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0206 - accuracy: 0.9971 - val_loss: 0.7051 - val_accuracy: 0.8299\n", + "Epoch 26/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0132 - accuracy: 0.9990 - val_loss: 0.7772 - val_accuracy: 0.8270\n", + "Epoch 27/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0152 - accuracy: 0.9977 - val_loss: 0.7688 - val_accuracy: 0.8240\n", + "Epoch 28/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0207 - accuracy: 0.9948 - val_loss: 0.7634 - val_accuracy: 0.8328\n", + "Epoch 29/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0258 - accuracy: 0.9938 - val_loss: 0.8014 - val_accuracy: 0.8035\n", + "Epoch 30/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0368 - accuracy: 0.9889 - val_loss: 0.7548 - val_accuracy: 0.8240\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.2)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v2_0_2_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_1 = model.fit(train_ds, epochs=30, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/30\n", + "96/96 [==============================] - 20s 207ms/step - loss: 1.2893 - accuracy: 0.5717 - val_loss: 0.7348 - val_accuracy: 0.7859\n", + "Epoch 2/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.8462 - accuracy: 0.7099 - val_loss: 0.6297 - val_accuracy: 0.8094\n", + "Epoch 3/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6839 - accuracy: 0.7585 - val_loss: 0.5952 - val_accuracy: 0.8094\n", + "Epoch 4/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.6017 - accuracy: 0.7898 - val_loss: 0.5717 - val_accuracy: 0.8152\n", + "Epoch 5/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.5271 - accuracy: 0.8123 - val_loss: 0.5566 - val_accuracy: 0.8065\n", + "Epoch 6/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.4703 - accuracy: 0.8338 - val_loss: 0.5297 - val_accuracy: 0.8270\n", + "Epoch 7/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.3811 - accuracy: 0.8732 - val_loss: 0.5545 - val_accuracy: 0.8211\n", + "Epoch 8/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.3688 - accuracy: 0.8752 - val_loss: 0.5263 - val_accuracy: 0.8240\n", + "Epoch 9/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.3110 - accuracy: 0.8950 - val_loss: 0.5617 - val_accuracy: 0.8328\n", + "Epoch 10/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.3031 - accuracy: 0.8963 - val_loss: 0.5686 - val_accuracy: 0.8211\n", + "Epoch 11/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2623 - accuracy: 0.9104 - val_loss: 0.5528 - val_accuracy: 0.8270\n", + "Epoch 12/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2410 - accuracy: 0.9159 - val_loss: 0.5476 - val_accuracy: 0.8299\n", + "Epoch 13/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2189 - accuracy: 0.9250 - val_loss: 0.5531 - val_accuracy: 0.8270\n", + "Epoch 14/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2125 - accuracy: 0.9302 - val_loss: 0.6035 - val_accuracy: 0.8299\n", + "Epoch 15/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.2091 - accuracy: 0.9257 - val_loss: 0.6291 - val_accuracy: 0.8270\n", + "Epoch 16/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.1880 - accuracy: 0.9355 - val_loss: 0.5961 - val_accuracy: 0.8240\n", + "Epoch 17/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.1720 - accuracy: 0.9400 - val_loss: 0.5699 - val_accuracy: 0.8328\n", + "Epoch 18/30\n", + "96/96 [==============================] - 16s 170ms/step - loss: 0.1609 - accuracy: 0.9423 - val_loss: 0.6102 - val_accuracy: 0.8416\n", + "Epoch 19/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.1435 - accuracy: 0.9550 - val_loss: 0.6321 - val_accuracy: 0.8152\n", + "Epoch 20/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.1396 - accuracy: 0.9518 - val_loss: 0.6171 - val_accuracy: 0.8328\n", + "Epoch 21/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.1311 - accuracy: 0.9560 - val_loss: 0.6067 - val_accuracy: 0.8416\n", + "Epoch 22/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.1231 - accuracy: 0.9596 - val_loss: 0.6105 - val_accuracy: 0.8387\n", + "Epoch 23/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.1206 - accuracy: 0.9583 - val_loss: 0.6297 - val_accuracy: 0.8211\n", + "Epoch 24/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.1056 - accuracy: 0.9664 - val_loss: 0.6449 - val_accuracy: 0.8270\n", + "Epoch 25/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 0.1087 - accuracy: 0.9658 - val_loss: 0.6766 - val_accuracy: 0.8299\n", + "Epoch 26/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0997 - accuracy: 0.9648 - val_loss: 0.6554 - val_accuracy: 0.8387\n", + "Epoch 27/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0930 - accuracy: 0.9690 - val_loss: 0.6971 - val_accuracy: 0.8358\n", + "Epoch 28/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.0955 - accuracy: 0.9651 - val_loss: 0.6436 - val_accuracy: 0.8446\n", + "Epoch 29/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.0929 - accuracy: 0.9736 - val_loss: 0.7145 - val_accuracy: 0.8358\n", + "Epoch 30/30\n", + "96/96 [==============================] - 16s 167ms/step - loss: 0.0761 - accuracy: 0.9752 - val_loss: 0.7174 - val_accuracy: 0.8299\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.5)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v2_0_5_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_2 = model.fit(train_ds, epochs=30, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n", + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/30\n", + "96/96 [==============================] - 20s 206ms/step - loss: 1.7922 - accuracy: 0.3999 - val_loss: 1.1657 - val_accuracy: 0.6510\n", + "Epoch 2/30\n", + "96/96 [==============================] - 16s 171ms/step - loss: 1.4048 - accuracy: 0.5130 - val_loss: 0.9569 - val_accuracy: 0.7302\n", + "Epoch 3/30\n", + "96/96 [==============================] - 16s 171ms/step - loss: 1.2486 - accuracy: 0.5629 - val_loss: 0.8736 - val_accuracy: 0.7566\n", + "Epoch 4/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 1.1612 - accuracy: 0.5870 - val_loss: 0.7757 - val_accuracy: 0.7537\n", + "Epoch 5/30\n", + "96/96 [==============================] - 16s 171ms/step - loss: 1.0790 - accuracy: 0.6053 - val_loss: 0.7731 - val_accuracy: 0.7742\n", + "Epoch 6/30\n", + "96/96 [==============================] - 16s 168ms/step - loss: 1.0593 - accuracy: 0.6268 - val_loss: 0.7370 - val_accuracy: 0.7683\n", + "Epoch 7/30\n", + "96/96 [==============================] - 16s 171ms/step - loss: 0.9958 - accuracy: 0.6333 - val_loss: 0.7164 - val_accuracy: 0.7771\n", + "Epoch 8/30\n", + "96/96 [==============================] - 16s 170ms/step - loss: 0.9602 - accuracy: 0.6493 - val_loss: 0.6757 - val_accuracy: 0.7830\n", + "Epoch 9/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.9615 - accuracy: 0.6441 - val_loss: 0.6642 - val_accuracy: 0.8006\n", + "Epoch 10/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.9229 - accuracy: 0.6535 - val_loss: 0.6841 - val_accuracy: 0.7801\n", + "Epoch 11/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.9175 - accuracy: 0.6503 - val_loss: 0.6299 - val_accuracy: 0.7977\n", + "Epoch 12/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.8785 - accuracy: 0.6679 - val_loss: 0.6358 - val_accuracy: 0.7947\n", + "Epoch 13/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.8635 - accuracy: 0.6786 - val_loss: 0.6512 - val_accuracy: 0.7947\n", + "Epoch 14/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.8354 - accuracy: 0.6780 - val_loss: 0.6116 - val_accuracy: 0.8065\n", + "Epoch 15/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.8125 - accuracy: 0.6887 - val_loss: 0.6488 - val_accuracy: 0.7977\n", + "Epoch 16/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.8038 - accuracy: 0.6965 - val_loss: 0.6214 - val_accuracy: 0.8006\n", + "Epoch 17/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7919 - accuracy: 0.7001 - val_loss: 0.6080 - val_accuracy: 0.8035\n", + "Epoch 18/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7787 - accuracy: 0.6992 - val_loss: 0.6097 - val_accuracy: 0.7859\n", + "Epoch 19/30\n", + "96/96 [==============================] - 16s 169ms/step - loss: 0.7512 - accuracy: 0.7093 - val_loss: 0.5995 - val_accuracy: 0.8123\n", + "Epoch 20/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7237 - accuracy: 0.7278 - val_loss: 0.5993 - val_accuracy: 0.8094\n", + "Epoch 21/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7380 - accuracy: 0.7184 - val_loss: 0.6108 - val_accuracy: 0.8006\n", + "Epoch 22/30\n", + "96/96 [==============================] - 16s 170ms/step - loss: 0.7150 - accuracy: 0.7265 - val_loss: 0.5720 - val_accuracy: 0.8240\n", + "Epoch 23/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7251 - accuracy: 0.7210 - val_loss: 0.5948 - val_accuracy: 0.8065\n", + "Epoch 24/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.7179 - accuracy: 0.7115 - val_loss: 0.5994 - val_accuracy: 0.8094\n", + "Epoch 25/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6883 - accuracy: 0.7317 - val_loss: 0.5730 - val_accuracy: 0.8211\n", + "Epoch 26/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6807 - accuracy: 0.7464 - val_loss: 0.5942 - val_accuracy: 0.8152\n", + "Epoch 27/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6469 - accuracy: 0.7428 - val_loss: 0.5732 - val_accuracy: 0.8240\n", + "Epoch 28/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6379 - accuracy: 0.7513 - val_loss: 0.5787 - val_accuracy: 0.8094\n", + "Epoch 29/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6426 - accuracy: 0.7480 - val_loss: 0.5866 - val_accuracy: 0.8211\n", + "Epoch 30/30\n", + "96/96 [==============================] - 16s 166ms/step - loss: 0.6414 - accuracy: 0.7438 - val_loss: 0.5989 - val_accuracy: 0.8152\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.8)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v2_0_8_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_3 = model.fit(train_ds, epochs=30, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = history_0.epoch\n", + "\n", + "train00 = history_0.history['accuracy']\n", + "train02 = history_1.history['accuracy']\n", + "train05 = history_2.history['accuracy']\n", + "train08 = history_3.history['accuracy']\n", + "\n", + "val00 = history_0.history['val_accuracy']\n", + "val02 = history_1.history['val_accuracy']\n", + "val05 = history_2.history['val_accuracy']\n", + "val08 = history_3.history['val_accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "plt.plot(epochs, val00, color='black', linestyle='dashed', label='0.0')\n", + "plt.plot(epochs, val02, color='grey', linestyle='dashed', label='0.2')\n", + "plt.plot(epochs, val05, color='black', linestyle='solid', label='0.5')\n", + "plt.plot(epochs, val08, color='grey', linestyle='solid', label='0.8')\n", + "\n", + "\n", + "plt.title('Xception v2, different dropout rates')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "\n", + "plt.legend()\n", + "\n", + "plt.savefig('xception_v2_dropout.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "plt.plot(epochs, train00, color='black', linestyle='dashed', label='0.0')\n", + "plt.plot(epochs, train02, color='grey', linestyle='dashed', label='0.2')\n", + "plt.plot(epochs, train05, color='black', linestyle='solid', label='0.5')\n", + "plt.plot(epochs, train08, color='grey', linestyle='solid', label='0.8')\n", + "\n", + "\n", + "plt.title('Xception v2, different dropout rates (train)')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "\n", + "plt.legend()\n", + "\n", + "plt.savefig('xception_v2_dropout_train.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data augmentation" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 341 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "validation_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "val_ds = validation_gen.flow_from_directory(\n", + " \"clothing-dataset-small/validation\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3068 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "train_gen = ImageDataGenerator(\n", + " preprocessing_function=preprocess_input,\n", + " shear_range=10.0,\n", + " zoom_range=0.1,\n", + " horizontal_flip=True, \n", + ")\n", + "\n", + "train_ds = train_gen.flow_from_directory(\n", + " \"clothing-dataset-small/train\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/50\n", + "96/96 [==============================] - 31s 319ms/step - loss: 1.1175 - accuracy: 0.6258 - val_loss: 0.6942 - val_accuracy: 0.7801\n", + "Epoch 2/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.7214 - accuracy: 0.7523 - val_loss: 0.6231 - val_accuracy: 0.7801\n", + "Epoch 3/50\n", + "96/96 [==============================] - 27s 280ms/step - loss: 0.5948 - accuracy: 0.8005 - val_loss: 0.5920 - val_accuracy: 0.7771\n", + "Epoch 4/50\n", + "96/96 [==============================] - 28s 292ms/step - loss: 0.5141 - accuracy: 0.8201 - val_loss: 0.5654 - val_accuracy: 0.8270\n", + "Epoch 5/50\n", + "96/96 [==============================] - 27s 280ms/step - loss: 0.4627 - accuracy: 0.8413 - val_loss: 0.5753 - val_accuracy: 0.8152\n", + "Epoch 6/50\n", + "96/96 [==============================] - 27s 280ms/step - loss: 0.4150 - accuracy: 0.8566 - val_loss: 0.6029 - val_accuracy: 0.8123\n", + "Epoch 7/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.3859 - accuracy: 0.8634 - val_loss: 0.5682 - val_accuracy: 0.8035\n", + "Epoch 8/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.3417 - accuracy: 0.8774 - val_loss: 0.6137 - val_accuracy: 0.8123\n", + "Epoch 9/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.3217 - accuracy: 0.8895 - val_loss: 0.5711 - val_accuracy: 0.8387\n", + "Epoch 10/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.3029 - accuracy: 0.9006 - val_loss: 0.5400 - val_accuracy: 0.8240\n", + "Epoch 11/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.2855 - accuracy: 0.8977 - val_loss: 0.5888 - val_accuracy: 0.8211\n", + "Epoch 12/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.2612 - accuracy: 0.9058 - val_loss: 0.6612 - val_accuracy: 0.7889\n", + "Epoch 13/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.2522 - accuracy: 0.9140 - val_loss: 0.5607 - val_accuracy: 0.8387\n", + "Epoch 14/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.2228 - accuracy: 0.9241 - val_loss: 0.5477 - val_accuracy: 0.8299\n", + "Epoch 15/50\n", + "96/96 [==============================] - 28s 286ms/step - loss: 0.2085 - accuracy: 0.9244 - val_loss: 0.5794 - val_accuracy: 0.8270\n", + "Epoch 16/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.2153 - accuracy: 0.9273 - val_loss: 0.6300 - val_accuracy: 0.8065\n", + "Epoch 17/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.1936 - accuracy: 0.9358 - val_loss: 0.6327 - val_accuracy: 0.8270\n", + "Epoch 18/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.1889 - accuracy: 0.9374 - val_loss: 0.6121 - val_accuracy: 0.8358\n", + "Epoch 19/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1713 - accuracy: 0.9423 - val_loss: 0.6349 - val_accuracy: 0.8299\n", + "Epoch 20/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1540 - accuracy: 0.9498 - val_loss: 0.6182 - val_accuracy: 0.8270\n", + "Epoch 21/50\n", + "96/96 [==============================] - 27s 286ms/step - loss: 0.1686 - accuracy: 0.9397 - val_loss: 0.6139 - val_accuracy: 0.8299\n", + "Epoch 22/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1620 - accuracy: 0.9433 - val_loss: 0.6559 - val_accuracy: 0.8270\n", + "Epoch 23/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1434 - accuracy: 0.9531 - val_loss: 0.7091 - val_accuracy: 0.8211\n", + "Epoch 24/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.1519 - accuracy: 0.9498 - val_loss: 0.6978 - val_accuracy: 0.8006\n", + "Epoch 25/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.1349 - accuracy: 0.9563 - val_loss: 0.6342 - val_accuracy: 0.8182\n", + "Epoch 26/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.1440 - accuracy: 0.9501 - val_loss: 0.6735 - val_accuracy: 0.8328\n", + "Epoch 27/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.1172 - accuracy: 0.9593 - val_loss: 0.6674 - val_accuracy: 0.8270\n", + "Epoch 28/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.1200 - accuracy: 0.9580 - val_loss: 0.6975 - val_accuracy: 0.8065\n", + "Epoch 29/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.1130 - accuracy: 0.9606 - val_loss: 0.6994 - val_accuracy: 0.8211\n", + "Epoch 30/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.1188 - accuracy: 0.9628 - val_loss: 0.7246 - val_accuracy: 0.8240\n", + "Epoch 31/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.0970 - accuracy: 0.9713 - val_loss: 0.6728 - val_accuracy: 0.8240\n", + "Epoch 32/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0997 - accuracy: 0.9684 - val_loss: 0.6742 - val_accuracy: 0.8328\n", + "Epoch 33/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1186 - accuracy: 0.9586 - val_loss: 0.6597 - val_accuracy: 0.8299\n", + "Epoch 34/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1012 - accuracy: 0.9645 - val_loss: 0.6816 - val_accuracy: 0.8211\n", + "Epoch 35/50\n", + "96/96 [==============================] - 28s 287ms/step - loss: 0.1175 - accuracy: 0.9602 - val_loss: 0.7728 - val_accuracy: 0.8065\n", + "Epoch 36/50\n", + "96/96 [==============================] - 28s 287ms/step - loss: 0.1097 - accuracy: 0.9596 - val_loss: 0.6658 - val_accuracy: 0.8416\n", + "Epoch 37/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.1138 - accuracy: 0.9589 - val_loss: 0.6562 - val_accuracy: 0.8358\n", + "Epoch 38/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0933 - accuracy: 0.9703 - val_loss: 0.6518 - val_accuracy: 0.8299\n", + "Epoch 39/50\n", + "96/96 [==============================] - 27s 284ms/step - loss: 0.1034 - accuracy: 0.9622 - val_loss: 0.7501 - val_accuracy: 0.8211\n", + "Epoch 40/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.0894 - accuracy: 0.9710 - val_loss: 0.7743 - val_accuracy: 0.8270\n", + "Epoch 41/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.0894 - accuracy: 0.9707 - val_loss: 0.7336 - val_accuracy: 0.8270\n", + "Epoch 42/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0761 - accuracy: 0.9756 - val_loss: 0.7068 - val_accuracy: 0.8270\n", + "Epoch 43/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.1091 - accuracy: 0.9625 - val_loss: 0.6398 - val_accuracy: 0.8299\n", + "Epoch 44/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.0840 - accuracy: 0.9743 - val_loss: 0.6789 - val_accuracy: 0.8534\n", + "Epoch 45/50\n", + "96/96 [==============================] - 27s 281ms/step - loss: 0.0949 - accuracy: 0.9651 - val_loss: 0.8494 - val_accuracy: 0.8328\n", + "Epoch 46/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0967 - accuracy: 0.9694 - val_loss: 0.8373 - val_accuracy: 0.8182\n", + "Epoch 47/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0840 - accuracy: 0.9700 - val_loss: 0.7499 - val_accuracy: 0.8240\n", + "Epoch 48/50\n", + "96/96 [==============================] - 27s 285ms/step - loss: 0.0772 - accuracy: 0.9746 - val_loss: 0.8410 - val_accuracy: 0.8006\n", + "Epoch 49/50\n", + "96/96 [==============================] - 27s 282ms/step - loss: 0.0723 - accuracy: 0.9775 - val_loss: 0.7202 - val_accuracy: 0.8387\n", + "Epoch 50/50\n", + "96/96 [==============================] - 27s 283ms/step - loss: 0.0807 - accuracy: 0.9733 - val_loss: 0.7050 - val_accuracy: 0.8211\n" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.2)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v3_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history = model.fit(train_ds, epochs=50, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "epochs = history.epoch\n", + "accuracy = history.history['val_accuracy']" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(6, 4))\n", + "\n", + "plt.plot(epochs, accuracy, color='black', linestyle='solid')\n", + "\n", + "\n", + "plt.title('Xception v3, augmentations')\n", + "plt.xlabel('Epoch')\n", + "plt.ylabel('Accuracy')\n", + "\n", + "plt.savefig('xception_v3_aug.svg')\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Larger model" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "def make_model(learning_rate, droprate):\n", + " base_model = Xception(\n", + " weights='imagenet',\n", + " input_shape=(299, 299, 3),\n", + " include_top=False\n", + " )\n", + "\n", + " base_model.trainable = False\n", + "\n", + " inputs = keras.Input(shape=(299, 299, 3))\n", + " x = base_model(inputs, training=False)\n", + " x = keras.layers.GlobalAveragePooling2D()(x)\n", + "\n", + " x = keras.layers.Dense(100, activation='relu')(x)\n", + " x = keras.layers.Dropout(droprate)(x)\n", + "\n", + " outputs = keras.layers.Dense(10)(x)\n", + "\n", + " model = keras.Model(inputs, outputs)\n", + " \n", + " model.compile(\n", + " optimizer=keras.optimizers.Adam(learning_rate),\n", + " loss=keras.losses.CategoricalCrossentropy(from_logits=True),\n", + " metrics=[\"accuracy\"],\n", + " )\n", + " \n", + " return model" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "image_size = (299, 299)\n", + "batch_size = 32" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 3068 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "train_gen = ImageDataGenerator(\n", + " preprocessing_function=preprocess_input,\n", + " shear_range=10.0,\n", + " zoom_range=0.1,\n", + " horizontal_flip=True, \n", + ")\n", + "\n", + "train_ds = train_gen.flow_from_directory(\n", + " \"clothing-dataset-small/train\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Found 341 images belonging to 10 classes.\n" + ] + } + ], + "source": [ + "validation_gen = ImageDataGenerator(preprocessing_function=preprocess_input)\n", + "\n", + "val_ds = validation_gen.flow_from_directory(\n", + " \"clothing-dataset-small/validation\",\n", + " seed=1,\n", + " target_size=image_size,\n", + " batch_size=batch_size,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Train for 96 steps, validate for 11 steps\n", + "Epoch 1/20\n", + "96/96 [==============================] - 78s 815ms/step - loss: 0.8443 - accuracy: 0.7272 - val_loss: 0.4424 - val_accuracy: 0.8592\n", + "Epoch 2/20\n", + "96/96 [==============================] - 75s 785ms/step - loss: 0.4331 - accuracy: 0.8455 - val_loss: 0.3982 - val_accuracy: 0.8710\n", + "Epoch 3/20\n", + "96/96 [==============================] - 75s 785ms/step - loss: 0.3875 - accuracy: 0.8651 - val_loss: 0.3825 - val_accuracy: 0.8739\n", + "Epoch 4/20\n", + "96/96 [==============================] - 76s 788ms/step - loss: 0.3356 - accuracy: 0.8774 - val_loss: 0.3828 - val_accuracy: 0.8680\n", + "Epoch 5/20\n", + "96/96 [==============================] - 75s 785ms/step - loss: 0.2973 - accuracy: 0.8980 - val_loss: 0.3681 - val_accuracy: 0.8886\n", + "Epoch 6/20\n", + "96/96 [==============================] - 75s 781ms/step - loss: 0.2736 - accuracy: 0.9038 - val_loss: 0.3665 - val_accuracy: 0.8856\n", + "Epoch 7/20\n", + "63/96 [==================>...........] - ETA: 24s - loss: 0.2177 - accuracy: 0.9245" + ] + } + ], + "source": [ + "model = make_model(learning_rate=0.001, droprate=0.2)\n", + "\n", + "callbacks = [\n", + " keras.callbacks.ModelCheckpoint(\n", + " \"xception_v4_large_{epoch:02d}_{val_accuracy:.3f}.h5\",\n", + " monitor=\"val_accuracy\",\n", + " save_best_only=True,\n", + " mode='max'\n", + " )\n", + "]\n", + "\n", + "history_l = model.fit(train_ds, epochs=20, validation_data=val_ds, callbacks=callbacks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's test these models now! (see another notebook)" + ] + } + ], + "metadata": { + "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": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}