diff --git a/docs/trains_examples.md b/docs/trains_examples.md index c500c6d7..d54c7385 100644 --- a/docs/trains_examples.md +++ b/docs/trains_examples.md @@ -2,7 +2,7 @@ ## Introduction TRAINS includes usage examples for the *Keras*, *PyTorch*, and *TensorFlow* deep learning frameworks, -as well as custom examples. +as well as *Jupyter Notebook* integration and custom examples for reporting metrics, configuring models. You can run these examples and view their results on the TRAINS Web-App. The examples are described below, including a link for the source code @@ -32,9 +32,10 @@ OR ### Keras with TensorBoard - MNIST Training [keras_tensorboard.py](https://github.com/allegroai/trains/blob/master/examples/keras_tensorboard.py) -is an example of training a simple deep NN on the MNIST DataSet. +is an example of training a small convolutional NN on the MNIST DataSet. Relevant outputs + * **EXECUTION** * **HYPER PARAMETERS**: Command line arguments * **MODEL** @@ -54,6 +55,7 @@ Relevant outputs of PyTorch MNIST training integration. Relevant outputs + * **EXECUTION** * **HYPER PARAMETERS**: Command line arguments * **MODEL** @@ -74,6 +76,7 @@ reproduce it with a new artistic style. The algorithm takes three images to resemble the content of the content-image and the artistic style of the style-image. Relevant outputs + * **EXECUTION** * **HYPER PARAMETERS**: Command line arguments * **MODEL** @@ -100,7 +103,7 @@ Relevant outputs * **SCALARS**: Train and test loss scalars * **LOG**: Console standard output/error -### PyTorch with tensorboardX +### PyTorch with tensorboardX - MNIST Train [pytorch_tensorboardX.py](https://github.com/allegroai/trains/blob/master/examples/pytorch_tensorboardX.py) is an example of PyTorch MNIST training running with tensorboardX @@ -168,13 +171,77 @@ Relevant outputs ##### Toy Tensorflow FLAGS logging with absl [absl_example.py](https://github.com/allegroai/trains/blob/master/examples/absl_example.py) -is an example of toy Tensorflow FLAGS logging with absl package (*absl-py*) +is an example of toy Tensorflow FLAGS logging with absl package (*absl-py*) coupled with parameters dictionary Relevant outputs + * **EXECUTION** * **HYPER PARAMETERS**: Tensorflow flags (with 'TF_DEFINE/' prefix) * **RESULTS** * **LOG**: Console standard output/error +### TensorFlow MNIST Classifier with TensorBoard Reports + +[tensorflow_mnist_with_summaries.py](https://github.com/allegroai/trains/blob/master/examples/tensorflow_mnist_with_summaries.py) +is an example of Tensorflow MNIST with TensorBoard summary, model storage, and logging. + +Relevant outputs + +* **EXECUTION** + * **HYPER PARAMETERS**: Command line arguments +* **MODEL** + * Output model (a link to the output model details in the *MODELS* page) +* **RESULTS** + * **SCALARS**: Network statistics across the training steps (e.g., cross entropy, dropout, and specific layer statistics) + * **PLOTS**: Convolutional layer histogram + * **DEBUG IMAGES**: Sample of the network input images + * **LOG**: Console standard output/error + + +# *Jupyter Notebook* Example + +[jupyter.ipynb](https://github.com/allegroai/trains/blob/master/examples/jupyter.ipynb) +is an example of integrating matplotlib and training with keras on +*Jupyter Notebook*. +This example connects a parameters dictionary, prints simple graphs and trains an MNIST classifier using Keras. + +Relevant Outputs + +* **EXECUTION** + * **HYPER PARAMETERS**: Parameter dictionary +* **MODEL** + * Output model (a link to the output model details in the *MODELS* page) + * Model Configuration +* **RESULTS** + * **SCALARS**: Training loss across iterations + * **PLOTS**: Sine and circles plots, convolution weights histogram + * **LOG**: Console standard output/error + + # Custom Examples +### Manual Reporting + +[manual_reporting.py](https://github.com/allegroai/trains/blob/master/examples/manual_reporting.py) +is an example of manually reporting graphs and statistics. + +Relevant outputs + +* **RESULTS** + * **SCALARS**: Scalar graphs + * **PLOTS**: Confusion matrix, histogram, 2D scatter plot, 3D scatter plot + * **DEBUG IMAGES**: Uploaded example images + * **LOG**: Console standard output/error + +### Manual Model Configuration + +[manual_model_config.py](https://github.com/allegroai/trains/blob/master/examples/manual_model_config.py) +is an example of manually configuring a model, model storage, label enumeration values, and logging. + +Relevant Outputs + +* **MODEL** + * Output model (a link to the output model details in the *MODELS* page, including **label enumeration** values) + * Model Configuration +* **RESULTS** + * **LOG**: Console standard output/error diff --git a/examples/jupyter.ipynb b/examples/jupyter.ipynb index 99325b89..3c15d4c1 100644 --- a/examples/jupyter.ipynb +++ b/examples/jupyter.ipynb @@ -5,39 +5,169 @@ "execution_count": 1, "metadata": { "pycharm": { - "is_executing": false + "name": "#%%\n" } }, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "TRAINS Task: created new task id=e8fc2b809a384c3f8ec3ded54a2aae44\n", - "TRAINS results page: http://ec2-3-218-72-191.compute-1.amazonaws.com:8080/projects/ec4476fb59c64d89af880ff0445c836b/experiments/e8fc2b809a384c3f8ec3ded54a2aae44/output/log\n" + "/usr/local/lib/python3.5/dist-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n", + "Using TensorFlow backend.\n" ] } ], "source": [ - "from trains import Task\n", - "task = Task.init(project_name='examples', task_name='Jupyter exmaple')" + "# Trains - Example of integrating plots and training on jupyter notebook. \n", + "# In this example, simple graphs are shown, then an MNIST classifier is trained using Keras.\n", + "\n", + "from keras.callbacks import TensorBoard, ModelCheckpoint\n", + "from keras.datasets import mnist\n", + "from keras.models import Sequential\n", + "from keras.layers.core import Dense, Dropout, Activation\n", + "from keras.optimizers import SGD, Adam, RMSprop\n", + "from keras.utils import np_utils\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TRAINS Task: overwriting (reusing) task id=8d23de406d0a4159a496b64c7eba0e32\n", + "======> WARNING! UNCOMMITTED CHANGES IN REPOSITORY https://github.com/allegroai/trains.git <======\n", + "TRAINS results page: https://demoapp.trainsai.io/projects/087f765c846c4c76a7e9f3d035667d82/experiments/8d23de406d0a4159a496b64c7eba0e32/output/log\n" + ] + } + ], "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib\n", - "%matplotlib inline" + "# Connecting TRAINS\n", + "from trains import Task\n", + "task = Task.init(project_name = 'examples', task_name = 'notebook example')\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Set script parameters\n", + "task_params = {'num_scatter_samples': 60, 'sin_max_value': 20, 'sin_steps': 30}\n", + "task_params = task.connect(task_params)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple plots. You can view the plots in experiments results page " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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3CKq/i4pntvsSrqKdaJkfX+N9DfxCx3rUQV547Txum1vV22V+vsr42DzplMtGnz3LNGiEIRXveTKFE6hmD8uNs8mEQ7Xmc250lttuG1z3eVyzh0g1OFP6E/Zl/zvy3l0b6/AWMju+0Eql3OHvaS201pwoTtOIQjL5JJXRMlrrGzJ76PFSTNTLfO7sa/zUoYcunVNpzWfPHsUQ2XaLeTCR4RuTp7i3Z5i9mRvrLtsOtCoSVD9JHI8ixiAiaxuEIgZi9qO1RsUX8Mv/GTv5MSz3HTegx2tzc80jOkisFMfPTZPPdi60KwhiTp2cwvPsTYuAnZxnzp9GN3tZ7WtIJV0mJ4sUi/UNnccyErhGL6PlT1Pxz2ywt1vHzHixNR26wcw2a4zVyqRtF8MyUGFMWA5u2PmHEhlemZvgyOz4pdeOzo9xojxDn5u6Yf1YCVMMMrbHZ868TKzUdndnS1HxPH7lv6DUBGLsbEvYr0REEGMAjB6C+qcIm09uUU/Xxy0r7gvlessi7GD63dGzM8Sxajtx1UoYdpVEZpaF2ctumJUQAde1OXligija2ENmGh6WkeFc5TNEamODxFZx8fQU7jqzcG6WII55bX6KhGVfMfsS/Pnr3SRbhYgwkEjz12dfpeQ30FrztfGTm96c1ElyjsecX+V0ZXa7u7JlaNUgqP4eWtcRY2BT917ERYxhwsbniPzt36R/y4r7zEKtFQreIRqNkOmZ8uZ3k0qMmTmPKRa1hkkUrf0RxzYJwpi5+eraB6+AbaSJVJ3x6j+wXfmElmN+uoybuLERPeO1EqGKrsqtorUirIY3tB8JyyaIY56eOsf52gJTjQrpDu1o7RQJ0+YbkzffjK9ThM3H0WoWMfo70p6IjRgDhI2/RKtiR9rcKLesuI9Pl+hkzYyZ6TICG/azL2EmJ0FiBAtBU2u010nXsRi7ML8pD4ZnDjLbfJFqeHrjjXSYKIyRG5j3XmnNaHXhurQAIoLa4MxoM/R7Sb41cZZvTpzGMW5cxJDWGqXVmgN93klyojjNbHPjhsXNShydIfa/BUZno8lEEoAmqH92Ww2pWzbOab5cx7E7c3lxrBgfX8DzNmdhihFieHMQL4ZmCvihAawtKrZtUq36lCsNchtcRxAxsCXDRO2rpO0DN8X0X0QWQ81uDHPNOkEc410bXqo12xHJ5pgWjSjiyfHT3Nmz/kXzdlBaU4lqVKI6pbBGLa4T6dZvTgDXdMiZKTJWkqydxjMvD3ytesFwbGGK9w1vbU6mG03U/ApIEpEtWMyXAVT4OlpNIeb2hCLfspZ7GMUdCyWr1XziWG06TE7cBVqPU6tfWoNeh7EoAsWF2qb6YBtZauEFmvHUptrpFI5nE99Ai/lCdQF72SmdYNzgiJ0lTEOYqFdbqXo7SKgixpuzHC4d51jlLBcaU9TjJo7YpAyPtJkgaXigYDYocbo2xuHScd6ojFIKq5eszoRp33J+dxXPEIdvgmzNTu6W4WQS+c9uSfvtcMuKeyep1ToQRSEaMzELauM+Vds2KZc2t+gnIhhiMdvY/hKHAIO7emnWb0yUitaaeb+xbJihGIKTb2+X8VZQC0OiDkWlaK2Z8Yu8XDrBudoEhhZSZoKUmcA1bAwxLs3aRATLMEmYLikrQcrwqIQ1Xq+c4c3qOfw4IGU5nKsu3FRrNZslDo8jCNWKMDWpaDa24NqMPuLg+W27b7esuNuWierQTS2X65u32q06SAz6cjsirMsVYNkm1Wpz09flGAXmm4fR65k2bBE79/cTBm2sKneAehSitL5uRqe1RiuN27M9GRHLYRNDhHq0+UEuVBEnquc5UT2PhUnKSmCtI/mZiOCZLikjQTmscqR0glJUpREFlMPmpvt3s6CiUxw9kuSP/8DnM38e8Mk/8pma7OzzIGKD9tFqoaPttsstK+6FbIIgXN/OzpWoVpqbDn8Us8ly4TuO3b5QGyJorQn8zYmhITZahwTbvJoP0DOYu2GFxGtRsGzgqQoVdsbGSmzPElQlaGKKQTXcXLROoEJer5xhPiyTNtcn6tciIiRMD9uwOVE9z1xYZL55c4XRbob5mVGe/JpDoQB9/QICjz8WdN7KFkGr7XGB3rILqjv6cyjVmS+qFdu+Wcu9BvrK0DvQWkgl1jsASYeuS2hGM7jm9u4+7BvOk0h5NBsB3iphpkop/LpPo9LEbwbEUSvSwzQNLNsikfZIZDysVRbRa+HylnFQCuh/aPvy70RaYQpUAh9SG0vDEKqIY5Wz+HFIyuzcDMQSk5TpMe3P8vL8GfZlezvW9nZSrdTByGFZreE+nYaZaVAKOll9UGuN1jdu/8SV3LLi3ldIr7U/qG1ag/nmGjPs+lXiHsWQ9BQbCejpiHUh0IgmyLmHNt/WJjBNg4c/eCdP/t1hvB1XDzSBHzI3Ns/s2DzVYg29OKhdmSZg6V7I4qzGS7nk+3MM7O4jXUhdFREUqvj6tMlKg9IU7t6+otlagyEGkdrYTFNrzZnaGM3YJ9lBYV/CEANHHL41+yYP9e9jd+qtX2A8l1cYAs2GxksICwswNCyYZmdnkYu/0o622S63rLgXsslWGTSlMDYZ8G4Ysigim/jijQjiy5ZpEBoM9W1gGi50xI0h2iJUlU230wkOPbCHJ//uZeJIYZhCtVhjcnSamQtzaKVxPBsv5a75PWqtiaOY6QuzTI5Ok8wm2HHbMD3DeSzbQqOv+wbDckBmbxa3sH11RI3FcNCNrqXMBSXmglJHLfZrMcUgZbn87YXn+bmD34lr3vyppFcjk/X4vh8S/uHvoVLR9PULH/2+zm8g0wjC9mxMu2XF3TINDu7q5+z4HD25zeVc9hI2jXqwyUXVqweHKIb+wvp855qWgNkdiN8XEZS+MQuZa5HOJXnw/Xfw7BOvUpktMTe+gGEaJNLeugZmEcGyrZaQa03oR5w8fAbbtbjtgX0YnlxlQ6lYETdjBt61o/MXtQ5sw1hMlbH+QTtUEWfq43jG+lJPrxcNZO0kxaDOt6bf5EPD927ZuW4EhrmTvXvH+fgv5AgDcD225P4JIGZndr+ul1t2QRXg4Xt243cgEiOXTRJuenF2qTReS9htC/p71te3OFJ4nr1p/z+0BglDbo6xXWtNb2+S0VfPMX1hjlQuSTKT2NSMS0RwPJt0PoVhGBx75gTTZ2aI4svfoz/XpO8dQyS3eXNO3kkQqHjdJf0AZoMikYo3tXi6Fnrxn6Rl0+dmeHbuJM34xiVZ2woM+zbQNUxT8BKyJcKudQwYiLE9bqyb4+neIvYM95DLJKg3A5KbSE6V7kSxD91K9A9CvWFy2+7muhduwjCmt68zQqSJsGT7q8iEQciX/ts/8tpTx7ntziFOH59eLJjRuYfNdm0sx2J2qsK8r0gMm0igcfIeg+8cAVoDTDOMqPkhlYbPQrVJtRkSK3UpfNIQIena5FMeuaRL0rVJuvamN8tlHQ+tW/9fD0prxpuzeMbWukgipUhY9qXBJ1aKY6Ux3t6zb0vPu5WY1kFCHm/tTN6iGY9W85j23cg2GVG3tLgbhvDo/ft4/FvHNiXuyVTrs1pvPLeMDpOIXUVFDqAZGVy/vz1Wily+M4Ks0STs4Y60tVH8hs9jv/Mlzr56nuF9rYx8caw5e3KGZNrZ9FrJlYgI+WSSaakydXKawkCOO//ZnUQC03Nlzs2UaIStmZTQcuvZpoFtWZfLkWtNI4woz5ZYWoKxDIPdfVmGC2kSzsZENmU5iEDaXt9vtBLVCFRno2OWI1TxVVWjMnaCZ2dPvqXFXczdGOYQWlVAtqqcoI/lvWeL2l6bW1rcAe6/fYTnXh2lXG2S3aAF7nk2+UKKSrmx4fwyKkxhOiWqdYN9O32S3voWz5RqWY+FQmdyfQuCt02+QGhFwnz+t7/E+WMXGNzTf8l6GhzJEyvN+dOzJFJORysjWVqwmq3dqAsjmjdm55ifiNBoXMsks4oBIAAimAZXFYCJYsXZ6SJnpov0Z5Ls7suRT63P/+2YFqYYmOu0HKrRjQmxi7Six71sVCRNhym/RCMKSNxkWSzbRUSw3A8T1v8ErTMdt961WsAwhzHM7RsAb2mfO4BjW/zQB++j2mjlh9koIyP5DedTB9CxR9M3SSYUB3av31/ZaIYMDOWwO5D/ROsYQbYtxl1rzZf+2z8y+tp5+nf1XfVgiQgjOwvsPzSA3whp1DuzsUQpTb3qkzRtqu/tYbrg8ObpSWyBjOesWoh8NSzTIO05pF2b+WqDF89M8MbFWYJ1lEYsBU0e6N3BQrA+sS5HVaytSHp1BUsuqX7vsuUuIgjCrH9zRFttFNN5G4Z1B+jOlsfTOkLrGk7yR7e1ruotL+4AOwfzvPv+/UxvIh96LpfE8yyCDS7QxkEC37e471CV9eqIWtwePzSY29C5r8WP58l5d29NNrw2OP7CKY49ffwqi/1KRITB4Tz3PbSHZMqlVvWJNrigrbWmUfepVpokd2WI3t5LyRFcQ3AFipPFjgwesuiPz3g2E8Uqz54YY7Zcb6vtZhzxsQP3ofT6wiGrUQNri/259ThgJJm7brFXa81c8NYWdxEDO/kxQNCqMymNtVZoNY7tfReGtbsjbW6UbwtxB3jfgwcYGchtWOANQ7jt4BBBEK1bDJTWVJsRBwayFHpL6z53vR4wsrNAKtVKbBXriGZcoxFXaMZ1lG5f+LTWKEL6E+9cdz86QbVY40uf+EcKg/k1p8KJpMNd9+9k/+2DaKBWbdJsBGvu0A2DiFKxxsTFBc6fmWF2usJsVvFctsnMRBld9Kk1AqJYUa82qWxi0L8WESHttXzoL49OcmJiflXBrkcBecfj7X07OZQfYMFvb4u/1ppIxxid2qm33DkWY+93ppY3KhodyIWz3RhmL076Z9G60vK/b4KWsI9hOQ9jed/ZoR5unFve576EY1v82Efezp/+/QvMLNTo34DvOp9PMjxcYHKyeElo10JpTdUP2N2bY1e6B4w3uDbmfTX8IMJzbYZ3ZJj1xyiF00TKv+7zjuGRd4bI2D1YsvK6QKRreGYfSWtXW+fvJFprvvJnTxKHCq/N+2cYwuBwjv7BLJVyg8mxIqWF+iWBX9qtupRzp1H3CfwQwzBIJG36hzIURxwm+03SvsYAjBos9Gi0H4HWTJybJYo12UIKy+7MbMaxTCzT4PxsiTCKuXNn37IlH+eadX543z2YhsGHRm7nvx77Jnmt2kr/u9XJButRQJ+XImNfv1YlIsTrMCpuZkxrP27mXxJU/xAdT4IxuG4fvFY1tJ7Dch7FTv7Its2Kr+TbRtwB0gmXn/zeh/nUF19iaq5Cf0963WFse/b2sVCs0WyGay6uxkpR80P29uU5MFCAUIgruzBT4+hgbRdLFCuCMGT33TbnGkcBsAwXx0hdFbWjdcuan26OMuOfZ8DdQ87pR64ZALTWBKrI3uyPbkuhjsmz05x48QyDe9a/kGsYQi6fJJdPtsIWmyHNeoDvRzRqAeMX5oijkFTaoX8wg2m2YpcnC8JYv0HC1xiLYphoQi0UIkewYojDmPmpErW6TzaXJJtPduT+GCJkPIeJYhVEuGtn31W/t6LfoD+R5uGB1kC7N9PD+4b289TkWUZWsJaXkMXQzK0iWhTuO/LLFxBRWmEbt458mNY+vOz/TFD/HHF4FCQFUljTZ651A9QcSAon9TOLoY/bXwQHvo3cMkukky2Bv3PfEBMzZerN9U0tLcvg7rt3YhhCs7l8OKPWmkYQ0QxjDg33cmCgcOkLD6YfppVDYPXzRrGi4dfpub1K3ZjBNjxcM4kp5nXhmCJgGRaumcQSm8nmGS7Wj19nWflqlpxzBwV3e3YXvvy1V7Fde9M/fhEhkXDI9aRQcczkxVksS+jtz5DNJbCsVr7yuYwwNmCS8Lkk7ACiIb/Qyr6sAdMyUVGMaRiUi3Wmxoubzrx5ZV8znsPEQoWTE/OXXo+UohL6/Phtb7sqv/x37biDvJugHKydXtcz3Usi3Ek0mloUckdukMQKaQYEIe90JnLrZkGMHE7qp3DT/wLD2oNWE6h4DBVPoVUFreotC13No+MxVDwGOsBKfD9e9t9hOffcNMIO32aW+xIJz+ZHPnQfdx0Y5O+efJ1KrUJfIbXstHnZzyds7r1pt/S7AAAgAElEQVR3F6+9dpF6PSCZvBwOFsWKehBSSHncOdxP0r364dBRCn/yHXgj30I1e1nOPRMEEUHsUzhYw/IEx2j/ITLExDVS1KMSF+vH2ZU8hCEmsWqJxa7MD2zLCn6tVOPYMyfoHelMhE6sFKePjTMzVSKZcjGuCZn0bTg/bOAF+iphX8IOIVOCSg6sxTE68kPclEsUKaYnFujtz5Jo0320Gkt++POzJfoySXrSHuP1Mh/ecZA9masrAXmWzY8deDu/+8ZTuKa1bGGRJbJWkhm/iN3hx7gaBvR7aYZXmz0I9Lkby2B5MyMimPYhTPsQKp5FxxeJo1F0fAGtm4CJGLswrAMY1giGuWfbNimtxc3ZqxuAiHDnviF2DRb42gsnOHpiHDTkMx6eu3YseyLpcN/9uzl9aoq52QqGY6IA2zQ4NNzLjkJ2xWlzXNlPVL6IlT2PahZYEnilNfV6gONYDByK0HbLl77+awPHSNKIy8z4F+l3R2iqGfZlfxzHzK+7vU7wxnMn0Up3JG5dKcXJ18aYn62QXqaerAbODxqIBnOV6NV0FQIH/ASYysCv+zhJF8syUEqYnS7TO5Al2QGBN0TwbIvXL0yzd2eO2wt9fGjnbcseuy/Ty4/ue4BPnXmJIS+Ds4LAZ60Uk/78su9tlGrkk7Fd7i0ML7sqFCtF1Q9o+BGlhYCaWSThWBTSSawO7km4GTDMPjD7MJ23bXdXNsS3rbgvkU66/MD77+WDDx3k9dMTPHV0lIVKa9XcdUySnoNrX12VXilNww+pNwMKAxnEMSnPVhlIJtg92EMisdbgIPiTj4IZYCaniJs5ms0IFWuGd+TJD2umg2lc2fi0VwRcI8mCfxFbfA7kPkbBu2/D7W2W48+fIp3f/DReozlzfJL52QqpzPID32xOKKeE5BqeDdFQmIeFPvBdwairlnvGNjEMwbJN5qbLmMN53E0WR4fWIut0tUq9kua/f+TBVa3yB/t3EWnFX509Qr+XwlvGPZKxWxuLdAe20Gs01TAgbbs80LcTa3EWq7SmWG0wU65RrDWo+yGBEZCLMvzJiZdAlnZuC8OFDPsGe7h79yAjvdmbykXx7ci3vbgvkUl5vPO+fTx09x4mZktMz1U5N7HAhakFJucqlxYnNa1NHYO9Ge7aP8TOwTyDPRlc0+TVVy/w4gtnKJXqOI5JMuniutb1OcS1JgyE8hvvwNvxNInCOL39OxkZ6iWVdjldfRnbcDec6uAyAYb4+OykP/HoZhvbMHEcM3VuhsLQ5osRz89UmBpfIJVZfst9aMLYgIHntxePZGjIzcJ8DhqWgVENsV2NZRuYlmBaBnMzZYZ29Gwq1bLSmmroM5LJkogcpis19vasvrvzkYE9eKbFX5x5GUsCet2rF3pdw6HXzlIMqyTMjc8uYq2oRQE9bpL7ekawDZMgipkqVrgwU8QPYwxDcCyTlGcjxNzh7CKTvLxrNVaKcr3J02+M8s3XzzDck+W9d+/jjp0DHdl412X9tCXuIvIR4LcAE/hDrfVvXPP+buCTQH7xmH+vtX68w329IVimwa7BArsGCzx4VyuKIYpiolgtpmU1sCxjWf/8ux49yMPv2M/o2VlOnpzk4oU5pqfLLR+3cClJkdKahOewc9cQe/b9NIXdpynG38SSJvXIJ1IBnrlxK7dV/aWCiE3Cfjel2KAel0hZ2+OSKU6XF5OBbW7aHgQRp9+YwEs4Kw5881lBiWC2EScYCzQMoWkJRkNjG+AnNZGKMRqqJWgJAxFFaaFGoXdjSdsaUUioFLfl+tibKTBXb/DU6Hn29qw92N3fu4ORZI6/OnuEM5U5Br30VW6aYa+PubC8Ietdo6lHIRrNHflBdiZziAjTxSrHx2aIYoXnWmSSlwcOn4CUTpDm6sHVNAzSCZd0wkVrTbXh85ffPEpfLsU/ffQ+dvR1ZgNel/ZZU9ylFbD528B3AheBF0TkMa31sSsO+4/AX2qtf1dE7gIeB/ZuQX+3Bcsy266halkmtx0c5LaDrRCywI8olupEUQy6VXkolXZJXZV/5Dbq4b2Mlv+ayfIRQr+GUoLWspij3MSyLYw1MiVqrdDUgRjT3IVt3YmIA+E0C8HUton73Ph8R4Kyz5+eJlYK117eRaKA6R4DJ1z7XKFAyTFAa0zdiv4wQtBVhe6xCUWhlKJZ05i2QaVUJ5V2cdpYj1nCjyP8OCLjeDzUM3gpXrw3meSVyUm+t3GIfGLtNZX+RJqfu+NRnp0e5QsXjhFrRcFJkrBsMlaSXjvLQlghaba3PqPRNKKQSCsKToK7CkMkLQc/jDg5Pst0qUrCsUlcc60KTaAj7mTfdWG2VyIiZJIemaRHqdbgd7/4DO+/dz8fuOdA14q/gbRjub8DOKW1PgMgIn8B/CBwpbhrYCm1Wg4Y72Qn38o4rsXAwMpZ57TWTI7OcOTJk5w4kkS/yyA9FODYkyhloCITrSw0YNsW6UKKRNq7ZAVrrYAArVuhlaY5jGXuReRy+KWBxbw/wc7k9pTUK89XNq3tQRAxM1lcNXqlmhJCE5JrRDEuCbuhuWqHp4igw5icdgi0oiERgRmjwpg4hnKxTt8aKSAipWjGEUprso7LoXw/A4k0xhURSoYIiPDy+DgfPLD/ujaWs8JNw+DdQ/u5t2eEl+fG+MbEKeZrdVzTYtjrpxhWV83rrrQmVDGBitDAgJdhdzpP3k0iQN0POXJ2nDCKySSWT3xW1w12MkCO9mcwuVSClOfy5KtnGJst86Pvu3/D2TO3kyvLOb5VaEfcdwAXrvj7ReCRa475VeAJEfklIAV8eLmGROTjwMcBdu/e3rwLNwOTozN8+VPfYurcDLZjke3NU3L7qM0NEFg+bmoe26tg2c1WuYQ4pFqqUi1BMpMglU0ghokheUxzAMscQeR6X7Rl2NTj9ac96BShH206fe/sVOnSwt1KTOdl1egYaFkhFUsQvcwmj8v1VHAxcLRDjKZhRDTjiLlyHTvnIIu+96WdsSAtN5EGz7LYkcqyI5Uj67is5PnvSSR48sxZdhVyTFQrnC7Oc65UpBq2csibhkHGdtiby7O/0MNIJsOebJ6s4/H+4QO8e3Afp8uzHJ67yGhljpyZZ8yfxBH7qlDXJQvbEMg4HrvcAsPJzFULtA0/5PDpMbTWpFbIjNnQPkk8drP+QuKWaTDSk+Xs1Dx/9rXD/OR3PIjn3NzLfaWwyLnaOSabE0z70wTKB8A1PPrdfoa9EXandpOzt2c23A6dusM/DnxCa/2bIvIu4E9F5B7dMisvobX+feD3AR566KHtqRp7ExAGEc998QjPfOEwibTHwDWZEUGIghRRsOh3lxjL9hEjRESjlGb2fIhlpdh/z+1ketaKNxY6kRxro6hIbar8rNaa8fNzuKtEIWmgmhScNfakRQLKEKxlbselLi5mhxDAQshgkxSLoBqxV2XJ9qeJrijiYYpB0rZJWU5b1ZSaccREvcLrc1PMvdDAtSySlk3Ssck47uV0CnHMyYV5XpmZAiDruHxgzz4eGBom47gcyg9wKD8AtFIFfHX8dZ6YOkrOSmEZxqW+pSyHhLn85rEgijlydhyt9XVumCUa2sfC5B4OYLExt4qIMJhPc2G2yF996yg/8YEH2t5XciOZbk7z0sKLTDTHAMEzXBJGgrTZmq3EOmbGn+Fi4yIvLDzHSGIHDxYeot8d2N6OL0M74j4GXJmIZOfia1fy08BHALTWz4iIB/QB053o5K1Es+7zd7//Vc6+foGBnb2Y1/ggjdBB2QGirnhdm0TB1UU6bBuCZsCrT5/g4AP76N+x8uagWIckzO3bcGK7FvqaZF9iRNjJMk6yhJuZwfZqiBmjlYGKHJrlXoJagbCRpVYyCYOI5Cr5+H0blAjGGpXmG9bK3mINLVFf5oBWfVOhMlPn0L6RVc+xErFWnCsXOVWab0VdIWRsl/709YvnIoJrWbiWBYuLl/Uw5LGTb/KFU8f56IHbec+uPZdCFpOWw/fvfoCRVI6/H3uJpGmTc9Yu7HJqfBY/jEgnrnd3KRR13SSJxz0cwN1koWcRYaiQ4fjFGV46NcY7br/x+Y1WIlQhR4ov80rxKK7h0GP3LjsYWmKRNtKkSaO1ZrY5w2Njn+f+/P3cn38Ae4urYq2HdsT9BeCgiOyjJeo/BvzENcecBz4EfEJE7gQ8oLNJkm8BAj/ksf/vy1w4McHQCulu7WqORv8ERrT2g+R4DqZlcuLwWUSgb4Xdn7GO6PU2JkidwEt5KK0AjZNeIDt0kvTgWUQUiEbHFiq2WLKdxYhJ9Z1v/V00xak0Y5NZ6gs7UPHylmPTXXtqoIFAwFxJ/9eIOBFDmF9M47te32sl8Hl1fopK4JOybUwxqPgBFd9fVtyXI2nbJG2bII557OSbvDI9yY/edS+Dqcs+8Ad79zOUyPM3F55nolGkz02vmANmtlRjslghc42wazQBIYGO2MkAuxnasMV+LSJCXy7F4y++yW3DvfRktr/Uox/7fGXqCab8SQpOAbPNpF8iQsbOktQxR0tHmfan+dDAd+JuIiy1k6w5L9JaR8AvAl8C3qAVFfO6iPyaiPzA4mG/DPysiBwFPg38lN5OP8BNypOffY5zb47Tv3N5qwDAruZB2i8KYlomibTHyZdHqZWvL/agdau4cc7evmljz1CedG+Jkbc9wc4Hvkhm8CxhI01QKxBUewgbWeIgSRwkiIMEUXPxvVqeoJpHrDL3f+cx3vWxb7DjjvOtQeEa6q4ga1jtAMgqlrvSmKtkhTREiJXCXyGn0EpM16s8M3mBRhSSddxLGR9t02C+sf5qSo5psjOTZbpW5f95/ilOzM1e9f6OZA8fP/hhPjB4F6WwwURjgUrYuMo1F8WKN8emSTiX3TUKRU03qOkGrna4n4PsZ0fHhH0J17YwBT7/3LFtdRdCy2L/ytQTTPtT9Ni9bQv7lZhi0mv3MtWc5KvTXyZU6y+huRW05XNfjFl//JrX/tMVfz4GvLuzXbu1OP/mOEe+foyBVYQdwKnkkdhCGRGGam9JxLJMIsvk1NFR7n33oasWL5txlV5nmIy1PVWXlPbx+p7l4Pu+ge3k8KuX0y20h1Av2TSbWVxPcfCRN+nfN8nxp+6mUb5s8TZdMNvMobVSwmWtNZa92j3XiBgEfoiXaM9FMV2v8vLsBAnLus4fbxsG9WBjQiAi9CZT1MKAPzjyIj/ztgc51Hs526ZjWLx/8C4e6TvIifI4z86eZLJZpDUEamZKNcqqTsK28XVrwBOEQXoYoo80iVXDHTdLX9ZjYv4UY7M+hXQTpZtoQgQbEQfL6MU0B7HMAWSVFNab5UjxZSabk/Q6qz+XayEiFOweJpoTHC0e4aGehzvYy41xcy9Z3yJEYcQXP/kkmXzqugRX1yLaIDmxm+qu00g93fYD5iVdqqUaU+dmGd7XstK1VjRVjQezH9myEC7fD5mdrzG7UGG+WCeKFKYhpFIe/X0BrvcZxJhFBb34oYVpXd2PpjKYCh0UQo8VkjOvj2PUWiNAHFlU5jKkcjUe/sFnOP7UXUydabmbVBuXJ4CpNHoV691YxXLXWrBE1iwWskTRb64o7Es9UnrjpRsBUotFtf/o6GF+6aF3sit7daimZ9rcV9jDfYU9+HHIrF9htlnhU2cPc0BSpHBxsUni4eJsqaDbZolc8jjpxCgJe5ZmEDAxn8Qy8osRPgataHq9OATJYrHyIRz7DjznISxzuGO/5anmFK+UjlBwejrSZkvgCxwtHmFPas+2L7J2xf0GMHpsjMp8lcHdfW0dn5gbxu+ZIfJqWH77Pkkv6TF2arLlzzeEcjTHntQ99LudDTuNYsXo+Vmee/ks58bmWXKImMblUEXLnOf2Q1/EkBCRXsTx8Es1UtnW9SgNR+oZXmlk0JdiEIU9ToN3pxdwl0vlCIDQrCYwrIg73/cqph0xfnw3WtqbD3ixpmaDoa9JCbH4H9NcOU5cBBKW2ZYQRCrm6NwkjmmuGEEjwrrK6q1EynYI4phPvX6Uf/2Od+OscA2uabMj2UNUFZxSkr29K++/6BwxKXeM3vQR0olzaAyiOIkfFdAIF2ZDdvUPr5h0TGuFUnVqzSepNb+KY+0j6X0Hrn1Ha4PeJji88CKueBtyxayEKSau4XB44UW+e+h7OtbuRuiK+w3ghSdeIbFKpMe1iDbInjtE8eBRYqeJGbT3Wcs2qdZ9inNlrEJMxu7lntx7O2bpaK05dnKSr3zjGNV6QNKzGey7vnK8ZZXZufOriJhEUYEwiqnEitJ0mR4gl0lwtJ7lcD1LrxWytPFWazgXJPArwndn51hK5SKGcd0mKBVZ1BbS3P6uN4hjk9ONXe143HEV1BYHoyt7rWOF5dor7gIOlSbr2Bha2soxc6o0T3PRx74SWi9F4WyegpfgYrnE10ZP85EDt6967JnJuU3lyWmXpHuRkcJXcawysXJphn1cucxnGK3fVKXeXHFhVcRAJI2xGJ0SxTMUq3+EIWkyyY/hOfdv6PddDBYYb4zT6/Ru9PJWJG1lGGuMUQqL2xoHf/MFmt5iVBZqjJ2eJLPOsn6mnyB36j5EmUSJGrot6QLDhvHpUTJWD4/2/TCOuXyCrfVSqTb57BcO87nHD2NZJsMDWXLZxDIPVszQ0JcxzYAoalmGtmVS6M/iplyKCzXOTVU4UkvRc4WwQ8uS7TFDJkOP6SuihRzHQqvr3RcqNqkVU9zx6DEKqQq6jWfcAJKRJhauuqNaadzk8pZgpBQGkHdd0KyZIbIcNDlXKZK2V7csNRqzg+6yoXSGr4yeYaq2ek3Ys5MLpNaRRmG9GOIzlH+Svf2fQySmGQ4QxjlWkpuq317BHBHBNArY5i4El1LtjyhVP0msyuvu4/n6eQyRLXFXigiCcL52vuNtr4euuG8xcxMLrS97Az8iq5mk8OYDuHMDRIkqkVtHrxBJo42YMFHFzEdEb/Tw3v6PbSr52JVMzZT5o08/xenzs4wM5kisIm653Gt43jhBcI3FIkJ2uIChYT52qTVDVHS9f10EDDQXg8sWr+1aK/pcVGQRBRaP3vcK2m7Pf52INd4VAq+VRkwD85rFVK01oVIoDcPpJIa0Fq8dd/UJ7/lKCaONMnhhrEi7K1v2URwzMV/mlbPjvHTqIkfOjHF+egE/XD6/gmUYmCI8O3Zh2fehla56bL5Iwt2cS2Mlku4YB4Y+RSH9Ks2wn1it/hu0TINidf0RQ4aRxDJ244evMlf6dRr+0XV9fqI5jruBWgnt4hguk/7ElrXfDl23zBYzOz5Pm0b3shixTebCQZKzIzR6J/F7pxYF/rKfGjQSWySnduHNDjB3pk5Qj0mkN2+dTc2W+dPPPodlCv09q+cUse0F+vqeJgh6WE6NE/kUJXMOaFk39UYAumWZX8niFV3RrrlqbppmNUHPQJm7do9y5s3rc7VciwCpuFUsu24JsVYkUx6gL50nVBqtNY5pMpBO4Jomvh+SySWWvbYlgjhmvF4hZa197yMV05NYptiI1oxOL3B+egGlNLbVyi+vtWah2uDM5BxDhSwHR/quy7TZm0jyzNgFvmvfbSSWSbBWbjSJIrUlhTWyiePs7H2CME7hh+3VybUtk0rD39D5RATLHEapOqXqH6LUD5P0PtiWITXjT5Mwty7G3jM8ppvbu4ezK+5bzPxkqWV5bgJBsBppMhdvI31xP8r1iZ0mWhSiTcymhxG6l/OIGE1q5fq6/PzLUa35fPpvXsAyhewK+dOvpFA4TCvVwfLCZlgmhb391E/NInZr6lpvhoghV2ULjBCG7MtTddM0sB2LOFYrpg1uzqe4/8BpLp7eRRCuLawCJGON1QhRSRsj4RAsRsEIkLItcq6Da15eQI3CmJ7+1Xf6TjWq6MW0BO2QvsaC1lrz5oVpJhbKpDz3Op+8a1sorZmYL1P3A+7bN3KVUNumSRgr3pid5u3DO647XxjFW+KKyCePMdLzFYKogNLtzwpEhCjeXMSQYSQRGaFS/xsUPmlv9egwpRW+Ckhv4a5tU0yacbMjhVQ2Stcts8XEYWcfJsHA9BM4lQJuuRenkscMvetC2FS8uSgMrTVPPHkMPwjbEnbTrJPNniAIVs+amCik6Onx2BXPU6aVR6XRCC5tZinFJnkzZMS+uoxSJpcgjlYRgcjEFsXI0OTaF7eIUhpRmh0jfezOZtifW/w3n2UwlcSzLhdaUUojhtDTv3qEyVyzjt1GzhStW/OulHO1EF6YKTKxUCaT8FZcbDVESCdcSvUmJ8eu3wjuWSYn5ueWv2a9qYnksmQSJxnp/TJ+1LMuYYelMgeb75GIjWXuoNb4AnX/yU23dyvQFfctxrTN6/Kq3AjWyv2+Fm+emuL1ExP0reGKWSKVOkUro/paYWVCbncf98oMI5Qp41LULhN1YTaySRsx35md49ruJ5IuYsiK7hkBorLLodtH29qpqoGwGZAfKWAvLi4urY3ESlMsNYiuGEyajYDBkcKqef211iz4jbaShwVxTNppzQyWUEpxbnqBpLtyMZIlRCDtuUwWKzSv2QiVsh3OlorLfs4yOhvJ7tnT7Oz5EkFYWHHGthpa07HIHRELy9hBpfY5msGxFY8zxMA1HGLa3PW2AWId45netqYI7or7FtM7nCcM1kgw3kG01iilSOc2vpgax4qvfPMNCrnlomGWJ5c7RhS1NxCYtsXg7YO8XV3kg8Y57rXmOKCm+VByih/MT5NZZqupYQjZXHLVe2mVbdLJBpnM6tEiGggbAameNOllMmrGcUy52iRYXPCNVWtjzdCO1SsnBSomiOO2ImD8KGJvoXBVlrK5cp1Ite8PX/ro5ELlqtc9y2KuUacRXr/7Nek6xFp3yFoOGen5Ckq7KL2xfCqxUovJ0TqDiI1p9FCufQqlVv4d9LsD+PHGfP3t0FRNBrzt3cTUFfctpm+kZ1PpbtdL0AzJ9qTxVilqsRbnLs5RqTZJtrnFXiTEdeeJ4/Z9/G46Qf9tQ6TDGrcxxyFjnmxYZjUjLpNLYNvmiu4ZKwYr1qRytRXbWBL2RC5J785rUy23cGyLHUM5Ep6DBho1nz37B0gkV7+nQRy3FRkVq1a5xoFrEobNV+vrToPr2haz5auvV0QwEKrh9SGGCdcmn/QIos1brb3pw3j2HGG88c1QYRyT2+Ta0LUYRhqt61Tqn19xEBv2RvDVGhXUN4GvfIbc4S1rvx264r7F9A7nF2ua3hjXTL3cYMdt6y+ocCXPHxnFW8cisOMsuQDWN4p5udT/z957BkmWneeZz7k+vSvv2rtpM95hDAbEDLylEwiJKzG0dBC5FJfLjdg/Cq32jzZidyMkBhla2mCQDEJciBAAwQwBwgwG47rHdk+baVNtyrvMSn/dOfsjq7qrulxWVVZXNTRvxEx3V96899Q13/3Od97vfWk72I0MQkQoKVfqq3ZsCtGoeYehXLY8IwC7Jsjkli9JSKnwqi7RTIy2gYWqnArdcHGcIon4OLnsVbo6Bulsu0g2dYHdu0fp7hpFhUMgC6CW14ORNFfQrvk+/ankkkAehHLd03htlQXJcJneAICBjjSVenPc8pXgmBN0pF7D9TenWRSGinS0Nb0YC6FrXdTcV3D95cszA9GBBg12C57Lxj4VA7HtNSR6ny2zxYinY/Qf7GZyaIZUbus11T3X5+ijBzb8fd8PGbw+Tfs6zKBNs8BGl+mcZJSOI33MDE5QK9ao1/1VZwyWbZDKxpidqWBaxtLadNmkO1HgbR3MueRUAaEXIKUk05slkUsiNHDsIvHoNLZVBnFLBEFKHRCEocTQoa3DATUN/niDoikU4IDegdB7QTQy8Gaq2fMBty+1dOHZNPR1BxupFNZy6wCCFRk7uzuynB7cHAe7I/0SobRRmw0hAqIruD9tardCQ9eylKr/Bds8ssidCiBtZehyupjxZkgYrX0uy0GJnkjvtrs0vZ+53wE89NwJ6uWtq+/No151iaWi9B/euHb7dL7cCAzrWOTSNI/NcDDMiE3H4V6inWnKhQq1cn1VXnsyFb1Zf1+yXajR4bv4ZmNZVYYSv+Zi2CbdB3vJdEZJJibp7jxLW3YQy6oShBZ+EMELIvhBhFBaeJ5G4OvkOnIYVhREBLQEaHEgDggIb6Dcl1He6yAn0YVatZNYKSh7Pgfbc8v6iGbj0RWz7ZXg+SHtyaXrK/Mc/eVwqK8dIbR1H2seljFD3L6xqXIMNFygIpZBwtka/XNNSxDKGfzg8rKfP5h5GFfWCVXrFlZDFeJKjwfSD7VsnxvF+8H9DmD3kV5SbQnKhZVrwZuFUor8ZJHHPnHfilzwZjCdr6w7exQiZLMLC0LTSPVkaD/SR6o9SbVYpVKsrlhfT2VjpDIxfC8gXFCWUArsMCQ9UqdIgFKKTG+Orv0dtLVP0t1xjlRiDCV1PD9KEFqohY+BUjcXbTt60sv3KAgBwgARb/ynyijvbZzwJBreiqWlmu+TjTjLZu0A2UQEU9eb5n2rObWzzszizDOQIZauk7KXr2Unow5Hd3WSL62/MxQgHTuLUhqbveauHzDQnlmTGbQZCGwq9ReW/azT6eR48gR5L9+S8oxSiryf5970vdu+mArvB/c7At3Q+fivPEOpUFkUiFqJ2akiPXs6OPHk4Q3vww0CRmeK6677KqXTCva0rmuEmsaRRw/wwLMn6D/Ug+/5VIpVKrNV6pU6gX8ry0qmo7R3pVBSUa+4eFWPoO7hVkMOhFG6u9vIHOom163T1fEeidgUfmDjBRGkWprVylDiuQGRqEVXTwazGRNnIUA4oCUaJtTaBJ4/CbdJ+c57rt7T2bHi+dU0jV0dGap1b9WZC8zNAuouXZkkzm2zgIrnM5BMr3odHz00gBcE6w5qmnDJxk/jBZsrOYRSoglBe6r58t9GoGs5XP80Ybg87//+zAN0Ol3k/c0FeKUUhSBPl9PFven7N7yfVuL9mvsdQt+BLua4K7QAACAASURBVB5+7gSnvneazoHlWRobhVv18N2Aj/3zp5d4sjaLyUqFPzz5KleGp8gpg06ar0NK6dAKStBCCVwnZtN/qJee/d1UizVqpRrF6RLF6RKV2YbV3XyRPJONEYQKN5BEkhLDbuP4h+5lgAqv6xeIx6cRQscLllm4U4owlMhQomkabZ1JIhtlGgmLrK1ztVzF1oYQejuICKFUVDyPE11dRK3V68t9bSmqrsfwdJGYYy3byCSlolL3SMcjHOhZKiNd8T32ZVdf6BxoT3Oor4MrY9PrCrAxZxhBsGytXYaK2TFFcVxRGFEEHhgmpLoFyU5Bukeg6Q0ZhUrdY393DnMLZBAWolFrF9T9M8T0Dy753NQsnu18ju+Nf5fx+vps9uYRqpCCn6fT6eLZjo/sGB/V94P7HcSTn3uY6dECV88O0dG/OeeXebg1j/zELJ/9zWdX9FBtBoOFPEW3jq0bjKoqx9fxXc9Lo5qRZFwDjYaWxT/TdY1EJkYiE6NjTg9fqTlGglIITbs5ra/XfKanLnL+jW7yU+Pcd/wnHIjW+MqNXUR1RdRQN78rFzBuLNsgkYvjRK1NX5OOiMaVioFSEsJRQnJUApsjHR10Jdd+YQohONjbTsQyuTqRJ5QSQ9fRRCMo+mGIQNDXlmJvV25JCU4phURxT251bRchBJ9+5B7+4zdepO75S7L/lRCxRri9UU2GipGzkisvS+qlxknV7TlJXwnjFxtjt6OCPY9qtB0OSUYd+truzIKjJqJ4/nvEnKXBHcDRHT7a9VHezL/J6eLb2MIhbsTXvBeUUpSDMq50OZG+l/vuQoPs99EimJbBZ379w3zjT7/P5bev096bWcPWbXUUZ8rUKi6f+Y1nOfjA2oJZq2FvJkvaiVDU63Sp9QkqNRQgBY0O1Y1nYlIqnCakaIWYNwVZ/PA5EZOO7jT7fvYjBPkf4dcFM9NtfMYt8vXxBF6oSBgKXdeIxiJYtoFpGRiruC+tF0kT4obAkxq6sqj6M9zT2UlfqvnFRyEEAx0ZenMppksVJmcr+EGIrmtk41E60vFFWjwLMeu67Eqm6Y6v/SJJxRw+/cgR/r8fv0N3bik1cznE7BECeWsGVCsqznw7ZPq6JJqCRMdKAVHgu4qz3wuIvq34wm/mmtbf2Sw0EcMPrq6q82JqFo/kHmVXbDdv5E8xUhtBCIGj2di6gz73QmssmNapSxdQdDu9PJh5aEfU2G+H2C6D2oceekidOnVqW4693Qj8gFPffYcXv/Y6dsQitYzhxVrfnx4tkO1M8fFfeeamrd5m4Ych10Zm+MrXXqejbX30sL6+r2AYJcJw452xlarLQG+Wgd6NzUAaZg7XyRndGFRBu3VehoqKvzmjyNcVnbHWmWQsh+FKyMkpSJiCo22KjmgJjMMIY+t5zzeKs/yL4/dzorO5XgelFN88eZ6Xz1+lJ5daNeAKQg73/r+4QQbQqBYUJ78c4LsQzawtkKWUwvUlURkhHrH49G9kyHbdmfzSD4doT//v6Nrq2kfzKHh5rlevM1ofYdKdwJONvgBLs+mwO+hyuhmI7iJt3Xm6oxDidaXUmnSc9zP3bYBhGjz2iQfYe3wX3/vbFxm5PIFu6qRyiRUX8ZRSVEt1yrMVdF3nsU/ezyMfvRerhaYLpq7T15FGzsndrueFMzt7lM7Of9xUcAeIb6KzNpSzGHIaXcbAWEwH7UsKfucR+N4gvHBdkbQVSbv1Ab7mKwJf0BuHvoSiI6YBCQguoISN0Dtbfsx5TNeq9CWSHGlrTm4XGrOEjz90CD8IOHVpiO7syhm8ZRRANGZngad446sBgQ+xLKy15iKVwg8k7akoiYhNKR/yrT/L8/O/m8OJ3gleh0YQjjcd3NNWhrSV4QT3Atz0utXE3cNBeT+4byM6+nP80u9/hsnhGd554RznT13BrbqNusNcs+OCJkraejM88dkHOXDfbpw1WuE3Csc26WhLUK66JGLNt4VXKntR6kcIEaDU+m8rpRoM8dgmfi8ZXCahRRD68m3fjiH41AHB0XbF351VDJUUcRNSNpuutVc8xawLtiH45eOCbBT+4zuSQCoMTQMRBf8saKkGu6bF8MOQehDwhXuOY67Ab18Juqbx2ceOEXUsfnxmkFTMIR5Zeh10vca83dXgqyGVaUWyc+3z5ochSkFHKkZsrmEpkdGZHgl47Ttlnv7ZO+DlqhRqFa2ZtXA3BfV5vB/ctxlCCDr6cjz7xSf58C89QblQZXo0j1vzkKHEMHUS2TjZrnRLs/TV8PiDe/na82+vK7hLaVMoHCOdfgfPa84IfCHqrk9bJo7dDP1wGYThDEKOYdsfZS3i9J604Hcfhfem4UfX4XpRYQhF2gFLbz7Q+2EjoLsBZCOCzx+C4x2CqNn4/rP9in+4oeiPKYQwABfln0eY9645xvVAKcVoucynDhykJ7GxQKlpgo8+cIj93W38/UunGZsp0p6OL8riBY3s1a0oBk9K4mtc5vls3bEM2pLRJS+dTKfO+Vdr3PfBGMlc69Y9loVQKJaXjfhpxfvBfQdBCHGTGbKd2L+nA9PU8f0Qcx2LjYXCfaRSZ9E0FynXk4E36Ig9nc1NmW+HlJLQe52ksQ9Na24x2NIFxzoaWfx4RfDaiOLMJEzXQIgGq8bSQRc3J1JIBV7I3KxK4BiCwzl4rFewO7203f/Zfo3xmuT0lKI3phAiCnISJccR+ub0f+ahlGKoVOSh7m6eGdiz6f3t687xW59+gu+/dZHXLg6hpCIdjxCxTcScxePYhQbTaCVZ6VBKQqkQAnLJRhlmuS01XSAEvPd6jYc+srV89zlDxS0+xs7C+8H9LoAvQyaqFSZqZa6W8kzWyvhSoiOIWhZ7Ehm6Y0m6onHi5ubLNbZl8OQj+/n+i+fpXkfADcM4ExPP0N39PPV6B81y32v1gGQi0pQpyLLHDc5iCYOIeWzd3xVC0BWHzxwUfOYglD3FZFUwVlYMlxtZeSAbQd7SoTMGPQlBe3Ttco6hCb5wQENKybsziu4Y6CIK/jnQsiA2p6kSSslIucT9nd384pHj61aUXAkRy+STj9zD08f3cfrqKD85e5WxfJFkpEZbMmD0vRAzopi3KVBKES7wLLAMnWzCIWqZa86CokmNwTPuHQjugrW9Bn668H5w36GohwETlTKvTw3z6tgNPBWgITCEjmMYaAgUiqAieWdqFE0IpFIcz3XxRPdu9iazm6KaPXzfbs6+N0p+tkom1Tw1slw+QLl8iVjsKq67dnkmlBIpJQf2dG6oUhGGZQiHSDpPIFoQ3OKWIG41SjetgK0L/tlhjf82KHlxVJG2dBJmiArHNsWemXXrzLp1fmZgLx/ff7BR128xEhGbDxzZzaOHBpicrTA1e5Zq/UXKEwI7Mb8o1FiIT0QMLMPANPR1NSbZEcHMeEDgKQxrK6mRAiF2Dgf9TqCp4C6E+BjwH2i8+v5UKfXvl9nmF4F/S+OSv62U+mILx/lTj/FqicFinsHiDBcKk5yeHmO6XgXA1g00IYgaJhk7QtqKkHEiRI3FWbpUigv5Kd6ZGqMjEuMXD5xgT3JjtEJD1/j0cyf4sy//hLrrN8U/b0AwPv4henu/hmnO4K8iCauUolr12L+nnWhk/Q+elDXC8DppYy+Gnlv39+8UTE3w+X06x3OKL1+UDFcccs41InofrHOhrh74TFWrZCNRfvuhx9ib3pzkbjPQNY2uTIL21CFGJ5JkHJO2jtbkhUJr1Lw8V2JYW5dZCwS6tvXnaidhzSskhNCBPwSeA4aAk0KIryulzi7Y5gDwvwFPKKXyQoidx+jfgfBlyHuFKX44fJnB4gxKgRsGDBbzSCVpj8TQ5kobSikCJRmrlBiuFAFoc2LsSqTJ2FE0IRpaHZFGvX7WrfMH77zEh3r38dzAARx9/Q9jR1uCz3/sPr7yzTfIpaPYTQZ4KSOMjHyanp5vYFnTeF6W20s0SinKFZeerjTdHevnCktZJpQF0uYeHLF1dmmtxP604Pfu13hp1OSF4TLTxRFiVo6EbWGsYs3nhyElz6Ua+MRNm0/tP8Tjff04xp3NRDWRQtMigNda42dx839bggYTS6Hr/32FpWae+EeAS0qpKwBCiC8DnwUWquD/KvCHSqk8gFJqotUD/WnDxcIUX774FrOuS9Q06YkmGSzluTQ7TcQwsbTF9VghBKbQb/pzKqUouDWmahVipsWxbNciFcCU7RAzLX40coWLs1P8y3seJmWtn4J3eH8XP/eJB/jqt98kFg1JNOmaE4YxRkY+S2fnd4nFruG6GdSceXIYSqo1j56uNHsH2tZVjlFKEspxBA6Z6Bex3P8CYnsdb9aDiCH4cL/O090B75VmeXVmL4OFPF546wU1V+24CUvX2ZvO8nhfPwezbVtSgmkGQggcZzdO/ByBZ9KC5R3CUKHrAie2lcG9hqG3o20BBXUno5ng3gvcWPDvIeDR27Y5CCCE+AmN0s2/VUp95/YdCSF+Dfg1gIGB7XUp2S5UfY9vXTvPS2PXSFsReuMN6trl2Wkuzk6TNO2mauVirkwDjan6q+PX2ZPMsCeZu/nwG5pGbyzFeK3EfzrzKr957DGS1vqfyCMHukjEH+Nrz7/N2ESR9ly8KVnhMIwwMvIpEonzdHS8gFJQLEWRSuPA3g662lNNB3alFFKVkDJPxHqYROzzCP8MINZd2tgJMPUMR1OXOdb3T0FEyNdrTFQr1PwAqSS6phExTDpiMdL29hotL4RtHqRz1xlGr0hMe/NllHpZ0t5vtMwkezlIVcExdoZS451EqxZUDeAA8AzQB7wghDiulFrkd6aU+mPgj6EhP9CiYzcNNwyYqJUpenV8KRE0AmDKitARia9obtAqTNTK/Mm7r5J36/TEkuhzQWmoPMuldQT22+EYJpZSDBbzTNarPNDWs2jK3hlJMF4r8ednT/Kl449v6Pfs687wP/7SE/z41Uu89vZVBIJMKoK1Ji9do1A4zPBomvb2N9jVf4O+7gxRx2RpjroUSgWEchqlfEy9m2T8F3CsYwghkMGVLWkIuiMQGkgN5CTC2EU2EiUbWZ+mz3bANHez7364ekZCdvPPS60kefQTrbfZWwil6ljGkS09xk5EM8F9GOhf8O++uZ8txBDwqlLKBwaFEO/RCPYnWzLKDUIpxfVygdcnh7hYmGKqXrmZAc1r6syX+5RqBMGDmTYebO+jJ5psabY0Vi3xR6dfRgG9sVuNJpXA41x+gphpbYrdoglB0rKpBB6vTQzxcEcfkdsC/I3yLD8cvsxHBg5u6Bi2bfLs00d48N5dnD43xMm3r5OfraFQWKaObTUyMKXAD0JqdX8udAvuOXiQB088S1eHhuu9SaX+Q4JwmIbQmJxjMjT+rtQcmRwQQsexHiDqPIGpDyy+JuG1mxZ3dyckKhxHGLu2eyBNw9R3s+tIF05sinpV35R0gOdKDFtj99Gt6bYGUMpFaBFsa+M+B3crmgnuJ4EDQog9NIL6F4DbmTD/Ffgl4C+EEG00yjRXWjnQ9cALQ96ZHuWHI5cZrRaxNJ2E6awasKVS1AKfl0av8cLIIAPxNB/s2cvRbNema5xTtQr/6cwrAGTtW1mKUop3pyfQhMBoUWkhZlhUAp/XJ4d4uKMfe8FCalc0zvPX3+NItpP++MYahgAyqShPP3aQxx/cx/hUkamZMteHZ5jOVwjCRkkhHrUZ6M3Q2Z6koy2xSFbAiDxD1PkgUpUJwjGCYIxQzqDwAANNi2LqPRh6J7qWQyyjr61kGeQsaBu3FNx2CBvCQRrLWncHhBCk4s/y+Of+mu//lY7dJxqMl3VCKUVhQvLMLySwI1tXVgvlNNHIs2iiNS+QMJT4XoAmBKZt7Jhy2XJYM7grpQIhxG8Bz9Oop/+5UupdIcS/A04ppb4+99lHhBBngRD4faXU8tYnW4wb5QJfvvgWY9USaStCXzTV1AXQhCBmWsRMC6UUM/Uqf3nhdXYnMvyT/ffSGd2YiW4gJX914Q08GdLuLM4yx2tl8m6V5AYWOldDzDAp+S7vzoxzf1vPzd/f0HSihsVXL5/ht098YNM3pmnq9HVn6OvOcN/R/rW/sABCCHSRQNcS2OYGDL3lJKC1tI3/zkI1Zh3B4HYPZN1wrOPsPWYz9JDFxTc82nrXF+SUUkyPhuw+anHo4a0rySglUUgi1uZensV8hXOvD3L59A0mRgpz1o+KSMymd08Hhx/czd57eptz7rqDaGo0SqlvAd+67Wf/ZsHfFfA/z/23LfDCkO8PX+S7Ny4SN2364xuX4hRCkLYjpCyHiVqZ//utF/jkriM82b173V2AL4xc4Ua5sOx4rhbz2LqxbhKYQhGqgEAF+NKfM/htlDE0oWMKE1s3mKiVGKkU6V2QpWfsCNfKBYYrRfo2kb1vO1R5K9lzLYUn65SDApWgRC0sE6iGxokAUnrAdPgT2p1eOp0+dLGzAsRy0LQoscjTfOBz3yf041x+p06202iqCSkMFDOjAX2HbD78S6ktXUgN5SS2eQJDX7/WETQM51/85lu8/fJFhBDEEg7Z9gTaHJnA9wKGLo9z6cwNnKjFh3/+EQ7dt2vHZPM7/05qAvXA5y8vvM6FwiQ90WTLqGJCCNqcGF4Y8rWrZ7hRKfBP9t97k464FkYrRb597QJdy2T9Rc+l6Lkk1sFe8ZVPLahQk3PGxje5xuJmnFMoakqBEIRScnKyjGXsoc3JIhCIuRLQK+PX+fn4evyWdhhUMO8QvWNRCYpMuSNUwkZfgi4MdGHgCBPmnJVCOc2l8ltcKL2JrTscjN/HvsRxLG3r6tCtQMx5lpp3ime+oOjaleCVb5cRApI5HcNcGtzCQFGcDglDeOijce77YGxLO1KlqgGCZPRzG/r+xPAMX/2TH1It1WjvSt8M6AthWgapXJwUUK96fOMvXuDig3v46Bceu2Mif6vhrg/u9cDnz86f5Gpxhv5YcyWY9cLSdfpjad6cGiGQkn968P6mAvy3rp3H1PRltx2vlhqOQk0c35MupaBIIH0QAmM+u1vZ9AYAQ4eq7/PG1EUGkhE6nR4yVhttToyT40N8bs/RbeNMbx47t3EpVAHj9evk/UkMYeJo0WXLR43SlEFKS6Mw8KXHmeIrXKqc5tHsc3Q4fdsw+uagaVHSsV9mpvgHHH+6l11Hbc69WuPdl2uEQUOaYC7HANFQnTz8SJR7HotsuUGHUoownCAZ+2V0ff1dqZMjef7zH3wXw9Ro78k09R0natE1kOO9t64ReAGf/hdPt9ThayO4q4N7KCV/c/FNrhZnWs5uuR1CCPqiSU5Pj/L3lw1+cf+9qx5vqlbh7MwEPbHlJVhn3BqWtvrpl0jKQYlaUEETOsYG/Bkdw6DsKUIpuFG9SsGboS+6G6kUU/XKsrOKuwM789Z1wxrXahcIpY+jxZq6J9WcNaGpWaS1duphlR9MfJVjqUe4J/nIjpnm3w7LPEDEeYqa+xKptl4e+2SChz4SZ3YqpDgdEPqgm5DI6qTbmivbtAKhnMC2jhGx1zQrWgLP9fn6X7yArgsS6fUxsYQQdPRmuHRmiFPfP8tjH93emfHdmrYB8PL4Nd6dGdvywD4PIQS9sRSvTlznnemxVbd9fWIIXWjL0hulUpQ8d1WBJV/5TLuT1IIKhmZu2CygYawMZU8S1WNUwwoXSu9SCoqMVzduXrDtEPqOq7m7ssbV6jmUkth6tIl7cp7nv/jaOnqUlJnjzOyrnJ595SZtdyciEfkkhtZOICcBMExBrttgzzGH/fc77Dnm0NZj3sHAnkcIm2T0FxAbeGZOfv8shakSyezGVCqFELR3p3np+beZHMlvaB+twl0b3CdrZb5x9SydkfX5j24W2lwd/iuX36Ho1ZfdJpSSH49eJesszwSohwEKdVM35nZ40mPGmwLYULZ+O0xdMFlrLOLZmoMpTCbqw5yaenfT+942iORNV6CdgFAFXK9eAMBssl6u4eOxfOOSLnTSZhvniq9xrXK+ZeNsNTQtSibxG2giQiCntnUsoZwFQjKJL22oHOPWPE794Cy5zs05Qxmmjq7rvPXihU3tZ7O4K4O7Uoq/u/w2hqYt4nHfKUQNC0+GfH3w7LKfT7tV3DBYcWzhnB/jcvCVT96fRkNrmbWXLsANFcGc5rYudBzd4UzhXS6Xt/cG3DC0dhBqxyyqjtdvEEi/6cAOYOBRYWUmhyZ0EkaWNwo/ohKUWjHMLYGuZ8kk/hWacAjk9shKhXIG8MkkvoRpbKz34er5UQI/xDA3H1PSbXHePTmIW/M2va+N4q4M7ldLeQaLedrs7etO7IrEeWt6hIllShsT1fKqni+NeLRMuQZJwWttYIfGVFEA9fDWS0UTgoge5aWpHzDl3n06b0KLgkgD7nYPhWpQIu9PYmnr42xr+NRYXarY1CwU8Fb+hU2McOth6O1kk7+DrmXxwyGUCu7IcZWSBOEICINs8ncwN6GRf/290ZZx1XVDR0rJ9PhsS/a3EdyVwf2lsatYmr6tC01CCDQEr01eX/LZjfLsiiUXAF0IlrP8KvmzSKW2xIxXAfVggfKgAls3cTSHl6Z+QCDvzMO4GfhByMRMiaHxAjdG8xSqHdTrs9tek572RtHFRu5HgcvaJYCEnma4PkjJ394a7lrQtQzZ5L8mZn+IQI4QysLaX9oEpCwThDdwrAdpS/6vGCsYozeLkWtTRGKto6AqqchPbt+Ma2dSDlbBrFvj7amRDXeMthJtToyXxq7xXN/BRSWYkcosEWPlU2vrxk1Hx/lw4Mo6tbCG2YIa+3LQgHpwK3MPFaQtnZgRZdqb5Mzsm9yXeXhLjr1RSKm4PjrD+cFxro/mmZwpo1A3Wf19uQonBq5RqpVJxGzSiSht6dgiqYOthi9dSkGhQXdcFyQg8Fj7Pm4kEhqDlXOcSH9gQ+O8U9CEQyL2WWz7BLPlv8YPb6BrrZXbVcojkFNoIkI68RvY5j0tSfTcmtdy+qJX376yzF0X3C8Vp5Gom4qK2wlL1/HrIYPFGQ5nbhkBuGG4avZtaBpRwySQElPTUEDRn0VfRkOlZRCwwOay0R3pNC5/2sxyZvYNDiSOEDO22stybdTqPu9eHuXltwYpFKsYhk7MsWjPxhexj4RxiIj1JoHSqbsB1yt5ro3MkIw79HWlySVjG9I9WQ8qQXFuMOs7jkWVIj1ImnuZR/UEVyvnOZ56fMdSIxfCMvaQS/0+1fqPqdZ/gC+n0EQMTaQ3NP6bks9qFiFsYs4zxJyfQdNad78apoGSK6+HbQS6sX1c97suuF8pzmCvwQ+/kxAChivFRcG9GaTtCOPVMqam4UkXqcKWMGNWwsLHqeFMA2m7cR51oSOAwfJFjqW3Tve6GrjMeEUm6wVm/Sq+ChAITM0gZyXJWgkK4x7feeEclapHKuHQ3b6yRIIXxJkq7yYbG6ImUlimjlJQd33OXholHnU4tKeDWGTrMvlqWELbwEtZIyDP3qa3N4RJOSziyhqOvrOlgadG8wxfnqBWcbGdPtr7vkSub4aa+wO8OS0dgYkmYggRXZayqJRCqRpSVVDKBQGG3kXC+TS2dXxLjDfaezNcvzCKHdmccfk8NE0jldu+ZGnnRMkmcbU4Q8xszclvBaK6xZXiNB9m/82fmZq2Zh24IxJnpNzI+qpheUOc3PVAcSu5rASSjqiJtYBnHzOSnC2+zZHU8ZbqmxS8CmcKg7xduELJryLm2u41Tbu5LiGVwnMDhs/WmbpWJ510GEh1YFprn5OR2aO0JW6JbwkBtmVgWwY11+f1szfY05ujr3NjGeNaqIbldZ8vHRePGNU1FlMXYn6Np+Tnd2xwv3FxjJe++SZDl8ZvdqVK2ehWzXSmeOxjn+TA/XHCcAQvGMQPLhOEYzdXnwRirlSpUEgMvQNHvx/T2Itp9GDovVs6a+nb18F7b12jFYpLjZmGJNe5ffpNd1Vwd8OAiXqZnsjmeKitRMy0uFbKL/KUbI/EuDw7s+pN0uZEMXUdV/p4obulWTs0SjL2XDB3Q8nR3GKmkaVZlIMiE/UxuiOba3tXSnGjOslr0xe4VB5GIEiaUTqc5QOsWwt5+7VJqkVFey5GQMjl8ghXyqN0Oml6ojmi+vKZWrHWSd1PYug1gnAxWyVim0ipuDI0RalS5/CeTrQWyy0EyscU60s2TGqMcj/r5TMoFJ7afnbQcjjz8kW+8zc/IRq36ejPLrrOSilqZZdv/sULPHT9GB/8/EM49gNzn3lz/PRgTsdfQwgDTUvecVu8fUf7+P7fn0RKuen7pDhTYdfBbuKp7XsRb3/heh2oBz5CiR1VczQ1nXroEy7I1HclsgRr1O40IdiVSFP23TvSaSmAiKHjhRJH1+mJLx+Qpt3JTR2nGrh8e+Qkf331H7lenaDdTtPhpHF0a8XA/tYLk9QrIfG0idAaZZqY4RDRLcbrBd6YucSN6iRy2f4AjWtTDxAxyizHQNI0QSLqMFWocPbKOEq2mlmzvv3puIRYFOld95EEbDszaDlcPTfMt//6RXKdSZLZOEIIQi2kFCtQSE5TTOVRHT4d/VlO/eMZTv3jreY5ISwMvR1D78Y0+uYy9I5t8TtNZmIcvHfXphkuSilqFZeHntle96e7KnMPlNpxLefQmE6GSmLMvSs7o/Gm1tf6YinO54cJZUPka6tha1D0Qp7oSaIvs9Boaw4T7iiwsbr7YHmMbwy/Qj106XTSa1I6w0By+idTuLWQaGLprdjwibWRSnG1MsaUO8vBRB8xY/GDP1neR0flMqnIKDV/qbSyEBCP2MwUKrx3fYJDuzs39PstBw19jsHTDBQWFa7zBJKNlBYXiMbtECileOG/niKRjmLaJr7hMpOZZCYzgRKy4XJG47E1fItMqo2fPP8GJ544hBPdOeXVeXzwMw8w+O4wbs3bcO19emyWQw/sZteh7TVuv6sy9y0mPmwKYsHj3R6JoQltzezdTz4rNQAAIABJREFU0nW64zq+FOtNANeFUCp0IaiFit64zb7M8s02tuYw6Y6vOztUSvH69EX+9tr30YSgvYnADnDtXInyrLdsYF8ITQjiRoR66PFW4TIFv3L7FlyaeBIQ6Nry1DMhIB61GZsqMl1onaaOo8cIm2zYsSmSZzdlujZ0LIUibm7cp2ArMH59msmRArFkhJpd5cqec0xnxzB9C8eN4tSjROqNP4WCib5hxg5e5ezp97Z76MsimYnx0S8+zsxkEc/11/39wlSJWDLCz3z+oW2vMNxVwd3U9C0NghuBVI28ZKF0rqnpPNzZx1T99iC0FFFTkbENaqHcst/Nk4rknFP9Yz3JFb1aDc3Ak+6c+UfzeG36At8ZO0mbnVqSVa+E4rTLtfdKRJPNrzU4uoUpdM4UBsl7iwO0G8S5NPEBouYsK51IIRp1+AtXJ/D91kgGx/REU+dLx0ViMsFxNjL9lCpEFzpRffv7Oxbi8pkbaJrAt1yuDbwHChw3iqaWhhZdGjj1KDLj850b/7BjG+cO3beLj3/xCfKTJYr5tZ9hACklkyN5nJjNL3zpWWLJrTX9bgY7a463BmKGRcQw8MIQS99ereR51AKf7lhiScB8rHOAl8euL1poXQ4KSV/C4uqsTy0IcXStpaWnhiGEwtI1fmYgTcJqnLfADalM1PFKATKUKAWaLqiadcqZCk4kxlSxzEShzMhMEdcPCEKJqTeMsHtzKdqTMUblBN+bfIMOO43RpImJlIrzr+exbG3dTjymZiAQnJ29yonMXhLGrQWridJ+srHrtMWvUvGyLHciTUPH80OuDE+1pDwTNRLgrv5W1vCxqHKNJwnZGC2zGpbpcga2pHt5M6iW6himzkT7CEpILH/1l7tA4HgRirECVypXOJjYmFn7VuPYo/to607z7b95ibEbM8QSDvFUZMmzHAQhs1NlgiDkxGP7eerT9+PcwSa61XBXBXchBLsSGa6VClj69r8ZAcqBxz3ZpUGiN5akP5Yi71ZJ2yuPtUFxU+xJOQzO1qnOBfhWzeiqvsQxNJ7rS2EM1Tn77hj5wQqVqfrNUtJ8TVQqScmrcqr4HwgNnUhXEqc7TXpPO1bEvEljDKTi1fPX8aTPhdIQ2WQEtbtOR2cE01w7+BQmXaqlgHh6YwwhQ9ORKM7N3uCB7H6MmzxzjffGP4ihe6QjIysG+KhjMjFdYk9PDmuTWiIRPYalOQTSX5bxJAiwKDHEI1TY+MskUB774yc2M9QtgWHpeLpHMZHHdptcBFVgSZvTs6c5ED+w7eWLldA1kOOf/d7HGTw3wus/PMfotSkQjWe2MTlUaLrOPQ/v5cQHDtDZt34lyq3EXRXcAfal2jiXnyCzSsC8k/BlyO7kUrcWIQSf2n2YPzrzMgnLXrGjVkfHx8fQBHvTDiNll+lagKULjE0sMigF1SCEasgTrsaV5y/jlXx0W8OKGSS6b2UhXhBSrNYp1VyUFRLTY0QUBFNVKiNFqm8N0Xa0h7Z7enAyDQMKqSSnC4PE4wahr3j3TIHz52bp64/S1x8nFlv51hq6VEZfxoptPbA0g0pQ51plnH3xWyqAUpmcG/0w93R/j1RkdNkAP/97T+RL9HU257SzMgRtVjfD9UGM27pNNXxsygzzIEU2LmhVD6vEjCRt9sbUDrcSPbvbeeFsASXUonWn1eC5AV3dbeS9PDPeDDm7eb7/nYZpGRy8d4CD9w7g1X3yk0XqNQ8hBIl0lFQu3nJ6batw1wX33YkMEtYsd9wJyLmFx5XMuPen2/hA1y5eGb9B7wqOTBEjRt2bxhAmuhD0J2xStsGNokstkJja+oK8Ug15XxVKYu+W6TlbpeIYRDIWyb7FnFupFMVKnXyl1mAzGKDhYJuNIKVbjT9lEDJ5ZpiJt26QO9xN7xP7maBIMagSNyIIA2xbJwwVN65VuX6twsFDKfoHYkvKLvVKwMx4nVhy87de1LAZqU3TZqdImbd4+6G0eXfkIxzu+gG5+CBVL4tUi4/n2CZDYwV6Ozbf3JSycsz44/jSvSn5a1JFx+MGj1Ji430DUklqYZkP5j6340oyAHuO9sGPJTRZPlc0BLW6+nLURJVqWCW3jmau7YTlmHT23x1jhbswuA/E03RF4pR9b13m0luBvFvjUKrhSboSPrHrMGdnJih6dZLW0mlrzIjfxi0XJC2DwzmdQj1gouZT8yVCNNQkDU0sLtkoCOdKJQ0pKshUFPxgEnPKp3d/CtNaWgt3/YCpYgUvCDGNhvyAFD56uLTpQjN0Itk4SilmLo6TvzZF8QGL2EBmUa6m64J4wiAMJefPFRgbrXH0eJp4/FZGW8w32CyteDHPSxdcKY9yX2bfosxxPoPvSZ9lT9urBNLEDRLMZ/GGrlH3fGp1n+gm280FGr3OXi5X3gXp4QZ1JoIkV4P7qKooGnkcXSdhWiQME0vTmrPfU4piMM2++HE6nf5NjXGrYNkmu4/28tbYFI4VXTN3r5XrZDoSROIONa96R8b43yvuuuAuhOBDvfv58sW3tj241wKfp3pW1weJmhb/w+EH+cPTL6ELbYl0gq05yz4QuhDkIibZiEHVl5S9kLIfUg0kUi3mVdu6RsYxiJkaxvkype9NoAzBvgMpzGVU7ip1j4nZMpoQWAsJ9kKhq5XrpkIIIrk4xXKR6j9OoB1xiT/ajbjNLlDXNZJJk0ol4JWXJrnv/gxt7Y0yWnHaa9l6AoCtmZSDOuWgTsJYXKpT6AwXjpOv9nGw80cknAmqXhqpzPkNqLrepoO7VIoJz+SdUjtnSmVc0YFLGggQNCQm5l+8CkXCMNmfSNETiWGuMKVvBPY8WauTE+nHNzW+jcCXAUW/QtpKrCnSd/8DR7n08kUqIzViyciy11cBtbKLZZvsO94oUSmliOyQtbOfRtx1wR3gWLYTxzCoBT4RY2vb9ldC0auTcSLsT63spDOP3ckM//LIw/zpuZMAiwK8o0fQhE44R3W7HQJBzNSJmTqdNIKDVPOdigJNzPukKqqv5Sn8YAJyJvvbY0SWCezlmsvkbAXDWN7fVZdrLYopKrqPkYtSv5BH1UMST/chjMUBQAhBNKrje5I3Xp/h+NE0uq+4/vYsoZTYlobhtIbxpAnBWG2GRGL5rs+ql+HtG5+mJ32WXbnX0YSPF8YQQqNUrtOW3ri4042ay7cnppnyJaaWJBM9wKg7RUzTMcTSe1MphSclb+aneSc/w8Fkiv2J1JzGfwNSSWb9KXJ2N0+2fXJd7k6tgCcD/vbaPzDh5tkf7+NzvU+vOtPYm9xD/6FOZrUak8N5BAI7YqLpGkoq3LqHDBXJXIyD9+7Gsk1qYY2EmSBr7axFyJ8m3JXB3TFMfm7vcf7qvTfoj6XueO09VJKCV+dLxx5fxG9fDYezHfz60Uf483OnqIU+OTs655AkaLe7GKsPE2lCEEog0AVLJGarp/Lkvz+B3mGzLxchukLGPlmsYBpLywJSBBhhBE2t/rL0ZICvQizdQOUc3GtFeHGIxFN9SzJ4ANPSqE95vPg318lmbAoTLrohqI65RLIW6d1RNH1z1y+iWUzUC+yJdy1gzizGfBY/VjxENnaV/sw7ZOJT+J4LKgY4zcv2qhA3rPDjmSKvFiFuttMd3w1aQ03IMtoYrl2hLqvYWmRRuUgIga3r2LpOqCTninmGaxUezLSTNE3qskItrHAwcR/HUo/e8cAOMOuXmXDzdNhpLpWH8KSPra88u4kbcfYm9jJ0ZIj+fV1MDM8wNZLHc310XaO9N0tnf24uq2+ci3JQ5um2p3fkOsJPC+7K4A5wX1sPb02PcCE/SdcdNu4Yr5Z5omt3U1n7QhxIt/O79z7Ff770NoPFPJ2RGJZukLGyjNWHFhlRrAelc7PkvzdOqjtCfzaySO1xHn4QMjlbxtBXqPeKEDNcW8HOk7e69oQQ6DkHd7CIFhkn/ujSduv6jEf5Sg0zojNbDxCWwIoYDf2NaR9Ulcy+6KZe0EI0dAQrQX3RwupyCKXFZOkgk6UDGAyzu2OQgwBylDlCKGCB0LjV4xeCCpgvrpRDwd9NxBj3++iM70bXFs924kaK/fHjTLhD5L1JQGEIC10Yi66vLjTSpk3Zd/nu2FWOp6PsT3TzeO5jtDvr155pFbJWgj2xbgbLI9yXOYjVhKjdg5kHuVG9gXIkAwe7GTi4cut9wSvQZrWxJ76nlcN+H7fhrg3uQgh+ds8x/q/iCysuVm4FZupVUrbDJ3Yd3tD3O6JxvnTscV4au8Z/u3oOqRRZO0rWaiPvTa9LztUNJaW8i/sP4/T1x2lP28sGSaUUk8VKg1O/3OciQJMWWmAjkY0ZxQrBth76iywEhRDoWYfa2WmsXUmsrlvBVSlF6VoNzRbolobnSdyaxIk0vmdENWp5n0RNYkY3V6JRQLWJ4H4LgmK9g4vjvTyTfAKlahBOoMIJkCMg64AH6CAs0BIIvZeqTPO3N96hoGp0xVZOKnRh0O3spt3uZdafpuBN4crasiO3DYOElmW8HuXDnU9ta2CHhr7/z/V9CFd62Nrygm+3I2Nl+Hj3x3l+9HlqYY2UmVpSZvSlz6w/S8bK8JGuj2BpO09b5qcJTQV3IcTHgP8A6MCfKqX+/Qrb/RzwFeBhpdSplo1yBaTsCL965BH+6MzLDd6pubVT2IJbAwG/euTRTdX6dU3jqZ49HM91cWpiiBdGBgnDBJVgGqU8HN1cMUh7oaIeSgKliOiCrjfLELNJZ1Z+uRVrLq4fLF48BWQo8fyAEBd/JEXem775mWHq2I5JJGbjRKybJQtX+ksW2IQm0GImpR8PkfnsfrT5LtiaJKhKjFhje9MUVEKJ64bYtj73EoFa3t90cDeExqxfpTvSPFVNSoUxd06EiICxC2HsWnH7UEn+/uoPybtV2p3mZKcNYZKzushZXUgl8WSdUAUoGl65lubcrM3XQ4+vDr1MxorTFdks/35zEELg6Ot7nrqcLj7b+1nemX2HS+VLS1Q8bc3mgcwDHE0exV7nvt/H+rFmcBdC6MAfAs8BQ8BJIcTXlVJnb9suAfwO8OpWDHQlDCQy/NrRR/mTs68SSrlqN+hmMO1W0ITgN+55nI5oa9xV0naEZ/sP8MHevVzIT/LiaJIXJ96i5opb03cx1z86t4CasnX6EhH6kzbicoV3LlVI9q+c7QdhSL5UxVwg1xAGknrNIwxChBVAOY6BDdaCRT0pqZZdKqU6mq6RzESJJhxCJdGXccLSIgbBTI3qGxPEH2tMyVWgEPMdfcxl66agWgmwrLnykIDQ27y1maHplIPlMuOV4XoBbZlmM314Y+YyVysTdDsbC7ya0FadmTm6hRv6fH34VX5l77OYO8hxrFmkrTRPtz/Nw9mHGamNUAtraGjEzTg9Tg/GCr9ToVLjpfPXuDo+Q3cmyQeO7KZzEwvd76O5zP0R4JJS6gqAEOLLwGeBs7dt938A/yfw+y0dYRPYm8zxr449wV9eOMVwZZauSAK9RV1jgQwZrZbojiX554cepD3S+hvO1HSO5bo4mu3kvm6ds7PvYsgMoWpUeTXA1AUJS79JnVNS8ZN/uEQku/q0uVzzFrkw1WseXt1vlFNsBYGNqsa5vYtT0zTmT6GSkvxkiXK5hkqqFeXm9LRD7cI0keNt6DET3Wq0aS9sODMsnXolwPMktq2jJJiRzV8rDUGwTsEzLwgZ6GouUM+4Jb43/hZtdnJLF/BTVoyxep5Xpi7wVMfRLTvOViOiR9gX39fUtsVqnT9+/lXKdY9U1Ob0tVFOXxvl1z/2GJ3pnSWUdjehmaeqF7ix4N9Dcz+7CSHEA0C/Uuqbq+1ICPFrQohTQohTk5ObM4W4HX3xFL9379M82b2b4WqRvFvblLGBUoqpeoWxWpmPDRzifzr+5JYE9oUQQvBw9kn2Jw6AUaAjatIds+iMWWQdcxEnunCtQnXKxYqvXB6SSjFbrWPqDdu/SqmOV28wGDRLQqijZtKgVg9WQtOwbJPAC6lXXMJg+SDaMKMWuFcKAOiOjpU2Ceu3MnPdbNT96/XwpnGGk2lF7VXc7BhuFpqAtkxz1/SV6QsIGrIHW402K8lPps5SDXam61Kr8caVYcp1l+5Mgqht0Z5qXJMfvzu4xje3F7XQ41vDbzO7Q5uxNp0yiYb55/8D/N5a2yql/lgp9ZBS6qH29vbNHnoJHMPks3uO8VvHP0DOiTJcLTJWLa6pq74QXhgyUi0yXC3SF0/xr088xXP9B++YCqUudJ5o+xD7YoeY9ibx5fKa0jdemkRfw2O05vlzDU+CatlFhhJd18AMIDBQ0xmQzd8ChqmDEFRKLmGw/DnV4ia1d6dRYePzxO4GFTCsS5RS6GZDFC3wJfVSQKLXwbBbMctS66LVBWHDSq2Z4F4N6rydHyRj3pkygaHphEpxvjh0R4633RiZKRGxFicp8YjN8Exxm0bUHGzNYG+8g5ixM9cPmklDhoGFvc99cz+bRwI4BvxwbrraBXxdCPGZO7Gouhz2JnP89vEnGKkWeXX8Oq+N30DSkLU1NR1HN26yRqRS1AKfQDVa/A2h83T3Hh7u6KfzDlMs56ELg8fbnqHN7uD1/MsIqZHUb/H53ZLP+OkCsc7VGULlmtvIkufq67oFaCFUoqhSDJbR3F4N8/R6IaBWqS/iLc9Ds3SCkoc/VsHqTWDGdLLH48xerhGUgptsQ6FAz5kkulvzYEjUihz35ZAvVrnvcC92E6qQ54vDDcPmJiWNW4GkEeGV6fPcn9m77RpKW41dbWnO3RgnHbu1Xlas1rl/7/ayhtaCJjQOp7bXbWk1NBPcTwIHhBB7aAT1LwBfnP9QKTUL3CR8CyF+CPwv2xXYF4yD3liKn917nE/tuoeJWpnJWpnB0gwTtTJeGDam2bpBdzTBQCJDZyROWyTWMAXZZmhC41DyGN2Rfl6Z/hHj9REsYRM3EpRGayhYtflHoah7AUpK/MBFd2iUYaYz4G2wDKIEmhIoDWSocGv+8lZpmsAfr2L1Nl6OZswgdzxOUJNIV+KUAvJTLtJpTmOlGfgypN1uLrNWSuEHIfcfaU6v5WJpBGeVJp6tQNSwGa8XKAU1kub2mSzfCdy3t4eTl24wMlMk5ljUXB/bNHjyyO7tHtpdjTWDu1IqEEL8FvA8DSrknyul3hVC/DvglFLq61s9yM3C0nX64in64inub9/Z2cDtSJopnu38FOP1Ec4V32G4doPR6zME0iNUFhrLdJsqiRt6hMIlCAKQOqoQg7qzZn19LRhSx9NDNF3Dc30sx1yi/KjZOv74YgcbIUSD7hjVMZMG5Vkfzw2RUq3bsGM5SCWbDoKzpRp9nWk6c2vPzJRSDNemiW4HdU/AlFv8qQ/uMcfiVz/yKG9cHmJwPE93NsFD+/vJxN/XndkMmlodUkp9C/jWbT/7Nyts+8zmh/U+FkITGt2RProjfZT8In81/hWSiRCpfDzl0hAlUDfN5TShoUkLqgm88RBTOLTK3klXOg1BrMb+fNdfYiQsbINgqoaSam6R9bbfRxekex3GL1dx3ZBIpDWLlNEmap9BGFLzfD7+1NGmZg3loE499EhtR4BVion6LHvjG/NcvZsQcyyeOrqXp+5egtCOw91HpN0EpJQUC1WKxf+/vTcNkuM87zx/T2ZW1n10d1XfjUajgQZxkDgIniIJUqQoipJIWYdJWVzZkjwaHzMxE9794AhHTDi8X2Z2YnbDa3vXo/HItnY8q3NkcW1K1EFKFEURJHiDAAkCII4GGn13V9edx7sfqgHi6EZfdTbyF4FAVXVW5fNWZf7zzed9jjyu7ZYbR+saPlMn0RImFF44w7ORiPpiuCMGm9uHMHwGtrJwlHOxdIEmGj4xGZlO8/70abBKl8WvrxVjfgFWAZomlIr21eKuC7jgZiz02MLujHCLDzOik03baxZ3V7loohHSl85SHpvKsP+WzXSllpeEVHRLdTsmdFl57L6HxwXWtbgrpTg3PMWp42MMn5pkZHga23Yu1iIpU56Duq5LOBKge0MbGzYmGdjSQWIFCS61QilFIVsk1hpGBHziw8fV4ZCO41IqWvgWqDOzFjSlobs6ruaiIbiuu/AMXUAtEjIJZTdNpN2EOZdSwcG8pEKk6yhsp/xen09fUlzzToneUGrJ0rSTs1k62qLcufvaZZovxbmivHItEQRbrT3By+P6ZF2Ke6FQ4tg7I7z0/FGmJjJomkYw6COeCKEbCwuAUgrLcjhzYoyjh88iCINbO9l72yZ6+5PlEMIGwHXVsrpQua7Cthz8/sqXRPY7Bjmt7A6C8oVkoUgS5Vw77lz3aWy8KcrI0QxIudnH7GyObK50cRsRiEYDxKLBBV08F/bQEVi4G9YFptNZAqaPxz92Mz5j+QvmumisIV1iTbi4+FYQAeThcSnrStwd2+HVl07wq2ePYFsOkWiQ9s7llQQWEUzTwDQN4pTFcfjkBMfeHSHREuajj+xhw0DlY/NXyoXFxyUF3v3gzqTS+FwdQXDnpXWhZDEFS5fQFYi2mrTfneLVX4wyPZNFM+Zn6/ObuEqRns1TKNi0p6JXCXzBKZEwIwQXWfBUSjExkyUcNHnik7cSj65skS5qBFEs74JaaVzlkvR7GZoeq2PdiPvkeJof/eOrnBuepi0ZwbfGrvaaJiRay26ZzFyBb/3d8+y+ZYC7H9hOIFC/anYigmGUmyDINUIhq3unIQQtk6yvuOilQ+Ca9l3YRjeERNJPpMdlJg3KEjDUxQuDNn/RLRUt0uk88cQHC5uucnGVu+iCY8myGZ/OMNDTxqP330R8FdEXft1Hixmm6Fo1D4cUEZL+pcswe3gsRNOLu1KKVw8c5+c/PoRpGnR0Vb55RyQaIBQ2efOVkxw/ep5PfvYWejbUr1Fua2eCQr5EKLL4AmI4UN3QPZ9rYLoORbGuEnilFMpVaOGlXULhkEE2W8RySmzcFWT6rM3sqIOmuxjmB6WHDZ/OXKZALP5B4lTOKTIQ7rpqIdVxXKZmsyjg4/t3smdb75pqDfWFUhyePVNTcXeVQilI+pe38OvhcSWN4UheJY7j8uyP3uKn//wmLa0REi3hqt06a5pGe2cc5Sq++Xe/5PjR81XZz3LoHeoinylcc5to2I+ma7grKL2wUoK2We4LesVRpCwXPWpeLP27EK5bdnMEQwaZ+bHoukZyg0nPNj+BiE4pryjmFa5TXtRUCiyrvNCad0pEjRDdwXKbNqUU2XyJ8xOzTMxmuXGoh9977C727diw5iJy2+J9WMpe02eslLSVZSDSgV+vTxtJj+anaWfuruvyzA/f5LWXTtDZHUerUBXIpYhEAxiGxv/4h1/z6d+6ncGttU8/7t7UwWvPXlmU83IMXSMS9JPPFTGr9N2IgkDRREXVxXBEAFVwMDdc21dsWS6xWDkB6kqffSCi0TXkxyq4zE06zE04WHkX21ZkZ20yuoUmQm8kwejkHJpoKBTJRIQ79+xg+2An4TU2vb6U/lA7YSNAwSnVbPZecC1ua9tak315rE+aVtx/9ewRXnvpBB1diYpkOK6EQNAkAfzjNw/w2O/cRW//ytrtrZW2rsSy2n12JmO8d3KUasmRbblEQkFCgQDjxVkMyglUynIwOq6d9FMqufT2lV1Hi61h+AIarT0arT0+LMslPV2ka0MQURr3tN5IWzhKOOAn2RKmNR5eVp2Y1WBoOne03cBPz79OZwUvGouRtQtEfSH6w+1V35fH+qUpxf3UiTFefO5d2jvjNRf2CwSCJo6jePLbL/GlP7yfYKh26enJnlaiLRHymQLBa/jdN/WleO/0GK7rVuXORrkukXgQv27S7k8wXpzBcVwEwexevM6Lmvcnd3WXLwDxeBDTb2BZNj7fwodkySoR32jQvynO4/330lZjX/Tulk28Mn2MOStHtIrZqq5ymbVyfL7/niXj9j1qw+npGY5PTuE3DG7s6iDqb8wqkFfSdEdPIV/iqe+/QjQWrHvseTjiJ58v8YufHFpT7fiVomkatz60i/RU5prbxcIBUq1Rilbl/cW27eDz+/AHyj7hgG7SFWxFz7mo3gCEF5835PMOyVSAUKi8jWjC5s0dFIs21hW2KhRzpRyFQIl7Nu3kS5serLmwQzlq5pGe28g4RWx3ZU1BVsJ4Mc2elkEGI41bbfB64tDIKH/5/AGefuc9vv/WYf7y+QOkC9de72oUmk7cf/mzw+QyRcLXmLHWkmQqxpuvnOL9Y6PL2n4mX+Dk1DSnpmbIlRau1b4ctt68CU3TFm2cAeVowu2DXWg+/ZrbrZQL0TCt7TEujaM3xCAhIQb3DlFwLLJ2AeeKDMtyRUZFf//l2b8tLWG27+hBAZlMgUymwEwuw0RpFjNg8G9ue5TPbbmbkFG/3703lOSe1A7GijNX9QetBFOlOVrMCB/uuKnhy2BcL3z/0GFaQwG64zH6EnGmc3lePNUcdfabyi1zbniK119+n/bOxgkP0zQhngjx9A9e41/82wcvNly+kjPTszxz9DhHRsfLjaGZ77y0oYd7t2wiEVyZaAUjAW66+wbefP4dUj2ti26XTEToaI9z/tw04koF3DMKq2STaItelUtQyhYJJMJsGthILxaj+RnO5SfJqxKGaPg0g2LeJRE3aWm9+tY2kQixc3cPozOzZIsFolqIuzp3cMfGbcsqClYL7kptp+CUeHHyKJ2BRMVcJ5OlNEE9wOf79zfMWK93lFJkSxad0Q9cjH5DJ1Nsjg5ZTSXur710Ap+p1ywyZrmEwn7On5vh5LExNt9w9e30O6Pj/N2BVzENg85Y9GKjENtxOXBqmEPnx/i9O28hGVlZLZs7PnEzR14+fk3fu64JN23uIVMokZvKYuigrdKdpVRZ2GOJENHE5QlBynWxs0UGH9qJaIIfkw3hdnpDSaZLGcaLM0wVsmRKJQb3BJgozZSzWOf7f1+oa5nwRbitbys74hvpCyVX1F2pFmii8UCAIAzmAAAgAElEQVTnbvy6j1+OvU3ECBL1rb40re06jBfTdAZb+M0Nd6378r7NhIiwrSPF4fNjdMei2K5L3rLYnKpfjstKaBpxz8zleeetYZLtjZmOHQqbvPyr9xjc2nnZLfV0Ls9/e/l1EsEgoStaiRm6RlcsylQuxzdefp1/e++dF4V/OYRjQR767f18/y9+hD/kX3RxORbyM7ShnfdknOJMHqtk4zN1VlKawLHLtdcTbRGiidBV7y1MZenYs4FI1+U1XjTRaPPHaDWjnLPS3Ld/E5s3Jcg7F1w2gi4aUSNImz9W8yzQ1aCJxv72G9kU6eTJswc4X5imxRdZUUy6q1ymSxks5bC/fSd3JG+oaacnj+XxmRt3YNkO701MIiI8vG0rN3Z21NusZdE04n7krbKfq9Fm7ReIxoIMn5lkYixNquODlPHXh89hu+oqYb+U1lCIs7OznJicYnNyZbOCzbv62XHHFt45eIL2vsXf29/ewmQ6S9Y0KGWKZOfKi0LGNasuKmyrXPXR5zdItccWLOtQmitgRgN03TKw6P7HZzMMdLSyf/tmjAYpwrZW+kIpfnfTR3ll+hgHJt5lqpTBr/sI635Mzbjqe7Vdh7xTIueUb+u3xfq4I3UDnYGWepi/KHnbIu9YOK6LJoKhaUR9gRVNPNYLEb/JV267maLtoGuCr0a9lCtB04j7awdOEIs3bmcWEUHThHffPnuZuP/65BlaQ0vbbeoGr505t2JxFxE+/PiHGD87zeT5Gdo6F66OqGvCTQNdvHr8LJquEU2EyM4VyKTLTTUWIxg2icRD81ExV5/cVq6EaztseXQ3+iJx5uOzGdpiYR7fv7sqwq6UYtbKM1FMM5qfZbQwi+XaaCIEdT89oRZSgThJf5RAhTM+/bqPO5PbuLV1iPezo7w9e5rh3ARjxZl5cRcu+J90Tac72MrmSBfb4xsaxgUzmp/j5NwUJ+YmOZmZYrqYQ5svha0EUOXqmL2RBIPRNjZEWtgUTdasaXy9ERECi4ToNjJNYXEuWySdztPe0TgLqQsRDvs5c3ListfmiiU6okv39vQbOjP51YVYBSMBPvtvPsa3/tM/MTkyQ1vXwgLv9xns3tTDGyfOUbRs4q1h4q1hbNvFLtkXywlrmmD4jCVm9WBlizglmy2P7iGUvNpdppRibCZDeyLCEx/eS7jCBdcKjsW7s2d5fvxdJopzF+QIv+67+NhRLq9MnUBDUMDORC/72gbpC7VVNCLF0HS2RLvZEu0GIGcXmbPzOKpc996v+4j7Qg2zhmC7Lkdnx3ju/HGOpycRIKAbhH0m3cHYVd+N47pM5DOcmpvCRRHUfdzduYm9yT5a/Y1xkaoEE2enOPrqCeYmM0TbImy9eZC27sa6s1ouTSHuUxNziNDw4WGBoMn5c+VEngsx+AGfge04mMa1v2rLcQmbqxe/SCLMY//zJ/jeX/yI0VOTpHpbFlw4DZoGewZ7OHTqPLPZPCG/iWHoi0b5LIRSitJsDkQY+tRewgtcdIuWzfhshqGeFJ+7+yZC/soJu+06HJg4xi/GDmO5DlEjQGdg6YJxjnJ5Z/Ycb06foT0Y45Gem+kNV2dxLGT4GzLqRSnFu7NjfPf9N0hbBUK6j57Q1WJ+JbqmETMDxMzywn3Rsfnp2aP8+Oy73J7q56G+bYSMxl8vWYxS0eIn3/gFR146Xp7cmAZ2yeaFHxxk2+1DPPjFe9ZcabbWNIW146PpS+qTrw5HbIq+PLqrY9rBiz1AK4mua7iOy8x0lrb5mey+vh5+deIUnbFrLwQXbJvdvWtLXLkg8L/8/ku89sxhYskw4djVsyq/T2fPYDdnJ2c5fm4STROC5sJulytxLJvCdI5oT4L++7YRSFz++UopJudyuErxG3fsZO/m3opmEY/mZ/nB8EHO5qZIBaKY2vIPYV00koEoSinSVp7/cuwZ7kptZX/Hdky9KU6FNZG1Sjx15jAHxk/SYoboCa2+nLBfN+gKxXCUy4Hx0xyeGeU3N+1hKF7/ngcrRSnFj//+Fxw58B4d/anLLnRKKQ7/+l1A8fHfvb9+Rq6Cpjiiz52ZwlxDR6Hp0CjnW9/HFQWiCBfi9E5swXCrM9OYmcxcFPdbNvTw/ImTlGwHc5HZcbpQIBbws6UCIVaBkJ+PfOFuhvZu4qmvP8voqQlaOuKYgcu/P02EvmSC1kiId4fHmcnm0TWNoOlbcBbnOi6l2RxKwYb9Q6R29CCXLG7bjstUJottKwa723jktu20Rit7u35kdpjvnj6AT3S6g4lV38mJCHEzRNjw88LEUU5kRvmtgbvWFNLY6AxnZ/jbdw+QtUv0hBIVWxzVRaM7FGPOKvK1d17gns5NfLxvx5orcdaS8eEpjrx07Cphh/Kx0tGf4vCL73Hbx/aQvEZOSaPRFOKez5cWbY+3FDlzjnPJ45hWAE3pKBRZf5pzrcfZMLGtwpZeXpYWoD0a4VM3bee7r79NIhggYpoXDyClFFO5sl/2q3feUtGV+P5tPXzpTz/Hq88c4pWfvcX06CyhWJBIInTZARwOmOwZ7GEuX+DsZJrR6TlcVQ7T9OkaWA5WpggitG3tpGNvP4FECKUU+ZJFrlC6GEmwd7CHfUN9dLVEK+5Ce3vmDN859SKt/nDFwiUNTacrmGC8MMffn/gFv7PpXiK+xsh8riQn56b42jsvEJifbVeDqM9PyPDx3PkT5GyLzw7sxmgSgT/6ynF0XVv0mL0YLHHwuCfulca2nFXf2s+ExxGloamycAqC3woyF5zG0ov4nMr6RRUK+4pU/9v6+4iYJj888h7n0nPMx1DgAluSbXx8x1a64pWP3w+E/dz5yZu55aO7eP/QaV7+8ZuMvD+OppUbWwdCfsyAD03X8IvGQGuc9kCAsfFZ5nJFMoUcrqnTsruXyEAbRsDHjHKQ6TlcpWiNhtjR38nGjha29qYq6le/lJOZcb5z6gCt/kjFo10AUoEo44U5/uHk83xp073rykVzJjPN1955gbBhEvFVdw1AF42eUJyDE2fQReMzA7uaInxydmIO3xKeAdPvIz157VpOjUZTHMXlmt+re6+j2Yi64lZrXl5dqXx9EE1kwRnA9s42hlIu52ZOki5k0MSlLRyjNRxF152q9uj0mQZDezexZc8AubkCkyPTTJyb5sy755gencW2HJSrMEyD7lSM2z5yEx0bkrR2JvBF/MzkCtiOi+uWI2l8hk5rJESgBgtMBafE90+/RMwXqIqwXyAViHIuN80vx49wf+eNVdtPLZkrFfj60QMEdV/Vhf0Cmgg9oTgHxk+RDIS5r3tLTfa7FqItYazites8WSWbaOvKMsjrTVOIu+HTrxmLfS1i+VZmI+MoW11cRLW1EqZjYtrVuQW/ECnjuLMUi69QLL2O5QwDiqCmCIYoxz67MDtXTrsXCWEamwj4b8P03YBI5YVMRAjHgoRjQTZs7WbvfTuW9b5wsH5RHz8beZs5u0BnsPq9RNsDMX45+g5bYz30hprn9nshlFL84PQhCo5FZ7C2IcSaCJ3BKD8aPsINiY6quYIqxdZ9gxz44euLTrCUUjiOy9DNg3WwbvU0hbi3paKcOj5GlJUveEVzrURzrcwFp9BUuWOPoNE7NlSViBkEwtFx0plnKJReAxSaxNC1DkQW96krVaJkHadovYVImFDgXoL+29G0xj4xqsn5/AwvTx2jM1CbJtGGphMy/Pzz8Kt8dcv9DR96ey3emh7htcmz9K0hImYt+DQdv2bw7ROv8Yfb725o/3v7hiRDewd479X3ad+QvCpaZvT0BFv3DZLqba4L/rLEXUQeAv4c0IG/UUr9+yv+/kfA7wI2MA58WSl1qlJGdvW04K5y5q6h0Te+lWxwlqx/BsMxieWSmBX2tQOIFOkdeAkj+FMKpSC61nlNQb/8vSa63ga04aoC2fxT5ArPEA09jt/c1dRCs1oOTh7HJ3pNE3/iviAj+WnO5afpadLZe962+N77b5D0h+p63LQFwpzJznBg7CQf6txUNzuWQkT42JfvAwVHX3sfw9Dw+U2sYgnHdhm6eRMP/c7+pjsHlxR3KavTXwEfAYaBl0XkSaXUpU08XwP2KaVyIvL7wP8GPFYpI9tSa1ts1NCI5luI5quXaRYIjZDq/QmaMYdp3rJsUV8ITQJoei+umyOd/a/4S3uIhj+HpjVm0bRqkLOLvDZ9kjZz6ezeSiIimJrBwckTTSvub0+fJ+9YDZE5mvSHeXbkGLe3b2zo8EgzYPLIHzzI2OkJ3nnpOOmpDLG2CNtu3Uyqr7LZzLViOTP3W4FjSqkTACLyTeBR4KK4K6WevWT7F4EnKmlkoiV8McKjEQuHReLH6Oz/Kdk5H9Fg35qE/VI0LYSoDRStt7DT50hEf39+dr/+eT8zhqtUXSoltvjDvDlziod7duNbQZJUI6CU4tmR94g3SEhn0PBxNjfD8bkJhuKN3RP2Qkx7R3/zJWItxHKUsgc4c8nz4fnXFuMrwA8X+oOIfFVEDorIwfHx8WUbqRs6G7d0kJ7JL/s9tSISP0ZX/0+wijGKBT8tbZWdaYoIht6Dq+aYnvsLHGeqop/fqJzOTmLUqQ6LLhpKwWTxg9C3U5kRvn7iB3x/+FnyTuM2aziVmWa8MEe4gUoBBHUfz40cr7cZ1x0VPXtE5AlgH/AfF/q7UuprSql9Sql9qdTKro57b91EYYlwpVoTDJ+js/+nFItxbLscL15pcb+ArqVQKs/M3F/jqsa7yFWaU9lxwnWszaJQjBfSF58/PfoClmtxLHOGQzPH6mbXUhyeOY/O4gk59SBhhjiaHidvN9b5u95ZjrifBfoued47/9pliMgDwJ8AjyilKj616e1PEosFyedKlf7oVaFpRTo2/AzbCqFck0K+RFdPC3oVy6DqWgrHGSObW/DGaN3gKJexQrqujTsM0Tibm774PGKEydp5lFIE69jHdSlOpCcJ1yimfbloImgijBXm6m3KdcVyxP1lYIuIDIiICTwOPHnpBiKyB/jPlIV9rPJmlmPHb/3QFtKzuWp8/Ipp6zqA7svh2CGUAtdVl9Vxrxa63kW++Cwlq3Fnj2vFdh0Uqq7ZjbpoFN0PJhKf7L6bW9p28tGuO9gWW7wpST1xXJdzuVlCRvWSvVaL6ypGc56415IlxV0pZQP/CngaOAJ8Wyn1toj8mYg8Mr/ZfwQiwHdE5HUReXKRj1sT227qIxwJkMvW1+cZCI2QaDtEaT76Jpcr0t4ZJxiq/oxJREckQTr731Bqfd7mrq3+Z2UQEZxL0qKjvjB3p/awKzFUsabYlWaymMVVqiHt8+sG72euj/WiRmFZoQBKqaeAp6547d9d8viBCtu1IIGgycc+tZdvf+NXBIJmRUvJroRE+xs4jh/QsCwHw9DpH6xdJICuxbGdM5Ssd/Cb6yNV/lIM0VBQ1ZIMS+Eqd0XlhBuBrN0YLsuF8Os608XGuOu+Xmi8S/wS9A+2s+vmjUyO1+cWz/ClicROYpViKAXFgsXmrZ34atyGSyRCrvAz1GqL7jQwhqYT9wUpuXbdbCi5Du2B5soOtl23OlnXFUAQSo6z9IYeFaPpxF1EuOcjOwhF/MzO1H4mEG05CvOFx3LZAu2d8apFyFwLTRJY9vs47vma77sW9IXayDn1m4kKQnuNyh5UCpFynaJGpRkqRK4nmk7cAYIhP5994k6UUsylaxsWGE0cwyqFyWaLxFvCDGzpZDkdjCrNBXdFyVqf8cMbIykKdQqdU0qhUKSabOZuiFaPQ3FZuErha8AExPVM037byfYYn/vih3Act2YzeNFKmP4Z5mYV0ViQoe09FytA1gMhhGWvz6iZ/nAKhaqL22nOztMTaq1rnP1qiJuBVZfGrjYFx6KrxtUpr3eaVtwBOrtb+PyX70E3NMZH06suLrZcRJsklyuRaI2y7cbeFTWVro49YSz7/braUC1SgRgbIylmrdonbGXtEncmh2q+37WSMIP4dR3LbTzftq1cNkSbs1ZPs9LU4g7lGfwX/+V9bN3RzejITFWSnJRSTE9mKJVG6e5r5YadvVVNVlough/XncF112cUwh2pIXJ2saaz96JjEdB8bIl11myflUJE2BBuIdeAUTMiQnuw9mtT1zPNFeu1COFIgIc/vY+tO3p5+slXSY/kaGmNYPrXNjylFNlMkbl0nr6NSe7/xE1gnGyY1G4RQdBQlIC1VQAslizGp7KMT81xZmSas6OzFIoWtu2i6eXuS6nWCBt7WulIxki2hImGA1X9LgYjHXSHWpkqztHqr74wKKUYL87xaO++pisYdoHBWJL30uPEzcZp9n0hiicV8MS9ljTnEbwAIsLmG7ro6r2ft149ySsvHmdqMkMobBKNBVckQo7jMjOVxbYdUh1x7v3oToa291C0XmAuW8VBrAIFoFZ3G66U4uzoLK8cOs3b742AUrgKTFMnGDAJ+H1IgIsZuOdGZzlxZgJUeb/dqRi37xlgc38KswqhoIam86m+ffz10Z9iuQ6+KleInCxmGIx0sKd1Y1X3U012tnTx1PDhuuYIXMlkMcu+ZF/Vfz+Py1k34n6BcCTA7ffcwL47t3Dy2Bgvv/AeZ09PIVIWKb/fwB/woWnlXqeuUji2SyFfKs9SNQBh+64+du8boKM7cfEkEVunQc6Xy5GV/Yyuqzh8bITnDx5nYjqDz6fT1hJest623zSIz3fDUkoxmynwvadfJ2D6uHVXP7fetJFgoLKp7+2BOA907eRH596gO9hStXC6nF1ECXyy9+aaNgepNKlghC3RFGdzsw1Rz10phe263NbeX29TrjvWnbhfwDB0Nt/QxeYbuijkS0yOzzExlmb41CQT42ksy8FxXAxDJxgyGdreTXdfK63JKK3JyIKLpZoEaaRYs7IvWqHJ8gtZzaRz/PPP3+bYqXHi0SAdydiqZngiQjQcIBoOULJsnj94nFffPsMj99/Epgo3N7g9uYWJwhyvTJ2gK5iouPjm7BKzVp4vbrqnJu6fanNP1yBff/cANECwT9oq0hOO01Ondn+VRikHnDMoZwRlHwdnFCgBOmgxxBhE9B7Q+xGtvg211624X0ogaNKzoY2eDW3s2rf6ok+63kFjiXseXU8hsvRZ7LqKN94d5unnjiAC3e3xigmw6TPoSMbI5or8w5MvcfPODXz49q0Vm8VrovFwzx5AODh5nI5grGI+8dlSjoJr8YWNdzEQaexmEstlcyxFRzDKdDFHSx1n765SpK0Cnx1o/jaRys2irNdRxZ+DWy4FrSQAEqQcl6LAGQfnVHnSJYL4bkX8tyF6d11svi7EvVLoWgoQlHIq1m1pLSiVwTT2Lrmd47o8/dwRXn7rFMmWCH6zOj97OOQnGDB5/fAwZ0am+a1P7iMWqczCnqHpfKJ3D6lAhJ+MvIVf85EwV98j1HYdxgppkoEoX+i7m+5Q9Vow1hpD03hscA9/fug5oj6nLt2sAMYKGfa29bIt0VGX/VcCpRTKOozKfwdUDqW1XBTrq448CQCx+ffZYL2MKr2A+O9FAg8saxJWSZrXuVgHRAx8ei9KNcaqqlJFfPq170Qc1+WfnnmLg4dO0ZWKV03YL6BpQkcyRnquwN//jwPMpCsXpqmJxh2prfz+0IO0+iOMFGaYLmVXFCppuTbnC7OMF+e4u+MGvrr5gXUl7BfoDSd4sGeI8/nM0htXgZxdwtR0Ptm/s2ln7UoVcfPfQuX+FoUJeg8iy7sTEjFA60BpXajiL3Dn/g+UM1Jliy/HE/cVEvDfjqvSS29YZZRyQDRMc+ui27iu4unnjvDGO2fpTMVrWkWzNREmX7T47//fQTK5ypZoTgVifHnzfTwxcDfdwRZGCjOM5GeYLGbIO6XLxN5RLnNWntHCLCP5GWatPHcmh/jDrR/l/s4bMfX1e/O6v2sLveE4YzUW+JJjM1nM8dimPUQbrHHIclGqiJv9BpReQWk9q/afi+ig96BUATfzf6Hs4Qpbujjr98iuEn5zN5nc91DKLl+d53GVouSCJuATqj5bcdxJ/OYudC2x6DZvvDPMy2+VZ+z1KNrUGg8zPjnHD376Jp//xL6KXlx00dgc7WRztJOJ4hxnshOczk5yOjvB+cIsivJtsyE6XcEEeyIpeoKtbIyk8OuN18yiGpi6zpeHbuP/PvIrxguZmsSZl1yH0UKGz2zcxfaW5ksEA1DKxc19E+z3ysJegXNHtBaUO4ub/S9okX+N6MkKWHptPHFfIZoWIuC/nULxRXS9i7Tl8vaczSszFkW3XJOvw9S4pcXHYNjAV4XZcnlmWiTov3vRbabTOZ5+7gjJlkjd6t4DJFsjHDs1zpvvnmX3tt7q7MMfJemPsqe17KJSSuEoFxFpyMYVtSRqBvjqDXfyN+/+mvP5NB2BaNUmHnnbYqKY4dMbd3F7E4c+qtLLYL2F0nor+l2JFgd3HDf/HbTwV6u+bnd9H/mrJBjYD7iczOT529M5fj1VIqhDu1+j3RQyjss/nS/y7bN5snblU+ddNY7PGMRnLOxvd13FU88eQjSq7mNfChEh2RLhR88drqj/fal9Gpp+3Qv7BRL+IP9y24fYHEsynJul4FS22qZSitH8HBm7xBe33MqdHQPN62d3J1GFH6C09qqMQUkS7BOo0osV/+wr8Y7+VWDo7WS0j/C9kVmCmpDya5jzs2MRIWJotPuFsaLLk+fzl7VrWytKFVHKJhr+PLKIeL393gjHT0/QGq9vnO0FLlxgfvTc4Tpbcv0S9fn50tDtPL5pD2mryGh+DrcCx2XOLjGcm2EonuJ/ufE+bmqtT9hfpVCFZwFVtcgWEUFp7ajCD1GqUJV9XMBzy6ySgzMJfFqMgJ5jobouIkKbCWcLLqdzDgPhtX/VSikc9zyR0G9i6KkFt3FdxS8PHiO+wpIL1aYtEebY6QnGpzKkWps/UagZ0UTYl9rAYCzJU2cO8+bUCKBo9YcIrGAdwlWK6WKOvGORMIN8YXAfu9sq45uuJ8rNoKyDKElVNZtFxAS3hCodRvxLhzKvFk/cV0HaynMkfY728C5KpQMoVUAWyBIVEQKa4tVZa83irpTCcYbxm7sI+u9cdLuzozNMzmTpSjVWRmDZ/y288c4wD9x5Q73Nua5p8Yf4wuZ9PFzM8drkWX55/jjTxTxKKXyaTthn4tN0NMqdnRylyDsWebuEmn/thkQHd3VsYjDatmTZimZBWW+DcpEaFI1TEoPSL8AT98Ziqjg379cNo5n7KJReWlTgI4ZwNr+2+toXhN00txKL/E/XXIg5eOgUptGYP2siHuKVt85w975B/Ob1EbHSKKQzed4+dp5zozNEQn52bOmmpyPOh7u3cHfnJkbzc4zlM5ycm+T9uSkydhHLddBFw6fp9IYTDMaSdIdidAZjxMzll7xoFpR9BCWh2uSgSwTcEZSbQ7TqZBE3pgo0OJd6KjUtRsC8jULpZZSbQSRyVeraWjybSpVwnBH85q55YV/cF5gvWBw+NkqqpTF87VfiM3Qs2+bEmUm2DTZnmFwz8u77Y3z/x69jOy7BgA/LsnnpzVPs2d7Hx/ZvvyjeveEEe5PViWhqdJRS4JwCqc25IyLgCrhjoG2syj7Wx/1UjYn7QrjKvZgso2lRgv4PoespXDWLUh9EI+QcRcq/8q+57F8fx3XHiYQ/TSzypSUXecan5gCF1sC3yYauMzwyXW8zrhtm0jm+9/RrRMJ+OlMx4tEgydYoHckYr7x9mtcO1y6ppqFRWXCzZX94zXDBnazapzeuCjQwrf4IA5EOZq0PQvtE/PjNPfjN3YCN66ZRqkTOgb3x5bsglHJxnAkcZxhD76Ql/seEAvctKyZ2bDKDclczotoRDPo4dc4T91rx1tFzKKUI+C8/BjVNaI2HeOHVE1VvT9kclKDGobOKcvRbtfDcMqtkf/s2/vbEzwm5fsxLFmAMvQtdS+I45xkvnCBuZOgPpHHcMJqEL8tqhQsJSSVcN4OifLEwfTsIBfbjMzavKNHhzMgU/jV2n6o2Qb/J2OQctu3UvQft9cDw+RmCgYVno8GAyfmJNMWSXfE6/M1HPSJ9LuRRV4fGVoIGpj+S4tHefTw5/Ao+TafVDF+sM15yYdqKEg/czuN9G4joZ7CsY9j2aRQOAihk/n8XTSKY5hZ8xmb8vu3oq0xNPjs6S6jBT1JNk3JP2nTeC4msAaGgiWUvvKDvOC6aCD7Du4EHE+ZdrbUK6RSkqpUilyXuIvIQ8OeADvyNUurfX/F3P/AN4GZgEnhMKXWysqY2HntaB2gPxHlx4iiHZ8/OizUEdB/3de5gT+sAESMA3AjB+ZoV7jQKq9waTww0CSESqcgBVShaRMKNX6hJRBYVHI/KctPWHt58Z3hB0ZqcyXLT1m7vDgpAQqDFKDfeqNU5pIFWvRozS4q7lP0CfwV8BBgGXhaRJ5VSl6YbfgWYVkptFpHHgf8APFYNgxuNnlArn9lwOw/ZRTJ2AR0hYYYXrKEtoqHrbVWzxXbcpkkkcZwGXxxYJ/R3t3LDYCdHjo3S1hLGbxo4rsvUdJaA38eHbh6st4kNgYiA3g/2MahB3XWlyhkD6NVrELOcmfutwDGl1AkAEfkm8Chwqbg/Cvzp/OPvAn8pIqJWUmi7yQkbfsJGfWfNmiZri7usEUL1q2Z6lNE04Tce2EV3+0lefP0kM+k8Amzf3Mk9t26hJVb/PquNghjbwHqrNjtTadD7FsyNqRTLEfce4Mwlz4eB2xbbRilli8gs0AZMVMJIj+XhM3RcV6E3+F22AgzPz1szDEPnQ3sHuW3XAPlCCdOne0lkCyDmDlTBQCkLkep+P6IyiP9TVd1HTc8wEfmqiBwUkYPj4+O13PV1QbIlQqFU2Yp/lUYphasUscj6y3BsdAxdIxoOeMK+CCJBxLwdUdWdkyqVBwkgvsUb7VSC5Yj7WaDvkue9868tuI2UY/3ilBdWL0Mp9TWl1D6l1L5UauHCV0CGzfcAAAhmSURBVB6rp7+nlXyhscW9ZDlEw35Ci4TneXjUE/HvB3xlAa4CSinEnUACj1S9p+pyxP1lYIuIDEg5fetx4MkrtnkS+O35x58Fnrme/O2NQmcqtqJ+ovUgly+xoWv99Sz1WB+IFkeCn0HcCVQVMgJFjYGxHTGrVzDsAkuKu1LKBv4V8DRwBPi2UuptEfkzEXlkfrP/CrSJyDHgj4A/rpbBHouTbInMZ701rsAXLZu+rtZ6m+HhsSji2wXmrYh7tqLnknKnQEJooU8v2ouhkiwrzl0p9RTw1BWv/btLHheAz1XWNI+V0hILkkyEyeZLREKNF++ulAIFA33VCwf18FgrIoIW/A1cVUKs11Fa99pb4rkTiJjl9nrX6HtcSbyQhXWEiHDn3k3MZavb4WW1zM4VGOhroy3RmFUrPTwuIOJDCz2O+O9F3HMoN72qz1HKAucMaEm08B8gVYxrvxJP3NcZWze1Yxp6Q2aAFooWt+/eWG8zPDyWhYiBFvwEWuQPyzWhnLMoN70sV41SRXBGEDWBBD5e/owqJjAuhFdbZp3hN33s3bmBl948SUdbrN7mXCSXLxEJm/T3eC4Zj+ZCjAG06B+hrCNQ/AU450BAKR0kSHmOXC4AKKoICCImEngAMfcgWn2OeU/c1yF37hngjXeGyRdKi1YErCWuUsykczz2iZsxdO9m0aP5EPEj5m6Ubxe4o+CMopzTKOccqBKIDpJAjE2I3gF6T41rw1+NJ+7rkHDIzyfvu5FvPfUKfr8Prc6p/hNTGXZt62FoY+38jR4e1aBcg6YT9E6EXfU255p406h1ytBAOzdu7WFiKlNXO3L5En7T4IE7b/DqyXh41BBP3NcpIsKDH7qBWCTA9Gy2LjYUSzazmTyf+shNhBswNNPDYz3jifs6Jhzy84VHb8HnM5hJ55Z+QwUplmwmZzL8xoO7GNzglZrw8Kg1nrivc1piIb74qVsxfQaTM5maZK/mCiWmZrN8+qO72bmlu+r78/DwuBpP3K8DWhNhfvvTt9GZjDEynqZk2VXZj6sUY1NzlCyH3/rEPk/YPTzqiBctc50QjwZ54lO38uqh0/zkhXcxdI2WWKhii5y5QomZdI4bh7p58K5tno/dw6POeOJ+HaFrGrfctJFNG1I89fNDnDw7VRb5eHhV8edKKdKZArlCuZbNYw/fzNBAuxcV4+HRAHjifh3SlgjzxKO3Mjo5x6tvn+GNI8M4jovPZxAK+Aj4fQsKtFIKy3bI5S0KxRIK2NjTxu27NzLQ2+Y1WvbwaCA8cb9OERE6kzEe3r+De2/bwonTE5w+N8Xpc9OMTc5dLe4CrqMIh0z6e1ro72lloDdJqjVSnwF4eHhcE0/cPQgFTHYOdbNzqLwAWrJspmdzWLaD7bhoIhiGTiwSIBw0PbeLh0cT4Im7x1WYPoOOZOMUHfPw8Fg5Xiikh4eHxzrEE3cPDw+PdYgn7h4eHh7rEE/cPTw8PNYhUotaIwvuWGQcOFXj3SaBiRrvs5Z442tuvPE1L7UcW79SaslqfHUT93ogIgeVUvvqbUe18MbX3Hjja14acWyeW8bDw8NjHeKJu4eHh8c65HoT96/V24Aq442vufHG17w03NiuK5+7h4eHx/XC9TZz9/Dw8Lgu8MTdw8PDYx2yLsVdRB4SkXdF5JiI/PECf/eLyLfm/35ARDbW3srVs4zx/ZGIHBaRN0XkZyLSXw87V8NSY7tku8+IiBKRhgo/W4rljE9EfnP+93tbRP57rW1cC8s4NjeIyLMi8tr88flwPexcLSLydREZE5FDi/xdROT/nB//myKyt9Y2XkQpta7+ATpwHNgEmMAbwPYrtvkD4K/nHz8OfKvedld4fPcBofnHv98s41vO2Oa3iwLPAS8C++ptd4V/uy3Aa0DL/PP2ettd4fF9Dfj9+cfbgZP1tnuFY7wH2AscWuTvDwM/BAS4HThQL1vX48z9VuCYUuqEUqoEfBN49IptHgX+fv7xd4H7pXmKlC85PqXUs0qp3PzTF4HeGtu4Wpbz2wH8r8B/AAq1NK4CLGd8/wL4K6XUNIBSaqzGNq6F5YxPARfqSceBczW0b80opZ4Dpq6xyaPAN1SZF4GEiHTVxrrLWY/i3gOcueT58PxrC26jlLKBWaCtJtatneWM71K+Qnkm0QwsObb529w+pdQ/19KwCrGc324IGBKRX4nIiyLyUM2sWzvLGd+fAk+IyDDwFPCva2NazVjp+Vk1vGYd6xgReQLYB+yvty2VQEQ04H8HfqfOplQTg7Jr5l7Kd1zPiciNSqmZulpVOT4P/J1S6j+JyB3A/yMiO5VSbr0NW2+sx5n7WaDvkue9868tuI2IGJRvDydrYt3aWc74EJEHgD8BHlFKFWtk21pZamxRYCfwcxE5Sdmn+WQTLaou57cbBp5USllKqfeBo5TFvhlYzvi+AnwbQCn1ayBAuejWemFZ52ctWI/i/jKwRUQGRMSkvGD65BXbPAn89vzjzwLPqPnVkCZgyfGJyB7gP1MW9mby2V5zbEqpWaVUUim1USm1kfJ6wiNKqYP1MXfFLOfY/EfKs3ZEJEnZTXOilkaugeWM7zRwP4CIbKMs7uM1tbK6PAl8cT5q5nZgVik1UhdL6r36XKUV7Ycpz3iOA38y/9qfURYCKB9Q3wGOAS8Bm+ptc4XH91NgFHh9/t+T9ba5UmO7Ytuf00TRMsv87YSy6+kw8BbweL1trvD4tgO/ohxJ8zrwYL1tXuH4/l9gBLAo32V9Bfg94Pcu+f3+an78b9Xz+PTKD3h4eHisQ9ajW8bDw8PjuscTdw8PD491iCfuHh4eHusQT9w9PDw81iGeuHt4eHisQzxx9/Dw8FiHeOLu4eHhsQ75/wHxLGcL+VFFYgAAAABJRU5ErkJggg==\n", 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "N = task_params['num_scatter_samples']\n", + "x = np.random.rand(N)\n", + "y = np.random.rand(N)\n", + "colors = np.random.rand(N)\n", + "area = (50 * np.random.rand(N))**2 # 0 to 15 point radii\n", + "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n", + "plt.title('Nice Circles')\n", + "plt.show()\n", + "\n", + "x = np.linspace(0, task_params['sin_max_value'], task_params['sin_steps'])\n", + "y = np.sin(x)\n", + "plt.plot(x, y, 'o', color='black')\n", + "plt.title('Sinus Dots')\n", + "plt.show()\n", + "\n", + "m = np.eye(32, 32, dtype=np.uint8)\n", + "plt.imshow(m)\n", + "plt.title('sample output')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Keras training example\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "# Notice, Updating task_params is traced and updated in TRAINS\n", + "task_params['batch_size'] = 128\n", + "task_params['nb_classes'] = 10\n", + "task_params['nb_epoch'] = 6\n", + "task_params['hidden_dim'] = 512\n", + "batch_size = task_params['batch_size']\n", + "nb_classes = task_params['nb_classes']\n", + "nb_epoch = task_params['nb_epoch']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": { "pycharm": { "name": "#%%\n" @@ -45,93 +175,106 @@ }, "outputs": [ { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" + "name": "stdout", + "output_type": "stream", + "text": [ + "60000 train samples\n", + "10000 test samples\n", + "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Colocations handled automatically by placer.\n", + "_________________________________________________________________\n", + "Layer (type) Output Shape Param # \n", + "=================================================================\n", + "dense_1 (Dense) (None, 512) 401920 \n", + "_________________________________________________________________\n", + "activation_1 (Activation) (None, 512) 0 \n", + "_________________________________________________________________\n", + "dense_2 (Dense) (None, 512) 262656 \n", + "_________________________________________________________________\n", + "activation_2 (Activation) (None, 512) 0 \n", + "_________________________________________________________________\n", + "dense_3 (Dense) (None, 10) 5130 \n", + "_________________________________________________________________\n", + "activation_3 (Activation) (None, 10) 0 \n", + "=================================================================\n", + "Total params: 669,706\n", + "Trainable params: 669,706\n", + "Non-trainable params: 0\n", + "_________________________________________________________________\n", + "WARNING:tensorflow:From /usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.cast instead.\n", + "Train on 60000 samples, validate on 10000 samples\n", + "Epoch 1/6\n", + "60000/60000 [==============================] - 4s 64us/step - loss: 0.2129 - acc: 0.9350 - val_loss: 0.1012 - val_acc: 0.9682\n", + "Epoch 2/6\n", + "60000/60000 [==============================] - 4s 68us/step - loss: 0.0813 - acc: 0.9752 - val_loss: 0.0684 - val_acc: 0.9779\n", + "Epoch 3/6\n", + "60000/60000 [==============================] - 4s 62us/step - loss: 0.0540 - acc: 0.9830 - val_loss: 0.0736 - val_acc: 0.9793\n", + "Epoch 4/6\n", + "60000/60000 [==============================] - 4s 64us/step - loss: 0.0387 - acc: 0.9880 - val_loss: 0.0859 - val_acc: 0.9761\n", + "Epoch 5/6\n", + "60000/60000 [==============================] - 4s 63us/step - loss: 0.0304 - acc: 0.9904 - val_loss: 0.0875 - val_acc: 0.9766\n", + "Epoch 6/6\n", + "60000/60000 [==============================] - 4s 64us/step - loss: 0.0220 - acc: 0.9933 - val_loss: 0.0847 - val_acc: 0.9793\n", + "Test score: 0.08471047468512916\n", + "Test accuracy: 0.9793\n" + ] } ], "source": [ - "N = 50\n", - "x = np.random.rand(N)\n", - "y = np.random.rand(N)\n", - "colors = np.random.rand(N)\n", - "area = (30 * np.random.rand(N))**2 # 0 to 15 point radii\n", - "plt.scatter(x, y, s=area, c=colors, alpha=0.5)\n", - "plt.show()\n", + "# the data, shuffled and split between train and test sets\n", + "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", "\n", - "x = np.linspace(0, 10, 30)\n", - "y = np.sin(x)\n", - "plt.plot(x, y, 'o', color='black')" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "m = np.eye(8, 8, dtype=np.uint8)\n", - "plt.imshow(m)" + "X_train = X_train.reshape(60000, 784)\n", + "X_test = X_test.reshape(10000, 784)\n", + "X_train = X_train.astype('float32')\n", + "X_test = X_test.astype('float32')\n", + "X_train /= 255.\n", + "X_test /= 255.\n", + "print(X_train.shape[0], 'train samples')\n", + "print(X_test.shape[0], 'test samples')\n", + "\n", + "# convert class vectors to binary class matrices\n", + "Y_train = np_utils.to_categorical(y_train, nb_classes)\n", + "Y_test = np_utils.to_categorical(y_test, nb_classes)\n", + "\n", + "hidden_dim = task_params['hidden_dim']\n", + "model = Sequential()\n", + "model.add(Dense(hidden_dim, input_shape=(784,)))\n", + "model.add(Activation('relu'))\n", + "# model.add(Dropout(0.2))\n", + "model.add(Dense(hidden_dim))\n", + "model.add(Activation('relu'))\n", + "# model.add(Dropout(0.2))\n", + "model.add(Dense(10))\n", + "model.add(Activation('softmax'))\n", + "\n", + "model.summary()\n", + "\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer=RMSprop(),\n", + " metrics=['accuracy'])\n", + "\n", + "board = TensorBoard(histogram_freq=1, log_dir='/tmp/histogram_example')\n", + "model_store = ModelCheckpoint(filepath='/tmp/weight.{epoch}.hdf5')\n", + "\n", + "model.fit(X_train, Y_train,\n", + " batch_size=batch_size, epochs=nb_epoch,\n", + " callbacks=[board, model_store],\n", + " verbose=1, validation_data=(X_test, Y_test))\n", + "score = model.evaluate(X_test, Y_test, verbose=0)\n", + "print('Test score:', score[0])\n", + "print('Test accuracy:', score[1])\n" ] } ], "metadata": { "kernelspec": { - "display_name": "PyCharm (trains-internal)", + "display_name": "Python 3", "language": "python", - "name": "pycharm-40126efe" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -156,5 +299,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 2 }