{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with the ``zea`` data format\n", "In this tutorial notebook we will show how to load a zea data file and how to access the data stored in it. There are three common ways to load a zea data file:\n", "\n", "1. Loading data from single file with `zea.File`\n", "2. Loading data from a group of files with `zea.Dataset`\n", "3. Loading data in batches with dataloading utilities with `zea.Dataloader`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tue-bmd/zea/blob/main/docs/source/notebooks/data/zea_data_example.ipynb)\n", " \n", "[![View on GitHub](https://img.shields.io/badge/GitHub-View%20Source-blue?logo=github)](https://github.com/tue-bmd/zea/blob/main/docs/source/notebooks/data/zea_data_example.ipynb)\n", " \n", "[![Hugging Face dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/zeahub/picmus)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "‼️ **Important:** This notebook is optimized for **GPU/TPU**. Code execution on a **CPU** may be very slow.\n", "\n", "If you are running in Colab, please enable a hardware accelerator via:\n", "\n", "**Runtime → Change runtime type → Hardware accelerator → GPU/TPU** 🚀." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%%capture\n", "%pip install zea" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "parameters" ] }, "outputs": [], "source": [ "config_picmus_iq = \"hf://zeahub/configs/config_picmus_iq.yaml\"" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "tags": [] }, "outputs": [], "source": [ "import os\n", "\n", "os.environ[\"KERAS_BACKEND\"] = \"jax\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m\u001b[38;5;36mzea\u001b[0m\u001b[0m: Using backend 'jax'\n" ] } ], "source": [ "import keras\n", "import matplotlib.pyplot as plt\n", "\n", "import zea\n", "from zea.visualize import set_mpl_style" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will work with the GPU if available, and initialize using `init_device` to pick the best available device. Also, (optionally), we will set the matplotlib style for plotting." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "zea.init_device(verbose=False)\n", "set_mpl_style()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading a file with `zea.File`\n", "The zea data format works with HDF5 files. We can open a zea data file using the `h5py` package and have a look at the contents using the `zea.File.summary()` function. You can see that every dataset element contains a corresponding description and unit. Note that we now pass a url to a Hugging Face dataset, but you can also use a local file path to a zea data file. Here we will use an example from the [PICMUS](https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/home) dataset, converted to zea format and hosted on the [Hugging Face Hub](https://huggingface.co/datasets/zeahub/picmus).\n", "\n", "> *Tip:*\n", "> You can also use the [HDFView](https://www.hdfgroup.org/downloads/hdfview/) tool to view the contents of the zea data file without having to run any code. Or if you use VS Code, you can install the [HDF5 extension](https://marketplace.visualstudio.com/items?itemName=h5web.vscode-h5web) to view the contents of the file." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can extract data and acquisition parameters (which are stored together with the data in the zea data file) as follows:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1mcontrast_speckle_expe_dataset_iq.hdf5\u001b[0m/\n", "├── \u001b[1mdescription\u001b[0m: \u001b[36m\"PICMUS (Plane-wave Imaging Challenge in Medical UltraSound) dataset converted to zea format. License: The datasets and code provided on PICMUS are free of use. The only request is to refer properly to PICMUS - The Plane Wave Imaging Challenge in Medical UltraSound and quote the proceeding paper.. Citation: H. Liebgott, A. Rodriguez-Molares, F. Cervenansky, J. D'hooge, O. Bernard. \"Plane-Wave Imaging Challenge in Medical Ultrasound.\" 2016 IEEE International Ultrasonics Symposium (IUS), Tours, France, 2016, pp. 1-4. https://doi.org/10.1109/ULTSYM.2016.7728908\"\u001b[0m\n", "├── \u001b[1mzea_version\u001b[0m: \u001b[36m\"0.1.4\"\u001b[0m\n", "├── \u001b[1mmetadata\u001b[0m/\n", "│ ├── \u001b[1mcredit\u001b[0m: \u001b[36m\"H. Liebgott, A. Rodriguez-Molares, F. Cervenansky, J. D'hooge, O. Bernard. \"Plane-Wave Imaging Challenge in Medical Ultrasound.\" 2016 IEEE International Ultrasonics Symposium (IUS), Tours, France, 2016, pp. 1-4. https://doi.org/10.1109/ULTSYM.2016.7728908\"\u001b[0m \u001b[2mCredit or attribution for the dataset.\u001b[0m\n", "│ └── \u001b[1msubject\u001b[0m/\n", "│ └── \u001b[1mtype\u001b[0m: \u001b[36m\"phantom\"\u001b[0m \u001b[2mSubject type, e.g. human, phantom, animal.\u001b[0m\n", "├── \u001b[1mmetrics\u001b[0m/\n", "├── \u001b[1mprobe\u001b[0m/\n", "│ ├── \u001b[1melement_height\u001b[0m: \u001b[36m0.005\u001b[0m \u001b[2m[m]\u001b[0m \u001b[2mHeight (elevation aperture) of a single transducer element.\u001b[0m\n", "│ ├── \u001b[1melement_width\u001b[0m: \u001b[36m0.00027\u001b[0m \u001b[2m[m]\u001b[0m \u001b[2mWidth of a single transducer element.\u001b[0m\n", "│ ├── \u001b[1mname\u001b[0m: \u001b[36m\"verasonics_l11_4v\"\u001b[0m \u001b[2mProbe model name/identifier.\u001b[0m\n", "│ ├── \u001b[1mprobe_bandwidth_percent\u001b[0m: \u001b[36m67\u001b[0m \u001b[2m[%]\u001b[0m \u001b[2mFractional bandwidth as a percentage.\u001b[0m\n", "│ ├── \u001b[1mprobe_center_frequency\u001b[0m: \u001b[36m5133300\u001b[0m \u001b[2m[Hz]\u001b[0m \u001b[2mProbe nominal centre frequency (midpoint of the probe's -6 dB band).\u001b[0m\n", "│ ├── \u001b[1mprobe_geometry\u001b[0m \u001b[2m(shape=(128, 3), dtype=float32)\u001b[0m \u001b[2m[m]\u001b[0m \u001b[2mElement positions (x, y, z) per element, shape (n_el, 3).\u001b[0m\n", "│ └── \u001b[1mtype\u001b[0m: \u001b[36m\"linear\"\u001b[0m \u001b[2mProbe geometry type (linear, phased, curved, ...).\u001b[0m\n", "└── \u001b[1mtracks\u001b[0m/\n", " └── \u001b[1mtrack_0\u001b[0m/\n", " ├── \u001b[1mdata\u001b[0m/\n", " │ └── \u001b[1mraw_data\u001b[0m \u001b[2m(shape=(1, 75, 832, 128, 2), dtype=float32)\u001b[0m \u001b[2mRaw channel data.\u001b[0m\n", " ├── \u001b[1mscan\u001b[0m/\n", " │ ├── \u001b[1mazimuth_angles\u001b[0m \u001b[2m(shape=(75), dtype=float32)\u001b[0m \u001b[2m[rad]\u001b[0m \u001b[2mAzimuthal angles of transmit beams.\u001b[0m\n", " │ ├── \u001b[1mcenter_frequency\u001b[0m: \u001b[36m5208000\u001b[0m \u001b[2m[Hz]\u001b[0m \u001b[2mCenter frequency of the transmit pulse.\u001b[0m\n", " │ ├── \u001b[1mdemodulation_frequency\u001b[0m: \u001b[36m5208000\u001b[0m \u001b[2m[Hz]\u001b[0m \u001b[2mDemodulation frequency.\u001b[0m\n", " │ ├── \u001b[1mfocus_distances\u001b[0m \u001b[2m(shape=(75), dtype=float32)\u001b[0m \u001b[2m[m]\u001b[0m \u001b[2mTransmit focus distances. Positive = focused, negative = diverging (virtual source behind the array), ``np.inf`` = plane wave (preferred; ``0`` is also accepted).\u001b[0m\n", " │ ├── \u001b[1minitial_times\u001b[0m \u001b[2m(shape=(75), dtype=float32)\u001b[0m \u001b[2m[s]\u001b[0m \u001b[2mA/D converter start times per transmit.\u001b[0m\n", " │ ├── \u001b[1mpolar_angles\u001b[0m \u001b[2m(shape=(75), dtype=float32)\u001b[0m \u001b[2m[rad]\u001b[0m \u001b[2mPolar angles of transmit beams.\u001b[0m\n", " │ ├── \u001b[1msampling_frequency\u001b[0m: \u001b[36m5208000\u001b[0m \u001b[2m[Hz]\u001b[0m \u001b[2mSampling frequency.\u001b[0m\n", " │ ├── \u001b[1msound_speed\u001b[0m: \u001b[36m1540\u001b[0m \u001b[2m[m/s]\u001b[0m \u001b[2mSpeed of sound.\u001b[0m\n", " │ ├── \u001b[1mt0_delays\u001b[0m \u001b[2m(shape=(75, 128), dtype=float32)\u001b[0m \u001b[2m[s]\u001b[0m \u001b[2mTransmit delays per element.\u001b[0m\n", " │ ├── \u001b[1mtransmit_origins\u001b[0m \u001b[2m(shape=(75, 3), dtype=float32)\u001b[0m \u001b[2m[m]\u001b[0m \u001b[2mTransmit beam origins (x, y, z).\u001b[0m\n", " │ └── \u001b[1mtx_apodizations\u001b[0m \u001b[2m(shape=(75, 128), dtype=float32)\u001b[0m \u001b[2mTransmit apodization per element, in [-1, 1]. 0 = element did not contribute, 1 = full contribution, negative = fired with opposite polarity.\u001b[0m\n", " └── \u001b[1mtransmit_only\u001b[0m: \u001b[36mFalse\u001b[0m \u001b[2mWhether this track records only the transmit sequence with no corresponding receive data (e.g. a shear wave push pulse or therapeutic ultrasound).\u001b[0m\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Raw data shape: (3, 832, 128, 2)\n", "Parameters(\n", " probe_center_frequency=np.float32(5.1333e+06),\n", " probe_bandwidth_percent=np.float32(67.0),\n", " probe_geometry=array(float32 (128, 3)),\n", " element_width=np.float32(0.00027),\n", " element_height=np.float32(0.005),\n", " sampling_frequency=5.208 MHz,\n", " center_frequency=5.208 MHz,\n", " demodulation_frequency=5.208 MHz,\n", " initial_times=array(float32 (75,)),\n", " t0_delays=array(float32 (75, 128)),\n", " tx_apodizations=array(float32 (75, 128)),\n", " focus_distances=array(float32 (75,)),\n", " transmit_origins=array(float32 (75, 3)),\n", " polar_angles=array(float32 (75,)),\n", " azimuth_angles=array(float32 (75,)),\n", " sound_speed=np.float32(1540.0),\n", " n_ax=832,\n", " n_el=np.int32(128),\n", " n_tx=np.int32(75),\n", " pixels_per_wavelength=np.int32(4),\n", " pfield_kwargs={},\n", " apply_lens_correction=False,\n", " enable_scanline=False,\n", " focal_region_length=np.float32(0.0),\n", " grid_type='cartesian',\n", " selected_transmits=[0, 1, 2, 3, ..., 73, 74] (len=75),\n", " attenuation_coef=np.float32(0.0)\n", ")\n" ] } ], "source": [ "file_path = \"hf://zeahub/picmus/database/experiments/contrast_speckle/contrast_speckle_expe_dataset_iq/contrast_speckle_expe_dataset_iq.hdf5\"\n", "\n", "# we'll only load the first frame and the first 3 transmit events here\n", "frame_idx = 0\n", "transmit_idx = slice(0, 3)\n", "\n", "with zea.File(file_path, mode=\"r\") as file:\n", " file.summary()\n", " data = file.data.raw_data[frame_idx, transmit_idx]\n", " parameters = file.load_parameters()\n", "\n", "print(\"Raw data shape:\", data.shape)\n", "print(parameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data with `zea.Dataset`\n", "We can also load and manage a group of files (i.e. a dataset) using the `zea.Dataset` class. Instead of a path to a single file, we can pass a list of file paths or a directory containing multiple zea data files. The `zea.Dataset` class will automatically load the files and allow you to access the data in a similar way as with `zea.File`. Note that you can pass `zea.Dataset(..., lazy=True)` to only load the data once a file is accessed, which can be useful when working with large datasets." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Dataset with 4 files\n", "\" (mode r, 1 track)>\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\" (mode r, 1 track)>\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\" (mode r, 1 track)>\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\" (mode r, 1 track)>\n" ] } ], "source": [ "dataset_path = \"hf://zeahub/picmus/database/experiments\"\n", "\n", "with zea.Dataset(dataset_path, lazy=True) as dataset:\n", " print(dataset)\n", "\n", " for file in dataset:\n", " print(file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading data with `Dataloader`\n", "\n", "In machine and deep learning workflows, we often want more features like batching, shuffling, and parallel data loading. The `zea.Dataloader` class provides a convenient way to create a high-performance data loader from a zea dataset. It is built on Grain and does not require TensorFlow. This dataloader is particularly useful for training models. Consistency of shape is preferred, which is not the case for [PICMUS](https://www.creatis.insa-lyon.fr/Challenge/IEEE_IUS_2016/home). Therefore in this example we will use a small part of the [CAMUS](https://www.creatis.insa-lyon.fr/Challenge/camus/) dataset." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Batch shape: (4, 256, 256, 1)\n" ] }, { "data": { "image/png": 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tVhWNRtXtdo102+/31Wg0FI1G1ev1NB6PFY/HbWATj8etumTvlQwO3sM/J5OJisWiKpWKxuOxgsGgbdiAO507d04//vGPj/kKzeyrskAgoBdeeME4iGCKvV7P2l4m2qyz4icej0fJZNKw7VAoZIkaLJzWmja71+uZmO5wOLShI3xdfjcYDOq5557T97///WO+Qsdvz3wFubS0pNXVVds6IIsziQ6HwwoGg5pMJtaaUC3iqPF4XIuLi8pkMkokEmo0GqrX69Yu0RbFYjHDh4LBoDkm2RsqEEF2fX19Bpo/xba0tKQTJ04Ylg09jBYadgSttM/ns44EvdFUKqXFxcUpxZ5Go2GwUDwet22wUChkMmiRSEThcNg2v6go8dHV1dWZ7+kZD5A+n08vvPCCtdRUbkhGSYecM1eBR5JlaNofNhV6vZ5hP+1227AhfhZsiQnkYDAwCSs2a/gZSOpXr161ozxn9vQYvifJ9vKDwaAFyslkIp/PZ4RujvAAvgmHw2o0GiqVStrd3TXfqdfrtuY6Go3M99xdbY4ljkajJrDb7/dNHxKe7iuvvPLM+94z22J7PB5dvHhRqVTKqDyQZzudjpLJpMbjsarVqmE0cCAJqGA8YDf8EzDc6/Wa07daLRPS5bXH47E6nY7t1NLmU0nQsp8/f14fffTRFCY6syfXPB6PLl26pEwmY61tIBAw6hiYNefJsCmDTSYTo4+5mDknFsbjccPKMfDuZDJpA8b9/X0Nh0Pb2EHEgrY9k8no0qVL+vDDD59Z33tmK8hcLqfTp09bNUjL0u12p7Aaj8czhc9Av3AVmwmqYInBYFDdblfSYTauVqtG6el2u6bhB9Du8iODwaBhUfAkT506ZYrSM3vyLZPJ6PTp09YOs0rYarWMEhYKhQxyoQWv1+uGV/r9fsO+qR6ZaJNg4eQOBgO1Wi31+30tLi6qWCyaj5PkSehs41AUnD179pn2vWcyQIZCIV25csXO9aAKrFQqxiWDJE47MhwOzZE5WtPrPbx88XjcgmkwGLSTDcEtOSuEwEobTkCdn59XKpWyaiAcDhuPjYPBXnrpJdvKmdmTa6FQSBcvXtRkMlG/35d0yHusVCqm59jpdFSv1+3/sUZIh1Gr1czXksnkFG+RTofBDP4Uj8cVDAYNH2f7JplMml8xIIxEIraWGIlE9I1vfOOfVLHPij2TAXJxcVGFQsE4YWwcEJBY32JYA44oySaHtDNkXnCbRqNheA4SU/w7iuOpVEr5fF6xWEz5fF6ZTEaBQMDkz2h3mEoOBgMtLy/r1KlTx3bNZvZobGlpScvLy8adBSuET8tEWpL5H8EqHA5bYgWfxFe8Xq+16Kwl4tPtdlter1dzc3NKJpOKRqNKJBLW7Xi9XrVaralAyeqsz+fTyZMntba2dpyX7djsmQuQsVhM6+vrBloDetPiMjzZ29szig/YDCA3WRlVZ/4/rwHFIhwOGw7p9/st0CKJ3+l01Gw27d85Gtblp7kHMp0/f95oSDN78iwajers2bPGc6QrQYCZpYJWq6V2uz2Fb9MyP+i3bsstHfoeFSO+0+/31Wq1VCqVTJyi2WxOnXwIR5KCIZ1OKxKJWPt95syZZ5KX65H0zKCvXq9X77zzjorFoonZejweNZtNeb1ea3PhOUqHLVE0GtV4PDaHA7dhjRCA3d2oSafTGg6H2tramsKGyP6uIyKDXy6XJUn1et2C6u7urhHOR6OR7t+/r+9973sGD8zsyTCv16tvfvObKhaLVgkyZYY6NhqNLAFyLAJ/Dw4YCASMXtbpdIy6Q/chHZLJ2+22arWaVaW03LFYzLBGJPdQoKpWqzYYZBVxMBgoFAqp3+9rd3dXf/3Xf/1M+d4zNcVeXFzU/Py8tcRwzLrdrmX0ZrNpwYkKkiwKBSMcDpvYhEvJqdfrRionw/P/cWKUWCCTA8THYjEFAgE7Q0TSP6FoxGIxvfzyy9rc3NSNGzeO8UrO7GFteXlZc3NzdqgW1ZgrFtFoNExoguqR1hvF73g8rkAgYDxJfKper5tewMHBgVqtlp2LRCeCaC4MCUn2+lCL8EmWG0jq+Xxec3Nz+uyzz3Tr1q1ju45ftz0zATIUCunSpUu2EQO1IpFIWJAcj8cqFosW2NxJIkGNoIhyM0fBEmQhfSN4QZsCLiTJBCr4/WAwqFAopEqlov39fUlHakJIWA0GA6XTaY1GI73zzjva2dmZyeQ/IRYMBnX58mULenQiCC0TJNPptGGGbNBQ3SF8Al4+mUyMYYE/MbGG9hONRu2/aeWBfA4ODmzxAS4u7+eyKCQplUrZs/DNb35Tf/EXf/HM+N4zgUF6PB4999xzSqfTNon2+XwGSuOosVhMKysrpggO5YEVQVeunsPYAbg7nY4CgYBRNUKhkOn38fucByLJyLztdttk1Mrlsu3Sjsdj28dF0YV2fmFhQd/4xjfsQZjZ42v4HnxbxCQCgYDBNkykl5aWbA0QXJokCufRJYNTOU4mE/n9fhM8QU280WhIkh0q1+12NRwO1Wg0DNbpdDq6ffu2Njc37f1IyNL0UDKVSunSpUt69913nxnfeyYqyGKxqAsXLtjiPsMW2pJcLqe5uTlJMooN4DiBjmDW7/eVTCZNXCKRSFh7wnpXJBKZmii6u9hkah4MAjGO7vP5lE6nlcvl1Ov1rO2SDqvgTCYjj8ejCxcu6OOPP9b29vZxXtqZ/Rqbm5vTpUuXDJfGSIKsp+I3+A7yZePx2PayXVoa2zd0InRFwESdTsfgIeho4JfpdNo2xhDHoMPh5E52t8G/U6mUVlZWNJlMdOXKFb3//vvPhO899RVkMBjUe++9Z+RssjYUC1psjsQsl8uKRqM2xSNTIoHm8/mmAPZ6vW7B1gXLCXYQeeFEQhvqdDpG3kUclbY+nU4bQE8Vi3p0IpHQZDLR/Py8fvu3f9sUhWb2+FkwGNQ777yjRCKhZDJpXUMwGJxqhWOxmLEbMpmM5ubmVCwW5fP5jD/LxHkymSiZTNpGV7vdtp1/sGr2thGkAON0j4IlgJK04UkSsKXDwRJ74Pwd67HXr19/Jni5T3WAZKVrdXXVxG5RQ2FjZTKZWECSZJSHXC43tUsNRgn+yP9j2s3vE/yq1aqq1aqprEiyoApvjQpS0pSYKQET0QIqx1gspng8rnQ6rWw2q+eff16vvPLKM9PuPElGa722tmYbK+z0k1TR/kTAhGoRIQm6FAIgyRTYhe6EQAruTVLGxwmGUNQYEEoy+g/apeVy2YaOFBMIQLNd5vV69cILLzwTvvdUB8i5uTm99NJLkqR8Pq9EImGZ2RUcHY/HSiQS2tjYsJa5Xq8b7iMdZnkA9Xg8btNoVr48Ho9hQ7TNnFkjyZwSDNMF1Glj+Lt+v6+DgwMbzKA/GY1GjfTLQ/fuu+/OtPseQ1tYWNBLL70kj8ejlZUV5fN5Oxsd6k273TY+ImpQJEUSJwwIfCwUCqlarVrLDfeWrgamBf8fxR4Em8EiqUpJ4AxkyuWyyuWyJWOSOIrkfCafz6fr168/9dzIpzZAhkIhvf7668pms1NncSB0C4n7QRUTd1UrHA4rk8mYiC0DFED1eDxuQxicFxEBWhroQ2zYQBni/V1OWSgUMsIuvMzJZKJ4PG5V8HA4tKGQdEhsf+ONN6wimNnxWygU0rVr15RKpQwHhLjNhkoqldJ4PDafo+oDF+QAOAZzqPDgLyw04G8EORYOwM35OyAfEj2dChWm1+s1mhCv3ev1VCqVrEJlgs5piD6fT6+99tpT7XtP7Tc7deqUTp8+LUlWbYHpQdmRNHVMK3ge7e3i4qJOnz6tQqFgK1pUkbwGALrLiaT9eXAizT8B2QmUkHdpz8GV2JvFiZFWc7cvSqWSnn/+efuuMzt+w/fAjOkcWB2VZOIm+BqUm0gkolAopNXVVW1sbBg/NpvNmg9T+UlHwx5XuR5IiMLAVaJCvYc2nraczoXPyoCS14WF0Wq1VK1Wbcr9+uuva319/diu9VdtT+UUOxwO69q1a1PCD2RU2t9arWZad/1+35RSIpGIlpaWlM/n1ev1zKlxGgIsclQPit+CS3q9XqMLkWHb7fYU3wxzOWds3TDFDAQCarfbtvJIVbm/v2+tTiAQ0Ouvv67Nzc0pLcuZff0WiUR0/fp148PSSUgy/VCwbwRSJpOJtcLr6+va29uzc60ZFIIR0qJXq1XbmAEnd4/2IOC5x3lEIhEbEEky+T0GiFScTNKDwaAikYharZZpCbBIwc+32229+eab2tramjrv/Wmxpy5AejweXb582RyJoxC8Xq8t6Luq4eArt2/fNkyQs6rv3Lmjvb09STJHBDvCyK5kd+nwIeG9OaiLoCnJcCYeHHftC+4jeGQ2m7UjP/nMbNgQhPv9vjKZjK5cuaK///u/f2a1+47bvF6vrl27prm5ualAhE8RYFgBrNfrdtIlBnvh4OBAe3t7hjdLhyuErl6jJAucyWRSkszvkdwD+pFk1SO+jP9TtTIocnUDJBnVjAk5m2JAAydPntT169f1f/7P/3nqfO+pC5BLS0u6evWqtbQMQCRZkEJKDBA7HA7r4ODAqrgf//jHFviYKEuyipPqYDgcTtF7kLwni9frdXsvsnu5XLYWx+VXer1e27QBb0LAlwdNkk002XjweDwqFAry+Xx65ZVXtLu7q5s3b36dl3xm/89Onz6tF1980XBDcD+CERQwF7deWFiwQ9za7bY++eSTKU4kfEXaYKpJt1PhZwlodCd0Pfg1QS0YDBo+XqvVjF0B9gmmia/m83k7mTOXy1kVORgMlMlk1Gw2deLECZ04cUK3b98+rsv/ldhThUFGo1G99957SiaTymazU4K34H3z8/NKJBJG4wFLXF9f15kzZ2zPmknf/Py8otGoHYvgVosISLA7y9ogKj/S0TEOkgyHwjn7/b79LNgiIL0kC4I4OvgQjhwMBk3YdDAYKBaL6ZVXXnlmtfuO06LRqHEeXXVwMMLhcKi5uTllMhkVCgUb0vh8Pi0vL+vkyZNKpVKGObqK8tJhFUdVx6APOpq7AgvBGxaG25kEAgFjRdB2M92Gg8tnDwaDWlhYmBJ/TqfTtqnj9Xqtaq3X68pkMnr77benVMyfBnuqKsiLFy8qk8lIOhIOZVhCe9BsNhWNRo17dnBwIJ/Pp0QioXw+r7W1Nd2/f1+//OUvp1a9cApJNsVmCu6eGsd7E1DhtlFVUAnE43GjbHi9XhsSuce/UuEizgslCPAfIYL9/X2blgeDQZ05c0Y/+9nPju0+PIt28eJFJZNJu/dUWQRHjg2m4mK3Gvx6bm5OZ86c0b179/TBBx9MYZXRaNT8kEDmQjpwIanqgF7oeEjSg8FAlUplKhBKh/7MoLDf7yuRSCgajZqaD+31cDhUoVBQPB43LL5WqymbzRr97fz58/rJT35ybPfhUdtTU0HG43E999xzisViSqfT5iw4aLfbVSaTsa2AdDpt/49l/Gq1qmQyqY2NDRWLRXm9XjUaDVMBdw9DosWhekOogv8HMT2VSlnLw8/H43FrwaEEhUIha53AgTiiweXMIYPFLvjBwYEFTumQr3n+/HnbfJjZV2+JREIvvPCCJeVkMmkTYEl2xG84HJbf79fc3JxVi8lkUrFYzJSg5ubmVCgUJMmqRfwLBgS+WKvVVK1Wzd9ZbSUwAxP1+30rEGBqwIJIJpOam5uzxQboPeihSrKgTKCuVqt2ciI84kqlokAgoEuXLj1VvvdUBEiv16tXX31VhULBtlagTASDQaXTaSNar6+vK51Oy+/320oh61Uej0ebm5uqVCqm9J3P5yXJtPfAlVwJfFpvFxdEQICfpzUOBAJKJpNG5sXpANclmfwUTgwJmJYGVWmwJypdpvC5XE5Xr1596rccHgfzer165ZVXTFKMBJlKpTQ3N6d8Pm/Bam1tzbiR+AErsMFgUPv7+yqVSkqn00b/YgkAzBJ8u1qt2rCk0WhY95BMJi2o4icEvWw2q1gspslkYi27e9omSwx7e3v2mpDP+VloZwwiqXALhYKi0ajm5ub08ssvH/NdeXT2VLTYp0+f1pkzZ+TxeFStVq0dYOJGZXdwcGDTREkWhCDfUinSslBVMnyBjxgOh+30OaaEnU7H2nDOGUYBBRAcQQCqPjAggHgeLrBM6RB7QmnIPVExnU7b+chsB7lHNbzwwgva2dnRL3/5y+O5Kc+InTt3TpcuXVK/39fOzo4ymYxh1u5aIKdj4jMPblixHw22mEqlVC6XbYOFIIffsDQAdQieI5Nr1Hw4v91lbTCoRMKMRQioO/B43cUGpuXZbNboQJzLTaeGGvrp06e1ubn5VPjeE19BxuNxXblyxYYnyMnTHpDlOAuGdpU1Qp/PZ7JRHo9HjUZDm5ubhuOAH+Fo7NJy9rU0vUZItcAB7LTxVHhMMCXZ+7qn0mE4LiRhftd9SNrttorFomKxmA2bgsGgisWiksmkrl69+tSB5o+TJRIJvfbaawqFQkqlUpIOBxbtdluRSMRa7aWlJVsy4AAuWAvNZlO7u7umIEX15mLeVIJsWTG9Rh8gm81OnVEDbIRCFR0KMA1qP9B34GFSkVIoeDwe5XI5ew54bQRdEomEtfkPbty8/PLLT4WQyhMdIIPBoL7zne9odXVVkgyA5mYjIcbB6+6h6slkUvF4XKlUSpFIxIjihULBWm0c3RWVYK+VyWG9Xle9XrfT5mh32WNFCABKD1Jp7hnatEQPipW6upJ+v9+wJle+iuoilUopm80qn8/L6/WqWq3qxIkTeu21147t/jzNFgqF9J3vfEfxeNwoMKlUSvF4XJ1Ox860ZjiDb8CcODg4sPU+uh0oP1tbW5a8SfzwXV1VHhgRiOYOh0NlMhkVi0VLohxMx9CPqTaDQRgW7pKDx+PR4uKiVlZWjJ6WzWaVTCaNMykdJgPELJaWluxnOS3x+eeff+Jhnie6xT5x4oTOnz9vEmGsbzGogadI9hwOh9rb29Pi4qJSqZS2trZspzmVSimXy9lAZ3Nz0ypO2ht3SAMQzuvCdQsEAjZ8oUpwHY8sDTjP1JPztl0SMKA6fwdkQFXqKkePx2PbtKjX6xZYC4WCCoWCKZXP7NHYyZMn9fzzz5uQBCcTSrJW9uDgwLZSGGQUi0WdPHnSOhH2/cEGESSBXE5nwJCFzsKl9biHxvFZ2KRBQQj2QywWM5+VZJoAcDHxQYZB+B3wTq1Ws2Cay+WMgkZiRztyc3NTGxsbunXrlnZ3d4/zVn0pe2IryHA4rFdffVWVSsVuIu0051ajxO2ev8HRBn6/3wB0Jsq3bt1SqVQyReZqtTp1ShwYDaosUHMIZEynAbR9Pp9yuZxNuGnhGfKEw2ETwaAacBV9mGIjeVUqlaxFk47a8L29PQPP4bAVCgWNx2Plcjldvnx56uzkmX05C4fDeu2112yVENI/WHE2m7UhS6VSUbfbNe4t0+qlpSWDd/b29nTv3j1tb28rHo9btci9TKfTisfj1hmxiuoS0knCJGYCF1ATCubuf7t6k7T0/B0YOkyOe/fu2eSaAIo8GrQ5j+fwDHeWL/x+/xN/nvsTW0Feu3ZN6+vrqlQqqtfrJlPW7XYVDAZVq9XUbrd14sQJpdNp1et1dbtda3clTbWjm5ubarVaJkyxtLRkRPBSqWRgO8OYXq9n3EcGNawx9no9q2D5eSgYtNdMweFLAoJLspafChGsEwdGvJc/8XjceJGQdtmakA63i9bW1p4K0PxxsOeee065XM4mxVR/nU5HlUrFBnPBYFCrq6sW/GBBoDm6trZm+B1nvGSzWZ06dcp+9ubNm3bMgsfj0dzcnLa2tqzNZlhCkQAUxLSbpMnJneiYUvm2221L1GyDkaDZJHNP+KRb4vVWV1eVSCRUrVZNlwDNAShNZ8+e1T/+4z8e2/36MvZEBshcLqdvfvObWlhYsGAXCoWUy+VUrVatQmPPlVPaCE6ZTMb+DvFSsD2cAlCdiR1YDed+kIkTiYThRGCHTAKRuHdJv0yfcTqCHi0aq4koR4Oh0jLXajWrGiC9QwfioWRI5R669Prrr2t7e1v1ev0Y7tjTY/l8Xt/+9rcViURUq9VMbJkW06XCVKtVZbNZOyajWq1qYWHBJOt2dnbML6kam82mJWMwQ7aupKO9fTZa8BdWXNE6dTsSxC3QbqSTYlsMrcloNGrVIZ0YbA6I4XRqbOVIhxqSHFmL74Gv+v1+vfzyy7pz584T6XtPXIAMh8P6wz/8QwPE5+fntbOzY1NjgGmI1uAxtB04MdgNGwfsXLP7jIo3lScqOrQt0G+gS7irXUwAwX9cBXEXO2Q3l+qBwQ8OTuvP6YtgPbRYtHFkeQYG/X7f8E3Ottnc3NSVK1f0/e9/36qMmT2chUIhffe739X8/LwymYy2t7fVbrct6cFEODg4sGpqMpnYWekEDpIlGynu2UV0QWDqrVbLEqqrG+luweB3dCyuPiPVJRARbAq6HlYh2YrBV11hX2hsvI/72be3t5VIJBQOh+1cd7bUGIyGQiG98MIL+t73vvfE+d4TFyCvXLmiCxcuWNZOJBKqVCo2IYzFYhYswFIymYxhMARFnIjd1Hq9rnK5bMdgSrIMC5k7FAppfX1dPp9Pn3zyiXZ2doxrJskmylQI0DlcfJTNAwJot9udqkQIgjwMUDT4LO5pc8Vi0SpguJC8VyQSUSaT0XA41M9+9jNVq1WtrKxofX191mp/QXv++efN9+LxuIkzMLEmsLBn3Wq1lEwmre1kzxqfcpWldnd35ff7tbq6agMdeIfdblfZbNaGIj//+c91cHBgFB23CgT7c4MoeKM7LOS/qQqZcvPvBMRcLifpEOZhuAj3cXt7W4VCQV6v11Yr0TLw+Xza39+3oH/69GlVq1V98MEHx3kLH9qeqACZyWT0yiuvGBm80WgoGAxqZWVF3W5X29vb1uZyjkaj0TAJeQjkm5ubU9if3+/X4uKiLe270lS0v1SC4XDYTjbkHBkcjkEMwZnWmCDn7kvjnD6fz1ohnNaVo2KPGyAd2lG73dbdu3fV7/ftQHraomQyqfn5eU0mE929e9eoR71eT5cvX5612l/AstmsXnvtNcPm2u22Kb03Gg3t7e3Zrj3JtNfrqVqtKp/PW5Ld2dmxYERAzWQyOjg4mDpulcQKhYiAy150PB43tSeSLX+8Xq9hlsBEJGim6rT10iE9jsEfFSqBFpxzOBzaNL5cLisQCGhubs4KD757PB5XLBbTzs6OUZykw4R+8eJF3bhx44nSjXxiAqTf79c777yj9fV1q6Qo1/P5vObm5kzCnq0ESUaiRlCUIMnAptls2r40KieoocCfdAOiW9WdPHlSd+/eNYdzZcs8nsOD4NH2azab5kzs1SJESusPvUOS0YmQx+dzUyWCN+bzefuMrVbL+JYIE4xGIyUSCeNdJhIJnTt3Tj/84Q+P7V4+aeb6Xq/XM78AeikWiyYywr1CpKTf76tcLpsCEzCQuz5KUHJV7l2xFYY+rJem02mNRiPdunXLBkOwIsDX4SSCa7L9RRcDHIPGKZUo8BS+BMWt0+lYd+L1epXJZKbOshkOh7aue3BwYNcJDUv+nD59+omqIp+YALm+vq7Lly9PnazGoAWHDYVCajabKhaLpuIjydoZHBEMBu7h/v6+kb6p7tg3dTdhAMT9fr+WlpYkHTrGJ598Ys7lBirOFPF4PCaUQevC54Zo2263Da+RjhSBpKNNHVfIFMwVDApdSrAoVtSoeFlVZLJ/8+ZNy+4z++dtY2NDV65cMSyPgOIK0hLcFhcX1e/3jUrW6XRsWEFSR4dRkkEkkLxR5WGjBYyQwaPP59P8/LxGo5FqtZpxLSGKE/Sgmrnrqfid3++fOmKE6tGlDOGvDC6RXuP3KRYI5lwXsHjOiGfDiHOUVldXtbm5+cT43hPBgwwGg3r77bcVCoXUaDSMO+ie5+tmTDBI+Gm0MwsLC1pdXbWg4vP5lMlklM1m1ev1VC6XzQHX19dNOo12HOdxzyKOx+NGF4KM664MImnFGpiryOxq6qEyxCYQFUm1WlW73bZDwqhOer2e9vf37QClTqczpb7SaDSsagWPJMgmEgldvnzZaEAz+3wLBoN68803VSgUbI0VAjZbLUAfkqwjof1kW2tlZUVra2vGZURBKpVKWRsNbu0uKFDREZAZqEwmExUKBS0sLCiRSCiTyWh+ft5ab/BAeJocQsfxIwR3WnBwRAaa4JZANidPnrSBICuKdGpMrHne8FmGlVTCXLOLFy8+Mb73RFSQ169ftzYGEjes/kKhYGK4uVxOiURCrVbLSnsmwhC7M5mMYXMMcWhvOp2O7ty5o93dXeVyOft9lFfC4bDpN4ZCIZ06dUobGxu6ceOGPvroIztUazKZWICjaiA49Xo9w43gNLqHgKGuAl/NrSjZAQcqAMdkgsrkmn3eVCo1hY9STfj9fp06dUq7u7v6+c9/fsx39/E1j8ejV155RYVCwbittKn7+/tKpVLma0tLS1P7znQAqEqh/B0MBrW7u6vBYKBWq2Wtdr1eV6PRUDgc1v37963FRrgiEAjYRorX69WZM2cUDAY1Nzenn/70p2o2m/YaCCkzqKEYYPoMTg9rw1XlIXCBuaMDcHBwYEkdxSh819WXZG/c4/HYQXeQ4xlmhkIhXbly5YnQjXzsA+Ti4qJefvllCxTwv2KxmGVjMEm/369isahyuWwgtauynEwm7fhVuIaDwcAmgggASNL29rYRa3O5nMmZuYcwdTod5XI5nTx5UvV6XZ9++qm15zgjTkELDERAkHNXxphq0rZB+YGGJB1iqrFYTB6PxwZE/DzXAbAcrht0IipevuuZM2d0//792cDmc2xhYUHXrl2zBOSSrZE2Y7WTau7g4EC1Wk2VSsUgGvDDSqUiSUYlG4/Hqlardv1ZFaT9zuVytnDAUAUdSdgOq6ur2tvb061btwwCoLuRZAEKuMYV3XXbZN6fgR7k92q1qlqtpmAwaOrlvV5P29vbJhnIwXJwKVutlv08cwK/369MJqOdnR3F43FdvHhRN2/eVLVa/Tpv6UPbY91iBwIBvffee7Yj7VaAYIJghgw0EHWAmygdZqxEImGYJS06Dk474g5wKpWKDg4OjGMIWD2ZTJTP53XmzBmtrq4qFospn89rdXXV3pcKwuPxWNUAHomaDxN0eJFsxdAeIV7q7vfi2OClQA61Wk2tVsvwTNpwfp/veObMGS0sLNjPJBIJXblyZQoSmNmhBQIBvfPOO8Z/BdOr1WpWdcEPBAtmtZX1VoYlBB4X/6PCJOGS4Ohoer2eDg4OtLOzY1J40iEtbXV11Y5DiMViWl5eNgiFoR+7+VSQtVrNBjYu3xEsG6gK3FM6OoPJLQgwt933+XyWuMEewb7RSTh37pxN0+liLl68+Nj73mP96S5evKi5uTlrgcmwsVjMKr18Pm9HElBZ7u/vW7BA0QcsjrYVoVkcikEKWA8Bt9vtqlQq2UmHANuQuqEspNNpnTp1ytoJcEoqSkn2cODIBC+CKRQguJxwNTm3BlGDg4MDSbKBDK0cWCcVDpPNeDyu5eVljcdj3bhxw44LTSQSWl9f1+7urj755JOv+/Y+1nbhwgVrqZPJpAWoYrFo21QrKysql8uaTCaan5+Xx+PRnTt3DJKhauO+sMJHCwpmjh+SgCWZj7qCKwSq4XBonZDX69XS0pJt5jD0wdfH47EKhYIlUQI9/kelyGsxaKQTgSHhHidCJwI2z2tRGVPlwolMpVKq1WpWiTJkPHHihA4ODvTxxx8fwx3+zeyxDZDJZFKvvvqq0SJY5QK8dqks+XzesiRBstlsWgtA60zQCwQCWlhYUKfT0dbWlnZ3d237IZPJqFqt2srg3NycBWkqTzT8wEM5HJ59bKbF7uogwYxqwV01xGndkw/dPe/xeGwYKFUIFSiBm+knGw6cb5xMJrW+vm4EY5IF+GSlUtGJEydsF31mh7535coVSbJqjGAG/4/2tFAoGI2rWCwqm83q1q1bxk0kuIDJ+Xw+FQoFE6lAxdvrPTw9kLXVQCBg0mVer9cwwO3tbXU6HRNG4T1QGGdjzD2YC2oPw5rRaGQJF6oZXQWFiCtgQQDsdrv2OvF43OhG7tHHhUJhSsmfZYZ6vS6v16t0Oj013Ll06ZLu3bv32PreYxsgL168aEMHt9qSZCRuJmbD4VDpdFr7+/tWkVFVtdttqzCZEJPRIZSn02nDTRYWFpTP5/XJJ59MkXaz2azdZFcKqtvt6tNPP7WWaHt72z4bwRwnI0jS0rMrTrtGdQG3TJLhnp1OxzAesEqqaMjiVBjSYXVZLBa1sbGh0Wikn//852q1Wtb6jUYjbW1taTA4PLpzY2NDP/3pT7/GO/x4msfj0QsvvKDFxUVrOaHBgFnXajWFw2GVSiVTa9ra2jLGRKFQ0O7urnEBXbFbOIa9Xk+5XM6gEKCbXq+n27dvG1UoGAxOnZopHVWXtVpNH3/8sQ0HgZTQInXhJHeJAb9hmOgmaRItcBXCKpJsScENuMiboXNJdbmwsGDVNomA4oP1XSCIkydP6qOPPjqGu/3r7bEMkBcuXNDq6qrJRLlKygwlstmslexsqPR6Pd29e1fr6+uGu6Hsw0ly8BO9Xq+1HKurq8rlciqVSqasQsvAwUQMXmgbwuGwZfdms6nNzU3VajWrAhuNxtRmhUswhifJEIbgD9bE3jVVIC0QDk3FSgBeXl7W/Pz81EoitJLhcKj79+8rHA6rUCgY7nr//n1beRuPx1pYWNDNmzdtQPCs2unTp/Xcc88pGAyaQhQDNCg3boLu9XoGiZRKJS0vLxsvEsbDg9Uk6jmj0eEhXYlEQltbW7px48aUcvdgMNDW1pb29vYMLgGuoRqLRqMqlUoWgGj50UYlMNJCUyjA0BiPx0Zno5CA3YGIC9WsSxxnuDM/P29nLCG0Oz8/r0gkokqlYl2d1+tVpVJRqVSa0k/t9/taX1/X1taWDbEeJ3vsAmQkEtGLL75oNAUEG3w+n2FCDDNQTgboJXB1Oh3boJFkgZWqEjoD7Q1Zmi2ASqWiXq+naDSqXq+ne/fu2c8wfUYAAmI4rQZTRBRUCLauMCnvSwDk/6MkBKTAzzKgYS2RafxgMLBDx9xJ4qlTp1QoFFSv19VqtZRKpQwWoNo5ODiwihVKyenTp/Wzn/1sqoJ9liwcDtvgAB1OMGGGePgFSWw0GqnRaNihVYhLcF99Pp+azaYl42w2q3g8rlarZTzBYrEo6ZDpsL29rWAwaPoA+D6nDyJ8cefOHRvwsXNPwIOrCfTCcNLdkWaAyLPDMMedcvM7VI5I+ZHQ+S78brFY1NzcnEajke7du2cbZ5KmBFU4/51n0u/367nnntPf/d3fPXa+99gFyPPnz6tQKFjGoy3g7AtuMlmSgEUl5pK6WbEj2LrANWtgTPrG47GWlpZUrVbVbDaVy+UsEN27d89oNbRdo9FI29vbtkPLpgNANy0IQY/qk4AHBsPPM/Ujs7IVAwjOyXEIaQwGAy0tLWlpacmUp0kiKKvgkID/VK/wSSORiNFKgsGgLl68qF6v99i2O1+1XblyRSdPnjSM11W6YQ8/GAxa9eeeOYSQgySjyXCMB2fPgDuzCMAhVwzRIGonEgnNzc2pVquZJNr+/r6JmsDDpKp0kzRtMlUdvFlJpm6F7+EDbF1JR1JorCO6GCztNJSddDpt1Sbfu1QqGYuD0xrBM+fm5nTnzh2NRiNTNkcvdWFhQadOnXrshFQeqwA5Pz+vCxcuqNlsKpvN2tQvGo0qn89ba0qbAZsfwFo6oh9IhzwygOdwOGyk7263O0XGZaqIpuTe3p6kQw5mOp3W8vKyGo2GDg4OtLm5aRSbTqdjN9h9DelorQvgnN1vVhjBIGnTeFA4E8flzeHInJgnSYVCQbFYTKurq6YTyZbRwcGBteLQUPjutF60fNAyisWiGo2G8vm8ksnkM8eNLBaLunz58tRRqwQdqnd31Y7z0hnK1et1U45CKIUqlKDDvd7a2jL2g1ttcuYL77W8vGzJL5fLaX9/Xzdv3jRc0K34oAxBIULrNBwO22fluSExcr42q4SBQGBqqInvxONxZbNZoxb1ej3Nzc0pm81at0ZFTJuOOAfFB8Mr/n+9XjeuqHTY5Z08eVK7u7uPFTfysQmQPp9Ply9fNowRYrdLd0ElBwBakqkiw/jHcdrttg1IxuOxEadrtZqRfXltdxqIvp97sFIul7PqgRaG1t7j8Wh/f9/a4ge3GAiYPGxUv5LM+V1pNLhjrCHCMQN3jMfjWl9fNwyIYxzc1s8lirNCyXXY2dmx6SbcOE6nK5VKCofDWl1d1UcffWSf82k3n8+nS5cuWUVHoEDEluseDAZtOAeOzRBNklVmaDzSPYABQuRn+8kVR+GIDXi9u7u7SiaTGo/HRh2bn5+3YOdqBLDRgm4oGqNMl/lvV5iCIMnaI8mcyjkcDiuZTCqRSJggB5J6iLdw9CzizmzexGIx7e3t2WcIhUK6e/eudnd3LTCy+RUIBLS3t2eaksvLy7aN9jjYYxMgT548aRQVpsxUXbVaTfF4fOqmNJtNu3kEQgIYADPKJxxHgMKOq5JMRUXQoy0Gn0PJBCckaLl8xdu3bxum4u5ZwzFjm4XgTraH2gDHjSDLxgLqLZFIRPl83mhHxWJRg8HAyORwylKplBqNhk269/f3dXBwYDjYrVu3JMnUiqB3MOnnc87Pzxt88CzY2bNntbq6airg3W7X5OLc3WYGfrTbqDXhW8jkEQiBStivxi9dTFqS4ddUkL1eTzs7O1OQjqsBQDCpVCra29sznI81VipSVgrBSl0iOJ+JCtfj8Vg1zPeGJlYoFJRKpQxn5RrU63XTqQSvjMVi2t7etupwMpno448/VrVa1d7enkqlksFffAeGWkADDEwfB3ssAmQikdClS5dsf5pKkLaw0WhIkpFi0WiEppPL5aayP/8Px0EXkqBE9QcVB84iE3NUuJms4ehkYPAjMCOqCCgdVI8MhNh/BesBW4X2gQwWpzFSQfPQIeKby+W0trameDyu3d1dc/ylpSUT3OA7bW1taXNz01r9RqOhTCZj4hYYu7Ncd6qOy5cv60c/+tFjy097VJZMJnXu3DnDy2AcwDEF1wNKyeVyhkG2Wi0Vi0WjXLGBAr6HnBmnCNK6Q6mZTCZGoGYlNZFImEhupVIx7BKcmr8nuYPxMVWnu6L1BmN0BVrgEPt8PhPNYFhDl0NlmclktLCwoLW1Nc3NzdkmWLlc1vz8vObn5402FAwGtbW1pa2tLfOj/f19W7VEvSoQCFh7joIQlL1gMKizZ8+aAMtx27EHSDTi4JBRdbGnzA0lQLFSx+qgJKOmkB3T6fQU/ug6GacagkuCHSIiy3uxjQLO+aAQL5mQVp4gypkxUCJoiXh4yJa9Xs+OdYjH40YyRmY/mUzq4OBAfv/hWd1nzpzRyZMnzbnX1tZsc0OSTd7h6ZFU4JsxsKLVYpIIbsVeuSRVq1VFIhGtra09sYct/Sbm9/t1/vx5wxbZkhoOh6pUKiYJBoUF+AO/Go0OD3QjATI0xGchZsNpTCQSNhx0sWf8CFk0v9+vXC5nCRXIBRYD1RscRo7ZoBNhqETix6cpOFiFzGazKhaLNtjDlxlexuNxbWxs6MyZM9bdZTIZzc3NqVwuq91um1TgaDTS/fv3jU/s8XgMGoCit7i4aC0/Sx5wljm3iTiwuLioGzduHLOHPAYBcnFxUWtra7azyo0Gv4OuQxCiOoN07fF4bHOGTRLaIfAZMB5uPpQYbgxDGdYRAarBCMFNwImY6DGdg8yOMks8Hre2iWEMAR/caTQaKZ/Pa2FhQclkUn6/38jFSGBRDTI44AGcn5+37YrxeGyfnyDJEaGcYUzWJtBDOKZqajQaprxSLpcNalhaWjJK0NNoy8vLWltbM06sy+sDKyTBoWiD0AgPcrvdtqOCU6mUbZdIMt5jIBDQ/Py8VX1wXUnOdDPwaAnQbDyBjePLVH9g9K4OaLVatecG1gITZboE9AoQvW21WnbcCFQmVzINfDuTySgcDlvBsbe3Z/oC/A6Bn79n3dbdPmo0Ghbg6booWHiGwCKPu9U+1gAZCoW0sbFhgwuEPQmG/Hcmk7HSHp4U2wzcDDIrslRk4/F4rFKpZHwyFwOiNYazSLviVpKSrA1m8sc5xfwOr5NIJIymwzYCzstnJaOCOZK1+YPMWTQa1crKijkdk0CcqF6vT01DmSKSNEgctEtUknwfgjLXxefzqVQqqdPp2IPHWSIuReVpMTiP4IAITrRaLavk4f9JR/vPYLx0Augrlstlm/pms1mr5FzBFO4F7WMkElE2m7VkDcdRklHFqPwIOOgsxmIxBYNBUyt39SOhwiFkS2VMcARKAW+EC8xr5nK5qe9I28/21e7urvb29gzL5J/g9DyLKFHxHcvlsprNpq1FuvqTdHOuetb58+f1wx/+8Fh979gCpMfj0ZkzZ0zrzh1ePFi9QYhGQw9Hoi0Bq4nFYiZqOj8/bxQYWg4cQTpsPRG1gF+Iqjfvj0O7mZXXl2Qq5a5qDgc2MWRhuijJppfpdFrJZNKm1gRsSL/gPUyoqQAYtLAtg2NB6aHK8fv9ymaz5rAoqbvqQdFo1LQNI5GIfSfEP2gdT548qVarZXvcT4N5PB6dPn3aqGQkaCaxJCsgFQQ/YA5A0icgMdCQZG0j3QMnY7q0HNTk8RmWIUjevB4/y8ICwyH0APjscF3ZesH3GGLSbTC0dPFmupNkMql0Oq1CoWDcSqrSWq2mcrlstCDel2UGn89n1W8+n9dkMtHW1pbG47E9t5ubmzbsfFAXASgNfiQk9aWlJW1sbOhnP/vZsfnesQXIubk5ra2tWZtCxnKzZjabtbakVqvZOdGokLTbbR0cHBigTNXp9/u1v7+vXq+nhYUFI1DTgpCRhsOh9vf3jUrD5E+SnZJIW8/Axd2BphXH2Wq1mrXB7M5CvaF1SqfTJn0G8R1OYzqd1uLiosmYNZtN44TeuXNH+/v7xvt0AzHBvN1uq1gs2poXAZDpZ7vdlnTYWnPmjrue6K6S8X37/b7OnTv32K6CfRFbWlrS6dOnrf3D5yRZVQ0W3mq1LFhS2TOZpuokybEhxWmXKIiz5koXJMnaTwoEuheWHGq1mg0AobdFo1HDBuE+uoMOScagCIfDmpubM7gA+hHDISpjpueLi4vGymDThu/vBjR3UYO97mazqXg8bkPAra0tSYfPF90bp38CV6CiTmvOf8OpJPCePXtW9+/fPzbfO5YAiew6LQwYI5xAMD2yLA4ArkdryxoUwQwn40K3223dunXL8DXacSgPSFIBdoMVuVUXlQCHru/v79sNRMKec7IDgYBpQpIRmRAThNl7xbEJbuBaKMOAWVYqFW1vb1srxGdmz5XvSQXh8/mm1J9pq3FMr9erarWqra0tw0alIyHVwWBgmCXTRZ/Pp/Pnz+v9999/7FbBHtZCoZAuXLhgWDD+BbGbNpO2lkEdLS/3jcpNkg3o/H6/+SxLAdCzXGVvhj/8AQohscFNddt/uiDwZpI0AyY6AypH/BIs3z3UjsSN+hR82WazqVKppIODAwu4Xq/Xui+UjChCOp2OifFGo1HdvXtX+/v7VomDkaO67sI/fHZwUiTfqESpfMfjsS5cuKAf/ehHx+J7xxIgT506ZUcaMERwAxetBpgcDzvDBC4+64CSrJIiAHA2CDgKK1aVSsVuOhJU7nSRaSQtQTabNR5YNps1DUgcFq4kFQbbCrTWtPTwIWmZw+Gw8vm8CZ1SfbhT84ODA1sPowoFB2W6iQMyEaXypJ2nAscJIelSwfBwUl0gDCLJgrTff3gs7uO4CvawtrKyYkELzBbfY4d4fn7eMGLw5UajoXQ6PcUv5Hqxq412I/QaICGv16tSqWQcX/BtJsIc3YB2KdQqNq5oeaGC0eZKMtEJd1+fpM5WGQUHPofQBcVFvV433QDU+KEoSTIICawVGIw/HFXCtH9nZ8eGK3yGer1u14yCxOfzWcvNtQHDBKOcTCbHuob4tQdISM0QnLnZUCUImGQZfoeKBn4gjgRuCV6XSCSUTqe1s7NjU1x3T9oNCPw+E8her2elPKKf8/Pztp0AXYbg7gLcbN/wnnAb3d3wcDisxcVFC3asFd67d0/37t0zx+GzERyR9UcEY2FhwfiSkI+ZjhJokbqCI0cLCRmYVTDkt9gf5rXAblmfDAQCOn369BN9pnY0GtXi4qItFVCx0xaz58xEl60kVvd4eNmyoiJkaYEzkTi33a0spSNFbjBlxFVIpHQx2WzWngXabboE9uhdhXmwcO4xsJWr7biwsKBCoWDfHWwUHVAGK9xrOjFI5yRtKkG4vOCprgoQuKkkS+houLp8YgZbbI3x2aXDISxV+mg00qlTp+yZ/jrtaw+QJ0+etBs8HA5tsELWrNfrSqfTVuW56t+ZTEaRSMQyFRQggh94RyqVUiqVUqVSsaxMRQBNJ51Om9ADmytQKwCxXS4XRylA+sWpII6PRiO7eQiGUinGYjE7sySTyajb7er27dv67LPP1Ol0dP/+fU0mEzuYDMyLh6Rardowxe/3m6I1VQLOywNGNUnLBeaEU4OZUl0yLKCScgO725qzCvaLX/ziiRzYLCwsGA+WyhjxWqo9hiMHBweW+PAFWsTRaKRUKjWVtHZ2dtTr9VQsFpVKpbS/v2/XDe4sGGI4HNbCwoLm5+e1srJiFK92u20JFgoahQTVGlUWqlX7+/tGByNgNZtN5fN5G9IsLi7a4XalUkmbm5va3t42Gg2cYXwoFovZxJ62FvgAeo6rjgUVCriIo14J7A9SkphwQydjUOXCGOgrsFiBoAfP89dlX2uAzOfzmp+fN+cE/GViRkbkwSQAoHZcqVQso4Dl0GK0Wi27GWwmICLB5BknYghE1kYrcX5+Xnfv3tVnn30mSabUXK/Xtbi4aNVqJpMxDI+MymSTqtjj8Whubk4nT57UwsKCKXjX63V7DygjCAMAKYAFMsEfDAbKZrNGwKWaBkBnSs60lF1ZAh4gP2IW1WrVtjDAuVjNpDVnx5bPDJSATD4n7D0pNjc3p1OnTk0R4yVZlUIVDVmZFpyFAR5uBndcV4JSNBpVs9k0hXuGPF6v1+AMOJSSpnbkFxcXNTc3N5Vo+Sx0JVSPdAcP7n+zWw0uv7S0pBMnTqhQKBjOt7Ozo7t372p7e9tei8kyAZyql7acROtyKJleU7gAyXS73SkclcUP3t/FYUnc4/HY2nYgMtSPmAHw+qdPn7Zzer4u+9oCZDgc1qlTp6bWtsiGZExI11Q3TIDdCojARpVERSbJKDQEQEkWPLgpZEKcgcC2s7NjmOXCwoJldJyi0WjYRJjKlxYGzAenQV6sUqkYzYE9WcR7oXCw9UKFQLDktWlNmAjicEANkqbOExkOhzZk6Pf7RutgAi7JMCsoTPyTYEwAINvzEHLA1IkTJ4x/9yRYOBw2GTOSGAHHxZNpq/ETqFjwWlutlpHCuX7wXfFfzozGlyUZ3s0g0u2Qdnd3ValUrEpko4rTBN21RO4TvhwIBEzQwuW5opZz8+ZNO6Jhe3vbsHLuKUpOqVRKo9HIJPIIgjwrdGkMf/jOBDAYFKPRSNVq1QoYEjlbN8Bn4PHQe+BA8rkY0MJa4XqGQiGdO3fOFiK+DvtaAqTH49H58+ftdEKX98jD6FZ00hE5vFqt2nYCLHxKbCZwtIfuKmIwGDRupCsYgTw8ADyZmoqC4Ly8vKxUKqV79+4ZPwt5MPhvVLfS0ZQYQQyW7pk47+7uqlQq2ednMoggRy6XMwelUqESBVMCy+TBdN/b3fFmbQzKCjglP0sQ57XROOR6kP15CHgoXXWYxcVFW3N8nM3j8Whtbc0qdVpo7jkUHwYPXB/W7QgAVD5ANVxr2llJVhFyvQiC+B/CI+B1PAOuhcNho4nt7e3ZoKLb7drxxFS14IVALMA5+B+w0f379029Hj+CV0mSwBdI3gRhEjSK9yxoQAAngFERQgfqdrsmatHtdu17wf11t3WAfUha4/HYukZXjBrsdnV1VZ9++unX4j9fS4AsFArK5XJGSSEDcbIeOCQVF84MHlGtVqf4YoDCbIiAoTE5xKncc0CYAIPnSbIgiTxYPp83dRsCZiwWM406AhWvAR8N4jUtEpNH8MRKpaJKpWLtOlUdn42WmgyPQ7FFw2cFHwTsBlIgqVBJEtwgJ1MNsNngagdSUdAO0goRJMB3Xe4c3M1yuWw734+rIbSQSqXsMDTpSDCCNTq+I9+fayLJEhbwBQEE8VgSKCwEAqkLI7nqTQwUIWonk0mjoqEuBSWMjoLESWDlvnMv2a/n7yaTiZrNpvb29qyjoZJ98J4jvkKCpJuhKOEzg92TUIHGWNzg+jINp4pOpVI21Ox0OuaHfAcGi2x18bxSdfMZSVonT55UuVz+WlZgv/IAGQwGdfr0aVvBQ56Myo+byk13lXO4WKz/pVIpk5/iZuKQAL5MwF3lEv7/g+Bup9MxfiIBiADCdg5bKZxVI8k+k7ul4yp9s9FCW4rTg7O42wM4JFU1FRwcPXdH3R0IuVlYkhF4ac/dh4AWkIk4mwo4uXvuDffMDcY4sXQkBOzz+bS8vKyPP/74sR3YBAIBnT171q5rIpGw6+7zHZ6xzv1jr56tJnwGQj/4LPcARR2OIEZARZJVQlwXsGKSNA+/dORLQBrQ1KBxsfYIrEP1CW+RyowNFKAcMDxJU5QfjOfEXXXlWQGf5FwecEoGmO7sgLVAAjm/y+uThCgISBbulB4oSTqaYnP9YVH4fD7DzguFgk6dOmXn73yV9pUHyNXVVeXzecMcyCbStA4igp1kIMi5lOA4TT6f/yfiowx4UFAmE7klPyojkszZmNxVq1UFAgHV63VzXKo3AgScyWazqWg0ap+Xzwd1AswULIqBUTKZVKVS0c7Ojm32UK22Wi2bRkqydpbgyilwrDzinAy73KxLO0bglWQPOnw98Ccc0E0MPMRQTfi+kgwTY5MpkUjo4ODAyMuPm62srNj3gPNIdU/14vIh+XuSAyuADE5IuKx17u3tWdAiiLq0IPf6s4ro+hSBlAQmyQJtrVYzzDgcDmtpacmgFNfv2W6B1wg9p91uW5BkrRbfIMgBWYFDgmUCq5AMCFpwhIGSWFfl99hFJ0EjPONWvHSF7sAU36X7wl+h35G86RZhkOTzee3v73+lPuSR9JWF4FgspmvXrv2TJXnKdTKDyxUkeNIWUF1B5OaCE+TA0GgXaUOgBoC9UbUx/WMrgCDJLirtFkGIzNbpdOx8EOgH8Njcvet4PG4UhU6no729PcvSm5ubKpfLU/JTOB0JgdZc0hStB4fiuzIsymazxtejBUH9hQTE9JAHlkBHECYAcu0IFJyWFw4fHnHKxBxskvvw/vvvPxbafa4lEgm98cYbFgR5EN2DrAhaPHQ8kLTYPNhgfVRsyWTSEiucVvA84B7YE+Dh4I1UinQtfr9fxWJR+XzeKjSGdRQCrVbLdq/dwaDf77cJOfeRgR4+AAH9zp07ViSwYivJXov3pKMi2FLdEQAZRgFnEbSoEqHvuVqVBHYSOJgjJHuX0sfsgOtJp0Ticon9g8FAH330kSWSr8K+sgrS6/XqxIkTlhnBK1yKDjeFgQHkXYIeTsVUi6zLAMPNfJJMussVtIBjxlofJGccOZlMTrXBrrgobVckElGxWDQeGetVPCwEZw5ZAgTHOflMBBG+B+Rx1sxwoFgsNnU2iCQLcAxjaIEgJMPRdH+H6Ts4Ky34aDRSJpOxSgC4ABYAFBRaaZRbqFQlWWUeDAZtywE6xnGbx3N4siPrpXQUXFM2Y6DFMPmHUuPCLiwxxONxJRIJ6wyAKgg6tNruRhj3Bd+m+oNlQJW6v79vcn0kJIJhLpczhe3bt28bzAN2CbTDoXZsa6E2TzUJZYwChCUAV9JsOBzaOd8IRQcCgalz5SVNYYIEP4She72eHZUM3AMPkufE7/dbMSLJ4A8CbSQSse9JAAeO4/ryXOJ7LnzwKO0rC5ALCwtaXl6eallZaqclpkJCJQT80c267IwScMhqiNYiPwZYnE6np2g0qECTgamwqLjgeXGDaGOZeqOsgsjEaHQoklqtVu3Ggx/CNZSOcCccHrUcl3xNkKdSdndNGTS503HaHfDMSCRiKjBkVypuSSqVSiZYwQONMg1VA0Etm82a+jWfg+vOg0uApM3iAVpbW7P97sfBWBVk4sowj6COaAhJkevAd8R/8vm8JWOXPQBdC7iBNpwWXNJU8HA5kNxH2ljXHyTZ0gTrpXQOkUjEtE6BA1KplHER6Vj4jvgpQUWSFR4MkJrNplWAVMkEdUnG+WSISGCiGwMu8Pv9U8IVDBzxW6pdihSXFSEdybBRvIDvuhgqAR2qFt9nZWXFNoK+CvtKAmQsFtPZs2cN2JaOLgICCR6PR6VSScViUcFg0DAXKDtUNDhDsVi07Iji9trammUrN6iCdVAdsXWDU3KDYeWjtk0wlmQbKUyv2Yuu1+vmuO6WDdgPwR/ogIDjtixU0Px/AizkY/hk7nTVnSzyoDJgwPFpaVxOHC27O7kmwIH5oE5eKpUMO4NlwFScIA+oDgbGxsfKyopqtdqxH9GQSCR09uxZgxZIhlRwzWbTpsNQYcC44KzSKpbLZc3NzSkUChncIMl8lDaSoEUlDpWnXC5PQRv4IQkHXxgMDo91pVIimXW7XTv/mkRFIh6NDoUraIHBux/ccqFQwPfdLTYqNknGr2WhgkUDRHpJhqxlsnZJZe5qkZKgSQYEXNppIAY22Oiy3KFrLBZTpVIxpokrU0h1zTOxsrJia4uP2r6SAMmOsaSpLEm2YXfV4/FM8Ra5GGwfIPS5v7+vTqczpYKMcjJtDk4tHSnTAEq7+BM71ZIM43HVQwhiLNcT1NHDYzUxn8+berfH4zEFFKoVJoNQP9x2mmzoqkFTAZZKJbtOfEZaOvAYhgHu9I/3JtO7ggfghjg4Trq8vKzFxUXV63VLNMAdVAcMmXhdHJjvwgApFotpYWHBtpCOwzwej06ePGkDOJINa4RUziRrNmAYGtRqNePqMuCDnM93JzAQBKjo3VbUJXW7CY5WlgOtstmsVfIohkPPcpM7wTiZTJouAfQ4KDrgeeCVo9HI2lqwQ5f4Dd4IvNTr9aZWd11IBawW/J3nzmWIsPRAxc72Fvgh8wMC4/LysgaDgba2tuz9ScCcIUUQdztMql1wejag5ufn7VC6R2mPPEAmk0mtrKyo2WzaKYR+v9+wO8Q5qdgoo7mQLpUBDINVQU5Ri8fjisViNjnkRgAOExAzmYxGo5Ep6ODYuVzOggtUH7d9REHbbTchAbukc4IdmZQ2mVaH9hUHIsNKsqNemRqTFWnDXFEO2iu0+tyNBRRfqtXqFEePSoNg505RM5mM8vm8IpGI9vb2jILiHsdJi+O29nx/qmG+AxV1Pp/X9vb2sVWRBGmCOff1QfFjri1Eb7ioQCSZTMYeQJIm1T0/w8PLhBz4gnsJnk1XQlXOfZZkWqb4PKpMBGgm7vgwwd7v95ughmvNZnNKWR4MlUqfwQ/J11X7CQQCUxtc6IFyjRgUuir1YNBer9doQFxbEjHJFJhgYWFBc3NzajQaunnzpvkVbTXPMrMASdYN8toETeloU25+ft6Omn2U9kgDZCAQ0Pnz56dIsgxd4KC5wwlJxtYPhUJG0Cb4gLO56tfcfFqIcrlsVRyAPFnPdX6CIbw2BASgeSBqikHUpXKgigLvoypmr5Xg4b4PlQSSTlQSTL2BDFwRUklTD7V7HjNVNRUQDx0TRpyRzyIdYaGNRkO5XE6pVMqI+O122zBh4AJW7ahu+Q68FwGdv6PSpAU8c+aMfvazn1mA+bosEAhoY2Njal8afA6MGfEE/I7AQPuImg9sB/A4WuZ+v69MJqNAIGAJyePx2Hork1paanwfygtVWa93eGoi74eQrpskUQ1yGRJ0EmD3LEUQkHldcEyeNT6DW3CAj4O7E2i83kM1ffwPPyHwMNSCg4vGAN8ZrJf/T1Lx+/02qd/a2jLIAvWj4XBoVanLPHBbe3dVmGeD7x4KhbSysqJPP/30kQ4LH2mAXFpaslaVIzNpAcbjsZX8Li4JPiLJWqIHmf9oMdbrdavuuFjgMjzIy8vLU9MzHI73dl/X3cBxiecuD4zpHRCBW6kSkFAmeZB+hGMx1aSS5CHlc/P7kgzMpyWGq+auefHw8gfckQk4mZuqlUklVQXQAEMZFFoQh3U5Z5LswQaI57vymbl/DLoWFxd19+7dR+lav9ZWV1dtYEbVh5wd7TVwBAmYn2OvHiFm2mGCIUkcojJbYbTVOzs7WlhYUDgctgROZ8P9IgjyfgzL3PNbXEJ4Op2WpCmcm+uN74F5gpci1UdQ5ecJkLBESN68DsnaXbSAPoRKPu0tVSr3mo6GgAquTjvN92MzC51InjvUqGAJuNQhl+9Maw8sQoEiHXat/X5f8/PzajQaj3Rg88gCJEMTaDhMoCnxCQpUaQRBMBeoAzgeGW40GmlnZ8dwQsjVYCbJZNJaXlfanUBAW0VFQZCTNLVOhmI4Gys4MTQksjIPEIMV2iB+hqwHXYQ1MiZ8TDe5+VR7YIs4LIGUwAdNiGvptonudwMPw5l4IAH/kYKTZNN4t7Vx99NpUxncQCOBnsGDQEBhAHfu3Dk77/nrsHQ6rXPnztnwAbyNCokq2+XNct/doAXei4QX03/+zj23yPVbOhdabK4NQYQVPTa0JpPD828IGhy/i2gEVTptvCR7pkjaLvQBDzKTyVglT/CB9uUKmPj9/inlHAoVgjQMBVZfXeqSi60yBaeiJdDDDeUaUR1TJXo8Hjsuhc8IrxKcl/voEtXBXmnB6X5ciGB1ddWGqY/CHkmA9Hq9OnnypE0Lode4LRiHHnFByPTusn42m7XKj4ks5Xa/31e5XLbNAUprV3LK3X/GIGPH43E1m027mLTk7t4zeB3VH9USAY02F6qCy/UiqPO9qVKYvpEJ4Uq6gqbwFtkiogJ0BzG85oNG1ct1d52bqs/v9yuVSmlubs6CeblctoAnTa92sVeMgxMkaWVIRlRhJDce8l6vp9OnT+unP/3pV74K5vV6tbS0ZNcaSIHrQItHdUaQ4TsiPkIgZVhIWyfJoB9gFzA3oBbYBkBIJBZXsgvIgTOoCRiRSMQCN75Ah8PwiIBMEGCQAWGdFpZEQOKne2MLjfsEF5NkTTVJcKRzIQEwuOF5A4bw+XwqFov2/ElHq5P9ft/wSreo4Du4lCSGonwPr9f7T45IIfHD3HDjB59nMplocXFRnU5HH3zwwSPxvUcSIF21YiI6wDUXmCqDm8ZFgJfFMQnFYtEqMDAd6AkEJKaABDLAeCZyXBicmqprPB7bShYBGJpPPp83LLBWq9lBQdwAj8djLSmrTgRJbhDvyc9LMkIynxWBB4BnEgKTPjeoUt24e6vAFVAlaKvd6hj8h4oUIQ6IyVQFBEpJ1tJBWXIfeCANVxSBZMjn572oUpj2f9XnGi8uLiqfz2tvb8+qMtpZrm8qlbKKye0E4BHu7u6q0WioUCjYw831DQQOJcUQp+12uzYoxEfBOIFzgIm4VwQPgjO+EQqFFI/HTeuz3W7bUb7g6CR+ql73oafiYrjGzxCkWV6ArM6zOBwOrcICLuK1+XwkZ5e6427cDAYDOzoYTqQrZUY3wu+6Yhxs8bDkAQ0OKAtOJH/PJhqVMJU98nJ0mrToFAOPQjfySwdIVKZppZla88VdrTcGK5FIRAcHBzYxBSNsNpva39+3EwjL5bIdckUwIlvToksy52o0GtYC8EBw8xKJhEkwgQ3xObvdrra3t5VOp5XL5ayS5bhLHjDek31cbig3dTw+OmgeTMtdk8Kx4FoSeFyeHtxMrsuDWCMBDofiYSALU63TmsXjcRsi4HStVsuER71erym1c11RtOl2u0ajoFKXZMA+DxHAPdUvAWFtbc3asK/C4MdKMhyZSsNtL90g6Q5OaLs5pN5VFHcHceDnDFFceIj3ch/eSCSiZDI5Vc1SPVGB89r9fl+7u7sGgzDMpNUEz3c7BQZ3kswfWFAgWUmy0xlZ35M0dcgbalSSbLhIgKQ7gIJGVewmf4Ig++NUnnQT4P7gmmifou+Iv/B5XT92sUb8mOrTHbwShF0sNxAI6MSJE4affhn7UgHS4/FoaWnJHi4yqJs5aFHRdaTqQCEHlR4CGoTxWCymfD5v+AMtApUbFCEqw1wuZxQd2h8yIvw+smu5XLYbTtClFYEOxAodh5zjMDg9nwuAn9aGdpj3drcn3BVAPqe7yy1Ni2S4qtcuNODySd3gCu0E6lEmkzFtSHQhXUyJ7wTu6K7cEfy4r1TKblIiWTCYcMnr3OOVlRV99tlnj7zVhvOIvxF4+GwEF7BYKjmGB8jPjUZHSkzcP3e6TbUMbxAMz036XCteA+4oNBnaYIIRmoy0ialUyj4XCY3KjwDPtaeql2RDFJdxIB2dsuhOzcGy+RxgktBv8FsCD6/hiiVLmnpdngdJ5uduF8QzCLYJn5Ek0+/3DZ/Hx+kI3Q6RZxgMngRMReuKamcyGXuN1dVVffLJJ1/K975UgCwUCioUClYJIuDq0lI4+oAHkRsIH499ZaSd2BBot9tGpHUvIIHEXdwnE7GBwMMChYKACubhsvnJyFS6SDsxfMlms1Nrf2CL7XbblHk45xp+HBgpN5SWhRaPa8Cww62+aVOo4sAjCVbuVN2tkGl1cBIgDz4TGG6r1VKhUFA8HrfvK8myuiQD5OGbuQEVbImBBH9c9ReuczAYVC6Xs+MpHqXl83ktLy9b4CYYEQSp4HlY+X5UdLlcTt1u1zYwWIuFLoOv4mck/wcJ0dLRGURuQsZPoaiB9zK5pgp01/xIoIHA4cFd0IncLR0+C228Ozxy1bjd6p/v4q61UhG7kAudDkGJttnjOSJ+M7V2t2j47u6Q1aXagMXy3OO34OYuB9nFGfkniQo8HM4lk23EofFpvuOpU6e+9CFzXzhABoNBnThxwjILN43KjpvJA+pecG4WFSGANo7h9/uncDr4bO5gg9+Tjs4NZnLK0Qk4N2t7AN/gFWByEJ65cTikKzfv8v44gQ18DmcAf6UdZujCtSGI8xl4PXeCSpAB8wPXkzT14OFIBFDpsJpNpVK2QkelAj3K5zs8p9nnO1TqgcdGJUjLzhADqXxJVmVATOea8ZCCO4NnEYCCwaCee+45/fCHP3xk/LRgMKjV1VXDV6lMuJ+8L5+Ze8eUnYqTwLa/v2/HvZKEGEyROLneJMrJ5IjLS7CCKQC8QSXNfYQiIx1VeUysefi5b3zPfD5v20oEUYaEyM3BeaTV5tmi8iV5g9NTheHXwC48V25HyM/RNZEoKUSoTF26FDi0uzwhHdHBYKAA1bD2yyYQSZ7ChOvIuT9Uvl6v1xgGVJEkAxZJ1tbWvhQv9wsHyPn5eavooI2AQ3HOLzeZqSBL+5TPXESI0bQ8OJk7FZaOFEg4RwTqBJNqSdaqU/WAAeLQ4C44KBkZ+gVVGGtd3ASyEAMkMEhei6qJ6SgDAKoR8EzaOHbMJU21vmRGtyVjovog2E/1RKJxKxEeDFozIA8oHfxOp9OxahnHgyBP9eW+h6Spto9A4k4lwUcJ7OFwWCsrK7p9+/YXdbcpYxpPYOG9qG79fr+1aeBnJFiqS/xsNDo8obDdbqtUKtlkmOtO5UZiIti5FRwQDtth/B7TcdpPJvy0rQRft8pnycKl8binaQLVACfgf9DaSLxAPqlUyqpJKjU4olRgDIHcZM/np0AggeLfdA74FLQeCOnuMyFpiigPuZzAKB0ObNyiic4S7JTCy8Um6fRcGTX8ga2ahYUFNZvNL7wC+4UCZDKZ1Pnz5yXJiNCQiMkAKOhwYaQjFWOcg+xDqR0MBrW3t2eTQKoBzoBuNptKp9NaWFgwHiFtMsAuFQLYEYMH6BQ4tzulo23G4XF+LjYPXKVSMecajUbK5XJWTTK5cydurvCBu0VEgiAJuJ+BLEswJBCDtUHkdh9alxJEVediZgws+Cz8OTg4MOdyD7znvrFxAcGYa0PF2u/37XhUcEqCLcGZdvzs2bN2KP2XsXg8rkuXLtn34xqTcKkM4RDiD5IM4McHmPTyHfm5UCik/f19S+ZULQQKkhHXm/vNtX8QEnFpOyRw99x0l27DMQK893A4tCNYuW8IShDYeF+2agisXBPpSLSE9wIecFtZ6aiyJUBy3wnOJBbuBd+NgEmC5Dnh+kqy7+smVIoRJutukGPBQZINutxhKN3lg/xQ7vPp06fV6x2exLmzs2PB9mHsoQOkz+fTqVOn7EEHV+NhoMTmHBmXoMoAhnLZdT6CJBcdrI0KLhQK2YSPCwvuwlSaG84DzENLBeEqX9Ni4xi04Aw7oAMRlKg0cTjaZx402lFuPEGLQMODQ3UB9keGhbDNmhkB1uVF0hryPXlIEomEPeDgUA9O+pmgApZzfahUCNbsDxNcqBr5JywB7ikVOdgebZnf758SS/D5fLp48aJ+9KMfffF2x+/XxsbGVJXBdYaK466MoprO5ycY4Stgay7jwgX9GdC5HQzUM5dhQJfANXaXIRCVIIjiqwRnAgFdETg+zwcdjXQo2kK7z3PB+UhUZ3Rl7v0kkbrfw8XqXII8Ahgu1u8OZpD/A0IbDAbWrTFvILgyqXYTCz8LHEXQ5ju4Svt8V+lI8gwYiGvIUNMd6tCxuDvkGxsb+uCDDx4a5nloRfGlpSVtbGxY+Ux7QsB0QVYuFhebh5rJKwdx8ZCC0XAxXSIyGy60zx6Px1RW+P+sluVyOXtoKcEJmlS4gM609xyfyY1xZdE4AoJqAcdytSXr9bphnQRVWlQyJlmYivJBbiM3mqoA5+MhxcH4nu7UlgzrTlOpsGhF+Fy04m4wIBC735uHEKelyqddfVDlhuvpwhlUBnyP73//+1+YG7m0tKTTp09bQkCByJUJ44zlbDZrVRfJj2vDuqB7P7gXdEP4JC0t14VqlWvI7zP8w7/xUwZVVFgIj+DvwDJ0DAw9XAUdptWshIKvuyuAJDtI2HRu/JwkewbAI6nG3Km6dCRTRrFAsOWZxXep8Ljv+D4dEcUPlS4cZn4P1SGCG9gmUANdE37ONXKDO10k8J7f79fCwoJVmS596Qc/+MFDHw/yUAEykUjo+eefl3RU+TB4cJ2DjMfP8YUlGS5C1qWSYzADHSUSiRiWCa7J64HJwe+Di0VQlI5wO6ZlUImkozNp+B1uNlNOt5qQNEWYJtMDvrM8X6lUbJoGdokCEVUk2BdtNUGK6sKtml0QHXyFqoOfw/EJ+FR2DGT4GaoaAhn3B7xuNBpZZSodSaeB2eXzebvuVKkMRMrl8hSM4pKfCbC0flQ8H3zwwUOfhhiJRHT58mWDSXiYu92uTfXxkUajYXvAg8HAfIyKivtKYIF1AT5Lx4BP4Ec+n8+UsF0KEIEXrJHBG9U61B1JFhy4vmzJAE24egQ8V1S4btADZnmw7UR2j+EciZeBKTxdhj4kNJIi14Vr5G4hkTjcFt/taPh8VIOwR8DFq9Wq+SsQGvAM9xNRbY6UBePluYF9wnPHs8IRz7lczlgmYKruGe7vv//+Q8E8D9ViLy4umlORmRnPA54yjMFpKZ+pOHA4viQBlUAnyVogshbcQzZHCMrIp3HTaKm5uXweLgjtqLuORwXEQIgsS3VLqxMIHKq9zM3NqdfrGaDPzQaPpMKUjjiNLu7HA+sGOzAxKhkc022BwUShUUC/ITvjhGRLNmXc9px/uoMw6egICKowVzGJSoaWMhw+PGZiNBrp/v371jZCvWCKTsvPw0yQyefzWltb0wcffPAwrqfV1VW71wR47jUBgMRAZeGSxklcLtbMvYI9wYNEMMXPXWiHxB6NRqd2u6k0wTz7/b4lZSos975wv1G4oeuClkbgc7eTSKJACpIsCBJI+F5cE36Hyo7PSzLg3uJ3BCuGQ5Ls+9JpAHFJmqLiMNCr1WrG6+RzMkBjgEgV7sqqcS/okjiGmQ6RwRscYBfbR4HIpRih9kWXGYlEtLy8rJ///Oe/sd/9xgGSqW6r1bJtGL6QS2GgZeNDcfHcqedwOLQBAWdgSDLNQ5emQMvsVj61Ws1+Z3d314I1mCfVXiaTsUqCIMcKJBle0pS0EwR1qEP8DJggARHuJ7QXAg8/NxqNrEJGol86anO63a45N98Xh3NbEDI34PtkMrH1RVoq3s8lH5NkXOyLoEJ1xO8wxIIv6LIL3F13zmbx+/3a3Ny0e9LtHgpYFAoFaxFp7VFfYrJPpZdOp62i+HWWSCQ0NzdnVQbfHSgGeIeAwpCJxMHU3iV5TyYTwy25BtyXSCSiTCZj3Qzfk24A7JpqH8IzXZC7ygpcwgMMNOOqt1Ppsvnh9/stKNfrdWuBwXZ5rvg5LBI5PF6E4QiVValUMp+Bn0rbyr0lmQ4GA5XLZcNKA4HAVDCiq3GhJuIA/003w/PI4oYbpIHn6OLoxprNpp2kSdDHF2GtsKHGkBefk2RsDxImcYr5wMrKira3t0356NfZbxwgFxcXFQ6HDfgGYJZkOBAgOa0Y0Z/ynfaUB8/v95tGH5hOJpMx/US3FZUOy28oHYDlVFRuRiOzu3SgVCqlhYUFhUIhEyggMNJyMLzgJtLK8V0JsDgx+AjfBcclmOGgrLG5eGmlUrHg4j6sjUbDcCQcBywSDNGlqPAZCeTumiVy+UzwcWYwKpcOwrCCjC5p6tqmUillMhl5PB7du3dPe3t79sC6eoJAKrTTYLHuwCYYDGp5efk3DpCrq6vmU1Q/dCYEPJKzz+ebqgbxP5cixtQ+nU7bSqCLAVMJplIpC5IsBHC9XQpUr9fT9va2stmsnWMNKZwBGNUt15FqinsqyapSGA0uwR0qEfeKDsweZGdABq8WKg/JIpFImB9KR1Qehogu1ERXRyAhuBHQeG+emWQyaZ0TwQ1ohWuED1MJ8v+45nwegqVbmaK2VSqVVKlUbECIT8EecbtD4pP7jA2HQ83NzT36AHnr1i0LSDxIXDimb9Fo1AB5nJdskk6nDTSlRCeAAkAD9FIxUYlyM10SM20pF5YARRajJaRKarVa2tvbUyaTMf4fQYL3DAQCVvW4NCGCkdsaEVxKpZJlR0nmJC5m6JLQqbRrtZq1TWCetIU4IK2RG9jcqoFBDk5HECHwcF9o18Bl+VlaTbdFdXmMfIZUKqXFxUUNh0PduXNHBwcH8vv9tifP93T/naEVCQRNRALnw/DSPvvsM/n9fmWzWQP9GUABMzy4IcOQgJ1jgj7BcTKZmIp1IBAw/Jhr7a62kpQg2tOe1mo1G5ZNJoenE9IyM2Gnmh6PxyqXy0bm54GHtUDnRMXK/XOrfHBH/J7kQLLgeaRY4O+BmkjqiJlIRxUXXR7PjkuVG41GBq1IR9NwYBmCJQGKe8PrAVe4SdTlHFcqFVOCoiMg2IdCIeXzeQ2HQ21ubloHy6Fre3t7U5ARr0kgr1QqU8nm/v37unHjxm/sew81pAkGg1pfX7ejUl38YDw+OnqA1pCpHfQBSl1KfBeHxBEAgGk9XSCYSTI3EIwQbM8Npu5GAC0VR7cC7KdSKXMylx9JQISWhAgwWCiVCQ8TjumCzjgCVSVVL0GxUqmoUqlYaweNgYDH+zBFpO11AxpDAI/HYy00vx+NRpXL5ey6SZrCQ2kZeYBcPI4WPx6Pa25uTktLS+r1etra2prCtLjmfDdoXiQkaC/892Aw0L179/Tpp59aQP1Nze/3a319XUtLS1MQAtc6FApZFZlMJm3gB6bobl6501P3OFLgivF4bIGOYMt3pENBuxGBEFpiqiNJFpjD4bBBI1Qy+DDwBJ+NBAN/FIikXq+rXq9bsMNPud4uT5X7wj+5X2yauRUjfuQmcnym3T46q93dxeaZYg/8QS0GNyEDIzHFZgofDAYN1+VMehIB0MX8/LwWFhbUbrd1584dYx94vV7t7u6qXC5b4uOZnUwmJslGEUL1fevWLd24ceOhfO+haT6BQEArKytaWFgwrMVdygfwdSspMgbYA5xJSnkqQBfgdstyWngmee5QgnYdbhfKItLRvjOOQwDj4Q6Hw8rlckqn01O8MXeC7GImBCpaPagafB84Yi59AgfBYaXDzLu3t2fHcoILEfDcayDJ2gl3uEOlQHCnXaeFBRJxByRuRcyWAkMrHEk6rBDS6bRWVlaUSqUs2BA4kW1jAEPQBAui+iKwVCoVdTodlctlffLJJ3YdHtb8fr/OnDmj1dVV62RIDq5qEg81uDJ+QWWG/zBl5xq6QsAMZ6hO+a74DW0uAYZnAz+UZDvx+IrLR2S4Jh3h0nQ0fDZJU+/r4uCTyeEWTKFQMJk33p/nht8B1+O/9/b2rGLl/nFtuDckDHBnfJjCoNFoqFKpGMTC+0oyxS1+ngodEWFkydrttglK012BZYNnHxwcaH9/fyrwwRgB3+Vacu/7/b4NZ0igd+7c0ccff/zQvvfQAZIbXCgUdOHCBVtB4uEi2LmbIXwRWgTwiXQ6bZmUMp52hQqy1+sZyEvmBCuCooCDkzEzmYzm5+eNSkHgJpPz8FK90rZzkXEYnKHZbKparVr7kc1mbTDD/6PSdHUX6/W6rTBSPRPEHgTSybL8HO0L2I/7QDHsIvkQCFjop0WmjaGq5L60Wq2p70OFBUUql8tpaWlJkuyz8f5UImBPVCJ0E3wOqsdGo6Hd3V3duHFDpVLJgsQXNa/3UCD33Llz1v5ScfC+BCDAfekoCEFRYlNlMpnY8al0O3xvoA4wXpfuQ4DmexKQYD9Ih0MTcEUCZKFQMCiEh5dEyOCBZ4RuwYWS+v2+qtWqHSJXKBQM+3QXNfisnU7HkiG4PtjwgwmO5xT/wyehjLmYnsvWoHsKBAJWnEDBIYFUKhVFo1Fls1nrlLgHYNbQdDgOlmEMRQf4P90jw9pMJmOzBGAcF5b49NNPtb29/dAkcekLrhqOx2Pt7u7K5/PpwoULU5NYHki+PFgC7QCZJhAIaH9/36Z/Pp/PHloyuSRrNaisyEhkCo/HYwRVN/CR6RnAEOxQhSY4E4C5iQQRKlcm2S6lZjQa2SDJbU86nY4ODg4MWiCTuu00mVSSTQ0l2XQf3NZVMaLicYF6MF6qYSoKtnTAZaiaU6mUBoOBVa0IeuDgHBlQLBaVTCa1u7tr2Cb3h1aNh4vPDskejl2z2bQWtNVq6datWw9N0P3nfO/evXsaDodaW1uzVo02Fd9jAspDBQUEfBu8NxgMam5uzu4b+BXHF9AG0xUxwebacl24Hy5rAEI7gQTclvO3XakysGiKglqtpslkYlqd6J3yueH70Wlxzx+EPtwFhb29Pdu9Jzm4QdBt0alUgQ8kWeDkGeGzEMgJsO7GEDvVqVRKhULBqEIuXQeGQy6XUzgcto7DHbiWy2VLHvF43BIE1S3PMR0F8NitW7e0vb39hf3tC1WQrmUyGV24cMGqKkBZnGE4HBpzH8yPyRkTtWg0atw5Wh6qO9o2qh4XY2Oq6gZMgqBLZ/B4PLbyBY+OjOjyOt2pJ5madsUdOEAdeZBOQkXG4VHdbtdEKnZ3d42TCPjtctW4ZtJRW1Wr1YxOwmslk0lT4HExHZIBm0gMRdxJM+9DtUdQhzifTqctoDcaDauGuHa0QTyAUKZooXw+nwXacDis3d1dffjhh1/Z2TTxeFwbGxvK5/NW+QFHuJtYVFRUhAQWEhEVPg+lWxETfLhWdEhcc3Dw4XCo/f19u4e5XE4rKyuW7GmZCb5u4MT3qHgg17vH8IbDh0dlANlw3fkMLAMw/YVRAlxEAcDQCqI1qll+v99Ug9xKkiAInMH3oBhxrzuB2J1y83d0aARt97W4XxRaVKMuVskKbDgcNooUlEIoZVT09XpdOzs7unv37kMvJDxoXzpASofYy/r6upaXl60yI/BAZ4BDBkDtcq/IOByDyWu6GwWSrIokm4N/or7Nww34TsCmPc9ms5bVuQHuFBCnd9sCeIGsdYGZAsbTfk0mE6XT6amqAboOLQXO6k55AYzdKSI3HdV1AhAVBdWmywgAT+Kau9gjP+MON3B4HJl2lNdmAk7lBeBN8GX90q0m3Hb7008/1UcfffTQw5iHNYY3tJpQcMBr+YxsV0gyqIXq3MVyqepd3q4bHEhyXDe+MxU+RsIh2ECyT6fTRoB2iwRJ9rMkVVphCNcM1lxtAooQcGMXWqHyhfJDawwnEN+UZHAU2B6BigTudlZ8XoIx3ZU7yEwmk3aEsJtYqGzxc2hF7ozChdCoDoGCWq2WUeSofmnH+Wx3797VjRs3DFf9MvZIAqR0mFGWl5d17tw5EwNlgBKNRtVoNKZWjWgnUaupVqtTU0+3uhqNRravSsDECSaTibWLOChBApCYKa3f79f8/LytpNEiQmmgqqCNgqMJs5+b5zo8MAJUHR4KWtFqtWoYpcuzQwiVh/DBB4wTGnEqHkIcA8fnwaa1hbNJUPT5fOa0rnQ/SQS6DwGYyotAB14sHQUW7gMreYDn0IRu3rypX/ziF4/EQX9T35ubm9P58+eVzWZtiuxi33wfF4ppt9tGx+FhpjrjYXWnuzzUwEcuxOHxHIp4ZDIZq/jpSPCrXC5nyYSKj24BnI/7SALiOWFyzCARZgiwx9zcnPL5vFX2wEL4Ia31eDy2s3XACLlGLi2HwOxueQWDh6uP9Xpd5XLZ/NGlBlH08Fy5/NJ+v29nyGAMz/r9vsUCngWGaHA/+/2jY2NhJ8TjcTvGQZJu3rypGzdufCG88VfZIzv2FWyo0+no4sWLWlxcnMIcuRD1et2yPL+Hc1GhcKFpZcjShULBXoOBDw8zJT6ORuVFSwk4zJQXHpq7mysdccdoMVDZcZ0JriDBq9frWWUJKRVogfbSTRq0ZUwGpSM8EtyLc6vhcvJdB4OBtUIQoyGY87t8Rqpnj8ejQqFgTupW1y72RdKB+8jOfCaTsUOqCIJ0CDzA9Xpdu7u7unXrlkl2fV02Ho+1s7Oj0Wikq1ev2nV2aWEEOwZSsAdCoZCJtWYymalhF/7EQMVlY7ibJdIR3QgqjysogZ9ALSIpUfEA5dDy8758B4KXdLTrzjYOG2TwX+lcGK6B2bEL7i4I4B/usGUwGFglTvAGe+/1esaBXVlZ0Xh8SCRnqMnvU0xQ4fK70pG4NTAHPlUsFu37AqtBxwJTRA7NHWK5z9uHH374yLBu7JFVkK5Fo1GdPXtWZ86cmWpbJdkN5KEn+JCpwNjq9br29/dti4NqD7GAarVqai5sjkiy1TgCI5UBAVmStScAvAw5XBoGWCnEcVSbmWASyKkGXAySHXV3Owc8lAqMoM7mETgWr+dmchyw1+vZ5+CBpYqhKkXxBdqKy32kaiE58H5UKnzGQqFgKk04NlkaXBhMDKD9Jz/5iT799FP7+eOyeDyuCxcuaHV11a4NmJYkw8KpIKly+/2+LRG4SZgBFPcRvNvd4hiPx1bNUNG5VReVFquA0Mrwu3b78Ixo7j1/12q1rOJydQL4LmgeQLMLBAL23fBdl5cJXQsZPBdPdXnFdET8rLuU4Vaa0GmQhRuPxxZAeU6lI+4k3V86nVaz2VS5XNZwOFQul7NJv9/vt38HDsIPeab5Q1K4ceOGfvaznz3yYz2kryhASoeOuLy8rOvXrxvQCsbj8sXgN7qbCy6gWyqVDGt0V4iYkoLjoUXHFBgnAuCGhM7xntls1igrtIkYD8FgMLBjWl26DA7CpI7WwKUc8Rr8LNN8Bi9UIw/yyKhOpMOqxB0SSLJ2hIxKdUQwBtDmGkmHu+sopNAmU9EALVChMjXl71DaptKFIkN1ViqV9P3vf19379790hSeR2VAKefPn7cq3A1UJAISlXREouffgV1crNmt5tyWmxaaxELVCXZNkOHUTFfVXjrqHlhRxE8lWccDjEFxgf+5+DvYKMMTd/hI203lxrSf549Wlu9PoIcWJ8kSMacTwnFlpZHv5E7J+W9ei06Sqo9nm89FkOeUU1gXvI4ke1bv3r2rH/zgB7p3794ja6kftK8sQGJzc3O6evWqFhcXrcWBlOxy1KBHuLQBsjD4odueANBKMuA6mUxaK8sf+GGdTsewwEQioUKhYDgl3EEeEpyU18XB+bzdbtdECXK5nN1g2lpwU1an+B2X3E0gJXC635EKguAPEE0F2O0eHlNLO0Hwdie0PORsy6AsQyXi0jloiRAJzuVyJjXnCgPALYSE+9FHH+lHP/rRl1YJ/6osHo/r9OnTOn36tAKBgFXO7hSY5QMCJVxDprzu0AB/qdVqxh4IhY60RvP5vJ1xDWZIIKHCBL90K1qwRygx4MHQzAjkbiIDoySYUdkT9Cg04CJT/bubZfi6Cx8A1bjDOj4jLfH29rb5MoNAt0ujAqXgAUqCVgaTBHOVolZWVhSPx1Wv1+28Ha5VrVaze/jTn/70oaXLvoh95QFSkvElL1y4YAMVSMlcQBeT4eKRtblhBDK//+gENG4GU1cmyQQi2gYXLyTb8prsmOM8kqylAIDnMzLwgNjO5wEXoZ2hrXB3msnKtBoEMab4fAaCFlgibSAOjUYmVBBJVpkA5PMzJKV8Pm/YI23U3t6etra2NBodqkGnUikVi8Wp9+QBJ6Pfvn1bzWZT77//vm7cuPHYVI2fZx6PRydOnNDVq1etteUaSbIVNx54OKsEP4IKAZaH2x0eolLk4rX4Mb4K/Qx/oMMgWeEvVGj4Fzgh/FewXwYe4/GhGEs2mzX6DomXQEjn4/JpCVouQRzfdod+EOYfZHaQ5N29fSpmgi4FjNd7JMxMEcCQj2II0juF0WAwMHHdQCCgnZ0dW1f9v//3/+qzzz77WnzvawmQWDKZ1AsvvKCTJ0/awITgBbZGOU9GQ22Yvwd/oy0lCLo33+v1KpvN2kSRdglOGVUdQQZeJTeZgMtrSjK80HVc8EFUb6TDlp5KDQwQAJspNbw8cFWmzQRUKkbaazBRKgHwKRRi3F1egigYWigUUrFYtKoQYHt3d1fb29sGiufzeUUikaktGCrLWCxm2zCffPKJPvroo8e2avw8S6VS1snQepKAGP5xXyAl892h2LjbL3QMTJOpGl0sl3ZXkmFqaBK4iw9UreD13GcGmNxzAhEDJAI3LTjSgUyyCUAMPXim8G+CNHi426ayTupikHQSYOPJZFLZbHZql52ghT8DUbCKSxVarVbt+gD5uDvte3t7Jupx7949bW5uan9/Xz/4wQ9+YyWeR2Ffa4CUjuhAV65csSEDbTcHR3HjwHFoY3Au9oLJlux188d1IFcdCPUVAkWr1TICNzvJKA7xGcAuGXLgoO4GDsEHRwJXiUaj1j6B/xDY+Xem32R0qkcGIC6NCE7haDSyFoSgT9vGg870cW5uzgBzeGqlUkkHBwd2RMTi4qKCweCUZiVQh3R4FsrPfvYz/fSnP9X9+/cf+6rx88zr9WpxcVHPP/+88vn8FH8RaheragQT7ieBhKoNlXKqNTimDwrkgssxvGJvmokvvETYEu6GE8HMxR35vWQyaRNit4MCOydYpVIpZbNZu/+ulgDdCv4HtOTyPx8k2Q+HQ1sZnZubk9d7JKvnBlwSN0sG4OTlctnOZ4c0TwUfDodtMw2puK2tLX3yySf6x3/8R21ubn7tvve1B0gsHA5rY2NDZ86cUTKZtCkXLbXX6zXlDjYCqMpYKcrn85al3M0Epqxgiu4uNw7/IFcLfM/j8Rig7Q5XCJpgeAyWcFhpuqqgtUfQA3zUFZWQjsi2tMuo4kD54PsBatM2dTod3b9/X/V63aaAfB94oAwDqJj4zLVaTXt7e7YlQ7Dm89HG8XN//dd/rQ8//NDa0ifdQqGQzp49q/Pnz1sFJsnwXpcQDZcXvNKlOpEECVL8HX7HNNsVjnXXByXZBNvFAnl/7iWfi20zPhvDGBdz515jVG+uDxGQ4EcCVRGkwJqpYN1gWa1WVSqVpjiuLicTLBH/pfOhNQdCgPpHggK+kA4TQ6lUUqlU0vvvv6+PPvro2Hzv2AIkFolEdPHiRV26dEler1elUmkqG6XTafl8PsN8CGSAzplMRn6/3zI0TuNypagMaenhR3ION60NFS0PDLiPdKS9iHOCFzFhc+XVaJVxNFYpCa48CLRDCDrQOlCBonTickOpXA8ODlSr1ayCdsUS3KNoqabBUsn2PPQowricslarpZ2dHf3gBz/Q97///S90XOaTYJFIRGtra1paWpraJ2cLRpKtbrrrgZD83YRLYGJRwIVN6AxofVm9gyrEgJKhJAMShjV0S/go0JJLwnaHRcA0YJ0MWdwtMfeYZHerheDO5ydZAve4EA9VK0kewj3Bn8qZ9UiCNANQPqd78mO1WtXu7q7+4R/+Qe+///5XQt15GDv2ACkdYnC5XE6XLl1SIpHQcDi0lUT+4GBM33AGsD0X3KWtoCWhWmTynE6nNRwOrZWlImCzhNaYdpUgyXSZbObSIKLRqNLptAVFN4uDA7nUC3dnFSiB3+V74LBgP8AELPMDeksyWo87SJCOsFP+KckCNds0PIwez6Fo7o9+9CP96Ec/0u7u7hPbTv+mBs/27Nmzmpubm8IauRcEJ+4zFDR+xuW4ErC4/gTIWq1mSY82lW4BYjvVHcPCwWBglDFX4JlETncQCASUTCan+JDQuVhnlDSlP0BgRyzGHU66QRrfY+mByTmfS9LUsIbr5HInCb6SrJJkM8br9doZRyTlDz/8UAcHB4+F7z0WAdK1fD6v06dPa3Fx0YKUyy2kPUYFhMyPU0BvILOB4fD/GbTgXJLM2ZlagmW6XDAmbUyy2+22TdkQ4SDjukMj3pfPDSBPlqaN4b35rhCJCeK9Xk+VSkXVatXwG34HsjzUJd6H4RS7ve6OMQ+7dLRBce/ePf3whz/Up59+epwucGxWKBR06tQpG3bQFUhH14iOQJpWjacVp+Li3vA7BLxMJmN74lCoCCwkKjZYCLzutJv1RQIRNBtXrg+fw/9IogwLSbZUcFDAgArgYdKBMOE+ODiw4QzDTlppdyOM78pgFN8HQ3dXLMFMP/74Y/3t3/6tbt68eQx3/vPtsQuQ0qHjpdNpnTp1SsvLy3bRydYEAKa4+XzeOGqTycS4iVRmTN64oQDVDIRc0qq7WuZOtF2uGFNiZMNwEtp7xFTd6bY7hCEzAtC78lBMFHFuVIWgOIAvutP5RCJhCtg8ULRzbEMQfGkN3RXNDz/8UO+///4TPYB5VObxHCo/rays6MSJE8rlchZg3MEhw4tcLqdMJmMLB9lsdmoXG6zOVQ0C5nFZFxD3qdrwDZKhiw2StPFF8HqU7+ks3EETr+suOoChukNNfo/nC6oY9B2v16tMJmNKT/wu/kZAlGQYJ/Qyd2WSYP3JJ5/oxz/+sW7fvv1Y+t5jGSAxpstnz57VwsKC3Wxk8l32Py03mAZ6eazfEYDgPLqbOVSU7smHnFcCR8ydNOOA7rAHDiSANxgj+I1biXJOD+/PDjZtFIGW70T2RSaK4Eb1wXu4Cu2SppwRfAiM7NNPP9Uvf/lL/c3f/I1pP87syGj9XnjhBa2srFhnwLSbfWe2aNhfZo3PFWEg+IE/U2269BsCEgmMyh+lHBgFBF3ue6vVsqCFdiIJkQqY6hOFexgZLjWJqhc8H39gaOo+I8BFkqZWZqG8oZ8wGAymxF8Iynfv3tUvf/lLfe9739P+/v5j7XuPdYDEGMYkk0ltbGwYoRSjpA+FQkqn0wb40hYxYXSHNwQZSTbggE8WCoVsugiQzfu5Aw9JJiXFdJI2BXVkF1dEYsp9H7eSA5Px+XzWVkuytouqw92XZSXNlVlzKww+18LCghKJhO7du6f//t//u95///2vXVTiSTSEPpLJpM6ePat0Om0bMSQ1dvuhioFRk9hcaAOoBlwOH+C+UXlSQTLwAxvFL1xsHiyRYSY+S+vO77srvXy2UChk5Ha2h3Z3d+2sIz4XBQGfXzpaPZQ0NYnmdxky0rHs7+/rf/7P/6mf/OQnKpfLx3ZPH8aeiADpms/n08mTJ7W0tKS5uTlJspsXCoUMH5FkrTWEYFdVhWk4QZPMS/YF52w2m/ba7koYrQc3Xzpqo9hueFAYlcqS7RtJU1XGeDw2EVOoR66MGy0Q4DcAON/ZxaQkmUJ4JBLRT37yE/3whz/U3/zN3zw1dJ2v2/C9+fl5ra6uGo3K3SpBlYbqz12/czFJWuVGo2FTbZcuRMAiibubXJVKRaPRyPiN8CZrtZrG47H5nStA4W61eDyHupK1Wk3ZbNZacybUFBt8Rn6P4Y3X67UK2n19V/2HQakkvf/++/rxj3+sv/3bvz12MZOHtScuQGKU+2tra1peXtbS0pI5i0vbYdABV9GdSJOdyYruZK5arU4dYE4FSFBCuAAMysWnmCRCafB6vSoWi1OEdRcecIO0JHNuAi0BHzyS7OtiSS5ORuDf2trS3t6e/tf/+l92jvXMvryxNoiaeSaTMTybg77clU4GOVSSrigEeCbtMNgmalO0wNVqVZVKxao3l+Tuil7gv9Bw5ubmVCwWTUQZqTxaYNdfCH4ubskq4nA4tMEowRomBN0U8FYymdTm5qY2Nzf1l3/5l9rd3X1ife+JDZCuwcjf2NjQqVOnND8/b7w+dPFwLDAdppMESSgRZHiqtAf5bFSP8NeYKNLCw22D9O4Ke8KdhPgOJMAetbvWyHuwhUDlSmXiPkgMZaBjVKtVffDBB7p165YJic7sqzHu4dramhYXF03ZCZgF/isVGveVYEZCJ6GinuTiyyRLOiE3GUNPgwzO3/OzQDj8PT7l7pnD/HChJzokKs9qtar9/X2jB8EQYRuLkwkrlYr+7u/+Tr/4xS+OncP4KOypCJCuxWIxZbNZraysqFAoWACCg+i2xNAqpCO5K/iF4DngKe4aH9gerToVHG07YDyv45LX3U0HgHsc05W8ko4I4y5/EiI3e9RgPnfv3tUnn3yi+/fvW4s9s6/XOCdpeXlZy8vL1qYy9QW2abVa1u24rSv0IfagpSPBFkmWZCUZFc1ddXzQt93hYDAYtJVUV3NAOjovh8/iBl8oPAhosHtdLpe1tbWlcrmsUqmkW7du6fbt209cC/3r7KkLkK55vV6jC12+fFn5fN6wSFfElyEPk2KAb3f4A++MrMkRCjgmU2myNOo47mSboMpnoy2m/WDCDp8N/JQWjd3Z3d1d/eAHP9DOzo7u379vJ+C55PSZHa8xWV5YWNCZM2e0tLRkWgK01eCS7sYX1Rt8Xfe8FnfgR9B78O9ou93XlKYFUVD4oe1HqcjVlsSfwMVZNtjf39ePfvQj7ezs6N69e1PHGj+N9lQHSNcg3cbjcS0tLRm2VywWlc/nbZiC02Aub4zqkWkd00KqAXdSSVB1p34ooqD6TZsP0Zv2DH4jfMu9vT3dv39fW1tbunHjhsrlslFMZvb4G76XSCS0uLho0nMkUXfCTNJ2kziMDLA/xDTm5+dtUAMWCbYYDAZtmcAVzOCMbDi/UJRgVUiyRF4qlXT37l3dvn1b9+/f16effmoajc+K7z0zAfJXmbsHmslkVCgUND8/b85DoIMWwaI9qi1s3UCTwBGpDvmnq5xC4PP5fFPHw1KZ1ut13bx5U7du3dL9+/fVbDatYp3Z02MMdGKxmObm5oxH2e12DapBhcndlyaBB4NBzc/Pa25uzoZ4sDCg5VBFEoBR+KFQYGWy3+/bbv9nn32me/fuaWtry/BshFWeRXumA+TnGe1ROBxWPp+31pezcTwej06dOmWSU0zyXIEMyNsEzF6vp1/+8pfW5ty5c0d7e3vqdrt2oPvdu3e/Mun4mT0ZBn8QWT6oa9CHer2eNjY2tLS0ZEdouDikq9okHan1f/LJJ5bo7927Z1qPu7u76nQ6un379sz3foXNAuTMZjazmX2OeX/9j8xsZjOb2bNpswA5s5nNbGafY7MAObOZzWxmn2OzADmzmc1sZp9jswA5s5nNbGafY7MAObOZzWxmn2OzADmzmc1sZp9jswA5s5nNbGafY7MAObOZzWxmn2OzAPkILRaL6Xd+53dMQ29mM/u6bOZ7X43NAuQjtBdffFGvv/66FhYWjvujzOwZs5nvfTU2C5CPyDhb+P79+zp79qwprMxsZl+14Xv37t3TxsbGzPceoc0C5CMwr9er9fV1O4s7nU7r7Nmzx/2xZvYMGL4XiUQ0Go1mvveIbRYgv6R5PB6dPXtWFy9e1IULF5RMJhUKhbS+vq7V1dXj/ngze4rN9b2LFy8qmUwqHA7r9OnTM997ROY/7g/wpFs2m9U777wjv9+vxcVF+Xw+pdNpbW5u6pVXXtH29vYTe6LbzB5vc31vYWFBfr9f6XRa9+/f1ze+8Y2Z7z0Cm1WQX8K8Xq/efvttFQoFhUIhO0SLA9MXFhb03HPPmYz+zGb2qMzr9eqdd95RsVhUMBg03+MYh8XFxZnvPQKbVZBfwhYWFrS0tCTp8CS6nZ0dVavVqSNmr1y5op2dHW1ubh7nR53ZU2YLCwtaXFy0kzh3d3dVr9fN9/r9vq5cuaLt7W1tbW0d86d9cm1WQX5Bi8VievPNN+00w3A4rFarpZ2dHe3s7KjT6SiTyWhpaUlvvPGGHek5s5l9WYvFYnrrrbfsxMNIJKJ2u23BsN1uK5PJaHFxceZ7X9JmFeQXtJdfflnnzp2zw+EDgYCq1aoajYa12fF4XKVSSRcvXtSnn36qH//4x8f9sWf2FNhLL72kjY0NxeNx871arWYVJCdk4nufffbZzPe+oM0qyC9gp0+f1rVr11Sr1TQYDOywpHA4rEKhoNXVVU0mEzuvutPp6Pr160okEsf90Wf2hNva2ppee+21Kd+TDs9ULxaLWl5e1mg0Uq1Ws7PVZ773xW0WIB/SgsGgvvWtbymRSKhQKCidTmswGKjX6ykQCKjX66nRaKjX66lUKqnT6WgwGCgej9vEcWYz+yKG78ViMRUKBSWTSfX7fbXbbQWDQfV6PbVaLXW7Xe3v79uRruFwWG+//fbM976Aza7YQ9r169d1+fJl+f1+RSIR++dwONTe3p4Gg4Ha7ba8Xq8CgYCGw6ECgYAGg4E2NjZ07949/fSnPz3urzGzJ9CuXbumS5cuKRgMKhKJyOv1Kh6PazAYaG9vT6PRSO12Wz6fT6FQyJJ2p9PR2bNnde/ePf3DP/zDcX+NJ8pmAfIh7MSJE/p3/+7faX19XePxWO12W8Ph0IByDm5vNBoKhUIaj8caDocKh8OWvV955RXdvXtXBwcHx/xtZvYk2cmTJ/Unf/InOn36tMbjsVWKHo9H8XhcnU5H/X5fnU5H4XBYo9FIo9FIoVBI0mH1+eqrr+revXsz33sImwXIh7B//a//tU6cOKHBYKB8Pq9AIKDd3V1JUrlcVqvVUqPRUCKRkNfrVTgcltfr1XA4VCaTscD50ksv6X//7/99zN9mZk+S/eEf/qFOnDih4XBovnf37l15vV6VSiV1u121Wi3zvWAwKI/Ho8lkomQyqUajoUAgoBdeeEF/9Vd/ddxf54mxWYD8De369et6+eWXVSqVrHocDAaqVqsKBoNTleTKyor6/b5isZgGg4FisZg8Ho/Onz+vTz/9VL/zO7+jX/ziF7p79+5xf62ZPQF27do1Xb16Vfv7+1Y99vt9dbtdDQYDeTweeTwexWIxLS0tGe7Y6/WUTCYlSefPn9fNmzf1rW99Sx9//LHu379/zN/qybDZkOY3sEQioXfffVf7+/tqtVoaj8fqdrtqNpuaTCZqtVoqFosqFova2NhQKpVSKBTSZDJRKpXSZDKxYAom+d577834aTP7tZZIJPTWW2+pXC6r2+0aK6Ldbms0Gsnv9yuRSCiXy+nUqVOKxWLyer3295PJRM1mU7u7u9bNfPvb31Y8Hj/ur/ZE2CxA/hrzeDx66623lMlkVK1W1Wq1NJlM1Ov1NBwOJUmBQEA+n0/RaFSJREKj0UipVMowyFKppO3tbd2/f19bW1va39/XxsaGXnjhhWP+djN7nM3j8ejtt9/W5cuXNZlMVC6X1e/3NRqN1Ol0JEk+n0/hcFjhcFjj8ViDwUB+v1+dTkeVSkX9fl/7+/u6f/++Dg4ONB6Pde7cOb311lvH/O2eDJu12L/GFhcXdf78ee3s7Gg0Gqler6ter8vn8005ZLVaVSgUMi0+n8+n4XCoXq+nwWCgTqejbrdrWX8wGOjll1/WzZs3tb29fczfcmaPoy0uLurChQva3NxUt9vVwcGBhsOhPB6P+VU4HFY0GlUwGFQ0GlWr1TL6TygUsiTe6/UUDoclHfrmqVOnVCgUtL+/f5xf8bG3WYD8ZywQCOjChQsajUaGMXY6Hfn9fvl8PnW7XQuK1WpVkqzFCQQCmkwmisViGo1G8ng8Go/H5ryNRkOpVEpvv/22/sf/+B8z1ZWZTVkgENArr7yi8Xisg4ODKcw7FAqZj3m9XpteEwwrlYphk8Fg0HDLyWQij8ejarWqWCymt99+W3/5l39pvzezf2qzFvufsXPnzunChQsKBAJKp9MKhUL/BDdkH9bv92s0Gung4ED1el2tVsvIur1eTx6PR51Ox8Qs2LQ5efKkTp8+fUzfcGaPq12+fFkbGxvy+Xzyer3yer2Ga49GI5PV83g8arVaGo1GajQaarVaKpfLqtVq6vV6hl2yxNBut1WtVhWJRHTixAmdPHnyuL/qY22zCvJzLJ1O67XXXlOxWDQ5e0mGP3o8Ho1GI/V6Pe3t7SmXy6nf71sg7HQ6mkwm2t3dVTKZ1Hg8VigUUqvVksfjUSQSUSwWk9/v10svvaSbN2+q3+8f87ee2eNghUJB3/rWt7S0tKRAIKD9/X0NBgMNBgMb8pVKJdv7H41GkqRarTalJOXz+UzIQpIF1fF4LL/fr0KhoHfffVd3796d+d7n2KyC/BXm9Xp1+fJleb1e24yBfAuOOJlMrO0OBoNqNpumvVer1dTpdDQejzUajVQul+Xz+bS4uGj8tEgkolQqpdFopIWFBb322mvy+XzH/M1ndtyG77Xbbd24cUPValWBQECSFI1G/wnNZzweG0vC7/drMpnI5/PJ5/OpXq+rXC4bm6Lb7apWq1lrTrJ+6aWXLLDObNpmFeSvsBMnTuiFF14wLlmj0dBwODSsZjgcKhgMajQaqdVqKRaLqdFoGB7U6/UUj8fV6/XU6XQMf+x0OrYz6w56hsOhXnnlFd26dUt37tw55m8/s+O0xcVFLS4uajQaaTwea3t7W8Ph0NprkvBkMtF4PDax3HK5rGQyabg4mHa73dZ4PDbubigU0mg00mAw0GQyUTab1auvvqrNzc2Z7/0Km6WNBywWi+n55583tRQAcapHn88nj8djmwk4H9SKXq+nTCYjj8dj1B+/36/hcKhOp6Nms2l4UrPZtL1Zv9+va9eu2aRxZs+eRaNRXb16VdFoVJFIRPF43JgQ/X5f9XpdkjQajRQOh9Xtdq2KDAQCFlSh+LDmCjYuHa4cUkFGo1HFYjGl02ldu3ZNkUjkOL/+Y2mzAPmAXbx4UYVCwabUpVJpKliOx2MLcD6fT4PBQK1WS71ezzBJRANoxaXDqWS73Var1bKVMI/HYwFzMBhobm5O58+fP+YrMLPjsgsXLiidTlvL7CpBAc00Gg2Nx2NJMh1IqGPtdtsoZEyvPR6PcSQJoPhps9lUpVLReDzWysqK1tbWjvkKPH42C5COzc3N2UQZ3IdWWJI5LBPCRqNhU+3RaGTqPj6fz6batNCsg/Hfm5ubGo1GSiQSyufzyufzGgwGevXVV5XJZI7nAszs2KxYLOrMmTMG0QyHQ5Mr498TiYR8Pp8ajYYqlYrS6bSCwaBVkeCK0MrY8qrX6wqHw5agJdmAEJin3+/bqZwzO7JZgPx/xjSZoYyLGQJ++/1+A7aZHEK7kA7JuIDd/MxgMLD/T7uOo9Ii8R6pVErZbFbXr1+fDWyeIfP7/XruuecUjUYVj8dtJTAQCCgYDKrf72s8HqtarZpfEAyBf6ACAdnQndDF9Pt9a8OTyaSRzNPptKTDM2zi8bjeeuutmW6kY7MAqcNgtr6+rvn5eRWLRTtnJhQKKRAITG0l5HI5RSIRqzB7vZ6B4gDoZPBUKqVgMGj0HgB0MvZoNDJ+GkG0UqkokUhofn7+2K7HzL4+83g82tjY0Pz8vPx+v8mTSYfHuvr9ftMTlWRJG63R8XhsQZO2GnX7eDxumHYgEFA0GlU0GlW5XFaz2bTtGkRWUqmUzpw5o1OnTn39F+IxtVmA1KEjXrx40fTzcEiPx6NAIGDVINm80WgoGAwqFotZpZlOp6faFq/Xa+ICVJ9gS1QITLP5uW63a1Pv69evzwQFngErFou6evWq8WLBDKkgSdCuCEU2m1UkEpkKhtJhFZhIJExQBf+ki/F4PEYPcnUiM5mMQT/D4VDf+MY3Zr73/+yZD5AIicZiMZv4QcodDAaq1+vmtGtra8pkMraT7fF4LPC12207m5gjGKRDcm673Vav17MgSKUALaherxvtIhwOK5VKKRKJ6NKlS7NzjZ9iCwQCunr1quLxuKLRqBqNhmGO4/FYW1tb6nQ6SqVSOnXqlPmFu0pIO8zfoXDf7/fVbDaNMN7pdAx/lGRDyHa7berk+H4sFtPFixdnvqcZD1IbGxt65ZVX1Gg01Gw2bQBDFUn7gipKt9tVOBy2CTYZOZlMyufzWRCUjrhq0mErzlSRDN7pdDQajWyXNplMmshuv9/X+fPndfv2be3s7Bzb9ZnZV2enTp3S2bNnDcfu9/s2NCFYhUIh9ft91Wo16zq8Xq/9zGQykdfrNVoZ3Y2rGQCdjKENfhkKhSywer1exWIx05xcXl7WZ5999syrjz/TFWQkEtHzzz8vv9+vs2fPmpAEauC0xOVyWQcHByYakE6nDRTnfBBaFCpLNm3Ym6Uld3mPZH+4lR6Px6qHSCSiaDSq1157bQaaP4UWi8X0xhtvKJVKKZ/PGyTT6XQM1hkOh2o2m6pWq7adBa+WIEdnQstM1wPODUxE2+7z+Swh46e9Xk9zc3O2mphMJpVMJvXiiy8+8773zAZIn8+nN954Q8vLyxqPx6rX68pkMkomkwoGg2q1WgoEAna+NZkbaTPaZhyu3W5bQIX7CObDZBv6BsA6BF8cvN1uq16v21AomUyqUCjoypUrs3bnKTKfz6fXX39dmUzGtrKWlpaUTCZtoIdaD9Zut61NdvFCIJ3JZKJKpSK/3z+VuNGNlKTxeKxaraZgMGgbXbTY/GG5IZvNqlAoPPMwzzMbIK9evaqrV6+q1+upVqupVCrZ3isbBe12W6VSyeTs3baF7YYH92JZ/aJtIiAOh0MLlrTnzWbTXm84HMrr9dpRsvF43BzzxRdfVCqVOs7LNbNHaM8995zOnTtn9xecGvhlMBio2+2q0WjYXjUQDXqkYNnD4dB+LhKJmMSZu9RQq9XsjHZac3BwiOUHBwemQo6vejwePffcc880N/KZrJ+z2ax++7d/ewpHRJKeKhBumXSIP/r9fpsaMmHkiE23QpRkWZzfw/HBlYLBoCTZqiJT7kajoWw2q2g0akpA6XTazrj5i7/4i5nqyhNurPWRFPf391UqleTz+QyHHAwGNgzs9XrGnsBfBoPB1DYNCViSCajQ1QQCAaOocYAXR8KOx2Ol02mVy2VFo1GdOXNGBwcHKpVKarVaymazCgQCunbtmv7qr/7qmdSNfCYryJdfflkLCwuSZNgM+9QuOM5GDC0x3DOqQIzpIa0NmZ4KstVqGR4ZiUQUDoeNOsSaItiT1+tVp9NRIpFQOp02ftz6+vpsDfEpsG984xtaW1tTOBxWKBRSoVAwGTPOOCKA4X9Qz9idphVmaBOJRIwnOR6P1Ww2beAzHA5t2Mj+Nj49HA5VrVYNEqrX6/L7/YpGo1paWtKFCxdUKBR09epVXb58+Tgv27HZM1dBrq6u6rXXXrMJcjabVbFYVKVS0ebmpv1cq9VSJBJRMBiU1+u1M68JpOA4kgzsxsGkI0JvNBq1CSN/gsGgTQ5RaAGLnEwmymQy6vV6arVaCofDarVa6nQ6evPNN3Xr1i0TLZjZk2UnTpzQ1atXjQkBa8Hn82lnZ8eSKH7FgA/pvFarZcGNFpohjSucQpUZDocVCASsFYcLySR7NBopGo3akNDd90YgGsjnD/7gD3T79m1VKpVjvopfrz1TFWQoFNLv/M7vGJm23W7r4OBAwWBQly9f1okTJ4yK4wpSSDI1Hv4gRoHjuW24z+dTPB6332dLIZ1OK51Oq9/vW/Dl/zcaDRNBrdVqqlQqBsAfHBwok8lobW1Nb7/99jMNmj+pFgqF9N3vflfRaFSVSkXNZlPNZlORSMREImin/X6/bcKwHsjmC77R7/fV7/eNVub6KdViKpVSKpVSIpEwFgUJPxwOG0GcrqfZbBoWiRo5vMyTJ0/qd3/3d58533umAuT169f10ksvKR6PW1XGEa7j8VjFYlHxeNxabI5LqNfrmkwmdjgSWZ0sDTGXTZxwOGwtt8fjsem0JB0cHJigQKfTsa0a6WhXm5YfqkcikbAq4MUXX5zJ5D+B9uabb+ry5cvKZDK2XlqpVFQqlRQOh7W4uGiCuCwaQPFhDVU6lESDG+kSxeE6sprIYKfVatl5SY1Gw5K6dBiQ0QNAGR8/rtVqqlar5od7e3u6cuXKM7eG+MwEyGw2q42NDVPQSSaTdjxCs9nU3t6ePvvsM3m9Xju6Fal6Jn2DwcAEBFKplAU/BjpQftiIcFcMvV6vYUysgBH0arWaJFngbTQaU1s5Xq9X1WpVzWZToVBI3/nOd2Znaj9Blsvl9Nxzz8nn85kgCUMWj8djlVosFjO/gp5DC0xVhw9KMnjGFW8maALf8D6tVstaac50h0pElQheCUd3eXlZqVRK0WjU9CO/+93v2smdz4I9EwHS6/XqzTff1NLSklVuZGLW+65cuaJisWhKO6wOEvyYRrMf64oEQP4m6BFEqRQAwV0tPrI2oqZMMVlblA6rAoKtdLQ3e/LkSV27du14LubMHsq8Xq9eeeUV5fN5S5SsATKMW1lZMd+g63B3912s0cUgGSpSYRJYIYlTjbpK5N1udwoWchWnXAgJsjodD9jmxYsXdf369eO8pF+rPRMBcnFxUWfPnlU2m1UikbCpX6/XU7Va1Y0bN/TJJ5/YlFCSBTpaEDBGVgGp7hqNhm3PdLtdc9J2u61ms2nBjoCJ6rgrZBEMBs2JXSzJba9zuZwdIev1evXcc88pn88f2zWd2W9mi4uLunTpknEJGQwmEgnV63Xdvn1bpVLJ9qHhOCK7xyQ6k8nYFg0tNL4AzEPlR0JF6JkKkQ6GChTiOcGTrqTT6SgcDltw5p/I/b344osqFovHcDW/fnvqAyQad1SFtLXz8/M6efKkBcG///u/1507dwxXdAMpi/ySLNM2m03DZ3AwtiBY33IpQ/DQqDQlWcBtt9tTii28HquJiGgMh8Op80feeuutqW2LmT1elk6n9eabbxrcEggEDPvOZDJKJBLa3t7W97//fd29e9cCY6/Xs45lOBxa1UjSpUJE75Fg2ul0bI2QpQQwbYY5kMc9Ho9h3o1GY2ofnJVHKEBg60BN6XRa3/nOd2za/jTbUx0gvV6vrenRMnu9XmUyGUUiES0sLGh5edm4kPfv37fTCeGVuYe0035DxYCfhvPSvkDHcLX8AN0lGVAvyYIlbc5kMrHjGNwpJdUlJPXRaKT19XU9//zzz9xk8Ukwr9erq1evKpFIWNAaDod2lnUymVQ2m7Wkx30GconH40qn01Nq9mCIQDPwIPE5FOvBIFGdQtwCOT7Ed+v1uu1eS4dQUSaT0cLCgrX3bJaNx2PDSr1er86fP6/XXnvtqfe9pzpAnjhxQteuXbMgtr29rVu3bplaT7vdNt07yLoER9atIIZTAbgHG4FDhsNhq/KYLpKFcWI2cHgtt6UOBoPKZrPyer12/oh02PKAF/FAuJqRoVBI77333jMFmj8ptri4qJdeeknpdNqq/mq1Kr/fbxxDgh8VYCgUssRLF0JFFw6HDWIhQdJqj8djxePxKZX7ZrNppyG2220LdgRYlhUajYYk2SAGeAjOLkIZkmwgRMfzW7/1W0okEsdzgb8me2oDZCgU0vPPP2+AdqFQUKFQUKPR0K1bt7S7u6vd3V2Th+KMGVfLEaK3e7ohQgKuUg9HvqLgzKl0oVDIsi8BDQdttVpWpbKP7eKUfr9fsVjMBj208rwmD0YikdCLL7741GfyJ8mi0aheeeUVg1kWFxcVCATUaDS0tbVlB7dlMhkVCgXFYjHzLzDvZrNpLTdJFiyQRE5SdpWlHlQUZ6joyqgFg0GDkMAYJdlKI++JRmosFlO/37dpOVXrYDDQ9evXn2rfe2oD5MrKirLZrEqlkvEK0+m0VldXVSwW7SyOdrtt0vZUgwQgqDtu+0NrTKWIQzIRZNII2RwMqdPpqFarTelHhsNhC4ZMr+v1utF83ArC3ckFmOe/L1y4oBMnThzj1Z6Za6dPn9bGxsaU+G0ymTTV+XQ6bfJlhUJBkUjEAqCkqUk2CReaD3QesHBJ5pe8F4ZGABARWGIoFFI0GjUiOUrmBFlJNsCkG0I5n9fz+XyqVCp6/vnntb6+/nVe3q/VnsoAmU6n9dJLLxn9Bsfb2dnRzs6OHZWJdH0kElE2m1UulzOskoO7aIddTiNVHAGQVUP4auA/VI4c4YnGJMC6ezzDg5SgbrdrD9FkMjFB3wdPWWRF7bXXXpvJ5D8Glk6n9dprr9n9lWSKT/gE+84MYRiiwFsEc8Y/CF6chcTQz9UalWTrqu66K7vXiDgzlOGz4McMHl1oqVqtqtFoWDseCoWsKi2Xy+b3L7/88lPre09dgPT5fHr77be1vr6uWCxmYHO1WjX6Q7fbVaVSmTrPg7OD3WGJJFsLJPBJsjVAHFeSBUpaj9FoZEGOqg+ch3Uvd0PCFctAyAIMk8n6wsKCTpw4YU7O4fDIVL366qtPdbvzuJvf79fv/d7vKZlM2pYURyNUKhWjdzUaDQtmHOaG3ijrf+DdDPnoKJhCo0TFH/yNgEdxQBeCb/l8PiUSCfNb/MwN4uCbYJ5gnDAw2u22iW1MJhMtLS09tTDPUxcgr169qhdffFH5fF5LS0taXV01cm46nTaajyTL0mRzlJglGTlc0pQoxXg8tlVCVwwXfFGS4TWxWEzRaFSBQECpVMqGNK6mJAIBBFvaKAJ2u91WMBg0EJ4d2U6nY2eZ8BCeOXNmdhriMdpLL72kF1980e4LGDaK3tLR0RtUjAz4vF6vHX8ANohEHmdiI3KCr+CDCKngp7TcHPCFj7rEccRaHjzPnYoVBgZH0QItHRwcmDgv/z4ej7WysvJU8nKfqgCZyWT0R3/0R1pZWTGniMViOnXqlPL5vImNUs1JspYEdRVkzdh6AHtBZJTXBfQGQxyPx2o0Gkb9kWTODIcsFAr9k4ztno/N7yBXNZlMFI/HlUgkNB6Pdfv2bW1vb2t7e9seEAIp1cerr776TPDTHjfLZrP6F//iXyiTySgWixl8g/gEiY+BSigUsiOEqQJZNQWGQfEbTFuSJVZgHgIYxHGEcdmIGQ6H9nO02rVaTa1WS+Vy2apaSdaNRKNRW36gCmaoyGtFo1Ejr7daLSWTSb355ptTx9Y+DfbUyJ15PB797u/+rpaXl6eysIvjpVIpy8yISpAB2XhxF/35PSg+ODnOyLRbkjk/dB7abWg7YDuA7KlUyoi3ODV0JJyfyoCzcPjvTCZj79VqtUxgdzweK5fL6fTp0/r4448NJpjZV2ter1e/+7u/q1wuJ+kQh9zZ2TE/Yvee/WeXrsNaKp0A6uKoPFFtsptPpwJu6fqdx+MxwQv+Hl1IYBv8l1VEMFAYE27bnsvl1O/3Va1W7XmAw+menkglvLa2phdeeEE/+MEPnhrfe2oC5Nramr7xjW/Y1Nf9I8k4hGR4FJoRpaCtwKGkowqQgItTQthmCMPPgin2+33DbMBwcFoEC/g9Wmx0H3FUFwsCO41Go9aqueIE0tF5I61WS5cuXdLu7q7K5fLXdwOeYTt9+rTefPNNq9xZH0UftNPpWMWYSqUssOF3yWRSBwcHNjyhCtvf3586AoFOhMSI71Sr1SltAUnWVfAzaD8ifgGVKJFIGE+SaTUsDhgVkmzyzdIFnYu7utjv93X58mV9+umnT81piE9Fi51MJvVf/st/UbFY1Gg0UqPRsIxHe8J2SrValc/nUzKZVCQSUSKRsFYGR2ZIA47jZmSyOcA3bTfgOAA7mZWWGSwxmUxqcXFRc3NztoMdDodtgo3uH87tqpLz8HCqYiKRUDwetwem0WgYfejatWtPJWj+uFkymdR/+2//TadOnbLW2RWh9Xg81kWg5ASmzCmYLmUMOT06HipMd8rMZJogRUDlNehsmHTDn2RF0R06MjGnU2q326pWq5bwoZIRnKlsIZHzuTgXZzweP1XcyCe+gvR4PHrnnXd04cIF45INh0NtbW2pVqsZ0RUeZK1W08HBgbrdrmV0Dsn68Y9/PHVmcLfbVTweN4eiGkSglIO13LbI5Z4R9Fy1FR6cTqej/f19I/cSWNmkYP3MfcjcExAJ3rQ9KALx+hw2f/PmzeO8PU+1eTweffvb39bq6qqy2ayRrFGbJ8gMh0M73wWcOpfLWUcCRQaoBIYDcM1wOFQ0GjWIhUDEv6MyxXvxvplMxuhGBF+wR3edtdPp2IFfDIza7baKxaL58/z8vLrdrq3Ugp1DSIc8LknFYlFra2u6cePGsd2bR2VPfICcn5/Xv/k3/8bOcKlWq6ZEgrZiNBpVOp02cBvQnAqNliOfz2t7e9uyLFM/t8WBVOse4E6QIoiivSfJfob38Pl8Vg26BPR0Om3vRQZnu4IqQDp8iMrlsrrdrm0AQTyPRqPa29tTJBLRaDTShQsXtLu7q1ardZy36Km1hYUF/af/9J80Ho+1t7enTCaj3d1dHRwcWLBghRCMG5I/AxGO1XCDnHuSIRUbwxP8BeoNyuL4niTrfjjRELFnjmCAw0iAA/Nmas5KIoeJRaNRqxBJ/O57umu6FAEvv/yy9vf3n/jjQZ7oFjsUCulf/st/qXA4bGdVs/rHgezJZFJer1e5XE5LS0tKJBLGNWs0GqZkwoADDJBBDgMZ6D9w1qhWXY1IWiCm5Ez8aJkIim7AG4/HVjm48lLucQ68j6skhDoR08ZWq6Xt7W07Z9nv9yuTyejNN9+cKf58BRYKhfRv/+2/Na4iSRkKGDAOGCBHIbCTXSqV1O127XfT6fTU7j4/D0TkEsglWXXJz5AwI5GIrS6iREXL3Wq11Gw2lUwmzedJ7igIua+LH1K9VqtVqxz5PODj/X5fOzs7dm1Onjypt99++4n3vSe6grx48aKee+45O/5Akm2bBINBpdNpy8qNRsN2S5nWHRwcGE+S1qJer6tSqajVapkSS7/fN5yQhX2yLMHTdWqqT0jf/OHgJZwPqg7nY/M93B1vNieYcFKRgkt5PB7l83lbC4M6BK504sQJzc/P6969e8d8t54uu3z5sr7xjW/YvYeBAP8VHmEikTDeIFxWDoSDVQHuWC6X7TRMgh16jtFo1M5SwtepGOkyqADpTKgKETNhcEIQRxCDBC7JKlVJSqVSyuVyikaj2t3dVb1etySQTqenlIhghUgyjD6VSmlubk7379//Wu/No7QnNkDmcjn9x//4H7WwsGAK4VASCCaBQMCGMTgDWoqnTp3SeDxWPp83565Wq8pkMkqlUjbVYz/btWAwaNPxYDCoRqNhfDNXTJc2hwCJs/P3tFLoQDKFbDQa6na78nq9do4xGCOfiUDM+cdu2w+UwCT16tWr2tvbm6IlzeyLWy6X0x//8R8bdoj4MUEKHU8Gfi7EQQDi6AXWBsfjsU6cOKHbt2/bUcDS0TSaboJgxIBFkiVGBnZUhu657BDUpSO+I9Jr7XbbXj+ZTBqdrdlsKhaL2WtxFg5UIrBxKl8Gni4N6cUXX9T+/v4T63tPZIvt8Xj01ltv2Y0igMRiMWtrJFnQ4uhK92gDKsETJ06oWCxOtUXJZNJ4YoDgYIo4vLvBQBB9UCZ/OBxaNdrv91Wv182ZmHIvLS3p1KlTSiQSNuyB+gG1yH04pCOuJu0QWGswGFQul1M8HrfAHA6Htbq6+syea/yozePx6Jvf/Kbm5uamJOvG47H29/eNFuNOrUl8tME+n8/uO1qM2WzWjmUgUCHczJCRoOPi2iRrt5UlCLs+6/P5lE6nlUqlrHNhSAmUhFqVu+NdrVbV7XZ14sQJZTIZBYNB/f/tndlz2+d1/h8sBAgSO4iFq0jtu2wpdiJHkzau006b6XTazvSiF7nt39eL3vUu05m2ky6O49iRJVmkRILEDhAkQZAEfheYz+EBE/9qWRvl8J3JRBYpLN/3vOc85znPOW+73dbW1pZdaAe1hUSI98/lclpaWtLdu3ff/Ea9ovVOIsiFhQXdvn3bnEMsFrOBoky5YXJzvV7X9PS0cYuhUEj1et2i8q9//WtJI04HUTmtgaQs0sjotre3bRiuH0xBSovzjcfjZtCQ3LR/YdBEdq7/hNuhswfpB1NaWq2WGbZvfQyHw1Zt9IN8a7WatSMOBgMtLy/ryZMnZ9rIl1yXL1/WT37yE7suGOdC2ttsNg31URmmM6XZbFqQXl9fN4kP13DgVOiE8ZPEyTp8KkwwxmGS9ZCR+MEpZEmSjDOHv2aiD+P+EJjX63UVi0VdunTJZp1KxzcrwtN7npQKPvbf7/d15coVffXVV6rVam9n015ivXMOMhaL6Re/+IVKpZIR1iBJhn1ms1l1u11lMhnt7u6q2WxaahqLxSwNDwaD2tzcVKFQUCaTUTabNeQWCAT06NGjMeJa0hjBjcHBT8I7UQAiBZGOR1hJI2dM5G00GsY3eWkHPCfEPAUaxmUxDAG0OBgMVKvVrE0Rcl0apWnz8/P6+OOP9c///M92mM/Wi62pqSn94z/+oy5dumRokbbWTqdjtxV6LWy73db29rYNS6nX60qn03r8+LGSyaTy+byKxaJ2dnZ0eHioixcvWvYCWoRHh56RZCkuMhxGpxFc4dkp2LGYMdnpdJRIJCx4U2U/2fxAjzhXH8diMS0sLBgvSSruOVBkQoFAQNPT04rFYnrw4IH+5V/+5Z2zvXfOQd6/f19XrlxRIpGwaEmUpvtEkkVhadSRgDPCaHBC3L3BtBx4lHw+r0qlYnygHz9PhD35fqC/o6Mje51msylJFsXpsvB6xr29PW1tbRm5T2rE2CmEwP4O7sFgoGw2a0j3+fPn5pQRvTO1xesmaUM8Wy++7t27p2KxqO3tbZ0/f16dTkcbGxvK5/MmueHeGKiYZ8+eWdq9vb1tHDf7tL29rXQ6rZWVFSvIbWxs2Mg9+Gs/5syPQMPWcKgoHqhs+0niKCJ8NZzCINOkYrGYDYDmjiUym263axna3t6eksmktdzy2ehECwaDNnO1Wq0qHA5rZWVFDx8+fFvb953WO+UgM5mM7ty5Y5W+VCplXAmOhZSUyLa/v69qtaqdnR1LcYjC58+fV6FQsLS72+0qFouZ+LVUKtkcR7Rr+/v7Jh4n2pIO+ysbaPIn7YeI9+R2Mpm0ip+fJk66DgrI5XIWneGB6vW6KpWKSTdw1hyg6elpq8LHYjGjFW7cuKH19XU7tGfr262ZmRn92Z/9mRKJhKWXmUxGw+HQbiWkpxonw+Bj0B+T5BuNhgqFgnWpbG1t2QVZmUxGxWLR5gDANXoOEg1jPB43R+ivcKCyjv6R7Iazwhg06CI/v9QPfaYYRKDFVjc2NmwI8OHhoRUpcdaSzHZBr5lMRjdu3FC5XH6nbO+dcZDhcFh//dd/bekl6QftVkQv0NnMzIz6/b4qlYq1UBEp4/G42u22arWaLl68qHw+bxxmt9vV0tKSOUBuj6tUKsYf4uSI2Gjg6KSAa5Rk1T8ctySL3kgkuNo1kUgYKc5wg+npaXU6HZOM+Ek9VLxxvqlUygb94rB9wWliYkLxeFw//OEP9a//+q9vfhPf0RUOh/X3f//3ymazpiAgLWWsGXwe7ae0l9Ia2G63FQwGValUNDExodnZWc3Ozpr94vDi8bhNnDo6OlK1WrVJUpIs2EKrEBjhKQnCCNAlWUMBxRemUzGXlMyF7CoUCtkc02azqcePHxu65C4nSTb3gBZY0CTvk0wmTY3RbrcVj8d1/fp1/cd//Mdb28sXXe+Mg7x586ZVw4hq/Jm0AudI5EVKQ2thOBxWs9lUNBq11i9S7Lm5OS0sLKher5sxFwoFHRwcaHV11bodEomEdQ5IxzP2/BWaIFSIchyeR4qgAXSRgUDAhN7o40jXvS4SA2biSjgcNgfa7/e1vb2tXC5nzwSJBdwUXTv5fF7VavXtbOY7tm7duqXr168b30cHDOPxQPkTExNqNptaXV01O4BqYc8JXPTOR6NRLS4uKpVKaX19Xf1+XzMzMzYK7YsvvjBnxSxHKsWgSQb0SjKwkMvlrBuHEWvoKuE0kf5AUZGJUXA5PDxUsVjU0dGR8vm8FZuYcYlGGGmZ1xjzOn74xsHBgS5evKhyuay1tbW3tp8vst4JBzk5OalPPvnE0lukOMzCwwBDoZCePXtm6QbRjbQTSdDOzo7xIysrK7aR9Ke2220FAgF99NFH2t/f17/927/p0aNHdlUmjo9oyVh6Ji5jLBgJRsmoKsTgFJa8fgxxL04T9Ala9YiTaxh4fX4Gkjg4OFCz2TTOVZL9/40bN/TLX/7SONSz9YfX5OSk/u7v/s7G3TWbTeXzeetzxgH5YSbRaFSdTsf2hgvh4LBBboVCwZxlNptVJBJRt9tVPp/XjRs3dPHiRcXjca2urqpSqahSqUiSVYlpOyS1p+EAx0mBB+kQd2pPT09bzzaZF/cj4RipSHNVMRI0dMQ+U6EYQzqPQgSEGQgElE6nbSD1zZs3tbGxYbZ4mtepd5CBQEAff/yx5ubmLDWNRqNKpVJmeJDeg8FAc3NzWl1dtYIMaThaQRxRvV5XIBDQ119/rUuXLqnVaunTTz+1lsGjoyOrNC4vL9vvggJ2dnZMfI4TBtVSSPGDK0jNQHaBQMD6WyWNSXRwhEgm/FQikGO32zWJE5+DNIgR/X5yC/2zFH2SyaSuXr2q3/72t9+b2X2vegWDQf30pz+1oAVN0Wq1tLS0ZH8mjZSONYdIz7a2tsxJ4rTa7bY1KaRSKZXLZUmyQFetVvXFF1+YTndra0uhUEiLi4s2OQfUh6PCjrjWg4wFkTo6W2gmf90InCYgA0ka+k0vTYvFYtaqSwESe6bdF/oJPtMXULHJK1euvBO2d+od5OLioi5cuKDhcKh8Pm/cIyLoer1u1V1JNh2lVqup3W6rUqnYdGXkD6Suu7u7Wl1dNeRXqVR07tw5LS0tqVarWSofjUYNie3u7lrlHKPCeDBGDowfdc/QDIh59JbMccSAfcSlWg7CZYYfhLkXjEsjdMiFZHCi6Ov8oFU4pNnZWa2vr6vVar35jX0H1vnz5/XgwQNJsjZBgnSn0zHU5IsonhNmuAlDHzxneHh4qLW1NS0vLysWi6ndbmt+ft70jkhw+v2+XVfcbDYtVcYB+v5+7JTrYAmmBOtUKmV/T2UbSZufeJ5MJg05zs/PWyEHG8WuSdE99wiV468l8ZfgtVot69V+/vy53bl9WtepdpDxeFwff/yxEci+/U8aRS6cJw5va2vLnA8RG5TlCWzSnG63q3q9rmQyaXKffD6vXC6nWq1mFexEIqFsNqt0Oj0miCUSo0v0UdL3W1MAoiJIRZHvIsmqihRoQAYzMzMqlUpKJpNqt9s2sJQUnGkrDPNFlyfJpj/7lJ1BG8lkUleuXNF//ud/nvpI/qZXMpnUn//5n9uwCfYUZ8CFcEdHRzYfEdnZ7u6u8dGeTpFkOlUKLQTCjY0NQ4mxWEyVSsW6XK5duyZJevz4sdlIs9m0VkJsnUKk19vS9EChDodFWyKpP/w4gR70igOHtnr06NHYXEreh8YJf1ZJt8nuCDCk95cvX9avfvWrU217p9pB3rlzR8ViUdlsVvv7+6rValbA2N/fVyKRsLaso6Mjm+AsjQwRcS7cD7wNGi90YhsbG9YKSEpQKBS0s7Oj9fV1FYtFJZNJLS4uand3V8+ePTNZhiRDi9zP4VsapeP7a0idQbGen5RkZDYcI5zirVu3tLCwoMPDQ21ubppBk1a3Wi1rR/MyopNj22Kx2Ng06IODA83MzGhubk7r6+tvYYdP77pz545dnVAoFKxBgD1iGITXB4KaQEz+Sg8QIK2p2MLR0ZF1PWGPvlJNL/Ts7KyazaZCoZAqlYplFARKr+ygYAj3zvLNBd1u1+wQLnJmZsbkYdAEzDOIRCJ2E2gikVC73VY+n7fB1FwRwpQpr+UFoSJfA/kWCgUtLCyc6kEqp9ZBJpNJffTRR8bpzM/Pa39/X8lk0irJzNrb3t62nutGo2HN+NFoVK1Wy4wBTZpv3qfA4iMtFcaZmRlNTk4qm81aijU/P2+3CnqinI3nsBAlOVikKPwbSXZIqExGIhET4jIGC+NELrS0tKTBYKDnz5+bcTIYwaftXBAvSe1223q6OZRwTYPBQFevXrXvdLakYrGoP/3TPzU9LI4qm80aPYH2kJSY1laGRsAB4yyYucglXOhz5+bmrAcbCiUSiSiVSikej1vrYb1eVyKRsCuH0+m0ibMpxnAHEsUigqwko2XIqJB/oaWMx+Nj98RPTk7a9RDMSIVCkGTzVdH6wulTIAKJwqn7s8dnm5qa0vLysqrVqp2J07ZOpYOMRqP6i7/4C01NTdlGcl0BGw4CLJfLyufzxgNOT0/r2bNn5nS4c8M7BxAYzor2p1KppEqlYgeDKt6XX35pTokLtLghEeMipSDlxaHRS0tqi3OC/CZtoyWL+ZXSSJzMoI2joyO1Wi1L8TgoFIYYhNDr9czR+eGqPAMkHky8ZhzXlStX9Otf/9pSwT/WNTk5qV/84he6fv26paz8f7/fVyaTsXS23W5bqh0Oh1WpVIybhmvELnjmFDRAbp1OR7du3VIoFNLm5qYV+Or1ujqdjp48eWIdN7VaTeVyWd1ud2yqODIzsicKMWRPnnrhs8JT5vN5szsQcDKZ1MzMjAaDgVWbGboSDAaVz+fHJu4Ph0Ntbm4aECB7IXhAQ6BBBphwed2NGzf0P//zP6fS9k6lg3z//ff14YcfGl+RSqXU6XQUj8dtbiPcBqkGMgKGMZCCkubiQKampsaucSX60cAPh+TFvTgY+Eo6dOCmgsGgob/d3V2LnOjQMFLSE6qIyWRSqVRKkqx3ms9x7tw5m+QSDof1+PFj+5wYEiPNSOkbjYbxnAxC4CDBQYJa0aqRmsG7/rFrI2/evGnOgeeKk5yZmTHnhqME9TFIme6Zra0tu4eIoSo0BqAsGAwG6nQ6+t3vfqerV69aoAV1Yf+dTsem7FBYIZWH3/QOkHNBoOW/GZ3HODZJY00MvV5Pi4uLSqfT2t3dVaVSMQ0m6gc/wm84HNo5m5mZGRs+7Xu6+RyMSyNLQ3I0MzOjYrFo1fzTtE6dg8zn83rw4IGNivIdC7u7u4rFYlYoAR3h8DCqVqularU6Bvl95wEIDgPM5/OmR6PyBmojJaI/FV4JA8cQvAyEzwdBj0GSMoOK4TxTqZQJxqPRqF3oRZRn4AFptHR8f0m32zXOaXJyUr1eT/V6XQcHBzas4g91XmDYoNjhcDRcl0Eef4wrl8vpww8/VKVSUaPR0Pz8vAqFgqXM7Xbb0Jckc2aSjHvb3t7WxsbG2NR5sgWvge31elYBJxvBpk/qX9nHYrFoeshwOGx31bBfyI5IeykgkkbPz8+bDXGDok+l/R3sVMWxK+Rm6XTarq2lpRIOnaExABiPaOnn5rySxUFBrKysqF6v28Sg07JOlYMMBAL6+c9/rmKxqHa7bXdAM74JFEZblx9thgaLG+M6nY45BiIZDhM0heGSAh8cHBjXyL3G9Hv7kfoYCAbmtWWkTh6xHh4emqFOTk4aV8lMyFAoZIUZKoaMo6L4grPmsyDxkWTdQByMQqFgIl16cknHOICSzAkzDCGTyWh+fl5Pnz59Wybw1lYgENDPfvYzpVIpC7w7Ozva2dnRxMTEWOeVJCtwwDkj16rVaobYcH6SrGsK9EfqjVwLW6/X63r27Jny+bwJyeH9wuGwUqnU2C2C7K10PH0HHpHPAa2EdA0KCH67VqtZ51a5XFan0xm7cA5BN3YJNUU2xbMAxXKFLcAEe8OpIzoHYExPTyuTyWhubu7U2d6pcpCXLl1SsViUJKvedbtduytmdnbWKmukM5Jscw4ODtRqtSwdYKSYl9tQsSPF9VO69/b2NDMzYxuIFAfim5FjoC+I5ZOREmRBCxgzHHd2dlSv102XiCHzuZgLiP4RwTpDKRhUAU/KsAQGBWD8pP5wjqAQxlZB8lOs8lXxQqFgBv7HtFZWVrS0tGR2h76Ve1fC4bBmZ2dtH+ES4aYRbicSCdVqNeskQafolQWIsKE/EomEqtWqOS7eF0rl4ODAhlxwLYMkQ47YqXTcZUNKHQwG7RwRWAEbcIvw941Gw1paoapmZ2eNP/d0FFwpZ49J4zho2l6ZewAyPOksuQbl6OhI586dG/t+p2GdGgeZTCb14x//WNPT06rX68pmszYhPB6Pm0ENh0NVq1WlUimVSiVtbm5amhmJRLS1tWWbiz5ROp7wjLzGj6ZCaM6INCQLSHBo3qdPm75YdF9UzZH2cAColFMVlGScC1E/m81aVbPRaGg4HN1fsrm5aQ4OJ0s0J2WnkkoHDvwX702q48f6M+8SxMMz9mnf+fPn9dlnn51K0vx1rEQiofv370uSDf0A2aMNRN6Vy+XMXtgPdIsEm0wmo6OjI9tPzwNDjUAJQZPw3wx9oLIbi8Vs8AVpO6J/6fh6YZAYvDfZQjQatQAvjdJ0hp70ej1z5v66j3K5rKOjIy0vL2tmZmYsG8KRM/gEaglVBKhVknG2jUbDwAUURb/ft6yJz0qx8L/+679Oje2dCgcZCoX00UcfqVQqWQvfzs6OZmdnLXWlWoZ8ot1ua2dnR9PT0xatSMExOKaY4PiYssNGUaTodruWcne7XS0uLppMBz0bzgrDZFIJ8hl4IirfbDBdNyDhVCplyC+dTqvf7xtfmk6nrZ91e3tb7XZbrVZL8XjcBmego2Q4hyfn/R0kyWRSBwcHluowRg3nD4LgSlLf/x2JRE4taf6qVzgc1v37923EWK/XU6lUsnTSF1RQPjDhnaBINtJoNOx6jXg8rlQqZXezQ3Fgo7u7uybbgQtGlYAjRUMIBULnFvId2vo85ULGdHh4qGq1qnQ6rbm5OaMOMpmMotGodZlRUKQTrdVqjYEEHCJXLcA90rTAZwQoEJyxIxz3yanozC7AwQIskPSdFm3kqXCQpVJJ9+7ds4iEw2I4rR/PxAVdk5OTdscLvdFcmcAEHQ49DoXhoHQ+4BTpbCFqDwYDZTIZZTIZm/3ITMVms2kpMvMhIdi9xgv+k9SkUCioVCoZ5xkKhbSxsaFarWbcKRVP9GqIfVOplBHgw+HQELKvsEujAle73R4beY+YHC6KmZcEHc9NSrIuofn5eTss3+dVKBT04YcfWm879M309LTi8bg5IBzG0dGRcrmcaVBxYM1mU91uV41GQ+122+gWP+YOp0ZQY28YQtHv93Xjxg2T3oBQ6/W6Go2GKSbgHaFJcEbcbAlHiDZ4ZmbGmiokaWtrS0+fPrXsSZKJ26GIvI5Rkr12KpWy4SrMNuh0OkZ9cf54H+RH2WzWUCIFTLIcsixornPnzpl4/m2vt+4gI5GIfvzjH491kyDlQSeVyWR0eHioWq1mAlPp+J4YkBUOZH9/31ITfo5ToFLMv2eIA0gA55RMJm14KY35jUbD0pf19XXjh7zomuofiDccDqtYLGp2dtY0c4FAQJ1OR5VKRa1Wa6y6TiHITx+nv5f0i44IUjpSZnrBeQ6SLN3302Yo+HAdhB+uKsm+09zcnB4/fvxG7eFNrkgkonv37o0JmtlTAo3ve4ZnZI/39vbUbDbH2lr90FqkLKSVBEF0vd5x4phrtZp1jxUKBfV6PT19+tSQpr9Ns9PpmEPzY9eQjsXjca2srGhqasrAQrPZtE4cSQZIyITg3HkOzDIAUPT7fXOWID7snUEvZGw4RM4kPD7Il+fFjYfY7HA41OLi4qmYPv7WHeSNGzeUzWbVbrcN6tNFgAZsZmbGIiSIbDgcGuSn2ggX4jWQbCDpNVGSTeFn6BCR85DeItmBk0SSgSRIOhZkM/A0EonowoULmpubs5Q3HA5rdXXVvgOIpdfrmYQHYTktWfV6XeFwWPV6XYuLi6YfY2Ygjo9Dg45zcnLSngfcE7IinhHPiVQbhIFWjyEJBIbv47p7966uXr1q2QZTonh2cIjFYtGecSaT0f7+vu0jdFAoFDIBNfw2FAiFF7ICr2WleIOWdmdnR7/+9a9VLBa1vLwsSeaQKfjgDBlsgsB7enpaFy9e1M2bNy1FHwwGWl1d1dramhV7cMy+OOi5UoqKNEJQNKT9kHoAHC0UAg0UdJDBq/oioiQLOpLGNLpeLjc/P69arfbWbe+tOshSqaQPPvjALjb3jmh7e1vJZFKDwegyKlqrAoHREFjEt6Q6RCv6qGu1mhkQ6A4nCRKgXRD0iswnEAhoY2ND0sg40+m0IpGIFhYWzOiI3hRBGHAKP7O4uKhsNmuC81qtZrMjkVsQhTk4IE7G9WP4BAoqqqBGHCkcGM+PayIwRJw6LYwYPN+ZAhbPCTFvNBpVqVQyFP59WvPz87p375619ZEuwg2jqUVwD0pEI0tBzA9oIO1l/3Z2dix7IWvBoZGewxuzz/DpOJVMJmNzT3EmpNAEMZAYciQoqMnJSW1sbJidNxoNkwd5m8vlcuYMcWqgai6k45xQXOEserUFny+TyZhUiExIkvH4pOVQVThcKCWex+Li4tgd4W9jvTUHOTExofv371sfKqkJMFySoUo/tiubzUqStdQhLQBZUuXtdrs2r06S8RveSVCg4WCQ9pCON5tNI7bhdra2ttRsNo24968PSc74KhAlaJHrXUnlMFBPGfgRaTgxtJZoyBgSDC8bi8XU6XTUarUUiUSUSCTGorbv4KENjmcFl8T0IVIfPhMFou/TMItIJKKf/vSndtUpygIyA7jvw8PRlRXoFfl9z2szpJauG+4BolhSq9WMa/Qj8kCqdLagqRwMBpqamrJiHReDMb4PO4M77PV6dhf69PS03XuNk240GrbnvDYOXRpdJEZAhUPEeRNw6eCBlvEOC+RKpZtzBX8IHUSRCWkZz6vX61kLMQVaztb8/Lx6vZ5+97vfvXkj4fu9rTe+ceOGzp8/L0k2XDQYDNqoJKrTPHimM3ux92AwsOtdC4WCAoHRNHDIZ0aWwdtJMsP3GjI2EMNjvNnh4aFxKtFoVFtbW9re3rbUlMiIlCGRSKhYLCqdTtuQ1Xa7rc3NTeuhZVgqkZPOHC8c91IJT2qTimBIpFEYMKJvP/qM90Ka5LkfHDzOk/egIAHKLpVK9ly/D+vSpUsqFAoKh8O2z5Ks+wikA6cLB84AEXR6ZCPMCQBJIU2Lx+MW+GkLJO3G8WQymbGiHsF+a2tLxWLR/ht7AJHhZEByBL1gcHRNa61WU61Ws+IR+l14Rop2dKjRJQSI4EwOBgO7TgJtL44T28IWkblBE3md8e7urpLJ5NhkH7IUnCp6ZaRMNFgkk8m3po18Kw4ylUrpZz/7mVZWVgz2e2U/aTbpsS+6tFot06nxMHngRFCcAekjDgUnCGeEJg2jyGQytlFsHAQ2lWIcCKJyScbPxONxe01SrF6vZ1OnOYikc6TQVCvhmJjxR3oDAiGd9z+H+Kdyv7e3Z+l+o9GwarYkqz5ymDi0IF60bhg/BaNYLGYFGw7qu7ri8bju37+vTCZjdIkko2i83CmTyajb7dqEHS94RlMK0sJeKZDR/QW3hqQFnh0kur29PYbspGMRNfw0r+m7Zqga44CorsNR06lC55lHfaBQzpnP2rx6Agqn0+kYV+8H3DJZHJqAz8z97bQVgrKnp6dt6C9nC1DA//shvFTEFxcX9eWXX74V23vjDjIYDOr69esG83EyzG70/ZrIfkBwiFubzabd9gciW1hYsE30QwJwdkRXBg3gbJhyA+cH9AfV8Tk5PKTGfBccK5vH+8F3YijtdtucOhN4iL4YL04Ox+5RJCiBtI+OGbqHcKDz8/NGkLfbbbXbbdPOec7WayRB015q5JEAadxpIM1fZgWDQX3yyScmt+IWSTR44XBY7XbbuGW4PD+kmYIOg0l8fzzBzhft6FYBDcGdT09P2x774cYgOoTY2CcdNyD/UqlkjmwwGF3fsLi4qGAwaMU47MdfssU5QMqDnpjuFwCAByrQMfV6XaHQ6Erh6elpKzYRBOjc8QNRQImJRMJAAxImX8X2Z4DzB0hJp9Pa2tpSrVZ74zbzxh3k4uKiLl++bA5jZmZGsVjMqms87GAwODbCC8cSCAS0tbUlaTSTLpVKjVXaePD0TBO54Yeo1JLiRqNRraysaHt725xxoVCwjWGMfbfbVSqVGhtnD+/D6H0/XBSkQXGFA8jAXyIx78mQDD+ODdTAz0F//J5vtUwmk0qn0yoWi8pkMnry5Imq1ao58UajYekNkguQBLwtBxp0BDLCaS4tLb110vxl1vz8vFZWVoz3npqaUjqdtj52X3FF0UAxAfvB8flhH74LSZJdE8x4sHQ6rWazOVaNJhDG43GrfsNrUmxh5iKKCQKqJLucTRo1I8zPz9v79Ho9NRoNo5fgSpHrUOCjIEKnmp92TjD2qNgj40ajYZlLMBhULpcbE3wDLuD+saVYLGYZjad5/AAQXpef9Xo9LSwsWOb1JtcbdZCRSEQPHjzQwsKCSW2azaay2aySyaSq1aoVS0BMpCLwgTgbCg65XM46UtBXwR2RhpK6EjW9zpD3QASbzWY1MzNj92pT5WMCD5NQpqenVSgUxtAg3Sv0gLdaLVUqFZXLZas6+oku8E6Q/vBCpL2gE9IgSWP8laSxyeGFQkGZTEbValXlclnFYtF0nr5qTjrIgUeuQYrEZ4GbBU1GIhEtLi7q8ePHdlDflRWJROz6jm63q+XlZTv06XRa1Wr19wbk+jZSDrAkq7KiHmAQLuiSVBqujn0nlSRoEfCxw2KxqGKxqJ2dnbG70hOJhGUGvurd6XS0vr5uDt07RhwlY9a8hlaSnSHoBP4MICHlp7HAzzzlNSgKzszMmDNF8oPaZG9vz7prCPRQVFAPnHeQNdkazxAB/7lz5/TVV1+9Udt7Yw4yEAjo/ffft9QglUqZ0yC95SpWOkF86R8nRkUW7R4bR/RnQgiRCAMGOdKlgxgdUS0yl62tLROj0wETj8e1uLho6bIkMywmlrPZIL1Op6PNzU1tbGxYGkeKxHcBhYCY4VupJoLwcIoEDQ4n0g+4WKQa3I1NpPZ39PDZEUZjqKBKL+eBn8Wph0Kj+3Ha7fY7NTcyEAjovffes44kBi2XSiVLcSn4oQPkwJJqQ9PU63Vrn0P7xxASkD/okIAdCoWs0ECHC5VqJjwRtLCTYHB0LSyyHWaH+kETSNAoyMF17+7u2p6D+EGyjEdDGO7BB0VKn40RJFBngDxBztBWAA6mYdF2SUAmUHAGUJ1Ix7NbeR2UJLx3MpnU4eGh6XLfpO29MQeZSqV0/vx52xTgNmkJ902TavA7pMHIJyi4UHxgrBn/TwqKfIVNZjQZPGC73VYymZQ0ajdDv0bPNIQ5xRR4TxAGwut+v6/5+XkNh0NVKhWLms1mc0zP5rlVelE5mFQDia4Yhp8dOBwOLag0Gg27lxnSGxRB6ghFQbugT5VweEhR0LgR+SVZoYsozkCMg4MDrays/B7xf5pXMpnU9evXLfDgOCqViiGrZrNpNkUBgZSRSjVcs7epVqul7e1t1et1+10/scen0qB0UBZnwd+pRNruM5FgMDgmNfNtsrxWp9OxYSpeLkNHFx0zOGJJxtfDebK/FFH4jDgunCMpOc6cbAc1xPr6uhUtKXJCp01MTJiDg5PFB3juEbQNVcV5OHfu3Bu1vTfiIAOBgD766CNls1lrrUOMCyEciUS0ubk5Nr0YB4pDos2Lfwdy9Jza9va2Njc3zRBIFZDdEIExVrhB0iAiGEaBE2LmJGS2JEs//aDdvb09PX/+3H63VCopFoup2WyqXq+bw2eDeT/4ViatMMFFkqFEKAJSX2Q3DCCA44xEItrY2FC1WrX0EETKLEt4WeRUXkKFE/FOE8QE31QsFrW2tvYmzOelVjAY1M2bN8e4abg2DjDfHykKPBlBDec1NTU1VpCDNx8MBtacwDXEHHZJVogMhUKG/icmJjQzM6NcLmdDdrlrSRrZBROCJicnbS9xTDiXTCZj4uuTPc50bPFzWmhxvqC/aDRq8iYABB0tSJV8B9vU1JShSarUcPDYKUiWS+xSqZQODg5UrVbNee7s7BhtQNAhFfcZFc6SwlapVNLa2tobSbXfiIO8cOGC6RRzuZwVaDis2WzWNglj5QHDxfBzpDhE8kajYc6RaJtOp40gp7KG/ozqnD8wk5OTajQaarVaJlqFUIcnBVmALP2/TafTFuHgZxCa855UtInMOBuIbNIWetCRIeGseU8cIYeDYRqRSESzs7M2IYbUhIoiAQE04TsW/IzDfn90pzGpKOhRkskuDg4OtLCwYMMZTvPi6lKvv0Myxh3SfEfsiIDqU0OyFz8bAI6RA8y8TgoU0WjUurAikYjm5uYUCoWMI0SYDoLHGfV6PUu9kdgcHBxoc3NT4XDYRq6he+12u9Yt5SdTwfP7afVU0ZlSRXbD9+A1aZbwnLh0PMyEAgvPA6eKOB0AwHmDz4d3RULmHTqfxdsgIARn2Ov1NDc3Z9efvO712h1kLBbTnTt3TDQqjYZwMs4Lw+T+ZnpeOay0X8Ep8uB8pHr69KnNXASRdTodE+JOT0/r4OBAqVTKJqVsbW2pXq9rb29P5XLZZEVzc3MmrAZF+T5RdGnZbHZsc6m6gQT9/cggUAh2SWNOmKodbVugWX7PVx4l2Zh7Whtpa2SIRqvVsgNBh0KtVjMj89VGnCMOgf0gqDDVhe4fPvP09LTdawzXedpWLBbT9evXLf0jEIHE2T+CSCqVst77wWBgTgg7I2hLsiIJ+wVnnUwmVSqVrFgijdQWaFoLhYIJ8eEkCbK5XM4c7NbWlhVV2u22oTWE4b4NcGdnxyZboW3lezIFChvArrELbAGqBccIegwGg3aVBA4OHpUhw4FAQJVKRfV63UTecLmgWoZZoM+kvRXHCXDwgAIgwzmkeHV0dKQLFy7YbZ2vc71WBxkMBnX37l3jHiYmJqxAQecBUbhcLhsU5+JyXzzAWCHBcQLoxMrlsqampjQzM2PXtNbrdVUqFYPmONtSqaREImHTi7e3t60FEWM/PBzdQX10dDQ20BTjxGDo18W4SBO63a5p0uAl2Uxa2RCKk97xc/RfCORxvKRnvntjaWnJrsTFCSMSZ5gHiJF0GTSMDhTeC5TA38HF9Xo9qy7iNEndisXiqWxDDIVCunfvnt1pDT/oBdIUuVqtlmURBFpolPn5ebsfHbQPdwsyIpPxnVhLS0sKhUKmYkADuLa2ZnbNnmDrBD5om1qtZsgeWyEzolgIIED8L2ms8QBOngyFgiVCc84EPDevg83hQAkY2B1j2ZA0+R7tra0to78ajYYVWSgY4eygu/icaCspVlGs9RpOnGw0GtXCwsJrv6LhtTrIubk5LS8v2wNGakLbGjyDV+5T3cNJhkIh62HudrvK5/PmVCVZ9QuUWKlULJ1ZWlrS2tqaFWskWQpwdHRkV7hCDJNSM4YMgplqN1EP1OUP28TEhF2nMBiMxqMxoko6FoH7gg1cIkWakyJbj+hAf0xKKRQK9nmYPwgSR2MJyoWcp9LKgcN5UoAharNOTnmRRkGP1CYYDOrChQtqtVomrTota25uTnfu3LGDdnR0ZIU7ED8iZsa+YT/ZbNaeyfb2tqampqzdEi7Od8yABHECu7u7SqVSJr2iuaHZbI5pZLHjSqWihYUF23+QI5kE52A4HJrkxgcvUBmpOIUaSVbwJPNJpVKGIEGN2KG/wxs5D84qk8lYFT2RSBhFgUMlzW40GuZ4qeJz2RfTpggwNDuc9A1kgpxtKAA+F8+/UCioUqkYqn8d67U5yEgkovfee8+a3A8ODiyF4L+ZXExzOxHQk8JwbMgwGo2GSS6oInvJAWgMw4bTIyJKsgeOdAXRLREKno8UIBAIGKdEVRJxO04PZ0a1HedCmgtCpXIJmvH8D5Ie0hLIcKrhcJih0GiEFlOCKM5Eo1HVajVL9eCgqEb6g+arrfBVUAm+2kog8FyV36eJiQktLCzo0aNHp6YNMRaL6Sc/+cmYKgAHwr6hnUXziM0h2WEC9/7+vtrttqampjQ7O2uDa3F0OASm9WAPHGgCm5fOgNCxOYJcODwaLItTwQnjPCjuINHhteFIqUb7YIaEjPvh0RrSUkqB0XewoPNF5B2Px1UsFi399s4WrhTlBp8NLpTnDNDgc4He+Xu+MxkO3xW0TaWeQirn9sqVK/rss89eW6r92hzk1atXlc/njfchonCxka+aptNpZTKZsStHcWyQxqA4P83bD5BAdgEqGgwGqlQq5iSIXmyyR2c4LZwBAnPmVLJJVH5JUWgNYx4lB65er6vVatmhIZ2DJyTieXkSqSufiy4JeBjScp4PRs137XQ62tra0t7enlUMe72e8WB8Lz8hyHfqUM0HvRBw0AHyvOG2cP79fl+pVEqFQuHUXNFw+/Zt3b592wIVtAPfJxgcXSwFzULBigndyMRIR+mooXrMvqbTacXjcZXLZUNatNeRGmK7FMOwNbIqgj2Fi52dHcui5ubmLFjyngQ3OOKJiQnTP578OXxkJpOxO9ZJbff29sYuH2MvQcXYPFwnGmJkePCMfOZ6vW5OCi6RQh/OE0UJDRCgeThHHDLZIwVPgjNSNuR5w+HQRqK9ruG6r8VB5vN53blzx9BhKpX6vY4Oelwhl/11pxCxoD7fJxqNRpXP520D4/G4XdLOZu3s7JjI1+sqaaz3484kGdlNVTgajVqKwoGCWwIJUGzxqQ2jreAsiXYnO3dwSnQLwLX4Fi+P9EhD9vf3rX3SHw5p1G6GhApaotFomP6O8VdwizwbAhBRGpRB0WE4HBodgj4PThJ+K5FImD7tbafaxWJRt2/fVrfb1czMjJLJpI308miOw5xOp1WpVIxOQZNHP38+n9fExIRp7+i2ITBdunRJ0WhUn3/+ud2XJMmeqefS+R8Ozw+6Ra/or++gcEkzA2k8XT1QGzgseM2TigvsR5J1CHEWvbQHyobKviSzSwKh1436SewETewCu+HvyY4SiYTZNeecCjZUAYGcgOIzGqgh6XgS+6VLl7SxsfFapk29cgcZCAR09epVQyxUSuPxuCqViqn4SVPRSrFZvneaf++jJ5waVVzurV5bWxtrUcQJ4QwwaAyI3yFdBI01Gg1ls1nrmab/2qcrSDco8MDb9ft9uxeZAgjdDmg4eV8cH6m2/3sKMqTSpCZ89nA4bNwpPdM4gUAgoHq9bs8QVOjTeFAOnKWf08fhpUMJx8xnpKrI9ajQH/C+X3311as2qW+9gsGgPvzwQ5PxBAIB5fN54wNRIXh5CYjE7yMBaHd3V7VazXSNJwslpKj5fF4rKyt6+PChBUa4OWyS9Fo6FkRLMvkQXTXBYFD5fN4udkODygV1SOFQSAAAJBmgwCmRylI4BGRgEwR+KC4cHUHW6y1pcsBh8VqeR0Ry5EcL8nlomYRn9MoNbB6bIuMCiHDu/FmA9yewLCws6Msvv3zlNvXKHeTly5d17tw5I3nRMTLIwQ/dJEKD6uDc6AKAz5FkVwwQiYiO0WhUc3Nzxmn6w+sdkCQzXiKxb7b3/A4839TUlFKplFKplHGpnU7HRKonyW76sUmXKDx5rpTqN5cUUXyCJyItQ/ITCATseWEoRFgie7vdHqsI0k/N98eYcAR8dpARwYhnflIzSLoGOqJjh8PFQZqZmVGj0Xgj+rQ/tG7duqVr167ZQd7e3lY2mzXeEfshlfQFAtoHCSAI7L2dYLM4PaQ+0C/ZbNa6a3gmvvmA9/VTf7AhJuWkUimbQUrFmPSz1WqNVcTb7bZNe2ffsANfiQYlMtYPp+Mr6Xwu/uyHVvhKO2m1ND7UmdSZwheOHPkURSU+w+TkpNrtthU/sXECCTIhSWPBwM8L8JriYrGoarX6ym3vlTrIWCymK1euWEULxwH/CP/m262mp6etsIEmDKPBocJNAO+3t7cNFRAJ4TER1iaTSTWbTUvngf7wnr4flQ3n0OCYMJJWqzVWcfayDopC/K4fmeXTZM9H4cx5b3pPqWST/pH2gOI8QS/JCld0BEENgFz85KCdnZ0x0S9ToEEKHCh+Bx6IQwZSoCo8HA4tHceRhMNhXbp0yeZ7vsk1PT2ta9eujXVL8T1yuZwkWeEuHo8b4ubA87t+IjipnzRqV8xms7aP29vbxpmTclJM8+iw2+0aZyYdz53EEbF/7Acyn36/r2w2a9QI/Obm5qZdFMbegNJweBQY/fg/Ah7vyevhWOEdScEZouupIYI8gIOiIWCEu5Wwu8FgYCk1FAeBdTgcKpvNGqokYHgHSeEG5+slaRQioYUCgYBN5XqVtvdKHSS6MwyNNjYiLPMIIaVZRFoeMhuZTCatE4aiBAiwXq8bN4HonEuw4Crh4oDq8IGkwGw8zgcejs2iCkr05N/wGVmeRCZiS7LDg0OemJgwvRmoDUTMweA54YgkGZmOhAgD73a7xuPgPCVZQQC5EfIeeF+iOY6USI0OD66RoAXvidZzb2/PWtWgHoj4kcjo7p4nT568StP6P9d7772nbDZrqSCI5NmzZwoEAiamb7VaNgYMNAld4ZUNoGQOG1xfqVSyLiVpdLUpxQwKFQRuCpQUUOiAgmcjePJ7FEFwICgZdnZ2VC6XjV7iyg+kSBQ94K+xRRwg9Iq3X7ItbIb/xr7Ya0CFdMxl0uEmyYpZvkWXz0FvNvYrjQLNxMSEARwys0ajoXK5PIZ+vRyN9B+ag0wNMIKGmok/r2q9Mge5vLysq1evmpzA91PD53juz2udkKb4VEY6dgwYADMZ6eQAgTE4goIOBx2HRGUYJEaBA4IZ58N0IBx4IHB8vYHnUyqViiqVijk4SWNRVjrmX0AHnk/0gmOI/5PiXj4DNAOfhfQPTgyJk0/ZeA86PXz3juffJNlnIrrzTEi5QTqdTse4ODqJfPTGsIPBoEqlkqrVqs2rfN1raWlJy8vLOjg4MGcujUbBNRoNbWxs6PDwUMvLy5qbm5MkPXv2zPSwXuoEWt7d3VU+n7fsh4wHlEMlnCIPNkQABE1S9PEie1AbHVeeZwNdkpkg3pc0Ro/QPcP+RCKRsQn5cKTYIooNtIUACN/Khw157Sh2CS/p+7xB151Ox4qbVLtpH4aPxP77/dFFY4lEwuYbtFot68IhWOAIoY84z1BxBHuKPWRi586ds3vEX8V6JQ5yYmJCP/rRj+yQMI4MPm8wGBiXMjExYREQLg1HSSrKg4T7wnkh3IWbyWQy1g0zHA7t/XCaJ4fXUplDsoFDQ8aA02VYAQMCcHAgBFAH6n8ctCeQMSjScK8fRNRNBMWhEfFx3iBFDNiPlSJ9J/0n5eBwgISR6FCkko4dJR0xRGo/wIPDCsUAyY5RgrgkGU/nHQQFG97zda1QKGRTojhAtPKxD0hKVldXDW1AuaD5Y1/Zr8PDQ5v4xPPkps2pqSkTaZMhTUxMKJPJWPMBaSI8NGkvTgWUiJSNc3SSS4a/plmCYOeDqZeyYUOk1z6lJyPwiP/w8NBSZP/+SOcAAeFweOwCLoI2yBCaSpKdfxQWvohIhrm1tWWfyb+/lzBxayTyIIJzOBy2ohpZFg748PBQ8/Pzpuh42fXSDjIQCOjevXs22Zu7nElxUqmUOp2Oms2m4vG4MpmM5ufntbq6OmYsOBqibr/ft3bCRqOhXC6n+fl5q3DRHRAKhawog0OFR6JtkAeP/tBzdVw5S8Tf3t62e7jZIA4IXCMbyr/lkJBugsIwVEm/p6Mk7fI93xg8fNhJJOeNB2oAZOB7waERfCeST6GolnLgSCUhwvlsBAXer1qtmlGTHRDc6CIB/SQSCeXzeZv+/joWign6p2k2AO2Bztk7qth8H5+OYoOSxsh/3yiAMJzUkwo0VWRAAcGC12LhqAgwzBiQRk6FajKyH4Im9gb14aUwODXSUHh9AimI0vPa3rl6oTZIlr58Mif0ujhMiiUEbl8wIUOBPvDyHYJ1q9UyCoEsizPgAQ7T3tkDbN4HaYCGl9Vxed6r0OW+tIOcmZnRnTt3TAQdCo36T32Vk6jRarW0tramxcVFzc7O6unTp8bf4FS8PpA0HDK4Xq8rEAhY2sr1rkQnjA1jIl0gioOQqMpRoZVkTpTUI5FIWFcDhknlkFQep4vcxQu3MRAMk8/AJmIM8EMgPiI/qIHPj0Py1VCcLVrH4XBoWk5SKUhvnxYRWT2yIULDTZLS855wXBwSAmI4HB6L1jiJiYkJLS4ums71daxsNqsbN26MCdzJAAhsoHjfjcE0HhAwKaUfBYYOlJ/DO1KgIysiDfTPFErHFx3ggOFypeMbNQk0BDmuM0DlAI3hpULw25wPED17Tvsp3WS+A8srPrwjlWTFOJ4PTpcGDZ4rz8dz0ZJMuQJ6pa2Y8+wF7Tg1AgHptSSj6pAD8hnZEz63JMvoJFmhaG5uzqaqv8x6KQcZCoV069Yt48qYNEI1mQ3vdrtKp9NmqFQP0TB54SyOhSoxB5qfgxRBPKCt/f19S8OpLnrhLRGOAg4OBYdVKBSMs+T7eP4RNOElCEQ7HILnXnGsGK6vYPt0i+8vyYyA9/X6O9/mGAqFTKd4Ms3jc+Pw2QPvtDmkIBB+z/eKw81i/CAU9oHPzL9hr8gemLs4MzOj58+fmzG/qhUMji5/S6fT9r7whSAO0kWPDPkd5m6CMkGBc3NzKpVKxg8TwHiOyGpQZOAYOIw8K1CkV3Lwuzxj0DaVdV8wwymTCXlaA5uQZFkXduj1xAAC3tc7bKbrsL9kLP6SMKgZeFD/XUh7/RlALyzJghVnBadLwKJwCqoE8ZMN4fD4PhSzPGJFleAlV3CU/f5okPWTJ09eyvZeykEuLS3pxo0bY6Pq+aLorEB5nU7HiFtgvzSq7OVyOeN0pqambBADwzWl4+GkXnbAe0kyw+cgc2g5+BgkxSAvSuUeYw6/R5neWbbbbYuYVNUwQCIzCApez/dCnwwUvjKP4+SZQLyD4HjGHHq6BjjkPNNer2fX4hL1vQTJHwbSekljshBSLvg4jH1yctI+D46Vfee5kkLyHIrFonZ3d1+5Pm1hYUHFYtEcjO9a6fV6RrOAXsLhsMrlslE5DFCAo4ZyGQ5Hk6/hExuNhknMQPYcTFJPggU24PkwODuQHZK1k5wfDhXbAa0ip0IbyWfkvQh00AU4PPYCeyXYYgPScRsrKJZzw8/hqf2Z9m2R0nGnjR8eA4JGYSHJ9LxTU1N2iyPPE19Axxz32hPU4cJ5LT435xW6I5VKmVyq3++rWCyq2Wy+VMHmOzvIZDKp27dvWy+x73dmk0iXQUDIUojMRDhJdmuZv4QK8tzzHtIxpCYdYmw8omf/wJmEDaIlKnr+k1SDP1P0IS3hNXAySC28DAGpCGgGx4IzxgHiXKXjKIvhc6sgBwuNmU8FJZnj5NCBVOGA+DkaSDpG6AKh8MVBxvhxmgQJEAzPMp1OW0GAKryv1PJ5JBkvOTk5qeXl5VeqT5uamtKtW7fs5kFpdNjoq/aj/LFNHBxXZ/iOLAIbEhTaY7nigp/TzcKoPlrfuK4BBQbtll6/B2/IucA2E4mEksmktQpim9gge893whFQsCBQQw3h5LAbbEY6LlLicHCYFCFpDcQGKCZin55b5DwQEPjMAJpQKGTzGn0nHLIoHDLVfzhGaAMPuHg9nsfR0ZFJiMgYOLsMqkGqVSqVzEd9l/WdHGQgEDBBOI4AfsFroTAOIgotU3AvuVxuTGQNl1IsFo1DoUrNRpKOIAHa3d3V+vq6RVPfuI+zAGIz6+/klBt6a0mLIfElWbcCfAlGgqgbVEfEA8HQHRMKHV/sjsGBsHxxB3mSJ8a9HpL34zV96sFB8Z1BpH+xWEzZbFbBYNC6PkjX+E4gaxCt5yr5zBifT6dJ13u90cg0DhjPFs51ampK+Xz+lcyNDAQCun79umlmCSqkZBTvcIJe1D4xMaF8Pm/aQgbM+iEcvuKKvXgeFeEyz2M4HNrsUfaGZ0KQ4DnwTEkz/SVY7C2OEvqD5ghpVFxkoAapPlIdFkgLFAi6J+DhKEhLCWogUuwXJMj74qxwmL5A5DON4XBoDhxf4G8b5dyzP14rzFmgYOULn7yWdDylyBeZoA4ouOGAmd3wXQs238lBFotFu90OnSORGqPg4TGoAlEnG0ZRhpY7HBNffnZ2dkzyA/eIwDQYDNqNgVS9cCgY1f7+vlqtlqWDpEqSTB+I+BoHDPpi85ATeMP2VTt4QLgRoh+91+i42EjSDhAlZDzOGuTHe5Lq45Q8SU9hh4PlHTRTqnm+Hh1gkOwBEZzvwn4iEuc78b6gMdI7nJC/FAr+jYNVKBTUbDZfenbf3NycVlZWJMmKVfF43A4gHVWgerhxDiAdRjg46fgaAagVMh9E86lUyopxBDacjUd93q5RQWA3pKsEPAITaauvwvogCx1zeHhovCkOrN/v277wOSRZxuaLIHD/XpyOs2dfkfT4YdA8H5w7z+RkYOU1qeKDUqXjwhVZCPpIaCHOEcVR35JIMOd7gPBRkfAsyBZ4P/YnFAppcXHRirkvul7YQUYiEV2/fl3xeNzmv1FlQlPn5QZczs5CSzgYDFQul606C0kNJ7S/v69kMqmVlZUxNT9dLED5ZrNp4+klWT83iADxLJvRaDRM+oMD9IUMOEKQRKvVMpQpychlUmN0WERgPxSC5wU64PB5p4VR+qq374Pmu+KESSU4pBDjpL043aOjI+MpDw8P7fbDTCZjjsVXL3GaXvbkdajwqQQwDJdiAIcZxwjC4LWj0ajOnz+vL774Yizte5E1MTGhlZUVc8o8dxwcaTMSGd+5wp70ej1tbm6aU4UeoBhBAEHSwyHk+1GVhf6B7/MFEPaRZwsHSRZA2kyGFAgEVCqVzCZ97zUNEJ6vw4GRGpMd4PB8NZgMgFZCCj7sZ7vdttcAldH2Kh1Ps/JZF8jSS8sAD7w3tuGBBVfCtlotO88EFyrjyO0o8uF8oZD4fw9s4Ft9tui5y1AopLm5ue80s/SFHGQgENDFixeVz+cljaA3qJAPwyw7Ik6j0dDc3JwdSm4xw4n5S7cw6qmpKVUqFUuDEHKD0Gq1mom9EQYzFQgkx+aR4gDR+Zw4Aip3pCQ8UK/JPDgYn8bNzDo2h9ScaAjSg5jGqNhcuDs2EefKvyFNwHBts8LH93VzOH3wwJgwWoTGvmOo3W6b8XtxL44YB4/CACfinzXVcjIFPzmH6q7n37CVUqlkgu0XXYFAQOfOnVM2mzUkxsHxc0Ml2WX3oH0OFpXpRqOhfD4/Ruj7yn6hULAA6e//GQ6Hyufzxg2DWnBC8HY4BRyCL25IMjCB5pGsxd9zjuOFowQx8vd+ViKFIuRdBEsQLe9L5sXZ5NwBckCyXrnALAT2n+Dr1RdkVMjSuPgOkCSNEG632x3r0OHzYTMAA4IPzp5zyXfmClxQJaPpeD3OvQ8SpVJJOzs7ev78+QvZ3Qs5yHQ6rUuXLtlBIzUmdSU9QxPJB63X6yoUCkaWwjtSgfXQv9/vW7sYN7U1Gg3rUcawA4HRRUE4NAyN9BSjx4BxQHQEIN3AGHy1kc4dqpv8Oy914b0wFH4HxIrjheTn5ximdNzsD7EP2QwaQvIAcsDhECgwHIoQvtqIHhOkBOdIxObfY7B8To90icp+tBqfh/2F/6HqiljcS7Y8JbG0tGR3rbzIymQyWl5eNn7U0x2HhyPxMcqEwWBgHCIBJRgMmjSKgg7FAoIKwa7RaFg6S0pNYEXGNhwO7S5sSVaYAx16SQrIFpQGEMCBZLNZS4v9AAgynMnJSXOS7B0ol+/Ke4KsvMqC3/eyJWgffo4d+JFmBH4+NygWNM7f8fr8na9kgzx5Rr7izZnibODgoAd8F5x03FDC+YAKwe5JvXnu3G/F983lcqrVai+kjfzWDjIYDGplZcXeLBAYjfGCM+Mwk175kWN0MHA9KvdE81A5xNylQm+lPwggkkajYUY+NzenRqNhVSsOL9GGFJiqN3/GeWWzWftuVBW9ZCAejyuVSlkrIbIPfp/f5aBSQeM1JY1pID0fhAwHI0TQS+rIa/P/PkUDPTK3kYEcODYOC2gLnSj/wwlTZANdYrDwdVTK+fwcalAGNAEVbb4jqOHo6Mimm/s2tdnZWT169OhbG2kwGNSNGzeUzWYVCoXsbheQEYgW8TMpKQeBbMVLaTiYUDnwlDgEJuZ4rtFPLjo6OrL0nOfBYfaFKop3pK5eL4pzhicl0OEEQElkR0xH98VEnK8kyzo84MCJgCS9cgHKhmIU6FeS7RfOkAB3cl6CL8jhG7A/SfYzT3lIss/jC30AFvwAe49fIfsiuHo+F/rH8++cHzSjyWRSy8vLLzQ3MiDpW6koz58/r1u3bhknxYeVjtukuMdjenraNstzXSBOHhDRlU4MvD4bgkEOh6M7O0B0DA/AWBiJL8mgdqPRsIoaG+Wrv6BOdJXT09M2po3UBw0hEpWTMgn4Q9AwER+j9QUQz31wcH20I5h4DSISIpwsshKue0W+1Gw2x66FPUn2e9QBMqQy6SvZGCFpJ8/Ad11w2EETvB/Pns/LM+VZ46BwLr/5zW/UbDa/lZEuLS3pzp07kkZZzHA4NC6ZVAxdKYek3z++pxrH7m0XW8xkMlbg4DDiYBC84yArlYra7balzvyerzrzTH1mBKcHL8f+8UzhvLmNUxo5KOgrUHy73bZZk3w23ofPtL29bcGKwEUA9EUWzhQB1ffig85O6iF9xiUdB3+KmjheQEM+nx9De0gB+U4+C8F5VqtVc5pIl0C1PHMCDRkBNozjPDkcBjBDNvDw4cNvrY381gjy3LlzmpiYMKiPw/MbzeVGOAC4LlAbDxI4TUqA8c3MzNhDI11nxD1OjxSqXq/bpoD+vJPhqkmcDU6NyI2jgGPj76iUw0fxkPk7Rn0RnXgOniwHIXhJBANbcVwnOTQi/sHBwdgGQwXA7fJ6Xi9HUPDfCRTD9yOKwx3iIODm+C7sGzwSpDmBjfeRjoMRzpUoTuGISiSpHo5pOBwql8t9awdZKBTs4BKMIpGIpfOgEb4XspPd3V3lcjnLKAjaPu0NhUI2tNXzx9AkvB7veXh4aK209PQPBoOx8V8EMzIWnBNoBr4O3pRgKx0XPrzeEAdHUATpgqx8JRrb8BkB6I8CH0jUtyhGIhGzTV/swHFxtnBMfB+vdvA24iVn1BBA03xGEClBDZ201wp7X+K51WQyafscDAate4zvyvfntflO4XDYLl/7NutbO8h///d/14MHD4yHAini4AKBkfrfi0UlWbQEuVHRJu2Fz2OzfFTAQcE78nAxCs+rgAg5hLRH4RhJ19loKtlsxv7+vkqlkjkTkCOFGgTuFFjorpFkxSZGsRG9/fvzPaVxUttXLanKQY6fTE263a45IT4nKJOUCJ4UtAB9gbFTqOJ3OcCS7PNRlEFrSqrtUyuIfv9n3wkEevJyKAy0Uqm8UKHms88+0w9/+EOb0sOhJ5Un0FKYCoVCmp2dteEPUAmtVkupVMpI/larZXo7X3ADwSB5IVBJoxFqcMt8x8FgYAOGpeOCl6cnQHMEbQbF8tlJAxGjJxIJc6bQN5ubm/Z6BDr2YHt726gWnCvvC7qFI8QJSRqbHQB1Q+0ABwjKo5hEMMFGQbikvDh70J6vQnvJEM8NKgMEje4Z9YMXjPNZ+Dek1zwrwIHXguJ7+v1Rj/qLzCr91im2NILad+7c0eLiot32VywWrZoNwVyr1SxiMy6KVHYwGNilW/CJOE3PF3DoB4OB5ubmVCwWNRgM9Pz5c62trdlB8QUMaVwbh3HgzLykg/FdONx4PK50Om3z+dggHGQ4PBrKUK1WrdgEAsBB+55VUBNpBw6Q6M5n98UhjJGfg1pAK4FAwFoOidC+EwSyHuSKwBwHwPIpIo6WNBjHiogaJ+cPPKk1hw5Uxn5xyHhudJoMBqObJtfW1szgv+2KRqO6evWqisXiGF2RSqXs/m/SZLg8OmHYT5A6w5wpIHCAKGThCFAwNBoNG9HHc+PfMkGIAQ3YLly4b8n0xTVuVQTV4tBA63CVPOfDw0NrnvCCab5vu91Ws9m0gAsHC2DwxSRUDb6QChLn/IHMpONBK3x+7IffoQ2Sz8978nrezvkspOJkROFw2Ca/gxp9VR7QwLPjdb1iBCkf9s/nhyaq1WpaX19/Idt7IQcpjaL19evXdfnyZSuWpNNpI+JBWkwk4VDhaOBg4BbRAqKsB+VxrQK3FJZKJRt532w29ejRI5uiQvo1OTlp6IfN439EFX+1AWR+PB4f400xJN+CKB3zQs1m0+gEr2s7WZGjrxSekc0GQeI8pWNxK4gNXoafQaKjIcMpUYDwU4U4kBg1zs0rD0D8OA9J1ulw0hHD6fHsPC8H+vCINBgMGkqp1WrmxFdXV1WtVg2NvegKBoO6dOmSrl69ageeYhejsUi/4KTRPnI3EvvE/voe50AgYNVjinTsE88Xh4BdYis8VwINwQWaiMOcy+U0NTVlXGIikdDU1JTx4b4YAlqDhuHSO2gtAjFp8tbWlk1g4t/gmAhyOBGfEZD1+eyH4O/vwAYETE9Pa39/NOiaoqvvvJE0pirBCRJMkQ7xuU6K+eE8AVQADYIsaJ9z7ccI+v0lve90Onr69KmNWXuR9cIOEoNYWlqydkOgOQYZCoVs2CgCZ1/C93Cd6c1EcLg1+jh9Az+bNByOpvSsr6+btgqHhDMhbfTRy3NUfB6Mgeqh51Rx4kQoohCHClRHhZHX5ZCBNvgMGB9pgOcukSrwfDn8nrvCeeP4kEMcHByYTAjDIUqHw2F7bhgmEZ7v6Yl0yHPSJ9QFgUBAuVzOHCb8IUYIAkIG5Y250+no66+/VrVafVFT+4O2d/78eV2/fn1MwA5S7Pf7lgng1OAa0XbisAge/n5oUk2cFEUhf+3r0dGR8eG+Gs7nI2ji3Hw2gMP0Bbp4PG4cJhw0AQY7kzQ2KRu6gkIiKgsq2jgM6AIcDujX2x5BAATOZ5Zk5wDwgSMDlEjHs1R9oRB+EOSMrVLg42x5hDgYDJTJZCTJKDFoE16Hz+QLjvwZR82fJVlKzf3wL2xv3+UfHR4e6smTJ2o2m7pz547y+fzYHEGiDIcKYTdthyCySCRi89s4pKTMkMhUQnGyjDKKRCLGTXrHyEHBEfsqNGk+Q1MnJkbDNdG1gYr29/fNWeJEOUA+ciNp8e2DPn32lXt4IVIvAgQO1XepSMeDLCTZe+PIceJU9Elt0fx5I5WOgwSId3t7e6xzBEMl0lOk8To8UnXPg3JwOFSgdl4vFAqpXC7r66+/fukWQ297Dx8+VKvV0p07dzQ3N2eHdnd314pppMSgWZw/3xe5EPvL4aWQ5h0hxRDE3Rw+ggB76zWa0vG9zfwuSJtnwZ1LBDD2+vDw0DhN3/PvbYMAzNgyz53ChQI8cN5kHLwGGZWXcfnKNxQA9A1FJ4BLLBYzlOo1vnwXnrXn3snw6NTiOUkjhQKvQ+A6Ohpdm8x5IKj5f8ff84zY762tLT1+/HhMNvSi6zshSL8mJyd19+5dzc7OWjmdwyLJok61WjXjI9WgEtVoNOzKA4o+cDxwhaAYkKLXQhFB2fSThulTCpwQOjbGnDEEYDgcCaILhYJNqt7d3bX2tU6no83NTbtsXpJ9b1JRIicbzWv4ySNeB4bBY6gcOKQ0VNH9yCo4HAIK35kUGE6QtjifwvNeFJ92d3etpx0+FIkQ6Q68o9fJITmieAbqIljUajV9/fXX5uhf9ZqcnNQHH3ygubk5I+UJIL7IBKIga8BpNJtN478IZGQ3IGhSOM+tgoJA7nTy4EDIWHhvgq8k40Z9Oyjj1UgdSdNDoZCdE1L7crlsQvtwOGzOwPeK87mwR19V9woHHJvfM84XQIZzxJ+9SoBsz7fBgmgJFDiucPj4Gt1arab9/X1lMhkrzuIsKazh6D3S5FxBIWD/OEwC3N7enprNpp48eTKW9n+X9dIOUhpFhjt37uj8+fNKp9NjbVE+pfQIDO6ICOk3jZQgFosZYqFCjFaqXC6bsp7ozx3TpPxwcRgnziccHt2Kxvy5/f1RszucKUNTPWHNa8FBgiZIYynAwDH6KOelPvwcp8JnI10EzXAokTnhsKRjjoXvDUJFxgT68dwb7+NJeRAnKRLSHNJuUKU03qZG9Od7cwihUAaDgdbX1/X48eOxgsrrWBMTE7p165YWFxcVDoetOOd1lwQp9h6OFfRIsQVUj52wXwcHB3YFKtlCt9u15gOery928RwoyoEwyW58AVOS3Z+Ebg/+EUdLAYTZqhQKCaLtdttABPsXCATGBggDDED6XteKXcZiMQusOGtqBsxbpN7gC6U+gNCVFY1GzcZwkgx/yWazY2PnCOCSjJelks7QD54lz4JgBmXBa5XLZW1sbLwS23sll3YdHBzof//3f9Vut3Xv3j3bYAhlzyOw8ZJsAjApAJ0hOEuG51YqFVWrVXOSpHaIxzFYaZy34xDzsOFbfMRi86hYsxmkz0TCmZkZQ1LIECKRiOkicU5EXByq7zDhAHjZgd9g78j5977nmeeESJjqNfwNz7XX65mECufsUQy/g5NkT0iLo9HjC8NArJKMBuHKTcbASceZwv7+vr744gvVarUXrlR/l3VwcKBPP/1U1WpV165ds4DrZR84fZw5xQGeK3QKciQCO5QQaJIxaF5ixn6BSin2ERihbOjiIZPBNrCp4XBoaMpX23O5nOkO0QqyN16mFgyOBpF4TSqBkEEwOFgyLWgx2io5G9Ixz+fRNZ8NpEmDCCiNMwbaxMHjQEG8dESBcH39gHNFzYDgTXru1RV8bk9rPXr06JVd2CW9IgTpV7FY1O3btzU/Pz82PHdzc1P9ft8iB9GJVID0gKZ9ppjk83nFYjHVajW1Wq2x1i1QI1VveBMOMlHTt0fhTPxIKFADspdcLmekOb3PkPXwffv7x/3TfogschI/dYg0VTqmBHDsfF7QL0jS85hsNq+PXImpLqQ9nt+UZKm6r+jjjBlawZBY6fieE/YCqgSROp+D7+izgsnJSVWrVX3++ecv1Ov6Klcmk9GHH36oubm5sWKVRzkgN543w1MkWecQe0mxYn9/NDaPwgwOlOfFYT46Gg1qwEHyTMk4cJ6goEKhYLz5wcGBMpmMpY+S7LWwt16vZ9pNvpd3gh798RkHg+P5ljgeXzGWjgsiFLxwnL5lFArH9+MjKyOYe56X58azJ/D6ieLJZFKDwcAKT5wX2gQBHThuiowoLMjwgsFRe+Jvf/tb+06var1yBymNkMatW7d069Yti15M/M5kMsrn81axlo4nZXvEFAwGjaT16KjdbtvfwzVxXQPpFNGbg086IR07AVJ4UAbpdiwWUzKZNONADhOJRMYmiZPWcA8OhRsMjCgH/wMnidP2s/u8MBdiWzpux8Qx8W94XT6nT0WInieLQHCr0ejo6lwEuhSi+PdwciAvSZa2UvWkiOCJ/efPn+urr7567Sn1/7UikYju3bunGzduKJVKGTL3joKqMUGVAhZKCTq69vf37Vnt7++rXq+bppMKMdQCeyDJaCWyA0+beBRPgc93xDAvUZKhV4JYu91WrVYzSRtZECjKoy1JBgqq1aq1z8IxS7LP7oXigBCE7L6RANvkeQJ0fAGFfwPFBlIG8bESiYTREZwbCqxQFAQaX6QFTHlQQOPB67C91+IgpZGRFAoFPXjwwEZEEZkjkYjxasgnINcPDw9tA3gwcEQYM44AZ0jaQsqHQXpJApwgHUBewCvJOMjBYGBpAA4Uxw1a5YB4ZwLv6hX//D+RUNIY8gJZYqDwOFAAGDOG6augIEl/8Cg8neSWfIAhQPCZfIsdnA7OkuIG1W8+O4aI8/ntb3/7UpXCV72gRD744AOdO3fOHIUv8BEcKM71ej1ls1mzm3Q6rU6nY40BVIQZjkKQ4tmCIkE+OB9fcPGcNqiKQpdvVEAbiE7XdzbhSDx6I3Dxml6T6gueVL29APsk1eQVFpwdggHnzWt0cY58D+gHHCOtmFAwdLIhMSMQQ1MRNPj+vJ5vG+Qz7O/v68svv3zhyVAvsl6bg2TF43H94Ac/0MWLF00QymElpYFXwdnRGuZ1hUhaiPrMj8MRnYT3PFSMGMP1hQ8MGCNBVgTHSeVPks1W5AoGqtFEf+88eG8q1XA0XijuNWj8jqu62PIAABFOSURBVEfA/D+FDx9BeQ1JdmsjCgDSMb43qRbvRR85Rs53BAXRS+6ruqRePCNpJB1aXV3VV1999crTmle1Jicndfv2bd2+fdvkXZ6bhGZJp9MWHE6qFra2trS5ualer2fDnj2X7SVfIC2CC84NKglOHj7PozD4d86ET1txeDs7O5auew5S0u+NuyPwep0h39GDBl/d9nIluHgcLekxXCu2j23hQDmX2A/BgnF4J6ddUZkmYHOdCvw8f09Bh6JZt9t9I7b3Soo0/7/V7Xb1y1/+Us1mUysrK7YhCFYRoEqyKjHas1wup0KhoL29PeMwiXREn16vp3a7bWJqdGFU2nAkvikehInjwplRPUf+QurJ4FR6p/0kZxykJHMkVOJBLBheLBaz6UK+l9ZzWtxC5yuqGDf6UC+j4pAQGEAuvhAFAqUohnFjgF706zky6VhL6eUdzWZTv/nNb+xiqdO6er2efvWrX6nT6ejevXtKJBJWYPPIHF1sNBq1e8UZfgKSIjWHL+S58N/D4dDSYw4zRQiqsjgHn4pTuKBQxN/DRwYCo0YEnLMvnMETIi8i9cRZ47go1OF0KaDwGrweGYcHJwxI8ciUf4eETtIYr4m8h0o39JT/TCBrzgXcLxP8CRSI6wE4BwcH+vzzzw1Qve712h2kNDKITz/9VF999ZVu3rypK1euGA+JhgxnALqigogWEpKWCi2OiKiG88QxUPnzhgESRXyKo/SkN1ds+rY60luP9HhPHA2dG0dHR+ZAfV8sqY4k22hSfSIjJDgRXZIdPtItqoJEf/7b97lieDjfw8NDk0j46mqj0TDFge/e4fOBgkAggUBA5XJZT58+fWl92Ztag8FAX375pZ49e6Yf/OAHWlpassPZ7/etu0aS2SLUxsrKihKJhD1Pf9Mgk8YJJKSVOAffh8zNkvyMTACESeqNQNtfeQCnh2OEO4V3ZwYC34mMKhgMWrra7/dVr9cNlaHEgNbynSo8C0/VUBmHZ/QaYxQVvskBR8eVCZ7m4Xnx+ycRLkiaM8+53dvb09bWlh49emRFtTexXnuK/YfW7Oys7t+/r6tXrxrKooABnOYObZxarVazDpB2u23CayI4fJJ0PGWbNMLzkr73VpKJxT2KJA0C2XlRKu+HA+LnoDGMhkpvq9WSJONMpGMpEkUYUhrIaZ+CScedNDhoLqbyfKUvDjHVBXSOg0UITmcEA4F90QWpCr+HGPjo6EiPHj16qTuGT8NaXFzUgwcPbJgw2kayhampKatsI53Z2dnR7373O5XLZTWbTbVarTEkTyD0Q5fRkBKkcJB+yhQBEV7QBylAA8WXarX6e50xHhTgwEj/Sd/hNP1n9WjT89g4OI+uCaqeUz8pZfKFH7qvPMLjc/HdeR4I+ROJhCF10necIpnT119//VZs7604SGn0kK5du6a/+Zu/MbTHJvj0EhK9XC7r0aNH5kQxVHRdcIts7kne0RcgqOpSySYKYlCpVMrkDBgW1UzSBpyjJ499t4nno6iOS8fXckoaaxEDIYMk+Xw4adBKPB63GYfQBfze3t6eqtWq6UNBK/77+oqrT198EQhNYLfbVaPRUL1ef6khE6dthUIhXbt2TR9//LHNEqBoQ9rK9Kj19fUxDrzVamlzc9MGH/hGBN+tBF1DoRGpDMUU2gxp98QhEtRAssPhaLBttVodG8IMTcVELOm49e4PqRNwkh7N+cBJao6zA0T4Walw7GQ4nDnQHioP5i9y1iRZld5XwqnG+wIif09R9G3b3ltzkCyKOO+//74uXLhg3CFOJJlMKpfLKRAYjUfb3Nw0OUOtVrPb+iQZekICBEr0+j2I5MFgYFfIYtTZbNaMk5+B7oh6TPJBMO2vXqCgATnuU3ovVyD98lV2HyA4SKTbfjSWJOMi/YAEntve3p5arZZVakEHCKJ98OBgMOIN1BONRlWpVFQul7W5ufnWpTuva8Xjcf3Jn/yJ7t+/b6krNoLcJxKJqFqt6tmzZ3aPTbvdVqVSsdSUwAg6o6BBkRDHQ6oKfYFz8JwgDiiTyVjW45EYfdzsrb+6FiUF9kQRjr1FZyzJzglZFk5OklWKp6enTWIE3+75bs6FV5ygb+50Omq1Wvb7/X7fupf4PNLxndu8J3WFV9kN8zLrrTtIVjab1T/8wz/ogw8+sBFFID8cABNLONy7u7v2315uA1/mK4zS8S1+OBr+jHFClIMMcM6kqVQRGb5BMQdjIYUF/YI8ac0jlQNReOIbGROolj5Vn17zLKg2I7hNp9OanBxdk4nmjUoqlVAcN0iSCiUOQTo+dI8fP9ba2tob5Xre5pqdndU//dM/aWVlxZALw1IoWvX7fTWbTa2vr2tra8ukP9KxPVEBJ2OgM4a9oPDB/vNvvFOFl0RDmUqlzPa8/pe9jcfjOjw8tIwKBDY1NWWFHvjmaDQ61irpO1SguvhcIFuvgMD5odrAmfuBHzhGpDd8Vgph2CEIFP6dTKdcLlsgOg3r1DhI1tWrV/XJJ5/o/v37Y9wfiAnjxGhoAyNd8FIirx3zKQtGwJ9JE3AckM78LJfL2bRyDJXRUnA8oADSGM8T7e/vm2CeohEGJx0P2AXp8X6gS5wmHQxoy/he09PT6na7Vuns9XpWMUX+Q/pCIPG92XCMDJcgLfpjW9euXdODBw/0/vvvGzeM9IeBKkdHR6rX69rY2DBqxT8vkBROlT1G/wfv7NtOCeZUtH311gdzzgPSGKgTHBCfgypwLpezzIcMBTDhK9K0mR4cjAZHe1UJTh0k6/8t3CJnES2jJAMqkkyShNM92bY4HI4m7zx9+vStdWF90zp1DlIa8ZPFYlE///nP9aMf/cicHQ+/1WqpVqvZVBMmmBNVSSdJGZD7+AGoCFzh8OjhxSmm02mrMMfjcZucTrtXp9OxUW1UK/1EIdAenCmI0qfFfF6cn++2IQWH2MdI+begBj8swxcOMFY/vovKLZ8P5LuxsWEyqj/2FQwGNTMzo08++UQffPDBWHGhWq1aG+nW1pYFvuFwaHQPRQv2y3d84ZwojqHng/fj9ycmJlQsFi2DwHYp2BD8QHK+EysUCplzojuIn5N247z9dC0cIJIkSYZ6PRUkyWgGnLjv4AJxQ4NR3ed7AxR6vZ4ajYY2NjaM5z2N61Q6SFYwGFQ2m9X9+/d19+5dXblyxTg1NqXRaKjT6ej58+eq1+tmnDg+SOjBYKB0Oq1sNms8B7KOVCqlcDg81q0TjUaVy+WskJJKpSzV7na7arfbRprzWXFwcJz0miIJwVGySFFocwTlIW3ybWAgTgpKnU7HJkhzuLxWEUQA8Y+0hKo6d5xUKpUzx/gHFgXCH/zgB7p7964uXLhgfcCNRsNSblBlr9ezO5cpeMAHIsny1WYGUWBf2CsoMZlMmq3S3QNf3mg0rB+ZrAY+GaUFGYYkC/6k7gRg0lqfvWSzWXsNJrV75YOfYIXjJGhwdvxsArI2kDC/v7GxoXK5fGpS6W9ap9pB+pVKpXTr1i391V/9lRYWFqz9r9frGWHebre1ubkpSabV6vf7Jo9Ae0g6jUYQ7pCISwUQ54T8Byfoe66Zwgx6o6UKMTISGkmWfvt7THhdXyWUZJyUdDzeDETBoAVSeQZ++Ko4h450vt/vq9Fo6OjoyGYKnnbjPC0rlUrp5s2b+pM/+RNls1lLA2Ox0f3uUD2VSsV6pUFcBDZoIN+XjcQMYTr2Icn47WQyaRwhhbhOp2OKCl/A8ZOh4LVx0hRpksmk8vn82AwA0nWfUcE3+pmL3qmSKTFh6GT3FfIwCkWAmtXVVdXr9XfG9t4ZB8mamJjQ3bt3defOHa2srJhxMenDayO3trb0/PnzMYREaomxYgjSSKYxPT2tQqFg/apc9sV4MdIFjMDf4kiPNE5POr58jHtMcFg4eCJ7KBQyCoC0GwP1Mh8/mII/c2B9nyrFKQpcT548Ub1etz7is/Xia2JiQu+9955WVlY0Pz+v8+fPK5/PG1qHI19dXVWlUhmbZI+mkcDlBf1+qjiOg7/zQRLOHLUGTs3bJIoEuGr4ba9YgJfk97mLGyeKU/WzAjhDh4eHluLz314LioYR+z44OLD7xE9759UfWu+cg2RRuLh//74uX76s+/fvKxwOa3d316rAg8FAz5490+eff65yuTw29skXPpi8QoqKFtK33fluFjilTqejZrNpxDPIwXNDwWDQhpzSZQNvQ88vUZufUXVHNuGNGv4USY4XC3NQ+v2+Wq2WyuWydnZ2zvjFV7zghX/84x/rwoUL+uijj7SwsGCi60ajocePH+vTTz/V2tra2FwAbI1CDOkqPCeoEicKl83VIPB3tORSLGT/eX3p2OF53S0ZEK22aH4pLJEtwS9CzUAnDIejllW6ZOLxuDV6IO9honen01Gj0RibYPSurXfWQfoVCoVUKpV0+fJlffzxx1pYWLCND4VCqlar+uqrr7S2tqZOp2OGSrUP44Q0h+CemZmxn0GsE1G5ZY6+cUbN+xmRvn0LZECFfHt726QXmUzGCHNJxnki35FGsx8Z9YZB+vatg4MD02c+evTILhc7W693hUIhFYtF3bhxQ3/7t3+rpaUlTUxMmKrg2bNnKpfLkjQmpk4kEkqn0+bM0LnShcP+ZjIZlUolo2vIMLAfsoZ+v2/TrPz8U7pRsBOvCvFTePzNinSSQTFJskxle3vbbleMx+OWxlMT4CK901p0edH1vXCQfgUCAd2+fVsLCwv65JNPdPHiRUuHqXz7icpeyoEx+A4CUtThcGjc5Ml5faQ5SB0wcoS0RGIMl2IJ7V28biQSUTabVSqVsp5X0CiHBrRKdZIrLR8+fKiNjY1XdjnW2XrxFQgEdPPmTZ0/f15/+Zd/affH++4mgiVyIKge1AVeDynJWiFJ0f0IM7IQP4mIK0FQQdCJJR1XnxkV6O8vYnqVLxghX/Pvi23v7+9bcXRzc9MKVN+39b1zkH6FQiFls1lduXJFV65c0blz53Tt2jVrUfSaRTgaCiwnZ/3RXcGgT6ImU4n4bxCeTzsgySG0vUbRGyRVdn9gqKjzesFg0O6XXltb0+rq6phzP1unY4VCo5mUFy5c0Pz8vAqFgm7evGl8Hvw20h8C8dHRkUl6kByRhTBhHEkZMwnQU0oyO/PcN0oL+EOKlQRudMAgSOn4LnZaGhlJ+PDhQ9OBrq+vm5P/vq7vtYP0iw2nhezu3btKJBKKx+NaXFw0HRupDtzPzMyMSqWSoUkqi8gV0ER6B0iXCw6TNITCCQ6XqUDS8dj7TCZjN9mFw2E9evRI9XpdlUpFDx8+tIr1uzJN52yNFl0shUJBH374oaWx58+fN7E4gZSiXCwWU6lUso4YX8zBaTWbTW1sbJiUjM4d9JJkS75N1jdKTE9PK51OG+fOisVi2traUrvd1vr6ur788kt7vXeZU3zR9UfjIL9pJZNJLS4uqt/vK5fL6fr165byZrNZlUqlsasN6OGGkCdCgwBoRaRqx2Qd7wx9xOc1BoOBpVKfffaZocz19fXXOjH5bL29NT09rVKppEAgoNnZWV2+fNn6nxmcwpQqHCrtjwi8mXruu6u63a5arZbdIMjgEarafmwZbblwjKurqzYwBZ3tH/P6o3eQ/7+Vy+W0tLSkeDxuV3QuLy9rZWXFECCi8PPnzxvJzoDf//7v/7Yb6tCcbWxsGKfJ/xjldrbOFiufz2tpackq3BMTE7p06ZLOnTtnFWgc3cWLFy0VPkm9ICfa39/X6urqWKvrcDg00fnZ+sPrzEGerbN1ts7WN6zg//0rZ+tsna2z9ce5zhzk2TpbZ+tsfcM6c5Bn62ydrbP1DevMQZ6ts3W2ztY3rDMHebbO1tk6W9+wzhzk2TpbZ+tsfcM6c5Bn62ydrbP1DevMQZ6ts3W2ztY3rDMHebbO1tk6W9+w/h8To9/0yDIW0QAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "dataset_path = \"hf://zeahub/camus-sample/val\"\n", "dataloader = zea.Dataloader(\n", " dataset_path,\n", " key=\"data/image/values\",\n", " batch_size=4,\n", " shuffle=True,\n", " clip_image_range=[-60, 0],\n", " image_range=[-60, 0],\n", " normalization_range=[0, 1],\n", " image_size=(256, 256),\n", " resize_type=\"resize\", # or \"center_crop or \"random_crop\"\n", " seed=4,\n", ")\n", "\n", "for batch in dataloader:\n", " print(\"Batch shape:\", batch.shape)\n", " break # Just show the first batch\n", "\n", "fig, _ = zea.visualize.plot_image_grid(batch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Processing an example\n", "We will now use one of the zea data files to demonstrate how to process it. A full example can be found in the [zea_pipeline_example](../pipeline/zea_pipeline_example.ipynb) notebook. Here we will just show a simple example for completeness. We will start by loading a config file, that contains all the required information to initiate a processing pipeline." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b50da611101845fca286edd40839b4d0", "version_major": 2, "version_minor": 0 }, "text/plain": [ "config_picmus_iq.yaml: 0%| | 0.00/2.91k [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "image = zea.display.to_8bit(images[0], dynamic_range=(-50, 0))\n", "plt.figure()\n", "# Convert xlims and zlims from meters to millimeters for display\n", "xlims_mm = [v * 1e3 for v in parameters.xlims]\n", "zlims_mm = [v * 1e3 for v in parameters.zlims]\n", "plt.imshow(image, cmap=\"gray\", extent=[xlims_mm[0], xlims_mm[1], zlims_mm[1], zlims_mm[0]])\n", "plt.xlabel(\"X (mm)\")\n", "plt.ylabel(\"Z (mm)\")" ] } ], "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": "3.12.11" }, "zea": { "tags": [ "data loading", "cardiac" ], "level": "beginner" } }, "nbformat": 4, "nbformat_minor": 2 }