mednist_GAN_workflow.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN Workflow Engine with the MedNIST Dataset\n",
    "\n",
    "The MONAI framework can be used to easily design, train, and evaluate generative adversarial networks.\n",
    "This notebook exemplifies using MONAI components to design and train a simple GAN model to reconstruct images of Hand CT scans.\n",
    "\n",
    "Read the [MONAI Mednist GAN Tutorial](https://github.com/Project-MONAI/Tutorials/blob/master/mednist_GAN_tutorial.ipynb) for details about the network architecture and loss functions.\n",
    "\n",
    "**Table of Contents**\n",
    "\n",
    "1. Setup\n",
    "2. Initialize MONAI Components\n",
    "    * Create image transform chain\n",
    "    * Create dataset and dataloader\n",
    "    * Define generator and discriminator\n",
    "    * Create training handlers\n",
    "    * Create GanTrainer\n",
    "3. Run Training\n",
    "4. Evaluate Results\n",
    "\n",
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Project-MONAI/Tutorials/blob/master/mednist_GAN_workflow.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Setup\n",
    "\n",
    "### Setup environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU \"monai[ignite]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# temporarily need this, FIXME: remove when MONAI v0.3 released\n",
    "%pip install -qU git+https://github.com/Project-MONAI/MONAI#egg=MONAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -qU matplotlib\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 0.2.0+91.g04e1c34\n",
      "Python version: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 19:07:31)  [GCC 7.3.0]\n",
      "Numpy version: 1.18.1\n",
      "Pytorch version: 1.6.0\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.3.0\n",
      "Nibabel version: 3.1.1\n",
      "scikit-image version: 0.15.0\n",
      "Pillow version: 7.0.0\n",
      "Tensorboard version: 2.1.0\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import logging\n",
    "import os\n",
    "import shutil\n",
    "import sys\n",
    "import tempfile\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import torch\n",
    "\n",
    "from monai.apps import download_and_extract\n",
    "from monai.config import print_config\n",
    "from monai.data import CacheDataset, DataLoader\n",
    "from monai.engines import GanKeys, GanTrainer, default_make_latent\n",
    "from monai.handlers import CheckpointSaver, MetricLogger, StatsHandler\n",
    "from monai.networks import normal_init\n",
    "from monai.networks.nets import Discriminator, Generator\n",
    "from monai.transforms import (\n",
    "    AddChannelD,\n",
    "    Compose,\n",
    "    LoadPNGD,\n",
    "    RandFlipD,\n",
    "    RandRotateD,\n",
    "    RandZoomD,\n",
    "    ScaleIntensityD,\n",
    "    ToTensorD,\n",
    ")\n",
    "from monai.utils import set_determinism\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup data directory\n",
    "\n",
    "You can specify a directory with the `MONAI_DATA_DIRECTORY` environment variable.  \n",
    "This allows you to save results and reuse downloads.  \n",
    "If not specified a temporary directory will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/workspace/data/medical\n"
     ]
    }
   ],
   "source": [
    "directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
    "root_dir = tempfile.mkdtemp() if directory is None else directory\n",
    "print(root_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download dataset\n",
    "\n",
    "Downloads and extracts the dataset.\n",
    "\n",
    "The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),\n",
    "[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),\n",
    "and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).\n",
    "\n",
    "The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)\n",
    "under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).\n",
    "\n",
    "\n",
    "If you use the MedNIST dataset, please acknowledge the source, e.g.  \n",
    "https://github.com/Project-MONAI/Tutorials/blob/master/mednist_tutorial.ipynb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "resource = \"https://www.dropbox.com/s/5wwskxctvcxiuea/MedNIST.tar.gz?dl=1\"\n",
    "md5 = \"0bc7306e7427e00ad1c5526a6677552d\"\n",
    "\n",
    "compressed_file = os.path.join(root_dir, \"MedNIST.tar.gz\")\n",
    "data_dir = os.path.join(root_dir, \"MedNIST\")\n",
    "if not os.path.exists(data_dir):\n",
    "    download_and_extract(resource, compressed_file, root_dir, md5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "hand_dir = os.path.join(data_dir, \"Hand\")\n",
    "training_datadict = [\n",
    "    {\"hand\": os.path.join(hand_dir, filename)} for filename in os.listdir(hand_dir)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'hand': '/workspace/data/medical/MedNIST/Hand/000317.jpeg'}, {'hand': '/workspace/data/medical/MedNIST/Hand/002344.jpeg'}, {'hand': '/workspace/data/medical/MedNIST/Hand/000816.jpeg'}, {'hand': '/workspace/data/medical/MedNIST/Hand/004046.jpeg'}, {'hand': '/workspace/data/medical/MedNIST/Hand/003316.jpeg'}]\n"
     ]
    }
   ],
   "source": [
    "print(training_datadict[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Initialize MONAI components"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n",
    "set_determinism(0)\n",
    "device = torch.device(\"cuda:0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create image transform chain\n",
    "\n",
    "Define the processing pipeline to convert saved disk images into usable Tensors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadPNGD(keys=[\"hand\"]),\n",
    "        AddChannelD(keys=[\"hand\"]),\n",
    "        ScaleIntensityD(keys=[\"hand\"]),\n",
    "        RandRotateD(keys=[\"hand\"], range_x=15, prob=0.5, keep_size=True),\n",
    "        RandFlipD(keys=[\"hand\"], spatial_axis=0, prob=0.5),\n",
    "        RandZoomD(keys=[\"hand\"], min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
    "        ToTensorD(keys=[\"hand\"]),\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create dataset and dataloader\n",
    "\n",
    "Hold data and present batches during training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "real_dataset = CacheDataset(training_datadict, train_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 300\n",
    "real_dataloader = DataLoader(real_dataset, batch_size=batch_size, shuffle=True, num_workers=10)\n",
    "\n",
    "\n",
    "def prepare_batch(batchdata):\n",
    "    return batchdata[\"hand\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define generator and discriminator\n",
    "\n",
    "Load basic computer vision GAN networks from libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define networks\n",
    "disc_net = Discriminator(\n",
    "    in_shape=(1, 64, 64),\n",
    "    channels=(8, 16, 32, 64, 1),\n",
    "    strides=(2, 2, 2, 2, 1),\n",
    "    num_res_units=1,\n",
    "    kernel_size=5,\n",
    ").to(device)\n",
    "\n",
    "latent_size = 64\n",
    "gen_net = Generator(\n",
    "    latent_shape=latent_size,\n",
    "    start_shape=(latent_size, 8, 8),\n",
    "    channels=[32, 16, 8, 1],\n",
    "    strides=[2, 2, 2, 1],\n",
    ")\n",
    "gen_net.conv.add_module(\"activation\", torch.nn.Sigmoid())\n",
    "gen_net = gen_net.to(device)\n",
    "\n",
    "# initialize both networks\n",
    "disc_net.apply(normal_init)\n",
    "gen_net.apply(normal_init)\n",
    "\n",
    "# define optimizors\n",
    "learning_rate = 2e-4\n",
    "betas = (0.5, 0.999)\n",
    "disc_opt = torch.optim.Adam(disc_net.parameters(), learning_rate, betas=betas)\n",
    "gen_opt = torch.optim.Adam(gen_net.parameters(), learning_rate, betas=betas)\n",
    "\n",
    "# define loss functions\n",
    "disc_loss_criterion = torch.nn.BCELoss()\n",
    "gen_loss_criterion = torch.nn.BCELoss()\n",
    "real_label = 1\n",
    "fake_label = 0\n",
    "\n",
    "\n",
    "def discriminator_loss(gen_images, real_images):\n",
    "    real = real_images.new_full((real_images.shape[0], 1), real_label)\n",
    "    gen = gen_images.new_full((gen_images.shape[0], 1), fake_label)\n",
    "\n",
    "    realloss = disc_loss_criterion(disc_net(real_images), real)\n",
    "    genloss = disc_loss_criterion(disc_net(gen_images.detach()), gen)\n",
    "\n",
    "    return (genloss + realloss) / 2\n",
    "\n",
    "\n",
    "def generator_loss(gen_images):\n",
    "    output = disc_net(gen_images)\n",
    "    cats = output.new_full(output.shape, real_label)\n",
    "    return gen_loss_criterion(output, cats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create training handlers\n",
    "\n",
    "Perform operations during model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "metric_logger = MetricLogger(\n",
    "    loss_transform=lambda x: {GanKeys.GLOSS: x[GanKeys.GLOSS], GanKeys.DLOSS: x[GanKeys.DLOSS]},\n",
    "    metric_transform=lambda x: x,\n",
    ")\n",
    "\n",
    "handlers = [\n",
    "    StatsHandler(\n",
    "        name=\"batch_training_loss\",\n",
    "        output_transform=lambda x: {\n",
    "            GanKeys.GLOSS: x[GanKeys.GLOSS],\n",
    "            GanKeys.DLOSS: x[GanKeys.DLOSS],\n",
    "        },\n",
    "    ),\n",
    "    CheckpointSaver(\n",
    "        save_dir=os.path.join(root_dir, \"hand-gan\"),\n",
    "        save_dict={\"g_net\": gen_net, \"d_net\": disc_net},\n",
    "        save_interval=10,\n",
    "        save_final=True,\n",
    "        epoch_level=True,\n",
    "    ),\n",
    "    metric_logger,\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create GanTrainer\n",
    "\n",
    "MONAI Workflow engine for adversarial learning. The components come together here with the GanTrainer.\n",
    "\n",
    "Uses a training loop based on Goodfellow et al. 2014 https://arxiv.org/abs/1406.266. \n",
    "\n",
    "```\n",
    "Training Loop: for each batch of data size m\n",
    "        1. Generate m fakes from random latent codes.\n",
    "        2. Update D with these fakes and current batch reals, repeated d_train_steps times.\n",
    "        3. Generate m fakes from new random latent codes.\n",
    "        4. Update generator with these fakes using discriminator feedback.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "disc_train_steps = 5\n",
    "num_epochs = 50\n",
    "\n",
    "trainer = GanTrainer(\n",
    "    device,\n",
    "    num_epochs,\n",
    "    real_dataloader,\n",
    "    gen_net,\n",
    "    gen_opt,\n",
    "    generator_loss,\n",
    "    disc_net,\n",
    "    disc_opt,\n",
    "    discriminator_loss,\n",
    "    d_prepare_batch=prepare_batch,\n",
    "    d_train_steps=disc_train_steps,\n",
    "    g_update_latents=True,\n",
    "    latent_shape=latent_size,\n",
    "    key_train_metric=None,\n",
    "    train_handlers=handlers,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Start Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "pycharm": {
     "is_executing": true
    },
    "scrolled": true,
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [],
   "source": [
    "trainer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate Results\n",
    "\n",
    "Examine G and D loss curves for collapse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "g_loss = [loss[GanKeys.GLOSS] for loss in metric_logger.loss]\n",
    "d_loss = [loss[GanKeys.DLOSS] for loss in metric_logger.loss]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "is_executing": true
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.semilogy(g_loss, label=\"Generator Loss\")\n",
    "plt.semilogy(d_loss, label=\"Discriminator Loss\")\n",
    "plt.grid(True, \"both\", \"both\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### View image reconstructions\n",
    "With random latent codes view trained generator output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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heu9bodfmXFlL2W/nMG/aOMqy967TKrbsCpV1SoV12do4b1oWxVthfeP9ON9bXVv5Pq5Uvlsq7yaKmhBCCCGEEEIIIYSJkD/UhBBCCCGEEEIIIUyEsvWJskRKeGxXeUofb7/99hZT+shsLJS6/d7v/d7S87l7/Fe/+tUWUw5HmwOlWJR38llM8m+ZZVhOyrgso9Pu3btbvG/fvhZzB3qW2WTaq6hI6Ox5+AyMaTOjTNTscL1Zn8ZkhOk93hvPYYd8sxpZffdmOCGV42OsFmPeFWHfoTTUrH5s17QoUv5cyWqxWGwc73gPyxLHMWhMVibrU6SSzc5sF3ZfO95rv6qUv9K2pgTLy3fOdkjs/RibkS3E+uYYG6PZbfm8tC3T7mRjV4WhPapiz+ZYwHJwTjQrRK/9Z12ZXNY1n46xddjaaM5U6s/ec8XutC7JP6/D+YR9iuXkHMd4TNmsPbI/cQ08tAyzfHwGs131Wn7YZ7klAe1OLEPFnmB9oXeeGjOf9s6zc5g3x1j/KnW/Lgvpuuqy971xTWsZSy1D7zrHZuuDvDfLRNuzra0/CYzpjx+PmTWEEEIIIYQQQgjhY0D+UBNCCCGEEEIIIYQwEVZanyq7rVNexWwnDz/8cIspg/xH/+gftfi2225rMe0/PE65IuVCTz75ZIuZNeX69estfvnll1t88eLFFlMOSZlobzYVyskot6YFjOVnPXwYi1MFyyZFyRljWsKYjYPPxvoyi4RliSIVa4PZpio7tvfaRjZbmryZVLLxEKuzirS2cn4lC4plbOE4U8k+VMmywnJSUk3pNWNKoe2aqzKxUJZK+GwVy2FFwlvJVlPJoNDbhsZkleptl3Pum2xvHINtXBxjH6mMkZVMRBUbVOU61mY5zzDb2o0bN1rMuqLNuXdcWvV/lfZjGadIpa9VrMc83yxwvVmFKhlNeu3JlTZhWVjmRu+YVKnvXutML/wt5yK+H9qduEY1298YWzSzGtp9F4uN4wLhWNA75tszcxuFiuXKtkhYVwavMVmfei12c5g3K+MNt9GwtaJ9o/Rm0uudN8bQ216sbZrNv9favKp8vB+/n2/evNniK1eutJjzPcemObTJzaJ7TNukcoQQQgghhBBCCCGETvKHmhBCCCGEEEIIIYSJsNL6ZDJYsw/s2bOnxX/v7/29Fu/fv7/FFYk17UKUsfFetEfRcvXII4+0mLtN0x7F45bFiFDS25v5gjYjytUqGXk+DLwW65FZaRibXJlZn7Zv395iyttMfmk2KDtesZb17pg9JnvBnHfINyljr72sVwJqGdD4W9obre9TemxYecwmyfbOPkgZNscTs/qR4VhhWfEoK6+0MXs3dm+zHlT6VEUiXJHMjpEL30p58a3CZNg25lUyKxljMgWRXrsTMbseZdGUPLOdXrp0aelxzum0JfaOe6vOI7wHxxFS6QuV4zbfc9w0a1nF0lYZ33ttLZVscXNgXZnoKmNz5f1XLPaVcYCWB645mRGV2ZfWlRGG1+G8yTa7ZcuWFg+zPm1Gu+I1bbsBlsPW6IzHjNGbYYOqrBPmsI4llXplO69YusdY+yvHb6UlyuyAluWN8TozQNmanuPLmTNnlpbPsp1+Euidf0kUNSGEEEIIIYQQQggTIX+oCSGEEEIIIYQQQpgIK61PJpeiNJi7qlM+zGwO/O2bb77ZYsoSjxw5srQMJhPmfblzPCXT+/btW3qcEmtaLcyaY1Ixns9n2bFjx9LjZMwO/6vozUxgElNK1X/2s58tvQ7fRyWLBBkjqSe9mWjMolbJsjEleu0mY7Jd9WYK4nG2f2aCoGSSu8JT3kmZq9l0LKuU9Tve18axiixxmMGKYx8tVSwrpeG90seK3cneH5+NZWBd2278Y6yIYyyKc8tYQXpl+72ybfutMcaCU7GEWDkpc7Y5hLLoq1evtvihhx5aek1idtsqVg72hUoWGLP3VjK2jMnuVLEukjHWS6vr3mxTHzVj+pS9h4q9jPMDY85x1r4qsC3QysN2zWvS6kubO+dNsyHb83JusTUwn31YJmvPlXdmdieu+zkvs67tHdi2CL3z0Zj50ejN9DkHG5S1BR7/yU9+svR4xVYyZo0/hTUIx12zw7K/s/1yDVjFsmqxHrn+tPUt51OW6ZNMsj6FEEIIIYQQQgghzJT8oSaEEEIIIYQQQghhIpStT5aFgBK106dPt5gSp127drX41VdfbfFTTz3V4m9+85stphWCFqqvfe1rS8tz7ty5FlNizXJ+/vOfb/GLL77Y4rfeeqvFlI0Rk33x+pRYUvZJWSmlYWZZWKdEkeXm+6BVjBmwLl++vPQ4pbSVLE4my+PzV6TzY45XZMomfxy7K/qtoJKBhOdY2yPrskfxvrfffnuLuSs8M0EwIxulx2yDFZk/n5HXYXu0TA7EbBSr7Ep2b2acMnozi1SyB1HavnXr1hbT7sX6pfyb9GYusaxSlfMrlpC5SbiJ9U1SsYTaNcdkiep9DxZblgrOyxwTzGrTm6Vi7BxqFg7GlI9zjLC53GxKZh3qtfv12nh6bVaGWRTmMG/2MsaayffMeYBZNLkVADM0mRXVsP7IPsjycH7g+vzOO+9sMdfhFYst5xb2cVqeh21kmAVq2TP0rjnYT3fu3Nlim9f5bHwH/B4YY33ZjKxPH8dsiZU+1TsnVratqKy/xmRkWxdmVWdczVL6Pqvqiv2Csa3xuIbk+R+VJXZdfWqz+1qyPoUQQgghhBBCCCHMiPyhJoQQQgghhBBCCGEirLQ+md2AEizu4k7JP+VYPMd2m6c09IUXXmgxbUqf+9znWkx543PPPddiWploxeL133333cUyTG5psmhKOg8ePNjiPXv2tJg7zRPb4X+sfLiSjYB1x3qh7JX1aPVicjjC+1Zkeb2ZRSo76ldktHPL+tTbTnqzNY2RybNsbP/s78zIRhkqbXYVKbi1F7Zr242e41glkxQZ7qLPe1ACzj7C9s/+bzLRii3C+jXHJtY1r88+3msJ7JWM2nUqbXFuGaAq48cYa+G6LC82NldsatZfrC2zrd28ebPFbKcVO1Ulo0cVGwtox+D4xbLyHMusY/Nd77usvLN12QYq5SRzmzfXlYmu91533XVXiw8cONBiWuYvXLjQ4uvXr7fY2pe9H55PmzuPHzp0qMWcN2hDfu2115be16y3XD8ePny4xVz/r6pDzqGcdyt2TZ5PuxPrl9mgaMXkOoN2YGa9sr7Wy7rGrzHzx1RZ1zw/xkJqx6dQr7wX18mWtYwx5yuuT21OXyxqFkreg/2FMe/9Udlje9eiU7MWRlETQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEibDS+mSyIEqfuLs7M7l86UtfavGXv/zlFlNaSHkRpYj/63/9rxZ/+9vfbjGloY899liLv/Wtb7WYEkXuWk9rEqWOtEhQxmUWH1q3mN3pwQcfbPEDDzzQ4nvvvXfp+ZSbrlMOxmtRcsbn5DPQUkJ5G2WytjM/65rtg+dbVh6Tz1ayi9mO7WZTMXlfxQY1VSpWmN7nqFhYTAJckb3z/fOdfPrTn24x26m1I8umwjKwXfO+/C3vy7ZT6Y/DzFmUn7If0TphY0pvFi7LpEaJOcca9ndaUCqZPKxvVuwxdh2Tr2+WxeWjZF2S23XZznqvT2zMtvbL8ynVvnLlSovZ33lNnm/24XViz2OZe9jXKPPuzVBDeuc7y75RsbpV+vuY+WOq9NpJezPyEbPQMesT172cs3ic80mlPDbPsP1y7mM59+/f3+Lnn3++xbRQWV3xGdlm2Vcsu+CQSv1an+Kz0a5o2dy41uW2CDaukd4+tdn0zgdTwsrYa6e2a/ZmOZxCRiBi617bFoDjBuGzW7xYbOy3ljXW6p02Q2Yd5XfvurKqGb39cV3v2K7POiT23UqiqAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJEWGl9MpkP5YsmpX/mmWeWHqfMcvfu3S2+ceNGi48fP95iSqDPnj279DjtVFevXm0xd7fm+bQ4UX5FCRKlW4yZ0enhhx9u8UMPPdRiykdpDWN5WA+bJUvkdSlRowSWEluTzFZ24Ccm265kfapYLSoWCZOFE6v3OWSvqEi47X1WsoVUYstAwevzvqxvyirZvyifZjt98803l96X12S/tuOUXpoUmlh7H8oV2bfZ53v7jt3b+gKfh2MTrU8sg9kbey2BvTYbozfL1RR24O9hM7I7VeTcpHLcrtl7X+vvbHe0JPMcWvRs/LZ+uqpdWDvkuMD1Ae0PNpZZhipSmdcqWRFtvK7Mp2RM5jBSsclNlTHP3Wtrs4x8lvnU5sEf/vCHLeZ6294/2yn72uXLl1tMqz5hNihmT+Iam+t/9kdmtqK9y8q2WHjmResjFnOtwKxaXGcQrnvt22BM9r4xbIadZg79tLeM1tfGWFvGnL+ubGCVcZfn2xqT4wP7LM+3jKirykHYX9iXmQGOWUc5ptAybNuOkDH1XlnTrIsx5TGiqAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJEWGl9MhkVJYGUL1FOSCkTJVuUR9LKRDkW5ZqUjFLGSMkoy8aMTiwPy0wZmNmyWM5t27a1+MiRIy1mJilaDWh9sEw35MPIsiqSLcsARVkay3ffffe1mHVKySwtatYmTDLKd9wrqR8jVzNZpNku5rBDPjFpfKW9kcpO+Ca3NixDkdnjKvJGk/9TesksFdwVn9dnn6hYcFa1C1qfWKbezFKVTFosB8caZrjgffn8lHlbVgDrL702m4r1g0wha8a6GWNfqmTZGnOdXntZxepo7Zdt8OLFiy1+9NFHW8w5vde+82Gw/sh+YZbR3nv32mYqWZzIuuxOvXaPypgwJTbbSmL2WLOS8zjXurQIVLJEEb5DrodpH75582aLuR5kH+SaltsR2DjA90+LFucozj+LxUb7LX/DZ6us02gns3bO63Mde/369aX33QwrS6/NZl3MbQ6tZHeqjDe98yPpHTsrdbyu92Drzcoa075/uWZcLGptlWtofp+zfPyWpg3qtddeazG/+Xu3nqjU6br6cu/3idUV3xnrypj+zBpCCCGEEEIIIYTwCSF/qAkhhBBCCCGEEEKYCCutT8Tkt4wp56HM8IUXXlh6TdqmLOZ9f/zjH7eYdhxKuSinMkuFScJoHdi1a1eL9+7d22JmVqEk1XaXpwWMMTEp+zCzDOmVGfO6tKBQ7sbnJLY7N9+rZcqoZLswaT6v0yvltzZakVT22ns+Cuz5rD/21kdFulvJsjXM8vA+lEOzPFu2bGkx5cwmGzRbFvsOr8/rWD+gFJHlZz2wbMPfDyWky+7H+lrVz9/HnoH3Yt3xGTg+Umpuz0Yq8l8rf0Ve3Jv1aQ70ZsGqWDzHWFgqY+eYMbtSHrY7Zmak7YDXYbtmvzHb7qpMRFY+2iOvXLnS4gsXLrSYFgnOg5YBqpIByDJj2XUqxyv0tq1ei90c+umYrE+GnU8bDdsRrX9s57buoB2J61uO6zZesgwvv/xyi2nb5/XNts+sSlwDWtYYtgvLsrpYbLRm0Y5lWWBs3mHGLD4Dr8PMVefPn28x300l+4zRO+6PsUGtq41OlcpaYIwdmIyZZ+06vccr97X5jsfNYlnJ0jhcq1fWdbw3M9XR7kS75gMPPNBi9sfezGu9dqcx1sLeLE4Gx3rWSeVbPoqaEEIIIYQQQgghhImQP9SEEEIIIYQQQgghTISy9YmYpNfkSLTFUPpIWaZljjALA883GRHPobyT0C5w6NChFtPiREkX7U78LWVNJg0z6ZrZnYaSqDEySDuHZeKO3JSusUyUwNKCQVsaJeUVWXglGxSx843KTvFzzl5RaRf2HohJHSs2NTvOmG2H1+RxWorYZ2ktNMk3pZu0Kdi75X2ZcYOxSaHZbxYLt05Rkm19uzfjFPsdM9Kx3GYJq8hKK9aG3uNGZf6YM2MsJuuyKfVaSM2CMSbjEOG8z/7B+XTr1q0tZn9iH6pkixuWgzH7EY9zfOHaxayClXZeGTctK55J1Xul/2Ok+bZeIXObN+14JcsWsfqw7KiEbdgsO5wHuRalbcgy+Nl2BOfOnVtaTs4hLA/7Ke/L8rMMPIdr5mFdsc3w26BS7/wt1w3ssywTLWfcOmFMpqcxGUjH0NvH50ylf/XagSvfHL1j6rqye9l12N653uTYwj7Evm/fnpxPhltz2HesWQ75zWzrco4L/N7keGRbNpAx2YHHbBtSseHxfG6n8g/+wT9o8fPPP99i2x6CTH9mDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIUyED5X1qWJ9MvmS2ZoqUnpen/JGy6zE61M+TbvA4cOHW3z//fcvPYeySmadYMzrswxmy6KUjHI1SluH2WOsXijvrNhdeD6vw7LyeShDf+yxx1ps2St+9KMftZi7eZtkn1Sk+XZ+JbNX5V5z2CHfrIKWhYHvvCKTtvdTkf7xOnz/zKzCrGqE7Z9WCEoIKfXke+NzVbJNsZ/yvmZ9oiWC/WP4G44XZiOx8tl4yrrmvWnf4rOxL1Pabf2xN3tQr1WwNxvZx5Ex2R8qtqPe6/dm5SIV2bbNd2ynhH3Isih+GEm5/R/7IPtIJcOa2UR5fev79o7NWmXjcq8VsdKXx0jz5zBvksqYVznfjvO9cb1KmT/XnLSbWzYVvjfOg8ygYhnJWAZmj6IFgX2TWwHwXs8880yLzaLHdsH1LefQ4e8rWGY4rg84P9oz055fyX44hjEWjN42N7c+SPg+WWccm4mNYRybe7MgWXu2NXZlzVKxs1csO8M15/twrffmm2+2mG2fcyvhenBoz+QczLW4Wez5/mh3pN1p586dLea4wyx0Z86caTH7aW9W0MpauvKdU5kPbGuCr3/96y3+F//iX7T4P/yH/9BijktGFDUhhBBCCCGEEEIIEyF/qAkhhBBCCCGEEEKYCCutTyYVs2wLlqGpIk2ya1JGZBmUaHkw6wevQ0mX7WzP45R68TosMyVzlOpRTmYy5GvXrrWYkrG9e/cuSK+0lPXCHf+vXr3aYj4n64W2LtYL6/fhhx9e+jyU3L366qstZl1UJLOksmP7GOZmu2A9ma3ArDB8P2wvZkOp2Oms/ngvtnNKtSmBZBvk8dOnT7fYpKpWHlqRTFJOWalJW1dZ6yi95nl2b16X8lyzBbHN85ocI/gMrF/KW/m+K3LQiuWQVLIm2PmfJMZkcerNdmH3JTZ3j7FHEc4hlEizbVIiTcuhWTnMfrQKa9tmcWSf5TNwTWDrABuPrK55r0rmy4p9qbet9GaQmXO2RKM365PB37JNXbp0qcVsR1z7sS1wTqRF4ObNmy3m/M4+xedlG2cGJF6T5WS2Kc4ztCZwXcl7cd5nu+D6ebHYuM5kXVS2V+D6hvZ89kHWEWPLHFmhkqlwzPFee8yc7U7kH//jf9zif/bP/lmL//2///ct/qM/+qMW27zQu8VCZeysZPK1tTH73ZiMRpwHOCawr3Hdx+8tWo74W/bxYd+07IcsN69l39v2TcLtRR5//PEWc337k5/8ZOnz2PYNveN7BetrfPdc0zz44IMt/lf/6l+1mNs9MAPUiRMnPrAM059ZQwghhBBCCCGEED4h5A81IYQQQgghhBBCCBNhpfXJZMIV2axJvHozGFDixPMpJ7P70gpAKRZ3s9+xY0eLKcuyjFQVuTR3qmYZKJ1mmZkNh9ekLHSxcJnd+fPnW8wdpCkl/e53v9viV155pcVHjhxpMeWtzIZlNhJen+d/4QtfWCyDNihKdSty696sFr2sS+58q6Ds02TCfFeU3V2+fLnFtBtQ3lixO1XGAbbnCxcutPjee+9tMWWZzHxBaeSePXtaTOk4y0wsm4plbqEElGWgrZBtlucvFhuzL/EetEFyLGO9VGyilvWJv+W9aG/k++bz9LZzs5pwXLLzLbuHPXvvLv1TolLGio2oIo2u1Fkl24XNsxVbll2f1kL28YceeqjFtHuwT7GNU15dsckOj1sdsZ/SZnngwIGl9+b8zTme9g/LSGfYOMXnrGSlqWR3GmObqpR/zn1zXZlZDP6W1iRaFbh2s7Uo11ycQ994440Wnzx5ssW2Due8z/lh//79Leb8y/vSZkTY3zmHcu09nAfYDmkf5tqFfYr9kWXleMH1NC3T7Ke92aaMSpsYY7Ww45XtJOZmifrGN77R4t/6rd9qMdutbeHAfsQ1js1rlYyylfnUsm6y/bJstN+xDXKMt206Hn300RZbP+XYwnuxP3Iuro7fLCv7I38zzOj2PvyOZfYojkHsv7btyPHjx1t848aNFpsNyui1RzHm+969e3eLv/nNb7aY7+no0aNLy8m/O3D8NaKoCSGEEEIIIYQQQpgI+UNNCCGEEEIIIYQQwkQoW58q0lBSyQBgmX9MZmbn00bA8y1TCuVkPIfSJFoqaHng81KuRckZf0s5KGVcJtVbldWCz89nphSN0izej9AiQQk3peCEUjnKU2lHoaSNdc3fUtL20ksvtZj1ZVLF3p3ZyZjsGFPFrEmsP0oOafejzJ/t0GTCfD9sq5WsTywP2ynva9ZFthdKBSnjZN9kf2H/sHbEvkJbA2OTnbMfDH9jmSl4Du9NbJzlWEO7CGNKSSlDZX+vSKZ7j/dmCbL+aH1zbnLudWWW6bVg2BhGWXXlvpWxkP2CUm22cVqIvvSlL7X4/vvvbzHHIvZrm4uMaruwbHnsj7RgmXWR/Y5WEPY7szdWylPpU5YNqpIlilSscRXr+4fJwnWrsfHmVmbaYX/kmM21GPsU2xfXrlx/UWJP6w/XpbaVAe9lVlTOP5yLOSdyrrO+PLTJmj2SawXaumxtyetyvcLn32xL+5hMajbfVTLpVraTmMO8aVnSzGpkGfnse60yb1bWtFwDs+5tLco+wnLyOuyPPJ+2Jtqdhtb797l+/XqLaQ+iveZ3f/d3l16HZVssNo41lcykPM53w7GA57O+eC+OF5YdlXMx+7htqVGZT61vcuzju/na177WYm73wXHJxnqzuhlR1IQQQgghhBBCCCFMhPyhJoQQQgghhBBCCGEirLQ+9e5yb1IjiyvXNFmfya9MAkwbCHdrppSJsilKnk3aTUkmdyCnbI/nU6J15syZFp84cWJpeViG4XUpa2PWJ8pkTR7HeqT8jvdjTCkpr2nvhhLYgwcPtpjSL96Xz09Jm1ndKplIenfINwnfVLH2/+abb7b41KlTLaZ8j5m+2Bco5+d1XnzxxRYzM4XJDFk2xmz/lGJSqk0JJH9Lax0tFZRIm+2AbZnSadYb+x3HClqceE3KOReLjWME2xXryOSglPPaWMYy8flZbtavZauytm2Wh145d8XiYc9YudfHhUomtd6419bUi1lhaF/ieP/444+3mHJgWic4LrFt0gZiGR7Nbrgq6xNjs3GyP1Juzvux3Bx3eE2OlZQ6V2xKve/Sxt+KrYn0WhfnlvWJbMZ4U1l3mBz+5ZdfbjGzaHLeYHvkOuvzn/98i5nt5ezZsy3mXGnZD02Gz3mZc7FlveE6kdcctjvem2ME5zX2KR7nPcxGUtmCoZfejEuV44wt649ZuznvV+aJqcJ12rVr11r8X/7Lf2kx14pc17BdsL9UvhVIJSsc+y9jtkF+39kcx+Ncl953330tZp94+OGHW8y+xm8mWp+4fQG3GuDa3ixXi4W3Vd6P74zfpJzj2YY5XnBNy/GI5bDMyuwjfOZz58612Na9vX3TLFdPP/10i/mdQLsaM0DxO519lm3aiKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE6Gc9alyTm82j4pVxexOxCwylMBxd3lKN3mO7cpMySHP53HKslgeyr74LJT2UZ5KGflQnmc7XVPiZjvyHzt2bGlZWS+U39HuRNkc64VyLz4ny015H2W7lIVT+sX7WjYjw2SulTY3B7sTMasW64n2IusjtEGx7VBWTBuC7XjP+1rfNxsUn4XybFoN2DZp0WKmKpMo2thC+Sill5WY/Wax2NjO+QwmXbbsbOybhHJK1gXvRWkl5aB8Z4b1EZa5kj3IZO4Wm31jbjYK0pvlo/d4b9bFiq2td65nX6BN44knnmgx2ywl3LT9sS2zH/D6FdvcKrteZTzivM5ymyWKsm3+ln3kRz/6UYs5h5pttdeOxL7G+/ZmP6xYHStZ2OaQ9YmMsaqMyeRjNgJaByjb57qUx2mR4Fz25JNPtphzOudKzldcx5ltme+W9nqWn3Md41VthOXgmmNoLX4f6wucN81muNmMaSusL64t+P7Y3zmnv/rqqy02m+gc5lNugfCtb32rxS+88EKLabWpWJwqY1LFBtVbf7xm5Z1wPmE/4FzEOZHH2ffZNy9evNhi1gO37OC3I8uwWLg1i+Xmtbj+5Hvi9gr8LedTPhufh/2Cx2n/55jIbRpYHtpB+T5sXq7Ypfnd/p/+039qMd8N10YsA79hhvW+jChqQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBFWWp9MQmjyWLNjVGxQlR3yLauUyXgpjWNMCSGlm5SBUfbF3/JelKpyJ21KFynd4vnMyMOMUbSZMF4sNsqu+BvKz2gdIibzpgXFMk5RKsY64jmUcvG98pp79+5dWgbKVv/4j/+4xb1ycbM1mWXD2uWtlMt+WCr9iO+NVjuT8tEeR2mhZW+h5JYxpdo2JvAc2nQow6b1iTJJlpPtkTvts32xHnjcsj5VstJQ3jj8vWVk4LhDiyP70YULF1rM97Rr164Wsy74XrnjPeuF5alYGEjFmmHWUD4vr2PZwj4uWZ8qkmmbvyrZoEhlHDBri2UEqszXhG3T7H2W3Yj9g/2A57Pfse1QIm7z9fB+nAsss5RlbeTz8H4s0yOPPNJizsW8L/umZROpvBuzA9tcz3oY1tGy+5r1xcr2cWRd9ihi75z1zXmZfYTyec6htDDz+pzLzALLuYvvlm3cLPxm67D2wmsuFhvbMNfNvAd/w5jPb2tR2j94/q1st3wW9juOLVxXHTp0qMVHjx5d+luznJgFfQ6wfdI2ym0SaJExu5t9t67LVmzYPG7bKvAczkV8XloAOW+wHtgHORcRjg+0MfEbi21qsdi4fmOb5DzItfulS5dazLnPsp2yrOzXvC+3KWF90eLFeuH1+b1p3wksp2WB5Ptjm2PM67MeaOdje2L5+S1gRFETQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEibDS+kRM1lWRw1vGmcpxs7BYFhFKwiihMskwZWOUT7IMlHqZDYiySu5OTdkXpVW287ZlnhqWj9leWFaWj/czSSTrzqTXhPelJJfPyfoilNxxJ/Avf/nLLaYU7bnnnmsxJXpmTapI/Cttdw475FeyvRC+W0or2V7MIkQpMSW63K2c7cL6FMvM45RA8jitDWxT7F8PPfRQiy2TFPsH+x3LT+kl5ZPWpihdXCw2jjuWcYpSSd6Dz8Y+YnZK1jWtUnyvw7FjWXmMipSf9UUJN+uUdcLfspxmka1YCKZKr4VrjK2p1+5kGZQqYyHHUcqK2R/5/u2+nJcsayTbF8cTthezSg3nLrOa2DPz3iwf+yavw8wO7Ne0R37nO99p8bPPPttiSt57M6BVMrXZcV7frN22BrA1g9XnlKjYCcdYIay/mP3F1tWW0Yhjp2VM49rKrAO8ptnQeT7HctqguF7jvGfZUbkmXyx87c4+yLmP5/C6XDdz3cDxiDaNSjsYA5+Lz8zycA3BMeTgwYNLzzFrRiW72BzgWELLWiW7rFnwSK8NyqiMCZWxxY6zHrg2tKymdj7HB2aIs7UnMyYtFhvXn3v27Fl6XfZNXotzM79vLcuyZWjmc7IfcR0wHFOWlcfsxr3zLMecShZFW4fzW8XsamT6M2sIIYQQQgghhBDCJ4T8oSaEEEIIIYQQQghhIqy0PvVKAisyzkqmHdtxmVAqRTkh5caUaFEyR8kV5YSW5cDsC5SGmUSLklTLysL70kYw3KWfO/7TwmFyZcuYZZkgeD6f07IH0YLBeqe0jrJ4k+jRWvNbv/VbLWa9Uy5ekbFZNqhKtpU5ZH0iZh+oSKnZjl566aUWU4736KOPtvixxx5rMTOcmLSbGQnYXlgeloGSQLY7tmuzPrE/UuZMGbXt2k65Je/L/ss64diyWGzst2zn7LN8BpOPciyjrcnsH6w7q9/erHvWnlgXlNTTDkepNn9rmUs+jlmfDLOqVKjUQcUOOcZexjmU9gK2X8v6xbmskimG0mn+ljH7o/XZxWJjXfO6Nt+xH/EZLFOS2URZX7wOy/rDH/6wxZax0ST7leNmg2KZbd1jFnRruybznhK9bb7XIsG+VrGkVLLCWTYoZhGlPYS2RK7FeC9aya1PcU3LtmxWQl6f55tNdrHY2NdsbKLdyazBrHfWBbNe0U7D57RvjAq27uEYxKx4+/fvbzGzvbCcls2Oz8u1gW2DULH6TIkrV660mOsFPhPr1b7dbF3fa12srI/sHBsjK/3d2j77Mudcy2Js849lYRtmGea/+c3MPlhZT1jWJK7Rzfpl2whUshOy7gifq5Kd2jCrr80BbLscKyx744ZrfuAZIYQQQgghhBBCCOGWkD/UhBBCCCGEEEIIIUyEldanMVKximS0sju3yYso/aJUkBYcQukWZYMmByZmNaCEinItysnMjsMyUw7G48PdrCllo4SU5eY9KKVmWSm9ZlkpJaSckjEls5Risn4p6eR9+Wwm4+OO96wXyiKtnJWd1i3T2NzsTsSe257PMqDRUnby5MkWs/5ozaH16eGHH24x2+kzzzzT4hMnTiwtG2Gboo3AbEpsaw8++GCL2dbYVyippRSR7ZHSabZrlmFY5+xHlvGBz8bjlGiyrJb5xiwfvRlKKrJgvkvWkUm4LRMHy2xy+YqFbw6sKytT7/VJr0Wm8v6ZTYaWQ57D9893brJwyw7DvsI2bm2Bc8LQgsMxjtYnk2fz3tYmeQ9aMSkL5xjEcn/lK19pMefol19+ucWWCcIszMTanGUb6rXGsa5Mdj5VKmuE3rWrtRG7TiWbltmW2e64tjp79myLuQbmfM12atke2R5tbcX5iuvBypYFQzsC2znnV8tQxjoyWwTXB0eOHGkxM2bRNmbPULG7sJy083NOPHbsWItpg+J74rzPtQHnUG6dcOrUqRbbFgdz45VXXmmx2SjNFkMqY5vRm13R7ls5brY59lPLeMY5l9dh/+Vcx7HCrEvDcnLcYTvkOpBtlRZ4fkvYvMnrcxzgupxjEOuCz8/z+Qy2jQit95cvX156rzFrsspcyTG3QhQ1IYQQQgghhBBCCBMhf6gJIYQQQgghhBBCmAijsz5VJJomV+49TukX7QkmxTLLDmPKzPbs2dNiSkYpfaIsmlJKStcs+wzlinxGPgullLRZDf9N2R+lX5SPUu5G6RfvQQkZr8mYz3P69OkWU1rHMtBaRjkc65ex7YxNaSitNZTVsQwsc0WGWMlANgcqu9BXbFDss7QIUXJLOT8zFRw6dKjF7FM8h++WEk0eZzkpDzS7G8tMqaP1CT4L5cn33Xdfi9knCKWLbHfD/2M75HNSPsqxjBYnxmZV4JjSaxEh1kdYBo5NR48ebTEtiiapZz+1bFuVbA1zlnObhLZiR6pch/RaWCrZvdjfmf2N2b04n7Cf8lksKyLlzLwO+w1jXpNlo+1gaLtgO+TvOccT9tNK1gmz8ln2R2ZMe+CBB1rMjIrsRybxNxtvJQtk5bjZV2zenEPWp15LZaVeK+cTs4uZ5d/KbLblM2fOtJh9hGO2ZWWyTDqWZZVrYMs6yLluWG9ci9vzs+/wWjzO/s8xhWsC9k3aH1544YUWc6wx2yCvT8vGvffe22KuXVnvLA/nTT4764QWdGY+ZfnHZK2aElxzmoWQbd7WCOvKrlixvpHejHJmm2P/sm0tKtuGWHZXfi+zv/P8xWLjGpffdGbBsnWgfZNevXq1xTbfM2af4rqU5eRYxvGB3yH333//0jLw25btrDIPVrIuEh6v2KCiqAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJE+FBZn8xS0fvbivXJdsOm9Np2vaYclLI6SnR5HWaZ4XNRunXp0qUWX7x4scXcSZsSUErG+FvKqSjRooyLEuzFYqMci9Ipk4xTlkmJqmVB4vmUhJ0/f37pccrmWF+Ut9GaQVgGyuf4PijRY0YfPgtjk5CZZNAkxZVdvqeKPV9vtiv2EbZDZm6inJD91DIuUXLIdsGymRXPbH+UOrLtszzsX2wv7DeUVVLayfrk9YdtjWOE2Sv4e97DZL48h/W1KvvUB2ESXtY7ZaW0hB04cKDFtG+wDBxzWWa+P+trVg9zsyL22o5M8l+xtvRmUSS8JudWZibhuEtpP/tXxabEcvJetAcx5jzIdmQWDLbf4ZrE+o7NoaxHzuXE7Ei0LXAuM4n44cOHW8yxyazHluWtsiazcd+Os64rFkW775SojJeV9QLpzaTWO7bxPVi7Y7umLYY2WdqTudZlO+Ua1Wx8nCtZHlr72U7Z9odZn/hvW6Pz3qwvrj95D8asO/6WNl4+P+Fxjn2c437yk5+0mGsR2qPY33kO4bPTgkG7E8cHs2NU2txU4XcDx2NbF1Tmu0rfXFfWxV67E9elnHM5r7Hts6+xvdhzsQ/RVsvrs5+ynS4WG+dgfhtyfGEmSK4J2a+ZWYnX4fUZs0zMzsZn47ch+zvri8/J/shxkH38D/7gD1rMb3t+/1T+ZmHzqWV9qsyb059ZQwghhBBCCCGEED4h5A81IYQQQgghhBBCCBNhpfWJ0qnKTv+MKfNhXLFE2XHKmih3oryI8jlKtCi5MhkvZduWWYbX5Pl8LsqaeN+KZcEsAouFS3J5P5aJdizWnUmzeB3KvSi9pkTN5JeUf/OalPrREkNZGqWhLDNleZShHj9+vMW2Y7/tDm/ln4PVolfiWrF5WT1RYkz5LbMl8B2yv/C+tNSw/7IMvA5jSpjNDmltmTvq816Um7JsbHe0d7HvD2WivIe1H8o1LeMM6479l1JSy3bDMcvGF9Ydr8nnPHLkSIspE+U57KdmD6E818ZTm1fInLM+kUpGtnUdtzmX57N90eL0hS98ocVsp7w++wLfrc0zPM6xn7DfcP7hnEjpOK+zKksD7Rx8flo47N58TsvUwPGR/ZFzP8cX3oty8UceeaTF7Js//OEPW3zlypUWWz+qWOnM/mxZiCrZUOaQfabXAtKb4aXX7lSZlyt2bdY92y9tB7StW5ZGtjsrcyVzkWUZHY7l7COcL7g+qGTYMtsRy8E+yGfg2Mfr0BZhFi2uaS0zJcdZ1imfnWPUM88802L2d7O9VZjDHGrfR2zblayIY7IfkspYURkjrZ2yfTELLtuaZRplXdlWHmwvnMfYD9h+h2tafkvzm452JP6G61Kz+Nn3MPsUxy+OWaxrrqUt+xuvw+P87Re/+MWlZaPl8Ac/+EGL+Q56rfqWXZHjhhFFTQghhBBCCCGEEMJEyB9qQgghhBBCCCGEECbCSuuTSbkqsrEx2Ugq16esi9IhsyZZtgSeQ1sHd8unBOzs2bMtpiWI5aTskeWhFIvloezr5MmTLaY0e7HYuFM/5XGsCz6P2YgozaL8jM9JuTmPmwzRpF+UilEyRykaZahmleF9Ke9jJhq+M96XzC1LRQXrO5TXWQaZikWCMdsL3+crr7yytGwmgeR1aKnZt29fiylRZDnZxikn5LvlfU2Sz/7INk7ZNccW9jmWbfh/7I+sUz4z27DtAG8Sa9olWF+8L2WcfH/sUxxPjh071mKOOzzfJLksP8cNxlaeikx5bhnZKnau3swvZkkhlXpi2967d2+Lf/u3f7vFbBfs44wp1Te7Hi0ItFBVMj+yb7Lt8JqEbX94TbMlsz9yfqQ9kO3T2rllsGPfYbnZ99nX+D5YBtavScRtzDEbKuuIbYvnswy9GVbmhmXD680Sxd/29lnSm53SrPCWAYq23/3797fYso6yfbE8nJfMLr1q6wNbK7Jv2ljJMtGCYVYuxhyPzFbN8/kueZyZLPn9YGMCM0YxIyztTsw4Y5lMe5lD37S+Y9nHOFaR3gynvdkSe+dfloHtjuterifZBs1ia2td1g/r0LI22Vpvsdg419CCRDse+z9/b33Kvgf4nLRrcp3BPmV9n9+VllWL74NraWaC43zNdQ8zINs8Syp/H7E6IR+PL9UQQgghhBBCCCGEjwH5Q00IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmwso9amxvh8rxdaU4Ng8rY3r3LP1XJfXwuXPnlp5DXyl9qPTJ8V709tKjZr57xvQRMyXa8Fo7d+5sMffs4G/oVzQPO/1x3L+Av7V05sT2ZaD38Nq1a0vLY2kd+VumfmM56RE+depUi+nDNJ/vnNMBj0kbOuY42wL3deG7pe919+7dS39LbN8g7l1Evyn3gGLbpxeW7Ytt2fZ6qaSDtP1aFouN3lh6e22fLJbDyk2PMff+YJ81/zbfDeuR44alJ2fZeE3Cfsoxh+2A46b198o+aGQOfdPaku2JZXtG2b5iHP847/A983ym/mQ7+upXv7r0ONvviRMnWkx/NvdoYXnY33lNS9tNbzfbFOdBzpv0o/O3PId9bngt9im2T/ZBPj/nZvZf+uXZ5m1/OvYvXof71bBeuE8Q5ziW+aWXXlpaHutHlX3K7HiFda35NhPbE8uwvWusL1s7570qaVor+9IYfG+8Jts1y8BrcgypXJ/X5JzDOYrPPtw70Paa5DOzv7B+LQU4n8HmXN6XZWW9cGzldfhb9kc+G89nvXCN+t3vfrfF3IuD910Xc5g3bV8a2+vJ0r737ndq65HK/jaE5zPmWpFrTq7FuCcK2zjbFPsa4drT9j5lu2ab5d44nMcXi439hXXH9s/5jmsCzv2sU87ffGbO3ywr59/Kup/X535A3P+Nc+UDDzzQYtt3z+ZH22vP5k22V9sHz4iiJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBOhnJ7bjvemMiQViRplQZZC0ySKJmkjlClRfkU5MyVkvD7LaWnBCK9vacR5zaHUjVIuSrz4e17XZIKU4lHuZrI5St1607SbhJUWMpOTmcSO51O2eujQoRazfVB6ytikjXOQifamDa2kJrT+aO/H0oCyPZqVx2SiBts+n4XWAbYFSiZZBko9adFizLbGfs0+N0zPbWMT5dmUU9r5rCOzM5gNhuXjObw+y80y8Dom07fxge+eNiieXxnfjTn0R1KZa0zObVYLS0HJ9mVpn5944omlZSDPP/98i5l+k1J99inCNsVnZ/syK62lm6aMujKn29y6WGzsz6xTtlVLZWr9l+Xg+oNjH39r0m7K3znGsTw859ixYy1mHdn1x9idKtYCs61Old55046zbhhzHDULjrUdW2eZVdCOm7Wffc1sEZaems9l45WlwmY8tNJyzKLNw9YH9my8LvsCy8pzuFZgvRMeZ7+wccDmR7M7MT23lcGoWN8rx6dExRpvNvHK2rWS2t7uW7FAWt+hxYdjuaXk5pjA7xV+M7EtHzx4sMWW4t626WDMNcYQXpfXsm03+C75Pcw65fjI/sgysY/zmpyj+T4497N+Lc25jcu0KnPs41hPbLuESrsZbqOwjOnPrCGEEEIIIYQQQgifEPKHmhBCCCGEEEIIIYSJsNL6VNltfoxlpCLHoySMO0lTTkYpUy+2Iz1tB2YhYnmY7YI7e1MmaTJv2+V9aH2i9I33ptyLO1fb+fv27Vt6nBIvy9gxRkJJeRjjq1evtth2Oae8zySGlpWH9XD8+PEWm0R8blYLYlk+zDpjv61IQ00CeePGjRabtJ/vx+7F61M+SUkjY8oJKY1km2Ibp4WEWNY2jjlDKlYuk7pWdonn87OOOHbs379/abkpMeV9GfN8XpPjoMlHLZudyfEr9Fosp4Q9q1lGzFZgYzDnGmZwYKYgZvTasWNHi/k+T58+3eJnn322xRwvbW5iGzfZPtss2zLbO+XS1tYsgwwtTSwnbcuLxcY64rjDe5j9x7LAcEzh9Ye2q/ex/n706NGlx80uwnf/+c9/vsWsR46/Jsk2S4DVQ8VSMeesT73WBrZJxpwjrM4sE5HJ+W39WclGYvMpYbvm+Swbn5HwWfjstFFwHKOVY7HYOCeajZPzNOMDBw602CwxhM9Tyd5CLPsbxwH2fWZke/rpp1tMW6lZ3XqpzIlzWNNaJhzrC7ZuNHtKrz2qktnNvj9smwpmr2VbZnnMTsRxnf2j8g3HPm59ZZgdl/Ma+y3rkRkVOUaYHZTnsB4rW4dU/tbA+Zp1Z2tRvifbssBsXza22ncun9fWFUYUNSGEEEIIIYQQQggTIX+oCSGEEEIIIYQQQpgIo7M+GRWpbOX6lCNREkVJESXQZiOo3JdyJEodKVminIxSc1o8KO+iZIxSMsqpKhkYhv82uxOllaw7Su4oN6VEnuWmXI3XqWB1XbHQWKYbk8Xu2bOnxZTe8t1QLn7x4sUW833MQRpKerNXmIy91+5kMmHKJik5ZMw2ZdkiaHdje2R/MSxjGq0QbBfDDBTvQ+kpoYR1KBPlGMS2yjGLfdDk05SM8vlZ17y+yWfNMmqZXFges7RxbDl//nyLaaGhDaq3zRlmS5gqlTKa7NcycbHt0FrL92zZCWhl4js8ceLE0nPYvmwcqGS7MAsJ51mOwZadiVAmzLGF/YPPslhsbIdmXzS7BMvK8rFfWKYna+csH+dua+d8r2wftE1RFs8MXrwX68jWHL32qLllfSJW35ad0No5x1eurXicbcfs3Vxzsp2b3al3vrb1lNkaOC7xPbO9m12P8Sp7BZ+Ndcd+yjmU7X/v3r1Lj1vmG+tHZmHg2GRWavavkydPtvgHP/hBiy9fvtxiy4RojMnuNDfLcGWeN9tOJVOb2dosexRhG+Q6kN8ibLMsJ+/LtmPrTLYpjuvM8mc2I8vuxOPs77aVx/A3PM/mO/ZfzpscC2wuZl8zu27FDm+xWY14X9qguF2AfUdZe7I2ZNlUK8xrZg0hhBBCCCGEEEL4GJM/1IQQQgghhBBCCCFMhLVan3rlSCYpMvm/WQEqckIrg8mpLCsHM2gwy4rJ0WnloXyykmWI910sXE5KKRoltoT3O3fuXItp2SKUsVEGxucxm9mYDC+UhA0zBLxPJTOKyettd24rz1SpyJ4rWTh6pbJ2PuuS741WGNqOKB81O47J83k+37nttM/2yz7Ec8w2ZBL3YduxfkdrHscISkb5W9aXvQ+zJfJ8jpWWMYplMCk8M7IxYxotG7Rh9mayGCP/niqV7BKEbYTj1gMPPLD0t4888kiLaWVif6dk+o/+6I9azLGfY3lvhi72QfYjs36wv3A8pn3JsjFQXk4LGNus2YmG92YdWRY69i/2HZabMeffobXjfdgm2MfPnj27tGw83+wu7OPHjh1bWgbaoNivK9mg1mWXnRKWEcay9tn5ZkeyLJ9m02OmGFobWK9s52YhtHWszadmIWF7Zz9gzGuy32zbtm3pc5k9c7HYOEZYdiezJlmmH/tO4DlmqWDf5Pvg+MJ+xDnxhz/8YYs5BvV+n1SO2zlzszsRtj1ahCxDV8XuZNmjKmt/tiPOO/xm4tqS6zjOv7TacM5hW+a9uJ5iWzP7Hecflsf6Pts4YZ8dwnfDfsFtBVjXXAewvrheN4tpxUJb+ZsC+zjfAevXvtX5nli/Zh826yzfgdnaK99pUdSEEEIIIYQQQgghTIT8oSaEEEIIIYQQQghhIpStTxWpkcl/TNJrxwmllZQOUXZUkRtX7mtSSkq7Dx8+3GKTdlOO/sorrywtZ0XuRLnZYrFRTkb5GiVxJg2lhI6Zj/bt29diZoCihI7Pb9Kyihy6N0sB65TyXztOqTGf0bIUVNroVOm1jIx5P9Z3COuVsky2O8tCxn5ESSDbGqWhZk1izIwQfP9sy5ZFzqxFPM52t1hstAixXdHuRDk3f287+5OKJN3GZdYdxyCzGXL8ojWDmZ4oE+0df4nJms2GOre+Wcl6x7qn5JZSao79ZmGgpPfVV19tMd8n5w0bvw2z3bBtMpMD3y2fhWPz9evXW8w5h3N9JbMVr8N4sdg4J/K6Bw4caDHHBb4D9lPWL8e4it3PMqxduHBh6fNwTOT5nOtt7KOVjmOLWa+tnL3z9dyyPhHL7lSx4XNcZzv63Oc+12JaBGiD4nuzbFCMLVuojZGM2ab4jHY++zWfl/B5+SysE5uLhvczW4RlPCRWVpbJbGzsX/wt78Ux9KWXXmrxyy+/vPScSvY30js/9h6fw7xpZbdzSKVebX1ELMMvt7zgca6D2MfNws62TKugbf/AuYXH2b64ZmTZ+FvOv7Tx0RLE7LiLxca5lvbjM2fOtJjPzPLx/HvvvbfF7Kc8h+Vgn63Qa4Ni3XGtwPnRslDZNStbvZiFs5IBar4zawghhBBCCCGEEMLHjPyhJoQQQgghhBBCCGEirLQ+VaTrvRmgSCXrEyWaPMd2wO7NdEN4X8qwDx061GLagyzz0okTJ1pMOaTJjU3+SVvSYrFx92zbMdveh+36TcsGd/2mpJUSvcuXLy+9TmU3+zHZoChPpdSPsjHK51g2s3tUbAlTpVfKam3EdignJq219mx2AfYFWp9MXs7r8Bw+O98n+z7HDcqwKQ3lvUxeblJw9v3FYqNU0jLIsO5Mqm0ZRHhNns964TUt24VJ6ilhNWk3JfvWj8wK2zt/kLlllrHnZpu0LAecRzjH8X2yfVp2O0p6KXvuzTpCWE72KcuCwfNZZlqf2KY4/1gWRdYDn53jDNvyYuFZVzi/8n62hmCf51xj4yCxNsGy0abE8ZH34vl8Zpafa4j777+/xaxrto/e8tvxytrro8bmEbaxiu2OY6dJ+Nm+vve977WY1jr2F75DjvdWr2wL9h7YR9i+OFZwfcc+y3qorOHNZsTyDJ+FcxnrwrJesQ9W1gE2N/GaHJtYLxxHnnvuuRZzfmSf4ruv2HXHzI+Wqa03U++UsLUDqaw1rI8TWx+xPfIbiH2B7YLfT5y72R7NAsxzOM5wHWfrBPYDrhM4JvAcmx9t64jFYuN8zDGO46CNL7TMP/jggy22LL28F7ctYF8blu99bMyycwj7rG2vYeMpqWyzQuzb1oiiJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBNhpfXJqOxqbBYJnl+R3JrcizIo2wm+1x5CCRylx4wpS+N9Kec+d+5ci012btYwyj+ZQWMI5W4mx7L7UWpFOwolZ5SicTdwPiefnxIyK8MY2FZMPmjS9Eq9zzljRcXKV7EiVrLVVPqySS7ZdiiB3L9/f4v5HtgXLCsTd7CnbJWSQ96LsnPWD6/Jts9nN1ns8B6WQcSyX1BWatnsLNOT2aD4DkzeyixOP/7xj1v84osvtphZDSqyz175cq+taQ79lPJblpfvefv27S2mdcjsbqwnjn/MGnTy5Mml55BKdgKzDljWNmaksjmL7Y59lnJxtl/WA/uEZfzjdYYSabPNMmZ9VdpwJVNDZZ3EZ6Dtgu/VssVxDcDxh+OmZeup2C4qzC2zTGWOY/s3GwLbP9dNbEc855FHHll6TdqOeJyWGvYLrtfMAknY9tm+GNt8xXbHsYjti+dY1iazgQx/Y23VxgIeZ8z+zz7Cd8xzaOdnRptnnnmmxdzOgFn6KhnfSG92GBuXK3Ynu+9UscyeloWN79y2grDY7P9cW7FtWnYnszvZHGqWON7L1g8299nWHzyf5aftlefzWRYLt0SaDZvHaYPivbklBdcNfMdco3NMrGQttO0PjMp8YONjb0YxHrf2akx/1RtCCCGEEEIIIYTwCSF/qAkhhBBCCCGEEEKYCJuW9clkerbbth03qRElh5VsSiZl4nHaGWjHoPWHsjTKzygno6y0UjZK/nbs2NFiZphaLPrlUnY/lollpeyTO3VT3rpnz54WUzrO99GbeavXlkM5r+3O3ZuBYm6ZZdgWSEWO12s9Mbkm37n1cVrQKBmlpJHXpPTUdt036SXvxWtaNgqTqprUkb/lNReLjVYWnmf3Y1n5zOxrtJGwnfPeNr6Y/Yy76D/11FMtPnXqVIvZryvj15iMTpV5oveaHzU2FtK2QPsPLWiUA3MuYDuiRJk2W/avik3NsHdLSfKBAwdazLmS7ZcyZ1p5OOewrswmTFsPy0P7Ats4x4TFYmMb5m9oYbDxlIzJ9FSxKvD6fJ6hJH3Zcdapje9cr/S2j8rxOfTN3gyZlmWIfZNrJa5NnnjiiRZz/qLVkTbb559/vsW0Pn37299uMd8hz+Fz2bzG82n34TU557Iv8zjnK9qgWJ+WfYV9eVhW9kHL1Mn+y/5CWwTHQWYt5PjLczg2sd+xT1l5SGVO7LUgVdZqvbapqcLvoEq2pt4xySx0tu4wixvH3cr6iO3aMspx3ifss3wWzpX89uTYwnLyOjzOOmd7Xyw2PjPbv2WDs7mcmbGYCW+YOfV9mP2Oz8O+b98e1oYsE5h9F9vc0JvV1GA749hlRFETQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEibDS+lTJlkNJEaVDdtykRhW5mu3mbnK4irTV7Ay0MhDKxiilvHTpUospybTdv83ucfDgwaXHFwvP3sI6sme245SoUQ5K+Rll7sxwQDkd5eaUyfVKLivyaWuXhsnSzD42Bwl3JdPTGLsT+wglmmxrdpxY/+Wu8JSRs+9YJhMrJ9sdJZDsR1Yey3Zhsthh3+R4YXXKMYvjCJ+Z5WbMZ7PsTuyDHI+YxcnsTqyLipzXJKC99qVee+ocqDw32xvnHcsOQyhRZttZlV3lw5aT7ZxZIGiBZRukDJsxbbVsy5VsO3wuthezIwyfi//mb9jmeZzvozJHWPuv2FNtfcCxgvO+WYB711VW/l77xpyzPrHu2X7Y5tkWaHH6vd/7vRbTEsh5gPPa3/27f7fFNk8xZgY3xmyzPJ9txMZgPqNlZanI8IllmbEMUMM+YeXjXEY7EteoPM57P/vssy0+fvx4i/nMljWnkrmHWGZNMiaLU29fq2Q2mio2f9ma1uxLxL5V7XzL4mVzFqlk++F4Yhmm2C7sm5eYdZr3NdvrqjbO8yx7GuuLMccU2rOPHDnSYm7twXfDcZNrDloUbU3A5zH7Jddetua0ftS7rUfvWtCIoiaEEEIIIYQQQghhIuQPNSGEEEIIIYQQQggToZz1yaTuvTJ2k6vZjtzMQGLZZCoSOMJzKI9idifaLihNYnkowzQrFjFZ8T333NNiWo6G9gpajcyq0PsObJdz3otyOtpCaIPib5nRpGL9MjZDSv1xsVqYjLPST3uzOxkVyyHbGrPAWNY2jgOUPVIOSfmojRssG9ssqWSmMFn0UK5IqwbHJj4npdeWycUyTpnEnNZF1i/l8t/73vda/Oqrr7aYY4i1iV5bRG+2pt4+OIe+WYFtmPPOF7/4xRZTbk27E9sO27DVn0l37RyOLcw8duzYsRYzCwzbOOcBWu54vDIWsQycB3kvsy4N26CNR7wWbSRmx6B8vDf7SG+2oUoGit6+cCsz0UwVjqm2bmJ7o62J6zRmE2K72Lp1a4vZr80uwP7FuYa2VEr+LRMNsffD5+V61bKUsu2btdkyt7I/keH6nP+28Y4WyrNnz7aYtibW0YkTJ1psY0TlG8bWWJUxtNcGNaZP9WbunCrWxirWExtriWUTJmanNfuerb3NBmhrTq7j2PZ53La44Dcp+wfnaLMzmw1osdg4RrBeLBOzZWOlNYt2Ra5XCZ+Za3HLdlrJNmfZW21tsBnzmrWV0m/XUoIQQgghhBBCCCGEMJr8oSaEEEIIIYQQQghhIpSzPtlxkwSa5JKYfIkSL8qgerP9VM6nVMp24aZEy3adNxmqlcdsBLRQDO0RlJMxruxOX5HD0rLB56TthOXjrt2U31FuymtWyrMZWSQqmTvmlr2CmOyzV25v7YJ9weSpJhk2qSrl4rQE0S6xc+fOpdc8dOhQi9nvLIMb7YQsj0lYLdvOqqw6zEpl8nrK3Fk+PoNJNzkGUZJKKTwlpk8//XSLKQXnmFKRERsVq9QY+Xfl+nPD+ibbFWX+nPv4njk2V+xrFRuU9V/2QVpgeZ2rV6+2+JVXXmnxxYsXW2zzo82DZolg3zJr4LAe+H8sByXm7F8cL2jhsD5u451lWhmT4WXMPFWpayunXadyfErwHZodgG2eWThpg6LF6Tvf+c7S63NNRAm/WaV4X2ZHee6551rMbH5sm1xnmQXD2iPbsq3XzDbCMpgFYdXWBLbW5xjHcYRjzenTp1vMcYdjpc1xNhbzOXu3frAxyMbZinW8MlaQ3vOnBPuFZR21sbaS7crGM7bBSiZBK0Ml+x/HB9t2gmsA21KDz8g+y3mMfZP9kWWgPWrYRlZlUnwf28KA36fsy1yjPvbYY0uvYxmwjMq757OZ3cuyqZKKtd/GCltjcW4woqgJIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRFhpfarIAM06UZH1mYTMdmKuWIcMkyYxpvzV5GTMaERZ2hipPqVelGMPd86nbYHlq1ifKuUwWTilp8ygQ4k4pbqsF8pZK/awXll15bjFJp00q96UMClyxe7E565kMLDd5nmcdiHKRys7rLN/8ZrPPPNMi2m7oESc97WMScTeP69j7YXlH15/1e75y+5BWEd8fsumQ2ksszhRIv/SSy+1eLPtTuuyQX3csz6Z/Jb2B47rtMeZxcmsiJV+be+c97377rtbzPmRFowf//jHLbZMiBXs/bOuLOsTj69qI/w/Su0rNgeOa0ZF6mwy+krWiUqfqtgiKvPjmDXWVDELEp/vypUrLWZ73r9/f4srtnfOWcQsGFzv0ZJAG4FlO+21wpht0Ky31r9YHpvHqu2I17Jsi3xnnNesb9r9zBJTscqYRbzyXVQZfzfj+Bz6pmUTqmQNJr3jFrH1qo3l9p7ZXzhvso/zHLZfrocr2QVZTs657CvMWMc5fZXtxsYaUpk3eR3aQfktzXrhvcy+ZZZLW4fb9wCxuq7YhIm1FcYsf6VsUdSEEEIIIYQQQgghTIT8oSaEEEIIIYQQQghhIqzU3FRsTZb1qSIDNBlbRaZXkSBV5PMmoaJMiTJMyq1Ndm5lM2kVr0MZlF1/sfDdtsdkkTBZ+Ouvv95i2i5MNkepX2UnbaNXbt0r9Rwjb/uoMZlhJcNVZfd7yhVp2bGd4E3qXOn7LBv717lz51r88ssvt/j+++9vsfVfYlJVs0BSYmly7lXZK6xvMqbUlbHVhWXW+f73v9/i48ePt7iS+aKXMVmcjDHnzwHrm5QfWxYGtiP2QR63mNgYb9Jg9ilmKmN7pCXkzTffbDHnjd4sJSwD7Ve0OFh/qkr72RcoMafdhfemdZfPwDGiknWCmLy+ksWqd36vzA1jskfNObOMreW43mMbox2cz8qx9nd+53eWXp9rImYron3c7PaV7DPEMsrZVga0s3Oc4XGOS6wTPhcZ2vatzDzP5mmOaxxruC5l3fX2BTvem9229/tnzFq9t5zWVqaEZcqqWPxIJRtU75YJFUuUfcexjXNu4fm0LFmmJ8PqytbkBsfGxWLjOMg5mH3Q1rqE9cU1PbO9chxkdj1+Y3JssnmZVN7ZmHmzYj2298eymUWWTP+LNIQQQgghhBBCCOETQv5QE0IIIYQQQgghhDARVlqfKvJAkx+b5MekwWNsUEZFxkbpJqWedl+zS5i00GR4LA8lXZaJZlg+SsJM0mpUZGB8H5SY2s7bPL7KIvJBZeg9Z13Zaiptd0pY1qdKXfL9mKyaMeWatOlQ9lh5/9bHTeZP+feJEydafPbs2RZTNmh9s2L1YtnM1kCGMk/WBZ+HxyvSbj4zpfbnz59v8QsvvNBiZn3i+RVJZ2VstT5SOb9Cbx+fQ98k1u8oAbasaseOHWsx2w6tUjbvVMYzmxPZpzg/Um5MKx7bbG+WRsZmpTU7IKXTlGZXYb+j7JvS7ookfUxGs82wSIxhzPphDn2zYle3MY/v5/Llyy22uY+2QR7nfGp22AsXLrSY2VF4jq0tbV1qWecsy4rZkipZXKvrebM+8t48h2NQxWbZm+msYmsilb6w2VmfKvaeOWR9Ypux9mbjbuWdmzWHx8dkS6zY42jRY1/u3VKj8j57LbbDNm5ZqYYWqWXls7Ky/546dWrpdSzzHL+HuS5hOSvzbO+atnf9XLHtmYXbiKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE2F01qfKcZNBVnbzrhyvSI8NSiwpbaZEjVIvk55W7mu7sFPqRnnXUNZOuVclQ0SvfNrKymem3JwSXmYxYdlYZtZjRSbda7XYDHvUVKlkGzCbGtvbvffe22K2N17zn//zf97i//f//l+L/+t//a8t5k7t7Ecm8TOrkLVl2nooBefO9Ow7zFJh1kKT2hKrz6Fc0Xb/t/txfHnjjTdazKxqtJc8++yzLWZ2p5s3by69b2VH+jHjZm9/GTMWzY1K1ji2f75z2ltpnbjnnntafOjQoRZTMs1sMhyzidU9+wszMLDNX7p0qcVsdybdtXG3115AKxL7PjM10fo0bEeVzIac17Zv395iPhvntco7Ngm+WURsTu+1F60r81olS1Tl/Clhtl+280omI/ZH8uSTT7aY6yNen++ZbYHzJsd+y664yrbwPmZ5sKxPfHbLfkg4t/Je/K2tT4bXtXtwrKxYREhvlqDeTDGV+/ZmcapkLeo9PoeMbJZByMbRSjbD3sxdFpOKbY59jbCPs/2yXduzE1sn2zhma/tV8Nn4bmhfsvHI4HNy3cP1x86dO5dev3d9aO3GbJ+kd2zt7YN8Tzt27Fj+AGD6M2sIIYQQQgghhBDCJ4T8oSaEEEIIIYQQQghhIqy0PlXke2PsUSYRqsgPjYr9yiSWVk7KOy0jjD0XpVu2kzmvTzk25WCLxUa7BC0fJj9bl9yRdcf7MjMBpee2a/dmyC/HtAljDhaMSkYVSh8tc9MDDzzQ4v3797eYGWf+5t/8my1mZpb/+T//Z4vtnVsGpMrO62x3lFteu3atxewTlmHHskPYmGBjGuttKG1lOd5+++0Ws7/w3rRtUALK7E60srz44ostpk3DpKGVnerHWJ8q9MqUx2R/mxKVemVbYns7efLk0vOPHDnSYtoMv/CFLyy9l9mC2B+ZPWrPnj0t3rp1a4vZlnlNy4BUkZFbhg7aZwn7EDMQsh+wPle1X1sTcHyhPNvsH5U1RK/s3q5j9VXJPEXG2KLHWNCnBMfpSqY+W8vRisvf/p//839azOxpnDeZ7YRz2TPPPNPiP/7jP24xrX9spyxnJRONZVjj2oDt2jLUmK3J5lYybCNmsbe1gtnn19W2ey2Hvdmdeu1LpPKO54yNr7Y2s++piuW2N7tT5fvUthew9ss2buvE3qy5PN8yp9n2I0Ns2wIrq317mCWM6xJek2Mr65G/Zcx6JJW/BRiVPjUmsybLb99OG8rzgWeEEEIIIYQQQgghhFtC/lATQgghhBBCCCGEMBHKWZ9IRVZfye60rt3N7Tp2X16HsinKqikvpwS0UicVmxhlUJZVirLzxWKjJJsS2N7sU0avjYjyd5abknrbbdvKWXmWMfLsikRyDlLSSjunnYExLWu7du1qMbM/MPMJ2z+liHznJlFk2SjxM3m5ZWDgccokmTGpIlVme6E8k2UwSa2dM/w/lo/Pz7Ky/9L6RHvJ2bNnl/62YncyNtvuZNecg2VpXfRmjrC2fe7cuRabjHnfvn1LY7NysO2wjx84cKDF7NfMPkMLxhi7k0mhacHgWMR5mf2gKhc3SwbLweua5Nuy9dg41bs2Mol/b6ZMo3d8GJPpZqrY2FnJ2MI2SVsTf8s12wsvvNBiZvZg9rTnn3++xbQ9cn6wNlvZgsCyPvFZuDbYtm1biznm8L6cxxlX1gDVtRWvZRY1UlnLjcma1LtGqXyf9H7/9I65q7JtTRFbT9oajPRmfB3zHqwMtta17EnWZy07WyVDEe9r2U753cY18NBCZNsH8Hug0rYJf8vvWWZzZLZLUrHGmSXZbEcVLHNr71qav+XYmqxPIYQQQgghhBBCCDMif6gJIYQQQgghhBBCmAjlrE+2e7HJGiuSqIpssLIDdiV7lO3y/c4777SYsjFKsSgJs+wSJney5zKpGyXYtBAtFhvlsBXrUy+9Ui7bsd8yXRF7Z3bfXmkrqewCXzk+JWwHe8oGmRHm8OHDLWamicuXL7eYWZ+YcYz1x/Mpn967d2+L2V9oszLJZEWiyzLwOpRusjwmh6y0TR43qeoQlpvjiEnYeV3GPJ9jk8lke7OxVMaszaA3K82c6ZVhE75nSoOZAYzthRbdRx55pMWPP/54i48fP95i2qk4JhDanS5evNjiypxTsUWzf7HPHjx4cOk5lF2zT3DOWTVmm3Te1gF8flpcCGXllfnFsgf12jXH0GtfGpO5ZqpUMg9y/KZEnX2NdiG2Q9pY//AP/7DFnKdoj2IGKFoSbOy3NlJZS/O5mNmNbdysTCzPzp07P/Be1axP9n82Z3MsMBvCmLViZa1bOd5bHtKbwavXXjtVKhlyKhnzKt+Jdh2r44odjX3H+qzZcO3ZrfzErPqENmGyak1itknGZgnjOZUMWBwT+Q3MzFC8jvX9yreEvT8r8xgbo33n8/uKawkjipoQQgghhBBCCCGEiZA/1IQQQgghhBBCCCFMhJXWp0r2os04Xsmc0CtLM9mjya9oO+rdeb03yxUlrywDZV+LhWeNGSM5HpPloSITrUjCxmSjqEjwKQ3kdShvmxtmP6S8bvfu3S2mVJttjFamGzdutJgyTmZ7obXK5OJ8z5SLsz1bP6ocp4yTZbNd1a1vWpYKkyvy/GG7M9k3Yz4/bS18H5TOVzI92bhmctA52BPmTm/2OXtvfOdvvfVWi81+y8xNtDUxGxThHEeLEzOPVfos6T1OSwgtjZwT2Vf47GRVVhOrX5aJ92NMiTLHPtadSfN7rdq96wwypl+PserNbTypWFgsYxH74CuvvNJivjeew75DeTuzm1lf7h2zK+dwjma73rJly9KY4w8tinwWyyxTzfpUsVRUth7obavWB3u/N8ZkIK18n1S+cyrr9jlkfbLshHw+ruusnnq37KhYZ2zLDnsPZkcyO5FlNrO1q/Uv/pbjD6/PcYBj0bCN8N78jWVLrcxfZmOktZLftpVsW/a+LUurXdOsW72WYdaVta277767xbShGtPvvSGEEEIIIYQQQgifEPKHmhBCCCGEEEIIIYSJUM76ZMcrNqWKJIryLdvBviJvJHYdy75i8isrp+1sb7JVk4DRvkGZ91Dqxf+jTLxXfty7430vFUtUrw2gYpuqPFcl680c5Nxm1aF0mRI8ZnqipYbyw3vuuafFtONQ6vj888+3mG2YdgnelzYlk7MSy/bAMvD9UJJd2fGe/Yb2BZbZZJKs8+HO/DZ+saz2DJRzUzrPcve21Tm04Q/DHJ6rVw5vtjn+lvMUJc0nTpxo8c2bN1vM+Yu2R16Tffbs2bMtZhu0LDk2HpPerAi8vlmALXsFGZbHrIysa9bXq6++uvR+tIv0rgOsLuz83mxuvW2ObIb1Y6pUbHB853wnnDs4ZnMOom3ObHpmoTWZPLH3aRZdzmu33XZbi3ft2tVizks2/vA4z7cymE1h+Fz8t2VC5BrF5sTesal3jVr5zrHrV7L1kDFZnzY7c9xmYhZSs5JUxshKnfXWd8XWZ/26knGoMm9UrFucx3k+x4FVVl2WiZnh+Ht7N7SosS/b2piwv9NayfnXxuiK/cyojFmVzFY8TqzNVbbgiKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE+FDZX2ynatNBlaRBo/ZDZ1UZIYmmbSMMKTXflWRPJvdaSgNs92910Xvs42hdydtO165zrqsdFOCkkBCeSDlzZRn0x7FOjh37lyL9+7d22KTc7M9Uq5o/d2kvmzzZo/iOGOWJctGQVtDxdZkkler81VlJZa9gpZL26l/jL1xDrLnKnPrsxW5tWWUqMyb7HfMGGYZDxizDbKPVGwXlYxGFYsPn5FZ5zheUebMPmFlq5aJvzFr2WuvvdZijjWs94qdZkzGStI7b/ZaBcYcn0NmmUrmEGZCYf1x/GcGsO3bt7eYtmK2I1u7mT2qYs2ojCGMaW3etm1bi/m8fEb2iR07drTYLMA2Pqxqg7aGsAyJ1i8qa4tK1qdKpp9ea6GVp8KYrE8V+9WUsO8vWg7NPl6xf5GKLZeMsfdaO6qUzWySNncx5rxpVhvOs+xni8XGcYHXqmxTUqkLxvzGsLUIn62yFum1O43pL7YVhWXxtExmWs6u0oQQQgghhBBCCCGETSN/qAkhhBBCCCGEEEKYCGvN+kR6JcAVqVRveXgdszlYmW3X7gqVLEPrutdYKtL8MVkkxpSHmNSzIhevZFUhc7CK8FkpX7z77rtbTJkoJda0DlFifOHChRb/wR/8QYtpqfjhD3/YYkoRKV2kRcAk35WsTzyfz8Ly33nnnUvPsd3vmRnn4YcfbjHrh1JEy/i2isr4ZdJVynztfpX2OYc2/GGYw3ONsYz0ZgqyzFBs5xcvXmwxsze8/vrrLbYsClb+ity4YuNimSl5ph2Q/Zf99MPI/Dn3m7WQdUHrE8+nTNxs0pWMIL023nVlfapYayrMLetTxbJH2F74fBynOT+yfTLunUfs/ZjVyPoa1wb79u1r8eHDh1tMGxevw3mQxy1Dja2rybCv8N/sd1xPWDY7a2+VsamS9cmOV+xIYzKW9vbZ3jX8VLFsYlxP9o5Vve+51/JSaS/2/q0vV96VfTNyjrLMhPwt+9bQ2k8rIvu2rZV5b8uIZOMUx9NLly4tLZPZRCt1bXbN3uxvpPL3BT47n4XfMCWr3geeEUIIIYQQQgghhBBuCflDTQghhBBCCCGEEMJE+GCt4hJ65YFjzq/stm4yQ0qrem1WlV3r12WP2izWlf2hUhdj7FG92Z16d3i3zGRzxiwAlOoz5jvkcdoKrly50mLWK2WJlPJZ3+E5ttO5vTezC3AcoJyb51AiS0km3//WrVtbzF3tbdf2SjanxWLjO7Axi9JuWido8+A5vZmb5iBvDv8flXmqImO3mH2W8ubbbrtt6flkjKze+riN8RzHrl27tvQcPgsxmfrwuSq2I8tGYf3R7E6kN5Nlrz1qzLzcu5YilXYzZypWFcs+xrrhHFSxLto7sXq133IepMSec59lTrTsh7SlmAWyso4btin+26yFtGiaXXFVttRlx81i3ZsNqJJltnJ+xfpS6ZuV8Xqq2HYQjC0rGentO71Z7HrfoV3fbJXEvlesT9ka1cpGexO/C4b/x3Kwb9q62cpk63u+S86t9vy8psW8vo3FvX8XIJX1tm2jwHUYt28woqgJIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRFhpfTI5YUWSXzneuwt7RVbdmymlYp2x6/RaEyoyvA9DRdY4RirZK7msyP565dNjJPiUS5oVp1K2KUE7AO0D3DH9qaeearFlQTLZ4Jtvvtli20ne6t4ya1Xaqe2Ez2w1e/bsafGRI0daTAk3sSwYlCWS3rY/PM92mKfViu2wkumpct8wDdZlPalkWaq8/95sRb2Z9yy27G92fcs8ZX2iNzvkYuG2xEr9sp+a7bnXwtBrFTPLRq81bozdmFQk4lPC1rQ271SsPRUb3Jh+bfetZGejZYnrIB7ns5tlwTKc9Nrch32ZdcdMT+xrPG5ri14b1LqyO5FeC82YjG923OI59E3OF5ZBiIzpR2O2f7DzbQzpLc+YrE+V98x78duBFvzh/xH2TXsee2d8xzbH891b/ZLeMatSd2OyWdu9aDe1zHlGFDUhhBBCCCGEEEIIEyF/qAkhhBBCCCGEEEKYCCutTyYzHLNrckXqO8YeNea+FVvHGLlirw1k1bV67zFG9tybjWJdsurebAdGZed0ssriMhUoS+Tz3bhxo8WnTp1qMaV2JivlLu8mzavYnca0Ix6nPPvuu+9u8eHDh1vM3dMJr/PTn/60xdu3b1963942uOo8lps7ulNayvNZPkrBY2v6/2du2Sts3uzNUkIq8nb+ltaBN954o8WWOW5MBr9K1hTLEMdxaUxGo1VS4jFrgnXV0ZgsTr3zb6VslfJU6ncO82avFWbMms3u1VtPvWtOzj+33357izn33XPPPS2mJZfWBMu62Fv+VWXm3Pf222+3mPZsjl+991uXhX9dWZ8q9yLryrA2h75p84JZW9a1vYb9tnf9VbH/k8pa2jIgmU2n8k1j1xlmV6xYciqWUbM78TjvbZYle/dWBrNB2XVYL2aRrtSJvRuzkpW2LvnAM0IIIYQQQgghhBDCLSF/qAkhhBBCCCGEEEKYCCutT2RM9ore61TOr0iY11X+3uNjZJUfxu4wxsLRa/9Zl2TUpGWVsvW2IWYTqdig5mA5Mfki7UuXL19eeg7llDzfZJY8bvYKMiZzgkmsmaGJ2SsY8/qUGY7JfrAu6fFisfF5LMPFmPvNod1+Ehhj/dyMrCOEYyH7GuXAvVnbKpl0zAZlVLIqVa20FWuWWT175+YxtuIxWZY2Y4yrSOrnNm+OsaqsKxOTlafXNmjPQisTrcFbtmxpMedTlo1zPc8Z827522GGLK4/bB3Te49e2+SYta6d37s+WNe2AL3tckpYX7C+2fvdU80M+EFlq/RNlpnz6Zjx27Ik9WY/s+OrMo5WsluZvcjW9FYXvdZayzDF42al43eCfZNaW7F3THhf2lBpT6X904iiJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBOhbH0ybqWNqHK+ya17bVOVbFa2S/QY2eoqKfG66miMPLtyfiXbwZhMHr075PfuwP5x2SGfmRNMHtgr4yRjMtpU+ppZmVgeKxuPM/NSbxu0cq6yVxDej3Lud999t8W2G/xYS+Q6fjsFTC47ByrtojdrIRmTBYm2QUpxmRnKxs4xGYoqz1XJ7kFWzT+9ddTb78zy0GtPGLOGqMjujcq7qWQNmVvfNCrrHbMCVN5VxS5m16nYHNh3aINiH2ffJ5b1aUzGq1X2CmZ64zrm5s2bLaZdk1T6Xa/VzewPFXsnj9v4Ncb+WhmjKv13qtj4x3Zh61j7bcXSaFgZKtYcO9/WMrYGpP3QymZjfKUPrlpbsa7ZljhGVLLB2Xxkz2ZjH6lscWK2SmtDlTJU6pHwGS0LVWmN8YFnhBBCCCGEEEIIIYRbQv5QE0IIIYQQQgghhDARPlTWp8rxXtlz5TqkV+5VofLbSvl7n7cqke59B5V7VM7fjAxYFYnwGEtbRbpmzM0qYnI87ibeK8M3OW2lbtaVeY3Xefvtt1tMSxefkdJCZlJiW+Bx1hVjloHyScuUMbwHy0RpNy0llLqatLvybirnV367GfRmhOnNQDdVKu+ht4+MGfMI2yPtD7QHsi+w31k5K2O59a9eO/CHsVNRhl75TcUu1JtRo1JHVs5K+ckY+/OYjGJzwNokGWYmeh8bwyxjmLWpXls9MVuHlc2sCSbDH/Oebc3AuW44njD7Ie3APF6xnPVmy6tsW7Cu7RIq/dHiiiXbsmrNIdMTueuuu1rMdZpl9rT3xnNsvTcmm1ZlPcIy0CrEMtjxu+++u8V8ds7d169fbzHXpVyvspz8Lc9fZQnqzeJEbEzkc3JcYPkIzzfLUiWTb+/7q2TAZXn4LDzO+DOf+UyL+V6T9SmEEEIIIYQQQghhRuQPNSGEEEIIIYQQQggTYaX1yaR8m2H/qVCRBFbO7y3/FI6vegbSK7cekwVjXdfplSFWylPJWGFyU0oSpwoldSbVJpX2UjlO1tW2KaXkOSYV5HFKPWnf4Du0ndcrFgeyapd3k3fabvkVufxHZTdY17u388ccn1vftHdr2Sgq8yypvAe7L8cNy1hi40xvJsDe9tKbAam6ruidN8dkV6zYfu09rWt+r2RA6S2n1e9tt9229PiU4FxgmVZIr6Wmkn2rYpGxvlbJ2GKZm+zZzVo3Zn1esf4Mz+Ncbmsas4tYW7VxxLIyVbI42XV4nOXks9j4W7FA9tpcCbN/TZXbb7+9xWZpZ91YPZmNhpi11OqV75PHK5mLiGVaZHloAWOdsH8wZl1Z/VSyxa16BusLNn9bndq4Y5Yia9uVb57eNaSNs4zZtiprF7OM9doSo6gJIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRFhpfaJczjKTVKTBJifslTFvhiR5zHXGWHN6j48ta6/taF12p3Vlnai0lXW1oTnIRFlGyvEq0tBeq+BYy17P+SYV/OxnP9ti2y3edrY3K0cle1Ll/MXCd6fn8TvuuKPFlLTu27evxSYNZfYC0psRZrMzxYyxXlZk6pQOT5UtW7a0mBnKrC7ZVs2OZ++/kjWJ16c95cCBAy3esWPH0vjixYstfu+995aWxzK8WLY1a6eUJLMtmI2gYjcc/sbapMm5be1iGX0qmWXsuFkkKn3H5NyWKcMyd1COz3MYcy3IerjnnnuW3mtK0DZrmT3MElXJxETMekAqbapXYm+WKI5FV69eXXpNrivMqstrWrtmnbzzzjtLy7BYLBYXLlxoMbMi8lq0NNv6prLWqdRvxU5TOc42ZFk5zR5m59txYmupOdgSd+3a1WLrL8waxnfFucmsKr1rIrOlcj5lzPUd2yzXejxu1qdt27a1mO+QmdD27NnTYtYJs0GxTob97n3YTofWWJtfzPplbZ7ltvdhc9y6MkdWtopg+blesTW5ZfCqrFc5JpayTH7gGSGEEEIIIYQQQgjhlpA/1IQQQgghhBBCCCFMhE/17uweQgghhBBCCCGEEDaHKGpCCCGEEEIIIYQQJkL+UBNCCCGEEEIIIYQwEfKHmhBCCCGEEEIIIYSJkD/UhBBCCCGEEEIIIUyE/KEmhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIEyF/qAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJEyB9qQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEiZA/1IQQQgghhBBCCCFMhPyhJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBMhf6gJIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmQv5QE0IIIYQQQgghhDAR8oeaEEIIIYQQQgghhImQP9SEEEIIIYQQQgghTIT8oSaEEEIIIYQQQghhIuQPNSGEEEIIIYQQQggTIX+oCSGEEEIIIYQQQpgI+UNNCCGEEEIIIYQQwkTIH2pCCCGEEEIIIYQQJkL+UBNCCCGEEEIIIYQwEfKHmhBCCCGEEEIIIYSJkD/UhBBCCCGEEEIIIUyE/KEmhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIEyF/qAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJEyB9qQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEiZA/1IQQQgghhBBCCCFMhPyhJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBMhf6gJIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmQv5QE0IIIYQQQgghhDAR8oeaEEIIIYQQQgghhInwK6v+c9euXX/1fvyLX/yiHf/Upz7V4r/6q79aetzOMXj+X/7lX37g8V/+5V9eep1f+qW//tsTf/srv/LXj2rX5zPyOr/+67/e4kcffbTF3/jGN1r8t//2327xXXfdtbScvNc64fP8xV/8RYtfe+21Fl+6dKnFr776aovPnTvX4v/7f/9vi48fP97in/70py3mu2QdVdqBnU/st3YO4Tu26/N4pV1eu3Ztc17aSH7lV36lFZ7vfAyV98a48lvCdlp5D73vysrAPsiY12Tf7y1ntUyVslau01t3Ru/z9LYPxr3j4J//+Z8vPc7rvPfee5Psm9u3b28V++6777bjlTY2d6xdWB/kmG0xr8PjvM727dtb/OSTT24o02OPPdZi1vvly5db/Cd/8idLn4f3uPvuu1t8xx13fOA1Gb/00kst5lz8zjvvtJht3vr42PFoM+G7+cUvfjHJvnn77bcvXdMa61rTVsbp3utXrtO7NqiUZ53PZfVbubcx5t30rtEr69je61fqxOZTO+dXf/VXW3zjxo1J9s3f//3fbxX1G7/xG+349evXW8yxk2P2m2++2WJ+rzz44IMtfuutt1rMb6Af/ehHLWZ/ue2221r8a7/2ay1+7733PuBJNn4z8p1s3bq1xTt27GjxoUOHll7/T//0T5de/+bNmy1mPbDMW7ZsWXqvAwcOLD3n05/+9IZ7cO3Cb9rXX399aXzjxo0Ws71xHuT5fB88n/Mgv2H5nIz5zhjzmjxufwv4zGc+s/Q42yLr6Pbbb28x3yvrd+fOnUuv82d/9mctZr3923/7b5f2zShqQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBFWWp8owTJLETE70hgZYK8NgeezzDxO7DqUSlGmZDIuyrUqdbVOTG7OZ6ZUzOqRUjdKsikRrthI7B2bbYaMkcL2ynPNVmdywymxLrsTqdRf5T1Xrt8r4V+XzN/kkGzj67QX9D7bmDFxs6nUS8X21mtv5G/NEjUlKL1meT+udidibcTeP/ugjTmcN0zCTCjzXiwWi927dy89j++J86PJ1vfs2dPiO++8s8U///nPl16f8witUvb8lfYx5XazGXPSuuFajvRaUSv0zne9c2vveriXdbW1VeWsWOZ7y9RrfaqwLktUr1Wqck6lrmyMmhJPPPFEi2ll5Xco7SMcv8mVK1dafPTo0Rbz++bMmTMt/nf/7t+1mDYrWltofbp69WqLOTdxrjdrmn0nMqa9hutSxqwT1gOtQmfPnl16X7Ptfvazn10QzokHDx5sMW1XvDfrl9e6du3a0nJwLOZ7rWxJYJg1mut+q3fO+7Z24ZzO52WdcDsRvku2Id6X78OIoiaEEEIIIYQQQghhIuQPNSGEEEIIIYQQQggTYaX1qXdHd5M3j5Fl9krsTSZt5aE8yrJK8XxKt37yk5+0+Etf+lKLmR2CkqvNkNeugtI1Sh9ZLywrpVnEZGBWR2NYlyyYVNqiZdz4pDImy4NZINl2evt+7zsxKenevXuXnk85ayUTy1gqbdgyOJAp2A0qdpdKtrDNkKx/FMyhjLeCXhuUycKJSZtpS2IGjeG/mdnB3hNl2ywrMztwrqT8m/Jplu9nP/tZiymT5lgzd2vc3MpsYyrptbD0WnPWlQ1qXXbwCmYpYJ9gX6ZdYJ3W1c1ob2OsymOyR1WwdsM1gG19MIc5id8itMJwPOZz8HzWBzPtsD5oV2W2o3/5L/9li7/97W+3mG37woULLaZlh99VHOMr24OwX7Cc99xzT4u5XuX75Dxz6tSpFrN/cV6iVYpZhlZlUmYGKdavPT9tSratgN3PsjzaGpjPaRZovj9i1qfK9znvazYuZiCz8YHv76GHHlp6rw1l/sAzQgghhBBCCCGEEMItIX+oCSGEEEIIIYQQQpgIK61PlSwMdn7lOmabIr02q97sEmZtMBm2ZSChhNkyU2yW/NAkW5RXUT541113tZiSuN5sLMQsZGNkuGPefa/VYl2y4I8jVq82JrDN02pAaSilghX7TuW9MaZ0lvLRv/W3/laLKQ1lNrcXX3yxxdxFnxLOdWLyTj6DZTug9HQK7ba3DOuy0U4Js/nMoey3GqurypjDeezBBx9s8eHDhzfcg1kVKH/ftm1biyk3N6k9+yDLR6kzy8c1Ae/FMdGejaTdbA6987/NU73rnd4MpGPG1HXBdkqLE+dWWowJM+wMM/VU7DmVuutlTMbK3rXxGHtUpQ1V1mRzGEO4PqTthusu2lnsmdjGeD6/12h1P378eItffvnlpb9l5iKO69xewjI8Vr6NWB7OFawTwnUpY37PWfZaW5MM17e89/nz51vMdsV1vG2FwXPMvsd6tHbL7wo+g7UJuy+pZObjvTgH2PuufI9b9igjipoQQgghhBBCCCGEiZA/1IQQQgghhBBCCCFMhJXWp155HbHze2WAvVaV3l2cSUVWSXkXJaBmjyK3Yud13oNSVMq2TXZF6Zu914o8zM6v1NFm26OMzd6xf270yrYJrQOU+ZvMsrePV94PZYb33ntvi2+//fal5WR5tm/f3mLKaNlX1iXBXiw2Pg/HFLNsmGR0avLmSqafSua4W50tbyyWVWBMFrO5U7GDVd4zZc60Ph04cKDFQ4uKWaDNWsiYv6X0mrJqlonjDsvHDCU8bvPyFLK5fRypSNQra0vLXlRZ11QyWG72+FB5Xh7nszP7GTOqmaX+9ddfbzHnq+H9rHxj1oTGZn+HbPZxW5+TylYAU4LZ8MwmwphrS66b/tt/+28tPnLkSIvffvvtFtPK9N//+39v8aVLl1rMOjOLE8fpSkZZO86YNnw+I+cZlvOtt95qMeclW2PzXqv6U+U3ln2p1w5q962smWw9UVl/2nhtW6XYca5j7fr220rfnH7vDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIUyEctYnO16xtlR2K7fjvRIkO267cBMet+tQekeZGeVnlMlRrma2rFsBJdaUCVKuRrmh1VGvbLciMxuTJWrMTvhkbpllbiUVuSLb1M6dO1v8uc99rsWUa3KnfWZNsSxsve+EbZltnP2RVibaoGgzolWKcu51wvKxHLt27Wox+y8lvFOzAlXk6JVx36T567ScbRZTlplbe+m1C4y5l71bm3PYl5kNhNmdaFdkn10sXH5sc7yNO5apjmuCStu27FafZGvcraI3I5u1SZP583yzC4zJcjiGXguRrcvMUsF+wGfnnLtqDKk85xi7UOU6pPcd2HUqa9HKvMY2x+tw7WXPZfabKXHu3LkW81l5nGMw10ecF5566qkWnz59usVcN/F77caNG0uvbxYnW6OSir3bMgibnYpl41qU/ZHnVLZ/WNWu7TkrWaN6vxMrmbGIHe/NFlrJ5Fd59soWMHZ8mAlv6fkfeEYIIYQQQgghhBBCuCXkDzUhhBBCCCGEEEIIE2Gl9amSkaGye3Fl9/Fe2WCvJMqkp5TYmRTaduem1LMiC7/VNgWzNRHLOFPJ+mRUpJ4me7Oy9e6EvxlS2OD1xLZz7NixpTF3sKfcj/2Ixyty3V4bHO1OtCgOs1G8j2WAGQuvxb5J29iePXtaTPk4pbpTa6u9WfcsQ1Ll/KnC8ayS9Wmz7VyVNrIZ9VpZM3COsswJtDLt3bt3abxly5YWs88uFp6th33+5z//eYs5lpnFie+M16H0nJhtKtmdbi29WUH4firW/s3I4tSbtbJCr5WHz37PPfe0mJmeGF+5cqXFnPeHaz2zS/TSa9Vfl4W/d91f+f4xKx3XADxuz24ZeabKf/yP/7HFHMNpjec4/eUvf7nFbHu01dM2RX72s5+1mGtCsx2ZfZbYHGfjPecNvkO+Kz4X50EeZ/ntm3SstdksP1YXY7K59WYcHmPjrGzHUblOJfM01xV838zObEx/1RtCCCGEEEIIIYTwCSF/qAkhhBBCCCGEEEKYCCv1cCZL793hmPRaZ0yCNGbX9srxigSOcuY33nhjaWzSxVsh4a/Yi2i7YMyy2jUr0sox76BXelqxNRlzyywzBVivlHGeOnWqxbTvULZ6xx13tJiSTkqhe20BVp5r164tvS/7Kd/566+/3mJKZMdm32AbY//avXt3i2nhoLyVz8N6mUJb3Yy+bMzBKsKx0OTQlfmrt731ZlkyubHZZC2jDY9Tem2w7dMWQQkwn/3AgQMtZqano0ePLr0O4yEcXy5fvrz0N7R2sI6YNcQs05Q3s89W1jHJ9LT5mGy/UveVsWddWSV5HfYvZgU0O4ZlrqlYBwyzK7Lf8Jo8h31ieK/e9m/ZdHgPji98ftYLxwErT6+V3rL7sC7Mzm1t0e5beXazfE6V559/vsWsD64b7RuFcwGhbYr1wXVd7zYaYzL8ELP6so9zDchspMx4ZfOMZWGrZCBcLGrrTOsv9r1p97OxzPpU71rKqHz39WYytetbzPZtRFETQgghhBBCCCGEMBHyh5oQQgghhBBCCCGEiVDO+mT2n0rGnkq2n8oO0BWZfK8EyeSBJsWiZJIZarjzdq+Eap3w2SglZ1ltB2/aQii5o+Sbz1+Rq1lbWZcNqjeL0xh7VNiI1Sv7ETMUUbbKdmTZDIzKu7JMUpS8Wt+3cWBshiXej3av7du3t5i2C5aDfZBjza1st5Ux1Ha8t/dq5/O4ZV6ZKpWsT2SMfW1M1hGWk5Y7ysjZj2zuY5s9e/Zsi2k14LOzv3OOYqYPxiwb+4pJx4eZl2g9sHmQv2H57PkrWUPeeeedFtNOyeOZd+bDGFn9GNgeuUaj5YXtl+3xvffea3HFBkVs3KA1w8Zm2jE2y3bD+7FeaMdin2VdsA9atldSyfBCbCsAs2WZrdTm1spa2tbkU4VrHLPa8VnfeuutFtNub5YfYtY0o2KJq2R4NOulvWfrm7bGYPvqzUw3XIf0ZqbkvS3DEW0+vduXVNacZIy9s9eeamtdK3NlbbzhXh94RgghhBBCCCGEEEK4JeQPNSGEEEIIIYQQQggTYaXmq7JjsVlbTPJDiSblUbwOpWiV3dnXZZ0xiZ3ZOiijprT55s2bLaYMk9IwykfXJZFdBeuddc0ybdu2rcU7duxoMbPmmPXJMAuD2aMqVjeTqo7JLJOsT04lcw2hhJUSaNrpaGGgVJUx7QW9WVPYTi9dutRitmv2QfZTjgMmV1yVvcJgX7vttttazH5nYyJlvhx3xlgnxow7FVus9f1V9fhB15yDVYQWv8ocOsZSUembJhNm+2dmpSeffLLF58+fbzHnNbZl9qnr168vLYOV0+Z3yxrDvsIsGKzzYRt57bXXWsx+xHvzN7wfxyPLXmMZd3gvxpR/zyGL2ceJMePHZow9FSs210pbt25tMds/+yPbO/sjj1u/MzhusG/ScmR9yCzyHwazUHL+plWSc2XlW4JshsW+8vw2VvZuHTC3rE9mBeN74zk2rhO2PbZJXsfW/hVbduVboWL7tqyLtABb9jeOCWaXt7nOsqItFv7dxJh1yvX93r17l97PxiPLAFVdf3/Q8YpdrWIVs/JYpk+zgDGmDdOIoiaEEEIIIYQQQghhIuQPNSGEEEIIIYQQQggT4YO3u16CycAo/6FkyzIRmTyyspN2RZpUsc6wzCZL5HFek7vIU2JpckvaQPjslHCu0wbFZ6M0lLI5PgPtGJTTMauH7drdm5HL5G29u6uTig2KmH3jVljRpk5lt/XKzuVsX1bfJg8ck+GH7Yv2Bco79+/f32L2QWvvLNsqCbO1JcuyYxYss5CNkY/3ZrWo0Dv+koq0dYxN6KOgIlevYGMkqcyPhHMCY7bBq1evtpiScmaTYZ+1DE1spzzHJPm8JuefgwcPtpiWSSvDsH8MJd3Lynr33Xe32MYgyyZDONawfjm+2DvozcoTbi29Y9KYMZXYGpt9xKwTLEPF9kds3mc/qEj7yap6qNg4rUy0XXAs4HPa1gxWhnVlDq1klbLMQ/adULEezy1bolmBLLaxlu+c9cc1FKnYrC2uzL+V92/X5Pcy+x2/1Th3s+1z6wCWwbagGJatsm0FxxpaoGlR5rPxnXE84nHra2MyT/duiUIqWasrWcos41eF6ffeEEIIIYQQQgghhE8I+UNNCCGEEEIIIYQQwkT4UFmfSMU+Ysdtl+9KhpAxkiWTpdlu2JRZ2Q7klIUzpuyascnw1mmDsucxaR3l37t3724xd+p+8803W0xbi+3abeUxGac9c6V9jJGqJuuT02vloOSS9h1mjaFkktkraEfg+SbVrvQRyirZN/meWQa2ff72xo0bS8uzCpaPGTJ4v9tvv33pdW28qOxOb2XoPV6xYPRaCO36JiWdW2Ycy9RQkU+TisXTrGO8PvsaJdO0+LHMbHeUVfO3lGHTQmg2YZ5vGQhtLqKtgdfhOexbw/Hb1hwcp3gtyybDcvOaPJ99mbJ71h3LypjXtMxQmZtuLesaOyvXt3fLdmftgu2O0nv+9vLlyy3mvFYZXy1LJ9s++5ZZeT4M9g3AOZTjF2Oz6veuCa08Rm/GUvsO4XsyW47NMXPLlmjWEKtLtjGO5WaPIlbflW+Xytqk0kYq5bQ2y3fO9s46se/WSkbjZf9+Hz4bxx3O05zXuBWI2Sb5/NYOxqyZKjb83v5SscZV7Jb2twASRU0IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmQlmfaHIkSnsolTJp5RjZYEXyXzlekSWa9I7HKSt95513WvzGG2+0mLImSs1tx/LKzvmrMAkWpW+WTYQyd2bXOHLkSIv5bMwOQkuJtZWKRY1UMnJV3rFloiFzk5SPySjRK8s0+V7FGsl2zr5AyTQtD5RP0i5gmVsqUlWTgPL6jHft2tVi2v5Y5iqsR0q177333hZTPso6Yl8zGSqpvNcxkv3K2Fq5vsWVcX8O2SsoRaa8t1dyW8mmZddhW6UM+ejRoy2mxY/nmEWR57Od8nnNZsU+zvZOLNMgbUlWD6vmGbMWVuZ+szhW1jqWsYPjAJ/ZskWwrpMNavOpjKO9a87eTEG2dqOVmLC/M2bbYbujhb1i32B5rJ2yjXMcYDxc01bGNbMS2NqV53PdYDaKdWXwGrMdQyVboh23tYF9p00Vez6z9/J81oFZ9e06tqYwux+xNjVmbDZ7F/u1ZQOzOdEyClqdrPo/yw68b9++pWXidTh+8RnsHVTsRWO2CBlj1V9XH2c9GNNf9YYQQgghhBBCCCF8QsgfakIIIYQQQgghhBAmwkrrU0XmU9mp3jICrUtiWilPRU5o59vO2Iy5u/y1a9dafOrUqRabnWTnzp1L70vp9PA3VlaLKYHl81OWSvk7sUw0/O2FCxdazF2+zXJl5TcrndmgKtmgei1zc8gyM0ZaOWZH80omNb43Sh1pYaAdgbJlvmf+lu2IfY33qrw3SjJ5L5aN16S0k+19KNW0tsd70E7IZ2Y/N2uGSVd7LUu9WfR624rdtzJejxnfpwSlwWxv1kYqsvdKpgabX/bs2dPiY8eOtXjHjh1Ly8+5gtdhX2DGP5N/WyY1y1LC8liGNMteQfvRcN7kO6BNy6TklXHE5lY+G60gfB7WBcvArHJ896+//nqLObfG+rQ59GbPG2ODqox5bI+cE9le2EfYls1WzH5gcwvh2MI1YKU/VW0KFesB51M+D+/H8vHZzEJI1pU5dIzVrZKFqGLdsbXaVOm1eXGs5bzA565k2TKLD9doVk6bjyrwvmybltmNbdzsxuwftjautrXKdyK3CeCantdlRi6WyWyJvWs8axNG77hcsThZXLHeVbY1iKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE+FDZX0y2Z3FFXqtKiYX75W92c7eFRkjpVt2fUrjKHGifIwytrFZTezd0NpB+TSzNZlMy47zmlu2bGkxs9WcPHmyxZTAVXZIr9iaTKbem/XJ2tZUuZVZnyi5pLTSJMn8LY9bTIkps72YDe61115rMbOtUT5q7591xX7H8rBvso3zOO0Ii4W/D0pG+ZwmEyWWWaZXml85x+wopNda1Vu2Sjses8P/rYIWgIqF0Bgjn2fbptWG9iLaJTiX0S5hfZPPyDmO7ZpjM+29ZmWiZYz9zs6nRJxWLPblxWLj+GXZaKz/VrIWWlY8WjPYf996662lZWXM52HMZ56DDfDjRCXryBgLacXqyDmO7YJrK/ZBtpdK9sZKRlTrH5Usb8PMiXyeCuzLjC2jlW1V0JvJhazLBkV6581KW+G7HI6JU6SSlYtUsl1Zfdj17ZvDMvNwHjcLYcW+xvmXfZnw+pZVqTfrE68zrGfWBdcTnNcY0+rL5+FxUrE7rWtNOCZL1JhMppXyVNaC0/8iDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIUyEctYn0itTMhnYGMlSJYNQ5XivTM7Op9SL8myTp5pMjDGlpMPf2K7RlQxbtps35dlmzaKEkpLWvXv3tphy+e9///st/s53vtNiWkf4zGSMvNjkZBUZ28ddUl6RE7L++J7ZLsx2ZpJUZkMzS+CRI0daTMsG+w5lqJRuWpvi81IKfvPmzRbv27evxXx2WvqY9ebq1asLYlnVaOE4ePDg0nNYPtoiOHZUrE9GZdf6oST9fawee3fIr5RnTNaMKbFKTvxBx43KPMj3SesBMzNwbLYxkjYCtnm2ZfZfYhmWOHex//I6ZlEyCyTbJvvKEPb5bdu2tdgskSYlN9sRz+dz2nzK7G+sI16TknJaPcM0GJPdacx12P45n7JNcc7iOMA2aPaoyhjPuc76kGU8G2ZkY7+1sZ1rTq6JaafkdZkZi/Raa8hHZcvtHfcr49gc6N2ioJIxrbLdgvU1zkfE3knF+kTMomfXtHKa9cnKbNsULBYb5132L/ZBjiO2LQL7o20L0msrJZX6rdiULLZ2ZluCVLaA6f3ejKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE+FDWZ+ISZMqFhzKoEym15sNqnK8cn2jImE1yRjlc7aL+CrpGiVutts45WS8FmXYlKgxaw7PMQk7M39QRs/sUZYdhNd/6qmnWkxpt9lm7NkrGb8ss1dlN/apMiarQKWvUbpMWxt/S+sBj7NN8fq0JtHKxN/ed999LT58+HCL2QYZE8qo2Q/s/fM6Jtu23e5XyUT5f/fff3+LKdXmdS2LFeuLcvMxWSdYTo5HfDb2HcpWTWLL99fbLivy5TnDerKsKyZRJr0ydrZBvmeOx8z+x77M33L+oiXKMlOwXfO5bB4w+7DZG9lO2d9ZPxcvXtxQJvYdjinMgMW2TWk3n5PPw9jGI5bb6pRwfLhx40aLL126tPQ6Juv/qJjDvEnGZE7cjMw/hq1R2a7ZTtnuGJudvVJOzqeM2WfN6shxY7ju7V1P07pI2wXvzb5s8+a6MnKRii1njDWOVDLF2NwzVSoWanvuij2lsv2F3deyLJFeCxKxDKpmo7b3yTJYViXbymNYP5XtMuxbl/3O7L2VLKIVuxPrq3dcNnrt/JXrVKydRhQ1IYQQQgghhBBCCBMhf6gJIYQQQgghhBBCmAgfrLn5ACq2Jjuf2PnrkqRWLDIVG5RJ1HgdSrKZHYayT1oKmJXDMkMtFhulnpSZUUrOrDGUpXFnf5aV8neWiXVEyazJuVnvlMZSqvrkk0+2mPLyP/zDP2wx7R69O9tXsj712qamyph+UdlRn/JGtjva2swuwXfI9sV2SmnklStXWnz+/PkWM5MF+wh/S6kn72sWQJaT/YPybJ7P9susTSdPnlwQZpBiffE37Efs56xHWp8Yj7E5sG3zvdL6wXJy/OJz8l32SrV75aZzzvpUkVtbloNKlodKzL7GtsM2ZVJq2ovY/tn3aXUltMNyjGf/Yv+1jC4mtTYrMedK9sXFYuOcaPMdxwuWz6wTJv+2OZHv1X7L82k3pTWSY5xlCgk11mVHMqvFurJWVrIvvfHGGy3m+M15k+P9nj17Wsz+YvYNW9uzLVsGGR4fWjYqNmz2eWae43xqdieue3lOJQPQuuxRvTae3gxhZj1el/XuVtFreVlXRiA739oz50HOp5X51743+Vtahdi/2N5tLWbzQMXKM+yb/LdtPWAWSsZc31of3IytTCoZ0Na1jq3Urx1P1qcQQgghhBBCCCGEGZE/1IQQQgghhBBCCCFMhNHWp4rUqJLdqVeaVDnfykP5lUk6eyWptju37XhN+FtmfhjuBk1JJ+VkJr2ltJsSPUrbLUPAm2++ubSs/O2WLVtaTFsXZeSsa8pwWUdnzpxpscnOK23CLAc83vuOp4rVwRiJK9vbvn37WvzlL3+5xRUpMY+bxYfSUF7zxz/+cYspMd26dWuLaXNge6S9gvJvvn+2WV7TLI38La0ZtDcNn4d1d+zYsRbTFsL+Qtk6Ja18hjF2PLZnPj8zUtH6dO3atRZTFs9+zeclJgG1LAWVjGyVTG1TpZLlgPTOa8Sydb3wwgstZttkf6HEmv3ObAtmFeRvCfsLY44Jdg77O9sv+ynPH46NLDfvR1sy2zOtFnx+9l/LhGg2KLNcsTzsXxwfaPfgmEVoRTNbR8V+2Dt/rCs7xq1izLxZWYvynTM2K66NZ5W+z3M4z9r6lnMlbVBsX7yO3Yttlu2a17QMUFxjLhZu2zG7Lvsa+4hZ763ejTFWtMp1Nvv69s4qmWU+aux7kFS2pxiTHZjwOOcEjsGcB2lRpd2W3zGGrT95ffYpsxOxvVesQqssgNYf2e8437Fvc01PzJq1rvHXrjlmHuxtKxbbs1e+N6f/RRpCCCGEEEIIIYTwCSF/qAkhhBBCCCGEEEKYCGU9XO9u4hVZGqlkfarI3irSpErWJzuf8i5aIUwWbZYr3pcyMcrKhs9otiBK5cweRXsFy8TzWSaT51rmHv7WZLi0cvC3rFM+P+81RjJasU2ROdgrKlkLxkC5Mq9P+bRlEqMcku+f75Mxr3/hwoUWv/jiiy2+9957l5bBbE22Kz77qWVxYb+xrBYsw/B5mCmH9chysI+wb166dKnFtEj0yrZZVo5Tu3fvbvGBAwdazLqzzF6E9igbQ80SUNmNn/D4HDKysb7ZPilL7s3iZedbnbH90/pEKxv7FLPAWMYWe7eUhbMdsczD/vI+rBOzTfE6lJSzb7Ffs88uFovFkSNHWmzzEeuUNmFaqlinnOMtO4ZlAOIcx7o2ezItJeyPPOeVV15psWWqMm7lOXPGLOZcvxw9erTFbIeW5ZBtqndsq2QapZWe/ZTH9+/fv7Q8bI9sp/YtwL5cyYq1WGwcR8xmSWsW2zz7P8tqmatIb1bBMXaaddmjerGxe6pU1rQVi4mdb8etntgG2XdoE+e6j/MX10dcJ7N/Wdk4nnD+4bqC86BtNWHPYvP70ILD8zhnsXycd83Gbt99tqbpzerbay20dUzv3ywqx23e7/3GjKImhBBCCCGEEEIIYSLkDzUhhBBCCCGEEEIIE2HTsj5VrCeV4xVpUq9Uaoz9ilQyepjFiVmV+OyUNg93aqeEjM9DuTV/Y9krWA5K0Xg+y8HfMruEycksQwBlebwOYX2ZFYv34jXtHVvWJ3t/c8j6VKn7yvmWkY0STdp0KPU0WxDbI2NiUkfKOGn94Y76bKcsP+/FcyzjEPvB66+/3mJKqvnsLNuqnfzt+fkbSlotS4Vl1arIqi1bFW0UfGdmM6RVqiJttXJS9mljC+FvrY1OlTvuuKPF7COV91mR9FpsFjH2I/YLlofZJXiOWR74Pnkvs+GyHtjvLCMTy8CxiGXmcdb5cMyhzYP9i/ejdYJzE/u/ja2sC8JxhL9lXbDfWWYNjhWHDx9uMbO28d2cP3++xaxrW6PwHdi4xnoz5mCvqPSd3uvw/dBOSjsh2xStA6dOnWqxZbkk9g75ns0WsX379qXX5xh/8eLFFtu4zv7O52I7Yv2wHwzHb3sflumJ1srKGqg3655RsS8Zle+iMd85Vp65ZX2ydVpv9t4x2RLNymdlYAYorrO4nrQtLyrjMW30tsbmHGW214olaFj/7IPsw5xrGdOWyPmU97YxrvKNbe3D2nZl/VSx0lXakLU/WzMTWz+Q6a96QwghhBBCCCGEED4h5A81IYQQQgghhBBCCBNhtB6ud9fzdR0nFQkr5WeV4yZLJGa1oJSpIlnnM1IKPSwbpahmX6G8lbvl83kohec1KfXjs1ECx/PNBsVnYNloX2GmG9ux32SClXqsSCF7M5lNiYpkr9KnGJutgFJqZlCh5JDvilJwtkFKvk2SzzKwjVDyzDbIvsZ7mV2L9+J1eA7bvlkRh/YK9h3bSZ/PzPuxX/B+3TvD416UqtL6wV36WdcsG+EzUyLP+qWN0zJ7EY4/bDdmDbLjU4VjMNu8ZWviOaxvs91U7F9mJzT7LO0FbDuWuYhlM3kv+yZjlofPy+tYpkWWgePS6dOnWzzsm2a/tLGDZeLcx3mKZeJYadnjKm2b961kSeN7og2K55uFlc9LmK3E1gZzpjcDTwWzzLMtsL7Z7mgFoO2Ibadi/WQ7unHjRov5/tmmLDsb5yLOCby+9XfL/Mh6GGZkY11YpkLaP9jveG8+Z2UOrbzjzc7uZOWpZDOy+YD3suw+U2Wzt9HoPc52RCuTfSfRPszMbmzzHIPN7sY1Gq/J869fv75YRiXDrVmFhvMk7801Dc9jn+d6nTEzTbKfVixq1m6t7sZYFHu3Pqm0UY6DZg2t9M3p994QQgghhBBCCCGETwj5Q00IIYQQQgghhBDCRChbn0z+YxKkijSpN+tTbxYnu75JnCoWGcrYKAHj7teUrtEGwpjnU8JMKehQomb2IsrpKFejnJ27k5tMls9JianJvy1jhe26X5Fo9kpACa9vMtwxO+pPiYqUz+R1tnO+ZVow6wzbKiWNlC2z3bH9s82axY3ti/Yak0LzOK9JiwTbgll/+IwsD+Xr3OF+CJ+zIolkPfLeZgm0tsrrMLvTrl27lsZ83xyPKv2X8F3SLkHJK5+Fcn/KiCsy+l472EcBLWLsR6wnwnZLa+Fzzz3XYtZfReprcKxlP+X7J3wPLD/bo2XeM2kw72VjFM8xaxjl32x3tNYtFhv7qmVTsgwXfGaeY1Zinl+xunENwbZCWL98TmYV4rvkOMX5nVJ+1gnHVo7jHBN755s5sC6rirUFs8pyfDh69GiLaSWn1aKSHcUymPH9Hzt2rMUcU1k2tgva+S0DFNsIxwq2cc5pw3mT7ZbzAvsC+ynHCD4z72d2qgoVC0ZvRpjetWvFTmXXt/JYxpwp0TvGmCWwN5tWJQuwrYnYntnO2Xes/9p7Y/tl/+B4wjHe2o61O665zNq86nl4P66t2Qc5Bpnlv3ebhooNivRa6YxeO5XNDZalrNLup/9FGkIIIYQQQgghhPAJIX+oCSGEEEIIIYQQQpgIK61PJp2q2IjMzlDZxbx3t/VKmXvtUbwOpWiUZNMSQnlmRXpp8qtVUizKzyijosyMElVi0kfK4CyzA+9LTNJHSSrhccZ230q7GWONs+vMwV7R284r1+E74a7tzARBuT3bMO0ClnFo7969LabEnrFZk3gO5eXsj2YjYPuyjCscryjBZnn4XHyW4b0pS7V+ZxnTKKU1m6GNTbSZPfDAAy2m1J592WLKbTmesGx8XrY52ii+//3vLy0/r8nfst4r48lUoZXpwIEDLba58utf/3qLv/rVr7b4X//rf93iH/zgB0uvY9YhayPM9rJv374W0zLLPmW2PPYplofHzd7KPkG5tK0ZeA77IO19vNcw2xgzcPA52a44f9t8x3KwrbI9U+bN69M2xee0emF5eC/Lxsh3ZtmmLBPHhQsXWkyrjPXHuVmcyBh7ip3Dd846pi2AcwLb8L333ttivrdXX321xWZfs7me9rvLly+3+Pz58y3mvMw+a9Z5s06zr3FOG2Z3ep+hxZL1wrbKMbSSkY19p5KBtbcd9GZ+qfQXPot9M9i3FtuBrXv52zlYnyr0ZmTrfZ+V7QLsXbFfcy1m6ynC61iWQrYXs0pZ+c12v8paxP7F57GtAWjX5BjEeaTydwGzBbHcVhf2TWrZl4j9tvI3AjvfrrlqvbKMKGpCCCGEEEIIIYQQJkL+UBNCCCGEEEIIIYQwEcrWp0r2kt6sT72yQbu+ScEru3kTKyefkRIwysgptzQbhWWNsOwKQ5koJXG8t2WvoaSK8lYeZ2zl4H15Pp/NMrYwtowSvbt/k0pbrLSJuUm7e3ciNzmh1Suli2w7V69ebTH7Avsj5dN8/4cPH24xbT28JiWNlFjac1ECzHbK8rCdsq+wvbBtmk2Bzzu0PrHcZjVivVCuymuZ7YSSepabsvDt27e3+ODBgy1mPXLcYF+gPJcSXlKRlJ87d67FtMzREmVWR1LJwDdVaBnge3jwwQeXnvP3//7fX3r+N7/5zRb/m3/zb1p86dKlFlcspLSsMfMLM4DRasN2yn5E6w/PZ98xaTPfJ8vMfmMSYDufZWO7ZltbLDbW18WLF5del337vvvuazHbOfuFzcXMRMUxlH3W1hZ8HsvAx7HMrFicW1kXHGdpiaG1hr8l1gfn1jcr5e1dL7DNc5ymFYjHLeMnbZJsO3wntP2aDYpzHC1utF2wr3F+4DzA8Z73tXtZnfA6vNfwfvxNJSuqZQbstcesi0o2PrNo0erFZ2TMd8n2ZN8zfDfDDLJTpzf71hh6bWpWr9Y22df4WysD51OzEHFM4DjDNYBlGTJb8dByxXmNfZDt1jKTVr6xx9hNezNJ8zk5trDM9p7MZjjGBkXYbowoakIIIYQQQgghhBAmQv5QE0IIIYQQQgghhDARyno4k/xUbEfryuJUkaT2SqIqGTQoPaZcnDvnUw5WeRZK1Cr2neH/UaZGKZdl0eC1TIrKXbt5L5NbWz3y2WyHbUrnad9geSzrBKlk1apIJytta0r0yj57s49Rcnnq1KkWU5LP31K6a1mMmDGKUmrrg5bVgW2Q59hxtke2KZPBs3+YBZA2o+F5HBf4/CwT5apmG+Q9rl27trQc7C/sUxwTeF/aWoiNIWZ9Ycx3SeuTZZCxLE4mo69IyqcE3w/bAjO80B7HNmKZDSxbmdmBKaf94he/2OJHH320xfv3719aZsa8F69vlh0eZxn4LCwzjzNmn6XMn7BdU5o9lPnzGTiu0RLFDD08n/M93xmfk33W2rDNy2b9Mtl2xXZCy6H1R5Osj8mKNIe+SSpZJe2ZzObDsfDMmTMtNts7x13L2keYuYnv0yyHLM/Zs2dbzLmCfYdWJGaFs/GYYwXPYbvm2M/rLxYb+5RlTmRZbV1n2dZuJfb9YJbRQ4cOtfjo0aNLz2FdM/Mf+69tNWDfG1NlXdtlkEoft7mM7ZF9hH25kmXL1lPE5m6uE3l92uBolapkAONz8V5sj8N/M2Zb4nxqVl+zzFfmnTHbVnAM4rjDbxXL3mjrAWL1W8lgxd/a9Tfc6wPPCCGEEEIIIYQQQgi3hPyhJoQQQgghhBBCCGEilLM+GZWsTJtxvDdLFKlch/I27n7NDC08bhlkTIpIeSbPtyxRi4VnoLDrUgJtUjRaJ8zuRGkWf0u5KZ/TMnIRq+uK1LiSgcwkgNa2eL7ZtaZEb7YNsyFQrkj43pghhO+cWSruv//+FtPuYRmKKHm2jEuE741WRJOnsp3ymib5N3sQZZ68F/vH8Pdm4TDJrEmXeR0+s8lzeb5lyqC8npiEneXne6Kl4uTJky3+yU9+0mJKSc3eRir9dA72Clp1KFdnHbNfcJzm/PKf//N/bjHrnvDd8r3RsvObv/mbLWbmqUqmEbPoWrYLs0WYbc6sUnwW1ueVK1dazD5OafYws8yOHTtaTLsIy8fjln2KVkQ+v82nbPNmZ2Dbtkw/Nt9xrmd2J9okKeHmc5k0n5jdxeZWk/VPlco41GsF45xCKznbLcdmWp8479gcQjgm0CJhGWSY9Ys2ZI45bKeM2V7MnmnSftoOhm2Nz8z+yHvb3GSWS7Oy9Ga+7MXsJZy7jxw50mLanTgvsw1xXOK7rKx7beuDqTLme7OSOZZYFj62T47rZr+z61h2XPsesn5UsdNZW+D5ZjHmOPDQQw9tKBPnVI5ZHGsqmZ44r3MNbFmWLFOSrSHt2Ti2Mpsm+x3nSo6PtIyS3vlgTBsl85pZQwghhBBCCCGEED7G5A81IYQQQgghhBBCCBOhnPXJ6N2Fe8xu3iZJrdijKhYcysYoyaRUijJOnk8ZMiVdlumFMmfKrCidptxssXCZNKWxfGZK7myXf9YXy8Hr0LJi8nRK1yr2M8oKTW5IGZvtEG42pUpb+ThmfarIR1nfrGO2YbYdyvDZhmlJMFkmM9oQ9i9Kry07BstDK4NZJ8yKxHLSFmA7xLNOWA+MF4uN7ZP9kccph2Z7Y3+kDYZ91qwjjM0GxmczywPHR0qsT58+3eILFy60+KmnnmrxxYsXl/621w5pNtFK5oYpwbGZ0lpmGeI4R+sYLYHf/e53W2xyY75nzh20OD3yyCMtpgyf1l32L8toZJmbTJ5csR1wrrTxnn2ZfZbtl/UzxKTtZqFkTAsZr8N3fP369aXHzVZsVkezOdhYxndm2aAqGSVMRk9Mmj4HKyKpjCWV57M1qtlWCN8n24itg9gGOZ+ynR4/frzFtMHZ+o5zN9ssxwTaBTgP0j5scxFtB6yToS3R5l2uD3gtq2uONbwH63FVRtUPwtaKvC/vRUsJs/0x0x6fl32ZcwYzhzHTj62Hba0zh3mTVPqmUfke5FqR7Yvtn+tM9kF+D1nZeH2zPFe+Yzg/8jrss5XsijzHrHjcvmCx8K0EWCZbZ9panHVna+vK3wjMcmvf7V/60peWloHXtzWE2ZcqFriKDTNZn0IIIYQQQgghhBBmRP5QE0IIIYQQQgghhDARNi3rU6+tqdfuRCp2p8qOy5RucsdrSsUoZaJci1Jly6RjMvWqLJFSMcv+QNsFJaqUolH6ZRYkPg9jvg9K4FgXZq2iBI4yPsoNKQdlmXkv29m+N+sTmZuc2yR1lpGB0k2+c9pWrB2aLYbv5NVXX116L5MZsi0w4wHLxrbMMrCN8L2x3fH67B+sE2LtwsaKoVyR/6aM2eS27GssH8cOytz5zJbRiscZV2CZmeXrxz/+cYu/973vLT3HrJfWj3qtBWQOmWVoMTh//nyLH3/88RazH9HOw3plWzB5LO1x+/bta/EXvvCFFjO7E9sU2yP7F9+n2Wv4HigZNqsU57thxrT34XhimaEYs35o9+Dcslhs7Asca3gerSAsK2P2NdoQLFsTZdhm2bB5luXheGrycrY5XqciF7f5g5j82yTfU8XGp3XN/6wbtmfaoNimrD9azLbMLC287wsvvNBis6IOrbvLymaWXMv+ZplFeZxr6cVi41jAa9m8xuPsR1xncEy0cph1qBe+G2aFo92UNiiWh89OixPtxmw3PN/6NenNLPNRM+b7rjfbL+uM7YXffcxeym8Uzk2cKy1TX+X98DqcW/jOzVrIMYTHWR62O64lDx8+3GJaHYf3YznM+sSY74bzLPsp52zLaFZp53wevrP77ruvxeyb9u3MOdTWW/Z3BGtb9jcInmOZbsn0V70hhBBCCCGEEEIInxDyh5oQQgghhBBCCCGEibDS+lSRy22G3akiSTV5lB2v7NpOWRZl5JTGUeZNCRglVGbX4vmWMWrVruaUWPNaJlWnLJPyS9sl3KRlvBclYZSuvf766y2mfNbuRVk4z6H8jlI0yvjMamHys16rxdyyPlX6At8VZfWWTabSN/keKNE9ceJEi9kWKMukTJLSRfY1WvdMSkl4fcK2Y1aOik2HbWpYBvZhw7K38DktAxx/a/3UZOisOz4/Y0rb//f//t8tfvbZZ1vMbBQm6ey1TvRmL5tD37TxiW2e7YfWJ1qleB27JiXDHFMpAeZxYn2Z4wD7jr1nloG/tWxCZmm0DGm8vj07f0uZ+vA83o/jINs/xyP2KT4/xztm7eL5tHmwX3Ou5HU4DnDO5bjG8ZHPYmOL2caI2QksO5v99uOSWWZdz8HrcH64evVqi7nmZLszu73Z+J588skWs3+dO3euxTZv8Hyew/GK1gGzANv1zY69WPj60DIY8jj7rF2TthCeX5mviVmYaSt9+OGHW8zsfZzHuYbnu3nppZdaTOsL342tV1jv1vfnYH0ilb5ZWaPaOsW2rWB98x1yDLZsXfwtx2l+01g2VY7ffG9cn3MeZ7t+4IEHlv7WbPRsj8xCNrTg8Fp8NtajZXfis/E6rBfLgkrsHdu35LFjx1pMWxfP4fxrWxYYlTGOZbb+yGev2PmjqAkhhBBCCCGEEEKYCPlDTQghhBBCCCGEEMJE2LSsTxXrSe/5JsU1STavw+OUZFPGtnfv3hZTVkoZMi0kVj+WuYhyJ0rJzPrE3y4WnqHJ7Fgm+WadUtJHuTyvbzYr1sX169eXXpPSXkrSWRd8Llq0Ll26tPT6Zu/ptTX12qOmhGXqMCki36f1I7PsVeqDUscrV660mJJL7sJOOTPlkIxNqm8ZYXgvShopdbQd6ylhJryOWSKG57G+2M5ZPmIS+cp4R9iGK32WdoyzZ8+2+JVXXmkxZdi2E34l05OdU5EvVzIoTAnLAMh2yDZM+wtjs4gxplyXUnq+W1p8OAazbGw77733XovZr9nm7Z1Y9iib43gOy2zWWJOdr7JXcEzhM1vmI5aV1+V4apZOyyJp2WrMMm02UbOGsh5JRaptknJSsZHbeD03eu1clfnR+iztOJTq83zOIZZFk+use++9d+n5bC98VzZXmN2HbYRt2Ww61odW/YZwfch+x7HArNSWOdHWxoZZGh988MEW8/uB9+W75PvmPEtbsb0D9uXKfMrnmsO8WbFH21xT2brAssVyfuR7YBvhu2VboG3Z7HQ7duxYei+uxXhNvn/LhsTvU17TvmPs23PVdwH7mmVd5dgxzOi2rBx8Tn4nsN6JfdtzHGGmLo6hXOvwOhwHud7iu7T1PLE51DI82rhZmTen33tDCCGEEEIIIYQQPiHkDzUhhBBCCCGEEEIIE2Gl9alCZRduUrFB2fUNsxcxplzryJEjS39LiRp/Syk4ZWZmi6Dskdfns1AGz+tTbkbZ22LhmY9ofeK1KA+j5Ju/5XHLLmBZaZi9gNlKKE+k/Iz1zuuzrimfY+YtWp8oV6PcsJJNhlQyjU2VityVdcO2Y/aE3vuynijnpmWNbZB2Bkq17fpmTTALIGPCNs5n37VrV4vZ7tge2fZ5fCgTpQTUskOxLsyCxzoy+wOfk+ML+y/HJpaNElP22R//+MdLj5uctyK9Jptho50qbG+EYz7HdtrO7LeVDB58zxybKbE2OTPHAUqAOW7wvuyDbGs2/7AfUQLM+dSyk7HtVzKcDNuatSVi1g7+luXjc/KdcVxjHbFMnEP5zljXtDgRy/5mNtdK1qeKddzG4rnNmxXGZH2q2KDY12iFuXDhQovZT+0dMuZccfTo0RazPdIaaXOFZXexDITsp+w3bPtm3VssNrZ5s3Pz3pY1hmVl+bgWp63e1gSM+Ty0mnzxi19sMe3c/AZg+TnGnTlzpsWXL19usWWcMbtpJbbMj1OlYvW1MaaSfZiwPtgGT5061WK+c/YvZoNiO+K7YswtJdjX+B3D4+wvFVsi2zv7B4/T2s9nZz8dthGWyTJmmY2K61jLDMc1N8tqmbpYp3wejnfMsGX1yL5m24ZUrHfEjlvbNfu3EUVNCCGEEEIIIYQQwkTIH2pCCCGEEEIIIYQQJkI561Mlm4fJzHptUCbFNfsD5V7cAdpkVrQ8UMZFiRYtApSCU1JOKRZlXyZ3skxKFg/lkGZnsIwahNeiXI+/pVSM8jNK9inbpkWCdhfbUZ3wfe/fv3/pcUoPGVPKP0auZswh6xP7BSXAJuHnO7cdzXvtYoTtjm2KljW2I/YvltP6vtkb2dbMpmDjBuWZHB/MisRzzGa1WLjUk7/hM9ASSJksbYMmXWbdWcY3SmyPHz/e4hdeeKHFzEDBMbFiZ+jN4mS/JWa1mEP2Co61LC/bMMdayqp5jlmTrC3wnTOjAo/b+zEpuEmviY0tnCt4X57D3/JeZtNh/2XbN0vQYrGxTlkOvgPLUMVnsMwZ7Kd8lyyrWdosiwkl2Vx/0MrCdQ/HKd63d31m1glb09jYPVUqc3slG2TF8l+ZK9nXOCfyPfPdWt2z7bBd25qO1j22ZbMyWVtj/7DxwebxYZnMdmUZnWiJMmsWy00rPevx9OnTLTbL5RNPPNHiRx99tMW0VvG3rJeTJ09+4L1svVrJ9GTMLZNpxfrUu71G5frWL7g+4jcj2z/bCLEMP5YNjGM837mtJTnPcF7iuGFZ2/iMnOtoT14sNs4j7Jssk40XLJNZ8MzqaPMO+zLfB7/57X1wDcS65jP3ZjO0clr2XGIZVI3pr3pDCCGEEEIIIYQQPiHkDzUhhBBCCCGEEEIIEyF/qAkhhBBCCCGEEEKYCCv3qKmkgOv1D1Z8hebLtPS+Dz30UIv37t3bYnrI6PmmL49+tZs3b7aY+6BYulKWgfelt4/QD8f9Osz/PfT80sNv6UF5Dn2J5sujd4/3s71yWC/cf4b1a/uVEPoZCVOkMw3eb//2b7eY9cW9NWy/msr+GJbieqrw+VjH5iW1fYzWta+A1R/bJtuLlce8oexT5uEl9MjSz8q2TF+77fNDzyv9xdU2Yntf2P5WVg6Wm8dZd6wL9lPuq8Vxh+Od9VPbE8bSRff66ElvWs2pwnfFd8u2wDSt7COWppXYO+HeDGxfnKcIy8Y9FWyvJ8J5kOW3+YRwnOb1+f5tvxpLn8v2MqxD2y+A92NdWFpPzpt8Zj6nlZVjH+ua/c5SXbOu+ZwcB7iHG/fMYd+3fXKsn1oftD4+h/TcY9aolbVu75hnewFybGYqWu6TZvtX8Jrc04X7Wlj75brS0vhy3rT9VNg2OS4xHsJy8Pe8t+0FSWwOZd2xXhizjzOd9yOPPNJi7iNHuAZmKvSXXnqpxaxf24/P9uWs7JVh58yhb5Le9Nxj+iyx/vj000+3mP2Icyvbl+2ZxONsa9zvk+XkXGbzDK/P/sG2ZvXGew3na0uTbXXHvmZrWq4P2O/4bc8yce5mf+Q3v+0dafvGcGzlO7DvEMa2/4zNg/ZdxPdXWvN94BkhhBBCCCGEEEII4ZaQP9SEEEIIIYQQQgghTIQPlZ57XXanivSPMjPaYhibLcKsQpQaMSUiU0ybPJlSLFohWAaWn2XgNWlFspSsQ7maWVb4G96P5zNFL+vOZOhmX6J0jfJZ+y2vT/k308dSqs2YkjZKDFl3lCcyFbSleiTWdueQAtjkoHZ8M+xOFSjPpoTf0uJZ+lGzylk75fmUVbI8bI8cE5gGkLJSs0QMf8+299prr7WY9gTCfmT9mtfks5nM0qykrHezg5rUs5K6tzJPWNmsD5oUdqpwHmF741jF+cjGbMI5oiKBZlvjcc5TZp9lzD7LfsH2Yu+Qkmc+l9mMWDbey6yIvKbZBIf3I5ShWypTs4+y3inhNlsif8tnM+m1jZt8Nq6N9u/f3+KjR4+2+PLlyy02Wwvva9YJGxMYz6Fvkoolulfe3ju38jpcW3EMofWJ7YvtzsYN9kFadvj+uS7lWGRphXkvWjgr6ynOxcPysW2zHbLPW9uzFOBm02I5uObk2Mo1J8dT3ot1Z3YnrgF4/UqfqsyPtmYwm+dU6bUvjbFBVeC74vcT+yb7FNspv1049nMu4vjN7yGO07ZmILwvn7Gy9YFZjIfnEfYvjgvW3tgHWQ7rX7wm1y733Xdfi2mDsnUG78W1LuvU+iOxea13PrB3U7HzT/+LNIQQQgghhBBCCOETQv5QE0IIIYQQQgghhDARylmf7LjJ9yybAzEJNGWJlESZzIyyI5NtU2JMKSKtT5S3WRYUk3BTfkW5lmXkMZmnybSH17KsTGbBoETTskjYLt8m8TJZl1lZeF+TEvLdsx4pJTxw4ECLH3744aXX5/uu7F4+t+wVxGTst/I5enfRNxk++xElk5SJUjbINs7r8LeW9YaZkXqzUA2zTdHOwPM4ZrFvWyYA1qPZ9+wc9n0+j1k2Ks9ZsUFVqFgLPy5Zn2jNZB3Tmsl3ZdYnm2dN5s92bvJhm0PYHykTph3DbMV8V7QTsi+zv9CyQVhmzu/sN3x2zgks2zB7BZ/N2q1l6WDM52SZLIOO2XKZicnk7GwHrC/r75Rnc81EmzPnXGtzlTnDstIM7aBTZzOyO1WyglTGcq7X2F7YFvieOQ5Y2Tg+8F1x3cSYfdYsSjYXW3aX4bzJ8ZzPwHWg2WxtDcy+addh+fhsPMdsU6wjZpA5ffp0i9nfWc4x86zNm5Xjlh1zSlQyC43ZjqOS3c6+XThO8z3Tcsr5i3MC5xD2BTufGaC4lmCb5TzOd2tzEWPOrWzLbO+LxcY+ZdmLuO7lfMdnY7nZ5nnvffv2Lb2X2XtpB7Uxl3XBtT77LOvF1l5GJYO1fV+TyrwZRU0IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmQtn6VJHXVWRmlCUyy8Pu3btbvH379hZT3kxppO2gTIkT5Ye0O/Ecy+5kEmDLssF6MMkVZeG8r1kqKEEf/h9lYzyP9zMopzMJNOuCki3bad+weqQ1ifYzy2RgmXEovWN74vWtPHPYCd8wWwQxK2LFskbGZIlim2Wbv3jxYouPHTvWYmZg4PukbJvPQsm/WRH5W/ZZth2Wk9fkdViflF4On8FskJScsu+wHHwGk56aVdKspDY28ZkrcutKm6tYCCu22Iq9dqpQTmsZhHp3/bd6MhtKZezkuMv+yExBZie1zF3s49auLTOU2QHN3sh+usr6wd/zeSrZxDg/2tzE8YXlsDKQSnYnlo3v5uDBgy3mfE25ONdSHFu5ZjDrE9cJrHeb9+dgS7RxxayZ1s4rmet64W/5/inVp5WHtjabH9ju7LdsCxwrCNs+r8/+y/fPtsy2OawfswmY3ZEWFMscybHDbE18Z5YtkmUw2/4rr7zS4gsXLrTY5u51zbN2vll3Kmv1OXArM7IRtm1aEZmtie1l7969S49zrmCfOnLkSIu57mX2MGsX7BNmjzK7NPv7MFuibZHB39tYQzgfcU3Ae1sGR/Zly/TMuuBYxvd09uzZFrO+KhmBK3/jsLW0zYm9VsTpr3pDCCGEEEIIIYQQPiHkDzUhhBBCCCGEEEIIE+FDZX2qSD0tOxKlTMyIQlkTd4xmGWynetp9KH2iLI0yK7P1mFSfZeCzcNd9Wg14PqVolJrTBkX5GKXjw124LYMS78HrEkqtKHNnufkuTa5qVg7KpCsWGr5LZn2qWM4oZ2VbYXtinVDqVrE+jZFI3ioqks6KBLRynV75qJ3DNkWZJd+Vlc0y11ACyTZu2dMsE01FCs3zV9WDZS5jzOsO+/n7WBYnwnfMsYz3Yh2xz/KcSja3SrYma3O9VgFrB3OwV5hVh+2c57BdVbIoEo6LfM+sP84ptANbJgQby3kday98Ls7F7I9mOWJ7pyzcysN+ZhmgFouN9WuZYszSbFlteB3WO2XbZk+mZcOydzDm/MV1jM1rLD/nxD179iwtj2W2YnYTriv4zqwdT5WK1ZLYWFXZCqAyFpKKnZ99kOOJ2WptbOH5XMdyHLA1Lds7rQxmv2J7GdYb2x4tErTpcezjWMD2z/vZ/GtZljg2sax8H8xoQ0sF19LsU9ZWereTsDrtterNYd4ck8Wpd01r9zUsmxDfP79F2Kes/bJ/EZ5vfZDzDMcHyxx36NChpb9lX2bZFouNfdAslByDzArE9S2fzfopy1fZwoDzLMcE9tNz584tPd/6Re88QcxKbJb/yrwZRU0IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmwkrrU++u2ia7o0yJFiezO5l0k7Imyroo17TMSlXbwjL4LJRnMuYzUhZN+RVjyq4p72KZKQ1bLNzCYNYp1hdlbaxrZquh3NykrpVMGRUoA2N9UWLK90r5HGXulinEdiY3KXAlY9mUqGSvMNtOb1aLdcFrUv7P98yMCnzPbCPsL7ZbPOWNrAe2BfZfYnYHszEtFhv7M69LWbXZtyzDBZ+Bz2mZYnhN1pfZAAnrqNL+TbZtVlKT9ROeY1kq5tA3Wd+k0qdsPjVJvrVtXseswRxrGbON8F2xPOwXbL8m9TUZPq9DSbnZeVkensMxYTi/c17j79ln+cysC5bV7IfMAGdZn3gdPifnZZaH9cLn4fn79u1rsWVj5LuhTYVjES1Rhw8fbvHLL7/cYmYfYQZNWunmkJGtQu/8WLE1jen7HMv5/jn2s71ULBVcK3G+Yhu0/ss2ZdsU0KbAdjdcM7LvWEZYwvbGNQSvy9+y3Oz7Zs2yeud9z5w502Ku6Xm+rSfH2J0q1mNibXeq9NqazEpS2QpgTNn4njk3cQ4xWzn7LNe97COWnW3//v1Lz+f4zcxj1k937drVYvaJofXJsjjZuoRjh2Ut5DUtayPLyr7M2DLMsU7ZNzlnWbup9MFea+G6+t30e28IIYQQQgghhBDCJ4T8oSaEEEIIIYQQQghhIpS9K2N21aYEizJkSpx4jsn2LUsFY8qgxtidLOMMd/O2nb0pL6fkisdNgm3lH/4fY7OBUcpmsq7t27e3mO/GrEM8hzHPqewwb1YI1hElppSd7927t8WUBlIaxx3L2Z4sm5WVbar07kpekZJuht3JYPuyrAuVzDhma+C4wZjnEytDJavOYrFRPm5ZYHgtHjcriL0b3ptjB9s5xwFaOVgX1mfNymJZJ0hFdly5jj3vHLJX8J1YJh97vl6rhUlraQtgZgq2BbYX2nEqFhyzIrLv2HzFZ7HxmPMA7ZCc06w/DuvE7FiUrfOZuc6w8Yi2TB6npYTXZP3yvpcvX24xn41thesMzteUsHNc43qAz2vvaffu3S1+5JFHWvzoo4+2+LnnnmvxU0891WKOM3Oj1+Jpbaw3A0+vbYp9jW2Tdhyud4hZpC3bEu9lcyvnB9qs2L54nHMd+8Gq3/DelkWP8witIDbnmnXR7MYsKzOTMub5FbuTvQ/Su4az+5plY6pUnq9yPqlkWOu1U/OanJuYZZj9lN8rhHOFZdPlvWgT5Dns+5xP7F7sK7QoMivxYrGx3XIuY92x/3JOtGxVZqfkXG5bithWDoTl5NxkmWVJxVpoFmOz6tuWE6TSN6OoCSGEEEIIIYQQQpgI+UNNCCGEEEIIIYQQwkQoW58qUk+TNVEeRdkkpYiMLeMDZWaUllGuyTJULCwVmSHLbxJTy6xCGbJJL00STxnXsBy8FmXulE9b9pFz5861+PTp0y3ms5l81LLPrMsuxGuafN/kZ3xnlN4xZp3YruYfF6zPmpWk19I4pjwmB+Y7Yb9mW6CMk5JJ7pbP69t7ZhunrJL9jPJnk2EOf8Pr9sqVGXOs5DU5LtAiwrGGu/+z7/C3vJdZ0SqWV7PukF6ZN6nIR6eEjYtmeajUcSXDHt8tMx5wTuDcZP3OrFuW9ckyq7A9Whvn2EybMMtMu5ZleCBsv4vFRkk2sbULy81nNksRs26wH9kYZ1lD+Dxmh+TcTUk95zWuDfi+bW3EMYT3ouXqwQcfbPH58+dbbJbROWN90OxLlawgletUxlezvDPmlgKcvxhb1sFhNsP3Yb9mO2Lf4hzIudjsUcP/s28G3o/WP7Z5216BmIXBMsvS4nT27NkWWxa63qxFVrZee1TFIj2HebNiUxpzvtF7vmVxohWRYyrHe1qNOFcM+8X7sF3bHG0ZkDgmcD6lZZZj9nCeZNvm3MSxg9+MNpYNv2Pfx7YC4Bhka1HWhW2PYn8jqNj2baywPmV2fsvW2Wu9m37vDSGEEEIIIYQQQviEkD/UhBBCCCGEEEIIIUyEslbVZGYmjTYpMWX4lCzxt2Ydov2B8qvKrskVeZHtmE5JGGWVlC+xnJRMmqyYUBrGeChXtMxSlLWZJI5lZT2eOHGixZQ6HzhwYOl1aDWxLFE837K0VGS+fMd8XtvZnLuZU3LWKyudQ9YnUsmQYxaMyvHNoJINicdpf2A5mbGE/YXXYb+zdkoJNu9LGSavP7RXmBTe3oFZGzie0grCMYWSTtYL5am0SPA472WyTMpwbXd9Pm9vRokxGVPmYFes9KlK/7J6Mqk7x0u2C7ZHjv3W5lnHlTGB92Wb4vxu1jraNHhfrgEsY5K1waGEm/XI/2OZ2M5ZDrNkU8LOc8xOxnqhddMsGGYho6Xt1KlTLWZGEGLrD44hfC6Wc//+/S02GzKffQ59s7IO7LWw2PjXm92pYvGwzH62lrZ5huvESrYmns91n9nleU2bQxeLje3N6suyPNL6Z1no7F6sF/6W/YUWP86/HI+szBV6z69kPzI7xhyyJdr8uK54M8rJPsW5iesvznHWL6wtmDWQ5/Ob7MiRIy022yPLaZlyhxw/frzFHPM5T/F5LJujPT+/3Tg/ss/aVimWFY9zlq1jK9s99GYg67XOJutTCCGEEEIIIYQQwozIH2pCCCGEEEIIIYQQJsJK61MlM0llx3HKrsy+QyhlMmmt3atSzorEybLAUJZlGZooQ61kRuJzrbLmWF1zp+9K1iuec+XKlRZT6slMFtzBn5J0ylDNarSuHdjZDmj14rPccccdS39rmW56padToiL1NKmdXWczYB2zjZhMmse5O731L1oBeB0+F+WdLI/JgS2T2CqZc8WOwn5e6bN8To6V7LOUv5slir/lvfg+LOOGSUbN8srjthN+RUrK8pi1ZKr0ZurotVpYBg+ztvA67EdmL7PshJa1je+ctibG7IOWqY3PWGlfbPurMpxQPk55N62+ltGJfdCOc73CslrmQY5xlWx8fK+c+yhht3fDdmAZSjinnzx5ssWUpvO5eE0eH9pBp8gY6foYu9OYrE/E2iDbhfVfvme2R5sHDI7rO3bsWHqOtXf+drHw+biybuY4YhlnWb98Tl6f/YIWpzNnzrTYxgerL2tPZl+x47autnvZWmIOWZ961wgf1ZrWrM1sR5a1kDYotl+OtVx/0RLEe7Hv85zDhw+32Cx9tNTzO4/bCCwWG9cK3AqD4wuzsLFP8dlYL/x+5ljAZ7CMkmZJZl2b1bf3bwQs23DMWoZl/rPj9ltj+r03hBBCCCGEEEII4RNC/lATQgghhBBCCCGEMBHK1qfKOSYNpSSK0jxKouyavXK4Xhk5ocyK0i1mVLCd1E0KPsb6M5RE2e7/tsO2ZQ1hzHdw9uzZFt93330tNusIZeusI0rOWEcVmW9F/stn570oTyW9NqGPC5U+shmYpNd2mGc7on2NmVW4gzslmdYPKLe2TG2WIY2YpaBqfaIsk2Vlv7NsHOyP3HX/2rVrLab1y6Su9j4s6xMxOwapZLyqZFiz/jjGSvlR02upMPkt4W8515jFk+ezjbA/WtYnnm9ltixpZgMySxQl35SL83z2cfYhtvdhvdGewfGF1zVZNe0illXL6p3ydF6fVvBKpiT2I/6W9iWzTLL8jFlfLD/tZBVLDOtkDpllxrCurCCE9WrjpV2f75BtgesgZjmzdRlh/63EnGeJZXkbtiObL9i3+cy8N6/Lccqsy1Z3nENPnz7dYlqMzU5WsVFU7FFGb3uy+pxD37R2XsmKcyvXBdam2EaYmZb2Itr5aYOyNRTh2M95n+tk9ndm7aNN1rIRDmFf47zJvmkZ2SzDGud72p24PqA1i7+1dT+/DWjLrVifyJg1auU6fK+VbSnIx+/rNIQQQgghhBBCCGGm5A81IYQQQgghhBBCCBNhpfWpkiHH7DWkV8a2LnrlcHxGSrF4HZNCU65F+bDJr6ycqzInmOyz8pwm12P5KOfm83AnccrTWUcmo++13/QeN2sV6d1pf8709lmyGbvoswxsO7Q7USZNySTbO8/nzvGUgPL61pfZZyklpayS16+2EZbbLIrsU7wf7VGUsP/whz9sMTOysZ9yPGIZ2K9Zv2Z9Yr2YVZX9mudYHVXGgUqbq8iCp0plfjRLqL2rStYnXnNVtrJlxytzBbFnZH9k+SmdNhuQyavZb2hfsCyQw3vQFsSMFXwGnk8ptWWstP7O57e+Y/ZLk2rbbyltZ9kY87ksM50dr1jy5tA3e+f5ii27Yn2yMoxZB7Gt0Q7L+YFzKK0JlqnMsjFavfEc9iGbc1ZZzC3rKtsh53L2F5bD+iPHFFpTXnzxxRbTYmzZzSrvxsbEMdsxEJsn5pz1qZePygZdyd7LtsN1H+cftkG2d47rtAeZnZ1tn1sKHDx4sMV79+5tMdebvO9w/GZboo2Kz8/ycW5i/6cVk2W171n2Nc5ZN27caDHnu4sXL7bYvr0r1r9KBlI7bt+hNg70Zkv8+PXeEEIIIYQQQgghhJmSP9SEEEIIIYQQQgghTISy9cmOm7zuVtorxsBnoRSLEkuTpVGKxp2naUeoPJfV2ypJVGW3eYvtHZjMjDt+0xZCKS0ld7RvsI7GSEBJb5ao3p3z50Alo1clI5uxrrph37GsT2an4zPyOCWN/C1jo2LxsEwmllFhWCZKQCkzpQTUsk5cunSpxexHvA7HF8q5zXrQa5Uxu6bZaSpU5gyTjJqla6pQ9muZXOxZK+M/r2MZgWzMYxvhXGbWPZP0VrKB0XbAMrN+7JpmgeRv+Sx89uE4YHYsXovji1kfLcMc69FsRHx+y3TF57H+xbrmmMDnt7jy/mzOsH46t4xsvRk/KnanyprW5mt751bflvGNc8uZM2dazPbFdRzfOcdUtn2WzbI7jc1uZPXIZzO7smWE4W9pA+M8+/3vf7/Fzz77bItpIeu1elbeMRmT+bSSWYZjKOtkqlhbmMO4slj4Fhn8NmT75TzA8/ldxTUAMzqxvVvf5PVZn5xPV/Vfri2ZoYpw3uSz2ZqN5xOzXFoGZZaN1jLOiTavVWLLmGb92sppY2hv+46iJoQQQgghhBBCCGEi5A81IYQQQgghhBBCCBNhpfWpYiWpyLl7LS+3UupmkmzKz0y+bpJMHu/NAmDZJxaLjTIqyrArssZKxhFK1K5cudLi3bt3t9jk+L0ZlMZkRxgjX67Ec6C3/kyuXpFb9/ZNXocyxp07d7Z469atLd62bVuLTU7I/lgZZ9iW2VdsJ3jLpsLjlIwO5czczZ/X5b1pd7pw4UKLX3nllRafO3euxcwKQKmnWUpsHOEzrLJvfRCVjGlmA6jYMCtyUEpqpwrnEc4FFams1atlZ2BbMFuEjZ1mu2C7YHksi1fFUmPzjGWfYTk555jty6yHw39bZoq77767xZR5m4SbczPtEhzjmFWK4x2zKNLqaGOFvQ+zXJmlkVQyFdmcaNecQ98kvWuBip2Q851l0quUh1RsUJa9k1YAuz77F/sErVI8blkazXZTXVtZ1ijLkGKWRs7FtPC/9NJLLf7Rj37UYrM7VexLZvfi+TbmVqxidh2Lbcyd2/p2jtjcZ+uvyjcNrYjsj1wP2jejrR8Ym017+G+z8FS+By0DHOe7Svn4zMyqZZmeSGV8t2e0THM8x74Z7Lk4N5h1jURRE0IIIYQQQgghhDAR8oeaEEIIIYQQQgghhIlQ1qpWbEoV60SvrJSMsUeZbJ9yJFowuMP27bff3mJKnCizsmwPVgZimW6G0ki7h72binTZ3hnls7RmHDhwoMXcVZz1aJLOCr22pt5zKsxhl/mKDLbXukgq1kUrD+WQO3bsaDHbzr59+1q8f//+FrMv0I5AabOVh1JKYjYNSimtz5r0eCgTZdYNkzvSOmVSdco4mQmgIsm25+S77LU79WZk680GVZGO85y5ZX3iO7fsAaRXGs/2xWuyD1auY32nMqebbYpzpWW+YBu/8847W2zPYu3dMh0Ny8Fy8948znIwZt+kpYTZZI4dO9Zik3ZzbcEx0WTrZgElHMvM6kXMclhZ2xFrH1Ol8hy2VjQLPC1u/+Sf/JMWHz9+vMXf+973Wsx2x3mN79DG8sqajm2TGVFoCbL2ZWOU1c+Ydfjw9/bMXPeyjjgXsI/z+ZkB6yc/+UmLaSs2K7GtxXstc+s6XjnHytk7738UzDHTEzHrMfsgLe9Hjx5tsVmk2Tbte8uyndo8YLZ4lmGx2Djf8d4sH9frlimY16Gt2NZyXCvQAsw++/LLLy+9vj1nJbZxzWxsrPdKFk+uC7u3k/jAM0IIIYQQQgghhBDCLSF/qAkhhBBCCCGEEEKYCGXrU0UyatJQMkY2uRlyuMoO1pSq2m7TPMfknBUpKcszlIZZtoyhZG0ZJis0uTKf03ah37t379LfjpHD9lotxkhPSe99p8QY+9JmZF6jTJKycMbczX7Xrl1Lf0vbiNmJLBuDSUN5PstgVC19lh2Hz2DSR5NwW0afigy7Yj+0DFA2ZvW2mzFWqTlnlqlkbjLribUjG4OJ1Q3bIN8Pz2e7s3doNitSsdpYGfhby+7CuY5WDvYVPstisdEuwSxLtnbhuGCZGljWN954o8WUanO8s2wRlYw+9u7Nhs2yVTJyGZYVrNceNSV6LfzELPO/+Zu/2eJ/+k//aYtfffXVFlOqP7TmLbtm5f3YOM3rs73Tose2SYtxJTtVpTzVc2wtynKwn7OvsV/zmc+fP99iZnricbOZVd79mLVir2Wpkk3VbKiVrFVhfdj4Smv72bNnW2w2f7Z3xuwTnKMqWcU4n1pWomH74rYF1n54nLZcW8fYuo7fz4xpp+IYSlsp68jWUhXs/dk6nPeytV3l+mZdI9OfWUMIIYQQQgghhBA+IeQPNSGEEEIIIYQQQggT4UPpyE1qZ1Liij2qV044xppBGRhl4ZQgUVbMmJYgypxtB+vKDvGUQvO5htekJIwS0Ir1qSK5NOkpn5nSPUrCTKraK4ddl+1ozE77c8heUbEpVeTqvX2wIgG2TEeG9Qu2a7Y1kzpath3KRHlN9idenzvWE9qyhhJxk5lyTOF1mU2GEtNr16612MaXdVmKTIppv7V3T2zct3dcsTKxrueQ9YnPxDZZyUhgdifSK9Vnu7VMZzZW8Fms7ivZ5Xh9Xsek3eyPzALB880mOLSWcK6kDJ31wn5KSTozNPEcjiN8HqtTysI5PuzZs6fF7Pu8ptk0LKuHZamovG9i1gnr13ObNw3WH5+bdWlzAX/LLKJbtmxpMbPA2PV7519rF+w7Fy9ebDEzLdIGxQynbOO8PvsHx7eKNXL4XDa2s59zHuTzsP+bvYQxr1OxFlq5K2Nu5fgYC/qq7LDvY7bSqTLHTE8Gn4XjN7M+MQMSxwrGNvaTisXH3r+t1xaLjX3Bsq7aFgNm57fxjllTWUe0O9HGyG1HKluuVOB1GFfWnFYGGx9ZD5Xv9yhqQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBFW6s5NWkkqdieTfFdsUGRM9h7LxEL5GctGudZbb73VYkq0KEur7NJvu0TzvrRHDK9JSTelX7YrdUWWbFYIy7RBiSnLw+tU5HpkXVmI1mXvmUPWp96+aRZFO15pR5WMJSbPZ9vpzWxAOSH7Kdspz2HfN1uW7YpvUuihTNSyO/G6ZrmkLYKWKI47U7AY9EqTWU6zTpBKu5xD1icrI9+/zRcmsbc6sJjWDFr/WIZKFiqjYqGrZCPhccvcRItixXI0lCqzH3He3L17d4tZF7R/MGa/tvbJeudvKQs3W/GpU6daTHuMrTlYF5WsbZU5o0Ilk+VU6Z3/bQzjdU6fPt3ib33rWy1mVkyzw1bm2THrGrYRswDyXhwruDZmu+b1aYOqzONDrE55Lcv+yH7OjFa0O9EqVckIY2ugSjmJzXe982CvncqYQ9/8uMLxnnPRuXPnWkwLLNusZda1TKbsK7a+rY7Ztl61NQr7F8cdPrNtPXD58uUWMzvbyZMnW8wxy6xcY7IA91oUzaZuW4iYDZnrCiO9N4QQQgghhBBCCGEi5A81IYQQQgghhBBCCBNhpY68Iq0cY6mgdMjk1r0SXctYQenmrl27WkzbActJ+Rmlx9yNvpLpyWRmJu20TErDe1M2ZjL6iky2Yl+xTFQmP6tI58dkZeqV/445PlVM4tcr6a3I/UglK4LJxS0rEzEbBWWYtAfxfOub9iwsD+/FvmV1O+xzfE7bLd8sW2ZrsfMrNqhKe+7tv5XsPlYGi00+avEcsleYbcHsKWaJrcB+xOvTtsDr8xzaaAybo23eMMshMYuT2YNsTrQ65DUXi43zt8H1Ae/HbD1cKxDalc0uQosIbZ/M8LF169YW06JlGS56M7hZ+6vYVCp9dg5Zn4zK2GNzKN/P//gf/6PFtNZRzl+x/Fv/qszL1hY4r9n60eZoW2OyP1YyPA4xm7StM806cenSpRbTQmgZSCsZCSvn9FotKvbDXps/mXNGto8rto0EvzGvX7/eYs4/nKNtbrXMQqTSZod93ObXSvZPs/vxeTgPco5mdjqOm6yXyr0q/ZdU1r2Mh+uMnnuRSlapKGpCCCGEEEIIIYQQJkL+UBNCCCGEEEIIIYQwEco6a5MRVexOlg3Kjld2vK+Uk3aJu+++u8UmC6eElTvkU65FmVLFQmKyKZOcsTyUdi4WG3fPrmRWMulapXxG7/nryqBUecYxWZzmYHci1h97bW292SUqEkjLREPLEu0CtiM92zstCIT2AmZKMZsCy0OrBWEmFrJK0mj9mVJMHqeM8913321xZTd7ezdjdrnvlUb32hsrFi2+G3vGOWSv4Hs2C16lP5pVlu2WdcaMLTyHEl3LSEAsA5S9w0o7ouS718rBczhW2PjDey0WG/uzScx5P9qRaJ0wyyXXE/fcc0+LueagzJtzt2VLtExXFetX77zfawc2a80c+qbN85XsTmbBpIWB7/nGjRstZlurjHO92aAqxyvWt4rFjfMVx5wPA+/Bvk07h9kNzDpimZ4qNrYxlvxeS1TlOkalfcwtI9vHFRuPObdwjmL/4ngynNeWUckUWbXH9a4zzYLEtQgtTleuXGnxhQsXWsysbayXSqZMK/+Yb9sx1kX7LZ/FvkNIem8IIYQQQgghhBDCRMgfakIIIYQQQgghhBAmwkrrUyUjTEVy2WuPqkjmK7Ijy8RCbOdpHje5MTHJWW+WEpPRLhYbZWAmZ6+8GzJGVttLRaLW+9tea1yvTWiqUBpMKtLuMTJ5SiitXdDuwZgyadoL7Jq0AlD2yb7MfkC7AK9DOwJlpSyzZZPg+bRTDTPm8N/W5y9fvtxiSrXNZlmx/1QyvlXkmvbuK5llTBa7LvuS3XeqVLIqVDJlsY+z7zBr4b59+1q8f//+Fps96tlnn20xJca0GRJ7/4R9p9d6yd8yM5Jln6nYF4ZtmRJz9mf2NdYv7ZQcpw4dOtTia9eutZjWJ9o3Kn220n95DscWs7WYlbrX/sqx2+w6lbFoSvRmS6ysRe09mO2RbcTeSWWdQiprbL5D2vLY9tmW2e/M1tCbpW4VLDfrjmMEY/bl119/vcWVOZT0Zr4cY7E3bI62d1khWZ82n8o3jfVljuWcowjbMq23tFJyGwEbZyoZz4bfyL1bkJitmmMiv2m55uB8yi0MLDsqsecxrPy9f4PgWMRx0Oz8hHWdrE8hhBBCCCGEEEIIMyJ/qAkhhBBCCCGEEEKYCCt1i727Go/J+tRrSalIsUwyTPkV5ZOUWVnZTH5lmQIqz0sJlVk5hv+uSKZJpe4qcvwKY87vzb60rmxNFYnslKjI/SzjkEnjTW7fmyWK96WFgdaB7du3t5gSa7Z/ywLBc2z8oZyb/d0k3LRdcDd6lsGyWS0WnqWF5TPZJ2XbvA7lraSSmaM3q0eFdVkgx9yrt8wfBTZHsA1zPmJb4vNR0rxz584W/+7v/m6Lv/KVryw9h1Jq9q8dO3a0+Omnn27xc88912K2QVpx1yWlN4tDRf7Nvl/NJMW65rVYL6xr2j84DrLurM/Snsz3wf7IMYVls75vY3Ql61OvBbLX1sTyzzmzjK3xbOyp2JHMet9rF6usgStjs81flXLauLvOd25WRvZZZtKilZj90b437J31Wssq9rleW13lXr0WD7tmuLVUbEfsj5zjOL6aBZBzKOcWW/NbHx+2kd42w3tY+bjuvXTpUovPnTvXYm5BYragyndub9+vfEdVrIg2VpoNvmIfTe8NIYQQQgghhBBCmAj5Q00IIYQQQgghhBDCRFipuendQbkiva9Ia3vtTnac16ScirJiyyxDTH5YkUNWZG8sJ6WdQyk4/23S8177Q8W6NuYdj2lDFXrvWzl/DjvkW3YRo1cOb32wkvWJFg9motm7d+/S8tN+yJh2BHsW2omYDYfyQ2bAsSwzvCbvy2dhmYdjhf2fvRtKQG08smxzlUwDlfNNdm+ZYswONyZ7WuV8yyYwVfhMzCDEsrNdsU/xONvhli1bWswsZtZWeS+2f9px2DcpPeb7pPXJJMBmqaD90LLMEJbf5qJKhqmh9Jh9k5nbGN9+++1Lr0t7FGPWL+1OzOZ24MCBpWVif+S74XhkWSRsfVPJAFWxCY+xQs9h3rS1j1HJLGbjqMnbK5ngTFZv2bcqsnq2X8u81ptNdJ2Wcc6DnB+ZBYb968KFCy1mH2TfrFiQKtak3r5TOd6bMai3bHPLljgXescQ+y3nO84/nJc4P3AeY19eZclfdt+qHdb+z7bwMLuuWblo9bX1cOU71xiT4ZTzta05K9lwK+vbZH0KIYQQQgghhBBCmBH5Q00IIYQQQgghhBDCRBid9cmkPZVsR2POr1hVKCniOZWMCr07/5Pe7CiWzYmSsWX/7mGMTWkzjm+Gnap3F/1eq8iUqFjZbHf3ivXN+mClL9Dm8NBDD7V43759S8tz8+bNFlPSuXXr1hbTQmJZmdjft23btvRZeF/2O0pMeX3LADOUEltGJ9YXn5MxzzcZpO0kb2Nfr4WhIocdcy+zL5mV1OxXc+ibRkUea9YGnk/5/8mTJ1tM+TRj9gXKqpmFjdfkb2kZNnuFybNp/aGtx8YcSsHtHLM+rZKjs07NFsJ+x7JatkjWC+MTJ060+PDhwy3mO+AYZ3YnYn3Hslr2ZneqYNfkfeeQkc2oZA5lu2J7MVsJ+4L194qVs9eubWMk2x37LDMwcnxYlRHmg+5Vhc/DdTn7FDM60eL0xhtvtNjmzTFrRTu/ElfWW71lI722tGR92hx6v10Ix3uuddkHaXPmXME52uy5nE+tzKusgZV1oNl4ubbmGMd+zXU2t/VY13qV9H5Xmg2q8jcLy7bLejDruJHeG0IIIYQQQgghhDAR8oeaEEIIIYQQQgghhIkwOutTZQflShYk20G5N3uUSZaMXkmj/bayAz/hccrEKP8c/tZkV6QqB/+g4/ZuKr9dV9andb0DO99sPHPILENZtbXz3vfTu7u52QiYfemJJ55oMbO90MZHOSSlmzzfdstnlqgdO3YsvQ7Pp3XCxg1mp2IZzEq2WGyUorJM7M/MXkEbhUkiiUmaScWmVOmPlomkYn2q9Fm7pj0jj1d2yJ8S7Bdm+bKsT4S/PXXqVItZH8wAxYxOltWF92VWKdoLLJsS3w/HojvvvLPFDz/8cIvZ9nlNloeSb2LHTZK8at637EuVjIwcp1jv7OOXLl1q8fnz51vMd0OJOCXvjDme2BxX6S9jLE52vHetMyV61xGV7EuVNmXri8ratVe2z7ZpZbP1A48bm2Wj4VhAOzBtEexrtmWAteFe+9YYa1KvDaay7UIlq1olU08Yx5itGhhzTmBMuxPnPp5TGdctIxPhemM4b9rYzvHF7NyV7E5cE9i2HpWxpnfuq3wnVr6pbEuF3q1bkvUphBBCCCGEEEIIYUbkDzUhhBBCCCGEEEIIE6Gc9clkO5UsThWpbEXqWZEaVeR+Vk57rsrO0L3SJ5OJWfaGVeUjvVJnu36vtG5M1qR13ZeYjLhSh3PYIb9SdsoSK23b5NmsS0qjKZuknP+xxx5rMTM98bc///nPW0xLAbOjsDx8Fl6HGSt4nJJRPguP0xJCWLaqRJH1zrp45513ll6Lz2PvzKxolf7VayEcI9W24zZ/mNy/ct85ZJaxjDC92QPYPikTprWOMe1LzFZGmx0zRzCbCq02tC8xgxvbI+0I7HeUZ7OvXbhwocWUYfOaPM4sb+xPtDTa3D0cv81OSasRxxHe78aNGy02qwWP8z1du3atxcx+x7pmf7///vtbzKxwp0+fbrFlrOzNjFaxM48ZN6ZKrz26YjcfYx+vbAVg4waxtW5lzmF/N7uxXXMsZseiFYJzKPsX1w32DVCxtK0aO97Hvk8q549Zq1ey29j8Mbdsib3fAR8VlfZVsd9xjj569GiLOT9w/mU/tYzGZmWqfJ+uaiO2HYmte9l/eT6zS3KO4/kV+1Jv1rNKe6r8zaIyXls5K+thLdsHnhFCCCGEEEIIIYQQbgn5Q00IIYQQQgghhBDCRFhpfTLG7HpdsUeZTKsiM+yV2JN1ZQSw+1aks1XJX28GrF7ZLqlIRivn97aJCr3P2ys3nSpmCzKpM+umNxMNoY2AUmVmSvqd3/mdFtNSwOvzvpb1xt4ty0D5M20dtI2wnJapivYFXoc2KN53uEt9JdsCj5sdg5aSMdmdjF7L0rr6Qu/YyudiHc4he4XNWZXxj+fQsmTZANn+mVHB+hftSG+88UaLKaVmtiJen+XhvRjTpkDrD2PCsrGvcXxjn6DVi+cwHmbOYv+3jFM8busPPpvZEjnuUOZNCxVtXVZuviezfvXKvHttSpWsWHOwCZMxmUx7rfRsC73rHbPO2FrX5nSzKpu90bI+jbG4rZpDLKMfxzse51hQyZZoFjKyLouXnT/mG6B33rS6nsO8uRnbOYyhN3Ns5R2yfz344IMtPnToUItp/2ef5W9tHqxsS8K+xbmlSu/cYfZm9uuKVaxik+9dx1ayMlW2Mum1QZFkfQohhBBCCCGEEEKYEflDTQghhBBCCCGEEMJEKGd9GmNrqtiOTNLZe30rv9Gb5aDXcjTGRjCUSZq8qiLrsndWke4ZvXLN3jZBeuWgdl+TtNn5U8WyEJi816TRdh3C+qBNh1lmjh071uKDBw8uva/ZgJh9hde0zF2U/99zzz1Lr8lzTHrK82k1oBSc1zH7yfDfjJmlgtYRnsMyWWYOG1OsTvmOe+X+lfZfGXNMklvJtGfjwxyyPpmd0Noen492CT4rbTo8n1YAZlFgliXrR2zPhOezPHwutmWe8+abb7bYbCBmM+IzWj8wu5L1oeF5tC/R4mUZsM6ePdti2pf4PIz5Ps6dO9fiZ599tsWXLl1aei++M16T59g7M5tNZayoWLitn5oFd6pU1jiVddaYLHn2TsxyV5lDTZ5PGy/nZWZjZPsy69NmUbGI8Nls7VLJGmtzUMXOb9icZfTalnuzPlXOmSpjLHVjvl3WVR77Leue/XHPnj0tZn+kNdayONkWBNaWh3bgdWF1bXYn67OV99d7ToXeMaG3T1WsiMn6FEIIIYQQQgghhDAj8oeaEEIIIYQQQgghhImwUqs6xtZksj67jkn1TdJpx638leMmS6REqyJVrVhwzK6yyuLTm82hco9KmdZlXbPzre4q5bS2aLI68mGybU2divTP2m1FTkgJ5V133dXiXbt2tZhSatttnhleeB2TXnN8YBkqGTEqlhOez4wYZtkYZn2x/6NFhLYN283fpMu2W75Zruw5rV/w+Stjfe/4bjExKb9J36cK68Ayk/B4RT7Mc9gm7fxKuxgjMbZ7WRuptAtmsqjMXZVsF8Py0Zr1yiuvtJi2kP3797f49OnTLWYf5Duwfkqr1NNPP93i3bt3t5hZN2iTZDn5PJTFV9Yxlcw4Rq+1YA6ZZWycrthGK32nNwOptWGzkVn5bUxl+2K7vuOOO5ae37uet/uaVXd4HfYXy1rHeXNdGZGsfHadyvq+t3+N6Y/2LDYuz4HKdhak91uht72M+WYitDvt2LGjxZwHaGWy61vGM2Ysta0ALLuaZRFcLLz927V4Dstk2RI5l/E6lTV3xSbfu3ULqdhK7b5mz7QsV5VvzyhqQgghhBBCCCGEECZC/lATQgghhBBCCCGEMBFWWp/MnkJ6MwiN2SG/N7OQlbP3+Jjy9+4YXZX/2b17M2aNKWuvxatig6rIUHuzO1FGbHJAnk+58FShXcayEZmckrYbWpB4Tcr3aE1idqdDhw61mNYnXt/aMOuYMd+VyZMtOwSlhWxTlkmnt22azHOx2FhfPM+Om+SU2af4XisZQSr2EspwSUU6b1YBk62aNJ/lZFvh89r7YNaxqcLns0xGllmJVKwZdtzK0ysZtrHcsPNtDDarCNtFRY5u9sHFYmN/4ftgfO3atRbT+kjZNu0i1s4t2wWPsw/yuGWPIqxHs6+wjnidyhpuTKZPjmlTxTIokcp6p2LBq1yH51QyNnKstb7GvsC5e+fOnUvvxX7AtmnlGZPJdIitDyxLjb0zs4rZuGPtoGJRs/OtTfD8ip2msr2CldPanM37U8LaWyWrb2VrhN6svhVrrWUx4rqJWwE8/vjjLaZNySz/FfuhrcXYxivfN8O+3Dvf2zcGY96b8yzLXalrs0FZ++/dFsDGmTF/s7D3V+mbUdSEEEIIIYQQQgghTIT8oSaEEEIIIYQQQghhIqy0PlGyRQmtWRIo3zLrCeW9PG47JZv8n/el5NaOV7JKmYy8kt1pXTLJVTLGyk76vfYlu3elTBXpZuX6vdLQyvOajNLKU5GUTwm2bduRnW2Yz8e6ZJ9lzPMpmWZ2lH379rV4z549LWYfZ//l+6EEku+k8p5N6mjPaJLDSt+nJJOZWJjNabHYmLHijTfeaDF3v6cElBYeezbuis+MOLbjv+2Qb5Yby57FtsXYxnHWI8tp4zipZFjhdTgnTRXWK20FZp3jO+nNoGTn99qHe7M6boZllticYOuKVfJnk2fz3bBvX7p0qcWUZ2/dunXp/ZityZ6ZbcLWBIRyaI7LlMuTu+++u8XXr19fen3LqtU7zhKWjbbNqWIZCW0usDbG98kxktdn+2Ib5pjN33KMNCsP2wXLz/mUdqe/8Tf+RotpVd62bVuLK5mkbN1XsQKsyhZo636exzrl2ox9s5KJrJL1qXK+lb/XKlixtVQyN1k/NQvcVOF75vrK1rSVbIA8zjUXj7NuuK5hljTLrMT3wzUwx4fDhw8vPYfbBZiFlOW0McfGisq8b9khh1imJx7nNgqsR96PdfH222+3mOvnq1evtpjPxnfAd8zfcs1s4y/tzJW/TVS2Tui1y/bOm1HUhBBCCCGEEEIIIUyE/KEmhBBCCCGEEEIIYSJ8auyu7SGEEEIIIYQQQghhPURRE0IIIYQQQgghhDAR8oeaEEIIIYQQQgghhImQP9SEEEIIIYQQQgghTIT8oSaEEEIIIYQQQghhIuQPNSGEEEIIIYQQQggTIX+oCSGEEEIIIYQQQpgI/z+CtXlR9WktfgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 10 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_img_count = 10\n",
    "test_latents = default_make_latent(test_img_count, latent_size).to(device)\n",
    "fakes = gen_net(test_latents)\n",
    "\n",
    "fig, axs = plt.subplots(2, (test_img_count // 2), figsize=(20, 8))\n",
    "axs = axs.flatten()\n",
    "for i, ax in enumerate(axs):\n",
    "    ax.axis(\"off\")\n",
    "    ax.imshow(fakes[i, 0].cpu().data.numpy(), cmap=\"gray\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleanup data directory\n",
    "\n",
    "Remove directory if a temporary was used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "is_executing": true
    }
   },
   "outputs": [],
   "source": [
    "if directory is None:\n",
    "    shutil.rmtree(root_dir)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}