{"cells": [{"cell_type": "markdown", "id": "3a14c069", "metadata": {"papermill": {"duration": 0.123758, "end_time": "2021-12-04T15:54:31.899114", "exception": false, "start_time": "2021-12-04T15:54:31.775356", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 3: Initialization and Optimization\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2021-12-04T16:52:46.401516\n", "\n", "In this tutorial, we will review techniques for optimization and initialization of neural networks.\n", "When increasing the depth of neural networks, there are various challenges we face.\n", "Most importantly, we need to have a stable gradient flow through the network, as otherwise, we might encounter vanishing or exploding gradients.\n", "This is why we will take a closer look at the following concepts: initialization and optimization.\n", "This notebook is part of a lecture series on Deep Learning at the University of Amsterdam.\n", "The full list of tutorials can be found at https://uvadlc-notebooks.rtfd.io.\n", "\n", "\n", "---\n", "Open in [{height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/course_UvA-DL/03-initialization-and-optimization.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/PytorchLightning/pytorch-lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n", "| Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)"]}, {"cell_type": "markdown", "id": "cd07a418", "metadata": {"papermill": {"duration": 0.119122, "end_time": "2021-12-04T15:54:32.133365", "exception": false, "start_time": "2021-12-04T15:54:32.014243", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "44a29573", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2021-12-04T15:54:32.380621Z", "iopub.status.busy": "2021-12-04T15:54:32.380141Z", "iopub.status.idle": "2021-12-04T15:54:34.883678Z", "shell.execute_reply": "2021-12-04T15:54:34.884084Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 2.626824, "end_time": "2021-12-04T15:54:34.884364", "exception": false, "start_time": "2021-12-04T15:54:32.257540", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"seaborn\" \"torchmetrics>=0.3\" \"torchvision\" \"pytorch-lightning>=1.3\" \"torch>=1.6, <1.9\" \"matplotlib\""]}, {"cell_type": "markdown", "id": "6502723b", "metadata": {"papermill": {"duration": 0.116681, "end_time": "2021-12-04T15:54:35.117128", "exception": false, "start_time": "2021-12-04T15:54:35.000447", "status": "completed"}, "tags": []}, "source": ["<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/X5m7bC4xCLY\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "In the first half of the notebook, we will review different initialization techniques, and go step by step from the simplest initialization to methods that are nowadays used in very deep networks.\n", "In the second half, we focus on optimization comparing the optimizers SGD, SGD with Momentum, and Adam.\n", "\n", "Let's start with importing our standard libraries:"]}, {"cell_type": "code", "execution_count": 2, "id": "7b848761", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:35.354836Z", "iopub.status.busy": "2021-12-04T15:54:35.354335Z", "iopub.status.idle": "2021-12-04T15:54:37.277810Z", "shell.execute_reply": "2021-12-04T15:54:37.278195Z"}, "papermill": {"duration": 2.046832, "end_time": "2021-12-04T15:54:37.278357", "exception": false, "start_time": "2021-12-04T15:54:35.231525", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_875/1682095326.py:24: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", " set_matplotlib_formats(\"svg\", \"pdf\") # For export\n"]}], "source": ["import copy\n", "import json\n", "import math\n", "import os\n", "import urllib.request\n", "from urllib.error import HTTPError\n", "\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pytorch_lightning as pl\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.utils.data as data\n", "\n", "%matplotlib inline\n", "from IPython.display import set_matplotlib_formats\n", "from matplotlib import cm\n", "from torchvision import transforms\n", "from torchvision.datasets import FashionMNIST\n", "from tqdm.notebook import tqdm\n", "\n", "set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "sns.set()"]}, {"cell_type": "markdown", "id": "34ff3be8", "metadata": {"papermill": {"duration": 0.117573, "end_time": "2021-12-04T15:54:37.513508", "exception": false, "start_time": "2021-12-04T15:54:37.395935", "status": "completed"}, "tags": []}, "source": ["Instead of the `set_seed` function as in Tutorial 3, we can use PyTorch Lightning's build-in function `pl.seed_everything`.\n", "We will reuse the path variables `DATASET_PATH` and `CHECKPOINT_PATH` as in Tutorial 3.\n", "Adjust the paths if necessary."]}, {"cell_type": "code", "execution_count": 3, "id": "35b849b3", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:37.749217Z", "iopub.status.busy": "2021-12-04T15:54:37.748745Z", "iopub.status.idle": "2021-12-04T15:54:37.951118Z", "shell.execute_reply": "2021-12-04T15:54:37.951502Z"}, "papermill": {"duration": 0.323033, "end_time": "2021-12-04T15:54:37.951669", "exception": false, "start_time": "2021-12-04T15:54:37.628636", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Using device cuda:0\n"]}], "source": ["# Path to the folder where the datasets are/should be downloaded (e.g. MNIST)\n", "DATASET_PATH = os.environ.get(\"PATH_DATASETS\", \"data/\")\n", "# Path to the folder where the pretrained models are saved\n", "CHECKPOINT_PATH = os.environ.get(\"PATH_CHECKPOINT\", \"saved_models/InitOptim/\")\n", "\n", "# Seed everything\n", "pl.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.determinstic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "# Fetching the device that will be used throughout this notebook\n", "device = torch.device(\"cpu\") if not torch.cuda.is_available() else torch.device(\"cuda:0\")\n", "print(\"Using device\", device)"]}, {"cell_type": "markdown", "id": "94ce8a6e", "metadata": {"papermill": {"duration": 0.116608, "end_time": "2021-12-04T15:54:38.185784", "exception": false, "start_time": "2021-12-04T15:54:38.069176", "status": "completed"}, "tags": []}, "source": ["In the last part of the notebook, we will train models using three different optimizers.\n", "The pretrained models for those are downloaded below."]}, {"cell_type": "code", "execution_count": 4, "id": "8e55f09c", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:38.430163Z", "iopub.status.busy": "2021-12-04T15:54:38.429683Z", "iopub.status.idle": "2021-12-04T15:54:39.458838Z", "shell.execute_reply": "2021-12-04T15:54:39.458384Z"}, "papermill": {"duration": 1.155736, "end_time": "2021-12-04T15:54:39.458964", "exception": false, "start_time": "2021-12-04T15:54:38.303228", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD_results.json...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGD.tar...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom.config...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom_results.json...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_SGDMom.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam_results.json...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/FashionMNIST_Adam.tar...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial4/\"\n", "# Files to download\n", "pretrained_files = [\n", " \"FashionMNIST_SGD.config\",\n", " \"FashionMNIST_SGD_results.json\",\n", " \"FashionMNIST_SGD.tar\",\n", " \"FashionMNIST_SGDMom.config\",\n", " \"FashionMNIST_SGDMom_results.json\",\n", " \"FashionMNIST_SGDMom.tar\",\n", " \"FashionMNIST_Adam.config\",\n", " \"FashionMNIST_Adam_results.json\",\n", " \"FashionMNIST_Adam.tar\",\n", "]\n", "# Create checkpoint path if it doesn't exist yet\n", "os.makedirs(CHECKPOINT_PATH, exist_ok=True)\n", "\n", "# For each file, check whether it already exists. If not, try downloading it.\n", "for file_name in pretrained_files:\n", " file_path = os.path.join(CHECKPOINT_PATH, file_name)\n", " if not os.path.isfile(file_path):\n", " file_url = base_url + file_name\n", " print(f\"Downloading {file_url}...\")\n", " try:\n", " urllib.request.urlretrieve(file_url, file_path)\n", " except HTTPError as e:\n", " print(\n", " \"Something went wrong. Please try to download the file from the GDrive folder, or contact the author with the full output including the following error:\\n\",\n", " e,\n", " )"]}, {"cell_type": "markdown", "id": "30caea5b", "metadata": {"papermill": {"duration": 0.120012, "end_time": "2021-12-04T15:54:39.700194", "exception": false, "start_time": "2021-12-04T15:54:39.580182", "status": "completed"}, "tags": []}, "source": ["## Preparation"]}, {"cell_type": "markdown", "id": "231720c6", "metadata": {"papermill": {"duration": 0.120402, "end_time": "2021-12-04T15:54:39.942112", "exception": false, "start_time": "2021-12-04T15:54:39.821710", "status": "completed"}, "tags": []}, "source": ["Throughout this notebook, we will use a deep fully connected network, similar to our previous tutorial.\n", "We will also again apply the network to FashionMNIST, so you can relate to the results of Tutorial 3.\n", "We start by loading the FashionMNIST dataset:"]}, {"cell_type": "code", "execution_count": 5, "id": "f973c6a6", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:40.192819Z", "iopub.status.busy": "2021-12-04T15:54:40.192347Z", "iopub.status.idle": "2021-12-04T15:54:45.087304Z", "shell.execute_reply": "2021-12-04T15:54:45.087688Z"}, "papermill": {"duration": 5.025264, "end_time": "2021-12-04T15:54:45.087853", "exception": false, "start_time": "2021-12-04T15:54:40.062589", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-images-idx3-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0fdf1596470d464fa9a290533416cb26", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/26421880 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/1/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/train-labels-idx1-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "68ab1848fe7942fd914800ef133729fd", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/29515 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/1/s/.datasets/FashionMNIST/raw/train-labels-idx1-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw\n", "\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-images-idx3-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "56a2c8c742d5459e92de2eb14a2908ed", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/4422102 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/1/s/.datasets/FashionMNIST/raw/t10k-images-idx3-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw\n", "\n", "Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/t10k-labels-idx1-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b34dadf61f28405fbaf0146f3b1a4611", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/5148 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/1/s/.datasets/FashionMNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/1/s/.datasets/FashionMNIST/raw\n", "\n", "Processing...\n", "Done!\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.9/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n", " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n"]}], "source": ["\n", "# Transformations applied on each image => first make them a tensor, then normalize them with mean 0 and std 1\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.2861,), (0.3530,))])\n", "\n", "# Loading the training dataset. We need to split it into a training and validation part\n", "train_dataset = FashionMNIST(root=DATASET_PATH, train=True, transform=transform, download=True)\n", "train_set, val_set = torch.utils.data.random_split(train_dataset, [50000, 10000])\n", "\n", "# Loading the test set\n", "test_set = FashionMNIST(root=DATASET_PATH, train=False, transform=transform, download=True)"]}, {"cell_type": "markdown", "id": "62d14135", "metadata": {"papermill": {"duration": 0.132716, "end_time": "2021-12-04T15:54:45.352571", "exception": false, "start_time": "2021-12-04T15:54:45.219855", "status": "completed"}, "tags": []}, "source": ["We define a set of data loaders that we can use for various purposes later.\n", "Note that for actually training a model, we will use different data loaders\n", "with a lower batch size."]}, {"cell_type": "code", "execution_count": 6, "id": "3d28e67a", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:45.623150Z", "iopub.status.busy": "2021-12-04T15:54:45.622650Z", "iopub.status.idle": "2021-12-04T15:54:45.624735Z", "shell.execute_reply": "2021-12-04T15:54:45.624337Z"}, "papermill": {"duration": 0.139138, "end_time": "2021-12-04T15:54:45.624842", "exception": false, "start_time": "2021-12-04T15:54:45.485704", "status": "completed"}, "tags": []}, "outputs": [], "source": ["train_loader = data.DataLoader(train_set, batch_size=1024, shuffle=True, drop_last=False)\n", "val_loader = data.DataLoader(val_set, batch_size=1024, shuffle=False, drop_last=False)\n", "test_loader = data.DataLoader(test_set, batch_size=1024, shuffle=False, drop_last=False)"]}, {"cell_type": "markdown", "id": "b7b214a6", "metadata": {"papermill": {"duration": 0.132224, "end_time": "2021-12-04T15:54:45.888887", "exception": false, "start_time": "2021-12-04T15:54:45.756663", "status": "completed"}, "tags": []}, "source": ["In comparison to the previous tutorial, we have changed the parameters of the normalization transformation `transforms.Normalize`.\n", "The normalization is now designed to give us an expected mean of 0 and a standard deviation of 1 across pixels.\n", "This will be particularly relevant for the discussion about initialization we will look at below, and hence we change it here.\n", "It should be noted that in most classification tasks, both normalization techniques (between -1 and 1 or mean 0 and stddev 1) have shown to work well.\n", "We can calculate the normalization parameters by determining the mean and standard deviation on the original images:"]}, {"cell_type": "code", "execution_count": 7, "id": "f3c58d88", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:46.162358Z", "iopub.status.busy": "2021-12-04T15:54:46.161597Z", "iopub.status.idle": "2021-12-04T15:54:46.309115Z", "shell.execute_reply": "2021-12-04T15:54:46.309503Z"}, "papermill": {"duration": 0.288556, "end_time": "2021-12-04T15:54:46.309671", "exception": false, "start_time": "2021-12-04T15:54:46.021115", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Mean 0.28604060411453247\n", "Std 0.3530242443084717\n"]}], "source": ["print(\"Mean\", (train_dataset.data.float() / 255.0).mean().item())\n", "print(\"Std\", (train_dataset.data.float() / 255.0).std().item())"]}, {"cell_type": "markdown", "id": "205aa3d4", "metadata": {"papermill": {"duration": 0.134209, "end_time": "2021-12-04T15:54:46.580091", "exception": false, "start_time": "2021-12-04T15:54:46.445882", "status": "completed"}, "tags": []}, "source": ["We can verify the transformation by looking at the statistics of a single batch:"]}, {"cell_type": "code", "execution_count": 8, "id": "eb1baf45", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:46.853983Z", "iopub.status.busy": "2021-12-04T15:54:46.853514Z", "iopub.status.idle": "2021-12-04T15:54:47.034146Z", "shell.execute_reply": "2021-12-04T15:54:47.034531Z"}, "papermill": {"duration": 0.319767, "end_time": "2021-12-04T15:54:47.034711", "exception": false, "start_time": "2021-12-04T15:54:46.714944", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Mean: 0.009\n", "Standard deviation: 1.012\n", "Maximum: 2.022\n", "Minimum: -0.810\n"]}], "source": ["imgs, _ = next(iter(train_loader))\n", "print(f\"Mean: {imgs.mean().item():5.3f}\")\n", "print(f\"Standard deviation: {imgs.std().item():5.3f}\")\n", "print(f\"Maximum: {imgs.max().item():5.3f}\")\n", "print(f\"Minimum: {imgs.min().item():5.3f}\")"]}, {"cell_type": "markdown", "id": "b800ca4a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.134539, "end_time": "2021-12-04T15:54:47.304930", "exception": false, "start_time": "2021-12-04T15:54:47.170391", "status": "completed"}, "tags": []}, "source": ["Note that the maximum and minimum are not 1 and -1 anymore, but shifted towards the positive values.\n", "This is because FashionMNIST contains a lot of black pixels, similar to MNIST.\n", "\n", "Next, we create a linear neural network. We use the same setup as in the previous tutorial."]}, {"cell_type": "code", "execution_count": 9, "id": "eb8177c7", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:47.584349Z", "iopub.status.busy": "2021-12-04T15:54:47.583870Z", "iopub.status.idle": "2021-12-04T15:54:47.585900Z", "shell.execute_reply": "2021-12-04T15:54:47.585499Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.147005, "end_time": "2021-12-04T15:54:47.586011", "exception": false, "start_time": "2021-12-04T15:54:47.439006", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class BaseNetwork(nn.Module):\n", " def __init__(self, act_fn, input_size=784, num_classes=10, hidden_sizes=[512, 256, 256, 128]):\n", " \"\"\"\n", " Args:\n", " act_fn: Object of the activation function that should be used as non-linearity in the network.\n", " input_size: Size of the input images in pixels\n", " num_classes: Number of classes we want to predict\n", " hidden_sizes: A list of integers specifying the hidden layer sizes in the NN\n", " \"\"\"\n", " super().__init__()\n", "\n", " # Create the network based on the specified hidden sizes\n", " layers = []\n", " layer_sizes = [input_size] + hidden_sizes\n", " for layer_index in range(1, len(layer_sizes)):\n", " layers += [nn.Linear(layer_sizes[layer_index - 1], layer_sizes[layer_index]), act_fn]\n", " layers += [nn.Linear(layer_sizes[-1], num_classes)]\n", " # A module list registers a list of modules as submodules (e.g. for parameters)\n", " self.layers = nn.ModuleList(layers)\n", "\n", " self.config = {\n", " \"act_fn\": act_fn.__class__.__name__,\n", " \"input_size\": input_size,\n", " \"num_classes\": num_classes,\n", " \"hidden_sizes\": hidden_sizes,\n", " }\n", "\n", " def forward(self, x):\n", " x = x.view(x.size(0), -1)\n", " for layer in self.layers:\n", " x = layer(x)\n", " return x"]}, {"cell_type": "markdown", "id": "8635213a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.133716, "end_time": "2021-12-04T15:54:47.853607", "exception": false, "start_time": "2021-12-04T15:54:47.719891", "status": "completed"}, "tags": []}, "source": ["For the activation functions, we make use of PyTorch's `torch.nn` library instead of implementing ourselves.\n", "However, we also define an `Identity` activation function.\n", "Although this activation function would significantly limit the\n", "network's modeling capabilities, we will use it in the first steps of\n", "our discussion about initialization (for simplicity)."]}, {"cell_type": "code", "execution_count": 10, "id": "9f352558", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:48.130790Z", "iopub.status.busy": "2021-12-04T15:54:48.130299Z", "iopub.status.idle": "2021-12-04T15:54:48.132296Z", "shell.execute_reply": "2021-12-04T15:54:48.131898Z"}, "papermill": {"duration": 0.144009, "end_time": "2021-12-04T15:54:48.132404", "exception": false, "start_time": "2021-12-04T15:54:47.988395", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Identity(nn.Module):\n", " def forward(self, x):\n", " return x\n", "\n", "\n", "act_fn_by_name = {\"tanh\": nn.Tanh, \"relu\": nn.ReLU, \"identity\": Identity}"]}, {"cell_type": "markdown", "id": "8eeb9167", "metadata": {"papermill": {"duration": 0.133812, "end_time": "2021-12-04T15:54:48.399825", "exception": false, "start_time": "2021-12-04T15:54:48.266013", "status": "completed"}, "tags": []}, "source": ["Finally, we define a few plotting functions that we will use for our discussions.\n", "These functions help us to (1) visualize the weight/parameter distribution inside a network, (2) visualize the gradients that the parameters at different layers receive, and (3) the activations, i.e. the output of the linear layers.\n", "The detailed code is not important, but feel free to take a closer look if interested."]}, {"cell_type": "code", "execution_count": 11, "id": "16bbefb4", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:48.682589Z", "iopub.status.busy": "2021-12-04T15:54:48.682093Z", "iopub.status.idle": "2021-12-04T15:54:48.683729Z", "shell.execute_reply": "2021-12-04T15:54:48.684129Z"}, "papermill": {"duration": 0.151108, "end_time": "2021-12-04T15:54:48.684257", "exception": false, "start_time": "2021-12-04T15:54:48.533149", "status": "completed"}, "tags": []}, "outputs": [], "source": ["##############################################################\n", "\n", "\n", "def plot_dists(val_dict, color=\"C0\", xlabel=None, stat=\"count\", use_kde=True):\n", " columns = len(val_dict)\n", " fig, ax = plt.subplots(1, columns, figsize=(columns * 3, 2.5))\n", " fig_index = 0\n", " for key in sorted(val_dict.keys()):\n", " key_ax = ax[fig_index % columns]\n", " sns.histplot(\n", " val_dict[key],\n", " ax=key_ax,\n", " color=color,\n", " bins=50,\n", " stat=stat,\n", " kde=use_kde and ((val_dict[key].max() - val_dict[key].min()) > 1e-8),\n", " ) # Only plot kde if there is variance\n", " hidden_dim_str = (\n", " r\"(%i $\\to$ %i)\" % (val_dict[key].shape[1], val_dict[key].shape[0]) if len(val_dict[key].shape) > 1 else \"\"\n", " )\n", " key_ax.set_title(f\"{key} {hidden_dim_str}\")\n", " if xlabel is not None:\n", " key_ax.set_xlabel(xlabel)\n", " fig_index += 1\n", " fig.subplots_adjust(wspace=0.4)\n", " return fig\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_weight_distribution(model, color=\"C0\"):\n", " weights = {}\n", " for name, param in model.named_parameters():\n", " if name.endswith(\".bias\"):\n", " continue\n", " key_name = f\"Layer {name.split('.')[1]}\"\n", " weights[key_name] = param.detach().view(-1).cpu().numpy()\n", "\n", " # Plotting\n", " fig = plot_dists(weights, color=color, xlabel=\"Weight vals\")\n", " fig.suptitle(\"Weight distribution\", fontsize=14, y=1.05)\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_gradients(model, color=\"C0\", print_variance=False):\n", " \"\"\"\n", " Args:\n", " net: Object of class BaseNetwork\n", " color: Color in which we want to visualize the histogram (for easier separation of activation functions)\n", " \"\"\"\n", " model.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=1024, shuffle=False)\n", " imgs, labels = next(iter(small_loader))\n", " imgs, labels = imgs.to(device), labels.to(device)\n", "\n", " # Pass one batch through the network, and calculate the gradients for the weights\n", " model.zero_grad()\n", " preds = model(imgs)\n", " loss = F.cross_entropy(preds, labels) # Same as nn.CrossEntropyLoss, but as a function instead of module\n", " loss.backward()\n", " # We limit our visualization to the weight parameters and exclude the bias to reduce the number of plots\n", " grads = {\n", " name: params.grad.view(-1).cpu().clone().numpy()\n", " for name, params in model.named_parameters()\n", " if \"weight\" in name\n", " }\n", " model.zero_grad()\n", "\n", " # Plotting\n", " fig = plot_dists(grads, color=color, xlabel=\"Grad magnitude\")\n", " fig.suptitle(\"Gradient distribution\", fontsize=14, y=1.05)\n", " plt.show()\n", " plt.close()\n", "\n", " if print_variance:\n", " for key in sorted(grads.keys()):\n", " print(f\"{key} - Variance: {np.var(grads[key])}\")\n", "\n", "\n", "##############################################################\n", "\n", "\n", "def visualize_activations(model, color=\"C0\", print_variance=False):\n", " model.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=1024, shuffle=False)\n", " imgs, labels = next(iter(small_loader))\n", " imgs, labels = imgs.to(device), labels.to(device)\n", "\n", " # Pass one batch through the network, and calculate the gradients for the weights\n", " feats = imgs.view(imgs.shape[0], -1)\n", " activations = {}\n", " with torch.no_grad():\n", " for layer_index, layer in enumerate(model.layers):\n", " feats = layer(feats)\n", " if isinstance(layer, nn.Linear):\n", " activations[f\"Layer {layer_index}\"] = feats.view(-1).detach().cpu().numpy()\n", "\n", " # Plotting\n", " fig = plot_dists(activations, color=color, stat=\"density\", xlabel=\"Activation vals\")\n", " fig.suptitle(\"Activation distribution\", fontsize=14, y=1.05)\n", " plt.show()\n", " plt.close()\n", "\n", " if print_variance:\n", " for key in sorted(activations.keys()):\n", " print(f\"{key} - Variance: {np.var(activations[key])}\")\n", "\n", "\n", "##############################################################"]}, {"cell_type": "markdown", "id": "307d0193", "metadata": {"papermill": {"duration": 0.134573, "end_time": "2021-12-04T15:54:48.953411", "exception": false, "start_time": "2021-12-04T15:54:48.818838", "status": "completed"}, "tags": []}, "source": ["## Initialization\n", "\n", "Before starting our discussion about initialization, it should be noted that there exist many very good blog posts about the topic of neural network initialization (for example [deeplearning.ai](https://www.deeplearning.ai/ai-notes/initialization/), or a more [math-focused blog post](https://pouannes.github.io/blog/initialization)).\n", "In case something remains unclear after this tutorial, we recommend skimming through these blog posts as well.\n", "\n", "When initializing a neural network, there are a few properties we would like to have.\n", "First, the variance of the input should be propagated through the model to the last layer, so that we have a similar standard deviation for the output neurons.\n", "If the variance would vanish the deeper we go in our model, it becomes much harder to optimize the model as the input to the next layer is basically a single constant value.\n", "Similarly, if the variance increases, it is likely to explode (i.e. head to infinity) the deeper we design our model.\n", "The second property we look out for in initialization techniques is a gradient distribution with equal variance across layers.\n", "If the first layer receives much smaller gradients than the last layer, we will have difficulties in choosing an appropriate learning rate.\n", "\n", "As a starting point for finding a good method, we will analyze different initialization based on our linear neural network with no activation function (i.e. an identity).\n", "We do this because initializations depend on the specific activation\n", "function used in the network, and we can adjust the initialization\n", "schemes later on for our specific choice."]}, {"cell_type": "code", "execution_count": 12, "id": "cc7ba656", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:49.227365Z", "iopub.status.busy": "2021-12-04T15:54:49.226898Z", "iopub.status.idle": "2021-12-04T15:54:53.850761Z", "shell.execute_reply": "2021-12-04T15:54:53.850280Z"}, "papermill": {"duration": 4.762201, "end_time": "2021-12-04T15:54:53.850902", "exception": false, "start_time": "2021-12-04T15:54:49.088701", "status": "completed"}, "tags": []}, "outputs": [], "source": ["model = BaseNetwork(act_fn=Identity()).to(device)"]}, {"cell_type": "markdown", "id": "7f1e34a7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.136138, "end_time": "2021-12-04T15:54:54.121872", "exception": false, "start_time": "2021-12-04T15:54:53.985734", "status": "completed"}, "tags": []}, "source": ["### Constant initialization\n", "\n", "The first initialization we can consider is to initialize all weights with the same constant value.\n", "Intuitively, setting all weights to zero is not a good idea as the propagated gradient will be zero.\n", "However, what happens if we set all weights to a value slightly larger or smaller than 0?\n", "To find out, we can implement a function for setting all parameters below and visualize the gradients."]}, {"cell_type": "code", "execution_count": 13, "id": "e3710c8c", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:54:54.396221Z", "iopub.status.busy": "2021-12-04T15:54:54.395746Z", "iopub.status.idle": "2021-12-04T15:55:04.530115Z", "shell.execute_reply": "2021-12-04T15:55:04.530499Z"}, "papermill": {"duration": 10.27465, "end_time": "2021-12-04T15:55:04.530680", "exception": false, "start_time": "2021-12-04T15:54:54.256030", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"source": ["def const_init(model, fill=0.0):\n", " for name, param in model.named_parameters():\n", " param.data.fill_(fill)\n", "\n", "\n", "const_init(model, fill=0.005)\n", "visualize_gradients(model)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "8da1230d", "metadata": {"papermill": {"duration": 0.152167, "end_time": "2021-12-04T15:55:04.833884", "exception": false, "start_time": "2021-12-04T15:55:04.681717", "status": "completed"}, "tags": []}, "source": ["As we can see, only the first and the last layer have diverse gradient distributions while the other three layers have the same gradient for all weights (note that this value is unequal 0, but often very close to it).\n", "Having the same gradient for parameters that have been initialized with the same values means that we will always have the same value for those parameters.\n", "This would make our layer useless and reduce our effective number of parameters to 1.\n", "Thus, we cannot use a constant initialization to train our networks."]}, {"cell_type": "markdown", "id": "65128840", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.151605, "end_time": "2021-12-04T15:55:05.137267", "exception": false, "start_time": "2021-12-04T15:55:04.985662", "status": "completed"}, "tags": []}, "source": ["### Constant variance\n", "\n", "From the experiment above, we have seen that a constant value is not working.\n", "So instead, how about we initialize the parameters by randomly sampling from a distribution like a Gaussian?\n", "The most intuitive way would be to choose one variance that is used for all layers in the network.\n", "Let's implement it below, and visualize the activation distribution across layers."]}, {"cell_type": "code", "execution_count": 14, "id": "d6173456", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:55:05.442031Z", "iopub.status.busy": "2021-12-04T15:55:05.441560Z", "iopub.status.idle": "2021-12-04T15:55:13.063195Z", "shell.execute_reply": "2021-12-04T15:55:13.065428Z"}, "papermill": {"duration": 7.778761, "end_time": "2021-12-04T15:55:13.065600", "exception": false, "start_time": "2021-12-04T15:55:05.286839", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"source": ["With a higher standard deviation, the activations are likely to explode.\n", "You can play around with the specific standard deviation values, but it will be hard to find one that gives us a good activation distribution across layers and is very specific to our model.\n", "If we would change the hidden sizes or number of layers, you would have\n", "to search all over again, which is neither efficient nor recommended."]}, {"cell_type": "markdown", "id": "b785fdfc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.165929, "end_time": "2021-12-04T15:55:22.105283", "exception": false, "start_time": "2021-12-04T15:55:21.939354", "status": "completed"}, "tags": []}, "source": ["### How to find appropriate initialization values\n", "\n", "From our experiments above, we have seen that we need to sample the weights from a distribution, but are not sure which one exactly.\n", "As a next step, we will try to find the optimal initialization from the perspective of the activation distribution.\n", "For this, we state two requirements:\n", "\n", "1. The mean of the activations should be zero\n", "2. The variance of the activations should stay the same across every layer\n", "\n", "Suppose we want to design an initialization for the following layer: $y=Wx+b$ with $y\\in\\mathbb{R}^{d_y}$, $x\\in\\mathbb{R}^{d_x}$.\n", "Our goal is that the variance of each element of $y$ is the same as the input, i.e. $\\text{Var}(y_i)=\\text{Var}(x_i)=\\sigma_x^{2}$, and that the mean is zero.\n", "We assume $x$ to also have a mean of zero, because, in deep neural networks, $y$ would be the input of another layer.\n", "This requires the bias and weight to have an expectation of 0.\n", "Actually, as $b$ is a single element per output neuron and is constant across different inputs, we set it to 0 overall.\n", "\n", "Next, we need to calculate the variance with which we need to initialize the weight parameters.\n", "Along the calculation, we will need to following variance rule: given two independent variables, the variance of their product is $\\text{Var}(X\\cdot Y) = \\mathbb{E}(Y)^2\\text{Var}(X) + \\mathbb{E}(X)^2\\text{Var}(Y) + \\text{Var}(X)\\text{Var}(Y) = \\mathbb{E}(Y^2)\\mathbb{E}(X^2)-\\mathbb{E}(Y)^2\\mathbb{E}(X)^2$ ($X$ and $Y$ are not refering to $x$ and $y$, but any random variable).\n", "\n", "The needed variance of the weights, $\\text{Var}(w_{ij})$, is calculated as follows:\n", "\n", "$$\n", "\\begin{split}\n", " y_i & = \\sum_{j} w_{ij}x_{j}\\hspace{10mm}\\text{Calculation of a single output neuron without bias}\\\\\n", " \\text{Var}(y_i) = \\sigma_x^{2} & = \\text{Var}\\left(\\sum_{j} w_{ij}x_{j}\\right)\\\\\n", " & = \\sum_{j} \\text{Var}(w_{ij}x_{j}) \\hspace{10mm}\\text{Inputs and weights are independent of each other}\\\\\n", " & = \\sum_{j} \\text{Var}(w_{ij})\\cdot\\text{Var}(x_{j}) \\hspace{10mm}\\text{Variance rule (see above) with expectations being zero}\\\\\n", " & = d_x \\cdot \\text{Var}(w_{ij})\\cdot\\text{Var}(x_{j}) \\hspace{10mm}\\text{Variance equal for all $d_x$ elements}\\\\\n", " & = \\sigma_x^{2} \\cdot d_x \\cdot \\text{Var}(w_{ij})\\\\\n", " \\Rightarrow \\text{Var}(w_{ij}) = \\sigma_{W}^2 & = \\frac{1}{d_x}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Thus, we should initialize the weight distribution with a variance of the inverse of the input dimension $d_x$.\n", "Let's implement it below and check whether this holds:"]}, {"cell_type": "code", "execution_count": 16, "id": "85fe111a", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:55:22.441463Z", "iopub.status.busy": "2021-12-04T15:55:22.440990Z", "iopub.status.idle": "2021-12-04T15:55:35.037471Z", "shell.execute_reply": "2021-12-04T15:55:35.037861Z"}, "papermill": {"duration": 12.767059, "end_time": "2021-12-04T15:55:35.038029", "exception": false, "start_time": "2021-12-04T15:55:22.270970", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n"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name.endswith(\".bias\"):\n", " param.data.fill_(0)\n", " else:\n", " param.data.normal_(std=1.0 / math.sqrt(param.shape[1]))\n", "\n", "\n", "equal_var_init(model)\n", "visualize_weight_distribution(model)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "ca9c629c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.183436, "end_time": "2021-12-04T15:55:35.408994", "exception": false, "start_time": "2021-12-04T15:55:35.225558", "status": "completed"}, "tags": []}, "source": ["As we expected, the variance stays indeed constant across layers.\n", "Note that our initialization does not restrict us to a normal distribution, but allows any other distribution with a mean of 0 and variance of $1/d_x$.\n", "You often see that a uniform distribution is used for initialization.\n", "A small benefit of using a uniform instead of a normal distribution is that we can exclude the chance of initializing very large or small weights.\n", "\n", "Besides the variance of the activations, another variance we would like to stabilize is the one of the gradients.\n", "This ensures a stable optimization for deep networks.\n", "It turns out that we can do the same calculation as above starting from $\\Delta x=W\\Delta y$, and come to the conclusion that we should initialize our layers with $1/d_y$ where $d_y$ is the number of output neurons.\n", "You can do the calculation as a practice, or check a thorough explanation in [this blog post](https://pouannes.github.io/blog/initialization).\n", "As a compromise between both constraints, [Glorot and Bengio (2010)](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf?hc_location=ufi) proposed to use the harmonic mean of both values.\n", "This leads us to the well-known Xavier initialization:\n", "\n", "$$W\\sim \\mathcal{N}\\left(0,\\frac{2}{d_x+d_y}\\right)$$\n", "\n", "If we use a uniform distribution, we would initialize the weights with:\n", "\n", "$$W\\sim U\\left[-\\frac{\\sqrt{6}}{\\sqrt{d_x+d_y}}, \\frac{\\sqrt{6}}{\\sqrt{d_x+d_y}}\\right]$$\n", "\n", "Let's shortly implement it and validate its effectiveness:"]}, {"cell_type": "code", "execution_count": 17, "id": "886f5024", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:55:35.773825Z", "iopub.status.busy": "2021-12-04T15:55:35.773355Z", "iopub.status.idle": "2021-12-04T15:55:48.170979Z", "shell.execute_reply": "2021-12-04T15:55:48.170552Z"}, "papermill": {"duration": 12.581276, "end_time": "2021-12-04T15:55:48.171111", "exception": false, "start_time": "2021-12-04T15:55:35.589835", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Layer 0 - Variance: 1.1692266464233398\n", "Layer 2 - Variance: 1.520001769065857\n", "Layer 4 - Variance: 1.585775375366211\n", "Layer 6 - Variance: 1.9146416187286377\n", "Layer 8 - Variance: 3.2868599891662598\n"]}], "source": ["def xavier_init(model):\n", " for name, param in model.named_parameters():\n", " if name.endswith(\".bias\"):\n", " param.data.fill_(0)\n", " else:\n", " bound = math.sqrt(6) / math.sqrt(param.shape[0] + param.shape[1])\n", " param.data.uniform_(-bound, bound)\n", "\n", "\n", "xavier_init(model)\n", "visualize_gradients(model, print_variance=True)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "920b63e6", "metadata": {"papermill": {"duration": 0.203664, "end_time": "2021-12-04T15:55:48.573926", "exception": false, "start_time": "2021-12-04T15:55:48.370262", "status": "completed"}, "tags": []}, "source": ["We see that the Xavier initialization balances the variance of gradients and activations.\n", "Note that the significantly higher variance for the output layer is due to the large difference of input and output dimension ($128$ vs $10$).\n", "However, we currently assumed the activation function to be linear.\n", "So what happens if we add a non-linearity?\n", "In a tanh-based network, a common assumption is that for small values during the initial steps in training, the $\\tanh$ works as a linear function such that we don't have to adjust our calculation.\n", "We can check if that is the case for us as well:"]}, {"cell_type": "code", "execution_count": 18, "id": "dff6e720", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:55:48.968877Z", "iopub.status.busy": "2021-12-04T15:55:48.968403Z", "iopub.status.idle": "2021-12-04T15:56:01.734151Z", "shell.execute_reply": "2021-12-04T15:56:01.733706Z"}, "papermill": {"duration": 12.965651, "end_time": "2021-12-04T15:56:01.734278", "exception": false, "start_time": "2021-12-04T15:55:48.768627", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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print_variance=True)"]}, {"cell_type": "markdown", "id": "3609de04", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.215252, "end_time": "2021-12-04T15:56:02.163103", "exception": false, "start_time": "2021-12-04T15:56:01.947851", "status": "completed"}, "tags": []}, "source": ["Although the variance decreases over depth, it is apparent that the activation distribution becomes more focused on the low values.\n", "Therefore, our variance will stabilize around 0.25 if we would go even deeper.\n", "Hence, we can conclude that the Xavier initialization works well for Tanh networks.\n", "But what about ReLU networks?\n", "Here, we cannot take the previous assumption of the non-linearity becoming linear for small values.\n", "The ReLU activation function sets (in expectation) half of the inputs to 0 so that also the expectation of the input is not zero.\n", "However, as long as the expectation of $W$ is zero and $b=0$, the expectation of the output is zero.\n", "The part where the calculation of the ReLU initialization differs from the identity is when determining $\\text{Var}(w_{ij}x_{j})$:\n", "\n", "$$\\text{Var}(w_{ij}x_{j})=\\underbrace{\\mathbb{E}[w_{ij}^2]}_{=\\text{Var}(w_{ij})}\\mathbb{E}[x_{j}^2]-\\underbrace{\\mathbb{E}[w_{ij}]^2}_{=0}\\mathbb{E}[x_{j}]^2=\\text{Var}(w_{ij})\\mathbb{E}[x_{j}^2]$$\n", "\n", "If we assume now that $x$ is the output of a ReLU activation (from a previous layer, $x=max(0,\\tilde{y})$), we can calculate the expectation as follows:\n", "\n", "\n", "$$\n", "\\begin{split}\n", " \\mathbb{E}[x^2] & =\\mathbb{E}[\\max(0,\\tilde{y})^2]\\\\\n", " & =\\frac{1}{2}\\mathbb{E}[{\\tilde{y}}^2]\\hspace{2cm}\\tilde{y}\\text{ is zero-centered and symmetric}\\\\\n", " & =\\frac{1}{2}\\text{Var}(\\tilde{y})\n", "\\end{split}$$\n", "\n", "Thus, we see that we have an additional factor of 1/2 in the equation, so that our desired weight variance becomes $2/d_x$.\n", "This gives us the Kaiming initialization (see [He, K. et al.\n", "(2015)](https://arxiv.org/pdf/1502.01852.pdf)).\n", "Note that the Kaiming initialization does not use the harmonic mean between input and output size.\n", "In their paper (Section 2.2, Backward Propagation, last paragraph), they argue that using $d_x$ or $d_y$ both lead to stable gradients throughout the network, and only depend on the overall input and output size of the network.\n", "Hence, we can use here only the input $d_x$:"]}, {"cell_type": "code", "execution_count": 19, "id": "d4b7f43f", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:02.594108Z", "iopub.status.busy": "2021-12-04T15:56:02.593625Z", "iopub.status.idle": "2021-12-04T15:56:15.072435Z", "shell.execute_reply": "2021-12-04T15:56:15.071951Z"}, "papermill": {"duration": 12.695197, "end_time": "2021-12-04T15:56:15.072560", "exception": false, "start_time": "2021-12-04T15:56:02.377363", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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x=\"759.989006\" y=\"37.8\"/>\n", " </clipPath>\n", " </defs>\n", "</svg>\n"], "text/plain": ["<Figure size 1080x180 with 5 Axes>"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Layer 0 - Variance: 1.037200689315796\n", "Layer 2 - Variance: 1.0582876205444336\n", "Layer 4 - Variance: 1.0638010501861572\n", "Layer 6 - Variance: 1.3167966604232788\n", "Layer 8 - Variance: 0.6909096837043762\n"]}], "source": ["def kaiming_init(model):\n", " for name, param in model.named_parameters():\n", " if name.endswith(\".bias\"):\n", " param.data.fill_(0)\n", " elif name.startswith(\"layers.0\"): # The first layer does not have ReLU applied on its input\n", " param.data.normal_(0, 1 / math.sqrt(param.shape[1]))\n", " else:\n", " param.data.normal_(0, math.sqrt(2) / math.sqrt(param.shape[1]))\n", "\n", "\n", "model = BaseNetwork(act_fn=nn.ReLU()).to(device)\n", "kaiming_init(model)\n", "visualize_gradients(model, print_variance=True)\n", "visualize_activations(model, print_variance=True)"]}, {"cell_type": "markdown", "id": "6af2f0ac", "metadata": {"papermill": {"duration": 0.227785, "end_time": "2021-12-04T15:56:15.529106", "exception": false, "start_time": "2021-12-04T15:56:15.301321", "status": "completed"}, "tags": []}, "source": ["The variance stays stable across layers.\n", "We can conclude that the Kaiming initialization indeed works well for ReLU-based networks.\n", "Note that for Leaky-ReLU etc., we have to slightly adjust the factor of $2$ in the variance as half of the values are not set to zero anymore.\n", "PyTorch provides a function to calculate this factor for many activation\n", "function, see `torch.nn.init.calculate_gain`\n", "([link](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.calculate_gain))."]}, {"cell_type": "markdown", "id": "0d736eff", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.22843, "end_time": "2021-12-04T15:56:15.988215", "exception": false, "start_time": "2021-12-04T15:56:15.759785", "status": "completed"}, "tags": []}, "source": ["## Optimization\n", "\n", "<div class=\"center-wrapper\"><div class=\"video-wrapper\"><iframe src=\"https://www.youtube.com/embed/UcRBZbAP9hM\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture\" allowfullscreen></iframe></div></div>\n", "\n", "Besides initialization, selecting a suitable optimization algorithm can be an important choice for deep neural networks.\n", "Before taking a closer look at them, we should define code for training the models.\n", "Most of the following code is copied from the previous tutorial, and only slightly altered to fit our needs."]}, {"cell_type": "code", "execution_count": 20, "id": "b9bfe5e2", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:16.464612Z", "iopub.status.busy": "2021-12-04T15:56:16.453313Z", "iopub.status.idle": "2021-12-04T15:56:16.468178Z", "shell.execute_reply": "2021-12-04T15:56:16.467704Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.251694, "end_time": "2021-12-04T15:56:16.468294", "exception": false, "start_time": "2021-12-04T15:56:16.216600", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def _get_config_file(model_path, model_name):\n", " return os.path.join(model_path, model_name + \".config\")\n", "\n", "\n", "def _get_model_file(model_path, model_name):\n", " return os.path.join(model_path, model_name + \".tar\")\n", "\n", "\n", "def _get_result_file(model_path, model_name):\n", " return os.path.join(model_path, model_name + \"_results.json\")\n", "\n", "\n", "def load_model(model_path, model_name, net=None):\n", " config_file = _get_config_file(model_path, model_name)\n", " model_file = _get_model_file(model_path, model_name)\n", " assert os.path.isfile(\n", " config_file\n", " ), f'Could not find the config file \"{config_file}\". Are you sure this is the correct path and you have your model config stored here?'\n", " assert os.path.isfile(\n", " model_file\n", " ), f'Could not find the model file \"{model_file}\". Are you sure this is the correct path and you have your model stored here?'\n", " with open(config_file) as f:\n", " config_dict = json.load(f)\n", " if net is None:\n", " act_fn_name = config_dict[\"act_fn\"].pop(\"name\").lower()\n", " assert (\n", " act_fn_name in act_fn_by_name\n", " ), f'Unknown activation function \"{act_fn_name}\". Please add it to the \"act_fn_by_name\" dict.'\n", " act_fn = act_fn_by_name[act_fn_name]()\n", " net = BaseNetwork(act_fn=act_fn, **config_dict)\n", " net.load_state_dict(torch.load(model_file))\n", " return net\n", "\n", "\n", "def save_model(model, model_path, model_name):\n", " config_dict = model.config\n", " os.makedirs(model_path, exist_ok=True)\n", " config_file = _get_config_file(model_path, model_name)\n", " model_file = _get_model_file(model_path, model_name)\n", " with open(config_file, \"w\") as f:\n", " json.dump(config_dict, f)\n", " torch.save(model.state_dict(), model_file)\n", "\n", "\n", "def train_model(net, model_name, optim_func, max_epochs=50, batch_size=256, overwrite=False):\n", " \"\"\"Train a model on the training set of FashionMNIST.\n", "\n", " Args:\n", " net: Object of BaseNetwork\n", " model_name: (str) Name of the model, used for creating the checkpoint names\n", " max_epochs: Number of epochs we want to (maximally) train for\n", " patience: If the performance on the validation set has not improved for #patience epochs, we stop training early\n", " batch_size: Size of batches used in training\n", " overwrite: Determines how to handle the case when there already exists a checkpoint. If True, it will be overwritten. Otherwise, we skip training.\n", " \"\"\"\n", " file_exists = os.path.isfile(_get_model_file(CHECKPOINT_PATH, model_name))\n", " if file_exists and not overwrite:\n", " print(f'Model file of \"{model_name}\" already exists. Skipping training...')\n", " with open(_get_result_file(CHECKPOINT_PATH, model_name)) as f:\n", " results = json.load(f)\n", " else:\n", " if file_exists:\n", " print(\"Model file exists, but will be overwritten...\")\n", "\n", " # Defining optimizer, loss and data loader\n", " optimizer = optim_func(net.parameters())\n", " loss_module = nn.CrossEntropyLoss()\n", " train_loader_local = data.DataLoader(\n", " train_set, batch_size=batch_size, shuffle=True, drop_last=True, pin_memory=True\n", " )\n", "\n", " results = None\n", " val_scores = []\n", " train_losses, train_scores = [], []\n", " best_val_epoch = -1\n", " for epoch in range(max_epochs):\n", " train_acc, val_acc, epoch_losses = epoch_iteration(\n", " net, loss_module, optimizer, train_loader_local, val_loader, epoch\n", " )\n", " train_scores.append(train_acc)\n", " val_scores.append(val_acc)\n", " train_losses += epoch_losses\n", "\n", " if len(val_scores) == 1 or val_acc > val_scores[best_val_epoch]:\n", " print(\"\\t (New best performance, saving model...)\")\n", " save_model(net, CHECKPOINT_PATH, model_name)\n", " best_val_epoch = epoch\n", "\n", " if results is None:\n", " load_model(CHECKPOINT_PATH, model_name, net=net)\n", " test_acc = test_model(net, test_loader)\n", " results = {\n", " \"test_acc\": test_acc,\n", " \"val_scores\": val_scores,\n", " \"train_losses\": train_losses,\n", " \"train_scores\": train_scores,\n", " }\n", " with open(_get_result_file(CHECKPOINT_PATH, model_name), \"w\") as f:\n", " json.dump(results, f)\n", "\n", " # Plot a curve of the validation accuracy\n", " sns.set()\n", " plt.plot([i for i in range(1, len(results[\"train_scores\"]) + 1)], results[\"train_scores\"], label=\"Train\")\n", " plt.plot([i for i in range(1, len(results[\"val_scores\"]) + 1)], results[\"val_scores\"], label=\"Val\")\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(\"Validation accuracy\")\n", " plt.ylim(min(results[\"val_scores\"]), max(results[\"train_scores\"]) * 1.01)\n", " plt.title(f\"Validation performance of {model_name}\")\n", " plt.legend()\n", " plt.show()\n", " plt.close()\n", "\n", " print((f\" Test accuracy: {results['test_acc']*100.0:4.2f}% \").center(50, \"=\") + \"\\n\")\n", " return results\n", "\n", "\n", "def epoch_iteration(net, loss_module, optimizer, train_loader_local, val_loader, epoch):\n", " ############\n", " # Training #\n", " ############\n", " net.train()\n", " true_preds, count = 0.0, 0\n", " epoch_losses = []\n", " t = tqdm(train_loader_local, leave=False)\n", " for imgs, labels in t:\n", " imgs, labels = imgs.to(device), labels.to(device)\n", " optimizer.zero_grad()\n", " preds = net(imgs)\n", " loss = loss_module(preds, labels)\n", " loss.backward()\n", " optimizer.step()\n", " # Record statistics during training\n", " true_preds += (preds.argmax(dim=-1) == labels).sum().item()\n", " count += labels.shape[0]\n", " t.set_description(f\"Epoch {epoch+1}: loss={loss.item():4.2f}\")\n", " epoch_losses.append(loss.item())\n", " train_acc = true_preds / count\n", "\n", " ##############\n", " # Validation #\n", " ##############\n", " val_acc = test_model(net, val_loader)\n", " print(\n", " f\"[Epoch {epoch+1:2i}] Training accuracy: {train_acc*100.0:05.2f}%, Validation accuracy: {val_acc*100.0:05.2f}%\"\n", " )\n", " return train_acc, val_acc, epoch_losses\n", "\n", "\n", "def test_model(net, data_loader):\n", " \"\"\"Test a model on a specified dataset.\n", "\n", " Args:\n", " net: Trained model of type BaseNetwork\n", " data_loader: DataLoader object of the dataset to test on (validation or test)\n", " \"\"\"\n", " net.eval()\n", " true_preds, count = 0.0, 0\n", " for imgs, labels in data_loader:\n", " imgs, labels = imgs.to(device), labels.to(device)\n", " with torch.no_grad():\n", " preds = net(imgs).argmax(dim=-1)\n", " true_preds += (preds == labels).sum().item()\n", " count += labels.shape[0]\n", " test_acc = true_preds / count\n", " return test_acc"]}, {"cell_type": "markdown", "id": "ebc0236d", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.226171, "end_time": "2021-12-04T15:56:16.921208", "exception": false, "start_time": "2021-12-04T15:56:16.695037", "status": "completed"}, "tags": []}, "source": ["First, we need to understand what an optimizer actually does.\n", "The optimizer is responsible to update the network's parameters given the gradients.\n", "Hence, we effectively implement a function $w^{t} = f(w^{t-1}, g^{t}, ...)$ with $w$ being the parameters, and $g^{t} = \\nabla_{w^{(t-1)}} \\mathcal{L}^{(t)}$ the gradients at time step $t$.\n", "A common, additional parameter to this function is the learning rate, here denoted by $\\eta$.\n", "Usually, the learning rate can be seen as the \"step size\" of the update.\n", "A higher learning rate means that we change the weights more in the direction of the gradients, a smaller means we take shorter steps.\n", "\n", "As most optimizers only differ in the implementation of $f$, we can define a template for an optimizer in PyTorch below.\n", "We take as input the parameters of a model and a learning rate.\n", "The function `zero_grad` sets the gradients of all parameters to zero, which we have to do before calling `loss.backward()`.\n", "Finally, the `step()` function tells the optimizer to update all weights based on their gradients.\n", "The template is setup below:"]}, {"cell_type": "code", "execution_count": 21, "id": "8115a144", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:17.381962Z", "iopub.status.busy": "2021-12-04T15:56:17.381485Z", "iopub.status.idle": "2021-12-04T15:56:17.383062Z", "shell.execute_reply": "2021-12-04T15:56:17.383442Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.234647, "end_time": "2021-12-04T15:56:17.383573", "exception": false, "start_time": "2021-12-04T15:56:17.148926", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class OptimizerTemplate:\n", " def __init__(self, params, lr):\n", " self.params = list(params)\n", " self.lr = lr\n", "\n", " def zero_grad(self):\n", " # Set gradients of all parameters to zero\n", " for p in self.params:\n", " if p.grad is not None:\n", " p.grad.detach_() # For second-order optimizers important\n", " p.grad.zero_()\n", "\n", " @torch.no_grad()\n", " def step(self):\n", " # Apply update step to all parameters\n", " for p in self.params:\n", " if p.grad is None: # We skip parameters without any gradients\n", " continue\n", " self.update_param(p)\n", "\n", " def update_param(self, p):\n", " # To be implemented in optimizer-specific classes\n", " raise NotImplementedError"]}, {"cell_type": "markdown", "id": "11fc5bd8", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.225818, "end_time": "2021-12-04T15:56:17.836069", "exception": false, "start_time": "2021-12-04T15:56:17.610251", "status": "completed"}, "tags": []}, "source": ["The first optimizer we are going to implement is the standard Stochastic Gradient Descent (SGD).\n", "SGD updates the parameters using the following equation:\n", "\n", "$$\n", "\\begin{split}\n", " w^{(t)} & = w^{(t-1)} - \\eta \\cdot g^{(t)}\n", "\\end{split}\n", "$$\n", "\n", "As simple as the equation is also our implementation of SGD:"]}, {"cell_type": "code", "execution_count": 22, "id": "7c5f4955", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:18.300285Z", "iopub.status.busy": "2021-12-04T15:56:18.299810Z", "iopub.status.idle": "2021-12-04T15:56:18.301728Z", "shell.execute_reply": "2021-12-04T15:56:18.301262Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.237501, "end_time": "2021-12-04T15:56:18.301837", "exception": false, "start_time": "2021-12-04T15:56:18.064336", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SGD(OptimizerTemplate):\n", " def __init__(self, params, lr):\n", " super().__init__(params, lr)\n", "\n", " def update_param(self, p):\n", " p_update = -self.lr * p.grad\n", " p.add_(p_update) # In-place update => saves memory and does not create computation graph"]}, {"cell_type": "markdown", "id": "0f34b439", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.226849, "end_time": "2021-12-04T15:56:18.754221", "exception": false, "start_time": "2021-12-04T15:56:18.527372", "status": "completed"}, "tags": []}, "source": ["In the lecture, we also have discussed the concept of momentum which replaces the gradient in the update by an exponential average of all past gradients including the current one:\n", "\n", "$$\n", "\\begin{split}\n", " m^{(t)} & = \\beta_1 m^{(t-1)} + (1 - \\beta_1)\\cdot g^{(t)}\\\\\n", " w^{(t)} & = w^{(t-1)} - \\eta \\cdot m^{(t)}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Let's also implement it below:"]}, {"cell_type": "code", "execution_count": 23, "id": "48028221", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:19.214485Z", "iopub.status.busy": "2021-12-04T15:56:19.214011Z", "iopub.status.idle": "2021-12-04T15:56:19.215560Z", "shell.execute_reply": "2021-12-04T15:56:19.215939Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.23626, "end_time": "2021-12-04T15:56:19.216068", "exception": false, "start_time": "2021-12-04T15:56:18.979808", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SGDMomentum(OptimizerTemplate):\n", " def __init__(self, params, lr, momentum=0.0):\n", " super().__init__(params, lr)\n", " self.momentum = momentum # Corresponds to beta_1 in the equation above\n", " self.param_momentum = {p: torch.zeros_like(p.data) for p in self.params} # Dict to store m_t\n", "\n", " def update_param(self, p):\n", " self.param_momentum[p] = (1 - self.momentum) * p.grad + self.momentum * self.param_momentum[p]\n", " p_update = -self.lr * self.param_momentum[p]\n", " p.add_(p_update)"]}, {"cell_type": "markdown", "id": "f9f83484", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.2256, "end_time": "2021-12-04T15:56:19.667175", "exception": false, "start_time": "2021-12-04T15:56:19.441575", "status": "completed"}, "tags": []}, "source": ["Finally, we arrive at Adam.\n", "Adam combines the idea of momentum with an adaptive learning rate, which is based on an exponential average of the squared gradients, i.e. the gradients norm.\n", "Furthermore, we add a bias correction for the momentum and adaptive learning rate for the first iterations:\n", "\n", "$$\n", "\\begin{split}\n", " m^{(t)} & = \\beta_1 m^{(t-1)} + (1 - \\beta_1)\\cdot g^{(t)}\\\\\n", " v^{(t)} & = \\beta_2 v^{(t-1)} + (1 - \\beta_2)\\cdot \\left(g^{(t)}\\right)^2\\\\\n", " \\hat{m}^{(t)} & = \\frac{m^{(t)}}{1-\\beta^{t}_1}, \\hat{v}^{(t)} = \\frac{v^{(t)}}{1-\\beta^{t}_2}\\\\\n", " w^{(t)} & = w^{(t-1)} - \\frac{\\eta}{\\sqrt{v^{(t)}} + \\epsilon}\\circ \\hat{m}^{(t)}\\\\\n", "\\end{split}\n", "$$\n", "\n", "Epsilon is a small constant used to improve numerical stability for very small gradient norms.\n", "Remember that the adaptive learning rate does not replace the learning\n", "rate hyperparameter $\\eta$, but rather acts as an extra factor and\n", "ensures that the gradients of various parameters have a similar norm."]}, {"cell_type": "code", "execution_count": 24, "id": "3f028169", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:20.130949Z", "iopub.status.busy": "2021-12-04T15:56:20.130446Z", "iopub.status.idle": "2021-12-04T15:56:20.132399Z", "shell.execute_reply": "2021-12-04T15:56:20.131929Z"}, "papermill": {"duration": 0.238498, "end_time": "2021-12-04T15:56:20.132510", "exception": false, "start_time": "2021-12-04T15:56:19.894012", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Adam(OptimizerTemplate):\n", " def __init__(self, params, lr, beta1=0.9, beta2=0.999, eps=1e-8):\n", " super().__init__(params, lr)\n", " self.beta1 = beta1\n", " self.beta2 = beta2\n", " self.eps = eps\n", " self.param_step = {p: 0 for p in self.params} # Remembers \"t\" for each parameter for bias correction\n", " self.param_momentum = {p: torch.zeros_like(p.data) for p in self.params}\n", " self.param_2nd_momentum = {p: torch.zeros_like(p.data) for p in self.params}\n", "\n", " def update_param(self, p):\n", " self.param_step[p] += 1\n", "\n", " self.param_momentum[p] = (1 - self.beta1) * p.grad + self.beta1 * self.param_momentum[p]\n", " self.param_2nd_momentum[p] = (1 - self.beta2) * (p.grad) ** 2 + self.beta2 * self.param_2nd_momentum[p]\n", "\n", " bias_correction_1 = 1 - self.beta1 ** self.param_step[p]\n", " bias_correction_2 = 1 - self.beta2 ** self.param_step[p]\n", "\n", " p_2nd_mom = self.param_2nd_momentum[p] / bias_correction_2\n", " p_mom = self.param_momentum[p] / bias_correction_1\n", " p_lr = self.lr / (torch.sqrt(p_2nd_mom) + self.eps)\n", " p_update = -p_lr * p_mom\n", "\n", " p.add_(p_update)"]}, {"cell_type": "markdown", "id": "b6db76a2", "metadata": {"papermill": {"duration": 0.227049, "end_time": "2021-12-04T15:56:20.587369", "exception": false, "start_time": "2021-12-04T15:56:20.360320", "status": "completed"}, "tags": []}, "source": ["### Comparing optimizers on model training\n", "\n", "After we have implemented three optimizers (SGD, SGD with momentum, and Adam), we can start to analyze and compare them.\n", "First, we test them on how well they can optimize a neural network on the FashionMNIST dataset.\n", "We use again our linear network, this time with a ReLU activation and the kaiming initialization, which we have found before to work well for ReLU-based networks.\n", "Note that the model is over-parameterized for this task, and we can achieve similar performance with a much smaller network (for example `100,100,100`).\n", "However, our main interest is in how well the optimizer can train *deep*\n", "neural networks, hence the over-parameterization."]}, {"cell_type": "code", "execution_count": 25, "id": "99328e5e", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:21.045265Z", "iopub.status.busy": "2021-12-04T15:56:21.044794Z", "iopub.status.idle": "2021-12-04T15:56:21.057645Z", "shell.execute_reply": "2021-12-04T15:56:21.058024Z"}, "papermill": {"duration": 0.24428, "end_time": "2021-12-04T15:56:21.058155", "exception": false, "start_time": "2021-12-04T15:56:20.813875", "status": "completed"}, "tags": []}, "outputs": [], "source": ["base_model = BaseNetwork(act_fn=nn.ReLU(), hidden_sizes=[512, 256, 256, 128])\n", "kaiming_init(base_model)"]}, {"cell_type": "markdown", "id": "1636493f", "metadata": {"papermill": {"duration": 0.226015, "end_time": "2021-12-04T15:56:21.512106", "exception": false, "start_time": "2021-12-04T15:56:21.286091", "status": "completed"}, "tags": []}, "source": ["For a fair comparison, we train the exact same model with the same seed with the three optimizers below.\n", "Feel free to change the hyperparameters if you want (however, you have to train your own model then)."]}, {"cell_type": "code", "execution_count": 26, "id": "83cf00ee", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:21.971222Z", "iopub.status.busy": "2021-12-04T15:56:21.970734Z", "iopub.status.idle": "2021-12-04T15:56:22.354403Z", "shell.execute_reply": "2021-12-04T15:56:22.353972Z"}, "papermill": {"duration": 0.615668, "end_time": "2021-12-04T15:56:22.354532", "exception": false, "start_time": "2021-12-04T15:56:21.738864", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Model file of \"FashionMNIST_SGD\" already exists. Skipping training...\n"]}, {"data": {"application/pdf": 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Skipping training...\n"]}, {"data": {"application/pdf": 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copy.deepcopy(base_model).to(device)\n", "SGDMom_results = train_model(\n", " SGDMom_model,\n", " \"FashionMNIST_SGDMom\",\n", " lambda params: SGDMomentum(params, lr=1e-1, momentum=0.9),\n", " max_epochs=40,\n", " batch_size=256,\n", ")"]}, {"cell_type": "code", "execution_count": 28, "id": "2d326436", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:23.673283Z", "iopub.status.busy": "2021-12-04T15:56:23.672814Z", "iopub.status.idle": "2021-12-04T15:56:24.028499Z", "shell.execute_reply": "2021-12-04T15:56:24.028068Z"}, "papermill": {"duration": 0.601587, "end_time": "2021-12-04T15:56:24.028625", "exception": false, "start_time": "2021-12-04T15:56:23.427038", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Model file of \"FashionMNIST_Adam\" already exists. Skipping training...\n"]}, {"data": {"application/pdf": 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"stream", "text": ["============= Test accuracy: 89.46% ==============\n", "\n"]}], "source": ["Adam_model = copy.deepcopy(base_model).to(device)\n", "Adam_results = train_model(\n", " Adam_model, \"FashionMNIST_Adam\", lambda params: Adam(params, lr=1e-3), max_epochs=40, batch_size=256\n", ")"]}, {"cell_type": "markdown", "id": "d48d9738", "metadata": {"papermill": {"duration": 0.242293, "end_time": "2021-12-04T15:56:24.518095", "exception": false, "start_time": "2021-12-04T15:56:24.275802", "status": "completed"}, "tags": []}, "source": ["The result is that all optimizers perform similarly well with the given model.\n", "The differences are too small to find any significant conclusion.\n", "However, keep in mind that this can also be attributed to the initialization we chose.\n", "When changing the initialization to worse (e.g. constant initialization), Adam usually shows to be more robust because of its adaptive learning rate.\n", "To show the specific benefits of the optimizers, we will continue to\n", "look at some possible loss surfaces in which momentum and adaptive\n", "learning rate are crucial."]}, {"cell_type": "markdown", "id": "650284a9", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.243267, "end_time": "2021-12-04T15:56:25.002092", "exception": false, "start_time": "2021-12-04T15:56:24.758825", "status": "completed"}, "tags": []}, "source": ["### Pathological curvatures\n", "\n", "A pathological curvature is a type of surface that is similar to ravines and is particularly tricky for plain SGD optimization.\n", "In words, pathological curvatures typically have a steep gradient in one direction with an optimum at the center, while in a second direction we have a slower gradient towards a (global) optimum.\n", "Let's first create an example surface of this and visualize it:"]}, {"cell_type": "code", "execution_count": 29, "id": "b739a90c", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:25.513474Z", "iopub.status.busy": "2021-12-04T15:56:25.513001Z", "iopub.status.idle": "2021-12-04T15:56:25.514972Z", "shell.execute_reply": "2021-12-04T15:56:25.514568Z"}, "papermill": {"duration": 0.271487, "end_time": "2021-12-04T15:56:25.515085", "exception": false, "start_time": "2021-12-04T15:56:25.243598", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def pathological_curve_loss(w1, w2):\n", " # Example of a pathological curvature. There are many more possible, feel free to experiment here!\n", " x1_loss = torch.tanh(w1) ** 2 + 0.01 * torch.abs(w1)\n", " x2_loss = torch.sigmoid(w2)\n", " return x1_loss + x2_loss"]}, {"cell_type": "code", "execution_count": 30, "id": "31ad8756", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:26.015141Z", "iopub.status.busy": "2021-12-04T15:56:26.014659Z", "iopub.status.idle": "2021-12-04T15:56:27.270117Z", "shell.execute_reply": "2021-12-04T15:56:27.270502Z"}, "papermill": {"duration": 1.508289, "end_time": "2021-12-04T15:56:27.270685", "exception": false, "start_time": "2021-12-04T15:56:25.762396", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_875/1102210584.py:5: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", " ax = fig.gca(projection=\"3d\") if plot_3d else fig.gca()\n"]}, {"data": {"application/pdf": 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plot_3d:\n", " ax.plot_surface(x, y, z, cmap=cmap, linewidth=1, color=\"#000\", antialiased=False)\n", " ax.set_zlabel(\"loss\")\n", " else:\n", " ax.imshow(z.T[::-1], cmap=cmap, extent=(x_range[0], x_range[1], y_range[0], y_range[1]))\n", " plt.title(title)\n", " ax.set_xlabel(r\"$w_1$\")\n", " ax.set_ylabel(r\"$w_2$\")\n", " plt.tight_layout()\n", " return ax\n", "\n", "\n", "sns.reset_orig()\n", "_ = plot_curve(pathological_curve_loss, plot_3d=True)\n", "plt.show()"]}, {"cell_type": "markdown", "id": "3a07e352", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.279049, "end_time": "2021-12-04T15:56:27.829838", "exception": false, "start_time": "2021-12-04T15:56:27.550789", "status": "completed"}, "tags": []}, "source": ["In terms of optimization, you can image that $w_1$ and $w_2$ are weight parameters, and the curvature represents the loss surface over the space of $w_1$ and $w_2$.\n", "Note that in typical networks, we have many, many more parameters than two, and such curvatures can occur in multi-dimensional spaces as well.\n", "\n", "Ideally, our optimization algorithm would find the center of the ravine and focuses on optimizing the parameters towards the direction of $w_2$.\n", "However, if we encounter a point along the ridges, the gradient is much greater in $w_1$ than $w_2$, and we might end up jumping from one side to the other.\n", "Due to the large gradients, we would have to reduce our learning rate slowing down learning significantly.\n", "\n", "To test our algorithms, we can implement a simple function to train two parameters on such a surface:"]}, {"cell_type": "code", "execution_count": 31, "id": "5d2f28de", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:28.389879Z", "iopub.status.busy": "2021-12-04T15:56:28.389383Z", "iopub.status.idle": "2021-12-04T15:56:28.391395Z", "shell.execute_reply": "2021-12-04T15:56:28.390988Z"}, "papermill": {"duration": 0.285299, "end_time": "2021-12-04T15:56:28.391509", "exception": false, "start_time": "2021-12-04T15:56:28.106210", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_curve(optimizer_func, curve_func=pathological_curve_loss, num_updates=100, init=[5, 5]):\n", " \"\"\"\n", " Args:\n", " optimizer_func: Constructor of the optimizer to use. Should only take a parameter list\n", " curve_func: Loss function (e.g. pathological curvature)\n", " num_updates: Number of updates/steps to take when optimizing\n", " init: Initial values of parameters. Must be a list/tuple with two elements representing w_1 and w_2\n", " Returns:\n", " Numpy array of shape [num_updates, 3] with [t,:2] being the parameter values at step t, and [t,2] the loss at t.\n", " \"\"\"\n", " weights = nn.Parameter(torch.FloatTensor(init), requires_grad=True)\n", " optim = optimizer_func([weights])\n", "\n", " list_points = []\n", " for _ in range(num_updates):\n", " loss = curve_func(weights[0], weights[1])\n", " list_points.append(torch.cat([weights.data.detach(), loss.unsqueeze(dim=0).detach()], dim=0))\n", " optim.zero_grad()\n", " loss.backward()\n", " optim.step()\n", " points = torch.stack(list_points, dim=0).numpy()\n", " return points"]}, {"cell_type": "markdown", "id": "34d620d2", "metadata": {"papermill": {"duration": 0.277293, "end_time": "2021-12-04T15:56:28.945849", "exception": false, "start_time": "2021-12-04T15:56:28.668556", "status": "completed"}, "tags": []}, "source": ["Next, let's apply the different optimizers on our curvature.\n", "Note that we set a much higher learning rate for the optimization algorithms as you would in a standard neural network.\n", "This is because we only have 2 parameters instead of tens of thousands or even millions."]}, {"cell_type": "code", "execution_count": 32, "id": "41d2cd89", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:29.513423Z", "iopub.status.busy": "2021-12-04T15:56:29.512941Z", "iopub.status.idle": "2021-12-04T15:56:29.600337Z", "shell.execute_reply": "2021-12-04T15:56:29.599941Z"}, "papermill": {"duration": 0.369932, "end_time": "2021-12-04T15:56:29.600456", "exception": false, "start_time": "2021-12-04T15:56:29.230524", "status": "completed"}, "tags": []}, "outputs": [], "source": ["SGD_points = train_curve(lambda params: SGD(params, lr=10))\n", "SGDMom_points = train_curve(lambda params: SGDMomentum(params, lr=10, momentum=0.9))\n", "Adam_points = train_curve(lambda params: Adam(params, lr=1))"]}, {"cell_type": "markdown", "id": "f8145cab", "metadata": {"papermill": {"duration": 0.28425, "end_time": "2021-12-04T15:56:30.165657", "exception": false, "start_time": "2021-12-04T15:56:29.881407", "status": "completed"}, "tags": []}, "source": ["To understand best how the different algorithms worked, we visualize the update step as a line plot through the loss surface.\n", "We will stick with a 2D representation for readability."]}, {"cell_type": "code", "execution_count": 33, "id": "54af4578", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:30.745262Z", "iopub.status.busy": "2021-12-04T15:56:30.744720Z", "iopub.status.idle": "2021-12-04T15:56:31.275773Z", "shell.execute_reply": "2021-12-04T15:56:31.276161Z"}, "papermill": {"duration": 0.832259, "end_time": "2021-12-04T15:56:31.276328", "exception": false, "start_time": "2021-12-04T15:56:30.444069", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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"plt.show()"]}, {"cell_type": "markdown", "id": "0e880578", "metadata": {"papermill": {"duration": 0.281894, "end_time": "2021-12-04T15:56:31.843290", "exception": false, "start_time": "2021-12-04T15:56:31.561396", "status": "completed"}, "tags": []}, "source": ["We can clearly see that SGD is not able to find the center of the optimization curve and has a problem converging due to the steep gradients in $w_1$.\n", "In contrast, Adam and SGD with momentum nicely converge as the changing direction of $w_1$ is canceling itself out.\n", "On such surfaces, it is crucial to use momentum."]}, {"cell_type": "markdown", "id": "23673b0c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.283817, "end_time": "2021-12-04T15:56:32.414094", "exception": false, "start_time": "2021-12-04T15:56:32.130277", "status": "completed"}, "tags": []}, "source": ["### Steep optima\n", "\n", "A second type of challenging loss surfaces are steep optima.\n", "In those, we have a larger part of the surface having very small gradients while around the optimum, we have very large gradients.\n", "For instance, take the following loss surfaces:"]}, {"cell_type": "code", "execution_count": 34, "id": "93f0e0e9", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:33.000196Z", "iopub.status.busy": "2021-12-04T15:56:32.994290Z", "iopub.status.idle": "2021-12-04T15:56:34.120588Z", "shell.execute_reply": "2021-12-04T15:56:34.120986Z"}, "papermill": {"duration": 1.424954, "end_time": "2021-12-04T15:56:34.121153", "exception": false, "start_time": "2021-12-04T15:56:32.696199", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_875/1102210584.py:5: MatplotlibDeprecationWarning: Calling gca() with keyword arguments was deprecated in Matplotlib 3.4. Starting two minor releases later, gca() will take no keyword arguments. The gca() function should only be used to get the current axes, or if no axes exist, create new axes with default keyword arguments. To create a new axes with non-default arguments, use plt.axes() or plt.subplot().\n", " ax = fig.gca(projection=\"3d\") if plot_3d else fig.gca()\n"]}, {"data": {"application/pdf": 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MFOlQso9AQjvhYCx7KpaQ7dOVNOoan2twz/J1OPuRrvA12EnjDeCKqvcvDEffR3QvDcRhFbPhBW8RXTmlrt86NRyW+liFs31XvNuFPvycWb8s5D1rYguEGTam23ZnplA0u473kH0GCplLlyxcZlpNL+NsPJB+sB1XuHvhZvs69aSDIWMkb/lOEMNPZ6C0PqdcByJJ7T6xqUkeDK/Dx2/lsQZ3M67D5kWb7R+j8NQBPuBsLH0nKO9U7C61M8Qr1/Eqtd9oII9NhpzXW4U++imz48Cb6+BE+D5HIUiE0QF4qtS6ZdRbChsZxCFUal/DYt34/Soz8fY4SjBSrd7XI/Tkoa2f8dG+OwXXwYmInA4Xr8XwbpE5rEvD7rBu8HBu8QLuJ1XMrKvcV/q002WQky5EYXnWH8KDzBdI6kNDshhTVAU/qCdKUYnDW8+sCYmeQYhVcKyyIUiM0uv9IdB1ZLsPlAEsyLuknXU4/NWeC3Wm57ExwSr3xyl2tNd10HWV6R0KdaFBErNS5Dp+NEcWn4Yx4WnPZoAYmnlNeSeI8P37CjWDXVTNUmYvPX++k6uPrlh9RdrOtwqxFcHX5/HWb1Gw4em+D/nhrItgqtELMbfUctW17Vr3w1d09dEVqy9f2QVWJLI50BmZWpVXsW7qJr0JXr5Nff8OjWxWYn+QnM5FBaamPp4myhOn6yB+gbgGRuRkdHreTUSMMdl0w3YovlwZEeb10pqlOdP3FgOIpTWjt27Z9qTvOkgrMAuD2rZnu+6supUResBYfSX6n0oW4veLyVVNrtOpUMuF+4/BdFAVmDHHExi64nQdxC8SV9+ynSwhd96+1UgrpZebSgdbxQCXqaKNO8ERbXqdijs9QJUrVFcsg77A+mrgpDZwAHbV663Bamy7jh9PUbkSKVfEgXfB8UpLJaaUvJ74vElK1dDNawkP0dQB5kwxmKrPrtVoG0/bx9r1pI2B5evQJcDyD7v9BHQetFlcAoBCUmWF11lMvlYtQ0+gPDWlcyiuwxmKzpyryU6KD/Z4Vn1qZorHDTvx6cfSbYDojJfnt3mYtm4P00IfToZ7iq7DqVukW1KyM8+UB7m8YRxKqroI5tlJWJLXS8GS9KVmbwZH6INi15MuDoZsPwG6eyiuwyGKDp2vY0OdzNzZjJsu/VQ0tnabyp+rdGBL8u2B4lBN+7ATOq0+OYWSeT4IMDfdbqaSLZwxA3fxX9VBwXbis73D18DHx1cupv0XPqBEqnHSN6DBy+0EYNiafNJya7fypwluWzIbF+Qz3RLkRl+iYE3EWCTmyQEOCsUD89jHg+v8FWznaZTroIAiheVbgthyQMl0WPksj10VfsGy+zQWAUy5Di+v7/u5ZTY96Dhx+DrlQQ26OuU66KBIZ3k2NjLIwleQDNKHZvzL21BDrbStL4NdXuWu0yFfX1Ti6cL2J4zyVMp1UEGRyvKNWWz8BlaEL65v1XsxbUJ2r5Xa9MSlAqX5YkPx4N10gwp99IeK83TQFeusmj8YK0663Uy8VGMje59ZQSNfA56yhj8qc0fxcfWbU441/MGKgbzVeODbtl3aV17km1K5QhUUaizXyIYmmUlqu9n3q8xPfZEuJPk1E43lQ8qaFuqVWoa+DHmj/TAdTKju5J7kZp1k9yVX4VrhcOohmnuoK+xa/NjExiaZefbQGV2ul7HNZcd9BhRfqbKGifPQ7LGttqYfglD44X/nTd0AHbxJ41AHQ9Smaaj4j28+K632qUH6kpLb+DQMS5nXrI4RCyoW9mHdrESh2muSudlYWnR/90aaocvvYc92v30GfkLq/8YCMFqG95jdCAKwP6GgPY14HTRopHF9GHCCDb1KhEc2v/SpNG1hJk2awiZSlpn3Y88dQuXK7mvx4ycJ+F0jXrEGjRSuj65iNFZI4/RuRyr1vW5bLSlrmqLdYapejhcXmVSssYE1PxW0pxCvgwKNFK4PZQ7IZ9DIqr4A1C3jdnL2/Y3oicC23RHviXMtrDvBBE7CQ4+4+vM66FuoD+hS0wx1yPvxOjVMG0WvJyxfuX1B1a02/YSd2qOZPofe6Xnop4UuLE9g7OnE66BDA5XrY4QTpphU7lM/FfDOuJ0yxn5qk+s4Dl1BGLmiORlEk8X11ucjCySvzsoyGqIlhxj/a2LLVcOFf3zzIaX6NqTwsmoyHLnPgAjOjxRtDnjMmzWz9zFg4UiWrEx8qr3PYcAobBhAVh/h/gS96JiT62B9Imvl46sTHhtMLlc1AINrsQZmnc/Zhsoq9GCdtzWcu9+v2fhlHg6qqxavgxoN1G6AZE7IBwcgb31bfMeuUJbcOEuPdXVpNWOP8Vx4MqOrcNcNrfjAb65qvA6qNFK9Pko8oUp85pb0KOLf9VfsHSuvS0sAN7yk9YrgAtg3O6Iz9cdBnDDowuFLLbUcNhxb8wYPf30LzorGAXEiOssvOn0K6NsbJUNbbfpxA45xXa+EiAU/gu7aYOHdLOWm+n1wEoGZICLvB/B/JsD83apcByvkG60AzJ7AL/zLsXX7K05uv68X2GE5DEDlhOg3Bkspm+LGwayfihjnlPqRGdcqGyzGUY++sPyoN/1XZNU1+vAdWkdY9gB8saqR7InZCrp5nWG0XeT1x2SFlT2SBJs8kysfea3P/Sudeyupc62Qt4AmYd2QkhnhH/X0snJgq4rOUglvlmThBLLyItO453kHSf38BT/ZLU6OawwgUmhEG00txxGuDW87tOAHVtQAKrk5QJS+VHpxTuYPQWUXfVwHtMKQvtQ/yXZC5ygNLslt8PBM03cpyeJ1mtWaWfvDheLL6YeUQMbSQBj2Hx5jfVoGD05cB/gRwRUHYMdofH4QR5hZyKxAyHaT1uaWm0MkYZmMtu4fUvKnKwOGWs9curT48RIBIIkz6kzWlJpIxn9VamakxdsRlWBhz8K1RgzESpDR0m2VPT4uZsBOjJmfE+TlECkaYOHZlCg0lNT8IX7NRRPXAX0EaCVwTmJnhhXkzOlQ+48PbxYb+8Q6MZVhmOXy+d0H9K4KBr9KfXhwLpq4DugjQiu+HxD7DfBgU01J7RGFXnWL4Hs2i5C3pd0Z9tgGazP1MnZzp4dhcMHEdQAfEVjxXazYJWON/27VPi0s+rhzuakfxRpiV6AK2v1cMapZzyj2ND3ViWMTr4MJjUyu78zEzk+V813ssTgqt080xmxqyTIt2R2QanPWpL+GNOengfbM4nUwo4HV9fF4jN9Z4dlX0XftbNtx4/oON8heJ7Gnij21DqgfPSiVnWUebIBrCq6D6VjMxLvXhtWY01bh2bEdhIpGXUkT9PBzMgPbRCMNZTqYXT13bvrUAXnsT/LV0+3XwRZEtsN3EU4uBVkPEw18yzpvBgkoWBNw8WFh0iz/mcg6pabnsJY1n57krwRz3FQazUVwM6ujXCKIL96qVqvGxH2URGdiEe7AlJwuin/uOx+BEH826byS+srGoeEzZbIQhPU46c2yATcFnWeJcsSkkx/D7552vw7WILIenn8Ve2NAeqwhtNMOT8YQoJB6iTqGAQCo8XXbrF62ehPa2OXpSXu6/TqYgsh0+KDsBOKg9lLTRcCTU1FgQk3KogawhtXZKcgeu1Kf9pYZ36I+TY2n2q+DKYhMh+/pHBwjAIlhGqDwXe9MCT4L8F76ulTzQxjj3cy91AMKsJQffsgv+dEOS37FpHpEwtN4YafZhmEQ9LA9m53DORZDBGw9BPc3Wf5KkXZo9HQ6M13ZZOFljvwcQy8n8SfCyozqMnNY4C1OBwCWhZWbdnhoODSTUO+ESoNU8lPquevpeY6hgr5V9xJXbECi0vohPZQYzKkpmSDWBFguy2e8L5Wl9k5Sk9SWez577CmfPGsY1rH03EHrblXQqYh7R82FA0BSUdFRotJrdHVFQ+EfPStOPHBzxVAoQk6ua3XyxAD/d9dDiRUZp1TofadFqoUGd2aDNUXKWUYxBTXZj+2BtRxwcx2wUISdfO/g5E0wBq3vunEQ2k3RMl906XWiIw0zME6PJw3TT309grceFLpC4ARzPbC91RZWDbI3gb7J5K6UZgH/Jhx7M+3OnF9VPOyjg2+S9IVwcNp+ppJ6wOaKgVAEnAJ35eDd0Nka9lWBVdQ1FvNW2CtGvio+6g3XUplENPJRc8vPtB/PNF8HUx6Zft9XOfk2k6B6qrTml3vfWS0xkx7QAiM27mAEsBe7tonUs9Di6ZZ6xvmKbXlk+n1YHaJwVtvMWzhwXPSdeBvo89r1Ouuk2k0b5b2Tbk9vu3zJv3JM8xVb8sDw+/7WwTvbA3ujzwQwstpvwt6BU6jX8SxLl2GiBDP/9Dqko7dn8o2nW6+DLo50t+9wHfwz2C7hmIvknuV8JzlAFdemXVDxgaam4TJZohIpyu9rYg+kx5H2tOt10MaR9vZ9m9gXYjR4VDsP9ExevJ6WEMgiOlve2WNLaxL0p4jJcx874anXK9TGkfIO4HkM50lHjmUWqBKvvIp59hDdIMtvvdz5VEyVMCmj9X2cB1cjXwcNzlyGLU0i+VL4NKpoGknpVPW74qUteNjY3aLxxXk9TYq7XC8fNEC2D3WJAD3D5t807HVQyIH+9l2G2MVo7MbIF5d3KnVbclTF7UdlSpj0+U1WR1QlqXAP/RT0fT8Zi1+JT7teslJOfoVOhLb5WUjQAhNXlgT0didH0/EkKB78yWjtFebfUvtA6jjhfPY/54V7XvsPFUR/s07XwZpF1s93uEL/jG1LZ81qwUuq+dWjpDGnSwUAmLO0O/ejMDlzqNZgk7cnkPLM03UwZ4H186FRCKWwFY1ZZyrFcMiMoMd95p5pqYobpd3SDc9pDzEbXESt9eFYb1GGEhvhzXAiJT69NyurJL1eno0ba2zPc0Ni2B+7CYUpXXXp+Weo4uW20PnUfejMFr4fi68tTV2p+Njp+aFMXCt7HaxyYMR93B/7CU0E0WQsvRAzG/w1rYDiIvCHffvYTfJlZQ342ZcY5a+QdF5c8IrDiFHU0S93O5XH9dbYWJWPhQr7LLVlLXhvok1w83kD3sGGZ1taucGpHuuR7POL6S7fAcp1ADQB/vF8ptjDYnJMIlEjD4XVqvfQggYTYyJcl4ZomXqTpI2HnM2d636QWx42uQ5QJoA+vn9y8GY6jM2wMzWt0SV9rgWkb03vazXbydeu7IKrK4C6TF/TXL4jk+uAZCLk43snsTfT6ICwwlYsN9t+vgr7qHd1cSXPmz5ljnrTX3e2QXz4Pi6SuWLgo1FlU8OV/TnlVdnkDjKSTDBoTKf9np2VuwpGg7E2vNXoqFb9+Rwr5yd16yGT64BkIuTjekwnBwvfutpmA5HcFDB7HghNyet5Ys+LASt2irLrbWUdb/AD0W5X+ViDBreWN6r99SMEUUCBXiFzqNlXWzpaqM0UL7LP3hVxpdpSv4t4Wu3aTbaI/Ws/lILhQpPrAGV85BN4nCcPFbZKek1IPQ1s+J31TWTbFRGlfp8svlolaSxKgz13n0myHjK5YiQTIh/fpzj5IGy6120RragyxuWS8q35AGaqcsbsJ7tXb/qaELsvZYseNLkOSCZCPr7XHHvZDSqb6l6PFnbK0isqzUiRDZ0s9W2GxNgFtJMQF80Hjb0fOs7FJtcBy0TYx3ebD1427FLfph/YKfN2+pnst3QRC/6oOR5N8mGzvmbePT/DCHgKTTYldGB5ojFkCIV6XvFf34Jc3zsfE6aEvTerzBl4FSxia4iVFjMZln19SWt8Z7t8OKXjZaPc1MUo0zHOIvayjn8mc+ZPocl1ADIB8PHJhhM3wVScYocqpfRZ9I1/ykpq6UJs40I0jadKqT4PJ+HMs3bYASdXjGUi6OP79SceYJds3gfc9WkpCEWioKSyuATo4Hmz0MBh29QOXKT+0IUOMrkOOCbCPb5jenJk8YmHqbbM3bkxPJTFWLqEzkFGdwYiVL6wbkUaFZeHv+UimeuAfCYjk9OWPKBhNXepUVeTodTNxA8UiLfFHIkpap4ZFbJ+vQ9BlkkqnIv5rMp2scl1wDIR9vHd5di9ZkR5VlOpaUtwWe/T4E8LlzU1H+hOOSlYYrXVsR/0p3hsdvpguzW918hCXm8qcvn38uialH53fffYWz4417D0w2Qp4RSI37s3kwKzHdBGEev6UCa2JH39Caj5pXeFAw+uGE2E6MN1lU+e9W7CPUzW7+1ys2MkNYvKdmNa710wSGPZTfO00p/n0wUHV4wlIujhovrYCejEm33YeSut33eBSaxZXxVfYeybdG04nLYENsSZj5PgQoPrACUi6OE7rbGLOyVNRw8tvsS2y4w3d90duOCmg/EyTBW+l1DyszmBiwuuA44IYIfvs4YuLjMlytj6SosN1u7bEP+byMM3+XxqYSszfUsOBvrSJcIzcFdsEEMD6jutsYvLgYNZBWmQQb1DCjBm6hgyew1G7M72Zfq2fKLJsZJfSvRcC3fFBjGyn757GLuTnYoqN33swAnU8y/zBMfWd2WKystss/g020HP6Yl4XQt3HSxiZEF9Dyd0iIiN9jIJhrOZXh08W6tZCBF2+t/tla9U2B3AZGzt+kxEdC3iFVtQTgZg8bLZEZ2Xx+ukB4da4kmKa+lhZGI4Xt2kBpBpavZC25LKUPTw9lrqMzrmWrjrYBEDCxr4XbGfxh4lpZdqlonxDbsP3S9R+Pi2cGj2nRI68mbmkqyuNQ2fmMX1zNwVWsXQiPpu18lNk/a9ep2Vt/XOq+F4RzFmrDuGgNyPTZBi0015MLr+0JeeqbtCwxiZUd/rir20LrrTpBKf+04O47ysIf1guQZ8unGHnxor0u38cnzaM07p2borNo2RJfVB4glU0nEwu8USRzV+uJ5KXmIUqSpe3AR71AEZ6+9x9/1sjOBauys2jr4l9V2g2GUS7mS1aqd3jVc9ZoO7KawfOyuScLWnlm6/Zi7kM3vCtXXXwTb6ptR3gU4eE8e3dpUwfPWS7ppLRkGXrYCzQ4o9deBthm7O7qquHj3gvpm662AafUPqekCxw8T2cdNUIUdW5NuPEvOlL8omVfWV4Ns4Jk+PSDUoditWx85dB7MYmVHf0zh5JizCGCq8KS/rcsEUfCb5NHnZtuB+vKq9GB/W65xE9JnEwlkhY2TTGSz1kCHXm79LEnrho4ll+G88tyd0NSLHhKcAu2HaH3LfzF9pFcep68unqlHXzTMAcKt6dsJ2PCvMXCt9Hay6DwICT+PkmXAEnS1hMSnsJmMB25dI7mAd8Jx32jvzl5sugljl07D9Gk2WWFkmiQKcEccwx53lmDkC7J2jkXHqLamaw8CWMOocXCO17nc2bN7Sl45ZxRDk8tlp2Gn5FHWIiirYGLDDRyJnNypfp3xSRb/GlLl46DrgJ2YP07LLfratM5kZIth8LVNrdU9V5J3tbpiPpfuWytDIKW4DoKHixQ7wZT31mgtvrgMciuCT7zyenE22GsrdIElhLb1ehwlX5509spu19eEoL84e1fccAmsfFtIDONcBEEUAyvUfY3eT+nl1+3hwRSyIzc4+NVWzMNgU653NXDvId1IM0zmZ7GsF6neEcx0QUYSgfA/y5HGygV1VvUSIve/i1FRr2roVMJI93anPcCTz0lVjNemZoudCnOsAiSII5XsJoVfRqX2KYoZBPvDmCTe9+Nuqc3qS5Rqz7zmHiKkKZ9++xznn1L9N/QVro3qMtPo0LRf+UbX89+YBVps/dErbZHlc6/lVm882R1UGew2G2u+ih07mR2IEsOntjizOVGWi3WIiSrekPxoF2FKsknfHhqR+3yXqm+T1WfqJeICHD68DngzwZ8AhnDiHjnVUUxBwCOerLVwZUj3OAzOTNQTTbMs99fewg2k/Q+QeRrwOmDLCoC6LcOActsyk05PFKeh3fx+IfzVMBM2u78qn4oRqYgPWAAX4LAwSx5HBUCA3q/x57WH8R/M3dFyrxpyqjYUeNplJqnygYdttp7+Xjv8WlGrHhd1+xY1boKMPxakZMvSPI8gefQZ/MTLuYesrhuIRcveZlxNTMyvFWrVRy7vexBHLnYSE5Oeqr4Ymq2p7TxoYLuCR6u1C6+sAxSPo7lMcISXSGCJg7xwVxz2n2nQWgUCUBZmwt93uikxZbYvvUszAQLHVR/LWH1fBLnLUoCQrfDhdcLFqYu19Q8RCfcypsE2mL6a7CL9zRooAU1qUeddF+p2dok5QITD1cOy9emiZQeEckMAlJxROJt5Llx//9c2PGVsLNFaBMBq9JP89397ngq8iw4lhvKo1aqIhA0Rjf5HF9ubtNTfAtVehfYua6fjNd379CLu+2RV6cr7fF1FjJyqtV5nvKLJfhL34TRg/3F4pDhl1nORgMxWWg7v154nZr8+KNs81uw6uXOT6+dzYiUuD3kk3bp39sy02W4FnXQM+7rwjZAL+zCzBhj/W4Loj18F96TjUzGPQdwI0U+XEdAWolKnfomehGPX3UFmD06yoGcmD6XVpCaQU/GCNYnk2l3Ddi+vgjkTui0/YxQQfzbMMJ1KPwNI1aBnF95NFUDdppjke22gODbSk+izk8ZyL6+CMRM6LR9id6D3Y2GLQJPMIvPqEw4+2bRiZpttcI5F1lZldxpfCa9e1uA6uSOS6uJRdyPAxM5uulAosZ6GV+zaQmVx0GzrnB9lTISysnBC7xTTTZ0zXcyyu2BEJHRffPz/58xIotlM3ZL71b0IjwLROVTSM6GlmDR5LEdPLAMX56XK64Pg6gOkIfPvM44mphEgUOYY8vOMzMaqQlFGp0aonfWzK0qtVFNA92usznfU7Or4OaDoA3y7xGPOUPNGzq8jshpNb7S6jEWTpCnhO7ygja6FuHYrd+kx++TVWza3juQ5V9lFVPj/X4IGjAd9EhPf0COYE0eDPJSmU+zVLBb+fOo8byjO1+z5+nZBfV/QDBp/5crAKzbZiwZOhwac+pLazjbMwpsshh9RtTPTObLxSJ6XfxFjh3pBSzeomrT6qqCm8yUpsEKE6El7BcyqIB/Gvg0cQeRA+bRvSvEXkKi8D5di+cvMReFbTy9hdI484O5DqyNTLtkmnn7m43xH+FXsEoQfhs3wnVrCz5ZB+O9ZBGetIXcjGZboK2EjN0WDdOUlKW0WDY/CMs3lY7TpguwgL+jTfiRaEtsn2toMjmu5s3AwPOanaYyZzusO5iS6vXm/0pp81sh5au2JwF2FBn+WLWUHqw5ymCbl+fb0PlcTQxUHDz89RTGUu3blW+JUe4NEDd9cBDJIhXMWEvFBHq02UtEcZj8H1kJWedh3u2Rr6dNb8axJSH2JDbQ0ztWfHShesXTG2i6CgTzyeiMqJxdl+Ds5OqXY9jXVrFdjupLxLE1Fcdh0nvz+bZbh47TrguwgP+vRZSLcxwRcmYBjEghyWe6pY3aNL+LFBzycb88oEYqmE0FVAKzyHQXmA7YrxXYgHXf7sRLdBrFVr2WTAezwCFEmTI9SYG2FSKSXnUGJ6TkuXKrtPGXfw2nWAdxEc9P2dk3+0x3gBlHYP+MP1Au9BrCg7vDE9wp6b2DpQf18gXs8uUB5guw74LsKDPp8V0l9DEnqqWnxmOPc7sZnBX/ZHZ9oJ9EC/YWhLOzX91sBi5Uu3dQ+yXTHCiwChz2ad2C+Or6jFwILUWur1xPO+ZRE4yP2OxzPFw0SeFdfPfqw8WomV4Z12T2s5B8snDECGf1WHcnMozBa6DCt4+ZNkC5PSX7Xf9dteFPe3MG4ax1mjzqIRnczk9MlkA0B6Ktz8MwELF7NdIcIL8KBP50TsD69DbJQ+wKGCIZx2G4Ac8fqx85B8cUH2kvFwRX+coLK/tMt08NoVw7sQDrpczon6wTmTjnQ8UTDv5W4Fhnuvrktgn7p9tw5jTYstDT+v4wG8XLh2HeBdBAc92uREsmyoi1QNXlXLjWVpJvZeNB5di3v8GHtYjLyHrYGjj99/hK91g3TXIagXBQELK6AT6w5XBWTDpt9QizMbhcllvKL0V+cGZi4xRDNpFm+pDT1Ev8/bTwQNPaR7HZBxhKR91ilmqWgx2T3JMBS04t2dpk94wGpIWOT9QvA4O7upaCwyHk/a1oO61wEaR1Da550ONNUEiim2iEpka9dZwNl1EW3tV9oAvfBpa2AY6JHZ7CLj64Ck94dMTFRT3K1ECuIB3AdVZVqp20TzwVBIl8l+1HD1jrR0liHttM24wc498x9dpHsdkHGApH0qLCTOGM4Z5Uama7QXj9eA5KraZ0CerJEBlvTOfSvvjuU/E1Q8mHvFsDiE0T4TdiDOemLmmIG6XtodqppraLkAKwYZhrXH9i1tUET1QeE+W2K4OPc64OIIR/tc2Ik7I8sx9dNiIyzRhTMj25Yx5FwFwxt3GJ1NW7ZCWSkjfUiTC3SvAzCOgLTrK8aupeQfqaFm5cK2JrZd2Ka99KOym1a3p7Kh1a2BOAH085TuyliWtHnjvSAdMotmcwgdrOS077f1+7n8WMhKRRwWNCWNXzNUioO09Sa9tmk2kVPvRH7xSPgk3fQkHNBn+wEXpV8HVB84AT4jdSCwJvT97KZ6AFjuEgwcxpV0BbBC+zVxD/aoLV0wC1WfbrQH0q8Dpg98gICPivkrNoYueasQjf3gtTj4xdbQWS9jj8WmKddJrL76Yx88sHmF0DSGsj4ZdSKvGttv2hKg19YdcIWRmEulIsECWK2DzJlrW9+TMfvnYXbx5nXApxGeddmoE3lFB2ypOu82oI8nAB/btG0ixfbqkAUEZ4aYFjw/Qt0MO0oKDtQmAQC3ckBh3k16wr+++YXk6n5PqHr6VotZBsWatogfvEivZp5nYN9XW0coXqLOvEn9J5sfFmUGRIkEYa36n5a1/wBkdFH6dUD1kRfg02UxvTaF7TI55UBpTVdmnbLa30oFOuZd2NGW5M7K1gOEftKczIRiDyZuLoRjsksJ5I7N86yHSfjXtyBpRfPsNrt2cOdne2XTyQAiWF3scCOmbq/6wCXTq2SHGzyG16w+r0FB1M8gyohxE2h+pjnTd3x+HfB8hP99lvHESo4pGQuK/WZJd3+mTVXWdPuZoXs3xZwsA1EzQTb4S4sxB6BfBzzvw/+AZYxZycysi2UmG+7yuPs84QMXWwLse0t3VUyp7AqqtmXSiH4uwQX018EBKPiwa5rzwQQWzWAZUuQ57MPCLNmHHaw1y+V+OvZQrciQVv7LnttKHc85rC5Evw6QPvAAfOrzRJUOZiyYqcKDFG13Tj8U36ayKbP5qp1efE+GajNzc54NZz2Efh0QfeAA+MzniSldHMnXDI329LrOTvFFFzFpWm9/BFeroSSg7vbs6eAC9OsA6CMHwOc+Q6qUWfJr2+IKBK7ejYUhYtL4mdWKW8ay6mMbIEY1u/01E9jD59cBzkfw3/dwTx7xKnNOQ6XduKwutUIqNAyiV2vm2VnFkDXCwXHD49HfxMXoV4joA/zvk5AhYzmYxWNvP2i/3iVqvmXIkL48LFG3J87SVEYZoyz76Tm7+Pw64Hkf/gcEZExYco7DZjaEPDYRB9l9Grk8VZMdCK++BhcDIiSVrcW2lQ8h8vD5dYDzEfx3GcgTYQlxUvRKSF1a/exgZbvAyaL19jlg28QUYgGNhP/nAlx0fh3QfIT+fQLtRLgtNrIdhqu2Tahid2qOhNBtaJxu8aqArhCyonqK2ScPvPXHU+K99pPqsvrdKqPuln5Ht1MHOPxLVolKmcYUXWpOKx12MrSNYf179AuzQoHkp7SFAjzLn6U5vzjf2vNtrtATivwmn3sMqcrJ3tzNRJXVW+OmMGlch+5yBeCsrxHCJWmBMHaffS4fPruD0K8Dno/wv0vznVjBlbRrjiS+2ojNxqjYLl2vAypYcWNjVXBXsokVxH09G+G4MPM6wNIAxgY0X0wLMvqQ7u+NPbvFlkcBxk7fp+C922ft+Cyq0Dlm6QkZXaB5xbg0grE+yXciBeFAK39RWTdv3YI4664DYeraAGWqtfNi1VXTNA3+UwjEQ6G7uPQ64NgmWZFdv3ilyyz7PRiPwau317HSgz/UFa1qThp7bk67D+NG0/QbPld94t7vQPM6wNIIxvrUY8hUsgyxKkquaojvBO3KlmMqytD5dqg7VXcvyexPsu6thlU44QznItsu4RsQqrD0B0fW5LJoEZvLgkbcY0xULmngJo9LLW27muZM9orELwqNaDelQEA2BZK4HurFhcjXAVJHENwnHmOiUuqLlsr1gKa9LQEHPZmgwPZbgzLWMUEZ93arzv1UkS5Cvg6IOgDgPu94oCknQxm2BOCZ13VIh2JOccWsEQ3exmgpWcOyzNb7EHgQ+QoRdQTAAw8z9kg5/LxtO7Edy1Skgvt0dtVWiU7sdnGHwqQ3rS0CcLk/ILuHlK8QV7sgPGJOY6aV7TWqdqEhMKxmbRPb1mLD+TJ099YL+gO2LVsYS6ifw2i5BVVSlROz3hVOFevOG//RQmWry3QsajuowdosVMbW8MRU0GiMp857foU7NSsYYHwaeMwcjtw1IQVo50VuzS1ty6nd4SimT3JrSyaZycR6Unu/hLU8H+M6+CSRD+PzzieeGpYwmxPZOOlISXOOg22607Qwy6h/yQcoJd+mc/bn7E3Hy7gOPknkw/gEakS3MusD4HmYW2BJjI3jj6p6HYURpL3u5gSsu7Mzy8mQz3Eiro9xHXySyIfxScCYNGStJbty6sHqt9pjh/kiQJGr4EAeQ3l002c2/xnL6U+45WLl64CtAyju04An2nBqYiXPKqk2u5rYgtmWgA0vrxmncPvNbYMlrE+e1AXLV4itIygeEG4RPVcYvL5hSuHgBrsJVKosrIi1WHdqAVt6DtPyzRKTP8tsvuPk64CrIxzus20xO1elIeOqN44Z6dUov0tH1cKwQH7NE4IlHbq0jYP5bP/owuTrAKsjGO5ybSdqDg7MMn8oswnjzRRyUO/UNXCH0z2MBzZJG2HLIvaTy3NR9XVA4TiJc4xlaNtcAAa3msTW5S4snZv2a2xBliXza+HoFMP4g0Fr3f3MpMpnmfOvlf586zOvdsKfcxtN5KmUadZR05Jx9HS7SwOAJmTu5CRvDw16B0sTHZgplT+1WsYx5WJx1DNuw9hTr+UebwZNK+XP7KMJgPozhb6+h3EdPJLAgXG505hqXUJ5VXMOIMFKqbMP97KGgzhWwHbK+XTWnm0NPNIk5oeNcR2NK/JKAhfGp08jspVdK4QrVUxtan+zEgGG0XRCVb285ZQ1kf9CCr61h2VxXYwrdEgi98UnTk9E62ATp6UnfN95CLj/ZoN5/czAAPVuxDDnur8+zHP60mzS8zCu2CMJHBifOo141sK6f4UJ1Of4gtNuAp9E5nlzAfy/ezJT2epLFSGcnkNeXO/iOngjkffi8wonHmKahPKOxQZi4HKq0HV6BjiRoNlTF2tIbRNgNJ6Jo2KJqySlw/B30XEySchSZ8O/aquSTcnTosW0U9JGrMxGb7NKtjibldQ9zv1E3BG7sW8R+CKu76KPhbaSGs3B5gNdpwP9RCTcc86ugzMXOX8+cR4T7ZXdbPLUYwgpT/uuiRss99D3gc7c+XYuAcp2VeMIY5afAVvXw7gOHknkwfgE8Ikw5kuNbQqu33w0w721mHzDUlhfXrF8qyd9T0DP8hzm4boZV+yV+C6MzwDHjDFDCrnd5iXjzHW7z2TroW1LGGalG72IOW1p0PnlySC6PsZ18EkiH8ZlgE+EMXMYbji39j1wWnsIdZUaCfe9ik3YAUb1TPvKkrtOxhW5JJEDE/C/MV9c2Bd2Jd0H5hfVm7/WBBtZQavW74iD7lrWCnuugA32PpfgeRlX7JP4DozPs554WZzbee9Bl1ZPv2nDjtX6ti2Yd4CaRQJjmOJpbNz9LGj/w817XJCqqsAfMxmVmzeZNCK5StjKcee6SX1OkTZWK6luu+tUqRekCqp/sC+PBY4jEOyD5h8oSEoyl2GbsWQ9tCT3cp7zbAZJ8LAl39sjzEOmOuK1yT8ODbFIkX7WlqhFM6VURNkxWEQXT6TdNDeHLVG+5K18cy2vgx8a+a0+SX0itRcra+wwwXVud5ssHKUuWUgcTAtgks0vFv5XIVghq/S1HvC7b3kdfNHId/Vp6pjWZrPmXrYZF8jfa54ajoOip8yxP23dje7Z/n7ptrFhznxoQdcbvWLndavPqO+0lj0D/t3m1hXTpXCIsl1nJGPfXzAnY7U3RzslA7csmvui1jwX6Tq4VIELFnDnJ659c7rQMvmoRlIM5tduDRVl4p+quaJ4LmcYmwUpGvh/NCH+5ipdoV8VeGE+d36g2tku03ye1oq2xdmLkKXJ51SQm6w5BQ6D9qQrTFvbz7Jjz0+6YrcqcsMC7jfmigtbMDdDWpw4um8OubL/45I18D/aqz0FgLp9CenY8SzF93yl6+Bb+Z6YT/+e6GKYhWmfrg5Wf8p1ut2Wk5cl22PbQzmOuZnph1/0rILwXKUrdqwiP8znlmIuCmLKxHh7KgNIy+5DXbL1JEwONCr2XIh/tQ+alqUbfrbd/Q72r4NzEDkTPoN9Yrwld8QkvBYjuBITNbYIPsTzc0QR62IkKiaL4GjIh6mmO5zpYEJU2cKGEBWWZaupPvz1zU+QVwakFyl8Yp49QxbmTnIAgqYnc5IfNv3VA5YzdPBZEvVtsQ5dkrO2WbmQWWA8cu13h242z8EiJF0fCy739ag1n9PJ7ycCVZ6bdB3cqsgN8+n/OFzA6oK0zJlkYm65e5MMildRAWDQdtpzW96p6wFlK8bPEa2E/4nql1u8WPiQBA1N+wKHv74FefXK7BcAHts6fPz6CiZu2CueGA5O2muX10TAya4bIgIQqPQaRetXbEQVHkHivpvm/xNVGJ6XeR280sCLDeInh3CLoDm9jhvar/Fy7GszbP+nhTYaeeelkSIWTEMWHvsPOYUIsc5i8f+4w0xSsyqMw1/fgiN3xUc0OtK0G0we3HI9pbuinR+DEzL0MsRt3ZR6BYrOWrBTgcfLuT2010z6Z5o9f/fPr9ifj/z/IPYUBKrobjXN+8kcU2jmHDepeJmku1y5L3ftC1SwZvhlllbkZ2zO88+vgzsfuf9+4OkUqOIcZTs4iekbCurJQaycFNPhm/R60w6cb2dQD/hvPJItfqWEzC32vw7NAaJmAsy2xr4t0UjYqlbuOT8Nd2GaxCbHWPK6u8hPKHrOZVgsKJdZKXrdbzfnt6f7iTCSQ3BcBz4k4k/cOE8cFmLvwTG2inxie/v7No0JtVt2ujEOPO2xwOSaCMGMFKCLT0DsuupX6NgHNIAf7QlDQxwJM7Y5pVr1SRsIwZ96BFnyOo1gYDwr6Y+hI/Kz7ZrrpV8Hrz5iAfxQTxgZWlgu3ELzPNi85i437n1mUxeQ9V7vvDwGdpaCiAaN/kjY8p306+DUBySAH+05BYdWYgTEXMB9uyrUeVW76UIX4oy2O2Fvp6R1sVlo+KdP4nr114EFKHQ5UjG/dGTLqZsEd+YVZJYidfv1LMsEGM6jki48zUQ15j1tqJxnkNF10a+DSx9RAH4IKoxYkeSuOluUxRg320iKcbZtn5X9Im/igVSHmSRg3Jk/O/K5bvoVOfURBxBFUE4RFzhMuZrCYH7snf4HXNyrrqGUlxOWgLvEpRXLM3AGnjO+PT/9Ovj1EQ/gR1BOEZdOpW1flmN+b/qVzGHV6wmGJ91dRjvgdtFVs1b1ScviXBT6cGx31YV5zpJDY1mJ0R+1Qw0nPUOt0ofiUPlXMhjRNxHagoWF8/EaAfOtqdZvURerU9Mrd+SCM6BBnwmY06Q1F1Mddqk/5Oh5DMd1IEQiAsWPPcWxqkmAMfON3LIJBTsusheUvg5UZ973IFzpSaxC2tna99GiqUoAlkBeJm4m1uuMe/3xH7XcmHPn6lQvL+Etp7VIo4slKJ/tp1Pud8ttYDumfBO2d4aT723eXULxxEKwglY4Hw0gCAcWRKOxaBm70hFsYTz6MybzKwLg8kPXgU+K+CefHz7xybAUfVc7/qNpae7gHHlWv6oEsID0zsFixKSo3m7PGcweOXQduKSIe3JDj3GkkpTPatleaO/5ug2wXRXDnVh9XMerIxVDH7rgwvmOz2SxX0iVcPOYr0Pec5Qn3aQheGdOBEsb+l1q7JTI6c83dnna/AeOd3vFfrCZnJYhQlHqPd4HyG4w3DbYT6q05/p/iazyyKHrQCYF5FMQuT0EegFoRjKzurq11a40t/AjVTBIQ++b22KDPjtpgAH9WQ3rMhxXTIgE9Ikfuw1DvXB08W3N9vdRrGqeLgW87GorALi7q+nx1VcyE4w9+dIx1PPSr4NX71IAfgAvDvix49S8YS9Oey13n4sJ3CUPZaBjal6OVNmzaUzV07lpVT+lsGsLYumkyMFxEAi4ezeMOPz1LSD01NmBTuUo8SJtpdscn6eqUY+XJGOzqlbeZPb+7qJIk5xCm+UTUswRJR20ePE7wtx8w5Kpv8Q7rM0jowBg/+Ib4r8qkJIxtEP5ADiy2m01kRBpQ2xjlRnIlkni2d7Y1sW2MZgyGXQzJZJoHBktcA9m7bNM6pfUED4y0xWSaSFYLmm7tLa0dRhmPnBQdViplwsQhuDjgD0EcG3VNEAjU+r8Nu3m1LB1Yl65+iyLzCUstxkgaIbxJVv5j/eZdYuctVUpJG0xJ4/FtalZ41wvu/m3KJ34lH3M9Ed2xJnSKeXOA5G6UvpvdEzxnxYIJ0O7WVqjc4kAUtMP8UwutXcdqMCIOvTj/qc8gQ0MbsiHNbOKUiub5WRTcSYUfChQks78yhz1Ocoj7uqSU9eBzIrILz/AfgrI46gbVQOdUlR9QwAWlLSALqwBOmPevfYGsEvR22Af+/o6cOE7wXMdCCGfPwoi1KeINtZf7cvifpZ9z1DBmFmSGRIFroybn2IaZtcNAp7sn9P9fKLtilg5n8Hz0wSipIJFo1ENPcFQ12Xp5FA/WUiMxCnM4+4WCC2ept6EEZ9nCoJLTl0HMisiv/wAexiPlzyFvJtB69Stol86rs+tG4ANy+vO6q5lGtRKdGcfVaYemXWFzJdMnDF3dd1TzmcXYqkarIK5Ml+oi0u7TDHPktQbngQ9Q3vXZdIIX5JPPWbqOhBZAe/lx/tP+QHwnu5Xwj9VuhKXcyHtb7K7reE+3mZtxbzUljIx6Nlj5DsxdR2IrIj48sP9YXZAo7OyTKfhHJua5Yxgdufdig2ZxPpqYFit3oKoO33t6eXxUteBx4p4Lz9kfQpxA/Sn2W9pna9uv4UzPfRtgcpLvtMdE+ekq5ABjn/dCs87vQ7ebOD9BqG3U6hupCTilMQV/ZwRK+kRWWq4R301Y2ODjmJLK7M+CEuXI7oOnFLEQfmx4zjWvNkBN1msg1kw/R77wZiFaB8pzLZcx8YRzUvbMCV2MdgP6tjnOa6YF4l4lIBkDzl5nBuS02Yi+7Ag3KRt6ynpJ+/57io/pX1Os/O+GNh9JlgsFqXTI4KTW8is4lsx8qcJFvFf3wIqTRPS2XJ4qL9EB6PdYwVgdgRx4LzjoNrEIVZmFHpAkmDBwYn9jki7jRH9NoohUecTez+QYeRG9K9DBkCQMMCuDYOFFJJ3APOd7kIHTogkHZ9ov29twtY2fK58FeCp1F71D27w1+88f4NmOnwKi9mBU5gq5gQYaI7/+hZ46NfBo48YAD96d4r24b+zwWPWCo77OtSppGGzco0pTDedtQvxmiopwvYf6rzkEkXXgVgKeCg3gSDON2BgeMykqhgacI27zKHR6jRTuji8d5kDvqi2jaMHtfczycKliq6YWQp4KD8EfgqZT7p4BjcA5vY9ahQKZe7bJuLVij12MZZy28T8qFT13eUrdK4DV9wPhEdh80YHsHdDGhvw1vre9Nq0kpLZaE1dtE0MlO7Lg9j+mdT+zdm7Dp5h5En60eM42sxefJAiFaLFCRbD7jNJMw9zee6OCHhuh5tfdMEdn+9Zve06SlfsWIWOmB8ijEOKTEkkKa3fO8Ma3EqPncNoUrK0ZlX8IW06JqeoiSaAtmzP7jSey3qFHm7kEAcB50N8mh2dmtptAih1QQZxGI6tvk3BDcudGE9HbBlYoUF5dJ/3fL4r9BB9fzIKN5/C05zPuM0dptJ+l8z4uWwv2bg8NW1izzLP1gz1Nnzl/cAjv9J22S03UnzvT8OM0pkYCF4820QS/EAmbDKEWqIxO8m0XhvTN7DQxlkXTJfKec4X7vfLmfzyp5+IBXvu8nVwrwNv3I/VnmK7Y2jfGNJ4NttA2n1jQ0wY8Sls1mJjgiwcwmy4YX3pTeP6dtfBFwxcRz9Ydwrukaxb+lLwDNq489xxEuSTiiZZNhCDzD5TKlTD0F/Oj0W4PMF14BVIhzcdokxq7+7sD7msrNucL+OgJ3SSRZN4g9qZNDWiN+majpmGnUQ5rM8eG99d/ytmCiJiwQnkxlHfxYhJG/ol+v2deBMI2ComHG1az11iwVzMi2HtYf8htOo4qlfo1YZOsB98ioNVlSQH3UaRHqbU3HmyjODQiuclFE+/k8GThgz4+zyl2PJnQqwe2XAdyImIzPBjoHHMdHPagJIoctjn6z4cDz5loTDDeb50LEyGueLsILQeunFQz1L/L5pJ6n/COwBk+QCHv74F6f0WY2dTYkbrWOu2bJZgZiWyNM4vDM/juM3bm2D/KC4IIgV9ozYwDAZGwcO4esCrNnglGfzxUWZUoFBESzPNYRyGdmtmPE11s6gt9q6986HcTKbJtHp23BMokmZ55cO43Ruibg9edwhV5xB/STKYMqoxjR9y1lyW54pZoYhFCmLop5g7joVZxzZlkJ9eZq90uiE0IEWcG30sdPHMBqXJADw/ADYnT1lilUQ4PLFk690X//Et8I6vgzcded9+7COOleBh0ntYH1vKuDN/ZaZOFg0Iz6JpZS7bMwNvsZNOJolYH/0Dfi26O+llDXNscDCW5Ngwsl52Micx4YGiud1YcBiBDeK1WxgkzV9ijlhfQuHhJmNK7FVs0VIXmsU/7EOrBhEKoT1wvc/uXQc2MCAP/ThFHNeAsmOY0VYA9ZXuLGIKBF9WKJVpifZTfOthXiLUaq7PRGyPoLgOhEZEgPiB0DBqCrCZqxm+WpYNGG6sOxW3jhYIz1E6tlbZnWzABFixPNkSj5+4DnxGxH/4YdA4bAoHZb2gW2U/3fs+kArKuKyiVwMknfCXTrqtQuMy5iR6JMUVUhoBAeKHEeOgI7zIZQek1K5dA7YMgmQiCt8egLRZpQLUtKZGURlC1T5VwC9gQJcWuA40QkQ7+BGaU0QHdlxgJtfJ1uqvBhZ7iaOVl67/LqhhS9+hsgkbXPYPwUCX3rliOiiij/wA7CleywrNPM27GlP1CKcsdB1tyw8Dkc33YGaAtdJVrNliPD8yXcZYjDoQ6OUt3UpwdpmirzAw/utbEITQ8Bl0OTO1CAPhNxquBcZrw8jjrJ39NTWATCrTMflzdjxpd1cqv4g0KjqNYxxeTOSWAA4EabLHhXFipvdgDyxqc/jrW+B6XAdXJXJtfFY9ZuHZ/6lm4d7Yv79aikUTModMYKa/mcq6PaeOcy+3h08AbfDMdfqVHi4eLXVFJFbMefmh5lNomt7iMqMPGKrrTOxdpgNlxePb02A2dVmWXlM8GdD8X1oY/4oj7DEa14EBiRgTP8YbRYQ3bXxrqup2yTZ0ZDGTWXqbZfZkgcN/P7SwXViVn882NcP5J9LlPV71OvCwAW3rpkvE2RXS7XZsvTtAQ9bPMshZAm4U+VxQx6XczQCBtbaxS8kmLtzVEqllWSC8UlokwszN+lYtl4j/qn0hKpCUKRzopbmsLQREDvBEUjw5xya9Eja+pYT+FiZhHnI2/YH1QSkOO8oIC8BYZjcZ+gkf0KMkr5jBjAhPPzEgTiRYTLzohrjYavN1n8bRZXrIoYiNNGtDuuA3PaIkCZ/cosf9XQeukOGL2yRDq1kgiSVRd5UjkQCHAtj1nKWKUJ4NnWUuwmI0Ldma4X48Z316bN51IP8CsjDIVTjlNsycp9ksWHnDZ7gOSy3NmGXmVLNpTGMJtDG4ATySy09ZVc+dvWLvN3CW/fjUKZw1IG5JlWplhFFxaJLJ311s6tyvMRVbUgf4eWlT4RV8DsIiWktVcuIhz1KhwvSbm1uL//gWJLZr1ubiLignhjVaJhs1Hd6JylC6X0gJir42awvoL3PINsyfdVpgoHPKLC5mLbV+L75w5HMTJAepOXZx8Pu4/ASx6hCZV8x7RjxpEKQ/BfXnEPJNnosbql2qwgIUExW2+ah3e/fFNDxTNQA982FQ5+S4GdneqdABNyKDp5sf//UtYD6VWKKFyLrLQNbWLwmixwJEMshSaDRbvnN5OX9LdrmQpmh5nqQllK6ozMIvy/iJLgUelXfFzF9EFPo5DnFOBPvPizyItuAf7tmYMGu93Qg8FSM0pSZuNfNCOQ36aVbhlAtaWPRIucK0WOCv6w//+BZA+CtG/JGD4DN/J6ZwcZxgk8eyh189ihe5hik2hD0M667jh4hFh867Iu4vYgr9BJE4nQTAexc7+r3I0d9SoHB/VtYHWpkKRGSYyetMR3zYbZfHuw68X8QT+nHHU5wSWHKalgKqNGA1OWc+kTTURTSLRrEuiNxAtVXkp/IGzJKtIcTi1mTp6KMbF/3tLXA9rthTcd2aIGB3CvAB5EtRbma2a175phMA2JQJY7fTVef9VNiwIiYRphza7NlcZrFbtVhlaMJCDZ2Y91t08Ye/vgXxLc3eY26igASZ9WYR0chP949VfAwjqxDFz9xw208wYR7/ex34Ypdc9rOi4iwqKLC8pVGEgGtjfTnDiz5aNqPN9C17ZibrmM0/sB7bny07v3O/14ErDrjlIKPolIE0mPowjZrYydrpMDiSm0EhmZhyT2WYMEHm47J/1Hy07HQo4CvkiwN22U8pihKQWNIsVYhKdO0icT3cpLJXplqrzSlH9kj2SyqqVpkU8KXoOct076SNFLMYRoDqm4eJ//oWHPnroCIileIzZfGJDQ54FO9mF5EiOUJCBfb9JOJIb2lFLbNhxCuC9353kz789S2gVjUnI1u3Jr7UttSQSEUGL3hakB/wj6hbl+n9CSLOo/6uA1UYUYt+KlKcusQWIL2bpI9258OxzJcRuyWCBF2TXs+tdPuUi+scL/XImnRjGNch5hHFSPzMj0OiiOgNfam97rlqsB1Z5sQaWLSJjszFw9ZuvV7bs4NOYkYtcSR53N7EZORVXiPxDn99CxzOK3RPfVeWlNuUI59ECZrLIsNWZxIXh1XIZZVXOiyONo0r098SA6123a0w8DsY/gSf5sVvrkO8J4oP+SmBcQoh56OWWlQqGJVS4lBG2E0pp6RU0Me7x2rj4MksbNHieTznPGFpVRrLYJ9X7wKUWIVgXyD+61uAc67YO4mcGZ+aiZgcGsUh04SKqvl9JFYC1/on2HSPv74OfHfEj/t5f4c0Qbi1it3Fhtd9X4cOkbQN7n9PFq3hJO7dpecp/Yoxv56AP+hBeJtzHVi5iMXzo71xdBgGHNCCsX3KSs829VI62Q9VlZ35mtbYh4Uac6StoghE0p85db/YYvY7f30d+O6AHw9ynk45UtjMWXT7gQOyRh84dRCandid7FAZScWl0Yb0anx6lva3nxbMZbyvA0MOwJG7xMT5VlWGEPE6exJwaqopw2r53ovdUboCyDSlkoBXmYjPUhpdA+D0o/Ei227CSBq1wya20o+BwZDOhG198jRe201tCjOKwvQjDhkyJgBwQlkZXC6aH8swHM6cxBTwwMSOa/oFYGaew5Zd8v06kPURue/nEsW5R8ycrBYwrTCO/a6X7Eny4WQJ61YJeCysiWTIcF/2/FKS47Go14F1jVhaP5koTj5qHFXQl+qpLPPK7D5tMZFIIRhO3LpLvmDuyTmIwPde8tM9/oWAhgsGrwN4jMCmH8g9BX5nbZJIBWXFpAIlQXEdwjdvngEg2YZjMXe9C3KgEsYWfEkW+gXL7j7xOrxhtCKfwon9Gd/9iRJR8Fj2vBL5ZuP+okXTP9N89zuPfB1454indnPRTqlrC9bNKMBEAtQuM51VvV87251dh3q9ffZZy2dAy+VDr4g7DYhWPxMtSFtjHnVd9aY/8rT0uTaSniOe3KrcJR6IjZKKLGEbynrWwLg06HWgTSOa1Y3ZxiFeBoamTOflCjrdV7vNZEmnARHoBt1whuxb4yhlZUq5yQ9qAFhk6umC4mpyujr/Q6mB+K9vAZC9QtwboWQf+JyAEgva5fdssV0/I42e0+ZHK38gQ8ljoq6Qt/JZriiBKE44klKepeeLmPLueIsTMKeaAXiRmp8m9f7dcFbjAOHHKCGXzbxi8jPiSv0cwDBnkANAe83JYPQcrd65hJNTjY2+KNnq6/FcYJyRDCySE3iSqx6ZecXkZ0iWuok8cd4PM1HnDc4AW8w/HEV63CaVCVirZT2SmTCwFGwxuaGWJ7b/lW54bj+B69B/IGhXwAb1Tfh+ph7ha427WQpAH3t5lVRxHsZQ1q5MTQEpN1ehavTUq97rbX/rIDgYU7VNkv4VEH/iZFVB4R+1JWmH7yfGj2pkD3UKgHMq1WsVHSH+2V3n4uMT3zyfzLmfhxo1z4C1ZDhlCEVU+hjph+hJl0q7YuYt4On8vIMwT6Gyx0aS2ZsU9ExNZPdhI5WtTm8nEX8/Fu6cZAEITv258a0ul3QduKeIq3KTuU65XxNLyWZUISjlvk0qc9hnr+OVcyYDqVc2QM6gyXOUlUeIXAcCJeBb/MSpONFqc4BMMnW14G1q7dRgYL8zQCGLwEmv92OlEF0Xh4vlmZjUpe8t4XLDLug2lZugOfxV3Ri8ZOYD2QgpdXNGyZmTNFeT3hkPuBtRfT/28TGLj2XUAydqPy3TccednNJna08x/qMElQd0rgODFDFOPnt+Yts7Ox4PAV7wIUq/O2phwSp1XbJAbkKLQRux3DLqU33HnzjDHiF0xQRSSDj5qTOnVBu8WC3FvN6cbJaSmEASHEXSlJL1MexMI0jJtF7/k5ZfDoF0HeimxnpGVlzK2pLFkpek4Cpwodrmkbfr+HKm5agpNElpcV5tTZa8w8TwzwoMlxW6QgrJ5ZuCvJQgiYXVJnveOm630uwecBFu8AOx2vIFiAN2v1fa2Arl6Vt5fNAV00cR2+RlpJzyV+gvTnv/bl0Gmf1MH8Lev1flTPBMvElP9wJqeiZq/fE8TS88dB2iSVH0qbO5BsfrynU4EOuO7EkRQJPr0C+93/3ysk5k5/3ht9xaJWxw5fXD+uUCWNeVuULHJ3KT/GDoKXi6Jit5VBXSG71T3KGFhJxlCk16XWcUWnCuRP/xDvmnEl48DvE6cI4RR+knpZySWLibzHWWL0YfVq5zEqH0Q8VC6aqW+7GS/aofmNj+WfmxCJmX0AaZTJ3QBjefF//xzXXir4PLH1EEvqkKLVuXmX9iBwoHWMxXaCcK7HqB4J+oe2MqbJMShyT9BorOg+TwlN7YJYF5V9h7y55wgVMMVE7Axp/DEfUOpzPIx+H+rK6vPwQFXB7uikg7n+DzU4HixCGOZdgGdYvVtmxVAknPN5SKXoVK7SQj1RIwt/Y5m/lPqc8rZkkjUtXPwYpztiRKuvut5pKhha5tHqYeZhp+a7k52Z2HVQyyWj72sQCXQLxiwjEiKIOYWRxh2xBGgynkq9TsLqbaa0UjNc80/22R4e0j6dIKZ7s87c4fTo92Nf11sAyRJfEjHTHzEDEVkYtDWp+2553cKlOxf2q8h8vAXTFjFzJ8fhbHKetj4YUM6kEia76vZ3LnKgAwFmXcDCJjCNUkILHz++cX+JVZtG6rMU2S8ictRmzX/OBwEBx7atMO8dFAObvyzCSd91leDNhsxDgfVpiXnaFiyPK97hO1MvNan/1EoohHYF4HwjMiSP1Mjjjzo3OU0c3yNnY6GnafNbq0Rc5CReZ+T5cbtfeiqoo92J+emEvCXAfSJiJ5/ISEOIGBOeld5j8KKBpp3vdpVOVNdQW0iUk4o657WC0FbkKh/kzZ87i0K+beQq7Ojz6fotWQ6Z30uRzDaciRbTi1Iycx72Ky2rtygRxtZIidXtBnK5w3yCc8g2rOE2BCk0XQnWVema5hUf/J6AwvoSJKZAiTHpqk8coWtCRr5XwT/FbALTtzWhsMPK6XZtaQZfXPBAmXBLti0iwi2fwMhlPCAwSqLL0O377t+zp2Og/VhoPjtJSTow83lViEmdzUF++/Tkk5QPk6MEYRw+TTvieamKXLevuWbq+t8ENUpSzo7sDXzvZY7HruagvppNcfSplyGaDrwBhFDJMf+vbD5J01F7Wo6HL26r7vMdlTR7EoIMFUwcIzORbVMsoXQ+CP7jEe6XKFFI3P5/jR7zhWTs1myi/x9e3WkqNtJCbg4rZeTJOSZ4o9MTD+efY8tus6kGNwWfAaTb/cYHmPYkVqCAARO8I4qUrvLkbEszSMkYff9nwRiyUDQHA6e3qiY48GukLSKKKY/GB8HLynJOCgGJLHd1dTM+kF7mFKGuYg3ZVZWHE2HU2e50vT0j8eZ/Icoiv0ngJfyw1HR8Fr6W+b8swqIpA3zULGXZjWnRWQYxtWtYa1EKJtpwRbqUUnP4GLHQbkOhAmEcHiB7LjwDcTCVq3RKXdt5VaVYk3Djv2TFlWh6TyG7W+p9m4/qUnJ5w+OhY01Y1F1eywW0khKSkR/9XGipBQ7UIm4HQVDTEmZlgyJkvCkEFpTYd3OYyQM4gphmicWdTrAgaV7RmrZfi3PB6i/8fn1vLYUVTZGhHvB7FN/S6b5wC9KWO0GgHIqxD625Cu396isVjxGC3pcphk7BYj+924AKYOMSrS3umYL3zndeemw23YbAQ5ZbpxeVbA/EpLSm/QpJYg+nMpgzGWg54T+9OThmDYdd4F5d+/72+Smg/8x80l9mFU89W5qKnQSy8NWrm7criyASDuPyvzPNdP9Wt22ZTrwL4EbE2QBXLKGpHaCz3RG+dx3deBTZTdpAZoadxsEM6gmnFp/qvR0tumOnzKdaBffLLGjzeF4Smgf/bhtWTLJchDbzPJKJgOgwrXxod4KORMDTaHa40vYPZXUtVcKvc6UL8BU+wjzhNCBSRaQ0s62KVORSEMwvhBm/sDbHZaka5/1P9c4aDjpOsP/6gqfHJko1bfMadYPWKJes8ty2enMGIKU7J+3MNnn05slR/Aj3hr0iiQeAoSh49YoP5nmq5/Z5OuA/sUsVV+Ikec+MFya7ZoNqy5V7rvc0PWyeYzd9pcpetYuoF9uMvPGfIuHXId6JOIbnETOeK8D/GO2ZNYFAsz0rfdpnK2gq6Bjvv9VLi5yZbcuZ/P0adTvgYTXfEooeB6TXd45PDXt7vtMLTgZucNaYRlyV5A8DBE0hQLSOVO7oAXkgGdgQcya0/vXBBoJ45N2IJahnU63jIhE8dcOvz1OwBCRQ3zuN7ZazYTkNnlzCzB9g6fAZtm7WuYf7LpovByZ8vTZ3rIH8/zdzHvFULkGFL7Ycv4uEfqwVMn+lz2wpbYC8PGDFL/0PH1KLTrQLkFFF2QwnFK+YBdG13PKQsptDACyGZzyoV+YZJhRiUCeu6x75wdaIr5eX5dGuoKKCuf3fKTJuIUiwVZ2fo8/LMm2hSXAd6KfctUWWn4LswZDdHQX9cx0zOk+ysjuBK7hNGvyFu6x4xiEX28dJcMgs7cjL3v1Odvve9+i7rNhb3pGo88mw1IgRh0ihmkKlgrK3WOf5ZfjSIl0yLLNDD4ciP9UIKiS9xdB6IvIgb9dJM4PaWxEUHKtvcWoOdtBmx00l0GNLU87smyxz6qbX7OTy7UpX6vA1UcUct+1kCYZCCjHdv9Uo3RMbsNJ/TdtT0k7Lc9lmIo6IRTudOzWswloq6QtgpILj8CHISLYRrKnPapM8MhQ+/BKURNI4R4XrpLUDj1rtg3hiP2oPN8EvA6kIYewehHf0/RYlize9dxlqy9EN03HJasNC2UUbWOWUwigEozTMPsoGf9pke6XQeSTvKQW7WNqdN8hM1Sf1NS8D6sNfpWxGD6GMikq3qg1V5FHRmsYbFW8fOdPBbtOpBuAUkXxKQPIezOfGx9V5nZoREEegJ9Z5PefFOJhJmzmHBhpe1LxPsXqgddr+WKafiAtQ8CQHHAKDNAvC1EDo9VZx3yPgMKsWmRPrYqvZriNdym2vW+vvRD/IWeeF5q23XIhIucNornVkyRmO/Q9/29SpexkrwOSGXhw8LuAmwMoy3xmBF6h3v9Ygu/OONn+ox/5xKvA/cYcJVBSD4O4XP0bRuWPAHnesy7jzlU515aCobNs4EIeC48pGlxJGbTPqcuuHzIdeBPIr7FDyrHQWim5LVlOIrnfN9Nh0kwqBUC9K7zpnkKEGk1yzs07/YnmgVzKNgSCLQ5nXwxP0ede3IygoHo5U0jMUIM1CSJR9oCD9Gt9VU07I2YiSbSeBNs9LFpi9qjLYCaGGP8jBi7dNJ1oJ8CtsoN/5yiRUw0NnRCRW8mR1rnaM70oJtqI14XK0/yMvMI16g/y/E8PuA60AcR3eCnFJxSEKAZdzEPEiKin6jJqDMFEFA/UITWAoaj0fYYKty77vIpxW+rSDdccy/xAbXNyBIqJfc7CgxrJsF+LzUgCsrHEfwNiNyqOcCcBqOXS212AnG8iyCsxYntS6EeC2seDTZ8LuM6UB8RVeKH48Pw/WTLURb26AIK+1XbfeaWuXMKEkmC2HPxqLQtAr85S/nzEC023hM3DJ9NzF3iVGXTIoe/qpMGid+CrKCpGvZ8GcfAkITAj8qOT3+GAvAZvJjxizJboygSZw8PAcKJFWx7/RTF4Lr014ECiCgDPy4fx/Eb5yRqwiopK2lHqveBPKdhneHYe6PYczONlqqXsctnpzfftb8iIiCgDYLYfBTJ57y0op9BJDJXC/DDhdFwJPk5mH5rdMrw9LYi8JS/DAz4hT4QLji7Dgx8xNi7YPOETTc7QmnhKOxh/rPYz8OKPxCg97zKK/RBfYfVj6HHEXfYxT21MwbgtQ1uVjO1lEuEpK5qzjBUh8VT6Ap9prr9Ir/iERpXzH9EdImfOXFItGCtxdRTSBHQrkMM1XKM/TTfyaLQfCwr9qzJAdOC+5Nf8QiN60CABHyJnwJwShmAWtP+DZyOu60FKadac9azXscnr+roVObfrDLuxe1UHw6+SwtcBxohoh38MGYY9hwSZO5mKtcdbuB9xswmtYXlstUey/FYom4pkywS/lyEyyNcIevA1uGWWkm2l30d5LpUqW3ze9Iq1pOK8JwDOezZBT7gtPuwMbrpdE4pbo/qYI8XuGIaIWId/NhqHIutVAYmr1AWGv7CXSrhn31VQCbVg4v4ps9qeKXb5KT7fDcyaKK6N3ECkw0LvRQ93/Ff33x1coXKx1dVUWDiFMhwQY+HkbRXbRm12U1mnT+k21x/+Dr4z5G/7UcVD0FIlojW24Mp29z5yvDEHHrC2TW23Y9Nmf6/fvVBCPP5AfAEGhqSRJz7/S59N4Z1v47/qDOg8H2kNydelB1W6z0DCouhI8UkIWCPekPD73lKv4WZQXEmUTRlJwhMyJzrnXUWbMKGPtzaX5mF65YXXIfmGxF/tMlhSvUUA6jEhMswDZyu0q2Zd5/z7uHQoTeFnqocwtHLnePgVy/41Q4/07byG5FyHXiXiKfx49GH8PWgR2auL76cmh/YXIDtaUYxwTLcT51krU3llJn2k9b4w4WwLuK8DuxpxLb6QZFTEAVWLImOg7MPqbsHbhJuN838wMe3ZgR4nUVPtdhr1keDRBrmIpp9si2DhAVZsWebH//RGhZj77ums0EmmNmmCTuVIf4trNOE3/Qqa8ZqyI5IXRo5k/tQlIlzpqxTr9bYjpnp0NOzyhkfBfCw28/JTywJAwIJzH23UKARqFmoNMDB0l8FpfjktQvX1eZPNZfySaTrQDpFJJUf/4jjJaxuqGOYVEC61BnlfVIaW5EL3tggJ2PXfYxsbh7bRz++wC/lerD7RWJjcraZ5xDqfLcE0YmmBF8cKnsXsP5pZshvUT5GmL0xRSSx41xVvvt2D2mBii/C8R+Z5Zz2QCadYLW4TEz7U9mGLvN2HZi6iNnzMxHCzIXOpN+tTsVgBNzG+OI+WH3qWmMNFbDvZq3YK50PM2R8QX/QEA7/doVkXUDt+ekIUfICXgefxL4aHq2JabjJqNLdm+9em+VF4BeVcEdBLE7/fAQFXertiqm6kNpzI+qnADwhnZkRiG2764TYglUwGpcwTP44jh2C2wyI977mow60LLEQzHxYVc4Ym9sUE8DDX7X/RAEKARyRT0EPed/taNh2ggm2MnJDGUlXUYeqMdakJInIoame3szYsduMKiCF1yFmLb3aXjBsr2nQBShs/Uw0wSWurpDmCkgxP5gfh/6BW273L8E0Kw8H9EVOQk8ezpdOGMAjSaYLJd2Vgn/UPrqs4XVgGSNW0g/mx8F/KtjbBYBP0dSedEIIybYTNJHT6DfpyWGsQ3/POVvP+h+XRbkOrEtA0viR8zDQLrVPK9nOZ6mw1dvQ0+mKiCQE1+2pdL3sU0BpP4a1BSTKdSBdIpLGj32GsdJGH6TduI5hwnLHUCVao4oE32rp6ipDnfgfqiPXTv0ZlvuF2UV7iV8u4dQkAy+69sfszIjncjKJgvbyuD3XOuL74/hAUJQS+YaCPti/Q8aMpFHnDyE4j366YrYqZLfcqPEhxgwDvao5B4zE3CHvzIw83eRRbXLFkMSQtcy4cLbDwxC6bNV1YLcaw9LNdACbriqCYtLU6MrVyFwpywbbbGM057wFzwp/mJE1kiYfcGf3/pJG5BFQV0hXReSWH4uNY7ddBkKkm/mYNqVnMXSQt7LaHZ973GUSo2JjVettDXh8hno9GuU60C4BS+OHYuPQLfH5ktmVAuGh0IrdZ2xGAnURVO73VA+G7lU2FnzIR68QtviAm387w/x+gvA45AeHS1fAsy8Y0o0LR/HYMHbbWEmrd07V5r1xfxk8VLluHARgD4ROL7fGxkFNDxPpevJX7PgHNIEfiY0jtwSuUv8gKyiQqfs+ktxjVn7Y+Ek8tTDrQ0VrrjmenNof73PiBqauOJAVBb4Cvz/mCYp0gmnKqMLxbPO+z8AmN53/RGC472QwI794HWp/Ps0SbDpekAg2d25kEoC5rBTo8FftGA+FyvxeKpNSpIv4JV4h24RCCInRN6uz1ERsWmPSjcI8wcet98QpKDUefqrXzUQjrX9j+DSTiKGWhoW0L8DPDVeDw7gnCwC76Sjpc8WZhAKU2U5g3jUGNDCcB0QADej0FeHjPEqZz2pS94MPTY/QAH70xzff+73iSge/LoLjtuAlVDjc7K3ZU7+nczEpBXBY0oCAb9WKs3ku8NmSn7PYT8FnlikMchfCPAqaPRPIM8EVXQJ+Rnos/JdYFY/FuGLWI2JJgnjNKb7D+LthrD27Rj95Hf9G86TZsetGooudtHGI1X5MSsfjCzQGJiReQrKCcg54xBRgTcWP//oW0GSa80guemiLm8zUzte8L7cFDaE1aydoxRsur5tqZv/NObS/2KtXUGFzPhnFLhzxHX84sHAOaffrkNSnyK9DSmYQxg+000mbbU5R6pad0LVjz4GC9xj7H2gX7arnK2Z9I5LYjwTHkWM29mfzJTNy3YqWOcaz6kQxvA5M3FJjiefC75UskUY4l5/zRKGxE9c165Immk39ctV80d+0egz+HQlQSfVjum+3+rE02TNOqITKAsq7m/Am71ilAhlrTP2uIMtduGtp0NzuriaRoQgNi2eI9LmsnuDvF38/f2oGssvlXTH1F1KFbgbSKWFpQCZuJMlWHVqPwBNSelYio3LK3D03itU99nsYhfRlCvwv5Mh4hMoV0i8BWeMmskRZL5MN09JU9NTZ+rrqPVg7mxUM4W5JexRy7BrLju1wMlX/Rzbeo0GvkDP1GVY/cytO85IROoa/bSALOyRVTYzr0m9ftP1iNwxpHqBOFLDu10bA3/nPK6ZLI3bVz7+J83VojnHJnNMuCEjvM9nLRWW27G4syqQ/zhbEKrN4yf7w+FwW7IpJs4hj81NXDpkugNnDdDrDIOnuDskeQEmvMyuz3PVBSadg8XRI36KH9P1CCocn7Vd4NoKT5OZYHDIyPJbJo6S0B2jnFGC5x5zsrvEz+tZlDq8D0+gTk0FyzSkZZ5Z5w8chHKNdh5OdllphWLy074lC2LSVVadx+sOzLahLHF4x0RgSk36WyCmrhCHNrUewr3uynEwtTTeXlfBlXpNlBtMpddWVp+ZxBD0G7DoQZgG/5gf74+QAcpCzm+aFXbUmQUPo76n4BkemprudYh2a0cUVrDUeK/DosuvArlFz1GGDpbHZVraxyfeWZkAvGUVH1ryPZGqNu1fsJpyoWIzggqqYDyLFY7+uA1kWkWt+DDmOOTOHFmhFlwD32lqSs6pk5iE6mL1pinbA4XNplor+vrAk7JFd59Jf14Eui+g1PyB6iJ9Soq3kHpJULVVLklI1NtmoA6b2J2hiCKad0cwig0e81aPBrpA08xk2PygaRlAX54/YVLxVtI+0Ego2ZZdpB80KPXajCtFX710P8mefxW/813WgywJ6LYiHnuKnkFiZLy4rgBnU3ydxz24Hif2lb1qPsaRym6WZ80OIXEf+Ojj+EVHgc98hVc4eLEuG2XAR9a68pDOtCUDqPyVraoLHcnu2fgy2l3oEvl14dgVQLoB9fmgyCmRWVlDraAxySzMNC6liu7YeAFg6DVSScoFftm/HdY6njXJdievgekSuih+WjMOYFIXWTHihKpONxGV0mmPqdQMSnRF7LofAGfSCVD+nALnY8jpg0Qi7+nHJUxwTEtXsugjdsOtwC0vSnRimrSeVGEek6q5N60v2yviB3BEI4ndtCx0l7X8s4Sf6o7a+5wxH9k1jJE4H32gD7pnY3EU4xyHm8TetMoTosG2cUGTprhvC/2CNM5x2xgUbPoQmCAXdYILeMX46EY+bpNdu6V6wf6rtskuDXAfaJKJZ/FjuKfY7lqS9Kg6pad2FBPhJ1dep0gtPnzoFYSlgLxIq+TyCnldxHbwQ32kJQpJhBJNjQTjPVA/UEjyqt4GAF2OQerkb7NMY2/RXahFg1gex9iuTG1yi9Dr09g6yaatMhxQXBSdijWWT6Cr0H/lTCWtzrlmxX/e1JRmUWc75nvIR8rAubfsDrQRc4vM6hH2iMJFL/J14wiWTZ6R5/sCZanYZYpO7eoWDkUoNZhXyQrPpdTbAWD/k6bmu1XVwxSLXzY8ch4HmBkjN3owq/mwAdRd1dPYv0e/YluyYPTZlyVZiH7Nanz1Cf6UG3e1iqZNbOpviVkkag/lN+Z7c8j3J7LcwreuUBgZNwARwXi/FUsGL5l+xZeUioH4NhqnsJF67xtT42X8ossDGRml37XOHw1nS1CoVnGvdzcRIljLCPg0R5aKccle8Ogi3bMLK9aY0upMQx8jrh6yX65BfBwc+cvjdhINTfsJm1xGDj+zTf3fWw2nqUoLNGGQ1bNeqeHFjGiYbjznIb1MqZIdhL7Y7mFI4C5lNzL/RZwMDiU/kZT+EWQdxjgId/2G2F7IoUQdGAhhNNdvLkGPTRwK99GH6b6vB/2wL+I1NuA7kQ0RW+CkHhwyFyQD4MPheqvowg7zeKvb5mSPR7Kkc4WSFtWM+ZycGBMQVsRXsrw9ny2DRalWMCstvAN2bfek8tFudVOUkDhHXJxvG4SHsUrai339ryOuGAuzg3MXYzyw1GlkWa1Ag/qvkvRUinqXDJrFQSeCWwdBJWrexZxnEo8WHNSgqOpQg+a3UvjdeU26UrTqb9GEmwnqG138hr8BJlL/CrPogBd+N/B/yBIDxmCi8p3Zg3nZvEqowNNIahT6SeQCc/gEMQewD7/hLQs2v2H6Pd7oOPFXEa/lR/zBLgOX79BItXDQsfMhif2vm36QJvVoXtoDJJGr0FEhm9icJ5rFO14Gl8kmtIHIXR/oIakX+DO7dpcFJ3HCDe0W7suhjcdS7wXeGWr8MP/qFyKDDOVwhQRHQGX70Lo71kQHvehni2LWTnORSNWF0iFX2NKZkSIqNzojSIPwPhAUduu4KuT2fCPSDd1Goj7MxtLEEOwHCiNogF0j0qmoxyJtWYx4558+EoO1ngYlP010HWi+iAf24XRzna8wV0O7+/ElqxqFUNubUoKmUJ5qVwXN5XA2kcA+fDZ0JVxN3p+B4Zv5HTwImVNvGf33zCQ1Vt7C4XIXq2Hl3a2K/iwYQ9p5lDA05QdOhkHZpSy8OB3Ttnd02Nkc7yXU2C1YmMlP5LzZGlJYuPanuitvnOs12f0DnugzndWBEAwLVj2+c4iHwYE1Njs3hNnY588yoNGbOlFaMwYb20rRBRJhVLY/1Q5fiMj2qxf+jjyWo1/yt+K82KBK7Oab2/Npwdm62jLa9a+YU9GnPd28vvymXEF1DqncGWZs07qo3P/clypXxcmt+e9OhD4tppZpkJxmmv5zJ46ZMXocUyyglk2XfRVbPFtoArdrxzZt4wMuV85KbVECxpKtt7W2UCfxTlkQeoSUtSyxzECJ7XEmCD1vt/NTUe5dfvg58dMRfuyH/U4YA82hNf+NJ6qB2xqbWNO+EPUhfE43YJKuo+DNDeDwgv0MvXwcyOiKv/dh1GOtePEdTmjtSIcPjezXfnFBaWRV7upH1ZIZiHkZIcWjJTyVFOIb+ClFBgCH8xIUgyyHgIhziQh4Iq1+n5jdCXz58ZVreUZrOGpC+1Un6JeQbaMV/ffMzHq9DgmSUUOkzeTHzxw4YEFG9DzOX5z3UjpG8pOVFAKwj32XtjcZQqBi2qXkWdi9yFrKxjF8IFcq0eFv/4a9vAe97xV0XggxR//3i5ZQP9pQmbCFAgNHWhOWQVfY4aFs9h9tItTZbRQ4uFHqm3T0NDn/VPvbwJ0hX0sGti802rY89re2UjwK8b/bT9V+jXt2n3t5+H0yvb+bNq00Z98lhwhyB9kMNLZy4ynWIwgRRmyDRJU6Mgf9Yd1Joyg57Nq2S41BHqVrxQcpWf16Fd9WqycYg8Zc+lV5g5TrEYYKwjZvockiLgZ3QhsRYATyrdTdaLVyOrYBOv2bpUFNsLb2G+wjlMx5hVpcbvw5cekC9+1klpywUmCybHAOnq1iFGhMzljomsgtV14Cnzp01bIZdGFnbfHx2C/3OcV4HTjTiUP2skjgLhTXO62UOM1uN2n044lU+RiWvNm/qlu0IhgEDcmSfAMBlOq+QFw1YVD+3JEhEYUbvasYAU61mq0MjBhLeBU9hC56hT8QDh50b+Dbts+2yz3JeB1I04FD9rJIwCwXXmQXRt60Ax/S+D9RuSdOWUI07Z79eCNGwNdRHvMvlRa+YROWcqD7KHTmtOuGSxopRxGbqoGgiiEYjm3pEzNfs2o6Ftyl4kn23smZ6drn3qKsrJLpCXszPcomzYipTS7ddl/SMZvcBxh92NJl6oCaZz2XMSdXOhl5aP8NC+ejlisOcUVTUD9RHYX3AipX0oVVanea7ZqNb4k9hF9D5agJY2/7/WfuSJFl2Hdd5rCJXUKa+2dPV/qdFkJSH8iWpwYn4Zt/qPo883qohARBM/HbRMYui+u84n5gw0HIwIwdgclRCrqQI2o6k2wdtCdsVlcZa0hg8w0FasasOTXHUjzBmO9UPFmK5Lginh4iaAqGbnoi+dFaEEqzo3N6xaFIV5dHqgGmCXnZkmkiCJM4AP6AjI7NArHUBvTyQzJYIeYIi8AZlLyGUCE2JTTsMRKaCnhX18FEvSlnV0K2GQtNyGk59oD+KbAVOIypKAeyui4SsHE5iUt8zxPfDliv952mEfEGRaWZkWB/JJenfYZHjZjg0N8++Wh+haRZ+tXy4y0PHbErNI+AmuKyioy1UdbGb6KKLylFJgemibV+Uvs8IGijRu5pHUGdCEMtHLDyAw9YHeXIiOkx7y9RNjqIkkV5XHijCNKvz4nY8BcQQFMfP3Kz9tPn8g0AsH7BwAQ5T4nFThAwognRFQLXrbsWYYDWiq2DrgvfjdiZQJfl7CiqlHnzHpVZOsfwUxMtYbKr+Ru0jMN+XhZmRnB/N/TQWz5w8y0PQdSnGyVNVSTRm5zsu4q6iKQYpNRmgNPAMHZ3nKYBSOoJeJkcPpnDA4+t9ch9mhUmvSAsNLxrcuCpm1U4l8PdTrgjmq0u4Svn2ODX5Zk60LjmUk3LZbL1L7gPzrW1qCD2nZNKNxZhBMC6sokMvCZehKo9bkZAfgbWZEC0/f/LSLZPudMlRCjJoxtWp+cuT5KFpcxWHdGwKFEs1vWoCfCcznJaucnwCKxlal9zJy7VssvNGjk6MHBkurSdtJFyQzydp2pIRuRZxdiiIBsPUmV8yOyQ8yZmVEy03g7LTLZPA89k+WqzKjlho1k09B4LgDZPS+6vaJGLU/cdYa+cBz33CA1ux33IjRTustMhah9dFvC+eXNDJwX1ETpCh9xp8mNbUsa9G0y8GOTzil6gEM3VcfqrppqY203ljRmmol6FpR2pD/fES5r9I8DmK1dOgqqxJXwmcMf7qHEBbWOSPOvCD9Aaou3GA/yPb5g96nX3jzrQMR7HNp89RWWFEf95ymgq4W2C5razzdHi2DsgSDUn9cC2Ze6bDnD/OL0GRdoK+/Hy+wGE86+KV9gBmewKaqkNX6659FnE8oIZIP1dSLQtOTwHTbDKTGk2IcFBCZsa9Lhm6l9HbvKvP01ZuEjF2uhRa3eehJULabeIWlI7h60bA9fJ0WNbPYhQr+VqXZM1L7mz6zKfb0O23T93EKijbqOepbDOAdQMVm89lIRTQl502o/a2r/uT/a8LWGBDCzaUfIOeUSCqQWeCqmGfJ9B0m0kXyqrek3RVeSrabPPZsNLKHdcl1bQTU5v7c6nCiXrzrO+TNm7pDcld6ik+jNKBhLa1+WTDtC3nps86Sj5QpE+02FwQz/aBfG3wzgyjwb6Kol6oBHuE62MRdZa0V4QIF6HZzFmpVTSvHxOCdSzKmIwSWiVY2GGJYg3AoD1SgHL6e8oJ2RAJvhW1y4KO/rs0okdlSQLFN01kcDBLhbRz8J9TONXHl7Y0M4VcXsZpp6ceUnlDNmHHNvXzhxE2bkrjqiadSGWq+TrIvzKixumQJdZTGlNr5qcDU88M4sB+J8/u/fZy+L91oT89utQG+S6Y4Ghsk890Mf57N3ClmL8LEY11OuziD3TRjNoEpwUxutMyRCv6WV6oZIdVNnvtUt3mVm8FBnxB+t6hyqlpZLQvoZ82aLBcjMFDJGzqy6fKkhgf6Tbdp0DrDa27khSSJbb00j+H7ZeUv6OAJ85+EmsWZLAuEIMHSdjUl0+V0WCF55WmmYNyur7Pg4S86RaRg9opYHVOmvwHsNSH34gJGawLxOBBEjb15TJlDetR00YkFKgAFJHT4ORDJygNdU3eMB2YxZNNfaYDBDCBg+XCDA4oYdNfPlk2A+3jev8Uzhc9Ca1rs8tkbgEtQ38Y8KDlVgBEdLYI4WTW/oIGy0cYHDzC5L1uNNlga1m5e4q54q58QIvLKbdPw1arjmD7RC8h6duPMFo45oEFGqwLyGBjEg7NdKOlGMfUTwCBRNPjgTbAKmt5Rb+ILTentUSX/tHVEHrj6FVrnCCPxHZDnwuriuDo7o8vWwm6XN2oKzPFNpLRCYbDE7pnmSfcjQc25D8oLZtdVAb0AJADQrGBmR5BrehZbDtC073wOwZbf7GW5WMzHpbj8DQ+r5Ng5TSKJs0UTIkYAb0L+5CmTwm9t6o4x9B14UjWNHqmEXQYAZiYy3IRGgfPsekyh1ybEDmjvk4GI4Vkeo6CHgZylPLjtPXiCei/rolonnLC6AZ+sC5wgwdP2DSTy0pVbh8XdEWgFSnsdnOUwMkHTpxCa7s53qiqLowUe797ejvJ87ok27D/rV0R6wpTDEnOsXzlujfGvA+jV0gSmiahHrjxh2QfbZpzVZ9hzlyPjATFb5NRlAI5HuTpaWypuv+jdhalL5LRr4gW2pGSdLcL3GFlwucOERtN6LLrHW1dlt1A+NZw2NZCW9ppuW5Brg84ADlpOQPyT9IRC0VYF9TBQylsBsxnzJDXVckwsctqMRXC4E4ruHz+jAVya+EppEhV92pYj5wEmwEirAvm4GEUNgV2o8zoTsKQzJ+2q/FYoYUwatxLQ1NkocJximaXPkSc9cwpP+gvaigl10VX6ekw7czHTZSKeJM0OQ2kLLtHU0dHF6kUpm8R5pZ5VvSFzGyKT3lA/tK+ZmIY64J52BCJQx76ZCPknUUqiBNX8iU9zUCpjnxkCl41kOqTKd6gMBEknMfXpxxWZbmUt+JJKciEPJ4f3/+R/etouwaBhLeKp+I6KbDgJZcoHQjo0w9eRjwk16669CSutiDW1M+KtDsLjYrYqsQvASk2eLF8rMODRmzQ1weJeUHfSQ8aCYy0zxP7Dsfpf/ba9boUcoYi2Srd7jhLi03wcPlgowdOOqSxTzInwGdBYxoaVU1GKZo3DjHlotUDJupb1Zt465ZlmQbQUeLdIcSosm4Aj4mdlXEdnXJmLbLBZ7iSYZiYDLZLHPs0M7irUXQ7KIPXGLYu7EWTapoUNeglKY6V4kf6GlVdG3dKZ8EJy0cfPLDCJo49mhmjq2f97hTKTdHINyh0sHnKE1BIVndLjcgrCT8BBREnKW1BCesCPXhQhckb32hmAKdtZ//t4bwRIes8COzA+yNACLpHBrnLQlnSuf7TNkFvA+hxYlE5VO4VxjSS0/m/vhy3pcWyppC4DSs7etB0KofcCWw99qSSUxCIGLsSFk0K1dA3igLYsC2V7epEr5rRcXOyzZ++kdhZOMy64DYOzGNz7j5HP7CiSmMj5O41anO3jqqnzuMRiF+p2tAVhn57AKAu93h8SihT5y2uBfaFp/CkFX16/8eX4ya0fPchp6rEDE78WCZjU0MSgT9vADWnnoaGEzZp3j8otpIivIwyRbT55puhOVG/lNSbGNZyES8HHzMFC566AZ6wMUzN2mdvHInDV5OyqCCmtrMUaZiNkkM0gpYgKXUYCB5LrwVhrQvk5UBkDv/u0vWwSKGPJyOx0oyWelCYAPU+OHRDGIRZrJfFbJ3q44t5fgxduMgw0ETrAiAFEFAdruPS8cD/9eWwVYsdixBPszMRJvEWqZpORv+51kE3qyFUsCLrhZfF3K0NsH10ME8A5jJ34NPL0oRAeQz7pAat3PyGqNzILdaFs3Eonj9Uq8/KDsRy4leIjb+/2dpG/0hpGVrJsvCMuXGjxCDkVADU8p1WfiZ+tBywyUOmDNWNL9Bp8M6X5+tJeiJMZqAkFcK6WATendz9WRJqqCBbPrtcmZjtumC8HiZsCxV8YUPhREyflm5KPgXOU0saVfcbtCveWHSiuFCDRNhvtWPRpbCS9g1W4MDdWz5O2jzm5deX0xpk+Z1EHMM7Z1e47SLgm5qUYWbaS8Nu4DHmGJq+YQ6NbbTXKO9D6ffEsJ/pO0PXRm7XBen1kGFb5XERhdC5Y5Q5jX5pZR+PqA/X4ZrQVO1HgGdae1PYU6GJifvb0fQv+rkuaKkDrppKiZuwgqKMpBlWBqO1u0QG+IHITKS9qbUN6Xb4Eshd0teP5QRjLbR0+eDqBLlYNY2YNEA4ksXaS0O162loiokPpHDOqWtmgQ1MUmDuxjR3JEeR99nk04Twlo/4eQChrUHwNAuJC2NBkOt6GrS4bWLyjRj0xWZ9Nr4uysw1T6GHONuwmRDeukB+DkRoc9I+hU1bT4flq4IIqOLQ01B4zxhCRB841Voh3aV1VRc+2s5Pt9sPDUP/NsJdfgdyr1WSuQC56xUdjyASK/uKQQ4k2Dqdhk7fGIunbR3cadTLsiFXZsMxeouHa9uHrlkWiLd8zM+DCE0y3+f+C0NlXec69ut9GiAO7CcQRcakV0Uc3vTzJyaE3kuDhUetC37l4V028XIjaujTcnsoPAQF27ns81AYkOUhKMBpU45DZ9oYe+RQLxzBtY1HrQt85cFdtiLhpmBoNejWENpUSxY0wSzK6IOP2f1eCxB1+i4argZFGr5gpRMt95Z1cXtxzGFQyF7w/CD3aM0YEpmYajz5e9p0kGxz5fkMmxGHBC/BRxh6AdRmyGnAljAAQ+kBZVm/a7/+3bnXDE+XF8s6ga8tivIkVDQLemGweqIXIEeY8G1K8nmZMCni5QcHNsqQo4izYPMST6H/v/uQmNdbzr05z2Frs30lt8kWWNwCX5ISUoBGODeAnPgd3bcJ3y4P7HWgYVu+42l9gESWobEem+tXOQetIaVpB73RJn/ZAWEC7f46zWn2nLVBJnS7LlCvDQzb6h1f7cMtxbNyipSX9biLnkYqUtXGHe7V9kmspLrmsx1L2EFHmODt8rFeDxo21Tuu2Aee5DTMtGEhXnB/RED0dYbk6rTttrx7rKB0R5dhSMLSKZKz4Md1gSsdeNORodxkK0jikz4FDC2qHocAUhGHDBxtX5fiaeFHcErN8lTmYsFpywXfHKjO1qL4yhUKRXqsmoHEEfTcdBnJ3gEtTelTP6S5ns6FRvd+iHRsJG1dkDcHqDOFKDfdCn2X0OVWB42WtmU0NE9KleOZ4lb9e0RNKTD1FyG7KadSzoQV1gWG8GALW7pwkzrQRwg7IKJ9PGzlBdrlRJVJV7qdqNelKKdqGFhRs3NKI6zceF1yaSf1NqULvtKhQ+I4mq4yaBCe9TS0ncUkxyNa1ia9ahZwXwOrfNZPm7nxuuTSXu5tU/AuZY8mtIPCMrlbSkz0C4GrRDNimZyUhoy0iynAlusKAKv48ylMhHFdEEkHwXS42At12weKunQvS/lxWKJoQsTY9Px1bFkHKiJyazJgQua868kqLERgXQAETkvnhv9pteK1T0CJVlLV5Q2d1vk4/WN64xqsYVTyuOH/QaFBkjEOq9uzptVM8ZcLCfgQgk3V+9Q+lqoo7UCQlmfpss7nCWoHFKH2T63qdSFBnPJq2UP/VHF90uHC4qnWRaPjSHpssPsGjsMKokgETAtrTPs8cbIdIhCntGHNgt6IWHtxmMKMUb9DgHaYqVJepvAIbQxc2M8tiNiKTfBf7UxoyhVckYAvKSi0GqS96FZphUErEq2RI8mICFFdMFDalZuY79NIptF+OpsY4M66QEEedGQpBG56grYLYun+6Q+0ww0t+u1ZuAOdMG7ACs0DhgxdtGI7jU1o60SNCpaRArkQGHv2dhCoyf/15TTPkKbfGe14mrTZnmyl/p8DTflg0A08ou0CiQ4jk2jAupuHB4iURPRUYaMmxxvMpBGBcMVnb6fX5gdkAl0EvW2ZSoaWd2RuLoC63ZgT99XAhisQmaOx8lwBfbWqp2611LCcNlKeENnnMYLET98yHbSgrXVBwjzkzBaX+GIUTme7jGYKX4r0jgYRTgu6KiAn5vpucMPEi0RelAocbt82sLUuQJgHnNmougvCo88EOjvIZkJxpWyVYbDJW1GDVfiHV70sjbIau8bxqDE7kiYL2Vo+EObhZpbEwxeEdOwle90GaJz2WdDLPkoxHCPCTa9KMVnXR6CPe8Bsds6+3AzfwQNsuYIvbhgUduliCb9pPRpgTFfl7nMcTRuw0ESMahYbwb6fe4CVrq9Leu/BAbZU4SZtmJNh/P0IZRcjZVry+DhHCF0+ZQOYRDGkPgWEfGeRnZWwLz/BdwEBm7m9Mb0DLWHk7WW0p0/7PBSMJLEVhi2jMsxMp7ch54mhSuucbxjfY+ZCbAOolB4nbONzynpQAw2xWkIl0S4OML20Kof1FChNxru0LZXVw+Q/1ZWBc/jh5XtAz64nH2hpJc0J8dpU5pa5lQA2MryUCQ/mryzmJlKwXFzBRiEc6YEnVIhsVcADJUg3U5VLwGOxyhSkG5HAH83jaZ/fMxN23AcfbOI064LreDiQzTr7LDVIIjglyqNR9Dp22RTlGa1JcN1DyNsljzLBqmtRQFZ0KKpMqGNdoBEPSrFpZ5+mbuAjhi7lqC0d+zwUb8nnDFigh2zAdN1CC4JmnxN+fMdqaEId6wKNeFCKzfH6nDCFV5TNZ91gmhoCIxZrmloHnqciDMVlaZoFfQgKKMrxKUysY12wEQ9LsVlenxPuFGNqrBIojg27hiVirZUPUSEIlOOdR0PZMEAMZ/MuE+lYPjLiIik2VXlhNmn8BN3LA5ZOPRywnPOt0sQTpT4uSrtTH7LKD8rf3zofB1JYFwgC/kCo6ZRboonPcP029tcIKQlZLscRhlW5KyjAhR+FtJq2R4W0aNrPcdyVCRGsC6TgQRA2e+qzrQz/hz1kZ83PaegOdBSUnGWjxGUbZZJRRj5dc5w2wyaEtC6Qk4NQ2eypz7ZmbjybJDxEMJ70NBX1ELroFybY5KoUkVYd+Q29it9LjZmwLze9t8EAhxfyWaQZZA3D3bcwtNABzei1AwIWBT5MN0LbaFEDCHj15YOuMbP1dcnuPTTAJoV8Egntp2eWm+IWl/s0hfabqXBQTdp/pQExnyIM5Dg0nqGvmXQtP0fzUjqbFPJJpAljmZzjTjIUGhsMHneNGTNFU2GnklzxrsMow6HlmM9W1rX8LM1J6hyc84aLTmyp+hC0GMWhxxPiYXkImiFBBGC4LAUeSWYspWoypXbsR2sxG/bQ55fgDhz6BiDdH1+OYnFddPKert6GCD1AEa3y0GRPepJSCFV2ZSL6jUTxPKM8VR06M3t6bJobK/IvFf5Hznx/483lRqdOLGvLCXzxASUd7N2MV4KJojJfJGtD3hTtmJLATlaOcu+7AE/Q2k747qPaEytXX5fc3sMCbDb0xp5WZDQ7tWtaboWmgJQ+6J4EA/axsQbaJodOd7RCHe9YzUzZl5ffO2CATYh69ClMvUbRTamj7FSLKwrgJbn7CI2LFlfQ6iwWc7TXTOR4xy5g5erLz+09LMChQ336NGH6SdYdMJ+HOm9jE8dZ5SHoSnNft9I2M/QT0Ax40/afuSSCwGg82WmZLqIRaowgVtGuoEVpU0MlU9JiFqn6Fa0WNGvhuOLgRK80yspIuVp86y0+rNy3wI11AUM88MTmj32+GTargUul2CClaBfwye2fZ5L1i7aG+nQwoH2vawpI4aI4CH6jeOTvrrAupJRHYtmEp0+Qcqu4qjExxcZBiAZ0yqkUNMrt0EqW2ibPKBThMkwc7wAHvjMCLIRgXQAFD4CwyfcbWQ8nKY1g0b1I1hU6DqW53g6tAO1pGzABmmhEhaF2JBEmQrAuiIKHQNjk+42sh1u5Dle6p5G2eADJSJCno3cqyjncz6QMWlfsDDb5DJ0+aLZmbdnL3eCdcMDW5HnLnbM4GispXzGILSWfuR39ij52QvwLqywfhbFBG49Vv7HwnQIWTdgr/e+niUwY8HkTJWXVYllcFu2Y5c/TEEWtZo9wOhlTE56KLZazx4JPV5QsjYELkv+zaWqXHPap5DnS7p9Hm7K0N+iwOqnsHhn4PUm3lc4l0K3nZ61KB7pjYkLLx5BsxMmhhn0qmYmVoctBgYgh63lYGibzgDawLFF7QzJfo8I+9JnqybymwrkQ/cdEjvnDnwEgmvbpdH992Z27Fxtz0RcLgw24YD0q9x09vy5IjDJ3vuBa2zq0rye8RfJA1RfKLecQi3GWblNMATIA5eSZG9DJeRoyHviEwThOe0tFXpNRr/0DqB964f4dvN7E05aPvjlYncmN+lQqOnmFPrqmSqnLLommeBFXla9PaXrUiyZYdeyAuIwj8rWxt3XB6jrWvbqxF/gy8VIRueNG0y29oW1M1L9HvbSGEvTHAluiM3qmwDDJ35cwx7EVfNCCkLkZFNpG4eWz9OmBv3VOuUlZ7sRs2MyP3dcWqn7EfeJ1m3dvdzSZjyzu44qVVLRXFZhqSruD9CBE8zI1MoCAporRi+CPshIH7nPV4HOHoC+kcD7/R54uFg65fNjSQzltrtvnxmHf0HaImnXG82mwKkrWTInkkGERGlefaO4JHOQ0ozJxyOXDlh7KaZPdN3K8U2pU9LLQumU9HgC+ybZGQyHW3cak9zyzPHMovZ8uexYauTzo0sE5bcLbo8czJ7HhMcDNYuAH5WNgCAV3T0MzalOSvJ260GxrnBaBNg65fNjSQzltrvvGjUOYtRODVtUwpoNdUAlep2hDouIGPKwXTS9Y6Xks6xQuAk9G3tEqF4rQokOXEUGV++PLKflffJwfObOeCpw634Ypv/rPkzv56qgObxasxDhOi2mXzSs13tTkzwvsyKZeFZ2zi5y9THoNZ5e6T/Y0C75dF7jXgYcdiYAvKYgAlYoOLtpRmrajYtsutKfgeL1WgRZw3ZYQMEq0OUKdJ2ll4bfrgvd6+LBNQvmkVUGuvkOCiL5qaZ8nsRkyHoKmY34uW3PWkiRwEyeoZmFZy0W+HJzMpux9gh8y2acYSjIByARBYEsCTtu2mqF0LlWLurRMmprnKm7hiMvHHT2c0qHrXXYfKquQs36A3NUukk6D1h2zKIhQ1Mm5sg6YyWGETXOmX51HJpgo/F9uhQDUjIb3xtTcH19Oie66WBB4lgU25O5D9Lw78C6Gv6eBKlR7RvAZkAJzLFBqVnAFNJZUs0JMAyv4L2WnJoq5Lqinh5LaSoebMmIiV5WNMMwYxi56QcpbdQSkXhS1helwGm1D+2me5jT0puCpjY12BLbQSuiCp2/g8uvLCf/WJVz0wssBiAGe+KifLoi0JBHCC49YqrmumjYTETaglWFG/0zdwRS0wHlQa5abbEllSi6A63KXZjkegWB+ZwiYiOa6IKAeYmrrRG66komelklDs6KeNthZS0GWxgvZCOqKM9HSsnJVOoYAdPsHsP6JD0DgwA1sA/eniKxJFXfcCbe7znkKpUVFdcWeey0gQIpmJxcOUzoSVKPmZTBexvM3P5KrUsCNyGYC463lS/WoNqK5Lgioh5jaIhtflDPAGqPFN+9jKDEbeh6ahtwOApMEgppdVkU569Q8hVaes6bTBDTXBQB1AFNHZuOqcoBl0Zfquhc37d49uIUzWpzyM1RNqnBZemb0NeUYAwDRWTxlgevLB+M98N4Rg9zEI2jDOuRuO+SUu9gK9U5Vsio6y6aksQmnplORcpjxXoxsiHFdEEkPwbRVOb6Kh1ukhQ2shdC3Y2mLIKTkXoMYsuCqNN44e8KHgND0HZmaOONyUUkHw7RFLZ4EBu8xZQ3fBhymkpyE8qDUZdCjfpcDdLpkydAcyEBpiqK9vT7/Yozrgkl6GKataLkpYOiicdfuS+ojx5FaNQnf6M2q53CDuZuG27WGkU+RtYGUrQuw5gFxtp7lpn+B802TkQ0fgi3HCQkGjTIRKEqWVWRAQJq4oQ2egXa/duxJH3RwsFTN66KCdkTTNDJhcRJ/JkQkSbtEoQaDtu1IeSlKZ4JiK7BQoPC2iNMnukRlPUlEXzcGbEFNhH1JSvhq+xFoGO0kvrId2XDkusCXmAD1GRIpCYWUEltqPkvz7NK8A8cxhFpRyilPsUKm808K01WVQxtNPTfYyN7kP8BBaVCyOWtlZIif6PKruu9HMGII5Ck5UPFIAGXG3RyQEDR4qm0T/JYHwk2ump2taE9qCgcRGtOgSGC8YJOhYcRfZ1j5+x5Q/sjNNigJazt6sdtmI+lBpv/Dho1gy7/V6NBCGJcPSDr4ZbRlXTcZGC06YYMSszaBUwFEZHbowlxGSXjZl6WYNQpWUeoJVTkA47oAkh6AaSt0fEXPADTAOhxGVqZWf6OVLZIsebgwi6LWBaa7lUtjGAbo+ZddSy/IUoMII1hoENIG7f0fX04quy7qNFvLZqsEbqqCCXl15rMPGBHv4wnxilw1IurUi1LW3jk/75BdxfidRckEaJcL5zrgr61v8tVQncuDFI0q0gd9QArdy5RxCMCCQ8HBDV6Sdjug1WP86udkgLPLh3Id5NdRNvlKKESPFHtqiEVDJuzzQIeumyZCv7ivGzmV4kdAFH7UvVno1nKgMAc3c+SBnpgwcVurKEOMIpHaVNaY0Xks6njvWokTUQwjw3TCp+us87DA0XUBUz3w1ZYY+ZIkDMO0N0Ba/VTjAQspNO8eKj/NZezrMhSnHwDdct8LoAmOrguW6mGvJqnoc5DsMh40dIRDsv59E4dh0WtMtA8seln0uEryDIk+5jy+hIkvrgse6cCXttTHlwYVpF1Z18LRxI8Sp6GBJHkSYg8I1fWqFPoVTU1oUcpnjmgiZOuCqHkInK308ZVBnWscFEgGgSL9K2E0QGFjLTpPqoD1uG6FDFD+PmERP31pse8xLkszj7VvkS1/tJTc//UFYSIsK3DjLEycnZOziT1bnDSRgHYuqv/PLfa2Ww3dGhPZUJAFHbGmckKQNuRmKA9qB2BNHymwyDzw+v3Dr//hzi+/Sr1VRi3A4KqALpUQFW4BCU+DpLMPVcJBq9kwF2AJAyJemtfDTwDtNn/gqYeu4tvDM3E21fkr9l1nVWmeUUgD4RnNrOcUSInTD+APug0lAQBLDcwiuHzTY3xNA2ohguuCIHqIoylyciRR8FWBukD3FrqbPLfVakf7uC4DPo3WHpwzQCDD2whimCP2oD+f7IsekKzzR4VTsFYdXH592d9RTFopB0I5JscbmPv6yVIJCBCgwKeATqYpVE3IAX6G9Fxpu1cp7YfAVFGkTGlj20V3E7AuDbMMkH+E6A8Rczhtagb6CdaBUNrDE5kLSzUFvvz6ciwtlu+f6vqt2uTJjWxpuQv5U9jmVbLmzMJxjsfQ/bImDV0ZGxeCDSAuDaqzZdsHMhmza/Nymzx7PaEdW/ubDf5EW4XJRAN9UMkDcRxmXzg/PBppCRSoM2Hq0cjn0wyKA+eXfBK5XrrkskO+yP0+mAmiHVQiggmwki5nahRdZaCvIyxIk5sGQGVwdNeh/unciRk5IjLKKpesnI9KGDVaOi1dTRB7XUBvByR3dIE3HSF9+jr0GWLK7Zm7ocj2PQDESvdmmb4h6/1QdCyGuF/QHVoo02LeC+ki7XDQStA2nTZN9kel+J8rC7zJCCk0QjqO473O0XczU5r5HYBawuKWs+D3PIRYs8MgyRaxfsdc+S8DsC6MgUMw2NLAi5IQcImCW/iXbTd6hjULx/wDkayyGhF3wBXO+PoULYdD4/ZBuyJskmjsha5EtC3MoZwSLHxAYkmXoZKDAgOIxgqmqshL4dD7UG2jsewUQH5XbN31lPAsKP4aVshFUYo2Gt9Mh2X8dzSnJumwfIrCYzRsBd5NsQfUrsiKhPiz7eakALmCRCyUAvSa9br4k1E1bYU373sFM8mfdSGLPHLJluD5kj0kOOJIytdtVWvh0PFWMk1w5HP3nShovJE4LcaSM9E55v0VI63nBTtrAeXPa1QFmC2wrfvjy2mItWRdyzShAOeC/pyS2UYBk1HKgVvNlMPIPsvkT4AFMV4d3PElMeHyJKStXElOud/YhylDgSydZWgdtfn7NFabZo6II9w2cXyi5eV3ZKom9bQ8osqhtRwJoy94pOCec1J8eqRZWmIeaqxR4BXEKbyoI8opOSsEQC/i3H9NzmldOCqP07IFjK7esSOEY498Bpe7LCk4DUDZIDdbUUqgV+XVU3MJqGJPYc+/orUGSLVcQMuBv8x6GLd2pjJvhKP0DmXPhZqGoqkhf0yxV596vUprQ1CMNgJV/tKOa5F0y+f0PArQ1iy6GkfuZRSnBqTwYmxbc1khOdVRS8Oi7tJ7VgZO+Q41SVW4wmwmO7UubFZi2ebcggKFN2hQ8RKkLAdNXeEH8Pc0kXOQVRT1sdI4NKFunwJ7uS4tcr+aX/1752nLS39drPc9q/6OUqLCjaRhl9w06TFzJDkeKMpQaU1G29ek50H/UE560O09x7KvG4GBTcliKDXo5+N/Uuxg6bmWr//y9GKst6SwNbPuDC+m7cKcv70T5O9hnp/5xU/KXoQ5AaPGtWYMD2cd80x4Yvnmm6T1KNf+lUlps23LJedcLs/W4LqSXcgU59y5GYWWJe/TULgiAU/po9dtSEEbhOg34U9Y07v4yqHa1oWac6g8R8V6U73SmM/KoaEioW8TAYr2uaMYnqJwLCbX7SnOneJh6T02k3+vPG8IJaXFJG1XYnDEpee0NkrRJfbULCyOVyBue2u6Vpw2mGtCv3zNQqk4y5GYepNS/y/QfhbZtlxqziHybAW2p9dGd3accUezj5NCZm2h7Mc0/TnkH9ynXnO6mcPZ+Mti2daFlfNYPFt5e1Pq0u4ugRewhd2ksaNNBetI+AlQuNT0uuhp2RRdScj9309hclXrwm15XJgtHvXFppBxs7mFBJctSLSN80Q4gel3CJuBa7VJYjdYKPXLeORfDWfMCGp54ZYdmtnGDL6NgzmnrRWAwz5aYiOvi6DjavxSJGeze+vCBnrsoS3p8SVAk3FglRVQHJd24SR98aAZZg6y8/JV6ZVr9xladXI8y44tdm/5ZKDHHZqiV18jixg4DU3p6bNKmR1YI2gPZPIl7iegV6W3FWbXnLqe9m2mymJdRBmeiMNWi7rqUnR/jzMFuWylxVMgL3SLR+cfGeITUedudlokGeLbBGdzOi9Y/OS68Jke/2krd29K34kiwaaBNq0x+zw0g1KULwS/9rmvG4DWyXE6XwznOmiRTsslqVxOy1a9XkSykwZrlS/dS9QCUyDzMOCVF96B++2L5pA0I6cd+VeVymfG/3+pmnWpo/Hqbmx90EVOVCG9lJWWHrJKyI2+vXAfT3xZ7iy+L1vpnfMAbegRmsN3wGSTgVguX+GwG7Zm2FcYDwq7dLtGT/UuRyN9gizbH43QxiFvx66Y846rc65nkZlJPawLVeFRG5Zc2BcXo3AL+nF5AHpn2mIKZsNMm3LPMBoDZRsz0I6Yh6zXJYV5mE9Z8Pm6oO0OOG+rhX11MbbBxo2B8AiIZrbYmXZsdUGhjFp9XemqBcusjGL6PdRj+H0gYEXY3TKmBw99Wj+415QE6YCPeYK2qt/Jkrv+5+pLfT1q4n5hlKmyoo1Wqke3YTIF8BrkQgcUASMDT1+KRkz4fF3gdg+etwWmdoosxzMqdYfug01EJDIfYXEk3z8EGS583dD3cMloVHO0jzHh83WB2z143haY3gSpFPXHrtdNPIXleAA/N3U3D2qCWCB979xFGfsjJYFv+0IbCV4ubuygzLZW0lNWFhT5cUaB4YkwWFvt4Q3qFkPvSTRgKAmkRwmy6tAoDPVAEiwUeF1AYwdjNnWSN1klDdgdTBfUCO3T0Pojvm8AYofsHrjqgGpEhjswt3bwKbzeRdZsaP0CerYErdm//PpyyurXpUmJ09TEkVy4Cg00QgIkyccpkQsyAjOMfgY7JQTgQlWcD3HZ1OS9ANxMtXyLErYQ4eUjyC7ibAs1b8JOWmty6bo9D7Waguoxyo7H2KnG5bhsQI2ZbPJzzLekxwGQ1wVw5t4aQY/T08gimShkoJhrymkaNLpRjydIS/IO5sVXHqcJFAFqEEHDTAj8Xf05WSEHSVGPzIrAjbZrcHn59eXoYDAqIbeFsoARVAwOGZSW88R/rtnDxRsCoNuU472orT4djohCMk8FNggQEQ8cbrL0/ancXfBLTaBNSHRdEFQHcLV1tzedbg+MigsaFNN2bAj1ibjhVyKDCK13YfstW1sEinHYuFjY2HKRNBt3cyTcntwbfSij3A0FTAIfjCli+c43j4Azan9KmmXyypB00Lc4JMkmmLt88NcFi23RsC8yzuzaq2kohZmj7L4OlDlw1TeqxyZlGvu6tEo2/WA0i8rZm84E99YFDPTAQ1s07IuM0XVSJFjjDUzhNAWOprKydVpbnpaawLFG2x+uvWW5Jra3LkighxyaMlBfNQqyre7vk4AiTT0NDV32FcJgmtrpbHIx3tSlnHLlHr4T49vGb4v5JdBRaLqFZlR0w7IOovsnWgjQcVpBpxp+RJSMFnwGbKOUKiR5fLTmZtNVrmmjnEW2GvrzNrnTFXZjSoi6kleeq5ztQveNZdDA+JaLCHoAosOi+qxrwnafdVzTahmlDQPOU6oEtFwwdgCXnf5Klhg0xn13tXl1COdz0MlGi+DkHnMd+s8m2SXYUnRnQ85uyStdVaOvgZzatYNDhSLJAFxCIM2Q4JhOIZU2sF6mZ68acVJcVY4c28Qolw9pegioLWj0BZAwAghTzk75qWI5kLjHVvQJ0I5l4661R/bfxyOUdPbSNaG95UOBHnToqPJcER96JPNE5kegqC9scR9InCpLCK3wWhEyENWyvQRvshNq2Xd8Z4J7y8cCHeTQlpb5UrTKZn5Z1zW4PD0StSSuvXiGFMPTlLTW2VRFMujfhmMomUjx8pFlF4m2pWU3KRptLxuYoWtXPRqHMGtI0dFEY/esnWDfZDJTkNQP8N1E2pYHyzkYnimQctRUAzLeWnSZoM2ZA0zYZGQEfHLvVUyWIc1InLDxnfejt5YDsK0LIOcBeLY26iKlAtCpuzcibomcGxwDNJsBArobWzUsXQmbGT9D61Lps3dSGtHa9gztGXivpMxAFS+XX19O/fYSESoQFREIQiIwHgCssYQEUV/tTYwmeRumtHCKcDDRh5I9luYKqkBQc5QTHGTC2EaZgVZxRsDQ9md3mLnUh1v15N9IdA2Acl3wTA//tJVSN2XVgN9B1v2FRuFumpJ4AcJhvMW622NKVwqZfiEcrSJtkG1dQDkHxHOkJRclChrE162Oo2Q+7dNQiJTl0VBNGZ7LYqOVSLfBL/o0oEBjh8QDtqLRL2qAJiazCLj9X8UVCEEg2vNxBl+01Kwh+IeZEgJd2kXjdlEH3g3VKihiCCmrbqPIIGhxRrwMjGlvulaRklfS5BSywfQCTmM/GG+xzW85zpvQ5LpAmR70aQtyfAHPhOJpVrnu6JtvovPQg4YpgyLHoMrigv9R9TCtoOVoxG7ik8tFMx3s05CX+EqUBh2yZu4RVYM/XMVOAUaV4UkPJ0sS3JTFjYR39V9tzk1Ycvkwpgd7OroSX4eCTm4SyXbwTCns01SgU/KS6fIh7DYfFOt2zc/qjHUeZCdFm8jvUMgx2HesohV33apz78eXI/NeF1m4oyLP6P3A9iwIYuAhthMrVNKi7rXAbXiDvhHFFQNvHSxmQlHnPm5leWjCljGiWGiJuuizbGRkNC4AoMLlHfhAtJyUveq4v75YHRHQA6D+cN4N02UVTVD8jLxqovY6a4hgrVHuwuAuI3bx7J86W7ke3QT0NhCb1tS+1OPDRI6WhzPZmJQtq/ZE2JlNcHUCBgoNecWC80CjWbobWeXB8AtqQ4sG58AfQGIdYx1aWQjQKShC9sawAoVlOtj9X19OKCPmi4j66R9yWQltlHN7ftMOwrUXALxGidu5CNbAsEXCmkiTPW1Zq1mSEbwSDqvkQy5LYw/OdoB/KdwN4fzwn7W3+YvBLx+y9xB+W4DmydUqKjlzV3gM+GDUs1S0etXVmibO2zCC4nKF02hNqkedkI3YrwvCT0NoTt38KLzv0sgi8d6fNREPJUh3Pvz5QAmJDL4CwFMgEkzykfeCra1b35b5f3Hk5cPOHkptK7xcQRgYvyjTB55wPaXnNIGLunj+0HTsDzgOg0V5r6hzLMcy+lHPIEuhsi6KFkcAYyurfCUWxmQImmtlVPrt5kOULvZa9HZo4el6WUpFBUNtgKZy/FItiAljrwvs7cHktkDuJqijYSLcckfTgPG0RaY/2cszfZ8SNgzftMUsPkjjvfEb/XZMMnRdqtu9anhb0+RroGiLpKdO8kkD/b3kchlJ6MgcI1egMPrCMrxs8+DsH5jRGF8K4k0OYPmUgccw2LK8m4yPPmIpsrM2hHa7pfSs0pQDO+5ArqjXRfGDbgAFDQQOIMtiAdaFNLBJBkeYd9PxoXX2rBqODEHrQYdP3TQCLHw2gUHDqegiBs/+t8+LA2SvC/DtAeU2/XujixswYd2yYK7HeTR41MnSIO4JNYtolvm6NFGSrGg5xaPZiQ1nLxf8dqByWyPnKeporjVtKdohGBbsB2Y+iMaz3D5tMJxPg06jfV5R6Yw/OHgtC8peLvDt4eS2Ps6X0yWEJ7XqA6SuMi06DVyis36AOtULuXGr2KELdqIJeCLrJpS9LtC3g5TbCjlXUMcSqKFzGefeZ6H9cXRZEoHR9H1RpAo6TIBv9EM3YSLZ64J8e0i5rfKyASs5HmgZ1ASWxl16usdkWr/1Q6DG/ikWp22tBXnVtE6/Y30Hyl4u8O3B5LbEy5eEUZqHLsG6xNC2JksPnae1olvNoLWgbXSeJjhbO/Ne+7sO6hOjcKN4bvmVdl5hHm3HmdFYYCZjqKOBVSgphzGxS1fbd1qpNv2amVT6kdoWGjZRr0oDERQ37pGW2/IuCvpMg2wwActhDTyKwRbH+VI6WNCWrhuLtgYEOYZpNCXwoE11KH3RUU0hYyRCEna2ELdIgHUhDRyOwZbG+VK6ge8ubQwRD6vcHudB+6Ch8TA0tPuyFDmEpA8R0Q/p+Hj/XA5upfXLBQEcyMCu9vFqgyBNZ6JBwskpPdi5kVatvOlUjCdZvCCYGiqRaYgH46lc/rBRzh/of12YAodZcCRxvoQusklC0+vSZJaBQueBIqLr4KXsN+3r4nVpbNVKPwhZE/pfF6rAYRZsCZUvuSrYZ3qQ6UfvREqF+Ty0rU/Z/eDDNfdl6RuOrAFu7AcP8ZFo1NDPrUsrJq91kynqvGlAOxdq4jA86SXqT9y/u3PIh7asQTra424GDSfOKuDJn1r+jjzPxv7XhStwuAVHv3XTe9GfJMUsKG6OQpVXYIHigY/PT6+xbu6CsgTJJdgmfxyiAosFWC5l4BAMtozOE91B+tGalkPSQqTSb0RMpcnNU84qXhcwiQdrMzSgUwx4qzosBmD5hIHHL9jas5tWjWZuU34abWm2cg7gv86+0MVuFVcdWFj09WdoMo400gL21gUItGFDW3t2kar1PnasSFMnvk9D0zQ2/QihP1glCjd3OAfjo1N2bMGA6wIbgi5KQ4ZvxzNyJpyRCceiQzEPLAR8nJsJsDUkLk4hVGl6moi0QhZyilDzkZbYwN66AIE2buioum4qMJbSCZJA0/rRpNWUeYlraG7SxN4SFwUgLJsrOr2/nT1eDURsGroR0mojr7Wj8nHUvl9fLRy5mwozV9flqcBoxUncG0Y2O2GsQdfRbtskvIAzK69PYPdQEtQ0xg/hV2ZroWrrgsJ5qJ2p6nI1YAAawswaN5VeH/9TyjtEhcNWAPGho6KuUACZfpEnn3TUtmKp5UZeTpxmWyf4RgsU3ArXmkHPSVKPGV271NngJLSIqVkQmLosh7G4pC9FdSaYti7gmwPWOUo4XzmHJtxJQEyUoofdHAjSiSwxVwOfKYIoXDcB0ZHR21tIB9FtguLrAqI7oLsjI/NlZ9L9swqGNOjhZF0Gf9yabMcNljzqTJkh0G4z6LpS8ykzMDDBdQEQPcDRVvP56j8kxU3ysMZujm2XMdNeJWgg+4z3/hQsA2OWR+jxzC8cTHBdMEQPczQZKZ/AArpS9n5H4zy15ywpJvkMOXfRq/FVuxiEYudvoEefZzBxweWiiA7maKr6XAVglwpO2axpVkQ5TMsunLr55mmw8o7F4DrNWDnckOOeybkFCa4LhOhBjraiz1cAFuj3epS7GigB2pXDNOElJ2jYpUPdkCb8TDVwafAWOdkqiOs5i5kFqnRkMSNug53Lry97/VzuauuszbbluOdQjnpZytZEC4sMM1VWI054wqB124+YBQyxhJ5AMqrYi2S4QOfyJabORFOXD746UK2p5XOVfxhOME3UqKK0LQiEeeyUKUZ/PNru5l5n2QEr6ONTL23hqOuCu3o4ra3kc5V/kXVDTd8brYU1P+cJOQ2JZOka7V01nHkL5qegmDQeGYIFpa4L8uohtaYi7SZgG7Qy6srbi8aTOI5QNCseRHuaFMrT7UyEPDtIBKHyHoUD+z3DCAOm0JhiHZG+gAzujy97zC93ivhTyuzQ6LdzpFksjnOoLEnqbAy8gb4lp7/wSIOaUOfrQFl54pvhDf87pS8moLpc+NUBay1FnyP+q2xwp5E+nBmrngGKBhmBEeNcLxc7qEv54nACPlJzE0ddF9zVwWkdMd9N/Af9m5LftBmJMAvH6RP3LOsKeuiE3WOdzrIfjh6tnoGgCQquC4jogY62TOcm66GXFzU6pbCp69/DRHlwp3um8Kd2kEePUpxHZ184+5J8qFWwULh1qdx2Cr1NLcFNegCyvIgUYoSiJtIZlS5VAHD2iCz7orSxDA5i6F9WbYDwDbWOdYProqXwtBe2nOYmv6k0D4Mcp/VK99iMfSwJ0FMZoZGCBLpuS3z/OE7L6PyOVsNGX9YFrUGTwyi15nhm4EY/gu7Q7Mvah5s2WelA++5g9Be5Xhek20PGbdnWTeZFKUoPspvRa9NIP3JwyQ1O8XnpBfW6r5s0VUcAry5nTyOsPwjmcuFOGxx19Jy++hM6gfLOC3+4BBquZJzJVX43QWHXypVhEnugu9V7hHxSjfC39cjy25Q4TU0y9l74xk6oACkf3uVHlCiitogLYtDkaf85jVTEO4BOetXeTzAhQl1C4U6bUJgnvSbsb+gDonJh1D6/JWSz8PZ1wec9PN8WmvnCNHHHDc9A2+AXZSIwyk384efU7gV0WVqmk8L/Ob09HGnJpxncee2Hb3wW6TcCCwnM/F9fTjS0LtGTHWsVUGWI+9ggEqpyqYyyer//x7JkuiTltxzJYaNSfTC8+xJqWCTww9TRq9L2zBwVou9aRv/O17fQ/uVzAx6VYGvMfE0aYp2pgGWlZ6pFT1MpYGriJUn/svVdbU8jZQPPCVacZwuRT6oB/vaZWm5TKrM7kT0v3VkMyIwJG1o5aEPQanScJHFBHzyjKRza14tsf4GythR+tXv6sNvVX35kXfgUh36xdXmmiA8HAZBWDVApKlJOBoUStTRZ5tEiTmowAQdII2l8cVo/2knVWETEcmkLj+SwFXm+gi9zoL8xWGSwu8S+7xiSdqra4nxK9SEz01h91KN1s81GLI+6cHgOW5TnS/hGptG5g+ssygBeAMqec7Bp5TcNuq9VtpjGzbdfgKZNRKwLcWHzHI4k7ybha/S8quLIKDgWbTWQqSCWJYgVdnvxygXMQaM+VCu9u7U6mPq6YPAeZm+r8m4qvlbH2OhfwTfk45QVUNycRSCNZLFurgDZm2aotBXkU3DV4dnEejLYLkSZ6shcpM+h/+vL8apfF297xwvfbtB16+dVKXxMibfQBqZAvDcgiaA1p/CmSDmyppcR4E7iEjFESvQRfhFcn3XR+Qvnrwv879EFpqTRRu7kzxEearaekNDvs1AWG4RB6+ARt9UBiq2UXAiwVDn7RlusyrqwMB5rY4saLxpIWOoXGcXwZxCEonBuL0hc5b40myuCxmYUjerKIR/Aqp01QqM1krtx0ucOimz7P76cMbYuPea8nnS2/ZVvlwWhb6hFbOXR9SRuGy3aCEOTAC4B7W16XTipookOVEPSf+ErY9ikc5bP/nhkka1DuOkWaCmMukFQelZlzPN5kthE0feXWrp9XX5qHsQIqg52yWJ1lssBmYSRo8/01JwAflRoADfbJtaH2BMKG6vx3VfuL8xXTLAyli2eZoZ0mNogoUXnrAv949FFtjjTF3Nid4JFlDwDvLm2yBNiIbVkp/AqpU1TBTSc1Virg8o6tkOLm1g+leEQH5bO0FUlThiRR6EwEGXQ1456For+pm6FTFBXvWiMMCeTJ0j9cLC2eYnl8xgu72HpDH1VYmYITaEvsJ5jNwLBCt8V4kLwt30WQEtPjRb70UvMoSXWhcbwaA9bK+dq6yraqQVNxQMQqaKnabllHUgUNg9tuMaVjcKmQ1OFle15CAtmXy4mbwP4tlzO1dahxZ4uEiFp25rBi5Vs1exirfOeYcXRRdMCr4WzC9GAspPbQtPywn3bKE6hfEeBBP/Xl5O6r0uq70ED+JADGLPo8dXILFhdfuTPe+UsjPtGo8vKU1E8KsdSA4S4Mo0ILFBiGLUdSp/1O/uQTU2sC5XhUR+20NDVJaJMgekXwTiHFuh0ycr60O8PL0y9LIWqe2mhzzLHyXL9O8huApbLATcdINRWqfua9lnZvYcFVzKsMGTokPYihgYraUX/pHRaxVm0j9b4JQDJ5HLWhfvxuCJbnOmLObFZNhHSI6CMIt0A8kon7/tBY1FKC4bpND1lOUC/14Mgs4HydQHWPSDeVkL6ykk2ac9V44pScpn7PKDwJFrOtOBLnhGx1m16pAdO0R6hJXSIMHOQZ2AQYb0aq1dFxU1XpvHO8bipynTFkL50csBKOOpuTGescphbK+jNC0QIgpIGtE47WqNqPqq+LMx3XSBiD1K2dJA31SRtsHWHQwEd7/Q4vQ0RsmNeTQU5K9Iwxub4EWiLO535LOhy+UinB4yaer6b/I/CwhzklsBepa7H0etZgw8K81LYeOzA6qf8Wor1jABNVm5dWDyP9bP1fK7+ryAKrJr1okneVgXmouBQhR21Ok5kCMpS1o0dZOxpgG7iicvFHx2w0lbz3dR/9FoFIAVzWlVwg0w7yfup7OU6NhY6adAmfc+9/DIl+Oc2anauunwnaMc32q408StTuEZUYKvIga4M8YQ1ssQpcrFMK2Tdl4VXEAfSALhzbN/KhC0gdl2AWw/otYWQN+EkrR5h4zZtBD1/RVgzqoxUQIf1ue4oQv9XyA7DL/tzK4FefsLtJegOVS2Ud8lPDhUEfOLTFEkmsWqHGt5LgwkSLxdSdgBoW6PpKTo5H0jyjujacvcdT8ImO/xKu0KEHYJZhMfyRkMNZ4MmEx5ePpzsws+2PNOXc8Iiq4kTRoV1VnnazJSUdNGm/yVBAi5LwfKMmrHMVMIBuZvo8LqgyR76bEoNfWUittSpr64N9D/ZZwlFAqYKI7B3XxpYI2moWkqN76XNATiXj4d68KktNPSFiQh22tB9DV08ZN5y5fAUqWdlN8A493Ujpy88FSgaOT+EBXCuCx7qwKe20NAXJjI2JEodTK+k7iEFA4u2dHml0LII4kKXpSnfc9XpPA73WhPkWi4k5gBotm7OFdm1HPb0bF2IKhrSFIEKyVy443DVC8J2u0swSHvLOIpVbXBrXcAwDzyzFXO+wg6hOgU7umjXrnB6A7U1hfougAaTXpUCZkliKxa1E+204a3lw2EufGbr5Xx9XUL60HXlqWrXy6cJXSItRNZDMXG6LEaujiFa8uuvlhCf6NEMAdy66OU8fZ0pF/PVZTC8GNI+kO0opWcdToMmOHq8hJZlKOTCVA4nDnQ8t/Jud4Q6tQRNMmKyLo8aYXyoVviXX1+Ot8K6eDE41g12T+lbD2pKUmggCVaUS5D3G7Gn960rRjP6ui/b2AKfj6PIrZ2owT+7BVgYxXLwDAf7sCu/vDox7O5zqmwTXY6qWgg0LFBV/xo+6HLJgsr9yYfR5O9LUisbE14XDNnDnG393k3v1yqQFpk/TRN16BLptcQiAxy9ZdpTyZ9ySfIlEt33L+nJBzpcM4lYbjW8VzzvKGVdYS3nc7JXZeQIs+3TgB9nwoWWTBix7PZPFEUIWZnY0vOc+HBb5tQrgUGh/wv9T1B7F//Hl21KLHEoJesUbnNHOgpXJI1G90LKtdkrEmFVUeYFVUtoc8H2LwypS6yJ/nhJ+laUqVEgQlYaH3Dghqa/dUWHPQUjcAaA+QMN2VJuX9IYmkTCcnkHl6awNIAXwSDsk4eGorQa9n0c0qaqGm7wCLsZ+kBuIJOTAl+Rb2rIZLEJy+UebKLClK46OleEAhSRdkU5gKRmOUdCDakscPDTnWpdENDbV8NxejFnswITCl8X6NxB2m0V3k21B3GofgCaf6qHRbJGdx/1A9BYVkIgo39P17gvojLhBHYsYHf5OLAHG5u6spsMbaKXtxY10/NLgQ5OE1EbJt8BXslZLxvg2CbPQDN5zCP/MWHd5cPAHmzsKMt8JVoC8ds0/oY9Z3sUathtdDRFza9w2bxBRcrLDtNQC9xdLhDswMa2tMwTomUk43FEDUKzMCgdi18pGna3ST8UuSQs8qucJCDdOqv5LWR3XZBgDzm2dWW+Dg1dJ4rGo6ifkbizDsZJiz4DM7Z62QRHaPl71Cj2Y/v5SOxskILrwiF6nCOgKIpBMx+ndCTLccO9TI7S5VBYgbMPGsMCHLJ3coV8ke8ma/cNdmuEFTUfrp0bYXyHobRQ8eWj6B7q7gjyfAEf+6MLy12RYw81B4hIX9JUp9SchBPBdWmmlyCLC1TYZ9sIC1RePgbtQda2psyGaP57yQpem5o+REzrrYjDhjw0WB713bWnVOwW8gxRzbze7XP+YvvLYwJc3sDWlF0kaBXVloodUpQpcF+JqGEpSRwqKhqIFL1sRVuwrjlGm+N/TKn/gLvrAgZ74LGti/J1VCD0wpYi0SRK4jDNsP0Qy0M8Re/huS49RJBFBsqlQ3BiwqnLw14doNbWRvlKqkb7lQKHKLjjaA3G+KlETdxosAhE3zs7UuT9CWo/VcgWjrousKsH09qyqJuMqqEFnT5CL1WD5wJF0lC4AJ4neh6gGqUEjXfySXyaOOryYVcPpTV1UTcZFSXVTccubYlJqlc5jIxZxxAwsadsHfvR0GiHdoFf6gGaBrPwoouWQD8aIWjV1eXXl52BLzdfd7J72znA9xkYNTfJKOmeigAHUDxTFCEZYoAegw9L0xlebDM3M/xWx28Lel4XqNqBtm012U18RnGwBGpAwKZ0N+TTwFJMXjjtPFFLOSb266DHO1bP7zX/YetZ1LHTDJVy+AplBq1i9WfACTvkuYl6o1MGCgnoMyX02hip9cc66X8LeuQoTRYkgAM1LiXtOIuCPhovP/ydo6Y/uBwsO6OU8YV5WgB8lAVbWP26YPseF2DL725yvQkIUPfiUqTHJI7TdlUU/skdcfaPcA0TLSjkONjJdLT9sUD75SH8Nhtga/A8xV6Da1RMMgYnVisV8mVEibIYgOoaWuRPa7xAZmBeWj9t6gywfvnQvkcFOBIyX3KWYNTRqu5i7GSm58kIKuVVUmAy2244FHpNuutRpJUOw3MbrV8+uO9xAbYgyhdQYXsKUvoH2IT7Hst5aARJFVihvGVIdRXj1+jdJd+HvuU4SW4TeVwXpNJDNk1BlK+fgmi5ph0Mge2K+zS03nTZ4CicCXXoZRNEOBqWtFjeliafNcPlqkosOADrx4i0zHAmhh04wc+dW8jAH0NzogHqOTGMBxa9aQY1e6A15Ye7OoXdd4+Wc/rQQCdQa4PyFUFYImDJgagaKoyEz7YTMXHbBQMT4CE/9Ko0PJHzotyBotSSviMabExuzY0A0eceWEMa5J7w5pKPj0Iq1qoZwjZXUGaLz9D7tApczKhZ4PfUsAkj/pCvjnYBUa6Xe2tZA2mFp3T1s/C35aF1HrZnizE96Sb2coqCk47BkrsqOum9CTIF5ES76040/cpNMQUktYdE3sY+1wUrdaBVUwnnC+eYxW161Q53JT0LRflVG4XQApnjbiiORkoabVba9Q4MzUYP1wVt9NBJWwrnS+fQkoBuUr98CpIN4jwdu7Es1rSnjrKvC5Wrrn/4z5PFNinHdaEoPUrTFsPdxHN0mroHd9u+lZnr7SRYhioKChq9Lm2AqevqTvPiTMBMFHRdUFMbZfUEcb6ADrwNhTTyFICfhP8KzJgMHRucOBa9Lv1FHFknRXxLvm0QdF0wUw9jtUVdvggM3OpsG/xJUmHPp6HnCZrjyFojl6XoWTMzWiTLqasxc/91wQocbMHRgKn0rOjySRlEe5eMAdnR9bMf2YEN0C4XznXAX1vX5avAKNufO+KgNUJx5YmVUt9ojpLz9MyY536jFGPNc6G0oNnlI7kO8OtIui4KMCBuOq6xc2gBGHfXGVmOs+nbvmzHxqgxMK14Zy3DBx2GJ3DiEaAkLVgca2G+bgKrYo0+wLEI5VWX5Jm2S/T2YuMgiocFDod8FMhF4fgh1aZ5F5jIEuglMVcZwfMM/XtgFFma49HeJekCfAbA1OA0nXUTUiyB3m4UEv5gkaI32OvJUX/iF2jBsusC43qwr62Fu2nngEPur0+vqO46cgrUahYzKpDn0lEMmRjehew8Gezj2V+a5lYCEcBCSzxjxYNocn759eX0i17ifRLRtuQHuTNUK08faajF0BKk8fYsfFTkxRyzgtNySqnDrnwyeyx6LRm9btR29+ov+B5bYNDykCMbZvLc9Hz3PRad1izKCOhSy3blQ/F7VmlEhOurXJbGMI8VelewsfiO87EN6K8LAeARBraI8ia6nLTGaZBIS1lTt0mQYVG8sDKDxEWOczy6SRJaofqvongL018XDsChDEwVpS+6BJjaQtXgu84qsWbBSG2li8scTdqR91UDkBLdQOOvljImJL48AN2B2x0tpae8TFiOkuYUtAdLUDLYA6LJF6AASzoXDrDEtFjLw0bk/Sc2b4Hh6wKee2C7LaT0ZJfwH5k7IimxaPfS1qWFszwBvVRZn5CHwjFAM5Ce6jixeQsNXxf03EHbHRmlL7ukxSw3Rd8o1BLDdJyGplxg7BaehLTNVL1smk0h3UgxSTz3wU9EmpY6al3UVI74ylZR3lSXlD5LsIOPRY8mxzMnoFlhftoHt7arI2MQKVimPw7fiQNsUHxdQHQPdLfVdL76rnAxuobbNP5EPoTz0HqqCyEFy7KI8GXpPrvcZuicgL8xeuAC0KHBs0xQqDL69qy9/CpyCbAf6Agui8eUnlbAzQuLaLjbN8WGsixW9olqQ5sYU0itrUyBS88O/5Y4pCesRMeewM3Ww1nqObkqLOiES0hwXP1SDwATHl8XON2B3x1hmS9Ei1yjJJdNA14EehqgDkXGf4cJStPLlp4eIKik/q6nslHy5WLqDgJvq8t8LRr6ue+NmSbq0Jr+DrxFJZuUtEsbHfQLZu093z1tAPXAuk2EfF0QdQ+Bt6VlvhSNY4bYHthDQ9SOBWmMOPQhihjz47oU1IwHPtE2D3sQIphjLThNZfGig3PGXoT9X1+On8y6+M94fjV21d2tSo8mUmR4LHIe85wH0pIiYSqtidrJJEHkMSOv5gHJ7S+kfIrz3gwRtDyCbfqHOxT3fns5d7cuNYdOjaIj6L0JgJGgNP17YDxdj8MFlfP/9H9cgiyuQmj3PmQrT1iO4ncMqmx2ZV3YGI+9sXWJNx0j4lUF0ihJ0lCGmcFe5H1RkjL7U3VPo1b37Embwzw0W/9e0Wo6gi3fQcx1HLPdl25uTbPCJomPU5SplYEJ+xGiabbhzEEa4jHJMitgHm6cDKvQ78QgJsi/XErAIRDsega/+mHM1KKGGbQI8hI3AUIEAYIz3ImZK6cbQRuvXDUUrIdhs8OqrQsL57B2th71ol7tcGjdgHwSr1Q+C8VJQdZuth55PBsyM2Q8dunBjiTMpIiWRyiZ5JMjSfUFrHAo2qQIqECV0gYagzK/KhgePFODzU/P6l4+KDiup6mCxa8sn47x2BtbjXpTrw6YruplKSB9jqOTTdX3n4tIvHA7tAbMIccH6O5j8fznlgumvHT5alRHu2o3u701x22UyoBhBVgqQ0cYWbQCqLJyhChJL0jbxnwmm/GEEr7UStdmpdaFxXJYL1uKelOu0iBV8QWWlLrPArubIqkrzUeB8XFVtCfpIq+isCr+2jdzBTmAnWHIAyIF27Iy98eXg5mtC8bmYXKm6/LNpJkmSu0SY1VU927LaJo2GpSAdxF5MSIU1DhqEFNUKfrgtzCGQ0YGS94kqVl60Hv/VykvDIjBQdwnFOBzzID8EVE6EHMsx0ESBc7haCjSMwDAqmP3rIjAqZlGRe4RmbLm41yKhreCLnhJS7Ii178AmoV+iJbGpjMFCyFLGUE+hqJ2Vkj4cG46OQVtAyYBX0rbTRJwXUhDj2S0Fcw2QCrH4bFYNZOJiE/1PJmiAx3k+GzSzAvXBcKmGy1tnvNQKpmE7LoQuA7h64iYfdFzBAQo2i9OmlrePeBo+09F7ofSmxw30Uw7QQxFI4Bw9n602czlk58eV2prmH3Nc2e/ZM014Vetfw+3gLnlnLQ+zvIYdVT4/MtDYGr88r74yx8uj2x0mElbx+ypngc4wVi6RtNZqiE7k19jykaZm+JOHSBmbprD9MBSorP12V/qcF2oRoeZtDTMvuIZywsD9fwAaPm4Fdj0/rPeakZHzG2IkSDG0LTil1TMpr/WhS7z6DVbxOyLntHXhzcZAe2ziCPRU7hTLKFfgZKVslk9upex8Y0SpGXKu4fXXxZjXVgPjyWxBbmugDeigDZ0LZSl+LCGR9iLSFSGGPpWt7SvS3F0UcEzfauzkNDkMdaF9/B4Eltg6gtS0Wd75CTzE32X+jbFgHKub+W3+rRgF2usgRfVdv+l4DLIjOUyHw5PYotMfUkqOmnqktqb2gwNgPjo/8GHYQjLi86AjDMH3RYqZU3pAMQsImNdeA+TJXH0pTc9aoMkRx+AYp8g03xCW7gfAF2Spl6UVvbYNNCP4RQlfIAFWCYF62Jq4Jkg2G7ivvv4xAqKnUKC9CFPiSSWFlNOL7D3Du2RFWF7mzqboQ/22kzfwoIsAmj5fJFHL9nC3JuQF+9FZxOYL5l8qAic4NHl89PASZu+orWydfn8pWlbwneLpD8kxrpwHh5HYgtzb0Leic1PbpaS8Vi2rhj4gNxOGPDG35eFAEDybeAQ7znYELCoKSGChdJZxd9AcreoS0Wi2IWHvSl19TSmviB1UJCly25Dw1U9R6Bdr8tiQINOxEgN8ySHvIOc3M7KO5NFWD7p4HEUtkbzpumkTS9r8s+mTlmP06IbFEACLyxLToVENpTYNdwMYx6LCAWleHeBBYmssYYHeNuklv+rSGARG1P4DoSAdswg3UDxROisUZh1Ql+TrA8U0JiQmzTVgcY+PwJaUOKPT4JGCPT8jwLWqFRxqlr+VMDs95TQ3BB3AXPhLxWfmPTLcskam9pxxMW+FBmWensfKNoaDOxDbwrFzyn8Ad3HxKyUYZnZqvE9aE3aZV1oGo/WsTW5voYXrd/q7g4IeVzYfYoAEOnoBGklGFJnSYk0LcVT5Pzr633gLWygr8uFah1g16xl8wrfYIieQxEEBW2weNHHHJhBsMzEy25WKDmjzE80UJNebP2Su7BF+q+LRsDTFNgyZl/2jN4Js+qbLfRUfcuhyxB6kd5KpQGy2yVCPtrldmhdyefoNUmT5XMsHiVjipg9zbOwfDQ6qsaRFDS2fZqUBP0Fy0HZ3m7ylCq6fsqbpvTo2DZs8HxdwHYHmzdVzL7oGf1GW1JomBOZuE/D0Jk8Aw1IraVGP1Owk/IM+bdfFb34xq7tBXk2Y2Zoea2Amvuj4ompIrtigSskjVXxRHiPJKmPoZsrW+KXK7JUoFwBwePPf494EpxV5D5VGx1EPo16OD5eoDTf4CPUVTg5OvEl0VJgklIQ1bhUhzby1Ld6kPLwhrqhjJWq1PEd0wQbM1kXjMXDZGyxOGvU26g6OWjR6f05jaaOCG5CPZpJW2zIcqkTh2ix9d+eWhy3nHtXBJuitKSNueAhKHOJdo3CYUAHS0jjRV5NQje8Uw1AK3YQ6SkIbCDXBX005Fu4P75sHHldYGcPprZbALgdA+AFyfotnIb+PO8OhoWuykswZg38CfWqmHgs8Ef5Ey0kX9oLTAJpXQgnj6CyVfOuyr5BHpWrQp8FU6vqeRpFH0UEwWjvIgt1RT16TJoA0f//VTQdsVmx8rrjlqG/H88L8H/chFZGOy8IRxF+i99BDGzNAs6pIsUIMW5Gq6EuPrEqO9Ce9FQHAtehPBp4XEtTwBdI8WlVTXI8oQO30ADQF3dOaKHWrkmMPvk0HE6wPwe9IAmB+aqhDFFlo/Zh/mI1AhCugJoC5J3sjwawQlkN99cXQhLKWgdMauDzTssnPz6N1Vi5CR7b9fQitY6TqycDkxcT3iCqSIdDG4dr3CMFi/PmYlrmBoxA2DNcoXcJA7Y6MPsZmHXXLB/niRAdQX+IiE8MCgC3RIh4GRKboX/J2tpm0JZPuHn8nF0y4JcYoBd7qEPDclo0kp6mTzVqQbeOoor8gTaWdWrMAQTlbLJgEiHrQpx4RIut/PaV4hT80SrWNSqnEO+tIC9TkDZ6INo98yZ4UPQdNZgb8QzmTB5kXXgTj2expd83qTjtgV1DzEzDrm9TG1iaqFCJ7kdYN7osnaTusJ7yvbeVn00kLJd2sEkKR/7tasVpVxpPcBlm10ZeALCnxNMUKEjZFF0RjZE17ENOMg9Zg0kirAvp4JEUtvj7ohXnqEozEfou2qsS7klJQ1EUwTwUCAOiMthpXv4qnDRJhOVzDh5FYUq/b0px9FXR6JjmizSfxVnoUxb5ChQF605ONwMiPGiUjWr1b9n/WjK6dZHdeTI9W/vtSsXRCHC0rhO+8hCT08CiRF1GU4QaRS8LmWSWv8/I/L7FUVv0y/LpGpfesbXfrlQcs6nq0I6dlq7tFgNPxCxhbMVysEmlABseUTVOwAFnrzKDfFkXrsbjdmz5sit3puMhKuYD2HkkQQzpPLTzp1oUmgBKqtelDazOpEgG0OHnIUwOZrmMjcPv2BJmX/A8YZaoi9sc8rwDbpRRPLcQcDWBnOmSFM5FnRH0GDkefJxFwCyfr3H5HVu97KudESnGPHRzmU/frgLTay1Jo+2gb1aJRljRujnYlYeD0DJZhHVhHTyWwlag+opV1BsFCToAPGeRukcGz0LVwjqaOUlL15EjTkl3E4Z1Othdi0VYF9LBIylsJaKvXESvxTQfKLWW9pyHtgIdS5CJz33ZxFWasvylFo71/JNCEctNeF3Mhx2vYsRdAV3wwOlh0Zbh2dgWryKPTZzLx/C00Rw8LlimB7eQqefJFOHT8OeMGUH+tkjGYkQzj32dSpnfCc5NHma5rI3D8dhSTlv3SRM8oV3e3pnzIwelaab5KI2dUvWCaDqRqoaIocyDkISVGLuyQfxXBffipVw+u/+rdO2ie0CmBzI0JxFa4TA9CnB91mNTKC0rfWWmC0UnqCIo6Gf+8COBIkVKpiAW663PTab8LT+S4xGWUeUH/qAFXKaeJtNSRKNqgtQO+zBmWEIjBZyF3nzPX6oTN7mrdeG6PG7MFsD6gln0MKJ/INHTbPyK9D2mqoMCtbp9vwCsyxrGoQntd+oDTS5gucyBwzOYZcyXome0A5YFjAJ5NQJA0UfNTXSUyNB5S4D+NnYBlRMo7/CrIuUDUb7BYC2P7XKoMVs374jskWf2ofE5l7lHOUdDCCKHKTCRHoQTyDstejI6Gu1kZ02ryVytC9PlMWO2cvem9K2IBbV4HnuFnGeg7Klo8TzqkzbxRmH3HrNNDdk+95u0y5fWpdzJK48yC378+iDUC2QZMqwEF+8AnAZqTg40mUtQ6S18Lmgch33ZNsv5AgIvHQCJC1J3yGQYpZAX4P/6MoXo6yJb92TudmMQv5EIo2FTIWcK8bTpJ8s+WuJ+JxMNxMTWkrFINHsQzDmlKUH+A6GipSXz+jgp70oNC59iqO6vL3lSGJ5ENl4DCyGLNp4U8X/+EcFPE0EJMzg0hTo2LcQqQ03vI/czZh6msGJSwkdEghT/MSqKWhUKGfeLoaUhDwZF4SiQ99kHYMcfwZVmiBuK7ZGDXx5otIJ+qQOJzf4tly302UVbvGxjfPL3nd5KlolBe1xW5ycIJULS3QJOY891KZ0Zmno24JRH0mDRsOvC2nosr61dvmmd4fFWZbUaWOy3qRY09kkejh6m7ctCYts1paNBeCQ+Noe5Lpynx5Ha2uWb1nlADLYfojfx68B5YBa1HyJV7TUANJNms+bVvfR4usygaR6NsY7eU0yfwh6obAdE78eXbQIjVvQVSkwK9qEKLkUmSgA2iZeF5LYDIhbHoIlQaGICAbabWZg2n/pwqBKTWtmXbVCJANfIHRvDVyaiRRwul2V0OElb+O3LxKHrzZoGTPQV+mHPLArxuo5pWsfyUL6T9oikG1mtzGa+RWQW87V8psxl1kzd900mPsqG6TpY5qGHoS/QJ6D5Fh53AVpQNR6mE46DQbYpjOUzHh5BYqu+fZV4E4BoJ2NVy7ABLSHmkNuhFzwkeIfSuOoqWGaKv1x+/r0+yuJul0/1OsSw3ajh0tcBr0T3+hYgEebjnfH5IRedYGO7XpVyHAGDkYnSu/iOkNYmftaFKPKIJVss74vrJ7qdZN23MOvCcx7g+7IaUIgSZenFdRMXnfHnp/+XvwTYVPSGRQsZjnboCWjVUR9QVKrC4Ato0XhcQP/k+X5mfUnEKZBE/2s0eptZURlPYMlQIa6EAYLZWc4IsHIkilUeWi4myiOWU7W2c399mcTwQg6JrQ1HsfqUFgTqxMijvQmRmOA5eT58MY0CGPJiMUQv+C39+eucJsehaWyywaBcVVbGC+9s8dTf6MBmlLCvS8W7VyFv+z34/hBwIwB2xH8/RH8h52n0xpqIVxqdUEA/BNsUEXHSi3Ds7Pzwoa2LwZiuC8HqEbJ2kYlbk9LY1kPRiPZ0diw8WZJmukEbDxVsN7Eo2gyLnHE4a1qs6fI4VoeQtatMvJoUIDY0dWV3oo8k4CSdhNbFqKEhRWdSeUFXDBQcKPycQYKcnIXBl64LveqwsWaNiVuRgqaZaWcZDVBd1rNUqPbyBpTURQPqfOxsCqW1eLRMsenS5bKrHhnrlEn4ZRVA8nnwSUTWtfsdnQdtwYugXzTh2+7dStH2VOIIZNC7tNph/JZPEDp0olkl4dZUcONp6USU4E8vBRJ8ljxTEGxkomvVc1GogOQj0NPK0qtFGNiSgBdpVtIK00ZNGCt9dzUybGQWbLh1El5VBQpO0BRcZuRgnwE+SZmoR5Wbp8nAy1fjGoqqt06jqpwNpC2mcvnMpsuE2jUSfk0FWLm2mdDSVRJFpxmUGCVZsaFl2jxrZXxXniEDVnmewWQrl8ttOkyorfl3CgQQVCZx3MBMmGIzORBU0t9WuXmNWOiCBa1i5Y9R9B2P928SlculNT0S1Nb632oD6D6TwuU0led2mpqzRx39FLZE+YiQp6KZu/w5pSU9H4uoqVhYF4WDI4iwdesXmTtNtKnLInCusfsnw3m9q+sStwXXqwK474Ju0q4sRN9mfi2mdV2YWY/JtYXrN6E7bYNT1xVaQtI+DW0ukvGixUJWQ6UGtLor0Etv5jcU/c8tkC3OYjkEh0eHeA4SruFEAlSUeNSgZpCChX0ayj9mFqtaNCYdTxDa0ZiGjxd48HxHymsy1MsntD0C3NH7+/UBeKnsCSd0w96xcJ4+xPcc22ndfTaBnZek9AQqJd/1BA5iti4Im4fI2VJ3Vtijyaqmf6MK9MqnyULjY8tRj7OPQQGzoHX59a9OuazjheVaZ0XgGrLOYf+joL/oaaAdD0P/fjv8oVVqgjkRH0dt9oGMf5AWmnLmdVE/e2rpAgv9lvgwRWOlbLoAkkZ4WDe01CtqpVLY/q4O1jSnrmWEriML0yLsF1TYjPzYFD6TMBjKgeXJDBxNgl304FZItDa6IuYYXVnVDjTocpcFB537qtpqgTvZeQYsk97buUmDLpc0tRlWu37Nr3bD3aigiUaqnhqGc60phUj7kZgITVD6tO5rhEi5xK+OtAZnvy4UvycJsEsObiUKtBa0HT9MpizleMo1RAmnKOpUtXKVvVf+PKCl+Wkx+0k1rtGCbbn92szebi1wIdL4GWgJjt7xPwI518TACor/pyzBggn3DAtZ9Hwd0toNh1F2lRlrqpR4p+1wRjkmuwaiDgD+R78M9v+9lIHhsoiSr4oL1lS07SGFVyUUWQ1KrxyDmIUPbrGBV5qAHk8D4D7CMsivdu9Eq3UDnHRpH5qdeY8Ee9hvGZMZhP/y9QGensCpNPArEyAQm5v9gC1k1tOgz7z6iFLcPNqWMaBUr+qApxOeG79JWq4LyemRomahgV+XgGYoLSlyhfbTCjcjv01VFs2WuLO2XpWChylzmS4/TvWuxVkun+L0KFGnzsAtS0AqE2bXaJrCLJmtBfJA2iUkMoVoTriBgrE8Y9qxTg/H4mlSV8sluhxazK418CoTKBZqUyOyCMnjkHOg2kDvstYqDhzQ1MBfUFZ+2sNFoLNxMIO1Wj7H5VFidpmBW5XAZmxD94MIpfKuVmgtRd14kSoJykuXhShDMwAmLA7xGJrschwFM/DCQgtakeouqPV+fNlVrMstefUKZOE4x8FQxGiL0qzI7Kwnf83dWwZ3kZkpai8zcGERqan400HvuKsnU5ooQOBq81HOMsYPFCgIlkGsNl62UxXRIBZzWmMhy8kcCDZ+GKf1DTK2QGMAtpSIw2Ep2jTDsyqPnTJlq6qZr5kAedKLynA9RF7znbXfJDnXhRT1SFS7MMUvZBlchzw0faY9RFf/jtCs6upPO692JoCgH1uQDHr6LId9lE3WrQu555CBTnHFrRiD4rHeNzYAY8p9HpxUFp8iBaRyXfh2bCfsedqqG6TDulAUDqNhF1bcCjFaCFL8H7m9lFbIdUzeUPUBYNhW9LKNso8qyBilTKeLl62cW77SzlPmOTU+t5ogWoyGYiWNG4rr8QgTIhlL9F9debLJdGOWrCCjWcDzECZ/sly2xeFm7AoRv56kwPlyBwUpdO3T2yN6K8hddu7iwZek+w2aDYSZ3gy8Q56sC9liczNOecitnKTT698hQQmPdxnjx3z/tOm2TfwMyhSKfq3ZfrOIH9CopmphXVQOtijCcQD2/IJRBjYQYDK6WcF76VkAJyW5GW7bsGVNUFEmIXVpGYpfKmAzWad1Iak8UsusqrkV4YBw0hSbYpra9vE01cwS87luTBdhRilTFglk+G8ZqUM8rQtR5fBadlmNW4WDevVR9u5SilQ78GlCqXqvqQ0pF+Gr0h4T5O+hWHtj6Cb7tFyuymG27PoQt5ikzS4SGeyNqSW1IqPFoeUtn64S6oLUpehDlxW0gjt8VU3iafk0lcdqmeUNbjEEO7A3XSjQ8CttcUqOo6jgmhJCNdGD9KWVKEOk6t6qq7dBPS2Pp7JJLUer7yv7B16YLsc1i8njjNLNT24dHMZQwgwtNaKMJ2CmR8WaTTstn6XySC1Tp++q+iEYiyKxiOxstGsMMGlC0gegqHm7bNFhneyoKS1n+fEHa7epOl8XlbqnaretHm/WkBVRlazevenWEBFfZRar4jg6ZD2acZj6CARe2eDzKymI3R98ya5TC/SbuFc8kILdlmj+P1en7sraC/xAAqNKtO/VnB4v/QSwRPo+Is57vPfhuI9EhNeH9KX24ybXt3xm0CYSPY26DQ/9xwbkMP/T0JTiLTHT5/PQEGwyrymdUHdljE3aSxQUgSr1l/rzX6lHm+5bF3rQYRNto5+bMRBKOrswPRVNWLZrPjqVJFGSUQpc8mYrO03TIOdpQD2/ZKZi8tXrwm87dLit73fLARjTnlU3ctqQyi4TqHnswQjDnJr1qmmyY7LkRQDj38GLSVmuC8XpUaK2vN8vB4BZ+ExVWQ7oIZ4yAYwqGdxohdz2ZVseGu3QzOnv7nw2Y7JcfsVhY+wyP68oEEszfRGNFGetQe0OUbEXRGs8sQsUuSRi3KRhTUtHZzWTq1ous2XTYLbK3tPkQ3ESh6aYrRRJVTusCLJuqug5mJV3QwXV0BdPefTZWiTSPszzg8IsVE1DHYz2UJu7cH99ORVRS2rCaHUHT4xku2wBnNcAgj5n7NyoDJk+2jntTbIN7L1cojHn1G6ZkbVFWxFbSxeH4FvFlVGg9Y2yX4sjWxdOzeHgnPoEt5wBKMfYlYgtx3ezxA46Q1dzlMcnvSwF8ntC4nv9egEfuLeYkt11kfh6kmC7yPFWFDmBksl159z1BqyT6HXoxkJvNOxWeBPgU5L7zGhm8p0hYNJGy2eZPFLKKu64lYKACRoaD6OkSo6L3ZQmgRSMvb2+IJTRhSHnOQ4RokkarQvH5HFSdonCraQBIt6iCRWQqKjHoXWZMnYmOyTs64ag+E2iZP1XOwKLNVoXlskhpWyxtS/Nbuh9Kq8bY0oSzsImmF2FLBNlElMvSmFP122fQsFw7CEWcbQ8lsmmpBy1ta/Nruj+p2ArUEF2tsEiUiU05XZahXkhsLIU700ZQigpOfE/izNaF47JoaRMrfVNmo3QQMYPbfBqjtvgRppajrr21O3pWEH1jKSoCjomHhycSQCsC2HgEAyO2NrVZiP9Qkape3NVzedkknVHzhRxilAZlwUApPlBi+ndXdDB/9eFL/D4BVtu7cuzIXEW3hQVBlBi7dNQmKuttOgBxA8YV4UlkSYRMed5wFic3qtMtKQCu8X1arKJqgAZHuXin2Ipvz3FtS/P7nRLI+rSWKXRIBRb0l1QMhr9Y6wVKAvb8XnsBw9hshfrwnY47Igjt3bV2RGGNzkr4E3hd95acUgMo77+ot6IuCwCWXlgDL78ngYm+L9cqsAhFmzJtavPhtdCljGV4AMtR2lwlyL7O006YdAGtnGKmuT20NnnyIMs3H/5JIHHKdhia1+cDe/MIYQlHG6U1cF5aCrWIHsABUlDJnwHLYsVUh4hh3L0k7DR63VBuz103JYqu8pmVFpTFrEX96aOlRBilSrQafg/cdOQq9KC3HeKhG66xzQw6a91ocs8es2WjPsSc/YxaxoV0zB/n6fR/GgCKtSxZWEZX2K0XJ4Y9Zc78T9jKqZcdV3krY4a1q56u1XJ0RI5FDtJsB3aVXtoCTN350J6Zh7QCe+UNm29LkLR72AqNn2xLnSHzY44iuubQhsYjM4RWpl3ZzjQvaXvjo4hylzAZftALC6jOBcRBm5QxQAx1gXz8DASW6HNwnDA8zsiz6I34PPQ0skLMMZRPcIsk1lZLg9jkjaOftZT2wLkG1139kiLjvSrBTbXBCjitpNCcLfJgtqigTuY3FPeatEq60LDOKyNKT/11apA1LKYE2FRoKA462lG5nJkfoRG73lftQKJ0OARooz3DmPSK8slYxzqxhYBe5JhNpfcG0JAiZ2eBLZMugjUwINtYkFLQ5dlSrIlStq7o0WtLJ+JsXkbRw15U0/ClFV3FvCnUjBOx+ET02RnoSBlyoZGl0VXFk3+ALDmg6Y14fF1gdM9+N3WQ17kk2hjp6dn2Gro8TRZPMZLdA/hcbih/2pFB1HgotVvoPUWPL4ucLoHv9t2Cr79AmfkbNLIW0WYjxkEBZII+lhrMON2UEdHJIqJ9k43j/4hn3l2meqGdVFD2NoJ21LqZkGFsv4pJF4NiI9+hJWiYZ5K1q4KI8V9VUi9q/x9mT18y7PJJAjWhVCw+QdHS3vT3qKlkP497aBjH0aJcJFJTClI1JpB7KyoypNNDD3pj0XUwteXi8bb2L1Xj3arX6OJoLhgg73DrqYL8HSTGRKHOK+DwYX4Yc+n2PJ7F7Yw9uUC8g58b8uBffFwlUIifmOUwvL07o3uMu1eH3nOvl18lD2Q5RLr5XvwoZhpgATvvUNGyzADYhIefJdfX8LyNVa3AEtJqD3eyw/NJXRDR3aDxEiGAS0DjQYxyg+wySDqSLps0FVojf7h7IlWIoHNPGMnxwbKMI2Si0JcgroJiKjmDPM7U88E19cFjHewe1tGfZNdo/ObwnH0n2EfpnBYhElBAPOkV4U8M8nQpThoHEPXxofXBU/28GdTFeuLaBmRS2EHhl2XIZwmUJYqxylK1hx8IAhOkkFitqZfjaRNfHhd8GQPf7ZUsa6GNvJciEFm20DvoK3phevsTJp2hR0bDKRvvWrqnIpUsWyoyEKI1wVRtvFnWxbry2jxHYIQwvQMuYinA84DrizrM1A2LvQDROm0iutSQktKP3u50YY4xNON9reILRtY5zYduvz6crx0lvRYqaha56WkwJf58cYpA9AK25MiF+TD0GAgIvwBzEMLww5MrJXqv6fvFhOKAI9HfSx5bKse29rn89XEBNmXC8k7AL4pyfX1uyruEbGKsiqD5cNZk2B6U4yNDCQmYw92Gj31oFpMfH35cLyH3tt6XEe92zH9QuuqMBlN3QzglY0uT0ND2b5t5gZGT9Moo3JZx/sRTJpwXWhFj4Y0Fd2uADxjmw77G9DqMPq2hG1NVzua8a3uixb0RtMlni4TjqXcJAjWhVBw+AdbFnuT0aIqpiowS5myLFBwMaanT/J9WG2waQ/6sEnfdKGvMM79+J9TIVMot3xdnSfDM0vI3YpzQEmpBKlnh/GfZhlww2LnSZwcwfTQi0JqwdXyk5/9S75VJrOyfB7Go21MOfFNfUwrsM7ARvNkK6HjzJp6RqlN1WvCQ1XmH2biwTJY5MpymRiHt7H1xJ76mCY8fQoNoQGTBu3InHsZirYFGvycwKObAD1/ltdbEndVeQNZFq+yLjyMQ9tYcuKb+JjuLaiggvaqLBFaVQGXPkGnmHDqRSlU1PiixDCPzvEmubI8JsZmbWxFsaM/prWKZkXZiHWU3ZvOAZhLdh64vUtjFZSvt6ExSka70GPvMVmVdWFhPNbG1BPf5McoBs0bQkY9oh6nN72d7yC2k4CXLktbeq8a2GLvOJBQi1VZFxLG4WxsSagnIeUbBD+kW/pUZQNWrMluq4rtqEACV01RXJ2x+5Q5DqrN5FTWhYPxOBtb2HgTQg7p2C0bd1U4gI5T4J03cpXhV/ojnBB6xenfp/Y/GL/FDKwLk+AxD7ay0RVCooAxlKnRFXrFpX2aMmvTZ0gP29GirqMJsvrD/c/C4ZeL2jsYv60OdKWEFXCOIp602HOg3KFqylDz8a2jUWmTK1YAMBKjUJjR87mOWhD8ukD2HsRvq9tcNVxDkDVEggXUthXtb4/AQBT8EwL2qodRCTyiBsg5jncDVwuGXy5k7wD8pq7UF6H2STnIXldoGRlyOMD8SAIQ+AQx4T6533nWsYNRdpbcmAj8uiD2HsJva8t8LRobUnaV4tFCKksvXCiBYMnop3NU0XTC3wM1NPL+I3daeM9gE4FfF8TeAfhNaZmvRINetQW5aB6hapltxWIDJosfgYKTVvWildI3DRhoLRyngb0FQS4fsPTwTVsh7yvq4cvDXXR4xlZ4bel56C2CaeUNJqEtsl4XYb82EIYD7+HAaAPBy8eNPZjZ1Mf5crrMxrj6GeAquI0luOpX9gKUz+82mLCs1S2L3u9RQ+Cw5OvCqnssvC009YWp6H4kjv0IILiNipyGYuGo5DydRGOBhAq3PjTVgPbpBHfRjxOpArK4JD4L/d1P2P3x5fRjEnIWznNcd5PZoWC3XaLYFREBN/aFIYIc7/Q3cC5mwxlacErca2jhYlC2HeiUQUpZT+W294N9G8C9FJGY+u2e7PZQnxNrFpG1LsSXR5TZRba3olx6fth88myjvVxn7eDb1KCM0nwd2nTdRmtBkb9HXBC+g0eZXMJymQeHpzAlor6eFCr39qAbkYdEh6yyTk3gKUyUhZtuhM7bhgba7TTh+axxGUxWoJkoP6DPG3MUi/F1eMEUtiJPXRl2v58cyoUoxZH+cxSfijCQsbIOsTT3qwNQXLcAFjJjGGswYhFH2BpV1Fxy1V0Bc6hdEBjPjDAs0cL4b3kwmTzC8mkHj6WwtbW+FhdlVLTCaWQ4t60X3iPd5yiKsIz09PGj2SAAHOIWWibPBg4Wj7AuvIPHU5ji2psWF3YrGsAmPo8eB4oXtO4fTFrSy/ZOUZ/cDrD9g5uxiYTlEw8uUWHLay9q3Em3OOT0CbK0R6ULqkAmIkUQIuLky6Yn0YIq5xyJ/47nWVT6ulDvHlVvF8r6hbUNKSAgcr4uPb58K2wvcEZTM4YxtNEk162iGJIdLRHajK9MRPQfqDP0rnne6GJijWSefSP441NoxuSnpZJ21cmOlBmmla0qR0+7nAzShtWLIkmJ8+pkEocvSJO+1Z1fl3Jq503+aF34Jo+fMpXJNyFz4yVFHoFGRtzthWmrKvpN62gCfuGyPaWhso8JgvGdSlhEyHJZE4djseXJnpiZ3lqIU3dAytgHD8kBSS/Fi3r3hRN5viRc1cvux1LjaQJt0iDLp00cksXWJt+0zBPcrI4htHre2upEL0UXQGQx8t07FoEE9Ts/AzyuTu2zRSGsC+XgURS2OtkVMze427e0EVT0VNqnCSDaBLwP+o5w1YRgUUO8Wk5czGByls/7uDyRrUy+KZnhSr5hSBg0bWU1xQxJw1fK/FQFnrEZAnaSXRa5/3s2m4j+cuF/jyywxbW+GBfJZkwKZdHky49zCqU4XfMyTiK20wrkw1PjxN6FVdzA3h8kbV1gNwelM5W4LABW60Asm72oWBl2daNrBkFjNxzaS5NmWC4p4VAYtrrW1+JWON2qBA8NOnl5Z00MFjFJ74PQCHTJiiakXQdEjWd/pQ/bQf+v96o4G2K40rYGWpIWLtmMrVjcj34vwTJlG/znMbMMVoloUCsJURJbMING3z3DaHZH7OlohY7F6kvmAiY5sy5kjkf+2KJkX8TcQMOgDlDS1h7rTp9pkkExLUFT1/Wdrqt+dfz5YWrxXtMsmmY5lI7H/9jCZE/ETIsqeHfZpMXnb7KQNwcNDzPO/MPEEn3ppPlw2+rATRZZ7My6sDke+2Nrkn0NM0jgEnQ2oW5FS44GxytRjkOs0neH2xKHbvhIwE5zVpOfWR6d47M/tiT5JmGm6BpxnKDhMQY5Drl0G/tDUNzeNutE2XXS22yU5ZzAxAcV5ha3vlwm3iXu7WIXvzgGZl+F9UkMzFSZiLAi7ehQJq5AQDyTXraiQFFOg0705TtpgU1srQsR5hFntiTXl/Di49KzygsbFUG3nofWM2QhLJ3uXN8g180F/jdidXf6C5vk0HKpJJt3skWtngQWJqKwqdAYLfSqphkp16giEJTeB70iTCyUeahIkA61skWtLJeIcWgbW0/sqY8ngFZ9w7TOxarnQPs81bHTFFSvkrQbL0wsIuFs8G0RWsvnv1y+zJaU3iSodHtF28QNbcNKgQD6pe2Bk5TPaLwAR3muivTkkGmZnNDyKSSPcbLlpDf56UAAqrFlVXtoHKf50nVrgt36JrQgWNBOmVX0gl9Z+0wyZl3IG4/ssesxbvUb9EBQ0vK3CrML/A6hNyUpMO3iLp9NPfOlb31Ou8qi5nKgsx+25/xLqS2fgfMIO1uM64t3J0zRNghN0Y3w+TgN5KPypRFIjcexghYtRYUooJFIdKMjBh+1fPbKI7tsLe5Nu0uZQNctCf8xtpIYfZSqzE7kAlEvS0FA1ipU4IenwVqi6G2ymLcACASLRLHB7u18+fXlNDBZfr8TpzsKYrYc0TUdJpU9xsdfDx9fFcBpi0gpPQR6F6Uly0xa9E1nodmFd0KhME2avLG+KokhoKbIzR2+MnxNMmN51IdDlNhaXEe3i/LZERX5QgtiBicGSoIyt4fAN6cBKOX72PBnmhpbzf9n7Tqyazl25Jyr0Ar+SW/2pNz/tBEAkqzXQtTg8Q76t3SLKpsGQBjIxv5ARMNS/Hkp3bNSf8zE5czdrZGqL/qjuCkFzoNu174oyXJZXVAxsVclr+pJltSebYB+tfxGKNzhqB1D+YgcjMvH1OksFY8y8+1WllFq3BKj6e8Vg3r6dWVfWt2aL8v+1z7UmyuGoQ9HrRnIHTOZOe9Zc6b7utI3B1tuRZuZqCNns6AO9+KtAy2w6PP/dej8LxZzOHbDsB7CY+a8Z1D2640JJNB0tl1GRJM9hEiSbfVr9yIRyvTlV6Kk+qiMxljMecFuCNQTc3IphbfrvpUcepFxki+1F4K1OW8SZBQHXFXWuuGpYcEa8fB1iXCFQ1GIGLKIabmcxIsy3L5IUErKWBkNCUFOzYcRUE67oss79OaHdg792cIjROFwBIIhFjErl5N44Z6yk79RFLi8vgPQUiedLRCgBPhllXSsjyCJyrNDkfwHZt+XEC0Z/aPc/hz0oOneZOmCIAZbdsFaPlz3plF+UcrIWnX3W8TrKGR37RkB9xeHtiuSIJRgkCxi69+3igeRgrYdknUCTTVvOxN4FmG+wzUOicxNO7C1o6idIYBxVxBkKUAJ0aRNYtTxGcFtCMYcCt0QoCemNFP+s+x4xVcOeRk2SeXnhFZ6BkdKhKnlGLkP2ba279ElQ4//M2pDGOa8wDYM5onZzJz9DNl5ubsGxCXlWuXI94Rllj2Ettmy6yK19SoeNLJPnmKEw5wX2IagPDGdOS6v2d+jzX26+LJ8hvt76pbroU2Ld+rAVedc/tdoQdU+Vf6KcOjzglsznDvWorxIV1CV1sh1qUC8eFiNHpbFln64CrWLrmONXvZlh6R/H2pGGENY5wXyYhBZTATnxHHQy1a5RKs0bFuvcGnt+9p8L6e5VIh6R1v2GrGFPtxhYwzrcMyLYWQxFZzSxrekiH5HFV0Jvtubys46bACjYlvHvaZEDPcJEG089DoRanQYxkQQKUIGZ9RxSAXz9hp6rcmaICHH0ymtd98ssJm2+PgYkUD4D55zCHqcF5CEgSohD/yFNT4aXAA8ttdS6r/KeNwtDZso8gY8dehITFPzm5RF6AHuh9jHYUBJiKnERHBGG1ffz969mI0exO5Eg1032ZIgAfdUEGorqqrb+lazifEkBoeQx+EQCYNUCAv8jTWOQNFfjtzTSJfEXpQFbAuVzPZ5IRs05fMRnkp7ht8h5nFeMBKGqcQ8cM4b74DQLjghU9ecovU82KftS8D/7duNps47tfcc6YFB/KIdLoBb1K+W+i/IKCm6BEPP0GWrzaqNXqCE2+D5bztL2kSSt5yUL6xgUsPN1Vm8ByvME9UmGuWbbpg/PPCS9nQFkAktwh/e+L/ZfCKw43BwhIEphHr+wlSXyGJ4roQh0G5nRBlh3pICraFtZ8MdpO5rIvwNx0Og8l/U4BCAgaARjPn/phSYUEBZlWLuC+6jmomOOPZ7Qvnazdln/c4mdYf42XhCxOZQfIegQTF3mDGNUa5S4oqFcljz7SQDe6C9fCjcNCyXSzYY5XsunHp/4C8R5HEoPsLQlJj5ypmyaFxS7zfP2grYz1M3ioP2AH1e3xH0YJINx4aPpP5jPIqfTXLdqV3C5Q+1da9MLsxGnT0vR82rSV66LlO4KdUFuldTlR1IvRyAAXryhMwtlYQsEttAhYTlujXhzWCZnQhd12250rQhU6/6O1aJft2aJJ5HcowgYpZvy6ewRzcE0SXZz7Lv7PIgvvyqdBZBxYcjywyIjgUfbwIRyYiL7wESOaf7s6y/xdeOutVMwi4LuMZ3bxmsTz0FUOUNvvySPa/jScvGh7PHfzn6FXv5WEs0IAOo7U20uh/ODEwKnKEhPeA0iVY9xczAqrX3DrbuKVGppW10rLCxFY1Fuy7+XHGCibQozQ/tHRFYeF6wRYZFxpxxzjFHWgbmisfCe+/bJxOsm2bDEavavBY8DdpJn/5wXHkyF35TPY/AwvMCLjIwMta8cI2MjrzpKboMjOv9ApbN1nk3rQh7vfBwk9kiQrm79CyeoxNpR/Oolpd+YmwesuJY1EQPukuArCu4vbKsW6PO/46BY5GvzvmdPWFAgVnW56ltXCVHc8VJV8ZbxXKGJ0geZyHUSVheVKAiIb+T2TqqYwXlr6z+nm3fCuM0ugiG/JJsal5vgl6UJ46UXy70MeA2gkoPR1YJEBurDd7ECaPpzmYYkRqymXnTRjsnS9sb2hHfq0rS6sn8HvXHpvGrY5lsME21hHhbY6iOuTz1RW5lnDWtI4T0fUqb5yT7gQLMhdG3lQ9RgJXodthEahIYT78kwl5PZeSe9nhWrAOY97ygwgxFjinznGK/YBd/g0LZldz1rEHkhG3HnqH0ahXuBv3//K6njJweNN4QtjwU5IwR0Zg2z0n2S6sRtn3KbE5+Epd66O4p0dh2tFXS0Nbsr5GzP4OHjPZmAMUT7NT+0bYA35A5P/gVd8nxkh9612TtkoNgNL2B2mh4XReUZwNmMNWdxDJ2BdC0/wGSi74XLmzBg03ooVHxRxkp3cZ2rAdP1LPnE5K0COg9L8AwAZKJ2oCrEyBoa8vL07u7YzLOs9RPVD8+0r7bwxZm1l4LlV/X06gkBPoOxwUZjBirDV7ECauo55GVbeG25b/LUl+bVQyqs6xwVXkto3rZdrQxP7T5h1D1eYG2GRQeyq3e1FndrK30svKA320FJYvGUNPNf6Vabxg5ANgv/V2C9PnTFPaX4X+EVh8ObodIONFpcF0HPPDkK1+UKBfLFauCUa3bUC34L4tftfXtUOhI3pfirl9VjTeUzt+r1oTk/333BeNHv4je+2ixVN5EQ7hdO0o1HoaDkgiMceliUrG6W5ILL5/drRsk5vh3W6wYYmSQZARh/uvtuCAh0uhH3o9VOj9h0BVh5OcFUycQfKxy4aqYhf+4XHhqFA+X0JCj2cqJ76+1Jb/qGNm/fx5tPVJ/iW1lMUR+B0IE/qHIjSz//i9Hv2K7RusqWeEtkDWbk/ddrPqqTXwnQl8YjiLUuXRDWflUPK0kqNTdJ5b5yjIfWuYGGbtHfuDzhyjLeUFlQggnlhTh9wKBjWMFWZsT+UmwxzWPE0f/CeZj2sOhJAlCqYhlQkxUJLFLbflWlioCRjsJsjx/N0XWse6XTBLo+9Dtu+UnkzkiPJwXggQjVMRClzdhzJSMzacxyhXfQh1JE6fHBLLK+sKENEteW7L9py+ZyD+fIILwD8X7Q3IA0bswdQxUkUAZbBTi3NlOIrMXdSTNeGWY6rSXK8psy54NdTQdelCxQ+z+vGD9jBsQal3epDEScq7mQda8jISpnFL/LrJ3p3mvmpoKW/UZ5LWtx4xWaRKC7dHQahurmCQi+bvTJT2q7UkLzMyGGccuNIYzGUCWSY2J21H2HjYhGFCB2gzkzWZ7lqbGnRiIpaL/unZB3GtUh0ZgrAzn+qr0WANAEaagS+jQU8OQpf7j/VAzikrd9OCfqmJFdIfzwo6I2RREHcTFRMBL4BtsX13SWAPGkznttOwjd3lXYh30o1dP72W4PBuPRJj94Qg/IwREApc3OYwkkiXZAFVMKvvvGrn53MvFu0tVGFANy6cROyG3/n6CEHk9FKclqG4sFWGyEnSN8OVWctGpmZD8DIg/WxQt24sZhm54n4zmMT2YNY/u8gFgfyi4T6gAsT6Hq3nAIet2N9gDdDcFt0n+yRcSyRGzBt7TQCxfSBrE2E+HugCsPy/YfkwFiCUuVBAD55G7s6CYVW6E3PBvjp4NVQ7YNQvcQezvZSaW8li1Q77O4fSemAxE1G1cDQfvAG2SbWBDvR2t0v+qfNOsw2diQeh+VRkA2cc+/PIeAGaM2J8XhJ8xAmKVDhX1NKBb6dbLZVGzibrAYmyIW/VmJdWs35dNafkkqP3h1fzLMnoEm54XmJXBsrFGh2t6GpaUNi01lss6MS6hLVmzeEVyrL6tT4d2jM/aXdWMTKutGZ8oJQd0h/PCjmBsilik8ybqkfeetsdy31aB2rpDESnsxTKkLldjyVsvPuYlnZmP+DVA7s8Lzk9oAbFEhyp6kD2t4iXXLh/cmwOA5fm9iqPmO69tnqz0edlSrQnTw2k0gr8PBctjaJ3oTbg6Ze11lwWJaJJ1kcT0yLXZ7cuuYKvINPT93j0Eog9rkRD8Pi9gOQPXY63JmzZFFlkfFG2oycC/KqHZ2BW3PcNSIzC7rGxYKXmeDlTjU6tIAH6dF6yMYGtEafKmTEGBpdpkgQWAtftOah89NI+BnGh5PS7pjj2bLToJznIfguND/PO84KUEXyVCjTdhx84qLrEAQzb/+zs06Ms2w1Wd0I372fLKbK3Ls5gZib+AUZSHo9UspNuAOXAL9gJejn6RoslR/FMuokRi2dvg3mqbu/yOUgWWUXl3fVTvVpmQsw9ouLB1ysAY+37SEMGniD9p9hP3BvpERTaiQBzOmGAEi1huxOVJMvMwvP2yMqDXNf6coKxMj4Zqv8af2Gl8HZNl6seIJYYxzwvqyVDSWKjDhT0N3cKGB/GIC23Pw3lk/7+PsNZ1mtN2JdtXAzWqeXjhRGjmodgnQUpjtQ7T9jRUBZaHUJLFGYCqjKtUZ/cvAIGwXVKGZfGIWuKa8tSThUDmeQE+CVAaSXXedD1zpVtWwpy2un4HnQ1kAnsCDzpxTTQc8PgbbXbrA9b5BaUL5Q4JirXACjBSUvRhVRBVqy5dWlBx0tgHOSIg1q4cHYnUkjIu8GSS8mHFhbBk3FZ9KWoJpOUREOwQz4OhJYM+3/KIpLNakIEDq0lqtrpRDazOKEoi3PtMISREgA/Fiwm6HKt1iLRn6ofuXmKUnVPzGPCEJohiloPvkq1c2NU911eGhHH5yGMjBPO8AJ4MII11OlzXI/NupTHvI0wrzspp6tKkUcZsar7xWcU+e5Qvl0nPLDACMM8L3kng0VClExa5/tV+DLIOpxtRyhJz/eImLmQfQDZGe1i5pAThWsZDWL2VhPYoPTY0mVE5BAyGFDcD2cdLj/So7dwqms5GB4CZ7fCdu0qQhhGCvkVFGef/ummxFtWUA4Cavm+4MhsyVi7Z8tZ3+pCUhaggYwUbUELnu3EPNIbcSsLWhb776aHKUyo3OlP4CMTtdBTgDayD9Pdp0SujbmpwMsGMB9VcfkEUYc3C+FEtf2FSKM+zqqShWfVLNjrQPTDOWrOlPlyh6LpAFhG0qc7wyEYBqM1midNGCK3AnEpHQOLqdsUkOwRmHrIGNc7/DIQeYdbnBeNmmHiszeJaLiS4yqPRAQ5O2KWZyLZThsXssPz+dk/IqObpnxewUh4RK4zBk24nMjAa/kHyJPRgsc2GH/0ilN3D+L2MDAwPCcsipwIL+wrWtvqQ/WOSk+ZbJ1oibY17slUMDSmlrhSaV+KVQLG8cvsUByZE7M8Lws8YAbEw7UXHhtuqtszKe6j9nqYkLwUN8N9MO6etk1OzrHL3bPW/G6xGoPPhGDWBtIkw7U3IBnNKD1aR8+Z8zyP76fR1G0Ln7+vK2GuWiIHx92iwEmKVhyKbMQ5K5GlMyibzbXT7Lnl4NjKAlVlhFktBN6tnOGtMbfyeDRZK+7H2yHzJOsAk1p+m7oZk4hvtoke/yJ5wNJ8vMiNRqpH8pbTcrsFFAocWP6drmPuvsX0wLNA9QDtfef9pmcFVtu1qW0VH28rbT7nLdZdZxcgXmj5b0WwHFEw1aJHAv+xruNHRLzprG4LhvRY+YdwY4bvnBQ9m+HEo66MiwAlEY3n5J/fu/uNyFsmnV/LSKmDG7VeVOzDIHI0LJYt9gEYRynsoJkwQ5Fjcx6WA2Ma7rwdoipvtZ2hbi20omk3oVo12ziv5PgO9635q1yOE93BAmOHHoa6PygBXUk97r+7L5VO+8kDk3enWsdpVzOPpi8WI6im//mh0/ve7fwi1HQrMhSBeLKxiOizNv8rKzoWTZNbiB4xnScambfvYeoxygGmBll5WqRsynJ91mt+0uP8vvHBe4AgGX8S0/hcVwEBz8aWX7UOy4Es1QlNyXQi6evGOi5rIzraVazPQ9m+uD1GHQoz7vGDiDEMP1aAv2lHJEJdfNcFyuPnvEmRnrzC0ieKsX1Xe1qxevsylt0e2FqLc5wUVZyh6LG0kQsgG2VxbDmomrDjTzzKAl9q9ymbTrAgNtxFEIjZ5JwzIHsrDCC8+FF0mWHSoDuRSQsnOYPusqyeaE7uXZQYfxTZ5eM6b1rGhPlTbXT1Bxf75ACFafDi4TKDoWBr4JiWUb3oBXllk0v42WEbXUXuEChXB8svOstqwySj/uOoDs4nQ4vMCLjMwOlS4vQniOsJtf6+7ulQX3Vt2r74Hy7Tsfh7UUHbyPQ/2beWB+4Vo8XlBlwkYHYu0uKgLu8e0zROB4XA1xwK3tk17BvnPbqc2AIhr35qS7MkP4mAIeB4OjzI0NZTaMGEOnNSgh3CQT6aCOVXBeQ0OUJ5s7t7cxxFlLdCe7Ankv81PIvvf70YRTnU4rkVxsJhnznnpMvNkbDbbjuQ/bek6xlXQq4ZuR2gyOG4ALrmnloLk9531TX+kLYnswXI0e1VLUl01gEPfLnS09QBQJozOklAzRZVKRNek9rTqtq3fHh5ty04i6/melgOV6e3F4cohy+0txyX0VviZfyHWfV6wcQKlxyqlN1UTCofZJtpqMgua/y47pU9LOI3br6iCr/vXsq488IYQ7T4UGydIeqxTYqqmCgr2rraiLQz/bidBSwrfh9ACShdxGHP0ZSfB20BQ+9iHIqD7cFycweix1IYpc6Y1u7tbhyQi30IhWWGKB3/y9Ue7l5UFa/s+BF77E3OUNSkpmiovCBaGAG4GGF8GuvKjXzECcpQHKjMDqwecjEcx6g4eWTZTMD7RSAbO3cUDc9DK8IpBemqXzByR7D2+7xNvCPUx7BXjO76P8JUYjvkEdzSCuM8LJM4g9FilREVNFXGqWcsgMhreXGgXY0nZV87oUvCN3KNTgX38lPIzko1Q2vMC6jIQOBTavOlytu0FBvLsZCXYgjWwqEYPjwDqyvbLQlNVPMKtq/7RzyWCac8LrMtg4FAt8iYukfedfGWQKLfU74bxcof+fVCF+nanlOi9+gLfIHb6iWjj8t15Kfex8mAsklBlhiyod6XqCI7ueYoXFpRBWx77S4ggH4o3E3Q6FD5wlQQ6wXYbrWP2pWktatJJtRuajcvAM51xU/nQfaVl16c9YIQenxewmYHTseaBayTgjiRJoIUUklKscoXuXVaCYb9LwlPXNbuUqbqTPwRg6Z/16e87noadHI0SBobrtE6OyOyd+gXPmaU2OEVrlg6vyIeCEk5ROMQ55XahQaCQAKEj7W31hhjo7tO3gqgF0PbM41LF4k6RcWfJD0DaEZJ8KO4cg9SxZoQrTCaiG19qZchY1zRZ7Cu8DzTY3QAp3UwTGVKz715h1faJmD6sZx1a/uLlspjv+saPxeKhrwrUIoWKbRffQA3xrtD6Yu7vXV9CLJ3RXbs7fshQMUDfD4fqGbIf62zedDlovuLZs+whrsybaJiX/B0mGENPvywqcF7Yhl7yj45MKL/g6XJSqzJsuDI07NnZsS9SyzsvtT9SKow5ipzTuNUY2K6a0Y/z2pTIojB1z8Y2stu+rYUlIkrZxkMBqvOgQEJKO/GEa46ma1rGxu2uAi9Hv2KVpTWTkIUK5rogH2Jmt6uabLLL6iTOWlmzhSoB5YYS/R+QJLusseZgRAmDjGDIVZyR6vMTAWnA3Dic50FYIbHW6E2btJtHbPIyXbahZU55F77mwTdffwa6ITunBRQVOclPLhjiKIeiLgSjicU6Id6g5IbSvR41MLynG+7Jf2dXhFpquQhNhis8UPRXCZX/UHAGCPJ5wZsJPB2rpLiqCpZCMuHti8sOnBz97so4VSL6wOfJ9vpr0zYh2RPEVR5NBX9J5ojIE+eFbMHIGbFW5kVa08E49JhKst99zT4W+GJ2O/BRdvssFKEuqiHrWnqW0X5jCBCZdp8Xj2/mCT4grdn40A2E51rqDapARkdY3JoanK5LtF9NGy8pEwLayvdgroHqrTkOGCJTm3R8Yu2JAPzD0P6YGRAr1biubWP7tXHVtmZO+nNu/X53FL1NYZeUPpY8yZG169ndN4SfzwtcTdDtWKn2pmybsj14RgbjYCsTdJRJhu1LMLBZw/MKlHpK97+XO5vPxqwhgns44ksR4lDnxWVhVRuK3sS8mbweZ2mSb7TpQWKycjiuKuFKv7nxeqpAYyzuvGB3BOqLlUpU2DS0d4fvOnV1s23GaSTitVI33Ijy+janbQOCe3uEutOzIBhicYdjdwTpi6VKb9Im2a6S5+m13/4j8rssstN34TmGJf24m+8ENWsK8XB1kvAQRLXdm5b7OsT7N+GkB780793gnC41eIM63JuBNlNXIhFF/Xxt7yFhWNA/CmjUZkGJzMW6EkhsW1N+R7tRj4d9YIUZnATvK92fU0dLCjXRa8WwKZykoahV9E5kWFQLVXBFowmoG5TkMOuJIlStYGnl2ZY37Fy++vODXyTFOS8pEUuhYtCdg/TyLzJVFWBADSx5eZ5GthV6Dvtd1k/Jcj/URSLEcA9FfAk+HCvcmB5uq9fA8kBSwn0/R991T4vnoC7SgYK0bYwbQkoaOMqziB/Bt+cF7mXwcKxu42o4CT6Ll4NQoL6RQS/jPkDGpu/XLBgn7SZl+Y+N+68BwBBwOy8AHQP0QpLsG6d2WFswnRmSqeXLN10oYW+bSRIwedeHCtDTRhK83nP+kJFRCBmfF4SZIdKhrI2r4BC3puX0ktLBdPLTwJZ628qAIWvNX3BZSU4cQpMhtuqziB9h9+cF62fcgFCbRZVcIIbXW//OWGWLn6XAp9A2zl6cQ1lQNC4GKaMDgSxDj9hb/l1LmbLJIoHF+ooeFvYF+cEvkpYfo/Fnb1aGhsDWS4mXJWA307KGqkXdFZ36ibW/DE2xMjK46YlKAkbYsl12q07Oz8NkgpGs8D4+CAO6u1REzgnQLGaWPT49+EXW+vOyN7C9JC7LvJVxwtfLiiRxUeUTfLKIMnE4xYIxMmJN35sGUEIBB5ELkrlyTyNfKdngH3K1H4NWtS2wxARo78/jy+q3zGtMkiFtUyAPWG7J7+XoV4ypqgM+Sia43MIHLLNejLTDCGbqV5NcwKYsvBYkuVW1AKqjQM08YAh9y5jNGQVsI3z392FXNDQPL0iS+iXhlrxxUYYkCbqqQmg67VvSlYKsLPfbo+un4uuS3C0t6cosST71X45+kbXm8LWJrmWSJix0W9JRIcm2qYe0klHmxlqBSYu+upelI5GJ4fuwfxqlXDp8bFoem5x/gL0TcWYOY9gwPk6sh+XqWTS7qw5RL59ZkPdKfFkt4YXruTOAZDUqFs9jLD/S3Ygsc16oNYyKE2thqXQW5PFZxiUJ7FnuaYa812WBB3p5ruuPLHtK2V5QKW7o5+SdiDJzKMGG0HFiZSfXgS6FPix1LYZuK8EPLUvt7rPsXc70ARKmWwPSRhUq/IT9EVvmcHYNI+PEuk6uAh1brXn0AeSpbIpA6SULjz9AVTzUrikfaOT7BM30t7/GCKMC92HVcFI6jwnynE4vE8WuiPiwmA2F4wGaxjUQPnZ2lSOs4pOlB0W23E/pzkKSzHkh1RAOTiyGpdpZUCB6GzakEj7pPQ1eimdZEPlbaUSuKjM6dVs4ZBvNT83hb6SmSOsQDJr8L+vKrRALmA+4v6me/dM+cSBMZYLQTdWj8u0UfEITbHnHt6VR2EUJwSxKcOrdraPrQ989ZBYdzkRizCUiCWQCwoK6bvJFOFV9xXaWKV/B5x/Eee62h/Cs7Gm/y3d8ePzEvKLzwkNivKVYEMgFhLBK2+XmnpIyjHsaOeYYE8ok2rhDL1ucSWHPMH7K1CEV4jDiBKFZEGEVk2FJzDW6VfdlMNZkZWKw4qxEqVEfGka6ASk4UA7ytaVtpH7StKHuDMhEt4Fh4Ixcf5qXo19x6H28+9Sy7h+yN8zirmWMNRGX8N5KfjH4TSL7OBH4AD4UEbgOpXsRclisB6XiUSjH+/aSSUnLzyHRfr1ZADBIJ6nJELdGFkOdf9fTnjeibp0XqhejhsWCxjcBJFDf4QHk2s6oRiEbL9fuVkEG+3tFSqpHazLNbKxfiCggo5wX7grjusSaQK4h1HTMMPChyJmVBOCtNsfyOlHtw0HwiXy6bMeOxq7zafUREQsOYSEwzgJTd3E1GPzYa02+H2+T+eppmo5kfYI0R7lUCSxbyYunDf3Nfp4gBOfPC5jPwP9Y3vUmBwMpeV1MbgwL/Stq1aU56iCrnztV1aHN1/27QeBQnrHn36btYch3aIAYh5Mxe4yvmmSRDashKnBDtdX+uDQz3/99rSbOj89LPs3yb6D4hnKg7lyLY7pZK0BJzw8gxFeXjFLgRAkVuqwOCH36aVh/uagf3QeisJCWcTiNg7A+iCaNa9jQKMry/qFv1DoSGvXZjGv7/6DF93o3HOE16tUwdTy45iG/4FA2AuEuxNJGJoSU/Xlo8cdygWzL5IRLilZRddbWUXX2I+tEmdG5frP9QXSImAWHMxEYc4HoGrkOsgARKD6JtWX19PP0W3MGFlZLudeFB6Evt7IxpP2MQmBdY/VwGSc6x2SzzZemxI9+kQrYeanpsRpgjEdx/KqBipY1MYRpncKc7xW2qCL3CbwrYmYcTuRgvI9YUvmiwIRhmI882Vd9YZb1SJueDV8Q1HrCLithXvNABn3N+1OxGVEzzguVgzA/YlHlmwhTNqU6bCZC02UCkaSFge7FG3nKG01gKUvVk3uJgOYfXv0FGClauHe0SwJNZwBmcPcSetSEDVO+KSZ5K1igmyWzUDDUpUyL1rRxWLtdG2R7MacrYBsyBm+4KOsGiksVgd8weF4JGxJbIuAoyOxqNWha/lyWTuhMsHQiDEn39wS3fGVVgLJmY1juBrI9zBFk0xW634+MYUTRMlW8rDenlhcOpJzQmfiQBDlAu8hFAk8qq+QiTBnT7cKykt9p4gFWigyy5qM3NUMOQD+R/Xne7D6vZ7v2kJNzXjg8jPMTqiqZBFOCpHQZ4ANx7bVQHtceGHXA5LC1XBPChLJ9x3T5tu+BEcfiEEIGYW8QVSXXYMI1a98UHs589rMktn3YXWrUoGQP9BEr2X+usqg9/S0jfsVhXAxG3Ij1lG/6yzlq2rafQQ7pek1sqpqp2wMUt88ZqOj04pF3Re/LJ9ljKHcScwOTTVeLui9N9eXoF9mfzgsfg/E3Qh4tp90CV7FGddpUfdhCk1X7oIEmdt1xYcesXUdmtt8XGPUf2vwiesXhdAxG34gllVyBqRBBzTco277LbazXW1sYogYlk6peD2a4i3tsV+U/fBYjIn7FeaFjEPZGLKl8k2B2dBMa/slXc1sKZaVmo7itNT0UlKsCKOj2yLJjz+cGHqLkh4PqDIOPFZVcgdkVu/P1ut+2HHoerASOADfra6GXRYxn+718y8cGTuqfh5ZLWXWV6C9V9zlL8m0C/Vb97xPsPvq+e04pT+/hAIU8FLIkAGesqeQKTJAO1r7Dwojm8nNG8xnLzzd4GN4XVuLz7NlJQenoCYhGGOThmCWBOGM95Zv8EmrgZWtZ25q32++yENskhyxjW/sF3A0UEP6O0ano2QIrAiIPQy0JxBnrApmKUEKuKQGnjYeKbmHurtsl7GpOPZX3VN1dt+BhbfDI1H7iSr+x84qq6YfW3kmlPtTXcDEO9rHmEGEb1u4DSNaGJ5FtcDKTrQcVFKfbvgBqsMNqk/7gHRE7Hg3VPLU4GLCi8kaA9OBXbJt5Xlw2mSsnSnBJtVjKkC/ehThDVj8nyPjI2/JVwyiEI+94KdEGHSrm/V0WzeEN/woCPb8seCTIRAaGS87lQ+zJCLY+Lyh3jInHakgqngQivPJdd2/3EBRE5E0US9WX1TPsmgglpg2rrFjtIw1DcotwCTBYcw01wFxLw/hRb9pnIQF2R8lXcxretC+pR0mGL1n1jra0Q3lVWgWmDlrbQDRvv4f0eMalj7j3dlF5r1qpKagMjT9d7H4DpEfQ9XmBugkyHgsCqX4QIglZRGxLxl5h5QiFgxQl14+f3cfRyu+lXEY99qeHA1cIw54X2DYGeWNR3ZsID35YvoHI+u8Ov7WpVUq2KAS9YMu9qtzKBR9k8PzBnpJ/6V0bbGcrtcu/zYtI8oPmoVZU7wwuZ4ftuVmooXQ2gSGBRLiWKzBQqysLzcWVHjpscmFDT+puo7JWGKnr1AVFXj4C3gmWSElt3Bygwt8EJuZa800KW9vvMsE7FkYJb+EjOu5VhzaI1fNIhPaH6bgMuqLFkbW16KCAK/7B4Fh+VJ3+e9L7A1wqCY/RJlUcVtWfB7H8sEoxY6nEQAGHFSJKJjHFUUSna20eG79EYR9yrQnxz0PR0hBaJXI6Jr7LKGCv7XO2zLT9JF2imOIpZ8vWd32gjIDeDLbdz/2UiMXA53kBShmwGmvpuPZOTXCWx1jmOqWiJEmctgU9EsS4QwI0THK+7nUJhYV/HiGEPc8LTEpg1VhK96a8kyWqenCNRM40USiiXKewjk1sW8wwtR/3vqUh2JY/DBpy1R7DkqNIlKmAl+ShADxdKkuPmtwSVBSs4U0ujqKCoRDKfFTPwIbVv9fy/ZrlzvECoMhEaL/8m2Cbw8YuO7Hk7F4BY722WW/usJe3XRdxGMZUhYZW5vdzCkqKnG11kQ2+6+oCINmnID/6Rar+5wUlYKhCWCd5K6sMmTK6ZWeYxhTj7DPYMEYZP0HZj3Dzw2F2hsrHSkyu3OwwJ08OQ5XsslA5C2TQFjoMzO5yr4q+Tl6cGRKYPSaA5vuqSQHBHE8KyGTcJjP8qGnlJXFCLtKB9u5vpTwU5Nm6AWxYK76F9BofwYpHu0MULFr/GHaJ0jhiaWQGspLu+/cSnyIgh856e8M6lUdKVodXCym55I7pDiEJUbQyNVCqre1THhUh2+C8sBMImyGSsHK9q6oUavWiycouuGzAJcastgeBfWC5v1wUnoO3MFfrTk/47e/8WcLU9NBElqS9MTOWRSgkngmCn3+//OrTFENQ843PhBwhVH0osE1g8FjCyQWfS6J+373Qe8NCkWmTyb83wDU1LEb0UOuyUo9Ev/uxX4csgcNJBYyDEMvfXtRy6BPgZVVn3SOHW6DvWlktQVRS/aLgG3nqAH/P+SAchTD7eYHlGYwfa1BfJKsLBcbuxSZNbex3ebxs6y36e1ttQK66zGvIKsltP0af3GXX4lMHHUO7OUje1q8snR/90oIdHrMZWjpl5JfsZcW91VhUG5WO6f1/QnSV45kU/ixYzzU6VLRk3pOghodlDoViGZfeMSFhVdZsDC1QUET8FOYRQeznBZInCH6sQOWKVaUMrrt+yZj4VrJ2CWBcE48w0IIQVCbhMO4ZPDKGxwv4jS1USHM6L7QoEg4potPwPZG/rZQMxMvw+pgoZOufz5LqbTM0dtLUFZ8AXWpuu6K4Bhg28vlEtBXRE84LnYHQH2IJI1c8ZryMuxdjh/45TZLBY2twkxfa572srB3D5v9c69EYMgTaD4XlYxA/FmNR4dbOuTooIAFV0015qP5uOeuwaJVE2QEyXEqz7WOilvnkjEtOPIfWIyqeGjFBwh5vRQp+VGU6sgsvtdfFKFMj2WMWVxJBZU0fJEw2fgHZ1eNCAis7EOde0ukQwfvWjmgAPtDr6kNEwZCYcDiPISY9xNq1N63bLrvbfEBZ23boBii/XqqpxIzlWmnvXkwAjYSjjEfUEFITDiUyENpDLF9jYrcGvr4uWwq5qSZQT9JvxQQN1FJVVEUuqc4A9tcyA3p+lFlCYsJ5ITIQ4gORr1G1Wwbds/m9yk7i+tSJMTeNnN81S2rXNrsikLogV91/yMR+w5OIUujDiQyE9xAySd94p7IoJf2wsDZaTsNERR/lZiMnQpxS7lWHjM5sAbtEfXN8aPqFxITzQmRgxIdYx/amexswCXAgXlJgKyfjPDIwfG+o2RU/el2JTm/u3tXO8HsORhDooYBpDK8SPRBXD6mdWbPsbCQrGS1Emqk0y/z6TOaoucz8zEk3BV3gHkKHiFhxXngYhLcRS8q4Ag3lwLH9qjIV9xWmyTnMbBFpIqokftUEgqD9Lq8//WHMHfAqzgsNg9E2YkUZV6Bh9arTM4yF+OXbyDu1m+bBK94jlYFNt/urHrB9eVAqIibDobwHwpKI1UVMi6SVoOSjaF1e6sBe1Xq/36Dbtj+wIianKeAP8qM/REhiOC+kB0aSiIVFXIjUNHOfNj8Q1ZYLdsJSqdzyBWhOft1W09281FHpWTKPoN3DkWCCG8e6ojcdEsLRYVdFo1XfvPBhLT+Ev4TEqd+XXdlVCciIobj8eYYQoTwviGYIgDJpEZciyQCAXNUfohTbanEe+TzNhzUMOsv3ZXPOHuzMVsYjLAgRyvOCaDIENFYWcSUSNBW7eqiFTMBdVLEadBPjdtTI7prXsXtqF0cVag9b3G4u2TIEjsgWa9NqQZbhPzwueDmqJTwJ+ZoHvBLzVQ3rgIgqGmy2UdkV0VH0ToNmHmKHrcdJG+CmRA832hiQ8n+ohBeBdYdCewQIDLUpXMiyigIjtrWnoYvyrBr8ekgF1wEtDmpcXe6wTTWlB2EyxOkOA/UIAkg0KW8aFpkSo1xYYWw7P1JICYn2LeGhAPePoVBb9hAPCmUIzqdyJ0Tpzguqx1DAWFfBdRgY2fnypjpyzWsH2oGN2P3AMcerQQpbDXu4imTpEZFizCMhQGdWgCQSZiwvm7NDTlm2Rb4lJH8pZ6/e9LEwLxrqcvOa+YCo2VBStF5HWOb/sWpMQ9ABwwHgWhA73Bi6YsX6RwuQMBy4AMgt9qltjNNStAikHRBUNFUunoya0ZqouqKSBz74h4wLYnjqUDCLQV+hFOVNuTJksU42Y3pLY9quAXevajUUjJhqTvJ6WZzfhoMkxfPBlMWeP13Fh/BqLe00iBABy5zdqpxPI4pQ20ElFVyAsSRarT5qZZZk/zmjEGkwKpYzXWnkRmSJvTGSrGH5R21GIKLzAikxCCqWVLxJMGTx255hdMAg/nOB42SxZ8jTzYcxCVDG8sCqwzr1MQB/A1YH5dDzAiYz8DlOVnlyi+Z6RYtcUOrhW90Jy8qtUXn2A1tfhBkdijARPCrWpBAFywIzC1x+285k0CkTbMLuS764qZtlBlRdw6YaNWQfvnnWP7xNA7DlvGAzBMqJBSlcwKIZTHeUsl0ewwBA0FO2wbuGNquxq8rr3r4nFizmj6gzBL0OhcgInhbrEah8ARBjGh5QSJ4y5xVTKKHElpAu067cqyLkLla6UZ7Xs3r/tz5NYeHgsDIDK0rE3GfGlK5oe27KeixZbnO1QYmctiMUtd3WHQFTE7lD0b9Gq59PVYBDnOi84EoEhyJamjftzZRFzmuQWGq+nVwT2jG44le2TpsxKOLs3n2rzAAQfqDXGOw4L+AIA1NiKQpVrsC1qm8HKuTsJU8/Tdve/hHDtOS072UlZC42oCT/y3+AT5a3Gi9UK6VJq5T2FenBr3jQHDrEyICMaQmUwxBCIyQVq4DcllYIAcWo4/pHoraw1HReSlOklEXUPioy6l5YRYQz6vTTqM9it+ig7GcxKAKvDkW6Ylgs1u8wtQ98ANu6yMOwHXwUXciqL5US12iuBppRgzrNVnlY/T7ynwiDORyxYQBPLN2hUh+A8pBs2Vuu3s+hoe/8NrSpwbTEW5c0rSMh49YnkJH1cO8KYZhDQRsC8YTqHS71gUak3uJINxoD0D5kbrYHy/RcBirhU6y70yDGf9oTQ0GgzsMDfdnBr4DR5GVf0INf6uqSlFSm3keyNqhn2d5aZlXqBWL/6sk3qlvFOm7Ujgrlbt+2VMD2u7Y5kSWimxAXLlYyLsY/Rs3fxeDAhL521sEKYbREH/m2IsMfYayr9L0k6yS04d0Nc3eVuMvk+pTfSQhcnRegi+BisYTlTfIiqeHw+BEYrr0X+T2DtWMB5FheHsLtrJWWr0rw1n66TchIaGYoKE84kCeg0FhcYvBy9Iuk48dJEBvUEkwnLINGnQdpYsH7R9P0vbeRwhVgQ72xaZouQ7nNH6uWovUMY7Cvm1RUbWimsqyBus09Dcv2o+rA9+jHJ0mIXZdGaGoIeZ0a6MEvYrxgCiGNUTEQ0a/TCstIlNWJQInOc2x7J12F/tr+pIGODwxff4941FaTl49SvSdQgXGEn4a5OgQmEJ8AESPQ7ryAfAwUjDU8XPMD3nYy068OVX+91pySr1uA2P7nfZLwB0s2MftRXn157joh6nU4SMYwtVjA8yb46fImfCvMq9zuYAU+63vb0o1trd3rIgTvw2sC3t/v970hotT8cJya4toxO5GzGSuQM21qhTG0W7Hlu6ArjgkhirLDvScxWuhsW73AXEzpj6Zmv5DKRIDbofAcAfNiPQtXv6zs20XGWNr2Y4JbhkV1/VJf0eQaXVTsNW0ojh6YWYi0nRdkjiF5sZjlTfwiI3c5CobV0btjqcGGcTWQuCcHorHMdy1na14l4WV7KPRCrO28YHMMy4vVLFT9Ans2ebgbjW6vLstpBjgA9sp7cu7i1O7Sy/dVGTJpPWpPv5KYRp2MDm98RNokoct1BlfKur1KYnU7k/1HAWgQiCzQCy8F3rIS9n43OIOHGdI520G2pdjYrNAUb6svkGTh+9nR81fF7wilPC+oJkNBYy3Hm/ZDYsw7O+WzTOsQro2U681dJFe19FnD02aBeQPNvj0sb2OU8rygmgwFjUUJVMTQkDs2/eqK0axL/dgqTneTRBhmdb9sqsW6MjXUQKzjjX9FpJXWiGBon1gMXGXf6Vd8OapVgJTRJx6o0cZfuDXAlMhYNVDIonLqnpL/F2GlwCaDQZEjLm06CXFMsz7kxM26IhvTCinqq7L59fGRwRshlYfCmgQEDYUJTMWg2aH1t0AsY320p4IHVjnSjuBZp8oEsNcUP8PXls2tPZYsCQdla0FQLyPcELms/brNYZwf1a/dhwpCQc9b6OzZreaDbWeZs7capNgnKUALlsbjkuyV+/1ACsDbQDi+m1l5KQ8C2nZNJuRtWfyjyFVBt0gk8pB4NveNkMRA7a/71PymufeEfK2hyibI7UBn+0y8GyK75wUJZshxKISgsgloCXNuVo2QXNf37QG7gbGXfWBZH7Y5CMhVW3MdjXx4lMKeOHOE654XHJjhxjGb/439jwB5e+FK9p19xQi5dYue0DbZbMJwWdjHW7FioBnSs5UUyuHqALl0lQOlGrYKTrjmR78IOf+8kPkZGgUOfZmunQQ3Yd42tpL2KuVvQe+bnapcNBBQMjFeHVy9h5+HGYBEhiGf2HIjiPe8QMIMQg7p/JT9j5Y18Bry8vooe19VgAyRliw8LOjBl/2yqDJ68CtTfjz6koRA76GwMAGRY1Y/kQCAibz9LldLhrx0dAfMd6ctcrLuqLUsaHl5KCx78bMtSYjwHg4IM/w4pqdTNjtYgEAU7QnKmB5cILqWONiiAjleynUOlGlpAbWKZ6zEdtWxsHNRG2S4LwEVqOry4+pYftSchGA0JDeI3S+pbFbDBVSQfUvv7g8RtrOgbSQ2aTqhTO6mu43sWCu3a0YUbU0FZcmGBUJSOHl5n+Jch+DuYVAwA45jVj/XAMio99E5UcfyU+85PdmUHTUrN28Bcqxz3Zx/jz8UhhGwezgOzGDjmM//xv/HouwTCcRgKxQMkBhNTIdnkE3W8x34Z06TKTS1M3nuG7+wvwqL6oeW4EnBPqaDM/K4PKNM72XI60IRa9hJYIZu7At5r7aMLvgmGJeiYvdan2qnE+Lh5wU/J3B7TCN/o53LH1Xf/0F5sJ8hw5D1tNpzgp6z/apdprXvaUW+wtN2LkR2zwsSzJDjWIzAxQuSeZRuXxAh+N73NJI1JJto2ZB9xTjlffY7wGS9ftopRrDueYGBCWoc8+DfePMTb8Je3Fj5svjTKiqr1Opd9SvONbMXX2F+86hVhYjgYfAhwRpDFjynzC8ki46DSAK1/BzJUzPoVrxj5oDrceteuGoSsT1BYnT+0lRIgpqhTksLt+S1HXrwy7KWpWghINhezOwQjPW81TkNFbzm5HKtdQOw1NQHNf1rQFQNcawKbZpHJIPRCOoWYnTW0Klk5dR2bSvUP6VIDgHUw+BWCs7GkoE3icFeJnaHN3bZ49qySWhkp5ev3j2+kavKpFVocVu75/3c5/+WEREnDOeF7kbocaTo/VIjH2Wq+dRW7NFbVrN8hOQvn4BEIwzyvGCWDOOM1RZcnYFAEKx0D7phQefnQaMv//zyacqFVmV0TV+GwUzojxj9v1jS4bgTg6lCpcWbMAOloOT3r82e/PfcgAP4nrc9M7XWPNUzanRTeQo5IiTpvCBPDKmKhRZcmIFob4/kmQ8aDlQ/D6b7fYg85r0sEM70nT/90Tn1r3smb8O9tGCKNnnTYD20FaslKRMG08Ud1RnzLeYOMaZRrBAhxU5ZkuT2kjnFSSaQP4RnRTjSiTEngk8RoQVRZejSOjxnkCnUlp+jySdWKiYi17mNxgpnVYU08LJkvXmysEP06LygTQydilUWXJWBmjuKhPYI0MZlPw+4StOfAXDg9ZVrFk/pU8Bb6Vld/PsMI1zYzwtWyrDVmKj6RmyF5cu2/QeZijEpStN9KZn3oWy+3UPwitLtcMchWaTMk+sTG0eAvR2K1FFgLxSovOlZZCVpN+6Wlda2WSTaYHhZpgFhszWgWtj+pwUfDXHLc+r+pf4FacJQrkgD9aUl61ia8bmzDtkGpz23BYzkMv9SgQoXtADCbkrHk99hB7KuOcroEsg3DVslgPR28Fldw5F3gvOyQL9/fPa/V+JH+eyhyW+cKhOSMqc0h9tAtGnoJeWEtgOiuTcMGz4y3kOs8bxAkwzKjKU5XMqj9j/9VtzHsPgJ5xkIE5q9RGxmw69bh2YSOuIlcHpI7+CEvPqN9FuVLBhxRtci2ijOlMSoV9+WSChE9TlMzaMp9vZSTJVrasVcjZO9uV4FXG19SLpKUazxhy6fsz84FBFSejiwSoHYWJvDtTwFUVjxUmBFYeBqfGDAa2uOvGfrdoDLyuCufhoo0X9SlRj+OwwrJMBiKDDhahS5NVOWgNza5nVeg+TX5+AARO6YJfI/33ZgE/xkf4QY1nnBvAhEFstL3uQo+2dIF4ffhyotPGqB+5Wx0+DVuNAn1Z6gOJnv97B56Px4XrzfCVSERhJwpx6Kv8P1dt0MEi8Fb0uCT8lPxrruJPKNl/oUZ1R19jfVkjlLRk6Un5CABODfecEKGbYYKzS4omMj3x/Lq4UVxJF7npw85AO/PF9jNMBAPi3hFvfItGL86nC8i+JjsULjTdEheVnygFlyk/R9HpkmRiKpGtr168q1ZFb67+jIW38WwqCmd2j5jxULY2mCKiLqvsuIRGSmRMN5KvxlfNOW5fBh7RICa4egcDFiR/QGXJ2AUMfXKUQQGlahcrdX9jiky+RTVASR1ARH2dNZWfIfWUCG2bRShledwwjzeazLpudHvwiD/bww3glBHhnW1k9XUZlthtonIIJbcSKwgUANHP7nsFdG0NfQakx7rtjZx4DTMwqH6JzubVULFCzIv0HfyDD1/8iCECFTh6FYBPKKZQ5MFKHb3b5R5rBOOAvRWMquZJUAoTe/osyunn28qgTxB9SNYNTzArsSlDaWmHBJChzfmrkXNW2g/i2QATnOlrIuYV2+2HBq6kdsftKAph67cYSqHY7CMdSOqA24OqEg8Tav9qbS5HrPM78jup5kjlywUNLE3PwhJKJ7cvl/gUzH4gTbkSU7UuslLAiwwbi129BcWbvaJm1JiHchby59t7WV+AGDE2w2+S7ju0iLmkU182a48PvPofYhlkp8gtD/H2TqcBSLgV4x4Z6y8yX7zjawJJnz5nMehfoElGt0B8mwcXlyX9X84GmFFIKDh2KJDHokZHtOzkczmLW8gCIbXbr+W1Vm77BRCqJy9asmlD+93DLMkddhsgCeOhTLIshXzBlnDPOFBKXcsuxEM0Q7SZvunIKGPtvIfGih2CRC9hWwwnXs5/VHOMvhqAwDcWK2OGeXmzgy3+r48ioJckqZGP5Vqrz9dPvo9Iruh/YMWKUeZf8IKzgv0AKDImK6+Au7XEbrTzQx3ZAWadlorfhnkJdo+eAEh63X6kNozvQEuyO44FB4gcERjPJMGdLwo0lWp6265NV7FpgG2SOgJUIp96qS092qo5zqR48cowXnBVxgYERMeH7hR8vE0ObteISa3Yi+TnDOPRjwcmLF7EMtzePCXZ9W6r/hUidtzofAYFXdU2c26EIiD80mEPjI6JoOXfyHeU0Zz5vRo+WjoP+RqhCr9jnixKuMLgNJyVsb8WZ/Ni4tqPQoHi7TfgKdge7vguUvR60UIAmDovDyfUDaTd1LARUexkPrmg0B1Ddbto/SzL9H0ra0bi1A/gjEP+0HokuDlQLAOUGghCRCthFbh7C6rqW9RxBjJYsl8DOaU2IEVhQoJUs1QhIqY1VxHhkW8Jzez8atv2CHRZjJoQhLDMfElG9OEJ+6U9tKCAs0b2m/e0lejwXDRy+o1Jfi81jO256BYwiWHA6uMDCGUJc51RnDP989FsTPes8jX9Sw/aoCTf8dLTrr9GIzOG5PJ5Ww5H9eIAIGKYTcZcp0Bm1uTV9fsOsaNIpERpYHvxv5L30gy1Vhm1lt5Wx/iDmxO2NcWZyFaFfm3J197NgXaajjxPauTmldxRdl3tcru4HKgdAGbDn0Blr71ja0Q932faenE5VN7GghsKvK/tENJUkIgD/VrCYqfJ+XQjkrrMeM5TeG85Z3nuzLgwBjLC2cR8IN3/aBtdXvgj6gEhvVrT5dTBNIXsAGAIhLAmWdSQrWAnsD/OgXIYmcF1IJ4aDE0CCHEps1uNp6enRusiGORobbALGlfF3be3BZWBFg80FXJchmPrP4htjBoUgDwSVi0jeniE/kItmDuGohJeBuGPXbipZQdtW/1tYBdpKKJag+G45FsMF5QRkYKhEzvjlDHDtPMuYy6qXLnJNxntq8zighbp7XHg0xu91k3TAVeHy73+Dm0eg6L8A2A8JjDinnnC5tS9RvXOhNRArKmkm7gAJ/rA7H4bKQUGn+Cw9eiEE+EzlEwMuhME0M6hD2N6GKo0Y5VLOqDnCyM/k5ZIrsYR9iGwlBr1iwOdrkwpL/sA4MQZdDERqG58S0b04Tlzkp367YyIUlgZujIXmWdbDYE+xbKkCuXW1HxxOkPzxXf0Eqj+qMhxUlSQUzJH5TkriME3MixhCUb+WmM/IeTeCj3zt3r41CEbW8tZTaIXwG9QlglsNRGYbiEMY0pVdvSC49X5Mlol6TMCCf0784KHAmewPRoFe/yYqGHQ9u7JpqkYRdM8NGURuPXrNFflDt3uTW9J7xjymZUe1WfkjWFVn+HM/yDyUkxOQxzjQLRbhx3giCSlfm6vwf1ojy9Ej5RUPIAe+m1TEeizJcssUXKJrKk7iRSZMbqVfGMeBMKO+jYd1pZV3Zh6xzaoqlDlC7fBeiwSrOBprARW1c+UhHkaGYnlT24Tv9JfFE+Vcl2pI1pDv9JSdGuI6a4UjKmvmMW1gEyp0XEI+BfjHLnlDykQkn1U5r/VxW+mspJy+oOS1m5zodIayQH12EUBaj/keXG0hqtQUTLJOXDet9+xC/HP0iyPXhokgqoowDwLeAESVDFVFiaQet/p83ZDxG0j9AzIxgxENBRwJRxjoFImrAEtpb8ywRDVvsFA0a7emRezY1K/rEySbilS9ZUZ7GZiGWdF6gJwJVEcL9Cz+/Q07mTyD/ZHNayW3J2mMUY0Bf0zYJzbpvmyWpKuunsBtASecFeWJIVUwapxxzfJpSPLpp8u8mAJ/o8dp3t1hwSW7snYDRJNuYGcj5B2wtf54hREUOB1FCwCUkjXOOOeBI2Qk8dpPowb01ofLSgaCxgfzBdw/4ftlWKmbY6cm2CiCRwwEUhrfEnPEXijk0lX5N9Hy/lk24k2mL4FSKpF912PDQ9AVo8wOgiXCRQ1GUGHOJadCcMw3zTF84Csr+3rUbTXrad86Tq8M58olyan7z26DyW6WLIJHzAqEwyCWmQHPKNGhO7SZK8nO9VjboqLTtC4yuWJZdNqEvhm1bCQapz0Tp71qzhjHuoRFxHD7HTFAWpoUxXRgA6gVlhVn64UAwfjpL/pJUHWFI5wVziiGqmPbMWdIZNbyWfNhK4G0Y6lKn/aIhiYbpjvohK21tFlsQN7puPZ4/w2IcwMHIy5p/1vHdX48fNFdREEvRcrGhqtZNc41/6TLH8PQNIPX25D3sJPovbd751uxTOTTye1FQJRshsYNvkND2HOWDiYa+xa+bJSVC/FYX/GPGhypbMQB3OF5H0L2YA/zGGZYJXHwhyutSkrHv6Z6g0w1T5boxJZjMeUSKsO5n79Z334cHAqWPqouuvHp8Kgdy4fSoQG5ISKY0YE4anqiX5bsPJ73Prvc2so3ePqE/wc/aokPzJo2nJXB4Wgz+f/TwvCCNMTBJ+L+ULpzVJcI3bRlzbV27qCqZ3n37HZ41flXZMpRPgvuXhOHHaiSEgw7FjgjSFHOAGWNYcs+NBm52+/IOlhOJJYAq/u6RnQ67omZNNxqs1sr0t9pd2KVhj1DtxVKdXNWMNaHJwTS/T3yV7S2FE9b1nnHTMIOU/7FgOENlNVTjLJ9FBpKrL2EkiRZuin3IhmgstewdWIcR6+RPXCKbkWFP9YeFWEPeifP50FN9b8t7ZWzuT4k4QwTtvCBuDKGLydOUa404Q4IgL3/LEmKx3bDuZNninaYd2PyyfWpLJY2jvRfA/fq/aSm9UIfXSFw2ggoZyU16RptqDYB4EyFzeVvYkcVIEIaFE7StZt1WkfNIFq9FYhk5Kw2DcvC7zmL88Z5y2e4nQdUUu6acfACAv+7VBYva1JuRpb63DxHBQvDxvICVBNuMmdOUaC2BIpq8evgKZlv208iSNIZFB2043xRXlT9axRY0iZ7aw2YnhtHOC+xGYDpCnOZEa0TykF7YdWUWpUv8XmAd+FPkVlzXg0pGm7fOKgn2fNDLw2LNeSnuhJWgmGqtBG+Z28u157KzepMCfbEWNGKGScj2syOEENWhgBaBv2ICNaFbw8YyJ1NYK53MN95te6T9LKOo6fTvaCBh6UnBPj5/bA1ijOJQRCOGP0IeMCcNgyA6/W5k5yjuzCOzuDiEKoN060SCHgOt6V2xhnYXT5zvv8DaeYHhGGwXc4BfKMPocb9vhVxmvJ1e/UdthyiIDh0uQ6E19VRvYl2esvQQYDkvgAwBcAgFmDKGJSKSrONGOMC/btthSYO3BxUVdXq/KrwEfbihrvhn19b/4nuHw4EEPYx5oC+sUSWu7juKPLUuW+0Duz2YhB3Jtl1sE+AN2YMBy3y6Xoegx3kBSUJEJebicu4uJAbTer9CHSjTpvh5FrgJtiyWlcq32U6HrbKXB0p+ZsQDK7Wq/0cyTplsvqjsGMGOH/0ipG0z6JhbRXCoCm5tQv4vV0UVqEjBK1YjQVmyVro1Z1lWYHuktW4JEQzGkt+LLtPKnGuygBsn9IUTHnHIr39nAg8J0etQfSgcOjfokObfyY9+xS707hstoR32oYHaXan92zdahshSEnqV23aBPHqZyohsbp+mvlZW1kEXUKxsHdikvODlfy/7P16GZtmz13J/j03uY1P8D9Tioxr4YQXzuLgeE6E5bVpu36rraKO9h+4MgLjl9pwtAz2y/oruBLP5strxWp+e31Hx+7wUy0ltPSZBc9J0Uw+C7xBlfnOpQRnw1SdDnXyNeyQzMY/ughpSe85d8OEWynNzTljGQ8QuodD2ocuPWndWiAGbjkX4b93urBVTt+rIle9iER3zh0W3G82CEVeX1nQpJf5LxKwpsnYyD2OJC7AH6Gfsf5Bjf1XFiwCD8wIwEDwiJI5znvkEDjVuINXN6QRnWTIGfCVHHGsr1lTCTfFtpcm298fj/71FQIzvnRcCGSOcxRycN86O7EzTzg4RuK3wmLhldSvWooewO280LZ1qDiz3L5Ozfujzh3DLeYFnYjiHse45Sz/rmtHturIaG66O89TpiIGEHZIOfMNIWMCHv8ZlSs9bCIsQl8MBGgLnxMT7mKSPHvOjzFttlujj6gXqUh2Y3n7HH/kl5ZMvX+Ykh02PTCyEXA4DaBicE9OoCecahZeWbgQEH/Rm55CZ4a8RXhjTGyTjUVvxcHyMnw4/BGs5HJohSA7hT7/xrbvE+L7zdfla7ZruIAmoxivdxnay62L0F98StzKUH/WcMtR2DbYFExxmdL7pFwfgR41mhp6lMHOCkcmetoAMNUHRspXkyDBTda7xVGICQt4C9aman/9reYo80LYyX8mXgKBOsdpvR3mOssJ4T+mt3DXUwSuUGNOJCbp7oa2zMlAq3PnuZeX5q7ZfgHClfKqeE+Eu5wWnYbhORD7nVPVmtQgvI3TXI6iwBRXeZZ9fBpHN4WVxw/JsTDakZzknwg4OhxoYMhGTqDnpWvfA6pMQNZ65/TwLDDF7BFXy+lVTMyy+KMv2D6uU/6IHh2INBJmIicCMNgzH9l28+NDQQjXbSfRGut48aGq6OHWloBgIiS6VZT/z3hA9OC9oAwEnQhow5wzLDfn63dAjyk6tgoy87eVLju+ANSrsMkZu1gtHrB/sPsIPDgUbCDQRU1k58VVWYeM1FE0HmjcQlilkmYbcvYQdzXs+jJ492JKFaT5BpLD+fV7q5aS8HvFYOet1ghqVl1d45C/aZeGCFlFs+EyYsl7NlKxb2QtvGfHqY/X6BU02qpUdWlkjdbiQzcqorzJvxtKwB2hvGdUZuHV7tAlhjHmuLlQGiz111oCkPRCoXzbI/W/Z+7yUyUlVPaayUurr1k17+F4hW+m0zRznyfDkss/u83mh0tTHvh9HY54PEGWn1rqzN/suzYJFXG1p21h1LZWXpiE2YVXEfFbCfSUCy0iOqRfMKLwVJS6iSfhTilPHdrs32FrhCWWVRh8Vy3D50S9SPrFIDVwurF8DbOfiHgwSSmnCiTi0jeThC7JmzKR/MInh2mE1KybcIjKvSBVmURp6UCsYmdU35KdE8QsNWqS4PFygyfScMjkSFJOamuPDeGglfyR72zC3f7DG6rcUIHVTockiK3c2bgQYU5DRdKyqWy1M+ZCsfoojHOFD5wVPYvhTTOPltF+1GZxeh5Kcy4Qieh7ZRJLtU9Noj37dMu9OmtCO56dCFUIyhwE4DO6J6aicvDpkwrThScO2dRkaRHTTsKVdprNREeWSEnKUm/vJ+eqDYhmCGucFBCGYScxFpdzVjvRFw10LG4frYRsWszGLbUQDWtPil5VJUu9DSJT4TPYiVOO8gCAEM4m5qG/cVTCwxmXHTYe6J65lxkXqtojG4H5ZGYzFg1D0a3tyjkNU4LygCAx1iAmpnMCqGdh2oEXW+JkumVaiPQPTslE6/LKS7lj4CE756vkxmkJ05nA0h4E/MSWVU1groqe87N1h2vXbXRQdunxGAMnI97L5FgfAS5H17Rm4yXkNTJFhr6t2lv0138CNH/0iPPvzwstnPP64ssYrcYAsJBCrGiZskMCu4BQx+7Dryo7e8tUPyBqj3QOhQ+grP11dYcGjlkEwVdUnRL9hj2HYsS+CHJ0X/z2mSojv7u1pZJucGsrAfgnj/J83ZCpGsj4QwYYAweF4AoMfYkbwG4N4aJ9am3dFHTsNyZKlaC0r6MqHSd/9lzG88vSE5kkimvJSNUiT0KApTSiDjehl65ej6my7ZUXUUYiA3EzIwSKWRRh7HqopsgCuyQNN4rexiTtH3EMparjkBOUF/lLWHkR7fihxiYCtQ1EwgpnFTHbGe0cQnG6CKLGbrqeAK8ewTRsgazMHG6S1tStpTqsHkmA9q2x/yS8PcsPD8kiSdMYccM4YDzOhKG/SSw6wE4ctrB09bT4zxUMw6HDsiEFNMQGcEsaxUFUJA7wKDm/s7edpyIetNKRub35ZufWeitdc0nOo/4YniDQKRQ95sVlrf8XStYGqZIMMGfbeCIusRBXVoWnh96VMvDSTUuejqjUQP4lk7ogKtdE7esz6NWGI26yrL2R5f0jvf9e997840HnBjRjOFPO/3/jiEHl6KVUmvjf7RWheU3XATraPbxMq+XZpVg9w+sP1BURP9Ji52UxDyfJ8KeVgTY9yB0psymaLuOiUAc744jpPtge+q8zLIq9I4O0m69bQWK8oH9L6b6pveRtPcnmEZJ0X5CtCyWL69xtdfMgw/wncreKDrF2efPpzyWB1MWe1wmbzcqHsW4/OVjEUdDh0RKGmkAnMicPAKZq9uIzOw3ciIjAabVgsLAtzs1BjZK2NeybW29PyNETkDoXvYqwv5rFT0vtoyV8oqtfJ/ZyWBOO+9EkcYORPxAdl2eao0eYfjX4kscCJsGUl62VU8NEvkYofVS07mgahYAOviapokGrZ5Z91JZc9QBZ7o6SxICTutrhJb8a4x3DUkNgqek016AiGJDfMn7LJDPGr84J3MXws5i+/8Z1HLxc4mBb7IMZHLOUfuEzNROyqEgit6nl3mn+wEX5juBJlXYeyJRi3IqYockojYrw8fKEoui/6ecB70RmOsmdzJmXR3dD6oMBequfRPvL5I+jvvACFDFiMad+UJg4JCOyaLWpcKJANP0/bN3kvsq3Ua0aV0Z/A7hINPR5S0Li2d15qgax2GBPFwU6XHezGW3tlszfW8xTvfJ2xdf1EIjEqeSiGSRDPkPvNieISLuTqccF2MQ/IYghBvFNbGub2i6xSNlVfX3tDm6GffSzEJA+HMGO8M+ROc6r1UFK6VzhlmbBNvkEasM0lHXsYuon4NVGt8RXhezfxPSxCJQ/DMAniSejrjOxeUMXMnnZ3tMpzdyHswPqxsBSh45FdMpdulhgI7paRdi5AGmGS5wXDZJhnSP1+IYoP0A29k4x883VBhQ0Ost0N5oG7RwJU2MULENjK9mNLKgDDVZEmi85QfKSgc65jJ/zoFxGUgdQMDmlXb/yGbbd70TfsOfQv6/Lz1hQIABVWn6GhiDMKs4pEkcUgE0oogFtzIXnXxToKAOgCtfZDW/J/wcnDoUwKfYacc05R1wivVP/8ywXhOtN3vqurDBZP53Q1yXXYFiaZxnMEh7X+w7EBiiXE5Ns3su4YxXrdZDXV9e6bGUSZtptXNHIfV8KMmmOxVUQ2kR+KVoy5HIrQEDwnZkATujQ4B8k8GrMVwqqdo8qQ8yVQvlfScBpRfzJ5KhaZh78NwSnOC67BcJCYBctZs7q/zdxvZuF8eTmPbHaWEyfYmzmlGyavqF/4hg9U+4H9RDjFeYE1GAwSEjnfeJ+osy3nQKTmfm04TVEmvT5D9n1XLwv7hOKxXMtPn5MQpzgvuAbDQWI65xv9c/S9PThpEGtYAqvdTEytjUlXtoXpFRzqsZz5ISd52FGSYvN5KU6zYnbMieQcSomTJRGuPj7ayjYuO0D1Xqf5USCDLve6cmJT6GqJszxisrCEeli9NS7OEmYqpbHKHPdtXJKFPN0yBdQm78QpH2971Vc+V/ZdNqNt5QO3QB6mvhATMZfulzKfvr2o+dGvuO3JeemSwrqqQGkwQfZQNwkYgppqOxKX29/Dckp3X3RyS2btht/h1ICBD4fkUX16JRg4toT9B1+3zfShBDeqoh5edKVF2pDP+Ub/hOTDt8eK9gC3RWOCqbuhf9ga+vUkgce170RQpfdHihMWAw+vHbJSY8joZPRPOObM4am4jN6aLhkV1YDtFjHyJ/Paasj3LtmmlyRhz8pkWBA8rHpISo0xpZMTQLtkf8k+TNnLqJxdHR729vef2nIzDPjHeMQlS/5Yj7JeWAw8vHjIio2E0skpoAU1wearLvjWxU/T1GxeH0GiLKMs4bLZG98hTHB5/k9zuv/WAs9L7ZDVGkNe5BuNcsiL8dxZokdvCK6QwtoOMVVvPQhTOAkvlw+tUZ4pdVgSPKx+GNcaQ1oq47AuNC7b7hcNQsaltlbYwNkGCgel7ReUaa1mF5rojf0hS+Awgzw03yTZaczrpCRQWcZW8m8DZT6GxMKIkEDUsDt5R7YjLhA65n0f2HfGh9C4sBZ4aOWQ1RljTidjgKJVqwyL4duwRFz3LB3tmm1kIvi0RASOtZJNzOmBbTfh6AXjMCdQekajG0XbZHO83Zv4wS+i2bASgKxeWilCJ6CJ9rD//MtdQKe2aEOQgpS5tmyoLQhLGDMo5cC+o2xrmK3MS1BBp0UFMg6K/xxLQmIJySfqkkEl8PDCIS00xvy+Nz7glPfrwb+8y2l+GhkZQi6eQEpis/L1wYA/SvVoTsbLk270G2EzLL7g4TM0TpI56iMATpXIRU3ZnORLzkuvm9iaACdo38i5vt27htKzYHUnIYRVoLvuLeb9szAtti3Mas+ydBo1MEkuObBDGmbdgRoqAuvHZAxeegjn0CcXS9mHnJXDyt/hhUJWV4zphW90RNn8t6dzEsIZJxbTYZsTkK6K0EFd64ghb6N4QLFyerSiDKuvh9Zq48ouYXkyTmhWz7DRPJBsBkWAtLrb9kUdmF3zSybkiT5+5a09ZEpx6e/wUiGpLBJ2JGdTIjjawyHuBGe370Uro32Yb0zYxvyyReKWbM829uyPps4xpnJeMBiC2RBm4RsTUYbX5flB3GHhW8U+sc1eCLQb5Np+Xck9e7dPIR/r2dM0LsOdl7IdK/PFJM8XTuiENN0vmyy9126OdflqKCuBW9RDHLVQX7K50P/YDP6OixQTA88LLZLRKGMI7Q1yk7gaFWalKkFIcDsuSGgHl0ZcV/Ja97UrqskZKKyABtp11n+GeBgVL89LsZMVRyNmIOcRwoQLQN7NMtwqBM4aS7WyFkIlXwc79v9ppT+8xz8joYU9SJm1FfwiqGPQS8h5p/Sgep0i/HV27ZzIAMzrVOInxPqqIAIgrXEuYYzFCheuh4lIjREDUq8IfoOdWVK7+iGX9bDoe2iJOK4nx+RATiWUQTvG8lA25+X2IHPL7mLzpqKSMeyKHVaA9rklW8gPeXlc7z0v9WFWTw6pbi/EuNlcS2Z+OfN2MAQ65WRE8LO9+iyPNr6HuGSy649e4H/v8RCmSocmVnEWFrMc2Qgn8yGaPXrFDN5Rs7VzaU3iM2tVVJg9L4VcUvglFKU3StO01ry2+iwbpfg9qRG9euzLh/cGnGrrXj2pRgPwP/QaUaX/UGSAIQmEqseZfdinZ/MJKG/I7Ir1NDmt5IGB7Fx138vKsjs9pEPv8h/0SsLVKtv1LTOjYbhSAWHslU27BGJLNuZSSBykfD3O7pO5NIfv/ntYwIPEBIuBzTL0TdS3A9fiAhTTM6ren5FnCFOcF1iDwSAxX4/z+5S3XJetdpL92ZagSXXf/qohd6nXwEMGcrmfQJaAp713k5jO+kjAlgu5fgWrxM2BXo5+kXTOEFQZ/XWaGhJFzX1tNUuqWCMQDoOblm9DAkxPfELU3CQ5ccQl8M2yP9/wAGyazl2DU5oshrnlB7pABRDJYXBKDL3EREFGKwTXDRuefXUEwsNOIi/AelQkBYnuFZPM67vWIBR/cGfCAv95AQQYgBAy3jhBrqNdpjUQwn7crmspug20u32nhWbbflnUsLqHw1WSiJ8VMK7xnxdMIEYQYtrWC8tLFvxebf2AZUu/pDN593tPX6xzKt8X7cXoMNAdt54fu5gkUk179yzU7fAPMrdgKaTD7+Wotq2ViaJWmfCeam1ZYyMsgVURBegJ0SXBJdghYxOtaIcqV4caDRrVi/l4EdevyCPMuibJgMUigOGf9Yt8qrfPf+tc56UuxupoITdOKXkS35TuW4Sm2XYamQTFRykoAfPBpQsgm0PxHYIGxXw3xo6Dq2hPvgANlNG9nyFIW9NuBCaRxS+Z7i6pBZBnLBEAHecFFmEwSsy14twsNPFwHxbsK8OLYYjxTWyxVNXbTJgytF+R1sKR042Znl3tQrzpcHiKoVkxY++N4TdVH+Txgdu+NfgcyAJjsfVGs+fhl0Ur5uaPjPY3348QYjaHIjwxHkQ4e5zhJxH98kBYkkiDmjZ8ZFauHtR3x6WWSqyr5yfTWLV3IRugDelSVZrS1mVnmw4i8INfsdnl8YxffjK9nQwx579hjkhAjVEAJ5nVPCaS2SBBrK5k2CjHqMarCd0aqLUDd9KMnDc/AR9FkM15gXhCQGiHNLc3VtySNzQ8qpVQ28trGWETZoheR+633IsuVIJt9MiXsB4Vd+ZFNefzUqMmNe2Y5sZZcdiV1/AgGLHnuIp/2Yf177UkVra7I204MiPg0Ier5aGJCevOh1apSU07orpxWpwmE16HKgbPwtK/j+xN2Fat27qQaf1jjxvXu/XyHX2SFiudRiY1StBJm2pdx5eXo19En3xe9MxM/xyXFXkZcqifN+YZpJkD5Fk/j+yp6o63dMFd7bpLSkagCQ9AYuRpn7L5jKr156W6z9CAmCP4ximUsHlmm/ey7roHrwX5GnssxS+maTQm1CopeaybkJ8+q0C/0JaEpdLzUlplpdiY1fbGgpvoi2vXRS5QbpMqGCZZxVw2y5/LThCebG5gn0wfEpQCXQIiZmLNBhen4dUgiSEWyjJw7UcKYNUaSLfVIB0CFKC4P04/U2dp7sh5b5EIrh5ZEZYCRMIl4lokSuq8ayDM8rmBItFUCpek+TD1b9cxqBWVcAHLUd/AD5lehTjJoagKgWBifinnoy7g/MCRbfxfioCcZ6BgrF8ZYy7dLncgK9lyCQr2j9EZwarOC7bFsLCYXcrZqLIUyURwIEmSQgez0VgVXhs2skFY3tOvi8JScq+N28nl6gLR6EwrLR2CXURJVabW1QXyo9AiAKqtGjLLsEJnpn0N1iuIdVnrM/DJSNdhPcFHo2jQhW7ytixD6anVbl2H8Mlt+UVbcxkvTWOuCZn18r+XwHNMUxjK5P/2V0dO2ew0eFsWK2dUmVNXsoDc2Jz5CT4VSUCUaiLbW9WiNQygig/hl6NuItQQAisNQWZuNfqkskNSKiaikLnZ6oXb/0tb+JfyBN54BRt1vq1O6mtM703Empq1pWXLbOefaBDyNL5AbUz36gm6npJIUBA13wt68Cv+ntbsVXZ0zddhsO+acO5/X7DEah1+bq3z2RwuGAtLLXQXiABtWUSFPmvT7LAmnPzVW+59uETD6wMwVIT+HIoVEWQp5jbHRGhQjdquHr/hFMX50bD09c1b/kBfLAzS0HLZ5r08Se2Pef/XXjfRVzwvXQ+YF00cW73FYrMhNTGEcXdb0OkgiQfVBwKXCDU5LyALA2ViOicjfzY1UZhWhOgF5Xk/iwQXSYsTSztJG+AMYzUZMs02tCSj8bnm/7LN5H/xwvOCL8ZwJKF0cwq4TGL7I0tGINny80g0rL74WrUBm9EvK+MdBpgWXtYHZVziMEQYuq01dDPGQi8fJnvy8nL0K7ZWtH5pcPtAVWlmtVyo3/4Da6p9yxy6n7q9INxLtZMyNjFtTmDjpSOiUcck7GIVlKY7LnpWm0Kd2FCK+O8yqBD0Yq+R2DD5+bGRTAQPQ7eIPT5UBQ0Rt/OC0DFEL6bEv1Ho4ZnjZTuJdrzlFc6T0OjKW4VPi1z1urLrXYxYgsQH8SnE3Q5D6RimFxPjKYteftqekeXsikj07UhJK7W4ewkptEgJ8AjB8fZamPlv36UbJKWh67Pk6gqv5/TdJvXlqLqt1lZtFcXogtmWua1KmJiKW6VKROPOQwGpY8duUpt6T0UmEaGlhHkdaT0Z832BOvghvmQEUZ4XRJMgoLGK4E10MKFkcCRdhni/Rizgshb7uhIHrHtVbCE2FJYsrT99cGK47VBwjkB5MQ+fs/bl1vfNfoCr6c/oNqYcOTOsMgccuJCh07vdu2w4Tz//X3lQRLWc80JBY5S1mEFIGYcVy4LVAzYYs8PKfGUrxDyMS9J7zh6eLPXRV0s/GM3O9dxvftmh8b8g5XkBNRkIGrPx39j7Cy3Xu4eP0wmrqMChuNfdpiwPe/FyXcl0fcWSUKCVH4ZmWNo/BAdgoEGspWDKC1hFLPeXrBil284hswjSAvVSk+0nuZsBaGM+HeXweNaZI0zlcAiGITYxFZpTpydMlO88mxkuin4eyR2qF8uTGhTbZUs3xojuI2jR+v0MMUp8XlDlGIQmhH4uAEDbpVL9piQ23z/ngWvY8K9TLWbRy/aMZlCayEBR+ai3RPjceYHzCPxHCNFvBGrZUpsPJVkIyjWKkCioYEHGUJL/KVf1Ib82G0vyf49mZjFGeiiiSvDXmJXOOewdTcvu6JW43Jt1zrSnew1WoCzLLglXzuUfYKLm9IiaoVTWvFCyuKrFoAkVjEfN/OgXCVPPS1jLwuAJ12+r/WgPzPXt7o0OAPiq8ruEkm61ikEDHLMrJUj2fTeUpa7imL5DV7eOrLrmR63gF7QJdJ1RvfrSE080NlLeXAaAaHb0Q9W1VhQJS2u8mEWLX2guCDKGvRcQUW21lvN0ADJbazR5amnVrltHViAaPRNL/UOw9pt6f4SPnRc8jeFvMZX6jXotf+3bUpKNJ9+OdpI09GwsTwmPPHyAwd/SjpA6rcEkeYSgv8kbQxKavQD5ETVZfKGNt2h3UjABlas/gU2sVO8Dbeh7h31RGRi2frHujazZI+W4RZS4T9T7I5T3vKDCDEWOdBRcddGgFUoWJa7sTojaC3HejQbEWnfDgDyudo9tOsyOHpRFyXQktNTnqypKlCCx3I/PD37F4NJ5waIYdhUzcxmRV7eEpQ03NSbtt1U55n6qmhlPpJ9uStXV475rHDQR8c35+PZjrKSr2pKVSx0V0BVse5345ehXxIYwoEsZSNXK+GjVUfy2JSNSyEniRlkP2n1IrTBXTRllGGf/nRVYWEGGtsaIOml8oEocoeuHQfEEt48FBUx+MGCdfQGfCUHHspPI7EsOcmVUn5w/sJcsMx7Po/T4NMuJgOHzAiQz4DmWE3D5gWzWsos7bWIggr9iCJkZGuYoO39dbW6XfUuXSA1oEDA8JBcRvHde0MAYPWSk8jcS+pbYqlnICjnS9++AKpdvSvjiRpzRkNWK2bq5r6fPx2+azYRNTs5LUxTSRGVCbpnUzx8dqXazIh9mmBppbJ09RdKtfvtxS5SrvaOBhCG+7n4eeY3aoKqbTYdLtfHmC1YS5fE0faZPzEIY7kn0UW5qsdAK8HxJNiWBpXEHrSqgEysUCFBaPifxb3QqtYmPxg6agIAkLh9i2aCQidrUlEHuZGdNtCwnao+SH8F2zwsWzLDjmJP/QuGf8iKK7yzgZevPXfFH38/QD8KC0wr3Vdkz7YkL7uu5ff4GdIlwjvOCi8QoSkzlfGF+LgmGsz3mQJHyngY0f1XMTe08lC90I1u45hRLrQjKLh8ZvBG+dxgYSJDDmJfPWfwdYmsbjB1eLN7rdNY57nfvXheYCNvSnPb+FHJ81r7+NmoKizWHVXZIGSgWc3HpF/CpaR9W0hXTMC10q5j6ohEvSZSmgQq6h3XZ160UKKOipA+FyiGodV5AMAaaxTqGN93D2qXeUGBfp175HeYLLicGJcLq2CuroYpDLKWq0eJP0SkCZs4LkMOAn1jKwKUPKGIsZU3gKZoM0n7Pk5rV1xeYUsOLXR1ytXrjIlA0n9NWEnXtNitrkRpj64J9hY704Fc8ag4dY2REhgpaoraNQZwI8dHrJfQjagqzQ0C1x0cGb1zcOy/FQFY8jGUPqraQaN4jPXXRHfc8El9mu25Kf6gWI8TlcICGAjohj57T7tE5YIN/bAtjsXhBjVhRiLKVDT7r9XZSLQMBoz0Cen18P0IIFB4KK8YYZCjG4MqN5p7lVrAxfAFC+S5r/fACSbfeRR1alzWG1z1nqn+0Fvr7zDmq0xxe1mFVoJjGSVmfA4H+sH6SvcPoz88CjpeGbogB5P0Wv6pETPoGNaEe+UNFkwhrOxSYIzBeqJ9gYgukI0NpGRrBeS84JNwoGQx/T9mqlKhLyafO/hXWbg9fvhCpOC/ABgNCQgL9G99eMiBle2nQln9OgwCo3vW1lQu/TJCGPbCU3/+Qaf69zD2ushxelWFFnJjJ88b8QZeFYpeVGMEtJwfMnsy6EbyqWr8biC50KVg6pKvsgvkzZY8YJzwvuCLDIWMVCFWNaKSD12dTHYru7ueZ7eLIkqt6ix28vN78bmQi9R9yIAF5DseEGIQUs+g56x79sXL2b7u1d4CdBiKIZp2goJC2dGxm9UMaPhQkU/hp0xojPYfhQgRFIlIQLhzZkF5tDwvn0rUFFNmpxkA6M1uyWgPKgE29bSxEl1H9EFlIoITSHvin0/LDLGMquUz85egX0WxZ2RUGWMXa4Cne9o+VPxGGaIwAx5Mxv/13ZVYvrSFDJo/99h9ep2RlzVgRFsvHPjD9QqLCobwGSoOIyeNvZHOJhPPIPnTHuH5P8tpGsY0WwKD7QyX981186Obn7Aur9IfW9BkCEHL4Xxj/Wz71timZL51wo1nXtEUFRaDloDEuKl+4emYk//CsGmf577QWDl9x1X/U8c3T4ge/iHrDHEnlJjRYzkoaT+OyA//bnexf2hGMdxAbiJ/0tVSk0VCi+nlW2XBiQFCbUfyoft2GdtE4D75KM/Dqt7ZWEQf4vOi3mN4rJuW8kXh08FoBZ4y7ThegIbozqr/mVnjXrouOht3SM4lS1of0YWGp/tDCPoEBYv43JYvD195T+SQhgVYlUTfe6gtiG2W3SBjDBH1E7eelxoGPB28wV9OseAOzwMctqE7Yg/OjXyTEPjwiJ/F7jGS9IV9LIuNs2XAvTsijO8IAva5qnR41P3Ub+NmyOvy0dFNCnc2+bLnj/uXoFwF9Ld3BfunselQlXSEM9wSJLPC6tOGCybHxGuXnjhmBvem7XzWVMTPZMwGVYwz6E/TyCN85L3gQw49i/vcbX1yCoD78ul0+6v1dUouWLbaqBigaPoWopXkK3z3M/nW+G9aWz0stmpSuQxYSJy1tddmqNi9WBjvQTyNjYm6bjV0WXytgACFNZu6NhRiS3U9Z20bQ2HmB0hj0FrO/OVscpKGd0g25cyq306S86eFF6Nrq/Xt4BJfiPBHI5PP6yBKAq5hzxwJ9UD4LskaNRbKuZxbEvLGod9wPj3fPC51bUuDzopfUXoGGwsumU/pn6oYhLHYoiBZDbjFtnnHsYRYie4/FnsAmu58D2j2vdVQZLRqqNgCyKjXUDy6foD7rixEidjiARuC2kABO6eK6katDh+EjvpTJWTSUtEkuK3nOtz0nBFHFnyD/8eH+esGK6iqHl2FY2YbwrjhPCw2ws1q8YwVyJB6nkcVw6vLZkeV/Cykr8GZbr9ZE8+nHw//CyiAMEQ6PKEgAQsowtGqDjH+oqgJw2O7Jm53RlDTKYD9Qn4wAwUPhQwI2htIBKjNA56Jyh3zW9wHGY8af2IgfEjZpeRLSQ7Pz1QFf1noO+V9EqdFnOS+1N1ariwFjDjB3Y6dnvewC+HjVgRI1jmmmrGNi4Pl1K0JC01h2oKmfa4z6X0TwvCCIDHEMRQNcYyB5xzQOmOYd3p1S5/pSh1d8f4karksc6EzVYjqEhusx63+lAYzYw+eFbczYyVgO4F6qv0P2/R2nLYApXXdwuBd/029kGGoUhM5MkiEajMHoOkgB09j6e9KBlJ4dGP66PIE0BQ3dIDRHKj+cywu8GSjaP1pPyrmvb8qqpBL4c/CXUfO85hoZ2ziqGdhqZ/a/R3pZdf3ISEZXzTcbAUcSVaQKJfr0agY8Q+R/tAODqtOKZztIU4zvgT00exPvT/jvRwjeeUH8GEIYyzZeVB6SbGuahtikzvbdTjRNjXa0cAHViV91pVa8MJy39q74wecjPPy84OcMb49VG1TlkbEA5Ox2vKoR+BadyMrgKVhGCtbudeHf7NNYJv2zv7FsyUt3Z5C0su7Os137Cn7wK94sz8veSrbieI19W5O3PIHWcEDlRIs+/z2msca01w/s3RGmdygCGOOFsXKKy6xkUGvPXoze7vTGZQXxbEQJWW/NsGuivonxYH+NWshj915TbR+SuhgWi8r6Ddn4wS9SMjn6Owos9h3B0++3MhJjvnENiNeMCOjACzJRAeealkjCk7WpXdYGlPBqH+m75R0/arBsQ8dHGWhA/tVSzHFZedHZG8JK0uO6tamzB58B9iX4fBdPxRxGNlERsOfLUYfgJIGnouVUWJxcj5PR9IsjWZKPucdtKSKbocZMsqhWtBP/Bmar2lypPcpn8PSQvHAo1YEQI2KRFJdU4R00n2Zg8Az7WfY2z9HkjZgZCDa45SxmCRTBH3rYNvymY07YA8m6lUjaqG55oMh2Y9NRfi4C/taUTYA+LSP5TjMBCqS1zKK1otmUuc4jTZPofenfZw0q/DysxVLUkukT+quosTH0V2iG1nAZTc62fDVrYBzptf5lAqk3PdWqBa99qj1J/5Z3pYk1Rn/HrLezgOpmBZ2BFpHg8z4GvmTf+mHBHtCRjc6bl4xAD37FafHhSTTLuWOayxstJlxRWc26oM+BBslgqqVV8ud6Rv8H+j+cKUCZBbE6iKuJUE6Ts3nBpWj7bDsPsJhk26Ms1itdO01UvTxfqdoM4YeKEIH/h3MFGLUg1km86Sokqc/f0Fqe/RoMlgZM6R+jMcL6xRcCWdhr8kqyhIuP1sshA+BQvgBhF8R6FaZugXUkKgKeA66mqcRQ2uRWAgcy71S2XxJJah/+tH0+g61f+G7E4d15CQdZ+NhVL6vv1zwh5zVBhlho2e9oy+Rt7rHbI1fV8BHNZGwcvNjMRLY017S/axsBPOyomh5XcEj8Dbwc/foCsI5BYlCSGiIj29iAGwqqBwiwYRqnz7OxmSZ9LZrcpaavZW/1kkHQBQKgTC7jayZoKzdeqTb4kalmKA0EO1ZQBbtqFitWbxRRVQFjcmIwXuya8t+lZAqwisrqU/sr28BWqgnqElhqigkpbO/hR7/Uv3yrfzIWe3lLysDZmvppCRtrvSzD5mqOFXSpmTgUMxI5aKAo76nJcoO1BG7BssZrJZm9J/JWo2+gtusAHZG2D3XJqM86CZZL1WOMnqvur70ouGBlAn7URL+yMyY8OfwPsvabskYNE5WvocGjZHer3fItjF6RYxWk1Ts7TROLX4b1JNzfqnp/vxYWWCEiqFvYZcE6xgKOBF52+vyIOn4leo14I4fTTAgpJdaFcR0ZoEDU1i2MrCMZaWdjBah5eKYFo/rrftlRH7ZlTxacMh87T8jdOZzqw5hBsTiIi4m6inWa7Z4VbXiv/WWvVX0dgDigN1316xbZReb2XbU+uKgNu2FDw47LuVQirfy8dipe6QDkZ1BPpBWjCi2m5wLvPq/hzCYZh0ptQmGnastpQ1rb0KdFsCurmA0TmT+PpSfmYBzK2AjpHUSiwwU98k+13ECk9O7KInSA90Rd0gytvqnzo6zDXmSS5/+D/Py38onY2e9wI0DmGxhzdDinBzY51nBKziOZjysZCypyyUrVDY0nfDgWDNOMZ9aGaBJb/QEy/HXIHgYA5wUDYJhBDNW/QfvyWpK+R3nTMo+d7cgCjDgg+QQVJKJeHE7VYNQOolLiqqaCFcJCMhi2pjmvBeEC9OdF1i4xa/fryhqXsoXCG/1qPmIaEH6hw8kajNxB6MFvdGI4kG97TrlqsQUcKnqsClYAhf/S5Y7In2uWN/D91hNi+I3mMypqn5caOCmZxwR/qgdQkocScoEUyxCyYd5wB1t7Jilm9W3Fi5aJEyRaQ5Zlpn0GYoiZJ+eFqcKYLbG8jcvhNIVfnqSVnpOX/1Ekq7t0H/6lFb8suqe7Nz/A0vZojxJyEc4LdyGmOsTCqDcdFVzuPI1LIEVdWZesYFaXRjl2+CWXOwqpNEH23p8HiEGJw0EMCnqE6jSI4qDTyx4kZEtQ9CzIAjxQw9z8CWZCgsqhdBZCfon1WlzdhdaLd0zImqgxlxaKttokYkiUbcWwjubj2rtBhwQsTj5jBhgiBYfiCgSFiGW7TOQrUUiZKQ/fPqvVgKGslTtRxFmih6KkYUU4ZPgmfyEobX1mLwwJDoexIWLmRKzV4squbwxQZaVNS4AoXXiJG7zDZYLzriWD5GRbSBD/+QROg1IwKBEaWIzVe7pN/tblNrb/WYuFfznzJy4q8iIkI9oyNybtdtTdDLWMB8L6q3KJLC3oJLanUQzk2VFgO/q75ERa5+qIz7Ll8DmjiWZWjZG8oQRqvC5vsvBswFReOHB9qMzI/4EWWMzIA9okSxGhuq1TpeQSUaJBrtUs5TQdu6j5tM3SXHWBBaxa8y0YgsgSWz8D1UScjvPCAGGMkVDeRcVgyjbIO3su0xymt3Ks83pgV+cR54Svfmnbt45txJML1/SiDKIJ+Zq2sJT97j47P2iRDyyz3bOrFy3fHA02UJbGYB7wc5gu0gn7EPxLff/f+gR0yY+QcktqLAv7DXIYBoiicYGOG1CiOi9+yO8jzM7Oi0KDKDpifRGXIyHOKNs3AWwkNrkqylqjzOZRIbQzftmC/chuU57HeDp37ZMrTGMOlqozPcHnzVHal6NfcAmQoFKr6w0a96y6Q6QBWHUNUWmQ8qqWn0j8QzNlwhkmBOOAjKzXAwnDIe6cUv5MyBthoocCqARujdWERHsIJFC+8XKkCy5czU7Swd7snkvX3R3hlWmbsg2+Cefax4Oj5cXSErAEGm1o/b8B/bCvzY9+kWr8eanes2R8wS2ujfvNR3JzM/TJkNvt+rts1U5Lgt1Sb93Z3Fnup3y3SCRumpH75gcYlWF+dV7ysTh9I0TfF15wGEtEoYetj/DWmlYbkUil9SfpXzsP44wJmw/U4AkPZJx/evDLWtbJzGgWdID0YNaQaFnnjXoXVkyZ8e3t/uJg5C12iZ0nSbtktJ3pozk2NOTNf6inQIyInhcElSGusRzzTb45JAwsDgNWiTIMAVb7LKUnYHGFwfr1npQgY3muiFroU64rMRGcWBDQKZgDCbja4Fu4x4/KG5CsOy3FI7CFywyeppZPoAH0hao2AJ6NwA53kjDqdRvSrtOyJxjVOKEkgAq6YiN1bdvxw2gSfw4MWEuWikRmT2Nxmo5guSoWBpmHhXtJjdGQz2mBUmbmHy6Jf6f3DJOpQ1MvkqjFhjbc/ibcSYNtV6/YU7UCH5qTWUr/AZVnBEIfClkTgDuWYnLhpsRbLd2IdXoDoaHbkZsSFVC19eemwvrp+X1qPT9woZACcF4oA4xiEEvpuPQONaXU8y1EVVfqTVQdtO8zhkuexbjXcEvBPLislTXa4/PJ+G26GUkMmrUUi9u9AMXL0S93MlF5vz6z7K+1OJNuz6TIzESarI0k/iXLPF9Xedt60uYemhv0qpjadg2Lw7rbVpPpl41sVEvOz/ZfMgJgmgeybAOqhp2jguFlb4Af/YJ0bNVsFqsw7wL1Gci27DdDVgzrwC0/237D+nU31JnUGkExD9nM9GeCgxPUPIDY9YqSEWr2N4HNTOt38YkNK0JSD0VeGVLLZIRcdghuWc0+5yaYdcvPgyJnswmcVbJgl5W99TuQ3bJ+PVxRQyT1vCCvDKmNhGVvMrSVzPhW3c3mmteCUk597wZGMKbiqKj31+RFJ6TJP/3cYzj1UPCVQLWxtI8LAede934mvDGanXtriGtfANQ9RYcBlPbivm4NnYmfG+Zfo6NhBnBe4DsG94XoDcN6ACNLQqibDexK6pWvsfwizkd+XRsMW/2dl9aArEtcDN6+gb1Tq/H697XfsQ6PjubnKSq0sPAKDY3XUE+npNWHzyTKERB/KGpPMP5YHMekdBX2kbv6pB1T3ysQxAbsw6bs2EbcwwoMvqHFNbJDzh/VSYDBnBfAhgE8sbCF6mBkd7Dm6ZYV7ryvPmaUMrcFnllWK+/hUmARl02MuYCYPGzNQhj4vMDGDGaO1WlMy9ahmlu+wHd0xcl+loqA3+F9CUEc9EZhRu7VvoGMpaetGcy6jXhQ5EL4h5o1iLBEhR/9irlzhzHtODGvIB8a1arYMi+sWJ20KQECGBhSJLiw+1/L8gICBOqe4OYYV0POLnFNS3Yz2jf10v4mypvd3ajr+kP39deIfLQqnhfAnAHsMUeWc2obbDGzjd7+f6x9S5Iksa3kvk+hE8j4J+NO4v23Dw6AVVGv4Vx05ZjNjJRdisiMIEH4B8A4rjCtGMy1GmUiwbB4Pl0wHrq4gwWTLz/kZgsl4H1RjJnCHBd03QrAZu2nm0uXvLac6yTsTXswz/B7SkCyunNg1VFfLWlDvXJTdZNooXGNBinoGP9FvaKr4x3lQNWu0QsSVvvm1advdJ3cYiFE1u16dRgIlLdNVTqi6cUFVbz86hnKB+qDlLeVvEGkfOXk33yOx9CXYrh0oKzsiP72rkfyyb6oLbE4E9vjb256WXrVpQL5VS6WoLFF13FRS+s3XBBUl3w1+yC0JDkjXqkK+rIlpW/7QJaNKlM8VcNI9B9Ny5myuiCcTYx89XiJxFVWyTDGV17tMBdjiCk5huOYDyaDqX1xhs79yfNgwXgENWZQwTGAnE3OpZQ+EzNCKn0z4p3R9LEtn5v4Fygwu2Xt2bgrKGZ9DG+xAFOCNc/COSe5hy0TtGNOr2wlFH72RShiwlJoTr15WeXAy+c7ycpd/rEENPMygfZBywi/K4q1PIuXnfMuuojJzH0hPxlZGruEuat4wZD+OEdftHH3uU6R7NUcmZJhZssJcN8yT5YmOf16g4XftGIN87x9yQtJGhl7vW7eMDS51r9vCA5jnOugi3u1Rt0L+XH2NHWB6NDLyI5NP6R0SX00tiwAb20aJBv8TOy+/OsfcsN9+YLsB8X6B9dLiH5BiTCtTCzFYCQM/B8CTJLQN52Q6VnBQo3G/gOzv+x3z2fAbGieGLrFmUebG7plG6THjs9iU2P00qr2+k7NaO2Aj9XI3HP2TCZbFHf/d0TObkrlEuI3dmkTS/eD6Gy1yjgLdR8Cy6vv1tPQbk0WtVSm6pRCpV11lN1nSqYiEXJfREtCQkxMD0tZ00odXt/qV23qalZtgaNweKXyROJQlcMD1Sz4r56Jj2FTDtLE4xMO6IjS3pwBJ4Q5MShzQzMGshfoJ3r85O5lJeAwcn0835YYur5GvGeUHnlAL1YrfE7QiNbbFxqQ0YaxTTP2dDaIa2uUc9bPdtylI69kn+ZSfFYOOM5uOTr25Ht06i/Ln6K+JPvSx4T1PcGcODVqYM4jKMR0ukklTJK17q5IrvLpQSCLtuBZgT1dY9bTbRijYUCDSSBqCHLV79vzVPqqoQ/XzO0z/aFCXnpfaGxGe8cm44snWWDbk30BSwrTTvM/We+WweBR+wooyi90j2xIvN4h7N/pgpBF3JcSE1aSEruMbq4kSaPOaSIpbbLjWz5PaJ8+jDDIPoED98VAHWV1lWay5r4fmPsX9iXavI8R7XsEUrVayO9qXE6n2rjjJ2lRbUM/gfPnsqYetdMULY9+7lPK4qlmn1j//5+i2Bc6g9AfxAyuFnRJ38vjKYYswNOPp0q215bHM3ijXieyJFjqQpBnWPR9IZL6j+H/+IeMmN36OcgGNQUJZFUh5X+Eo2ecOGfQGwwHTcFIhuW+2plW4OLGEO3/fBnW/KaqhfhNx8rjM28ylHc2E4OIdESM8cxGjx2zUBlmJ+czNRzA5NPHcFuETnYrdkv5UslX2kSzjxd7FBH1m7H6RAIIbd7MEf5k+S+2Ah/4Yqt9LJuvdNOgZI0WzR+QNEnIWR5+BTf+aM747xV0UT64uYjFNK+YTuX0K0Rnm6kuYEsH6x5alqWbUXp6TGUDnhXTw5qO186ge9xDevnXP7B0FvDPJoZJvlH1pWv/PfmzrltE0FzWs5f0e31QPqK9jfQHSJxNNy2fKP+RT0BviRUzzSaBdt2f6vcRiSL7IqLEkkvs7r6YwYGkpjdTQTHm6Vkosc/5xoqT7twTB/nwpLnN+d2TM2ZZN+NkCYMbF6OwyhUg5HZ6uBSUjTx2jQGHuqUMS3bK8DtK6qhjJBUJvM7LmNvelAmPafPYact9uTB6euiT32/ESTes4+xOgYKiDmEFBIZuJEGTrfejSOcXpvUwImyu3zC5JzYscYMTzqNeqr1RgSPVkyl0Nhra2gkLX/auj9ztIMcsLe1oDj4/lO2E1Pq+MPGMuY9dltyV2fB2m6f1oF2++vJ09FZzc4McoNYbBzEN/T0tPLQ5LAE7+W7W/BVsfsGooQSjRTodBS//+odU1Wz9HL2Q8ci1n9+c9kvDplT/o32gaNsoKLxZiwun/TY7WjKWm6YEUPvRZK35XfH6qyXfyn1/xM4YEeyb0vGEvI8th8ygiF9iTY5gLvQKP+TwmFvvUxOxwrznj7w6u/RAgc13nwJCSu8LiR2T3sxxdTFowQbrfvUukLR/GbfQM1u3KKaqGNOCuwpmdQYaQOC7Ow+RBzZXE5j4EBuuuEELtHjqxR6dpEpOi1cMohnl2GZRYXcaeNTq5+rALOo3XxQSpJvSqYR8jU1XzKK1NAgOi58dLUpOAw8JWt1Q8VPSzH7LbFZkfQUDfv7PHB8BO7UplRXTXqFv5WZzkQzIhiV29BRoVl0in8vCtrbUcGQuC6dNvViKkpEIJ9U6PxI4ImZ5xyx0TFjHjiNuTwJZUU74Tz17LxS5rz3RjnqpaZVD2nrsrFaJsvNd4/fvFjZZ6DOjhE/Tfskbkhn4UPtXMcJSgbCkKaWfGr9YNIo1k5vGEpuTmfAMM/2j5k6giIyCse/fL4A3K+5A+NDjsKEoz9/65V/NPt0xg7uo2yd3HTm2XaK2meZQsR95hcc+HXYKBfKV8DOM7ZRb9na6OsXNpFjzqahZlatefaEyD38iDzXnD/H7IaG+LwQ8I+xDs9fNGzYwd9EhCcpmz5hn7SVmiaGkE96LFpZJSTuK7U90Sfqx8f95vneELTZFIgS3hLUZtI5DUkALKPgYW9w+RjRIpxZkGGeEbjYVMF2vrPTaux0cGhbq3paTDPlgkdU7Pc7zf/xDWquai0yetAp3MLxjjuRp3xKTlpimMOCE0HEucgM7jqfW5RzELqd0Xsd1tlaGzQSQPeM4K198RdS6NW71+vuqAZXdq+w0y0slFrdZPPSlrv2Z9EulaXR+aKH5H7Os3BwuHaSc5drPQFblgTUunkZX06z+zKG5RR6fOe5iGWJfZAuicsRWt9gWh2Smu4KA1oZOFWOOwXrG8N0zx9cNUy0O4TMOyNdPh407KwQSXK7nW1JXsR96/F///DF+zyxcBc1XrW2+8ij69fUMQ9sXnTUUVx/HE4gYfxWzXSE3ptViCO/Nbon228+HbFARabspxUsI4cgeyK2E2D7ra6CMs8Q6uM2DZW1NFxjqQ9cXsSCP620fCknbTSnekA6OzYHMSriAzQ7EkdWp7r85tbmXa1RDrfd2P/ne3U+FJHvg7d6KqLd9YeoIsRc7A5mREPOoJOnwfnMluxlYW5I9fdmzHyhSnX7TImHZD11J99oPPPGPEzSjFH5fMn6GEEJHEs9FWe4a5bp2WwwMbVZOgSjzVt7/mfkPD/RNj/84Vwh7HNB+CGH4ioKd3g9uA70I5gLVH92RftNFIOxBDoEO3vzeixlpEsxFpsRFgh4X0S6aG4bwKumPBmPlXASV7tZmQvagYf8CvmOhDYmiDJtr+6HeEX+TvZtSwzGRTLygzDmqHlAnF3tK5mA1jFN97ldbNqC3w5qznmLhS/bUHK84FQrzmwv5TPgnPlDuGy0o2pgH8mK0bvHrIA/rFqvk6j4LomopSM1OcMtL+NHr8RftH0JxbF/ENCa+xdz2jQsXAGqfo6fAWF/t059lJE/W6Rvt+A0GJGhTo2X9rPVevv8+7vhv7Xtfqs1YdVrs4+C+D1QBwGdr10HOnv06fSyLfLP6OVVwgsqTso+LXGR8SjaJ3s2+6BpMBwltaDfX2pRfNXynTm8JMh8rFSz6UGRxtHlUFvlzm+EGjrqu98DPX6kmkUyxL7IGk0Fi//TNbw0DmE9/SBgbXf3zDEe7LSMQJc+5LwocfADSXEgvv2IYyEF5Un4TWeSPdqhrGbR8r57jpZX0qYV+XGqDZaZZiC395I/o/2PuXeDiblRxR09Fw61yywQuMDvUkYz0jWn+eYpUaFnaFxcpswHEoYVGIiyOR+XU+WjaMs5liCMqMlB9gLuNtJbNhBmi4sQWYm44XpJeezR7sJ59UjnIRmen5cEYO43OSkj1fdHKIf3dnJdIFPsiaTAJJLaQcsOpLJnq3wnFjOuMf82WVCPArGS5edXKhKp7BYOTXLU4/UkDoWhfdCUiQ4UGSO6XrJgt1J/TgzoZbNXLQC+y07dgsl8+d8XzcQvNAyT3+gkBCbE5ZRGxG7HjDX8raM1xXsJbPFeQeOO9oASYzlcYi0SfTSWiWFAiVixu3BIolZxnXGWaa7s9qJgsHoVxPX2WTRss1P51aqX2snlFEsamggeRR2I7FjNvoYTm67CYBv71Ioi1pTtylFO52i3lvHnmmYqb1rsXy79zqmEh8L4UDpM6Y+w7hQhqoW7TbNZhbyZ8jl4kNjYMXXiguhe/TKs9KQMLMqANvzwqwTv4cDCWD9rXfch4ETL3+8L0M2UgNiVRD5Nq0FaCgpkJ9buEHdqEcx1JUNQ8ty3PAUEV7Qe+FfiQ4NmMDYqpo9iWxD1Mj+ApDwO1NWsluTQ3b4/xAQvHktfUyn7SknINbbN9mxQjVm1TBo7wdbEriXmYJmBC7faTRstWiwSPFeid5Zn0sqGBqq7plrATZFiE+cxUuQ7qY+lUuTp9sBZmUGBISdO5q5hBWE6H+iTPSesh0Nnt8YFbkG/lxQEBoFFfkUQue9Yih0vTrmWgepsre8hmEI+79R2VQ7uf6XRVkiWfhoP2NPVUBgFzYrlDNFjzh797wdZq+R0GlGh+90CWtOSP/+sfn/darL8oKjx00Mz2ea/Z+qooTWjIjRJhscjKRVlWPUyUkqZnVnLNt8yRPtWkN2I1NyVBGWUaG7JuBi7Bk9XTto6WL8U/z2hya83eHszmK37bhfoi2+rwbb28liFjswm7Q5ig0FnEbUgCHm3euc2dtGLm8l80CWnG2KIKRpfAgPbapodY9Ix8d9ePeJp94XUYDxQ7cm4OniVB3YfbI1oa0VDRygMlCUYozGwFeBWvK50efChrGa8cPISb+wJPGZyNvVHUSoXRxvJoqof/7DWrmKg1WvL4D5hvKQMGcCGRsxeBwvTnQ/xHkPbvC0gIIQXx5lArDxLybo5nRKpp3iZcZqJbinHTFfun+k3R82Xaiy0IIB8qzI3A+qbQnhABsSWLG7hQK+vHtfKZ+jGyVznVvXGWwFTd2gidVZ6rpzLjZX0gXNGm1BJjooi/gvsxwM+3dtIrmJOmXwcjXr2c21fGwhkuubF9CH9Ee8W+CDRvCrEJII8dUsRPhYxYNoOHP4lSs/lF5FfU5TKMQGy1tqAGuLevXSehpL8broIcVa0Zj0m7qPcJ+Gl9jPi//iG5iFWHwIaLo1gdA2WcKo14YG3B+CPtudsxR2Nld200TfmKNUqVyFHshAYwkowPJyOwhKzbcYbxxKlOnBp9oClCRCztCyPOGPSYF+U8Kqq6NXUDOIHKmv0yGAKzjAOvaMtwiPuCUpyln2eU6X3KhRGRFftCbjAyJDZLUG/F0OEMfnQKpm1nuo685BOo5Ry3jEnvins5HHm0XuCLrYg4gs0YBcY/xOp/7BSoSrH7YZqtikovgSIqF1wlv0jdbygroHiTXTma54+1+5tu4dE5tS/nGjkGY/35pleHWX0EAuyUheCcTQ6Xs+OZn5EMQnZlUy6GMDexdYIbLeTp62gIbP9ebB9OJLbNMzuJcjhtlRRaSZvI6YuXJ/amef69oi8SoPelRpTVlMZSFtW9VvHwI/n1TGcaIIaNdh8fA73DpDb9uU+1ldOGzhn6LubD5BKV7wUda0IoPw3l+1bMx//1D7EB7kutMxEC8AWfot47JNJwJHuczfg92fR62dcGzTAvWnAJznw7GU6xJHcZRq7Ej8x++puX2ZTFIZxP7OlkDlDMAppqLNasFzqVXUR2yMoWfNZAbZPdUuJQT5ZoVXmbn2o2HNJp+0K/EbYudhrdnEkLQdu3vGw76/dkHTeN9QaPKMv1kITrwZRRR06zfgoh/b0x90W9Jmo3MS/czA7yEEf2E1lQrSUw6Ngh8ND9O5JOu49Uvg8a7OpjqRD6W30LjL/x9QRVX1s/XugTr59i5oVlkqEL6H/UesOMOrKa5S3jhIV9Dz2tp19FXoXO48BJAfDW/K5ICZeV+Fbo0+9EfUnk0NNcElBtLFBh0/CYf/lXo9jkoTb1RWsFllcVyiE9lj6krGqjoMjjFv57eNL/6LAiPtxo/leWOrz2kt4AoNtjbBjJ0uawy0h8a189wwp8x91aXWAW7We2f0jqbkoBE8I49h9xt9IY3ui7IRWzcdsdIzXSsChZGwzadschWLFa3JNtakXJh92JKLl9ofAY5RebD7hZAeOAs2l/OC5yO6WisMiUdoiMZNKM3BY/vrqlZiJP/voRGFYJ6cEijezsqe3jGsasyUpwVVMClxZ1hk4I6j/gbgU56ubyr49cQ5uN4VjL1ggR0+yagsqGWXJW0qPYoRpZ6RA/4hU3ZSEJZxmr6ExzB+Wbmt9SgkLRh4nJxQWdTPRjdU/aHTMcbPWE2efdIL1AigR0l6tUxbLgob7qi/i/Wot0GOOw9Coa2klW27xFusRQrJgKW01yN2SkZXDtgGoN6CWzlo/nk9Xowyz0MopksvaVrTmdMcxdVi+iGOaLd1B3r7CB7hQ2I6ytbhVkyjBY2OD/+idOAPfFOEJ8JjH+vuH1NdDPSVP0IXAkn+vE+WWcj36g9xC8zmByh2oh5dsDXbWsWJtRYG8k70sTVqX8j9aB8LoRNqSPnUuYRCmb9/mPdbZ7xmeo1ZCN3xfynpH9sf+EuVVQfvm0r4la2Vl7uQpIP43TGGc+63Pu2pLbPBqg7Czv1qURGb8peU+4fuY/4X4VyDxorGxHWvO+lbgOurM5p4XmS0dhQJT3tpgVZMr3b4iZnc2JIEIbhaYVNcnIDbOjojSMlsRVxkjLY20B0/mqsY6o7k2JcUKjx0YUZltBUl7UWYEniv5CPpBDko9DTqMJznLmHkJX80xDzuLy0mvl12B3oSsWpGVE/TlQ92dnAv9XnAmSKc8FTb6ips1namdtMIxCnP8ADgzkEzbbvOIddfw9yISvnBmULhAgjha1UvjktAG67dFRRg3O5cf3Flozz6rBGL+tyqlU/Do4kJE0dpT/jW4N/OS+6IaN9S0vS67VPtSQL6TZNyPlGYUfu3i450eQVvY93J1PBp2x4E0xtCTrtno/0im72a3N8uhrfVtk/rnUNILsmwJ8QgfEhRu8zAORxVviNvdcKWs6lebHx7AVeVMeFajt0kXnIX2IO/hLE9oXAYkJTqFv52bzeZL289DnV7+IA4tEzRCyPNblfUgLFIrhzxtlN+92LB2trfSQT7axC2aVn3Ys/F//kCl2m0+9Y1PyyJDj21BktCiYPrgLiMEv/6Cu3TIPyaKr1Wkn9LvH+EX9c3l5z3xBx1/ZU0AM2DEAy1RF02KvA1PCenoSWG1KKmeyYwMJ9ZuwQl6WYqkv4cH21OZHCAefwc6RMLGpjBFKHrF5ilutOijs6ktfMl79WI7ZRyKjCQ2YD6LHdAfYdT27KU5+F14G1PqmPDxh7WPXGvO4YY6uHOmeNKjBSq+BBtjVNBV5lV7pCd9+MeAKZWzaEfg98eFvjnBfOEXCQRLnDnf6SKaHFppOwmb0lfbryL421z0Walq2UzErXBCHMx0J59fbkDp718Hsq6lTGkPcRjsHzuVf/xDB2Ph6QUsKkDoacWcfXUrL8HU8pWbx8MnLgxyncmlVnUICxIkWadU/TrJAis0GlB22xikjiPXoWL8+ggXmD2p0mbgVeq9InHb4xf/xDwmN+zJfns2jj3pVXRpbTUBXg3zyraalU/J5KtZwF48TLe/1Y3Qi7V2jl2T8rc53Mde/TwmKoPHmyjcRyolxg7o85MhNDq/kCeTnfK7tqCw3ETjuPSJ0EJW1BkTO8qy3I/TfC2+j7GazVIjkTXEJPivYR6drzznB/nQbj6vdTAWFzf+YUJuMqoQc8mC5/Md03VL6hyaXB8zoZjQqIV1jpx+zBUr8a8v0RxA6rrDKNdBgbTjKbmj7Y3dEY/1uVZTyBJ539c8/dwR+sLgEiBbldCQIL1VmMKcnaQ7iEoHuWdYDNB7RSed5YturHRa212wk8KMwXv8WE6e6uUpxFMhBMEzBAM3RP6O+xwrQvihGTGGKfZHURlnR5yu7viRXsaRKriKpQfJi/NrO9DG5awVN/vhaaPn9zh/MXsP/D1+6Zo0D1bT2zuk//ol31qb7MN61ceNXtkLi5RStPY0HeDHNboh2Ue1TVZqRcrC50MB0idhGym2n0P6WdU/EsqgrHTsqmKvpYLSs9lW8mSQ9rPbWkXb98Ev9xqYaMXj7wvjF/GDsJL05T+Utqg6JlhKCuQ0zQpOpc/mhArLhaxbtI3tq2i5JzZv5nnxG8kGb5zwFzmnOUjoyE0to+L/+CYPMphEpDl+S2Urij3E4kszgCaoCKa8MHeenNTJCsYS637GC4MpZZuxA2Z5fpOD9WkkAFAB1RD5N530NK/AEoVo+wyBEktOm+hRRs0ITLXfc6oAvu7ScWwahlCYfyX2JHS9A74j8T2KCvW+MFP9+36HitKk8RcSs2IHK/aqSB4yv3uLpzOSRlWGsVoXYZjU/8kXQ/s4zQoxKfG/Vfx4NHAj6m3bpIC09YhsTtz110NC267KO/M1n7q46hs0wJ3Gp+N/LOil2hCQkyJ+athDw8/tC5zP6P/Zc3jyaQxDO8PRK/sOwv9dcyyB81bTw3HbI4ec9LdDAK38L5iEjvil/HrPtxHjJbZod01I9sSjAZ//RutSJmbz2sfwPnffHAFgTMvHtMTv6fcL+wtQZhvTNjwB2ZBDbJSe3GBkWkWd2XwFvT3MXaEU32o+s35AT34xAJ2x7bL0kPk1w/dB2LADBKeTXaAs1F/pxGcWcbFBRJQ10H9ZA8fgn4HIYUzY1XoYezdh6xa1aqBefy9g4AUPGHqBJqFF0mPjQrT89bli7T7ZDS/oP2c9jMnxfyHNGtseuO+7SQ6v5pNIV7jux4s515FDstvjQ/HScwdMwZZwJ6a2+25M1OPCsxAKel/+gV/Fo0x/A5V9tMKqEl6G+LjQRaybFISuYA6k/2jQWUHW2G5nvasqPVpyLbrH9zBcMO8XSzrJRJ1q7p2SEsFaAHMPw6vra6Yh8OvIL36Vne7/lNPy5/OufOInbNOUjCSJmVTx6omsHjcccYOGEbv1r2ciGrjKea9Xn+kzt94CjQrY9WHENI4/OIMjNupo0GPs+c0aHbPIm1DPhqYnTlvtywZj7OSYPppyq8ISh67azn/LYO58YM+dmeTtUvq3rMY2/KelPJILYL0fMdYB/yYRZBLyRHp+agXGIw36qxLmsoLijeuyxRg9qaMvvHje/6dUcTo7bl0lzZDIduC9ZtNqrWYJ9HebLDnV5+1jWijbcg32mnMvjMhKgkKGhqgUjXIvfVv5vA3OiPeDRweBD8D8SEPZFcGACRWw05MZEaCute0ULEj/r4IDrIEm1k1BOPJutrvdFk1v7nhik+C1o/KlT++E6OJZvpE1IK1osdI1+KvTOoZE/dD1SsyF1JsqScMj2rMc/k7zzycZJQ1bWJ4Z61/w8w5udNW1e8wXkIl5zUxaUcKax05DaEmVdd09zOyzKPrVC8qWheAl3KTasSe6IDqjNK0oep66OhP2gKZ+K1GCKsLJmfw6Wu/zrH9JLZF96j7BeJZDrEhwL0FdwjJvuEkrk9vcgz/DE4cmQPzfhLek4knTccxM7QT8Ht6F/jY5fmD7/GVAQ+gb3xWfIfImx4kEFkgfb9DH8W2R/G0uFhsNo9FkVEshiSum4IVt7tCYdzfYhxb5Zp39PFGJZcF+6ebO6HFj+td2rSYHp8TJNrItHB3nhT5bkhvY5zGCYGfUfM5KmXE89UCg7xirlJ8j2iN7enA0n3HlsM7zZEiU7nJ4GAEPY48KAAXnpy2AJ7MJG98q3kV9h0Qvbwnt0fw+X+Zus3RdyNyaDmdHw4ktEN/Fq8RcBcR2XpGSNq1sQE4BrRj/cFtMNmkOAqc/rI9xxtMv2RbUMJc7QJcRNRVi15RDhqeGH+WVWUvSpcBNuszNboT3W2Q1mwFnzZ4BeyALtC2nESKbY0Ym/l7A5fH/g4CinLBljEaqvyOcndvn3KaCR9L4vI6jIxKq4XOhWXrR0roeGWpxkwz9O6Olv3d2rrBT3B6CBQdPh0pCSJHp/CL6ELPSmnDVhuGN7KzfDNnmevpCbSuPaxQLtYJOxJvJjn+V3xLhOT0QEzZoz4qvbw9809GacdUxwE4Mms3NK/o5gODwbTUYWwuUGY7xhr2nJPvwd8i4fW8iyS9b3mJWYjNyUuiREZ2y34uYsDA997LtjcrF18ZPvtspwNnLkmb23H0zA3ZPaMd+R89+HBIWn+b5U5bIqizg/4fnM0lNEs4WlVSXXXCHOLT5AFkRE5Ka0Zcxxxj417mqTzKU7ZMkCDGyCASpq0+hOA3bH23j2kgzpEsHpNOePAv9fFDtH5MSmVAYhPmJTMbMgN8wOq27HkYdkdSjqdG7+RVK1HaP+4WVFhhACx6daIYck5L6QlozkjG191Ab4qILWevPYIxtqnetIMK/2rJqkFsZLLJ0bZUUayKBcTvv9gBQd9wa9cVrbYIkp6XS2SH3ZTpVN+xTvP0NFithSe7PgxlVRURWV3Re3UiAwdOrch9ZASG1tSoSFrBnxO3F3VNeOYpYp1mGdZvvzX+TU0xIotEtZfkcJk3NZooil+Er0AwlhM7mBiROxb+dm85Ecf1afuVGXWXzxORoHZtujcjdz3eHLrMdmscgufl6u3V8Nmw555E1Z55ijlo2HZkLqV9YmF973K+7vap/L8tDhHeAdJBkb5zpV8zT9NkkQa7e7SmI2ltnSn+f5UBOzmIzbF/KOkX2xeedm9pkYEzX9mHDuBpfJX+lVzc2Fzkfb2qTqqct46e4xK7djAi+m+mIjBrNtKJdhqA0zaZZV6kGKlaeZ7FvIluvWZxkcTE/Fwq0snPqjf+VvTE0Rnt8X/M/4gth2xG1KC2Pr8rKh73gTRq9V5B+ztDNhBvm637ctl0NlL9TW6keWbshnbsp+xlRpaGNhnhf0yc7F1yZmFBQvGa9omGehUwJzLl53nuBOseAmT+NHR5F/d8hEKHNfml7ELTICE8vN8bIa2ojq5wXNn48BR47/hc/RNXCu4jM7MoiZp1lmDvf6pzwyEfmzL2QR4ZZiEwv1vMAY9ti6Llpc4ywFUteuTTywP4p1sNGbon7Kz975HroaQ9/NcDIB1bGLhXteMILed8nM2Rzb0JCVANXvnqo5MNBPBOeurVm5tfXW/r1PIlpZ++K9Yl6tyNDAzQ9rqd9Gw46Ep68JMeidZA23tEeHOShQJFpmt1m0QMyfUo3/Jgw2JRcIFRG6V7jVRZ5Wdrw3IGd4Kau8mTHtjaN1gtXDQvJow9GQRKtXd+GYb9wXfpLxmaEPg9s2htbd+ZeS/2U6V4F4263NA8boPKfCU3Zq7vYtkzzpH1PofuPqik60fTkByYlJXFccADHAFAEsu6+cXF0hHWZGJhtS+OvK47/x86ZYmyDzuGqF17hIpHp8HrSA11X8ImjtuMygL/nWOrNv5cRp1afRlDXqh2BcQJNtyqkRBi62M3DzQxvTCjUR2Wq2XkYdj2msE8K8OgTNWeRB+ospGAD4et0SIHVFT/SQ086xEnnP7+b/+Id0AECHASRw2nETKw070JiKsEnN/2hfGN5H5kFxGcRhWJh6F+Rw+svIy0HGAg8HKD1rcw14JYcJvicOPRxYn7E2aQ9O+Q7dkKCcBssUa/TgbGN1L86Td2XfhDIMYV+UWxuVuB1Y1D7MbotJntMs+vLYzd7wgbMq4go3ZRYJDxnbkIhpaWm3/fzME09MZVERu9tAtAx3RVOWGHWIEj6dtcwodnjj4H+ve4zI5E21dKK8xzocl+2K8uXVwl0D3D5VkpIUKZDUrlBObCO0y5k6LWjKMZufz7z1mCrcnFqkVGRs4LkZfiS22vBRPLBR7DnK5wlJn530qK2eX/fNNtkKG1KC7jd/BdTbrcxf024dWrj/oE2W3MOZKtQsKfsSuYmoj4e5fnDan2wU455UZIIjCSXgtnCR/WuwrOjI13I9YGGN7w6LMV23GbnHqMDYx8NcP7gg5j7Yt5cLTp/kUCTJGrbg0XH80IwP6uiyp1irfPf4IHTdvtB7jA6MjQjcuNC0fM2yMAkyxSeMVh042C1rq3Ban1EOsgZN0cwYTtq+ve+EeNqcp2K0VuxD4L4FtPNIj4tvMK21Lz9DrY8BI3TTMXIQt9Uia/3JcnJ/1z7HgHRf8CuBu7H4rqK/fF2PYqhZbF+XqbOus+B7Hy9qLiLFNqXQCOEWC8NcRkZLUseD8sRs1UHmFhDoK1s2f9IjXL6JQDKTpbJqB+8arIjZ2ZQHIqxRrA0zHVlVDT/e2tl+GA+SbbNmmJvNKoj6U2diMxSZ50eR8L8NdA1z7U0zc5LHx5WsrO71AUaoOE3lr7vce/lFZCmsx4q+JGucxaGDrJuSzTyHAU/5M3RixIhsRp8QsiWW0onsXtDD/OwB7RpR7BICg2dtnhQko5E7oB/6odguk53x3aI1xvabMgGEN4gFYSYfDwzlVJVCey6mYtI0Mtxuh1DGXMnZ/JZgXJMDZgCLV5/VENhvzgMw2iAWNm9C6EQ88hRz1Mf83Avt6nRYmzFBWqZl90W24p9rq46vAzwCapvCOgICQ3WOS3na++TxZ5bTdCc+iAYvfiw5WzGCfBEJRs/h2WTLvqYYhan2Zok5S+NDWZ0p8PLjTNvP1onG8cHzNJtRgXuM1rzfDwodTzln07ko33J1ado7dWlZdtIuK9nVE/pvf0g7q827X7FmWWEXFtqzRSAlhGqrnMlrupoqV2kY9WmfS15t+xR3ld+teByXkXygfkj0izjQfaFMY4I1UmOpdltRU4UuTvbWS3POTK4yFiaV2BEi56pNyi7Io+VL+okjiPvdTe83LckjgXVf9Fim38YTMW8TNIckLt7bHJvM5LGEWDVnsa8zZOOYcJOwN5LmW3AmaXPvz7z9EF7tCxxj8C3UZLmEC2rDZp+hXUx/zkUkclYHwQktSKrfFH3uPFtv6K7ykTY/UVaxWQ5CMhbSnoX2cpFD2N7jQtftqhtCPobc8RiXC8L0sVwIBTnLGGHZCuv5TBFQCEs3wbAE8BIpmwvfsmef4mf+qoacmrb7K/Y90oT6Y1ga/UMf+xha2NM+8LZj29+mJkFiKSTMy4WoGfAQd72MJAzZnYmPmf1Ni02og3/8tgM9u5sup5LshP+1ATM6hfal0RjpSxbbkbl9GaMtipmp5cdM7Ydv14FyAXSEz1HwlP22cq4qxYaccqJj2uvN/8YfEfEAm9MGjGUI7Qvc7YAeuSm51jPWsAIuXAZbw9YKGMxyijsEhZlaDL9KHx8qyA+ZnE15H8IShR4GbnjoNc2T5CPWa40aqkAwa8B2PLz/fscGNs5fjiRXr/D+y97pknIkGJdQ5iPJbhreO71gaoBz8mPUO8cez8S4zdCIa7+DUnG77URRV9bbyq//0evnV/UqQVDaF7KZkdOxqnpTYWeay9K7XEpyUIbePt0o4YTS59rObQdaAnjTLbQp/tBY34g525RoY8Qcs3FcXB8ohRzHyeAMKT4vElar0RWPbEhLh+XroM7T534JLBjfonXMVW3KbBEeLPZyEOMHavMfTX5VxGg2ugS+lLGyVypKmm6sQ0MXIgl4jyNkAKLvl/ebyZbyPaasLYSFrj7L7gIcOrA9WjNdIYg3G7Lckdk/Cl31KbTHhPWOPSBp/FIls4xsPBq6mUqWinQXWvqjtab256hkf6wPgxx2I31dBh12s+111K8Nv60ccVbCjZ4e8zMiVOin2hf/FfNrxVYW6nwZKMczNweMVNBF/DIS4622Bs/Z23LpyLohB7xFGNR3ftuIf3dkBTTnZpxoyJ/GbhZmfcEkdlmMnnNiMxa7hgSK5T8PVlfdN5i5KqDF4yfa3f/opvLPGWqYR21aNcaKzGLPHbfooUVHT214ijqP8SpjSU3LUIFE+pmcVR0nyOe1tzlfZsZ/bsiog6LlYsOMLvJVel7eNqnDGOO3K9bOmvpfiKGfy/NMzo/kf7svNo5W/GlzkTcoQ3eErM0nFv4PAtpCcHTDBf/XPyTA7EtAIvGrapNaMGpoOAUnpH2OlLarg70h6ZTdZa0m0IppKiRtSmd1xa+4DjqGwQ3b0RpymHCGu8pB8di4ICjc9QMUXFiNvy/V+6zaP+5ATzvWZ/TZRzmGuf1lD8zTyV6zpep9bZxhx33lAjrMfGqh8/OpxrsR07+pLhCLCLEViBuHBA9qLoosMfnsgI4yo6LjOfUA8OXTkUw8T/PRpG31H73ufjG+OcJC+wKdGNSKbW83m1wUVcIgZLfFaJhinFCVIP2hFDUUSTaVVIgAEzthuG8G+8gXBF6JHidwikusHJady6JLSvhOjBsUqG4PViLRem94OMFsbsUzkjZRF9g8z5a//OufkPTalCGL6bS4WShvLdrQstPYBXkEhsoe+Karzt9E0w3BXvYxeOwn+TGXGmL+J5iYqPHEvvSpYLWqIU/EaaUHDEDyBygh3n0Q2BMIH0sD2tOOLUOuiR761b5OxjP+1CCXSFHbFwWOKHax/4fbhZDJTWs7h5QNc2P8MhLinmzxrqMApPptETccqfXVvxFZKE5tKmWFsldsV2XmVlnP8k77iYVZrf+oCM+S3dgfYxbe4zKbPAttYYM/lt//GrQeSoKbCoix3EgMQMwuhHQ6p5MryzfrPrFzoHmNPeGetMpdbynb3DqDyP4B+f4yF4X60OZ6EtWfYvfPzS0kX3XYGlkIOcs/lpum5VSwvM9x5K1HQFH19yXZentlCVNn1CMPQi2OnoYpfbVupv/4h8y+2JdZGaTHTby3b7FA8sN5MufiLWfk44RBpk01MnRPs0RTC/ln7dbRB/6c+aHQEcpDm4pJTHoKbVMXkxV6FvoUh1T80crHGJLy2JIAG5HPuFb8uX8OWW9+OwNiAmhfCCPCL8UuK3xeJFBWi31AtuvQVOg12u228tfluyQg1GA2FWyIvBMbp5jNCj0r0DLGnsJ0izd8YPJGny920CfuFU0Xi308i6QUr35qEZ+8KftMuOrYO0WMVnJ8CXg8X744QaJ0cVrOtKJ93vRRsfC4N8+mJ+L19174TT81FL1LaqcjvtD3pHUr+ETyIOcVTpahCLZ+jUWN5+bEu5Xv7qFVWmABB8DWTNYfZ2LaEeABunhpguZ3lcecHhs4lpeOrP+IQSsiIzelLkOek9iouOlKHv/yDuPaEtE7Vz/tsdJovKHn0f4EgNLAp7ZMOlbgt5UwZJU25aAIYxXbqLjpaqBPtH/7Ko9OiTNUqfS0bH2i2skuUpCMQhVWQn7l9yEcsvibk/5MI4gcVNxvpW2ZksM7QX0OB+UqYBEew2XyVSxo2ODPah1XE9zW6UdP+n8fRxyihU2xBUEicVda3sNWgEhzkIO/9mkLKDtQKkIeZSs2y+9Bk041IOsWfnQY5vcgsNKn5Rcwh+M3FjQYd7h2+dc/Ng9YXt9ovpetobuOD0Z32GxaV6mun9NexEDYS90VXYNctkDE6HvC9ofigN1W/kvSPoMZBufePkNIh8TIpjQKIV1i7x1z6sHe2odDb8kjl+J3uUiX9VyNr33gzrQ7CkhYj2XOgpvnqyAixPabMgExb0Dcd8yrl9HVuXmYaJI96/tUHVKeopla0GnBusDDGH1+qWyZ50fO+IsGRBFI2gxREfgVu4y5JxktPJViz14XuJTMmtP+VJDOVAyE8YwaKuwK5flYphzKNfsi7xA1KPbd3Xx6cvGafA3m51nnOpKOKzTEYynJyWT5Oo+lSroYJBh8ZEpuzIJsbsZh3p1Yl+Y6NoorZBHa+5RE3wGX2iWqzgrCjkO7y3PbgtFKRjE1RJFP6d4Rq7EvLAhjTWLj3c2oJ1u+pZM4dMv48XnpWf8eK11yC694MeDTDoLOI712ALBZUvqwj648IoyYZ2Iu/UcbWolzKJlKAeK6Zh9aKWHQRA1EURVM/0eyaZ6/8mx3Qd/L2stYwN346mUrh3HVIxJy7LIaVZUiMBCs6VklKWH6UOlmSK5sSsUQ4iY05XAHD3aBUx7Q9ZN/nOF/9QiXctP1MwCzsA8sPMlGfTX3jmmVzVkYRtrEbqqL+ar3PL1ORZL7MU9hmSyk4epoOcJ31UKv4nt9Ii37ZghjemBzNoFwD7EvhPlIYF+RbKGu4w2QZ3T8Jdpj2X7acLIRd0WD6erafX1+qNi/af4W2pj3xfbMbNJRM27eunvoCOTiHkfZrBbGUcSGtrRLE2hZIO7ewnikNUz37kbNfsZUHTIZm/IehCWJfTXchQMTghMN8gS7PhO4hMCe+0ggScGHEzMjSY5sB1vKKNf/WrwhlbEp8RGyJMQgwe0UvWjnGtvw1cY8gV+AB8Kw3UCS4YPMJANTBVCTBkjwr2X7zw2BoZbLo8UZgIqMBs5DAQ4SRMkZbDFPDH45HQtjg0BckM/NhcSLGFkX7bYFDpB+2NsfCvS/+40Cs8e+eEOYlyT2A938Q0v+o+uqkpEvOzILqj+LLnOY7TEz+HhVEHp8+ltyeuAjBX9/0zibkT6MIopFciapy3tvXjyFR4WOvnYRiYXW3O9B61snq9AkqBwBGhbkdyOt39jsIqPHvvhCmI9kQD1QFhXk8UKXaP08mrhin8v/Vhth4nE+M/v1cZ2qEBm1cXNOOwxxX8jBS1M3Odjr8yEFPuLANmXMCL8Wq+RMU0fRcD3AIoHz84tIapWWw8SCcbF2y2SNhHSrtf4Gif9W2Rs78PfFsU8c/sQMdvGOrfrYZSSMt7k8DGADVBtdh3OmW+89fJ2V8zAya6IW5kPTGELecF94RsZLRjLxTVQW7JfO0dXGU89VSn30CECUlU3lPbQmaqe1fSFOwJbrm8v651ZOERuxY+KCcBxxZVJcxvQoG6U1FqipKcNIJrlG75KTFjfmFBsHuDBc3h7d1Eq6l/L+O+I24ks3I1cJExs2XuVdWiWQaVaDYhlZ0XoaYCaG3DubfxAtU5tzv0r1WZ9j9PT7THSLyMJNmUXCQ8aiPJfwQe9WW0poCda8DXGq1SkgtFXz6ueJ5NDz0+oudvdZRFThpsQioSFjOwQ3TwhqSdO3gQR9Bb0og6nP9GxH/u/06uex0mF7MA/5I+RVBDj3BZ8SOBsb1rjBbYEQKGYllmVS8xlDXaeObdEIjBx3+l3lHaS8fKu39WO9/nNH07D8eV/KpUl1dVxIdis8m8uaKuJsrtUDcEbntqKnPax3cySb4ZKbusyrXR+qaf6UxyFiyvaFWWNMXGxE4MYFVPwhZDufkYaVcMt1JDb0autIkMvTT+tnzK0rNomhlzSft1EnInz3hSAmhDJx0nDnTUYzxnLOS/Sz8MtAY/OcOvXmtuCCBFtO+OwnoSz7V8fLiDbZlGXhrEzsvsDfCzxSzlWTgZ7KYXfk2SWP2LWW9aMa7J+LC8KtvPnOJ4Ei9r/f7PIheg7Rtt12KEdkQioOko91iP6bityUuCQ0Z+znYO4POchlZ3ZbdhKylntCBAg8jy3ShPpKuyG4k2b7rDXvz/KJyXORrWtfLKHEQkrUipu6sdBi/Ky3Yk5eJYRmsU0nYbdbyjxtAni29Ezi2A8t5DfzecMZm/syk5OM8ER7GXCz+ufodGj4diCTGMAxuDyoLp8Qqs3ja7JGlhlekK/rAL43kxrOAAjcVpbNApIC1O31Z2/432Cd6AXti1zF5K3YU3HzYDzInE8iM320HcTXbJYz7Rr5+AhHdDlOw7IsCNMSZV5lkBGTuSnvSVjS2JXDPDxow6ujC+z5rNx9vBW6chanpmR7W5+EiVn1zc8SNFl7R+1/xy1xLcrmtSu01iXuIHPrODPR1WHajtTY9D/tigPlvFjYgOT2ddsBxdzgzpSN9SE2PaSBNyONGcUcOpqY/Ql18VVPcrz4PCzfx+Sq+mghFl68gEY9siaed/8Cw917whwNc0pYe1QxkbNM654w5vxUr/N//WOzbLK8wMe6yoyO2SvbZt+g7+pUnVFCiLUWo+Xl8fTv27Tw2NETGID+p9/mSXL4meo5JDEtL8Qu3wcdlAAbYdL5j16wHhnp8q9/SI9gnFy61LTqHgv5MedTLPpymZXLsuhL3tUGIp9LbumduHEd1Nhm/f3oze6dQnTQjZZuABKXXp7PgIGQCt6UOCY0c+wLoiYi3KU7ahRsPryyDLXFzYidOawzjHwPNF/w+J6zj2Y6R7ZgtKJJSVEfMfIiFCn5kc3/9Q85U7c2pZa300AwIdwKJLdUI2xW/T/aHPrWTFreh/Zg76prjXqaW6N4sdkbVo/0OvcV8InTB+3d5J18aLpFyARvyhsTljn0VHEDliwG7Q+INy/Z8eO1YpJSreVvXhCXhVOEDkyCtxx2tPUm6f6ZmIwowc0IRMI2xmVbvMhLEhHLaUF/lvJ48ya5XLY/xiGoaPTBasVIA0NEAkDLZ6BJrHbtizrG1LTYT8X9VzpOMTvol1/frK9HQZ/bVLo9xP6l/RTkycloD8wiaK/x4ITF3pz1Ziw5MQVxExEqIWdO/eSZj+n+D8rI8vTlNE4zkwcgPCkHoQ9bEpgX5xgwfZvSgoREjG19zAQ45H/ZLXtGOil/4DMUJIVL3T6GO2L4LdGEzHP5hVHQr+quvynWzehYQt4SZw7z8RSd5O0MCfQRayAEI0BuPrJC8n6zcyCZUDOp/dKCPfG9ekKOZnNKhzFAsS+H2ngwKNYaOmFFywlRTvtp2bCj2EpHUuKNodDZMam1YypT0b/nzhGybF/INUbGxc4cauR5VNpI/uimppJ+GTllfdzTSGmeu6JE+Ihz8qLela3/zN2GwsZmMggRTVinQ94ZEUWVQ8epqCnvcTla2Th5AsbQJjmqXZORj3yYpIS5HxnLbzBKwOlsxv8Qsii2tXATzIN2Et6M8HGQBzsiCmctatRlJagwI64xvQYeO/3HSJh/L+GOrVX7YsVi1q24r8KtD4PkhDpWQt+6wFFbVBPF1z2ZpUsSyOlzF1FRkDRa4O8bGvi9nsAvOmyFyvW+KN1EGA99LdwGI+HpwXQ+u7rEr3Quo0DSODdAmq+OMLB3D8+KSm6fWfchF7IpcxLzLMTawo0w6LU9uj/B1HSNS34v+G1634CC+exO7cCps7IniTn9wGa/WPphgdzm1bKsuDbmPQlLiuS9P3Nk52okScp+FTl3bI52UyfK111h+G1W6ivvbXxqkFnEhWzKnBCeJTK2cBMMBLfSTuKjXeQhKXRcz1Lyp9sRh98qj/jxBKRB4X/lqRH/uC98JeM3Q3cGN3Pgm2Tr1gR6M3mvTfgu0ETUPhc87TNkF9SVdH4bxIgXHRoh8k3he4z1Y+091umRA63uYtDApOJsl2iy7Nv56sOG/nSVXGf36e05v4tef9ErJoJlm2I4gvji+jhWTQe3lw53wB/L78t2Cais9qc6Bqzb/SRDXtpRBu6K8qGBPCEC3wyvE3QfWxWIraGgsHQ4PuoYI9rtErIqerbfh4Tj3BD2MocvaCGa3wlxAMH2BbExhBeLzVychuCSl69LNOweRyxva52mKdBwXRSHkbadZF/ifnsnxP/s5AjdA/viNmDuhNAFx01zjxb5dPPwdRS0L79MH0gV7bbl8clictsCCdG/Djodf2qedABA9gWvhPCGCeIqxMsVvYfTlL+xljm4DJqZ2AIGhn5PNo+ojX2hQhh1ElsHqNWgwmef15m9i7mdZ4x5Geq40oSteFogX+dZ6nbQJQnE+e54n9XOAWVLy0aSjolvp9Ea/1cbawcfjZ5a8CvnqRMe1LSL9jVD8xn0+7cBftSHHjetpSIE0ywijcNuC9EiWfGHfP30oV7IIbGyKQ1DSJvYpcg9jRPj4J04RAyfdm05PbJ3jpc1ZqPxBo62Xv1M/VEQ98sO2H8zGvvCgBDCJDZ+3YxiWuVsx4YWEJ45Xuvpp20+2mTZEVBRmvIM+3NJtlP9zDEaclKbMliE74pNGtTRsTCbzRYDnInDPsXA1Jp9ww9rkyDfQxDi6UdXLA39VAvo/w8k9sWcwcwcsTbPtXxQo20tO2vklPJtrYPVnz5O+iVb59xXIt/sJ9Oq7UMINWJENqVPYrKFKLxMD0aVZTe6G2EiWS0pgmd7zJI4UYZs7x3W2nqGSkioWT8s3v/q7o80+s0lfeYAiF2O3BXZYdK0sxUx/hQLZTgzzbk61CXmHwMeN51dCNAnSWD/kG4acQGbMgeEZ4jVTSKFNnjouvM0kkI13b4dDhuY6R2DjGIQC21cZNf6of/k/qOZ5G+aaUZRdV+iMIvasQuPu/Ye9WR34yWqbCI73TFpRNKtx/g4eKZspegETNlI/uqHt0v5AFqKCIHN2ANCNYTiJlNCgXvXGR0yrT4c2fvQ6mp93AJEbI6t7BsBYTOfoPhzs//CIxQ+5315L+Q9xsWFt1rEOGeMcky7reRP4ILVJCo7MH3mdI+Jz30hShmxGiucN0UUkw4fR8Wle0mIfJ61MNrKpbr3E5CvI4HWEJEcAlY59Zmmov+/H8G+9Opivb1itpCzi6CKUvdyGEnj1zqsY81d+xFOvJdmRTV2WMh3tTJv1Mh9SkAPObR94dwIR0e0YSoloylK1xptReDl+ZKY4cbL3rAdwwofv63EWmP/Jw7I/KO46Rcm30ju2hd1jKlpcUn8rYS+j348f5D/znizuSSpsaa1C3a87LfFKKlkiQZGLn+Ijg6JyE1py4jiJOo017IFy45sEV42ij1YubIklW3ZC0Z1qJ4HHTfP0/9aUt6S3wTTLxjRgIrchLUkDGdYd8aL1CTz10CDP55QSOxj2RDDryHoYdnHcEfJxS0LHVC0P5PfhVTkpsRlzHLGRgpuuwBh4Xdc2Nv611XbRTqaEfiSnFYdGB5otbIS/ua3K4xwkPvCWTKOM1bxL6L/kkTc6Wk0vJ2nv6nktodclAg26qFQ5ZzW1qNKIqIy8vtHRKzV5iQXJcViN8XNfSGHjw4RVwzlqTo8IqvW7sQpxip7q5cEsrs6E9oe5OVfPyKiXzblamJmh1jCiH8M/eHS0/xQqP24yrr8j5ODKcnK9AXghEKrVQsZcEq89s5v2viF9fX7Uo/P6vdxlskeK+rFnIKw7Z2G5Rb29wuJhrdegjN7fV0H05XUWylpVba2GHrfhNpO82Li0PkMIxAxMJvRNTG1ExsqiPti6jx1p93nRCdSu4akJoeBASg+U9bQk3TaX2MG8HyT8f9ICIS+iH3xURDfBTH586IAde+WI25P7zSdUWm1rDM1nNfTyWC5Ldo0P/axBOr3bJXfzIELDb9bHeZy0ONIh2W8d2/JEzvP/0et3twaDqQ/dKYLxueBX/66DkbXV/1cQnF2AzLk2YzgPbQH/qd8GrH2s7lWxLSl2Ehy850sDFFzUTR5zVTR8boqBihRVIvNzcOcI2BsSztkEzzvplohd7gvXCPjJmNhnQvx8sAgYjuah1Vt+nWaHN+PLXoBtq5Wyn0zTq+TlaVvh2ZEAm7KGBJ+MTY3cCvEg2XX/AXLmT7tYznztZE3vuNoeugAxKeukx8Vtqb8XdUdk1mbUl8xTxY6A7iNQDKIuuwYRec6JeCQmPSaDOksdHL3+3XU1jrzBYvlG1v+glML0e/mXhzi3IkJbk6IVzSnOltB/toMjDZeG3Nf9K4dR+qpzkxy12kRG+fph8qvQi5rE94r5siIR4A7CgRRFvdzwMHRvEABVZnFyeUpy8X5t442NdkD20rfI+EJEN8X4E5wfugPuNkJ5lNTPuxPOTl1VcOJwSQ5Ok8JP9ol5e6sAJb/qxtbnKDvS0JP8v9QFFcpXmCvpxsSmszZjqtIej+yD9yRSPzd4CBmt/aFDGPkWSxyU00cFrpu+A7G6TW/LiPr87h6Jct5TnPZtrJOKkOIk4T2ZQILgfpmsJ6RALFoxyQ+9HkD6ehf3+WHgckq05IeUO3VUDKqk2drLppIMvJ810OHuHNTkEogbaQzE0kaA4ZqSvZU4Oy0ggaIOctYXhSQC9hrdr+MBlm2dB5MI3k394yg2r5AOwYFQ52Yy8r4UoLTbDFIoCkuaADSTK2BBNFRkldvy20xeWXY44dJ7Edr3l/osZEYti/aGdPaYtPOzeSzQBj6MmvJqf2MiJNN8IUlvvpYREwGl8Qs2zFbweC/HoCsUdlwajGZiFbJTi5vzXv51z+k7HJfuufGvXZjLplzzxPuX/NWZRzoy54votvCfe06iMOnw28HRl/mS5G/+GFE+FdPWEiVbUqsERourGIgJQ8otEChjGcSICt0BclF0KZyfXG31vgCgb9YvyT8tezq8tao/j2PCHmOzUiRmEAJLZbUjrnQ/N+WOxzZzT5NaGTsD1SSDONGs1YK+Y/uqLz5DlkhUN8U1sccQCzzU08Afnv1lFByTP8UY126/c7WNMNUdmHK7U5aBPT4IS05JPE35/yZRBCXXvBSDZwK6Axk1x+jfpWOSOw3yaJrwVo+0gQkqGp/L88wf6hhTASRNsVTBH3Fii/Xh8EVF9vYC3UQ+nFHC53zRWSL98dHusjeLP6xnAu5vVK0EJzuC5hl4DeWrG4S15Tj0rOuOVHn7Z8Dkj22TiVB8LYnC7aDalkRfn6xvz+JZkQUbM4rMBoill2pSttQy53OXUuxknlcBhN8q/02OZptAAZuK+fp+QkSxL7HOMRwdTNsS4BwLB8xsQlulZF8FI3E9Wpt+dDZEzSqf3nJ3IrdUhkqDytj2aN0uiLCXJshNIbnYjmEaCcKe3XkIBa1XOFxSaVi7oYfSurc0htK7lLy9Fyq/WiU9ZscLeInN6czGfsZG5wufqiJxqLlnN+PlUTmglLu5oEPjYGy33WAYrEAKtG2fPcdIYntviTCLHGOnTo3Z498EdONOryVBn3rUDU928dyVnpTKbntkNDqP2I8/We7z9810vsLbO8LNmdYPtbBuG4mb1TOMn/uA0fCGQyIKQWPPQBYD2y8Ke4rJ9a096FN8F9oKYK2+4KEY+BMZDAum2W4AMqX789bK+A68k2HN0t95OF/edgTAIitEQSc8R1DQny7KRom2DlWRbiGMuWkeeyp9bpsBh6Yec1K7NunfmC5nBOP548QyF8TG0OIuykgjtFzqORx2W8CFZzYgOz/P+ogf6BNeUCqTXXShZu07PlCxlyf74MnTHU3y4vjHDoW8JncjxDnliqkPXWoICgX6RKus9GkksEOr6HCwGr/PYivr8aLIce5L5wo41AjIv5G20/ZI82fZsouUC4YYSQ4e6RDO6BjiQYbnrywXfHbd+iaskBUe5UEqKocN8o4bZ/4P/7Rtky1pCdZpUSvZ3ZIA3BYOnYXaxZDhfXXs0loHR1ipgmxE+01LL/uGV3cVJKSJKTIVjUvH4axZT2hoD0hLprig6JdORBBTKApYvIpqk0VNRiFgeLlsKjvercJAlxLZyUD1HMWKDZ5P/rLv/7RShG8h8cQP9rpN/v94LGKDq0Q0CNIoHrnSdKWKh4ExwfHsccbvQ67rTxe6xKlGPUzTqYQ5m2CCWP8SFQcIvkUDGyoHhDg/Dc8o20Ykkux8mHRzTPRsECir6co0Fo/U0cfshGbkheU64gNZtyQVmFYGb63JU65Ppu0V15xHxyCmgGGlHSGR7PH0p76s+/vP6rzIWe1L/5K4sckGtBNMxrgkawjIGqADC7pdJphbnEYAtEIy+8ry22O7t/n+dTAnJiF3BfWkrGcsYBMBWeML5g51S9UmvsZdzBy1b7DeGKjLqPYMB4BtbbTgUGb4/evP8yNN0+lWeYdm6upGbsl6LmGfiu4lfXVmbcP615U4Zov8zQIl9NkFWvRUScmlf/nI4QwJjxjqi3umOW3Ne9PKp8vecRKcSNbkv9ibnRWMhg3IqQHBDtPovPHbqvDuHxIFeojPgNVQn5nUzaIcEexDspVU7iIPTmUxLlaeIcKN/JJaSBC6GGABBWtQ23VI1n9UFFiSOrtCwnISMPQOHTzGSGQnYAoP9jE+LRQgJySXQeD5E9XGfnxj9dHCXh8xocKpENWbHMWjbFuREKmirM2yev+ORzf68jfkjpbkxLsjr5cDU5gebQjMJ6j1ot8p+shtN0XKMygc6w54+8xQ9mhUunDyxIe/NKUhn2r8f9mQf2itUsUATcPmDTAxiXMt5LnON5F8dEC+Op+HEM9bWV+qpomIjr3hRglPGqswFPBXt4nvoX9Obp7jnMZAY3PAWDY9Kc5Eig4X3co2F4v3jWiOzclR2MqlYjwXLIf4PvmyUlL8lKBhZDuwsgzu7O0fWRPJGdvP8Z5/UpejayI+9LVlnXBjQVQLpjC2jVbshoxNCntR76VkGU4DD09hncZx30zYIx2XZ09f2btRkzxprRyzEHHBgZud5Bo2zzpRkmWLobV8J6G0x6y988NJVsffsBIBpveQ8wkUENY0mGlTX8h6nJOAdjlX//EMH1zVE9ZAMEfEqke66HcgBLN7R0MibOPJUxoRy/1fS8bUonL9JzRug9gWs7jbC1Lus7uGWeersSY+Rl1OeYm94XLZNxnbPy4GUUGkJnXTksAG6dU4UF7w1NhKcf58PsOCZDZcil5cu1jHudosMC+DCJggwvipsa8CTKaWyo1OXAKoeXuuQxGB1R71ZItW4kdbluQbBsvgy31maqgiN7dlAsmzHGspjPtfemcBnfPy9o3v9tASVXJrkjLF3k0SRtK0Rb31EuK1vKL84xEnn3RhIiEFLt2uMtnqN3Y7zoxt9Av093BiiHK4Lz8pgUH+smD68sTE1LUm/HZhP0mPgzm2igSsnM+xQsYWeJmDrm3BfUOoD0VxDw6Has7NSQ3ecG3kKTelNEm/HfsnOE+G4kPyb9lFdBVvPBO8FHRHEJOkF4e+xgAEISU/SZZlq+1E3J1mzJ7MQ0Ymwm49UByjzGbZ97V9jzIoif7SSiR0II+cmJZK37m1ZnXeJXDhez6vrDxjL2PfQTcd7B0UG31OIyc9fgRUMqU/AU8B2wse9PFV9XjEvTZvRFftDm/RPmo0ElwMx6gs7av6Q4D7bkMHPBe3rvQzejQXfKLih9KKKkqH3I1hoTpvhCsjJCN3Tw390+XVMepCQGhXqeSGpobpm5kGKhKL3yugPvttOGU4Jw+5BaKqJNNiZaYlokNDdT8gFzT36WsFV1ZOqoWVgB78yhsL3Y/lPY6xag1sC8GA1NUluYVE+Kw/bByqMvLv/4hWcG+jFJgoxfCyidaKKVtKEB8ap7RixM4uEo3NX0geE+fhjd0jimSfuQfEmr7+pTNIKJM9oViIYxMbAPgtgGEzX72iSS+ybmVhpYPrRpLO1UF8rvKV/UsVZKNUt92/ghg7wsgJ/g9tgHcbAMdrXn9N2Bqh34sy6UOozWAZJYXBRd0R1zWUxo1iU/9rowMQfamiJzg99gJQGwD2qZ6ZD+/0Rgu2zVk1Td7/LiajY0r/02opz1tFXxwrD/+iHXcnKRkpCbxA1H7kETsJOdl8+/kTa1wGQk76bt1d7X0P2McwJrVxKK5+vyuB4/A7qbImODo2MxAnA8LNitry4eDrJuLCdObJeD56pfjKJ0RCxCRsv0kAan9eT3+EKntC7JjSDC2MnHrEzoOSmJoR5S8rdSOJUp2eR2WgcgyfA4Alff1pO8/X2/o/JsOcZHcty/yIJMTY0MEN1A05QrzOiuqWGxb4EfXGqcZPEb++H0LqnItMOBEmR/qnhJyWvtSNMCKDGKBlAqqS3Wkbn8uh9tqp5u3NhtoenlAnHRqG0oZrRjhK4h1/eAOfjFpLCzC37xonxX5kyGGfOhh+S9kumVkwMCMjHWugyEZ1ZqISkRcX80FJkQ+JRtqSp9qGRdi9k0RfswHEHcE91JIftI97OfslXpIYdCC78Tfx4riBjrc5jJt2p+8y/Q9tCOGvJsCZAKnQ0sWtW9JnM2jeER9rKH7A/deNnsEoqQgKZ/JJFc7Pwlt6N6DXyKyYXNqgjEZoTJ/E/LlP+lCwg+Az7D65xiY6D8M69T+fKAZqtm1m8b2t/D/716vkCfdF16V0LBYslVRDSqs06z2tcNeH/b3S5aS9uiFoiRPovvnkgyqORatTuX+llrgtnnoCGOkzziePlQUFNEdm5IjMZXCXDncxYOmZ2Aq/ChtdZ7roKOzw2b53yglLHdtOR0oibX3GrkUUR6b8iOETYnldSbGT9BoK9np+KBnkI9NwFG9PGt4PG3A4+ut+vhlJO3jPbErIjv2hRwhZArRh7meLDg35e65JDrqzi+dWcLgMGNTwTR7MzZBgCqqaeEVjPFjclSEefYFIjFIFcrJULHl8aXh2TlaBJ8B6wPz2HxQkZzd6w2pIgZmXxgbxvDEGifXRHFRm3CL+8r7yaffoSxbT+rlOfuLKBAFx/lcUvH5rp4PSZR9IV0YSRP7J6jdAgPJ8/C8U5Zey8eGgZ1dLF2SnLb68CA5a0dNHpzlC6TXVLqQENmMPYmZlli0IwofpnDO7vdDbNEWF3INSX+yJ/uQm4rXxkgOs/rXTN62PtT2OUIR+wI6GEiJXV8Xk5ikeUWRFtqUKP1snye4dezv5cF/dwdHE/lmfy/5bHo+5CgMWYRNSQfGURC5jst7BcA0PydSgTPz68gq7s2snLIVmgWdmvTErfaaKlq8fG/AiEzYlHmIaYpYdOISlWRs1qhDvv3qlhmCYpPQoGRthWRguRiUPCRa9tcVg0l/dFf7G4pvCtxDlE8kGy7wQPYeHsvkJNZXgumTBW3z7MtLTnoYhNG/dp4OiX0RwSg8yTpRfj22yeS8eM7U4su//iGU5b70gmK9oyKDxM1OMSXuIMYOHZHizB8SlIZen/p56Y+DaR0NLmtqmn1dQOjzoV6sIYG0L4QT4adisfGiTcqPqH7gNmyb83lCX+eir38BMha/65S8ItnizVYJ/bXxIgy2GWJj+C7WvYhIVtHOofkAPHmYy5pOo2uQZBk2TAwNW7u3mJd94zMSJba0F50Vsif7wrYwdiYWvG4CmRzruR778bQ2I/gcMy791AZnY40alsar8XhElBj0GsMaAslNYWeMUWPNlCmsEq8wh8geT5FXbuOEB0pdZ7PlMx44GuyOVnSs+6vn8WrOEmLIfYGcBKISsY6Lexmjn2yEIH4Bpnr4dQaGk1kAhLnBkuqsE8JOUp1gMfwWxEM4sSn4IFAlVp24RvWAlrcdWeQLmQQNa+/T1LpVUXBvWBDVgaCm/EwC7fim1AMkty/AjwHFWC3k6iKeY3keXxcze3BOKHZ8PFtqeKvVcSjG8TpdKKdeGm9Y/Zs5AFG+tS/5WZzOEYcpd6Qiy4F4pZ/Lt1kWsxv4x2J4GB2pZ/oqNVCN7tHPJWSkTxmrQyS5L8iTIdVYOLvobAOq/Qm+0G/084KnYUwc1lRZX33FZYfXYe8DfqkfbbYC0LYvGI9hwlg440IbFk87eZ2AqdnOddB9yXacHFXTmyhgkohs0bOKccp8BsOEPPimtDll2WPd4KYzoMV+zb5du/u8cR10VjPWH/g3HRZfEgmtO8FQ2yT7+kNe1gg0bgoxYzwaa19cKesQ830os6wVXVoLFo3hcwHkjfV6xp/L+vGTHepIfb/43+gxUZK7Lx5j5kmOBZObwKKN9OzvE3r8+scZ3YaKhrUu/7vydVvJaPW5YO21T5lJYqpjX6iRmEmJNdubxIunnqZDpd6/qmvklLHkVJuADqP90D245OrliRIBXpJwjHj3BSEzRB3rnheZVC5o4+5lUWKvn0ZvEvgtaFZFvP0AdvnJxU9KWfz55WkMge+mMJmA6li5YToPWtRj/rOfCPJ4ql1EfurymCTPstt0AliXBVN6cp1ASH5D8Aj5boaTGaqOtRum9OC7Yay3ffsEB6pdRN7+M+ylZNzGgTz6IuThacdcP3q21o7OtggRyzaIpHj1pD/0H/+QdGNf0hOWzsQVf7xCEGusJoVA2vXN2ijgOvI/tlmtSMbHOIWZgiesyTZa59T1oVZrMejdF5BMMHWselGRDPlBSav5eYCw5ZcBleovH+OTu98Uti1fuxKFyqt/VQh8N4XJMaaOtQ+ulMjzL90CgaRFtbhmk4qbHaCbStx0vC7bYp1ROZhZ+w0/QtC4KcQkgDSWnbhIJRfu1aNY8ktjDnt6lCosoJascS1ax3ZnYir66KUfBEIE2PcF4DNCIBZtuMgjh/KzVju/QX5k9usA5A7bzc8XCJH7ohPr8FMpe6uYIwpFGfe+ZOgkoY9VHvw5piOV5etzpnJaApehUU8Xu0DY774QBIrvC3RnUD+WbS4qDwrEHj/j5MV5k3eFpZZI43Gt54tJABvrOxOCyrv35296e4QC874I0kzAjodD8GESC726xzQ/CWoY/frqGsehaVytDz7sajJvVkslMOJpn2nuEXIZmzEfhCchmhFXmAT793aibO1aFK/eLu8RUjAL0cecwFuoG1XTDMy/fAmtEY2xL6wHY0live6m73WkRPbUqiRu1tgg9f8K9jPNVLIULX/z+3aQTfb3sFb/aN36izF70UG+L+c+yxOi1jG3RjNyvTytEFN2tzX7aHC5YHKZYqSasqPRhlgrJ6B9jvEBHxohEcH/fWELCLtAFK+bQoYKtOFvH/XRFht1vL1aZ/D2YU0/rMZwirhqH7vX2Rbh2E1BL4HIsXDEZKZHD3fPicpaVqgFg4W5aJT4AvPlDb0SSrbs9MAIr/Kzpd/fEGZfIA9BSLHmyjVavPdZPZtGJ4J1JkFLojH99JC4kF0DlrxOFl2y34bml692mDGW3Bx7Uqwa6zdc70Hs0pIsi2x5fqlPbaD20d5EQuLn981yAhQndTAZ5qUPRWhsU+wWI71QxOGCj2zkxzdflnx0ef9g+dlz2B7okpMttydiQufjDQnki3xPmSQ01r7QXowmiwUcLvhA3cPcW1vcEkk8TwQorrbBC8brzHXu2wVs+robOAK+30CIKDfFnwSthioOl3wQsrPvzTWfoz3Jws/ZXoGkwV3XD9pMon7Hz9fyrPUKw3YKgzYcWNlArwUw09Rl+o9/SFXt5kW4rGY3rofh9TOPXcdbsyebEojLAEcgIuFOqBsv57YSyLAu5Qs/E57gDyHbAEvuC/QkSDWWvm5SGYbIumySWnHQkJHP+Zhk1JRkW6LatgpKtoVAtG9Zr9+/MFbUcoyCR632AfwHe/38X/+QU3/zJIHlFHH3Ptrs79Faff144Jzz9jINE0Z6Bm5B33M06Dy3zUMn3OhKky8zP5RAR2h8U+xOkH4sHTKhERNr2nKNSiBYNXJhAl48wz6utdq8IS18yjY7WoKWXDy/qowjOL4JdidAn4mGVGNEoGw21g5ngezo4ZeRDCU5BV4wb8Nv2iQzb6eyIbWXLSKG4ptDdwb1iezFZTL5hi1ZKQ5+xGMtpHGdXp+i+VJBLjb6uW/JaljQX4ESsJeuuDTxwv9fQVVgg41az5hl+o9/SHjctBqRFi/G3hvu1UG3IXmmJofICeP9Tad11s66yzDozDu6ZBTfDUMXD2Zyf64t0N98wb7wC4SOiAXDi77Y57CMHvlKaWfC9hKo7OivyC+22FPQX+IpruFgi6b3u8/o7aNhFm2GVXeSoO1y5OVf/5CilH0pYmFFL3EHDN4xo2to7da5p6Hn1/LrNHSI77Yak9ae+X0FGDVbdgXDCz8lyEZC2Oa6GZHZiGDKBVaUGFUbH4tXPfz6IEiwXbJ+HUyvnKcMC5PZHptvLknxap9JP0LWZFOOhTAysWxKNFZLL5cdM+jvd+YRy2Poj30RSQKsqwlS+N6b/TFKi19pf8iW7Au7wtiYUPTjGiHOvPI4RJHAUExzSYBQpfh3Rb+J4/GWb22tnOWpSqjt7+D1G005Cq77YpxkRstY9OUisc50MA1R//5bPEY/tGHiktzqaWfwkGAgl7gThsZ8ir+JCJPNCRZKyMSKKVdYQSbkeVKiLBuz+nUkk0vLg4Zkl5YcVPSIGsPXdl3th5viN5b4iLDYF4KDESKxa+nmcnrKMzwcyc72obHyOSiGdlZMMYoB30fyNF2pkCm79jP+SPiKWKdNOaqY0SKiLZd4wT40u6VAt2zCFDbptEwCSqycdX5LSVuTxyg50eZLzo6pmn2hdhgVFAuPF50SU8eSR1UJVD6BTSv512Onj6C1Uc7UD8l6DigsmAT0LQGFjM2m/A5hg2LxkUmVelZYE0OE1FSaK5jtDGhFYtNtgifcGSm5YWaM+gO4Ig1bGmBBrBo0Pcw5/8c/BCjuC7CMcShpH3drNzfl/evnKITo7i3vaJQta/jRFLnBBnpui7arRmdgntOH8oaQJNqUUiIEVKzaMo0XsLVkX4XYr/qoHoimxRCWPJxuKekD1NrS8Ulhqtkb9UUE3b4QeowAjAVbLvBikFbuxSF2e746NElwN6tmhv3NSR043YbT+BJ+h0Smd5PaCCruC7RkUDQWeKEry0vtw66PnDufVrsgdB4P5sjwXpR+SCBtTjgxgoootlzhLZhw/5yjsPT1Nd57yg47DQ9S9iNP7tuq/L/Fn61kTS+vRkSFbEqcxDQLUR65Tgl6rNv3KThqfdCwxPu5ugOCYpIE4Am0Y3sF04e5n7w4IEL2hTchNEusOXKNErG6Td+1cm1nLuQ6BUWo9gs6lpR9jiKZ5amCbIr67qEWQuHNkTPB2aFwxnW2BydFdswn/3X4RVrq/Xz/NL46Bql32jOgVV+uiBDLbIp8YpgUC2dcZoMZyPWR1Gpz76tEKhuNg9TXrR4Yn4tT3r77nOr7+z4U/nUQaJz9bZ4tsuySuMio6UyzDjUJq1kjG6pp6LxYR/Xe06uvfJz7gmenXRxz7PqHLGoR/tsXuEjgJZHpbrIe8vVqAQ7TmixVhKMQWMEfYitmwNEeTFpzbWdN7S9EHQKYfQE8DCDFgulNYJVIsJJ9KVmmxaRS+VwObj/awTx4qSomno3pCxiJ4Lt5e8ih7AvnwjiaWK3j6h6kRNmF/mT7+bZL09LmyCBbC0i7L9Cyf09Ia9+/4ncNsyMP0L5YhojFiLSm4a1sMkYc6AMDqyeZ3XNa6+B8eYxjbmhe3Py+2kPMJLUCku0zuWkExTYFbgTmxXInE0cbtMLmB7Is4JlcM209GfeTdXJi91nAkJw9g+rIgV9nX4jC9gW1EZAXK3Zc4cNs32RYS0HGcdJO2UNOeaJ5fPG9LLeVd23WAGSi5flu3RRCsU1xG0F5sfDEZSrM4a2++2S5Vbu0HJbZgmBa1Xoiq/W3FV8grbX+bpwb4IlNwUcMVZjodNGoHoGS2k1P/qKfyuCEA7Zkj4sY2li8L69grJ7tz2Vv9Xf6F6KJzcEHwSpEcbopVHLcGlWLoCwI93wuv+H8Nvnp/RsjJe2ypT9CkvQ3uAnT9n1J8xksiLUTrrWgnKycdFvpVb+MvMNqOYacfslMBAW1dMXYnfxfHYfxnnnwC1U8xPP7gv8ZXxD3OeJ9kdC1s0yfOASyPJ9+SZKSoVcavk9Gx41z3wpHfTG/BNoIfqhSLAI+m8IkAqoi9YApDRWTp4aHXdledZ5Zvy0btabtEYrfrw6r4MHYNvUjfEaPijj0feHcGUcfC0ZUYMLjnh5sdZeemRkPitKeYSu/FTTi8vsKAuh64CUEhDQ+c3jHeHFf8CXDo7HucpFpJDW1uWxaU+2aEQpGFJjo65dc1LLupFX5jx9wGV0yXz//F3XOIWzZHOUQTBRaZrnDVqPBVDLzQV8WH04ll8FixarA55I8rlP+k+Rw87cvqcOrx8uvm9z8hbY3heYEyMd6y02ekf9tbnZCNbRFNZiBL9vP2p/LRRG5qwCu7DBXnlF6942NIPdm+JyA+VgzIAoDztdsjQoy2h227teQd2L0VgITb+325I6pzeKZQwKmGZ9Zt5FzY1+MHswYEmqFN2lR0s5zGYl3PjUT1YpLo6J8jHZobkeRo7Eb6YT133r50W/1Ny6+CG5vjs4ZmI+VFq7MwAWtE14NIDfXiQS6pt66Ra2Bge3Lbyvn6Hyc/EAh3Bv9B2h7X8A5A/Ox2MfFQQxJ82eBuWOS/J7rDEyCrRYJoWEeEkGggO0DBcCzvF/iP9dYhtbPzZ2ixFgae5UuzqYH4lg3X+GUAPjleEoaAgaGcHx5VvFgu/0xprJ9SEYNceqmqJZg4FhyofqMZNGPL6oMerPbxxID6yz+zt1g9WiZ9OoWFpA4t7eaExEs+0LIMAInlltu8gzIU08ZZF3UdvoDSS5lMgEolJLPbac8wmoxTPKO9Ub9IUzaF1jFYFiszuBzOUHXsIOpoMfGMRw2SeOG7SaYI771UQL+94UsIOQCUVtu6syDidAeE7wffMF4KB36og9Wzux1qAv5oWmdJwtj0tdviCiAzegCwi2EaguTZjSxMoU7aQvCcqaZpjb8O0ocsrYrGNIm9/G8fqJF7tvhFOH/feELCL0QSi1cmYGG0rKfHg+6Qg+/DKRbX9dw541jvIWAV4bnoKvMdzL8i+q00A+4L/5B5jeM3fs3tz+KMbH8tAXC97wwOYmxVPAfO2iIM19MglnpBoJTt7KqT2TDAe2wOUnBOI1QqroIW+j47pnjWvl0Z0XN8LIwUXozjk479zxznXcvV/rx2/9xfnSIO/YFpzBcE7v2by5/zI0cdsBKtKsGNBpmfXat8MPpDZ3j8ftKilKzfU9U1X6oi3TEOmxKURBCI5b5uCgo691kYAlEEp6Si5PyYXJvoDyZpOtBvonEERv2meD9re+zLATNm2NsgshDpYoKW+gMI2eAl6xOiUdmTZfL1PWk4qWssqe+7Jpowu0rFy0y3nUBEfbbF6jIoGWsVFFhS8tHk792eBjXGeEoaXQt9kATcuHit8VQGj895OKv4QGEu9mc66HcUCxU3YStKUnDcG4oo+eify4rSc9cZLJP81H2C2UhAvH8DQnI/WbjQxy7CehlCDmWW7g4Iw+++unXZVNojJE8CQpKd6CZRnHXn6zilu3ryUmV39WtEYDZF8BDAFKsWHOBG5OZH2tdDg7fmVq5iqyVUixGIra5+RV13Ll5YgIC8Qcm/sXpHXLG+8IxM0467F1AOx1MdGK1EpWBY817pnVMj+yKWnBIo23lmTQgiaxxAvL5mHV9iMsKUfjmoJ1h/FhouwlzS3KT5tsP061P75u0mvYKwhKWn2xcIVrooFu/r5eFHPlrEYeAbFP4FmO9WK6i6hbCVinNgiBOMx8j2acEc/v2kssZ8S93LLBn2ibI2FbfwSNEYvsC3BjQi6UqLm09gOyHoahrmka1kFJkPXnxE8B5Tb+t5BlpZd+BkC++f0MIejaFSAxQxUrVRdjC9ssOVuS5WW5YEHJaGx5DmrdpRKOtx08tzJD/URv5i7H1oXNgX5wGzJkQ1w/d6o1k8fbqnI/EjXauk5AfWpmbhBmrs9Sip2w+mKG9CGb6UHVohBo3w5gEkcZiFRW2EBeqrTY0Vm3/OVMcIZ9p3i1pcfP+thPsW7aI8jiLebLACC7uC7xkcDSUWrgyg1q80v0HyA7+sieWMfwXyFF5GgGiEAiYwn5CayV9qDYjoqD3hbFmDHcstN2EOXlAzWt8qqx3YyXBRSIWGlIRnGJZgY7Gndq7EF8TefiHdLwQMW6KLwkYjbUWJs3IxwUVXpYPy9FuSx2XQfeHOrwiannnMblrxmTw6RlpSfmN3/4VvUZocV/QJUOjoXefWv17AnhQzw2Y50dVf7sMuLFp1LM8QO+YILetaBB8OGYEms+A1wgzboowCR6NlSqua0m01tQRRxBGw3nDG8F12Oq2uh8LKZjXOHxugbz2VOo7aQgB4+YAkwLSWGy5iTOz4jSy3yBnjrObsMNq500lNxb4dr+vBNzV/TBuzzNf0TeEjPsCMQkijcWWmzjzSJ7fXXiG6b365+hXPy33wUiaA3jlUa9zsMjD/d6ABLJsjnAYIIrV6pu6PYA9hj9w2ST2OSp7U7WGFO6eknsiDC1/Ofj+LytZgBk3BZgEjsbSBxNKOv6XNmUNYlH15Llh8becPfo3gxYPyj2QmtsDkNDwHuIawvV9gfeMDoh1DyqTYDxa1XlSuG1DQY5fRp3jxbm2r17YOlJGtx9iuyzV8cav/+xm/1uB2xe5jqh7sd5M5Wm4NqaOtoFYnubIR7YW+Pss+y44WcrRFDuqOe3g6PLX7xnYvzm8Q6iyKbJhQIiIS/h7jLZ3TJO0o4B9LKdG8uAhWLi85ekQv+4L3mX4OFaLuLqEk0POVQsHC3LB9OuMsXw9ZglH44xzrLMvf7ldluPbXBqh2E0gLwPITHC5CTTyY3q2rzS+MoCEbbqmv2XMM1veY1XiTU32Ghq6Xb6dmQEG3BwxEnwZywZcZhhqc/OvBF7vS36AsWt4TMZgudOnQ6JZ9dCOcYsvVTNEM5tBnxgmxfw3Y8vRZ1XLDXRJyCk2fIKarNV6vjzGzPkdMe8l2dcrM835JqR+4dEJ9avN5S6mjsU2hNizMJW89ENT3ouzdFg6o9lGQHLbfeID/pfyN89pJCkn2IeaBUQAcHO4yNBlrBrcVAa484q9+gYjzRkguASpKKmpU3Se8tUZtGuDZzvkfrT5+XdvS0h+7gtZSrjVsGLjVuAB7eMxWmSh1ssug/Rp1GEEsEQ1OxXlpjj43UuMpt0/XLW/axTxN/7bHC8yeBnLLTd5RmJQPS+/oO28fy4nmebgChCTjzWpuFfTkg0cKQKTn9f5F0L4zRE/IQhitYWLM7Az2QgAjUanW6VcRjKP2i33kIwgl8NLSBgrjjafiYf6AUgdYdjNMS/FyLFd/WZvR1Ps1j1iFu9326GvL+1wjuvjqFmOwR95to/dVxKf/KHwFSLZTXFvjJJjtYooWyDGc/L1gLNXA6P2P5MzzuKW5L9FvakYo1v8y4GKfpUCxfh1X/Auw8exTsF1DQzDHIq/sXJloa9zHfT4sHUrzyMfy6QAruLISU7Pl6swhq/7gnYZOo61Nq7NPdoKfZ2AME995YKs/DzGMPUJK4Dft6GTSXGI2tsPPusXbOZf2HdfcDLD1bFbgrsr0HXBT0igeR9V0VUaVUcx6LvkAayhz7ic6w6GOvrqfmTrheB5U6hNgHmsU3FVSxbrONjCeDD9OGFwjEXBniG1GejHf6qeSyydBP3udfE3bt4XnE1geSxScVELmSHiq/0ESS/TGbZY0Qq5OpbNySLtUl/WsORxDbn+u6lEALU2R2YMycU6FVW15EQsOftylWeavOoAIsrsyfZC07k5jhPRO+FxEkASvee9AP/x5Aslp32RqJikFXrUuKUNvJ/3CsObx1hYv8xAa247J+U0rOeuqq/5573+mK/wq7Q9wqj7gmkZBo6FHqoLaVtrS7Ywyfe4j+QyU86KbutUQraTB2iuIaGhOjH0Y/WGIHVfQC0DwbFacVM3QB0ujwvPs7wmcuoxv2yhFnlcbtId6MdgP0AQ27tRe8hA7QtjRQiuWG3i6pRETczNOknmadgm15GF0C2GoKhk+E3liw4/FauEgO82oTHY3hSaEyAfCwdcZkADY9d1cFAt78oB1tWftCTM1qoZrnT0TvJHLIHt1WQrxov7gi8JHiWiARcZsk7bbrZWZp/ty20n0LSMg+vHyqe9hMT6uewdSOT94UgLgc++ACUGrGLZgKoMmAIsm+XYQF0ux2UGZsktRxWnKVdFHxZJKO1HN8jur0G+UQa8ab4cJ9cx8c5pepzg05b7wIbwEWRIEYqFaKRvVm8Bi1t5fKVUeV/15SYJYdu+wDwGC2PW/cbSyw+b/goEZ+d6PodRORnwekDUPQ47JVWsqfhebu1H/64oidiXpIMkKYRwBs1dG+5p73gsV72WRkZ17mHVC5RsL2Y0ABb7AkMYbIn54wvdjG1ePCcckHH9c1nfo9mJ/FTINI6KZAf2x5aHvClzbp0NGgGLzYEIAy5EFosltKJNx2xFylPP7WvyrmD4bMt7aedWv6Uc/trGAD8gyVt9t7T7BT0agvp9IQEYaRA7Cm8ORAE3rTyeVJ1OWx2z06qah0GTdW38aPdFbx3/PgV92d7J1m9QXoStNkViDLfFxoKbEUHOm+ap6cJIsNOLX3Kb+UVL5+k4cqDOwUY9TAg46z2OJWSWNyWiY9I6Fta4EFd1Jpi9wAflUEcVbBLQkr2ohsix/J4J3RBtaQ9Jdz4FVSOAuCmcJOAzln+YWgSVCvS4x4wGg5Z3cZT4YUBE729Mudy04BixxYR9vPKHtm/AKm9OQjPSmnj5uPcPJdqPtk/EbVczgR7XkXPYBsDJdSQGmkg3oC3V9ZyvUz40SCLGpfuCYwnsjaWzm9Qm+0MHktgQoQPCK1y/7YwEq8NbosttZU+uk/6mNN6N/0J8tS94jMC3UEbhqgvCdF/TlnABkDvdHwDjsx2iclQuF2abPADJ7f1whTj0HnIe4at9wWMEvxE5gMsHKIWQ1N59ZPNUR1XkfHl6RJSjw0crqrg4zo9ImvB+I76/Ida+ADIG4GI14KYeLMmwfHEkbwOctcPmNNwlgTTJRjiuRtQDFLfIyWZ7JeohztoUlREMFxHbnARHAxbnsCQ8dJujhj8xw6NqEc+YfsNW1zn05Oh93nW3EbzaFzjG4FvManMOfM7Ziz99eYd2XmX0wu1+3lb0TT83nZiSaD8Bg5TTayeH4GpfwBjBbjGlzSlw+AbHc87iUb6pcUl2+7TVjsGotvkq/LjyriwpkzTkR+3qL3KpiCnbF2KNEXGxIM4F9Mf0bHspGI/UT6lrhWO82LlTu1dm48xCT0BPSit65nwmF4ng6aZglkDfmNpmRPiAqnAcs5LxWFSRi0gUbfYLi1zM0pz2X6yx6kmYbOdX2VmMS/cFxzLcG9LanAXXoVdz2epF435bpegRu9oa/hvQ0ubcVtaJPEP7ESj+/Vm9/zcG3BfMSCBmTGxTIhzF/g9aSjtyq9MOiYwsY2g7CS3I1pIgu62An8d9JnJ+jfnC+iEO3Bw2RggzloZuUpJgfisXmfpIvqeho9LWtoo8E8c1MES26qG8QYz/UDewEAHtC2IiCIvI0lzGRqtUm2eEuPScwYkdvr1Ss+e0vXuQlvsiSPmylZwu//Bg/iKIRCBiU8zBMUpEz9/IfNksww+2gSYap2W1PILp6QZW1NdcM6AHf0ultv42bP2zAzXE//vCFzB+IfbVUBvOgm89F3/JEk7LmVDd5NR0xUkepxfidLRNkbTTThY5eseP4cS/m871N4TYF8jBIEogbHAVBCluLh750Xw6nQ4IDUyG7/7UT4lKx6I4x4u8rpRfITikADZnDBjBEEsbXAiRjW9XQdhoPr0MjVb7k4stqNV8vCmGb8gxkWxZZzjS3+UQEQbaF8zEMFasDNyUBDkr0kmbZflbSX1C7MsGW9GPqZhrUpsLSO7S/YiUxVzfBo4AA+0LZGIQK1YGbkrCY9PgLPeXEHKuo6Uhno356V8hFrTiB2GeLvx9IhmNyIh9IS8Y2RHrvBdZGFRPNrJjwlZw5sUVmCfstvINHPEOHN5FO7/Djthaa585R0IMthliI/iOKCtMh5ElNOWbOGrq2YaiPWi+1Oe0n4gnMr2RQAJLYwEB48fTKwcK8e+mcJmhayKqcBEGTWX7eWqYsGChE05pOVyexyO+MiF2X5wPfZ7zsb9l8DCH3Zecl+XIsQiDv0fKOh9HUMPbJyzlLAzZg3Gr6y1JRth2X6BwDJxjUYWLMLLoB16RPQp5nc+Z44QW+nbCSh7sveMyTPQpe5afJPyXl+wUItt9QcIEORNh5SbEIP57GiJ58SzHtIbBmL65hyZhfl85wNXWgFgz82uGYYjRNgV0BP7F1DInoo0Sskg20nROHIyks8Ly/sfjyHKi74T9prVQcPJaQxGw2RwIUeAUU6OcSkVh20rnvEh1nCr9ijnsdrjIGerj7ApqSJqdtKAA4AV94awA3OwLFmLYKRZnuJgzQVeV7EkosrPs1xlN9r+3V5Sna5Sf3FdOr3IgCPbaK90KWY59YUUIi0IUmpuiM7AqPLOS1NDOeRS9jF6XJStF9l06JRSCKaZ/LpGgPS/qLIRZ+wLLGIyLhUIuLDZYz1I73yr7zPFe1d3pTcfRSG0e+Jig/NvnEhf6mwAM0dK+oCuGxmKynZPzWJlp+LvAEy9+GXTuc+GlykpPx9Elh23xsIRGJu8ePmHWvy8ogaGKmGy/cPNLArufBI8yz/65bInm6xtdYZbfdSLBsBcn+Ov5MSD5F4ljCPr2BSTGmDIWvW4aGWZO+5B31MnXM0lEnvnU4IIjV4O93XWlr6F7ErOsq8AHIGgEmfYFYcV4LJYpuKyhhNhs/Tzc1M7g1Ym+qvb2ZbvNdGx1FV/tVPHM/h6lEAKmzQEWA2SE7qfqANrz1+zvcMLHf5xmDXUptqIAuMq5LTwWy1ZOA6Pzxm9/A6Z9gVcMjoVkP9cGgEHKM1wKlpvmUysvL99ODcRDzJr12zbQo75Z5Ph6/YQw8d8UJhBQEZPWlOGWBzofC89DNk5z85kk5jqrGlVcEjXdTYYx574VK3bfO3ePMv59QQgMUcR8Nee3wXuU6XFNQp9DgIbpBm34T5hQEfy2GW/SkwaAhJ+Ddf7KrvclGWfJe0xX3+jtR0vzdHOOdbYNxrGkWqzETpbxcofmsAkXx8CdytuIGyLnfUHaBJjHqg9XiXDAKq9pt80nf9Jkx38DJlFZYir/BbXezTdIHWW8TWYRQNgcUFAAEvPulKav8DWeKCkr6cnHf9bL1D6TOFRkmxunIbeVI67aE5Xn9m5LGMKEzTBFDEAIbcxZZjQXWefV4yi2cpuEXhc2ldJM/jZFQO4qWyGfONshT73ymggjbA4pGAKJyc8bWYrCoG63RSc1hyayfIt/WSyk6XFB7jvKySYymn689kIIEfYFUjAIEqsXVO3oqFbLpdrnBX9xrlNAhE/9EZKFuaOoIWsFSrXdkLIRE4fbiSDCvkAKBkFiFpeSvg0jwp+efXkfKgNDWFGKZ78hre/29YKtjRLr8N/MHw1fo9x681ScZe4xhcspX9mFT9PGt/oi5JQ8Nh8BGK3ai0Dgal+tweRtVf8RYCNeeVKIcvYFFTEUFeshN/0EPvxsSS1GaVvrHflcvs3ZE5LTZ6u46pheJth9+F55XnJjjBA2BxQEfxAmmvHW2uq9+hPKgNHDrzIE/3gVuVzc+YwK9W7VZu9Hvkz7nsFJstx9yYpJFk3YSM5eAvO3YwhEKvSc61StmdBv29CL85iSJGjM6e9Bdnt/Z0xRirJ5RsMSoJi8VNI01ezXKWaosetMQ9S2f3t9Pdkw9d6XVJ1k9jEdeWEvJYqUNDwZKK4ZYJg9zpSzvofXYqMuuKsdSp+rgMn5OrhCCLgvkJFBzFhq4NJEBZcHXle/1ZLcdvp1MJDYd29BoU31+8qhMoq9CInv5f+NWvkbQewL4mAIJeZUOQeLUZ+SHXZfld3zvqrt96qH99rQ/cTvKwlUt40qX8f6/XgOFOCITUFHjFBiWpWRsDgTUku2YOSvbcM9QzflY9+9yNHUvCo2Q1z3kyw/Nkrou6L07+x782Sd5PaEUr1RsMhAPXZDovHSvqSJm30bWZVe2YcC1/V071sqmcWPOd9h4roviS7Ji2NG9cbAYrriyTclt3i+hoihrNQeuCwdy0PxdeRgLp5SSNR62it1iBDEvgAOBlBiQpUSsCisbOisaqtjTp8RBgOuZP7dXlAa1TzxxnErmaeHQOq9v2jnMAXfl5SdpfgxF3njLiXvKP70JNWYFvTkc/nGyR95Ql+TY+Ffs3vEbUiF3o2dohx8X3J2luPHZCTlLiGYVW17bVnzF5HacVRY6JE4nb+8HKUoFvPONO3dMDDKwDfP2GmGHxPbNyJ8yGryxBLP0HLvjj7ejx19TS1YX606Jf8xW67sd8Eh/6/Y9q8UfF8ydpLgx6TgjUSUFNMYbfkN5VT6YQyvbGUVHVB2mX20wQTjULK/N/RUnu8dEaXg+5KykxSfsII3FnFIVCi2fzER0D7XaevD4gPy9uWNYrR+Nj3Lj+//6+vbdW1Lduvy/ooTWoHkej9SA3LgTFBnggLDlgEDt2AJ0P/DHCRrnVq9SeJGd/U+c86q4mNwFB8EfN5VmAD2BIDXhsc2LejTiGgEXwUEIFm6JB2Uhr7xU6S7odG1hlMV9Wl7bdlrDDV6r4fNMOIEYYcXptgcu8/Jb+4C0tUtEzbRplDISMjSAJnMWmIhkfdWXMtttVltv1mgJgg/AWj3QL7NEEaM4kS6iLyX/r72O/siYa5w0VWow8YcWgCNou6DdOttY2Eh3hMgZAdQ2wyhzyiCXaIP6XoSSGzS54zGfcFpCWS8d75JCJhKWQR0JCQ1v9kGFlI8AbJ0kKjNrkVkHPIxssh4QbZv198TuUO2Dw2V1E3mS9JrJ4bay+Loc/KbimKC9hOAfC8osHlOlxaFpcJU+YuQ1qepRkvcAhFvIuOlHhYTm1edOvgCPdN/a4SJeo+LkR1EbXOEAaVIL7zGMqNHgfKbCYuRM+h5ao7GpNcMNbhvNx4b7p4AHXto2mQHfTIRI8E0nu2c67IuyUiYdOqAkYbriQvikRvHtoKWUEt/K9VMuHsCeOzBaZsd9NnEBefclHYAWJx31hWZk6STvlvSTIaGlhUtLxmJNmnv0nex7E+4e3x47MJpmx4M2EQCErPIt2J687h1txljcOUkAO4vWCdTxp23sYaM5J8Halhg9wTg2APTNjsYsYnk9reKPaGl+ztOfZQ7xoYsr5g2lDCOLAX/uJ9c9U0UNvHu8eGxDaYdZi1i4ijYrVPXQCdebwNE3IJPWUMZUytE6Gswieia29rb26HQhLsngMcenLYZTp8RBYZukloMYHSZPnoOugwPga7oNzZut7+CSZ+i2LiQTC//aAHeEwBkD1Db/GDEJw6kiykw2uCq+PdF5mwLpccWbJT73r55IIh4jVn+UsD0A6CcAM94+MfkE/Ez4dWdVW7IsdTbpJvs8VaYjbmWX8W9Fgg/Pmh3Qb7NEPqMIgiR2e7OEghpl2nsXG7BG0sR1X0r6ewS4Wi0xvUIh4nBT4DZPYxvEmsRDzfUo+Ewpw4mqkCCZC/l9wSYeyOLvtC0SNZA+vr0oLYR+PERu4fwHYrTp0RRiC6N4AdXcJR0K+ULGnLhORX30toMuqNjDnCXeoPvjkgWej0B2PXAsU2xeYxcQbtyCn5U6YaO2Myoc1YetgJ9aCNIVNoh2Vy8LxnyL+7LBK8nALseOLYZNp+RAz4ocuHZuOuvuN8mtZlVDmLB0S59L8l2LvKdpe6vKkMTvZ4A7Trg2GbYIkZuQsaLyvie6XaQpiVLS9iKGHynm6pAhnaquczICfoNpn8i2OOhXQ8cO+Saz8UV5meG6mi+d/yoMWe3TpYarWqzvrMie1BUJ0MFX+bOArAnALweQDa5tYiKIze2r36O8RkVm1CbKEO4Jo+a1reSgUpJDoCs30iPUbLg6wnQroOOHWbNZ+Iyhz0aYxW0zq76nJaRssiLGGNp16bGHTSKgjikR3xBBwO/ngDuOujYZtZ8Jg6IDk5J1tCHmoXJtII8B5GSmrCJILctPrbK9zbjIVpM9HoCtOuhY5tY84m4TU5mTZVwXEeUe/ePxvNJNALYViILlJ911FnLKnp5Z+aZ8PUEcNeDxzaz5jNx9IV573TnT6Ohnz6HUMceYpO4aupeszdQ5SJlJKD5vQg34esJ4K4Hj21mLWLiNtn8WlVR77S8jkwAVEDx17ahqIs+Z5H8dfmZohox6FclLPR6fLTroWOHWXOJOLKuIPyHIiMeayuPIZgra8a8miY9sPHaQogky+9zl/VejRhA8QSw0oOhNrEW8HCLDHmVnSM8qVADlrxI32GuRpzKiMPZ5XlXgNL4F7daSPH4wNLDoTat5tJw6A83W71aSi/QsVLoc5OEfK4cJ6x7D02KnKfgKCZ63rtCCyieAFh6QNTk1SIabnBpiLyWNn/dlAASJdEriEJWa0KvHST3U05oyODJt1joJ1Q8LrL0cKjNTUVcFg8okreuooMxC7lFMFTyM7o+f+6jO3RaDFO/LuX6agsnngBXejjU5qZcKgvJJqV2eS39eU2X4iqrVd1vMsJaidwQmUInZBFJBwD9LhX6iRNPgCsdHOqQUy6XxUFTqyL5JG1730ohZNerbJOe5HwrhQgmjiRoqqG6/VmEiRePBy49JGoTUxGRtYHr5ZPGKuU2xUI+epcNpSXq1XwC+mmjyjcSqip/KbD5iRaPDy49LGoTUz6RhW0tQjUDG+2y7hxKMsA8nA0ZQuSS98XA6IOXZHFc9/0swkSLJ0CXHhq1iamIyJqbDJlIPjox51uwRR6IHTiKrWrS6+uMEKg2OQkkVbzY1UCLJwCXHhi1WSmfxWKLv5IuAfkpW5+DciTOOiABYk5TXgvErBik9PJOaTCd+wnAgIMdbBILf46raBUZ2od0q6QhPSLAKFj+gtMWfD0B3HXgsU1K+QzWLF16w1fc5vZ1L9Nxz8sReUGmgDbmlEogiWghga3lx8aY4PUEYNcDxzYp5ZNYuGXmWeG8CkKw9Vbf1FWlnUlBvv3u93qcvHcd8vfk9uvLh1rg9QRY18PGNiflclgDjnTJZMCKZKBa7+34IsuaREUpANdcgY7WGFKgVBGxf7XPNqHf8ZGigyttTsrnsOiTSD/VfpdFBv8WtwPPsckFwT92v3AWV0eKDhLZ6fwohIn8jo8UXWRpc1IRh4WeV2rAcZsoJpTbSKXChTMF8esQ59fg1ps8B1FgLW/Cuwn9TgAVPWhpklI+h4Xa5XmjFOSd7EupdTIURbxWJ9XK97VgSaasAUN0n5Mwsd/xoaKHLG1ax6eBOtKblwIgMv1bqG96Tpt5sY8rBHTWnWgPXW5ry9oWZj5814D8xH4nwIoOtLRpnYgGQtmX7lEiMV639TG5dUkgKNgvPf+GYreSkixiAvS+CV8W9jsBVnSgpU3suDwQTms3BTpaLEKHTJH4ku+s9Jn9Xsyifp61FlVX9A9fFGohv+MDRQ9XmpROxAARGBYXWjDRI/VbXUL+c+oS6Cw/A+DbWnK9SxJOJvdtHGDivuPjRA9XOoyOzwChv36SWYrAyUnxJj2nVKVHyj98WmgXTJlaY8qaBzI93r4BBuw7AUr0UKVN6Lj8DzJRhlhizHGoenvXcemOkIl/z2A7LpitBE9U8gjj1mc2p4n7TgATHVjp8DkR/0OitKeCCuTX3zF3GXNN5RwwgH7pa1fGiEBZA3mLp/GsjbNOgMtsFGcTOj4B1Ng56DfR998kcgTwaU09h3TPAVUftNVZfqfz/spHMFHWCVCZh+JsRidigOZAn0yRcDq4dkeeJVQXykHQItLnvSRl0k8UWZlA0o9GWDDr+LDMhXE2peNTQBWrrvKxA5M5iz6mzcJ/ndGfLn3eSo5MWhIXxLrpi3y0cNYJcJmD4xxOx+eAkFq5peazcNejcdt7kqRIE6TM0Uq/9c8kcXLbVBACfK3CxFknwGUejrNJHZ8Ews10l2EEBf5RKzrQ10a6VmAVC+W5Rd8Ly6ZyVkp9ExhMnHUCXObhOJvUiUigjYqwpdgo5X2rHsgQZD2KCXm95dq7yKhEqPzYbxaqCbROAMw8IGdTIhGFsqQVmWCLPT6NGJPMKcUaxFYITsRYnCRrSGii+16dW0Dr+MDMA3I2JeISKAAUtF3y1gLluLeL6KK75RwGSrhupTABr6xCT4HZeOkKE2wdB5g5GM6kQwLyZI4qFxMw/pp3hMxVUQOuJbvgkIIRGa2XkQ/05nlZTvEEPtTzuSZxgp/pZKU/GWqde/q4bkSz/PdIkKjzZSks1HcCkOiBSpsKcakTrk/QaQn47nv7yU3Yl24fycb8jINOCwPbZVurWp3f5QY/QN8JMKKHKW0iJCJORmlFDX2Git7mlWgzy4YioyV5u+MHSI1zEWEnTJZfjsJEfSdAiQ6oNHkQnzbZuNGR+kjcjHT1bR3H3JJ63EbYS+M69MFFmq6soXDI/vYE/ImYToCwPERmMyERc7IrGkHIKtJQs5Oh5TzEEl9LByuN7bhuoPMEctaIChf9SJOFmE6AsBxE5jAhPnOScecpYpk5F7jeIbcDTZjl95rT+NSk4vpqqgla+StV3IJMxwdYHh4ziZCIN1mjCrkNqh7mQX8n9ywdltHDDyGtvnalJIKRwZ6U/phLEzCdAGB5gMxmQiLmZMIQqpAnTkr/GxM8JGZJDwJSme9l4ABalO/sdJ5v9xwTMJ0AYHmAzKZCfOoEfXlrVlXFVPpxKRUMwhALSz52fIZS5VTF9WHcKx37m6tmAabjAywPkDl8SMSfjCaUGjBOmkJizsyXP0tcWuoapGJwMIrZ5YAITJT24jcDL50AXnlwzKYSPOahopvCaKoSI6EiVB+DPFEGUCgOGEp4FGQQJNmJjMLF+t4gm4DpBADLwWM2lRBRD7urBCMWajfdnASvsVNBo/yijZ56kboV1fZVv9qq/YRNx0VYDhyzSQSXc9icDlOmuuNSpXSNHoMEYjYjaILAWdzyVtr12URxCu3rW8dlIpTjAhoX/9gkQkQ6EHJLqs1Nmhf/TQbYbO4KwIug99b73rG5IzCrObTldXEGQjkBoPEAkM0h+JwDWhgU6V+T0Y1H2eKKCtVWWXXIcFBEv/S1+WJTnk/Q3m5YJkQ5AaRxIJBDIgScwyQLr8KNUqt8k703gFeWNeQ70ZHHqKUmj8HgyndYlAlQTgBoPABkUwgR5UAhipBLJOLjQ5GRSkxt90oCVLJOxUVyONpCq6aT/XtDCBOgnADQeADIpBB8xgEXDmSJxWNxV9HLRBTaoyIqQQBJ86cbZj0laXNMi8D13RNDmBDlBJDGg0AmheAzDsjTLbsrpuhaz4BmYWh2KtLU0a3tXlg1EhjOO0uwsq9lNfHJCfCMh39sBsElHFAMVlYSi5gGuZM7+A6+IolWk3VI9Q7KK4pvEhPsb0cIC52cAMw44MchECLCYRFSaQooUK1077ESAaEia6gEheu9r5pgoZcsQnpP/HZxBjw5PpjxsI9JJTi0A4bl1rLFl23Mw1r6jE5bNeR3TDlZ953oQa2HMNbYry6YnvH4ntRxvA7xgD+fFD5tldOKecP6OwlvVsUlcVxfhX4WYDo+wPLwmB2Fu0E7ougifVPQdnBov96Kk0qSC5lQ5KxMZMW1O+q5RXHRsuEt9LMQ03HxlYfG7CA8CtqHzDjgNdDa5Tq0o6BE+Q6gl60DLjEnpzaVjU5+8i3kMgHHCQCKB2jsGNyP2YFBithbkh9SUB3AjuQGCihEVtNkkkjeWwi0Vzk4GLd3nLeFOE4AUDxAY4fgUchOzmh0Vbsxb6k/eiss2VKMaC231g1epcqSK/n692LABBwnACgOnrFDcD9kp9ABdbXysSQ+S0v3ce/AoBmpPmOrE4BFA50gx0DGbb7diU28cXx84uEZJwL3I3Z4ZkwDEhHHwJZ7dVN35r8HXCQ1bvfqhtxdVpUojRTg0WoTcJwAoHiAxg7Bg4gdnWjVSjc6IDEDrZNPRFdUWQTBqHbTetFmSw1oBjB++8NZiOP4AMXBM04I7kbsmXkatfi00k9Sb+OuyrwEJDjXm0ucUSAm55ahYq+HsADHCQCKg2fsCDyK2CnEE7iU8Kb6eQ63v1y/JAiYEpIXLuPbCh2AR79oYAtwnACgeIDGjsH9mB3Yuw31EZC8cjs0ASKKWmOYYb+vrWhTLWuj029vk1wTdZwApZiQxg7Do7B9ry5VIrQC8mL5JtaWQUZ186eCwfwM+9pIh8iyBHQXeiNky7+fAA848MEOYYOIF503xcxgfNK4maEUZPP0KPrUNucc96UkzEXVZBHEeYu5TPd+AjjgwQczgnUDXmwX0m+HyDcFQJq7jTqAxRQPFoGx2VVfS2CGb7mg61uv43+3/vnp3k8ABzz4YEewUcSL+gBsHqeuDp3GltEnBk2c+PeKcbv3KoOUaQyRMlSAju/kzZ8e/nh4wEMPTgDrB7woe5ZBUFgDXELR52CmpXo52nkdxtaAkzbPpsCZo/XZswbLwZ8AD3j4wYxg/YAXuW2D25NCxMdTlVwLuQ4R/YLJ2V3hCaG+JErdefD5m7j507ufAA146MEMYKN4l6wVQiVxZlNzYDAwnpx4kd8pcuj3rRPkiHwl6bT0SPvdPeencz8BGHCwgx2/+vEugG5KaizJEq/8aaoj+f/Mu5EofS4ZCiyI/N7IHn7lqlnu/fhowMEOdgwbxbwkbdzeXEyaigxGSpLWF1FpZIHPC1mgGVskvmLk6He35Z9u5QRuyHFbTtCLWLsSkmiygwuTlG4GHYWUTOlDavbs4zsd8wfmOAFEcSCNE8VGUS8BQ/QDks2YQwnixBEMa9zCKAhtg0LvpYD9Oq5KG/D2Z7UwxwkgigdpzDDWj3oH2pXzRTvWUJpOGuqo6K/cdwGt/DC+5t6h8Kg3tUBkHN6yfNNlH9/DO3jAjgD9iHFhlkBR61DpW/t9DkIiXcOsuX2Id7LRS9cA0/jV78Ty2Mf38C4isGPAIGTcJNdVXWxpNyItkG2ed4gqE9rSS7yjkaGuIWEu5duv2PLXJ/DvDh5wIsAoYkT8ukWaMDetXuZ9tcJVj7QIWGDBj/RechFcPQlNgY17mzxYHvsEHt5DBHYI6IeMHQrSVYrRolPIMhSsYoh4l1XUnMdtqJJ24V5tWAVty1fxh+WzT+DjPUxgR4FR1DjpxJJKubDzf/tDZh3jlou/lnRUu9hiwk4noCWrW10HaP5OA/zhtk/g5T1UYIeBbtQ4kYfH7XYg5fTWfpsJj1YrW63Ftav5M9IZuduq8eT3viaAWG77+G7egwVOGBhEjYTIuI0ExINe2+7vBS1tRbMrPVCu4OlzyMygCwAvgk7kbW1q+u0T+HkPF9iBoB84oqNH5ewkrILeq+LX/2Glzu2eMOWNTIH28kPVDzd05OK4tN/RAqaHPIFH9TywHQr6oWMDIZWaejXYxazPocCjyseS8U8fx4+yS1WVTqt8i0BMF3kCl+q5YDsUjEJH9L6Tj6WlrQ8PTvEtdyXCE2uqnwEjqGfCfQQWkdp34r7lI0/gUx0f7ERRftQFOqciN5jlo9JD73NI35lp6DzJrM9bGEASBAPNykJS8t4oW07yBD7VccFWFOXHXChQ5e59vK90JOV2f4V/AxeIFRRySENfSrh9LZF5Op79QljTQx7fo7oe2I6j3LCLXCbmd6gzWE0Vix7TCOGg0rBjiNbQzSggQ3trsuhCPqJ/J339dJAncKieA7bjqCjuIgB2j4IEXOcqoKxv8BRgrGLKNG55LwpD1QzQf3snLJn+8fj+1PO/TiDlB164yV3XpZEn1R6qaDY0+KYCayhbrcBERENPUXVP9atu1rTKJ7DintW34y6eUTllNjhbxrTSTSWHlLFqYXBp2e/MJNNtn8DNO6jAjqPcuKsCabR1NwNR5X0OdoZRMqayDnWBmMrat+gcX4G198rAdNsncPMOKrADqSjwIgODkau8iJpGuhT2QlOnzItgxHOpcNz4blkcqel8JzRaDu8E/tHzp3YkFUVeC7Ns5b1pVb30p9/R9bvIIjCkL90RmfREvjOHGUp7vcm+psc7voN0/KkTSrmRF5fZfqTjUwSCwAmqIopaMT/r09l+9JXkIKZ2qf6dRPTT3Z3APXru1A5BopAF8+hVOhb5nXnJ050qT+noGCyFqyx9bycIoL+PxBTnb5NpubwTuEjPpdohiB+yLKZYp4oHmpNfWrVzUQj/DhPyqZ/PpLi8q/Qnvdf38sP0eSfwkY5LtWOQKGZZs6D/PcsHGef5GZZIJzTkiJBL0K7LnsiolN/p/d9515bLO4GL9FyqHYS4QUvn8WlJ7D4Q7L6NFyfQiPyOAqt5M5jQgyGJ3BNqL/v1XpbPO76L9DyqGYL4EQt3KeaBolgDPTHdYioMduXfMTXrTnydPO9Ml1bRXvsVJsu3HN8Xub7LDkGCiAUk+JI19Nq10WUFmTx46gvCkIXbJX0tPN2WcyiouPmuEP/pW07gizzfZYcgfsgy0R+eCyF5vghFUzeC6rC9uopa8i0dL5i5qItIs758nulbTuCLPN9lRyB+xMIopasEVDpa0SwU1CDne/IiBhzSZStbzkn9B/rgvAGO5VxO4Iwc32XHIH7M0sgGbdkLnEna5T6njj3RxQNrIEeVL1mJA5XHpDTqX2p6f/qWE/giz3eZ6N0H+wPtnIrqaa7c2kAeQ+qU9RwanZvkbDQ0WNoDPC+ErL8smOlYTuCIHMflgPcI7GNyue4r2YBym8llZHuJZWqIy296LX03j0DGEkjX376Ipj07gf3z7KWN9XmqfZ67y+86mWnKWLAqn4SGNu/4S9PXncA3er7URu4+0oc1WiJ6Az1QRv4MJSMxbrJFFQ1ebg/jRl5UN3ZmNUxXwC1nd1zf6LpSG7n7SB/1UoXHj2ERSFWt+hzULxT+maQt32ZyiWGwHAShunfQoeknTuBXHDdk43Yf5wMzdXDEvK+rt3FrUwkPFlFFTNDKNwMEd3lNfieY2N8espaXOIFT8ZyQidsjmI/hSxwicZumO4VicytRDgux+jTy9XGY9pDkHBpB66+h55aTOL5T8ZyQg9t9nI9EZgLG8rVkTookKaOoYQ3OnuncDz99kifI+2yVJviB9yRMN3ECt+K5IRu3Rzifgj6JL0B4pDLuc5L4E9iVpaySdGzb8pGl9/x1B2V5iRN4Fc8L2YA3AsggJtVeIumnXQqMIBo/pnMvgnKTKBdMvvx5TnO8pSqmnzi+W/G8kAl4I3w86Hu3LoH8ifZ2RJIeRy2c6z3v8B+ksO51FaXXr0DZtMgnsOCOxXcArw+QUSe1uqKBUnud9zl05GOLQvRVb8Ol1Beng3ceCpTe9GvDIJ/AfHvm3ka7ATieexYVbzJ6dd00+9SkmyUWwIO99LWTbNfWJaAI6/uC/4dBPoH99uy9iXUjaExLwA0EiwamcTT9ndSfOwUj9Fh55utOyKtBk3j5dDov2DAt8vEtuGfxbbAbYeOB/hUi9hv1bJdPWlISiTUUNAbXt4Id1D9Hat07w8G0xyew3569t8BuBI0XiUOTt0LEPhfsaOPGyonUz60i3EB9Nm4hDBkjN/QWPRnW+AS227P1Ntb1sfHmMo2hsjGXtgRGI+eCYWWyBvID6zbHQWQ4VNETC85vy2rYshOYPs9U2uiYZzt0pTJlUNq+Mx+KmoyOkY79q9+24SJO4FEcD+QARR9WDpKHLSJAUq/FzMjgRlUdVgASeY5y34qKnyzHQAI23h4IhnU9gS32bLcNEwNUSU6ck2h750KEqT/T3zI8RFedPfIdAjFT4aZMsMhpzP12ErOs6wmssWO8bZDog0oKMgl3N/ko1DR/CJVJnoIfj9bpXfvXsCBwggkLfS/jvc4zzesJzLFjvh2QGIHKUTpXaGJryZ9+8r/WaJJ3wQyUjtipi4tfljwnIbHwjdkN+3oCc+yZbxMlepASWtnX1BWU2+WUW/U37taEdoqknPleSJcivTq4KC71r+5Vlnk9gTn2zLcNEl1MCXZZagaxrbffSAOZh/s3/tZW2u9LVRR0NlkD4ZOvKdWmeT2+OfbMt4MSXVAJf7jSlp/JeOt1OHqlZkmk564Q6U7h48y+hMpaKArtXXkBnGXMTmD8bFNpIywfkaHt8ubmodjYmxNDj8FDsui0DpyVlxZSrSRLGJj3+wI4w5od3/Y5ltLGVz4ewxzrJr67oSdcW/c5ZDYxN46vQz+jNSra1Baw+yxi3Gn3bc3905adwPY5ttIBWC4ey5xzV2TnxmyaO84ZqlMejwuhvfrlA8gCtC6ygt6vrySZpuz4ps81lTbC8hEZWj+2NGXzyN5rHTCnVFZJRuT+ie3DH2QKSdRgYYjl6+VMc3YC82cbSxthRYiM/mUbsgb6413urR99ILcWwhryzJ+xy5hJmeQr8+717b5qWoITWA7H0JiITJosC0Xa0CruJt9hJOXcLMNoHFrm16WwZWGPb5Ad820jLBeRdWRmD+HyGmfhf5pdV1QaybcOVBwUfS0FfGvI3yMJ4x0VbFqn4xszz/bZACUCNIBXW37POKfbdbiUyrNBOaG+r8+oDOBFWdtq7etC2DRPJzBnnvmz8YmPZzb3sEPLBEjHLGI6OCevjsF4llBKnuWTL59QJ5jlhMhVvvdfpoE6gUFz7J8NUSJIs0oR+oIWgfZjS39P5GrYMtEjB1eo/k0LuCe3bMYiaK/fkmnTPp3Annn2zwYpEajhLvYiIHPyNZX8npc0igR/Q3ayXvuKAKXI32M6/F+uj35aqBNYNMcCOiglQjUUQ/IcLgEf82aU77ok7Qods7b+OYqfKaKeckSNHM+bWmbag+PbD9fe2DglwjV0jFyj0KUv+bj5VanxIKyOdM+0b0nXBMAV8SsYu/liCMscnMB8eObGdvIRKMCcELX7FUM4psZtm4wp29LCHz5u3NYJWarYJ9rqr9xdyx6cwH449sZx8hEooEMs+l4KVce6lxAbXTLyL7nDWnrrXLnLrmB8FBcumVD5+5Lgp0E4gQHxDI7t5n1YMMAc96vC4OyHPoe2f7B1R6f1pu3AMACCFs1KxG3Z5gsjTF06ruq5mmoDA+4gWHj2LR81UrP1MbRDPI8GU9PJer7XL6aROoFR84yg7ekjZIA5enqiaNHV76A2jCjiRVNk1OUc6K3A+kvWgEELf6mK+6t2n8AWeLbDdPM+Kui4sFVtXDdviH096aL8DP/0zKVXJqhwPdPXsC9LtY9vCjzT4fhH358Wvh6UvcZY33ndbF/ka/CYTGJAK976WtqswZwBCtdWelvcmpp9AkvgWQ7bPfruFGK4hIwpnGbd9TEwKLxHyLKm9VyWG/RlkT8nK7PfhiWmYp/AEHiGw3aPvjtF4jQDWl4EMMfNXmxkaTlRkFbReK6UvDdjrEOTVVD08FUK/VOJTqByjoo67tFzppn/pMk7SWZ1FCnGJ1ceONO5afee96UoslvyO/1Y394GphodV+k8FbU9o+9JkSnUUxL5Jiu31p12Sy6DGztxWVdSiQRVQLrEPgtdvHJv35ziTy06vtJ5Omq7FN8FIRCtXTeWAGNud0JpmegIx79njQul9yn35ON+KRRrvQ7Lkr4TSKsj3Y4L4pY47S6BfFff9zG4H9XtRl3a21LR0uvjmwHPatgexfM/E7lHPHgePB6JguaTDdScDd3UhKL0qW9NifZV/h7zSL+k21CJE2iQrW+2R4k8ED5bl8Bm9d7lkk1OnKeZuIv6ZwABbVvqcvb078rX/ailEidQIUflHJcSuSA0VeZLZAiVJo+izAz5sIUXQbozx525R5FPSyIshHfTm0BjqMQJFMhTONsUR6Z7as+Gzl1jtN/KQFzRhLVGOWzTOzYMcses8yEHlPp8Z86aEngCibXl27bGgfFGGe+ST9319sPBzLPMDa25zeRs/TN9F1dfSf5+9JHf9immBJ5AYj0Jt81xZL4pbh6MrsjV1bTnRXZbxgNIde/NMEIkQaEECwDqubQZym8K5acIHl9iPQG3DbJnvjGkQZgvtGcZ/dOgF1Ac54Yc/dFTvbRNq7vw4UPxZlov9rEE8AQC6wm4ab7xcxpA4bLd9EmfAo1amhw+KVXd3zzCT4E6vgA68urYMt/2wYHsIYJBcVWet0XqpGOCpoM4Jf0S191R+J24RJFLIsrbes2UphNInyettinzTR+hulYZjjcUsM72mV+BQ4OXaVwYt+VKAykfbejaBhrevvygJVDHlz9bWG1D5hu+wb3NNr+TQoiRPoMvgTFkt2l7NN8BCYO47uefcTv1NcfOOrrjn7QnGLYJ8E0GoQ8K04ZsdkPB89Ln0MkwP8H9hlqud/pyaoNb4cAkl1neUzCP7vhH7YmGYwZ8s4EkptWqvLekomOKh9gz5ACiRIiCnPZJc6U9gjfhLsRk+F9cbJ3f8Y/bkw7LbHAG0MI0OdkiXH1nfcrCTLYpG07/P78EgnUcJzg+77hNHYpUjhQ6Zdk/CtebBlhIbxeWqiGNAiGZvnbiFqzz3+NWO33HGz9P4wSn5522qUeR2qG7VpLDbgBO9y4A3UqgKjwhFZMu9LUDzYcz/z3uuL9h5c+Vn2CnvJ21JdCVWNzzkjdB5kbjquV5behE5D74Z4IcM18bivumJH+OL/hi8K2Vn2CnvJ21ZZZvSOEW5DlouSvIHzeqdMxINmy4Lh3la2uttRx36e5OmcfnnzbqIjP3eGFE2u61Aua6ZMYODWOcblDLnSDlnAeo3fGVDGj8oxO8w/sme5+YNEbuf+fHz7zbhz1Da0f5pvSU0P/Tr//44z/+yL/Sr79Pv/KvjSbOPISqTO4SuXJGX7z/df74b3/++q//nT67/Prz//yR6B/8+b//+Jdf/+Wf//Pf/u3ff/2/f//P/3v+59/9+tdff/6PP/7xzz/+if73/wEDcflcCmVuZHN0cmVhbQplbmRvYmoKMTIgMCBvYmoKMTI3NzQ1CmVuZG9iagoxMCAwIG9iagpbIF0KZW5kb2JqCjE3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggOTEgPj4Kc3RyZWFtCnicNYyxEcAwCAN7T8EICEOAfXK5FMn+bbBjN+glHQoEMYWNgyRTpxOt/KC3wXXS05BO4EHKWJmxbeq2Wpdc2d6r9sjar89Df+1Oar1IJGYCkamxB+92fQtHHgEKZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PCAvQmFzZUZvbnQgL0RlamFWdVNhbnMtT2JsaXF1ZSAvQ2hhclByb2NzIDE2IDAgUgovRW5jb2RpbmcgPDwgL0RpZmZlcmVuY2VzIFsgMTE5IC93IF0gL1R5cGUgL0VuY29kaW5nID4+IC9GaXJzdENoYXIgMAovRm9udEJCb3ggWyAtMTAxNiAtMzUxIDE2NjAgMTA2OCBdIC9Gb250RGVzY3JpcHRvciAxNCAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2Fucy1PYmxpcXVlCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDk2Ci9Gb250QkJveCBbIC0xMDE2IC0zNTEgMTY2MCAxMDY4IF0gL0ZvbnROYW1lIC9EZWphVnVTYW5zLU9ibGlxdWUKL0l0YWxpY0FuZ2xlIDAgL01heFdpZHRoIDEzNTAgL1N0ZW1WIDAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9YSGVpZ2h0IDAgPj4KZW5kb2JqCjEzIDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNTAgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyOCA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTcgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxNyA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA4CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5OTUgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjE2IDAgb2JqCjw8IC93IDE3IDAgUiA+PgplbmRvYmoKMjIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzNDEgPj4Kc3RyZWFtCnicNVI70ptBCOu/U+gCnlney3mcyaT4c/82AjsVLLBCAtICB5l4iSGqUa74JU8wXifwd708jZ/Hu5Ba8FSkH7g2beP9WLMmCpZGLIXZx74fJeR4avwbAj0XacKMTEYOJANxv9bnz3qTKYffgDRtTh8lSQ+iBbtbw44vCzJIelLDkp38sK4FVhehCXNjTSQjp1am5vnYM1zGE2MkqJoFJOkT96mCEWnGY+esJQ8yHE/14sWvt/Fa5jH1sqpAxjbBHGwnM+EURQTiF5QkN3EXTR3F0cxYc7vQUFLkvruHk5Ne95eTqMArIZzFWsIxQ09Z5mSnQQlUrZwAM6zXvjBO00YJd2q6vSv29fPMJIzbHHZWSqbBOQ7uZZM5gmSvOyZswuMQ8949gpGYN7+LLYIrlznXZPqxH0Ub6YPi+pyrKbMVJfxDlTyx4hr/n9/7+fP8/geMKH4jCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMDcgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagoyNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI0OSA+PgpzdHJlYW0KeJw9UDuORCEM6zmFL/Ak8iNwHkarLWbv364DmilQTH62MyTQEYFHDDGUr+MlraCugb+LQvFu4uuDwiCrQ1IgznoPiHTspjaREzodnDM/YTdjjsBFMQac6XSmPQcmOfvCCoRzG2XsVkgniaoijuozjimeKnufeBYs7cg2WyeSPeQg4VJSicmln5TKP23KlAo6ZtEELBK54GQTTTjLu0lSjBmUMuoepnYifaw8yKM66GRNzqwjmdnTT9uZ+Bxwt1/aZE6Vx3QezPictM6DORW69+OJNgdNjdro7PcTaSovUrsdWp1+dRKV3RjnGBKXZ38Z32T/+Qf+h1oiCmVuZHN0cmVhbQplbmRvYmoKMjUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDkgPj4Kc3RyZWFtCnicTVFJigMwDLvnFfpAIV6TvKdDmUPn/9fKDoU5BAmvkpO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plot_3d=True, title=\"Steep optima\")"]}, {"cell_type": "markdown", "id": "7ef1175a", "metadata": {"papermill": {"duration": 0.332877, "end_time": "2021-12-04T15:56:34.786176", "exception": false, "start_time": "2021-12-04T15:56:34.453299", "status": "completed"}, "tags": []}, "source": ["Most of the loss surface has very little to no gradients.\n", "However, close to the optima, we have very steep gradients.\n", "To reach the minimum when starting in a region with lower gradients, we expect an adaptive learning rate to be crucial.\n", "To verify this hypothesis, we can run our three optimizers on the surface:"]}, {"cell_type": "code", "execution_count": 35, "id": "4f8818bb", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:56:35.429165Z", "iopub.status.busy": "2021-12-04T15:56:35.428679Z", "iopub.status.idle": "2021-12-04T15:56:36.013195Z", "shell.execute_reply": "2021-12-04T15:56:36.013583Z"}, "papermill": {"duration": 0.911295, "end_time": "2021-12-04T15:56:36.013752", "exception": false, "start_time": "2021-12-04T15:56:35.102457", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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optima\")\n", "ax.plot(SGD_points[:, 0], SGD_points[:, 1], color=\"red\", marker=\"o\", zorder=3, label=\"SGD\", alpha=0.7)\n", "ax.plot(SGDMom_points[:, 0], SGDMom_points[:, 1], color=\"blue\", marker=\"o\", zorder=2, label=\"SGDMom\", alpha=0.7)\n", "ax.plot(Adam_points[:, 0], Adam_points[:, 1], color=\"grey\", marker=\"o\", zorder=1, label=\"Adam\", alpha=0.7)\n", "ax.set_xlim(-2, 2)\n", "ax.set_ylim(-2, 2)\n", "plt.legend()\n", "plt.show()"]}, {"cell_type": "markdown", "id": "494d99c5", "metadata": {"papermill": {"duration": 0.323237, "end_time": "2021-12-04T15:56:36.662651", "exception": false, "start_time": "2021-12-04T15:56:36.339414", "status": "completed"}, "tags": []}, "source": ["SGD first takes very small steps until it touches the border of the optimum.\n", "First reaching a point around $(-0.75,-0.5)$, the gradient direction has changed and pushes the parameters to $(0.8,0.5)$ from which SGD cannot recover anymore (only with many, many steps).\n", "A similar problem has SGD with momentum, only that it continues the direction of the touch of the optimum.\n", "The gradients from this time step are so much larger than any other point that the momentum $m_t$ is overpowered by it.\n", "Finally, Adam is able to converge in the optimum showing the importance of adaptive learning rates."]}, {"cell_type": "markdown", "id": "753e94b5", "metadata": {"papermill": {"duration": 0.327421, "end_time": "2021-12-04T15:56:37.310853", "exception": false, "start_time": "2021-12-04T15:56:36.983432", "status": "completed"}, "tags": []}, "source": ["### What optimizer to take\n", "\n", "After seeing the results on optimization, what is our conclusion?\n", "Should we always use Adam and never look at SGD anymore?\n", "The short answer: no.\n", "There are many papers saying that in certain situations, SGD (with momentum) generalizes better where Adam often tends to overfit [5,6].\n", "This is related to the idea of finding wider optima.\n", "For instance, see the illustration of different optima below (credit: [Keskar et al., 2017](https://arxiv.org/pdf/1609.04836.pdf)):\n", "\n", "<center width=\"100%\"><img src=\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/03-initialization-and-optimization/flat_vs_sharp_minima.svg\" width=\"500px\"></center>\n", "\n", "The black line represents the training loss surface, while the dotted red line is the test loss.\n", "Finding sharp, narrow minima can be helpful for finding the minimal training loss.\n", "However, this doesn't mean that it also minimizes the test loss as especially flat minima have shown to generalize better.\n", "You can imagine that the test dataset has a slightly shifted loss surface due to the different examples than in the training set.\n", "A small change can have a significant influence for sharp minima, while flat minima are generally more robust to this change.\n", "\n", "In the next tutorial, we will see that some network types can still be better optimized with SGD and learning rate scheduling than Adam.\n", "Nevertheless, Adam is the most commonly used optimizer in Deep Learning\n", "as it usually performs better than other optimizers, especially for deep\n", "networks."]}, {"cell_type": "markdown", "id": "7c5857ba", "metadata": {"papermill": {"duration": 0.328086, "end_time": "2021-12-04T15:56:37.960730", "exception": false, "start_time": "2021-12-04T15:56:37.632644", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we have looked at initialization and optimization techniques for neural networks.\n", "We have seen that a good initialization has to balance the preservation of the gradient variance as well as the activation variance.\n", "This can be achieved with the Xavier initialization for tanh-based networks, and the Kaiming initialization for ReLU-based networks.\n", "In optimization, concepts like momentum and adaptive learning rate can help with challenging loss surfaces but don't guarantee an increase in performance for neural networks.\n", "\n", "\n", "## References\n", "\n", "[1] Glorot, Xavier, and Yoshua Bengio.\n", "\"Understanding the difficulty of training deep feedforward neural networks.\"\n", "Proceedings of the thirteenth international conference on artificial intelligence and statistics.\n", "2010.\n", "[link](http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf)\n", "\n", "[2] He, Kaiming, et al.\n", "\"Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.\"\n", "Proceedings of the IEEE international conference on computer vision.\n", "2015.\n", "[link](https://www.cv-foundation.org/openaccess/content_iccv_2015/html/He_Delving_Deep_into_ICCV_2015_paper.html)\n", "\n", "[3] Kingma, Diederik P. & Ba, Jimmy.\n", "\"Adam: A Method for Stochastic Optimization.\"\n", "Proceedings of the third international conference for learning representations (ICLR).\n", "2015.\n", "[link](https://arxiv.org/abs/1412.6980)\n", "\n", "[4] Keskar, Nitish Shirish, et al.\n", "\"On large-batch training for deep learning: Generalization gap and sharp minima.\"\n", "Proceedings of the fifth international conference for learning representations (ICLR).\n", "2017.\n", "[link](https://arxiv.org/abs/1609.04836)\n", "\n", "[5] Wilson, Ashia C., et al.\n", "\"The Marginal Value of Adaptive Gradient Methods in Machine Learning.\"\n", "Advances in neural information processing systems.\n", "2017.\n", "[link](https://papers.nips.cc/paper/7003-the-marginal-value-of-adaptive-gradient-methods-in-machine-learning.pdf)\n", "\n", "[6] Ruder, Sebastian.\n", "\"An overview of gradient descent optimization algorithms.\"\n", "arXiv preprint.\n", "2017.\n", "[link](https://arxiv.org/abs/1609.04747)"]}, {"cell_type": "markdown", "id": "717a390c", "metadata": {"papermill": {"duration": 0.321185, "end_time": "2021-12-04T15:56:38.605839", "exception": false, "start_time": "2021-12-04T15:56:38.284654", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/PyTorchLightning/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[{height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Tutorial 3: Initialization and Optimization\n", " :card_description: In this tutorial, we will review techniques for optimization and initialization of neural networks. When increasing the depth of neural networks, there are various challenges...\n", " :tags: Image,Initialization,Optimizers,GPU/TPU,UvA-DL-Course\n", " :image: _static/images/course_UvA-DL/03-initialization-and-optimization.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab,colab_type,id,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}, "papermill": {"default_parameters": {}, "duration": 129.529808, "end_time": "2021-12-04T15:56:39.938276", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/03-initialization-and-optimization/Initialization_and_Optimization.ipynb", "output_path": ".notebooks/course_UvA-DL/03-initialization-and-optimization.ipynb", "parameters": {}, "start_time": "2021-12-04T15:54:30.408468", 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