{"cells": [{"cell_type": "markdown", "id": "8ecd5f2d", "metadata": {"papermill": {"duration": 0.013104, "end_time": "2023-03-15T09:54:13.367259", "exception": false, "start_time": "2023-03-15T09:54:13.354155", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 2: Activation Functions\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-03-15T09:52:39.179933\n", "\n", "In this tutorial, we will take a closer look at (popular) activation functions and investigate their effect on optimization properties in neural networks.\n", "Activation functions are a crucial part of deep learning models as they add the non-linearity to neural networks.\n", "There is a great variety of activation functions in the literature, and some are more beneficial than others.\n", "The goal of this tutorial is to show the importance of choosing a good activation function (and how to do so), and what problems might occur if we don't.\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/02-activation-functions.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "3e50a070", "metadata": {"papermill": {"duration": 0.011958, "end_time": "2023-03-15T09:54:13.388750", "exception": false, "start_time": "2023-03-15T09:54:13.376792", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "b5fc1714", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-03-15T09:54:13.433230Z", "iopub.status.busy": "2023-03-15T09:54:13.432760Z", "iopub.status.idle": "2023-03-15T09:54:16.770682Z", "shell.execute_reply": "2023-03-15T09:54:16.769697Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.348973, "end_time": "2023-03-15T09:54:16.773391", "exception": false, "start_time": "2023-03-15T09:54:13.424418", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"matplotlib\" \"torchvision\" \"torchmetrics>=0.7, <0.12\" \"setuptools==67.4.0\" \"seaborn\" \"lightning>=2.0.0rc0\" \"pytorch-lightning>=1.4, <2.0.0\" \"torch>=1.8.1, <1.14.0\" \"ipython[notebook]>=8.0.0, <8.12.0\""]}, {"cell_type": "markdown", "id": "0349ac26", "metadata": {"papermill": {"duration": 0.006921, "end_time": "2023-03-15T09:54:16.795535", "exception": false, "start_time": "2023-03-15T09:54:16.788614", "status": "completed"}, "tags": []}, "source": ["
\n", "Before we start, we import our standard libraries and set up basic functions:"]}, {"cell_type": "code", "execution_count": 2, "id": "a4f668c2", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:16.811446Z", "iopub.status.busy": "2023-03-15T09:54:16.810861Z", "iopub.status.idle": "2023-03-15T09:54:18.829720Z", "shell.execute_reply": "2023-03-15T09:54:18.828931Z"}, "papermill": {"duration": 2.029888, "end_time": "2023-03-15T09:54:18.832292", "exception": false, "start_time": "2023-03-15T09:54:16.802404", "status": "completed"}, "tags": []}, "outputs": [], "source": ["import json\n", "import math\n", "import os\n", "import urllib.request\n", "import warnings\n", "from urllib.error import HTTPError\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline\n", "import matplotlib_inline.backend_inline\n", "import numpy as np\n", "import seaborn as sns\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torch.optim as optim\n", "import torch.utils.data as data\n", "import torchvision\n", "from torchvision import transforms\n", "from torchvision.datasets import FashionMNIST\n", "from tqdm.notebook import tqdm\n", "\n", "matplotlib_inline.backend_inline.set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "sns.set()"]}, {"cell_type": "markdown", "id": "9529842a", "metadata": {"papermill": {"duration": 0.007055, "end_time": "2023-03-15T09:54:18.851025", "exception": false, "start_time": "2023-03-15T09:54:18.843970", "status": "completed"}, "tags": []}, "source": ["We will define a function to set a seed on all libraries we might interact with in this tutorial (here numpy and torch).\n", "This allows us to make our training reproducible.\n", "However, note that in contrast to the CPU, the same seed on different GPU architectures can give different results.\n", "All models here have been trained on an NVIDIA GTX1080Ti.\n", "\n", "Additionally, the following cell defines two paths: `DATASET_PATH` and `CHECKPOINT_PATH`.\n", "The dataset path is the directory where we will download datasets used in the notebooks.\n", "It is recommended to store all datasets from PyTorch in one joined directory to prevent duplicate downloads.\n", "The checkpoint path is the directory where we will store trained model weights and additional files.\n", "The needed files will be automatically downloaded.\n", "In case you are on Google Colab, it is recommended to change the\n", "directories to start from the current directory (i.e. remove `../` for\n", "both dataset and checkpoint path)."]}, {"cell_type": "code", "execution_count": 3, "id": "5d927953", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:18.867337Z", "iopub.status.busy": "2023-03-15T09:54:18.866808Z", "iopub.status.idle": "2023-03-15T09:54:18.962441Z", "shell.execute_reply": "2023-03-15T09:54:18.961464Z"}, "papermill": {"duration": 0.106589, "end_time": "2023-03-15T09:54:18.964754", "exception": false, "start_time": "2023-03-15T09:54:18.858165", "status": "completed"}, "tags": []}, "outputs": [{"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/Activation_Functions/\")\n", "\n", "\n", "# Function for setting the seed\n", "def set_seed(seed):\n", " np.random.seed(seed)\n", " torch.manual_seed(seed)\n", " if torch.cuda.is_available(): # GPU operation have separate seed\n", " torch.cuda.manual_seed(seed)\n", " torch.cuda.manual_seed_all(seed)\n", "\n", "\n", "set_seed(42)\n", "\n", "# Additionally, some operations on a GPU are implemented stochastic for efficiency\n", "# We want to ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.deterministic = 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": "a98df241", "metadata": {"papermill": {"duration": 0.007149, "end_time": "2023-03-15T09:54:18.983792", "exception": false, "start_time": "2023-03-15T09:54:18.976643", "status": "completed"}, "tags": []}, "source": ["The following cell downloads all pretrained models we will use in this notebook.\n", "The files are stored on a separate [repository](https://github.com/phlippe/saved_models) to reduce the size of the notebook repository, especially for building the documentation on ReadTheDocs.\n", "In case the download below fails, you can download the models from a [Google Drive folder](https://drive.google.com/drive/folders/1sFpZUpDJVjiYEvIqISqfkFizfsTnPf4s?usp=sharing).\n", "Please let me (Phillip) know if an error occurs so it can be fixed for all students."]}, {"cell_type": "code", "execution_count": 4, "id": "579eaee5", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:19.002259Z", "iopub.status.busy": "2023-03-15T09:54:19.001898Z", "iopub.status.idle": "2023-03-15T09:54:21.622999Z", "shell.execute_reply": "2023-03-15T09:54:21.621526Z"}, "papermill": {"duration": 2.6349, "end_time": "2023-03-15T09:54:21.626218", "exception": false, "start_time": "2023-03-15T09:54:18.991318", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_elu.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_elu.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_leakyrelu.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_leakyrelu.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_relu.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_relu.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_sigmoid.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_sigmoid.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_swish.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_swish.tar...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_tanh.config...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/FashionMNIST_tanh.tar...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial3/\"\n", "# Files to download\n", "pretrained_files = [\n", " \"FashionMNIST_elu.config\",\n", " \"FashionMNIST_elu.tar\",\n", " \"FashionMNIST_leakyrelu.config\",\n", " \"FashionMNIST_leakyrelu.tar\",\n", " \"FashionMNIST_relu.config\",\n", " \"FashionMNIST_relu.tar\",\n", " \"FashionMNIST_sigmoid.config\",\n", " \"FashionMNIST_sigmoid.tar\",\n", " \"FashionMNIST_swish.config\",\n", " \"FashionMNIST_swish.tar\",\n", " \"FashionMNIST_tanh.config\",\n", " \"FashionMNIST_tanh.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": "0d06221f", "metadata": {"papermill": {"duration": 0.007582, "end_time": "2023-03-15T09:54:21.647128", "exception": false, "start_time": "2023-03-15T09:54:21.639546", "status": "completed"}, "tags": []}, "source": ["## Common activation functions"]}, {"cell_type": "markdown", "id": "57d4c25d", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.007361, "end_time": "2023-03-15T09:54:21.661915", "exception": false, "start_time": "2023-03-15T09:54:21.654554", "status": "completed"}, "tags": []}, "source": ["As a first step, we will implement some common activation functions by ourselves.\n", "Of course, most of them can also be found in the `torch.nn` package (see the [documentation](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) for an overview).\n", "However, we'll write our own functions here for a better understanding and insights.\n", "\n", "For an easier time of comparing various activation functions, we start\n", "with defining a base class from which all our future modules will\n", "inherit:"]}, {"cell_type": "code", "execution_count": 5, "id": "f47f14a9", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.679006Z", "iopub.status.busy": "2023-03-15T09:54:21.678608Z", "iopub.status.idle": "2023-03-15T09:54:21.684614Z", "shell.execute_reply": "2023-03-15T09:54:21.683573Z"}, "papermill": {"duration": 0.016657, "end_time": "2023-03-15T09:54:21.686010", "exception": false, "start_time": "2023-03-15T09:54:21.669353", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ActivationFunction(nn.Module):\n", " def __init__(self):\n", " super().__init__()\n", " self.name = self.__class__.__name__\n", " self.config = {\"name\": self.name}"]}, {"cell_type": "markdown", "id": "1e0818b2", "metadata": {"papermill": {"duration": 0.007485, "end_time": "2023-03-15T09:54:21.703648", "exception": false, "start_time": "2023-03-15T09:54:21.696163", "status": "completed"}, "tags": []}, "source": ["Every activation function will be an `nn.Module` so that we can integrate them nicely in a network.\n", "We will use the `config` dictionary to store adjustable parameters for some activation functions.\n", "\n", "Next, we implement two of the \"oldest\" activation functions that are still commonly used for various tasks: sigmoid and tanh.\n", "Both the sigmoid and tanh activation can be also found as PyTorch functions (`torch.sigmoid`, `torch.tanh`) or as modules (`nn.Sigmoid`, `nn.Tanh`).\n", "Here, we implement them by hand:"]}, {"cell_type": "code", "execution_count": 6, "id": "6c456d7c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.720516Z", "iopub.status.busy": "2023-03-15T09:54:21.720118Z", "iopub.status.idle": "2023-03-15T09:54:21.726930Z", "shell.execute_reply": "2023-03-15T09:54:21.725793Z"}, "papermill": {"duration": 0.017736, "end_time": "2023-03-15T09:54:21.728824", "exception": false, "start_time": "2023-03-15T09:54:21.711088", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class Sigmoid(ActivationFunction):\n", " def forward(self, x):\n", " return 1 / (1 + torch.exp(-x))\n", "\n", "\n", "class Tanh(ActivationFunction):\n", " def forward(self, x):\n", " x_exp, neg_x_exp = torch.exp(x), torch.exp(-x)\n", " return (x_exp - neg_x_exp) / (x_exp + neg_x_exp)"]}, {"cell_type": "markdown", "id": "03510bcc", "metadata": {"papermill": {"duration": 0.013545, "end_time": "2023-03-15T09:54:21.754949", "exception": false, "start_time": "2023-03-15T09:54:21.741404", "status": "completed"}, "tags": []}, "source": ["Another popular activation function that has allowed the training of deeper networks, is the Rectified Linear Unit (ReLU).\n", "Despite its simplicity of being a piecewise linear function, ReLU has one major benefit compared to sigmoid and tanh: a strong, stable gradient for a large range of values.\n", "Based on this idea, a lot of variations of ReLU have been proposed, of which we will implement the following three: LeakyReLU, ELU, and Swish.\n", "LeakyReLU replaces the zero settings in the negative part with a smaller slope to allow gradients to flow also in this part of the input.\n", "Similarly, ELU replaces the negative part with an exponential decay.\n", "The third, most recently proposed activation function is Swish, which is actually the result of a large experiment with the purpose of finding the \"optimal\" activation function.\n", "Compared to the other activation functions, Swish is both smooth and non-monotonic (i.e. contains a change of sign in the gradient).\n", "This has been shown to prevent dead neurons as in standard ReLU activation, especially for deep networks.\n", "If interested, a more detailed discussion of the benefits of Swish can be found in [this paper](https://arxiv.org/abs/1710.05941) [1].\n", "\n", "Let's implement the four activation functions below:"]}, {"cell_type": "code", "execution_count": 7, "id": "81fe25c4", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.772120Z", "iopub.status.busy": "2023-03-15T09:54:21.771408Z", "iopub.status.idle": "2023-03-15T09:54:21.779331Z", "shell.execute_reply": "2023-03-15T09:54:21.778425Z"}, "papermill": {"duration": 0.019365, "end_time": "2023-03-15T09:54:21.781766", "exception": false, "start_time": "2023-03-15T09:54:21.762401", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ReLU(ActivationFunction):\n", " def forward(self, x):\n", " return x * (x > 0).float()\n", "\n", "\n", "class LeakyReLU(ActivationFunction):\n", " def __init__(self, alpha=0.1):\n", " super().__init__()\n", " self.config[\"alpha\"] = alpha\n", "\n", " def forward(self, x):\n", " return torch.where(x > 0, x, self.config[\"alpha\"] * x)\n", "\n", "\n", "class ELU(ActivationFunction):\n", " def forward(self, x):\n", " return torch.where(x > 0, x, torch.exp(x) - 1)\n", "\n", "\n", "class Swish(ActivationFunction):\n", " def forward(self, x):\n", " return x * torch.sigmoid(x)"]}, {"cell_type": "markdown", "id": "78b51f88", "metadata": {"papermill": {"duration": 0.007626, "end_time": "2023-03-15T09:54:21.803774", "exception": false, "start_time": "2023-03-15T09:54:21.796148", "status": "completed"}, "tags": []}, "source": ["For later usage, we summarize all our activation functions in a dictionary mapping the name to the class object.\n", "In case you implement a new activation function by yourself, add it here to include it in future comparisons as well:"]}, {"cell_type": "code", "execution_count": 8, "id": "bda57f03", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.820353Z", "iopub.status.busy": "2023-03-15T09:54:21.820076Z", "iopub.status.idle": "2023-03-15T09:54:21.824793Z", "shell.execute_reply": "2023-03-15T09:54:21.823869Z"}, "papermill": {"duration": 0.014946, "end_time": "2023-03-15T09:54:21.826308", "exception": false, "start_time": "2023-03-15T09:54:21.811362", "status": "completed"}, "tags": []}, "outputs": [], "source": ["act_fn_by_name = {\"sigmoid\": Sigmoid, \"tanh\": Tanh, \"relu\": ReLU, \"leakyrelu\": LeakyReLU, \"elu\": ELU, \"swish\": Swish}"]}, {"cell_type": "markdown", "id": "f2f001f4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.008929, "end_time": "2023-03-15T09:54:21.843375", "exception": false, "start_time": "2023-03-15T09:54:21.834446", "status": "completed"}, "tags": []}, "source": ["### Visualizing activation functions\n", "\n", "To get an idea of what each activation function actually does, we will visualize them in the following.\n", "Next to the actual activation value, the gradient of the function is an important aspect as it is crucial for optimizing the neural network.\n", "PyTorch allows us to compute the gradients simply by calling the `backward` function:"]}, {"cell_type": "code", "execution_count": 9, "id": "975c6dba", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.860277Z", "iopub.status.busy": "2023-03-15T09:54:21.859359Z", "iopub.status.idle": "2023-03-15T09:54:21.865005Z", "shell.execute_reply": "2023-03-15T09:54:21.864127Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.015385, "end_time": "2023-03-15T09:54:21.866350", "exception": false, "start_time": "2023-03-15T09:54:21.850965", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def get_grads(act_fn, x):\n", " \"\"\"Computes the gradients of an activation function at specified positions.\n", "\n", " Args:\n", " act_fn: An object of the class \"ActivationFunction\" with an implemented forward pass.\n", " x: 1D input tensor.\n", " Returns:\n", " A tensor with the same size of x containing the gradients of act_fn at x.\n", " \"\"\"\n", " x = x.clone().requires_grad_() # Mark the input as tensor for which we want to store gradients\n", " out = act_fn(x)\n", " out.sum().backward() # Summing results in an equal gradient flow to each element in x\n", " return x.grad # Accessing the gradients of x by \"x.grad\""]}, {"cell_type": "markdown", "id": "724ce840", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.00848, "end_time": "2023-03-15T09:54:21.884609", "exception": false, "start_time": "2023-03-15T09:54:21.876129", "status": "completed"}, "tags": []}, "source": ["Now we can visualize all our activation functions including their gradients:"]}, {"cell_type": "code", "execution_count": 10, "id": "38f4e357", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:21.901503Z", "iopub.status.busy": "2023-03-15T09:54:21.900663Z", "iopub.status.idle": "2023-03-15T09:54:23.922192Z", "shell.execute_reply": "2023-03-15T09:54:23.921164Z"}, "papermill": {"duration": 2.035263, "end_time": "2023-03-15T09:54:23.927297", "exception": false, "start_time": "2023-03-15T09:54:21.892034", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["def vis_act_fn(act_fn, ax, x):\n", " # Run activation function\n", " y = act_fn(x)\n", " y_grads = get_grads(act_fn, x)\n", " # Push x, y and gradients back to cpu for plotting\n", " x, y, y_grads = x.cpu().numpy(), y.cpu().numpy(), y_grads.cpu().numpy()\n", " # Plotting\n", " ax.plot(x, y, linewidth=2, label=\"ActFn\")\n", " ax.plot(x, y_grads, linewidth=2, label=\"Gradient\")\n", " ax.set_title(act_fn.name)\n", " ax.legend()\n", " ax.set_ylim(-1.5, x.max())\n", "\n", "\n", "# Add activation functions if wanted\n", "act_fns = [act_fn() for act_fn in act_fn_by_name.values()]\n", "x = torch.linspace(-5, 5, 1000) # Range on which we want to visualize the activation functions\n", "# Plotting\n", "cols = 2\n", "rows = math.ceil(len(act_fns) / float(cols))\n", "fig, ax = plt.subplots(rows, cols, figsize=(cols * 4, rows * 4))\n", "for i, act_fn in enumerate(act_fns):\n", " vis_act_fn(act_fn, ax[divmod(i, cols)], x)\n", "fig.subplots_adjust(hspace=0.3)\n", "plt.show()"]}, {"cell_type": "markdown", "id": "098f8b4e", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009763, "end_time": "2023-03-15T09:54:23.950124", "exception": false, "start_time": "2023-03-15T09:54:23.940361", "status": "completed"}, "tags": []}, "source": ["## Analysing the effect of activation functions\n", "
"]}, {"cell_type": "markdown", "id": "2480a14b", "metadata": {"papermill": {"duration": 0.009868, "end_time": "2023-03-15T09:54:23.969887", "exception": false, "start_time": "2023-03-15T09:54:23.960019", "status": "completed"}, "tags": []}, "source": ["After implementing and visualizing the activation functions, we are aiming to gain insights into their effect.\n", "We do this by using a simple neural network trained on\n", "[FashionMNIST](https://github.com/zalandoresearch/fashion-mnist) and\n", "examine various aspects of the model, including the performance and\n", "gradient flow."]}, {"cell_type": "markdown", "id": "8f3190e1", "metadata": {"papermill": {"duration": 0.009688, "end_time": "2023-03-15T09:54:23.989320", "exception": false, "start_time": "2023-03-15T09:54:23.979632", "status": "completed"}, "tags": []}, "source": ["### Setup"]}, {"cell_type": "markdown", "id": "150cbfa8", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.00974, "end_time": "2023-03-15T09:54:24.008854", "exception": false, "start_time": "2023-03-15T09:54:23.999114", "status": "completed"}, "tags": []}, "source": ["Firstly, let's set up a neural network.\n", "The chosen network views the images as 1D tensors and pushes them through a sequence of linear layers and a specified activation function.\n", "Feel free to experiment with other network architectures."]}, {"cell_type": "code", "execution_count": 11, "id": "24662d8d", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:24.030778Z", "iopub.status.busy": "2023-03-15T09:54:24.030155Z", "iopub.status.idle": "2023-03-15T09:54:24.042044Z", "shell.execute_reply": "2023-03-15T09:54:24.041327Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.025612, "end_time": "2023-03-15T09:54:24.044218", "exception": false, "start_time": "2023-03-15T09:54:24.018606", "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", " layer_size_last = layer_sizes[0]\n", " for layer_size in layer_sizes[1:]:\n", " layers += [nn.Linear(layer_size_last, layer_size), act_fn]\n", " layer_size_last = layer_size\n", " layers += [nn.Linear(layer_sizes[-1], num_classes)]\n", " # nn.Sequential summarizes a list of modules into a single module, applying them in sequence\n", " self.layers = nn.Sequential(*layers)\n", "\n", " # We store all hyperparameters in a dictionary for saving and loading of the model\n", " self.config = {\n", " \"act_fn\": act_fn.config,\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) # Reshape images to a flat vector\n", " out = self.layers(x)\n", " return out"]}, {"cell_type": "markdown", "id": "b7762902", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.009696, "end_time": "2023-03-15T09:54:24.066966", "exception": false, "start_time": "2023-03-15T09:54:24.057270", "status": "completed"}, "tags": []}, "source": ["We also add functions for loading and saving the model.\n", "The hyperparameters are stored in a configuration file (simple json file):"]}, {"cell_type": "code", "execution_count": 12, "id": "e7eaf18e", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:24.088781Z", "iopub.status.busy": "2023-03-15T09:54:24.088323Z", "iopub.status.idle": "2023-03-15T09:54:24.101355Z", "shell.execute_reply": "2023-03-15T09:54:24.100487Z"}, "papermill": {"duration": 0.026883, "end_time": "2023-03-15T09:54:24.103615", "exception": false, "start_time": "2023-03-15T09:54:24.076732", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def _get_config_file(model_path, model_name):\n", " # Name of the file for storing hyperparameter details\n", " return os.path.join(model_path, model_name + \".config\")\n", "\n", "\n", "def _get_model_file(model_path, model_name):\n", " # Name of the file for storing network parameters\n", " return os.path.join(model_path, model_name + \".tar\")\n", "\n", "\n", "def load_model(model_path, model_name, net=None):\n", " \"\"\"Loads a saved model from disk.\n", "\n", " Args:\n", " model_path: Path of the checkpoint directory\n", " model_name: Name of the model (str)\n", " net: (Optional) If given, the state dict is loaded into this model. Otherwise, a new model is created.\n", " \"\"\"\n", " config_file, model_file = _get_config_file(model_path, model_name), _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", " act_fn = act_fn_by_name[act_fn_name](**config_dict.pop(\"act_fn\"))\n", " net = BaseNetwork(act_fn=act_fn, **config_dict)\n", " net.load_state_dict(torch.load(model_file, map_location=device))\n", " return net\n", "\n", "\n", "def save_model(model, model_path, model_name):\n", " \"\"\"Given a model, we save the state_dict and hyperparameters.\n", "\n", " Args:\n", " model: Network object to save parameters from\n", " model_path: Path of the checkpoint directory\n", " model_name: Name of the model (str)\n", " \"\"\"\n", " config_dict = model.config\n", " os.makedirs(model_path, exist_ok=True)\n", " config_file, model_file = _get_config_file(model_path, model_name), _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)"]}, {"cell_type": "markdown", "id": "a5f7b5eb", "metadata": {"papermill": {"duration": 0.009661, "end_time": "2023-03-15T09:54:24.127629", "exception": false, "start_time": "2023-03-15T09:54:24.117968", "status": "completed"}, "tags": []}, "source": ["We also set up the dataset we want to train it on, namely [FashionMNIST](https://github.com/zalandoresearch/fashion-mnist).\n", "FashionMNIST is a more complex version of MNIST and contains black-and-white images of clothes instead of digits.\n", "The 10 classes include trousers, coats, shoes, bags and more.\n", "To load this dataset, we will make use of yet another PyTorch package, namely `torchvision` ([documentation](https://pytorch.org/vision/stable/index.html)).\n", "The `torchvision` package consists of popular datasets, model architectures, and common image transformations for computer vision.\n", "We will use the package for many of the notebooks in this course to simplify our dataset handling.\n", "\n", "Let's load the dataset below, and visualize a few images to get an impression of the data."]}, {"cell_type": "code", "execution_count": 13, "id": "27a19288", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:24.148072Z", "iopub.status.busy": "2023-03-15T09:54:24.147838Z", "iopub.status.idle": "2023-03-15T09:54:29.079651Z", "shell.execute_reply": "2023-03-15T09:54:29.078922Z"}, "papermill": {"duration": 4.944169, "end_time": "2023-03-15T09:54:29.081442", "exception": false, "start_time": "2023-03-15T09:54:24.137273", "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/15/s/.datasets/FashionMNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fc029e21a04448809e9f636574ba2e2b", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/26421880 [00:00 first make them a tensor, then normalize them in the range -1 to 1\n", "transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])\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": "7d3ea236", "metadata": {"papermill": {"duration": 0.010699, "end_time": "2023-03-15T09:54:29.106806", "exception": false, "start_time": "2023-03-15T09:54:29.096107", "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": 14, "id": "5683a862", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:29.129731Z", "iopub.status.busy": "2023-03-15T09:54:29.129339Z", "iopub.status.idle": "2023-03-15T09:54:29.134498Z", "shell.execute_reply": "2023-03-15T09:54:29.133835Z"}, "papermill": {"duration": 0.018252, "end_time": "2023-03-15T09:54:29.135838", "exception": false, "start_time": "2023-03-15T09:54:29.117586", "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": "code", "execution_count": 15, "id": "063cad91", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:29.160966Z", "iopub.status.busy": "2023-03-15T09:54:29.160367Z", "iopub.status.idle": "2023-03-15T09:54:29.486771Z", "shell.execute_reply": "2023-03-15T09:54:29.485808Z"}, "papermill": {"duration": 0.340617, "end_time": "2023-03-15T09:54:29.489312", "exception": false, "start_time": "2023-03-15T09:54:29.148695", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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0A1aHHrFbINCkYNe2IHLHDxgMwZkTiyIMSk0G/65yj59eixs+w/FDFJGSDuY1/1j98nMNr1OPJ5Fub77iXpypDgMRHJKavCNdWLEuEhFpNUFNz8BaLYC7t17+G7QjugxA9onEcZpSjqG/a3Clzy/lJ1PYCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCAyMzkgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTVDJbQQxDPu7CjUwwOgcux4Hizyy/X9DygmSl2hL4qHylFuWymX3IzlvybrlQ4dOlWnybtDNr7H+owwCdv9QVBCtJbFKzFzSbrE0SS/ZwziNl2u1juepe4RZo3jw49jTKYHpPTLBZrO9OTCrPc4OkE64xq/q0zuVJAOJupDzQqUK6x7UJaKPK9uYUp1OLeUYl5/oe3yOAD3F3o3c0cfLF4xGtS2o0WqVOA8wE1PRlXGrkYGUEwZDZ0dXNAulyMp6QjXCjTmhmb3DcGADy7OEpKWtUrwPZQHoAl3aOuM0SoKOAMLfKIz1+gaq/F43CmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCAzMzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVJLcsUgDNtzCl2gM/gH5DzpdLp4vf+2kpNFRg5g9DHlholKfFkgt6PWxLeNzECF4a+rzIXPSNvIOojLkIu4ki2Fe0Qs5DHEPMSC76vxHh75rMzJswfGL9l3Dyv21IRlIePFGdphFcdhFeRYsHUhqnt4U6TDqSTY44v/PsVzLQQtfEbQgF/kn6+O4PmSFmn3mG3TrnqwTDuqpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvTGVuZ3RoIDE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDg5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVNuRGAMAzrPYVHwI9IvA/HUYT9W+yENJZOnxHKB2vkAYLhjS8h+KIvGYS1Cw8q+0h02EQNZxUkE8OvLPCqnBVtcyUT2VlMo7NBy/St7W+DHro/3Y4cCgplbmRzdHJlYW0KZW5kb2JqCjE2IDAgb2JqCjw8IC9UeXBlIC9Gb250IC9CYXNlRm9udCAvQk1RUURWK0RlamFWdVNhbnMgL0ZpcnN0Q2hhciAwIC9MYXN0Q2hhciAyNTUKL0ZvbnREZXNjcmlwdG9yIDE1IDAgUiAvU3VidHlwZSAvVHlwZTMgL05hbWUgL0JNUVFEVitEZWphVnVTYW5zCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0KL0NoYXJQcm9jcyAxNyAwIFIKL0VuY29kaW5nIDw8IC9UeXBlIC9FbmNvZGluZwovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNzAgL0YgNzMgL0kgNzcgL00gL04gODMgL1MgL1QgOTcgL2EgMTAxIC9lIDEwNCAvaCAvaSAxMDggL2wgL20KL24gL28gL3AgMTE1IC9zIDEyMCAveCBdCj4+Ci9XaWR0aHMgMTQgMCBSID4+CmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL0ZvbnROYW1lIC9CTVFRRFYrRGVqYVZ1U2FucyAvRmxhZ3MgMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvQXNjZW50IDkyOSAvRGVzY2VudCAtMjM2IC9DYXBIZWlnaHQgMAovWEhlaWdodCAwIC9JdGFsaWNBbmdsZSAwIC9TdGVtViAwIC9NYXhXaWR0aCAxMzQyID4+CmVuZG9iagoxNCAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNyAwIG9iago8PCAvRiAxOCAwIFIgL0kgMTkgMCBSIC9NIDIwIDAgUiAvTiAyMSAwIFIgL1MgMjIgMCBSIC9UIDIzIDAgUiAvYSAyNCAwIFIKL2UgMjUgMCBSIC9oIDI2IDAgUiAvaSAyNyAwIFIgL2wgMjggMCBSIC9tIDI5IDAgUiAvbiAzMCAwIFIgL28gMzEgMCBSCi9wIDMyIDAgUiAvcyAzMyAwIFIgL3NwYWNlIDM0IDAgUiAveCAzNSAwIFIgPj4KZW5kb2JqCjMgMCBvYmoKPDwgL0YxIDE2IDAgUiA+PgplbmRvYmoKNCAwIG9iago8PCAvQTEgPDwgL1R5cGUgL0V4dEdTdGF0ZSAvQ0EgMSAvY2EgMSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCAvSTEgMTMgMCBSID4+CmVuZG9iagoxMyAwIG9iago8PCAvVHlwZSAvWE9iamVjdCAvU3VidHlwZSAvSW1hZ2UgL1dpZHRoIDYxNiAvSGVpZ2h0IDYxNgovQ29sb3JTcGFjZSBbL0luZGV4ZWQgL0RldmljZVJHQiAyNTQgKP////7+/v39/fz8/Pv7+/r6+vn5+fj4+Pf39/b29vX19fT09PPz8/Ly8vHx8fDw8O/v7+7u7u3t7ezs7Ovr6+rq6unp6ejo6Ofn5+bm5uXl5eTk5OPj4+Li4uHh4eDg4N/f397e3t3d3dzc3Nvb29ra2tnZ2djY2NfX19bW1tXV1dTU1NPT09LS0tHR0dDQ0M/Pz87Ozs3NzczMzMvLy8rKysnJycjIyMfHx8bGxsXFxcTExMPDw8LCwsHBwcDAwL+/v76+vr29vby8vLu7u7q6urm5ubi4uLe3t7a2trW1tbS0tLOzs7KysrGxsbCwsK+vr66urq2traysrKurq6qqqqmpqaioqKenp6ampqWlpaSkpKOjo6KioqGhoaCgoJ+fn56enp2dnZycnJubm5qampmZmZiYmJeXl5aWlpWVlZSUlJOTk5KSkpGRkZCQkI+Pj46Ojo2NjYyMjIuLi4qKiomJiYiIiIeHh4aGhoWFhYSEhIODg4KCgoGBgYCAgH9/f35+fn19fXx8fHt7e3p6enl5eXh4eHd3d3Z2dnV1dXR0dHNzc3JycnFxcXBwcG9vb25ubm1tbWxsbGtra2pqamlpaWhoaGdnZ2ZmZmVlZWRkZGNjY2JiYmFhYWBgYF9fX15eXl1dXVxcXFxcXFtbW1paWllZWVhYWFdXV1ZWVlVVVVRUVFNTU1JSUlFRUVBQUE9PT05OTk1NTUxMTEtLS0pKSklJSUhISEdHR0ZGRkVFRURERENDQ0JCQkFBQUBAQD4+Pj09PTw8PDs7Ozo6Ojk5OTg4ODc3NzY2NjU1NTQ0NDMzMzIyMjExMTAwMC8vLy4uLi0tLSwsLCsrKyoqKlwpXClcKVwoXChcKCcnJyYmJiUlJSQkJCMjIyIiIiEhISAgIB8fHx4eHh0dHRwcHBsbGxoaGhkZGRgYGBcXFxYWFhUVFRQUFBMTExISEhERERAQEA8PDw4ODlxyXHJccgwMDAsLC1xuXG5cbgkJCQgICAcHBwYGBgUFBQQEBAMDAwICAgEBAQAAACldCi9CaXRzUGVyQ29tcG9uZW50IDggL0ZpbHRlciAvRmxhdGVEZWNvZGUKL0RlY29kZVBhcm1zIDw8IC9QcmVkaWN0b3IgMTAgL0NvbG9ycyAxIC9Db2x1bW5zIDYxNiA+PiAvTGVuZ3RoIDM2IDAgUiA+PgpzdHJlYW0KeJzt3XeULG1V7/EBUUQEQQEVJAfJSpAoOQeVICBBcnoJEiW/RAGJguQkooiIARThCggqWeCSQRQQ9QqKEq4BFIG77tqfXqv3eWt193m650zPnDO/719VXdVVu5/61qy1Zz9h57EhbIGd/Q4gHA529juAcDjY2e8AwuFgZ78DCIeDnf0OIBwOdvY7gHA42NnvAMLhYGe/AwiHg539DiAcDnZ68/8dJB6767jeXTynuH9xv+J5xeuL3ce16RX2hJHm+nax9Og/FT9VvKp4QfGU4qaFo9Ov/E8xuf7ks4i2+7g2vcKeENE2JaKtRUTblIi2FhFtUyLaWkS0TdmVaA8vLl5cqbhWcf3ix4tLFS8uZmd/q1g/rvXD2kM2ba6vFi8sbl/sFGy7cXH+4jLFpYvbFL7x1fXjimgRLaJFtFEi2qZEtLWIaJsS0dbikIn2qeIdxQcKed//Lt5bvK+w+8HiE8XHio8UHy589z+LDVquhWHRdYr7FlrukcVJxc8Wf11MvzfAiSPaKws+/XTxsOLHCt79a/Go4k6FA06+Oh5QfLIYjSuiRbSIFtFGiWgRbStEtIi2FQ6FaF3aeEQhsHsWVyvkfbK9yxUXnHOJ4iIFJa5bSA3fWGzQcs2fFT9R3LJ4dKEE9dDC7d5abPBjj0/R+hF9rrhbcfdCde7exdMKKl20uF1xx+KGhafo8t7Vkxy+RaFgNbnJorgi2npEtIjWRLQJES2iHRsiWkTbCodCtOZxhUAeWPg9P18oXvg9ty5uVejnJOH5mUIuKD+UEG4g2tsLhmsqF1R9EpFLPaHQwu75RLy2+Pdi4CbHnWj9+HUVI8cD5jy48GDuUzy7UHNy4BeLnyyI5jN/R+4km/cVMn6+mNxuEldEi2gRLaKNEtEi2laIaBFtKxwy0Z5c3GfOHQr5iYfOrJsXnvKNCimNXIdjss6/L4ZF+3ghkaT0neZQjmh3Lgy0cBNHVaS08L1bvG6vpRx3ojUnF/SSfNNGLq6lHJD6e0211OmKMxf8dPJdoc20HtHsro4rokW0iBbRRoloEW0rRLSIthUOmWi/UnSJgkVdX2qz/IobFJ6vX3GTQsKjK9SwaH6t0ShPnKPmJMPUpI8vXlE4YLi6o7Ozn16407OK1bc7nkTrDPCdhVb3t4Ax/hZ0CUoe6dkR8nzFFQoaOvnujQ97y8iWNxdL44poES2iRbRRIlpE2woRLaJthUMm2i8VcjwJp0oTx9jGJ2Z1mkk+VhLtqsXylpvw6cIFZVB8ekjx8DkOuIohF3aJZlom33g8vbSur/zfYuk9jyfRGgm5X+fNm1Sf0D7JypUKH1R0YkqsWX+0Fk/WKRP1L4PtTFsV0faLiBbRtkJEi2hbIaJFtK1wyETzK9z0LnOETSp9z4xzvnYhLnpRk4asHGk5vLqQN/nxtrQmf+SVfCLRbxV2tY/s9/chEROD272lWHrPY9lc/VC+UMiL31Z8qPibQl85B/6iGAhrGteLCmYZd04bW20W7GpHKaQehFpKy3iotmZZJ0s55uwuRmn/RXFFtIgW0SJaRFtIRFuLiLYpEW0tItqmRLS1iGib0lMiyI5vN8dj9Y8I/+hQQ9O/zugan3HxisVRWq6hkvKl8/p/HA7wx8oppgDWVGZb0AvzkoU1VF7PQ1/2lV8rlt7zWDZX8/JCX0xvi3//qAWft/AO+hHaVsMZr7QorCPiUjR2VcPT6KUZ/EFwrf7PRtcxDV7zmfpnz5/gu8S6vfFR/nI4h4J89eC76hnRIlpEW95yEyLagrAiWkRbTkRbj4i2IRFtPcQuEHH+QiEX0cGQAxpIEmRQlNTztsU1i3sVy1tugodCKtmSn9x3Yo000wF6yd9eWvxmYVLlF/uKFjdS6pnF0nvujWh+hAFiMnUp31UKk8iJX6YuyisX/1gsCuuIuPTo1DRmQm7RlD5tscgp95zjALlPmuMzd+fUnV3LhHeer8twzIkvKSZxRbSIFtEiWkRbSETbkIi2HhFtQyLaUZjMhqW+ITBbRPCApZ58kvDIWQh5vUKfRJkWB5a33AQJFXPlkNcoHjmnB0W9YA75nlq0hi8QprkTnL0fokGPy38o3lXY8mZ4JemlP+nqsKZxmWDC8DSvMk144Zl0X0aO9XgownQS2tUnB+6qi6v/JQjOxNRS528Ui+KKaBEtokW0iDYjoh1DItpyItoxJKItZyKa5EZMP1eoKvWcyDJM5aYrzRGrZNV4qO50NSIaaZWgeho0u511GuHErKfM8ZmhUCR9tpTP+Cvfc87Se+6haIvQKhaakYSevVi07ONIc1mysmuDUtvWq7e8eHTqTJRjR8yL0EvQeIJfKRbdM6JFtIg23HIRbUFYES2ibUBEmxHR9piINqN/89cLcXJMOcXIE73QJDxCullxkzkmRLvqHMuuD7ec5NIcCLacrAX0MLOrCkM0enX++ZxGBzQpsZEVvrL0ngNhGXPyd4XxJTLpb621cDt6bmkpvFE9ktDVYU3j+mYx+UwByePo/xO0VD7rTmldlvIZDe9PVbns6h8R0SJaRDtKyzURbUFYES2iRbSIFtEOtmiddX6kELEeVTJAD09yc4tTovkopzWlVsZi/FFxlJZreEHu7lKmWWSP5HvqnK4+PX2OatMzKaglSbsb0Qwx/2zxxcJn2sOI89eONquwLlt4qnL2TUeqf3POpOG0f0+b7NnJKH02qU2BaEbxPNha9dL11b8kokW0iHaUlpu0V0Rb3VwRLaItJaJNiWinJKItjWtj0SbVJ4/Hg+qIPTzjTTqRUQLxG1WkDLnQFU2/NYYcpeUaKmkC59nSY0sdSur5hDldkep80+6z1JyM1xDm6uVTVoc16Y2F9xSP71/2rTmLrmBtdw1iWfpeEHLy3f8qFoU18hi9khrcMCEd0NykB/n0rpM9T5/N1mL3aCdTVK1urogW0SJaRItoEW3R0Yi2tLkiWkQ7qKJNc5g/KDx0d5ZlGXnSA9etoaIYZTSKNEprqkNJTFWuNO5wyzlKDmmaFiEas0TENo55E0jUCeeT+4g2lVDtZnAKTT5QGF8i1XT5e7Ysq/nTQoHHD7Mlo1/9tfVE82MljrLaTtd7QuC7z+FdJ6FWU3mQPxqUG40rokW0iBbRIlpEW3Q0oi2NK6JFtH0WTVrZac4ky5zx1sKXjTYXrJRSA6kvKS0xi3JmOWIg+ZzcCyo6OtxyVPLrlZY0hrKXA72curTSKSTqrZloRqgIneejWads8o+LDxZfLt5R+HWd8/aAmNlV/6Mwvaw+a58s3jPnwoXJqgxNP0NhII0GFrV4Xc/W54r1RGOW/N+r1ZUmcjvqMzpNktCZaDadvfpOES2iRbRJhBEtokW0iLb6ThEtou2faAtVWoBxKF9/TfGk4lcL8xiJQWczz1sG2PMf3aMQsZ9i1wgJLhJtvdXtWKQduEMTNz55jqIY5VyKWJ68XHA2q5XRIy4j6RrNOpnVySVeVzx7zqsKa/A9H2Rxu5cVhpr8bmGVPVufKiYLk5vPyu2+MsdndFX2Wk80z8TfAnML3HNOd0+D83zWj+3BvRY721bfKaJFtIg2iTCiRbSIFtFW3ymiRbT9E82RLxVvL/6seGdhV4L528WsL5fnYZD3L8+xkAH5PDclCiFSTpcvo1aE2ROpyj9vOmdYNEddkHJEU8nimHqY/PPRc4jFSpnc7PoWwXa2NvXrBlpu0VEZZe9+Y0L3QrPVPn2t6KMuMzHri3O+MMdIeD3T1hNN/q/pvYMtmsfm6UxEO2nOLOvsw5NLT/5aRbSIFtEmEUa0iBbRJkS0iBbRmoMgmjrIKwsrv0meSOQZ+Uza9FLLFxDtuYWSiNTKI3xaIRMVuwxThB6oSpPqkx/FFT9eWWQS4VFKUL4skZRhUlWLyHRd3wHJUqejqkOzTT9K3Uisuxmpvm+sJ5pm9hC0CsfaIg3HJH8LNGHbNpu2SkvpRzgaV0SLaBEtoo0S0SLaVohoEW0rHFeiSWmkZGojLyo4RSfJmKOzkd/s84A9LVeQ9xmaLpqu/zx8jkzUMBWLUCg82fKZqZoUVkZarqetIofGkNo6IAQRaU27HYckVGwPteq1EzWfd2ug5QYe6PYYFk1WaO4wPjGLaNpHS7VoXXjStppnlnXalLeOxhXRIlpEi2ijRLSIthUiWkTbCseVaCaQta6EG9DE0gcc02VK1zNp5fOUmyxp4Jk9ohCnhyc6EyNZAu3SxRXm2L1cYYteVm+7fPGJYqTl3KlLUJ0JiYM67XoXo5wsiZ7VpfyyblO/bqDlVrfwlhkWzSwDRgNRqUeaEM2fgR6V3sWoZpZ12uz/EQzEFdEiWkSLaKNEtIi2FSJaRNsKx5VojvxeIZ2wIp3RIsaZd5LyGHBOFtqZi85g1yguUtDmagUhr1gYhH2p4pJzfqzw2Y8WBnyMtJx0l16yX3oRrbtV6TIlNt51wunKPnugpNo5fuOBWup6lKM0V/cSI5qXv7sM3ndOS9QvXjvW+eesGmXiq9G4IlpEi2gRbZSIFtG2QkSLaFvh+BOtMZ+Sh8I7/lBHknhZ2sghfSiHpKVwDGqZXHCnOFNxjuKHix8pzlWcvyCaod3DopHDCHRtoz8af1q0yYh5jdTzNZ0kqT6xS1AtmiEv/UdjYpYt8nXraSMHbM2M1FPNBMSjcUW0iBbRItooEe0IItreEdGOIKLtHcefaJMZkyaQz5j1t5q2yiBvu6ZbWn1TkzaZ3clUr5JLiz+oORl13QtEj4imMcySZXyJbm0mfXXAcBNt0xllF1bkTk65k9RZrUrK6t0aaLnVP3bLHKW5egmSvy00g5oc0XqyKlu/WLDNZ52Odqe0+/awldG4IlpEi2gRbZSIdgQRbe+IaEcQ0faO40+0g8KIaDJH5RRzaBnecppCizigS5wBK5zSec2Wz/TL+ikvgASLtIbeDMS1F796Y4ZF+2jR9SXVRaUkKaRRKwqJdqXwZhTQjj5z8i01n8kFRuOKaBEtokW0USLapkS0tYhomxLR1iKibcqIaIaRqC/pUuYzi01vcD9FLJmpDHYgrg1usnccpbm6BKV9DEN6W/H2Ob377sI/Bkxj/JbCKn6vaX6j8KKPxhXRZkS0iLb0nIg2J6JtSkRbi4i2KRFtLY4r0ULYO3b2O4BwONjZ7wDC4WBnvwMIh4Od/Q4gHA529juAcDjY2e8AwuFgZ78DCIeDnf0OIBwOdvY7gHA42NnvAMLhYKc3j3kBbjc8djdxGchkpgNrwJjx7A2FEt3vFL9eWBzmRV8t1o9r/bBmrHW7UXbVXHtIRItoWyGiRbStENEi2laIaBFtK5y4olkT3kxsZyvOWpy6MEvzuYtereX78dvF+nGNfuNzhXFH9zx7cb5ChFcqDDz6oeJihYmjTUinJ+L6YR3UxxjRItoeEtEi2laIaBFtK0S0iLYVTlzRTO71fYXFWy5UnLc4T2EKtRsWVy8ug/cW68e19JRvFb178WKm9AWKixbmh7ZYjEmniSbWCxZ2TSttErlFV10a1kF9jBEtou0hES2ibYWIFtG2QkSLaFvhxBXNFGeeljzvWnMknDcu2GW5F5Mj3+b5xfpxLT2l5x+QOP5gMVsvkvtWh7HLOy/AjxcXnuOohNlcbmuFdVAfY0SLaHtIRItoWyGiRbStENEi2lY4cUWzLqM6zrWLqxSW+ZDn2VX0eVRhJcEH3bZYP66Bk29UWC5SqnlRjlHJFu/6AC44x1qTfs5aYR3UxxjRItoeEtEi2laIaBFtK0S0iLYVdiXatzutGuC/iv7G5LuGjKyOcL2WO20hr/zJQpcvz/IHCr3QrAdi6Y9ZCepxxej11wqLU+dvOuts2xqfdc1MvzWiDQxniWgRLaJFtIi210S0iLYVIlpE2wq7Em2YjxdPKv6+8BnRum/Vw4pXF3Z7RfcNWs5Vv6PQx0uB56qFio4qj4dn1Io1F2eFoV52ZYDRsF5ZqCLxRwp5DqJd+JRcZE5nok6WeurG9uxiNKyIFtEiWkT7tdGbRLS1iGgRbStEtIi2FfZStJOKhxdL07lOPT9WdA2oDdyg5f6hMAScXg8s1JiMVfHILA1utIgV1y0XfrNjPTgFDykoTScFsHMZi94ZJqTEnYQSjaB2z1KIcjSsiHYEES2i7Z6IVkQ0RLSIthUiWkTbCpuKZtHxczUvKQwOf27hHL2/rlncr/hMsfqqv1rY+kYxiXC45Uw/RTR3J5q+Z5I4Caet6xVXKGxd76XF6E1Gw3pkQRNFJaWkHyHaJOsk2iTrNFJd1nnmwtsyGtauRPtmYev9hSdrQmCT//5V8RFYfL3/lzAQV0SLaE1EGyGiRbTVV41oEW1CRItoeyhaJ4kfLJ6GuxSGWZ+hsPvIObcvrl948kaET676d8WXi/sXSyMcbrlfKjwZE1Ndo9AN7M7F6wvBGLUi9bwctOToTUbD4lQbrofZeSeOUbCzTru2fM+WYe4m3R0Na1eiddb/juIBhZ57/sLozWeI/+UN/1HVc9iznFxhEldEi2hNRBshokW0iBbRIlpEm7PPoi3nbwqDvk0I5TlqL+mcShDHhOTA1wrJpRxGmOZy/fNiUYTDcZHbuPBbF1crRCTx/VBh98qFdHT2+F9bjN5kNCx34piXUee4C0wGosgrpZkXmuOAFNU7YrIt1xoNaw9LUF8s3t/5qD8QNygsGOiczlsncUW0iDZKRJsR0SJaRItoES2iDXJMRZM1/Pecb8+Z3vXlha1/KzTQh4t7FNrG3LEyl3sX1pezkMQHCnUiD10aqAPXrlrOBc9Z0Fze912FoyonwjKEXUas1PNDPTZmgNGwDIw/z5zZSPULnZKWzy7vWrTOVvdQNI/Wk2bI6lUzFvJTxS8Xdl2mhYloEQ0RLaJFtBkRbSURbRJXRIto2xZt4C7vLGab9ylOVXhw5oIyRez7igcXRo0rUMkFOXCxOU7+6YIX5ns1x9WmLafmRFpLRauSuGo3qUcmyVVOmT1+xanRm4yGZQz8eedcYCJVmyXrdLR3laAI6jMN90/FQFjDzWVWgO4A2Jgla9H639OTDfUxK4C32GdfLxbFFdEi2uRARDsKES2iRbSIFtEi2inZc9EckYG8plBeMorbSARZoQel1HN7VSXFHWmmxhCiTO6+xd8WOlNZttl8stJM/cUkicaIsIEhxkBs2nI9E5X6kq5cbO5TxK8HWCemF/u9YvQmA2FpFHfX6e2I4hNtJqKJhvBOdICbtnztewtjbQbCWh6XhLAnBmsMMnlz8dfFYwod0JYPLPJvBG391mIgrogW0SJaRItoEW3pKRFtUVwRLaLti2jKRp68yZIUGDSDXanbbE04AvlQ7NrLXFAuo4iiK9pfFFrcQ++ak65W1y3+uXh38fhiuOUmyDoNTvESfGcxGXgi+zWZlIWwZzPL/noxepOBsGTv6m3my/KS+bHn7sITx7SrmpNykwPakaU7c0S9+nVYr7k8xj8slN9eV+i+95eF9nhK4en318zLexP4T4NHO9pcES2iRbSIFtEi2tJTItqiuCJaRNu2aH6K+ozOWu4iu/hS4dIqS/eCMQrSuxcU/1mYpklyqbEVUXTMkp/IMPVHU/+RWvnJ/1Ew0I/foOUgGfaiyNBMBPuyok/xJnj8jyhmc2/9fjF6k7XCkr0/tZj1fPOTO/VkFtG6TCXNFOGfFuuHtTwuw8nJ8ZVCc8nUTSTsmT+vMIznFwr9C53n6fdcZPfzyBbdZGSkekQbIKJFtIgW0SJaRItoEW3Loj2r0Ay6csmJTEj7L4UDakcnQy8uz9E5eqH9SXH6Qjoqm/Q98qn3mCjK87XwhcKHcS4u4KIfLTYQ7Q6FDO2WxamLFxV9iuaTycmDZ5WgFxajN1k/LGiU03dyyafe8kr2UaGvdf0jmsu7+405fY4f70loYbN8EU2r36kw3wAh/YX5lcIp3OjegkfQha2+sU5plItoES2iRbSIFtEi2oSIFtEOqmiqDhJHqafyjG5m8gxPRvVGNDeWjFhQztkikVrR8HSFXybDPHvhWvIrDssPpbG+YWJctRc93zYQzRUUnm5enLGYZJTPKIxel0H5TRd/Y7H+E1193iT7mtWSzj+H4cyyde45Bqc4eem8sKvDWh6X4eT+Y0A0eqkmKi0pRplo7NOFPy63KvxnYbR5lscV0SJaRItoi661OqyIFtEiWkSLaFsUTRVJEqRs1MOq5URKJT9XPBl6nFmXztPSfPqo6Xam5qQhHZgM0YYD8s/uiOUm6lAbiPa7hWekZ5p7vqnoU3S18rYYnDIrDOmEtX7LrT6v53I1a+8RonW5ya6sXDMQTQtof0tKTK41ENZj/ZwnFr9WyOudo7PfZQr9Dc07Kyz39GfGtAKOemulqP6i8N9fANOQPcDq5P7g6PHnT4VBLW4iUdX0ES2iRbSIFtEiWkSLaBHtOBFNNF8oLGDm13oo0sVuBiZdRa7JGOMwmGVXhnmOOb6ncXuZhm5XzWzLVfye/1NsIJpfpuUMTjlNccQ8W4UkVGsKf/b4J0tpjD7R1ef1iPBPFJrwnNrHj/cOds+0nqeq+6PJBV1hPdHsv6v4g8Ia5E8quGNaAe48rlAHpI6/HvqjSc1fUbyt0IQfL+TBZiz+N0PaDWqRrZL2NXPsfr6IaBEtokW0iBbRIlpEi2gRLaJFtKUtZ9YsveO0gB6Lmub7oeWMNOKOXR76H4eG9M8PnRA90N4ywa7/QWjmZxaUVjFd2HIDGJfl+gqZqrZvKPqU/1UQzY1nj980bqM3GQ3LPHO2jGby687GohatJ0cgmgOa0CwKvRrJMap17gcRLaJthYgW0bZCRItoWyGiRbStMCLaInSH023uo5ZC/KPizworbXy2WPQ9ZVRbcpa1IhxuOUkR0WRV3oz3FH2KZExORzSdMi8r0R69yWhYnSlKy1h/lkWioTuGOsUkb3ct1g8rokW0iBbRItreENEi2laIaBFtKxxFtIWLqG+XDVrOW0A0g3ks4qiI0qdwSoon2ZuVoFYvULg0rtXndQnqxYU88kzsVonrwWCUM/qLgU6RzN+gWD+siLYeES2ibYWIFtG2QkSLaFshoh0G0Q4AG7ScRM/UYhJOhbNFq6KYQs2znKV8G8Y1EIytlxZHiCbD1AXP3SmnQNUjwgRnbun1wzqojzGiRbQ9JKJFtK0Q0SLaVohoEW0rnJCiQR2HSqY7eHYxOUXW6ZkbxPLjG8a1+rwWzXzN39d1rx6Icr45HOtMtCepWz+sg/oYI1pE20MiWkTbChEtom2FiBbRtsKJK5oHanyMss5kTXV8T2GEyizrHBj7sSiu1edZPMSW5UdmWaeRJ1JKdShTwnFMKcyBPsWCk+uHdVAfY0SLaHtIRItoWyGiRbStENEi2lY4cUUz7Nx8TZZ6N9y+j36msKCLXmIvwYZxrT6vlxyUdZpD6zSyye8tyL4zx0zF5h52wFQAUuf1wzqojzGiRbQ9JKJFtK0Q0SLaVohoEW0rnLiircYq77u6wmhYk1R29hXzeNk32J/xfY6qlaXNzeK7YVgH9TFGtPWIaGsR0TYloq1FRNuUiLYWEW1TItpaLBQthL1jZ78DCIeDnf0OIBwOdvY7gHA42NnvAMLhYGe/AwiHg539DiAcDnb2O4BwONjZ7wDC4WBnvwMIh4Od3lyvuGAFi7cWq8/7ZPGWYsPaxQY1la8VFtuwjPUb5/xxobeaRa/fjA3jWn3eE4q7FA8pZl95amFNaVsvK5zoqKX9njzn4YUDtyg+VwyEtWkJytqAIjKaxrKGjy9eWGjRDa4a0SLahIgW0SJaRFtKRJvEFdEiGg6WaK8vDKa+sHlijfe+SGH3XsVtC+tFXLwwZaujvva+Yq0IN2i5uxc7o/Sq1GvFtfq82xXioMmtcVLhQwpakftqhaH1dyicYutmhd2fKZ5RDIR1lLisVdJzar2z8JxMmmVKrV5qz/gYnxlZv/5QnogW0SLaNMKIFtEi2lIi2iSuiBbRDqBoVrWeLdusgUzAeqrCI7tuYSGGfozmZrpiYTKmDxZrRbiBaLw2H5SXQEOKo7fMXjuL8HXF6KUHwpLa0uQ+BbFOhkTSh0S7xRy73PS9BxX3L3zZlvP+othVc7VjRsCYMldLWWPbrvl1Tf9lxHwvwOGzv+gYBhZBjGgRLaJNIoxoES2iLSKiLYorokW0AyjaJwpxnUs41y7OWJy2+M7iNHPEeZ1C0sLKvy4G7rQr0cwMJfElmnTXri0T11JuFuY9itFLD4TFi5MW4QjlqES5PmDXAWWpzk6fW9DQi7Gr5mo5/NG4WHGZwtPxUCnnXdU8PvPXQ5vdEus3V0SLaBEtokW0iLb0lIi2KK6IFtEOoGgfKkyz+j3Wi1A6uU0hN/mugm1cdMADdrIDWxHN8yDVRedoPu1qy2cWwPshlaDRSw+ERQ4JJjn0RyPRfZSRup/ZM+bQ6+Fz9Ecjn60+Tznoq8Xum0sHNH8BtIoS1E8UvHtwwfV+L+WkFt27lH5+fa2la4JEtIgW0SYRRrSIFtEWEdEWxRXRItoBFO0dBZ2+i0rySvnKTxc3n3Ojwq9wit5NviZvHbjT7kXrcpOGbO+IZneWP6+13vXqsDQ40SST7JJlyiOfYPhJl6TsMqvdpBy9bDmPqq8p/JJFPdOO0lyTYtHjCn8a/BnwuvlbQCXL6ukD58lal08er80uCY/2x4rJj18aV0SLaBEtokW0iLboaERbGldEi2gHUDTD02eFG+54UGcr1KFIpbea6PwAPZnE5acYDTFwp92LdonCk7HVJahWTnXlPJcvRi+9OqwvFXqOdbXJybP+aBRxhDtW2LNlNAq97Hb+efIc36XvvxfDzdWP/z8KfxD8eP8xUKLTCq2cnobXKnQj9HNuUPRbezEvp0d7q2KguSJaRItoES2iRbRFRyPa0rgiWkQ7gKI9u6DT93PsR+a4qYhlLgoaXDzHHAbKWwfutCvRvrvgk4bUNGcupFFdlhLgOa9RjF56dVj/UEjYutB08kQWR9imn5kDxrH3ZFUTyCcJpeamzXXXwi/2akm+6aXI5LHZvWlxpeKyhbKU/mjskqyeXZNqXF/5l2J1XBEtokW0iBbRItqioxFtaVwRLaIdQNFeWszGd/cMR/Ace8y6LmvtmFMc+GwxcKddiUZpLacOJQTtqmnYppBiWqYfvn4xeunVYenjpRdaO3aEbWauta84pQRlV2anSuW8iWgMdIBtmzYXObhj60JFv41Sc01x5YI/1LxbIS3++cJQ+pt1Ss9SXqyOK6JFtIgW0SJaRFt0NKItjSuiRbQDKNqbiploPdOpJFRuoqksFSFzOV3hFCfT8CvFwJ12JZrsVx6sy5R4n1fcr5CTitfbcWZjaEYvPSCah8KQFm3GE+YwRhJqd/KVScra391UNL0AvWAMuUphl2heSd6pOZlbQGLqKV61kIQyUDHqBj9ZGKbiCksH+0e0iBbRRlpuNRFtQkRbSkSLaBEtokU0RLQJx51oHyu+Ay2ajo8Knu4sgXbA7Mj9HxDP+8vFwJ12JZr/XfQAfjd+T/Hywq4mNanbaZc+vdVxLTr63oI1E02OEK3/WeG/HT4zMmpS3Dx5Tn9XiXSD5vKWaRD/kqCS3e4JqqXUXP2vxWd3LPwn5t6FHzZbA+Z6ha6TJlCwtTquiBbRIlpEi2gRbdHRiLY0rogW0Q6gaJZRlD2eSsdHAikYGuJ0+kKuKRNVY+yl+FQgB1ZhH2m51RjUL+vUrsz6n+JdhV0Z1Cx/9rxHLz0gmlonLx67iNarlfMwu38jejyUrznZKKivF+s1132LnuhMItkTxqlrSkfl4t5BkyVfew41OaXhrtxdIn3ozV7dXBEtokW0iBbRItqioxFtaVwRLaIdQNF0fePUD/ZsCFSinM9aL58ZZS8nNU3CI4qBO+1KNOP+xaFd6eSArNmuKsxMtKW99lbHtegokaWQTJq4MsscFZ5oo+NjZ5N9dmedfTvnEe3zxXrNdcOiJ4VgUXeBpIlhYbcv9GxUgjJj8uOLhxZ3Lu4J4qnvSTj9k2F1c0W0iBbRIlpEi2iLjka0pXFFtIh2AEX722L2eLjzw3P4NNGrj3YJauAmIy23mjsVApRL9Y3/vrDbHdV2dKBbP65FR81ozAancIUhUs2n0stECHYd9pmz7z+nlZskr45+qlivuaSGepwpPGkVNSd66ZnmgG5n1y18w2QRhHSeNjNx2rWoerE5ZyhWN1dEi2gRLaJFtIi26GhEWxpXRItoB1A0ExrMHo9EY5FPi0TD9kR7dCHASdb5tcJuG7hjxM36cS06ajJponXOaOvhrQ3Ruu/ZZM60Fk1ZqjVs0YyAWa+59Aw08wGfbFFJFUkxSoOo3UlRZZ2/WGjMBxS/UMwWhDQ4hbkUNN5HkW9pXBEtokW0iBbRItqioxFtaVwRLaIdQNGM8Dgi61SM6k5pE9EmB3ztv4u1HugGov164Z7acGK43e6otvPhYvTSq8OSdfZIkz55NsqkpTLCw5a+Z6o+yjos9ZkHSs2W1AF1rvWaywqWqk8G8dsy9bFEUs89LSUT1VtNwmlVFGHpo0Y+Gt7QW6yDm/rV9xWfK5bGFdEiWkSLaBEtoi06GtGWxhXRItoBFO3PiuGsc9IzzdcsNLjWA91ANO+DG1vBZZFo2npnrZAmcS06+spiUoKy9eReFmXp6icGnbDUEusT0RStiPaHxdKwpnGZJ4xock1/H2y1aNpC9mjRFzUnOakk1ztx88LWjSGN1f1QXm9ubHIsjSuiRbSIFtEiWkRbdDSiLY0rokW0AyjaG4ojRJsUmSaiNZTztY8XA3falWgfKdxTlItEU4rZmRxZK65FR1u0SUeymWhKS68oll7f9yShnrKv+cwW0f6gWBrWNK5PFiYaI5URQmpHfOqFL6We6lCGopPIHLaE5J3UU056VdfyZVmn6b9WxxXRIlpEi2gRLaItOhrRlsYV0SLaARTtVcXs8UhfFmWdtiaiddb5tmLgTrsS7dNFj55ZJJqGPOaivayYiCb1nA1OkUj+SbH0+opYss4e1PLkOUxdL+t8X3HqglmehB9vIIrR673w/I0KaylKJs1my3oR2b1MT1pLPLv6o/1WsTSuiBbRIlpEi2gRbdHRiLY0rogW0Q6gaC8qjhBNf7RWqUWbeNdZp7xs4E67Eu3fC9WnHygmOplkV1jfibUuPSBa145OnjPzxOy2Hyyc/a1isuVFVIdqvTrr3EC0Py5knZTo2caY1esAekQyzKsVzjNWpRe2Y+Vs0jJ56+XnaNIXFkvjimgRLaJFtIgW0RYdjWhL44poEe0AivbLxTTrXCraohLUc4qBO+1KNGgLoyYmovlMWx9z0ZSXiNaOOXlmjNztM4Wzv1HYatE+Wkj0nnBK5J8biCYXVCIy8oQmHgfRNJJxKSy6QtE1Jr3QDEYxlRXbVKku0YPb5bKadNHCIBEtokW0o7TcKBENEW01ES2iRbSIttei3auYiSbYLkFNHJuI1gWfRxYDd9q9aEZbfG8xEU1s0tHZUtdrXXV1WHLqRaLNZJFN/kPh7EVZJw0XibZp1vmUwu9UafKCscg4cwmnOWwl6ayRa/rMwnZEU5vqYe3nlq1yk74eskUvlsYV0SJaRItoES2iLToa0ZbGFdEi2gEUTRpyGkxEW02Ldtdi4E67F01OdMZiIpqMySANna4uvtZVV4fV01Y5ZZp1OtIrVS8STfXMkHDf6HWxe8qr9UR7WOFhMctAFEUmr1qvd80sU9qyzQHnXbMgnwPsuqB2dUGiGQPkr9DSuCJaRItoES2iRbRFRyPa0rgiWkQ7gKJZx0Am972L0sylCES9R0I1cKfdi6bRTl/ojdUHDKzQUsdcNA7coeBYTz47LJqjrtCCdglqA9HuVhBNpqjmpI+aqax4Z9qqVo5Z/jQQzVRWP1VIPc8Cv0StylcktPcrlsYV0SJaRItoES2iLToa0ZbGFdEi2gEUzQXNX3rWyVLXq3Gyh+5XDNxp96JZktkQak3aB3Sr8rIcc9F6aAk5WrTZV8xE1WcvEk0fNVeY1K+6BLXetFU3KaikyORxOCAX5xPbJJNsM0KFPxJ33/Xg7UpWf8AyfmazZa6zvR5L44poES2iRbSIFtEWHY1oS+OKaBHtAIomGl2aziaROftKWjS7Zy5ULAbutHvRtLBqmZbrA+porD/mor230B+t55s6Iusk3sBNCEk0hSfXelbhxutlnU8vtL964R0LByxCYZy5ISnGsXu+OvL1Yndwijd09kB/r5BmkkFCeywHp0S0iBbRJkQ0RLTVRLSIFtEi2l6LZpzzTDRhn/WUONDn/OAcJ0tX9GEauNPuRfPjd+b0Aas4+0y6df61rro6LAmhZEx+aP0JU9rKI+/uw4GbOPn+c1zGdzlsbqylYU3j+svCQPPrFW8pHLh3cctCRzIGemzePimqx+aFdeN7Fs67ozT574qrFK7fl14UV0SLaBEtokW0iLboaERbGldEi2gHULQLNow57yk5zykxplmRo2dJVdAYuNPuRfuVwpoLk3TIQtiSLM18y7WuujqsTxWUkGXKGaWLs6xz9cJ2k5tIUZ8xx2X8EiNgloY1jevfipcUMsWeKmv3uJasVlt/oVgaV0SLaJsS0RDRENFWE9EiWkSLaNhUtBD2jp39DiAcDnb2O4BwONjZ7wDC4WBnvwMIh4Od/Q4gHA529juAcDjY2e8AwuFgZ78DCIeDnf0OIBwOdvY7gHA42OnNTUteaoePLu5b3KN4TPHS4uPF+hd97Fpx/Wuhw+FaN7FE/It0CFw/rrXutNes11yLsELmzxa3LayXYv4Ko7h2H1dEi2iIaBFtgIgW0bZCRItoW+FEE+3bxWzzXwqTIBtvbyUSS2ufeY4JiY2CsizHNwvf7enABiIcietLhWAM11l6Xnf7+9NC580LmBZg4CaTuEa/sRV2JdrnC9Mh08tkZ0aJXanwHnqTdxVXRItoES2ijRLRItpWiGgRbSucQKK1Jpb9/ozR856tQVFmzTLpQYt2hsI8yaYesATH+hEOt5wWMbnXLxVLz5MlWx/8uRhdzGUS1+g3tsIGzeVtf3VhaUv/LHhEYYYE0x8Yz2Wk2KMKC658+4g/NuvEFdEiWkSLaKNEtIi2FSJaRNsKJ6Robv8QGaalCqWUtyissGeurNMVVieRjlp+cXTRlEmEwy1HGDOUWfz714rJKV8pJFRfLH4fPX/wWnGNfmMrjDTXJNfvGdweXyhB/WJhTrZ7zjGZg4kPfOMJa5WkIlpEi2iTCCPaABEtom2FiBbRtsIJJFrzMJyqsCbJleeYBu1WxWnnWIDvkoXU87eKtSIcjovSf17Y5dMbij7l1IWZxuxaFPyqP1+M3uQ4Fq15XcGnJxU9g5t5zno6tmcX/ZlTzLF870Vv8eq4IlpEi2gRbZSIFtG2QkSLaFvhBBLNSBNzIH8/pJSW4jMdMpWsvacXmqxTV7ReUNFRLjpZLyiX7oR205bDM4uuPr2/8Dr8VWHdxLsV/Q2d1+4zkXE1J4Ro5lwmWq/2KK+0K/9UjLJLNFs+ewAkqv9TjMYV0SJaRItoo0S0iLYVIlpE2wonhmhqF3JGos0WH2SMdfaYpSuaw9cpyGdX1nmJOZcu9GC7U3GMWg53KPjfY1CMkxfgA4s+2RonhL/kQ4vRmxwU0T5adBzDzeX15hhh2iJ6Pa7oddx9Zre3ZgVIX/5Isfp2ES2iRbT1Wq6JaBEtoh1rIlpE2wonpGjW7ZNqShwv1OiKZri3XUNSfq7gmCHPF59j3Iii1aUKRauBCEeeqOW8rzCnpbIrQInvBwoHfrowEvtK1lgfGDw/iWvg5PX4XGEJ9IHxH0ba/2MxCesocXmWd56jImjKMf4oLkpCH1yYhkxmLuF8fuHAg5XtDO1ZfbuIFtEi2noth4gW0SLaKBEtoi2Ka+Dk9YhoJxV6e0kmZ2uqc0z+adda6TLRuxe2mNW22TVuRLJn1+RXqyMceaKGWVs//dqF21HpNIVTZFp2LVYufAnydYT0m8XAnfZQNON7vIPeZzdRM7tNcYNCs3rlf7dYFNZR4lI74pgpqkzie7+CaPedY5iK3NIIFQbqozYr24nV4dW3i2gRLaKt13KIaBEtoo0S0SLaorgGTl6PiHa1Qo3JXFWzhJNjF5hDNLnmjYrzFpOs0+5lClsu8KFidYQjT9TzMBOV4fDySFPTejwmXpInGTHvWV69YOW1L1c4Z+BOx1K07s9lDl5NaJy/Yp1mNenXOQpbfpPGnLwYw81lTir/GPCzDT8hGn/aNrklk2z5mi5rt4d0XSr6T8VAc0W0iBbRIlpEi2gDJx+FiDYhoi2Ka+DkoxDRJvCHY2al1SAX7mqUrNOWE29aEK27ojHrknMccPLSZG890e5S6OpGHW+GLT45YIat68xxgJXXVKYanVPrWIrWnFxcvzARrzYjgl/SbSbUqxRKUIvCWh7X1wrvm8UrPFCicUxW3lNZ0UsyzzZJqE5p1hC8sacv6vcVA80V0SJaRItoES2iDZw8TERDRFsU18DJw0Q0mK5Wfy6izQanUOmCc5aKJhfsuau6xKKcYgzE6ghHnqi8STZ2zTmeUSeX1yrsyklt+exaSlce68CdjrlovbyGObRsaUw99yinWVX/iCb/fGOxKKzlcX22MILHj9UMKlnyyIfMIZqt7remgc0C4AFeSiKsH9vbioHmimgRLaJFtIgW0QZOHiGiNRFtUVwDJ48Q0ZrvKPw8cz8x5Nz2uwTFNi1iXYlJ1tn90eSfnXX+QrE6wpEn+oLCpUl1jTlXn9O7tmg4O0cW59kO3OlYivaxQtTawosoDq0nV7argZ3iDfKaThbwG2ku+aG+bSYYeHmhi5meaVJIfc96nbvbFt1HTUc+PdFuYVlD49pfWww0V0SLaBEtokW0iDZw8lGIaBMi2qK4Bk4+ChFtgolg2WVSqLP1KteawDgPovmMaJqFWUTrDMp5mkpr3q5YHeHIE/2dwmy5BNLtTBJnOLzOU1c+JeyajVSXyvkRA3daX7SFw87Nm6tVPFFPyxRSbNOsjhJNc8k6vUa69MlO12uuPyl0RfMArexn0ImKFHNpqBhFIouT806Nz8AW49ifLOu0tMVvFwPNFdEiWkSLaBEtog2cjIgW0SLagRFNKxGNOmaD/Z5exuKCp8QKFi2aXFMraT4jK1Sy2k99w1ZHOPJETUdl0izVFX3P3M5ubzU+m01yJTiCDtxpaVhayuRXxpws1OvLhTm1LAH44sLa3BaZ01xaRZvJOuXn3nHVP1kzLzT13xbDzaXv300K+r6+UGlSNLSCtXIT0fRHk47etVClelThKr9ptDxpX1EMNFdEi2gRLaJFtIi2qKUi2tLmimgR7SCI9n8Li0Zrge8u2HZGsuiZJmyH/Xo/pUtQUk8t59metSCp7+rctDrCEdG+WBBNrqasIy/ztKRpJqey5bNeXWP2MPd4pLrH8QqP0Evx6UIRiDvSQE0odG1rSx6vMaXJKkG+Qb7nFcPNZX1CI2C0hYqUfmY/WzymMMuAZNJoFN313FMxysmvhuYT0suKgeaKaBEtokW0iBbRBk5uIlpEi2gHQTR5DdF4YQXrU6NFA3f0UVO38NmF52g02R4XNd/55qyOcOSJfrWQCTFX/ka5y8xp0dAjZS7lRdGIA3daHda/Fh8uVGaM3rCK3yuhCCSf01zaVVWvJ/jq6pMw1cjU6TxvIkiVvUYKQ8PN9ZxCjzZZuVExak7SWFlniyZ0VipB3bxQLfN2vFyialRPRItoEyLa6ggjWkSLaBMi2tLmimgR7SCI9vaiRfuuwu6OHkk+JIssSH3JQhISJQmnluvHKr8yOMJ3JaGrIxwR7euFAfVu4kHJKCeFJy1MOefNnBOrdh640yQsHeEM2jDmvo96UCaFkrhLi7/4zMLEUUpvotE0QtCEtn50Ts8Q5iV2sq/5ieabGm4us05pFWPR31TcumCNWG3po0YqohmwYkt/tBfC2+Nav1EMNFdEi2gRLaJFtIhmN6JFtIg24UCLZlKiLkHZ0uHsZpqgKyRayfAQc5xqlq4+dQlK+mJxDKIZ+r46wuFajxLUpebwqZe/btEga5ttCn118rQoLn3PpGlSw35Gf19Mvqbk9On3Fx4wQWWmktBeqMLImR5V3wNpjLXxaDt05w03F3c8Rj9W9mgpQlNZPa3wI7w3snD/QNAVzTobZrO1Ht6LLHDh6eukNtBcES2iRbSIFtEiWkSLaBFtEQdaNEUTehluYmu2vpnRKFLPc8+RdRLNbs9npcqjbSQ3ba40dnWEw6KpbnkURLPlnj04pUVzyiwV9aK8phi4SYfluenL1qPeW3OJtcu361foxT1knZ6eUTGXPyVSSt9wVddv+XyDd1r5k8VIc32wIMtfF0acG18ihZR2+zNAHYmpcSkGrLi7wezPgFH1PDDGZaC5IlpEi2gRLaJFtIgW0SLaIg60aK8qeKEHlZ5oJ+EsxTnn8FDr6t1ktwtUGlcmKtcx6lorOeUfi6URDovm+nLN7s/lUS8S7Yic1Psg6Rq4SYf1X4WqjDEcvO63SjDqbRpKPn72fi+1z5nndD3PFfgkZ3eeC9pynlb2Vim6va7YoLkkjhJOGWb/EqmzwpMSF9ssuqeGNVv0YvQmES2iRbRJhBEtokW0iDbaXBEtokW0iHZoRDOMnmg/Mme2jMZpC7/eh/5HoAmU//rfG05RGKWX/oH+AaDpHVUCXBrhcMvpREmqgX9v0NAwnit6jiYHXqvlFh395+K9hX+XmLLA3GePbh43xxTJHyoWlUdNPeC/DIYveUONpVIs/cvib4rh5uop275RGOxkHJXpD1Q4PTux0dDdjYJiICFp+LAvFJOrrm6uiBbRIlpEi2gRbdHRiLa0uSJaRDsIovmNRJPmyNWeDh9SiWhd6zRgxq6Es8dDGfwvc9GhTu9H3zXUammEI6J9rTAlggg5RiVSXW4BDsyyTsFp9oE7re//Vlivuf67UPWUXBJNMuk/Bp11OkXWqb5qi3wP+s9irbgiWkSLaBFtlIgW0bZCRItoW+G4F02SeKZCqURt5IkgmoIJvVSobOnTRy+i9UxppynE/tzClMtSzzcXu2o5S5IIs6dBszUpQbVjTpmJZp4B84at1XIDJ2+PkeaaZJ0WUTcKijr0MmW0Hp3+s2ByZ/mnNtPR0aitB1N1rbgiWkSLaBFtlIgW0bZCRItoW+G4F83YGN2eTF7gpgbN3Ik2VNIfrXtZWfe7u6L1KiCkMuj+zwuLMcplF41AWk80Mzd4FSynPhGt9WrINytBOdGkDWu13MDJ22M90Sz0qObEJ8Oe7OqephearNNnnrkmvEVBvvubnXpy1dVxRbSIFtEi2igRLaJthYgW0bbCcS+aW51pjhEMM9FknexjDBn1CCOVcpBdW0ZpmP7Ar/irwiosDkhCN2i5RqWMyCYUa734JLe84ikx14CTr2zTl79ZrL7TCSEarJuo+qQDms5mCnGKUbb8mdGJzqwMKoesfNR/FIuuujSuiBbRIlpEGyWiRbStENEi2lY47kWTJJ56jrxjduTFhVX4blh4ZKb7VagwPETBSnQqHSYI9t2PFka3WKR90Xrm64lmXi992+h16Tmdf07qUK3h5TWit8Wg79V3Oo5Fm2AFFxVBL7/kUhIq4XRUBzR/Voz911vNdGl3thyMy0S0iLaaiLZBy0W0iBbRtkFEi2hb4bgX7fPFHxUmxV00onpT3l1QbnWEIy33yEI2qQTVUi1KOHsW4qs2vmI64dV3OnFEe1ch4X9+4R8KJu/ind5qnZhablG+aZz8m9aPK6JFtIgW0UaJaMuJaMeQiLaciHYMOa5EC2Hv2NnvAMLhYGe/AwiHg539DiAcDnb2O4BwONjZ7wDC4WBnvwMIh4Od/Q4gHA529juAcDjY2e8AwuHg/wOoYcgWCmVuZHN0cmVhbQplbmRvYmoKMzYgMCBvYmoKMTQ5MTYKZW5kb2JqCjIgMCBvYmoKPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFsgMTEgMCBSIF0gL0NvdW50IDEgPj4KZW5kb2JqCjM3IDAgb2JqCjw8IC9DcmVhdG9yIChNYXRwbG90bGliIHYzLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChNYXRwbG90bGliIHBkZiBiYWNrZW5kIHYzLjcuMSkgL0NyZWF0aW9uRGF0ZSAoRDoyMDIzMDMxNTA5NTQyOVopCj4+CmVuZG9iagp4cmVmCjAgMzgKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDE2IDAwMDAwIG4gCjAwMDAwMjI4NTIgMDAwMDAgbiAKMDAwMDAwNjcyMSAwMDAwMCBuIAowMDAwMDA2NzUzIDAwMDAwIG4gCjAwMDAwMDY4MTMgMDAwMDAgbiAKMDAwMDAwNjgzNCAwMDAwMCBuIAowMDAwMDA2ODU1IDAwMDAwIG4gCjAwMDAwMDAwNjUgMDAwMDAgbiAKMDAwMDAwMDMzNyAwMDAwMCBuIAowMDAwMDAwNjY4IDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMDY0OCAwMDAwMCBuIAowMDAwMDA2ODg3IDAwMDAwIG4gCjAwMDAwMDU0NjIgMDAwMDAgbiAKMDAwMDAwNTI1NSAwMDAwMCBuIAowMDAwMDA0ODUyIDAwMDAwIG4gCjAwMDAwMDY1MTUgMDAwMDAgbiAKMDAwMDAwMDY4OCAwMDAwMCBuIAowMDAwMDAwODM2IDAwMDAwIG4gCjAwMDAwMDA5NTkgMDAwMDAgbiAKMDAwMDAwMTEyMSAwMDAwMCBuIAowMDAwMDAxMjcwIDAwMDAwIG4gCjAwMDAwMDE2ODQgMDAwMDAgbiAKMDAwMDAwMTgyMiAwMDAwMCBuIAowMDAwMDAyMjAyIDAwMDAwIG4gCjAwMDAwMDI1MjQgMDAwMDAgbiAKMDAwMDAwMjc2MSAwMDAwMCBuIAowMDAwMDAyOTA1IDAwMDAwIG4gCjAwMDAwMDMwMjQgMDAwMDAgbiAKMDAwMDAwMzM1NSAwMDAwMCBuIAowMDAwMDAzNTkxIDAwMDAwIG4gCjAwMDAwMDM4ODIgMDAwMDAgbiAKMDAwMDAwNDE5NCAwMDAwMCBuIAowMDAwMDA0NjAxIDAwMDAwIG4gCjAwMDAwMDQ2OTEgMDAwMDAgbiAKMDAwMDAyMjgzMCAwMDAwMCBuIAowMDAwMDIyOTEyIDAwMDAwIG4gCnRyYWlsZXIKPDwgL1NpemUgMzggL1Jvb3QgMSAwIFIgL0luZm8gMzcgMCBSID4+CnN0YXJ0eHJlZgoyMzA2MwolJUVPRgo=", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["exmp_imgs = [train_set[i][0] for i in range(16)]\n", "# Organize the images into a grid for nicer visualization\n", "img_grid = torchvision.utils.make_grid(torch.stack(exmp_imgs, dim=0), nrow=4, normalize=True, pad_value=0.5)\n", "img_grid = img_grid.permute(1, 2, 0)\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.title(\"FashionMNIST examples\")\n", "plt.imshow(img_grid)\n", "plt.axis(\"off\")\n", "plt.show()\n", "plt.close()"]}, {"cell_type": "markdown", "id": "53f4e7b7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.011522, "end_time": "2023-03-15T09:54:29.516884", "exception": false, "start_time": "2023-03-15T09:54:29.505362", "status": "completed"}, "tags": []}, "source": ["### Visualizing the gradient flow after initialization\n", "\n", "As mentioned previously, one important aspect of activation functions is how they propagate gradients through the network.\n", "Imagine we have a very deep neural network with more than 50 layers.\n", "The gradients for the input layer, i.e. the very first layer, have passed >50 times the activation function, but we still want them to be of a reasonable size.\n", "If the gradient through the activation function is (in expectation) considerably smaller than 1, our gradients will vanish until they reach the input layer.\n", "If the gradient through the activation function is larger than 1, the gradients exponentially increase and might explode.\n", "\n", "To get a feeling of how every activation function influences the\n", "gradients, we can look at a freshly initialized network and measure the\n", "gradients for each parameter for a batch of 256 images:"]}, {"cell_type": "code", "execution_count": 16, "id": "f974117c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:29.541016Z", "iopub.status.busy": "2023-03-15T09:54:29.540811Z", "iopub.status.idle": "2023-03-15T09:54:29.548722Z", "shell.execute_reply": "2023-03-15T09:54:29.547850Z"}, "papermill": {"duration": 0.022536, "end_time": "2023-03-15T09:54:29.550946", "exception": false, "start_time": "2023-03-15T09:54:29.528410", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def visualize_gradients(net, color=\"C0\"):\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", " net.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=256, 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", " net.zero_grad()\n", " preds = net(imgs)\n", " loss = F.cross_entropy(preds, labels)\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.data.view(-1).cpu().clone().numpy()\n", " for name, params in net.named_parameters()\n", " if \"weight\" in name\n", " }\n", " net.zero_grad()\n", "\n", " # Plotting\n", " columns = len(grads)\n", " fig, ax = plt.subplots(1, columns, figsize=(columns * 3.5, 2.5))\n", " fig_index = 0\n", " for key in grads:\n", " key_ax = ax[fig_index % columns]\n", " sns.histplot(data=grads[key], bins=30, ax=key_ax, color=color, kde=True)\n", " key_ax.set_title(str(key))\n", " key_ax.set_xlabel(\"Grad magnitude\")\n", " fig_index += 1\n", " fig.suptitle(\n", " f\"Gradient magnitude distribution for activation function {net.config['act_fn']['name']}\", fontsize=14, y=1.05\n", " )\n", " fig.subplots_adjust(wspace=0.45)\n", " plt.show()\n", " plt.close()"]}, {"cell_type": "code", "execution_count": 17, "id": "4c921ebd", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:54:29.581928Z", "iopub.status.busy": "2023-03-15T09:54:29.581615Z", "iopub.status.idle": "2023-03-15T09:55:02.431219Z", "shell.execute_reply": "2023-03-15T09:55:02.430163Z"}, "papermill": {"duration": 32.900432, "end_time": "2023-03-15T09:55:02.469690", "exception": false, "start_time": "2023-03-15T09:54:29.569258", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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", 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", 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Seaborn prints warnings if histogram has small values. We can ignore them for now\n", "warnings.filterwarnings(\"ignore\")\n", "# Create a plot for every activation function\n", "for i, act_fn_name in enumerate(act_fn_by_name):\n", " # Setting the seed ensures that we have the same weight initialization for each activation function\n", " set_seed(42)\n", " act_fn = act_fn_by_name[act_fn_name]()\n", " net_actfn = BaseNetwork(act_fn=act_fn).to(device)\n", " visualize_gradients(net_actfn, color=f\"C{i}\")"]}, {"cell_type": "markdown", "id": "2faa1e67", "metadata": {"papermill": {"duration": 0.030258, "end_time": "2023-03-15T09:55:02.530727", "exception": false, "start_time": "2023-03-15T09:55:02.500469", "status": "completed"}, "tags": []}, "source": ["The sigmoid activation function shows a clearly undesirable behavior.\n", "While the gradients for the output layer are very large with up to 0.1, the input layer has the lowest gradient norm across all activation functions with only 1e-5.\n", "This is due to its small maximum gradient of 1/4, and finding a suitable learning rate across all layers is not possible in this setup.\n", "All the other activation functions show to have similar gradient norms across all layers.\n", "Interestingly, the ReLU activation has a spike around 0 which is caused by its zero-part on the left, and dead neurons (we will take a closer look at this later on).\n", "\n", "Note that additionally to the activation, the initialization of the weight parameters can be crucial.\n", "By default, PyTorch uses the [Kaiming](https://pytorch.org/docs/stable/nn.init.html#torch.nn.init.kaiming_uniform_) initialization for linear layers optimized for Tanh activations.\n", "In Tutorial 4, we will take a closer look at initialization, but assume\n", "for now that the Kaiming initialization works for all activation\n", "functions reasonably well."]}, {"cell_type": "markdown", "id": "88cbda6c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.029945, "end_time": "2023-03-15T09:55:02.590672", "exception": false, "start_time": "2023-03-15T09:55:02.560727", "status": "completed"}, "tags": []}, "source": ["### Training a model\n", "\n", "Next, we want to train our model with different activation functions on FashionMNIST and compare the gained performance.\n", "All in all, our final goal is to achieve the best possible performance on a dataset of our choice.\n", "Therefore, we write a training loop in the next cell including a\n", "validation after every epoch and a final test on the best model:"]}, {"cell_type": "code", "execution_count": 18, "id": "a27ae1e8", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:55:02.652240Z", "iopub.status.busy": "2023-03-15T09:55:02.651996Z", "iopub.status.idle": "2023-03-15T09:55:02.667902Z", "shell.execute_reply": "2023-03-15T09:55:02.667063Z"}, "papermill": {"duration": 0.049384, "end_time": "2023-03-15T09:55:02.670281", "exception": false, "start_time": "2023-03-15T09:55:02.620897", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_model(net, model_name, max_epochs=50, patience=7, 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(\"Model file already exists. Skipping training...\")\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.SGD(net.parameters(), lr=1e-2, momentum=0.9) # Default parameters, feel free to change\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", " val_scores = []\n", " best_val_epoch = -1\n", " for epoch in range(max_epochs):\n", " ############\n", " # Training #\n", " ############\n", " net.train()\n", " true_preds, count = 0.0, 0\n", " for imgs, labels in tqdm(train_loader_local, desc=f\"Epoch {epoch+1}\", leave=False):\n", " imgs, labels = imgs.to(device), labels.to(device) # To GPU\n", " optimizer.zero_grad() # Zero-grad can be placed anywhere before \"loss.backward()\"\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()\n", " count += labels.shape[0]\n", " train_acc = true_preds / count\n", "\n", " ##############\n", " # Validation #\n", " ##############\n", " val_acc = test_model(net, val_loader)\n", " val_scores.append(val_acc)\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", "\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", " elif best_val_epoch <= epoch - patience:\n", " print(f\"Early stopping due to no improvement over the last {patience} epochs\")\n", " break\n", "\n", " # Plot a curve of the validation accuracy\n", " plt.plot([i for i in range(1, len(val_scores) + 1)], val_scores)\n", " plt.xlabel(\"Epochs\")\n", " plt.ylabel(\"Validation accuracy\")\n", " plt.title(f\"Validation performance of {model_name}\")\n", " plt.show()\n", " plt.close()\n", "\n", " load_model(CHECKPOINT_PATH, model_name, net=net)\n", " test_acc = test_model(net, test_loader)\n", " print((f\" Test accuracy: {test_acc*100.0:4.2f}% \").center(50, \"=\") + \"\\n\")\n", " return test_acc\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": "75a7d6e3", "metadata": {"papermill": {"duration": 0.030118, "end_time": "2023-03-15T09:55:02.735413", "exception": false, "start_time": "2023-03-15T09:55:02.705295", "status": "completed"}, "tags": []}, "source": ["We train one model for each activation function.\n", "We recommend using the pretrained models to save time if you are running this notebook on CPU."]}, {"cell_type": "code", "execution_count": 19, "id": "b6ea5164", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:55:02.796272Z", "iopub.status.busy": "2023-03-15T09:55:02.796040Z", "iopub.status.idle": "2023-03-15T09:55:10.980439Z", "shell.execute_reply": "2023-03-15T09:55:10.979576Z"}, "papermill": {"duration": 8.217528, "end_time": "2023-03-15T09:55:10.982888", "exception": false, "start_time": "2023-03-15T09:55:02.765360", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Training BaseNetwork with sigmoid activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 10.00% ==============\n", "\n", "Training BaseNetwork with tanh activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 87.59% ==============\n", "\n", "Training BaseNetwork with relu activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 88.62% ==============\n", "\n", "Training BaseNetwork with leakyrelu activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 88.92% ==============\n", "\n", "Training BaseNetwork with elu activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 87.27% ==============\n", "\n", "Training BaseNetwork with swish activation...\n", "Model file already exists. Skipping training...\n"]}, {"name": "stdout", "output_type": "stream", "text": ["============= Test accuracy: 88.73% ==============\n", "\n"]}], "source": ["for act_fn_name in act_fn_by_name:\n", " print(f\"Training BaseNetwork with {act_fn_name} activation...\")\n", " set_seed(42)\n", " act_fn = act_fn_by_name[act_fn_name]()\n", " net_actfn = BaseNetwork(act_fn=act_fn).to(device)\n", " train_model(net_actfn, f\"FashionMNIST_{act_fn_name}\", overwrite=False)"]}, {"cell_type": "markdown", "id": "39cfc4e1", "metadata": {"papermill": {"duration": 0.02983, "end_time": "2023-03-15T09:55:11.045690", "exception": false, "start_time": "2023-03-15T09:55:11.015860", "status": "completed"}, "tags": []}, "source": ["Not surprisingly, the model using the sigmoid activation function shows to fail and does not improve upon random performance (10 classes => 1/10 for random chance).\n", "\n", "All the other activation functions gain similar performance.\n", "To have a more accurate conclusion, we would have to train the models for multiple seeds and look at the averages.\n", "However, the \"optimal\" activation function also depends on many other factors (hidden sizes, number of layers, type of layers, task, dataset, optimizer, learning rate, etc.)\n", "so that a thorough grid search would not be useful in our case.\n", "In the literature, activation functions that have shown to work well\n", "with deep networks are all types of ReLU functions we experiment with\n", "here, with small gains for specific activation functions in specific\n", "networks."]}, {"cell_type": "markdown", "id": "4740ecc3", "metadata": {"papermill": {"duration": 0.030316, "end_time": "2023-03-15T09:55:11.105690", "exception": false, "start_time": "2023-03-15T09:55:11.075374", "status": "completed"}, "tags": []}, "source": ["### Visualizing the activation distribution"]}, {"cell_type": "markdown", "id": "97b06315", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.030267, "end_time": "2023-03-15T09:55:11.165908", "exception": false, "start_time": "2023-03-15T09:55:11.135641", "status": "completed"}, "tags": []}, "source": ["After we have trained the models, we can look at the actual activation values that find inside the model.\n", "For instance, how many neurons are set to zero in ReLU?\n", "Where do we find most values in Tanh?\n", "To answer these questions, we can write a simple function which takes a\n", "trained model, applies it to a batch of images, and plots the histogram\n", "of the activations inside the network:"]}, {"cell_type": "code", "execution_count": 20, "id": "b6996c0e", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:55:11.247807Z", "iopub.status.busy": "2023-03-15T09:55:11.247405Z", "iopub.status.idle": "2023-03-15T09:55:11.260052Z", "shell.execute_reply": "2023-03-15T09:55:11.259194Z"}, "papermill": {"duration": 0.046311, "end_time": "2023-03-15T09:55:11.261272", "exception": false, "start_time": "2023-03-15T09:55:11.214961", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def visualize_activations(net, color=\"C0\"):\n", " activations = {}\n", "\n", " net.eval()\n", " small_loader = data.DataLoader(train_set, batch_size=1024)\n", " imgs, labels = next(iter(small_loader))\n", " with torch.no_grad():\n", " layer_index = 0\n", " imgs = imgs.to(device)\n", " imgs = imgs.view(imgs.size(0), -1)\n", " # We need to manually loop through the layers to save all activations\n", " for layer_index, layer in enumerate(net.layers[:-1]):\n", " imgs = layer(imgs)\n", " activations[layer_index] = imgs.view(-1).cpu().numpy()\n", "\n", " # Plotting\n", " columns = 4\n", " rows = math.ceil(len(activations) / columns)\n", " fig, ax = plt.subplots(rows, columns, figsize=(columns * 2.7, rows * 2.5))\n", " fig_index = 0\n", " for key in activations:\n", " key_ax = ax[fig_index // columns][fig_index % columns]\n", " sns.histplot(data=activations[key], bins=50, ax=key_ax, color=color, kde=True, stat=\"density\")\n", " key_ax.set_title(f\"Layer {key} - {net.layers[key].__class__.__name__}\")\n", " fig_index += 1\n", " fig.suptitle(f\"Activation distribution for activation function {net.config['act_fn']['name']}\", fontsize=14)\n", " fig.subplots_adjust(hspace=0.4, wspace=0.4)\n", " plt.show()\n", " plt.close()"]}, {"cell_type": "code", "execution_count": 21, "id": "eacc199b", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:55:11.322418Z", "iopub.status.busy": "2023-03-15T09:55:11.322254Z", "iopub.status.idle": "2023-03-15T09:56:33.546465Z", "shell.execute_reply": "2023-03-15T09:56:33.545262Z"}, "papermill": {"duration": 82.362352, "end_time": "2023-03-15T09:56:33.653667", "exception": false, "start_time": "2023-03-15T09:55:11.291315", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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Dp4RAhLnwxJLDd2PjymuwIVEP7df8codr+mSpUsbCksc3Y+Pu3do2JOOjfaxN4wGnnhnkpHpizoiHd2PCXOvrCCqVccFxlV5wOCUWMDC01h1Fmons8N3YuPKabDQGCae6zq0HzoAnKRI15saz5PHd2JjerW2jBcYL51WSweEMF8MgETRe0Nk54Zuxcek12UiMkqpP664DPwlRnFhy+G5smHdrjjonhcQA0/UA8MAZYthqE42DdHZO+G5sXHlNNuSWGcW6jqIHzgDK3BlntbLk8N3YmN6ta43WGbn4vJc/cIaHhjbCyJ6lccZ3Y8O8i+so2gPvD8Oq5uBwRhLHIFSw/uDtnPDN2Lj0mmwk3h8ugXgfPI5dCCU9TdXQs+Tx3dgw755WX73w/jA/rb4OnHuRwMMMcSxx9eXx3dgw7+I6iqK599bKKvfgcG5Leiv4XccS7Jzw3dgw755WX8x6wL7r6b7xwDU4Hk7Y6uuwc8J3Y8O864sCAkMpA7NR1p5y4KnkWxIJyZHEjAAHb8bFpc/kIt/i6H1Ngnc4U1x6hwdt4cjju7Fh3q3CcbqACnU83aMcuJ6Jd5EYHUs88fD4bmxM757aRuMNYlwzGxyeIvOKqmWNODsnfDc2rrwmG4M3iGlNEnY4JWIK+kMdK0sO340N825de+UQeYP4tJ93eAqCTWuR4kji0svBe3Fx923Nns8h8/6wrioSDmfCYqrVUsmcnRO+GxtXXpONyvtDKXFl48CpMRlVUujE0gnfjY3p3VPbaLdReutPbePAR6EKeQ0nlsYZ340N864t61DmqQU093Xl5fGRbg1bs1QcS03O+GZsXHpNNhJvEMNT0pvDBwVnWNt3Zcnhu7Fh3q1aRDkW3iDGVZTL4yNQhS5Hz1KRM74bG+ZdW0dRVqlsLYexsnHCWwmaLb2YucO7cWG+xbZy0Xl/WNablAXPMKfxfoudB74bG+ZdWVtGCrxCrG0dQ894zI1mP6x2HvhmbFx6TTYS7xBbkJWNM94o7v1E0h3+2Vz4HLB3Ktrx8pnMPIt2rOlDteP/agg9nj32e75t7L2JhdAXrfc9L6YFP6naGgyhHzlI0/FRmki3WHIeZ4yaZ8Q9nslORzPMt9aTaQcMrQBKODKzUKikySm4dD4YcSzbainBFjAy2lCdBzioVRWGBSTDj2oHbBl/hGh1ltqtJQkWs4tdkoTCvsp6DDCjG+tM4julILBr4HOxurOFceJpBq/VA0tVtaCSpAxHwtOwFLLqdApaWpmhfSziSNUJvrHQJzeFudoB6yxNa6u9NDsULSAW70WFbyl8ge9a5FO6dYZWs3Y1FRxyrfdAj9qwQIla2xlorvb5fqPISLbaEplX22pfYAejN1UkOs+SiqiGCa/CR44jW4XjkClZMa9BYWcUkwIB+znb51mbU1I2yY/YerDjGRayAMd3KZCcza2G1thVqqNTc6IYmY1pqBqyQ3kQvAOlvkVSxjpICscy7JW0hBdIRVw1jVdQrYG0jDcYpZumfI45WWR6Kxpxy30xFSPRcLPd0DBLcRQV9tDAy6j6Yby50eoLvNHBT2Gpl+M8tc+lJGyR9Mi6ZTE7HUsgHtjylL8zesCESXiSG2rgQ5C9ijaqohN6ASI9RMspx9us99M9UNP4pio7GH6vz6MdmJEg2hIa3LXwSIqddLTYoS0K7CUVkCFXvTE4kKXY0aSDngTlwD/wem3jm3mRrWs5tGqYH7zpDTyIj3XiWXswz7O5Zx6lSJ/7AnToGmyXFGPHS5wr5EyuRLthuUvo5IBRH0NXNxHLnLCTsOehug2Ib6YcEkpOtrbEqEVRt6G9Hy+12Ao8MsqtD1tkVbR3Mx8p4YJRYaZuYYZVeRNOvTw7b1MwrgZrgGja+DL1YmzsYq5yvximqz7Z6/gXKIG8lo7+mnQETF9nqn98VYUCX/mKpPfrB/jkr3yJMginDzRejOORk9sXSIPEl+9efv/+Tx//8v6Pv4Y2SG6UQ1I9kxOV+NSNk6gsS2GHn/hydq61QR4fiOD6q5W5rrUgMChFTFV5XbQd+BtoQWRKcoVKySfNjnpVC4JSRtwkUprpQgHgr7DNYYoN4fjXHz6+wMJ3v/nhz9//699///2PP3338f2Pf//p5Td/efndTOf+BRksFNTismLZBDj8DRjEtA7TsCYYyT9TTWPNaf8lSegUucp52SIf8FtQwLqXMFYxjzOv8DMoeBKP+CV70heOBl/Zk0o7O/BZaiK3b9kWHBG8Y6K+B3w/EeHwNyKCieoDq7TO4SN+WmvnV+ICK+bE5VReuDjwt+KicgGCFSU1Cz+lKBJ/rXbBRWbOsek28TQtPvC34gIreSzXQwuBCnKf4qK+oiqSqVHnW9rXq4p8Lbtfd3KBbdtXnVw0LuNjWouzc0mAJW5Z8zkXHCvxoDKci50H/ji72IONS6/JRr718FSv2sFs1jVa3ru34vHduLjwmVRUinLKWVDiwwlnjbIWUkgLRw7ejYsrp0lGx8J09FVFw+Oql1lieSLJ4buxceU12KBIJ8/W1lsBh/MwruoJyYmjB7oZE5cek4nEaT6sEW0eHzy5TFElSk8MOXw3Nq68Jhs85nwue+1xngFLyOWJJYfvxsaV12SD+sk9P00mDufJN/Y+eiJ+Ysnhu7Fx5TXZ6Ldan4tiexzNAY7JeGLJ4buxceU1VseBzf25RrbHGXeR9Mx9Ycnhe7Fx7TXZSDc83VPFbI/ztLn3qqfZJ5YcvhsbV16TDez+23P1bIfzrH6kMPRSyNk54buxceU12RCGPT8V0nY4tUOwcYuqpOJZ8vhubFx5TTYG456famo7nF4PzVl8ZumB78bGlddgg3es47nAtsN5DcED/raSdMCbcXHpM7nIjHouqzaAwyntJ5FS/wtHHt+NjSuvyUal/G5dY3UcriUzsMbS9bdnyeO7sXHlNdlotxrXwtQfPM7IBZ5faJCEZ8nju7Fx5TXZYIWR53LcDmecxchlqHaIZ8nju7Fx5TXYSPHW8nNlbofz5Lk2bEbyypLDN2Pj0muykRn3HNY4aYcnKbck1aJRPEse342NK6/JRmXc81O9bocnEUZ1mPbliSWH78bGlddko/MCJq+D6AOmz4knffLM0QPfjYsLn3kLHm5Rnkt4O5zxVQwPsun14MjDm3Fx6TTJwBQpz8W8Hc50xTyGysp4jhy8GxdXPpMLPHxbS11/8PhJOeTEkcN3Y+PKa7LBZMzcVw0NhzO+jzVlNCzzxJLDd2PjymuywWTMGNb8NIenjtFh1GojqGPJ47uxceU12GAI8eixrHPrgTOkVR5DqGPJ45uxcfduDSkqLL9X01r82OEM/MWys3TxLMkZ342NK6/JBlPuUonr9HrgVA7haY7Gl5xYcvhubFx5TTYa7w/rqg3gcEZYh87Y7oUlj+/GxpXXZIOVDZ/LhDuc2QMpsojomSQH78bFlc/gokbYfa4X7nBmcPB+IOSFI49vxsbdu5WMfONdanki44FTNyRa4ZvDiAN342E6to4XtfLm8KmGuMOZ7gNCguYWODsnfDc2rrwmG0w+qU/XJw+YiVhMR+lx4cjju3Fx4TOpGLw2zKt0hsP1XoAVZ9OZIw/vxsWV0yBDIm8NnwqMO1wLVtdW7dzPkeTxzdi49JpssOazyJrn7HDmTcFy1BzeE0sO342NK6/JRkVzz+1pKjlw3jRTm0zGypLDd2Pjymuy0W9haF3xhY0HztraGqGSV5YcvhsbV16DjRZ4bxhWNSaPU3KIUQgLSyd8Mzbu3j2xkXhvGJ8OhR3O+M/cutaU83Y8vhsb07v1vAsPzwC2VRPA40NuQXqznuLseHw3Nq68JhvCe8MSntrGgc82YOEJniWH78bGlddko/Pe8CkT1eMj3agrYPEJniWH78bGlddMoQu8N5SnnuJw5j9jpJC4suTwzdi49Jps4AWX0MN6MnzGW+1UJviw2nngu7Fx5TXZKNiEYiRc968L3gd+5ZmlB/6z2XhLNZElbQhfr3VoYgW+WMM9tCs3oSiFpdY0LBXmBRNmyZI1ELYz2LH1NpUAes9axEOzT5I0koMO0vHV/EjEKKVowcJCvY+kChW5c9WeNdwQg23MQ7LZabcQcmZAEXUkc+qmEACcGgtl4jFPgQMmN0iK00yobeY88KQpTlWSVDGa6a8ObhvQZNKUztKoEguE7wlcZvVqtFhUdCIPapuCt6J4h1kVb2SoOLxq1FliqHhMYtkFwocXXXWo1qNYYHnjM5Yhs2xnSKaGMDofMrPUEBVEa4/zVwe5l2KcUSlHOS5UCRxogEOXcsEUew3H02cxHF801RMGpmaO2pSQCIJdQpto76IV8ChKkZOqh5WQ4VINLOoUQBnLzuoaIVSGY2XqekSupvEK7WG4YEh6xhfx+7303iaOxzIdzHQDX6FbJGSjs2ghGgkpHW9f4U5fB+U+ImXr67y+DwOdG64WLWqfRIaFl0btAonBtpHyHcz/erFwOhAZKXGe4DZItis9dqlBbcUXVkKszAlNM6go9qCRV2yh1Wq9FdhEX6RIhhZ2StWyaRl10znqUx5E4C1bhV6sB5LZmYmKDXrraJZtxqXAaCPLQp0HNLQ+IzRCMBFhKbeQpmBLSSxhmtFPVECm4LXY8XFCo04YIGZ0Q47RRmCm+nZ4K4rj91NJE2/oBaPp2Tu8z3Ymn9otlcAfS9gQZDTJ+btN99GsakXNmK493gIGQLNG8Ta83TqsdnlJgzTrDWnjV/GAdjuG5k7lHb1TL1mrPNv1clCNlMHWhR7QJlpzydlKGMBaUw0W5vYHyuVETTDtrGJ6xytlcUxyJlK9J8/r2pg0DJ2fFzypHaJlZjmxyqPap7zEvOisJGdEu/nHksa0jXi1l6l6QtUd9H42arsAHWAQDY9hEwynKanFiQ+8IcpiU90Gw5K15UL9udoopU41HPRjTRIoJVKnIUXDIzqWdQng4EHmVWPFRtV6aEn0t1L1qHEMjFFFkgpPihNFPRRnDTo7QAUOf7MY3rDZs0ZbChtJG1ZwqWEYCX3i6CmwpJd3FJdU1Ra0PjYSYV/BPglDb7ZGVRg/M6plW7PEhD0lC4u2pOfaGMgwt83rH5bKE2lJu1Z8BK4VsIl3GyyocaCNaLFJjDlkh1lpiYSwslyfONjBvPBCuacuOVj8BqvPCcidONpysWNkVShne6QdfKDUOw52LMgy8oW2eD92jp3qKNM+t8YTbwxSrnpJAdbGPJpVafiiSkDofW2WDcC6mK1H2OXYOzC1Ntt4Bz4nJaGo9BJEjHyJfEx2OIZDY2Q17jFK4GmKCgQ1zgXlfuKDTkjVNmrECrWhzIqJAk0ZLY4K/X4KwAQ/1dHCc2GmNvLRiRuG3zZ3ehl+qLONcbehiE2ro6P53vdCcDxb+mSHmfs+sneb++tNhHJFCnMEbcLz3lF0PLR+2zgJF07UmmyHUdgCOrG2SLMwJ9N2831s79yOhjbXSLAS7XoVaws8jMyrOGayWz9cF2TNVLFew79ANeg12YnXRGVg+lqR4uOr+jQUGvpycYvrB/jkr3yJahCXa1THqkk1379ANSi9fPfyz+9//OH7v/0aokGY5m6VK5OFSXR8DlhnGu/giShn4Vou6PhAXi55fraGA8zSwFPtMIe/gdJLxYIjYmGSeBdZP0/s5lZWsYVfkAaM++jccT3/PeA3IEEw6GKC4aDExcXnkSDfkIQvaslf2RASKyK5p/8stZtfhwNq2nDplBwFB/ZGDFDmBvsaLKdCiJ+Sdkm/EgmiC2MKojsWDvCtaKAeYsdeWGrAAvh1Gr7loHAal/m4lYvhj24Qf4BvRQNXPKNipMRefnyCBnlF2qZif1t96/p6aZuvJfYrRXkrhtkvP0irWFZ37A9X8UPsT3RdKMsh64LnESQYfrLzwB8HaZuwYd6tyZK1cNQqbS057nGskiMI0KMIb8fju7Fx5TXZwJZR4ljjYjzODUwsugU4kXTAu3Fx5TO4wE4Xjx7WkuMe5/lGiUniM0cPfDM2pney9pPKkvISR1nZOHAG549Y9HTV2/H4bmyYd2taca3lpmrPeWXjwIceiBQ9NfF2PL4bG1dekw0eLq9l6j+ccD2P73YGs7J0x3djw7yTlYxOvfVVBMrBVHZuoc9u8jDi4d2YMN/WFLAq4dbDWqL+wwnXm5Qkekbp7Xh8MzYuvSYbPNTFtu6JjQMfvE5T0feFJYfvxsaV12SjsuMvZeo/eFyl12ucY8Zh54TvxoZ5tx4jVOE1nMRV+MjhKlQSk5WBdXZO+G5smHdrumRl/QRMD6WubDxw3koWsJA9S3HBd2PjymseN7LkQ8hr6SWH8841lSlVcmLJ4Zuxcfeur2wwA3QtU//B4xRuicrLB2/Gw7txMX17OoiurKAha1qxwynbkkWsroqzc8J3Y+PKa7LRWKmjrQXHHc4bRUaoTDYOljy+GxtXXpONwZiasaYVO5wVVbrUlBaSHLwbF+bb006+8yB5LVH/weM8ZE3DjiudGQ9vxsX0bS1QhsU1Q5eWknYfPM7QGNbW0dtcZ+eE78bGlddkQxiGn58axh1mGA/MVk2jPnHk8N24mM6tZxq937DRKGvqvcNVvqb0VMSTtOC7sTG9W8gYmBFCqWsemMMZydUy3BuepDO8GReXTpOMdCsxPpXxc7gW+cqlajCNJ8nju7Fh3q2J93UUVvrra76kwxlXl8sYmrbh7Jzw3dgw79aqN1VD/OpoT2w88JOEjbNzLW2zCRvmXX9ig809x6dz4QNnG0gsRVgcSzzt8PhubJh3a8KkhMCbw5SXnavDKWETGHPWHEtMmPT4Xmxce002Em8O8xo+5nCtvsigmrGw5PHd2DDv+hMblVeHdT0bdjhDnDGAiAawOzsnfDc2zLs1DUwYXCpR1tRih6fGuFQVrPjg7Zzw3di48ppsjFsSLaC7sPHAGSHdQpxkOJIOeDcuzLd1Hy/MoGisirxwceAscyt1ZBWjdXZO+GZsTO9CXNnIvDkMa7qkwxnFDv+SzqPOzgnfjY0rr8kGs2hCXCN5HM7Y+iAwICtLDt+NDfNu3ctLbLw9zPGJjQfOgtPoG1ZU19k54buxod6l0VY2Bm8PSy4rGw9ci37nMbQgtLNzwndj48prsMHELzzYGsvjcBZkD+gPGpfhWfL4ZmxM79amway22Ft4IuOB8xqtiWX2HFY8uhsT6tpTATNhwl6qq2DvATMbCJQkzYlyVk74blyYc6v0kTC5MMPMysUdpqQPpwwtbOisnPDduDDnZNnHS2ZZ+xDWskwOTyEDb0XPcpydE74ZG5dek43E76W1zJ/DmbScMGdoxOOJJYfvxoZ5l5/YKLw3zOvRsMdH43l41/w5b8fju7Fh3q1JcZK185e1iIbHmRWfZZToWJJ0xndj48prssFE9f5UwMzjWpprhHrB0gPfjQ3zLq9sMIGXqasrGw5XlYnS03AsTXmoB74ZG9O7uu7WmF6Nrj/WnuLwkW6BsgLNsVT7Gd+NjSuvyUbl7WFY43g8zmskzShdWXL4bmyYd2tOjpTG68MoT2yc8JhH1Zzpxc4D/9lsvKW4zT1xKM2a2IJ/iiaZMH923EV3pKLHawwKU2uq3iLzFReJYuF9dDa2GKwyKH/aYqdJDseIpsNFocBGnLhQAsPUZwb4C48EjZGlW7Y9ulMfd7xRw2PiqQbRC24qQ9dUNTBXbvBS0h1vFDiy7HyJyYQdmPJAxaGpbiM5q+gNY/9LoryFysxQw8ZgHsa0MROMiBrMZFrp1bRwItXODMcvYQCIhlOGotmPcgYptVksPd5sK5Zx0OhUyfaz2NhbmQDGWLee0szX6LEHI0HQqfhmbezF70eV4KsS+VJMXQCL3Dps9cJ4XKxksr6UQWWXpKoXlfmPsbVsa0Ipeg1MHHunJiqBQiGbpMdTxLGPGPC+qyBDSWWMOnE8P5WFGOHaWon3UM9Uc6RWdaRwUTQxkkr5lAjLuknteD3z6SmexPskCstgCO15ouw8WQMk0YKztY8WKIyjgiYJj86mrUZa4rZPhUtUEAbv2wLIhFRSMIHHaVJyH/f4IdipvBbHW4tFb0EtlAZ2KCHDuKJC7Ql99k4pmpZUBSawa9xDbzJebdWQRiEFWaxddjqOheZQVZrU0BL0OXtVqZs8TJUmUiBC8UZ3uQZncAL2KffPd2rC5GF4D9EEGYgPvueoqjSNOYEW4zCozZTIAwUbMTmFPK/7uS3udv4eO7zT52duR2tVz+V5utTtEKVSVWlUijMl9vMea28Th19CcaGRTPUizytj9MJIng+FJ8Phb6XoEPpnYhwCUepQYcgydOBV1jbxjuYVJx6S1TnkFWxukcIeakUwNPaJU6KGQQz4amA1VbvKpZYTT4RUrkY0ZhB/YyiSxldFSa+erWQzb61YoaPNuz3wKXe8YZwJpgHD0xXbK2DzCFcLXyE1QDC23WF42k2RBg1aol18lJsOG0RHbWPMq6GMfsG3TTkaNGfbogKnjBDfNhpuST0MM8LTvqi1MImjY5cJw81SzUwDffNWsrERdH3ZhWEe8wmtI8XZBko2PSpeRwSqeWZtk7Fi2JR5MA8CsvadrmO9TfMxqS6YysXw6XOIZeKYZaLKxbCcVms9ziPtzNM5q9Un1JzLE++FQSiKY2SIyXDWkkmqUExcMLOleRisQ7DhA5+fdippoE4ZfzeUEqLhogpGHM4K75xCl3mKymGDXZmzX0T/TRMHf2yJjDUtJcZhj98xzrTBdsOtAHgNduZGQVSh9A3Va/Dqi8Yto3Hy8ZlwyeGvYwixt4VxCY8vfFu88kCDtnaWVGiJQkJ6YhXQ/vo8rxDBwFh0NA6CcdZW4Jyy0ZBtVGeWY55bWHSYmm0SwJw+5rYG7Q5U8/CZKSrh3rwzOgz11AwPrIvX5naH3s6ZlrF08/OcyZvNqMDR0+xQOnMmbxTFiSo9lWuOc6MAoGfDOY/ryKgbCPRgMZwMiv0uZ+waTTdHxzllDa8IbnXGhbKYdk0mm4WRnAmLKnlBfTksrnSu1VVXGzqnYixsmrdFGD+FrjpTI7EiKXYvORdvM+UYrTTb07+Cf4EQzmtyFK/JpcD0hVLFx2vNFXz4S+Uurn/3dftfIn7DZWTDMI6Jk0KfXyB+k1++e/n9+z99/Mv7P/4a6jeF4nS8quZhmPGB5n5rGAxVQ+wOf/BwLCpbafi0cMIeyjdP5r9E1MFa1bulVYWbJrb7J75jp2f4OkEHjs9YZmJcx9rrhWuJK4UXrcZCYb5WH68zHq/vry9cbNBmcP/6w8cXWPjuNz/8+ft//fvvv//xp+8+vv/x7z+9/OYvL7+bmg1vT1fkZC0UAjq/4wN+A9IoOqnGCpVMXyPtE4Iwv4DfleqAOaxt+4DfwO+IkSOptYKlyGc4Hn95xz+/Q3+dzymZfSyP/2Phn9s3eNPTIraSGOxDiWeHD/hnOlwpZM2F28A655M+12/mc8fejlqF/ezzAf9Mn7Hnq9hKtMDKhp9q1t/wPUcuajP2+uXstMN/ptdRl/eDm0VsBT7pdn1FxifqCc2LcHj4OvWeL+Qvm5+fecZ3PyDEDqzHiwPC0wnau4tU+hu2ZT2fp5TKBeMYUk+DzIFSobXYyv4w4MDHseD/0a5Pd/rZdSyoeRxycvyOdSw6oy223bcduofj6g5Fb73jQlHUYmqwh+sHip0atTctlfFhwaNbOD8dWhq85FtNMct5Vj1QXjBnk8o4DDhwD9cvnOSW6Ybd+bmn37HM5PZhglOODIfu4bi6U9YGz7ypKmuD92jrPA99EDItPNA9nDeH9NTOOT94ONmWEf5AM88RQ1FN+MOCR/dwfjp07u2Nquyjj3Jy3qEdc30yCXdnwaFbOH936NzsW7mFimXg2fc7WHiunHUSP77uwD0cN2/S4rhgm1NiPff3A61B5dx1e3FY8OgezptDa5Nn442s8XBy/oFq6H5u2rgPCx7dw/kLNz++67xqZN3Ik/MHSun2loKeSDtKHLqF83eHzs2+8960VjkvaQ+0shQFlq/dUdJO6B7OT4fOzb4Xlv2QcZ7mDpRFy1scqmV0WPDoHs5fuAnnecEz+qk9fHBowy/0rHVQHCMHuIfrF07CdRbNlbGM9QcKf7FXreVMiEf3cP7CzY/vWLooljDOU/yBDk7s0SSrDgse3cL5KzfhPAauFDVE2Dv/QGOMPO3MuoY9TJzgPdy/cBTus1BQz0vDP1C8bynFEo8OCx7dw/kLN+E8g01qGeep7kBjYKWdIaOfODnBe7h/4Sjc511w4gX9yf0HGhkfxfrZZWHFwXu4f+Eo78EivjVaPc93DmYhmpGtrPxhw6NbuH/pqN0DYvnSx+L+HY2M3mExsnhm5YRvQsCzp/SfxYgoYLAQ8IBZ9i+UUuKJF49u4v6Fo/S/3RpLVsni/wOmNk4Ry7N0Rjy8CQHTpeVuNAxGYqanHvCA+UUs9SzM11k54ZtQcOErKMAyZnQWrThTcMCMkczJyoQ5Ix7eg4C7S0swRCy8j6q1LgQ84JiviElnfBMKplPLPBgZiTakL+PAAUtGay9WZdQZ8fAmBFx4SgL6LTMGcBkHDrixKis2+PnMi4c3IeDCUxCQ8DXN0TgTcMC8xMJooBUcnREP70HAlackIPGyKizXWg5mVVQGR4+FFwdvQsCFpySg8MIqxWUtfMCladCxxnU7Ix7ehIDp0jIGsAJu5mnG2f87qrFEw/J3nAkPb+L+9Cgu7ndNPuhl8f8BC3NDsp7sOxsO3cT96dCyCsgaHKtR+t79Ay5a7C5M/x9GPLwHAXeXlubPYJwqms10IuABZ7zzmoomKTgjHt6EgOnSMgBmVjDNvS2BzwdcEla8zbIxnBEPb0KAudSXEQDL2cRExGUNcMAsvy3MXHK8wIiHNyHgwlMSwFS8Hp+6wAF3eFrbKGdePLwJAdOlZQxgmete09oFDhjdvrQStQS8M+LhPQiYLq1doLBgfCprFzhgeIrBzgQhnBEPb0LAdGnxv/Jmq5bV/wecWbs9WdrwYcOjm7g/PVo7QNOMtacO8IDR6UfH/xFPi5zgTQiYLi2TIJ55xNzDsgtwcL8l7PdG9rzUE7wJAeZSXHpApY5FGGsPOGBM/DEy097xAiMe3oOAK09JQOEtV1i7wGvwJV2bEDBdWpOEhPdcaU2Aew2+MLINAebSUxfovOjKy0bwhI4WumdlmrjDm7j/7CeTSvitoenX3v0D5spfqkpDHCYc+LXOn7NgKGzyuaIvF8ImM8eCigG3SvHlpFXLmN4uQxdsPUe7waI8B0awJAzN1yw+k45gGtHozdA2lUKoo6Ep/0QHRQwM7bXh3RMNoTRLImaoBDgZ/LUkRYXS9bxQfz+i0VhycryNpmuLcMtSZ8YyC7mUrp8cTbIm9jCgXKgcwC1HGXg+zRTncXRmsg5zwNOY6f48oUkVO3SimSIPLxqVjSErREVrqMMUibGFLSBJH4GVT2XGdSeqslIQ/4bnEpW8YGjzgF9F0Z6bqStwHpDOAHgqolBLDWjjK8POSJnFBqlpDDg1PFptRVFqaqgOCBXDQ41sXwnMY1IhDdT1yAVv+SWzICmnWKKZHhcBOmChN9U1pEpDTXg9L4W1TvDO9dcqfRN8olItQpi/pgGbqeFNAVVBe6sDTLWQMUp/wQul9K7G67fGVz4iP5kkpzpjHrElbmCkUrOi6osAOGIDuy8YFLEuwgskOkgC9XNYUxUf1YAZCpA0CaJo4l0qP9sDSShDUUGjbaSGoiS5sMfwx0I3TRygVQq1FCpPIFPXQpw9kwS8whehmMToFqWY8apSQCPC65NUWpxBfUzcz48QNn3tnXolAQ2RQX2hNSuF0ZnTNRj50vTAL81QqEIxkM5wmMobkWSottj+MmhWikqQUIEEA2UeL5TYgVVVXQAKJyheQmWRrmKVQBOdAE8MtMi1NO2RQOEEBQWIJlYTfNGQHHQkKqEMPAMMaNA1rWFFjj5BeSF8S7UZqFLSeG7PXxtYuuuGbVClQQ9wgQo2NSpUQI0SKvIkWkAT41EPUNDQOz3ScICqFXIVxpvvVB1gajHai9loHLKoohJNG1+lDqhOQr0WCy+B4WIHCvgDCwmqn7BSTYlBedNYEzw/uksH86MPlcfmz8eaqX3SuQgN1lQIgyTqrIDaSJ0Du7pP5KPi9ymgwjZotjMJGQMw2kVH/ysThkXG+VLxLueoTVajHqjaAhjtG1O+CqLwWpyZh+hhlJBqo4T7bfmIemoAdzB4WPuM1DIpHDQ0ji7bEEaUmZZ62ZpAa7Mf7GwzMM3noEqVnb0HPW6gItCgpFBtj6vZMHTspQpSziZZQxgtnrHZ9LHVajc4TIDMaAJE0ZPljmJICykxqRtzQrXTzoiHikmLJIMaDtHmIlMlW6RYFP9ZOxp1nzj404pFFDmpw8Qs+EfOJosdKWSE380Tx4ckFLWvch12/VbJVQf7HJlSnIevFDxpKoXNQZNSI3YtKZT3aFVeBNSOLNZ+oko4Ut0DfX8UBq1OuHLCVrhJ72ZDx/ScO1GMazZD8bonmYaWaBJf633CrST8zcGmyhydMbGRFiYECSVhKBL0Ylcm8JJjG1D0TWv1lEvBw6KnMm+glnm+zIqp1IgiqpU3DOYI2pm3X9n86/00upAQVheg6ZQsXCMJHacAUiV7UzWHcKXmfuaAjiXAbBCJnatmUViw6uhpwhwv0Vk5/Lcp8Mfz78g78ELbvaEh3S8LhCIYheM3U6KsHWOmBE8UJypo0cz9U/q4CAmRbZ1CNKw6ZEfLVIcJ2FdydutVTEWMcJeQsryoBmMbdhOH2RL8CefCrmOTvTHKvmDsRnvk2USJY/4kHis01trkjl1sQuZRJpjidSbQMEt9YwbGS8fKLVraXu225OOvY2hg5TW4FcscBfLQXB+JtuKKs0HRhRCpOqKf1gQR7htZwDDUqimBGEoscZmiBGhbqel6kjkmxbbZhSfMeExLpEP7LBOugjZceQYfhk1fUXX3KiXKVCQ9tFAmjCVoSXpgX3QWVZhLwgKWCSdWa2gTFiY6CH+R87w1Vuz3edcbdTlClatoMIeyFGz9R9W8dN8cY+wsqojC5dpQbS0MKtgohDQ0QRSrhXZHB5fyulrsZVQb6yoXh51Rm4MSdfOlM9cAS8KqCbZcTc/VONeHMs+1VVD/vkULGPBtjwLLYu+AwmcVKy69CCp4Nfmxo8H7sew3oXpKn3ArWHPpaW/Aq7E3Vjn5JBWJQgPAfGNEobOguUSstXg0WuOwiYXLZ6zBIuVm8DoiPTB4gL6iMOjrutL0OxHuJLodul2iXyDMcyERcSXiAouXyhEfX5WCwTe+SH7i+Vc/aftLBHnAkTBEHd2XKr5sn2bpP9LjKS/fvfzz+x9/+P5vv4YcD+UFMW2mfN6QY5WDsUqlhTyFDj5x5axcy/I8PqD70K/cn767alsUIMOEKOcjpQN9A9UVbLaZaiM58KjhM9RmbmVVV/gF/VddQpljykHAAb8FA9glpUBriVvAz6FAviEFX9SGv1J5B1uS6p7+s9RofhUKMLLXxvnmxMADfSMCMJPlgVVVwmLgU+o06VfhgJqvFBNcmsEBvxEL2AcKRV6pudI/wcK3HA/cUExd1sLdyYmFA34jFriFoaoqVlKsAvI6DfKaeg2F8w57P0PF5it5/Uq564Ll/pfLXVN1N5cUzokR2NNxkcWdnR+tHIxZCCvboidkhw2PPo6B96BherZMXNjK1opetfBwwDxGTalEz07y6F40XPlLGihznMuifeHhzmOLqhEg3oiDNyPiwmMSwd1gqIsOhoPZA2pgLfYTPx7ejAh1jdK/ZyL01KzluBDxgOlxFrsdcEY8vBkR5tqSQkftXx6yy8LDNXphYjsanj0DCxjwAueFxeEDtpnBRHoPGx7di4Yrh8lD4fFoTCsPDzjrkakpVHt6HLwZEebaIpZEqeyMf5/EJT54uAjl9YdqhTgjHt6MCHNtybBLGhqW81iJeMCt3SLWnN3RAxsO3YyGC395BMIAwVAXQRkH16I5pZpI44x4eC8irjwmEenG6hRLyrWDK+XsMUOOhR8Hb0aEuSbLxFnwZrPerJyJeMDCe8WQ9SbVGfHwZkSYa33Za2HI6yWOJQnbwSLUGWHG5QdvxMObEXHhMYnoeiC8KHE4mNK+HcvodObHw5sRceExiKiRuRVxycp2sMYK1F5tOXVYOeF7UTGdWxdUlWebOadl53nAKsRSs15COiMe3owIcy0u80bVsJxSVyIeMO/XhVqTjh9W8XDwZkRceEwi8EdrdZGscfDgbXHqtvM8jHh4MyIuPCYRvPkobVlYPlAqNETezS70nPDNiJi+LROoxBsFy9cl1QEzcqn2YWEDzsoJ34uKu3MLE5mhK2M9dzhgjWXD24+eoBO6GQ0XDpMHxklKTEvfOOAB13ssbaXHwZsRYa7FtUE05mdYPThPxANmIBqDQzT0yFk54ZtRMZ1bFlUtMFMj92XeOODOM9picZ7OiIf3IuLKYxLBYmutLkIHDqbAyWhT2sZZOeGbUWHOhWUP2grMFlm2oA+UsbIxWsE0Z8LDm9FgntWVBsbLx7akfDiYQbQZe3INx3NWTvhmVFz4TCoYLdnHkgDr4JPokafoUgxpDyrUuT6WAZPJCJVFf85UHLBgZGyth3wwRCMe3ouI6VpZiUi8xot9JeIBs0OwhGh0/MCIhzcjwlx7CuMqvMZbQxkdzESikJImFzgjHt6MCHMtLVNoZ9B3K2WZOA4YkyYzkDT82xnx8GZEXHg8A/s6a22debijWm67yOwYDxMe3owG80yWHSi1AEdcS0Q4GHtOfNfSQJwRD+9FxHQtrkSwbnTveTm5PGCsqwOGSAuYOYx4eDMiLjwmEZXV68e6pDpgzWtJotkazoiHNyPCXFvKCSTmwMUcxjJEHHBmdtiomgXhjHh4MyLUtRhXIgav8lJZiXjAJbNGcdGwWGfEw5sRYa7l85lEZopianmpGOdgRrgzYa87fnI9wVsRcXetl4WIzKu8umiNOFjzxnPTiy1nxMObEXHhMYlgDnWURUTDwQyPqdXy+s78PODNiDDX8sIDEzR7E1l4eMCZbYCxlY6efEI3o8E8O191sXx4qHWE86Glgwt+I1ZLHjtseHQvGq4cJg+Jl3hhWUw5uDBduVlU6ZmeB7wZEeZaWmaMqHn7cSnI5GBNpMQ6uzh+Uj3BmxFhrrWVCBbYajmuRDxglRAZlnbsjHh4MyLUtTTWIaLzDq8sGj2vwhdGtiPCXCvL1JkC7/CWq89X0CsTu9Fw4RlZSLzBo+rFmYYH7AONDxtvGX7ss6HeqXrRy2eS8qxedE+oEV35xJayNM3KwQsMYaSiU34ZLVreSb8NzAe1MO8Ee+ig6eeajoKtZleY6afDgtDhfexStGJpqsEkV5i0EUoPpr8DVkTlihIojNiHZV17UOOolglLFj4W1Qrw28E+zbCT2qzKMfbzSUUUNA9iVKqt6FIlx2w/yWvnjIfRbP8uYoFdgHtMg4q7GY7lbiEtiUftiS+Ln4bv9Q73Rv0rKlClEuaDNFJij0fdGyUkgT4J0QQNqEQxszR4HDNqVrikElVeh0HqeDRm1jNXvQy7BaK8UWip6qoLG/ZQ+oTBhvQ82Uv9Hv8eRaJJx7Yood7jvsFHLSpz0KWNbFHQ+ZYS3mox0YHQk326kCZqWRVK/tQ4P13JBwvQFSas9RrvIcSD++rGJPBGhSoz0uh74VFLuo0R5wmM6jaOjnfNS/AWGXJr8bc1DspcAUab6OMejQrnqWjFRH70HEkTlqwx25USPFGawawPxtU7dTbSveUAxTN3UT0N3iLZZ9EUVREEhqM2dwNbLZS7opQWVWHUQypOt8Tq4ZQ/iWM29wLH0amkMHZaJXDUQ1XcGKxV1CmaYKtkNHs+G9vFUP3qVOqEqaWATjAoGVKKul0jH44iVNSKiSlYjwGM3UfITZWHepFo4SVwRU83qSHTBG9oTLhjH485iBo96Ghj3CO14A0TYiiC07HRt/sUikWFFO8lWErp1tkpLYUJLRqOhwnTIyEXDH8afKw6H0bFmliLdQzTIpIJ19YpX6U6Qr3PFkj2BcxZXECM2GWliYOXMgxHH7HSRwnvBczoaBiomjKms8LeXzj0qLOsmKZ20PYyh5aiCkLCWPcJ95apIThUvyVOK5kU1FA1V7ZJtW7Mohs5UraKn5ZhEkdJQJhExgUznMUKzVtIA88PWMcOj8KqhTYMipAZ0eggGEQjtqYPHANY6Hc8D7kHBFDunxF1AaMsXqANYkK/pQdVsEKfrhZkJONGp03uCt2tTev6clTvirJfqUe7RozkhWMNfxRwUiUt4iAma9QW2ilrzKr1lsgMRa+IN4ww8xKOUm9J4zqAY+mXVLkptUxupCe13zAB23ttrF8SecrEz492T0xpRcPOi71XDKDVoiJY5zb0Dhcp8jOwtLZGDFxyV6ErzAwZHciGLmYxSOMYpSUz8M+YJs4RPFmcCUa0bMN243l4xab2pUeqlwXdzBHu1LJSHaSMQcvOyFh7FcNAay/wI1fNJzYYpLEEb+Ooch9GYbEE6sqprlkMo5kRFmkPVFghqaXMAYKFLdH4u8IYsKvNFaz6ielNh57UJXb7cAUtzOTmW6egkH2WRYF7QLdls8eU2+0HG1sG+jxD+sXSfwzGyIptCwvJDgwb3ZzpZKRlhSulhdKEO6UMs8rHYbS3obFzWMakr0pxo6taEM/QAl0vLFjDjUCf/Yu1HDFToZfiAYN0K+LAU/nCSbrqVIH5PI0Jo61Q8F1UTMyqW6ehhTC4yWA4WZySPTzRBn+CpVdVUbBgywm0ejBV0KUwHrJyrNyPxUFVxmstFWtUyZb9MEhQo3wUhYHUAaJyo2gg+ClUFezRoqA5YqNnRT0IwFBh4fIs2tf1NrZQSi6b8l2icBhG93CHa8sT7lS5ofLSoE6J/eAgfU3VBrkY08fIIZAm4bJBKBvXxI7pEh0faSosljzB2il/p0pAnLJe7CCLzbBnW2G0oSJXhIWLRhOEDCJ2CFRI0TwMo7hhnii1qpoeCrISUbJnq+SCSnGaiRysPk3mBIO16NCzVLQl0+vKgcvKErrKOAO0VkMYqy8GBFEJKner8YJfpt+jKFw7OnucMFZUyhIXDy3ZFpT5K5GKmjzPx09qy8NOFSThd1SGip/V5sudO1epSZd4nSJE+tiRq0qqoKnIKH5Dnxq7W7QMSvypYBYGQ7PB9WBhF6VGF1Y02g7wlPSx2A0dhe6M1dh0hTnFjijyaY/NlWJOdj7XsIgOY8KDNX+13n3ASkw9jzyhpj6c6uCjizTbfEQKIhfTOoKLQxc1hOFjN/VLrFGy6mRxq0KVrGaCnzPPx29geC+E9b68Dn+BBtIrIiWvKefA8qV+ycdXNXjwjS8WQrn+9U/+xpdoInFThWGJGqLcOnyBKFJ9+e7l9+//9PEv7//4a6gi5UbdRVVy8kSWoeedFOXzRDr4xJazcq2K9PhABNNfq9p7qQJSMBxhOyVLtLKD30AVKOsaERN84KXia6JAgxvhht9EHz3kgerxWv8K05yy2QaOf/3h4wtsfPebH/78/b/+/fff//jTdx/f//j3n15+85eX302xj1+QPh0jRZZ6LQ5+A/oKN8dY/GKfwTXI54gqPSkK/YIUiG0Lw0LBAb8FBVwcNVgrKhX5ORTUb0fBl40CX9mHqObnnv6rdKW+CQWNJXc0LfhEwQG/EQVCTU0sA7FZl0+xEH8VFiji2npfG8IBvxELFKgV7JG4n/2UpFJ6RVIJi0hPxs+QVPpKWr/ujBqvHZvILz+jxna1mjT8aeHTefKlApmnQ+oD5hJZq6rrdP0w4uHHKfUWRNxdW26xOuWla12v816DL4xsR8SFaySCEtnpfMvw4XX4kp/NiJiuyUIEmngcfVGHeBW+MLIdEdO1JSSIou1Pnr2CXpnYjYYLz8gCj9xKWFJ8X4Wv2NmMhunZMlCOwuySlJZ73NfgCyPbETFdWwKjBosG9LzEmTt4zpEnerJHN6Phwl/SwOu1upRnPNCGjtDn6eRhwaGbkTD9Og+RhfUwqCB1buYLXGPQ0/bFyB3eioiHa8vOL+jtV1uiPBzsmr+zsWunuPSXNPA6WPoiluJgFizokvXO6MzOA96MiAuPSYQwrWSJmjxQGXoHWBZ2PLwZDc/+kgVWgdET3jMND5jVXBh8pNe6h5ETvBkR07eFicgKZT0tYhAOZiGKLHrf6Ww4dC8a7o4t4wOL7oRa4jI+HHBj0ZaRNdbDGfHwZkRceEwiKovU1poXIh7wKYXZWblObd6DiulcXKhoDJaRvgwSB4xRkaEcGnnmjHh4MyKma7IQweJY0mNfiHjAXcvzZI28cUY8vBkRFx6DiBRZBnDU897SwXXcWgh2P+yMeHgvIq48JhGZWSVh7RoHrNXNUhMNf3JWTvhmVEznltulVJlZktbOccDC6AZ0h+4Zqid4MyKma8sUymgHTBDLxtPBWtstso6XJ6id8c2ouPCZl64MHE3w7kzFAVMup7EU+pkhD+9FxJXHJCKxOqzEZRt+wKwpSuXeZ34e8GZEXHhMIvDoTdqS1+tgxp0GbL019NFZOeGbUXHhM6lgZb3cF4ktBzMWFv/QODdnxMObEXHhMYnQQLAQl0n0gIX1BVUQ/sSPhzcj4sJjEMHUgEFFvTMRB8wQ01aDLSYcPw7ei4grj0kES8Ku8ucHOpgXEyznxpnw8GY0PPtLFgpv7kpcZs8Dxr6ijGEh+ocNj25Gw4XD5KHx6q6WZfI84GYpahrQ7ox4eDMipmvL/rMwgEukLSvLA2ZmQWxdA6adEQ9vRsR0bekZzDHKsLv0jAMW1ke3nCZnw6F70XDlL2nI/GMsShAO9sEwzsi2MTKXHpMIlv7uoS0T5wHfPb6mZz8apmPL4pqJapX5OQsND/hEw8PGvjSYY8tykul3gkdfB4dX4Gcb29Fw4RnDfaPm+y2iWg7WhOHYNf7TGfHwXkTcXVuW1cKkUZFFOcjBWXO4miYnOyMe3oyI6dpyHCNMc4MDy47zgE/zxWFk3/niymMSAYeYtbrsOF+DL2nbjIjp2jJGMNwFNhYpiNfgKyO7EXF3bVlRtsT7u/UI4hX0wsR2NEzPloUUHl1j5Zat1gGfRojDyL4jxN21ZYRoKhBQ1jnjNfjCyHZEXLhGIjov79YExFfhS342I8JcW5dTPfDuTtbJ4RX4yshuRNxdW3jQ1LEeliHigH0g3cPEtnF0V+6ShXLDGnHkZSA44NNA+UTOmwyUbymi9Mig0ST4FoRiBMw1YLh4YBFe5sYLpewZKApYamCAGBbJ2Ep3CybujBdsnVsOCliEZDn6PeOxWrblRO81lzvckzTjEIaH5WuDQ3ymDqOWshH2aWzUMlbj9zVts+TzwVON3izRrxRmNxraqNyQzHQ1LUzGfA9V2wA6mFJ/DwWnAg5DfdhrezbFyKEyGaUDHreeUyp3uFP8QV3He5kyWYNTADzTotaVejX2aeyyWau2UpSh5MdPYpzMVCugHNHAK1PBkzwaU/YLtuI1UkwipTbhykrpBkvM0aKWqRQUM6tg4r3HPiUeKWkThhSVdqDqiN24B3YEvVzOg9I+Id1hMFXtcAA/HTV5v4RIqih0w+qitZkECmFQldF8YSSLmDAA40RBlf5gwyuwQCCAvZPsQvEKNH77vUye6DglVOI8q6R4TKlMVih4CrShbE9RyB535xUtKwerPcVUaLCH1sbqCaO1YTcBAaQOfU2NGkDRhDuKCv9EmHzpTKUbFt1NmJoEaM9UL5FKCgh31vuiLk2nlHDLPU8YpIJVHq8HGc06dqBokSYPci0UeUOnES/aD7UybVDRbgsbBMyYj67SJ9R/MOdjBHsqS8CqKZw24oTB3zy/xZuLE06kilNsx3ssZQ4xEU6EksRuUCtwOw4HDq5ElZTQsUKcMTkVXqA5Thhb/xAnDg6rVkdULSLTYWEwE4UpKstHgs3W40TZMjqZpdyJmD8UwFC5NarIjBAsmBgwWIvYRjEeaphYEroGnS/ZbjMwUsc6YTiP5kNYQunRQgQoCCJU6eqUa6BcmMLoW61yOOJLS/iIOpkoAlN4Q4ZXjO8W2+Envm8qv7z0CttgpEwYrjPXvXOUCsV6QoLDeCoqJXHQyKbSRhiuc5RCNxSM0NZiMbBQVg0eM6cAvWzkCfNQoc5LKjhsP9nRIDoLDzZWpQxWS6tgvAEnPHig5BB+fKQJ994o4KIKSMW0cBgtQIeDwXhtsU14FI6qNIIXmrQ8Mm/UAwVeKBnUKKWtIKVC0Hjji4rfpGr8Aa4Uj4pUawp4EGvGFAJMGh2vskxah9dg0fK7/DQG33mDX0kfYxmolEMBu/stbqLKTn7RRL3I+uYfNEs/oWNgAGl4NfGOMQAXPbKxOGOY4XUYlzJGFdE+1lR0aMKdkibW9SSZ6FDBcFUoGTIvi9qdu0GSMHZp18MopQpNvEsLUnhw3FUdtoY7DD44UIA9TATdmKbmeKJYhjbKlFtNEwYfHH0pvcSwN/t0uakKj4bSUlrKnqSoiF2wt1U0Q4WokI9qKdxUNioTBSMMI+Oog8YX7aKDzbZR+Qw2ItpcNpjloRs1sgCHkO63RmxxIqbOJPDWmgLmI7xRDvfY7TcMpzbCY4ZhZI5ocQYMJ8V6QWVRvMysusLzQxl2R19Z/UnfSNHqT1FFaAgLY70qZdqxDsnGX8UfDDavOn9EKga+2AF1TzDYKPCHkTfMawyO7RjPuqkDaV02wp3PzQBclQfKpdyPNQNmnqJwhjMWdAW45pqawlT2sZcgbDKlqUgOVlCpKyUCfipeoKom4hV0VZUqAifheVYJdrznYS+Hek+sd5UIox2a4BJP03gpOZKtD1TD3mC89pbmp9Mc0DCv8QcpnFhUGu0BU5GNGkiZkZRzXmGR5zqCCSf2XooNrdJIScOKg+NMbFb8WE9xgs7cqavspI1cgEFJHbp2w8Bm+koUjUAfo2ogFYIq2naZcB3U/1IZH+1Y82SE2keNS6bUTNoLfIIRzhkq9xjq7B+UCkOfDAoLVovhDjeMrFx5sCSORBv5KVqGgUsUxpIwjPvOG65nU3+KlCFSlDHInSHdfLgiNgY0RtphjorqIaYzWxhhdi2U6tTNu2AVZR/mcpMrYl1XVpkTUFMpo3nWXjg2twmDjtZMGakmUwjEToCzUTIdJUzUMh96sEEx9pPPh8ana0VuljBaBRZYozJZsHVo4aDC0s224OSaSX+y8/CFSpbaoBKXnBOmJgmGzsxVUuGqmzAXs4OVMjDyYiivNs1y8O1DdUwxzaE3Wa/hdBm7isZjGK7Nlm5uc1PwRtFYPgF/gb7SK2Idr2nwwPKljsfHV9V8qMj0pYIg17/+yd/4En0lpmVT765SUU6+RF9JXr57+ef3P/7w/d9+DXklNFuMXku6/cd3aPI3an54Eu/YiSb3/WthpeMD+Xyf+7OlQCr1ITFQLSGJDn4DWZwqOvVWvFDuSj5HFqessh2/HAXCYOOsop6nt3fAb0CB6F6B1ijQ9jkMyLdj4Ava71c2gMTY5ePRv0oV6Jv4z6rno1GD8PD/wN7If6otR0oiRx6RfqYYzjejABMxk7awgT0oOLA3oqAxexTL/tAZ2v46Bd9wHPCjMKVsKVPrKDiwN6Ig8pQpcx+LleSnNJF+nZEgUnNSGhePjoQDfCsWEn6xswAsdqLtEyz0V5ShsFT0ZPwMZaivpPUrqxfoJvKLj97RCTkV1UX1pZasy/hF2cfBLG0zVNL4gzfi4cfR+x5EmGs1L0QIR6q2ZNe8Cl8Y2Y4Ic20sPGBjKno8fnb4FfjZxnY0XHgGHngLgI/W8zLmNfianr2IuHKNRCTmysRF+uhV+JKfzYgw1/oyQrAsQs95iZN+Fb4wsh0R5lpaeODNUNDTqpPDr8DPNraj4cIz8sDyC632dYR4wDywqtlumpwRD29GxIXHIELCrQety3Ei4oBPa4jDyL5riOlaXEYILVoQ+xKn4uDpcRXHD4x4eDMizLW1QVSeCI9F/sXBpwbxsLFxe7hwmDzwslzikqjsYOF1aMvtzI5DN6Phwl/SMJgrk+raLR4wd4eMPMhndjy8GREXHtvhKG8i+jnOz8E131JlBNCZHw/vRcTdtYWHzG/VMBYeHvBItzCynRYcNjy6GQ3m2SJuUVtl2aZ1F+ngwaVC7Bom5ox4eDMiLjwmEe0mTKpcRogDjkljmvTa0Bnx8GZEXHhMIjQEasR1hHjAjLqrrad+5sfDmxExXTvz0LXqWljqDzuYJaHSiL17ek7oXjRMz5bc/doZspTXXaSDOSaiM2gcmjPi4c2IuPCYRPCiPTztsg+Y9dtCbhrp44x4eDMiLjwmEZ2XSqWsPeMBnyTznJVrKb09qJjOnZkYDAQrdYk2cDB8p1Jc8vx4cC8Spl9tGSHQ00uMbUmrcnBlaSssn8SRAyMe3oyIC49JRGHR3PU05oHGgP1VnlGGzsYJ34yIZ4/JgzAmdrS1Wzxg1miRni3Q1BHk4c2IuHCZTHTmyzxtvw+YrUCCBvI5Gw7djAZzbEk6kxB4acfKiqeAogPGjjv0Wnpz7MCIh7ci4u7aMmHgnfLSbsmyO9DGlA0te/zBm/DwZjQ8+0sWKq/sznvRDx7mXqLMSNvDhkc3o+HCYfLAAsFR8srDA2bORivVdpfOygnfjIoLn0kFFsnS25J26WAZN6yhLZ7fGfHwZkRceAwimDzU6ojnmwwHc0xkXee+8OPgvYi48phEZN7ahUU8z8HYaWeqtsuZHw9vRoS5JnkhgilcIfalaxywXVr0II4fySd4MyLMNVmmjah106k/fibiAfvbG2dk20udS49JxOC1XSnrGPGAz0Rc8rMZERceM86ZWbXxvCH98Cp8TdteRFy5RiIKb+7WLfir8CU/mxFx4RqJYDpr7Xn1+AGfBsvDyL6D5ZXHJIJJu2ksEiYOPo0RKz87jhFXHoMIJpKVEBcJk9fga9r2IuLKNRKR+LXzlvTD6/AlP5sRYa7JwkPh3V1edLpfhZ9tbEeDeSZLkhAeXbBKCqvDD/g0QhxG9h0hrjwmERQy6LKeyrwGX9K2GREXroEIvNne6tMm/BX4mp+9iLhyjUSk29CM/IWIV+BLfjYj4sI1ElF5dxfyMkYcMBtBqnpz4Ww4dDMaLvwlDY1Xd+cN6QcPMysFv6kqIM6Ih38uEW+pDuWyaHK2S4eomReUnxijVsKFAhF6l80c0jEoZUUtKTz/0Hhi+hmbLhE6JRqG+klYQ2MUhmN4oJeZp9HqMPmxFqbaSSWHucwq1yUGsTiCMm6ppnsMf+xiEcw85Ql3Xb4+gkq6MNC9pBEtwlsqjMUJj9orU8SpgNCGKgLVyhOSBn9URiHHYii3Aq0kVR6Io7Z76DgevFdVYsj4sGpC1ArPm1BOgkn5YE/1MBhgHXONoKHwe6WKfZrNpISg6ZjUBCt5wi1lBkjge4kyFOp57fS8i8Kq5nCPWW49UVwEP05lomw+DvwRW9Lb1DTAr14lSSBRtbcXvA2YMAkiwuAMW54XrS6gb5pwJH9U0ylUs+khyYTBX28TrikaDLZ7z6BehaBS0XsJJkqDQMrmUBFlpGCRUkL5k4ahAO+/ok+IRVgWEqhqWVKm6AJBCRQkA01obxZgJJWcFtEaZGjgplRUGaA6Sk6R6ZisxXYPa001x55eer/VzgqGEwZ3EjOVbCJaxwx37eAuBbyNUW6st2HvRZi6H0vTT5dQTLSr6kkz+nWhThM1fIzpFshdo5BUZG2fPgPEwDQ1vqqqwuTpYaNkTmdfb6oUVOwltqTyFXgZ1KERHToMBnVU0qFI06jWvVomSxR2YBYm+kgRsw32qNcDnBpRvZiGG0PVIjVxOnA63Ks1KOBY9VMzgzhaY7TW2oQcFgpMYQACw8M6CJ44t5hUDYh+lgmiQWGoHazmU62eLIOhSg6hq/xQrOiaFhIT6WZuiQxGKq3dQ4aY3hYUDuxj+nud0QHUASJcYw73sCN4yYFroEPCclKqeqEzGQ81qBGVTDAKnZ++dN6zVj6fvTKgreeEJjMaxmfeoyjc6CBGNxBLvY6e58cbfeTARRwjQyz3eA54ye6JIZCDQ7IAB+B49T3ZNc1IaFn3qJDKtm5qQ0Wk261vpJ9awZoDgkkCERXKkmkNCMrI2AgDpylvFVRsqOVu0jWEwQq8B2mUzVIC2ah7Ri/W50hZjNfB4YOiTICpaIR+VicOa9SUYbpwJi0WryGkpVf7PD6A9zlxTDGpZ22HrGk1P09llMBBUKWxqAKnMBPHB6Xk9BlzvKN4WtbDYkoK4wh1JRGYZ9s6s7dFxQPvKCY0qhpQ+SXnqiE0+C1ywi2aNl0MWXHCVJthtkujyk9K99tELE0wUvE5egumWYRRh2RxrMPrbqPaNCRkIuZMhSg8H2ba+SSFXPFOkm0TI2AzGNQ2tL2hLTkl09YhTLU3zKVqO9tsISy+C0tCxazBkjf2fJSyGRzYOqZ3/HOa7iSqUSJqqH5ZNniwsBr+VsEx9PlwvxYCU2IxaaWmJHYSHKiAhxGNtvE2VdSGKIgqTdGEnqh6L6K1YNFoNYAN/9JqRERBX8YflIZFt7CrycjgT+o1me5WaXYXwRpXYKdS0LBjYqx1wlycoFdi8kc7Szrw8MC+cIIVzlnFKp0QFmrOpSCcbjKal8oKCcYsuMhJIjNaHz3Fzm+oHYaZRGWFQplyfzzxTKoImPEP6oa1iTZVWcSUT9Uce+qkyn+jmGoU1pF9omin2bSkejG1Sh4g4pnFxKE6Fnr9Dg/wSnEouFIxWZiNSlcq1xgYgsYwHTgevoGOURXGYi4YH8wup+qfwhT4mXDTWnrR1h5Sw3w+6mLyCfgkg9pE5mK/qYqhqgrFGGxW5ukOi95SHAoNJMRphCnyaEQmDtUoZaZdKVOHrrDtUwYUNJltJSJTUDBxKJzNmgslagN0XV7hp1XckjDeIoempJUJkg4LoipZGPT1fWEzKUZg5nCNZcjgQpSVsMqYMChpumgNaFoGcmE5ompoRS5fJwg60PlZcY6v3lCG4AnHskQdMXB+hzF8UAiQ/qGZZnOba1AM97qs7FjZ9Im2flfaxuJl2GspqldVp2JUwLiXJkz9LlOM4uhusR2FC1msN4otTavNyYRBUpdiK+pmWqSiUqNJVV4w3ICFcofxgtDctf0mMRlR3X5QlEDYM7A0mr4XaiN2jlQU4BsyO2PhmhWrOVO4xWq+pwnjLTMDCJ/G2idbUz12PCzb1mxSvka/QDDqFRGX17SFYPlZ3+XjtT4RPvtlEjHXP/q69S8RieK2ixJ8DHHjeuzzRaLay3cvv3//p49/ef/Hs5bEwzYG3RsaJTs6GslJl6Q4izT5j//5BYNqw2aLS5ku7/7TH/79/f/8/t/f/+XHlz++/+nf//b+//u7/vFvf/nby/fHf/u3v//4B/3H8STvHiIU7/5/O8yS7wplbmRzdHJlYW0KZW5kb2JqCjEyIDAgb2JqCjIyNDI0CmVuZG9iagoxMCAwIG9iagpbIF0KZW5kb2JqCjE3IDAgb2JqCjw8IC9MZW5ndGggOTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNYy7DcAwCER7prgR+DiA94miFPb+bYgtF9w96YnzbGBknYcjtOMWsqZwU0xSTqh3DGqlNx076CXN/TTJei4a9A9x9RW2mwOSUSSRh0SXy5Vn5V98PgxvHGIKZW5kc3RyZWFtCmVuZG9iagoxOCAwIG9iago8PCAvTGVuZ3RoIDE2NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9kMERQyEIRO9WsSWAgEA9yWRy+L//a0CTXGQdYPepO4GQUYczw2fiyYPTsTRwbxWMawivI/QITQKTwMTBmngMCwGnYZFjLt9VllWnla6ajZ7XvWNB1WmXNQ1t2oHyrY8/wjXeo/Aa7B5CB7EodG5lWguZWDxrnDvMo8znfk7bdz0YrabUrDdy2dc9OsvUUF5a+4TOaLT9J9cvuzFeH4UUOQgKZW5kc3RyZWFtCmVuZG9iagoxOSAwIG9iago8PCAvTGVuZ3RoIDYxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM1NVcwULC0ABKmpkYK5kaWCimGXEA+iJXLZWhpDmblgFkWxkAGSBmcYQCkwZpzYHpyuDK40gDLFRDMCmVuZHN0cmVhbQplbmRvYmoKMjAgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNVI70ptBCOu/U+gCnlney3mcyaT4c/82AjsVLLBCAtICB5l4iSGqUa74JU8wXifwd708jZ/Hu5Ba8FSkH7g2beP9WLMmCpZGLIXZx74fJeR4avwbAj0XacKMTEYOJANxv9bnz3qTKYffgDRtTh8lSQ+iBbtbw44vCzJIelLDkp38sK4FVhehCXNjTSQjp1am5vnYM1zGE2MkqJoFJOkT96mCEWnGY+esJQ8yHE/14sWvt/Fa5jH1sqpAxjbBHGwnM+EURQTiF5QkN3EXTR3F0cxYc7vQUFLkvruHk5Ne95eTqMArIZzFWsIxQ09Z5mSnQQlUrZwAM6zXvjBO00YJd2q6vSv29fPMJIzbHHZWSqbBOQ7uZZM5gmSvOyZswuMQ8949gpGYN7+LLYIrlznXZPqxH0Ub6YPi+pyrKbMVJfxDlTyx4hr/n9/7+fP8/geMKH4jCmVuZHN0cmVhbQplbmRvYmoKMjEgMCBvYmoKPDwgL0xlbmd0aCAzMDcgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZJLbgMxDEP3PoUuEMD62Z7zpCi6mN5/2ycl6Yoc2RZFapa6TFlTHpA0k4R/6fBwsZ3yO2zPZmbgWqKXieWU59AVYu6ifNnMRl1ZJ8XqhGY6t+hRORcHNk2qn6sspd0ueA7XJp5b9hE/vNCgHtQ1Lgk3dFejZSk0Y6r7f9J7/Iwy4GpMXWxSq3sfPF5EVejoB0eJImOXF+fjQQnpSsJoWoiVd0UDQe7ytMp7Ce7b3mrIsgepmM47KWaw63RSLm4XhyEeyPKo8OWj2GtCz/iwKyX0SNiGM3In7mjG5tTI4pD+3o0ES4+uaCHz4K9u1i5gvFM6RWJkTnKsaYtVTvdQFNO5w70MEPVsRUMpc5HV6l/DzgtrlmwWeEr6BR6j3SZLDlbZ26hO76082dD3H1rXdB8KZW5kc3RyZWFtCmVuZG9iagoyMiAwIG9iago8PCAvTGVuZ3RoIDI0NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkU1yBSEIhPeeoi/wquRXPc+kUllM7r8NzbwkK1qF5gPTAhNH8BJD7ImVEx8yfC/oMny3MjvwOtmZcE+4blzDZcMzYVvgOyrLO15Dd7ZSP52hqu8aOd4uUjV0ZWSfeqGaC8yQiK4RWXQrl3VA05TuUuEabFuCFPVKrCedoDToEcrwd5RrfHUTT6+x5FTNIVrNrRMairBseEHUySQRtQ2LJ5ZzIVH5qhurOi5gkyXi9IDcoJVmfHpSSREwg3ysyWjMAjbQk7tnF8aaSx5Fjlc0mLA7STXwgPfitr73NnGP8xf4hXff/ysOfdcCPn8AS/5dBgplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9MZW5ndGggMjMyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVRSW7EMAy7+xX8wADW7rwnxaCH9v/XUsoUCEAltrglYmMjAi8x+DmI3PiSNaMmfmdyV/wsT4VHwq3gSRSBl+FedoLLG8ZlPw4zH7yXVs6kxpMMyEU2PTwRMtglEDowuwZ12Gbaib4h4bMjUs1GltPXEvTSKgTKU7bf6YISbav6c/usC2372hNOdnvqSeUTiOeWrMBl4xWTxVgGPVG5SzF9kOpsoSehvCifg2w+aohElyhn4InBwSjQDuy57WfiVSFoXd2nbWOoRkrH078NTU2SCPlECWe2NO4W/n/Pvb7X+w9OIVQRCmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0xlbmd0aCAyMzEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNU85kgQhDMt5hT4wVRjbQL+np7Y22Pl/upKZTpDwIcnTEx2ZeJkjI7Bmx9taZCBm4FNMxb/2tA8TqvfgHiKUiwthhpFw1qzjbp6OF/92lc9YB+82+IpZXhDYwkzWVxZnLtsFY2mcxDnJboxdE7GNda2nU1hHMKEMhHS2w5Qgc1Sk9MmOMuboOJEnnovv9tssdjl+DusLNo0hFef4KnqCNoOi7HnvAhpyQf9d3fgeRbvoJSAbCRbWUWLunOWEX712dB61KBJzQppBLhMhzekqphCaUKyzo6BSUXCpPqforJ9/5V9cLQplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1QO45EIQzrOYUv8CTyI3AeRqstZu/frgOaKVBMfrYzJNARgUcMMZSv4yWtoK6Bv4tC8W7i64PCIKtDUiDOeg+IdOymNpETOh2cMz9hN2OOwEUxBpzpdKY9ByY5+8IKhHMbZexWSCeJqiKO6jOOKZ4qe594FiztyDZbJ5I95CDhUlKJyaWflMo/bcqUCjpm0QQsErngZBNNOMu7SVKMGZQy6h6mdiJ9rDzIozroZE3OrCOZ2dNP25n4HHC3X9pkTpXHdB7M+Jy0zoM5Fbr344k2B02N2ujs9xNpKi9Sux1anX51EpXdGOcYEpdnfxnfZP/5B/6HWiIKZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvTGVuZ3RoIDM5NSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UktuxUAI2+cUXKDS8JvPeVJV3bz7b2tDUqkqvIkxxjB9ypC55UtdEnGFybderls8pnwuW1qZeYi7i40lPrbcl+4htl10LrE4HUfyCzKdKkSozarRofhCloUHkE7woQvCfTn+4y+AwdewDbjhPTJBsCTmKULGblEZmhJBEWHnkRWopFCfWcLfUe7r9zIFam+MpQtjHPQJtAVCbUjEAupAAETslFStkI5nJBO/Fd1nYhxg59GyAa4ZVESWe+zHiKnOqIy8RMQ+T036KJZMLVbGblMZX/yUjNR8dAUqqTTylPLQVbPQC1iJeRL2OfxI+OfWbCGGOm7W8onlHzPFMhLOYEs5YKGX40fg21l1Ea4dubjOdIEfldZwTLTrfsj1T/5021rNdbxyCKJA5U1B8LsOrkaxxMQyPp2NKXqiLLAamrxGM8FhEBHW98PIAxr9crwQNKdrIrRYIpu1YkSNimxzPb0E1kzvxTnWwxPCbO+d1qGyMzMqIYLauoZq60B2s77zcLafPzPoom0KZW5kc3RyZWFtCmVuZG9iagoyNyAwIG9iago8PCAvTGVuZ3RoIDEzNiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNj0EOAzEIA+95hZ9AIEB4z1ZVD9v/X0vYdtMLHsmAbFEGgSWHeIcb4dHbD99FNhVn45xfUiliIZhPcJ8wUxyNKXfyY4+AcZRqLKdoeF5Lzk3DFy13Ey2lrZeTGW+47pf3R5VtkQ1Fzy0LQtdskvkygQd8GJhHdeNppcfd9myv9vwAzmw0SQplbmRzdHJlYW0KZW5kb2JqCjI4IDAgb2JqCjw8IC9MZW5ndGggMjQ5IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nE1RSYoDMAy75xX6QCFek7ynQ5lD5//Xyg6FOQQJr5KTlphYCw8xhB8sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvTGVuZ3RoIDk0IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCAzNDEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0xlbmd0aCA1NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNjNUMFAwsVQwMjZRMDY0AmIThRRDLqAIiJXLBRPLAbNAqnK4oMpzYKpyuDK40gAFGA4yCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0xlbmd0aCA3MiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0xlbmd0aCAyNTggL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZFLcgQgCET3noIjgPzkPJNKZTG5/zYNzmQ2dpeo/YRKI6YSLOcUeTB9yfLNZLbpdzlWOxsFFEUomMlV6LECqztTxJlriWrrY2XkuNM7BsUbzl05qWRxo4x1VHUqcEzPlfVR3fl2WZR9Rw5lCtiscxxs4MptwxgnRput7g73iSBPJ1NHxe0g2fAHJ419lasrcJ1s9tFLMA4E/UITmOSLQOsMgcbNU/TkEuzj43bngWBveRFI2RDIkSEYHYJ2nVz/4tb5vf9xhjvPtRmuHO/id5jWdsdfYpIVcwGL3Cmo52suWtcZOt6TM8fkpvuGzrlgl7uDTO/5P9bP+v4DHilm+gplbmRzdHJlYW0KZW5kb2JqCjM0IDAgb2JqCjw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9Gb3JtIC9CQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvTGVuZ3RoIDM5Ci9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nOMyNDBTMDY1VcjlMjc2ArNywCwjcyMgCySLYEFkM7jSABXzCnwKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvTGVuZ3RoIDE2MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDsSAyEMQ3tOoSP4IwM+z2YyKTb3b2PYbFLA01ggg7sTgtTagonogoe2Jd0F760EZ2P86TZuNRLkBHWAVqTjaJRSfbnFaZV08Wg2cysLrRMdZg56lKMZoBA6Fd7touRypu7O+UNw9V/1v2LdOZuJgcnKHQjN6lPc+TY7orq6yf6kx9ys134r7FVhaVlLywm3nbtmQAncUznaqz0/Hwo69gplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggMjE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1QuY0EMQzLXYUaWMB67alnFotLpv/0SPn2ItEWRVIqNZmSKS91lCVZU946fJbEDnmG5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+uco+fXosbPsPxQxSRkg7mNf9Y/fJzDa9TjyeRbm++4l6cqQ4DERySmrwjXVixLhIRaTVBTc/AWi2Au7de/hu0I7oMQPaJxHGaUo6hv2twpc8v5SdT2AplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9MZW5ndGggODMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRYy7DcAwCER7pmAEfib2PlGUwt6/DRAlbrgn3T1cHQmZKW4zw0MGngwshl1xgfSWMAtcR1COneyjYdW+6gSN9aZS8+8PlJ7srOKG6wECQhpmCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0xlbmd0aCA1MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdIAMTM5YIJ5oBZBkAaojgHriaHK4MrDQDhtA2YCmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0xlbmd0aCAxNjAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZA5EgMxCARzvYInSFyC96zLtcH6/6kH1kei6QI0HLoWTcp6FGg+6bFGobrQa+gsSpJEwRaSHVCnY4g7KEhMSGOSSLYegyOaWLNdmJlUKrNS4bRpxcK/2VrVyESNcI38iekGVPxP6lyU8E2Dr5Ix+hhUvDuDjEn4XkXcWjHt/kQwsRn2CW9FJgWEibGp2b7PYIbM9wrXOMfzDUyCN+sKZW5kc3RyZWFtCmVuZG9iago0MCAwIG9iago8PCAvTGVuZ3RoIDMzNCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUktyxSAM23MKXaAz+AfkPOl0uni9/7aSk0VGDmD0MeWGiUp8WSC3o9bEt43MQIXhr6vMhc9I28g6iMuQi7iSLYV7RCzkMcQ8xILvq/EeHvmszMmzB8Yv2XcPK/bUhGUh48UZ2mEVx2EV5FiwdSGqe3hTpMOpJNjji/8+xXMtBC18RtCAX+Sfr47g+ZIWafeYbdOuerBMO6qksBxsT3NeJl9aZ7k6Hs8Hyfau2BFSuwIUhbkzznPhKNNWRrQWdjZIalxsb479WErQhW5cRoojkJ+pIjygpMnMJgrij5wecioDYeqarnRyG1Vxp57MNZuLtzNJZuu+SLGZwnldOLP+DFNmtXknz3Ki1KkI77FnS9DQOa6evZZZaHSbE7ykhM/GTk9Ovlcz6yE5FQmpYlpXwWkUmWIJ2xJfU1FTmnoZ/vvy7vE7fv4BLHN8cwplbmRzdHJlYW0KZW5kb2JqCjQxIDAgb2JqCjw8IC9MZW5ndGggNzAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzM2UzBQsDACEqamhgrmRpYKKYZcQD6IlcsFE8sBs8wszIEsIwuQlhwuQwtjMG1ibKRgZmIGZFkgMSC6MrjSAJiaEwMKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvTGVuZ3RoIDMyMCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UktuBTEI288puECl8E/O86qqi777b2sTvRVMMGDjKS9Z0ku+1CXbpcPkWx/3JbFC3o/tmsxSxfcWsxTPLa9HzxG3LQoEURM9WJkvFSLUz/ToOqhwSp+BVwi3FBu8g0kAg2r4Bx6lMyBQ50DGu2IyUgOCJNhzaXEIiXImiX+kvJ7fJ62kofQ9WZnL35NLpdAdTU7oAcXKxUmgXUn5oJmYSkSSl+t9sUL0hsCSPD5HMcmA7DaJbaIFJucepSXMxBQ6sMcCvGaa1VXoYMIehymMVwuzqB5s8lsTlaQdreMZ2TDeyzBTYqHhsAXU5mJlgu7l4zWvwojtUZNdw3Duls13CNFo/hsWyuBjFZKAR6exEg1pOMCIwJ5eOMVe8xM5DsCIY52aLAxjaCaneo6JwNCes6VhxsceWvXzD1TpfIcKZW5kc3RyZWFtCmVuZG9iago0MyAwIG9iago8PCAvTGVuZ3RoIDE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvTGVuZ3RoIDEzMyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFj0sOBCEIRPecoo7Axx/ncTLphXP/7YCdbhNjPYVUgbmCoT0uawOdFR8hGbbxt6mWjkVZPlR6UlYPyeCHrMbLIdygLPCCSSqGIVCLmBqRLWVut4DbNg2yspVTpY6wi6Mwj/a0bBUeX6JbInWSP4PEKi/c47odyKXWu96ii75/pAExCQplbmRzdHJlYW0KZW5kb2JqCjQ1IDAgb2JqCjw8IC9MZW5ndGggMzQwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSOW4EMQzr/Qp9IIBu2+/ZIEiR/L8NqdkUA3F0UpQ7WlR2y4eFVLXsdPm0ldoSN+R3ZYXECcmrEu1ShkiovFYh1e+ZMq+3NWcEyFKlwuSk5HHJgj/DpacLx/m2sa/lyB2PHlgVI6FEwDLFxOgals7usGZbfpZpwI94hJwr1i3HWAVSG9047Yr3oXktsgaIvZmWigodVokWfkHxoEeNffYYVFgg0e0cSXCMiVCRgHaB2kgMOXssdlEf9DMoMRPo2htF3EGBJZKYOcW6dPTf+NCxoP7YjDe/OirpW1pZY9I+G+2Uxiwy6XpY9HTz1seDCzTvovzn1QwSNGWNksYHrdo5hqKZUVZ4t0OTDc0xxyHzDp7DGQlK+jwUv48lEx2UyN8ODaF/Xx6jjJw23gLmoj9tFQcO4rPDXrmBFUoXa5L3AalM6IHp/6/xtb7X1x8d7YDGCmVuZHN0cmVhbQplbmRvYmoKNDYgMCBvYmoKPDwgL0xlbmd0aCAyNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoKPDwgL0xlbmd0aCAxNzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9MZW5ndGggNzUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicM7U0UjBQMDYAEqZmRgqmJuYKKYZcQD6IlctlaGQKZuVwGVmaKVhYABkmZuZQIZiGHC5jU3OgAUBFxqZgGqo/hyuDKw0AlZAS7wplbmRzdHJlYW0KZW5kb2JqCjQ5IDAgb2JqCjw8IC9MZW5ndGggMTQxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2PwQ7DMAhD7/kK/0Ck2CmhfE+naofu/68jS7sLegJjjIXQ0BuqmsOGYJvjxdIlVGv4FMVAJTfImWAOpaTSHUeRemI4GFwetBuO4rHo+hG7kmZ90MZCuiVogHusU2ncpnETxB01Beop6pyjvBC5n6ln2DSS3TSzknO4Db97z1PX/6ervMv5Bb13Lv4KZW5kc3RyZWFtCmVuZG9iago1MCAwIG9iago8PCAvTGVuZ3RoIDIxNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9GaXJzdENoYXIgMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxNCAwIFIgL1N1YnR5cGUgL1R5cGUzIC9OYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTYgMCBSCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ1IC9oeXBoZW4gL3BlcmlvZCA0OCAvemVybyAvb25lIC90d28gL3RocmVlIC9mb3VyIC9maXZlIC9zaXgKL3NldmVuIC9laWdodCA2NSAvQSA2OCAvRCA3NiAvTCA4MyAvUyA5NyAvYSAvYiAvYyAvZCAvZSAvZiAvZyAxMDUgL2kgMTA5IC9tCi9uIC9vIDExNCAvciAvcyAvdCAvdSAvdiAxMjEgL3kgXQo+PgovV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMTMgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTYgMCBvYmoKPDwgL0EgMTcgMCBSIC9EIDE4IDAgUiAvTCAxOSAwIFIgL1MgMjAgMCBSIC9hIDIxIDAgUiAvYiAyMiAwIFIgL2MgMjMgMCBSCi9kIDI0IDAgUiAvZSAyNSAwIFIgL2VpZ2h0IDI2IDAgUiAvZiAyNyAwIFIgL2ZpdmUgMjggMCBSIC9mb3VyIDI5IDAgUgovZyAzMCAwIFIgL2h5cGhlbiAzMSAwIFIgL2kgMzIgMCBSIC9tIDMzIDAgUiAvbiAzNSAwIFIgL28gMzYgMCBSCi9vbmUgMzcgMCBSIC9wZXJpb2QgMzggMCBSIC9yIDM5IDAgUiAvcyA0MCAwIFIgL3NldmVuIDQxIDAgUiAvc2l4IDQ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HfDU1KrtrP2GcGBJdmobCj7ah7CzaNmasoUbCkWGI+hxJ45aIpipVfDU1ZqwRm4r0FxuNVuOC57oDerGTV9wBnbKGGn6zOaVLT7ngsZ6cXOzEFU9OpqyhRti8CTle1FD43lO0nTV7ypR1jeIm6+21pyjcWstTlSq+mhqV3SWyP2ecGbrLVpfGDzWUnUXVqOx0nr43FmeGXveUK95G0YudFUfROWuo4XFmGHQW1H18rt5qaszYQY2AM0PWeftXvM4dVzsrzilz1lAjbpkQv6bV0Piuxlyl1dSYsRY1EGEeQr70iHv4HfUWU6Oxs0Gr4XBqeFnPX/E6Wl7tLDmKNnaXnmIJp4ZW5/Xfx2d21lNjxg5qMI4NvdHjxl18rtJqaszYQY2Eg0O6pMjexecqrabGjB2ShQxODsOlR1zwNqdc7Cw5p0xZQw2Hk8NoLmpovI2iFzuvNIr26V8PpS7I7QO1udYF6dOGckcWpSzImOyolErAHWwtsSZtWTwplGvPtFFOIewJN1keuZTYpFKloIZoOLHDBulgCKrHxTamhs2Ka+qZU407Z+dq2LkXHy0QmVaNkq1z9eMR5lPz66ONuB62xhTK52OLh/EcUdUVAQC8BQQpV1WxzRRLAxW5UMHqWGrWkm6XVygPGZ/An1Er4V7W7r3kejE9T+h9vJunL3/ygtzg+QM8+S3PqZ2AoifBszQhRk3+ZxRPsLc3t+++vlGZZ1Jr01+9/fZrFNBBwCTjf/H21Z+/RHEFH1EephSEGJQmNNeSPj4q3eGDnJ2deXGF4wM2qHPsj04O9TltLsfL9Zwd/goJ9T6RDBVJHj7gcpL7CfWlxqZ06BTDPIv672LdWBg23U8/PN7Exptv3v71+3/95Y/f//jzm8d3P/7y8+2bv93+0DJAP6GG5GXUkqHHq5PEDn8FDcnZLTux5rMz7qUFKj6lDIzCuf5y33WHv4YMgUraIgr8SCN5YYGKT9mjnjkqvLBHkRkJfGBths/ZIDol4FvEHHIehTjhV9IBpRkYKzSxLEP2U1K4L9QoEN8egoXbNGpx4q8lhnhEPmKOTCiM8pQY4Uu1i4gNfI5WN4wTfy0xMFA4nLiKK/J0J4lfqGVgdBSjqCX2qJ2Fhr+WGOLfY1mQWb7CP1WdQY0YXXUGccW21Jn8iOoML5X3hWvBgDX689eCMWMN57Ka5cUkFgakM0gvuCx9yspY22n4sRZcQ40pa6ghrcKEoOsR3Mfn6q2mRmWns33kiVFaUE/5d+GZlfW0aOS81iKJv55VPb/3FzxYKjUptJ0dX02Nxk55gz6jMAtnfbOfxk81RjuLqrGz03utKJ0pc5TOitN4yKHU+tR2dnw1NWasoQYhotTpdB+NB06lFuhFpYavpkZjd9mJQFHd5C8nVgo/1RjtrKpGY6fn15xklRBIZ1prPCb58l4lHvHV1JixFifZoCyrY53PoHBrXM0hVnYOfC01DnZaDLfJw0V9O6bCLYVU9t1HMwe8mhaNXNBi0JZiufBTiTHglqMvtbiVnQNfTY0Za6jBiCi93KascBdjJr6qdOCrqVHZ6bwfKnXXrdUzisJ9MjSo1Owc+GpqNHZ6l9paRJRebldWOOqXlzLxys6BL6bGlDXUQFW8yy3LHVyqswdTsgJ7Kz2+mhaVnM5pIHkmcaQu1y33OCqCpWRL3mxvp8dXU6Ox81qNiHPky73LPS6OuJE/dbFXyY/4amrMWEMNdP3r/cs9noN4FSaR0yp1+GpqNHZ6RnF2i97nS085cYROgFup0dHZGfDF1NjZaa/cecSTXu5j7vBy4zBnKrmBnZ0BX02NGWuoERBPermXucNRVDBG531SKvX4amrMWEONhHhSrwfRA3aM6+oTl3LtvUY9vpoWE841PMCyvrr4fY8j98llgwIvg0Y9vJgWOzk9u0pTd3y9r7nDcfmQlf9XipR3dgZ8NTVmrKGGPHzUdxm/73EkkIYEP1yp1OOrqTFjDTV4kxd9ucG5wzE6iF8RXVYq9fhqasxYQw1cVKPvNn7f445wAZate+K9Sj2+mhqNnZ5dyeD88HKjc4cj/JBQizX1KtGIL6bGzk6vXsnhBFHf7HzCiPSU9Xqoe16nlQFfTYsJZ0iBW+70pcfve9wZ2phTLLfodRr18GpaNHJai4jDw0DaIe9wRHwz1Sv5OjM9vJoWjdylj6B0JLPOhOxxWaE64lKuuDfTwatpMeMsWgTcuymG9cza4dmLa8GhBM0PGnX4Ymrs7LQYfsMpKumJtcNxlaOnXO467cz08GpaNHLaAw24DDQZnQXZ47i/kmO9hLS30+OrqTFjDTUizg2drkvQ4whJ8O0WUaXSga+mxow11Mg4OPSX6WTEmXGjilKpx1dTo7HTwwbXLauoh40Rp5C4np+Mdg58MTV2dnp2ZY+TQ9ZZ1Qr3Prrqg452Dnw1NRo7PYpy2HKST+lRdMSdDBAl+UzZOfDV1Kjs9FKN02ayQWqPEmPAEddVynqPZg54NS1mpEWMaHByeNzTfIih8JzrXQDazo4vpsbOTjeN6HByaL0+g1a4tIZyMbu2s+OrqdHY6flVHh4BbFHPrwrfY7m0nTVjvOasoUbJ3yWda63xPQZU21kzNvRgp/fJY8LJ4fUAReGnGqOdVdVo7PT6NRmcHLIeREd4j5XWVtaMod7JsV6jJLcl+WzWaxSNt3yTi50l81Aau2R0MCQyjinmi+el8UMNZee11HjNCg2X1CFKLShY/KNk9sNWa2w6AtdCdq7taDj503pcZNIWxYVg2pcgNu0hXynxvmWW2ac9ksWwt9hizXazNvu65rcsbpkt9fMFj+BhW6yHPE4LiIkxp1KikGySV51j2VSSMTql4KkFQ7icAm5az7wxudgOwkWoEMm7sg2Jal1138VZGeDZZX9zxgse2setvNkQHWAWD8nlHNt5ujgKoUQd+E2UpRqR4pBVJ7/U3DXnrDnOnGX6kAn1Jm9lY3np1R11jExjy/W2XQypDY81ZCGWrMXIRHWd59KWZN1vU7lPPkV5D/XxM2iV06ksj+9CzKHhKaGcheAsP6JCbjvpFLpscjnpzJRzQ7M0Up/7Yh3ARRyUwSg3MPskLzm1A0KDQsTSduSNkAn7saG8A49bsvFiEfS6H6BZlOdFgmaESmzroWuABOVkmhkJx6l9nkvwrEy5kIatPGO1HyGBiHzD/WfeWERe10MYoUoZEltZDRvpA2V7EfmGwVkumcHi7zpuW/OGCbspiA+RCd5yxT0eM+MybyNNLnAI+xa/tDL5F8GlUeB6sfr5csNp2axBgRAh5dsOrzxN3ewMG6EunGv7e0GMI91C2jHiQvedrojwx9Ci3FBIruClom/xRwSX5hVd2/yQDrEndaVcb1XFLoBQTa02tIwqpj4Mo1IMtWMuotw2lGRhKMZjS/8R58bVnXp5Dag9vi+SiKt7LL3RIgW6ucfcmrYe/KRLpSfwZ9Q2uZf1fq+2hZieJ8RfMl+HcijPz62fP8CT3/Kc2ibIUrNcDo6SDJvPqG3ibm9u37778e33P32J2iVBuqyMlaxzjzi47XIjwg4OQnUW5lVLzg94taH60UnTQQYKma3VXZSPPf4KpSakv2NPW6xRyQv4kIobOrX5U4qAQUnGT50w1eGvIUKC0wxrMvbGD6u38RlFEG9BfKYQdexyh7+CCOIIbRnWogyTT9WvOUWgz9kSntOZX9gMxBkz/dN/UL2RL6MBbjKHFxtzp0EHvpIG4jFuqKyRxTEI/E/SH3oZGBXLDBYZnQwn+FoyhLQFK/5ZCHDt/+l6RKkDbQPunehkOMHXkgGlyH0Qz1e82aeKrfCdehpBFiehb10vr6fxUmFftnJnlhXe81fu9+dwD2dU3TH8vsexDkIhsbL+6uwM+LFyX0SNxi5pNRj3semLNE7YmYAOLcv2XqM04qtpMeEMKZK42tdrlTscoVvOypLaKo06eDUtZqRFjICSmOKTqF3xDnfGoa6ms6xF6vDF1NjZBa0GKl2xTUmrceDIs0nOcalC29kZ8NXUaOx0RwnYXfPeOa3GiWPPKBOXHY3eTo+vpsaMNdTAJqghXWmlx7FVJrhNWqUOX02NGWuoIcu5jK1arcaJZ5y7sy8u4qBSh6+mRmOnewpL5zdHzeBTjQ7Hhqm3MQ0q5RFfTI2dnRbDbdnapNPiezyLj2qwc9+LNMKraTEjDTHCZgRJ2gvt8Ix51PtyTjSI1OGrqTFjDTUiAnB0LH0HZ5wcIWtRa9Thq2kx4QwpMqJ+XbgMGScupH1yme1FowNeTYsZaREjWkyQXmf69njGKJGkNWiROnwxNXZ2FzX8hq1ie1HjxBEbbSmWA9HeTo+vpkZjp8eMKM8ka7BLR+nwUsYtmEC9SnHEV1OjstM5jCHGjVnchYsaA54jLmDoVGp2Dnw1NRo77YXGvEW+3rGs8GS8I+5VohFfTY0Za1EjWUT9Gp3tq3B2THWFMto58MXU2Nld1PAI+7WXLa8RDya5Om6Mdg58NTUqu6TdjYRQFlSt1moMuJc1CflOpWbnwFdTo7G7qJE2WZSTvn9L4UKhOV+jnQNfTY3GTq9eM2JqrrcsK9xRdtX7Gu0c+GJqTFnXY2+yNlrdNkZc3E8cWl5V2vHV1JixhhokDmWJSVFqDLhN3mR/VenAV1OjsdNisMySIUftfI24pRxKEZHRzAGvpkUjp/3ynBD3a3X9CIVby6mcjio7B76aGjPWjw+M7X8yTucyKtyaYKu3oVTa8bXUmLOGGg7nh/qKsBG2JoWyFrlo1PDVtKjkdN4Jm4Djw2AuLWPEU6vrp+0kXe9vETUqO6vFiDg+ZLJajBFPth6kKTM7vJoWM9IQQ9wFvt4QrHF2iOO9irTjq6nR2Gk1rMUyIxu1OaxxFncz9SrZEV9MjZ2djju0HqeH5jKfKDx4Wyo+ajs7vpoaM9ZQI+D00OqUX40HMiUV5KJSw1dTo7LTge1sI04Pnc7Q0ri4FK4TiUd4NS0KN68z49liG49IF1q+4D6Xy2Yvdhq+mhoz1qIGcpfkwaJWQ+NcE2gudhq+mBpT1lCDcH4YdbWVCx59qet3sdPw1dRo7PQKBXlnLiTvtRoKz3Wn72Inr7gDeLDTY6hDEqHL2vVSMCU7aJRHfDUtJpxFCm9wfGh1YvwFFxJ2olGDF9OikdNSOJwdqjuv31/xfTYZraw5l+zcLs2CcHLodT2iC35ooewsqsaMNdRgnBwGc1FD4d6WRMurStYvOJdUdjL0azUSTg5ZFwi94JbYnyqddhq+mhqVnb6fm8lsKYbLMv4OPLWynBYzcjVdLifUFNdaKHyfS5SZNeeSKWmIEXBqaC6zicb3XqLtrNlLpqyhRsSpoWU9Zmj8UGOu0ker8ZqVNIakof5wBFdmc6mt5RJKB8QUy9EpqgPnXG/Vxr3Hkd2ecEMOF93Uu8ydbaknfsshIUjFmbQhaIEqjtjyyJFLjQrnXCp7xEE0Q0iUzMHI2/FccvFrngKqksgfo5KByzZSjVG3eJxobMlfkM4Xyo4iYteNfAbi5bQxdljq58UP9vIZKiU2shP993jmmJzLrbhtMPUu9sBWaGWiWuc1cnClvlZgD/sR/nQujV50axGfzJRwrx7uuvGyHC2nipxwl0WpgYgCEyGmikcUSMiRj/jAWHVDAQ0nL6PeIGSspVKIIMgfS1sJdJzVOld3z5w8P9l87BelchpzbZuhbqncwZ9R6OBeru29dHgxPUnDfZzn1MuHn5vLO//e+/afU9wAzUzeJcVa++IZxQ387c3tu69v0txdNKl10K/efitQ3ihmXEqPykNf/flLFD+QZ0CBHFezO4pcVlpwtNGb1MHve9hSqRBU8WZhwI7CBxfzz0lnrY3uYWx0Sfq4DQ6lZ7pnPtHhOV6Wzhp5SzAmA4D4xPKSnlvp4PVZW1S+keGA4/iqTvgVeFsn4yGscUYU3a8TD5+e+Ie3z5dxdq7a95v99Rz+7TO86WZRhhu2npMdCZ/wRxKW0Z+8xzRu8lMp2uJIfTbOMuOlxDhbHzif8EdyRikgI+OgQdmuJzlf8vM/GWdr8LhZnKKRdId/JOuSqoYtMPEyniTNn4+0E9/Pu6R68wl/JGXrpNkGbxnl055u3elOCQLLmxdL7GTWflnlgWeq5yvPD1yj7AucKL5hmCxwhhXAwyQJEEXhknfDwBrgEeY8TK3vNSpuek0bHiw09FjY/FOTb4TSSL7ExNqR+gyb/PUqxAsdjPc9caxvxHnwI80RDZbpFGS3sKNLkJ/RxC7gFpxMqDSSH1ChZNxFkh1dg3wjNPZ3rFRdKqUHe/I9SqhsRJ0kbkDXIN8IjSsGjlv0gVEYsSffozFkX/YABgsHugb5CU0hL09MLrIfyZ9okrftUHl9kKRD1yA/ofn4EFEcNSfV7E9U1lTiK/m6q3JY6NElyM9oCnnaTCgX0gzkD9TL9GY9JSVJh65BfkJTyPOG0EMep7oTlREe25BlpXFa6NE1yE9oCvkkrmwpmDyQP1BrHSoq57JLepoY4DXoT4jWHaKIvjzQP1Hr5Ynx9Dyq0sNL0J8RFfpOZu0Qwjjdnag1aZNFtillpTtVengN+pWS2h4kFP/GxvDA/kCzQznuUIpSHwZ6cA3qE5bCnTfOeXzo9x0qLxtlKcrhxWmhR9cg3wipXi/NVxpvGKe7E+WEw6DUCUIdtgbxCcXHh4xtfzJ5nOpONPAWcdxFgxw9ugT5ndDI3W/GWefG3n6ipf5buxb6MNCDa1BvfEbnNqNif/JhnOZOlLCITTiRft9Z6NE1yE9oCvkoRjFgD9x30POWPdW7os6/79E1qFc+SbX5jF04dqq/H6j3W7LJFzfmtNCja5BvhMYpzhqL+wFiGHcuO9hbcWNyzq4TxQ3oEvQPRknx95t4LCkr+juKU0JKJYyis9Chi5CvfJI6AzUl4MKopt/DGfsWHLgTBUY6eBEBJkwhQJRZWx4sKAFOGKEnPpX9yt5IBy8iwIQpBJBnjrhdRQlwwri9QN66v+hywIsIMGEqAli75RRodHg6NMlE72uu+qjKAa9Bf2fkFX3CYVQIpPj3cEwhlYCx0cgBLyJAo6TmP9w+hTMpNQIMMG6JCqnXxQ3wIgJMmEIA6cmGxa4SoIdDKn+ndTngRQSYMBUBnHzS+hzUJDjAwfhY9q+VLju8hgA7JTUGOIcDKpPUGDDA5E119kYjB7yIAI2SGgMc4ZCqXBg2CDDAqa1wlZG00sJ3p6T5M86pvJ4ERjh6LrcAjDZ2dBH6lRHpEQDenBOHXvEfYKbqBIw2GroI/UZILYHKJYCZ1aaPggPXQprKyA6vIUCjZHUoqN9M4Eiq/ys4U4m+10YavIgAE6YQANfP+aSilgc0CKl0VWWHF6HfGCkXyEecWRmr+Y9wSW58fzHS4EUEaJRICYAaj8lSVAKMsHg7vteFBngRASolvQrALm4Kl2WwgluonjayUgTfQUlthJDH8RVZtQxS8N4ClJGlWsCMKQTARUc5kPKBFHwIMNVlEQEaJeUE4f5aw5yUF3APnhhZRoBKKSo3SJ45W48LfEemd+CJkWUEaJSUFxDcZpxRwV130JmJVeg3RqRGgIDrqpOJaiNEwfsIoIwsNQLMmEIAxuGW08lr9+CpXIsIUClZtRESsKntvHaD7sETI8sI0CgpN4hxroWr/0amCt7dIGXkI92gMQcGidkfmrQ+ScweUywIZd1rxDr7WP15stGWEHyW12hNPRmTZY189lYidx0KhZVBHznSJZQJecghGVeOBYJJ1rdA14xytrgkdENSWCkOjtxkYo/WIf+eE26kKHGhwciPhAPUKGuHGjNXkp1ZvhmLKzFQxhJBA5fSdZ62FB2ubRdUPpF9ycmNW8BdoVRRWagj3lRcM2kTqRRSiNJFg0MyDgnJHFxoEaucLI52A+rklZcWg4hgrTi6uIHI+FTiGSIj8dZIw08I3kZeOlCRVL4KWUcWWebycmKFE+pIpZIMmeT/uRYP6FOMyJUWGp5DvRVO4CxvAMXqfNfUIm6rZ0RMY5nFOGyxFc6RDYLtRE2BbbkWKZXEYuQ4o+Kfj7VUqqCim0fVUMOyVLehBNwnhxuoheUtxy3LKy+56EmES85Ed8vi08qUVuRIAcSF2k3+3RuHML8S7CNP4aUbsBVbMv35GgYjX4c0ZJETeWdlQwj57mSdNBuSl2twWS5QeZHGyCPICkLWD+I/AGRIAcVB2eFUoUYaoJ4OLpynEjhfuOFqUsMWB/C45zWnIiauoWWSX25eSAprLjs1xoBxotLYRHZnqR3jCrksLc8jOT7bUvQNsDwbyUsTifGFpsLyyOLqtkNfYVQuYYKyuGYF7k9C4wy+2k7yRkLiGvHuObJvB2cGVW3LsaG8Ml/PEpCF5kMux6nykykq45DJyffUjfcsTxKp7TtHcjbX3tvaMjYjs7Quapkl5WwSKzSHnVlumVcySCdq6xaB8+7MulBur9TjWxku7sLPyPKfZODOUr7F4jQx9/Fu4rj8xbOye6/f+qTt52T3i0jMWV6ijEnSESntln4tuZ9ub27fvvvx7fc/fYnkfWlhm4mXpCaPJseMOgW9hB08aNVZmSfxHx8oc94L58Lp3dQoFOJxnc/ow3TwK6S1yxiHzV6DMSnal+Tzf0IBZGh2ib0W4IRfQwBckgNrjqNMzy/I6/+ULeA5TfiFdQ2yPFX39B+U6/9FJJDlSGKLqtmDBCf8ShIEUxwGPLZMI/dVsF9EheTK5YAujSqc8CupgCwPXJ9l4BA9ocKlAsJnUQH+RjbwVAYVTviVVEBAWHTksytrifsy+C8jg3jW4uOI06t0OPHXEkK8UfHYZC1C8MHvC0H3agagHtFp7yNqB7xQ2ZcVSZM5IcyW379SJO3u3I3VT7Z5XH53sJU/TG0Z1s/0HXwsv9cQYsJYhHAWkaZeZRh3MEl7w9LWj/r08FpCzBhDCFmesnjWVglxwFjukyyRtD4dvJgQjZpTQshIK6682obuYFlRW5dzKVLdGenhxYRo1EgJga4eo0rG7GBkZRi3t4jDSA8vJsSEMYSQyTyROrE9UU+o5l62oHpxTnQxESqvoMYHL48uI5/KzuxgbChSKkUXOhsdupYMOzGrZCBEpVpHSoYDxm6rGCnXB3VGengxIRq1oIRgRKc6lavcwR67lTaVG7Y6Iz28mBATxhAiIUrVXzrGCectBU9l67o30sGLCdGojTqQvFhnghsPqXoYl/G5xLGXZ0DXkmFnpqZNwu5/ZJXc2cNJlnKx7JD3Nk50MRkaMTU+EMmAR1FF+vUwzvRiTtSrEwZ4MSEaNd0vGCWF86VfnHASx9Eg//P9YKSDFxNiwhhCpLI1fukYPZxNjOWkZDRywIsJ0aipEQJVijEHqhXGACcro2IvD/XoWjLsxJKSAVu8qIytZOhhjgGF7N4rIwe8mBATxhCinAGTSowe4ZBivKjTwMVEmLCFCPJLjCGp0WGAg7e4CPqizQ4vJsSEMYTA0Q9FlSk9wpRKNI7W54AXE6JSs2p8YIuaVkkFfo4wic1enhZx09C1ZGjEVMC4Y79xTllvPwwwWZNLhNBo5IAXE2LCGEIExMFaNV30KBlvyr0MSp0dXkyGK1+oEBEO61QOqYJTyMxanQNdTIYJYdEhGkTFeu1EjXBk6QFXeXZ4LSF2aloIJ+uEGHS/GGG2HHyvjx3gxYRo1NSKM5KYxc2iSogBDvLyY69PGODFhJgwhhAsvyDOTwkxwMFRaxGjPg1eTIgJYwiRUDwnW+VTKli6QL7qs8OLCTFhjEglg+M6oycNBR9CTPVZS4idmvIpEZrL/rL0VrBMFL7XJw3wYkJUaioDwyXCcZ1XKQUaRt2dTp/dSIMXE2LCGEIwTuxIpVxo2GdrJ/o0eDEhJowhRMKZXUh6jBjhQ4ipPosJMWEsQuDuNmQMqDFCwTbXwVIZafBaQswYQwi/2ZwuC3AFy9KbJ/o0eDEhJowhRNiQMhDVGKFgK8PkRJ8GLybEhDGEwJ6CtypDVcOHEFN9FhNiwhhCoCyhcaTHiBE+hJjqs5gQE8aPD94gPyl6lZup4UOIqT5LCTFlDCE8Du6Ccqg03KaHiz4LzhoHNS1EwMEdK4fqLjwxspwQjZrKjjBxyyROUlCM78ATI8sJMaEmQlizmRCyUWPEHXiuz1pCzKhBCIfDO0NqjFBwGxW1kSUHyxljCFHu2bXKodLwIcRUn8WEmDCGEIzjO68cqrvwVLbFhJhQgxAJ53eksyHvwVN9FhNiQg3JMwYHeCGO2/n34Lk+awkxowYhHI7womElxAjvY4Qy8kpjRJ8F9VAqRdw+UJZrpYg+i8Z3RF1JqbGU8e0oqdDW03i1xmTcdcJbcsTZNziL1xQIl2H4XIKsW2YKKgIQ7jCP7Ez9NGSxCTdAkqxKSRQq+3lI35AZVkREzQDyuaIyvMj3oOo8Sh8Is7LV43CCWkKbeXPJmFIcwnl5VINsfFQCyDHnGgCP9CxHuZWdMAZVWwAHcOd6cRseg2wLf2SP4v4ltz/ktu9KpQoF1aLYznnOeyiUoWxqwKW8tFzqQTqW9udCixQjz6Z+OmZ5Wvme1lCCK2UxLu0nlISNO/AzMvDv5Mjey9sWy9P02ce7GeDyF8/Ow51/+5Pf8ZyMfOcYpRdQR0QEe05Kfri9uX33NdqqTGip9aav3n77NWq5UMyW8b94++rPXyJl30favLukBsmHkPQVefRYO3gQs7MyT9k/PmDDeI790amZZMGJxytTH3v4FTLWZbQrhWfk/0THl6Tsf0IBZMw0xquQwxN9DfohF2MxR/HJPoC+/Yz8n9eCXyaApzg8/Qdl7G9fRoToNlR0kXFwEOGEX0kElvmKHBK1k0yBT+lwyVf/LDogfzcYj3syByE6/JWUQKEk+TUEQyhp9ZQU91K1hb84Pqe2L0/VfqG2L/SDXRRTz/eDxWERF9apNEyxCM+kVEvq/eARhrfGZc4ZjOzw4QcvIcROTe2eJJSNCkFlKI8wGV/rQ41GDngxISaMIUQQ78Vx1i2ih8XfrdXhlD47vJgQjZraRkoJVcXSpWv0cHDGM/X6+AFeTIhGTS0ZZCAvb151jQGWFZIra8XRyAGvJcSMMYTAdbBkstphHeDobSj3iY1GDngxISaMIQSVXRCVvD/CKaPk+kWfA15MiAljCMHlAj2Vp9zDJeHQ1sGyM9LBiwkxYQwh0hZCoKzHiBPOwg2+q9KngxcTYsJYvF4Uq0TxyzGFv4O9RenI6k+dNjp0KRkOYqo8pHGbPFxUty53sMftOzUauLPRoYvJ0IhFJQP2GvWtnCdKMjKyDAmp1yYO8GIyXPlCBUYUm1FJyh3MsnI3SD8d1OnRxWSYEIYOKB8rD8ZKhwPO8hO5VLKqOiM9vJgQE8bYKcRJhLiIql+csPVzgQZ8LSlmnCEFCjMHUgmZHWxlXBDXMTqlUAcvJkSjpjYP5ZHEN9I3eHZwcrimj0q1n85IDy8mxIQxhJBlk82scpU7OKIWaMyl3HVnpIcXE6JRU/vpNqOMeLp0jQNmLgeNgzypRxeToREbVXCo7eRzGM/mOzjkzXqbWnPYbfToWjLMCEMHlHC2RiXXdHBwmw8heqXOiS4mQyOmpk4XEMDm1B1vHUy8WWkA5TqFzkgPLybEhDGESAhg8yrrrIMJtypwNFHp08GLCTFhLEJ4I2/Wkbr2toPLPXeB4yhPh64lw4wvZMCiSd2J+b6HZW0pA4H1SpwTXUyGRkwtvHE5SeSo0mo6WP6OZFFVSpp0Rnp4MSEqNa0DCx+fVJpyB+87LraTJwzoYjI0ZkqGhBM7o/L3OxgRU8ZwHNQZ0MVkaMxUc0AsWL7cDNrDGYf1pq4uOiMdvJYQOzUthMOBnYtaiBNOuCgqhdjrEwZ4MSEqNdUxCBV0LxeF9rC0ATK+RBh2Njp0MRkmhKFDxHFduHSME0aQpPXVnxzkOeDFhGjUlP9AqLDNfOkYPZwp1qOK0cgBLyZEo6Z2pIIVs2JXTRkDnHymXp3cgWuJ0GgZLYLHL1klTI0wO9y41mlTjRzwYkI0aqpbhICjOn095ghzEFeBen1ogBcTolLT64sQcVbnrFpmDXDALr3t9EkjvJgQldqla2Sc1nnSQvQwxUDlBsLRyAEvJsSEMeJ9LQ7sSHeNAZaVRU7pqs8OryXEjDGE8DixY3Vf3wgfQXJKnyVj56aMIUTYchICatE5wLKcCHXWGPXZ4cWEaNR0i0ibySZF3SIGOLqav6OM7PBiQjRq6sg3GhzYGd01RvgUYjCyphAzxhDC4cDOei3EAAeyZqLPDi8mxIQxhKCtxICpMWKEjxYxGlm0RUwYQwjGiR1dusYdeCrbYkJUalYLkXBmF/SsoeBga5iQMtLgxYSo1LyKlkoGp3as0pAVfI4Rg5E1x4gZYwhR8seS0UKM8N4ilJElW8SMMYQgbK5krzZlRvhoEaOR12oRr5mmfWTQtAu9cZd4SzVgZ+vRf2CfU42eldV04HY3mDxgLEsGhBGTUCDGJpxJljm1oNrgveGySRljtK4aQRS+xZ2p2Mu2xrk99FTW57gKxCPD2da71n3OWzSZy0GIrFrdHp8ZOQXc9+5EeM/langydksoM1QufDemVQNHHKM8Nd4CjhOspbSHfqaEm+tvPm3OIgy8wF4sBgSH49JDSm3D1QjHSLjYhzDexXozBRl8kc/Bl/trZenQvjKAupfVtqy7s2Pf4ueQfiALLvEmnCxEy60vyCtloYKcUnmNSWZX32LMnAiOyylxN0oWz9w2XLgLz5vFBfbGcEmgR7Kry8mFiltjXF3mWWSVRShky4LXRlNxh5cmrG+4zz5bk8i1QCYhmny+SSPgCL0KTFu5xF5gEqLR1ONbgYVovCWsodjnFgGE3HZ53CjUvKe6A2HjZpNBrfaIQ77Ivn5fwurLyqwCr9vn2hzIiRZBVihULkK3hmo4ibN4O1FaUkBtyphzPWQnfCXuh5C3k3FvRrEthLGyw/VcoiYbV58ERwfYAMk4c2aSBU9qZ4+4ocrXy93qHU3yq1iWj5bEfycW3X4+x7jKqBzbsfTTBuPqdyNsAKNNWW7HWIikjaUeQKIyJNVTnZRklcHoCdLTs61nHFakDiW+AHGGXMoVECoaOIfKWfL40vhNbYAUYaPtgsr7N7TvgxLqftcUB+nBJZIb+4LCpt1vD2W4bZJJew7EdZNM9LJtpyQT6sqX5SAKEJRPszyU9NSWhhRFmvKNSLxKWZSoA4mxkScjYys8fQd+RgGCOym/97LQxfI0G/jxbj47ShY8N614/u1PfsdzChAgp8tyOffBDSDPKEDAtze3b9/9+Pb7n75EgYHgEQHnxlIkjw/SgTZZag8i7tggU/f389IC5wdkyowvmkjvJBPLnI3v09eedfAr5NbL7A/TYo0QJviC0gKfUICE8CRfqpP0ApzwawgQca2NWLORZCD4dQHC5xOAHdwR1glGHfwKArD1zZpB2emXVFf4hE3gw7vvC1+/+DT9o7+wtMJnUUAmRZNRqKhT4MReSQGxKw4ZIYhcPJanRPiMHaETAeWwPdyBU4MDeiUJUIM9ZNRFEh/9qY7whdqBtfKj+Dvih58idOAryWDhpktfkHWQeEtP6hDu1JUIsqIxp8WPqCvxQmFfunA3MrY+e+F+d8LGsu5yT2kHD5k8/fw+zfBZQ4oJZ0jBmwn6lPBECSu9mIwf9enhxWS48oUKsqjmy0WlHSxNQJblmdKoTocuJsOEsOgQDCJujcoB7GCSn0zOySt5OngtIWaMIYQrV2WpHMAOFi89kimbNp2NDl1MhglfyEAIudX3lHawjIeWSxXDUZ0OXkyICWMIge26UuRzFOKAPU6Jky+H5L0+HbyYEBPGECJtJUVeTZ0n7IU7tW3VXp8OXkyICWMRgs2WDKnToBNFMVBp/yXwsleng9eSYcIXKmBxYlNQE+cJY6PUZV/2cTt1OnQxGSaEoUPYjL3cVNrBSN0Qk4aVPB28mBATxhAiIubWqksNOhgHOhH7n0qfDl5MiAljCIEjG++CHiBOOAsFn7LSp4cXE2LCWISIFr/o20p7OEsfSLVa36jPAa8lxIwxhPAb9nRVDmAPpy1F3PSu9OngxYRo1NTyO8ojySKKtBAnXDKcShnv3saJLiZDJaabQ9yYL1eV9nDaPG5gdJ04YUAXk6Ey02vvWM6ws8ro6OEkU0MO1YXojHTwYkI0amrqTBYRt0Ylx/YwihfauqHYG+ngtYTYqamekTwibq2Kzx3hJIZi7PUJA7yYEJVa0udqjLMEr9IARzhG4pIgPho54MWEaNSsEgJBL5FUVscIR1dCb94rIwe8mBCN2hifGxAVJE1d9YweZc+2BO+MJg54LRkaM3V3UMiIV7LRKhk6VPynXOqqjCYOeDEZGjO16syEK04SqVXnAAdPqVxbMho54MWEaNTUQJl5k5+yujhthEmGxLo9Nxg54MWEaNSUM5UTom2tdqYGuBOiN7KqEJWayuhgg9g641SK7AiTD7ZEt41GDngpIQ5qQQmBoNjolTM1wuQQtvle2djRxWRoxEjJEHBmhyS2UYYeJkvZxV4dGuDFhKjUrO4YOJq5XEaq4MwowvReG9nhxYRo1JQOCAJOMbLSYYCTq4HQo40dXUyGCWHRwdrNx5DV5b0KjjGWCF5lZIfXEmLGGEJ4HNoZckqIAWZyzFd9dngxIRo1FcxYQvZL6OEoxACHEuCmTFRwMREaLatEQEh89CrRT8EhBut6bewALybEhDGEyDiz00tvBSNrIl/12eHFhJgwRoyvxaFdUDmeGs6m5DtpIw1eS4hGLamu4QiHdmrxPaKIXudOnTTCi8lQmRk1XzjerAvIjRp1GOG9PSgja7aHCWMIkcSsy9qTUrDzJXdMG2nwYkJMGIsQ3uAXJO8NQij4aBGjkSVbRKNmVTIAynWi6pgaIhQcy4172kZFF5OhEvNqxijVW8nrkfIePDGynBATahCCcWh3WXzfg6f6LCbEhBqEQH5rkn9VjEd4HyqVkSWHykZNO1O43jKGqJ0pBQcu+3DaSIPXEqJRU1EAsnBEom5WxRTuwhMjywkxoQYhQsmb0u6UgveuoYws2TVmjCFE3EquuFp5KnjvGsrIK3WN16wq0afPWPF5DCWRC0kXTlaIOQc8DOoSULB7Gg65lCyjCIA4Rlyjy5F9EuRTjIw4F10qWfaBEGsdOZZLBDhYX1YYAbrIetyUXBUy8qy5wYii8bEUdOBYwxCR0BFD5nK5lYy3XNM8RHGckqWa5yGjUDWRcKaMUD3pjagQUGM6Q9k6T+LceFxuIa5dMYLsvZACl1B5VGKsYSwhbGxCRG0AeQ+RbLkqJwS8ZDKmxEtHcqYmWjDKXWSc5MJJEMs1VI7xIDGUMtnlPKsGjpF8vYyhJYyK5UkbLJ+WHuPqrX8xtgjMmHFBjcECLW02Zi4XWgVk3BlmLgOxdDNpWe0IkeXN17qRmKNT2y238hprKCjhNsWyie6MPF/gfW/IcimHoRsyBbqLPqMEwJ281HvZ4mL5mrL6OM84l88+L+t1/qX3rT8n7T8E5FqLo2hRJ+Y5af/x9ub23dfoDi5Ku6od+au33wqE+hDZMv4Xb1/9ecwdPL7aMm2Z65fJwmUbbram7ivxnb/9Wj4vTVh6oNCVpvHVD/9495/f/+Pd3368/fu7n//x07v/+0v55S9/++n2/flvf/nlxx/KD999/SCPKgN9QjeRZrs/arMoaH3UhyMr8eH/A1oQHngKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iagoxNTEzOAplbmRvYmoKMTAgMCBvYmoKWyBdCmVuZG9iagoxNyAwIG9iago8PCAvTGVuZ3RoIDkxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDWMuw3AMAhEe6a4Efg4gPeJohT2/m2ILRfcPemJ82xgZJ2HI7TjFrKmcFNMUk6odwxqpTcdO+glzf00yXouGvQPcfUVtpsDklEkkYdEl8uVZ+VffD4MbxxiCmVuZHN0cmVhbQplbmRvYmoKMTggMCBvYmoKPDwgL0xlbmd0aCAxNjQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPZDBEUMhCETvVrElgIBAPclkcvi//2tAk1xkHWD3qTuBkFGHM8Nn4smD07E0cG8VjGsIryP0CE0Ck8DEwZp4DAsBp2GRYy7fVZZVp5Wumo2e171jQdVplzUNbdqB8q2PP8I13qPwGuweQgexKHRuZVoLmVg8a5w7zKPM535O23c9GK2m1Kw3ctnXPTrL1FBeWvuEzmi0/SfXL7sxXh+FFDkICmVuZHN0cmVhbQplbmRvYmoKMTkgMCBvYmoKPDwgL0xlbmd0aCA2MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNTVXMFCwtAASpqZGCuZGlgophlxAPoiVy2VoaQ5m5YBZFsZABkgZnGEApMGac2B6crgyuNIAyxUQzAplbmRzdHJlYW0KZW5kb2JqCjIwIDAgb2JqCjw8IC9MZW5ndGggMjMyIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2QS3IEIQxD95xCRwB/4TydSs2i5/7byO6ZbJCqwPITcRwTZ/OICKQc/KxhZlATvIeFQ9VgO6DrwGdATuAaLnQpcKPahHN8ncObCpq4h8dstUisneVMIeowJkls6EnINs5ocuOc3KpU3kxrvcbim3J3u8pr2pbCvYfK+jjjVDmrKmuRNhGZRWsbwUYe7LDPo6toy1kq3DeMTV0TlcObxe5Z3cniiu+vXOPVLMHM98O3vxwfV93oKsfYyoTZUpPm0jn1r5bR+nC0i4V64Ud7JkhwdasgVaXWztpTev1T3CT6/QP0wVcdCmVuZHN0cmVhbQplbmRvYmoKMjEgMCBvYmoKPDwgL0xlbmd0aCAxNTYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicNY9LDsMwCET3PsVcoBJfQ86Tquoivf+2YCeSEU/2zBhCBYQMvFgxzRFy4M2jbpp+g3MuuhZJ1U2UVQR2hkiCOXAO8YlUKAnmhFbVOYdVQIGF96uT3nJXXgFuO3D1bEfT/vYZapOE3cSRmFJ+Cnjd+tE2iYqGea6FeoDuNVgpmoSsBeyxDcy0EmrfDqzWyvPZ/xrf8fkDYBM2+AplbmRzdHJlYW0KZW5kb2JqCjIyIDAgb2JqCjw8IC9MZW5ndGggMzA3IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2SS24DMQxD9z6FLhDA+tme86Qoupjef9snJemKHNkWRWqWukxZUx6QNJOEf+nwcLGd8jtsz2Zm4Fqil4nllOfQFWLuonzZzEZdWSfF6oRmOrfoUTkXBzZNqp+rLKXdLngO1yaeW/YRP7zQoB7UNS4JN3RXo2UpNGOq+3/Se/yMMuBqTF1sUqt7HzxeRFXo6AdHiSJjlxfn40EJ6UrCaFqIlXdFA0Hu8rTKewnu295qyLIHqZjOOylmsOt0Ui5uF4chHsjyqPDlo9hrQs/4sCsl9EjYhjNyJ+5oxubUyOKQ/t6NBEuPrmgh8+CvbtYuYLxTOkViZE5yrGmLVU73UBTTucO9DBD1bEVDKXOR1epfw84La5ZsFnhK+gUeo90mSw5W2duoTu+tPNnQ9x9a13QfCmVuZHN0cmVhbQplbmRvYmoKMjMgMCBvYmoKPDwgL0xlbmd0aCAyNDQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZFNcgUhCIT3nqIv8KrkVz3PpFJZTO6/Dc28JCtaheYD0wITR/ASQ+yJlRMfMnwv6DJ8tzI78DrZmXBPuG5cw2XDM2Fb4DsqyzteQ3e2Uj+doarvGjneLlI1dGVkn3qhmgvMkIiuEVl0K5d1QNOU7lLhGmxbghT1SqwnnaA06BHK8HeUa3x1E0+vseRUzSFaza0TGoqwbHhB1MkkEbUNiyeWcyFR+aobqzouYJMl4vSA3KCVZnx6UkkRMIN8rMlozAI20JO7ZxfGmkseRY5XNJiwO0k18ID34ra+9zZxj/MX+IV33/8rDn3XAj5/AEv+XQYKZW5kc3RyZWFtCmVuZG9iagoyNCAwIG9iago8PCAvTGVuZ3RoIDIzMiAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UUluxDAMu/sV/MAA1u68J8Wgh/b/11LKFAhAJba4JWJjIwIvMfg5iNz4kjWjJn5nclf8LE+FR8Kt4EkUgZfhXnaCyxvGZT8OMx+8l1bOpMaTDMhFNj08ETLYJRA6MLsGddhm2om+IeGzI1LNRpbT1xL00ioEylO23+mCEm2r+nP7rAtt+9oTTnZ76knlE4jnlqzAZeMVk8VYBj1RuUsxfZDqbKEnobwon4NsPmqIRJcoZ+CJwcEo0A7sue1n4lUhaF3dp21jqEZKx9O/DU1Nkgj5RAlntjTuFv5/z72+1/sPTiFUEQplbmRzdHJlYW0KZW5kb2JqCjI1IDAgb2JqCjw8IC9MZW5ndGggMjMxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVPOZIEIQzLeYU+MFUY20C/p6e2Ntj5f7qSmU6Q8CHJ0xMdmXiZIyOwZsfbWmQgZuBTTMW/9rQPE6r34B4ilIsLYYaRcNas426ejhf/dpXPWAfvNviKWV4Q2MJM1lcWZy7bBWNpnMQ5yW6MXROxjXWtp1NYRzChDIR0tsOUIHNUpPTJjjLm6DiRJ56L7/bbLHY5fg7rCzaNIRXn+Cp6gjaDoux57wIackH/Xd34HkW76CUgGwkW1lFi7pzlhF+9dnQetSgSc0KaQS4TIc3pKqYQmlCss6OgUlFwqT6n6Kyff+VfXC0KZW5kc3RyZWFtCmVuZG9iagoyNiAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw9UDuORCEM6zmFL/Ak8iNwHkarLWbv364DmilQTH62MyTQEYFHDDGUr+MlraCugb+LQvFu4uuDwiCrQ1IgznoPiHTspjaREzodnDM/YTdjjsBFMQac6XSmPQcmOfvCCoRzG2XsVkgniaoijuozjimeKnufeBYs7cg2WyeSPeQg4VJSicmln5TKP23KlAo6ZtEELBK54GQTTTjLu0lSjBmUMuoepnYifaw8yKM66GRNzqwjmdnTT9uZ+Bxwt1/aZE6Vx3QezPictM6DORW69+OJNgdNjdro7PcTaSovUrsdWp1+dRKV3RjnGBKXZ38Z32T/+Qf+h1oiCmVuZHN0cmVhbQplbmRvYmoKMjcgMCBvYmoKPDwgL0xlbmd0aCAzOTUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicPVJLbsVACNvnFFyg0vCbz3lSVd28+29rQ1KpKryJMcYwfcqQueVLXRJxhcm3Xq5bPKZ8LltamXmIu4uNJT623JfuIbZddC6xOB1H8gsynSpEqM2q0aH4QpaFB5BO8KELwn05/uMvgMHXsA244T0yQbAk5ilCxm5RGZoSQRFh55EVqKRQn1nC31Hu6/cyBWpvjKULYxz0CbQFQm1IxALqQABE7JRUrZCOZyQTvxXdZ2IcYOfRsgGuGVRElnvsx4ipzqiMvETEPk9N+iiWTC1Wxm5TGV/8lIzUfHQFKqk08pTy0FWz0AtYiXkS9jn8SPjn1mwhhjpu1vKJ5R8zxTISzmBLOWChl+NH4NtZdRGuHbm4znSBH5XWcEy0637I9U/+dNtazXW8cgiiQOVNQfC7Dq5GscTEMj6djSl6oiywGpq8RjPBYRAR1vfDyAMa/XK8EDSnayK0WCKbtWJEjYpscz29BNZM78U51sMTwmzvndahsjMzKiGC2rqGautAdrO+83C2nz8z6KJtCmVuZHN0cmVhbQplbmRvYmoKMjggMCBvYmoKPDwgL0xlbmd0aCAxMzYgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTY9BDgMxCAPveYWfQCBAeM9WVQ/b/19L2HbTCx7JgGxRBoElh3iHG+HR2w/fRTYVZ+OcX1IpYiGYT3CfMFMcjSl38mOPgHGUaiynaHheS85NwxctdxMtpa2XkxlvuO6X90eVbZENRc8tC0LXbJL5MoEHfBiYR3XjaaXH3fZsr/b8AM5sNEkKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvTGVuZ3RoIDI0OSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxNUUmKAzAMu+cV+kAhXpO8p0OZQ+f/18oOhTkECa+Sk5aYWAsPMYQfLD34kSFzN/0bfqLZu1l6ksnZ/5jnIlNR+FKoLmJCXYgbz6ER8D2haxJZsb3xOSyjmXO+Bx+FuAQzoQFjfUkyuajmlSETTgx1HA5apMK4a2LD4lrRPI3cbvtGZmUmhA2PZELcGICIIOsCshgslDY2EzJZzgPtDckNWmDXqRtRi4IrlNYJdKJWxKrM4LPm1nY3Qy3y4Kh98fpoVpdghdFL9Vh4X4U+mKmZdu6SQnrhTTsizB4KpDI7LSu1e8TqboH6P8tS8P3J9/gdrw/N/FycCmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0xlbmd0aCA5NCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFjcERwCAIBP9UQQkKCtpPJpOH9v+NEDJ8YOcO7oQFC7Z5Rh8FlSZeFVgHSmPcUI9AveFyLcncBQ9wJ3/a0FScltN3aZFJVSncpBJ5/w5nJpCoedFjnfcLY/sjPAplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9MZW5ndGggNTQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzYzVDBQMLFUMDI2UTA2NAJiE4UUQy6gCIiVywUTywGzQKpyuKDKc2CqcrgyuNIABRgOMgplbmRzdHJlYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9MZW5ndGggNzIgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzK3UDBQsDQBEoYWJgrmZgYKKYZcQL6piblCLhdIDMTKAbMMgLQlnIKIZ4CYIG0QxSAWRLGZiRlEHZwBkcvgSgMAJdsWyQplbmRzdHJlYW0KZW5kb2JqCjMzIDAgb2JqCjw8IC9UeXBlIC9YT2JqZWN0IC9TdWJ0eXBlIC9Gb3JtIC9CQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvTGVuZ3RoIDM5Ci9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nOMyNDBTMDY1VcjlMjc2ArNywCwjcyMgCySLYEFkM7jSABXzCnwKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvTGVuZ3RoIDE2MyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFkDsSAyEMQ3tOoSP4IwM+z2YyKTb3b2PYbFLA01ggg7sTgtTagonogoe2Jd0F760EZ2P86TZuNRLkBHWAVqTjaJRSfbnFaZV08Wg2cysLrRMdZg56lKMZoBA6Fd7touRypu7O+UNw9V/1v2LdOZuJgcnKHQjN6lPc+TY7orq6yf6kx9ys134r7FVhaVlLywm3nbtmQAncUznaqz0/Hwo69gplbmRzdHJlYW0KZW5kb2JqCjM1IDAgb2JqCjw8IC9MZW5ndGggMjE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD1QuY0EMQzLXYUaWMB67alnFotLpv/0SPn2ItEWRVIqNZmSKS91lCVZU946fJbEDnmG5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+uco+fXosbPsPxQxSRkg7mNf9Y/fJzDa9TjyeRbm++4l6cqQ4DERySmrwjXVixLhIRaTVBTc/AWi2Au7de/hu0I7oMQPaJxHGaUo6hv2twpc8v5SdT2AplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9MZW5ndGggODMgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRYy7DcAwCER7pmAEfib2PlGUwt6/DRAlbrgn3T1cHQmZKW4zw0MGngwshl1xgfSWMAtcR1COneyjYdW+6gSN9aZS8+8PlJ7srOKG6wECQhpmCmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0xlbmd0aCA1MSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdIAMTM5YIJ5oBZBkAaojgHriaHK4MrDQDhtA2YCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0xlbmd0aCAxNjAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicRZA5EgMxCARzvYInSFyC96zLtcH6/6kH1kei6QI0HLoWTcp6FGg+6bFGobrQa+gsSpJEwRaSHVCnY4g7KEhMSGOSSLYegyOaWLNdmJlUKrNS4bRpxcK/2VrVyESNcI38iekGVPxP6lyU8E2Dr5Ix+hhUvDuDjEn4XkXcWjHt/kQwsRn2CW9FJgWEibGp2b7PYIbM9wrXOMfzDUyCN+sKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvTGVuZ3RoIDMzNCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJwtUktyxSAM23MKXaAz+AfkPOl0uni9/7aSk0VGDmD0MeWGiUp8WSC3o9bEt43MQIXhr6vMhc9I28g6iMuQi7iSLYV7RCzkMcQ8xILvq/EeHvmszMmzB8Yv2XcPK/bUhGUh48UZ2mEVx2EV5FiwdSGqe3hTpMOpJNjji/8+xXMtBC18RtCAX+Sfr47g+ZIWafeYbdOuerBMO6qksBxsT3NeJl9aZ7k6Hs8Hyfau2BFSuwIUhbkzznPhKNNWRrQWdjZIalxsb479WErQhW5cRoojkJ+pIjygpMnMJgrij5wecioDYeqarnRyG1Vxp57MNZuLtzNJZuu+SLGZwnldOLP+DFNmtXknz3Ki1KkI77FnS9DQOa6evZZZaHSbE7ykhM/GTk9Ovlcz6yE5FQmpYlpXwWkUmWIJ2xJfU1FTmnoZ/vvy7vE7fv4BLHN8cwplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9MZW5ndGggNzAgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicMzM2UzBQsDACEqamhgrmRpYKKYZcQD6IlcsFE8sBs8wszIEsIwuQlhwuQwtjMG1ibKRgZmIGZFkgMSC6MrjSAJiaEwMKZW5kc3RyZWFtCmVuZG9iago0MSAwIG9iago8PCAvTGVuZ3RoIDMyMCAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UktuBTEI288puECl8E/O86qqi777b2sTvRVMMGDjKS9Z0ku+1CXbpcPkWx/3JbFC3o/tmsxSxfcWsxTPLa9HzxG3LQoEURM9WJkvFSLUz/ToOqhwSp+BVwi3FBu8g0kAg2r4Bx6lMyBQ50DGu2IyUgOCJNhzaXEIiXImiX+kvJ7fJ62kofQ9WZnL35NLpdAdTU7oAcXKxUmgXUn5oJmYSkSSl+t9sUL0hsCSPD5HMcmA7DaJbaIFJucepSXMxBQ6sMcCvGaa1VXoYMIehymMVwuzqB5s8lsTlaQdreMZ2TDeyzBTYqHhsAXU5mJlgu7l4zWvwojtUZNdw3Duls13CNFo/hsWyuBjFZKAR6exEg1pOMCIwJ5eOMVe8xM5DsCIY52aLAxjaCaneo6JwNCes6VhxsceWvXzD1TpfIcKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvTGVuZ3RoIDE4IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iago0MyAwIG9iago8PCAvTGVuZ3RoIDEzMyAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJxFj0sOBCEIRPecoo7Axx/ncTLphXP/7YCdbhNjPYVUgbmCoT0uawOdFR8hGbbxt6mWjkVZPlR6UlYPyeCHrMbLIdygLPCCSSqGIVCLmBqRLWVut4DbNg2yspVTpY6wi6Mwj/a0bBUeX6JbInWSP4PEKi/c47odyKXWu96ii75/pAExCQplbmRzdHJlYW0KZW5kb2JqCjQ0IDAgb2JqCjw8IC9MZW5ndGggMzQwIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nDVSOW4EMQzr/Qp9IIBu2+/ZIEiR/L8NqdkUA3F0UpQ7WlR2y4eFVLXsdPm0ldoSN+R3ZYXECcmrEu1ShkiovFYh1e+ZMq+3NWcEyFKlwuSk5HHJgj/DpacLx/m2sa/lyB2PHlgVI6FEwDLFxOgals7usGZbfpZpwI94hJwr1i3HWAVSG9047Yr3oXktsgaIvZmWigodVokWfkHxoEeNffYYVFgg0e0cSXCMiVCRgHaB2kgMOXssdlEf9DMoMRPo2htF3EGBJZKYOcW6dPTf+NCxoP7YjDe/OirpW1pZY9I+G+2Uxiwy6XpY9HTz1seDCzTvovzn1QwSNGWNksYHrdo5hqKZUVZ4t0OTDc0xxyHzDp7DGQlK+jwUv48lEx2UyN8ODaF/Xx6jjJw23gLmoj9tFQcO4rPDXrmBFUoXa5L3AalM6IHp/6/xtb7X1x8d7YDGCmVuZHN0cmVhbQplbmRvYmoKNDUgMCBvYmoKPDwgL0xlbmd0aCAyNTEgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNDYgMCBvYmoKPDwgL0xlbmd0aCAxNzQgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjQ3IDAgb2JqCjw8IC9MZW5ndGggNzUgL0ZpbHRlciAvRmxhdGVEZWNvZGUgPj4Kc3RyZWFtCnicM7U0UjBQMDYAEqZmRgqmJuYKKYZcQD6IlctlaGQKZuVwGVmaKVhYABkmZuZQIZiGHC5jU3OgAUBFxqZgGqo/hyuDKw0AlZAS7wplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9MZW5ndGggMTQxIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlID4+CnN0cmVhbQp4nD2PwQ7DMAhD7/kK/0Ck2CmhfE+naofu/68jS7sLegJjjIXQ0BuqmsOGYJvjxdIlVGv4FMVAJTfImWAOpaTSHUeRemI4GFwetBuO4rHo+hG7kmZ90MZCuiVogHusU2ncpnETxB01Beop6pyjvBC5n6ln2DSS3TSzknO4Db97z1PX/6ervMv5Bb13Lv4KZW5kc3RyZWFtCmVuZG9iago0OSAwIG9iago8PCAvTGVuZ3RoIDIxNSAvRmlsdGVyIC9GbGF0ZURlY29kZSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNSAwIG9iago8PCAvVHlwZSAvRm9udCAvQmFzZUZvbnQgL0JNUVFEVitEZWphVnVTYW5zIC9GaXJzdENoYXIgMCAvTGFzdENoYXIgMjU1Ci9Gb250RGVzY3JpcHRvciAxNCAwIFIgL1N1YnR5cGUgL1R5cGUzIC9OYW1lIC9CTVFRRFYrRGVqYVZ1U2FucwovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdCi9DaGFyUHJvY3MgMTYgMCBSCi9FbmNvZGluZyA8PCAvVHlwZSAvRW5jb2RpbmcKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ1IC9oeXBoZW4gL3BlcmlvZCA0OCAvemVybyAvb25lIC90d28gL3RocmVlIC9mb3VyIC9maXZlIC9zaXgKL3NldmVuIC9laWdodCA2NSAvQSA2OCAvRCA3NiAvTCA4MiAvUiA4NSAvVSA5NyAvYSAvYiAvYyAvZCAvZSAvZiAxMDUgL2kgMTEwCi9uIC9vIDExNCAvciAvcyAvdCAvdSAvdiAxMjEgL3kgXQo+PgovV2lkdGhzIDEzIDAgUiA+PgplbmRvYmoKMTQgMCBvYmoKPDwgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9Gb250TmFtZSAvQk1RUURWK0RlamFWdVNhbnMgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0FzY2VudCA5MjkgL0Rlc2NlbnQgLTIzNiAvQ2FwSGVpZ2h0IDAKL1hIZWlnaHQgMCAvSXRhbGljQW5nbGUgMCAvU3RlbVYgMCAvTWF4V2lkdGggMTM0MiA+PgplbmRvYmoKMTMgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTYgMCBvYmoKPDwgL0EgMTcgMCBSIC9EIDE4IDAgUiAvTCAxOSAwIFIgL1IgMjAgMCBSIC9VIDIxIDAgUiAvYSAyMiAwIFIgL2IgMjMgMCBSCi9jIDI0IDAgUiAvZCAyNSAwIFIgL2UgMjYgMCBSIC9laWdodCAyNyAwIFIgL2YgMjggMCBSIC9maXZlIDI5IDAgUgovZm91ciAzMCAwIFIgL2h5cGhlbiAzMSAwIFIgL2kgMzIgMCBSIC9uIDM0IDAgUiAvbyAzNSAwIFIgL29uZSAzNiAwIFIKL3BlcmlvZCAzNyAwIFIgL3IgMzggMCBSIC9zIDM5IDAgUiAvc2V2ZW4gNDAgMCBSIC9zaXggNDEgMCBSIC9zcGFjZSA0MiAwIFIKL3QgNDMgMCBSIC90aHJlZSA0NCAwIFIgL3R3byA0NSAwIFIgL3UgNDYgMCBSIC92IDQ3IDAgUiAveSA0OCAwIFIKL3plcm8gNDkgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNSAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDAgL2NhIDEgPj4KL0EyIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDEgPj4KL0EzIDw8IC9UeXBlIC9FeHRHU3RhdGUgL0NBIDEgL2NhIDAuNSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCAvRjEtRGVqYVZ1U2Fucy1taW51cyAzMyAwIFIgPj4KZW5kb2JqCjIgMCBvYmoKPDwgL1R5cGUgL1BhZ2VzIC9LaWRzIFsgMTEgMCBSIF0gL0NvdW50IDE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", 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", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["for i, act_fn_name in enumerate(act_fn_by_name):\n", " net_actfn = load_model(model_path=CHECKPOINT_PATH, model_name=f\"FashionMNIST_{act_fn_name}\").to(device)\n", " visualize_activations(net_actfn, color=f\"C{i}\")"]}, {"cell_type": "markdown", "id": "2417b09a", "metadata": {"papermill": {"duration": 0.060702, "end_time": "2023-03-15T09:56:33.781081", "exception": false, "start_time": "2023-03-15T09:56:33.720379", "status": "completed"}, "tags": []}, "source": ["As the model with sigmoid activation was not able to train properly, the activations are also less informative and all gathered around 0.5 (the activation at input 0).\n", "\n", "The tanh shows a more diverse behavior.\n", "While for the input layer we experience a larger amount of neurons to be close to -1 and 1, where the gradients are close to zero, the activations in the two consecutive layers are closer to zero.\n", "This is probably because the input layers look for specific features in the input image, and the consecutive layers combine those together.\n", "The activations for the last layer are again more biased to the extreme points because the classification layer can be seen as a weighted average of those values (the gradients push the activations to those extremes).\n", "\n", "The ReLU has a strong peak at 0, as we initially expected.\n", "The effect of having no gradients for negative values is that the network does not have a Gaussian-like distribution after the linear layers, but a longer tail towards the positive values.\n", "The LeakyReLU shows a very similar behavior while ELU follows again a more Gaussian-like distribution.\n", "The Swish activation seems to lie in between, although it is worth noting that Swish uses significantly higher values than other activation functions (up to 20).\n", "\n", "As all activation functions show slightly different behavior although\n", "obtaining similar performance for our simple network, it becomes\n", "apparent that the selection of the \"optimal\" activation function really\n", "depends on many factors, and is not the same for all possible networks."]}, {"cell_type": "markdown", "id": "5bc523fb", "metadata": {"papermill": {"duration": 0.063588, "end_time": "2023-03-15T09:56:33.904271", "exception": false, "start_time": "2023-03-15T09:56:33.840683", "status": "completed"}, "tags": []}, "source": ["### Finding dead neurons in ReLU networks"]}, {"cell_type": "markdown", "id": "0dcce3bb", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.061694, "end_time": "2023-03-15T09:56:34.037378", "exception": false, "start_time": "2023-03-15T09:56:33.975684", "status": "completed"}, "tags": []}, "source": ["One known drawback of the ReLU activation is the occurrence of \"dead neurons\", i.e. neurons with no gradient for any training input.\n", "The issue of dead neurons is that as no gradient is provided for the layer, we cannot train the parameters of this neuron in the previous layer to obtain output values besides zero.\n", "For dead neurons to happen, the output value of a specific neuron of the linear layer before the ReLU has to be negative for all input images.\n", "Considering the large number of neurons we have in a neural network, it is not unlikely for this to happen.\n", "\n", "To get a better understanding of how much of a problem this is, and when we need to be careful, we will measure how many dead neurons different networks have.\n", "For this, we implement a function which runs the network on the whole\n", "training set and records whether a neuron is exactly 0 for all data\n", "points or not:"]}, {"cell_type": "code", "execution_count": 22, "id": "2f02086c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:56:34.161890Z", "iopub.status.busy": "2023-03-15T09:56:34.161413Z", "iopub.status.idle": "2023-03-15T09:56:34.174223Z", "shell.execute_reply": "2023-03-15T09:56:34.173619Z"}, "papermill": {"duration": 0.077644, "end_time": "2023-03-15T09:56:34.176493", "exception": false, "start_time": "2023-03-15T09:56:34.098849", "status": "completed"}, "tags": []}, "outputs": [], "source": ["@torch.no_grad()\n", "def measure_number_dead_neurons(net):\n", " \"\"\"Function to measure the number of dead neurons in a trained neural network.\n", "\n", " For each neuron, we create a boolean variable initially set to 1. If it has an activation unequals 0 at any time, we\n", " set this variable to 0. After running through the whole training set, only dead neurons will have a 1.\n", " \"\"\"\n", " neurons_dead = [\n", " torch.ones(layer.weight.shape[0], device=device, dtype=torch.bool)\n", " for layer in net.layers[:-1]\n", " if isinstance(layer, nn.Linear)\n", " ] # Same shapes as hidden size in BaseNetwork\n", "\n", " net.eval()\n", " for imgs, labels in tqdm(train_loader, leave=False): # Run through whole training set\n", " layer_index = 0\n", " imgs = imgs.to(device)\n", " imgs = imgs.view(imgs.size(0), -1)\n", " for layer in net.layers[:-1]:\n", " imgs = layer(imgs)\n", " if isinstance(layer, ActivationFunction):\n", " # Are all activations == 0 in the batch, and we did not record the opposite in the last batches?\n", " neurons_dead[layer_index] = torch.logical_and(neurons_dead[layer_index], (imgs == 0).all(dim=0))\n", " layer_index += 1\n", " number_neurons_dead = [t.sum().item() for t in neurons_dead]\n", " print(\"Number of dead neurons:\", number_neurons_dead)\n", " print(\n", " \"In percentage:\",\n", " \", \".join(\n", " [f\"{(100.0 * num_dead / tens.shape[0]):4.2f}%\" for tens, num_dead in zip(neurons_dead, number_neurons_dead)]\n", " ),\n", " )"]}, {"cell_type": "markdown", "id": "6e2c2f19", "metadata": {"papermill": {"duration": 0.068375, "end_time": "2023-03-15T09:56:34.309265", "exception": false, "start_time": "2023-03-15T09:56:34.240890", "status": "completed"}, "tags": []}, "source": ["First, we can measure the number of dead neurons for an untrained network:"]}, {"cell_type": "code", "execution_count": 23, "id": "69d5b0de", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T09:56:34.467185Z", "iopub.status.busy": "2023-03-15T09:56:34.466657Z", "iopub.status.idle": "2023-03-15T09:56:41.410582Z", "shell.execute_reply": "2023-03-15T09:56:41.409884Z"}, "papermill": {"duration": 7.019756, "end_time": "2023-03-15T09:56:41.412015", "exception": false, "start_time": "2023-03-15T09:56:34.392259", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e3439b3a6c444ed78a6377560cd2e588", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/49 [00:00