{"cells": [{"cell_type": "markdown", "id": "829d77e5", "metadata": {"papermill": {"duration": 0.139908, "end_time": "2021-12-04T15:58:00.173929", "exception": false, "start_time": "2021-12-04T15:58:00.034021", "status": "completed"}, "tags": []}, "source": ["\n", "# Tutorial 5: Transformers and Multi-Head Attention\n", "\n", "* **Author:** Phillip Lippe\n", "* **License:** CC BY-SA\n", "* **Generated:** 2021-12-04T16:52:50.580472\n", "\n", "In this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model.\n", "Since the paper Attention Is All You Need by Vaswani et al. had been published in 2017,\n", "the Transformer architecture has continued to beat benchmarks in many domains, most importantly in Natural Language Processing.\n", "Transformers with an incredible amount of parameters can generate long, convincing essays, and opened up new application fields of AI.\n", "As the hype of the Transformer architecture seems not to come to an end in the next years,\n", "it is important to understand how it works, and have implemented it yourself, which we will do in this notebook.\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/05-transformers-and-MH-attention.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/PytorchLightning/pytorch-lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n", "| Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)"]}, {"cell_type": "markdown", "id": "4bd97ecd", "metadata": {"papermill": {"duration": 0.139186, "end_time": "2021-12-04T15:58:00.454648", "exception": false, "start_time": "2021-12-04T15:58:00.315462", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "6df421c0", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2021-12-04T15:58:00.739046Z", "iopub.status.busy": "2021-12-04T15:58:00.738548Z", "iopub.status.idle": "2021-12-04T15:58:03.208213Z", "shell.execute_reply": "2021-12-04T15:58:03.207638Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 2.615354, "end_time": "2021-12-04T15:58:03.208358", "exception": false, "start_time": "2021-12-04T15:58:00.593004", "status": "completed"}, "tags": []}, "outputs": [], "source": ["! pip install --quiet \"torchmetrics>=0.3\" \"pytorch-lightning>=1.3\" \"torchvision\" \"torch>=1.6, <1.9\" \"matplotlib\" \"seaborn\""]}, {"cell_type": "markdown", "id": "a5126640", "metadata": {"papermill": {"duration": 0.138671, "end_time": "2021-12-04T15:58:03.486977", "exception": false, "start_time": "2021-12-04T15:58:03.348306", "status": "completed"}, "tags": []}, "source": ["
\n", "Despite the huge success of Transformers in NLP, we will _not_ include the NLP domain in our notebook here.\n", "There are many courses at the University of Amsterdam that focus on Natural Language Processing\n", "and take a closer look at the application of the Transformer architecture in NLP\n", "([NLP2](https://studiegids.uva.nl/xmlpages/page/2020-2021/zoek-vak/vak/79628),\n", "[Advanced Topics in Computational Semantics](https://studiegids.uva.nl/xmlpages/page/2020-2021/zoek-vak/vak/80162)).\n", "Furthermore, and most importantly, there is so much more to the Transformer architecture.\n", "NLP is the domain the Transformer architecture has been originally proposed for and had the greatest impact on,\n", "but it also accelerated research in other domains, recently even [Computer Vision](https://arxiv.org/abs/2010.11929).\n", "Thus, we focus here on what makes the Transformer and self-attention so powerful in general.\n", "In a second notebook, we will look at Vision Transformers, i.e. Transformers for image classification\n", "([link to notebook](https://uvadlc-notebooks.readthedocs.io/en/latest/tutorial_notebooks/tutorial15/Vision_Transformer.html)).\n", "\n", "Below, we import our standard libraries."]}, {"cell_type": "code", "execution_count": 2, "id": "31508898", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:03.772463Z", "iopub.status.busy": "2021-12-04T15:58:03.771979Z", "iopub.status.idle": "2021-12-04T15:58:05.871634Z", "shell.execute_reply": "2021-12-04T15:58:05.871226Z"}, "papermill": {"duration": 2.246581, "end_time": "2021-12-04T15:58:05.871763", "exception": false, "start_time": "2021-12-04T15:58:03.625182", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/tmp/ipykernel_1492/2689201066.py:34: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n", " set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Device: cuda:0\n"]}], "source": ["# Standard libraries\n", "import math\n", "import os\n", "import urllib.request\n", "from functools import partial\n", "from urllib.error import HTTPError\n", "\n", "# Plotting\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "# PyTorch Lightning\n", "import pytorch_lightning as pl\n", "import seaborn as sns\n", "\n", "# PyTorch\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", "\n", "# Torchvision\n", "import torchvision\n", "from IPython.display import set_matplotlib_formats\n", "from pytorch_lightning.callbacks import ModelCheckpoint\n", "from torchvision import transforms\n", "from torchvision.datasets import CIFAR100\n", "from tqdm.notebook import tqdm\n", "\n", "plt.set_cmap(\"cividis\")\n", "%matplotlib inline\n", "set_matplotlib_formats(\"svg\", \"pdf\") # For export\n", "matplotlib.rcParams[\"lines.linewidth\"] = 2.0\n", "sns.reset_orig()\n", "\n", "# Path to the folder where the datasets are/should be downloaded (e.g. CIFAR10)\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/Transformers/\")\n", "\n", "# Setting the seed\n", "pl.seed_everything(42)\n", "\n", "# Ensure that all operations are deterministic on GPU (if used) for reproducibility\n", "torch.backends.cudnn.determinstic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "device = torch.device(\"cuda:0\") if torch.cuda.is_available() else torch.device(\"cpu\")\n", "print(\"Device:\", device)"]}, {"cell_type": "markdown", "id": "940525e1", "metadata": {"papermill": {"duration": 0.144686, "end_time": "2021-12-04T15:58:06.158145", "exception": false, "start_time": "2021-12-04T15:58:06.013459", "status": "completed"}, "tags": []}, "source": ["Two pre-trained models are downloaded below.\n", "Make sure to have adjusted your `CHECKPOINT_PATH` before running this code if not already done."]}, {"cell_type": "code", "execution_count": 3, "id": "a7a72c1a", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:06.442497Z", "iopub.status.busy": "2021-12-04T15:58:06.442012Z", "iopub.status.idle": "2021-12-04T15:58:06.793103Z", "shell.execute_reply": "2021-12-04T15:58:06.792670Z"}, "papermill": {"duration": 0.496404, "end_time": "2021-12-04T15:58:06.793239", "exception": false, "start_time": "2021-12-04T15:58:06.296835", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/ReverseTask.ckpt...\n", "Downloading https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/SetAnomalyTask.ckpt...\n"]}], "source": ["# Github URL where saved models are stored for this tutorial\n", "base_url = \"https://raw.githubusercontent.com/phlippe/saved_models/main/tutorial6/\"\n", "# Files to download\n", "pretrained_files = [\"ReverseTask.ckpt\", \"SetAnomalyTask.ckpt\"]\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 \"/\" in file_name:\n", " os.makedirs(file_path.rsplit(\"/\", 1)[0], exist_ok=True)\n", " if not os.path.isfile(file_path):\n", " file_url = base_url + file_name\n", " print(\"Downloading %s...\" % 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 manually,\"\n", " \" or contact the author with the full output including the following error:\\n\",\n", " e,\n", " )"]}, {"cell_type": "markdown", "id": "1e17404e", "metadata": {"papermill": {"duration": 0.140163, "end_time": "2021-12-04T15:58:07.075728", "exception": false, "start_time": "2021-12-04T15:58:06.935565", "status": "completed"}, "tags": []}, "source": ["## The Transformer architecture\n", "\n", "In the first part of this notebook, we will implement the Transformer architecture by hand.\n", "As the architecture is so popular, there already exists a Pytorch module `nn.Transformer`\n", "([documentation](https://pytorch.org/docs/stable/generated/torch.nn.Transformer.html))\n", "and a [tutorial](https://pytorch.org/tutorials/beginner/transformer_tutorial.html)\n", "on how to use it for next token prediction.\n", "However, we will implement it here ourselves, to get through to the smallest details.\n", "\n", "There are of course many more tutorials out there about attention and Transformers.\n", "Below, we list a few that are worth exploring if you are interested in the topic\n", "and might want yet another perspective on the topic after this one:\n", "\n", "* [Transformer: A Novel Neural Network Architecture for Language Understanding\n", "(Jakob Uszkoreit, 2017)](https://ai.googleblog.com/2017/08/transformer-novel-neural-network.html) - The original Google blog post about the Transformer paper, focusing on the application in machine translation.\n", "* [The Illustrated Transformer (Jay Alammar, 2018)](http://jalammar.github.io/illustrated-transformer/) - A very popular and great blog post intuitively explaining the Transformer architecture with many nice visualizations.\n", "The focus is on NLP.\n", "* [Attention?\n", "Attention!\n", "(Lilian Weng, 2018)](https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html) - A nice blog post summarizing attention mechanisms in many domains including vision.\n", "* [Illustrated: Self-Attention (Raimi Karim, 2019)](https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a) - A nice visualization of the steps of self-attention.\n", "Recommended going through if the explanation below is too abstract for you.\n", "* [The Transformer family (Lilian Weng, 2020)](https://lilianweng.github.io/lil-log/2020/04/07/the-transformer-family.html) - A very detailed blog post reviewing more variants of Transformers besides the original one."]}, {"cell_type": "markdown", "id": "c7a1d465", "metadata": {"papermill": {"duration": 0.14018, "end_time": "2021-12-04T15:58:07.355646", "exception": false, "start_time": "2021-12-04T15:58:07.215466", "status": "completed"}, "tags": []}, "source": ["### What is Attention?\n", "\n", "The attention mechanism describes a recent new group of layers in neural networks that has attracted\n", "a lot of interest in the past few years, especially in sequence tasks.\n", "There are a lot of different possible definitions of \"attention\" in the literature,\n", "but the one we will use here is the following: _the attention mechanism describes a weighted average\n", "of (sequence) elements with the weights dynamically computed based on an input query and elements' keys_.\n", "So what does this exactly mean?\n", "The goal is to take an average over the features of multiple elements.\n", "However, instead of weighting each element equally, we want to weight them depending on their actual values.\n", "In other words, we want to dynamically decide on which inputs we want to \"attend\" more than others.\n", "In particular, an attention mechanism has usually four parts we need to specify:\n", "\n", "* **Query**: The query is a feature vector that describes what we are looking for in the sequence, i.e. what would we maybe want to pay attention to.\n", "* **Keys**: For each input element, we have a key which is again a feature vector.\n", "This feature vector roughly describes what the element is \"offering\", or when it might be important.\n", "The keys should be designed such that we can identify the elements we want to pay attention to based on the query.\n", "* **Values**: For each input element, we also have a value vector.\n", "This feature vector is the one we want to average over.\n", "* **Score function**: To rate which elements we want to pay attention to, we need to specify a score function $f_{attn}$.\n", "The score function takes the query and a key as input, and output the score/attention weight of the query-key pair.\n", "It is usually implemented by simple similarity metrics like a dot product, or a small MLP.\n", "\n", "\n", "The weights of the average are calculated by a softmax over all score function outputs.\n", "Hence, we assign those value vectors a higher weight whose corresponding key is most similar to the query.\n", "If we try to describe it with pseudo-math, we can write:\n", "\n", "$$\n", "\\alpha_i = \\frac{\\exp\\left(f_{attn}\\left(\\text{key}_i, \\text{query}\\right)\\right)}{\\sum_j \\exp\\left(f_{attn}\\left(\\text{key}_j, \\text{query}\\right)\\right)}, \\hspace{5mm} \\text{out} = \\sum_i \\alpha_i \\cdot \\text{value}_i\n", "$$\n", "\n", "Visually, we can show the attention over a sequence of words as follows:\n", "\n", "
\n", "\n", "For every word, we have one key and one value vector.\n", "The query is compared to all keys with a score function (in this case the dot product) to determine the weights.\n", "The softmax is not visualized for simplicity.\n", "Finally, the value vectors of all words are averaged using the attention weights.\n", "\n", "Most attention mechanisms differ in terms of what queries they use, how the key and value vectors are defined,\n", "and what score function is used.\n", "The attention applied inside the Transformer architecture is called **self-attention**.\n", "In self-attention, each sequence element provides a key, value, and query.\n", "For each element, we perform an attention layer where based on its query,\n", "we check the similarity of the all sequence elements' keys, and returned a different,\n", "averaged value vector for each element.\n", "We will now go into a bit more detail by first looking at the specific implementation of the attention mechanism\n", "which is in the Transformer case the scaled dot product attention."]}, {"cell_type": "markdown", "id": "d9697f03", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.139603, "end_time": "2021-12-04T15:58:07.635701", "exception": false, "start_time": "2021-12-04T15:58:07.496098", "status": "completed"}, "tags": []}, "source": ["### Scaled Dot Product Attention\n", "\n", "The core concept behind self-attention is the scaled dot product attention.\n", "Our goal is to have an attention mechanism with which any element in a sequence can attend to any other while\n", "still being efficient to compute.\n", "The dot product attention takes as input a set of queries\n", "$Q\\in\\mathbb{R}^{T\\times d_k}$, keys $K\\in\\mathbb{R}^{T\\times d_k}$\n", "and values $V\\in\\mathbb{R}^{T\\times d_v}$ where $T$ is the sequence length,\n", "and $d_k$ and $d_v$ are the hidden dimensionality for queries/keys and values respectively.\n", "For simplicity, we neglect the batch dimension for now.\n", "The attention value from element $i$ to $j$ is based on its similarity of the query $Q_i$ and key $K_j$,\n", "using the dot product as the similarity metric.\n", "In math, we calculate the dot product attention as follows:\n", "\n", "$$\\text{Attention}(Q,K,V)=\\text{softmax}\\left(\\frac{QK^T}{\\sqrt{d_k}}\\right)V$$\n", "\n", "The matrix multiplication $QK^T$ performs the dot product for every possible pair of queries and keys,\n", "resulting in a matrix of the shape $T\\times T$.\n", "Each row represents the attention logits for a specific element $i$ to all other elements in the sequence.\n", "On these, we apply a softmax and multiply with the value vector to obtain a weighted mean\n", "(the weights being determined by the attention).\n", "Another perspective on this attention mechanism offers the computation graph which is visualized below\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", "\n", "
\n", "\n", "One aspect we haven't discussed yet is the scaling factor of $1/\\sqrt{d_k}$.\n", "This scaling factor is crucial to maintain an appropriate variance of attention values after initialization.\n", "Remember that we intialize our layers with the intention of having equal variance throughout the model, and hence,\n", "$Q$ and $K$ might also have a variance close to $1$.\n", "However, performing a dot product over two vectors with a variance $\\sigma$ results\n", "in a scalar having $d_k$-times higher variance:\n", "\n", "$$q_i \\sim \\mathcal{N}(0,\\sigma), k_i \\sim \\mathcal{N}(0,\\sigma) \\to \\text{Var}\\left(\\sum_{i=1}^{d_k} q_i\\cdot k_i\\right) = \\sigma\\cdot d_k$$\n", "\n", "\n", "If we do not scale down the variance back to $\\sigma$, the softmax over the logits will already saturate\n", "to $1$ for one random element and $0$ for all others.\n", "The gradients through the softmax will be close to zero so that we can't learn the parameters appropriately.\n", "\n", "The block `Mask (opt.\n", ")` in the diagram above represents the optional masking of specific entries in the attention matrix.\n", "This is for instance used if we stack multiple sequences with different lengths into a batch.\n", "To still benefit from parallelization in PyTorch, we pad the sentences to the same length and mask out the padding\n", "tokens during the calculation of the attention values.\n", "This is usually done by setting the respective attention logits to a very low value.\n", "\n", "After we have discussed the details of the scaled dot product attention block, we can write a function below\n", "which computes the output features given the triple of queries, keys, and values:"]}, {"cell_type": "code", "execution_count": 4, "id": "f65c1f5d", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:07.923041Z", "iopub.status.busy": "2021-12-04T15:58:07.922459Z", "iopub.status.idle": "2021-12-04T15:58:07.924745Z", "shell.execute_reply": "2021-12-04T15:58:07.925122Z"}, "papermill": {"duration": 0.148521, "end_time": "2021-12-04T15:58:07.925250", "exception": false, "start_time": "2021-12-04T15:58:07.776729", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def scaled_dot_product(q, k, v, mask=None):\n", " d_k = q.size()[-1]\n", " attn_logits = torch.matmul(q, k.transpose(-2, -1))\n", " attn_logits = attn_logits / math.sqrt(d_k)\n", " if mask is not None:\n", " attn_logits = attn_logits.masked_fill(mask == 0, -9e15)\n", " attention = F.softmax(attn_logits, dim=-1)\n", " values = torch.matmul(attention, v)\n", " return values, attention"]}, {"cell_type": "markdown", "id": "10dc2e7e", "metadata": {"papermill": {"duration": 0.139995, "end_time": "2021-12-04T15:58:08.204876", "exception": false, "start_time": "2021-12-04T15:58:08.064881", "status": "completed"}, "tags": []}, "source": ["Note that our code above supports any additional dimensionality in front of the sequence length\n", "so that we can also use it for batches.\n", "However, for a better understanding, let's generate a few random queries, keys, and value vectors,\n", "and calculate the attention outputs:"]}, {"cell_type": "code", "execution_count": 5, "id": "c417bca9", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:08.490138Z", "iopub.status.busy": "2021-12-04T15:58:08.489665Z", "iopub.status.idle": "2021-12-04T15:58:08.498288Z", "shell.execute_reply": "2021-12-04T15:58:08.497857Z"}, "papermill": {"duration": 0.15228, "end_time": "2021-12-04T15:58:08.498402", "exception": false, "start_time": "2021-12-04T15:58:08.346122", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 42\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Q\n", " tensor([[ 0.3367, 0.1288],\n", " [ 0.2345, 0.2303],\n", " [-1.1229, -0.1863]])\n", "K\n", " tensor([[ 2.2082, -0.6380],\n", " [ 0.4617, 0.2674],\n", " [ 0.5349, 0.8094]])\n", "V\n", " tensor([[ 1.1103, -1.6898],\n", " [-0.9890, 0.9580],\n", " [ 1.3221, 0.8172]])\n", "Values\n", " tensor([[ 0.5698, -0.1520],\n", " [ 0.5379, -0.0265],\n", " [ 0.2246, 0.5556]])\n", "Attention\n", " tensor([[0.4028, 0.2886, 0.3086],\n", " [0.3538, 0.3069, 0.3393],\n", " [0.1303, 0.4630, 0.4067]])\n"]}], "source": ["seq_len, d_k = 3, 2\n", "pl.seed_everything(42)\n", "q = torch.randn(seq_len, d_k)\n", "k = torch.randn(seq_len, d_k)\n", "v = torch.randn(seq_len, d_k)\n", "values, attention = scaled_dot_product(q, k, v)\n", "print(\"Q\\n\", q)\n", "print(\"K\\n\", k)\n", "print(\"V\\n\", v)\n", "print(\"Values\\n\", values)\n", "print(\"Attention\\n\", attention)"]}, {"cell_type": "markdown", "id": "a9d22885", "metadata": {"papermill": {"duration": 0.141388, "end_time": "2021-12-04T15:58:08.781187", "exception": false, "start_time": "2021-12-04T15:58:08.639799", "status": "completed"}, "tags": []}, "source": ["Before continuing, make sure you can follow the calculation of the specific values here, and also check it by hand.\n", "It is important to fully understand how the scaled dot product attention is calculated."]}, {"cell_type": "markdown", "id": "09bc5c7a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.141201, "end_time": "2021-12-04T15:58:09.064542", "exception": false, "start_time": "2021-12-04T15:58:08.923341", "status": "completed"}, "tags": []}, "source": ["### Multi-Head Attention\n", "\n", "The scaled dot product attention allows a network to attend over a sequence.\n", "However, often there are multiple different aspects a sequence element wants to attend to,\n", "and a single weighted average is not a good option for it.\n", "This is why we extend the attention mechanisms to multiple heads,\n", "i.e. multiple different query-key-value triplets on the same features.\n", "Specifically, given a query, key, and value matrix, we transform those into $h$ sub-queries, sub-keys,\n", "and sub-values, which we pass through the scaled dot product attention independently.\n", "Afterward, we concatenate the heads and combine them with a final weight matrix.\n", "Mathematically, we can express this operation as:\n", "\n", "$$\n", "\\begin{split}\n", " \\text{Multihead}(Q,K,V) & = \\text{Concat}(\\text{head}_1,...,\\text{head}_h)W^{O}\\\\\n", " \\text{where } \\text{head}_i & = \\text{Attention}(QW_i^Q,KW_i^K, VW_i^V)\n", "\\end{split}\n", "$$\n", "\n", "We refer to this as Multi-Head Attention layer with the learnable parameters\n", "$W_{1...h}^{Q}\\in\\mathbb{R}^{D\\times d_k}$,\n", "$W_{1...h}^{K}\\in\\mathbb{R}^{D\\times d_k}$,\n", "$W_{1...h}^{V}\\in\\mathbb{R}^{D\\times d_v}$,\n", "and $W^{O}\\in\\mathbb{R}^{h\\cdot d_k\\times d_{out}}$ ($D$ being the input dimensionality).\n", "Expressed in a computational graph, we can visualize it as below\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", "\n", "
\n", "\n", "How are we applying a Multi-Head Attention layer in a neural network,\n", "where we don't have an arbitrary query, key, and value vector as input?\n", "Looking at the computation graph above, a simple but effective implementation is to set the current\n", "feature map in a NN, $X\\in\\mathbb{R}^{B\\times T\\times d_{\\text{model}}}$, as $Q$, $K$ and $V$\n", "($B$ being the batch size, $T$ the sequence length, $d_{\\text{model}}$ the hidden dimensionality of $X$).\n", "The consecutive weight matrices $W^{Q}$, $W^{K}$, and $W^{V}$ can transform $X$ to the corresponding\n", "feature vectors that represent the queries, keys, and values of the input.\n", "Using this approach, we can implement the Multi-Head Attention module below."]}, {"cell_type": "code", "execution_count": 6, "id": "9a857f4f", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:09.357800Z", "iopub.status.busy": "2021-12-04T15:58:09.357266Z", "iopub.status.idle": "2021-12-04T15:58:09.359052Z", "shell.execute_reply": "2021-12-04T15:58:09.358641Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.152505, "end_time": "2021-12-04T15:58:09.359159", "exception": false, "start_time": "2021-12-04T15:58:09.206654", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class MultiheadAttention(nn.Module):\n", " def __init__(self, input_dim, embed_dim, num_heads):\n", " super().__init__()\n", " assert embed_dim % num_heads == 0, \"Embedding dimension must be 0 modulo number of heads.\"\n", "\n", " self.embed_dim = embed_dim\n", " self.num_heads = num_heads\n", " self.head_dim = embed_dim // num_heads\n", "\n", " # Stack all weight matrices 1...h together for efficiency\n", " # Note that in many implementations you see \"bias=False\" which is optional\n", " self.qkv_proj = nn.Linear(input_dim, 3 * embed_dim)\n", " self.o_proj = nn.Linear(embed_dim, embed_dim)\n", "\n", " self._reset_parameters()\n", "\n", " def _reset_parameters(self):\n", " # Original Transformer initialization, see PyTorch documentation\n", " nn.init.xavier_uniform_(self.qkv_proj.weight)\n", " self.qkv_proj.bias.data.fill_(0)\n", " nn.init.xavier_uniform_(self.o_proj.weight)\n", " self.o_proj.bias.data.fill_(0)\n", "\n", " def forward(self, x, mask=None, return_attention=False):\n", " batch_size, seq_length, embed_dim = x.size()\n", " qkv = self.qkv_proj(x)\n", "\n", " # Separate Q, K, V from linear output\n", " qkv = qkv.reshape(batch_size, seq_length, self.num_heads, 3 * self.head_dim)\n", " qkv = qkv.permute(0, 2, 1, 3) # [Batch, Head, SeqLen, Dims]\n", " q, k, v = qkv.chunk(3, dim=-1)\n", "\n", " # Determine value outputs\n", " values, attention = scaled_dot_product(q, k, v, mask=mask)\n", " values = values.permute(0, 2, 1, 3) # [Batch, SeqLen, Head, Dims]\n", " values = values.reshape(batch_size, seq_length, embed_dim)\n", " o = self.o_proj(values)\n", "\n", " if return_attention:\n", " return o, attention\n", " else:\n", " return o"]}, {"cell_type": "markdown", "id": "b56973f7", "metadata": {"papermill": {"duration": 0.142069, "end_time": "2021-12-04T15:58:09.642785", "exception": false, "start_time": "2021-12-04T15:58:09.500716", "status": "completed"}, "tags": []}, "source": ["One crucial characteristic of the multi-head attention is that it is permutation-equivariant with respect to its inputs.\n", "This means that if we switch two input elements in the sequence, e.g. $X_1\\leftrightarrow X_2$\n", "(neglecting the batch dimension for now), the output is exactly the same besides the elements 1 and 2 switched.\n", "Hence, the multi-head attention is actually looking at the input not as a sequence, but as a set of elements.\n", "This property makes the multi-head attention block and the Transformer architecture so powerful and widely applicable!\n", "But what if the order of the input is actually important for solving the task, like language modeling?\n", "The answer is to encode the position in the input features, which we will take a closer look at later\n", "(topic _Positional encodings_ below).\n", "\n", "Before moving on to creating the Transformer architecture, we can compare the self-attention operation\n", "with our other common layer competitors for sequence data: convolutions and recurrent neural networks.\n", "Below you can find a table by [Vaswani et al.\n", "(2017)](https://arxiv.org/abs/1706.03762) on the complexity per layer, the number of sequential operations,\n", "and maximum path length.\n", "The complexity is measured by the upper bound of the number of operations to perform, while the maximum path\n", "length represents the maximum number of steps a forward or backward signal has to traverse to reach any other position.\n", "The lower this length, the better gradient signals can backpropagate for long-range dependencies.\n", "Let's take a look at the table below:\n", "\n", "\n", "
\n", "\n", "$n$ is the sequence length, $d$ is the representation dimension and $k$ is the kernel size of convolutions.\n", "In contrast to recurrent networks, the self-attention layer can parallelize all its operations making it much faster\n", "to execute for smaller sequence lengths.\n", "However, when the sequence length exceeds the hidden dimensionality, self-attention becomes more expensive than RNNs.\n", "One way of reducing the computational cost for long sequences is by restricting the self-attention to a neighborhood\n", "of inputs to attend over, denoted by $r$.\n", "Nevertheless, there has been recently a lot of work on more efficient Transformer architectures that still allow long\n", "dependencies, of which you can find an overview in the paper by [Tay et al.\n", "(2020)](https://arxiv.org/abs/2009.06732) if interested."]}, {"cell_type": "markdown", "id": "c9ea3bc6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.142611, "end_time": "2021-12-04T15:58:09.927152", "exception": false, "start_time": "2021-12-04T15:58:09.784541", "status": "completed"}, "tags": []}, "source": ["### Transformer Encoder\n", "\n", "
\n", "\n", "Next, we will look at how to apply the multi-head attention blog inside the Transformer architecture.\n", "Originally, the Transformer model was designed for machine translation.\n", "Hence, it got an encoder-decoder structure where the encoder takes as input the sentence in the original language\n", "and generates an attention-based representation.\n", "On the other hand, the decoder attends over the encoded information and generates the translated sentence\n", "in an autoregressive manner, as in a standard RNN.\n", "While this structure is extremely useful for Sequence-to-Sequence tasks with the necessity of autoregressive decoding,\n", "we will focus here on the encoder part.\n", "Many advances in NLP have been made using pure encoder-based Transformer models (if interested, models include the\n", "[BERT](https://arxiv.org/abs/1810.04805)-family,\n", "the [Vision Transformer](https://arxiv.org/abs/2010.11929), and more),\n", "and in our tutorial, we will also mainly focus on the encoder part.\n", "If you have understood the encoder architecture, the decoder is a very small step to implement as well.\n", "The full Transformer architecture looks as follows\n", "(figure credit - [Vaswani et al., 2017](https://arxiv.org/abs/1706.03762)).\n", ":\n", "\n", "
\n", "\n", "The encoder consists of $N$ identical blocks that are applied in sequence.\n", "Taking as input $x$, it is first passed through a Multi-Head Attention block as we have implemented above.\n", "The output is added to the original input using a residual connection,\n", "and we apply a consecutive Layer Normalization on the sum.\n", "Overall, it calculates $\\text{LayerNorm}(x+\\text{Multihead}(x,x,x))$\n", "($x$ being $Q$, $K$ and $V$ input to the attention layer).\n", "The residual connection is crucial in the Transformer architecture for two reasons:\n", "\n", "1.\n", "Similar to ResNets, Transformers are designed to be very deep.\n", "Some models contain more than 24 blocks in the encoder.\n", "Hence, the residual connections are crucial for enabling a smooth gradient flow through the model.\n", "2.\n", "Without the residual connection, the information about the original sequence is lost.\n", "Remember that the Multi-Head Attention layer ignores the position of elements in a sequence,\n", "and can only learn it based on the input features.\n", "Removing the residual connections would mean that this information is lost after the first attention layer\n", "(after initialization), and with a randomly initialized query and key vector,\n", "the output vectors for position $i$ has no relation to its original input.\n", "All outputs of the attention are likely to represent similar/same information,\n", "and there is no chance for the model to distinguish which information came from which input element.\n", "An alternative option to residual connection would be to fix at least one head to focus on its original input,\n", "but this is very inefficient and does not have the benefit of the improved gradient flow.\n", "\n", "The Layer Normalization also plays an important role in the Transformer architecture as it enables faster\n", "training and provides small regularization.\n", "Additionally, it ensures that the features are in a similar magnitude among the elements in the sequence.\n", "We are not using Batch Normalization because it depends on the batch size which is often small with Transformers\n", "(they require a lot of GPU memory), and BatchNorm has shown to perform particularly bad in language\n", "as the features of words tend to have a much higher variance (there are many, very rare words\n", "which need to be considered for a good distribution estimate).\n", "\n", "Additionally to the Multi-Head Attention, a small fully connected feed-forward network is added to the model,\n", "which is applied to each position separately and identically.\n", "Specifically, the model uses a Linear$\\to$ReLU$\\to$Linear MLP.\n", "The full transformation including the residual connection can be expressed as:\n", "\n", "$$\n", "\\begin{split}\n", " \\text{FFN}(x) & = \\max(0, xW_1+b_1)W_2 + b_2\\\\\n", " x & = \\text{LayerNorm}(x + \\text{FFN}(x))\n", "\\end{split}\n", "$$\n", "\n", "This MLP adds extra complexity to the model and allows transformations on each sequence element separately.\n", "You can imagine as this allows the model to \"post-process\" the new information added\n", "by the previous Multi-Head Attention, and prepare it for the next attention block.\n", "Usually, the inner dimensionality of the MLP is 2-8$\\times$ larger than $d_{\\text{model}}$,\n", "i.e. the dimensionality of the original input $x$.\n", "The general advantage of a wider layer instead of a narrow, multi-layer MLP is the faster, parallelizable execution.\n", "\n", "Finally, after looking at all parts of the encoder architecture, we can start implementing it below.\n", "We first start by implementing a single encoder block.\n", "Additionally to the layers described above, we will add dropout layers in the MLP and on the output\n", "of the MLP and Multi-Head Attention for regularization."]}, {"cell_type": "code", "execution_count": 7, "id": "23103d12", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:10.224411Z", "iopub.status.busy": "2021-12-04T15:58:10.223932Z", "iopub.status.idle": "2021-12-04T15:58:10.225971Z", "shell.execute_reply": "2021-12-04T15:58:10.225565Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.157144, "end_time": "2021-12-04T15:58:10.226077", "exception": false, "start_time": "2021-12-04T15:58:10.068933", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class EncoderBlock(nn.Module):\n", " def __init__(self, input_dim, num_heads, dim_feedforward, dropout=0.0):\n", " \"\"\"\n", " Args:\n", " input_dim: Dimensionality of the input\n", " num_heads: Number of heads to use in the attention block\n", " dim_feedforward: Dimensionality of the hidden layer in the MLP\n", " dropout: Dropout probability to use in the dropout layers\n", " \"\"\"\n", " super().__init__()\n", "\n", " # Attention layer\n", " self.self_attn = MultiheadAttention(input_dim, input_dim, num_heads)\n", "\n", " # Two-layer MLP\n", " self.linear_net = nn.Sequential(\n", " nn.Linear(input_dim, dim_feedforward),\n", " nn.Dropout(dropout),\n", " nn.ReLU(inplace=True),\n", " nn.Linear(dim_feedforward, input_dim),\n", " )\n", "\n", " # Layers to apply in between the main layers\n", " self.norm1 = nn.LayerNorm(input_dim)\n", " self.norm2 = nn.LayerNorm(input_dim)\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " def forward(self, x, mask=None):\n", " # Attention part\n", " attn_out = self.self_attn(x, mask=mask)\n", " x = x + self.dropout(attn_out)\n", " x = self.norm1(x)\n", "\n", " # MLP part\n", " linear_out = self.linear_net(x)\n", " x = x + self.dropout(linear_out)\n", " x = self.norm2(x)\n", "\n", " return x"]}, {"cell_type": "markdown", "id": "7820dae1", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.143711, "end_time": "2021-12-04T15:58:10.516987", "exception": false, "start_time": "2021-12-04T15:58:10.373276", "status": "completed"}, "tags": []}, "source": ["Based on this block, we can implement a module for the full Transformer encoder.\n", "Additionally to a forward function that iterates through the sequence of encoder blocks,\n", "we also provide a function called `get_attention_maps`.\n", "The idea of this function is to return the attention probabilities for all Multi-Head Attention blocks in the encoder.\n", "This helps us in understanding, and in a sense, explaining the model.\n", "However, the attention probabilities should be interpreted with a grain of salt as it does not necessarily\n", "reflect the true interpretation of the model (there is a series of papers about this,\n", "including [Attention is not Explanation](https://arxiv.org/abs/1902.10186)\n", "and [Attention is not not Explanation](https://arxiv.org/abs/1908.04626))."]}, {"cell_type": "code", "execution_count": 8, "id": "5619cdd3", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:10.816718Z", "iopub.status.busy": "2021-12-04T15:58:10.816231Z", "iopub.status.idle": "2021-12-04T15:58:10.818205Z", "shell.execute_reply": "2021-12-04T15:58:10.817797Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.158399, "end_time": "2021-12-04T15:58:10.818313", "exception": false, "start_time": "2021-12-04T15:58:10.659914", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class TransformerEncoder(nn.Module):\n", " def __init__(self, num_layers, **block_args):\n", " super().__init__()\n", " self.layers = nn.ModuleList([EncoderBlock(**block_args) for _ in range(num_layers)])\n", "\n", " def forward(self, x, mask=None):\n", " for layer in self.layers:\n", " x = layer(x, mask=mask)\n", " return x\n", "\n", " def get_attention_maps(self, x, mask=None):\n", " attention_maps = []\n", " for layer in self.layers:\n", " _, attn_map = layer.self_attn(x, mask=mask, return_attention=True)\n", " attention_maps.append(attn_map)\n", " x = layer(x)\n", " return attention_maps"]}, {"cell_type": "markdown", "id": "8a328d53", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.142344, "end_time": "2021-12-04T15:58:11.102765", "exception": false, "start_time": "2021-12-04T15:58:10.960421", "status": "completed"}, "tags": []}, "source": ["### Positional encoding\n", "\n", "We have discussed before that the Multi-Head Attention block is permutation-equivariant,\n", "and cannot distinguish whether an input comes before another one in the sequence or not.\n", "In tasks like language understanding, however, the position is important for interpreting the input words.\n", "The position information can therefore be added via the input features.\n", "We could learn a embedding for every possible position, but this would not generalize to a dynamical\n", "input sequence length.\n", "Hence, the better option is to use feature patterns that the network can identify from the features\n", "and potentially generalize to larger sequences.\n", "The specific pattern chosen by Vaswani et al.\n", "are sine and cosine functions of different frequencies, as follows:\n", "\n", "$$\n", "PE_{(pos,i)} = \\begin{cases}\n", " \\sin\\left(\\frac{pos}{10000^{i/d_{\\text{model}}}}\\right) & \\text{if}\\hspace{3mm} i \\text{ mod } 2=0\\\\\n", " \\cos\\left(\\frac{pos}{10000^{(i-1)/d_{\\text{model}}}}\\right) & \\text{otherwise}\\\\\n", "\\end{cases}\n", "$$\n", "\n", "$PE_{(pos,i)}$ represents the position encoding at position $pos$ in the sequence, and hidden dimensionality $i$.\n", "These values, concatenated for all hidden dimensions, are added to the original input features\n", "(in the Transformer visualization above, see \"Positional encoding\"), and constitute the position information.\n", "We distinguish between even ($i \\text{ mod } 2=0$) and uneven ($i \\text{ mod } 2=1$)\n", "hidden dimensionalities where we apply a sine/cosine respectively.\n", "The intuition behind this encoding is that you can represent $PE_{(pos+k,:)}$ as a linear function\n", "of $PE_{(pos,:)}$, which might allow the model to easily attend to relative positions.\n", "The wavelengths in different dimensions range from $2\\pi$ to $10000\\cdot 2\\pi$.\n", "\n", "The positional encoding is implemented below.\n", "The code is taken from the [PyTorch tutorial](https://pytorch.org/tutorials/beginner/transformer_tutorial.html#define-the-model)\n", "about Transformers on NLP and adjusted for our purposes."]}, {"cell_type": "code", "execution_count": 9, "id": "9a4179be", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:11.394604Z", "iopub.status.busy": "2021-12-04T15:58:11.394124Z", "iopub.status.idle": "2021-12-04T15:58:11.395735Z", "shell.execute_reply": "2021-12-04T15:58:11.396116Z"}, "papermill": {"duration": 0.151358, "end_time": "2021-12-04T15:58:11.396243", "exception": false, "start_time": "2021-12-04T15:58:11.244885", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class PositionalEncoding(nn.Module):\n", " def __init__(self, d_model, max_len=5000):\n", " \"\"\"\n", " Args\n", " d_model: Hidden dimensionality of the input.\n", " max_len: Maximum length of a sequence to expect.\n", " \"\"\"\n", " super().__init__()\n", "\n", " # Create matrix of [SeqLen, HiddenDim] representing the positional encoding for max_len inputs\n", " pe = torch.zeros(max_len, d_model)\n", " position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n", " div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n", " pe[:, 0::2] = torch.sin(position * div_term)\n", " pe[:, 1::2] = torch.cos(position * div_term)\n", " pe = pe.unsqueeze(0)\n", "\n", " # register_buffer => Tensor which is not a parameter, but should be part of the modules state.\n", " # Used for tensors that need to be on the same device as the module.\n", " # persistent=False tells PyTorch to not add the buffer to the state dict (e.g. when we save the model)\n", " self.register_buffer(\"pe\", pe, persistent=False)\n", "\n", " def forward(self, x):\n", " x = x + self.pe[:, : x.size(1)]\n", " return x"]}, {"cell_type": "markdown", "id": "ab85f000", "metadata": {"papermill": {"duration": 0.143264, "end_time": "2021-12-04T15:58:11.687431", "exception": false, "start_time": "2021-12-04T15:58:11.544167", "status": "completed"}, "tags": []}, "source": ["To understand the positional encoding, we can visualize it below.\n", "We will generate an image of the positional encoding over hidden dimensionality and position in a sequence.\n", "Each pixel, therefore, represents the change of the input feature we perform to encode the specific position.\n", "Let's do it below."]}, {"cell_type": "code", "execution_count": 10, "id": "f3c3e710", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:11.979041Z", "iopub.status.busy": "2021-12-04T15:58:11.978542Z", "iopub.status.idle": "2021-12-04T15:58:12.357315Z", "shell.execute_reply": "2021-12-04T15:58:12.357708Z"}, "papermill": {"duration": 0.527144, "end_time": "2021-12-04T15:58:12.357871", "exception": false, "start_time": "2021-12-04T15:58:11.830727", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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gMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDkgPj4Kc3RyZWFtCnicTVFJigMwDLvnFfpAIV6TvKdDmUPn/9fKDoU5BAmvkpOWmFgLDzGEHyw9+JEhczf9G36i2btZepLJ2f+Y5yJTUfhSqC5iQl2IG8+hEfA9oWsSWbG98Tkso5lzvgcfhbgEM6EBY31JMrmo5pUhE04MdRwOWqTCuGtiw+Ja0TyN3G77RmZlJoQNj2RC3BiAiCDrArIYLJQ2NhMyWc4D7Q3JDVpg16kbUYuCK5TWCXSiVsSqzOCz5tZ2N0Mt8uCoffH6aFaXYIXRS/VYeF+FPpipmXbukkJ64U07IsweCqQyOy0rtXvE6m6B+j/LUvD9yff4Ha8PzfxcnAplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggOTQgPj4Kc3RyZWFtCnicRY3BEcAgCAT/VEEJCgraTyaTh/b/jRAyfGDnDu6EBQu2eUYfBZUmXhVYB0pj3FCPQL3hci3J3AUPcCd/2tBUnJbTd2mRSVUp3KQSef8OZyaQqHnRY533C2P7IzwKZW5kc3RyZWFtCmVuZG9iagoyOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDM0MSA+PgpzdHJlYW0KeJxFUktuRDEI279TcIFI4ZeQ87Squpjef1ubTNXN4AlgbHjLU6ZkyrC5JSMk15RPfSJDrKb8NHIkIqb4SQkFdpWPx2tLrI3skagUn9rx47H0RqbZFVr17tGlzaJRzcrIOcgQoZ4VurJ71A7Z8HpcSLrvlM0hHMv/UIEsZd1yCiVBW9B37BHfDx2ugiuCYbBrLoPtZTLU//qHFlzvffdixy6AFqznvsEOAKinE7QFyBna7jYpaABVuotJwqPyem52omyjVen5HAAzDjBywIglWx2+0d4Aln1d6EWNiv0rQFFZQPzI1XbB3jHJSHAW5gaOvXA8xZlwSzjGAkCKveIYevAl2OYvV66ImvAJdbpkL7zCntrm50KTCHetAA5eZMOtq6Oolu3pPIL2Z0VyRozUizg6IZJa0jmC4tKgHlrjXDex4m0jsblX3+4f4ZwvXPbrF0vshMQKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE2NCA+PgpzdHJlYW0KeJxFkMdxBTEMQ++qAiUwgAr1rMfzD+v+r4b000F6GEIMYk/CsFxXcWF0w4+3LTMNf0cZ7sb6MmO81VggJ+gDDJGJq9Gk+nbFGar05NVirqOiXC86IhLMkuOrQCN8OrLHk7a2M/10Xh/sIe8T/yoq525hAS6q7kD5Uh/x1I/ZUeqaoY8qK2seatpXhF0RSts+LqcyTt29A1rhvZWrPdrvPx52OvIKZW5kc3RyZWFtCmVuZG9iagozMCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDcyID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXEC+qYm5Qi4XSAzEygGzDIC0JZyCiGeAmCBtEMUgFkSxmYkZRB2cAZHL4EoDACXbFskKZW5kc3RyZWFtCmVuZG9iagozMSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDQ3ID4+CnN0cmVhbQp4nDMyt1AwULA0ARKGFiYK5mYGCimGXJYQVi4XTCwHzALRlnAKIp7BlQYAuWcNJwplbmRzdHJlYW0KZW5kb2JqCjMyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjU4ID4+CnN0cmVhbQp4nEWRS3IEIAhE956CI4D85DyTSmUxuf82Dc5kNnaXqP2ESiOmEiznFHkwfcnyzWS26Xc5VjsbBRRFKJjJVeixAqs7U8SZa4lq62Nl5LjTOwbFG85dOalkcaOMdVR1KnBMz5X1Ud35dlmUfUcOZQrYrHMcbODKbcMYJ0abre4O94kgTydTR8XtINnwByeNfZWrK3CdbPbRSzAOBP1CE5jki0DrDIHGzVP05BLs4+N254Fgb3kRSNkQyJEhGB2Cdp1c/+LW+b3/cYY7z7UZrhzv4neY1nbHX2KSFXMBi9wpqOdrLlrXGTrekzPH5Kb7hs65YJe7g0zv+T/Wz/r+Ax4pZvoKZW5kc3RyZWFtCmVuZG9iagozMyAwIG9iago8PCAvQkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzOQovU3VidHlwZSAvRm9ybSAvVHlwZSAvWE9iamVjdCA+PgpzdHJlYW0KeJzjMjQwUzA2NVXI5TI3NgKzcsAsI3MjIAski2BBZDO40gAV8wp8CmVuZHN0cmVhbQplbmRvYmoKMzQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjMgPj4Kc3RyZWFtCnicRZA7EgMhDEN7TqEj+CMDPs9mMik2929j2GxSwNNYIIO7E4LU2oKJ6IKHtiXdBe+tBGdj/Ok2bjUS5AR1gFak42iUUn25xWmVdPFoNnMrC60THWYOepSjGaAQOhXe7aLkcqbuzvlDcPVf9b9i3TmbiYHJyh0IzepT3Pk2O6K6usn+pMfcrNd+K+xVYWlZS8sJt527ZkAJ3FM52qs9Px8KOvYKZW5kc3RyZWFtCmVuZG9iagozNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMyMiA+PgpzdHJlYW0KeJw1UbttxTAM7DUFFzAgfiXN4yBIkbd/mzvaqUjTvB9VXjKlXC51ySpZYfKlQ3WKpnyeZqb8DvWQ45ge2SG6U9aWexgWlol5Sh2xmiz3cAs2vgCaEnML8fcI8CuAUcBEoG7x9w+6WRJAGhT8FOiaq5ZYYgINi4Wt2RXiVt0pWLir+HYkuQcJcjFZ6FMORYopt8B8GSzZkVqc63JZCv9ufQIaYYU47LOLROB5wANMJP5kgGzPPlvs6upFNnaGOOnQgIuAm80kAUFTOKs+uGH7arvm55koJzg51q+iMb4NTuZLUt5XucfPoEHe+DM8Z3eOUA6aUAj03QIgh93ARoQ+tc/ALgO2Sbt3Y0r5nGQpvgQ2CvaoUx3K8GLszFZv2PzH6MpmUWyQlfXR6Q7K3KATYh5vZKFbsrb7Nw+zff8BXxl7ZAplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjE4ID4+CnN0cmVhbQp4nD1QuY0EMQzLXYUaWMB67alnFotLpv/0SPn2ItEWRVIqNZmSKS91lCVZU946fJbEDnmG5W5kNiUqRS+TsCX30ArxfYnmFPfd1ZazQzSXaDl+CzMqqhsd00s2mnAqE7qg3MMz+g1tdANWhx6xWyDQpGDXtiByxw8YDMGZE4siDEpNBv+uco+fXosbPsPxQxSRkg7mNf9Y/fJzDa9TjyeRbm++4l6cqQ4DERySmrwjXVixLhIRaTVBTc/AWi2Au7de/hu0I7oMQPaJxHGaUo6hv2twpc8v5SdT2AplbmRzdHJlYW0KZW5kb2JqCjM3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggODMgPj4Kc3RyZWFtCnicRYy7DcAwCER7pmAEfib2PlGUwt6/DRAlbrgn3T1cHQmZKW4zw0MGngwshl1xgfSWMAtcR1COneyjYdW+6gSN9aZS8+8PlJ7srOKG6wECQhpmCmVuZHN0cmVhbQplbmRvYmoKMzggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA1MSA+PgpzdHJlYW0KeJwzNrRQMFAwNDAHkkaGQJaRiUKKIRdIAMTM5YIJ5oBZBkAaojgHriaHK4MrDQDhtA2YCmVuZHN0cmVhbQplbmRvYmoKMzkgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDMgPj4Kc3RyZWFtCnicTVG7rQMxDOs9hRY4wPrZvnkueHjFZf82pJwEqURDFEnJw1O6ZMphfUpGSI4uD20aS2y6PDdCU4eKgqlrieqUq5mmzFMsTdDz3lmu5hjge1U31N/0iF4CkVGCVWGBDpA7uGD42WsmbFELIjGGUDOAacIKc7gSMQQZjLVnGJQqDE7VzypX+y+nZdgqsHgwnSI/sppop1+6HHjrKQdC2NyVu3ohTQjujQZjzCxcd6mynQAcTHSZiYxYvA3H0yEMDV6aBqxw1o2YILEbI6UPXgcZ07B3RR51txjxvlvGlLvVz31RfeZd7R8IwRsn+HsByhtdXgplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYwID4+CnN0cmVhbQp4nEWQORIDMQgEc72CJ0hcgvesy7XB+v+pB9ZHoukCNBy6Fk3KehRoPumxRqG60GvoLEqSRMEWkh1Qp2OIOyhITEhjkki2HoMjmlizXZiZVCqzUuG0acXCv9la1chEjXCN/InpBlT8T+pclPBNg6+SMfoYVLw7g4xJ+F5F3Fox7f5EMLEZ9glvRSYFhImxqdm+z2CGzPcK1zjH8w1MgjfrCmVuZHN0cmVhbQplbmRvYmoKNDEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMzQgPj4Kc3RyZWFtCnicLVJLcsUgDNtzCl2gM/gH5DzpdLp4vf+2kpNFRg5g9DHlholKfFkgt6PWxLeNzECF4a+rzIXPSNvIOojLkIu4ki2Fe0Qs5DHEPMSC76vxHh75rMzJswfGL9l3Dyv21IRlIePFGdphFcdhFeRYsHUhqnt4U6TDqSTY44v/PsVzLQQtfEbQgF/kn6+O4PmSFmn3mG3TrnqwTDuqpLAcbE9zXiZfWme5Oh7PB8n2rtgRUrsCFIW5M85z4SjTVka0FnY2SGpcbG+O/VhK0IVuXEaKI5CfqSI8oKTJzCYK4o+cHnIqA2Hqmq50chtVcaeezDWbi7czSWbrvkixmcJ5XTiz/gxTZrV5J89yotSpCO+xZ0vQ0Dmunr2WWWh0mxO8pITPxk5PTr5XM+shORUJqWJaV8FpFJliCdsSX1NRU5p6Gf778u7xO37+ASxzfHMKZW5kc3RyZWFtCmVuZG9iago0MiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDcwID4+CnN0cmVhbQp4nDMzNlMwULAwAhKmpoYK5kaWCimGXEA+iJXLBRPLAbPMLMyBLCMLkJYcLkMLYzBtYmykYGZiBmRZIDEgujK40gCYmhMDCmVuZHN0cmVhbQplbmRvYmoKNDMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMjAgPj4Kc3RyZWFtCnicNVJLbgUxCNvPKbhApfBPzvOqqou++29rE70VTDBg4ykvWdJLvtQl26XD5Fsf9yWxQt6P7ZrMUsX3FrMUzy2vR88Rty0KBFETPViZLxUi1M/06DqocEqfgVcItxQbvINJAINq+AcepTMgUOdAxrtiMlIDgiTYc2lxCIlyJol/pLye3yetpKH0PVmZy9+TS6XQHU1O6AHFysVJoF1J+aCZmEpEkpfrfbFC9IbAkjw+RzHJgOw2iW2iBSbnHqUlzMQUOrDHArxmmtVV6GDCHocpjFcLs6gebPJbE5WkHa3jGdkw3sswU2Kh4bAF1OZiZYLu5eM1r8KI7VGTXcNw7pbNdwjRaP4bFsrgYxWSgEensRINaTjAiMCeXjjFXvMTOQ7AiGOdmiwMY2gmp3qOicDQnrOlYcbHHlr18w9U6XyHCmVuZHN0cmVhbQplbmRvYmoKNDQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxOCA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKNDUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxMzMgPj4Kc3RyZWFtCnicRY9LDgQhCET3nKKOwMcf53Ey6YVz/+2AnW4TYz2FVIG5gqE9LmsDnRUfIRm28beplo5FWT5UelJWD8ngh6zGyyHcoCzwgkkqhiFQi5gakS1lbreA2zYNsrKVU6WOsIujMI/2tGwVHl+iWyJ1kj+DxCov3OO6Hcil1rveoou+f6QBMQkKZW5kc3RyZWFtCmVuZG9iago0NiAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDM0MCA+PgpzdHJlYW0KeJw1UjluBDEM6/0KfSCAbtvv2SBIkfy/DanZFANxdFKUO1pUdsuHhVS17HT5tJXaEjfkd2WFxAnJqxLtUoZIqLxWIdXvmTKvtzVnBMhSpcLkpORxyYI/w6WnC8f5trGv5cgdjx5YFSOhRMAyxcToGpbO7rBmW36WacCPeIScK9Ytx1gFUhvdOO2K96F5LbIGiL2ZlooKHVaJFn5B8aBHjX32GFRYINHtHElwjIlQkYB2gdpIDDl7LHZRH/QzKDET6NobRdxBgSWSmDnFunT03/jQsaD+2Iw3vzoq6VtaWWPSPhvtlMYsMul6WPR089bHgws076L859UMEjRljZLGB63aOYaimVFWeLdDkw3NMcch8w6ewxkJSvo8FL+PJRMdlMjfDg2hf18eo4ycNt4C5qI/bRUHDuKzw165gRVKF2uS9wGpTOiB6f+v8bW+19cfHe2AxgplbmRzdHJlYW0KZW5kb2JqCjQ3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjUxID4+CnN0cmVhbQp4nC1RSXIDQQi7zyv0hGan32OXK4fk/9cIygcGDYtAdFrioIyfICxXvOWRq2jD3zMxgt8Fh34r121Y5EBUIEljUDWhdvF69B7YcZgJzJPWsAxmrA/8jCnc6MXhMRlnt9dl1BDsXa89mUHJrFzEJRMXTNVhI2cOP5kyLrRzPTcg50ZYl2GQblYaMxKONIVIIYWqm6TOBEESjK5GjTZyFPulL490hlWNqDHscy1tX89NOGvQ7Fis8uSUHl1xLicXL6wc9PU2AxdRaazyQEjA/W4P9XOyk994S+fOFtPje83J8sJUYMWb125ANtXi37yI4/uMr+fn+fwDX2BbiAplbmRzdHJlYW0KZW5kb2JqCjQ4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTc0ID4+CnN0cmVhbQp4nE2QSQ5DIQxD95zCF6iEM8DnPL+qumjvv61DB3WB/OQgcDw80HEkLnRk6IyOK5sc48CzIGPi0Tj/ybg+xDFB3aItWJd2x9nMEnPCMjECtkbJ2TyiwA/HXAgSZJcfvsAgIl2P+VbzWZP0z7c73Y+6tGZfPaLAiewIxbABV4D9useBS8L5XtPklyolYxOH8oHqIlI2O6EQtVTscqqKs92bK3AV9PzRQ+7tBbUjPN8KZW5kc3RyZWFtCmVuZG9iago0OSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDc1ID4+CnN0cmVhbQp4nDO1NFIwUDA2ABKmZkYKpibmCimGXEA+iJXLZWhkCmblcBlZmilYWAAZJmbmUCGYhhwuY1NzoAFARcamYBqqP4crgysNAJWQEu8KZW5kc3RyZWFtCmVuZG9iago1MCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxNSA+PgpzdHJlYW0KeJw1UTkOAyEM7PcV/kAkjC94T6Iozf6/zYzRVh7BXIa0lCGZ8lKTqCHlUz56mS6cutzXzGo055a0LXOAuLa8L62SwIlmiIPBaZi4AZo8AUPX0ahRQxce0NSlUyiw3AQ+irduD91jtYGXtiHniSBiKBksQc2pRRMWbc8npDW/Xosb3pft3chTpcaWGIEGAVY4HNfo1/CVPU8m0XQVMtSrNcsYCRNFIjz5jqbVE+taNNIyEtTGEaxqA7w7/TBOAAATccsCZJ9KlLPkxG+x9LMGV/r+AZ9HVJYKZW5kc3RyZWFtCmVuZG9iagoxNyAwIG9iago8PCAvQmFzZUZvbnQgL0RlamFWdVNhbnMgL0NoYXJQcm9jcyAxOCAwIFIKL0VuY29kaW5nIDw8Ci9EaWZmZXJlbmNlcyBbIDMyIC9zcGFjZSA0NiAvcGVyaW9kIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgL2ZpdmUgL3NpeCAvc2V2ZW4KL2VpZ2h0IC9uaW5lIDcyIC9IIDgwIC9QIDk3IC9hIDk5IC9jIC9kIC9lIDEwMyAvZyAvaCAvaSAxMDggL2wgL20gL24gL28gMTEzCi9xIC9yIC9zIC90IC91IC92IF0KL1R5cGUgL0VuY29kaW5nID4+Ci9GaXJzdENoYXIgMCAvRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250RGVzY3JpcHRvciAxNiAwIFIKL0ZvbnRNYXRyaXggWyAwLjAwMSAwIDAgMC4wMDEgMCAwIF0gL0xhc3RDaGFyIDI1NSAvTmFtZSAvRGVqYVZ1U2FucwovU3VidHlwZSAvVHlwZTMgL1R5cGUgL0ZvbnQgL1dpZHRocyAxNSAwIFIgPj4KZW5kb2JqCjE2IDAgb2JqCjw8IC9Bc2NlbnQgOTI5IC9DYXBIZWlnaHQgMCAvRGVzY2VudCAtMjM2IC9GbGFncyAzMgovRm9udEJCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9Gb250TmFtZSAvRGVqYVZ1U2FucyAvSXRhbGljQW5nbGUgMAovTWF4V2lkdGggMTM0MiAvU3RlbVYgMCAvVHlwZSAvRm9udERlc2NyaXB0b3IgL1hIZWlnaHQgMCA+PgplbmRvYmoKMTUgMCBvYmoKWyA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMAo2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDMxOCA0MDEgNDYwIDgzOCA2MzYKOTUwIDc4MCAyNzUgMzkwIDM5MCA1MDAgODM4IDMxOCAzNjEgMzE4IDMzNyA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2CjYzNiA2MzYgMzM3IDMzNyA4MzggODM4IDgzOCA1MzEgMTAwMCA2ODQgNjg2IDY5OCA3NzAgNjMyIDU3NSA3NzUgNzUyIDI5NQoyOTUgNjU2IDU1NyA4NjMgNzQ4IDc4NyA2MDMgNzg3IDY5NSA2MzUgNjExIDczMiA2ODQgOTg5IDY4NSA2MTEgNjg1IDM5MCAzMzcKMzkwIDgzOCA1MDAgNTAwIDYxMyA2MzUgNTUwIDYzNSA2MTUgMzUyIDYzNSA2MzQgMjc4IDI3OCA1NzkgMjc4IDk3NCA2MzQgNjEyCjYzNSA2MzUgNDExIDUyMSAzOTIgNjM0IDU5MiA4MTggNTkyIDU5MiA1MjUgNjM2IDMzNyA2MzYgODM4IDYwMCA2MzYgNjAwIDMxOAozNTIgNTE4IDEwMDAgNTAwIDUwMCA1MDAgMTM0MiA2MzUgNDAwIDEwNzAgNjAwIDY4NSA2MDAgNjAwIDMxOCAzMTggNTE4IDUxOAo1OTAgNTAwIDEwMDAgNTAwIDEwMDAgNTIxIDQwMCAxMDIzIDYwMCA1MjUgNjExIDMxOCA0MDEgNjM2IDYzNiA2MzYgNjM2IDMzNwo1MDAgNTAwIDEwMDAgNDcxIDYxMiA4MzggMzYxIDEwMDAgNTAwIDUwMCA4MzggNDAxIDQwMSA1MDAgNjM2IDYzNiAzMTggNTAwCjQwMSA0NzEgNjEyIDk2OSA5NjkgOTY5IDUzMSA2ODQgNjg0IDY4NCA2ODQgNjg0IDY4NCA5NzQgNjk4IDYzMiA2MzIgNjMyIDYzMgoyOTUgMjk1IDI5NSAyOTUgNzc1IDc0OCA3ODcgNzg3IDc4NyA3ODcgNzg3IDgzOCA3ODcgNzMyIDczMiA3MzIgNzMyIDYxMSA2MDUKNjMwIDYxMyA2MTMgNjEzIDYxMyA2MTMgNjEzIDk4MiA1NTAgNjE1IDYxNSA2MTUgNjE1IDI3OCAyNzggMjc4IDI3OCA2MTIgNjM0CjYxMiA2MTIgNjEyIDYxMiA2MTIgODM4IDYxMiA2MzQgNjM0IDYzNCA2MzQgNTkyIDYzNSA1OTIgXQplbmRvYmoKMTggMCBvYmoKPDwgL0ggMTkgMCBSIC9QIDIwIDAgUiAvYSAyMSAwIFIgL2MgMjIgMCBSIC9kIDIzIDAgUiAvZSAyNCAwIFIKL2VpZ2h0IDI1IDAgUiAvZml2ZSAyNiAwIFIgL2ZvdXIgMjcgMCBSIC9nIDI4IDAgUiAvaCAyOSAwIFIgL2kgMzAgMCBSCi9sIDMxIDAgUiAvbSAzMiAwIFIgL24gMzQgMCBSIC9uaW5lIDM1IDAgUiAvbyAzNiAwIFIgL29uZSAzNyAwIFIKL3BlcmlvZCAzOCAwIFIgL3EgMzkgMCBSIC9yIDQwIDAgUiAvcyA0MSAwIFIgL3NldmVuIDQyIDAgUiAvc2l4IDQzIDAgUgovc3BhY2UgNDQgMCBSIC90IDQ1IDAgUiAvdGhyZWUgNDYgMCBSIC90d28gNDcgMCBSIC91IDQ4IDAgUiAvdiA0OSAwIFIKL3plcm8gNTAgMCBSID4+CmVuZG9iagozIDAgb2JqCjw8IC9GMSAxNyAwIFIgPj4KZW5kb2JqCjQgMCBvYmoKPDwgL0ExIDw8IC9DQSAwIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4KL0EyIDw8IC9DQSAxIC9UeXBlIC9FeHRHU3RhdGUgL2NhIDEgPj4gPj4KZW5kb2JqCjUgMCBvYmoKPDwgPj4KZW5kb2JqCjYgMCBvYmoKPDwgPj4KZW5kb2JqCjcgMCBvYmoKPDwgL0YxLURlamFWdVNhbnMtbWludXMgMzMgMCBSIC9JMSAxMyAwIFIgL0kyIDE0IDAgUiA+PgplbmRvYmoKMTMgMCBvYmoKPDwgL0JpdHNQZXJDb21wb25lbnQgOAovQ29sb3JTcGFjZSBbL0luZGV4ZWQgL0RldmljZVJHQiAyNTUgKP7+/v7+/f39/f78+/77+fv7+/769/r6+v749fn5+fj4+P738v718Pf39/X19f707v7z7P7x6vT09PPz8/Ly8vHx8f7w5/7u5f7t4+/v7+7u7u3t7f3r4f3q3/3p3P3n2v3m2P3k1uzs7Orq6unp6ejo6Ofn5+bm5uTk5P3j1P3i0f3gz+Pj4+Li4uHh4eDg4P3fzf3dy/3cyd7e3v3bx/zYxPzWwfvUvvvSvPvQufrOtt3d3dvb29ra2tjY2NfX19XV1dTU1NLS0tHR0c/Pz87OzvrMtPrKsfnHrvnFq/nDqfjBpszMzPi/o/i9ofe7nve5m/e2mPa0lvayk/WwkMvLy8nJycjIyMbGxsXFxcPDw8LCwsDAwL+/v729vby8vLq6urm5ube3t7W1tbOzs7Gxsa+vr/WujvWsi/SqiPSohvSmg/OjgPKgfvGefO+beu6YeO2WduyTdOuQcuqNcOiLbueIbK2traurq6mpqaenp6WlpaOjo6GhoZ+fn52dnZubm5mZmZeXl5WVlZOTk5GRkY+Pj42NjYuLi4mJiYeHh+aFauWDaOSAZeJ9Y+F7YeB4X991Xd5yW91wWdttV9pqVdloU9hlUddiT9ZgTYSEhIKCgoCAgH19fXt7e3l5eXd3d3R0dHJycnBwcG1tbWtra2lpaWdnZ2RkZGJiYmBgYNRdS9NaStFXSdBUR85RRs1PRMxMQ8pJQslGQcdDP8ZAPsU+PMM7O8I4OsA1OL8yN15eXltbW74wNrwtNLsqM1lZWVdXV1RUVFJSUlBQUE5OTkxMTElJSUhISEVFRUREREFBQUBAQD09PTw8PDk5OTg4ODU1NTQ0NDExMTAwMC4uLiwsLCoqKlwoXChcKLknMrgkMbYhL7UfLrQcLbIZK7AXKq0WKqoVXCmnFFwppBNcKKESXCieESebECeZECeWDyaTDiaQXHIljQwligskh1xuJIQJI4EII34HInsGIngFIXUEIXIDIG8CIGwBHyYmJiQkJCIiIiAgIGkAH2cAHx4eHhwcHBoaGildCi9EZWNvZGVQYXJtcyA8PCAvQ29sb3JzIDEgL0NvbHVtbnMgMzI3IC9QcmVkaWN0b3IgMTAgPj4KL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0hlaWdodCAxNjQgL0xlbmd0aCA1MSAwIFIgL1N1YnR5cGUgL0ltYWdlCi9UeXBlIC9YT2JqZWN0IC9XaWR0aCAzMjcgPj4Kc3RyZWFtCnic7ZwLeFVVfsWxNDBASSM4VhJTaMhAQJJGoFQEDWEARVDGVkZGSAciL4tggiJSKhCeJr4q+EYMiTjQ8QFSWqRgxWl4DAxDAUFRgXZ0kI48Ooi8o/Nb+Z/ck5MXES9fbu+31/f5qeFmn73X/7DW+u+zz21w2WWXbQXvv//+D8DevXu//PLL/qCoqOhrkJ2dPX/+/PPgqaeeuhvws8WLF98CTpw4wcf3tmvXjl9+nzEaNmz4W3D48OHW4L333jt9+nRvsHLlynPg3nvvnT179jcgLy/vPsDPVqxY0RecOXNmHejYsePRo0c/BTExMb8Cu3btSkpK+hgwq5tAcXGxZjV8+PCnn35as3ryySdHAQZ9+eWXbwPM6j9ASkrK7t27fwkaNWp0EHzxxReJiYn/CU6dOtULrFq1ign8PZg7d65mNX369NzcXA365ptv/hgwq5+D9PT0Y8eO/QY0adLk12Dnzp3x8fGfgOPHj18PlixZ0sDx6HiMJB4ZYBaYMGGC1vrWW2/16dOH9Z9ev379X4AjR4589tlnDQDLEl/JyckfffTRl8Z2kbEN2dkLFiwwsp8aOXKk1lpYWDhgwACWdeLdd9+F7Hasa8uWLQ2N7cPGtpEttv8FGNlzgJGdl5OTw6DLwcCBA1nWmbVr13YMsQ3ZMdu2bdsVYptlHYfsV0CI7KeN7CdHjx5tZL9sZJ8Ikb3byG4UIjvRyD6VkZHxr8DYnmtsQ/b0iRMnGtli+yyAbMej49HxGIU84jOLQGlp6Q4wYsQIJvLfgE9Krzt06DBt2rQPAAPPBFxmzZo1UmNWcivIysraDpg4pKLETV544YWvABVAtnuCkpIS6TicpaWlTQLQoEI888wzLVu2RKSX8MsQsm3IkCG33367XIG5awmXX3450xfTe/bs+Qdw7bXXUsWPuPr+/fv/GkCPqstiCgoKvgc8Uzt06NDYsWNhfyC109WXLVuWkJCgOkP2/4HHH3+c4lKYtfzpJpCZmUkVfwdOnjwplnxqcJYRlanpEKDG8eh4jCgeGUShqkuXLvcDVoh0PAdiY2M9nSExDQWDBg1CKT9jkNWrV18FCIPeCv8RXHPNNYRJ8XvgwAGFKrIml5d8LFy48I8BWZEJ/i9Abn4I0CWurojWtm1badjvy6D/4gf/DPhjPqTP8iv6TQZgmBWMxqAaG21Ej0dyPS6rqzMJpnINM2Jimh/T/DPApPlT5VsWchdgWSxOa2SpWjELZ/liATLESYAabosjfOJ5wOe9HOtT43h0PDoeo5BHxHQy2LhxI3paynVZli5z9dVX01jP//zzz7k8/eUxoqXo6dGjB9orbecy/wUwAnXmjPX2228rwaHyakYffPBBZF8tOXPR2MQ/QunfAezjCWBVkeZjKerpU1NTcQvY38nY+hf/16lTJzXzfEIc83kVE4+Ii4v7KcC8zhq4inJss2bNuPKDTIB5aDrMSn03U5w3b54mzNia/ksvvQRD/QDWpAUyNg4nJ2LtogcLUiGYOuxgaKPhiuw4kYoxtpzynXfe+VuAgTkeHY8RxWNgxp1sxlqaN2OWy6o1YzjQdBmVGYseWPJnfM5mDJuDbMbz/BnDOzPuQhECMz7oz5ja1XnG6mLRJcZGAPcsXryY9i2jW7duBD5pGGNLSMmmbdq00e7X0qVL1eShZps3b1YGJeLSBU/Yt28fanvCdv/uAWRZmj5t1zG2YiHSKWkms7722mu0jF8wtv5FQE5PT6d/7kP0/RDwecej4zGieGTGg0F+fj6X2cxi+KGuy4LERt++fQkZdGu3M5ZkjzUxnLSKiWg/5eabb24KkFQUR1vr6OA3NhMI16p79er1p4A+bfny5UoedI3nTXXp8RRzrrvuuisA1YIXdYgUTGPs3r173bp1dF/TkGU4v3r8+PHaGKLrgwONAXNQ/M6jjz7auXPnJEAIUqdJFamKsgnJSBFqypQpKSkp2sKiWyUErab/O2eAGm6Bt6kbrR4fSuGztJHLuL2+NjDaq2DUqFEEsrYQy/2jy1rdzjMXx6Pj0fEYhTx6tG3atOkRMHDgwIYNG/4IIKb/A3za9u/fj3g/gxM1btz4J4DObAvwaSspKaEJm52ZmfknYMyYMUi/vAop92j7+OOP/wl0794dUW9JkiwsLAzShptpy7lnz55q5saNG4c1kWE/92nTbt5jjz3WtWtXTCcBv1Nrt337dmjTGNCmTnPq1KmkUaymTU5Ozr+BAG1rAL1ex44dtVH/0EMPYUZLfdp27NjxM4DfJQM8ZdKkSf8ODh8+rAGsu9W+3n333ffnIC0tzfHoeIwoHtFHtKcH83kR0GahBWeMGw0JPXClUfPy8oYA9IEFaiOcxUirIPsosIVqI5x5Ip37IBHFUStJ0FQ2Rf0QXG1AE/9Uu2/KoAAGldKwOXPmZGVltQfIKbp78wMPPLBixQoquJ8lfGWyyoVOQifREopXIlRqG8m4xFZFUfRY2kYKZFytgEsom8I0UosN5I8YMYKFpyH7yDIs57zxxhs6agDTXsXh6pg9+hdZRGhSsdQ5Pj5eiZQ6o8A6Y0CA9S7heHQ8Oh6jkEf+0aYWbTCJcSPxjQ5UXWWLFi30RAy3WLBggXSZ3Ce74RpcSS0vIU6a/9xzz2EH4whV+M+NAHkmSb5EeES5vZLoElyUhCpXIA8qs7Zq1SoxMVGmRoSUF3z66ad09ufsEup8WRoJk+T6Goqux4OYoFr17OzsgoICPGk9VT9todeK/gEBVlWfOXMmViibuPLKKwcAEiZF1+M+K7pXdZ1HoOg6aoDx9OvXrxkgQmp7b+7cubIsihwqepkn8wN+rIMBVF15k19xPDoeI4pH9FHb2fR/el5E/CsuLv4F4MPal/KSnVEhKSNbkTUlQcgpKW0qDaGWFhsbi26iONfR/z0OUB06J7WSCKa3T27acw6GjtjBob17974FyJT3gptuuok0RjkaI6dSosmTJyOpasnIpjptAFFnQ1PSf/ED/XzDhg18SAmYMKjfZACG0WEdBlWduQRSqwObXFZX506wGZ33tuZglMmq3UPatQIWQtLtzrJY3F8CKqMVU0Lipo4OQIY4gRrHo+PR8RiFPHo5Ea3WOUKyIilLpwRnzJihpwkZGRmkJolv586dtX1GpHv++ee18USC0w4+3uPJtpcfSV7qiD/55BPCl1ry3Nxc5U26WWxCG0/YmB7tEfWKioqIrO+R9nQQ0WvVbUpSdIwKa1K0pJl/GAwbNkwnr2jOSYzdwB133IGDPPr666/T/2pDwCtaaEbn7fH+UWwSy1LknD9/vp4r9O/fn+77jwB5VQeq8EesQz6IReocuN1GZ0O3ke4jPXDEj+FRUXTKlCnK1N4+hePR8RgxPKKP5LhW/O3XCxgwhwAoGhUWFkoE0SUSmZ4ookQKg9YGeWvVZViuHgpyXQRDb28wERSkgO4JwdVlyJSS4KZNm9Lu6VgyMU0P/+AX1YKC18mOUi5oIJRq1cZEORXWDUrDqJEOKBI0aeS0iY2UkW8XUF3UTGcJyLEkxitjYmISEhLU8RJUdXaRIqCeOohN9kNON9hrJZI3W9XXoVVpWfo5f6xNPWijl9VvMoD25rkFUGDVMykpSWpOTR2PjseI4tFPNeqvECkEQFsZqJb6Kxo8ZIww8RDSpJezaINQJ53vb926tWIObZ3khpyAbmoDiF4R6Txw8OBBkoRaSTTT6/X83KOjBkgZqqXdHLRN8efZZ59F65SAbrvttr8CxBYk9fsgNTVVD97p1tCkKcQzJFyb2EiZNuUJIN4xPW9b3Bc16iztJRwhtWollyxZoq2tvLw8FFFHmXr16qVt8ebNm2MDehJ2/fXX/w3IycnRQzi60lWrVr0LaCql/IQm6uxtP3lwPDoeHY9RyCM+0wIkJyer84EojGE4gDki5AzaskWLFunJOvKsU75MBu3VOyJMzlPpUnvk7s9fYm0nHvUJPqjP82v8ssZAtjUiVKDcem8LauQF9F1ES3WZ2JKeKDZo0ABr0tuzXbt21fuuFE1vqE6cOPGJJ55QNqX7ZJGrKATGo10xKiMnYq1UUNaEQ/lV9F1LJ6RgQ/ve1FP00BTu27dPW4HYnk5Pc1/oNOSkSZPGjx8vY7zllluUWTt06ICT6cU0qNfZAy8/Oh4djxHDo7/y0tDKd9vKt4VWrm1hFq63m1g59E6xles4ANRr5RTCFt4gLi7OX7layXHjxuktAFaOAOpFKRaubRVWTmL8ra1cqZGVs7qTtnJ/1d6zd22Q8wl9kJUjv78JrHz58uV664BEOim0cgVKFi7Ns5XHAlt4WmZm5p133qned/LkyTp7QGjGDdQX0odqy4s8rNOQGAb5WHvtyK8Sc+CW8WTY8eh4dDxGIY/4jI4vEtH0cI2YdsMNN+h5IR0kiXEYZHEZvbCFHajvJnnReutqpDc91OeCetJI5IR2bSvRi6sQ9h6oWnI7VV5a5fJ++fiEsh8lJAWqW2cMbXOzCgqpbpg4q2KSaIsB9czPz9cL79RUZ75HjRqFNyoM0k7DUCaNNUlXJkB19RYuJGJZepqPTVDjroREfEN1vueee5SAKTUhWC+7UA51/SRanQPAw+j99bUe2Ji+hARPovB60ohJ/d7szPHoeIwoHquEPtakxcOBJCGwJvWLrAnmRCA8ak2kQG2T2Zr0GIhuS2Fw6NChrEmtZM+ePXW+p23btldccYXWRO10QvKqq65CuDqD7t2765QO6XDs2LFI23jW9BggvSGpWhNxTmtas2aNGjR/TcikNuVZE7KpZpNMKCG1XvR8dTC1PcknWL9ogA09+IIbGNKBTVRbJ5RgTzKO9q5cuZKg+gaBUpt0CD0KrG8gIsqqgbz11lsdj47HiOKRNWlptIbaFmet7du3Vyxg8fr2KpQSNpRvsrKyRM/IkSNpGSWYRAV9YQWaqUc+CxcuRDdF7KuvvqqdGNRs/fr1agOhXu9CbNmyhVroAZKJ6E5qZDr6AcIoffS11HvXoIqeBqseKrwqz++fsuLvMZHdtWuX2lFuAQW4rVu3MgcpOTPSszXuhGXLlmlPC61fDAg93A96SWP27NnqiR9++GHlO+6L7OxsHfhBfolKd/bt2xcFxkduoJPW+R6MxfHoeHQ8RiGP1c2u1Lcbm92HluwUC2122212en0L79FRl5KSEvxHzwvpqsQjfWRRUZGOFBA5dfIGJyKD6oEbgU/vzt999910ZUqpQ4YM0ZN4POnGG29Ul4kt6RFjQkIC1tQcxMTEaKOqWbNmlwNSYJs2bfQ9FARBbcrTgQ4YMEBHKekG9fIqXpWbm/sAwK60q40/YFkvAAxDm+80p6tXr9bJTvpKfSkQ5nXgwAGdJcBq9D7uoUOHfmdBERfSBpvZ2HHsyZzsK29PDjgeHY8RxSP6qO+tozXUtjjtYXx8vM7gtGvXTn/1UUpaLB1XNrH8IculZZRgEhAlGvRjequdzgzdVI+GrEw37URn1ErCoR4soT+QKiVCRLUfjjShUGr3YF+SRSEohzSMwiisUiZPUqmb13PqGJIpq3TQxPVDam366vWhZ32N9VCL1FYRXf9W8rX3tH9HmQTrpjIV1o3leHQ8Oh6jkMfah/va5uMNFzQeL6nZcF5a01q9wAYBJMZfmg2JG3MiPYmHNrFn8U2MQqz4hWYLcQVQrwpQCKxJVaE4qhGl0tczUzfKpypSTNWU0pLqVGfKrWRH8bkFdCdgV7ovuD2wLN0r3DK6c7iBGjVqpJOdcXFxerk2MTGxdevWetklPT1dXX+3bt10rrlfv379+/fXCc5BgwapnR4zZgxOpuMKEyZMwM1y77//fsej4zGieGQ4ne1t3LixtsVjY2Nbtmz5fZNJvSfODJiIXp0k0qlfJNwxR001IyNDO8/MXScnTTd13JHuSS9M0keSDtVK0lXpXQj6K0KjOq2pU6eqsSRJ0n3pHMCsWbNmm5hCoLpM5JRGc+GiRYvgln6zkChabLKqtxdMWXXe2cR1HcUxff2F9aEbTGPVjlJInQAyqVWBTW0ltxR+pyEouqa7ZcJr2mvyW1F/DX4L63h0PDoeo5DHOieqkOmU+uP43nParuZd+gPPgwIpy2a8w9xIK2FB5ke/YpFbzJRY+CbzJbEBKVCjaGnuJHsSe+ZQYtRMqhiaIVvNPNSrAhSCcjxifqUaUSoKpu0wyqcqUkxKqj6fAqvOlJui62AAt4DuBG4I3RfcHtwkfc3HdOdwA5mT9eCm0r3FLeZ4dDxGFI+ePhKoGptGNm/eXKdgCFr6qiyUslWrVlLKpKQk5bDk5GRaRgkm2ayTiaZOTvq6SQ+prxIkyPXu3VuRrk+fPprF4MGDmZI2lJmc11gOGzaMSWcRBH9qYspi1GWyLOVEX1LJjlNNVvVaF1SQKWeYus4ydZ0zZ44i57x58x4xkSWGvmg6q9fqfaklqBaZ3L7yyitLTHG1Lb60DMtMeJVv165d62uv17dqf58gvMFgKiwZdjw6HiOKxwvoY81iWUEwq+hmBe0M7JacqqKi5TpaJqRei+npaWVJ9XpOX1m97nOrwWtCNxt8jQUsvMSXWlNbyS1E/dwUV+wZjz8z4RW/0OxpL9QXmv7q6CJVMQV+kVLlmww7Hh2Pjsco5BGf0WFksxp5TZMmTZqa3TQ3y8Fx9A6GmY5cJ95g3pNk3pNM99jOYBYkD0pNTU3zbCjkRHpN1MyozI3K/KgXoay3mVIfgzWaA31rMneSPd1lfecwg5mUXGq4wbwq27xqpPnVaPMroBfTzLXKbMv6UzOuaQHzEmbOnOl5mNlYmY+FrCzfUGB25nh0PEYUj99GH+smmtVqZ3UKGhDRoJbWJKkBZa2grr7A+hpbWWbLpdZX24Dg/joguh587fX6Vl+CNxk2GhyPjkfHYxTy6PuM12Z7nXYTQ9Om5aYTa2hRBs94vA68iv94/bjfkntO5JlRe0NKiudHMqRUz5Qq2pJ8ybMmc6cyewo4lJlUmUtZM1/Fq8yuBptl/ThgWz8x3GUYWgbfv8oMzOv8K9rYCH8bwHczx6PjMaJ4/M76eBHSWSctrUlSaxHXqgJbWWarVduA4FbR3aD2BvXXl2HHo+Mxonj09LGyRJbLpK+UIbEsbxkra2a5bvrSGVBPD0ERDelomZAaAnL6g0C/2b6KsnqoqK8VRLaLj4DUlsttSHCriG658Bp6B/Q3IMEeHI+OR8djFPJ4KX3m26DOnlRnh/o2lnUB86rdxgTHo+Mxonj09bGCRtaglFUEs4JoBnSzgnbWoKA1CWkVPfUlNSirvrLWJLBVdNaX2kpyWy645aisu6mpVfQ31L56Mux4dDw6HqOQx/r2l7DhIo0qTB7meHQ8BlHPPAb1sRqVDCpltYJZrW4GpLNaBa1GR4NaWq2kVlHWagW2ssZWldpqBLd64Q1ob7US7H3Ph+PR8eh4jDIe69se/l/gwp7keKwLHI/hQR14rE4fa5LJWgTzQtpZg4LWoqW1SGpNylqbyF5AbmsQ3VrkN6jCjkfHY0TxWN/SEyVwPIYHjsfwwPEYHtTmM3V2nbr5T13NqG6+VDeXqqtl1d3AanAyx6PjMaJ4rG9hiRI4HsMDx2N44HgMD+ruMxdnPN/NjS7Snr6TZV2chzkeHY8RxWN9C0uUwPEYHjgew4PvoI+XRDoviapeEsENwvHoeHQ8RiGP9S3QUQLHY3jgeAwPLo0+1q+Q1ofcOh4dj47HKOSxvgU6SuB4DA8cj+FBPevjxaMelbU6OB7DA8djeOB4DA+cz4QHjsfwwPEYHvwB7ZHo5wplbmRzdHJlYW0KZW5kb2JqCjUxIDAgb2JqCjU4MjkKZW5kb2JqCjE0IDAgb2JqCjw8IC9CaXRzUGVyQ29tcG9uZW50IDgKL0NvbG9yU3BhY2UgWy9JbmRleGVkIC9EZXZpY2VSR0IgMTYyICj//v7+/v78/Pz/+/r7+/v/+fX4+Pj/9/P+9O/+8+z39/f19fX+8Oj+7+by8vLx8fH+7OH+6d3v7+/t7e3r6+vq6ur+6Nv+5db949T94dDn5+fl5eX938793Mn92cT818L807zk5OTh4eHg4ODd3d3c3NzZ2dnW1tbU1NTR0dH70Ln7zLT6yrH5xqz5wqfQ0NDNzc34v6T4u573uZz3tZb2s5TLy8vIyMjFxcXExMTBwcG/v7+8vLy7u7u3t7ezs7P2r471qon1qIbzpIHyoX/wnHvvmXnsk3SxsbGtra2rq6unp6elpaWhoaGdnZ2bm5uXl5eVlZXrkXLpi27mhmrlg2jjfmTie2Lfdl7ec1xckZGRj4+Pi4uL3G5X2mhT2GVR1mBN1V1M0lhJ0FVIzk9Fy0lCyUdBh4eHhYWFgICAfn5+eXl5d3d3c3NzcHBwbGxsZ2dnZWVlYGBgXl5eWlpaV1dXU1NTTk5OTExMSEhIRkZGxkE+xT49wjg6wTY5vjA2uyo0ulwoMrciMLYfLrMZLLEYK6sWKqUUXCmiE1wonBEnmRAnkw4mkFxyJooLJYQJJIEII0JCQkBAQDw8PDg4ODY2NjIyMjAwMCwsLCoqKiYmJnwHInkGInMEIXADIGoBHyIiIiAgIBwcHBoaGildCi9EZWNvZGVQYXJtcyA8PCAvQ29sb3JzIDEgL0NvbHVtbnMgOCAvUHJlZGljdG9yIDEwID4+IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlCi9IZWlnaHQgMTYzIC9MZW5ndGggNTIgMCBSIC9TdWJ0eXBlIC9JbWFnZSAvVHlwZSAvWE9iamVjdCAvV2lkdGggOCA+PgpzdHJlYW0KeJw9wedaCAAAQNFLoRJSKbQLTQ2hoa0SRYOirIxUKEpICUVoKYpoKD1pf+7nHLbEP7EpNsQf8Vssi1/ip1gSi+KH+C4WxIh4I4bFa/FKvBRD4oUYFAPiuXgm+kWfeCqeiMeiV/SIbnFL3BQdolk0iUZxRVwWDaJeXBIXRZ0oF2WiVJSIc6JYFIlCUSDOilyRI9JFmkgVJ8RxcUykiGSRJGLEUXFIRIoIES4OiP1irwgRu0Wg2Cl2iP8CxC6xRwSJYBEq9okwcVBEiWhxWBwRsSJOxIsEkSgyRKbIEidFtsgTp0S+OC3OiApRKarEeVEtakStuCBaxFVxTbSKNnFd3BDt4ra4I+6KTnFP3BcPxEPRJR6JUfFWjIl34r34IMbFhPgoPolJ8Vl8EV/FlJgWM2JWzIlvYl6siFWxJtbFX20DycmcqAplbmRzdHJlYW0KZW5kb2JqCjUyIDAgb2JqCjMyNQplbmRvYmoKMiAwIG9iago8PCAvQ291bnQgMSAvS2lkcyBbIDExIDAgUiBdIC9UeXBlIC9QYWdlcyA+PgplbmRvYmoKNTMgMCBvYmoKPDwgL0NyZWF0aW9uRGF0ZSAoRDoyMDIxMTIwNDE2NTgxMiswMicwMCcpCi9DcmVhdG9yIChNYXRwbG90bGliIHYzLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZykKL1Byb2R1Y2VyIChNYXRwbG90bGliIHBkZiBiYWNrZW5kIHYzLjQuMykgPj4KZW5kb2JqCnhyZWYKMCA1NAowMDAwMDAwMDAwIDY1NTM1IGYgCjAwMDAwMDAwMTYgMDAwMDAgbiAKMDAwMDAyMDM0MCAwMDAwMCBuIAowMDAwMDEyMTQwIDAwMDAwIG4gCjAwMDAwMTIxNzIgMDAwMDAgbiAKMDAwMDAxMjI3MSAwMDAwMCBuIAowMDAwMDEyMjkyIDAwMDAwIG4gCjAwMDAwMTIzMTMgMDAwMDAgbiAKMDAwMDAwMDA2NSAwMDAwMCBuIAowMDAwMDAwNDAwIDAwMDAwIG4gCjAwMDAwMDE1NTQgMDAwMDAgbiAKMDAwMDAwMDIwOCAwMDAwMCBuIAowMDAwMDAxNTMzIDAwMDAwIG4gCjAwMDAwMTIzODQgMDAwMDAgbiAKMDAwMDAxOTI1OCAwMDAwMCBuIAowMDAwMDEwNzE2IDAwMDAwIG4gCjAwMDAwMTA1MTYgMDAwMDAgbiAKMDAwMDAxMDA1OCAwMDAwMCBuIAowMDAwMDExNzY5IDAwMDAwIG4gCjAwMDAwMDE1NzQgMDAwMDAgbiAKMDAwMDAwMTcyNSAwMDAwMCBuIAowMDAwMDAxOTY4IDAwMDAwIG4gCjAwMDAwMDIzNDggMDAwMDAgbiAKMDAwMDAwMjY1MyAwMDAwMCBuIAowMDAwMDAyOTU3IDAwMDAwIG4gCjAwMDAwMDMyNzkgMDAwMDAgbiAKMDAwMDAwMzc0NyAwMDAwMCBuIAowMDAwMDA0MDY5IDAwMDAwIG4gCjAwMDAwMDQyMzUgMDAwMDAgbiAKMDAwMDAwNDY0OSAwMDAwMCBuIAowMDAwMDA0ODg2IDAwMDAwIG4gCjAwMDAwMDUwMzAgMDAwMDAgbiAKMDAwMDAwNTE0OSAwMDAwMCBuIAowMDAwMDA1NDgwIDAwMDAwIG4gCjAwMDAwMDU2NTIgMDAwMDAgbiAKMDAwMDAwNTg4OCAwMDAwMCBuIAowMDAwMDA2MjgzIDAwMDAwIG4gCjAwMDAwMDY1NzQgMDAwMDAgbiAKMDAwMDAwNjcyOSAwMDAwMCBuIAowMDAwMDA2ODUyIDAwMDAwIG4gCjAwMDAwMDcxNjggMDAwMDAgbiAKMDAwMDAwNzQwMSAwMDAwMCBuIAowMDAwMDA3ODA4IDAwMDAwIG4gCjAwMDAwMDc5NTAgMDAwMDAgbiAKMDAwMDAwODM0MyAwMDAwMCBuIAowMDAwMDA4NDMzIDAwMDAwIG4gCjAwMDAwMDg2MzkgMDAwMDAgbiAKMDAwMDAwOTA1MiAwMDAwMCBuIAowMDAwMDA5Mzc2IDAwMDAwIG4gCjAwMDAwMDk2MjMgMDAwMDAgbiAKMDAwMDAwOTc3MCAwMDAwMCBuIAowMDAwMDE5MjM3IDAwMDAwIG4gCjAwMDAwMjAzMjAgMDAwMDAgbiAKMDAwMDAyMDQwMCAwMDAwMCBuIAp0cmFpbGVyCjw8IC9JbmZvIDUzIDAgUiAvUm9vdCAxIDAgUiAvU2l6ZSA1NCA+PgpzdGFydHhyZWYKMjA1NTcKJSVFT0YK\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["encod_block = PositionalEncoding(d_model=48, max_len=96)\n", "pe = encod_block.pe.squeeze().T.cpu().numpy()\n", "\n", "fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 3))\n", "pos = ax.imshow(pe, cmap=\"RdGy\", extent=(1, pe.shape[1] + 1, pe.shape[0] + 1, 1))\n", "fig.colorbar(pos, ax=ax)\n", "ax.set_xlabel(\"Position in sequence\")\n", "ax.set_ylabel(\"Hidden dimension\")\n", "ax.set_title(\"Positional encoding over hidden dimensions\")\n", "ax.set_xticks([1] + [i * 10 for i in range(1, 1 + pe.shape[1] // 10)])\n", "ax.set_yticks([1] + [i * 10 for i in range(1, 1 + pe.shape[0] // 10)])\n", "plt.show()"]}, {"cell_type": "markdown", "id": "d5cc9a34", "metadata": {"papermill": {"duration": 0.154874, "end_time": "2021-12-04T15:58:12.659676", "exception": false, "start_time": "2021-12-04T15:58:12.504802", "status": "completed"}, "tags": []}, "source": ["You can clearly see the sine and cosine waves with different wavelengths that encode the position\n", "in the hidden dimensions.\n", "Specifically, we can look at the sine/cosine wave for each hidden dimension separately,\n", "to get a better intuition of the pattern.\n", "Below we visualize the positional encoding for the hidden dimensions $1$, $2$, $3$ and $4$."]}, {"cell_type": "code", "execution_count": 11, "id": "a476a12e", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:12.994247Z", "iopub.status.busy": "2021-12-04T15:58:12.979090Z", "iopub.status.idle": "2021-12-04T15:58:14.181132Z", "shell.execute_reply": "2021-12-04T15:58:14.181526Z"}, "papermill": {"duration": 1.37322, "end_time": "2021-12-04T15:58:14.181688", "exception": false, "start_time": "2021-12-04T15:58:12.808468", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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ipSJwOUe3OQglT1SCIISBDUCoFIDLibrNMSg5sRIDkSnbAINa8XcFYbc1CiIDWKAg84LXozBS+F1B222OQsl3ligIoV8DFGpF3xXk3eYolOxuiYIQ9jVAoVLwXcHgbQ5CSWWXIAghXwMQasXeFTze5iiUxH2JghDuNUChVuhdweZtjYKQKciAgBAvNEChVuRdzemNhHjtOb3Nbyki+iK4PRF+WU3tjaR3X4Pf2xHFilU5orie5huJy67n+nbEouL8ZPBuNeU3QmM17zeS2m3D/jVE/pkDTIMIKwCEASNgyt/j54eH4WssnTeX5XbVEMlYHTqlddDO1XK7BLck6ANzJgI6AQ2iFTtS27nMLigGqRbbOZiIusP4l3uwzxwajoO0TdwME3/3pvMu1Uo7g/MATiKFkdCOk1V88sGOhHY6KP4mx7HQDm8SvCPjap1d4Bcjh6grmZ0H8BkHRqhVdtgjI1EY9khxTvmgSGOmJhnenzVkFRFY3uguvXFeeVfG+Hl19G9+shdIGP5SUkxUDZvMdO3h0RvHO7qfrk6GGR4XPOngZrSO2eGyZsGhm9G6URwfJ5PmNG4IcyyQidhJpg2l4atG+Vsy9QvReKntzDlz0WACmDxdG7MQ/rlGx2fUvsHSxJx1mJ4zKmPZeasxlzl/b9b4YEUn3gTCnNHHSsLge5zojub0nDcAoBhmIJ6xxWOBZzgPsuNXCe9kCPYMXVTp7l6lok7owPnU1UikJxnB8+2vEN21IQTrK1D3f0nK0CEKZW5kc3RyZWFtCmVuZG9iagoxMiAwIG9iagozODY4CmVuZG9iagoxMCAwIG9iagpbIF0KZW5kb2JqCjIxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggODEgPj4Kc3RyZWFtCnicTc27DcAgDATQnik8AuD/PlGUItm/jQ0RobGfdCedYIcKbnFYDLQ7HK341FOYfegeEpJQc91EWDMl2oSkX/rLMMOYWMi2rzdXrnK+FtwciwplbmRzdHJlYW0KZW5kb2JqCjIyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTcwID4+CnN0cmVhbQp4nD2QSxLDIAxD95xCRwD/gPO00+mC3H9by5l0gxRjyy9EV3TslYfHxpSN92hjT4QtXOV0Gk5TGY+Lu2ZdoMthMtNvvJq5wFRhkdXsovoYvKHzrGaHr1UzMYQ3mRIaYCp3cg/19ac47duSkGxXYdCdGqSzMMyR/D0QU3PQc4iR/CNfcmth0JnmFxctqxmtZUzR7GGqbC0M6o1Bd8r11Hqu8zAR7/MD30E+ZAplbmRzdHJlYW0KZW5kb2JqCjIzIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzA3ID4+CnN0cmVhbQp4nD2SS24DMQxD9z6FLhDA+tme86Qoupjef9snJemKHNkWRWqWukxZUx6QNJOEf+nwcLGd8jtsz2Zm4Fqil4nllOfQFWLuonzZzEZdWSfF6oRmOrfoUTkXBzZNqp+rLKXdLngO1yaeW/YRP7zQoB7UNS4JN3RXo2UpNGOq+3/Se/yMMuBqTF1sUqt7HzxeRFXo6AdHiSJjlxfn40EJ6UrCaFqIlXdFA0Hu8rTKewnu295qyLIHqZjOOylmsOt0Ui5uF4chHsjyqPDlo9hrQs/4sCsl9EjYhjNyJ+5oxubUyOKQ/t6NBEuPrmgh8+CvbtYuYLxTOkViZE5yrGmLVU73UBTTucO9DBD1bEVDKXOR1epfw84La5ZsFnhK+gUeo90mSw5W2duoTu+tPNnQ9x9a13QfCmVuZHN0cmVhbQplbmRvYmoKMjQgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMzIgPj4Kc3RyZWFtCnicNVFJbsQwDLv7FfzAANbuvCfFoIf2/9dSyhQIQCW2uCViYyMCLzH4OYjc+JI1oyZ+Z3JX/CxPhUfCreBJFIGX4V52gssbxmU/DjMfvJdWzqTGkwzIRTY9PBEy2CUQOjC7BnXYZtqJviHhsyNSzUaW09cS9NIqBMpTtt/pghJtq/pz+6wLbfvaE052e+pJ5ROI55aswGXjFZPFWAY9UblLMX2Q6myhJ6G8KJ+DbD5qiESXKGfgicHBKNAO7LntZ+JVIWhd3adtY6hGSsfTvw1NTZII+UQJZ7Y07hb+f8+9vtf7D04hVBEKZW5kc3RyZWFtCmVuZG9iagoyNSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIzMSA+PgpzdHJlYW0KeJw1TzmSBCEMy3mFPjBVGNtAv6entjbY+X+6kplOkPAhydMTHZl4mSMjsGbH21pkIGbgU0zFv/a0DxOq9+AeIpSLC2GGkXDWrONuno4X/3aVz1gH7zb4illeENjCTNZXFmcu2wVjaZzEOclujF0TsY11radTWEcwoQyEdLbDlCBzVKT0yY4y5ug4kSeei+/22yx2OX4O6ws2jSEV5/gqeoI2g6Lsee8CGnJB/13d+B5Fu+glIBsJFtZRYu6c5YRfvXZ0HrUoEnNCmkEuEyHN6SqmEJpQrLOjoFJRcKk+p+isn3/lX1wtCmVuZHN0cmVhbQplbmRvYmoKMjYgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNDkgPj4Kc3RyZWFtCnicPVA7jkQhDOs5hS/wJPIjcB5Gqy1m79+uA5opUEx+tjMk0BGBRwwxlK/jJa2groG/i0LxbuLrg8Igq0NSIM56D4h07KY2kRM6HZwzP2E3Y47ARTEGnOl0pj0HJjn7wgqEcxtl7FZIJ4mqIo7qM44pnip7n3gWLO3INlsnkj3kIOFSUonJpZ+Uyj9typQKOmbRBCwSueBkE004y7tJUowZlDLqHqZ2In2sPMijOuhkTc6sI5nZ00/bmfgccLdf2mROlcd0Hsz4nLTOgzkVuvfjiTYHTY3a6Oz3E2kqL1K7HVqdfnUSld0Y5xgSl2d/Gd9k//kH/odaIgplbmRzdHJlYW0KZW5kb2JqCjI3IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzk1ID4+CnN0cmVhbQp4nD1SS27FQAjb5xRcoNLwm895UlXdvPtva0NSqSq8iTHGMH3KkLnlS10ScYXJt16uWzymfC5bWpl5iLuLjSU+ttyX7iG2XXQusTgdR/ILMp0qRKjNqtGh+EKWhQeQTvChC8J9Of7jL4DB17ANuOE9MkGwJOYpQsZuURmaEkERYeeRFaikUJ9Zwt9R7uv3MgVqb4ylC2Mc9Am0BUJtSMQC6kAAROyUVK2QjmckE78V3WdiHGDn0bIBrhlURJZ77MeIqc6ojLxExD5PTfoolkwtVsZuUxlf/JSM1Hx0BSqpNPKU8tBVs9ALWIl5EvY5/Ej459ZsIYY6btbyieUfM8UyEs5gSzlgoZfjR+DbWXURrh25uM50gR+V1nBMtOt+yPVP/nTbWs11vHIIokDlTUHwuw6uRrHExDI+nY0peqIssBqavEYzwWEQEdb3w8gDGv1yvBA0p2sitFgim7ViRI2KbHM9vQTWTO/FOdbDE8Js753WobIzMyohgtq6hmrrQHazvvNwtp8/M+iibQplbmRzdHJlYW0KZW5kb2JqCjI4IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjQ5ID4+CnN0cmVhbQp4nE1RSYoDMAy75xX6QCFek7ynQ5lD5//Xyg6FOQQJr5KTlphYCw8xhB8sPfiRIXM3/Rt+otm7WXqSydn/mOciU1H4UqguYkJdiBvPoRHwPaFrElmxvfE5LKOZc74HH4W4BDOhAWN9STK5qOaVIRNODHUcDlqkwrhrYsPiWtE8jdxu+0ZmZSaEDY9kQtwYgIgg6wKyGCyUNjYTMlnOA+0NyQ1aYNepG1GLgiuU1gl0olbEqszgs+bWdjdDLfLgqH3x+mhWl2CF0Uv1WHhfhT6YqZl27pJCeuFNOyLMHgqkMjstK7V7xOpugfo/y1Lw/cn3+B2vD838XJwKZW5kc3RyZWFtCmVuZG9iagoyOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDk0ID4+CnN0cmVhbQp4nEWNwRHAIAgE/1RBCQoK2k8mk4f2/40QMnxg5w7uhAULtnlGHwWVJl4VWAdKY9xQj0C94XItydwFD3Anf9rQVJyW03dpkUlVKdykEnn/DmcmkKh50WOd9wtj+yM8CmVuZHN0cmVhbQplbmRvYmoKMzAgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzNDEgPj4Kc3RyZWFtCnicRVJLbkQxCNu/U3CBSOGXkPO0qrqY3n9bm0zVzeAJYGx4y1OmZMqwuSUjJNeUT30iQ6ym/DRyJCKm+EkJBXaVj8drS6yN7JGoFJ/a8eOx9Eam2RVa9e7Rpc2iUc3KyDnIEKGeFbqye9QO2fB6XEi675TNIRzL/1CBLGXdcgolQVvQd+wR3w8droIrgmGway6D7WUy1P/6hxZc7333YscugBas577BDgCopxO0BcgZ2u42KWgAVbqLScKj8npudqJso1Xp+RwAMw4wcsCIJVsdvtHeAJZ9XehFjYr9K0BRWUD8yNV2wd4xyUhwFuYGjr1wPMWZcEs4xgJAir3iGHrwJdjmL1euiJrwCXW6ZC+8wp7a5udCkwh3rQAOXmTDraujqJbt6TyC9mdFckaM1Is4OiGSWtI5guLSoB5a41w3seJtI7G5V9/uH+GcL1z26xdL7ITECmVuZHN0cmVhbQplbmRvYmoKMzEgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNjQgPj4Kc3RyZWFtCnicRZDHcQUxDEPvqgIlMIAK9azH8w/r/q+G9NNBehhCDGJPwrBcV3FhdMOPty0zDX9HGe7G+jJjvNVYICfoAwyRiavRpPp2xRmq9OTVYq6jolwvOiISzJLjq0AjfDqyx5O2tjP9dF4f7CHvE/8qKuduYQEuqu5A+VIf8dSP2VHqmqGPKitrHmraV4RdEUrbPi6nMk7dvQNa4b2Vqz3a7z8edjryCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA3MiA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlxAvqmJuUIuF0gMxMoBswyAtCWcgohngJggbRDFIBZEsZmJGUQdnAGRy+BKAwAl2xbJCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA0NyA+PgpzdHJlYW0KeJwzMrdQMFCwNAEShhYmCuZmBgophlyWEFYuF0wsB8wC0ZZwCiKewZUGALlnDScKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDI1OCA+PgpzdHJlYW0KeJxFkUtyBCAIRPeegiOA/OQ8k0plMbn/Ng3OZDZ2l6j9hEojphIs5xR5MH3J8s1ktul3OVY7GwUURSiYyVXosQKrO1PEmWuJautjZeS40zsGxRvOXTmpZHGjjHVUdSpwTM+V9VHd+XZZlH1HDmUK2KxzHGzgym3DGCdGm63uDveJIE8nU0fF7SDZ8AcnjX2VqytwnWz20UswDgT9QhOY5ItA6wyBxs1T9OQS7OPjdueBYG95EUjZEMiRIRgdgnadXP/i1vm9/3GGO8+1Ga4c7+J3mNZ2x19ikhVzAYvcKajnay5a1xk63pMzx+Sm+4bOuWCXu4NM7/k/1s/6/gMeKWb6CmVuZHN0cmVhbQplbmRvYmoKMzUgMCBvYmoKPDwgL0JCb3ggWyAtMTAyMSAtNDYzIDE3OTQgMTIzMyBdIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzkKL1N1YnR5cGUgL0Zvcm0gL1R5cGUgL1hPYmplY3QgPj4Kc3RyZWFtCnic4zI0MFMwNjVVyOUyNzYCs3LALCNzIyALJItgQWQzuNIAFfMKfAplbmRzdHJlYW0KZW5kb2JqCjM2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTYzID4+CnN0cmVhbQp4nEWQOxIDIQxDe06hI/gjAz7PZjIpNvdvY9hsUsDTWCCDuxOC1NqCieiCh7Yl3QXvrQRnY/zpNm41EuQEdYBWpONolFJ9ucVplXTxaDZzKwutEx1mDnqUoxmgEDoV3u2i5HKm7s75Q3D1X/W/Yt05m4mBycodCM3qU9z5NjuiurrJ/qTH3KzXfivsVWFpWUvLCbedu2ZACdxTOdqrPT8fCjr2CmVuZHN0cmVhbQplbmRvYmoKMzcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAzMjIgPj4Kc3RyZWFtCnicNVG7bcUwDOw1BRcwIH4lzeMgSJG3f5s72qlI07wfVV4ypVwudckqWWHypUN1iqZ8nmam/A71kOOYHtkhulPWlnsYFpaJeUodsZos93ALNr4AmhJzC/H3CPArgFHARKBu8fcPulkSQBoU/BTomquWWGICDYuFrdkV4lbdKVi4q/h2JLkHCXIxWehTDkWKKbfAfBks2ZFanOtyWQr/bn0CGmGFOOyzi0TgecADTCT+ZIBszz5b7OrqRTZ2hjjp0ICLgJvNJAFBUzirPrhh+2q75ueZKCc4OdavojG+DU7mS1LeV7nHz6BB3vgzPGd3jlAOmlAI9N0CIIfdwEaEPrXPwC4Dtkm7d2NK+ZxkKb4ENgr2qFMdyvBi7MxWb9j8x+jKZlFskJX10ekOytygE2Ieb2ShW7K2+zcPs33/AV8Ze2QKZW5kc3RyZWFtCmVuZG9iagozOCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDIxOCA+PgpzdHJlYW0KeJw9ULmNBDEMy12FGljAeu2pZxaLS6b/9Ej59iLRFkVSKjWZkikvdZQlWVPeOnyWxA55huVuZDYlKkUvk7Al99AK8X2J5hT33dWWs0M0l2g5fgszKqobHdNLNppwKhO6oNzDM/oNbXQDVocesVsg0KRg17YgcscPGAzBmROLIgxKTQb/rnKPn16LGz7D8UMUkZIO5jX/WP3ycw2vU48nkW5vvuJenKkOAxEckpq8I11YsS4SEWk1QU3PwFotgLu3Xv4btCO6DED2icRxmlKOob9rcKXPL+UnU9gKZW5kc3RyZWFtCmVuZG9iagozOSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDgzID4+CnN0cmVhbQp4nEWMuw3AMAhEe6ZgBH4m9j5RlMLevw0QJW64J909XB0JmSluM8NDBp4MLIZdcYH0ljALXEdQjp3so2HVvuoEjfWmUvPvD5Se7KzihusBAkIaZgplbmRzdHJlYW0KZW5kb2JqCjQwIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjQzID4+CnN0cmVhbQp4nE1Ru60DMQzrPYUWOMD62b55Lnh4xWX/NqScBKlEQxRJycNTumTKYX1KRkiOLg9tGktsujw3QlOHioKpa4nqlKuZpsxTLE3Q895ZruYY4HtVN9Tf9IheApFRglVhgQ6QO7hg+NlrJmxRCyIxhlAzgGnCCnO4EjEEGYy1ZxiUKgxO1c8qV/svp2XYKrB4MJ0iP7KaaKdfuhx46ykHQtjclbt6IU0I7o0GY8wsXHepsp0AHEx0mYmMWLwNx9MhDA1emgascNaNmCCxGyOlD14HGdOwd0UedbcY8b5bxpS71c99UX3mXe0fCMEbJ/h7AcobXV4KZW5kc3RyZWFtCmVuZG9iago0MSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMzNCA+PgpzdHJlYW0KeJwtUktyxSAM23MKXaAz+AfkPOl0uni9/7aSk0VGDmD0MeWGiUp8WSC3o9bEt43MQIXhr6vMhc9I28g6iMuQi7iSLYV7RCzkMcQ8xILvq/EeHvmszMmzB8Yv2XcPK/bUhGUh48UZ2mEVx2EV5FiwdSGqe3hTpMOpJNjji/8+xXMtBC18RtCAX+Sfr47g+ZIWafeYbdOuerBMO6qksBxsT3NeJl9aZ7k6Hs8Hyfau2BFSuwIUhbkzznPhKNNWRrQWdjZIalxsb479WErQhW5cRoojkJ+pIjygpMnMJgrij5wecioDYeqarnRyG1Vxp57MNZuLtzNJZuu+SLGZwnldOLP+DFNmtXknz3Ki1KkI77FnS9DQOa6evZZZaHSbE7ykhM/GTk9Ovlcz6yE5FQmpYlpXwWkUmWIJ2xJfU1FTmnoZ/vvy7vE7fv4BLHN8cwplbmRzdHJlYW0KZW5kb2JqCjQyIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggNzAgPj4Kc3RyZWFtCnicMzM2UzBQsDACEqamhgrmRpYKKYZcQD6IlcsFE8sBs8wszIEsIwuQlhwuQwtjMG1ibKRgZmIGZFkgMSC6MrjSAJiaEwMKZW5kc3RyZWFtCmVuZG9iago0MyAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDMyMCA+PgpzdHJlYW0KeJw1UktuBTEI288puECl8E/O86qqi777b2sTvRVMMGDjKS9Z0ku+1CXbpcPkWx/3JbFC3o/tmsxSxfcWsxTPLa9HzxG3LQoEURM9WJkvFSLUz/ToOqhwSp+BVwi3FBu8g0kAg2r4Bx6lMyBQ50DGu2IyUgOCJNhzaXEIiXImiX+kvJ7fJ62kofQ9WZnL35NLpdAdTU7oAcXKxUmgXUn5oJmYSkSSl+t9sUL0hsCSPD5HMcmA7DaJbaIFJucepSXMxBQ6sMcCvGaa1VXoYMIehymMVwuzqB5s8lsTlaQdreMZ2TDeyzBTYqHhsAXU5mJlgu7l4zWvwojtUZNdw3Duls13CNFo/hsWyuBjFZKAR6exEg1pOMCIwJ5eOMVe8xM5DsCIY52aLAxjaCaneo6JwNCes6VhxsceWvXzD1TpfIcKZW5kc3RyZWFtCmVuZG9iago0NCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE4ID4+CnN0cmVhbQp4nDM2tFAwgMMUQ640AB3mA1IKZW5kc3RyZWFtCmVuZG9iago0NSAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDEzMyA+PgpzdHJlYW0KeJxFj0sOBCEIRPecoo7Axx/ncTLphXP/7YCdbhNjPYVUgbmCoT0uawOdFR8hGbbxt6mWjkVZPlR6UlYPyeCHrMbLIdygLPCCSSqGIVCLmBqRLWVut4DbNg2yspVTpY6wi6Mwj/a0bBUeX6JbInWSP4PEKi/c47odyKXWu96ii75/pAExCQplbmRzdHJlYW0KZW5kb2JqCjQ2IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzQwID4+CnN0cmVhbQp4nDVSOW4EMQzr/Qp9IIBu2+/ZIEiR/L8NqdkUA3F0UpQ7WlR2y4eFVLXsdPm0ldoSN+R3ZYXECcmrEu1ShkiovFYh1e+ZMq+3NWcEyFKlwuSk5HHJgj/DpacLx/m2sa/lyB2PHlgVI6FEwDLFxOgals7usGZbfpZpwI94hJwr1i3HWAVSG9047Yr3oXktsgaIvZmWigodVokWfkHxoEeNffYYVFgg0e0cSXCMiVCRgHaB2kgMOXssdlEf9DMoMRPo2htF3EGBJZKYOcW6dPTf+NCxoP7YjDe/OirpW1pZY9I+G+2Uxiwy6XpY9HTz1seDCzTvovzn1QwSNGWNksYHrdo5hqKZUVZ4t0OTDc0xxyHzDp7DGQlK+jwUv48lEx2UyN8ODaF/Xx6jjJw23gLmoj9tFQcO4rPDXrmBFUoXa5L3AalM6IHp/6/xtb7X1x8d7YDGCmVuZHN0cmVhbQplbmRvYmoKNDcgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyNTEgPj4Kc3RyZWFtCnicLVFJcgNBCLvPK/SEZqffY5crh+T/1wjKBwYNi0B0WuKgjJ8gLFe85ZGraMPfMzGC3wWHfivXbVjkQFQgSWNQNaF28Xr0HthxmAnMk9awDGasD/yMKdzoxeExGWe312XUEOxdrz2ZQcmsXMQlExdM1WEjZw4/mTIutHM9NyDnRliXYZBuVhozEo40hUghhaqbpM4EQRKMrkaNNnIU+6Uvj3SGVY2oMexzLW1fz004a9DsWKzy5JQeXXEuJxcvrBz09TYDF1FprPJASMD9bg/1c7KT33hL584W0+N7zcnywlRgxZvXbkA21eLfvIjj+4yv5+f5/ANfYFuICmVuZHN0cmVhbQplbmRvYmoKNDggMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxNzQgPj4Kc3RyZWFtCnicTZBJDkMhDEP3nMIXqIQzwOc8v6q6aO+/rUMHdYH85CBwPDzQcSQudGTojI4rmxzjwLMgY+LROP/JuD7EMUHdoi1Yl3bH2cwSc8IyMQK2RsnZPKLAD8dcCBJklx++wCAiXY/5VvNZk/TPtzvdj7q0Zl89osCJ7AjFsAFXgP26x4FLwvle0+SXKiVjE4fygeoiUjY7oRC1VOxyqoqz3ZsrcBX0/NFD7u0FtSM83wplbmRzdHJlYW0KZW5kb2JqCjQ5IDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMjE1ID4+CnN0cmVhbQp4nDVROQ4DIQzs9xX+QCSML3hPoijN/r/NjNFWHsFchrSUIZnyUpOoIeVTPnqZLpy63NfMajTnlrQtc4C4trwvrZLAiWaIg8FpmLgBmjwBQ9fRqFFDFx7Q1KVTKLDcBD6Kt24P3WO1gZe2IeeJIGIoGSxBzalFExZtzyekNb9eixvel+3dyFOlxpYYgQYBVjgc1+jX8JU9TybRdBUy1Ks1yxgJE0UiPPmOptUT61o00jIS1MYRrGoDvDv9ME4AABNxywJkn0qUs+TEb7H0swZX+v4Bn0dUlgplbmRzdHJlYW0KZW5kb2JqCjE5IDAgb2JqCjw8IC9CYXNlRm9udCAvRGVqYVZ1U2FucyAvQ2hhclByb2NzIDIwIDAgUgovRW5jb2RpbmcgPDwKL0RpZmZlcmVuY2VzIFsgMzIgL3NwYWNlIDQ4IC96ZXJvIC9vbmUgL3R3byAvdGhyZWUgL2ZvdXIgL2ZpdmUgL3NpeCAvc2V2ZW4gL2VpZ2h0IC9uaW5lCjY5IC9FIDgwIC9QIDk3IC9hIDk5IC9jIC9kIC9lIDEwMyAvZyAvaCAvaSAxMDggL2wgL20gL24gL28gMTEzIC9xIDExNSAvcyAvdAovdSBdCi9UeXBlIC9FbmNvZGluZyA+PgovRmlyc3RDaGFyIDAgL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udERlc2NyaXB0b3IgMTggMCBSCi9Gb250TWF0cml4IFsgMC4wMDEgMCAwIDAuMDAxIDAgMCBdIC9MYXN0Q2hhciAyNTUgL05hbWUgL0RlamFWdVNhbnMKL1N1YnR5cGUgL1R5cGUzIC9UeXBlIC9Gb250IC9XaWR0aHMgMTcgMCBSID4+CmVuZG9iagoxOCAwIG9iago8PCAvQXNjZW50IDkyOSAvQ2FwSGVpZ2h0IDAgL0Rlc2NlbnQgLTIzNiAvRmxhZ3MgMzIKL0ZvbnRCQm94IFsgLTEwMjEgLTQ2MyAxNzk0IDEyMzMgXSAvRm9udE5hbWUgL0RlamFWdVNhbnMgL0l0YWxpY0FuZ2xlIDAKL01heFdpZHRoIDEzNDIgL1N0ZW1WIDAgL1R5cGUgL0ZvbnREZXNjcmlwdG9yIC9YSGVpZ2h0IDAgPj4KZW5kb2JqCjE3IDAgb2JqClsgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAKNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCAzMTggNDAxIDQ2MCA4MzggNjM2Cjk1MCA3ODAgMjc1IDM5MCAzOTAgNTAwIDgzOCAzMTggMzYxIDMxOCAzMzcgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNgo2MzYgNjM2IDMzNyAzMzcgODM4IDgzOCA4MzggNTMxIDEwMDAgNjg0IDY4NiA2OTggNzcwIDYzMiA1NzUgNzc1IDc1MiAyOTUKMjk1IDY1NiA1NTcgODYzIDc0OCA3ODcgNjAzIDc4NyA2OTUgNjM1IDYxMSA3MzIgNjg0IDk4OSA2ODUgNjExIDY4NSAzOTAgMzM3CjM5MCA4MzggNTAwIDUwMCA2MTMgNjM1IDU1MCA2MzUgNjE1IDM1MiA2MzUgNjM0IDI3OCAyNzggNTc5IDI3OCA5NzQgNjM0IDYxMgo2MzUgNjM1IDQxMSA1MjEgMzkyIDYzNCA1OTIgODE4IDU5MiA1OTIgNTI1IDYzNiAzMzcgNjM2IDgzOCA2MDAgNjM2IDYwMCAzMTgKMzUyIDUxOCAxMDAwIDUwMCA1MDAgNTAwIDEzNDIgNjM1IDQwMCAxMDcwIDYwMCA2ODUgNjAwIDYwMCAzMTggMzE4IDUxOCA1MTgKNTkwIDUwMCAxMDAwIDUwMCAxMDAwIDUyMSA0MDAgMTAyMyA2MDAgNTI1IDYxMSAzMTggNDAxIDYzNiA2MzYgNjM2IDYzNiAzMzcKNTAwIDUwMCAxMDAwIDQ3MSA2MTIgODM4IDM2MSAxMDAwIDUwMCA1MDAgODM4IDQwMSA0MDEgNTAwIDYzNiA2MzYgMzE4IDUwMAo0MDEgNDcxIDYxMiA5NjkgOTY5IDk2OSA1MzEgNjg0IDY4NCA2ODQgNjg0IDY4NCA2ODQgOTc0IDY5OCA2MzIgNjMyIDYzMiA2MzIKMjk1IDI5NSAyOTUgMjk1IDc3NSA3NDggNzg3IDc4NyA3ODcgNzg3IDc4NyA4MzggNzg3IDczMiA3MzIgNzMyIDczMiA2MTEgNjA1CjYzMCA2MTMgNjEzIDYxMyA2MTMgNjEzIDYxMyA5ODIgNTUwIDYxNSA2MTUgNjE1IDYxNSAyNzggMjc4IDI3OCAyNzggNjEyIDYzNAo2MTIgNjEyIDYxMiA2MTIgNjEyIDgzOCA2MTIgNjM0IDYzNCA2MzQgNjM0IDU5MiA2MzUgNTkyIF0KZW5kb2JqCjIwIDAgb2JqCjw8IC9FIDIxIDAgUiAvUCAyMiAwIFIgL2EgMjMgMCBSIC9jIDI0IDAgUiAvZCAyNSAwIFIgL2UgMjYgMCBSCi9laWdodCAyNyAwIFIgL2ZpdmUgMjggMCBSIC9mb3VyIDI5IDAgUiAvZyAzMCAwIFIgL2ggMzEgMCBSIC9pIDMyIDAgUgovbCAzMyAwIFIgL20gMzQgMCBSIC9uIDM2IDAgUiAvbmluZSAzNyAwIFIgL28gMzggMCBSIC9vbmUgMzkgMCBSIC9xIDQwIDAgUgovcyA0MSAwIFIgL3NldmVuIDQyIDAgUiAvc2l4IDQzIDAgUiAvc3BhY2UgNDQgMCBSIC90IDQ1IDAgUiAvdGhyZWUgNDYgMCBSCi90d28gNDcgMCBSIC91IDQ4IDAgUiAvemVybyA0OSAwIFIgPj4KZW5kb2JqCjMgMCBvYmoKPDwgL0YxIDE5IDAgUiA+PgplbmRvYmoKNCAwIG9iago8PCAvQTEgPDwgL0NBIDAgL1R5cGUgL0V4dEdTdGF0ZSAvY2EgMSA+PgovQTIgPDwgL0NBIDEgL1R5cGUgL0V4dEdTdGF0ZSAvY2EgMSA+PiA+PgplbmRvYmoKNSAwIG9iago8PCA+PgplbmRvYmoKNiAwIG9iago8PCA+PgplbmRvYmoKNyAwIG9iago8PCAvRjEtRGVqYVZ1U2Fucy1taW51cyAzNSAwIFIgL00wIDEzIDAgUiAvTTEgMTQgMCBSIC9NMiAxNSAwIFIgL00zIDE2IDAgUgo+PgplbmRvYmoKMTMgMCBvYmoKPDwgL0JCb3ggWyAtOCAtOCA4IDggXSAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDEzMSAvU3VidHlwZSAvRm9ybQovVHlwZSAvWE9iamVjdCA+PgpzdHJlYW0KeJxtkEEOhCAMRfc9RS/wSUtFZevSa7iZTOL9twNxQEzdNNC+PH5R/pLwTqXA+CQJS06z5HrTkNK6TIwY5tWyKMegUS3WznU4qM/QcGN0i7EUptTW6Hijm+k23pM/+rBZIUY/HA6vhHsWQyZcKTEGh98LL9vD/xGeXtTAH6KNfmNaQ/0KZW5kc3RyZWFtCmVuZG9iagoxNCAwIG9iago8PCAvQkJveCBbIC04IC04IDggOCBdIC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMTMxIC9TdWJ0eXBlIC9Gb3JtCi9UeXBlIC9YT2JqZWN0ID4+CnN0cmVhbQp4nG2QQQ6EIAxF9z1FL/BJS0Vl69JruJlM4v23A3FATN000L48flH+kvBOpcD4JAlLTrPketOQ0rpMjBjm1bIox6BRLdbOdTioz9BwY3SLsRSm1NboeKOb6Tbekz/6sFkhRj8cDq+EexZDJlwpMQaH3wsv28P/EZ5e1MAfoo1+Y1pD/QplbmRzdHJlYW0KZW5kb2JqCjE1IDAgb2JqCjw8IC9CQm94IFsgLTggLTggOCA4IF0gL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxMzEgL1N1YnR5cGUgL0Zvcm0KL1R5cGUgL1hPYmplY3QgPj4Kc3RyZWFtCnicbZBBDoQgDEX3PUUv8ElLRWXr0mu4mUzi/bcDcUBM3TTQvjx+Uf6S8E6lwPgkCUtOs+R605DSukyMGObVsijHoFEt1s51OKjP0HBjdIuxFKbU1uh4o5vpNt6TP/qwWSFGPxwOr4R7FkMmXCkxBoffCy/bw/8Rnl7UwB+ijX5jWkP9CmVuZHN0cmVhbQplbmRvYmoKMTYgMCBvYmoKPDwgL0JCb3ggWyAtOCAtOCA4IDggXSAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDEzMSAvU3VidHlwZSAvRm9ybQovVHlwZSAvWE9iamVjdCA+PgpzdHJlYW0KeJxtkEEOhCAMRfc9RS/wSUtFZevSa7iZTOL9twNxQEzdNNC+PH5R/pLwTqXA+CQJS06z5HrTkNK6TIwY5tWyKMegUS3WznU4qM/QcGN0i7EUptTW6Hijm+k23pM/+rBZIUY/HA6vhHsWQyZcKTEGh98LL9vD/xGeXtTAH6KNfmNaQ/0KZW5kc3RyZWFtCmVuZG9iagoyIDAgb2JqCjw8IC9Db3VudCAxIC9LaWRzIFsgMTEgMCBSIF0gL1R5cGUgL1BhZ2VzID4+CmVuZG9iago1MCAwIG9iago8PCAvQ3JlYXRpb25EYXRlIChEOjIwMjExMjA0MTY1ODEzKzAyJzAwJykKL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuNC4zLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuNC4zKSA+PgplbmRvYmoKeHJlZgowIDUxCjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMDE1NjgzIDAwMDAwIG4gCjAwMDAwMTQ0MDEgMDAwMDAgbiAKMDAwMDAxNDQzMyAwMDAwMCBuIAowMDAwMDE0NTMyIDAwMDAwIG4gCjAwMDAwMTQ1NTMgMDAwMDAgbiAKMDAwMDAxNDU3NCAwMDAwMCBuIAowMDAwMDAwMDY1IDAwMDAwIG4gCjAwMDAwMDA0MDAgMDAwMDAgbiAKMDAwMDAwNDM2NCAwMDAwMCBuIAowMDAwMDAwMjA4IDAwMDAwIG4gCjAwMDAwMDQzNDMgMDAwMDAgbiAKMDAwMDAxNDY2NyAwMDAwMCBuIAowMDAwMDE0OTIxIDAwMDAwIG4gCjAwMDAwMTUxNzUgMDAwMDAgbiAKMDAwMDAxNTQyOSAwMDAwMCBuIAowMDAwMDEzMDEyIDAwMDAwIG4gCjAwMDAwMTI4MTIgMDAwMDAgbiAKMDAwMDAxMjM2NyAwMDAwMCBuIAowMDAwMDE0MDY1IDAwMDAwIG4gCjAwMDAwMDQzODQgMDAwMDAgbiAKMDAwMDAwNDUzNyAwMDAwMCBuIAowMDAwMDA0NzgwIDAwMDAwIG4gCjAwMDAwMDUxNjAgMDAwMDAgbiAKMDAwMDAwNTQ2NSAwMDAwMCBuIAowMDAwMDA1NzY5IDAwMDAwIG4gCjAwMDAwMDYwOTEgMDAwMDAgbiAKMDAwMDAwNjU1OSAwMDAwMCBuIAowMDAwMDA2ODgxIDAwMDAwIG4gCjAwMDAwMDcwNDcgMDAwMDAgbiAKMDAwMDAwNzQ2MSAwMDAwMCBuIAowMDAwMDA3Njk4IDAwMDAwIG4gCjAwMDAwMDc4NDIgMDAwMDAgbiAKMDAwMDAwNzk2MSAwMDAwMCBuIAowMDAwMDA4MjkyIDAwMDAwIG4gCjAwMDAwMDg0NjQgMDAwMDAgbiAKMDAwMDAwODcwMCAwMDAwMCBuIAowMDAwMDA5MDk1IDAwMDAwIG4gCjAwMDAwMDkzODYgMDAwMDAgbiAKMDAwMDAwOTU0MSAwMDAwMCBuIAowMDAwMDA5ODU3IDAwMDAwIG4gCjAwMDAwMTAyNjQgMDAwMDAgbiAKMDAwMDAxMDQwNiAwMDAwMCBuIAowMDAwMDEwNzk5IDAwMDAwIG4gCjAwMDAwMTA4ODkgMDAwMDAgbiAKMDAwMDAxMTA5NSAwMDAwMCBuIAowMDAwMDExNTA4IDAwMDAwIG4gCjAwMDAwMTE4MzIgMDAwMDAgbiAKMDAwMDAxMjA3OSAwMDAwMCBuIAowMDAwMDE1NzQzIDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gNTAgMCBSIC9Sb290IDEgMCBSIC9TaXplIDUxID4+CnN0YXJ0eHJlZgoxNTkwMAolJUVPRgo=\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["sns.set_theme()\n", "fig, ax = plt.subplots(2, 2, figsize=(12, 4))\n", "ax = [a for a_list in ax for a in a_list]\n", "for i in range(len(ax)):\n", " ax[i].plot(np.arange(1, 17), pe[i, :16], color=\"C%i\" % i, marker=\"o\", markersize=6, markeredgecolor=\"black\")\n", " ax[i].set_title(\"Encoding in hidden dimension %i\" % (i + 1))\n", " ax[i].set_xlabel(\"Position in sequence\", fontsize=10)\n", " ax[i].set_ylabel(\"Positional encoding\", fontsize=10)\n", " ax[i].set_xticks(np.arange(1, 17))\n", " ax[i].tick_params(axis=\"both\", which=\"major\", labelsize=10)\n", " ax[i].tick_params(axis=\"both\", which=\"minor\", labelsize=8)\n", " ax[i].set_ylim(-1.2, 1.2)\n", "fig.subplots_adjust(hspace=0.8)\n", "sns.reset_orig()\n", "plt.show()"]}, {"cell_type": "markdown", "id": "323263e4", "metadata": {"papermill": {"duration": 0.162339, "end_time": "2021-12-04T15:58:14.500062", "exception": false, "start_time": "2021-12-04T15:58:14.337723", "status": "completed"}, "tags": []}, "source": ["As we can see, the patterns between the hidden dimension $1$ and $2$ only differ in the starting angle.\n", "The wavelength is $2\\pi$, hence the repetition after position $6$.\n", "The hidden dimensions $2$ and $3$ have about twice the wavelength."]}, {"cell_type": "markdown", "id": "2a423f6a", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.150745, "end_time": "2021-12-04T15:58:14.801934", "exception": false, "start_time": "2021-12-04T15:58:14.651189", "status": "completed"}, "tags": []}, "source": ["### Learning rate warm-up\n", "\n", "One commonly used technique for training a Transformer is learning rate warm-up.\n", "This means that we gradually increase the learning rate from 0 on to our originally specified\n", "learning rate in the first few iterations.\n", "Thus, we slowly start learning instead of taking very large steps from the beginning.\n", "In fact, training a deep Transformer without learning rate warm-up can make the model diverge\n", "and achieve a much worse performance on training and testing.\n", "Take for instance the following plot by [Liu et al.\n", "(2019)](https://arxiv.org/pdf/1908.03265.pdf) comparing Adam-vanilla (i.e. Adam without warm-up)\n", "vs Adam with a warm-up:\n", "\n", "
\n", "\n", "Clearly, the warm-up is a crucial hyperparameter in the Transformer architecture.\n", "Why is it so important?\n", "There are currently two common explanations.\n", "Firstly, Adam uses the bias correction factors which however can lead to a higher variance in the adaptive\n", "learning rate during the first iterations.\n", "Improved optimizers like [RAdam](https://arxiv.org/abs/1908.03265) have been shown to overcome this issue,\n", "not requiring warm-up for training Transformers.\n", "Secondly, the iteratively applied Layer Normalization across layers can lead to very high gradients during\n", "the first iterations, which can be solved by using Pre-Layer Normalization\n", "(similar to Pre-Activation ResNet), or replacing Layer Normalization by other techniques\n", "(Adaptive Normalization,\n", "[Power Normalization](https://arxiv.org/abs/2003.07845)).\n", "\n", "Nevertheless, many applications and papers still use the original Transformer architecture with Adam,\n", "because warm-up is a simple, yet effective way of solving the gradient problem in the first iterations.\n", "There are many different schedulers we could use.\n", "For instance, the original Transformer paper used an exponential decay scheduler with a warm-up.\n", "However, the currently most popular scheduler is the cosine warm-up scheduler,\n", "which combines warm-up with a cosine-shaped learning rate decay.\n", "We can implement it below, and visualize the learning rate factor over epochs."]}, {"cell_type": "code", "execution_count": 12, "id": "da5c81cf", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:15.111318Z", "iopub.status.busy": "2021-12-04T15:58:15.110838Z", "iopub.status.idle": "2021-12-04T15:58:15.112740Z", "shell.execute_reply": "2021-12-04T15:58:15.112354Z"}, "papermill": {"duration": 0.159462, "end_time": "2021-12-04T15:58:15.112851", "exception": false, "start_time": "2021-12-04T15:58:14.953389", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class CosineWarmupScheduler(optim.lr_scheduler._LRScheduler):\n", " def __init__(self, optimizer, warmup, max_iters):\n", " self.warmup = warmup\n", " self.max_num_iters = max_iters\n", " super().__init__(optimizer)\n", "\n", " def get_lr(self):\n", " lr_factor = self.get_lr_factor(epoch=self.last_epoch)\n", " return [base_lr * lr_factor for base_lr in self.base_lrs]\n", "\n", " def get_lr_factor(self, epoch):\n", " lr_factor = 0.5 * (1 + np.cos(np.pi * epoch / self.max_num_iters))\n", " if epoch <= self.warmup:\n", " lr_factor *= epoch * 1.0 / self.warmup\n", " return lr_factor"]}, {"cell_type": "code", "execution_count": 13, "id": "a0668690", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:15.442783Z", "iopub.status.busy": "2021-12-04T15:58:15.438778Z", "iopub.status.idle": "2021-12-04T15:58:15.738564Z", "shell.execute_reply": "2021-12-04T15:58:15.738973Z"}, "papermill": {"duration": 0.475589, "end_time": "2021-12-04T15:58:15.739126", "exception": false, "start_time": "2021-12-04T15:58:15.263537", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Needed for initializing the lr scheduler\n", "p = nn.Parameter(torch.empty(4, 4))\n", "optimizer = optim.Adam([p], lr=1e-3)\n", "lr_scheduler = CosineWarmupScheduler(optimizer=optimizer, warmup=100, max_iters=2000)\n", "\n", "# Plotting\n", "epochs = list(range(2000))\n", "sns.set()\n", "plt.figure(figsize=(8, 3))\n", "plt.plot(epochs, [lr_scheduler.get_lr_factor(e) for e in epochs])\n", "plt.ylabel(\"Learning rate factor\")\n", "plt.xlabel(\"Iterations (in batches)\")\n", "plt.title(\"Cosine Warm-up Learning Rate Scheduler\")\n", "plt.show()\n", "sns.reset_orig()"]}, {"cell_type": "markdown", "id": "54af6807", "metadata": {"papermill": {"duration": 0.154142, "end_time": "2021-12-04T15:58:16.048540", "exception": false, "start_time": "2021-12-04T15:58:15.894398", "status": "completed"}, "tags": []}, "source": ["In the first 100 iterations, we increase the learning rate factor from 0 to 1,\n", "whereas for all later iterations, we decay it using the cosine wave.\n", "Pre-implementations of this scheduler can be found in the popular NLP Transformer library\n", "[huggingface](https://huggingface.co/transformers/main_classes/optimizer_schedules.html?highlight=cosine#transformers.get_cosine_schedule_with_warmup)."]}, {"cell_type": "markdown", "id": "a8c7d2a2", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.154296, "end_time": "2021-12-04T15:58:16.357362", "exception": false, "start_time": "2021-12-04T15:58:16.203066", "status": "completed"}, "tags": []}, "source": ["### PyTorch Lightning Module\n", "\n", "Finally, we can embed the Transformer architecture into a PyTorch lightning module.\n", "From Tutorial 5, you know that PyTorch Lightning simplifies our training and test code,\n", "as well as structures the code nicely in separate functions.\n", "We will implement a template for a classifier based on the Transformer encoder.\n", "Thereby, we have a prediction output per sequence element.\n", "If we would need a classifier over the whole sequence, the common approach is to add an additional\n", "`[CLS]` token to the sequence, representing the classifier token.\n", "However, here we focus on tasks where we have an output per element.\n", "\n", "Additionally to the Transformer architecture, we add a small input network (maps input dimensions to model dimensions),\n", "the positional encoding, and an output network (transforms output encodings to predictions).\n", "We also add the learning rate scheduler, which takes a step each iteration instead of once per epoch.\n", "This is needed for the warmup and the smooth cosine decay.\n", "The training, validation, and test step is left empty for now and will be filled for our task-specific models."]}, {"cell_type": "code", "execution_count": 14, "id": "a392e677", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:16.678659Z", "iopub.status.busy": "2021-12-04T15:58:16.678143Z", "iopub.status.idle": "2021-12-04T15:58:16.680126Z", "shell.execute_reply": "2021-12-04T15:58:16.679738Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.168727, "end_time": "2021-12-04T15:58:16.680237", "exception": false, "start_time": "2021-12-04T15:58:16.511510", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class TransformerPredictor(pl.LightningModule):\n", " def __init__(\n", " self,\n", " input_dim,\n", " model_dim,\n", " num_classes,\n", " num_heads,\n", " num_layers,\n", " lr,\n", " warmup,\n", " max_iters,\n", " dropout=0.0,\n", " input_dropout=0.0,\n", " ):\n", " \"\"\"\n", " Args:\n", " input_dim: Hidden dimensionality of the input\n", " model_dim: Hidden dimensionality to use inside the Transformer\n", " num_classes: Number of classes to predict per sequence element\n", " num_heads: Number of heads to use in the Multi-Head Attention blocks\n", " num_layers: Number of encoder blocks to use.\n", " lr: Learning rate in the optimizer\n", " warmup: Number of warmup steps. Usually between 50 and 500\n", " max_iters: Number of maximum iterations the model is trained for. This is needed for the CosineWarmup scheduler\n", " dropout: Dropout to apply inside the model\n", " input_dropout: Dropout to apply on the input features\n", " \"\"\"\n", " super().__init__()\n", " self.save_hyperparameters()\n", " self._create_model()\n", "\n", " def _create_model(self):\n", " # Input dim -> Model dim\n", " self.input_net = nn.Sequential(\n", " nn.Dropout(self.hparams.input_dropout), nn.Linear(self.hparams.input_dim, self.hparams.model_dim)\n", " )\n", " # Positional encoding for sequences\n", " self.positional_encoding = PositionalEncoding(d_model=self.hparams.model_dim)\n", " # Transformer\n", " self.transformer = TransformerEncoder(\n", " num_layers=self.hparams.num_layers,\n", " input_dim=self.hparams.model_dim,\n", " dim_feedforward=2 * self.hparams.model_dim,\n", " num_heads=self.hparams.num_heads,\n", " dropout=self.hparams.dropout,\n", " )\n", " # Output classifier per sequence lement\n", " self.output_net = nn.Sequential(\n", " nn.Linear(self.hparams.model_dim, self.hparams.model_dim),\n", " nn.LayerNorm(self.hparams.model_dim),\n", " nn.ReLU(inplace=True),\n", " nn.Dropout(self.hparams.dropout),\n", " nn.Linear(self.hparams.model_dim, self.hparams.num_classes),\n", " )\n", "\n", " def forward(self, x, mask=None, add_positional_encoding=True):\n", " \"\"\"\n", " Args:\n", " x: Input features of shape [Batch, SeqLen, input_dim]\n", " mask: Mask to apply on the attention outputs (optional)\n", " add_positional_encoding: If True, we add the positional encoding to the input.\n", " Might not be desired for some tasks.\n", " \"\"\"\n", " x = self.input_net(x)\n", " if add_positional_encoding:\n", " x = self.positional_encoding(x)\n", " x = self.transformer(x, mask=mask)\n", " x = self.output_net(x)\n", " return x\n", "\n", " @torch.no_grad()\n", " def get_attention_maps(self, x, mask=None, add_positional_encoding=True):\n", " \"\"\"Function for extracting the attention matrices of the whole Transformer for a single batch.\n", "\n", " Input arguments same as the forward pass.\n", " \"\"\"\n", " x = self.input_net(x)\n", " if add_positional_encoding:\n", " x = self.positional_encoding(x)\n", " attention_maps = self.transformer.get_attention_maps(x, mask=mask)\n", " return attention_maps\n", "\n", " def configure_optimizers(self):\n", " optimizer = optim.Adam(self.parameters(), lr=self.hparams.lr)\n", "\n", " # We don't return the lr scheduler because we need to apply it per iteration, not per epoch\n", " self.lr_scheduler = CosineWarmupScheduler(\n", " optimizer, warmup=self.hparams.warmup, max_iters=self.hparams.max_iters\n", " )\n", " return optimizer\n", "\n", " def optimizer_step(self, *args, **kwargs):\n", " super().optimizer_step(*args, **kwargs)\n", " self.lr_scheduler.step() # Step per iteration\n", "\n", " def training_step(self, batch, batch_idx):\n", " raise NotImplementedError\n", "\n", " def validation_step(self, batch, batch_idx):\n", " raise NotImplementedError\n", "\n", " def test_step(self, batch, batch_idx):\n", " raise NotImplementedError"]}, {"cell_type": "markdown", "id": "2818f62b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.154523, "end_time": "2021-12-04T15:58:16.990037", "exception": false, "start_time": "2021-12-04T15:58:16.835514", "status": "completed"}, "tags": []}, "source": ["## Experiments\n", "\n", "
\n", "\n", "After having finished the implementation of the Transformer architecture, we can start experimenting\n", "and apply it to various tasks.\n", "In this notebook, we will focus on two tasks: parallel Sequence-to-Sequence, and set anomaly detection.\n", "The two tasks focus on different properties of the Transformer architecture, and we go through them below.\n", "\n", "### Sequence to Sequence\n", "\n", "A Sequence-to-Sequence task represents a task where the input _and_ the output is a sequence,\n", "not necessarily of the same length.\n", "Popular tasks in this domain include machine translation and summarization.\n", "For this, we usually have a Transformer encoder for interpreting the input sequence,\n", "and a decoder for generating the output in an autoregressive manner.\n", "Here, however, we will go back to a much simpler example task and use only the encoder.\n", "Given a sequence of $N$ numbers between $0$ and $M$, the task is to reverse the input sequence.\n", "In Numpy notation, if our input is $x$, the output should be $x$[::-1].\n", "Although this task sounds very simple, RNNs can have issues with such because the task requires long-term dependencies.\n", "Transformers are built to support such, and hence, we expect it to perform very well.\n", "\n", "First, let's create a dataset class below."]}, {"cell_type": "code", "execution_count": 15, "id": "d73f1841", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:17.308123Z", "iopub.status.busy": "2021-12-04T15:58:17.307642Z", "iopub.status.idle": "2021-12-04T15:58:17.309563Z", "shell.execute_reply": "2021-12-04T15:58:17.309179Z"}, "papermill": {"duration": 0.163367, "end_time": "2021-12-04T15:58:17.309674", "exception": false, "start_time": "2021-12-04T15:58:17.146307", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ReverseDataset(data.Dataset):\n", " def __init__(self, num_categories, seq_len, size):\n", " super().__init__()\n", " self.num_categories = num_categories\n", " self.seq_len = seq_len\n", " self.size = size\n", "\n", " self.data = torch.randint(self.num_categories, size=(self.size, self.seq_len))\n", "\n", " def __len__(self):\n", " return self.size\n", "\n", " def __getitem__(self, idx):\n", " inp_data = self.data[idx]\n", " labels = torch.flip(inp_data, dims=(0,))\n", " return inp_data, labels"]}, {"cell_type": "markdown", "id": "5640c756", "metadata": {"papermill": {"duration": 0.157285, "end_time": "2021-12-04T15:58:17.623302", "exception": false, "start_time": "2021-12-04T15:58:17.466017", "status": "completed"}, "tags": []}, "source": ["We create an arbitrary number of random sequences of numbers between 0 and `num_categories-1`.\n", "The label is simply the tensor flipped over the sequence dimension.\n", "We can create the corresponding data loaders below."]}, {"cell_type": "code", "execution_count": 16, "id": "8e1d16b7", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:17.940041Z", "iopub.status.busy": "2021-12-04T15:58:17.939564Z", "iopub.status.idle": "2021-12-04T15:58:17.956756Z", "shell.execute_reply": "2021-12-04T15:58:17.957137Z"}, "papermill": {"duration": 0.177314, "end_time": "2021-12-04T15:58:17.957279", "exception": false, "start_time": "2021-12-04T15:58:17.779965", "status": "completed"}, "tags": []}, "outputs": [], "source": ["dataset = partial(ReverseDataset, 10, 16)\n", "train_loader = data.DataLoader(dataset(50000), batch_size=128, shuffle=True, drop_last=True, pin_memory=True)\n", "val_loader = data.DataLoader(dataset(1000), batch_size=128)\n", "test_loader = data.DataLoader(dataset(10000), batch_size=128)"]}, {"cell_type": "markdown", "id": "7a21b9dc", "metadata": {"papermill": {"duration": 0.156233, "end_time": "2021-12-04T15:58:18.269306", "exception": false, "start_time": "2021-12-04T15:58:18.113073", "status": "completed"}, "tags": []}, "source": ["Let's look at an arbitrary sample of the dataset:"]}, {"cell_type": "code", "execution_count": 17, "id": "aeda9084", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:18.587916Z", "iopub.status.busy": "2021-12-04T15:58:18.587443Z", "iopub.status.idle": "2021-12-04T15:58:18.590343Z", "shell.execute_reply": "2021-12-04T15:58:18.590793Z"}, "papermill": {"duration": 0.16296, "end_time": "2021-12-04T15:58:18.590927", "exception": false, "start_time": "2021-12-04T15:58:18.427967", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Input data: tensor([9, 6, 2, 0, 6, 2, 7, 9, 7, 3, 3, 4, 3, 7, 0, 9])\n", "Labels: tensor([9, 0, 7, 3, 4, 3, 3, 7, 9, 7, 2, 6, 0, 2, 6, 9])\n"]}], "source": ["inp_data, labels = train_loader.dataset[0]\n", "print(\"Input data:\", inp_data)\n", "print(\"Labels: \", labels)"]}, {"cell_type": "markdown", "id": "e5c8430c", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.156663, "end_time": "2021-12-04T15:58:18.903232", "exception": false, "start_time": "2021-12-04T15:58:18.746569", "status": "completed"}, "tags": []}, "source": ["During training, we pass the input sequence through the Transformer encoder and predict the output for each input token.\n", "We use the standard Cross-Entropy loss to perform this.\n", "Every number is represented as a one-hot vector.\n", "Remember that representing the categories as single scalars decreases the expressiveness of the model extremely\n", "as $0$ and $1$ are not closer related than $0$ and $9$ in our example.\n", "An alternative to a one-hot vector is using a learned embedding vector as it is provided by the PyTorch module `nn.Embedding`.\n", "However, using a one-hot vector with an additional linear layer as in our case has the same effect\n", "as an embedding layer (`self.input_net` maps one-hot vector to a dense vector,\n", "where each row of the weight matrix represents the embedding for a specific category).\n", "\n", "To implement the training dynamic, we create a new class inheriting from `TransformerPredictor`\n", "and overwriting the training, validation and test step functions."]}, {"cell_type": "code", "execution_count": 18, "id": "9e00bf72", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:19.222837Z", "iopub.status.busy": "2021-12-04T15:58:19.222299Z", "iopub.status.idle": "2021-12-04T15:58:19.224262Z", "shell.execute_reply": "2021-12-04T15:58:19.223880Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.164853, "end_time": "2021-12-04T15:58:19.224372", "exception": false, "start_time": "2021-12-04T15:58:19.059519", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class ReversePredictor(TransformerPredictor):\n", " def _calculate_loss(self, batch, mode=\"train\"):\n", " # Fetch data and transform categories to one-hot vectors\n", " inp_data, labels = batch\n", " inp_data = F.one_hot(inp_data, num_classes=self.hparams.num_classes).float()\n", "\n", " # Perform prediction and calculate loss and accuracy\n", " preds = self.forward(inp_data, add_positional_encoding=True)\n", " loss = F.cross_entropy(preds.view(-1, preds.size(-1)), labels.view(-1))\n", " acc = (preds.argmax(dim=-1) == labels).float().mean()\n", "\n", " # Logging\n", " self.log(\"%s_loss\" % mode, loss)\n", " self.log(\"%s_acc\" % mode, acc)\n", " return loss, acc\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss, _ = self._calculate_loss(batch, mode=\"train\")\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"val\")\n", "\n", " def test_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"test\")"]}, {"cell_type": "markdown", "id": "5bbaf3a6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.155556, "end_time": "2021-12-04T15:58:19.534838", "exception": false, "start_time": "2021-12-04T15:58:19.379282", "status": "completed"}, "tags": []}, "source": ["Finally, we can create a training function similar to the one we have seen in Tutorial 5 for PyTorch Lightning.\n", "We create a `pl.Trainer` object, running for $N$ epochs, logging in TensorBoard, and saving our best model based on the validation.\n", "Afterward, we test our models on the test set.\n", "An additional parameter we pass to the trainer here is `gradient_clip_val`.\n", "This clips the norm of the gradients for all parameters before taking an optimizer step and prevents the model\n", "from diverging if we obtain very high gradients at, for instance, sharp loss surfaces (see many good blog posts\n", "on gradient clipping, like [DeepAI glossary](https://deepai.org/machine-learning-glossary-and-terms/gradient-clipping)).\n", "For Transformers, gradient clipping can help to further stabilize the training during the first few iterations, and also afterward.\n", "In plain PyTorch, you can apply gradient clipping via `torch.nn.utils.clip_grad_norm_(...)`\n", "(see [documentation](https://pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html#torch.nn.utils.clip_grad_norm_)).\n", "The clip value is usually between 0.5 and 10, depending on how harsh you want to clip large gradients.\n", "After having explained this, let's implement the training function:"]}, {"cell_type": "code", "execution_count": 19, "id": "13fba27b", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:19.861262Z", "iopub.status.busy": "2021-12-04T15:58:19.860777Z", "iopub.status.idle": "2021-12-04T15:58:19.862292Z", "shell.execute_reply": "2021-12-04T15:58:19.862689Z"}, "papermill": {"duration": 0.170942, "end_time": "2021-12-04T15:58:19.862822", "exception": false, "start_time": "2021-12-04T15:58:19.691880", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_reverse(**kwargs):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " root_dir = os.path.join(CHECKPOINT_PATH, \"ReverseTask\")\n", " os.makedirs(root_dir, exist_ok=True)\n", " trainer = pl.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " gpus=1 if str(device).startswith(\"cuda\") else 0,\n", " max_epochs=10,\n", " gradient_clip_val=5,\n", " progress_bar_refresh_rate=1,\n", " )\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, \"ReverseTask.ckpt\")\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = ReversePredictor.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = ReversePredictor(max_iters=trainer.max_epochs * len(train_loader), **kwargs)\n", " trainer.fit(model, train_loader, val_loader)\n", "\n", " # Test best model on validation and test set\n", " val_result = trainer.test(model, test_dataloaders=val_loader, verbose=False)\n", " test_result = trainer.test(model, test_dataloaders=test_loader, verbose=False)\n", " result = {\"test_acc\": test_result[0][\"test_acc\"], \"val_acc\": val_result[0][\"test_acc\"]}\n", "\n", " model = model.to(device)\n", " return model, result"]}, {"cell_type": "markdown", "id": "11c5e6ce", "metadata": {"papermill": {"duration": 0.157063, "end_time": "2021-12-04T15:58:20.175228", "exception": false, "start_time": "2021-12-04T15:58:20.018165", "status": "completed"}, "tags": []}, "source": ["Finally, we can train the model.\n", "In this setup, we will use a single encoder block and a single head in the Multi-Head Attention.\n", "This is chosen because of the simplicity of the task, and in this case, the attention can actually be interpreted\n", "as an \"explanation\" of the predictions (compared to the other papers above dealing with deep Transformers)."]}, {"cell_type": "code", "execution_count": 20, "id": "a41d7448", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:20.491778Z", "iopub.status.busy": "2021-12-04T15:58:20.491301Z", "iopub.status.idle": "2021-12-04T15:58:24.308685Z", "shell.execute_reply": "2021-12-04T15:58:24.308229Z"}, "papermill": {"duration": 3.977688, "end_time": "2021-12-04T15:58:24.308813", "exception": false, "start_time": "2021-12-04T15:58:20.331125", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", " rank_zero_deprecation(\n", "GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:901: LightningDeprecationWarning: `trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.test(dataloaders)` instead.\n", " rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/Transformers/ReverseTask/lightning_logs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:111: UserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b9ed1cfedbeb4586bab29909930e47b1", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "89c0145b83d34648bacf9da6ad15d74d", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["reverse_model, reverse_result = train_reverse(\n", " input_dim=train_loader.dataset.num_categories,\n", " model_dim=32,\n", " num_heads=1,\n", " num_classes=train_loader.dataset.num_categories,\n", " num_layers=1,\n", " dropout=0.0,\n", " lr=5e-4,\n", " warmup=50,\n", ")"]}, {"cell_type": "markdown", "id": "ccc85090", "metadata": {"papermill": {"duration": 0.163772, "end_time": "2021-12-04T15:58:24.638088", "exception": false, "start_time": "2021-12-04T15:58:24.474316", "status": "completed"}, "tags": []}, "source": ["The warning of PyTorch Lightning regarding the number of workers can be ignored for now.\n", "As the data set is so simple and the `__getitem__` finishes a neglectable time, we don't need subprocesses\n", "to provide us the data (in fact, more workers can slow down the training as we have communication overhead among processes/threads).\n", "First, let's print the results:"]}, {"cell_type": "code", "execution_count": 21, "id": "02e867c0", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:24.972467Z", "iopub.status.busy": "2021-12-04T15:58:24.971991Z", "iopub.status.idle": "2021-12-04T15:58:24.974029Z", "shell.execute_reply": "2021-12-04T15:58:24.974648Z"}, "papermill": {"duration": 0.171511, "end_time": "2021-12-04T15:58:24.974782", "exception": false, "start_time": "2021-12-04T15:58:24.803271", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Val accuracy: 100.00%\n", "Test accuracy: 100.00%\n"]}], "source": ["print(\"Val accuracy: %4.2f%%\" % (100.0 * reverse_result[\"val_acc\"]))\n", "print(\"Test accuracy: %4.2f%%\" % (100.0 * reverse_result[\"test_acc\"]))"]}, {"cell_type": "markdown", "id": "389ff9fb", "metadata": {"papermill": {"duration": 0.164894, "end_time": "2021-12-04T15:58:25.304480", "exception": false, "start_time": "2021-12-04T15:58:25.139586", "status": "completed"}, "tags": []}, "source": ["As we would have expected, the Transformer can correctly solve the task.\n", "However, how does the attention in the Multi-Head Attention block looks like for an arbitrary input?\n", "Let's try to visualize it below."]}, {"cell_type": "code", "execution_count": 22, "id": "f740e35c", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:25.645172Z", "iopub.status.busy": "2021-12-04T15:58:25.644677Z", "iopub.status.idle": "2021-12-04T15:58:25.650646Z", "shell.execute_reply": "2021-12-04T15:58:25.650153Z"}, "papermill": {"duration": 0.178894, "end_time": "2021-12-04T15:58:25.650756", "exception": false, "start_time": "2021-12-04T15:58:25.471862", "status": "completed"}, "tags": []}, "outputs": [], "source": ["data_input, labels = next(iter(val_loader))\n", "inp_data = F.one_hot(data_input, num_classes=reverse_model.hparams.num_classes).float()\n", "inp_data = inp_data.to(device)\n", "attention_maps = reverse_model.get_attention_maps(inp_data)"]}, {"cell_type": "markdown", "id": "82e9b36d", "metadata": {"papermill": {"duration": 0.164929, "end_time": "2021-12-04T15:58:25.982199", "exception": false, "start_time": "2021-12-04T15:58:25.817270", "status": "completed"}, "tags": []}, "source": ["The object `attention_maps` is a list of length $N$ where $N$ is the number of layers.\n", "Each element is a tensor of shape [Batch, Heads, SeqLen, SeqLen], which we can verify below."]}, {"cell_type": "code", "execution_count": 23, "id": "ba03c69a", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:26.324791Z", "iopub.status.busy": "2021-12-04T15:58:26.324320Z", "iopub.status.idle": "2021-12-04T15:58:26.326971Z", "shell.execute_reply": "2021-12-04T15:58:26.326543Z"}, "papermill": {"duration": 0.178216, "end_time": "2021-12-04T15:58:26.327081", "exception": false, "start_time": "2021-12-04T15:58:26.148865", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/plain": ["torch.Size([128, 1, 16, 16])"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["attention_maps[0].shape"]}, {"cell_type": "markdown", "id": "1ff46b13", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.16452, "end_time": "2021-12-04T15:58:26.658828", "exception": false, "start_time": "2021-12-04T15:58:26.494308", "status": "completed"}, "tags": []}, "source": ["Next, we will write a plotting function that takes as input the sequences, attention maps, and an index\n", "indicating for which batch element we want to visualize the attention map.\n", "We will create a plot where over rows, we have different layers, while over columns, we show the different heads.\n", "Remember that the softmax has been applied for each row separately."]}, {"cell_type": "code", "execution_count": 24, "id": "f23fa88e", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:26.999173Z", "iopub.status.busy": "2021-12-04T15:58:26.998690Z", "iopub.status.idle": "2021-12-04T15:58:27.000240Z", "shell.execute_reply": "2021-12-04T15:58:27.000618Z"}, "papermill": {"duration": 0.176536, "end_time": "2021-12-04T15:58:27.000749", "exception": false, "start_time": "2021-12-04T15:58:26.824213", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def plot_attention_maps(input_data, attn_maps, idx=0):\n", " if input_data is not None:\n", " input_data = input_data[idx].detach().cpu().numpy()\n", " else:\n", " input_data = np.arange(attn_maps[0][idx].shape[-1])\n", " attn_maps = [m[idx].detach().cpu().numpy() for m in attn_maps]\n", "\n", " num_heads = attn_maps[0].shape[0]\n", " num_layers = len(attn_maps)\n", " seq_len = input_data.shape[0]\n", " fig_size = 4 if num_heads == 1 else 3\n", " fig, ax = plt.subplots(num_layers, num_heads, figsize=(num_heads * fig_size, num_layers * fig_size))\n", " if num_layers == 1:\n", " ax = [ax]\n", " if num_heads == 1:\n", " ax = [[a] for a in ax]\n", " for row in range(num_layers):\n", " for column in range(num_heads):\n", " ax[row][column].imshow(attn_maps[row][column], origin=\"lower\", vmin=0)\n", " ax[row][column].set_xticks(list(range(seq_len)))\n", " ax[row][column].set_xticklabels(input_data.tolist())\n", " ax[row][column].set_yticks(list(range(seq_len)))\n", " ax[row][column].set_yticklabels(input_data.tolist())\n", " ax[row][column].set_title(\"Layer %i, Head %i\" % (row + 1, column + 1))\n", " fig.subplots_adjust(hspace=0.5)\n", " plt.show()"]}, {"cell_type": "markdown", "id": "5575de2c", "metadata": {"papermill": {"duration": 0.165585, "end_time": "2021-12-04T15:58:27.339327", "exception": false, "start_time": "2021-12-04T15:58:27.173742", "status": "completed"}, "tags": []}, "source": ["Finally, we can plot the attention map of our trained Transformer on the reverse task:"]}, {"cell_type": "code", "execution_count": 25, "id": "70711ff5", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:27.678174Z", "iopub.status.busy": "2021-12-04T15:58:27.677704Z", "iopub.status.idle": "2021-12-04T15:58:28.093751Z", "shell.execute_reply": "2021-12-04T15:58:28.094142Z"}, "papermill": {"duration": 0.587062, "end_time": "2021-12-04T15:58:28.094303", "exception": false, "start_time": "2021-12-04T15:58:27.507241", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["plot_attention_maps(data_input, attention_maps, idx=0)"]}, {"cell_type": "markdown", "id": "163001ca", "metadata": {"papermill": {"duration": 0.169201, "end_time": "2021-12-04T15:58:28.432387", "exception": false, "start_time": "2021-12-04T15:58:28.263186", "status": "completed"}, "tags": []}, "source": ["The model has learned to attend to the token that is on the flipped index of itself.\n", "Hence, it actually does what we intended it to do.\n", "We see that it however also pays some attention to values close to the flipped index.\n", "This is because the model doesn't need the perfect, hard attention to solve this problem,\n", "but is fine with this approximate, noisy attention map.\n", "The close-by indices are caused by the similarity of the positional encoding,\n", "which we also intended with the positional encoding."]}, {"cell_type": "markdown", "id": "d8ac4d91", "metadata": {"papermill": {"duration": 0.169547, "end_time": "2021-12-04T15:58:28.770515", "exception": false, "start_time": "2021-12-04T15:58:28.600968", "status": "completed"}, "tags": []}, "source": ["### Set Anomaly Detection\n", "\n", "Besides sequences, sets are another data structure that is relevant for many applications.\n", "In contrast to sequences, elements are unordered in a set.\n", "RNNs can only be applied on sets by assuming an order in the data, which however biases the model towards\n", "a non-existing order in the data.\n", "[Vinyals et al.\n", "(2015)](https://arxiv.org/abs/1511.06391) and other papers have shown that the assumed order can have a significant\n", "impact on the model's performance, and hence, we should try to not use RNNs on sets.\n", "Ideally, our model should be permutation-equivariant/invariant such that the output is the same no matter how we sort the elements in a set.\n", "\n", "Transformers offer the perfect architecture for this as the Multi-Head Attention is permutation-equivariant, and thus,\n", "outputs the same values no matter in what order we enter the inputs (inputs and outputs are permuted equally).\n", "The task we are looking at for sets is _Set Anomaly Detection_ which means that we try to find the element(s)\n", "in a set that does not fit the others.\n", "In the research community, the common application of anomaly detection is performed on a set of images,\n", "where $N-1$ images belong to the same category/have the same high-level features while one belongs to another category.\n", "Note that category does not necessarily have to relate to a class in a standard classification problem,\n", "but could be the combination of multiple features.\n", "For instance, on a face dataset, this could be people with glasses, male, beard, etc.\n", "An example of distinguishing different animals can be seen below.\n", "The first four images show foxes, while the last represents a different animal.\n", "We want to recognize that the last image shows a different animal, but it is not relevant which class of animal it is.\n", "\n", "
\n", "\n", "In this tutorial, we will use the CIFAR100 dataset.\n", "CIFAR100 has 600 images for 100 classes each with a resolution of 32x32, similar to CIFAR10.\n", "The larger amount of classes requires the model to attend to specific features in the images instead\n", "of coarse features as in CIFAR10, therefore making the task harder.\n", "We will show the model a set of 9 images of one class, and 1 image from another class.\n", "The task is to find the image that is from a different class than the other images.\n", "Using the raw images directly as input to the Transformer is not a good idea, because it is not translation\n", "invariant as a CNN, and would need to learn to detect image features from high-dimensional input first of all.\n", "Instead, we will use a pre-trained ResNet34 model from the torchvision package to obtain high-level,\n", "low-dimensional features of the images.\n", "The ResNet model has been pre-trained on the [ImageNet](http://image-net.org/) dataset which contains\n", "1 million images of 1k classes and varying resolutions.\n", "However, during training and testing, the images are usually scaled to a resolution of 224x224,\n", "and hence we rescale our CIFAR images to this resolution as well.\n", "Below, we will load the dataset, and prepare the data for being processed by the ResNet model."]}, {"cell_type": "code", "execution_count": 26, "id": "5ff1954f", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:29.115622Z", "iopub.status.busy": "2021-12-04T15:58:29.115139Z", "iopub.status.idle": "2021-12-04T15:58:34.276229Z", "shell.execute_reply": "2021-12-04T15:58:34.275775Z"}, "papermill": {"duration": 5.338193, "end_time": "2021-12-04T15:58:34.276369", "exception": false, "start_time": "2021-12-04T15:58:28.938176", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz to /__w/1/s/.datasets/cifar-100-python.tar.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "efc763b5cd4e4ed9b3e3881bad434e2b", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/169001437 [00:00150MB free disk space.\n", "So it is recommended to run this only on a local computer if you have enough free disk and a GPU (GoogleColab is fine for this).\n", "If you do not have a GPU, you can download the features from the\n", "[GoogleDrive folder](https://drive.google.com/drive/folders/1DF7POc6j03pRiWQPWSl5QJX5iY-xK0sV?usp=sharing)."]}, {"cell_type": "code", "execution_count": 28, "id": "68fcd0ab", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:58:37.486821Z", "iopub.status.busy": "2021-12-04T15:58:37.484217Z", "iopub.status.idle": "2021-12-04T15:59:11.196064Z", "shell.execute_reply": "2021-12-04T15:59:11.196470Z"}, "papermill": {"duration": 33.893166, "end_time": "2021-12-04T15:59:11.196649", "exception": false, "start_time": "2021-12-04T15:58:37.303483", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "336b58f1c17b4c2998f77978c46f92b2", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/391 [00:00= anomaly_label:\n", " set_label += 1\n", "\n", " # Sample images from the class determined above\n", " img_indices = np.random.choice(self.img_idx_by_label.shape[1], size=self.set_size, replace=False)\n", " img_indices = self.img_idx_by_label[set_label, img_indices]\n", " return img_indices\n", "\n", " def __len__(self):\n", " return self.img_feats.shape[0]\n", "\n", " def __getitem__(self, idx):\n", " anomaly = self.img_feats[idx]\n", " if self.train: # If train => sample\n", " img_indices = self.sample_img_set(self.labels[idx])\n", " else: # If test => use pre-generated ones\n", " img_indices = self.test_sets[idx]\n", "\n", " # Concatenate images. The anomaly is always the last image for simplicity\n", " img_set = torch.cat([self.img_feats[img_indices], anomaly[None]], dim=0)\n", " indices = torch.cat([img_indices, torch.LongTensor([idx])], dim=0)\n", " label = img_set.shape[0] - 1\n", "\n", " # We return the indices of the images for visualization purpose. \"Label\" is the index of the anomaly\n", " return img_set, indices, label"]}, {"cell_type": "markdown", "id": "2fc781ed", "metadata": {"papermill": {"duration": 0.177543, "end_time": "2021-12-04T15:59:13.783170", "exception": false, "start_time": "2021-12-04T15:59:13.605627", "status": "completed"}, "tags": []}, "source": ["Next, we can setup our datasets and data loaders below.\n", "Here, we will use a set size of 10, i.e. 9 images from one category + 1 anomaly.\n", "Feel free to change it if you want to experiment with the sizes."]}, {"cell_type": "code", "execution_count": 32, "id": "d089f9e7", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:14.150564Z", "iopub.status.busy": "2021-12-04T15:59:14.150080Z", "iopub.status.idle": "2021-12-04T15:59:15.603980Z", "shell.execute_reply": "2021-12-04T15:59:15.603533Z"}, "papermill": {"duration": 1.641455, "end_time": "2021-12-04T15:59:15.604117", "exception": false, "start_time": "2021-12-04T15:59:13.962662", "status": "completed"}, "tags": []}, "outputs": [], "source": ["SET_SIZE = 10\n", "test_labels = torch.LongTensor(test_set.targets)\n", "\n", "train_anom_dataset = SetAnomalyDataset(train_feats, train_labels, set_size=SET_SIZE, train=True)\n", "val_anom_dataset = SetAnomalyDataset(val_feats, val_labels, set_size=SET_SIZE, train=False)\n", "test_anom_dataset = SetAnomalyDataset(test_feats, test_labels, set_size=SET_SIZE, train=False)\n", "\n", "train_anom_loader = data.DataLoader(\n", " train_anom_dataset, batch_size=64, shuffle=True, drop_last=True, num_workers=4, pin_memory=True\n", ")\n", "val_anom_loader = data.DataLoader(val_anom_dataset, batch_size=64, shuffle=False, drop_last=False, num_workers=4)\n", "test_anom_loader = data.DataLoader(test_anom_dataset, batch_size=64, shuffle=False, drop_last=False, num_workers=4)"]}, {"cell_type": "markdown", "id": "70f843f4", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.178644, "end_time": "2021-12-04T15:59:15.961336", "exception": false, "start_time": "2021-12-04T15:59:15.782692", "status": "completed"}, "tags": []}, "source": ["To understand the dataset a little better, we can plot below a few sets from the test dataset.\n", "Each row shows a different input set, where the first 9 are from the same class."]}, {"cell_type": "code", "execution_count": 33, "id": "cf7e1059", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:16.325926Z", "iopub.status.busy": "2021-12-04T15:59:16.325451Z", "iopub.status.idle": "2021-12-04T15:59:17.628117Z", "shell.execute_reply": "2021-12-04T15:59:17.628507Z"}, "papermill": {"duration": 1.488315, "end_time": "2021-12-04T15:59:17.628671", "exception": false, "start_time": "2021-12-04T15:59:16.140356", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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MAnb/UFQQrSWxSsxc0m6xNEkv2cM4jZdrtY7nqXuEWaN48OPY0ymB6T0ywWazvTkwqz3ODpBOuMav6tM7lSQDibqQ80KlCuse1CWijyvbmFKdTi3lGJef6Ht8jgA9xd6N3NHHyxeMRrUtqNFqlTgPMBNT0ZVxq5GBlBMGQ2dHVzQLpcjKekI1wo05oZm9w3BgA8uzhKSlrVK8D2UB6AJd2jrjNEqCjgDC3yiM9foGqvxeNwplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzM0ID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxOCA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA4OSA+PgpzdHJlYW0KeJw1TbkRgDAM6z2FR8CPSLwPx1GE/VvshDSWTp8Rygdr5AGC4Y0vIfiiLxmEtQsPKvtIdNhEDWcVJBPDryzwqpwVbXMlE9lZTKOzQcv0re1vgx66P92OHAoKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE0MSA+PgpzdHJlYW0KeJw9j8EOwzAIQ+/5Cv9ApNgpoXxPp2qH7v+vI0u7C3oCY4yF0NAbqprDhmCb48XSJVRr+BTFQCU3yJlgDqWk0h1HkXpiOBhcHrQbjuKx6PoRu5JmfdDGQrolaIB7rFNp3KZxE8QdNQXqKeqco7wQuZ+pZ9g0kt00s5JzuA2/e89T1/+nq7zL+QW9dy7+CmVuZHN0cmVhbQplbmRvYmoKMzUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTUgPj4Kc3RyZWFtCnicNVE5DgMhDOz3Ff5AJIwveE+iKM3+v82M0VYewVyGtJQhmfJSk6gh5VM+epkunLrc18xqNOeWtC1zgLi2vC+tksCJZoiDwWmYuAGaPAFD19GoUUMXHtDUpVMosNwEPoq3bg/dY7WBl7Yh54kgYigZLEHNqUUTFm3PJ6Q1v16LG96X7d3IU6XGlhiBBgFWOBzX6NfwlT1PJtF0FTLUqzXLGAkTRSI8+Y6m1RPrWjTSMhLUxhGsagO8O/0wTgAAE3HLAmSfSpSz5MRvsfSzBlf6/gGfR1SWCmVuZHN0cmVhbQplbmRvYmoKMTYgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9DaGFyUHJvY3MgMTcgMCBSCi9FbmNvZGluZyA8PAovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDggL3plcm8gL29uZSA2NSAvQSA2NyAvQyA3MCAvRiA3MyAvSSA4MiAvUiA5NyAvYSAxMDEgL2UgMTA4Ci9sIC9tIC9uIC9vIC9wIDExNSAvcyAxMjAgL3ggL3kgXQovVHlwZSAvRW5jb2RpbmcgPj4KL0ZpcnN0Q2hhciAwIC9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnREZXNjcmlwdG9yIDE1IDAgUgovRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXSAvTGFzdENoYXIgMjU1IC9OYW1lIC9EZWphVnVTYW5zCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDE0IDAgUiA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnROYW1lIC9EZWphVnVTYW5zIC9JdGFsaWNBbmdsZSAwCi9NYXhXaWR0aCAxMzQyIC9TdGVtViAwIC9UeXBlIC9Gb250RGVzY3JpcHRvciAvWEhlaWdodCAwID4+CmVuZG9iagoxNCAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNyAwIG9iago8PCAvQSAxOCAwIFIgL0MgMTkgMCBSIC9GIDIwIDAgUiAvSSAyMSAwIFIgL1IgMjIgMCBSIC9hIDIzIDAgUiAvZSAyNCAwIFIKL2wgMjUgMCBSIC9tIDI2IDAgUiAvbiAyNyAwIFIgL28gMjggMCBSIC9vbmUgMjkgMCBSIC9wIDMwIDAgUiAvcyAzMSAwIFIKL3NwYWNlIDMyIDAgUiAveCAzMyAwIFIgL3kgMzQgMCBSIC96ZXJvIDM1IDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTYgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvQ0EgMCAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMiA8PCAvQ0EgMSAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9JMSAxMyAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9CaXRzUGVyQ29tcG9uZW50IDggL0NvbG9yU3BhY2UgL0RldmljZVJHQgovRGVjb2RlUGFybXMgPDwgL0NvbG9ycyAzIC9Db2x1bW5zIDY3MCAvUHJlZGljdG9yIDEwID4+Ci9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9IZWlnaHQgMjcxIC9MZW5ndGggMzYgMCBSIC9TdWJ0eXBlIC9JbWFnZQovVHlwZSAvWE9iamVjdCAvV2lkdGggNjcwID4+CnN0cmVhbQp4nOz9Wa8kSZYeCJ5FRFTVzO69vkVkRlZVJslpVg+7MSgOMA0Q/ef45+ZlyHlgz4BVLFZlZWRkLO5+V1t0EZGzzIOo3cXDI7IKM8AAREh6hpvbNdOrKnLkrN/5BP/jf/yP8Mv4Zfwyfhm/jF/GL+N/lEH//76BX8Yv45fxy/hl/DJ+Gf+/HL+Y9l/GL+OX8cv4Zfwy/ocav5j2X8Yv45fxy/hl/DL+hxrh+T/efvFvLl/9BhEA8McfxWf//fHPHAAA/PFj/umHEREAoV3c3dePP7s44rNP/uTw85fW23R3gPu7b68//r69byB3/k8Oiu0XqZmpmjoCEsUYU+qYI1MIIYUQYwxMWHIueXEDBAIHcHB3Fc1LLqWKGiCGjtMQhm3s+9h1MVAkD6DkAm6ISO6u7uCOTjF0XeqJAiK1R0+hDxTHcZymSawA+WYzpL4T1XmZbh9uTuN4yb/i86L863/9r//6r//6Z6bi//thZiJaSimliIi5BQ4xxhhjCKFNblsrRGQiDvzzq9PGhw8f/st/+S/tNTP/X/+3/73vN+7tSu6AbfXOMuMAdv4t4IDgq6ggAoCbm5u1jyMiAgEgAPoz8cGz1LTf8Il0rd9dxe+fOxz8v/0f/8/j4aH989e//vXf/M3f/Au+DfC57dIetrouS96P052BhMAcYuDOzE0hpYu+u3KvVU5LuVnKPWEKvBv6d316g5gA+F/wGAAPDw//6T/9p3VWEP/i//I/D1c7bBsSgQAYkRGp/Xed3Ke7f9y3bZs7eHsGW5ezXem8mu7Q/nceiOgIBmDrD+Hl0jwfT9P12U98+1//4XB9115vt9t/83/61+M0H/Ynd2AOuH7LHcDbaGKFgE2roDeBfn55t/Um21/rF9Gf3Yz7T93vo+LyR9WG51mCJtWmqlIRYei63Xb7+tWlO/y///bvzKxd4d//+3//q1/96nPX9k+m4dltmLtVWUqZVYubHI8P9/e3d3d39/f3+4fjvCyvX1/96tdf/NVf/vZXv/qKOQCgqtRaS8lqEmPo0tB3r1PcICEiOSA4nZ8Af3o1nt79+7//+6+//rq9jv32L/7d/45E8KjI12kHQiAAQnAHt8cLmIObqZu1zUnICKBSax7H480y700mBEkcEFykmKm5O6BjQIoc+pg2qd+lbhtjj8gOYI4OSCESRyTCdWnAAZrsOYCfF8gB0MGk/vBf/+9aS7utL//yr9988dsmPT9+bvzcX7DqpbPhfHoFjzoJnv/4R+PZBvqJgc+u/PTO+i9CIMT33/3xmz/8/ePPX5j2q9e/+eqv/uazph2f9jk+vvP8x8/FsN1nUxDrxdqT41kLnLX8swd7uld8/PenY/38WUed33UH90fT7qAP/o1DZSRwNxMRqVKBkBL3NGzSNoYucOdpoG6gPsXIdRz1dAKDJopoYGpS6jQfxzLnqk44xLSLXbro6XJIm74LffQeSrAMrgwAalZNwIAchy5tNymEjigAOKIP3S5Sf38PjrkoYPDLN2m7G7JUOM51Oh5PNzt/y7guyldfffUf/sN/aA/4UwZ1tWT+qH4+Ix/tu3hWW9Y+7eAOolJLHcdxHE+lVlXr+24Yhr7vU0rr1c0RkZlDCDFFZjovwePy4Se397d/+7ePpp2I/s//699cvXpj6rYuPDqgrzLijuamZuJggGAGZgCIhESM7bbNzF3BnZoBAm4JJ3d8vIFVs/uz/z1OCDo+zsQ/wzU5y5V9849//2ja371715bjpweef+NzR/f5m+4uZlnqqeT7w5FuH47q2vWUUpfi1gxE4GLz68uLv3Aoud7vj+U47plCl3avLv/ycvevmC8I+8/l237yuf70p2/+83/+z6vzBPDF//S7q998QYSESOCMGIkiYiQKiAGerDtCU8rPjBaCuRu4uKuZO8Jq2vHsD68DnvlihqAACqup/aypfHTuHfCFa/C4HAD33398NO193//uX/3u7u7B/RqB+mFLSGe5dnc3czN381XvEiI08bcXjpeut9n8Q2tb5FP775+9JXjcXI4ASI7Nj3FEBzM3cJOS52kE06Hr3ry6+t1f/SU4/Nf/9vePpv3f/tt/++/+3f/yuce1Zw4wgj9OG7qLWh6nh+PppuRTLfPHD3Ot5f7+fpy+vbn9eDicQvzqL0L88tf/5q//+ouUOnAsJS95HsdRtPRdt91evbr47XbzJsTIHBwjADcrTEhnaTpvqaeoDR5fHA6HR9Me0vCb//l/oxBh1fer7DMCIQQCBjBz07bv20YQ0apazR2QIkcClLJMp1v+iPhQJC/kMCQk91JVpJq7ARkihcQp9puLzcW77fb1sLkiDOaojoYcuiF0G6KAxOhNM6ADOKIBGqID2PmR6jJ9+G//j0fT/uaL3/7ur/9vq/ScRfVRhZ7/BnhprFYRe/rnOgNNotePv7Scz+0s4qPL8SRYj5Pd/INn13xxBQRghBZy/aRpX32b5uXg03uPr/DlZ58W+qXU+wsvZL2x5tv6o+P+40t9uuF/HPd83of1H6kKZgIkxoDuRsjMMUUKHPsUU4iRiQBBRBdfFLxzS4CWhogAhJRSYmapNc+MUWnAMGdH2F0NF682u8uuH4KjitVAkUNACwqsCmJQxdwMHZAL1RwdiES0qtTTOBHwPC5zmcQymo/TUaEULdN8MCuBXpjIF1rypXX3czxt5qJaq4qoqToYrSqsmXFj5pQSEQGAiOScS6211qbnRbWUMp7GcRrdnUPY1k0VneaFicABEVOMKaWu6xDJS20bxczUtE01M8cY8Zngf7Ig7t7U7Plx3Hx1nwEdwcVyzlOtS5FcZamaAYkp9v22S1umDiGoiomCAyGF0DMnXH1ydzBY3QZHaLa/RSHn8HNV8G0Wf9Y7fpS4n5BV+CnLhC+2wWpGHcAN3AyKQ3WQUo7TeH04fH94+NPD/tuH0/eGOQ2cYh9CjxgI0+X2N+P02y5tiXw67o/7eyLq08QQkXhIX6X4hig8Zi/O7vNPPteny2Hqqg4EhM3QGoAAgHmzJwxAgARAzSddddqjQ+0Aju7kq9ZvIe45ml/nw8+OoXkL2VvU/mTaX7qDqwo9O3yfewp4oSKq1Lu7u4eHw/F42u2udhdXIaRmgv2scx79T0RvUXu7ErbQutnzZ7rbz0GWmlYRb24LERM3Z/HRYznfyKqjsE0TIDhaU9LuAEbgyzze6Pv9/c3D7c3psA+IXZd+Lg/wMkR6+bpNKqpqLtPD/sP3H/7x8HAzHve3t9c31x/v7x9KGUPU7QWlXtVPD/vvv/2emdnNay2l5lKqmTLzMFyMV9PlxZfb7cWw2XXdZQgdQEAIL4JLfAxxn98YrpP17C1CYDxP79kzQAAChOYxiYhUcHWXWqdS5iqLSFETcEhpiBxcpcz7mvcqo1s2L0uu5GZa3RUBEBRcXFRBCgjo4nkv0xaJzd2cDGMaLtJwSZyQAhMjEjavlSNwJA6OtKoGf2lQ190h54QPPCYPn8wqnmP0RwuLjz85x9argMGjpW/Tcv7CWeLOX3lMocFT3P8sdXJ2EdYXzwKV9vWWdQsvc3kvTXtzHFYt+ELKXq7qyzef/LnHN5rrjGeNh4Dun+7NZ+HTc4/wmSb658RXn4tWkQgBiRAJKBBBRCSKXUp9ogBI1gJBlSylIiqzcaAuBSIkpmHTxRhzJpoBkmEPNIG776663WU3bBMzVMngLhgRAyCbWxEvteZa3QXBgQg4RFcmLrWUsoC6K0hRKaIuyA6TZmXxMi+zaaUfWZ1H0/7s4VZXxszUrBQtuc5LyUuptZgrM+Bqec3NUkqbzSaE4O7LspxOp2mal7y4AxKJWq11HKd5mQOHru9KKUvJTTIJIISw2Ww2w+DuIqvEu0OrcRBhs/pExPyTWeKzSV8NurmZNfMiAApouUzTdBzn/Tjup/wwlyMghtBdbN9dXnzRxcvAQy0ipZoZAW+Gy77fBg5IACjmRS2riisgxRQ3gXrAFoLAM8XUzNKfE6tmqBwe46o/syJnb+YxI9U+B4Bg5iaqS9VD1ZP5PM03d7d/vLn+p48ffv/w8N1xvgGu3YZCTMwpcIqh322/eth9f7n79aa73O8/PNzfIErX9YCMIeFuYN4hAFBYt7YT4Mst9Gce0MAMCNwIEAxA25O6GwADMkDLzDMgITogPeYbm2JAb4mX1abDObp7nFx88o1amGznrMV5cp7f76MJ9p8x7fCiBAMqst8/HA7jPM8Xl28uLl+lbmP+aKmbrWla158iJ3qMz4HWYP6ckGjuMjoAFKnzsjhA4FagSgDwlO+CR33jT3dsq8Gz1QlwRGD06bSfTqfbjx8+frg+Ptxf7ra73fZnTfvTnIDDy/TM6hiVkg+nh483f/rjN393c/39/v7heDiOp7HkUmvloD0jh1Jkf3v/J/MRm0bQqqZ+3gpd2kyn8erq/urq9atX766uZKArpu4cTfL5Nh6THM+NAH0qb9hiR38erbZJQDBQVZWSl1pm9+qWl/m4LIdSZ5EsUsC9S0OMiQFqmWW5dxnBi1ut5uiOboSABLS6CQpSxbLXk+W+xg4RDdwwAHY1X9V8SZSQInNgDsSJQkdpy2lD2BMnaN4/widal8AIdE3b4JOVbTuA1vTPy6LFWibER9O+Wt9zSeL8A3j+5uogrJK6GksCf5EZePQMcHVFHt2mp/QAQEBggPizph3w2X/h5erh0/28kL9nC96cYAGvBoLghMEhArS4ql0vIAREADR3ddc2n4QRMeALGcKfj0Uen//Hn3FDdReTQNStMWcX+5T6gKyOxUxVvWSrRSkoR0sdhcghBo6MjIpiqp6Mt9SF6ElUBWMtOmEuSKhayckISKtnLgvMs4iqghIBM7XUfLI+xuRgENRctOpS8rJUc0WC4nOsRMGrFbPqZp/ZMs/MxvN31DTncjot47hMU57nZZ6nWjOimotKdVNwGPrh8vKSiGqtp9Npvz8sy1KlPoqJg4uIqTLznOOyjOnYERETdTH1XQdgKuVw3JtqlWpmCEhExDwM/W63I6Ku635umejxVzkQooNbzeV0nO5zHUWyyFzq9LC/ub17vz9ej/O9mRGlq8svXl/9ard506WLUmvJxdSZ05vXv3796othGEKgUo9LOc7LoeRFKnbp4vWrr3abtynumBIAnXNQ/ijPf1atnkO9f7ax/GS9mn/t6ljMj3P+cPfw++Pp+1z343h3f3d9f3fzcHu7PzycplNIvr0IMSlRQSTCcINToOvL3RcX29c1n/L84KapS0i7bvPFdsi42iV/5mn/C271XP58ej53t9VqY7MfBk4AAsiAjEQOBNCs5GOgb08G6sm/fiw2P87FY4hHDvb02ed3/GxZ/JkK/NydP452G8zcdf1me7G7eJW6jTo60PMo6KnYt2ZkV31KBKs6Rm9a2lxFVEzV1ESqGjOnYbMZ+r7rAND0uVe3TlQz4+dnRnc8lwQAwZmcCS52u2HoicBcRaWq/DkZfFSAL+YBgRxMteyPt998+49ff/Pf//jH3x/292XJphBDLwKWRdXUyjSdHh4AwJZ5BAIEJwYiJEIiCjFAlZs73R/uN7fbN29+9ZvfzG/f/Ga3eUORYC14Pd8un1j3H3m3AMzAjNjC08e8rSuYSJ3qfBpPD9PpAFDRay2nkkfVxbSYV3fTmQk5cHQTyXvXhdmAGHQt9xIBswN6gNVqIDqiuJyWcmyLSqFHrmWUMh/NwB1C4BAZKXHcpM3rbvu6276O3QVTAgxtKz1/kBRp15G6q7m3qlTLtbfNRmdz/KLODgBI7UpPefrzV89h/7MfAT67DJ7zb4S+fhyfrgtPCQAE9Oc/OhcGoFUo+WWN7oVpPzu559/9o8V72lxnl+J5sHDGQy1iR/fZXQgiQQ9gDgpIhJFpizg4uLuoTWoZHAgDw5YpnTMgdAb3/BmFtd6Ff3q3pqgGrkaBsAsp9ZvNNg0xdIxcHdFMRBRQHTRE4GgcPXTAEYlRXEWleFUUTBCZIUQRBzOBjKKAICKgWFxRZi+cZ5vnYg4UMcSQQgA3FTGqTgMzYQA0B1Kxksusqo5OAqFCSASgVYqqAH0ao39a7DBwcDUT0Vzq8TTu96fxlMdxGsdjzrN5Vi1SC7gR0Ga7necFCfOSj8fDw8M+56wqjgCExMzMSEhIRIhIIxMzM3GMcdP3m2GoNTNTzqWUXEpxN6KQUur74eLigohSSqLC3Mqyn9fISIAOhi0tl3M9Hsfrm7vvDqfbeT6oF4fysL/5+PG7+4ePh+NtLQJOV5dvX7/+8vLyzWa4FJFSqirE0M/LX+by1cXFRYg4Lw/jdDfO98syS+Ht8Na8OvjFhrpEhMGfVCT6eesBPtfRZ+H9RNg/p39/9ID+8v1WqRX3arqoHEu+3u+//uH9/3F790/zcjeOh+NxOh3y6SDjqUyjhgSuHpMSqbureq17lQ+7zfXF9hW5gRZXi6nrd++u3h7lqgAYeLUWUzyDwf0zDTyd1dAL4XoEGbXkua9JRXUgV/RH3Ay2ONcfU+6wFobMzcGJiIjO0cjZgQQkB0Mnx8c87qNyOkPrHhXZp+Hgy5l+epuJQghd3/fD0A/b1G/VaPUlHisILQx6adqpVY+oeQHrHYmKU5Fiteqc82maUtf17sTcdR0hmX56T35e8vX+8RPTDgHNtQzD0HWRAwG6tRr858eP5Q2fvY3uXksZp4ePH779wx/+/utv/vH7H74vy4yOXdykuCEE02xK5pBzOZ1O4JBzBnQk77rQdYEIY2Sk3kzm8ahCzOlwegD0lnnYbigERiRwerYU+DN2vQ0mYPI1IQ8t/+EqonXJ834+3hzvr4+HOwRhUtPFZHEr7uIgDipi7hi4Q4CSJzdBIEJ2J3dseg8QifwcKqyoqFJqyQUQmUNwYEAREfEqVUVigBAIKFLc9PmoMqMLWkXeICfmiFqeP1EXcNuzqIm5OZijP9XQW2nq0ebCoxQ/3g+cg+zVFjfBewq1H5MAa8Dd3qfzTn5850nPPHMOztn48/Y6m2N+dCyejU+jdlivjj/pOT9eEAHOz3KWvCw6Vf2Q5VvVe7MRjN0iICFxCEOKF134MvJrNa8yL/VjlQckDxwjvwq4dQDEEHnDvCHcIMSfCZ3O28v9R59wdULiGFOKMUZOTBEpEkemgMioJqgCrJyMCI1MQF1cSxYzAzcHM3OHwIGYKHDASARMxMyqlrOWqUouqMwQpXrWSsSJU+yx6wnR3QsEdELHRBhjSCHGwrp4qSrViol6VmJ3sJKrCnj48aT7OTuCpi5qqiqqYlaqzUs5HafD8TQex2keS56rLGbFVAGciRuoNMQI7qJm7mJWqxgYELZEPREbttK8mam7MVGIoYuxi5FDIETVFaZOzCkltR4ZYw6nKcQYUpeIKMZIz2X/PMyrgwChiU7TaZzuT9PHu4fvPt788fbu/f39jYPEhKXMp/EwHQ/zfhrHJWc5PcyHh/3F5W6z26wAAiOmbp4+3t1e9X3HAaqMVSe1WVXN0jTfOVit1d7Cbgdd2jLFVV780W19iuB/TrT+BeNcg4JiNoseSrmb54/T+OF0en9/9+37H/7pYf8+lzFLEbVSXdTdnJBAMJ9Qk1NQMxNpWUxY5pPV4ipaFQz7fvt6Py+nqjm7jlXMrKopOBJtmDchbJjj2WX5uVC+gW5ahOHPLOG5eorn3LgDgJi5Wim1loIAHDil1Jb7nKQHUStS57yUWgKHFGPqUgyRnoXZSEiA5q049yxwx5bd98c44Sfj2Zc2H5FijFGcq+EKGngsCPuj2V23zrMEB8J5+R0MgfBcGnVU8Lnk+4f7h/3D/nBIKYpm0zeE3qeeMZxzo89z8k/rD09TeE5XECGhr0AEAzBcUVz/ooEtua2i9/c3X3/z9//0h//6+z/83cfr7w+HRasSoHVgifJMJTPREFMMbG5QK+Cs5gqgKipViCxEMjPCOE1aspthzsXdlrzkpbx7q1eXnBITRmiP9BTPPU7sjwM/D+gEZq7gZqt1c8nzfHqY9h+n/fvT4WY6PRBZZCcyRAUXcEFXAOOGb5ciass0VylEhEhupApqBgjMEAKGQCFQYI4xpNjySgEc3dkNXA0BecViGqiZu0M1KbOJlknmfeovkQcKm9hvG9jy8UESwya6EIiBGKiv7RKPIfKa5nny7rAhOQgB0R/D9DUsP2fjzy5Ak44n091yb/i4DVfH6IUGfUrc42PB/plhd6D1t79YlJemHc9eCXyqnR/H8yDFwRsMqnnuYqdcb3L9ZpHfq966nUzBhJEicYppZ3CFuCAuIlBkXsq3S71GUqYY8RXjDoCYh75729EbxAgYPnHhX+Sln93Tiw85gBkShBBjTBQZGZzMUW1VJmSIhgCBCMHNq6lWhwrLknMpgAhruZ5SgsBk6s1jpBBCCACGIKqSlwqmMSIgADsG5wSho7RhADcRIgQiAHJn9ICI6ATeus5EvAqIgpirm6PTp0W4hpYDa7hxEa/VSq1V1MGXXOc5j9N0Op5Op2Ne5lIWkWwmAE5EHjDXCtMUYwrMYu5EgGgO6r527BE3tS5qUqWULFqZMQRaiJmxQaQREAlb6VFVHJyYEbGVaTkEAN9sth4C4Yqwe3yCw+mWIsQ41KrjfLrfX9/cfX1z84ePN19fX/9we3cNYMM2IbiKlGkpkyynOk25ZhHJRY5zidzuwgghTKfru5hCIGIAqIAVWZEQsJ/nk4pXMQMqIhe7NylumAJiQAzYYkp0fIxLH6um5xcEq6X5qfFMCP38zVZV1iLHUu7m5ftp+u50+uZw+Pbh/v393fXd9e3xeKxaFRwCq6AKugEaq8JckKOF5Aam6kwUGKoXKYtWkaquXAoe7k/724f97kOkZFZFF9HsgDFe9f3b7eZXXXexJpqfCoH48oYBAAJgPCuWx1AZHQifqQ4Ad3T0IjLPy/F0Op5ORJS6tNtutr6NkRtAyQFyztM87w+HaZlDDH3X73bbTT8EDoxtpoGIoeEr12IHECIQEqCjN2uPT6WTH03/j2w+IjAFIkEEBzc3dTd7oZTW3MI5DGq+iLe8pUHDC1QzAEf0KnXK8+F0vL2/vbm9edg/MNPx9JCXGd2vLl/thm0TuDanZ5/hGT7Az52bZ/uHSO2T5u6u7cboZ2TrfOH1mf1Jwkw153x7e/2Pv//bf/rD371//93pdMzZTQBctVaJtRRwjV2XNlsCKkiVMIKzqZupBABQtUzkZhhY51nyoiKQc231CBN0izHsmBIyIdHqdz35ND/llzjo4o6q1VxbDRER83QcD3fT4WY+3CzjfV2OROYBYgBicFMzPad+GlLBVFRVpIq7m4Gqi3hpPi9hSqHvU5dijNZ6d9wRKbQWMHdUVVrBtO7k3jAGbgYAeV7Gw3y85zggDdzt+u1rJPZnqBpG79gZvEEHxFdHcA2yz7sFH0Pfs01eM2GPtvwR1fEUxz9F8y8Ee80otjzT83j8bMYfv/vC4J/3xPorPrXYn0nIP6slfCZ+bHsGV/EV92KQ3Yt6nfP1afqmyHfm7xFnRiMEIAKsiMVsKXIAOKq+d4jmAHSPtBfJWdzkHr1jDl13CSjEIdAOcXiWFXgU/U+tO346Ww6uDuau5mpA6lotW6moCGgOVlWqVFuniwjQzUVkmZdcSiskN2RsDkJEAAYAIVBKDgMzhWHYogfCxRViighoatBSSBFDZERTNnJHUNOqFaWaZ8h5qTWrVnNrWAtTr6qmAuck5/MJd3M1q6K1aqlWq9WqVURMl2WZ5mXJeSlzLkupRUTAgYiQKMbQdR0HBsQiNZeiqgBAgTkGVzBTdzSDEEIMyb1UV3cCbyU5dgAzIDqLs0OV1QdW81p1HOcY4zROeclLfvv2rXcpAcCy5MdHMLd/+P3/6+r1mzevv0ppk+syTsf3H759//7r27tvDoe7eRyZicgQXKqWRUyAPESGoeu3m2EzhL5j1apVtSqoaJXKjAhETgGR3KECOXGpyVQwl3IaD29efffu7W9229d9t0tpG+MuxD5yB0AtfMTPtJCBP4tT/tw4f8zBXUWm4/H7h4c/3N//w+Hwh1w/TNP1/v7+8DBOh7rMksUV3YO4o1WshcpENaMIcMTUE0cgcmMQdWYgQnBGQDWY53r94QPj382H09XVP5iLWlFbkHHYXr1+89vffPU3zL8LYYscP28dzyMgprVG0jBfcFY+65/HopA6LNPy8fbmw/XN9d0tx7C72L16dfkqX3QpxsDoZOqncTocj7f7h+N0osD90L+6urq6vNz0QyTWWsEhpUTELQRiohRD33UBw+pJrUDlz9r1c3DyclXcQdVEraqKmbiTeYvtEBzdcfUZnus/QIIVHAXg4KWWaTkVyYhQSj6Np/v7++ub65ub65ubm1oyM//FV7+ppf72L63vusiMAGsj+Op0N7DgenOrJX5Uums433wNdzd0Q/8p6/i5sXooUCWfTvfX19//6Zs/3N5cg1PkQUmyzSXnZT4R1K7rhmFzddVfXEXzWS2nFEPkWrNa7Tpy1Gk/lZJNQ0pQskoFd1a1aTrd3X0EC4T9dvM6hLQZiDCtua7n1v1zmE03LdONgeUyq1RzQyRCWuZxPu7rcnDLTB4TgZuDFlEv2rANzES8RjVEjAH7TQqJa5VSatVapC5LEVEHGIY+hijckoxVFUIIzKE1c5qpiSgRIrobgKrV1vxcRVTdHTl0SJ1jx93F5nKKsX8OmEU3VCEHcuCzMAE6raWcRzu6RsBwfvE8an80w4+h9qOlfkx5n4XgbPGfXj8T+mfvvMgUwMuLIOCnC/KpaX+86Wcpg6eLPd946CBmi9hB9L7qKDKP84fj9I3ZDfMhBufAiExEgAZeRbSqidxXHkLYEiWkzDzXkmupJR/cQup6DKb22r04KICdnaJH5+Yzz/zpTkGIkQzdXcSQFFDMMpE4AJibmJYqRaSZ9UBMQCJSc1lyLqWEEEIIREzESAURiZCZQmQzZLYucQihH9ZMGTETIsDa4BUShRSJLFhr6UV1c1RzMHdHxQAEQA4OaGsb7KOH98mOWY3oNC/TnJdFSpEqJqpVZMnL4Xia5nlZllJyrcVMGYGIOYSYYuw6JBSRWmstxax5x0CBGQGV3EHEQ0AABm/IaCZyIiamtaOs+ZRrr50KoJpX0SXXVpVfVlYfdce+7wDwdDo9PoGZ/f7rv93e7r54e3d5+a5L/bScDsf7+4eb/cPdPB1VDZzrUs28FqlFTZEpdF3ou77vui5yYHBVUTVxE3dXNwM3QGdCIFCviB6SqbqK5TydTrfj+H5Z3l9evN0Or4bNm83m3WbzBvAVc2oYcHrmSD2K2M9ZxU/k7Nm+cCu1Hg6Hbz9+/G8fP/zdfv+1wX0ux+P9NB6lzlwqiaESWOurEVQBqZhnyItzABPqeoiduYGRoyEEdCM3MHVTPTw8oNl0fBj6zdr26JkTXr5+lfN+u7ns+80wYODdz7FMIkRsUTs6oCH4Z6y7g4GYZpH94fj+4/W373/44fo6pHBxdXFcxnEZ+y5FZhCoRQ+H0/3hcH/cn5aJEvdD/2oaX42ni802cqjLAu59P7QWLAKMgTf9cHmx2/R9CoGJzoptjRr82SI8LsiPF8Xa7lDLVZbSsl9M6ETekmXkeIbGuyMS0VqyJYQGWJCyPzwcxn2teVnm03g6HPb39/e3d7e3N7fjNJqoiOx2F1eXV+/efRFiBCRRc5ezi0CPc+bYAvdzLA9uBqvpt7Xdfq3D/wtGa8nQXMaH/ce7uw+H/UOeMzMTpgZldHDVoubDkDbbfrPt+yGYuRrEGEIIzGgemL1Klgp5sRTM1UpVFXADFQMQN68F+u7iyy/+Yru9SF0fGQFCo7J50sGfS2mZyXT4wVyXPIpWM2twhrLkPE+SJ9OMIMzQqn5VitQqIuaeUgwA5tpwPMzMISCxOWAVM1cVNVFVB7AWXBhqC3EFABEZEcDc1MRUwdYY1t1Ea6kll5JzKaVUcaJE3GEY0uAQhpjsxYqs7lfr/KQWoQMC02PeGxAACQgRCZHOkPnnMLqzvJ6D72cNgU9G69yL+VK2n33q03nGF38BwHMv69MPf5KQx2emHQDwGdqoyS89MoGollL3U/5hnL+Zl9tpGXM5VDkEnrouIwBTYHTioNpMURGpIVCMqR8uurQlgsimwS22HhnuutSlbQw7pqGJC4IjNhBa257rba3piCfk+NODEdLVq12WeVpyKaJeSyViCIE4oJqVKqVKqeqIiNwC0lpLKaWWaqaNjo2YEcncAZADpy5uoNdgOVc3DwxE0G3IreGLYeWcA05dil0KTIRuYlqdmSEwdOQVKHW8sTnjUnCp2cQjY/DInBiZXsIcHVDNc5HDcbq7359O0zhnbeV201LKNE3zNM7zXEtREQSnQEhMgZFIzVqxqdQ6z0vrXiPmEEKIiRItS8m5uhczUlVzRArMxIGJQKSaKbU0o7fmdAMAVqUqgQNzCBzc0R1VbZ5zlxJzuL29e3oEtx8+/sE/+h/+8PWb17/+3W//DWAlxhijOyBySskdc7Zaa8ni6uDIgRNTjAgmWhUBXRyVwACAGDlw48DQWquomDsHJCZXrzCaZZWT6UOdf7jrdl262O1+9er1X755+7s3/tuuf8U8MAV/Qqf8RAnxz42zLTLR05LfH/Z/vL39p4eH70/HO8dJaikLSKZSQBUwYowEAVVBqgMCsjlCFTDHUCgwBHYnACRzAiMVqdXMgMjRq9Vxf7fsndzIHQxq7Fm19kO/3//DZpc4IgVC2gCGn3oWQmSg1Z981r3V/k+NT8T9NC93h/33Hz98//Hjx9vbh8MeGMdlmpdpHI8pRkIoc53HfDiOx3HKWhUt9V2fay5yOJz6lBhJSgH3mBIRmhoTdSle7i7evn796uLyYrvddF2XEiPC6pK8UIGr3+GfWR1ENIciPi7ycFpiZHfmgDFADNjYK6iZ8Vb4bc50a0MhAPSq9Tgefvjw3fXNh+NxX0qe53mcptNpPB1PJVd3mOb5OI7jMmUp0ROBj9M0HidCjiH1/dClDtb2AvcznZ0bgpsD1eoiqmqtd67hDX8aUPAjAXNwV/U8zQ83t9+M8912uynlap5zXvI0nZDqxUVCJIS4u+h2FwyUp3lEqkgKKgZEjAxeZcl5IeQUt4idKWvVZSnTNBHC7jKqmlTaH+6Ox7v59dvtbhdCACQEc280c/hTW8S0Ptz+0cHUylobaQ2vYiqiNWvNZsVczES1lpJLLu6tIBiQ2BVEzWoFkBgckKqCGIkBIHep65IjYoxdC5M5xJi6mDpEqKLVqnsFV3BvaRVogZxW0VprzbVMcy7VKEDquovtdnPxerO9irE7vKiCPCbU2wOj00qV16w7rnliIIImYXgGlbSPf256Wj0enko36+o+BfPtRz+e3Bfv/Et8wh9H7eei1JPXcQaaru6omhW1Jdf9NH08jn/an/5wnK6neVQtiNp3iuiMROjOTmiiVquUXKsUCWBeiZHIEdAMAJTZMDmAx4TMAFBVT8Uc6YAYmVKAgSjhOax8CuM/O4uEl1ebubh6XYpIw8IDhEgxkpnVqiKm6tpozsxctdZSazE1AARnOjcRipqvGbbIzMwBWlc3OgbuuggAa6IHoG1uIFB3JiZmAHEXJMQADAhOPKSwQMwUZvRRdamkhAhdl1JINCGck0Mt3VeKTnPZH8a7+8PxNM5Lbka21ppLWZY55yy1iIirIgI5mruZV1FRb36mKuQqOWcVjTF0PSRkZDLDUhVAiVp1gpkJyIBA3cRcVdtUP+/ndnBqbZ/oTihi07So2pJL33UpdeNper4ipczjcjo8fHd985GD73Z9lQXQEYk5BE4iVmopi+dsiBAYQ8AYCdG0WnXygmoggrW6NdgIIRK6QWO0AmBiBmdXFy3ic4ZxOd2fkAN3KWwuLm+mcTSFEDaXEIeha+Cmn4Zp/qR8Pb46Q7fEbC7lZpq+G8fvx9OHeTos8+xeRKzMWBdaZnf01BEnxEBQXdCBnDsPFUJFAkRyM695jTga00YtUKsTYYygVUsuWkHE3dgdzDX2HBLuLm72h68vXm37zesQLwJ2iJ8BZJ4f4NOHXrNhfm4DAyhqh2l6f3P7w8fr65ubh8NhyrO5LXmuUnJeQgjosIzLeJynOS+lGAIG6lRVTUSXOTcgS+P5iikhoaoiYhfD5e5iysucc1Wx3QUR4UpivCYyHc/Wst3h89rj+R0kMsciNi714TTHFAhjjNQlMCdwZyLGJ0WGZrZKTYvEXM2qyjie3n/4/vrmY85zKbnUWnLNWUwdgae8jPN4nE6H8ahuCPhwf9jfH/rU7zYXzcDDWnFvBEpubq4tkwtSTdVtZeVtP/4XyZubq8gyzw/3+x+WZT8MwzzV8VhqqaJ56GB32aWUmGLXhdSpSC515uBM6FrVsKNIgUANCft+06XAHAFQFYlcanHT1CFhURkPh/v7h/dXV5ebzYCEMTQzdw4PfyKppSrHh/cOqlbd9VFhIDIju1WzolrVauPeNVU1o5UIMSA0WJWLiJmZKmIjBSTEEAIEAqKGxSQ3K7Wao0FQVzcVKeCFsDBhYAZbhcXB2+ybmVSr1XIxVHPy3sicP0992DbgORZ3BCQgQiY8J+ShVbT4DNeHJ5/n7JJ+eslnPzwHzfiUcm5dXj9jun/k2z6WmZ4qWS/G56N2OAPyzlD7VW7dRPVU5H7OH8f59nC4Oxw/Ho4fp/mQawHSGIEQAiNDBOsqOaCamZmLBFNGUlUoRU2nNufMyIwpMWF1GEVv5qJZPoIRYuDQxXDRxTeRXwW6YNoyDghhrZLimRXheY4CYbPt4+AU4DTmcW5d3GgVxQCACCgFihFKlVxKFZMqUs0VEZiYIndd7FPXcaCixd1SHzeb4eJi2w/dmflUADCmnjk0WRSVUmopUuqSi/edDn2HbuZKaBiMAkbmYDFWToXjTBZEqdZSALAfYp96WujRtKtZrjIt+XCaD8f5dJqrGHPgwI4uo8liVaTW2oICc0WHqmDuuVZAQqSYUt/3gMGMqkDJUqpXgRgthFCLiHoMCNiKVQ3qZCJZpIpUNX1sLkRszDTNHnMgTjH1XU+IZiZVC9bAse+bT/MkU19+8euH49397R/v7n/45tt0cTGonHI+MSOEhBRRdeXgUSWGluQCdBXTYkosGEqFUixXNfe6jf1AFBRJDYQZOYQQAyKZqohLriUrmAfkFHKKVZaopWe8SvEd00XfvcbwPEEFP6m0fmqbnV+rTKXcnsZvjsc/1HJHoOgAxlKpLlgmXEafRvHgvMVIRASAIG4eIHXIPcUtuSIZSvFpMld0I0JDhFpdBGKCvgdytwox9IGigZuqmWiFPNp4nMbT9Tj+sN39LqVfIV3is/j307tf6yyw9qb7E2jWwQFIzJcq+3H6cHP78fZ2fzzmsrR6TM7Sej9iCARUllpEibnr+yJVVbWIUO27hAC11FpLrZVWDDGLVDWbF8i1FmmsiA4OgRm7gTisCvUsOY+gHj/b++faiogNYBH1pXRj7q1PKTg5KhDZ6p23LvdVhSMiKgCatWDLHLtu0w87wLDkcnt3O8+juamYSiu1paUsU5nvjw/ff/whhaRVT4dxHpc3r952sRdRqXa+SW9+dcN/oSOBt3gdWr6W/Mdwpz8jaehmtdZpXvbjdFvqmFIKgWst5tL3uN3x7iIOm5Rip6a1juoFSIiRCUXUzEOAENMw9EO/cYuuwYHcfLOpXZfBUs6Z0ExBQE6n/Q8/fBMThwjmenHBKT4eL/Booz41JO62TGOVZV5GM0Fsmzj0fT/0A5Ehqdacy9JWlohS7JhS4EQQQJkhAEanUrVKUQBj5sBh6DrwwOhEiIhVdFpyqUuLrgCDg7lJF33ocLPpmIObq8pZiogoIAg6EUQCE8V5Lnb3UCqUKn3q8bm31TxBQgAkQkds1IxEQK3iDo/VdGvMwo/Qt0/T8S/W8en9My6jAUFaDHk2tyu24mdF4ulK54z+5xpfP1NrhxXF97iE5q4O6lZFx1xu5vzhOH17PN0cDqdxPMzLQTQjGhEwIThKpdm4ZAIQ87ImLoAQmAgRTdVUas5Vzfqehz4gIZHmMmopGU+IAcCQKMQYw6XYuy68S+Fd4FfRr5i2TD2u+PlPaU0BAAkC0TB0aijSErf4CJtCMCJCRjMQEgGAdR8yM4cYu9g3GCZHwgrqtdUD3a01lZlpLVUjpOR9x0wRXF1AqiyLqFTA3HelbPpASOSMwIRACkEdCQIwYDSOHYcFa3FTNVUzfV71KbUejqeH/enubv+wPxxPk7khYUdrACSNY8ONiAijIrqZmlepqg5AxAEpEXddH4ZqolCyNqLy5hUZEHPi2HFMiAhg52t4FVMDd2qcL+d0bbPvRMyExBxiTExkvp79oapV5HFfNTl+/eodEKT0/fF0fNhfV+kYi0pBYOZAFI0E0ZAUSZkxJiJ0U9MqVq2Yg4MIFYEi3tpZRLEfPPUeAnBY78kVRLAsnmebR/FqDJKidR1IOansU7obNrf98G67nREphgBE5xzVo9f886p33U5rnthNdFnywzR9HKf3OR9VqorVAjVTnmg++XT0aQbsvCvA1clcqps5MFBC7jAOZAU0e60+Ly4LurYsoLuTQyBEIZodtcJmG/uuB1BAQQQEc0cVy3nJeam1qoqv7uHnVYS6i6k1EGwVc2cOzMztfA6VcVpu9ocPH2/ef7y+vbs/nU5iFVdiDXQzKeLqCNSOFOIQIhKA52quqrVqrUZkKmba8tJu7qa11OYBLLlU0WbzwCAg4QXGzZbpyXgYgJuLq5itxPXP5ArAbWV3EC86F8PojUuPFIgc0VsQTQ1h5dBIk9xdVczU3HJdVAExgFOtOp7GcTwaNHFGhMDs4zTeP9wPHwcHDxRBAZ0iR2aOZ2zgejIDObSqeqO1e1bMPCdDCT8JRP78cNEyjvvD8fZwvF2WfaBLZksdDh6QumEAjp46GAZcFlnyJNqoZCNgOOfaiCmmFJjZJEjFnNXdUopMDJamcSnlZFIJfVnG65vvQnRmFVUDvtxi36eVn/BJqF5Kl7vUknOex1FUEIGIA0eEpvTNXBwUcYVP4dnPaZ060HqPGAiZkWrNDhYjM6M7gyu4IgARYsacRWs5zaUKqGErhG8GIowxhsDmbqIGqyg1A8GBu5SiuUmRnEuph1LFTTfDcPmixwqfLC2dhZ4AVyTdI2kM4KeuJvyzYoPW72+Ql5xzkVrdLaXUdSnGQKG1YDzlpZ/P8kuS+XO8/wRBfTE+bX57Hrk7uHs1L+7VbK5yyOVmWr47TT8cxuvT6TBNRbV2vWwCckiI2FrCRSEvqnURrWal72gYQt9D7D0lDIlUoIrVYiLK1FoVg5vNU16yORAzpY5DgGpWJYl9rPF1p+8iv438NoUvUvgy8Jaow0ZB+Bzy5z6OI5A6UODQpx5da3Hi0MhVSs22OtUeI7sFMCcgN0ip6/qudcMH5tYlZSplUa21LjmEgNgQehpD0oLbrW+GjTvWBfJkyyi5lKoSw9j1aehi18VWmOdMxAxOqIxAJmaq7lByLUvRYnMoX5o9Lsk4TR8+frx/ON7dHQ6H0zhOVaq5DZs+RJ6XpdYsKoCYUhdDcLOSyziNOZtKaxRhor7rdzGGYXMR070ZimpY4+5ASETcpaFLvaioVF9ZldERiSIABmZEUK0O2qoELWmhwKrqbjF1XdeZaWucOx720zQ+F6rt9kK8Xl29cncmVlF1cXW3yBRi7MzKygkIkCL2HUoteZG6WM0uS9FakTvkpA4GME9Vzbsudil2AzCjGkoFKVhmnEdYJswTaQVUiAGGAUwdqA77eXO/32xvh/7CrWw3Gw4RW7UP+eyOP8uufW5rnv+0DWOiOZfTUqZc5iXP87yM4zKeihZYpnDYl/GkWZCNpiOpAaIArq13ohQjph4seAEz8FywLOgVmSAGiom7yBwc3PNseVm5Y4gA0J2dE/ab1PcD+sZkQBiIunNfzOcewkHcimoRWUo5TZOoxpj6vt8OWwCYp/n65u6P3/3wp+++/+GH94fjvtRMDCFyF+KKr6DggG6AjkwUmYjInRFIVLUueQIwCTH0KXqMAEAOIqql1lyKVInCiPeADfYCqgy0HYaIK82eu4tpEZlKnvKSpVaVKS+Pz2FmeVnykkuu1BsAO7A6iVFVBzA3VwZlCGGlzkUARFeTaTpN87gsS66lSJnGpVZrCE0pqlr9DM0yw8Ph8P33P5RSj4fjpt9u+u2vv/z1b776zRdvv3xz9ZopItCKkkPw1jyJrVpFgSgwces4WcOmFgb+M+umDmClLnf3N9fX7x/ub0+nY5+UAr79YqgFxIL6VGsuFVKHZlltyTnXqgADh0AYOXCKmxQ3gREAzHRZysPDUVSuXm2Gfvv69asu5uvrUkUpsUo5HG4ccq7HaVmqon1JX6RtO+bqfFefilaTS0QiiqhuauCgAMtcRCwE4ODMHMLGzFWtZBFRU0OoXTeEjrvEIQZTEsFSwV1T1w6jchUtJYMDUUCODkF0HKcitYoChRYhECLWquD50XkyB1V1d6LUDwNHIJYsR11m8WxWIqvL5mL7es1JAKwh9BMg/sxIfP4D8ISEb9mo9YSCF0v6ZJefWBxwLTC5Qc317vb+9ub2eDyq1NevXr158+ry1dVmuyHmtQz+gqNinWR8fAlwDvkfRe/F+ISy5uxTYqvzVtGj2tFsFj3WejcvH4/T96fTzWE8zPNcqhJ5Stj3oevIlfPiuVpdrBSVCrVUUZHK4Ijk55N4wI3MEIBxJev21nJeVUspqsoBiVskIGakNokcJT5Evgn8uo8HAAH8MtFrxPiJu+QO02lBNuboQJGDB3JTZMYQwF3d7UxrjYwcOEQDRxNgZqIAQO4uIqBaS6lSWmGjUMGWTnUAxxjUKmp23bobzPM8z/M0L1lK1UqEceI8dMOQQkAixwDIwB6jh0ARgcC1hadlqVI8k77b2CO6eRynDx+v94fTfn+a52VZ8rJkkTrPkQNXqVWrijIzEcWYwNGMzBaR6s4BUwgDc48QiGLXcdfNHBNgbRjUECJTaGB6pDWRfN65xBzXzgBmcFeDRv2OaM1RdneSmksJMXZdAgQzKyXXWvMzFQwAohnQNpuh1srE4OoKUl2yM7dkEnNMCYACp+ApglaRYnmxPHuZtBaPiUMX2rEkjd+EiWMIjAJuLuYSUDtQsApa0Y21Sl1qIVdxR6Uo42k5Hg431+/N7Hjc7XbbYXPRDxddv41pwxhWR/zPON/+LG+GiCGEoete9f0XXTdzOKnSPFmecD7Z8WDL4o7kzMtEqoZoxEaRmv2JEYbewd3MEJ0Dc0BRQEaK1PWp76KZSK2lgrmGVDBg6ixEY/Z+271+++btF1/sLt6k9LqdmvPzceFSymEaj+N4OJ32x2OpNcZut92+evWaicZx+v7jhz/+6U/f/fD+5uZ2yZOZBKaYQtd11CU3NFo512opqtVCiIERPBI1Kal5Ma2p71OKzIxI7divlQYWwM1rlWmatJQUwtV2O+Y5S2VmdnIAdR3n6TiN+9NxP52mZVlqlecu42M51wGdAMiMRMBdVS2QZfLAFNhi8MAemJiB0GrN94eH/f5+WeZSiqjc39+dDsdlXOpSNVdRAUAKzQrLPE73cNv4+PhduLq42m23l5eXQ983qBbAemrF6vQ1BWqIjaN3NeqPd4ufRF9Pyvfp7ydQi7nmPN/d397d3Z5O4zJPABA4dj0SI2S0CiIuVZupBHfEBoTtzicpBLBUM5Us5mJqpeacFzU1Txw8UJBibmRKhIEIADTn0/29cOhiukhpt9u9wYGZ+mdBFH5y80QYQkipA4BSKgCCo4qLlJgwAXUphhCkipghYjPb4EAMHIADpIgeOVoMou4UIhMTAoqgg6jYyuxOCOCqYlrNAFvHifiyqNRKRMyh1QTNodHmpxhDpBBDB6GvpVr1vKhMy+zoxTevnp4GEZDPr+ClMX1s/IZHHoYzCu3xg/7yDKqmKPxM6YAAoKLzlK8/3Hzzx2/ub29LWd69ffPlr7746quv3rx70/V9SJGYz5woZz7j55P+5Lr7c4P/fLxMyOP5XDp0tSx6XOqHXK/NDqL7Wu/m+eF43J9O4zSXqgZoFAzJiDxwrGJ18WWyZWlEJu4OqlCKTaQKUhV4oRBCTBxCSF1AUGJB8NYrQRSIrYqAGTgjBPRk5qVWoQdNR6IbpqF2D44ZSUPoCRAwPH82dz8dC7KGYCHEyDEyaXBDEJfitXrrg7WV65+QAqOCq1VVz0WUWQGruUuus0jFBqFAAkdVA8DIbMGtWp3rfJhFbFlyqVVUHc0ZFFyKSNWSCwciBozKEQbqEHsKTsiExuwM7uqlSAXw4elBpmn6cP1xnvI8LyU30NySl+V4bKB8RQRi7rpek1qAhjV1I4QYYur7zXa7C7HLWUQF0XMpZureYvJG30LQGutNVM3W49Kgsc+GkJiiG6kIQFF1c3F3avCP1pdlpioiBQnaadAlLyovKGvu9t9nWTjgdrN1I9daVbTWccyAta/KMcSUKLJIDajoYoo1Y559mb0WUAEnU6rITmhdpGHgrkuEtEyLSHXrCPpElxiChGRxZiyu8zxKreagFDX0dZ6XcTrKh3pz+11KvBnS2y/+4otf//bN299cXQViQvwMP+Nnxpl1DIC7eBW2oYv90L9C2Obs1++PJe8f7uW015LVHLhjAK4ZTJzWU9VMFEpRJhx6jC2vQ3z1inPnZQEm6lLYDrFL8XTQnLWqOvlSTpgnSpQCxy7urjZf/dXvfvNXvx02u83uXYjBXc5nlXx+HKZxRHn/4ePN3e3+dCqlEvN2t/vi3RcpdaWU9x8+fvvDDx8/Xh+Px1KyiTBBYO661HVdDDHGxMQAME7jPE8hhtTFvu9TlxIHJVzyMi8zL0tsX4mRkRExpUBMUVIjjNGqxbyqKnqxeiyjgAYOAC4mH++u319/uH24vz8cTvM05/ybub+AeFZWGGNMMaUYAzMBmpqaOihAbUqJkIgoMsfAKXIMyKQ5j+9vbu7vr82qlDJP0/3N9c2HD/u7++U0yVJVFZnACdnQsZYyAew2W0J4c3X1u9/+1dvXb4hgnE9LXoZuGLohhEgUAHGtS/lKXtPyn9iiZVNtnV9/vvntyUpILfM0Pjzc7/fHUlTUap3M0B1KlmnK5koUVbkUV0WiuN1smPquG1LqTEgEltlPx3lZRrUybGIIGFOIyDEioIospVap6kbM3dDHYYvE6mjzcry5/X6zeXV1+YYIt8MbxPiM/vNFySFGYg6IHa4sZsgczBrWqrVxshtJtVKEAw/D6j3FEENEJDOoxM4BMITVf3/UVEy1ai45LzIvMi9TrUsr6gOoqkyTLZO28nyIKaS45uCIEElKYYWuG0IXrniXenrY67IsUufF5XlJ1x2tsRr5euLLWvXFZsThzPMObkAIQGuLtp8ZCdvjr3H8Oc3hj2cUOZRcj4fThx8+/vGfvrm5fr/M48fL3c3HL6bTaZn/8ur1q93FLvZdTJFaUPjYMnk25ysc7nEV1kDjhVz9KCGP2AAnajmX/XF6Py3fqj6Y701PpUy55Cpi7TBiclWrVWNoAehqAOEMn25ljtYGjdWBADIieT/AMEAXMARCYoDm0COsHD4A5qpmakQMDqZmVh2UaGwnSofYx3CZ4jukgEjPn8rdl6UiW0qEwITuiMRkCI3A2cDVDdyAqDXztXSGgauqGATX4IhsDqJufqYmaGFWEQF3sADmrqqitVRVz7mqqLphIEYGRHOrVc08Rg6JAjt7ExIH1RacMEPXx80GagE3fq6Ql2V5uH8Q0QYapcYPSqRV1QRb1sPB1UrJCMQcS5GVYpYbZU1MkRFcRc1FVVbv7UxnaObuKmB4Rkm3UzwapI15rUHZ+fRrAkemGFLg6IZm3ljlHSyEdoC6mb8gbgTwabnPslQVNQAlN0FSJANUdxVBYA8cCTEw0zpFjBDcXEXdaIWsmHOAmGizjbuLmFIE8zLjsripMooGNaVawBTNQA3UwdShGM0F4xQeHjAwMYpVZu+7MM4TMKdus929ipSew8d/UgGfN5KrqFaVReqYl3kZyzJKmV2F3UKT2NgZMYWOOJBjA2qiA6q6ZM+Tu0Jh7DrqB04JNzuMnZfs6B7QY28cmgPdGNZcrBbxJKSWiGNMgSPHrttsX213b7t0wdwhPj8ECj+59/v9wWb6cHN9fXt7PJ6WktWg67rbu4eUkjvs94eHw0MuC4AHJsfQHAWtmjUL10L5mWmfkTGmWDZl2Axd1yGj1bXfqKUaCIBjw2cgM4TgzcIRQgwBEZaa7w4P1WqfuhgiMZnr+5v333344f7wcDydpmVZcn3LX1ystMFARF2XUkoxRCJydzNXMHN1MCRDAkADhKAQ1IIIk4HlaXr4/vrj3d0H1yol52k63N8d9g/LNEouWmWVXrLWF28qedFpOo7HwzSdlnl6oLvxNAaOKXWvr16HGMj5zLl9Jihpf9Ej5X7TFtbGJwL16QqtQaGXstzf375//913f/ru+uNtntsFykqFZ8ScUE1FlslrrczAnJhT5C5QIkgcEwKNp8N+P87zCVCIL2LsU6JW9q6l1OrTXIuIK0rlwLtXVxdIdVoOInI43t3dfXd5cUHojJTSjqlfewf9xV37OvPYDnpGQA7BFF0AHEQcQES01KqqMcWuT80kIri7qIErkTuu+SxTXUlAVa1UybUuS8lZq5haS/U25BNYBRMx0cAcY0q9JXcKgTgQUFPlrspakSBGQky1dGDS0srP97o5quL5xFfHlddsZfeAM2x+xeYQoJ0L8K2j42x1n+Nz1w5T9yolL3J3/fDDdx8/frjZ3x/KUqzqMk2H+/vb7SYQ5WWepsvNbjtsd5vdJqRkDqag6i1VgI1kjRqZWJMfQ0eznzHtcC4guFVZpuXwsP+wP32reg94CizgZuBITgzoqAImrsUDuCULBJuB3bGddqymgIrk2NihANVICqqAVnMRHxx6T5ERWIXNwMXRjRHcvRZB8NhB4wZ1JxNzMuRa9ZTrXRdvqz4g9IHSi63hUItQ8JjQAGtrCub1wXBNm4Gv51KSuol6da/rOZ4WMRhRYGDmGFKrO7e+WBFBc1NVWLtY0VGdHYECGTisTArczpRtlWnAdlg8dwP1EJJx66AzVAywvdx2CWtBF2J/6muvtdo4NvUQmBvKokudqKgZojmCqZpBWZaSCyOrmcgCDgiBUAJbjMiMIrrkAi6B0VpxCLwltWptKRZKMcYYAcwMc66lZjVLScBDc2DMjIhi7Lbbqz71pp5zPp2OpRRE6LrUdSnE4JAEa5HyuOfVc5XjaZzyJIwhMMboqdetowgAqJtLUWiK0BGcGVOKGhgQnZkZuGGnU8LNli6u0u4ihMAqWnKcR69FTWeGe/eg0vj6csnVDR1AVedlFlexOi8nYnRQYuv6yKnfXr599frXKtWDPdbM/CkH9uOBCOZeVY55fpjGm+Pxw93d9zc33918/OH248fpUAN1m00JsYaAHJAYAL0dqEmIZpQLSHU0K4sv1WrPBDEl7AZLvXWDaxEVMHCxSlH7LWAhMSNEVy9ZS5TtDt1tv7/e3G23uy9Suuq7L1N8Qxh+stYOfnN3VwPsj6d5XuZlmaZ5yUVEv4PvmTnG6Oal1JRC10VCZOI1OVNFcqmlZpkbdKzUWmo1d6BlnpZ+mC8uLrqhMwFUFKkuphwtJEoYAiOieztUGRAgxNB3iUM4nk7TNCJ6SrHv+hgDENw93N/sb6dpWmo2V2Z8TtGKRF3fpy5zDIAgqsQKGJrmOx+X02h5UMClVpW8TPv9w/X3Hz7c3b6vyyRlcallmuZlak0yDd+Obu7G4ExkpiL1eHj4wBgClbIM/S6l/ury1Zs372KMm92OgoEZnol7n4j5GxQLyRt//tr39RwM+Iy45Pz3Cn1zm8bTt9/+4b///X/7h//+j/d3H/ueAKHUaq5EtNteXF28Xub88LDP46xeN7tut+vR0aW6EiQeNheB0zLf39wcqyxdx2Aphl0gM68qc8nzskynUWoV0zCO/ubN5uryrzhWePhhXqZlGu/uv08JECQQXex+3XcBKTZb9UzrWi6Lu6uaWjv9kmMMqgSIDVtZcjZTQA8BG1rWrRHbuxswRSIGMEAjAjOrtaoaOKlqXU+bF1V0ZGD2QG6+HphpJkWkaAhRtZUTYyCOMSFyg9EAgEgxrSEyIvR97w5+mk1fbHM1L/LEI3Y+8cVXIjo6h0BruR3p3FIGuOYYWtl+Bc2vZXNHBAM7naa72/0f/+nbb/7w3cPtvTu+unqdIjFZilzm5ebDx9N42tzvdldXr96++YK/HIjVXCq0fYaIxBgYORAzAoK7gTmYi+rzB/lRQh7BQc3ykg/H0+1+f3t/uHM7EC0xOSLUCrVAKSjiao0R2WuAWpSShQjMbm6lailSq4l4dCd2M/IVnUIt41qygoNpCNz6ycgcVMHcTU1mr8VD9RApEDaMPTg24hS1ol7cBdB+XFuspaI7FzFEMqPAyAyro2mqKqLuzu5OINVqFZHW3952lThQO7k0xo7DyiTl7khIjK6GK3skKBi4YEN4AFnDSri1rzf3llpuxhGU3YMKu5m4ZNAKRsbEMSWCQLg81eEQkBGJOIS42Wz6rm85kipVRERrlbosy9pB1I5VdmVuz1DM5lIDByNKiIJghB4YxNfmPVVq8UPLG8YYuy6JlCqtIQpCoJgCGBNAYVJFM1N1BCIKhCiisKYaVUSZxbzV/J7FJe7tzLhaZ9GKIXjDxrKH4O3r1proDVuZgdHdnZlDpBjZiQGiejtAiRDJFGo1rSjF5xNOR8hZpTaKOgIw83Y+pwO0cMqkimgVLcsyciRmjB2rDvuH4+3N3bsvjvO8dGGTQlu+FXX6Y9tuJqpLXh6W+W48Xp8OHw/7H/b7H+5uP9zf3TzcHU77qRRztS5CA+A7gIiJQFUFtMBgjrVAnb3MKAXAmYhCAERTUVEr2fPsNWsMmiKDITFyQBVS9Ua+0CUkjCmmwMwUQtjEeBHDjrn/OYyWw+3d/UI6z/OyFCmqVeqc52lZlgUAUkotExhiaNo5hdR4dBefS3ZVEanWuFfMEai1fqqYiqGTirqDmopUQGAncgrEZypyMPMGkQZGd8vzPI3HKlW1dikOmyGmSEynaTycjrkUEXF3MPRnWS0EQGJcz0RwNXUz5LPCJeTQfgm1sNlFXVTqMk/H0+Hh4f52Ph1qntHMai15cVdEIMLGegZgKzQaANRKmfd7iDEA4Nu3X7598wXH2G82nDpDUvNznP6YvG05tVVPtIjPYe08/PGirHCrtSbfsl96Go9/+tMfv/7DP11/vFnmmXkDgNOkZsLMfSKmLoYQgy4ZcjYkIDSw6qqbQeCC+sFipJhCipHYUxeZN67dNE25ZMDFPEv1JZuoSXGV6fCqSG1klElknJcJwJiVkFzx9at6dWH9cJFS7/C0zd291NJOu2xaAhDYGh+slrLknAGMCEMipAhgKqKte7eYKjAZIlXJ7hoCg0NupKHqpto0TBFTY8MwFzCIHDBSakdfuJuquRuiBNEoWqu4UzvRDtYpFQQPgZnJYaWvM3nhw5uZmrTk+bmlARDbYYgOjw2Aa2v7+Vw4XLPU6zlvcMbYA6wswwSqdjicfvj+w9d//Pbrr7+tSyYH3HZdF1LELhIDaM6HksdxmpdsgJuLCwqdOYiCKpgBApCiBQzu5gTgzbS7ub70UT4x7YgIbkXkNM33h8P1fv9w3E+Ildk4AoCVqqWCFHInWkNTFIFajEk5mLgupZ5OdZpqOzvUOkd0DhijdQnCgMyIaFKlVis5crB2HlWTflNXsVLIXClI6nGzoS6FFCK17lQPANzQYUwrNvK5Lami7uq0sBmnwB4DAiO1zi6RUqq4NVYZr6XmXE0buA/cvKqaKDozxaGPKQZpDV3kKXCIAwKaai1ScjU1NyFi5khIgUBV1Spaw6oRMwO6SMUFQMkFtDG8Oo6m2SoDBaCA/gl/WAphM2yYOKXuzZu3V69emVopknNe8jJO0ziNUsVUW7XRXEUcAUQNIddqp1M13cBuS0RMFgiYQGGVXQcjijFFohg4boYhxjhOYtmYOcRht9sNm8GVSy6qxbSUsuRlWZaFMTKx27mcCKAq8yxqUmtBB4annPAyaanmgBwpJmdSczOFdixpS3eZYs1asjBhCGxmQBYidj2ZsCmjgaGDkxQ47WuePFA0hdNexlFKllpNRdyByBzUzJhjFxMCuTc6xFJlyRljCl2XmC8I0jLr7e3h/v5wPI6b/mLTmwM2jfUJZ1iL49WWJd/d3vzD9fv/fnvz7f3NDw8P74+Hm2Ua53EZj2WZjSgREQcjQBFfZj8ddZ6sVEP2NAAiqPg8wXh0ZtrueHeJm0tFtPGkp5Ofjr5MXosPvW63oU8cAgMyAJVsoghOMqTAw2a4eHX1xeurX22GNyHsGlPNz4DoHOD+/v5kxc1N1dXQkAxRzJeqohDEuhg3HbiBGxN6MFyxX66uCmboDULMjRSmCtTqZjXLDLOWduyCqymAe9F2LE7aVDyflUKIIbDWKLWI1HmeSi1m0vf9ZjeELiJTLiXnXEVUtC2FdU8g05a0VTd1c3c1WylCARmRCSJhCBSZiZAAPHiGWAO1Kk4+7U/7+2WewAzXQ0M9BAbnBsRp7jIiECM7Nbqew+GYus0XX3715a9+/Zd/9dtf/eo3XTcgB3EyNVoDtcZw1fzQxruC7WCqcxPcJ2tyLsr66gB4U9Rax9PxT3/647fffjtPCwCaoBrkxc0gRijZSzbCbrt5KxpyhbJILbUso5blzduLmKLqkrru4qr/lb2u7WjKuF1m+vDD6Xi8i10NyQOzWqNKlmXKw2Z7c3O/uwg5S57r6TSpFCLVCuMhv30zffFFfvf2q1ev39mzU6DAvdaiKrVUVTUDlQTualZyPY2naRpTiptN3zoGRGSeVbXW5igqMJsZTNNJpHRdR0R5kZJrLlVFwV3Nq2FVLkbm5ND13W47BKl5GU9WJ4HZzUVdRatIlclgQYgA7E3jgAIqtXplSmZeluUTbkAHVa8rJ0ILQtYsvAM6mq/0Cg2n5QRrWvqc7z5H62vWHs7wJQcV3+9P333/8U/fffj2+49gHgPPOS9L9+7VZtNthtQRwcPpdJrmqspd9+r0LnYbx+ZTEACotx1IZhDcAdxMW3/CzyXkG+H+ko/H6WZ/+HA4Xk/jKc8F3BCdAgB6VagCWgmRUocheoyeEnBAJLe1nOTt5AYVRwciCOwaQZOGp5yaI7o7VrFSpMriXomFWZjNHVqt1HFlLCQkJiAiR2TqAvWBB6aeMCLyC22GOPQbxWpnuhyVWk2IAMCWJZdSWgmcsDXftrN5EczJ0Qwd1MyN0QOAEBBJtlorgIdAjI1gHj2AiJq1c1ZNTFe2KbDm3xFT16V+SA5upu5k4lIdxJkJKSJWRCcMjNx6bp8vBxHF1p+G1HAMKXZd6mJkIiyltMRfY+4Mkc1qJTUjqKJaVQ3ACjmTE5OZrUy07ew510c264YpdRBRm+dxGo/E0A/dZrO5vLwygSVm02pWzBYzn+fZDWOIpusRT4QUQogxiFZoePpnh1tHvnRkxs50BphVq1TTCiosRUtWq+CCKmbqGNiJ2n0yOwcPxExd7Lax6x1FLM/TcTotfQqMZEYrA5WtTJ6raUYk4kZLqQKqYmZgAu6MZoEIQuQdeFcy7B/Gjx9vuzh0McXWL/B0SMqz/QGmXkWnaby5u/n6/fd/+Pjh2+Phfp5OUkSy5sVqRgRACjE5IpYKZYEyQ11AFM47FExQM7gQR+wH6gfoeivZlwXGox/2lmcwaVAddIUY3d1F0JTROVAK3LuxKQe86NLblF6HsIOW0nyRiv/kOfx4OB7KvCYlDLRWWYrmqrlKFUWRKqISuhhi1CpalVvDZ5UGAzN3QgocAkdCzrkgYDvCoNbqbo+ocEQwRCl1meYq4ufjL7kh11VMas7L8XSqtTq4qREj1eqNPVRE1VTUmzfI+qix3F1askAVWNXc1RB05f3Ac4XUkAkNXGstuUijazBw9Vpqnhc3RXACp3bYF3NMERApBCJsZxw3hjk1W3I+Hk/7w/HhcLw4nLrN1BWPUSNboNCaZ1aSHCBGM/ZcpYg2qlk6VwM/P9aTaQ3AzGop82k8Hg4P43hycyIWcVUwZXcADyqYswZyV9bKNSNxjJFMai1Fxd1VrKjOIehmFwADOGvR8bAc9tM45R1RiIzIKXEMRFSWeZqm493ddZWkPo7jfDqMpRCiSvFlkmmqh+N4PD28Hb86nh4e793MptNBqpRa3ACA+t44tCBXERVRzbxWYHZiJ4oA3E58WJZSi7oXVZvGU5XaTHstVouUom7GRAZYlYpCVkAOMXUh9DEm0Am8gleH6i6qXoo4LmKuiswdc8fYmuvNV8iRMS+AUKvyU1NfEywDF3cEwOaDroyesIJdGjnz6qCRr45qy7o0TMoZvo4N03Q+W0DFlrkcj9PxOB3HGR0ic8m5LDO7JPQ+hphYxKpIbVzoVUyVQiQiBDJybFltN1BvzoX7ORv2sgD3wrSba5HlMN7fPXx///D94fixLJNW02pmjgGA0IBVSYUDIUQPDJuBhi10AwFBFTVXRA8BY0Qw0OoqUCuGClwdXE09RAgBUopIXLIvWU/jWKrHBF0Pm40zIwUiQI6YEsToHJzYGtA8hC7GbeANU48Y8SUXDxG9ef0m2zLX3LCPVaRKMRVvfMYiKm7mAEpIaND+uDaoBJoBoqMAVNfspeo85Zxnd42RvboNXYiM3ogP0UxE1vp742sLkZiBgvdDuLzaNIaZskiZzcEcjEKIKap37BypZwi6VJeX+Bo8F3WljtPIjNvtNqWukciI1FpzU6MhUIxsZg4chVRBVMEcmVTyNEk7LRGBGmWzra06Ym4uguhM2AKkh/3t6XgYhr4f4tD3lxeXWj1wNCluxSyXvCx5EdGusdy4MzMHHoZht9uZyTRPeV7mM9csIl5sv3JcHOaS78fx/VSyVK0FrdI8yuk4azZ0ihxSjAEpELVaI6IRWuxos9l+8cVfXL56O07T3cP9dJTxmHFLQxdTAB5IU98Un6qYL2pq5pH7rusAILsitQoYcPOVACP3Xbrq4hXhcDwsP3z/vk/9pu93u4uu76E197+wkG6N7sVKLfMyHQ/727u767xkqW6VTFbvuhRUtZoBEFWxVkenGIAYjVaPFowQIUbrOuh6iwmJ0R1rwZw9Z1NlBHaLUsNsnrOtZVyIKabNsBn6XirNk0odwF+F8DrEHSAaGAHBUwfOyyDRYR7n03QyVTBgIhfP81Ln0hgI3R1ywcycQkgxpbjEJYRARKbq4iau4hQphNR3fQyJeQZ3d62lcTeDWaPhJA7MkYmp5AqlqisRdilCCtQCbpUyL3mcRBWJNCUTU3UxVXVVU3UTq6VKrbpR6J+ZdpFapVZBUjVzNTcFA1JTBa1Y2BitZRDyMub5NI05V3eMHDoHUjMzBVVwYYQuhMAUQg9EyKy+8p66GwIikVSbpvn9+w+A6TTV+8NydfX24uL1drMdur6ZduIma8jENdhcylJLERFVIlup1V8syLpPfAXbtZCkzPNpmo6lZDNDZHCsxdup5UTMFNxxWSoBauVprNMku912M+zQmBxDDIggkpeCVTOQDEMHTjfH6eHhtCwzgPd9v91GZogp9H0/DHmZi/l8f/+hSqKo4zgej1MqROSmKOLH4/jDh/cfbz+8ffubm9snPmk1fbi/byYJgWJKzGiaOFAI0PcBMJZS5uVkXgEHZo4xmbm7lCzjaS5Va5WSs5rO04JEKqAKKsDEXSJHckMzNgtEiXBA6MCi1lIzSsWWf0GwJZdci6irYWMVS/0mxqDVaxFXq1LEqgNwCF162YvYTsNc3cPmxLZE2CPEfT3Zr2Xf4RzPrWcUrEvqANTeXDEfZiIm1WppARGouknNc11OBrKg1a5LF7TxsxVhIgJgxBiZOACAGSi5mqmYu5u2Ekxrvyf8GdM+Tvvbu+9u7v90e/f18fhhGo+lZBUQITMgcFgh02sh0ttRO+JSPVenlpQi6zqoBWoBXWNDAEdVrAW1QkXvescNOSBTAEdTr6Xk3I75cCZKHQFQDNgPlDpgMjeQKo1quhWL3arqQjC5uVl5MoiIF7urzrpYllnyIqWKapF6PvMUkEJrrPNWLFzzFaYto7EiHk2t5Oo6AWDj+SJyVyCsrhi6AAhqho4xREKrgKbuBoYrTNzPWD1iQgpSDdAwOJAZSzuvycycBZCYEDmsIrTKWAOkEBJO83HJp+HUx5REZF7ycTzOeSp1MTODGgo1WFwuuZRSSiHkwGyAusIrKAQMROIO4ubqoA3P1PrX3KHWOk/HnCdiy0saj8cYooqXXEpZAHzohy4lJupit93uUggIjb3O+q7bbrZEuNvujofDM9NOv/7yd8bLMl8f9st4ZC2ghayQCblEr2ZiYACKYP6EOvFGyI+cMPWYBux6XIoRauig3/LFVdr2se2WWlGt5R5MLYiIiiMEJhRp9ETIkRExBIgxpdRvtpevXn+xuXiXtlddtxH1UuqSyzAonBPQL1SwlVrv5/n98fT18fjt6XS7zEepi7Vjo4mQwN1U3RVcQMwdXQ1UwcGRILJTBO4AEa1SLVAWCMkBoBabTlALInHqYLMhrQQWYoiEoaGNVMUNYsQYUsuUMAbGIcbLlC6ZEz2Zh2dsrT+KE8kA1GQpKsrErl5yrqW2ENjMABGsHZVqmrW2c42xwZZbl6ShoYBUKKBWcxYpboLoDZ9BTBw4xBBjjCkRkapLQzuYFjB3AYgAxhxMtcm6i5U5z8wNdwBOBIEAjCJSI079BPlPz/ndVs1qYIAKgO28tQYkRjAgQ6rqudRcailiDb7h3lADACCgYERM6AYGYlbbMeFnDhqwDAB397eORLEP3QXFXRyABIyN3BCchIgcAJm8mM2lzE31qAVYe9s/TaSc0fUrxN7dQUuZp+l0Oo7jOLkGBCJaAeFEUMDNcs7KEMFDKZmIN5vt61fvIpJJBdN51lKPDqdSC5ATX3Wpd8iOOSbrOF69Gi4uk2olhtR5r3hxlVRszkcKaUMR0Zm4VjnsT+6AhLWeluX6OJ5u7m69XgEMZ2UFqlpFqyghkK54Ohc1FWmR1VrmaIZNS6mqWotW0VJlnktZSQUAHJFXI1urO1PXh8ABCLVCVXXJZTaQpc4kec55EZW167yxuiM66Jkfs4hGJjARVwFrdHXi4Mjk+IKLrqWLz/4WAYLbmmZfq+r4ZOLBDRBddb1Ia5VbSexWEFijdahV5ilP07LMWarBGc9t6hVknJfDKRynKfYdx24X+WJ3sdtu+xQTU2RGRjV3dERnRGRaKR3O4Hv4Eez3hWk/HG8Wy9e339zd/0nqUWWRoqJuTo7kaEQGpAyOLVNhXqvBrOZeFWJqvgb2vUuGvMLefMUMGmnBuloZiF3jV2H3CIDojcCyImjkhEDMyB1vNpgSmJgWKVo4eNdxCEVkqvVQwp2rEfaqT1wWCLTpLnrq+6HE+WSnh2oOBiYu2Yg5pdRahtS0SrXajnVXMW1AucCBmNW0LjJpbpzQDVZmhGVR08JVkRDIYuDURwSsVUuWXKopVABkjwZVJdcSISCyuTs5JiB00aVxzau4sXXB+pBiiM89LzMTrV1IHOF4PIzjsXXTOICZF9FSG6OO2KgAEAIjQJVaa5VaWv/x6nkSh0AxhBhjaxtRqwq19SBVqIiLG4hILbNZkQLT6XB9/X48nVoq0k2JsB+Gvu/6rr/YXbx5/XroOnSf5+lwODh4l7qu67ouMYYfvvv+bFvwN3/5rwznjx+W8Ri0kGTyQiAMwuw4hE01F1GzuuSiZqbGjEjszivTUVKx0zTDNB+r7vttHXbh3Ztu24XpUMajA1pA2m5TCFw1tkMBavGSzdvxAA2ATh4YU9f1m93l1et3777cvfpV2l4hh9h1xGEl/P4cr4jasiw/HI+/v73927u7f9wfPpY80dqv2dLMkGeTuqIt2+nhrbRkboDAwboNbC6QI7phWWAeScRUbDw6jN4O493tQooqhbQyekRgM68COddadBgwxaASXGOgTd/tNpuLrh8QVHVBZMDgbUOds4YvwkSE1olZPauIubpZOyzAW124ZRYV12NOSBTV1k6kM+oesWVA65KJoNRSpRgYMcU+bDZD7FNKKXYpxsghAkDNtSwZ0NsBm2aE5HQGGDMxgZZa53FRsX4Yun4IHBADBUInw96CRo5Pz4HIjRsvRCcmIiRqh7qtHTmOANTalwg59p2j+BGz1Gme53kxc0JSXGk9zayoCCDTGe7nrrZWUd3NwEhErDqBE+5ev3tTsgBC6CuyqSM4tk5NcEAg1gi6SFm0FlU1P5/a/tKuvzTz2KDnJrks43jaHw77hxNBIgqEjIREjgiQ1V3NNXCIoUPmYUivXl2+e/cGXdtBwod9yWVZShYtMVGI+OoVUJB+A47cJX77bthexGmSWqu5Mvur192ySK25qhOHrovDZpim0/F4IsZhSHPOx9O8P410ffv64n+62PzFeTmAKTA7KrTGQTWoVdy11lxrFimAGEJgjkRBREUWVavncoWIlKruSIzIHAK3c/TMRQ2RYuhScHSsJeecpyw6rR1PBu7m6mQrc30IHAOySBVzL3UJjGjBajEtgEbkzOgIjUXtxXKYu7RDQZsaOKMgENCJqPEvAQA0Q7vq41aEXfvBCM67jwhjZA6Ul3I6jKfjOE2zVEEgIidwB0Y3M5tLGZeyVd1ut7uLi8tXl6+urjZdn5gDoQE0xdi4mQNzg8q4tTQwYpPQnzLtuZwy5NPp9nR6UCmu2njN28qtBOy4Hl3UXDBVRwHIruadeurAFU1BqpVsps7sfYebLcaISNw6DTYDDAN2HRC5C2riYdMBeuvJluIAmiJqInQEQ6kklcxodbK9iB7n8sGdCa7Qhnm+fb43CHrCFOJA2BF1MRyOpyN5ICghcOo7YjQ0dWl1cQxIhozYMiEt/ShVvaqBeavARe761OrcLa43U62CBAN3zIEomGEuaqoAoOIiVnNdQnaDEKCB2hVVCF3VXZGAiFRl0UWLBKhXbo+BibmKlmCI6qUsp/HU+nPAGxiXHVYm+VrXzjRwUJWWykQA1dqwDSHEzdCl1DEHkSJSimR1acAIcAWojX/GVNsJHWa2zLPbmpBytxgjc+i6fhg2KXVmrqqB2pkxoda6zP8fvv6kSZIky9LF7sTMIqJqZu4ekZVDFXp4jbcCHhF+AP4/ETYgwgYEolfdVVmZGYOHu02qKiLMfAcsWMyHrK428k1EUnq4uoow3+Gc7+zaTXvf9/rdQ5ZEA817165KEEuSuUhBSD4nO4kpqLpZs6iIRujqTbWiRBKUFIBb60/ut64b0j7PKglSbgHdfDevgMRMkpwFDJRcKUACItBsAKuZKIlwznk5ne7u3s3zPSKLyLIs83KeT6d37+6neWaWN9DEd3Vwb9vL098+f/rXn//2f/701798/Om322Xdd1cPDQQHa7Berd4icTAjkiC4qrY2kOooeTS/OBT+rUM3qLvXqu6BRNOE88xDXI1CggxB7uF9/GW7aqh6a3q9bIlhnvN2XS/Pvz1+/B/r+tt8eljOH+b5fZ4eRMqw1/57VR0CEGHKggzDf+we2nXf91b7OHdyyoAYA4bQNdTCDA5NESJidO/eFAPQ1dVBB1W1TGk+z2UqktOYZYz/Sy4JMQwUyNBChCQnToxMaONmRgwMC+seGQiYghFwQBUYKQnzFyfvl+uEMSWCIrkIMhswAAT4m5nc3M1NHTS8tb52bYCYp2k+3ZlpuLVqMRq+0f68hed4+DgOMAAshvsv3JHCzWIwTKPvfb9sF+6CREIoREJFOI12Jij6sHuPYwk8/p2k5suPu9V2q/VW6/r6+vzTzz9//Pjpdtu1OxO4AYANfcToS1Vba3vOcjrp6f7u/mFalkTsEb21tu9bt91DHY6esu7aWmOB03ksqhFJzQ1IOfnYnqSc0629vmqAqjUkmiYxS2opPGrdVRXQW91a70vZ75Yv3wVxmsCgq7beY2232z7lLAREgeRIyEPCEqBqhAGAg5bhHojALCmBqo8ZagQyC2TMnQDAhtgJwEwhNLxbb+4WEEeG+hFtwSx8YF483EPNRpVnzOCG4JJIUi5SOHEupeSJvnlFxqA74E2aBmHuPvbaNFSjNMwTTMgEzEAUQA44rM7o4WFgHsccNhKC9NbrVutWe+0QMTDmQkjATGUu9HB/+uF3P/zD73//8O7d3d15WebT6ZRTAg/rZhFNtampOhydQ5ibu0UYABBSre3bx+m7q71rddhbW3utvZnqUWISAstgriMCA5C7QgRRjFqhd+jd3TACwKltuK2+3TzAywTLCR4eUBIR0agKljMtJ2RBAKcwCEKSnOdarfcwM98VHEtBawBOdQdzQhSBAWyuaq9r9b1ewO5Bz7ft27sEo2dGlCS53J/PH+brM8JHpkvOTRLlKXXrW93UVIfaWFCIJDDlnPMgYECrndrBnstJypSX05xzQgj30O5122tTHDYPISLsB706QsMYTK03rdxG2q+qtd4gxlAFGCnlBMJt01br3jcwvFv8S2K7u/VeicKMa9tbq63tfdgbATklYhkQAg9X7cPTNmJqEYMduzWi0bLjcio5FwC8raHaWm02Uml8lL12fNkIwklo0KHC1DgzEZkNKwgRMhGr6tPTkxDNJUeEmbXWaq0RjogDh/nlp7Wt+W2r171VCyEu0/RDllNKhbFAlAh2C3c1b9221tfL9bHfnhhcGEQMYq1160hmJhySSQSt31q321prt5Gh4lHBsfet9xG/KTlnM+G9QyBzKmmap9PD3fuHdx+SnGrtapaSvHv/7sMPP55PyzwVYXkbG353BLe6fv74519//vNf//XPf/vXXz7+7aW3CEeFsADrrhXWi+kOywzTRMIUENpt36M1Z6Fckim1zdV9XbU1VKVti/Wi7iAMtiA7I4b7+NZkSKE6qJu6RwSaRt3Ve/feS07n0+df/vrPvX0CoeXu/e//8H/94R/+t/cfsnAe08I3Vc+Xows8PDDynCeZcsmSEiGp2u1622573SujnKYFAupet23bju+ejnQgpNHjuqqDBZijBQUJMlOe0nyaylTGfr33TuTMnFNCTgoCklOwCJdpYiJwJDXkEfBjAIRBI1Zi6HK1m5vllEXSd7oBDKThu2EqMs0JWczZ4w2qOFTRpq1trW21ra2ura3EtJzvH1qLCDe1vltAOKAfadzjajH3MegCB9Me6qHqSOAshCUlZvTo1/XFicahX3KZ8zwXjPxGmA2wQB8ePBpGLPz3N3sAAES3/np5eXr+7eXl8+PnT7/8/NtPP/2yrXW4gQDIdOxlOhGwUKt13W7zLHmiXO4f3i25QG23dbvebteX58u+1/mUp1MRzsKoCq1qEk7nVDoHaG237sc8MhdiTgSZmVrfzbX3LaWcSwooSIEUtXYgyCV1Ddv79+wdlDxH89rXy2Xb9wYRgnyay915WpY0LUKEAGhqEZ1ZmPjgrAEAYsoJkSOaajdVIpfEmYdbGFRr1+oBpuGDOjYIOSOUNYIAhTnlLCJq1nrTrqpdzXz0/sSEIIJMlKdyPs/LaZpKSanQNyVjuA82rTl0926DkDOWqnQkTTghoBAlwalQSjTMWz6M2QaDM0aIkpgACdGa9iGLc2PCklMSLkkSeUlwdzf/+OO7//yf/9Of/vFPDw8Py7IwD1gomY4Rq+9dW9fWzH3A7YYvxEYGKSD/fUP17T+MT6HafehQbBSZFjR0m4M7gwDgFghgY2vrYB6mXjNodwDSSq25haUUecYyY5lYhAnJ3AFCUoigJARAVxOLiVgSlFnqnm/XqmYRDcIBC9HbzGSMuiJGPDliI9yFQrB867AEwMSnnNK8LFzQSQP4ettUDQgl0zQJ9miG0MAG5Y0pl1xyPp1O8zJDDBx67037cMAz5pxO56XkDBhurs1vgt32N24oIAAwEA3WnDsjGOBBqU85ZQhC5AAHdDDDiMSJIB2Ckm5hiF8hjrDv+3W7jECadb3dbjdVHXPRAEBVGkoLxLHH8reL/ejUwnWA2ZAQUURSagBYa40AD9DuNqoEZhnRtOHfAmghQkTevXs3z/MYDDCzqrbWOmCrOwPqNB3dfJ4gcKR2foOihIh4fr4Z1PBcpnd397PNZUrvc15STsJFeCLKBGKmrW/Xy9Pz82+4spuJQErjxNcwsBj7V4QOpmHqrVrdoXUAMIeGOxBB1xYBzAkD3Q7nSGhot46eEoYLQY5AVSOkaZpOp9Pd3V3JSYQRaRBD/u78vV2f/vV//PNvv/zt5798evq43l7ddEwDwcJ7hbpDXcPa0GkoMVr4ulmtg+4AplR3uK22176uBpjLNCXh09m2te7rRmFZPCckIWZm5t6tda81agXTgWUI7cNOZo+fLykxoD09/Rbsy/3D7Vb31iNgWR7UOnPOZan7y7cfZ4BZgJEYKXGaUkrZh3ssvFvHAGQEj8PV/cZfGfNzIiIgDT+OTAhkEsIkY9E15jhETCTk4Q6GEQ6EBKkkyUxCLCzMplb3puFAlEpmSRCD5EBDpDQStyKs1q3WTZczlENHh+O5FU5JKEvOgpI80mG2j8HMcOgW+75vz8/Pny+vj23fWt21NddOEIwIHt7Nex/mIXzbOIxpFDEfe4pwj6CI0dX13m63y+Pnj7etpvwZiFnSw927h/sPfiYHAQ5yUo/WtHVTDXeAcVjgd7OHoV4arPjffvvlbz/9+fHp8+Onz799fP7ll99u1900JDMhBwEOM446KtTa9r0Rh1lPiU/nuWt7enr6/OnT9XqrVU3JlCIoZT4taVmWXLIN/lqAu2/77tBEqGSJsFJAhIhNxKObmjJDKrxIziWrdrWeEnNOiB6gKX29PhAppTlny2Uq3WKAlB0QmSjlPC1zAXBzHzaqITgfA/laW92bB7lBOAJwhLm7ahORnJMLqkZX7d16t96sd3M3JBShsadgGhAXGlDMw3QImFMiYpbExHGQ/RmRiZiAXF1DI32tGT1cTc1BDWq3vWkdHE1CPOS9hI4YyAgl8ekky5IXTMzoFqoD6KK9mzCFJ8JxB3tKclqWdw/37pFznnIqWTB65nj/7vy7H9//8OHdu4e75TTnnEejNQxY6qDuTa01q02H1wcwaOwL4u0R+o5Y8/dXu/Xobu5vHP4hfHUfMZFhjgA0VhDMgBYe4R1aj7ZHEtcOhGQmTQ3YuECZKRVgGZRJHJMuADigFYTIxqLEnCYE4Fxy7+q7IimQIwsLJQfnCHRi8gjrXdWImojy9C7lLOnrXYKIk5znspxPD1Kww1ZrK2nZZdPQJJizBFk22esQMSIRTWW6uzu9f//+7u7sHjqgMKpt4GzCRPh0WnLJozqzHkTQ2h5hxAQYQTAsLwAQFkNSR8BCKUsueUoJyuxAHqGt7tY1URLK0yTg6G5gwJ/pi21s29fX6xMgDoGbdh14qwOLqz0ARZiIAd7kFDjEDUDEAdC7au/u0Jv2boPUPDgzEKgagDjEB6UkM1MzIhz9+nBkppQ+fPjw/v37Wuu6rrfbrbUWEQjQaxuz3POCU5lLLjmX3nvrzbR/c7XD0+cbiCLenc+SyN2y8JklSYqUck5LznOSoqrbuhrU11cI664NyL/UdeEQRugCQdrc3XqP1qF2UoNAI2/mOv4yxlIlHFV9uKvNXZu5M2Gre+/NKBlnI6ZpKtM0lVKEv8ms/HeD08v18Z//+//75dP16efr+uq6yVt4qnp43WLfoFV0BQBvvTu4uTd1U/AIRDBFs7jc+u3Wtt2nuZSynE+nnOXx8+P1dd33Pk8qIlkwZSbiWr3tXjfolcIDx5TZHNx399eXFRG66vKUgvX08LxX66qEcDqd9n1Neb5/+P31un7PBA2HA5xobh5OQsSUinBlYDDVpg10uN1G4siQBw2fJBGSB4EeMmImTixZSpZCSMMshoScKJDH+NrChkOSE4+e3t23bR+3HgoNUI5HmDshmisSEBNJhPm+7q3W/u7Hb19z4TSQ4SychDGxg3y7cjS1wAjY6/7y/Plvv/3603q59NYSJwS0fQfV6Oq9W++h45MiEXNi4czCQBhHFM5XGbSq1m19ffpsHiSfgDKQ5LLUf/gjADLPJLNrAJFI6Na3XVs/mq3/ydUOw52se11//fjLv/zr/3h8fHz6/Pz4+frpt8+369Z7lITEDBjmBACq6m61tt5NlVSNiHIuz48vP//0y+Onl9t1H1m7gDRA16fTdH93nub89Pn5eq2MbK573c2bCE6zeKi5l+xmTuzkFmEOSJJzKkzzttV1XXNJ8zIRBZLlnL//OnLJ02lZiGieVZu6+pTSNM3TPM/z5K61VQ07Bu4Wvdte277WddsjKIIhGJAP/kxrAD7PGVGwmfqI62p1V7OxchZmSpJEGIHG4tvNwxwjCECIcim5FEkZiXtXMxsWFVXf9xbmTPxuji8YjoFS7QZNY6u6VdurVrWv92Eg+vhlU+bai/lMDDnxaAi3bdDYLYkcUjlTDyg53z+cf/zxAxHf5m2e8pRF642i39/ND3fLaSklCyGYjcU6mIe6j+huc2zdejPtpnoYo4iRiAEoxvv5H13t4ebePAzQxz1BEEgsSZDAXVW/rHVRhAKQCD1Qe3T1iJAKhEfkpweqxd4iV0/ZsiARaXfzwxdgDiIRATSAeYDhRG+2MGYfZtoYe1QCICQejfvwhTFhEs65lME1Ox4ywLmcpjwnnmLwJbuNXQwTEQMLTilzumNOIpP1DuGMTEHeXasOlTE6UCD6ANobuPfU8E12gICS0rxMHpaKELE7cBIRYbahQDf1VrVKE9oRWEpKSUhGUDhZ7wSJURgTAqq2UAeCL1f7WzrmMSYZnahIQsKIqL2P6tLDv2REj3Hrgc544+W5RwPDrTUyADwUK83cghMnEWZEHCFsSESIZDouoyH51jeNPYyW3cyO6DhiJgY4sttEUko5a9H+3danrkRlKnNKs5XkvVnbe11fbv3KTDnPwvmNGK/r08d2/WzbS9SbBxoLJSASV9AG1ty7h6uHBaAGdgMLIMYAcj8KNQR2x970dut198FpQsIIq22/rpf0mpfwENbe3xTujEQQDt973r78aO/b+nq9tm1zVUSmRAESCHCECVqoghmpowcBoTm2Htp9/Mf33ZmJsDATQifiKeeH++XubkKol1dh9unO5jMsC+VEhEU7bNxzceKRVh3gCI5hkJmX5XR///Dhw4fTuVh0wLg9rb/CX6h7KWnbb9Ny//7Hf7ru386xgYWJacgt697qtvfaU0oQkVKapqJVwWMk+KnqmB8c80FiEWHifdsstFdzc3AarLXwwXa05EBEJZWC4EOyo25q5q5qpk5CNBw1xMTDlMhE7KpqPTwqABMxHakUzINz9Z1swH3grSzUuipC9wgL8+gswByAHtBbu63Xx+vzb5fHX9bLpe2VAsPBtPe2920FVbQjfA8AHQAdBwZiDCaCkJIIIyfmlACgt3Z9famtA0qgSJ6Wu3cf3n9AQiC0GMzH8IE2VzcdQ44hjv878YNHqFvvbb/dXl+en16eX56eXn77+Pz0+Lzvzc23bRPpRB5hKZE7HecwMgK74fXSPn182bYansDFHZlIEg3Gq1pYMJCzADMhUK2q2jwQkREonFSjdyPUCGAhwAQguTALDEDXvu2vr+vUszvWTtrB09cXxcxeXy9qDcOnzHMRU7OuQpwEtNfX1+Zh7i6SkpRxEPVmrapquIGqu8FbBokha8ohaRjLI4ZsIjwCiNB9DFLM7NCs8/ggMRaSQyI1fjMY+xVwMDVTdTXraKqE6GpE8o+//6pwat1ue+0GtUftvjfb21GZOQwdBoIjekB47aSg3bV7H7dy77pvtXdzcxHTiL3i4OoPSuw0lWnKqr0kTkJ9t9r2deXLdbpd12laJRuxIPIwQXb11ro5AHBXq/toNo2YUhIRIiGiAOS/a0X+DlljER3AgALI0A+5eJ4mItz36r7Wuqs2IsyZiDMzH5OPgffshITuoQYW2DrACiIhYlZAmLWHdjRHM1SLUoIIkXHApdUQgpOUKEioRGZu5oiIyMg8lKtIiEBEVITnlOZSishXLDMizmXJqTBy7X1f97pXiGAiERYBZipZFp5yWUpp6/W6rys6WrUNNq8HcGPQDaq23vtoB4nQ9a1KYEHEaZ4DTIQQcQCVWITQECwstFnFCm6ururL3WkS5rH6BgiSMaIXTggBDQzt27PL3VU74hDgICJJkpILMUeM58wPG6CbH/zKA+8TBMhDGDGmuNC7I7g79K6qOsaDTJQSE4VHRwQmoGPrgSO7z1TXdU0iANhbGwt1VZ2maZmWkjIFMPMA18ibAn/brt88VdCrCBaaUk6IxTa4rK+/vj59vD3/DK5lmiFIezCxcNpvr+31k2/P0FYPUsqCmXMxB+te1963btoinHICEQ3w8XQQEg07FAGQ9ti3dnldVYEpDzV1OKrXdbsQg1PwlGvbe69uOvr04Zl6A39+BxgJoIAMgxPNIRhcgrOjg+kxse0KqhZAATgohBGm7oRkBr05FZnyAqC933Likum08Pt32Sw/vwpyPz/Ycop5piJAnvbVJDFyICGLI4U1sI7epUi+v7//4Ycf//CHP9zdn3ut23q7Xl8e1496vZJE7XU6P7y+3hRO3/qthmRNW9/3qqoksp/qvMzTPDHRVEoLsk21a91reKSUUk4pJWbGoUVkRgr1ZtZNPb48h+a9q/Tu5khcSuJEEN5qXy97b9ZbN7eg4MS55MFKQiC3AV1Ad+utDT7ZeNGmPJVcJBFCJv5mJxpg5qOSoKatddCtG3btFq0UzoXDe2vbent+ff7t+vLb9vq5Xq913Vrt2nRYVAEcIWgMqQDj8PSaKg7uMSETE4kQCI+JCqGpbrfruq4RCMjldCdJED3nzMyB5IFDUmXHr4H6+Z8Ba8LC1az2ttX9tm3r7Xp7fb0+Pj6/PL26dYhwvTGjJJTEKUuE1LoTEWIiTK708rSa/5akCJ2YK8LODClHoAKqeXQlj4qUJBEz33rtrecyQNrMPKr4UPUvYmImkIyS0LW3atfb+vJy27bWmptLV16SvXnfwMwef/sM5JwgF84lQUbX0SHatq17qwAgnO7u7qdSTE279ea9uzsC0NA7IjkAmCuxihC+IVLj4FH7ENyN0a2ZNQhzNyIhYZLxEB7XwME2td7A1AHJDvqIKUSvGBDajFniG45b7Xpda7VoGk2j9THTGSsV9+F5c4wIDG+KPXrVtrU6FcnC4d4GL8iBxZseknMRZqHaOsCYops59q77tm7r1axHQCknQEm5pFxSmYjFPVq3vTZ3IBbt3mprrasZM0G4B5MjsxBB/C9As8NRTEhM7GRAx8rUzc1Rbex5EYARAJEHsgkJhYOQwzGGl3ToIpAB3A1MUQ1ZAcIHXYuJhBkDTcPRkQKZECRAkFOZC0tyN6TdLFSdyCgwYpxK455DJhGahCehQvhNQDjiskypFMrJWnvTlHoulNMiGSQDEkREopiyqEgndsNatVe7wkpv1hd376ZBMC0plzLPsxC3tdfWzCsz5TkTI4T1rnXvdRugdA8DM6/u2nrdoO5t3+ve9qmuKYuICBLT6IQC0nBQ2N8jKGO0DMMOM9xVYyJ6XKVjQogIw1x4GHQjAoOGN5yQkBwDcdhI3nT73YmGopmFOUKt61u2gQGgW3hAkDfm9XohCCI2szAdOtBj3sU8eHljQvi2GvBavwo6AqDWm3ojTubCQq3qvtX1cn19egRvy1wg0NRzLjRNFFuSmrPnfHh8pzSdz/dasCbfaF9hvbzatqtEcACKJMGUIGVJKRHK2F+s23q71X1vRKmUsiz303xnavu+J6GUAamFr729brfH2/Vhvc6lzMzi7ubmYePnywe5v/vdjz/+P58+Pn+aP12fn7fbxb25+r7Fvltr2BsBkCSKQGacFiZGLtGahEMWuTvNSdgs3EIIrLfX16dcmqQZaPvxdxwUkgHJWnPvDW2vezNVyV6mSBkkYRi7ilYEE7P+8vIiCdfbBYOsa10bse9U85xTPkPky8utgwXcffk+bq+X15fnfa+tNjMnYt+trW2bNmIyN1f3HqZDZArDPFmmknOWNBaa6OgafdARmAgYLLyboop0H0qI3kZiRbStra/retu1q4cDQyoJAyTJ6MuJaEz1Q62zwGBMBICFtg4WBwXkmwLlKJrMR7Jcotjbermsl8vzul2nOS1Lce/b7fWnf/uXn//y5+dPv+23a6+79aat9aYDdEyjGERE4Qge4ZABPtRJGAQCiDjG/oOrfxC79fgzEicMY4QBuCVG4qGYBsZhkfJRYA/R9ffCIICR2bFdbrcX7dW03W7Xl+eXy+WybVuSQIxuXR0MgGXKpTCj+Vz31puaxe3au11vm8/zktO0b2oWKUPKMJ3KtExlwXlmIo/ozCFp/Ed1VPci41sNJmRO7q5dkZCzjHgURECKUtLd+aTq6602bWr0cPq6d1O1n3/6SBx5onnJ81KY8C17A/ZWa63EkhOKKEJ3i95CG4UmBsiCwg6IRAIQqoCkZeJlmZZlhiBV3De1btZsdKhjoGIeZK5EiUPIx6hkSD7Hgege6DrEv4SUspQysdDYum7QB3bsywe5re3T09osmoEODJKG2eigwODNvhiBEOSgQd2je5RqJRHGkVnqDohGOHqzAAx3u15fn1+enx4fL6+vIwir7jfrbdum1tyCXy5bKdO8LPf3D/NySikFIPiAIZqbQwQRMhAxBYKqujpiI6TW/2MZHQBAICIxkzPBsXH1rt0CelczR2BGIMLD8yQkgi6AEKrm5mMufFwrY5gcZIZmMIyCzJgTJRlEdw/XwBhDQg9CSjkn5tR6BVDVjujMNv5+iFl46FgJR747CAbDN4FpiJgyp0wgiOoA5qABKhnLqSAHgKv13tXNERwREcjNtIV1tZG2EmE2hqwucyrz/TRN59OJgvR6bbd9Xbc8pXmaMkvvzWrfr2271n3tWjUsYlA30ZCitVbrvrU1r0VEEucpT3OeUmYpbI7EQAHk/z4ncTxJgUAEPnDHCF8NTWP7DoiBX6JXARAcD5XFMFwfyt/RQFiYBmU8TirC3n1kzCDGUep6AICTEcDtJhBGLBBgZhhDlR9uhhBjkFtrPfR9Ee6+rrdvH6m2v0AHD2k95zzVfa97rfu+rxvGJtgJwc2BO0NgajF576CdzIEJlnl+//Dejdvst7wy8vW2tb4ZRiYUkcQsCXLOpZwQU2++77fb7XK7tda15CypnO/fffjh96Z6u74idBFIJYir9pfb9bfL8zwVPp0eclnUtGvr3lS72tfNwsPD7//v/7f/x8f3fzmVf/748799/lX3a2w3u73C68XdOYARU07s4JxwOXOZaD6jNleNzPJwPxPAetlbVSbvvb2+VKALcl7u6N0HdOCm1lvU5ugVFLdNTXspUDLmEpJAiCFS27FtsNd1r9u6vSzLtJRz5oThOYsrC59Od++Cea2tWoUvVzvA69Pr0+Oz9XFSILFHj74rJUIeWg7i4PGCAACZxcBul5RLRsKAKJ4tZg9D8ggAoqAY8/axC+zNVDuCA0Tb6u3lOjzrQMiZCdDGHFFG0UpClFlCkkpCD3Mamybv1qrCmyz/+6vdwp0AMuOUsO77dvn0+defn54+TVM+nSbVul5ff/q3f/n1p7/09WptjyN+arxTb5iYN+koEh4qqqE0DBwe80BHRslj3cZqan3o6gIJGUMYEx/IsRiSEEYETBjEIBREDuhxcFS+u9rdWm+3y+vj6/OnVle3vq6319fXdb21XoVlmBAhghwBJWXMKY8j1m3rXXurezVe+zzrPGnbm5kHADHe3y8PHyYSlxyI3nuz0ACN6B4KIIgjg+NNqBxs6vveiZBRRMYZQyx4Os3C0+tlfX297dVah96/zkpN7ddfPxP5fJLlVE6nafiEhyGsdW29S4KIxNTDKji5o6sQEDKlxCTBCZgFAHqnAEuJp6nM0xROvcVKNSzcDAnHoNgi3JwIg8e5efjaAb86GQfDCDgYIQtPUz7fn0rJ3azWxlyHtO7r1b6pw9499I3zMDreCBxFmR8inDFqAA3QAHVsElVoDPbjGDF4vGFdWq/7vr6+Pj+/PD0/PV5eX0z7gBwL016t9qgtHp+upZS7u/MPP9T379+fzueU0qgk3NUtCFF4uOsRCIbrKsIRoLX/+GqPsIB+/KFHZOk4rMc21+1t0czEx+bSLADRFHqPkWN+wDYxiA6daRj2ChgBKYQRZYw7wcwDxjw1QhkMR/YXhJnbWNcOwZqk8WIjQiApU7BAuBBed34mWGr72rW72+eX3zglFL7V6+Pl82V9Xus1ujYns157dbNDZRi4r7VtTbu5OQHSUOKYebeuauEAoVuPZikkUdqiQoP9Wvve52nyubhb36yt2tbe1mpmRzQhhiSWhCPZE9x63bVyx67SNbfpVEpkCWIhNERj+YaMhG98o6NSHH9thzfj+Gc8XMbfSA0OYNLhjTiKNQKiAV8Y1jkkHMJGgoDedb1tA7BwcNgGt596hCVhhCDisR4DQGYZMXlmOlT0cQS0h5l27V2/ia2M6O1RQffmuZZ5eYAIYitLWh4WdCwTE0a45YRcHMzQDNmRQRhzkXnJ5/MMkXoxBN13ZqEYIl0NUCdFMRrS8XBs1ereB4UFAdy91qpmktP5brl/mME3iF0S50mEb+vlrx9x3daP57v383w2cPXetXbrrX118ZUy/fjj77fbU1loOU3t/h10qrew3msldxwm4FKExKT4craUPavbFGYoDNNsYNCblezzFGw0MujUrHcnBvPo3a1DGBMGchcxEQ+ntuK+eoClBCIBTmbQVQHQFMATk5eJRoJ5KjmVlKcpkCzAO8I34oe2t7Y3AmSWMWrnJMN8VVvtvYUHB4eDmyOh9tjWcG9pTZISJ2FmIpQkd3fneS52vC42Ygm0a9sqAYwwC/DoYwBuAAEMlDklShSsVetaxwrJygSTq9oQqiMRBwGCxSEOGbC8b54rCFcCT4xLkYdTCevXia4ZNjJot7VfbrfL6/Pj86dft9eXGG4RH6YVCXpD1YyVLBMgOgQQCWcaw3oEGCOFITVq/XiFzNyHGVdYWHLJScD18vz5069/ZZYkUqZTSrkQN+UtUWZg8jcj4ndX+/Xy9Okj/vUv//aXv/z586df9/WmvY9LBDGQjTgoVBKdTuW0JJYIc2JAjAEaIkIPt16JmQndFQncXTuYhQeMo2zIZtfLfrvst1uDgHku4bJvBqCIY4dgQ1CVkrghYGERZkpJhqj+tEyIhNfdb+1byoC7b+stZSoTwaG6GK5YN/PaelfNGcA40wRpOF84HMLdAxwByZCCaPhO0YPCvbd+u20IBOEl0+mUckHiZO6tax+hB4jMWIpMJQuPIaJrNxjT4UQpSUlJJGEAIuWMIpBLySUjiukb2mm8HT2C3GI06IeuC45EArC3bmsscd56JgwLC+8GdpQJEW/5tr21vdbber2ul1r3vW632ncNU3d3AkyIGdmRuvm+133ft23b93q5XO4f7k+n8zRNQ/Iw4uKZkAB9SONsECQNIr57O/7d1a7uFUBHy/cWzfL2NIbjd01jmA2MKatCrYPcFMjBb3mIwwITjq2NEW4QQBCYQ3/D3DM7EbpRBLuPoGQ1670PwnSYRLKRHTnymYzFU/aIDfEa8dg77/vXp8zdf3v8FYWAeeu3l/XTrT7t/eKgcIta93VdPYKIxuJbm7Xae1O3mHNJuYSZelc36Ie0pq9Nb50eIImwEfToa2sU6+sa6oiou/Xd+q7a1EOJnRnGA1cmYUEiGPgoN/Nw7+ZdkZw4kBKEeMdQOH0PPRyDdHi744dCJPRw7H6ZT751y6NIPNy5Y2MIMPL5mBnjC8YV3gIFIty91b6tOxGIMDG+VRKOQOEmTPHWtY9VXEql97Zta++NCedpJubhGo0IHSXeN4+V26vaptG7ZkIVKSw+nzPiPUSS5BgarsKEbNgNugE5UBBRypQSp8SEzAjbhiJBAsfhpRYVAJ0PyHlz07pr3auZjZPZzfZtq3WP0OV0Pi/nsKLtlROkzAG39bput4+Pn6bT+WE53SGTo3etar23Am9ZY5LS3f39cpryTMt5iU664e1FiTuSuzk6MvFUJM+Qp8jZScJ5aHoIwQGbQyB1Tp4LkBOlxIJmXuvYxXrvMWJKgIOoc4qUgoC18m3rtWqeYJ6xTImZkIEAcpKS8zTLck6nZUolAaU8lzxNSEIs2OD57WoPAOvm3SillGQ5zWUqkpKaxs3rrvttta4EwsSShGAwHrS2FQmJWPKxnJrnuUzTTLOqtt6h1mjq5lrbft28aYSHq5v5CGIEHF4oIWFgUOi9r+vqbszks6KHR/Rh8HTgIYgkcHTtWrf9+8MrEIwxCuOpyMNS0HW/n9q6eJ3X9bqtt/Xl88vnj9vLk24ruh3ZNE6MiCzDon+Ee0GMMfuQjDDCMesfbrwA12GQS0ORBB4kLCIpp5QTI1irL48fIXAq02meT1lOKS1CTfmWqTAyBkKMMca3p+7L82eM65//9b//21/+fLm8bOvVvRMCETCHJEwJmGGa+N37OefsbiOnx1zDjQhSIvVoru7VnJhRmAC8Nt33vq9i0dQaYDXz6+u63aopppznGRGg1mamRG7RB7gaEUvJETRgatNEIqRoAZaLSCoe1NWJ/k7qb0RUSi655FTcvbbDq1Zbb6quxKixhAhPU8o5RYS7qrn6eEzsbfjnA4Kpaq31IdpNhc532TwRs7t35dq49w6AzDTPZZnnnBITb3ureyWilNM05WkqpWQhGX5mcwdQSYUTaw/V76BOOjKk3qQ38MXHOxLFwYeODgBoZI8SHSkmg4k3uPI4XA/eertcL88vz6+X1+t2AwRE0qDgBDDW4xjCXOY0LZITINR92/Z927bL5XK9Pjw8PAz7MbEwC7GMEz78sLWHx+E5+F/Q6Nyb+RrRCNUxhhJ7hCZ6vJUhgwAQMNLNiYckHtXGFNfR3dCZkZmIkJkAwSw6AAS6oimqB2dHjLEAYRqCzOQmFtijR3QmH0cBRJgCM4rIID7iKFmU2w5a2wqXdisA6cs9d9suQWCIu94u2/OtvtZ2UevhIx94DJ9xSH8RiJAJI8DGfW8Rb3lrxBBk6Kv119bODRPVy963NoD8Zt67IsKAvaecw1GtufcxRw8niCSUJEnAF7lREKEIA0CYM4iAVO1a7VuV4yh6DyNxjOb8CKCz8K4DXukwmHRqXTUcmBlJAAhiaEaIUACFmSKQyN7KMmu1mykSbNuuqsPyMKJxjp1VWDQDhN4bMQ96FCIlqSNHgxC2rRBiLiWlaZ5mQDT152d6efn0Vp7AVECCAhOLZDHihmE0UU4nDwrfTNVUA0ADPXrAcBKHmdbaLpcLUyYU6369XLd9jTAiHBNzNOzOHtw1JbYIbE1VK4TSITKAcO31dr0+ns90f3cPoIYdwt1BBySvuVqkVEqZy1KksLl6WOn/ieAAbhFJSqeHd7//05/+25w+P6WXuvvlun/gNN3ry8t13/aUgtDIHS1AEYEYGCzqrrXtrYNZDODagJi2ramhO2aFnuGN6RHW9bCfOqeU5zIJZwdXs5yn8935H37/4+k0vb6+amv3p/NpWXJJlMQQmKjM83z/7v7DP4iU3lVu7efXl/Fk4Zc6EQMoUIAzpyLsqCq9p5KSwbGBzSlJFhEODI8DSzniDlptvXVhHgHnfSQlm0UAMXnXJjJmTKYKgCXnQXYzte22DfBCt95aBYSUuElLPXW1fWtqHh7CkliYiFnGquDbwwoRhCEJZqbCPAvjMtn7d+w6C7y8Pr88U98ua5IbDqLQ0OUJkjAii6QyDJvJw7dtrXUf6bHDY0FEZqbWwSxoREx4DDolM/HoKZuZ9dY1q3bVrhDwcnd/Ny+nxFx4KkvKtAgVppEWOfjm336Qz58/r1f/+OsvT58f62BSuSE6UeQi796floXNWs58d57N4fK6r1tdr733SJlEKGVWN1YXsZTbMs1Tztu217pdXqqaOaiZqoUdQvHSdttu1tYLEmrXlOndh6VMkwiPOV9E7GtHQATSE0yT39b99WVlLinNiDBNOclXpL8I//GPP85z/vDDu2WZk6R9rxe4hVWFICAGSsRFZCppmdM8p5R5tJrUETr27rX2Vqua0ReuAKKCDXkDMy7n4m6qhkgLzeNOHvuUwRweSs95EbfpeIKTMI9QV89AiLRufa+9G7jT7db8+zUooAMaHsGkx6s/2HYATiPYgEmIEmFmnLIkPmhEMXr8Y61jdV+fX54en56eXp5bVwtMZZKUgTPnWYSZOQKE6f3d6d15PmXBsOvlddvWMG+13q4XBA/XfVmmMucy5TKxCBKPWhQDGAkZmIjo24SFf3e1q24RhmhIAIThIwht5BWNJay5hTuYBVkQIVFEjPH9qAEMwkUIMwXD2LW4D3sDKmEXYHMxyAkw0WitAAd3d2gGFKKzGAK8IVyQCUsR5gH9cbOAyNrF1LStoPjlah9jYY/QiNprbXut27ZvvTe30fYedXNwhEBKLEkiAhE4MQl64IhkZWECQgDfo722fdldfH29tb0REeVRikdAAIEkzoFE3Cq2PgYdFCHhglCERmLb2Ho4MRAjIYQjObFz9Gbtu373mPzgoZRDQLOBfPiamgv+lnBvbqYQgDTwOeQB4QZBROA+ggs9juWhR3czHaCB1qqZEY0F1hdl+HgdwHZrbWceLBdEpM5VJKWUhHFdiwhLEhE5nU8iCQLNvjO/EVjCoMTERNDDRrfhowXpbX/Df+lx442grRjKzf35+aXuTshhsO91XTczQwQz7epoaE4R7KZehhbdIBqhIwMgjw9lut2un29XvD8BRG37DcXZj5iK9bavawVHZj4/nJbz5GgA8X76Y357XxA4yd27hz9BwJR/Zfp1362qvwtUi19//vj42yfyTqjh7s0dRpoumXq99Zdru96sW8ibj0stWnM1AmB3FCMRYILeom6jUPM5z6eZl6VM89R6Ve/Labq7v//DH//w7t3d50+/7dt2Pp2nMo16u5uRwSx5Ws539x/KfHYHer7Bv71+aRYPY3W4hwU6sJMAOqZEJYnl7BxJEktKSYZcFxD8ywgvwD32ve1bHQEYg098hL0O/6S6EcPQJ/dOTIyYxtNv3kfCzEECNUnMiQJC3Vpr67b1NoL7JKc85Zx4yDO/faYOyiwTJaZEmIgoZzudQxtEx1Cr61xyYWI4xPQY6Al5cHeYSsnL6TSfZjPtve8jCD0w3IGYkQBM1d0tAIa4EswgIpXMmAzcNAIaEvXee2/aOyFenz/d7u/qefbTlE4pCxahzCRIhIFfpkBvP+tt7VW3tZo6ITMJIxIFJxDm+4fT3X0Ob8xQStrWZl5rH4LzNM1zzpISBYI5IIIkv7+X82l5+uT7tl8vdd2rh1uYqgPSaZoJpNe6rfVZdzONiIeH+f7+vuQpFSeOiNjWenldtVXr2Kvrnd1u6+vLVZLOEwDJXLJ8f7X/4fcf5rm8e3dfpoJIwtybHnglESFeynSapnkqU5GUSRKYOYGheYT3ruutruutN2XhnFMphZndTQk8tBQpUzYL7ZWY53kqOeckg681VM9DWyRSksi46gEpPLZtM+uAjHTkfnkFM9z3DkDfGhcQncmGb32UwUMZr6rgygSCNHHOgomwCE4ZszDikJGGQwSCq3bdb+vz5+ePj8/Pl8sVueTpJKmkMnOOHDHPcymTuTPiu7v53bmcs6D3oa7etzXMemvrDcJNe9Olz24AkSKTpBgB1sOhgTRm5P/x1X70E8dkd/y7iLGvBRZGZAAyPUSkQ/Huh3oVjz2wurubHq4DIkMMQGBCJuxEIpQhkIknmSZmBlOoran2cI7AACXWJEEUboYIxDRNsCyQMxGxKbedTJNpVhPtgR5fnjIk+vDuAwoG0WV7AdzNNu0buAWiKagZIBBSSimXTANknfIYDAbF12pIMhqBAqjtq7483pj319fbbo1mLuc830+pCAK0ppA67IYNkYNbAIxkSEYca2BPPKq9jAKSCQVMNSzazR0NGonmr/MfGDgZRnzDK7n1roOHDDQy5UbhNKIyEFEgYCBezHXgBYhwbMVrrW7QWwwX6diBDeZXgEWAu/d+4BiIiQiIxmZJzR0wCMljRAkMNYnvRJer5JweHh6mqeScAahV1W/SacPjl7/9olbTYJqKIBHgSPVFxGq+Q3QKj3A/tA1EmERItdZae71dWQULAvc+5u2ufaCeIyeRzIMjBB6cgAubSK0KgTllIgZEzuS2Pj1+rNsLener0ynPd1MAavPbzW9XB0Mmcm+1eYA6+N3v7evVTiml+9NZWBbCM9GpzO9+/4//FRBV21//9V/+9uf/8fL5t/X1WXtvbpIFOQDYPczQuqhi617DRKAUJsKSB58VASgUaz+iFK1jb6gKOEEmbM1T6jnD3YnLxImi3q5X9DArOS+ncy5T3Wtr1dURel+r7x1VF0nTfAfxNVXleKfNAcO7Wm2dicwjwmoD8yIJBI+IaCI44MIjDXFshygCWzeHsK4wzpcAJMrEOaeUkww4kmnXUcuGtQqmY/DzxWoTBMgkJU3LnJc5ldyaWte+N+2qyJ1rT0mIa62t1sPXNJ4rAIVhAR04o3E3rJ8/P/70y0+Pn377/Pnjx18/Pn5+Wm/byGbFiFCw8OFM1EhNKVbV3vft2vdNVREDvAsTE7sbtBZ+eJpHaWzQR3IOMI1NPBKNiB0RDTcAC+v77bK+znXOCWHIGhAJwQn+Pnzzhx//9O5+Pi8/Pv/41LU+vTy19n9ertfTkohR5BANe3jdq2qTBMtZJEMEMYIIJMFpmksWB42w+4d0WrK2vF7Tutd1681agM9LmeeScgYnj9ZatN3cXBhBBT0zTik5kmnv4eSdt11fni7X+/bhhxkQEk/huO+tTFjmIvKteBmSMITt+82sEXFrDUKFYZ74NOck+bTMy3mZpuxm+6oO3rV17dq11n65rLfbuu/VzCTJeKwISU0BQhLumcuman3ftiRiHWwyz/m4pcwtnBhFeDnNWVjbYIm7dRtbHkRxR1UASEyZiBEzvW0wxw9jZHLhgbhmIlGN1mztrWsX4SJpYkiMBE6h4OCmSORuZr22vrV6ud6eX1+fX19fL9emlsqU0pzyiSmHEyKxEMtMMoU5IDiIOTugDK5LzsNkIkzCyAjh1tsOEKot5SKpILIPZZ/HGASZ/ccD+YGx/ILsf+vUA3C4zjgIAYnIVS1GvDn4YQocN7uaqZuGk0cfwKsgAhISocRsHO7BBQAgSaSMrtCb3269VoNAIuAEI0IXKTCMCFKGPMU0R5lQRKynxLnuUp3DwTQI4ovllZB+ePdBSiKR+ZZbv/Z207ZyBIQ37IOtLSLTnKd5AkHDSMiACQMwwgmCAJOgERpHi3bdW7Xr6wqIt2039klSnlJZcp4EADCTMwAjJRIBkxHCQYc5jTDCwUcYADNxSkIJFZq1rjXClIBTyLev/TCYHiN897GJcg9kHCEFcOgch3SORBiP1KtwtUPB4jDG+BEAQQEEwRE05AwwwK0ERDF8SgMQARiHJeiAso0a9q3GiFAPCN8rIMA8TyNytPduFttW97p/81DF42+Pta1lmnPJnGSUpZIwZ2RR4iqswuBDiKDkNtZYDKGmULWHO6MzJhvosEMHGURQppRLctMvDKOU2Y0IjQlPyyJJgNDCNfR2e355UnBD9Pv39y7MnN1ELZlGGDrwtoKGBmrgaNi+PFfMPA3NVb/3CLp7+NHdmbHrflpyZv23qG17rdUsdAIAAlXvitrBbVTG3q06AAsW5lwQOQDDDdywt2gtEAiB7EhGJ+1Y907oEFYKZQFwvby8tH1joXlZcplSmVrtoa57w6YNuU7P/f7JyyxpSt83vDGizQG8o9WmhNAVArR3DMgpIY7IC/Q3wjccfo3xsB1k+ohwc1clAEaSkchUpqkUThyAVQHJidI4X713BIqRkTIMLkyUJc9TOc1lnlIS2Sojont07a6GrRMRoKqpfudFjEHTCzgOKwvtutf2+nr7/Pnp8+fHx8/Pz48vl8uttu7jqIIIVwdHFgjoxtCsVuyt79vaew0zxFBDYEahwy1qQ9k3Xmd3UAVwD8pCiYmZiY4IrhHVGWq9rtfXa871/sw5ASAhvwV4//1A/scf//hPf/r9jx8ut8tl39dfPv709Pz49PzJrDmYu7U+jK4Gph42zZInDnR3jGHzJrk/Lfd3i9re+r6cpUyYZ8qZbxvWarUbUtw9yOlUhKW3GJtW6xEGEOSKfQ9tMC3Ds9bDMVz2tV+ue90sHM7nqUyTmmmzMkXKRPL9igRCu64+6LDZzZljmhgKT2ma52WZl2kuAWBu+5im1r22quqt9fW27fveVQG+3kGIqKoRQYwiJNJNe933JKw12tT3nA61nnsAcOJSZIjDaq3btrdmA38OQAEcwQFMlEUmljQknN9d7RCZPDOUhKWwsNTmq1sLM+uDLyYIciQjuqqbASJ063vdr+v6cr0+Pr18fnpZ96oWqczTsuR8Elk80A2IxwwrRcjgY5nTyGBCAiQet3sYJcY8dKsU7tpaqPbeGqdGlMbiNeB4Jd3+YxkdYhCFmg/Omb8BIwXpm3hECEIRiaAI/CLRO95zG9kTABY6NvNmyMyJYRIhlITTjOczns5IFHXXVr1W33fvPYiCCPzIXwEWF/GcQRAiVI1ZESlQvCwAAKaIGGNg9mXOhYineSlzTiUj99vtrtWzt3UHcFfCAAoSSlOal2leJo9Qdx7QbsQI3/etdQQgcLIKikoNArxbg6BwIGIyhA6+qwKMuKlcWIRCo+/QNoMI5JE7yUIsxNAp1CPQOkpCYpkSASdvHXqQCUUaiP4vl6IOFJyOOcqXLxE9wNXMB70yBu1aZFg3EWJoIMBHSRE+sL5MyCQA5A4p8cgRhwi1AYJCGKmxiOP7HO57oXQEeAUeCe9h4aHooAAQL69P/DO9vDzlMo3ff9+2bx4qmKeJ2Qf4REpCRDWzplu3lK1MHoQR6M6mrh20ee3RzQNBUmIhCA5FV8MAGnnIQ7pCNE/Tci5mHQFSSjnlnIXQlplzofP9nBIHQlPfqq63ffWhnsVhop2mOUmZss5TtxZuDqwozimLEPO36ysL3/bb58vlt1YbugslFC5TRjjdfvjd5YcfPv/8E5O480BLqvu2+e0S644ePM8pz1C7mOnIl0qMFlAPIBeGEwaXJKXIlAmQppxK4lptvY2cFbQc1nvbLed9XkqS5NbRiaxSveH1xc3q9nLZLx9vT9e//kvO91dPXxWXAGZmpgjoij7edg8ACA8iLmWKwNq6uSFBOI4Qoa5HBPCIPKu1W1cMSCQYwYgJKSNlJEGEoXoOF6FcJlOrt7VrD9cAxFHfJklTyUtJc06j4BOalundh/ucZL/tIxhGu7URGaj+fR4JHI7PobqCkJROy/nD+x/2uieWRMla37c9PAjRTIdbbjjWAMFMD9qTKlCwoAEgoAgnYWGKkYaAAyRGgOSDMa6GHuTOnggYKQkhM5dUhHlf1+enz6Ex56k3izSUmG9ul3/HrFnOy/sfPpxOp/bhXWt7XuTXT395fvmk2i/Xy+X1mjYqU8qJEvOcS5kzCTo4MxOLK2nDKeVlyq0LAmuPWtfbbWvaACOXlCZJhR4eltO5DFYHkCOGCDugu6/r+vHjJ6MN8jKfGBkHGlwk5eSq8fxU3eg+hJPkLJJkhBh+e1j11j2MMNJZlnlmIhsYSEBhSSmXnHNKGGgW3n3r+3rZn18vtY0cajU7QvIGvbBrHxECCECGXZGwa7dWO6HueyTZE9P4Codpu0w5T/l6a0hPAxY+VjcIFEG1mTmmPJ9OtJzyPM/u+sWG9PZQATkIYEKSADJHVVRjQKHRb6DqkZNBCBDuZr3X2+368vL8cnl9vdzWve/NAplkIp6JF8Ry3BMB7oAGpjBaPyIcqZtmwBAAMEhzKJSFsnBKzMIkAogQFOGt7ogdKTELcRoP1/8qr50YSCJ0SBPVDgAnHTGmb1HwSMA40tYxAsxD1c08wiGC4mAFhrt1612R3QKECQozQS5QCuZEblB32zerNVTRA4mBIdzC3FsP4Zjm4ARjx69diQLQRIJHoh7weLEBv77zCMARApAFlySnUs5l7mkiM3MiAhSkwnlJ86nMS3ELbS4kSWSs8hmDBQDRHRtZBLqCY7hZqCMgBZESVrBVwQMTYsKUiDIRYOPgGGmJmDLnkoukhGJ79M1VwyxAgRNPUxYJld2bkib05Dt+scaYRxvhdGqjNacjdgsBv+zXHQIIAwSYGIgJEQkDxjgdw9H82MGzYBKEAV88LAtDxum9w5uukJBw3D2juZLEwjJ272OxOmydEaEGAHG9vZrrkzwRcko5l+nbnQ8C/vDhvepCLClJyskjau2tN7VG6Ekgy0gRNGcbEAQkI9ZcKOWMJAjcN9VdgyHJkIwQgAdBEi4pRUKEYEoiI8SbWWg58/27IoU9Yq/GKyF4mHWBCJfEo8ufppwlTaX0ptpUAwKsTCMD9+vVHt61vbw+/duvP/9zBAvfIWXiTD4zB5qPJW6M2O6gMfpbd79tURsh4lQEmZNKrW3fdzM3p25+27S1MIXMec4ppTyXQQ2DJEwIL892edWcaCqDyTxSrjmlDDEMrk7WsG283bDvUKnuL4+vvz1hUc+tvIt//D/gS9QL4XiWeKg/fAyUcCiKSylqUVsfgls3165q2to+KviurmpqHhaMo5YE9MAAiiAPNHcfeA8nxpSZGXslaKHaYaSkEaaUprnMp4WzgAwMYuQifH9ioiTcm/bWt20fWcbD8/ntaz6yVuAQM4ekdDqff7Afieg0L3OZtbXttkI4Ig4N0LjOR9DNWNAeCU7IAyqCEAfISdhUgYCIiBIhA2JX85GqZEoe4cCDgStChBQIHvu6XiiVtIz8TQQZ6d0A8T+52AEEoQjKJFlKS3F3mu5Oy2maBdmade2aSIhGhPmU82lZJFOg55LneQpLvQkDM+Ltdq27Xa/rdV2vr1utNRDTJLnwvKTTOZeJWjdkQzLkEGFz0K7btttTw1xP713KnIQHeEcS5ZJa7ZdLQxARLwtlHvyr7wz6CMhEYR7uCFRyTkki/Ii8IyKkRJKYGNgRKgk5atXttt+2vbUGcbBxiAGQPYbwcTytiIbHvEm9N4OAfXfCwdiwCGPmnKR1T83Vv2ilIaeUJCGyG97W2g2WxVlygLMA+YE3+HoJDiC7xwCKWlivak0xYGwpPaKrgo1wD/Cw1urtdn15evr8+bfX1+ttqx5MaUq5ME/EM1IBSoAMESNkbTCSXUOEGA/3xPDaEUvKmRkYoiTOY8wrRDKEzCP/Wj18CAxkzJMQIP7jgTxxpARdkRhB0cwDnNDjgImO79BxJOoNpryaqo0GEhFlVJJIMIB/AeFhEKHqzm6hBq3DtkFAuKF2ahVUIwCBMDyCgnCw4pWT87GJRPfovQ8hmKlgQF1x28IM+fsK0t2ffvt1WtK8Ts127C1HZOSgZGPYgYoZRXjiNKGQMCCBAVjQAbApzNDBO1jKQ62ZPGNU8D3IgRDFiRr4zdCCJ8ZABJBCOSf2QLW9tW5KiFPKcy6zZEioOW7Xtu+WAjPwInmaKSYK1zAK48vP4F+vdu9dx6CJmJlp2CuBABElJRYeo5FDFXh4YZFw+GxwoG4osvuYoCgiMI/UYYAIJEKUlMksHSxbESLqval1/BJHeWCCQHysxA8t37HDOfILAoJq67TvWdJpKsc7T/Rf/sv/juhvRSKYRe/eu5l1pC65ExtCuINqaNfee61136t7hAORIFDb977VUIdAQK7dfv346fV6661tN8gZRChM1d20S4ZCEAQkThyujmCMPhXih0lN3C1nRKttM2+3lAZoLSyZewTglDmnxPT1udK+X19+/uWv/99//v/9v1wppQfmQpxTyhHw8Zeff/7bXz5//NhqBQgmcsPeyA0BUZgBZcRXjMQ1DOgafotd4bahGRDgacr39/NpzlNhhAhQpkC0QDP04Ex5klJSZgCTIvPdu9PDD/PpIRGs8IzuBSIzTpkJXdfrVi+33fVU6U//x5dXZDmd7to9C4mQJCHCgGCRnEvKWVKm7q7eqZtpdK29D1zB6NqPPY+HGwSYB6AHAZC5RjRACENBIAgwg/BqEYCEJOytuVu4ciQiFOacEgk5erhpdwwAgTRL0JQt3FyuMiS2Hv5t/gUippFbIDyip5i5lPL+3Yd5Xu7v7u5OS9vX6/VliGpLyaVkGtAHDHPrratpAJjqvm3ruvbaDnzEaCbM1Vwk5VKYEyBCrWqGPuxvNlYbvTZOSXIa1Tbc4+l0fz6d37//sMwnZhkvSYR/IYV8e+qun359zqqqte4v15effv3l17/+9enz8+X5VtcuiQqXiUthYQBrtr7eOBFnYiSa8O7h4Xz6kVG02l/+8tefrp8/fb48vby02l2dRCRDWWQ6MbKpaaASd0pG4q4jIzvCrXXdG+1tnRVYCqAHdhST7LVH22LdPN9srRW5P3gJmYvolwuEhX/3ux9r3bZtJcQ24tLCJaVcCh68lzgwMgYckYVPU9lPs7u5jlIyCPFITz1i1hCQwiEgzGzQR0wRgYLAEGBM1AkhyBxr8+5dzboZYjAhkRIBoXtAQI8Ic6n9+nL5zf2aEyZJw71+XIKEzNjVWmvjQT/YAB4I6KGhIy3Bx0tRW73eri8vz6+vr9fLpXVzEJZMMmOagDNwAhJkAZbRdEMQ4hBqYBKcMi6Z5oSDHzSdEuLsWhEsMyVGIRqvwBDqs7CYmA0aY++mI6LQ/T8eyBMCMYigCKkSkQ+63EhJhIESQAeMUS+Ymb5N4wfJRtIBohzAlEH6A9MDCmjQFWoD2sIM3MAVzcj9KL/jWLETixOHSAz3KSJBoBkgBY3o5ObbTdcrmqIHJPrWM+bb66M19lYCjXrNETMxSbZwARMSEszCE+WZCgYHYFe13kdsdIREhFvTUCKQTIRsHE6h4WTBjgyYnLNzChYcxChPQJlYcuYZCKm2NktZ0nTK0yyFE3sKso2sZpYZZeE0JyYmQHUHN7p9HNFhAABu3rsd/FciGhaOCA8Y3jlEYVMzdjOMo+9hOhT1w3yLGIikCmYWPixmR5DXSLkZAZySGAARSA79FFJHjz5CAyPiSHpliBiBv45vinpiZEZmjiCzqLWBw9erHfEf/vDHlAabHjzcLazjyF2OqEH7UERFoDuZq3mrba91Nxt1PyNgq1vfNzDFgJSn2kx9lAG6u2XOIhjuR/IdEmfmTJSGaVkxnNGngnNJHuzuCIig3loFYFwkLyjg7qYGDoUgfy9m7n1/ff75t5/++7/98/9HG5T8LpUl5WIGdbdPvz09fn66Xa/aOxMxkBuGoRsMFvqA6aJ7wIA3gBmqYVNSlXBHBhGaJ54KJ0Z3MANgBwQSlwyUCFkGgIPIpykv5/vT3bv5/CDgJc/OiZAyYGGKsFrrfq2XW/eY7r75IMtpubfOR2wUBoS5EXHKmUXc/TgjHEKta1dtIyDuC9rTY/TAoe5gjhEMIAA6zlgwdgqGHqbowIjELCw5UWuuPnTHw106zB9EBBRAMWwlIMjBx4RqwMXCR4jnt1e7MI/R/phDAaFQkpSXZVmmUhK/PD8+PT221gPxtCzLaUnMxGP4eQQdIWHd96enJ+26E491GwANQUIEEHHOhVjCo2MfGhrGtxEIIHpQAAMTsnBaltO7h/c//vjjDx9+nJcTRQDgGz/+e1guAADU508rbl3bbbv98vnjX3/55eNPPz9/frm+rq1q5jlRTpQSipC76m3fONG05JKSq5WcPny4Z8x1bT//nGrV263erpUwJJEUSFOUBfMEAOZhkqDMVCbaCzaNaB7ggYPPY4BG7JIhHPI0CjfYK3hga3Fbu+MetMms8x1+K31govuH87ahu0bAXttYgZcyBfBhX6QgDA919eEMWeZicY4wCOtNw40EmYmYAdEiIgicAsDdXMEGF8kJ34qkAGdCATLA0FDXaDayYyQRESWRKSdmDgDE6OpSkEl7v+61M+Uk3295EBCg9d5aHfyAkYo06gcIU3N1bb1t+77t67qul+v15fV5XbfWFSnlMnOapMxSZsmT5Cw5kSQiHuTsoSAdaDFhmBItkyyZMFwY57mkTO4NwxKCEAhCQFioHzZ0cMeu1rH3bmamQehk34LC/l5Gd9RfVAoMgZUbh0MMQfawOgcgBjN5RHQYEi4RZiYREU44hKQAyJRyQgjsaGYYaAqtI9ax6HpT1seRzTPggRRABCwhAsxvSDvkYXJkogjat7g81+trX2/kgUh4nkte3r4bgIKe3LFFgLH1CdFzSYi7KliE2pzzXV6WXAqlfW+3297XvbdeSpFS0DAMtGkPxYIoEQgoyBMjBJhiByZcyvTjh3enuznN3KBe+9XA0SJzmu/KuSy99yJ5ztMkuXBmECDgmecoTJJSmoJTJw7B4d94GzAeV7t7VyXEkQcTPmTthoQCjIlTYkk01Isw8m+IRZiIINjd3vhgw6or7m4abqrqY8vSwQDaKByEmRjMNQaBnpBiDEuaKpZSRnL80NJH+HAkEvE05fPdqZSTcK67Xq8bf8uAQAABkIMLQTE2bwRG4OHOHgQHJZwiRiqt2xgGqZvDAIu4VusVoiE4Ea9rfX59uVyvL88XUxOcp5Q9AIVkzvM5nz9M84lLxrBu4Iw9MwCOAogBGIIgcIRMCAEfeW9uqtZdOwMjp6/yTO319eW35+fH58dnbVGmeHhw4bi+ro+fL8+Pl9fXdYRxSU6A3NV7d+9DIR5qZu24LNS89wCglIQECMi0h3e0bq02UEVwbx69IOUJpgWD0dXMa7gQpXnKp/MyzblMeTmdizDW/bXu15fnveromPdmTcMMwb+TOy3LrGHjXiemiOi9q1lvbVu31ntvas21D6WHmvUxLYqAN6bF2PVEqIUZQgwvr0Oom3d3DQPrrkGYppwLSUnMyQKsKyCO5Ve4tX1PVHIpUpJkabXt66bWWmtJJEma5mn4kiNigE+/PllEgDjQHd2M3AZ4OQBIUllO797/+Ic//skjyjSLSEqCb/kvAHB3z0wUALfrtVXdbntONdxKnhCg94bASUpOU5Js7q017RoBJeUyTSIyDFaScp7m6XS+/+HDh9/97k9/+tMffv/H3/3uTx/e/zBNs9Y60Djf/Hx3tafWph05eq239vL5+um368vreqttV+sRE4Git4CMJU1N97pfxXCe8zDW7+vldvnEmOquva8scD5PkrBMPE3kaEYuAsSGEcy8nAsEWmNr9NL2QAt0EpiXdHc/P7xf3n04nc+ztZiyXC719bXWHnlDN7itTYqWGZHCvUd8a1gY42sHIu3Wt9a79q45W61QylRScSYDHd13rbVbzym9e7gnppzzvu296/BkDA0dqLsBIAcgBgXgmJOMZW+EHeguJhd6K7UwCJlREk8p3d3N7x9OD3dzSgkJtq3VrshMIsNft0wpp/xdKJeptl1b1d4QMCcWScQ0zEhqptrXbb1cL4/PT5frZRthDL17ALKkwRAvS57mPC15Wso0pVKQGAIcAsHDcRjQIpyQUqLTaTpP0muDiAFcEJkZA8MobMjP7C0M1y3UYizUmLW21lrfd/1fgWaHWx0AhJFKKilFgBm0bq256Zs+9qC5jBiMI41nGPARGA5r8pDWM2FGRO14wGlQEFMEuh1P+QjqGkQWZkgCwiA0qFUjyIuJMtMgpaEp7JtfXvTlxW9XB0AWSngPX652hPNEwjBmTYycsEyJt9bWVreGe4eTTO/yeZbESK56qxZVvWkEDw2UqVozC2WgQVgMH1J/JEFyYICJ0/vT/bu7Oym46s2tbV6tGxdeSqFEaCEgGUUwJRAGRkTONLsCIhMzICtQINHQBSN+Y34bysQR5ekWeoxJ7NC0uQMyje9jDFTgDU77doK8VbYIgCJ4rE48zJwIiIdhw5iIhSOl9Pa4DfbyqLtUbYzxAQAR3f3QrBICDd0dEFPOUvKE0Ove8fudIhfk/OVEQxze0SA0CMcxGIID64hBAXRMLU1HygIhUHgLbwA1ooMDCsznUiYZ4QJTLuflFBScpZyn+X46vZ9zRgz1VkECHQk7EfCRUTrkhFhr673nQ2eBTggS6E6QKPjbQ1i1r7eX9Xq5XbbevDeZyjxNU9332/Wy75ubARJzSmVBlHDVUEITOtqI3k1HYACCCLHINGVAUDVt0bslDjd1BRKKQHNSAzKXRCehtju4TbPc35/uH06n05JLNrdaaxirhwP0bto6SgRE7d4NLP5ekj3PxUD5WLKE2hvLtfdeW621VbXuegg4LWCEOI8cFwwHR3AbeG4YvC5EBAIgCAJHGCmmGoZBODjrRBDILHDQPyDcRrQwCOaliKSUs1sg7G4xNHoMnCTJJG1u2jrzd87dQPCRmzYWTu74ZlFGolymh/cf/lD/iSgtp7tjyz5mU4SSZJ5mYVbVl+fn3hQB53mBiGnKvbbX12frysyn8/nh/qGrXm+3aeoRcT6f7+7uyjTlPLShOU/LfD4//PjjD//wD3/4wx9++PDjPJ2nvORUvPe3/Jkv5Ojvvo798noxVLTrfr28Pl0uL+ttrXvvzcwiFKzHvjYmyJnMXJsCkGtot23br5fXJEgovUatV0Rbljyf8nKSaWaFptHcYQQnEtFAuJzO3h7AOxJgbywJ7j9MP/zu9O796e5+muYUJZgAGAy8NtxWX29aW6UEKScWxu/lZxFRu6q5BXSN3rVWbU1TRe08T2SFGX1EIZr13puDTfMkOec0lTJCiPSLj8vUAt0QADgCHIICEZQihqz/Daw5lI0eAzwzhirII8Pw/nT6cP/w/t05J0bCbW576xqhfuRujcHld5egqUZVbW49SRKmJETM5m5qvbd1W19eX56enz59frxer027RyARpyx5ztOpzKdSllSWMi9lWlLJnNLb8NsxCAhgcFfDiSAlXpZymssGaKpEjMQpiQiij6vdwhStOzoBOAWRGw1zBg7+2PCB/odXu6q3OpweNE9lEAPM4uWyRuwRY0vAzMRJiPDuTOY+PINjWAcOQBjM7mHuRyYfABGnnMo0lVMpc2YePL5ACKSRbDp4c8ASxEZoBEIRDMjEiTNzAuCu2Cpsm7beW923dQdAEdb56yyCED+8W0QgBrUOfPht9l5v+3bdtrXtJefTVBgw1AgiEU45CWFY1G2tas10xErG7t69R/dwQidFjGBgDmJHVqQWBE7q3AHcFTojQsqTpEkyO7MROpHzWLVOwUUO/PCgEqMNTjGB/92diABDOg7aDfHgggCjEbbhcT9ODMSBxI1RpHNKiQhYaHjeIDAEOynE8f6Nv31TV9VDTxEwrLIDezf26KN9x0NWPTZPh5ppLEqIaEj8TG2eWm/edRfiL48WIi73qcwUb/gDCMQgdAALdwiXAb8bx7O5WhiMmgUBfIzI4hsTYDfX5qtHG4EQktLp7u79j+84UypSTqUsSSYmBggzScwiuXatREOsW1KaANAM0r7X2vhAWREhxUxgTFEwkvPXSOoAc6+mpi321ay39aTTghEp5fl8n8vkrbs7lmlmySQuqY8MXIBoXRBR3VJhkgiMXGSeEwK6em/YdrKOESRpvj8van1vW7P9emvzgtOMiUk4//FPH/70p3/64Ycfcs6fPn16enr89PjZevXbJV6e4vqSreWAADQHc3QH8u+eqZLIXUbsxL5vVre2rb0rE01CDCkjVvIKYTa+KkxZSjlyXT3C1FtrrXYnAk+j8kspcUopCwsnOHYOSJSnQsym1lqv+25qGEeMOaVjl1iWqUv1cGtKMearBN27VkzBxII45fwtb+tN6m96OJ/CInAkfgMAIEt69+6DcFqW87unD7f11lrLWaZpOt+d5mmSJKZ2ub6e7x/KvPz+T/8YZoggzI+fP/3bn/9c6z6V8rvf/cMf/vgnM79er4SUS37/7v2HD++nec45ExESc8opT9PptJzPp7vTPC385o37cqsjfA0b+fYl/9eff/ocV2Xfvf32+vKqdTPrFhbogWqw7/22tusKa018eB2w1uYYrfehWiJk7XG7varuIiQ5p8QkOJdCKbtDb3592Vvtt+s+HvV37853y71W761xgvv35eHH/PA+pRyqzc2BoMxyD0vr9Xbb92baOvE0TUspIom/+zoCavVaozbYm7dq2l117PPMtLUdDwvvEJ+FBcTWK1K7bftWmzsCJBqMMAoSyBgDLm4eaopghOQEb/lkxxBxRBiMaHFEHNPifLTP85SnQpkCvTs5k3vf21rrXhsi7qdlnn3+3VfWrJuZd3AbSe8QQzXmqr7X+vL6+vTy8vj0/Hp5va2rmtNbJiDnkvJU5tM83+WypLykY48D2jUC/dDJHSQGiqH9DhEuJc/LFGa1gnu0pgCRjZOMZQYbQN93MxvO4Zyz2SCTwsD2pZTsqtf+NU37+4G8jzKJRHiZ5/N5SUl0MLDDmNEsiESEc5ac0zRlRByY37126yMKFCKwda2tE4QQ9qrabJqn03k535+W8zTgToPLhjioq5SEWEa8U48YnSKmRKXINCViCeBOhA42W9w316srR6CI5Fy+Pbxy5iSDqwKBPNrYkXImjLkyEQliqFnrYcaEOTEjVG1am7q72VCQu0egB7pDAAYGSlAmzsgcGLWbVOzh3rEHenhYsEMORiws7IQK4QgeGIAxJtNfk4kiYIgmR2n0fXIEwhG4fhzODuMPQTHiuMCOhQ0xArgBROi4tw/ZZCASIckgflFXHf51QyIRHvuwUUkjRIzIeISjHIYDXDSu9oiR3jESnb/MFhGR0vj9kU1drSIk+JLkDFBOaVqGXOf42DhA2g7hGCFDmmemMBKXzEbK56hsEEY4EKmRKWpHdwc0EpAkOSfhfH64e/jhnRRMmVJmEhytPwABJShIwuJCRCI5pznnyQNNjSRTqoMOlFJKkpgyYaHIEOn1Nfev0gerW+21WQ/rg1Yy5enhBAV4MnOz2LeuBvOySCq9tn3b19utt4aIXS0VVu+cg5OT+MCDDyWAde5Tao16T9M05TJ7RfNaG3SDXECYkDFnnE+ynMv57sScAGhb99u+9rpx3XlbRTvD8ESSxbEZ+7a7QsCHu9P5VKZpioiX1xdGRzdPUqZp+BK3vV1v9UroYQCUs8xLOZ2WeZ5KyUPQ+vp6eX299KrWHZGYmA8Y0ZBvjHyq45e5b+u273urzc0IyMlMlU0kkvVurVsSQhSkVOZkKAZ1r71q7U5I1vU79/E4riKGRaprV+tkigQRBjEkSjDNS0pZUp7n5XK51FbneTqdTg8Pd/MyI0JrbXm5m093d+/eq/YRggYRf/vrX/ba9n0/n8//9E//l//yX/4rAN3WVVimebq/u7+/vz9WVISIxJKIEx/ZOUTEATRM/ObetffezJz5QHZ/+zF+fvz88/pJxTvFbnpR27o2VQ8IIA9o3VqvGiGTz5RSEiI2c9vbVltv1rcukgBouAmQBMk6BTDJxKWkiBEls+/ryHsNwlRKLueZgFU7c8x3Mp+BOcy7WwfAlBJzkhyt47raXmutQ1f7NkP9RtaISNN0z6wirWRtTXvT3g0CEVmkiGR/m9a+jX0BAdy9GVtkwNFuwyAkHSN+dW2qZu6mOXUVAEUeIPNxPkBrvdUeBEc+MPM0Tadlfn9/9+7+7v58WuYyUPWmikHWrW6wbWO43bWnf/jxa3JoHDN/h7FlGLt1863Wy219fH5+enl9eb1s+65m46sXSZyylClPS5nOeTrlPKc0EQsCjQj5AIjAwz0TBwfOEYlgoIJTFsnSBzGmm3tEDkJOLMw0lqu9KxrmnHMS5sFKNzNnkZzz2tdvn6vvrnYhLqlISmWa7u+X03kSQTVzV2ZszcyAiJk5Jz6dyv39OZcMAXvtt+umaggYHqp+vW3X6y0JzVPa17at7e7u9P79w4cf3t/fny1Mj1AAH5grPrDN5BG9tSGZkcSlpJxEhAalJYLDxRR69+fny+PnlwAUyWA59OuXs2+7SvjbDTJGB+YGahI8S+lqbW3axzt0uKV9hPuMJHIkM0cLHLI+SkiBSAJUiCdOM2chqPtG3oWxo3oMrwtRI+4EDooaiqgATjj+pwMt92VUfjBBAsDCNMBj+uYLYcA8ViA45IRhEQMVNzLlRzmAPhbtnHBgEdW33kbDm0RyoZLT6XRC3Nd1GydLSmk5Tb0LM3VtY7etX/Jjvm4Ej9n4gOR+w+4Y/XqMN5BQhHNK2b25q31/CJMQJR5++DCDCCIgAOSBCBolC6sh9GA1N8oiOWVCQjjyC3trrVPv0DtUxl77aVnO51PdIEk5vT8v7xdmA1T3Nj6Qx4CFEDGylMRFJEnKwpk5ewSIClGwMHNKOeeccxGZmIrwxJjWff2SYtdbe3m6bNfqHRnzlE/v3v/hj//437q1dbuqjnV1RNCyLCJc98vt8vL8SNttI2KLuO+16tZtA1JO7u6taRiSI2CkAtMpkZxBU6/9cl0fn24WxpnDeSTdBLTL9enXX1OrmtOy3nbmfH8/MUbRGq9Pa22w39wp3jYz47369uv40+9/nyc5390BwOPj48vz8/V2A4Dz6YSIe63PL5ePn55ZCDE40d15effu/v2Hh7v78+m0hPte6y8///LTT/j8dLnpPoSCY4vYuoN1SSlRnkqWJF21t1b3fV93UwUftx4HsgAnZgYENbaYOJ3meU6lbvWaL0+Pz8/by7ZuYyVESPH9yPGot1pNdd32NYZUVt3UhVhEhBiJc8mLnyUld5/mUkopA5KKkInuiabz6YPZ8DVguHcFgM+fP9da371//5/+83/9r//tf08pjwzTo/KNMAA3EOQkSXKRlIfsz30sy0Y0e+xtv/z/2fuvJVmSLEsU20SJmbsHOyRZZbHu6umZO3NxgYFAIAIIvgEi8wI8zL/M71x8B0TwBsEDBhjSNT1TJDNPHhLEiZkp2XvjQdU8PE5WdTcuni6ktKIi40S4mxtR1c3WXut4OByPJWUXsIl5X17CU8rLacqklQEcnxROKc8pqwERKQATOu/HDd/c7YbBNcVmIMg1TynPhzKzjJtNGMeSqBbIZRFbXORh64AH9gjAtdgyyelQBkFTJRBz1bsaAvrAiKqap6ksUp1X72mIw2bYAVAqUqVpqZWcCqClpdbCn6WyvQ+/+Plfw0ptJFWnaT4ejmkpUnUcNtvNjpixu4EUvGfnpNaS8+E0TcvSUr9EwIye2dTSkuZpmY6ziDjHSE3WBeJAxNBJi4vMS5rnxJ7DEIiYnbu6urq5ut4MwxjCOITgWEVTqkbzUqeGMnHei2gpHd57MXBNl7Z4Q3NKp2l6eHp83O/3p9O0LLWqGjRKOWaP7AAdUWA/+Dj6MDgfib0Bqnbut8ZtYGiKdr5LjXeTHWH7IwEQtiJYy4M7xuAbUoqRejkGAcdhdM4752qtuWQS8t61guqfNu3juBm3r+MwjJvx6mocN4EIVGSI2+urXHvhk4nYO9xswvXNLsaAiDnV07RI1db3XIueTtPxODnPQ+Cnp9P+8XR9ff3F29dffPHm1d2NmJZSWgdNB8W3Yh1SiwlUFQkdc4yB2fUYsftzDoEU4PHqabd5MEP28fC0PHw4rrYIUiq12Jr66Y+rPaom5YRFJEvNUqtoJ88kBnDsglM2qK24UE3UqhgTIUGLjwO7gcPgvXeoWkoWZVICQnLgIoCvnhNDE/oTQGkNg0jkjBpRbNeJab0FrTghJhVeePRE7CgAEiA6RnKgRmYCZ/4ZRmyt58zetYiTVTTnNM9TKWKgoOQcELngh+y03WpybrPZ3N5c55InfzydTvPcqiqN1Elbwx9AA0+1tDyc3SRQbm26Lf2ASM6F7ebq1es3y7I83/Q+bJ6PgtScACgVzVxTkwUDAG3UI4gN8S41g2ZATyzcWHaAzVA0iyTRrFYNhRwO23h9txNjdn7YeY6AKCo511RTqUVqMVEEJvbsg/PRe6dV1DtzThW7ngA59t7HEEKMIUTngnPBu0AUiJ+5d0ou+8f9dFpKNlNAoM3m+tWbb4w01anUpm7uCF2MDqHsH3/wbqmlM0aJmjFaQc2dt1QrlGRSALusIgwbH5zmmqelppLbrSVErZhmdV5U9enxMc1yfFq8G1MqohDGYTeE7TCSZImh5FkVAZCg3b6f4LaCj0PcbrfMVGs1VWQCgM1mg4jOh1w1HucY4zAWH93V9e765ur65urqarfZjqbqFjfuxxB9c+9a7qY552o9acSOfYzeO4UOGSIEZEeuoW6d8xyGOG7GGIJDckABefBhM44OSIucjhMi1SrLkppOwWfgcjNLOe33D3PJSjhur5wfcq5pKd6FIcYhRse8LNM8zyIVAIrEVDxNDe4MBo1ECpCJmYGAGnM2Ajn2EOJmdDEYI3r2zE3j7nQ6TdMkIog4DMNmsxnHMYQoKir98pHbHLenh4/vP/748Hi/LEvgsDLxX+7BQV08LaeTZPIuJUm5glkIwbMLITiHxLzd+e1uGyNqKc5zHGMqhU4zFA46BN5FP2Rv3ifi0GBnKrYs1UjRXJr1tM/ToZqSCSKoeHWEjmAYHKDlkqUkrMVHRAvmiMATsQXZbKTchPkU56nmBMtS5onHmWUwWLOlRLzbXpEjdoSIBnY8nBy76TSXVK6uru5u7+IwtEDNOTcM0XsvteacT6d5mpdUSpWKAMwUnDe10/H09Li/p4dS6jhG5xFJQsA4MqGKSk5lnjPRjOicd8Nm8N6HEO9evXp1dxd8mzXsmcGwioZNCptl2J2u5yXXToLUVcDWIWpFtYq0P5dSTqfj/nB4eHrcHw9zzlUEqVGGr2xk5Ik9ueD8wD6SC0DOsGGfW/gDuGKgmrY2gnX2MUTEhoQurY1C1VofE5hlT9GLeIaeeXU55478dewcI5qBYzYzuOT9/dy0392+urp9Mw5dsDEE38TfapXW29cUDhGREJjJB27Ro2z05koagL5pJNWqUg1QReSd+yDl09Xu9vr67d3NF3e3d63yf5ksxE5B0XD42vsZ273rxSpYGxO6dC5b1MJqxD7W5eEBVtMOUAoItn7Ehs/qtIXWSsqIoopCpI4VTatayyM6Di5ybKUdB8RKqQo0cEijNXLomYPjGDi4RgxNBK2JhxF8QHPKvDAA1KYPrda4XBWtkfAbgGrXkmr5bcDWK/lyySOPbmhNM4zGZIqqiiJqis7HwQfH7F1T0YgxDt45MJim6dOnT0c7lVpN2ZRNCaARPCATe+9vrm/evn2bUnryvlaZplm09/KqCiI04SXuJDkttNZm3K131lnPfzmOMbx+/eY3f/23acnfjz8cD/vpdDjvvz+++wMHQUAUw6rOIBKjmWiXglFCI6wmWUqVolaD9zEERiYjsibwUWutYrVJkFWzuOPbL3d+GwHQDVrkCJZrSWmeSxIpsMx1OpUiikzOuxAjO8fOD+M4bjbMQChIwIyAgI5QCXqvdlUrwA7sub2nlnqsx/mUclIVizGDuWG48VsnVEURgIewDcGjpWX6lE7vrS6mRSSnkpac50VSKaUWEQVFExbxtWhNueQitQzburmupdg8qXPj67e3IlJrLjnvHxYXwAVEOBAujp7QOKUqaiGOb25249evrqgEh+aoISc9kwpk+DzS/XD/wEdeavU+HA7Hw2k6HKday9NpQkSpejxNSykCQN4579G5onqcliKyP55UNef8+HSal5rruQcD2Dkfg6gYWhyGYRhjCOxdVfEhDOPo2DOzdz60XFwToAmOHHMjG0LUKjmnItXAkJlb25cIODbHcNH81uB4p+Pxh3ffZdXv3n83bHYhjNOUTtMyhGG73e622xD8fv+03+9zSdA0OYbYRCkBwQxKUzMMzocwhugAIZcP7959/PSjAZpDYzikY/ARAFNaTqfp8elxv9+bmvPu5vr65vZ2u9uGENOy5JR7N59zyASmx/3j97//+8eP78o87QbXM3UX482bt2nk0x//MD0cii2liBXdhDCM2xhCy1G7AJstx+AcqSDeXF19/bNvgOk0JcvO1ZGdA4+fHt8j6+766vrmZn883T89nJbHw/EJlGvCw+MyHwsYajUASK5Y67ilDbPlVASqAyPCSpZRZivOA3pkwhDh6jqURPef0mGf94+KWK6HfLPtV6Gqp2lBBnItMY5zmue0TMtUUx43wXndbHm7HUIIwfs4xBgCAJrpkvKylCXlUkRFiXiIgyrsn/beD0uqaUnjdvSeAAqxIqm25p0Ky6LzLNNU2WkV2GzQuYHIsQ/kPTkm713wMY7Bh7eAKpZLy/C3hk6pVb7/4/szAXsuNZfW/JanaZpOp+PpNE3TUlKpAkjsmrACA5IBIzKxdz46P7gwEIdWRgFtPi0SNlm4VRCMoDN+Qat9mqrkklLSBoPqAZYqqC4MniAwOYYhDipSclbVeZ7NdBgCInjPKmZq/5A8zO3N3dff3MYQY4wtYYK9Y7ThTpuPQbgaZDtXjPv3Ho6aaiMeKLXO83w6yGPMQ7zajLeb8W473gGYqKzK4u3wnSivNf1Zg0QANjrRjis9LwkCA3AcPQdRJPKfKwcjAwKAADTweBOn6iWP5h8wMhKxZ1JmU8RGotvhWlWqQ2ZgWgpqJbVqhmYBaXRh05o52DkgBiYjQHKIHlERCcEJdetnjUpmraJ3QRc823VVbTi4FgC/4EVC8ujETM0Y1KEZohFUIwSKHAc3tnBzjHGIwxCHELxjtwwLgYtxPy+JmMZh3AxbJq8CJVcA8C6Mw3a3vXFuybnGMHk3g2EFUimNhI6QWtNDS2G0Qj91IjMwMKlCZI4ZCJBsGN3d3U6qaSkfES9N+/7+I1AyNVJzCgEokQO1lHPrxwAmYywqWUq1oiiNMpaAsMOgoUpTeLAGL0dgRQgjADtQM0vLrKZFSs5LKkm1QprrdFpy7l4kEhkSEMc4jOPoHTKr9xwij2McxxiiD77JIbN3jtjnfHdWFCylHOf9PM+1qilUqYgU4mZ3fcObYOAA3TiMnmE+vS/pUy1pmU5pPqXlNKV5yXVZqAoaBilaFiFwzg1kolXzkpdZUk5LUQUwo+vb27dv3pZUjsfTab+fp1KrcTGESqhMFQBTqqpQioyMyxw3AZjAOaq1cRT2phR7aRDfffxYrD5OpxjiPM+n0+l4POZSGqUQIeVSTzkVEyUoplNKxeS0zK0S1NKtp9NcDNB5H5WI2DsXvR+CAwXCOEQ/BPRk2BtHXXDEXfoiDjHE4L0nJqK1cx201ALJsmQVy1YhUNgOIwGNgRgbevdylRtYqXmaj/vpCMdPLkTn47SkeUohDJtxs9mOIfjD4XA4HkrOBjYMIYTQupSho7lMEanh3H10gJjL/tPDh/t3yJwpn+rx4/EDs0PAJS3TadofDqfjERC899en6+vT9bAZnfPLspSUCRCRkBkZAXSZTo+ffkzHR67ZbNuSXZcjxAHqWIsdnuZSxRSc0RDj9W4XQsg1GYpn9I6io+AZ2F2Nm924ieNwe4OsA9chlXLKx2Hxu934xRd3X3z5xcf7gwHmuuwPhzQty0nmU5UMGghaVYpgmbLWIiLek0hGVmJSoiwmS572hxB82HiFpLUgmGMA1ZJqjSSF7YIVu9T6x+8fGk174/JL83J8OuR5kVJ8cNtjBBLR1E17jg2ECEiiJipVasp5WbKpxZgBaF5yrmpIRiyGJiJSDComMZUqssxlmsq0yLwYsVathpV8Pc0pnGYfC3sXVSoaxhA8jXEIPrQ51+qiIpJS/vGHT2fTvuS0nJbGP9/MekqplGII1igUAFs6DcmzCy42TrIhjFvvB2YH2IUSsFvN3hXRgnYgNAMjQwAiU9OSyzIvaNWkE+uAgopV1ZxsIYzeUXTeh2HQUmotuZRKhI6RHRMBMwHTZ/0jL5bKzdXdN19828jUibivOWgp2eeQGZA6vAu63VwLsnAGfxIxEaWUQQ6eHxkHx6N3G+c2zKOpWpsXhk27zBQ6/lmfy9AAzTz34nQ37dgooHGe07xkNaVaykVLHyLGwRNxrQ3tb6JKlUQqVO35Q1h7Ubq57c6LrjkUFfWNrRQSW6kA1Uy1Rs9Xw+ZqiDE4R+SAqGH8gQzJVn2sDoVt86f5LtbsnGqTFXiWXelXS03clp89ejRoMHLoPDlNuIWCY6Yw+M3gN9756MLgxzHE4PzgwziMV5vrzbg9zqfjdGo5QyKqIlJ0OiXn3NW2bRTRkXkexrjbbWrOKZeMtiBk77nxkhFpypOW0rrdWuMYAFXRXARAY3RVq9gsdlB79G64ufbLfFH1MUuHSfUkpZBaZFZyQq5kmaaERGEc2DllLFJSzYoCziCoFWn9i1KkZCmldsk+pkZA1tJaBAgGeapWG62W1AJSrH2REZppqfNSpjnlolUQiYmcI3BsMfIw+GHjxzHEwXnPYNbaLhy727t/7f1Nu46ac3p6WOZZezLNiND7eHX1dnv3CigYEjuTetjvl/3+0+Hp8fC0Pz49nY7HVGsRNgmORx9i5lLTgYi3m6FWMdVSKixlXuRYiw9uu/N3r+9++etv05TuPz5+AKxFS8laJUQfhhAH7zwXUTFiijGGXOtiFdHYYeMGbHPcMdrlmjf44/sf9/Nx9/AphJBzSWmZpqnWikjeuxBiw8ZWq8XKktMxH9syaRQLhI04ATGE4Yr8OLS54QbvYqP5ZecdORbUqpItFxTwiI6AwFxLJKmyAoP2IBeKak0FCgIjIBkgDLRxu3izEVUkIMYwhsv9ChGZ0Tk0LDnPUz0oQqlSqy7qJvG8OGIqJZfSmrAtJaTSzPoziFUJgRmJHRELYKqnp8PT4z0yn+w4pE2cxlZqlFpLrbXUipWIDHmfy7I/4IkAsEptMgdoaNR7wzTnPB9AUjRTbFqLL0x7znme5sP+tH88mppnt4njVYx3m5EdPx7nlJeiZkMMPNxsxuhCDCEdjw5xd3u7CTsPw8f7T5/2D2KnOMJmy9utr7JLSVJe5mk+3t8fHk9WnMMYeNgOu9vbayB5//77T0/7/eNpGPy4cZttQHVacKmS5mmZ9j66zXVw0ZDr6VhPx5LmolU8xd1w6/0zMCil8v/6j78Vq4bGnn3wjZcDRAhATWpdYgzOt/nRCg0eCBHZsTOjeUnH4/z0uF+W3HKRzN4UWn02S6mS5nRSyYg9CysVaoWcLVciAyHTpQjNBR8OSwqD99HFGMZNvF5ON7vr691ut92NQwzBU2PjFmtak+cLmeb54WGa5vk0TcuSSimIRL71+1FXCgJCcsTBxXHYbIdxG4eN8wGdR+S1ANZDXmxlduyhaZfsxKYEY1JlWZYTiVXv2AO6lqJvHU8lW6aaYvGOgvPDMCDAPE/zPNdSM1MwbT0/3nn/kvXhxT9CiJtxh71jsc9NsyZndJ6rANZ5drF1M0ErINsa3DezRGCoArVarVqrlCI516a3XWstJfeL7334a2tTp0Nph38+ojUi5n5HCAmWcjJMBmpQAMqLNc+tVtxoNRAMkAzFIV/QUPdYGRuh9Nmh6AVgNWYmZC2gFUiNzNDxMPjduNkMgQiIkIEYiBUJzmJZoNL5vDrKHDr2phehbUXO4eoBdCYg+qwrhti5MDSeGuIK3UHQRpHu2bsOVfXeBe+CY2Z2rW81RL/Zjbtla2bO+ZTyfn/IS05zggCmUHNdpqVKJaDNuCWkZZ6neUIAStCY2FsWQ6rVqi3j0+VHDU2xCcqYmVk1yzk/Pe3fjX6jaojPnpYZHO9PtRy0VEarziUiUFpSnU4JiYcxs3dNtlJM0RsHVAeV1LoSSV7mPC85ZWlyYcFzaAzwTME5MFimnJdam0yXmFStolJVxGrRUmSe8+mUliw5axFrJ8+MMbg4uBg5RAqRnSdQRYDGRvIv/9W/PGNTai3Tfr8scxFlIkUTS1UPSLP3FdkDMTnMGAyoNopqH4wdujj4zUgj8/Wwvb26vUsp/fjuh7xMjhFS9dF8Rp85LbYkDeNwfff2m5//8jd/+8/SPH94/5E85rocD1KLjlu+fTVc311tdjukDdJAQAH0Sk4h7bUQFFRGBWiE130ZXc6r0QE5iVicCYEREwWWlsRyLkYmdqaiEqRJENW2MJplb1IV3nvHvgE522xn57x37J1z3JiJ1bSqxjpIlUZLi4htjnrvQ/DMjLR2WzRbi2YE2JBOyD1p14FNMJ4GyBfLHAAJOtGfLbnWCtpKPABUhFHXtlAwJINWX9G+qTSS/C50CARKBERVbanzcpzSEZktYXU1U2oM861SbgjmAYiEJZuUslg5S1w3StUuAosIUERrck1GF/RZTnMdp+N+//BpOR01F4dudO52HF9vtzebwRDShMjoA99d7b754u3d1c4jSalLSZCzUwlk0SnAPM2POR+NrJRlWaZSFAGCC0PYEuy1QiC32+y++uKrL754e/vqZknT/Yf70/7eII+byrgdIoF4MJSMy6k+PSZ2JVcZNuQDlMVqNhVFAwLHEOnCfIjIjx8eqlQjYEfee0IgsyH43RiJA5EvWaZTqiJVdVUAwCaWjMQll2le9k/HeU6I5DjEOHgfnfOIoFZynuflJFK64VAwIzAWMVEgbFAe1SUXszlnH52PPkYXp3Cc5v3xdLW9utruNtshxsiMiKAmNTdHuI9pmR/3+3lZlpRU1QCda5zMDpFhXQdIjtj7OPph4+PowsDOQ497277Xg1RE6PE5IBh1a9+lFIAIm16XigF3yC+BoYHUqqK51GUpwfEYGzTed+owFUJU0WrmmBu0+nKZf9b8Zg0KB4BmYtp1lcC6nULotm9NHWPD8wOs+XjrarDtj9M0P+33x+N+no7O0dNTZNZcTimnJc2NiFy0s6eeHxciAAGdm+LXNquu98XQ8nii1Q2lwbnZX9LnWrXKYIrazhTMiBAdsdF6xxuCygAMiahD4zseqDWcO2YCzEtNSUouCBBj3G6GzWYTIxcpBmZM2HrcgRHZDKQT9phZLzL08KkJpJKB6OrTgJKZGmD/MnyxBXMIw9V1qaXWArAYtpSGgCl3VBI2pwgJyDXWplolIXsfnItDHFzr+U3LskzTMk15SYxci5yOpwbOJMKr7Xi92xyPB8cAVtEE0MC0ZK1aUqq5CKKyApF1L0ux7cqMwGiEdZ72P/74uzGMBG6aL8U37dOPx5KOzswzllBMLeU6z3WaCwDFOBEzETnXyA9cjCwkqlXUiuhpmo/H+XBcprmAEZEbvIueo6ch8GYMSHA8LacpT3PJWUSxqqpWgNYWiCZQRU3YpMtLpCxNpnVO4mZlBmJgh8yAoITQ1tDf/LPnC5Fa59O0LCnX6pyvIKk+zssfplnjvGd/jXzt4Q7Mkd/5zavN3dvddJqUccib4Wqzud5sbl+9+fKrn/1ySvNvf/sff/ju9/efftSSKKQwymgumyXF3dXtN9/++td/8y/+9l/996XMb99/by6d8r3wkpZy/SV/9fPhy29ev37zzW7zyxjuEIvN+/rx+/ndd/t0sAWCZ2MzQ+li5hfldsSvf/OzW8otHSpVqnQtDUB07BrlsGiTfmyolyaRch680gAwdtSFmjb6WOx7TKPSWC0wAqL13B4CrJl571zTEOtqLX219q/mF1+WqAx/m+HTxUrvZX4FUEU1VMTG+oQtgwY9Omg8Ou1k1l/2rOPaX8kAhAwEBFoB2JrcDZAhGzoDMtTGldi7VqlxK6ECWSemaAGOmDXhrBaYkPbdxaALMLwAmcLTw8eHD9/XZRqYNj7e7nbf3N68urmOQ8xSZk/Rx6vb7c+/+eqf/+avbzZjPc2Hp/3jk0CpdZoKIcec836Zn+Y0K+L9/UMtmhKeTqJVox8HvxlDHv3mi1ev//nf/LNf/vqXm+3mw4cPf//b39X8rtbKYHpNqB41IBCqBzHJWcT4BGBkwlaYETyrowIKNVmr2Z8fRS1QlYCa39/IVG0Tw83Vq6/fvvnizd0yzQ+PT0/7w+l4XFLJtRF6NCPXqs6aUxVtYqlWijhfYhiQQCSXklLO0uQ+m35ab3DGlr9TxGpmRcSsiLjCPpdpYTfNj/uT936IQ4xDQ/Axd1ELALhkzE0pHU/HKmIALkRmR213AARiz47YN9pPJO+8JxcMWQ2hudGmZze1RaqEhAyqaAjUQvU2W5CcwxD9EF0M5D0H753ziF4FElJGKDmXUucFgqer3eB8DN4PQxxizDnVknJeUspMNMTwmQf/wrTfPzz97vfftZ8bBU3732q2m016DsyxY9rOJr8XqnsXn1lKaZrmT/cfTvPRUIktldPjPhbJpeZewEdARF71oIi6PACs9Izd2OIKo2ciUkT0DD74VsZw/tmZN4NcM5/7Ry82h7Ywu8QzqKkZAFlffKBwJoS01SYDooLlUgQsYmDncGVbMVCTM+ygV29UTUS0SiO1bpuUoenaNoO4xuc9WGiNZdTKTigXnRhoQJVYGVSlqibRxawiWCXMZWqIhFKdYU7FOybnKATXOLIQsUtcVHl6POz3j/M8mYrUmpb5eCTR1ARBhnEIwbNDH3nITsQ3qJcBorngNo4DkjGT986HEFwgIlVlhuBpdzXcXO0chdMhV4/exVZDPz+OD++OaT4MRDHwMDhROU7LPJclKQB5l5sl8N4PsdEyOgWtjU2+yrykaUnTnJelGBABBXbB8Rh4jJy2lQj2x/k0pXnRUk0aFwQqM0ZHnpCRArNj9E6CqyVKqdZ0DLtmDVpnDWhKJ2goZ/+7DxHNqfXYolY7zeXh8f2P7/4D4A/LdB3H13F8u736pQ+vhuhevfoCAcbt3c3rj8u8DD6Ow3a3vX31+psvf/brVGrcXd+8evX9H//L+x9+//HH72opweHgWdV/+fbur//qqy+/HIZxZj6O29PNbXnzhZnhstDda725Kzc39e7OXt3EzbgROZSDTFO1WBdv4pCRpFqpZqb4ErWFAF9984VsYPX9z1rXXeaxca9WbYRdrfTQXK8+sLO0tH0REeC5hobwzKna7PKZia2b9lbaQ2b2LTHCLUCnvrespt2ey1jP53/4/UOC+eJqunfeKSOwVw1x/aFvLu1EoGchz/be1uJfE79DJDzvyKKq1rRcEQl6fw61Yzz767jW9frKBgAGhC6eZNDTBo2Kby072ktp2oC2ZbwbQtjBNmxvt1d347jzDsGqCKk4tMHx4FwAHIgxDhBz9qGCQa51SclUqrDzXkEB82IP5VQr1gqm4CnEEK+2V1+9+fJXP//VP//bf/bzX/7COUfor3evgttpXRzHm6vXb1+/2t3sUiofT/c5AYgzs5qxEJE6ETFxnnWzYc9OK+gLjxG9H0gBG/ETIRN4h1+8/eKvfvWLX3379TdfvJ5O06f7+0/3D58eng7H6TQvTa26gXXa3a+j1NIEq80Mici1dDYROIc4gDUSLeyeaAOEARiBnXFZCIDWCAYYmdABYCPqrrWkBXofI7ReehrNn62EqFZVQGJm5wM7v27ZrbHOswvNXhlw1zpvReWOM2/m45yBNmumHIHQDBuLK6yA0LbzO+dcjG6IwftA6EQMoAu0FxEqlqsv0tp5WoiO7CgtUGupZUmc3eJK+fMc8n/8/t0P7993E917s1TXGLcJQwDa6lXb2qzde8u6q9Iz7M3OqVRdUs65VCtZ5qejD9G1ZAwxMnMIPgYfQhxiS7Q6dl1aFIkAVnj8c+68Y+v6ujIwgOArwOG84FNKzD3LQmtjXfc81p6zft/V1m2gVRcQAddtBXPWpvM5LXM1HTaDmOZaBaBIUVMGKUgBvEMiWAWqapXaGBMJOgcGaJ94aC3wPc+DTpLfIZewPPteKkvJjwCAJqYnqUfVBFAVagWpIognAyDEpxM5R47QOQpd1xTBmmk3FZ3nfDxMyzIhglqdpyNgqRJcYh/ckGOM0UwR1QUO6nQWEWV0PnjvuQX3bROOcbi6uh6iJ1Jm9WzDEDabLRiVUosG0EGkwpo5NbMf35/mw3Hr3XYM261V1f0pTUspxRCJWcBARRxzCMEH5xyLaREpVUvnjFDp+VNB0FzUEZXCpXJVJbTDcZ6XKsraVIWJ2FPwNDgaPIeGNqHGN26NZqSR310mg1vzEoAQqmNiZn9RpTa1WhohEOViclx+/PG73/396fBINzfu+ubN7d3P7cv//ubVb3ZxHL/88s3rr9Mv8vH4aVmepC6MHOPt7urLm7tvDd148+btl19//bOv/vP/e5yP++WwEJTR0eDGX319/c9/c3Ozfdrff5znp8PTvccf397NgWxJuLsqgztS/WCLw80O3VSWT8vhU57vrTx4KhqwVrJG6W2KXXTp2ba/ff3K3XqmVjIHs0YucAb6IAB0hb+VRLa1tnVR4WeL1jFFsJbqztZuBYauG6a1g3UQa0fr4Xrv4cymuJr29U3nJGEbC+1f0GSDPZv3tZcI8JxkbEJJ509pgUQ/XdMGlq0mTbOQiQAJLGtZas0q1YgQlVAJBVeHYQUg4ZqV6MkFbGoa2Ps5+4RZb0brkdHm478M2uGLq6s38mqr7ujzxm+2cRyIqUhtGbMlKZuWupym+x/fu3x7O25HHzZhSCoGLMVEK2K43r0eFMRompbpNHfvA9gRRz/wjf/NX//Vv/rv/uVf/c1v3nzxhQhMU767++L66g3YfrfdfP3Vz3/5q69v7m5+fP/xuz98mo8FjNBACxVkUCfVSgXv4hDZu8GULrUJiGi7vVIjct4xMWP0vB393/z6V//Dv/zbv/rlN1+/fVVy3h8On+4fPz08PT7tnw7HJeVSq0HXJ1WVlEpOJaWcS5XSu1MNTNRblx5GQmpu4TjGzTgggagUkdKaoppeQ9dlwhBCiME58p5j8N77xoVVShVZJzhcXggze+KWcmcibpa5QUi4qbwAqZloY85gJGbuVNkASPTMxo0rloqIjYBQUKhFkyaoYiIiasw8DHEzDjEGMypFq+iSk6JVE1aooqVIqRpVuXGYWDCzZUmisCzZTFP68xzyS5rrkhsyrlnBFwYbTEHPufGeIafG+dfav1r3vyH1PBoDhujGrbPWONr6XBpBfJOhbdzj0Q/Bx+C9d85Rk19b/WVa3WF43jX6kyBaa+R4AU0xs2VOhLLeaFpL3aamjZyyp+W1d8N19L09H6EFGKXq8ZiWeamlGJiUnFOaQdmBmgCYteZhAuv9w83VUOQ1gCA4gxE7LatCVwewvus0OHqbMZeTrJRlOt13rhhJorNpBRAlqKQK1ZB7XQSam4XM6B3TmngABVFQsVIkpzovWbRKUhFZigsL++B88CHG2OrMxGbMfhssECv25uTgg1s7+zH4MITNEGII6B16BzEO47gxpcwVjIk84Qlgf76QGILGIQY3DGEcBgMDpCGqGhKxcyy1piURUaMyJSZAbGoQsjI+ICMyrL14RIAOIXjajIHRbq6XWo05IDkkJsfOs/cUGT2jbwBAR20qsXOuaeZVaW5ze+BVREQQm7whIOJ28wwUsiZiROgcVdVS5LA/fPgx5aNMG8ivH+20pzLL9M6Pr1x8FYbXLnqoJ7J9sidTA52lSi2O3Dj45fYGyK6Pn27fXW/KvQfNqmCkoCdJPzx9ylKf5uk4HafpdLJlilCZ1SnoXBfQp1ztVILfLMs+H4/ytOixqAkxMSArsCixdqN0MTbDEDexYWUv40042+E1MO1lNniuKTXDv6715qLDObA92/T2n54PabFBV/Jew18876fNQGIPk3tuG6FbyQvDDuBeJFLaBDdRrSK5lNI0ZLs/0Azxel6wtvOsuQEVrW2zrE24slWXnFWVJacp1VIJuaaSiWqtzfFu2UVsMIB2N/oCR6S18tD8HQFEY0QUM1IktfM5vGx+MwAg8jHEAqY25zTn2dAqYFYVRB8HP2w5bBSDueh2V7yxaz8IMQ8Dem9MV7W+KUUAVfH+/uHh/r6xJez30/39nonB4TCOu6vd7np3dXNlQDdPt1c3V+N2PEwnQyaKAKEUmqe6f5oen44mQI44ssvq2kQiAkaHLoZxO1x7f4FqRHRDNEBywTMFRze78YvX17/8+Ve//PlXX719dXdzJSrb7Xi1271+dXc4nY7TVEqttbEmqkqtVUoppdSSa5MpalDoqpJrNTAkaMWgEFwMYbOJ280IYFVKo79raIgmftAItpx33vlGKx+Db4EkIouoKiCwiP63v/+91J6TJ2LnAraWjMa41+aGc41yZ4WHtZroCpZUBQDVBj/CRsgNF3tLB4siQkedo5gVgHm2yHC18YDYsHC1Nt2lWmojVDMfSIGqWpV2IGTHYKrq2TlASjktaZnm5XJevTDt7I1Z10426wm17lijQlvKtLa2nxXZkIlcpxchhOeYmhiZkHo2r0fi/Wb1HFcvcPYW6v5h7e3cFSfOu06jvgBqQTWiiVzwul2s+DQlgKoC1ipo0PeXpoIhtfS0wvrWrrNpz5F9S6lV0ZxKzhWrMJPmnE8TlIYTMiYCImBQRiQD6naHXEswgrUafyfHaOT7JAImjd4e2vbanjm34tTF4yh5Ph4+rogDRZB2CwwrgBhkaA4BgrVW937jYI1csKeh1KSaCNQiRUSqymy8oA8UhqZUHr2LPo6tjS74IY40jAhiDBhD8N73RCwiEkqmYuhwAPKgnmBk2rngxwFFTarmC1AjIn715atyGwfP2xB2m4EJSy0GSA155VxK6Xg4AoD3vs0u570PATvUcJUMduR9I2pAUKi1IMBmjN6T1oIAQ4g+hM566huYuwFXhHo4QUSNqMypSK0VV3IeU2tJQCAwRDUV1d9/2kwXuC1FI8bgSQRy1TTXp3uTo9RoPj+Gkmz6cPr47/14O2zf7m5/zmG3P3w6nT7N84OqsbvdXH1Z8x9d2EitZrYJ9mpnX14PaQjVTqnUGZaHT9/94b/tg59rPqap5llLrioV0IhRCqeF6z6daP8J34FhqdWKucpBfbTBkUcg9ujNSlXMAi8xHI7Qd9eaVnvcYs9eULNeX4IeTDe57BZS67oam+OK4KijX7DDbw3Oyx+ZWquPqqC0hp/zx60hbjteD2ihlaG6I3GuuPdvjHx5IdYJvjWXMi9LktI3dLVVjuE5vl7zdJ3dQlVrk+vplYc1eQbQ2opNjZDTtNRSDJsuFJEj9kyOyCF7Jk/UcQgN+gcGaIjaXAoFUiIR4QpkRg0QYJfOCgAcc6kpLwCJMKUlL7lKETVD4hg2V7vN3avd6y93r19vXt/GV7fh1S2T41cShu325pWLA3jXWkQJSU3f//ju/Y/vEKXW/Nu/+2/7xwkMRHReluM8Za3gMca4udlsrge/8UaWajlO6dPD8WE/ff/Dx/v7/cPjodbK3sUxsPNMPg5hHIMBAOjd9fDq9s1m2JyvAhF5YDNERnLkPd++2vzi27fffvP6zd1miCRSDMx7vr7abDbxVb2u9bJWr6UUqbXXSNRqrSmXUouKplrnnIpUtca47ocYx6ERxAczrVJyzbkWW3uPzKApEWPfBg0BfcPLbzbBD4gOyTnyKZfvfv/Ds2lnxy5Ah3cyMZNr4TsBQGONbZycSND4sJv/2trnmu1tRa7P8CnOuxZD9iK3iVVFKaz1eutrHQGBCFs/3jQv05zmVAhhRAZyYqjNQ2ACM2UmFiKH5FI+TdNx/gdM+/X1sLka+tJdweLnZHjPd9H6D2xltEZsgozA52xdc4IRkOA897tRhxZo9iR+R7y3rH4z3z18Pnv0ButvG+IFQJ5fifCnTLstcwaTNQVYu81Wa3mS2kBDF5+yAgUuvq0t6c6Q2LWnFIBYjUQJwAE5QIfkjBiQzy4PriVJhDWdgO30qN8wA1M0ArDm9nSh0bVaeL4QESkpI770eNa/EhoTeGJELNCqPXB+QOvO12BGrYWfHPrgGkgX2KPz5GOryntEtgpJslQsDhw37wUMiUDAyDkGBnZMQKCoSlKxEiFQSoBYnQNEalCDnC/Uegj/+tdvya7HEAbvBteKiJXZhXHw3hNRY4cwg97SBtAw1KuDrADQ1kYIwTuH2NRmi5mG4L1jQmPG4ENrEOhl0cbZDK3TsudGOgyMuAlCnmfO2cghkyE27ogfDnSGZKtBNahqVU1EpWpZIJ+M0MYBBqsuzaf3x/37Txg+uPHH4eod8rg/HOb5IHpCBPK7cfvu8eN3zkcpwsSO4un9Q8inHerMMJPOJZ/2Tz9+PznKklJZrCREsKaLSAyGhqzeG7OhmonVYqg08GCNx/05NY3ExI7BPVtEBBzQb6i1qTGeK1xr8NHqVE2eBwBXXCysPSvdgreYnJFaM0pnd1uz1B0f0zUKTcmUzPo++3wm55/aImwIc+tuQ5/yrTq+1twvonZE9q4Le1fJKeeSG/S3G9i2oFYXfuV/1B5xaZtZrR8NehICezdW29ucdyE4INSWt7hI+v+pr8uqBAHYGsH0JIJ2rA9c0gwAwPWbN3jlD8clTLnxlzcWbgUI4+bVmzdvvvzqq5998+rN3dXVuLsad5uRicUoxO24veEQkbl1EphKTsvDw4OaeebWx1hqrbWmnO8fH7/74fvhZky6xDh8+Pgp6zRs3fWr3Rhj3AyidjgdTtPEnje7UVTZsYseAE0RuTFDSxFd0jLNh1h3ATpnjXP81Re3AIQrDubbL1/97OvXr++2QyCAWur6HJu+C3MIz8UdMBPxKnre+FWt1lpVTLWoLO1ns9b7GrwLjYDTMZiK1iq1SFmXMwHgBe8fmiICOReDj8MwhjDQatrnJdEFLQoiEbtGStM505xDIgOzLpNtLVfP7Mm5HrmtPdVtEVnfjLHvNg126lzzAKw1PZkAQkUotfWO5bRkBFiWuixpXvK85CVVIhoEilipVkWriDNiJgYTaakFJ4LLIrlcYsk/Z6PbffWzEc/pXLBeM28LkRAR9YWpxb5fAjRR6IuqXtd2Wxcxrik4XFloYLVFl+bsxVo3BFzrVufwes0OIq5Z9LZun99tkBYBq6poKwkdGECv5tRamyZgTzAiWm9z7Rdma/hLiIS+zRJFNOfZMXtmz8yEnnoTL7fSAnZEVnuc/XL7ntKqBtRaGhFZSc2MELGDQtCsi5Y/3wUjBNd2m3Pg0gIcBvTIgf0YRnK05JxraVBm5x0So/VMZVtMCASNjB2J2TGz8+wCs2N2zgyr2GlapiktaV4gcdOhRnbMEmqt1XsfmvRW0/5DMgMRBaiqlnNpD7I9p1yfhQqI6J//5otNsM0wBqZmFWutPoTt1c716S6tNHV+0s36itQO6DNzjXavN8MgGJRammvMjpumMDEycfNqq3RduWbgW4qsGXgEM1NCQKZSasPcOsfOBeccMLWoXVVpVSEAAAUogqVqypqKSrG6gC2wvcU3V3S34ZHw073e73PBRWiP/oeqeDyVUiVE8wE5BB9+jJsBESUXMvYU5Wj1sYySbgJMDg65pEnuPxUGswQlkRTwTN4jsQFaVjWs4xZjJDYygbIIGbvIgk6b9GVrtzBAJO+ZHF8CbEaKOxo9O3Yt9dbTMQb4HLav7qR1AgZ7UTxvcDnqoU1bLpfu9Tl59LyuqGHP+oL9bK030Qw103VDPnvbZ+f9M8wAEfkQnPdMBGqSitZyxr62C2sNptozkAYrjhZbW00LyPrVt6xrg14RN1Bvo+hpO0dzlVzbS4laVbHvlYiKa2kMwQAVzJDMSHuTjJoRWhfMeLnfff2LX26CfPz4cJqWq+12HKKJimgRjXHz+osvv/jqZ199++3u5oq9dQonQESPFJgjEPe4wLSUepqmjx8+/vGPf9xuYoz+eDzN87ykPM3z+4/vycEspx/e/8EFP83L4XS/uXJfurvNsL19dQVg8+OpSr663Y67AanFCVBqzak6R+xBxVTkND+ZSLy+2t7dtasInn/z86+aL+kdj8F9+eb2my9fXe0GAKmSYS2y2AUWu+/jPYA0cqtHB8CALjgD11JFTYht9S6BuvJkS4ATmiNGpwzdBW0xJIFRJ2jBxvceGl0rk2utlQhc6svn0WusjK1uxx7bHJBSRUUqAnrP3gdyHojbAkG0ViDWFb59DvLOdp2ZRRVb4F8zILS4DozW7txZal2SznNeljIvZV4KMy9JUtRcJBfJtXol57tdIGJiD+hEUfXFdbxscncco29LthnStdCp0HS5WzXZ8GwBW3i+VsbOQTZAD3tXx+miFH5+2Vrg60CUs+W6OKN1EtjZiPe0+eoc9HSfygtwIEBT2Gm5aDG13tzWeiF7sqHrT684o4ti+3ph67QgaGUeR46R3apotfp13FGLZyd0jdxXZ7CfNyEgqJq0jj/V1UcyM1RAfVkUvbq6+fbbX7egqkct3aNBRgzI0bk4DOS4SK2qik0B11OjY1Az6dSwvXWxxSQNl+xaWwchsypUscbkXFtOmp3rjkvLXvfRa+HnPZDPG+NL7yzlaQUyI8Dr691uwCFEJjJRkSpSnfeb7QYJpVYzBgjN7LZ71Y5Yai2V2z1qaVBm1xtlzDy7lrlBIue5BXCI0KYrU+cYsAap7SFVmzdgpsht4REANzaedi2tZNGV7dWd56kqpGIioIaqKGKSURaSCetEC5l6mWdLBZNaxYq5AAIBDYGGEUNEJmNKlIqK2iIlQy6sJ6oHtJP6Klu0W0dXnl8HFyPxDsuiJUnvsAwEDpKYmPkIxKAZalHVRmTIHRkHKKqiikCE4AiA6XJ5EDpGT8Co3BIC2qdHM+TUlpf1qbu64C+WMCgqILZY1C7Mr50d7RcVZewr37qzi+cjrq79ObZee3NWT7tl3FRNNecX9BWMxIDO0Bl6RVBanQ+ktTul2fMWDBqZofb0Vqv64+rXAPTkw1o+pLVNeA20EQBROtMYVkW2tcCIiNqzZbh2uimgAhlCEc5Kgo58dMGxJ3aXPBzb66vbK18M3TDf3lxvN6OJ1Cq5VHZxs9v6IQBhrkVSKmXJeVFRMxYlEdI15AGw+XR4evj0n//Tf/j7v//tZgzDGL//7sdPn+73h6d5TuwQ2Qouj8ePIXoDKLKMO7fZxmEY3QAlZcUErvgBSLDB06pUQGGvPvgwIFNgpMGNo/M+PAe7wftf/fLb1Y6R93y9HW6vNzF4BW3eyMX06fGedfwmdE51aKmWFasBuBp/tBWGZACmdhEQ9nodkTNb4zF0iEyNBballsjR+Tt2YRvsWyJf2iciZhcagI5btbjnrsAMiVwr6TUWk2d342IOW88yvsjHdxdmRXypylnwVwFqtZwlZ2HiWk2qliKtQCEKKdcl5cXjMnBKznts6jhqRty4NTebTUqWTvWMJf+sr92sQQvwOZHVgP2GTY2vrYS+ZqzbY+3xMz47pGa9gxxVoSe8Oo7VWtV+XdT91wDPgcGLxX6BbWtpLQNZIe7YV6pqfWnaHbO11kc5uwP9BIiRgVYr1SrhzeR34vqeAWw+2GrdCYEYnacejSM2Vg4zOPf4rm2+fSPk9dmeLV+rh7WablM/byWZFlVoJyh6voq7u9d3N2+e394zlYCADOiJAjF7T8xrrwJ21EPDKbWO34brM1NpwP+2nXEjBSEmQNLGDJNLLg2FgA0YcZ6cPXN74btgB4X0u9Ge/Vq+QTrK44qiQ8SNjxsHTI6RwaGRCgsxErCJSmNFbS0g7ADXXV6VscOce62jZ42rwtoCvYYBqnhGfqlp82DUtNQCZswrFLxNQVsvkqlVBM7DzHLKS0qlIXzk5rxGVC1la6loQ1A1rSQLLwc8MugsPsKCoM41ZLl3GAL7GMLI4wjRG4NgVclWZkwIS7HlIOWp5r3VZFRhY/Dauzfb8LO7eHXjhsBpzumURQqg0ugguqxUFMSkVpkPdcrW1pdj9MyOGA0bigiBmVDRXswqAABn4ESp4dbgOZSy82JpCae1VeSz6jB0AISBtpnVrPHz3O4T+zwBALAX3dekOALSKlxzEd9bF8hufI3noiA0GQu5xAC3vYYN2TAYRWOHvf4FXbjowptb0QZrthHOswnXDHB7KRq2UBu1y09wdxSA1uOcXQM8L+4WKOL51KCZdgS0qi4LKkbnRz94H5jdRekCwhDH7TButwo4bsZhGEQqliIAgJBq2h+fFAEJUzrO0/E0n3IqtVpOmrIWqWLSIqzj4fDw6ePvfvdfv/vjH0J0Qwyn47zfnw6HY84ZGYCtwnSa47AZQgzswmYTQxy990C5wgxcyFesFaCqaal5yckMmIg9DmPYbIbNMGzCdvSbYfsMMg3e/+2vf+EbaxtTKxdS63KHxlZC58fRkmfrPehhG0G7vW0D7qBkXO9zt/9tnvbU7aViFQD0PjfillRxTUuMkHGtlxrQiwJPCxtfhiXEznlrzDGEDJ07BNSwoYOYHbIzwFY+oHOTNqxglbXK3toAcQ0szFZZsJY6QlgLVlBVq1gVUGnhJ2jVxqwNpimVxdPsbFk4ZR8yERkaiCozDeOw2+1EdZYJLmRdX5j24z6//2ENwtdQeW0vtdWFOjv4z6Wll049wMudwmBFvcL6uNothdUX7o8YLpe6wfnTnw97djW6os6ax5uOl8AtEr42XFksqLEWQUvH9XXIrE0ZGM897GvWEdbL6pdLZ0tWmsRwo93QFV1syMYka4y++qSrZ4DPtnm9L2qkwl0gpgc7oEgGpBeFOGZm7z4PiHttBAjIkLQ1zOK61TXfzFpL+tnPWjEEAM3lJRUEbImCtp5UQU06AAigceEboDbiuXUzfLE4cQ2C10ytrQmzJZ0unhncn/wpAzVUce8ERSRwDtRAKltLnBEyP79LW0Vf3bkYjOcJcZaRb3Ott0mupl2tPWEzqJXAjIjAzIx6YgAQzIjBcUuWduh3SxnkAqVQrSyKpT7f/Dhev/32X7TLLllr1q2n7cC4wXkECcbeKkNFrAqGUBnBkUVvnowgg5EJgCpaZSvB8kYzaGWT0awCGARFRPKvXL3yaWD1VLEWKqJioBQcBK6K0jQRqgIIew1F0NCGIfsI7EGxhGoCBNTUISVenee1ATy9f0pTOtfXz2mxc4gMzWc658Rf2vVzir1F4L1dvb+/c0XrijUCOFfo173vnLEkes7a4/N5rDvg5aeaNla8dEEtkvMP3/0wHefdsPvy1VfbYdfkGC4isJ8uncvfnz/5IsfQ9zJaT8kMgKxn+J/3JWumBZ4LD+egph15zQD2XnYRMvTOXV3fquGS62WC8P5xKUX2h5oSKJTj3MDi0rrC3HIKRwkPM6DlvOS0pLyUIiJai5UiVVVN2q69LMuSwYer27uv2ZHz7KNsduV2SSIyjOM4DnHkEF2I3gfvfPA+eB+ZGcycc1c7cu5qtyutqNzg6mBAzDGGRpYSQ4h+CBzogmhWRN5/vHfNvOJF7qKHLeddcN1EEM8bf88LrW7WOVRYH8ezV9/MhFqP7Neb2N5GBLwGY40EZ02qAF3CIdpBzpYlpVTrc2S4GfnVLawUiU1oANRIlc3onNvrKwAVSQgNSdv5NcuNqNytQm2VnUbeICwKEqGoq47FsxCRo6SS5nl+wuM8exFLqaQymy1MFRFNclrcwZzpU8rjMDjvG8UPSNVabFmWeV6Wckn5APjv/t2/+5PT/y/jL+Mv4y/jL+Mv4y/jf46D/vGX/GX8Zfxl/GX8Zfxl/GX8z2f8xbT/Zfxl/GX8Zfxl/GX8/9X4TP+4/ccQjKj3jF4gPi4KnPDZj//wOBeW/snv+Ccc0C5+VFVdKf4R0Xt/UWc71xCfYf3P+KBnWONaAlzfaM8wflxpEHQF9rwo43W8kdnFab0Ya93+GSbaz8bODQV9XJ55K8a/fMHaG/CMCVi/nRGOrXj4fKp/8ozW4uLFOZ7/8Gff8icO8tNj9BoZ83MvNa+AKfjpy//sTz89hT9TOf2T4wIb9eLb+Qxfvvw88+3lnxuFRj8kUVO++3Of9mdP5ad//gmC/M+On1a6L3/9cvpcvPLiNjaUxYVCTJMe/9NHhRX6Yp/99vPjrv/6p13FnzyxP/37f2ioiq0X0nCXf66c/o8N+4fP+/Kwf/Kl/1M/tx8v52ciJHaOEFdSb0Bs+FW82JzOeIXLGvTLg8Lzo9CO0W5PfiUqWAFnF4CvM+7BGrK29UZ9dtxntNPlnOqfaS+eo10A13tB+7kavqKsLkE7lzcZ1v0QV7QrNlWNfsyfPIQO9VC7PBI+l+vbC84r5Awa6TV+u2h7Z7oMcVc8D/yJDf0ZONxwFi8W4MXrLw7xckt53oDOz+L5zet9vsC09W6RZ/J2uIAVPwNioD/EC7rGF6Z9grCoucO7naVf/e3/5tu/+V/G12/d7qqdKT6f0QqfwvVGwcX3M6iro1St484Qzh0APx3Wv6OdqSL7TUB7nkR2CbURU1GRlGtKf/i7//hf/v3/ox1qHMd/+2//7TiO68PVXJc5HU/zwzTdI6L3Q5rT8TDllHNKj4/7+4eHDx/vH58OIYRxHHwkdLLkQ6k5uA2BT6nsD6d379/vjwcg9RE3O+8Ya9GSJM1yPMyP++MylZLUilkFxAapRWK+ut7cvtpc3cbttTPCqrrfL/un5fi0zMciXWTLnOP/0//x/3x7c9su5He/++1/+o//z6Yp0REaImbae5HRWvOuY+dCvL6++eZn397c3CHxssxPD5+m06GWpFLMpHchGpjiszMDDZQI2lSuVifp/P9GmtmfQtsCzBqKxQxEnzcHAyRy6AKxY+a3X3z13/2r/6EvG6Z//b/6m80YWk8vtZbiBndxRIjQxCTWXjRcgX2wNjJ3AGZDNK6gpcvV1ufaC9hN37+qaSkl59Ig28ytN8I1yVpY8Z4iUqrklEupjTSwYe//r/+3//Dp/tg+59VXX/3Nv/5f9wZ6alRVtIJqzhDYNotfgKvwBVAMteupQEPeNoW7LtFysZJ6O/bZ/wRQE1GrqiJW+xcUtaxa1ApANRREBW5CpWhW85ynA5weh3f/+by5/+t/8b+7ubpT6BqNBnDWQ+v38oyY1z5JzpizMxLqubUMVk23zvOEtuKbXgDhzl0w64JfXWm43PbOPvDzlo52vpP/5bf/9/v779uLv/rqq3/zb/7Nxf74D9jqC2vz7MRZrVJrzTk3SPM6Mxw+zyAwA1mHmjKxa63wnRPp+eT/CaNvw4+Pj//j//h/KaUjf/+3//v/w5u3X/3ht39894f3+VS2280//1/81Zc/fy1ap2l+uD/kVEJw2+1wc3u93Y5DjMy8Gs9+BqJSRWutKdf7T+njp3Q66uFQPn748cOHd4/3n6bTMXgeN+Pt7Svv4vHxeP/x/v27758eH3Ipd69f/+2//Bff/vznt69fhRBUpD2wkvI8zbWImblGz6yNC7bUWhU/GD30W6wSp0diQsdoZrXYfILpyFYcmw/RxdGPt35z64eNi6PzntkTOUAuRadZ333M909SMokAmFzduF/++vrLrzbbq+AJJdXeGUNkjqtprvn9h3ffv/sDoowbz4xIGLabuL2K44ZdmI/TdDpJLVrrMuU56ZO4mQcar7LUx+9/Nz98oJI9wt/+6tduDUUSHhbbT/M0TdOypJxzrVVNnLcYebfbXO1211fXu80u+i1BrJlqBTHJdU7pVOosNZVSSy5PT8eH+4Ohee8aORh1g03e+9dvXr16fRuHgASH/eF4OKU5AeB2u7u+vrl7/SqEMJ2W+w/33/3uux++/+HDu4/LvCDh3Zu7r7/95upuF8dQpczzMk3zPKUvb7/55Rd/fZ5tL0x7FUlFLCfFRAgxxM32yl/drqa9755nRPyzOX9GSgNgl0lcZXgaI5h1nOKzY3A54VejjWfTbqvXcNbkghVe3vnfFVRM67xUZO/8+XiIeHNzs9lszFS0lJJ0WTTlqnOxiYAYQKEYZrGlSqq6iOaquUr25gBNtFjNuc5Fkhog+JTKtBxP82FajuwBmHItaigFctaUZFnSPC9pqVqxmXYARTBidgY5p2kC8kmZyaMipJpSXeY8H+dccpWqCOZd0IvoChGRycQM+/YpUqUW6LfGCIwQicnnQGD7zWgqRJxTOu0f5/kkJakWM1kTCwDafe5zmkK7bFZrN+hMBk1pt0kBrd1RjQvQ2rNqJmHVFcFG/8Rm1PQo+EWhJwaO0REaoTVJP27SYY0coNMA9FZ5xAbuN1ilG9asx6WeyBpJA8LKgLTC9i82ZdWqUiuVwE2Op5l259g3HwLMVs7RUmv2JCLdtINp65pbB7Hz48YxszszJ5OjlVy/N1zpc1rlwrSfvXPrvhQoNPFBq2ooSq0Lst3T1tH7eZbCmp79C7teNYuxKmmLenBVEyQEQFUyQ16AXvCzeu9jjNJ0UZABVqg82mrZe9jRlY/NLgx7P6t2cY3v8QySNzNdSRVXG3pe5RfdaOf5BwCrYMuzcht2He/+WjTr1ClG/LxfOedub29XuPI/mjvAs2lvp6mqpfG01WpmzOy9b7QNl+6CGTTzX0VUxTsfY7ygOzwf/J8y7Ow9X/52s91e31wP4+hdKGiMfhw311c3gBLjoMJpySHwdre5u7vZbjcxBiY6m/b22Y1Iv5SyLCUty+HIx8OSliUlqUWZ/BA2zrN3kTCYcSmwzHI65tMpm4EKI0SkAXEA8AZ19XvRTMGqqZqwout81RlrRXTP6ogI4J+5htVMQKvVzFYcgFPyFgJaIPJNaMUF5zyRA2BnWlkQVAVESbukkXcuhDiMYwxMwsWKnU17MaXC3ntYcfjYtEKZOLCPwYdYa3U5I5qAMQKoVsCEjvyYsZyqnuYZ0xQBzBSgr5GqJcuS6ryUeclzSrm2VgUwZFeExYJhARJySqAo1qjhjVAMFM1AUUShlJqWPBETB0R25KBH4SogoKjkiAO37VOsFsmqFiSIFSMFBmQz1Gol5zTPp9NpQoK48SlPQ/WsVrUUXXKdlzIX+fPyMDkv87RgnpVLLVNJk0khtLZ20QAI0ax1CQFA13W11brb51E7rSrvCIp63oFpdQaeF+RF1N5DRHvevqGbE4RudlRacw+iQS06JUsvuCyg52rKko774/2H+3fvPn6/P31I6ckxD2EjxZa5LPO8zMs8p1NKuSaFRmPiSplSnRaZiiSEquKWuRyPU0oJTIML0TuHRkYA7BGVbHC8C7Rx4CmAEYiJVNUaQgghGopBTqnoKcUNc6QQbbvjml0tYiZm4NgPMVymaHZXN9/8/Fe1FqlFRaXWvEw5zVprI4U3lZZXqiWfjk8/fFc/fXxPxGYmtahU0GqmTdHgnN/r+yueH4atHT3Q9K6bZJJRTxKdOxGxUUIQXhAsty5SRiLnvAsxjuOw2ex2N5fPopZcCxAaY1tka9LB0JSMGnWaqDKAIgEaIF0k2Tpbkq5x3XlWILSTvwjc20WqqdRzBxEQdWZIOjOSrGF2uyeiWmslAm6ZBGoiDy8oQVU1lVJESbR1NjIRk3byIkDsE3f1olocDAAAvS+yO092Ydqbuneb1J3llQEa4xl3BURC7J6uGSmBMIhYFaikueqiSqKoCgqmCAYKrXewckm+JpULHnyAVKcpB7VqiEwbomBGawB9uSS7ilHz7bDd7Bcxckv0n71t0/6HCxMO53vx4perRe9P8eIvF27BRcLXAOgnBvzCBv8DxtVefnRL0mittZSiqoh4Scf0MmUNjZo6pVRqNVUcMMb4WSr1nzx6BvWz91JjwWoMzqql1lrFzGL0ALhsCpPznsdhiDE65wChk+2f7197VGo9I6RaS3l6+Pjuux8Ph4eS5+vdOL6+bZsyMedcU05LXkrNiLjdbq+ur2McAGjJNVfVWkGNAGsuORWpYqoVBbHkklPT1ag17kpcQyoiHGMwBCHUakZojd3PlBkaFzk3970nHZnRE5IZEQCBAppSJ4BEAHOgaIpmiEpk7Jr0GBIqERmQsiGJgprmIobsCMVArLXwOkAHxMi+rQ6rpUBc2LPbZCuz2Gma7PSQoZEo9FFKmfOccpenKbW2VmI0AuNGe9RTVFjbLQdi770z71zM2XJGBKxVGzvIEIfbm9vNbowximhONaWiBkyB3eDdAACEkynXClI1pbKkssy5Sde04qb3zgfnMpu11teqms0QoCJK/3q5Pl6Y9jTPx8OBZS5BD4/39x9/8HevwvVtE2LDlUAALpfhRVCCa+t6YxnCtUEWm55pT+EZgK7h1bru7LyALzaT1Rk/JyRbANGpIg1a1k4UsAq+dITNtEpe0ulx//7Hj9/98P4P373/7jjd1zp5dmPYgpFUzSkty9I8tNMyL7k4F5idWKlSq0iuJaWSkqW5LHNKJQOYYxdDHAOHMATaMgwgoRZLqRC5GAYCMjWpRSQ3y3ecDsfTg/IMIXEQ9OKcMJE2b1jFzByTc3y56sfN9s3br0VKrUVVTTTNU1pOJScpWUqRWqQJv5iC2bzMS1r6jtcjbEXrTPvtxuBKo/ssi3tpgbrjRUZd7wiJgFyLl8/Ue0TMzjvvvY/eB2JHTXPF+zDEOAxE/vJxYFukzbMGavIriIBA2CbDZerHELubeHGC2DMV62TrCSRo0TyuDiSgtTKNSG0SG03hwTdOoZVZ+DwTn4sTBmBt72kfYY3//+JxWOtwVcNmmqH5OdKlH/FMl3WuYHa/tl1nLw41067n3DW0ePvMcA6mDODIwDVeYkRq2Wlb09VwTmwYIAOyAT1/GSkgGJl4EEOpKBU1X1zGh0/f74+f1CqxHzevxnjtw5bYm9mF9XyeNN1bN0Q8+9vnAlkv4yt0jsqLW3a2qeeDrJvPT8zi5Y3GNQRYs/3P8/OfEJ1/Nl68fo3XpQXrLXpu+6Zzn8Xr/fWdmFrqGcrz/+P4aQ7fMXvvwuBD9EBLVa1VTY3ZxYHHcWwJrRCic24VWejJs9UZw56atzXZIjWnOS1HAtlEtxtjDDGVnGsptaYkIpWY4jg672+ub+7uXg3jhshJVQGVUsGMEFsdQtrENTWAkktJJeWcc3bD5T1B51zj1QUmFQbH4B0AgAPz3jyrY2M2ZiU2ImNqbLxACKSKIGhKps2NQ1BEBRREblojZF0Bm0kUoKJ2/dbqKpIjhwSGWkyyCCoaBhfYB3NeTwtTQWMjbzyYspIXJACQpjqwjlLLsswppZxzybmUAobMaHq269D0gEvJAFKqETqPyNy2VTbjkmmtmLPzIW6GzWYbh1hyBchVAMSIg3ODcwOAMQciB4CimkqZ5mWYpkaZhIxhCMNmGMex5JyySinzNPvICmKgueSSSy3lsyn6wrTP87x/fBzckk0//PBHxdHfvB3vvhjG0Tl3xnKshc31kXat5I6T6CEeaGezADuLM1M346sI9Dl0b1siNHNkSH1Vw1lYZd0BEYCRgKGRjxCiEhd6sSABQLQsKT3s3//w/ve//+633//4xx8/vV/SCbR6FwY/e3ZMJFqLlFNaDsdlf1qWU6lFSy5xZAqM5kRgfzgd9kvJtZSqUn3wxDyE7c3V9avb169vv7zdvbna3AQ/MrFzwQePQD1slJxyneflw8f37z++K3CqOC3lac77JU+zWwgIEJoKOBp+tuXFONw6pyramAABS045p5yWnFPJqZRUS5GSpVatjZaw06SCtfsv3SlanaVnsWkEagn3Nf5d/6NNFJVdcHFwYeAw+BC88yGEEGMI0TkfQgxDY7AYmJ0BnLnIAXFZ0sPjBeWh4+AZ0ZiA6UIMkLGJ5yGuXsQ5E43YeUIu9EIBkAifMw6rXUcgW+eSKORcaxUwZXZhiL6JiACsG32bK9QC7EaOBm2Ld8457nG10XNyv7+N2LmWTq9VqzTyzJ4FbCyl7SZ2hE4vJffV0XIDsiqT9UJG541BWLNULfW1Cpit9r9b2gaoAVUQMSlWSy1Fikht5Efa1wgBBLItG0dUwVTg/ZojM7C/+/t/L1pEJcbxzdtv37z+9u7u62G86g5zO7PncnkDwED3ds5Fc2h6cM3An2ERn9n18555zspd3s/2vR/uHFnjmTnq7H2BgZl+FpWcT+9F/u/yBJ4RcLhSHNYqtZZSiog2duEWrDPTWsex83tXuy5mze1z3rtzxuflx/0Th11878M5HmLc7rbbq+3T/dzpNrVpR+EwhDb5ne85nBfQtItd+HxPW0zpHG030bshOCqpztP8dHg6zROQa9v+dnflmAnx6urm9tWrzWbnnAfANveaLwdIyIxmnUdzzfqJSClZPnN3ut+JaIzMEJyaF0RiNO/Ve/CMgdST90iOkBtsjwzMHCuhoAmoATWaG0AyJDVUQ0OgTtSO1GQDwERqzlk0Ow/BArNDI8u1QsIMbLhzw2YcGADmfFok1BA4EHpl8sMu7q6RamhUZOuopZn2nFMqKZVcEQgcK6NZn5lVJOesYmBUizn27MB5AhQkBTBtuBgFREbmlU2nwd+IkAyRKTBH5oBg3nsfPHu2Yrnk03Riz1XrMET0OO6G3c3udDzmnHIuaclP90+1lmEegEBUU0ppKeX6z5t2yTkvcw5lBk3pw1z91c/+6uqLr1WuY4xqLf5oaSVtmpgNs3oOrxDbZdVaSy3FVAHMEQfvr7bb3WYTQvArmei6gZ8X4jotuyN6/sNz4Z1aBASraTeoSER0uWUYWMrHqsvH++/ff/zDh/vvHvbvT/PTkhYtGrhWD0P0MbBoXUo+TqeHw/F0THlWUyQkI8dmqZZlqafDcnyaVJWIN5vt7e3tN19+9cXbt69vX726e/369sub3d1u3EU/OO7xG7Rik4poXZZ8mpbNOO62G+MKvk7p8bQ8HqfHp/3Djx8/piTOMWBDy71c8+zYOTU105ZNlrqpteSS11xRkVKk1QxLLjn37zVrLVqrSlER01UkR7U9kYaEOBNnPu+qiEDETNvNbnt9t72+HbZXLg4+xOBDCMH74JzDRhXtnXPeO28AUquagmnfOF9yfTtCZiQERmhQOaIzPyOuEiXdjK8bxBph94eLz45lL+jh+d0I3ZWWqrWKVGuLisk55x075DVbvm5/0O163/2pizcyE63syKj2MmzvZwWo3edUNVMBUEQ5Jw9wVS1a32TQyG3BRE3OyeszrfLqK69fa37DDEyVRZq/0+JNNVEQgSpWREvRXGVRyWrVoBpIC97IkMyzeVUlVbpIs5t99/1/O52eqkqM49N+P88LEt8RsR9aNWedDnZxCeeF2MGCdk6kXVoqPL++O/Hn+7ce9HNb2J/mOV5YXQFspKO4Ru8/+ah/dNiLiwZVla5fIGZGhE1KocHi1kkFqz/QA/xa5ez5heCdc825/KefxmeX+9NLaAi+cRw2240PoWZRtSZJyIzMjTG5RUvWpXX7oV4c8jMlDB/cuImooiLH4+npcT+lqUp1kVrRfRhYtjsm2my2u6urECMRG0DrN1orLo2EFBRUVGqpy7LM8+l4OkzTcdjhFTwX21tVihgByUxFuDJSg8g6BsfiqBISozEJAoIyIFpDbJwrO6pq3IWm0AxFTVTPTJ+qFaSVZEVFpBZVMVEEICBU0yxFkiYNzjkf2MgxNlWvaDwimAoiXF/fBP0G83WwShdglKYjJVVqrVJFRRBAsfO/9onR6LG1gpEIIkitGYAAtVYp7auISI9ORWopGaHBFKpUMWOVhp0BIkCiJjuGDkVlSQscUFQMdwRIDn30cYg+eESsuU7H2cCKKjk2sC5U/iJz/dK0gylKLVVOVvOUDjO+/v6767dfpnQXh6H5aOwcgNVS0rJM0ymnJFJhbXBCBCml5Lws8zLPVcRMvfObcfz5z372y2+/fX33atz5hq+mHrEgnIFynTxauxPRBKwAepnvWWmStMsJrFmEy8Sp6Wm6n/Ph/cfff7z/7jQ/ii3kBbPVKijmAMwbolQtU5r3p8PDfr+cihYKLlat05x0KankaU7zseRFHdFmu/3q9Ve/+MUv/tlvfvPtN9/cXt9uN7voRya3amsWTcuKL+9zVcRAdYzD67vX49WwvR4Vcsqnx/2HH378rqT/8OHHgyp2DRsQu1z4K6TIABt3InlstOeq4xnD3Ajpay2l5JxTTqnkVHOqJdWcSs5Sci1Vaqml1FLNpJVIepr3TMmMgGjk2blw/ertl9/84s0XX13fvnIusG9KMYSAbb+rnSlcUso552VZalpqzXlZUlrYx+3N6+frQGPsES6vrMq0Gm9adUV7gL5CqdZf4mraV5O/WoKzaQdAU5NalyWXUs2MsPlCDhRElBpIgPrHQo/J1Kw2O9Xl+9od6UZMyT7fw5s5JkY2MMVqWhuuU8Uuih6tA2CtOlvTCTlHPR0mukqy9GexUkwYAgEKQiXM3Ll5W8a+ilXVKtCA8f27WgETs/NKAUJg8wQGqlCr5qrl0qJ8+vju/uF9rcouPD7u52mJceNCvL5+xTxAo8zEMxjhZULswlqf7w6uIfvZtenewMrrv760v94u3wlrNQZgvQBoBn316FpGRsH+AbuOl0f97DfneF2kl7FbiruZ9rMQzDlF0L43101VzAyRnHMhhDN+/s+fyT9l/MS/IYxD2GzGEILWrArSfPHVtzy7rdwaMF9EMj3RAWv9QhGAKEQfh7h/eHi6f/jw4f5wmIbBb7bjZrsbxiumEdE1LUTnvY8DsyNEbbZV1sQVAKIZqJjkkpY5Hff7p/3j/vB0Oh2u7q4Brp4vqvGuOwdUVVURiymZAVJDzwqiYhe3BmiaXkamKqBaW3pSpamVkDVMmqAWUTRumRcVMZGiVVRrASkggqAMQGooaqgGVYoAFHUeohACE+VSDTEgbEwkz8459+qV3m4YhLXS0we4kDxYOZit5d4QrCn/4iWQWwwACQiQEJpAezXQnMqyNDBjKVUNQKqkJYFp4qUWLVmlGJHPKc9zIiLnQM2QiTyzZ81SapXjSaQig3euNgkJRw3A23p5kAnZcwCkLln7md/80rSrSqmTlmolzdWV/fff/4G3283VtQ+h9sQUdy8lpXk65ZybaV/3TlCRWkvOKedcalVTQo4xLtOppDR9PX3x9u04bEIIatSEJ9dSkZ1RSAZwTlDjuqX21GUH69maZuus1ZcX8f7Td3Paf3z4/uHw4XB6WtIsUk1VxQRbsc1y1jktx9PpOE2nZS5FSR2gImqpacnzPOeUJNC4vbu9u7l58/rVV1989fUXX357/fYOYzgs8rTsRUsuS15yS43X2sJkOQPRgcyotV5trjbb660LjGglO9It25ZsNGGppiKg1S72LwRoxeg1iO2DmW1tsFqjTxORKqWFzLWUWnItuaQl527ga6kll5KzqYBK2wesBTEq1iRQwQjNKQI69tH5wbEHgOaItlxNLeVchsq5LCmntCzzXFKqrUBQ6+7m7tK0N3FI6kINq7x3A+Q9kzWflbM+G6stP/9x/WcjpTfDWmSaltNpOR2nWsVR13ht8Vj/6gJ9jp1rknZrUpxoFbjrSW81IjBrcsLPq6WKnKapzXQREDFVbCqkLdq4aO2oq7RUm91KqGjG0FVUnpPyLTBpuDdoaPNe1Lc17Ws9v2cNIiSGYqSAAusPdE5rrUoUaLnUqSaen8rTfZ5Ol7HiPE3Hw7EUBaRlzgC8u37t/ODYXV2fFc/OXvdFocg6ir4LD/1krOn0nnDr2fvn1PU58r+8syswd/3lc2rFzucAq9D5P2zdL5/XGv6vNZfWvwbQ8kbURJ3aZISLUP2zd6l2+Ve3qjv+5IP+0XF5znaGG55HS6ORcy74XhgwWMsx3ZESUQAruTAh0aqDs6Yz+22E5+BXVVMqp9P09HR42h9qleDduBl3V7vN7ioOV0QRwKmIARAzO9/UyahBOFRUWsHJADSnNM/TdJym47R/enraP07TaUlLKePlhZT2BkVVrAZFNJVC2CTTHGDlddqfcRRmJoKmplVAKpiYARi1y1hFeRXUkAnN1KrknEqqLZRXjY4NzBORmZYCagosAqog3rd4BomWUgBpIECoqR4rxjhEGnY+BDajw/3ZtBO1hKR4V9UpCKwCMmamtZScFgIzUe/Ec2CKgCBVkACs6bugSmtYbsgxlVozGiJJ0VpUqxHq6XQMT4+1Jh84laWKIJJz3hRNS8lZagFQxyxF5tPcOngBwFRL1hYqeQnsHVInvb98HC9Mu4qWUmuqoFXFPNQ//vD9qRTvIxBXqabW86gALVpUFVkri4TIvP4ZzABEpYiKCAAu8/y0P9w/Pv7iePzZN9+8ef2Gzo42AvYGol7cW0tw3Wlf/3WRLIO191a0lmIXVR8R+cP3/zXV4/3+/eH0eDgeU84KWmvL9qtqzUWL5MM07w/TaZ5zrWpICETg2HKtKaXTPmmlL15/8bOvf/brX/3yZ19+dT1uRyScp+n97z5+vJ+eno7H43E+HZZ5LjUXzarFtBhUAAEwIkM2YkQCIheDH+JmM242m3EzKth8RJANqFfBWivoy3wdds3prp79XO08G8C+AxMBMztzFqOINjENaXY+p5KXWkWqlFJLKVarajWpKiWnHuWXlKUkkUpglGWa0mF/ZLqfT0upqZbSU1Qll1JKSbmUXErKNbW28ZJrKVILEznvyF/0IsJaXyfoRXY8S9ufc/IXofuFEb805PCsodTDdyIGwFr1NOX37x/vPz0dD5OKjD54x2uS3xoIgB07750PPsbNdhy3IzsCAsAmME2OyQBMFYBUDRrc73Lnyvnx4QF6JokBmNgzh1bToLW5W1fAeI/IERjMmbE1wK+irupmqqpiK1lJbzsEyKJZLBtmhapW1RrxgREBOSCH5IGdkQNyQKyNrAKA0LgpR6steYH5Afcfy8N7LRlceF7mClJBaoMCnYDeh+G3xGGz28UhjMPIxGYKKybuRZRuZwPbdEzPsJj+MrqIIQ3s5ft/YpltDdVXo28rerabdrtA8Px/MZ4ttJlJLx2qmVHXz26dl3gO68/fL9/VUveIuMb3jOdmr8+TBP/YCTVcZZshL+tuYlCs6yOd6/h9GkGPmqWKKqTcZMQbM8mLCsbq+TS7DCXr09Pxx/efDo9PeUnb3Wa72cQhxHHww9b5ASyYkRIDGBK5lrJaQyxQk1pFKpgCyDSdDoenw9PhsD8eDofT6SiNaeNiqEGqLRXW1eZTLmlJ1OgYBE3Y+QqDggqYtLWu0rpTTEXAClqlJslshFbROm5tVblXk1LzvMwnUTMkBt3GYEDRMarUtCg5Qa7VRLVUV4qbFybiakhIo4NghaUmyDreuDgM2xuHOONzyYrZBR+hCfcpkuVapGU0zSTnxazknENIgx9i3GwGh4St6ZWQm9gdk1/VY6Gl60QU0ar0iiGY7vdPAjbNYxy9gagVAHQ+EDhQyMuyLLmkBIBSJC9lOS6l1J7AK1VUxTSIhDH64J1nfEG889K0DzFeXd2gVQAFQMfOeZaSwAyRSq2i2vIb0TvvOIZg0PFBKsKE3rWKc090iWoqNeWSSk0iP376pIhVNQ7x+voqhIjU0ExA+ILJqLnrDbTzbNcv6n3r6jKVqjlrfTbtqvLHd78rOh2Xx/3xcDjOpVSAtq+CkOS6qBpQbcnkhpRRITTNJZeSHPHV5mrr7gJvvv3yZ9++/fKL3c1NUTp8ysdTfXwoTw/L48N83E/T6bhMh7RMpSaxZJAQKkAFLIgFqSgUA+h0LYyOY4zDMG62O+fDfj4dD7NkW2vNL7aDNRX9vIusdv2z2BZwbQ6HlrwVaSbYlRxiBNs2uVpV6B1WKlpLrWWZptM0zadpnqac5poTgCK7JZWH+4d5Wpg556Xk1Ja61iJSqxQRVQMxqGariKeYqveegQxfTLIeNyMw4aXU4bnWDitqjtYK+/qXc9z+jOhvDqoZ5iLTnD9+2r/78f6P372///SYl+KJ7na7zRB8604H7SiwFrh753zY7Da766txOwxjHMYQo2ciagiSc5UHwF7SMEuV+TSJaJVqRkhuiGOMo2vZAWpzWPt3UEZgNEZgAG7AWQAGo3UzNgXR1vGDaqoKRWqpdT/npyWlakUoGxYjNVJAU7aWzOSGlNA1MkZsyVNAbL3tqiCCRTAXzgWl6oVpb9X63hCk5XA4/PD9H0McX716PcbIr96McVyb4f4hg4prihwvrNyK8uqm5nmx/iSNfxHD29lE4bOFxbWaD3CO/f+hnLz9NAl/jtfP9fLzOC82+1PHXOs1L0L2y7fghQfyk9P4E0dbT0ZUtZQXvYhFtIi2fEK7RDWTlfCg1JpymZfSshvMFGMwbuHgc5XDwAzEOo9XlVpNlRA347gZwmYYYgxqWkoVWFgoBs/sEHvKoItVm5mKlJLT0lDiteZa0+l03O+fptNpOc2lChOFEBqW9vIaZ1FFE1GTarmUXLUIM7ARVqtW65KLT4QMZsxMQFZVq5pAzopWohMGNEIC81xB5rIAgFkBdYQq0vqz51MVNUAtaROcAbpzLU3VEJtRYVNqspUADr0nZgK1ikUQpEqkuhlMPdJy8dioP3FW76x6kNb9ay1rAwAiiihEUlndSlCKSI6c88ExkNWacfE1uZJd5lX+87m2ZaYqOSecjga1iGeHDVQYfERPjp3WliU1UCB0jtEHc76SWwBRVa0apJaM5NaG+9nqeGHad1dXXxp7RrdWRL1zIYQQByTOpdYqCOYd7zbDMA4xDui8GrZaLxMOvuGWenG21rrkclrycUpPh9PxNH/4dC8iX7x987NvvnLeO3R6oV8L3UdfA7P1X/YyWd98KITW5iHyEvcvKt+//77aUnSZpuV4KrW0RUFMaFJqTY6JGWoPmczUWuv4NKfR+9vr6+urm6vx9d329Vc3tzfOy8Pj8ePvph/elft7mk5cskNzICNkKVlLxqps1ogRK5IQJkBoWLxUcqtMW09jIiKzJ+eVWNBqzd45BGth6Ocbw4ppP9t1AOyckB2S1pLPDRDEZlZymWBqcaF3LgbnffA+EDlE7tuH1FrK8Xja7w/7w/FwOKblVNIspZiUWu3x/r6lklJeas7aSfE62AWJ2AXyEX1gHxobQ6vbhTj4+CJTR9hL7GcB+E4huabjn632uSKOiEBndHy/BdAdAgASscNp+eH9w3/8u9//3d9/94fv3j89HjzgzXb85s2bu6vdJrRewt7s1BOciEg4bja7q6vbV7d3b+5ev7kb44aR6Ez/1sV/0dYEVBtq2iAmyzKbGRPDZuvsysfogg/M3qEn82yRwTM6MkfQsQBAjBxIHRKTESh1Yj9oHmczQSXneVnef5psOUrWKmQYAaP5ABzESIAMm0Y5WUt0nR3fxvvYWSXMAQ7E3gcLsVR8vHgcoiid3AZNMS3p46f3PoQ3r95sx81mGCN7R4z47EHDxd5nawm9lyzOmbf1783SPBvm1Tzb6rTD2SzBnzSFax6gvaj99Kde9SfetrojKwiu/tSun7nk4M/Ydegcz920N5hbI062vo/jat0vP/rPnNOaEhCRUkqtZVleUIsUkVR7qcBaMh5M1YqIFltSmZd8PC2AAAQ+uI022VCAZ/X4FuY3YsMsNWstwbub62t/d+MZpZac0uG0zCkrhjCU27th6zeIBI3StbkqplVKTmk6nU7H42k+zdNpnk+n0+F0OtZSQGwYh93uehhHH4Zx84w+M4AkVk2zmdVCtUBVNPDoRhfVqBatc0o0iUouiYkQCERBzBRKBQIcfeO+QDIaXbF8SMelJM3OPBGqaCk5p5TmXGoVrVIHbg1xPThQJAESNiPwznnXci2M4ACIURQFuILJlCYkH7yLrWN+HYhAYEzomNQ584qGBhaC94EAjAiIGYmRHbZHhkjkQhiGuDEjz1UKprmkJaec2GFPyAAYA3PrW1IzqbXkBAbiPLvgXIgNqizDAKbsWKqCIaGToktMpjSf5kQJobQHLlWkKlcV0s+yQZ+Z9l3c7GLw0btm2h2Rcy4OI5JbcimloKl3vBtjHKILEdgpkIjUUrzjzRCcawxdgGC5Vp8zxYIhV3JKzhOGYWDnAFvnNJqCmrZeh55FbQDlHsvBMyK3FZ8An9N2piYipdWJz2sJcilVS1apFVRJzcAabMNURaV674L3VaytDxQAATSOfnN9dffV6zdf3NxtYBwNN/ePsCz106d8f18eH9LppKV4h5vd6AZG1jIvx8cT5EoAbMBgxISOGMDEFquWW3LHVvAaILQegkXJKSGQOCL7E3E7rInNNTIyAABm8s4PMTZcT7Pq3OaZWSkVYUkpE1dW11IpvoUdxIiuVzeB2XgYCdDH8ermNteapOaGU1EpteRlmU+nY9nva9Jak4qsGB1i9Owix9HFkZ3HlmPHRlwT4ri7vArmFqp3087dtK8Vc3o27AomvYsLARrGnVsRsJXp1Wwp9XhcHh5Pf/jh/X/744//+b989/s/frh/PJRURs9a6sCOVHE7DsExgZlCa0ZXBQNFXGSqqZaU05IIbAiet4MLzkwBSUERiC44Vc7PggE8gbExQnCwG2A3qPeFSdAUiilqRTPS0qj31ifOAMERBXQO2JknIDSiRqxBYGhqJlq5jlCSX454XKQsCbJFw1GDgAdFZ8DAiNj5Dzrlb6uwIuha0UY0UvBAERno80ydrGrqiEhMZiClHPeP777//c3u6vXtq00cKAxErD3Re7awsH47w+Qu5+kznL29BNf2v/56fH5jy+z/1B4+/6bXKc8h/Gd//rPjMp2uPRimFnx/Ztc/G23xnY36+b2XEt3nj7j45z92TmvzSzPtKS0pLZcuxfO+Rh1z0KJ2UTORlBspWiHCQYJC78I2AAC1NbVnZiJacslLVhFHuBmCXW+CYyY4HQ5priWntCRgYF/NADp6sJEumIjkPB/2+6fHh8dPj4fDYZpPyzKnNOecSskE6NmNw3h9fT2MmxCHGDJAOd+FYljEkgiIODUH6NkP3kc/1KpSpaa62ORydoGpXa0qiIGhKfrqdkKmiIAEHHKypyUlJNZCUJo/JVVqVSlWxUQdAnPH0bb0nyHZ2grDqk4E0RBbvQvMUpXMUlmRsNBWYghxHF64jtbucctn2HmLcs6F4KFhhpwLIcYhhhB6o5APIYxD3JgiWok+e+edd9478ui9Y8eExKwMFa0gCjFxC+W08QGpkKpDJPbEcdwgOWjNc+RrltPxlFN1PvQL7oA+ADt//fla+3a7oTBshjiGgNz4ydl7H4cNkpuXXEpBUEc0RseODVCRFUlEpZYY/NVmdI4NQGotJRsINKKySNsrdGEcg7+7vrq6vnE+EDsgMhBRQxSCNWSjlg7sFftzyK6m/RqwEddAA2KovHBYEIDJi4mJmCkxsYqoliq1UR/XGkKQAbtfbsiKHrzn4e7q1ddvf/arL998sd3qh2N5/2F6/14fH21eVCo6wIGXyGUXw5c34WrwDpfHY4GP5ZSMyERMxHmKg2NTKXUy4cKg5AFbtB09eocp1yVJqpJVm/dd4YwUvhx9c2xVR0MDA0c8hnhzdbXd7nzw3gf2HgBFNOc8wVxyQWRCJtKWp61VS01mRYEa8ULLDiJCHMbd1bXzDpvzQYzQUl/5aX+4v79/9+6duQ/LtK9lBlNEJHbehzgOYRjDsHE+MHsmph4XuWF8ztQBAvcsfEuKU29hxzOIrufrG0igNXKIGBjE6IchOuh0tIaUS3k6zH/44/u//90P/+nv/ut/+d33P75/2h+TAQZmI5Ra5uk0Odo6jDwwsXMM5MDMtFPX5SJpWh5Tmk5H73C7HYPnGAMigykCAciKBnt+HoQYnQsYdt4GD5uBd5s4Dk5Eap1TXnLOorWjOq3rT6Aqg0W0TaC7rb8aaQwUXWsIZPIRiRulDal4S4jzFo9bOB1lPi4C1VUdNGYdBDiii4RAxI2arTZKYEAwVMT1jM0QGpW8M0RAeTmrGoqvp0iQEMBMal4+vPv+dnf9i5/9/Pb6xjsPhPLcKv8cjEMvjp2N22rBWzWgcaOtaXGElZ/+uR/bnpF2f25cuA8Xpw3/qB09J+FbvN5cxrNF/8xC/7nRUoEXVfZn0/7novw/d2LnisIatdeUUkrpp6/s3i2hESqcscHWCLVSqt5z13rovIwrKB4QgLRqTnU6pdNhrrl6h7sxOFBGMJUJVErWWkDVeXTEz851u7MmpZbj8fjhw4+fPny4/3h/PB5TWkSqdvg1Bh+GYdjurq6urodhdCF4b2fT3rAX1TRXJW20SzQ4H12M7FE0m2mqKU/CKH09Q8PYkSECBXARXAdVAnNGSFq400KUZtBUGwrPmZEBMXnvGvZvJbBu/aktzWeECoitnbWK5DxpmU0qKCKe+EbC9fXgX2g+nUsbaqqg2kr9ROydD5GYqFEMxTDEEHz05IMLwY/Rb2IYGsFumzTM7Dyzpwbycc7VooVqj9A8s2fHhIimJkUKCrF4b+zYhdH5GMLgvXfs85KZ3Gk/9ThqhatiX3jPOI3zeMlGdzrVp31id3KMCMjU8qvDZudCFANEjMF777wP5Fi0BdF90iNAgx+r6FLL8XiY5mlJSdAZBUb2gcfNZre7juOGnYPuTdmKDsMqFaWj4Vu3unXO7WeZHuxJ+U5bx/8fxv60y5HjSBeEbXP3iACQWRspSurue+adD+///zMzc+90t7olUWKxKjcAsfhiZvPBA1lZ7GUGJw8PRVVlJhARbmaPPQtzHAcJb+3PEIxduVXIm65L7XisNu1fptZaKZv2g2Dg490PH0ecDuH4+4/vfj+O903j+dLOL3Z9buW66XzVfK11Vby6vZSWcPonOvx44NMYTVsW3hAR0RDcnQhD4GZg1apqbmbW17dGBjHwrsRG63au2jEp9N8eO7sQp/dmBDuHups0dpovAWAza7nu+E7tdhzasU9ENLfSqpuruTk5cFdadYWeMEfkgEIcb94oDMiAiG4ypuEu3tXgPK3Lcymza0P0rmoPMYaYJMYggTlw91MnJqIYvruvmPEWBIP8jQzXVwwIAK7u5mrQ1HO2nNu2rKWU092RSIgjAa+bLdv6y5evf/3b5//7X3/+13//+9/+9uvj47k1P4RwPBzupvH+kI5DGIOchjglGQMNUVj6w9M5v/0jpd1QSVsrteTSmilgK7ZtZVvXknNvit8K9APRaRBBSERTsik6szrM53V+Pp/Pl+u8ru77Vh+R+7mOZgI2EGhEyqKJcsLIyAiI5MhNrVYFMCEYGBJD3RZqOWgevBRjt9qUoGFI92kcumMQUmggi/pqXs1aN3sH11uZMGtgujvFfb8V3jkNu6iFEbADK9paba072dutrjq+lVrdgPKOEbi/AZT2yAhD7JDLvhrw7zgynffwfdv0X9Raf/MI/IelwH/6p81eae3fsPS3Y/f3k/ftrPi2At07g9e63v/uq0ud76yG/08ju+0+BN0NBFSttd0C5DewAb2mINwK/A6um7p5LaVsOedNVbYtlpK0GQS4QZrQ/8zXry+//P3Lw5enl+crYhCOYFXIulMugw8hvDsejyNwmmQ4phjoZmEHCGbeWlvm+eHr1y9ffr28nEsuakpE3chRmGOIKQ4xphiChCAS3srBHcAAu9+OmzlaP7g1l1WxNbfadUOI2G0ewAE7DR6gS99ZiHdiPDACElm3weSuaeiGL/1z2ieG3StqX+ICEQRGeXvPoqMDNtei6qXVrVKt2Mx1M0P99FEDv53aJUiEhExA6ADW7Ub6bxGkl3YURu6TCoUQYkgxDCFGETFQ3rWa1lm03YmLBUUYAV2deHed72b7ttdRRiRmbkEAgpkTMUmQkIIEcAwp9f6Aid8WdYL9d8H/prSfvz68fHnA3qwgIDPFFMbxcLobD0dJaZjGu7sTw7EJM2Kzvua3rqkOzL7f1rpu6+Pz0/X8ktclpmM8vHMUQGEJMQ0SAjE7uGmPoPC+9am1mdn+RIEgUEcddI/ug9sj0AWcgA4hhPF0CEN6+0asYatQsi9zvVyurdXdAMPJ1V2hqa6tuiKRvP/x3T/9/qcfD+8+psMBbfQKL89lWeG6Utlo9ErhF89/q/Vpq0/r9nCZ7/IB/jgFGGNMNeBqurbKRNaatgaGjGBuuelS2rwWcyRkcCV0ogDEW625NSBC7uELiujfp3jsZ01n9bvbza50v6qqupXiXs2hzyj9SAKAVpt1q2FQ1WalqVpTcyfA7gJF1le8Ag7k2JqhmpoZIAOIuZhx0QTybroPnI7bdl/z1bWA6747uiGcwsLUneSx2yazfHeT3Qjx/qp4u40/CL6nn6mbGqrBtukyl5eXyzxfzfFwOElER3y5bL98efg//+c//5//81/+7z/9/PMvDzU3QX5/PH16d//jh/ef3t+9Ox2GyGjK4IEgBRlCIEYA6AYDvUFkhoFEwXtIa6s117aVdr3m88vlej4v19laM9Vt+8Z4EoZTwjGGU5JDrIOUXLbLsl2fv37+5euXh+eX67w3w2kMcSAgQmT3gI7kJAgr5IBRMBAQgpnXZvOynq8LuqXIH+8OH98daq5am3gbsBlVhtocEfAYx7sTjWOIITiEbPRU9CXrYnXV1nXBhoCAhmTetGXVCqZqCm9zbghZCLwfB8zEiBAkckwskUIEZrvJVaDTWm5Ye2+zO7//pvgDRAZiQobOisVbndor+VtA/3Xsf63rtyf69fVfDNb//b7d92DnnTT3Hwvz/5d5/S357j8S6P6r7/CbYt/h/KraWusuC8zs5l08HCTEEN/+dXJgR+3BPv33NOjmRA5mXciaizVdF8lbVJ3cAzoBgJvnrV2vy1/+/Mv/9X/86+e/fTm/XD9+/PC7H39gArDuUt0E8TRNd9MBWUCSy6gUdJ9Q+7fx1nRdt6en5+en57yuAC4SUoxpSGGHLlikk++442y/6cocsFcqMHUyN7XSstpmue8qu/XCXvO77qd3iUTEJmgBKoAigFuX1CD3nXCPk9nVhwjgRL3W46sjNji6EwEz7Qhhp7j1S1O1Mpplrs6lNcrNc2lE5XqmFN6CMSHGMR64FhIGQDVvag6AQijcS76hd78ZAO+GBzHEEISZwBRufEZwxd1U2widCIxuI7ebWZeQGiG6IaBZQ21Umzh4UydCeyXp0S20MuyTvt/ErjeO8m/vzu9K+zZfz1+/WK3eGgIBsYlAiGkc0zDGYRin6XR3Op2O03GKQ+oWTTEmR1BVG1L/DfqVK1pzXsp8JuCYjoZYEW5hS9pa65h6d1DvHLl1XWut3f1xSKOI7GHDr/S5fVK4MerMiTFOg8RvU7u7L/O2lnVZt2XZlmVVVREWEgQSIGKKKcaQyCVQ/IdPH//p/Yf3IR4BqG6e57YVrZWieeSGcb6Wn79s//L8cs56zWXeNjrE5uboirZpfcnr87Lc6K1GiSYEBahqW2nzVs2AkYlcGIoyKW3V1qISkAJLQkJ2Bgnhu47+Zs3yeqr0abepLtuWa4PzZQeacjazEGRMw3Q4EGNrbV2XeV1KKdraPsoAIbJwCBIRCZxq2VbELuzZZ56+U8aIFAHZHdyVCEMIjCOCYJdvv5L0dzYc0X9yd93ex06J3OGd/R+ODqBqpbRcai61NG/N5zn3JB537flmj8/Xy7z85efPf/rz3/7073/968+/nF8uAfBwmA5pfDdNH0+HH07j+ylNkYXJ0E21qvZJnYkAvda65tz0NpYiduPQl5cLhPh03TCEp+fz8/NLy9lbFURBLG+m9lrXy+VvOMaRopM5aN3W7Tpv13O5vtTruV0WRVaWQlci6YNIYBoCcSB1e8ibtoYELBxEHLy2tm7bsqxR6O4wEPFhOiAIywBUVFvNuVZDqQIac4gbR89RBoeATiclqMo5Y85b3a+0AhBRdV0t1+1q80sDgMP71zfCjEx76gLdNIIkIaQxjVMahhADIoA1UL0tw26E1VZryaXmbVtz3kptqk4SQxyOp7vD8ZTGQVLcr3aXY/Vyjn0Hupf2vbH7T3jzb9by+2D3ffn4/tXvpj1ax/bF3NthnfaItt/O6//xVu3v8NVZ9m1D8Fq839SAt38fX8f91mouueS8btuWt476pJSChF2fEUJK8e1P9+5InmsrpUs0EIAchJBYDlNqRbsjdBRmJHJ09dLqumwvz9cvvz5+/uXh3/7087//6efredamgnwchnEIwoRuDBCZxbsShQzdXG/hRP3TMLfu5EMxpCGNXfa5F60YpY/N2A0g9uH5xm79/o24ddsMhIamaMaIJNGdzLD1riuyxBBjEJaO5AmLMAm7MEhA4Q7nCzEyBeaBOQoLIoE7I4mIA5gZMYfADm6mRMIU9ugSAiTosc2mbu6jYHLD56s+n/PLBS4Xl4snUGzNytv7ikOIPBIzIlmDVqxSN5XoUFaHGV0Nu0oIEZlwp+uRmTe1plbNGnTiAHQOhzdtTb1p12GCmUJ1EN5tOERCDDFKYERy0L217hQlEIpRYgophRCFmMy0o0yuZqqu5P8Nja7mbbmcLRcvFYEdeUMoiLsqOIY0DNM0HY7Hw+kwTFOYxul0PN3fhRSBaBwHdwdEYgICBddWtWyQKna6v3vOeV23bV23bQshIO7EPicytbzmdVuZeRiGwIGJ9joCPW1n17LfoAx3MyAMQ+T47Y24e17LlvO27l8AQMgARCiRwxjSu7t3H99/iDREDz8eh9+NMbYNt7lsL3mbm4Ehh4NgihXq1ddf1uXPDy9bg9pt5gCFSBDdbKv1ed2+znMnpxEhtXAArwDVfWu6bFXNhTgIAVJzrIZbs7W2RJwihYElEgaU24i5Hxhd144I3ikI0EWetWmuLddWq5pZLW3bFgA/HU8f3r2bDhMillov8/X55WXbtu6odQPz+9M7MAkitx5t5OBA+8Ti4EAsiSWFkJi6iX1zq0weWBBcbzd1P+EQEfmW5/fbDel+CuLt0twu4v42S9Z53q7LepnXLbdc9HJertclBj6eJjVci375+vRvf/nb//W//vQvf/rL0/NlW/OU4u/e3Z3G43EYxiCnMd1PcQpI2ppCM2um2hoCCDchQoacy2VetpJLawAuhCmmIY3VX16Wms2vOX99fH45XyLRFMPdmKYYKofX3Mqcr18eLu04sZ1gkMJ+vS7PT5f15dyWBUuWVkyxGZSiqs5MMYRpiDjGOKallF8+P75c12JOIQzTiIzNqmq11o5jUpT7ihUkikgSuG6l6rosy7bFuAWvHlChVRmBI2IADAMmMIBt8XWxbW0le6sOrsQFbbbqZavL7BLD4d1rNdrNec3MTfa9AKJwGIY4DjFFIQSr3pqW4qqopq3VVkvZcl6XdZ6X6/P5+Xw+L9tWqkkcpuPd7//wx59++gPThxhusgBHhz1TZs+fv3HM/98B+TeF379fKHxXTnbzR1Vt/TvTm9d/AcL/5qfsN2bf0L9d0v+GOvebnwy3I9t97wm2vK3rOl8v1+vlcj0vy1Jzj1abjsfj8Xg6Hk/DkEII33UyalZry7luxXtCqAMhBOY4iN8dBSRJbM04ECOZel5LyeXXz49//ve//+lf/vqv//KXL58fnp/OzHSYxnXZ1nkWHDlF2jEzVHKzZloMm6J7iEDkjuq7dVW3DT0d77zWbYvuykwdjKfdapR6HmG/uPi9NLQLutwUvHvIKboRQpQ4pKMZlqJuWVU5SDwdxsM0DKlfpBAiCzk0Eu/8POJIJEhEFIIMxJGIVbXkKszTODXVdd04SDoM2lreNg5DGsamlrUBOhKgMBG1XE1tujvEIP54Kb8+XT5/9cC2mAXSCEb69tJ2ihwRI5AWK1KJiume3gDuPfuMgJR69KbfCHc9l7Q2LU2rWrfF7MwiNDdrrVWrbXeC6d+zS52CSEopDjGkyIENkKr1VK3ucsuE3Kv7EGIUFlQFcOuZlVqJEP+7eJj9bO95JA4GXgEKAkIrWKVvfeZlfrmkIYVhCGMajtPxdErjIDEul6vmejhOIeD1cs7bptrQXYiiSHbG5jnn5+fnz0kY7P2H98fDsW/rOxgVQwAz7A2bu+tO0e1Tu+9N5m2Kvx0WN6LO/iLi3//+95fl7A+/rmUDIDNzBUBk4kOcPh7vfv/ph58+fRJ1X8ugm52vWyutbtdtnsvSjInj/TGOQxTwGDkwBUQj6NjcFHgMIYg4UHMo7pvto4ODhxyPpe4UFHN1VXMECICdQwbwzaichWMKYRAnI/nOLJCJQhB3AteuXjQHNWjWvUOxIno3JQUw1eu2+cu5uLv7+Xy+bjmrtT3A23ohNvBmWlolVADs9nU7Y8d3Ma8BEguzBIlE7OaATuQiJN1u9pbo0gV4Pf+76+72ZAAw8PZ6dn7bIO7E45tTG6A2Xbcyz/k653nZliVva6mliYgD/frl+ZevL3/52+d/+8vPv/76eH65DiTv7sfTGMYYkCQQjoFOY3h3Nw7DMK91yy1r58BQEJGY4iBDQJyX81qz6dosCU5RhiHFFLYtX1/my1Yu6/ZyXZYtJ+FDClamNiY+Msh+hNVWn87nmtd1XsTcanl5vjw/n0vpdj2mTmuu21ZLUTUQoRRVETiFYwgGcDX/uuRrbk4SN+dAjkZggkCJCkpDMQiODOgGpAal6VY6W8PVIectSUohBY7CkWUkR1xmn69tnvO2Ljk7WBQ2wureat22TdLxhx++VZNdJaU33RzBvhfWUrfr/PzrCxSrRfNWt9Vbu9lqV9XWrLRaWtnscm6Xs25Fq3pIVRe7H7AcqQ7UAiD12HhAMu+X+9WDDL6v66/1+7X16P8bb3Zet4Uwflec3b21BgCqfbMOr8v1bqz2H0H4777/7du8PfxewfwOHHZ7efjPX7tzbc4l522Zl2WZr/NlnudluW7rsm1rLVlrRYDLkKbpcLq7f//+I4DnXL5jyJuadYOrfdfW4QdCDEHGMbmhGqxrLrU8Pjyfny/bur08v3z55fHvf/v69dfnx4dz3TRKHId0d5zGlNBRq1WojIgGrUGrpi2bVQU0rFqhUVZk7eoMU2vNzVNKh8MxBHFtNzLad1aY2Blct//4/UeiBB4YBYHdWEhkSONxOtybkRS167WtC04DHUY5TjKO0Kf2YRCR0jYj4/tjOEyIAVAAEJiBE4aALFZqxllZ4mEstc6tpCRhGkrJs5YYGYZYmm7ZHI0EJTAxrdoUYBhjmCZtXtayXq7LLLkxCUJg+p5GR0jAgo6oXrgEEgbUncEIBLfccQNTaFVzKcIbAGoITJhzXkvOtbvMIrMEYRHZafoG2per/pq5sxPLdhlRd4HqV0Qtb4t7azW7ed62ppkZQuQ4BDO1Zu7WWoOyqwfeXo3vSnu/YPD6SDjA6+Po5s20tbrlFef9F4khpJjGMU1DGseXh8fr08v9u9PhOC51W66zlcqIIhJijM4CWkp5zht51ZqJaEgDESMCmBPAmFK8xYghgNtrcFmf0V/3s7sk+KYbMXjj3cjM/+Of/un58ph1vcxXZnFVMGKXROE+TT/df/jj+w9/eP/Ol3nL13J5mde5mmewl7KeazaVGDAYJOYAcghyiuFdjNlB3d31lMIUQ5AAXUYJ3ACrY1FrqinX+1KaQfcQA3cHM0C8LYL6amQfd7tqNnB7HWlfbzKmEIMbgbOICEsz8L6RBkMgps5YQWmtum+l5np+ma/eE65UHZmk25Ub3o5JRGr7Qshbq02rqfZIspu9pfUntxATkgMgYYgBMTkMyLHbtQYJLEzMKaVpnIi4v11wrCWvl9fkt113ZACqfgMBd6WP6s11Obd5LpfLrM0QUQ1ytV8ePv/y5eEvP//yy69f0XEM6ce7+x/ujmNAdD+vm2qNHI5TuL+fJA7nssyqWwMzIkKWQabT4RTvJ6IQnq4NMihiSHJ3NxymQUJYH84vL5fn63JZt9LMzKv5BrBJDUTDwV+P9tbaNi/rkh/8sl3Wy9Pl8fH5crmOYzodxmlIhDKX7TJn9Z7pgObIQJUFpwlrLRIuDk+5VVNRZBESiEJjoAmkUTAKjgJO7s27I5BZUa1mxWzNLczzGOIQ4xjTEIchjO6o17leL/lyXZd5Xjd31SiFGJBK0zW3dIBPbyr7DXbeSWEA4GBmRcuyXh6fP/8VLg+6zW1dyjK7ts6RcjekPp04unGZY51NG5lBLZyd8gtuL5BHTwFIHHezvBsIv3tw72fKPvPh24J7u/X74WM7H4tu+8k3Oe6vxdXdbxlubzfrr7L1fqzBf/m60e737qUnC8FuX9gXu3j77fa99I69t1JK3rbr9Xo+n5+fnp5fns7nl+t8qSVrq+B7ogp2oHsYLi93eVuR0PzNewUAN3DlPa4GYTeKNSAQIUhRm69SVdfLeb6cr9fL+vj16cuvX54eztfnRZsT8CDpOE7TlI6HMYXozfKWtWC3Wt6y5ty8ZbdqCEZSsSgm5+DAZmAGrtpaE+FhHIPwzn6xm74KAaC39N4Jv/RbQN7RlMEiIwOgKgbkNITTMd3dA4gUK5G2M+CYcBxwGmkaO8rLh4lD8BUNDKcTHQ7uYk67J2QIHkMIQZlyzo0lpZgRViYXGlLIrguRMpFIMVsRgJAQuxHViq4IRSTFWFPMKa5R1sCFURhBCIW/mwwRidgZiSFyCCTs2OnHcjPD7gOMNS+k67b28zYEEeaSy5K3rdSqao7MQYLEIArQOru5c0iRYLfdxd3+fd/4mCvoDkSZWi1lZRFwr6WWsiF7HGSYkruVrfYGt9/AvdN9ff0mHmavmJ2OcyNZfKOn2v4koKqhNdBa6rZta7iKxDg/Pz9/+Xo4TuNhMoLibQh0GiOERGmIKCPrBq6bnecFAU53d9M0xRACc3vdit0sS7pUE17rt99+nX3n9ao99dvX7WBACIIhoAQQAQYmlEOYPkynT8e7392dfjqko2V7/lKWZb68zOu6bLk6VPdLbktVAKyg11xTlinaKPR+DD8eUzE0BwD94TDcD2EMbNRjVgiAARyAwf1me94BwteEefMOEyHSDTZER6u2XrOvXr0h4VsVHxPHGDvoEySIBDbnZmKa2Bq11tRaK9Sfq07B3c0spK/rEJzIoQu6Oum3f4LYOyVmFncFNFBDNOyc3v1Eo36OI5FwFNk3hUOKaUwppWGIKYYQhiENwygit1xAfHl+/su30g4ppWEYTLX7rtsOsHa1aOmNZJ/RHHytZclFz+dmdr5cX86XspZTjHdpej8dPp2O7w6jMBStS87mfjxO797fHw5TcbyW9rxVQxYJKQSIqZFQjKe7wRzGQ44Fo8vxNPzw6fTu/hCHKMPBKMXn8zTPvkv2SYgGodjzpF+feaLIUnNb5vXrr89fPz8u66rWODIwohARGYIRjdNxnI5pTMMQpzGcDoOMU7ZFEavpVvKaFbeMSMSUIrcxHoPkUrWp9wG9NVdFB0RCFEAyk+o9GcN6RkDNW+XFHa/rNi/LmtdctlKzmWrNSOTA1SBXg/DdM9/ThFQBEUvN5rbrg+q2zS8Pv/6s4yDW2JVcSbotgRChWqvaSqm55For7NMbm3up5enhKyCt1e6LpukUhgMH3Gnc++nyVq/VBVz+Wt39tQbfevZaS62ti4bfsMi/nVW92Pa6fvOO3V0L3/6xb6/f1vibQ3trpZRScs7FwUUCGWlrztYFO/uPM2utllLXdVnm+Xq9XC6Xy/l8vV7meV6XJddcW3XvKWQAZmUrpo2Za1PT3taaw3drUQk8DJGRytZCEKT+bN7QTLct18eHl7//7cvnz1+eHl4u52W5Luu81aJMIgKdRkYAbcvnkp8f1Nxi4GGI7+6OKcbPX56eXi5j5CEKMavjWkExpPFInGozMyBEbdpqAwdmYSIAuW069kTzV9LMdxuV2wdtbXNraNXbqm1pyJW4BKyRiSNEwDaiFhoHPkzhdIyHqZNewjSGEDwN7sbD5JJUUQ3V9x9JLDxO0zBhHJllHKdQC8YUoxyOU5yqjJNIGtLUmg61ADox9giJOBRVnU73PCRcKqQEwwApQQiOVk1Lq29vEiFmDoCuRi3ELEGYWyNBCSTIhIBq4AbN1S1rqyXXnEoMQURa0WXJOdda9yxgIYkhOmEzQ2hgFRp6AwMH3GVyAGjqpTRUg91mwFXNmt3c+VybqjWJcjgdmXgZlvm6lNy0qZlZsf92avf9yHfzbpO120jf6E8I0HdnCOBdT1RRa62ZCHF+OT/x1xBTGgYeAg3h/fu7GFMDUkRkjpG0Ra112Tat9fT1IYVwSCmKIAGxDOMQUwoSkfbQ69tYcWPSwd5Aw03nsqP533kOeGub6oZYmSCSsMR3w92Pp3f/8P79p0N6nxDz9fqyLOt22bZLtVm9Oap5LpaLIzmQzVsZNhoDjcLvhvDpkJqiOSDYpzGeEifB3D22Xq09HGkPLifa9Uj7M+DgtyAMuCEjhA5WtORSrFVoyGT6DX4g5hiTaXPXEFKQaGaBdGjNoKpCda1at1atVaiF6x4R3Psj7+3FjYXYP6jOa31FEhzRiLthj4JqN072W/YXvgarhCASuZsmxWkYp8M0TuM4Dr1sd46oSOjUJQb/y5vzdC/tZtYzTqz3iE6AIYYgwrfzrKldt/Xry/n5PF/mpZYCph+m44/v3/0wnT5Oh0MKQxQKuDZ+vC5Oejwd7u/v0jhsS3lZ8+O8cRwGCsyxkWQDQxqmoTYfx21YvQKfTsdPHz98/HiaDmMcTxyn4/Hx/PIiBJF3Cqy2qqoz4WtVZKQgUVctWzk/Xx6+vhhaGoQDhyQhMQICATAd7k7vP36ajtM4xhgoBRZhWPNuKW2tlawK7kiAOgS2IR9iq9VNEczVtWbTCm4ERCTu5C5N3U21aWOstRTmDdkNrrXOpeS2mzI282oKDoaihk09/ca0vFlrnTystRYzFUIg91bycnn8SjYOxyhTkiGFGPeuUphzKW1d8qqXJdfWzMiQnLCpa2n14emylk2xAr8DPoQEHJi+A6JuBXZnycPe//prw35zqXJVraXkvEEaXkP5frMt78wv3MHMPRD7N3/g9hPhzb/4d3/EvHNO13WtpSBRjLty7ZsOHsFUtem6rssyPz8/Pz8+Pj49Pj89zvN1XRdtam7YWeQiQl2EorVCrSYCXeFj/lTy1pnkr79BCJKGJCxlrRKZbplX7uAGter1Mn/++5c//9vf/vbz56eHl+W6uUGUEEJIh7EvLjt/radxPp1fztd5GOO7d6d/+MPvTsfDv/31r3/9+6+f3t9/uD/FEMz8Zc4GfHf/KcRxy80MgoT+NgFgD6EhcCPEznTuA+a3cvCbq+puXhe3Bla9ra0uVWLFVBmKUIgCTlAS6sDDKIdJDgc5HPomOgwpiFAc3Z2TOJEZKEJD72uWKMwphZDSYY9vUdU4TiI8pqBmw7ERCUvseipHQPJuizNOrZkN4wAIIIszuwiEAMRmlmuBsr59M4wYiBDAGAqH0N1ViQNL4Nj1xujWXLVp85azZSlDLTHGwEGrbWvNW21FEdEJEChwQKEAQN7ACKp7M1VHtMAiLASo6s0Vdhu1HUnraCq84tQAMaUg4XA8jIclpMt8WZbrWnOtrWn7LnboN1P7DpvBvt8GBKebAQV2phQA3hKZZDc4jSxCvBvMEQuG4IRmcF03f3xWjtmRh4NzUnUwaKXlsv2l/dvLr58H4b5iHqbppz/84dMPP55O9ynGb+FHN2Kt+26eYTeH515RDeytG52Zff3yfN2et20V8nfH6UCHn04fPkyHE1Gwps3yer1cz3OuS9W54WZd4dtdenoA5+5nhc6RaBAehDJgH4w72ubWqvqWy2WZz/Os3hPfrCvLrYfT7kle/e6/7Z0B+y7FvWehOREIMQd5O3Awc4xJlVxbCCFIxNagKWyzXs75erFl0dq4Nsk1lEqqnbEJuxMJdgSmqfZNUW86dgauv8Yj7xJE9VdU/psNaG+Zhau0wKrUZZ5mWktbl5oixUQxegweosfgIsLsl/Pb22qH75gQAQ2I4JY9gmkcDnen81y28vL4cvnl68PX5+eny7WVJg53aTyl+GE6vJ8Oh5iiULUG5h9Od8d0WgHmrY7TCMxqPm/56/PLz78+A0sahrvD4e443R+md0cqbTIAEg4xJsCUUgwhBolRjsfpQ/FWNi8zewtoDORGm5Hrd48KOLoSoySJUxoO4+BoaZLDEMcUgrBqFxG1UsqWVxR3LGpEGMY4psSn0/jh/cnNxpRbc3NHh5jkMKW7QzpOcRpkiOglt3KtZW511Za1labQjJq6qb8yciNzJEKEYpZVFXoupDB3RiSQgxuwGv8235Hcul0JgyP0HUxTV62lXC5n0AqH0Sg1BFLFDIAIDuuWL8v2fL5erjP3z5JEgXJrpTaiOkI5lHanZkhIYoBg0B0MbwfM6+5879N9t7zducddEtIh6dZqrZlZgvrNgfK7FxE6dLlVZ8X7zaDPX5fZ322Jvy/qHZ6stS7z/Pz4NM9zrZWYYkoiskdp4c5BqaXkbZvn6+VyuZxfzufzMl/XZamtqrZOBEYkIDaOLh1mUJNmtnuTAgUzXJa1Z4J9ezr6I3vzQ0AAU+sRTiXr5WX++vnxb3/5/PXvD/layChJREARDrvNJPU4UEJ8eXpa1vU8r3/7+jCMsZh++PThQHjN28P5HIc0HQ7ADgaqrqZaKjrXrZqBh64l6eZ9+8bVjAEBlcxat+fr48LtPH7zLtywXMkrWEMrCFUoBOEYUxrGmCZAzqXmXMIwyDiChAYIXZBj4Oa1J4t280wG7PpLRIlMEswx51ZKJZZhBG26bTWKCVJV3Uplgehs6toUCJAxEBJCaVrdxR3c87ZeL+fL8/n8dFnPM3oh4VQ3tPTa/9Va21bI0Zu3nrXd3efCMMQBiMzdrRk6goKZeVOwxo7gxqrFS24tVy0K5tCsCJcocYwcOAhrlBalVe0D3pCGYRiAyBCa98Q9tVs4PDIycWeZAQEJ8tgd02lb8+F4fH56eX54uZ6v7aq/WVh9/9D/dp3Vh0zvAsbbnQ6EhLuYPqVxTMMU0kASkdhgrysGqt7M23nejJ+qwXC4i+OJJQFgK3W5XC9ff8VaBIzAHeHu/bvWWgxxiCmydMeGVyj+xrvuPDqzvZZj09a0tPpNpGRmX78+rvW8rish3B+HD+H40/F4YAnarGg2vWzL4zKvVbPT1qgqJcEgKIzg1NR2NyIzhI62ohDmVycK7RYUramvJZ/X5bwsnf7LCN1sXzvxqVd32z/b10PGO6EUDNmZEZkhoMS4d0/95GIOMVEDI+iSRgan4r4u9emhPj3A9YJmZCCO0UBV/QZhAXfqJaoZ1WZgQHuecteLQjeqNuiEPrOu1PRXCdEOC7sjM3KAEqAUr1VrKdsGQZSpidQQSu87wk3dKSFv3wVg3DAN7KJUBOC+tnWgIJxiA3++zr8+Pv38+evL+bJt2yHE9+Phx9Pph9PxkGIScUR121pm5N9NP7z/8G5pEK6rhNDMS23Lmp9ezp+/PhT1EMPd8fDh3V15/+7jnWz5qAaAJDEMxDH2kxAQYUjh7jgul7idkdQDGAKbw+ag9pv5DlyRQcaYjtN4dzw4tpBwGmIKEljAFBy06ZY3ma/Nc23SBmYc76aQEh0P47u7o6nFGGtVNUPwEHga4mGKhyGMA8eItdZWe2lfWt1azVuz0rxUaAqORMRBJAYfogRhJ3JEZo8hYDfu7IzcvlSvTcL3s6yR2c2PrCsjrFsaWa11NkOEENkYs5m7tX0NbctaL2t+uSzzuk3T8e5u5BidZPVtU2VHdKxATozMwGwA1gXM8Jbq+lqje/71nkbap4rOzuqm+s10t4D37rSB3w/tXWpqTZu50x4QjbeRt++mvhHm4bcvd/emmnNe5vl8frler9qUmGPKLPz6o8xMW9u2bbnV9XWe13VtrZopEkhgJ3YkIwYKIBFCAhQgpaS7QZowheBgtWZVc/hmYWHdV3YXe+NOhihaq9FSnh/OX395/PL3r+fHMwKPYZjS7lRC3XE6hhTjEFMUMbOvT89rbY+XayqRY8hNnTg3nbdtra0YhD0EnqBnt0JHdB0BYQ+A66R6YWHarbSwExb9Rmm6ncjffZxUr+jKqIiGAkk4hRAlxphiGoh4iYlYSAKyVHMt1QyQ0BBFeVs3dw8pxKFzjbszJElgQCpVy7ZdLleRcLq/a63Nl3mIAd1yaedliylOo7WmJTdgJKHELAhLycWNhIVxXdf55XJ+ejk/nctlRsjEXut2HH73OlPlUlTnLvHacmnNADBwSCENYXRENQPrztAKnerp6oaq4GZaQatqVS/marVqYcpBEDESEkIPmRQmQ0GkEG7qcXdt1Ry6N76rEmPkyEyvo7VwiDGmGFOMtdbDceqqbzXNOb+tHfAfSvu3xfY+Ee/aRwRAR+9i4DhMp/fvTu/en969P9zdjdMhpAElQnf8AwCEpq22suU1l40IKIiDWFPwAg41b3mZ1/NznS9oDd2cKNf60x+el3lupVq8PdRvmDC3+r7/ggjoYJfL+fHx69PT07f3AJBz3aqW4qAgDABlWV4MSMxjxJRwq600d0BhSkBMxGBoDay5N3cHp55J5G4G3syyaVYo5mZeABRJkapDdSjmZW9h3QCqWVVzx9fe6xtjjogJX32PHczRJCGNJCmElN6K35g4hEBgiiAhcBABQ6GGuz0fAIYQiNiZ0UFL242NwB09DGm8OzrQtjV3ICGmblcSWKJ2adONBWAGzZq2PoQ0s1Zbvly2bd1IhEW4BW3FtdayLEiMEMEjU3dLiiGkEELo0G2YOQB9MxHqrSjizgvtn0Svxw9Plz/9+W//61///L/+7c+/fnmar2si+XD/7sM4fhoPp5TGGA1gbbWCF7PSSoJg6BKIBd1t2RYnO/nUbX/cdcvbsnpe57ItXstP92HL7wiECGJgFIkhEHVkppoBggraEHAYhySSM11X2Lxd2/dK0c5wNSMAJhKhZrCHagMSUwBMMaQoBGZatGgjUQ6gkTt928Ga5bVeL+uylaYNAWKUeojHMaz9IOnQqqtZM+shkK1UXYvn6rUBSghBggzD6fTxw7vDYdgFW3uabCfieqsl52Xbtm1d0/fmgA7UdcZuoGhdtOj7IWUAYN6qVsy+5u4SqN2Av1Q3h85kSOMwHiaJA5CwSBxSDHI4HKZpYmHAfbPXF88dJrot2vdXL9X9xHDwm2s3ABA47jZuxAao5g6Gr752+1FlOWdVra31dKVOCOj1/vXowO+dZ24neO9nrLWWSy61OECIMSYMQWKMxPx6IJpZ68YYTCGEcUg5byXn1qqququ6VbVi1jtoZL4FcMh4GC3Jrg4gMO++VAhvtqKtatmad7s6c3DUZiXX9bplgodfn54fz1pUvuUSswQWYY6RU2QJzNIlRTxEFAFkd1KHZl6altq6Tt/cq1pTZ4D+gzp21pd3N4PW3cd+l94Csof9srWOje5Oct9DIIDgUVsSPAwDBXIGHicJAm45F5QkkXOt13mpXZENZAZqhkSn0zFGOZ8v7s7CHAIiGmBPjDflRoJQLuf58+cvMaXfmeZSvz48Hsax6ft53R6eX8bpcH9/l7e6LhswcuAphIj4Ml+LKSGcjpN14EutC88YXLQFrW/fy5bLatfuSlO3WreiTYX72m0gZgMPFISkIFZGM3Qw6uwwMzdHM3FwJCBkQlZoa85khuqIu4wO+1bZW7NaqhNqN+Nobe/HW8MeKGA3O0DuXKfY86hI2NGPteacSy4115i+s0L6LSDvr3vtPll25/a+hQdgkRCH6d37H//hn374/R/e//jj8f7dOB0kJmR2vDm7A7amudRlWeb5WsqidUPs+v2+5lVTrXlb5quW7KbILCHOl+syL9u6xhBba/vu7Rvl9fWG6i05udv5fPn555/PL89vnnnIpebScjZ2g2CtbZeas2EAGi0CxapuhkidRcmiCFZdi1t3GTIH7v/etYzFNatmw2Lu7g1AiYyowR7hqm/uDgVoe1vUP0fsiPRuj7XzBIF6qgF5SBwmkRRjim99gJkpBqmgiCaBQwgMBiKVSZkgJQbgEDyIM6J5ydWqdQ6Gek2n8f73P6CkdVFwFBEiYIQQB0lTK1tZZ3N3IHMyg9ZKLVvdSs1F27au1/PlnMtGHhiMzQwU0bBhqw1No2tAYuYYwxhjDCLEJAE51MMJPvz0rSaamint7QyCWwPfcn0+X//6t8//85//9M//8pe//PXzuhQGvj8cfro/fRqH9ykxkhnMVherGby6NVUyNoB9uvO2lubkY4qIEISF0VrLJdfNoeVIfrmcSslJkAmisBDHwIQIu72Go7dANgY6TcMwjE8Xbblkp8UwvomndXfT6qbuimCIoGbaus2fMxILDlHGKMzAYGSIZmTOjoJkCOhkzUtu87y9XJZSG6APKbSW7qa0bLmW9rqDuuFD1prWqrVZMzKSGMfxcLr/8OGHTz/88Y8/3d+futZGXykyalrKulwvL09nekFVkbfynlupvfUlXdaIt7RYJmByAG26Gxh4v42RkFBEYnADTCmlFEOMwEKMScOQ0uFwGIbUP1vVpu7miE7/VWmHVxJsT0WBDvnu94k5dBqj+j5W2nd7Ny+1qFqrDQFUBQDcadfFvslyDSK9xe7pbTvo1/NKtm2Z521dwVQIQwgxhBBCtynsTbKqKqJAEsQUwmEctZVWaz9/W6tV21rKWktRbwbdVDowM1FK/ConBlV34yCqluv6OvXW0ra1uFreOibtrem25utlNW0Pvz6dny5gkEIMgdMQ0hBSChJFUuI0AElXm2ptwAzc1QLSo02bajeuICTzPaYBkb4do3AjOLzu0XFHUXY2M7uD8425/B/ZCvvVdE/ajiF+GAcZYguoIZqwueWcKY4kodQ6r2t1M+xeVdrUiMhU4xAfH5/c/e7du+lwREJ30B7QqsykbnB+OX/59eswDtM0Lrn8+vXheDzEFM/X+ZevD8dTMcJtydfLDAQS5RhTInp8eSmtvrs7TTGBdYbw/mKAAXwCe7t4q7UueXN1b95KtaqmGpgZOXKIIQJSoRCIN4TK2JzMm+3xM11q7II3a9oev5SLkyo2YDLsqLCrmilwLgAO1GXJrePB3VQRALDsVA9y7oYx2H1riTCAeBym8XA65jXX3FL6zpL1N+38K+jdx7l9pWwA5qjuQ0yH9/c//MNPf/zf/vjpd78bj8eQAosTZ4CbEwmyU0QOIBE4YRhLmWtZGJXJuwtNdypkAhGan5/yMncuXC7lcr3++vB1yRt0f/jdtOhmqLcPPz2W18xs3bYO4b4p7XZdr8u2XJccAGJk7mlegEA8oJCAKAi7ASLC3kRaQ1ACJTI0RzRCI7jZ3t1gde1INQLsQpDdU2d/FLrfpogImwMweRf3+h6NtoctddycSIKEGIeYmLllLaW8nROZMAVCh+YeA8cUCNUie2AfB5mmQMxE5tbKVmvmmNCQka1WXy9hmk4fP8XxuG4OwCyh2y+mcRrGU9nm9frSVA3RnN2plqXmZVu2vGy1zE3cnqQShEASGZlQiCIykSGj7jHoSqwsGliZ/DXC7LuHBb4lczi641bq+Xr55cvjn//y93//89///Oe/XZ6uE/H96TjFdDcMd8Mgwqu3Zl7NFd2EImNAVJVhiN2PiYhEGCMNUzocxuZ4GMIYeAiITkIYhVyrtgJuRBiE1cGJAyOh9421IwzRDkOEcTgMKcQA5AW9MXsIb4VT5q36Yqa11ly3XHIuBdBbUVcngEA0CI+BYwwpxjjIMMRpGk7jNMRBG4xpHIdpSGMKW+Dapz5rZtW9AFQEJTJGF3QGI1XU5q1193u6u7s/3n/6+MPvPv3404+/++mHH3749OnD4TDZHnJmqqat5XWdz+fHr59/Mc/rhjZ7+87zJQQOgUyt230jIjPGKNOQpjEG8jGFKSUO8oo59We7VFuybkW1ac7buoRaGzF3J84CLsK15FZLziuyGLI5gzM6Upee36LXXwfofcdOe2nv564bmUHfa1GruDe8bvaN6o+IHTJm5hikw1dEu/pDVREgq2prAD0FmW4EvVZzKXkr67ou83w5b8tSS3azSpz3e3U/SvrWX23Pz+w4BJiBO5uRm7gFBwlhCJIBq4OqAVKKQ5Bwq56vQh4Ah1rKfP3LK6Kdt3q9ri23+WXe1qxVS67rsj0/vdScH74+X88LOg4xxSQhcb9pa2tVzdeCu406u7nWAm7ClFJgRt79ZZCReuAEuBH2aCJEBTMtrXZcPPZ53f31GvViT8QdUyFV+60B3ZvSDhDVR4cDMjFnwcrUCK3nDvcBp8e5SBAJCGaMiHazA2YJ0R1ZInLol7vvgntgpXUfzdi964VIiQOyOLMzAwsQQ8806zlX/RPv1GbvwZrMKCyhh6C4cHQ+CB9Fzm+alZ500e3wAdxcoZ/9qtQ0BhIKMXBEFqasUr00ra213r8hKaAjGTNGwCioZBlN1UpWIzLAVqEVq7mqWg+zI+lb7I6lABOCBMC9Ge0O//tK0Fz3TSq0pmbIEofxcLqzYfwuSvs3U/tNqrVzv/qOlAxAEQ2Ih3j6cP/hxw/3H0/jMRDVVkvOCm6w991AMko6AU2IY7+niJDYCV0IWBiRJjsAeAiYkhBBj5En4a2U5/OLEZ2XmRFjjMdxDD3rprNMqAfJdwthdoNc67KurZZwc8l2gFy3LW/bVtWpujaBiGjUE4acGImB9jdK7maq5IqgiE7gQrecgX6j7NJOMIQ9sXw/8fCWbuPg0LXgTByYRbiZ7yYBAMTYUe2OCvpuh9mRwiAc0aguxbR+L37DGKiPiMIkwqDshM7sMYXTnUwHcWglr+cn2hADMEoMg+WsVmUYD/f34+l9LAAgJAkREGw8nKbDXV6uMsTamgI6ijvWPOc1UlwxBFox64ZRPCAlliEwkjD3NEYhAiVxRkDH4DFACEa9ZXG97SFuNxW4mjdzRHNozV9e5j//8uVP//bzv/zzv//6y8PleWagD2k8DsNx2ClMCrZ428yquzCn0FWjVFXjEEgEO5UsMI9xOoyHw6AOpyndTbHkmBkDkwgLWufqMGMSBgBDFO6oCfZp1aOPKcAQUxDsyAYTRxFHpPb61Pc1OpgBGgukJJ3UOcQ4xnhIMQrnw8hmKY1pHIchpiGNYzoeD8dhrM3vjscP77acGwKmENY111pF6DjGu2k6xmHgKMgGTCAIjMAADE4AHkQ+fPjwh3/8H//wT/+/P/7T//jd7//w6dMPh+OUYsQbFqhNSy7r9fry9DimpKWWraxLUQpvD+QQOCVpraGaKgqhBI5BUgpDFEFLQilwTIklEHOPozD1ZatA23VZzbTmssoSaiHinX2riuBrGoY0MEttzZAMhCAiMAHwG8sMwL4ZxN3WbIcFDQkJOobkpe7npWruEFpr3zgciCjCgBi8P1zdKA1f/9+OeNZa932+mSJpq12Nvlwvy/m8XC/b9dq21bRBN+C/NTKd1HPT2JruaYC4e+N06KMHgDDFGCTFEEJlrlUdIKUoIYLfgLs+yxACYF63V5kAAJTatqVuyzZflrJVbUq15S3PF9rW9eX5Ml9X8YDCDqjq6lWzNtXe8zFzDCGGJCzeNAifjuOn9/dIfnccpxQjc6dhd8ZDFB5TZKRSDcBLq6Wq9fAHJ3A0B77JAne80fdI5Tf94W/ndgQYHJJCaIZqxQlv0UKv7GESCTH1LyIDZDMnohBjT15wQA6ROCDsoZ4E3aKbgDDEMExjjEkkiHlIKcTE0l35hxCTSFBpIQRHJ2FhYaTAAZ0CBcGOa+/rQ9g4OR2TnJJcEV83JEwSOBqoGipW7IwzgN5vinoiEkDi4GjACM6gAlAbNDADBEfrd3FEGBgquqKreaumSM3JFLRZra3V2kF8NmZhoJ2hTsTEiD0GjBAQzK1Wa2qtNtXWQSxVa9o/s2E6evzvpnbcq1dvTR07yVGol6sQ7t/d3X84ctAvv/7l4ctfwb0nEANYCIGYEGk6vTu9/yGOdyzj5XJ9evq6rc+1XNGNmVM6xOHAfV94fzpMw+l0WK+XdZnNfc7rL1+/LLUO40joUWQaUmAhxI5MqhkAHE537z98OByOgQO8YXO/3mRpiFWbZBPDKDIwDWiRPDAwu5MpWIUuNNNatdYWGHo8IBkKc4rxNE3HaQxMZWdz75xzdVPVnqgMqtCMzNAcwYhIyAXwNVapr9mChOM0HMZxSInRTdttbiEAqsWt1PXaWtW3U3snwzGTKrtTrWjZ2qatqpuRO3ceVJfbISMhhCTjwZnLHINIlBBDMEAHIQkIQLCvbkQ4iiBAA+jJDcBkTI1QCJUgEE6R6ijDFGMK6E6IQrhj4YQExkjAIUggZgB3q27mViAOby6Kz0+zNHOk2mxZy8+fv/4f//ynv/3t1/PjWar9dLwbQkgxMSEimGtxVYJGiEQRIVCPjAUHr6Dg1MAdMQY+DEHGcBjjMEjT+PF++t2HUyBft+zuiBADD4mFJbCMwdndABK6IBB4t0oEpsqizN0kIga8vxtNGm0t58Vsf+pd3YsNMYyn+OFwr79HVUP0u+N4f5ymMQWiD8dT3qqEuAO7QWKSNMRpTE0N9f5ulH/84X5ZS861lFpbA7DA9O4w/O7d8fcfT1NAEIiBYgrDMIxVsztVRYk//vDxH/74+z/+8aff//S7D58+3t2dYgx7s9gpnOIi0i34UkrTNN29+3B6//G6lvIGfkiDjFNQZTUxNUIYgkggN7XmgAYFbNuQJMU0juMwjQhYaxPZ1GxMIQkzADYFAKTdhaiqudoFyVudr2eKsQEChyEdowxM0u229ukZ3N0JiaV3SIYEhNB3irVq3lrR1rSBN/fayqa1MAfCbwS0bn4P3eSR+E34VbeABzXtqsCWMwKAtnWel/PL5eHh8vh4fX7aLue6zFZLd0VF4i4t2XcH3eSjw/vgrStYAdBeeTROwhRjuDuG+3t69w4OB1dr6q0Z4Gq33GgiZO4Xi2r5jmSKTuDUAVI3Q7eO6Ha5c6ttWTYrGzgQo4E1rbXVXEt3LB1iPI7j/d3d6e4UhX/8+G5I8Q+//xHRhxh+/PQ+BhmHGEQAgAjevzv9+P7eDZatfHm85vNq2gxId649+U3mDB1gxlfYZsdy4T97EcCAzE3rdTErmwY/AMbhJh4EQhKRkKLEKCEiGRCbOxOFjrrE5IDM4VuElAH146Zre2II4xBiohgCwDiOwzBECSmkaRjHOAwhUjNI6gQUeJAYgMbUVCyGxBSQGIUlhpgCRIku4xCnMWL+1qvEGA8ytVYrZm/VWkWkIJSIIiDX5m1rbhW0QK2gjVCNzMmN1NwUzfohSQAGpkB+S5FD7/FuhMjeSBFbl08R4J6xi4A9RVckBIkpMOOe39Cq1lKzazUwRGRTq6W1qoAYYmD5rpr/FpDvaTTfElQ5SBpdAscwDuHu3XE6iPv68OUxryuYl1LW9YoAMSXmQCT3Hz6a5vF4zyE9P3z99Ze/buuzltkdECUNp2G6n4534+EU0yQpHsO78TBer+dtXau2l8tZEVLeCF2IUuCuMsw5z5c5l2JqHz798A//+D8+fvrhOB1Kzq0WU/uWbYUoUbgIEhKgECXGiUAIiLxnUyl4NXd3NATogUEojABOwCnEwzDeHw7HcYisrWM0iHyrB+iObuSO5gwQiBIzAQlhYh6YBxFCi4SBKBCPMZ7G8TCkIQTQdjPaQXdszZvXqrpcq7a3Ij7oGX9MQmTmaM1a0ZabVgUz3L1yOt2FqSf7SZCUvNPuWIRZmMU6Tk8EQDuLWPszSggCaODmTgDcGW5IShSYx8g6yjCGmIKrwb6xY3MAcOn3qAgzAWBfYKI2dH11mQUAN/j6y/P6MitirrrM+S9///xv//rz89OLqJ/i8PF4HGIipmaWW9WuIEQ07igNhpsoqNcDde+cscTokUMKUwpDFDD/3YdTzfndFJc116YOHgQ/3h3HGAdm4JbI1GwE6JZvbkaA4n1Gxk6LCpJOB2lASPRY19cros3Waw5HkoHG45RSQkACH4JMURKzEE7H0cahZ1D2i8dCAsZtI/d3A4482vvRHBGomfdtrZsmggPTIFDXZVuWXGptN1oWigQKw3B/f/r08f3Hj+/fvb8/Hg5pSDtyvC9JEQWJArN0I4Hj8TBOx5jGr48v//75+tozEqEEEiEHMTMCCMzM1B0noXepG7tEnqYkchwTABamWmpkSkJJpK8q2Z3dCXY2HJnVbb1am+cXIyxuKPF0+jCNpyiDSLxd0v1u6RMbApgpERBjZ2LmXJcll1arllKWmuea51a2j5/+YZrubk/HDdV3vJlE7Xdcl59oqzXnvC7edDfr2rbr0+Pl4eH5188vD1+Xl6d8vXje0No+voSIzB2IfjWa6pSjfk+7O+wNfTNVd6MgYRjG5f2oJQUkIa2eiy6lltbNNJEZWXj3B2DWpm/55a1a2VrNrZVmTXt1R4C+vKtN13XN11qrOnhnuuZacin7nDOkejoI4BBDTDK8O314fw/dzppgCCHnPHTzCEQmvD9Ov//hg4icr+u85qeXeQ/f7PjjzXrutwV8N8d48x9ue8hv1d12NkpzKWQYQ3RnQMd9IxAkxJhCTBIisiGpuxGThNApYg7ExHBbwBDuE1I/myhKGJOESFEEcRyGrgtIMUzDMKaUJHAwTGqEFDhJEKBhUFMPIRIxMiPv4TQQJILEIcQxQvmWQhREiMZamQCsVdeG5jHEyBIRUdVM1ayCNrJGpg621wS60dSQkIkbuSIZI9xo00TAgEyEEFyruircUmp7HhF0lAD6Y08xhhC4SzQ7safk4g0YBZE60aTVBm7d3f+/Lu3Y18uvpd2RGEPEmEIKaaAgbvVSTLf5oWwrIYFrlOLu3kptDMDbDOsFa35Sh8fHh8dfv2jdENQVzOmKF6IvElNIQxyPcZjGcYohEWEaR1VDYldrtYqQuq/WNncwvZzPXz//er1cai3vv36ar9c//vEff/jhh6eHr89PDyFIivffqom5aqslo4EGpCApgSAaAKKCGxiQkYMz43BIwgzW3GtklhimNByG8XAYphQCeiFMjCNjAwPwiDARRsJAmEQOKb07HPTOGZkRA+G7w+F+OuTWtk3vh1ynNg3D/TjGGIRQDRo4mGmzmhsBNdDS2paL95isN6UdSXqClYGpVmultWpu+/EcEzmzE3EgKtQDhJmBiG9f++HvQDtvCVtrtm5ty7U2BwNCV+1bH0RiEg4hWEpDiimmFIYUUorW1B0JgzmBApoHMiEAgr6+dFPQFhgCswd+FSOa2b/8r39nhOze+d/XeY1K74djYh4lhCjO0NBbH526kz7eAsC7y7Ht/v0RJaJQM881qQWiyGEUGZnSFP/3nz58moalk56bAYAIffpw//EwRoUBtHk1U1EJitjIGpo1L2a1oXuPY3bDpjawKftbOcm65p///vA0xYeXFFOQIIIYAI5ER6IJcbhtj6k7PPQL0IMgGY25BTYRDIwSkFmBskPV0rYNS3mu7atqbHZd1ufr/PU6P1zXay5bUw58R6N5D7VmZup57wCwu0wAeq9GCMiAgClFskN5d7/O75vCv/96fb2zti2vaw6yZz93RmfvnbooCtVaLk1K3UodSi3F3XMu27Zu2+KmKfRSKIFRdlOjGMcDh9RjRHPZtpbXVjAEIQ/kgQERzQ0Noe8wTdmZqrubtoboxKhViCTnuizbVtatrPP8vM7Pphmsnu4+vpZ2uPHp4bYVAwMDN23aWsnrOs/z89P1+amuW922mrd8vV4eHi6PD+fHh/nyUrZFaybTQAgpUmAYGWMAos7EQ/PeQ3M3e8MebK5eihX1HozHDqhiWfKV1pGYtmLn6/br4/O8biGmIQ3jOMQYq4QQQozxN7qx8+MZS2o552XLy9ZKa0GqaWmt1FpK2dZtWbZWWj+ZDczNe/uIhENMQ0hChKaCnGIYxiGOqfsDoNq5toB9YoVOQBlTGFIy88B8QxZhlyu8Noo3f5Od02m6O3Pts/u+h3x9mfmyLANToKHrackc1BiAOSSWSJxCGFOSGEMMpIqEbopEzMiMItQtiLB/ZzNwAwTzXXKNTBSZgpCwOHQ1ShBOIkOMKUhk4iCg0RhBSCQwkAzJmpOIEwISMocoaQgYQ7RKkeF7aSghITN6IAdUDyhgPkqIMREJKtzsv14dkjobDRGDELugq2NURo1oic3Au41JU0cg3w3oHBIguoLBjYCF3uWg5u6tORboKQbCjIHA0RVaUXfvYu+eZmRNu7Pnb+6r307t+4rdbzpypJ0DHYNIQ9vKmquXbX6ptYqICIZopl6LtdbMaLm+hKDI1Fp9fDq/PDwjuDADkBv2YPFOqeA0xeEwne6Px/vD8TiksWN1WrObogUnMlcwc9Pr9fr09Pjy/Jy3db7O21ZardbKw9fPL8+Px+Pp/v61tIOqaTProg9XYR4TC2Ez7Bt0ARYUJEiJp+M4jqmWXMsWYkhDHNM4pTSkGAOi1oCQCEaChk7gTnggjASR6RD54+n4jz98vIujECMAo//44fTp/riW2tS0qDgNMR6nkQIBeSMjk1EkU2MA11fNmeFvAtsREeVmnwmOiqC7ywMyiIAEcHY1ZO7sSSQiAO8DPd5Me2F3H9pVTta0eCtbaxXR0KFrrMCUUQMbCAZg1rAdYrBhnFKMcZ/aKZhTUUC3AMrojqhqtaipEnsMPKRQB/lW2t3/8ufP1moGAKIoAQEHDIcxpiDMCD3eqIdLvDJY+5LSO1Vxd8ggpIickLGZlSKtsVlCTMSRkIOET/ef7o/VvKlX3Uv7EMIUAy45QjOvO2KhhEbe0BU1q7eG7syMUax6cx8ZIRK/qe216ctlXvL6PBMwOLkgJoAT4B3CEXFCECRGIOuNFHa2HiAZIQ4x3B3D6RCmUcZEIVaWBWhpdZ0v9Xyx84xLprWtuV62+rTmp62sqg1gmiKnWmrTPhTddNAdKd3LXH9e+9hAwEyU4jQOp8PxMn233G21taKBwm2Xh4TOgmkIY6CRICARShimOBwkjSix5Hxdy2XZ5nXLpam7IEURQWDyKJLG4fjuXRoPy3Kdl4tumUEZGwIwZIbMWBnFwdyg+yUAAILgPgdXB3cnc3VsreRW1rIt23qZz4/z9QmsIqi2b/YVO4MawA2gZ171BLaSy7at83W5nl8eHy9Pj9s8b8ucl2W7XuaHh/n5abmet21VaIAewBFZSJQdBUzAGMHAFXriFhEi77iYq2JDJwVkJzcFJwLBBtZa5bwRh7zV68v16+dfztf1dHeP9+/GmASZHMiB8fswK4DLy8VWtta01LJWc6tq0lqppdZOJwcCIzCHjlULdAY2AhEdhnQchiTSYfxB6DjG6TghuDXTXFfCSBSJEktkJncw3Z2R8Tuo/XaH4JsvuDEOdjP5Hl3S6Qdva4mZXdfVQ0gpeddDKEBTNojEETk4DsRjjL2amLG0ZsZEkIKIiEYD4Ni5ke4OCEyAjozEANTjqHtqD6NDjBKFAmMTTkGicCcEWRQjcCEWRmCMgdhB2AmdEPfvwxg4GBPzN7j326mLzIJABJLiUZDGGO9Opykmz63WhtYQDFEB0E270I0oAAUnMkYQY1ARiJHMDVprRav3u5wYiBBiqA6tWlWwPWunH37UeaANK5Ta+cIR90RdISQza7V0u4Xuq9Y5Xv49efm70r7z4Ts5vjNK0DuJnwi0bZutWM29zUszoxCDIyYmcy8VuqVG0bxsRRgI9HJZr5dCRClRCMAM1FuHLj5ZW97W5Xo9D893d3fH42kaxyChR4+1NIhE6gtYd0SWOIQ0NLUtl19/+XsgCujn8/N8Paf0La8dHLSBGzJyYhpjOgzDaZLA2BQkDCEOVmmLliIfT/HufjocB22t1ipMgYlZgkhKIuQtO5kmtAndwAIZI90xJvcRaZhG+pHi/x/XJRMRuiPYcQx3x+GSN0k4DfLhfuxZBcDoCFBj3ZIQjIGNSRG5ATZA7qj/t8e+83X6TEVMwkiJIYmK1NoMqSGoaeucgR2cdGvVWw9+3sH6Xp4InBwQjNzcq1lBqwANVKFmqIXBGSxBc6oeWptg+jiU6SgxhRDYnR2QpPf36EbOnRBYq60F1IUYRCREOWP8Zkfn/nKdXRtGkRgJIDARc++h97yRnh7Qsyi6GgEAHHqv0qFXuTX0DO7WrBbKGVWxNup+w0JxigI4IFkPEe0knk7K8kZojArQ0z8ZQF5XqWBG4FFIokBXXQEP4n95U9pFeJpSc11rWbeyaUWEADihTwQTwkgYkQKRONGt/dabkGiajj9weD+cDjgOcuI0riEsQEt+/HV7uD5eti8PeNlorrnoXO1S26VqBXfG6prGeL1c5vN5u17rutpUQEdget0L79JVeOXBem+UhIi/bxmZQuAukA4iuw5wHNKHTx/en8YpcmBBCofTu/uPn4ZxRKYvX359/np5XNvLZi/ztszbh9PpkEbwZrUZMxIM03C4uwMmR+DIBxucTaKMwxRTZDbCiuhVa15nABhSGjhFQSNvoEAogsgI4K5WRQPVQC2xK7O6mhm+WfSq+1q0b9dba+jaSq7rtszX5XpZri/z+eX68jJfXrZl3ta15q2sS3l5yfO1tg2oUSAkYEBEbG6aizlYZkUyd1ATgNg3TkFcqCmiGWoDMwdXIkMCFpfQUNQJDFFNSytb3pa15BIk3N29+93vfn863Tk4M4UQaqtfvzy+YchvUK+g5s3UDAhVrZRGa9VqUxg+3t3VMGiXTPmt+hB2MdMQw2FIKQRBCogRKQEk7w+5dqvYIDKleJzGKaVayvnlbGrr1lppPYVEHXoSzE76g53Uvg/osFsa3RytVE3JvjnYAIC5L9vmAEmNHR3FDa00aCoOrMZVo+Nhv/UCuLswuDPRMCQRSRQc+TgN45C8E5SbIjgxoJAzu9khBhGJREpWqc9p0GlS/d+BvMd6OHdSoLt0MkMfFBwRCIzRkTEg4y2Z8E2PoqYKwEgpjinKdHc83R9Pn47TSLReLtfLVbctl+Ld7qQBgAUSgODAbWexAziCJJlGN7WSI7aGik2bmdcu3EDm4AQIugdXMCGhOalqa9XMWq0FyQ0Qqdv5m4M205Z7g6XdRhSx57b/l6W9Xzu37mYF4MAAhLCrtrRpzdUUgBAHConDyALIGc36XzHwUmttObIG0rzVnJUZkZwYWLAnPXaNsKqrZvN1Wxdv2VsBPcYYa1F3FBlCSikNIcZutpKGcajNHLZ5vVyujw9fpyGu27ytc2v3b94HkotgSJwSU5I0SBxYImMD7+mogViQI8tBwkQ0gBthIGEzMvNajJsZK4PmYrWANnJFUzYTRna3XDHXQ7QhhMP7Uz2OnTGHZkjg5A0skg2J3EPXDxoAAMVABw8wDtF9AV/djMUFkAm+L+3uZq2ZNjcFcEAEb4i7fWeupW0zKVip1s3pa4GSbVs8b7jOdBi6hbxpVUM3Y3TxRla8FchZc2ZoDA3rijV30JFqpdaYEUO7PwUdRyNB5AguCHtcJgAYmvY8OSgN1wg9JqB/FRPIb54WdEBngsAYhaJID/a75frtVtHo+0b9NoAi3pIImbBLrsFRGBHdTb0WaIraXiUHxETEwJ1fCOiAZlq6WaExOjGg3YYgdAVXtVqqtuZqBCYEkckFApFGlDcuZsw0TcNS8rKVudRzXgFACK/kiWFkHIQnCQNSQCSHWluudaulqTLRe8Sh6R1KCNOYTjwcTCIBZFqei325bpfniz3PtLRSbGu+qW3qRsCBiH1dt3VZ1uslz9eyzO1wsjQSBGQBuNkw78Tu3eath3rsuVJvXinGMQ1jGoYUgwQidLVpHI+n++P9IUUmFqDAhzuc7jRGM7tU/Hot56zZaW1+zeU4GhC7areNq6rVtLk3B0NmCYEoDhyTBAmErK5ohdnVitUFwSmAOAZER0OqgMRdTwoorJHNAoAyjolN80allLcW8ap2Xtau7/Ky2bbk+bpdL9fzy/VyXq7n5XpZ5+u6XHPeSsmtZs3Zltnq5t5QgBOzcABEda1Wc8mlFcSKZO6oFhAH5hhDSGZCSojupMqtYVMDMiQDQidSCM21GnJrWy7bVnKxpkQ8DOPxeHc63XczcGKC743u1WrzjEBIcPN+cK2mpUGzyOGYRkMx3bV34LYrC9zdLQhHpk4LFaSBZUCO5rk1LT1+DELkaRpOh/EwDG6+rplJtqK7JbZ2T4RumGO77O1V4b6v4ffaQN3wxfS3U7v7pTYMekBKJIDBFMqyoSwkF8iusjVTtAbWY0137QcjCaCIRXMXx1qscMeowQzBEYkUwFpQGwEFMJqpW2NIhIHAGAfhxBjYwSAIGqExgaAjYkB0Qul218AIkWkQMuGohK9o5rfSbg0USZgihbs4vj/cf3r3/v27u2kg4Pis/LKdrwir16JtbdqBq8TG2gBRAZohIgOFgeLRtJEJWw3WzKtZaa2pNxBkEUN3AGJ4ZYKSdxHVvrlXNUQFNNfOPEUFb9p8D3137VkgaO2/yWu/4fx78wa72GM/MglEOMbIIQyT3BMfOCTC6n6GrcXGICSA6JVgFdgYNmEDqDsfxcDUVb21PlNg92xhAGYnrKZryVjLusy5ZDXHENPpdHc4HsfD5IBpHNXdANVR3Yvq+XKpdWtN32IRCHCQEcVcdCAMGFHFCwI4qWNVC1W31pq1pVa1+TJvZKZmzbApdLMAoTiFMDKzLcu25jo3vdZWqnIgyfVyXk9yGaDG6vdrtla7EQC6L6091/owz3+b53PVvBtku7mT43tKdxAOBkisWlZXCRhjcOk8tjfPfM1lfW611FJad7fd1rYutZWmLT8/2vkpEgciAyZ3uV70fNbl6qUwOo2Dl1pKXta1NEeSgXSEwtsTzI+1tLXhJD6IYtu85grUDGzboJU4pGEQDmYSVoWqLmDS/Wl3GyloBqbg7s2xd20OpEZmWN8ufRDff7xDrQAWmKcYmKVjyh0don2B5+hO+x4LuidWHxx2b26CTkdKKcQkJAigZtVBd+AeyZ1c0X1fPJMDO6AiGSAQU2AGcobAEGIhrubblufrCmVjb60AB0QIUSSCAH4HyDNxSqm6c63grA0BgZAqIQpyEhkSToeYpojizdd5nufrs+qmjchM/HeJdQw0DjKMIY0ZAzRrxqviRf1BLd/007WZVlBzJowAtbXWqpaseWvrXJdrma8lJJwOe7V7U9rxJoDtene17z31AI7TCHrspT3G0LGQIUVE2HLZiqlDM4KnK39+lBCJ6MuXL5+/PiFSGg9xrTxvTtyDF4w4N6vL2h6enta2rbnmTKRRYPTohhaQyM2AmCEgugmBm1vLrbqQmXurpfuOEQkSo2og5yGOkQ8xrWk6ny/X6/wWfqit/fr4HIUCQT2/rI9f1uen9fJynS/zMue81rKVkkvJTVvTZrVaLWgVXQlBpIvGRBy9aCslZ71oWR0KdNqqRcRBOEUNRS2IMpG7mEmrrM2RjUVRgdArorHwGqrl61zm1Zp2X7xa25aLbFlbMzcEr/W7vHYSkMAxREbWatqs20ixoRmiujeDpqANe8ztHp0MHR5Xk9qbO0YmPqRhkhictq3ldVVEdZMUptN0PE6HaYwpIrM61M7hbK21ashqar6nuPrNraOPBv1Hvlb31/n+LY3OAVbwQFhiCDGRpFZL2a6XWfEpRx4SB2A0QQzCItRpmICCFJhZxJghhXx+4XHAwEjEQEIURISFmdE8NZPgEbd9uU0Q3AHRokShgVCEGEQJjcmFFdEiu0MQFPKAMArDmGId8iK4ETuhfm/DDG7g4OQe0Ydi46bDqkPjI4xBLEY4BLwyLX6dtV6bEZONcYqAzWuwkhWNDRFFxPfIbzY0IOtTUbeKJ0J0QdQeywnwaqHigCidqSqBeu4RgCMwUQiyC10N1FzNte3Te63/dahrl3A2t7onzjnvl48AuyFA5BDjeCfjDxxPTGK21mrqLVjAxgoIXsgDO5EZ0taPaHc0I1VoDV9TZW/rhd2jT7XmvJrB5bIsS65FQ4h5W0vZ1N6lYYppQBEMEfuGFuEyL6qlqb49vAjpw3Q/QOCMoo2UdGnrWtUUmnGIOLZWwYtp03JWbZu37M2sGdYKqg3BAsVjSncx3aXslpuv6tdqWzVGiM2WbOulpNLC1mzdrNaeLo/u11Iftvzrsvx9Xq4AVRhFiKnW6k1dGnEix91lEJpLIGbS3cnj9Y20uq3Xp9Zaq63UWmq1kr1kq01bLSWrNRSmmGg4sruvCzx+ta9foFY5HPjjx7otBeDr14dlqw50CPA+WFwf4PLLudjFAo+CB2ItVrMpbQ3asmAt5FPkkQURqFbbqjZ3QQgCgFAVavPW/WNV1XtenJs1NWwGs0Q4fLvKH9/dgeZtXcAhCCJh1R18h5uPB9ycPWjn+vc7wwmdiYSJhZk5BElDHIMERkN3125BigBooM3cHRh8t+l3V4NWsVmnUDqSoRNxNz5qqvO6vbxcgusYUDvnYI+721MT3vYoHddwQ22g1SXszW4cJAxBxiTTkeJoBhXaSvmKfEbaEBnhQJAZVQgCcxCRICDUmjtWg83har50ix41beYKqBC4z8Y969XJHVShVstZ86bd8gFeT4a+wHZ0tNZqqSXnnLfyvdrqMA5o09hDckSIEByYSVubtdRWStWtWlaoDYhZWC6X69PT0zAMiGhmgNjU5q2QV21FzWzZnrNiONeirhYEh8SH0rYhhFvwubDE2F59ItRaa1g7hqfNHVzJqAGiqrlqZwQKc+S+VJW38ENt7fHxMTAEtO3xy+WXn9fnx/VyXvOy1txaa7p7e92oZwqmZMY3j0thEhHxDrFDrbqWdlXLjr20J0IVUYUI6E4mxP0oq05VgQGcWo+nbU4Kgbm1luel5c1bc8eW8zpfLy/PWmtraqZgqvbddMVCIXJKUShUqg2aNiN3MNdmOdd12yznbhvQS+0tXbtHADkgOSEIdTmcVoWi67xdr4tHbmAh8GEaxhRDYCJ2wGb782tqqmoI2lprjYiZm1AzYXeG/Ue+8ufNVJVaa0zEb/PrHLEKW0p0PNB04BBqqXnZstZGNYAkJ4yEkbpUsc8kDCSOPRqHgkAKOA0wRAiCPXOsW8wwhxumYxJxGIypkWsMlpIiVYOevG4A6jvObMLIxGqAhHlzZSoltNYjX9W7Ug3QvsO0zPp3YAVxFVLaKuQK2ThhVAGL5Ik9BwjsjI7G5EM6DGhNV1Hg7mKKSIT7MtxhF/G9mgM4IvQoXtU9z8PRfOc8A3R5DYddMAnuZgpAXZVixmbWmqtqU9XWG/n/urQbeHXLrsUUANiY3GRv1RCBHcUouiSKE8cRAcgqeZBIwQdsDLuJbgNjA+ywJyIZiLuYoTZvZTc634PIAN2wMVQyt9ZUt5y3bSu55m1rrdRWmtq7D/ju42EIx+lowzBKCNv1uqyLab2dabdHhegfP/40p2vMVOcrrWXeLnV+lpyleTpM8d2phhFR/Jrzy0zrgnm12rx1YpU1RAuMdwN+nAKxD6zOFXh1XBTZ8OBUMWzGT9emL9tyvtYtm7YuhJ2bPtb297w9bGVl0kRplBDjvLTtWoyhsBFgA3+2ehXrNqC19pb82zspOV/Pz1pbra3/w1uD1jADVJXaxFowEyRK4AjQGqwrPr9gbYEYtm29XJ4uL//2pz89vVxq83eH4ffvprHNuDxfq12N6f3xLh3IsCldGl6KbxmgeE3YCnKDqva8tCUrGQhiFEC00rw11z4NtZ4Z5tbUarXq2tzeA/zvf7gVRPhwOnjlr2XNpVXtuTOtmd3YpoC3nhXACVEEA5EwBiIhDMyRmREFMQZJEgeRwFB6EQNHcDanqrYVcOAUkQnVvTXPxVXBrfs6Nm1WGwuwcVUortfr8vj0cowSj8PupOdN3T0AMr/13jGzUlrZSllLW6ttGiXdxcM4DcMhxkFilEBSi27zNl+3y7pcylaaGRKiG+A+R1sDUEZncEFndEQwAL3pqtTQDXvjYz1mF8mRJcRhGIcYAzG5e2mNs+lvpEpdCkZaa97WZbmeLy/zfHk7YI0poqXUTYA6lQMQwMq61Ja3LW+5LLkuW523Yo6I1Fqrpa7C1+enPjJc57mV6lpVi5oZuNGLoZghAQ9RhiGMQ0yReZe0YwhhSDHGnodA6FDNqDVCwC5Ed9da1KyUWmpTczOv1UrWbVm1fufpr7VeHz4TGHhZHn49f/65XM51W7LVso8xfcDsRHpyd3ACIzTs5Ls9mtGhpzg1g6pWqmZ1NUMzYAoAGsQdCJlCZEA2xVbtNVUJwNyrtk1VGEIrOZdWi2k29e36cnkIpC2l8Tb9GhDyeHg1OmTkQCLIDOzdiBmquzeztdan+fr48qI5s1sQ5r5pAoBuG+fgRHATPl+W9Ul4ZSGHp8v1eV3CECCQIE0xMIFpa8bVGdRq3xDsy1GvtSKS9TAeN2SQJOi094r72zXdOZxu7mN7U0sIeZri/d3h0w/TdNBmuK6g4FUdqrZSqjmqsXbejEtAln2zXxXUmRhFfEyagjJbl24wUwjCJA7S58s+4gdugTGwxOAcnEMUToFY9saZWSAIBDFiFCl5M6L69OLXGW2DbdV10W3z8fAbGp0bqpETdu0PUa11q3Xd8shM61Kvmy0FqhFQkDCEUGOEIR4G0Boci5GogjlAM63bVXtwBthNQU1dyCQkRKSuaM3MHAx3/7ybUYlwilFEesS5Kja0bl9mQVQVwNyba3NVV4P/Zteu7s2tmhVTABSzYH3/AjsJ4aaBBkQiIDTHns8pyKkpcVNrasrQqHdL3WEUd78XAhBEQydEEBZm6t8NgFXR3fZQGAAgMsfadNtKWNbxUFyNEzBzCCxM6r6VCuCB5Y3VNyDiSYaY3E81A/G66mXJj1u5XkM1uDPCAMfAMcBmds5wWXFdvCpoN4Z3d0BmUuAg+NEwBUdS5AKUwYJTA24ks8Pzmi8v89PjZVuzdlm6eXaf1Z5aPddWY0DCNKUYDgvUrSxPVjZURHKElawZpupE0LJ+48n00l7K5VxMzV5hMnNQF3N2GKl73JFIoBS9tUKIqroVbC00JTWttWhZr+f55bnUJu14FWheYLOl6ua+ZV2Lo0EpMDdfim8ZrJDMZlbBrVQ9z2XNDZqTgzAhYG3emlvtk5F3Ho7V5qW4ujdkOAyvlwNgCsnQhSWjNlM3b7rjxNRvJOgbJiDErmYJhMIUkCJx6GYT7uIQHYNDcEDv1v0IgOjADqLmtbg5k6MRmFtplrOpIaG21mptrZlrAI6I1b1WzblsW04I7kBITKRFuy888fekU3f0RqBMHhgG4VFkEhlFEgdGIketTWubl2Ve11yLmXZLGWGXvknb71BERgEahCeRMYQh7M4/fltg7cS4zgJUKA1qI1VyYwBCA1BruUJRbWptb9/73hQAaq3ben14evj18+fneXt1GkGAFANoEN59rrq7q7u2VrVUb81VQdVbtZL7urY3BrXVum3dRTHnLW+b7giymbsiGTCCMEVtset/S2FhEqEgFEO3nYruEUA6WYsQkpDs9Atk06bgQu4I1azr0UvT2qx+t9zVkp9/+dm9ueX1+WF++FXzYtoagiLa7jeD/w9ff9YlR5Kkh4KyqaqZu8cCJLKyFlaTnHvPnHmc//9XZuY22d3Fyg1AhC9mpqqyzIOaBwLJS8ZB5SkgE0C4u6mKyCffEgC73WowMCIbqiPYGyt8N5COsHC1gd27eVCEEeieeDzy6XZroHEUiZBwBOqgm3f32snQuzbz7q6qti3XC6L3moczXQREcEpPfzu8IRBZ8pTnJIWQ0Sg4THXQ1Zr267p+uV7qsqB7yUl4FyruJqyEKVsOQO3QOUKttUkSA162balNtFMibYoBvbWrWdWeakqSa9Xah89/3KEzdzdXVW6m4lYYBd/4vIgAZGa9a+/GtR2fGGC/eIloejxNjw/5dKJc+m3bR9UIisCuuLWIFtgHP5ZzKbmkIHb0TaOZAKKIzQVLduEgckAjosQGOGx+E4QiAZEV0SmHEDCHpJCSGDPjcJEtJIkEE4Ows0ASKwkwzq+3ZVmAu+rV2wqu1hXJIL4temxkuaIGdpMVnLfES5HlNoXD9dYu13q51XWrrTcwy5zmLIdyyNFh3QzRY1CGwN2i9x2VuNuXDbLZkKsxIdmOut1pR3fnPkLhXRGAiOGmGOAc4gAOCG7WGysqQozR5X/HkI+36m6Ow119fO4DUseA0DAPTaArCYkY0eZiLslQ1MkUtbE27BVdkQmzEAAw69jXp8REBQCJaCpZRJqque0W4wHDRrjMKRXczVhTDuDe+rZcwzcAXy/X5fKyLUvrnlKiVIi/hd6ER78uHPHD6RHLzLWu3V5++aK6SVPpmGz3m4VBz3MiI3BHoMwkDNbDFZNiVsxBNtAFokAK5NE7KuHZ/fe6/rxcfr6dl7Wa+QgJcHD3cVkAAiTkw3R4fvrQu16vt2XdLq0G7ekBghLu3kCr3a/le2mv/ew63OdTmVLK2prDRtaL6yFNRQhEcJr56dFrW0tBYWUC21PmsshB+PnhES2063E6TPlAJirBoHMEmqzXcLPevBq4BTbETstrb+dmqr32ura2NevmajCcvg1c3buahvndyyIMvQMwYM61f/dceSBgzqWOxs88whFhmEEnJiEUxFHOM3Nh5qEABRJEcSAIUmMP6S4OPGeAkfpOEIiBApAB0MNMo4cbhqOPFLuICKytLeui5sF0SBOVbK6uBh6DvAJAlCSVrLqFdnQnEnzv+8sxFQ/CEMY0lZVLksyGWnVVbaPNH3djJ7KcYRzEwCCEY5qKFJFMLEACJInTI8mH+fDD8eH1cDofDm2pVWrQTnjHADdQgHXz681eL/p61tvqvQ+rJPTae7f1eluu1/V2W5Zl3dattaZWtbW2XG7nl9cXBUnP//I22ydhFxnpOKPYwN0yLKWcJE0TnDwear+u27rVujU1e4uAGzPjyB0ZIYHj5ickJmRiEc6JhYarp5QsOfHYSgkTQphp7zEWz0yIJNNUZE9mu68F1Wrr29av1w19a1vbH7X7V6/bb//+e4C6N91uertgOA0kkDl8V/ggkCRhZpBAVlAAD/RGw9ocY1gCGIaO+NrQ2Jkbu0GVhXcz6y0AGUJNuXV2R2AiZLj7dw7+CaKhGbiGd9NtWxnAtSXmYRIVAGmanuJf3pCWeZofjg9EAkEtmlu02mIY4blufXtdbueXs2oXEeYhKwFCFEQhnspUpm3EVVyu0+thnnNOLAZhHtE3d+vaW2/qam4BiMw5TWZwXtZuNqj7JUvOiWjYlmtEN2tEiMCDbOWUnKOr1drMVvd4/nQCOOylnfn5w/PheASmprbU1tUIWRgxRNDTaFPRMVzCH6k8JS4gYuiKYcSAyOJp6mVqWTqzj6aCkc1Fm1gXMwBzIkXsEgphptrD0Bq4gXVEAbKg7AgYBmBESugcPexyWWvv6YgyAbOmxKq9xxZxeDsdptq3bVRDQPNklwhGnPKk3V/Py8vr7Xy5bVt1t8R0nPIpy6EUVqi7qVDfO79wHiYOMKhFYzqzCANQDKAgHOQgBNqPEA7l6vAtEBaRBBEOQOjMAmlE7lp4mHqYRzcHs90M/X9Z2sN3A/qd3+T7Xt8hnNlFrEibE5zS5ymtLIGg7i0ogZAa9daVqvGyxuatZfFDJoNAMSJFRCGWlBCQiIauGRh0H76GOpclBJmAdqMFQgyE3nVdrwiSBLwvfV1clVkkz2k6cPpmn4uI05SKpDIdWM3O15evL1dK7owjARiJcKiG9lc40IchUUuZuplZJMAEJIg8OlbcPRfv6xLUiGtvX+v267ZetvqGUwEAQkhQQhJz3rchNpShzW2zHoFEPhNNiaeUmFlZO9j7tU+3ADMRSFmAElIxMDVgVdCWCCdgYEKiLMk8nNmQh2EhI3CEdDswfZJyyLNCz0hTM1drFUQ9zLAty2Uz1dZ7t+gaY+5s7mFm2rWptW6tax+LPPBACHANV3MDi+HMTzCuR5JgjK6ndy/EzRhgytnct03NnYGIRm/DmSkjCmJiTESJKRMPYJIBOXayxt37L9ic9jkKA2jPAR3MI/Po2k0VMBxUXZsCIibu5lV1M1MQIj7OE7aGsBICIXqEugESiwixQ2d1dsXvGGgBrAyeAQ7AKJGICgeTDXlmjPnOPWOkzEgJiZB5WNc8H0+P0yGThHltDWg1Ug1A1yI8z9PheLjeVl42a+5kow5EhAbU6rdFX17W3z9fPv2wfvyhH58CBqcsurXWl2U9n6/n1/P58nK9fL3crtuytfW23a7LLR0e/svTv3wnq4T9W92zQGIM0ESEhJQCHYA5IYlwylzVbIDcZjauBDNT5xjxlYjD6otZRLJwTpKTpJxlymma0vDDHUq90QIQMQkRCQ5msIxMxCGFGPlPXmpP0kbI6brw+r1Bmplu1xfzbt6hV+wqw8SWhFmY0H0kUuMw3yUGQIrUoguSsXhiEkECBIHEkBkKgwWloS4mTAiZkCHAzbtGILiHdXKlAEFMRI5oCBGO4CLB4uQjr25EdmqtFdyMCe/CtUDYacqjZSQRTogcHjCYCCN11A0BWBgZN9Nl3YCGp7AjBgIIkhCVPOWyBmJEzCUfp5JTSiJIBAiq3VSJYNjudutrbWqRJCNI79F7mMeUy8Pj8XQ6iSRmIt4V9DGsId0CgJg4ZLwfpoNd/y2PhBBPx8M0FUQ030PFhic1AwliHl5oaAguiDPHUbA4iI/Xs3NnjYmyQCkoYsPZAyH1nlFTYHIEDAWMlP3hoYtU981j1QjvFCYBBaLUlqsOPV9HQIiKZtb1uqoptZSeU37IksjCzHoMRc44G+HoitHGpQIortYVWnckvS3r9XK5vJ7ruhEETSLpKJHQqveqvfZetTeIQBFGyoIaw0zDIzRCIXR0khAehuAtYlz7wzGU+J6UsuM6u2KBkJgpQAACPEJYSs5hHhrgERb0vfLiDzS64UMXHvv+7248ZAggbIdiz8f6/FCfHnrJxUHMQc2QWNJrV7hpt6QgnX31benZfI4eYOiAioBD0nyvwephSMYYiEhAiMJOgcghmA8sBUXCzNraVNfllqWcpikTgCkj5+mU59N0OKb0BgADMf34lz8dpul4ONmynSEuORmhEoFwJA5hJzCwcCXraEq7ygKIiYQiuWG4IAowIdNOBNyNoyMQgAMFQDzILHp31bG4sKEMBYgwigDtsMHr60t1v1wva2vqtjOeAkvih3n68HAadKbWjd+prTyg7583A5B6bLVvyxrrLdVVXZ2Qj6eEVB6e3GELZEAb5yQAm9J5OSb5qUMz7BrYKi+b9t63rbfaWw/TRa3vjIboY8w13VMxzN09zGJPtx0hmzgiQcPcgxzGDmMIuVE9FL+brgJAtTPHsaREsEQNtASYkIQpEWWkhMAB7ECA7MDow98D6M0fa/jvBBKRIBA6giM57qZsbh6O2CNW72Hd3Ry6Wu1GSTKRAwVSc7tVm1H4cEJe5baMN9xUW+vqDsRFkrBj9+jx3a4dvIf26IaKYpkiU0wMSUiIATgcVM3UkXAw/nLOuZRSck75OM3Ph2NG6lu92Mv1tjSgxeN8u3VXSlyOcznNeV29mVZ3HcMgekDvsaz6+cv5nz9//uHTpx9+uj25PyZJw49sW21LVpNuslFY3T7//tuvX7+e19t1W26tPn7o/+X/9e2YDwa+m0HcLVnw7rBzVzO7D6NCzCI4jdOKPpJaR+zH0C3skX6E98xiGTJwFqYkzDnJVCRnHv6t7mN3L/ffPNwc2CE0jCyQJDEjAgQhCACYujZdclrTiFf51p8Eg0eoKwFkYkEaESCYcwCawwjEE0IhECIMNkZnFGTJlDNJJgrqRpbJC0Oko4e/eZ1GoAUioYeruQO6gXVCF6bCVIQDyGlsNWLKSAV7YGIQRHTy8T2QWexYDCCFfQecjjx4AHeP3puqalNVZSEhfDgdHx8fvr5er2vt2s3HBGERNqJTmDdiGVd1Zikp3Y3wISBU1d3nUqYp55TM/cvLy3VZGRNTZirMOeV0fHz8+OnDn/706XiYU07EcLttX79e16X13mJnLCCLpJyKlTv96tvHQYhTSpmJwANRRIzZ7uWJICQcwQMMKYQpi2d28SAPNHMbJjeo6ErQhXoWHz2GezafkGaiwoEIloQfH/Pf/tZSvvV+qe1cm1sj7yfhR+R8XjlWNsNwBWjhFekGkUHWCEFKLDlLZKkG/Q84NsXEQwbvjoHCpRxSPgFPFtS7tVb7ttq2AjiAYHFfbTu36LXeXtt27b3R6HNTSszV1HtT17AO3hGUUAUVbZA1NMDHvUZIQsOrDgfrx9TGNL+rWIkpwNDdHAFTyjAcuYEJOKX03Qv5rrSP8RzCIRDQ78uhCPMx2QZGKKPOyaa8dU3VUS2E6WGS2r1tze0u1NEWg70ZsSdIAI4qNf7UEU48uP5EY3OEAEjIQIk5ISdENnB3aKo3V4JObtfrutXmMElhRHLz90A2Ef30tz8f53meDsvLZT1fYi46TXUqppSzRCKgXdgx1ky82/cOP0C3QVEg2HVXsMsC910IAAIQoABkhDQEmuGjjMW9ugMSBWB4mK7rrbrV1twMAca1IQinqfz4/PDXn344ztOXl/Nam8g7dxG8r7hip3d11do6bhsuV2ybg8/WSAiWDwhM6rt+blAxW4+vLwQ4vXyR263VzVVB1XvH3rE36N26qVpTa+Zdvaub6VCs3rUvAOEBYYPPRRxIHmQB5tEBOroBGoxZBAf89Z2GDwDDOCARCjMxh0AOTIiMmABSBAeOrR2NQAiIsVCM3fUScTQrw1WaaGBLHrD7Xu0iHQs1VCcPDCAPb163jhaYJQCIaGv9t+sa8zw/Pc64Fylwt4iuOqAACiJH17Bq8N2pBwAf7l3qXrsbhlIkNSHFYAjU7m5BxMLgQIEGpIiMYatX7365rcKCxI7UAFaLc6tf1vX1elm32kc+kMNYFO+CdYDRX/VutfWupj6yeoRFkGg6HkAbukKvfS2FKbTXbb3dbpe6rr3n43cMefMYq587DL8/1PvfeRdYuQMRSRKSvd10Ix0EHyZJnEag2BDn3McKSTICpRCYEIUoZxZGZAAIDxpBIUhjsTVsM8AiaBxd2nt8AJAgDxIZc0zge8MaAEQQGbAGMEACkgDWSBkFBYWDcKhLwg2rYiB2w9ZQFcRgPNhmEY5uEjEjgrA6Asvw3R0Ltp3oeXcBwojMNIkUSZlzIA8HKDNTNegjb3p4CUR4eLg5EfhuTQHu8Z0b+7Js7JdhzWR9txgb2wcRPj0cHp5O85c5Lcu2tNa7h7kbgDMSM1IomA/tqJK11gNGepV7+AhifTidgkhSJhYPUHVgh901lwxh623rrVkrkRIysnj4si7n861uHYlyEmYmoDGNE42on3dAEOKcJDMLICANhgUQD9MKRhAEoWE+4yyYCnIC9D0oewAZuBvewRCmOwsBgMGOoABMAIigBPlQDj987IeZeu/X5Xq9hVYBO83lo2TCr7A2gWAHi+iADbAEYeAtcDyonMWyWCc1wHeShcSMOQlmoRI0Yz6Wh4fp+ICc1N0CIoY5i7Mr9m5br7RcLEXXbblZ7xAwmtyci+QMrXbtGIFuGM4wdokQYD5mEHRAiTG5IAkLIoxr2NRGksjd/ZcA9mB4QBRJCEwoI68ipf9tXvv9xzud7M7hwtZlRbkgzjl+eHZEc4fesFaXJPPMSAGg67rdrtuy9XXra4O1YzeyO0kKnILHxTFEtz4WdTxiWsmQsiPBUMhoN2+u6l1dta+1Ltv5Zdlqu9yaZGG3aGvrtT1mgOfxKpjpp7/+5TjPTAIBfJztdGjPD1vbaNvKlEAYiEbsACEJckIGCgDz1npzdTBmhwh6G8FHaO8+00AABySkSXhKlBgTo4+yNMBNRAFMSMQY5KZbsxYG4jEgSQvPSB+O83/68cP/81/+8uHp9PuXr5fbWtK30s6Mexds3ZQdk5ua2a3W7bau62WJ/gwNMsntE9MMagMn3J1larXfPlvd/PffdLloayMGspk1026uHhZgARpgDjtVz8zvB2z3jomIAIdQQAtSDA3vgTW8AlSi5tHdg5GEQRKmPKdvzxUCCEAOQHceDBPCFDhkZBxAAYRA8Db/wVA27iSrIRlBHPJ3JApCC1C/c0JHK2oWgOGGEYmIEM2hq5uqo3MXZiDEdd3+8c9fz1tT9z8/PTxlCQvXcIwh6w0P0oAeXs227yjZjFAIwwmDW9XXm5o7BvCQxCFADPRnmKozCw1yuDALshAnkSFpASJHVI9qtqrder9t9bpsy3Wp16pVRysPb58DBhGkYQ8+lVQyi6AwpiQs+HASiIRArlrXx+N8mqe55NwkezKC9O7jeOvYCAl3I98B8b3xqGKXLiMCMSdiGMZX5h6OwcyppJKlJE7DaxqJEAYgJ1lEeFjRj+UNjrnIfbBlifFbzufADMgjHJBSSiQ0SNl7DJshgJvrSFN7b39GCFlcApISWXA4qVO4JC8zppwwc1eqG9TrTa+bdodmvq2hzZM7oG3OxhwYzaFp0mCnCNq/gwD3YYG884AZhy4ysYxEk4llCuLu0Zu23tpVrSpIcqCdxQ5mgR7uQaPWA8B3hBqA15fXFS3nlLMIse902UCiROl4Ojw+nR6ejpflumzL8NuPMXSI5FwQcdxOCCCITNgHo9L2SsQsRJRzPh6PWURrzyS5TMSyVd+6N20v59d//Pzz2taH0+F4mKdpul6WX3757fX1tq5tKuXhdEpJGNHMVDUiEO9OiPdOaxYWokQIgJKEEyMTeKATIybEIswTuHgI8sSUEMxj158FBoGHO967OBo7hQgPCiJgDArD0X8nkscDPD5mM0jcXN2RONLjw2mafav99y+iLuAUgeEcQRbqwMOlkwhENIkC2L3vupf2lGkucsjpiOmBp6f84YmPJ3OofQtAFsk5sSVSI299uV279QUoItQgJMmIXJnKNKdSLILGmOfAMXbRKTDMrdtQJxgAABOEj3x6JLTo7m7qiI5w3wg77CPNsGofvXTKY+VU/jehrrDDOPvLvCsZdxGJY5iCtmgtthZJ0EOYOCdIIkwZoIaZ1d6W3qrWHrVDG0wrR0QDRA/VuxGCm4XHyAOOnYk//PuJU2bJgBLmRigExmgdPXSr3o0oTZwy0vCaad9J+hBLnnOeELEc5odPH3/4+98uW8Mpv/7z5yoxYpkZKYiQ2JlV2BEdSQzYLCAI7sx+AuQ9P2M4SY+cAiEuzBPLRFyQ0p6Ouvu9DoSTGBMjMdgIbERyRt3NWCARHaf88en0158+/vTD03GS18vtyvx2e42QaXfrHuQEAm4Wbk3VWtuWrXlLmU9bg64kThGEEEIQxETkHusKyy0uZ1iuoB3MhseB3+eR2GULO8vSd5cpH/6vb4W/O7SADtAxOkYHaAENoBN0jA7ew5lSLiXPc55mmufvnqvh12MR5hQjfgEZAEdph7i/1URjK/hW1/HbT+/34U53QEAP6gZr7bxUIQ5EUIWwIEFCwtGck0d0HVQTa123dev+lQnx0w/49LBtag7E6IGt27Z16Qrm5tHD349XieiplIk8g3oFpRjqBXPvQ09sI2NsPDuD9480XkfQbulP48LCQLAINe/mzbyr1aa9dt26V4ddrg/EKEQly+Nx+vB4+Ph8eno4HIcCbkQ6E3DKOE3RapmmeSqHuTwcp8fTvHkNDlE8vLNhvhOe91XeWL857Mcvxk7Ox5O8IydjMW97vUEWkiQpiYxOZWQ5EzEijz6GOUZvPuaCkXM3+PhAEXt65Zj5wg12/st9N3A/RwPMIyZiNPDau31XFJ28jwhUCEft0d08rIrVxpkkAxEwA7lDa1ANm2Jt7j0gnNEacAQ5YndQZ4PkREBkCDjICGG7OWcwgcBYde8tJ6MwTzgdRMTzwbbFoA8HG4IGxPtd+s4CZuxOPb7Dgq7nG6oejtNhnuepDJRwRKBKooewj9r+XFcHR4bzWbZtU9XwGJbiiASBkiRLmnPOide2LnWJsPHxItJcihAVluM04cfn58fjNM/IfFna6217vdyqtl9+++1yvRwP8+EwH+a51fby9XK7ba2qmiFhFqHxtvgd8PxOPwKuHhSuHnTngBMR7PcSEyWWLGwpIiGL7PHk+xMJeJfz4f1D3mmVg2wDEXvkOACEga/gzqCJvUrMEhEhiMeJpglyQiJEoPusyuAYPg93iEGvQrQdsfru42ARoWnOh3k6cT7xfEqnA85l2doG+2DGzMCEBhDm1gy7ImTmUlKWOZdTKqc0PTDnGFU5DEaXOB5sQNj99iLe3LXj/ofTIHeN5xrcwjGIBg/jDqoPOQ/LzlhhYeaU/9eAPLw78/fj47GrrxzJCQzBTeO2ADPnNM3TPIPkQm6gzUwdzNgcFE15lJIw2O3C0GJX2wAC3Jk1BLufaCA6MeSS8zxLPhGmsDup2rpp095U+/CgZJEkybS7Dc7ht4esN2ukRCgpf/rznyin6emJpvxyvbZ2swgGTETOHCKapIcphbJMliY16iYQjEyIyEiCJCACKVEySEKJqYhMLAdOR5YZ+RbksMek0phKBEAwCc6MGOiBzbEZtF1EGImxZDkeysfnhz99+oAQOaW18tugGG5uXXsPswRIAMN5qJtX9a259/gwg3ccebJj6IUhVyGEe8VGd4wgpKCAAA7mCCdg3/uHcbh2BmeEBjqSIW0em8fmsDpUiArQMBpGB+iAPjy0CQY8XoTSfMiPjw8PD4fHx/cfhzk1MFcDsz0ykxhgHOa9jo8uCgjhbWS//9ghk501N3zlmIkcadPo160DgYMmpt7JAdkRdhOPVLK79q4OZqrmJsx1q7/9/Lt0o623TdWJmDRobXa5bakFmzuEEd5NbwEAisjxcOxqW7I5+AiyNWtqa+2r96Vrr9qat26BdMd37oqW/dYCGFRR2HM5wGNXV1u4h6u7OmigDso+ZaG5yMNx+tPH018+Pfz0w8PHp+PDcSopMe6TJYxFySCyiUwlPZ7mD9usUDlFaThP3535seGG+7bN3fXbfQ1vVRUB0Ee/N+RgBgg7+JAEWYDYA8Ni9+wjREcfjxtGDE90c0RkZuZCSDaWVbjHtnu4WScgTpmRAcBH+iGSIwYSUEhKKWcHWHt7b/YSHt4ajqFYu7Ua3SHANzRGFc9SYg8XcnInczIDdxgAhGIoOgAFkgZZ8JBQRtBIyHI334eSQCQK4l125t0jHAszFzk988MjqpJW0Vq1al/1eiY+vxkODLsEuid2+vfL3XVZfG0YkYiLZGIYNocpJZlzZHIBYJgO5fhQPn/+en49L8taawMLGuAw8aHMD8fj8+PDw/FwXa/n21kEk5Cbd7XWlSIYfEp8+uE5ZZmPMzCfl/r76wV/xS9fzl++fP0SMJVpnss8F0I0ddWwCFVd163tZz2GBwEifvdxRKxrN6cQM6YdYEFECHLnXVmahEAIgpBB3AliWJbdE3N2SSLsvxD3XRziPbQsxgnaTNf1FoesJRk7FQRgEFKhBt/gIAADAgxAcwFP4Cl8TCzuYB6qpvodtZyYk3CZpnmeU5llLjLnyNK78t0oE8Y3iHsDw8wp4zxND/PhMD/OhyfOR5RDa76tW7iGaYQCGNCwG3rX6sVAJYaUcQf5YI97gX3iusthR0bP+AFMTJLy4LUQIgr/r/PaiVlSTtnMcTBLWRhp2CN4Ej1M+nj0h5OnNGixRdIxp4klEFZCn7PZbKThFH2FQbqO/Z0IoEDaU27eogPvcxjswj8klpRyTjkTSXQMJgh2F7fUtQzXVYfAe3JRuNE79lkE1KqMzIKIOB2mR39s3Q5PjybcOzlxIkojLYRxZbixX8A2iBnhyHgAPhJIkTmzJCoCB4aTRE0oQZLwiFDUDpg+Tcf+/Gk1ma+3S621926qESMMSBgeiT8gM1IEXt2vYMZigCsgD+dgTtPh9PD8QR0pTf/4Bd/sAlVNdQMz9IDWyWlbt2VZt9bVnLpZU93Mqoe6pjirf2522bprn4qesj1JlACCYAjfN6oxJjEHVIgO0AAqYiVskhSSIhliB6oR561fmq4RFaIjdMAxr2uMP42GCQMgAgclTkVSZpbvEuwc4LfLTVzdlCIKixARqiAmwDRCxgkIgwN5CGzvpvJ73z4OeQQGMCBFCKAGvKzbb6+vsMi8rs38eZ5TOA0xEgUAdfMOtDkstdVea2uvSwsgc1u29vnlMvweevNSZAOvX+K81Rwkhtg91Fu8n9r5VKYunqm7hvUAb72bGwymcevRu/e+H72IMSLs+OtdSD3w7/uTCt8krbAbSAQYYGBO6TDl5+P88XH+6ePprz8+/pe/fPjTUz5mQGt9W5drHvtSUI1WdVu33lqYYQA5c+SEk2IEFfmO+uDuux3M7mkW5nGv7btmDMDfpGg7hYqEGIUp5yQpjeT1sfSL+0vwAPM3Ek24jztKiNmDBqeGmUUS0PDBHm4xGB62q7AweBAvRo71/hvGvv2Nxjy+7d4UPUCjt163zZqCA5sliEyWo7MIOFhTN9/nDLNwD4odqRh77+ET834s9AHHj+wgiOHXBQDEgTh8GVhWyzOejJDlME/0RN6krdvtdauNWO4IRLiZIca9tP8hxiMshsu1NqvUlKk3RcbeFTIBRpnThx8eOeN8TB8+Pl7Ol9ttWZetrrUvrVfV5kx7xUGKlPg4l+NcDnNxt9r0clt6NyYmomma5kM5PBwoST4d+JBBOJX0+uWsXVPKpaRpSkwUEW5o+yp8d67bsT4kRPR3fEAz//z1XMo0u2CZlKnWvm2V1p469G7NnHo4BjpRYFgomnTgIAFA9G88puF+GhgRNCJdYp+v78gHekRXQzMIFvTC0d0jvPa+KKZucud6jrM2WjJEw32vPsp9NO21R+C3Yz4gQoJgDKYQgswBEnPCnrlm1kQmCIzgOELoJTEnSFOejofjw+l4fOB8DCxwXZdb9+iISuwMCL7rKWGknPsdXt9l7vTGFEUkRL+ThCMiAmMftM3NfOwqRg80EOX31Af4Q2lnkVwmD2LOEcjEuWSRQZOJUuzhUX/8YE8nlxTMyJRYplxOzM39VpI+HDSBTxnwGjqygRxcBy8qAmMsUu6mC/s3ds8LpgiGEEIhSsTEuKeVAQ4GsrCpJFEzdd/9mCAAfIgx3878tnVGykAsSOTCkBIyow06GCdkTgRBoIwv7L+h/qLtpeuE/Ej8MdGPOaVZHuYkwkR4onjmiAwbEgs9opV1PUZ6mB/mPz9MH3/68Xr9/PKyXK99WbWruW9uHfRTpD9HzsgG+Dm2ZJVZkOAlag8mQwuicpgePzxConzg37+C2r20976uw6EcukGv18vt9Xy1rVG33A1rt7W1pfWmje2Xqv9taf+81NbbQ6p/zfP/UUIweM89invOw2CNxgqwRqwBK8PK7CV7KZCyiWwW16q/xe2lrxXdEJApELuZumsERAiBwOB9oAhNRUpCIQuvYe/DN+PfPr+AdgBLTIdUhDDCGWgWKczDShEhhCgNIvWYCwCRge5tIAUgoACIcVHfwH+9nP/77793hOPhqIHtMSYRInRXB4SR/xZxrfp1WS/Lcl22rbbu6MgW/vW2XZdqDhYwlTTXnG5LTlwkZxZywICayluYBxHlVAgNHHMykW4Rt9YvtV1r37pVdd3B7b2q764Qd6L9/SffGAx3otwd+N//FSHRoUw/Pj/87dPzv/z0/F///Pz3Pz0+nvLxADm2vpxvX9O2dg03N3AD7V5rW29Lb4v1Tataw1BBE3Km7wQLXa317kMZhvvk8I5VigNGh7H5ICIa+rGdPTByNcdYtF8ub+pQADOzuxHq3XWjIJH2Dhg5i+QkOZlpr9tINwGAruo+HLhIhCOAx8px7EWJWWS42b9/rpraoEqstd2WtdfuHqKa3Yv33OpoQWBrpC5q3A1GEooPGkAAOALGHlfgo8sJxzErjeXU0IZgArsrYM1UB68FEKYZpiM+zTIdEZ3zFGZJzkyyi5Huzd1elr6viADAzEiMgdZ9tW3QOlloWVdBNfFgn4+5zE9Pz4e6/bCudV3qsmzXl/Pr59eXL+fXl6ubb3W93MKhQThiDPAmImrTQFxrH3IyJwIWYElTZmY5TvkwPz4/XD5dtXdmSllykoAh9whT2Na6Lps2jz7EEwHuA9N5exWq9utvn8t0eDDKJ6NS1nW7XS689kkRHTGgqVE1KSxZiF2QZpDZeWAPgB6Ee6cVMNjXBMCOfFfm7PsQQIjBvwSGKBgHtCW0dt+ULt5PWxMDHOjCblMAjjBApUCIfaMHTXXrFulb0zic/cy6aWNt4I2iC9mhcGiyLfnG3oiUAlkg5YQ5OYuTJCpZ5pKPs6QSkbZ1cW8QnchH/vG+WtDwRtH3B2zsuegesBz3+wLv8h/wCHSACNtj90yNAM3MzAhx+CrBuwblj6WdiDmVmVKeRh/BKUtKhSSnhClRSVAKTAWTABMgqXAjWhEb0UbUEqsn94jSokhkDiFQHo6gA8zYnWU9dk3TgLEhMEAAM0KGEHAMC0eH8EE53XnCd5vDO3Xfv9+H7l+9dUuC04gOHdinEUUqCZQ3j+RemHOWdCph6039q+rv1gjoC9Jr4hsAUMkEE0AySB4HAGecHQjj2Du+nPEox/nj8ePz8+PD33t/+fqyfnlpX1/7smlt17oubXt2+gElkRjSE+UX2oQ5CH+G5YWcFGpVdSSZjg8i+UB8BthLe1dbt8rIiWEQjq3bbdmWy6Vfr3JdDqoL1/N5+frbOWb915frvy/1V3UNfFQntR9Vi7lGtIirx+qxmi8ON6AFYQVcATaATmxJZJrkMEPOhnSr/dLtSriKeGaSPB8mYeqt1VqXdXNzERoSWBbKReZDPh7LPJeUc8rveVtxaTW0E3oyUo+udr5dEfChTIcyzTmbad0qM5VcItzU9qLCSISDpzbQe2bOpTzUGsLn1lf3c60vtQPJeWmZBQDVR0kdfDy4tfqyLOd1vS3VzRDuZpmmYaqOhpSHs4q87Y+JACng4S9/kbLrKrvZeV27em16XvtltddFz4teNlurdXU1v5fyO8GIYKwK/Y3AArumeZ+Xd971Xb16R+kQMKV0mKfH4/x0nE6ZZzTpGyzWJa9OrRrkSzVVN8bdBbeuy+16/nJ+eb1dbtvatNk489/ztnR324P9b70LDHYpMu42JTusScjMkngv7Xdq9L4tAULEcSeNDZJ7wF0JRyRArBbogcwsnEoiRtWuphExWJJDNx4ORDtHNRxcgNEBUHV444w/+g9HPRzCIFr4Fr6Z9W7kIeZSN8kiSZiYmnKz1CypJTdBf0v0w3vM5/ALIQwe/PygoN1he5R+GJthBwAYVxW5c++wbbbcYDpEzkA43JmYOInkJKN9I49994Tvno23Wxdx1PtWu0cFxJyFkLZWGTtMyMw5C3IusxyO5UGP2rw3Xa4fLj9cfv/16y8//34539Z1W9uqtqUkJafW+7o1c996X+q21m4QCt7DOphnikwinEt6xGMp6enxYG7Dq4eZ3KN3NQu32JZyu0lfuzYdmpphQvheR+3h19vSzTFNM0smar0vrce2bi2UxDghaJhCKHpn5kLyLBjAADzKdvjwwrKoGsjogYQURBZswQ4cAQFmIIFELJSYJYj7WA4ioFmvbmr7nwsYgAQYwEESJAFjN4CliBbAFf8gglFr5pUgAmKGAOHcjxzTsZRMheOQsU3U12RaHR0KI5EGgoKv2lOr0rYJhYgc3cnH9pdpvEzEAO+o4NqoxU4NfXus463Ndr/LqBF2hQbsnfQIAR30UhsutLuo5v0L+a6040hjzEIshEg4MjkEKKccSSqRIDDu0sBArkQX9zVAEVeA5mERTkM4NsjPAIjDiuVewgNNwwyIYuSJj5EdgFkyRQYn72FogbtgOXYOVow+ZaQGDzrB7kz7vbWImXn4uF58MLisM8PxNDetq5mosciUU3o4Ul+2DRfyM5hax4AX4Is4+zS5l+4nplDIAScAQyCI0pp+fW1dSJ6fHh7/9l//S6S0vV633z7Xn39rr9d2Wy7n8/VyzuoHRxJx4icu1zYTc0cA5/AKGstS16oWNB9OZQKm//b2MsxsWxsiJ4kpzZNwOKxb++3l8vr1JXoTgIeQZ7l9yr9zKb9+/vplWa9IwLgFHs2/9p60s/vN4rP6q/mrxQ3xhrwibYE1ogOQsKR0LOUwT0GkHhfVS2sVAnLK83E+Pnx8fpqK9G27Xi5fPn+ptYoQEyJCyjRNcjiUh8fDPB9E8jR9x9VUCA9PEBa+WX+9Lf/911/d/MPx4fF0PMzzum6fv34l4nk+mFptFRF5lBEmQeCIQTYhopLLD+ePp9OxachUtmVdlls3/OXrdajAdc+n9CFV2rpeWl9q21qniMx7P6nae+8tUIEYMRNNpaSUdCQfRxDA//vDx6d7ad9a//r1tWtszV8X/XJtny/95WZbtab3YEwfnhB47yt23RQGvo3FQ1QKDoADE77f9DvxIMamm4VSEmFEt225vvpVM+uUrUWvhku1XBZt6p5YCKLXti7L+fz65eXz1/P5ti5qffcZ+h4BHoy/Mf3Ee2ZODOYeCjOLMI0v5EFtEGa6+43CQJfHTD8aMFTdextkysIimZAjsFYlpsNxnuYsicz6Vlc3Gy41iBgQ4YEA4fgmi2XzYTlQW6u11db7jjS8v68Cd7APjakRrh5eO1QdFExiYmKyEPPsMQXMiHOiEpRg56YQIsVd0efAhHd2e6A7mO+fFxIgR2A4MDKm4YsD3Jsvt56LAYAghLl2IihF5qmMNQfunrd7B8ffm5aPNsndW2tNFRnT9IDCrXeASCIEQCIsFA4iPOHIJiXr3n5qzx++zMfDz//89ddffl9vt9taJ5+I+bY1NWuqa223rVbV0tvW29K21VpkjswzY8opZy7lAE/HwDE9BYywY9Wx3G21H5fctt7W3ptq021tde3vlboR0VtFhLotMhUpydxreOsdt95TUeYI17CqphFZ5ChZSe4+08QQ4WDNlXvHag6UxhPC1Ix0r+4AQBZ4J3QKpiCpKE5AGKQRqmBGw9I1ggMCUFECIygNZR0zTDPDRPkm1PzdngeadbObWe/WNRQYcj3MNh/mIvM0i59yHHMsl9gW1zZ2YqYGm6uvN0cMZHWcChp4CEIizEy+c4MB0IgAvG+0AfQ92/p+Dj0Qd63STjvYnxCACHpjGDPi2GINeCPuCPj/urQTkXDKInmH/oWZJDAxufq0VP16du19zilJZmEcQh4CJGnbdLueWm2923mx26q1DwreHv4WgW6harWadhe5L+0QzQwgUk5AxrUBMKkiQYTdKaZwJ9yOJKcd8fwGd757HdOcS0nj4tjVBsIfnh/+5T//5XVO/eV62bbXr+sjygnxjHEbw2uEAgLAxQO6zrcVXnhJ8DhJW7QbIwzWJ5jHy1pXPb/4rw8pP8xJptm21m6Xut1qvfW2Lb0u2qErm4eyEa1dt66gqBC/9+0CmnqvW7u+3l6/nPs8EdL7y8vV2raqBQCyRxwO3rsgI6ACbB7hsVW9XZYz/coi19vLsqxNLSD6sv4z4tjbr9Zia7dmL+oXi8VBhTxJiDiJDdCECYRrgC61mdeuy23ZtgbAZTqcPnx4/uHHT59+mA9Tb/3y+kqlrJczozNhSpxzmkp+OB2Px1Mp07BqeP95sAhDJPAsnHN2pKdta61JEodoZqvqao6B4a5um+q4XzTCwncy25iAAHNKr+o/tv7x4fRweJiu223T61bPS11ab+oWI698txjq7tVczcDicS5Ph8NcErNstV7X9evW1todAwNyRATUpmtt5goB+g473br9ctm2astm51t7vfbzrV3X3ocQeW8mAHa0785GxR1H3B/Ntw79zrEav7QTCgbzER3AtW/Lcnl51SlucCMteMpynOdjg4NRduaAPCBvJjPbel+W5eX8+vV8vm7b1u/W7g72/fm4TwY+NgEIsJtcD+x9ZG3lPKR6e6FDAAja/7t39zmMRKqh93UElJxIBInMolmLACKRnMpUWKi1TXtz91GdMPZJ3+67/71XYGYmEWbCurW61d77sAx+/yosfBhjGzgwICMKRr+nLrjDjtkFOSSAAnhiUhQIFkIWHrZtjEA8zL/fNCMQEEE02p3YpS8Eo6khQEQnCcTeu96uq0ddbshEFIwG7qfDQRB8j9/AO4syIkLyhN8rwokppSQpZQgg4pSGrAMxMo3tx86SoT04jYk4MpQiAU4M8yE/Ph5u12vdtiQpp4IBrtZ9BbRcSp6mw2E+HudpKvNxOhwPKSUaNOywABhSfHxbgCEh4TDIGU9IEs45mbp1m6u22qfDdx08EUC49dbrSit7eD4cMdhTM8AbUaB0gAVgQ5WwqympiTigROLk5BFX09tyW1vTJJy5MB+QNSKbzkP+QxiMpqqXRVOijWvffKtCxCzMIHIHZWJP26EACPKQHlQ9AhwwkIEToRC+W+bCaLyIA6N7W/uCK/NlEpEpl3xKfEiF5gztSu1C27Jstbt2baHh0M2AUspNiuaxNBi8QebRRgISAxEAeQcRZ3If6rD7lYC798sAqYiRmIR3dxNTsDB2SsBIwDtuSsBAhu/XVfA/lXak4cfG8rZjYxLAhBhd58sSrcH5qlPinIQ5sTDLblDZKi5Lqq313m+3ermtrXdzDQd3jGFy0qJWu91qqzZNpZSQJIjY1QGtgABlJHEPZA7EN4HFALJ2vNMdx+Cz3/bftStIeHqY5ykTY0AAIBNPOf346QPif/7tMP387z//8z9+/uXLy4Okp3n+Te3iUQ3Md5Mti7g0/4/zclP9vfXnQ5k9Jk8FMBMGo4fetNb1Ypcmy+1we0nzTAH9ttbzuS1b31rbal2rtr4b4CB0C3UbnOSbqzI929G6XV+vn3/9vEyFEN+r+My0beuy1d4NtXl70O6FpeScctq6dlVXa8uyqBGh9s2sDcMZ7esvtbVVEpj2unW7aTQLBSzMB5ZScso5Mw8bGIxoTW+37ba1Zd1qbeF+PD4e5vmHjx9//Ouff/jTn6bjqXvMLy/AsLxMpC0JzSXnnHPKJedSCksa1sffPg7ALAkAEvhc0ul0OhyOJGnbtsG38ggglJRQhHNyQnYfOdJra2tr6uGAwzMnIhLV5gBEn56enw6Hl3lb1r6pXmv9/XJbqvrY3N3VJgHgGIJ0IP5wmP/Lnz49n05TLtd1+/31gl+/bnZmjDnxcZpKymZRa1ML8+/ySDb1ny9tWfv1Vs+Xdr21Wq21PbvnTuIft+K+J8Nd6rKn0kPcDSN24vyuLbs/4GMMHcfbWl/P5y5d4iZtkm2SUymn4+E5xGR+nE7liIcyS06EtLVtxVvv9XK7nm/XpbV+f2/HTfbdARklZmwPhpiTkZGIgIlEJOU8lWnQ5RAgwtTUTHde4zdxLAz2rptHOBOnlEuZOafWem2t1oqIj4+P86GUKZnp7XY17cPknAnCXff8QFNVMxv9hbBI4pJFhOpWt632rvfYqPv7FXt2iw7TD0ZOlIwCYlR39QFY7OsZQsxIPcgZc/iMKCxF0gi1DYzAUAj1uywJ0AMN0XdKwli0w3BXAKIgNsDW22Z+WW4LEhJJotOcD5OcTsenh+OIwOHh1hB3ZgXJH0u7yDTP8/HAOQVCrXXrazeVwJQ4JRnQxli97qwGdCKgFI/P0zR9eno+/vTTx23dWq3hGAZ1qcttpVchEmTOU358PD08nA6nOZdMPIyF0cNaNzP18DHZIQ1+8jBTcA9nREhMhJL2tLihmT8c378KGIFDYb1ti2NQSseHR5uOtvW2tbU1JzfmFfDmEOo3NSFjciQMkhxkZl9qe+l6jeiMnGViPiE3kbnkWbgwI5ES1dbq71+jViysoOZdSimzCHASFOJRJ+JNgxMUEc1iM4MR2Tt4uWNl/w7VSjkVnsdj060t2w3PXxj58XCieS6SpkMWL+TZu/QGS+vVWjdzBwyi1Ce3A0AwoTAyAREQ7klYwyk/mJKgcAiH7RmP400cfS0QMDg6DOXn2MJDAGBwDLWTEAEnEmYRAQBn4Lvh4f99aR+MVBbZI7nuKtzx792xdQwF67GiBcTIVRIGTmAGtUpr0Dquq9XOo1ANrEE1erdts3Xr26q9WTiZgiRj4QAijhGtga1axPiLdykUwH0MGj8bH9j/tGO/31xEgei6xxq5u6ckz88Ph4M8HA+n0yPm6QZ4u1xfbtefz9dfr+t5a019NH6BoRHn6tXbFnyr+FzySVIGzAAcoRGvQIv7Zg1fXzM45wSOvda6LL01VdOu2kxVv8XJ73xvB3LKPB8Pz58+fPr4YcpirVe3wSd4eyGt23mprTZXrUkqoTq6agyzadyJTBC9NwUAdyOi+XBKKRMSuF7b6trVrHm0QEOAwESIsvOWATB2c6wxxjmCM2JOTJQfHo4fnh5+eDp9OB4OSRhBtVP0Y5F0msglEeWcs4wHbPcc47tk+a1kta7eew9TMwtgkUPJU0rhvmzb63XZWmvmhM5q7js2wszzVEpmdegO16ZL14hgiAAniFn4+TD/9PhIAKv3L7dlVa2DYzk0lxABw1KNH8v06Xj42w/Pf/3w+HQ4lpRfUwqgz1vN1zUzPM354+l4nOdjKaeprLV21fQOclyb/fPrslVd17YuvVbVHsNSHd7YPRCAEMOdBe4JUHea+X61f6vmsU/2FHt4MwHvhw5zAmZ31M393M1CzxYH4Pag6DGxYC4pl5QyQvTew6N1vW31sm7XdWtvPWIE63eAPIYjBhPA7q1DaVdI37fk97hG2knyxIHh35yEENE8/G5px4giKUkSSR5htTdVjxgJtIfTLAnXbRne2pk5iwgiuLkPdufYsO2OxgGmZKKMEABSW1vXddtqq/072djeQ42lK+eSAAAJuVkTpY7YYMi3HAAiHKADbBFiNjeVtSOTazBx9OhL0820efjejo0hwu7Lk3EBjddPzCgMwe681W0xv6gt7kiYc+qPJ3x++Pj8cDoddleRwd7YjwVa4Jf6DsgZfvUlTYc55dJdz5fzbblxQpEkwkwEg7AWEeBuY67ZG0giKJMATiw4t6xdCZlB+qbbUi/n2+22RQQxTXOZD9PhMJcp757CEWoqWFsfK3Qbfjiw71qGeNYigAazDcFpAA+MgCzfRZL4GNp7c4BuxtPEZXIDDVjdl96DMQgbihMYWUDciM6E6L5YE1PV/rXWl95vHorAjQrhEVDLdOAHSROIAMbN9fz68rJWOB7ywzSw4FRKmQ+T5AnotC0UlsYOP/YuOgK62WY2JuNG3JE3g22fBfavzDnnt3BbJOQw07rptti2pFIYXLyLd7Rmvdatbq2aByILC0tOZcrTnKdJze/ZCHj/tNCRlFFGYaUY8vux8xp8ShIExEEBCHSHMNhBanVV7wFOcg/tYULCAEACMIR3GvA/WNbsRh/MQjhILkA40ggC0REdwzx6MzSD2kINETAlKvOQQ2BX6B16BzPYaUKB7tC7b5uva99Wbc3doIWZQVJPeQTxcQCaG2q1sD0EZ5dQ7vPPO9rgG96J/xMgHxFqBmrqFgEB7iIylZOk0/OHDx9/+BPNh4X4//pv//0f/3H5Zdte1rZ1s4AEwz+FAmJV3cws1NwQOSgPBj+CN8dX5CtBNfBa6asCgFv01mutOpxlfEgn3W3wcTGnVBIzURJ8OM0fPn7469//+p/+01+eHk5C4Krq30GOtdt56aDKbtZaF+qBXQcKAEwEPJpv3x1FAjgfHh4/nE4PIlLX29fff97qNuzlgIgjAECYEtPQQpq5mcXQl6CnhDOIEDkUSfnD89PH58cPp8NDYQm3dWnXi93OGXrOPLpJISbmkVe6s1B39se3T6O2rrWBKyEstR7KdDzMcylI1My3fr5tbW2dPYAYAswDAJnllMtcOIA29V/ON70uBD4nPk5ymtIhp8dS4vnpOOWK/ni9qlk4XJbaoJvtOrLEfMj508PpX374+LcPjz8c5+OUmFg9HyabUhGROfHTYf7x8fR8PC1Hvda2rGvtPcu3A7I1/fnromq9m7V7TN+OVt9FrkPutI/nd/nqIF3Dt2sQ9wcX9tX1DrRikhGPhkwwJ5wTJqFgXCA2d+o6tU4RB5aPpcA0UypMPABVNd+63Wq7rPV12brq29nh/s7QCQAheFREZhkFWfbZFXZmOw7tXrgB4bA6G0bWd/gMIEZpD0TcjfJTQuS1tdbVIyTJ49PpcJhFSLXfrmfTPo1ce2aM8Ah0JwD6ZmJA+5ht5h6D0VhrW5Zt22pr7T1pABFJUiAxWEqDKMSSrGdNXWvtzNS7ckfT8LtBU4dYzS9VCZsb1GyEZBrr1mvVqmY+UJZd9ud35AN2EgQioeQkSdgliNbWblu91rp1RcRcMlqbMn36+HQ8HFJOKY/4cN7pmYjN4uv/OH876QiIKCIpJUnSN12W5Xa7PX04lVLGtnm4KQ9CsWGA7XMC758wiVCeEiVwl5xKSRMaWfNtaettpyogAieaSil5SFSHNb4lTo3bYMhurWrv5oZEYxyMGEgSRYQFjB5sBO4gfdsvjTYI3MO7moN2NCMND1T1ZWu3rUUmJAEmpIyoQdA4XZi76UvXaLX3em79qrpFKAD1SACHALN2nIgndMIAeNX+2/n886p8PD7+8AQC5p0l5TzNpZxy/rRZCk+4O4jvXtTg6t7cERBJKqUatPRYqvH0jSGfKR0kDZ3LmPkJKNSsbrbeAgwxoK/QN6tr37Zt3bZWAzAl4pxymefDcZ4PZSraWxpUIYTYyS0wnEd4v3KDCBBJ3pV25AHRhI/goYAwhT0OUVUVh2aKAAfz/671/9/t2neaD8CePIPD15sQkDCYVKhmWjE2a9F63G5Wa0RgyXgyLgVzwpzDxTL0m7fWrVn0Dq3ZtsW6WW2uBne56ojQSKlkSUVyYk50X5MMgyy8J4Tsht/71YR3Q9f9lH//KkB7Zwx3R0AmZEkMzALMIMlFyv8jQqby8dPTpz99+Lf/8fP/+OW3y3UdcZgjqlRHChOgO6pBc6i762dAQDU4a1ybbV3NA+ug9caOK/qInxyIz/7sM+J0mD8+PT49zE+nw8fnh59+/PB//svf/+UvPz2e5imLqTU1ou3thTS169oS+IS7rfcY1lOSacpEoJ0xADzMHNENIKd8PD6cHp6YCSEkZWI2VwQU3rNC78yoPSErnJhh/MthKm0BSJxyfnx8eno8TUUYgrS5GbUb9yWsI/hY9OwJH0TExDv1Ct/jjYA4z0dLKUzHFeVAtXnXqhGvt+1W+1p1bQrNtqYQMJhoWfjj0+nvf/owlykC/v23z//j8wtRHEv6y4env/3w/OFUJgE85OMkWOjj0yERPs7z76+Xy21pvY4My5LSwzT96eH08XRkpvN6W3sllpel/XZeLtvqAcySUzlO89PxkHsXBole0eTdalk9lqZuuwH70DoDAQEN4hshIgZAuJvvhpC4g4Jvz8GOD9771XvAE9GwTsMkUASK4DHzIXEWIsLuUX1gVmgp8TRPx9PheBJE8DD1anHZ9Mut/vq6/fx1u257ad+Bovydh3ySsU3jvZyMnfbIJ4l4W3gDgpmiw6ADMtGwrrJd8rQnqonIoMtsrZttHsFMp/kwzNWIYFu3VjcIzyJTSsJkZuAWu38NMFNGYeFk+3MOAIQgwiPdtbVuavFd3wuInMvRzVhMzJKaZVdz7dp7z7m3XFvrvarp3ZfdHCIMYlOjrZn5ujEAqfnadO1abcgXAd50iPEOaBmoBmMBK1gKozAqhIKpqfYWCIjRejMzZi6l5JJT3lWDPOYspuj2jnMxpPWhbmur9XK53q632xUgypSnuTDxWLK7mw7q0tgu0F2/QCJMACxMHmDW1BywMzImSodMIrnm3rppDzfrfRmt3oBliBBxStMsU099k23d1nXbPAIN1Ky3kZKE3W3weJBgmgrN9IeRysfN/AYwRAuFkZ9Va1P1QED0nEiYDTAIgKUTteimVbelbduiWt11t1cFhqjugCavftXl4zQB4q3b12pfqx8KMz8CQwtHa1J9dm29ZaMDgBASCg29GQRSUCYw2RA3BauwGbyudt30sXyrIWEQHfbV+E5ZI0GUCA7l6LCT/aq1qq1pV++ORJwoSyopZRFGQFPUTtpYGw04yAlYAilMwTu4IfgYsUSIRiS091B3iNZXNSW6y1M9zM1UezdEZLt3inRXoQIcMOV39+4fS7vfFXWISHtoMQMQYjB75j7LBras3VV9WXVZ3NWnQhjEgHOKkoARxN2bWQ/r0TZcK2zVtxqmMMzzmSWnlHNOpaScJSUSofttd59+vhXz8e3dR/W3w7b/v+8OPYSZuiECsFASESHh8ZucKJjT33P69On5b3/99J//85//9b/9x3/7t3/8/uX169fz19fLy/ly3ba1NRjsftxJ13UYcUAEYTW4NbvVtrWm5vvCInAHFsfbeEc0iSgQmfjhePzp06e//umHP//44dPHx58+ffjPf/vzT58+CAOEq2rvxvT6Jn4z89qUKCDtqzZAZOEypSNOSUi72p5H4uiB4SnlMk1lmghBcyo5NxFXDQTa2SKxjxAiOe/TtggKjU02joynkW1wOD4cDnMSxjCwCtpZN7YW3iGCgIc9027pmoWzEDIGIr9bwgEcH04wZDTurhpqTb33tmm7rFvVsCBzMNWt9jG3EdE0ZZH048ePPz4+TpKOOZ+yMOFxLn/99OFPzw8HEsEQEkkyP0w/uk0sj9P0P+bycrmsdW2q5jhJeijT8+n0MBW1/uv1ikwkcl7q5/NyqzUQgBhRWFJOomGJceLA75dXEdH1fkQQgPGNv/pGJh81XPs+te9A7hh2cX+ccR9Ux8WKOKi8BEIgDIlhFjwkeihyLMJMgajduzogZkZKqUzT4XA4HGY009Z6xNLtddPPl/rzy/rz13VtVc1iBxIwHXq8I+2lJJ6TyKCxpnv4RAyYbZT2PZjD7c7eIhoU5ggzi4CU0lRymaaUsqrV2pZ1671NczlM5cOHh8PhGOHrut0u197a4TDNJefE4d60hRndIThiSshpd+x5w+ACIEarrKpu/oephIincvJh4DH+52Fjsulacq25tVp7boPU3buqmpqFWXOPFl1dECNQLRbVzWwz152a+2Y18B5yBkJkppnABCEYgAZXeyzGBvlgBLISk+SUc5Ysgxk4bj0k4vh+FAGwiKaqt9vnz5/P51dAP5ymMuVcxmYNiSjMrblqN3cg2LM/EZMgpDREDBQe1qv12juxCIlQ5iLIxMJasbfaamuta1cPEOaU8zTPI54wSpRUhBM4ttbDw5vWpQ2aQ+196x0wWDh8vP/fdfCOI/4RcCjfe9cWHiOCz8LDNRCcADKQIzkDIXfEGr5pr9tW17X7m/MwIoZBNHBrpra9rPKUMxLVgC24Ug6JxzmFwNbM1dG19lDVGdMJk9AwEgf0oFDk4MxosgJuzW8328i/3vS66Okx3hZvrq7huBtDj/BhzsgJI6ExWISDa1h37a4KO9MDE9Ju2QWA1r2uUW/UFmwL9dXdnShMjNhUTTuEEezOmsw4kqW7AQJa+L20I+Lu2Tzyh1QNAnZbZuJxjQAAYFCeTu94jf/T1D7Eo99mC0IkhAQoAbN6u9aq1a9XfT3r5RbrGqawVtua3Va4LjDlyAzb6uer39a4Vagdm6IaeCCLJGRJI/EySXoTzAreXdvxDZ+Dd35kOMhR+IdU2j+cPQC4I4QJ4Y1QOlqCYQULhJCTMBN+ep4LPx/nv/744fOX1y9fzy+vly8v559/+/zr5y9fz9frWh1ss3aprOAyTJGHzVnbj4mZfbtzYl+r7u/+yPZjGVQ+YDKAqnZdm5xvxDScnI9zmjILUyKEd/OuMM8lJTCiGCHXyOxIE4UzClPdegUb4wxCYKiHb+uVGVKWiF6K2FzGOv0tB3iey/E4H6ZSSiIEAKedgHm3FUNk5iQiSAwxfJLD0LWDK0UMGHAwdQEROfE0y6GkU4oIq/qdDmM0OGMtFAAph7mZd+2smVJOZXo49q211nrrvfVWh6I27Hy7/vP3LxkpPT08HSfEj8w8l/LD08PpMFnrm1nmJDkdD3MOn6fbYco/PJxOOY2K6ohCXEgw0Dyuy/blukzT9PQ4P55SKRPlG54XhLis6y8vr7W3rW21bQIh3wdgwM4BxviGTsNOeht7Id+JZT60uwY70fPOPtth7fEbdjv5wW+954UnnBPNiQ5Ch5KmLEhkAWx9p5hEwB4Fad4tTGvT29rOS329ba9LvSx9qbpfxvsp/o7nDwAiEjntlWZ/gbsoa1wi96l9r1gDrdon9ggRkZSmMpU8ecS21WXd6laRaD7MT0+nx4fjXBJ4v12XZdlcPXGack7Ceyykabj73Q50mI0N5jmMMN99HxA+2PPqcJfRvvssmPPpXc7U+ApVVe7CWaSVPPXex1PVW++ttda0d3Azj81tmHeqx2be3NXj/6a0vz3IIyLKgnoDDAtvPZl5H6GB9/dx/7bdAYKYUhIe2MZQHCABdnw3tudSkIqatVpfzq/rcnv+cDqdDpJ4mC8Mz2sKSVRI2O/O4rvpEDihsSRmSVSco1lT62rd0EGQGJmRS0pCpeQ+9VZ73Wqr3cza1k29rjWlkbwLGCSUg1HNMHo4hiMChqE1V9OAbj3aZjlNh0O+33zolGJQEmHslomQwCEgGFCQbO9izB2CMRyN1Sx6793Mx16CgQB2B3gcYsMw8NUNW29dAbEDQZ7pIJCzJVG0m5ubkoGCB8VJ5EzoZqt1cZSIGT3IO0f1eKn96+viyBXx5fNVt/7nn7591l0b9P6WUZxEUkoUht7BOkYanS6TCKXC6ZBG68STpASIXfvturZmCPXy2l5/t+urLksP70RGoiQ9QlUZYEp596sZyxHthIZODuMSsRjsfh/C7+FEF+4RaneW7rhaIgBm6PC/Ke0D8RlkvPsiEQE5EC1mNe21rUtczvV6bdc1WgXrAGHXxc/XeLnAXKJwtGa31bcNto66c/1G8FxOKZdSUsopCbEMLcDdSf7b10APdk7ovbojfKNo7f8B/MGEBwBwmqappHFCh57lOyLywKUxHg7TcUofTvPffvz4+np5PV+v1/Xl9fKv//0//n//9h//9s9f//n567W2Zv3aoIcWliRCzE219d57097V7O3be48e0Juhh/AYewxgae3L5dbNX6+XL+fL2vSybJ8+HD88Hk7zJMLvL5Sc+OFQwLq4EzOScEqZ2ZjeArRa744WRAGOHuG2LmfEPseMEWUS8BkBwgPHByA8z+V4mA9zKTnFHvU12jrcm24AosHsBHAHs4ErmyqEM8IwkhURJAEkSoWmk5ym/CSm3XABe0/9HpuRYKI0QKTACFC3yftB9dFM1Xu32uq2bUvdlm29bVvt7bqs//HLbxlBMFKiD49HkVxSKTlb4LV37XrMJADADA7dPSIOJT/N09M8pywxFIse5+v625fzWvt57SDlo6QPp+OUJE+vBp9fr8vr7da1fzlfaq/h+vE0P87z+0mRCIUHfXMgr7AzPfxuvm8jwjP20c8R7v8NfmsBYI9xIuChpWbMiCXBJHjIeMg0yUgUTDlJAKIHdxNUwGAEigCzAfK4at3addler9vrbbssba29vWHKb5K67w+ICEOSexZLxGCOjaXCrvkaLvO4S1TijU8PxFhKnqZ5KhNzul6X2+12uy1d7fHp8eHx4cPz4+k4mfZ1Wa6vr9vWUipTKVkyoW+9997c/U3vh/s989ZC7I/M7ktlNqw49kSsdx8HIuV8uEfSvVX3IFKmId5JA4jvXXvrvdbWqmxbq1vv3Xofk6SDa8QuL35zNL2/ed+RRvZ/elcdW+rKFQBHozXUEQFg7l219lZrSzmnSIlGoE4eIWz2/dRepoI83bbtfL3cbjezNs/l4fHAo7g6hgOFCKcysEeKZr22Td1iJPeYBQcjIyMKjG68mzqoAQfJMCWgnPa9Ydd12dZl3Zatba0udY0NADilXDIEDO/84XM4jHYBCYPAUKu33nu1bdHnZwbIbx9IcI6gtzXoHl9kgfcVB45E2zB1B6YQUuxAZs3AgpCT5DsQBohAEIHRGSDctG2m3Q0CFCkLHFLCnIO5u21mbkYGhoBAN4QzY23Ka2WLHPCUQMBu3q+mX1b7RX1rUQPaeWH7LnVIezddxiySJKHnIERXsA7WIRyJaTf7KYcyqbu6kUhKOTGDarvdAq7Vut7O+vpFl5ttWwNoRJ1FSQzRHITokCeDYZAXAe4OoU5AY1kx7pj49rVv1D1c1SN855vdPTN6+o5S811pH5nDwwQG7txeJ0CsCIigplC3tG65du12r2eE7uSO0cMAtuZC4YaqYIHIVBIRU9rxv5xS5pRYEhIjcXxrIL6VdoD72X/bF96P27c97vj89+Hk/Zg4LikaxI5BA7nPArv11uDqjEBQIT5MRZAeDrN227b64/PjX//06V//8c9//cfP//3nX3/58nWt/bLVhQYHUtR9u+ts7ygD7u/822MSEe5qCm2Ajryu2ytxbXq5rcJYsvx+Xv79l88fHg8fHubH0zyVXKu8lZO55I9PD6027R3ybGmWnEWoJA0mt6jVhvT6bYtCCEyRBKcpCaKSZYRJ2COAeexlUk45CY6YcriHUo0/AgZtG2iHfBhJgCWQzbuHD5MAcCLhPM8k2YExH7A8OFJt3U19hHG9uxCv20YBh5IpIcdY4yITpRBj8pEt66E2q2rTXntf67bVTc0i4reXS6ttnlIuKaXCkhDA3K7b5u5P8+GH5XGJ0LD/6+ffvr5eDin9cDwep3yYsg/mhke413ZYel/NhKhtNT0+/Pj8FEC9q/V2uV56b1dEdWWELInZDvENqSPCkgXurLn95ndwHXakI195mDa985ogGHLwkcUAw/LyrolKhIkoM0xCx4yniR9mPmSZUhJJzKIGVS2pJiPJ+ThPx8M0TSUlISID7O63rZ5vt8uyrHU170wuCRElEIYbhPB3p2NYyr2N7LGvtMZNMpCdXf9114c7MaXEKVFKPD6/Zd1au2619daneX6eytPT08PpmBi3Zb3dbuu6mFlKUkpKicyG/l/38/jtpGMMJOSNezjYWDHssk2ID2Wqufdu75UXiCSc3c2GV+F+PQTx+IYjYRAhO/Mw4iq59ClPpdZSt621OjB6MCNzVgcPGrafsfN53qr8fj7uo8LoidxcI+DuNz9ehXu02i6X66+//kYAp9PxeDoeT8exQCml5Jxd3/GYAYjFla635fX1DBjH0+FwnHJJgGGqiEiBQTECPlgICLg39wDrAbGr7e+blJIKIjBgw25mCYSDKJAQ7zYogEx5ysx8mA/atNW2LOv5fLnebuZu6mrDtAmaam01IJDI1DGYgDEsDKzb94IFhJQDCSxwJyOPXV0QIhsBOfpOo7kbkEKYo4d4ILBR+B7INKrKmNoHQhbCKMjkI5SQmfMeHgljipbR2CCSsrScVpHrbWm3W7stVOuHTBPHl9p+Mfucy3mCTpMhuiHFdzCwaY9tJWLmRBFOhJAR0M21q6mN7VvKeZ5nc5eSgzCXiUQAaHC4vTevq9eNTAWCiRiQmYY9OwP6ngPOGjaq+47tMSITICQSdVM39SFDCCQgIEKnQMA+FK2j9g8C0B8MjL8v7e5DXWpm4YAAPrLvUAEB3FStVeqdzXksWJMQIoRTBAIMI7Boe7oTCWMmSsI5cUqSJElKJAlJANmRAugObAbAHtm9l5gBHbyr6/D9tu0+DY1rCf7wFbCHVEYE4oiTGtGAsY9We5qeR4CIJJGH44EAwuPT89Nf/vTpx08fP354mqZMCD///vWltta6AxJzAHRViwjc5Yrju951tDvoET40f+6IZCS4ruqR1sosCEFIP385H+bycCgPx/J0mg/T9OPf/09JO6oylfz8+HBbt2VrkecuB0qSMiXvgdibIrUBcQ24khCFIDGVxHNJQqiRM4Jn2WMpARCDBjQevjcF8e2NpXtoJd9XCSSJOCNRmA0azJA/S0rleORycEzOB5Nj92qXG/gG3hEJ3vlAXJYN99YBSZCG3wKBAAFTDIL+uEZxj8lqvW+1Xm638+221O12u00llamIpEDaWltqXXsHhKfD8dPl9tK6uf9//vFr3da/fXj+dDqWJHPJw0vMPXrJp4M/dl3VWu+1VvA4lunjg9+27ev5/DvhLgaPQKbmsdl3Jod7ad+hozu1wmL4xo9SNFxUcGSd4z1UdKTojFxK+FYemCNxJI7COAkcEj1M/HxIhylPOSOnAK4a1jT3PoXPx8PHD49PTw8PD8dpmiQlVVOPW90uy21Zb71vTDYXnDKPmFp1V4ucvjOaGamUIrKv8b7pMt5hZvcOeDALhGWayjTlUqT1vq7r5bZcLysi5pyfnh4/PD+fjnMSXm632/V6uZx7a/NhHl0IIujIhdyLwbsOPvZzeT+032ACM3M1YT7O87b11r5LgUJEFkGje+JnjOy5iHA2QB5QobuzSNpN7iy1kmqRLUvdemu9d+2d1XhoJnXYXI5ZaG/h4j48DQXBGxSD+505SGb3CSqitXa5XH79RXqtx+N8Op0enx4fHx5OD6fj8XA4HNS/Q/s9oKtdrtfL5XI6TQ8PhzJlZrTwsCDiIBhGJjkVEQbGCKzURzcxju+41RInFmJCQU7YOuoAycZlNBzLx3svWUopghwabWtMr7frrW7b9bbslLdAJLYINQV6o8mKs7uMW5m+H6gQpAAI0DB4wD3scKQ4W6ARjZxDGJkWCCN4zEOChFgZNWgstQhxSLRGXhgGCBDTmFGQiYkLBL4JH4gYmIQpkJy557Sw3Nxe19v1y5d+uf6Q6ZDorPZC9HLCVSY3RBbCxH+wmnUfoRlETuHjqA4ag6qrWZgAoojM0xwIopmSzIcjEPWudau1r9rVa8XWKIwQmUWQnAWFmccTuyseuquFDS+5t4iYwRrvpluvEOrDTB8wCNy9jfTAGBTq2N1YLb7PCv5jqCtEhGnvdXs3KSONlJkhSjFFgMSCuQizZ9vhtZ2zuS/QeO8/7gwjHjt/Gv46w+2fgHZwIODbgXnD2yP+UNf3w39/kADuMuH4I+Y4av5wOHGPeyJvxLCy3P+uITXY24OdvK1mvbPQPKUfPz66/yWL/PD09G///O0fv/7+69eXl+uy1dZVbWcTBxEmYWYCIrMRvPH2Hu83J2JEQG3V3BsnZkEAImqmtfV1reerfHm5TiU///m/yt3JTZjnki1QMSlOC0wIxAgiAGBJmBn2UxsIOBxfqAgJevTNEN06DEr8sCkYbtgYAH6vpfsMOvh+SAQBSJhSSXlKuUjKKU9IuEPM47BBpDIdnj6Ux48gx035vNh6WbfrNfoNQY+Tn54+vL0F12UdOImaWcpTSkk4MQmPS4MA0XfQZudyZuYpyZTzw+FQa+29RkSAN7XW23VZr1u99a7uX8/LL1/P//H7V0K4XK4PU5nL4eH0OB9PuaRuXd0NvINubi18iDg18LW2f54vrXeH+PDw8H/8lcZjYWYRMcRK/K6WEGJK94VvQESounbVbtrNdY83wAjEYIbBYkpC7zLoaV+1067+Z8JEmJiOhY4TnSY5zTIXyUkMsI2uDSFPOR+mH3/66S9//du//O1f/vzTXx6enkVytNZd121Zt6v7lpM/P2Q74JQJMbp5V+9qp+k7v623+k1E7j7Gd9rXLwO5M3dHBGIqJZUyWFbFzW/Xer5cL5eruSPz0+Pj8/PT4+l0mIurXi+X15eX2+1GhPM8H4+HnFOE30OF7A1fhG99POyr/fta6H74w7r21jAipTTlXEvnd5GCiJg4OfJQ2/gwTvPo3InI3SLuq0mH4WwfEamUMs3lcOi99tZVm3bdf7TeWx9L6nGEcQeueNyAe/u73zB7H2Lmw891Z68DmHut7fX1rL3fLuV6vN4u18t9dp+mCUnCv72Qy+W6nuN2W8ws5zTPk7mttQ6ua5JUcsk5p5SYOAKsqXYNi0Ei3/VRHuGBAsICNFpy6aqqI7DXdnQEYO8r6I4zUojQ4TB9/OE553RblmVZl3Vr3VSjaTcDCGDCJCMUnpOO5TLn8obGAwCijPCk3Tk50A1txIeABVqMyCVAMNx1zezIAUwBxMgOabcZon3YCxwRUbtnOSEFAgJxAHWPZla1K3h339MiiUCkMS2EF4gXs/PIAneYGm4Aa8pOKaXJiREpp1zuLPTx9XA8zkcZZRaIWJLkjJJCslFqjqThDgoYzIE0VLbIHQBrrdu6bcui2xJ1497FfFj9ESfhhCRODPtcEA6OiLb3bqPjJpJdl9hVEZCxjy3p4Oi6eyNM6OoRgOqhYOgQGH/goP1R144IbtZb+8ZWwxhh6rBbWwUjoiRhjp1u4kSw20Pu5ANmBBkT7dh1Inqg+R7dN7w0Yzjd75jXHxbmsSPviONMjvP+jn1yX4Xh/1Ta7z087JzVuN8nHu44HC73TmO3fUEAcHMzDW1mGk5CT4/HnNKHp8e//vSnv/zp13/9x8//3//4x3/88tuXr6+3de19aKBwd/xIgkxdh2MuvO1G7i/O98bZVFiZZdgH7N9ouLv3bnV4Gt2/mDBnyQESUj33SAJYMJgiCYuMPHnY16U7lYGTMENY2wzAzTGGfyjS3ensjrzvXMnBcbj3XwyAxJxyydOUypzyzLkAgquC2Pg9TlDm+fD0cf74E5anuHVfvmybLq+r1iu44qPA07cPZNn2q0/VtNih+JTSlKQAJQEZ2rH927irq4lA5FCKn45NtXWtrW61LuvSu0FgOJjG2vrZ1oCz/P65ME/Mp1xSypyLIq0eTW38eN3q13U9r/U2InGRPl/XRp9HtsrT8fjh+CCJibGrddXWVc2Evx8Th0Zw9zwOVeu9a3dVd4OdzIjADJwwJ8qZcmImgPA9j3mQKwcFGAIBBDEJHjIfJzlOcpxyKVlENoUwH1ZypeQyzz/99Ke///0//fmnPz9/+DilKSzUrfW61VurN4p2yCBPE0Iqidxta711ax2P+bvS/m44H4ATvlXZ2BfcZmbMlDKXIsdTyakgynVbz6/Ly+v1elumw/T0cPjw8eOPnz7mJOB2uV1eX15eX19760+PD8fjcZ4LM7ZW3TVC3WNv3e4P+bvx4c4FeneKzaz3PoLOiVn4exM3QGZBctytOvds+UFGH/iJxe74cAcLwCMsfDI101HSbXzevffW2lpbrX2toyLicOfkvUHje17O/b0Cjxgtwdbq1rqamQdBuMe2ba5a13VdlvV6u8zzPE/TNOWSJc/lh7+/cQIvl/P5q7ZamWiaSinZ3GptpUwpDYveaSpTkkSA6tpb16ZhDoAUO8FnjIEEyMwEiJyUhXvfYmvWBsET8Y4tvCEMoeCA6KWkjx+ejod529ptuZ3P12Wt69bWbTz/MSJqck4pOHsiJkmc87tqgoicAwx5N1x0NBswIRBSAPlwSBl1exj0UYA4MgWyB4CHo5m7Y9ztMQIxGC0YcKTsDCtAQ9KI2nWrTcFrUwJ0AiZmwAoAYRf3S/gVoAGqe/JBSJY8zelwCClIlDnyt7YNAOB4OHwox2FxYxGBlEumJJCyS2pBaBEABhQsjlS7uXcPdIdt27Z1q+uqdY2+ibc8DJN2AV1CEAyG3ScmkBwcaF9HIyEzMqEgoocHQsaEBOOzo2Ea6p4REoZ6GFBXa4Fh7mTfQXN/DHVlGj5RsKu37h7ssLvcIQRwwB6huu+chmndPuUw8lvkCwYA7pcTIPi9kO9PIeyL8Hd8K/hGREN8Y2K9AXf4FoQDiLiHyQGM6M/3L2T3cNl9qePOJNrfH3oj5sJOwTVX6117772b9ogASYecjw/4wePHn/qPP336T3//y3/951/+/Z+//OPnX3/+7cvvX18u12WtzSP47YIkIhphEN+6jTvkCIiB97xCIRHCwjEnfDrNjw+nx4fjYZ7KO/f1AdgMnMXcW7g5O1BEgBsisKAIsVDoWF8NUITGaiDCrRtAMBIIITANXyQmHMEuOJKoYB/Zd78iYcl5msp8OB6fcpnVvfXGBD6KGxPJ4fD44XB6TPPJuYTXtl2sLYjohtvaS34f6gpm3ppaRDNr3dbWp5wOOR+nPOdUEoyCR8OBcyQW770RBUBJ4hO4z2rWW9taW9d2WdYvt9vX6+2yLmurEc6EuWQn+Hy9+D/jf3z5HSBUtburwXndvlxul2Vb1k7ERfJrrenycprK4zx9mI5P08xCyGh+Z6JGXEjeUVP2WCLTMHPr1pv2PlDcfblOiMwwcutHWGJiTIwjJELw7u2D48pzhhhT+1zkOKV5yrmUlBOyEDiagsWIOUmDvmyuW12vN+Xqqpfzy+161nrjqLMEzOyRCSMxdjNGZOgEmOSPopJRXxFxHOWI+LaJixDheZ5SlpITCarqutS69XVp29aZ0w8ff3h4Oj09P5xOB4S4Xs632+12OW/rysLzNB2Ph5xSuHdzvdN3BqN7R5C/7drfFXgaREMMdwMFRHWvW6urXm/rsm3m77bUOyj8NkVT0EjXIGaOfZQdvP5h6HRvt8c4Mn7ZLGJ83mpde611Xbfbsq1L3VZVDQ+C4IhENDaKIomZkCgA3KP3VltLW0qpDtf+gT3hYJxE9NauZttWb9dBtcrlcPzbx78h7v3W7bYst15KOhzK8TClJI4+GNoiUlKZylxKIeIwH46O7o6BfDdTAERhTsgMRAHDrxxYIMBER54WRDj4WHqNBREj7oBGBGGkxIRFhFOinGVb21b7utZl2WpvaooESM6IjDx80Oj7YjKK1c7WGSlhCLtRLzgiYoz5Ymw5cL+CEYECnBkiRzgzuL/bXiE5oQP5HgkRO0EZgMB6X16vCl57Q6TEwsXFHT0q4qK9EUbOdDgERCAxixyPx6fn6cNHmWaI6LczWP9un0uEiXfuFwASpSychFIKSQoI/3/2/qTHkiRbE8TOJKKq914zN/eIyHzvZXEAmuwmugGCXHDLNVckuCMB/pf6X9xxxSWBXhFNoqpYb8iMCHc3szvoIHIGLkT0mllkFlFF1qaJ0PSIyAhzM7+qKiJn+oYARAZBAsBaPWDbipq5e9nKtm3rutayWS3smtGSCIto41WZWhhSMxsiJAqkZnvmXS+xxzvvA3RzcMQgQpAdJJMosah7Nd9AQd2JnIg/BsHfmLqSCLceT7tbAuyKHLu/Qeue017xEhIzMDcjmdZiBQpvmFfb51OxN4Mwdihs9H7Ljpe7n+b3rduxafeN/L5qbxlo4M49/3hX1vdtd9B5yxE6YZKaEXSEmZvWWmqp21a2TU3dQ5KknPKQxyGJpAD48sPnP/zhh7/76fPf//Tl3zw9PB6nLPQLIiGWqt6yht7Sbs3l/qje3U3HjhA4gwlSJhgED5k/n8Y/fHn86YfPD6cTvDdTgtZJa0KZDSbRWJqBEd0eQIi5YW4xWpOrKwP3wwvCA5F6+tPLEBbZadjRm3M7tEkkSWvCjsPxeBzyYV5nqxsBMCEEMdMwTuPxlMejpKEAu5uWm+vGRNHkaOqHsU80rlZYNTePYr5pFwXa+U9AgdTGwC2f9E4natLH1LKSBgcxK5te5/Xw8pJTYiGeqWphwjxmIHyZb7dtaQxjc7cAD7yu5WVe57VuxcY0HIeIxQLrD49HIfwyHqeUWTAoHHjHyeGymfo9RWsN2Gh4fq3N2jK8T5SwAX+EKWfJo6SEwpgIEuPIlJlTJwtCw7cRYEJPhEloGnga0pBzSpklATO6AlkzJmsdFYzwWsu6LiQFSWu9Xi7rfPW6cujAQbkJgAMjbrvmQTgIfaza33ZbtKS0lFJrrbW2/5JSmqZpHIeUxbyu63K9Xl9fbqUYBD88fHp6+vz05eHx05EZ1fR6u37//n2Z5zB7evz08PAwDpmZWqPaTe9yD+4d9bRvWHwTOGpVBCJ1Q7dGDoitlNu8Luu6beUDbqt/CxBR6941JCAienTOvPdFYG7qzZavVa1t+tB8zdrDcHe1Wsq2zHO+iDBCbMuqUZu/iCBmkZyHnLOIUEu0IkpJLAKIDePlTcsvPLSGVWibUHXbtgWpcUqnav/wDh20beu21ePp6eHxNI4DMfXRTbOpTzmlLCkToIZCwN7477o1LWFvUmbd+xjbx8HYnWQamAIdIoK4OccR7YTGdkwxESVkRmbMibZh2LY6D8OQ8+12uy2zYwD4rpq2133vVxT0wN32gyMF9TBPjIANLNmQctE7CB0sBUjQmvDhju57WyQAkILJAX1XXWgzfAwgD7Xttmh4MSWiEJdO6whEXE0NCXPiaEISJJKG4+l4Oj08PgyHY7hfbCub/xY0gL233wBlLExMKAzM1vgafVJDKKsDVK1Vq1ubIDWipdaqHGoYzjAge2D1qKa1OdUySZuIARn0DLipM/QGc0RA0E6UZUYIIGmLgiAlNaeqrqFEgph2HeO3+/jX//pf3//lepuXZd3T6N6o/s37u/dy9y7C/ptagb8/JOzjSNir9P6uoOPu98Xw8Z8f/wX/9u+It49xv8YhT1M332Siv//pCxPe53bvf/heKfSftNNsdlbsG9acuLWpsclyuaqVWpo0x7Ku87KVWutufA2AbSDd380HoAze/9GfUie9d+7kkFLOKackIsHp3qlzLabFPMyhBnmQCCUGBIemTWRWSp8M4s7OEqZd0K9ZCnV5wY8X/dVbvVdOjUJPxJwkE7HthUI7B1t7glOSYUIWByqlrPOstXSKhWqS9HA69icecT5fzbvLRodeIDab0I7G2BmaLeXqeVEP7f2FtPXWhyseal66w6SqWaOCCPUBOe4jyD1bRHOv1gRMgds0BDwgsvCQ0ihpEAHsmeM+DYL1nV3wvC7fz9/bT/QPG7AvS+yvGHYR9pa6QEcKdwHX+8oNgOh6V/0kb3qT3L7TvDNRWwrU1M2GYUzSRGbAI7RqI2tXLebv+DAtuFj/CSz58dOP93etZW6TqXvV6x1L1+DH0B0fm+NquHcdmC7CJ734ZBEGaPlGrbU0rbfUNEj3vLE/o14x78/qfbX+4Zm8deZbI6ENi9sCNI/T01Mep/Y7CTEJ7z+ytfr7enu/sPc/eR+NvduQ8fYf+mnVwXvalnFtTx/3Nd8LVXrbPtGjo9+bEm9XP0zifuDt2x+J5fT05X6u3i7FaqTUhAGJmKKjx4hZEjf/PWqdd3NvLAOI2LF87xLzjujtuzkizHrw3rkH/RG3996q+fuzj/vzelP1bzQFM7M2yOvLGhEAJMHdB8rdr9f5/UEd+6nfLXX6w+5IFdj3M94P+vvme9sgsL/bty/F/tpizwSjKfoBEFIrhXv6XM1Uw7yN/zsGmDkNA+dMzBDRfAjzkO+vIwsm2oME7t9FxJwaFGlfphjR1OXX7ukV90jS/4YRhE0lgqMxKXb+Be6EsB3q7bFPjuE3wQP3UNsLt/7w29prvM92JRmGfHj7xveh/ffr9+v36/fr9+v36/frv+/XXym7/X79fv1+/X79fv1+/X799/n6PbT/fv1+/X79fv1+/X79/9X1AUa3+a3G8n7KgP0fwUhd7r3B7NwBg5kA30CvbQyCARAdMv9ucBZvI6f9f3D/q01d4C4IixFgFstWztd5qzXaSJJ2gyxCZux2Wm3KEkeKx3YXHn4uz9FxOW8eHrCPn7sOx+7HVbbSrKDD47fgvb++8P046T/6S/9xFxL+6e//Pu0g+aAKXA7jcBjHRA37bhCmWtTUQRwT8ACUIsDczWrVUrQ2JQ7TPoTnPivCfQYNXX6hei1WGxzMHAAIISUaB348yfFAQ3Jhi9CIsOCqNG+0Viw1IpC4QdlBq5W11upWwQ0i8Hic/vjHL/ttxbYtAb5PKQkQrHvQdPc4DzMtplWtWnO4b8BdQiIWScLSAHaJKCEyBnVY532++g5X0QZl+3hVEbWZSSMI0oDEO5yhTSTvQ1DoSL42zyJE+r7Usms8YRh5vfsq0o5faHfUPwUEADYW6D43hgY7jQgzI6SUEyJ2ZMc+1aSuBcrtS+bWQAk7cAixwb483kMS3pFL+kbAfQ4KO/LU3dRhfUek/vHT55xyECMA2UY6Y71gVGQKYo2kCrUYBhKJMymjN/5rQHiXKtm2dSsrQCCFeZNzLWYK8A4htvNZoM2kG0yhYUCbmwQChDloRIXOG2zA/DHzqNW1KDEzkUN4OGG+A8sj3Oq6jyf3M+gOydtfDiD0QT1EewUQ0BTdpYMJ3gAB/TN/mHXeN+f+snf9jn687HNsiDfIQrwBcPpY9I2BG4FIw3i4P5ovh2FMHBHmttZtrdta182quzvsAnL3j9WPWiLARDJwGiSPkveB/E5q6n+7b4Y7aq0Pnd/Pku/AMUB4s9iFJrq7wxICILzbhu+P63neLls3FTSr388/d49oaCYUKacU0AgytZTaBJCbvvJuVvtmmtvBBGYQkFJKKeWciVBdmyaXqm9bKZtqsYaBYKJGESLGpsnz3lD6TSulPxSgu2eCh1t0s2KSTz/98a6GdCux1ne4a7xDA367Fvb328+1/Q7i3YuGDhTdsTd9r97BJ3CHed8hUHiHcdzxH/15348sfDv23gIr4pjw8M767UNof9F//F7/fZvw70+fiDBjTFk+fzo+PBwOx4EZVVekmKZMQm7a34wHmKMRWhrTcZpOxIhoBrZP+2M/ExuuCPF+R9CAj4YOCFQNrov9+z9//e9+/X/+/O1ZIUgwZxwGGAc8jDRlZgoMBTcIH+y/GvV/0RdZ6H/38t8qOEtC4AhgQOlG4FZL3bbqCugYirb515+/ff3527YWrY7Qvcugo0qaMlXfUl3kIfZtAfsjbkEG4/5d/19dmJL8n/6P/4fPT0/t3z1d8PTtD3/6u//iT0+fx8NJJMUaej2/Pl9utxWPVb7g8UdPn9ViLettfn6+fPv59fL95fn7y8vtdivrRgTjkKYpTynnzMLoHrXY9ba9Pq/PX+eXb8v1dV1vBdyz4A9fxj/9/fF//j97+B//NP7x8/owzW7nqnXR47fr9G9/Tr9e+OurV+dxyEwU5pfv86//8vL6a7k+x7agKv1X/+X/6H/7v/tft7uIiG/P/+KuOackaZAUiCu45Ok4PebDNIzDVm/L9XZbv90uz5fr5Xq7bdtWrQ45H6bjw+npdPyU8zjkPHJ6FD6hjeAYdlcchIYFb9pEzcXFLQAM6Ir4irQSBMGR0x9kPAClfnoFozMGMxNnpIycRUQk5zwQp//Lv/3+de6HF3sZtm/n8/nyehbmYRzlne1H51CFE9FxmsZxbGDEiCDExGxmS51F5MvjlyS8Fqu1qmotpWoZJB+n4zhKSmlZ12Vdc87jOKqaqTXgey2qVVmkmec2ABF0QE2YKUZ05/WuA4OmvqzlVuEft+l+Kv0v/6f/9Y8//CGGA4KNl5/z679Nz7+Kf6Njrul4tU/nC7z+uqGmKR+243g+pS2zEpqRK3FKyPiXX/7pzz//DFSJrfjtVs7PL79ebi/Q+LV7Ut/+RAsvtRS1YqFKppLT8fH0JIIeS7Vz1Regwokej5+/PPzdHw//w8PxHy7f1/l8ycMwjoOGVq85/STc4Zmm5Xb5S4N4edfYQaYOSRRhSYklAeI8z/OyRLiZzfPi7g8PDw8PD8fjIYlYLU11qIVljAZ26pseEf1umZNSSpJSzllSSkzETB03BeHuVdVKba81ukoaAaIHND8kVTN1kfTHf/Vf3jOw/+bvP/8Png5qetvmf37+yz89//Jy/Zdfr98WLcVqd3/qEdAjAhwFZMT0OT/+4fD5p8cf/3788Wk6Hoc0Mqcms9GS+X7U7j5EtBOn330FiJEJmNsvpATEQBLMTp0dBeHoHlqiFtDqtahqNf2//rs//7f/8q3dxVpu/7f/+//5tmzXWyGSw3j48unTD09PATSv9devL79+/V7qhuDjkA7TcBinMacmLuBq4cGMbn673sLh6fHph8+ff/rpxzTKrVwKKCQ6X+e//Mv37z+fz7/OtikHHA7p4XE8nIZxSqXUZSlhiE6tbiMIQpCUJAkKUMI0siSEAK9WrsU3SJDG4fi/+t/87/PQUdj/8mr/+GIQ0bCrSNhI1B3JCIGAIruhKiJjcydPROgeuxiDuVv7CSmllJOIcGNMWoOFqkcDZ9e+urjxKoWIWvT29jtVGzG10UMaThICmm6DmZp1/dN/9Zn/iz+8caf/ytTVDRGpCfO3lR5gDmqxVd+K5dGJG4GspcGB0auWVlVQQIMo94QDfc85PuhjvKst7omW31OgMFtLWbdNOzKz4wIjwtRVwZRIsCdsEAQM+uFGAsyDCHsVRghu7o3nDwIQ4bDO6+11vl3nWpp9Je1wzXdIzZ4mv/8Pv43ebzKZ/z/V7Xtitl9JeBiH45gPo+SEDEbRtUzN1dA0DFzDi1rs0ixNhsPMQj202TugAlWk4sFJ0A1KtbXWYgXI8oCHQxO400RwHOk44jCEsAaYukewAzlmoCwpjQc+AWpwSsKITVp1mbVuaynqEFCRPmqkNK7hThBEZE4gLIlYEKkdi2paVLdaayPi9nSU3kqR5t+MwDvgHHZ0bYO030sMAADEIFb3xe0adnWvTJzEiQOiqVrtDBzYW1O9KqOdGXEvx++v515pN/NQQoxGoQ4HAGJKJEkk5yRMLAyAbto+HeLuCWYWLMzc7nrH/UZzJyWixthsx0TDviIhejQeYESYWnDfd9jScKLG4g5o3G5sH7VpYX20I4HIHtmQlavJpjwjzhPEiZKQMa1zftHjy8I6HHJajArRWm0GMyBCGQWYhCgAXa2Cq3sQCoCEM0RTDumGkxFWSil1K1o7/z84otUBEIAR5EZVG6snjcPj09MfPj/99Dh93m7P5ufz+fLy+uLoTvHD50/30I6IIhKEhGjQPhyJCO2v0t1DawCYGSK0Ot3UVJVp3+nQ4qW/tU8QpdEDAD2a5kVDzBuoaRVNZposGwv3034/nxCRmdwpYrfGQIrWmLjTBXom8FYGtmCwlPW8XJ5vr8/zeS5LaeHu7RyK99DxACsBV515bU0NcjDAgJwBU9sG0ruP1D0dwz2AHJq6SAT10xECQOC+wcIg7tbr3Iv494sHmnRodD7FfY8DPvDACXikCBAwXefrSxDnAJlyfnp8vM23dZ216gqRiDIjgSeGlp4zoxPkjKYBzcJUC3v3dzV0Bw9qASaQgQBYqFkSW3UrbpuFIbgTBlGQUE4yTnkYB5AACUwAHGZu5iqo1UvVxvl8OzkI71boRAQIWi3Ceh8kGrsSeiYPAFGxYAvcuK+ZiECIloI7grp7VWx5onmYRTQlJuIQcHMPC2uqvJmZRQCAJIiUmbJI++PwnYajmqlpraSq7dO/19eCv9aQb8kvdLZoe4FggUVhWTWlOtQkCXqz0Hp/CYGicUMt7n98dN6oB7h3rD/vuQ52dtK+ZHvVTp2SY6Hzslznuaj6fsAjYripWq1QxZmahAQyAUeG7f3ycwCHsPadTMgIrmFq99CuqvNl+frLt+W21WJNOqFRwuFjhMb73yLup+Q7qZ2eysF/7iuLPB7Gh0M+jjJQsBm5hqt7VasFtVCFujksplHrWupa6tblMi3UopqFV3cLJCSJYHd0g61EqVrdOMHxlCZhnyxq5bAvD/J0osPkktRCiwJEVufqk8HAOU1HiVEMmAURCBxJkhl7LNUWTMZbpOmjFBIxoPdutwhxImLOo0hqDGNzK1W3WtdSS21SYAHRCzBsokjU+LgghNy7JgiwmwMQYgA2PYXG+kcqEWfXl7q+6gYiI04myZtcc4eZdPrZrtHQIjvvfOAPKtnRdV0c2nFcChHJrq3WVNnHIQ85caOxiQBAMb2TBsPdAKqapBCRCN8KQKdNQjvMWkyCbuiwh3boHw0AWgpPQcgEgCytRCN3MwBvPJ99/NSb4B8hNT5ojIUBpZgslc+AlyMiwMCIG72e07fbw9c66HQYUnJeEj+zXmx1oTxkGSDRiGQArrVaFGIkyoQDxBBhANDTDUazWsq8LGv1GuBATUwGmzd7BHqwGtVCLGmA6Xj48tOPf/rx8z+c0ufLS7X49fvL6+XyioIsdJr+Yae4AhHmlDyY2ZQQK4hITrmdLJ211R6gKQHknJg5zEvBNlQkfBfdW53ihszNw4qJzXTbtjaDqS3JZU5Jas2qmfltyUhqhq3ILO1IjF7zo++uea3S8a51/XZp1XXdzsvl6/X5l9evv16+n7d5M/VOXcX3OX9jjLWCz0I3K9W1NEs0hIAjQgCk9ox797bLvzg28iJ6U5HAQHRCCuSdYdV1v9vh6RgRXQUHPvaRY5cmfbsRRvqSDgeQA9eitVq19fo6X1I+jtOnKef0ZSQi1VK3da0lE45MORELOEFEsIQHmJGWADS1UnUTRxJkxApqqEGBApiQHBlJsuScCNCq62q2NscrEAZOmJinUR4fD9Pp4GhKaqQVVEtUiyqwMZRNUy3v3wchsaCICDMStaB2FwnFXabHdtKjapN8BSISlva6kJCJAFiYDMC0153RqMwRBJCTpKYXD7jVqqGBiNLUbYWYICKI0J2hizgiQkuZA0BVSy2lci21mTdmeZ///Y2q3T9YQgUAggEixlZt2cpYWHIaEgBiky8kQgiwCLfW8yckigDT5v8SQCRMSNI+Vp9CIjS9znCLiH3ISYgUQNXs+fL6/PqylrXZHlDvEmAAmkOtzhTMkJoRoeYPwbgLyAAzCBNDhBmEd8sCxfW2XV+v55frOhetHt5S1l7INYmk90lyqxG7kUWTlGvF1o4U2Jmr/1kvd1AFqxgbOIVr+OZWatV1swVsrTVscabwKHVb521d6rLouti2eauSiCJlnEY5nVr1z+5Rqx8Gfjg4R0qRSRGq+7ah1YcTf/ksjycEik2pmgAManxe+PkWz6/1UtwIMRG39U/ClIWGnKdhWuZrLZt9+WF6/zaEmSKYqZHIRUaQjJJJMiE12nSt3a1Da6tV+5SMiAD74ULtV/huCPKbBgpCE29EVsQCcbE4a73UbakrR0opdSXzYAB6Q1u8jelxHz0SfOwCAECt9XY+b1tR1fanJ7PYQ7uklFIax3HMKdygRw5svfqeLjO3AO9mbQ8kSYQ0eG6zzQYEQcQkAoimpqZuTgmpublJL/e9hyIPjS6+tjcP7qt2L/rfJaTtMeGZEDkGcvPyarohDZAEElW9btt3LZXAWCAPKCkQa6m363J24SkmErNYl+Va6wYAwpmEGGIaH2sF61rxXb69qJWitfZxaQS4ea3KoKoGwO6oyqXyQCx0Goen0+mHw+FTpsMwHcfjUV5fPdCL1hpNxXm/C2QhDgxuYkFAxP1A7BIoXYb8LlAV3tw+dtBNOCIxYQgzkxFCDSZmIRZOLIhgZkSldVDUPAK7PHQjlkNvR0XXAweArrO21y3YSh1mImNCN/DftPaWbX2J5dfr1z+ff/n1+v28XjfrGgLvmoeA9xFra8gSePjm27lcPFqwr1v9pMdPD+N0zGMXaoXdP3dH2sCdWd6HDoZB0D6XQYvagA7uwM3MmbtmgPuekLVGx91VCACAkB7yY5aSo851Wba5bGs11bquIZID0zjmfDqe5oCyzl5Nt5IoMTMiOARiEIXktgXdsWw2YzUkMHYPRVRi4xQ5AwAPJEMWIgwP06bb1p3g8sCHkzw8Tg+fDofjMeU819WqV/fqUSyqRXHTMKcIsfdvJKCrThpAK/C3spVSA5EQU0os0opUZA5VV62mrbUmYkyMhBxMSAGg1gTH2iQaARGY+1ge0AOEJaWUh8E8LAICXVW78EmbGGJmvr873EEUJpwT1yo1lYgAxGGo8K7A/a09zH3bQKsAEDGQEM2jqK4llg1T9lYqdDUNiAhQd/DA3cwPohcQRG0WIURCyE2wqDfsAcNBwZvVT5sPAZI6bFpfzq/P55e1rNGDPgEYBCBQeFR1IiOWYWSWEfi9Vy0yYSv5hFEEwcLcwpGR3UE3u77O3355nq9LLe7WMELvJuj9FXf8BHYsXpM03r+Ed+hWh2T9Zy/bXU23zcpqdXEmNwOrarVUXTebQ2ests1OjhFay7wu82Wbr3WZrWxm6gEhiQ4H/vQ4fn6ajlMeskBvfbhrjDIe5CBBZFGX1co65BhHGA5Vo26FzBBxKFW+veq3F/36WudKNHqe4BDMBxyS5AONeZym4fQ4LEvdVv00vA/tICwAIImGlMacUxqBB5AMwgZu5toctWutpYkyRRfk2Lvid+kjbNPQ3g+M+xSjpc0BGEgVZQO4hb6Gn63OWqsqEoVZuIFboO1YlH5E727b2PuV7zSR75fWejlfmoJHS5xbSd2+SkQ552HIQxKt0FyUCEGYWiMMEaWF9oiGNQIASZJzYiRT01LabAIJJaUum1KrqyeRpm7UBr4R0R5YhLtBrbshindDJYTe/N8FVt9X7cH+LLZKjGzudo7YKEvk0RMXq6uCmadEkRgfBI8QQ9X5tq7PzoxYAsqyyfV6LmWTJDkPxBwQhwkgslqtVsq2Vl3M6lZq2dTUSRABw8Gql8XRq06GGB5kRqo85EHkNAyP0/iY0oEwD9Ph4dPT7Tqv67Zti1r9TfZMraroNWe/u92lGKjJ2gFEkEJzibHo4CZvKA1CEOHmEqXSBon3fuyO9tqNc+7tmSZT1p1x27QPwsx3qai2IDvACd5+qxsbGuLHu7iuN9u2P7/+8i+Xn7/Pr3NdtUXSuP+wXnq/DQeRdvyfLbYUrWpl07VqaRkHi2Bw81br+nRw96O9N7xgX+MOgWAIAdCKMfSg/iUMb0+xoZqaXH8Xs3nfx0Y6DA8JqlClQoGKYAheq23rbA4JMCV5fHzs9g8BtdQxM0YLdX0+g4KMgBBOtdiCqsQYEEEBWJktSeSMSDS2kSBAmHu1cCCioGDG6SE/fpk+/3B6fDpJyubgZ1+1Vrfq3jqa5hZgkjDnD8uqT2ZKqYjhrmbbVmqtDkHEgTgyI++WBoTFtM2kwAPNAVGgqfVR8/RrP5aZEaj7zjZUrJl6ZOFxyCllRFjWbSvFq1U1JmYiaXKDiKnNiJrTIAIBiFAG1sRaJQAIcaT5Pxja+7hxn5RDPzix6/QbFPVScSvBpGaEgUwhTADUrJIYdtRoF0EmzsQiLALY0AHesqJ2NsY+62qVWRuM3bZ6WxZ1AwYWDAdsvntNNB4p0D2immOpvFSiItX4w40AI3YRp2iySM0KUNaynZ8v5+fLfF3LZvuyvytRt28Ph7s8bvcmfD8ei7fEvcWd1qW5w0n/467fjNb/qug31XVZy7aqbo4c4eZVTUuxZbPFbfa6+rX6DKZay7ytL7f1fFnmZSlWA1wIDmN6epx++Dx9fhwPY8pJGLC5A3NQliHzENVtqwvFghpRl1JvVoxKbRZThutav7/Ul9f6etGtIkrN43Y4TsfTeDqNQ07ChGDj6JLweJID8vu7EkJmOU3DMAwiQxBry1+JIaCGNYhAC+oNY4xNj703cprpZ8PFNGx8f3Qe0AyXsUGEiBRwtZitXEqdTRUJeUiYcpachkTSPS73FG3Py+7T9vs/AX/zSrD7+TabE+yWHG0mhTmlaRxb8E7CwQTQGr29G98ANe0nafNoxv4FwGa0Kn367ogI/qZGF/ucFmPPk4oWd2tN0TbbczNsGq6E99ZpGzUw7DNhAACY5udjekVjmMHq5hjumUrii/hSYAU3LBAb25YrTGk68KPHZ9GrlnLVbVuC5TrPtapIZs6I5BHjQEyjuqqWhS+3OUrZtk1rNdUQBGiQnTWWW5iWcSwAQiJIiXlgGYQHpowozZ1vPB5+/MNPSfLT05daVtV6PD2+2z2hWrtOvFnTwgPoGnjtNbY2OELrjkIECDMzNfR1EmEWFm5tbzFmIoBoob5VbvcMkokQOTcl4N5759T15NFMzW1fRq0eIAAgwtj/KzWNZ3LiDxiOr5dvYK+/3L49L+fVir07F+KefO4esx+WI0BQ8/QslwrNXrhYXa3W8M+H08N0AJKWebz/Xur5AcLerOwPtJlkRgDtUBa/47ED3KLbJ2jRMtdVvb77NETDyMADiqGpFyLgLKmCKQMyUkiiTILxkBlDK4MDokVHxgRGS7gMHCPUQI3YIgEHQoABGFEIY2YOR4gw1WqOgYSUE4UEJkwjP/54/Pzjw+PT8XAYVG1btuJ3qCQQUBLBHCiSSKbh8F6bM/YmYm/OISJxGgYAIOacUzPmgh1tPgwDS3NK22vq3QLVHSKMewbYAZXhAWHoQW4MUJmzx8SSUyKgRFybNdEuJNxxkExIDL1X3sA40VzHuCPYGvzg7fptaL9P6ftUpd0tRoADhjoXxbUGAqoQAwlHCCGlNtwJ3A87AmaS1EB/jMwRjTrR0gWLcMQI6KeXY4d6bKqX6/U63xwjZcmDgEafoyI0V1gkAnSzWKMGrOZw9HJ8u4+2E6Jl4RHeFE4RJUDKen35dj6/Xre1hu990zvhA+DO4XO0gNgH8LhH9q5h3Fk87bG1eeaeYP8nXR17/y5ruF+qts5bKZvaZiwcCGHqVsxL9eKxqd62sqxqZa1lW7byutTzrc6mQS45ktBxyp8/Hb58Onw6DVOWzJwoZZJEKXEiEAhc5/Wm7gBFfa1l063AVqAW81Jh22y5xetrvZx1uVnZIGKVJNNxOZ2mx0/T8ZjGsQlpuwjywBngDdWIwEyj0OM0jcOoIVtgdSM3gT2Gmamq7TgraMkZdVPge2hH7D35huOxcAOoiN5MytJAkjVi29abl5vp6h6UJCdCGkSGlBInQaJ3cxTY4/odCvWO1/ThIqKUM9TakJ2ACNEMdQMRc87TOBI0sdWECFprjdpHC4BI2ApuAGgVT99u0Q5YYhaPqq13DQDwNpN192oa4Aw7a8hNrUYANB6dO0TcEYed39mdkITtQ6Y1XZ4Ppr65beAFPSQ0R010Y9hWWiIUa+iCZZAVh3Q48g9Oy4BQ6tdtXVGU01bVPQCJUBouSyRJwoAwq8RkrtfbubY5sDsiBmApsS62zGFal2ljyaMkSSn7mNLAnBGbJGc4xXg4/PCH/PnpBytqtbrq1+fbvHTCgoeXsrm5t3zQ/a2o7ULBvRpsvgl9biJCxHnITa4/iQB3R8Q2iwzvJo1qqqrWLeBAhJHSOOScUydHEokICyO17/CG1NjHhjuMbk80OvWT6c6zatevt2/b+su53m62tjiH/Zi5/69HXL+LZ7ejAhG8OXbE5lsppbrOdS2hjTpCRJFGkDs+eD+hofnB9mKujaEC2kCoUUB9/713GF8r1NWsVq1zWV6322ZvoR0QIglGCESOZHlAAg5xQzdUJbNgAhZJx8OUuG6Llg0ILAKJgNDRLKK6mxkYhEeqlIZO1fOwAEMIRhRig3BrJgmQOQ0pNWc1OcjwkJ9++vTpx6dpysK0Xm+Na1CtdcepeXUOTIw05jQNB3znc+PutdZlWbZtI0RJaToe8zi2ECvNpBnAo5Nmc84DDW/QtJaPB8DuD8kM3AbkAeauZm4GZgwhLSwRHaYDM4/DIMQ7w+KOUiQPtPvoJ8CjiUobNPXuexIA+D58/I2G/B0GfF9GRNS8093QDEyxEroHWGUCGzglJBIEdy3oLihCGiAR6A5hLSG7Vzlmpt1fGbzRDAgJINR1XpaX8/lyu3p4HuQQY1IPp7Z4mJE5evoRERHbVkqpxNvx3a2ENbw7EqI7EAqnvK16fr68fj/fzre6VDB4w0fvkbX10hzd0RytF2ZOgYhB9+AO3cENmAmDQAF9f6z/iTP3D925j2W8qqmXbSu1FhUUosBwJE5pnCaPMTQXW1ZfyvwyX6+3pdw23yoZkgyYGU9Tejjk45imRJlgIBolDTwMMjIJAm9bnZfl+8v52/Pr+XK7zktpsssEhlQDq8WmsGjMBqvCukGZTTcjsPXFl7HeDusw8TCQpGCJPEg+pKdpenx8d49IxDLkQ0pDKbbUeqlVkklKgL0B1nzHvBPa29SGmpB/q6EJg/e4jhEesZmu7iuBQRpSTswsYgHmFpqcBDkSc7Ory83foZ0ieI/bvbzrxN69Xqe3zv+7lxOhZlWtVG2S8GqNqo4YwADdLiqQsI0nq7f+JYCb7UOlt2UWEa4NQB9M3NDvu2cJsiShBECbb/O2rPWWEg0puStzUi3h2ix13BWaQD1y22LqFhDNBIiJK76LJQH0fMHzYretamg6hA+wCK6MhUV1UFeLZvGz2ZIx5zQcR36ahstqtEWSnMYp5TALkeyOZq4eAEFMKUsSRnS37fU1dcyVgSFEoBt5RS0VwtaljAdj4ZQkT5yFmakbAIURepryNB0zZAGOaqY2r//mHtrDo2z13v7uCDPoEKHeLQ7E7uOBhNSQba2ZKizNxZikuzqFOyFb1QaLr7vXh5sJMwpJ6rbp9262hyH0Io1FpIt+IAL63kD33tjbR0i/7dXBFtscs5IhNHtMJABiTtLsnzDC2/xl02odXt8zSyRqQ33HCIIVStgVZypos61zmb9Mj0/T6ZDHMQ8gPUgCOAV5OBF0t1sMRIoWHjCgJdMtutzR/aauutbtVuZfby+/3p4vG98jSERYnUMramXdRF3di5uqWQ03BKNgda6IkMBbZmRh5t3xL9DdQdXXRW11YWGUNERygEAz0ApW0SpYCSvuHtwePgIR5JHTScancXo6HJ8e8uFgDutczpflcpm3rUBYY0YmabG2l96/0f5vZQYAJBFmlpxbK84bIlzh3tJGRG6cNWIRQUR3r1FdDQIASYigNSZL7VL80YKyhTuBM0AtRWsFxKraeKsOAQ2UEe7uYFaschUW6X90BAI0G449IIG588fezN+A0cFesu8jK4pwAHInM6w1ti0gECO0ViY3pzFoyIzhVguGD5I8pKVfgYZOQNgoS+3waphT76MvZGbEALO1lPP19nK5XOebh6XMB8ZawzRaXYIcLA3cSa0JWYvWqtNQ3m4l2kkK2Oh2gQRJYLhu5fu315fv5/W2mjrufamPVwB6oAcH8V7QGYb2rv79aA4MYmQhDHpDdP0nxXXED/t8n869W2Rea11L2WqdBvbmHEOUx/FoienAVaqt21wXu+r2UpatFoiYOOcschjT41Eej+NpTGPiTDgQjZxGGZLkCFLz27J+fXn986/f/vL9+3kuS1VHAiJKhEyOoegb+kpQiJVC3esG9eZenMBnLpe0soBkYAERGA4yngb/6fFPjx/uFFGER6KsfltquSxzMpvGiZliB1P26itgH7R3X4YWbxmAMRgcAwHCwletZy03DI/hOE4jYkIKRGcBTkRZhIlyc24ScIHKiNzPr/4a7wH9XqojAP4GeAYAzY6l1SzViAKRvLvSoDQX411bphXcbSbZXqy5tfO6Z/cAFG5mnVcDgYINRNLw1AAgSMxSqla1ZbuqzuPIx3Ew1Sxj1RJhgNIaXwBAItjQAhEWjogk3Cjw3FurfZ3p822z83qZiwN8cmSnhfCGtAA6JHEBw6geobAxVuYQoZySsDAa5Wl4eHIgVXcH86ihptXcAaGhK3NOwzBQ69V1YBZhCIVQWKhp+LYWVSOmacoomTCIoMHBLKpAliyHfDzm04GnqBHV/92//zPA8/2wqtUQ22DzbYbSwdsRZgHNjs8D6P6CcZ+h96sZsnNDDgWBQ/P0vcs/RYQw3/nsLAwdnBYW1s3OmJiQqZXwSEC7n1Jgx821M2L//++3OaqxIgc7e3X3YKQschyHKedBUrjd1vmKi4XFDpXERhpuyAGI1h3w8AU2Ky+zL3Odr9vtH04/atWnw0NEDJEhEhNDEBBQdA/J1mTt9VLvdOxlSivAIsJdq9a6Xdfb9/n85/PXf758XbZPAJ/2ZeVeZtgKrgW1kml4bRhKrZWc2CWwGgkxoaBQEOGmWN2J0KltO1CNsvp6s0QwDX6o4I7o5EamqAV0Ay1h1SO8c/QZKVE+psOX4fDDcXo6DuMEJMtlvb0ul9f5dlvMKkQwAVMIgQgQsjvU6g2+9vY63M2dRZrFn6S0j+HMIjrOpkVGBGchImzONIgdae0RAZ1DS6SqpTQPJ90hrRER6I7glbix14rqmHMSudsSqlsDFQdEcwFswCPCBl3mQfrYr11MDu/G0h9Cu5mVUn5TrwMAAjmGVg+LWpYbFxEijHAVoWW109EfTpH2cYm7hRuweUQYNEIyhHUXz/3UdA/VhphSqWYRl9v1fL3N66pmAY4YTIEJnCmCPBzBgZwIiAgCfJ958EdKH/TkxplY0mgFl0t5fbm9fDvP1yUc6H503ydJLSajBzollDGNx5wG8Rq62nYpZt0RsvU5GwcrWisOeo/+P60Z/4ZmePsc7y8122qZ13JdtmnIQ/PrTTgdMCQGnJKixuA+oI8s43CgqeIxBpOcxvz4MPzwefzyafhyGk6THBJPeRjTCMBqdttu53l5Pp+/vp6/ruezLTN7QURmktTOKERHcwJlN96QJBBreESNKGiOAWgUHf/AQBR5hHyAI1X4n7zfLaABpfHu1IrWagWNqimA7JUKAsBddgm7W2Wrt4AwGEPa1CPCAIr7pWyv27xgoKuMRx4UOJAYOHMySRuSE/fQzl7IlLofMQDs5+yOr2h/u0cI+i0bfB+XMhN7+wEsPI7jYRwP05BEwpsj+b4Eu3Ic9c4sUUrp7o9urmEWVgkoSWLqk/k7iG/ZynVez+fXl9eXZbuZl8eHoRzHmddBhjwkSQmD2+QL7u6oezreMuZukfuxMfSX67bWVVcl4MMKkwBXZ4sI19Bi1aiSWJ6G8TR6lhfdfp5v/3y9fN10Aznkw+n0mVMCwFqtVF3Wsqzr9XZZ10VtJQbzcrud59ut1urmhDykKfEEMVGU29Wrt+FndVfARhpqPY5qXs2rgVqYRTNSfMN9vd897sF0V4Zrs76eGNIdDw4IEB7ewjh0MR9i5pxSygkQCJCBAsPJDSl2lEPsOSYzp5zy0HtDrTENb7kC7Uw3vK+TgNb/fDOTpJ6q+vvJLgCIcA4hDwBX9F4mYF8zwhyEwkm4EBGS075W++tsa3MvxyBCQWfzWHyt221dXm7nH49PPxw/fTqeTtNxSsOYcqC0Ifxel4S32v0OH/ZGUIRoIitq8zZf5uvP129/vnz758uvf5m/TyQH+XTfP9m7IaMt67qtm1cFj6ZC5oFu6AFhQREEKERCCZGQXKETFgKFUkrogzEhZQ6hdrqCMyhFhVZlEQZQSALOkE4yPUzHHw6nL4fhNEhOupVlWa4vt9vrrdQ1zBo4oj0zNysGAGYGtQYzvT96GyZGmEWkmfG1mhghqKfqDndef8AG6A7m0Bo/5o4snfOtHqGl1lqqmXnch3+NnEuEkIRJpEbMZStm0jxkAd2t5QRaq1qb31HLS0UkSxqTTKlpV7WRnFHS4//n0N7pMvswiQgjMMw1sFhoKeHQQUvsOdO0qlskYRoocZs8q4MFWINmIDFF806O5uQJABFoFrVqKVXdAbGanq/X27IUqxZ+D+0tKwJow+2mDAW7IEazPrT0NyYLbXciUypVz6+35+/n8+t1WyoF+W9j6g6KAkcKyTyc8qcfHqfjWDdbL2voZa0b9DqAggEFgFts3uflsc/4/qOvd2V67GHl3Q2EF9N1q/Nct4P7SIwkiScQypEhs4VFjhgJDmmwQ6knhdWTUZY0PJ4OP3w+fn4YPh35OMiUeEhZOC2lLmX+fjn/cn5+uV5fbrdL3VYyywQkKIkkMwsjOTiaBVKYeoYq1qOiQyiAke99a29Crghrchnqj4/67h7BImrzaA/a1KqpmZqrmiJieGAgI3ejyYgOQ6IOVO6hHWAP7VDBF9NLWc7rrUBI+KTFLKj9HGYWT3kkM6LcUc7mFEhdpib24smhz1nueAvEd+CL9++DiIaUwMEN3BwRkqRxnKbDOI1D56SZ+05gi+br3BcHElPr8EE/8w3CwZRZhkRIqBCgYQFq4R7rtt5u1+fnb99fvm9lC/SqB9eS8DbI8PT06Zgemj35XSkCd0gU9QdHezT8kDX+ZdVLMVQciXnDsQatgeqOWqmUmA2dE+cxjcfpwvR9Wf7pcv63L6/LJkiPpzSN43E8HFikVttKzctKfJ3n27at81I8atV1Wa6327WW4uZJeBqO0/BJ8ES+vMii62JFy1Zq3VQJhQCM3Kpu1TaNmsA0arVSahHPWBE0ftM73XdOA+42+LYDBkE7x5syLjTegTTX7V2nr2VaKaUw34Ecvfa69yxjx0T1Yz6nlBOzQC1qBnvAxntQjz4jbxmdd0xIR/oQEzlxE4x+dzUVhAAn7yszwK3pV7iLe4fTNePutgPfWJsdmNJgdtG7Fl7dqpbrOl/m2/P1/DJfz8vtp/L5S90eD6ejT0PkLImDmAihW8TjPmdvOcL+RK2YrmV7ns9fz8//9PrzP55/+WV++b5d/u7002E/dxEgNQq0u9a63tbN1ZqTMSI4hAaogWmEOTolpiycM4lo5z4hISVJPjKAESONDAmdGtgA0akJ8zA3cBulgYdJpqfh+OV4+nI6fJqIydWWy3x9vt5er8ttJQIWIkEE8nA3V28QVfRAM0zpQ7OUiXPK0lXksjB7uLkx92+/w1rBwyHMPFC9C9ZoIAXmaAosVVWbCGGns70daDs2ToSJ0COWWrFWIkpd3TLcrNRaylZK0SY618gzKQ8pDUmmZvBMDBFulg8G49uN/A3Jmga4bQG4JabhYRGmUIuvs9XqEECCKeM4kVqMQ45o+Q4S4/4qAnurokOOzK0liGauquuyXW+3y+Vyvc1LLdUMsEkMtxPKiUJ2p9tWUnmQKgAGUuxYFiQmNoY3ymuXuHfz+ba8fl+uL+Xl1/n8ctXSYlBgE2fq063W04KAAApiHA7Dw6fj6dNxOh1CY82jb+4l6moQIDlhBhcNDiSMGmoetrdi75/h7R/7bOyvL7x/EXcO97uvEgCHBRQlcwHISEgY7CpgBsbhIp4GzMdhJGO3MdBASMYhHx8Ox8+n4+OUjwONiRMTAqj7bSm/vpx/OZ9/vS5z0c3JOUsWQgISksSSpImZRxgRGoKQMQo7UyGi1qmAAHCEaDrnDXoTFh4OWvz9XRatgLGWVcTUu8JDQx6padFqHZ/RlfLDfe/Gt6uL0DEAIGj4TcvrOl/W5batQMR57FsyTcSDeSX2lCcyBWjIZBGMBCph1E+tNv/onN8dRreP4f9WbM8pDY+P87IxLm0aJ9J0KL3WKkyMUKuqGre5KyMRBWGvxQNUm4AVmtZaVqtFCAeRIQuyYMBsdV7LbVmXpdxul+vtvJWlWC0e7rBudpMtI0BW8yMhCkvju8OeJuK7SGNmYeCE5h/q3XmYgJRLZaMoAVbqpbp5TGiiFc05mIUDo8Si29ft9pfvr39+vRI9PB4HRypaSUsS5Cyj5AAqRQnIq5u5uW7bus5z3YqbYUDidDqcHg5PAiffZOS8+KJbXW/L9Xy1KJIB0ZhgXm5b3dwVMcK11I18dgyoEBWqvYdk90mz1WrQHkDjsyEQcTQg217KQ7izu9Fdkgr36AUWncHYMnsnCeQAdnQgvuOTqQ1nA1zNaq0sItAIUPuyCWjHfR+r7OQIgH6wMzMAEr+pgQLApnUupSPIILx1Pq3Y5qtuiRkCzLSqWlMkxDaQCmyz2D4KvOM3OkOtQednWt3Cw1fbzvX2bT49TMeH8TiN02EYxzQMKSdJwkkkUVOWhSaabGq2aVnrdtnm1/X29fby6/X56/LybTlf67JYUXuXwSMUQctcLalldw2lJlpiDUHS6WFBDRDtQAZkQoIkjJmcFdASBoAhIrDLGJDNqUKQW0B4FsZDgkGYQBLlQxqP4+HT8fh4zGMGgOWyzOfbfLkt19mKIgQieWDZVNW0y7W2/ltiyWkap+HwvqbadxOYGaoiADOlnFmY2tijP+/GYY0a1NSCwDXAzUHRa7W6rVqqaUPFonBLJRsig/g+FYRwj2rVXCOCiRxwQE6pQ9BzSiWXqqrqjgTEgKyAXrWUwkiyI99P6cOp+9ti923Kvk8hAdA9rEbdfFt9ma0UdwsSHCb0YKIo1fbQztJLa/Mwfp8MWkSEhZqhe5jqVrb5Nr++nr+/vDyfL5vq8eF4OE55GlgoMIgwpUb8D4BAJvdeXCMGEjA1OQiQQu9Ce6/JELFs5fnb/PJtPn9b58umZq3viQjMb5rvb1AcBGIaxnw4HabjNB5HDkqSt9tWVnVbTF0SUyZjoARpSK5eoCiY9RzkN+gr+ACVu3cKWlCJJkmPHcz8cayABJQwkMw5IgEmIqRwoiB3CiNQROOEMubMkBGAiTnlNB6Gh9NweBinQ0qjkBARYtWy1OVyW76+Xr5e55e1aERgIpHMvbJgbpYtjAhNXxEEg9EEipiIIFXALsQAb53S3qMOC/Pw+n6RRWPirWVN4Wrq4fdFrR5r3aoZIgmJcDKrAdawxEw7j6SLXoJHbO6XbXtZ5su6rGWTlLyjoxLLSJy9BpGKjIjVQwlJWBLjwJF0w1o7NOhNcIgA6Q6ku1fvQO/zLBDm6XggRHAvhaIt/YhaK7glIWFS09p0yyKI0l0fuYGqVLUBj01r3TYKz8w5p9QUcM3WWl4ul5fX6+U6327nZblyQsniyBqwKWxFkd0EIJyw00p7EXEXzELE5jGjagCEqEpvcCeAGA+QnGIlVdzMV7XLihg4Zk2hDMCEJBBUV79q+X5+/fZy/n6ZD4fDA4u63darsQ1k0/iQcjb1LJlJKIiA3cnVrfEmIxBAOE3D4TSd2I+LaEIhh2J1m5fbOakxpiByEbzebuu2mhlCeFi1LRRq1Lqablrq9v6N4N7/aMEbIDAiiFpA7ypq+66LMHcEIgLsYllhFqhePQzdkYAFGSMFqkNRoM6tIWJGJiCMu1pwOAPsEoZI987BPklt45V7qKA9eUdEJH6faa1Vb6UEokMY9NCsEcUVKmC8tV0w2jipSYJEOLR4eccSNrJcH+gbgoeGLgEBvnm51vl5eT3m6TBMx/HQfh2GaUxDlpwk7QLh7m7Vaqn1VpbzNj8vl+/L+dv8+n05X3VdrWh4M+y534UDbIkNpIJXHIECq6Caad2qVggNBXeKSIRCiODoyG7sLpRIEggFmIAjEguCuEyB2ZwgKlb1UCeCYZIk3MhTw3EYT8NwHPM0hEFZy/X5evl6Xue5lMJCnCQQzWNZ6ro1V62ghpygxCxpGPI0fii7EADJIcAdzBApJck5jXlISVpARMQ2fFf3zaCaa20VXhSP0tXhVa2aWVOulcR5SEPOOaU2RGrYCDMLU3Ov6hHuHOzhAESSRDB5DINZVdWibgEGpB6qprVstbS2SDuy9WMG/1fkNyRosn/clTYhotbYFmtCYxhMSLgDC9wcoXNxIlDSwGBet2oVtjmllDABIQQTEjcCaKPhtuEKwTiOp+PxOq9rqVq0pMqZSBLtxPSIADBoLDRCQGkKt4iOb/OueP9uWECSnA6HQjGnShjV1mJrdYsADhrHYZoGD29llqlDy4eJOTGnxMyBEOFOSJmmz8dqbuHbbQu0CGeC6ZCffnwkgPkyX1/X2/Na1vDufbXXfkRE6NCZiO+fdu9k93EABUXQh98gwkNqPcBElIgyAAAYgO597mZphsx5wMScJMmQhzGnKQ9jSiNj5mACJAiATe0yr6/zcp7XpaoDATHt8IdWJzMzN4sRRPNQoBDyhJogZeMslBi4eocPtZOlkQMJIZrk8G/m1GYK4Vvd2tQKAgC6xpyZLdu2lK2aIuGUBgiFrtjASEzIrdHcPuRW61br8zy/zMta1Ryakid0+HGjpdaGdmEiDOomVJSGkLQhmoG+k7/GDvCk+697f/tjo6VBiTAiMUPqSpPruiYmSBIxElGShNAIzTvjjSIAqfW9TBteI8KJWVCyiOQhWOZSvp7P//Tzz//+z3++XpdtU3dDpJyHNA4Krm4eYBEskAeSxETc8L3tftsUwMyaaQ20JpwZAFTj2Hc6Av44Dk+m6VaHUvIy62pV3TMjQ2SJcSJmoHGVNKs+z+v5Mq/zFlUxDGBbq9fr6+LjaCcLP47UQC3TMJ0OjxZVbQBQ021db7VsCIhBYeg10NyrN7nNCLei27IGCmUAMma43ebbbS61tAfecIZr3Z6/vZ5fzttc372OUNVoo42u9OweYWa1JSfUmye7CGZP29qTMdVSi6NW2AKVJHKWPI4IkrYBRattAeDa7MVERO4/Ig2ZmIRFRKhRDXvwbgQ3tL3y+5Dlx94//4i1tQC9dxIQfG8ntNf1Bq7tIkpNqLRpcBk2QeYdw4v7ggZA4EAKbJaPDEG2xWq13myWjeWWsqQxj0NTfOCUWmu4TXlNi9dN66xlruu1bre6LrptVjTM+if9CC0irOOgjJXACEBQ1NDM1nWd5+p1rRbkBDEwZyFGQoSqCr5mBIHAAZCBgVMWSQcawsc1WM2jFluuut1MNx+GnE/TdBrHw5AmkZGBoNRtu5b1ZV1f13rTMGRKyOhIVW2rtqxlKxUIWViGYRiHNEycslFsru/fEhFz6iUWMpM08ZjWIfd9RRFABCK7C7qjIbpTOMJsAE5MaZCD1mRa20RmHGScOhKz8eat8zY7RIcal4eZmAFI3ck8MaUkkoeIUHc1L+ql1i2iOGkQeBCCMKYm0PBu9PYhtBNgG8u3zsBOMQM3rAWWpZbNxoFEeK+MDcMJOUJrKapGxNSCjxaLGpERA4AwCKDr1/relgcIIjocJkC43pZSFftXHTG6q0sjXKLv2vRAxOFgZg080k7g9/UVAo7jMIz56fGTjmgFtcS2VURYpVrxMBiO6fQwudu6buvivhp2TxDsAtMQZlqNBAOEhtN4tNBSAdxUPUKQxkE+Px0k83ggFKhWFNxWB2vadhEBzCCJHULdvIZbh9l3JuDu3YVNYu3jTJSFxpRTTiwJSQCk9QUCOACbuj9CMFJOg4CkNA45T0MeMw8SiUAa8rRJuwTMpbzM8+ttua5bce/KnC2Mt7SuqWo06i2iUSCQMxpDTcFJODEJASOQB/r7Ywq7jgvsGu1vl7tDeNECRN7BpZ2FqaZr2dayqhaIGFLyyB7asG+0N8nbmQoOq+pl2y7LeluLWkQ0QW01q6bFtACG1WJeGwaPurg7J6YUzKYISztnEQKofeJ3zLc+tH6ztnx/F7VuEJGEEKEautu6qCfhPRsm6soz2Mh7iNiwSQhuFtHNWwiRpVnIpCAuFq/X+c9fv/3LL7/85ddftk0jKInkPKAMnEeqDlQDHdAl0Tim1KXToAUz1dpcYe4xPiK01mYpYR8adfBZ6A9AyYBXtdtaim8sNQkmwpx4SDgk40lBbhbf6/Y6L8uyelXXpdpZV/dSk06TPRKJ0IjGTHSYDvbpKdBVVyJ3L8tyLduqhuCsJQoZe9Wqrt7sJ0y1bBU4OChIkXxeltv1tm2lFeINbFhW/fr9l5//8vNJPg0y7YEyTBt+u1nO9nrZzdxNEZvkZzPn63t7b5y7W9WKBWqEygZZU8J8GPJDTpSsQnBeCzdBYCbKOXES6kaYyJQxZ0QkaCZYDg0bvVvTtOiO78CxHR36N5G22L8TAAIcetehT9Cho9l6L8D3CbjtVLr7Gr3PY6BPmIAAmJGJhQgJFK1CbaNmAEAgERESJuH2V1MRbWMyr5vr5rqZ1vDaPgQ1EAwCdGGe+00EYElckWqQAQdncid3oWCv4Oqm1tjpFECQCBhQzaK6bSBgKVhy50IejiOPsDFsvlotZdNlKXX1cBiEh9Ph8OkwHgfOBBwNb6ab2uYJh+F4AAZIYOQKdpvXeluAGNhYOA95PE7DNEoagEjNwfUd5glImDM2pyXkdtYRIrqHofMuZYIdPxMIkBhyoCsUBFF0x5QkMGmttRY3A/eWQIkQUafOd4I79tfGRE2AAZEcQtUwkJGIeUjSgnI1K6VuiAKRCFSwzS4TS04pJwMo9xv52JDvy7K3ptvECCGEJaUWahXJiJG71TEQO4DWulxvL8cjlTokge6YZNZaV1KDxJGleS0Tk1uz4AxmFElI+PT0ABTFFLgJPTYFx45d3PlK0bZoeKuGmmhA22xvrWwi+unHn8Zp/OHzH5mGH3/4uz/96fb8en55vrx+v55fr/NlZsJxFHcfCtHZHSsCEKJFeGip67JiZAUxj0SYgjBP+enHpyGll+eXUrZ9WOvEPBzSVIdpmwwWxwIWgthKExkwjxgA7Fhm8KUdWdgEc++bIwBaI+P92xDhNOWchYgCUD0IiUK8k6iJEBPRmCilTJxTGgdJWUjYBQqFI0AEKXB1WKp/u16+ns/nZSlq8b5Wvau19JFik3dFgHC8w46QGIkRpbVPIjACHQGhWz8CAgFYdJHWd1drR7uRqSPuRAlqbWQ3q7Vs68zhGUlERhglcWMcA0IH0Dk4xlr1VmrxAKCmo+nmtZZluaT5JTAT56orgBMwC0samnpxY9j1Rxyxg6f3D77zfvaTGd8wafvlbqVsTCklQYqAOpetlAIxjEO+j1NBYFckbRbz0MVBiACwRf12ABOiAa1bXbbbv/z69f/155+/v54dYphy4iECPagq4OYNCt5ezTjK4TCmJC2U3ZF00Az0VFW1OShvpTDROI5CjOUtbTyU9aHMaVthq0vRLVDHVI5jTCMmZgyRQR5OK/JrKd+u/rUuL8tt2TZg41RocBDLfnKsU344pEeBkRhPD8dxypLIrA7fOKBer9f5tpoVVZrniromxFJra1W3LoQWo0QkDECBvm31cr2u6+ruTWDUParWebm8vn4fHqd7aG8dGgC/uzv24qGTgdrcOxAhCeeU7s+/oaKsluZCEodNKHhMdAx+GIZE6BSUljWZRYkQkpwTS6KGntj7ORHdof2eFkLT3EZEQo4Okm8b+52cyG/QmTBInmKMgAad02gaAb7fZQfoQu/z7YU/YETLQd8OEdwh+gjIzbCE2hps0NAmpIzUdwAaukGNqKAA2mqO8AgL17AaphCO4D1nh735QTta813iC7HYVnWrVhufs00nOMt0OjRC5HJbtttaPUIrpMRJRDAc1Gtdq4LkSDBib/QSogtUthXq6qaGTMM0nh5Px8eH8ThKJqCIcIqUWI6fP08/HB6mTw+HB0rk7Ksut/X2/Pz8/fvz9+fn6/UKhJJkPBxSTuZQm4IhfZTqY0mZTZsqPZh3R0UibHIF2gB4EOg+kB8TPAo/JLDNXmNtI/eE7CxFmKjl9IEIqlqrWoCZtdb1fdbSTtg+V2v9XST0KO7oju7d1S0gCyehw5jVrWEduKsc4sTzfzi072cbvA3dHZBYJOfIg3ioZJQEidsMHgk9vJbi86y3WbZyABB38zB3K4ERVBWJg0WomdFieLi5BjgRiBBz/vTpAQku81xC6V3vrKGliahFQ0TgVlACNgmq+7D33S3Q8fA4HabT8XEcjp8/009/tGXbXl8v37+9fv/2/PztuZYVQiHcNHMOZDMFN4xi6l4UeHPaDMXMB2YDEyQ+HEd2mG831RINp2KOCJJSHnM+pFSLGoBBIrAaVpQHksmRgR3doW4ednec6sdBYzneCcpv70Y4jUmSIKFFVAsi5K7vzIjEwIkxAIGzcE6SEpM0O7ZQCAsIDayOc/HzUr6ez98ul8uyVnfgO0LofQu6+WoAtW57ADVZyj56DiJgAhZgBucmIugYAV3PKZAAOfDjZIEQA8Ld1BVI+uSntdLN3VRrKWUlCJTESEPKIiySmooTITIgAKjHZr7ZvleAIszNtZR1uaZhQholTe5KCAbOQsxCJAEAYLGP2NtBiB0E0ZAg/dDdEXT7Cny/sAIggAhFOMBQW/Gn2aTDT5vnQg8zvYJsgJkma8LB1JxiAgDCHarZ5TZ/e3n9519+/fOv34puSXgax3GYthLramZgGsI0CI8S44DTmKdxSizUEf54L9gAwFQbA8rdyrqlnBGR3+n+AoSsS96WXGqYbe5OVBPVMcWQMYsL+DD68bAQnGd7IXvW5VyWdV2NtpBZavAAGhVRtvFapxWZmFLKlHIexhxhW3283h7H8ShyqYputG1OriGmHp0uCejePEeazDkhUK31druty6KqJIKIu+VvVd083mFlAaLbOrWcqg0PMZpOlnfiFiAyUWr0/sCK6uoeWq24b4BFoFImnEY8EEwJUlAIrcoDcqImJZaEOUnzIL7HUTfbMWywZ1cYvXpnQIAmGL/7+BFSdPLFh9DOTCkkAsgD0MIBw7zperY7oH313TPS/r4RmycNtp7Z/QAHhH1rMxLuljktw9zTgjYvaDTu94rGjY5vEAadvvm+rXU/K+LjjUTEZmu1olbDnBzQAQOAKY9Dm1djoFav67ZWRSQWS5JYqBZTV9BAwQQeEi5AhOiCLmCMQSllHtPx9PD4+dPh4TRMmdrvARDMAw9Px89fHn/88fOPT0+fUVCx3ubL+fLyy6+//PLzz4e/jN+/f1c3ZMrjiMRb0aja1KLe73MiJGbrbRFXi6osZNTUzs06FDaCwQeGKeOnFJ+zF9MS2+A0BFOWGNIe2bu9sqqWqrXa3iJFAGgTutY+7UL00BqfEe4WUAEIwLwxGajVPEToHtUbkIjbcZPexXX4Wxry/R3eb0EYRJAmPtmQR0xN2yla/hvuVqpzAeZtXtNtOUYMGEGAxAmBLVCrRjHkRrsXIux+9aZND4GZj8cjEFUI3xYAsN5DbLzczr/ruWrnlkDTD0FAQiKzO+o0IuZbdedBNlNmFmAaxvzEn4bD8PA0fv7xcLte1uUa7hBx/JQfnsbLebmeF7uFbuoQal4rUA0zQ+AwJucBUoSP42Cma11qAzZ4Y9CyOyBBmogABaVu7hghFlx5ECaqBWnGCAjb9zjsAJk9cry/iCmlxM1Gz6KqI7kggVMAYwijZ3ZCBAIilTDxJtnW/BtCHRa166Yv8/Z8m5+v88t1XlRbocftF3cR4r4G2hM0hyYOYA49czR0ozDByIKRSA06FaTVAwSIwCk4Rxo/3EjrBDXUSNMK3jWW3ay2X2oKZm46pjTmlFNOTeV7bxpUIIso7RMFdPKxRQQo+baWtM6S5163IQQYMZk7CQhTxhgARXfj3l0HHnEv299dfTd8rK4kpen0AM3fxa3WioR5GEjEwtey3W7XliUxEwKaGSDknCVJn703mU+PZsVeqy2bfX1++eef//LL88t1XpPwkIdpGIc8uFshZJTM8umUHiZhosR4GFPiQbhT4dW98UeJOXYOOBNhRGus3Su9+6VLLVuloEi5iBeIzbW4iQhORx8EjoMP42Ll5nq1crVy87I2B9fFBsABmbAW3moppsVRInSeb2XbhmFAolI3JOY0pDSV0hwgEpBQSpyERFCYgpmJJTEnQkEyJHCLZV2WddnKljizJLcggnHMD4/Huwh/3yDYULStwddtIO48bUIUYUKMMFNFBkSmQEJ0cIWtxhVxyyOkg8iBIS8baK3nqHSb4bpGUQ7MrYsjRJISIoZHVdXa8Q1tyxBzt/UDAHdwp2YmYoZmqsYEIUDWWuHvMy2obsWUSICJEATDPBrSvu9H7K0xagO8PT/Axtjrg7x9ueJbSd8cF5y8S8befwfsviANINPweTsytgkrYAP4wt7CbTHvTScB76fFvskjsAYU8y20dZqb/R4hoiSRlM1iK1qLbnVTW4va4QDjOPA4EA7IQFlwEMgMCSERmaQ8jBYiGZ94GMbD6WE6HsdpymMehnSYDg/H09PDly+PXz49fv50+jQOY8piocXWeT6czuOYZCQGdfRY1rW6MwkQR0IWAQSh4X2O0vGnjZ8bTt0xp6OHGsS8tZBThKdgAyy1lttyudyeX5dClU70gMPxlCQxunl4YFWtpAAFoWIXYkGAMDOPECZhwTfd+H1CA0CIjt7wS4gYCAZW1d2agztamx+7J6kwvDuvPhzBH6r2N9gHETLz8TgMA2FTM1aotZ1xrmaqoYalLPN8JfAkkjgxMQaHo+2ywIhkqbtSqqqHAToRsPAkCYiu67Jq6fUrAuCuvtuCA+4zDoCm9Nn2cAC8J7y6x7dv55SXMsN0OKYhDYchHwYSzBMfaaSkPChfa9v8D9vh0w+n1+/X5++X8bwsSwEOTpAyEYRbDVOviM5IRkHDIKpp3m6l1LKpVkfmMKjV3IGTECIBoxIQOJiGJknDkHSBkkMtmjXl7jV3v34zam85WtdCUvei1ug3bTSBwIQi5G1vYii5QtVm810Vi2MNuBR9mZfvt/nbbb6uZanqLfj0wTDLDqSLN65AY7cRuIM1yV1DM3IT9wyhBChQOVTCoCmsdn+WNFI+4Hj6cHghYgMHNLPq1tgDaI0pNatuGqamagaMOObEzDll7pIMFIg1MCJqoAP1OeMOjjaFZgprWlwyEQOgdyAzIDZenUkYs/V5U6tikBqgrz9V2OP629/f3o8wT4dDz0o91JSYhyyJCQlr1XlZCIH3hExVe3UOQcz3eq9tymo6r+X1sv76/dvP3355uc6lQpJdZkpSEk4JE/OY5MvD9IfPowdF4JCoERlE2APNuwUlEbdFhIhN58RUqFshf+gGbastq6ETIBeiYlpqqbUAIudMp2McBxzENnfEYITMnrhW9AjcDFhYSAh0c6vmpiE1Aub19fX1FaFp38ayruHIPIiEOTAnksQpcU6SJeXkZDw0wzehnUdj7uu6LcuyrsuUJ8mZCIhgmoZPj48pflOK7P2kvesE0EjPnU3eDUPdTRUDibGzw0ANiuLKUnhI+YB5BMqusLp6mf0207xl15GbUK57RFB3SrdayrZtXQSQOjClveJWrEPflF1J3j2cgoKh4VPe6/4CWLiGMzgB76NsorYb0ff8f+/19Pl7hw00xXrv0Yb2PsUbUCQwnHZIzw6F2SlEgN5+GrXuvTcZoJYYd7X5O57vPaxppxV9aD8EQPUoZpupeQ0mFs5CSCJJMrGYWYMtN6YSiPCQh+OUhtwoUcg4HIfhMMiURDisFx8QkHIehmk8HIfxMIzj8XA8PRyfHp9++PTlxy8//fjlp9PhOAwjgJvVrS60VVfRIR3ycBzGQ8qZ0hYlqllYcOtjQBN5+3AK9+jdjmgAhGh4cXNsKHB3QBTCJgXHEVHnZXk+vzy/fHs9F1z4NjEPn58GGTImC7TAommTyoSlTda4n5Cq6h5J5H4Q0w6+2Ff5LjmI4BgaFua1qpu1ErEBOcNinP7DoR3e4nrnv+0pcBBBzszsZuEWra1jGggQQogkwhG4zCsT81EACJrrGgRGUB9LNcRutDMdIJjbfhdzZvU2ZW8SVNCsKPfPAwh8V+JARIDU9pw7OLwXeDKzf/P/+MfAOB4fDsfD4WE6fDqcno5pZORQXUtZyrYq1sM0no5HRg6Ln/5Yl7nMt7LMZd2Woqu6qmlZrW7WnGcdgFBS5lQ5wLbNrrcrJxzGqRYtS63VJUkAqYFW0sLAERiHowzD0Q9VDzzrZqU2e8VGGdtr9t8MFhqcMyFyAKp5qYpAwMDN7mHnbO1NNAvVUpbtdlvmclthCzZOV9Xvy/V1Xc9lrRFBxJxYWHaWG9253NjmAruoV9MYMgczMmc1UUvuQ0Rga8uHRhg3ASeSzGng8STTSR4/+LUDYkcbtVOjyfFDE+XQYrvkcKPTVi5VJWDqxokNqx9oAA7oreMf2ESK25gfgsOxSRoRobAAUmAQSwdHChBUqjP62lz9Gte8x4ffYOh65/G9cCIAABIlTkQUEY1xkFIapgMjMkYAVFXuFFNoaPlGsVKtiRCCWoa+J8RxW7avzy/fX15u81WtskhQFIPNiF1I4jBRIjhkfjpNP33+pIalunuxCBSRYQinQM9qAEjCbg4IRJhSw/V2Q6rfDHquFaYNbHPebKteTatv28hWixDJ4ZCOh5RgAn+cpi+Pjz/99ENB2Ji8aHFMVaxkpxFSDmvOzg7k1ebL7fvL83WeN27q92sNpJTHBCiSJbMMLMppTKMOAiKZx2NGAQ9rb8HNy7Yt8zzfbqfpNA5TGw6Ph/HJnvSa/ANG3rsLHwJz7xYToTu1iU3zWLuTMhjQIDRq8VKhgkQaOU9pGIY8TFkE0UutWlbdwopZ9ajGpLWhKJBUddvKtm3NSLcpl71pkTC1EBiO3f6UA4LAMBCajPK7GL1vc0IkfNN09YDoGExAinuY3SdHHWayr86+lgER3uZK7YuIgU0KDu4EuRasoONf3DGww70DzT0Mzdveb894P30RItzaj9zz4A+xI8K0mlattaxFi+U85JTTmPMwsggiBU4gkCd5/HwkpCzpeJwOx3GchjSIgwMBJWYR4oYdMbM8mEVjt1IKxDwMX7789NNPP/3hx58+PXx6OD5MwzQOg4XdbmezUuu6rtdluc7X6/X1/P3X569/+fr9l2+vX5/Pt9tSKiUGJoMIAmTMeYyf/O6aRkhCTILSvH4RhViYAUktalU1Q0QQplGyIGO1bbu+vvz69evPv76cNzMeWDB9+TQ+fMZhUJQatGlsSVaRkuqu2YGI0I4Ebmjm2I/cCO75OwOiRvfWMPOiRVVrUTML71V+2wqaPoyr/oY9zL1ejzemRqvJRATK5sXualoEKBCNESrgaVtNWLMYge3grAACbMp7LWMOaoVGq/PaIdQH6S3JBYjYe0/Q+X8I96XbUVgiAk3Mz530LRE28z//89eqdRieDw/T6fPp6cfHT/YpT4zs5kV1dTOE4MRjmGQZpmE8Hh4/g26+bTrPt3m5rWVb13W+bgttm9eAyKnZOCZgn5ZsYIHm0FCO4eauEEjm6DV0BS0I1PDRMg4HOFQ9ghbTomBvcNmeP9Jv5XKJWCQTCwCZR3Vlb/hFR3QMCzetWrcGCVcopdxu5+eX1/PyevOCCQ+HOeyl3G62rWGYKA1Z6M7pQO4nRMAu8tqVMR3Dwc3BDNVJjc2SWW5dIAxmkITKGEQ8puE4pEnSQcajTA/y8DjAX13RTLJ7+zAasl21qDZCTXvP1kbyHr6Ph8D3NLMfOHsx3WYxAYFI7dO6VwhjJuIUFMKMHTjkGIZQMBTDdllvaH4YvRi5t+LfOvUfWvLY56kgKb3ZeqaE0I7lcHMCiiBoAmRCFNEsGokYyauquQNgNV/W8ny+fv3+crneHD0PjJKYxYA1yFGGgdMBMvoh09Pp8On0oI5bsbUs7goszUQsAWVLQIQoCrqXr01HktoU9TeThQq4BUg1WdWLg1lQ9W2rVR0ghpGHEclySsc8/PDpYfY/aOIifHtZ7aqmbDWHZIyMzuEA4EgGZBbl5fz927czYWLOjImpJ5AiRIxAgQJp5DEGQKCMKbOBuWorR1tov823y+X86fHTCR4QgxiGIaPjbfVS492Kggaaa4KVzeMEERpctzdlsEuit36zgtXQ6sW5phGHQxqmMY9TTlMShnANAC1ewyq00E6kBOAeW9Fa67Ztqmpmd5fenu3d41079frIdJ+89Rr6rSV+vxLnEQb3sA4+tjZUbzMcv38jIiK1VLZlFbhT41oJhPtM/m0pY0RTBYW2pe9Qk07QIWpNCWhpcjgRGllT3fR7BgDYUwJ37/h46PX//fKIspZatTXS3AGRJKVxHMbDhMTeznQMET4cRkJOIsOQhjGPU8ojQ0fmIgC2MNbmnSDN0omZRFI+HE4//vjT3//dn/7up787HY4pZYQIs21b1vVWtqWst2U+367n2/l8eT0/f335+uvz6/P32/W6zOtaaxAGoaE3vdtxsPcIeSZMzNDc2Ps4uLexTbW53wFSM4Ays7It9Xp+fX7+9dvz1+fXrVoaMswPafk+jSz8YJg3lMSUkTNiYY43piS6NTg5EUHzydJSw6NRmYchIWIbWBbV4m4Wrq2LEOERbUrT67GPKOzfHL+9XI/YcRq9SUDEIgQgZg7Fa6m1BuHIHASAzuAplJVoW+ISqx8cDp4TEbfu2F37gZnIEBGaQjIBgoep+S4W5N5WKyJgN4tr2txorRMfgdD4mhjo5GFBld/fw3LTdVtutK61hEA6pnGbjMigqG5aNzOD8Hkp5+vtMB0O02FIQ5ZEIGmgA09pSif3WurlfLu+3K54c43Hx+M4jgA0LTmyqtXjw3g4THkYtqIppU4TqVY30828BlA4RQI5DiMb61G1VFPT1b36LvkSiCiJU07vpz5EnGRgkkYB946h98YudC+1rPNlu57L7VK2a/VV1/Pt+Zfvz6+3l8U05/z5UTPeYFO2GDBJ3/RNn4Z3duP+kO9gmo6LdfNQQzMyE7VkPphHBCGIoBICC495fDwcv5zyKfGEPIIMcEj5t+sKAroVRQd/9VVcizdvxvA+VuwQZ3PXbisCrfnexIqtKTAzIUY7mYgQwV1rKWUZbDzIIQ/cGrWA5oAODHc2Wj/nY29cvrHY/7ps/80pvPd4MaecRBCxKU6Ea2LmjkPBJumYcwaAUoqatVbtWqoFIMltWb9+f/316/dfv7842MPDowwMzGaDaiYSInk4TU/HIZOPEo/Hw5jHABoyDJqKVQTfVDNnZh7yQOIeCIgppXA3VW/TnKbXVqAx/nosGTkZ0bVgqana4DAyuKEahKMjG5KGBoQwPj2e6JjlOPI4/kzfv6/n8DBFCGFIENxobEQwHcfT4zGPycKWpYQvOY05j8MwECUgd6zFwKCkgSbK0JQMsXPcm7WNu5dab7fb6+vrl89fPj99RgIRzEnAYKYKd2mqno41nHY3EIS9A7MzZ6HpzjZpmUAwt+pVQYM9H9PhMR9Oh2k65DQxUbgxBEYFM69g1b06oIYbbMUjVG0HcWKzCFPViEgpeTg4euxotF7IRIfME1PbxO4f5zxwHI4pnkxtK9ttvdVlc9PWf2qhPRCCAJlYEoqgMKMICiBGK7q7tBP1NjvcJ32hXs11b+juA/a2wxv3s+E++gd1AuDOsL0DnvbtCxGE6M3voQ093o1BLS7nAuCJBs5DYp/GaTqcpsNxPBwCoKpVNY8ushIRpWitdV7mNGMaUAYgacAeMAtVL1oBMOWc8zgO05APp+PDD19++Luf/vjD0w+H6UDEqmpaa13X+TbPl3W+rPPlen65vj5fzq/nl9eXl9fz62XeViCTgRLJVrXUahDAICAuH3pajJS5aRZwh0ZCuEVVbRqZ2B1ao5Ry2Rao3+z155dfv31/eT3PizB9SsMJS16f+QpYF+SxkUqJBiZOSWzXGfLwNnkRxE6hdSp9cJ6HlMYhI6JqLYirB1OwoAAysHIfxLTGLbgTv9sdfyU0G125sBtatjVIO1JEACKlXNlbEszUpmSAwaHJKzlACQBTAmACAE57NsssjWXKKM2We8dwgYeX6vOyzsu6biUYm5FDdIlZ8AhwcPSWjRJS8y4iBgj0TiK9xxHQEmX1QE9jRmCRxFmQwTSqeanu5hFR6jYvdVl0HsuQhyHlJrvYehbELBlSSpKFEhPF4WE6Hg8OgQOUOFro6TQNwyCSS6mH49CMlRWdDNjDEUhiOPDxkA+TuKuMICOmQuBNcLWZyzQlP07Dh9DeZJLbsBkAusoUBoC51bou8+Xy7Zf5+9fl9XuZz6q3up2Xy7fz63V+3dTGPMyAp6SD0wElcSPJEjijMzoBwY5UsLbDe3SHcMQAsEB1rMrVRM3UcgQQcEJncmIecj5Nx8+n00+PcuQYPNiCNMEHx4V3kLR9zLOrhqgWM/WwLtPRqh3cP5QTNQNX93AN9ybcghh9itx4QQS7vOG1FgEYiCBQHLi6gknyBOzEicSADdAA7V10+E1Qf5u8f8xPAKJrNLYkNRpgVjXcCDvc0KxnyE0fwN1Va6OzFDM10Kgvl9uv355/fT6/XpdxksdhHA+ZiEvlgiwEQnEY0ufHY6bIZEPOO4oWgQCNVEujUxMRcRCAqUOEsAR5RJhZA3a1LOX9fYyjj2aIJWwjDYkYgdxoK+EV2BmBHEDDKlTJ+DkdIbGkQSrpRctt6VrU1Mj11N7wMA6nx9Px4TRO53W5rGvxQCTOg3BKeWBmitBAk0zBAgjmWppKc0tyWypmvizL6+vL7XattSROKbGncA2iN2VThN7l2nU27zPmvd1J99Y3Qnc+CQ3TsCDjAcdTPn6apuM0DFOSkZE8lMPJBSxc0WpgdSCAaF6zPXXo8/VWyZnVWtsDCesqs7GvFt8ZrUQozBqgbtEK4v2djDIOdPKkhaV1yDQwzLD37nG/B2qirEiJMBEKIBrsrfidH7y3nDpLA53QpSWy1H0BW+fMMUAAwQNQwdVaJ5YAKJAC26Sj83ERGo7LAajX/L+53OP6uokQHxp/G1MeiASIGyLAI0qxZd7KWrRoNJBIKKAPI+YJxyNJxghyw1qjViu1ANEwThA05sM0TT/+8MMffvzDD59/fDg+MImZl7KVsrRGz+36eru83C6v19eX2/nlerncrufrPC+6BIOMlAgVILRa47MEgcRHY7Em7tbc6Qj7KdgF1gKgYTYBkCE8tKjdNiurvq71ulXVOqZ8GjlB2c5fo6yUDy6j0Yj5QPkgMiENCqSBfm+QIxEwt/YPABMHEFNz9GXGpj8SlCATq0Qx25IW9dqEhiPAI4iY/sMN+QhvUjDNrro3Kvu/gjsI8zhM4FI3BtdwZCQmAmcvbEAc6G7VrVAsVFqHhUmEGn8kCw9EgkCVGNEbQ8MN5nl9ebk+P7/O2zo+TJTYItCbS1LrWDdryMDWLW4RMRr9X8XsHXALGYQpAcU0HT9/+eGHH396+vLJWXGhADRDpEAAt3CzZbFtvSHcCGHMw5ASNM9OIvfYlm2e16WsjOTkkAABUAEkEsvj5+M0TRFU1B6/HKYpJUTbvCxat2rV8oCHh/TDT+PhFEvdnGaQwrnpyGGDDDZROE4o+QOgo5UkrR2GrUeHQRAQbrXM58v3X77/8797/cs/316e/fpq9VZtrrFu26azWR3KEjepY3pKw5QSJ8FAV3TgN5w4WEA1V3U1N+2FO0agA3uwBm/Om3qpSc0xKFFODESYUzqO48NhejoevowwQIFS3NRqhL5fV28xE7A33pv0j1a16q6xey4ANH4dUcuSwsC5DeFN1a26e7gDBHU/w4hwB1e3qMabj5tpMZWjwmiQPMQhp5xDRIbMFSNzKIQZ4i5i+5vCHfZg/9vjK1rMVDN3a9V5rRXCqXPKa7gpIiLI3rFXrapVaw3EYNmqvV7nby+X7+frZV43DXY0pzButqxIMbANVEfySTgLCooDraqCjo0ZDyHIKN3KzCNqrfOyuccgwpwgwNy367XB7WoIvAPYnIbyoMvMc8ENQDB4AiEVWtAWSEaI5Agr1Jd6JYyJpy/H6dN0SgXqZf7ll20+XwEOIpCEhQWAIkIkH46nz1++3K7btuq2XR3c0UhgGOXh05GZ1mWtHtQEKiEirAk5IXpzKAdECC9beXl9OZ8v67rIkVNOXsOKf4A+IIi0mqcPs5iZWe7YkZZERvcvawcYVDMDpUzDMR0/jcfHw3iY8jAmmijQvRZQNA411zAFVAeG1uVvmnQ55xbaG98xIlprpAEdYE9jo0d+v6u3MZGDWVUPTMe3jZ4au04SwHAYZT2Nddu0VugIgd7MJ0ZOAsgGKYADuBkqmrr1jhYgN/GJNv4g4qaqYm2iRT3B7k06gpBGsUN1LRG19wWjz/WbiTo1Jc3ex90FmPoNvtsbFreXNWURyDhJSuQea9lgxaAW1/V8vnz/+nq7zOu8gTewnjHD8TGdLDmmIYRIwtGqa1FVBSQmjSGSpKfHpz/9w7/6+7/7h8eHRxFR1bJt6zYv63VZr7fry+Xycnn5fn75vlwv6zyXbalRacAxjWrIFfRaQA2w9y+gac+RvO+iMCETuOmqFRA8QrspDCDgXSsKwyVYGCSBWo3zK6XbmJZjxuMobuXXrz8jfZM0Eg8kQxqOaTzB+BD55JSCBCQhJQAKYndSA3QMD3UMxzD0Cu4qBIIuxGlshV9U07WWuehSrKqZmWNEBP9GzPQ3R1fXTOwjo6ZRwrBT0YOAWYaBpgnAVUtAAAFRCHpCI1AMUI9SyZi8d32Tt4Y8N+lHyq056q6AhoDhvq7b7TavW1XzlhlHgJsbEFB0VGgfsrfJtrdB6d+aMgACETBQpJSnw2E8HvI4OKcJHJCIcjgQUli4u2m1Wk2LmjlDCJmGhptXdzd1QwgmR1i1UFkAaTN1BBbERCAQBtCywpRPQ44aZdGyllrq8ZgensbDpyRDxVwiFUgVxEACJdAcIEiIUgOHfbiNvgVbFy8Qupyqh5tVrWtZzsvLL5df/vny9Ztfzq6zUo0xIvz/zdefNjeSJGmDoF5m5u4Ajzjyqn5bZnb3//+ekRHZDys7+x5dmRlBEoC7m5ke+8EcTEZWT1MkqyIkmREA4Waq+uhzgHqouXGHnPLDxE40ohXDKIjBCQwi3EE9eo+m3vsgtHm4UziFZ0fUwOq0GzVl9yKUT4yZMQsV4TnnOaUzUFYlN68GzUAdfugfP56cY51/gNZqpv5e10e8JnNKwvcExgPVvDvO3DuAAaS/Z2uZhQ5MXptaN9fqdDKYLAoPkRIulGeaZlt6GIT2QDrMdj60Hu8vFuHvxX1c3+4DkgUiGp5MCAAjJCbA0ZhQmIHIzGB4nx50L0ICVX29XF9e3263tfc+lsFdQQ1ZpAhLwiXzeU6Pc5qTCA+5HNSuBsgAg2IKBDjMHmKs6BzcEYCFmdhtWF0NFMZ7RNxLOwJMvOe0rnnrqbsGAlJINvaG0Cl5CsiVmzFVBAoT7UuZz+dz//nT9frF9KbtmjKVkkvJOadhNA1IOZXn58d9b5fLVquxyLJMj8+Pnz8/Pz0/AcT3b99q30kAD8KYRzhgMPM8leVhRgx3hYDb5Xq5vF2v13mapzKrOIt/hB+O9fldPPqhe0REjCOcYYi5xlwz/J7NyaTgtOTTeT6dlpLnJJNgBkOPCONQDqMhARtIjUcIiySZpmmaJvpL3+EejgCj0uPdFW+wUP1u23VH3YZzbvfDigDvtYRySqfzkkvWrnVv+3rbt63Xptbvfh5BjMwMlBynwBRB3WLv3lr1Vk3NXZEAEoGwJ6GcMIlIEsnvj/X4JyAiHCMYItwF1YkdOIDCBjnK7jZ34RY4cjjhHq0Td/Leh2s3IurWTT1JJSIWsrDQgB5eLQJ619b31ute67pto7QnGbnhBEBmoBoiBM6mburhIzKLk+TT8vD89OnL56+Pj48iaTgy7XXb9nVdL9f19e3tz9fXP99evl3fXrVWU3X0EMIQ8AgMM1eEwwccCCGGubuk9PGcx/FBmbqNW2dcikQkxICBI7oNiJyRMgDg9CDLQ1lfU7vMEplCe71cVw9gTsI5cc55SmXB6YzlDGXBslCZKU2A4iHAEZGGHYMHOKCHm5paJILMmJkzH2S7UfccwAMIwQk8KCKEfxio/ja1w7CfAwweeVmcRAQABx6FxMyYEi3LRGjbrVt3BCCQhHNC5gj0HgHeVVk9oSsFcSA4hbMj0HArZZbetxFOb2a11dYbMWUpOWdhMTfzQLRR1ekw2KVDDRX38zzIeB/O/OCvvMvE4/C8ME48LYukMk+KQQwyokt7r61uvdUwPS+neVrWut+27bqt6o1zKqkgi/b2tm3XtjPnAOiB4HhZt713CFnr3rRPWeanzEG6275Sq/T0vHz66RTcdr1GrjRbbN1YDcMwnAARpQgncrRu+0cBnEfocKYNFAwjdHMnC+vWeyiEou6xXfT1e3t9tVAsgEmEAcmUNVAhGRYQDopmIU45mGIwHMxCLZpFbVGbt+Za3dXCnEATODqSBW4eu0U1AShL4ZL4lHHiEHSGIGuw1bp3sBo9IGgEmX6EHz6kZsBwuwo+RO13XvBBOGDOKeVcUsrMgiPPYFwhiEgCPrwwDGA4QoWHO1iERlh4hKl3C3NmAmQIIXQMRSTKC8IEimEYbYW7jdg79QnulKcPv/7hdKgNRtLhyQtu4YGIAThu8SAg5JTzXAqLwPGtDDhi7VHVbtf1dr31VgmjJGbGpi6KKcs0T5+W/LjkhyU/LOd5ykNtZQ4G7hHsYe+Zy0yQeIAHhDhNBQCFeMzCAJhSGirTH8J6AAh2wtXKXmfTQKhcPDiwGCeXKbJSAfFUzjKfrd26qVkXjC+fTv/P//031bW123keHJV5mnL32rs6GQI+Pp49/O1tNUMWenx4+Mc//u3r159O53Nte231tl+RIMZWGhwJhDjl/PT0/OXrZyLovb69vtzW2+Xt7e3t7enx6eEsLMASH2VjEdF6P6AXikFGCXMOOJzDEAEO9vB9Xx2OCmypwLzIaV6Wcs48kpWSu5uSKmkncEmEIWhDeITAIvM8n5bTvMwRoaZ+ZMjCoA0NNgkcHNEwdRsTVQQPdrDZ4Tz6Y8LCeGLOj1+enj4jpN708vry9vr9cvlz26/mCmMDxYhMnIpMjyQzodRmdKth0LXG3r2tRAEMQazCOC88xA7TNGg1dxjjbvIXDmHuLmwgnTh1FVLWztA7kII66KBB3DPKB6HuOK931v3947Bu7npbASVkQgwJwG7mrQOEWZD4ckruhTBMFSPOJzmdZD5zntDIzI0CIFDVVYMppVSmfDqfnj4/f3l++jwvJ2Su2lSt1lbbvrfttt8u15dvL3/8+ed/rOul1Y2QaGIKBvPeeuv9tvd17dumrcdIAgIkIU6pSCofD7qZdx2u8jhIYEdgKaKD997clId7EWEA92CQMi1nPi26UwaDXl2jq5oFgSl349r3lfANJFOep8fn+fGTxAPGQpScE4mhlx5hjjEc/8HVCQI6ojr3oO5B5OMDcEDAsQ+DkEEIwPxfAfL3uoJ39m/OmTmZ2fBe85EZSykXDidTbxGg5uahEXhYlwIhWI9O1tH74HMjCB6+UYfFGJm2gHCze0SmLMsSBCllHEqAcHMnQHpH2z8MV6O6D5Ev/SgVvQs3w8x719Z7N8MYoEGCghTMKCVPOaW6b9t6DVdCPC/nnAq9valzcC7hIgzg23rb17X16g7MmZATJkC9bY2bEqXazQANoUMAI2QBlXCLzJDYMLobZMhnKd1coUcouGK4gxQgGSET9hHkMveqKgiByAiJqVsntGhVazUzQmISQjb12hSdCVkHf8YAwKFa7OpbhwWgAAMlEWEBRLNo5k2j9tir79Xb5m0z7x5qgqbox+NcjZqi+bhY8rnwQ46CHd1Cu5u6a3OFMAwiGtnI8LfPIwYH1w7/QMQY4UKHpXUgYmKeSpqmqZTCkgbMO4yRImIIeu5Y4P0DhjsHGA1AD7K6OhoSFuIMkRHNDM09UCAvsBTYd0gTqB4+O+8q4I91/k62/nh5RQSOyBp2Ee+OR+CfgztgoMgRFDlPZbi7D/cAOEZUGA+kmTJ6EipzAkmOZA7qKCznZX5Y8nnOJSUCtDh4WON1OMTQS/swc1YLiKo9IHLOTBwOh4sQwDCQaK238PeDHgANvbJbCZtjV3cPNcgEAoEWUg1bcJacluX03Jm570SCTMtp+YXptt1aa4KPp+WhlEkSawUfQw55zvz4uPz881fhiTnO59Mvv/7y+dPXnOfL9fWQ896HYURgpjJNDw8Pv/z8y6+//mra3t5ebpdL2+vl7fL68vLl80+fnoOIJZePSecR0dQIURjRAtHQHACFWWQ4Vd+JQkiA4CNOioFS5JmmJU05Fy4ChUIA2cPMoLfoFbwDAyU+UpaJISXJOeeSc8kDez+eB8D7r+/ThB/I1P1zeH+9d9PCwUR5f7KIgSmoBM1IGcEhdyyNemUA9OHbes+ixgSYUj7N07k0R7hFc6UVEG340wQM3T51woaYBCVxERY5WMlj4qYDEwwwc2cz5k6aqAuxkHTtXUmVDNRgeCJ6vFf3fxnaAQCB7jg3IYtwEk4Q4F3bYUXZ1ezIlxGWnOjhqTw+JsmOrN2H8RV4gKpr9xAvwPN8en789PXLT8/Pn3IuEVB7q63t+77v67bf3q5v316+//n925/fv7e2u+kgCoODqdfatr2vW993bbv3qlbNuwEAEUPwh+oCMPjJ5ncCByECEwTE4A2O+BMMiDHGIkAgcTqdz6CPYOdsdUJ3cI5wszsxCCHMo0Vffb+oV/UK7UbTySmZ5Ggz5GJAQEnyhJTVwZwi0JCqixo0Gq1tIDrRgWEeEMr4+X98qP4Ty5r72MIiOZdSZmYeeg93szD3I7xYEqSErkPqB9WZHBNNRImQKBhdoodWYHAmoDKsQw8RDDAi0mHtpD2l9PB4Vhgeh27hePicRHgEQQC5Bw1PuqG+HvI4QgFB5r+9kfH8qeq279teS5/ICBmypMSZQAh4mZd5ngnBtE7ltMzLnBYIWndLSU/Pn/OcgaBrfX35zjlPaggkPAGQ9rbX67p+j+glswUB52bxcq2JnSFXxb0FbD2uOyY1Qix5BiRMRaxnb8m2a2vVOCHKCPr6oR6q+967IAYhEyajqt0ddFt1W8GVhJZzOT9OedpZehhGYB/6WfVw97X1jPuEVJZ8mouUeUqS0SGa6ta09qgNti321far1tWsGqgnjEKDzxaiJmaEkQtBRpwIMhh71Vat9eF0MILfhEe+wDDr+bG2u0dAkIeCI+KwlDH3kRwbIjSVfF7mZZ5LngbRwV3D7R0hByQE93eAfqwYYRDrnDAwHB3QnJwTrsEFYnaw1qI17RYomco5prPnGWPHYSZ2MOcOc9l3VQj87fMYnHtiIk4J1KMbdhvhNzEUWJKmeZ7nZZmn3FqHkTo6pAHmam5mCJiYElKZ5fRQOpWbiiONfZ4QMzI4mjpAH3i+UBKRsTCjgPAYRIW6tdpr7ZWEnh8fpbCNURIAcJgYem2thXw86FeWlJJOBjNqo91ipUgSCxr0mq7XuDJmydP0eP6sZYK6ZmHgnJgf0vSP31Sk1DVHP6WckeBgv5gGOnNME//260+fnr4E9FLy50+fTssDAGOwmqnpweSCkdqcHh8ef/31t3/7x7/99utvb28v220Lw1b79XL9/u379adrayqc8zRCsY4vD+hqTHSQLg+z9sgpAWThMeiOXCwmIgUOMAGkQmXmaebEzCEciSIBoEGYgjZvu1lzDBEmIQZCYko5y0iIGXPJcPz+gCgBgL/vXwYKRUTMMYB6v2OJ+MOwCwDBohSXvTe4ArKabdutmpokmh/oiD4YuV9qRrZZSjKVz1NGgQla79sbY45SEHyI1AEcsWP3uEVXg+WB5oVEiMiO8I77yhiOXbKIsmbmxJbNeu+9t0q9967YuyGOBc99xz428j9Ac5gTMeR5mpfTfDqXKSF711rbpl170/Xabi91vdS22+k0zQ/T+TyfH5PBZt4TDXsC6h6m1lon9XmK03L6/OXLzz//8vz0iUXUrLa2buv1dh1b9j9fvv/+xx/fvr28vlwHObm31nYdMnvtquqHTLlFr9pu1boioAfOGmA/DCIW0dwF6HBPZQwIBz8YNncfjEPAFe4Rwnx6OM/yuZSN94v3Bt5WQiLwiElwntPYbWjt1qtf+96uPJ2pLErJOWGZKBeQkqZ5fniWsqhjd3IUde5uHRRQAmgkaxE5gzMMlYYZYARR7ssHZdLfpna4d2Lv2sXBp3hnGt03kQjEkDKGEwZi97Bqjl2JOMvYDoCjByg49WADy+QZncEgwM1Ue2+9195qNyDgJHGQokY7godciuDO0oe7Lv54vfF+Wn4kZB//2kG7beu+rdu8F04AwQTAyIMgKJnnufSeeJdc8nJapjSD0zRNS2vnx8f5vHRr64a1FPRIeWJMZqRda9uRDkuJnEtvFrB27QqOmEimUHDStYVfqxSjTDGicJacMZSii6a073sLjoAwA4YfqomZt26OADwiiGIweOt67euVW1dVyXR6mB4eptsWbUdo2Du4BTlhBDT3TfXa4GmaMM8yT5IDrPW6V9t3rR1qg+3m28XWt14v3apH94yojCERFClc0CU5AioZswJGD6/ROqih+51jQyOoAJngbyDKIMZ7DPtMJKLhbWBj1SpMIjRNeZ5LyWXMduH36NU7EDiWK8eTgcNMywAMyImc2QmHw2yAIdgNeEJ8cOPWYiu19cpkzIIkiIyDJnRIhUaBp2PFQwfA9Dfx28DXR4pBjDvcHY/tHQLCWN0Ry/tkOsTwHNAtttpvI7/WgZCQRVICShmFOS1Z5sxZmIhiGDIaDiLCYB4EIBIfq8DAvdnL2+2yXpu2MuVpPkmCY8U+tBcAwyz6b3ljV8wJJ8bIyIIIyDthDfO6xfUNvv0BRavMRrmcpsIMlDKhSB606+cnSDLfLlzXNIpXzjmikJK6MioJ5qezn0VtZaGHU8mJawvTrq1pb8Mdj5BSTqVMP3396bdf//Hp6TOB1LVfXm/brVr3fd1fX98u1+tet2WRnMrHPJL7B/IOuoSZjyubmSEQeOCUPmKE1LWDxrhIZ5nmnJIIi6AgSgSAa2hYd+seBjQM5lCQmIRzTh8CPYF5sDt96EoG/8OPif39Fn33mzz2TaMngPiBLbtr2/e6mfN2CyBz73036xh2PFWEiIIsAg5B4AlRiItgwpn2+VbyFLYFNWYQocP5xM0jtDfXMI1edcSyEyELpcSUEiI5AI0Wm9MgIrIms8bciJi4ITclRALrADqEiqNC/IDNjQ2RJCnTXKal5CUnQfKI6NjCu6lp7b119MhEU5KpCCd0cg8HgkKJQaxjG4oZwFzK+eHhy5evX798fXp6LtMMgV211rbv276vt/V2u11evr9++/P127e3y+uqTU2jbn3fmnZ1HZ8OkggSg4HdReLEBB4M9Dcanbo3uyvdiflu1TmGyqHqYpYDfwZipBnhzPC82NPZ4JrW15dQq1UzUgCcTvn8MBOhm2ltfWuta283te7brQcZMkrinFOeeTl73306uRNSkjwzF8LUg7tTAAUNpxRHAiZwt2amFhY4kcH/bWkfE/IB57uactc43OiIWISRMCFyIBBBykTIOaVoDrW7Q7UgwwQTYRZkDkQNwBZcoRfoCYDcwrw33bd9XfetWa/q3dkCW9duHhAOeHiSBgUB3CeqMbA7DJJjBHlYeBzGGX89Z8doj11tXff1us7XQhRmPGKfhFOWAmiSSdLg7EOgEyMLL3M2n8/LVHK+bZ3MU6CU+fPnn1Ka395u19sa7kno8+cnSUxE67qTvG77bm4p5SnNgKlbqG6Xa00KxWnYpgpznikIrHieU9337k2ta78bNty/3KN1cwJwYAgM6y3A+nZ9a7c3bgp7APH5nJ8/LXvHt1er7tYcnAQFgdwNW+DuqdOJ5hMvmWS3re66Vx2lvVXcr3Z76+tL216b7Q4tMpMKRSIXmMRycidDgD2aOYSScXQwIzgSzQKGg8qg6NKPipKD6ThmVwMkw5Hs4hbgTChMU8nzNJVcRBjxEN2MjjLCYKin/QDoEYAIzO9QPDmxEwfR3Wqnu3YCWpytq+21l7zudU1cmezurP4Xd5/wvRM/NP/3nLuPD9Vf3bqbtdp6a642ImjsaG6QEM299ehqevciBcBoel3379ftbW97tyRIRtmYiUtKpyk/n+anUymJ+T1d5v6lqniX1EeEuXfztfV/vly+vb5Y2MPD6enZS0azwfU78GBzZxaBH6zXLz4lX54VF21moME7cFWL29WYe8HgdYdTpqfz9CmliXNKxJmTuZvplLg8n5ec9lla21V3ZipTqrW2vjluSFHKhFj2qoheEjLZrq23tbdde4+hfEE6LefPn77849d//PbLr+D47Y/v/+O//6//+f/7j7e3qxuMwf16uazrbZomTtMHPB4Q4Ui3PjIiEcEOyNvsOM/gBhHckXBXq9CBdRIsJU9zSSWxsEBCEANHR9cBdwUGCBGDECVioXRQO4czwvjFoEm+S9vj3mfEwTUbE73fsetAPAoD/rhrv2yvtb0SCQ6pekC4Afj9wWSmlCRPqcxpIRQ1oCTmIYlyOeV8Yp6IkrmM/vjocky1GzTtfdetBV+nqczzNC05LWXixIkc2QLBHCnIfawxmLkr3+1aCId/Lg5IAjHcwQ+Z6o/zFCMlkSnPJc3ChYmRPJEpdyUNrxDBELlImdNyznNB83arhqyJMUvOkLdq0T0cUiqfnj//+stvv/32j69ff57mhZBUrdVea621tVb3bbtcrm8vl5c/L6/frm8vt+2272tve+9Vx52BBEQkJUtKjIQe6EEAgpSZS0ol5Y/H3Nx712AKj8QjxzYiHDwIMUkSEWJmRCbKTCXxiacTpWfCXwDjDV+ieeu9ahckpsen5enTAwtFhFZte399u72+rXtvTeverJkTUkoJpoWW03p7ozwrCJV5efxUTg9zmRvy5qFOHgyICM7MSDJYE928W/T/wo0ujiSpwUlxNSPtFgFhw/kBkCPYY+QyACVMghAYXT13axZamzHuEk4EmTEhMnEmK16pE0MmFuhme2/XrV72vYX1iOH71M3VHJFicPLHMgjBbQi/8JjmcWy5ggap47BY+PhGBpcOtfl23W9v6+mxMEe4uJm2LpK6tGkqJafaqrnVXmlFDCiSiX0qVASLAE6ZfI5WI+LxdCLO17druDJBnuan5zMztdbMfJ5LLplTTpKEZJtWEdw3bm0ljNDoqhCWmBMxEpAAF07IHOIBroAjIOJDaXcN4kGYHK4D6q3W1mtT6sYGxKnMdHpIy6rbHnU1HWWIhMYJNMDdYbW4dDv1Tt6pD5PnMIwO1kKr9d3q2uut2+agEcyUODkkQGMIIkgBCVUMSB0xaKS14yGX9UAgCiZgRvnb1B4HueiAttCd0CFiiMRIMIvMpZSch7HuOzR0yIgiYiSjUgyEA4M9RoDN/UIdZjQB5tHVWwdpDakDjSvJzU2tmbWIDqbgBuEABIe+8wPORgT3CNsfpvZBvXbvI25Zu6uGmYcF0tDgH/AYIDKj2h3tB0Iyi9fL+v3tct02NQ3k5FgVlyRLyZ/Oy8/Pp6d5WkoiwAhgEWYe+Y8A4Ees3GH7U1u/rPu3y+3bdSslnTABZ5KCSAZNvQ1kGBBTLoH54/noNJs85Ckvc43aVaM5kwObku6ga9/g+rJJVpjotJwnykApPLuZKXCSlAhL5khC0pWQDNBqrq0Vh4LkWR4QchEHtPM8R8iOTRCmnE/zRDiUtPL86cuvv/7j89MXAvn2/fv/9f/5v/7n//yP69vqGoLJLNbber1erte3h4cz0unHT4PSR27zXdE+uv8DFT+4bQFj8YdKo2Qy4oBGIg4D13erzDsCODQYNKqdDCXZ0XKNwf1gxY8g7d7NfZiD011Yet8cHfYVbuaH0fIPpb32uvYrDr9Z+KjFBAgwAELpliMmiplQ1MMw4golnQRk76uGqbupMYO5IzKMJGQKEk/oIwZ7zjhnm5ImEcK7/cK7Y97h4s/33CgRccAACsJgdCZgit6g9/DhuvLhCwEEKAEnSplywpyQAd1RGRMCIyAjZMEMPCWZ8kje0HBlBkQWkuSym6NhkizT/Pnzly9fvj48PEpKvWnEZha11m3dtm3b1u36dnn54/v3P15e/nx7+3a7vm3bdd/XNswy7vI25AREQ1QCw46aEUrmueSSJPOPWFAAHKnb9/U0ACFxIiSUYVR136K7E0CwmWDfk0eRXKbz6WzdAaSbIcH5YX48FUmCCKrRqgkLsVxue6y7qlpXhEBUcowWb3VtwR2Yyvywvp2fnh+en0s5MSVnAR4yd7GA6gYAB4Z5B4j+89IOd3LEXbthDTqbAw7XQyJEH1A/jkhQYAomBifv0Datq/a6ttW0OzgTpMyZI5O77bapW8EkUD3Wbm/X/rpV5TACg7AgHx6LQHFX4o0NLfIwpggEolHgwdEPb+z4K9Lw45NGCGg91stWZlkfCzNAZO1aeZjmJgQ01YNqVOu+ba3uy1TCh6NGJ+Cn83SaBby11qdEZqZt1bay0DLNzw8PAf69bRCWEj3Op+fPX5nEet/WshTZ17Lvc2+19brt2qpaJk8Mw1vc1cFTGeJgYBTij7wtcItAIjkSeQfpzAMCeejcKXEuUWYuE6K401gHkRMx+GHd2N0udf3jEtC5sk8RKRhIgHoYmIWGd7dmvXbvAYaMdDDXD3obyMw0Q2Q0iRAcVImRHzsW6QNuZGeGfwHk71cv3Bfkcfi/U0mpJCk5l5T5uD0JAOGQLg2TED48H48bCGPsRYMgCDGNDmDkynWD2oFa5OaSjAYHWxDpAA0iOlpHUwyHd6PZH77eb9e/2wwgkerhNmqq7hYj58jDI5ipVmkt45lyLoOZMsa5CGxdXy7X769v274hgkhWi64AwKdSvjyefv3yNCdhQLfwiJRySmks0wbk+17dPWCr7eW6vty2W9Xp4WE+PU7zeZpPGNbq1lQ9DBGZRXKCEKh/vRFKJyGcsJ+pgt7CWnSsihDIDCmFQd2uV5fW2fWx4/wJJQWiGnbD8DHJsFDCEikTiSN6Tl17I26Igb5EsBUijrk8qMIqfc7l89NTRL9eVw8oZfrp6y//7bd/Y07Xy/rf/7//4//8P/7f27YiQqICnNz7ttXL5fp2efv05VOEfty7IR4SAB+5sEhIJHfZ8Z0XCQDo4RHm4IE+wjZ8IB9qvftoSAMRggZX7cDQPYLiWNAcnSMcf919uX6vwNBVW2sszJJSEjwsfm3sjyDAzIfDwZBBfjwYbqahI311TMpCMnI6Ncy0tXBC1po6ZyRWBN6n6/Ytyynh1Nat6tq09laHXRxTAcrhKUBySbMkSi7J56IlGzI4h4Jab44So/kddPmwQ/x6KNuJQQCDMRJFFlTBynWn6M1M3910jg8kAUmQGLKTAI/8DAxCR3RAABGiwhmoMDAHosGYoEe3FYLOaETAy5SXp+evX74+P3/KKWvXt8sbkQBg631bt3W93a631+9vf/zz27d/fn/783J5XbfL3vZu1cCCffT9gYDkJMBymOUHATDjMuXTkqfE6cfKTohMxEck7ggLDkZMORGRmo0NvpmCByFl5hu0a7xR2R/P/RmwLOenYElz7xpguaQinLOkJGrYcnAqeTnl1yu/XYlXpj3AWKjMbOgvL2/f17o7oeTTyx9fPn3+h/7y/Pnrcn7isrBAzpJLuVb7/VrDAZBxTPL8Yyb43y7g+1ru3eF17EYDYMSAOZoDEPCIJcYjolOYmQPcXMO7eWjgVgOhY7SIDJCAh2gSQ6AabRUvr/b91iIBJHIewQl8RGXDoFSPezYwMBxcR18NYz5DGDqBsXP/T2oJBoRF33V9W1/+ydrbvE5SmIRFJEny3eu10pD6hyPGvt6uWSRJEtnqWvb88PjAwsEGKQwVhM+PM0k4xOk0nU6TmV6Fk/A85Yfz6fPTs6ldehOAueQ5MTye921b1/UmaU3beIOqXTv0Bm7ISICcEib5wY1ucCEZObHMpSxFQKUzMUKlhD2oBzcAbWVCyY6kDl2BAqACDBqsWGADu7b992tYTS3Jp5yfM7IEujEpUxZPYsLIjJSRg0uRXDgVkonSQmlBWYJnwEyQCISACIFi4N/drTsAOMMxX/8LRf799xEB4ENdmVMi5JJTlqPTOlQa9xvjL34bDMaPH5GcMCgZEE5HFJWHA/QABt8VWYHUyYyhJ1mITlM5s5wQ02HIc7Tj8N4F4scXOq7s/2Sxi2ZWa2219t5MbQjKR6cCAdp7bbXrmOVjcBnNrDV9u96ut7X3miVyltOcU04IBA4UIQRZSBhD4/CGJso5D/HIe0WBCHNvrb6+vf35/dvb9VItRNJyOp2W0/n8wBh1T10VaY8AFk4peRDWv36oskxpwsSRPtWS8TTteI1WIQxgZhDYQMnq7aqrt/2691Nb8lPmcwBYqGRPEyIceT3AhyUak5BwSoUJrBc3BEKWKDJ3gKXop6fPKPbweLpcru6Q8/T89Pn58fl23d6+v/35z2/f//guzJ8+PaUiwH7dL023dd3e3t5u1+v29DgAjPtnAe8m+RAAFBgw8mHuz1nAONlj04iBw3KWkJgHAI4ogALIAR5AgIzMxEISGHxkno+pnWm4qqnqMA848OrxqMRd4DKWMhFDF30w5gLMtLZaa+va/67mOVTwGDQwhCOHDSLCu3lTVwRyqAqMRI7AkrTfdioUyZp1qwqbo2pQU8opCzJxEZrydJ7m+XSWeXbht4jr2rZbr7fuzfcAinfG0OGddxhSDqfnABvUPBy0CmEkATTEaOg/UB8itCsErNcbE2MEJwa02re9XrtWUBdinDEN1VYCSB40hFfsOPLmiCXNSz4ty9NPP/36269ff/oyL5O53l5XVTuUzGrren17efn2x7ff/+OP73++3q5729UUwgmDCQ833JFDy8wFWQLRPcKZMQkvU5oyo6v1+vHjoJHPDeCggxWGAH637u9matpNzRzcIbCD7r5vfU15fdBu1CbvptGCFMjdde9N9eTziRZAlsQPKc+nUy4l55SYs5C5jmgc87DQ2uuth8G+1+rWKfS2rrI8cJmY8zwvD+eHzem6++qyQ2lA6vRfx8PcNUD3jne4OI3FpzmgIYDCIfUEuze1jERCKZMrEriLWbvtffO4mC/mjxEPkSZIiRDdoinvG15e7eWt44Q8EaQgQaaBx/q9VwZBGqmOGOBjTBp2szRiI3hglPAvpR0BECjcvdn2tofrflvnhznPWXLKOaWc9rd6SVdJIsIkSAwXMuIoc8k5Bzgnfu7P8zw3a4C4+1rS/OXn5yc9r9uaUypFVGEquWsRptM8n6Zpvd7abeutMsD5/HBaln1bL9fLbT9ttdbW9q1Z31StV/JO2Bk7y8JI6WM9wcCBb2cpp2l5Ok8U3dp0K9M+tegAzfy2Rb+kjJIcqBuoAQ89lkGIOUKkwLj1BtfQ5JokPc5P5wMD4IDCWqJNXifpM6NzYimTTBNNiUrBMkr77DQBFsJEIAhIYege1s2qWTWPIBZzN/CQ+Di3j8dlPE8ejgg0lAZYCCMJCx0qo+MixIOPdJDWiAnJXd0OkxN3N4vhT4pACCM7GCMcwJJSMhd1cYVoiWkqT6f5S0mfiE6gDEGIjGg/0kUP26/AI8cC/+aniYPo77331lprzYcv3iDWASKhmda97q1yS7Vr62pmtbbLdf328rruG4CdZ35Y8sNpDirVCNz9ME1SjTD18KH0O5IfPy7dxxC/bdv3l+9//Pnn2+XCuUiS0zyfT6eH8zkJ7SU3VWIxN0QUIf3IAUaQc0pPxA9IVtIS88L5d7NLqIKV1DkyRQazffv29vKW3tbldp6+LOWZiIOcs0lx5gfmB07IKSghEYILAzHkRIyYh9sfUxAUQVomJE5Pn09bu16uV1MXLiXPOc+Xl9vl5Xp9vfa9nZ6evjx/nk7FyeNbtLe6b/Xt7fJ2uTxcrqPR+fhYAUD44ArGQX28ry2GSzXePS5G9MCo68JJJIsU5sKUESVcfQxsnCRnyM4ohMLC43uHfMXj8BtkYhxub3SP+iCEI6xs+A1bmB8dYozpu9VarRvJDzql8cT52OfCWMpDxBF95mFxhDCC+rEvCmeUvQWZQthIYHZMERgGHgSShDCLLNPycH54/Pp1eXgAV7lt/dpv13p7q7FpeBzlD0eUaACGj246IgYV5nBlpiACltEH8PD4wx8gxtj3ilhVtdZ9u11ZGMBVd9UdOTiTZJacmIEIgD3YPczU0KM7K7oLyVxyms6fP//062//+LffPn36jEi3dX17+369XVUVCUTSelv//PPb7//8/fd//nF5vdWtmwGSMCMJYQQDjMQ2uvdxZMPlKFColLSUnJms1zYEV+9fPkwkNADtMIgBQqy1IqIfSxu8E3yjqUHva628rae6VWqTt7ABB3u4em/u/fPzYyCnnERkXuZc5mnKU5HhKt21d3cFADNmZhHvvXUdqvNWm/z+zSQFChI/nM6fnz/jdGoyNzl1PnVMGvSUfriv/mVq//E2u++wwyHQB2UT0ZAAfRR4G/xNICYSyjOlhGBgNawq+uZgzQJbuFXV3DoxQYvaunogknh4aCAN2VsHUHegEWiaBo5Bg1kV7qpaezd1c08plzIJCzOWHn9/J2NbFhhOXr1G82b72lMWTmkkR7OwCB+mWlOSTEEa4mUueUpIwIm7a5kLMTLL3tpclvPpgYmJw0PX7UqIy2kiwn2vU05CUBI/nJdWMcJPy3Q+L4jW+oay5Llcb7v72jbDUOu13aJa2xjaSZZT+M/x8TPBGDFvQIFCnGms41MStRa29d6NhCRhnmg+5eVModgRmru5F/CMJCjiSDXg2oxUl6TnyWcCjsxJcrICfbK+lFAPRyZKE8qEuVAqRBkgA2TCBCiMTIHoDq7Qq/dN+9r6qhSsHNYiDKUoLH97sAYSdNjX0CEPzwh+ZNAfj9vYRCIQjCHsvvkbSdsaoXbQXj3g2Joel1LEMIVUs9ZBOlAzrJ0YszwIfRb+zDRjMHJCSYNi9f7q4j2F+v1///44weDKjv23mxOCCJeUc0qDbNPN3Gxdtwgc4GZYqMfIxjDTcMNAHlQsScR5SpwpaMjUAd0BkJk4APSeQDLq+n1fABGu2lvbW6uJmIlzLjnnlLMIZYjl4SGI6r4PHx33HxSv2le17lNBAdkZN/GL2xbq0CEiPEVMCai3/fZ61ctKtyW/LuVTypkTSnHJLvlB0pNk4kypiEjCSIwpXEoSUwwjYGUPkIBhhpE8i4sAI0Vgkom5IPBpOj8/PP305Wtb9+enx99+/rmcZkNv2q7rtTV9fX37/v1lnpfWPpCZI0aojAiPIM7BqiMiHBEFNlzffQgxDf3A5I8sFeaDNCYACQMQGZCZpKQiJZgyYRLJkpPkNKrd+OgtnHwMcofv6rD3GmV5POGMGEPXMJTj9wxiwOHD/sMd6+B38h0EBkRXRMKB0dxXpPftL0EMdleM9HcmEiFjkuAAxkhJOW1EAWhqfdvWt7fZjLp9X+vby+122ba1e7V7eA0dfiSDBEhIRAIA99IeQAboCAZgBu4HxfkHXVLACKEwc1Pt+76JjJ8vMINIylkwYYg3sGHqEWHqGuFIo2QCJVkez+fH56+//PbTL7/+/NPXZTmbmamWnNY1ar2pdgC8XK5//PHt+/dvb5e3fW9mAYAxfO0YGCnRyBzkwyqdPCgG9TIlnuYicwbhbmraP5a9Vuv10saanZiHMQ3c55MjQpiOACIIcDXWmqx5tKvu3TZou2rvZsNOxnp1bZtFBXw8zQ+nqUwTkyfBnIgoHEzNm3pzaBqMqUhkQTWLgKoWW8VmxgwkxLJ3u+6NyhmnM52MlqQEGmT2g6Ln72507xZDd7uOwe0MiCOjBe56OEIEIEdQBcQYVE7hRBg81kTNdXetuwc2dbfcekpMxGDo3btkmmOq0RR0aKNUm1uMxW3KDFCmkpJITuO4mqv32m+3ve6NpcyTDfYVgk//UtcRMIAjAAx81722uLZxrQ6mFAsl4XmZ5qXMy1SW5GKQfN9qnnKaRApvbR+pryLi5lOamta5zO4ODvu+ztP8+PCYU2JEZnRrOfOXL0+15lr3acqpEFUAjpxSomIBtapIJ2zecbtavW1gtp3T6aT2//J3t+/D2dHDdfjCOGaUlB0EKJS8eVW+ISNnLLM8Pi+tm5sbaN2rmhOEIwnnTMiusHeH3qfbPrGfkk2cz5Lm4jla7rpkiFCzwJAMXEBmkkLBZilIkAYsieNhDG3edt1vbb/Uet3JqIlqc3eU6B9L+1++VfFXXb97yToecXZDQAQDDYeAw8d/XNYIAO7ezbsN9OZwcjkckBwQwAEEQN1E1VoP2MNZETHzgv7I8MxUSAxTQclhPawfKsu/bqk4lLv/IqjEewcy7G4BQJiXnB9Oy2meAlDNr+u69b7dtt6dZAyzhznL2GmFmjXs3dVwyryUsmSeEzKGqQNAAAkJpeQAtTVV1d7dLCLexVd812CFmnUFQGIZ11AAkshyOgegWzTfXbsr/IWiRNTr9z1V1RPOWWajGYwcTcHQCFG7BE6ECdTa7fLWXvfvib7N+bnMc55Snj1NkPKD5CtnlsJlnnOZKLJQ7p1LTugKIYA7M/okTCPOF+OwFxWmNJUzYQ7Hp8fnf/z6b+h4KtP5NP/09UuaJ4243q5/fPtTm72+Xv7883vKJddPeD8ew+0KEVKSJClLEkkyok4jamvqu6qa6siAc45II+H0GEzwWLwQIpMHAmMgEeecwZA5EWbiNKxCDwJjh7E/VzdGQEIPP6KkRCJ8jNuIQMIAFAGmw80HCUUYwpH4b+RlP15VHL9poQhBePCFx5J04AKCKBzCKISByEiEiblwAFmQK4GlVCVZ4OrAW8V15estSWbFusd+rXUdS+Bh3wgAQTQMG8bWC2lc9O7H7izCA2001mra3XoMHsHf3kaomzXt2ra6SpJc0uk0T8syzVmKKPYatVtXr0CGaEfiNyABI0uay6evn3/55R//+Me/f/n60zyfiKV3tVkfH851X6+X1+122/bt9fXy57fvr28v2752cxhrXKQgB3IWSTKgQHSMgDDyYd5ImdM8panglAyxdsewjwd92/aX1+vYA0pKzHJXOzgAIPNQYyCTSCJEdk3eFtasvdZ6W9fr9bbVvVrPQkuR0Ga93tRXi58+tUBLRZipd+3aat/Xut022xt0p+4YkbLQJGTWm3lA9MEJYqaUSEo1uLzeULpMOkdZ8pOzqIcbAPxl/PCf5bW/WxK89zIR93zLcUt73J0HbfjxGCIiMUtm4RAKMI+CVtg60oHUIYEj+Jg53Fue4lRSNHWFu5cODS2uu6q2CC9FcqKQgXjRyOxFqHXXiGgr5NxTTjy3x38ZE0eTG8gjq9mHWwTC2OsguZEZq+3W17qXVYqEREikKZUl5TmnWVBQCk+nuZREiJYUPNahvQ4M8y2trTYmNvWAuu23IRMEDJko2KptlOP8tDiCeqSdmAHCXa1ubbvufR2ejuit+Yd1CTNKZmECgNb1tlc1YIJu0RW0g+rwxBTKuczT8uinpmttNNzg1SiwIlRAtkhm4iqhfKn0bYu94yTSJfec9172PgNAYQcOQmRgBkTqERahEICMQGAEjt2id2g19puvF9vedLt0cqqJFdRFp/LjqR8H42iESYYtC+Phm39YZPkdkn+Xzb5z5wbO2c26mfrYYd4JrAflLTAghkW0BXdz6hF1ckgYQbFl2U+zJpqQMnIGTkgE9rHNjb//4u+MgSEeOVjFw/b88bQ8nubTNI08b21thHmrWpJMxG7R1ffaamvD5l2dqtKtBRWYEy2TnCeaksAIExlzm3sYDDPeEWI/miMiEkmlTI+PD8/PT9+uezO/Xm+vb5fbtp/PJxECQJaUy1RKhwhD6jD8KY+vvq99vcHqyMVXtc20dTMIZ1D32l0ixCE6Qw9bt9V223beyjSXJZcKeUFJG8lGiTlzmZdcFoTExMtMJSeGB4gcsDPjPNWUTgAMYEGrR1M1EHQ2kiDkOU+fnj8x4qfHc068LDMw711Py7nk+bJfXl9u0/wdiX/Ky0z30u7RehtyMmEGADM11YPX1ptqr61qt5KmnBNwjM1u19Zaq3tLWCk2T0xY1U1tjWgISuiBfHh6/CWU+Is3N0ghER6Oh6QdkZnjsM3xsPewC7jfWy6Sj0Bylo80DiZKKISEg993CDzfWU80PrpxMBzfL7axN3UAJ/ccNAUmQAEkBzS3FMpW3av5vqPtOKxBu3s/ZAP4V1zI4I3dTxyNbIN7wHygB5gH2TGqB4ya/LcDEjF6GhaWLGXKZS5lKjTljlBrbVG7VyQX4aXM85QTc0JOnJYyfX7+/OXz159//vXr11++fPq8TAsEWbVeW99bdAcD776t+/eXl+v1pqqcaDpNVLt2Cwc8wr8BCILcCWAYnGMgRhBQ4kjDHhSvW9Vu21aJ0kdAHklYCiIiH84WFu7ud+q4IPK4z1SN0STazPVrjtSibf261T9ve/OO4iDIHBFoRhczv62KXrW/ru00XVX9trZ//nn5/W27rr626HoojTSidhsTDDGlqczLNC0zlxml1A7rpg5CaUopM4l7WNf/qrSPin6XId+VmTi+HeHQBAyl2eF6hQ4IZAYAKIlIOKVI4off58QUwpAFkwATkKm15v1Ww/eUGFnain0/Ln5mBCD3rqqt7+Y6zzknZg5kRkRhmcu0S4tY6667bcxdRJbP9eOYeDz7EAgDTiEMoVHXjzVRgDmgG7q32rd9iJmDAgTTLHnOZcl5ES6SZqmrzss0zdmT13UXkZILEYcFAb1+fym5TPOsbnvbiLmUvCzLskwtmjXLKZ2W2Sz22jkhortrb3Vf9/22RwMyrGbRh8XI/bNhKmXYamE1i22X5oiuFqYQiqEOEJGFppIWn9Sn1vNqtINvrt0r8o5wBQ/3orqITRh5M3mrsKslpibSJLWaa1swWAhTApGRi6CutVuAiwMGgbM0BMfDnnaP7Rq3i98utl6MIlo2F4c5zvrDwGthHk6ByCDIx/DJI5X1vVU8Rvs7z+5+iw60xgek3Y/AjTFa40GzQ2Q86m45HEM9esfAxby4ueqryPfz6SnLlKYFOQWn9zTQd4nLgFL/hTz31+l4z6cDJBaZp/nx4fw4lykl7YYOe5KmUgM9BuYgptq63tZtXbdwJ+TA1IKhR1YngrnweclTTghkDhoR5kHm4UJD4B9jzT9+ICIyT9PnT5/e1v0/vl3q6+3l5e2fv//x288/PTyeSyQRRuKU8jTPhKjcOnS4U+QDQNuu+w1uQF7bd22v3iu7MZqYR6+9s1ly9JbYmdRj19aba9dVPRuQAaI0oApCKJz3JZcZgJho3mEqifERo3jsRDhPa8kPIhNhAK4R3R0sOUOGPB4EOc2npWT8+fNo/as6bvs0zVNZXq6X675KfvHwh59+mZfHex3xPgjuEUxMpKamvQ+enLr23mprpl7SklJxAWXz8N6t7m3bdooUzsWciDysa/XYItoIZzVzJIhB4LXBaj8euvsLGPy8IeM8bOTd72GEAXR4bQETh4xfDwbnD9aZwlIgD2a/xuG9bPGODcWdz3wstQiADmbz8D1VD5TgU/AckAEDyBE1YRPw8BZaN9t69MF/G4jlAb4ezTHTsZPmuxDlLlSho7cGM6tHiDwCETLp30o7IjBxmlJZynSap2Uqy0SEHr7t27at3SpAX6a0lOWnh89fP31acskijDSV/OXzly9fvn758vPj06ecFwyqTbe93tbtersNVVvddb3W79/eWm+UeDpNKLyu23rbTH04+oQihNvQTyMGRXAAhBMyszEakquv12277XXvScoPt24u08L07iFhZtARUQqJCFHGYLeRqdIBuuB2TvrTA+AN/sN17/3SFROczzkVRkZQAI7mYdrrm72tW+FrIjGD1v3ltr7c9stua/Xh0zjkPD1QAQywZJnm6en54enpMc1LcKlKtxrdCCDx9MApWXNXdUP44Fnzn2yox+Pk4eDv/CcEHPrHQd81D8U4iPIOI+mP1FCNUgJkRqQIpbHVpJQ4JxJBBoeRJNFB9/Cu3dwC4D2DcAheEbBr9UNA3FSD0Ef3O83ldF7OmyK0tgcAu1P4v17IAXDMKvh+ewfSOKAH+IsAAObuMTDd4ZLlVXX1eumciQqlIuW0z6dyepjKlIhRhPZUD/YdAARwklLWIO+hgEErlq1MtzKEMylJzqXkQiSEQ0nQtTXTHm4YBIDh6D8wHAEJSWiQkZzwvoiNw/4qAMCJAbOkueTmUhuXnk5Udpk09UzJBIyqOakh+kycU8rI3EBVgzrLNBVzdTNPFHPishSZ5m3X21bX3hU0PAypJwTAfcwkAb3DXmOrse9Ra3Q9qMeJKCP/jQQ8NIyIQMQiJCkNl+/R/BocUggAO+5LAIJhREIAaO69t35wLIahyEE2OiD5wWILGlq9ERZiRtAngKmFu11v+eV6/l7KPJcFDwzqqOvwzqZ7X6S9s+p+eBfRzTyAWJDNDQ4RqAiLdHWLYEm5RG/qEOP6jIDW9e1tvVxXgJjnMj+cORcfStJQgkgiIjJ4TBCOyMKUmBNhuAVAAAHDnR0GSLQs89Pjw+P5dF332+Xy++9/vLy+PD89+DJNJaeURGSeZ0aqiGLwXtoRQCaQEmBV16av2t9I98kq9m2vYLduG4UtSALlJCed3NlqJltIRDJxDkxAgiBgaM7aAQYHiI+sNGHoGMWsgUG1XfY34cKEhB0xELgnNcOcuvAUjtpdGHMWN+3d3DwMMufTdM78av1yfV1N7d8f23sHj4SSZKjFHEJVW2t1rwiD2hY+gglSOp2Xp8enPS47dBVGciCCO3x07GKPhwADwMxdDc3C1bCRMwz7ZCKIoBEn9/ESGXM8IhIjgKM7DN+gYS6JI7WZIsiJhQF/8EsQSU6FCAgR3CgC3cjfj3kMQumo9I6hcLeshZG2HQEWozn2QA8OdCStpAgW6AjBI34A7+LuUbbhaIyJiYRJhFkID8D5/p2IFOARpsiABMiITNAJlD9sFpCwLBkFpvM0neayTAN2VtXeqvbqpgwhIo/z6evjp3/7/NtvX38+T3MRcVMiOC/nc54KMwVo62p929t13S+X28vb5c/v3//488/f//zj2/fXdW2BnpNISigChECorVs3raruvXs3QxycbACGCCemNBJjNVx9vdT1svemkfEjw4yZJY0dDvSuETCw4jJJSkKRMBgMwjsEJNAHpk8nenyQ3bh5d46HT/N84qcnITBtTTswMwJikAfeHC+7aeu9e2123dtt77W7WgiRCCK/5wQOx5AwM+1q2rm30GDMp1yQC/EEaQ5idDeO9COV/O8M+TvsHve4ykEjGLlVAQAeBoO/AYF8QDMOBEFq0DqkRAAMhEBBOHgqnEQmyVkyI5t6D99M19t1q7V1dY90yGYYAC1lQKhtPd5U70nioL2knJOcbKktEOoNuhkijOTZv9X192Upvv8CgfDOwP7xXcNIRx5Fw1roZkE9KFCQM+dpn855f5zmc85TksTMe865lDIEUYFAgpSJMhmoakdGvnP0iCmJfH76/LA8ghu4We/aW5geKBb+VWY+vIdwHKG55ESG4BiHTR8MMY+HIIEkK6kb3jolyAvPKh2LzsEmuHu7dXTjgEgiJRMjWHhXd+VFZ4VBYikUgfEw5XxevtsGt2Y9zNwcAMEYVKN30wBgNoP2nhenGMHOYil1kSasH2M8ABzCwZmYGCVJTklEEGnoltAs7mozv6dPDH0SEkKgm/XeDj7Ze6v2jtkfgUPkEOMOI0IECme37FjCQ/ttvb1cb4/n01OcPh0zcOCdHx8HP+autjs2dz9+Iu5ee7cAZEHSGBAgQCAFkUaoB4lkohqb23C9NXdvTS+X2+22lQzTnJ+eT5Rya5oY8IhIYaJDlEoIwphESuJE5IpjUT6EIO8SuJLSw+n0/Hh+vVyv2/r925/fv798fn4aOh0mypLSNDFiuEr7Ie2xnLksAND67v1qdpGopVe/Xfeb93W2rZBG4gyT5DORCEBfJB4AydEhdcxGpUDKCmqgwd3JEN3FPIVJAgzwyUFNY6+3cGFMTEwYg7qamtamSfZEEwS5R8kJMZtara13166J03l5mNKCnrZbr3tr/97/OrRIZZoOVgSCuXfV2vqYoUdgOTNlyQ/n8+PTA1ZV3UKYBQ+CfE6Ss3ACwDBEMkSOILUwdVQz7Bg+JEAiklM+DumxPzomHwIERA9EwkAkDw81tQij4UWIhAwUyEEeI0r4Qy0ZuasYCMGB5EFO5keWqN8JUMO0KtDtECYDYRAMyZx7QI9oFmQhQIHYG+4BVVAJUYgB2DGcAvn+4B8naNyfhMIog4k4Cv6xg0OKcA+9WzkJkxA0QuUP3GUinB8LJ1oeT9NpylNGBDON1rRtoV3QJfE0peeHx69PX355/ukfn359nE9FUq2rWkss7OBd67apt9r8ttXLdX29XP/48/v//I9//v7nH99evq/7bmGc2AOFpQgPBnfba91qDG5eN1NHAFRECiAICGYOgRC0iF5tv7X91lxd4q5RPN4IsSAcqxU3N8mSipSSUhJ2oWB0oCAOm6g9JHpeeDrJvuLmjTL89Pzw9JQ+PVLd19fX2iks0VBaqnPveNV66/u+9m3va9XWjRCzyDKXJSdico+tabNwQBaJQOumrWOAR+U8z1MqUyrzElx6sICz8yw/FJC/xcMgDtZfBLjj8aAiBbjTPfbYCSDEQhKgIwEhj7bXHGobhxiSsDAd2M3d0ok4MQki5LKU0nBrbrsb+cDM75QogOHnl3yQo8K6OXQkRkBHRmAok7TmrQcpQgjz30o7wF3wjn+loMfHjePxjo+7nQAAYxyUwx7cjYA8FKxHa+a16qrrxFJYEhGj5DRNhRMf8BUFZeJCh74fAwnnZV5O82CNkXHs0baGBt5da3fz++t0BAT8YUW9t7ZdLsJZ7m7XzEDs4CMPNpAGB4J4SqlnmWtaePJkDDSx9SATvfSdzAlq9RXtEl57QFcLBwYKFEBWl6oJMSOdPbj3utet1oT8lObN+7ZFs97I1ohORJncojXrzdyAiFNhLEJL9kmaYP+XT2M4pktKKWWWNKRCf63LAUZdvy+yj5BAgI8pMgdv7k4uGslhf32SBDS2noSIw40rZKhlA0NV6761tpt3HpyusbM5bDru3NExsiDeX8tfX+beem995E2EwzC5xe6R3H1YVREBRHFH7YTh1mvd933veri/Rqh5Z5SppId5ejovy1SGMxogCNMRsU0DINMIg2P3euisRtfNhKep/Pzl0966//NP1f795eWPP8/DvSkionhJOeIeo/PhB5UYmbGDV/W+Y2wkG+oevbYt6pVs79wDQ2jOCyYsxAme5/QFmB2t495ph0SRWKEpdOShKzMRzBmSCAWFgXWKqt129YA4XAuZUpZFqBHuWaYs85AUTmVSX1y91g5B7phTejydz/NpyXM19ME1+3AF55KHRGsQcZCIWepeW6tdnRyTyJDWqKm5BhgxcEJKgBIgI6OcEARj0OrZjczIAhjA495LO7mjmx0bIvtLsBARdNdQj2uQPNADCMAdjo0RQCAJMhAEx9/mEATAw8IunGAkowMBOGH4OwE1YLgo3EmZAe/QEkJFAPAKIRhECgib06agocEOCJLQFO29FXqv7O+xSOP+C4o4spGGIg7HoQJiZKB0zCHBAEom7yMSMT18OnGm5bxMc8klQ7gqJfQE1juoIScuc8lzoSRN7XJbOThKmIUHdjWvtSPiVpvCuve3y/ryev32/fWPby+/f/t2ud1aV2JhScPbffC5U85Iw3BYkhTNWm+9cuu1a++hMV4eEwtPKU1uodbVweK49T9+GoTIhBDgQyAfhIjh6B7gjmEMI5qlsr8luCRf3ey2863flDWf6PPn6elBTsXJqQp6DzW38WKTlJKM3NlBIBJgpiXo+fHh6/PTL1+/PD88siRz2GrfmzUdRlWeM5/mBOF1rxFIHEuKh4VlKsBTVd/rlLN/pP7+WNoPAgWEh+GYJoOQ3BHN4m5fjRARGqGIIxVbACmA1CNa8MBSJ06FiQ9Xo9EDAjCgICHLJGkmukVwBIXDMD+BwwdioCJpjHPm1tU8gDwCLCgghDOmTJIZESGY+G+69uEh4jBcaY8TNNzN8MNM/z7WE96vdgQa9GiCY20WI9KwWrt1kiABFCBGzpymlEpKRZDR0UmAMw3r1cELq2fTFiwkxLBjL0ZGoTCCKNzuXph3YO3jZ7PXetW3lHJOJSVJmVMiEWB0xkiDR4ABiBREk/DEssiECIXyQ3IDUN5LdW8KvUPcwsVV1MC6EJaSjh9+s9jbLPkhaG4Kvpfrumz7dF4ole97vOztZW+dsDK1JAjh5n1XqxoGzJKKwJxgSTFxk1D6eweFSDIUMCkx8RD73vkQcDeriSMfAA/NWziGD1f0UdqJhsPGgT8e4Mu7zTDer1IcIhigGJcsoJm31mrbe9/IGg2TMDgoBTwMjcdtfdfYx4/BBO5eW69dB9CERIFoAN29qjkgSuIkhDCFYQtE2Hvf9m3bd/fRhph5a3WTJMtcHk/z8+PpVAoijUAXYUr5MJEP1yFcgyOEBETSyGl1M0KYS/7562cL2PZ22+rb6+vvv085izCN/4qA3GwQDz+cDBh+v1t3rACVeRfZSaq5WoW+uu6eHAwkTUthlmwyp8+P5984Jxdf/bbq1cRcXFEMG5ETB7Mzu/BQsxew1Db3iH3fNVp4hIMpECR1IDTwlqiWVCHCrdc+q1c37M0S5yS5JDkvy3lZlmkiRQPjD/mbSJSnMpKBiUhYpkCf/e3trfVmpu5ASBFRW5X11nwzasTBCUk8SB3VsQcWBATgCHIjcxyBVPG+dB5BROBxR9jHBwL3vfuxaI8YLlnEwoM3fzxLAw4ayBzBoC/9uKQ+bl6Ee/86tA5Id6EGHomiYwevY0oZ4/ZwbFICHeCpBqIHaQdUBwzj8JRJGPs9SCvuRJW/yCp4P35+/Bs44mrhWH8db5MhAIEiMYBQ8MfSfv60sNA0lVJySgLhRp5CMqbavBtQTmkqlEUxXrababSqj8vCA/U3h15j2wywNb+t9fvr9dv3tz++vXx/eXu5XC0iT1MafziGgR5tEUsZKA1JlAiNmtuWtuvrrTe17hGQMjKmnKec523bux3JVIM1+BGeQwRG8AiEo7SHg6t7BwtPpITB0dlW0m+EbwB7J7sE39oNUsxzfv5UHiZK0YyxJKm7ubUeAYRTTmUhSkly5Ay5oPbIUv79t9/+9//27/+P/+1/+/mnnyXNFrTtulXddt1rbXVH0JKw7be3l5f1dut7O6F+LrGcKU3Zgczj1vdL2/7z0j50NUe2xMBGedRzg4jBk4xQhAA1AEOKsW1kjOESDACtAUEniIHrcMIx0Y5UvTgoIOrRPdTRgAIieu9DYpnSQFiZmd1Hx+2ByuEMEGiORlACEgpIgoGn/t3g6eBawxEDgvdO9e+GtO8V6N6bvv8W78usgyJ6aJhAwzmAXMlBvO3GpcugSwzwfIgGCQeXZl90P/XhirOm/S3d5rSQJ+8omAkkogNQHIvWH15d63rZ1ySWxCRJSiyJUsLEURhcIBiNICC66269RXe2tDBOnNxUwxpYUNGMaC7Qqr2Zkil0LcwueNk0vW113epWE5KLd61Ajdf9rPYYwIHRvK99B98ZW8kGrt5713qtsSs7SUoylZhFJ7KEzmM8eL+3QIQBMaUsko7CGQGH3QwGIh46nAO9GVscoWRh4G7atVcLj7v2hEb4h0eg/9Wk4VGuYexS7xtQGPbYHqr7tn17eZMleo6a0RMhRZC73Iej+4V3IDcfUR5zr3trqk2NJeWSKSUDaKoDKg3k0ZNMUxYZu3ntvW51s+jEkTKx4LAchRJCNKWUEw8KMkKYGfYWdqBXg8D0PnQPUk/v3c1EuGT+9PQQQOvef//+Wmv9/vLy/Py4zBMhCnNJJcxMze0HEsd+9ddNb72WKzy8nR56yZ4A0QWNyKcEU6Y0IxboYnvYrfvUYemcU1pElfcGCu6kxEMonEQOBBOcgHJOD8C575tq1Lq2iimllFIuwpQTzwgSRgSMFGa96+69662FYxgs8ykLDW8cKZ4mZCwolPIPCPA0TYNtNqZ2CDCzUkspRTuOdrC1+vb2utfV0w5Ly2SUGEgda/cbKQQ5RXIDtWaxByoJUmLhTMBwZ2UwCR28y5E07apmbge8dCBAozQTi3iEHVdMvC+QxiXiP47t4wYdBA9Hi1HkATCcRg2Gux9OhIV5CIATOmMIAh1CDoJAHY0cxKEzOTyfXIRQUAI0IJyG/P4+sdNgIUW4uY0sYAxk+FDzR4j2wVgiQuDhcv3jtUtMAGG972oVwLT33ro21eZgQDhR5pQ0/HW73Xx/xcta6/P+kHkcUNPQ7tpNm+q29+ttv1y3t7fbbatmA7Z9V9R4hLtGBz9yqZByLpQRnbK0xGLd2l7DPRxSTtM05VyIWLvVvZseRMW/5Y9AeNhhImmqZgZA5OgA4QEciN11DXsT/x68glgEqgslf3xezst0WsokQD3mvMQ5mW21r9ZMFbSq0E4IE0dwkICU/LCc/9tPj//+y9N/++3zz7/8kssZuHTFrtAVWuu1buA9cayXl9//1//45//4H7/f/hfWi9SSChZxD1Sz/Ueq1t+mdhrkphFXBGPhMCwVBkMDDNFw+NOpHdp95oBASgQEQT0i3AmDGZk5J0YCoCGf6A4IiBZNvVq0AEVycO9qZs4sPFwciZnYCM0dTANaoIdDmFookzFOgUyCDBj2L8na9+p+vItx8MZVCXfrlPfK8/5/gYHwbox8KE7gTqkKOJ4qDwdzGvFiit2oIco4BgHm4ygOA9K92Dr14aQqiJnT4/K0lLNVYMyEcvBm79PBx+re1da9MruwiQgLS6IkOGfwjJiJMo54q6a6W9+9GxnnxIjsQGpNXAKLZ+TQhHFrddMICAUDRIOXrRncdFut1oRSsHfWsQMpBEvXBLjufd76CaMlVg4L11DdW79UsCApWFhK9km0RKRwVscfnrIkAhyHSPRO0zzo6PeB3eMIbkfCsbwRYjcLV9emvQ3O8XDlRWI8BDxx/7TvBLwAoxgf9oc7Fd21t9vtCsx7E5rRF9TA4AA+DHAOHt3x6cf94N+/zHyvtZurB6eSckFiNd/DDeFwyAAcQHFKqGrrXtV669VdkSMXkkQe4eYYIEhFWJjVBpEQ3ay5MVNiHn/e6DAGNew92cHdElAWzlMmyVs3Q/rj999v1+u2rbWexq69pQamg6bw8ePoa9zM60XTFWFNkxWHFBwm5EIwEeVCNIFl3yFu3W69x9b7TQA5pcBm2sybgQqiCOUkiUm7q0FARpgTPSOmm1Ovre6sXUo5zcsplylJIUrgFBZu5qqGBq7qvfcGjgQ8E3MujCbNygzLmcfAV8pfEXZEVKTcwTYEgDA3tVLKPC+dWbWFhape9IIbyNnL5JmJEwObQ1NH1AgyCgnDbmpRAYOZKEnyjMH3nJCge2hLABBiEA1yJ40Liw5NeMQQXvC77f+QgeB9R+/hHj+08XeyyGh4hz8u4IFNHQM/HWi/i7u5ITqTCYVQDAozIiNydXe1CHN3DBBAgRAKEQBGkeAYuUgI72uve1LC0UH60II73Vn0cQw4h03j8G0iAib8OFEFhLmFmXoLdevaemu99egGxonTlFMgIO29a9+tOTmtW73OayamgG7arFXdm1Y17WqteW2676oWTERChIDhYRowIGQPG3K7YGIZzg9ASVgI675tWxorm1LSNJUkgoC9ad2rqh6xYvEO3wIMSMbNzUzNVN0MYKRZKbhBsoDd+gvZK8MbYEcgQAlMOcvzp+U8z+fTlAEiEHNC8K1yvkXrzU21WgsrmTNTEIjAaUqfH6av5/zplJ7P5flxnk5Pks+OGUIAxdRb28M7k1++/V7Q2+X1z/9ebd31xs7qUT3CujrPkB7e38i/hLrezY8OB5GxiR4tEh60IcBDi4FGqBSNxI1ZmVJQAiQEas02qsIpZ4oxLKMHqAcGoHpTrw4NSTEcyc3cwt2ngWMjBTOSobmjO6eRoB2A7tHBEZACEhIzcyD+a5DzQWPFH9/f6E/v33JwSeKu2T++2e9cGThu+7g3BEEHkoyETiPegBBwsFsBwQF9rO0HS8u9QnezGohKAYy1F1hzB2dXDKf7+pDGyPrx5bp5azoAIhGXJNzJEjFQZh4MHodQhwbQMZwhBEEiEFwtOHjCBBIAktCnRC2kg916v1Rf+9ZMa71o81qx9451U+QIAhOOkpEtknBbq6g/lAQoXr1br02xdtrVgby4FTAEYOREJAYU/BGQR5SUkFyYCcnDYtgbHACghVuYhvVwA4hhKzsoqmG9t816A3PEMdh2C4MBkvjh+XhvvjDQ7yCNH7nEBmNPGDUs3sxee5+2uSwlN8SzwwwyDTJJWNxhVAQc7q4fG3qPaN2G4ngM0LWFGyTCzJgTJXIwdfBEPubmdPBNAskIbJBOgFLJ+WGaTlPJkoQlIlgkp9J63/cdwgmCSQzBzc2dJbOIeZgpDasUAiBgxmlKDw+n523frq8QEabamqXU6n6FsN636+XWPN6NkAA+nabZT28NYccip8QzAlp0RzQE87Dq/qpQyYVdiUVQQmNdW4s1rrre6tUJKDFi5kDftTqYMsA0LZ/n6bmkRZv3ttadTBNTXubPD49Py3KSlBHIzU173db1VpF9OiUiJJLwcIs0BaWeBc7MP+kZ5q8oIiVP8THCDhmOdIGxaA9yQ5rnCSC0Z9NDDrf1vUdDdhegJJJ5aFuY7s7wjsfubUQlDT+XMRwMo65DczYq3JC0H8J2MPTDmgzxeBoREPwITrpvmcbrvjPbf/j6q7KMaR0Os1y4X2GjU73D6ELI5EIkFEQj+x0JCYjBOIjVBnkEh8yOCQ76EAYTBMQdZRhI9IHKOwACHWI3PAR341/dIbbBdAEAcjC89yvH6XB/e71o79HdhwPFOD8MKEScKLJ3rjertW978+7ouH+v3/CFfFjtmqNjCmRAhoDh4U655Hww8/Fw4Rued64BHgQ8pIYyKN9x99iJNMnpaaaE1i0viQtqdK99r1trzW34VB+cnr8+DR8LYHMzBBAmAmPsAhtZdW8WO9qFcE2l5RIykeRAdgDILI+n0+enzxxR8XbRda9rraY2zO0lzLwH5VQkU2HHviRJ4H19u778vl4+1U/PZXkYWUEITChOLpiHI0bOo78Vhmj7+vrNe92m0wKI7t6Wz/9laTfzYcqMR3jqAGMGbQmOJ22Ab4CuoDR4wCyWOJCROAVAN4ddU8JpEs93Kgm4g1lA99asOSiSUQxXhrFYVA8NYEQnHpncERFExALMDugRDqAeHYCYeTQCfyvt+KF6H7z3cfMfZyvey/idTwIHNRvv+6jjv36f4cbvaZBNIN5d7o0YiHFIYA8Q9a+/nMDBO1izwUEkx86xSp/yTCAR48+8/3U/RiC7h3aDiA4h7KIhQmGcWTwxAiGxh2uEAhpRJAb3YIiIw1URGRiQKSbBM2bnAqLXXr/v1+/X2/dbbd1VwRTN69avHUiN3ZYJT4X2vSdCswjmUiRAttq3qnVrvZqrK6NTdHMK4OHLKIEY8qP7+pjaD1O50T+iw32L7KZu3bQDGAJApHdDbrNmursp3p21w9QNAAWRDm/Ew+sT4PjxxfjHrYejqSMCO6g17K13bC3Vfqqns6dEJIxTYXE0D/W/0oEx3IYH3IczH6oaiIB0AOOOiqCMlhhIgM16iMOIej9Sk4iIgsgDYiyakKXk8rBMp6kkSUTM5FnyPE8AsG9beISTmY3zGBCJKeWMpgFOQXioYpwQWPh8Wp4f6/p26q2Caa+1C+8Rvdbe2r7edkeQvzwOvz7kB5xyR2209Ek2djXr5gP56G7XDn33KXROmgmyRPYet23Xavvq+25Ncikyg3Mo1b1bdcRTLvMsX87zTxistradaoWIknJeTp8eH77Mp5OkBBCmXduuVvXaAjQPBnLObm7dkgAmTcw8y0/pcf6UQRhF7Pfst7/OOH7AiIUpkAgxojChqbm59r7X2jetuhmbMYEAybCYPfgcSAwwejsIC1dzHW7XoyDau6RshAodcI4fca0O7o7j39HdzG3U1XGw7X1MAhwW5n/buw1YFA6N+cE2Oo4KHC3wu4iXAIhYiBI5EdDBiAIkAqQgCgRCUDvYqKNLGCMbohN7ovtG/941AGLctSEIdL9MY3BD8R37QqK/epDw4QD5flmZX96urTbv5hpuQcgkwplFhKAQFGu4t35b99tWwwAdLvWGTaMrmCEDF5rPuZyyFBl2fsQkUpgHuglmg8I4JkGzcLin9TkiEAWAg5l3C00Tn3CmTL33khMntOi1aWtVtUMAEzKO7PWPt66bWYQjAB/jSKNYBS7sN/DqXjk2yZpzlAmlIKdDS8Ah52l5fvwE5tDh9VLXve21q5o7ABxiBkSRXBgxhDIiQa/r6+UF314/P12/LA+fSxgedcoIQBgdyR2YKeeUUxLmW2vf67bu67QvzCO+Zoa/KvuPpd3NVcfsMiCbASd54H3reOCefmAY5oEGLoHmfax2eDj0A4Sq71XXtc9pximNDVIEq3vttrc+qEfMkCI0gTl4qFmVSIBOHMTIQcSCR7riKJx+nKJgihSQHBPRvzLkAeH9UcQDnkf/2CTD3fL5EDrdMfyjjkPg8a0H9zXAPWi0sKOLJSaWGFyD8LE0hzsYgO9n4p5oDgigzbztocCUwF0YTT1Gnt6P/P0ICAszd+9GoT1E2NULQ09kniLQAg0pWCBlLpMcQasRSONse4KcPDSiuziyIyfhPOES/MCt9t603lpf21VpN4RANJwqTYYJLeFQWxJFixQJ6dmnDGmK/mrtGrGrhzuDI0bCkAhGkI/uTgAsAuQDsbzP6sOWw4a/tlk37xA+TNoRh02NmjUPZYIsMhzoDkuQI7sFx06dxvfHnQ9uI6KyRiAEEYV7IDWwFgGAMtqdOYrmFEyA4tA1uo31KI6dUfTBcvzr4wjzEcEe2hti5JwpiQV0daYOCMIEjmgAjKkUuqdDChERp5SSJCDOglkwy1iy8sHm6xruh/0uYK1dtRNRSilgRNAQOZGThXcz9ciUKMk8L58/gdW63a6MUNeb9ZZSEmGIcNOPsQQI8FzaF+nTk2w7wR/r/v2q3/uuLebOERLRt9Z6XSdZPxX7NOFcMlmxrW3rtl47eQhRwojcaq299Wvzhg8PT8v5cclPUzrte933tu977z3ltJzPy+mhzAtLIWYc7mBOHrZuN7NqnhymQ0ZGIwTUhg80T+cznkkYRf644u32/nG4aT86bBiOFUAEKTFiMiJtx88zwNW7q7KiOrtnNVU1QqMRZ+poatbde4RCGLqGhmIQgAMFELrDSG/xgdgOL/lRVO7ycyQcVvPhf7E0EOA9OX5kqv0L4Wf09ERExx9BIw/rYOYhxPDxhMNpbaRpD6Aw7nwSHFckMhMg05FuyERE5ODoju40CCSDAHCH///qj/CuGnkHGeL9InzX/o/7D3kMNB8uq1ZHljcRc85JJEsSZEJGYaFgba5dt623aghMQd7Vq0bvECaIiBIEh1Uv4nj54y8Z749ZAMKDxZg6qZmZgYd3c+gGGGaIYGZAMZzCZeKuJsQErNXGCpgTSmJGpiDJf68diJhzOaQqqLa/envL+MpxC23oJmhCkIQpEQohgyBk4kJlKVOipKbq0VVrb127342ngiJSmLhxBzRgAwhgwyRAdd8vt+vrU9sjbCTRuDsCCgMKe2Sdp+V0Op3Py/n8+vZ2edvW7rOP90LprOXDu/ihtJu5dgMMAD+OXzgixqAbH1j2fQuJ4OaBjiNpDAwCGNmYA8QhwL013VZsk4Yh8PD6Jg9oGq0PSwcmRyZICVA9wM3beKCJghnd6TgIcdRmHMmv4+l28RAHpr8v2/9qjfGvh9Lfl+z41/fgvfwOMijA/XE+uHVwvHEctEl0AB6zPxGOvDgiCBhtHhwcsdERHFzYETA/fqKorqY9HIR0rI8dRiQpxI+lfZBiTUM7GAJzmHi4V4GWsWtSFwOwwEBBAc5qBxwFiXFAe/A+KHcjc9TAhDIJLTM/Sm1Wq8Vb1be9r15XNwc3SE7SgT0EvHAUNu41pZim04lyBmdszXx3dY0w5wgOByPCkHD5gVoOzBI4xAAjKVu7NjeNkbQd4a7uQ+JPcaga7vUenBkJ2I5tkeO7VR3dWUCHY93QALt7mLlpQCAxR0CAY3SITgRdI2m33l0QHIAlsDuohcZ7/4XgEHrXxL1/Gu42AAVX7RDCjDkBxGhD2IggHNA8FCgD3E2+WJgDIjEnYWTKgllAGJF4sHTdrNbq7sMzFRH3Xfe9ppw4pTvL+ogOd4WuGoCUYyJZypykkNpbSut6bXWv+8YipWSRMXf8UEtm2h7TTtMsOa5tbZe9v7Rm7gEUIB24dr+1faZX7v3kSFDQc2tdL5tdYLhwewmz3kG3aNcOnR+XVORU0iyUTNd932vdPWwu0+l8mpY551HXD3BrECzUeu8N0ALc1IabAYgTAFAkHjmNwjlxkhfp744UHqGtjqdA3CHiPWKVmcMC76EnxxrYuzq6m42QF1UnMTQMCwvrbt1cAYwoOIasfQDsY7keAINgrNp7194BgA5TuKNmH2z1GJFfR5rb/bGMCDge0h8xLbwLc+AIkX1H94/7gCCEbOBYCIDAMJRycEg4/EC07rQ5JHD2uM8ehBhBFBgxdMbH+DLCFX0Yzo5V0bE/8Pem9k5nOaaUY0cbdwreD8fcNSKIWXKapmnOKbPI2I7R3QFmW1ttvasLMSFFjNCeQAgQooSYEPmQAt8hh3AwDBSWJESEAcFKSNhar0MbG90hHBGYgCDAkWIQt2RiNafAMAAPFkyFJ0uZE5OAYy75I9Y6nINZZJqmkoggmlbXS6ELx6akEZGYErMIszBQEKEwTiKnNC25MHD3bm6DEmihEYOGDcSBCZxNqQMqYjccUD066L6v1+vbtt3mthEhgoIDEzEPCiflnOdlWc4Py8Oj5O9VL2GqWFMSYT51+78t7RHgNoB3PzJ8R6s4AnAFYQjC3sd2BA1FQCejgGADNwyjsMTEBGZem23buq4Zp7mUMlyZkAZhbYge4u6tNp4dcPfR+x4/64M24WY2OnohTxzo99n5b4/Y+5H5l18ccPqx0n4v/+//O0r5xybufgrvtX40NwMaI0JmZI5DEQ8w5shw8MML6zi6xzEZUEhQhFnXQCfkgajRe2v8w+cB4BDqrjFQgfBA8H33NfuyCScyQh+eUXdC64j5osMobeQ5jZQoh27QzFmdDAkkExiRZjrl/Lj06/+fvT9rkmQ5sgNhXczM3SMys9a7NbqB7kZzpoeUnke+kSOfDP8XfxlJEVL4wOdhc3oB0A3g4i615BaLu5vp8j2oe2RkVl3gogGSMyMwFOpmRUZ4+GJmqnr06FGtO9lfH+r9JMCERKaoyirUhMGHjC8J+5zdLPyTBmaZXMCa6axCxlmY1PkRjY6IDM1czZpKVZlbm80UHAmXfuu47EspKEXL/nLKdmOobxuhIznT0njpTIVhFREJ2DACmiD7MCytrTilhDnxkNNl4YGJl23ZACIMWiS1HUDNnkTtgdpwopTZ3d2XVtaMxAQlp5JSzpwICSwxpZSTQope38juyuCFIHc89ClnomAfAxBRiKnRuowBIOdsZqWUkgsAtqYQjo25qrWmRJxTv9lclrLR3skAAUWaSFN3BCOEnKiUIljg7IFM43hou92uHQ8sJo4NXUxEjq4CmCgDbEqeBvTiM9fmfpgrzwfXyWFOUIBTKGNl7LrcUVEHIiwmbiIic63H1g6AkgtuLvrhos8lUVrzLFJbHefpmAhfvXw5j8e5zrvb8c14TURdztt+2A49J8ZEzgiJylBKn8fx6iSlqaq73WFBsRPnnEspJedYdUu9xVK0hUQhionEEAJf5rqa32ZKUk2qgRA7MxUjEjFV86WvGy2LzGzp6jvPiJg4JU5MYGoCErG0RcGsWmtSaxVRPFXRmavZk3oeZsaUdCm2RHBgpHDwAAKO8ExLvvCUGvel0BoshBdhyU8ERG+ISz9xc/U4KhIS8rLHRnZgVXcOTWSCZfuI9bQAZMs+vIY9i4aOgT/s0KftNaWEXdf33dB1HREH6B/igGDmrmo15EejEoYKuVG05k4Dco+QTEE0KgEYmADBoqNXCMGHn4TgS9soA2miIuieeFk+hOQElIgYo+4YHdEgQcqUCLFtWgr1dYWS+/N8bhMZ5yl2bTLr8Fh8Rzz22RipMpt6JswdpRxuFBBSSWnTd5ebTd9nADMVMCMwJiNypKXojIn7zJnAXcyam2BoFTbNtZVx7Pe765u3SpmwT9yVVHLOc+OQ71SRlHO/uRgunm8ub7eHcZonEQV3YDd9FFF9kGtfnpivbqo6OIMv1OTl2S5cooCaFjYCuJuAaUIQQsbMSKpe3ceR95kIPJIa0aHSPVw/BtdwvMyMDM1AdFEqWZxfcG3u5mhO5ISOjAyLQh6c6tYfT7Ozvx+/7gRga5IOzvLy8d/HqPhHDrD4NISwNJIncIyMP0bJQDTAXYteFlB/4cUsspZo5uaaCCkaUSyKlI9H1CaomxiGHQCDhtOsebTDMVMmzNEHakHqcNEijVbngKEKTUCIDI7JnEUJGzozWXaIBNymdJfYDj5tajUdTQzYkUXYm8hBXJTENiqpc3NwgUl8at7QTAnErZqO0swpK7Fofhy2Izi4aFOZVSaRqbUK7oQpauEiKULEnDJzyIyAr+DHihsaQvS4hcSQeMm3aDQtXZS8ESE0wZBSbCsYwj7ERJxKxr5L2y5dldQz4RLiByuZl64YAOCu6k3sfPfCRSqOSk4xYzHYI4wIxIQ5ccmJmcgtEXJKLMYppZSYyB0KQZ9w6POmLyUzEbpZUI0BoDVJKeUcyT8spQBAKSXnDAAiCrDsuCutiXMufbcp3cYNQU1bO+zvpc1qysyJMDGVlBD5ZNrd4XY/A4z3O52POTtyAkxu1XUWFYDC3Kduk/MF8sY0tUmb6hGOe3bJ7E5MrEKqBH3XDd1VVjdCps4UpDXgaZ6PrU3E3g15c9FvNkMqKQrUVFurx/G4H8edtrmkItjq8Xj99v7t2/eJ+WKzffnc8FkCamo6WxPQ1KXcpX5KvJp2U93vD/FcmDml1PVd3/Vx90KvIJy2QKlT4lyW1kQRCga27o7WsFXXiq5EnpiyEiiIQyg7A3pIGa9m1V3NCDEYLbiIjZsDOC/5VFFrTcZxak0QMTIzUZ+O7H622JkYid1V10Qfk+dVCja6a9CKixM6kS9Me3B3F0O1BVBEMiKLPcoNVUMGHOHk+cdUi/QjrXnDRdKRAFfYEsmit5upLQg++Eq6WtQ+7MnqwJwyMQ/DpnRdziXIMsSYEoKpmpmJqJiHmwHIoQqNmJEI00DckZOLKXjIPTOAoS9NbIzieflCBgs01UIGV8GY3AmAiXzR2eeAbWjRTMXCECtUmnDKQGwGibtzWMvcRLW2msg6r8j7gseSW8nBlUF1J/SUkRMQApijQyz3UhITSKvSqpkgWGJI7IQGqODOSDniuTBv6o2A3EmVUfpZxmm6290qJqa+5H7Tb1LKgMDEKZeEiZD6YXN59eLy2f1ufzD3eRp9qYt8ZAgfC806+qpKDA8s+dhHAv1ZO1CFrxfRt5ijGzpDKGZ5Tsjk4GKuzexwNLIWZY6UcnM4HPbzPGmI/6KDq4rU2lRRjdQI0UWaroiogzuEc0FIQJDBEji5oaqJ2JNqqydVZGebc7z4JGqHE/x+9kE/+9XpqEvddCT+F9OOtja7XcpCllWwfIetK8nXthCrqXIwNV8PDB/AD2YgYuHAIhjQIrNeVQ+zpiMbexlKKiWowkyYCBVd1Sw2e1SCFI5QKPBzSU45cxnnWessTcyFSy4595m6nFudFSogA5FWrUebFGozIjQDGuuuoplPre7dK5IZUgU/qrp7Nc+qrHN5ZNrFmtjU6tjapG1UbWZKKw0UcWG5M3HmkihH+pmAYqVZTNwAkNAAgKKhOZI72EJBd1o6xjnGvYh8PBqSIRklSxmGIV1cdBd92TCSazVLzgLqugAHCwJj0FSryDkiz0RDV3LOOacTOjuPRya0xB0DlMSUc0pgiqFZBJBzLl3JmcBw09HVkDfbzdV2s+mHxMnMYvdl5r7vYU3KRLE4IkY5T1SGqKmpuWHiRD2nVJjYTNUMgXJKQ99dXmyZluqD2PxFmzyeV/94Db36NBtM9pzLdoC8BVWw6q7ujpq5Pcv23NOVp9RwmlUn0wYIGdgVZHbPzNh35WLTPRMAJSx5g8hNRCat82jWuq5wStvtRTcMzBkB3EXaOB7udve3u93deDhMx/nudn/9/u76/d3d3X47bPB1/8nz7fPLzxxsnI/z4f543Nebo1r7o6vPL1fM0cynsUU+OMQLp6kd87x0bV+V0M0NEVKilDln7LpUusS8dMR1aw6mldoMNjPURMJoxOCJAQk1iGW8YPsMDADWdQBASIk5pZSIbaXVhRwIOAbpcprmcZzMnIhK7tLi5/kKEK4bU+SVIfD0hbZPaMHiU/GmgtCYWkrO0VfJVMWb+tygqssSLMU6T0TJFFUXUbmoATplrwABcBGKX2QkokcH4pK0pIi3DTH2SndwR3PzqOk1U5NHbV2RcBh6Yu76jpkXSZiIrZma6DRPda7aLJrpYNT3sGJRJswppZKASQTAPTMzZ4ZMEPAFAaCZiTQADwNv7q7GZF3hUvpSci6FmQnI3N2AmImyIwbmiWTswAk59apm4Qo5Je7OAflS8mZjDE5eGaZM84atw3hmnlLobSlxiBwCoSu4sMy1HcaxErpMdZ5rnRG963KeG3FjAue1ck+icDDE/lAdR3EGc8qcutZ0Gse+kALNDqPbXGdwKLnb9Bfb4WLo+08//fSw39/evK/zBK6gjzlkAPBd/dpXHGYxOLFhQohnnNr6UYg2RXhlQIYETNBlGvrMhKbN1Ny0Vj+6IYCqAqdmtj/s5nnm0hMzUZCWQDU6YUS7AlANlmeovod6GKIHly6DsTutAsqLe/54PJjtlQ7vZwT28Gwfxe0P+fGzW3IKu9cjLCQ7XMJBX03IicgSGavlz4nyvnw3rpY9THv0mF2SbSvtbh1m3sSilGWpV0EA9OYGYjROSr5xHxwKZk7ISImToBiEfJsGwTYiWUNfkr+YEhdHUgh9KyEyJiemRHz5sncSIHKiNsu4w1nnquKNm/K9NhIzh2Y6AQiSG0EDP6qZaXVlFba6sfM7qFJFp9bGVkeR2U0REYGZAq0I2iYxpcxd4szIgLb+blFCOVU3rLHGiQIEAEAAdpIdpMD5o1pSgRTZOEMpvB3Ss013kVLBqKNtyVgwLOxC4AmWkajNIudxSUp8ud1EK4MIGJpEy5oQ1snh/AanGBYj7SmnrsslExpuOr7clIvtsB02Xe6I2NTAldbC6GDnBq0+pQSwVDRrsApFzZwImXNOyCmZa62TATFlcEuJN8MQcWOTNs+zh+QTyvnj+Ok790nFsBP8vNELpG1yzVgbCKAy157GK5wv3TslrzSNaM3dndmcTVkdQRJDyTyUcsGGCpBSB8jSxERrnc2sK6Ubhr7flNwRsbuZzHXej8f7/f3N7fXN7c3d3c3u5v3+9nq/ux/Hsdrz9GyLhS9fPPvcXflwP84OOk/78XAcX3dyKuIzh9bMT2sJYKYWUleJljRryoxsCEBMiT0lTIlT4kjAL4C7u85UJ/SZuKEJLcVuSy8V9EgaIcCS/QUvhcI2IXG8pGC40GVC3Szi+NbaNM3SBIl9iCMQP0UZHcBpqTujYJKCG4Cia1DwpVUAgdSYojonbL+Tm5nVpnMTsWX65ZQSh8onEaUw7YvgNi4AfPDmACmE/BKSEhKgL/Kh5BCdzmGhvkaQaWtxtIm7rqo8sSFiVzISJY5YTRGBFmaFq0qd5tbE1BEIgpOOhmzAnjPlTITshiKORhkTcSZPFDX+GNkws4UjYL66dKVLgTqklKPHkgOYNFFz9wSIiZEIUdFDJtSIE7uLuS4sg0dUlC7nzQAgE3tjGDOOhaWwN4kyVEImAEcwAHN1RzdCER3HWWWHOktFUzWbRYUZU6acSLMTAlMIC8Ciu+xoQG6gTSoBpS53gzuoKCRHc6mtST2OezeX3JNhn7qS88sXL+9e3n57edHanBKpNFc979YDH61rXzx+N3dnXqX4lgT7usOuneEQwM2RnNxSR5uuXF1snj+/BLM6ja2BiidmTkndx2lqZlNru/2xNulznzmrIZOllHNWdwnSfMqcUtSGRVcMBXSOVeYIyhZ1jkiEQGiPwxL4IGR/4tHQ+uoKCixmk1bC3ZPPLj+sBQkhUBhMFkPwQBPC9yYAo6i+XFkuDkE8WVIHuALNAGuZ7HIOjzO7oGZVTMzXAj8GRmA3xoZ+qFLBqntzvyDqKSNxZoDsBNBqNTMERUAAcgYFEnQEzkiJqOsyYo9oDioqc9MEHaW8fT6UTXEiB2xVxosJiXI+6uQ2g1dvTVRdIMSVEcBdzWYN3MbIFUGuHrlHIpPoJG1SqWCKgEtjM2IEixozQk5cutTn1BFQZNkj92MnXGNxNCHUYd3JHC16Zq95CEdMwStCRVJOjdiAvZS82eSrTfe867KhiTadq9ZC2dTN0JxOaRN3b6pzexS19323+eR1k9aaRHmEQVFTbQ1MCcEjrEBEs0ikI1Gg9H1P5HwxpIu+XPSbvgxMxR2DZ01mgYiamYgsarKRkAIw9+ho60vjk5JzCi7CeNxPteZuzqkkcLdWCoPnuc4QJVlLIPkwqd39b7+eDrsRiDbO140+rfRipuw+sWlmuKTxCu82bcfzfj5Um1k0Izgn8KSSkAqnzjV7S6iJPDuiESAxALTWxESqgmEufclDogyObiZa27gbD/fTYX/c73f3u+t3t2+/vb6/HY+7Ok4izd04pX4Yri4vXwI6pV6MRMk1uWamh56VCEiUFskN8+CsQ9Olpo0ocer6nAvFBrXEBstmENuXuaNVbZPXEX3i0oAE1AzUQpueeHHeIdS3ARGAE0eC7CFUCGBuIZfhmlxf9gxzZ3dcm1vC6bfxUQcAZ1IgAFc3rM0FLJESKrozKlAD0ESOAKaQE3dd6grkZo61qVQx1QaBKIKBMyMloszKzAYe7YtEPJJXkX0PsJEpJVzIBBGy0MI7ediizD0KJANeILSUjOUsoHIAtQjPQqEs59zlgujSWqvSqpoARQ4DIBhWnICYc6FEpIKq4AZoiSATpuiou6TIgouwIHzhkYRGTUaKSmAiZDVvTabjvD8ccmndoN3Ql65wYiJyb7oWt7lD9AVQwvONN2fcdATkKC3BCLY3GxVrCGrxUklAiEBuBJHGIxU4tHrfbufRp6MQQumA0M2NCPo+E7E0JzJmC/qTqba2dIhMRsil6y+G4RIpJyyZukwFzJEydgMiltz1OYEKYdr05dmzy1evXxPTeDyqVFPh7Vlf8480dV0yKrBmWCKwCt4pESUEBXAK+2RLaAJoRFBS2vb9xWa42AyuSq4EJuiZU0oFAJvaOE2HaZrmKktZV3DDI9vDZooAxGHXw8lENxel4IEGXbIJqEhm6YqdqOdn13AW/eIJfljJIxCaSifo3Rc9ipiewRpdqVsrh+t8/sZLdhJoDD5dtPA8reYF91qwhpNdWoVvT0CCrxiBOz7WqwEAda9q5ie7zsCEKdrY6uxam+pUDYlyoZRKZkZEdlJDFFtRNLTwwSBMfUimZWbqsnpu1mSamzUFT+A85LzpkRMgq3q/GZC567t2lHqU+dDmsensLiuFloKgh2B0ErW36ZF7pNpUqmkDV4Ro5ZCY0pITdUegMIIl9UwZARrUJaO3mPbF+8KT1xQ0KEN/1PQnbC4iGWFjlpKFEgDR0NF2yJelbDGZ69RqtSpeFW3V2GLEqOdBc2iqs8p5X5VSyovN82kax3F0AFowB5/GsdU55M1F1D1YQewOhNiVfLHttV0Q8MV2sx02m36Tc4/Oag4eqs7K7MviWwcAIGKAj7WGP4EppUBQzVxMaqtW59Yk51w4LhSZQ4zcVrITOTwqQf7VTXt/PVPCLeJkODvNkIZEktAHwmcwXuhdaXs8TnJ0VTRiTgwZPBkU8ILeoxRo5BW8udvKu7Go1GxmQJhz6rvcMzKYmUqrh3F/t9/dHna7w+6wu93f3eyu3++O+7nNLgIOzCmXbsjdkLsNIvQGm7lezK1Wc0s5PajRIQKniE0NYEnBurst88JMlRmYEyIkZDDVWWU2rQaIHPIY6m10OVA9Ek7K6qyuGtTMyEeT0wJURiVSjGWDWlPQKzrn7hZoJiIycUqcUlKzkNfklVdxPhaKHSiAhbeqCgoA7okwozMBJweIZB+YOjCGuiWRq3kVaSpNAByYKCEk9MyWE+SEiUAdavSBtwgVIphwMfNYj0C48t6DmcBI68kGf9fFTNQ4qAFoBEZnaVB3b7WqG4ouzcAwJ2LVVudapypNTJfCu6hhBUdizCWnTIQc7DomSlRS6qPO7WHb9EXUL2TIU+KUcsmllIIrK9sBUUwVTGGeRAXAiCAlTEAJCdWgiYmKakhDoInzQ7k+AEAmHJKrNPAj2t58pzQpNo8GTUHJic3fFvINU0Lk2ux4aHd38/3tISe8vCxdSSkREZaSCF3I3AWC/m/uYtoMkRKn0nXD9tlmezVsLsAoUdelvks9AgCqQxf5SjBs8xStA4a++/TTT4btME9Ta1VbHfP2eDavPt6vPcwgnZgXDon58nLbdxlACT0xgbs0CWkItWpeN12/3Wz60iUEIyw5gxshJc4pFXBQVYOqtmzU81RrAwcSNRWNGCXMd+SNlpmPkWWOSk9r6tM4H/e1K3K1RU7JVyLAwzzDkwppNEpaqodgyR8t63ChWONCJgEIz9b9oQ4enhj2ley+clMWZ9GBcBWXsBO/BCmKUUMYapl+Ide4GkYLvNxDpfJxVYwDKuLSIzklzMGbQ2RzNgc1sMnAq/LYooCBGAmwEKWUTUnRTx2clitcFqoSOEerscys5OpValUJNljhTelKwdRv+tLly6ttPbZ5Px934/5+3N1PfhRrhhZov3OUJRrFZkfyRNN/kYCINGLkAsOue4CRlBJ3mTvmzMhB0hHVAABtFZKJuRhgAYZ/SWtY6tH3LXTJkEmY5pykZKOUiFPf5W3uOmCuXmsb6zx7s2i6SZk5B4OP4l65i3u1R9VvzDRsOk6QEtnafn7ZecA5Z6QUZ0y5EHKkLC83A8LLix7R5os+9cNF122JOxUACEkQc9c1zbMk1wOfZ+bWmtQaatzMzEQezZSi9M/FVcVHlwaJjYmX4oKFqWruoGpk535jFR+buZgSXXDaZ9xmskI1gw5ul20abIfTrLOgETFRISoJCmAh6jKVjF2yDmfSg8xwNGAD0iRk5ABqRsiUcpeHjntGBG21HsfD3f7uend3t7vb393u726P+11t1cyQGBMTce4v+u5ywEKTVgCftIXF74cBEFM+M+2EuUQAAKEJutALARHAglpm7u6ZM6eu1jrd1+NmTom7i8QdmVep2PYsh+THnJpZsJuWvi6FmInYEQxcFzH/aIdJSzEiBrIaJZluEkgnIRAzd303tMHcmRkAc47Y+ElKFKJdAKy6tCHAE9/BCEyeSDGlsOBmBgTqWqUxMzMPJan17gBGTFgyZ8JEzgzMwU6nJuAOFVeI0TH4fLTIdLmDecxEkITASkSICQDdogv1wnT2RInikuFxaajZuB/FlRKXrh+2TAhu2mqdDuM8zq1KlLwYOBBgYoalTzwhE1BKyMhccqbS5cLMDhoakeZqgESUcko5pRxaQwicjJAg0g0OjkiWEnRD3srgDkhgqtIaMRpgM69qtYo0QUN2zMCcHuG0CTVDM9m38RrghnifyRKF1D8mIl4Y2IQIHBX8nBGTmVNqavP+OCcGSu7oPWYmTowQ0H1sVmZuaM3BoHTddnv5/Or5Z68/ff785Wa4AEWG0udh6Dd9VzghoIi2Nrfj7nh/t5OmiIRMn3362ef8BSDUOo3T+PVu+vnd/HAhj2bZo3zvA6ESABh56PrLy01iyom6nBBAqkgTERGdRefNprvcbLtcQvuQKWV2dE6cmUtQxsxRzM1Azac6mdeUOyT2RdN4LUhSXaxmGASIokU3BVetsx4OkzbInErJxHRebfVAGEAD8KXLcOj1KBjAkiFCR0JKBGS+euMKsBRcfQSZX1flAyQAtPLpFnWbB/5cuC9rIO9neAG5h8g5rICeLfHak0R/hP8LjsdMzBixbnJgB1B1E1VvzpMQ1UKcO0ruDEicnCiqfZ3JkCy+B9xANaASt9Bsj8SdoigIht6bM0FKmXPJxJtuyG2Sepz7XS4XmYec9vN0bNLUzXDlG7giGJo56aNbt8SgxOSrfMtiQQ0AiRJzyWnJsiOiuYd2vLkuIawDLPBRtKmGJRUYjxiXGh6IBA1BYsk852w5IWdgTn1KPaakqE1qraM0RUiLAnqXUlkSBEgAqG7NbD75FMt6AGbMOQH4Ui/HjIAqzVSDcCutgVlid0B3YKZN1+VEl5vi1hJ6ToVTB0BhJ4gYPRTGgHltVgugGpvrEqPQwjcEXCIeWxUWA9RqhqqeQFERZelsv3R2D7LC+UIPbV9IBCnRpsc+W6Ha+dh77Vy6NrOMPqsrEDkk9QTQZdoQ98x9odxjLt5xQznMY9tjzpAyOuNSkcQ5l5TK0A0lF/fW6jge7vf3N3fX13e3d3e3h5v3+7ubw3E/t+oAlDvOpe+32xefvLh6ccUljW1SkcPxsD8epnlW16XP+Pl2FQ0FQtqa1vXm7uYKoqEcbJ4pYepVZplkvDXw2o+eBnJwFbSRfAKc0Q2X0qolmsCFiocnuC0WaaRscOkYFtSPRYe4qggTLyKERDmXvjMAdPPI6Uau84RDLbtDgDOrvCJHRTpSIiqZSoLEIjqP475pA3A1r61lsJwwMW5KUi1umJn6zImB0IAsUHe1JUBhYmZQWyFEdMJg3S9ZV3OQgNBi1wQwNwUVUw2aXkxrdzU30+6s2srd53lWF1TmxIgdgKh6ncdpGmudRcI7AkNbqw4icUHgCTB6hyTGnKgkzkQEqAZqJuiEbsxc+pJzJuY1Uora2VPPmNDT975jsz5EoYlji/UFwXRUQwt9FUBmKumRZA1aIzl63el0T3SwPJmRGgM5LcIzS9GgAzgieAJI7rwmv8gRDVwdRb2KZoLI3ywyQmvciY4M1HfD1eWzV69ev379ydXls22/tebkOadScjcMm5RJbbbR63w8HI773a7ODREvrq6eX11tLi+6oW+tjtNx/Ortz+++PV3Ik37tgARkYCthJBghCwPMIXO6uthuNn0fNQRAptaiG5ZVZuj6nDh4KMExychMlBGymtQmtYGoN7Ha5Dg2UdheUN+HsEYiBqKg/ig5AkA0X0BnVzXVk1YCIZi1Wkf3xoyS6mMDHJx5Y8aSOeWUczJ1mVXc1BbFXE6YCkUf15CBEjB0NzV7JAn55O/IDOBSJRCkuYUQjUS4lPbFuo+yE4N4ycmdDDg+GagBmoFrVGs+4gQgIWeOJg6UkNLSNSWSUIAU3p+ajVNj902iDhOjEFoiZErA7AASf6JeFcwcdUn6m6iJrqfLBEvUUr0dxEW0dbljYu4hUpdlmzbPhu1zOezmw36ajlMdJ63qDayhRpcVg8foQzhtBJQRMVEKHPtkLxE4c8lcmDICupmYRCvSiAsCyMHljhnRwmExMHcFNAJjXu40oTN6SdoX5YTGxMw5p4LMgtZ0nNuobQZNnDsa+nwxlE1XSkohTkUB7za3anoOyEdBs67FZ+HJRZxdug4woFT3pV+UuQMh5QgyoAMI1VgwQ1URWajCKeWcozhOz0fYhpTS0PdMOKekwT8wExX3kN+HpegSAcBFtAZ2L3LCvcwsGF6nQe4FsWyGF882r59fvtykAapT8yKNZIRWTRQMMPpAcm2QU+rLpisXXe4zpuLUQ8pKsp+qz3kY0mYgLxzhak4ldaUMw2YgguN0HA/7+7vbu+vrm/c3t+9vb653t7fHu9vpOM6tacqpH4YXr169/vTTz//oB599/mk3lGke9/v97e3Nfn84jkez6t66y+elbM+eiMbUIkaiFOIFoYNzKhNUsQIlUx6SV0vzXZ32c+pb7lPKKacuQ0nWkefkBQwdAZkQ0REUQwIRV2rNsgOE++VuywKP8iuzWus0juDAzGthNzBzTtnMomjC3MEe2RJV0VYNFcgWZBmZ0Jk0Jxr6fuhKSigyAhjWo4iqNlExSwiAQEwwZCbHxNRlJjQHE7dmXhWrgIirEhKVlKSJqAUZFYkS4klP3wGrARkXZ0ZEMjGrGuquykBMjIhqVpvMtXVg25N/AqBuCsaITuqkYrWqTXWc2xTFTouLTk4JUsaUERBMAR2JE4eGTCCAjgjEiVJKgDkS7cScSwka4jnsSgjk7qJu4uronhNuN506GKwZBiYgJKQMCc2NGN0zcp+CEHAGzbbJpnufdtAOlIUJl6VdVdGYiJzXsmwURvNG5O7UqtdqTHx1eUkEXZ8BdZ6rgKRgV1sg+YviWUJLiS8226urq2eXV1cXlxfDZlM6xQBgODKDtek4jXc3t+/evj3sDtKamxNSq63Nsw89I6auKyUN1/vzefWk81tY92WThDPqtptJayZacrnYbIeudDlnzm5e59qkqjZAJY44QXRhmSISAyRVOh7lfncY61RDC2LxgCI5SoyJATkTkpk1X8M1XJ1KBwI3cMIEXVe2254QU0JmZwZ6CmVrpFly5m7IpeSUWKpaU1zM59K1HgkxAzIS4wLMO0oDWMIeOuPWIUBopQazbynsPHnzK86x0j4WqTR0JydHdyAHckwLyB4wFEFUD4LbY7EcACbKmQEICZmRGOLPokITfBJ0AWitzegiAh6hMYRwZcrJkdijjn7RYlU3BDd3NBeN0BgcCdiRMBxysdnaIjHRpZI5U+LERDlxTqmDfttvx2E+jPPx2CbR2ea9jjupo5m4P86PINJS6ga4pNh9qdVZaMacE60hu1no0KlK7J64ulNxMAhOBIiDASijMluiJRPJaImtzz50iJwqMHEqnLIzNZeqc62TizB2VDbpYps3Q9eXHEQbhsXjVnEVfyw0e5IUXU7D3d3AQgvWAMGciDTYu6ckQqTxOQGAgKyNIm3pNAcYOxScskS+cFAiZI+K7ZhbtTZVDRfhQRwCYCWDuqrO09wW+VVacmnu51cBAFd9j5cXm1eXz19ur676UhxExVtlm12rqrhFKbU0bBXq6F6wpMQ5d9T1lApQNiKBNtbahJFTP6Q4X+KUUteVrmRCl1YPu93N7fXtzfXd9c3tu7vb9/e3N/vdfhqPImIA2HXdxeXlq1evPv/88xcvnueS5zqP4+H6+ub9+3eH42Ge55wtFZBNe7RClutaAPRwhmICByoUtMRWoxFMn4HbtJ/bOJOlYsOWeei4Gwp1kdGObE/w4Q0Dc3YCYmRfCHi4glC+NjFfegWJtFrrPM9uHrX1OZclyo8CbFu6xDwR4kA1RPNo78SRL1RcSjg5Z8olp5SRoLTeXBHEBaItWSNlAgQqzNwxBUHdUQ3FYBKrSqKI4MwQaqBJUdAi9xeZ45SoS6nPCZHFSRVB0c0c5NSWAR2IMIcDhTg7VHWlB5QREZCBIBKDZt6qSGsy1VlUzIISjYTAmVMHuVDAVKqeiJm6nEvi5BZbVyYmTsgZkCyStMiUUgZYlhjgah1AMUQtozRPzBVMQSPKcEQXMooaxoSpzwQ5OssAr/vGaWgd5Xhn8wFkxmQYZeeGKm5umjQFQ8hA3EG0SkUQM1QBbQ7uQ9/F3q+q2szXWiAidkSLWM48M3PKQ9cPXZdzIozWTHZSKDf3pqoq+/24P4zjWM2hdMMqN+BS23ycEhPnhIT+eF49zbVHUnvhAK4YIDqY6fFw7HJ+9fx5hLiMoe0OIUwe1UqJyUFV0eOmOoGTKs3V3r27/+bbbzFp6ZCYEueLix4w55CfdAfEnJnJ1UAN7dSgwYMV6cRRl0mZ+WLTAyy6MUzYc3l8HUYUqup5GLqUGcFV4rHbahvMXEUlZeYUgjyeCjFQJL4cFnLxqh8LCEu46QYiCuJJEJLFtKOl1COWsyMSM4cEQOA0hhazP52amwGioZObACoufRTXwUxd4VhWzAGTLaI9gIC2cEaj31lkfzPj0GUGrXVuZoyFExMs7YwQ1NXcvbmRAZg3czFQJwPyRc0qPBcAk9ZUpUoqhUtJHQGbRHcDHi7S9qL4y0HqRibRye6vx/ff3N/bXKuYPZIZiCxVQEIrLLloJqx0hUTMQSBWN7Uate/g4XWvLSqWTFXsuYqgTFqSZrbEgADmzmxDsr6jvs+Oyb0j7Doo2QiaNZHRWkVHTH3qL8v2ottuyiKKGZVMEhrh0XziHEQBZE4LxrKkzBaxvJSSAUZvCmON8ioD1wX/MWkSFUGqZqYASIsAgjZRtTmcwsD0zos5w34Hc4h5eTFsSZj/eM9CiTGPgL7rOmZePIinvC384uWL15e4/exZf5XBp0mPDedq01HrvEhaMSG2ZtMo84RtplYaWs3eBjbuqE8Zm6uKibhoItp0XRk2PHQahdVMiDaN435///btN++v397f3e1u7nY3+/3dNB2rzAYOiRk5XVxcvHz54sWL55eXl+B4f3tX2zyOx3fv3717/26aJne9et5dPevOq60AgqsMtEbTsAqjh/secldmpnurtfWbIechO6ilVqs1It7kfFHKpmCO9U2wrEsP3bklhYVLb11bKXMAAKfsCbp7a63WuuRB1uLhpVLMTESkKcRTZ6bHW/CgRMAzQUUHcofgs/vyzdDMsxiaO3PqypCTU521opu1CsaQoml8IkdsDmLQBA5Vx7bIRQwdFYZZwJulhAlOpTtRVYtMvMmlyxkoz+K7WWtrcbMLkCI5QSLMDH2mxGxmswj52dRCzF0yRC6E7E2qqtVZWhW1SJQTBgDeYxmIMgBCKNBQn7t+6EuX02raMREhsSNp1IgFzGAhPhEkn4cwCkEVFNEI1LXZ8Tjvx7mqijtyoO6pz/miGzZ935eBmVS1SZ3q1NqjvJvWsR5ubT6SCgI5sBiZuDUHcFMHBUJ2ADOtomKiCq4RTiIjp5TUvVYxVTTIiXPKhRNjUsOqsS14Sbnvh0jwtTofD/t7Ltg8c5fTAJRARWeore4PowhcXD5n5r7r2jwfdjsEMNHDbnc87IKxcNjtzufV06g9+g8RMdLCkMK15ktaq9Nc51maQFfQQcXqVPf7wzSOVVopabPtAL1pUxXVaMcs8+z7g7x7e/v27e3mIj9Lm9KVru+QM1JyoGiYgOSJmdnJgQxV5ZS3jqgEomE3ce5SShxQOKITQpECZz2piTElKl3u+ly6RAxmtjbuXLh6AGYhGIeR2Ao83ZGRMyQPYH7Z1J9k32OdW5BpxSE50+IRweLo0lL5vkgArR4VLlXXeDpsVKetqlDnj6Nkvtj2ZmgO0WOEEzIvZPxoRuOAwRIMbIUIciYCm5qZO5uAB72ZUjAsDRb/Nkq8zNXRHN1pkeLBACNhERKS5qrG6uaM2TSCTE+cUsZEhN6jujcsXXI3JEKau82jebVITy0AEKw1h5HHpBXeIAiFRm2tBQ60bJSR0Vpu+1K7EblDS6yJtSTLTIzg7szQJSgJmcgpZSvkOSmjgIpUaaOJMhGlzKVPXcchwMy4NOx1NW3axJpYO9c+QiSiRavAI3PgBu6RJglnnICMlZat31TXZLevaVSm0EtZEX0z89Y07jst/SXP4MGTqV/tN6z2HuDBhIRH6fAgLRbm392ZU8Kz9jAIf/zJK/COX3RS5O64n9sRbWw+zy4CCJjAUMSnox32Mk+okq0p2ZxhKsQdUE8MZmKydu8MBI4IOTqoqDSRebe7u7l5/+7dm+v373f3u+P9Ybyf6iguyJj6AphyKmW73ZbSmcFhfxTdj9M4zcfjeLi7v7vf3QN51+dU+n5bOJ0tkFWfebVSiwuDIdtCSIQBJYuKihJmMHZhlIzi4AiSoDEqI/NywAVKi1kbyzWyb4tLvyK3D/uAL7KyoqLuzkRIQZmO+exqKiK1NXAgYs6O9Gh1XPIwJD9gG6E1aHLiwQC4i0idkRwauIICAhMBszEWBfFowIlLM5hm2AxUyQzMGcETc5d503FJZLPNpqGBAAsHyJZCPExIKaecOTN7W5jJzmZA6kZOTowp01BKyUkUxmYs/BgPIkQPn7VVqbPMo2gz1aD1ADNzgiiPcQ9jSZxyyV3fD0MpOWVwCtOOCAAKEACEGcYj5vBVEwbpCxOZB+HHoumbt2a1Sp1bM1UwsgWEQKTS0zZ3235IKTWt4+TzdFyEWtdhUrWOYA2jPtDQxZEcDRxAqzWwlAiA3EzFpyoiUTuLCYgy5UQmNs9irpkQiDilzDlRnsWj3YUqYJdy6QCx1nave5k1GSdLlxepFCJmJBSV2kTVUy5933dd1+UyjUczB7WcaJrGw2EvJo5+Nz9yfB+b9qCbISA5IS791nChH5OjmY/H6Xgct8Og6vM83d3cff31N/d3u7nW7cX29ScvOVGTGQk4Ua1yHOt+X+/uxjdvb3a7sfQppbLdXmwvNwaoCqIgGsQoJ0KiKN5GQTI3BArAG8xMjTglzl1X+q6kREiLIC4c88m0IwKn5Ohd3+UuUQplWYtyqSi1QghdVliyAhzz3GINU6IMbLaS3R+ocY9o+yEIhcnpJPgMiwcQRiuAcJXYndcG4Iv76WBKQQIhD9Lo48bB0PflVboUA5G12yw5knNQDQDFIfRxZJVRAVCKR8ggood5LKZDNwARJ07gHEJOS18hUEd1Mg9WF8UKJFxBqsjmqzRxEGA2xIRA6qbW1L1k6gt3fclccuGUU78dhovx2ctHFZaIFKSZRdEq8CzwKB9cmkc7REpvrvNcp1ZbxEBRqhPRWFg6XPhyVBi7BIk8kXeMhSPd6oAGSOIJLLHnpIkETVVEJ22TGwL3mEK+J2Rpl/SeA4I3lVlqldq0upfzRxKyBYEiARK6OQCnlHNRBzNPxJ7MRBYxzKXNEoQeWd/3UQ0jKvM0RbOZiK3NDJYS00WycLX0oOFhtXay6yczrysOEDXxiNh1nS7dTxZR+q7vibrzLfiHn37CMNzh+F6OUzvs6oF0NtBKDpQYs4gdD/Wwb/udqlB4eW4zGKg2MAMENjTV0URcjzKXedIpFyIjcPJWx9rGd+/eXl+/u727u7u9393up/1ss7li5lIyYcq5K7nvU1dE9N2792/evJ/meZymJrPY3LQ62NWzi5evrz79/MWrT656fRDDj9QhQNQ8wdotzKPkOCU25ZBNcwN1ncZJWlC5FBGZyE1Dcp857DFHm7IIEAmZ1jQ7RG4wAhCKzQLMlsLlU3we9LmclsI0C7KZiDRptZkZEmUvSOmcRfeqv3rRbfc67W3awXy0KigAxuhgNs/zPKuqI3pm4KDYK5ATQIi0oziqoijM6s0gIWbkPuOQOWcqmbucAAlb8+BuPcjA+HItwFU5KyaGQnhZUofUkN0JBZENCSAhFhqGXEquRscGyfkhoHJoM0BCSuzm2qwebdqrNovyAWAsnQHlIm4NVU3dy1CGbjMMw9CXPnclRDcwxFjBrZmrgyqawOIjoyEiJe5K3jA5QmsuNXQezKpqVXPEXHKiHHkZAiych67fDpvtZrsZhsRcNUUjxycQI7iha2RMPbr7Nie0Ao4AdRZX6DoCjpwNqrqpkTszl0x9x13JVetc1d2wEMTcyokpkZmBi0Nz6ImAWdQOx2OdtHDJ0F30V884DcOQh8GRx3lGhNKVlPLV1VUpJdDOoTYC6HKuTe92h93+vtbxkAforz5u2kvqNt0l0bpIQvsNIIAhBOy4gGatpJUFqE4wHW086PGgc1Umm0fkhE2JE4GzNJRqbZZWiaDr8kWfL7t82ZeroWzUQMDInUJHYe0wR+iAxrQ0uQUADFU1MIaUcMjUZcqJiDmyxmbUnZ4PIj2/eqYuw6YvhVNCBxVp7IWss97BCNCcl+qHMqTS5/AdoDkoIrAbzp20auAcIvoAeCqxcwQHxWxpMO6AC3Bm4hyoauzUSz91c5WHSqQ174lLoIMUzZbAkYwyFT4L3AuXi3Qh6pIWaM/RAC0hEqIBCgC5MqggdwwJMlpxzU4cradNUSEpJWJGQDJmQzcNCho4sFsGc1RCBVAAITBeBOxA2Rbo2YkhsxfCjEjuFjr45JQw59R1qeetuxS0wthdbrdn0woZMy7ilaDhXYOGWY8mOwjJgc0JzMzRnREzkQEaPSj6xS0LEmxA8TUnYRJiI0rMlIiQXN0MSC0T9uSDezFjM2veBFAxMRbAQb1U41GQq80gbI3ZAXCUeT8thbjn6SsDqBpZzOAgoTlHA3UCVgB1RwRHU4waEIjaOnBTJ3JKwEQZmcxAIhpCUnABijpqM1ouO9wXIDBU8dasSTi+tKSSiRBRFk4KoBMYIkVdEolqM6wWml7F+czxBeyHCwbaN4c2AvSIG6CE6JkQOSfqSG2mKVHrkhoyYknU5VSIsnsSYzEONxOSA6hCqgrc3GeFRSKyTXOdjnWe1ITQC2OfEyMAZsJgTZWcui73PRBZ1O7X2maRKmYOQJm7VOjy4ur51YvL7YvtcMHjmWlHyrlbSmGCkYoLKwfcARghOOayCMkTQzQJxYQEKTFzRmR3Mlt+C0gG5L5gl0thaoTm7u6hPk9ruvyBabPIziYiwJwzL/0GFcmRMidhi5w6EmeiR+VWSIUQ1sIXRCek5hAxOWojc1cxRPBCTIQAouDiEGvF0AjFoTo0BTGIsjmO6nbH5IiaHAiUUIUXERBamP4hwQWkLQmyARFhCkK5J/eFakWLsg6xZ/KUAQsiw0MtIiFdDM8geTckAFfSWVtqTdnd0NCBPfepH/Iw5L4kVVXzfrjYbC4v+otN2XYpZ85EOcrEwcEluauhKbhGYUfw64gy910eCN2dkZunikCEYpotN8SuZENGSgFoQCYeSj/0l313UXJHTI6pZOvyEVzw7HmknLthIMxIgikspBN6JsgIZI7EQB1xYvIEkrUSaQIoibqcu64rpSuacwF3yx2nLnPJlDvCTG5swpacjcsW8+DE5j6Lithx1kOVY9PSNHMz0OM01bmpSiGazVTE1KYmszkhusOotq+ym+ZpnNomQX82r/7tv/23D5uXq7k9Sc2dD1yoEEtFSEyNpX/A0p0DTzjzYgX9hD0uru9DX+B1+MPxH738mGd9esNCInt8gKg/W/4RLRfP3vhwDh+5KDx98+MTeXj3eUT+CC5dP/5woKeHOaNhfeS7z9/kiAiJ0+nmLKf86M1Pb9X5FeFaartkbR/eh6fr8/PjnKWYzl8/v77zb0f4YGosmYWlTABgLeJbc8nrYT6UAf7IYU5vX/dNgI/cMl8vafnn6emdHeRs/j0+50c3ZWWKIjyaUSstcpmwZye5PuFH1LrzGXD+8mnurNERwsNc8/UBrO9/OC988pAfFhI8+e3jc8EPXlk/Egvg4SRpOb8ggdjjD51DzaeXHy07hMfL1+GsTvbR3Fmy0b7av8dn/uT9y0k8TOj1u5dURbjN59kK/+hyfvjl09vxdH85Td3zJ4j4He/+2Fx8tIL87GYhrr89uyZ/uJHnqTdaKTwrPOhn732YLWd3Cx998eMz88fnff6kPtxNzsfD3Xi4oo9cddyvlWWA5xNGtJ2OtD7MD75l/ZqHuYm4zqin/4ePPcOzQ+H6zetbzh66f3D58ZHz2btOUOCzFElIaZy+9sGanS7r8V7xcJEPC/zUkNdPO0t8q59Nh3XjebhQWrivhCtp+OwG4KPzXjNQ9sABiTM781HOr5yQCZ/2pf/oOF/2tIT257fw/G5+sEY+GN/1++/83HdP0PhUPpO2+H/vWJnhv+FNj8Yywz/4zVMn5eP//A0H/65x9nUffdwfCu39+m/Fk/H+nifwcBa/8dhn7/fQ/Dp99Ds8ybPjf7BV4Xd/8bmf9/ht/sF7zn/zZJ/+Xt/1Hb/5jrmzStMhAHznev/+d/6x7/H46/H7LP/vOz48/q9ZHB/xQ7/fl3znHf5Na+XDk1m37l+/hu3B88Wzv8Ef/efJ3PjOIz7dD/zX/fbpeOp9feQTJwfm6U6CmNMTLvPvPB7yn7/2TR9347/X4T98NEQE33u/+k7L9R2/eNjZPva6A6jDE/m173ceH1lmv9We+4fxh/GH8Yfxh/GH8Yfx//TxB9P+h/GH8Yfxh/GH8Yfx/6nxCJAfPnnRPb/wU2pnzcmtuP8J4cdTevUsT3JWjhMCLisotuazztCpUFxZE44ICxP17BBw9qGHzjynrJ3bkg2NY9Xb/fTuLt7Tdd2/+Tf/pus6+H/bEJF//+///X6/iAr96Z/+yf/6v/4FAoLZPN7vdu/efPPTm7e/ABldZVKvathqr+0F1ivUjhFTntMlXv2g//wvy8WnzB1TYgIxrS0aQxgT5qWtZVD+XFVNVaWJ1lBUUXNdqXunJ+rLVIhe2EuzekRCYkQWldpqE1WzpjrWhbjl4G/mezEDdwIkTASh+usAunTVMgAHUzfxudZpHPe73X6/A1ciYA7FT0dcpQxCgyqylks5PsHaenNtg3CedF5zXGsO1Bbh+VOCMxKjUUK7XDcg/O9/+r9fbRbS6R//8R//y3/5L3/vD13NVDTkmpEw2iSmxL8Wbv7txvv37//jf/yPy6pE/Nf/+l+/evXq93Ts/6Hjv/yX//KrX/0qfn79+vW/+lf/6ne5SY95KthaOxwOd3d379+/r7Uy89XV1atXr7bbbdd1RL+3KGi/3/+7f/fvVBdCYF8Kc1rST35K0P/24zs+eU6wWfPSFsW7agoOoUVPnAh53abxYVF80JFyqbR0D93DeLFJ+/rbrwF81Up6+C+e2uGdXifCRxmMp+SpR18W/4l/PU4wPCTY/WQOlm3KwyRpO+zu9vc3h/u76XgUAVGook1NEULhpHTd//n/+z9P9mI/z8fWQlEqp0SI0mqb51YnabObuAtEw08AAgRHFWtzCw2JVHLpCyYycDVTkTq3OjdV9VA9Zywlxx9mbk1qXd6Qoh1vSoioS4mNi2qttUkTETdAz8+fffLF5z/6oy/+9I+++GHfbTHa+XB69+4fvv7qv51uziPTnrdD//r5souf6BGxnzuEBMSia4kUFhcharYcT1Y92Ndrmvhk1x9Iditx4sQuwbUvdTyeVakrcoGP2FF+Ns7asKA1gdW0p5T++T//55vNo/qrD4e7q5mqES46Vr+/vfSfOFpr//k//+eTaX/x4tlf/LMfkaOL7ndv3r87jvfzDt663lubTUGa0DRDHXs/PsN22SGV4ZBfwVW/vYD+1bbkiy73paQmepyaqgBYSdBn4qjiVjN1FdEmrU2tja211pqoipqF5Irq6ak6gAOtwni0yPgRI6WmMk0QGhVQfVwvyh0OMlcVdCdMmYAXoogBKrkThZMBpq7i4zwfDofrm+vr929VK4FzgkQLeRkIF00+DpVeJApRKkaIvpQQwvIn6/5AXfGlMY+DLW3RIPj66ybv6I5mpovSIP4vf/S/nEz78+fP/+qv/ur3+LhjDs+1juM0TdM8VyIqJfd933f92ibm9zAtf/nLX/6n//SfTqb9xz/+8Z/8yQ9/H1fwP3j43/7t355M+3a7/au/+qvf8eacYgkz2+/3b9++NfO7u7vWGhFtNsMnn7z+5JNPnz9/nnP+fVn39+/f/4f/8B9Opj1xKjnDmcmEx27H976Y+OvpB09R0brfujmYiWizOps75szQ5ZRXQUY6ecdxwJCuO82f9WB+ugQAMNXb+xsACAlFejxwlVuIEWp9p2eHZ5biyXjgZ4ZF+uC2+KNhpwjEAAhMW72/e3/95qubt9/e39y2BnPzcZZJVQAUwAE2m+3/8a/+j5Npn0Tup1nU3KHknJCkzm0a63SsdTSb3QRBES1FeZyhzDIeZ1VDojyUbttjInVrIvNcp+M0HWdVcXBiSJn7vvRDN/R9Tmma5/E4j+MoTXLJpcu5ZGLWtftDbe04HqdpktZMEb189olutp9+/nm32X56efGSqeTcldLN8+Fr+A7TvkYrUZcV1F4AgIeyrdUxxEWojlZdZLeHndTdbWkuBLB0dT8j1z5mKa4zw2ypM1lnNUUV79nTffIsz8/7t/Vz1V1Ed7v9br/PKW2GfjP0Xdc9OBzfMdXOT+PDt/22n33y5ievN5kOxxtQk1rv7765vv32frzf6SytSW2zQ1MjcZsgH8Ws6pbywPs8Sr65f/OL0nzory4unl09e6GWxiYqBq7gWBLwUufAiEvRe8qUlaU1EWmiIioSwqhLD7bocmy+unmOgOpKiIzUwC2xawjZ0nk8BBmJUnRAyjkNTHlp1xTNBuKWL32RYKj18uJi6PK2S9Nx36bR3QDMUR3V1955AQHAQptGiMLJmHOAq3UHiFjoRHj38PxxjeCD47uiUwsIRXTiNv339PZak+PxeH1z/ebN27u7+8PhwMzD0F9dXT179vzy8uLiYtt3XdeV0D+JGfJPMGb/FDvx/+nx5H7U2u7v77/66qu/+7u/+/nPf/7VV78ax4mZP/nkk2+/ffvjH//4L/7iL54/fz4MfewPv98YIObluV1/8tvvfaCP8zJPry6bGwKotvE4He73u52IlK7bbC7w2Yt+s8VcYBHLxwVG+Nj5fORUVxmGD+36Gr/jk38+mO3Hpv18E4Z1/w+q9ilqP53G6b6d1raH1YiA9KEg61S7dXbKj/9ajqwgVad5nucZHBixcMpEOQ8pFbXqrkyeGQszGkjVmhrhZGaYiQpjJjUTbbVaraJioR1EjCHWj8gmKNVBTSbTWa2qNVnlajWXkkuGxGqmqtEVHjgbICiryGF3f3395s23X42HMefNdnO53V4udWHr+MC0n9eerA/38YN0j/asocGiaqLmZmDElFKiRUgijhHduB8i7w8f6uIMPmArJ9tOp4cXs+H8tycX8rfZtpaJ7gBm3lTvdvtv37wb+vLi2SUzdV13frRfs4bPb8iZN/Lr3M/1zfDR1fLRj4zj7t37X5pIm6fd/dub66/ujrcHmeY2tzorEhiQuM9+c6fWhBqV2nblONG1b36R6jT0ly/qZ8LGdDlVMgE0Y0QpEIItwIRE5IDJ2SgZaxFdTXsTkdYCLD4z8Lao+hkAWAgVBo6TCJStqiI+UoHIxIyQcyl5KGXDXBAWq7x0kgJwwMVoq8B2M2S6yHS878f9TkXU1EAUommMKaIC6rp+wTFCDfDYAQx8rSd6qFlZ/rivK3z55SlgP1UW+IIv/fcxiEt8YXY4HN69e/erX/3qF7/4xfvr6/3+wMybzebFixcvX758+fLF8+fPr64uL7abvu9zLuerAH6T+/g/dXyfG/c/6OTX1N6jV+As7myt3d7eff311z/5yU//+q//6y9+8Yubm+tpmgHgzZt3Nze3tbZSOlV99eplKSUM2O/x5p+Q5PX0PpZI+t7HOo3TCeLjf7uZqbTxMN7d7G7e17nmvterF11OJTMkRiRHjk88iEV9jxFqS6egPP7+qGk/vbKUUHxg4J9McsLoAXrSE/APbIGv0Sc4AHpY9xDKOn3Xo7M9Gaknv5DW6jiNx8M4HaUKAV4M220/5JyZEyI7WmIoibuU0ICgujYtydG5I8wEBE3VIamAoDkDZ6Lo9cWMISiPnLAkZCNSAgE1U2hL4Z0jcSmEFN35ACCaAJGTG1mTw353e/3uzear6Tj1/aWJElJrj3qkPRGaRcTFyOLpsh/F2IiIKjqP03g4Hnf7aRzbXNUU0PvN5tnzZ5uL7bAZENmWWw0e7XTXGfyh++Znqw3OfLEPg/XHj/MMrvktFpoH8q9qx3m+2+1Fuq7ki+3W3UOh8zceIr43dMFEREUAMZKkvwuC+uSD1zdf/+3f/8pEVVqd99N0uzu+m+Q4yaQqKUUXUiM3FThOcAOOVe7KPMGdbb9CvWcszw/Xh6YXmy8SPydPaFDEmxgzLT2zQh3Cw9l1jFaZZMSWkkoui7hZa2sHX1Ex1KX5kZufrKEjIKh7dfdzhmbk5DPnXLrcDYkLAK9Lytd2Ko5u5EpuSEBdHq4upLBeblVN3QRETGqt0zwfah1bm93ayRo/9Op4lLg7bRHLDYZTtimi+zXb9PB3bAmncvnfvwVCxNba8Xj8+uuvf/azn/3yl7/86uuvd7t9rTWl1Pfdzc31119/PQzDdrt58fzZq1cvP//8808//Wyz2fzTGCT/D3YC/icMXF281mS323/7zTe/+MUvf/KTn3z55Zfv3r1rVa4unw9Dm6a51vqrX30NDvNc/9k/+4u/+Is/f/369dXV1e8x7w4ApxT4k0Dlw+3ugyt5OkFPx3l4x2keryZTw505Hur+Xg87rdWlTkTTxabru1Q6TgmjsWR0aHSCxf3+dQEVrlH7R6H4J3Z92SRPdj0M/7KZx3karIZg/RXhY9N+Hlw9WHcAB7Sl3zE4h0jRmkRG+41rej7udzdvm1SVak3doZrgPHPKyOwEQNEEG3IidDQxqdqqMCNn7FLpNxskNsNxmo6Ho9TZWnOIRkOMKTGmxPli2Pal01rH4/76+u3+/sa9AoqbWzXN6oZiIecHsIrnu4GIzNPxsL+/v7tBIxXvcj8M2/P8CHzYr31N+KyEucdzJF6ba7u7vr1++/b9m7eH+12bZlUFhMsXzz77/LPXX3xWSsESArHLZ06B7enRnL4QVuzf/SOTZrXdj+buE5/uIy7ZbxwO5t6ajPPMS0+tUzXhKc77eDwdFl1VW5O6Dmbu+0iSdvhhS471mB8sxl83dvt3Y/vKzF0NoKrOY92pzWqiLsWhgHesnBwLY01jg6Zw3WQsBz++U7uTaneHY9P+9cvy4tm2MKN6E2jiKQEnRkyICXy1sKuoPqMTmTFzKHWaSM4itbXKQlKNJfRM3cAWEMxV1ZpUkVkVAR6MEAECEHNKqaTSpdSvCt3gHq3LndzRhbQRVnQrjNhnyoTbQS06ZqhYm6ZpHKfbw/5uHEFqpPt8NcruKyTvp/TQk8e+xOKnQtKPRCSL0f+ukux/spl82JWmaXr37t2XX37505/+9Fe/+ur65kZEEDGl1Fq7v9+FamxK/Ozq6vXrVz/+8Y9F9LPPPjv3HR9Pzv+5xvt0E3+X0/ioS/0bD/jr3/DQ4n0xS4HqqM9zvbu7/+abb37yk5/+9Kc//dnP/uH29gYAu65st1tRBdgdDofD/k6ajOPcakX0aLa72Wxyzr/3e/5kc3ts2h9j63El/vi1ZW/xFajAlfIMJ2Emc1PVNs3z8VAPe52O1pqp1Jzn8dDmi24rHD0oMSThn55k7NiE+GHxdShphg0PUP6RVUfEs9cfYNt1rBMb4wpO94EW40wx6c9w0vOQ0E8v+3oXImonWntULLH72SV9bMa1Os6HO0BHMDQxsblVgWPI7FPOlB602KNdGRi4QyIuqWz7zdXli1x6Bx7H+dAdpDZVWTTOmYkTODHytt8MXU8bl81UsLvPg8ixyTzLbODkBYzAgNwTOrBGosQJE2czq3Uex31JPVNpraqI26Nn8ti0n4CNFTheZ8gaQzu4+Xg4fvXll7/8x5+//eqbw/1Oa3NTAH/24vlxtweEq2fPOCVfk/VnW9DqogGGJvrJKiOik6NDKDI/zKQPgvLTZn0+N75f2H6Wa0HAtV0s0nd4Bgtq+yihYGa11nGcdrvD/nA4HA7jNLdW+65/9vz5yxfPX3DCEh1iH27hP22IHqW9cUMwcBB3AawpeU5IglmlA3ueYHuFOQ160R33Os9a3UcNv6WO+2meMuq3jK+vLn4AjOYg5k2xODlmoAKUg1i2ZKqouSFgyPoDhJQyYSKiRJxZJWsxaSpNpAWh3tW9tXY4Ho5tmrQ2zeemHU57EDFxppzh1CrG1qjFDRXRldxRhaSyVnJDBE/syApohj3AAMihPz+BtXmODnFgABgC7wB2atr7mJKxzOrzP4/PcN1Vl+D+9w/JR9P3m5ubX/ziFz//+T9+8803u93O3YdhGIYNM7n7bre/v9/N86QiX2e++HJ7d3e33+//xb/4F+E+RptX+Bja/D97fP8b9t8n2/Gxg0cCJDqGJ04AMNd2fX3z93//k5/8/U9++rOfvXnzZhynnLvNZpNzAgdTMfMQ8p+m+e3bt8xU23w4HGqtX3zxxcuXL1e1f/gdbfw5nPkkGF1PHm1RykcAp6UB1dlY46743Iq34qkxNy7eKrhaG+dpf5yPxzqNJs1VHNGkSa2tNRMxNQ8WWvCkg94SxLr10A4E8CjswUXsBcKuMxExhzl/CNkf4/OnrfuxaYeIt+MWwEPEHcHAY3O+3qcz6x5QvLs7ugETEzJF81YP9fQzlx6Xtt2P7qUxKTEB8KxiLnOdrJkKIKYybLp+KCUTk4EBISdKOSXOF5vN86uLZ8+eXV0941TUMfNMkG1YOsWBOyVGpPE4jeM4HqVNx8vNZjtcbX6wlU9/MM67w7i/P+zGNgORI7BpzlJSNTNEBkAEIkzMOaUcpCcABzf8YLt6nGsH8LWs7DRjcPlxCRRMdRrH9+/ef/v1t+/evD3udlbFVcFtnisQXVxdvnj1ybY2IFqMI+CaslmTouFGhUcFgICEwEypFMqEj2cuPpjJR/vv2a9+48Anlx2PdJ14Z57BWQPi5S5EE+Do4NTaPM+Hw3G329/d73f7w3Ec57ma6mazUUdOuRt6AE98msRwtvJ/uy3AoTnsl4A2uOygCAbq0IxcOvIXA73cctmUOsA7bzsTlTaraZ0nseN+FDpu6FinigjE6I5A5MBAHaUeOQOwua1i5BZ7gRNg9MWL1jVOSEiOlIhTUnFmYVYmIVZRA3PXVkXn2mZpBvSwXhxEFAALIEb3qJRXeA3BEAzBDdzQDQXQlEzIGmlFN/DoGkzBoSeR5NYhDonHzJNT7EUA7ot1h4X2/oC9nwLm9ceVe7fmi07z6pRBOnn/v8eBAK6q+/3+3bt3v/zlL7766qvbuztR7fvh8vLi8vLSzI7Ho5mO47jf7cbxGJmX6A3z8uXLTz759LQLwiNE1h9/0X/X8RGrfAqVYthpq/2AGfBoWz9z0L//d32fE3P38KLmeY426q01M+/7npmPh+mrr77567/+b3/7N3/39Tdfj9M09P12uy2lINI8T3WuKgaAxElN94fj1998M82jqnr0AUpps93mnAnxd73566c/xJmXC3HQNSTHJdiwlS8K5/bphDMtyTFcslMIAG5gbq3V43E67OfjUebZItQzNzWNDvCqdipxepAtfKCnxlENkT64VF7y4sjRnBxxse4xYc+t+7rzfsdsOLuNq/0nfDTPPwDmH4WkEdOjAyim1bRz9P/FB+Tu8Z9lmIq0OWPHlBIlJwNUdyMABGMDdsjIiRMSMjOnlHPOqWyGTd9tcioI5Et1oZkBYk6pJ2Ja7gdoxQm0SVWwoQOgbtt3KdFUx2E6luNhrNPSfjs6i6m4e3gs5iCq2oQ5+xqAfHT1PKXR2YkUtjZTggX7We7f3No01yoCTJvLC2Kax9laczPkNM/t7Zv3XH4ybDfECQCiYs4R1EzNlhvpS+cMQgI1dE9M2+32kx98fvXyGZdEhA7RnyYaNy7/WxIF67M/f6q/aQE9ulBc/6yxmZ9m8WmEE61qtbVpng+H/e3t3d3t7e3t7eE41mbu0YmciNgcp7nuD8furmtzzYn6vkTb7O/tfzwdzEAFpblUD1Rem9dZj7tZ9q1PXjb0YuDPtomajmTz0W+rs5hMdri1CUD2tNn0m3Sx7bebvvR91kZdSd3Ql36Ty+COKibNVIwQGBOiA0Uf2ZgxfrKRS99qAHQnxwSEmCgZG5BjI+xMZnBQPUeGHOA4z2hWHIBjvaTFn/NgoxOaoTZwcFU0I1M0RRNtTURn9aoexD5tIqIVrLkReklUHSVMCRg4ArjjWdQODw7harfDb8eo6XnYVx/svK/Ryu/RSC7fM03T23fvfvXVV1999c3N7S24b7eb589fXF5eDsNwf38/z9M8z6oi2mqr8zQB+O3t7fXNzbt379+/f4eEgFhy/l2m1u/heh6ndRGiNaNJFDuLSNiJaNz3ODILVsoTKvVpe//1X/Q9z05Vp3l+9/bdmzdv3r17d3NzM46jmT979rzvh3Gc33z75r/9t//7q6++UlNm5pTV/H63N9W2XoO5r+6511rfv3+PhLXWuVY1/eKLL169epVT+r2k3v1jxir+toDeF7TaABSiP1NMd8RobLPi70uY7oBgeNrA0V2b2HRsh/u6v2/TqK2G5Vmy3qH2YBHvrrsj+ikcPEdeg5GEj7fL81w749KK50NY/vyVD128h+OtL56b/vVefXjDFkfSHGhZx4AAwLTIbmAE7gF9IOJal/7YrgPAeJhur3fDxoZhm7nrhgE7I4BEmSkDZuLEzKUrfd+nnB8iTsf9YRynmfjOAdR8nNo4Nea+K9uSu5wKEyNBbYopd6kgYUO6G+vUrOScS7+52PaXrw3dPOrgMbw2Mw0zNE7T4bA/HPZEDIvvhtE168lIT194fEPXWbdsTKI6HsfD4TDX2RG7zQaJAUkquxqm1ERvb26rSNf3pZS1ng3MvZmKRmfcpRsPE5GDq4J6Inj2/Fnqc7/tu4TMCZcM6kqCfgSg4glFOF8Yv+148lR9nSkRc6haE5mm6XAc73f3t7d319fvb29v97t9a0JcSun6fqCckNgBa5XjccppP02cCC4vt8+QSsGU6DuCj9+0W/nS8EjUzVzEa7V5kmkSG9U7TD0M7NtigGJZcnJGdTURm+5RUym+uepevbz85Nn2qsupy+yc+q4bNtu+71PuRNS8NfE2KycsiULiJtbJYtrdQiwmQjMHMDCLtoDkCMiJgbhn2ILOqodpAn+02SkyYcJUOBXmRMQUZSlOkbECEFR0MxNFVfLYZrS1Ok3zYZZj1bnNTWZpqmqe2TN7l0pOHQEkNiM1iDL8KNz9eNS+FrrFqlj/rF7jI8D+9zIejmPuIrLb77/55puvv/7m/fX1PNfSdc+fP3/9+nUpWVWnaby9u93t7sbxOM9zbdXBc8o5F2ae5/n29i7l4g5D35dSUuKUEq676hm+9d93nFZb8E4iLJ7meZqmaV7+11oL5Qla0q9LuXMIpKSUco72pw8/BJNgUVA5M/a/jXV3AAho5P379z/7h3/4h5/9wzdff/vu/fvjcXT3589fDMO21nZ7d/fLX/zi/v5+2AzDMKiZL8IgGn3u8bFBE9Xa6pu37+ZaHTEyXkR8dXU59D0iriDPbx/Bh/V9vKGt93lBn9HFlj7fDaxhsE3DtEM06AzJJl+a267hy+oQAJrL3KbDcbq7nvf3Oo+uuvweoxYpHubSGyUu/3Gu/SGa/vBZ4JprPw/NaWXLf5Bzf/jh40c7T8SvPz190k9te1BxH5Lw5O4UgDwxESGcyHsPN+ZDk+hEkDL1fd72pcucwYwACmem1MQt2pQaJsqJsqiqRfmQhNbXMm2QRLxWZ5Y5ac4lc1m8nsSUE+fMzNKkSa3SSpMLSptSui4jk5qEac9MJSUHr60djgfEu1bbiCMCParf/eBSnjDkHyw6LkSHhxuHiK2129vb6+vrw/6oIiml0pVWq5sDGRGZ+/5w2B0Pfd9vhoGJI22i4FVaE/HFQ3QAIEByAF1qUA7Hw8svPnn2+kXqcyrJzmgvcW4Lzy5Oc5WoWxfDbxe1n/bvYEL7St8Q9yjzatLGcTocjrf39zc3t9fX13d3d8dxFBEmyqX03dCVKEmK/s2uqvM87w/ke1Wpr+qLlAsSr9GVr0vg+45arc6iAsF8NPNpkuMoTYGJQzhNa5O5olRrKg1atTp5U3bK/eXzT1588cMvfvwnf/TDV89fJqLElPtuM2wvtpcRbZgBoKpZFU1ASEw5ITMAoAGiuofgAABYuGQOGIiQqamamlPKKac+9Z5wlEb7PTxmSwyXz6j0/eYylwWYInACImIECuIqmpoItAaqCK7g4ja1tpum+0PdjXVq49TmJmIO1JU8dF2hVLoL7gcgc2pNx+NU62QK0e8SYKHVnXv5Yd3X+nhak2++2v/Tzx/hEP0TxukY0mS33799++7LL7/69ttI7uaXL1++ev3q1csX+/3+2zfffvmrL7/+6qv73f00RuyuV1fPPvnk0z/7sz//0Y/+dLPdHsfx+v31NE3D0Pf9MPRd3/ellGgkCo8txH/XmD4wrdbaOI7v3r9/+/bt+/fvb25u9vv94XgYj2NtNXb2xJzTYrxPZjuse9d1XdcNwzAs17P8HT+UUsJx+a0uJM7qm2+++bu/+7v/6//667/927+7u707HscmCgBd/3XOxQFEZH84qFsVgWkWNUJc63kVEZkTLA2a1aNqIyVRvb27/8lP/+F+tz9Okzr86Ic//PyzjglPDs9ve9vxzIQR0RNYHtwJ3F20TnU8tvko8wjWcDXt5rh4so7oTr5gkOvmBhBurLm2JlOdDod6PHibCQGJgdkIwT10LEzVT4nb3zIldR6142rAca2Ce2Lgn2bcn9yTs9fP//5wPLHrtv4EEKVvDzw6IkL05ai4iFuuR3k44LOr5z/4/IeXV8+220t00KbzYazzPFlzm2ttTcTBOafD4YCJ2wJPqYEZxMzh0vVdv+GUeyZXFJ1bm8AdGTnRcLHt+y1mREZ0NVdtaiZpInJrMzv4XGdVAYKc0zZom4Qm2ubaalM1DNnMOP2PuShPovaze43LjYv69TDt0trxeNjv99M0ttaYGHz1gDBcQ9cqTYWQhmEgJiYGAgYgY1Y9QwOWbdVVZW6tzWOdp2mudQ6v+XTDT7P85IA+eRi//cBzZ0/Nqshxmu8PxyYy19paq7Uex3G/P9zc3l7f3N7cXO93ezVjomEYkJiYiRd3cJlbZrUJTrNqq62mUjb7Q0qp5ERMcAIhnkZX37l6jge53U9ExMQ5EyJq9GNmzgUQVVVr9Xk0UplmOUxwrKRWEg/bcvX82Rc//KM/+5M/+rNPP/n82eVVSbnLud8Mm2HT9T0hrjlqE9W5NcPMngzICYAMKUoWNabOgs3j4t03ERUNIh1p7tiN2MAN0BzMg8+6PLarF6+p9P32IpWeOGGo1C4EF0QzAHWtIDNIRW0RfTezUdp+nnfTdD/OxzpObVYHZO4AiTPmrvQDlQ45u/M8NzA0VdEa4jonh+oB4cTlHyGBuCSDwxouLgCuYD4uxT+/84jpMc3T+/fvv/76m2+++ebu7s7dN5vNy5cvLy8vAfz27u6Xv/zll1/+8s2bN+M4iggRD8Pw6aef/tmf/fmf/PCHr1+9ZuLD4egO5iYS+pRlmqa+77uuC0N4ind/99N+chHn/4jDh4Lbmzdv/uEf//HnP//5m7dvrq+vd7vdfr8/Ho8iUnIucWa5nAJ0AFhtJw/DcHl5ud1ut9vtMAybzSb+3mw22+02/hmOS0T23wecE5FxHL/++uu/+Zu/+Zu/+Zuf/uRn0zSrhr4GEhOuiQB3J6KQSlVTJo61aO6nyWFuUZlJQIDYRKe5zrUejseUS04FEXNOlxfbvutgCVt+O02bc/sWidO4zEVt02OBNpkOx7vr6bBr09EXGoqBu1r8AVu1nB5IoCtpCR3QzMW8iTVxFQQlWpqZuoNFUVxoTJvhQoxaYe0PI9sPHgRiVNLGD08F6b6PaX9y084s0a+7myc/CM2WbPKqfIYITrFVMwc3H23F/R0Q0D/ivfddf3FxOfRDStzmNs/zOE51nMHc1VqtaurglJM5IFNVUTcFAzRn55CIZsqly6kjSKouTUSbqiIZMgE1pAYLFVHIRVVUoQFMrRGgmdU2i4oT5JJcatf3KedWZ6lVW3NT9xRP2VZ45gml9mld+ylvFJp95uHFalTjiEirrdXa5trmSgZmpk1c1c2BAA2RqHB3cXX56tNPNttN7gplRuZwJDEIdCEk5gBm0mQ6HN9++xbAOLFqoELLZhzf6yfhmkBQHc7UxZZz/+6n/5Fh5ss8Vp9r2x9GxNv9cZym6TiOkWmrdZ7n+Tgep3FSMWJOKQfOZKp1buCEgCnlmHtxp0QVgCjlKnZ7d19yHoa+LLPXV4rA9yI27+7rl1/uhqG72HaXl13fM3NO2RJRJ47zVMXGCQ4IpLgb6Wb0XcvQPb/avtp8/umnP/iTH/3wzz57/fmzZ8+GYZO7buiHYdiWrkNCM1ONKSW11blVTKSIsujHZiIgIyBRA3C3wJgcQzbZ3avUcZ6O8wiEXe0VcGp+d7+balOlskLyiPjq1WfU9UwpuDWABMjBtkEwRHObre29HVAn0OomZiomk7RDmw4yH7QeW6uiqfTdsOkvL7dXl8NF3236VHrKHWIqU23N5lphHl2XfW6N2s+lFAEA/UGKIyApW7M+6GC+IPm/B7sevoSI7Hb7r776+le/+tX79+/DHj9//vzlq5fu/ubtm3/8x3/4yU/+/s2bN/vdrokg4osXL1+/fv1nf/7n/9tf/m+Xl5c556jpL6UEoaa1VucZcVdK6bouDOFiSX9/qqi/Zqjq27dv/+Zv/ua//vV//fuf/GS/2x+Oh/1+fzgcpmkK32UYhqFb4u8YrbX9fh9KrpeXl69fvw6eQUTwp2h+s9lcXl4+e/bs6urq2bNnL168uLy8fFK5++Fwh9babrcPzYA3b749jkc3wIW8iwCwRCqreEYcM4BA4iXKjKuLYyJSSuQeqOta9Onw5ZdfxiIy1R/98E8+/eSTwBh+29t4snm45gTjxIgW70KqWKt1v99fvz/u7nSewBq4hjUWsRYVqr7QURe+yBkZOTZNVEeLwmxnwpQ4BV+WEFVDsGLZ6s3RDB5itic3+YSCPVogzI/q2k/W/WTXv49pxxV2is/g2StPbtrpZJZBS/39YhcipcIrXX8J3vFRFvbDHyLIkLnuZruz8TiOh0lnBYWCKQGBOwFQSpxySRkTU0oK5gTABslTyV03DMPFdrhKVIK9BwDuzbwaiIMhE2EjczCmZkkUW4MWHe8ZTnkRdErozjOqyZxyV5tEkdvCifCVIYR4aqBxGo9N+3K3TobzpDgXph1iHpuoiUptEaBFvQQs6mTIzCmnru+G7eby5bPN5Tb3Xco5pE3BnTwoVIDurjYex13J4zy12oBwEULToGqvXQpWR3gJq36HLTd044/jtNvtj8dxrhWaqPlcW055HMdxHNe1v4j7qygCJk7h+iGiGUgTBErMSBwVNWauaipGiTmVpna3O/Rdt90MuOlLThHzwrKEf/MWME9+dyNamSENnUPHiXNXIDvm5ja3seF+xgIOwruJd5a1v7q6+IKfve5fPn/x7OWzy2fb7UXfD33fd/3Q90PXdZzYwcMPaXWe56m1KiYOgERNXU07hAJsrqgLtXJdFEiATAQIYjrLPNbR3OY2G1BVqq054EkYNR7XMGyp60OLGJZUDAMyIIA5aHUZrR1BRtQJrLo20TaLjNIOrR61TS4VXIm7ru8vrjaXzzaXF7nPqZRc+pQ75kJY+37KhyPCTm3JoeMKPsFZhhgeOHKxPenSNTvSVqf22eDwPcLEXzsQQhzpcLy5ufn666+//fbbw+EQVu3i4oKIbm9vfvGLX/785z//+uuv7+/vpTVi7rvu+fNnP/jBH/3xD37w+eefi8jxeDwcDqoakHVOiYkCKwroPpRIunVEmBt72u92CY/GAnSphoX+6quvfvrTn/7kJz/92c9+VudaWz0cDuM41loBICQf5jJFviC2+2mabm9v53lGxOfPn0dTlmEYTu85h+s3m83z589fvXr1J3/yJ5999tk0Td91XqefWpNxPN7e3rx9++7+ftdaQ+RIA598uwjyiOi8ztbcEjzcsVNEERdtZqJy6lshIre3t+4e+nQIwEzPrq6GYfhtrfsjC7eKtMCD0UICcBGZ5/lwnA9Hb7NrAxORJq2pmi7OaSQF3MEW+sgZIA8OZI4OjMgARuhuQMTIDoRmFgk2MzOnYOB7wLyP0/+wJkcBHm9ivyFqX4rLl74IdN4x5tFNWM43vofObswHK9FXIPRUNPihaY9TiYZLKSEpwAeX9HioaZW5SWutzlOVJkwpcy5cCiZ0JERMlLrSbQZM3KLGgMCTU/ZUcilD310M3YYhRVKACQGTQ3JUBzUzU0AHiqpCTEpuqMmBwNyUPOgTS9EeuoOakwFAimuhE4X2O5GsD4rfYk6sts0WboXFGlC1haWwzHQH99A0gbid4icPycG5pO2zi2G7KX3vCObuoiYKAdq6axM9WlNxRsysZq3V1ppIw6U3Bp8IkWaGIX8CALCsgXj9+2zBgULUKuM03tzcvX1/fX19ezyMan7kMXbDWmtrjZlTYqZEiRpWBGJKuCAOi4uhrqjSmhCnxAkA3cHMmmhhzilLq/vp2JU89B0R5pQQf8vslZNLkYrTUdvGYUNdKokTioNq83SUfDuDGbRKx5qn7kX//IvnP/jTdHE5WQX0Otcg+5Su32y2pe9Db8Cj05vINE+H42Fus7txYs55nqZWJy8ZiaCqtwbWHBUSLsq/BDkzCxloM2neRLSKIGanwsRd6czQz+UTlmodx8BtYpkDojtYszZaPXo7oI5k1a2ZiqjMIkepB2mjSQP3nBJyf3m1vXo+XFyVYUMJkRggI+TEnWfuyqaUDhB9Me2nvfyU34mtYyn0XfZBNAcFsGgP44v2LT58/J8+HACatNu722+//fabb76+vr5W1YuLi1evXnVdf3t7+4tf/PLv//7vv/zyy/1+LyJItN1uXrx48cd//Mc/+tGffvrpp33fv3v37u3bt+M4ImKJkXPf933PYZ/CwM/zzMw551JKYNp93wcG/rtfxWm01qZpur+/f/fu3S9+8Ytffvnlu3fvDvtDmJow5yHtEuVnblZrPa3WcRzv7u7iFXfvui5OPhyRyCmILGJQIjIMw8uXL//yL//yxz/+8f39/W84UXdVDVpfrbOImBkhWpDFHR0B0NepvODeEb0ws7uHZsDJwC9bnYiauisusDMSkaru94df/epXphLh8o9+9KNSyjnp4fuMp1H7Wfzq7m6AgG5uAburLefUxFozaeCYiIFDIBYW636eLT/JtHik5aOY11UNQ74Zmexhr194dHBaQSvb6eGM13/h+VUAEcJZrv3MsC+mPH4Rrzxhxz+J3ZfvOCnUATwFDxz8QZou/JglH7FAvu7oYEhEiVNOKaeUkQRQTiyGdX0/WuUiOtcq0sykdHm72fTdps9Dx11yBnEwB8JUUrcdKHF1EVcjB3ZMwCkxdyWVRImcwu8A8yUHmYipmJqToyEaMiYgEmjGtmyL5gi27JGJnNmYgBOkwuZIydxra7CUbqx+zAcb1ceEZle7vv55gL5XDyEK7BzDop/9Gs3MbdkU3Ygo5VRK6bvOEdTNWI3V9ZTDMkdXsIAUbGVziAhDimzIGSCzpEf8VKC3enMf3YDPfuGOIE3mOh8Ox7v73c3N3fXN3eE4qbmKiqiZrS1J1c0QgAtHPhiRQs73LBhdHPlojRanEhnc6I4XbU/N/Hgcb27vck5932VOH07fXzOeP3v553/6z9xnwpoTg0V6381NDMSyqx+M0VmJddh2V5/nq88vX732BOP9vk37Oh1MG6dU+r70fQouRmAu8zwej4fD4Xg8OkDXdV3Xd6VTNVVjzoAwzW3e71wrJei3XeoyOfjSPNXVtGmdpbpBSakrXSqbJs5c56rTeIad+rJRIKx1j+4ERi7Qqs9Hr0eUGaWCNdem2mprY61jk1F0NmvgwAycFWAWhWmq5lFhk3hOqeu6Gtu6O6iaiIQY8/l9Xv8TWAEujwzNUYEM0Zfq+qUs7qOZuO8zHn1KVcfj8d27999+++3799fjOOacw+g2ad988/WXX/7y22/fRLu/gNOfP3/+6aeffvHFF1988UU/DOM43t7evn//PpzOMJnzPD07PrvYLtnosEMngC2MYoTy2+3WHstU/VMuaY3U68pECXzreDweDweRBgABZYdvcTqfWmsDJKKcUtiqeZ6naQoBx3mej8cjAIzjGHB9ZBOmaTrl7Pu+f/nyZSD28eZfMxAh/IOu6wMJOHn/J/0WWJoDndqZOQLGLToZo/OrVlURdbAISU9KNWbWWt3tdkRYcnJTiNj92bNhGD6YCd+52HERzXqSagbwFQ8PGbJT6BXXY+ruTIxISGnps7zsK34q6o1DnSBxdFAREzGROJw5oC1CYWHXfZWF8IWBDyszaElTr0vj6RXRY6FZfLDiix0/AePrCw/GG3GlbC0P6TxqP7spD0mLJSMQOXNfhcwj9bAYCHMKEDmXVPpUOuLm0B60S1Y21/mTYkoldYkSgpdS+m7oh01XhoSZjbyZqzk4M6WSkRGREpiiLVgkJcZExisvLDxKA1VXdSNIkHx5rgBISIDknNCdgmOMQIGSElFOxqxIRgyUAJXMT5oCp5zEklx8vF09BeTXp7fY9ki2wxk89cCix1Ph86l71/qxNRkAZpHgcTWg6AcW/bbR3VwhBOFOeYJoY6eqqoZkSERmfpaVgYVdEpSoB5rUr4FY4nemdpymu7v7m9u76+ub/WE8HucmljhH7tzNxRuAI4CaQoOcmJhxSdJQOLMLwLtgX7CWQS9uESy9bhEcErOX0kSub276Ll9cbHGg8ttk4/7kj3/4lz/+o7u7d7d3b1q7Nz0COpqbuFRsmhV4wpLzkDcX/fDy8vKPuL/CZON0d9i9K6nU7ZWr5lJK16WSkdjc1bTVejgc7u/uDofDPNfN9uLi4mK73fT9QJS60vfM0OphnG/e31ibuo5e8cuck0P0ejZVbdrmWsdpKrnbbC6uLp8Pm8upSt4dd/txGg8P8wqRlinuDICgZEBuaM3a6PMR5hGlogqqmpmozU2OtR2rTmrVXACYyAmP4zROgpSImBKF8hWnJZLlRE1aEEJMmoPjKp2zuv6IixBieGPoZIBG7IgRz6zVEohI3412fb8RRRP3u92bN99+++239/f3ZhYEMQC4u7v7+c9//qtffXU8Hohou93mnDebzatXLz/77LPPPv3s1cuXqvrm5s3bt29vbm4AIKU0zfPN7c3wzXB5cfHyxYvXr1+9fv06zAkRiUiY3sPhMM+ziKSUTk21f5vx6MJFJCL13W4HAMxsZqXrcikp5650m80gqmYWQPoqEWMq6mbMTMMQ+MEJ7kbEsP1hVHJe7HHXdfv9/u7ubrfbRa5hGIbdbnd7e/s9ThtLKRcXF69evfz008/u7nb39zvVQGEc8cwiLdDM8q810WZB7oHVT3oo4SNgirZdJ18BCclM52n68stfHnY7NwPwP/uzP4v+7t9zpYfVxZUH8ASAXEAuDOFysKDPuZkZMeWUiBiQA4XHZYNaU7AAC8mZiBOXUohZWqtzlWky0ShRcYcI2fXBuscRzIHWPfYsTF/MylPG0JMQfLHfhA8UdSRei9lPGfhHofv5fwEw6tBPN2Qx/GuuAk5ptXiei/HRVSrJwY045ZLLUErPuQOaFkW61TMAAIRHju/QDc+3LwmRGXPKuXSl71IuGDFoUtflCagIGGKmSFC6AyiiUoSghkoLxwzAXURVqwIYU0mcmXEREhAzFAMFZCQnzkyO6G5ISKmnlB0JgBzJdBaZmqiIUIBDDrhwI55GIk8BeXj870du5+JDBhmBkQiIwNzJQNfM5bIsFt9qxfeXoyEAYYCobkAGikyL2AIiICwCPu6rZ+CxM5/s+pkVj0Mvs+C7VlH4AK21cZ7v7u6vb27v7u53+8M0N1ElIk45wIOY07gya9xPNTBcSolgKJCDpXqAmBNHCocTES3kgSZNR21tWoSH2jyp3Az9ZrMhxHJx8cH9/s71//LFyz/9wct3777+9ttye/PNbm8qVU10Bm1okICx0abxlrqr1F1h7hVsPtweDu/beFeGy4RWcipdl0pBZgdQ0Wkad/e72+ub25tbM0spd313cXHR933OCYlTLgVREXM3pG4QcGBASoi82PWgQ6iKamvCXBKnUrq+65iLO4r4DRzOLsUBDE1JnSC6sTZwAa0w7XE+YqsgCmpurmpNdRabmjVHC2kcAEqMhPM01/koAu6wlNEBEqec8zAMV88uzCuAE4IhgIE/tCaKibS47Babfbj86KgQ3qktGyQ4hDv3Tzft7t5E7u937969e/Pm7fX1Ta2ViMIG393dffvtt998883d7a2qBQl8GIbtdvv69Seff/7Fixcvuq57//767ds37969C8O28MxLWarIx/Fw2O92u2DabzabUxmxqk7TFDmm3960P06vukcv85ubm91+f7HdXj17Fl6LNAGH0nUXFxex9apIE6nzPM/zOE21VhMlopCBC27aOQrYllFLKe7gbvM87Xb7u7vbcZxUJedU6zyOx91uNwx91Jp+MJbNARFzTtvt9pNPPvnjP/7B+/fv3717P42zqhGslv1swflJRvrMQkQws/CHg2S3BjOw4udB13d3aTLCOE9jq/Xi59sgDBLR1dVVxO6/0cCfxy1nu9waYsFintcwb71cRGYqOfx1FFVTxQdccfncCbWEkIBNS6UuRHdxSk5sS107LKHcgyzE+snFmHtk7c9O8emFnC7nCUuOkB7l3h9z5h+u/RQ7xgHpoc5mCSzP/nkOJC8wzFocsHScAiLmrus32+102Hb9nviwJtrO786jUVK37S/ihNeKzUzIAGAUyTs4wdmUqFDBzB6S2Y5uiEAEnDAxEDuYmYYit5gDIKFnwEKhpkM5ASZxNCdHQuTMiYP84K5I5mROYlH+OI3TVGsLx+4UV55g7PPxpPhtwTIe5tv6wxqyr0+KGVMi5kj/nI57ejcucM4JUIrXF8O4guy4cFIBCaNHp5tbMCBwKd94YtdXf2OFnk4p8I88JV9oA+M4Xd/e3tzcXN/czfNsaoTIiWmlJBDBaRlH5i/0qMAhJSbsdSGYRBEgxVTNpXR933Xl/0/bf7XZkd1cwiCAbcIdk5ZkOZWVNN/b/TzTN/P/f0FfTZvXtkpVLMf0ec4Jsw0wF9gReTJJlqTunhBVJJNpwuzYABYW1nLWAhIzpxRDmEKIOceqqtqmyTnlHO7v752z3rlV2x5JT/+No23aVxeXBlhylDSFsR+GnKYQA+ckiIacE1snqgkrYAjDIaVw2L2L04PksVqv26Zq26aqa+OcAGZOMcX9fndzc3V9dfN4/9B1q/PzVdd2Xdc574nIAhoCA4Aim9MzQzANe4TkmxoNaeo1a3UVxg4zsCCzcGJDpmvacXgWSxAEmSElTFmheBSWHDiNEg4YR0pRkkhGZsxZQsohc2BksGQrS0wgxhICZJ6GYZzGmBLr8tRHYo3t1h0R+wrJoPeWUDhL6TpyQewAAVAZIgrnUQGz5p6PlK/QT6X/k6JdF9LNzc2vv/56dXV9DLnHGH/77bef3r69u72bpskY8t7Xdd227Wazubi4ePPmzWq9zpl3u93V1fXd3d3j46P2g1er1WquHe/u7tQmbrVanc3HdrtdrVbMfDgcNGwu06T/0LHsnsx8e3v37//+H3d3dyHGTz/9dL3ZjsN4d3e3PxxyZu/9ZrNd5tOYc0ppGqfH3WN/6FNOBMUxue97nKve+TMjs0zThAjM1TjmlPJut9vtHnPOiJRzijGO49D3e+es97/3+oiIMbZpmouLyy+++OLt25+b5qcQUkxBZYiVc7188pFsxlN8Xbrvy6ZHRKXxzYAo3ldV5UOIQcksKVXexhh/e/dOPz9n/uqrL51zxtolZ5jP8eVOdfyjj5l9S8txjnpL2ERCAkM6f4s6cpw5xIgAtmgQlxK+5AjEAhJLCQnGGHCO0SAZQUowC7lq3Tv3YBFk1mv76N1+cR3Hl/M8ss9Cs89D+7PojtrHnLfypeB7djteRvenA/R9LvUei6h2Rl3XuF6FfrXbPZaEDJ7i+twCeLoMa6x3FQkRoBFDbCACZynLNuUUo6QEAoaMQ9fYxvoKiFggz4I2hoy1BnSoMKU4BmZGAQSDYgSNkDHeV1VVta2rKvXICFFY0BhLiFk4hNSPY4iZgUOM/dBP0xjTEGKAeekKvLwHTxfy/FHNhaS6ZOLs8LrcuvJ/JCIy1jorxJwSCClDpcAr2kY/+oFFsfsoui+LYH6WCAWQl2cvwfOYPZ/Gi/fkw9uWvmMhhEN/uL+/v79/POwPMSmfVNmayjlydmbuaIdSv5yZM2ZrrXOmrmtE1OpHk19rnfXWWQcIk47J9f0wDDrxL8zK7zVEZFDHZdqm2azXTV0t0R1/ly2PIIbAWVO7qqm6rl4DCwgSpIRZLBJYsl7QZGFJk8SY4pjGPXBsKr9eb07OLlbbE1c1SFZAQoj7/e7u7vbq6t1+t8+cq6rabDZFPZvoKYEDJGvr1RoN+rGRHL1Hwgw5K68T0Xhb1VXbxmStF8EY0oiDMR7er6tyAgEIo8QMIMDMHIUnTgPEEeMEOS+ts8QSM6ejdFG3KVOU03Em9yfd9jILAyaTrLMxBussoaL3BMw6aUFmXmZlwlcMCBfajeJxohW6aMtBgfkPJ4x/80AAUah5v9tryf74+Bhj1Ck11Ur75Zdf3r27GoZBRLx1qtyyXq9PtieXl5dvXr+p6zoVydMIioHnMoYKAsZa7zwCpBQVtdYVqEvUe6/LteDGz43eAeDjDayXV5xSGobx6urqL9//5eHhUb9PSnm/3717d3V3dy8i1lhC9FXlvVfe/jRNhoyA1FUZ2XTe5Zx3ux0gDMNwOBwAIGeOMeh74JxvmialPE0hxhBCEBFrraLi0zQNw7Barf/GrT8aCk9JuxMxp8g5gY6tI5ASw+caePlV7suRPdVR+JmVtuaVYYw1hhFNzpE5O2tYYBjGq+sbQBqnoNvkycm2qZsXe9gHTxtnRhHiU19UZO47Hj0fBAQi5Ujr9lrm3cp3l/kpFkCz9OlFmDOAa5rGGhOHKU4xZU5czFhkGYx6AlqfnfYx/v5+IHmqAp+X7IVD94wR/+zT9D9aoMEyn/asokOAgjbTEQqi5zBPM80nVO4aIgowAhlbVUa6vm3ruibzBGo8Fe3Pl7x1rm4IsnLcDBGRITCACCxsxFhyYDMBeoXrfUXGAiKzJMyCQhads1XlkEAkh2CCtzkzZzWuQmsMkSPjyXgyznhvnAcyLnLOoNqmyJwk8BQSR2WGxkLuO3rCx0/n90P70UUjouA8BlS+7MiYCBGtMc55ppxD0L6zgvUEOHMy+Om+H0ew5U9LXjYv6yKPw4XbshwfW0/yBBF9eKvKzGro8viw2+8PIcSYMjOTtdZ5Y8g7W1XeWae0XqUZ64Yo81yAGrY652KMIKJ+ANY7JBLOwzju9o+3t7c319f7/W4cBwLSvcx73zRN27XDOI4hbFar05MTQmybGv6OqJFSGIddmEZhrly17rbGGO+qGEIs0Q8NGiQU0f2LOUUL7Kq626xOz1+fvf50fXZhqxqIcs7DON7d311fX11fXyHgql1vtpvt6UnTNkTEIpmZEEhzZ2NsU6MlU9Wco8EsecQYAC2Ss7ZqKs4CSJYFRHCcphyTtdE4n2J8diUxCiYYe4gJEIEz55HzBHnCHCEnlDyzTSQLJ/VV0AwxJ85ZEAidNdZSYUAwlaSVVSMBkArBfdFvKGWIpimqgqZcY7WyKr47AMxHya8uQ8i8LM9/+CihZRzHh4eHq+vrm5ubYegBQIldwzBoKX93d5tSds447+uqaup61a1OTk4uLy5evbpMKd3f3SOANuARcRzHlJIy6Zy1Tdu00Ijw0lyfhzusSrnFGEVEof5/6ORhzqERMYTw8HD/62+//vDDj4dD773v++G3337b7w+arwgIGcLZR9F7DwDTNAlz5atV263W667rvHMp54eHB+0RAMAwDMw5hGitcc51XXtycjIMwziO+LQhsAbpEMI4jkfOyx+/AOEY4263U+n4/X43TUPOYgzPDmjKiSGcVw4uDLQCSgs+x4ph3oy4NKWFs6iPYQatFQAAM8uh7/fff397d68zV9rO0++CzyD3Dx/Hn1MClMDCJZ9PBYgQDIFI0hcKEBENGVSKK5X0AOf+k/YhRICINut127TDvh8Oh74f8xSfqnUu+XXRdwCYufV/11ugdMAX9PhjdvwLlB6Ph9uKDOxcRB/x68qqVG3y+f4s0bw8lKfXF/RTWUra4JxzUOsbYcgsIDY8lY7PHon1HhkgEWYisIbIOCKj90+4YhDGLJaodhUZysIikkVI2AoTgrXYNLZb1dYZQGCWzBJCClMMU4whq/QtGstoogCyEKH1BsnoIF0WgQSMKIBZOMaQUoDiPm9ZDCR8SqZ03b4XAd/za5/r4LlAf0JdZGFYzCRwYwhVHJEIsPjXIsMRO/OoM146R8/r1FKEa+McjqgcM4Lw/BzwiK93dNIfRBsRQKQk7+r0YJyvkHLK2TpX1ZWvKueLzcaSAOq6lDl/zjkTogppOe9FhBBZRFuJ+/3u4fH+4f7+9u727u42xYCIXdO61Vot8mJKY5jaEATg5vZ2s1oZIu+9NXScBH/wVckpTONeOFhDbdMSQl1XIXRayXFiZlEqoyhDIGXJhNnXdbu5uDh78+X64k21PiHns0iI4XG3u7q6un94iCluVpvT07OmbnLiaQosQMYimcU8BRDRWuW/QDbASSQzGkEiss74ykFiCYmziCEjLFOcpikA2n48Cu0iPI2IgNMAKQpSlsRpkhyQg3BSQQBhTpxiDjGFKUwxTjkGThFyhMy6SxEUAmnZb4talshi1SiFSCNZdEhXIVQu4H2Z2dR+it5/zpLysqpBp00RwcwFxQcfzd88Ukq73e729ubu7na328WYiEh3+YeHB1VtG8fJGHLW1b6q67qpm9WqOzk52W5P1uv10A/G2vVm/ebNm91ut9/t94fDOI7GWEBKKYcQnTVEuHi8KlBERPv9Xn/WYsTyd572ch/0Dyml29vb77///u3bt7e3t9M0eV+puKwOpynz3Ds/46m06L2EGPUE2rbdbjd13TCz9y7GcH9/N44DM6cU1DRqAe+0uDzerAtFPaV56uF3jg9iYIXbz2JZ2BqZ2+XPw4x+PZabsOCFS4JIZKyznAt0Mo6TvrbOeeccEoWYWAQDppRT5u+//8FXtbFGRDbrVVVV8Ltx/aneOWo2L2dSohzOBbgUGRTNXAENETpnjzqYS8xCKB8t01I613NkBPaUzKnVmMBTn+L5vYWnc3mJxpe7twT14+iuXWWcR96Wf8X5FUMq3GqgOSUp6fn8AoqAAMETY74U7ADwvIsxAzBIKIKolEheYGRYZv1LylJymeMHgQjGGHIWvEFnyRiLRMDIWTlgzJizQSLnQDiFmDghirNmvaqayjVt1XVNt2qdd2gMIDHjFOI4Tod+HPopRc4MAIRk0BkxlLHMi6ljSMg8xTxOU4hDimNKo+REKGQRyQK6mMyT/s7Rgjk+XjDkcUmIjjD8pz9yqanLPoqIanwmCpUWit0i/oFQhA+ehXp5vm5mIKi81SoRd5RFLEEWnn/VkjJQ+S4fOmQeiLLWNQ2KQMw5pui8r5vaWqvZiG4eypuz1vLcCQSRpDPVznrvBYBZUkqTamJcX/3226+3t9cPDw+Hw34YB+/dZr1erzots0KM4zge+p4F6rq5ub331jV1vVmvCb0xT6N0H8yLc44h9CBcOWupbSofU5tyzDGkHCWzMGOpM1LmyDlzBmTXrE5PX3968enX7elr166RbB6nfhju7+/fXV0Nh4OzbrPZXlxeEtndbjdMU1U3Tdc1Tfv07oAIoYBRsU0EyUBZQACVReQBQ8rWRBS21uek04UxJp7Ss8fBYUABigOkxIggiZO6XCRQs1rOzCnlENM0xWEKQ5iGOA0cJkgRmAEJGWZpJx1I5zkBnZfsMq3JrPNvKSlpCyVF5syZYk4hBjRknSNjACiGFEPS9x4JiNBYNBbJFPj/g+vq9w/tst/f319dX6uAuQhb66y1+vG7u7thGJUW7pybddObVbc62W7Xq1VTNznlyvuL84vNent3f397e1s9POz3+5QZAMZpEpDKWdVg1Vb9ZrO5vLysqkr14Lbb7aID87G3A573LI8IJagqKSrG/uMPP+73+5RyTplmehERxRhzTspYWUZpmFln3621ufKa07RtjYiIst+vu65r2yaEaRhUEpFzziFELdljjEU/Y257H+F/f/vmI6Ixtqqqruu6rq3r6nDYxxTKxqVAOc764s+C+1zZyFy7Hv2RiJx1YkRx/r4PxhjnbFVV1toQpilMUwAl8YSYfvrlF0EwxojAl3/4YhH5/8BTWAof0GAsSxSdLwhxllJDtdJUzu+cghMC0iyTW1ydSt+zpLSoNTUJ8zAMSfW0p5DUxF4vusy1l6pqHn8qOcL7weMoI3r2kVKOH4vQzeH+ZVyfPwMRAUkIZwxl5mPh0noX4Pkijn/ee39cQrtSFAAga5oZYopRmGEx/Zwr3Rf5YhZOgMY5a1pH3hpjCBAlKyueOacMnEQYWSTF/dAzR2dNW7dnZ6vzk9V203Wrtuka5yvjHKBlwSnEYZp2u8Nuf+gPYRxiYmRAMSiEDMApckw55DGmKaQxxnEK49CHMEqKIGyQjLXGO0A7RVOkQUpChO9HkJemrnPaLk9l+1GGJLLYu8xBV3Pg8v8Z1Zq7NfjxnGJ+EDORAWDGBWbl5KMf/96jfLnIPgYZiUjOLADGWoeGyNicXYpmlrMu5g+5DL08aUyyMiFZ67yUM0/TOE19P+z2+4fHh9vb6/u7m7v7u/3+sT/00zTFFHNKiOhdVfmayOacEzMiOeubpsssdw+PN/f32+3mZLPtdPgVlxzl/fSFhRMAkAFEZf0ZZsfeMxe5QdSqVRJLypyFDWLbrs/PXn++PXtV1S2RQeAwDY93d48PD2M/WnLb7Wa7PfN1e3//cHV9Y53rVuvXb97UbQcIasSpkttk0IElAo4pAaccU0woYtBaS1WFLZCgOGdSCL1IDGmappABwMN8YTlMCMI5IidAAM4iCSSLEuJz5pyyDm3FKUzjNA5hHPI0cQyQkjAzUE6JXXEIXe7Q0ULS9cKgA29qLcus+48I6HNOKaaUUAwgkQAAp5RCjNrARoICaqKBWaT2Hz0Uit/tdre3t7c3t7v9PoRJV/Q0TYfD4eHhYb/fM7MxxlmrDfi6qlVmrm3bqqqMtXVdb7Zb3cZ9XXvvV+t1f+gPh8MwDimlGEKOkwbapmmUku29F5H9fl/XtfLyPuj9ukRfjaY6aK7UUU1wF47927dv//rXv97c3Cj/VEQ6AFWEZWYVjlUQeHl3xnHUuTst0qw1RJhSCjHc3d3e3N48Pj70fZ9SnFN5FuHd7lEr7JQSIlhrcp5r1SeBs98HUeZeobVnZ2dffPHFL7/88u7du1wOZs4ZCCADRI1Exyjxi/fvKLSXvx5hCeUe5idVC9RkKAOQMSIyDMPVu6t/szalaAgIYbPZKJfwRYZSEEvFzLUUKiP38z+W0DrHQAAdLFeEWGeyVN5zvgcyw9pLhAdQSeecOCUiw5lz4sw60TtPaOm5cUG/58QAXwTR39mHcYY6lns7hwYsZLrn9xxnEB/IFOW1OZDMX1eUWZAZmfFJYe9ZtY6EWpsfocSgvh2cUhynOI5pmoSz9ixo6TXDy313CqEfmGuLlWfALEygEr4QhSc10YnBCFfWSA79OIhkJG8Mdm212XQn21XbNr7yVpvoSAzkvfGV8Y7axg2rOE45ZUgCSSSLxJxCjGNOQcIQ+34chjCFMKY0ZYlAEUQIUMRytDFGzhMZDyV/mXPT5xfyHJCfS9W5Un+iKuguOW+sKPy00LUxQvqtl/dhXr1zQozL0n1aCrDAT4hPXI+jz5ASYmTu/B0vo6fv//HcQUBKXkrGIBjriDOa0qddivXMLKU5swBdBc/RhGMKMeXx5ub26vr63dX17d3N48PNOB44K1cnZWZESikf9j2hEUDvPZEOZdVdt9puT1KKh3G8uXtYdbfOuqaqyCwTJe8tsZKBSpEPKG+FBSGwRsSW5LqUGgUrEnRkV+3m1cn5m9X6zBunCjdjv7+7ud49PObEq8364vzNenMG5G4edv/x1x+MsScnJ1XbnZ5fAJDOcQCCNWDIABkmCRmEcwxTDJMjqxp8lXVQVUTgHcZpMoDTFHLepSRg/bKoOE0MIpxUZBGEQTIUDIizztIlnYGawjSFcYjTmGKQlCBnSTkL6rydLFRNWHYxnIM6K5KkdYKutGUHUfyG58SRVVZBsMw+ZBYRYgBBJmEq3au/q048Xm4AzNz3vRok3t3d9odDitFYy8w6rv3w8DCOIyJY65RrXVW+qqoltCsZrW4aMhaQWISMtdadpDiN493d3f393W636w+HcZo0m0wpaY2uMbvvexXG6bru/WJRWTk6KX5zc3N/f39/f6/6MMok1XRhwR6ur6/3h0OKkUWVflAn2TQ7McbknETYGgMAh4PaR40ppaqq1HYWAPr+cP9w/9NPP719+/a3d78+Pu6KrKkwJ44h6Fktw3vOOWNYRLShoLDERybfnr81iM65i4uLnPPt7a1K/YQQ+r4PIWVmkQLS0GwgWwLSXIfA0uKF0vDGGYpYNiKVrss5pRTJIIBS8/JSAqWUHne74X8NfX9QZX8lPby/VencFEEhfMzrqFSUugTnwEimbAcLoz2XMWFYot5Skz3vKsyxfpzBegBCY5GsmKUvWnAvmAdDn8LGezf5A3f+Wd1+FOOX4P68sl9eVSQCY4QsGKvV/fKJREjqcpkT5CQ5ohztlkQoDKzSZk/A8NJeQeAUYhiHMAxpmiRlA2IQDCFyqdbN85g4jOPD48hsBI3DYMAYFmRghAwypjilmMNkgBvnhNMwDQbFV4QEvnJ1XdV15Zwrl1HUUMSQVI4s1W3t00ZSlsiQsoQkIeVhmg7jkCH1SeI4jbwbwn4KPUASjEgZdH/PyIHCJDGBw1kuu5TuLx/K+732pd/2BNMtq3xeNCBSpEvmb7bMO2KJ0EozLnNERTKivBdHqRXKkt0V2LDIJ5RV9nQCx7H8+ITnjf5jVTuwcIhx6PsQEwDoe654g/b1AVGtX6xzy4ITkaw/lwhYUor9MNze3b67+u23367uH+6GfpfSJJL14nRgwFnrnNfd2fvKOd807Xq9Wa3W3nkylBINw/ju+qZrmq5pqtpb+3u71fwOqsodISoibea3v1w7AQOwAKCpTL3tNqdNt66qyhjinKapP+wfHx7uY4ht027WJ+v11jofU55imkJiDoJ4fXOz2mxWbVt5Zw1Zq1qwgAQCklMIYYxhTCk4VzgWlgjQIbEFRidN27n9gVGdso6uQhKCILAyl0WYFuGNnHPmlFNOOarIcAwpRc4ZckYWYgGFZ5lBb8Fs5YTabCfd1ATn3YiMcc5VVZU5L/WB8p+sWMcM2tsDFAFEY4xjnkcuCcgImZcp8O8eT+FfRJTDpQ6nu91uGIeYEiCmlIdhuLu7OxwOqv5bVVXTFmF/rbDVEKVpGgA0RL4iQEosYIhBkNBX/uRkW9f+9GTbH/q5kR+NMWqyojxQa23XdfoRXczHZ/jjjz/e399fXV3d3t4uku9KQe/7vu97lYnV+KQfzFnFtyWlPI7jfr/XgJ1zZs7ee2vMNI2Hw0Hn9MZxNMZUlffeM+f9Ybfb7e7u7h4e7sdxABDnDDirkSullGI6dnNXioCWE03TnJ6efvLJJ19++SWR+Tsx+bZtVXn+5uZGAYmbm5vdbh9DPk7lnxWRRFDaiB8oFmQ2kgEAHfPTFn7KWcbJWBVHr/RWxxgzc0opRrq7vfv++++9s5ofrJ4pWzx9c14w+Seu+9HyOib8PWFVitDPFKp5gzymIS2ZL8yt0fmTtSTiwo0qNdwMms7V7Mdegw+kvHPV+KzoWsI8fvgoZ40EqHo2BmcHWChJvJLrBAo4/7xZAUBAjIxUlGiKHJUAqLgFQEpxHMYwTTlGh9BZRw4qzFlH+AFq544vM4Tp0B+srbyv0AqgySlzTJlz4hRyDDnmFAE4JkKUTNEaHBF7GfrUT6lNuXKZCOaRWiwj2lY1GckwIANlwSyQGGLmmNIYwsmw3h02j7v1/UN3dfPrw0Ps+yGkESAjChpkRBbKBpmg6NQo5141/59jjC9NXZ9aHfK0zp49yBLAyzqYuxvqxlbQYe1v6mBREZWVXEjJQAAEWQAQND1E0t2aDAEVQTrVrjHzKNyLzPH4r++JEb5cglp8PDw87PeHGKZpmsZpmkXvAQCcr6qmXa3W3eoJKSLEhaMhIDGlYRz3+8eHh/vHx/vDfpdSYE7MmRCNcd5XTdPqgHhdV75SfbRq1a03m23btohU+dp7H2K4ubndrtebzRoNGWt+P4ggAOIsBDhnxoBQus7aaAPWTcm4xncnzWrj68ZaZxBSisPhsHt83O12wrJebzebbV23GWCYxpzZWJfCNIzT9c2tr+rzs7PTk23XNoRW76COzcZpnMY+TBOnKNYjAgIZIjAogsJMaKq6cd4LAgM/D+0Zy9rgzAk0XWYGJVaU6J6Lr2BMOWU1HAJVjiwLWJTP6L2PMYkIIAPO2lAixs4iEOi0l8qcl03kSQgJYdaXAGaZafWFkQfAAonnpfnRhfVymZXftbZ7eHhQkZn94TBNIaWEiDGmu7u7u7u7cRxF2DlfV3Xbtm1bQnvXdWpx1jQNKKUBiRGZU4ipH0cQtobW69X5+SkAjMPw80/N1dXV4XDQr22aRpFzdVVZr9d1Xb/faP+3f/s3Efnll1/u7+8RUfXjBEDH55Qut7w7RfXZWOs8AHLmvu/v7u7UcyHlBCJKFDgc9lri73Y7772K6XrvpjDtD+H6+urh4UEnBeq6MjqFgQSAOttmja28N9ZqQbwMp6xW68vLyy+//OqPf/zju3dXj4+7331XAAq7za3X608//VRzl2EYASBn2ec+xqBhDGb0XjMJOu71fqgwXcoMtaONMQKA5g0VOM1jEFHpApCSNaaqqnGcfnz7FkAWkfyPfVt4OaCoVftSDRMi6aLHQnNFQEJSO5inCqeAp7jcEVQQDl4Q5GYQXs1hYQ7uUnyclvofn5+RvPjD0/f73eP9f4ajb42AUFAJWmYN9ZOUKYtLhfr+U0ckWBa5ilGBAAoRAqYUh3GYppFTrBA33taA0eTEkkVExHt3/F1VRKGphxgbZ0hQEo8xTilNKYXEKUnKnBg4MJBB441Y02PeZfcwtuvBt40zCGiZhEmsDh+iNeisdc44j8YAGUYSIAYSRC16Qo7jNO6H/e3tux9+ND/9kn9716dDTjlqESzGMCIzSAIxZXMEPI7uT8fLdfb0HOYFvjxILKYaJYsCtSugsi0Wc18sTRIyxnrHwmPfW1sIY8xsyBAaFlAorijDIFhnbXaK9M8o1MvF8KGH+oHF92Ih5px3u8e3P/54fXMzjX3KaYEDdPC3atr1+uTNp582baMDr3Q04a2XhoTWmrqpm7Y2lpDAGLLGG0OVBvVutVpvurZr29Z5a20RJLDGaf0RQqibpqpqYY4pPex2Vzc3xpD3TrtJ+D5j6ymOAxKBEBpLVHQAZ+qEbkhCgMYYWzVVt62aFTkvRIIwxfD4+Hg49MxS1c3J6WndtCGl3aG/e3hImU/PTlNKKuzz9u3bFBMiWmMrX4PyaJFzStMwjEMfY4CcUopoLCDFIENOAuIQnbPOeWOtAu5HF6EGR6W6YBHJSe0pQFNhbbdzTimHGENIIarQtTALJ+GsuDs556q622xOpimEEASSqDmRCAD4yq03K+9tzomlEm7njsVxI1Gxe+GZzSGi5btmpqxlJEPOwgD4/kb8sQMRmEWH0JQot9/vwzSlFFNMzDyOk4q/Kshc13XTNk3dVFWtgVDVZlRBIcSImIVoivkwjHcPDw+Pj3Xlq1W32W62mzWIqEVh0zTMXFXVarXKOauFzNnZ2cXFxQfjuoj8y7/8iwq5A8B6vVYdjxCjGgqkWX+NhTPnmFKMwTADoSHjvDWGAKRtm81mczjsD4dD3x/GYbi5uVHmf4yhaeqq8sZQjOFw2A9Dv9vvpmlCwKqqdMZkHnUmpSZZa6tZDF+flPe+adrz8/NPPvnkn/7pn/74xz/2/fi7of3oaSBox/3LL7/URgMzh5BylpQ4xrTIzCkgX2B5pDKC9eJ7wRIkUaBkPCLFV0yT1pSy4g1aHqhXpkoxDH3/7t27yjsAWQT//95VBQUdFyQg1TfDGWDSzrto+33ZDeFFf2+5HCxGbjLXyrpdF96d8nGPeItATELa/0c86orOoO6zM53v1XsHfaxmXz7/eAM/ygKWRsLyEj/lWyJPp7ScgFnqTAAEFf2HmNIwjWOYYgqWcldBRSp9CZmBAVwFx89DZiWlGMdgka0NMkSaxGeomBAdWiNUPO0JwGAECXm6GR/9HertzOt00qwIwBZ/AIAMgCgYBUEyCaI+TWMtogFEIXTG1YYaZxuDBqR2xht8d2Xu7u+GaciS1BM+J8tin+52ufsvw8d7fu3zw34/nD49WtSinoUZgOZNc5ZoRkRUVxgHIuMwOGsdaWsqG2uNsSlzEXNNOXFGbUrnnFJ8gcCX1XHk1HT8dJcH/DGUTn/o4+Pux7c//vrrz8PQI4JzDgBFeJqmEGLTrk5Ph9V6fXl5ucT1IqbAKlgPhqiq/Gaz3vebu7vbaepzNs6Ypq5Xq/V6s9mst+vNtm3aqqoUKlbDlRSz1m3jOPqqssZa61l4dzi8u75pu7Zbdd4ao73hD7wtiMXtbnYOsg6LwKpyxREAScCoQGzd1c3aVS0aq3480zQ9PO6GYSAy3Wp1cnqGZId+vLm9e3d97avq7OwMEWNMv/32283NDQh6X7V12zadKVNmnEIYhn7sDykGlBxTVL7ZGOPDYQ+IXdMSka0cEOZZD+DpQlgKl4ILc05SkpQlZyj0eJWOTyHGKcYp5BA5ZimWCrlIADrn1puTuuk4c84pc2ROSUvxYgRhECXGACBER6qbx2Qoje4oMGselqodQLlJrG+9sADYv3ciXDQ/UKF17V5rRMkppxQ5ivLV1Kfce69W5nVdV1VV1bV6wW02G+2XhxCESJB2/XD3sLu5u3vc7YzZOu+32+3F+WlOqa4rEDjZnug4xn6/v729Hcex67rz8/Pz83MtIt9/I77//vthGFT5Thv8McackpLpENE6S0SYKTMrFAGQcjbO2qryzltjab1eXVycA4gSCMZhuL29vb+/n6aRiLx3VeUBZBj6h4d7taMFrXfruqlrwnkmquiLo3Pee7+0AZFos9mcnp598sknn332+Xfffff555//27/9+9/3OLRHRpvNhohUXHIYhsP+UAYimEMoNbcC8qocR+Zp4rHcOjlSatE5S8GcWU1pEcE5a5hUgM+YgvkpEQFADBEiphQf7u9TDCKy3Z60bfsyLh6FOjnqey5rS32wEQ0gsSxtTt0b5oofZis0eIqVMvPpnphQy7/M8VNmsF6eBEnmYRNihdqfFeuIH0Bzj67ivcmDZ78f/9MS2Y86RkugelnWz0mALDfqOLqX5gQAAhYyLSIiJs4hxZBClmQtWwS2wAxJQ7ugrZ5hAaqvkXMY4wAOLJpJhmii9WSdas7i/FA4C6fMIU5TCmEIKY45RRCBlC2jEbBARCQooEYqOXOMDMICaAyVOt4BEiJZMAbIOe9XW4/UWGtEMEm/6/fTfohjYgF0AJVaYyw3EQpO/uwV+HBRonFNAJHx+IOoqAk9pVpze4ZLEJpzCEtUKUfIOzeDbIhorCVjStcYxDprrLHWcM4xBP2GxxS8p/X0AQho3gVeLNrnn6FzscwZEIwzRGhs6euTJeudtU4b/E+rBJUaiILIpXIVY6hpqpPt5tXry6p2nNg5t+5WXbdq266uG+8r9a1KKU7TCIDWOmNokRUbhkGjc103KafH/f7u/qGt6+161dT1Bx8DEWqURCJCKyicIycRAGMsoREBBEJyaIyxtfWt8bVxvqgfpDgM/X63Tyl3XbfZbFfr9RTSGB8fd7vbu9vNdrverOu6qSq+ubExxsfd49X1tbMeBNerrqkcc4xhnMZ+HPucAoGEFBgAcjwMw93tHZKhEzGEQtz3+8fdfc5UVydP1zE374qIK0uegzZwLh33lFLKKXFMPKU8xRRT1on3zMJESroy1mj/GLS8VmMfENWZUlBfw7dzluiIt7nMusACXS29pPld1d4CZMGcRQDkaEDxY4csizPlpCW7cuD7vp8UXogxpayacQrJKn29qirnXdM0m/X69PT05ORECe2FqS4QWe7vH65v7w6HgwB0XXt6etJ1rXPOGmUgmZzYGHM4HB4fH+7ubgFAwfnVavUxyKHrOt18QwgPDw8xRvWBPT09dd7fP9yPw6BSECJirG2IyBhnbd3UigQIy6E/0C3d3FxfX1/3/WEYBoXxlaVfVZUA7/e7EKahHxQKooU0NbNrDNnSXrfGO/9k625tXVWffvbZF1/84fXr15eXr87PzxVg+NhT+OBflXPw+vXrGOPh0E9TEKGs3UDolUEbwoSIOuHUUGMsLXNk5fHOq3j+jwDIPEeAznlrjQ5XqTatNji884DAwiEEQwjCj4/53bt3P/zwQ9u2x7nvsss9D1QvLgwBSYgEUEEOkoSzt6Esv4515GaQfymRjr+ltvgACTAzGTAWLbicmPPsyVnA0+Wc3k8Tf+c4jt4f78HPheLzsh0AeDHim9OFJyRiSbmODik7S5nZozmMIKF1vmrqqm1zGiUbyImZmbPh4j9q/DMBMeXxTWHKB46YPLpoUqY8WSAiC4YYVbgSlYgd0zSOQ99DSj3geBjCYewf9sNZf7HennRr55y2m9VjHREycwYmq5wgb51DY4gsoJ2907LNobN0vuoeu/YXpDxMu4f7kJhs7dzKewdGURgqv5Dg+QN6jyH/VBkjIeX3sKlSRc8PSmYZo8JqLPdfDBnvq6ZuqqaqZtMnEUFjyJAp8RIQEbI458bDgIBzOiowD78/NaIAeDZlgvIaqOgjA9DvVO2cswiTIecdWUQCUgunggQAM1oVKC/5IDxdpUJnwoBIRJWvNut1zmm97jixc37drZum9d4DUM5CJUlPfT/klAmpbbuqqkQgDuM4DACw3qzrph6GQz+O9/cPjfeVc3VVA77cnxZgCgB0aaScc4oxRWbx3htjlQiGBsFVZLxxtbEVGgdock4hhGEY+0MvLOvtZrXeVHU9xUOIsR+H/aF3VRVTagidKcTOKYTbu3tCKywIr71ZSQ5hGsfhMI095GAQIkDmLICH/X73cGeMa5xHkjHi3f3N3d0Nmfrs5PgxlE4fzLsPswjn4hDIOeesc1eJOQlkwZAlxKz7TGYAq1atWUSMQeecMaQdeBbWtj9LHoYhS1JXCatJ5IutTptwujloIXEE4jEKowhmRi5jQR+lZL/cWbS/sz/slUd2OByGcZymKcYUQwwxTtOUOSNiVfkltOv49dnZ2enpqdbQR98tjyE+Pj7e3d6mFOvKrdfrk5OTqq6IEMgaY+uqVSnlh4cHJe6t1+vVarVareYECJ5v64CIn376aYxxv98vDq3aLAdE69zD48M4w/XKF3NVtbi1KnofU7y7u9MpgJvr677vNT+ofKVIhHM2xrjfPer3mdVGn+mP6ge0z10koZzqQvmmabbb7bfffvvnP/+/Li4u1uuNNqo/FF2O6j15+RFEdM6dnZ0R0TCMMcYY8jQF5VQOw6BStACFpOW9c+CtMVhyx2XzKXjzscaHCAOQtaaua53KCzHknJx1xbVdZApTiKGuvAiGEB4fHn/+6ae26z5W735ooSmehGoEyiBK0ANOUM6hTGmzPDHqygmX+P2staqBFBdZHiQmg5YJaB4h0SElWX78x1qhvxPs/2YecBzaZQ7/yzUvNkKgLltYWFwfePxHAUL/lQD4qLiv6qpbb3IciYSzEqQ4s+QyFgPkKjxqkaip6hSnIY3J5trVbJGdJMjMYNgQg2RGZgMIiVOI0zAO/ZDCtMtp2B/G3WHYHcZd/7g5vVhtm7p23ntrXcFmOUtmFPWRd94574x1ZCwaC0RIxWSO4rRytKl9bRBi6O93Q0jWxaYxtmvFC7B62Glof3lvPpAFyzMV5eK/K0vveQZ2ZM4ESuGuCrPIwIwsRi13Vquqa1XxTe+8vtwlI9S+vEXIYIigeA6VX7KssWer4cWSKm3Uj1XtunpEJMU0jlMIo4AQoTHWOatKkAAERojQ6tzskv3OGQYRGWuMEHNqciNb6dqVAlqGjLXOGJdSWqBIAPC+EoBhGImsMRYRVZcqxpBSEhZrDIH0fX9ze9u1bdM0yqF9cb1EpCmswpaQWHJCzgbBIjhDCGoMz7MFCGpzCwRz4mkYwxQAoPL1arVCovuHx92+DzH5qt6enFhrD/uDiNRVtdls67ruh3Gawt3dXQqxqXxTGUl9P+zGcT9NPQkzIYBgTlmEU/CWCIHTeHf9eH+4/+tPP75798tqdfZyTWlcLzJYUoBxUWOLlHKYUoic2ZBrm8473h3i4z7nmBMzADLmJDGmSW1CvFNn46flkDFxGsMUYhRhY6whZ8nIku4f4X6Cyw6H83+1alfTTDW7EQH40CD1h2hEWCbF9/v93f3dbr/TKjaEpJP0KmkSw4REiLWxxlhTN9V2u718dfnZZ59dXFxUVaUz8QBQeS8QpxDSNIbhUNf1drVufEWI+mpozUtIIvFwONze3qrsa9t2miJ8cJxdb9Z/+S//pa7rw+GgcLSG0mmafv3tt9/e/aaoAwAQUUrJOZdzXgRq9PSUmjqNo5LUREDn09q28b5CxMPhME3jFCZmdsbi8wb2spHLcag8kqbx3quc/na7bdvOe4/aVP7dgHEcvI6vV40SvvjiC2aJMWuCmFIEkHEcU8opxWlUo3dHZLxzs7t82f4XKpvMvdicy1krUASFloEirIvBOWuMUbWYpq4MkRrs8hHT/oNP52ihlRUmAECkwiQoLDlyDpwyKxt0tigVYTVswzlnLZXWDKHru1J2YJwr4Jm/bQyq1qOWzfN9xKd6+bg38ZFjQbCevR0fzg70mz4rygFEODNIzgkE0NkyYP+UtT3DnY8K0VI6HAUm3Q5N3a5OQZqmCeNlETcomqeSRbdWQ0cDokySiVPKkjkNUzSAaFkg5Bg4CwNkwcwWoDbWAGJiy1iBIWOZREgGma4OdzmG+/vbX13buapxvqvqpvIac2xlyRk0QOryQ2StMdaStWCopF0EGZhT9BK3tT9pm2tnpynlmKKJuUq87KUlr2N4Xm+8NHUVeJEHLRVsAfcLAFnE40RJJcBcJH6AgBl1lEgDmnOAJmlyiaDaqMp4LNs9EcccpxhD4JyPwurTe/+E4Myw1fxQn878Y4fW9zGl/jAcDrucE85Ea7Xhcs4743WqigiXoK73iogI0DoHoBr3YMiICJFTdQ49zZxzjAERAcRa09RNCGGagjGjEmeUTpxSTDGk5K0xYCiE8PD4eDgcpnFjimXTs5VfKhwUxCfRRUNIhM4Ybw2RFcExcE6JnzQBEARyyuMwpZCscU3TdKtVTHJze30YpynEqq4vLy915EnrtlevLj/99JOr6+tff3v3+LAbh+H15fnptsth1/eP03iIYSAAJhJhRGQWkNzVDgAljXe3V9+//cvbd7/eXt8Q+ucXUqocnSlgnvdFYJacJIUcpxhCThnANlVnu4RmDDlGyNMkgMTIGXLKIUzDeAiJSBV8SP2qgUFijmMYOWejmnLkrLG6LQPoPAGWHaDE9DLcB3OPbsbqeWECPrXrj1+TuRla3gQouGvf94+Pj/cPD4fDYdRQHmPOKWYd2Q8hBGMNIBhD1pqmac5OT16/evXmk09WXYeIOrpWeV83jWM2iARihFd1dbbd1N5D2ZZ0DRACpZRmXvoeAHSC7mh++uWeioj/9E//9Pr160VVBgCY+edffvnt3W+Pj483Nze73U4Fa0VEy3FzNJKkL+Dj46Oa0SFgfXQgoo7/9UNfRHC1TXx0JsfvtmalMCfuyldv2/bk9HS9WTdNY+3LlPe9xyFLRFnGypeL1aOq6tevX1lrwxRiiOM4juMwf+2YUoox4KCZum2bRmcxQCRjie2ABcMTkJwRoEzophQRwZC11lnrAFgHArUNr83IpiqhvW1bdaP4ncs5flRFOEu3TpqDsiTgJCCCBGRBBXE4A2fILFmKexeZBedQERIA0fRFQ9K8ewqLkDHWmYKM6POB5xMi74fnjxKcnj+Xj8b1+enMmd4cUlgzJgAQISzCM/MpvfczlzPUmK91mWqzsAAiVXXrfbVeb3XmMXOZeS6iCiIikOQpDmbkaHLGDCI5pHxAgyKZEqcpxxSz5EzCFZL13pA1gkbJYgQZAUkS8UM49IfDDVMnbm382labul3Vraucq2zdNVVXAzICk4hFcMY6Z8hZsDrwwGQJCDMChnFTuZO2batqfwhjKlosMuvKgHD59TuhfXlgmh9qEfj0CACWBcHHsoTMwMw6UozAOecYH+8ff/3pl8MwVG1bEgIouQOWWaNChQeBME3XV+/6w76ufNvUBSGF8tiXh/dUo82sj6cH/NEFVtgVOfMwjPd392pNoTCgQqOb7UlTtzhLK8w4lPo7FcZrmT7XwakszGytAwFt1+lZKVeZmZnJe2dtIeKO49g0TV1X0yg6Gj6NxrSd954ArSXmHOPE7F+oiyDohD0v3SVjnScDIIRoXUXWIRJnyRJAGFFIfZ6YGSSHFMYAAqtu1Xarumr2/f0vv/12GEZAs9lsLk9OdrvH25tbLTE3m/V2u7XO1nUdpkBIxpJIHqdDf3gIoc85CiCIAfUCIfLO1t7lFMdpiONu6h/S1BPkZ4xTgJQTCovK+qWQc9KJN2FOWrOnPMU8TOEwBVPX3vm26xAMwn0MHJPS1cvOa53RLqG11jlPZAUgccrAxjoEIkEy1hhnjAVdwwRHuN/8+1yylAc7o/QMRZJUPhLa5+sSAGCWEMM4Djrwdn19tXt8LBLrs7ib7vUquQcIIkyE3rvVqjs9O92ebNumUbaXTscpRI+IRHh6snXG6FBcXauWGSCgGtWr8szV1dX19fUwDBoInXNHHOwP7L9KoFt84fTn3t7cfP/9X3/44Yf7u/spTACgEV23DwWulSigr4SmBfoedW3Xta3W9+p/OE0DMxtTUPR5j38WfZmFSBBZ+9YqgNN13SeffPKHP/zhyy+/fPXqVV3Xf3NIQUR0Ln9RwVv8WPVuKF3RWntycvLll1+mlBWcRyQFj6ZJUsoq9WOMdd4jUdd1Rhk5x6EdERHYkLOUEsUUOKfAXFWoojrG4OFwSDkaosq7qqq8c26+DycnJ59//rm19vr6+jj/WP4gL+Il4twUR8Ay9W0NUuWJrGtav9pQVaE1zDGHcTj002G0xlVVpXoJMMsjhmmKKRLRZrPtus54B4Q5phCmcRxTzkjGlVkmmfFYOa7Uj2P0grX8zkOZf3vqiB19g+f4u/5IzjKLvhoCMjrNisU3ao7uslCn32tqFLz1CMYVQESjnjnG6By4IDPnjDNIxCI5Pp1lghhgQoMGEIQlsAyMTM5YAQwQGMQgOIOWwBI4QAAyQpEpCjJw5sQsWYQzsWRnTGcpgQxhfNg/hhhMZV3tfWXryjXOtd41vhLnwCYhEMigZwmcOPcxYEwOwLBI5hiZbJqLijLIr7oAv9trn/sTSx2jT0FDu4g8fbXKLmZVyWFgVl94RuCU0xQe7+7ph7d3t/e+qgAL70BEir0XlFlkpeDFGPvDHpDPzk6PfiS8AKie/oI62QBSMLoPdBqevgrUycYgYoxJmbGEKRrt7krXrXUVHf0QLDy6uR8x06mQs0xjFBCa6R5qo+mcq+taJT6kTMVY1bUYhsF7b63N1qSIMcZhGK2vfdU4T7UziMQfctRe8JLln8hYsiqsQWQckBUA5sxFkxHnSQLmDCnGOAUEXHXrpuuss9MUbm/v9v1gfbXarNebDQvvdoXw1ff9MI4i4mu/lrU1pm48QJ7Gvu8fUxyFow47AjBYY5C8Nd7ZOHEYIuQReDKQHMHzWX1JOQMnySnnlFPkpOKyzBrac46ZQ8rDGA/94ASxqryvvKvjlPvDlPqRdXMlY61x1jJkJHTe1VVNaFlA4kTMxjACIhORM8ZZ4/Q+6oSgkD7a4m+gffZiebl034vago76AmZ6PzgilhIi5xxieHx4UD89DbGHvp+mkDSNSZk55awDek8STOoluF6tT05PtcjWdXJ/f//u3bvNZqOoEgBsVquTzUZ15XRTm9U3SVj0S/Tn9n2v+NDHoPjl0K65c07XlS7a29vbX3/55erqahiGlJOuOpWt1fR0GAZjjI6zaw6qq7ryvu26pqqZeRjGh4fHw2FvLCkiTURaobNw2UOQuAgA4wxKkd6QumkuLi6+/PLLr7/++vPPP1+tN1VVfSy7WnKOaZpUWe/x8XEhEOilee91xF+pDFVVvX7zCgB0ClHV4Gc8qRjjEh2stdYY7xxVFZEKE0J5FQuL2Iq4EAknCWFKKXjnDGFTVyrREwLWlW/qerVqvfcIZI2t6/ry8vKLL74gov/23/7bCybdRx/ZXE3pXqTgLaLzVdOdnK3PX1WrjrzLaZqG/cPt/c48eudX3Xq9XnddBwAxpd1ut9/txnEkovPz89Pz86ppkHCcxv5w2D0+juOQspQsSlv48rxE/htI/Acu5Cj2yoeq/qemTInskMuEPmkKb4wOuSxJBsgStJ/VdaXoR017lRS71H7aUy2fhKwSo0iCApoMvDgtRs6UtF1LiVAAJkERX1kyxiCwQUPsDDpjLKFVy2kmAoQMqQAPwIhsJAtQ7apN59Bh5MPDw83tTeCEFrtVt153m7bZNM26aRtfoTFFvABy5phyCDkEzsCsQ+zqdmZymd95YnY+LZOn46VkTXkwBFr7LRZsSy07594izJKyBnlkVhgEEVLOKabD4z4DuGpnvMfj5kdhLcFct+v345ST85bVS3NeDi9AIWYuZyiINHeP5q7pR5caYdM0F5eX4xRWq5XqhxRpYwAy5vT09PzivG5qvUYismb5hk+zMABgjHOOnXMsknIGxK7rxmkc+oGZdTfUjXWxzVbEW3dS62y7Wg9T2A1jpgMDbderZrWqu5WrG2M+UJ2odo42QwAYEAXL+mUhFNLBL++dtd45JQswa6soJwExxlZNTcYOUwgpkbFoTGaZxulwOIhI13UIOIUwxfTu6hoJjTWvXl+uu27dNTmHaTyM/SGFiXMiMEIgVLorGhkNYeVM6+2qtn1FIYI3z+JhTFFykpy4/FLQRxF5yBlSkphkijyMacx9ZFmvt23btV23OYlZoB8HVabR6gpIDBprra8qBJNShohcdiQyaA05Y7wxVtt5ZfifnnYeLPy5o6q95MCz9DSIgOBLHYinQ0T2+/3Nzc0PP/7w9u2PqoseU1J9Q21OspSh/UXGWCljauWyPdlu1uuqqgFA8XzVlRORxXx9s9mcnJyoB4mG7aUoTynd3d39+uuvt7e3Dw8Pj4+POjj3+3H9xdsB83bsnNtsN6cnJ9M4zgIvWSt1LbVxzmWXMTalvHnnDVGM8XA4aOs95+ycsUaNMEXgSTpI32OYAQ8N7Urp/+yzz7786quvv/rqyy+/PL+4WK/XzrvfCSkiEmO8v7//+eef3759+9NPP81Kf3HmnYDe6ouLizdv3nz99deffvpZ0zQXFxffffedlNmZLCLKHgshqjLBbrfThH61WqlavuJns01ryZ3q7ELlDgcch0GEU4ycsyHcrFeIXde1TVv7wvm3bdNeXFy8efPJJ598olvQxx4HPI9b+g+ldT6nFyBmhkyMtdY4R8icnJ0V/WbydaXFj2ItOHe1dV9XgA+1XYIEkOXIH+gIVJgR1Ocn+cEnsnzOx5KVo8rp2dfpSKwAGmstoY6YIS7SenO4KA2KZzyEpXkLS6B5cWJLqC+5wdN/52/1dDbema71Dr1jZyOZQJKBIwKIs+isQ2etA0NCqqrJGZJYEWRBxizEgERkjfVoanRnp6efvHrTuRojR+Dr/cPj4/6wH5o4rGLf7Kuu8uumWzdtW7dtVTeNvlA5ppQK2VhAQHXdCgTOIlKkdZ8i9PPLfjHXXrB4RMycYe6KPd2g+TcUgKJGVyhvIMIIwMA555RzP8aUyQ1kbaFH642c99TlwZTgSWiNKTBDaYIeAQcvoaoC1c9L40MLbf48Imrb9vLyEpBOT0+lyEIZxAKnd117stlUVQ1SDP0QVFRjvifzYQw5572vUuYQAwC0bQsI/aFfWpIqUBNjVDAwxqiAmPd+5TZV0/YhDUPPLCS8bmvnvPOV8R4NwYtxcDVrKqOnMHeRQM3u5nvPAGKN8a5oiajiQoyqYaRMah8zHw7DFGLV1BkwscSU1Dy7aRrvqxDj/nC4vrltuvrkZHt+fnp5dpqnsX94HIfDNBxynCSnQmIRRCEQQWAQDe2urdyqsoeKxgDV8bISCDFKjpKzCtAwc4mcUkJ7zpiYEmNiyEMIMRM4ZytrXbda9eM0xElgkZKIaABN2eigeK4risT4hMY7Y90c2hVQPW7vIaDq/cCiTVkC/FNoBwwfWFtaLI7jeH19/cMPP/zrv/3r93/9yzL6RWSptHIMIaWjDqKGdrVe1Wm3rltpyT6Oow5/K7k6hKBdEq2b1bC8aZolxuuiur29/e2335RDt9/v1+s1APzNqv3FQUTW2a7rLi8vb29vpxDo4SHGWJL72c3eWrsQFwHAkPFeJ1ttzhxDeHx83O93AKCBRCt7YeGSRM1vqjwd1tqqcpvt5vPPP/vuj3/685///Pnnn7969aoEVHw5qnt85JwfHx9//vnn//7f//u///u/f//994+Pj6oIVGKYiN7zk5OTN2/e6O39/LPP1+vt559/RoR9P+jIu76kIhBCTCn1fY9H7KKqDAiQtcY566y1zhhrADjn2hCgkp5zEs6GcLVa143XMQERMWR8VZ+enH322edv3rw5Pz/v+/7FtbwIhMfbnf7DHF5x3hOePEx1sYGo+DKV4ULFDF15D51dEJT5YJ6VaoqjOkBxw8qzx95ca+P7hdP7C+y4j/niX1/EnfezhKeUAlFpHYUJzNqLRC0CeIYTsLSJ52J++UHzX95HQI8+KC8++OJTm8qdrFuD3oIzE9EIsc8xcA4ADM44a4xziIY5BU4ROBOLFbRAFiiDYWCD1llfGdfa6mSzvTi/WNctMj/2O3dThx3fj4ce0wGTG423tjsc1nW7bdfbbr3NrXcUw5jyxMBZcmAeppC4iA4CAL+ARD6U0Hy4al+eED57E6Vglrq+kPTGH4kZLTiOLNvkghooH03fbf3e2seh8t6YqqoNmSX+A75cEHPpsGQGsKRdH9sCijxI121PTkLKisy3baulzziO4zB676q6Ug6/dnMzsAgszpLHN4fI1HWTWUIMijE6W6B4bRZaa3VfVuiyqWtDxCLDMLi6tr4CTiYP0O8z72OLYWVjV6W2yU5eII9z7NJHqR9Rm18DgDlzSlPmBMDOG7GWU+KUhVKSPE4h5WgrK4xTSo/7/vrmLua83Z6sN5iZx3G8u7tv23a9WXfr1YponKbDMJAlEfbe1ZV/PDwe9o9jf4jTxCmBZEAEYoLCbyk3GckrgG6xMdIY9vSUowhAjJFTWOgesy2wzPAPAJHxlW/YJx7HMYawe9znLM5Xs/5JRUQ55RRTjMGAoWzGcdQTSZljLC18VH8/46319im0l8IdiyiTZphPvXaeQzvMQZ1Krz3OHlHz5Ygoc+3q6uovf/nLX77/y2+//XrYH5CKHbBIIqKq9ik1qluixoGqX2atXa1Wp6en5+fnJycndV0BwAIpI2IBUWMEAOdcCOHm5kYLr9evXy8Cc+M4qrmL+rv0fc/MagGn9PgPvw8feUeaujk5Pfnss0+Hoc+cq8of9ocQQkqZMzOIRYJiv0alfjUFIAoxhHEchmEY+pySryrvneLwR0ug/KbF1+ye7E5Ozj797NNvv/32T3/60xdf/OHNmzebzaaqlN4PTyDih45xHP/jP/7j3/7t3/7rf/2vv/zyy8PDAxEtU4VEpLH/8fHx7du379692+/3t7d3/+X//V++++67ruvevHn95z//kZmV36iMCBGJRTpwXMYEQDM2RGtIRXt85bx31hKgNLXv2ubhYR9CNAbr2p9sN92qJSpX6n19cfnq9as3n3zy5vT01Hs/DMOLayl45nvRHZcW6TLiPfNFyoaMaIzxzjEJ5Mo7b51TbEEHH5QVEUJQPSJmrryv67pp6qLtDZJSTDnFpNR0ZZjJUgy+v7e+V2g9uwrFL48/CsVs5qmaPz5kxk9EGJBAsnDixOryUNquOqrADEejW8s9W+r15b4sf31xbi+uouwGz/9l3XRkGciiGBMIvQwwjRxCZk5gkrHZOrCGhJ0RNJzJGvGIQjYiRA5JL1aAEK1qtJFxxhBR21SbVdt2lZss1iZ7EoMReYiH+zhcHXbNve8qXzmDkBEZEGLO/RRud/ubh90wBZYlx5sr3Pk2v6hxX1bty2N7OQ5Z7tDTp2mYFBEgVu/gmegIy58UBNXvoT1ATRjVKU6U5GCtdlC990Rmjus4n8CzYn0+zfkqllHOD600AFB10rquurbt2z5Oo/N+vd5UdWWM3e/3AGgIkYwIKDpHRGKQdOKa+YlGgiKCROR9lXIeBhtTBAHdUNQyS/du/YOmoTquO4yj6pbknC1wjcnEgx0A+irv27jZxrhh5hejVnqPNcdhgDmvEkOIQJIk58w5ArK6DrLK+YFJDOM4iUhVVSnLoY/3D4+/vntX1c3lq1dVVbPAu3fvbvZ7BqnbZmVtVVXWe0S01lTee2cJIUzDYb+bxiGlADkRsNCRSsIMwCAZa523rjJUW2wduGeRRWJKOaUC4BdorWRuGuoRyVW2QurQCFJKeRinEFPbdXXdEJJ3DhFnF/aEBjnzNE3MgmBEIHFUCQqDZi5anLW+zLkdAfJz6YkzzqkusAUPUUdYnlNTxPRiVWlH9vr6+vvvv//LX/7y448/TGEq45HGaGDIzMYYX3mFfKdpnL1K0Xu/2Wx0kH29Wi1d9r7vx3Gs69p7rzNm2n7OOT88PDCz9/7k5ATmjVUH3lTb9eHhQVPJtm27rjuaaP+7DmNM3dTb7fbVq1c6tGaI7v3D4dBP4xRjTCnPWTiZmXutqbawZM794dAfDmrG6qzxzpG6mOg2oL3qp9YtqY7TZrP5wxdf/OnPf/7zn//8xz/+8fz8om3b525J+N4fno5hGDS0/+u//uvhcKiq6vT09PLycrvdqt9dSundu3c///zzL7/8cnV1NU3hcOi9q+q6+errL9eb9R/+8Adm3u12yqpLKavOQoxJo+BCG1TegyHyzlaVb5qqqStfO2dNWK8OhxXAb/f3j7pfVZXr2kZEZ8RhtVq/ef36zZtPzs/P27b94CP4YLmJM9w8B0Vt8xPOM3gLkXnejK0xbrZNAlToyFok8pV33hlrMWc721iRMSwSk1MUEwBm744nUkjpVgMsg4cfraKWRODoK5/+DWAeOX3qAS3YLXPmlASFSISzZMyM/KK2nJvLS2pTwn75ns/C+XGYL18sIsJqC8THh7K5jxZY5+raoZARQPIkhikATAwcE4NJZJKhaIq0jDBKMiJWZeyNVQl1PUlCKlNXIAhsERpvt6t63VXN4Lmy4IkNskgYpjQFTGIEvDHekHfGWUNkYsr7ftwP424YQmQGu+Bf+MEXYz6eh/Y5DSjPRo221MNGIEuGIhUqgEjWkHcikvSzUZAIjWEyyrViEWQh4TLNLmW8Uu/6Mqd1hAgsKQXRso6PTp6obM8yA/eAQEDHraAXhyYIBtECe0mVJMoIKXKygBhSHMaBSAU1ARCcc76qiMg4m2cYYu7ngBAgIhnylW/aFsYhpsjMykRVWA8AqqomMjmnaRpFKpUSNdYZMsDcerNZ1TROTkLLI4WDxJFTFGbBF8UWFRYPAc2QCSEZIgTjHCKQZURk5501RkRiCClwZIgx+aparVbTlHa7se/7h/v7po0qZWqsJQQWVmFUIuq6rq78J29en5+fvro8q5wbh/6w3/WHfYoBikRg0YOmp2N5San4rXnXeUfPY7tawCy9+fLuCaMwS2LOaKx3FdWNa1fGe0AaDocQguz7OMWcMwnolHHmXFTjhXNKAGiIAQFVvoGEVJLIGmOssVbm9GMO7bSsIpg581Ii+gLFg/I4BABxAHiaQhYpvdhff/31+++/v7m5YebNelM31RSmvh/6fj8MI6KOrSmH3xljck4550Wg5vz8fLvdlkH2YdSR9KZplNqmtu7q26asb5WI13JcP0GheDWXe3h4CCGouOxqtfrHVMoBiKjyVde2m8369PRkv7vImY2xTd1OUxj6YX84xBi0rhIBHZ9WuCPlFEPUiQBrjXPeOrfMXM3BXNt3hSjTNPV2e/LJJ2++/PKrb77++utvvnn9+vXp6ekMSLz/In/41Q4hvH379vr6ehzHzWbz5Zdffvfdd19//bWO9RMZ5nx9ff3zzz//y7/8y3/8x/+6u7v78ce3Xff/1VT0m2++Xq9XX3zxxeHQi/DCq9e9SHOycRx2O6ujNJV3iJU1pvKubepVp6LSHgmnKWQ1JQ7T/d1t19Z15aw1dV2t15uzs4vXr1+fnpxofvD+wfykg/ksIs7bHCwAHhpEA2BFUmaOKUxTbwenIqA56qgzJknDOByG3o+1EoNCTlmZH8wxpRAimIkshRCVdZgV8ANaXi6eQyKV6hieGqofPko3HEVYmORJgFyf4Vylz5U4iEAuglUpMic1YuYUsxT1fSiElZLcGN1xnu7Tgk+DFFIWLmKm881TjiTrC5gyp8zFWlR/LzLVfllmTozHCskAEQokl4Ij69BmlAQkyEECRxwZTRKeeBoyZyEidWMTINFU3xEZIGThnCNng4S1kZW3G+823k2W2GAmiDkzhxCHHJNwRhGD5Iz1WpwIhpBiFkJjEDkzpFns64k2cASQz8cH7GGelpcAAQrSrB5WPLXzYoTqXIFTmAEEDRWQp9TVi+pLudlPYXLGETT1hJwFMWWroVGZHTS7Fxyf2/yXJ6RqnmP64IKT0jwAIGErXKGIMKcYowGRaZq05AIA52zKOcYQY3C+MtZQzpiRNborOqtYkyHrbNM2LDxNoxbHuuFqKWOtU9jtcDhYy0jGW2tFEDHFWBvqmsZRtJkskajr2dHrfXy9hAYMwjzsQdrXQIOgAjskQkRgnUFjMueUQ+SUgYhM5XzbtIjBmJLopRjHcbDO+coba7quY+Ywhd1uxzl5X21fXV5enp+erCXHfv/Y7x+n4ZBz1BeSEGZElmYXjFK6IxIZ61zVVLXkIM93MWbOwiis9vPzgtTQngUYENAaaxxUFHOeximEyOM0TVOaAiICEXChMs81BWRmzBlAkBBQlAKPCMX11VoyDmZFDpizkrlwL6RZOWLSLVB8weeVfPH80NB+c3Pz7t27cRyrqj6/ON9ut3f3t6pksNvtrHHWOUNW9yMkVLS3ruvNdntxcXF2dtZ1nbU2pdz3/W63Sylp5Nb6XiFrlSJXMp0auym+qiX7cZddJ7suLy9Xq9Xz4be/3XQvTau2W683J9uT3ek+hAgCla+mKTrnBKDvYRxHmG//MsCmU2chTMxsjFd31+PXVkSKsEDBz+qLi/PPP//im2+++dOf/vSHP/zhzZs3OkO/fP7zd/mj56/3Yb/fxxjrun79+vU333zzn/7Tf1KLHaUjPD4+fvHFF5vNpmna//E//scvv/z617/+YMienZ1vN5s3b15fXJx/++03Oefdbj8M4ziGcQwpZQ0GIYTDYe+dreuqqSqQ1hj0zjZ1terabtU0TeW9Tyn3+8Nhf7i7uzsc9of9br1qN9vNqlu9evXq8tXr09Oztus+HhSfQOwXoR2WD6EaAapAtwFBVWtWQyUTgiELQgiGkJT/GGIMMQqhAOTCiwEVzEkpYYooJqaU1J8pZUlZUCQX/VWZ4/SSdfzO+ZerKIOCmg+Ucq0gxUv5zAKYRZTLJXPIjcxMxiLozNjixVyIKkqBAmMACBEWTfDihqc7Ci/VuTzTRYGiixVTjInjTGFn1bpkFsDaP8UZYjQ6REzAiSFllEzEOvUjApw4hcyQAAPAiGlgyUBkrEFrEMkAGrLOOENGWFKKIQzZIVrjCDpvV951ziFxALWjBgOMkkRCzCmnjACWvHe5ykhksppjGccpJU6QuUhvAB8tmZedhfcka47Xll4uC2eOMQ59v9/th74PIQiIscZ6JyI5RqZUHDysIWvIGC6+R0RIAFCYjTjT8o7q7JxyShliBJSUuqWeL6SnssifFtb8h1kfH95vGyyXIwsTQFgIqfI+CmWWOMUU4hRC6d8j1nWz2WwAse/3HVHVWDIGzTzdJyJqtcCgUFFVVTmnw8Esw2+bzSaEEEJglrqujbHel0khYw0A9tPUjxO1vmpW1niQlJwH6iK42UP02fmXVF1QXxkAoTnf0eyICIksGXLeCmAcU0oZyFW+qdtV066ssd7Ber26vLyYQhinMI0T84Ovqrquv/7qq0Pf73e7aRzjOL558/rs9OR0s26r6vF+t3u8H4d9SiOoca01hkj1QBXBO6raEQms9VXddLGzJNHX4fmD0PdNFeShoDwqbQRElIDVqz0wTjEAkXHWOpemEFMUASR0Fi17dQtWI0MCJfnq0IDS24vHAZnSEBY14Z79Ome9Uw3tZXHMw3DAUMQulqo9P/fj02C23+/Hccw562DVp59+cnJ6QkS73Z6IFNq1Kasum244ObMxZrPdXl5cnF9caMkuANM07g/7x8dHbZMjYkrJe7/dbo0xOiOuUf/s7EzhXPVdvbq6urq6urm5URH4tm1fv379ySefaMbwgZfh4wciKptyu9nut/u7+4fD/jBNQQSNCTNViVOKKRV6PCCq5LHS0IjQOWetWxh8y2sLACxZzenPzs4+++yzr7766k9/+pPS5eYKm45PRn//e85cfxwzT9O03+/1XhGR9tqlaORVzvnNemutI7L39/c//PDDP//zP3ddW1X1mzevXr26TCk+7naHQ399fX1/j84ZZsoZ9NXeH/bOubby282KEL2zTeXbxq/aqu0a7z2znJ+f7Q59iGG3249TGKf4uukuXr1+9fqT07OzqnpqkbwfIOd6570OsTxFxRmKNoBGO9cIQIQ6C+e9c6YCxsmPvasQgwo6We+tcwJgnSNjkfAo4BWMH2cbJVF5nFLSzjWZvl9/5xMpbw7MBT/qxJbmgsULAhGi0n85c3H30FMxLDmbZUNZ8H9NDg0tZwqziW2xI1CvFL1zi69N4fMgsEgWzpxTzpEhMjBjsX3L2ruX+rgSScIhaQIUY4oxTmOQmCwZg5QyxMg5pBCHlA8IU2UYDTABMpIYMsaQcegsWsgc8zQOh6GvggWm2hjwztaVb5wNHEJOhiwY01iPLo0CBiABgqA1trK+ct6QZeKMnCAzSeSMx8nS03m/hOefh/ZjiVn9RJacUpjGcRiGvp/GKaekYorqNS4sWOSQdLjHkA7nFYNx7anPEX0GmPD5D83MkiVGyzMmBkv0P0r/5zdjYUjO3+Yj3Z9yIWWhGGO9qzthYDCJOUb1cjCGrLN2tepOtttx6MM05ZxA2Bi01nJ+UrsVEQZGBiovjl8Gf5etWV1AcmbdYhRBJSRjrYkJIDLabNuEDaIAGbSNWIf0QVazujEDmsIp0KAEQsKl1iyNC0BmjmGKkcnVtqq6uqrrChENma7rLi/OEfHm9u727v6wD+M4varrs7Mz51wMYRrHEIM1ZrPqmtob4DD2/e4+jAfOEYCJ0JJ11mibzlprrSokluYfIVrrq6rl2FnMo/HvhXaVgeHj0E5SLNhZgHMaU+5jTiFlzoKI1kBApfUAIs3MXVUi1tGPssgXhB+KeDHppIi1gEXGa6bRaT5C9ITGl4kNFcLA4w0KhOGl1a4OUqvwUdM05+fnr169Ojk96fu+fddaa0EghBAxMTMhTdMYUwSAuqrOz88vX706PT3VOjWEcDj0Qz+klJT4tsxWNE2jTX0RUctXRdqVGnZ9fa02a0qg05L99evXr1690u/8NwusFy+I9vU3m83h0J/cP/SHfhynnDOA5FwJFAUO1T+IOg8egnLLhbmua+f9scz7sucQmcrbrludn198/vnn33333bfffvvtt9+en5+rwM7x7vQPxXV1ZL+7u7PWjuP47t27t2/fnp+fK8xa17WS2XWcvWu7YRinKfz3//Y/7u7u/tf/+l/qj7dadU1bb7fbi/Pz8/Oz9XrVNA0iLBB9SmEYyBrTtc3JMOScVMCq8q6uXFt7550IbLfri4uLnLnr9t2q22y35xeXF5evT07PV6uV8oY/diG6675ftR8F+yeKE2BhpGlGSoTGkDXWGgvKglhiYymsSEqeOpOHZvqZ9udN+Tx9RZmLjPzSY3+5VH7niYgwMgqiMAoSI5c7CXnmtsyfCU/CwgBgrLWWZFaMLFdZBqvKaWRmRAFgmAn8WubMuP0Re0Z5HfM1FKt3ssYZb63xlBlSKnhBkfs7viwGjJjHvKxwThH0p6MhQRRBFokcxgAykRdjjehYjDiLXj0ECSimcYpjb2lfuW3lcmURwTqj80TDGMfMYsQgWSCHVgyTgAUGQEvOGV9ZZ9BmyYmygBhQQ/syQXBUin/gubwM7cepAAIw5xjC7uGxP/QiQgDq4IBlOO2YbAxAaIiYaHk4ul5A55MWmGTZPOflYowRBJWFeeI3wMtiXFMELkYJi/Dt8hjfP8oSN9bZurWJwSe1BcgxMYQKxFtDRHVVb9br09PtwdJOWDhN42R9VXmvpmTHufTSNTDGrlYrRDwcDsysw+tN02rGp2NOzKxK3da5Vdu0TeWdB+uiSFbOWtVUVVV7a9/TKy915pGK89KuUre7nCTnpOVFSmnoDyGxS6nyniATcM4JBCvnTrYnVd0g0n5/GMbdMI7dqhunSUScs3Xl2VDbVE3lJachDMN+N/X7nCaUjMKEYK113jn1op83hBmUJwKxzldVm8NB0kDPXVU0MgCzcJF6EwEq+vzGO0ssKSWZpnHowxSzOsWACKEQqUgmCyhDlhmZkXWOdDaeEW0EZwCDSIaM1V9IBOVVoOJSUgTNVfnnyCoGlcdbKH4MBUl5saQ0k1MYqa7r9Xq9Xm9Wq3XbdHXd1FXtnJvGaQrDNI0AoCpp1tntycknb968efNmvV4ba3UQ/OHxIeW0Xq+13Fd8W8epdZqu67r1eq0iNiKiynG//fabUufUsv3s7OyLL7745JNPTk9Pq6r6h+L6clhrV6v16WnY7/d9P+z3+3EcQiBjyDm72Wy6rnt8fCysvb5fdGqdc847/1yMlmfnrqqqLi4vPvvsiz/+8Y9ff/31H/7wh8vLy/V6vdD4//fOFgDquv7222+nafrhhx/u7+//8pe/6M355utvv/rq68tXr85OT+umrry3xp6enf7pz3/KmXeP+77vf/vtN2vN2dmpteby8nKcisbDyclJ3w8PD/cw6+SrRPChPzzuHu8fuu12lfNZYb0QGUKDyAh1XZ+fn5+dXxhr27bZbNaXF69OTk/rugZ4T0D6+XG86y5xd/mrPFGQnn/JMUtMhIUlcUwxhJA5WbLaSE4pZc7TNI3TFGLglIRZAT8dQ9AkfU7IChG9FO3laf4+W+vppCAn0QQFUYiZIaunYyyC59qKpaIRYA0VyUgkC2iFjBCBKYjaMt5chG8VPM+LUjULxKVEL8NgRCr3qzOA+kKzGEDra2dd7XxL1qfEKaaogmUizPlw94twodQYsAaQiQW5KBAKS8whjRkIbW3Jg/cgOU4mRomcjANPtTV1U6+ddwZJUDKnKaRhGPbAO2eHrsmrVkCIqPa2q6p9mmwOMaQkmMbMEUisRwMOEZBUjVoMCjADJOaUgVmJRGQMEh1pzcr7D+nDQrNleamOZggP9w/7h0dVhjJI1hpAYGbOSUQ9PwqEA3L0HaCU1grLvAjTy79DYS3hvLxKg/4jpbg8fW156L9Xs2ulZowxzpOvETPmbFK2gsxMCAgWEbz3dV2t2tYAoPAwhRBGJbdHY5PR/E6TUL2+Iv6qW7CSn7V2X626cZxU9EP5z6LidITGWCrIMDAQGuPruu26tm3qyhnzlO4sp18ymOVGzaG9UB0N51yGUWOMOQXJWQglT5InzrUAIFhrDDXWV/5wOKxXXQjxMIwqqykizrnNZu0Mbddrb00KY797HA67aTikMOo4p9bW7gmNn2vfovyDBGSs81UTpzqMVtslyxNbOCuSZ+LC3HQxZL31BJCRDjhCUjuBxFkABSwhq0JWaXtrKQ1S2kyFk7dAgIIAWr0YMtZYC4orEM4xvsT5ebQddd8s6h2lIBIB1jLmg4FnrouWwgh1/K9t2q7tmroZ+kGVdDnnMUzM3DTN2dnZq9evtVpFgHGa9vv94+MjAq5XKxHRESz9iYuaStd1FxcXS+xfiPEqeqhtoM8///yrr756/fq1ciSX+Pq7x4tPECJTVfVms7m4uOz7Yb/fKYOPCIiwrit1OdJlo/NUOWdVRNf6WFNT7XCpoM1qtbq4uPjyy6+++fbbP//5z1988QdliX+c5fcPhPm6rr/++uthGH755Ze//OUvV1dXb9++PRwO9/cPt7d3n3762evXr9eaeW3WdVOdn51/8803P/zw4/X19S+//PLDDz+en/+LMeaLL/bWuhjTZrP59ttvuq775Zdf3r17B3Map6oDu/3u7q7ebtdn+5PNpo2x0+ZtLlqD3Hbd6dm5Zld1XatTzt9Deiixeda8evHx45tTalKliKvhppKgmEUSBw4hpJgEWUTURxgixpTGYYwhKuSeco4h2OjRGFgoZ1K62nlhs7wn0/Y3DgHJCRiZBDQQA+RcVE5wToZIe3vWe1d5VzlbGevImOJOo3k5zuIq80qePSiypMQa3SWx5JxjzjHlJCklxfgFBVCIrLYJhURQ0BjTVNW66dbWNwvSrq8Y59jf/7rc6JQ4BY5BYpScsm5IYZrGGAVN1dXOOmONMBMZEYgxWUQAsuScrVQBk3NKWcKUhj4cQA7VMI5BfZK1p9PWvgn2EDBngSSQAFIROpfCRwJmzpwBMMeUY1ZIW5uN7zFV4fmoOMCL0K679Zwult1yCvH+9v7u6nrdraq6FmNQ2ckxxIlAIKXIOTGzpCQxKX8PRYxRDhEpx1H0B2hoPNp9CsgqwJxL+fTUAZon8XDJ4pZfT0v/Y0tQI+KymbNAFIWA1OdNG+klKSVAY8x2u25q/9u7q4fHPeeMHpyzzH6aJh1lAQBEhbby/MIpN49jjM75tu2IzDQVpXqlDs0SViKSJbMAOusa70627cXpZrNq6sobQzk9G7XCojJb/vy04AEBwVogImOUhxeRwHk0mYwVlBjGHo23lSkkSAbO3NTV61evfFU97g6CpPSryruT89PTzWa77ixhP/b7h4fhsIvjEKc+xckSGGutMa5A8aVqf8ozAEHEGOt8Y2wtYJ4jXFBGZZX9oXdR9LkUeTWPZK0PIe7MIWFICq0b/R+hpcxM1uiiodJsF8pAJEALIgRKnCSjZheOjMPZPrTE9Zker5UBKNF7dnplmAv30qT7QGjXgSjdhXUe/fHh0ZABgNVqpeJxGvZSSgHAEDlrN9vt61evLs7PN5uNs1a11h8fHx8fHquq2qzXuorUr0j5XzlnnVw4Pz9Xh3Xtsit1LqXUNM3r16/btv3uu+++++678/Pz3zF8+1sHIgghNXVzeXGRUhyGYZrGvt8PA7IwMJChqvInJyf7/V4XABR95co6i8XKhBHRWtu27fn5+ZdffvnNN998++13n3/xxcXF5Waz8d5/sPH0j58wVFX12WefMXPOeb1e//M///PDw8Pt7e04TL/9enV2dn5+fq7DCJ988ubV61fbzXa73X711Vd3d3ePj7vr66t//dd/Hcfh6ur6zZvX2+32u+++NeZP795d/fM///O///u/a8m7JDH9ob8xN01TNbWvKtc2tTGYOcUQphjB1E230fEH1d9dbtHfPJa4vjRDl4/DDEmXN61ATvqvBd3MOeeUYs5xjNMUMmcyiIgxhv1hn5lDjCFMzMlaK0hhCo+7XQKocrbW5JTSYoUmJQeXhV00ZxNHCwUXUOHF2UoIAiiUE0YGDIlDSkjGed/UTds2dVXXdV352leVd5W1lTGW0ACiEMziEwqfabuBZt4rkiCISpsz6J9YnQ2mEKYQpjGEUdOXzJwjgyjLhoWEOLuUmWNmSTknyRlKu52F87Nr2Q/TeD/kFFIIKUwxjNM4xDAJoq3a1la+blkopixksggKMwgSAGr7GoUlaT4wxmlME9JU5zBxjIwiiOItNd61zjXG5sxJhMAQYGZJnAuckFmHfg1RYSUkNcGkhTFXdt+ZI/fikXyAIa9rSotEQcjMfd8/3j/wFNtu5bv2qU7UxDZGTkmHkjKEHCJkLjlm+f9TBH7RGZDCooQnrpXSrZGWID7vBct/529U9nP8/Z2haC3pSLWwqMoJIRGKEJdqkACAELum6Zp6vz8c+l44axPUV1XKOjpRCkNm4Vw4BzFGLVOstXVdKUQ/DKOiSXNRR9qEwJm/3bXNZrU+P9mcnKzbprbWEOILr0eNQPpAlj8X2iAAFRERQBIAZaVa4USIBCmGnpwnVymlVgfHKu8vzs985X1V3z3sdrt9XVeVd+vV+vWrS4MSx3447HcP94fd49Dv4zQKBzLOzqbadlba1IIVAMrTZTbGWl9b3xjXED/xUmQutefVpwUyEhZzEWetNbYCmqa4b/qcYk4xoQCiAWRBsiZntsrhAAAQbYrjjPDMqtE6DUOkQJ91xvpnob303UvXXVO/Ja7ryyIA2rNX/7f3AXnts6gqnCLAS+p2dnamFa2uh2EYxnFUZTYFtIkoxxRDZOEUI4gYYwhRMSHtstd1rTp0RNQ0Tdu2bduqDtLDw8O7d+8eHh6madLvqdy0b7/99vPPP99ut0djb/87wRIRnHOr9foixXEcQxjV+DWEMeUsQZhlacWofVlVVc55QtIGmjGmaZrtdvPmzSd/+MMX33773dfffK3FelV90OjlfxON1591cnKikJj2wn766aerq6v+MLx79+7u7u6nn95uNtuzs7NPPvnk008//eyzT1erlbX2/Pz85GR7f393c3MTYxzHKee02WwuLi6UOeGcJSqkGa0fQphCDLvD/urm2jpDBoXz/f2qqasYQ2Y5u3yz2pzVdd21rRqo/UPXclygy8sZgflOlbCuUqNQON5zRI4xHg79OA45Z+sr7z0ABjXWjVNhRjUNZE4p94ceDAEBNg3AcXsaCroNS6P6wwzl9w/mvLu/B0QyjpGyIAMJmqaru26zXq9X3apt26Zuqqp2ZZLCFJRdLTVp2d2XMKUYnc4cAwoV0N0UUoFSOYs50DQN0xSnMU19Hvsw9cJZBDIYMDUYZ31jU0LKSSduVXmAVZPp6UJCSodp4hRTDGEcwjhwjoRUd127OunWp67qQkxIAwNHTpICkQxhtKESYw1ZYUkpxRxTZBFkxpQkxhxiMpBBhBCdocqaytopZQNikdhoTa326JR15hZJKcN5bjzwTBWcK+Ryn95DfN9jyPMRa0ObIppOTuOUpxhC3BJKypCzcpqFJc9jdpkoxDJHQWSU5jT3keZfesyJ31FRjvPI4lOyBjNHaxmQnZ/3DLXPIO1HC/cytfXUZdWZe+I5cyBCFNIfIaI+jNvNepymcYrTOLTduna1ttw4pZySFAvXKcWoeJ01VDfNZrNer9Zt2yJiTGmaQs6ZA5eJNUPWGm9N29brrjvZbLab9Wq1aprGWAJY2DFHJw9IRNr6LbOAGpGeci9AICJAdEhMmJiFBBE5pzGGgVzjKktkCIBzdtbWTeuqCo3d7Q+7x8ccm1VTG8KqcnHsD/v97vFh93C/f7jv94/CwdknNxG1g1xCezmd8lARiayrfNU23SZE/1zDTaMoKWMFC+3OuBni99YT2ZOO1W2dOcecGEDACGLKnJgRkAwJAc8UukKkQ5CncTUUJDLOWE+megrtSFoTzFVPmdkrOHxJGnUxqQ5d8R6eKbnLckLluLVt2zTN4XB49+6dSppcXl6enJyoH4ke9/f3+/0+xIBETdMIy93trTUmhjNfVYZotVoZMjFG5eVpwM4zj6lpGiXcIWII4XA4XF1d/fLLL/v9HhHbtt1utxqK1O5dR+P+t0r2+REJIIFFq+7mIjJNIeU0DEPY78c4xZjCFEIIiKi1qTqq6QZpjKnq+tNPP/3qq6//n3/6f7777rs3b94osV8N1z8WtP63DyLabrdK73/9+vXbt29//PHtjz+8ffv2p/v7h/v7h6ur67dv3/71r3/VAH9xcVHXNQBcXFyM43h1dXV3d2eMWa1WX375JRGtVu1q1VpriEg5jOrapy3oGOP9w0PKse8PV+/erbqmrioiarqu7rZ1XVfe00yH/Puj+wLIHwOZy59xpiHjHN1BSESxbtYuIRlMOR763TD2KafOdV3XAVHMSUmyTd3UTV05Dyz3d/djGGm0rnJd1xpjg9PxF4B5UnohmRdNxudn+8GrSDH98vNPgGR9hcaDsU23Xp+cnZ9fvnr1puvWVdV4X3nv9PZmzqrWPE1jjCHlhLPt8gIsLzsMzEoUAFB53zRt07RN01hXWVc7n6sqNjGtYhz7x3FP94fd4eF2GvsYo5Cz9QoM+bpmbhg8i2TJWXISFafm40sSBCYASyiUmCNzVVWr1frV6083p6+QupAg7R6ySMxpimMKfYwoIiHlLrElj1zSJAD0rrLWAVBKHKZoKSndAVWq2Tpn0FHOBiyida42JMrnSkkyI0LmPE1BGa1ZcswZY8yc57FynLXkX/Ien+fRJXmceRtYlL2U8DKOIwB0m42SNjhlyFnKsteCG4WyAkyAMylsCVil7uRlmS4LpSzu/JSLHI1VzZXqct4zbw7nCbrf3y/mJTGfy0LPWP5R4VkAKPIdfr1eTSHc3N33YxRhInTOWGvGsSiL5ZxFsuIkOhbcdauu7aq6Iqsz1U4lAJTE6ZytvKubqm3q9brbrtab9WrVtd57oxr7Hzl1IKO1KR4dCgCIiI4hgKBFA2BBTE5KnRfJMYch2j0iWW8Q0BCRdd5Xus66tmuqqqnrtm68tcB57A+P97eP9ze7x9uh36U4OQtqO+N9IcYfl+xzKCxoEKExxvmqbbrtMBLMOtkz+0/hMVFY0RAVVSxrZxktt6qbvGEEMQbGEFQ/gQG1n5aLapTknGIkJGDIWQxxqblZUMQAGGO8tbV1tTV1sb4gWoRmFdmcsYNCoJOZkqu2bzMmwO/XLBpIlNq22+3u7u4UR91sNm/evFF1T5Wmub6+fnx8DGFiEWtdVVUpxIf7B87ctm1VVdaY9XqtssRK1GDmw+GgM1dN05ycnDRNIyLjON7d3SkxXm2/Ly4uXr169ebNm8vLy7Ozs9/3aP97j5kUq5MdMcYQJhFOMQH8dn9/n2LSqT8zozi6GNQnZrvdvrq8/Pbbb7/74x///Oc/ffHFF+v1RkMpPAsJ/3eiuz5K773mnev1+uzs7PLy1fnZ+XZ78vPPP797926/PyhjQEV5Va6u6zplvQ3D8PDw8PDw8PPPP3///V+7rqtrf3p6dn5+/vXXX/f9gVnS0iMjqOvKe+e9Y4Dd/pAzp5arqqpbzCxZkzLm5yq//2DD+nngfAF9I2DBrNU/k2FxTGHOMYWUI0tWZowAxCRqmQyeLZF3Xp0nUowphqz658YQCMFShy2sNPlQKfjRg5kfdjvjXEsGGFOIxjdkTFXVTdtVdWONRzIimDIg55hiDNMwDuM4aGgXzk9vnEihEKuAvBJoyBhDKedlQIvIiggzChgiNAZUKCbltOsPu4fbfjiA8VWzAd+YesWmdowxS5xdz7Wl8QzMRtF3X1vdzIxkXFW1q023Pkm5SkNggcQ55RhzDDFEABbD6MmlyhhkUCCY0KCtkDCrxOEUhBJnNXtW9w8FYDjmPGUgS87Yuq69tVhEujimOI7TOA5TP5gpUMxWM8iZ4ai72PvUiBdVu8ycCr3DQkTO2rqqq6pKIehUKyqPmFlilsxSbOFVbAxL6mXK0tQpI0FUHRFEyFDkF0CEiJRulHICAzknDWMLU0Deezlm7KpYaZXI/pHovsT1slpLVx0A54YrCJWAX1BeIlqtOuY8jOM4xZwT5miMcc5yTuM4aIpT15XzvkK0zqsfpa9qQEophxhjDClFYTYqcLHqNuvVZrParDqlzTlbstffSUtUfA4En4V20MZoKVh1LSITkUF0Sv4CEIQscYw9AAuiRdN45wEtMxKapm7Oz05TTF3bnJ+dNJWdhv3j/c3t9a93t7/tHq85jpakqaqmqeqqUmI8qadsmXk7botoVm2QrKuaBqVChiMLDCWcyawjbclYa7wz2rpHMjpr3lSVscZ723VVP07DFPQdCClNMU8phZxAJKc4QU55Ms4qWRSIBADRIBkRa2zjfOtcbV0N88jb3MmjmYpUIjrMixYQGJDKmHwxnflY1X5ycnJ+fv7rr7+q5drhcHjz5s0333xzcnKiSMzJycnV1ZXy2HNKSmeNIYZpuhrGqqqU3rXebJSZ2DSNarDc3d0hYl3XmkCoGtJut9O4vt/vtfv++eefa7Gu4X8xWv2/chCRc/b09NRa631ljFVCqDJOAEDReDP7jKlajk61qR/r6enparXSyc+/J//+Pzyqqjo5OdGA/cmbT7755ruff/757duf3r59q/Lyfd+/e/dOmQrn5+fn5xenp6faSru7u3v79m3O+eHhfrfbf/fdd59//unl5cV//s//mciEMAEAErar9tWri27Vee/DOMVx6rp2s16vum69Xntf7XY77V9os+bvP/mlkvnoZxxRmxVpVFqUAM4s9CSSiWQG0VSJBeZJEgbtM9skIlrs5xxzDDlORqykKJyQeYao5yE5mbtc8LdTFAGICNa7erPOjOO+D8WsOY1hYkDCaSkJECBxSinqAJp+9xBjSnHBJRHRGgsAs0Jz5ZxBNMw4joH5EGPW/b9ox2bOOcUwjjH2Ke5juD7s7x/uAF1dR3YbsashW9fEmHJUbbqsDnjcMi/XhyAInFNMYeKURSQzx5T7KbhhYqSQckwxppA5ZclZJLFIyD6iiEes1F0eBcCQMUYgTzGNU5imSYg5x2lKw5SmmFPimNIUwq4fDjFTmhpZVU3ddG1bNdYQZ44pjtM0jmPoh3GYhimKrUxVk7HzbrZQg58dLzTk8dkKQ0QiQ8Y5Z50r9SIpsoqqWygpQS4rSVV/iUqRpJUQL5D7U1Ses8MnnL18obKwn4Yzy8J++rS5Oa/L/e+F+J7qhbLJoDGUs9GJ+vIZUn4YIlbed123Xq+mmGLOYRpV5V/hkRgDkbHOt01rrXXeV1VljRWAGKN+AiLUVWWaumubddetN6vNerXu2q5rKu+PCqz5yj504Fy1ozxnjIoUSe4FLSMhMUSWiYWTrleRyMKIhkxtq2JVK1womqfbrTO2rnzX1SRx//B4d/Pu7ua3w+NdnHpHUnlX11VTN+pzM1dpx7PLSgGap3PKcnHO13ZKAHG5OEOWTdagSUSWjLfGWdXetUUXgdAANdYYg3VlxilMIWp+G1KOMY0xTSklNdlCVgsfUWSKANFY56qqa9tN227qeu1ca53H56FdjriY8jTUXv6KBeJXeykReRnaQRXX61rBcFWeUZ0ixW+aptGKtm3b9Xq92+3GYdC9KcV4OPT7/X6322XOfX/QlFQb5Nq/V3rd4vKiA2/qBPPu3bvD4YCICg98+umnb9682W63bdseMbb+T8LnM9gVkRbhW71qfb4xJjUaQUStlU9PTz///PMvv/zy22+/VaL+6enpkmqUhBrf/xH/1w5EXMCDtm27bnV+fv7q9eVnn3362Wef/vTTz9fXV5oYHQ6F279adRcXOq12/h///u9XV9c///zzNE0p5b4/jGN/cXGxWq2++OKL/X5fN835xfn2ZPP5F5+tN2vv/dgPYz80TbPuVk3TeO9jCkqGUE+/1WpVOhxHe8/Hzv8ZBja3K+F5Ba97qMxLF0hRY9BQnVNiTsIFaU4pakaSQsgxzpTnSYfXYww5hhgojOPY92zMNI4pBM4ZMksuQs7aUFs2HcKn3XNh8h9vYkjUrFZN2zXdKiQ2IZFzxjlBDDGxTCpEvuhXlnG48v0AmZklpkSzPcFceqFqhIiojbsnMiISYxIZSXW4ZRaOLWQzTGAimH3Id/uROfgeMl71gerbvfF1Yk4a15kFwBJ9+2ZjTXnTi2BfzDEmHQOMMR364e7hIYH3NacMgWOSzChkrasbYSSo0FRElbGNNYgggoxW0DJxyByGKT88DhYip3Hf7x77Qx/CIcZ+irrXjSHmlKfElqzyg5qqRkTrXGOsdxVXbWjDOMWJMRpnVLFAORHv0ePh/dBOSFqFaeRQzjwZY8oQEZE1IoKGBEHdIUCF+2YsUAjRkhhiHcmeJ9SfFEJFEHAeT8fyZhqq69LCNMYeEd9fIHkoM7Iw//Z70V2Wq56hCyq1vl10hOfu0jJBKkbJwNstC1xd3/VDb9rWEiqLDJHI2Lppu/XGa9gzJqc0jmOMIaUEwnURmu42m9V2vWrbpqnrYmL98mw/vt+VxQ0IZcCyYDCwdJb1y7XZgsbobLfo/0GyiKQwMDxWQlXjyFDRVDe03a632zUhoOTdw+7u9t3t9a/3t7+FYU+QKl+1bd02jRqWaGg3xpCOpixx/YlDoXeunOyLDNIYK8yCTICE6Ixxdim5DRoSooxiAAmxctbbZuVr9apQHcycOCSOOUfJE6chT0OaxhQnTlEgAxnj63q1PTk/PX212Zy37db7xhgLJE+9diz7owZ4KX8FmGl0KLqnCSmHTvLLJwJgjNGa+/Ly8vXr17e3t+M4anNdXxQ1gKnrerVaTdOUQmRmBEwp9UP/cH//7upKtd/vbu/2+33btuvNRlHirutyzjo2plT5cRyHYbi+vn737l2Mcb1ea3NdI6gWiP//K4ittWpWpvQC772SkkMI2qL+wx/+8M033/z5z3/+7rvvLmaVvXkQ7u8r9/5vHAswYIxp27au68128+mnn3z9zdf3d/c3Nze//fbuL9//5aeffu4PfdM0r1+/+vrrr5u2ub+7b+r6f/7P//njjz/+9NPbYRxubm+urq6+++67b7/9ZrNZ/9M//dObN29u7m5Pz7afff6Z4ig55RyTd8670rl4+/bHd1e/qaeAc67rur//meiyOQ7tMLfbl+1yJo7q/rXk+Dh7QoUck+QsKeUUwzSOwwEEYkwhjDFMhtAg5BRZeBr6ECZEGRAcoTUmDKNqkTGDRncdUi3ggA4ZLbf4GXr4dJHGmtOLS++rqm0x5iZBu1o1XWecTZw5CiqJSrnAZcYLtcU3K+kgcwFiYUZYi9gEqOGTd65SPoreooXnyMycQZhZUNCSqdG2SdxhlBQTQH8Yf3t3syfrwVAZ2JlvdeXdV5f/H2sK85ezxMQx5ZRYAAVonNKUD0Gu+pC3Z2xsFTkwCJBxVd20LYKRbOtqZVztfdfaChEyJCEWw5IGCeNh4qvbPeYhhsPj/nF32AXmCDBmHrJGURzGYb8fpn7cPe4OZ2fr9bqyznvvK19Xlatabnic4iGmXWRCAhaRrCqaAlme85s+ABwdw+Dz7C6hQt9EzvvK2vXJdhrGyfZpnCRm5szl3SKyBo0xzhpjAJGF9cvL8jhaHDQfxhhXudVmffnqUom+qP2Dkij+/9r7kiY5jmNNXyIi16rqBWyggUeRkkY2txmbX6ifJx10kZkuOkgU51EkQVSvtWVmLD4Hj8zKRjcgcCRRes/a2UZUV1dlRmZGhLt/7v551s05FEiglX/j9NJRfyAolKFWQMhBWmtt4ZxNiQBCCMF7kUQjycZ0BwybpqljSvvd3vddHHofI6TEzNZZZVogNkSMoMVwESQZQlvYwtmqLNumWbRN29R1XTlnrTkq9YeuzMcEcXQq8yrTyyHJZOzTlQsRMpuUElLEqfpbogQfZe+RGZldJFNqKTZby2yC7w77/ebu6vrd95vb9dBtEGLhbFWVukUWrjD2ob+OKHLMj3yQIDluVg839NwMVIhIMSolLcwFrJi0Og8FMSmsxBlcR2BEm9uspgQhQYDkJRzicAjd3vc7329DHATJlnW7PD397OzsomlPyqpltsSM2ltx9MsB9MbhRDErMypZ/Q0laY27jJbUe09E88BPTk5ev349tW85OTmZKv41xUzJ4xTTA8CU4tD3y8Wiquumaa7W6/vNpus77cfqvVd9UNdVURR1XTGz9uLUOPFut1O62ZcvX3722WdqPcz0+j9eg+qRJ+tBPWNlvluv1xpc//LLL3/xi1988cUXr1+/Vt0/fltmx/inymRDTH4CImJZFm3bLBaLF+fnL19eXF6+Ojs/vbx8dX93b4z55f/45c8+/7xdLHa7bYxBWVvevn273W6+/vovh/1us7nf7bcamF+dLFeny7Pzs1evXtZN46zVum0m0qLHfhgG34cUtG3EdrtVC0Bh+b+JLD5W7UfHZWIH1bgcoRbGACEAaGv5IGm/Pwxdf9js+66Lwe+3KDHA2KXQe5+iD0Ov8ZHucAjBg7Kvem+Ykw9+8DEEEZKYJq5HHMm+MTNe5tE+BhUAgJDqdkHKe2JNuzDtYllWNVun2K3SpIw0pUcAgIhG/ayblvozikqiTIpgzOmeYIOHNwpG/4KQLNvSFg1SGcUMIaQYfdjv9h4IR6gxTd+uimKeSIfERBYxABokYEOQRAgHH3aHvT3sXJlSSgr+Ipqcy8xmbFtK2m0PgIUTsESUFOK22+2GXfL7MOx3++3hcABCMJyIDZIldoQH8X4Y7sOm74dh8O1mWzhXlVVbN4uqXhQ1A1kyFoExKgp/3Nve96eeTqOD2a3HiS1ccxeKqqya5uLyFRNvb2673T72PgTNeQJlHgDO3R81txSJjGFjDI97nzIcMhs2bI0tyqJdtKuT1en56eJ0WRYljg6qQkHauDgn1CG9p8c/wluDsyhDjKHvOsNUFoWG2A+H/dB3KSUUiCnCWK4nAEhQloUIHJZ7P/T399vd/qC7QJHjHBBCAJHIOeBjSYqyrOtq0TaLxaKqytI5Yw3zPDQ9jetvyzhwUPpUyCRNMKvVyniEiEBCMmIkpaAuspr7IBIkdLHDLomLwVWJrEMwKJhSPBw2tzfv1u++W7/7dr+9weQVb6ibuqoq5cBXy1rtOzWT5q0YkkZWxkCJzCsdj6slU+bqbOJZCYQWvkSt6EHKNP0SGZARDbIhNsyEBMACKAQRJUAcxHeh3wzd9aHbhiS2rJers7PPTs9eNM3SKWs3UVbikyI/3vzjCCcDCcef8e0nZpXuLOq4X15eGmO0aO3FixcKreszUwWvNygfTqSqqrKu6rZZnaxOT0/fvXv3w7sf7u/vr2+uN5v7ummUYkVvu0jqusN6faUd3rR6+/Xr1xcXF6enp4r8//P0+lyYebFYqO9+fn6u46mqarVaXV5evnz5UiEH3XB/miE9FjyulqMoolaUxXK1ODk9+eKLL/b7HQBcXFxoOUPfL4zhxbJt2vqPf/zj119/fXd3t93cXV+vv//+2//4/POf/ezz16/fvHr1arlYlEVp2TAxgpqfeUOyzrz47IWxRusXNptNWZanp6efmP2QVXvex8a8uby4cEwTwpx5Q7lPtgAMQ9+FTu7vE4iEJF4Z35L3/X57r8fQDGfscE95+5AEgBBj8MPQ7XaMyDDCAmRneh1IFCDIAYPHqv3BDUc0xglAiImNa6umbZdlVRvrkBglh7wm/XOMuyOmlDSnTTRrhxhyAF7xdpyYemUmMLMcETVvW0AQWYytXNEaVxOXQL3kIEOQOC32o1UQjZmvdKXeSkGCTwgsIoVhYAopSUrD0AGiJGEyjEYi9r4nIMuYTIjJxzREsQQsmmZvAIQj4b7v97f30e9j6OMwxJissZat48KxxRBEhsGnEKUfhmHYdl1/bW+tsXVZrtrF+WIly9PSlQKUEqAAjbeQOHea/Jhq1z06oyWZD07nL2huG1tb1tXq9AQQm6bZ3N4dtrvh0PV9N/QDIBpr0BhUfnGk4H0YNAdNYV2X/TWT88i1rLkoy7Zt2kXbtE1Rl8bq+pkiSyNNm4zDeYjVj2rlQ5KzvAyzy6wrJFoOa7hwLqZkbS5FlZkHQIhFYVfLhaSYt6ueAEnzFwgRJKKgQSqsKSw7Z+qyrJu6qauqrhXCzrcPcgW2HuZjC30mMYUhdATMYAB5pnre38VQYycgwEbb8cUgmZoVEopPATLJK0SOJRkXe/Ax3Nysr374/urdd7v7G4lD6aipi6ZuqrouytIcc/0QR+82sz7O/i/jm0ft/vD+a00BAgoBCMQEhEAgKNlYSJgSsjatBEgIidVmJkQZ+al0WyDtFi8GwCBaREtoCROxMbYoKldUxlo2rNTxMs5iHQnOFPdMzY/jzR7gPHb19LzSiLvykyjfquaoTyVPR8RyCtYLiGE2bKx1RZHrkcpivX53dXXV9/1+t0tjoHQYamZOSbQNjDZ/u7i4uLy81HKyf0A+/MfkvWOKhmM+++wzTVXb7XbKbL9cLjVd7uGE/In1+nS6Bw8r63piHsssF4uF94OI1HVdFKWywb969VLbtp6crFar5V/+8pfvvvvu5vbm0B1ubm+urtbr9frm5vry9vKzi4vlYlE3TTlmn0zcuhp/Kctyu93e3Nwoz7Rz7lMYhAiJifUiJqWlep4kRUJJkCCxMWQNGgYi5UqMKYaQ6SgxZeZmRJGYgowYZD6skj3DCKqTsgYmgAjIyiMCjJBQkozancYW0nRE5LNq13Ko9y4tivZtEUZyrnRlZVxhrFPIV6nQRjB4JJs1hnKNNVvrEMg5R8wELADWWMUr9BnS2GhjDhuMEDCgSCIRECY2zlpXsHFIDIBjcE0AEImnCKMao87ZeUpNDHEYgvcxJtFcM2MtEEY/pJTC4AmYkB3b0pWhd4euS8kn4Bh67w+9YUNIhoUxpZR8CsPBd7ttt992+xQ9Cihqidawdc6WZCxSBOA+xC6GfvA+xJCkHzwj+X5gwYaLVAs5JrYBE6Uw7Yh5x32EMD4ByGftDtmsgTEijYZt6aq6Pjk9bRbt2Yuz3Wa73+66/X6/2+23O0QsytI4S9YwEQL6vh/6jtm4wpWucIUjY8gYMtllVwoUpWxzhbPWMPORwBMIjjH8ae1OVfGgiRiPFvX8YrSMAgxTUTj1pQBAkiCIc26xbFNMxrCzBiZQZ4xjM/FyuSicdYUrq+rm9n67OwwxJgEmNgSWoSrsqq0XbdM2VVkW1uXGaDitqo+N72MSwnDoNo4dmgLFEgoIiybHjmbsaLUKgiRiddwFBCAFLwIpE7BEtVxjiJ5sSbbovd8edu9++H79w3e7u2vf7WpHbVW1TVM3TVGW1hXETCNNjGY5jMD7TLXru/lX0HK1B0QDAn4YQvDqsidhBY8EMSEyIIIEQMY40vsAIjBChJQIE2FM0bAgJUFMMUWJPnkf/RCGLgxhGJKQsICgMt5qXp4mjT5U2k/oQnnwcq7RP2YvatKWcqFPSeMz1f7Ubo6CgMxcEGlFeNXUi+VitVo2TfPu3bvr66u7u9u7u9u6rtu2RaIY4s3t7Waz1e4vb9680dS5H9uR/e+WbPIorexqtdKcwYy+jelyPwH4/gnjhPee2gTo6W4uUsFRPwkR1XX9+rU7PT199erVxcXF+fnZ73//+2+++evNzc3t7e2333771VdfvXz58vPPP9cWtEpgsFwuNdVRDxVC0MyvGOPV1dXNzU3btnNY/mODJqJHsfbRPyWUBERAEKyx1rIxwCSIEUQzlHlk76SxrElQIJvXojvnWPoE41rNPRNwvEVJRDBR7tQGytGph2UiItZa5jGgmvHw+RMXkSF4ESS2SgmZmaPYMjEh6CwBSWPcJMd3ENEYcU5EILpkjCWiYIIIWGsEgFmbW5bW2OnBPeG4Qw6lAQlbZmu0QiyBRImAiCCAbIwpm8VisWjb1jqbolhDc3ylH4btbhf6IYZg2RhmIAKEJJCixCEkjMayY9OUdfL7MOxCjJhCisPg9wdGwMSRE4EX38ch+j72h323OwRPqEhS4Zic8moZx2yIBYgPMe6DN8b7GAFRRHKpnA8CYIwti4qNi8GzMui938f2gfztIg0ZVTsbdkVRlEVVV0UqyiL/3DERkZZ8Nm1rVX8TIUIMIXhtjmKssdZY5DxZpkg7MxvO7cQmB1E/MU1+nLwsGXMsjtGDjwT0UAF9RiAEa3hkXzGJk4gFBGuNSDJMdVUYw5pSoJetc1n3awG01lVVfeg6H3PzGuUQrQrX1FVdlWWpLVzH8iqA492eRvhjVHzXdTe314UpSqd8y86QY7JEBvHYikOvP6NdyMxW1EoXUIpkEABIKJIixJRS3yWhbbe/327ubq72m1tIvnK2bYplU9d1U5aVcY6MzbH9sSJBAx8T9p6OClBGQsGcyvse3B1C9D4QEkWKnBJRRDSgMJLibpoqeGSlN0QGOTAEAsNgkgBFQYkSfQx9UMKgMEgKaNkWXC3KemGLiq0FxuzaA0C2M2ScRXlMcuRcVuaHfA3TdaUPP6vJddA8cJ2FE8A4XvTjOZkfFqGMmamk9OvOuaoqy7LQMq0YwmZzPy3W1Wr16tWrqQuqFp3/9Hp0Wpha+TaXh/GwfxMFr3J0A9QYmjt8KqpgiqLQZoFFUVRV/dVXf/7P//zm5uZmu92u12v1xb///vsXL168ePHi9PT05ORksVhoC11E7Pte6+uurq40NfLi4uL8/FwdUx3Jo7GNA0Aa/VEcm5Hmb0hu8pgoobgyVXXXNKauYb+L/SFEJYIU/drxmo/26TQdR+RQGTE0iK1EynkuE5Ix1tmy5LJk58gYNdyIOQ8PR9UONDZyP15OEtntOiLjClZ6lr4bnOtBEEwOptIImk1nHR8NWmuRWESIDSLaGCHXZUAsCkJ0zmpwEzH3pEjxuEZl3JmSpBB833WH/b7vuql4HbWAHAEpAqJ1xWK5apomCbDCGKNYa6u6GtjE4BEQiZImjIMkSCF6DqSNzJjI2bIqG6+E1sxBkk+BJWCKKaY+9J3vJAVIEa1xTWOYrTWOWW3zBBDICLEAEBsnUgNEZONKAMgUosa6smJXiDUDSvLDwQcfEysYDYzAkH8ezK4Pq3ZEHPkLtHLfMDvnCuecsyklJmLAOPiNABO5tm3a9uT01JYONUcqBz/SETU5ziSATBSMxDnyjjh26Tt+FvGpxTCp9vlon74IAEZkQsvoLAOyK5w2ZDPMRQwxRgIxhhZNVTgzlkDMQjgAhrlpGs2cyuWQSuaLyEQj9kBzB/fDo/oRur07HK7WobC2LIqqKMuiLFxtbWXIMZsxM5FmMXkFnNiAzaaQpKhQfE4QCzGGvo/7w3C/295v77vDHmNfWlOXbtHUi7ouytJah6xWjt7t0YrSOpPJo8hP96gWs15/FPYJIQUfEZMCXAGRc1ogzHj4UB0F3WYssyXjWCyDYWEioCQYfYx9DIfBdyFEQDHMRV00y3J5vli9KOuFcQ4JgbLvcmxUk/NyppdHx3z6SUe9joJKX/MxUXX+nvfwiaIAEjMZY6q6Wiza1Wq1Xq/X6/Xm/n6328WUiHixWJydnb958+bNmzcKxf9TU+I/RaZ1d3SY/vX++sfkPefy8fvqgDZN8/nnP1sslpeXl19//cs//elPf/7zn7/66ittdnd/f//tt9/Wda1hiNVqtVqtJqYgTaC7vb3d7XZE9PLly5///Odv3ryp6/oTxkfKo46IIvNEIp2lwhJTIioSNE23WBTLJR/2qT/EFCXmvYYg53RoWugUFJshe9mQldEdQM1DyUzMlq3DqrGLpVssTF1zUbC1isqoIzHm9CJiNvTnNzbFtN3srXWEdqAA0CFsQciXoXDaX3Nsyjg6dTHGscodNZgLeQtFNgwAmh5gABBHilImBMCUADDlNARNJ8jc8SGEvu/u7+5urq93m+3QDSkEiVEzbpJ4AA6DlySFK9tmCcSMyouRpW5bkLrvu77rg/chhBBDCJIQgTBC9GkgPR6hdUUNqxhDikkQBDkSRUJACCn2IQyDRwJrbOFc1ZJlY41lIgSIMcQQ0+jJAohjbq3jovbBK8IRQ7CGm7q2VR0N71I4HIbBx0Gw1Bg5GkSDyID8oCnXo+K32TLIhlBKSdnTEzNr60Zt2RklhMHv7jdrjQWuVkRknC3KkrQzDE1h5tk5csTl+AeaMqtGUQX//vyfNTp8Ynl8II8OEY0xTV2/OD/rvQckbUEpIqqhJSn9GTVVVVfVDJmZw93InPlWRWvhR9x+cmWePPlTo/wRm6D3ab8dBhP6g+9cXxadc31hD9aW1jhjjRoVytOERGqlC2h1nskl50gpxCTaViEhCEFgiAVJbcmBSwUXzlVFWVXak1SBrCe0Wo6mP8pafIhcqzalhx/AmNlhBCUKSEpRibF0WdOo2hFAtwBdA5atcsEbJiARSENKPklEA2VVVrWrm6pZls3StauqPanaJdsCmAVAy2amGy6zDU7bV+vz0K4wknu95WJ3EY0sfJJ8QLH9bRuOCBENorZvNFr8tlwu7+5uN/cbVe3L1ers7Fwp5x6mzv2IEz29ZH6cHLFuHCuXHv7p31PysOdaHB4pe/11yp9QMsHz8/PLy8s3b94osd39/f1+v/feK96uH55a0041gYh4enr6I4eIU0FbLjx+ICJAjIQGsIK4SjGIc1WzWPXdIflBnUKQhJIA1BsfUaPJj5pFM2ECM3OfDlJmN7bOVXXdLNrFSXtyWrYLU5RkHGq3maOnfbyXc78rpXR7c+tcCUApQhjCYXdYv1sT5mKYUTXThG9NKNex0cPYznUqh8oXMPV0IgKATFET9d9RvWtjj+CHvr+5vb26urq+uvb9EEPSvk+SPRPfHfb77faw2zV148rKsDmScABcvHzj/uNkt91ttvfbzWa33w59N3ifUgQRRGJCdQFNURSxijFIikmbmwEqy6aAmBi4LIvQaEoHEzGyIWZS01yUOignABACAMdQhNAGn1KuchJJTOisLcsSyioCpoRoxKExRYVsQb2kDEx/zGvHowbNULwoDUCM0TpnrBYUISLGEHfb7fV6/d03fwVEY+3y5EQAgFBz5dQulOlwegIRkNznJKMxeAThp7kzxZE/LYb39M4luXTSLBaLsihjigKgOXygiiqJpAwqaNeWnEz3EGt67wYZ5ski/vCm+Q/Y72KEvgffhx79gXtrD84enCsKV7qiLItiZNlU8hcDyLl3BGT+H0RENB69xCBpgAREYg1CwQaL0kFKlYgYY60C/saSZjjk2rkHtxNmCYuPUhen37Nt/+BCAFMGQkFSCjH0wxCiTzFpwzGakHlM2rI563VjrbGGrVVXFTGIJDK2bqvlanX2Ynly1rSrsm7J1exKsgUZg5RbCE+w5JOPZQwlYKafy/WhmBAEMIkmDfxokU/tqJHnDhE7h8aYuqratj07P99uN/vRa2+adrFY1HXzAb3+r5F/k2H8f8jjkc/fQURNJmia5vLy8ssvv/zVr371179mVru3b9++e/fu6urq7u5ut9vNy2XVu9X0CyU20FLAT0mJGAPYWZ7Y8UQAiQwZsoy2KOvVyfl+tx36LoQhxZBS0L0MlINJjpMQx0SrCZ4/nimr0pwaTNZaW7iicmXlysq6IrOHwTF3F466Pa/1aYwxxuurm7IsCSmFZK07HPr7+03fDyEE1MZM+bQPtvo8SGWDnt0HmJ3x4WMawXeRKVis+T+ibrv3XdcpsVLwforBAQgkSBK63W7D5m6xKItiSeT4wTN69frzVy//5+3tzfXNer3+4ebm6nDYDUOv51X1qWS/MBJXTu1aBEkIcTQ+ZCzgpmMeME2JtTL7T92aJCKQ6WdRqwUyYaZSkkoUcU4EicCwKck6IkPIuf/VR1T7/LYKQJSonQ299zGl2tmyKo2xSISSQvCb+/vN3X2/74qqdM7ZwuFknh2TKkf4brQjcXydLWiiKcj66CmOAUrAhxbCcaqNQPDTose0iIY56dQfIf8po2Q6zYc3rEfbwXRRmNuPPfrqB42DR/KR0RtA4+OQgkfwzKFw4nzq+mC7vnCucNZZ45y1rjDWGXZTmVq+HmIywIhAmKJABBEQEsOIzhiTA+dEhsYQvibSyKR5xhS5McPw+KcHKf+C4+p54no677th0NUXY/TB96EPUXv4oiFi0gADECITikE0WhlgxJjEJmk+jrHOFbZqmtVpuzptT86axbIsautKYCtkgA2oQQmgWMATmaM4hdJFJgT+6LKLCM4ogX60DvtkrXfcxo+8UMyuKKqqGpa9iAAqS12pKWBPrI6fWv6LavQfsRJV6ygxkcbgNdfh+vp6vV6/G0WpirQ7HABoZWDTNKenp9rK9vXr3GjuE0b3vl4fd7aZbY2IxIzkipKMsUVZt4sQQ4whSZLM7DZmi+Q98UEtUb4Pk77HTFUCEybPzGxIWyZyDrTrYpoIyqe593gSppT2++3gB0QMIdR1632IwXeH/W63S5LGC32gZcZDaWn28W/zk8w/PF5Nhm8FQGaqHcY2t9qN95HfpZ03Yhj6w353d3NdOOecc4bmetAVdduexoQhiQ8iyGW308IKGOMZudY+82KMiIBGQzA7jZqQhSMmAzBep7qED68lv84LPG+0ODUMAEiQQhoZ3YEIjBZ4lWXT1G1ZlPPyHJVHgPwY/FCDKEkKMfjglWWpLEu2BgkhoQ9hs9lsN1uJSSkei6okw6OVQrnbFtBk7IE6Z0dDUtEINeWIpnzm45ObdUDKfQfhKfmghz1/wUCjvs2oFIyu5HSX8zz7NJnPnE+I+3/s609+BYnJlD7KENTBTSFhSEgUCPvCsrOmsFw4W5SFK0rnKmsLrTLI9WIEBGyQECGSIEhMaTRuCMSCKjAkGOvrRATGFNQ8zlG1w3jrjmOXKQYA04p7T7+LyGHw+24QyQjQELyPQxJBJiZyiAYECA0TMJIlsSzGiXGJbTIuskV2ZAoummZ1slidLc9fLFanRdW6otRoTgROSIIkAAKaNYNwBOTH+4tjWAFG83jU4hmW10C7RvKeNrv+UepN5/7RBEREVeFFUeQlnGuK+dHu9uPG8C+1Bv4LyTHooDPZOXd2drZard68eTNF09fr9du3b9++fau9A7z3IlJVVdu2q9XqxYsXb9680RrFtm1ntf4ffARzp/3RH+UYw0MEADLWsjG2kLpVJqcZmJYbIo7r74n45XEZTF2zdY3gpGIpI+B5QIKQPmX2iEjXH3DolaOeiIiMdZaYYgze+7F99vuqGsfkANVpk16HGZ4xf2d+xrl2z/U62ml+3klvBIBhTBdKKfXd4f721rlisVhUZSHCcFQ0TOysLcuiaZohCZRlHcKQPYHcLHLU6zA22dUuPZh9nDRDEzRjHOFowEzK7wEGCijH5ncyU+36VoqSoiiPHiIQk2FyhavKoi7L2lpLD3kUHqj27m4rM5c6peSHgXbDy/akecVt2y7Jyd3+4CWlBJvDkgs5e3HiqrKuVrZyfYL7Qzx4IY6j0z651zCPn+SHAw/ePyIuT+nw0X0c433TuwgA/WY3fdB7/7vf/e5xHu+DY8H4HOfv/qsLeJRYdPp1GOLdrg8+Bo+SCAAHwYNPCIAYmaLhYBgNs7Edm73JFvdYh6pHEWWhSCkFJW4VzfDXZNl8B2hsnIOjzX8E5KfJlxfStGFM2n72ls7mfR+mq0CiVxcXg/e64GJKMcYoUZexht80DTfDPay5NppDYIj0I9oyng1G8ft+cw3+cLCO2eC4KUxFd5nwFuF9va6DPRorMF3E5OpogEtGvqrUH6Zvrtfr3/zmN//YJz6N6uFvD62+v3tO3t7eHg01kT/84Q/ffPPN33nMf4lcXV1Nr+/v73/729/+ZKdW8PJwOOz3+8PhgIhaRqtcp2qTaeO+t2/fKnWgbkGPH5/yD06/7vabqS0KTGHy4y54hBVhjlwe/aPZvzC3wD/sCsHoQ8LoJM7+cHyR8S+Zn+oIBIAMfT99rSyK//O//jcAWmvLsqrrhoglpcOh2+/3cdZdbQYmPDijPDJeH8QUplHDdCf0xQQhjit7toAe3PxjjQ8QUVFWTduenjZ1xfNPXV3/3yRpGPq+Pxy6w9B3IYaUIsDsaYxbo3rqYwhk5gBNKCcAjH1XUSsTYD6i2VFldDWn0aJkvz9vUNrMRgCUM5OS8DBsJbkkN11XbrfvHtzWX//61/Asz/Isz/Isz/Is/13kpyS+eJZneZZneZZneZZ/ujyr9md5lmd5lmd5lv9W8qzan+VZnuVZnuVZ/lvJ/wN4eZBNCmVuZHN0cmVhbQplbmRvYmoKMzYgMCBvYmoKMjQzNjk0CmVuZG9iagoyIDAgb2JqCjw8IC9Db3VudCAxIC9LaWRzIFsgMTEgMCBSIF0gL1R5cGUgL1BhZ2VzID4+CmVuZG9iagozNyAwIG9iago8PCAvQ3JlYXRpb25EYXRlIChEOjIwMjExMjA0MTY1OTE3KzAyJzAwJykKL0NyZWF0b3IgKE1hdHBsb3RsaWIgdjMuNC4zLCBodHRwczovL21hdHBsb3RsaWIub3JnKQovUHJvZHVjZXIgKE1hdHBsb3RsaWIgcGRmIGJhY2tlbmQgdjMuNC4zKSA+PgplbmRvYmoKeHJlZgowIDM4CjAwMDAwMDAwMDAgNjU1MzUgZiAKMDAwMDAwMDAxNiAwMDAwMCBuIAowMDAwMjUxMTE3IDAwMDAwIG4gCjAwMDAwMDY5NjYgMDAwMDAgbiAKMDAwMDAwNjk5OCAwMDAwMCBuIAowMDAwMDA3MDk3IDAwMDAwIG4gCjAwMDAwMDcxMTggMDAwMDAgbiAKMDAwMDAwNzEzOSAwMDAwMCBuIAowMDAwMDAwMDY1IDAwMDAwIG4gCjAwMDAwMDAzOTkgMDAwMDAgbiAKMDAwMDAwMDczMyAwMDAwMCBuIAowMDAwMDAwMjA4IDAwMDAwIG4gCjAwMDAwMDA3MTMgMDAwMDAgbiAKMDAwMDAwNzE3MSAwMDAwMCBuIAowMDAwMDA1NzAyIDAwMDAwIG4gCjAwMDAwMDU1MDIgMDAwMDAgbiAKMDAwMDAwNTEwNiAwMDAwMCBuIAowMDAwMDA2NzU1IDAwMDAwIG4gCjAwMDAwMDA3NTMgMDAwMDAgbiAKMDAwMDAwMDkxNiAwMDAwMCBuIAowMDAwMDAxMjI0IDAwMDAwIG4gCjAwMDAwMDEzNzIgMDAwMDAgbiAKMDAwMDAwMTQ5NSAwMDAwMCBuIAowMDAwMDAxODAwIDAwMDAwIG4gCjAwMDAwMDIxODAgMDAwMDAgbiAKMDAwMDAwMjUwMiAwMDAwMCBuIAowMDAwMDAyNjIxIDAwMDAwIG4gCjAwMDAwMDI5NTIgMDAwMDAgbiAKMDAwMDAwMzE4OCAwMDAwMCBuIAowMDAwMDAzNDc5IDAwMDAwIG4gCjAwMDAwMDM2MzQgMDAwMDAgbiAKMDAwMDAwMzk0NiAwMDAwMCBuIAowMDAwMDA0MzUzIDAwMDAwIG4gCjAwMDAwMDQ0NDMgMDAwMDAgbiAKMDAwMDAwNDYwNCAwMDAwMCBuIAowMDAwMDA0ODE4IDAwMDAwIG4gCjAwMDAyNTEwOTQgMDAwMDAgbiAKMDAwMDI1MTE3NyAwMDAwMCBuIAp0cmFpbGVyCjw8IC9JbmZvIDM3IDAgUiAvUm9vdCAxIDAgUiAvU2l6ZSAzOCA+PgpzdGFydHhyZWYKMjUxMzM0CiUlRU9GCg==\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["def visualize_exmp(indices, orig_dataset):\n", " images = [orig_dataset[idx][0] for idx in indices.reshape(-1)]\n", " images = torch.stack(images, dim=0)\n", " images = images * TORCH_DATA_STD + TORCH_DATA_MEANS\n", "\n", " img_grid = torchvision.utils.make_grid(images, nrow=SET_SIZE, normalize=True, pad_value=0.5, padding=16)\n", " img_grid = img_grid.permute(1, 2, 0)\n", "\n", " plt.figure(figsize=(12, 8))\n", " plt.title(\"Anomaly examples on CIFAR100\")\n", " plt.imshow(img_grid)\n", " plt.axis(\"off\")\n", " plt.show()\n", " plt.close()\n", "\n", "\n", "_, indices, _ = next(iter(test_anom_loader))\n", "visualize_exmp(indices[:4], test_set)"]}, {"cell_type": "markdown", "id": "48c08ca7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.1952, "end_time": "2021-12-04T15:59:18.016890", "exception": false, "start_time": "2021-12-04T15:59:17.821690", "status": "completed"}, "tags": []}, "source": ["We can already see that for some sets the task might be easier than for others.\n", "Difficulties can especially arise if the anomaly is in a different, but yet visually similar class\n", "(e.g. train vs bus, flour vs worm, etc.\n", ").\n", "\n", "After having prepared the data, we can look closer at the model.\n", "Here, we have a classification of the whole set.\n", "For the prediction to be permutation-equivariant, we will output one logit for each image.\n", "Over these logits, we apply a softmax and train the anomaly image to have the highest score/probability.\n", "This is a bit different than a standard classification layer as the softmax is applied over images,\n", "not over output classes in the classical sense.\n", "However, if we swap two images in their position, we effectively swap their position in the output softmax.\n", "Hence, the prediction is equivariant with respect to the input.\n", "We implement this idea below in the subclass of the Transformer Lightning module."]}, {"cell_type": "code", "execution_count": 34, "id": "cad86c76", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:18.412870Z", "iopub.status.busy": "2021-12-04T15:59:18.412388Z", "iopub.status.idle": "2021-12-04T15:59:18.414272Z", "shell.execute_reply": "2021-12-04T15:59:18.413887Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.20176, "end_time": "2021-12-04T15:59:18.414384", "exception": false, "start_time": "2021-12-04T15:59:18.212624", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class AnomalyPredictor(TransformerPredictor):\n", " def _calculate_loss(self, batch, mode=\"train\"):\n", " img_sets, _, labels = batch\n", " # No positional encodings as it is a set, not a sequence!\n", " preds = self.forward(img_sets, add_positional_encoding=False)\n", " preds = preds.squeeze(dim=-1) # Shape: [Batch_size, set_size]\n", " loss = F.cross_entropy(preds, labels) # Softmax/CE over set dimension\n", " acc = (preds.argmax(dim=-1) == labels).float().mean()\n", " self.log(\"%s_loss\" % mode, loss)\n", " self.log(\"%s_acc\" % mode, acc, on_step=False, on_epoch=True)\n", " return loss, acc\n", "\n", " def training_step(self, batch, batch_idx):\n", " loss, _ = self._calculate_loss(batch, mode=\"train\")\n", " return loss\n", "\n", " def validation_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"val\")\n", "\n", " def test_step(self, batch, batch_idx):\n", " _ = self._calculate_loss(batch, mode=\"test\")"]}, {"cell_type": "markdown", "id": "d732d394", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.190651, "end_time": "2021-12-04T15:59:18.796683", "exception": false, "start_time": "2021-12-04T15:59:18.606032", "status": "completed"}, "tags": []}, "source": ["Finally, we write our train function below.\n", "It has the exact same structure as the reverse task one, hence not much of an explanation is needed here."]}, {"cell_type": "code", "execution_count": 35, "id": "261279ac", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:19.193945Z", "iopub.status.busy": "2021-12-04T15:59:19.193455Z", "iopub.status.idle": "2021-12-04T15:59:19.195328Z", "shell.execute_reply": "2021-12-04T15:59:19.194922Z"}, "papermill": {"duration": 0.207853, "end_time": "2021-12-04T15:59:19.195437", "exception": false, "start_time": "2021-12-04T15:59:18.987584", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def train_anomaly(**kwargs):\n", " # Create a PyTorch Lightning trainer with the generation callback\n", " root_dir = os.path.join(CHECKPOINT_PATH, \"SetAnomalyTask\")\n", " os.makedirs(root_dir, exist_ok=True)\n", " trainer = pl.Trainer(\n", " default_root_dir=root_dir,\n", " callbacks=[ModelCheckpoint(save_weights_only=True, mode=\"max\", monitor=\"val_acc\")],\n", " gpus=1 if str(device).startswith(\"cuda\") else 0,\n", " max_epochs=100,\n", " gradient_clip_val=2,\n", " progress_bar_refresh_rate=1,\n", " )\n", " trainer.logger._default_hp_metric = None # Optional logging argument that we don't need\n", "\n", " # Check whether pretrained model exists. If yes, load it and skip training\n", " pretrained_filename = os.path.join(CHECKPOINT_PATH, \"SetAnomalyTask.ckpt\")\n", " if os.path.isfile(pretrained_filename):\n", " print(\"Found pretrained model, loading...\")\n", " model = AnomalyPredictor.load_from_checkpoint(pretrained_filename)\n", " else:\n", " model = AnomalyPredictor(max_iters=trainer.max_epochs * len(train_anom_loader), **kwargs)\n", " trainer.fit(model, train_anom_loader, val_anom_loader)\n", " model = AnomalyPredictor.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)\n", "\n", " # Test best model on validation and test set\n", " train_result = trainer.test(model, test_dataloaders=train_anom_loader, verbose=False)\n", " val_result = trainer.test(model, test_dataloaders=val_anom_loader, verbose=False)\n", " test_result = trainer.test(model, test_dataloaders=test_anom_loader, verbose=False)\n", " result = {\n", " \"test_acc\": test_result[0][\"test_acc\"],\n", " \"val_acc\": val_result[0][\"test_acc\"],\n", " \"train_acc\": train_result[0][\"test_acc\"],\n", " }\n", "\n", " model = model.to(device)\n", " return model, result"]}, {"cell_type": "markdown", "id": "3a251275", "metadata": {"papermill": {"duration": 0.192005, "end_time": "2021-12-04T15:59:19.580159", "exception": false, "start_time": "2021-12-04T15:59:19.388154", "status": "completed"}, "tags": []}, "source": ["Let's finally train our model.\n", "We will use 4 layers with 4 attention heads each.\n", "The hidden dimensionality of the model is 256, and we use a dropout of 0.1 throughout the model for good regularization.\n", "Note that we also apply the dropout on the input features, as this makes the model more robust against\n", "image noise and generalizes better.\n", "Again, we use warmup to slowly start our model training."]}, {"cell_type": "code", "execution_count": 36, "id": "adc7b1bb", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:19.979138Z", "iopub.status.busy": "2021-12-04T15:59:19.978661Z", "iopub.status.idle": "2021-12-04T15:59:25.752247Z", "shell.execute_reply": "2021-12-04T15:59:25.752638Z"}, "papermill": {"duration": 5.981928, "end_time": "2021-12-04T15:59:25.752808", "exception": false, "start_time": "2021-12-04T15:59:19.770880", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/connectors/callback_connector.py:90: LightningDeprecationWarning: Setting `Trainer(progress_bar_refresh_rate=1)` is deprecated in v1.5 and will be removed in v1.7. Please pass `pytorch_lightning.callbacks.progress.TQDMProgressBar` with `refresh_rate` directly to the Trainer's `callbacks` argument instead. Or, to disable the progress bar pass `enable_progress_bar = False` to the Trainer.\n", " rank_zero_deprecation(\n", "GPU available: True, used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/trainer.py:901: LightningDeprecationWarning: `trainer.test(test_dataloaders)` is deprecated in v1.4 and will be removed in v1.6. Use `trainer.test(dataloaders)` instead.\n", " rank_zero_deprecation(\n", "LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: saved_models/Transformers/SetAnomalyTask/lightning_logs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Found pretrained model, loading...\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.9/site-packages/pytorch_lightning/trainer/data_loading.py:453: UserWarning: Your `test_dataloader` has `shuffle=True`,it is strongly recommended that you turn this off for val/test/predict dataloaders.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "9c0f4106b2fa430d807e9c34f98e5bd2", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "983722707bb64460bfbdc38056f7d32d", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ab0dc6637c5348a9839eded7ec182961", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["anomaly_model, anomaly_result = train_anomaly(\n", " input_dim=train_anom_dataset.img_feats.shape[-1],\n", " model_dim=256,\n", " num_heads=4,\n", " num_classes=1,\n", " num_layers=4,\n", " dropout=0.1,\n", " input_dropout=0.1,\n", " lr=5e-4,\n", " warmup=100,\n", ")"]}, {"cell_type": "markdown", "id": "b752953a", "metadata": {"papermill": {"duration": 0.212151, "end_time": "2021-12-04T15:59:26.177915", "exception": false, "start_time": "2021-12-04T15:59:25.965764", "status": "completed"}, "tags": []}, "source": ["We can print the achieved accuracy below."]}, {"cell_type": "code", "execution_count": 37, "id": "0c9ae3d1", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:26.590345Z", "iopub.status.busy": "2021-12-04T15:59:26.589872Z", "iopub.status.idle": "2021-12-04T15:59:26.592352Z", "shell.execute_reply": "2021-12-04T15:59:26.591950Z"}, "papermill": {"duration": 0.211453, "end_time": "2021-12-04T15:59:26.592461", "exception": false, "start_time": "2021-12-04T15:59:26.381008", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Train accuracy: 96.33%\n", "Val accuracy: 95.92%\n", "Test accuracy: 94.41%\n"]}], "source": ["print(\"Train accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"train_acc\"]))\n", "print(\"Val accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"val_acc\"]))\n", "print(\"Test accuracy: %4.2f%%\" % (100.0 * anomaly_result[\"test_acc\"]))"]}, {"cell_type": "markdown", "id": "93d2718b", "metadata": {"papermill": {"duration": 0.201663, "end_time": "2021-12-04T15:59:26.995547", "exception": false, "start_time": "2021-12-04T15:59:26.793884", "status": "completed"}, "tags": []}, "source": ["With ~94% validation and test accuracy, the model generalizes quite well.\n", "It should be noted that you might see slightly different scores depending on what computer/device you are running this notebook.\n", "This is because despite setting the seed before generating the test dataset, it is not the same across platforms and numpy versions.\n", "Nevertheless, we can conclude that the model performs quite well and can solve the task for most sets.\n", "Before trying to interpret the model, let's verify that our model is permutation-equivariant,\n", "and assigns the same predictions for different permutations of the input set.\n", "For this, we sample a batch from the test set and run it through the model to obtain the probabilities."]}, {"cell_type": "code", "execution_count": 38, "id": "dccefbde", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:27.411424Z", "iopub.status.busy": "2021-12-04T15:59:27.410941Z", "iopub.status.idle": "2021-12-04T15:59:27.581026Z", "shell.execute_reply": "2021-12-04T15:59:27.581442Z"}, "papermill": {"duration": 0.381038, "end_time": "2021-12-04T15:59:27.581610", "exception": false, "start_time": "2021-12-04T15:59:27.200572", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Preds\n", " [2.7691365e-05 1.8979923e-05 1.7386470e-05 2.7843047e-05 1.6143023e-05\n", " 1.7020926e-05 5.7294892e-05 9.9977750e-01 2.1365197e-05 1.8681889e-05]\n", "Permuted preds\n", " [2.7691472e-05 1.8979976e-05 1.7386521e-05 2.7843154e-05 1.6143069e-05\n", " 1.7020990e-05 5.7295114e-05 9.9977750e-01 2.1365277e-05 1.8681943e-05]\n"]}], "source": ["inp_data, indices, labels = next(iter(test_anom_loader))\n", "inp_data = inp_data.to(device)\n", "\n", "anomaly_model.eval()\n", "\n", "with torch.no_grad():\n", " preds = anomaly_model.forward(inp_data, add_positional_encoding=False)\n", " preds = F.softmax(preds.squeeze(dim=-1), dim=-1)\n", "\n", " # Permut input data\n", " permut = np.random.permutation(inp_data.shape[1])\n", " perm_inp_data = inp_data[:, permut]\n", " perm_preds = anomaly_model.forward(perm_inp_data, add_positional_encoding=False)\n", " perm_preds = F.softmax(perm_preds.squeeze(dim=-1), dim=-1)\n", "\n", "assert (preds[:, permut] - perm_preds).abs().max() < 1e-5, \"Predictions are not permutation equivariant\"\n", "\n", "print(\"Preds\\n\", preds[0, permut].cpu().numpy())\n", "print(\"Permuted preds\\n\", perm_preds[0].cpu().numpy())"]}, {"cell_type": "markdown", "id": "e44810c4", "metadata": {"papermill": {"duration": 0.202216, "end_time": "2021-12-04T15:59:27.986209", "exception": false, "start_time": "2021-12-04T15:59:27.783993", "status": "completed"}, "tags": []}, "source": ["You can see that the predictions are almost exactly the same, and only differ because of slight numerical\n", "differences inside the network operation.\n", "\n", "To interpret the model a little more, we can plot the attention maps inside the model.\n", "This will give us an idea of what information the model is sharing/communicating between images,\n", "and what each head might represent.\n", "First, we need to extract the attention maps for the test batch above, and determine the discrete predictions for simplicity."]}, {"cell_type": "code", "execution_count": 39, "id": "a3aa3c54", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:28.404309Z", "iopub.status.busy": "2021-12-04T15:59:28.403839Z", "iopub.status.idle": "2021-12-04T15:59:28.411125Z", "shell.execute_reply": "2021-12-04T15:59:28.410728Z"}, "papermill": {"duration": 0.214883, "end_time": "2021-12-04T15:59:28.411233", "exception": false, "start_time": "2021-12-04T15:59:28.196350", "status": "completed"}, "tags": []}, "outputs": [], "source": ["attention_maps = anomaly_model.get_attention_maps(inp_data, add_positional_encoding=False)\n", "predictions = preds.argmax(dim=-1)"]}, {"cell_type": "markdown", "id": "53fdeaca", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.203066, "end_time": "2021-12-04T15:59:28.821317", "exception": false, "start_time": "2021-12-04T15:59:28.618251", "status": "completed"}, "tags": []}, "source": ["Below we write a plot function which plots the images in the input set, the prediction of the model,\n", "and the attention maps of the different heads on layers of the transformer.\n", "Feel free to explore the attention maps for different input examples as well."]}, {"cell_type": "code", "execution_count": 40, "id": "73a6c7b3", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:29.243841Z", "iopub.status.busy": "2021-12-04T15:59:29.243370Z", "iopub.status.idle": "2021-12-04T15:59:32.280160Z", "shell.execute_reply": "2021-12-04T15:59:32.280550Z"}, "papermill": {"duration": 3.254644, "end_time": "2021-12-04T15:59:32.280702", "exception": false, "start_time": "2021-12-04T15:59:29.026058", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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MAnb/UFQQrSWxSsxc0m6xNEkv2cM4jZdrtY7nqXuEWaN48OPY0ymB6T0ywWazvTkwqz3ODpBOuMav6tM7lSQDibqQ80KlCuse1CWijyvbmFKdTi3lGJef6Ht8jgA9xd6N3NHHyxeMRrUtqNFqlTgPMBNT0ZVxq5GBlBMGQ2dHVzQLpcjKekI1wo05oZm9w3BgA8uzhKSlrVK8D2UB6AJd2jrjNEqCjgDC3yiM9foGqvxeNwplbmRzdHJlYW0KZW5kb2JqCjMxIDAgb2JqCjw8IC9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9MZW5ndGggMzM0ID4+CnN0cmVhbQp4nC1SS3LFIAzbcwpdoDP4B+Q86XS6eL3/tpKTRUYOYPQx5YaJSnxZILej1sS3jcxAheGvq8yFz0jbyDqIy5CLuJIthXtELOQxxDzEgu+r8R4e+azMybMHxi/Zdw8r9tSEZSHjxRnaYRXHYRXkWLB1Iap7eFOkw6kk2OOL/z7Fcy0ELXxG0IBf5J+vjuD5khZp95ht0656sEw7qqSwHGxPc14mX1pnuToezwfJ9q7YEVK7AhSFuTPOc+Eo01ZGtBZ2NkhqXGxvjv1YStCFblxGiiOQn6kiPKCkycwmCuKPnB5yKgNh6pqudHIbVXGnnsw1m4u3M0lm675IsZnCeV04s/4MU2a1eSfPcqLUqQjvsWdL0NA5rp69lllodJsTvKSEz8ZOT06+VzPrITkVCaliWlfBaRSZYgnbEl9TUVOaehn++/Lu8Tt+/gEsc3xzCmVuZHN0cmVhbQplbmRvYmoKMzIgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAxOCA+PgpzdHJlYW0KeJwzNrRQMIDDFEOuNAAd5gNSCmVuZHN0cmVhbQplbmRvYmoKMzMgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCA4OSA+PgpzdHJlYW0KeJw1TbkRgDAM6z2FR8CPSLwPx1GE/VvshDSWTp8Rygdr5AGC4Y0vIfiiLxmEtQsPKvtIdNhEDWcVJBPDryzwqpwVbXMlE9lZTKOzQcv0re1vgx66P92OHAoKZW5kc3RyZWFtCmVuZG9iagozNCAwIG9iago8PCAvRmlsdGVyIC9GbGF0ZURlY29kZSAvTGVuZ3RoIDE0MSA+PgpzdHJlYW0KeJw9j8EOwzAIQ+/5Cv9ApNgpoXxPp2qH7v+vI0u7C3oCY4yF0NAbqprDhmCb48XSJVRr+BTFQCU3yJlgDqWk0h1HkXpiOBhcHrQbjuKx6PoRu5JmfdDGQrolaIB7rFNp3KZxE8QdNQXqKeqco7wQuZ+pZ9g0kt00s5JzuA2/e89T1/+nq7zL+QW9dy7+CmVuZHN0cmVhbQplbmRvYmoKMzUgMCBvYmoKPDwgL0ZpbHRlciAvRmxhdGVEZWNvZGUgL0xlbmd0aCAyMTUgPj4Kc3RyZWFtCnicNVE5DgMhDOz3Ff5AJIwveE+iKM3+v82M0VYewVyGtJQhmfJSk6gh5VM+epkunLrc18xqNOeWtC1zgLi2vC+tksCJZoiDwWmYuAGaPAFD19GoUUMXHtDUpVMosNwEPoq3bg/dY7WBl7Yh54kgYigZLEHNqUUTFm3PJ6Q1v16LG96X7d3IU6XGlhiBBgFWOBzX6NfwlT1PJtF0FTLUqzXLGAkTRSI8+Y6m1RPrWjTSMhLUxhGsagO8O/0wTgAAE3HLAmSfSpSz5MRvsfSzBlf6/gGfR1SWCmVuZHN0cmVhbQplbmRvYmoKMTYgMCBvYmoKPDwgL0Jhc2VGb250IC9EZWphVnVTYW5zIC9DaGFyUHJvY3MgMTcgMCBSCi9FbmNvZGluZyA8PAovRGlmZmVyZW5jZXMgWyAzMiAvc3BhY2UgNDggL3plcm8gL29uZSA2NSAvQSA2NyAvQyA3MCAvRiA3MyAvSSA4MiAvUiA5NyAvYSAxMDEgL2UgMTA4Ci9sIC9tIC9uIC9vIC9wIDExNSAvcyAxMjAgL3ggL3kgXQovVHlwZSAvRW5jb2RpbmcgPj4KL0ZpcnN0Q2hhciAwIC9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnREZXNjcmlwdG9yIDE1IDAgUgovRm9udE1hdHJpeCBbIDAuMDAxIDAgMCAwLjAwMSAwIDAgXSAvTGFzdENoYXIgMjU1IC9OYW1lIC9EZWphVnVTYW5zCi9TdWJ0eXBlIC9UeXBlMyAvVHlwZSAvRm9udCAvV2lkdGhzIDE0IDAgUiA+PgplbmRvYmoKMTUgMCBvYmoKPDwgL0FzY2VudCA5MjkgL0NhcEhlaWdodCAwIC9EZXNjZW50IC0yMzYgL0ZsYWdzIDMyCi9Gb250QkJveCBbIC0xMDIxIC00NjMgMTc5NCAxMjMzIF0gL0ZvbnROYW1lIC9EZWphVnVTYW5zIC9JdGFsaWNBbmdsZSAwCi9NYXhXaWR0aCAxMzQyIC9TdGVtViAwIC9UeXBlIC9Gb250RGVzY3JpcHRvciAvWEhlaWdodCAwID4+CmVuZG9iagoxNCAwIG9iagpbIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwCjYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgNjAwIDYwMCA2MDAgMzE4IDQwMSA0NjAgODM4IDYzNgo5NTAgNzgwIDI3NSAzOTAgMzkwIDUwMCA4MzggMzE4IDM2MSAzMTggMzM3IDYzNiA2MzYgNjM2IDYzNiA2MzYgNjM2IDYzNiA2MzYKNjM2IDYzNiAzMzcgMzM3IDgzOCA4MzggODM4IDUzMSAxMDAwIDY4NCA2ODYgNjk4IDc3MCA2MzIgNTc1IDc3NSA3NTIgMjk1CjI5NSA2NTYgNTU3IDg2MyA3NDggNzg3IDYwMyA3ODcgNjk1IDYzNSA2MTEgNzMyIDY4NCA5ODkgNjg1IDYxMSA2ODUgMzkwIDMzNwozOTAgODM4IDUwMCA1MDAgNjEzIDYzNSA1NTAgNjM1IDYxNSAzNTIgNjM1IDYzNCAyNzggMjc4IDU3OSAyNzggOTc0IDYzNCA2MTIKNjM1IDYzNSA0MTEgNTIxIDM5MiA2MzQgNTkyIDgxOCA1OTIgNTkyIDUyNSA2MzYgMzM3IDYzNiA4MzggNjAwIDYzNiA2MDAgMzE4CjM1MiA1MTggMTAwMCA1MDAgNTAwIDUwMCAxMzQyIDYzNSA0MDAgMTA3MCA2MDAgNjg1IDYwMCA2MDAgMzE4IDMxOCA1MTggNTE4CjU5MCA1MDAgMTAwMCA1MDAgMTAwMCA1MjEgNDAwIDEwMjMgNjAwIDUyNSA2MTEgMzE4IDQwMSA2MzYgNjM2IDYzNiA2MzYgMzM3CjUwMCA1MDAgMTAwMCA0NzEgNjEyIDgzOCAzNjEgMTAwMCA1MDAgNTAwIDgzOCA0MDEgNDAxIDUwMCA2MzYgNjM2IDMxOCA1MDAKNDAxIDQ3MSA2MTIgOTY5IDk2OSA5NjkgNTMxIDY4NCA2ODQgNjg0IDY4NCA2ODQgNjg0IDk3NCA2OTggNjMyIDYzMiA2MzIgNjMyCjI5NSAyOTUgMjk1IDI5NSA3NzUgNzQ4IDc4NyA3ODcgNzg3IDc4NyA3ODcgODM4IDc4NyA3MzIgNzMyIDczMiA3MzIgNjExIDYwNQo2MzAgNjEzIDYxMyA2MTMgNjEzIDYxMyA2MTMgOTgyIDU1MCA2MTUgNjE1IDYxNSA2MTUgMjc4IDI3OCAyNzggMjc4IDYxMiA2MzQKNjEyIDYxMiA2MTIgNjEyIDYxMiA4MzggNjEyIDYzNCA2MzQgNjM0IDYzNCA1OTIgNjM1IDU5MiBdCmVuZG9iagoxNyAwIG9iago8PCAvQSAxOCAwIFIgL0MgMTkgMCBSIC9GIDIwIDAgUiAvSSAyMSAwIFIgL1IgMjIgMCBSIC9hIDIzIDAgUiAvZSAyNCAwIFIKL2wgMjUgMCBSIC9tIDI2IDAgUiAvbiAyNyAwIFIgL28gMjggMCBSIC9vbmUgMjkgMCBSIC9wIDMwIDAgUiAvcyAzMSAwIFIKL3NwYWNlIDMyIDAgUiAveCAzMyAwIFIgL3kgMzQgMCBSIC96ZXJvIDM1IDAgUiA+PgplbmRvYmoKMyAwIG9iago8PCAvRjEgMTYgMCBSID4+CmVuZG9iago0IDAgb2JqCjw8IC9BMSA8PCAvQ0EgMCAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+Ci9BMiA8PCAvQ0EgMSAvVHlwZSAvRXh0R1N0YXRlIC9jYSAxID4+ID4+CmVuZG9iago1IDAgb2JqCjw8ID4+CmVuZG9iago2IDAgb2JqCjw8ID4+CmVuZG9iago3IDAgb2JqCjw8IC9JMSAxMyAwIFIgPj4KZW5kb2JqCjEzIDAgb2JqCjw8IC9CaXRzUGVyQ29tcG9uZW50IDggL0NvbG9yU3BhY2UgL0RldmljZVJHQgovRGVjb2RlUGFybXMgPDwgL0NvbG9ycyAzIC9Db2x1bW5zIDY3MCAvUHJlZGljdG9yIDEwID4+Ci9GaWx0ZXIgL0ZsYXRlRGVjb2RlIC9IZWlnaHQgNzEgL0xlbmd0aCAzNiAwIFIgL1N1YnR5cGUgL0ltYWdlCi9UeXBlIC9YT2JqZWN0IC9XaWR0aCA2NzAgPj4Kc3RyZWFtCnic7P1ZjyRJlh4KnkVEVNXM3D2WXCqr2F1cLsm5d4AhB5gLAvfP8c/Ny5AvnBk0u5vdVZWVW0R4+GaLLiJylnkQNV8il64eDDAAkZIRkebmtqioiJzlO985B//zf/7P8Ov4dfw6fh2/jl/Hr+N/lkH//76AX8ev49fx6/h1/Dp+Hf+/HL+q9l/Hr+PX8ev4dfw6/qcav6r2X8ev49fx6/h1/Dr+pxrh+Q9vP//XV69/C4CAP/FSXP/9qd8BAIB/8mL3p7fg41sRARz8/OrzmxDPX/H4DT/+In/xNfj09N3dtx+v/9B+MpA7/4ODISC4u6qamakDIGOMKaWOOTKFEFIIMcbAhCUvJWd3QEdwBAd3V9G8LCVXMXfE2HEawrAN/ZC6FANH9uBCLgCGgOTu6g7u6BRj18WeKCARAiBCDH2gOI7TNI1iBcg32yF1napOebq9vzmN4yV/yedF+Vf/6l/9+3//7wHA3RF/9rb/fz3cwcxUtZRSSq4iZhZCSDHGGJnD+ipzAEBEYmJmon/aHHz//v1/+2//rT1m5v/r//5/DMPWzN0fVx0B8Lyc7uDgBu02ObgjICIgoAO4u5sbuCM4IgEiOJ63GAAgAj6/P+6+/mb9/MeNBQDrTvsLb9Df/j//6/Hw0H766quv/sN/+A9/4TufPcYf/crcitmyLPtxvjOXEDlwZO7cQM27eNH3V2a16mnJN7ncI6bIu6H/vOteE6ZPju0/Oe7v7//Lf/kv681H/N3/5d8PlzvEdouBEBix/SFo9xfaLV7X6Xw2HQCwTcAN3Nzh8YgDPt5zd4fzlwG2JQUDMFiPvfsnt2i9UW1lvH3HT93Q7/7mHw4fb9sz2+32X/+bfz1N035/cofAAZ4JCHc3B3f3JoVw/XR3A/fz5yEAuPlZLAEgtrcAPn3pOqOfGatw8zbR9QkH9PU+uamqVEQYuu5iu3316srd/9///W/NrH3Cf/yP//HLL7/8qc9ut7DNCR8vBBHdzUFrXUqZzappPR4f7u9v7+7u7u7uD/vDPOfXby6//M0Xf/Uv/vrL33wVOAKAiIjUnLOZhBi7NAz9qxg3RISI7rSu9tNper5O/mOB/Pd///dff/11exz77e/+t/+DiNfbcn4tIlD70875k9h3B3NTa/IFkIjRwbSUZRyPN8u8N5kIJHJAcJWipu5ugI4BOTL3sdumbpe6TUwDAjmgOxgQhcAhAhIitVvYlvyTP21YLe/+5v+utbQfv/wX/+7153993sNP647PhAi+ECjrHnhUcojPBA76+TU/vn/PJNH6Mv/pX56XAPHpeXz2W0QkhA8//PnPf/z7x3e9kBFXr3/71V/9B/zJq/iRaseXv35+s56Egp+ntEpqPB8wf3688RMT4PHnT8d5X/iLubu7uz2qdgd98G8dKiOBu5mISJUKhETc07BJ2xi6wJ2ngbqBuhQT13HU0wkM1q3oYGJS6jwfT3XOVZ1wiGkXu3TR0+WQNn0X+ug9lGAZXBkA1KyagAE5Dl3ablIIHVEAcEQful2k/v4eHJeigMEv36TtbshS/TjV8Xg83ez8LeO6KF999dV/+k//CX5Rta9ibJWlnwogf7z3j894k8jtLSAqtdRxHMfxVEpV077vh37ohz7FBGcxjYjMHEKIKTLTeQkelw8/ubz//t//+6NqJ6L/0//5P1y9emPqti48elPaTR+guamZOBggmIEZACIhESESuruZuSu4ExIiI3ADnFa5fb6CdlFP/z3uGfRVciP85ard3f78j3/3qNrfvn3bluPnx6NAfPxqfPmku4tZlnoq+e5wpNuHg7p2PaXUpbg1AxG42Hx5efE7h5rL/f5UjuMDU+jS7tXl7y53/5L5grD/KbztZ+f17bff/Nf/+l/bkUOAz//N769++zlRk3zOiJEoIkaigBgACZ4OLwEQng1zBMdVr4u7mq3mFq6G2lmZPjveiIhgCAqg4LaaXT+hKR/1a1PtP36FA9z/8OFRtfd9//t/+dd3dw/mHxGoH7aEdN7X7u5mYGZu3q4BCXHd/vZctYOul9mUmvnZKnh+nNx/8pLg0e5xBEByRGyuATpY+y4peZ5GMB267vWrq9//1b8Ah7/5u79/VO3/9t/+2//1f/3ffmq6tkrSdqlPhjG6i1oep4fD6WPNYy3z9Ye51nJ/fz9N393cXh8Op5C++l1IX/zmX/+7f/d5Sh04lpKXPI/jKFr6rttur15d/PV28ybEyBwcIwAjEAAR0nk3nY/UC9W+PjgcDo+qPaTht//uf6cQYZU5695nBEIIBAxg5qbt3LeDIKJVtZo7IEZOBChlmU63fI34UCQv5DAkJPdSVaSauwEZIoXEKfab3ebis+329bC5IgzmqI6GHLohdBuigMToTTKgAziiARqir4YmIEBdpg9/9/94VO2vP//r3/+7/5ubudmTiYqPcubpp+fKat1ijz/So56H1S7DT5Xycz2L7ep+UrWvGtfbDn00Uh8/AQEYgQkR4GdV+2pLna3Qp7U9P8KXr31a6Je73s9n9GkmjmeHzH98Rl5o7E+f/unvfP5y/5GoYCZAYgzoboTMHFOkwLFPMYUYmQgQRHTxRcE79wRoaYgIQEgpJWaWWvPMGBUHDHN2hN3VcPFqs7vs+iE4qlgNFDkEtKDAqiAGVczN0AG5UM3RgUhEq0o9jSNBmMd5LrNYRvNxOirkonWaD2Yl0AsV+UJKvtTufnaNzFxUa1URMVUHp1WENV/XmTml1FxtEck5l1JqlbZjRbWUcjqdpmlydw5hW6WKTsvCROCOSKkBHV2HSF5qOyhmpqbtVjNzjBGfbfxPFqRdij1Nx61JC2xC0cVyzlOtS5GlylK1ACJT7Ltd122ZOoSgIqYKDoQUQs+cELE5SA7Nh2wuWtP9tDp/q9Njq7fjiPjp9vt0nHXxT+5V+DnNhC+OgT96uG7gZlDcq6OUcpzGj4fDD/uHb/b77x+OPxjl1HNKXQgDYiCMl9vfjdNfd2lL5NPx4bh/IDz03UQQkXhIX6X4higgrCr4bD7/7Lw+XQ5TV3UgpxUVMQABAPOmTxiAAAmAmk26yrRHg9oBvHl5Teqjt7m6A9ij+lnNTjNfXXY7m/SrkfHCxvJHF/WlmHn2CnghIqrUu7u7+/vD8Xjc7V7tLq5CSH7WQgAAjo/2J6KvGEW7+GalNEPPn1kVZ2ddTatK2y5E1JzR5xbLM+9zfSf6avs4gq1zdEJbpvFG3+/vbx5ub06HfUDsuvQLMMAnLtLLx+2moqrmMj3sP/zw/h8P+4/j8XB78/Hm5vr+/qHUMUTdXlDqVf30sP/+ux+Ymd281lJqLqWaKTMPw8Xpcrq6/GK73Q2bi667CKEHCAjhhY+JP3Z0Ec6wzvPrIwRe78WjYnIEIEBoFpOISAVXd6l1KmWusogUVQGAlPrI0bWW+VDzg8rols3Lkiu5mVZ3RQAEBRcXVZACAjp7fpBph0TmoE6OMQ0XabggTkiRiREJm9XKETgSB0daRYP7JxiRm7qKr1boGXeCZ2p1hTWenNzzc2dDvlmJz5Gc8205vwHObsb6FnzmdDzZ0c9/PBu9iHCWYE/f2CC3wC+20SfIXkOimxR8scterurLJ5/succnmul89hgdAd0/PZvP3KfnFuEzSfSX+FfnFz9fHiRCQCJEAgpEEBGJYpdS31FwJGuOoEqWUhGV2ThQlwIREtOw6WKMORPNAMlwABrBwXeX3e6yG7aJGapkdw8YEQMgm1sRz6UWKe6K4EAEHKIrE5daSllA3RWkSC1qLsgOk2QN4mVeZtNKP9I6j6r92eRWU8bM1KwULbnOc8m5lJrdtAGTZupqbpZS2mw2IQR3n+d5HMdpmpYWekASs1rrOE7zPIfAXd+XUpaSzzsTYgibYbPZDO4uIu7WnP6G5BNjjKnvOyJiZviZcVbpq0I3bwESUVcHBbRSpmk6jNPhND1Meb+UAyAG7i92by93X3TpkmmQKlKKqRPyZrjs+y1zIAJAMS9qWVVMgSimuGHqEJvyg2eCCZ9k8S9tqXaT4dGv+idW5GzNPCJS7XUACGZuoroU2YudzJdxurm/+/PNxz9ef/jD/cP3x+kGQ+02FGJkToG7EPqLzXcPFz9c7r4cuqv9w/XDww2CdH3vyBQi7gbmHQIAhfVoO63a9S8cbmAGBGDU9JC2mbobAAMyACMSAAMSogPSWTE2weDoeAadV8n45KW3O/JkGzU32c6oxfnmPL/eRxXsv6DaAfD5jVeR/f7heBznebm45IvL16kbrKnzFaF8lJnrhSEAUBOOQNhedBaweDaXEQCgSJmXxQFC4BhSiBEB2taHF4Dj2Uzx1e9yRzu/CNEZfTrtp9Pp9vrD9YePx4f7y91mt9v9omp/uifg8BKeWQ2jUvLhdP/h47d//va/f7x+d3i4Px5O42ksudRaOWjPwKEU2d/ef6c2IYGbiVYzdVsXqEub6TSexrurqzdXrz57dfXlQFdM3dmb5PNlPIIcz5UAfbrfEBhh9ZHPqsi9WVEGqqpS8lLL7FbcyzIfl+VQ6iySRQq4dWmIoSMEKbMsDy4jeHGr1Rzd0Y0QkIBWM0FBqlj2erJ8X2MHiA5uGAC7ulzWfEWUECOHwByIE3FHactpQ9BTSNCs/4bQPhsERqArbPO4Oc6qm1b454XmbbvsHMlqOvLJU3/CMJ4pbzzvzbZBCVdlSavF+YgTnN/VPvP5hzyzDAICA8RfVO2Az/6Fl6uHT9fzYv89W/BmUQh4NRAEJwwOEVpYpd1ICAgBEQDNXVogF4EII2LAF3sIf9kXeZz/j1/jhuouJoGoW33OLvYp9QFZHYuZqVrJVotSUI6WOgqJQwgcGRkVxVQ9GW+pC9GjqAnGWnSCXIhAtZKzE5AWz6EsPs8iagpKBMzUoPlkfYzJwSCouWjVpeRlqeaKBMU5VqJgVatZ9SZw8dM5fuKyt2fUdMllPC3juExTnqdlXqZaM6C6i0oxNQQY+uHy8pKIaq2n42l/2C/LUmttCGLTYaKiqoF5ztOyjOmYiIiJu5j6rgMw0XI47k21SjVVQCJC5jAM/W63Y6au635pmQiIziEDbEew5nI8Tg+5jKKLyFLq9LC/ubl7fzh+PM13Zs4Ury4/f331m+3mTZd2tUgpxdSY05vXX72++nzYDCFgqaclH+Z8KDlLhS5dvn711W7zNsUdUwIgXLWFP+7nf1Ksnl29v1hZfrJezb52dSzmxzl/uLv/w3H8Ptf96XT3cP/x/u7m/vbucHg4TacQfXsZYlKiijgjhBucAl9f7j6/2L6RfMrzg5ukrgPa9ZvPt0NGRKAGP+CPjuxfcoUt9vk0P3e3VWtj0x8GTgACyICMRA4EAPgshnz+d/2A8207q8mne/Ho4pGDPb32+RU/s/H9mavyU1cOTy8Fd2fmrhs224vdxVXqtraaHGcv6Fn4cNW9Z3lKBHi+Ce011rxKUzU1warGzKnfbIah7zoAMH2u1Zsb5U9IpwMArfEAaNQRD+RMcLHbDUNPBOYqqlXln9qDT1/x/D4gkIOplv3h9pvv/vHrb/7+z9/88bC/K0sxhRh6EbQsqqZWp+n08OAAtsxjC14xAxESIRGFGKDWmzvdH+6G293bN1/+9rfz2zdf7TZvKRKsAa/nx+UT7f4j6xaAGZgRn4WEHQBcwUTqVOfTeHyYxj1ARZdaTiWPqotpMa/upjMRBuYAppL3rguzATFoIz85ETADoIXV2iBERxSX01KObVEpdMhDmaQsJzMwhxgohICcOA5x86bbvu63ryNeMCXA0I7S84mkSLue1FzN2146808AYdXrLyOQzzX0qm5hBRThHOeB88k4a/Qnz7+ZBXBW8Ijn6NDZHHgEAPCZdn+m/hsgD8AvY3QvVPtqZDweix8t3tPhOpsUz52F86lfxA7ui3slSAQdgDsoIBFGpi3i4ODuojapZXBADAG2TOkRaGiX+k/K1vUq/NOrNUU1cDUKhF1Iqd9stmmIoWPk6ohmIqKA6mAhAkfj6CEBRyBGcRWV4lVRMEFkhhBFHNwEMooCgoiAYjFFDZ45LzbPxRwoYoghhQBuKmJUnQZmwgBoDqRiJZdZVR2dBLhA7MhBa82qCvSpj/5psMPAwdVMREupx9O435/GUx7HaRyPOc/mWbVILe7GQJvtdp5nJMpLPh4OD/t9zllVHAEIiYk4EJ0D20gjExEH5hjjpu83w1BqZqaSS8651mJmzCGl1PfDxcUFEaWURIS5hWV/RiIj4BMsl3M9HsePN3ffHU6303w0y4714eHm+uP39w/Xh8NtrRWcLy/fvnn9+eXF283mUqqUWlQhcD/NN7l8tdvtYqQ534/T/TjdL8sslTbDZ+bFwS82lBIxBn8SkejnowdPpvpZfr24zz9rA/xogv7y+RapFfdquqgcS/74sP/63fv/dnv/x3m5P42H42Eaj/l0kPFUplFDQjePSYnU3VW91r3Kh93m5mJ7RW6g1dVi6vrtZ6/eHuWqABh4NX902c8cuL9MwRNgYza92FzPqEYGYL6CiupAris5As5SjcAfIXdYA0Pm5uBERERnWffoLiM5GDo5PuK4T8LpibnxzB36ZCl+tBoIwEQhhK6Hfhj6YZv6rdrKBYPHCAL4WZQ+qXZq0yAgADxfkZh4QSlWi845n6YppdRvt8TUdYmQTD+9Jj8vefseQHTHc7QfECCQmZZhGLouciBANzDzn0CDfmKGj1903mLuXks5jQ8frr/709f/4+tv/vDDux/yMpFTFzcpbgjBdDElc8i5nI4AjjlnQEfyrgtdF4gwBgbqzWQeT1IphHQ83gNaIwNvNxQCIxI4PVsK/AW93gYTMDmc4x2OAO5qonXJ88N8vD3cfzwd7hCESU0Xk8WtuFcHdVARc8fAHQKUPJkJAhGyO7mdUV8EakyAM/RCaKXUkgsgMofgwEAiVQSqVBUJAWIgoEhx0+ejyUwuaBV5QO6YI2p5PqMu4LZjURNzc7BVuz+p1Ee9/mSErvbiaiQ8AkFnvX5G5vHlf48O/dmMInh65knOPNoHq5Puj4bBozrm84l+Pn6CanvW6z9jOT9+IAKc53LeeVl0qvohy3eq92ajG4ElQEKiEDYpXnTh88hv1LzKvNTrKvdIHjhFfhVw44CIIfKGeUO4QYi/4Dqdj5f7j17h6oTEMaaUYoycmCJSJI5MAZFRTVAFWDkZERqZgLq4lixmBm4OqgYAgQMxUeCAkQiYiJlVLS9a5iq5oDJDlOpZKxGnkGKPXU+I7p4hoBM6JsLYkL3CunipKtWKVfWsOLmDlVxNwMOPb7qf0RE0dVETVVUV01JtnsvpOB2Op/E4TvOY8yyymBY1RXAmVnN3CCECuJiZu5jVKgYGhCFwcHBiw8Y8MlN1sFVopphiDCEQoqqaOYITc0pJrUfGmMNpCjGG1KUGzq9C8xNLy6uDAKGJTtPpNN2P0/Xdw/fXN3++vX13/3BrXmNHpUzj6TgdD9NhmsYlZzk9TMeHh4vL95vdgA3gM2JKy/zx7vayH/oQoMpYZRSbzcwsTvO9g9Uq+hYudtClLVNc94s/mq1PHvwvba1/xjjHoLyYzyL7Uu/n+XoaP5yO7+/vv3v/7o8PD++XOhYpIlaqi7qbExII5hNqcgpqZtIivADLfLJaTMWqgFHfb18f5uVUNWfXsYqaiZqCI9GGeRPChjme8ewXDt8ny8EIjNg8DH+mCR/p7mds3AFAzEytllpLAYAQOKWUutQswfYNolakzstcag0hxBi7LsUQ6ZmbjYQE6O6NQ/V0n9fv9Uc/4Wf92Zc6H4lijFGcq+FKGngMCPuj2l2PzjOAA+G8/A6GQGfB6Y4KPud8v7+/3z/sD/suJbFi+obQ+9gzhTM2+oiWv1h/eLqFZ7iCCAnXhBswAMOVxfXPGuveV9H7+5uvv/m7P/zpb/7wx7/9ePPDYb+oGIFbB5Yoz1QyEw0xxcjmDrUCzmquAKqiUoXIQiR1I4zzpCW7GuZcDHxZ8jKXz9/q1RWnxIQR2pSe/LnHG/tjx88DOoGZK7jZeXElT/NpP+2vp/2H0/5mGh8INQYgMkQFF3BBVwBjAHN0KaK6THOVQkQIZI6mqGaAzowcMASKgQJTjDHFhisFcHRnN3A1BOSVi2moZu4O1aTMJlpmmfexvyQeKGxiv3VrIaN1JIZt9EogBmKg7o4Oj7p0tQUf4fjmLPgaAUJ/dNMfjYBnEYpHA+HpSDbs7QxuITUH/eWRfYIF8DFg/0yxO9D67S8W5aVqP2MNj1bBj8dzJ8VhzRVrsSWxU64fc/1mqX9Qu3U7mYIJN6Ua087gFeKCmEW8yLyU75f6EUmZYsRXjDsAYh767m1HbxAjYPjEhH+Bgzy7phcvcgAzJAghxhgpMjI4maPaut/QAA3AAxGCm1dTrQ4VliUvpSC2iAoSUUoQmFTb6hKFEEIAMERRk5wrqMbYQg2O7BwhdJQ2DOAmSlSACIDcGT0gIDqBgzXivlcBURBzNTN0+jQI19hyYI03LuK1Wqm1ijr4kuu85HGaTsfT6XTMy1zKIpLNBMCJyAPmWmGaYkyBWcydCBDNQd3BDRGJ2nZBEZMqpWTRyowh0EwcGB8p0iteFqOqOHhL6mthWg4M4JsNegiIK8PucQaH0y1GT3FTq47z6WH/8ebu6483f/r48euPNz/c3t4A2rBNCK4ieVrqJMuxTnOuWURKkdNcYqOAgjFCmE43dzGFQMTuIIgVWZEQsJvnUcRFzJyq1Ivd2xQHpojIiLFxohAdH/3S845+dNsJ0OHH5uJPbkI/v7NFlbXIsZTbeflhmr4/nr45HL7b33+4v7u++3h7PJ6KVgOHwCqogm6AxqowF+RoIbmBqToTBYZqRcoiVbSqK5eC+7vj/ub+Yfs+UlIrIotqccCYrvr+zXbzm667QKIn6O9Rm70cATDiWfefhRM6ED4THQDu6OhFZJ6X4+l0PJ6QsOu73Xaz9U2MoRGU3CHnPM3zw/EwLXMMoeu7i91u6IfIzEiNWUfMgOi2gteISIjQ9D160/b4FDr50WX/SOcjAFMgEkRwcHNTd7PnQglWbOHsBjVbxBtuadDA+2pN40KVOuX5MB5v7m9vb28e9g/MeDg95GUCs1dXr3bDrvHpmlh9dNn9UQK5w2PW4Pqd1F5p7u7aLox+YW89fTA0xfFoRJhqzsvt7fU//uPf/vFPf/f+w/en0zFnNwFw1VprrLWAW+y6tNkRYgGqhBGcTd3MJACAqmUiN8PAOs+SF60CJVczURFTBA8xbpkSMiGRtzv4ZNP8nF3ioIs5qhZzazFERMzTYTzcTofb+XCzTPd1ORKZK4QAzOAmtsoKb9kW5qaiqiJV3N0MVF3EimiTXinFvk9dijGyOQEyOCIFgNUOAFVaybTu5G6qVc3NAGCZl3E/H+84boD6kC763Wsk9mesGkZP7ATeqAPiqyGI58A2PrrOj7bpE4QAT7qcHr325744fGppPx5Uf8SfH9X2099PXXlYLd0z1PWEJTyOnwDkn4cBfnIJff1iBxD3YrCYV/M654+n6Zsi35u/R5wZjRCACLAiBrNc5AhwUH3nkMwd6B5pL1JydZN7gBQ4dt0FoBCHQDvE4Rkq8Lj1P9Xuz42gdZO5Opi7mqsBqWu1bKWiIoA5WhUpUhtcSEgE6GqiusxLLoWIiBmJmDgHISIHQwAOlJJvBmYKw7BFDwSLG8QYEdDUoEFIEUNkRFM2AkdX06oVpZplyHmpNatWc21cC1OvqqYCZ5Dz2UzAzdWsipaqtVotVkWriJguyzJNy5LzUuZcllKKiIADESNhjKHrO+YACEVqLkVVAYACcwygoKbuaAYhhBiSW6mu7gTeQnKMAGZA1O46ukOV1QZW81p1mpYYj+M4LUv+bMlvP/MuJQBYlvw4BXP7H3/4f129fv329W9T2uS6jNPx/Yfv3r3/093tt4fD3TyNzERk6F5F6yImQBgiw9D1282w2YS+ZxXRKloNVLRKZUZ0JGBGZHMQICfONboKlpJP4/71q+8/f/vVbvu673Yp7WLchjhE7hwIWu78T5Vs8md+yj81zi9zcFeR6Xj44WH/x7u7fzgev87l/TTf7O/uD/txOtR5liKu6B7EHa1iLVQmqhlFgCOmnjgCkRuD6BoZRWcEVIN5rjcfrpn+djqO76/+wUzUithCjMPu1as3f/W7r/4j8+9D2CLHX77ogJgQWwTRVtWxSm5sqOB5D6rDMi3XtzfvP958vLvlGC4utq+uLq+uLvsUIzMCmfppnPaH4+3+/jiNHLnru9evXl1eXGz7TWSWUhAgpo6JWxI5E6YY+64LGFZLaiUq/6RePzsnL1fFHVRN1KqqmIk7masZIKBbA0LR8ZzEho9u0VNRBPBSyzifqmQgqCWfTqf7h/uPN9c3Nx9vbm5KKcx099Xvaqnu3nd9ZEZYzX5wMIe1WsP54s638/ylqzvfbA13N2zg8l8+zrGKKvl4evh4/e7bb/90e/sRnCJvlGq2ueS8TCfC2nXdMGyurvqLq2i+qC8pxhC51qJWuo4B5eFhKjWbhpSgZJUK7qxq0zze3X0Ei4T9ZvMmhG4zEGFadfRz7f5TnE03LdONgeU8tXw2RCSkeZrm474ue7fM5CnRmpQvWoqKiJkyE/Hq1RAxBuw3KSSuVUqpVWuRuiyligLAMPQxRGF3V7OqCqER5QgJ0UxVRKnJKwdQtdqSn6uIqrkThw6pM+xCt9tcTiH2zwmz6IYq5EAO3GxfdECnNZTzqEdXDxhW9AqJms366KafEfQ1dwIe/z3vjPULH3/zpMsfN/2zZ5708bN3PsJfP/bEP1HtjxeNL3XoaoQ+k3roIGaz2EH0oeqpyjLNH47jN+Y3zIcYnAMjMhEBGngV0aomcl95CGFD1CFl5rmWXGst+eAWUtdjMLU37sVBAexsFD3ekCdz5vF/n54UhBjJ0N1FDEkBxSwjCQC4uYtqESm1AiESB2ICkiqllLwsDVQMHIiZiJEyIjXmfIzsioG1SxxC6AdABDcnbi7lmuAVOgopElmwltKL6uaoDqDuTkqhWZjogLamwT6ad5+cmLMSnZdxznmpuTQdZ6Ky5OVwOo7ztCxLLlmkmCkjEBGHEFOMqUNCEam11lLMWmAUKLAjgJI7iHgICM7gjd/ARE7ExNTqe6wVCtZcOxVANa+iy1KZmZmXJZdSVdUB+q4HhNPp9DgDM/vj13+zudl9/tnd1cXnXddPy/FwvH94uN3v7+bpqGrgXJdq5rWlDygyhq4Lfd/3XdcFDgQOKgombuLu6mbgBuhMCARqFclDMlVQtZyn4+luPL3Py1eXF59thqvN8Haz/WyzeQP4ijk1Djg9M6ReQmx/yXg8WQjgbqXW/eHw3fX1319/+Nv9/k8KD6Ucj/fTeJQ6c6kkhkrQyu+4oApIxTxDXpwDmFDXQ+zMDYwcDSGgG7mBqZvKYX+PrvPxYeg35m4m5gsnunzzKuf9bnPV90M/fBl59xME5uenA5vXjg5oeEben2l3BAcDMc217g+H99cfv33/7v3HjyGFi6uL4zKe8jR0KTK7uGTdH08Ph8Pd8TAuE0XuN/1+mq6uLi8220hcc0b3ru+Zg6sRYuKwGfrLi4uh67sY6FwFqd17fALQXyzIj+dj7XSo5SpzEQUxa7vXCYEIyB9ztNY0NjcwcCKERliQsj8+HE/7InlZpnEc94f9w/397e3t7e3NOE6mqlV324ury6u3n30eYgQkUXOT1UM/U/GbSQi+khAaaGAGq+q3M7bp/iOB9cujpWRozuN+f313/+Gwf8hzZmbClbUP4GpV3Ychbbb9Ztv3QzBzNY8xhBCY0ZyZvYqJQF4sBXO1UlUF3FzFAMTNa4Guu/ji89/tthep6yMjQHAneJROCD8JaZnJdHinrjmPjYqPSAhUcs7zJHkyzQhC7GZmplVKrVVE3D2lGADM1d2IAjNTCJHYHLCKmauKmpipO1hzLgy1xdcEABEZEVoFHDFVsNWHdTfRWmrJpeRccikiRpSIewxDGgDCJiZ7sSKr+dUyP2nlYiAwPeLej5F+REKkM2Uen9Hozvv1rK2eJQQ+Ka2nOjU/cuOf9B386FfPn0b/qWcB4EeAPD5T7QCAz9hGbf/SYyUQ1ZLrYc4/jPM303I7L2Mux1r3HKa+KwjAxIyROKiaqFYpIjUEijH1w0VL241sGtxiy5Hhruv6tI1hxzS07YLgiI2EhuBnfiCc4Ygn5vjTxAjp6tUuyzw1leMxV2KCEIkDqlmpUooUUQcE4uam1Fpa6oiZhhhTjMSMSGbuiCFw6gLCoMFyFjcPjETQDbRmYjSAERCAU5dil0IgAjcxrR6YPTJ05D1Q6niwWHAuuJRs4pExQGRKTJ/WenNANc9F9ofx/uFwPE3TlFVVWhW5WqZpmqZxmedSiokgOAVCYgqMRGoGBmpWap3npUptpOIQQoiJEi1Lzbm4F1NSU3NECszEgYlApJopNZjRW3K6AQCrUpXAgTkEDs31V7V5WlLXMfHtzd3TFNzeffjarv3rr79+/eo3v//9v0GsxBhjdAdETqlzh5ytllqKuDo4ciNIBAQTrYqALo5KYABAjCEwAZi5llpVxdw5IDG5eoXRLKucXO/L8u427bp0cXHx5dWrf/Hm7e/fvv19118xb5iCPwPIfjqE+E+Nsy4y0eOyfNgfvr69/cPDww/H4z3AJFLKApKpZFADjBgjQUBVEHFAQDZHqALmGAoFhsBNkJI5gZGK1GoNO0EXq9P+Lu+N3MndDST2rFb7oX/Y/4/NLnEkDoS0AQw/NxdCZKDVnnyGJ7e/LSIu7qd5uTvsf/hw/cOHDx9vbx8OD8B0WqZ5mU7jqYuBEPNU53E5HMfTOGWrCp6Gri81ZzkcT31MhKilOHiMiYjcjAi7mC4vdm9fv3l1cXG53W26rkuJEWHN9HohAle7w39idRDRHIr4uMjDcY6JwZkDxgAxYKteQY0PhIBrAi6igYMjIaBXrafx8O76u48fPxyO+1LyvMzTNJ2O4+k0llzdYVzm43Qal7lISZ4IfByn02lipBi6vh9S6hqm1dy7dRiCmwPV6iKqaq1WlLVD9Bcrd3dwV/U8LQ83t9+M0912uynlap5zXvI0nZDq7jIhMGLY7brdBQPlaR6RKpKCVgMmRgavsuS8EIYUt4idKWvVZSnTWJH84jKJmFTa7++Ox9v59dvtbhdCACQEc1+xu587Iqb14fbPDqpWvVHQHNxBGzmoZq3ZrJhXM1WtpeSSizsSMWJAYlcQNasVQGJwQKoKYiQGgNylrkuOSDGm5i9ziDF1MXWIUEWrVfcKrk0rIBG0rAetorXWmkuZ5lyqEWPq+4vtbnPxerO9irE7vIiCPALqbcLotEK7vBIwmw8MRLDusKcCEy805sut6quYeUJ34FzMBs5wyE/c3BfP/HNswpde+1Pw/7yE+OiaOK7mqJhVtSXX/TRdH8bvDqc/HcebaR7VCqL0nRE6IxGCsxOaqNUqJdcqVYKbVWIkNEByAwBlNkwO4DEBMQAU0aObEfaIgbkLMBBF9NDcyic3/ifvIuHl1WYurl6XIiICCggQKsVEalartfou1ipqmblqraXWYmoACM6MK7lX1Lylc3hkZg6hRcoVHQN3XQQAUV3DbI7g6ATqzsjEDCDu4oRowB2CEw9dzBgX4gX9pLZUUkKEvksxJJoQzuBQw71L0Wkuh+N0d384nsZ5yU3Jts26LHPOuZaiIq6KCOTYQhEgWtUbW1kVcpWci4qGGPoOUmIMpAalKoAiGQICMgcCNCBQNzFX0bP9+5TP7eDc0j7RnVDE5jmbes6l77qUunGcnq9IkXmcT4eH7y+21xx8t+urzACOSMwhcBKxUkvJnrMhQAgYGGMkRNNq1dEKmYIIVnEzIIS1UF2D51UBmZzB2dVFi/ic4TQf6UQcqEtxc3F5O42TGcawuYQ4DH0jN/08TfNn99fjozN1S8ymUm6n+btpfDeerufpkOfZvUi1MmNZaJnd0VNHnBADQXUBB3LuPFQIFQkQyc28ZmuXxIRIWCvU6kQYI2jVkotWEAE3cgdzjR2HhLuLm8Phz5evtv3mdYi7gB3iTxAyzxP4dNIrGuYrJmcARe0wTe9vbt99/Hh9e/twOEzLbOBznmstS15a+c95zNNxnua8lGIIGKgzVTURXZZMCG6mtQJATAkITRURUwiXp4s55yUvImoXF0jUceAzxgmNmPTMs3kkMT1dMyISmWMROy3l4bSkLhLGGKlLYE7gzkSMT4IMzWzdNSuJUM2qyjiO76/ffby5XvJUSqm1llxzrqqAwHNexnk6TqfDeFA3dHy43+8fDl3sL7YXhBxCwjUj3wEbJmOmDckFqabqZ+8a/9lOO7i5iCzz/HC//2HJ+2EYlrmOx1JLFc1DB7uLLqXEFLsupE5FcqkzB2dCV1ejjiIFAnUk7IehS4E5AqAZluIixU1LJ4TFdDwe7+/uP1xeXW02PRLG0NTc2T38GVBLVY4P791VrTpoExhmQMSM7FbNimpVK+6maqZqZudCiAFhpVWJ1EbmRXQzcCfEEAIEgpbIA0huVmo1Q3NWY3MTKegZoQbGwAwIoE2Rerv7ZiZitVouhmzO3huaPyUrvjwPiI1hhIArNAPc8gbPeh3XJ/2xMvMnUPKPPvLZL89OMz5Bzi3K/gs740e27WOY6Wfop58w5J+j8c0O8bMl4uDgJqqnIvdTvh6nm+Px7nC8Phw/jvMh14KkMQIhBEaCBJYqOaCaqZmLBFNGUjUoWUxnUXczZmTGlJiwOoyiH+ciuV6DE2Lg0Mdw0cU3ka8CXTJtGQeEczUSPFdFeI5RIGx3fVKnAKcxj3OV6gBogtKiOcAppBihVMmlVDGpItVcEYGJOXHXxT51HQcqWtyt69Nm019cbPuha3nZagKAMQ3MvBaKFimlliK5LCV73+vQd+DmroSKwThgCBw1RKGYKc5kLEqNe4zdEPvU00KPqr0BDNOSD6f5cJyOp7mKMQcO7OgymixWRWqtatosFHSoCrpSYxGQUur6vgcMZlQFShaqJgKxWAihFBH1GBAxrBsX3cGk5ipFpTam/Wq+YqtMQxxCIA4t8b3vEalFuEqugWPfN5vmaUt98dlXD8fb+9s/3z68+/b7dLEbVE+5nJgRQkKKqLrW4FElhvMZcq2mxZSYEUuFXKxUNfdS4jAQBUUSA2VGDiHEcL4Sr7nWrKAekGPIXSp1CVI6xqsU3zJf9t1rDM8BKvhZofVzx+z8WGUq5fY0fnM8/qmWOwJFB7AgtdYFy4R59GkUD847jA19RhA3D5A65J7ijkAQDaX4NJkpghGhIUAVF4GYoO+B3K2unQgM3NTMqgrk0cbjeDpdj+Ob7e4upS+QrvCZ//vp1ftazd9WetcTadbBAUjMl1r3p/HDzc317e3heMh5AQA3K1nmZueGQEB5qUWEmLu+L1JVVYoI1b5LCFBLqbXWWhFxQETiKtVMJ8TcTOlam2gLxNQPxGEVqOed80jq8bO+fy6siNgAFlFf6mHKvfcxBSNHBSJbS5FQ44kAgKMjImrTaQjAYI5dt+mHHWJYcrm9u53nydxUTMTBiSnNZZnydH+8/+H6feSoVU+HaZmWN6/e9mkQUa12vshWl6lVxAJ0dHBVM2vgDq2laP8ZxqQDupnUOs3LfpxuSz2l1DFzrcVc+h63O95dxGGTUuzUtNaTegWqxMSEImrmHCDENAz9MAyu0S24kxuolS5lsJRzJjRTEJDTaf/u/Z9T4hDhM7eLC0qRztmVjzrqU0Xibst0qjXPy0lNkYCQiLjvh6EfiAxJteZc5rNVRjF1jClwIgigzBAAo1OpWqUogDFz5IBdBx4ZjQgRsYpOSy51EUVABgzu5q4p+tDhdtMxBzdvFe4AmtQKCIJOBJHARXGei9495AK5yND1+DwdsVmChABIhI4IZ9ecWsT9HLtCtFZZ+Hx+fgTHv1jHp+fPvIxGBGk+5FndPnIrfmFLPH3SGdH/qcTXn4i1N5/48bA7GLg6qFsVHXO5mfL74/T96XRz2B/H6bgsB9NM6ETAhOAolRajupBDdShErfopITIREpqaS64lVzXrex56RkJCzXXUUjKeAAOCIyHHlMKF2Odd+Czx28CvI18ybZkGXPnzn5Y1bTPhQMPQqaFIA27bmWqFN4yIkNEMhEQAwGClZDGHGFPsu9B3qeNIWEG9MiMirLFmAFWttWqElKzvIlF0VHeQKssiKhUw90spQx8YCZ3bnSE1V0ACBkoYjGPHYcFa3FTbn+cmfS71cDw97E93d/uH/fF0mtSNCDtKSKiqoiIq5kZEhNGQzFTNq1RVB1hz1pm7rg9DdVEsWVuh8hU5BOKQOHYhJkAEMEQ3U3MXcTXw1e0/G420Ft5s0QpiDiExk63UZFfVWkVEni0Fvn71FshT98PxeHzY31TpGItKQWDmQBSNBNGQFMmYIUYi8hbm1GpojuZVuQoUcQNVc1XsB0+9hwAciIipYXqCZfFlsmUUF2OQFKzrQcposk/pbtjcDf1n2+1bRIohAJ0rkj3O8p9Q8GdmNAAAupvovOSHaboepw85H1SKVqvZS6Yy83y08eTTDJi8K8DVyVyqm3nbBtxhHMgq6OK1+ry4LOjqrZOBOzkEQhSi2VArbDax73sABZSV6u9o4iXnJS+1NtKDvZACL4e6i6kCqHupYu7MgYmZGAFU5TTNt/vD+483768/3t7enU6jWG2HBxHdTIq4OgKJtFBIiEgAXqqBqtaiNRlR887OYWd3Uym1WQA5Z1FZcQJzRkLHuNkyPSkPax6rq5ipmwOIyrN5uJmJaC7iRedirQg6MZC26h/ujpFbJnSTwK1C3mPs1nLNqg4YzKlWHU/TaTz6SodAhMDs0zTe7R+GDx/MIXJ0BXKKnDhwTImYG/7uDq30rq1V+J7Yco+RWXykTf8zhouW07g/HG4Px9t5OUS6ZLbU4cYDUDdsgKOnDoYBlkWWPIu24hMRMMBKliGmmFJgZpMgFXMWc0spMrNbmsallJNJJfRlHm9ufgjRKaiqmPPFjoY+rvUJnzbVy93lLlVyzvM0iQoiEHHgiI6MiOTm4qBPRVIJaa0EAG4IRCGEyC2MS7VmB4uRmdGdwRVcEYAIMWMuolLGqVQBNWyB8M1AjDHFENjcTdTWW78qCA7cpZTMVYrkXHI91CrgUvvh8qVv+KRpaa0mgwSEZz7hGQXCT01N+It8A0cHcPWcc86lVnGzrksppZgCBz5f9U+4/y+LzD9hqD9JEPqJ5LdH193d3at5ca9mc5V9LjfT8v1pen8Yr0+n4zRl1Zp6HSJyCAjUaoWLQl5U6yJSzUrX02YIfQ+p95gwJFIFKVaLiSgRhIAhuINNU87ZHJCZU0chQDUT6cSua3jTxbeR30Z+m8IXKXweeEfUYStB+Jzy5z6eRmB1oMChTz25luLEIaUkKrnm1agmj5HdApgTkBukruu7PqYYYwiNgw1mKmURrbUumQMTkrmraAxJM263vtls3LEskCdbRllKEZUQTn2X+j71XWQGJqBMxIxGYERAKm6q7lByLUvRYnMoX5g9Lsk0Te+vrx8ejnd3+8NhHMepSHW3YehD5DkvtWZRAcQudSEENyu5jtMpL6bqSJgSE3VdvwsxbDYXKfVmKCotSM4htMpUXdd3sVcVkeotqgDoiEQRAAMzIqiecbaWemCAwKrqbjF2XdeZWSmLSD0c9/M0Pt9U2+2FeH119drNmVhF1cXV3WLLTTUra01AgJRw6KiWnLPWxWr2uhStSNwhp6a15qmaedfFLsV+AAqoilJBCpYZ5xHmEctMWgANYoB+AFMDlGE7P9zvN5vbfrhwq60Eb6svjchnc/wZuvZTR/P8p51kE825nJYyLnla8jLPeRyX8VS0wDLz4aCnoxZBMgpHEgUiAVxT70QpREwDWPDiZuC5YJnRBZkgBkqJW/MOcM+L5eyADmt4z52dE/ab1PUD+sZlgzAQdbgCdD81CQdxK6pFZC7lNE0iEmM39P12swWAeZqvb26/+f79N999/8MP74+nQ6kLMYbIXYjrtqHQoGd0ZKLIRETujMCiojXnCUElpNCnCDE6ADmIiOZaSylSJQZGuod9LVVyAbUAuB2GiGuZPXcX0yIylWXKzWaRKS+P8zCzvCx5ySVX6g2AHUgdxYjVM5i7q4EahICPBbAQXU3G6TRP49zerHUeF6nWGJpaVLU6AGJABDM8HA7vfvih5HI4HLfDdtNtvvryt7/96qsv3n75+upNoABAK0sOwVuNNAdwQsCAFBqDhlZKdSNV/xSA+nObzUpd7u4/Xt+8v7+/G0/HLgkF+uzzoVQQDeZTLblUTx2aZbUl51yrAgwcAkKMIaS4SXETGAHATJcl3z+cVOTq9WYYtm/evOpS/nhdqiglVi2H/Y3Dkstxmuci+NWXlNKWKD65iT/aWucigEQUUd1aLTeAZSkiFiIwN5bPxsxVrWQRUVMFqH03hI67xDEGVRLBUsFBU4orgVal5ALuRIE4OgaRcRyL1FoVODQPgRGxVnVfVoChES1V3Z0o9cPAEYgly1GXWTyb5cjqMlxs3zy+ZXWhHwnx5/JLj38A4Fn++qO+f3Kiz5JhfeKpigOuASY3qEVub+5vb25Px6NKffX61Zs3r69eXW22G2Jew+Dozz5qvcn4+BDg7GA8br0X41NAHlbx5g5mUEUPaie1SfVU6u2yXB/Hd8fx5jge5nkuVYk8Jej72HXkynnxXK0uVopKhVKqqogwOCI6hVZlGMzIWl0BBHAw85Zy3rqVqCoHJG6egJhNqpPEo+h95JvAr/t4cKiAX6Q1/f3l/B3G04LBmCMARQ4eyEyRGUNoNHY7l7VGRg4cooOrSUsb4+bSigio1lJqLeAOSIXKU80vhxDUKkh2ye7u87zM8zzPS66lWiXCJSzDkMrQMSOxYwBiYA/BY6SIQOBK6K5WlirZF9bPNvaYkDVO04fr68Nh3D8c5zkvOS/LIiLTFEOgqlKlqioTI1GMCRzNyGwWre4csAthYO4BQkO/UjdzjIANnQghRKbAHEJISK3m2HnbEDFHRCJk5gBuatAq2iCarRxGJ6Fccoghdakh6i1UmZ+JYAAQzYA6DH0pO2ZGV1OQ6pKduRGNmWNKABQ4BY8RpNSaLc+WZ8+zSvGYOHShtSVpxWOZOYaAIGDmYi4BtQMFq+hCblVF6lILuYoDGkUZT8vxcLjp3qvZ8bDb7TbD5nLYXHT9LqYNY1gN8X/C+PZnuBkixhA2Xfdms/ly7HOIo9p+ni2POI92PNiyuCOxcJ7I1BCN2CiQI6hZigDD2t8S0UNkjagOSMiRuj71fVSVWmupYK4cCwZMnYVozN5vu9efvf3si892F29SetW65vzy9S+lHMbxMJ4Op9PheCwiIaTdbvvq1WtGHsfx3YcPX3/7zfc/vL+9vZuXyU04UEqh6xKlDlu3ZEA3r7WKVIshBEbwSIiOalaWRbV22sWUODABNRImtlZygGBeq0zTLKV2IVztdmNeshRiZicAV7dxng7jaT8e9+NpynkpRcfx+UTOhHNo/crMUARaF+dAtpBHpkAWgsfggYkJiKyWfLd/OBzu52UupYjK/f3daX9cxrnOVXMVEUCk0IS7TOPUJkyA8bPw6uLVbre9vLzs+tSiVyt0+9hHpQlQQ2w1eldY6MxWWWsI/sS6PMVh4ckhM9Ocp/v72/u72/E0LvMEAMwh9YSMkLFUUHWp1lQluBMxMwfuAvWAASGApZoxL+ouZlpqKXlRU/fE7CGGks2NTIkoMCGg5Dw+PGiIfewuu+5it3uNAzENz5wo/OTiiTCEkFIHAKXUljem4iIlGqaOOo4hBKkiZojITG2mxMABOECIGCJHi0HUgUJgalcj4C4q5xZQiACuKqq1dV9yBxFbFqq1MhIHJg6+0pXUHWKMMVKIocMw1CJW57yYyDIDevHN66fZtFDN+dGaE3ZeED+DSo91GM4stEet6y97UDVB0So3rCaRVp2n5eb69puv/3x3d1vz8vbtmy9/88VvvvrNm7dvu6GPKRIxtiSec4WGF3r+yXT35wr/+fgRjW6tquxqWfS41Pe5flQ7qO5rvZvnh+NpfzpN01yqGqBRMCQjgsChCtaGhc5qqz0BqlCKIamBVANeiENIiUMIKQVMRlwR3KwFYAKxVREwA2eEgJ7MvJQqda/pRHTDtKndg2NGshB6gi1geD43Nx9PBVhDsBhi5BiZNLghiEtxqS5m4mZEsGYvBEJtRREVchElVsSWjVUW0YrI5x6YqGoAGJktuFerc50Ps6rNc661ioqTO4GiSxGpmpfKASkgReXoA/UD9hyckAmN2Qnc1YsKAvjwNJFpmq6vr+e5zPOScy2lLMuSl8VaWU+wpqS71EunGoCJEYMbIcQYu64ftttdiF3OIiqAlkuxVggbH1taEQCaWjVRNVvbpQETY8IQElN0IxUBKKpuJu5Nszq0vCzzVmACEarUUpaSsz4D5N399uGHIjMH3G62YGRaq4rWOo4ZoPaDcgwxJYosWgMoupiiFMyLL7PXAirgpEoF2Qm9izQM3KVESHlepFa3jrBPdIkhSDhZmomK6zyPUqs5KEYNfZ3mZRwPYvXjzQ8p0WZIbz//3Re/+es3b3979SoQE+Jf1gp97RGGANzFq7ALXew2/Sv0TV7s+t2x5IeHezkepGY1h9AxANcMJk5rVzUTgVwkMA4dhdDqdPLlFefOywKM1KWwHVKX4umgJWtVdfKlnihPlCgFjl3cXW2++qu//t1f/b7f7Da7z0IM7gKPuds/NQ7jOIK8u/5wc3t7OJ1yrcRht9199tnnqUsl1w8frr9/9+76483xeCw5mwoRBOYupb5LMaYYInMAgHEa53kKkVOX+r5PXYwciHDJy5LnZVliil3fxZgYGRFTisQcRVvBGK1azauqomerxzwJWCAGBFG5vvv47vr97cP9w/FwmqY556+W4RLiWVhhijHFlGIMxNRqNZoCKEBFMsK16m1gjoFT5BiQSUs+vb+9ub//aFprycs03d3efLx+v7+7X8ZJlqqqSAROyIaGAmUGqJstIbx+dfX7v/6rN69fE8E0j3nJQz8M3RBCJAoN8V/7xMPa7vbs6pmZKpjZyqj7xfGkJUTKPE0P9/f7/aFkFbVaJzN0h5zrPBVzJYqqXIqrIlHcDNtAXdcNMXWmJBWW2Y+HZVlG9bLZBA4UU0jIISKgiCylVKnqRkz9MMR+A8TmqPNyvLn9Ybt5dXnxmpC2G0aMz8p/PsufQoiRmANijyurEJmDmZqZWUvjZDeSaqUIBx42AQARKYYYQqtOUYmdA2AIrVDtk6RiqlVzzjnLPNd5mWpd3AQRAVRVpsmWaS2jGWIXUoQ1DE5IJKUUga4bQgpXr3exp/3e5mWWOi0uz0O67mhAzUYjACBAg7UzkTdzZXXfH9mYRishbi2G24o8Pm+A6SsM33RxKfW4Hz+8u/7zH7+5+fhhmU/Xl7ub68+n4zH/1Xz1+tX24iJ1XWhbls5cvyes8KlK/+plro7Gi331KSDf0hcdXHVZyv44fhiX79TuzfZmp1qmXEoVMTM3cHJVq1VjIHBjpKYAV+G3poKAmotoqQ6EkBHR+wGGAVLEFBCJAcD0XJmxmYXmqmZqRNwY6ebVQYkQ8QiIIQ4xXHbxLVILAj1bG/BlqUjmXYv+uyMSkyG0As4Grs1rd2rJfA3OUHBVFYPgxI7E5iDauiY3ghmAmRURcAcLzdNS0VpE1XOuKqpuGIgjA6C51apmHiMHp8DAgNgAb1UnB1Bm6Pq02UCt6MrPBfKyLA8PDyImYm5G2FiapKKmhuQNu3SzmjMCBQqltCiqE2OMoetiiozoqmomqnW13tY6z2jm7ipgK1iD0Lp4qJmZMq+n2NxN3cyJHJFiSIGTO5paKcXdzDUEbkEcO/dDfFyQOd9nWapIK2LrJkiKbEhqpiII5IEjIQZickB3AiZodBh1b8VVCA04YEy42cbdRUwpgnmZcZnBTAlEgrhRLaCCpqAG6mDqUAznTHEM9/cUCBlVhdj6PkzLBBxSv93uXkdKz+njPyuAzwfJTVSq6lzruMzTdFqmsS6TayW3YF4BLfZGRLEjCgSkDeoDQFGX4mXyrJ7J+576gVOizQ5SB6V3cAvosVcORMGIm6hztVrEk5BaIo4xBQ7EMW42V9vt2y5dMveIz5tA4SfXfn846ELXNzcfb24Pp9OSs7mn1N3c3aeU3OGwPzwc9rksgB4CAyO6I4KJZiuSpRC3QvHjNM7LjIwhxc2mDMOQ+g4JrbZ8o1pLbf4uRyJmRuTAIYSm4Vo9c0RYSr473FcrfepSiESkru9vPvxw/cP9YX84naZlybm+5c9hLRsMRJS61MqSEZG7q5qhmauDIRkSgBkgBIWgFkQYzT1P48MPH6/vbt+bVi05T9Ph/v643y/TJLlolbXsqBmQI7mp5EWm8TgeD+PpOE0nRDgdT5FjSt3rV69DDORMj3yUhpq0E7amQoMDWLtCsx93FPx0hVan0HNZHu5v3r377rtvv7/+cJsXVTXVshYmNWJOqKYiy+i1VmZgShy6wB1TJEgcEgKNp8PDwzgvJ0RhvtjF2CUGMmhUx+LzUksVN5RKTNtXVxfIdZoPInI83t3efXex2zX5k9IFU994Wy+ywaE1ETCktdEzIgYOqugC4CDi4LVWaXSQlGLXpSYxENy9pRkhtfREdDdTVXNAJFMrVXKpcy4la1VTMwM1UGjFAyqYiIkyc4ox9Z7cqZF9iZrV4KZsFR1CoM2Qau3cpFZRedGtxxxVVwjGven1R226VrBccTEEQ2h1W85QN5xJ6PAIga/ppWup4JLnevvx4d331x/e3+wfjiUXU8vzcnx4uL3+GJiXeb54NQ3b7bDbbnbbkJI7qD52J2qcPlyT7tr+cQNAs19Q7XAOILhVzfOyfzh82B+/VbsHOIWgLakKyYkBHVVAxbU4u2+TMcFmw+6oplVcTQEVyREbpwTVUEp7l7mKD469p8gIrMJmYOLoxgjuXosgeOxaAAfdycScDKlWHXO97eJd1T3CECi9OBoOtQgFjx0aYDUDROd1YrjCZuBrkyhSN1Gv7rL28bSIIRIFbpBvB2jtgLqDiDTqlAK0XhDmqM6OQIEMHLQRJrndfQdzN8DWLJ67nnoIyUKj5BkqBthebrq0lYKmxPaU115rtXFq4iEGTjEGDl3qRMTMgBzO/Nucl5ILE6uayAIOCIGwBrYYgZlEdakFXAOjteAQOICLiFQ1B3BKKcYYAcwMS66lZjVLScAbnaaaGRHF2O22V13qzSAvy+l0LKUgAkDquhRidDDBWqQ8Siq1XOV4msY8CWMMjDFa6nSzQxVwUHeXrE9dS5yJUowWGBCdiZm5oUkp4WZLl1fp4jIEZhUtOc6jl6KuEyGBBVERLSKl5OqGDqCqy7Kom6jMy4gBHYTZUxc49tvLt6/f/Ea0uttjzMyfMLAfD0Qw9yr1WJb7cbw5HT/c3n5/c/PDzfW724/X06EG6rabEmPlgBzWII+uMVg0o1xAq6NZWXwuJgMTxJSwGywNlqprUZViYGKVonYbgIJiSIiuXrKWKNsdutt+/3Fzv9tdfNGlq6H7IsbXhOFnY+3gN3d3JcD+eJqXZVmWaZqXJYvo9/ADM8eY3L2UklLousitHUfjnpXaSO9Z5saqqbUWqeYOuMzTMvTz7nLX970poGDVamLK0UKihCEwIrojnatjhBj6LnEIx/E4zicETyn1XRdjAML7/f3Nw904T7lmd2XG5yVakajr+5gyh9C8fGJr4chWShFb7xdAQxRwqVVlmaf9/uHjuw/vb2/e1zxpWbxKnsZ5mVqSjLu5Gzq6G4MzkZlKrcfj/YcPwIyl5GHYdrG/vHr99s1nIaXN7oJCCzecuc8rWRqxUbGQHNDc0U3XupPPJvIYvz7/f80zRZvG07fffv0Pf/93//A//vH+/nroGRByqQ5CxLvtxdXF62UpD/cPS53V63bb7S76NltX8hQ2m4vAaZnvb2+OVZauZ7CUws7IzYvUOeclz9Np1CLiGsbR37zZvrr8a4oV4Id5mZbpdH/3Q5cAQQLjxe43Qxewxd1fOLte8tIyAtRa90uOMZASIDZuZbbcqu2GgMQYE7mpqNTqbsAciRjAAK1xlmutKuYNVly7zYsqOnIL3ftjw0zTWlSKhhCtkaxiDMQxJqRw7qwGUotpDZERoe86Nx9Ps+mLY67qRRzBznH05vD6YyE6WDPfW7i91avxM0mnveWxBsATSo8IBnY6TXc3D1//8ftv/vTdw+29O766et0lYvIYqCzzzYcPp9Np83C/u7x69dnbz4mGhniLt7uEhMQYGDkQ8wqfgrmbi+rzifxEXruDmuUlH46nu4eH24fDnfuRaInJEaBWKBVKQRE3Q3Ay8BqgFqVkIQCxm1upmkutxVU8Rid2M2pRCCZCEFMsWcHdNDKDO7tRs8TM3dRk9lo8VA+BmJEJEKjFLFrHCvXirm0ffBJsqKWiO5VqgBiMAhMyrIZmS1RTd2NnJ2gAkUjLb28hEXEgQCKiGBOHxzoUTozE6OZoa2xNwcAFgSggI1njSrgBEK3YaxNJ7oag7BZU2czUJKNWNzJumxCUMT/F4QiQkZg4hLjZbPu+V2nZblKlqkmpZe3QuqYyOYIyN+uuqM2lBGYlSgiKYIQeGKRlnFpVpRYxwtaVL8auSyKlypoQFQLFFNCDABRmVWz5qA5E1GjbAivUqCJKJO4qIq2zzuOhL6XknEtdxCpy9ZYDxB6Ct7ebtlQudGvNS8DNmSlEipGdA3hQbw2UCJFUvWQTAKk+nWA8eV5UpKX9EYCaq0pt9XVauEpqFa1Vy5JPHIgDxi6oDof98ebm/rPPD8s892GTgq/JA/gjhAsAAMxEdc7LwzzdjsePp8P1Yf9uv393d3t9f3ezvzsc91Ot7mopAgcgWpObq7ioAhoHcMNaoMxeZpQC4ExEIQKiiaiK5ex5sZo1sqYoaEiMHFCFVLwd4S4h0ZlWyyHGTYwXIWyZ+3PA4KeGw+3d3Yy6zMuyZCmqRepS5mlZlgUcUpdCjBxDiC1fJKSQWh1dN8vZG93S1l5vjtDyMhowZAhk1RxcTbVWRVicyImJV5QSoHnFROyMbrbM83g6iFTR2nVpGIbYRWI6Tafj6Zhbgqa5O/gzVAsBkBjWnghrgQrkVeAyIYXGwyZCIHATdXGteZlOp8N+f387jYeaZzT3Wkpe3JUIiFDNWyuXljaDAKBWyrLfP4QYEent2y8+e/tFTGnYbkPXtXDjGgRZ65ICODRW/lplFtbmef4pprUuysq2wnNM3t1Mx/H47bdf/+nrP95c3y7LEmgDiPOkZsoMfSKmLnKIQZcMJRshIBpYcdPNIBcXNAwWI8UUUhcpQpcC80alm6Yplwy4GGSpviymarW4yHR1lUuhjiN4kjrOywRgzEZArvj6Vb26tGG4SGlweDrm7l5qMVeVthUcAJi5pZTnkkteAJwIQmKk6G4qVa0VylRTYDZAlFoaBAjgJddWj0rVTKWKVTFxMohLdoPIASN1JkVqgaY13Ai1WfdUqjWVCtzkr5kAWgjMRIDo5qZm8sKGtzWx+dxA/QziIa0tXs+qvcWv2xbBs7L3VeWfsb/1leCNWHM4nH54d/31n7/789fflqUQ+G7TpcQxUBeJATWXQynjOE5LNsTNbkcxqaEqqIIZgAIpWkB2D06w4qQGBvrSRvlEtSOiuxWR0zTf7Q8f9/uHw2EirMzGAQAtV60FaiH3llqFBKgCtRiTcjB1XUo9nso0iYkjgHWO4BwwRuo6DITMiGhSpRbLUQIbYABY+WtmpmKlkJlirF1Pm4G6LqQQqZUZabVrIBIFpvBIVHncZVXUTZ1yMeMU2UNAYCRAP9c4FDdnMmKvpZalmkGzLdzcVVwQnZni0McUg4ioCpAzhe1mQEDTlkRRTc1NiJg5tgxKNVWrSMQYQ6DWOkWkYgZQcgUVcgBxHE0XqwEoADWi9vPliDFsh4E4pNi9efvm1dVrVau1LjkvS57m8TSOWsVUYwzM1MQvAIgaQpaqp1M12wDsiInJAgETKJiaNA4SUYwpEsXAcTMMKcXTKLYYM4fY73a7zWZjSiVX1WKaz+H+hTEECn5uAgEAqrIsoipVCjowPGHCy6ylOkCzFZxJzF0VWlvS5liZomQtWZgwBDYzIAsRu55MyJXRwNDBSQoc97KMNXI0gdNBx1FzlsYkAnciazEc5tjFjhDNRFWllFKXsmDsQtcl5guCtMx2d3u4vzsej+Omv9z01kA4APikZpivePiy5Lvbj/9w/f7vbz9+d3f7bv/w/ni4WaZpmZbTseTZCBMxczACFPFl9tNBpxlrNSDvNoAIKj6PMJ6cmba7sLuE7aUC6Hiy8eino8+T1+JDr7tt6DqOgQEZgHIxVQQg2aRA/Wa4eH31+ZurL4f+DYdtq1TzCyw6B7i7ux+turuKuhg6kiGK2VJN1LNYF+OmA4+t6osHw5X7ZeqqYIbeKMSBiRmhCtTqZjXLBLMWaZVcVRXAvKpWAfc61FYazsyIkEPQGrXkKnWe51qLmvRDt91tQorImGtdchYREW2OsHVPJFMHUDdza2pfzdYSoYCMQISRMASKfK4CHbxAqIFaFCefDuP+fplHMEe3VmmGmT2wFTkrYEcEYmQnc5uX+XA4dN3w+Rdfff7lb373V3/95Ze/7boBOYiDqRGs2biArekN6lp3Bde8ZnyWVvxM7sJjkOdcz85cVevpdPzu22++/+67eV4AUBXNIC9uBjFCyVayEXa7TRQNuULOUmst8yh1fvP2IqWguqQuXV716q9FhZBT3OaZrt+fDse72NWYjDmYNi9ZjtMyDJvbm/vdZchLzUs9HUeRTGRa/XTIb99MX3ye37796tXrz8yewQ/uVYqK1FoaqqhRmzDPTVeNp9TFzWZgCkSoKtMkaiIitVhT7e44jieR0vcdEeWl5iwlV1UFM3WoitUoWzEngNR32+0QtS7zeLIyCcxuLq0ndxURM8+AEZzbHgGQlt/LTClGcyjL4i/PuYOq11axbC1EsEap/bnybqlx6AT01Jd9RezPC71G4JuzhqDi+/3x+x8+fPv9+2/fXYN6CmFecs7dZ1fbTbcZUkcED6fTaZqqWkj9q8/m0G+an9lKNZq7KZhRWItTuJk2y/GXAPlm9yz5eBpv9ocPx+P1PJ3KXMEN0SkAoBcFEZSydmUOyWP0lIADIrk2PKYRvdREvCEZgV0qaDImOR/Oho2jiNciVWazykGINbA3gr4ZoIKZOThhI7iSIzF3gYbAPVNPGBH5ZV4iboaNQHVEtWYoSDVtKPSScylFqpo5oRFZC127IZiToTk2T9IYPQAIOZFkLbUiWAjMuKZaeABae2uKyiNID+52zlCyrk993wGYmoKTqUt1EGcm5IheCZ0pMDIafGLOE1Jo+WmIBEgEMaQupRCYEEvJbUsyUZdiiGxWiVSNoIpqVTUALxmYnIhbr7nmu1MTjL5+IxExBwepVef5NE4HZuiHfrvZXl5equIyZ9PiVsxyc7PAIIZkqr62j6MQQoxBVTCDqcKz5tYxXAGFQL3qDD6pVBGTCiZci9asWt0FTdzUMXCr8U3ozM7BAzNjn/pt7HoDEcvzdJhkGVJgJDMiDIEBzBotA89BL2pgCKIKqIqZAYiCM5kFIggx7MC7kuHhYby+vu3i0IUUU8LWrO9HatHB1KvoPE2397ffvH/3x+sP352OD/N0kixSNC9WMyIgksfoQM07h7KALCDaoBgAABPUAi7EEfsB+gFTZyVDnmE8+WFveQETAAcmNAMNbu6q6MroIVCMPLixKjNepvg2pTch7KBBmi+g+E/m4afjaV/mNTBlLkVkKbpUW6pUUapaa1UJKYYUtIgWISJwr7XFihvrggKHECIh81IQsdZiqlIruK3GtgMRmJoUWcaltSvE1kIpcAgBVEx4yfl0PNWWeGnGTFSrIYhoFVm9QDVXU9ZHieXuqioNgSNVc1cD0NayYo2FOqChEyKY1JKXXIuYGhg0ev+yZGgVggDWZl/MMUVApBCI0EzXnHVzB8u5HI/jfn+4f9jvLg6pv+w7C7GLnCJHBKBGvlqReDP2RaTIWmqW8Dkb6kdj7UxrAGZWc5nH8bg/PIzj6OZErNXVwLSVggwqlBdldhPWSiUjc+RIplVqadpatKguHHSzDc190qynw3zYj+OUd0QpBaIQAqVERHVZxnk+3d5dF03m0zhOx+NUChGZFF8mmaZ6OIz748Nn42+Op4fHazez6XgQqaVUM0ckd+PQoGlDVCQzq6UCsRE7UQRgUxexnEvJClBUbJxOIrXrEhHXoqVoKQLmTGRAVbEoZjXkEFMXQh9jAmXwCl4dqru0ytyGsyqoAXO/1vDGRi0SVQGwJTAAiChReLEibuDijo0sRUi+Vo5fe5y26PlqoJGDOayorD8lzvrq1rc6pk3Fi9gy19NxOp3m07igQ2EpOZdlZtdI3scQI8sKzVaptQW2W9XwVvUcFczB3VQbONAQxtWgeL6bXqh2c62yHMe72/sf7h7eHQ4fyzK2imDmjgxAaBDUUIWZ0IMHhs1Amy10AwGBiJobkgfGENENtLoq1IqhQq0Orq4eAoQIKUUizhmWrMdxqtVjgq6HzWAckAI19k1KHqNzcGIjYsAQuItxE3nD1CPGp4S0phGJ3rx+k3WZahZwB6giVYqpuKmI1KqqjcuiBIQOZOAGrqvB3aLzKADVdTGvOk8lL5ODhcBevRu6EBm9FT7EZjyoGAASc0OSmYGC9324vNoQgZrlWcqsDuagFGLsolnHzpF79qC5en3Jr8GnoO44nYhwu92mrmvVkauUUrN702wUI5uZA0chVRBRcEMmlTxN0mL+LUbAiLam6oi5uQiiM0HOtYo8PNwdj4dh0/dD7Pvu4uJSqwcKTbWr5VKWJS8i0sUOEVsSWmDeDMPuYqeq8zzmeZlOa61ZRLzc/sYog0+53I+nOpVcq0pBqzSPcjrMUoyMQghdjG3lHQwcEI3IItNms/n8i99evXp7mub7+/vpWMdjpi0NXUwRmFgFREDV1cQ8m4qax9B3fQcO2RVpjYBxq1gJGLnv4qsuXTFuTsflhx8+9GkYuuHi4qIb+jW/64WGdANxr2allmmeDsf97f39x7wUqW6VTLB1lSgZVa0EQEJRlOroFANQQEcHcjcEI0RI0boOut5jAiI0x1oxZ8/FVQmB3aLU4OYF7UwDiCmmzbDt+04qzZNKHcBfh/A6xF0DZAmopeT8+MCDwzzO43QyUXBnZFfP81LmImXtpAm5Yi6cQkghx5xSan2AVNRb8rc4RQohDV0fY2Kawd1Nq6r72oAYCbjlTkYmplKq16JmzJhSTClQc7iVyrws06SqSKgpmZiqN4Xe0DcTr6VKqbpV6J9U+zk4JciqrR2KKRiQmipIxUrGZEQArnmZlmmcx5yrO0YOnQO1ViWg4qaBoAshMIXQAxEyq3vjtbhbi6tK1Wma373/4JhOU304LJevPrvYvdptd33XN/uAeK2OxcQSbCplqbVpdyJdfa0XC7KeE1/JdgYtf3A+TdOxlGxmiAyOpXqrJ0HETMEdl6USoAqPY51H2V1st8MFWSDHECIiiOYlY9UMJMPQAdDNYXx4OC3LDAB932+3iQhSCv3Q98OyLMV8vn/4UDVxsHEcT8epZCIyVRCxw2l69+Hd9d37tx9/e3M7P85CTR8e7mutpQohxZiYyTRxoBCg7wNgLKUsS3EXAGOmGJOZg0vJcjrNrcVbLtlUptAqS4IqqAAjdV1wJLeW3xuIEuGA0LlGraVmlIqNi41gOZdci6ibQUrQd9xSO6R6LeJqVYrMFcA5xC59ckAcz72Bm+ZEauXeHinu5xXzc5VDe2qh1T5hLSPbzmDjfJipmFSrpdU4B1c3qVnrPBpIBit9Fy8utg7QioG1hiKMmGIgZgcwA+XVZ3Z3U2s9EqhFKn9BtY/T/ub2u5v7b27v/nw8fpimYylFBUTJzgkBQNAY5QitcYGJeKnOxZDJjJCsSyAJagEFV1856KpYM2qBQt73jkwOiBTAydSklJxFRd2AiZIzAMWA/Qa6DgJZS/NnRm7ln0zNiupMMLmZ2VMXUUS62F511oe6LDUvkquoFqm1SBUAAKTAAQK2MAW2gsdmqromCaAjgqnlXE1nBMzLUkWQ3AMsWE0xpgDkqkaOMUZGriBq7gaGruoc3FcozpCZiYgRCDA4shuLube6UebCDY3ngE85lA1E0VZLa5yO83Iaxj7GJCJLzqfxuOQxl2yuBoUzI7hazTnnUkopLWPfAFSl4UeBMRCJO4ibqWMTNK0TYnWHWmWej6VMzJbndDoeAgcVL6WUPAP40A99lwi5T912u0sxYmM+mHV92m52hHix2x32h2eqnb788l86Lct8vd/nUVkLWCErZEIu0cVdTFtws2UOQePRYLNHOWHqMfUYO+CsiBI7GHZ8cZW2fdRitVipqEYcAoCLBlVRcfDAhCItaIocGRFDgBhT6vrt9ur168+Hy8/S5rLrN6ZQSs2lDKqdP1XgezryVkq5X+b3x+OfDsfvTsfbZTlJzWbmK3ULvDQgA1xB3R1dDVTBwZEgslOE0AMgWiXJUDJwdACoxaYRSkEk7jrcbNQqgTW3OLipuKiKG8SIMUREaHyaQENKl113wRzpST2sjK6GD36KBhmAmuSiooQE5iUXqVVFVdTMABFMVcyqaTAJtcUc12BZs4sVGSoDmWjNWaS4CaKHSDEGCsyB49p+MDWScxUxUxUtruABPAIYe3BVdHc1FchTZmIODETgxIAM6OStxDG/ZP6vBPS1ive52oBBIyybgzQzhwARDMmJq3kutSF31ugba91dUwVxcWNmRANQF/Oq4nZObV5zk/H+/taRKQ6xv6R0kQZkAeMWDnCWdlVO5MlsLmWutRVoacXhflxF/myCNZy35UBrLvM4nU6n0+k0gbaWjCshHAkA3CznRQgjWCwlM/N2u3v9+m0gMilgOs+ay9HhWGoBcuKrLvUOBbDEBH0Ir14PF5edSCGGmKwf8PIqSrUlnzikzTYiAjNXkf1hdABCKvW0ZDuO483dncsVwOYsrEBVG45CCMTe+BeripCi2lIPVtBC1UqpKlqL1qq1yjznnOVcINaQVyVbqztTRyFwAEKrUFW9LsUV6lwD1TLnvIjKmnm2Br8RwRqzqkhJGpjApLq2zGpTFQdHZv+koYsZ6BnabtlEtkqk8wfDmgXXaCOIDtosgbXYKVArTttQeWZEglplnpZpXJa5SFU815gxdxGZ5uVw4uM0x74LqUshXFxc7LbbPsXEFJmQUc0dHcEJIQRqWNK5tkOz+n8ekD8cP8623Nx8c/vwrdaTyiJFRd2cHMnRiAxImRzdCdHNpdo8q5qLQkzNZcWhd8mwIHCDNtbMe9KKVdc7Frtmf7F7bGaHm0upCBo5oSMH5I43A6YEJqZFihYO3nUcpIjMpR5LuHM1xF7lqZYFAm66i4763obTctKjFstoYAJSjDi0hBmOrCZVqtXW1l3V1N2RsHV0VdO6yKzZdV3aEMio1WotpSoRAlqM3HcJEtSqZY2+gwNQ8GhQRXIpEVrapjs6doDoVRYpNeci4sYGwfqQYojPLS8zE61dSBzheDyeTsdG2mzCu9bWwb1WqYeTAkIMAQBqLVKr1BpjjDG1DDdijkwxhBijmbmaWlGQtTBtKYiLG4hoLbNZqdWnkW6ueTydfI1nKhP2w9D3/dD3F7uLN69e931C92WeD4eDu6eY+q5LXWLgd99/fxbA+Lvf/UvD+cOH5XT8oJUkkxcCYRBmpyFuq5tWUytLLmpm1urYsntAcgpIScRO0wzTfCy677Z1uAifvek2XZz2ZTy6o0WkzTaFyCJBROpa7dW8tQcgjCkROTOlvus3F5dXr99+/sXu1Rfd9go5xpQoRLUznelH9Z1Ul7y8Oxz/cHP7N3d3fzgcrkueaM3XRERCgMVNqrsh4do9fA0tuQFCiNZtYLjEENAMywLzSCKmYqejw6mV64rbnadoUlBrAA8IbOZVIOdaiw4Dphi0ZpcYaNOn3TDsUjeAq+qMGAAbFZAeUcMX80BIHBKF6tlEDMDVW7UTb1AfuDuQ4prOJKIo1jbKWnUDAdEiuLgsBdFbJ0UHp0CpC8N2SF0Xu5RSjCkyRwCoRfKy4Oyl5lqLuyB6qwCLCEwsoFrr4oup9f3Q9UMIgTBgYAQ0Ug8WnrWiR0RuvQdDcG5EfjLgdfFWDULtISLHrnMQP+IidVqWeV7MnJC0sZ4AzayoUtXG3Wk0dV3RTvQWY5SqJo5gRBdvPptKFkAIfUU2bRVTnQAJHBCItIAuUmZtQKGfu7a/1OsvfmpGhJtJzvM4nvb7435/JOgYQ7PyCQEISlZzdddAIcaEHPohXV1dvH37Fk2n00HqfHgoS1mWsoiW2HGM9Oo1UpB+A4CcOnrz2bDdxWmqVaq5cvCrV92ySK25qhOHroubzTCOp+PxxIzDkJZSjqd5fxzp4+3ry//lcrN5vhxqjooAaEBiUKu4a61LlVylImIIodUAEFGpi5rVIqJrS6Fa1R2JEZlDYHDyVt3UEDnGLgVHx+a9THnUEex8UM3cnKz1WA0xcAjIgiJqXuuSGcCK12JaAY3ImdERWrvIF8th7qJrCdlzil+rC4NOTIxIbi290ddKz+juugZjEMHbrsMWe4qROVBeyukwnY7jNM5SFYGInACAA4Kb21LquJSt2m63213sLq8uX19dbfo+MQdCA9DVh3IEDMSG9oiROSDip0mVL1T7UsYM5TTejce9SjFrhZbbysFagP0cLnI3c1d1FAB0c0vqXQI3VAEpVhZTdWbve9zuMEQkYlUHh80Aw0BdB8zuCia0GTpEUBFAr9UBNCbUhOgMilJIhMyocXncs+hxKe/diOESfDMtt89VO3ofMIU4EPVEfQqHw3hEnxkiR+76hISGLVHQAB0DkuFaFZ05BCaiWtWrGpgTBOYQQ9/HGMOZNIlqqiJIMPAqZcww51YKHlRcxGqpy5LdIQQQ0Vqrogqim7prAy1VZZFZsQYsV2aPjom5ihZWRPLy/+HrT5okSZYsXYwnEVFVM3ePIe9Y1ej3Xj9sgAUIfwC/HzssQAQC4VVX1x0zMzLCJzNTVRHhAQtRj4y41VVGSbnxCA83VzERFuZzvtP223o5lMnjy8QBaOZqwyrqbXQezYbBBhHMGuHQr6V5KTkVZlFtqq1ps9CheAcwBA0fq8fGpcPd932PADo8rD4WaSnTNM0pZXPTrsI8Zu29923dtWtufdu/o9GxcAyVce+qBLGktBQpiCmWZF1MUdXNmsWOaIhuVrtWlMgCkhxwb+3R/dr7TrTPc5cEItW9q+3mFZCISZKzgIWCK3FIwggyIySkYCKRJCWl5XS+u383zXcAJCzLvEyn83I6v3t3P88zsxybb3xXB/e+vTz97fPnf/vxb//y97/85ee//3K7rHX3HmGBYeAdbhdrqwuzMBJJgGvX1q13R0JJhIDgaAa9e23QFfbd664eQUjThPPC4EBgIiTIEeQe3gcZyFVDu7eq18sqDPOc1+vt9enTl5//5Xb9eT4/LOcP8/KhTO9ECnx17sA/vogwZSFBYkbAcO9d61Zbbb0bvUVnuoep9q6hHmZvDBZEROiu1owC0NW7h1EiZs5TWk5znktKCd7MQ4iYsgBmDw0ytBAhmRInRiK0YZNHDAwL6x4ZCJmCERDMPYCRaAibvz0FCZgxJYIspQgIewyFhL/ZO83cwtSje/TWbl0bEuZpnk937hphbbcwffMeD0uXe4CHj/0AA+DQEGkEIsEw5gy91da21/WVm4xdX5gTFeEEg7bh0WMwNMadzr8KXP79y9xau+31Vvfb6+vT3//+06dPv6y33XqMlimABwxfFiCBaqtty1nOp+V8f3f3MC+nRGTmvbW6bWu3PcAGDh8JatVWmwic7iTnYEHErmaAyuJMLIlzyre1vb5qgKo1JCqTqCW1FBFD1Qjove2t62mqb5d2QEROEyg01d66R7te99echJEoiHyYFQDIHVTtoK76AQdFBGZJCVVtaNkjkJlyJlWGALNeWwCgqUJoeB8n1HAaEo4NgAZ0dqT7jTuJ2chPDmMZugpJlHIuUiRJLiXnib5xVY4Gzmipj/u7+Qj/HNFvjEhwoAaRGXhYwWmIlzwAPNwN3EcIAWEkDO6171ute+2tQUQSZkQhImChPE/8cH/64bc//O73v3949+7u7jwv8+m0ZEngMQZdTbWpqXoEEtLo6bqbu41pUavtPzzaVXeHvbW1t9qamR6hu0TIctg4GBgg3A8psg8gbYPew8whIJzajtvq680Ao0ywLHD/gCkRIbkDQCxnWk4oggCO7uiBKCljq9o7qGlzhQDNYA3BqVYwZ0QQQMBwqGov624bvqI9gJ7X9bt3FZqZULKUCU7nD/P0DPAz07XnxonyJN36Vjc1VTWEIEEhEsAB6R9NBq69NRwT9JxlmsqyzDkLIIS5dt+3vTYdgxYWYkJJNq7UYWEdLFlvWrkRMgGrWusdIhCDEZgo5QTCbdPW2t53MLxb/GsRaWa9V8Rw59r21modQvDjX8wkAsd9YhDhwo9sF0cMM+zWiICIRXBZSs4FAG9rqLZWm41UmnFVgwPiCAgiiSkhkrubGmcmInfAt9VNxF370+MmRNNUxlymtV7rmAvivn8X6lr71v221Wvt1UKIpyl9KOmcUmEsECWC3cC9m7duW+vr5frYro8MLgLCBnGrbesNzV3YJbMImq612brVvVmgEKJHDcfet941HAFTztmMudKA2RaZ5ul8f/f+3cOHJKdam5qlJO/fv/vw8TenZZ6nIiJxFK/fbcFtv3359Keff/zTX//tT3/7t58//e2lN48gG0QE9V5hezWtME9AMzETQnS1bYvenZlKSaZUNzf3ddPWUJW2NW5XCw8msBNyMOLQqTGLRISpd1A3dY8INIta1bWZaslyPn35+e5fevsFmJa797/7w//+8bf/7cPHLJzG9Ru/nfEAQICHB0aZMwrlKUtKhKxd19u63va67YKyTAsEtFrXdQuzwyw+cjWRxh3XVQc2xHFEpiAx5SlNp6lMhZhbbV07mbNwTgk5aQiknIJFuJSJicCR1JBHwI8hjJwpEUxHqEg3N0PJktJ3ugEMxOG7YSoyzQlYLNj9DaoYh4a2ta22tdW11rW1jRBP57vefoAIN7W2W8Cws35Fwo4LkgilsbdqBzNXBaRwEcSSEjOa99v24k+ExMQ8pWku81wwEuOoVgJsfGMaxFmA70kvb88EAFytvVyen59+eX758vjl808//vL3v/20bRWAEQSATVVNzToRMFNtdVtv05LKRHm6f/d+zhlqu67b5Xq9vTxf932fT2U+Z5EkjKrQmibh5ZS8sIfubW1mQ6yTCzInwsJCre3m2vuWJOWcAaZBrWy1A0Euuavbrt9dExElLVC99fVy3ffhTiNa5unuNJ1OaVrSOEFVLbyzyMCRxRvCLeWEyBFjNqRELokT8xygBqq1aY0A1RF4bzHYRIeCbeSEcko5JelqrTcdWgwfY8dQstHKZuJU8t3dvCxTKVNKmX4FyEOMoNIAc+jm3ayrWcSIvRxKdXQkIEFMiUqmnEkSIsIwbJiHqms3IvJEYxZuXbV16+ruTDSVlFhKYiEvCe/v5x9+ePdf/+v/6Y//9MeHh4dlWYiJiRBR1UK9u+9dW9fWBjkXB4DBQ91tDKf2rX67qL472s20H3JyHxIGj4CBcXjDvJi9sdQALAA0zME9VD0n0O4Y1Bu17g6eUpQZy4ylsAgTkrkDhKQQQUkIgKZmFoWYc0yz1JqvV1fViA4QgESE42oxatWI6L11NcJGuAuh4PTmVzpWWeZTTnleFs7opBF0va6qBogpY5kENZohNDA3QmSmXErJ6Xw+z8sMAe7WmvamfTjgmXJOp9OSSwIIt9BmLNRtJ0JkOGT/BETj9+POCAYYxMiJU04lgIkkwIZ5EiMSJYLEnHM2UwND9F83r1q36/YqIoi4rrfbbbXROB1AC1Vk5oPlZ6Nh6O4+oNYIAKG9h/m4OjFzThWA6l5HeandLQIAhZkHIMydDq4jjS1HRN69e7csy2BIMbOqttYQoO2VEHvvw1pdygSAQwv87RYcES9PV8M9Ik/T+7uHky+5pPc5LTmLSGGemTKhmGlr2+Xy9PL8CW7oZiKQhJls3JssEIIIGDqahnbvzfYdeodAC2i4BxKo9ghgTgjoOvwqY9kooacEYYyQItDUiahM0+l0Op/PJWcRQqQI+HeNUrjenv71X/+Pzz/97ce/fH76tK6vbob+5sJqFdoGdQ3rw2HRUdA91t326qqRE5hS3eF6073quhliLtOcM9Gdb7d9u20EltlTHjwKZh4iYa971B1MB5YhtJt1A/enx2tKHGBfnuZgP90/XNe2V42AZblXU+aUy2nfn799O0NvhozMSMKppJyymTuGuXdtEG98TTiU5vBG53g7o0h7mPuYnrIQEWXJORVhIUJiIkYSGiRijHAgJEglSWEWHsAyU6tb03AYYeOSD95TIhhoJQKkCLW9rbWCLmcoh44OAYVFhFMWypKzoCSL5OEeCGAQ4ObQLPa9bi/PT59fXx/bvrZatTXvDSMGzd67etdhxXkzsOFA1BJTvKFjj9o3ws17r7fr6+OXn2/bLvkzErPkh7t3D/cf/I4cBCzQKHn0qrXZ8IMFjYHtd72HIzEkvNb9l19++tvf/vT4+Pnxy+Mvn55//umX23VXDclMJMGBQ12ojgi11r12EjBVET6dlq7t8fHxy5cv1+utVXVjNwqnnPh0SqflVEoeysPhJenbFtBFKBeJsFwiMSKpiIeqWhBDSjJLyuMvWpPMkhKRA1pKvx4fCJTylLOVsnd1QLJu6KPZknOZlqUAuJmFh7mjBUKYeW+97m3fWzj6m3sxwtxdtYlIyokDtYer9m5DNq9q5kdRQsSEOKILGengyHmMbkVOQwQqxBweiIHMiDx2bVND75F+3bA8XFUtoBvUprVbbdotkAeQFCEOHTIDlESnUzotacHETG6h+nZwdGOh7Gngd9w9JTmdlvcP9xFQSi45TVnQW+L48P78m4/vPrx/eLg/L8uccoLxDvzQpnbzpt66taaH1+do/4/DAADgHyCH/3C0m0Yff3O4Ogcdz10DHCDMMWLELiIzoIVHeIfWolUXCVUgJFdp3YCNM+SZUgEWYibEIZPyGAKf4etiI9EslIAQOO259e6uSB3IkYWFxIE8AJ2IPMJ6V92Jmojx9D7lzOnXswQRJ7mbynx3euBMHba91pyWJJuGimAuEmRFZa9DSIxENE3l/nx+/+Hd+e4uzFW1D4927101wkT4dJpLyXCgDZ0IW9/cjZgGHBUJiBEAYmQfORCQUEqScy6SyjTPgB6grVbrXSgL5mlmcAxzcOAvBG9m0W3fXq6Pw8MzklLf+pvgAK49AFmYaAAZxi81BhOUiAfXsUePCO3Wu4okRDp4MoGqAYjEnHIuJY1OPhIOl8X4vKaUPn788P79h33ft227Xq+ttXGd7bURYFjQiaYy5zzlVEY+t2n/5miHp8cVRBHuTndJ2MMy04klcYqccs5LTnOSoqrbumpsry8Qpq4NyAmPblk4hBG6QJBWd/eu0Tq0zmoD7d/MdfwymEWEwlHDzA0gzF2bhTfGti+9NSMxzjYe/TRNpRRhHmO1g+n6/fXqenn67+v/6/Xz7fGny/biuosezA718LbFtkFv6AoR3poHhrm37mrj+gza0SwuV72tbd18nkspy/m85CSP9HR5Xeve26SSRARTZiKp1Vv1ukNvNCQ7o8sM7rv768uKCGq2PKcQXe5f9mqqShSn5bzvt1SW+4ff3S7rPzBBh2HCAszVIyGTMKWSOBMwmGrXDjb04QYRiEfwOTGOJLMR7j1kxIycWIqULJmQwodsEDlREI+608IQSUQ4cZnKODLXde+uGo5MRUQoecSots0VCJiJPIJ8X/dWa3v/m28/5mMKxiwsLMKYmEAift3iTC0gAva6Pz9/+dunn/++Xi69tSSJAq1u0Hv07l2t9RikU0Qi4iTCeTAQYxhaj+vlgaTc1+3l6VE9SD4DZSDJZam/+yMAiczIc1AAkZjr3veqbUzS4n92tI/V7brX9edPP/3bv/2Px8cvj4/Pj5+vX355vF437REJCQmYzQgARgHd2viokZoRUcr5+vj8448/P/3yvN62cBFJgOQRkvh0mu7uztOcnr48326VgDx0r9W8ieA0iYfO7p7DzImd3CMsgIhzThPTtG11XSPnPC/TwOvlnL97HJRz1mVZiHmeplGATjlP0zRN8zxNQy2tYUfD3aI322vft7ptuztBMAADcoSbebQG4POcEQXR1EdcV6tVzQIAiYV5IOgZR+xqhI/I5wgCEOJcci5FUkHicT9BDA9U9bq13Z2Q3y3xFcNhbs26KjaNrepWdWvaug/duAOAAzihA4ZPmasWj5kYc2Z3b61v696aajdJAhEQbtoDsOT8cH/3ww8fiPk2bfOUpyy93gj63d18f7ecl1KyEMYw+NgRoxrdTC3Msav1OhI4DmMUMxIxHOb679bV9752N/cWoEAD2RgEgSwiCSl84AV6N3dCTAkDiAgtUDW6RkS0HZCG/gs8UC32Grl6SpYSEpF2dzscgmYgKSKA5HD7hxMRiOSUg9kRI0IDkAY+iHGIXcfhhcCESSSXqQyu2bHIAKeyTHkWmiK0N9VuYxbDxMTAjLNkTnckOaXZegc3BsZAa9a3DnA06MARfIC1LNybtCMdABGAUpJpmd015REwDiwiSZhdw8PAureqVSqTYJCULFkoIWJiJuuJYGxICQPMmnf/tuNo1mvdRkk2dAHMSSQhYQTU3tTUDUYh9iaXjBh3EQAMHmIid2hgiJ3IAcDMtVvr5hacOI2xDzpLEOEwUJp5+BBXe++q2sf3VtVaq6rKeA0ELBDAEMhIzkVVe/+uNbSvzCWVOaXJS/Jete1tX5+1XZmwlJkpI0o4mOr69KldPtv2EvXmAcaJEhCJK2hFa+5qbhrhjmiB3cICiDGA3I/7FgK7Ye96u7a6H2RPIoyw2rbb7TXl1MNCuPfufrgWkejfiZd/fXXt23a53tq+hRohU+JAHhiEOMSGCmZogWEEhObYNLQ7ABJG3Z2FiCZhJmzMMpX0cL/cnSfE+noRYZ/vbDnDvFDORJBNY7v1XJxk+IMDxs3GIIksy/nh4eHjxw+nu8m8AcX6tP0Mf6Xuuci+r2W5e//Df7nt3/axgYWJqWtvve173dfaTj2lBOEppWkuVg0seu9131V19A/oeHESIeadwUNbM1cHH1clCAft3rsmK1S4JCkYQ7Jj6qY2IidUjYWRcIREkCBY0KgYVFV7hNcAZqa3VAoWZKNvZ6IA4A5uYWrRrXfFaAbhrg7KAswBaAG9tevt8nh5+eXy+PN2fW1bRUAwUO3aat9W6HpYM/wAFqEPHAZYODgEIWURQU7CKQGA9na7vNTWACVQJE+n+3cfP3wkRiBygJFS5hAjA0LVzSIE3phz372PCHXrvW3r9fXl5en5+eXp8eXzL8+PX17q3tx83zaVhuQRnhJ5UO/N3QkZUdzweqm//Py8bw08QaRwJqKUaDBe1cKMgZQlsRAC1aam3QOJGJEjyDR6N0INBxZCzACei0gCQFXr27a/vqylmRm2Tr2Hp18/LGZ2eXnt1jC8ZJrK5GrWVYhTgt7ry2sdXUVJKaUyivHerVVVDTfo6mHwBkcyop4KyAgTpAgwd/PwYeEdJ7i7qR2adUIewdyjeQkeOFCQAeHuqoAxMAjezTpYVya0bkz8z7//VeHUuq17awpNY2+2N6vNWjf38MNMAeA05Putk4Gqa7NeMhOidt332ru5uZhpBNdGBMQDvoJlKqXk3ntOLIJt11r325Vep3J/veVpkaTM6auUqqu1PtLnqavXfeAZnZk8iQiREFEAcvynyBqL6IOwCGToQy7OeZqIcN+r+1rrrtqI0J2IMzMPjxfAaMvTsGKrgwW2DrABS7BYCRDm3sIULdAdzSOX4KHaQoxANYTglCYIRDIiHTdJRBz9Q0SM4SogYirCc5K55EnkVy4SIs5lyXlilNr7vu51r4OrJcJDTis5zTyXolNpt8utris6WrUNdtudeLwLULPWW9PmrsTIhGGGg+MrCRCnaQLwMVkYQCVmITQECgtttmMNs9E9Xu5PcyIGYRbMESQQNPIyEAIaANi3kid3V+2jjAAARJYkJRdiDhiha6MJMl7HbDgiCCkCkBFj5NGAG/RuAO5+3OA9DuFJSkIUHooIb+DOA5wREKq6rbdLEgTs7XiZKk7zcl5KygRvdsSAlBIipJTWtXyzqkArA2aa7nJCLL7F6+31cnn8dH3+EVynaYog68EkwrKvl3b57NsztNWDlFywcJ7cw9Tr1vvWTVuEU04gogE+VgchEQw1AAB2jW1rl8tNOzJn5iTMHqje1v1KF3QKmUpre2/NhiUG3zwkxy/ye7oIMmAZDhjiIALOLtnBwRQOprpC1/GBQGYGiqhmHogwpHNEMuWMkFqPlKgkXGZ69y6r5+dXQe6nB1sWn2cuAhRpX00SowQSsjhhaAdrGF1yyvf3dx8+fPzd7/5wd3/qtW3r7Xp5flw/6fXCErXXcn64vK4Kp2/fx5j59tb3fVc1Zt5P+3JaylyYaMqleTfVsUlBREop5ZRTHsHjkhIzI4VZV+umx443KFW9qzR1dyQqJXEiCG+1r5e9V21NzQ3IOUkumZiYBcEdA4GQ0N16b6rdzQCQiacy5VyECUuib2R0EXAYhbuRaOstlLpVtWbecuEysXtvbb1dny/Pv9yeP2+vn/fbta1b27s2dR/XsEAIAggY8OhRpQ1Fr4MqjbGnJILh5xMkNNX1eoXbzQOBeDrdp5wQPefMwkHkjh6BHm/Xr1H+/c9oNTGim2pr276v23pbb+vl9fb45fnl6dW9Q4TrlZhEICXOJQd43YGIEBOhmNLz46r6S84T04m5AuzMkHIEKaCaRzfyaEhZErFwv9XWeimUpLAw8/DahuogUA1WBaaELOiqrdrtdnt5XfPeW3ML6cqnZPCWVGlmX375AuScsRTOOUFCT+OGaNu21rqPC8Dd3cNUiqlpt968N3dHAA4zMx8AdnMl9iE7pRE0efCoHQHGoWM2Zn/h5sqUSJjEPVz9sDCPZ2rWWxg5AJm5mwWYQvTaI6J3E5ZvD8Xa+2Xdm0LVaBa9j4DyYQs9nmD4oNd4M2jed21rq3ORLOTD1GceDsJeddy7XJKw0N77mz1VzaAHbtu2rxezFgGlnAIk5ynlnMrMLBbRu+21uQOxaPe299a7mjFThLszHVOP+M8U8sOATkjM7ORAx+DXzM1B1cxGs5IRAJEPZBMhCxByxGHaH5UpIo+xtBuaoeloqgIRCpMwj1FooCECCiFIgCCnMiFLcjek3TzUnMgoMCIQh48fEZFYhKfEk1Ah/FWSjYjLMqWpcErW65A6InmeKMssGVIGIIgIISpJukgndoNate2KsB6WoQhz72ZAMZ1yKWWZZyGpa6u1mzcSKnNmxghrXeve69rbptY9DMy82mBgYd3bvu1726Z9TjmJsCDL2Ck5QpwIBkjru0/+sDa8wY2ICGKQ4ziOTOCANwsnjqvjUIxgIAzuFxJQDMtcQHgc0c7diYmJk7AQRah2PeLg0ABxOHWCvBHdbhdEYCJVC9M3bGIQgAgnTkSo2s2stToqkv0bhXwA1P2qnoiTubBwq71udb1eL09P4NXmCQBNPadM88yx5dRK9p4xMDBiyuXu7l4L1Wwb1xuulxfb9i4hPFKdBVPClDmlTMTh2HvbtvV2q/veiPKUy7I8TPOdqe37LkwpA1EPX1t7WdfH9fqwnuZc5jfNgdr4/zdJHvd3v/ntb/4fT5+eflk+X56etuure/Me+x77Zq1h7wRAkiiCmHFemASlRG0SDlnk7jRnETN3C6HQXl9enlJuLBfA7YffcGBwBiRvLaxVtG3fm6lK9jJFLsCC4OLKuiO4qPbn52cWuF4XCrKu+9aYfGcuc8nlnmC6vK4aHnD39XlcX64vry/7trfazZyIvXpfW5oLER5K8B5vsgngCCLOU84liwiP0B40i+6ggEZEwGDh3RS7iJppmHprnQ0hvO3t9nrbrnvv5uEoIEUwYlQJQzotxMISZp0F3W3AGT20ddB4253imw9HmLqbgwcjJIK9rdfX2+vleV0v05yXU3Fr23r5+5/+9cc//+n58y/77aa1Wu/aW+/9kK0PBQEiCgfwyDg8gqLAkWicEzIa/0lY2A8KxvgZkSRhmCDwVyASASNggBAExujeBvjggX6LXh/7awzA4vXFdDfrt+v1+fn59XLZ9i0JIEa3QA8HlFRSnpiz27Lvo3MX63WgwX2aTyWVfVPTSBkkw3wq02kuC86LEHlEZwoRANBwHT1tERlMFCZiFnfXrohIlIZoZ4icpinf35272rrWrq0bPZx+nbup2o9//4XY80TzkuelMOFRJ3vU1vZWmSRnFFGEZgq9hTYKSwyQEyVxACAWAFAFQJtmmpdpmZcINMV9U+um3RB4VNIOYRZEzkzOzmSjdTQkn6OVNghuRErEhJyz5DKL0Ijl3PeG8B3M9Lb2z49b8+gW6mAWdiRcwMAYxptFDCDYoUdoRLfYq2UhHL3vw6VmRB0O5VO10Mvl9eXl+enx8fL6ihgEXvfNe1vn0npY8MtlK6XMy+n+/mE5nSRlOOavo3fsAEGEDERMgKCmbo7QiKj3/1ghDwAQiEhM7KyHFQRctVtEP2I/eCCaxxkjQiIoDgii3dwOI/04VsYx4E5mqDrMwqONTikRHmpEAwgORqYIQso5C7O0vgeoakdwFhtXSSYQGTrWgXIUBPlHCzJCyiyJkAPUI8xDA1QS5lOhUfANw5gelAkMNDX1GGy/ocJwd/Mw8DRJWVKZyul0YuB+tXbb13VLU1qmKbP03rz2/dq2a623ps3CwgcRqxmS91Zb2/e+pTWLpMR5ztOUSyoimTkjMZAHBX2vZT62sKGUQHj73cKvLb3jWCcMh7cfexh7MQi/fv1N+RvmMRYrMSYZeibs3XvrAP71Ow8xNrMSxHpN4M7MQ/N4UK7d3QwiUmIIqLWaWcT4gm/btwr5aPUlFC24tpzL3Pat7rXudd839E1ICcHNgScGoNTcvU3QO5sHMy7T/P7+vbu03W/pRkjX69p6GEYiHDkmkiDnXMoJMfXm+3a73S63W2vdSkaWcrp7+PDx92a6Xl8hekqRcjBX6y/r9dPr81QynU4PuczjOtEHYdV+3bweHn77f/2//N8/vfvzkv/lU/7TF+zbJbbN1xd4vbgFAzBiyokdXBIud5wnms/Ym5tGYnm4mwlgve5tVyFvvb++VqQrSj7d0cN7dODWrfeozdEa2LpvatpLgZIw55AEQgSResa6Qa1rreu6vZyWaZnOmTOGc5ZQFl5Od++DaK2tWoWvRzvA6/Pr85en41wMIvZdo++K1w0ZAYMAKUT7CAhGM40DHyZpKqPLmT1PMXvY1yzLoOP6qd1VrVfVHogOEW2r68ttve5mjoScmQCNdES7jI2YiTJLcFJJ5KFBx/bdrboOGd+395KIGGMyAkiMU6a6r9v18+OnH58eP09TXk5Fta3Xl7//+d8+/f2vbbt620MNvr0+H5q5IQ4kJHRwOIKG/OByjSwyRhlKc2I1NYvRq0AipkiMMpwN2iOU0IUQABMGMggFkQN6HHiT7452s9rq9fr6+PL8ue2rW1/X2+Vy2ba1tSosAyACERQIJDkjQA4fncKtN+29bdV502W2qcy91hHPQox396eHjxOLcQoAa62aW4S6q4cCyHjviEMiROGs3fe9D2FaCI9fDwstizBPl9f15fW2V2sdDgLYeBdqn37+TBzTIqdTOZ2KJKEjMiBa7001CUQkxh7aIMgd3WScUomYU5CAHHAOHKOHMpVpKuHUWySqIxl3YFAQYSjyKPDYFSEO4/9xIBzGn4FlZKIsOE357u6Up6xqtfXrdXfHb2ki11UN9mGX9ENeASO61APtEOEcSjRy1AALUMfeIzExAsJIxX7z8CEiQuv7vq+vr8/PL0/Pz0+Xl2ezDm4IIERLs9Zhb/70dM2lnO/OP3ys79+/P53POefDyBcaNowAxExAMPCvrbchL2n1P1bIR1hAH65QGpGlcTSowgHeRPZAMNx948MFiKbRW4wc8ze0XhAdLd9w7PWIQBVGlACAcDBwDwM0RAhlMHTHYR0bXZRx0VSJlAgAw8dMRYmDGbysiFfhJ4S5tl+vie7++fkTJ0GRW7s8vn5+3Z7X/ercq5FZr2N9jwZs4LbWujftFuYEyCij+eMtVNXAMEK37s3SYG1Fgg77dW+1LdPkc3b3vlm7ab21ujU3G0ILxODEkkQSsiCEaavWtGM36T3XeZlKJAFmJnBExRS/rrKDTjXW6IGwihi8mXE5f7uz4zeZP8ct/m3vO4o1GpNmgNF7epM6j9zZ3nRbN3xjZAKAh0cAI4N7EgZwPqppHy55dxu7OISLpAh/26dsKOm+XVW9PSpobZ72vJzeQQCxlSWd7k8QNE2EGGGWM/JkpoZuKI4cIlgKz0s5nWeM1ItD9H1nThSI7mAa2J0ExWjYFiKwVat7125ugYBuXveqainLXVnePczhG/gumfIkwut6+esnWNfrz3d376fl7OBqvVlV7a39WqOUMn384bfr7UueYT5Nd3fvo3O9hWmrjc2HGFNKSZxUii8nS9k1u5VwRyGYZgMLbZaLTxOwESViQTXrzZHAwntzUwhjwkDukkxk0FhxWz1AU4IkHk6m2E0BoBhCOHNMM5Wcc06p5FRyLiWILSA63b4p6Nve2t4ISVhSzpKSiARC19b22lsNDwqBoTVH1O7rGmYt3RKnJMIsQkwicr47z/Nk5upHijZEaO91rTjIcGHhrk216uh3M1DmlChRsO662w4BhKhlgtm1q3vEYbQERrThJOp9oM6+XVfhSuCZ8ZTl3VLA2nXmW8FdPPptfb7ebq+vz48vn3/eLi/RG7ihAwYySvAgPhxXBGQCHPB5lkwEPq5dh20NCdy1NYiAFG42vCQiwiIp55QETF+fP//y04lIkqQynXLKhbgpb4kKA9PowDl8f7RfX58+f8I//+VPf/nLnz9//nlbr6pt/DGkQDbioNCU6HQuy5JJPAyJAXHsv06EDt57rcxM6K7jDfQ+yBVhbtG0dwfE9bLfLvvt1hBwnqdw2TeDUCADGCAD7b3nnMwwIDMnFh4VvLsvpwmI6bL7rX5LGRgFfUpUJoLhpdLoPoTu3npvXXMGcE48TRlSEh4hoeFHoUWGFEQBEMzoge7eWrtdEZEhPBdaTilnIhEb5UK3roqIzDhlmUoWEUIe9BsExsNDwSXlJAKOSJQyikAuJZcCIGaB39zau0bsbvDWXYmv+zAO5ZXDmEAHHXs0YmBoWDjaWyz00J+bm9nI57ytl+t6rYe4pddBN/ZAxIyUkR1ZLbbatlq3bat7vVxe7+8fTufzPE0sCd7i4sfK9aM9MAiSBj52+//waFf3CqAEQYcg8G3g+JYzcxwf40+bBxgZm0KtMYz+REFvUIWhvQnHVgMj6Li1gxl0A/dwcOYgQjeOEPdRqne33rsfzAEJ10Ck8dUIY/ZUHGAjvII/9cbb/usqc/dfHn9GIRDe+u3l9vlWn2q/GHTE2Ou+rusot8ettTdtu2pX15hLSbkEmHrXMNAIDwfrt2a3Tg8gImwELfrWo8ZtWd2cEPtuvZpVs6YeRmxDr1eKlGmEvIC9ST/cPbp5V6IgCaQEId4xOsg3R/txvkPA2xk//G2hB+8zjujRN4/juGu/IXU83AwAgpkQmRnfPF0DQ/r1+PfW2rbuRCDCRPAVvmlAHiZCET50+KoOQDnn1mTHVXsjwnlemJmYECjA1fR7Skq4v6ruGq1rYXKRzOLzORPdRUgSB9AwFUZkRzToBuhAQYSSKCVKQkMqnzKwHIwID1e1wAByFkFE12be226ji4AAhOhu+77VunnofDrfnU5huVeUBKmwx229rOvt0+Mv5XT3sJzukMnRu1a1rnWCN+1sknR3f7ec5rLI6e4USrrj7WVkGrl6uBMzT4XLHGmKXJw4hN0FPAjBAqpHAHYWzwXYCVNiQTevzQdaSjXAGYGAA6mzeMpAwdb4uva9Wpn6POM0ZWZEBkLMKU0lT1OaT+l0mlLOyJKXkud5ZAJjjae3oz0AvJurUWJJMi9zmScRMbPbanXXfd2sKwEzSUoJiQC8t721DWnsjDmXMs/zPM+lFJpnVa29Q63Rupv12nfavA8BwqHuDYeD8oEkJAwcCq31bbsNp68tSgHm0buajcqSkIbOy1W1bvt3RzsAgjFGZlxKul8yxrTfzf22eF3W22Vdb9vr4+vjp+31WbcbumMAAuOoYAnwcOkfq9XB3Q0RJTEjuJnDwSoZy9p6HwAYjwCPIx4np5QSI1ivL18+geNUpvM8nzKfUpqFmvItUxHkA172vYAD4OX5C8b1z//23//85z9fri/benVXRCACZkgZUwIRKBO/ez/nlO1rzKnrCKJNidW9ubpXc5ShPgRvzeret43Nm1pHRDW/XtbtVq1jznmeAY+WmyKHe29993BELMXDiQhzkomQhRAtwoZh1R2bGn/PcUNwFp5KnsqUU3H3rj7sarX11jWMhBSWEOFplpwSRLirunfzgy5kX5E1Ye6q1psysSTJmc532T2Q2N27UuvaOo3p+zyXZZ5zykS877XulYhTlmkq05RLLszca+9d1TxCRQoL9+76/XalHgMn+naZwmMDpnEgjkCBUfWBEGVGIWSEke8VhwwwAMLdW2+vl9fnl+fX6+t1vQEhIinQGLyhOyCACJclzUvKiSD2YUba98vlcr1eHx4e3r17N88zcyIWFjmozyPJ2Dw8Ds/BfzJrd69mt4iOpCPwZog8PcJGr9ViEKQj0EYzhJAo3FEt3GO0IsicGQf8kZkAwTxaH6UcmWL3EPUD3EfIREACIW5sgR4W0ZkcEBAZIsyAGZPwr2VvuCm3CtraGpe2FoD8VqDEbbsFhRNt/XbZX9b6urerWRtjpK46ZtVKRsyExCSOEWjELMwWcbBZkBiCDH21/trauaFQvWx9b8Mz5zbmUuB++E88ULW7t/AIxHCGSEwpJTlARR4RwYgjFinMBUQg7b31avH9NNHdRrMQAokwwnvvw+44sMweb6J3ta4aHsyCJAAE4RYGSEQCKCIcXyO5IEyt1mamgLFtQ/ROAeiDhjAaWWHe7AbReiXit+Ac6j0xi7AQwbZNRFRKTmme5wUBzfz5mV+ef/n6iZ8ypsSBhVPKyYg6ghFSyWd3ct/M1Lo6hEa36IPJHDF+yPr6+joiOq379Xrd9luEEWFTbaqk1I08JJckYhDUmqruEEoYb1wqbfV2vXw5n/D+7gFDEXuEqaGZ9T6cspEeSylTWYoU8VBzm/S/0htwC0mSnN89/O6f/ul/n9OXp/Rc97hc9w+cp4eHl+frvu5JglDRHTWCEJ0YGSzqpnvT1ndTAHfV8Aj1sK2pohlmhZSGcxKth3YD6AiBLknyMs3C2cHNLZfpfH/3+9//cDrNry8vvbX703lZ5lIyJTEAJizTvNy/e3j/W06ld0239uPrcxx+xsOmPoonFOBMqQgbqom21FMyJEGRNF7CiQc37Oj3ECFi22urXZiIyEy7auuqqkMUEq03kbGCVRURSi5EFAGmtl5XRAwcPLWGFCmJdBna321vqh4RQpJFxgB4bJ7fHSQIwpAEi9DENAvTMtuHdxw6J3p9nZ6fSffr9iI3HIo7wwDikaVALJxLyVNJJUX4ut5q3S0MIhCICBlJzXTAamiU5Abm4IHD7+7eazPVLr333lvvrWPAy9393bIsSajIXJacaU6UmRIRQxDgP7yRL1++rFf/9PPPT4+PrVft3d0QnTjKxO/en5eFzVrOfD4vbvH6ut3Wtt5675EKCXPKpA5iLmIp99M8l1z2ddvr9vqydzWHkZDnQyzlWupm+8329UIEvWvJ/O7jUuY5Jfna6963joAIpB3K7Lfb/vqyMpecZkSYpyzyK9JfEv/xjz9Mc/748d28zEnSttfL6w0MDIKAGDkRF5GppGVO85JS5nAzA1KMhr37vrfWmqoSjpwtRkQAY/IAZ8bTqZibqiHSieehBCD8Oh3msZ/PC4fPcixhOWLjzaAQIPW1t9q7gjvdbi38e1sMBuJodvoxCA0a/eOhjCeCxCREiTALTkkS89DseQwPGnq4mtXt9vTy9Pj09PTy3NQcMHHJOaNkycoiwhwRzPTh7vTuPJ+yYNj18rptq5sNCShChGtdTmWaSplymVjS8PJEOAIM+9tIWfhPjvautkU4Dqk2YTgGun9FlcEoFcI7kIV5jKN9JHnEgQ20AE9CmCl4/ACDvYfWURm6gKjLBDlBHkRkHBmaiBAIFt4hGosdSTQAAMiEpQgzAQ58YEAU7WLdtG+g9PVoBwAPdQ916Fpb22vd933rvY1bsx8iCAwOFhjXl6EPYCES9EAgICYRJiAC8BrttW3LbuK3l7XtjZg4MzIFRQQAgWQePvHWsDUfnXAIDheEwlToyP7zEX4w5OjhiEMm2MObficUGlXgmJQjDg+KuUeEx9AGGLgFjBhtN1MIGGl0AOgxcLZEBO7iYeNhHJL6cHMdY/Peq5kRQRxaxVH9HYMY37X1nVgIaUyOek8ikiQJ07pNIpySDCCDSIYA1e+mPhQGYCxMDOgtvIc7ocuU3FNre4Q72biEh3kMcHeQmtpWn+Gl7s4oYbDv+23dTA0RzLR3QyYLClAzLcUJycwgOpEjAxAPirjbfrt9ud3w7gzgtW1XEicHM+tNb9dtW2s4Csv5YZnPU6AHuMx/yPx2tIMkOT88/BEASvrE/NNevVm8D1SLn3/8+cunX9CUhpe+RyBHEBOZel3766VdVlMNERyuELVozdUIkEfVKwmIoLeom2nXcJ8ynxeZ5zzPU+1VTedlur+7++3vfvfu3d08l21d7053pUwRHuDdjAxmzmU+ne/fT/OdO1C+wZ9evl4WD2N1hIcFGqATBwKKUE4y5+wCwkmSiKRcUi7p7WgfHkswh32r+3aDoy8ymJcRg1xJ1M3tDabUeydGBkw5H+BCVXf3w1XgklgSjFDI1tu2br2rW4gkTWnKRZjf7lDfHe3j/iBEQpiIKKX75WS9hrWw2vbrlCQxDU6pa8cAR+JxFjDmkpdlmU+zmrZW9x3iLReYkImIwAZ7GQHczQ4lVaSSGZOB2yB6E/XetHVTZaLby+fb8309LX6a5JSSYGHKRIxEGPhGt/r62rZdm9a9hyNTEjYmIgoRYJb7h+XuLnu0sQFuazNvTdfaKmIq05wzpzyKcgQMEb9/kPNpfvps27bfLm3d2kBIqzkgnaaZQHqr262q7m4aEQ/v5vuH+ynPkp3YI2Jb6+V17a1qh179dGe3db28XkV0ngGIp3842pl/97sP05TfvbsvU0EkJuq1W1ftBpISyTLN53lepjIVSYmEw9AsbOTd9abbWm+3tfcuwinlqRRidjei8MBc0jQltOi9MvE08iqSDGH12NcAgQhHhORoygKie+xbN22APLzZrXYHMMN97wj8rXFhbMeIjuh4bKTuBqod3JhAkCbOWTARFsG5YOIxFPU+iGwAZr3pfl2fH59/eXx5er3cSEouZ0mT5IlSQMS0LKVMbk6ED+fp4TydC5PrKJ33bQvX3uqGAGGqfdHF3WJYG1k8IMzDx1h2TK/+Y1+7W1iPNycVHtTi4Q9BYDkIPmQD5jg+m6NUCQT0OPpE7u4KrtZr0AhoxxAkJmQmYcrhk/AyyzQzE6hCa633Hs4RAGAkmiSIws0Qg5nLBKcTpMxMrEp9Z+3JNKmLtsCIr6uMCN+9+4hCQXjZJoBqtmvfwA2OSlzHCsgp5ZIpCQhiTuHMTGNPx9EPlIKGYADd9lVfv9yY99fLbbdGC5dzmR/K4NP1ZrA1EANB5GABjKBBz0QyC+0mlA5WlyBnGhipMG83N1DoJFaOHM5jkRGP2KC3G7y7RhwBgoNScFSKOJyBMjQvCDAowW5GhGN+tu+7G/bupiMq0c1t4JUgLADMvXcf5umxDw2bd4SZO2AAsh+TpxhPf9tZLlxyevfuXSmllBKBrXb9pnEaHj/+9Ue1PZUsOZPIiD8WwVwAsbltEJ1g6I41DMGJMIuw6l5bbXW9sgoWBOlda7W6m/Zw80DImSXzmCGAByeQSVSjVkWhLIlIAJEzuW2Pj5/22zOEutbpnJe7OQC1+22N9RJgSBTmfW8RoI52/zvP/HVdSUoPp3OStBDeEZ2m+f3v//l/Q0TV9pf/8a9//dO/vnz+dHt56l29m2RBDgB2D1M0FVNqzfZmIjAVJsJSghmZEYDCsA7CjoZ1bA2tA0yQCdpsKbWc/O5MZWKh2G/XC5irlpLn8znnqe57q9XNMXrL1faGqrOkeT4D/JqqcnymzQPDu1ptnRndwEP3BuZZEgCxyDgEgRB5oAJwDOUQiQNbU4exgG0sIiZi5pxyygd9QXVwzwwitDcwO7bgr8OkUUCXXE5zWaaUc2tqqn2r2q1j7Swt7ULcam21uX0jowPQr6RMhzDvrd9u65fPX/7+49++fPn0+OWXTz/9/Pj58Xbb3tg7ENosDFCYQ73WTn7r2vu23lrdTDsigPVgZiYfhsXRIv56sxlDsggg+jozCI9una2HG4CD97q+3i5TXVJCIEBmQSQEJ/jH8M0ffvjj+4fT/fk3L8/PrdfH58fa/n+Xy+W0JBprA2EcMHXfVbskON1JKhBBNFoXCacyT5NEqIfeP6TTknvL6zVdt7pvvWkL8OVUlrnkksMoovUWraqrCROooGeCklMAqfYeRt5l3frL4/V63z58XJAgy+yB+1bLPJU5ifx6lgxeOoTt21W1EnGrDaILwzwJLyVLXpZ5OS/TVNxsW9XDurauXbvV1i+X2+26jVGaibvjoLsOGyILpNz3PZnqvm0ibB1scssJ3gaLHo5Mkvi0zFm41TpiolQPjCeimA9BdzrKLRz///WJCEYmF/bEIImJRBVa1VvvvXdJXCRNDJmRwCk0DAyIEEd60N7bXuvr9fr08vpyubxert2slEnSksuJKIUREpEw80xcAhwQRlS6BxIii+Scw8wNE5MwMgG49VYBQnuTXCQVRA4gP5ppgAj/+az9QOC6v9moRgzfaFEhD1Ai6XG9AsTj7A8IfEt8VDcNx5FzohGBjCSYmJOwcEQKcSBEkUiCbtCq39a+7wMsAyyQh1QcA8mIQjLmKcoUZQIRMs2Nc92kOocP6m18nfsg0sd3HyQLJZmvufVrbzfrK0NAeO8A4YjAIvOcp3kKRh8qAAQaH1+CIKAk4ITK0aLp3qpdLisCrttu4hOnNEmZc54EADCZUYAQCYmANxhGtaFNH/d1DCTnAURMSSihQrOmViPMCDiFfIuzGAZTP1Tn4RZqFh7IOJh3gGMIcmBARcYapRi+Lbdx04iwjh0CICiAIEboiPXeAYbaEYjCDRSC8MgnHpYgAjj+iSHdgIjAMWuEcKyAEPM8qTbVXutuBttWvzO/RTx9eaptLdOUS+E0TH8oaehZlKiKqPAgMLsbuREAESHECGLv4c4YhMmP6V2MuRARlJLKlEyVBgBIOGVxRyZlotMyS0pAYBHdbbu9vD5pmBLGvd1DSswpXMyze4RRGO87GmigBvrXLJ63SquMX/bpTt3h7v6jhzOT6rZMKXH/s+91fanVLXSCIWH13kEV3BiRAVm1BYQIFuaBzQYMNzAF7dBaYBAihYEbhJEr1k0JHcJzosQQptfnl7ZtkmhelpxLKqXVFma6Nay9IbWytPMXS4U4yfcj6nAPcwDwjla7IsE4gXvHgJIyEAHykOrEwUIa1rPjbA8coZbh5q5GAIzEhIXTXEqZCosEQu0A5EhpgB167wgYgD7Y9qOLmqXMpSxTnqeURPYmhA0i1Gz03faKiNqHjfmbknFozQfeYXw61Frt1+v69PTy+Pjy9Pjy8nK5XtfWNI5BRAzCDnJAQPcKzWtF7a3uq/YaZoihhsCMQuEBpmDmPuCZCBYO1qO7ByZmFOKDmPd2fxqOon29vl5zrvd3nBMA0oBRDy/B90f7xx9+/1/++IeP739/u173ff3p09+enr88P392awZmbq0h0sgJ1gibZykzB8bAso6f9e68PNzNqrXpNp+kFMyFUmJYsVWr3ZAjJTmdJ2bpdYxXwzqEYQC5Ytu91ygLDSpGBIZz3drlsrfdwPF0N01TCTdVK3OkTMcu9PUDEqFdb9YGfdM9hAEnmYqUPC3zssxzmQoAqFutba9127e9NVXrrd/Wbd+rqg4jjzsMgZupBjgxsnCSNlQXSVhb1NJLlqEXHrJHSZxLGl6hWuu21dZU+8jB5QiKYAAhykkmkRQR9DakHC+CyOSZoSScijBLa74a1DDzLoEJQRAYBwrHVc0MAaFr2+t+XW8vl+vj8+uXp5etNrVIZZ6WU84nkdkD3YGQERKARMjopZlTV+gWSIDEklKYhqEw5sRJmCnctbVQ7dyHX/sYvMbbkvq28P3Hox0RiEPNm76N1CGQUEbDPNABCCEIRSSCItRHGOpQPdhhMx0UXzMdEkEU4iRYUBgl4bzg+YynExJGrdqq77vvu6sGURDBaMr2DsI+BEeCFqFqzIpIjhxlBgAwRcTBKDD49WjH0zTnOacpI+t1vWv72dpaIdyVAICCEqWS5tM0LZMHmDkxCQshgPteqbaKQBFkFRSVOgR4tzawkxRMRtDAdlUYQqvIE6dEMUnfoa0WEG9BcpxIBDkUoXsEWkcRJJYpMYpZ7dCdTTAS2q+LzCP6WwayH4LGw8R+9F7D3P0wu5PICPJCBGLi40l9vW8g4RhbAlA4WOZsCOEBccAfhouOEBHH8xQhZEo5DblRBOBITgAPVwNvEADw/PpEf6enl6ecC5MgcK3bt4tqmSdhT7mknFJOgGhm1lS7pWTTHEEYRO5sGtqjN28tuo3PapJEERwdj3HpUXcgASDRPE2n82TWAQa2NuUkSPk0Sy50dz9JlkBo3bfab1cMdx17KxKRTPOSpUxZt6lbc7MAVmSTnFkORP+vD8TXbf1yef3Ua0MHJhGSaUoA88cPP7x++Phl+TuTuJPZQB75uvrtGuuGHjzPKc/YurzlWEditIg6Jn8K4IzAJaWSZSodEaecivBe7XbrRC6C1sNa67umLPNSRJJbIyeyneoNr69uvW3Pl+310/XL9a//mtL9NdLQ88LRLTRTRUBXdDVXtfBRJzJJKcUDa2s2UlYc3czVu/a3YVCYR927dcWAREIABJCQxn8CCG4RjuEilMvkZvtt7Xt3c0DCQZDPSaZc5inNKY1+ltC8lPjwkFPa066qpq5de++965El/91Zgm8ARg8ISWk5nT+8/6HWliRlSda1rjt4EKJbH1Zbhxj+KLM+7CauBuQsaAAIKMJJWISHMBhwzDAJiAedZwTckg+SNCMlISSWkkpi3rf1+fFLaExp7s0iTTCMSmPV/Ttmzel0ev/x/em0tLq3tpeZP/3yl+eXz9rb6/Vyfbnsiac55USJueRSlkKCAT42F1PShlPKc0m9JwDqLfa63ta19QoYecppllzo4d3pdM5m3lWBHDFERl3p62399PNnww3zMp8EGVmYmSWlnF01np92M4IHkSQ5p5RkGP6/qbSi9z7E+TmlZZ6Z2W1Yd1DGd0q5pASAruHdt77fLvvz66UOmreqDwomHfJL0j70XgiAhtS1ElnXVhsB1d1FdmEKNzcdPJYy5TyVy7UiPY0wHQAiIEAOx9rcDHOZlxMt5zwvi1tH+O6NYAB5CFBC4ggyB1XoyoBCI9JmRMWOSg66g7u1Xm/Xy/PL0+vr5eV627a+dwcSkkK8EM+I+TgnAtwBHUyDcDTBMBzc3Q18eAWIJCUUKkJpHO0iJIxIQwrV6g7IxEKcmBP8qt37D452YiCOwKHUVXP/avgc2z1i4HC1Ifqg+QeYhR4o1gAIAmTCYZ9yNe2KRu6QmCCYCFKCnDEJmULttu++V1fFCCIOhnAMD+8aIj5hpOGND9OuiAHIInCwx4Aj3shpX58NALozRCaYhE85n8vUUmFXNSJETMiF8pznU5mWMsYQQpxYiBDCmYI5AskDGlo4uqEjhJorIiAFkSI28JuqBybEhCkRJSLAzkGh5jYc9qXkIjkR2x59i97BLMBAnMvESUBl86akCT3Zhl+tMe7e9Uh7OvxAo8AchJo4iioIIAtgZOKRT4N4tAzgoP4ZBCABCyRBQBz2jQgancbevHVEQqZBEMFh6Q9AIpQh9xhdLwIaFd+Q0Dt0het6MTd5eaIRe14m+r738PHDR7XTiNZOOXlArb31ptYSWxbIKYiD4DDdOyuJsWlGTmUiEgTuW9ddQ8A1RsE0/JRJOCeJBAiDZzdieYkFlzPfvy8ps0Xs1XgdycjWG0SEJGKCxFSyJJYp595U+0hNhFKmXDJ/o0xx79peXh//9NPf/w8IFr5HysQZbGIC7/2ArQZCYAS5oxpsu69b1E6IOBVB5qRca9t3U3MzahbXzVoL75CF55xyKsskIkgSWZgAtme7vGpKNBXBI1Ogm3kuGeGwMJJ1ajvvV+wVGtV6ebp8fsKsltv0Lv75/wZvUS9HfAuNTEkED8dABGJOKedpMvXaOrgDhasrdTUdsX4Do9UPiXYIMiKhB0KgB3mgB46RHBiAE6NkDsO2E2CYKYzsGMIkUqaynBYqPKghiJGynO8XIRQm7TpsmWp9OIm+FZkiAMNXeXuMo/10On38+AMxn+ZlKZPWtt3WweEagmBzDXckhOGyGlokJhkFiRtCfCXTGwQokBBTQmIA6qbeu1uEKfkResPAlBgJMMDNt3UVSlM+uQUEIfBwRcO/08aPF4Nncs6UKAnZMuXTPM2lMLA169olURpVuXBJ+TTPnAjIS8nTPIcn68LABHi9Xvddb7fbdd2uL+veWiCmInkq8yLLOedCrSuyIhtyiLA5aNdt3+2pYa6nDyHTnITHWSCJypTq3i+XDtgl+TRjovGE3yIUjseBzDzkEwhYUk5ZIhxpwFWIkBKJMDGwA+wkFGjN97Wu215bg9EPIiCGY1Wavd01EIdq3UO7azMI23c7KKZh4cbCKUnTyD3UtGv38OEfSZIQ2Q2ua1OF+eScsoMxBQ3PwjdnOwHQkNCpqyuEtmbaOwaMz4tHtK549I/Cwmvbb7fry9Pj45fPr6/XdWsOTGlKZWKeSSakCSgD8qgdhlrZDZxABL9qRAcMh1mglAFinoST8NCU8KjDAroe4HN35wgc4FFEiP/41k4cKUFXJEZQNIMIQ/IUB9tvYAMBDuG9jtdxPXdElJHVR3zc6wYBDd5u9C5m0DrsGwSEG2qn3kCVAhAIwiMoCJEYkFQkWJAYhxSi9X6k1KhgwH7DbQ0z5O8rSHd/+uXn6Sbzae62U68poiAHpsQkIkIZE4jwxGlGQRFECguwIB+2mMKMDay7pTzUmmIZYgffg8Y0wYkqOBha8MwYiABSqOQkEdC9ttpMCbGkvORpkgwJLMf12vbNUmAGXlKeJ4qZwnV0g19//DXAxzxa1zjoE4PWDog47q2ShYN0XGWGrh18IO2G/DgQIRCJGciOV0cMZnlrpQWOj1yeimU4uvpChEfECxqOP8EHJkjkmPkfdtRBUnY176EBga33ba85pdNU3o52+l/+1/8zoQ86/Yg+bM21m1pH6pI7DWiZhypo195brbXWOkayRIxAbdv7VkeIDlKqzX769Mvr5dZbW69RCgpTuKq5aZcMhSAoSBJygBqCE/pcSGhSS+6WM6LtbTWr15xKSknYLY+bBpbMOaVvA8Kt75fnv//0l//Pv/x//5+mlNMDcSHOKWX3+OWnn378+19/+fSp7RUgmMiNekN3AgQRBhAMxABBUiQM0B63iNrhtpOZE9App4f7+bTkKTNABChhIFqgGXrizHmSklMWQJWclvP78/3H+fQucQAQmk8AiWkqTOi63bZ6uW6up8b/9OvdZD6d71plIRaSNMLN4lCM5yKSlH22GPzdUK29qeuo9cNDfdx+wx30cLwGB1B2BegIEEaCQBBgFmEwniKSsLfmbuFKIUg4BJjE5Mek0iAAGGSSCafRZufCyBgYHv6tTggRBzNChMf6ZOYyTe+Jl2V5uDvfnZa6r9fri1p3jzLlMjDfCAEj2rGpKQCo6rZu23rrtX31+LqHmqu5SEplEhEAhFpVDX1Ec4/8TtO9UUqpZDc3dQi6O93fne/ef/iwLCfhFAHuFuGDFPJtgQIAt88/PaWuqnvdXq6vf//5px//8tenz8+vL9e6dklcOM9SMieBsKa31ysnkkSMRBPcPzyczz8IJG36pz//9W9///z58+Xx5UVrdw1KIhnKLNPCSKbWA41ESYzEXREoHA6dWO2417UpsBRAC+zIxtmjRWuxbZ5vtu4VuL3zAjKXpF8PEBb+7W9+qHVbt5UQh9A9wiWlPE1IOMRZg9QfFhxRmE9zqecl3Ny6D9QaIo/YMKLhPUPgQ/Fjrqqm4YYI5IFfNzEmHAycVl1tjNcNMZjG5AcIx2xt2Iu4tsvrK7tdc6IsAvHr6T6CRrtaq330rod6cjRNPTR0hACPe2uvrV5v1+eXp8vL6/V6bd0DhSVTmlEKcAJKQ/AJLAjBMbKiDiZ2EpwSLoXmBAlRiNIpI7lppbDMx7gdEQKHrCSYOQmouam7tW46Qu/c/+NZOyEQgwgmYVUncj8g5Qfv7ZBPH/OyYZc/NNs4nLAiwsxI4BFDoO+OphYRBmbQFWoD3GIM693AlDxg1LzjnyEiFicOTsGCRIhAEOgGTmEa2qJX3266XsEMIzDRt54x3y9P3jh6CTTsLUdMJCjZwhlMyEgwi0ycZ8oY7IBduzYbhr2IFBFm0V2JIJUh+w3HMHC3YEcGSsEFJCELDWKUCwysUqEFGYl7m1JZ0nTK85IKZYoEpCtry5wmkoXSnMbGNOS3eP3513jaQeQ+uDRENCwcEaMpLcJIwmJu5mowGk8IB50GKUbqNwYiA4Af+AiNCJF4o9QBEguRDOwUkEgavQFF8HjTTg1JIB0wClBwdCQY2QFjDMYkAGQaowD/5mjH3/zu9ykRHoBuNwNTcAU3C2iBO4DGuDwGmat5q22vtR79OWIE7HXr+x6mCJBSqc26DxKXVrciefjvY3hLibiIFGEhxIBwDBN0LjhNKVzcDRARzHv3DkKLlAUZnN3UwKNgZPiODtj7/vr80y8//uuf//v/WxuU/C5Pi6RiBvumXz4/PX55Wm831T4STd2OeTkC0qGBdzAfjtdwMEN17Eqm7I7EIEzTxCWRMJiBOwA7EFByKUCJcDDhSiKSaS7z+W65e7ecHxi9lGdPmZASQCFyt7rv261drj1i+pVFB3A6L807Cx15xBHqRsQpJSI6eJYecOxerY9W9rDOxpsQxyE81Bzc0cMRBEEPAqWTUzD0UMMIHe56kZyo91AbC4+OC9kIDaOhAzkcNYIUfOgtR48wfPhLvz3aD93KSHyAYEIhlpTmeZ5KTsLPz1+enh5b64i0LMuyLAOMHeFm2lobUYd135/oSbsSbcd6Qx57egQQSR7BDR6IHccG9QaGGvAzGXZBTiVPd+e79+8//vDDbz5++GGZT3gcCYdc4d/Htffnx432ru223n7+8ulvP/70y48/Pn95vb5ubdfMKWEWTAlFKNz6ellZcDqVkrOr5cTvH85Mud6asLSq69q2tTFCKsQF0xTTgnkCBHMwSTABTzPVQlU9mg/cHhAMQg6Lp4IQVGbwACDYKzhga3G79cA9eMuLLff4rfSBie4fTusKoy+y780j3DwXd5DDvshB6OYe5qaKEctUPM7uBsPW62OmTiMHzAP8jQ0T/lYAWIQTQNDbLIYZcUi1e6hpdPOIAJdEzJxzmkti5gAggm4umURc7da6JsmA5dvHMWQkrfehwnP3kYqENDCMo+DT2uu2b9u2rdt6uV5eXl+3bWtdiXIqifOc8iJlkjxJLpIySaJB6h+qjBjNCEgMU6bTJHNiDBPGaS45k/uEYIIhiELjNBgz8hgDe+3WULWrWlNHNP42b/Mfj/ax5lioTAAgfDwHiKCIr/YXQAxm8ojoMPKV5ddkRUEAtAgEFJIQgKCOaoaBqtAaAmMgmAWCj81zCBKGyJkCkEAkWEJ4NP/HyTpSoTGC9jVen+v1ta839CBkvJtLXt6eDcCELuHYPMDZ+oQYOSfEah00Qm3O+T6fllQyyb6323Xv695ax1KkFHQIDe3avdNEIOPtIE+MEKGKCkxwKtMPH96f75c0cYX90q8GhhZFZDkXK7N2zZynPE2SC2UBBkReeI7GLElSCUkdOQQP/wb+Q/tB1YbeaKQODe0CEUowpjERpHB2M/AYusskgxQI7m42skqGBkgi3HRUZDGwmgAW0cbaHX0Bc42gMZunQHforatqKWUkx7914x0AmZGQpimfz6epLCKl7nq9bN9iqgABOECG3A8AgJ0kMTiCRTg7YIQBjkKBAiNwCAyGVgMIGRFdq2nF6BBGxOtan19fXq+Xl6eLqSWc55QdAplkzvN9Ob+f5hPnBGHdwQU9ZLSjAZGOxluQdlW1RGNVAoKbqnXrfQcCTv5Vntl7fX355fn58fnxRXtMJR7ehTBcX2+PX16fH6+X19UjCFlyBqSu3rp7j7Dj0tGaDfKkqvcWgJRSohQEZNrDO1q3VluoUph3jw5IWWCaEQhd1byGC1Oa5nI6z/Ocy5SW07kkxrpf6n55ftqrjmnl3rxruOPXQft4LcvcQwfPYeBYeu+m1ltf+9Z7b02tufVhhlZ3BQigw4UQg+QSGB5jFEcQw8vrEN3Nunt3A+thQJCmksskJTEnD1RVABiZVeFW9z1jyaeSpsxJemvbbVVtrVXhlFOa5klIhs+H5Tvn7tsBEOqupuRvKWAAJGmaTw/vf/j9H/7JA8q0iLCk0axyd88Id/fCxABxvV7b3rfrltME4VOeAKK3hkBJptHRcfPamnaNgJLzNE0iacDoJKU8LdP5/PDxw8ff/OYPf/zj73//x9/88PsP7z6Wada6D0TG8YJvNVsAANLqtCN7r23tL4/XL7/cXl632962rj3CCAy9BWQsqTSNul0k07yUcG+1bevlev2FIddde19Z4O5uToWmwmUmRzM0ZiA2jGDm012BQGusbdW2B1qgscC8pLuH6d375d2H0/m8WPOpyOvr/vpaW4ttJTdYtypZS8YRBBrxrWEhuquFA6I69L33rq1pzlYrTmUquQSRoQ3Bemu1actJ3uV7Yiol79veex/ArQgw894dHAAlAgOYAy1GSMxgXozAFjciFzqsO6MaFEyJp5zvz/P7d6eHuznnhAjb3mrXw9bMnLMsU84pfxfKpdqx9rZrb0iU03FdjbEhm6n223a7XC6PT0+X62WrW62ta/dA5CR5ytOSpzmXOc9LmZY8TbkURHrD3jo6jktRhCNSEj4t02lKWluEi6RcsqSFMcgVw2nEjw5bsIe5mwYhMXFjwoMhubf2HzPkhygRAphwmqTkNEbprVlrrh4Qo9weRy0BkIiFG+JIO2ECBn8DnkAAMCEQAimRjFAYGakDXw3bo+4FQCRgwSSQ+CjAR6AjoTDlUfcBoHbYtri86uuz364eiCyU8R6+Hu0I54lFAgh8RPZSmbPsra2tpkZ7w1OaHvJpliyIrro2ixHjB+xB42CxZgORjTGuvMEEJEiCHCAAE6d3y/2701kKriqmdTM3MCKep8x5QgUBTqPiBmFgROSE05wQiIgYkBXo6DkPxvSvq2wokIEIjlS9ocbUI5jOPYAJEZgQxpk4/hYceUkIAYPfhwCYhEYIvXuYOREQk5uZKxGLcBJ5W234DYDQ1Qwdvk6dI2K02o5h/qCOE6QkJRcI2rf2PdMfZELJCOPajjSIhRiEBuE4FiyMUWQgYAxExFh7boOvR+E9vEJUiB4BwD6dUimCCBBYynQ+nYJCipTzNN9Ny7s5Z8RQ6xUUMJCoj7atCBMJwOjgtdZ6OqJtIZwiAYULZIL0rZjZtG3ry3a7rtetVetFpmku09RqXW+3VmuEIzBJynkGSrGp9s5oic0PfoCZBTAhQcrESaaSEFHVegPtljhc1QkIGQLDSQ3QXBKdEg3//7zIw7vz/f35dF4kJTNdt1U7967q3rpaayQSEFW9G1j8oyR7nrLFREeDKlQtVC3ce9faaq29qbZjsbnb0CcLCRIxoNMRO2gePvppeCgbgQAIA8FG3z4cx2LE4aQEYqZxFx4p1hXUFRjyqTCzpOzmCOge1g0dHSlxkjK1edbW+Xsox7gc2Fseh7kTHVoiJEqlvHv/4Q+1EqfT6U5NPRxhoDgoJZmniZm79pfn596NSE6nOwCfSqm1vr48WVdmPt/d3d8/qOr1dlsWBYjz+Xx/f19KSTmnlFLKeVqm0/ndDz98+O1vfve733388MM8nUues2Tv7evB/kaV/O5xrJfXF31VtNf9+vr6eL28rOtWq2p3twgF67HdGmGkhGqm3QDD1HvTnfbr62tiJJReY68XRJ3nNJ/SvKRpJsM29PzuYM2PUC3i08nbA0QnJtYqkvHuQ/n4m9O79+fz/TxNEnlkUIRD1Irr6utN99qXBCnnUeV/+z4iog24s0PXaE1b661aa6iddSItzOgY4aZuvWvzsInGTKXkZG6IlMZ3NXMMDxpddHLAAHcAAiWIgGNbhrcj0s0PPYMDEhELE805351O7+/vPzycUxZE2Gvbm3Z39aO/F2+xpd8cgmq9mnZ3TZSEMQkRk7mbeev1tq4vry9Pz0+fvzxeb9dDnDPQd3nK06nMpzKdcpnzNJdpTiWzpDHiQfCAtyrEI8KJICWal+m85A1BuxIxEicREUQ3CmMY0++O7kGAHoTBxMY+wHRm1jv8g5Lju6Ndu9cDiIbzNE9TYWazeHm9Beyjp0TEzCRJiOjuTO6u2k3H/BXAD/7e+EXwQH0iElnKOc9lOk1lTkwwihg8nHUgjCwggsRBrEROIBTBgEwikpkSAKtiq7Hv2ntvbd+2HQCZuc+/BhUQ4vv3cxIY0ctDsu8Be6+3fbtu69b2nPN5KgzoqgQujHNJidDN67rtZs3U3RAQNrfmPbqBMwgpogcFExA7sgI1pwA24w5grtgZEFIukqZSxImMyAltSAZgCi5yyGUxABXAxo2Wxu/jmxeOxesOvRvi8AM7AChhbxoQeFRHiKMOCFM2EclJiJCZgwKCxvehZhFtWDwHi2Bg7JDGjQsCRuMdDhMvHBASABg5YG/GiWGHG953HggmU5um1rt1rUz0dWkh4nKf8zw88hDD/xQEB6kC3NOIrBthWub61i1HojfpyRjtHEywbqrNNo8G4Igkic93d+9/eMeZ8iRlKXlJMhExgJuJCEvvqWtFRJGUU0lpgiAz32tNtRHRqM2JaF4IXMgzRjLKXx9IhLs1U9UWdTPrbT1bWTAg5bKcH0qZvXV3xzwvLJnZRHobedQAvSsSmlsqjAJAnrPMc6IxAq3QdtSO4ZTScn+3dOt7Xavt9dqWE04zCWPi9Id/+vjHP/7zDx9/SDn98svnx6fHT19+0d7idomXJ7y+Zus5IIDMQR3dgfy7NVUSeaSUJAK2bbO2tXVVVSachARSI2pke4UhziDCnKWULEmE2QMGyrBVDyOQBIAjKFlSkpxYKAEEHD7jPGVi6d167/u2uxoCDHoiJabEyDSd5iZs7taVAgXIgEG9WwOJIQ4uOX8rfTik/m8zwaFdDzcYNwVAlvTu3QeRtCzn9+8/XG+31mvOaZ6mu7vTPM8sbGavr69398/TfPrDP/1zmI0+/+Pnz3/60/+o+z5N029/89s//OGfzOx6uyFSKeXD+/fvP7yf5znnfOiMJac8TafTfDqdzqdpmomSYGKS0bH/Kl7992K6P/34t89+NfbN++fLy6u2TY8ABA9Ug21rt1u7rrDWJGmcYFhrD4DWNSLCjIi1x+32qlpFSHJOiUkwl3JKxQN6tcvL3va+XncWJqZ37873pwet3lvlhPfvy8PH9PBeUgrV5upAUWa5w7k1Xm9bbba3RjxP0ykXSYnoG/9IBOzVa429Qa3eqvUDeXB0rOo+NHZjB/MIB4yt70h42/a9Ng8ESGOqCAQkkQnCwH3IeRRhoDkBjovRIASgHTOkADyY/zlJyXkq01LmOc2ZMzm6O6qgRt/1VuteGyCcT8s8x/LbX1mzbmY+glsCRrPU1cFVba/15eX16eXl8en59fJ6WzezYE7piE4qqcxlOk3zOZdTzrPkQizhoKpDGB94sNvDgY6mR6TEZUrTPA33mnm0phGRE2XBIY7XDr3vajr2qJzFzKH3DDLWX8rZLnptl//50R6B4IQIIrIs8915kST2FkLPTGZBJMKci+ScpqmM6OJatdZmauHD4Q61aW2NAIRQq/Zu0zSd75bz/Wk5TwERrh4+3uLIcxMmllGDtIDDsJkT5cLTlIk5QjohBcaiaB38Cn6NQBGZyvTt5pUTJRnoGQigwXERJkJggpwEERnQ1bT1MGfEJEwAVZu2ZgNBFUExeG4+wOuOgQeaUzIJB3rtyhUELDp2RwsHC3LowYiFmY1QIQzgIPYhvd2iDvHCYRrGITj/PjkCAXjYZiKGgpJicCCBzAO6jYm5MCPAQUIKBx9MzaMZQkQjaLpS15HmBUbEIhQQ5ofMEbAH2FsvZcwFx4+J4+j2iGFAGleQcRUBUARKkkUyk6i62o7fM1LKImX5CmsczdyvvGCMYA+KcFUAddAAtTEhAMCI0fcZH2BSJVX0cCRnwZQll5w4nx7u7j8+pIKSKBUmxoARwUhESTKiEFsipsQ55TmnKQLUHGWn1AiRWSSlJIkpMxaKDJFeXtPXpBs33Tdte7ce1ke+zjLN7wFnlNkt1HzfVA3mZUkpt9r3bVuvt94qInWzNLG6cnJKTgKSMEkgeChYYZ1Sa9R7KlNJebId1KhWUIcyoTAiY844zTTNaV4m5hwB+1bXuvW6cd25VlblOPIX7SC4fXe7QsB39+e7mKZpjoiXl5dXAg539wERU9Vtb5dbJVrDNZBLlmWZTudlnqdSsof3pq+vry8vl7537f5GBWUYelZCJkJOzERCxKTmvdV9qwOqQ4hGxKrsSTC5qrUeIoCUicu0ZKfNsW611RbNOpKp0j80siEOuX7vXXu3jtaRDsHOYMOXaRZJIqnM8+lyqa3NczmdTw/3d/M8A0LrbVrO8/l89+69ah/yAwj/21//utW6b9v5fP7n//Jf/tf/5b8B4LptzDJN093d3f3dOeciKb3pTDNL4iQkwkxEHEAYFCMYuvfWm5kxH2qQb9/Gj0+PcftsEp18d7uYrV1rNw8IIA/o6r13hcgNUCRLYiY33/cWtfWmfVORBEDbWlX7oOkCehDJJLlIBIKb275vah4pB0HKWcppZmTtnSXms5QzELmZmncCTDmxEGfojdZN99b2GiP+ligNRcKv6wp5md/l1Ese93XtTXsfHXrOUlIq42jHrxBvQoQwd4MM1A+OBwHT4JNCqKu6tgHhtGJZLQd04mBGJgLEcGhNa2uAgHKMFqdpPi3zu/u793fn+7vTaS7hrt2MFIFMte2wbwO4pqbqv/l1ZDUM/0cfMcJNq6mZrbVebrfH55fnl5fXy3Xbq5ohEkvilFmylClPc55PZT7lvIhMxIyDKmM2lOzDPXPk4QQ6Do4LpcQpiyTuSuZRh8/UiVASMwmhqXloNzPPOZcsI3tF2didhVNOt36D11/X1XdHuzCXXCSlMk0P98vpPAmjunkoC7ZuboDIzFwSL6fy8HDOJUNgrf163VRt9BlU43pbL5dbSrRMaVvbvrbz+fT+/buPP7y7f7hzt65qph7jcDqCZBApIlpr7oaMSbiUlNKIV2EEimAIMYXe4/n58vT4HIEi2S15+/XhbFvtfFjiPI4wNHMDMwmeOKtaa7U3HdmuQ5jmg8sSQQiCZOaoY2xAzDLkPBJciGdOM2cmaPt6syaCinZQAIBIiBuBg4KGIupoTwy8G8VBs/LDjRsIAA5gYebh8U2NAgyYh8GHxjjCjxqLDkgdjEgAh2Ai4YSDCGq29TrQuZJSyTSVtCxngP1228bOknJalqlrYqbeu9lggoyhCxDCsJWM9T2EqoBHbty4qMRA7A7RJopwllQsqrnR98hDFKLE42gPtfEbJgZkPBwXAQ6Mhqhu3cIoiZSUkYbElTGw9dY7tQ69YWXsVZdlOZ3PdUORfH5/Xt4vzAag7rVXM7MYFmZGFmIuqUwiKUlmyczZ3cNUEEOSMKeUci4pF5FJuAhNjPm2X/tbcmVr/eXpdbvt3pExT+X0/sPv/vDP/61b27brELT0HhG0LIskrtvldnl++oLbKkRs7ve9VN26rUEqyc29VQ0DcgKMVKAsmdMJNPW9v17Xx6ebgXHicAagYca8XJ5++jnVvZe0rNedudzfz4wxWfOXp1trsF3NR1sIEQfU9rvH8Yff/a7M6Xx3hwCPj4/Pz8/X2xUBTqf/f3tv2hzJsWwH+hYRmVUFoNEbycvHu79FJo2N2dj8P/3J0WgkvdG7updbsxcsVZWZsbnPB88soKllPkvWwbYmiUYDyKqIcPfjx885IOGy5Lv74y8f7pgJUVno6mr/4vb65cubq+ur/X5npsuSf/rp5x9/+Onu02NrCyAgkiGo9Vw7duQQIsUhRYmh1lqraz0vTlE0RGAGMAEJxAIIrZPawHIYd2Mc8ryc4vHO7suc52VqrSHiOsD2+erac11kmeblDEgksTdtTYXZ54GROMS03+9DCGo6DCmllGJgYUCIRNcvaDwcfLKOCdFMazODDx8+lFxevLz93e/+8Ic//X0IsdbmEyp+dBtYbyoigaLEFEL0Y6MKqh3RUUOd8/x4ejwejyVXjujA1PNHeFjKfJ4raRMDkUnhXOqcsxoQkQIyAUcZ93Lz8jAMAqaIhmxLLdOSp2OduY+7fRrHWqhVyDV3XSTxsA8gAwcEo1psPvfTYx12YJ0QmwkFCTFgCAykXcs01aVViRqExmHcD1cAlFvvir1rqy3ngmg5t1YF4TNV/xDCb7/7k98S3vKbpvn0eMpL6U3HtNvvr4h5U9fgEIOI9NZKzqfzNC2LmwIRATMGZlPLS57Py/k09d5FmNgAmgRIozcDzdsT05yXJbNwHJNryV9fX11fX+/SMMQ4phBFtGvOVU/z0idiZ6PG1nut5HjK8+tqg0vd17vnJZ+n86eHh/uHh8fzec7Zx9qJhVlIXMpBiAKHMcRR4sghEQVzS5XVG5YA0NAU/VJd71UibzwDoCIBIHZTdScx4yCkwU0NGJF715abY+reC6qt1lKYKYQQTp8VVJ+F9t243x1gGIZxHA9X424XibBrH9Ph5tqFnx2vIhHcjfHqZp9SRKSS2zQtvXdEcFmo83k+neYQKEZ+uD893J9vrq/fvHn11dvXL29fdOu1tm7ddBWpdc4sIKmbpqsSoQjHGIUFnUzrXFpHMwFuDo/74cqAOKTj/fzp/XGL7JBzayvzzNXivVY0x50RyGrvWXvtva3oMhkJQGeJwdig+ZAVWOvWwFjJf0QhjiKJwxAlMKk2n59UAkIKIAoYW+DCUK1ps47Yt2yN2JAv7+uKMNuq9Natt88HY4g4bPJnzMgCW38TEJERhV2BH4U5iFecbKo5l2k6t1pcSCwIIkqMqZQOsGoZ7na7Fy+uSy0icj6f5rn27o4/26jSRvvZlIbX+RBEBGV/SX3GGpFE4mF//er1m2Ve0Mj0+VmxaXpsTnPtZrWRmqtu+iuhsFpitd5rK70X61UhACVWJ8syArbmps+5azHrxDAe0s3tXoGZw3AQjobYei+1LTXXVnpr1joCkUSWKDEGCViFJHCQbqjemXA0PsYYU4wxep0XJBCthtO+Wq2P98f5lGtxNJHG8fr25TfAmttc/XiAEIUUGaw83v3EdC5Z3F4BVQNZR2tuGN6tN6gZejXoCqYINowSUi+q56WVVlauNmFvkKfOQXvX+/v7ee6n+znImHPtCnEcD2McU6Td2GKomXyPEBATENmvyl0WlhCGYWDmpeTamp+SYTciIhLPucYU4xBTG2KUw/X+cHXYHw77/W63H30ufBiSRPFuPeKGDhF362omCCwSU5IYdGVvIKG/ou6FyiIcxzTuxhiTIAlQRE4hjENig17aOZwJqfW+5CLCEuRX5HIzy3m5f7g759xRh/2VhKHklpcaQxxSGlIS5nmel2Vy+t4wphgDMRGugjd9Y4SyUEAmNc8ugZADS4ooVK0TGgi11mspp/N5Ok+9d0QcxmG32+3GXYyxb36MAEgkQIhmD3cf373/+e7+U17mxOm/rtopDhjHaTmd54yh5aylNARIKQYOKQURROL9Ie73u5Sw1xqCDLshtxrOM1aOOo5xl9LQOsyxSDBAUjQwXeZm2NEkz3Y+lunYwMg6IWgPJkRCllJA0lyL1gytxo40RAvez5UQaBzb1U04n+M8DTljntt8rvOOrpLCxi4n4t3uiplI3MgKTscTAjFPNdfD1f7l7YthGD2ihyAppRhC762Uej5P07zkWlvvXubFEFTtfDo/3D8Q3bVah3EIgkg9JBgGRrSuLee6zAVAzDhEGcYhxBBjevny5e3L2yhBmKOIMAPgrlvc5/Fq2V9P07wUV59VdXfBZ8miFtXeeuut9V5LOZ/Px+Px7vH+eDrNtbSuSOwjxD5dRBSIA0mUmDgk4gDItjKO19EIXDNtdG9tR1U2bxDr2luv3bqtM8Dm1V8UjEI9CCAFCYWllNJqa615/xCAwYTVzED+B/Ywty9eXr94MwzjbhyHMaUYXAyo9bXJa05XRyI0ZhIhYkSAvrObKz8j5vpOvfl8sPVef+L3vX48HF7cXL15cfP29sUtgPXeL/0NDxn+lOCjQWaIPgq7etitLqSeUiEaAFvSQt2IY2rLJ4AttAPUCh1d2shsGzhY/QMcWlfFTqzBFE1RraGPP0ZJrGpaWw/As3GuDZprWLnqCwXhFDhFjkICK7yE6OSIENCCCi/su0QVUI0AyUjJ327X1NGu6rJW/nAGG5fs8t6QDDx4Z5vJmMxQu4KPhElIQ0jCHIPEEIeYBp++NZim6SN8PJ5PtTVTNiVTWsmjhkwcYry5vnnz5k3OOYTQWp+muTs3qlvXvmqH86pgA+j70wlQ62OoemOeJXBM6dWr13//p3/MS/lx/Ol4fDifHi/377uf/8ahe0aKTYNBIkFYO2TdVfOIqvbSa9ei2kIMQ0iMTEBoBIZ+0Lo1BQWDbhYPfPPVIRwGAODkHnW5lZznpeXWKyxzP59Lba4sLXGILIFYhnG32+1YgLATATMZASihInRwDLVbARKzJw5Hre1cT9O05EVNLcYCxuN4Lfuwp94VAGRI+xQj2jKfPyynd9qy9dJ7XmpecpmXXmotrWpXH8fTHlrtNZdaaq9l2LfdTavFlllD2L1686L3XmtupTyUItEkEsIRYQ78AEpeP8Q0vr652n378ppaFLTAzpwMjNrxM9YsAAC8/3THJ5pbiyEej6fjaTqeptpaOE2I2LueztNcSjdwkBlZmuppmktvj6dzVy253D2c5qWWpn2dwQAOIcbQtANCGoZhHGJKLNx7iymOwxA4MHOQkFIKMUgQjiJBmNeOESNq69kpZGAgzDFyjNQVhCAwPMu0HC46n08///xD7v2Hd98Pu0NI4zTl6bykNOx3+8Nhn2J4eHx4fHwsJYOZm5Mw+5kFNaiqhkhRQkxjSmKApb5/9+7Dx3cGqALK9ricQogIuCz5PJ3u7x8eHx/NTERubm5ubm4Oh0NMcVmWkgsaEhGKIBOYnh8ffvzbf3748HObp8MQ1g7cs/X69Zs88ulvfzs/PlSzVtVq36U0DvuUIhIygwTYHThFYVJAvL66+s133yLzeV4si7SRRUwg3v8CpFcvrm9e3DwcTx/v747z3fH4CCYt4/FuWU4VDbUaAJRQzTUGcMcMNdcOjcE6YUMr2CetISIKMmGMcHWdWqZPH/LjQ358UMR2PVQ4rE+hqufzjAwkQExENOVlzvM8TzWX3S6J6G5P+31yyciUhpgCAppZznXJZcm1lObePEMa1ezx4RgkLXNblmW320kgwErcgZwnTrXCsvRp7vPUctHWYNyhyIDkk+XBlVolxiGNMcS3SKpQqyP8uk3h9b/9l5/6BqWU2q3mWmvOeZqm8/l8Op2neSo1194BSYQAyaVXDBhJiIOEFOIQwkAcvY3ihsCw2nUZAMNqygAADn92b32q9lLywi4T11en1q6muiwghElYGFIa3GxJVed5AbBhiIgggU1N+0qn/m+H9hc3t7/5zW2KMaYUQ2AWH9TY0kxPPDZb0DWMbppOAE4S2QIzEXJtdZrn40NPMQ/xahxf7IYXu+HW509gmw0FM1jVacGeyCZrKY9e1z8/DgiGwBSZAhgyMn5up+QCSIrN0HvUus7jdlsTKEBGRiZCYlMxAUSkrbhXbb0LMSMTFNROunr4JuRdSLuYxhiiiACxEQMZkCAGROdzc3fNdfWnUQAAhe58MFRAXVdXz922AvhzXSQMyN1MzcSU0cBrOCMETjwMYYwSUgxDGkaHGkMUlmVcGGVIuykvRDyO427cCQXtUEsDg8BxSLv97lp4KaUNaYphBqMOVXtBQwdnRJgFzJrPYgMaraRIcHlaVA7ChoCkaaCbF6PWsZfCaM9D++Onj0AZ1EiNFSJQoWCquRQDQGHPXKq23GuzZthDkCVGcmqhonVrvfXeDc0VYBBJAcJge3LRjWWZFLT1WvKSW1GtkOc2n3Opjp8hIBkQEKc0eBEgpCFyjDyOaRiTd39WdoIEJqn51cVRsNV6nB6WaXFEp2sjpBj3h+sXsosKgijDMArbfHq3TL/UsiznU57Py3yeyzznnjP1joaDdi1zI+Agg0EvXWsuy6y55qU56Mw3L4e3b96WXM/H0+nxcZpa79aqIgKhNTY0zKV1ha4wB87T0JLTVrBVBTNCZkKm51MXYGA/ffhQrd6dzzGmeZ6naTodT6UVQp8Rp1rbVHOx1smK9XNeirbjPDmpHgx60/M0N0MKMQxGRBJEkoQUGQ0JU0phiCio6GwK5CQoJBxijGlIMUUJgdkHG11GRmsrljX3rN2qNggUD8PIQGP0cWcOvxrWtdbqvJwfpuP9+ROnxBLnpcxzjiGNu91+N4YYTsfj8XSqtYBpGtaq3a8eNWimhoRRQohDjGKIpT1+uvvl08/IXKic2unD8T2LIOCyLNM0HY/H8/ns3a6H+fpquhrHkYPkJZdcyDEv8URE83S+//hLPt9LK2aHjcLytEJIFlIt/fgw19bBQIzHFK+v9jHGUrNiDwyBMQqlICjhehz3KaVxvLm6YRukDUstx+WYEu+vhjevb958/dXwaaeGpS2n43E+L/ncl3PrFawRaghJmCFPRWvrtYdAXQuJEZMi5W5tzme0EEPciWFptYJ1QjDVWnovYo2fD1XW2t5/+ghowMbCIpSXfH485XnR2kLk3S4q1FJnNxBKMcUUiBgA1aDVvuQ6z2VZsqnFOCLSPOUltw5oRM2st9p6NqiEXU1768tSp3Ob5j5no6bdmlGjUI/TIsdzSEGCxB4GUwgOEqUYI3tr1Cs+7TmXH7//5RLa55yX85xznuf5fD6fz1POufVmCOBK5IBu44IU2SN6GkMa4riTODAH75WqrWIwbu4OuBbt4GU7eWcTVLWWssxI1kw3vpSCqjXVTBYYc2RKIYRgw+iyy621kpEZRZgIkBncP/2/F9pvrm6/efsdMTGt7dwttCuA4QYqADhxGbdIrGt0tzXiI6y80ZyL9WPke8ZReAyyE9kxj6qK1jyamV3cRtxfxjbZc08gOmxf3ZsUrgACCNOcl5y7GrdS65OLKCLGIRIqdR/9WkN1d0VhT1NcF81f+O33DSY3A9Peg4iQkDFZTQbNTK3HQFfDeBiHFDiQqzESuVUjkQGqT2X6dyHvBGzJ0Sr/426ptvWw1//0VND93NYHcYp/BzDFbqxu3IIoQhzHsEuyCxKSxDEOQ0iRwxDSbhyv9te78XCezqf5DIhDGoiwtd6rTqdFJFwdJFASSp1AfC5914rkUgrCXKDEICFyjISkuUzqPWREn5wBwNa1lA5ohtK0dpu6Hnu/DzLcXMsyP+v6mJXj1PXcayW1gdlIOkktfZoyEqVxJBFjqL3lVpS6iQtDKBqaQqu9ll5rq70jIjGKsDDBZlSCBmXq2jqYWddWrVXrzXo1AkQjbW2a83nKpWrrLpHCzCBkKckwyjiGcYxpkBDZE0wnzL989X/GuIb2WsrycLcsk14AEEIJ8XD1+nD7CigYMbO29vh4vzw+fjw+3h8fj8eHh/PpXFprytCT8BhSKrXW5chEu31yemKtDZY6535qNSbZX4XbV7e/+8O3y5Q/fbhHwFp6rUVrj0OIQxzGKJFrc+gkpRRLb0uuRCaMveqKCiMIoz0/8wY//PLuYT4ePn2KMZRa85KneWqtEVIIElPyebxmvVnVoqdytm1WldlVYgmAMMbhmsM4+BiFpMBDiC5xEYSZO2nTnq027BAQmYDBgqmYshqrsl8WSAhFteUKFY1XKSUYaYz71HdqbpsBafyVuggyowQEbEudezsqgovNL8pTD/ezEFFttbXql9iSkTZC3gqWIigSMLl2E3XA3KaH48P9HTJPdh7yLu4GRDD1kezaWu/YiVBRHktdHh/xTAboVwwZwlqwASJoqWU+Qi9gpqgrC/bZKiVP03x6PB/vT6oQWXajXMV4uxtZ+L7OS1mqqg1DkvFmPw4SYwz5eBKkqxcvdvEQcHj/8cP7+7tu0zDCeOD9Xlo75NxzmadpPn76+Hh/hirCKfKwH69e3F4D6rt33z8+PD48nIYh7HeyOyTsooB17nme5ukxRN7dpJAMuU3Hdj61ZS5ae6C0H2+jPBGDcin/7v/5567NyCRwiAHUtDbQzgBqrbac3gcJvj+csRp8aFI4GNA85/Npvn94XObMHESicDDAVrua5n6qLS/LuWvZ5mWgN2gNarHSCA0aqS610Vzh7rQsYQghhSGFcUzn5ermcHV9uDrsD+M4RAnea+vdWD/TKZ/m6e5umqb5PE1Lzq02XFNXAfJJXS/FmThK3A37/TDu4zBKSMiCKJtwwXrFo7fZcVMbNrI1PQBk670vcz6TWm/Cwf+6f7KZtmKFao6OcIU0DIAwz9M8Ta21WgjdiDpwkOjTy//t0B4kjsMOn7DxTXLeCMH7oe7ljbZNZyKAGT3Rnp8KeVSF1tUv5VZbKTXnPM/LKZ5bq7UWj3+bh6zpKour62T/09zkBTRwgGMFwXM9GxYgNSSAz4R43JKaAJW8zw1ChgzYLwiE//To95/3HfRCGkMwVWYmIG2gDYsqmQHJmOQw7neDD2kg+7CeEuHFLAscagcw84EXNB/IcVAD+voK4ZYaramAgwbPq3aWkEaXbyF2b3VDUHKJdBahwCRMIhyDRNdPFzekcVZz3pkZc8g5Py6PZc7LnFNC61BKm85z740A97u9EM3zPM1nv5F8YMGZdL1ZrQqgxsS8Euq8keFvv1k3K7k8PDz8NMRd62b23PkNHj+deztprQLWgyzIZpiXej4XJB7GwkGA0E28KABHNLFG6qPteSnzUua55OLCtxyjRPFhMIpBQHGZcl5ab9q6abfedBViVGtVa+vzXOdzWXLPVVuz3g0JmTBFTkli4jhQjCyBnLUvIiKy25ctskNrdToel2VpvTOxonZbWn8EOAnvUdCb9gbJiDsQSKSYMCSOfUyy55HlZty/uL59ueT87qcfl/nMaCU3SRYGDFXKbLnosB9fvHz77W9///f/9E95nt//8p4Dlr6cHrVV3R3kxavx5vZqf3WFfCAaEChZ2/dzmB96IygovBFYVjXOzwdeD0lixVFUGJtxtJiMe0ckb4ISk5/E1tsqKgsGBkiu/iZMEkIUFgADNXepZhEJ4upVblPkAmJj27nktasWrBd7DCEElhUZR9cMBHd6NXRTYUfjLo04svE4wFMODyuhwNs1lqu25kRTtI7ctGBD8JFo1/j1zWvr7Lu3GAHJCFUJjLgTNrXc5nyaypmYoVAPvXIBdHaT+iG04JKkWqG3ll3e3hU9UDeLBb9gateexVyQVy85/eUpTqfHx7sP83S22gRlF8PLcffqcLgeB0NYGEExpfjq+urbr766vdoHwFbbUjLkQr0F6km62TTP9zmfjKHkeTqfSjFTDRzHsCd4hI6Bw9X+6jdff/P2q7cvXt7My/njLx/Px08AWsYeaD8MBBoASGvLUzveFQ7UuqUdhYgbL8TrGGaLCM9a1F0/fLxvvRsCC0lY1VHHGK7GIYQhyqC9n+bZc6/VMwAQkX2QrtQ2z8vj42mZC/rlF4cQ1jEEU584OfVe0b0gDEDZgLRbVyAE6LBU7Uttdp5rCSnEJDFJGuJxmh+O56v96bDf73ZjGiI7Z9K0lqbPWiTzstw/Pi55WXJRVSAkEZKwzqgAGBASIwlzCHGUtJM4ShhYgj3pEdm2FH1WT83IJdpWcgozMYO7frs+AzL6AC6joUFvTbuW0uelhMBjijHGGCSIMJH2RojataoKMUek/wEgb2q9ri1wW2WnNtKHeWTdqs8tLjrBeQuJnk15gxsQbZrmh8eH4+lxmo4s+PCQmC2Xc67LkmcfRlV15tbKc/OY58EbHDXb+FwXFVV/+q5NhurdDA7P5XOtWQMwxZXvDWZEiEbs6oTgEujrrJgrXoIBmW2oCYIxEyNQXnpeWikKACnG3W7c7cYUpWox/7rAbMxAiGwAXa33tfSHbZQVL5eXrTA9rQ3KdSbCf9lnXQfgGNPVFdfWWgFcDDwH6mCdPaFCcENLJwEimUGrmhEkRAkppZF7194tLy6JONVcGKXVfj6eEEwCE+NhP14fdqfTkdlAG0DzK7uW3npdllZrR1LW1VPDiZ6MjISCKAiEusyP73756xhHApmW5+ab9undueaTgAbCEpuZLbnOc5vnCkgxTsyMK5dN0hDSwICu4W216XlaTufpeFymuZoRk6Tgv3CMstsFRDydl9OUp7mW2nvHru5GD0KEgKbQmll3awOtpefS3TBvzihzZa7EwALEQGCE5kfoH/7p6UF6a/P5vCw51x4CNdPc7qflr/Pc0vzA4Yb4RuAWjFkOcf9yfPn2ME2zCo1lHK72u+v97sXLN19//Xe/n5b5n//53//4w3/5+P4nbZlCjmPfKRc10r6/evGbv/vDH/7+X/39v/43rU6v3//YeTmVj53mPMP1V/T1d+nrb1+9evPtYff7Id0iFJsf6/sfpp/gMT/OM6TIqqscEPiI6VM8xN/8+dtbLjFEZNSmrbXaqsPSzCLCANB0bfn5QafPlmeQwsJ+/i+OAj4RuDWyUcHWXhPiOtDrioNEIhJiDEHEpV9xA7jwySPNcaunKIgA/yFDfmI/bDmxGnTDbqiIulYgtBE/0dU0dStGLiOdW48PDVzYlJCQsEBvAGw+PAZkyIZiq0yiXbhRRmRIZq6n4qKztsrTgxtneQ5BiqsTzQoB62ckU3i4+3D3/qe+TIPwLqTb/f6bFzevbq5jSrnXSShJur7df/ftN//05z/d7MZ6mo8PD/etQWt9mioRp1rL47w8zMuiBJ8+SauaFzxPXZumMAxhHGMe4/7tq1f/9A//8Ls//H7cje/fv//n//iXln9qTQnMGpMF1IRAqALdeq29GwcAI+islQRRWIUrKLRs2uz5W9EqutySGakCghLYYUg31y+/fvvm69cvp2m6u7u/f3icjue5lFJd0MM1WnwQzmpxqwJtXWvVIDXGgQi7llKXkmvXhivXx8FWWuERQkPqZlC7mpXeQ2tSmReWaWE5h/AwxCENaUgphEBrPxYR0J7NLOSynKZT72oAEhNLcJVZAwRiYWEOxME9VVkiSTRkV6NxxPaCCXl+vGn7oCms6lvggiTEgWIMQ5IYKQaKIYgEItEOjFQylFJKa7RYiqyHQUKKIQzDMKRUylJLLmWppQhTGuKvOByfhfYPn+7kLyt25xHaVmfXtYu+gtZb8wBwy73WZ9lCu29e01zKNM0fP32c5rOhIutSjuOYqtbaqg9xI67Xgaz3wiVlv0g4bWxtL5S3c8hCMSX/QUJ4qtrNoLbiWACsQ9nbDeFbQjdS3TpRjqSr/tnKxFeAVfYZDKCb5Vq6WRojCQNiB2tr+x4MDQwU12itai6kAbAqczkWo7CVH7jW5x7WCVc7d9dnwf5sEgMVsCJ3gq69qi6qi1ntoA0pl6BqiJgrd1vmEmSdKZAYQoiCSE7Uq1UfH4+PD3fLPLkBYs7z6YRds+sT2DjEGIgtRk6DdA3ONDFAwpDiPoYByUTQ660UEhGbdSKIgQ5Xw4vrg9AwnVoLJTLW8tmZ/+XnY54fB6Yh8DCErv00LdNSc+4AFKR6ChdCSEMaUkhJFLT1XrvWpvOS5yVPS1lyAyNE8vG1MdKYJO8DETye5vNUlqylmZpDS8pMQwAhEiQJGAPEoCm22rQ6kQ1oEwH15ocBGEJnMDGUz6FT7VqW0mpXw9zstJRPd+9+/un/VvthOl2n8eWwe3u4/n2Ir4bEL199xUSHq1e3bz/meU4Sx2G/3724ffXNV9/+MdeWDlc3r17/+P0/v/vhL+9//r7VGgSGQGbym7e3f/7jV2/fhhgfEU4pPd7cLG/eKBguM92+0psX9foq31y1ly9oN4beliJ5OuYeahDrgh2pd6vVi4bPTjwifPXNG92hiCChk17dK+JyEl3twKXi1+FP/4NtQvXyf05b8Yjl3+W5oqqtp9shRttKLXCPQRfAcQmOVdAFDLZBjK2E2P4BAIDHeJfhWWjfIMJtXslPF6yk2/V/EcEQCC9B/QKXrpKlCn3t0sHqJ+ocV7fCXt2D0Q09Lj8bbDO1l+91gRe6GdpaVqgjoESwajn4tfH81h0Ze+Q8pqHjPu5f7A+3w7BnBrPaO/UupIk4InG3YJhihDgUiRUMam85L6a9qwMuClQz3tdza1gbmkGgOMTh6nD9zZuv//C7P/zTP/7D3/32t8xCIDdXr4Z4Pfc5Snpx8/rt65eHm6sl519+/lgWg85m1jJVZLTQe9OmQeJ+J4GDNtDPMkYMYWQDdI4iIRNEoa/ffvXnP/z293/3zW/evpqm6dOnuw+f7j7d3T+epvO0NNVuXsutgEptvdXemndmgZBFCBEQCEMQQgO7BARhCa5+vbotXrBmADJmYiJxFz9CAtei11abrtAp+H4e7Ul30mUWgYiRJUSW4HmiHw4JkR02J3KVkdXn15vKa81LAGtcdz44rP1sW92xcTUehHU6i4NISmEYYggBUXoHM+i9W8m999qsNPdUBmJhZkRgJkJsrbW25FxFlvo/0JD/4cd3P/3yi0c03cwg1vBuvtvXOain/gGZD81uBb3Xw8/CfLdcaqm1TaX0+fEUYmJvLLtRXYwhxRBDSjGkKDE4prwF8O30+IldvcM+J/SBfR7awZa8MJm/rJfltIBVg1jXtrquMXflOvgZXhEIxVq0tJpbnZa5m437QU1zqw2w9mqmFVoD7tgFGJHQVkug3hog0Oqx6kw68xvCaG3rg/MIt5KemAEZ5qfQri3Xcg8GaGr9pP2kmrdxvN60AZwNgBAfzuguMyIuesxMq0RM76Bd57mcT/OyTIim2qbpZFBaD5IlRB7KkFIyM0OVyLGHrh06MAaWcNgLCzGDK3SmlK6urochMimTBrGU4m63B8NSWusRw9Bagw05NbNf3k/z8byPsh/Cfg/d9OGU51xrNQASUTDTpiwcQw5RRKib1qa199q1dW2qW+2paFqbZqTauHXtpoh2PC3L0tTYjM15cIFCoDFQEnbrdSRyx0JngrhPFyLQKmewziwAdEIVQhaOz7rUptZrUzUDqtX6Mb9798P1fz4d7/HmWq5vXt++/M6+vrt5+ff7tB/fvnnz6ptc6vn0cV4eepsZKKUX+8Pbm9vfGIbdzavXX33zzW/e/od/l6bjw/I4N2ij0BjG3359/Y9/ur7e3T9++mWe7h8f7wR+eX07R9Il4/6qJDliY10E6x74XOePy/Fjne603geuGrE1MrcyNPXi6fkxf/vqpbwIHlPtQgS5oGPO2nUAfYXfPDyvkX2NnJe+3RYrt6j62YG97AHb9GUJ8JJP+TfbYEI/jM9KdrNfhfaZHp/h8bAlALBB7U698I/iGnuBtu+yQonrbbXaJalPmZsLLSBq0bq0lt1nAVEJlVDx8tge5Lfpni0Rwcu3MwBQXDfMOjKLRuCoK7oC//P1+nB41V/uu5xC2YXdLo6JGFtrrdeytJyVTWvL5+nTu1+kvLjdH8YYd2nM2hWkV+jaidLN9etRsRlO5/l8ng0UkRCYSVJIfCN//tMf/7d//W/+8Oc/vX7ztqtNU7m9fXN99crs4bDbf/PVd7/73TfXtzfv3r3//i8fpnMxJUTojWohUO5da4PAcbySwAmU1+cEAAAi2u+vDIhcZIUxBd6P8c9/+P3//m/+8Y+//fabty9rKY/H46e7+4+fHu4eHh+OpyWX2twcVg269p5zzbnmXEptvXp95nJe0elrnlq6MPY4pt1uRISurWov3fNRbV27dgNDwphSjEGEQ6AUQwyBmc2wVR+kd4vMp4XEzIF4HWzzep2QvAUqIRCxGaq52BciMlFnVgDQrc9gthpx4ZbWup4TaEdEICY16Oitw96NhNOQdrshxmDGtfbW+lJQ0Zp1VqhdS/XrRz1uBggGuuSlKyy5qOmyfHY4Pgvtuc69FltbRWvV6ifLj7pe9KLdJg6ADNh8/GtF6NmQDbZOPALiARLAOknFciHiEDOy+HhLGGJIMTjHwu3XEBnXChcvUXe9Nfz4rqHdLpnRdt4tz5lQV4yQLmRAV3NxMZPVdFzXxhs+73+5JIWj26dzXual12oAvdayLLN1FvSxKEZEUkIwVL/2vLm+GvDSygxYx/tonaZV195A/+vrzcnI9tlwD9Q6T6ePfl/0nr1kB1Ds2EgNmoFsnHqjVRMHhdcLGMC1vkG71dpL7ksuXXsvS+ttKRIXDlFClJiGGBP7WDwIhV20xKIIJBRSiiEKrcUVhhCT7JLEGCgEDIIppXHcm1HgBsZIwnQGeLgclnEYsJddlN0Q97sBEJiltK5GRCwirdacMyGFEJiZmJwhp6sXphkAssM54AoHZMgIUWg3RiaY56U3ZYlEAYhJOAQOgSJjYBREYVrNEREcTe699+pDj6uJlO8NQFsZJYjD/oko5Io7RBiEmlpr/fh4/vCullObdlBfHWE+UZ37+ScZXobhZRxeDxS6fFL6lPVB1Ro+1pxLBpIxUL4+VPxq9/j++qfrXf0YTEtXVexaH/P0/d370uvDPJ2n8zSfz32aRdtIyh10arMpLk2PJci45GM9n/pDtlOzrsTEgGzAXYkVP2fIA8CYUhqTn8PtoG797LUgXU/FJe7759AlauN6up1Pu2lOPAvWW2MLcfUxdQcj8Ob5lh+srysAuHOiqQOaG10XL8wY/5fg88ld8zK4q7bWc3NTuXW3OC3nUt7BCgH4cznFp7fSem3aunklSMzI1qznkufcSkPkulREbLUir0IB6B5gvNXz3kzAtQxZeUhbGkFI2NVIkTYhRvj18JsCGCDHIMma6ilPp2wG1hCraUNKQ4q7gwwH4AHiLhxuZGfXcackPI4YAjDd9Pa2VEVShU+f7j59+uS0n/uH06cPjy6LElMaduPusNvf7AH45uXj1c3VsB8fzycFNJCunLOdTuXh4Xx/f9IOJChJJHTm7r5KIgjAKY778SbGZ6xGRBmiAZLEwBQDvTjsvnp1/bvvvvru27dvX7+4Puy6pmGI+914e3NznKbzNG/ooBr4LHmrdf1VSqvrmJp1V/429YLQRedTDLvdsNsNANZ7za2W1lxWoa8uaQpmskqeIbOH9hRCJBIf50ag3vX//U//0traevPGIDIjbd6IrqftvXHADfQF86oWAQA2dHsN7arqtnhr9FkLOCeOGGhXhG5QDCbSyHa1EwBwj+PaTE1ra6W2XJuphUgGtPrDrOeQGYy7EgsgLXmZF5ddf1qfc+oiyGbLbk/hc0W01c+l1264Biom8hxNVsuY9fT74SVG9pEa78CtVbeHO/KKn9zKlskFoj1aIyJt9lzrzlmPKxjQlsob4takf3ZazGA5ZwSXVkPH0zxb6b331tyqb/2bumX9WyazFfOmar1pLq2Whl1ZyHIpTFZLcCccWkURVRoSA5hnOMSI4tI0a7j1b+EGlL2DdUNUNHdhJwLywXewz0N7mU/Hj1s2owC69T6aYQer4MKMgKt79gpAbtvNEAFVoatph96h1V57d8F/XlASxRRjiiKLSIppjGlMaQxhl0YmQOhGgCnFENx1z+9r6oUqIEMKFIECwci0F4ljQrexKc8AeUL45uvbmuMYeBfjYTcyYW3VAFmCG27nJR9PRwAMzlxFCjGEEGFjPSEACbFQCBLE3WKstYoA45iikPZKACkNnhy4Igqzp1gK2olpRTNcLl5Ee2+teRFga6Ha1dxdB1wP6l/e76ZnqbCiIWMI1LqWpmVuD3faz9qjxnqf2mLnX07v/68wvhgOrw8vfsvxcDx+PJ0+Lst9VxW53V19Vct3Ie57q2AwCrzc61fXKQ+x2jnXXmy5+/jD3/7yEMPS8qnMLc/aatPWgYwYe5Vl5vaYT/TwEX4Exdq6NQido8bBBqGAQGwYotVuWDr8isNB6FXJ5k36lBivIOIWXbdz4YZVzyv0lQbDCOxzItvu2AZQAJF4dYVxJI+MdEsKnop1sLUC8IJ2hQEuUhbbgfATz5+rHLr5XFfNtc7LknttznRTpwCtyOwWyy8NRjUA7dpa7dU7D7Y2P13ftK+oHhGXeWm1AgEykiAHpkAshEIciAPTykNgZO8qeLefAAkVSJF679yAzEidIX8BIXyda2u1LoiFMeelLKV17xeRpLS/vtq9fHl4/dXh9avd6xfDqxfx9gWzSO1xOOxvXsowgIitXQdU03c///zLu58RW2vlP/3Hf3n4dAaDrjoty3E6515BMMY43ozj9RB3Ymy5t/NcPt6d7h6mn3768OnueP9wrLVL4LjzpF+GIY1jbN0M9PZ6ePni5TiMl6dARBlYwQ18KQa+fbn77Xdv/u43r97c7oeIrRcACIGvrna7XXrZrlvvG1zkOHltvV3w1NZ7KaXW1lVLq1PJtXc1FeGU4pDikOI4xCFFd2srrZRWN2cyMHNZEQcWUNUQMEhKcdztdimOiIIowiGX+td/+f4ptDOLRMO1MUy0SqA7SW2dV1Zc55mYmBkRLwWIHx4vErxIJSJyjZsg7umroKBWtWkz7IWtXR9i7TtAIMLeW85lXpZpXpZSEWFABpKu6Ppm5MPHHnpZkKSUfp5O0/zfr9qvr8f99fgMk15P3gaBE2xOBxdy27qvCZmALyfSEMnZN2uAJyJyFH4TRXcKHoJdPnMN3xs6aLCNpD19dB0f2DC7DU00089Cuy1zBesbBOhkto2x15obl9l2R8HWSrgQZLZ/ASpEJAlBmYkxEomaqDGCIIphMBKkVYxg00X2PuRn1xZ432gbb7NVa9+3z/o6wYYVbqt3raU9v3yfYZxACEwYRZCoKjTbyh9acUHHIdDA6yoWCgQprNwjFpRIIYUQApIAiHbMU20VgxizCLErMjgNTZhZRITJFROVtGNrThJQgOqaxq4xmfNTfwSJ/vzHt2RlF8MQJAqjQe+NhNM4BglEVEqZ58kMPFMBgBBCCOH5XexnI8a4dtfUSq2mFlMIQoTGRHGzYMQ1TVx5TaB963x425iZWG0dtfyscbOa3jsRT3+4p0toV4Om0NRqt9a1N6tLr2cTtr3ADnuoeXo/Pfxyh/ETD78Mh1+Ah+PxOC8n1QkIOByG3c+H9z+IxN4aIwun408PspwO2GeBhXSu/Xw8vvtpFqo957pYy4jmSbARg2FD6iGCsKGZNmvV0HikBBJEcAOBERBd0hGfeaEi4IBxR0m8SbFi7X7CYU1vLmf+Mma0Ha/LRnRWyjYju/KRYCuvPSo/NdnNFN2C8Fnu+hkO+kQp3vYxPjsLl+P9LLQTyipTi9p7zbm2up55XVlABgi2vsV64TXpSmFXVVAjuEARK30Ag3iYlyAxChCqbTZXQLxyeT0jv6hpIW3XErn5udcliJ6O6/a7AvwKRLl584Zu0vE4D3NxZLq5TJpBHHcvX795/dVXX3/77e3r26urYb8f9ruBidUoxP24v+EQQdhfLdOW80z0vtQSAwmLGdRaa21LLp/uPn3/4/fpKk31nIbhw4dPuZ2Gvbx4dTWkNOxHA3g8Had5ClEO14euysKSxMedUBAZDbT1Nuf5eHoIh32EvT+FCP/m65cGRMxBeBfl269efvvNq5cv9jGgQatN1xcIjdgCkWzHzu9g7azPuvemVltzdb+qPbfWtLuidggcxN2IWJgBrPfWem29bZAIbn7u/rXQkXPmGMMwDGMMCUmIhCnMc36uxIxI6DppLjnHQsxIZGZOozIFWj8u5A54YBf2HGxJ5CU+ur0Ci5CImTkKob2hKSJ0hNas5LbMZZkzmi1L94GgeanzUpmpNqjNarfatPUejJiRYCWmEUlXzLnX+pxL/ms1uv3X3+42VsvTWrcsrZS5p/7c+sOv++rSrzbzT9QnMuqKqz916J6uAfjsvD+d5/Um0Evo1TXKX2LdGsB+rd1okHMHbWa4ytSooVfofQXk4YJI+ODz9tPYBZf3K49dDAdd614CCbOTAXxfCIsQb12EFQD04OHcuPUVVFVH52zlDCiBB3uXPwBEM+if5/OIhCj0XCNoC/AMGJCThCGOJLxQLq05+dhN+cDAumnr68vveRcwEq3zcYElsgiTiBm2ZqfzfJ6WXJYMxbkn/qTae289xhAM2HmlRN5w6l0BmqrlXC9Jh5mVOl2eggj/6U9vx2j7cQjE5mrUrYUUD1cHFllPzKrQuc4NefR1hK6rmhlzCBJDSMLBYV5HX4j8lIlPUvmLD/6n64ieIcIFIsM1c1VCQKbWWu8NEUVYJEoQT7R8EpPwaeZVAYqiu7CXoq1aW8AW2N3i6wPdjjwAfrjvnx5rgdzpAeXnpnCaWms9DhAicrgL6V0c/4KAvVYyDhj1DP2+Db3cBDgHeKytTP3+IzOYFawZe4XAHAISG6CWrko67mgYkI2sQc0dlSRJJzYjp+SaeQFBEgQDP+eejRQPNDjFnbY2+rrHDJQVbMOznzW8NzYqrH/gRq3PEs8t7YbtRd56Rf6u0lN8/uy0rleGT5/6L13hMwC4UHi3q+hpX63QjjARqPVStdV1HgXWRNvA2S/gHSsFW18XAERkYc+2icgZAJukB631FhMRqW3CYn6Rrp43uEKU5q0jvxkBEUHX70q6cuY9mfbuv7Pqnt9333z3232y9x8+TdNydTjshmRde9faNabx1Zuv337zm6//7rvDzRWxoismAiEKYiRKW10AYL20fDqd379///333+93KaV4PJ2meV5ynufllw/vkWHp04+//C1EmeZ8nD6NV/KV3O7G/YuXV2A6301Ny9XNfjykNc1FqLWVUlmYI2g3bX2aHz+0PtxcH+ClP0UM8ufvvvaaPQiPSd6+uvnmq1dXhwGgt14uF/uWvuG2T9YIgGhOd7iwGCSIgXgs6ehERHzWa9nYDebKrxiM3cDaaxkwQiDyTh4ycWAOIoF59bbxoasqv4o/BMheEDtfHYkMoGt1izkEdA18kgDIGw5vjr7rFoyez5P4VCgxr5mldm2VAJDJXZFr7cucz2fprS9ZPa4vS11yI+ah9Fy1lF5qK7WGSBLESSTkBj7oNf1nz/FZaHd3eo8l9nSc1tuN1pJ93alrqEHaygTbGC0rAGaGBms57WHam9pPtfhlrVHL4NdxfjvNT/96OuH+LvpLuTLSn64LD6srX91DO5jhk2Gzo+OEhLT+PLpuqPVLXBgbvG4/MnZshokZGVe0xavZC0EAtv6Kv6+Xx/Cv6UivE9dX1dv16kR1uZtn6/rqxe9++ye/dy9oBW6hPbqafUosspruIQITByEiNAQ165d6y6lUnq54BU4s3l4kVWxdp2mZ5qV1MwOfTXYCs3i5LiIiay98e/CNMY2/zs6oTFvfBwFur/aHBCklQbauvbfemwQZxxEJe2sqhBA8zXVo3LdabbUJb0dFmNY3DQHNQIjNCBBwje604j7aAQwJUUFVEYy2HAu3HNtddpjZP355HARsrTW9eIWGy5WkCqVYX0eX3VMc+0LtTOWEE2gLNi9QGmaDBkpNgSAIxRjGHcbkyFbF1rWZLU0LzIX7mdoR9KzS+h7sVug68ZsxDIkYsM5acyc0YuPIJli6NTOJwAw9WzVVRfJh21UuDnvvXQ2BGEEIgOj58SAMhBGBQckQFdfezRpCFT1/Xk/mU0Rdb9Vno6+KW/A1eHZhbLOfTzU8AOCm2LHdfbh1jy7Lsf+V5vPsnBs4xVFL+Uw216m2YhgMgxIqbcQB9Fko//qKZkBrtoOX0VxEhA21wEuZcmkd0orAriirs4fRANWwKzZEskuD8RJqANEnZUANPLRX5azcUSgmScKBPtf63l9fvbiSosZpvr253o+jt4pK7Swx7RIJtd6m6dx6rnUpZeldTakr9Y5qoKC+refpdH/38T/+h3//L//5n3e7NAzxpx/ff/j48fH4MM+ZBJCt4XJ/+hBTAITWy+4Q9odhSCMnLTkrZpAmg7lZlqfhhp1EQ4Q0ilBiokHGUYY0PIWPFMIf//gdszglPQhd7dPN1S5GcfwZkD67JNDWKHOp83B991bl7bURvMIiYaOM2PqmPMc3EY2BgAEA2bEqBLdf8N/FkzIkcQ4nuB3oqitEz78WEbPElUbHTMQG6MMiZkgcnFLno8b+c+P202xlnKI7utDTgo3wYdsMBhN7qFWA1q1UrVWFtLssR+u19dqUFHKu81IGwXGgXEJwG1Q1NWOiYUi73T7nki2f23/X1NW06wqc29Z3XrsXq4PaRtHago2/NS55/4ShmRuiOpKzxnS4nA9db41Limbb72tRj08v1CUJWLF19R7KWggbrVLAn4d2YTY0A3WNmvUqAQMwYmQkEYkhrKfYfPbXEPTpR/KHXTkzhASu6seM4OP1yI5WeO92G8/Fy7X11GjZOATeH9jEQPrF/lydu+/WP892/+2LVy9fvMEN1bzsZlqrdgrMIoGEjdD8onKymG8ln+eCFfBwGXyzleju/UHPXrwfn0stpbla0Ep0/Hx34q/XSmTcyrZLUoR0bHcPl2OHu5DGAIKBkFBQKXTuxETA1rU31+cnzyC2N95UldFWIJRw7fEYgDavwxDwEi/MsPe1vvNZUhEx09YqmDmNY60DN2KXP5SIbLgZIZKZ1VKWnKur3/Vr2Axqu1quBoBAZG5v1ahlzkc8MdiiMlhG0CCqYIAcIEaOKaZRhh2kYGwNXZ5t0QVlqboce3to+VF7Buq2M4Qgr3fx2xfp+iYMifJUl3PW3gw7jQIpFMPaQa3X2iftvbh5HghjYBZiNO8mGIEwYUf7/E4FADYQPz52oZP7JOflsODa6npWLK/vsW0v5IaX+QzZGo77Bow9gau+UwjNr5K1A7pWvfAMDvA6YqO62BMOb9Za773n/BTa/a5hQzYMRslYNqLJxhvESzFxgQ9dZGWLGU+ftMG4iIaOe2wim+A8mC1wbxAvXMZ3Np4srnn3etM4O84QmkrpaDiEOIQUQmQOAMulvAkpjbs07mcFHMYxjUNvFSspVEPItRxPj4qIBMtynufjeTqXXFu1UjTnXnrv1vxePh2Pd58+/PUvf/nhh7+5RMR0Xo6P0/F4qqU6u7LBdFrSuEsxJQlp3KWUxiARqHZYgAuFhr1Cbd2stJLzYubtMEoj7Me0G8d93A9hl3ZPNLoY5R9+/9sQhEWcYkHoOp0uj3JhTWyX/EYu3pBSo+2VQzCvI2Fr/26vMKxbcn17aXtTL3maIyqBSYhkDeq4Uh/XEP70Fm676/P8klgkuB88Oy3Mq1vnZDu4jiwG2LsB6ia6tEYsZ3SsWeL6ZxfYdW0D+UlDXCdFnRZQu7UOfe3ZQG/aW9euapBLXZa8iOVMpcRaVmqsqhLTMAz7w6F3nfv57gkt/Ty0P97lC969wdMXbMy2/MnjOj5D6Z7At0te9SyD3w7Ndh6fEJenA+EvNXweIrZb++kHdHTNtnvE83Azs9Pjs9CO1PjWSJXMSFH6ZVYWN5cqI25b/ffUl75sve0td/D8coB5TfQM+2UGb4XXqdGvnUxp/WcFJ1dMw39sUiVVuWh9mKEhqlF/Nk+CjEy8vrbP6x9A/0AjU2i4utn5hULYtzcKnuhTts7gg4Gtn7BFZd/rauD38Yq9KBmg6vq5nwVvvPwDz35f4Rz/tGU5P71nBu8eJa7ufd76YDVEQmYwg9YZvAJj5K1jaQZm2JU3d/jt+z9Vg+v53sCj9cxu1TmQi9k1Aljnkf1KIOfDAzABs8ev7U1CM7PasFbpHVW1tKf3dNzf/uaP/4c/cKvai46BrxLRiPMALRiJdYaG2BQMoRJaoB6kMRUD7kamoKpgna0OVkErWx2t35h1AINkSIDyQvKVHBMvgdquNW6q3UAxMATpht2gq/agRp0HTa7Im1KWqBzAoI2OVPHa7IiHy6Y2sE8/fjzfn9cH3rb9Z0XyZceZfX4An97UbTOv+ffTf+jTRO+zw/B09/nZvey9X+E9fuU8yyfWj7qMSV+ejvmy5L/+5a+nx/P17ubbt9/dXr1c5UGefVf49b393/r4llXA8639BFp4THHWEMB2EO2ZGs/6RfDpa/tRX8+8OiEaAsv19XU3mnN5Dlv+8vF8nvLpVEvRpeYY3ECut6YGyKdjeCgxngCh1lzKUkr2HnRr2qp28/lPRMSy9K5xd/Xmq28iC4rw1XV/+arlUnrvKQ3DEOMgMbk7eBAJEmKQ4D2sENLNTRxSrq32rmarsC4AEHGMIQ0xxRhDjBIDR5YnGl2t7a/f/8TM24CybbMAnuNcrv2nTO4S8GDlbG+ZGVyixFPEwUtoX98sRHg++oE+s0VIuFbD3oKkp+QL8elFt217A5RSn0+EH/a8ZR5rSNb1xhYz47WHs25mQCVsSIq49rmdHISIzErUCIuzyJ0er6KKvVM17UxNuDKRMPc2TecjajoGUYVcai6LWRbugGh9zos8KLcWT1NKUSQwOrzZrTXNS1mWfF5On+3yf/tv/+1/vfe/rC/ry/qyvqwv68v6n3TR//+nfFlf1pf1ZX1ZX9aX9T/P+hLav6wv68v6sr6sL+t/qfUltH9ZX9aX9WV9WV/W/1Lr/wNTgSgDCmVuZHN0cmVhbQplbmRvYmoKMzYgMCBvYmoKNjQxMDYKZW5kb2JqCjIgMCBvYmoKPDwgL0NvdW50IDEgL0tpZHMgWyAxMSAwIFIgXSAvVHlwZSAvUGFnZXMgPj4KZW5kb2JqCjM3IDAgb2JqCjw8IC9DcmVhdGlvbkRhdGUgKEQ6MjAyMTEyMDQxNjU5MjkrMDInMDAnKQovQ3JlYXRvciAoTWF0cGxvdGxpYiB2My40LjMsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcpCi9Qcm9kdWNlciAoTWF0cGxvdGxpYiBwZGYgYmFja2VuZCB2My40LjMpID4+CmVuZG9iagp4cmVmCjAgMzgKMDAwMDAwMDAwMCA2NTUzNSBmIAowMDAwMDAwMDE2IDAwMDAwIG4gCjAwMDAwNzE1MjcgMDAwMDAgbiAKMDAwMDAwNjk2NiAwMDAwMCBuIAowMDAwMDA2OTk4IDAwMDAwIG4gCjAwMDAwMDcwOTcgMDAwMDAgbiAKMDAwMDAwNzExOCAwMDAwMCBuIAowMDAwMDA3MTM5IDAwMDAwIG4gCjAwMDAwMDAwNjUgMDAwMDAgbiAKMDAwMDAwMDM5OSAwMDAwMCBuIAowMDAwMDAwNzMzIDAwMDAwIG4gCjAwMDAwMDAyMDggMDAwMDAgbiAKMDAwMDAwMDcxMyAwMDAwMCBuIAowMDAwMDA3MTcxIDAwMDAwIG4gCjAwMDAwMDU3MDIgMDAwMDAgbiAKMDAwMDAwNTUwMiAwMDAwMCBuIAowMDAwMDA1MTA2IDAwMDAwIG4gCjAwMDAwMDY3NTUgMDAwMDAgbiAKMDAwMDAwMDc1MyAwMDAwMCBuIAowMDAwMDAwOTE2IDAwMDAwIG4gCjAwMDAwMDEyMjQgMDAwMDAgbiAKMDAwMDAwMTM3MiAwMDAwMCBuIAowMDAwMDAxNDk1IDAwMDAwIG4gCjAwMDAwMDE4MDAgMDAwMDAgbiAKMDAwMDAwMjE4MCAwMDAwMCBuIAowMDAwMDAyNTAyIDAwMDAwIG4gCjAwMDAwMDI2MjEgMDAwMDAgbiAKMDAwMDAwMjk1MiAwMDAwMCBuIAowMDAwMDAzMTg4IDAwMDAwIG4gCjAwMDAwMDM0NzkgMDAwMDAgbiAKMDAwMDAwMzYzNCAwMDAwMCBuIAowMDAwMDAzOTQ2IDAwMDAwIG4gCjAwMDAwMDQzNTMgMDAwMDAgbiAKMDAwMDAwNDQ0MyAwMDAwMCBuIAowMDAwMDA0NjA0IDAwMDAwIG4gCjAwMDAwMDQ4MTggMDAwMDAgbiAKMDAwMDA3MTUwNSAwMDAwMCBuIAowMDAwMDcxNTg3IDAwMDAwIG4gCnRyYWlsZXIKPDwgL0luZm8gMzcgMCBSIC9Sb290IDEgMCBSIC9TaXplIDM4ID4+CnN0YXJ0eHJlZgo3MTc0NAolJUVPRgo=\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 9\n"]}, {"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["def visualize_prediction(idx):\n", " visualize_exmp(indices[idx : idx + 1], test_set)\n", " print(\"Prediction:\", predictions[idx].item())\n", " plot_attention_maps(input_data=None, attn_maps=attention_maps, idx=idx)\n", "\n", "\n", "visualize_prediction(0)"]}, {"cell_type": "markdown", "id": "c342c0bd", "metadata": {"papermill": {"duration": 0.217865, "end_time": "2021-12-04T15:59:32.717940", "exception": false, "start_time": "2021-12-04T15:59:32.500075", "status": "completed"}, "tags": []}, "source": ["Depending on the random seed, you might see a slightly different input set.\n", "For the version on the website, we compare 9 tree images with a volcano.\n", "We see that multiple heads, for instance, Layer 2 Head 1, Layer 2 Head 3, and Layer 3 Head 1 focus on the last image.\n", "Additionally, the heads in Layer 4 all seem to ignore the last image and assign a very low attention probability to it.\n", "This shows that the model has indeed recognized that the image doesn't fit the setting, and hence predicted it to be the anomaly.\n", "Layer 3 Head 2-4 seems to take a slightly weighted average of all images.\n", "That might indicate that the model extracts the \"average\" information of all images, to compare it to the image features itself.\n", "\n", "Let's try to find where the model actually makes a mistake.\n", "We can do this by identifying the sets where the model predicts something else than 9, as in the dataset,\n", "we ensured that the anomaly is always at the last position in the set."]}, {"cell_type": "code", "execution_count": 41, "id": "7d06d854", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:33.159783Z", "iopub.status.busy": "2021-12-04T15:59:33.159310Z", "iopub.status.idle": "2021-12-04T15:59:33.162672Z", "shell.execute_reply": "2021-12-04T15:59:33.162185Z"}, "papermill": {"duration": 0.227646, "end_time": "2021-12-04T15:59:33.162786", "exception": false, "start_time": "2021-12-04T15:59:32.935140", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Indices with mistake: [49]\n"]}], "source": ["mistakes = torch.where(predictions != 9)[0].cpu().numpy()\n", "print(\"Indices with mistake:\", mistakes)"]}, {"cell_type": "markdown", "id": "20752dd7", "metadata": {"papermill": {"duration": 0.217506, "end_time": "2021-12-04T15:59:33.599090", "exception": false, "start_time": "2021-12-04T15:59:33.381584", "status": "completed"}, "tags": []}, "source": ["As our model achieves ~94% accuracy, we only have very little number of mistakes in a batch of 64 sets.\n", "Still, let's visualize one of them, for example the last one:"]}, {"cell_type": "code", "execution_count": 42, "id": "aff3ca25", "metadata": {"execution": {"iopub.execute_input": "2021-12-04T15:59:34.041610Z", "iopub.status.busy": "2021-12-04T15:59:34.041142Z", "iopub.status.idle": "2021-12-04T15:59:36.836836Z", "shell.execute_reply": "2021-12-04T15:59:36.836406Z"}, "papermill": {"duration": 3.018115, "end_time": "2021-12-04T15:59:36.836962", "exception": false, "start_time": "2021-12-04T15:59:33.818847", "status": "completed"}, "tags": []}, "outputs": [{"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Prediction: 7\n"]}, {"data": {"application/pdf": 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\n", "image/svg+xml": ["\n", "\n", "\n"], "text/plain": [""]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Probabilities:\n", "Image 0: 0.07%\n", "Image 1: 0.11%\n", "Image 2: 0.07%\n", "Image 3: 0.11%\n", "Image 4: 0.17%\n", "Image 5: 23.27%\n", "Image 6: 0.16%\n", "Image 7: 48.91%\n", "Image 8: 0.10%\n", "Image 9: 27.03%\n"]}], "source": ["visualize_prediction(mistakes[-1])\n", "print(\"Probabilities:\")\n", "for i, p in enumerate(preds[mistakes[-1]].cpu().numpy()):\n", " print(\"Image %i: %4.2f%%\" % (i, 100.0 * p))"]}, {"cell_type": "markdown", "id": "fafec094", "metadata": {"papermill": {"duration": 0.236848, "end_time": "2021-12-04T15:59:37.319392", "exception": false, "start_time": "2021-12-04T15:59:37.082544", "status": "completed"}, "tags": []}, "source": ["In this example, the model confuses a palm tree with a building, giving a probability of ~90% to image 2, and 8% to the actual anomaly.\n", "However, the difficulty here is that the picture of the building has been taken at a similar angle as the palms.\n", "Meanwhile, image 2 shows a rather unusual palm with a different color palette, which is why the model fails here.\n", "Nevertheless, in general, the model performs quite well."]}, {"cell_type": "markdown", "id": "3ca49cba", "metadata": {"papermill": {"duration": 0.234811, "end_time": "2021-12-04T15:59:37.793046", "exception": false, "start_time": "2021-12-04T15:59:37.558235", "status": "completed"}, "tags": []}, "source": ["## Conclusion\n", "\n", "In this tutorial, we took a closer look at the Multi-Head Attention layer which uses a scaled dot product between\n", "queries and keys to find correlations and similarities between input elements.\n", "The Transformer architecture is based on the Multi-Head Attention layer and applies multiple of them in a ResNet-like block.\n", "The Transformer is a very important, recent architecture that can be applied to many tasks and datasets.\n", "Although it is best known for its success in NLP, there is so much more to it.\n", "We have seen its application on sequence-to-sequence tasks and set anomaly detection.\n", "Its property of being permutation-equivariant if we do not provide any positional encodings, allows it to generalize to many settings.\n", "Hence, it is important to know the architecture, but also its possible issues such as the gradient problem during\n", "the first iterations solved by learning rate warm-up.\n", "If you are interested in continuing with the study of the Transformer architecture,\n", "please have a look at the blog posts listed at the beginning of the tutorial notebook."]}, {"cell_type": "markdown", "id": "88cdba30", "metadata": {"papermill": {"duration": 0.240818, "end_time": "2021-12-04T15:59:38.270965", "exception": false, "start_time": "2021-12-04T15:59:38.030147", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) on GitHub\n", "The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool\n", "tools we're building.\n", "\n", "### Join our [Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-pw5v393p-qRaDgEk24~EjiZNBpSQFgQ)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\n", "\n", "### Contributions !\n", "The best way to contribute to our community is to become a code contributor! At any time you can go to\n", "[Lightning](https://github.com/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/PyTorchLightning/lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* You can also contribute your own notebooks with useful examples !\n", "\n", "### Great thanks from the entire Pytorch Lightning Team for your interest !\n", "\n", "[{height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Tutorial 5: Transformers and Multi-Head Attention\n", " :card_description: In this tutorial, we will discuss one of the most impactful architectures of the last 2 years: the Transformer model. Since the paper Attention Is All You Need by Vaswani et...\n", " :tags: Text,GPU/TPU,UvA-DL-Course\n", " :image: _static/images/course_UvA-DL/05-transformers-and-MH-attention.jpg"]}], "metadata": {"jupytext": {"cell_metadata_filter": "id,colab,colab_type,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.7"}, "papermill": {"default_parameters": {}, "duration": 100.878464, "end_time": "2021-12-04T15:59:39.519049", "environment_variables": {}, "exception": null, "input_path": "course_UvA-DL/05-transformers-and-MH-attention/Transformers_MHAttention.ipynb", "output_path": ".notebooks/course_UvA-DL/05-transformers-and-MH-attention.ipynb", "parameters": {}, "start_time": "2021-12-04T15:57:58.640585", "version": "2.3.3"}, "widgets": 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![](\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/05-transformers-and-MH-attention/multihead_attention.svg\")
![](\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/05-transformers-and-MH-attention/comparison_conv_rnn.svg\")
![](\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/05-transformers-and-MH-attention/transformer_architecture.svg\"){kind=spacer}
![](\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/05-transformers-and-MH-attention/warmup_loss_plot.svg\")
![](\"https://github.com/PyTorchLightning/lightning-tutorials/raw/main/course_UvA-DL/05-transformers-and-MH-attention/cifar100_example_anomaly.png\")